Summary: Researchers identified a large-scale association between multiple coordinated blood leukocyte gene coexpression modules and the multivariate fMRI response to speech. Associated coexpression modules were enriched in genes that are broadly expressed in the brain and many other tissues. These coexpression modules were also enriched in ASD-associated, prenatal, human-specific, and language-relevant genes. This work highlights distinctive neurobiology in ASD subtypes with different early language outcomes that is present well before such outcomes are known.
Summary: Previously, they gained factor scores using factor analysis to CBCL of ABCD data. With this hierarchical structure level, they tried to figure out the association between these factors and resting-state functional connectivity. Using the hierarchical linear model (HLM), they found a significant increment in variance with the p-factor model & 3-factor(internalizing, externalizing, and neurodevelopmental) model.
Summary: This study aims to develop and test the generalizability, specificity, and clinical relevance of a functional brain network-based marker for a well-defined feature of mind-wandering. The result was that SITUT is represented within a common pattern of brain network interactions across multiple time scales and contexts.
Summary: This study shows us that the contributions of subcomponents of visuomotor activities have not been studied in detail (i.e., The contributions of subcomponents of visuomotor actions have not been explored in detail). Here, the Authors designed a Kinematic control experiment using hand. And they conducted selectivity analysis and compared it. They found/elucidated the different subcomponents of hand actions and the roles of specific brain regions in their computation.
Summary: They utilized machine learning models to find associations between functional topography and four correlated dimensions of psychopathology and overall psychopathology (general psychopathology factor; p-factor).
Summary: A CNN model of the ventral stream was used to predict responses to the ROIs: FFA, PPA, and EBA. The model accurately predicted activations and the result was shown by synthesized stimuli using GAN. Moreover, they generated importance map for photographs where each ROI showed difference
Summary: This study uses their recently developed Spatio-temporal fMRI constrained EEG source imaging approach to perform source analysis on a simultaneous EEG-fMRI emotion regulation paradigm. They found cortical dynamics underlying negative emotional responses and reappraisal-based regulation using a newly developed multimodal EEG-fMRI integration technique. By using this method they could get detailed timecourses regional activities and causality analyses based on cortical current density could be identified.
Summary: They propose a novel framework to combine features corresponding to functional magnetic resonance imaging including dynamic functional network connectivity (dFNC) and SNP data from schizophrenia patients and healthy controls. For each subject, both the functional and SNP data are selected as features for a parallel ICA based imaging-genomic framework. They also assessed the correlations between the polygenic risk scores (PRS) and both dFNC and SNP components’ loading parameters.
Summary: They combined linear regression with CCA in a coupled manner to extract discriminative features for schizophrenia that are co-expressed in the fMRI and DNA methylation data (epigenetic data).
Summary: In this article, they tried to find a relationship between family conflict, parental monitoring and children’s behavior problems and cognitive scores. They utilized sMRI, CBCL and KSADS to get 20 behavioral problems scores and 10 cognitive scores. They visualized correlation between these criteria and showed structural differences according to these scores. Furthermore, they performed longitudinal association analysis with baseline & 1 year later data. Utilizing CBCL scores into account in my study later seems reasonable.