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Brain Sci., Volume 9, Issue 12 (December 2019) – 53 articles

Cover Story (view full-size image): Clinical data suggest that deafferentation-related disinhibition tends to occur primarily in the aged brain. Therefore, aging-related disinhibition may, in part, be related to the high metabolic demands of inhibitory neurons relative to their excitatory counterparts. These findings suggest that both deafferentation-related maladaptive plastic changes and aging-related metabolic factors combine to produce changes in central auditory function. Here, we explore the arguments that downregulation of inhibition may be due to homeostatic responses to diminished afferent input vs. metabolic vulnerability of inhibitory neurons in the aged brain. Understanding the relative importance of these mechanisms will be critical for the development of treatments for the underlying causes of aging-related central disinhibition. View this paper
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22 pages, 2901 KiB  
Article
Neurologic Injury and Brain Growth in the Setting of Long-Gap Esophageal Atresia Perioperative Critical Care: A Pilot Study
by Samuel S. Rudisill, Jue T. Wang, Camilo Jaimes, Chandler R. L. Mongerson, Anne R. Hansen, Russell W. Jennings and Dusica Bajic
Brain Sci. 2019, 9(12), 383; https://doi.org/10.3390/brainsci9120383 - 17 Dec 2019
Cited by 15 | Viewed by 3771
Abstract
We previously showed that infants born with long-gap esophageal atresia (LGEA) demonstrate clinically significant brain MRI findings following repair with the Foker process. The current pilot study sought to identify any pre-existing (PRE-Foker process) signs of brain injury and to characterize brain and [...] Read more.
We previously showed that infants born with long-gap esophageal atresia (LGEA) demonstrate clinically significant brain MRI findings following repair with the Foker process. The current pilot study sought to identify any pre-existing (PRE-Foker process) signs of brain injury and to characterize brain and corpus callosum (CC) growth. Preterm and full-term infants (n = 3/group) underwent non-sedated brain MRI twice: before (PRE-Foker scan) and after (POST-Foker scan) completion of perioperative care. A neuroradiologist reported on qualitative brain findings. The research team quantified intracranial space, brain, cerebrospinal fluid (CSF), and CC volumes. We report novel qualitative brain findings in preterm and full-term infants born with LGEA before undergoing Foker process. Patients had a unique hospital course, as assessed by secondary clinical end-point measures. Despite increased total body weight and absolute intracranial and brain volumes (cm3) between scans, normalized brain volume was decreased in 5/6 patients, implying delayed brain growth. This was accompanied by both an absolute and relative CSF volume increase. In addition to qualitative findings of CC abnormalities in 3/6 infants, normative CC size (% brain volume) was consistently smaller in all infants, suggesting delayed or abnormal CC maturation. A future larger study group is warranted to determine the impact on the neurodevelopmental outcomes of infants born with LGEA. Full article
(This article belongs to the Section Developmental Neuroscience)
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<p>Pharmacological course (graphs) and corresponding representative brain MRI cross-sections (right panels). Graphs illustrate the timing and duration of various pharmacological treatments for preterm (<span class="html-italic">n</span> = 3) and full-term (<span class="html-italic">n</span> = 3) patients between PRE- and POST-Foker brain MRI scans (vertical dashed lines). The POST-Foker process brain MRI scan for Preterm 2 was obtained before completion of sedation weaning due to transfer to another hospital. Representative T2-weighted images in axial view (at the level of the body of the lateral ventricles) illustrate brain parenchyma and segmentation masks for divisions of CSF: extra-axial space (blue) and ventricles (yellow). Qualitative evaluation of the PRE-Foker process brain MRI scans showed increased CSF volumes in extra-axial space (white arrows) and/or ventricles (black arrows) for all except Preterm 1. Furthermore, POST-Foker process scans showed a mild increase in CSF in either or both CSF compartments (*) for all subjects, including a case of novel subdural hematoma (Preterm 1, POST-Foker scan; obscured by the blue mask; see also Figure 2 in [<a href="#B17-brainsci-09-00383" class="html-bibr">17</a>]).</p>
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<p>Body Weight and Intracranial Volume with Advancing Age. Graphs show body weight (kg; (<b>A</b>)) and intracranial volume (cm<sup>3</sup>; (<b>B</b>)) trajectories for preterm (<span class="html-italic">n</span> = 3; black circles) and full-term (<span class="html-italic">n</span> = 3; gray triangles) patients between PRE- (filled marker) and POST- (open marker) Foker process brain MRI scans. Both preterm and full-term infants show an increase in weight and intracranial volume (an indirect marker of head circumference) between the two MRI scans.</p>
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<p>Brain and Total cerebrospinal fluid (CSF) Volume with Advancing Age. Graphs display absolute and normalized brain (<b>A</b>,<b>B</b>) and total CSF (<b>C</b>,<b>D</b>) volume trajectories for preterm (<span class="html-italic">n</span> = 3; black circles) and full-term (<span class="html-italic">n</span> = 3; gray triangles) patients between PRE- (filled marker) and POST- (open marker) Foker process brain MRI scans. Despite brain growth in 5/6 infants (<b>A</b>; similar results found for T1-weighted analysis), this growth was not proportional to intracranial volume (<b>ICV</b>; <a href="#brainsci-09-00383-f002" class="html-fig">Figure 2</a>B), resulting in decreased normalized brain volumes (<b>B</b>). Reciprocal changes are reported for total absolute (<b>C</b>) and normalized CSF (<b>D</b>) volumes.</p>
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<p>Volumes of CSF Compartments with Advancing Age. Graphs display absolute and normalized extra-axial space (<b>A</b>,<b>B</b>) and ventricular (<b>C</b>,<b>D</b>) volume trajectories for preterm (<span class="html-italic">n</span> = 3; black circles) and full-term (<span class="html-italic">n</span> = 3; gray triangles) patients between PRE- (filled marker) and POST- (open marker) Foker process brain MRI scans. Based on T2-weighted analysis, all patients (except Term 3) showed increase in absolute extra-axial space (<b>A</b>) and ventricular (<b>C</b>) volumes, similar to the pattern observed for absolute total CSF (<a href="#brainsci-09-00383-f003" class="html-fig">Figure 3</a>C). Normalized volumes as % total CSF volume) are shown in Panels (<b>B</b>,<b>D</b>).</p>
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<p>Total Brain Volume and Corpus Callosum with Age. Panel A shows 3-D renderings of total brain (red) and corpus callosum (CC; yellow) structural masks based on T1-weighted brain MRI segmentation. Graphs illustrate volumetric data for preterm (<span class="html-italic">n</span> = 3; black circles) and full-term (<span class="html-italic">n</span> = 3; gray triangles) patients at PRE- (filled marker) and POST- (open marker) Foker brain MRI. Panels (<b>B</b>) and (<b>C</b>) show absolute brain and CC volume (cm<sup>3</sup>), respectively. Normalized volume of CC (as %brain volume) is shown in Panel (<b>D</b>).</p>
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3 pages, 172 KiB  
Commentary
Reports of L-Norvaline Toxicity in Humans May Be Greatly Overstated
by Baruh Polis, Michael A. Gilinsky and Abraham O. Samson
Brain Sci. 2019, 9(12), 382; https://doi.org/10.3390/brainsci9120382 - 17 Dec 2019
Cited by 3 | Viewed by 3516
Abstract
Recently, a study published in “Toxicology In Vitro” (Kate Samardzic and Kenneth J. Rodgers) was entitled: “Cytotoxicity and Mitochondrial Dysfunction Caused by the Dietary Supplement L-Norvaline”. The title may be greatly overstated, and here we provide several arguments showing that norvaline is not [...] Read more.
Recently, a study published in “Toxicology In Vitro” (Kate Samardzic and Kenneth J. Rodgers) was entitled: “Cytotoxicity and Mitochondrial Dysfunction Caused by the Dietary Supplement L-Norvaline”. The title may be greatly overstated, and here we provide several arguments showing that norvaline is not as toxic as reported. Full article
(This article belongs to the Section Neuroglia)
18 pages, 1056 KiB  
Article
A Pathway-Based Genomic Approach to Identify Medications: Application to Alcohol Use Disorder
by Laura B. Ferguson, Shruti Patil, Bailey A. Moskowitz, Igor Ponomarev, Robert A. Harris, Roy D. Mayfield and Robert O. Messing
Brain Sci. 2019, 9(12), 381; https://doi.org/10.3390/brainsci9120381 - 16 Dec 2019
Cited by 6 | Viewed by 3894
Abstract
Chronic, excessive alcohol use alters brain gene expression patterns, which could be important for initiating, maintaining, or progressing the addicted state. It has been proposed that pharmaceuticals with opposing effects on gene expression could treat alcohol use disorder (AUD). Computational strategies comparing gene [...] Read more.
Chronic, excessive alcohol use alters brain gene expression patterns, which could be important for initiating, maintaining, or progressing the addicted state. It has been proposed that pharmaceuticals with opposing effects on gene expression could treat alcohol use disorder (AUD). Computational strategies comparing gene expression signatures of disease to those of pharmaceuticals show promise for nominating novel treatments. We reasoned that it may be sufficient for a treatment to target the biological pathway rather than lists of individual genes perturbed by AUD. We analyzed published and unpublished transcriptomic data using gene set enrichment of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways to identify biological pathways disrupted in AUD brain and by compounds in the Library of Network-based Cellular Signatures (LINCS L1000) and Connectivity Map (CMap) databases. Several pathways were consistently disrupted in AUD brain, including an up-regulation of genes within the Complement and Coagulation Cascade, Focal Adhesion, Systemic Lupus Erythematosus, and MAPK signaling, and a down-regulation of genes within the Oxidative Phosphorylation pathway, strengthening evidence for their importance in AUD. Over 200 compounds targeted genes within those pathways in an opposing manner, more than twenty of which have already been shown to affect alcohol consumption, providing confidence in our approach. We created a user-friendly web-interface that researchers can use to identify drugs that target pathways of interest or nominate mechanism of action for drugs. This study demonstrates a unique systems pharmacology approach that can nominate pharmaceuticals that target pathways disrupted in disease states such as AUD and identify compounds that could be repurposed for AUD if sufficient evidence is attained in preclinical studies. Full article
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<p>Cell Type Enrichment Results. We determined whether genes preferentially expressed in specific cell types were enriched in the genes differentially expressed between alcohol-dependent and control brain tissue using the userlistEnrichment function from the WGCNA package in R (see Methods). The human alcohol gene expression datasets are the rows (brain region of the dataset is shown in the first column) and the cell types are columns. Yellow indicates that the genes preferentially expressed in the cell type are up-regulated in alcohol-dependent brain tissue and blue indicates genes preferentially expressed in the cell type are down-regulated in alcohol-dependent brain tissue (Bonferroni-corrected <span class="html-italic">p</span> &lt; 0.05). The <span class="html-italic">p</span> values associated with the enrichment are shown. If a cell type had more than one cell type marker gene list associated with it (from multiple publications, for example), the most significant <span class="html-italic">p</span> value is shown in the figure. See <a href="#app1-brainsci-09-00381" class="html-app">Table S2</a> for the full table of <span class="html-italic">p</span> values resulting from the enrichment analysis for all datasets. Some of the cell types were enriched in both the up-regulated and down-regulated datasets. The direction chosen for the figure was based on a more significant enrichment and greater number of enriched datasets for that cell type if applicable. These occurrences are denoted in the figure and described below. * Type I microglial genes were enriched in the down-regulated genes: purple_M4_Microglia(Type1)__CTX (<span class="html-italic">p</span> = 4.61 × 10<sup>−5</sup>) and pink_M10_Microglia(Type1)__HumanMeta (<span class="html-italic">p</span> = 6.43 × 10<sup>−5</sup>). ** magenta_M8_Microglia(Type2)_MouseMeta genes were enriched in the down-regulated genes (<span class="html-italic">p</span> = 3.11 × 10<sup>−7</sup>). + Astrocyte_probably__Cahoy genes were enriched in the up-regulated genes (<span class="html-italic">p</span> = 0.000178). ++ brown_M15_Astrocyte__CTX genes were enriched in the down-regulated genes (<span class="html-italic">p</span> = 0.00131). <b>#</b> Oligodendrocyte_probable__Cahoy genes were enriched in the down-regulated genes (<span class="html-italic">p</span> = 9.75 × 10<sup>−5</sup>). Note that Oligodendrocyte_definite__Cahoy genes were enriched in the up-regulated but not down-regulated genes for this dataset. BLA: basolateral amygdala, CNA: central nucleus of the amygdala, PFC: prefrontal cortex, NAC: nucleus accumbens, VTA: ventral tegmental area, HPC: hippocampus, Glut: glutamatergic.</p>
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<p>Drug-Pathway Prediction Results. We determined whether genes within KEGG pathways (within the MSigDB v6.2 dataset) were significantly up-regulated or down-regulated by drugs in CMap and L1000 databases using Gene Set Enrichment Analysis (GSEA). We downloaded the drug gene expression signatures for CMap from <a href="http://ftp://ftp.broadinstitute.org/distribution/cmap/" target="_blank">ftp://ftp.broadinstitute.org/distribution/cmap/</a> (amplitudeMatrix.txt) and the L1000 signatures from Gene Expression Omnibus (Level 5 data; Phase I: GSE92742, Phase II: GSE7013). Histograms of the number of pathways predicted to be targeted by drugs in CMap (<b>A</b>) or L1000 (<b>C</b>) databases. Histograms of the number of drugs in CMap (<b>B</b>) or L1000 (<b>D</b>) databases predicted to target pathways. The blue dashed line represents the median number of pathways in A and C or drugs in B and D.</p>
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16 pages, 1437 KiB  
Article
Typical and Aberrant Functional Brain Flexibility: Lifespan Development and Aberrant Organization in Traumatic Brain Injury and Dyslexia
by Stavros I. Dimitriadis, Panagiotis G. Simos, Jack Μ. Fletcher and Andrew C. Papanicolaou
Brain Sci. 2019, 9(12), 380; https://doi.org/10.3390/brainsci9120380 - 16 Dec 2019
Cited by 7 | Viewed by 3869
Abstract
Intrinsic functional connectivity networks derived from different neuroimaging methods and connectivity estimators have revealed robust developmental trends linked to behavioural and cognitive maturation. The present study employed a dynamic functional connectivity approach to determine dominant intrinsic coupling modes in resting-state neuromagnetic data from [...] Read more.
