Gene-Specific Effects on Brain Volume and Cognition of TMEM106B in Frontotemporal Lobar Degeneration.

BACKGROUND AND OBJECTIVES: TMEM106B has been proposed as a modifier of disease risk in FTLD-TDP, particularly in GRN pathogenic variant carriers. Furthermore, TMEM106B has been investigated as a disease modifier in the context of healthy aging and across multiple neurodegenerative diseases. The objective of this study was to evaluate and compare the effect of TMEM106B on gray matter volume and cognition in each of the common genetic FTD groups and in patients with sporadic FTD. METHODS: Participants were enrolled through the ARTFL/LEFFTDS Longitudinal Frontotemporal Lobar Degeneration (ALLFTD) study, which includes symptomatic and presymptomatic individuals with a pathogenic variant in C9orf72, GRN, MAPT, VCP, TBK1, TARDBP, symptomatic nonpathogenic variant carriers, and noncarrier family controls. All participants were genotyped for the TMEM106B rs1990622 SNP. Cross-sectionally, linear mixed-effects models were fitted to assess an association between TMEM106B and genetic group interaction with each outcome measure (gray matter volume and UDS3-EF for cognition), adjusting for education, age, sex, and CDR+NACC-FTLD sum of boxes. Subsequently, associations between TMEM106B and each outcome measure were investigated within the genetic group. For longitudinal modeling, linear mixed-effects models with time by TMEM106B predictor interactions were fitted. RESULTS: The minor allele of TMEM106B rs1990622, linked to a decreased risk of FTD, associated with greater gray matter volume in GRN pathogenic variant carriers under the recessive dosage model (N = 82, beta = 3.25, 95% CI [0.37-6.19], p = 0.034). This was most pronounced in the thalamus in the left hemisphere (beta = 0.03, 95% CI [0.01-0.06], p = 0.006), with a retained association when considering presymptomatic GRN pathogenic variant carriers only (N = 42, beta = 0.03, 95% CI [0.01-0.05], p = 0.003). The minor allele of TMEM106B rs1990622 also associated with greater cognitive scores among all C9orf72 pathogenic variant carriers (N = 229, beta = 0.36, 95% CI [0.05-0.066], p = 0.021) and in presymptomatic C9orf72 pathogenic variant carriers (N = 106, beta = 0.33, 95% CI [0.03-0.63], p = 0.036), under the recessive dosage model. DISCUSSION: We identified associations of TMEM106B with gray matter volume and cognition in the presence of GRN and C9orf72 pathogenic variants. The association of TMEM106B with outcomes of interest in presymptomatic GRN and C9orf72 pathogenic variant carriers could additionally reflect TMEM106B's effect on divergent pathophysiologic changes before the appearance of clinical symptoms.

Spinal Cord Injury Enhances Lung Inflammation and Exacerbates Immune Response After Acute Lung Injury.

The severity of spinal cord injury (SCI) is closely tied to pulmonary function, especially in cases of higher SCI levels. Despite this connection, the underlying pathological mechanisms in the lungs post-SCI are not well understood. Previous research has established a connection between disrupted sympathetic regulation and splenocyte apoptosis in high thoracic SCI, leading to pulmonary dysfunction. The aim of this study was to investigate whether mice with low-level SCI exhibit increased susceptibility to acute lung injury by eliciting systemic inflammatory responses that operate independently of the sympathetic nervous system. Here, we employed T9 contusion SCI and exposed mice to aerosolized lipopolysaccharide (LPS) to simulate lung inflammation associated with acute respiratory distress syndrome (ARDS). Twenty-four hours post-LPS exposure, lung tissues and bronchoalveolar lavage (BAL) fluid were analyzed. LPS markedly induced proinflammatory gene expression (SAA3, IRG1, NLRP3, IL-1beta, MCP-1) and cytokine release (IL-1beta, IL-6, MCP-1) in SCI mice compared to controls, indicating an exaggerated inflammatory response. Infiltration of Ly6G/C positive neutrophils and macrophages was significantly higher in SCI mice lungs post-LPS exposure. Interestingly, spleen size and weight did not differ between control and SCI mice, suggesting that T9 SCI alone does not cause spleen atrophy. Notably, bone-marrow-derived macrophages (BMDMs) from SCI mice exhibited hyper-responsiveness to LPS. This study demonstrated an increase in lung inflammation and immune responses subsequent to low-level T9 SCI, underscoring the widespread influence of systemic inflammation post-SCI, especially pronounced in specific organs like the lungs.

