[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

Classifying schizophrenic and controls from fMRI data using graph theoretic framework and community detection

  • Original Article
  • Published:
Network Modeling Analysis in Health Informatics and Bioinformatics Aims and scope Submit manuscript

Abstract

Schizophrenia is a psychiatric disorder characterized by symptoms such as disorganized thinking, hallucinations, disintegration of reality perception, and delusions, among others. Resting-state functional magnetic resonance imaging is a promising method for studying changes in functional brain networks in schizophrenic patients. Graph theoretic representations can effectively distinguish between healthy and schizophrenic subjects. The process of grouping users with similar interests in social networks, which can also be used to group diseased subjects, is known as community detection. In this paper, we propose a method for classifying schizophrenia and normal subjects from fMRI images by employing graph similarity and community detection algorithms. The fMRI images are first preprocessed to remove noise, and then the automated anatomical labelling atlas is used to divide the human brain into 116 regions. Following that, a region connectivity matrix is constructed, and a weighted undirected graph is generated from the connectivity matrix. The graph similarity algorithm is then used to determine the similarity between each graph or subject. Then, a network of networks is built, which is a weighted network in which each graph is a node, and the top k (threshold) similarity scores between the graphs form the graph’s edges. On the newly constructed weighted graph, a community detection algorithm is used to detect communities that classify schizophrenia and normal subjects. We applied this proposed method to the COBRE dataset, which is publicly available and consists of 72 schizophrenic patients and 74 healthy subjects. We achieved an accuracy of 86.5% and compared it to other graph-based methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Data availabiltiy

The datasets analysed during the current study are available in the Center for Biomedical Research Excellence (COBRE) data set (http://fcon-1000.projects.nitrc.org/indi/retro/cobre.html)

References

  • Algunaid RF, Algumaei AH, Rushdi MA et al (2018) Schizophrenic patient identification using graph-theoretic features of resting-state fMRI data. Biomed Signal Process Control 43:289–299

    Article  Google Scholar 

  • Anderson A, Cohen M (2013) Decreased small-world functional network connectivity and clustering across resting state networks in schizophrenia: an fMRI classification tutorial. Front Hum Neurosci 7:520. https://doi.org/10.3389/fnhum.2013.00520. https://www.frontiersin.org/article/10.3389/fnhum.2013.00520

  • Blondel VD, Guillaume JL, Lambiotte R et al (2008) Fast unfolding of communities in large networks. J Stat Mech Theory Exp 10:P10,008. https://doi.org/10.1088/1742-5468/2008/10/P10008

  • Bou KR (2012) Metabolic syndrome in patients with schizophrenia independently from atypical antipsychotics intake. Presse Med (Paris, France: 1983) 41(5):e238–e243

  • Chen S, Bowman FD, Xing Y (2020) Detecting and testing altered brain connectivity networks with k-partite network topology. Comput Stat Data Anal 141:109–122

    Article  MathSciNet  MATH  Google Scholar 

  • Cheng H, Newman S, Goñi J et al (2015) Nodal centrality of functional network in the differentiation of schizophrenia. Schizophrenia Res 168(1):345–352. https://doi.org/10.1016/j.schres.2015.08.011

  • Chowdhury HA, Barah P, Bhattacharyya DK et al (2021) Identification of potential Parkinson’s disease biomarkers using computational biology approaches. Netw Model Anal Health Inf Bioinf 10:1–16

    Google Scholar 

  • De Vico Fallani F, Richiardi J, Chavez M et al (2014) Graph analysis of functional brain networks: Practical issues in translational neuroscience. Philos Trans R Soc Lond Ser B Biol Sci 369. https://doi.org/10.1098/rstb.2013.0521

  • Fornito A, Zalesky A, Bullmore ET (2016) Chapter 3 - connectivity matrices and brain graphs. In: Fundamentals of Brain Network Analysis. Academic Press, San Diego, p 89–113. https://doi.org/10.1016/B978-0-12-407908-3.00003-0

  • Ghanbari M, Pilevar AH, Bathaeian N (2023) Diagnosis of schizophrenia using brain resting-state fMRI with activity maps based on deep learning. SIViP 17(1):267–275

    Article  Google Scholar 

  • Haq N, Wang ZJ (2016) Community detection from genomic datasets across human cancers. In: 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP), pp 1147–1150. https://doi.org/10.1109/GlobalSIP.2016.7906021

  • Kalaichelvi N, Easwarakumar K (2022) A comprehensive survey on community deception approaches in social networks. In: Computer, Communication, and Signal Processing: 6th IFIP TC 5 International Conference, ICCCSP 2022, Springer, pp 163–173

  • Karataş A, Şahin S (2018) Application areas of community detection: A review. In: 2018 International congress on big data, deep learning and fighting cyber terrorism (IBIGDELFT), IEEE, pp 65–70

  • Koutra D, Vogelstein JT, Faloutsos C (2013) DELTACON: A principled massive-graph similarity function. CoRR arXiv:abs/1304.4657

