In clinically normal older adults, lower functional connectivity predicted more rapid decline in PACC scores over time, particularly when coupled with increased Aβ burden. Among examined networks, default, salience, and control networks were the strongest predictors of rate of change in PACC scores, with the inflection point of greatest decline beyond the fourth year of follow-up. These results suggest that rs-fcMRI may be a useful predictor of early, AD-related cognitive decline in clinical research settings.

Introduction The use of mobile health technology has the potential to increase healthcare accessibility, promote self-management, provide education materials, and support self-monitoring of biometrics. This may be of particular benefit to those living in the community with chronic neurological conditions. The purpose of this research was to characterize perspectives of individuals with brain injury or stroke, their care partners, and neuro-rehabilitation clinicians to inform the design of mobile health app-based interventions that address the need for ongoing community-based health-related professional support. Methods Individuals with stroke or traumatic brain injury, their care partners, and clinicians were recruited from a large university hospital with a specialized neuro-rehabilitation program. Data were collected via semi-structured focus groups and were examined using descriptive statistics and content analysis. Results Five consistent themes emerged: (a) all-in-one app with customized features; (b) communication with healthcare providers; (c) cognitive strategies; (d) app accessibility; and (e) user education. The clinician focus group reiterated these consumer focus group themes, with one additional theme: (f) logistics of clinician time and effort to provide effective app-based services to patients. Conclusions Results have directly informed the ongoing development of a community-based mobile health app intervention, and may also inform the content and design of future mHealth technological development in this population.

Objective To establish a cut score for the Montreal Cognitive Assessment (MoCA) that distinguishes mild cognitive impairment (MCI) from normal cognition (NC) in a community-based African American (AA) sample. Methods A total of 135 AA participants, from a larger aging study, diagnosed MCI (n = 90) or NC (n = 45) via consensus diagnosis using clinical history, Clinical Dementia Rating score, and comprehensive neuropsychological testing. Logistic regression models utilized sex, education, age, and MoCA score to predict MCI versus NC. Receiver operating characteristic (ROC) curve analysis determined a cut score to distinguish MCI from NC based on optimal sensitivity, specificity, diagnostic accuracy, and greatest perpendicular distance above the identity line. ROC results were compared with previously published MoCA cut scores. Results The MCI group was slightly older (MMCI = 64.76[5.87], MNC = 62.33[6.76]; p = .033) and less educated (MMCI = 13.07[2.37], MNC = 14.36[2.51]; p = .004) and had lower MoCA scores (MMCI=21.26[3.85], MNC = 25.47[2.13]; p < .001) than the NC group. Demographics were non-significant in regression models. The area under the curve (AUC) was significant (MoCA = .83, p < .01) and an optimal cut score of <24 maximized sensitivity (72%), specificity (84%), and provided 76% diagnostic accuracy. In comparison, the traditional cut score of <26 had higher sensitivity (84%), similar accuracy (76%), but much lower specificity (58%). Conclusions This study provides a MoCA cut score to help differentiate persons with MCI from NC in a community-dwelling AA sample. A cut score of <24 reduces the likelihood of misclassifying normal AA individuals as impaired than the traditional cut score. This study underscores the importance of culturally appropriate norms to optimize the utility of commonly used cognitive screening measures.

Diffusion MRI tractography produces massive sets of streamlines that need to be clustered into anatomically meaningful white-matter bundles. Conventional clustering techniques group streamlines based on their proximity in Euclidean space. We have developed AnatomiCuts, an unsupervised method for clustering tractography streamlines based on their neighboring anatomical structures, rather than their coordinates in Euclidean space. In this work, we show that the anatomical similarity metric used in AnatomiCuts can be extended to find corresponding clusters across subjects and across hemispheres, without inter-subject or inter-hemispheric registration. Our proposed approach enables group-wise tract cluster analysis, as well as studies of hemispheric asymmetry. We evaluate our approach on data from the pilot MGH-Harvard-USC Lifespan Human Connectome project, showing improved correspondence in tract clusters across 184 subjects aged 8–90. Our method shows up to 38% improvement in the overlap of corresponding clusters when comparing subjects with large age differences. The techniques presented here do not require registration to a template and can thus be applied to populations with large inter-subject variability, e.g., due to brain development, aging, or neurological disorders.

The fornix bundle is a major white matter pathway of the hippocampus. While volume of the hippocampus has been a primary imaging biomarker of Alzheimer's disease progression, recent research has suggested that the volume and microstructural characteristics of the fornix bundle connecting the hippocampus could add relevant information for diagnosing and staging Alzheimer's disease. Using a robust fornix bundle isolation technique in native diffusion space, this study investigated whether diffusion measurements of the fornix differed between normal older adults and Alzheimer's disease patients when controlling for volume measurements. Data were collected using high gradient multi-shell diffusion-weighted MRI from a Siemens CONNECTOM scanner in 23 Alzheimer's disease and 23 age- and sex-matched control older adults (age range = 53–92). These data were used to reconstruct a continuous fornix bundle in every participant's native diffusion space, from which tract-derived volumetric and diffusion metrics were extracted and compared between groups. Diffusion metrics included those from a tensor model and from a generalized q-sampling imaging model. Results showed no significant differences in tract-derived fornix volumes but did show altered diffusion metrics within tissue classified as the fornix in the Alzheimer's disease group. Comparisons to a manual tracing method indicated the same pattern of results and high correlations between the methods. These results suggest that in Alzheimer's disease, diffusion characteristics may provide more sensitive measures of fornix degeneration than do volume measures and may be a potential early marker for loss of medial temporal lobe connectivity.

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When do prosocial actors experience positive versus negative psychological outcomes from helping others? In four studies and an internal meta-analysis, we tested the hypothesis that autonomy shapes the psychological consequences of helping others.In Study 1, prosocial behaviour was associated with a robust pattern of negative wellbeing outcomes (i.e., depression, anxiety, stress) for individuals low but not high in autonomy. In Studies 2-4, relative to reflecting on a neutral interpersonal experience, reflecting on an autonomous helping experience increased sadness and happiness, strengthened intentions to help in the future and raised support for social welfare. By contrast, reflecting on a controlled helping experience increased negative emotions and decreased positive emotions, but did not affect attitudes or behavioural intentions. Collectively, the findings indicate that autonomy (or lack thereof) shapes the emotional, motivational and attitudinal consequences of helping behaviour.

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