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Abstract Background Cognitive impairment and dementia are frequently under-recognized. Health system strategies anchored in primary care are essential to address gaps in timely, comprehensive diagnosis. The goal of this paper is to describe the adaptation of a tablet-based brain health assessment (TabCAT-BHA) intervention and the study protocol to test its effectiveness in improving the detection of cognitive impairment, including dementia. Methods This mixed-methods, pragmatic, cluster randomized, hybrid effectiveness-implementation trial is being conducted in two 18-month waves with 26 Kaiser Permanente Southern California primary care clinics, with 13 serving as intervention clinics and 13 as usual care clinics. Patients 65 years and older with memory concerns (n ~ 180,000) receiving care at the 26 clinics will be included in the analyses. Primary care clinics are provided the following practice supports as part of the TabCAT-BHA intervention: brief education and training on neurocognitive disorders and study workflows; digital tools to assess cognitive function and support clinician decision making and documentation; and registered nurse support during the work-up and post-diagnosis periods for primary care providers, patients, and families. The intervention was adapted based on engagement with multiple levels of clinical and operational leaders in the healthcare system. Effectiveness outcomes include rates of cognitive impairment diagnosis in primary care and rates of completed standardized cognitive assessments and specialist referrals with incident diagnoses. Implementation outcomes include acceptability-appropriateness-feasibility, adoption, and fidelity. Results We identified seven themes organized by system-, provider-, and patient-level domains that were used to adapt the TabCAT-BHA intervention. Accordingly, changes were made to the provider education, diagnostic work-up, and post-diagnostic support. Results will be reported in fall of 2027. Conclusions Our engagement with multiple primary and specialty care clinical and operational leaders to adapt the TabCAT-BHA intervention to these primary care clinics has informed the protocol to evaluate the intervention’s effectiveness for improving the detection of cognitive impairment, including dementia, in an integrated healthcare system. Trial Registation Clinicaltrials.gov: NCT06090578 (registered 10/24/23).

Abstract Background Alzheimer’s disease (AD) is the most common form of dementia, progressively impairing cognitive abilities. While neuroimaging studies have revealed functional abnormalities in AD, how these relate to aberrant neuronal circuit mechanisms remains unclear. Using magnetoencephalography imaging we documented abnormal local neural synchrony patterns in patients with AD. To identify global abnormal biophysical mechanisms underlying the spatial and spectral electrophysiological patterns in AD, we estimated the parameters of a biophysical spectral graph model (SGM). Methods SGM is an analytic neural mass model that describes how long-range fiber projections in the brain mediate the excitatory and inhibitory activity of local neuronal subpopulations. Unlike other coupled neuronal mass models, the SGM is linear, available in closed-form, and parameterized by a small set of biophysical interpretable global parameters. This facilitates their rapid and unambiguous inference which we performed here on a well-characterized clinical population of patients with AD (N = 88, age = 62.73 +/- 8.64 years) and a cohort of age-matched controls (N = 88, age = 65.07 +/- 9.92 years). Results Patients with AD showed significantly elevated long-range excitatory neuronal time scales, local excitatory neuronal time scales and local inhibitory neural synaptic strength. The long-range excitatory time scale had a larger effect size, compared to local excitatory time scale and inhibitory synaptic strength and contributed highest for the accurate classification of patients with AD from controls. Furthermore, increased long-range time scale was associated with greater deficits in global cognition. Conclusions These results demonstrate that long-range excitatory time scale of neuronal activity, despite being a global measure, is a key determinant in the local spectral signatures and cognition in the human brain, and how it might be a parsimonious factor underlying altered neuronal activity in AD. Our findings provide new insights into mechanistic links between abnormal local spectral signatures and global connectivity measures in AD.

Abstract Patients with Waldenström macroglobulinaemia (WM) are at increased risk of severe COVID‐19 infection and have poor immune responses to COVID‐19 vaccination. This study assessed whether a closely monitored pause in Bruton's Tyrosine Kinase inhibitor (BTKi) therapy might result in an improved humoral response to a 3rd COVID‐19 vaccine dose. Improved response was observed in WM patients who paused their BTKi, compared to a group who did not pause their BTKi. However, the response was attenuated after BTKi recommencement. This data contributes to our understanding of vaccination strategies in this patient group and may help inform consensus approaches in the future.