Intrinsic functional connectivity networks derived from different neuroimaging methods and connectivity estimators have revealed robust developmental trends linked to behavioural and cognitive maturation. The present study employed a dynamic functional connectivity approach to determine dominant intrinsic coupling modes in resting-state neuromagnetic data from 178 healthy participants aged 8–60 years. Results revealed significant developmental trends in three types of dominant intra- and inter-hemispheric neuronal population interactions (amplitude envelope, phase coupling, and phase-amplitude synchronization) involving frontal, temporal, and parieto-occipital regions. Multi-class support vector machines achieved 89% correct classification of participants according to their chronological age using dynamic functional connectivity indices. Moreover, systematic temporal variability in functional connectivity profiles, which was used to empirically derive a composite flexibility index, displayed an inverse U-shaped curve among healthy participants. Lower flexibility values were found among age-matched children with reading disability and adults who had suffered mild traumatic brain injury. The importance of these results for normal and abnormal brain development are discussed in light of the recently proposed role of cross-frequency interactions in the fine-grained coordination of neuronal population activity. Full article
(This article belongs to the Special Issue Human Brain Dynamics: Latest Advances and Prospects)
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<p>Determining dominant intrinsic coupling modes (dICMs) between two sensors (S1, S2) for two consecutive 2-s sliding time windows (t<sub>1</sub>, t<sub>2</sub>) during the resting-state MEG recording. In this example, the functional interdependence between band-passed signals from the two sensors was indexed by imaginary phase locking (iPLV). In this manner, iPLV was computed between the two sensors either for same-frequency oscillations (e.g., δ to δ) or between different frequencies (e.g., δ to θ). Statistical filtering, using surrogate data for reference, was employed to assess whether each iPLV value was significantly different than chance. During t<sub>1</sub>, the dICM reflected significant phase locking between δ and α<sub>2</sub> oscillations (indicated by red rectangles) whereas during t<sub>2</sub>, the dominant interaction was found between δ and θ oscillations. Significant values were subsequently integrated over groups of sensors roughly corresponding to underlying lobar anatomy to obtain indices of the dominant type of interaction between hemispheres for a given lobe or between lobes for a given hemisphere. Finally, from the set of potential intrinsic coupling modes (PICM), we derived the dICM for each pair of sensors across all temporal segments. For further details see [<a href="#B29-brainsci-09-00380" class="html-bibr">29</a>].</p>
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<p>Dominant inter-hemispheric frontal coupling indexed by δ-band amplitude envelope correlation (AEC). (<b>A</b>) Topographical layout of statistically significant sensor pairs for the six age groups. (<b>B</b>) Mean subgraph strength (MSS) and (<b>C</b>) fractional occupancy (FO) derived from envelop correlation across the six age groups. Significant differences between successive age groups are marked by brackets (<span class="html-italic">p</span> &lt; 0.0001).</p>
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<p>Functional brain maturation curves. (<b>A</b>) Individual flexibility index values (FI) for 178 healthy volunteers without history of learning disability or brain injury (aged 6 to 60 years) reflecting the degree of short-term stability of dominant functional connections during the 3 min resting-state recording. Chronological age is shown on the <span class="html-italic">x</span> axis. The best-fitting curve for FI as a function of age is indicated by the blue line. (<b>B</b>) Flexibility index as a function of age for healthy participants (<span class="html-italic">n</span> = 178; HP: red circles), school-aged children displaying severe reading difficulties (<span class="html-italic">n</span> = 25; RD: purple circles), adults who had recently suffered mild traumatic brain injury (<span class="html-italic">n</span> = 30; mTBI: blue circles), and healthy adults who were retested over a week-long period (<span class="html-italic">n</span> = 10; repeat scans: green circles).</p>
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16 pages, 982 KiB  
Article
Experience Matters: The Effects of Hypothetical versus Experiential Delays and Magnitudes on Impulsive Choice in Delay Discounting Tasks
by Catherine C. Steele, MacKenzie Gwinner, Travis Smith, Michael E. Young and Kimberly Kirkpatrick
Brain Sci. 2019, 9(12), 379; https://doi.org/10.3390/brainsci9120379 - 16 Dec 2019
Cited by 8 | Viewed by 3775
Abstract
Impulsive choice in humans is typically measured using hypothetical delays and rewards. In two experiments, we determined how experiencing the delay and/or the reward affected impulsive choice behavior. Participants chose between two amounts of real or hypothetical candy (M&Ms) after a real or [...] Read more.
Impulsive choice in humans is typically measured using hypothetical delays and rewards. In two experiments, we determined how experiencing the delay and/or the reward affected impulsive choice behavior. Participants chose between two amounts of real or hypothetical candy (M&Ms) after a real or hypothetical delay (5–30 s), where choosing the shorter delay was the impulsive choice. Experiment 1 compared choice behavior on a real-delay, real-reward (RD/RR) task where participants received M&Ms after experiencing the delays versus a real-delay, hypothetical-reward (RD/HR) task where participants accumulated hypothetical M&Ms after experiencing the delays. Experiment 2 compared the RD/HR task and a hypothetical-delay, hypothetical-reward (HD/HR) task where participants accumulated hypothetical M&Ms after hypothetical delays. The results indicated that choices did not differ between real and hypothetical M&Ms (Experiment 1), and participants were less sensitive to delay and more larger-later (LL)-preferring with hypothetical delays compared to real delays (Experiment 2). Experiencing delays to reward may be important for modeling real-world impulsive choices where delays are typically experienced. These novel experiential impulsive choice tasks may improve translational methods for comparison with animal models and may be improved procedures for predicting real-life choice behavior in humans. Full article
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<p>Flowchart of the progression through the session in Experiments 1 and 2. Participants were randomly assigned to different orders of task presentation at points where the chart branches off. RD/RR = real-delay, real-reward. RD/HR = real-delay, hypothetical-reward. HD/HR = hypothetical-delay, hypothetical-reward.</p>
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<p>Illustration of the computer setup. Computer monitor screen displayed the two choices, a keyboard was used to make a choice (F keypress for left selection, J keypress for right selection), and a dish collected mini M&amp;Ms delivered via a tube from a dispenser hidden behind the monitor.</p>
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<p>The proportion of LL (self-controlled) choices as a function of the delay ratio (left panel) and magnitude ratio (right panel) on the different tasks. RD/RR = real-delay, real-reward. RD/HR = real-delay, hypothetical-reward. A 0.2 ratio means the LL delay (or magnitude) is 5× longer (or larger) than the SS delay (or magnitude), and a 0.8 ratio means the LL delay (or magnitude) is 1.25× longer (or larger) than the SS delay (or magnitude).</p>
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<p>The proportion of LL (self-controlled) choices as a function of the delay ratio (top panels) and magnitude ratio (bottom panels). The top panels show the delay sensitivities on the real delay and hypothetical delay tasks for participants who completed the real delay task first (left) and the hypothetical delay task first (right). The bottom panels show the comparable conditions for magnitude sensitivities. RD/HR = real-delay, hypothetical-reward. HD/HR = hypothetical-delay, hypothetical-reward.</p>
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13 pages, 2745 KiB  
Article
Reduced Apoptotic Injury by Phenothiazine in Ischemic Stroke through the NOX-Akt/PKC Pathway
by Yanna Tong, Kenneth B. Elkin, Changya Peng, Jiamei Shen, Fengwu Li, Longfei Guan, Yu Ji, Wenjing Wei, Xiaokun Geng and Yuchuan Ding
Brain Sci. 2019, 9(12), 378; https://doi.org/10.3390/brainsci9120378 - 15 Dec 2019
Cited by 11 | Viewed by 4054
Abstract
Phenothiazine treatment has been shown to reduce post-stroke ischemic injury, though the underlying mechanism remains unclear. This study sought to confirm the neuroprotective effects of phenothiazines and to explore the role of the NOX (nicotinamide adenine dinucleotide phosphate oxidase)/Akt/PKC (protein kinase C) pathway [...] Read more.
Phenothiazine treatment has been shown to reduce post-stroke ischemic injury, though the underlying mechanism remains unclear. This study sought to confirm the neuroprotective effects of phenothiazines and to explore the role of the NOX (nicotinamide adenine dinucleotide phosphate oxidase)/Akt/PKC (protein kinase C) pathway in cerebral apoptosis. Sprague-Dawley rats underwent middle cerebral artery occlusion (MCAO) for 2 h and were randomly divided into 3 different cohorts: (1) saline, (2) 8 mg/kg chlorpromazine and promethazine (C+P), and (3) 8 mg/kg C+P as well as apocynin (NOX inhibitor). Brain infarct volumes were examined, and cell death/NOX activity was determined by assays. Western blotting was used to assess protein expression of kinase C-δ (PKC-δ), phosphorylated Akt (p-Akt), Bax, Bcl-XL, and uncleaved/cleaved caspase-3. Both C+P and C+P/NOX inhibitor administration yielded a significant reduction in infarct volumes and cell death, while the C+P/NOX inhibitor did not confer further reduction. In both treatment groups, anti-apoptotic Bcl-XL protein expression generally increased, while pro-apoptotic Bax and caspase-3 proteins generally decreased. PKC protein expression was decreased in both treatment groups, demonstrating a further decrease by C+P/NOX inhibitor at 6 and 24 h of reperfusion. The present study confirms C+P-mediated neuroprotection and suggests that the NOX/Akt/PKC pathway is a potential target for efficacious therapy following ischemic stroke. Full article
(This article belongs to the Collection Collection on Molecular and Cellular Neuroscience)
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<p>Infarct volume reduction by control treatment, chlorpromazine and promethazine (C+P) treatment, and C+P/NADPH oxidase (NOX) inhibitor treatment. 2,3,5-Triphenyltetrazolium chloride (TTC) histology depicts (<b>A</b>) the cortex and striatum at three different levels supplied by the middle cerebral artery (MCA) from anterior +1.00 mm to posterior −4.8 mm to the bregma at 24 h of reperfusion. (<b>B</b>) Percentage of infarct volume reduction (mean ± standard error (SE)) with no treatment (50.0% ± 2.5%), C+P treatment (33.7% ± 6.0%), and C+P/NOX inhibitor treatment (28.5% ± 3.0%) at 24 h of reperfusion. While no significant difference in infarct volume was produced between cohorts, there was a significant reduction in both cohorts when compared to no treatment (<sup>##</sup> <span class="html-italic">p</span> &lt; 0.01). In addition, at 48 h of reperfusion (<b>C</b>,<b>D</b>), infarct volume in ischemic rats (39.1% ± 3.1%) was significantly reduced by C+P treatment (23.1% ± 5.5%) (<sup>#</sup> <span class="html-italic">p</span> &lt; 0.05), while DMSO alone did not induce any neuroprotection (37.1% ± 4.4%). MCA, middle cerebral artery, C+P, chlorpromazine and promethazine.</p>
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<p>Apoptotic cell death photometric enzyme immunoassay in control treatment, C+P treatment, and C+P/NOX inhibitor treatment. ELISA quantified the degree of apoptosis via 405 nm wavelength absorbance. C+P treatment significantly reduced cell death (mean ± SE) at 6 and 24 h, and C+P/NOX inhibitor treatment augmented the reduction in cell death at each time point. Cell death level at 6 h: no treatment 1.5 ± 0.2, C+P 0.8 ± 0.3, C+P/NOX inhibitor 0.5 ± 0.2; cell death level at 24 h: no treatment 2.2 ± 0.4, C+P 1.2 ± 0.3, C+P/NOX inhibitor 0.7 ± 0.2 (<sup>#</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01).</p>
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<p>NOX activity luminescence assay in control treatment, C+P treatment, and C+P/NOX inhibitor treatment cohorts. C+P treatment and C+P/NOX inhibitor treatment both produced decreased NOX activity (mean ± SE) at both 6 and 24 h, though there was no significant difference between treatment cohorts at 6 or 24 h of reperfusion. NOX activity at 6 h: no treatment 1.3 ± 0.1, C+P 1.0 ± 0.1, C+P/NOX inhibitor 0.8 ± 0.1; NOX activity at 24 h: no treatment 1.9 ± 0.1, C+P 0.8 ± 0.1, C+P/NOX inhibitor 0.8 ± 0.1 (<sup>#</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01).</p>
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<p>PKC (protein kinase C) and p-Akt protein expression and western blotting in control treatment, C+P treatment, and C+P/NOX inhibitor treatment cohorts. Brain tissue containing the dorsolateral striatum and the frontoparietal cortex were processed and used to determine protein expression (mean ± SE). (<b>A</b>) PKC protein expression significantly decreased in each treatment cohort at 6 and 24 h. PKC protein expression was further decreased in the C+P/NOX inhibitor treatment group at 6 and 24 h. PKC level at 6 h: no treatment 2.2 ± 0.1, C+P 1.4 ± 0.3, C+P/NOX inhibitor 0.6 ± 0.2; PKC level at 24 h: no treatment 1.8 ± 0.2, C+P 1.5 ± 0.2, C+P/NOX inhibitor 0.8 ± 0.2. (<b>B</b>) p-Akt protein expression was elevated in both treatment cohorts at 6 h and 24 h. p-Akt level at 6 h: no treatment 0.6 ± 0.1, C+P 1.8 ± 0.1, C+P/NOX inhibitor 2.1 ± 0.2; p-Akt level at 24 h: no treatment 0.6 ± 0.1, C+P 2.1 ± 0.2, C+P/NOX inhibitor 2.2 ± 0.1 (<sup>#</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01). Original Western blot images are provided in <a href="#app1-brainsci-09-00378" class="html-app">supplementary materials</a>.</p>
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<p>Cleaved and uncleaved caspase-3, Bax, and Bcl-XL protein expression, and Western blotting in control treatment, C+P treatment, and C+P/NOX inhibitor treatment. Brain tissue containing the dorsolateral striatum and frontoparietal cortex were processed and used to detect protein levels (mean ± SE). (<b>A</b>) Cleaved caspase-3 protein was significantly reduced by both C+P and C+P/NOX inhibitor treatment cohorts at 6 and 24 h. Cleaved caspase-3 level at 6 h: no treatment 1.3 ± 0.1, C+P 0.7 ± 0.1, C+P/NOX inhibitor 0.6 ± 0.1; cleaved caspase-3 level at 24 h: no treatment 1.3 ± 0.1, C+P 0.7 ± 0.0, C+P/NOX inhibitor 0.6 ± 0.1. (<b>B</b>) Uncleaved caspase-3 protein was significantly reduced only in the C+P/NOX inhibitor cohort at 6 h. At 24 h, both C+P and C+P/NOX inhibitor cohorts resulted in decreased caspase-3; caspase-3 protein expression in the C+P/NOX inhibitor cohort was further decreased in comparison to the C+P cohort. Caspase-3 level at 6 h: no treatment 1.3 ± 0.1, C+P 1.2 ± 0.1, C+P/NOX inhibitor 0.9 ± 0.0; PKC level at 24 h: no treatment 1.5 ± 0.2, C+P 1.2 ± 0.1, C+P/NOX inhibitor 0.7 ± 0.1. (<b>C</b>) At 6 h, Bax protein expression was reduced in both C+P and C+P/NOX inhibitor treatment cohorts. At 24 h, both C+P and C+P/NOX inhibitor treatment cohorts exhibited a significant decrease in Bax protein expression. Bax level at 6 h: no treatment 1.2 ± 0.1, C+P 1.0 ± 0.1, C+P/NOX inhibitor 0.9 ± 0.0; Bax level at 24 h: no treatment 1.4 ± 0.1, C+P 1.1 ± 0.1, C+P/NOX inhibitor 1.2 ± 0.0. (<b>D</b>) At 6 h, both C+P and C+P/NOX inhibitor treatment cohorts resulted in a significant increase in Bcl-XL protein expression. At 24 h, both C+P and C+P/NOX inhibitor treatment groups also produced a significant increase. Bcl-XL level at 6 h: no treatment 0.6 ± 0.1, C+P 1.1 ± 0.2, C+P/NOX inhibitor 1.3 ± 0.1; Bcl-XL level at 24 h: no treatment 0.9 ± 0.0, C+P 1.2 ± 0.2, C+P/NOX inhibitor 1.3 ± 0.1 (<sup>#</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01).</p>
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<p>Pathway of neuroprotection from phenothiazines. C+P suppresses expression of Caspase-3 and Bax, while enhancing Bcl-XL expression via inhibition of the NOX-Akt/PKC pathway. This regulation results in decreased apoptotic cell death, leading to a subsequent reduction in brain infarction and a mitigation of neurological deficits.</p>
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9 pages, 1256 KiB  
Article
Characteristics of the Uncinate Fasciculus and Cingulum in Patients with Mild Cognitive Impairment: Diffusion Tensor Tractography Study
by Chan-Hyuk Park, Su-Hong Kim and Han-Young Jung
Brain Sci. 2019, 9(12), 377; https://doi.org/10.3390/brainsci9120377 - 14 Dec 2019
Cited by 11 | Viewed by 3995
Abstract
Many studies have examined the relationship between cognition, and the cingulum and uncinate fasciculus (UF). In this study, diffusion tensor tractography (DTT) was used to investigate the correlation between fractional-anisotropy (FA) values and the number of fibers in the cingulum and UF in [...] Read more.
