Resting-state functional connectivity correlates of antipsychotic treatment in unmedicated schizophrenia.

BACKGROUND: Antipsychotics may modulate the resting state functional connectivity(rsFC) to improve clinical symptoms in schizophrenia(Sz). Existing literature has potential confounders like past medication effects and evaluating preselected regions/networks. We aimed to evaluate connectivity pattern changes with antipsychotics in unmedicated Sz using Multivariate pattern analysis(MVPA), a data-driven technique for whole-brain connectome analysis. METHODS: Forty-seven unmedicated patients with Sz(DSM-IV-TR) underwent clinical evaluation and neuroimaging at baseline and after 3-months of antipsychotic treatment. Resting-state functional MRI was analysed using group-MVPA to derive 5-components. The brain region with significant connectivity pattern changes with antipsychotics was identified, and post-hoc seed-to-voxel analysis was performed to identify connectivity changes and their association with symptom changes. RESULTS: Connectome-MVPA analysis revealed the connectivity pattern of a cluster localised to left anterior cingulate and paracingulate gyri (ACC/PCG) (peak coordinates:x = -04,y = +30,z = +26;k = 12;cluster-pFWE=0.002) to differ significantly after antipsychotics. Specifically, its connections with clusters of precuneus/posterior cingulate cortex(PCC) and left inferior temporal gyrus(ITG) correlated with improvement in positive and negative symptoms scores, respectively. CONCLUSION: ACC/PCG, a hub of the default mode network, seems to mediate the antipsychotic effects in unmedicated Sz. Evaluating causality models with data from randomised controlled design using the MVPA approach would further enhance our understanding of therapeutic connectomics in Sz.

Protocol for magnetic resonance imaging acquisition, quality assurance, and quality check for the Accelerator program for Discovery in Brain disorders using Stem cells.

OBJECTIVE: The Accelerator program for Discovery in Brain disorders using Stem cells (ADBS) is a longitudinal study on five cohorts of patients with major psychiatric disorders from genetically high-risk families, their unaffected first-degree relatives, and healthy subjects. We describe the ADBS protocols for acquisition, quality assurance (QA), and quality check (QC) for multimodal magnetic resonance brain imaging studies. METHODS: We describe the acquisition and QC protocols for structural, functional, and diffusion images. For QA, we acquire proton density and functional images on phantoms, along with repeated scans on human volunteer. We describe the analysis of phantom data and test-retest reliability of volumetric and diffusion measures. RESULTS: Analysis of acquired phantom data shows linearity of proton density signal with increasing proton fraction, and an overall stability of various spatial and temporal QA measures. Examination of dice coefficient and statistical analyses of coefficient of variation in test-retest data on the human volunteer showed consistency of volumetric and diffusivity measures at whole-brain, regional, and voxel-level. CONCLUSION: The described acquisition and QA-QC procedures can yield consistent and reliable quantitative measures. It is expected that this longitudinal neuroimaging dataset will, upon its release, serve the scientific community well and pave the way for interesting discoveries.

Comparison of electric field modeling pipelines for transcranial direct current stimulation.

OBJECTIVES: Electric field modeling utilizes structural brain magnetic resonance images (MRI) to model the electric field induced by non-invasive transcranial direct current stimulation (tDCS) in a given individual. Electric field modeling is being integrated with clinical outcomes to improve understanding of inter-individual variability in tDCS effects and to optimize tDCS parameters, thereby enhancing the predictability of clinical effects. The successful integration of modeling in clinical use will primarily be driven by choice of tools and procedures implemented in computational modeling. Thus, the electric field predictions from different modeling pipelines need to be investigated to ensure the validity and reproducibility of tDCS modeling results across clinical or translational studies. METHODS: We used T1w structural MRI from 32 healthy volunteer subjects and modeled the electric field distribution for a fronto-temporal tDCS montage. For five different computational modeling pipelines, we quantitatively compared brain tissue segmentation and electric field predicted in whole-brain, brain tissues and target brain regions between the modeling pipelines. RESULTS: Our comparisons at various levels did not reveal any systematic trend with regards to similarity or dissimilarity of electric field predicted in brain tissues and target brain regions. The inconsistent trends in the predicted electric field indicate variation in the procedures, routines and algorithms used within and across the modeling pipelines. CONCLUSION: Our results suggest that studies integrating electric field modeling and clinical outcomes of tDCS will highly depend upon the choice of the modeling pipelines and procedures. We propose that using these pipelines for further research and clinical applications should be subject to careful consideration, and indicate general recommendations.