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22 pages, 2244 KiB  
Article
Mismatch Negativity Unveils Tone Perception Strategies and Degrees of Tone Merging: The Case of Macau Cantonese
by Han Wang, Fei Gao and Jingwei Zhang
Brain Sci. 2024, 14(12), 1271; https://doi.org/10.3390/brainsci14121271 - 17 Dec 2024
Viewed by 590
Abstract
Background/Objectives: Previous studies have examined the role of working memory in cognitive tasks such as syntactic, semantic, and phonological processing, thereby contributing to our understanding of linguistic information management and retrieval. However, the real-time processing of phonological information—particularly in relation to suprasegmental features [...] Read more.
Background/Objectives: Previous studies have examined the role of working memory in cognitive tasks such as syntactic, semantic, and phonological processing, thereby contributing to our understanding of linguistic information management and retrieval. However, the real-time processing of phonological information—particularly in relation to suprasegmental features like tone, where its contour represents a time-varying signal—remains a relatively underexplored area within the framework of Information Processing Theory (IPT). This study aimed to address this gap by investigating the real-time processing of similar tonal information by native Cantonese speakers, thereby providing a deeper understanding of how IPT applies to auditory processing. Methods: Specifically, this study combined assessments of cognitive functions, an AX discrimination task, and electroencephalography (EEG) to investigate the discrimination results and real-time processing characteristics of native Macau Cantonese speakers perceiving three pairs of similar tones. Results: The behavioral results confirmed the completed merging of T2–T5 in Macau Cantonese, and the ongoing merging of T3–T6 and T4–T6, with perceptual merging rates of 45.46% and 27.28%, respectively. Mismatch negativity (MMN) results from the passive oddball experiment revealed distinct temporal processing patterns for the three tone pairs. Cognitive functions, particularly attention and working memory, significantly influenced tone discrimination, with more pronounced effects observed in the mean amplitude of MMN during T4–T6 discrimination. Differences in MMN peak latency between T3–T6 and T4–T6 further suggested the use of different perceptual strategies for these contour-related tones. Specifically, the T3–T6 pair can be perceived through early signal input, whereas the perception of T4–T6 relies on constant signal input. Conclusions: This distinction in cognitive resource allocation may explain the different merging rates of the two tone pairs. This study, by focusing on the perceptual difficulty of tone pairs and employing EEG techniques, revealed the temporal processing of similar tones by native speakers, providing new insights into tone phoneme processing and speech variation. Full article
(This article belongs to the Collection Collection on Neurobiology of Language)
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<p>The electrode layout used in this study.</p>
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<p>The stimuli based on Guangfu Cantonese tonal system used in this study. The dashed line indicates the divergence point (160 ms) of T2–T5 and T4–T6.</p>
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<p>The discrimination rate and reaction time for different tone pairs. (<b>a</b>) shows the discrimination rate for different tone pairs; (<b>b</b>) shows the reaction times required for recognizing different tone pairs. * denotes <span class="html-italic">p</span> &lt; 0.05, *** denotes <span class="html-italic">p</span> &lt; 0.001, **** denotes <span class="html-italic">p</span> &lt; 0.0001, “n.s.” denotes “not significant”.</p>
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<p>The brain topographic maps of standard stimulus, deviant stimulus, and difference wave in the time windows of 100–250 ms, 250–300 ms, and 300–340 ms under T4–T6 condition.</p>
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<p>The grand average ERP waveforms at the Fz and FCz electrodes under three conditions, including the standard wave, deviant wave, and difference wave. The topographic maps on the right side of each sub-figure represent, from top to bottom, the standard stimulus, the deviant stimulus, and the difference between them within the respective time window. (<b>a</b>) shows the ERP waveforms and topographic maps under the T2–T5 condition; (<b>b</b>) shows the ERP waveforms and topographic maps under the T3–T6 condition; (<b>c</b>) shows the ERP waveforms and topographic maps under the T4–T6 condition.</p>
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<p>Differences in the mean amplitude, peak amplitude, and peak latency of MMN across the three conditions. (<b>a</b>) shows the differences in mean amplitude across the three tone pairs; (<b>b</b>) shows the differences in peak amplitude across the three tone pairs; (<b>c</b>) shows the differences in peak latency across the three tone pairs. **** denotes <span class="html-italic">p</span> &lt; 0.0001, “n.s.” denotes “not significant”.</p>
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19 pages, 3997 KiB  
Article
P300 Latency with Memory Performance: A Promising Biomarker for Preclinical Stages of Alzheimer’s Disease
by Manal Mohamed, Nourelhuda Mohamed and Jae Gwan Kim
Biosensors 2024, 14(12), 616; https://doi.org/10.3390/bios14120616 - 15 Dec 2024
Viewed by 684
Abstract
Detecting and tracking the preclinical stages of Alzheimer’s disease (AD) is now of particular interest due to the aging of the world’s population. AD is the most common cause of dementia, affecting the daily lives of those afflicted. Approaches in development can accelerate [...] Read more.
Detecting and tracking the preclinical stages of Alzheimer’s disease (AD) is now of particular interest due to the aging of the world’s population. AD is the most common cause of dementia, affecting the daily lives of those afflicted. Approaches in development can accelerate the evaluation of the preclinical stages of AD and facilitate early treatment and the prevention of symptom progression. Shifts in P300 amplitude and latency, together with neuropsychological assessments, could serve as biomarkers in the early screening of declines in cognitive abilities. In this study, we investigated the ability of the P300 indices evoked during a visual oddball task to differentiate pre-clinically diagnosed participants from normal healthy adults (HCs). Two preclinical stages, named asymptomatic AD (AAD) and prodromal AD (PAD), were included in this study, and a total of 79 subjects participated, including 35 HCs, 22 AAD patients, and 22 PAD patients. A mixed-design ANOVA test was performed to compare the P300 indices among groups during the processing of the target and non-target stimuli. Additionally, the correlation between these neurophysiological variables and the neuropsychological tests was evaluated. Our results revealed that neither the peak amplitude nor latency of P300 can distinguish AAD from HCs. Conversely, the peak latency of P300 can be used as a biomarker to differentiate PAD from AAD and HCs. The correlation results revealed a significant relationship between the peak latency of P300 and memory domain tasks, showing that less time-demanding neuropsychological assessments can be used. In summary, our findings showed that a combination of P300 latency and memory-requiring tasks can be used as an efficient biomarker to differentiate individuals with AAD from HCs. Full article
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<p>A schematic diagram of the sequence of the oddball task.</p>
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<p>Flowchart of the EEG data processing steps.</p>
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<p>Mean peak amplitude values (µv) across the 32 channels during target processing in the HC, AAD, and PAD groups.</p>
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<p>Mean peak latency values (ms) across the 32 channels during target processing in the HC, AAD, and PAD groups.</p>
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<p>Average P300 waveforms recorded at the C3, CZ, and C4 electrodes during the presentation of target stimuli (shown in black) and non-target stimuli (shown in red) for the (<b>A</b>) HC, (<b>B</b>) AAD, and (<b>C</b>) PAD groups. The measuring window spans from 300 to 600 ms (shown in gray shadows).</p>
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<p>Correlations between the P300 peak latency and neuropsychological tests in the HC, AAD, and PAD groups and the whole-group correlation during the presentation of the target stimulus (<b>A</b>), and the correlation with frontal/executive tasks and among the cognitive domains (<b>B</b>). The level of significance is shown with (*)/* <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01 (FDR-corrected).</p>
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<p>Correlations between the P300 peak latency and neuropsychological tests in the HC, AAD, and PAD groups and the whole-group correlation during the presentation of the target stimulus (<b>A</b>), and the correlation with frontal/executive tasks and among the cognitive domains (<b>B</b>). The level of significance is shown with (*)/* <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01 (FDR-corrected).</p>
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17 pages, 4943 KiB  
Article
Cost-Reference Particle Filter-Based Method for Constructing Effective Brain Networks: Application in Optically Pumped Magnetometer Magnetoencephalography
by Yuyu Ma, Xiaoyu Liang, Huanqi Wu, Hao Lu, Yong Li, Changzeng Liu, Yang Gao, Min Xiang, Dexin Yu and Xiaolin Ning
Bioengineering 2024, 11(12), 1258; https://doi.org/10.3390/bioengineering11121258 - 12 Dec 2024
Viewed by 500
Abstract
Optically pumped magnetometer magnetoencephalography (OPM-MEG) represents a novel method for recording neural signals in the brain, offering the potential to measure critical neuroimaging characteristics such as effective brain networks. Effective brain networks describe the causal relationships and information flow between brain regions. In [...] Read more.