WDR44 Loss-of-Function Promoter Deletion in a Male Newborn With a Ciliopathy Phenotype.

Gain-of-function variants in the WDR44 gene have recently been associated with an X-linked ciliopathy-related neurodevelopmental phenotype. Here, we report on a WDR44 loss-of-function (LOF) variant identified in the genome sequence from a male fetus enrolled in the Prenatal Genetic Diagnosis by Genomic Sequencing (PrenatalSEQ) multicenter study. The phenotype is consistent with the described X-linked ciliopathy that includes developmental delay, microcephaly, congenital heart defects, kidney abnormalities, cryptorchidism, musculoskeletal abnormalities, craniofacial dysmorphism, and effusions. This is the first report of a WDR44 LOF variant in an affected individual with a prenatal presentation and supports LOF as a mechanism for the X-linked WDR44 ciliopathy-related phenotype.

Prominent loss of striatal dopamine transporter binding in frontotemporal lobar degeneration with the MAPT N279K mutation present as early as at prodromal stage without parkinsonism.

Our research found out, from 123I-FP-CIT SPECT scans of three familial frontotemporal dementia (fFTD) individuals with MAPT N279K mutation and similar autopsy findings of frontotemporal degeneration with severe neuronal loss in the substantia nigra, that prominent decrease of dopamine transporter binding (z-score < -5.0) was present at prodromal fFTD without parkinsonism.

Language abnormalities in Alzheimer's disease indicate reduced informativeness.

OBJECTIVE: This study aims to elucidate the cognitive underpinnings of language abnormalities in Alzheimer's Disease (AD) using a computational cross-linguistic approach and ultimately enhance the understanding and diagnostic accuracy of the disease. METHODS: Computational analyses were conducted on language samples of 156 English and 50 Persian speakers, comprising both AD patients and healthy controls, to extract language indicators of AD. Furthermore, we introduced a machine learning-based metric, Language Informativeness Index (LII), to quantify empty speech. RESULTS: Despite considerable disparities in surface structures between the two languages, we observed consistency across language indicators of AD in both English and Persian. Notably, indicators of AD in English resulted in a classification accuracy of 90% in classifying AD in Persian. The substantial degree of transferability suggests that the language abnormalities of AD do not tightly link to the surface structures specific to English. Subsequently, we posited that these abnormalities stem from impairments in a more universal aspect of language production: the ability to generate informative messages independent of the language spoken. Consistent with this hypothesis, we found significant correlations between language indicators of AD and empty speech in both English and Persian. INTERPRETATION: The findings of this study suggest that language impairments in AD arise from a deficit in a universal aspect of message formation rather than from the breakdown of language-specific morphosyntactic structures. Beyond enhancing our understanding of the psycholinguistic deficits of AD, our approach fosters the development of diagnostic tools across various languages, enhancing health equity and biocultural diversity.

Boundary-based registration improves sensitivity for detecting hypoperfusion in sporadic frontotemporal lobar degeneration.

