Although schizophrenia is considered a brain disorder, the role of brain organization for symptomatic improvement remains inadequately defined. We investigated the relationship between baseline brain ...morphology, resting-state network connectivity and clinical response after 24-weeks of antipsychotic treatment in patients with schizophrenia (n = 95) using integrated multivariate analyses. There was no significant association between clinical response and measures of cortical thickness (r = 0.37, p = 0.98) and subcortical volume (r = 0.56, p = 0.15). By contrast, we identified a strong mode of covariation linking functional network connectivity to clinical response (r = 0.70; p = 0.04), and particularly to improvement in positive (weight = 0.62) and anxious/depressive symptoms (weight = 0.49). Higher internal cohesiveness of the default mode network was the single most important positive predictor. Key negative predictors involved the functional cohesiveness of central executive subnetworks anchored in the frontoparietal cortices and subcortical regions (including the thalamus and striatum) and the inter-network integration between the default mode and sensorimotor networks. The present findings establish links between clinical response and the functional organization of brain networks involved both in perception and in spontaneous and goal-directed cognition, thereby advancing our understanding of the pathophysiology of schizophrenia.
The functional architecture of resting-state networks (RSNs) is defined by their connectivity and metastability. Disrupted RSN connectivity has been amply demonstrated in schizophrenia while the role ...of metastability remains poorly defined. Here, we undertake a comprehensive characterisation of RSN organization in schizophrenia and test its contribution to the clinical profile of this disorder.
We extracted RSNs representing the default mode (DMN), central executive (CEN), salience (SAL), language (LAN), sensorimotor (SMN), auditory (AN) and visual (VN) networks from resting-state functional magnetic resonance imaging data obtained from patients with schizophrenia (n = 85) and healthy individuals (n = 48). For each network, we computed its functional cohesiveness and integration and used the Kuramoto order parameter to compute metastability. We used stepwise multiple regression analyses to test these RSN features as predictors of symptom severity in patients.
RSN features respectively explained 14%, 17%, 12% and 5% of the variance in positive, negative, anxious/depressive and agitation/disorganization symptoms. Lower functional integration between the DMN, CEN and SMN primarily contributed to positive symptoms. The functional properties of the SAL network were key predictors of all other symptom dimensions; specifically, lower cohesiveness of the SAL, lower integration of this network with the LAN and higher integration with the CEN respectively contributed to negative, anxious/depressive and disorganization symptoms. Increased SAL metastability was associated with negative symptoms.
These results confirm the primacy of the SAL network for schizophrenia and demonstrate that abnormalities in RSN connectivity and metastability are significant predictors of schizophrenia-related psychopathology.
IMPORTANCE: Alterations in multiple neuroimaging phenotypes have been reported in psychotic disorders. However, neuroimaging measures can be influenced by factors that are not directly related to ...psychosis and may confound the interpretation of case-control differences. Therefore, a detailed characterization of the contribution of these factors to neuroimaging phenotypes in psychosis is warranted. OBJECTIVE: To quantify the association between neuroimaging measures and behavioral, health, and demographic variables in psychosis using an integrated multivariate approach. DESIGN, SETTING, AND PARTICIPANTS: This imaging study was conducted at a university research hospital from June 26, 2014, to March 9, 2017. High-resolution multimodal magnetic resonance imaging data were obtained from 100 patients with schizophrenia, 40 patients with bipolar disorder, and 50 healthy volunteers; computed were cortical thickness, subcortical volumes, white matter fractional anisotropy, task-related brain activation (during working memory and emotional recognition), and resting-state functional connectivity. Ascertained in all participants were nonimaging measures pertaining to clinical features, cognition, substance use, psychological trauma, physical activity, and body mass index. The association between imaging and nonimaging measures was modeled using sparse canonical correlation analysis with robust reliability testing. MAIN OUTCOMES AND MEASURES: Multivariate patterns of the association between nonimaging and neuroimaging measures in patients with psychosis and healthy volunteers. RESULTS: The analyses were performed in 92 patients with schizophrenia (23 female 25.0%; mean SD age, 27.0 7.6 years), 37 patients with bipolar disorder (12 female 32.4%; mean SD age, 27.5 8.1 years), and 48 healthy volunteers (20 female 41.7%; mean SD age, 29.8 8.5 years). The imaging and nonimaging data sets showed significant covariation (r = 0.63, P < .001), which was independent of diagnosis. Among the nonimaging variables examined, age (r = −0.53), IQ (r = 0.36), and body mass index (r = −0.25) were associated with multiple imaging phenotypes; cannabis use (r = 0.23) and other substance use (r = 0.33) were associated with subcortical volumes, and alcohol use was associated with white matter integrity (r = −0.15). Within the multivariate models, positive symptoms retained associations with the global neuroimaging (r = −0.13), the cortical thickness (r = −0.22), and the task-related activation variates (r = −0.18); negative symptoms were mostly associated with measures of subcortical volume (r = 0.23), and depression/anxiety was associated with measures of white matter integrity (r = 0.12). CONCLUSIONS AND RELEVANCE: Multivariate analyses provide a more accurate characterization of the association between brain alterations and psychosis because they enable the modeling of other key factors that influence neuroimaging phenotypes.
