Artificial intelligence applications in psychoradiology

One important challenge in psychiatric research is to translate findings from brain imaging research studies that identified brain alterations in patient groups into an accurate diagnosis at an early stage of illness, prediction of prognosis before treatment, and guidance for selection of effective treatments that target patient-relevant pathophysiological features. This is the primary aim of the field of Psychoradiology. Using databases collected from large samples at multiple centers, sophisticated artificial intelligence (AI) algorithms may be used to develop clinically useful image analysis pipelines that can help physicians diagnose, predict, and make treatment decisions. In this review, we selectively summarize psychoradiological research using magnetic resonance imaging of the brain to explore the neural mechanism of psychiatric disorders, and outline progress and the path forward for the combination of psychoradiology and AI for complementing clinical examinations in patients with psychiatric disorders, as well as limitations in the application of AI that should be considered in future translational research.

[1]  Michael W. Cole,et al.  Early-Course Unmedicated Schizophrenia Patients Exhibit Elevated Prefrontal Connectivity Associated with Longitudinal Change , 2015, The Journal of Neuroscience.

[2]  N. Minshew,et al.  Patterns of visual sensory and sensorimotor abnormalities in autism vary in relation to history of early language delay , 2008, Journal of the International Neuropsychological Society.

[3]  Guorong Wu,et al.  Multi-resolution Graph Neural Network for Identifying Disease-specific Variations in Brain Connectivity , 2019, ArXiv.

[4]  Y. Liu,et al.  A neuroimaging biomarker for striatal dysfunction in schizophrenia , 2020, Nature Medicine.

[5]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[6]  Vincent Frouin,et al.  Association of a Schizophrenia-Risk Nonsynonymous Variant With Putamen Volume in Adolescents , 2019, JAMA psychiatry.

[7]  Li Yao,et al.  Longitudinal Changes in Resting-State Cerebral Activity in Patients with First-Episode Schizophrenia: A 1-Year Follow-up Functional MR Imaging Study. , 2016, Radiology.

[8]  Philip S. Yu,et al.  A Comprehensive Survey on Graph Neural Networks , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[9]  James C. Gee,et al.  Interpretable Multimodality Embedding of Cerebral Cortex Using Attention Graph Network for Identifying Bipolar Disorder , 2019, MICCAI.

[10]  Mark Jenkinson,et al.  The minimal preprocessing pipelines for the Human Connectome Project , 2013, NeuroImage.

[11]  Xiaoping Zhou,et al.  Development and validation of an interpretable deep learning framework for Alzheimer’s disease classification , 2020, Brain : a journal of neurology.

[12]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[13]  Matthew W. Mosconi,et al.  Alterations in the functional neural circuitry supporting flexible choice behavior in autism spectrum disorders , 2016, Translational psychiatry.

[14]  P. Jezzard,et al.  Correction for geometric distortion in echo planar images from B0 field variations , 1995, Magnetic resonance in medicine.

[15]  Russell T. Shinohara,et al.  Childhood trauma history is linked to abnormal brain connectivity in major depression , 2019, Proceedings of the National Academy of Sciences.

[16]  L. Yao,et al.  Two Patterns of White Matter Abnormalities in Medication-Naive Patients With First-Episode Schizophrenia Revealed by Diffusion Tensor Imaging and Cluster Analysis. , 2015, JAMA psychiatry.

[17]  Yuan Gao,et al.  Safety and efficacy of magnetic resonance imaging-guided focused ultrasound neurosurgery for Parkinson’s disease: a systematic review , 2019, Neurosurgical Review.

[18]  G. Pearlson,et al.  Multivariate Relationships Between Peripheral Inflammatory Marker Subtypes and Cognitive and Brain Structural Measures in Psychosis , 2020, Molecular Psychiatry.

[19]  Xiaoqi Huang,et al.  Progress in psychoradiology, the clinical application of psychiatric neuroimaging , 2019, The British journal of radiology.

[20]  Nikolaus Kriegeskorte,et al.  Deep neural networks: a new framework for modelling biological vision and brain information processing , 2015, bioRxiv.