Introduction: Frontotemporal lobar degeneration (FTLD) is associated with FTLD due to tau (FTLD-tau) or TDP (FTLD-TDP) inclusions found at autopsy. Arterial Spin Labeling (ASL) MRI is often acquired in the same session as a structural T1-weighted image (T1w), enabling detection of regional changes in cerebral blood flow (CBF). We hypothesize that ASL-T1w registration with more degrees of freedom using boundary-based registration (BBR) will better align ASL and T1w images and show increased sensitivity to regional hypoperfusion differences compared to manual registration in patient participants. We hypothesize that hypoperfusion will be associated with a clinical measure of disease severity, the FTLD-modified clinical dementia rating scale sum-of-boxes (FTLD-CDR). Materials and methods: Patients with sporadic likely FTLD-tau (sFTLD-tau; N = 21), with sporadic likely FTLD-TDP (sFTLD-TDP; N = 14), and controls (N = 50) were recruited from the Connectomic Imaging in Familial and Sporadic Frontotemporal Degeneration project (FTDHCP). Pearson's Correlation Coefficients (CC) were calculated on cortical vertex-wise CBF between each participant for each of 3 registration methods: (1) manual registration, (2) BBR initialized with manual registration (manual+BBR), (3) and BBR initialized using FLIRT (FLIRT+BBR). Mean CBF was calculated in the same regions of interest (ROIs) for each registration method after image alignment. Paired t-tests of CC values for each registration method were performed to compare alignment. Mean CBF in each ROI was compared between groups using t-tests. Differences were considered significant at p < 0.05 (Bonferroni-corrected). We performed linear regression to relate FTLD-CDR to mean CBF in patients with sFTLD-tau and sFTLD-TDP, separately (p < 0.05, uncorrected). Results: All registration methods demonstrated significant hypoperfusion in frontal and temporal regions in each patient group relative to controls. All registration methods detected hypoperfusion in the left insular cortex, middle temporal gyrus, and temporal pole in sFTLD-TDP relative to sFTLD-tau. FTLD-CDR had an inverse association with CBF in right temporal and orbitofrontal ROIs in sFTLD-TDP. Manual+BBR performed similarly to FLIRT+BBR. Discussion: ASL is sensitive to distinct regions of hypoperfusion in patient participants relative to controls, and in patients with sFTLD-TDP relative to sFTLD-tau, and decreasing perfusion is associated with increasing disease severity, at least in sFTLD-TDP. BBR can register ASL-T1w images adequately for controls and patients.

Prediction of Post-Operative Delirium in Older Adults from Preoperative Cognition and Alpha Power from Resting-State EEG.

Postoperative Delirium (POD) is the most common complication following surgery among older adults, and has been consistently associated with increased mortality and morbidity, cognitive decline, and loss of independence, as well as markedly increased health-care costs. The development of new tools to identify individuals at high risk for POD could guide clinical decision-making and enable targeted interventions to potentially decrease delirium incidence and POD-related complications. In this study, we used machine learning techniques to evaluate whether baseline (pre-operative) cognitive function and resting-state electroencephalography could be used to identify patients at risk for POD. Pre-operative resting-state EEGs and the Montreal Cognitive Assessment (MoCA) were collected from 85 patients (age = 73 ± 6.4 years) undergoing elective surgery, 12 of whom subsequently developed POD. The model with the highest f1-score for predicting delirium, a linear-discriminant analysis (LDA) model incorporating MoCA scores and occipital alpha-band EEG features, was subsequently validated in an independent, prospective cohort of 51 older adults (age ≥ 60) undergoing elective surgery, 6 of whom developed POD. The LDA-based model, with a total of 7 features, was able to predict POD with area under the receiver operating characteristic curve, specificity and accuracy all >90%, and sensitivity > 80%, in the validation cohort. Notably, models incorporating both resting-state EEG and MoCA scores outperformed those including either EEG or MoCA alone. While requiring prospective validation in larger cohorts, these results suggest that prediction of POD with high accuracy may be feasible in clinical settings using simple and widely available clinical tools.

CSF Proteomics in Patients With Progressive Supranuclear Palsy.

BACKGROUND AND OBJECTIVES: Identification of fluid biomarkers for progressive supranuclear palsy (PSP) is critical to enhance therapeutic development. We implemented unbiased DNA aptamer (SOMAmer) proteomics to identify novel CSF PSP biomarkers. METHODS: This is a cross-sectional study in original (18 clinically diagnosed PSP-Richardson syndrome [PSP-RS], 28 cognitively healthy controls]), validation (23 PSP-RS, 26 healthy controls), and neuropathology-confirmed (21 PSP, 52 non-PSP frontotemporal lobar degeneration) cohorts. Participants were recruited through the University of California, San Francisco, and the 4-Repeat Neuroimaging Initiative. The original and neuropathology cohorts were analyzed with the SomaScan platform version 3.0 (5026-plex) and the validation cohort with version 4.1 (7595-plex). Clinical severity was measured with the PSP Rating Scale (PSPRS). CSF proteomic data were analyzed to identify differentially expressed targets, implicated biological pathways using enrichment and weighted consensus gene coexpression analyses, diagnostic value of top targets with receiver-operating characteristic curves, and associations with disease severity with linear regressions. RESULTS: A total of 136 participants were included (median age 70.6 ± 8 years, 68 [50%] women). One hundred fifty-five of 5,026 (3.1%), 959 of 7,595 (12.6%), and 321 of 5,026 (6.3%) SOMAmers were differentially expressed in PSP compared with controls in original, validation, and neuropathology-confirmed cohorts, with most of the SOMAmers showing reduced signal (83.1%, 95.1%, and 73.2%, respectively). Three coexpression modules were associated with PSP across cohorts: (1) synaptic function/JAK-STAT (ß = -0.044, corrected p = 0.002), (2) vesicle cytoskeletal trafficking (ß = 0.039, p = 0.007), and (3) cytokine-cytokine receptor interaction (ß = -0.032, p = 0.035) pathways. Axon guidance was the top dysregulated pathway in PSP in original (strength = 1.71, p < 0.001), validation (strength = 0.84, p < 0.001), and neuropathology-confirmed (strength = 0.78, p < 0.001) cohorts. A panel of axon guidance pathway proteins discriminated between PSP and controls in original (area under the curve [AUC] = 0.924), validation (AUC = 0.815), and neuropathology-confirmed (AUC = 0.932) cohorts. Two inflammatory proteins, galectin-10 and cytotoxic T lymphocyte-associated protein-4, correlated with PSPRS scores across cohorts. DISCUSSION: Axon guidance pathway proteins and several other molecular pathways are downregulated in PSP, compared with controls. Proteins in these pathways may be useful targets for biomarker or therapeutic development.

