Biomarkers in Neuropsychiatry最新文献

{"title":"Cortical abnormalities and identification for first-episode schizophrenia via high-resolution magnetic resonance imaging","authors":"Lin Liu ,&nbsp;Long-Biao Cui ,&nbsp;Xu-Sha Wu ,&nbsp;Ning-Bo Fei ,&nbsp;Zi-Liang Xu ,&nbsp;Di Wu ,&nbsp;Yi-Bin Xi ,&nbsp;Peng Huang ,&nbsp;Karen M. von Deneen ,&nbsp;Shun Qi ,&nbsp;Ya-Hong Zhang ,&nbsp;Hua-Ning Wang ,&nbsp;Hong Yin ,&nbsp;Wei Qin","doi":"10.1016/j.bionps.2020.100022","DOIUrl":"https://doi.org/10.1016/j.bionps.2020.100022","url":null,"abstract":"<div><h3>Background</h3><p>Evidence from neuroimaging has implicated abnormal cerebral cortical patterns in schizophrenia. Application of machine learning techniques is required for identifying structural signature reflecting neurobiological substrates of schizophrenia at the individual level. We aimed to develop a method to identify patients with schizophrenia from healthy individuals via the features of cerebral cortex using high-resolution magnetic resonance imaging (MRI).</p></div><div><h3>Method</h3><p>In this study, cortical features were measured, including volumetric (cortical thickness, surface area, and gray matter volume) and geometric (mean curvature, metric distortion, and sulcal depth) features. Patients with first-episode schizophrenia (n = 52, ranging 17–45 years old) and healthy controls (n = 66, ranging 18–46 years old) were included from the Department of Psychiatry at Xijing Hospital. Multivariate computation was used to examine the abnormalities of cortical features in schizophrenia. Features were selected by least absolute shrinkage and selection operator (LASSO) method. The diagnostic capacity of multi-dimensional neuroanatomical patterns-based classification was evaluated based on receiver operating characteristic (ROC) analysis.</p></div><div><h3>Results</h3><p>Mean curvature (left insula and left inferior frontal gyrus), cortical thickness (left fusiform gyrus), and metric distortion (left cuneus and right superior temporal gyrus) revealed both group differences and diagnostic capacity. Area under ROC curve was 0.88, and the sensitivity, specificity, and accuracy were 94 %, 82 %, and 88 %, respectively. Confirming these findings, similar results were observed in the independent validation (sensitivity of 91 %, specificity of 78 %, and accuracy of 85 %). There was a positive association between index score derived from the multi-dimensional patterns and the severity of symptoms (<em>r</em> = 0.33, <em>P</em> &lt; .05) for patients.</p></div><div><h3>Discussion</h3><p>Our findings demonstrate a view of cortical differences with capacity to discriminate between patients with schizophrenia and healthy population. Structural neuroimaging-based measurements hold great promise of paving the road for their clinical utility in schizophrenia.</p></div>","PeriodicalId":52767,"journal":{"name":"Biomarkers in Neuropsychiatry","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.bionps.2020.100022","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91728613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hippocampal subfield alterations in schizophrenia: A selective review of structural MRI studies","authors":"Na Hu ,&nbsp;Chunyan Luo ,&nbsp;Wenjing Zhang ,&nbsp;Xiyue Yang ,&nbsp;Yuan Xiao ,&nbsp;John A. Sweeney ,&nbsp;Su Lui ,&nbsp;Qiyong Gong","doi":"10.1016/j.bionps.2020.100026","DOIUrl":"10.1016/j.bionps.2020.100026","url":null,"abstract":"<div><p>The hippocampus plays a crucial role in the psychophysiology of schizophrenia. The past few years have seen a rapid growth in studies of hippocampal subfield anatomy using MRI-based volumetry, with exciting new findings particularly in studies of schizophrenia. As findings varied across studies, and given limitations to conducting a meta-analysis at this time based on the number of available studies and their variability in methods, we conducted a literature review to investigate whether volume alterations of hippocampal subfields in schizophrenia are shared across subfields or specific in terms of severity and location, and their relation to genetic and clinical features. Current studies show that severity and location of subfield volume changes are related to risk for schizophrenia, discrimination of psychiatric disorders, severity of psychotic symptoms and cognitive dysfunction, duration of illness, and treatment response. These diverse findings indicate that MRI-based hippocampal subfield volume measurements are a promising clinically-relevant biomarker for future schizophrenia research.