Many studies have examined the relationship between cognition, and the cingulum and uncinate fasciculus (UF). In this study, diffusion tensor tractography (DTT) was used to investigate the correlation between fractional-anisotropy (FA) values and the number of fibers in the cingulum and UF in patients with and without cognitive impairment. The correlation between cognitive function, and the cingulum and UF was also investigated. Thirty patients (14 males, age = 70.68 ± 7.99 years) were divided into a control group (n = 14) and mild-cognitive-impairment (MCI) group (n = 16). The Seoul Neuropsychological Screening Battery (SNSB) and DTT were performed to assess cognition and bilateral tracts of the cingulum and UF. The relationship between SNSB values and the cingulum and UF was analyzed. The number of fibers in the right cingulum and right UF were significantly different between the two groups. The MCI group showed thinner tracts in both the cingulum and UF compared to the control group. A significant relationship was found between the number of fibers in the right UF and delayed memory recall. In conclusion, memory loss in MCI was associated with a decreased number of fibers in the right UF, while language and visuospatial function were related to the number of fibers in the right cingulum. Full article
(This article belongs to the Special Issue Dementia and Cognitive Ageing)
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<p>Diffusion tensor tractography (DTT) of right and left cingulum. (<b>a</b>) Left (red) and (<b>b</b>) right (yellow) frontal pathways in control group showed connection up to the basal forebrain. Activity in (<b>d</b>) left (red) and (<b>e</b>) right (yellow) frontal pathways decreased in MCI. Both posterior pathways in MCI ((<b>d</b>) left (red) and (<b>e</b>) right (yellow)) were thinner and shorter than those in control group ((<b>a</b>) left (red) and (<b>b</b>) right (yellow)). Changes in the structure of forebrain, anterior cingulate cortex, and posterior pathway in MCI shown. Fiber connectivity between bilateral cingula shown in (<b>c</b>) and (<b>f</b>). Note: white arrow-, anterior cingulate cortex; yellow arrow, posterior cingulate cortex; curved arrow, connectivity between right and left cingulum; and white arrow head, thinner region.</p>
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<p>Diffusion tensor tractography (DTT) of right and left UFs. (<b>a</b>) Left UF in control group. (<b>b</b>) Right UF in control group. (<b>c</b>) Bilateral UFs in control group. (<b>d</b>) Left UF in MCI group. (<b>e</b>) Right UF in MCI group. (<b>f</b>) Bilateral UFs in MCI group. Tracts in MCI group were bilaterally thinner than those in control group (white arrow head, thinner region). Note: UF, uncinate fasciculus; and MCI, mild cognitive impairment.</p>
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15 pages, 7550 KiB  
Article
A Hybrid Feature Pool-Based Emotional Stress State Detection Algorithm Using EEG Signals
by Md Junayed Hasan and Jong-Myon Kim
Brain Sci. 2019, 9(12), 376; https://doi.org/10.3390/brainsci9120376 - 13 Dec 2019
Cited by 54 | Viewed by 5473
Abstract
Human stress analysis using electroencephalogram (EEG) signals requires a detailed and domain-specific information pool to develop an effective machine learning model. In this study, a multi-domain hybrid feature pool is designed to identify most of the important information from the signal. The hybrid [...] Read more.
Human stress analysis using electroencephalogram (EEG) signals requires a detailed and domain-specific information pool to develop an effective machine learning model. In this study, a multi-domain hybrid feature pool is designed to identify most of the important information from the signal. The hybrid feature pool contains features from two types of analysis: (a) statistical parametric analysis from the time domain, and (b) wavelet-based bandwidth specific feature analysis from the time-frequency domain. Then, a wrapper-based feature selector, Boruta, is applied for ranking all the relevant features from that feature pool instead of considering only the non-redundant features. Finally, the k-nearest neighbor (k-NN) algorithm is used for final classification. The proposed model yields an overall accuracy of 73.38% for the total considered dataset. To validate the performance of the proposed model and highlight the necessity of designing a hybrid feature pool, the model was compared to non-linear dimensionality reduction techniques, as well as those without feature ranking. Full article
(This article belongs to the Special Issue Brain Plasticity, Cognitive Training and Mental States Assessment)
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<p>Block diagram of the proposed method [<a href="#B15-brainsci-09-00376" class="html-bibr">15</a>].</p>
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<p>Decomposition tree of level 5 discrete wavelet transform (DWPT).</p>
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<p>Descriptive illustration of k-NN algorithm.</p>
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<p>K-fold cross-validation process in k-NN for selecting the <span class="html-italic">k-</span>value.</p>
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<p>Boruta feature space for Dataset 2.</p>
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<p>(<b>a</b>) Classification performance accuracy comparisons by boxplot, (<b>b</b>) performance chart based on accuracy comparisons.</p>
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13 pages, 934 KiB  
Article
Neuroendocrine and Inflammatory Effects of Childhood Trauma Following Psychosocial and Inflammatory Stress in Women with Remitted Major Depressive Disorder
by Laura L.M. Cassiers, Peter Niemegeers, Erik Fransen, Manuel Morrens, Peter De Boer, Luc Van Nueten, Stephan Claes, Bernard G.C. Sabbe and Filip Van Den Eede
Brain Sci. 2019, 9(12), 375; https://doi.org/10.3390/brainsci9120375 - 13 Dec 2019
Cited by 9 | Viewed by 3833
Abstract
The dysregulation of the inflammatory and neuroendocrine systems seen in major depressive disorder (MDD) may persist after remission and this is associated with a higher risk of relapse. This vulnerable subgroup may be characterized by a history of childhood trauma. In a single-blind [...] Read more.
The dysregulation of the inflammatory and neuroendocrine systems seen in major depressive disorder (MDD) may persist after remission and this is associated with a higher risk of relapse. This vulnerable subgroup may be characterized by a history of childhood trauma. In a single-blind randomized placebo-controlled crossover study, 21 women with remitted recurrent MDD and 18 healthy controls were exposed to psychosocial stress (Trier social stress test) or inflammatory stress (typhoid vaccine), or both, to investigate the effects of childhood trauma on the neuroendocrine and inflammatory responses. Childhood trauma was assessed using the Childhood Trauma Questionnaire and participants were dichotomized into a traumatized and non-traumatized group. Serum adrenocorticotropic hormone (ACTH), cortisol, interferon (IFN)-γ, tumor necrosis factor (TNF)-α, and interleukin (IL)-6 were measured at regular intervals after each intervention. The effects of trauma, time, and intervention on these parameters were modeled by fitting linear mixed models. Childhood trauma in itself did not have a main effect on the outcome measurements. However, an interactional effect of trauma with stressor type was found in the remitted MDD group: trauma was associated with higher cortisol levels only after adding immunological to psychosocial stress, and with lower TNF-α levels in response to vaccination. This suggests the existence of a vulnerable trauma-associated MDD endophenotype. Full article
(This article belongs to the Special Issue Neuroimmunology of Major Psychiatric Disorders)
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<p>The six treatment conditions of the study’s crossover design. TSST: Trier social stress test.</p>
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<p>Least square means of the log-transformed cortisol levels after each intervention in the remitted MDD group. * <span class="html-italic">p</span> &lt;0.05. TSST: Trier social stress test.</p>
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<p>Least square means of the log-transformed TNF-α levels after each intervention in the remitted MDD group. * <span class="html-italic">p</span> &lt;0.05. TSST: Trier social stress test.</p>
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10 pages, 219 KiB  
Article
Spatial Neglect in Stroke: Identification, Disease Process and Association with Outcome During Inpatient Rehabilitation
by Ulrike Hammerbeck, Matthew Gittins, Andy Vail, Lizz Paley, Sarah F Tyson and Audrey Bowen
Brain Sci. 2019, 9(12), 374; https://doi.org/10.3390/brainsci9120374 - 13 Dec 2019
Cited by 39 | Viewed by 7756
Abstract
We established spatial neglect prevalence, disease profile and amount of therapy that inpatient stroke survivors received, and outcomes at discharge using Sentinel Stroke National Audit Programme (SSNAP) data. We used data from 88,664 National Health Service (NHS) admissions in England, Wales and Northern [...] Read more.
We established spatial neglect prevalence, disease profile and amount of therapy that inpatient stroke survivors received, and outcomes at discharge using Sentinel Stroke National Audit Programme (SSNAP) data. We used data from 88,664 National Health Service (NHS) admissions in England, Wales and Northern Ireland (July 2013–July 2015), for stroke survivors still in hospital after 3 days with a completed baseline neglect National Institute for Health Stroke Scale (NIHSS) score. Thirty percent had neglect (NIHSS item 11 ≥ 1) and they were slightly older (78 years) than those without neglect (75 years). Neglect was observed more commonly in women (33 vs. 27%) and in individuals with a premorbid dependency (37 vs. 28%). Survivors of mild stroke were far less likely to present with neglect than those with severe stroke (4% vs. 84%). Those with neglect had a greatly increased length of stay (27 vs. 10 days). They received a comparable amount of average daily occupational and physiotherapy during their longer inpatient stay but on discharge a greater percentage of individuals with neglect were dependent on the modified Rankin scale (76 vs. 57%). Spatial neglect is common and associated with worse clinical outcomes. These results add to our understanding of neglect to inform clinical guidelines, service provision and priorities for future research. Full article
(This article belongs to the Special Issue Unilateral Neglect Assessment and Rehabilitation)
14 pages, 2406 KiB  
Article
Cortical Thickness Links Impulsive Personality Traits and Risky Behavior
by Rickie Miglin, Nadia Bounoua, Shelly Goodling, Ana Sheehan, Jeffrey M. Spielberg and Naomi Sadeh
Brain Sci. 2019, 9(12), 373; https://doi.org/10.3390/brainsci9120373 - 13 Dec 2019
Cited by 23 | Viewed by 4792
Abstract
Impulsive personality traits are often predictive of risky behavior, but not much is known about the neurobiological basis of this relationship. We investigated whether thickness of the cortical mantle varied as a function of impulsive traits and whether such variation also explained recent [...] Read more.
Impulsive personality traits are often predictive of risky behavior, but not much is known about the neurobiological basis of this relationship. We investigated whether thickness of the cortical mantle varied as a function of impulsive traits and whether such variation also explained recent risky behavior. A community sample of 107 adults (ages 18–55; 54.2% men) completed self-report measures of impulsive traits and risky behavior followed by a neuroimaging protocol. Using the three-factor model of impulsive traits derived from the UPPS-P Impulsive Behavior Scale, analysis of the entire cortical mantle identified three thickness clusters that related to impulsive traits. Sensation seeking was negatively related to thickness in the right pericalcarine cortex, whereas impulsive urgency was positively associated with thickness in the left superior parietal and right paracentral lobule. Notably, follow-up analyses showed that thickness in the right pericalcarine cortex also related to recent risky behavior, with the identified cluster mediating the association between sensation seeking and risky behavior. Findings suggest that reduced thickness in the pericalcarine region partially explains the link between sensation seeking and the tendency to engage in risky behavior, providing new insight into the neurobiological basis of these relationships. Full article
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<p>Sensation seeking relates to lower cortical thickness in a cluster of the right hemisphere, adjusting for age, sex, and BMI. Cluster spanned pericalcarine cortex, cuneus, occipital pole, superior occipital gyrus, middle occipital gyrus.</p>
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<p>Impulsive urgency predicts greater cortical thickness in two clusters, adjusting for age, sex, and BMI. (<b>A</b>) Left superior parietal lobule, precuneus, superior occipital gyrus, and cuneus. (<b>B</b>) Right paracentral lobule, precuneus, and posterior cingulate gyrus.</p>
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14 pages, 632 KiB  
Article
A Comprehensive sLORETA Study on the Contribution of Cortical Somatomotor Regions to Motor Imagery
by Mustafa Yazici, Mustafa Ulutas and Mukadder Okuyan
Brain Sci. 2019, 9(12), 372; https://doi.org/10.3390/brainsci9120372 - 13 Dec 2019
Cited by 6 | Viewed by 3909
Abstract
Brain–computer interface (BCI) is a technology used to convert brain signals to control external devices. Researchers have designed and built many interfaces and applications in the last couple of decades. BCI is used for prevention, detection, diagnosis, rehabilitation, and restoration in healthcare. EEG [...] Read more.
Brain–computer interface (BCI) is a technology used to convert brain signals to control external devices. Researchers have designed and built many interfaces and applications in the last couple of decades. BCI is used for prevention, detection, diagnosis, rehabilitation, and restoration in healthcare. EEG signals are analyzed in this paper to help paralyzed people in rehabilitation. The electroencephalogram (EEG) signals recorded from five healthy subjects are used in this study. The sensor level EEG signals are converted to source signals using the inverse problem solution. Then, the cortical sources are calculated using sLORETA methods at nine regions marked by a neurophysiologist. The features are extracted from cortical sources by using the common spatial pattern (CSP) method and classified by a support vector machine (SVM). Both the sensor and the computed cortical signals corresponding to motor imagery of the hand and foot are used to train the SVM algorithm. Then, the signals outside the training set are used to test the classification performance of the classifier. The 0.1–30 Hz and mu rhythm band-pass filtered activity is also analyzed for the EEG signals. The classification performance and recognition of the imagery improved up to 100% under some conditions for the cortical level. The cortical source signals at the regions contributing to motor commands are investigated and used to improve the classification of motor imagery. Full article
(This article belongs to the Collection Collection on Theoretical and Computational Neuroscience)
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<p>A brain–computer interface (BCI) system.</p>
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<p>Flow diagram of this work.</p>
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<p>Selected brain regions: (<b>a</b>) top view, (<b>b</b>) midline surface.</p>
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<p>The 0.1–30 Hz ROI occurrence chart: primary foot somatosensory area (S1F), primary hand somatosensory area (S1H), cingulate motor area (CMA), primary foot motor area (M1F), primary hand motor area (M1H), supplementary motor area (SMA), presupplementary motor area (pSMA), dorsal premotor cortex (PMd), ventral premotor cortex (PMv), _L indicates the left lobe, _R indicates the right lobe.</p>
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<p><math display="inline"><semantics> <mi>μ</mi> </semantics></math> band ROI occurrence chart: Primary foot somatosensory area (S1F), primary hand somatosensory area (S1H), cingulate motor area (CMA), primary foot motor area (M1F), primary hand motor area (M1H), supplementary motor area (SMA), presupplementary motor area (pSMA), dorsal premotor cortex (PMd), ventral premotor cortex (PMv). _L indicates left lobe, _R indicates right lobe.</p>
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18 pages, 1077 KiB  
Review
Co-Morbid Insomnia and Sleep Apnea (COMISA): Prevalence, Consequences, Methodological Considerations, and Recent Randomized Controlled Trials
by Alexander Sweetman, Leon Lack and Célyne Bastien
Brain Sci. 2019, 9(12), 371; https://doi.org/10.3390/brainsci9120371 - 12 Dec 2019
Cited by 138 | Viewed by 10606
Abstract
Co-morbid insomnia and sleep apnea (COMISA) is a highly prevalent and debilitating disorder, which results in additive impairments to patients’ sleep, daytime functioning, and quality of life, and complex diagnostic and treatment decisions for clinicians. Although the presence of COMISA was first recognized [...] Read more.
Co-morbid insomnia and sleep apnea (COMISA) is a highly prevalent and debilitating disorder, which results in additive impairments to patients’ sleep, daytime functioning, and quality of life, and complex diagnostic and treatment decisions for clinicians. Although the presence of COMISA was first recognized by Christian Guilleminault and colleagues in 1973, it received very little research attention for almost three decades, until the publication of two articles in 1999 and 2001 which collectively reported a 30%–50% co-morbid prevalence rate, and re-ignited research interest in the field. Since 1999, there has been an exponential increase in research documenting the high prevalence, common characteristics, treatment complexities, and bi-directional relationships of COMISA. Recent trials indicate that co-morbid insomnia symptoms may be treated with cognitive and behavioral therapy for insomnia, to increase acceptance and use of continuous positive airway pressure therapy. Hence, the treatment of COMISA appears to require nuanced diagnostic considerations, and multi-faceted treatment approaches provided by multi-disciplinary teams of psychologists and physicians. In this narrative review, we present a brief overview of the history of COMISA research, describe the importance of measuring and managing insomnia symptoms in the presence of sleep apnea, discuss important methodological and diagnostic considerations for COMISA, and review several recent randomized controlled trials investigating the combination of CBTi and CPAP therapy. We aim to provide clinicians with pragmatic suggestions and tools to identify, and manage this prevalent COMISA disorder in clinical settings, and discuss future avenues of research to progress the field. Full article
(This article belongs to the Special Issue Insomnia: Beyond Hyperarousal)
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<p>History of research in co-morbid insomnia and sleep apnea, including Guilleminault and colleague’s 1973 article, and a lack of widespread research attention until two articles by Lichstein and colleagues (1999) and Krakow and colleagues (2001).</p>
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16 pages, 1224 KiB  
Article
Helicobacter pylori, Vascular Risk Factors and Cognition in U.S. Older Adults
by Víctor M. Cárdenas, François Boller and Gustavo C. Román
Brain Sci. 2019, 9(12), 370; https://doi.org/10.3390/brainsci9120370 - 12 Dec 2019
Cited by 19 | Viewed by 4738
Abstract
Previous studies suggested that Helicobacter pylori infection could be a risk factor for stroke, dementia, and Alzheimer’s disease (AD). The authors examined data from participants, 60 years old and older in the Third National Health and Nutrition Examination Survey (NHANES-III) to assess the [...] Read more.