[21]  K R Thulborn,et al.  Magnetic resonance imaging of children without sedation: preparation with simulation. , 1997, Journal of the American Academy of Child and Adolescent Psychiatry.

[22]  Steen Moeller,et al.  The Human Connectome Project: A data acquisition perspective , 2012, NeuroImage.

[23]  A. Mechelli,et al.  Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications , 2017, Neuroscience & Biobehavioral Reviews.

[24]  A. Yang,et al.  Functional, Anatomical, and Morphological Networks Highlight the Role of Basal Ganglia-Thalamus-Cortex Circuits in Schizophrenia. , 2019, Schizophrenia bulletin.

[25]  Ming Chen,et al.  A Multichannel Deep Neural Network Model Analyzing Multiscale Functional Brain Connectome Data for Attention Deficit Hyperactivity Disorder Detection. , 2019, Radiology. Artificial intelligence.

[26]  L. Yao,et al.  Functional brain networks in never-treated and treated long-term Ill schizophrenia patients , 2019, Neuropsychopharmacology.

[27]  G. Pearlson,et al.  White matter microstructure across brain-based biotypes for psychosis – findings from the bipolar-schizophrenia network for intermediate phenotypes , 2020, Psychiatry Research: Neuroimaging.

[28]  S. Lui,et al.  Neural hyperactivity related to working memory in drug-naive boys with attention deficit hyperactivity disorder , 2014, Progress in Neuro-Psychopharmacology and Biological Psychiatry.

[29]  J. Sweeney,et al.  Cognitive dysfunction is worse among pediatric patients with bipolar disorder Type I than Type II. , 2012, Journal of child psychology and psychiatry, and allied disciplines.

[30]  N. Hattori,et al.  Improving the Quality of Synthetic FLAIR Images with Deep Learning Using a Conditional Generative Adversarial Network for Pixel-by-Pixel Image Translation , 2019, American Journal of Neuroradiology.

[31]  J. Sweeney,et al.  Integrating functional brain neuroimaging and developmental cognitive neuroscience in child psychiatry research. , 2008, Journal of the American Academy of Child and Adolescent Psychiatry.

[32]  Enhancing magnetic resonance imaging-driven Alzheimer’s disease classification performance using generative adversarial learning , 2021, Alzheimer's research & therapy.

[33]  Zhiyuan Liu,et al.  Graph Neural Networks: A Review of Methods and Applications , 2018, AI Open.

[34]  J. Sweeney,et al.  Imaging-Based Subtyping for Psychiatric Syndromes. , 2020, Neuroimaging clinics of North America.

[35]  K. Lim.  Psychoradiology: neuroimaging clinics of North America , 2021, Psychoradiology.

[36]  Ivanei E. Bramati,et al.  Brain Imaging of the Cortex in ADHD: A Coordinated Analysis of Large-Scale Clinical and Population-Based Samples. , 2019, The American journal of psychiatry.

[37]  Alessandra M Passarotti,et al.  Differential engagement of cognitive and affective neural systems in pediatric bipolar disorder and attention deficit hyperactivity disorder , 2009, Journal of the International Neuropsychological Society.

[38]  J. Posner,et al.  Attention-deficit hyperactivity disorder , 2020, The Lancet.

[39]  S. Lui,et al.  Clinical Strategies and Technical Challenges in Psychoradiology. , 2020, Neuroimaging clinics of North America.

[40]  Li Yao,et al.  Altered White Matter Connectivity Within and Between Networks in Antipsychotic-Naive First-Episode Schizophrenia , 2018, Schizophrenia bulletin.

[41]  S. Lui,et al.  A Selective Review of Cerebral Abnormalities in Patients With First-Episode Schizophrenia Before and After Treatment. , 2016, The American journal of psychiatry.

[42]  Lauren E. Ethridge,et al.  Identification of Distinct Psychosis Biotypes Using Brain-Based Biomarkers. , 2016, The American journal of psychiatry.

[43]  S. Lui,et al.  Widespread white-matter microstructure integrity reduction in first-episode schizophrenia patients after acute antipsychotic treatment , 2019, Schizophrenia Research.

