Dynamic functional network reconfiguration underlying the pathophysiology of schizophrenia and autism spectrum disorder.

The dynamics of the human brain span multiple spatial scales, from connectivity associated with a specific region/network to the global organization, each representing different brain mechanisms. Yet brain reconfigurations at different spatial scales are seldom explored and whether they are associated with the neural aspects of brain disorders is far from understood. In this study, we introduced a dynamic measure called step-wise functional network reconfiguration (sFNR) to characterize how brain configuration rewires at different spatial scales. We applied sFNR to two independent datasets, one includes 160 healthy controls (HCs) and 151 patients with schizophrenia (SZ) and the other one includes 314 HCs and 255 individuals with autism spectrum disorder (ASD). We found that both SZ and ASD have increased whole-brain sFNR and sFNR between cerebellar and subcortical/sensorimotor domains. At the ICN level, the abnormalities in SZ are mainly located in ICNs within subcortical, sensory, and cerebellar domains, while the abnormalities in ASD are more widespread across domains. Interestingly, the overlap SZ-ASD abnormality in sFNR between cerebellar and sensorimotor domains was correlated with the reasoning-problem-solving performance in SZ (r = -.1652, p = .0058) as well as the Autism Diagnostic Observation Schedule in ASD (r = .1853, p = .0077). Our findings suggest that dynamic reconfiguration deficits may represent a key intersecting point for SZ and ASD. The investigation of brain dynamics at different spatial scales can provide comprehensive insights into the functional reconfiguration, which might advance our knowledge of cognitive decline and other pathophysiology in brain disorders.

Exploration of scanning effects in multi-site structural MRI studies.

BACKGROUND: Pooling of multi-site MRI data is often necessary when a large cohort is desired. However, different scanning platforms can introduce systematic differences which confound true effects of interest. One may reduce multi-site bias by calibrating pivotal scanning parameters, or include them as covariates to improve the data integrity. NEW METHOD: In the present study we use a source-based morphometry (SBM) model to explore scanning effects in multi-site sMRI studies and develop a data-driven correction. Specifically, independent components are extracted from the data and investigated for associations with scanning parameters to assess the influence. The identified scanning-related components can be eliminated from the original data for correction. RESULTS: A small set of SBM components captured most of the variance associated with the scanning differences. In a dataset of 1460 healthy subjects, pronounced and independent scanning effects were observed in brainstem and thalamus, associated with magnetic field strength-inversion time and RF-receiving coil. A second study with 110 schizophrenia patients and 124 healthy controls demonstrated that scanning effects can be effectively corrected with the SBM approach. COMPARISON WITH EXISTING METHOD(S): Both SBM and GLM correction appeared to effectively eliminate the scanning effects. Meanwhile, the SBM-corrected data yielded a more significant patient versus control group difference and less questionable findings. CONCLUSIONS: It is important to calibrate scanning settings and completely examine individual parameters for the control of confounding effects in multi-site sMRI studies. Both GLM and SBM correction can reduce scanning effects, though SBM's data-driven nature provides additional flexibility and is better able to handle collinear effects.

Dysregulation of working memory and default-mode networks in schizophrenia using independent component analysis, an fBIRN and MCIC study.

Deficits in working memory (WM) are a consistent neurocognitive marker for schizophrenia. Previous studies have suggested that WM is the product of coordinated activity in distributed functionally connected brain regions. Independent component analysis (ICA) is a data-driven approach that can identify temporally coherent networks that underlie fMRI activity. We applied ICA to an fMRI dataset for 115 patients with chronic schizophrenia and 130 healthy controls by performing the Sternberg Item Recognition Paradigm. Here, we describe the first results using ICA to identify differences in the function of WM networks in schizophrenia compared to controls. ICA revealed six networks that showed significant differences between patients with schizophrenia and healthy controls. Four of these networks were negatively task-correlated and showed deactivation across the posterior cingulate, precuneus, medial prefrontal cortex, anterior cingulate, inferior parietal lobules, and parahippocampus. These networks comprise brain regions known as the default-mode network (DMN), a well-characterized set of regions shown to be active during internal modes of cognition and implicated in schizophrenia. Two networks were positively task-correlated, with one network engaging WM regions such as bilateral DLPFC and inferior parietal lobules while the other network engaged primarily the cerebellum. Our results suggest that DLPFC dysfunction in schizophrenia might be lateralized to the left and intrinsically tied to other regions such as the inferior parietal lobule and cingulate gyrus. Furthermore, we found that DMN dysfunction in schizophrenia exists across multiple subnetworks of the DMN and that these subnetworks are individually relevant to the pathophysiology of schizophrenia. In summary, this large multisite study identified multiple temporally coherent networks, which are aberrant in schizophrenia versus healthy controls and suggests that both task-correlated and task-anticorrelated networks may serve as potential biomarkers.

Cerebral and cerebellar sensorimotor plasticity following motor imagery-based mental practice of a sequential movement.

Motor behavior and sensorimotor activation of the cerebrum and cerebellum were measured before and after motor imagery-based mental practice (MP) and physical practice (PP) of a sequential motor task. Two-button-press sequences (A, B) were performed outside a magnetic resonance imaging scanner and at 2 Hz inside the scanner during a pretest. Participants (n = 39) completed PP, MP, or no practice (NP) of Sequence A for 1 week and were posttested. Sequence A performance improved 121%, 86%, and 4% for the PP, MP, and NP groups, respectively (p < 0.05), while Sequence B improved 56%, 40%, and 38% (p > 0.05). PP improvements were accompanied by increased striatal and decreased cerebellar activation, while MP improvements were accompanied by increased cerebellar, premotor, and striatal activation. The efficacy of MP for activating cerebral and cerebellar sensorimotor networks suggests that MP might be an effective substitute or complement to PP to activate compensatory networks for motor rehabilitation.