Previous studies suggested that Helicobacter pylori infection could be a risk factor for stroke, dementia, and Alzheimer’s disease (AD). The authors examined data from participants, 60 years old and older in the Third National Health and Nutrition Examination Survey (NHANES-III) to assess the relation between Helicobacter pylori infection and results of the Mini-Mental State Examination (n = 1860) using logistic regression analysis controlling for age, gender, race/ethnicity, education, poverty and history of medically diagnosed diabetes. Moreover, we examined performance on the digit-symbol substitution test (DSST) of 1031 participants in the 1999–2000 NHANES according to their H. pylori infection status controlling for potential confounders using multiple linear regression analyses. In 1988–1991, older adults infected with CagA strains of H. pylori had a 50% borderline statistically significant increased level of cognitive impairment, as measured by low Mini-Mental State Examination (MMSE) scores (age–education adjusted prevalence ratio: 1.5; 95% confidence interval: 1.0, 2.0). In 1999–2000, older US adults infected with H. pylori scored 2.6 fewer points in the DSST than those uninfected (mean adjusted difference: −2.6; 95% confidence interval −5.1, −0.1). The authors concluded that H. pylori infection might be a risk factor for cognitive decline in the elderly. They also found that low cobalamin and elevated homocysteine were associated with cognitive impairment. Full article
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<p>Flow chart of study populations.</p>
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<p>(<b>a</b>) Prevalence ratios of cognitive impairment by <span class="html-italic">H. pylori</span> infection among US older adults, National Health and Nutrition Examination Survey (NHANES) 1988–1991. (<b>b</b>) digit-symbol substitution test (DSST) scores among US older adults by <span class="html-italic">H. pylori</span> infection, NHANES 1999–2000.</p>
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11 pages, 1971 KiB  
Article
Protective Effects of Scolopendra Water Extract on Trimethyltin-Induced Hippocampal Neurodegeneration and Seizures in Mice
by Yun-Soo Seo, Mary Jasmin Ang, Byeong Cheol Moon, Hyo Seon Kim, Goya Choi, Hye-Sun Lim, Sohi Kang, Mijin Jeon, Sung-Ho Kim, Changjong Moon and Joong Sun Kim
Brain Sci. 2019, 9(12), 369; https://doi.org/10.3390/brainsci9120369 - 12 Dec 2019
Cited by 5 | Viewed by 3217
Abstract
Trimethyltin (TMT) is an organotin compound with potent neurotoxic action characterized by neuronal degeneration in the hippocampus. This study evaluated the protective effects of a Scolopendra water extract (SWE) against TMT intoxication in hippocampal neurons, using both in vitro and in vivo model [...] Read more.
Trimethyltin (TMT) is an organotin compound with potent neurotoxic action characterized by neuronal degeneration in the hippocampus. This study evaluated the protective effects of a Scolopendra water extract (SWE) against TMT intoxication in hippocampal neurons, using both in vitro and in vivo model systems. Specifically, we examined the actions of SWE on TMT- (5 mM) induced cytotoxicity in primary cultures of mouse hippocampal neurons (7 days in vitro) and the effects of SWE on hippocampal degeneration in adult TMT- (2.6 mg/kg, intraperitoneal) treated C57BL/6 mice. We found that SWE pretreatment (0–100 μg/mL) significantly reduced TMT-induced cytotoxicity in cultured hippocampal neurons in a dose-dependent manner, as determined by lactate dehydrogenase and 3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyltetrazolium bromide assays. Additionally, this study showed that perioral administration of SWE (5 mg/kg), from −6 to 0 days before TMT injection, significantly attenuated hippocampal cell degeneration and seizures in adult mice. Furthermore, quantitative analysis of Iba-1 (Allograft inflammatory factor 1)- and GFAP (Glial fibrillary acidic protein)-immunostained cells revealed a significant reduction in the levels of Iba-1- and GFAP-positive cell bodies in the dentate gyrus (DG) of mice treated with SWE prior to TMT injection. These data indicated that SWE pretreatment significantly protected the hippocampus against the massive activation of microglia and astrocytes elicited by TMT. In addition, our data showed that the SWE-induced reduction of immune cell activation was linked to a significant reduction in cell death and a significant improvement in TMT-induced seizure behavior. Thus, we conclude that SWE ameliorated the detrimental effects of TMT toxicity on hippocampal neurons, both in vivo and in vitro. Altogether, our findings hint at a promising pharmacotherapeutic use of SWE in hippocampal degeneration and dysfunction. Full article
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<p>Protective effects of a <span class="html-italic">Scolopendra</span> water extract (SWE) on trimethyltin (TMT)-induced cytotoxicity. SWE treatment reduced the cytotoxic effects of TMT on hippocampal neurons. lactate dehydrogenase (LDH) assay (<b>a</b>) and 3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyltetrazolium bromide (MTT) assay (<b>b</b>). Values are reported as mean ± SE, * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Protective effects of SWE on seizure symptoms in TMT-treated mice. (<b>a</b>) Schematic diagram of drug treatment, tissue preparation, and behavioral test. (<b>b</b>) SWE treatment ameliorated TMT-induced seizure behaviors (<span class="html-italic">n</span> = 6 mice per group). Values are reported as mean ± SE, * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Protective effects of SWE on microglial cell activation in the hippocampus 4 days after TMT treatment. Representative images (X 200) showing microglial Iba-1 immunostaining in untreated control, TMT control, and SWE + TMT group (<b>a</b>). The graph (<b>b</b>) depicts the relative intensity of Iba-1 positive cells in the dentate gyrus (DG) of the hippocampus sections. Values are reported as mean ± SE, <span class="html-italic">n</span> = 3 in each group. * <span class="html-italic">p</span> &lt; 0.05. Scale bar = 250um.</p>
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<p>Protective effects of SWE on astrocyte levels in the DG, 4 days after TMT treatment. Representative images (X 200) showing GFAP immunostaining in untreated control, TMT control, and SWE + TMT group (<b>a</b>) The graph (<b>b</b>) depicts the relative number of GFAP-positive cells per dentate gyrus in the hippocampus sections. Values are reported as mean ± SE, <span class="html-italic">n</span> = 3 in each group. * <span class="html-italic">p</span> &lt; 0.05. Scale bar = 250um.</p>
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<p>Protective effects of SWE on the incidence of Fluoro-Jade (FJC)-1-positive dead cells in the DG of the hippocampus, 4 days after TMT treatment. Representative images (X 200) showing degenerating cells stained with FJC stain in untreated control, TMT control, and SWE + TMT group (<b>a</b>). All tissues were collected at 4 days after TMT treatment. The graph (<b>b</b>) depicts the intensity of FJC-1-positive cells per dentate gyrus (<b>b</b>) and entire hippocampus (<b>c</b>) in the brain sections. Values are reported as the mean ± SE, n = 3 in each group. * <span class="html-italic">p</span> &lt; 0.05. Scale bar = 500um.</p>
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11 pages, 240 KiB  
Perspective
Noli Me Tangere: Social Touch, Tactile Defensiveness, and Communication in Neurodevelopmental Disorders
by Daniela Smirni, Pietro Smirni, Marco Carotenuto, Lucia Parisi, Giuseppe Quatrosi and Michele Roccella
Brain Sci. 2019, 9(12), 368; https://doi.org/10.3390/brainsci9120368 - 12 Dec 2019
Cited by 25 | Viewed by 8328
Abstract
Tactile defensiveness is a common feature in neurodevelopmental disorders (NDDs). Since the first studies, tactile defensiveness has been described as the result of an abnormal response to sensory stimulation. Moreover, it has been studied how the tactile system is closely linked to socio-communicative [...] Read more.
Tactile defensiveness is a common feature in neurodevelopmental disorders (NDDs). Since the first studies, tactile defensiveness has been described as the result of an abnormal response to sensory stimulation. Moreover, it has been studied how the tactile system is closely linked to socio-communicative development and how the interoceptive sensory system supports both a discriminating touch and an affective touch. Therefore, several neurophysiological studies have been conducted to investigate the neurobiological basis of the development and functioning of the tactile system for a better understanding of the tactile defensiveness behavior and the social touch of NDDs. Given the lack of recent literature on tactile defensiveness, the current study provides a brief overview of the original contributions on this research topic in children with NDDs focusing attention on how this behavior has been considered over the years in the clinical setting. Full article
(This article belongs to the Special Issue Recent Advances in Neurodevelopmental Disorders)
13 pages, 1238 KiB  
Article
Individual Differences in Ethanol Drinking and Seeking Behaviors in Rats Exposed to Chronic Intermittent Ethanol Vapor Exposure is Associated with Altered CaMKII Autophosphorylation in the Nucleus Accumbens Shell
by Sucharita S. Somkuwar and Chitra D. Mandyam
Brain Sci. 2019, 9(12), 367; https://doi.org/10.3390/brainsci9120367 - 11 Dec 2019
Cited by 3 | Viewed by 3500
Abstract
Chronic intermittent ethanol vapor exposure (CIE) in rodents produces reliable and high blood ethanol concentration and behavioral symptoms associated with moderate to severe alcohol use disorder (AUD)—for example, escalation of operant ethanol self-administration, a feature suggestive of transition from recreational to addictive use, [...] Read more.
Chronic intermittent ethanol vapor exposure (CIE) in rodents produces reliable and high blood ethanol concentration and behavioral symptoms associated with moderate to severe alcohol use disorder (AUD)—for example, escalation of operant ethanol self-administration, a feature suggestive of transition from recreational to addictive use, is a widely replicated behavior in rats that experience CIE. Herein, we present evidence from a subset of rats that do not demonstrate escalation of ethanol self-administration following seven weeks of CIE. These low responders (LR) maintain low ethanol self-administration during CIE, demonstrate lower relapse to drinking during abstinence and reduced reinstatement of ethanol seeking triggered by ethanol cues when compared with high responders (HR). We examined the blood ethanol levels in LR and HR rats during CIE and show higher levels in LR compared with HR. We also examined peak corticosterone levels during CIE and show that LR rats have higher levels compared with HR rats. Lastly, we evaluated the levels of Ca2+/calmodulin-dependent protein kinase II (CaMKII) in the nucleus accumbens shell and reveal that the activity of CaMKII, which is autophosphorylated at site Tyr-286, is significantly reduced in HR rats compared with LR rats. These findings demonstrate that dysregulation of the hypothalamic–pituitary–adrenal axis activity and plasticity-related proteins regulating molecular memory in the nucleus accumbens shell are associated with higher ethanol-drinking and -seeking in HR rats. Future mechanistic studies should evaluate CaMKII autophosphorylation-dependent remodeling of glutamatergic synapses in the ventral striatum as a plausible mechanism for the CIE-induced enhanced ethanol drinking and seeking behaviors. Full article
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Figure 1
<p>Individual differences in operant responses in ethanol drinking and seeking behaviors in low responder (LR) and high responder (HR) rats. (<b>a</b>) Schematic of the experimental design indicting the order of behavioral studies and time frame in each sub paradigm. w, weeks; d, days. (<b>b</b>) Active and inactive lever responses in LR and HR rats during pre-vapor sessions and during chronic intermittent ethanol vapor exposure (CIE) sessions. (<b>c</b>) Data are extrapolated from panel (<b>b</b>) and represented as percent change in active lever responses from pre-vapor session. (<b>d</b>) Active and inactive responses in LR and HR rats from drinking session conducted during abstinence. (<b>e</b>) Active and inactive lever responses in LR and HR rats from extinction and contexual cued reinstatement session. Data are expressed as mean ± SEM. <sup><span>$</span></sup> <span class="html-italic">p</span> &lt; 0.05 group × weeks interaction, <sup>^</sup> <span class="html-italic">p</span> &lt; 0.05 main effect of weeks of CIE, <sup>%</sup> <span class="html-italic">p</span> &lt; 0.05 main effect of group. <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05 vs. LR, * <span class="html-italic">p</span> &lt; 0.05 vs. inactive lever responses within groups by post hoc analysis. <span class="html-italic">n</span> = 23 LR, <span class="html-italic">n</span> = 21 HR in figures b-d; <span class="html-italic">n</span> = 23 LR, <span class="html-italic">n</span> = 8 HR in figures d-e.</p>
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<p>LR and HR rats differ in plasma BELs and peak corticosterone levels during CIE. (<b>a</b>) Plasma BELs expressed as mg/dL during weeks of CIE. (<b>b</b>) Plasma peak corticosterone levels expressed as ng/mL during weeks of CIE. Data are expressed as mean ± SEM. <span class="html-italic"><sup><span>$</span></sup> p</span> &lt; 0.05 group x weeks interaction, <sup>^</sup> <span class="html-italic">p</span> &lt; 0.05 main effect of weeks of CIE, <sup>%</sup> <span class="html-italic">p</span> &lt; 0.05 main effect of group. <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05 vs. HR by post hoc analysis. <span class="html-italic">n</span> = 12 LR, <span class="html-italic">n</span> = 20 HR for plasma BELs. <span class="html-italic">n</span> = 6 LR, <span class="html-italic">n</span> = 9 HR for peak corticosterone levels.</p>
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<p>LR and HR rats differ in the density of pCaMKII in the nucleus accumbens shell region. (<b>a</b>) Schematic of a coronal section through the rat brain, showing the location of tissue punch (colored area) taken in the nucleus accumbens shell. (<b>b</b>) Representative immunoblots of proteins from one control (con), LR and HR rat. Coomassie staining is indicated as a loading control for each sample. (<b>c</b>) Quantitative analysis of proteins from control, LR and HR rats. <span class="html-italic">n</span> = seven controls, <span class="html-italic">n</span> = 6 LR and <span class="html-italic">n</span> = 8 HR. Data are expressed as mean ± SEM. * <span class="html-italic">p</span> &lt; 0.05 vs. LR by One-Way ANOVA followed by post hoc analysis.</p>
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14 pages, 572 KiB  
Article
Inflammatory Biomarkers are Correlated with Some Forms of Regressive Autism Spectrum Disorder
by Margherita Prosperi, Letizia Guiducci, Diego G. Peroni, Chiara Narducci, Melania Gaggini, Sara Calderoni, Raffaella Tancredi, Maria Aurora Morales, Amalia Gastaldelli, Filippo Muratori and Elisa Santocchi
Brain Sci. 2019, 9(12), 366; https://doi.org/10.3390/brainsci9120366 - 11 Dec 2019
Cited by 22 | Viewed by 5070
Abstract
Background: Several studies have tried to investigate the role of inflammatory biomarkers in Autism Spectrum Disorder (ASD), and their correlations with clinical phenotypes. Despite the growing research in this topic, existing data are mostly contradictory. Methods: Eighty-five ASD preschoolers were assessed [...] Read more.
Background: Several studies have tried to investigate the role of inflammatory biomarkers in Autism Spectrum Disorder (ASD), and their correlations with clinical phenotypes. Despite the growing research in this topic, existing data are mostly contradictory. Methods: Eighty-five ASD preschoolers were assessed for developmental level, adaptive functioning, gastrointestinal (GI), socio-communicative and psychopathological symptoms. Plasma levels of leptin, resistin, plasminogen activator inhibitor-1 (PAI-1), macrophage chemoattractant protein-1 (CCL2), tumor necrosis factor-alfa (TNF-α), and interleukin-6 (IL-6) were correlated with clinical scores and were compared among different ASD subgroups according to the presence or absence of: (i) GI symptoms, (ii) regressive onset of autism. Results: Proinflammatory cytokines (TNF-α, IL-6 and CCL2) were lower than those reported in previous studies in children with systemic inflammatory conditions. GI symptoms were not correlated with levels of inflammatory biomarkers except for resistin that was lower in ASD-GI children (p = 0.032). Resistin and PAI-1 levels were significantly higher in the group with “regression plus a developmental delay” onset (Reg+DD group) compared to groups without regression or with regression without a developmental delay (p < 0.01 for all). Conclusions: Our results did not highlight the presence of any systemic inflammatory state in ASD subjects neither disentangling children with/without GI symptoms. The Reg + DD group significantly differed from others in some plasmatic values, but these differences failed to discriminate the subgroups as possible distinct ASD endo-phenotypes. Full article
(This article belongs to the Special Issue Advances in Autism Research)
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Graphical abstract

Graphical abstract
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<p>In the left plot, the Principal Component Analysis in gastrointestinal (red) and non-gastrointestinal subjects (green) is presented; in the right plot the PCA based on the ASD onset is presented: subjects with early-onset in red, regression without a previous developmental delay in green, regression plus a previous developmental delay in blue.</p>
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23 pages, 2590 KiB  
Article
Get Set or Get Distracted? Disentangling Content-Priming and Attention-Catching Effects of Background Lure Stimuli on Identifying Targets in Two Simultaneously Presented Series
by Rolf Verleger, Kamila Śmigasiewicz, Lars Michael, Laura Heikaus and Michael Niedeggen
Brain Sci. 2019, 9(12), 365; https://doi.org/10.3390/brainsci9120365 - 11 Dec 2019
Cited by 1 | Viewed by 3195
Abstract
In order to study the changing relevance of stimulus features in time and space, we used a task with rapid serial presentation of two stimulus streams where two targets (“T1” and “T2”) had to be distinguished from background stimuli and where the difficult [...] Read more.
