Abstract
Past work on relatively small, single-site studies using regional volumetry, and more recently machine learning methods, has shown that widespread structural brain abnormalities are ...prominent in schizophrenia. However, to be clinically useful, structural imaging biomarkers must integrate high-dimensional data and provide reproducible results across clinical populations and on an individual person basis. Using advanced multi-variate analysis tools and pooled data from case–control imaging studies conducted at 5 sites (941 adult participants, including 440 patients with schizophrenia), a neuroanatomical signature of patients with schizophrenia was found, and its robustness and reproducibility across sites, populations, and scanners, was established for single-patient classification. Analyses were conducted at multiple scales, including regional volumes, voxelwise measures, and complex distributed patterns. Single-subject classification was tested for single-site, pooled-site, and leave-site-out generalizability. Regional and voxelwise analyses revealed a pattern of widespread reduced regional gray matter volume, particularly in the medial prefrontal, temporolimbic and peri-Sylvian cortex, along with ventricular and pallidum enlargement. Multivariate classification using pooled data achieved a cross-validated prediction accuracy of 76% (AUC = 0.84). Critically, the leave-site-out validation of the detected schizophrenia signature showed accuracy/AUC range of 72–77%/0.73–0.91, suggesting a robust generalizability across sites and patient cohorts. Finally, individualized patient classifications displayed significant correlations with clinical measures of negative, but not positive, symptoms. Taken together, these results emphasize the potential for structural neuroimaging data to provide a robust and reproducible imaging signature of schizophrenia. A web-accessible portal is offered to allow the community to obtain individualized classifications of magnetic resonance imaging scans using the methods described herein.
Abstract Early emotional stress is associated with a life-long burden of risk for later depression and stressful life events contribute to the development of depressive episodes. In this study we ...investigated whether childhood stress is associated with structural brain alterations in patients with major depression (MD). Forty-three patients with MD and 44 age as well as gender matched healthy control subjects were investigated using high-resolution magnetic resonance imaging (MRI). Region of interest analysis of the hippocampus, whole brain voxel-based morphometry (VBM) and assessment of childhood stress was carried out. Significantly smaller hippocampal white matter and prefrontal gray matter volume was observed in patients with MD compared to healthy controls. In particular left hippocampal white matter was smaller in patients, who had emotional childhood neglect, compared to those without neglect. For male patients this effect was seen in the left and right hippocampus. Moreover, physical neglect during childhood affected prefrontal gray matter volume in healthy subjects. Both emotional neglect and brain structural abnormalities predicted cumulative illness duration and there was a significant interaction between emotional neglect and prefrontal volumes as well as hippocampal white matter on the illness course. Childhood neglect resulted in hippocampal white matter changes in patients with major depression, pronounced at the left side and in males. Most interestingly, childhood stress and brain structure volumes independently predicted cumulative illness course. Subjects with both, structural brain changes and childhood emotional neglect seem to be at a very high risk to develop a more severe illness course.
To date, the MRI-based individualized prediction of psychosis has only been demonstrated in single-site studies. It remains unclear if MRI biomarkers generalize across different centers and MR ...scanners and represent accurate surrogates of the risk for developing this devastating illness. Therefore, we assessed whether a MRI-based prediction system identified patients with a later disease transition among 73 clinically defined high-risk persons recruited at two different early recognition centers. Prognostic performance was measured using cross-validation, independent test validation, and Kaplan-Meier survival analysis. Transition outcomes were correctly predicted in 80% of test cases (sensitivity: 76%, specificity: 85%, positive likelihood ratio: 5.1). Thus, given a 54-month transition risk of 45% across both centers, MRI-based predictors provided a 36%-increase of prognostic certainty. After stratifying individuals into low-, intermediate-, and high-risk groups using the predictor's decision score, the high- vs low-risk groups had median psychosis-free survival times of 5 vs 51 months and transition rates of 88% vs 8%. The predictor's decision function involved gray matter volume alterations in prefrontal, perisylvian, and subcortical structures. Our results support the existence of a cross-center neuroanatomical signature of emerging psychosis enabling individualized risk staging across different high-risk populations. Supplementary results revealed that (1) potentially confounding between-site differences were effectively mitigated using statistical correction methods, and (2) the detection of the prodromal signature considerably depended on the available sample sizes. These observations pave the way for future multicenter studies, which may ultimately facilitate the neurobiological refinement of risk criteria and personalized preventive therapies based on individualized risk profiling tools.
