Resting-State Functional Connectivity-Based Biomarkers and Functional MRI-Based Neurofeedback for Psychiatric Disorders: A Challenge for Developing Theranostic Biomarkers

Abstract Psychiatric research has been hampered by an explanatory gap between psychiatric symptoms and their neural underpinnings, which has resulted in poor treatment outcomes. This situation has prompted us to shift from symptom-based diagnosis to data-driven diagnosis, aiming to redefine psychiatric disorders as disorders of neural circuitry. Promising candidates for data-driven diagnosis include resting-state functional connectivity MRI (rs-fcMRI)-based biomarkers. Although biomarkers have been developed with the aim of diagnosing patients and predicting the efficacy of therapy, the focus has shifted to the identification of biomarkers that represent therapeutic targets, which would allow for more personalized treatment approaches. This type of biomarker (i.e., “theranostic biomarker”) is expected to elucidate the disease mechanism of psychiatric conditions and to offer an individualized neural circuit-based therapeutic target based on the neural cause of a condition. To this end, researchers have developed rs-fcMRI-based biomarkers and investigated a causal relationship between potential biomarkers and disease-specific behavior using functional MRI (fMRI)-based neurofeedback on functional connectivity. In this review, we introduce a recent approach for creating a theranostic biomarker, which consists mainly of 2 parts: (1) developing an rs-fcMRI-based biomarker that can predict diagnosis and/or symptoms with high accuracy, and (2) the introduction of a proof-of-concept study investigating the relationship between normalizing the biomarker and symptom changes using fMRI-based neurofeedback. In parallel with the introduction of recent studies, we review rs-fcMRI-based biomarker and fMRI-based neurofeedback, focusing on the technological improvements and limitations associated with clinical use.

[1]  Janet B W Williams,et al.  Diagnostic and Statistical Manual of Mental Disorders , 2013 .

[2]  Vicente L. Malave,et al.  Autism as a neural systems disorder: A theory of frontal-posterior underconnectivity , 2012, Neuroscience & Biobehavioral Reviews.

[3]  G. Northoff.  Spatiotemporal psychopathology I: No rest for the brain's resting state activity in depression? Spatiotemporal psychopathology of depressive symptoms. , 2016, Journal of affective disorders.

[4]  Jean-Francois Mangin,et al.  Cortical sulci recognition and spatial normalization , 2011, Medical Image Anal..

[5]  Takeo Watanabe,et al.  Learning to Associate Orientation with Color in Early Visual Areas by Associative Decoded fMRI Neurofeedback , 2016, Current Biology.

[6]  Vince D. Calhoun,et al.  Identification of Imaging Biomarkers in Schizophrenia: A Coefficient-constrained Independent Component Analysis of the Mind Multi-site Schizophrenia Study , 2010, Neuroinformatics.

[7]  P.-Y. Zhang,et al.  Biomarkers and heart disease. , 2014, European review for medical and pharmacological sciences.

[8]  F. Tong,et al.  Decoding the visual and subjective contents of the human brain , 2005, Nature Neuroscience.

[9]  Mitsuo Kawato,et al.  Decoded fMRI neurofeedback can induce bidirectional confidence changes within single participants , 2017, NeuroImage.

[10]  Andrew T. Drysdale,et al.  Resting-state connectivity biomarkers define neurophysiological subtypes of depression , 2016, Nature Medicine.

[11]  E. Walker,et al.  Diagnostic and Statistical Manual of Mental Disorders , 2013 .

[12]  Michael W. Cole,et al.  N-Methyl-D-Aspartate Receptor Antagonist Effects on Prefrontal Cortical Connectivity Better Model Early Than Chronic Schizophrenia , 2015, Biological Psychiatry.

[13]  Mitsuo Kawato,et al.  Predicting learning plateau of working memory from whole-brain intrinsic network connectivity patterns , 2015, Scientific Reports.

[14]  Jarrod A. Lewis-Peacock,et al.  Closed-loop brain training: the science of neurofeedback , 2017, Nature Reviews Neuroscience.

