Baxter, Leslie C.

Background/Objectives: White matter (WM) tract detection is critical in the presurgical planning of tumor resection. However, standard-of-care imaging techniques including T1-weighted, T2-weighted, and Diffusion Tensor Imaging (DTI) often fail to identify WM tracts within edematous regions. In T1/T2-weighted imaging, edema increases extracellular water and reduces tissue contrast, and in diffusion-weighted imaging, edema elevates isotropic diffusion, reducing sensitivity to anisotropic diffusion along WM tracts. Advanced biophysical diffusion modeling techniques such as Neurite Orientation Dispersion and Density Imaging (NODDI) and the Standard Model (SM) address this limitation by compartmentalizing the diffusion signal into free-water, intra-neurite, and extra-neurite contributions. Here, we test if biophysical multi-compartment models can robustly identify WM tracts and recover tractography streamlines within edematous regions. Methods: In this study, we use multi-shell diffusion-weighted MRI data obtained from patients with meningiomas—a pathology allowing for isolation of the effects of edema without the confounding effects of tumor cell invasion. We compared FA from standard and free-water-corrected DTI, the orientation dispersion index (ODI) from NODDI, and P2 (a scalar descriptor of fiber orientation coherence) from the SM fODF in edematous and unaffected contralateral WM regions. As a proof of concept, we visually evaluated the tractography performance across models. Results: Our results show that (1 − ODI) and P2 values in edema remained close to within-subject contralateral measurements, contrasting with substantial reductions in FA and FW-FA. (1 − ODI) showed a small but statistically significant increase in edema (~8%, p = 0.02), while P2 was unchanged. Conclusions: These results highlight the potential of biophysical diffusion models for preoperative mapping in edema.

ABSTRACT Deep brain stimulation (DBS) of the anterior nucleus of the thalamus (ANT) can reduce seizures in patients with drug-resistant epilepsy. However, seizure freedom is rare, and there is no early response biomarker to predict long-term seizure suppression. To evaluate a short-term biomarker of therapeutic response to DBS, we studied 18-minute trials of ANT stimulation in patients with drug-resistant epilepsy undergoing intracranial electroencephalography. We compared a standard high-frequency periodic versus a non-periodic pulse pattern. We found that short-term ANT stimulation within the clinically used amplitude range preferentially altered the first 150 ms of anterograde ANT to anterior cingulate and superior frontal connectivity. Non-periodic ANT stimulation selectively modified early cortico-cortical evoked potentials more than periodic stimulation, and these changes were driven by the response phase. These pattern-specific changes in connectivity between the ANT and spatially restricted cortical areas suggest differing mechanisms and are a promising biomarker for patients being treated with ANT-DBS.

Objective: Age-related hearing loss is prevalent in a large proportion of older adults and is the largest modifiable risk factor of dementia. Previous studies have shown that hearing loss in older adults is associated with reductions in brain volume and alterations in brain connectivity, but there is a gap in our understanding of how hearing loss may impact brain activity, specifically while listening to speech. In this study, we hypothesized that hearing loss would be associated with (1) reduced activation within temporal auditory regions in response to speech presented with and without background noise, and (2) increased activation in the cingulo-opercular network in response to speech in noise. Design: Twenty-seven older adults (mean age = 68 yr, 17 women) recruited from the community completed a clustered sparse-sampling functional magnetic resonance imaging protocol while listening to speech presented with and without multi-talker background noise. Regions of interest–based and exploratory voxel-wise analyses were used to investigate correlations between functional magnetic resonance imaging activation to speech and hearing ability. Hearing was defined as the pure-tone average of hearing thresholds at 0.5, 1, 2, and 4 kHz from the better ear and ranged from normal to a moderate hearing loss. Results: Our results indicate that age-related hearing loss was associated with reduced activation in right Heschl’s gyrus and left middle temporal gyrus in response to speech. Exploratory voxel-wise analyses suggest that hearing loss may be associated with alterations in activation in other sensory cortical regions and portions of the frontal cortex, including left pars opercularis. Activation within the cingulo-opercular network was not associated with hearing loss. Conclusions: These findings suggest that age-related hearing loss in humans is associated with altered brain activity in response to speech, particularly within right Heschl’s gyrus. It further provides hypothesis-generating data for future studies on the effects of hearing loss on brain function.