The underlying neurobiology of major depression (MD) is likely to represent an interaction between genetic susceptibility and environmental factors such as stress. We investigated, in a multimodal ...high-resolution magnetic resonance imaging (MRI) genetic study, whether reduced hippocampal volumes and other brain alterations are associated with the tri-allelic polymorphism of the serotonin transporter and childhood stress in patients with MD and healthy subjects. Patients with MD and healthy participants were investigated using high-resolution MRI and genotyping for serotonin transporter polymorphism in the promoter region of the serotonin transporter gene (SLC6A4, 5-HTTLPR). Region of interest analysis of the hippocampus, whole-brain voxel-based morphometry (VBM), and assessment of childhood stress were carried out. Patients carrying the risk S-allele developed smaller hippocampal volumes when they had a history of emotional neglect compared with patients who only had one risk factor (environmental or genetic). In patients, childhood stress also predicted further hippocampal white matter alterations independently from the genotype. Moreover, the left prefrontal cortex was smaller in patients, whereby childhood stress resulted in larger prefrontal volumes in those subjects carrying the non-risk L-allele, suggesting preventive effects. The findings indicate that subjects with both environmental and genetic risk factors are susceptible to stress-related hippocampal changes. Structural brain changes due to stress represent part of the mechanism by which the illness risk and outcome might be genetically mediated.
Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder that often persists into adulthood. Core symptoms of ADHD, such as impulsivity, are caused by an interaction of ...genetic and environmental factors. Epigenetic modifications of DNA, such as DNA methylation, are thought to mediate the interplay of these factors. Tryptophan hydroxylase 2 (TPH2) is the rate-limiting enzyme in brain serotonin synthesis. The TPH2 gene has frequently been investigated in relation to ADHD, e.g., showing that TPH2 G-703T (rs4570625) polymorphism influences response control and prefrontal signaling in ADHD patients. In this (epi)genetic imaging study we examined 144 children and adolescents (74 patients, 14 females) using fMRI at rest and during performing a waiting impulsivity (WI) paradigm. Both, TPH2 G-703T (rs4570625) genotype and DNA methylation in the 5' untranslated region (5'UTR) of TPH2 were associated with wavelet variance in fronto-parietal regions and behavioral performance, taking TPH2 genotype into account. In detail, comparisons between genotypes of patients and controls revealed highest wavelet variance and longest reaction times in patients carrying the T allele indicative for a gene-dosage effect, i.e., the WI phenotype is a direct result of the cumulative effect of ADHD and TPH2 variation. Regressions revealed a significant effect on one specific DNA methylation site in ADHD patients but not controls, in terms of a significant prediction of wavelet variance in fronto-parietal regions as well as premature responses. By the example of the TPH2 G-703T (rs4570625) polymorphism, we provide insight into how interactive genetic and DNA methylation affect the ADHD and/or impulsive endophenotype.
Reliable prognostic biomarkers are needed for the early recognition of psychosis. Recently, multivariate machine learning methods have demonstrated the feasibility to predict illness onset in ...clinically defined at-risk individuals using structural magnetic resonance imaging (MRI) data. However, it remains unclear whether these findings could be replicated in independent populations.
We evaluated the performance of an MRI-based classification system in predicting disease conversion in at-risk individuals recruited within the prospective FePsy (Früherkennung von Psychosen) study at the University of Basel, Switzerland. Pairwise and multigroup biomarkers were constructed using the MRI data of 22 healthy volunteers, 16/21 at-risk subjects with/without a subsequent disease conversion. Diagnostic performance was measured in unseen test cases using repeated nested cross-validation.
The classification accuracies in the "healthy controls (HCs) vs converters," "HCs vs nonconverters," and "converters vs nonconverters" analyses were 92.3%, 66.9%, and 84.2%, respectively. A positive likelihood ratio of 6.5 in the converters vs nonconverters analysis indicated a 40% increase in diagnostic certainty by applying the biomarker to an at-risk population with a transition rate of 43%. The neuroanatomical decision functions underlying these results particularly involved the prefrontal perisylvian and subcortical brain structures.
Our findings suggest that the early prediction of psychosis may be reliably enhanced using neuroanatomical pattern recognition operating at the single-subject level. These MRI-based biomarkers may have the potential to identify individuals at the highest risk of developing psychosis, and thus may promote informed clinical strategies aiming at preventing the full manifestation of the disease.
Neuropsychological deficits predate overt psychosis and overlap with the impairments in the established disease. However, to date, no single neurocognitive measure has shown sufficient power for a ...prognostic test. Thus, it remains to be determined whether multivariate neurocognitive pattern classification could facilitate the diagnostic identification of different at-risk mental states (ARMS) for psychosis and the individualized prediction of illness transition.
