Dataset Information

Fronto-temporal connectivity predicts cognitive empathy deficits and experiential negative symptoms in schizophrenia.

ABSTRACT: Impaired cognitive empathy is a core social cognitive deficit in schizophrenia associated with negative symptoms and social functioning. Cognitive empathy and negative symptoms have also been linked to medial prefrontal and temporal brain networks. While shared behavioral and neural underpinnings are suspected for cognitive empathy and negative symptoms, research is needed to test these hypotheses. In two studies, we evaluated whether resting-state functional connectivity between data-driven networks, or components (referred to as, inter-component connectivity), predicted cognitive empathy and experiential and expressive negative symptoms in schizophrenia subjects. Study 1: We examined associations between cognitive empathy and medial prefrontal and temporal inter-component connectivity at rest using a group-matched schizophrenia and control sample. We then assessed whether inter-component connectivity metrics associated with cognitive empathy were also related to negative symptoms. Study 2: We sought to replicate the connectivity-symptom associations observed in Study 1 using an independent schizophrenia sample. Study 1 results revealed that while the groups did not differ in average inter-component connectivity, a medial-fronto-temporal metric and an orbito-fronto-temporal metric were related to cognitive empathy. Moreover, the medial-fronto-temporal metric was associated with experiential negative symptoms in both schizophrenia samples. These findings support recent models that link social cognition and negative symptoms in schizophrenia. Hum Brain Mapp 38:1111-1124, 2017. © 2016 Wiley Periodicals, Inc.

SUBMITTER: Abram SV

PROVIDER: S-EPMC6866816 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

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Fronto-temporal connectivity predicts cognitive empathy deficits and experiential negative symptoms in schizophrenia.

Abram Samantha V SV Wisner Krista M KM Fox Jaclyn M JM Barch Deanna M DM Wang Lei L Csernansky John G JG MacDonald Angus W AW Smith Matthew J MJ

Human brain mapping 20161024 3

Impaired cognitive empathy is a core social cognitive deficit in schizophrenia associated with negative symptoms and social functioning. Cognitive empathy and negative symptoms have also been linked to medial prefrontal and temporal brain networks. While shared behavioral and neural underpinnings are suspected for cognitive empathy and negative symptoms, research is needed to test these hypotheses. In two studies, we evaluated whether resting-state functional connectivity between data-driven net ...[more]

PMID: 27774734

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Project description:Background:Electroconvulsive therapy (ECT) is arguably the most effective available treatment for severe depression. Recent studies have used MRI data to predict clinical outcome to ECT and other antidepressant therapies. One challenge facing such studies is selecting from among the many available metrics, which characterize complementary and sometimes non-overlapping aspects of brain function and connectomics. Here, we assessed the ability of aggregated, functional MRI metrics of basal brain activity and connectivity to predict antidepressant response to ECT using machine learning. Methods:A radial support vector machine was trained using arterial spin labeling (ASL) and blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) metrics from n?=?46 (26 female, mean age 42) depressed patients prior to ECT (majority right-unilateral stimulation). Image preprocessing was applied using standard procedures, and metrics included cerebral blood flow in ASL, and regional homogeneity, fractional amplitude of low-frequency modulations, and graph theory metrics (strength, local efficiency, and clustering) in BOLD data. A 5-repeated 5-fold cross-validation procedure with nested feature-selection validated model performance. Linear regressions were applied post hoc to aid interpretation of discriminative features. Results:The range of balanced accuracy in models performing statistically above chance was 58-68%. Here, prediction of non-responders was slightly higher than for responders (maximum performance 74 and 64%, respectively). Several features were consistently selected across cross-validation folds, mostly within frontal and temporal regions. Among these were connectivity strength among: a fronto-parietal network [including left dorsolateral prefrontal cortex (DLPFC)], motor and temporal networks (near ECT electrodes), and/or subgenual anterior cingulate cortex (sgACC). Conclusion:Our data indicate that pattern classification of multimodal fMRI metrics can successfully predict ECT outcome, particularly for individuals who will not respond to treatment. Notably, connectivity with networks highly relevant to ECT and depression were consistently selected as important predictive features. These included the left DLPFC and the sgACC, which are both targets of other neurostimulation therapies for depression, as well as connectivity between motor and right temporal cortices near electrode sites. Future studies that probe additional functional and structural MRI metrics and other patient characteristics may further improve the predictive power of these and similar models.

Project description:The 22q11.2 deletion syndrome (22q11DS) is a widely recognized genetic model allowing the study of neuroanatomical biomarkers that underlie the risk for developing schizophrenia. Recent advances in magnetic resonance image analyses enable the examination of structural connectivity integrity, scarcely used in the 22q11DS field. This framework potentially provides evidence for the disconnectivity hypothesis of schizophrenia in this high-risk population. In the present study, we quantify the whole brain white matter connections in 22q11DS using deterministic tractography. Diffusion Tensor Imaging was acquired in 30 affected patients and 30 age- and gender-matched healthy participants. The Human Connectome technique was applied to register white matter streamlines with cortical anatomy. The number of fibers (streamlines) was used as a measure of connectivity for comparison between groups at the global, lobar and regional level. All statistics were corrected for age and gender. Results showed a 10% reduction of the total number of fibers in patients compared to controls. After correcting for this global reduction, preserved connectivity was found within the right frontal and right parietal lobes. The relative increase in the number of fibers was located mainly in the right hemisphere. Conversely, an excessive reduction of connectivity was observed within and between limbic structures. Finally, a disproportionate reduction was shown at the level of fibers connecting the left fronto-temporal regions. We could therefore speculate that the observed disruption to fronto-temporal connectivity in individuals at risk of schizophrenia implies that fronto-temporal disconnectivity, frequently implicated in the pathogenesis of schizophrenia, could precede the onset of symptoms and, as such, constitutes a biomarker of the vulnerability to develop psychosis. On the contrary, connectivity alterations in the limbic lobe play a role in a wide range of psychiatric disorders and therefore seem to be less specific in defining schizophrenia.

Dataset Information

Fronto-temporal connectivity predicts cognitive empathy deficits and experiential negative symptoms in schizophrenia.

Publications

Fronto-temporal connectivity predicts cognitive empathy deficits and experiential negative symptoms in schizophrenia.

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