Citations

Abstract Background Diabetes mellitus elevates Parkinson’s disease (PD) risk, via hypothesized cerebrovascular mediation. Whether the diabetes/prediabetes vascular-risk phenotype concentrates in cardiometabolic risk or macrovascular events across prodromal and clinically diagnosed PD remains unresolved. Objectives To quantify the vascular-risk burden associated with diabetes/prediabetes across the PPMI diagnostic cohorts to test whether this association differs by cohort. Methods Cross-sectional analysis of 413 PPMI participants (76 healthy controls, 145 prodromal PD, 192 clinically diagnosed PD) examined diabetes/prediabetes (n = 73) and seven vascular risk factors. The Vascular Burden Score (0 to 7) was a priori partitioned into microvascular and macrovascular sub-scores. Modified Poisson regression estimated adjusted prevalence ratios (aPR), adjusted for age, sex, and body mass index. A cohort-by-diabetes interaction tested cross-cohort consistency. Sensitivity analyses incorporated nigral diffusion tensor imaging (PD-risk biomarker) and FreeSurfer white matter hypointensity volume (cerebrovascular marker). Results Diabetes/prediabetes elevated Vascular Burden Score (β = 0.53, 95% CI 0.29 to 0.77, p < 0.001) versus non-diabetic participants, with a non-significant cohort-by-diabetes interaction (F = 0.29, p = 0.747). Three microvascular factors survived false discovery rate correction: obesity (aPR 2.28), hypertension (aPR 1.60), and hyperlipidemia (aPR 1.45). Macrovascular events showed no diabetic amplification (β = −0.06, p = 0.25). In the imaging-phenotyped subset, Vascular Burden Score components contributed classifier variance distinct from nigral microstructure. Conclusions Diabetes/prediabetes operationalize a microvascular cluster stable across prodromal and idiopathic PD. Cardiometabolic phenotyping may complement established PD-risk biomarkers (dopamine transporter SPECT, nigral diffusion), pending longitudinal validation linking vascular phenotype to dopaminergic markers.

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 Magnetic Resonance Spectroscopy Imaging (MRSI) offers spatially-resolved, neurometabolic information, acquired non-invasively at whole-brain scales from human subjects. Analysis of MRSI however, is extremely challenging. The metabolic information is highly convolved, and sparsely distributed across millions of spatial-spectral datapoints, allowing for little direct human interpretation. Conversely, the overall low signal-to-noise with high-intensity artifacts can confound unsupervised machine learning approaches. These technical barriers have left much of the potential of MRSI unrealized. We acquired MRSI data from 4 human subjects with a diagnosis of multiple sclerosis (MS), incorporating experimental design into an informed machine learning approach. MRSI acquisitions were registered to anatomical MRI to label 105k spectra from brain tissue and 162 spectra from white matter hyperintensities (WMHs), an imaging biomarker associated with MS lesions. Spectral labels were then used in contrastive principal component analysis (cPCA) to filter artifacts and background features in the MRSI data from lesion salient features and clustered into statistically significant states based on features that could be interpreted from the original data. Our approach renders MRSI data into testable representations of neurometabolism, enabling the method for fundamental and clinical research. Graphical Abstract Analysis workflow for neurometabolic profiling of MS lesions. MRSI and anatomical MRI is acquired and processed in parallel for spectral data and anatomical labels. Spectra are then labeled and separated into experimental vs background data for contrastive PCA. Spectra are clustered for similarity, further labeled, and projected onto a brain atlas for a neurometabolic view.

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.

This study investigates the sex-specific role of the Hypothalamic–Pituitary–Gonadal axis in Alzheimer’s disease progression, utilizing ADNI1 data for 493 individuals, analyzing plasma levels of gonadotropic and sex hormones, and examining neurodegeneration-related brain structures. We assessed plasma levels of follicle stimulating hormone (FSH), luteinizing hormone (LH), progesterone (P4), and testosterone (T), along with volumetric measures of the hippocampus, entorhinal cortex, and hypothalamic subunits, to explore their correlation with Alzheimer’s disease markers across different cognitive statuses and sexes. Significant cognitive status effects were observed for all volumetric measures, with a distinct sex-by-cognitive status interaction for hypothalamic volume, indicating a decrease in males but not in females across cognitive impairment stages. Regression analyses showed specific hypothalamic subunit volume related to hormone levels, accounting for up to approximately 40% of the variance (p < 0.05). The findings highlight sex differences in neurodegeneration and hormonal regulation, suggesting potential for personalized treatments and advancing the understanding of Alzheimer’s disease etiology.

Abstract Falls are a public health concern for older adults, particularly those with cognitive impairments, like mild cognitive impairment (MCI) and Alzheimer’s disease (AD) dementia. Executive function (EF) is linked to falls in older adults, but little is known about the relationship between individual EF assessments and falls in individuals with varying cognitive statuses. Using cross-sectional secondary data analyses with binary logistic regression, we examined the associations between EF assessments (Trail Making Test (ΔTMT), Digit Span (DS), Stroop test, Clock Drawing Test, and phonemic/semantic fluency tests) and falls (fall history and subsequent falls) in 466 participants (aged 66-103) with either intact cognition, MCI, or AD dementia. Surprisingly, a significant interaction effect emerged with AD dementia and EF assessments (ΔTMT, DS, and semantic fluency), revealing that individuals with AD dementia with worse EF had a lower probability of prior fall history. These findings were influenced by a low percentage of reported falls in people with severe AD dementia. Further stratified analysis of the interaction effects showed that DS and semantic fluency were significantly associated with fall history in people without AD dementia. While some EF assessments were individually associated with falls, interpretations should be made cautiously, especially considering the appropriateness of the assessments for specific populations. The counterintuitive result may stem from factors specific to severe AD dementia. In conclusion, this study uncovers complex interactions between cognitive status, EF assessments, and fall history, and the potential for further investigation to enhance our understanding of falls in individuals with severe AD dementia.