Optically pumped magnetometer magnetoencephalography (OPM-MEG) represents a novel method for recording neural signals in the brain, offering the potential to measure critical neuroimaging characteristics such as effective brain networks. Effective brain networks describe the causal relationships and information flow between brain regions. In constructing effective brain networks using Granger causality, the noise in the multivariate autoregressive model (MVAR) is typically assumed to follow a Gaussian distribution. However, in experimental measurements, the statistical characteristics of noise are difficult to ascertain. In this paper, a Granger causality method based on a cost-reference particle filter (CRPF) is proposed for constructing effective brain networks under unknown noise conditions. Simulation results show that the average estimation errors of the MVAR model coefficients using the CRPF method are reduced by 53.4% and 82.4% compared to the Kalman filter (KF) and maximum correntropy filter (MCF) under Gaussian noise, respectively. The CRPF method reduces the average estimation errors by 88.1% and 85.8% compared to the MCF under alpha-stable distribution noise and the KF method under pink noise conditions, respectively. In an experiment, the CRPF method recoversthe latent characteristics of effective connectivity of benchmark somatosensory stimulation data in rats, human finger movement, and auditory oddball paradigms measured using OPM-MEG, which is in excellent agreement with known physiology. The simulation and experimental results demonstrate the effectiveness of the proposed algorithm and OPM-MEG for measuring effective brain networks. Full article
(This article belongs to the Section Biosignal Processing)
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<p>OPM-MEG system.</p>
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<p>(<b>a</b>) Auditory oddball experimental paradigm. (<b>b</b>) The position and distribution of OPM magnetometers.</p>
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<p>MVAR model coefficient estimation using different noise conditions and filter methods. (<b>a</b>–<b>c</b>) Comparison between estimated values and ground truth values of MVAR model coefficients using the CRPF method under Gaussian noise, alpha-stable distribution noise, and pink noise, respectively. (<b>d</b>–<b>f</b>) Estimated values and ground truth values of <math display="inline"><semantics> <msub> <mi>d</mi> <mi>t</mi> </msub> </semantics></math> using the KF, MCF, and CRPF methods, respectively. (<b>g</b>–<b>i</b>) Estimation errors across 50 trials under Gaussian noise, alpha-stable distribution noise, and pink noise, respectively.</p>
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<p>Total outflow sPDC values of the cS1 node during unilateral whisker stimulation in rats.</p>
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<p>Connections in effective brain networks during finger movement. (<b>a</b>) MCF method; (<b>b</b>) CRPF method (displays the top 50 connections).</p>
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<p>Source activity and time series of bilateral STG. (<b>a</b>) Standard stimulation; (<b>b</b>) deviant stimulation (significantly different time points are marked with a yellow line along the <span class="html-italic">x</span>-axis, <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>&lt;</mo> <mn>0.001</mn> </mrow> </semantics></math>).</p>
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<p>Time–frequency effective brain network of MMF (white boxes represent statistically significant time–frequency points, <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>&lt;</mo> <mn>0.05</mn> </mrow> </semantics></math>, FDR corrected).</p>
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<p>Connections in effective brain networks of MMF with significant differences. (<b>a</b>) Connections in the theta band (3–8 Hz); (<b>b</b>) Connections in the alpha band (8–13 Hz); (<b>c</b>) Connections in the beta band (13–30 Hz).</p>
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<p>Total inflow sPDC values of MMF. (<b>a</b>) MCF method; (<b>b</b>) CRPF method.</p>
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14 pages, 2538 KiB  
Article
Sex Differences in Processing Emotional Speech Prosody: Preliminary Findings from a Multi-Feature Oddball Study
by Chieh Kao and Yang Zhang
Brain Sci. 2024, 14(12), 1216; https://doi.org/10.3390/brainsci14121216 - 30 Nov 2024
Viewed by 786
Abstract
Background/Objectives: Emotional prosody, the intonation and rhythm of speech that conveys emotions, is vital for speech communication as it provides essential context and nuance to the words being spoken. This study explored how listeners automatically process emotional prosody in speech, focusing on different [...] Read more.
Background/Objectives: Emotional prosody, the intonation and rhythm of speech that conveys emotions, is vital for speech communication as it provides essential context and nuance to the words being spoken. This study explored how listeners automatically process emotional prosody in speech, focusing on different neural responses for the prosodic categories and potential sex differences. Methods: The pilot data here involved 11 male and 11 female adult participants (age range: 18–28). A multi-feature oddball paradigm was used, in which participants were exposed to sequences of non-repeating English words with emotional (angry, happy, sad) or neutral prosody while watching a silent movie. Results: Both mismatch negativity (MMN) and P3a components were observed, indicating automatic perceptual grouping and neural sensitivity to emotional variations in speech. Women showed stronger MMN to angry than sad prosody, while men showed stronger MMN to angry than happy prosody. Happy prosody elicited the strongest P3a, but only in men. Conclusions: The findings challenge the notion that all facets of emotion processing are biased toward female superiority. However, these results from 22 young adult native English speakers should be interpreted with caution, as data from a more adequate sample size are needed to test the generalizability of the findings. Combined with results from studies on children and elderly adults, these preliminary data underscore the need to explore the complexities of emotional speech processing mechanisms to account for category and sex differences across the lifespan in a longitudinal perspective. Full article
(This article belongs to the Special Issue Language, Communication and the Brain)
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<p>A schematic example of the order of the trials. The standard (neutral prosody) and deviant (angry, happy, and sad prosodies) were always alternating, and the three emotions (deviants) were pseudo-randomly interspersed.</p>
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<p>The grand mean event-related potential (ERP) waveforms of standard (neutral prosody) and deviants (angry, happy, and sad), and grand mean difference waveforms of angry, happy, and sad for male and female listeners. Mean amplitudes of the midline electrodes (Fz, Cz, Pz) were used for the waveforms. The gray shaded areas mark the windows for MMN (200–300 ms) and P3a (350–450 ms).</p>
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<p>The scalp topographic maps of (<b>A</b>) MMN and (<b>B</b>) P3a to angry, happy, and sad emotional prosodies averaged across male and female listeners. The topographies are based on the latencies of peak values at Cz channel.</p>
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<p>The interaction effect of emotion and sex displayed in the model predicted MMN amplitudes to angry, happy, and sad emotional prosodies in male and female listeners. (* stands for <span class="html-italic">p</span> &lt; 0.05; ** for <span class="html-italic">p</span> &lt; 0.01).</p>
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<p>The (<b>A</b>) main effect of electrode region and (<b>B</b>) interaction effect of emotion and sex displayed in the model predicted P3a amplitudes to angry, happy, and sad emotional prosodies in male and female listeners. (*** stands for <span class="html-italic">p</span> &lt; 0.001).</p>
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19 pages, 2770 KiB  
Article
Intentional or Designed? The Impact of Stance Attribution on Cognitive Processing of Generative AI Service Failures
by Dong Lv, Rui Sun, Qiuhua Zhu, Jiajia Zuo, Shukun Qin and Yue Cheng
Brain Sci. 2024, 14(10), 1032; https://doi.org/10.3390/brainsci14101032 - 17 Oct 2024
Viewed by 1158
Abstract
Background: With the rapid expansion of the generative AI market, conducting in-depth research on cognitive conflicts in human–computer interaction is crucial for optimizing user experience and improving the quality of interactions with AI systems. However, existing studies insufficiently explore the role of user [...] Read more.
Background: With the rapid expansion of the generative AI market, conducting in-depth research on cognitive conflicts in human–computer interaction is crucial for optimizing user experience and improving the quality of interactions with AI systems. However, existing studies insufficiently explore the role of user cognitive conflicts and the explanation of stance attribution in the design of human–computer interactions. Methods: This research, grounded in mental models theory and employing an improved version of the oddball paradigm, utilizes Event-Related Spectral Perturbations (ERSP) and functional connectivity analysis to reveal how task types and stance attribution explanations in generative AI influence users’ unconscious cognitive processing mechanisms during service failures. Results: The results indicate that under design stance explanations, the ERSP and Phase Locking Value (PLV) in the theta frequency band were significantly lower for emotional task failures than mechanical task failures. In the case of emotional task failures, the ERSP and PLV in the theta frequency band induced by intentional stance explanations were significantly higher than those induced by design stance explanations. Conclusions: This study found that stance attribution explanations profoundly affect users’ mental models of AI, which determine their responses to service failure. Full article
(This article belongs to the Section Neural Engineering, Neuroergonomics and Neurorobotics)
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<p>Attribution stance explanations can influence users’ mental models of AI. These mental models lead to different cognitive evaluations by users when various types of generative AI services (emotional or mechanical tasks) fail.</p>
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<p>Examples of experimental condition stimuli, examples of specific tasks, and an organizational chart of trial numbers.</p>
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<p>The experimental process included gaze point pictures, AI stance attribution interpretation pictures, generative AI responses to the service, and allowing participants to judge the success of the AI service.</p>
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<p>Spectrograms of ERSD induced by cz electrode position in 4 conditions with corresponding topography.</p>
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<p>Functional connectivity diagrams in 4 conditions.</p>
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17 pages, 1968 KiB  
Article
A Dual Role for the Dorsolateral Prefrontal Cortex (DLPFC) in Auditory Deviance Detection
by Manon E. Jaquerod, Ramisha S. Knight, Alessandra Lintas and Alessandro E. P. Villa
Brain Sci. 2024, 14(10), 994; https://doi.org/10.3390/brainsci14100994 - 29 Sep 2024
Viewed by 1240
Abstract
Background: In the oddball paradigm, the dorsolateral prefrontal cortex (DLPFC) is often associated with active cognitive responses, such as maintaining information in working memory or adapting response strategies. While some evidence points to the DLPFC’s role in passive auditory deviance perception, a detailed [...] Read more.
