SMART (Splitting-Merging Assisted Reliable) Independent Component Analysis for Extracting Accurate Brain Functional Networks.

[1]  Yating Guo,et al.  Aging brain shows joint declines in brain within-network connectivity and between-network connectivity: a large-sample study (N > 6,000) , 2023, Frontiers in Aging Neuroscience.

[2]  Pengxu Wei,et al.  Comparing the reliability of different ICA algorithms for fMRI analysis , 2022, PloS one.

[3]  G. Kwon,et al.  Alzheimer’s Disease Diagnosis and Biomarker Analysis Using Resting-State Functional MRI Functional Brain Network With Multi-Measures Features and Hippocampal Subfield and Amygdala Volume of Structural MRI , 2022, Frontiers in Aging Neuroscience.

[4]  Vince D. Calhoun,et al.  A new multimodality fusion classification approach to explore the uniqueness of schizophrenia and autism spectrum disorder , 2022, Human brain mapping.

[5]  Weili Lin,et al.  Common variants contribute to intrinsic human brain functional networks , 2022, Nature Genetics.

[6]  Julia H. Tang,et al.  Electrophysiological resting state brain network and episodic memory in healthy aging adults , 2022, NeuroImage.

[7]  N. Shu,et al.  The overlapping modular organization of human brain functional networks across the adult lifespan , 2021, NeuroImage.

[8]  M. Korgaonkar,et al.  Default-mode and fronto-parietal network connectivity during rest distinguishes asymptomatic patients with bipolar disorder and major depressive disorder , 2021, Translational Psychiatry.

[9]  M. Zanin.  Simplifying functional network representation and interpretation through causality clustering , 2021, Scientific Reports.

[10]  M. Tian,et al.  Structural, Functional, and Molecular Imaging of Autism Spectrum Disorder , 2021, Neuroscience Bulletin.

[11]  Chih-Mao Huang,et al.  Healthy Aging Alters the Functional Connectivity of Creative Cognition in the Default Mode Network and Cerebellar Network , 2021, Frontiers in Aging Neuroscience.

[12]  Guoqiang Hu,et al.  Snowball ICA: A Model Order Free Independent Component Analysis Strategy for Functional Magnetic Resonance Imaging Data , 2020, Frontiers in Neuroscience.

[13]  W. Qian,et al.  Brain functional network modeling and analysis based on fMRI: a systematic review , 2020, Cognitive Neurodynamics.

[14]  S. Gohel,et al.  Current methods and new directions in resting state fMRI. , 2020, Clinical imaging.

[15]  Jianfeng Feng,et al.  Automated anatomical labelling atlas 3 , 2020, NeuroImage.

[16]  Peter Fonagy,et al.  Conservative and disruptive modes of adolescent change in human brain functional connectivity , 2020, Proceedings of the National Academy of Sciences.

[17]  Y. Stern,et al.  The Effect of Aging on Resting State Connectivity of Predefined Networks in the Brain , 2019, Front. Aging Neurosci..

[18]  Vince D. Calhoun,et al.  Dynamic functional connectivity in schizophrenia and autism spectrum disorder: Convergence, divergence and classification , 2019, NeuroImage: Clinical.

[19]  Hamid Hassanpour,et al.  Image quality assessment using a novel region smoothness measure , 2019, J. Vis. Commun. Image Represent..

[20]  Cuntai Guan,et al.  Large-scale brain functional network topology disruptions underlie symptom heterogeneity in children with attention-deficit/hyperactivity disorder , 2018, NeuroImage: Clinical.

[21]  Vince D. Calhoun,et al.  Model order effects on ICA of resting-state complex-valued fMRI data: Application to schizophrenia , 2018, Journal of Neuroscience Methods.

[22]  Xia-an Bi,et al.  Abnormal Functional Connectivity of Resting State Network Detection Based on Linear ICA Analysis in Autism Spectrum Disorder , 2018, Front. Physiol..

[23]  Yike Guo,et al.  A novel community detection algorithm based on simplification of complex networks , 2017, Knowl. Based Syst..

[24]  Ludovica Griffanti,et al.  Hand classification of fMRI ICA noise components , 2017, NeuroImage.

[25]  Guokang Zhu,et al.  Robust K-means algorithm with automatically splitting and merging clusters and its applications for surveillance data , 2016, Multimedia Tools and Applications.

[26]  V. Calhoun,et al.  Artifact removal in the context of group ICA: A comparison of single‐subject and group approaches , 2016, Human brain mapping.

[27]  J. Wall,et al.  Functional Network Overlap as Revealed by fMRI Using sICA and Its Potential Relationships with Functional Heterogeneity, Balanced Excitation and Inhibition, and Sparseness of Neuron Activity , 2015, PloS one.

[28]  C. Sudlow,et al.  UK Biobank Data: Come and Get It , 2014, Science Translational Medicine.

[29]  Linda Geerligs,et al.  Reduced specificity of functional connectivity in the aging brain during task performance , 2014, Human brain mapping.

[30]  Yong Fan,et al.  Group information guided ICA for fMRI data analysis , 2013, NeuroImage.

[31]  Erik B. Erhardt,et al.  SimTB, a simulation toolbox for fMRI data under a model of spatiotemporal separability , 2012, NeuroImage.

[32]  Li Yao,et al.  An Empirical Comparison of Information-Theoretic Criteria in Estimating the Number of Independent Components of fMRI Data , 2011, PloS one.

[33]  O. Tervonen,et al.  The effect of model order selection in group PICA , 2010, Human brain mapping.

[34]  Yong He,et al.  Age-related changes in topological patterns of large-scale brain functional networks during memory encoding and recognition , 2010, NeuroImage.

[35]  Vince D. Calhoun,et al.  A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data , 2009, NeuroImage.

[36]  Tülay Adali,et al.  Estimating the number of independent components for functional magnetic resonance imaging data , 2007, Human brain mapping.

[37]  Rajesh Nandy,et al.  Estimation of the intrinsic dimensionality of fMRI data , 2006, NeuroImage.

[38]  Jiri Matas,et al.  Robust wide-baseline stereo from maximally stable extremal regions , 2004, Image Vis. Comput..

[39]  J. Pekar,et al.  A method for making group inferences from functional MRI data using independent component analysis , 2001, Human brain mapping.

[40]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[41]  Robert A. Jacobs,et al.  Increased rates of convergence through learning rate adaptation , 1987, Neural Networks.

[42]  A. Belger,et al.  Multimodel Order Independent Component Analysis: A Data-Driven Method for Evaluating Brain Functional Network Connectivity Within and Between Multiple Spatial Scales , 2022, Brain Connect..