Neuroimaging-based classification of PTSD using data-driven computational approaches: A multisite big data study from the ENIGMA-PGC PTSD consortium

BACKGROUND: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. METHODS: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. RESULTS: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60 % test AUC for s-MRI, 59 % for rs-fMRI and 56 % for d-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75 % AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. CONCLUSION: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable

Controversies and progress on standardization of large-scale brain network nomenclature

Progress in scientific disciplines is accompanied by standardization of terminology. Network neuroscience, at the level of macroscale organization of the brain, is beginning to confront the challenges associated with developing a taxonomy of its fundamental explanatory constructs. The Workgroup for HArmonized Taxonomy of NETworks (WHATNET) was formed in 2020 as an Organization for Human Brain Mapping (OHBM)-endorsed best practices committee to provide recommendations on points of consensus, identify open questions, and highlight areas of ongoing debate in the service of moving the field toward standardized reporting of network neuroscience results. The committee conducted a survey to catalog current practices in large-scale brain network nomenclature. A few well-known network names (e.g., default mode network) dominated responses to the survey, and a number of illuminating points of disagreement emerged. We summarize survey results and provide initial considerations and recommendations from the workgroup. This perspective piece includes a selective review of challenges to this enterprise, including (1) network scale, resolution, and hierarchies; (2) interindividual variability of networks; (3) dynamics and nonstationarity of networks; (4) consideration of network affiliations of subcortical structures; and (5) consideration of multimodal information. We close with minimal reporting guidelines for the cognitive and network neuroscience communities to adopt

Brain hubs defined in the group do not overlap with regions of high inter-individual variability

Connector \u27hubs\u27 are brain regions with links to multiple networks. These regions are hypothesized to play a critical role in brain function. While hubs are often identified based on group-average functional magnetic resonance imaging (fMRI) data, there is considerable inter-subject variation in the functional connectivity profiles of the brain, especially in association regions where hubs tend to be located. Here we investigated how group hubs are related to locations of inter-individual variability. To answer this question, we examined inter-individual variation at group-level hubs in both the Midnight Scan Club and Human Connectome Project datasets. The top group hubs defined based on the participation coefficient did not overlap strongly with the most prominent regions of inter-individual variation (termed \u27variants\u27 in prior work). These hubs have relatively strong similarity across participants and consistent cross-network profiles, similar to what was seen for many other areas of cortex. Consistency across participants was further improved when these hubs were allowed to shift slightly in local position. Thus, our results demonstrate that the top group hubs defined with the participation coefficient are generally consistent across people, suggesting they may represent conserved cross-network bridges. More caution is warranted with alternative hub measures, such as community density (which are based on spatial proximity to network borders) and intermediate hub regions which show higher correspondence to locations of individual variability

Infrared Spectra and Ab Initio Calculations for the F-−(CH4)n (n = 1−8) Anion Clusters

Infrared spectra of mass-selected F-−(CH4)n (n = 1−8) clusters are recorded in the CH stretching region (2500−3100 cm-1). Spectra for the n = 1−3 clusters are interpreted with the aid of ab initio calculations at the MP2/6-311++G(2df 2p) level, which suggest that the CH4ligands bind to F- by equivalent, linear hydrogen bonds. Anharmonic frequencies for CH4 and F-−CH4 are determined using the vibrational self-consistent field method with second-order perturbation theory correction. The n = 1 complex is predicted to have a C3v structure with a single CH group hydrogen bonded to F-. Its spectrum exhibits a parallel band associated with a stretching vibration of the hydrogen-bonded CH group that is red-shifted by 380 cm-1 from the ν1 band of free CH4 and a perpendicular band associated with the asymmetric stretching motion of the nonbonded CH groups, slightly red-shifted from the ν3 band of free CH4. As nincreases, additional vibrational bands appear as a result of Fermi resonances between the hydrogen-bonded CH stretching vibrational mode and the 2ν4 overtone and ν2 + ν4combination levels of the methane solvent molecules. For clusters with n ≤ 8, it appears that the CH4 molecules are accommodated in the first solvation shell, each being attached to the F- anion by equivalent hydrogen bonds