In order to study the changing relevance of stimulus features in time and space, we used a task with rapid serial presentation of two stimulus streams where two targets (“T1” and “T2”) had to be distinguished from background stimuli and where the difficult T2 distinction was impeded by background stimuli presented before T1 that resemble T2 (“lures”). Such lures might actually have dual characteristics: Their capturing attention might interfere with target identification, whereas their similarity to T2 might result in positive priming. To test this idea here, T2 was a blue digit among black letters, and lures resembled T2 either by alphanumeric category (black digits) or by salience (blue letters). Same-category lures were expected to prime T2 identification whereas salient lures would impede T2 identification. Results confirmed these predictions, yet the precise pattern of results did not fit our conceptual framework. To account for this pattern, we speculate that lures serve to confuse participants about the order of events, and the major factor distinguishing color lures and digit lures is their confusability with T2. Mechanisms of effects were additionally explored by measuring event-related EEG potentials. Consistent with the assumption that they attract more attention, color lures evoked larger N2pc than digit lures and affected the ensuing T1-evoked N2pc. T2-evoked N2pc was indistinguishably reduced by all kinds of preceding lures, though. Lure-evoked mesio-frontal negativity increased from first to third lures both with digit and color lures and, thereby, might have reflected expectancy for T1. Full article
(This article belongs to the Special Issue ERP and EEG Markers of Brain Visual Attentional Processing)
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<p>Sequence of events in a trial. Participants had to identify the red letter (T1) and the blue digit (T2) embedded in a stream of background stimuli. At least nine pairs of background stimuli were presented before T1. In 80% of trials, three of these pairs contained stimuli that resembled T2 (“lures”), either by their blue color or by their being a digit. The three lures could be on the same side as the ensuing T2 (as in these examples) or on the other side.</p>
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<p>Lure effects on identification rates of T1 and T2. The upper panels display the percentages of trials in which T1 (left) and T2 (right) were identified. The lower panels display the differences between trials with lures from trials without lures. No-lure trials (upper panels only) are denoted by black lines, trials with digit lures by grey lines, and trials with color lures by blue lines. Trials where lures were in the same stream as the target (T1 and T2, respectively) are denoted by solid lines (grey and blue) and trials where lures were in the opposite stream are denoted by dashed lines. Both for T1 and T2, the six values on the x axes denote left-right-left-right-left-right targets. E.g., “L1R” is a trial where T1 was left and T2 was right (separated from each other by 1 frame). Thereby, L1R is the leftmost value for T1 (L<span class="html-italic">1R</span>) and the second value for T2 (<span class="html-italic">L1</span>R). The main ANOVAs were conducted on the lag 3 data (four rightmost values in each panel) and additional ANOVAs compared lag 1 and lag 3 other-target-on-other-side data (two leftmost and two rightmost values in each panel).</p>
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<p>Contralateral–ipsilateral event-related potential (ERP) differences evoked by the lures. Data are grand means across participants, recorded from left and right posterior sites PO7 and PO8. Depicted are differences between lure-trials and corresponding epochs of no-lure trials. Unit on x-axis is milliseconds, time-point zero is lure onset. Unit on y-axis is microvolts, negative voltage is plotted upwards. Waveforms evoked by the 1st lure are shown as solid lines, by the 2nd lure as dashed lines, and by the 3rd lure as dotted lines. ERPs evoked by color lures and digit lures are plotted with blue and grey lines, respectively. The scalp maps show the view on the head (120°) from above. Recording sites (small circles) are depicted on one hemisphere only because the contralateral–ipsilateral differences were pooled across left and right sides. Blue is contralateral negativity, red is positivity. Scale is ±4 µV. Displayed are topographic distributions of N2pc evoked by the first lure at its peak latency: 256 ms with the first color lure, 224 ms with the first digit lure.</p>
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<p>ERPs and current source densities evoked by the lures. Data are grand means across participants, recorded from the fronto-central midline site FCz. The upper panel displays the ERPs, and the lower panel displays current source densities, i.e., ERP data from which ERP data from surrounding sites were subtracted. Depicted are differences between lure-trials and corresponding epochs of no-lure trials. Unit on x-axis is milliseconds, time-point zero is lure onset. Unit on y-axis is microvolts in the upper panel and microvolts per square meter in the lower panel, negative values are plotted upwards. Waveforms evoked by the 1st lure are shown as solid lines, by the 2nd lure as dashed lines, and by the 3rd lure as dotted lines. Data evoked by color lures and digit lures are plotted with blue and grey lines, respectively. The scalp maps show the view on the head (120°) from above. Recording sites are denoted by the small circles. Displayed are topographic distributions at the indicated latencies for each of the six conditions. In the upper panel, blue denotes negative polarity, red positive polarity, and scale is ±2 µV. In the lower panel, blue denotes negative sinks, red denotes positive sources, and scale is ±15 µV/m<sup>2</sup>.</p>
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<p>Contralateral–ipsilateral ERP differences evoked by T1. Data are grand means across participants, recorded from left and right posterior sites PO7 and PO8. Unit on x-axis is milliseconds, time-point zero is lure onset. Unit on y-axis is microvolts, negative voltage is plotted upwards. Waveforms evoked in no-lure trials are black, from color-lure trials blue, and from digit-lure trials grey. Trials where lures were in the same stream as T1 are denoted by solid lines (grey and blue) and trials where lures were in the opposite stream are denoted by dashed lines. The scalp map shows the view on the head (120°) from above. Recording sites (small circles) are depicted on one hemisphere only because the contralateral–ipsilateral differences were pooled across left and right sides. Blue is contralateral negativity, red is positivity. Scale is ±7 µV. Displayed is the topographic distribution of N2pc evoked in no-lure trials at the peak latency of N2pc (222 ms).</p>
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<p>Contralateral–ipsilateral ERP differences evoked by T2. Data are grand means across participants, recorded from left and right posterior sites PO7 and PO8. Unit on x-axis is milliseconds, time-point zero is lure onset. Unit on y-axis is microvolts, negative voltage is plotted upwards. Waveforms evoked in no-lure trials are black, from color-lure trials blue, and from digit-lure trials grey. Trials where lures were in the same stream as T2 are denoted by solid lines (grey and blue) and trials where lures were in the opposite stream are denoted by dashed lines. The left panel displays data where T1 was in the same stream as T2, and the right panel displays data where T1 was in the other stream. Time-point −100 ms is approximately the peak of that preceding T1. The scalp maps show the view on the head (120°) from above. Recording sites (small circles) are depicted on one hemisphere only because the contralateral–ipsilateral differences were pooled across left and right sides. Blue is contralateral negativity, red is positivity. Scale is ±8 µV. Displayed is the topographic distribution of N2pc evoked in no-lure trials at the peak latencies of N2pc (282 ms in same-stream-as-T1 trials, 260 ms in opposite-stream trials).</p>
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19 pages, 485 KiB  
Article
Estimating the Parameters of Fitzhugh–Nagumo Neurons from Neural Spiking Data
by Resat Ozgur Doruk and Laila Abosharb
Brain Sci. 2019, 9(12), 364; https://doi.org/10.3390/brainsci9120364 - 9 Dec 2019
Cited by 13 | Viewed by 3425
Abstract
A theoretical and computational study on the estimation of the parameters of a single Fitzhugh–Nagumo model is presented. The difference of this work from a conventional system identification is that the measured data only consist of discrete and noisy neural spiking (spike times) [...] Read more.
A theoretical and computational study on the estimation of the parameters of a single Fitzhugh–Nagumo model is presented. The difference of this work from a conventional system identification is that the measured data only consist of discrete and noisy neural spiking (spike times) data, which contain no amplitude information. The goal can be achieved by applying a maximum likelihood estimation approach where the likelihood function is derived from point process statistics. The firing rate of the neuron was assumed as a nonlinear map (logistic sigmoid) relating it to the membrane potential variable. The stimulus data were generated by a phased cosine Fourier series having fixed amplitude and frequency but a randomly shot phase (shot at each repeated trial). Various values of amplitude, stimulus component size, and sample size were applied to examine the effect of stimulus to the identification process. Results are presented in tabular and graphical forms, which also include statistical analysis (mean and standard deviation of the estimates). We also tested our model using realistic data from a previous research (H1 neurons of blowflies) and found that the estimates have a tendency to converge. Full article
(This article belongs to the Collection Collection on Theoretical and Computational Neuroscience)
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<p>A typical stimulus and response pattern. In the first pane, a Fourier series stimulus with parameters <math display="inline"><semantics> <mrow> <msub> <mi>A</mi> <mo movablelimits="true" form="prefix">max</mo> </msub> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>333</mn> </mrow> </semantics></math> Hz, and <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mi>U</mi> </msub> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math> is displayed. In the second pane, the neural spiking pattern of the Fitzhugh–Nagumo model in Equation (<a href="#FD1-brainsci-09-00364" class="html-disp-formula">1</a>) with the nominal parameters in <a href="#brainsci-09-00364-t001" class="html-table">Table 1</a> obtained after Poisson simulation can be seen.</p>
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<p>The variation of individual standard deviations (or relative errors) of the estimates against varying sample (iteration) size <math display="inline"><semantics> <msub> <mi>N</mi> <mrow> <mi>i</mi> <mi>t</mi> </mrow> </msub> </semantics></math>. Other stimulus parameters are <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mi>U</mi> </msub> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>A</mi> <mo movablelimits="true" form="prefix">max</mo> </msub> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>333.3</mn> </mrow> </semantics></math> Hz. For most parameters, these relative errors show an improving behavior with the increasing sample size. However, some parameters such as <span class="html-italic">b</span> do not present any improvement or degradation in relative errors. However, in general, the relative error levels remain small.</p>
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<p>The variation of individual standard deviations (or relative errors) of the estimates against varying stimulus amplitude parameter <math display="inline"><semantics> <msub> <mi>A</mi> <mo movablelimits="true" form="prefix">max</mo> </msub> </semantics></math>. Other stimulus parameters are <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mrow> <mi>i</mi> <mi>t</mi> </mrow> </msub> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mi>U</mi> </msub> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>333.3</mn> </mrow> </semantics></math> Hz. Except for parameter <span class="html-italic">F</span>, one cannot see an improvement with raising the stimulus amplitude. However, in general, the relative error levels remain small.</p>
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<p>The variation of individual standard deviations (or relative errors) of the estimates against varying stimulus component size <math display="inline"><semantics> <msub> <mi>N</mi> <mi>U</mi> </msub> </semantics></math>. Other stimulus parameters are <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mrow> <mi>i</mi> <mi>t</mi> </mrow> </msub> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>A</mi> <mo movablelimits="true" form="prefix">max</mo> </msub> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>333.3</mn> </mrow> </semantics></math> Hz. Stimuli with small <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mi>U</mi> </msub> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math> or large <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mi>U</mi> </msub> <mo>=</mo> <mn>30</mn> </mrow> </semantics></math> component size can be preferred. In general, relative error levels also stay smaller in this case.</p>
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<p>The variation of individual standard deviations (or relative errors) of the estimates against varying base frequency <math display="inline"><semantics> <msub> <mi>f</mi> <mn>0</mn> </msub> </semantics></math>. Other stimulus parameters are <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mrow> <mi>i</mi> <mi>t</mi> </mrow> </msub> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>A</mi> <mo movablelimits="true" form="prefix">max</mo> </msub> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mi>U</mi> </msub> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math>. The frequencies are in KHz. Although overall relative error levels are smaller, one can prefer a mid frequency range, e.g. <math display="inline"><semantics> <mrow> <mn>1</mn> <mo>≤</mo> <msub> <mi>f</mi> <mn>0</mn> </msub> <mo>≤</mo> <mrow/> <mi>M</mi> <mrow/> <mi>M</mi> <mrow/> <mn>7</mn> <mspace width="-1.111pt"/> <mo>/</mo> <mspace width="-0.55542pt"/> <mn>3</mn> </mrow> </semantics></math> KHz</p>
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<p>The variation of the Kolmogorov–Smirnov test <span class="html-italic">p</span> value with the number of samples <math display="inline"><semantics> <msub> <mi>N</mi> <mrow> <mi>i</mi> <mi>t</mi> </mrow> </msub> </semantics></math> obtained from both measurements (simulation and realistic measurement). Here, the segment size is 500 ms.</p>
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<p>The variation of the Kolmogorov–Smirnov test <span class="html-italic">p</span> value with the number of samples <math display="inline"><semantics> <msub> <mi>N</mi> <mrow> <mi>i</mi> <mi>t</mi> </mrow> </msub> </semantics></math> obtained from both measurements (simulation and realistic measurement). Here, the segment size is 1 s.</p>
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<p>The variation of the Kolmogorov–Smirnov test <span class="html-italic">p</span> value with the number of samples <math display="inline"><semantics> <msub> <mi>N</mi> <mrow> <mi>i</mi> <mi>t</mi> </mrow> </msub> </semantics></math> obtained from both measurements (simulation and realistic measurement). Here, the segment size is 2 s.</p>
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<p>The variation of the Kolmogorov–Smirnov test <span class="html-italic">p</span> value with the number of samples <math display="inline"><semantics> <msub> <mi>N</mi> <mrow> <mi>i</mi> <mi>t</mi> </mrow> </msub> </semantics></math> obtained from both measurements (simulation and realistic measurement). Here, the segment size is 3 s.</p>
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<p>The variation of the Kolmogorov–Smirnov test <span class="html-italic">p</span> value with the number of samples <math display="inline"><semantics> <msub> <mi>N</mi> <mrow> <mi>i</mi> <mi>t</mi> </mrow> </msub> </semantics></math> obtained from both measurements (simulation and realistic measurement). Here, the segment size is 4 s.</p>
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<p>The variation of the Kolmogorov–Smirnov test <span class="html-italic">p</span> value with the number of samples <math display="inline"><semantics> <msub> <mi>N</mi> <mrow> <mi>i</mi> <mi>t</mi> </mrow> </msub> </semantics></math> obtained from both measurements (simulation and realistic measurement). Here, the segment size is 6 s.</p>
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29 pages, 6194 KiB  
Article
Brain Connectivity Analysis Under Semantic Vigilance and Enhanced Mental States
by Fares Al-Shargie, Usman Tariq, Omnia Hassanin, Hasan Mir, Fabio Babiloni and Hasan Al-Nashash
Brain Sci. 2019, 9(12), 363; https://doi.org/10.3390/brainsci9120363 - 9 Dec 2019
Cited by 32 | Viewed by 5956
Abstract
In this paper, we present a method to quantify the coupling between brain regions under vigilance and enhanced mental states by utilizing partial directed coherence (PDC) and graph theory analysis (GTA). The vigilance state is induced using a modified version of stroop color-word [...] Read more.
