MRI predicts remission at 1 year in first-episode schizophrenia in females with larger striato-thalamic volumes

Psychosis brain subtypes validated in first-episode cohorts and related to illness remission: results from the PHENOM consortium

Using machine learning, we recently decomposed the neuroanatomical heterogeneity of established schizophrenia to discover two volumetric subgroups-a 'lower brain volume' subgroup (SG1) and an 'higher striatal volume' subgroup (SG2) with otherwise normal brain structure. In this study, we investigated whether the MRI signatures of these subgroups were also already present at the time of the first-episode of psychosis (FEP) and whether they were related to clinical presentation and clinical remission over 1-, 3-, and 5-years. We included 572 FEP and 424 healthy controls (HC) from 4 sites (Sao Paulo, Santander, London, Melbourne) of the PHENOM consortium. Our prior MRI subgrouping models (671 participants; USA, Germany, and China) were applied to both FEP and HC. Participants were assigned into 1 of 4 categories: subgroup 1 (SG1), subgroup 2 (SG2), no subgroup membership ('None'), and mixed SG1 + SG2 subgroups ('Mixed'). Voxel-wise analyses characterized SG1 and SG2 subgroups. Supervised machine learning analyses characterized baseline and remission signatures related to SG1 and SG2 membership. The two dominant patterns of 'lower brain volume' in SG1 and 'higher striatal volume' (with otherwise normal neuromorphology) in SG2 were identified already at the first episode of psychosis. SG1 had a significantly higher proportion of FEP (32%) vs. HC (19%) than SG2 (FEP, 21%; HC, 23%). Clinical multivariate signatures separated the SG1 and SG2 subgroups (balanced accuracy = 64%; p < 0.0001), with SG2 showing higher education but also greater positive psychosis symptoms at first presentation, and an association with symptom remission at 1-year, 5-year, and when timepoints were combined. Neuromorphological subtypes of schizophrenia are already evident at illness onset, separated by distinct clinical presentations, and differentially associated with subsequent remission. These results suggest that the subgroups may be underlying risk phenotypes that could be targeted in future treatment trials and are critical to consider when interpreting neuroimaging literature.

Evaluating the evidence for sex differences: a scoping review of human neuroimaging in psychopharmacology research

Although sex differences in psychiatric disorders abound, few neuropsychopharmacology (NPP) studies consider sex as a biological variable (SABV). We conducted a scoping review of this literature in humans by systematically searching PubMed to identify peer-reviewed journal articles published before March 2020 that (1) studied FDA-approved medications used to treat psychiatric disorders (or related symptoms) and (2) adequately evaluated sex differences using in vivo neuroimaging methodologies. Of the 251 NPP studies that included both sexes and considered SABV in analyses, 80% used methodologies that eliminated the effect of sex (e.g., by including sex as a covariate to control for its effect). Only 20% (50 studies) adequately evaluated sex differences either by testing for an interaction involving sex or by stratifying analyses by sex. Of these 50 studies, 72% found statistically significant sex differences in at least one outcome. Sex differences in neural and behavioral outcomes were studied more often in drugs indicated for conditions with known sex differences. Likewise, the majority of studies conducted in those drug classes noted sex differences: antidepressants (13 of 16), antipsychotics (10 of 12), sedative-hypnotics (6 of 10), and stimulants (6 of 10). In contrast, only two studies of mood stabilizers evaluated SABV, with one noting a sex difference. By mapping this literature, we bring into sharp relief how few studies adequately evaluate sex differences in NPP studies. Currently, all NIH-funded studies are required to consider SABV. We urge scientific journals, peer reviewers, and regulatory agencies to require researchers to consider SABV in their research. Continuing to ignore SABV in NPP research has ramifications both in terms of rigor and reproducibility of research, potentially leading to costly consequences and unrealized benefits.

Thalamus Radiomics-Based Disease Identification and Prediction of Early Treatment Response for Schizophrenia

Background

Emerging evidence suggests structural and functional disruptions of the thalamus in schizophrenia, but whether thalamus abnormalities are able to be used for disease identification and prediction of early treatment response in schizophrenia remains to be determined. This study aims at developing and validating a method of disease identification and prediction of treatment response by multi-dimensional thalamic features derived from magnetic resonance imaging in schizophrenia patients using radiomics approaches.

