bluuebunny/crossref_metadata_2025 huggingface dataset:
Predictive Ability of Physical Performance Measures in Identifying Falls in Older Adults with Dementia: A Systematic Review and Meta‐Analysis
Authors: Chan Wayne LS , Pin Tamis , Chan Jason YH , Siu George CH
Published: December 2023

Background

Falls are very common in older adults with dementia and pose a serious threat to their physical and psychological health, functional independence, and life expectancy. Reduced physical performance (i.e., gait, balance, muscle strength, and mobility impairments) has been shown to be a risk factor for falls in older adults with dementia. Identifying measures of physical performance that can predict falls in this population is crucial for clinicians and researchers to select appropriate assessment tools to evaluate their fall risk and design effective fall prevention strategies.

The objective of this systematic review and meta‐analysis is to determine whether physical performance measures commonly used in clinical settings can predict falls in older adults with dementia.

Method

MEDLINE, Embase, PsycINFO, CINAHL, SPORTDiscus, Cochrane Library, and PEDro databases were searched from inception until 27 January 2023 (Prospero Registration: CRD42022303670). Longitudinal cohort and cross‐sectional studies that evaluated the associations between physical performance measures and falls in individuals aged ≥ 65 with dementia were included. The standardized mean difference (SMD) in each physical performance measure between fallers and non‐fallers was calculated.

Result

Twenty‐five studies were included in this systematic review and meta‐analysis (Figure 1). Five‐time chair stands (SMD: 0.23; 95% CI: 0.01, 0.45), postural sway on floor with eyes open (SMD: 0.25; 95% CI: 0.07, 0.43), postural sway on foam with eyes open (SMD: 0.45; 95% CI: 0.25, 0.66), Berg Balance Scale (SMD: ‐0.52; 95% CI: ‐0.87, ‐0.17), and Timed Up and Go test (SMD: 0.34; 95% CI: 0.03, 0.64) predicted falls in older adults with dementia (Figure 2). Sensitivity analyses showed that gait speed could predict falls in those who were staying in residential care facilities or hospitals (SMD: ‐0.21; 95% CI: ‐0.38, ‐0.05). Concerns about the risk of bias in the included studies were noted, and the quality of evidence was generally very low to low.

Conclusion

Chair stands, postural sway on floor and foam, Berg Balance Scale, and Timed Up and Go test can predict falls in older adults with dementia. Gait speed can only predict falls among those who are staying in residential care or hospital units.
Development of co‐designed resources to provide guidance for people living with dementia and their carers on medication management during hospitalisation
Authors: Clough Alexander J , Gnjidic Danijela , Manias Elizabeth , Jeon Yun‐Hee , Jokanovic Natali , Thompson Jane F , Schneider Carl R , Chen Timothy , Sawan Mouna J
Published: December 2023

Background

People with dementia are more likely to experience medication‐related harm than people without dementia at transitions of care. However, medication management guidance is often not provided to people with dementia and carers during hospitalisation. We aimed to co‐design novel medication management resources for people with dementia and carers during hospitalisation.

Method

This is a mixed‐methods study involving five stages: i) generate resource topics by content analysing a scoping review and a previous qualitative study exploring carers’ medication management experiences at discharge; ii) develop the first version of the resources; iii) obtain input from dementia expert advisory panels; iv) undertake focus groups with people with dementia, carers, and healthcare professionals; v) complete quantitative analysis of readability, understandability and actionability, and suitability of the resources through validated tools. Focus groups were content analysed to explore participants’ perceptions of the resources’ topics, content, and layout, and how well they meet their needs. The resources were edited during each stage, a graphic designer modified the layout, and final copies produced.

Result

We derived six resource topics including: participating in shared decision‐making; identifying common medications that may affect cognition; and describing hospital processes and the person’s role. The advisory panels recommended introducing informed consent and the discharge summary and medications list in the resources. Two focus groups have been conducted with carers (n = 4) and healthcare professionals (n = 3). Participants felt the topics generated would be useful and beneficial. They suggested: modifying the title to appeal to all supporters of someone with dementia (not just formal carers); listing more medications that may affect cognition; and limiting medical terminology. They recommended providing the resources at admission for referral throughout the hospital stay. The resources were of an adequate reading level, understandable, actionable, and suitable.