In this paper, we present a method to quantify the coupling between brain regions under vigilance and enhanced mental states by utilizing partial directed coherence (PDC) and graph theory analysis (GTA). The vigilance state is induced using a modified version of stroop color-word task (SCWT) while the enhancement state is based on audio stimulation with a pure tone of 250 Hz. The audio stimulation was presented to the right and left ears simultaneously for one-hour while participants perform the SCWT. The quantification of mental states was performed by means of statistical analysis of indexes based on GTA, behavioral responses of time-on-task (TOT), and Brunel Mood Scale (BRMUS). The results show that PDC is very sensitive to vigilance decrement and shows that the brain connectivity network is significantly reduced with increasing TOT, p < 0.05. Meanwhile, during the enhanced state, the connectivity network maintains high connectivity as time passes and shows significant improvements compared to vigilance state. The audio stimulation enhances the connectivity network over the frontal and parietal regions and the right hemisphere. The increase in the connectivity network correlates with individual differences in the magnitude of the vigilance enhancement assessed by response time to stimuli. Our results provide evidence for enhancement of cognitive processing efficiency with audio stimulation. The BRMUS was used to evaluate the emotional states of vigilance task before and after using the audio stimulation. BRMUS factors, such as fatigue, depression, and anger, significantly decrease in the enhancement group compared to vigilance group. On the other hand, happy and calmness factors increased with audio stimulation, p < 0.05. Full article
(This article belongs to the Special Issue Recent Advances in Human Brain Connectivity)
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Graphical abstract

Graphical abstract
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<p>Experimental protocol (<b>a</b>) stroop color-word task (SCWT) presentation interface and (<b>b</b>) timing window. In the timing window, the plus sign in black background is for the pre- and post-baseline. Sixty (60) min SCWT is for the vigilance group, and the 60 min stroop color-word task with pure tone, SCWT+PT, is for the enhancement group.</p>
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<p>EEG data acquisition and experimental set-up.</p>
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<p>Performance evaluation with time on task (<b>a</b>) reaction time and (<b>b</b>) accuracy score. Each time interval is averaged at every 3 min.</p>
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<p>Percentage of omission and commission error in the vigilance (V) and enhancement state group (E).</p>
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<p>Average weighted directed connectivity network for (<b>a</b>) vigilance levels, (<b>b</b>) enhancement levels. The connectivity network at Level 1 is measured at time between 0 to 20 min, meanwhile the connectivity network at Level 2 is measured at time between 21 to 40 min, and the connectivity network at Level 3 is measured between 41 to 60 min, respectively. The color bar represents the PDC thresholding values ≥0.2. Red indicates high connectivity strength and blue indicates less connectivity strength.</p>
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<p>Statistical analysis between node degree indexes of connectivity network at three levels of time-on-task. (<b>a</b>) Vigilance group and (<b>b</b>) vigilance versus enhancement levels. The variables V1, E1 represent vigilance and enhancement at Level 1; V2, E2 represent vigilance and enhancement at Level 2; and V3, E3 represent vigilance and enhancement at Level 3. Red color indicates highly while blue color indicates less significant differences between brain connectivity networks.</p>
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<p>Statistical <span class="html-italic">t</span>-test topographical maps of EEG electrodes between (<b>a</b>) vigilance levels and (<b>b</b>) vigilance versus enhancement levels. The variables V1, E1 represent vigilance and enhancement at Level 1; V2, E2 represent vigilance and enhancement at Level 2; and V3, E3 represent vigilance and enhancement at Level 3. Red color indicates highly significant while blue color indicates less significant differences between brain connectivity networks.</p>
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<p>Average global degree index of connectivity network at three levels of time-on-task. (<b>a</b>) Vigilance group and (<b>b</b>) enhancement group. The bars represent mean ± standard deviation between subjects and the * shows that the differences between vigilance levels is significant at given threshold value, <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Normalized node degree of vigilance at the three levels of TOT. The red line represents the average node degree on the left hemisphere, and the blue line represents the average node degree of the right hemisphere. The black ** indicated the differences between the left and right hemisphere is significant, and the red <sup>+</sup> indicates the normalized nodal degree on the left hemisphere is greater than the right hemisphere based on lateralized index calculated using LH-RH/LH+RH. The RH and LH stand for left and right hemisphere respectively.</p>
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<p>Statistical <span class="html-italic">t</span>-test topographical map of EEG electrodes between the three levels of vigilance mental state and the three levels of enhanced mental state based on node strengths.</p>
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<p>Average node strength across electrodes during (<b>a</b>) vigilance level 1, (<b>b</b>) vigilance level 2, and (<b>c</b>) vigilance level 3. The bars represent mean ± standard deviation between subjects at individual electrode.</p>
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<p>Average node strength across electrodes during (<b>a</b>) enhancement level 1, (<b>b</b>) enhancement level 2, and (<b>c</b>) enhancement level 3. The bars represent mean ± standard deviation between subjects at individual electrode.</p>
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<p>Statistical <span class="html-italic">t</span>-test topographical map of EEG electrodes between the three levels of vigilance mental state and the three levels of enhanced mental state based on node strengths.</p>
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<p>(<b>a</b>) Clustering coefficients and (<b>b</b>) characteristic path length of the vigilance and enhancement states. The variables V1, V2, and V3 represent vigilance state at Level 1 of time-on-task, Level 2, and Level 3. Likewise, the variables E1, E2, and E3 represent enhancement state at Level 1 of time-on-task, Level 2, and Level 3. The error bars represent the standard deviation across subjects. The marks ‘*’, ‘**’ and ‘***’ indicate that the differences between the two conditions/levels is significant with <span class="html-italic">p</span> &lt; 0.05, <span class="html-italic">p</span> &lt; 0.01 and <span class="html-italic">p</span> &lt; 0.001, respectively.</p>
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<p>(<b>a</b>) Average clustering coefficients of the three levels of TOT. The CCV stands for the clustering coefficients at vigilance decrement mental state, and CCE stands for the clustering coefficients at the enhanced mental state. (<b>b</b>) Average characteristic path length of the three levels of TOT. The PLV stands for the characteristic path length at vigilance decrement mental state, and PLE stands for the characteristic path length at the enhanced mental state. The error bars represent the standard deviation across subjects. The marks ‘***’ indicate that the differences between the two conditions is significant with <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Hemispheric mean information flow at three levels during (<b>a</b>) vigilance and (<b>b</b>) enhancement. The acronym LH stands for left hemisphere, and RH stands for the right hemisphere.</p>
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<p>Scatter plots showing the correlation between the subjects’ reaction time and the nodal degree in Level 1, Level 2, and Level 3, respectively. The horizontal axis represents the values of the respective graph metrics, and the vertical axis stands for the reaction time. The <span class="html-italic">r</span>- and <span class="html-italic">p</span>-values of the corresponding correlations are displayed in the figures.</p>
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17 pages, 2111 KiB  
Article
The Influence of Subclinical Neck Pain on Neurophysiological and Behavioral Measures of Multisensory Integration
by Antonia M. Karellas, Paul Yielder, James J. Burkitt, Heather S. McCracken and Bernadette A. Murphy
Brain Sci. 2019, 9(12), 362; https://doi.org/10.3390/brainsci9120362 - 9 Dec 2019
Cited by 6 | Viewed by 3417
Abstract
Multisensory integration (MSI) is necessary for the efficient execution of many everyday tasks. Alterations in sensorimotor integration (SMI) have been observed in individuals with subclinical neck pain (SCNP). Altered audiovisual MSI has previously been demonstrated in this population using performance measures, such as [...] Read more.
Multisensory integration (MSI) is necessary for the efficient execution of many everyday tasks. Alterations in sensorimotor integration (SMI) have been observed in individuals with subclinical neck pain (SCNP). Altered audiovisual MSI has previously been demonstrated in this population using performance measures, such as reaction time. However, neurophysiological techniques have not been combined with performance measures in the SCNP population to determine differences in neural processing that may contribute to these behavioral characteristics. Electroencephalography (EEG) event-related potentials (ERPs) have been successfully used in recent MSI studies to show differences in neural processing between different clinical populations. This study combined behavioral and ERP measures to characterize MSI differences between healthy and SCNP groups. EEG was recorded as 24 participants performed 8 blocks of a simple reaction time (RT) MSI task, with each block consisting of 34 auditory (A), visual (V), and audiovisual (AV) trials. Participants responded to the stimuli by pressing a response key. Both groups responded fastest to the AV condition. The healthy group demonstrated significantly faster RTs for the AV and V conditions. There were significant group differences in neural activity from 100–140 ms post-stimulus onset, with the control group demonstrating greater MSI. Differences in brain activity and RT between individuals with SCNP and a control group indicate neurophysiological alterations in how individuals with SCNP process audiovisual stimuli. This suggests that SCNP alters MSI. This study presents novel EEG findings that demonstrate MSI differences in a group of individuals with SCNP. Full article
(This article belongs to the Collection Collection on Systems Neuroscience)
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<p>This image portrays the experiment set-up and the equipment used for data collection. The participant is seated directly in front of the monitor with their right thumb placed over the response key. The EEG recording takes place during task performance.</p>
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<p>This figure displays the multisensory simple reaction time (RT) task. The stimulus presentation process is depicted above. Participants were randomly presented with an A, V, or AV stimulus, each of which were preceded by a fixation period with a randomized duration of 1000–3000 ms. Participants responded on a response key as soon as they saw the stimulus.</p>
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<p>A visual representation of the most active brain regions between 100 and 140 ms post-stimulus presentation for all participants as revealed by topographical analysis.</p>
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<p>Mean RT for visual (V), auditory (A), and audiovisual (AV) conditions between the control and subclinical neck pain (SCNP) groups. Significant differences (<span class="html-italic">p</span> &lt; 0.01) are represented in this graph by the asterisks, as significant mean response times were observed between the AV–A condition and the A–V condition, but not between the V–AV condition.</p>
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<p>Significant race model violations are represented in this graph by the asterisk, specifically at percent quantiles 0.6 (<span class="html-italic">p</span> &lt; 0.05) and 0.7(<span class="html-italic">p</span> &lt; 0.03) in the control group.</p>
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<p>This figure demonstrates the grand average event-related potential (ERP) multisensory integration (MSI) and Sum signals for the control and SCNP groups between 100 and 140 ms at the electrodes od P7, PO7, P8 and PO8. The blue line at 0 ms represents the moment that the stimulus was presented, while the blue lines at 100 ms and 140 ms represent the time bin for which the average brain activity was analyzed. Those without neck pain exhibited greater neural activity levels compared to the SCNP group, as shown by the greater peak amplitudes. The asterisk represents the group effect revealed according to method 1 analysis between 100 and 140 ms (<span class="html-italic">p</span> &lt; 0.003).</p>
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<p>This figure graphically represents differences in the grand average ERP MSI and SUM signals between the control and SCNP groups between 100 and 140 ms at the specified negative voltage electrodes. The control group demonstrates significantly greater neural activity levels, shown by the greater peak amplitudes, as well as significantly greater MSI, shown by greater deviations between the SUM and MSI ERPs. The asterisk represents the group by signal interaction revealed by method one, between the 100 and 140 ms time bin (<span class="html-italic">p</span> &lt; 0.005). The control group has a significantly greater divergence between the sum and multisensory waveforms than the SCNP group.</p>
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<p>This figure presents group differences in MSI evident at the Cz and CPz electrodes between 100 and 140 ms post-stimulus presentation (<span class="html-italic">p</span> &lt; 0.005), as represented by the asterisk. A greater divergence is seen between the MSI and SUM waveforms in the control group in comparison to the SCNP group, which is indicative of a greater degree of multisensory integration.</p>
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13 pages, 1759 KiB  
Article
Role of MyD88 in IL-1β and Ethanol Modulation of GABAergic Transmission in the Central Amygdala
by Michal Bajo, Reesha R. Patel, David M. Hedges, Florence P. Varodayan, Roman Vlkolinsky, Tony D. Davis, Michael D. Burkart, Yuri A. Blednov and Marisa Roberto
Brain Sci. 2019, 9(12), 361; https://doi.org/10.3390/brainsci9120361 - 7 Dec 2019
Cited by 18 | Viewed by 3659
Abstract
Myeloid differentiation primary response protein (MyD88) is a critical neuroimmune adaptor protein in TLR (Toll-like receptor) and IL-1R (Interleukin-1 receptor) signaling complexes. These two pro-inflammatory families play an important role in the neurobiology of alcohol use disorder, specifically MyD88 regulates ethanol drinking, ethanol-induced [...] Read more.
Myeloid differentiation primary response protein (MyD88) is a critical neuroimmune adaptor protein in TLR (Toll-like receptor) and IL-1R (Interleukin-1 receptor) signaling complexes. These two pro-inflammatory families play an important role in the neurobiology of alcohol use disorder, specifically MyD88 regulates ethanol drinking, ethanol-induced sedation, and ethanol-induced deficits in motor coordination. In this study, we examined the role of MyD88 in mediating the effects of IL-1β and ethanol on GABAergic transmission in the central amygdala (CeA) of male mice using whole-cell patch-clamp recordings in combination with pharmacological (AS-1, a mimetic that prevents MyD88 recruitment by IL-1R) and genetic (Myd88 knockout mice) approaches. We demonstrate through both approaches that IL-1β and ethanol’s modulatory effects at CeA GABA synapses are not dependent on MyD88. Myd88 knockout potentiated IL-1β’s actions in reducing postsynaptic GABAA receptor function. Pharmacological inhibition of MyD88 modulates IL-1β’s action at CeA GABA synapses similar to Myd88 knockout mice. Additionally, ethanol-induced CeA GABA release was greater in Myd88 knockout mice compared to wildtype controls. Thus, MyD88 is not essential to IL-1β or ethanol regulation of CeA GABA synapses but plays a role in modulating the magnitude of their effects, which may be a potential mechanism by which it regulates ethanol-related behaviors. Full article
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<p>Basal spontaneous GABAergic transmission is similar in the CeA of <span class="html-italic">Myd88</span> KO and wildtype (WT) mice. (<b>A</b>) Representative traces of sIPSCs from WT and <span class="html-italic">Myd88</span> KO mice. (<b>B</b>–<b>E</b>) There were no significant differences in the basal sIPSC frequencies (<b>B</b>), amplitudes (<b>C</b>), rise times (<b>D</b>), and decay times (<b>E</b>) of CeA neurons from WT (<span class="html-italic">n</span> = 41 neurons) and <span class="html-italic">Myd88</span> KO (<span class="html-italic">n</span> = 45 neurons) mice. The scattergrams represent values for each cell. Statistical significance was calculated by an unpaired <span class="html-italic">t</span>-test, and significance was set at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>MyD88 deletion dampens IL-1β’s effects on postsynaptic GABA<sub>A</sub> receptor function. (<b>A</b>–<b>B</b>) IL-1β had dual effects on the sIPSC frequencies (<b>A</b>) and amplitudes (<b>B</b>) in both WT and KO mice. The scattergrams on the left show the normalized effects of IL-1β (50 ng/mL) in individual cells (WT: <span class="html-italic">n</span> = 13 cells; KO: <span class="html-italic">n</span> = 15 cells), and the right panels show the percentage of the CeA neurons responding to IL-1β with an increase, no change or decrease in the sIPSC frequencies and amplitudes. (<b>C</b>) Representative traces of CeA neurons responding to IL-1β with decreased sIPSC frequencies and amplitudes. (<b>D</b>–<b>E</b>) While there were no differences in the predominant effect of IL-1β on the mean sIPSC frequency in WT (<span class="html-italic">n</span> = 11 cells) and KO (<span class="html-italic">n</span> = 13 cells) mice (<b>D</b>), <span class="html-italic">Myd88</span> KO mice showed an IL-1β-induced decrease in the sIPSC amplitude (<b>E</b>). The statistical significance for the IL-1β effects was calculated by one-sample <span class="html-italic">t</span>-test (** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001), and for the comparison of the IL-1β effects between the WT and KO mice, an unpaired <span class="html-italic">t</span>-test was used (# <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>The effects of the MyD88 mimetic (AS-1) in WT mice. (<b>A</b>–<b>B</b>) The scattergrams represent the responses of individual CeA cells to the acute application of the AS-1 (50 mM) and subsequent co-application of IL-1β, where panel <b>A</b> shows the effects of AS-1 on the sIPSC frequencies (WT: <span class="html-italic">n</span> = 7 cells), and panel <b>B</b> shows its effects on the sIPSC amplitude. (<b>C</b>–<b>D</b>). In five out of six WT neurons pretreated with AS-1, there was an IL-1β-induced decrease in the mean sIPSC frequency, and the magnitude of this effect was not significantly different from the IL-1β-induced decrease in the <span class="html-italic">Myd88</span> KO mice (data from <a href="#brainsci-09-00361-f002" class="html-fig">Figure 2</a>D). (<b>D</b>) IL-1β in the presence of AS-1 had no significant effects on the mean sIPSC amplitudes of WT cells, and the extent of this effect was not significantly different from the IL-1β’s effects in the <span class="html-italic">Myd88</span> KO mice (data from <a href="#brainsci-09-00361-f002" class="html-fig">Figure 2</a>E). The statistical significance for the AS-1 and IL-1β effects was calculated by one-sample <span class="html-italic">t</span>-test (** <span class="html-italic">p</span> &lt; 0.01 and *** <span class="html-italic">p</span> &lt; 0.0001), and for the comparison of the IL-1β’s effects between the WT, following AS-1 pretreatment, and KO mice, an unpaired <span class="html-italic">t</span>-test was used.</p>
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<p>Facilitation of the presynaptic GABA release by 100 mM ethanol is more robust in <span class="html-italic">Myd88</span> KO than in the WT mice. (<b>A</b>,<b>B</b>). Ethanol (44 mM) facilitated presynaptic GABA release across both genotypes, as represented by the increase in the mean sIPSC frequencies in 10 out of 14 neurons from WT and 7 out of 16 cells from KO mice. (<b>C</b>) Representative recordings of the CeA neurons responding to 44 mM ethanol with the increase in the sIPSC frequencies. (<b>D</b>) There was no significant difference in the magnitude of the ethanol-induced potentiation of the GABA release between the WT and KO mice. (<b>E</b>) Representative traces of the CeA neurons responsive to 100 mM ethanol. (<b>F</b>) The magnitude of the 100 mM ethanol-induced potentiation of the GABA release was significantly stronger in the KO mice (<span class="html-italic">n</span> = 4 out of 5 cells) compared to the WT mice (<span class="html-italic">n</span> = 5 out of 5 cells) mice. The statistical significance for the ethanol effects was calculated by one-sample <span class="html-italic">t</span>-test (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001), and an unpaired <span class="html-italic">t</span>-test (# <span class="html-italic">p</span> &lt; 0.05) was used for the comparison of the magnitude of the ethanol effects between the WT and KO mice.</p>
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13 pages, 1638 KiB  
Article
Neuronal Transmembrane Chloride Transport Has a Time-Dependent Influence on Survival of Hippocampal Cultures to Oxygen-Glucose Deprivation
by Ana-Maria Zagrean, Ioana-Florentina Grigoras, Mara Ioana Iesanu, Rosana-Bristena Ionescu, Diana Maria Chitimus, Robert Mihai Haret, Bogdan Ianosi, Mihai Ceanga and Leon Zagrean
Brain Sci. 2019, 9(12), 360; https://doi.org/10.3390/brainsci9120360 - 6 Dec 2019
Cited by 9 | Viewed by 4528
Abstract
Neuronal ischemia results in chloride gradient alterations which impact the excitatory–inhibitory balance, volume regulation, and neuronal survival. Thus, the Na+/K+/Cl co-transporter (NKCC1), the K+/ Cl co-transporter (KCC2), and the gamma-aminobutyric acid A (GABAA) [...] Read more.
