Eyes-Open and Eyes-Closed Resting States With Opposite Brain Activity in Sensorimotor and Occipital Regions: Multidimensional Evidences From Machine Learning Perspective

Studies have demonstrated that there are widespread significant differences in spontaneous brain activity between eyes-open (EO) and eyes-closed (EC) resting states. However, it remains largely unclear whether spontaneous brain activity is effectively related to EO and EC resting states. The amplitude, local functional concordance, inter-hemisphere functional synchronization, and network centrality of spontaneous brain activity were measured by the fraction amplitude of low frequency fluctuation (fALFF), regional homogeneity (ReHo), voxel-mirrored homotopic connectivity (VMHC) and degree centrality (DC), respectively. Using the public Eyes-open/Eyes-closed dataset, we employed the support vector machine (SVM) and bootstrap technique to establish linking models for the fALFF, ReHo, VMHC and DC dimensions. The classification accuracies of linking models are 0.72 (0.59, 0.82), 0.88 (0.79, 0.97), 0.82 (0.74, 0.91) and 0.70 (0.62, 0.79), respectively. Specifically, we observed that brain activity in the EO condition is significantly greater in attentional system areas, including the fusiform gyrus, occipital and parietal cortex, but significantly lower in sensorimotor system areas, including the precentral/postcentral gyrus, paracentral lobule (PCL) and temporal cortex compared to the EC condition from the four dimensions. The results consistently indicated that spontaneous brain activity is effectively related to EO and EC resting states, and the two resting states are of opposite brain activity in sensorimotor and occipital regions. It may provide new insight into the neural substrate of the resting state and help computational neuroscientists or neuropsychologists to choose an appropriate resting state condition to investigate various mental disorders from the resting state functional magnetic resonance imaging (fMRI) technique.

[1]  R Cameron Craddock,et al.  Disease state prediction from resting state functional connectivity , 2009, Magnetic resonance in medicine.

[2]  Thomas Stephan,et al.  Eye closure in darkness animates sensory systems , 2003, NeuroImage.

[3]  Sriraam Natarajan,et al.  Machine Learning Applications to Resting-State Functional MR Imaging Analysis. , 2017, Neuroimaging clinics of North America.

[4]  Alan C. Evans,et al.  Growing Together and Growing Apart: Regional and Sex Differences in the Lifespan Developmental Trajectories of Functional Homotopy , 2010, The Journal of Neuroscience.

[5]  Ernesto Pereda,et al.  The variability of EEG functional connectivity of young ADHD subjects in different resting states , 2016, Clinical Neurophysiology.

[6]  Rupert Lanzenberger,et al.  Correlations and anticorrelations in resting-state functional connectivity MRI: A quantitative comparison of preprocessing strategies , 2009, NeuroImage.

[7]  Jin Fan,et al.  Different topological organization of human brain functional networks with eyes open versus eyes closed , 2014, NeuroImage.

[8]  Abraham Z Snyder,et al.  Dissociated mean and functional connectivity BOLD signals in visual cortex during eyes closed and fixation. , 2012, Journal of neurophysiology.

[9]  M. Raichle,et al.  Resting states affect spontaneous BOLD oscillations in sensory and paralimbic cortex. , 2008, Journal of neurophysiology.

[10]  Reinhold Schmidt,et al.  A comprehensive analysis of resting state fMRI measures to classify individual patients with Alzheimer's disease , 2018, NeuroImage.

[11]  Jeff H. Duyn,et al.  Modulation of spontaneous fMRI activity in human visual cortex by behavioral state , 2009, NeuroImage.

[12]  Yufeng Zang,et al.  Assessing the mean strength and variations of the time-to-time fluctuations of resting-state brain activity , 2017, Medical & Biological Engineering & Computing.

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

[14]  G. Fesl,et al.  Differential effects of eyes open or closed in darkness on brain activation patterns in blind subjects , 2009, Neuroscience Letters.

[15]  Yufeng Zang,et al.  DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging , 2016, Neuroinformatics.

[16]  Y. Zang,et al.  Detecting Static and Dynamic Differences between Eyes-Closed and Eyes-Open Resting States Using ASL and BOLD fMRI , 2015, PloS one.

[17]  A. Marquand,et al.  Distinguishing medication‐free subjects with unipolar disorder from subjects with bipolar disorder: state matters , 2016, Bipolar disorders.

[18]  John Ashburner,et al.  A fast diffeomorphic image registration algorithm , 2007, NeuroImage.

[19]  Yong He,et al.  Discriminative analysis of early Alzheimer's disease using multi-modal imaging and multi-level characterization with multi-classifier (M3) , 2012, NeuroImage.

[20]  Xi-Nian Zuo,et al.  Regional Homogeneity , 2015, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[21]  Abbas Babajani-Feremi,et al.  Classification of patients with MCI and AD from healthy controls using directed graph measures of resting-state fMRI , 2017, Behavioural Brain Research.

[22]  Simon B. Eickhoff,et al.  An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data , 2013, NeuroImage.

[23]  Dinggang Shen,et al.  State-space model with deep learning for functional dynamics estimation in resting-state fMRI , 2016, NeuroImage.

[24]  Mario Quarantelli,et al.  Relationship between simultaneously acquired resting-state regional cerebral glucose metabolism and functional MRI: A PET/MR hybrid scanner study , 2015, NeuroImage.

