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Introduction: Mild-to-moderate traumatic brain injury (mmTBI) that lead to deficits in balance and gait are difficult to resolve through standard therapy protocols, and these deficits can severely impact a patient's quality of life. Recently, translingual neural stimulation (TLNS) has emerged as a potential therapy for mmTBI-related balance and gait deficits by inducing neuroplastic changes in the brain gray matter structure. However, it is still unclear how interactions within and between functional networks in brain are affected by TLNS. The current study aimed to extend our previous resting-state functional connectivity (RSFC) study investigating the effects of TLNS intervention on outcome measures related to gait and balance. Methods: An experimental PoNS device was utilized to deliver the TLNS. The 2-week TLNS intervention program, specifically stimulation during focused physical therapy focused on recovery of gait and balance, included twice-daily treatment in the laboratory and the same program at home during the intervening weekend. The resting-state fMRI datasets at pre- and post-interventions were collected by 3T MRI scanner with nine mmTBI patients. All participants also received both Sensory Organization Test (SOT) and Dynamic Gait Index (DGI) testing pre- and post-intervention as part of the behavioral assessment. Results: Compared to baseline, TLNS intervention led to statistically significant improvements in both the SOT [ t (8) = 2.742, p = 0.028] and the DGI [ t (8) = 2.855, p = 0.024] scores. Moreover, significant increases in intra- and inter-network RSFC were observed, particularly within the visual, default mode, dorsal attention, frontoparietal (FPN), and somatosensory (SMN) networks. Additionally, there were significant correlations between the SOT and inter-network FC [between FPN and SMN, r (9) = –0.784, p = 0.012] and between the DGI and intra-network FC [within SMN, r (9) = 0.728, p = 0.026]. Discussion: These findings suggest that TLNS intervention is an effective in increasing somatosensory processing, vestibular-visual interaction, executive control and flexible shifting, and TLNS may be an effective approach to inducing brain network plasticity and may serve as a potential therapy for mmTBI-related gait and balance deficits. [ABSTRACT FROM AUTHOR]

Introduction: Crohn's disease (CD), one of the main phenotypes of inflammatory bowel disease (IBD), can affect any part of the gastrointestinal tract. It can impact the function of gastrointestinal secretions, as well as increasing the intestinal permeability leading to an aberrant immunological response and subsequent intestinal inflammation. Studies have reported anatomical and functional brain changes in Crohn's Disease patients (CDs), possibly due to increased inflammatory markers and microglial cells that play key roles in communicating between the brain, gut, and systemic immune system. To date, no studies have demonstrated similarities between morphological brain changes seen in IBD and brain morphometry observed in older healthy controls. Methods: For the present study, twelve young CDs in remission (M = 26.08 years, SD = 4.9 years, 7 male) were recruited from an IBD Clinic. Data from 12 young age-matched healthy controls (HCs) (24.5 years, SD = 3.6 years, 8 male) and 12 older HCs (59 years, SD = 8 years, 8 male), previously collected for a different study under a similar MR protocol, were analyzed as controls. T1 weighted images and structural image processing techniques were used to extract surface-based brain measures, to test our hypothesis that young CDs have different brain surface morphometry than their age-matched young HCs and furthermore, appear more similar to older HCs. The phonemic verbal fluency (VF) task (the Controlled Oral Word Association Test, COWAT) (Benton, 1976) was administered to test verbal cognitive ability and executive control. Results/Discussion: On the whole, CDs had more brain regions with differences in brain morphometry measures when compared to the young HCs as compared to the old HCs, suggesting that CD has an effect on the brain that makes it appear more similar to old HCs. Additionally, our study demonstrates this atypical brain morphometry is associated with function on a cognitive task. These results suggest that even younger CDs may be showing some evidence of structural brain changes that demonstrate increased resemblance to older HC brains rather than their similarly aged healthy counterparts. [ABSTRACT FROM AUTHOR]

Throughout adulthood, the brain undergoes an array of structural and functional changes during the typical aging process. These changes involve decreased brain volume, reduced synaptic density, and alterations in white matter (WM). Although there have been some previous neuroimaging studies that have measured the ability of adult language production and its correlations to brain function, structural gray matter volume, and functional differences between young and old adults, the structural role of WM in adult language production in individuals across the life span remains to be thoroughly elucidated. This study selected 38 young adults and 35 old adults for diffusion tensor imaging (DTI) and performed the Controlled Oral Word Association Test to assess verbal fluency (VF). Tract-Based Spatial Statistics were employed to evaluate the voxel-based group differences of diffusion metrics for the values of fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), and local diffusion homogeneity (LDH) in 12 WM regions of interest associated with language production. To investigate group differences on each DTI metric, an analysis of covariance (ANCOVA) controlling for sex and education level was performed, and the statistical threshold was considered at p < 0.00083 (0.05/60 labels) after Bonferroni correction for multiple comparisons. Significant differences in DTI metrics identified in the ANCOVA were used to perform correlation analyses with VF scores. Compared to the old adults, the young adults had significantly (1) increased FA values on the bilateral anterior corona radiata (ACR); (2) decreased MD values on the right ACR, but increased MD on the left uncinate fasciculus (UF); and (3) decreased RD on the bilateral ACR. There were no significant differences between the groups for AD or LDH. Moreover, the old adults had only a significant correlation between the VF score and the MD on the left UF. There were no significant correlations between VF score and DTI metrics in the young adults. This study adds to the growing body of research that WM areas involved in language production are sensitive to aging. [ABSTRACT FROM AUTHOR]

