4 research outputs found
Meaning in the noise: Neural signal variability in major depressive disorder
Clinical research has revealed aberrant activity and connectivity in default mode (DMN), frontoparietal (FPN), and salience (SN) network regions in major depressive disorder (MDD). Recent functional magnetic resonance imaging (fMRI) studies suggest that variability in brain activity, or blood oxygen level-dependent (BOLD) signal variability, may be an important novel predictor of psychopathology. However, to our knowledge, no studies have yet determined the relationship between resting-state BOLD signal variability and MDD nor applied BOLD signal variability features to the classification of MDD history using machine learning (ML). Thus, the current study had three aims: (i) to investigate the differences in the voxel-wise resting-state BOLD signal variability between varying depression histories; (ii) to examine the relationship between depressive symptom severity and resting-state BOLD signal variability; (iii) to explore the capability of resting-state BOLD signal variability to classify individuals by depression history. Using resting-state neuroimaging data for 79 women collected as a part of a larger NIH R01-funded study, we conducted (i) a one-way between-subjects ANCOVA, (ii) a multivariate multiple regression, and (iii) applied BOLD signal variability and average BOLD signal features to a supervised ML model. First, results indicated that individuals with any history of depression had significantly decreased BOLD signal variability in the left and right cerebellum and right parietal cortex in comparison to those with no depression history (pFWE \u3c .05). Second, and consistent with the results for depression history, depression severity was associated with reduced BOLD signal variability in the cerebellum. Lastly, a random forest model classified participant depression history with 76% accuracy, with BOLD signal variability features showing greater discriminative power than average BOLD signal features. These findings provide support for resting-state BOLD signal variability as a novel marker of neural dysfunction and implicate decreased neural signal variability as a neurobiological mechanism of depression
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Predicting Task and Subject Differences with Functional Connectivity and Blood-Oxygen-Level-Dependent Variability.
Previous research has found that functional connectivity (FC) can accurately predict the identity of a subject performing a task and the type of task being performed. These results are replicated using a large data set collected at the Ohio State University Center for Cognitive and Behavioral Brain Imaging. This work introduces a novel perspective on task and subject identity prediction: blood-oxygen-level-dependent variability (BV). Conceptually, BV is a region-specific measure based on the variance within each brain region. BV is simple to compute, interpret, and visualize. This work shows that both FC and BV are predictive of task and subject, even across scanning sessions separated by multiple years. Subject differences rather than task differences account for the majority of changes in BV and FC. Similar to results in FC, BV is reduced during cognitive tasks relative to rest