[15]  Lauren E. Ethridge,et al.  Identification of Distinct Psychosis Biotypes Using Brain-Based Biomarkers. , 2018, Focus.

[16]  G. Rainer,et al.  Neural and neurochemical basis of reinforcement-guided decision making. , 2016, Journal of neurophysiology.

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

[18]  Bettina Sorger,et al.  Real-Time Self-Regulation of Emotion Networks in Patients with Depression , 2012, PloS one.

[19]  P. Schnurr,et al.  Cognitive behavioral therapy for posttraumatic stress disorder in women: a randomized controlled trial. , 2007, JAMA.

[20]  R. Goebel,et al.  fMRI Neurofeedback Training for Increasing Anterior Cingulate Cortex Activation in Adult Attention Deficit Hyperactivity Disorder. An Exploratory Randomized, Single-Blinded Study , 2017, PloS one.

[21]  Takeo Watanabe,et al.  Differential Activation Patterns in the Same Brain Region Led to Opposite Emotional States , 2016, PLoS biology.

[22]  Tyrone D. Cannon,et al.  Elucidating a Magnetic Resonance Imaging-Based Neuroanatomic Biomarker for Psychosis: Classification Analysis Using Probabilistic Brain Atlas and Machine Learning Algorithms , 2009, Biological Psychiatry.

[23]  The Fourth World Congress of biological psychiatry , 1986, Biological Psychiatry.

[24]  Takeo Watanabe,et al.  Perceptual Learning Incepted by Decoded fMRI Neurofeedback Without Stimulus Presentation , 2011, Science.

[25]  J. Morimoto,et al.  Identifying melancholic depression biomarker using whole-brain functional connectivity , 2017, 1704.01039.

[26]  M. Frank,et al.  Computational psychiatry as a bridge from neuroscience to clinical applications , 2016, Nature Neuroscience.

[27]  G. Jackson,et al.  Cognition-related brain networks underpin the symptoms of unipolar depression: Evidence from a systematic review , 2016, Neuroscience & Biobehavioral Reviews.

[28]  Karl J. Friston,et al.  Computational psychiatry , 2012, Trends in Cognitive Sciences.

[29]  A. Beck,et al.  Comparison of Beck Depression Inventories -IA and -II in psychiatric outpatients. , 1996, Journal of personality assessment.

[30]  B. Leventhal,et al.  The Autism Diagnostic Observation Schedule—Generic: A Standard Measure of Social and Communication Deficits Associated with the Spectrum of Autism , 2000, Journal of autism and developmental disorders.

[31]  B. Seymour,et al.  Fear reduction without fear through reinforcement of neural activity that bypasses conscious exposure , 2016, Nature Human Behaviour.

[32]  Catherine Lord,et al.  Is schizophrenia on the autism spectrum? , 2011, Brain Research.

[33]  Hiroshi Yokoi,et al.  Induced sensorimotor brain plasticity controls pain in phantom limb patients , 2016, Nature Communications.

[34]  Vince D. Calhoun,et al.  Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls , 2017, NeuroImage.

[35]  Jun Morimoto,et al.  A Neural Marker of Obsessive-Compulsive Disorder from Whole-Brain Functional Connectivity , 2017, Scientific Reports.

[36]  Stephen M Smith,et al.  Correspondence of the brain's functional architecture during activation and rest , 2009, Proceedings of the National Academy of Sciences.

[37]  M. Chun,et al.  A neuromarker of sustained attention from whole-brain functional connectivity , 2015, Nature Neuroscience.

[38]  Atta Abbas,et al.  DIAGNOSTIC AND STATISTICAL MANUAL OF MENTAL DISORDERS, FIFTH EDITION , 2013 .

[39]  Kymberly D. Young,et al.  Correlation between amygdala BOLD activity and frontal EEG asymmetry during real-time fMRI neurofeedback training in patients with depression , 2014, NeuroImage: Clinical.

