A comparison of methods to harmonize cortical thickness measurements across scanners and sites

Results of neuroimaging datasets aggregated from multiple sites may be biased by site-specific profiles in participants\u27 demographic and clinical characteristics, as well as MRI acquisition protocols and scanning platforms. We compared the impact of four different harmonization methods on results obtained from analyses of cortical thickness data: (1) linear mixed-effects model (LME) that models site-specific random intercepts (LM

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

Transplantation of gut microbiota from old mice into young healthy mice reduces lean mass but not bone mass

Aging is associated with low bone and lean mass as well as alterations in the gut microbiota (GM). In this study, we determined whether the reduced bone mass and relative lean mass observed in old mice could be transferred to healthy young mice by GM transplantation (GMT). GM from old (21-month-old) and young adult (5-month-old) donors was used to colonize germ-free (GF) mice in three separate studies involving still growing 5- or 11-week-old recipients and 17-week-old recipients with minimal bone growth. The GM of the recipient mice was similar to that of the donors, demonstrating successful GMT. GM from old mice did not have statistically significant effects on bone mass or bone strength, but significantly reduced the lean mass percentage of still growing recipient mice when compared with recipients of GM from young adult mice. The levels of propionate in the cecum of mice receiving old donor GM were significantly lower than those in mice receiving young adult donor GM

Physiological mechanisms of sustained fumagillin-induced weight loss

Current obesity interventions suffer from lack of durable effects and undesirable complications. Fumagillin, an inhibitor of methionine aminopeptidase-2, causes weight loss by reducing food intake, but with effects on weight that are superior to pair-feeding. Here, we show that feeding of rats on a high-fat diet supplemented with fumagillin (HF/FG) suppresses the aggressive feeding observed in pair-fed controls (HF/PF) and alters expression of circadian genes relative to the HF/PF group. Multiple indices of reduced energy expenditure are observed in HF/FG but not HF/PF rats. HF/FG rats also exhibit changes in gut hormones linked to food intake, increased energy harvest by gut microbiota, and caloric spilling in the urine. Studies in gnotobiotic mice reveal that effects of fumagillin on energy expenditure but not feeding behavior may be mediated by the gut microbiota. In sum, fumagillin engages weight loss-inducing behavioral and physiologic circuits distinct from those activated by simple caloric restriction

SARS-CoV-2 ferritin nanoparticle vaccines elicit broad SARS coronavirus immunogenicity

The need for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) next-generation vaccines has been highlighted by the rise of variants of concern (VoCs) and the long-term threat of emerging coronaviruses. Here, we design and characterize four categories of engineered nanoparticle immunogens that recapitulate the structural and antigenic properties of the prefusion SARS-CoV-2 spike (S), S1, and receptor-binding domain (RBD). These immunogens induce robust S binding, ACE2 inhibition, and authentic and pseudovirus neutralizing antibodies against SARS-CoV-2. A spike-ferritin nanoparticle (SpFN) vaccine elicits neutralizing titers (I

CFA2: a Context-Free Approach to Control-Flow Analysis

In a functional language, the dominant control-flow mechanism is function call and return. Most higher-order flow analyses, including k-CFA, do not handle call and return well: they remember only a bounded number of pending calls because they approximate programs with control-flow graphs. Call/return mismatch introduces precision-degrading spurious control-flow paths and increases the analysis time. We describe CFA2, the first flow analysis with precise call/return matching in the presence of higher-order functions and tail calls. We formulate CFA2 as an abstract interpretation of programs in continuation-passing style and describe a sound and complete summarization algorithm for our abstract semantics. A preliminary evaluation shows that CFA2 gives more accurate data-flow information than 0CFA and 1CFA.Comment: LMCS 7 (2:3) 201