Conclusion

Resources containing information on shared decision‐making, informed consent, medications that may affect cognition, and support to improve involvement in medication decisions would be valuable to people with dementia, carers, and healthcare professionals. The next stages are to conduct user‐testing on how well key information can be located and understood and evaluate participants’ knowledge and engagement in shared decision‐making after using the resources.
Fixel based analysis of white matter alterations in Mild cognitive impairment and Alzheimer’s disease
Authors: Bhattarai Anjan , Maillard Pauline , Decarli Charles , Fan Audrey
Published: December 2023

Background

Alzheimer’s disease (AD) is the leading cause of dementia, and is associated with altered white matter (WM) microstructures in the brain (Clerx et al., 2012; Jack Jr et al., 2013). Diffusion weighted imaging (DWI) is a non‐invasive method of examining white matter alterations in AD and Mild cognitive Impairment (MCI). DWI based Fixel‐based analysis (FBA) is a novel method that enables the investigation of fibre specific WM alterations at the microscopic level. In this study, we investigated fibre tract‐specific changes in the whole‐brain WM, in individuals with MCI and AD using DWI based FBA.

Method

N = 102 participants (>70 years) from the University of California Davis Alzheimer’s Disease Research Center were included in this study, including 34 individuals with AD, 34 with MCI, and 34 control subjects. DWI data were obtained on a 3T Siemens TimTrio scanner (resolution = 2mm, 60 directions, at b = 1000s/mm2) and pre‐processed using MRtrix3 (Tournier et al., 2019). Single‐Shell 3‐tissue CSD (SS3T‐CSD) was performed to obtain Fibre Orientation Distributions (FODs) related to WM as well as for gray matter and cerebrospinal fluid compartments in all voxels, using MRtrix3Tissue. A study‐specific unbiased FOD template was generated and used for FBA processing. Whole‐brain FBA maps, including fibre density (FD), fibre cross‐section (FC), and combination of fibre density and cross‐section (FDC), were generated. Statistical analyses were performed using connectivity based fixel enhancement at each fixel using general linear model (Raffelt et al., 2015)(Figure1).

Result

The whole‐brain FBA showed significant reduction in FD in individuals with AD compared to MCI, in regions including splenium of corpus callosum, fornix, and the left uncinate fasciculus (Figure2). Furthermore, AD individuals showed significant reduction in FD in the left uncinate fasciculus compared to controls. We observed subtle reduction in FD and FDC metrics (although not statistically significant after FWE corrections) in regions including corpus callosum, forceps majors, and uncinate fasciculus in AD(Figure3). We did not observe significant difference in FC between groups.

Conclusion

FBA‐derived measures demonstrated sensitivity in detecting microstructural white matter alterations in AD. These findings highlight the utility of FBA as a promising tool for providing valuable insights into pathophysiologic changes in AD.
Association of depression dimensions with cognitive functioning in community‐dwelling oldest old adults of LifeAfter90 study
Authors: Soleimani Laili , Hirst Andrew K , Gilsanz Paola , Whitmer Rachel A. , Corrada María M. M. , Beeri Michal S
Published: December 2023

Background

Oldest old adults (90+) are the fastest growing population with high burden of cognitive impairment and depression. Though depression is a known risk factor for cognitive impairment, it is unclear how each of the predominant depression dimensions correspond with cognitive outcomes.

Methods

We used baseline data from LifeAfter90 study, a racially and ethnically diverse cohort of community‐dwelling adults aged 90 and above. Participants are longtime members of Kaiser Permanente Northern California Health System without a dementia diagnosis in their medical record. Verbal episodic memory, semantic memory, and executive function were assessed with the Spanish & English Neuropsychological Assessment Scale and averaged as a composite measure of global cognition. Depression was measured by the Geriatric Depression Scale (GDS) and the dimensions (Dysphoria, Withdrawal‐Apathy ‐Vigor (WAV), Anxiety, Hopelessness and Subjective Memory Complaint) calculated based on GDS‐items. Generalized linear mixed models estimated associations between global and domain‐specific cognition and depression dimensions adjusting for age, sex, education, and race.

Results

Participants (n = 960, mean age = 92.4 ± 2.3 yrs, 61.56% female) were 24% Asian, 23% Black, 19% Latino, and 27% White. The most common reported GDS items were “decreased energy” (52.45%), preference to “stay home” (46.31%) and “dropped activity” (40.0%) and 25% reported memory complaints (Figure 1). Hopelessness was associated with worse executive functions (P< 0.001) but not with other cognitive domains nor with global cognition. Dysphoric mood, WAV, and anxiety were not associated with any of the cognitive outcomes (Table 1). Conversely, subjective memory complaints was associated with worse global cognition, executive function and verbal episodic memory, but not with semantic memory.