Brain inflammation co-localizes highly with tau in mild cognitive impairment due to early-onset Alzheimer's disease.

Brain inflammation, with an increased density of microglia and macrophages, is an important component of Alzheimer's disease (AD) and a potential therapeutic target. However, it is incompletely characterized, particularly in patients whose disease begins before the age of 65 years and, thus, have few co-pathologies. Inflammation has been usefully imaged with translocator protein (TSPO) positron emission tomography (PET), but most inflammation PET tracers cannot image subjects with a low-binder TSPO rs6971 genotype. In an important development, participants with any TSPO genotype can be imaged with a novel tracer, [11C]ER176, that has a high binding potential and a more favorable metabolite profile than other TSPO tracers currently available. We applied [11C]ER176 to detect brain inflammation in mild cognitive impairment (MCI) caused by early-onset AD. Furthermore, we sought to correlate the brain localization of inflammation, volume loss, elevated Aß and tau. We studied brain inflammation in 25 patients with early-onset amnestic MCI (average age 59 ± 4.5 years, 10 women) and 23 healthy controls (average age 65 ± 6.0 years, 12 women), both groups with a similar proportion of all three TSPO-binding affinities. [11C]ER176 total distribution volume (VT), obtained with an arterial input function, was compared across patients and controls using voxel-wise and region-wise analyses. In addition to inflammation PET, most MCI patients had Aß (n=23), and tau PET (n=21). For Aß and tau tracers, standard uptake value ratios (SUVRs) were calculated using cerebellar grey matter as region of reference. Regional correlations among the three tracers were determined. Data were corrected for partial volume effect. Cognitive performance was studied with standard neuropsychological tools. In MCI caused by early-onset AD, there was inflammation in the default network, reaching statistical significance in precuneus and lateral temporal and parietal association cortex bilaterally, and in the right amygdala. Topographically, inflammation co-localized most strongly with tau (r= 0.63 ± 0.24). This correlation was higher than the co-localization of Aß with tau (r= 0.55±0.25) and of inflammation with Aß (0.43±0.22). Inflammation co-localized least with atrophy (-0.29±0.26). These regional correlations could be detected in participants with any of the three rs6971 TSPO polymorphisms. Inflammation in AD-related regions correlated with impaired cognitive scores. Our data highlight the importance of inflammation, a potential therapeutic target, in the AD process. Furthermore, they support the notion that, as shown in experimental tissue and animal models, the propagation of tau in humans is associated with brain inflammation.

Deciphering Distinct Genetic Risk Factors for FTLD-TDP Pathological Subtypes via Whole-Genome Sequencing.

