A Hierarchical Taxonomy of Psychopathology Can Transform Mental Health Research

For more than a century, research on psychopathology has focused on categorical diagnoses. Although this work has produced major discoveries, growing evidence points to the superiority of a dimensional approach to the science of mental illness. Here we outline one such dimensional system—the Hierarchical Taxonomy of Psychopathology (HiTOP)—that is based on empirical patterns of co-occurrence among psychological symptoms. We highlight key ways in which this framework can advance mental-health research, and we provide some heuristics for using HiTOP to test theories of psychopathology. We then review emerging evidence that supports the value of a hierarchical, dimensional model of mental illness across diverse research areas in psychological science. These new data suggest that the HiTOP system has the potential to accelerate and improve research on mental-health problems as well as efforts to more effectively assess, prevent, and treat mental illness.FSW – Publicaties zonder aanstelling Universiteit Leide

Meta-analytic modeling reveals differential functional connectivity of the medial and lateral orbitofrontal cortex

The orbitofrontal cortex (OFC) is implicated in a broad range of behaviors and neuropsychiatric disorders. Anatomical tracing studies in nonhuman primates reveal differences in connectivity across subregions of the OFC, but data on the connectivity of the human OFC remain limited. We applied meta-analytic connectivity modeling in order to examine which brain regions are most frequently coactivated with the medial and lateral portions of the OFC in published functional neuroimaging studies. The analysis revealed a clear divergence in the pattern of connectivity for the medial OFC (mOFC) and lateral OFC (lOFC) regions. The lOFC showed coactivations with a network of prefrontal regions and areas involved in cognitive functions including language and memory. In contrast, the mOFC showed connectivity with default mode, autonomic, and limbic regions. Convergent patterns of coactivations were observed in the amygdala, hippocampus, striatum, and thalamus. A small number of regions showed connectivity specific to the anterior or posterior sectors of the OFC. Task domains involving memory, semantic processing, face processing, and reward were additionally analyzed in order to identify the different patterns of OFC functional connectivity associated with specific cognitive and affective processes. These data provide a framework for understanding the human OFC's position within widespread functional networks

Technology Enablers for Big Data, Multi-Stage Analysis in Medical Image Processing

Big data medical image processing applications involving multi-stage analysis often exhibit significant variability in processing times ranging from a few seconds to several days. Moreover, due to the sequential nature of executing the analysis stages enforced by traditional software technologies and platforms, any errors in the pipeline are only detected at the later stages despite the sources of errors predominantly being the highly compute-intensive first stage. This wastes precious computing resources and incurs prohibitively higher costs for re-executing the application. The medical image processing community to date remains largely unaware of these issues and continues to use traditional high-performance computing clusters, which incur a high operating cost due to the use of dedicated resources and expensive centralized file systems. To overcome these challenges, this paper proposes an alternative approach for multi-stage analysis in medical image processing by using the Apache Hadoop ecosystem and offering it as a service in the cloud. We make the following contributions. First, we propose a concurrent pipeline execution framework and an associated semi-automatic, real-time monitoring and checkpointing framework that can detect outliers and achieve quality assurance without having to completely execute the expensive first stage of processing thereby expediting the entire multi-stage analysis. Second, we present a simulator to rapidly estimate the execution time for a given multi-stage analysis, which can aid the users in deciding the appropriate approach for their use cases. We conduct empirical evaluation of our framework and show that it requires 76.75% lesser wall time and 29.22% lesser resource time compared to the traditional approach that lacks such a quality assurance mechanism. ?? 2018 IEEE

Harmonization of white and gray matter features in diffusion microarchitecture for cross-sectional studies

Understanding of the specific processes involved in the development of brain microarchitecture and how these are altered by genetic, cognitive, or environmental factors is a key to more effective and efficient interventions. With the increasing number of publicly available neuroimaging databases, there is an opportunity to combine large-scale imaging studies to increase the power of statistical analyses to test common biological hypotheses. However, cross-study, cross-sectional analyses are confounded by inter-scanner variability that can cause both spatially and anatomically dependent signal aberrations. In particular, scanner-related differences in the diffusion-weighted magnetic resonance imaging (DW-MRI) signal are substantially different in tissue types like cortical/subcortical gray matter and white matter. Recent studies have shown effective harmonization using the ComBat technique (adopted from genomics) to address inter-site variability in white matter using diffusion tensor imaging (DTI) microstructure indices like fractional anisotropy (FA) or mean diffusivity (MD). In this study, we propose (1) to apply the correction at voxel level using tract-based spatial statistics (TBSS) in FA, (2) to correct variability across scanners with different gradient strengths in DTI, and (3) to apply the ComBat technique to advanced DW-MRI models, i.e., neurite orientation dispersion and density imaging (NODDI), to correct for variability of orientation dispersion index (ODI) in gray matter using gray matter-based spatial statistics tool (GSBSS). We show that the biological variability with age is retained or improved while correcting for variability across scanners. ?? Springer Nature Switzerland AG 2019

Redefining phenotypes to advance psychiatric genetics: Implications from hierarchical taxonomy of psychopathology

Stress and Psychopatholog