Background: In the oddball paradigm, the dorsolateral prefrontal cortex (DLPFC) is often associated with active cognitive responses, such as maintaining information in working memory or adapting response strategies. While some evidence points to the DLPFC’s role in passive auditory deviance perception, a detailed understanding of the spatiotemporal neurodynamics involved remains unclear. Methods: In this study, event-related optical signals (EROS) and event-related potentials (ERPs) were simultaneously recorded for the first time over the prefrontal cortex using a 64-channel electroencephalography (EEG) system, during passive auditory deviance perception in 12 right-handed young adults (7 women and 5 men). In this oddball paradigm, deviant stimuli (a 1500 Hz pure tone) elicited a negative shift in the N1 ERP component, related to mismatch negativity (MMN), and a significant positive deflection associated with the P300, compared to standard stimuli (a 1000 Hz tone). Results: We hypothesize that the DLPFC not only participates in active tasks but also plays a critical role in processing deviant stimuli in passive conditions, shifting from pre-attentive to attentive processing. We detected enhanced neural activity in the left middle frontal gyrus (MFG), at the same timing of the MMN component, followed by later activation at the timing of the P3a ERP component in the right MFG. Conclusions: Understanding these dynamics will provide deeper insights into the DLPFC’s role in evaluating the novelty or unexpectedness of the deviant stimulus, updating its cognitive value, and adjusting future predictions accordingly. However, the small number of subjects could limit the generalizability of the observations, in particular with respect to the effect of handedness, and additional studies with larger and more diverse samples are necessary to validate our conclusions. Full article
(This article belongs to the Section Behavioral Neuroscience)
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<p>Schematic representation of the co-localization of the 8 light detectors (red circles) and 22 light sources (blue squares) over prefrontal and premotor areas of the cerebral cortex and the 64-channel EEG setup according to the International 10/20 system.</p>
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<p>(<b>A</b>) Grand–average waveforms of the ERPs evoked by standard (dashed blue) and deviant (red) tones (mean<math display="inline"><semantics> <mrow> <mtext> </mtext> <mo>±</mo> <mtext> </mtext> <mn>2</mn> <mtext> </mtext> <mo>×</mo> </mrow> </semantics></math> SEM) at locations corresponding to 9 sets of electrodes along the antero-posterior and mesio-lateral axis. Four ERP components (N1, N2, P3a, and P3b) were identified. (<b>B</b>) Topographic maps of the consistency of differential activations (contrast analysis between deviant and standard tone conditions) for N1, N2, P3a, and P3b ERP components. The contour lines connect the points with the same value of consistency of differential activation.</p>
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<p>Differential activations in the event–related optical signals (EROS) following a contrast analysis (<span class="html-italic">deviant tone</span> &gt; <span class="html-italic">standard tone</span>, Z-score <math display="inline"><semantics> <mrow> <mo>&gt;</mo> <mn>2.575</mn> </mrow> </semantics></math>, n &gt; 10. (<b>A</b>) Raw EROS data analysis. The upper panels show the axial projection of the Z-score surface maps (computed across subjects) on a template MRI for the contrast analysis at 88, 120, and <math display="inline"><semantics> <mrow> <mn>320</mn> <mtext> </mtext> <mi>ms</mi> </mrow> </semantics></math> after stimulus onset. The Talairach coordinates <span class="html-italic">x</span> and <span class="html-italic">y</span> of the voxels with the greatest differential activation are indicated with the corresponding Brodmann area (BA) and nearest cortical gyri. The corresponding Talairach <span class="html-italic">z</span> coordinate is on the cortical surface. The lower panels show the corresponding EROS grand–average curves (mean <math display="inline"><semantics> <mrow> <mo>±</mo> <mtext> </mtext> <mn>2</mn> <mtext> </mtext> <mo>×</mo> <mtext> </mtext> </mrow> </semantics></math>SEM), from <math display="inline"><semantics> <mrow> <mn>100</mn> <mtext> </mtext> <mi>ms</mi> </mrow> </semantics></math> before stimulus onset to <math display="inline"><semantics> <mrow> <mn>600</mn> <mtext> </mtext> <mi>ms</mi> </mrow> </semantics></math> after stimulus onset. of the peak voxel and its direct neighboring voxels during the deviant (red) and the standard (dashed blue) tones conditions. An arrow indicates the timing of the greatest differential activation with a sign (n.s.) not significant and (*) <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>&lt;</mo> <mrow> <mn>0.05</mn> </mrow> </mrow> </semantics></math>, for the significance level of the differential activation at the peak latency with multiple comparison correction within the associated ROI. (<b>B</b>) Standardized EROS data analysis. The upper panels show the axial projection of the new Z-score surface maps (computed across subjects) on a template MRI recomputed following the standardization procedure described in the Methods <a href="#sec2dot4-brainsci-14-00994" class="html-sec">Section 2.4</a>, for the contrast analysis at 88, 128, and <math display="inline"><semantics> <mrow> <mn>320</mn> <mtext> </mtext> <mi>ms</mi> </mrow> </semantics></math> after stimulus onset. At <math display="inline"><semantics> <mrow> <mn>88</mn> <mtext> </mtext> <mi>ms</mi> </mrow> </semantics></math>, no voxel of the differential activation reached the threshold level Z-score <math display="inline"><semantics> <mrow> <mo>&gt;</mo> <mn>2.575</mn> </mrow> </semantics></math>, n &gt; 10. At 128 and <math display="inline"><semantics> <mrow> <mn>320</mn> <mtext> </mtext> <mi>ms</mi> </mrow> </semantics></math> after stimulus onset, the Z-score of the differential activations was above the threshold level and remained significant (<math display="inline"><semantics> <mrow> <mi>p</mi> <mo>&lt;</mo> <mrow> <mn>0.05</mn> </mrow> </mrow> </semantics></math>) even after multiple comparison correction within the associated ROI.</p>
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16 pages, 991 KiB  
Article
Early Auditory Temporal Processing Deficit in Children with Autism Spectrum Disorder: The Research Domain Criteria Framework
by Atoosa Sanglakh Ghoochan Atigh, Mohammad Taghi Joghataei, Shadi Moradkhani, Mehdi Alizadeh Zarei and Mohammad Ali Nazari
Brain Sci. 2024, 14(9), 896; https://doi.org/10.3390/brainsci14090896 - 3 Sep 2024
Viewed by 1401
Abstract
Altered sensory processing especially in the auditory system is considered a typical observation in children with autism spectrum disorder (ASD). Auditory temporal processing is known to be impaired in ASD children. Although research suggests that auditory temporal processing abnormalities could be responsible for [...] Read more.
Altered sensory processing especially in the auditory system is considered a typical observation in children with autism spectrum disorder (ASD). Auditory temporal processing is known to be impaired in ASD children. Although research suggests that auditory temporal processing abnormalities could be responsible for the core aspects of ASD, few studies have examined early time processing and their results have been conflicting. The present event-related potential (ERP) study investigated the early neural responses to duration and inter-stimulus interval (ISI) deviants in nonspeech contexts in children with ASD and a control group of typically developing (TD) children matched in terms of age and IQ. A passive auditory oddball paradigm was employed to elicit the mismatch negativity (MMN) for change detection considering both the duration and ISI-based stimulus. The MMN results showed that the ASD group had a relatively diminished amplitude and significant delayed latency in response to duration deviants. The findings are finally discussed in terms of hyper-hyposensitivity of auditory processing and the fact that the observed patterns may potentially act as risk factors for ASD development within the research domain criteria (RDoC) framework. Full article
(This article belongs to the Section Developmental Neuroscience)
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<p>Schematic illustration of the stimuli and experimental setup. (<b>a</b>) The upper panel illustrates block 1, focused on duration deviations. The frequent duration stimuli (100 ms) are represented by black rectangles, while the deviant duration stimuli (50 ms) are depicted in red rectangles. ISI is 1000 ms, displayed in green lines. (<b>b</b>) The lower panel represents block 2, which is designed around ISI deviations. In this block, the green line signifies the standard ISI (1000 ms), and the red line denotes the deviant ISI (500 ms). All auditory stimuli last for 100 ms, depicted in black rectangles.</p>
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<p>Grand averages of ERP waves to the standard and deviant stimuli in children with autism disorder and TDC at Fz and Cz; Dotted line indicates the start of stimulus (<b>a</b>,<b>b</b>) Duration-based block. (<b>c</b>,<b>d</b>) ISI-based block (MMN, mismatch negativity; Fz, frontal electrode; Cz, central electrode).</p>
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<p>Duration-based block. (<b>a</b>) Grand averages of mismatch negativity event-related potentials in children with autism disorder and TDC at Fz and Cz; Dotted line indicates the start of stimulus and grey area represents MMN peak. (<b>b</b>) The scalp topographical map for MMN. The top topography corresponds to the autism group, while the bottom one is for the non-autism group (MMN, mismatch negativity; Fz, frontal electrode; Cz, central electrode).</p>
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<p>ISI-based block. (<b>a</b>) Grand averages of mismatch negativity event-related potentials in children with autism disorder and TDC at Fz and Cz; Dotted line indicates the start of stimulus and grey area represents MMN peak. (<b>b</b>) The scalp topographical map for MMN. The top topography corresponds to the autism group, while the bottom one is for the non-autism group (MMN, mismatch negativity; Fz, frontal electrode; Cz, central electrode).</p>
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21 pages, 8502 KiB  
Article
Habituation of Central and Electrodermal Responses to an Auditory Two-Stimulus Oddball Paradigm
by Gianluca Rho, Alejandro Luis Callara, Enzo Pasquale Scilingo, Alberto Greco and Luca Bonfiglio
Sensors 2024, 24(15), 5053; https://doi.org/10.3390/s24155053 - 4 Aug 2024
Viewed by 1230
Abstract
The orienting reaction (OR) towards a new stimulus is subject to habituation, i.e., progressively attenuates with stimulus repetition. The skin conductance responses (SCRs) are known to represent a reliable measure of OR at the peripheral level. Yet, it is still a matter of [...] Read more.