Methods

A total of 390 subjects, including patients with schizophrenia and healthy controls, participated in this study, among which 109 out of 191 patients had clinical characteristics of early outcome (61 responders and 48 non-responders). Thalamus-based radiomics features were extracted and selected. The diagnostic and predictive capacity of multi-dimensional thalamic features was evaluated using radiomics approach.

Results

Using radiomics features, the classifier accurately discriminated patients from healthy controls, with an accuracy of 68%. The features were further confirmed in prediction and random forest of treatment response, with an accuracy of 75%.

Conclusion

Our study demonstrates a radiomics approach by multiple thalamic features to identify schizophrenia and predict early treatment response. Thalamus-based classification could be promising to apply in schizophrenia definition and treatment selection.

Illness remission status and commissural and associative brain white matter fiber changes in schizophrenia

There is a paucity of studies clarifying biological basis of illness remission in schizophrenia related to white matter abnormalities, hence this study aimed to examine brain white matter anomalies via combinatorial diffusion tensor imaging (DTI) indices between remitted and nonremitted patients and evaluate predictors of remission. We examined DTI data of 178 patients who met the DSM-IV criteria for schizophrenia (120 nonremitted, 58 remitted) and 111 healthy controls. Remission was determined using Global Assessment of Functioning (GAF) and Positive and Negative Syndrome Scale (PANSS) scores. Analysis of covariance identified significantly different white matter tracts between groups, whilst covarying for clinical variables. Correlation and regression analyses were performed to determine clinical-imaging predictors of remission. Compared to controls, both nonremitted and remitted patients had reduced fractional anisotropy in the body of corpus callosum (BCC) and posterior thalamic radiation. Nonremitted patients had higher axial diffusivity (AD)/mean diffusivity (MD) values in the right cingulum than remitted patients after controlling for duration of illness, number of hospitalizations, and daily total chlorpromazine equivalents. The MD and AD of right cingulum correlated positively with the severity of psychotic psychopathology in nonremitted subjects. In addition, female sex and longer duration of illness were also significant predictors of remission. Specific DTI indices reflecting axonal processes and inflammation/edema of associative fibers (right cingulum) differentiated nonremitted from remitted patients, and together with relevant clinical factors, could serve as potential prognostic markers in schizophrenia.

Interaction of Cannabis Use Disorder and Striatal Connectivity in Antipsychotic Treatment Response

Antipsychotic (AP) medications are the mainstay for the treatment of schizophrenia spectrum disorders (SSD), but their efficacy is unpredictable and widely variable. Substantial efforts have been made to identify prognostic biomarkers that can be used to guide optimal prescription strategies for individual patients. Striatal regions involved in salience and reward processing are disrupted as a result of both SSD and cannabis use, and research demonstrates that striatal circuitry may be integral to response to AP drugs. In the present study, we used functional magnetic resonance imaging (fMRI) to investigate the relationship between a history of cannabis use disorder (CUD) and a striatal connectivity index (SCI), a previously developed neural biomarker for AP treatment response in SSD. Patients were part of a 12-week randomized, double-blind controlled treatment study of AP drugs. A sample of 48 first-episode SSD patients with no more than 2 weeks of lifetime exposure to AP medications, underwent a resting-state fMRI scan pretreatment. Treatment response was defined a priori as a binary (response/nonresponse) variable, and a SCI was calculated in each patient. We examined whether there was an interaction between lifetime CUD history and the SCI in relation to treatment response. We found that CUD history moderated the relationship between SCI and treatment response, such that it had little predictive value in SSD patients with a CUD history. In sum, our findings highlight that biomarker development can be critically impacted by patient behaviors that influence neurobiology, such as a history of CUD.

Prediction of early response to overall treatment for schizophrenia: A functional magnetic resonance imaging study

INTRODUCTION

Treatment response at an early stage of schizophrenia is of considerable value with regard to future management of the disorder; however, there are currently no biomarkers that can inform physicians about the likelihood of response.

OBJECTS

We aim to develop and validate regional brain activity derived from functional magnetic resonance imaging (fMRI) as a potential signature to predict early treatment response in schizophrenia.

METHODS

Amplitude of low-frequency fluctuation (ALFF) was measured at the start of the first/single episode resulting in hospitalization. Inpatients were included in a principal dataset (n = 79) and a replication dataset (n = 44). Two groups of healthy controls (n = 87; n = 106) were also recruited for each dataset. The clinical response was assessed at discharge from the hospital. The predictive capacity of normalized ALFF in patients by healthy controls, ALFF_ratio , was evaluated based on diagnostic tests and clinical correlates.

