Schizophrenia is associated with widespread alterations in subcortical brain structure.While analytic methods have enabled more detailed morphometric characterization, findings are often equivocal. In this meta-analysis, we employed the harmonized ENIGMA shape analysis protocols to collaboratively investigate subcortical brain structure shape differences between individuals with schizophrenia and healthy control participants. The study analyzed data from 2,833 individuals with schizophrenia and 3,929 healthy control participants contributed by 21 worldwide research groups participating in the ENIGMA Schizophrenia Working Group. Harmonized shape analysis protocols were applied to each site's data independently for bilateral hippocampus, amygdala, caudate, accumbens, putamen, pallidum, and thalamus obtained from T1-weighted structural MRI scans. Mass univariate meta-analyses revealed moreconcave-than-convex shape differences in the hippocampus, amygdala, accumbens, and thalamus in individuals with schizophrenia compared with control participants, more-convex-than-concave shape differences in the putamen and pallidum, and both concave and convex shape differences in the caudate. Patterns of exaggerated asymmetry were observed across the hippocampus, amygdala, and thalamus in individuals with schizophrenia compared to control participants, while diminished asymmetry encompassed ventral striatum and ventral and dorsal thalamus. Our analyses also revealed that higher chlorpromazine dose equivalents and increased positive symptom levels were associated with patterns of contiguous convex shape differences across multiple subcortical structures. Findings from our shape meta-analysis suggest that common neurobiological mechanisms may contribute to gray matter reduction across multiple subcortical regions, thus enhancing our understanding of the nature of network disorganization in schizophrenia.

While most of computational annotation approaches are sequence-based, threading methods are becoming increasingly attractive because of predicted structural information that could uncover the underlying function. However, threading tools are generally compute-intensive and the number of protein sequences from even small genomes such as prokaryotes is large typically containing many thousands, prohibiting their application as a genome-wide structural systems biology tool. To leverage its utility, we have developed a pipeline for eThread—a meta-threading protein structure modeling tool, that can use computational resources efficiently and effectively. We employ a pilot-based approach that supports seamless data and task-level parallelism and manages large variation in workload and computational requirements. Our scalable pipeline is deployed on Amazon EC2 and can efficiently select resources based upon task requirements. We present runtime analysis to characterize computational complexity of eThread and EC2 infrastructure. Based on results, we suggest a pathway to an optimized solution with respect to metrics such as time-to-solution or cost-to-solution. Our eThread pipeline can scale to support a large number of sequences and is expected to be a viable solution for genome-scale structural bioinformatics and structure-based annotation, particularly, amenable for small genomes such as prokaryotes. The developed pipeline is easily extensible to other types of distributed cyberinfrastructure.

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target the default mode network, a set of interconnected brain areas implicated in monitoring and memory retrieval. Conversely, variants of FTD target different networks; for example, the behavioral variant of FTD, which is characterized by impulsivity and disordered emotional regulation, targets a brain network implicated in reward and motivation. Methods: We used the Parkinson's Progression Markers Initiative (PPMI), a large open-source database of imaging and clinical data in de novo PD patients. We performed deformation based morphometry and independent component analysis to identify areas showing atrophy in PD patients compared to control subjects. Results: Striatum, basal forebrain, amygdala, hippocampus, insula and anterior cingulate cortex demonstrated atrophy in proportion to clinical disease severity, consistent with the scheme proposed by Braak from postmortem data. We also show that these regions form a connected intrinsic network. Moreover, we show that disease patterns follow brain connectomics, compatible with an epicenter in the substantia nigra. Finally we find that cognitive impairment at visit 2 is associated with progression of atrophy in the entorhinal cortex. Conclusions: PD, like AD and FTD, demonstrates a pattern of disease atrophy compatible with the network spread hypothesis. Disease spread from the PD network to the AD network may herald the onset of dementia.