The orienting reaction (OR) towards a new stimulus is subject to habituation, i.e., progressively attenuates with stimulus repetition. The skin conductance responses (SCRs) are known to represent a reliable measure of OR at the peripheral level. Yet, it is still a matter of debate which of the P3 subcomponents is the most likely to represent the central counterpart of the OR. The aim of the present work was to study habituation, recovery, and dishabituation phenomena intrinsic to a two-stimulus auditory oddball paradigm, one of the most-used paradigms both in research and clinic, by simultaneously recording SCRs and P3 in twenty healthy volunteers. Our findings show that the target stimulus was capable of triggering a more marked OR, as indexed by both SCRs and P3, compared to the standard stimulus, that could be due to its affective saliency and relevance for task completion; the application of temporal principal components analysis (PCA) to the P3 complex allowed us to identify several subcomponents including both early and late P3a (eP3a; lP3a), P3b, novelty P3 (nP3), and both a positive and a negative Slow Wave (+SW; −SW). Particularly, lP3a and P3b subcomponents showed a similar behavior to that observed for SCRs , suggesting them as central counterparts of OR. Finally, the P3 evoked by the first standard stimulus after the target showed a significant dishabituation phenomenon which could represent a sign of the local stimulus change. However, it did not reach a sufficient level to trigger an SCR/OR since it did not represent a salient event in the context of the task. Full article
(This article belongs to the Section Biomedical Sensors)
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<p>Schematic illustration of the procedure to identify stimulus–evoked SCRs (eSCRs). The raw SC is downsampled at the sampling frequency of 50 Hz and then the z–score is transformed in order to be deconvolved with cvxEDA to obtain an estimate of the phasic component and sudomotor nerve activity (SMNA). The right part of the figure shows an exemplary comparison between raw SC, phasic component, and SMNA to standard (red–dashed vertical lines), and target (green–dashed vertical lines) auditory stimuli. Nonzero SMNA bursts occurring in the 1–5 s interval after the target stimulus onset indicate that the observed SCR is related to the target stimulus itself, whereas no eSCR is present due to the standard stimuli before and after the target.</p>
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<p>The average SCR responses to the standard (blue) and target (red) stimuli in the −1–8 s interval with respect to the stimulus onset (black vertical line). The gray shaded area indicates a significant difference (<span class="html-italic">p</span> &lt; 0.05) between SCRs, with a higher response to targets in the 2.8, 5.4 s interval with respect to the response to standards.</p>
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<p>Results of the statistical analysis on the latency of sudomotor nerve activity (SMNA) responses to standard (blue) and target (red) stimuli (median ± mean absolute error (mae); No significant difference in the latency of responses was present.</p>
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<p>Statistical analysis of the percentage of stimulus-evoked SCRs (**: <span class="html-italic">p</span> &lt; 0.01, ***: <span class="html-italic">p</span> &lt; 0.001). Red plus signs indicate outliers in the distribution. (<b>a</b>) Percentage of SCRs to standard (blue) and target (red) stimuli, irrespective of the stimulation block. Target stimuli elicited more SCRs compared to standard stimuli (SCRtarget = 32%, SCRstandard = 16%, <span class="html-italic">p</span> &lt; 0.001); (<b>b</b>) percentage of SCRs evoked by any stimulus type across blocks. There is a significant decrease in the percentage of SCRs over time.</p>
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<p>Post hoc analysis of the interaction between stimulus type (standard, target) and stimulation block (B1, B2, B3) on the percentage of evoked SCRs (*: <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). Red plus signs indicate outliers in the distribution. There is a significant habituation to the target stimuli, as the percentage of SCRs evoked by targets decreases over blocks. On the other hand, the percentage of SCRs evoked by standard stimuli does not differ across stimulation blocks. Interestingly, the number of responses in the third stimulation block is higher for standard stimuli, compared to targets.</p>
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<p>Statistical analysis of stimulus–evoked SCRs’ amplitude (*: <span class="html-italic">p</span> &lt; 0.05, ***: <span class="html-italic">p</span> &lt; 0.001). (<b>a</b>) Mean ± standard error of the amplitude in response to standard (blue) and target (red) stimuli. SCRs to target stimuli had a significantly higher amplitude with respect to SCRs to standards; (<b>b</b>) mean ± standard error of SCRs’ amplitude over stimulation blocks, irrespective of the stimulus type. Amplitude in the first block was higher than the amplitude in the second and third blocks, but no significant difference was present between the second and third blocks.</p>
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<p>Grand–average SCR responses to the standard stimuli presented immediately before (pre–target, blue) and after (post–target, red) target stimuli in the −1–8 s interval with respect to the stimulus onset (black vertical line). No significant difference has been found between responses.</p>
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<p>Results of the temporal PCA decomposition on subject–average ERPs. Top: topographical grand-average voltage distribution (i.e., scores) of each identified factor across the three stimulation blocks. Bottom: time–course of each factor (i.e., loadings) in the 90–470 ms range, and its peak latency in msec.</p>
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<p>Grand-average ERP responses to the standard (red) and target (green) stimuli, and the difference between them (difference wave; yellow), evaluated at (<b>a</b>) Fz, (<b>b</b>) Cz, and (<b>c</b>) Pz channels. Responses are plotted in the (−200–1000) ms interval with respect to the stimulus onset (black vertical line). The gray shaded areas indicate significant differences between responses to standard and target stimuli (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Results of the statistical comparison between ERP responses to standard stimuli observed immediately before (pre–target) and after (post–target) target stimuli. We report only those channels that showed a significant difference between responses, including (<b>a</b>) Fp1, (<b>b</b>) Fz, (<b>c</b>) Fp2, (<b>d</b>) F3, (<b>e</b>) F4, (<b>f</b>) T3, (<b>g</b>) C3, (<b>h</b>) Cz, (<b>i</b>) C4, (<b>j</b>) P3, and (<b>k</b>) P4. For each of them, we show the grand-average ERPs associated with the pre–target and post–target conditions, respectively, in the −200, 1000 ms time range. Gray shaded areas indicate a significant difference between ERP amplitudes (<span class="html-italic">p</span> &lt; 0.05). Post-target responses showed a more negative N1 amplitude and a more positive standard P3 (sP3) amplitude, with respect to pre-target responses.</p>
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<p>Grand-average scalp map distribution of individual ERP components’ amplitude (N1, P2, standard P3 (sP3)) associated with the standard stimuli presented before (pre) and after (post) target stimuli. Amplitude was calculated as the average within the a priori defined time range for each component. Post responses showed a significantly more negative N1 amplitude and a more positive sP3 amplitude, with respect to pre–responses.</p>
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29 pages, 5464 KiB  
Article
Dorsal Anterior Cingulate Cortex Coordinates Contextual Mental Imagery for Single-Beat Manipulation during Rhythmic Sensorimotor Synchronization
by Maho Uemura, Yoshitada Katagiri, Emiko Imai, Yasuhiro Kawahara, Yoshitaka Otani, Tomoko Ichinose, Katsuhiko Kondo and Hisatomo Kowa
Brain Sci. 2024, 14(8), 757; https://doi.org/10.3390/brainsci14080757 - 28 Jul 2024
Viewed by 2206
Abstract
Flexible pulse-by-pulse regulation of sensorimotor synchronization is crucial for voluntarily showing rhythmic behaviors synchronously with external cueing; however, the underpinning neurophysiological mechanisms remain unclear. We hypothesized that the dorsal anterior cingulate cortex (dACC) plays a key role by coordinating both proactive and reactive [...] Read more.
