The distinguishing intrinsic brain circuitry in treatment-naïve first-episode schizophrenia: Ensemble learning classification

Abstract Schizophrenia is frequently characterized as a prototypical disorder of integration of brain function involving almost all intrinsic connectivity networks. However, a consistent conclusion regarding the most distinguishing brain circuitry in schizophrenia has not yet been reached. In this study, we used a novel network-based ensemble method to explore the most distinguishing brain circuitry in treatment-naive first-episode schizophrenia (n = 41) and healthy controls (n = 38) who underwent the task-free functional MRI scanning. Ensemble method showed commendable discrimination ability (84.7% for classification accuracy, 91.9% for sensitivity, 74.5% for specificity, all p

[1]  Jonathan D. Power,et al.  Evidence for Hubs in Human Functional Brain Networks , 2013, Neuron.

[2]  S. Heckers,et al.  Functional resting-state networks are differentially affected in schizophrenia , 2011, Schizophrenia Research.

[3]  H. Barbas,et al.  Medial Prefrontal Cortices Are Unified by Common Connections With Superior Temporal Cortices and Distinguished by Input From Memory‐Related Areas in the Rhesus Monkey , 1999, The Journal of comparative neurology.

[4]  K. Kendrick,et al.  Depression uncouples brain hate circuit , 2011, Molecular Psychiatry.

[5]  Dewen Hu,et al.  Discriminative analysis of resting-state functional connectivity patterns of schizophrenia using low dimensional embedding of fMRI , 2010, NeuroImage.

[6]  Huafu Chen,et al.  Partially impaired functional connectivity states between right anterior insula and default mode network in autism spectrum disorder , 2018, Human brain mapping.

[7]  M. Mesulam.  Brain, Mind, and the Evolution of Connectivity , 2000, Brain and Cognition.

[8]  A. Heinz,et al.  Dopaminergic dysfunction in schizophrenia: salience attribution revisited. , 2010, Schizophrenia bulletin.

[9]  J. McGrath,et al.  Schizophrenia: a concise overview of incidence, prevalence, and mortality. , 2008, Epidemiologic reviews.

[10]  Jungsu S. Oh,et al.  Altered resting-state connectivity in subjects at ultra-high risk for psychosis: an fMRI study , 2010, Behavioral and Brain Functions.

[11]  V. Calhoun,et al.  Aberrant "default mode" functional connectivity in schizophrenia. , 2007, The American journal of psychiatry.

[12]  Georg Northoff,et al.  How can the brain's resting state activity generate hallucinations? A ‘resting state hypothesis’ of auditory verbal hallucinations , 2011, Schizophrenia Research.

[13]  V. Menon,et al.  A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks , 2008, Proceedings of the National Academy of Sciences.

[14]  V. Calhoun,et al.  High Classification Accuracy for Schizophrenia with Rest and Task fMRI Data , 2012, Front. Hum. Neurosci..

[15]  I. Olson,et al.  The Enigmatic temporal pole: a review of findings on social and emotional processing. , 2007, Brain : a journal of neurology.

[16]  Rong Li,et al.  Epileptic Discharge Related Functional Connectivity Within and Between Networks in Benign Epilepsy with Centrotemporal Spikes , 2017, Int. J. Neural Syst..

[17]  Lars T. Westlye,et al.  Consistent Functional Connectivity Alterations in Schizophrenia Spectrum Disorder: A Multisite Study , 2017, Schizophrenia bulletin.

[18]  Wei Zhang,et al.  Selective aberrant functional connectivity of resting state networks in social anxiety disorder , 2010, NeuroImage.

[19]  Giorgio Coricelli,et al.  Impaired decision making in schizophrenia and orbitofrontal cortex lesion patients , 2010, Schizophrenia Research.

[20]  Huafu Chen,et al.  Differential patterns of dynamic functional connectivity variability of striato–cortical circuitry in children with benign epilepsy with centrotemporal spikes , 2018, Human brain mapping.

[21]  H. Möller,et al.  Use of neuroanatomical pattern classification to identify subjects in at-risk mental states of psychosis and predict disease transition. , 2009, Archives of general psychiatry.