Background: By reinterpreting the difference between a model prediction and calendar age, machine learning algorithms allow for estimation of "brain age" using MRI. There is growing evidence for sex differences in the influence of APOE genotype (evaluated as APOE ε4 carrier status) on Alzheimer's disease (AD) endophenotypes at various stages of disease. The purpose of this study is to examine the influence of an APOE neuropathology‐based score (APOE npscore) on the brain aging estimated from deep learning. Methods: Brain age from 274 participants (Fig. 1) in the Wisconsin‐ADRC was estimated from T1w‐MRI using a publicly available deep learning model called two‐stage‐age‐network (TSAN) that was pre‐trained on over four thousand scans from OASIS, ADNI‐I and PAC‐2019. TSAN uses a novel rank‐based loss along with mean squared loss and makes predictions in two steps which does not require a posteriori bias correction. This is a significant improvement from prior deep learning models in reducing the "regression‐to‐mean bias" typically present in brain age models. APOE genotypes (ε2ε2, ε2ε3, ε3ε3, ε2ε4, ε3ε4, ε4ε4) were weighted by the log(odds‐ratio) from a large study of autopsy‐confirmed AD cases/controls, providing a variable representing the relative amount of risk across APOE genotypes. Mood's median test was used to examine the sex differences between the excess brain aging (EBA) as a function of the APOE npscore. Results: Fig. 2 shows the brain age predictions. Fig. 3 shows that there are significant differences between females and males in the EBA for the APOE npscore <0. Median EBA for both males and females is <0 when APOE npscore <0 suggesting protective effects of ε2 on brain aging. The median EBA >0 for both the sexes when APOE npscore ≥0. Conclusion: We report sex differences in the influence of APOE genotype on brain aging. There is heterogeneity in the differences within the ε4+ (APOE npscore >0) and ε4‐ (APOE npscore ≤0) groups. These sex differences may not be observed when using just ε4 carrier status instead of the APOE npscore. This provides evidence for a shift in paradigm for the way we analyze APOE genotype because of latent nuances of the ε4‐status influence on AD outcomes. [ABSTRACT FROM AUTHOR]

Objective Methods Results Significance Application of cluster analytic procedures has advanced understanding of the cognitive heterogeneity inherent in diverse epilepsy syndromes and the associated clinical and neuroimaging features. Application of this unsupervised machine learning approach to the neuropsychological performance of persons with juvenile myoclonic epilepsy (JME) has yet to be attempted, which is the intent of this investigation.A total of 77 JME participants, 19 unaffected siblings, and 44 unrelated controls, 12 to 25 years of age, were administered a comprehensive neuropsychological battery (intelligence, language, memory, executive function, and processing speed), which was subjected to factor analysis followed by K‐means clustering of the resultant factor scores. Identified cognitive phenotypes were characterized and related to clinical, family, sociodemographic, and cortical and subcortical imaging features.Factor analysis revealed three underlying cognitive dimensions (general ability, speed/response inhibition, and learning/memory), with JME participants performing worse than unrelated controls across all factor scores, and unaffected siblings performing worse than unrelated controls on the general mental ability and learning/memory factors, with no JME vs sibling differences. K‐means clustering of the factor scores revealed three latent groups including above average (31.4% of participants), average (52.1%), and abnormal performance (16.4%). Participant groups differed in their distributions across the latent groups (p < 0.001), with 23% JME, 22% siblings, and 2% unrelated controls in the abnormal performance group; and 18% JME, 21% siblings, and 59% unrelated controls in the above average group. Clinical epilepsy variables were unassociated with cluster membership, whereas family factors (lower parental education) and abnormally increased thickness and/or volume in the frontal, parietal, and temporal‐occipital regions were associated with the abnormal cognition group.Distinct cognitive phenotypes characterize the spectrum of neuropsychological performance of patients with JME for which there is familial (sibling) aggregation. Phenotypic membership was associated with parental (education) and imaging characteristics (increased cortical thickness and volume) but not basic clinical seizure features. [ABSTRACT FROM AUTHOR]

