Personalized functional imaging identifies brain stimulation target for a patient with trauma-induced functional disruption

Chinese Verbal Fluency Deficiency in Temporal Lobe Epilepsy with and without Hippocampal Sclerosis: A Multiscale Study

To test a Chinese character version of the phonemic verbal fluency task in patients with temporal lobe epilepsy (TLE) and assess the verbal fluency deficiency pattern in TLE with and without hippocampal sclerosis, a cross-sectional study was conducted including 30 patients with TLE and hippocampal sclerosis (TLE-HS), 28 patients with TLE and without hippocampal sclerosis (TLE-NHS), and 29 demographically matched healthy controls (HC). Both sexes were enrolled. Participants finished a Chinese character verbal fluency (VFC) task during functional MRI. The activation/deactivation maps, functional connectivity, degree centrality, and community features of the left frontal and temporal regions were compared. A neural network classification model was applied to differentiate TLE-HS and TLE-NHS using functional statistics. The VFC scores were correlated with semantic fluency in HC while correlated with phonemic fluency in TLE-NHS. Activation and deactivation deficiency was observed in TLE-HS and TLE-NHS (p < 0.001, k ≥ 10). Functional connectivity, degree centrality, and community features of anterior inferior temporal gyri were impaired in TLE-HS and retained or even enhanced in TLE-NHS (p < 0.05, FDR-corrected). The functional connectivity was correlated with phonemic fluency (p < 0.05, FDR-corrected). The neural network classification reached an area under the curve of 0.90 in diagnosing hippocampal sclerosis. The VFC task is a Chinese phonemic verbal fluency task suitable for clinical application in TLE. During the VFC task, functional connectivity of phonemic circuits was impaired in TLE-HS and was enhanced in TLE-NHS, representing a compensative phonemic searching strategy applied by patients with TLE-NHS.

Personalised functional imaging-guided multitarget continuous theta burst stimulation for post-stroke aphasia: study protocol for a randomised controlled trial

INTRODUCTION

Continuous theta burst stimulation (cTBS), a form of repetitive transcranial magnetic stimulation (rTMS), targeting the language network in the right hemisphere of post-stroke aphasia (PSA) patients shows promising results in clinical trials. However, existing PSA studies have focused on single-target rTMS, leaving unexplored the potential benefits of multitarget brain stimulation. Consequently, there is a need for a randomised clinical trial aimed to evaluate the efficacy and safety of cTBS targeting on multiple critical nodes in the language network for PSA.

METHODS AND ANALYSIS

This is a prospective, multicentre, double-blind, two-arm parallel-group, sham-controlled randomised trial. The study will include a total of 60 participants who will be randomly assigned in a 1:1 ratio to either the active cTBS group or the sham cTBS group. Using precision resting-state functional MRI for each participant, we will map personalised language networks and design personalised targets in the inferior frontal gyrus, superior temporal gyrus and superior frontal gyrus. Participants will undergo a 3-week cTBS intervention targeting the three personalised targets, coupled with speech and language therapy. The primary outcome is the change in the Western Aphasia Battery-Revised aphasia quotient score among participants after a 3-week treatment. Secondary outcomes include Boston Diagnostic Aphasia Examination severity ratings, Token Test and the Chinese-version of the Stroke and Aphasia Quality of Life Scale 39-generic version.

ETHICS AND DISSEMINATION

The study has been approved by the ethics committees of Affiliated Hospital of Hebei University, Hebei General Hospital and Affiliated Hospital of Chengde Medical University. The findings of this study will be reported in peer-reviewed scientific journals.

TRIAL REGISTRATION NUMBER

The study has been registered on ClinicalTrials.gov (NCT05957445).

SUGAR: Spherical ultrafast graph attention framework for cortical surface registration

Cortical surface registration plays a crucial role in aligning cortical functional and anatomical features across individuals. However, conventional registration algorithms are computationally inefficient. Recently, learning-based registration algorithms have emerged as a promising solution, significantly improving processing efficiency. Nonetheless, there remains a gap in the development of a learning-based method that exceeds the state-of-the-art conventional methods simultaneously in computational efficiency, registration accuracy, and distortion control, despite the theoretically greater representational capabilities of deep learning approaches. To address the challenge, we present SUGAR, a unified unsupervised deep-learning framework for both rigid and non-rigid registration. SUGAR incorporates a U-Net-based spherical graph attention network and leverages the Euler angle representation for deformation. In addition to the similarity loss, we introduce fold and multiple distortion losses to preserve topology and minimize various types of distortions. Furthermore, we propose a data augmentation strategy specifically tailored for spherical surface registration to enhance the registration performance. Through extensive evaluation involving over 10,000 scans from 7 diverse datasets, we showed that our framework exhibits comparable or superior registration performance in accuracy, distortion, and test-retest reliability compared to conventional and learning-based methods. Additionally, SUGAR achieves remarkable sub-second processing times, offering a notable speed-up of approximately 12,000 times in registering 9,000 subjects from the UK Biobank dataset in just 32 min. This combination of high registration performance and accelerated processing time may greatly benefit large-scale neuroimaging studies.

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.

Computing personalized brain functional networks from fMRI using self-supervised deep learning

A novel self-supervised deep learning (DL) method is developed to compute personalized brain functional networks (FNs) for characterizing brain functional neuroanatomy based on functional MRI (fMRI). Specifically, a DL model of convolutional neural networks with an encoder-decoder architecture is developed to compute personalized FNs directly from fMRI data. The DL model is trained to optimize functional homogeneity of personalized FNs without utilizing any external supervision in an end-to-end fashion. We demonstrate that a DL model trained on fMRI scans from the Human Connectome Project can identify personalized FNs and generalizes well across four different datasets. We further demonstrate that the identified personalized FNs are informative for predicting individual differences in behavior, brain development, and schizophrenia status. Taken together, the self-supervised DL allows for rapid, generalizable computation of personalized FNs.

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.