</p></div>","PeriodicalId":52767,"journal":{"name":"Biomarkers in Neuropsychiatry","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.bionps.2020.100026","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"107774096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Latent inhibition and its potential as a biomarker for schizophrenia","authors":"Kiri T. Granger ,&nbsp;Anahita Talwar ,&nbsp;Jennifer H. Barnett","doi":"10.1016/j.bionps.2020.100025","DOIUrl":"https://doi.org/10.1016/j.bionps.2020.100025","url":null,"abstract":"<div><p>The biological heterogeneity of schizophrenia continues to be a major obstacle for clinical practice and the development of novel drug treatments. A non-invasive biomarker to define sub-groups of patients with common neurobiological underpinnings would dramatically improve detection, diagnosis and the efficacy of drug development, not only for schizophrenia but for a range of psychiatric disorders. Latent inhibition is one candidate biomarker for schizophrenia that generated a surge of interest in the 1980′s and early 2000′s but fell under scrutiny due to inconsistent reports around its construct validity and predictive efficacy for detecting abnormal latent inhibition in patients. Latent inhibition as a preclinical model of schizophrenia however, has long been established with comprehensive literature documenting the neurochemical substrates of latent inhibition and its links to schizophrenia. Here we provide a brief review of the history behind latent inhibition and the limitations of existing human paradigms, before discussing a more recent latent inhibition task modification and its potential as a biomarker for schizophrenia. The application of latent inhibition as a tool for use in clinical practice (e.g., to the detection of early psychosis) and pharmaceutical clinical trials (e.g., to stratify patient groups) is discussed.</p></div>","PeriodicalId":52767,"journal":{"name":"Biomarkers in Neuropsychiatry","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.bionps.2020.100025","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91728612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Antibodies against Group A Streptococcus, dopamine receptors, and ganglioside GM1 cross-react with a variety of food antigens, potentially interfering with biomarkers for PANS and PANDAS","authors":"Aristo Vojdani ,&nbsp;C. Christopher Turnpaugh","doi":"10.1016/j.bionps.2020.100023","DOIUrl":"https://doi.org/10.1016/j.bionps.2020.100023","url":null,"abstract":"<div><p>Group A Streptococcus (GAS) is a bacteria that manifests itself in a variety of diseases, from strep throat to neuroautoimmune psychiatric disorders, such as pediatric acute-onset neuropsychiatric syndrome (PANS) or pediatric autoimmune neuropsychiatric disorder associated with streptococcal infections (PANDAS). Dopamine 1 and dopamine 2 (D₁ and D₂) receptors and asialoganglioside (GM₁) are used commercially as biomarkers in assessing neuropsychiatric diseases. However, some studies have found these antibodies in healthy subjects. Since previous research has shown cross-reactivity between foods and tissue antigens, we sought to determine whether or not cross-reactivity exists between GAS, D₁, D₂ receptors, GM₁ and commonly consumed foods, and whether the presence of food antibodies may be responsible for the false positivity. We reacted antibodies against GAS, D₁, D₂ receptors, and GM₁ with the antigens of 180 foods using the ELISA method. Anti-GAS antibodies had significant cross-reactivity with 17/180 foods, anti-D₁ antibody with 26/180 foods, anti-D₂ antibody with 20/180 foods, and anti-GM₁ antibody with 47/180 foods. Our results indicate that the presence in blood of antibodies to GAS, D₁, D₂ and GM₁ that cross-react with food antigens may not only interfere with the accurate measurement of these biomarkers of PANS and PANDAS, but show that these patients with these antibodies in their blood may not have these conditions at all, but just have innocuous antibodies against food antigens.