Flexible pulse-by-pulse regulation of sensorimotor synchronization is crucial for voluntarily showing rhythmic behaviors synchronously with external cueing; however, the underpinning neurophysiological mechanisms remain unclear. We hypothesized that the dorsal anterior cingulate cortex (dACC) plays a key role by coordinating both proactive and reactive motor outcomes based on contextual mental imagery. To test our hypothesis, a missing-oddball task in finger-tapping paradigms was conducted in 33 healthy young volunteers. The dynamic properties of the dACC were evaluated by event-related deep-brain activity (ER-DBA), supported by event-related potential (ERP) analysis and behavioral evaluation based on signal detection theory. We found that ER-DBA activation/deactivation reflected a strategic choice of motor control modality in accordance with mental imagery. Reverse ERP traces, as omission responses, confirmed that the imagery was contextual. We found that mental imagery was updated only by environmental changes via perceptual evidence and response-based abductive reasoning. Moreover, stable on-pulse tapping was achievable by maintaining proactive control while creating an imagery of syncopated rhythms from simple beat trains, whereas accuracy was degraded with frequent erroneous tapping for missing pulses. We conclude that the dACC voluntarily regulates rhythmic sensorimotor synchronization by utilizing contextual mental imagery based on experience and by creating novel rhythms. Full article
(This article belongs to the Special Issue EEG and Event-Related Potentials)
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<p>Examples of beat and rhythms.</p>
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<p>Examples of beats and rhythms. (<b>a</b>) A neural network model of imagery-driven cognitive control over rhythmic sensorimotor synchronization. (<b>b</b>) A direct imagery-execution scheme in a feedforward framework for rapid cognitive processing without Bayesian calculation.</p>
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<p>Missing-oddball task for testing the direct imagery-execution scheme. (<b>a</b>) Mental imagery of a proactive-tapping event characterized by negative mean asynchrony (reaction time (RT) &lt; 0, NMA). NMA is attributed to auditory–tactile integration for coincidence in the brain, providing on-pulse tap feel. (<b>b</b>) Mental imagery of a reactive-tapping event characterized by positive mean asynchrony (RT &gt; 0, PMA). The PMA is characterized by an event-triggered motor outcome regarded as reactive motor control while proactively inhibiting motor outcome until the predicted pulse-onset timing. This reactive tapping will be postponed if the pulse is missing. (<b>c</b>–<b>e</b>) are examples of postulated tapping manners in the missing-pulse sequence. (<b>c</b>) A case of tapping promoted by imagery of the proactive-tapping event. Erroneous tapping for missing pulses is unavoidable due to proactive tapping before the expected pulse-onset timing. (<b>d</b>) A case of tapping promoted by imagery of the reactive-tapping event. Erroneous tapping is avoidable. (<b>e</b>) A case of tapping promoted by appropriately exchanging imageries of proactive and reactive-tapping events, enabling proactive tapping accompanied by on-pulse tap feel while avoiding erroneous tapping for missing pulses.</p>
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<p>Experimental outline. (<b>a</b>) An example regular sequence consisting of 120 real pulses with an equal interval of 1000 ms used for Task 1. (<b>b</b>) An example missing-pulse sequence created by randomly omitting real pulses from the regular sequence, resulting in a total 300-pulse sequence including 45 missing and 255 real pulses. The sequence was featured by the lag number (N) locked to each missing pulse site (N = 0) and the pre-tap number (M) defined as the number of real pulses between neighboring missing sites. All real pulses had a pure tone of 1000 Hz with a duration of 100 ms. (<b>c</b>) Experimental setup. Participants were asked to simultaneously press a key with sound pulses with their eyes closed. The pulses were sequentially presented by a PC depending on the task. A digital EEG system (Nexus 32, Mind Media BV, Herten, Netherlands), comprising an EEG cap with preassembled Ag/AgCl electrodes (&lt; 5 kΩ), consistent with the international 10–20 method, was used for EEG recordings. To integrate EEG data and event markers corresponding to tap timing, data (sampling rate: 256 Hz, amplitude resolution: 24 bit) were acquired by a PC running control software (Bio Trace+ Software for Nexus 32 Version: V2009a4). (<b>d</b>) Electrode placement of a 21-channel EEG system according to the 10–20 international system. O1 and O2 were utilized for evaluating the deep-brain activity (DBA) index while Cz was utilized for evaluating ERPs. (<b>e</b>) A sample ER-DBA trace during isochronous tapping. The trace provided a typical DBA excursion amplitude d and standard deviations at the peak and bottom, SD1 and SD2, respectively. These parameters were utilized for determining an appropriate sample size.</p>
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<p>Aspects of behavioral performance during Task 2 (missing-oddball task). (<b>a</b>) Sample RT distribution for real pulses in the missing-pulse sequence resulting in two populations identified by negative and positive mean asynchronies, corresponding to proactive and reactive taps, respectively. (<b>b</b>) Proactive-tap imagery showing proactive tap outcomes as a false hit (FH) for real pulses and erroneous tap as a false alarm (FA) for missing pulses. (<b>c</b>) Reactive-tap imager showing reactive taps as a correct hit (CH) for real pulses and tap avoidance as a correct rejection (CR) for missing pulses. (<b>d</b>) Signal detection theory for the missing-oddball task indicating the probability distributions corresponding to real and missing pulses. <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>P</mi> </mrow> <mrow> <mi>M</mi> </mrow> </msub> <mfenced separators="|"> <mrow> <mi>x</mi> </mrow> </mfenced> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>P</mi> </mrow> <mrow> <mi>R</mi> </mrow> </msub> <mfenced separators="|"> <mrow> <mi>x</mi> </mrow> </mfenced> </mrow> </semantics></math> are probability density functions for “missing pulse” and “real pulse” imageries, respectively. The decision variable (x) was defined as an indicator for “Pulses are absent.” The criterion c, as an indicator of decision bias, regulates behaviors such that a tap is inhibited for x &gt; c while being promoted for x &lt; c.</p>
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<p>(<b>a</b>) Histogram of regular and random sequences. (<b>b</b>) Sample normalized ER-DBA traces for synchronous tapping with sequences involving regular (ISI = 1000 ms) and random (Random: 1000–1500 ms) sequences. All waveforms showed dips corresponding to each tapping timing. ISI: Inter-Stimulus Interval.</p>
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<p>(<b>a</b>) Histogram of RTs for real pulses in the regular sequence (Task 1). (<b>b</b>) Histogram of RTs for real pulses in the missing-pulse sequence (Task 2). (<b>c</b>) Histograms of RTs for missing pulses in the missing-pulse sequence (Task 2). Distribution curves depicted in (<b>a</b>–<b>c</b>) were derived by kernel density estimation. NMA, Negative mean asynchrony; PMA, Positive mean asynchrony.</p>
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<p>(<b>a</b>) Box-and-whisker plots of reaction times of tapping in the missing-pulse sequence (Task 2) as a function of lag number (N) locked to the missing site (N = 0). (<b>b</b>) RT histograms are represented for each lag number in the range from −2 to 6. Blue and orange triangles represent proactive-dominant and reactive-dominant asynchrony taps. (<b>c</b>) Correct-hit ratio (CHR) and reaction times of correct-hit (CH RT) and false-hit responses (FH RT) as a function of lag number (N) locked to the missing site (N = 0) derived from the individualized lag-dependent histograms in (<b>b</b>). Statistical significance is shown for comparison of RTs (<b>a</b>), CHR and CHR-associated RTs (<b>c</b>) between trials with different lag numbers and evaluated using ordinary one-way ANOVA and Holm–Sidak multiple comparisons test. (See <a href="#brainsci-14-00757-t001" class="html-table">Table 1</a> for numerical data). **: <span class="html-italic">p</span> &lt; 0.01, ***: <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Error rates (erroneous tapping frequency for missing pulses) as a function of corresponding response times (r = −0.737, F = 33.4, h2 = 0.544, <span class="html-italic">p</span> = 3.32 × 10<sup>−6</sup>).</p>
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<p>Analysis utilizing signal detection theory. Lag-number (N) dependence of d-prime (<b>a</b>) and criterion (<b>b</b>). Statistical significance is shown for comparison of average SDT parameters (d-prime (<b>a</b>) and criterion (<b>b</b>)) and evaluated using ordinary one-way ANOVA and Holm–Sidak multiple comparisons test. (See <a href="#brainsci-14-00757-t002" class="html-table">Table 2</a> for numerical data). (<b>c</b>) Scatter plots of R(FH) versus R(FA). R(FH) and R(FA) correspond to ratios of false hits for real pulses and FA for missing pulses of the missing-pulse sequence (Task B), respectively. R(FH) vs R(FA) &gt; 0.4 depicts a positive linear correlation (r = 0.795, F = 36.24, <span class="html-italic">p</span> = 5.64 × 10<sup>−6</sup>, η<sup>2</sup> = 0.749). (<b>d</b>) Scatter plots for the error rate of taps for missing pulses versus c depicting a strong positive linear correlation (r = 0.86, F = 80.7, <span class="html-italic">p</span> = 1.3 × 10<sup>−9</sup>, η<sup>2</sup> = 0.75). (<b>e</b>) Scatter plots for the error rate of taps for missing pulses versus d-prime depicting a weak positive linear correlation (Pearson’s coefficient of correlation r = 0.41, F = 6.25, <span class="html-italic">p</span> = 0.0178, η<sup>2</sup> = 0.167). **: <span class="html-italic">p</span> &lt; 0.01, ***: <span class="html-italic">p</span> &lt; 0.001, NS: <span class="html-italic">p</span> &gt; 0.05.</p>
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<p>Brain responses during tasks evaluated by the ER-DBA method. (<b>a</b>) Grand-averaged ER-DBA traces for real pulses in regular (Task 1) and regular portions of the missing-pulse (Task 2) sequences. (<b>b</b>) Difference in grand-averaged ER-DBA traces between faster (RT &lt; 0) and slower (RT &gt; 0) responses. Dips in traces are marked by colored triangles (blue triangles for faster and slower responses, respectively). Black triangles correspond to taps. Faster and slower responses are characterized by deactivation (ERD) and activation (ERS), supported by the significant power difference in the epoch indicated by the gray area (<span class="html-italic">p</span> = 0.036, Power = 0.55). (<b>c</b>) Grand-averaged ER-DBA traces time-locked to missing-pulse onset compared between erroneous tapping (Tap) and CR (Avoid). Pink triangles represent ER-DBA peaks, while blue triangles represent ER-DBA dips accompanied by negative and positive asynchrony taps, respectively. (<b>d</b>) Grand-averaged ER-DBA traces at around the missing-pulse site. Orange and green arrows represent proactive-dominant and reactive-dominant asynchrony taps for real pulses in Task 2, respectively. Red line represents a temporal trajectory of averaged ER-DBA indices. *: <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>(<b>a</b>) Grand-averaged ERP traces for real pulses time-locked to stimulus onset in both regular and missing-pulse tasks excluding missing. (<b>b</b>) Grand-averaged ERP traces for missing pulses in Task 2 compared with real pulses in the same task. (<b>c</b>) ERP traces featured by erroneous tapping rate for missing pulses. Comparison among three groups, i.e., high (error rate &gt; 0.5) and low (error rate &lt; 0.5) error-rate trials. (<b>d</b>) Post-missing temporal development of the ERP trace. The portion of ERP trace of the highlighted by pink color is corresponds to omission responses and that highlighted by green color corresponds to CNV (contingent negative variation).</p>
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<p>(<b>a</b>) Integration of behavioral performance, ER-DBA trace features (ERD/ERS) as brain responses, and the corresponding mental imagery based on performance and ER-DBA trace features. Imagery updating follows event changes including both “real”-to-“missing” and “missing”-to-“real” changes. (<b>b</b>) A model mechanism of imagery updates during tapping for real pulses in the missing-pulse sequence. Inhibition of delayed proactive tapping by reactive tapping triggered by real pulses may induce abduction (retroduction), where the reactive response could be attributed to the “missing” imagery. This abduction may update imagery from “real” to “missing,” while shifting the criterion toward “missing.” (<b>c</b>) A neural model to explain dominant reactive tapping for real pulses while inhibiting delayed proactive tapping. The pre-SMA stimulates the subthalamic nucleus to inhibit proactive motor outcomes via the hyper-direct pathway triggered by an alarm signal from the inferior frontal gyrus (IFG) to the dACC via the anterior insula (aIC). (<b>d</b>) A neural network model of reactive response triggered by pulse stimulation while inhibiting proactive behavior by dACC via hyper-direct pathway.</p>
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20 pages, 1189 KiB  
Article
Detection of Unfocused EEG Epochs by the Application of Machine Learning Algorithm
by Rafia Akhter and Fred R. Beyette
Sensors 2024, 24(15), 4829; https://doi.org/10.3390/s24154829 - 25 Jul 2024
Viewed by 1004
Abstract
Electroencephalography (EEG) is a non-invasive method used to track human brain activity over time. The time-locked EEG to an external event is known as event-related potential (ERP). ERP can be a biomarker of human perception and other cognitive processes. The success of ERP [...] Read more.
