ABSTRACT Code comprehension and natural language share structural similarities but engage partially distinct neural systems. While language predominantly recruits the classical perisylvian language network, code comprehension has been linked to the multiple‐demand system, associated with mathematical thinking and problem solving. We report the case of a professional software engineer with an IDH‐mutant astrocytoma in the left middle frontal gyrus, a region where these networks converge. To address the potential risk of impairing computer programming abilities during surgery, we implemented a personalized protocol combining fMRI and intraoperative electrical stimulation mapping (ESM), extending the standard language assessment with a Code comprehension task. Intraoperative mapping identified sites in the left inferior frontal cortex that were functionally relevant to the C ode comprehension task but were not identified during standard object‐naming mapping. Complete tumor resection was achieved while preserving these critical regions, with the patient showing intact post‐operative language and programming performances. This single case study suggests that individualized Code comprehension mapping may identify functionally relevant sites not detected by standard object‐naming mapping, supporting the potential clinical value of tailored fMRI–ESM protocols for preserving higher‐order cognitive functions in neurosurgical patients.
ABSTRACT The cerebellum supports motor, cognitive, and affective functions, but the spatial pattern of genetic influence on its morphology and its relevance to anxiety traits remains unclear. Using structural MRI data from monozygotic and dizygotic twins (298 participants), we estimated additive genetic and environmental contributions to cerebellar gray matter volume at lobular and voxel scales. Monozygotic twins showed higher intra‐pair correlations than dizygotic twins across cerebellar regions, indicating genetic influence with marked spatial heterogeneity: effects were strongest in posterior and vermal lobules (notably lobule IX, Crus I/II, and vermis VII–VIII) and more moderate in anterior motor‐related lobules; cerebellar white matter also showed strong genetic influence. Voxel‐wise mapping confirmed this pattern and revealed within‐lobule heterogeneity not captured by lobular averaging, with additive genetic factors explaining ~45% of voxel‐level variance and minimal shared environmental effects. Social (measured via the Liebowitz Social Anxiety Scale‐Self Report; LSAS‐SR) and trait (State–Trait Anxiety Inventory; STAI‐T) anxiety were substantially heritable, whereas phenotypic, genetic, and unique environmental correlations with cerebellar volumes were small and did not survive multiple‐comparisons correction. These results show that genetic influence on cerebellar structure is strongly spatially heterogeneous and scale‐dependent, whereas associations between cerebellar gray matter volume and anxiety severity are weak in this non‐clinical adult sample.
Left hemisphere (LH) strokes often disrupt the language network (LN), causing aphasia. The right hemisphere (RH) has been proposed to contribute to post-stroke aphasia outcomes, but existing findings on RH functional connectivity (FC) changes after LH stroke are inconsistent. This study systematically investigates whether and how the RH FC differs in chronic stroke survivors compared to healthy controls, and whether these differences relate to lesion characteristics and language outcomes. Using a naturalistic movie-watching fMRI paradigm, we compared the RH FC in a large sample of chronic LH stroke survivors (n = 85) to neurologically healthy controls (n = 71). We first used a validated semantic decision fMRI task to identify a core LN and a non-canonical LN (labeled Semantic Network (SN)). We then mapped group FC differences within and between these LH networks and their RH homotopes. Compared to controls, stroke survivors had higher overall within-RH FC and lower within-LH and interhemispheric connectivity. These differences were greater with larger lesions. However, few of the RH FC differences were observed within the RH LN. Rather, most changes were between networks and were distributed widely in the RH. Greater RH connectivity was not associated with lesions or reduced FC in homotopic LH regions, although lesions of LH superior and middle temporal gyri were associated with increased FC between RH SN and LN. RH FC was not associated with behavioral outcomes. These findings show that LH lesions lead to increased RH FC, but the pattern of results suggests that these changes may not reflect meaningful functional reorganization that contributes to language function after stroke. TRIAL REGISTRATION: clinicaltrials.gov NCT04991519.
The postpartum period involves substantial brain changes, but whether these represent pathological aging or adaptive plasticity remains unclear. We applied machine learning-based brain age prediction to quantify postpartum brain aging trajectories in a controlled longitudinal study. Brain age gap, the difference between MRI-predicted and chronological age, was assessed in 55 women (25 postpartum and 30 nulliparous controls). Postpartum women were assessed at approximately 3 and 12 months postpartum; controls were assessed at matched intervals. Postpartum women showed elevated brain age gap at 3 months (2.5 ± 2.7 years) compared with controls (0.3 ± 3.6 years; p = 0.01, Cohen's d = 0.69), which normalized by 12 months. Within-group analysis revealed significant brain age gap decrease in postpartum women (-3.3 ± 3.5 years; p = 0.008), while controls showed minimal change; the between-group difference in longitudinal change did not reach statistical significance (p = 0.12, Cohen's d = -0.58). Across all participants, brain age gap correlated with working memory performance (ρ = -0.30; p = 0.03), depressive symptoms (ρ = 0.38; p = 0.004), and subjective cognitive complaints (ρ = 0.28; p = 0.04) at baseline, providing preliminary evidence of clinical relevance. Explainability analysis using Shapley additive explanations identified subcortical structures as the network most selectively associated with the postpartum elevation in brain age gap at 3 months (p = 0.006, Cohen's d = 0.82), with normalization by 12 months. Together, these findings indicate transient brain age gap elevation in postpartum women without statistical confirmation of a trajectory differing from controls. With brain age gap providing a quantitative index of postpartum brain structural change, these preliminary findings are consistent with adaptive neuroplasticity, suggesting that such change may be temporary rather than reflecting permanent impairment.
Understanding the cortical architecture underlying individual differences in general cognitive ability (GCA) remains a central question in cognitive neuroscience. Prior work has established associations between global brain size and GCA, yet the regional effects and directionality of these relationships remain debated. Using a genetically informed cortical parcellation in 11,289 UK Biobank participants, we examined associations between cortical surface area (SA), cortical thickness (CT), and GCA measured via verbal-numerical reasoning. Total SA showed a robust positive association with GCA. At the regional level, dorsolateral prefrontal and superior temporal SA exhibited the strongest positive associations, which persisted after adjustment for global SA. In contrast, CT showed comparatively modest associations. Using Mendelian randomization (MR) with genome-wide significant genetic instruments, we observed evidence consistent with a bidirectional relationship between total SA and GCA. At the regional level, dorsolateral prefrontal and temporal SA demonstrated evidence of MR-inferred directional effects on GCA, while GCA showed evidence of MR-inferred directional effects on total SA and perisylvian thickness. These findings support a polyregional SA architecture underlying GCA, with prominent contributions from prefrontal and temporal association cortices. Our results refine global brain-GCA models and highlight the value of genetically informed parcellation for identifying regional cortical contributions.