BackgroundFalls are common in people with multiple sclerosis (MS), causing injuries, fear of falling, and loss of independence. Although targeted interventions (physical therapy) can help, patients underreport and clinicians undertreat this issue. Patient-generated data, combined with clinical data, can support the prediction of falls and lead to timely intervention (including referral to specialized physical therapy). To be actionable, such data must be efficiently delivered to clinicians, with care customized to the patient’s specific context. ObjectiveThis study aims to describe the iterative process of the design and development of Multiple Sclerosis Falls InsightTrack (MS-FIT), identifying the clinical and technological features of this closed-loop app designed to support streamlined falls reporting, timely falls evaluation, and comprehensive and sustained falls prevention efforts. MethodsStakeholders were engaged in a double diamond process of human-centered design to ensure that technological features aligned with users’ needs. Patient and clinician interviews were designed to elicit insight around ability blockers and boosters using the capability, opportunity, motivation, and behavior (COM-B) framework to facilitate subsequent mapping to the Behavior Change Wheel. To support generalizability, patients and experts from other clinical conditions associated with falls (geriatrics, orthopedics, and Parkinson disease) were also engaged. Designs were iterated based on each round of feedback, and final mock-ups were tested during routine clinical visits. ResultsA sample of 30 patients and 14 clinicians provided at least 1 round of feedback. To support falls reporting, patients favored a simple biweekly survey built using REDCap (Research Electronic Data Capture; Vanderbilt University) to support bring-your-own-device accessibility—with optional additional context (the severity and location of falls). To support the evaluation and prevention of falls, clinicians favored a clinical dashboard featuring several key visualization widgets: a longitudinal falls display coded by the time of data capture, severity, and context; a comprehensive, multidisciplinary, and evidence-based checklist of actions intended to evaluate and prevent falls; and MS resources local to a patient’s community. In-basket messaging alerts clinicians of severe falls. The tool scored highly for usability, likability, usefulness, and perceived effectiveness (based on the Health IT Usability Evaluation Model scoring). ConclusionsTo our knowledge, this is the first falls app designed using human-centered design to prioritize behavior change and, while being accessible at home for patients, to deliver actionable data to clinicians at the point of care. MS-FIT streamlines data delivery to clinicians via an electronic health record–embedded window, aligning with the 5 rights approach. Leveraging MS-FIT for data processing and algorithms minimizes clinician load while boosting care quality. Our innovation seamlessly integrates real-world patient-generated data as well as clinical and community-level factors, empowering self-care and addressing the impact of falls in people with MS. Preliminary findings indicate wider relevance, extending to other neurological conditions associated with falls and their consequences.

Abstract Introduction Conventional Z‐scores are generated by subtracting the mean and dividing by the standard deviation. More recent methods linearly correct for age, sex, and education, so that these “adjusted” Z‐scores better represent whether an individual's cognitive performance is abnormal. Extreme negative Z‐scores for individuals relative to this normative distribution are considered indicative of cognitive deficiency. Methods In this article, we consider nonlinear shape constrained additive models accounting for age, sex, and education (correcting for nonlinearity). Additional shape constrained additive models account for varying standard deviation of the cognitive scores with age (correcting for heterogeneity of variance). Results Corrected Z‐scores based on nonlinear shape constrained additive models provide improved adjustment for age, sex, and education, as indicated by higher adjusted‐R2. Discussion Nonlinearly corrected Z‐scores with respect to age, sex, and education with age‐varying residual standard deviation allow for improved detection of non‐normative extreme cognitive scores.