RESULTS

In the principal dataset, responders exhibited increased baseline ALFF in the left postcentral gyrus/inferior parietal lobule relative to non-responders. ALFF_ratio of responders before treatment was significantly higher than that of non-responders (p < 0.001). The area under the receiver operating characteristic curve was 0.746 for baseline ALFF_ratio to distinguish responders from non-responders, and the sensitivity, specificity, and accuracy were 72.7%, 68.6%, and 70.9%, respectively. Similar results were found in the independent replication dataset.

CONCLUSIONS

Baseline regional activity of the brain seems to be predictive of early response to treatment for schizophrenia. This study shows that psycho-neuroimaging holds promise for influencing the clinical treatment and management of schizophrenia.

Exploration of Major Cognitive Deficits in Medication-Free Patients With Major Depressive Disorder

Background: Major depressive disorder (MDD) is associated with a wide range of cognitive deficits. However, it remains unclear whether there will be a major cognitive deficit independently caused by depression at acute episodes of MDD. Method: A comprehensive neurocognitive test battery was used to assess the executive function, processing speed, attention, and memory in 162 MDD patients and 142 healthy controls (HCs). A multivariate analysis of variance, hierarchical regression analyses and general linear regression analyses were used to explore the possible major cognitive deficits and their predictor variables. Results: MDD patients showed extensive impairment in all four cognitive domains. Impairment of executive function and processing speed were found to persist even with other cognitive domains and clinical variables being accounted for. Executive function and processing speed were further predicted by total disease duration and depression severity, respectively. Conclusions: Executive function and processing speed may be two distinct major deficits at acute episodes of MDD. Furthermore, the executive function is likely originated from the cumulative effect of disease duration and processing speed is possibly derived from the temporary effect of current depressive episode.

Prognostic Utility of Multivariate Morphometry in Schizophrenia

Background: Voxel-based morphometry studies have repeatedly highlighted the presence of distributed gray matter changes in schizophrenia, but to date, it is not clear if clinically useful prognostic information can be gleaned from structural imaging. The suspected association between gray matter volume (GMV) and duration of psychotic illness, antipsychotic exposure, and symptom severity also limits the prognostic utility of morphometry. We address the question of whether morphometric information from patients with drug-naive first-episode psychosis can predict the linear trajectory of symptoms following early antipsychotic intervention using a longitudinal design. Method: Sixty-two first-episode, drug-naive patients with schizophrenia underwent brain magnetic resonance imaging scans at baseline, treated with antipsychotics, and rescanned after 1-year follow-up. Positive and Negative Syndrome Scale (PANSS) was used to assess their clinical manifestations. A multivariate approach to detect covariance-based network-like spatial components [Source Based Morphometry (SBM)] was performed to analyze the GMV. Paired t tests were used to study changes in the loading coefficients of GMV in the spatial components between two time points. The reduction in PANSS scores between the baseline (T0) and 1-year follow-up (T1) expressed as a ratio of the baseline scores (reduction ratio) was computed for positive, negative, and disorganization symptoms. Separate multiple regression analyses were conducted to predict the longitudinal change in symptoms (treatment response) using the loading coefficients of spatial components that differed between T0 and T1 with age, gender, duration of illness, and antipsychotic dose as covariates. We also tested the putative "toxicity" effects of baseline symptom severity on the GMV at 1 year using multiple regression analysis. Results: Of the 30 spatial components of gray matter extracted using SBM, loading coefficients of anterior cingulate cortex (ACC), insula and inferior frontal gyrus (IFG), superior temporal gyrus (STG), middle temporal gyrus (MTG), precuenus, and dorsolateral prefrontal cortex (DLPFC) reduced with time in patients. Specifically, the lower volume of insula and IFG at baseline predicted a lack of improvement in positive and disorganization symptoms. None of the symptom severity scores (positive, negative, or disorganization) at baseline independently predicted the reduced GMV at 1 year. Conclusions: The baseline deficit in a covariance-based network-like spatial component comprising of insula and IFG is predictive of the clinical course of schizophrenia. We do not find any evidence to support the notion of symptoms per se being neurotoxic to gray matter tissue. If judiciously combined with other available predictors of clinical outcome, multivariate morphometric information can improve our ability to predict prognosis in schizophrenia.