Neuronal ischemia results in chloride gradient alterations which impact the excitatory–inhibitory balance, volume regulation, and neuronal survival. Thus, the Na+/K+/Cl co-transporter (NKCC1), the K+/ Cl co-transporter (KCC2), and the gamma-aminobutyric acid A (GABAA) receptor may represent therapeutic targets in stroke, but a time-dependent effect on neuronal viability could influence the outcome. We, therefore, successively blocked NKCC1, KCC2, and GABAA (with bumetanide, DIOA, and gabazine, respectively) or activated GABAA (with isoguvacine) either during or after oxygen-glucose deprivation (OGD). Primary hippocampal cultures were exposed to a 2-h OGD or sham normoxia treatment, and viability was determined using the resazurin assay. Neuronal viability was significantly reduced after OGD, and was further decreased by DIOA treatment applied during OGD (p < 0.01) and by gabazine applied after OGD (p < 0.05). Bumetanide treatment during OGD increased viability (p < 0.05), while isoguvacine applied either during or after OGD did not influence viability. Our data suggests that NKCC1 and KCC2 function has an important impact on neuronal viability during the acute ischemic episode, while the GABAA receptor plays a role during the subsequent recovery period. These findings suggest that pharmacological modulation of transmembrane chloride transport could be a promising approach during stroke and highlight the importance of the timing of treatment application in relation to ischemia-reoxygenation. Full article
(This article belongs to the Collection Collection on Molecular and Cellular Neuroscience)
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<p>(<b>a</b>) Experimental design. (<b>b</b>) Phase-contrast microscopy of control and OGD-exposed DIV 7 hippocampal cell cultures. DIV—days <span class="html-italic">in vitro</span>, OGD—oxygen-glucose deprivation, DIOA—R(+)-[(dihydroindenyl)oxy] alkanoic acid, T1—treatment applied during OGD, T2—post-exposure treatment.</p>
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<p>DIOA treatment during OGD is associated with a further decrease in cellular viability of DIV 7 hippocampal neurons. (<b>a</b>) Hypothesis: blocking KCC2 using DIOA may aggravate intracellular Cl<sup>−</sup> accumulation following ischemia-induced KCC2 downregulation [<a href="#B18-brainsci-09-00360" class="html-bibr">18</a>]. (<b>b</b>) Cellular viability of DIV 7 hippocampal neurons decreased after 2 h of OGD. DIOA treatment during OGD (DIOA T1) but not post-exposure (DIOA T2) further decreased cellular viability. Bars represent mean ± SD. OGD—oxygen-glucose deprivation, NKCC1—the Na<sup>+</sup>/K<sup>+</sup>/Cl<sup>−</sup> co-transporter, KCC2—the K<sup>+</sup>/Cl<sup>−</sup> co-transporter, GABA<sub>A</sub> R—the gamma-aminobutyric acid A receptor, [Cl<sup>−</sup>]<sub>i</sub>—intracellular chloride concentration, DIOA—R(+)-[(dihydroindenyl)oxy] alkanoic acid.</p>
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<p>Bumetanide treatment during OGD is associated with a higher viability of DIV 7 hippocampal cell cultures. (<b>a</b>) Hypothesis: Blocking NKCC1 using bumetanide may compensate the intracellular Cl<sup>−</sup> accumulation following ischemia-induced KCC2 downregulation [<a href="#B18-brainsci-09-00360" class="html-bibr">18</a>]. (<b>b</b>) Cellular viability of DIV 7 hippocampal neurons decreased after 2 h of OGD. Bumetanide treatment during OGD (Bumetanide T1), but not post-exposure (Bumetanide T2), increased cellular viability. Bars represent mean ± SD. OGD—oxygen-glucose deprivation, NKCC1—the Na<sup>+</sup>/K<sup>+</sup>/Cl<sup>−</sup> co-transporter, KCC2—the K<sup>+</sup>/Cl<sup>−</sup> co-transporter, GABA<sub>A</sub> R—the gamma-aminobutyric acid A receptor, [Cl<sup>−</sup>]<sub>i</sub>—intracellular chloride concentration.</p>
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<p>Gabazine treatment post-exposure, but not during OGD, is associated with a decrease in the viability of DIV 7 hippocampal neurons. (<b>a</b>) Hypothesis: The direction of Cl<sup>−</sup> transmembrane flow following GABA<sub>A</sub> receptor activation depends on [Cl<sup>−</sup>]<sub>i</sub> and its reversal potential. Blocking of GABA<sub>A</sub> receptors with gabazine during or following OGD can therefore either block the inflow or the outflow of chloride ions. (<b>b</b>) Cellular viability of DIV 7 hippocampal neurons decreased after 2 h of OGD. Cellular viability of DIV 7 hippocampal neurons after gabazine treatment post-OGD exposure (Gabazine T2) is lower than either OGD alone or gabazine treatment during OGD (Gabazine T1). Bars represent mean ± SD. OGD—oxygen-glucose deprivation, NKCC1—the Na<sup>+</sup>/K<sup>+</sup>/Cl<sup>−</sup> co-transporter, KCC2—the K<sup>+</sup>/Cl<sup>−</sup> co-transporter, GABA<sub>A</sub> R—the gamma-aminobutyric acid A receptor.</p>
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<p>Isoguvacine treatment does not influence the viability of DIV 7 hippocampal neurons either during OGD or post-exposure. (<b>a</b>) Hypothesis: The direction of Cl<sup>−</sup> transmembrane flow following GABA<sub>A</sub> receptor activation depends on [Cl<sup>−</sup>]<sub>i</sub> and its reversal potential. Enhancing GABA<sub>A</sub> receptor activation with isoguvacine during or following OGD can therefore either increase the inflow or the outflow of chloride ions. (<b>b</b>) Cellular viability of DIV 7 hippocampal neurons decreased after 2 h of OGD. Treatment with the GABA<sub>A</sub> agonist isoguvacine did not further influence neuronal viability. Bars represent mean ± SD. OGD—oxygen-glucose deprivation, NKCC1—the Na<sup>+</sup>/K<sup>+</sup>/Cl<sup>−</sup> co-transporter, KCC2—the K<sup>+</sup>/Cl<sup>−</sup> co-transporter, GABA<sub>A</sub> R—the gamma-aminobutyric acid A receptor.</p>
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19 pages, 1280 KiB  
Article
Endocrine Disruptors Induced Distinct Expression of Thyroid and Estrogen Receptors in Rat versus Mouse Primary Cerebellar Cell Cultures
by Gergely Jocsak, Eniko Ioja, David Sandor Kiss, Istvan Toth, Zoltan Barany, Tibor Bartha, Laszlo V. Frenyo and Attila Zsarnovszky
Brain Sci. 2019, 9(12), 359; https://doi.org/10.3390/brainsci9120359 - 5 Dec 2019
Cited by 10 | Viewed by 4150
Abstract
The endocrine system of animals consists of fine-tuned self-regulating mechanisms that maintain the hormonal and neuronal milieu during tissue development. This complex system can be influenced by endocrine disruptors (ED)—substances that can alter the hormonal regulation even in small concentrations. By now, thousands [...] Read more.
The endocrine system of animals consists of fine-tuned self-regulating mechanisms that maintain the hormonal and neuronal milieu during tissue development. This complex system can be influenced by endocrine disruptors (ED)—substances that can alter the hormonal regulation even in small concentrations. By now, thousands of substances—either synthesized by the plastic, cosmetic, agricultural, or medical industry or occurring naturally in plants or in polluted groundwater—can act as EDs. Their identification and testing has been a hard-to-solve problem; Recent indications that the ED effects may be species-specific just further complicated the determination of biological ED effects. Here we compare the effects of bisphenol-A, zearalenone, and arsenic (well-known EDs) exerted on mouse and rat neural cell cultures by measuring the differences of the ED-affected neural estrogen- and thyroid receptors. EDs alters the receptor expression in a species-like manner detectable in the magnitude as well as in the nature of biological responses. It is concluded that the interspecies differences (or species specificity) in ED effects should be considered in the future testing of ED effects. Full article
(This article belongs to the Section Molecular and Cellular Neuroscience)
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<p>ERα mRNA expression in rat and mouse cerebellar granule cells treated with 17β-estradiol (E2), triiodo-thyronine (T3), bisphenol A (BPA), zearalenone (ZEN), arsenic (As), alone or in combination with the hormones. (<b>a</b>) Relative expression level of the ERα gene was analyzed by qRT-PCR and normalized to the average of the control gene β-actin or Gapdh. Shown <span class="html-italic">p</span>-values were calculated compared to ntC: (*) <span class="html-italic">p</span> &lt; 0.05, (**) <span class="html-italic">p</span> &lt; 0.01, (***) <span class="html-italic">p</span> &lt; 0.001. (<b>b</b>) Relative expression of ERα mRNA in rat versus mouse non-treated controls, normalized to β-actin or Gapdh (<span class="html-italic">p</span>-value not shown). The data shown here are the mean ± standard deviation (SD) of at least three independent experiments (<span class="html-italic">n</span> = 5 per treatment).</p>
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<p>ERβ mRNA expression in rat and mouse cerebellar granule cells treated with 17β-estradiol (E2), triiodo-thyronine (T3), bisphenol A (BPA), zearalenone (ZEN), arsenic (As), alone or in combination with the hormones. (<b>a</b>) Relative expression level of the Erβ gene was analyzed by qRT-PCR and normalized to the average of the control gene β-actin or Gapdh. Shown <span class="html-italic">p</span>-values were calculated compared to ntC. (<b>b</b>) Relative expression of Erβ mRNA in rat versus mouse non-treated controls, normalized to β-actin or Gapdh (<span class="html-italic">p</span>-value not shown). The data shown here are the mean ± standard deviation (SD) of at least three independent experiments (n = 5 per treatment).</p>
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<p>TRα mRNA expression in rat and mouse cerebellar granule cells treated with 17β-estradiol (E2), triiodo-thyronine (T3), bisphenol A (BPA), zearalenone (ZEN), arsenic (As), alone or in combination with the hormones. (<b>a</b>) Relative expression level of the TRα gene was analyzed by qRT-PCR and normalized to the average of the control gene β-actin or Gapdh. Shown <span class="html-italic">p</span>-values were calculated compared to ntC. (<b>b</b>) Relative expression of TRα mRNA in rat versus mouse non-treated controls, normalized to β-actin or Gapdh (<span class="html-italic">p</span>-value not shown). The data shown here are the mean ± standard deviation (SD) of at least three independent experiments (<span class="html-italic">n</span> = 5 per treatment).</p>
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<p>TRβ mRNA expression in rat and mouse cerebellar granule cells treated with 17β-estradiol (E2), triiodo-thyronine (T3), bisphenol A (BPA), zearalenone (ZEN), arsenic (As), alone or in combination with the hormones. (<b>a</b>) Relative expression level of the TRβ gene was analyzed by qRT-PCR and normalized to the average of the control gene β-actin or Gapdh. Shown <span class="html-italic">p</span>-values were calculated compared to ntC. (<b>b</b>) Relative expression of TRβ mRNA in rat versus mouse non-treated controls, normalized to β-actin or Gapdh (<span class="html-italic">p</span>-value not shown). The data shown here are the mean ± standard deviation (SD) of at least three independent experiments (<span class="html-italic">n</span> = 5 per treatment).</p>
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11 pages, 293 KiB  
Perspective
Human Periapical Cyst-Derived Stem Cells Can Be A Smart “Lab-on-A-Cell” to Investigate Neurodegenerative Diseases and the Related Alteration of the Exosomes’ Content
by Marco Tatullo, Bruna Codispoti, Gianrico Spagnuolo and Barbara Zavan
Brain Sci. 2019, 9(12), 358; https://doi.org/10.3390/brainsci9120358 - 5 Dec 2019
Cited by 13 | Viewed by 4901
Abstract
Promising researches have demonstrated that the alteration of biological rhythms may be consistently linked to neurodegenerative pathologies. Parkinson’s disease (PD) has a multifactorial pathogenesis, involving both genetic and environmental and/or molecular co-factors. Generally, heterogeneous alterations in circadian rhythm (CR) are a typical finding [...] Read more.
