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Kennedy, S. H.

Publications and source records attributed to Kennedy, S. H..

3 recordsLinked to original sources

Cerebello-cerebral Functional Connectivity Networks in Major Depressive Disorder: A CAN-BIND-1 Study Report

ObjectiveNeuroimaging studies have demonstrated aberrant structure and function of the "cognitive-affective cerebellum" in Major Depressive Disorder (MDD), although the specific role of the cerebello-cerebral circuitry in this population remains largely uninvestigated. The objective of this study was to delineate the role of cerebellar functional networks in depression. MethodsA total of 308 unmedicated participants completed resting-state functional magnetic resonance imaging scans, of which 247 (148 MDD; 99 Healthy Controls, HC) were suitable for this study. Seed-based resting-state functional connectivity (RsFc) analysis was performed using three cerebellar regions of interest (ROIs): ROI1 corresponded to default mode network (DMN) / inattentive processing; ROI2 corresponded to attentional networks including frontoparietal, dorsal attention, and ventral attention; ROI3 corresponded to motor processing. These ROIs were delineated based on prior functional gradient analyses of the cerebellum. A general linear model was used to perform within-group and between-group comparisons. ResultsIn comparison to HC, participants with MDD displayed increased RsFc within the cerebello-cerebral DMN (ROI1) and significantly elevated RsFc between the cerebellar ROI1 and bilateral angular gyrus at a voxel threshold (p < 0.001, two-tailed) and at a cluster level (p < 0.05, FDR-corrected). Group differences were non-significant for ROI2 and ROI3. ConclusionsThese results contribute to the development of a systems neuroscience approach to the diagnosis and treatment of MDD. Specifically, our findings confirm previously reported associations between MDD, DMN, and cerebellum, and highlight the promising role of these functional and anatomical locations for the development of novel imaging-based biomarkers and targets for neuromodulation therapies.

neuroscience

Neuron-derived extracellular vesicles extracted from plasma show altered size and miRNA cargo as a function of antidepressant drug response

Previous work has demonstrated that microRNAs (miRNAs) change as a function of antidepressant treatment (ADT) response. However, it is unclear how representative these peripherally detected miRNA changes are to those occurring in the brain. This study aimed to use peripherally extracted neuron-derived extracellular vesicles (NDEVs) to circumvent these limitations and investigate neuronal miRNA changes associated with antidepressant response. Samples were collected at two time points (baseline and after 8 weeks of follow-up) from depressed patients who responded (N=20) and did not respond (N=20) to escitalopram treatment, as well as controls (N=20). Total extracellular vesicles (EVs) were extracted from plasma, and then further enriched for NDEVs by immunoprecipitation with L1CAM. EV size was measured using tunable resistive pulse sensing, and NDEV miRNA cargo was extracted and sequenced. Subsequently, studies in cell lines and postmortem tissue were conducted. Characterization of NDEVs revealed they were smaller than other EVs isolated from plasma (p<0.0001), had brain-specific neuronal markers, and contained miRNAs enriched for brain functions (p<0.0001) Furthermore, NDEVs from depressed patients were smaller than controls (p<0.05), and NDEV size increased with ADT response (p<0.01). Finally, changes in NDEV cargo, specifically changes in miR-21-5p, miR-30d-5p and miR-486-5p together (p<0.01), were associated with ADT response. Targets of these three miRNAs were altered in brain tissue from depressed individuals (p<0.05). Together, this study indicates that changes in peripherally isolated NDEVs can act as both a clinically accessible and informative biomarker of ADT response specifically through size and cargo.

neuroscience

Visual QC Protocol for FreeSurfer Cortical Parcellations from Anatomical MRI

Quality control of morphometric neuroimaging data is essential to improve reproducibility. Owing to the complexity of neuroimaging data and subsequently the interpretation of their results, visual inspection by trained raters is the most reliable way to perform quality control. Here, we present a protocol for visual quality control of the anatomical accuracy of FreeSurfer parcellations, based on an easy-to-use open source tool called VisualQC. We comprehensively evaluate its utility in terms of error detection rate and inter-rater reliability on two large multi-site datasets, and discuss site differences in error patterns. This evaluation shows that VisualQC is a practically viable protocol for community adoption.

neuroscience