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Biology subjects

Quarmley, M.

Publications and source records attributed to Quarmley, M..

2 recordsLinked to original sources

The Cerebellum and Striatum in Reward Processing: Caring About Being Right vs. Caring About Reward

Emerging evidence indicates the cerebellum contributes to cognitive functions including social reward processing, yet its specific role relative to established reward regions like the ventral striatum remains undefined. We hypothesized the cerebellum would respond equivalently to both positive and negative social rewards. This prediction is grounded in classical findings that the cerebellum operates via supervised learning mechanisms that rely on error signals rather than traditional reward-based reinforcement. Using fMRI, we examined adolescents and young adults during a social prediction task where participants forecasted others opinions of them and received accuracy feedback. Findings reveal that both the ventral striatum and a subregion of the posterior cerebellum (Crus I and II) were sensitive to social rewards. However, unlike the ventral striatum, the cerebellum exhibited a more uniform response to feedback, treating correct predictions about being liked and disliked in a similar manner. No age-related differences were observed. These findings suggest the cerebellum processes social rewards distinctly from the ventral striatum, likely reflecting its computational emphasis on prediction errors rather than reward valence. This functional distinction advances our understanding of cerebellar contributions to social cognition and learning mechanisms.

neuroscience↗

Data-driven Assessment of Structural Image Quality

Data quality is increasingly recognized as one of the most important confounding factors in brain imaging research. It is particularly important for studies of brain development, where age is systematically related to in-scanner motion and data quality. Prior work has demonstrated that in-scanner head motion biases estimates of structural neuroimaging measures. However, objective measures of data quality are not available for most structural brain images. Here we sought to identify quantitative measures of data quality for T1-weighted volumes, describe how such measures of quality relate to cortical thickness, and delineate how this in turn may bias inference regarding associations with age in youth. Three highly-trained raters provided manual ratings of 1,840 raw T1-weighted volumes. These images included a training set of 1,065 images from Philadelphia Neurodevelopmental Cohort (PNC), a test set of 533 images from the PNC, as well as an external test set of 242 adults acquired on a different scanner. Manual ratings were compared to automated quality measures provided by the Preprocessed Connectomes Projects Quality Assurance Protocol (QAP), as well as FreeSurfers Euler number, which summarizes the topological complexity of the reconstructed cortical surface. Results revealed that the Euler number was consistently correlated with manual ratings across samples. Furthermore, the Euler number could be used to identify images scored \"unusable\" by human raters with a high degree of accuracy (AUC: 0.98-0.99), and outperformed proxy measures from functional timeseries acquired in the same scanning session. The Euler number also was significantly related to cortical thickness in a regionally heterogeneous pattern that was consistent across datasets and replicated prior results. Finally, data quality both inflated and obscured associations with age during adolescence. Taken together, these results indicate that reliable measures of data quality can be automatically derived from T1-weighted volumes, and that failing to control for data quality can systematically bias the results of studies of brain maturation.

bioinformatics↗