Electroencephalography (EEG) is a non-invasive method used to track human brain activity over time. The time-locked EEG to an external event is known as event-related potential (ERP). ERP can be a biomarker of human perception and other cognitive processes. The success of ERP research depends on the laboratory conditions and attentiveness of the test subjects. Specifically, the inability to control experimental variables has reduced ERP research in the real world. This study collected EEG data under various experimental circumstances within an auditory oddball paradigm experiment to enable the use of ERP as an active biomarker in normal laboratory conditions. Then, ERP epochs were analyzed to identify unfocused epochs, affected by typical artifacts and external distortion. For the initial comparison, the ability of four unsupervised machine learning algorithms (MLAs) was evaluated to identify unfocused epochs. Then, their accuracy was compared with the human inspection and a current EEG analysis tool (EEGLab). All four MLAs were typically 95–100% accurate. In summary, our analysis finds that humans might miss subtle differences in the regular ERP patterns, but MLAs could efficiently identify those. Thus, our analysis suggests that unsupervised MLAs perform better for detecting unfocused ERP epochs compared with the other two standard methods. Full article
(This article belongs to the Collection EEG-Based Brain–Computer Interface for a Real-Life Appliance)
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<p>Event-related potential. The upper picture shows the common (standard) events in blue and the odd (deviant) events in red. It also shows for odd events that the ERP peak is higher [<a href="#B22-sensors-24-04829" class="html-bibr">22</a>].</p>
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<p>Machine learning (ML) techniques for detecting unusual data points [<a href="#B30-sensors-24-04829" class="html-bibr">30</a>]. In the figure, green balls are regular data, red balls are unusual data and grey balls are test data.</p>
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<p>The electrode positions on the human scalp for the EEG recording is shown in subfigure (<b>A</b>). The positions are based on the International 10/20 system. (<b>B</b>) shows the Ultracortex Mark IV headset with a cyton board installed. This was used for recording EEG [<a href="#B36-sensors-24-04829" class="html-bibr">36</a>].</p>
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<p>The recording of EEG data from a Test Subject in the lab is shown in (<b>A</b>). The auditory stimuli generated by the PIC24 microcontroller is shown in (<b>B</b>). T1 shows odd and T2 shows common stimuli. The tone duration is shown as PW = 300 ms and the ISI (interstimulus interval) is shown as 3 s. For every setup, there were 50 auditory stimuli and the test subject heard 10 odd stimuli and 40 common stimuli.</p>
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<p>Example of common EEG artifacts that are easily identifiable by human visual inspection (Figures (<b>A</b>–<b>E</b>)). Figure (<b>A</b>) shows the eye-movement, (<b>B</b>) shows eye-blink, (<b>C</b>) shows skin potential, (<b>D</b>) shows EMG and (<b>E</b>) shows movement artifacts. Figure (<b>F</b>) indicates that the amplitude in microvolts is in the y-axis, and the x-axis shows the time in milliseconds [<a href="#B40-sensors-24-04829" class="html-bibr">40</a>].</p>
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<p>The figure shows an example of common EEG artifacts that are easily identifiable by human visual inspection. (<b>A</b>) shows the movement, (<b>B</b>,<b>C</b>) (first epoch) and (<b>F</b>) (last epoch) show eye-blink, (<b>C</b>) (last epoch) and (<b>F</b>) (second epoch) show EMG artifacts corrupted epochs. In every figure, the yellow-marked epoch is identified as the unusual or artifact corrupted epoch. The y-axis indicates the voltage amplitude (in microvolts) from the human scalp and the x-axis shows the time in milliseconds. These epoch figures are collected from Test Subjects 1 and 6.</p>
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<p>An example of common EEG artifacts that are easily identifiable by the EEGLab Toolkit. Figure (<b>A</b>) shows the EEGLab setup and Figure (<b>B</b>) shows the identified ERP epochs 26 and 29 (green marked) as artifact-corrupted and/or unusual.</p>
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<p>An example of common EEG artifacts detected by the DBScan (<b>A</b>) and k-means (<b>B</b>) MLA methods. In both figures, the unusual points are shown as orange circles, and the normal behavior data points are shown by blue circles.</p>
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<p>Figure (<b>A</b>) shows that the ERP epochs are larger and clearer in the position of the parietal (Pz) and occipital area (Oz). Figure (<b>B</b>) shows the electrode positions on the human scalp. We collected this dataset from Test Subject 1.</p>
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<p>An analysis of the ERP epochs for the “ideal” condition. Figure (<b>A</b>) shows the comparison with HVI and Figure (<b>B</b>) shows the comparison with the best MLA (data for Test Subject 1).</p>
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<p>A comparison of the ERPs for the “visual” experimental condition for Test Subject 1. Figure (<b>A</b>) shows the comparison of HVI with EEGLab and Figure (<b>B</b>) shows the comparison of HVI with the best MLA.</p>
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<p>A comparison of the ERPs for the “audio-visual” condition for Test Subject 1. Figure (<b>A</b>) shows the comparison of HVI with EEGLab and Figure (<b>B</b>) shows the comparison with the best MLA.</p>
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<p>An analysis of the ERP epochs for the “mind-wandering” condition. Figure (<b>A</b>) shows the comparison with HVI and Figure (<b>B</b>) shows DBScan. The black solid-line ERP is the ERP in the ideal condition after the artifact was corrupted or the epochs were removed by HVI.</p>
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21 pages, 2765 KiB  
Article
Combined Effects of Moderate Hypoxia and Sleep Restriction on Mental Workload
by Anaïs Pontiggia, Pierre Fabries, Vincent Beauchamps, Michael Quiquempoix, Olivier Nespoulous, Clémentine Jacques, Mathias Guillard, Pascal Van Beers, Haïk Ayounts, Nathalie Koulmann, Danielle Gomez-Merino, Mounir Chennaoui and Fabien Sauvet
Clocks & Sleep 2024, 6(3), 338-358; https://doi.org/10.3390/clockssleep6030024 - 23 Jul 2024
Cited by 1 | Viewed by 1192
Abstract
Aircraft pilots face a high mental workload (MW) under environmental constraints induced by high altitude and sometimes sleep restriction (SR). Our aim was to assess the combined effects of hypoxia and sleep restriction on cognitive and physiological responses to different MW levels using [...] Read more.