Intracortical myelin is a key determinant of neuronal synchrony and plasticity that underpin optimal brain function. Magnetic resonance imaging (MRI) facilitates the examination of intracortical ...myelin but presents with methodological challenges. Here we describe a whole-brain approach for the in vivo investigation of intracortical myelin in the human brain using ultra-high field MRI.
Twenty-five healthy adults were imaged in a 7 Tesla MRI scanner using diffusion-weighted imaging and a T1-weighted sequence optimized for intracortical myelin contrast. Using an automated pipeline, T1 values were extracted at 20 depth-levels from each of 148 cortical regions. In each cortical region, T1 values were used to infer myelin concentration and to construct a non-linearity index as a measure the spatial distribution of myelin across the cortical ribbon. The relationship of myelin concentration and the non-linearity index with other neuroanatomical properties were investigated. Five patients with multiple sclerosis were also assessed using the same protocol as positive controls.
Intracortical T1 values decreased between the outer brain surface and the gray-white matter boundary following a slope that showed a slight leveling between 50% and 75% of cortical depth. Higher-order regions in the prefrontal, cingulate and insular cortices, displayed higher non-linearity indices than sensorimotor regions. Across all regions, there was a positive association between T1 values and non-linearity indices (P < 10−5). Both T1 values (P < 10−5) and non-linearity indices (P < 10−15) were associated with cortical thickness. Higher myelin concentration but only in the deepest cortical levels was associated with increased subcortical fractional anisotropy (P = 0.05).
We demonstrate the usefulness of an automatic, whole-brain method to perform depth-dependent examination of intracortical myelin organization. The extracted metrics, T1 values and the non-linearity index, have characteristic patterns across cortical regions, and are associated with thickness and underlying white matter microstructure.
•Intracortical myelin is a key determinant of optimal brain function.•We used a new approach to study intracortical myelin using ultra-high field MRI.•A new non-linearity index was used as a measure of intracortical organization.•Intracortical myelin concentration correlated with non-linearity indices.•Intracortical myelin concentration correlated with subcortical fractional anisotropy.
Several psychiatric disorders involve abnormalities of interoception and associated neural circuitry centered on the insula. The development of interventions modulating interoceptive circuits could ...lead to novel treatment approaches for these disorders. The 5-HT3 receptor antagonist ondansetron is a good candidate for the modulation of interoceptive circuits, as 5-HT3 receptors are located abundantly on sensory pathways and ondansetron has shown some clinical utility in disorders characterized by sensory and interoceptive abnormalities. The present study tested the ability of three different doses of ondansetron to engage neural regions involved in interoception to determine the drug's utility as a therapeutic agent to target circuit abnormalities in patients. Fifty-three healthy subjects were randomized to receive a single 8-mg (n = 18), 16-mg (n = 17), or 24-mg (n = 18) dose of ondansetron and placebo before MRI scanning on separate days. Subjects performed an fMRI task previously shown to engage interoceptive circuitry in which they viewed videos depicting body movements/sensation and control videos. The results revealed a highly significant relationship between dosage and activation in bilateral insula, somatosensory and premotor regions, cingulate cortex, and temporal cortex for control but not body-focused videos. These effects were driven by a robust reduction in activation for ondansetron compared to placebo for the 24-mg group, with weaker effects for the 16-mg and 8-mg groups. In conclusion, high-dose ondansetron reduces activation of several areas important for interoception, including insula and sensorimotor cortical regions. This study reveals the potential utility of this drug in modulating hyperactivity in these regions in patients.
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