Personalized functional imaging-guided rTMS on the superior frontal gyrus for post-stroke aphasia: A randomized sham-controlled trial.

BACKGROUND: Aphasia affects approximately one-third of stroke patients and yet its rehabilitation outcomes are often unsatisfactory. More effective strategies are needed to promote recovery. OBJECTIVE: We aimed to examine the efficacy and safety of the theta-burst stimulation (TBS) on the language area in the superior frontal gyrus (SFG) localized by personalized functional imaging, in facilitating post-stroke aphasia recovery. METHODS: This randomized sham-controlled trial uses a parallel design (intermittent TBS [iTBS] in ipsilesional hemisphere vs. continuous TBS [cTBS] in contralesional hemisphere vs. sham group). Participants had aphasia symptoms resulting from their first stroke in the left hemisphere at least one month prior. Participants received three-week speech-language therapy coupled with either active or sham stimulation applied to the left or right SFG. The primary outcome was the change in Western Aphasia Battery-Revised (WAB-R) aphasia quotient after the three-week treatment. The secondary outcome was WAB-R aphasia quotient improvement after one week of treatment. RESULTS: Ninety-seven patients were screened between January 2021 and January 2022, 45 of whom were randomized and 44 received intervention (15 in each active group, 14 in sham). Both iTBS (estimated difference = 14.75, p < 0.001) and cTBS (estimated difference = 13.43, p < 0.001) groups showed significantly greater improvement than sham stimulation after the 3-week intervention and immediately after one week of treatment (p's < 0.001). The adverse events observed were similar across groups. A seizure was recorded three days after the termination of the treatment in the iTBS group. CONCLUSION: The stimulation showed high efficacy and SFG is a promising stimulation target for post-stroke language recovery.

Fast cortical surface reconstruction from MRI using deep learning.

Reconstructing cortical surfaces from structural magnetic resonance imaging (MRI) is a prerequisite for surface-based functional and anatomical image analyses. Conventional algorithms for cortical surface reconstruction are computationally inefficient and typically take several hours for each subject, causing a bottleneck in applications when a fast turnaround time is needed. To address this challenge, we propose a fast cortical surface reconstruction (FastCSR) pipeline by leveraging deep machine learning. We trained our model to learn an implicit representation of the cortical surface in volumetric space, termed the "level set representation". A fast volumetric topology correction method and a topology-preserving surface mesh extraction procedure were employed to reconstruct the cortical surface based on the level set representation. Using 1-mm isotropic T1-weighted images, the FastCSR pipeline was able to reconstruct a subject's cortical surfaces within 5 min with comparable surface quality, which is approximately 47 times faster than the traditional FreeSurfer pipeline. The advantage of FastCSR becomes even more apparent when processing high-resolution images. Importantly, the model demonstrated good generalizability in previously unseen data and showed high test-retest reliability in cortical morphometrics and anatomical parcellations. Finally, FastCSR was robust to images with compromised quality or with distortions caused by lesions. This fast and robust pipeline for cortical surface reconstruction may facilitate large-scale neuroimaging studies and has potential in clinical applications wherein brain images may be compromised.