Neuroimaging clinics of North America
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This review summarizes current knowledge obtained from psychoradiological studies of posttraumatic stress disorder (PTSD). We first focus on 3 key anatomic structures (hippocampus, amygdala, and medial prefrontal cortex) and the functional circuits to which they contribute. In addition, we discuss the triple-network model, a widely accepted neurobiological model of PTSD that explains the vast majority of neuroimaging findings, as well as their interactions and relationships to functional disruptions in PTSD.
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Schizophrenia is a chronic psychotic disorder with a lifetime prevalence of about 1%. Onset is typically in adolescence or early adulthood; characteristic symptoms include positive symptoms, negative symptoms, and impairments in cognition. ⋯ These abnormalities are not sufficiently specific to be of diagnostic value, but there may be a role for imaging techniques to provide predictions of outcome. Incorporating multimodal imaging datasets using machine learning approaches may offer better diagnostic and predictive value in schizophrenia.
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Neuroimaging Clin. N. Am. · Feb 2020
ReviewWidespread Morphometric Abnormalities in Major Depression: Neuroplasticity and Potential for Biomarker Development.
Major depression is common and debilitating. Identifying neurobiological subtypes that comprise the disorder and predict clinical outcome are key challenges. Genetic and environmental factors leading to major depression are expressed in neural structure and function. ⋯ MR imaging observable abnormalities reflect cytoarchitectonic alterations within a local neuroendocrine milieu with systemic effects. Multivariate pattern analysis offers the potential to identify the neurobiological subtypes and predictors of clinical outcome. It is essential to characterize disease heterogeneity by incorporating data-driven inductive and symptom-based deductive approaches in an iterative process.
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Neuroimaging Clin. N. Am. · Feb 2020
ReviewResting-State Functional Connectivity: Signal Origins and Analytic Methods.
Resting state functional connectivity (RSFC) has been widely studied in functional magnetic resonance imaging (fMRI) and is observed by a significant temporal correlation of spontaneous low-frequency signal fluctuations (SLFs) both within and across hemispheres during rest. Different hypotheses of RSFC include the biophysical origin hypothesis and cognitive origin hypothesis, which show that the role of SLFs and RSFC is still not completely understood. ⋯ RSFC data analysis methods include time domain analysis, seed-based correlation, regional homogeneity, and principal and independent component analyses. Despite advances in RSFC, the authors also discuss challenges and limitations, ranging from head motion to methodological limitations.