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Background: Extracting multiregional radiomic features from multiparametric MRI for predicting pretreatment survival in isocitrate dehydrogenase (IDH) wild-type glioblastoma (GBM) patients is a promising approach. Methods: MRI data from 49 IDH wild-type glioblastoma patients pre-treatment were utilized. Diffusion and perfusion maps were generated, and tumor subregions segmented. Radiomic features were extracted for each tissue type and map. Feature selection on 1862 radiomic features identified 25 significant features. The Cox proportional-hazards model with LASSO regularization was used to perform survival analysis. Internal and external validation used a 38-patient training cohort and an 11-patient validation cohort. Statistical significance was set at p < 0.05. Results: Age and six radiomic features (shape and first and second order) from T1W, diffusion, and perfusion maps contributed to the final model. Findings suggest that a small necrotic subregion, inhomogeneous vascularization in the solid non-enhancing subregion, and edema-related tissue damage in the enhancing and edema subregions are linked to poor survival. The model’s C-Index was 0.66 (95% C.I. 0.54–0.80). External validation demonstrated good accuracy (AUC > 0.65) at all time points. Conclusions: Radiomics analysis, utilizing segmented perfusion and diffusion maps, provide predictive indicators of survival in IDH wild-type glioblastoma patients, revealing associations with microstructural and vascular heterogeneity in the tumor.

IntroductionThe acknowledged role of external rewards in chronic stroke rehabilitation, offering positive reinforcement and motivation, has significantly contributed to patient engagement and perseverance. However, the exploration of self-reward’s importance in this context remains limited. This study aims to investigate the functional connectivity of the ventral tegmental area (VTA), a key node in the brain’s reward circuitry, during motor task-based rehabilitation and its correlation with the recovery process.MethodsTwelve right-handed healthy volunteers (4 men, 8 women, aged 57.4 ± 11.3 years) and twelve chronic stroke patients (5 men, 7 women, aged 48.1 ± 11.1 years) with clinically significant right-sided motor impairment (mean FM-UE score of 27.6 ± 8.7) participated. The analysis employed the CONN toolbox to assess the association between motor tasks and VTA connectivity using psychophysiological interaction (PPI).ResultsPPI analysis revealed motor-dependent changes in VTA connectivity, particularly with regions within the motor circuitry, cerebellum, and prefrontal cortex. Notably, stronger connectivity between the ipsilesional VTA and cerebellum was observed in healthy controls compared to chronic stroke patients, highlighting the importance of VTA-cerebellum interactions in motor function. Stroke patients’ motor performance was associated with VTA modulation in areas related to both motor tasks and reward processing, emphasizing the role of self-reward processes in rehabilitation. Changes in VTA influence on motor circuitry were linked to improvements in motor performance resulting from rehabilitation.DiscussionOur findings underscore the potential of neuroimaging techniques in quantifying and predicting rehabilitation outcomes by examining self-reward processes. The observed associations between VTA connectivity and motor performance in both healthy and stroke-affected individuals emphasize the role of psychological factors, particularly self-reward, in the rehabilitation process. This study contributes valuable insights into the intricate interplay between reward circuits and motor function, highlighting the importance of addressing psychological dimensions in neurorehabilitation strategies.

Although the relationship between corticospinal tract (CST) fiber degeneration and motor outcome after stroke has been established, the relationship of sensorimotor cortical areas with CST fibers has not been clarified. Also limited research has been conducted on how abnormalities in brain structural networks are related to motor recovery. To address these gaps in knowledge, we conducted a diffusion tensor imaging (DTI) study with 12 chronic stroke patients (CSPs) and 12 age-matched healthy controls (HCs). We compared fractional anisotropy (FA) and mean diffusivity (MD) in 60 CST segments using the probabilistic sensorimotor area tract template (SMATT). Least Absolute Shrinkage and Selection Operator (LASSO) regressions were used to select independent predictors of Fugl-Meyer upper extremity (FM-UE) scores among FA and MD values of SMATT regions. The Graph Theoretical Network Analysis Toolbox was used to assess the structural network of each subject's brain. Global and nodal metrics were calculated, compared between the groups, and correlated with FM-UE scores. Mann–Whitney U-tests revealed reduced FA values in CSPs, compared to HCs, in many ipsilesional SMATT regions and in two contralesional regions. Mean FA value of the left (L.) primary motor cortex (M1)/supplementary motor area (SMA) region was predictive of FM-UE score (P = 0.004). Mean MD values for the L. M1/ventral premotor cortex (PMv) region (P = 0.001) and L. PMv/SMA region (P = 0.001) were found to be significant predictors of FM-UE scores. Network efficiency was the only global metric found to be reduced in CSPs (P = 0.006 vs. HCs). Nodal efficiency of the L. hippocampus, L. parahippocampal gyrus, L. fusiform gyrus (P = 0.001), and nodal local efficiency of the L. supramarginal gyrus (P < 0.001) were reduced in CSPs relative to HCs. No graph metric was associated with FM-UE scores. In conclusion, the integrity of CSTs connected to M1, SMA, and PMv were shown to be independent predictors of motor performance in CSPs, while stroke-induced topological changes in the brain's structural connectome may not be. A sensorimotor cortex-specific tract template can refine CST degeneration data and the relationship of CST degeneration with motor performance.

Alzheimer’s Disease (AD) is a neurogenerative disorder and the most common type of dementia with a rapidly increasing world prevalence. In this paper, the ability of several statistical and spectral features to detect AD from electroencephalographic (EEG) recordings is evaluated. For this purpose, clinical EEG recordings from 14 patients with AD (8 with mild AD and 6 with moderate AD) and 10 healthy, age-matched individuals are analyzed. The EEG signals are initially segmented in nonoverlapping epochs of different lengths ranging from 5 s to 12 s. Then, a group of statistical and spectral features calculated for each EEG rhythm (δ, θ, α, β, and γ) are extracted, forming the feature vector that trained and tested a Random Forests classifier. Six classification problems are addressed, including the discrimination from whole-brain dynamics and separately from specific brain regions in order to highlight any alterations of the cortical regions. The results indicated a high accuracy ranging from 88.79% to 96.78% for whole-brain classification. Also, the classification accuracy was higher at the posterior and central regions than at the frontal area and the right side of temporal lobe for all classification problems.

The usefulness of diffusion-weighted magnetic resonance imaging (DWI) in the evaluation of scrotal pathology has recently been reported. A standard reference of normal testicular apparent diffusion coefficient (ADC) values and their variations with age is necessary when interpreting normal testicular anatomy and pathology. We evaluated 147 normal testes using DWI, including 71 testes from 53 men aged 20-39 years (group 1), 67 testes from 42 men aged 40-69 years (group 2) and nine testes from six men older than 70 years (group 3). DWI was performed along the axial plane, using a single shot, multislice spin-echo planar diffusion pulse sequence and b-values of 0 and 900 s mm−2 . The mean and standard deviation of the ADC values of normal testicular parenchyma were calculated for each age group separately. Analysis of variance (ANOVA) followed by post hoc analysis (Dunnett T3) was used for statistical purposes. The ADC values (× 10−3 mm 2 s−1 ) of normal testicular tissue were different among age groups (group 1: 1.08 ± 0.13; group 2: 1.15 ± 0.15 and group 3: 1.31 ± 0.22). ANOVA revealed differences in mean ADC among age groups (F = 11.391, P < 0.001). Post hoc analysis showed differences between groups 1 and 2 (P = 0.008) and between groups 1 and 3 (P = 0.043), but not between groups 2 and 3 (P = 0.197). Our findings suggest that ADC values of normal testicular tissue increase with advancing age.