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1.
J Psychiatry Neurosci ; 45(6): 395-405, 2020 11 01.
Article in English | MEDLINE | ID: mdl-32436671

ABSTRACT

Background: Dysfunction of the corticostriatal network has been implicated in the pathophysiology of schizophrenia, but findings are inconsistent within and across imaging modalities. We used multimodal neuroimaging to analyze functional and structural connectivity in the corticostriatal network in people with schizophrenia and unaffected first-degree relatives. Methods: We collected resting-state functional magnetic resonance imaging and diffusion tensor imaging scans from people with schizophrenia (n = 47), relatives (n = 30) and controls (n = 49). We compared seed-based functional and structural connectivity across groups within striatal subdivisions defined a priori. Results: Compared with controls, people with schizophrenia had altered connectivity between the subdivisions and brain regions in the frontal and temporal cortices and thalamus; relatives showed different connectivity between the subdivisions and the right anterior cingulate cortex (ACC) and the left precuneus. Post-hoc t tests revealed that people with schizophrenia had decreased functional connectivity in the ventral loop (ventral striatum-right ACC) and dorsal loop (executive striatum-right ACC and sensorimotor striatum-right ACC), accompanied by decreased structural connectivity; relatives had reduced functional connectivity in the ventral loop and the dorsal loop (right executive striatum-right ACC) and no significant difference in structural connectivity compared with the other groups. Functional connectivity among people with schizophrenia in the bilateral ventral striatum-right ACC was correlated with positive symptom severity. Limitations: The number of relatives included was moderate. Striatal subdivisions were defined based on a relatively low threshold, and structural connectivity was measured based on fractional anisotropy alone. Conclusion: Our findings provide insight into the role of hypoconnectivity of the ventral corticostriatal system in people with schizophrenia.


Subject(s)
Cerebral Cortex , Connectome , Corpus Striatum , Magnetic Resonance Imaging , Nerve Net , Schizophrenia , Adult , Cerebral Cortex/diagnostic imaging , Cerebral Cortex/pathology , Cerebral Cortex/physiopathology , Corpus Striatum/diagnostic imaging , Corpus Striatum/pathology , Corpus Striatum/physiopathology , Diffusion Tensor Imaging , Family , Female , Gyrus Cinguli/diagnostic imaging , Gyrus Cinguli/pathology , Gyrus Cinguli/physiopathology , Humans , Male , Nerve Net/diagnostic imaging , Nerve Net/pathology , Nerve Net/physiopathology , Schizophrenia/diagnostic imaging , Schizophrenia/pathology , Schizophrenia/physiopathology , Ventral Striatum/diagnostic imaging , Ventral Striatum/pathology , Ventral Striatum/physiopathology , Young Adult
4.
NPJ Schizophr ; 3: 21, 2017.
Article in English | MEDLINE | ID: mdl-28560267

ABSTRACT

Previous studies suggested that electroconvulsive therapy can influence regional metabolism and dopamine signaling, thereby alleviating symptoms of schizophrenia. It remains unclear what patients may benefit more from the treatment. The present study sought to identify biomarkers that predict the electroconvulsive therapy response in individual patients. Thirty-four schizophrenia patients and 34 controls were included in this study. Patients were scanned prior to treatment and after 6 weeks of treatment with antipsychotics only (n = 16) or a combination of antipsychotics and electroconvulsive therapy (n = 13). Subject-specific intrinsic connectivity networks were computed for each subject using a group information-guided independent component analysis technique. Classifiers were built to distinguish patients from controls and quantify brain states based on intrinsic connectivity networks. A general linear model was built on the classification scores of first scan (referred to as baseline classification scores) to predict treatment response. Classifiers built on the default mode network, the temporal lobe network, the language network, the corticostriatal network, the frontal-parietal network, and the cerebellum achieved a cross-validated classification accuracy of 83.82%, with specificity of 91.18% and sensitivity of 76.47%. After the electroconvulsive therapy, psychosis symptoms of the patients were relieved and classification scores of the patients were decreased. Moreover, the baseline classification scores were predictive for the treatment outcome. Schizophrenia patients exhibited functional deviations in multiple intrinsic connectivity networks which were able to distinguish patients from healthy controls at an individual level. Patients with lower classification scores prior to treatment had better treatment outcome, indicating that the baseline classification scores before treatment is a good predictor for treatment outcome.

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