Purpose White matter hyperintensity (WMH) is associated with heterogeneous pathologies. In neurodegeneration, the prevailing hypothesis is that WMH primarily reflects vascular pathology, exerting an additive effect in AD. Alternatively, a subset of WMH may arise secondary to AD‐related pathophysiology. APOE4 genotype, the greatest genetic risk factor for late‐onset AD, impacts the blood‐brain barrier and WMH burden. Herein, we investigate the association between regional WMH burden and APOE4 allelic dosage in cognitively normal individuals. Methods We used deep learning to automatically segment WMH from T2‐FLAIR MRI from 154 cognitively intact individuals older than 50. We mapped WMH to 4 specific brain regions (lobar, periventricular, basal ganglia and infratentorial, further parcellated into sub‐regions, Table 1) using atlas‐based registration and FastSurfer. WMH volumes were adjusted for total intracranial volume and normalized using log transformation. The APOE4 groups were compared for WMH burden using linear regression, controlling for age and vascular risk factors (hypertension, diabetes, hypercholesterolemia, cigarette smoking). For brain regions where more than 20% of participants had no WMH, a two‐part hurdle model was applied. Results We included 13 APOE4 homozygotes, 50 heterozygotes (ε4/ ε3) and 91 non‐carriers. Compared to non‐carriers, APOEε4 homozygotes exhibited significantly greater WMH burden in the whole brain, periventricular (posterior) and lobar regions (Figure 1a). Homozygotes exhibited a significantly greater WMH burden in the whole brain and posterior periventricular region, when compared to heterozygotes (Figure 1b). No significant whole‐brain or region‐specific differences were observed between heterozygotes and non‐carriers (Figure 1c). Conclusion In cognitively intact individuals at risk for AD, APOE4 homozygosity is associated with significantly greater WMH burden, particularly in the posterior periventricular space, after controlling for age and vascular risk factors. The relationship between regional‐specific WMH burden and neurodegeneration warrants further investigation. image image

Abstract High-grade gliomas (HGG) have significant spatial heterogeneity, complicating therapeutic approaches. While multiparametric MRI (mpMRI) enables noninvasive assessment of this heterogeneity, most predictive models lack biological interpretability when used to classify or predict outcomes. Here, we developed an unsupervised framework that identifies novel mpMRI feature patterns through dimensional reduction, linking them to clinically relevant molecular signatures. We analyzed 18 advanced mpMRI features from 73 stereotactic biopsies across 20 HGG patients. Imaging data were normalized, followed by dimensional reduction using an iterative UMAP across varying parameters. Density-based clustering was used to generate stable image-derived clusters through consensus co-clustering. RNA-seq data from matched biopsies were analyzed using DESeq2 and GSEA to identify differentially expressed genes and enriched pathways. We then constructed a network linking specific imaging features to these transcriptional signatures. Validation was performed in an independent cohort, allowing us to extend clusters across full brain mpMRI. Unsupervised clustering of mpMRI features produced nine stable image-based clusters. These clusters were not fully explained by contrast enhancement, metabolic class, or tumor purity, shown by intraclass correlation (ICC) analyses, suggesting they capture novel biological variation. Transcriptomic analysis showed each cluster was associated with distinct gene expression patterns and enriched biological pathways, including neuronal differentiation, immune chemotaxis, and chromatin remodeling. Key imaging features (sRCBV, MTT, K2)—combined in cluster-specific patterns—enabled construction of an interpretable imaging–transcriptomic network. This was validated in an independent cohort. We then applied to full brain mpMRI, mapping spatially distinct regions to biological trends and patient outcomes. Addressing a research gap in imaging HGG, we present an interpretable network that connects specific patterns of mpMRI to clinically meaningful molecular signatures. This approach advances our understanding of tumor heterogeneity and supports future translational work in precision patient care.

Abstract BACKGROUND Glioblastoma (GBM) is a molecularly and phenotypically heterogeneous tumor, with regional invasion patterns driving treatment resistance and universal recurrence. In infiltrative regions, GBM cells adopt at least two predominant invasive phenotypes: Neuronal-enriched (NEU), with synaptic and neurodevelopment programs, and Glycolytic/Plurimetabolic (GPM), with heightened immune and metabolic activity. These subtypes differ in therapeutic vulnerability and microenvironmental interactions; features not captured by conventional contrast-enhanced (T1+C) MRI due to minimal blood-brain barrier (BBB) disruption. Our group sought to develop predictive models to distinguish variability of NEU/GPM subtypes within infiltrative GBM, utilizing graph convolutional networks (GCN), advanced multiparametric MRI (mpMRI), and spatially-matched image-localized biopsies with transcriptomic profiling. METHODS We analyzed 18 mpMRI features —including conventional, diffusion-weighted, and Dynamic Susceptibility Contrast (DSC) perfusion MRI— of 101 image-localized biopsies from MRI non-enhancing infiltrative margins of 64 GBM patient tumors. Features were hierarchically clustered per modality; each cluster was represented as a node in a Neo4j graph. Edges connected nodes sharing overlapping samples. Nodes were annotated with NEU or GPM subtype proportion. GCNs were trained to classify subtype-enriched nodes using graph topology and node features. RESULTS GCNs achieved high predictive performance on held-out test data (AUC = 0.95 for NEU, 0.88 for GPM; 70:30 train-test split) with similar 5-fold cross-validation performance and generalized well to an independent cohort (AUC > 0.85, n = 45). Importantly, performance was robust without relying on T1+C imaging, enabling prediction beyond BBB-disrupted regions. GCNs outperformed traditional classifiers (logistic regression, random forest). CONCLUSION Our results demonstrate that GCN-modeling of mpMRI features enables non-invasive prediction of invasive GBM subtypes at the tumor margin. Unlike convolutional neural networks (CNN), which rely on spatial continuity, GCNs leverage relational structure between heterogeneous imaging features, making them ideal for modeling sparse, non-grid data. This may support non-invasive regional vulnerability detection, surgical targeting, and improved response assessment.