Frontotemporal lobar degeneration with neuronal inclusions of the TAR DNA-binding protein 43 (FTLD-TDP) is a fatal neurodegenerative disorder with only a limited number of risk loci identified. We report our comprehensive genome-wide association study as part of the International FTLD-TDP Whole-Genome Sequencing Consortium, including 985 cases and 3,153 controls, and meta-analysis with the Dementia-seq cohort, compiled from 26 institutions/brain banks in the United States, Europe and Australia. We confirm UNC13A as the strongest overall FTLD-TDP risk factor and identify TNIP1 as a novel FTLD-TDP risk factor. In subgroup analyses, we further identify for the first time genome-wide significant loci specific to each of the three main FTLD-TDP pathological subtypes (A, B and C), as well as enrichment of risk loci in distinct tissues, brain regions, and neuronal subtypes, suggesting distinct disease aetiologies in each of the subtypes. Rare variant analysis confirmed TBK1 and identified VIPR1 , RBPJL , and L3MBTL1 as novel subtype specific FTLD-TDP risk genes, further highlighting the role of innate and adaptive immunity and notch signalling pathway in FTLD-TDP, with potential diagnostic and novel therapeutic implications.

A Natural Metabolite and Inhibitor of the NLRP3 Inflammasome: 4-hydroxynonenal.

The NOD-, LRR-, and pyrin domain-containing protein 3 (NLRP3) inflammasome, crucial in the innate immune response, is linked to various human diseases. However, the effect of endogenous metabolites, like 4-hydroxynonenal (HNE), on NLRP3 inflammasome activity remains underexplored. Recent research highlights HNE's inhibitory role in NLRP3 inflammasome activation, shedding light on its potential as an endogenous regulator of inflammatory responses. Studies demonstrate that HNE blocks NLRP3 inflammasome-mediated pyroptosis and IL-1ß secretion. Additionally, covalent targeting emerges as a common mechanism for inhibiting NLRP3 inflammasome assembly, offering promising avenues for therapeutic intervention. Further investigation is needed to understand the impact of endogenous HNE on NLRP3 inflammasome activation, especially in settings where lipid peroxidation byproducts like HNE are produced. Understanding the intricate interplay between HNE and the NLRP3 inflammasome holds significant potential for unraveling novel therapeutic strategies for inflammatory disorders.

Examining Associations Between Smartphone Use and Clinical Severity in Frontotemporal Dementia: Proof-of-Concept Study.

BACKGROUND: Frontotemporal lobar degeneration (FTLD) is a leading cause of dementia in individuals aged <65 years. Several challenges to conducting in-person evaluations in FTLD illustrate an urgent need to develop remote, accessible, and low-burden assessment techniques. Studies of unobtrusive monitoring of at-home computer use in older adults with mild cognitive impairment show that declining function is reflected in reduced computer use; however, associations with smartphone use are unknown. OBJECTIVE: This study aims to characterize daily trajectories in smartphone battery use, a proxy for smartphone use, and examine relationships with clinical indicators of severity in FTLD. METHODS: Participants were 231 adults (mean age 52.5, SD 14.9 years; n=94, 40.7% men; n=223, 96.5% non-Hispanic White) enrolled in the Advancing Research and Treatment of Frontotemporal Lobar Degeneration (ARTFL study) and Longitudinal Evaluation of Familial Frontotemporal Dementia Subjects (LEFFTDS study) Longitudinal Frontotemporal Lobar Degeneration (ALLFTD) Mobile App study, including 49 (21.2%) with mild neurobehavioral changes and no functional impairment (ie, prodromal FTLD), 43 (18.6%) with neurobehavioral changes and functional impairment (ie, symptomatic FTLD), and 139 (60.2%) clinically normal adults, of whom 55 (39.6%) harbored heterozygous pathogenic or likely pathogenic variants in an autosomal dominant FTLD gene. Participants completed the Clinical Dementia Rating plus National Alzheimer's Coordinating Center Frontotemporal Lobar Degeneration Behavior and Language Domains (CDR+NACC FTLD) scale, a neuropsychological battery; the Neuropsychiatric Inventory; and brain magnetic resonance imaging. The ALLFTD Mobile App was installed on participants' smartphones for remote, passive, and continuous monitoring of smartphone use. Battery percentage was collected every 15 minutes over an average of 28 (SD 4.2; range 14-30) days. To determine whether temporal patterns of battery percentage varied as a function of disease severity, linear mixed effects models examined linear, quadratic, and cubic effects of the time of day and their interactions with each measure of disease severity on battery percentage. Models covaried for age, sex, smartphone type, and estimated smartphone age. RESULTS: The CDR+NACC FTLD global score interacted with time on battery percentage such that participants with prodromal or symptomatic FTLD demonstrated less change in battery percentage throughout the day (a proxy for less smartphone use) than clinically normal participants (P<.001 in both cases). Additional models showed that worse performance in all cognitive domains assessed (ie, executive functioning, memory, language, and visuospatial skills), more neuropsychiatric symptoms, and smaller brain volumes also associated with less battery use throughout the day (P<.001 in all cases). CONCLUSIONS: These findings support a proof of concept that passively collected data about smartphone use behaviors associate with clinical impairment in FTLD. This work underscores the need for future studies to develop and validate passive digital markers sensitive to longitudinal clinical decline across neurodegenerative diseases, with potential to enhance real-world monitoring of neurobehavioral change.