Objective: Juvenile myoclonic epilepsy (JME) is a prevalent genetic generalized epilepsy with linked abnormalities in cognition, behavior, and brain structure. Well recognized is the potential for advancing understanding of the epigenetic contributions to the neurobehavioral complications of JME, but to date there has been no examination of the role of socioeconomic disadvantage in regard to the cognitive and brain health of JME, which is the focus of this investigation., Methods: Seventy-seven patients with JME and 44 unrelated controls underwent neuropsychological assessment, structural neuroimaging, and clinical interview to delineate epilepsy history and aspects of family status. The Area Deprivation Index characterized the presence and degree of neighborhood disadvantage, which was examined in relation to cognitive factor scores underlying a comprehensive neuropsychological test battery, academic metrics, integrity of brain structure, and family characteristics., Results: JME participants resided in neighborhoods associated with significantly more socioeconomic disadvantage, which was associated with significantly poorer performance across all three cognitive factor scores and reading fluency. JME was associated with significant reduction of total subcortical gray matter (GM) but not total cortical gray or white matter volumes. Among controls, participants residing in more advantaged areas exhibited increased volumes of total subcortical GM and diverse subcortical structures as well as areas of increased cortical thickness and volume in frontal/prefrontal regions, findings that were compromised or not evident in JME, raising the possibility of disease-related attenuation of socioeconomic advantage., Significance: Socioeconomic disadvantage in JME is associated with adverse effects on cognitive and academic status, whereas socioeconomic advantage in controls is associated with increased brain volumes and thickness, markers of brain health that were largely attenuated or absent in JME. The associations detected here argue for the need to better integrate the social determinants of health with genetic and epigenetic factors in advancing understanding of cognitive and brain health in JME., (© 2025 The Author(s). Epilepsia published by Wiley Periodicals LLC on behalf of International League Against Epilepsy.)

Unknown in the clinical neuropsychology of Juvenile Myoclonic Epilepsy (JME) is the impact of the disorder on the nature of the relationships of specific cognitive abilities among themselves including their patterns of integration, segregation, and topographical organization-that is, the network neuropsychology of JME remains to be addressed. Examined here is the status of cognitive networks in JME and whether similar network alterations are present in the unaffected siblings of patients with this genetic generalized epilepsy. Participants included 78 with JME (mean age = 19.8 ± 3.7 yrs), 19 unaffected siblings (mean age = 15.9 ± 3.3 yrs) and 43 unrelated controls (mean age = 20.2 ± 3.2 yrs), all administered a comprehensive clinical neuropsychological battery from which 15 metrics served as nodes for graph theory analyses. Calculated were global metric indices (i.e., normalized global efficiency, normalized average clustering coefficient, modularity) and landmark "hubs" by calculating betweenness centrality. Salient JME network findings included: 1) significantly greater intercorrelation of test measures (i.e., increased positive manifold), 2) significantly lower segregation (i.e., normalized average clustering coefficient) but similar network efficiency (i.e., normalized global efficiency), and 3) significantly lower strength of the division of the cognitive modules in the network (i.e., modularity index). Regarding the topographical structure of identified cognitive networks (i.e., their community structure), unaffected siblings and unrelated controls demonstrated three cognitive modules (speed/executive function, perceptual, language) while JME demonstrated two modules-one of which was undifferentiated and "g-like", and speed/executive function. Overall, JME is associated with less segregation of cognitive subsystems (i.e., modules) indicating less specialization in cognitive processes, abnormalities in the interrelationships of psychometric measures as well as the general configuration of their resulting cognitive networks (fewer in number and atypical in content and structure) which appear to be contributing to their generally poorer cognitive status compared to controls. Unaffected siblings show some penetrance of these atypical network features., Competing Interests: Declaration of competing interest None of the authors has any conflict of interest to disclose., (Copyright © 2025 Elsevier Ltd. All rights reserved.)

White matter alterations are increasingly implicated in neurological diseases and their progression. International-scale studies use diffusion-weighted magnetic resonance imaging (DW-MRI) to qualitatively identify changes in white matter microstructure and connectivity. Yet, quantitative analysis of DW-MRI data is hindered by inconsistencies stemming from varying acquisition protocols. Specifically, there is a pressing need to harmonize the preprocessing of DW-MRI datasets to ensure the derivation of robust quantitative diffusion metrics across acquisitions. In the MICCAI-CDMRI 2023 QuantConn challenge, participants were provided raw data from the same individuals collected on the same scanner but with two different acquisitions and tasked with preprocessing the DW-MRI to minimize acquisition differences while retaining biological variation. Harmonized submissions are evaluated on the reproducibility and comparability of cross-acquisition bundle-wise microstructure measures, bundle shape features, and connectomics. The key innovations of the QuantConn challenge are that (1) we assess bundles and tractography in the context of harmonization for the first time, (2) we assess connectomics in the context of harmonization for the first time, and (3) we have 10x additional subjects over prior harmonization challenge, MUSHAC and 100x over SuperMUDI. We find that bundle surface area, fractional anisotropy, connectome assortativity, betweenness centrality, edge count, modularity, nodal strength, and participation coefficient measures are most biased by acquisition and that machine learning voxel-wise correction, RISH mapping, and NeSH methods effectively reduce these biases. In addition, microstructure measures AD, MD, RD, bundle length, connectome density, efficiency, and path length are least biased by these acquisition differences. A machine learning approach that learned voxel-wise cross-acquisition relationships was the most effective at harmonizing connectomic, microstructure, and macrostructure features, but requires the same subject be scanned at each site co-registered. NeSH, a spatial and angular resampling method, was also effective and has generalizable framework not reliant co-registration. Our code is available at https://github.com/nancynewlin-masi/QuantConn/., Competing Interests: Conflicts of Interest We declare we don’t have conflicts of interest.