Introduction Despite growing evidence to characterise cancer-associated cognitive decline (CACD) in women with breast cancer, interventions to mitigate CACD are limited. Emerging evidence suggests aerobic exercise may enhance cognition after breast cancer diagnosis and treatment; yet, CACD remains an understudied outcome of exercise, and few high-quality studies have been conducted. In addition to knowledge gaps in effectiveness, the translation of exercise interventions to community settings remains challenging. The Breast cancer Reasoning and Activity INtervention (BRAIN) investigates the effectiveness of aerobic exercise training, delivered in a community-based setting, for improving cognitive function in women with breast cancer and gathers information on the implementation success of the intervention. Methods and analysis This Hybrid Type I effectiveness–implementation study is conducted at an academic medical centre in the southwestern United States in partnership with a non-profit, community health and wellness organisation. The study enrols 160 women diagnosed with stage I–IIIa breast cancer and within 3–36 months of treatment completion into a 1:1 randomised controlled trial. Individuals randomised to the exercise group receive a 6-month, individually tailored aerobic exercise programme delivered by exercise trainers employed at local community fitness centres. The programme is progressive in nature and designed to help participants achieve aerobic exercise levels consistent with guidelines for cancer survivors. Individuals randomise to the control group receive a 6-month health education control intervention delivered virtually by hospital-based health educators. Cognitive performance (primary), self-reported cognition, patient-reported outcomes, physical activity and cardiorespiratory fitness are measured at baseline, 6 months (postintervention) and 12 months (follow-up). Brain structure and function are measured via magnetic resonance imaging (MRI) at baseline and 6 months. Implementation outcomes are defined by the RE-AIM framework, which includes reach, effectiveness, adoption, implementation and maintenance. RE-AIM outcomes are measured at baseline, 6 months, 12 months and ongoing during the study. Ethics and dissemination This study was approved by the Mayo Clinic Institutional Review Board (#23-000020). All participants provide informed consent prior to participation. Findings will be disseminated to scientific, clinical and community audiences through manuscripts, presentations and newsletters. Trial registration number NCT04816006.

Hippocampal segmentation is essential in neuroimaging for evaluating conditions like Alzheimer’s dementia and mesial temporal sclerosis, where small volume changes can significantly impact normative percentiles. However, inaccurate segmentation is common due to the inclusion of non-hippocampal structures such as choroid plexus and cerebrospinal fluid (CSF), leading to volumetric overestimation and confounding of functional analyses. Current methods of assessment largely rely on virtual or manual ground truth labels, which can fail to capture these inaccuracies. To address this shortcoming, this study introduces a more direct voxel intensity-based method of segmentation assessment. Using paired precontrast and postcontrast T1-weighted MRIs, hippocampal segmentations were refined by adding marginal gray matter and removing marginal CSF and enhancement to determine a total required correction volume. Six segmentation algorithms—e2dhipseg, HippMapp3r, hippodeep, AssemblyNet, FastSurfer, and QuickNat—were implemented and compared. HippMapp3r and e2dhipseg, followed closely by hippodeep, exhibited the least total correction volumes, indicating superior accuracy. Dedicated hippocampal segmentation algorithms outperformed whole-brain methods.

The Program for the Education and Enrichment of Relational Skills (PEERS®) is an evidence-based intervention developed for autistic individuals to support social communication, peer interactions, independence, and interpersonal relationships. Despite a demonstrated effectiveness for young autistic individuals in the US and several other countries, PEERS has yet to be modified to support the needs of autistic adults across the lifespan. The present study describes how our team sought autistic voices to adapt PEERS for adults of any age. Specifically, we aimed to address the needs of middle-aged and older adults and adapt the curriculum to be more neurodiversity-affirming. Between two cohorts that completed the program consecutively, we evaluated the acceptability of the adapted PEERS program and made refinements based on feedback from autistic participants and their study partners. Results indicated that Cohort 2 reported higher satisfaction with the PEERS components and overall program than Cohort 1, suggesting effective refinement. We present a framework of adaptations that more specifically address the needs of middle-aged and older adults in a neurodiverse-affirming way compared to previous iterations. Our approach to implementing an adapted PEERS curriculum across the adult lifespan may serve as a model for improved clinical care and cultivate the acceptance of neurodiversity in the interpersonal domains of autistic adults’ lives.