Conclusion

Characterizing the dominant depression dimensions may be informative in predicting pattern of cognitive deficits. Perceived memory impairment in the oldest old was supported by objective deficit in episodic memory and executive functions suggesting that subjective reports should be taken as a valid early sign of impairment in this population. Further exploration of the longitudinal association of depression dimensions with cognitive domains is warranted.
Blood transcriptomics differentiate tau positive discordant and concordant individuals
Authors: Souza Lara Angi , Schlickmann Thomas , Machado Luiza Santos , Pascoal Tharick A. , Ant Marco , Bastiani De , Zimmer Eduardo R
Published: December 2023

Background

Levels of soluble hyperphosphorylated tau (p‐tau) in the cerebrospinal fluid (CSF) and tau tangles measured by positron emission tomography (PET) are used to determine tau positivity in Alzheimer’s disease (AD). However, a mismatch between CSF and PET positivity occurs in some cases. In fact, it is thought that CSF tau biomarker abnormalities may precede changes in tau‐PET imaging. With this in mind, the aim of our study was to examine the blood transcriptomic phenotypes in individuals presenting tau positivity biomarker discordance.

Method

Blood transcriptomics data from 64 individuals (cognitively unimpaired = 31 and cognitively impaired = 33) were obtained from the ADNI database. We divided these individuals into negative (CSF‐PET‐), discordant (CSF‐PET+ and CSF+PET‐) and positive group (CSF+PET+) to tau biomarkers. We also performed differential expression analyses (DEA) using the limma package. Additionally, we conducted a KEGG pathway enrichment analysis using altered genes obtained from DEA (p‐value < 0.05). Amyloid B42, total tau, Mini Mental State Examination, Clinical Dementia Rating Sum of Boxes, APOE4 status, education, and gender difference in the groups were described in Fig1. Data were analyzed with Pearson’s Chi‐squared test and Linear Model ANOVA. All statistical analyses were performed in R.

Results

Our analysis revealed that discordant and positive individuals had, respectively, 1191 and 314 DEGs if compared to negative individuals. Interestingly, we found 324 DEGs in common between them (Fig2A‐C). In the KEGG functional enrichment analysis (Fig3), we observed that the main terms were enriched in the tau discordant individuals were related to aminoacids metabolism and inflammation (Fig3A). In the CSF+/PET+ individuals pathways related to calcium signaling and viral response were the most altered compared to CSF‐/PET‐ (Fig3B).

Conclusion

Our results indicate that blood transcriptome differs among tau negative, discordant and positive individuals. This suggests that multiple biological pathways in the blood are dependent on tau changes. Further investigation of DEGs clusters possibly could demonstrate new insights about tau mechanisms involved in the disease.
A comparison of machine learning based‐composite cognitive test scores to track cognitive decline in early stages of Alzheimer’s disease dementia: ADOPIC study
Authors: Shishegar Rosita , Lee Matthew , Cox Timothy , Chai Tze Young , Dore Vincent , Bourgeat Pierrick , Fripp Jurgen , Burnham Samantha C. , Lamb Fiona , Robertson Joanne , Laws Simon M , Porter Tenielle , Savage Greg , Hassenstab Jason J. , Morris John C , Aschenbrenner Andrew J. , Weiner Michael S. W. , Masters Colin L , Rowe Christopher C , Villemagne Victor L , Lim Yen Ying , Doecke James D , Sohrabi Hamid R , Maruff Paul
Published: December 2023

Background

Clinical trials of early Alzheimer’s disease (AD) dementia use cognition as a primary outcome and therefore cognitive measures that accurately reflect disease progression and represent multiple cognitive domains are required. Current cognitive endpoints are computed by averaging standardized change from baseline scores (i.e., preclinical Alzheimer cognitive composite (PACC)). Here we compare the PACC to composite scores for which the combination of multiple performance scores has been optimized using machine learning (ML) algorithms in a large, harmonised data set, namely ADOPIC.

Methods

A dataset harmonised across various cognitive scores (Table 1) was used to construct composite scores using ML‐based algorithms: a manifold learning dimension reduction technique (UMAP), principal component analysis (PCA) and Latent variable analysis (LVA). Data from ADNI (n = 1470), AIBL (n = 1105) and OASIS participants (n = 412, Table 2) with ≥3 assessments ≤5 years before clinical progression/last visit were included. Participants were classified clinically as; stable cognitively unimpaired (CU), CU progressing to mild cognitive impairment (MCI) or dementia, stable MCI, MCI progressing to dementia, or AD dementia. For UMAP, all stable participants (including dementia) were used in 4‐fold cross‐validation training sets. However, for testing UMAP and unsupervised models (PCA and LVA) all clinical groups (except for dementia) were used. The validity of ML‐based composites was challenged by modelling mean (SD) change over time in the non‐demented clinical groups, using linear mixed model (LMM) analysis. Signal‐to‐noise ratios (SNRs) were calculated (mean change/SD change) for each composite. The mean change was measured in progressives relative to stable participants of each group.