[25]  Yingli Lu,et al.  Regional homogeneity approach to fMRI data analysis , 2004, NeuroImage.

[26]  Yi Zhang,et al.  Frequency-Dependent Modulation of Regional Synchrony in the Human Brain by Eyes Open and Eyes Closed Resting-States , 2015, PloS one.

[27]  R. Cameron Craddock,et al.  A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics , 2013, NeuroImage.

[28]  Yufeng Zang,et al.  Eyes-Open/Eyes-Closed Dataset Sharing for Reproducibility Evaluation of Resting State fMRI Data Analysis Methods , 2013, Neuroinformatics.

[29]  Kevin Murphy,et al.  The impact of global signal regression on resting state correlations: Are anti-correlated networks introduced? , 2009, NeuroImage.

[30]  R. Müller,et al.  Local resting state functional connectivity in autism: site and cohort variability and the effect of eye status , 2017, Brain Imaging and Behavior.

[31]  Y. Zang,et al.  Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI , 2007, Brain and Development.

[32]  Yufeng Zang,et al.  DPARSF: A MATLAB Toolbox for “Pipeline” Data Analysis of Resting-State fMRI , 2010 .

[33]  Yufeng Zang,et al.  Spontaneous Brain Activity in the Default Mode Network Is Sensitive to Different Resting-State Conditions with Limited Cognitive Load , 2009, PloS one.

[34]  Yong He,et al.  Functional connectivity between the thalamus and visual cortex under eyes closed and eyes open conditions: A resting‐state fMRI study , 2009, Human brain mapping.

[35]  Thomas T. Liu,et al.  The global signal in fMRI: Nuisance or Information? , 2017, NeuroImage.

[36]  Yan Zhang,et al.  Abnormal regional homogeneity as a potential imaging biomarker for adolescent-onset schizophrenia: A resting-state fMRI study and support vector machine analysis , 2017, Schizophrenia Research.

[37]  Stavros I. Dimitriadis,et al.  Mining Time-Resolved Functional Brain Graphs to an EEG-Based Chronnectomic Brain Aged Index (CBAI) , 2017, Front. Hum. Neurosci..

[38]  Keith A. Johnson,et al.  Cortical Hubs Revealed by Intrinsic Functional Connectivity: Mapping, Assessment of Stability, and Relation to Alzheimer's Disease , 2009, The Journal of Neuroscience.

[39]  Niall W. Duncan,et al.  Vascular-metabolic and GABAergic Inhibitory Correlates of Neural Variability Modulation. A Combined fMRI and PET Study , 2018, Neuroscience.

[40]  Chaogan Yan,et al.  Reproducibility of R-fMRI Metrics on the Impact of Different Strategies for Multiple Comparison Correction and Sample Sizes , 2017, bioRxiv.

[41]  Tetsuya Iidaka,et al.  Resting state functional magnetic resonance imaging and neural network classified autism and control , 2015, Cortex.

[42]  D. P. X. Kan,et al.  EEG Differences Between Eyes-Closed and Eyes-Open Conditions at the Resting Stage for Euthymic Participants , 2017, Neurophysiology.

[43]  X. Zuo,et al.  Test-retest reliabilities of resting-state FMRI measurements in human brain functional connectomics: A systems neuroscience perspective , 2014, Neuroscience & Biobehavioral Reviews.

[44]  Chunbo Li,et al.  Aberrant Functional Connectivity between the Amygdala and the Temporal Pole in Drug-Free Generalized Anxiety Disorder , 2016, Front. Hum. Neurosci..

[45]  O. Sporns,et al.  Network centrality in the human functional connectome. , 2012, Cerebral cortex.

[46]  J. Oosterlaan,et al.  A randomized controlled trial into the effects of neurofeedback, methylphenidate, and physical activity on EEG power spectra in children with ADHD. , 2016, Journal of child psychology and psychiatry, and allied disciplines.

[47]  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.

[48]  Hang Joon Jo,et al.  The perils of global signal regression for group comparisons: a case study of Autism Spectrum Disorders , 2013, Front. Hum. Neurosci..

[49]  Michael Brady,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

[50]  Edward T. Bullmore,et al.  Volitional eyes opening perturbs brain dynamics and functional connectivity regardless of light input , 2013, NeuroImage.

[51]  Jie Wei,et al.  Higher-order Multivariable Polynomial Regression to Estimate Human Affective States , 2016, Scientific Reports.

[52]  Thomas T. Liu,et al.  Differences in the resting-state fMRI global signal amplitude between the eyes open and eyes closed states are related to changes in EEG vigilance , 2016, NeuroImage.

[53]  Geoff Dougherty,et al.  Pattern Recognition and Classification: An Introduction , 2012 .

[54]  Yu-Feng Zang,et al.  PAIR Comparison between Two Within-Group Conditions of Resting-State fMRI Improves Classification Accuracy , 2018, Front. Neurosci..

[55]  Chaozhe Zhu,et al.  Amplitude of low frequency fluctuation within visual areas revealed by resting-state functional MRI , 2007, NeuroImage.

[56]  Chaozhe Zhu,et al.  An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: Fractional ALFF , 2008, Journal of Neuroscience Methods.

[57]  Yong He,et al.  Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI. , 2007, Brain & development.

[58]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.