[40]  G. Pearlson,et al.  Brain Structure Biomarkers in the Psychosis Biotypes: Findings From the Bipolar-Schizophrenia Network for Intermediate Phenotypes , 2017, Biological Psychiatry.

[41]  Michael J Owen,et al.  New Approaches to Psychiatric Diagnostic Classification , 2014, Neuron.

[42]  S. W. Rieger,et al.  Learning Control Over Emotion Networks Through Connectivity‐Based Neurofeedback , 2015, Cerebral cortex.

[43]  T. Insel,et al.  Brain disorders? Precisely , 2015, Science.

[44]  Jun Morimoto,et al.  Creating the brain and interacting with the brain: an integrated approach to understanding the brain , 2015, Journal of The Royal Society Interface.

[45]  B. Ahn.  Personalized Medicine Based on Theranostic Radioiodine Molecular Imaging for Differentiated Thyroid Cancer , 2016, BioMed research international.

[46]  G. Dichter,et al.  Reward circuitry function in autism spectrum disorders. , 2012, Social cognitive and affective neuroscience.

[47]  M. Farah,et al.  Progress and challenges in probing the human brain , 2015, Nature.

[48]  Jerzy Bodurka,et al.  Randomized Clinical Trial of Real-Time fMRI Amygdala Neurofeedback for Major Depressive Disorder: Effects on Symptoms and Autobiographical Memory Recall. , 2017, The American journal of psychiatry.

[49]  The 10th Annual Meeting of the Japanese Society of Biological Psychiatry , 1988, The Japanese journal of psychiatry and neurology.

[50]  Bogdan Wilamowski,et al.  Fully Connected Cascade Artificial Neural Network Architecture for Attention Deficit Hyperactivity Disorder Classification From Functional Magnetic Resonance Imaging Data , 2015, IEEE Transactions on Cybernetics.

[51]  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.

[52]  R T Constable,et al.  Orbitofrontal cortex neurofeedback produces lasting changes in contamination anxiety and resting-state connectivity , 2013, Translational Psychiatry.

[53]  Mitsuo Kawato,et al.  Computational neuroscience approach to biomarkers and treatments for mental disorders , 2017, Psychiatry and clinical neurosciences.

[54]  Jong-Hwan Lee,et al.  The Inclusion of Functional Connectivity Information into fMRI-based Neurofeedback Improves Its Efficacy in the Reduction of Cigarette Cravings , 2015, Journal of Cognitive Neuroscience.

[55]  Jessica A. Turner,et al.  Behavioral Interpretations of Intrinsic Connectivity Networks , 2011, Journal of Cognitive Neuroscience.

[56]  Robert T. Thibault,et al.  The self-regulating brain and neurofeedback: Experimental science and clinical promise , 2016, Cortex.

[57]  Huafu Chen,et al.  Alteration of functional connectivity in autism spectrum disorder: effect of age and anatomical distance , 2016, Scientific Reports.

[58]  R. Jardri,et al.  Current Issues in the Use of fMRI-Based Neurofeedback to Relieve Psychiatric Symptoms. , 2015, Current pharmaceutical design.

[59]  R. Schultz,et al.  Reward system dysfunction in autism spectrum disorders. , 2013, Social cognitive and affective neuroscience.

[60]  Thomas E. Nichols,et al.  A positive-negative mode of population covariation links brain connectivity, demographics and behavior , 2015, Nature Neuroscience.

[61]  V. Menon.  Large-scale brain networks and psychopathology: a unifying triple network model , 2011, Trends in Cognitive Sciences.

[62]  Timothy Edward John Behrens,et al.  Task-free MRI predicts individual differences in brain activity during task performance , 2016, Science.

[63]  J. Andrews-Hanna,et al.  Large-Scale Network Dysfunction in Major Depressive Disorder: A Meta-analysis of Resting-State Functional Connectivity. , 2015, JAMA psychiatry.

[64]  Thomas R. Insel,et al.  Endophenotypes: Bridging Genomic Complexity and Disorder Heterogeneity , 2009, Biological Psychiatry.