</p></div>","PeriodicalId":52767,"journal":{"name":"Biomarkers in Neuropsychiatry","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.bionps.2020.100023","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91728614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Auditory paired-stimuli responses across the psychosis and bipolar spectrum and their relationship to clinical features","authors":"David A. Parker ,&nbsp;Rebekah L. Trotti ,&nbsp;Jennifer E. McDowell ,&nbsp;Sarah K. Keedy ,&nbsp;Elliot S. Gershon ,&nbsp;Elena I. Ivleva ,&nbsp;Godfrey D. Pearlson ,&nbsp;Matcheri S. Keshavan ,&nbsp;Carol A. Tamminga ,&nbsp;John A. Sweeney ,&nbsp;Brett A. Clementz","doi":"10.1016/j.bionps.2020.100014","DOIUrl":"10.1016/j.bionps.2020.100014","url":null,"abstract":"<div><h3>Background</h3><p>EEG responses during auditory paired-stimuli paradigms are putative biomarkers of psychosis syndromes. The initial iteration of the Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP1) showed unique and common patterns of abnormalities across schizophrenia (SZ), schizoaffective disorder (SAD), and bipolar disorder with psychosis (BDP). This study replicates those findings in new and large samples of psychosis cases and extends them to an important comparison group, bipolar disorder without psychosis (BDNP).</p></div><div><h3>Methods</h3><p>Paired stimuli responses from 64-sensor EEG recording were compared across psychosis (n = 597; SZ = 225, SAD = 201, BDP = 171), BDNP (n = 66), and healthy (n = 415) subjects from the second iteration of B-SNIP. EEG activity was analyzed in voltage and in the time-frequency domain. Principal component analysis (PCA) over sensors (sPCA) was used to efficiently capture EEG voltage responses to the paired stimuli. Evoked power was calculated via a Morlet wavelet procedure. A frequency PCA divided evoked power data into three frequency bands: Low (4−17 Hz), Beta (18−32 Hz), and Gamma (33−55 Hz). Each time-course (ERP Voltage, Low, Beta, and Gamma) were then segmented into 20 ms bins and analyzed for group differences. To efficiently summarize the multiple EEG components that best captured group differences we used multivariate discriminant and correlational analyses. This approach yields a reduced set of measures that may be useful in subsequent biomarker investigations.</p></div><div><h3>Results</h3><p>Group ANOVAs identified 17 time-ranges that showed significant group differences (p &lt; .05 after FDR correction), constructively replicating B-SNIP1 findings. Multivariate linear discriminant analysis parsimoniously selected variables that best accounted for group differences: The P50 response to S1 and S2 uniquely separated BDNP from healthy and psychosis subjects (BDNP &gt; all other groups); the S1 N100 response separated groups along an axis of psychopathology severity (HC &gt; BDNP &gt; BDP &gt; SAD &gt; SZ); the S1 P200 response indexed psychosis psychopathology (HC/BDNP &gt; SAD/SZ/BDP); and the preparatory period to the S2 stimulus separated SZ from other groups (SZ &gt; SAD/BDP&gt;HC/BDNP).</p><p>Canonical correlation identified an association between the neural responses during the S1 N100, S1 N200 and S2 preparatory period and PANSS positive symptoms and social functioning. The neural responses during the S1 P50 and S1 N100 were associated with PANSS Negative/General, MADRS and Young Mania symptoms.</p></div><div><h3>Conclusions</h3><p>This study constructively replicated prior B-SNIP1 research on auditory deviations observed during the paired stimuli task in SZ, SAD and BDP. Inclusion of a group of BDNP allows for the identification of biomarkers more closely related to affective versus nonaffective clinical phenotypes and neural distinctions between BDP and BDNP","PeriodicalId":52767,"journal":{"name":"Biomarkers in Neuropsychiatry","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.bionps.2020.100014","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10540466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prefrontal cortical alterations of glutamate and GABA neurotransmission in schizophrenia: Insights for rational biomarker development","authors":"Kirsten E. Schoonover ,&nbsp;Samuel J. Dienel ,&nbsp;David A. Lewis","doi":"10.1016/j.bionps.2020.100015","DOIUrl":"10.1016/j.bionps.2020.100015","url":null,"abstract":"<div><p>Certain cognitive deficits in schizophrenia, such as impaired working memory, are thought to reflect alterations in the neural circuitry of the dorsolateral prefrontal cortex (DLPFC). Gamma oscillations in the DLPFC appear to be a neural corollary of working memory function, and the power of these oscillations during working memory tasks is lower in individuals with schizophrenia. Thus, gamma oscillations represent a potentially useful biomarker to index dysfunction in the DLPFC circuitry responsible for working memory in schizophrenia. Postmortem studies, by identifying the cellular basis of DLPFC dysfunction, can help inform the utility of biomarker measures obtained <em>in vivo.</em> Given that gamma oscillations reflect network activity of excitatory pyramidal neurons and inhibitory GABA neurons, we review postmortem findings of alterations to both cell types in the DLPFC and discuss how these findings might inform future biomarker development and use.