Functional connectivity patterns of autism spectrum disorder identified by deep feature learning

Autism spectrum disorder (ASD) is regarded as a brain disease with globally disrupted neuronal networks. Even though fMRI studies have revealed abnormal functional connectivity in ASD, they have not reached a consensus of the disrupted patterns. Here, a deep learning-based feature extraction method identifies multivariate and nonlinear functional connectivity patterns of ASD. Resting-state fMRI data of 972 subjects (465 ASD 507 normal controls) acquired from the Autism Brain Imaging Data Exchange were used. A functional connectivity matrix of each subject was generated using 90 predefined brain regions. As a data-driven feature extraction method without prior knowledge such as subjects diagnosis, variational autoencoder (VAE) summarized the functional connectivity matrix into 2 features. Those feature values of ASD patients were statistically compared with those of controls. A feature was significantly different between ASD and normal controls. The extracted features were visualized by VAE-based generator which can produce virtual functional connectivity matrices. The ASD-related feature was associated with frontoparietal connections, interconnections of the dorsal medial frontal cortex and corticostriatal connections. It also showed a trend of negative correlation with full-scale IQ. A data-driven feature extraction based on deep learning could identify complex patterns of functional connectivity of ASD. This approach will help discover complex patterns of abnormalities in brain connectivity in various brain disorders.

[1]  N. Tzourio-Mazoyer,et al.  Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain , 2002, NeuroImage.

[2]  Dimitris Samaras,et al.  Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example , 2016, NeuroImage.

[3]  M. Just,et al.  Cortical activation and synchronization during sentence comprehension in high-functioning autism: evidence of underconnectivity. , 2004, Brain : a journal of neurology.

[4]  C. Lord,et al.  Aberrant Striatal Functional Connectivity in Children with Autism , 2011, Biological Psychiatry.

[5]  Timothy A Keller,et al.  The nature of brain dysfunction in autism: functional brain imaging studies , 2010, Current opinion in neurology.

[6]  Ruth A. Carper,et al.  Autism and Abnormal Development of Brain Connectivity , 2004, The Journal of Neuroscience.

[7]  J. Rabe-Jabłońska,et al.  [Affective disorders in the fourth edition of the classification of mental disorders prepared by the American Psychiatric Association -- diagnostic and statistical manual of mental disorders]. , 1993, Psychiatria polska.

[8]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[9]  Scott Peltier,et al.  Abnormalities of intrinsic functional connectivity in autism spectrum disorders, , 2009, NeuroImage.

[10]  Daniel P. Kennedy,et al.  The social brain in psychiatric and neurological disorders , 2012, Trends in Cognitive Sciences.

[11]  Simon Baron-Cohen,et al.  Reduced functional connectivity within and between ‘social’ resting state networks in autism spectrum conditions , 2012, Social cognitive and affective neuroscience.

[12]  P. Mundy.  Annotation: the neural basis of social impairments in autism: the role of the dorsal medial-frontal cortex and anterior cingulate system. , 2003, Journal of child psychology and psychiatry, and allied disciplines.

[13]  Razvan Pascanu,et al.  Theano: new features and speed improvements , 2012, ArXiv.

[14]  Jared A. Nielsen,et al.  Functional connectivity magnetic resonance imaging classification of autism. , 2011, Brain : a journal of neurology.

[15]  Daniel P. Kennedy,et al.  The Autism Brain Imaging Data Exchange: Towards Large-Scale Evaluation of the Intrinsic Brain Architecture in Autism , 2013, Molecular Psychiatry.

[16]  E. Courchesne,et al.  Why the frontal cortex in autism might be talking only to itself: local over-connectivity but long-distance disconnection , 2005, Current Opinion in Neurobiology.

[17]  Karl J. Friston,et al.  Movement‐Related effects in fMRI time‐series , 1996, Magnetic resonance in medicine.

[18]  Istvan Molnar-Szakacs,et al.  Watching social interactions produces dorsomedial prefrontal and medial parietal BOLD fMRI signal increases compared to a resting baseline , 2004, NeuroImage.

[19]  Marcel Adam Just,et al.  Inter-Regional Brain Communication and Its Disturbance in Autism , 2011, Front. Syst. Neurosci..

[20]  Thomas Bourgeron,et al.  A synaptic trek to autism , 2009, Current Opinion in Neurobiology.

[21]  Marco Iacoboni,et al.  Reduced functional integration and segregation of distributed neural systems underlying social and emotional information processing in autism spectrum disorders. , 2012, Cerebral cortex.

[22]  Ralph-Axel Müller,et al.  Underconnected, but how? A survey of functional connectivity MRI studies in autism spectrum disorders. , 2011, Cerebral cortex.

[23]  M. Just,et al.  Functional and anatomical cortical underconnectivity in autism: evidence from an FMRI study of an executive function task and corpus callosum morphometry. , 2007, Cerebral cortex.

[24]  Vince D. Calhoun,et al.  Variational Autoencoders for Feature Detection of Magnetic Resonance Imaging Data , 2016, ArXiv.

[25]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[26]  Matthew D. Zeiler.  ADADELTA: An Adaptive Learning Rate Method , 2012, ArXiv.

[27]  Jared A. Nielsen,et al.  Decreased interhemispheric functional connectivity in autism. , 2011, Cerebral cortex.

[28]  Nicole Wenderoth,et al.  Promises, Pitfalls, and Basic Guidelines for Applying Machine Learning Classifiers to Psychiatric Imaging Data, with Autism as an Example , 2016, Front. Psychiatry.

[29]  Max Welling,et al.  Auto-Encoding Variational Bayes , 2013, ICLR.

[30]  Hod Lipson,et al.  Understanding Neural Networks Through Deep Visualization , 2015, ArXiv.

[31]  Khundrakpam Budhachandra,et al.  The Neuro Bureau Preprocessing Initiative: open sharing of preprocessed neuroimaging data and derivatives , 2013 .

[32]  M. Just,et al.  fMRI investigation of working memory for faces in autism: visual coding and underconnectivity with frontal areas. , 2008, Cerebral cortex.

[33]  C. Keysers,et al.  Increased Functional Connectivity Between Subcortical and Cortical Resting-State Networks in Autism Spectrum Disorder. , 2015, JAMA psychiatry.