A tale of two PG systems: A comparison of the two most widely used continuous probabilistic genotyping systems in the United States.

The development of probabilistic genotyping (PG) systems to quantitatively analyze DNA mixture samples has been transformative in forensic science. TrueAllele® Casework (TA) and STRmix™ (STRmix) are the two most widely used PG systems in the United States. The two systems were challenged with 48 two-, three-, and four-person mock casework samples, for a total of 152 likelihood ratio (LR) comparisons. TA and STRmix converged on the same result (supportive, non-supportive, or inconclusive) for ~91% of contributor-specific comparisons. Where moderate or substantial differences in log(LR) values were observed, 9% affected the conclusion of the reference association to the mixture. The PG systems exhibited high correlations for estimated contributor-specific template quantities (~92%) and log(LR)s produced (>88%). When the log(LR)s for only low-template contributors (<100 pg) were compared, the R2 value dropped to ~68% and the difference became statistically significant. Of the 14 contributor comparisons where the conclusion differed, two were contradictory (supportive vs. non-supportive) and 12 were either inconclusive versus non-supportive or inconclusive versus supportive. The differing results were likely due to dissimilarities in the mixture input file as STRmix uses a lab-defined analytical threshold (AT) and TA models to 10 RFUs for each electropherogram. When 7 of the 14 mixtures were reanalyzed by STRmix using a 10 RFU AT, the log(LR)s for the low-template contributors became more similar to TAs. This study shows that while both systems may produce accurate and calibrated LRs, their results can deviate, especially for low-template, degraded contributors, and the deviation is generally predictable.

Artificial intelligence classifies primary progressive aphasia from connected speech.

Neurodegenerative dementia syndromes, such as primary progressive aphasias (PPA), have traditionally been diagnosed based, in part, on verbal and non-verbal cognitive profiles. Debate continues about whether PPA is best divided into three variants and regarding the most distinctive linguistic features for classifying PPA variants. In this cross-sectional study, we initially harnessed the capabilities of artificial intelligence and natural language processing to perform unsupervised classification of short, connected speech samples from 78 pateints with PPA. We then used natural language processing to identify linguistic features that best dissociate the three PPA variants. Large language models discerned three distinct PPA clusters, with 88.5% agreement with independent clinical diagnoses. Patterns of cortical atrophy of three data-driven clusters corresponded to the localization in the clinical diagnostic criteria. In the subsequent supervised classification, 17 distinctive features emerged, including the observation that separating verbs into high- and low-frequency types significantly improved classification accuracy. Using these linguistic features derived from the analysis of short, connected speech samples, we developed a classifier that achieved 97.9% accuracy in classifying the four groups (three PPA variants and healthy controls). The data-driven section of this study showcases the ability of large language models to find natural partitioning in the speech of patients with PPA consistent with conventional variants. In addition, the work identifies a robust set of language features indicative of each PPA variant, emphasizing the significance of dividing verbs into high- and low-frequency categories. Beyond improving diagnostic accuracy, these findings enhance our understanding of the neurobiology of language processing.

Default mode network tau predicts future clinical decline in atypical early Alzheimer's disease.

