Biomarkers in Neuropsychiatry最新文献

{"title":"Is a paired-stimuli configuration necessary to obtain typical evoked response differences in studies of psychosis? An MEG study","authors":"William Oliver ,&nbsp;David Parker ,&nbsp;William Hetrick ,&nbsp;Brett A. Clementz","doi":"10.1016/j.bionps.2021.100033","DOIUrl":"10.1016/j.bionps.2021.100033","url":null,"abstract":"<div><p>Paired-stimuli (S1-S2) procedures have long been used to assess auditory processing in psychosis. Such studies have shown aberrant evoked responses (ERPs) following long (S1 response) and/or short (S2 response) inter-stimulus intervals. The historical tendency from paired stimuli outcomes in the schizophrenia (SZ) literature is for (i) response to the first stimulus (S1) to be smaller among SZ, and (ii) response to the second stimulus (S2) to be larger among SZ in relation to the size of their S1. An interpretation of these two findings is that SZ have poor auditory response suppression to redundant stimuli (“poor gating”). The present study sought to determine if the reported S1 and S2 effects in SZ (smaller S1 and larger S2 in relation to S1 magnitude) require the paired-stimuli presentation format. Participants (18 schizophrenia and 17 healthy persons) were administered the equivalent of S1 (after a 4.5-sec ISI – “long ISI”) and S2 (after a 500-ms ISI – “short ISI”) stimuli under four conditions (traditional paired long and short, randomly interleaved long and short, block of long, block of short). Neural activity differences were consistent between-groups independent of condition: (i) schizophrenia cases had greater activity in the pre-stimulus to very early post-stimulus period, (ii) healthy persons had greater M100 activity to long ISI stimuli, and (iii) healthy persons had greater activity after the M50/M100 evoked fields (recovery phase) following short ISI stimuli. Simple early auditory processing in psychosis may be largely independent of stimulus presentation condition, an outcome that may help re-frame future translational studies. Traditional paired-stimuli auditory neural response effects may not require the paired-stimuli format.</p></div>","PeriodicalId":52767,"journal":{"name":"Biomarkers in Neuropsychiatry","volume":"4 ","pages":"Article 100033"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.bionps.2021.100033","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40705822","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":"Biomarkers and neurobehavioral diagnosis","authors":"Joshua B. Ewen ,&nbsp;William Z. Potter ,&nbsp;John A. Sweeney","doi":"10.1016/j.bionps.2020.100029","DOIUrl":"10.1016/j.bionps.2020.100029","url":null,"abstract":"<div><p>Our current diagnostic methods for treatment planning in Psychiatry and Neurodevelopmental Disabilities leave room for improvement, and null results in clinical trials in these fields may be a result of insufficient tools for patient stratification. Great hope has been placed in novel technologies to improve clinical and trial outcomes, but we have yet to see a substantial change in clinical practice. As we examine attempts at biomarker validation within these fields, we find that it may be the diagnoses themselves that fall short. We now need to improve neuropsychiatric nosologies with a focus on validity based not solely on behavioral features, but on a synthesis that includes genetic and biological data as well. The eventual goal is diagnostic biomarkers and diagnoses themselves based on distinct mechanisms, but such an understanding of the causal relationship across levels of analysis is likely to be elusive for some time. Rather, we propose an approach in the near-term that deconstructs diagnosis into a series of independent, empiric and clinically relevant associations among a single, defined patient group, a single biomarker, a single intervention and a single clinical outcome. Incremental study across patient groups, interventions, outcomes and modalities will lead to a more interdigitated network of knowledge, and correlations in metrics across levels of analysis will eventually give way to the causal understanding that will allow for mechanistically based diagnoses.</p></div>","PeriodicalId":52767,"journal":{"name":"Biomarkers in Neuropsychiatry","volume":"4 ","pages":"Article 100029"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.bionps.2020.100029","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39344103","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":"How not to think about biomarkers in psychiatry: Challenges and conceptual pitfalls","authors":"Awais Aftab ,&nbsp;Manu Sharma","doi":"10.1016/j.bionps.2021.100031","DOIUrl":"https://doi.org/10.1016/j.bionps.2021.100031","url":null,"abstract":"","PeriodicalId":52767,"journal":{"name":"Biomarkers in Neuropsychiatry","volume":"4 ","pages":"Article 100031"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.bionps.2021.100031","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91686551","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":"Treatment response biomarkers in anxiety disorders: From neuroimaging to neuronally-derived extracellular vesicles and beyond","authors":"Jeffrey R. Strawn ,&nbsp;Amir Levine","doi":"10.1016/j.bionps.2020.100024","DOIUrl":"10.1016/j.bionps.2020.100024","url":null,"abstract":"<div><p>Multiple and diverse psychotherapeutic or psychopharmacologic treatments effectively reduce symptoms for many patients with anxiety disorders, but the trajectory and magnitude of response vary considerably. This heterogeneity of treatment response has invigorated the search for biomarkers of treatment response in anxiety disorders, across the lifespan. In this review, we summarize evidence for biomarkers of treatment response in children, adolescents and adults with generalized, separation and social anxiety disorders as well as panic disorder. We then discuss the relationship between these biomarkers of treatment response and the pathophysiology of anxiety disorders. Finally, we provide context for treatment response biomarkers of the future, including neuronally-derived extracellular vesicles in anxiety disorders and discuss challenges that must be overcome prior to the debut of treatment response biomarkers in the clinic. A number of promising treatment response biomarkers have been identified, although there is an urgent need to replicate findings and to identify which biomarkers might guide clinicians in selecting from available treatments rather than just simply identifying patients who may be less likely to respond to a given intervention.