Background: Posterior cortical atrophy (PCA) is a clinical variant of Alzheimer's disease (AD) that presents with progressive visuospatial symptoms. While amnestic AD is characterized by disrupted default mode network (DMN) connectivity with corresponding increases in salience network (SN) connectivity, a visuospatial network appears to be disrupted early in PCA. Based on PCA patients' clinical features, we hypothesized that, in addition to early decreased integrity within the visuospatial network, patients with PCA would show increases in SN connectivity despite relative preservation of DMN. As the lateral pulvinar nucleus of the thalamus has direct anatomical connections with striate and extrastriate cortex and DMN, and the medial pulvinar is anatomically interconnected with SN, we further hypothesized that lateral and medial pulvinar nuclei might be implicated in intrinsic connectivity changes in PCA. Methods: 26 patients with PCA and 64 matched controls were recruited through UCSF Memory and Aging Center research programs. Each completed a standardized neuropsychological battery, structural MRI, and task-free fMRI. Seed-based functional correlations were used to probe networks of interest, including those seeded by the medial and lateral pulvinar thalamic nuclei, across the whole brain, and functional data analyses were adjusted for brain atrophy. Results: Patients with PCA showed disproportionate deficits in the visuospatial domain; they also showed preserved social sensitivity and endorsed more depressive symptoms than HCs. PCA patients had significant parietooccipital atrophy accompanied by widespread connectivity decreases within the visuospatial network, enhanced connectivity between some structures in SN, and enhanced connectivity between key nodes of the DMN compared to controls. Increased SN connectivity correlated with a measure of social sensitivity, and increased DMN connectivity correlated with short-term memory performance. Medial pulvinar connectivity increases in PCA were topographically similar to SN (anterior insula) connectivity increases, while lateral pulvinar connectivity increases were similar to DMN (posterior cingulate) connectivity increases. Conclusions: PCA is characterized by preserved to heightened connectivity in the SN and DMN despite decreased visuospatial network connectivity. The spatial similarity of medial and lateral pulvinar connectivity changes to those seen in the SN and DMN suggests a role for the pulvinar in intrinsic connectivity network changes in PCA. Keywords: Posterior cortical atrophy, Pulvinar, Alzheimer's disease, Functional connectivity, Default mode network, Salience network

Alzheimers disease and frontotemporal dementia (FTD) can be difficult to differentiate clinically because of overlapping symptoms. Distinguishing the two dementias based on volumetric measurements of brain atrophy with MRI has been only partially successful. Whether MRI measurements of cortical thinning improve the differentiation between Alzheimers disease and FTD is unclear. In this study, we measured cortical thickness using a set of automated tools (Freesurfer) to reconstruct the brains cortical surface from T1-weighted structural MRI data in 22 patients with Alzheimers disease, 19 patients with FTD and 23 cognitively normal subjects. The goals were to detect the characteristic patterns of cortical thinning in these two types of dementia, to test the relationship between cortical thickness and cognitive impairment, to determine if measurement of cortical thickness is better than that of cortical volume for differentiating between these dementias and normal ageing and improving the classification of Alzheimers disease and FTD based on neuropsychological scores alone. Compared to cognitively normal subjects, Alzheimers disease patients had a thinner cortex primarily in bilateral, frontal, parietal, temporal and occipital lobes (P < 0.001), while FTD patients had a thinner cortex in bilateral, frontal and temporal regions and some thinning in inferior parietal regions and the posterior cingulate (P < 0.001). Compared to FTD patients, Alzheimers disease patients had a thinner cortex (P < 0.001) in parts of bilateral parietal and precuneus regions. Cognitive impairment was negatively correlated with cortical thickness of frontal, parietal and temporal lobes in Alzheimers disease, while similar correlations were not significant in FTD. Measurement of cortical thickness was similar to that of cortical volume in differentiating between normal ageing, Alzheimers disease and FTD. Furthermore, cortical thickness measurements significantly improved the classification between Alzheimers disease and FTD based on neuropsychological scores alone, including the Mini-Mental State Examination and a modified version of the Trail-Making Test. In conclusion, the characteristic patterns of cortical thinning in Alzheimers disease and FTD suggest that cortical thickness may be a useful surrogate marker for these types of dementia. [ABSTRACT FROM AUTHOR]