Neuroimaging markers of antipsychotic treatment response in schizophrenia: An overview of magnetic resonance imaging studies

Antipsychotic drugs are the primary treatment for psychosis, yet individual response to their administration remains variable. At present, no biological predictors of response exist to guide clinicians as they select treatments for patients, and our understanding of the neurobiology underlying the heterogeneity of outcomes remains limited. Magnetic Resonance Imaging (MRI) has been applied by numerous studies to examine the response to antipsychotic treatment, though a large gap remains between their results and our clinical practice. To advance patient care with precision medicine approaches, prior work must be accounted for and built upon with future studies. This review provides an overview of studies that relate treatment outcome to various MRI-related measures, including structural, spectroscopic, diffusion tensor, and functional imaging. Knowledge derived from these studies will be discussed along with future directions for the field.

Psychoradiology: The Frontier of Neuroimaging in Psychiatry

Unlike neurologic conditions, such as brain tumors, dementia, and stroke, the neural mechanisms for all psychiatric disorders remain unclear. A large body of research obtained with structural and functional magnetic resonance imaging, positron emission tomography/single photon emission computed tomography, and optical imaging has demonstrated regional and illness-specific brain changes at the onset of psychiatric disorders and in individuals at risk for such disorders. Many studies have shown that psychiatric medications induce specific measurable changes in brain anatomy and function that are related to clinical outcomes. As a result, a new field of radiology, termed psychoradiology, seems primed to play a major clinical role in guiding diagnostic and treatment planning decisions in patients with psychiatric disorders. This article will present the state of the art in this area, as well as perspectives regarding preparations in the field of radiology for its evolution. Furthermore, this article will (a) give an overview of the imaging and analysis methods for psychoradiology; (b) review the most robust and important radiologic findings and their potential clinical value from studies of major psychiatric disorders, such as depression and schizophrenia; and (c) describe the main challenges and future directions in this field. An ongoing and iterative process of developing biologically based nomenclatures with which to delineate psychiatric disorders and translational research to predict and track response to different therapeutic drugs is laying the foundation for a shift in diagnostic practice in psychiatry from a psychologic symptom-based approach to an imaging-based approach over the next generation. This shift will require considerable innovations for the acquisition, analysis, and interpretation of brain images, all of which will undoubtedly require the active involvement of radiologists. ^© RSNA, 2016 Online supplemental material is available for this article.

Overdiagnosis in the era of neuropsychiatric imaging

New guidelines proposed by the National Institute of Mental Health are intended to transform the management of patients with psychiatric disorders. It is anticipated that neuroimaging and other biomarkers will play a more prominent role in diagnosis and prognosis, especially in the prodromal phase of illness. Earlier treatment of psychiatric disorders has the potential to improve outcomes significantly. However, diagnosis in the absence of symptoms can lead to overdiagnosis. Overdiagnosis is a problem in many fields of medicine but could pose additional problems in psychiatry because of the stigmatization that often accompanies a diagnosis of mental illness. This review discusses the magnetic resonance imaging methods that hold the most promise for evaluating neuropsychiatric disorders, the likelihood that they could lead to overdiagnosis, and opportunities to minimize the impact of overdiagnosis in psychiatric disorders.

Magnetic resonance imaging and the prediction of outcome in first-episode schizophrenia: a review of current evidence and directions for future research

Magnetic Resonance Imaging (MRI) measures are promising outcome markers for schizophrenia, since regional frontal and temporal grey matter volumes reductions, and enlargement of the ventricles, have been associated with outcome in this disorder. However, a number of methodological issues have limited the potential clinical utility of these findings. This article reviewed studies that examined brain structure at illness onset as a predictor of outcome, discusses the limitations of the findings, and highlights the challenges that would need to be addressed if structural data are to inform the management of an individual patient.

METHODS

Using a set of a priori criteria, we systematically searched Medline and EMBASE databases for articles evaluating brain structure at the time of the first psychotic episode and assessed response to treatment, symptomatic outcome, or functional outcome at any point in the first 12 months of illness.

RESULTS

The 11 studies identified suggest that alterations in medial temporal and prefrontal cortical areas, and in the networks that connect them with subcortical structures, are promising neuroanatomical markers of poor symptomatic and functional outcomes.

CONCLUSION

Neuroimaging data, possibly in combination with other biomarkers of disease, could help stratifying patients with psychoses to generate patient clusters clinically meaningful, and useful to detect true therapeutic effects in clinical trials. Optimization of Treatment and Management of Schizophrenia in Europe (OPTiMiSE), a large multicenter study funded by the FP7 European Commission, could generate these much-needed findings.