Resting-state cortical hubs in youth organize into four categories

During childhood, neural systems supporting high-level cognitive processes undergo periods of rapid growth and refinement, which rely on the successful coordination of activation across the brain. Some coordination occurs via cortical hubs-brain regions that coactivate with functional networks other than their own. Adult cortical hubs map into three distinct profiles, but less is known about hub categories during development, when critical improvement in cognition occurs. We identify four distinct hub categories in a large youth sample (n = 567, ages 8.5-17.2), each exhibiting more diverse connectivity profiles than adults. Youth hubs integrating control-sensory processing split into two distinct categories (visual control and auditory/motor control), whereas adult hubs unite under one. This split suggests a need for segregating sensory stimuli while functional networks are experiencing rapid development. Functional coactivation strength for youth control-processing hubs are associated with task performance, suggesting a specialized role in routing sensory information to and from the brain\u27s control system

The community structure of functional brain networks exhibits scale-specific patterns of inter- and intra-subject variability

The network organization of the human brain varies across individuals, changes with development and aging, and differs in disease. Discovering the major dimensions along which this variability is displayed remains a central goal of both neuroscience and clinical medicine. Such efforts can be usefully framed within the context of the brain\u27s modular network organization, which can be assessed quantitatively using computational techniques and extended for the purposes of multi-scale analysis, dimensionality reduction, and biomarker generation. Although the concept of modularity and its utility in describing brain network organization is clear, principled methods for comparing multi-scale communities across individuals and time are surprisingly lacking. Here, we present a method that uses multi-layer networks to simultaneously discover the modular structure of many subjects at once. This method builds upon the well-known multi-layer modularity maximization technique, and provides a viable and principled tool for studying differences in network communities across individuals and within individuals across time. We test this method on two datasets and identify consistent patterns of inter-subject community variability, demonstrating that this variability - which would be undetectable using past approaches - is associated with measures of cognitive performance. In general, the multi-layer, multi-subject framework proposed here represents an advance over current approaches by straighforwardly mapping community assignments across subjects and holds promise for future investigations of inter-subject community variation in clinical populations or as a result of task constraints

Aerosol climate feedback due to decadal increases in Southern Hemisphere wind speeds

Observations indicate that the westerly jet in the Southern Hemisphere troposphere is accelerating. Using a global aerosol model we estimate that the increase in wind speed of 0.45 + /- 0.2 m s(-1) decade(-1) at 50-65 degrees S since the early 1980s caused a higher sea spray flux, resulting in an increase of cloud condensation nucleus concentrations of more than 85% in some regions, and of 22% on average between 50 and 65 degrees S. These fractional increases are similar in magnitude to the decreases over many northern hemisphere land areas due to changes in air pollution over the same period. The change in cloud drop concentrations causes an increase in cloud reflectivity and a summertime radiative forcing between at 50 and 65 degrees S comparable in magnitude but acting against that from greenhouse gas forcing over the same time period, and thus represents a substantial negative climate feedback. However, recovery of Antarctic ozone depletion in the next two decades will likely cause a fall in wind speeds, a decrease in cloud drop concentration and a correspondingly weaker cloud feedback

Discovery and genotyping of structural variation from long-read haploid genome sequence data

In an effort to more fully understand the full spectrum of human genetic variation, we generated deep single-molecule, real-time (SMRT) sequencing data from two haploid human genomes. By using an assembly-based approach (SMRT-SV), we systematically assessed each genome independently for structural variants (SVs) and indels resolving the sequence structure of 461,553 genetic variants from 2 bp to 28 kbp in length. We find that &gt;89% of these variants have been missed as part of analysis of the 1000 Genomes Project even after adjusting for more common variants (MAF &gt; 1%). We estimate that this theoretical human diploid differs by as much as ∼16 Mbp with respect to the human reference, with long-read sequencing data providing a fivefold increase in sensitivity for genetic variants ranging in size from 7 bp to 1 kbp compared with short-read sequence data. Although a large fraction of genetic variants were not detected by short-read approaches, once the alternate allele is sequence-resolved, we show that 61% of SVs can be genotyped in short-read sequence data sets with high accuracy. Uncoupling discovery from genotyping thus allows for the majority of this missed common variation to be genotyped in the human population. Interestingly, when we repeat SV detection on a pseudodiploid genome constructed in silico by merging the two haploids, we find that ∼59% of the heterozygous SVs are no longer detected by SMRT-SV. These results indicate that haploid resolution of long-read sequencing data will significantly increase sensitivity of SV detection.</jats:p