Aircraft pilots face a high mental workload (MW) under environmental constraints induced by high altitude and sometimes sleep restriction (SR). Our aim was to assess the combined effects of hypoxia and sleep restriction on cognitive and physiological responses to different MW levels using the Multi-Attribute Test Battery (MATB)-II with an additional auditory Oddball-like task. Seventeen healthy subjects were subjected in random order to three 12-min periods of increased MW level (low, medium, and high): sleep restriction (SR, <3 h of total sleep time (TST)) vs. habitual sleep (HS, >6 h TST), hypoxia (HY, 2 h, FIO2 = 13.6%, ~3500 m vs. normoxia, NO, FIO2 = 21%). Following each MW level, participants completed the NASA-TLX subjective MW scale. Increasing MW decreases performance on the MATB-II Tracking task (p = 0.001, MW difficulty main effect) and increases NASA-TLX (p = 0.001). In the combined HY/SR condition, MATB-II performance was lower, and the NASA-TLX score was higher compared with the NO/HS condition, while no effect of hypoxia alone was observed. In the accuracy of the auditory task, there is a significant interaction between hypoxia and MW difficulty (F(2–176) = 3.14, p = 0.04), with lower values at high MW under hypoxic conditions. Breathing rate, pupil size, and amplitude of pupil dilation response (PDR) to auditory stimuli are associated with increased MW. These parameters are the best predictors of increased MW, independently of physiological constraints. Adding ECG, SpO2, or electrodermal conductance does not improve model performance. In conclusion, hypoxia and sleep restriction have an additive effect on MW. Physiological and electrophysiological responses must be taken into account when designing a MW predictive model and cross-validation. Full article
(This article belongs to the Section Human Basic Research & Neuroimaging)
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<p>Performance to the MATB-II tracking task (<b>A</b>), NASA-TLX subjective scores (<b>B</b>), and accuracy (ACC) and reaction time (RT) to the auditory task ((<b>C</b>) and (<b>D</b>), respectively) in the four experimental conditions (Habitual sleep/Normoxia, Habitual sleep/Hypoxia, Sleep restriction/Normoxia, Sleep restriction/Hypoxia) and at the three MW difficulty levels (Low, Medium, High) * is a significant difference with the Habitual sleep/Normoxia condition, <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Changes in peripheral oxygen saturation (SpO<sub>2</sub>), respiratory (breathing rate), and cardiac parameters (heart rate and heart rate variability parameters) in the four experimental conditions (Habitual sleep/Normoxia, Habitual sleep/Hypoxia, Sleep restriction/Normoxia, Sleep restriction/Hypoxia) and at the three MATB-II MW difficulty levels (Low, Medium, High) * is a significant difference with the Habitual sleep/Normoxia condition, <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Changes in physiological Eye tracking parameters in the four experimental conditions (Habitual sleep/Normoxia, Habitual sleep/Hypoxia, Sleep restriction/Normoxia, Sleep restriction/Hypoxia) and at the three MATB-II MW difficulty levels (Low, Medium, High). Pupil size in raw values (<b>A</b>), pupil size in Z-score (<b>B</b>), an example of the Pupil Dilatation Response (PDR) at the three MATB-II MW difficulty levels (<b>C</b>), amplitude and latency ((<b>D</b>) and (<b>E</b>), respectively) of PDR. * is a significant difference with the Habitual sleep/Normoxia condition, <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>(<b>A</b>). Correlation analysis (with Pearson coefficient, R and P) between physiological parameters and MATB-II tracking performance in the four experimental conditions (Habitual sleep/Normoxia, Habitual sleep/Hypoxia, Sleep restriction/Normoxia, Sleep restriction/Hypoxia). Only parameters showing a significant correlation (corrected <span class="html-italic">p</span> &lt; 0.05) with MATB-II tracking performance (RMSD value) in Habitual sleep/Normoxia were presented. <span class="html-italic">p</span> values take into account multiple comparison corrections [<a href="#B27-clockssleep-06-00024" class="html-bibr">27</a>] (<b>B</b>): examples of repeated-measures correlations between MATB-II tracking performance (RMSD values) and heart, breathing rate, and amplitude and Z-score of the PDR response in the four experimental conditions.</p>
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<p>Illustration of the four subtasks of the Multi-Attribute Task Battery (MATB)-II and the auditory Oddball-like task: SYSTEM MONITORING (<b>A</b>) task in the upper left corner where participants had to respond as quickly as possible to scale fluctuations via keystrokes, TRACKING (<b>B</b>) task in the upper corner where participants had to keep a tracker as close to the center with a joystick, COMMUNICATIONS (<b>D</b>) task in the bottom left corner where participants had to only answer broadcast messages that matched their call signs and RESSOURCE MANAGEMENT (<b>E</b>) task in the bottom right corner that required participants to keep tanks’ levels as close to target level as possible (2500 for the left and 1000 for right) by managing eight pumps. AUDITORY ODDBALL-LIKE (<b>F</b>) task that requires ignoring frequent tone and detecting infrequent auditory stimulus. (<b>C</b>) A workload rating survey is not a task but an automatic evaluation of the temporal progression; no action is required.</p>
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<p>The study protocol. The order of conditions is: Habitual sleep Normoxia (HSNO), Habitual sleep Hypoxia (HSHY), Sleep restriction Normoxia (SRNO), Sleep restriction Hypoxia (SRHY). The levels of MATB-II difficulty (low, medium, or high) are randomized. Black square: NASA-TLX test.</p>
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21 pages, 1190 KiB  
Article
Effects of Transcutaneous Auricular Vagus Nerve Stimulation on the P300: Do Stimulation Duration and Stimulation Type Matter?
by Manon Giraudier, Carlos Ventura-Bort and Mathias Weymar
Brain Sci. 2024, 14(7), 690; https://doi.org/10.3390/brainsci14070690 - 10 Jul 2024
Cited by 1 | Viewed by 1270
Abstract
Non-invasive transcutaneous auricular vagus nerve stimulation (taVNS) has attracted increasing interest as a neurostimulation tool with potential applications in modulating cognitive processes such as attention and memory, possibly through the modulation of the locus–coeruleus noradrenaline system. Studies examining the P300 brain-related component as [...] Read more.
Non-invasive transcutaneous auricular vagus nerve stimulation (taVNS) has attracted increasing interest as a neurostimulation tool with potential applications in modulating cognitive processes such as attention and memory, possibly through the modulation of the locus–coeruleus noradrenaline system. Studies examining the P300 brain-related component as a correlate of noradrenergic activity, however, have yielded inconsistent findings, possibly due to differences in stimulation parameters, thus necessitating further investigation. In this event-related potential study involving 61 participants, therefore, we examined how changes in taVNS parameters, specifically stimulation type (interval vs. continuous stimulation) and duration, influence P300 amplitudes during a visual novelty oddball task. Although no effects of stimulation were found over the whole cluster and time window of the P300, cluster-based permutation tests revealed a distinct impact of taVNS on the P300 response for a small electrode cluster, characterized by larger amplitudes observed for easy targets (i.e., stimuli that are easily discernible from standards) following taVNS compared to sham stimulation. Notably, our findings suggested that the type of stimulation significantly modulated taVNS effects on the P300, with continuous stimulation showing larger P300 differences (taVNS vs. sham) for hard targets and standards compared to interval stimulation. We observed no interaction effects of stimulation duration on the target-related P300. While our findings align with previous research, further investigation is warranted to fully elucidate the influence of taVNS on the P300 component and its potential utility as a reliable marker for neuromodulation in this field. Full article
(This article belongs to the Section Neurotechnology and Neuroimaging)
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<p>Schematic overview of the experimental procedure in each session, depicting task presentation (novelty oddball task and serial reaction time task, upper part) and measured variables, as well as the stimulation condition (lower part) with corresponding time points. Symbols represent the following measurements and conditions: electroencephalography (EEG) is indicated with a brain symbol, electrocardiography (ECG) with a heart symbol, oculography with an eye symbol, and saliva collection for salivary alpha-amylase (sAA) analysis with a saliva sample symbol. Blood pressure measurement is represented with a blood pressure device symbol. The stimulation condition (taVNS vs. sham) is indicated with a corresponding symbol. Both stimulation conditions were applied to all participants on two separate days, one week apart.</p>
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<p>(<b>A</b>) General P300 grand average event-related potential (ERP) in the 300–600 ms time window (highlighted in gray) over a parietal electrode cluster (E53, E54, E55, E60, E61, E62, E67, E72, E77, E78, E79, E85, and E86) for targets and standards. Scalp topography of the ERP difference between targets and standards is depicted in the inset. (<b>B</b>) Grand average ERPs showing the differential effects of stimulation (taVNS vs. sham) over a significant parietal electrode cluster (E55 and E79) for easy targets and standards. Notably, the significant effect of stimulation was found only for easy targets, not for standards. Scalp topographies of the ERP differences between taVNS and sham are depicted in the insets, with the significant time window (308–368 ms) highlighted in gray. Barchart depicting mean amplitudes for stimulus type (targets easy vs. standards)under taVNS (orange) and sham stimulation (blue) conditions. (<b>C</b>) Grand average ERPs showing the differential effects of stimulation (taVNS vs. sham) over a significant parietal electrode cluster (E53, E54, E55, and E61) for hard targets and standards for continuous stimulation and (<b>D</b>) for interval stimulation. Notably, the significant effect between stimulation and stimulation type was consistent for hard targets and for standards. Scalp topographies of the ERP differences between taVNS and sham are depicted in the insets, with the significant time window (308–408 ms) highlighted in gray. Bar charts depicting amplitudes by stimulus type (targets hard vs. standards) for continuous stimulation and interval stimulation under the taVNS (orange) and sham stimulation (blue) conditions. See <a href="#brainsci-14-00690-f0A2" class="html-fig">Figure A2</a> for an additional bar chart not differentiating by stimulus type and a corresponding table with means and standard deviations.</p>
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<p>Bar chart depicting response times for targets (easy vs. hard) for short-duration stimulation and long-duration stimulation under the taVNS (orange) and sham stimulation (blue) conditions. The bars represent the mean response times, while the individual data points indicate the distribution of response times.</p>
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<p>(<b>A</b>) Bar chart depicting amplitudes for stimulation type (continuous vs. interval stimulation) under the taVNS (orange) and sham stimulation (blue) conditions, independently of stimulus type. * <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>&lt;</mo> <mn>0.05</mn> </mrow> </semantics></math>, ** <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>&lt;</mo> <mn>0.01</mn> </mrow> </semantics></math>. (<b>B</b>) Table with means and standard deviations in parentheses.</p>
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14 pages, 1958 KiB  
Article
Age-Related Aspects of Sex Differences in Event-Related Brain Oscillatory Responses: A Turkish Study
by Görsev Yener, İlayda Kıyı, Seren Düzenli-Öztürk and Deniz Yerlikaya
Brain Sci. 2024, 14(6), 567; https://doi.org/10.3390/brainsci14060567 - 3 Jun 2024
Viewed by 963
Abstract
Earlier research has suggested gender differences in event-related potentials/oscillations (ERPs/EROs). Yet, the alteration in event-related oscillations (EROs) in the delta and theta frequency bands have not been explored between genders across the three age groups of adulthood, i.e., 18–50, 51–65, and >65 years. [...] Read more.