</p></div>","PeriodicalId":52767,"journal":{"name":"Biomarkers in Neuropsychiatry","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.bionps.2020.100015","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38152583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Psychomotor slowing in Schizophrenia: Implications for endophenotype and biomarker development","authors":"K. Juston Osborne ,&nbsp;Sebastian Walther ,&nbsp;Stewart A. Shankman ,&nbsp;Vijay A. Mittal","doi":"10.1016/j.bionps.2020.100016","DOIUrl":"10.1016/j.bionps.2020.100016","url":null,"abstract":"<div><p>Motor abnormalities (e.g., dyskinesia, psychomotor slowing, neurological soft signs) are core features of schizophrenia that occur independent of drug treatment and are associated with the genetic vulnerability and pathophysiology for the illness. Among this list, psychomotor slowing in particular is one of the most consistently observed and robust findings in the field. Critically, psychomotor slowing may serve as a uniquely promising endophenotype and/or biomarker for schizophrenia considering it is frequently observed in those with genetic vulnerability for the illness, predicts transition in subjects at high-risk for the disorder, and is associated with symptoms and recovery in patients. The purpose of the present review is to provide an overview of the history of psychomotor slowing in psychosis, discuss its possible neural underpinnings, and review the current literature supporting slowing as a putative endophenotype and/or biomarker for the illness. This review summarizes substantial evidence from a diverse array of methodologies and research designs that supports the notion that psychomotor slowing not only reflects genetic vulnerability, but is also sensitive to disease processes and the pathophysiology of the illness. Furthermore, there are unique deficits across the cognitive (prefix “psycho”) and motor execution (root word “motor”) aspects of slowing, with cognitive processes such as planning and response selection being particularly affected. These findings suggest that psychomotor slowing may serve as a promising endophenotype and biomarker for schizophrenia that may prove useful for identifying individuals at greatest risk and tracking the course of the illness and recovery.</p></div>","PeriodicalId":52767,"journal":{"name":"Biomarkers in Neuropsychiatry","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.bionps.2020.100016","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25493962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"White matter pathology is shared across multiple psychiatric brain disorders: Is abnormal diffusivity a transdiagnostic biomarker for psychopathology?","authors":"Raza Sagarwala ,&nbsp;Henry A. Nasrallah","doi":"10.1016/j.bionps.2019.100010","DOIUrl":"10.1016/j.bionps.2019.100010","url":null,"abstract":"<div><h3>Background</h3><p>Neuroimaging methods have become important techniques for exploring the neurobiology of neuropsychiatric disorders before and after treatment. Diffusion Tensor Imaging (DTI), has emerged as a propitious tool for the detection of neurological pathology in a variety of psychiatric syndromes. DTI measures the dispersion of water molecules, giving insight into white matter (WM) integrity. We conducted a selective review of the literature to determine WM pathology across major psychiatric brain disorders, as measured by DTI.</p></div><div><h3>Methods</h3><p>An online search was conducted to identify systematic reviews and metaanalysis published from January 2010 to September 2019 that assessed the WM integrity in several neuropsychiatric disorders. We examined the WM pathologies in different brain regions to detect patterns that may be common among those disorders.</p></div><div><h3>Results</h3><p>Studies in schizophrenia, bipolar disorder, major depressive disorder, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, borderline personality, post-traumatic stress disorder, and Alzheimer’s disease all demonstrate decreased diffusivity as measured by fractional anisotropy (FA). A common location for WM pathology in all disorders was found within the commissural fibers of the corpus callosum (CC). Nonetheless, the heterogeneity of these disorders is highlighted, as patients present with different locations for WM pathology.