Connectomics in NeuroImaging

The underlying neural mechanisms of autism spectrum disorders (ASD) remains unclear. Most of the previous studies based on connectomics to discriminate ASD from typically developing (TD) subjects focused either on global graph metrics or specific discriminant connections. In this paper we investigate whether there is a correlation between local and global features, and whether the characterization that discriminates ASD from TD subjects is primarily given by widespread network differences, or the difference lies in specific local connections which are just captured by global metrics. Namely, whether miswiring of brain connections related to ASD is localized or diffuse. The presented results suggest that the widespread hypothesis is more likely.

[1]  Vince D. Calhoun,et al.  Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia , 2016, NeuroImage.

[2]  Bill Seeley,et al.  Neurodegenerative diseases target large-scale human brain networks , 2010, Alzheimer's & Dementia.

[3]  Ruth A. Carper,et al.  Longitudinal Magnetic Resonance Imaging Study of Cortical Development through Early Childhood in Autism , 2010, The Journal of Neuroscience.

[4]  Scott Holland,et al.  Template-O-Matic: A toolbox for creating customized pediatric templates , 2008, NeuroImage.

[5]  G. Glover,et al.  Dissociable Intrinsic Connectivity Networks for Salience Processing and Executive Control , 2007, The Journal of Neuroscience.

[6]  Yann LeCun,et al.  Signature Verification Using A "Siamese" Time Delay Neural Network , 1993, Int. J. Pattern Recognit. Artif. Intell..

[7]  Delbert Dueck,et al.  Clustering by Passing Messages Between Data Points , 2007, Science.

[8]  Eduardo Alonso,et al.  Phenotypic Integrated Framework for Classification of ADHD Using fMRI , 2016, ICIAR.

[9]  Evan M. Gordon,et al.  Dysmaturation of the default mode network in autism , 2014, Human brain mapping.

[10]  P. Thomas Fletcher,et al.  scMRI Reveals Large-Scale Brain Network Abnormalities in Autism , 2012, PloS one.

[11]  A. R. Rao,et al.  Attributed graph distance measure for automatic detection of attention deficit hyperactive disordered subjects , 2014, Front. Neural Circuits.

[12]  Andrew L. Maas Rectifier Nonlinearities Improve Neural Network Acoustic Models , 2013 .

[13]  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.

[14]  H. Zou,et al.  Regularization and variable selection via the elastic net , 2005 .

[15]  Dinggang Shen,et al.  Matrix-Similarity Based Loss Function and Feature Selection for Alzheimer's Disease Diagnosis , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  C. McDougle,et al.  Structural and functional magnetic resonance imaging of autism spectrum disorders , 2011, Brain Research.