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Addressing reverse inference in psychiatric neuroimaging: Meta-analyses of task-related brain activation in common mental disorders.

ABSTRACT: Functional magnetic resonance imaging (fMRI) studies in psychiatry use various tasks to identify case-control differences in the patterns of task-related brain activation. Differently activated regions are often ascribed disorder-specific functions in an attempt to link disease expression and brain function. We undertook a systematic meta-analysis of data from task-fMRI studies to examine the effect of diagnosis and study design on the spatial distribution and direction of case-control differences on brain activation. We mapped to atlas regions coordinates of case-control differences derived from 537 task-fMRI studies in schizophrenia, bipolar disorder, major depressive disorder, anxiety disorders, and obsessive compulsive disorder comprising observations derived from 21,427 participants. The fMRI tasks were classified according to the Research Domain Criteria (RDoC). We investigated whether diagnosis, RDoC domain or construct and use of regions-of-interest or whole-brain analyses influenced the neuroanatomical pattern of results. When considering all primary studies, we found an effect of diagnosis for the amygdala and caudate nucleus and an effect of RDoC domains and constructs for the amygdala, hippocampus, putamen and nucleus accumbens. In contrast, whole-brain studies did not identify any significant effect of diagnosis or RDoC domain or construct. These results resonate with prior reports of common brain structural and genetic underpinnings across these disorders and caution against attributing undue specificity to brain functional changes when forming explanatory models of psychiatric disorders. Hum Brain Mapp 38:1846-1864, 2017. © 2017 Wiley Periodicals, Inc.

SUBMITTER: Sprooten E

PROVIDER: S-EPMC5347927 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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Addressing reverse inference in psychiatric neuroimaging: Meta-analyses of task-related brain activation in common mental disorders.

Sprooten Emma E Rasgon Alexander A Goodman Morgan M Carlin Ariella A Leibu Evan E Lee Won Hee WH Frangou Sophia S

Human brain mapping 20170109 4

Functional magnetic resonance imaging (fMRI) studies in psychiatry use various tasks to identify case-control differences in the patterns of task-related brain activation. Differently activated regions are often ascribed disorder-specific functions in an attempt to link disease expression and brain function. We undertook a systematic meta-analysis of data from task-fMRI studies to examine the effect of diagnosis and study design on the spatial distribution and direction of case-control differenc ...[more]

PMID: 28067006

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Project description:In a recent review, it was suggested that much larger cohorts are needed to prove the diagnostic value of neuroimaging biomarkers in psychiatry. While within a sample, an increase of diagnostic accuracy of schizophrenia (SZ) with number of subjects (N) has been shown, the relationship between N and accuracy is completely different between studies. Using data from a recent meta-analysis of machine learning (ML) in imaging SZ, we found that while low-N studies can reach 90% and higher accuracy, above N/2?=?50 the maximum accuracy achieved steadily drops to below 70% for N/2?>?150. We investigate the role N plays in the wide variability in accuracy results in SZ studies (63-97%). We hypothesize that the underlying cause of the decrease in accuracy with increasing N is sample heterogeneity. While smaller studies more easily include a homogeneous group of subjects (strict inclusion criteria are easily met; subjects live close to study site), larger studies inevitably need to relax the criteria/recruit from large geographic areas. A SZ prediction model based on a heterogeneous group of patients with presumably a heterogeneous pattern of structural or functional brain changes will not be able to capture the whole variety of changes, thus being limited to patterns shared by most patients. In addition to heterogeneity (sample size), we investigate other factors influencing accuracy and introduce a ML effect size. We derive a simple model of how the different factors, such as sample heterogeneity and study setup determine this ML effect size, and explain the variation in prediction accuracies found from the literature, both in cross-validation and independent sample testing. From this, we argue that smaller-N studies may reach high prediction accuracy at the cost of lower generalizability to other samples. Higher-N studies, on the other hand, will have more generalization power, but at the cost of lower accuracy. In conclusion, when comparing results from different ML studies, the sample sizes should be taken into account. To assess the generalizability of the models, validation (by direct application) of the prediction models should be tested in independent samples. The prediction of more complex measures such as outcome, which are expected to have an underlying pattern of more subtle brain abnormalities (lower effect size), will require large samples.

Dataset Information

Addressing reverse inference in psychiatric neuroimaging: Meta-analyses of task-related brain activation in common mental disorders.

Publications

Addressing reverse inference in psychiatric neuroimaging: Meta-analyses of task-related brain activation in common mental disorders.

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