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Resting state low frequency (<0.08 Hz) fluctuations in MR timecourses that are temporally correlated between functionally related areas have been observed in recent studies. These fluctuations have been assumed to arise from spontaneous blood oxygenation level-dependent (BOLD) oscillations. This work examines the T2* characteristics of the low frequency fluctuations (functional connectivity) and compares them to those of task activation induced signal changes. Multi-echo spiral data were fit using a mono-exponential decay model to generate T2* and intensity (I0) parameter timecourses. Resultant correlation maps show that both functional connectivity and BOLD activation modulate T2*, not I0. Regression analysis also finds that both have a linear dependence on echo time. Thus, functional connectivity and task activation MR signal changes appear to arise from the same BOLD-related origins. [Copyright &y& Elsevier]

The Adolescent Brain Cognitive Development (ABCD) Study is an ongoing, nationwide study of the effects of environmental influences on behavioral and brain development in adolescents. The main objective of the study is to recruit and assess over eleven thousand 9-10-year-olds and follow them over the course of 10 years to characterize normative brain and cognitive development, the many factors that influence brain development, and the effects of those factors on mental health and other outcomes. The study employs state-of-the-art multimodal brain imaging, cognitive and clinical assessments, bioassays, and careful assessment of substance use, environment, psychopathological symptoms, and social functioning. The data is a resource of unprecedented scale and depth for studying typical and atypical development. The aim of this manuscript is to describe the baseline neuroimaging processing and subject-level analysis methods used by ABCD. Processing and analyses include modality-specific corrections for distortions and motion, brain segmentation and cortical surface reconstruction derived from structural magnetic resonance imaging (sMRI), analysis of brain microstructure using diffusion MRI (dMRI), task-related analysis of functional MRI (fMRI), and functional connectivity analysis of resting-state fMRI. This manuscript serves as a methodological reference for users of publicly shared neuroimaging data from the ABCD Study., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Neural activation patterns in the ventral visual cortex in response to different categories of visual stimuli (e.g., faces vs. houses) are less selective, or distinctive, in older adults than in younger adults, a phenomenon known as age-related neural dedifferentiation. In this study, we investigated whether neural dedifferentiation extends to the auditory cortex. Inspired by previous animal work, we also investigated whether individual differences in GABA are associated with individual differences in neural distinctiveness in humans. 20 healthy young adults (ages 18-29) and 23 healthy older adults (over 65) completed a functional magnetic resonance imaging (fMRI) scan, during which neural activity was estimated while they listened to music and foreign speech. GABA levels in the auditory, ventrovisual and sensorimotor cortex were estimated in the same individuals in a separate magnetic resonance spectroscopy (MRS) scan. Relative to the younger adults, the older adults exhibited both (1) less distinct activation patterns for music vs. speech stimuli and (2) lower GABA levels in the auditory cortex. Also, individual differences in auditory GABA levels (but not ventrovisual or sensorimotor GABA levels) were associated with individual differences in neural distinctiveness in the auditory cortex in the older adults. These results demonstrate that age-related neural dedifferentiation extends to the auditory cortex and suggest that declining GABA levels may play a role in neural dedifferentiation in older adults., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Major Depressive Disorder (MDD) is characterized by rumination. Prior research suggests that resting-state brain activation reflects rumination when depressed individuals are not task engaged. However, no study has directly tested this. Here we investigated whether resting-state epochs differ from induced ruminative states for healthy and depressed individuals. Most previous research on resting-state networks comes from seed-based analyses with the posterior cingulate cortex (PCC). By contrast, we examined resting state connectivity by using the complete multivariate connectivity profile (i.e., connections across all brain nodes) and by comparing these results to seeded analyses. We find that unconstrained resting-state intervals differ from active rumination states in strength of connectivity and that overall connectivity was higher for healthy vs. depressed individuals. Relationships between connectivity and subjective mood (i.e., behavior) were strongly observed during induced rumination epochs. Furthermore, connectivity patterns that related to subjective mood were strikingly different for MDD and healthy control (HC) groups suggesting different mood regulation mechanisms., (Copyright © 2014 Elsevier Inc. All rights reserved.)

In this fMRI study, we explore the connectivity among brain regions in a shape-from-motion task using the causal mapping analysis of structural equation modeling (SEM). An important distinction of our approach is that we have adapted SEM from its traditional role in confirmatory analysis to provide utility as an exploratory mapping technique. Our current approaches include (I) detecting brain regions that fit well in a hypothesized neural network model, and (II) identifying the best connectivity model at each brain region. We demonstrate that SEM effectively detects the dorsal and ventral visual pathways from the covariance structure in fMRI data, confirming previous neuroscience results. Further, our SEM mapping methodology found that the two pathways interact through specific cortical areas such as the superior lateral occipital cortex in the perception of shape from motion.

The present fMRI study explores the connectivity among motor areas in a bimanual coordination task using the analysis framework of structural equation modeling (SEM). During bimanual finger tapping at different frequency ratios, temporal correlations of activations between left/right primary motor cortices (MI), left/right PMdc (caudal dorsal premotor area) and supplementary motor cortex (SMA) were detected and used as inputs to the SEM analysis. SEM was extended from its traditional role as a confirmatory analysis to be used as an exploratory technique to determine the most statistically significant connectivity model given a set of cortical areas based on anatomic constraints. The resultant network exhibits coupling from left MI to right MI, links from both PMs to the two MIs, a negative interaction from left PM to right PM, and functional influence from SMA to right MI and right PM, revealing contributions of these areas to bimanual coordination.