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      Large-Scale Functional Brain Networks of Maladaptive Childhood Aggression Identified by Connectome-Based Predictive Modeling

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          Abstract

          Disruptions in frontoparietal networks supporting emotion regulation have been long implicated in maladaptive childhood aggression. However, the association of connectivity between large-scale functional networks with aggressive behavior has not been tested. The present study examined whether the functional organization of the connectome predicts severity of aggression in children. This cross-sectional study included a transdiagnostic sample of 100 children with aggressive behavior (27 females) and 29 healthy controls without aggression or psychiatric disorders (13 females). Severity of aggression was indexed by the total score on the parent-rated Reactive-Proactive Aggression Questionnaire. During fMRI, participants completed a face emotion perception task of fearful and calm faces. Connectome-based predictive modeling with internal cross-validation was conducted to identify brain networks that predicted aggression severity. The replication and generalizability of the aggression predictive model was then tested in an independent sample of children from the Adolescent Brain Cognitive Development (ABCD) study. Connectivity predictive of aggression was identified within and between networks implicated in cognitive control (medial frontal, frontoparietal), social functioning (default mode, salience), and emotion processing (subcortical, sensorimotor) (r=0.31, RMSE = 9.05, p=0.005). Out-of-sample replication (p<0.002) and generalization (p=0.007) of findings predicting aggression from the functional connectome was demonstrated in an independent sample of children from the ABCD study (n=1,791; n=1,701). Individual differences in large-scale functional networks contribute to variability in maladaptive aggression in children with psychiatric disorders. Linking these individual differences in the connectome to variation in behavioral phenotypes will advance identification of neural biomarkers of maladaptive childhood aggression to inform targeted treatments.

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          Advances in functional and structural MR image analysis and implementation as FSL.

          The techniques available for the interrogation and analysis of neuroimaging data have a large influence in determining the flexibility, sensitivity, and scope of neuroimaging experiments. The development of such methodologies has allowed investigators to address scientific questions that could not previously be answered and, as such, has become an important research area in its own right. In this paper, we present a review of the research carried out by the Analysis Group at the Oxford Centre for Functional MRI of the Brain (FMRIB). This research has focussed on the development of new methodologies for the analysis of both structural and functional magnetic resonance imaging data. The majority of the research laid out in this paper has been implemented as freely available software tools within FMRIB's Software Library (FSL).
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            Anomalous collapses of Nares Strait ice arches leads to enhanced export of Arctic sea ice

            The ice arches that usually develop at the northern and southern ends of Nares Strait play an important role in modulating the export of Arctic Ocean multi-year sea ice. The Arctic Ocean is evolving towards an ice pack that is younger, thinner, and more mobile and the fate of its multi-year ice is becoming of increasing interest. Here, we use sea ice motion retrievals from Sentinel-1 imagery to report on the recent behavior of these ice arches and the associated ice fluxes. We show that the duration of arch formation has decreased over the past 20 years, while the ice area and volume fluxes along Nares Strait have both increased. These results suggest that a transition is underway towards a state where the formation of these arches will become atypical with a concomitant increase in the export of multi-year ice accelerating the transition towards a younger and thinner Arctic ice pack.
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              The brain's default network: anatomy, function, and relevance to disease.

              Thirty years of brain imaging research has converged to define the brain's default network-a novel and only recently appreciated brain system that participates in internal modes of cognition. Here we synthesize past observations to provide strong evidence that the default network is a specific, anatomically defined brain system preferentially active when individuals are not focused on the external environment. Analysis of connectional anatomy in the monkey supports the presence of an interconnected brain system. Providing insight into function, the default network is active when individuals are engaged in internally focused tasks including autobiographical memory retrieval, envisioning the future, and conceiving the perspectives of others. Probing the functional anatomy of the network in detail reveals that it is best understood as multiple interacting subsystems. The medial temporal lobe subsystem provides information from prior experiences in the form of memories and associations that are the building blocks of mental simulation. The medial prefrontal subsystem facilitates the flexible use of this information during the construction of self-relevant mental simulations. These two subsystems converge on important nodes of integration including the posterior cingulate cortex. The implications of these functional and anatomical observations are discussed in relation to possible adaptive roles of the default network for using past experiences to plan for the future, navigate social interactions, and maximize the utility of moments when we are not otherwise engaged by the external world. We conclude by discussing the relevance of the default network for understanding mental disorders including autism, schizophrenia, and Alzheimer's disease.
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                Author and article information

                Journal
                9607835
                20545
                Mol Psychiatry
                Mol Psychiatry
                Molecular psychiatry
                1359-4184
                1476-5578
                26 September 2021
                February 2022
                25 October 2021
                02 May 2022
                : 27
                : 2
                : 985-999
                Affiliations
                [1 ]Yale University School of Medicine, Child Study Center,
                [2 ]Yale University School of Medicine, Department of Diagnostic Radiology,
                [3 ]Yale University, Department of Psychology
                Author notes

                Authorship Contributions

                Conception: K.I., D.G.S., D.S., G.M.; Design of the work: K.I., D.G.S., D.S., G.M.; Acquisition, analysis, interpretation of data: K.I., D.G.S., D.S., S.N., C.L., G.H.; Manuscript writing and revising: K.I., D.G.S., D.S., M.J.C., G.M., S.N., C.L., G.H.

                [* ] Please address correspondence to: Karim Ibrahim, Psy.D., Yale University School of Medicine, Child Study Center, 230 South Frontage Road, New Haven, CT 06520, Phone: (203) 390-9981, karim.ibrahim@ 123456yale.edu , Denis G. Sukhodolsky, Ph.D., Yale University School of Medicine, Child Study Center, 230 South Frontage Road, New Haven, CT 06520, Phone: (203) 785-6446, denis.sukhodolsky@ 123456yale.edu
                Article
                NIHMS1742513
                10.1038/s41380-021-01317-5
                9035467
                34690348
                dd1fb9d0-f2ca-4825-9a02-181722986c11

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                Categories
                Article

                Molecular medicine
                aggression,brain networks,connectome,dorsolateral prefrontal cortex,disruptive behavior disorders,predictive modeling

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