[65]  Niels Birbaumer,et al.  Volitional control of the anterior insula in criminal psychopaths using real-time fMRI neurofeedback: a pilot study , 2014, Front. Behav. Neurosci..

[66]  A. Abi-Dargham,et al.  The search for imaging biomarkers in psychiatric disorders , 2016, Nature Medicine.

[67]  Xiaoying Wu,et al.  Structural and functional biomarkers of prodromal Alzheimer's disease: A high-dimensional pattern classification study , 2008, NeuroImage.

[68]  V. Calhoun,et al.  In Search of Multimodal Neuroimaging Biomarkers of Cognitive Deficits in Schizophrenia , 2015, Biological Psychiatry.

[69]  M. Kawato,et al.  Connectivity Neurofeedback Training Can Differentially Change Functional Connectivity and Cognitive Performance , 2017, Cerebral cortex.

[70]  Nathan Intrator,et al.  Limbic Activity Modulation Guided by Functional Magnetic Resonance Imaging–Inspired Electroencephalography Improves Implicit Emotion Regulation , 2016, Biological Psychiatry.

[71]  Katherine E Henson,et al.  Risk of Suicide After Cancer Diagnosis in England , 2018, JAMA psychiatry.

[72]  M. Daly,et al.  Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis , 2013, The Lancet.

[73]  M. Kawato,et al.  Multivoxel neurofeedback selectively modulates confidence without changing perceptual performance , 2016, Nature Communications.

[74]  R. Perlis,et al.  Translating biomarkers to clinical practice , 2011, Molecular Psychiatry.

[75]  Dimitri Van De Ville,et al.  Connectivity-based neurofeedback: Dynamic causal modeling for real-time fMRI☆ , 2013, Neuroimage.

[76]  J. Delgado-García,et al.  Functional basis of associative learning and its relationships with long-term potentiation evoked in the involved neural circuits: Lessons from studies in behaving mammals , 2015, Neurobiology of Learning and Memory.

[77]  M. Hamilton,et al.  Rating depressive patients. , 1980, The Journal of clinical psychiatry.

[78]  M. Kawato,et al.  Functional MRI neurofeedback training on connectivity between two regions induces long-lasting changes in intrinsic functional network , 2015, Front. Hum. Neurosci..

[79]  A. David,et al.  Failures of metacognition and lack of insight in neuropsychiatric disorders , 2012, Philosophical Transactions of the Royal Society B: Biological Sciences.

[80]  Norio Matsuki,et al.  Operant Conditioning of Synaptic and Spiking Activity Patterns in Single Hippocampal Neurons , 2014, The Journal of Neuroscience.

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

[82]  Martin Walter,et al.  Detecting Neuroimaging Biomarkers for Depression: A Meta-analysis of Multivariate Pattern Recognition Studies , 2017, Biological Psychiatry.

[83]  T. Crow,et al.  Brain-Wide Analysis of Functional Connectivity in First-Episode and Chronic Stages of Schizophrenia , 2016, Schizophrenia bulletin.

[84]  R. Whelan,et al.  When Optimism Hurts: Inflated Predictions in Psychiatric Neuroimaging , 2014, Biological Psychiatry.

[85]  H. Karnath,et al.  Candidate Biomarkers in Children with Autism Spectrum Disorder: A Review of MRI Studies , 2017, Neuroscience Bulletin.

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

[87]  Takeo Watanabe,et al.  A small number of abnormal brain connections predicts adult autism spectrum disorder , 2016, Nature Communications.

[88]  E. Fetz.  Operant Conditioning of Cortical Unit Activity , 1969, Science.

[89]  Masa-aki Sato,et al.  Sparse estimation automatically selects voxels relevant for the decoding of fMRI activity patterns , 2008, NeuroImage.

[90]  Warren W. Kretzschmar,et al.  Sparse whole genome sequencing identifies two loci for major depressive disorder , 2015, Nature.