  • Lei D, Pinaya WHL, van Amelsvoort T et al (2020) Detecting schizophrenia at the level of the individual: relative diagnostic value of whole-brain images, connectome-wide functional connectivity and graph-based metrics. Psychol Med 50:1852–1861

    Article  Google Scholar 

  • Lin QH, Niu YW, Sui J et al (2022) SSPNet: an interpretable 3D-CNN for classification of schizophrenia using phase maps of resting-state complex-valued fMRI data. Med Image Anal 79(102):430. https://doi.org/10.1016/j.media.2022.102430

    Article  Google Scholar 

  • Liu J, Li M, Pan Y et al (2017) Complex brain network analysis and its applications to brain disorders: A survey. Complexity 2017:1–27. https://doi.org/10.1155/2017/8362741

    Article  MathSciNet  MATH  Google Scholar 

  • Li X, Zhou Y, Dvornek N et al (2021) BrainGNN: Interpretable brain graph neural network for fmri analysis. Med Image Anal 74(102):233. https://doi.org/10.1016/j.media.2021.102233

  • Mheich A, Wendling F, Hassan M (2020) Brain network similarity: methods and applications. Netw Neurosci 4(3):507–527. https://doi.org/10.1162/netn_a_00133

  • Nallusamy K, Easwarakumar KS (2022) CGraM: Enhanced algorithm for community detection in social networks. Intell Autom Soft Comput 31(2):749–765. https://doi.org/10.32604/iasc.2022.020189. http://www.techscience.com/iasc/v31n2/44541

  • Newman ME (2006) Modularity and community structure in networks. Proc Natl Acad Sci 103(23):8577–8582

    Article  Google Scholar 

  • Oh J, Oh BL, Lee KU et al (2020) Identifying schizophrenia using structural mri with a deep learning algorithm. Front Psychiatry 11. https://doi.org/10.3389/fpsyt.2020.00016

  • Patel P, Aggarwal P, Gupta A (2016) Classification of schizophrenia versus normal subjects using deep learning. In: Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing. Association for Computing Machinery, New York, NY, USA, ICVGIP ’16, https://doi.org/10.1145/3009977.3010050,

  • Rao B, Mishra S (2016) An approach to finding similarity between two community graphs using graph mining techniques. Int J Adv Comput Sci Appl 7. https://doi.org/10.14569/IJACSA.2016.070563

  • Sartipi S, Kalbkhani H, Shayesteh MG (2020) Diagnosis of schizophrenia from R-fMRI data using ripplet transform and OLPP. Multimedia Tools and Applications pp 1–23

  • Sebasthiyar A, Panchanathan A (2019) Diagnosis of parkinson’s disease at an early stage using volume rendering SPECT image slices. Arab J Sci Eng 45. https://doi.org/10.1007/s13369-019-04152-7

  • Shi D, Li Y, Zhang H et al (2021) Machine learning of schizophrenia detection with structural and functional neuroimaging. Dis Mark 2021:1–12. https://doi.org/10.1155/2021/9963824

  • Sifis M (2012) Graph-based network analysis in schizophrenia. World J Psychiatry. https://doi.org/10.5498/wjp.v2.i1.1

    Article  Google Scholar 

  • Sujatha CM (2021) Identification of schizophrenia using LSTM recurrent neural network. In: 2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII), pp 1–6. https://doi.org/10.1109/ICBSII51839.2021.9445189

  • Su X, Xue S, Liu F, et al (2021) A comprehensive survey on community detection with deep learning. CoRR arXiv:abs/2105.12584

  • Taya F, Souza J, Thakor N et al (2016) Comparison method for community detection on brain networks from neuroimaging data. Appl Netw Sci 1. https://doi.org/10.1007/s41109-016-0007-y

  • The Mind Research Network UoNM (2012) The Center for Biomedical Research Excellence (COBRE). http://fcon_1000.projects.nitrc.org/indi/retro/cobre.html/ [Online]

  • Wismüller A, Vosoughi MA (2021) Classification of schizophrenia from functional MRI using large-scale extended granger causality. Neurons Cogn arXiv:2101.10471 [q-bio.NC]

  • Yang H, Zhang H, Di X et al (2021) Frequency-specific coactivation patterns in resting-state and their alterations in schizophrenia: an fMRI study. Hum Brain Mapp. https://doi.org/10.1101/2021.07.04.451042

    Article  Google Scholar 

  • Zheng J, Wei X, Wang J, et al (2021) Diagnosis of schizophrenia based on deep learning using fMRI. Computat Math Methods Med

Download references

Funding

The authors did not receive support from any organization for the submitted work.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Anna University, Chennai, Tamil Nadu, India

    Kalaichelvi Nallusamy & K. S. Easwarakumar

Authors
  1. Kalaichelvi Nallusamy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K. S. Easwarakumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kalaichelvi Nallusamy.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nallusamy, K., Easwarakumar, K.S. Classifying schizophrenic and controls from fMRI data using graph theoretic framework and community detection. Netw Model Anal Health Inform Bioinforma 12, 19 (2023). https://doi.org/10.1007/s13721-023-00415-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13721-023-00415-4

Keywords

Search

Navigation