Results

Each ML‐based cognitive composite showed sensitivity to cognitive decline in the progressor groups (Figure 1 A). For the MCI progressors, PCA and UMAP composites had significantly higher SNRs than PACC (P<0.01; Figure 1B), however, LVA performance was not significantly better than PACC. For the CU progressor group, SNRs for PCA and LVA did not show significant differences with PACC and UMAP performed significantly worse than PACC (P<0.01).

Conclusion

ML‐based cognitive composite score computed using PCA provides a practical solution to track cognitive decline with improved performance in tracking cognitive decline in MCI progressors compared to PACC, while being comparable in CUs.
Will Changes in the COVID‐19 Brain MRI Signature Region Affect Subsequent Dementia Outcomes? Linking Pre‐Pandemic Brain MRIs to Long‐Term Dementia Incidence in UK Biobank Participants
Authors: Wang Jingxuan , La Joie Renaud , Kelly J. Daniel , Kornak John , Chen Ruijia , Ackley Sarah F , Glymour M. Maria
Published: December 2023

Background

Neurocognitive sequelae of SARS‐CoV‐2 infection have been suggested as potentially leading to a higher incidence of dementia, but the underlying mechanisms remain unclear. By comparing brain magnetic resonance imaging (MRI) between participants with and without a history of SARS‐CoV‐2 infection, prior work identified infection‐related structural changes, including reduced cortical thickness and gray‐white matter contrast. We characterized the COVID‐19 signature region (COVIDsig) in MRIs taken prior to the pandemic and evaluated whether changes in this region predicted incident dementia.

Method

Using pre‐pandemic data on UK Biobank participants aged 55+, we examined the association between the COVID‐19 brain MRI signature region and dementia incidence up to March 2020 (N = 39,119 dementia‐free participants with a median of 1.8 years of follow‐up). The COVIDsig comprised the lateral orbitofrontal cortex and parahippocampal gyrus and the exposures included COVIDsig cortical thickness and COVIDsig gray‐white matter contrast. Dementia diagnoses were obtained from linked hospital, primary care, and death registry data. We used Cox proportional‐hazards models and adjusted for sex, APOE‐ε4, education, material deprivation, body mass index, and assessment center. To test if COVIDsig predicts dementia risk independently of global cerebral atrophy, we further adjusted for an Alzheimer’s disease signature region (ADsig) or a non‐COVID‐19 signature region (cerebral cortex except COVIDsig). In sensitivity analysis, we repeated the primary analysis on each component of COVIDsig (lateral orbitofrontal cortex and parahippocampal gyrus) separately.

Result

The mean age of the participants was 64.7 years (SD = 7.4; Table 1) and 65 dementia cases were identified during follow‐up. The age‐adjusted dementia incidence rate was 2.5 per 1,000 person‐years. One standard deviation decrease in COVIDsig cortical thickness and gray‐white matter contrast was associated with a 49% and a 52% increase in the hazard of dementia diagnosis (HR for cortical thickness = 1.49, 95% CI = 1.19‐1.88; HR for gray‐white matter contrast = 1.52, 95% CI = 1.20‐1.94; Table 2), respectively. After adjusting for ADsig or non‐COVID‐19 signature region, associations were attenuated (Figure 1).

Conclusion

These results should be evaluated in independent samples and the persistence of post‐COVID‐19 structural brain changes assessed. If confirmed, findings suggest SARS‐CoV‐2 infection may increase dementia incidence via structural brain changes.
Gross measures of neurodegeneration from autopsy explain as much variance in dementia severity as measures from histology
Authors: Woodworth Davis C. , Nguyen Katelynn M. , Kawas Claudia H. , Corrada María M. M. , Sajjadi S. Ahmad
Published: December 2023

Background

Gross assessment of brain atrophy at autopsy provides important information about the degree of neurodegeneration. It complements information on misfolded proteins and other microscopic lesions provided by histological assessment. We examined how strongly these gross measures of neurodegeneration from autopsy were related to dementia severity as compared to measures from histology.

Method

We used data from participants in the National Alzheimer’s Coordinating Center database (09/2005‐05/2020) with relevant autopsy data and clinical assessment within a year before death. We used Clinical Dementia Rating sum‐of‐the‐boxes (CDR‐SB) or Mini‐Mental State Examination (MMSE, in subset where available) as outcome measures. Gross pathology measures included hippocampal atrophy, cortical atrophy, frontal/temporal lobar atrophy, and locus coeruleus hypopigmentation. Histology measures included Alzheimer’s disease neuropathologic change (ADNC), Lewy bodies, cerebral amyloid angiopathy, hippocampal sclerosis, arteriolosclerosis, infarcts, microinfarcts, and hemorrhages. We used dichotomized versions of all pathology variables (see Table‐1 for dichotomizations). We used multiple linear regressions with relative importance analyses for separate models (with either gross or histology measures) and a joint model (all measures), adjusting for sex, years of education, and months between clinical assessment and death. We report variance‐explained (R2) and relative importance of predictors.