Identifying individuals with early stage Alzheimer's disease (AD) at greater risk of steeper clinical decline would allow professionals and loved ones to make better-informed medical, support, and life planning decisions. Despite accumulating evidence on the clinical prognostic value of tau PET in typical late-onset amnestic AD, its utility in predicting clinical decline in individuals with atypical forms of AD remains unclear. In this study, we examined the relationship between baseline tau PET signal and the rate of subsequent clinical decline in a sample of 48 A+/T+/N+ patients with mild cognitive impairment or mild dementia due to AD with atypical clinical phenotypes (Posterior Cortical Atrophy, logopenic variant Primary Progressive Aphasia, and amnestic syndrome with multi-domain impairment and age of onset < 65 years). All patients underwent structural magnetic resonance imaging (MRI), tau (18F-Flortaucipir) PET, and amyloid (either 18F-Florbetaben or 11C-Pittsburgh Compound B) PET scans at baseline. Each patient's longitudinal clinical decline was assessed by calculating the annualized change in the Clinical Dementia Rating Sum-of-Boxes (CDR-SB) scores from baseline to follow-up (mean time interval = 14.55 ± 3.97 months). Our sample of early atypical AD patients showed an increase in CDR-SB by 1.18 ± 1.25 points per year: t(47) = 6.56, p < .001, d = 0.95. These AD patients showed prominent baseline tau burden in posterior cortical regions including the major nodes of the default mode network, including the angular gyrus, posterior cingulate cortex/precuneus, and lateral temporal cortex. Greater baseline tau in the broader default mode network predicted faster clinical decline. Tau in the default mode network was the strongest predictor of clinical decline, outperforming baseline clinical impairment, tau in other functional networks, and the magnitude of cortical atrophy and amyloid burden in the default mode network. Overall, these findings point to the contribution of baseline tau burden within the default mode network of the cerebral cortex to predicting the magnitude of clinical decline in a sample of atypical early AD patients one year later. This simple measure based on a tau PET scan could aid the development of a personalized prognostic, monitoring, and treatment plan tailored to each individual patient, which would help clinicians not only predict the natural evolution of the disease but also estimate the effect of disease-modifying therapies on slowing subsequent clinical decline given the patient's tau burden while still early in the disease course.

Relationship between cortical brain atrophy, delirium, and long-term cognitive decline in older surgical patients.

In older patients, delirium after surgery is associated with long-term cognitive decline (LTCD). The neural substrates of this association are unclear. Neurodegenerative changes associated with dementia are possible contributors. We investigated the relationship between brain atrophy rates in Alzheimer's disease (AD) and cognitive aging signature regions from magnetic resonance imaging before and one year after surgery, LTCD assessed by the general cognitive performance (GCP) score over 6 years post-operatively, and delirium in 117 elective surgery patients without dementia (mean age = 76). The annual change in cortical thickness was 0.2(1.7) % (AD-signature p = 0.09) and 0.4(1.7) % (aging-signature p = 0.01). Greater atrophy was associated with LTCD (AD-signature: beta(CI) = 0.24(0.06-0.42) points of GCP/mm of cortical thickness; p < 0.01, aging-signature: beta(CI) = 0.55(0.07-1.03); p = 0.03). Atrophy rates were not significantly different between participants with and without delirium. We found an interaction with delirium severity in the association between atrophy and LTCD (AD-signature: beta(CI) = 0.04(0.00-0.08), p = 0.04; aging-signature: beta(CI) = 0.08(0.03-0.12), p < 0.01). The rate of cortical atrophy and severity of delirium are independent, synergistic factors determining postoperative cognitive decline in the elderly.

Exploring the potential of virtual reality for the self-management of chronic pain: A scoping review of its use to address health literacy.

BACKGROUND: Individuals with low health literacy struggle to manage long-term conditions. Addressing pain-related health competencies is important in the management of chronic pain. Virtual reality may be a useful tool for empowering sustainable health-related stratgies due to its unique ability to engage users in artificial environments. OBJECTIVES: The aim of this scoping review was to explore existing research on the use of virtual reality as a tool to promote health literacy in people with chronic pain. DESIGN: Scoping Review guided by framework proposed by Arksey & O'Malley. METHOD: Articles related to "pain", "virtual reality" and "health literacy" were searched in four electronic databases: CINAHL, PubMed, Embase and PsycINFO using a formal search strategy. Studies were categorised based on intervention content using the Health Literacy Pathway Model which encompasses health knowledge, self-management skills, health communication and information seeking. RESULTS: Thirteen studies met the inclusion criteria. Several elements of pain related health literacy were not addressed in the research. Interventions addressed health knowledge, self-management skills, decision making and featured content aiming to address emotional barriers to pain-related health literacy. Other components including active information seeking and use, actively communicating with health professionals and seeking and negotiating treatment options, were not explicitly addressed. CONCLUSION: There is heterogeneity in existing research exploring the use of VR to support people with chronic pain. Existing VR tools to address pain-related health literacy do not cover several key components of health literacy. More research is required before a robust assessment of efficacy can be undertaken.