[22]  Hilleke E. Hulshoff Pol,et al.  Classification of schizophrenia patients and healthy controls from structural MRI scans in two large independent samples , 2012, NeuroImage.

[23]  Asai Asaithambi,et al.  Multidimensional pattern recognition problems and combining classifiers , 2001, Pattern Recognit. Lett..

[24]  J. Gabrieli,et al.  Hyperactivity and hyperconnectivity of the default network in schizophrenia and in first-degree relatives of persons with schizophrenia , 2009, Proceedings of the National Academy of Sciences.

[25]  David H. Wolpert,et al.  Stacked generalization , 1992, Neural Networks.

[26]  Vince D. Calhoun,et al.  Classification of schizophrenia patients based on resting-state functional network connectivity , 2013, Front. Neurosci..

[27]  Kent A. Kiehl,et al.  A cognitive neuroscience perspective on psychopathy: Evidence for paralimbic system dysfunction , 2006, Psychiatry Research.

[28]  Moo K. Chung,et al.  Spatially augmented LPboosting for AD classification with evaluations on the ADNI dataset , 2009, NeuroImage.

[29]  C. N. Macrae,et al.  Finding the Self? An Event-Related fMRI Study , 2002, Journal of Cognitive Neuroscience.

[30]  C. Frith,et al.  The positive and negative symptoms of schizophrenia reflect impairments in the perception and initiation of action , 1987, Psychological Medicine.

[31]  Abraham Z. Snyder,et al.  Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion , 2012, NeuroImage.

[32]  Rebecca Saxe,et al.  Associations and dissociations between default and self-reference networks in the human brain , 2011, NeuroImage.

[33]  Huafu Chen,et al.  Frequency-selective alteration in the resting-state corticostriatal-thalamo-cortical circuit correlates with symptoms severity in first-episode drug-naive patients with schizophrenia , 2017, Schizophrenia Research.

[34]  Thomas G. Dietterich.  Machine-Learning Research , 1997, AI Mag..

[35]  Qian Cui,et al.  Decreased static and increased dynamic global signal topography in major depressive disorder , 2019, Progress in Neuro-Psychopharmacology and Biological Psychiatry.

[36]  Manuel Graña,et al.  Discrimination of Schizophrenia Auditory Hallucinators by Machine Learning of Resting-State Functional MRI , 2015, Int. J. Neural Syst..

[37]  Indira Tendolkar,et al.  Paralimbic cortical thickness in first-episode depression: evidence for trait-related differences in mood regulation. , 2013, The American journal of psychiatry.

[38]  Jonathan D. Power,et al.  Prediction of Individual Brain Maturity Using fMRI , 2010, Science.

[39]  K. Vogeley,et al.  Resting-state functional network correlates of psychotic symptoms in schizophrenia , 2010, Schizophrenia Research.

[40]  Yuan Zhou,et al.  Schizophrenic patients and their unaffected siblings share increased resting-state connectivity in the task-negative network but not its anticorrelated task-positive network. , 2012, Schizophrenia bulletin.

[41]  R. McCarley,et al.  Orbitofrontal volume deficit in schizophrenia and thought disorder. , 2007, Brain : a journal of neurology.

[42]  Huafu Chen,et al.  Altered dynamics of brain segregation and integration in poststroke aphasia , 2019, Human brain mapping.

[43]  Charles J. Lynch,et al.  Salience network-based classification and prediction of symptom severity in children with autism. , 2013, JAMA psychiatry.

[44]  Qian Cui,et al.  Preservation Effect: Cigarette Smoking Acts on the Dynamic of Influences Among Unifying Neuropsychiatric Triple Networks in Schizophrenia. , 2018, Schizophrenia bulletin.

[45]  Katherine Prater,et al.  Trait anhedonia is associated with reduced reactivity and connectivity of mesolimbic and paralimbic reward pathways. , 2013, Journal of psychiatric research.

[46]  J. Ford,et al.  Default mode network activity and connectivity in psychopathology. , 2012, Annual review of clinical psychology.

[47]  Kaustubh Supekar,et al.  Brain hyperconnectivity in children with autism and its links to social deficits. , 2013, Cell reports.