First, classification of 30 healthy controls (HC) vs 48 ARMS individuals subgrouped into 20 "early," 28 "late" ARMS subjects was performed based on a comprehensive neuropsychological test battery. Second, disease prediction was evaluated by categorizing the neurocognitive baseline data of those ARMS individuals with transition (n = 15) vs non transition (n = 20) vs HC after 4 years of follow-up. Generalizability of classification was estimated by repeated double cross-validation.
The 3-group cross-validated classification accuracies in the first analysis were 94.2% (HC vs rest), 85.0% (early at-risk subjects vs rest), and, 91.4% (late at-risk subjects vs rest) and 90.8% (HC vs rest), 90.8% (converters vs rest), and 89.0% (nonconverters vs rest) in the second analysis. Patterns distinguishing the early or late ARMS from HC primarily involved the verbal learning/memory domains, while executive functioning and verbal IQ deficits were particularly characteristic of the late ARMS. Disease transition was mainly predicted by executive and verbal learning impairments.
Different ARMS and their clinical outcomes may be reliably identified on an individual basis by evaluating neurocognitive test batteries using multivariate pattern recognition. These patterns may have the potential to substantially improve the early recognition of psychosis.
Abstract To date, research into the biomarker-aided early recognition of psychosis has focused on predicting the transition likelihood of clinically defined individuals with different at-risk mental ...states (ARMS) based on structural (and functional) brain changes. However, it is currently unknown whether neuroimaging patterns could be identified to facilitate the individualized prediction of symptomatic and functional recovery. Therefore, we investigated whether cortical surface alterations analyzed by means of multivariate pattern recognition methods could enable the single-subject identification of functional outcomes in twenty-seven ARMS individuals. Subjects were dichotomized into ‘good’ vs. ‘poor’ outcome groups on average 4 years after the baseline MRI scan using a Global Assessment of Functioning (GAF) threshold of 70. Cortical surface-based pattern classification predicted good (N = 14) vs. poor outcome status (N = 13) at follow-up with an accuracy of 82% as determined by nested leave-one-cross-validation. Neuroanatomical prediction involved cortical area reductions in superior temporal, inferior frontal and inferior parietal areas and was not confounded by functional impairment at baseline, or antipsychotic medication and transition status over the follow-up period. The prediction model's decision scores were correlated with positive and general symptom scores in the ARMS group at follow-up, whereas negative symptoms were not linked to predicted poorer functional outcome. These findings suggest that poorer functional outcomes are associated with non-resolving attenuated psychosis and could be predicted at the single-subject level using multivariate neuroanatomical risk stratification methods. However, the generalizability and specificity of the suggested prediction model should be thoroughly investigated in future large-scale and cross-diagnostic MRI studies.
Magnetic resonance imaging-based markers of schizophrenia have been repeatedly shown to separate patients from healthy controls at the single-subject level, but it remains unclear whether these ...markers reliably distinguish schizophrenia from mood disorders across the life span and generalize to new patients as well as to early stages of these illnesses. The current study used structural MRI-based multivariate pattern classification to (i) identify and cross-validate a differential diagnostic signature separating patients with first-episode and recurrent stages of schizophrenia (n = 158) from patients with major depression (n = 104); and (ii) quantify the impact of major clinical variables, including disease stage, age of disease onset and accelerated brain ageing on the signature's classification performance. This diagnostic magnetic resonance imaging signature was then evaluated in an independent patient cohort from two different centres to test its generalizability to individuals with bipolar disorder (n = 35), first-episode psychosis (n = 23) and clinically defined at-risk mental states for psychosis (n = 89). Neuroanatomical diagnosis was correct in 80% and 72% of patients with major depression and schizophrenia, respectively, and involved a pattern of prefronto-temporo-limbic volume reductions and premotor, somatosensory and subcortical increments in schizophrenia versus major depression. Diagnostic performance was not influenced by the presence of depressive symptoms in schizophrenia or psychotic symptoms in major depression, but earlier disease onset and accelerated brain ageing promoted misclassification in major depression due to an increased neuroanatomical schizophrenia likeness of these patients. Furthermore, disease stage significantly moderated neuroanatomical diagnosis as recurrently-ill patients had higher misclassification rates (major depression: 23%; schizophrenia: 29%) than first-episode patients (major depression: 15%; schizophrenia: 12%). Finally, the trained biomarker assigned 74% of the bipolar patients to the major depression group, while 83% of the first-episode psychosis patients and 77% and 61% of the individuals with an ultra-high risk and low-risk state, respectively, were labelled with schizophrenia. Our findings suggest that neuroanatomical information may provide generalizable diagnostic tools distinguishing schizophrenia from mood disorders early in the course of psychosis. Disease course-related variables such as age of disease onset and disease stage as well alterations of structural brain maturation may strongly impact on the neuroanatomical separability of major depression and schizophrenia.
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