Result

Participant characteristics are described in Table‐1 (N = 984). For models including gross or histology measures separately, the variance‐explained for CDR‐SB by the gross model (total = 40.8%, gross measures = 29.4%) was comparable to the histology model (total = 36.9%, histology measures = 23.9%, Figure‐1). Results for the separate models were similar when using MMSE (N = 238; gross model = 38.8%, histology model = 39.6% variance‐explained). For the joint model, variance‐explained for CDR‐SB increased to 47.3%, with 22.0% explained by gross and 15.2% explained by histology measures (Figure‐2). While ADNC was the most important variable in the joint model, this was followed in importance by three gross measures: hippocampal atrophy, cortical atrophy, and locus coeruleus hypopigmentation. Results for the joint model were similar when using MMSE (total = 50.3%, gross = 19.6%, histology = 25.6% variance‐explained).

Conclusion

We found that measures of gross neurodegeneration were as strongly related to dementia severity as measures from histology. While histology provides important clues about dementia etiology, measures of neurodegeneration are just as vital in our pursuit towards better understanding, preventing, and treating dementia.
Music and humanoid robots aid in performing daily living activities in people affected with advance stage dementia
Authors: Khan Arshia A
Published: December 2023

Background

As the disease progresses the ability to perform daily living activities is gradually reduced. As dementia progresses music is one of the last things to go(Foo et al.2017, Beatty et al., 1988). Additionally many studies have shown music to be an effective tool to help people affected with dementia(Baird, & Samson, 2015). In order to help residents in nursing homes to perform their daily living activities we programmed a humanoid robot to sing, perform the daily living activity and instruct the resident to follow them in performing specific daily living activities. The activities that are performed are brushing teeth, washing face, putting on clothes and going from point A to point B.

Method

Humanoid robots are programmed to sing the instructions to perform a daily living activity and also act out the activity. The robots are programmed to perform simple activities of daily living that the people affected with dementia are forgetting to perform. The music is composed and lyrics are written by Professors Rudy Perrault and Rachel Inselman.

Result

The robots have been successfully helping people affected with dementia in a short trial. There were three participants chosen for this case study where one person was in the advanced stage of dementia, one person was in the moderate stage of dementia and the third person was in the mild stage of dementia. Each of the participants was able to successfully perform the activities of daily living by mimicking the robots.

Conclusion

As soon as the robot started to sing and enact the daily living activity the participants started to move to the music and follow the robot.
Podcasts as Open‐Access Knowledge Dissemination Tools for Researchers: Lessons from Three Years of AMiNDR Podcast (A Month in Neurodegenerative Disease Research)
Authors: Louadi Sarah , Koch Ellen T , Rowe Elyn M , Kuhlmann Naila , Kamesh Anusha , Ferreira Jacques , Onbasi Lara , Liang Joseph J.H. , Cheng Judy
Published: December 2023

Background

Researchers are expected to manage various responsibilities whilst staying up‐to date with developments in their field. This is an arduous and time‐consuming task. In Alzheimer’s disease research alone, an average of 350 research articles are appearing every week on PubMed. We present the podcast AMiNDR (A Month in Neurodegenerative Disease Research), as a means of easing the burden on scientists. We offer an accessible channel where one can find all the primary papers sorted by topic in distinct episodes and summarised in an audio friendly format, complete with a bibliography of the papers covered as well as those not featured on the podcast.

Methods

We assess the success of this initiative on three levels: 1) We track our listenership and reach through podcast analytics on Simplecast including unique listeners, countries reached, and number of downloads per episode. 2) We evaluate the utility of our podcast based on listener retention, growth through time, and feedback collected through surveys and engagement indicators on social media. 3) We also consider the sustainability of our project through episode output, team growth, and internal interviews with volunteers to assess satisfaction.

Results

Since the launch of AMiNDR in June 2020, we have published over 350 episodes with accompanying bibliographies in 40 categories pertaining to Alzheimer’s disease research. Our podcast benefited from the contribution of 51 scientists worldwide at various stages of their training and reached over 6500 unique listeners in 91 countries. We present the results of the ongoing assessment of this initiative and measures we took to continue to refine our tool over the past three years, with a list of recommendations for digital knowledge dissemination tools like the AMiNDR podcast.