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

[49]  Martial Van der Linden,et al.  Self-referential reflective activity and its relationship with rest: a PET study , 2005, NeuroImage.

[50]  Mark A. Elliott,et al.  Impact of in-scanner head motion on multiple measures of functional connectivity: Relevance for studies of neurodevelopment in youth , 2012, NeuroImage.

[51]  Jonathan D. Power,et al.  Intrinsic and Task-Evoked Network Architectures of the Human Brain , 2014, Neuron.

[52]  Edward T. Bullmore,et al.  Schizophrenia, neuroimaging and connectomics , 2012, NeuroImage.

[53]  I D Wilkinson,et al.  Neural activity in speech-sensitive auditory cortex during silence. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[54]  J. Cummings,et al.  Frontal-subcortical neuronal circuits and clinical neuropsychiatry: an update. , 2002, Journal of psychosomatic research.

[55]  Yihong Yang,et al.  Evaluating the effective connectivity of resting state networks using conditional Granger causality , 2010, Biological Cybernetics.

[56]  Ann K. Shinn,et al.  Abnormal Medial Prefrontal Cortex Resting-State Connectivity in Bipolar Disorder and Schizophrenia , 2011, Neuropsychopharmacology.

[57]  A. Grace,et al.  Dopaminergic modulation of limbic and cortical drive of nucleus accumbens in goal-directed behavior , 2005, Nature Neuroscience.

[58]  Thomas G. Dietterich.  Machine-Learning Research Four Current Directions , 1997 .

[59]  S. Debener,et al.  Default-mode brain dysfunction in mental disorders: A systematic review , 2009, Neuroscience & Biobehavioral Reviews.

[60]  T. McGlashan,et al.  Early detection and intervention of schizophrenia: rationale and research , 1998, British Journal of Psychiatry.

[61]  Abraham Z. Snyder,et al.  Corrigendum to “Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion” [NeuroImage 59 (3) (2012) 2142–2154] , 2012, NeuroImage.

[62]  Marek Kubicki,et al.  Reduced task-related suppression during semantic repetition priming in schizophrenia , 2010, Psychiatry Research: Neuroimaging.

[63]  P. Liddle,et al.  Does the salience network play a cardinal role in psychosis? An emerging hypothesis of insular dysfunction. , 2012, Journal of psychiatry & neuroscience : JPN.

[64]  V. Calhoun,et al.  Classifying Schizophrenia Using Multimodal Multivariate Pattern Recognition Analysis: Evaluating the Impact of Individual Clinical Profiles on the Neurodiagnostic Performance. , 2016, Schizophrenia bulletin.

[65]  G. Shulman,et al.  Medial prefrontal cortex and self-referential mental activity: Relation to a default mode of brain function , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[66]  M. Matějka,et al.  Connectivity of the anterior insula differentiates participants with first-episode schizophrenia spectrum disorders from controls: a machine-learning study , 2016, Psychological Medicine.

[67]  Daphne J. Holt,et al.  An Anterior-to-Posterior Shift in Midline Cortical Activity in Schizophrenia During Self-Reflection , 2010, Biological Psychiatry.

[68]  L. Uddin.  Salience processing and insular cortical function and dysfunction , 2014, Nature Reviews Neuroscience.

[69]  D. Lewis,et al.  Cortical inhibitory neurons and schizophrenia , 2005, Nature Reviews Neuroscience.

[70]  S. Kapur.  Psychosis as a state of aberrant salience: a framework linking biology, phenomenology, and pharmacology in schizophrenia. , 2003, The American journal of psychiatry.

[71]  Daoqiang Zhang,et al.  Ensemble sparse classification of Alzheimer's disease , 2012, NeuroImage.

[72]  M. Mesulam,et al.  From sensation to cognition. , 1998, Brain : a journal of neurology.

[73]  Khaled Rasheed,et al.  Decision tree and ensemble learning algorithms with their applications in bioinformatics. , 2011, Advances in experimental medicine and biology.

[74]  M. Nour,et al.  Dopamine and the aberrant salience hypothesis of schizophrenia , 2016, World psychiatry : official journal of the World Psychiatric Association.