Promising researches have demonstrated that the alteration of biological rhythms may be consistently linked to neurodegenerative pathologies. Parkinson’s disease (PD) has a multifactorial pathogenesis, involving both genetic and environmental and/or molecular co-factors. Generally, heterogeneous alterations in circadian rhythm (CR) are a typical finding in degenerative processes, such as cell aging and death. Although numerous genetic phenotypes have been discovered in the most common forms of PD, it seems that severe deficiencies in synaptic transmission and high vesicular recycling are frequently found in PD patients. Neuron-to-neuron interactions are often ensured by exosomes, a specific type of extracellular vesicle (EV). Neuron-derived exosomes may carry several active compounds, including miRNAs: Several studies have found that circulating miRNAs are closely associated with an atypical oscillation of circadian rhythm genes, and they are also involved in the regulation of clock genes, in animal models. In this context, a careful analysis of neural-differentiated Mesenchymal Stem Cells (MSCs) and the molecular and genetic characterization of their exosome content, both in healthy cells and in PD-induced cells, could be a strategic field of investigation for early diagnosis and better treatment of PD and similar neurodegenerative pathologies. A novel MSC population, called human periapical cyst–mesenchymal stem cells (hPCy–MSCs), has demonstrated that it naively expresswa the main neuronal markers, and may differentiate towards functional neurons. Therefore, hPCy–MSCs can be considered of particular interest for testing of in vitro strategies to treat neurological diseases. On the other hand, the limitations of using stem cells is an issue that leads researchers to perform experimental studies on the exosomes released by MCSs. Human periapical cyst-derived mesenkymal stem cells can be a smart “lab-on-a-cell” to investigate neurodegenerative diseases and the related exosomes’ content alteration. Full article
(This article belongs to the Collection Collection on Molecular and Cellular Neuroscience)
9 pages, 242 KiB  
Article
Cortical Excitability Measures May Predict Clinical Response to Fampridine in Patients with Multiple Sclerosis and Gait Impairment
by Rechdi Ahdab, Madiha M. Shatila, Abed Rahman Shatila, George Khazen, Joumana Freiha, Maher Salem, Karim Makhoul, Rody El Nawar, Shaza El Nemr, Samar S. Ayache and Naji Riachi
Brain Sci. 2019, 9(12), 357; https://doi.org/10.3390/brainsci9120357 - 5 Dec 2019
Cited by 10 | Viewed by 3842
Abstract
Background: Most multiple sclerosis (MS) patients will develop walking limitations during the disease. Sustained-release oral fampridine is the only approved drug that will improve gait in a subset of MS patients. Objectives: (1) Evaluate fampridine cortical excitability effect in MS patients with gait [...] Read more.
Background: Most multiple sclerosis (MS) patients will develop walking limitations during the disease. Sustained-release oral fampridine is the only approved drug that will improve gait in a subset of MS patients. Objectives: (1) Evaluate fampridine cortical excitability effect in MS patients with gait disability. (2) Investigate whether cortical excitability changes can predict the therapeutic response to fampridine. Method: This prospective observational study enrolled 20 adult patients with MS and gait impairment planned to receive fampridine 10 mg twice daily for two consecutive weeks. Exclusion criteria included: Recent relapse (<3 months), modification of disease modifying drugs (<6 months), or Expanded Disability Status Scale (EDSS) score >7. Neurological examination, timed 25-foot walk test (T25wt), EDSS, and cortical excitability studies were performed upon inclusion and 14 days after initiation of fampridine. Results: After treatment, the mean improvement of T25wt (ΔT25wt) was 4.9 s. Significant enhancement of intra-cortical facilitation was observed (139% versus 241%, p = 0.01) following treatment. A positive correlation was found between baseline resting motor threshold (rMT) and both EDSS (r = 0.57; p < 0.01) and ΔT25wt (r = 0.57, p = 0.01). rMT above 52% of the maximal stimulator output was found to be a good predictor of a favorable response to fampridine (accuracy: 75%). Discussion: Fampridine was found to have a significant modulatory effect on the cerebral cortex, demonstrated by an increase in excitatory intracortical processes as unveiled by paired-pulse transcranial magnetic stimulation. rMT could be useful in selecting patients likely to experience a favorable response to fampridine. Full article
(This article belongs to the Special Issue Advances in Multiple Sclerosis Research—Series I)
9 pages, 533 KiB  
Article
Efficacy of Verbally Describing One’s Own Body Movement in Motor Skill Acquisition
by Tsubasa Kawasaki, Masashi Kono and Ryosuke Tozawa
Brain Sci. 2019, 9(12), 356; https://doi.org/10.3390/brainsci9120356 - 4 Dec 2019
Cited by 3 | Viewed by 3270
Abstract
The present study examined whether (a) verbally describing one’s own body movement can be potentially effective for acquiring motor skills, and (b) if the effects are related to motor imagery. The participants in this study were 36 healthy young adults (21.2 ± 0.7 [...] Read more.
The present study examined whether (a) verbally describing one’s own body movement can be potentially effective for acquiring motor skills, and (b) if the effects are related to motor imagery. The participants in this study were 36 healthy young adults (21.2 ± 0.7 years), randomly assigned into two groups (describing and control). They performed a ball rotation activity, with the describing group being asked by the examiner to verbally describe their own ball rotation, while the control group was asked to read a magazine aloud. The participants’ ball rotation performances were measured before the intervention, then again immediately after, five minutes after, and one day after. In addition, participants’ motor imagery ability (mental chronometry) of their upper extremities was measured. The results showed that the number of successful ball rotations (motor smoothness) and the number of ball drops (motor error) significantly improved in the describing group. Moreover, improvement in motor skills had a significant correlation with motor imagery ability. This suggests that verbally describing an intervention is an effective tool for learning motor skills, and that motor imagery is a potential mechanism for such verbal descriptions. Full article
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<p>A schematic diagram of the procedure.</p>
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<p>(<b>a</b>) Mean number of successful ball rotations in the describing and control groups. (<b>b</b>) Mean number of ball drops in the describing and control groups. For (<b>a</b>) and (<b>b</b>), the error bars denote the standard deviation.</p>
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<p>Scattergram showing the relationship between the improvement number of successful ball rotations in Post 3 and the relative error value of the mental chronometry in the describing group (<span class="html-italic">n</span> = 18, Spearman’s rank correlation).</p>
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22 pages, 12818 KiB  
Article
Improved EOG Artifact Removal Using Wavelet Enhanced Independent Component Analysis
by Mohamed F. Issa and Zoltan Juhasz
Brain Sci. 2019, 9(12), 355; https://doi.org/10.3390/brainsci9120355 - 4 Dec 2019
Cited by 50 | Viewed by 6018
Abstract
Electroencephalography (EEG) signals are frequently contaminated with unwanted electrooculographic (EOG) artifacts. Blinks and eye movements generate large amplitude peaks that corrupt EEG measurements. Independent component analysis (ICA) has been used extensively in manual and automatic methods to remove artifacts. By decomposing the signals [...] Read more.
Electroencephalography (EEG) signals are frequently contaminated with unwanted electrooculographic (EOG) artifacts. Blinks and eye movements generate large amplitude peaks that corrupt EEG measurements. Independent component analysis (ICA) has been used extensively in manual and automatic methods to remove artifacts. By decomposing the signals into neural and artifactual components and artifact components can be eliminated before signal reconstruction. Unfortunately, removing entire components may result in losing important neural information present in the component and eventually may distort the spectral characteristics of the reconstructed signals. An alternative approach is to correct artifacts within the independent components instead of rejecting the entire component, for which wavelet transform based decomposition methods have been used with good results. An improved, fully automatic wavelet-based component correction method is presented for EOG artifact removal that corrects EOG components selectively, i.e., within EOG activity regions only, leaving other parts of the component untouched. In addition, the method does not rely on reference EOG channels. The results show that the proposed method outperforms other component rejection and wavelet-based EOG removal methods in its accuracy both in the time and the spectral domain. The proposed new method represents an important step towards the development of accurate, reliable and automatic EOG artifact removal methods. Full article
(This article belongs to the Special Issue Handling Uncertainty in EEG Signal Pattern Recognition)
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<p>The data processing flowchart of the proposed EOG removal method.</p>
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<p>An example for frontal channels (marked by red circles) used for correlation calculation in EOG independent component identification. Top view of scalp with nose pointing upwards, 128-channel Biosemi ABC electrode layout.</p>
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<p>Distribution of the normalized weights of the components of 20 EOG contaminated measurements selected from the Klados datasets. The red crosses represent the weight of the EOG (HEOG and VEOG) components.</p>
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<p>Distribution of the normalized independent component analysis (ICA) component weights of 10 selected PhysioNet datasets.</p>
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<p>Distribution of the normalized ICA component weights of the 22 datasets obtained in our laboratory.</p>
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<p>ICA components of a selected Klados dataset.</p>
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<p>Sample sections of VEOG (blue) and HEOG (red) EOG components from four selected Klados datasets.</p>
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<p>Correction target windows around the detected VEOG blink (<b>a</b>) and HEOG eye movement (<b>b</b>) peaks in the EOG ICA components.</p>
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<p>The wavelet decomposition process and calculation of coefficients. Letters F and G represent the output signals of the low-pass and high-pass filters, respectively.</p>
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<p>Wavelet decomposition of a target EOG peak signal window within an independent component.</p>
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<p>Illustration of the cleaning performance on one artifact contaminated section of the Klados dataset9. The two subplots on the right show the difference of the pure electroencephalography (EEG) data and the wavelet-enhanced ICA (wICA) and proposed method (PM) cleaned signals, respectively. Amplitude scales are different to make difference signal visible.</p>
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<p>Comparison of the artifact-free, the contaminated and the PM-cleaned EEG signals of dataset9, channel Fp1.</p>
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<p>Distribution of the <math display="inline"><semantics> <mi>λ</mi> </semantics></math> (<b>a</b>), difference in signal-to-noise ratio <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi>S</mi> <mi>N</mi> <mi>R</mi> </mrow> </semantics></math> (<b>b</b>) and root mean square error <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>M</mi> <mi>S</mi> <mi>E</mi> </mrow> </semantics></math> (<b>c</b>) dataset average values obtained with the rejection ICA, wICA and the proposed method. For <math display="inline"><semantics> <mi>λ</mi> </semantics></math> and <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi>S</mi> <mi>N</mi> <mi>R</mi> </mrow> </semantics></math> the higher, while for <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>M</mi> <mi>S</mi> <mi>E</mi> </mrow> </semantics></math>, the lower values mean better performance.</p>
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<p>Power spectral density distributions of the pure, contaminated versus the ICA rej, wICA and PM method cleaned signals (dataset12, channel Fp1).</p>
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<p>The grand average (20 datasets) magnitude squared coherence (MSC) results of the three cleaning methods. Note the higher average performance of our proposed method.</p>
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<p>The magnitude squared coherence (MSC) between the pure EEG signal and the contaminated signal as well as the various cleaned signals (dataset12, Fp1).</p>
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<p>Distribution of the <math display="inline"><semantics> <mi>λ</mi> </semantics></math> (<b>a</b>), <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi>S</mi> <mi>N</mi> <mi>R</mi> </mrow> </semantics></math> (<b>b</b>) and <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>M</mi> <mi>S</mi> <mi>E</mi> </mrow> </semantics></math> (<b>c</b>) dataset average values for the resting state laboratory measurements obtained by cleaning with the rejection ICA, wICA and PM methods. For <math display="inline"><semantics> <mi>λ</mi> </semantics></math> and <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi>S</mi> <mi>N</mi> <mi>R</mi> </mrow> </semantics></math> the higher, while for <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>M</mi> <mi>S</mi> <mi>E</mi> </mrow> </semantics></math>, the lower values mean better performance.</p>
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<p>MSC values obtained with different cleaning methods for the resting state laboratory dataset (20 subjects).</p>
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<p>A 128-channel EOG contaminated EEG dataset before (<b>a</b>) and after (<b>b</b>) artifact removal.</p>
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<p>Topoplot potential map (µV) of a 128-channel EOG contaminated resting state measurement before (left) and after artifact removal (right).</p>
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<p>Distribution of the <math display="inline"><semantics> <mi>λ</mi> </semantics></math> (<b>a</b>), <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi>S</mi> <mi>N</mi> <mi>R</mi> </mrow> </semantics></math> (<b>b</b>) and <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>M</mi> <mi>S</mi> <mi>E</mi> </mrow> </semantics></math> (<b>c</b>) dataset average values for the PhysioNEt P300 dataset by cleaning with the rejection ICA, wICA and PM methods. For <math display="inline"><semantics> <mi>λ</mi> </semantics></math> and <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">Δ</mi> <mi>S</mi> <mi>N</mi> <mi>R</mi> </mrow> </semantics></math> the higher, while for <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>M</mi> <mi>S</mi> <mi>E</mi> </mrow> </semantics></math>, the lower values mean better performance.</p>
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<p>Event related potential (ERP) signals computed from artifact-free epochs only (<b>a</b>) and ERP signals computed from all cleaned epochs (<b>b</b>) showing the distorting effects of the cleaning methods on ERP curves. <math display="inline"><semantics> <mrow> <mi>E</mi> <mi>R</mi> <msubsup> <mi>P</mi> <mrow> <mi>c</mi> <mi>l</mi> <mi>e</mi> <mi>a</mi> <mi>n</mi> </mrow> <mrow> <mi>P</mi> <mi>M</mi> </mrow> </msubsup> </mrow> </semantics></math> produced the smallest difference in both cases (dataset, electrode Fpz).</p>
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14 pages, 3560 KiB  
Article
Diffusion Tensor Magnetic Resonance Imaging for Differentiating Multiple System Atrophy Cerebellar Type and Spinocerebellar Ataxia Type 3
by Chi-Wen Jao, Bing-Wen Soong, Chao-Wen Huang, Chien-An Duan, Chih-Chun Wu, Yu-Te Wu and Po-Shan Wang
Brain Sci. 2019, 9(12), 354; https://doi.org/10.3390/brainsci9120354 - 3 Dec 2019
Cited by 14 | Viewed by 4248
Abstract
Multiple system atrophy cerebellar type (MSA-C) and spinocerebellar ataxia type 3 (SCA3) demonstrate similar manifestations, including ataxia, pyramidal and extrapyramidal signs, as well as atrophy and signal intensity changes in the cerebellum and brainstem. MSA-C and SCA3 cannot be clinically differentiated through T1-weighted [...] Read more.
Multiple system atrophy cerebellar type (MSA-C) and spinocerebellar ataxia type 3 (SCA3) demonstrate similar manifestations, including ataxia, pyramidal and extrapyramidal signs, as well as atrophy and signal intensity changes in the cerebellum and brainstem. MSA-C and SCA3 cannot be clinically differentiated through T1-weighted magnetic resonance imaging (MRI) alone; therefore, clinical consensus criteria and genetic testing are also required. Here, we used diffusion tensor imaging (DTI) to measure water molecular diffusion of white matter and investigate the difference between MSA-C and SCA3. Four measurements were calculated from DTI images, including fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD), and mean diffusivity (MD). Fifteen patients with MSA-C, 15 patients with SCA3, and 30 healthy individuals participated in this study. Both patient groups demonstrated a significantly decreased FA but a significantly increased AD, RD, and MD in the cerebello-ponto-cerebral tracts. Moreover, patients with SCA3 demonstrated a significant decrease in FA but more significant increases in AD, RD, and MD in the cerebello-cerebral tracts than patients with MSAC. Our results may suggest that FA and MD can be effectively used for differentiating SCA3 and MSA-C, both of which are cerebellar ataxias and have many common atrophied regions in the cerebral and cerebellar cortex. Full article
(This article belongs to the Section Neurotechnology and Neuroimaging)
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<p>FA map from a control group participant. (<b>a</b>) FA map without directional information. (<b>b</b>) Combined FA and directional map. Colors represent the direction of the fiber tract: red, left–right; green, anterior–posterior; blue, superior–inferior.</p>
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<p>Flowchart of DTI analysis.</p>
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<p>Tract-based spatial statistics (TBSS) result of DTI in the regions of interest in the JHU-ICBM-labels atlas: (<b>a</b>) axial view, (<b>b</b>) coronal, and (<b>c</b>) sagittal view. Colors represent the direction of the fiber tract: dark-brown, left–right; yellow and light brown, anterior–posterior; and red, superior–inferior.</p>
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<p>One-way ANOVA statistical analysis of FA, RD, AD, and MD of DTI between groups (<b>a</b>) Results between health control group (HC) and MSAC (<b>b</b>) Results between HC and SCA3.</p>
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<p>TBSS result in patients with MSA-C and patients with SCA3. The general linear model (GLM) model for age regression was used, two-sample <span class="html-italic">t</span> test and permutation test to investigate the significant differences in FA, AD, RD, and MD between the control and SCA3 groups. The red and blue color voxels are the regions with significantly decreased <span class="html-italic">z</span> value (&gt;3) and those with significantly increased <span class="html-italic">z</span> value (&lt;−3) in patients with SCA3, respectively.</p>
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<p>Mean (<b>a</b>) FA of and (<b>b</b>) MD of cerebral and cerebellar white matter (WM) for the three groups.</p>
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<p>Scatter plot of rescaled MD versus FA for all participants. Blue, green, red stars denote the control participants, patients with MSA-C, and patients with SCA3, respectively; the three groups can be linearly separated by K-means clustering with few overlaps.</p>
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