Conclusion

Based on our program evaluation, we find that AMiNDR is garnering a growing listenership worldwide and proving to be useful and accessible to researchers.
Antemortem MRI detects atrophy related to hippocampal sclerosis pathology in the oldest‐old but side matters: The 90+ Study
Authors: Woodworth Davis C. , Li Janice X. , Faramarzi Sina , Greenia Dana E. , Bukhari Syed A. , Montine Thomas J. , Kawas Claudia H. , Corrada María M. M. , Sajjadi S. Ahmad
Published: December 2023

Background

Hippocampal sclerosis of aging (HS) is the neuropathological finding of neuronal death and gliosis in the hippocampus. HS is more prevalent, and associated with dementia, in the oldest‐old (those ≥90 years), but cannot be diagnosed during life. HS is often unilateral and can be missed if only one brain hemisphere is assessed, which is standard neuropathological practice but may hamper the development of biomarkers for HS. Here we examined hippocampal volumes from antemortem MRI in relation to bilaterally‐assessed HS at autopsy, accounting for side, in oldest‐old participants from The 90+ Study.

Method

We included participants with both autopsy and antemortem MRI data. All participants had neuropathological assessment of HS for both hemispheres. We performed hippocampal segmentation of antemortem 3D‐T1w MRIs using FreeSurfer, adjusting by intracranial volume. We compared total average (average of left and right), left, and right, hippocampal volumes between those with and those without HS. For HS participants we combined the hippocampal volumes from each hemisphere with HS into a single group and from each hemisphere without HS into a separate group, and compared these to the average hippocampal volumes from those without HS. We used Welch’s T‐tests for comparisons and report Cohen’s d effect sizes.

Result

Participant characteristics (N = 89) are shown in Table‐1. All HS was unilateral (Right = 6, Left = 4). Average total hippocampal volume was not significantly different between those with and those without HS (d = 0.51, P = 0.11, Figure‐1a). However, right hippocampal volumes were significantly related to right‐sided HS with a large effect size (P = 0.002, d = 1.02) and left hippocampal volumes had a large effect size for left‐sided HS (P = 0.17, d = 0.90, Figure‐1c), whereas this was not the case when HS was on the opposite side (P>0.3, d<0.4, Figure‐1b,c). Combined HS participant hippocampal volumes for the side with HS (d = 0.98, P = 0.003), but not the side without HS (d = ‐0.14, P = 0.7), were different from volumes in participants without HS (Figure‐1d).

Conclusion

Our results suggest HS is detectable by atrophy on antemortem MRI, but most strongly when using hippocampal volumes from the same side as HS. Researchers should consider assessing and documenting the presence of HS in both hemispheres to accelerate development of neuroimaging biomarkers for HS.
Social Networks and MRI measures of Brain Injury in Racially and Ethnically Diverse Populations
Authors: Chen Ruijia , Pederson Annie , Wang Jingxuan , Hirst Andrew K , Hill‐Jarrett Tanisha G , Torres Jacqueline M , Gilsanz Paola , Decarli Charles , Corrada María M. M. , Whitmer Rachel A. , Glymour M. Maria
Published: December 2023

Background

Social networks may influence late‐life cognition directly via affecting brain structures or indirectly through enhancing cognitive resilience – i.e., the ability to maintain cognitive function in the presence of neurodegeneration. This study assesses the associations of social networks with markers of neurodegeneration, and determines whether social networks modify the associations between these markers and cognitive function.

Method

We pooled data from three harmonized cohorts of Kaiser Permanente Northern California members: the Kaiser Healthy Aging and Diverse Life Experiences (KHANDLE)study, the Study of Healthy Aging in African Americans (STAR), and the LifeAfter90 (LA90) Study. Social network sizes were calculated by summing the number of living children, spouse, and close relatives and friends. Neuroimaging markers included total brain volume (TBV), hippocampal volume (HCV), and white matter hyperintensity volume (WMH). Volumetric measures were normalized using a residual approach, which regressed intracranial volume (ICV) on volumetric measures to predict the ICV‐adjusted volume. Executive function and verbal episodic memory were measured by the Spanish and English Neuropsychological Assessment Scales and were z‐standardized. Linear regression models estimated the associations between social network size and each neuroimaging marker, adjusting for age, sex, race and ethnicity, education, cohort, and Activities of Daily Living (ADLs). We included an interaction term between social networks and neuroimaging markers to evaluate whether the relationships between imaging markers and cognitive function differed by social networks.

Result

The pooled sample’s average age was 76 (SD 11) years (59% female; 47% Black) (Table 1). In the fully adjusted models, the mean differences in injury markers did not differ by social network size (Figure 1). However, the associations of HCV and WMH with executive function (βsocial network*WMH = 0.09, 95% CI 0.01,0.18; βsocial network*HCV = ‐0.08, 95% CI ‐0.15, ‐0.01) were stronger among participants with smaller social networks (Figure 2).

