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Mild Cognitive Impairment (MCI) is a transitional stage between normal aging and dementia and people with MCI are at high risk of progression to dementia. MCI is attracting increasing attention, as it offers an opportunity to target the disease process during an early symptomatic stage. Structural magnetic resonance imaging (MRI) measures have been the mainstay of Alzheimer's disease (AD) imaging research, however, ventricular morphometry analysis remains challenging because of its complicated topological structure. Here we describe a novel ventricular morphometry system based on the hyperbolic Ricci flow method and tensor-based morphometry (TBM) statistics. Unlike prior ventricular surface parameterization methods, hyperbolic conformal parameterization is angle-preserving and does not have any singularities. Our system generates a one-to-one diffeomorphic mapping between ventricular surfaces with consistent boundary matching conditions. The TBM statistics encode a great deal of surface deformation information that could be inaccessible or overlooked by other methods. We applied our system to the baseline MRI scans of a set of MCI subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI: 71 MCI converters vs. 62 MCI stable). Although the combined ventricular area and volume features did not differ between the two groups, our fine-grained surface analysis revealed significant differences in the ventricular regions close to the temporal lobe and posterior cingulate, structures that are affected early in AD. Significant correlations were also detected between ventricular morphometry, neuropsychological measures, and a previously described imaging index based on fluorodeoxyglucose positron emission tomography (FDG-PET) scans. This novel ventricular morphometry method may offer a new and more sensitive approach to study preclinical and early symptomatic stage AD.

Medical training is stressful and has well-established implications for student wellbeing. Despite widespread efforts to reduce student burnout through wellness programming in medical schools, there is a paucity of literature examining students' perception of wellness and engagement with these programs. As such, we sought to evaluate: 1) medical students' level of engagement with a multifaceted wellness curriculum, 2) factors students perceived as important to wellbeing, and 3) associations with longitudinal measures of wellbeing and perceived stress. Intervention: A multipronged wellness curriculum was instituted at Mayo Clinic Alix School of Medicine-AZ (MCASOM-AZ) in 2017. This includes mental health services, curriculum-embedded seminars, wellness committee (composed of students, faculty, and administration) driven programming, and student proposed wellness activities that are reviewed and funded by the committee. The authors invited students at our institution to complete questionnaires at three timepoints during the 2018–2019 academic year. Questionnaires asked participants to rank eight factors from least to most important to their overall wellbeing. Participants self-reported their participation in each prong of the wellness curriculum and ranked the impact of each on their overall wellbeing. Their wellbeing and perceived stress were measured at each timepoint using validated psychological instruments. Context: As MCASOM-AZ opened in 2017, the student body at the time of study consisted of first- and second-year medical students. All students had the opportunity to engage with all aspects of the wellness curriculum and participate in this study, however participation was elective and all responses were anonymous. Of the MCASOM-AZ student body comprised of 100 students, 58 consented to participate in the study, 41.4% of which were Year 1 and 58.6% of which were Year 2 students. Participant age and gender were collected and were representative of the larger student body. Impact: Students engaged most with student-initiated wellness. They perceived unscheduled time as most impactful to their overall wellbeing with student-initiated activities as second-most impactful. Students with higher perceived stress were more likely than others to use mental health resources, which otherwise ranked lower in importance. Ranking academic performance as important to wellbeing was associated with higher wellbeing. There was no difference in wellbeing between students who participated in the wellness curriculum and those who did not. However, overall student wellbeing increased over the course of the year while perceived stress decreased. Lessons Learned: Medical school programs may benefit from allowing students to direct or contribute to the design of their own wellness curriculum. Additionally, medical education should work toward creating a more supportive learning environment with improved flexibility in order to better meet students' individual needs without compromising their education. Despite having low utilization rates overall, mental health resources remain an important aspect of student support services as they are used by students under greater amounts of perceived stress than their peers. Supplemental data for this article is available online at https://doi.org/10.1080/10401334.2021.2004415. [ABSTRACT FROM AUTHOR]

Background: Besides their other roles, brain imaging and other biomarkers of Alzheimer's disease (AD) have the potential to inform a cognitively unimpaired (CU) person's likelihood of progression to mild cognitive impairment (MCI) and benefit subject selection when evaluating promising prevention therapies. We previously described that among baseline FDG-PET and MRI measures known to be preferentially affected in the preclinical and clinical stages of AD, hippocampal volume was the best predictor of incident MCI within 2 years (79%sensitivity/78%specificity), using standard automated MRI volumetric algorithmic programs, binary logistic regression, and leave-one-out procedures.Objective: To improve the same prediction by using different hippocampal features and machine learning methods, cross-validated via two independent and prospective cohorts (Arizona and ADNI).Methods: Patch-based sparse coding algorithms were applied to hippocampal surface features of baseline TI-MRIs from 78 CU adults who subsequently progressed to amnestic MCI in approximately 2 years ("progressors") and 80 matched adults who remained CU for at least 4 years ("nonprogressors"). Nonprogressors and progressors were matched for age, sex, education, and apolipoprotein E4 allele dose. We did not include amyloid or tau biomarkers in defining MCI.Results: We achieved 92%prediction accuracy in the Arizona cohort, 92%prediction accuracy in the ADNI cohort, and 90%prediction accuracy when combining the two demographically distinct cohorts, as compared to 79%(Arizona) and 72%(ADNI) prediction accuracy using hippocampal volume.Conclusion: Surface multivariate morphometry and sparse coding, applied to individual MRIs, may accurately predict imminent progression to MCI even in the absence of other AD biomarkers. [ABSTRACT FROM AUTHOR]