[44]  Qiyong Gong,et al.  Psychoradiology: The Frontier of Neuroimaging in Psychiatry. , 2016, Radiology.

[45]  Lucas M Bachmann,et al.  Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study. , 2019, The Lancet. Digital health.

[46]  L. Yao,et al.  Brain Structural Abnormalities in a Group of Never-Medicated Patients With Long-Term Schizophrenia. , 2015, The American journal of psychiatry.

[47]  Nicco Reggente,et al.  Multivariate resting-state functional connectivity predicts response to cognitive behavioral therapy in obsessive–compulsive disorder , 2018, Proceedings of the National Academy of Sciences.

[48]  J. Sweeney,et al.  Disease and drug effects on internally-generated and externally-elicited responses in first episode schizophrenia and psychotic bipolar disorder , 2014, Schizophrenia Research.

[49]  Margot J. Taylor,et al.  Subcortical Brain Volume, Regional Cortical Thickness, and Cortical Surface Area Across Disorders: Findings From the ENIGMA ADHD, ASD, and OCD Working Groups. , 2020, The American journal of psychiatry.

[50]  J. Baldermann,et al.  Progress and challenges in deep brain stimulation for obsessive‐compulsive disorder , 2018, Pharmacology and Therapeutics.

[51]  Qiyong Gong,et al.  Intrinsic brain abnormalities in attention deficit hyperactivity disorder: a resting-state functional MR imaging study. , 2014, Radiology.

[52]  J. Duan,et al.  Identifying and validating subtypes within major psychiatric disorders based on frontal–posterior functional imbalance via deep learning , 2020, Molecular Psychiatry.

[53]  Wei Deng,et al.  Short-term effects of antipsychotic treatment on cerebral function in drug-naive first-episode schizophrenia revealed by "resting state" functional magnetic resonance imaging. , 2010, Archives of general psychiatry.

[54]  Mohammad Hamghalam,et al.  High tissue contrast image synthesis via multistage attention-GAN: Application to segmenting brain MR scans , 2020, Neural Networks.

[55]  Huafu Chen,et al.  Glucose disturbances, cognitive deficits and white matter abnormalities in first-episode drug-naive schizophrenia , 2019, Molecular Psychiatry.

[56]  Qiyong Gong,et al.  Multivariate pattern analysis of DTI reveals differential white matter in individuals with obsessive‐compulsive disorder , 2013, Human brain mapping.

[57]  Jian Pei,et al.  A Survey on Network Embedding , 2017, IEEE Transactions on Knowledge and Data Engineering.

[58]  R. Cho,et al.  Treatment response prediction and individualized identification of first-episode drug-naïve schizophrenia using brain functional connectivity , 2018, Molecular Psychiatry.

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

[60]  S. Lui,et al.  Association of peripheral cytokine levels with cerebral structural abnormalities in schizophrenia , 2019, Brain Research.

[61]  W. Deng,et al.  Aberrant triple-network connectivity patterns discriminate biotypes of first-episode medication-naive schizophrenia in two large independent cohorts , 2021, Neuropsychopharmacology.

[62]  S. Lui,et al.  Meta-analysis of cortical thickness abnormalities in medication-free patients with major depressive disorder , 2019, Neuropsychopharmacology.

[63]  Yinghuan Shi,et al.  Ea-GANs: Edge-Aware Generative Adversarial Networks for Cross-Modality MR Image Synthesis , 2019, IEEE Transactions on Medical Imaging.

[64]  L. Mewton,et al.  Association of Prenatal Alcohol Exposure With Psychological, Behavioral, and Neurodevelopmental Outcomes in Children From the Adolescent Brain Cognitive Development Study. , 2020, The American journal of psychiatry.

[65]  Ah Chung Tsoi,et al.  The Graph Neural Network Model , 2009, IEEE Transactions on Neural Networks.

[66]  J. Sweeney,et al.  The neuro-pathophysiology of temporomandibular disorders-related pain: a systematic review of structural and functional MRI studies , 2020, The Journal of Headache and Pain.