Effect of Regional Brain Activity Following Repeat Transcranial Magnetic Stimulation in SCA3: A Secondary Analysis of a Randomized Clinical Trial.

Repetitive transcranial magnetic stimulation (rTMS), a noninvasive neuroregulatory technique used to treat neurodegenerative diseases, holds promise for spinocerebellar ataxia type 3 (SCA3) treatment, although its efficacy and mechanisms remain unclear. This study aims to observe the short-term impact of cerebellar rTMS on motor function in SCA3 patients and utilize resting-state functional magnetic resonance imaging (RS-fMRI) to assess potential therapeutic mechanisms. Twenty-two SCA3 patients were randomly assigned to receive actual rTMS (AC group, n = 11, three men and eight women; age 32-55 years) or sham rTMS (SH group, n = 11, three men and eight women; age 26-58 years). Both groups underwent cerebellar rTMS or sham rTMS daily for 15 days. The primary outcome measured was the ICARS scores and parameters for regional brain activity. Compared to baseline, ICARS scores decreased more significantly in the AC group than in the SH group after the 15-day intervention. Imaging indicators revealed increased Amplitude of Low Frequency Fluctuation (ALFF) values in the posterior cerebellar lobe and cerebellar tonsil following AC stimulation. This study suggests that rTMS enhances motor functions in SCA3 patients by modulating the excitability of specific brain regions and associated pathways, reinforcing the potential clinical utility of rTMS in SCA3 treatment. The Chinese Clinical Trial Registry identifier is ChiCTR1800020133.

Altered brain white matter structural motor network in spinocerebellar ataxia type 3.

OBJECTIVES: Spinocerebellar ataxia type 3 is a disorder within the brain network. However, the relationship between the brain network and disease severity is still unclear. This study aims to investigate changes in the white matter (WM) structural motor network, both in preclinical and ataxic stages, and its relationship with disease severity. METHODS: For this study, 20 ataxic, 20 preclinical SCA3 patients, and 20 healthy controls were recruited and received MRI scans. Disease severity was quantified using the SARA and ICARS scores. The WM motor structural network was created using probabilistic fiber tracking and was analyzed using graph theory and network-based statistics at global, nodal, and edge levels. In addition, the correlations between network topological measures and disease duration or clinical scores were analyzed. RESULTS: Preclinical patients showed increasing assortativity of the motor network, altered subnetwork including 12 edges of 11 nodes, and 5 brain regions presenting reduced nodal strength. In ataxic patients assortativity of the motor network also increased, but global efficiency, global strength, and transitivity decreased. Ataxic patients showed a wider altered subnetwork and a higher number of reduced nodal strengths. A negative correlation between the transitivity of the motor network and SARA and ICARS scores was observed in ataxic patients. INTERPRETATION: Changes to the WM motor network in SCA3 start before ataxia onset, and WM motor network involvement increases with disease progression. Global network topological measures of the WM motor network appear to be a promising image biomarker for disease severity. This study provides new insights into the pathophysiology of disease in SCA3/MJD.

MRI-Based Grading of Clear Cell Renal Cell Carcinoma Using a Machine Learning Classifier.

OBJECTIVE: To develop a machine learning (ML)-based classifier for discriminating between low-grade (ISUP I-II) and high-grade (ISUP III-IV) clear cell renal cell carcinomas (ccRCCs) using MRI textures. MATERIALS AND METHODS: We retrospectively evaluated a total of 99 patients (with 61 low-grade and 38 high-grade ccRCCs), who were randomly divided into a training set (n = 70) and a validation set (n = 29). Regions of interest (ROIs) of all tumors were manually drawn three times by a radiologist at the maximum lesion level of the cross-sectional CMP sequence images. The quantitative texture analysis software, MaZda, was used to extract texture features, including histograms, co-occurrence matrixes, run-length matrixes, gradient models, and autoregressive models. Reproducibility of the texture features was assessed with the intra-class correlation coefficient (ICC). Features were chosen based on their importance coefficients in a random forest model, while the multi-layer perceptron algorithm was used to build a classifier on the training set, which was later evaluated with the validation set. RESULTS: The ICCs of 257 texture features were equal to or higher than 0.80 (0.828-0.998. Six features, namely Kurtosis, 135dr_RLNonUni, Horzl_GLevNonU, 135dr_GLevNonU, S(4,4)Entropy, and S(0,5)SumEntrp, were chosen to develop the multi-layer perceptron classifier. A three-layer perceptron model, which has 229 nodes in the hidden layer, was trained on the training set. The accuracy of the model was 95.7% with the training set and 86.2% with the validation set. The areas under the receiver operating curves were 0.997 and 0.758 for the training and validation sets, respectively. CONCLUSIONS: A machine learning-based grading model was developed that can aid in the clinical diagnosis of clear cell renal cell carcinoma using MRI images.