Differential Vulnerability of Hippocampal Subfields to Amyloid and Tau Deposition in the Lewy Body Diseases.

BACKGROUND AND OBJECTIVES: Alzheimer disease (AD) copathologies of ß-amyloid and tau are common in the Lewy body diseases (LBD), dementia with Lewy bodies (DLB) and Parkinson disease (PD), and target distinct hippocampal subfields compared with Lewy pathology, including subiculum and CA1. We investigated the hypothesis that AD copathologies impact the pattern of hippocampal subregion volume loss and cognitive function in LBD. METHODS: This was a cross-sectional and longitudinal, single-center, observational cohort study. Participants underwent neuropsychological testing and 3T-MRI with hippocampal segmentation using FreeSurferV7. PiB-PET and flortaucipir-PET imaging of comorbid ß-amyloid (A) and tau (T) were acquired. The association of functional cognition, ß-amyloid, and tau loads with hippocampal subregion volume was assessed. The contribution of subregion volumes to the relationship of AD-related deposits on functional cognition was examined with mediation analysis. The effects of AD-related deposits on the rate of subregion atrophy were evaluated with mixed-effects models. RESULTS: Of 103 participants (mean age: 70.3 years; 37.3% female), 52 had LBD with impaired cognition (LBD-I), 26 had normal cognition (LBD-N), and 25 were A- healthy controls (HCs). Volumes of hippocampal subregions prone to AD copathologies, including subiculum (F = 6.9, p = 0.002), presubiculum (F = 7.3, p = 0.001), and parasubiculum (F = 5.9, p = 0.004), were reduced in LBD-I compared with LBD-N and HC. Volume was preserved in CA2/3, Lewy pathology susceptible subregions. In LBD-I, reduced CA1, subiculum, and presubiculum volumes were associated with greater functional cognitive impairment (all p < 0.05). Compared with HC, subiculum volume was reduced in A+T+ but not A-T- participants (F = 2.62, p = 0.043). Reduced subiculum volume mediated the effect of amyloid on functional cognition (0.12, 95% CI: 0.005 to 0.26, p = 0.040). In 26 longitudinally-evaluated participants, baseline tau deposition was associated with faster CA1 (p = 0.021) and subiculum (p = 0.002) atrophy. DISCUSSION: In LBD, volume loss in hippocampal output subregions-particularly the subiculum-is associated with functional cognition and AD-related deposits. Tau deposition appears to accelerate subiculum and CA1 atrophy, whereas Aß does not. Subiculum volume may have value as a biomarker of AD copathology-mediated neurodegeneration and disease progression.

Using explainable artificial intelligence to identify linguistic biomarkers of amyloid pathology in primary progressive aphasia.

Introduction: Recent success has been achieved in Alzheimer's disease (AD) clinical trials targeting amyloid beta (ß), demonstrating a reduction in the rate of cognitive decline. However, testing methods for amyloid-ß positivity are currently costly or invasive, motivating the development of accessible screening approaches to steer patients toward appropriate diagnostic tests. Here, we employ a pre-trained language model (Distil-RoBERTa) to identify amyloid-ß positivity from a short, connected speech sample. We further use explainable AI (XAI) methods to extract interpretable linguistic features that can be employed in clinical practice. Methods: We obtained language samples from 74 patients with primary progressive aphasia (PPA) across its three variants. Amyloid-ß positivity was established through the analysis of cerebrospinal fluid, amyloid PET, or autopsy. 51% of the sample was amyloid-positive. We trained Distil-RoBERTa for 16 epochs with a batch size of 6 and a learning rate of 5e-5, and used the LIME algorithm to train interpretation models to interpret the trained classifier's inference conditions. Results: Over ten runs of 10-fold cross-validation, the classifier achieved a mean accuracy of 92%, SD = 0.01. Interpretation models were able to capture the classifier's behavior well, achieving an accuracy of 97% against classifier predictions, and uncovering several novel speech patterns that may characterize amyloid-ß positivity. Discussion: Our work improves previous research which indicates connected speech is a useful diagnostic input for prediction of the presence of amyloid-ß in patients with PPA. Further, we leverage XAI techniques to reveal novel linguistic features that can be tested in clinical practice in the appropriate subspecialty setting. Computational linguistic analysis of connected speech shows great promise as a novel assessment method in patients with AD and related disorders.