OBJECTIVE The purpose of this review is to critically examine studies that have examined investigated the Montreal Cognitive Assessment (MoCA) and functional or medical outcomes and other health variables in patients with non-neurologic medical conditions. METHOD Databases OVID Medline and Embase were systematically searched through April 2020, yielding 281 articles that were separately screened for inclusion. Study characteristics extracted from retained articles are presented in Table S1 (online supplemental materials). RESULTS Thirty-six articles were retained. Cognitive impairment as assessed by the MoCA was associated with adverse health variables including increased morbidity/mortality, poorer functional abilities, increased length of hospital stay, and increased hospital readmissions in 34 of 36 articles. CONCLUSIONS Cognitive impairment as detected by the MoCA was shown in 34 of 36 studies to be associated with worse functional or medical status compared to those with better cognitive functioning across a variety of medical populations. Further research is needed to better understand how to best use the MoCA to potentially inform treatment planning in medical populations, including referral for more detailed neuropsychological evaluation. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

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.

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.

Introduction: Individuals with traumatic brain injury (TBI) often experience long-term cognitive impairment that affects daily functioning, but neuropsychological tests are often not associated with functional outcomes in chronic TBI. To identify better neuropsychological test predictors of functional outcomes, we identified process-oriented neuropsychological test scores associated with behavioral dysfunction in chronic TBI. Method: Adults (N =?65, age?=?49.9?years, education?=?14.6?years, 80% male, 97% white) with a >?6-month (median?=?55?months) history of TBI (31% complicated mild, 20% moderate, 49% severe) completed the self-reported Frontal Systems Behavior Scale (FrSBe) and a battery of neuropsychological tests. Cognitive scores significantly associated with FrSBe domain scores (Apathy, Disinhibition, Executive Dysfunction) were included in hierarchical linear regression analyses. Results: Pearson correlations were significant (|r|?=?.25 ? .41, p <?.05) between FrSBe Disinhibition and Executive Dysfunction domains and numerous cognitive scores, particularly cognitive process scores, including California Verbal Learning Test II (CVLT-II) and Letter (FAS) Fluency Intrusions and Repetitions. In the regression analyses, gender, injury severity, premorbid intelligence, time since injury, self-awareness, and depression status accounted for less than 1% (Disinhibition) and 6.1% (Executive Dysfunction) of the total adjusted variance in respective FrSBe domains. Cognitive process scores were the strongest factors associated with behavioral dysfunction, explaining an additional 22.9% of the total adjusted variance in Disinhibition [F(3, 50)?=?6.28, p =?.001]. For Executive Dysfunction, traditional scores explained 2.6% added variance on top of the base model, with cognitive process scores explaining an additional 17.2% [F(4, 48)?=?6.79, p =?.003] of the total adjusted variance. Conclusions: Less frequently utilized cognitive process scores from well-known neuropsychological tests may be better predictors of behavioral dysfunction in chronic TBI, as measured by the FrSBe. Future prospective study is required to validate these findings and test the sensitivity and specificity of these cognitive process scores for identifying risk for behavioral dysfunction.

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 (M-MCI = 64.76[5.87], M-NC = 62.33[6.76]; p = .033) and less educated (M-MCI = 13.07[2.37], M-NC = 14.36[2.51]; p = .004) and had lower MoCA scores (M-MCI = 21.26[3.85], M-NC = 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.

Individuals with amnestic mild cognitive impairment (aMCI) experience cognitive declines in learning and memory greater than expected for normal aging, and are at a high risk of dementia. We previously reported that sedentary aMCI patients exhibited neuroinflammation that correlated with brain amyloid beta (Aβ) burden, as determined by 18F-florbetapir positron emission tomography (PET). These aMCI patients enrolled in a one-year randomized control trial (AETMCI, NCT01146717) to test the beneficial effects of 12 months of moderate-to-high intensity aerobic exercise training (AET) or stretching/toning (ST) control intervention on neurocognitive function. A subset of aMCI participants had PET imaging, cognitive testing, and immunophenotyping of cerebrospinal fluid (CSF) and peripheral blood after AET or ST interventions. As adaptive immune responses were similar between AET and ST groups, we combined AET/ST into a general 'physical activity' (PA) group and compared Aβ burden, cognitive function, and adaptive immune cell subsets to sedentary lifestyle before intervention. We found that PAinduced immunomodulation of CD4+ and CD8+ T cells in CSF correlated with changes in Aβ burden in brain regions associated with executive function. Furthermore, after PA, cognitive scores on tests of memory, processing speed, attention, verbal fluency, and executive function were associated with increased percent representation of circulating naïve B + T cells. We review the literature on aMCI-related cognition and immune changes as they relate to exercise, and highlight how our preliminary data suggest a complex interplay between the adaptive immune system, physical activity, cognition, and Aβ burden in aMCI.

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.

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.

To test the hypothesis that executive function process scores are associated with frontal executive behaviors in chronic TBI.

OBJECTIVE:To examine the utility of resting-state functional connectivity MRI (rs-fcMRI) measurements of network integrity as a predictor of future cognitive decline in preclinical Alzheimer disease (AD). METHODS:A total of 237 clinically normal older adults (aged 63-90 years, Clinical Dementia Rating 0) underwent baseline β-amyloid (Aβ) imaging with Pittsburgh compound B PET and structural and rs-fcMRI. We identified 7 networks for analysis, including 4 cognitive networks (default, salience, dorsal attention, and frontoparietal control) and 3 noncognitive networks (primary visual, extrastriate visual, motor). Using linear and curvilinear mixed models, we used baseline connectivity in these networks to predict longitudinal changes in preclinical Alzheimer cognitive composite (PACC) performance, both alone and interacting with Aβ burden. Median neuropsychological follow-up was 3 years. RESULTS:Baseline connectivity in the default, salience, and control networks predicted longitudinal PACC decline, unlike connectivity in the dorsal attention and all noncognitive networks. Default, salience, and control network connectivity was also synergistic with Aβ burden in predicting decline, with combined higher Aβ and lower connectivity predicting the steepest curvilinear decline in PACC performance. CONCLUSIONS: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.