</p></div>","PeriodicalId":52767,"journal":{"name":"Biomarkers in Neuropsychiatry","volume":"3 ","pages":"Article 100024"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.bionps.2020.100024","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38417472","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":"High delta and gamma EEG power in resting state characterise dementia in Parkinson’s patients","authors":"Anita Pal ,&nbsp;Nishi Pegwal ,&nbsp;Madhuri Behari ,&nbsp;Ratna Sharma","doi":"10.1016/j.bionps.2020.100027","DOIUrl":"10.1016/j.bionps.2020.100027","url":null,"abstract":"<div><h3>Background</h3><p>Parkinson’s disease (PD) is a chronic neurodegenerative disease with appearance of dementia as the disease progresses. Dementia in PD worsens the quality of life and poses a burden on caregivers. Objective markers of the presence and as well as onset of dementia needs to be explored to improve the quality of life.</p><p>We attempted to identify EEG abnormalities that differentiate PD patients with or without dementia using high density EEG. The differential changes in the EEG spectral activity could mark the early stage as well as onset of dementia in Parkinson’s disease (PD).</p></div><div><h3>Methods</h3><p>The present study was designed to find out the resting eyes-closed (EC) EEG characteristics in PD with dementia (PDD) and without dementia (PDND) compared to healthy controls (CON) of both gender using high density EEG. Absolute power of seven frequency bands based on individual alpha frequency was estimated by Fast Fourier transform algorithm in 58 PD patients (30 PDND and 28 PDD) and 26 CON.</p></div><div><h3>Results</h3><p>Compared to CON, PDND had higher power in theta and lower alpha1 bands while PDD had higher power in delta, theta, lower alpha1 and beta bands. Higher delta and gamma power with no difference in theta and lower alpha 1 power was the characteristic feature of PD patients with dementia compared to non-dementia.</p></div><div><h3>Conclusions</h3><p>This cross-sectional study proposes to use these differential changes in power as EEG signatures of appearance of dementia in PD. Absence of high delta power, but presence of high theta and lower alpha 1 power defined PDND group compared to CON.</p></div>","PeriodicalId":52767,"journal":{"name":"Biomarkers in Neuropsychiatry","volume":"3 ","pages":"Article 100027"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.bionps.2020.100027","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"97258816","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":"Neurocognitive and neurophysiological endophenotypes in schizophrenia: An overview","authors":"Francesco Luciano Donati ,&nbsp;Armando D’Agostino ,&nbsp;Fabio Ferrarelli","doi":"10.1016/j.bionps.2020.100017","DOIUrl":"10.1016/j.bionps.2020.100017","url":null,"abstract":"<div><p>Schizophrenia (SCZ) is a severe psychotic disorder that affects up to 1% of the US population and it is associated with progressive impairment in social functioning and cognition. Nonetheless, despite its high burden, the pathophysiology of SCZ, including the genetic and biological mechanisms underlying the development and manifestation of the disorder, remains largely elusive. Endophenotypes are subtypes of biological markers that are more closely related to the genetic vulnerability for a disorder (e.g., SCZ). Recently, research on endophenotypes has identified several parameters that may prove useful in shedding light over the underlying neurobiology of SCZ. In this article, we provide an overview of the most established SCZ endophenotypes in the domains of neurocognition (attention deficits, working and verbal declarative memory dysfunctions) and neurophysiology (pre-pulse inhibition, anti-saccade impairment, event-related potential deficits) along with some novel, sleep-based measures (reduced sleep spindles and sleep slow waves). We also discuss recent conceptual advances in the field that may lead to novel, personalized treatment interventions for patients affected by this devastating mental illness.</p></div>","PeriodicalId":52767,"journal":{"name":"Biomarkers in Neuropsychiatry","volume":"3 ","pages":"Article 100017"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.bionps.2020.100017","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9610682","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":"Change point analyses in prodromal Alzheimer’s disease","authors":"Alvin H. Bachman ,&nbsp;Babak A. Ardekani ,&nbsp;for the Alzheimer’s Disease Neuroimaging Initiative","doi":"10.1016/j.bionps.2020.100028","DOIUrl":"10.1016/j.bionps.2020.100028","url":null,"abstract":"<div><p>Change point analysis can reveal when a biomarker starts to diverge from the pattern of normal aging. This paper analyzes several biomarkers from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) to estimate the sequence and timing of their change points relative to a subsequent clinical diagnosis of mild cognitive impairment (MCI) in subjects initially considered cognitively normal (CN). Data on 379 stable CN (sCN) and 98 progressive CN (pCN) subjects who progressed to an MCI diagnosis were used. Linear mixed-effects change point models were used to estimate when various biomarkers in pCN started to diverge from rates expected in normal aging. Our results indicate that in pCN, hippocampal atrophy rate diverges from normal aging 12.4 (±2.8) years before MCI diagnosis, followed by ventricular volume expansion and decrease in Rey Auditory Verbal Learning Test of immediate recall scores about 5 years later. Glucose metabolism decrease begins about 5 (±1.3) years before diagnosis, followed by deterioration in other cognitive test scores. Planned AD interventions should note that irreversible changes such as atrophy may occur a decade before possible diagnosis of MCI.</p></div>","PeriodicalId":52767,"journal":{"name":"Biomarkers in Neuropsychiatry","volume":"3 ","pages":"Article 100028"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.bionps.2020.100028","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"111421530","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":"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":"3 ","pages":"Article 100022"},"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":"3 ","pages":"Article 100026"},"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":"3 ","pages":"Article 100025"},"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}