Conclusion

In this cross‐sectional study of racially and ethnically diverse older adults, larger social networks attenuated the associations between neurodegeneration markers and executive function. These findings suggest that larger social networks may promote cognitive resilience.
TELL: A web‐based app to capture speech and language markers of neurodegeneration
Authors: Garcia Adolfo M
Published: December 2023

Background

Over the last decade, the search for scalable markers of neurodegeneration has been boosted by automated speech and language analysis (ASLA). Participants are asked to produce natural discourse, generating acoustic and linguistic data that can be digitally extracted and analyzed with statistical or machine learning methods. This approach has revealed subtle patterns that differentiate among disorders, predict core symptom severity, correlate with anatomo‐functional brain alterations, and identify autopsy‐confirmed pathology years before death. Accordingly, multiple studies have described ASLA as a promising resource for patient assessment. However, this translational potential remains unfulfilled, as the underlying technologies are unavailable for immediate clinical use. Aiming to tackle this issue, we developed the Toolkit to Examine Lifelike Language (TELL), a web‐based app for collecting, storing, analyzing, and visualizing speech data.

Method

TELL version 1.0 was developed by an interdisciplinary team of language, brain health, and data science experts. We designed a speech elicitation protocol comprised of validated spontaneous, semi‐spontaneous, and non‐spontaneous tasks with different cognitive demands. Each task is analyzed via ASLA algorithms capturing word frequency, affective valence, and emotional magnitude, as well as rhythm‐ and pitch‐related features. On‐the‐fly results are offered through intuitive visualizations for each metric. The measures are complemented with a language profile survey. Audio files, transcriptions, and data matrices can be downloaded for offline analysis. The app runs on multiple devices, requires no installation, and offers multi‐language capabilities.

Results

TELL version 1.0 is now being used in 10 sites worldwide, mainly including low‐income countries. Preliminary results from persons with Alzheimer’s and Parkinson’s disease, as well as frontotemporal dementia, show that its metrics are sensitive for disease identification, discrimination, and monitoring. Updates have been implemented drawing from users’ feedback. Funds have been secured to develop TELL version 2.0, with novel metrics, improved speech‐to‐text algorithms, and a streamlined front end.

Conclusion

TELL turns basic language science methods into clinically usable tools, offering an objective, low‐cost, non‐invasive complement to classical assessments. More generally, it illustrates how digital technologies can foster equitable insights on brain health. Ongoing updates will further increase its capacity to establish scalable markers of diverse neurodegenerative diseases.
Associations between air pollution exposure and empirically derived subtypes of mild cognitive impairment in older women
Authors: Petkus Andrew J. , Wang Xinhui , Beavers Daniel P. , Espeland Mark A. , Millstein Joshua , Resnick Susan M. , Kaufman Joel D. , Gatz Margaret , Rapp Stephen R. , Chen Jiu‐Chiuan
Published: December 2023

Background

Exposure to air pollution in older adulthood is a novel risk factor for mild cognitive impairment (MCI). MCI is a heterogenous construct with different subtypes likely reflecting different disease etiologies. Understanding if long‐term exposure to air pollution is differentially associated with MCI subtypes may provide insight into the neurotoxic effects of air pollution exposure on the aging brain.

Method

Participants included women from the Women’s Health Initiative Memory Study. Data from 651 women without dementia who screened positive for cognitive impairment on the modified mini‐mental state examination (3MS) and a randomly selected comparison sample of 2,560 women who were cognitively intact based on the 3MS were utilized. Participants were administered the 3MS annually (1996‐2008). Those who screened positive for cognitive impairment on the 3MS completed the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) cognitive battery and the Trailmaking Test parts A and B. Average annual levels of particulate matter of aerodynamic diameter<2.5 (PM2.5) and nitrogen dioxide (NO2) were estimated at the participant’s residence using regionalized national universal kriging models and averaged over the 3‐year period before the cognitive assessment. Latent class structural equation models (SEM) were constructed to identify MCI subtypes. We then examined associations between air pollution exposure and latent class membership using multinomial regression while adjusting for demographic, lifestyle, and clinical confounders.

Result

Four significant MCI subtypes were identified (Figure 1): multidomain MCI (n = 147; 23%), amnestic MCI (n = 141; 22%), dysnomic/dysexecutive MCI (n = 200; 31%), and subtle MCI (n = 163; 25%). Women living in locations with higher levels of PM2.5 (per interquartile range [IQR] = 3.93 µg/m3; OR = 1.28; p = .04) or NO2 (per IQR = 9.25 ppb; OR = 1.36; p = .02) were more likely to be classified as having subtle MCI compared to normal comparisons. One IQR increase in NO2 was also associated with increased likelihood of being classified with dysnomic/dysexecutive MCI (OR = 1.38; p = .003) compared to normal comparisons.

