Breiter, H. C. | Search bioRxiv

bioRxiv2023

Sustained activation in basal ganglia and cerebellum after repetitive movement in a non-task-specific dystonia

We previously observed sustained fMRI BOLD signal in the basal ganglia in focal hand dystonia patients after a repetitive finger tapping task. Since this was observed in a task-specific dystonia, for which excessive task repetition may play a role in pathogenesis, in the current study we asked if this effect would be observed in a focal dystonia (cervical dystonia [CD]) that is not considered task-specific or thought to result from overuse. We evaluated fMRI BOLD signal time courses before, during, and after the finger tapping task in CD patients. We observed patient/control differences in post-tapping BOLD signal in left putamen and left cerebellum during the non-dominant (left) hand tapping condition, reflecting abnormally sustained BOLD signal in CD. BOLD signals in left putamen and cerebellum were also abnormally elevated in CD during tapping itself and escalated as tapping was repeated. There were no cerebellar differences in the previously studied FHD cohort, either during or after tapping. We conclude that some elements of pathogenesis and/or pathophysiology associated with motor task execution/repetition may not be limited to task-specific dystonias, but there may be regional differences in these effects across dystonias, associated with different types of motor control programs.

neuroscience↗

bioRxiv2020

Novel age-dependent cortico-subcortical morphologic interactions predict fluid intelligence: A multi-cohort geometric deep learning study.

Brain structure is tightly coupled with brain functions, but it remains unclear how cognition is related to brain morphology, and what is consistent across neurodevelopment. In this work, we developed graph convolutional neural networks (gCNNs) to predict Fluid Intelligence (Gf) from shapes of cortical ribbons and subcortical structures. T1-weighted MRIs from two independent cohorts, the Human Connectome Project (HCP; age: 28.81{+/-}3.70) and the Adolescent Brain Cognitive Development Study (ABCD; age: 9.93{+/-}0.62) were independently analyzed. Cortical and subcortical surfaces were extracted and modeled as surface meshes. Three gCNNs were trained and evaluated using six-fold nested cross-validation. Overall, combining cortical and subcortical surfaces yielded the best predictions on both HCP (R=0.454) and ABCD datasets (R=0.314), and outperformed the current literature. Across both datasets, the morphometry of the amygdala and hippocampus, along with temporal, parietal and cingulate cortex consistently drove the prediction of Gf, suggesting a novel reframing of the morphometry underlying Gf.

neuroscience↗