</p></div><div><h3>Conclusions</h3><p>Our selective review suggests that many neuropsychiatric disorders have underlying WM pathology on DTI, specifically a decrease in diffusivity. Measuring WM pathology using DTI is emerging as a useful tool for identifying individuals with various psychopathologies and may lead to early diagnosis and treatment. Finally, the mechanisms by which WM pathology may contributes to the generation of psychiatric signs and symptoms requires further investigation.</p></div>","PeriodicalId":52767,"journal":{"name":"Biomarkers in Neuropsychiatry","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.bionps.2019.100010","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"110152913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tracking a dysregulated gut-brain axis with biomarkers of the microbiome","authors":"Emily G. Severance,&nbsp;Robert H. Yolken","doi":"10.1016/j.bionps.2019.100009","DOIUrl":"10.1016/j.bionps.2019.100009","url":null,"abstract":"<div><p>Biological markers that track the physiological mechanisms underlying psychiatric disorders are desperately needed. Microbes that colonize mucosal surfaces, collectively known as the microbiome, and the array of genes that the microbiome encodes, have become a newly recognized source of potential novel disease mechanisms and pharmacological treatment targets. Much research is directed toward the search for measurable biomarkers that reflect both healthy and pathological states of the microbiome. Here, we review direct measures of microbial taxonomy present in gut communities and indirect measures of host responses to gut dysbioses. Direct biomarkers of the microbiome derive from the discovery and analyses of data obtained from deep sequencing projects of biospecimens from psychiatric cohorts. Indirect biomarkers of the microbiome typically entail measurement in blood of components of the toxic cycle of inflammation, gut permeability, and dysbiosis, which affects subsets of individuals with psychiatric disorders. As we progress in our understanding of the benefits and risks of certain combinations of taxa with regard to psychiatric disorders and their clinical manifestations, it will be equally important to characterize host phenotypes that relate to specific microbial compositions. The discovery, development and clinical testing of biomarkers of microbial taxa and of host responses will, in turn, lead to new methods to effectively and individually characterize and treat psychiatric disorders.</p></div>","PeriodicalId":52767,"journal":{"name":"Biomarkers in Neuropsychiatry","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.bionps.2019.100009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43157744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Measures of Retinal Structure and Function as Biomarkers in Neurology and Psychiatry","authors":"Steven M. Silverstein ,&nbsp;Docia L. Demmin ,&nbsp;Jesse B. Schallek ,&nbsp;Samantha I. Fradkin","doi":"10.1016/j.bionps.2020.100018","DOIUrl":"10.1016/j.bionps.2020.100018","url":null,"abstract":"<div><p>Investigators have increasingly turned to studying the retina as a window into brain structure and function. In neuropsychiatric diseases, retinal anatomy as assessed by optical coherence tomography (OCT) and retinal cell function as assessed by various forms of electroretinography (ERG) demonstrate notable changes. In addition, many studies indicate significant correlations between retinal changes and clinical features such as cognitive decline, overall illness severity, and progression of illness. Here, we review retinal findings in psychiatric (schizophrenia, autism, mood disorders, attention deficit hyperactivity disorder, anorexia nervosa), and neurologic (multiple sclerosis, Parkinson's disease, Alzheimer's disease and mild cognitive impairment, Huntington's disease, traumatic brain injury) conditions, in terms of their potential as biomarkers of disease onset, progression, severity, and outcomes. Consistency and variability in findings across studies are highlighted, and implications for future research are discussed. Potential confounds and methodological issues central to studies of retinal structure and function in neuropsychiatry are also considered. The review concludes with discussions of: a) recent advances in retinal imaging and their potential applications for studying brain disorders; and b) the potential for applications of artificial intelligence to increasing the predictive validity of retinal data.</p></div>","PeriodicalId":52767,"journal":{"name":"Biomarkers in Neuropsychiatry","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.bionps.2020.100018","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46806413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}