Earlier research has suggested gender differences in event-related potentials/oscillations (ERPs/EROs). Yet, the alteration in event-related oscillations (EROs) in the delta and theta frequency bands have not been explored between genders across the three age groups of adulthood, i.e., 18–50, 51–65, and >65 years. Data from 155 healthy elderly participants who underwent a neurological examination, comprehensive neuropsychological assessment (including attention, memory, executive function, language, and visuospatial skills), and magnetic resonance imaging (MRI) from past studies were used. The delta and theta ERO powers across the age groups and between genders were compared and correlational analyses among the ERO power, age, and neuropsychological tests were performed. The results indicated that females displayed higher theta ERO responses than males in the frontal, central, and parietal regions but not in the occipital location between 18 and 50 years of adulthood. The declining theta power of EROs in women reached that of men after the age of 50 while the theta ERO power was more stable across the age groups in men. Our results imply that the cohorts must be recruited at specified age ranges across genders, and clinical trials using neurophysiological biomarkers as an intervention endpoint should take gender into account in the future. Full article
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<p>Grand averages of theta ERO power across age groups of both genders.</p>
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<p>Correlational plots between theta ERO power and age in both genders.</p>
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20 pages, 1929 KiB  
Article
Socioeconomic Inequalities Affect Brain Responses of Infants Growing Up in Germany
by Annika Susann Wienke and Birgit Mathes
Brain Sci. 2024, 14(6), 560; https://doi.org/10.3390/brainsci14060560 - 30 May 2024
Viewed by 1137
Abstract
Developmental changes in functional neural networks are sensitive to environmental influences. This EEG study investigated how infant brain responses relate to the social context that their families live in. Event-related potentials of 255 healthy, awake infants between six and fourteen months were measured [...] Read more.
Developmental changes in functional neural networks are sensitive to environmental influences. This EEG study investigated how infant brain responses relate to the social context that their families live in. Event-related potentials of 255 healthy, awake infants between six and fourteen months were measured during a passive auditory oddball paradigm. Infants were presented with 200 standard tones and 48 randomly distributed deviants. All infants are part of a longitudinal study focusing on families with socioeconomic and/or cultural challenges (Bremen Initiative to Foster Early Childhood Development; BRISE; Germany). As part of their familial socioeconomic status (SES), parental level of education and infant’s migration background were assessed with questionnaires. For 30.6% of the infants both parents had a low level of education (≤10 years of schooling) and for 43.1% of the infants at least one parent was born abroad. The N2–P3a complex is associated with unintentional directing of attention to deviant stimuli and was analysed in frontocentral brain regions. Age was utilised as a control variable. Our results show that tone deviations in infants trigger an immature N2–P3a complex. Contrary to studies with older children or adults, the N2 amplitude was more positive for deviants than for standards. This may be related to an immature superposition of the N2 with the P3a. For infants whose parents had no high-school degree and were born abroad, this tendency was increased, indicating that facing multiple challenges as a young family impacts on the infant’s early neural development. As such, attending to unexpected stimulus changes may be important for early learning processes. Variations of the infant N2–P3a complex may, thus, relate to early changes in attentional capacity and learning experiences due to familial challenges. This points towards the importance of early prevention programs. Full article
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<p>(<b>A</b>) Temporal ERP courses for six fronto-central electrodes averaged across all 255 infants for standard (blue), deviant stimuli (red) and difference wave (black). Time points 0 ms indicate the presentation of the respective tone. Dashed lines indicate peaks of N2 at 250 ms and P3a at 340 ms. (<b>B</b>) Topography of the deviant stimulus in the time windows of N2: 224–274 ms, early P3a: 264–314 ms and late P3a: 314–364 ms. Black dots depict electrode placement. (<b>C</b>) Boxplots of the mean amplitude over all six electrodes (F7, F8, F3, F4, FC5, and FC6) for the time window N2, early P3a and late P3a. * <span class="html-italic">p</span> &lt; 0.05; *** <span class="html-italic">p</span> ≤ 0.001.</p>
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<p>(<b>A</b>) Temporal ERP courses for electrode FC6 for standards left, and deviants right, separated by subject factor groups (EDU0/MIG0, EDU1/MIG0, EDU0/MIG1 and EDU1/MIG1; EDU = education level, MIG = migration background; 0 low risk, 1 high risk). Time points 0 ms indicate the presentation of the respective tone. Dashed lines indicate peaks of N2 at 250 ms and P3a at 340 ms. (<b>B</b>) Topography of the deviant stimulus in the time windows of N2: 224–274 ms, early P3a: 264–314 ms and late P3a: 314–364 ms for groups EDU0/MIG0 and EDU1/MIG1. Black dots depict electrode placement. (<b>C</b>) Boxplot depicting mean amplitude of standard and deviant processing over all six electrodes (F7, F8, F3, F4, FC5, and FC6) for the time window N2, early P3a and late P3a separated for all four subject factor groups (EDU0/MIG0, EDU1/MIG0, EDU0/MIG1 and EDU1/MIG1; EDU = education level, MIG = migration background; 0 = low risk, 1 = high risk). * <span class="html-italic">p</span> &lt; 0.05; *** <span class="html-italic">p</span> ≤ 0.001.</p>
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<p>Boxplot depicting mean amplitude of standard processing over electrode FC6 for the time window late P3a separated for all four subject factor groups (EDU0/MIG0, EDU1/MIG0, EDU0/MIG1 and EDU1/MIG1; EDU = education level, MIG = migration background; 0 = low risk, 1 = high risk). * <span class="html-italic">p</span> &lt; 0.05.</p>
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18 pages, 4043 KiB  
Article
The Development of Global-Level Categorization: Frequency Tagging EEG Responses
by Stefanie Peykarjou, Stefanie Hoehl and Sabina Pauen
Brain Sci. 2024, 14(6), 541; https://doi.org/10.3390/brainsci14060541 - 24 May 2024
Cited by 1 | Viewed by 1156
Abstract
Adults and infants form abstract categories of visual objects, but little is known about the development of global categorization. This study aims to characterize the development of very fast global categorization (living and non-living objects) and to determine whether and how low-level stimulus [...] Read more.
Adults and infants form abstract categories of visual objects, but little is known about the development of global categorization. This study aims to characterize the development of very fast global categorization (living and non-living objects) and to determine whether and how low-level stimulus characteristics contribute to this response. Frequency tagging was used to characterize the development of global-level categorization in N = 69 infants (4, 7, 11 months), N = 22 children (5–6 years old), and N = 20 young adults. Images were presented in an oddball paradigm, with a category change at every fifth position (AAAABAAAABA…). Strong and significant high-level categorization was observed in all age groups, with reduced responses for phase-scrambled control sequences (R2 = 0.34–0.73). No differences between the categorization of living and non-living targets were observed. These data demonstrate high-level visual categorization as living and non-living from four months to adulthood, providing converging evidence that humans are highly sensitive to broad categorical information from infancy onward. Full article
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<p>Schematic illustration of the experimental paradigm and conditions. (<b>1.1</b>) Four conditions were tested within-subjects in adults and children (Experiments 1 + 2). In infants (Experiment 3), the condition (original, scrambled images) was varied within-, and the deviant category (living, non-living) between-subjects. (<b>1.2</b>) Images are presented by sinusoidal contrast modulation at a rate of 6 cycles per second = 6 Hz (1 cycle ≈ 170 ms). Category changes were introduced at fixed intervals of every fifth image (6/5 Hz = 1.2 Hz). Sequence duration was set at 60 s in adults, 40 s in children, and 20 s in infants to accommodate differences in attention span.</p>
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<p>Electroencephalographic responses in Experiment 1 (adults). Signal-to-noise ratios (SNRs) of summed responses for the categorization response at the posterior-occipital and frontal leads and of the base response at the occipital channels. Fast Fourier transformation (FFT) responses at the posterior-occipital channels. Data have been averaged across electrodes per cluster and grand-averaged across participants for display. * = <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.</p>
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<p>Electroencephalographic responses in Experiment 2 (5–6-year-olds). Signal-to-noise ratios (SNRs) of summed responses for the categorization response at the posterior-occipital and frontal leads and of the base response at the occipital channels. Fast Fourier transformation (FFT) responses at the posterior-occipital channels. Data have been averaged across electrodes per cluster and grand-averaged across participants for display. *** = <span class="html-italic">p</span> &lt;0.001, ** = <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Electroencephalographic responses in Experiment 3 (4-month-old infants). Signal-to- noise ratios (SNRs) of summed responses for the categorization response at the posterior-occipital and frontal leads and of the base response at the occipital channels. Fast Fourier transformation (FFT) responses at the posterior-occipital channels. Data have been averaged across electrodes per cluster and grand-averaged across participants for display. *** = <span class="html-italic">p</span> &lt;0.001, ** = <span class="html-italic">p</span> &lt; 0.01, * = <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Electroencephalographic responses in Experiment 3 (7-month-old infants). Signal-to-noise ratios (SNRs) of summed responses for the categorization response at the posterior-occipital and frontal leads and of base response at the occipital channels. Fast Fourier transformation (FFT) responses at the posterior-occipital channels. Data have been averaged across electrodes per cluster and grand-averaged across participants for display. *** = <span class="html-italic">p</span> &lt;0.001, ** = <span class="html-italic">p</span> &lt; 0.01, * = <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Electroencephalographic responses in Experiment 3 (11-month-old infants). Signal-to-noise ratios (SNRs) of summed responses for the categorization response at the posterior-occipital and frontal leads and of the base response at the occipital channels. Fast Fourier transformation (FFT) responses at the posterior-occipital channels. Data have been averaged across electrodes per cluster and grand-averaged across participants for display. *** = <span class="html-italic">p</span> &lt;0.001, ** = <span class="html-italic">p</span> &lt; 0.01, * = <span class="html-italic">p</span> &lt; 0.05.</p>
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