Conclusion

Our study illustrates that long‐term exposure to PM2.5 and NO2 may be associated with specific subtypes of MCI. Our findings provide further evidence that air pollution may exert neurotoxic effects on brain areas underlying executive function and language ability in older women.
Glycemic variability and dementia risk in a diverse cohort of older adults with type 2 diabetes
Authors: Moran Chris , Whitmer Rachel A. , Dove Zoe , Lacy Mary E , Soh Yenee , Tsai Ai‐Lin , Quesenberry Charles P. , Karter Andrew , Adams Alyce S , Gilsanz Paola
Published: December 2023

Background

Type 2 diabetes is associated with a two‐fold increase in dementia risk. Although glycemic variability is becoming increasingly recognised as contributing to end‐organ damage, less is known about its contribution to dementia risk. We aimed to study the association between HbA1c variability and dementia risk in a large, diverse sample of people in Northern California.

Method

Over 209,000 members of a fully integrated health care delivery system with type 2 diabetes aged ≥ 50 years were followed from 1996 to 2018 to obtain repeated measures of HbA1c, dementia diagnoses, and comorbidities. Variability was measured using standard deviation (SD), coefficient of variation, and average real variability. Cox proportional hazards models estimated the association between HbA1c variability in the first 3 years of cohort entry and incident dementia at least 2 years after the end of the exposure period adjusting for age (as timescale) and baseline health conditions.

Result

There were a total of 209,703 people in the cohort (mean baseline age 61.0 years (SD = 9.2), 47.5% women) (Table1). Approximately 49.6% of the sample were non‐Latino White. The mean number of HbA1c values available for each participant was 5.8 (SD = 2.3). The mean HbA1c for each participant in the first 3 years was 7.4% (SD = 1.33). Greater HbA1c variability was associated with greater hazard of dementia when measured using SD (aHR = 1.12 [95%CI: 1.10, 1.14]), coefficient of variation (aHR = 2.17 [95%CI: 1.85, 2.55]) (Figure1), or average real variability (aHR = 1.06 [95%CI: 1.06, 1.07]). When stratified by mean HbA1c, we found that ‐ relative to the lowest quintile of variation ‐ increasing variability of SD of HbA1c was associated with greater hazard of dementia in HbA1c categories of <6% (p = 0.0004) and 6 ‐8% (p<0.0001) but not in those with a mean HbA1c ≥8% (p = 0.42).

Conclusion

Greater HbA1c variability is associated with greater dementia hazard in those with good glycemic control. The absence of association in less well controlled (HbA1c ≥8%) patients may stem from greater variability including lower risk periods of optimal glycemic control. These results support current clinical recommendations to minimize glycemic variability and extend known benefits to include lowering of dementia risk.
Air pollution exposure is associated with widespread cortical thinning in cognitively unimpaired older women
Authors: Wang Xinhui , Salminen Lauren , Petkus Andrew J , Millstein Joshua , Beavers Daniel P. , Espeland Mark A. , Braskie Meredith N , Liu Joshua D , Thompson Paul M , Gatz Margaret , Resnick Susan M. , Kaufman Joel D. , Rapp Stephen R. , Fennema‐Notestine Christine , Hagler Donald J. , Elman Jeremy A. , Kremen William S. , Franz Carol E , Chen Jiu‐Chiuan
Published: December 2023

Background

Late‐life ambient air pollution exposures are associated with increased Alzheimer’s disease (AD) risk1. Cortical thinning in temporal areas vulnerable to AD is associated with memory decline, neuropathological changes, and increased AD risk. However, most neuroimaging studies on air pollution neurotoxicity analyzed volumetric indices that are not sensitive to early cortical changes, and focused on discrete regions of interest (ROI) selected a priori rather than taking an ROI approach across the whole brain. Therefore, the mechanisms linking exposures to early neuropathological processes in older age are poorly understood.

Method

We examined data from 1068 cognitively unimpaired women from the Women’s Health Initiative Memory Study who underwent neuroimaging in 2005‐6 (Meanage = 77.8±3.7). Long‐term residential exposures to fine particulate matter (PM2.5) and nitrogen dioxide (NO2) were quantified as the 3‐year average of monthly estimates prior to MRI using spatiotemporal models with regionalized universal kriging. Brain scans were processed using FreeSurfer‐v.5.3.0 to estimate cortical thickness in 34 bilateral regions parcellated with the Desikan‐Killiany atlas. Exposure effects on AD‐vulnerable regions were assessed by calculating an AD signature (higher value representing thicker cortices) of surface‐area‐weighted thickness in the bilateral entorhinal, fusiform, inferior temporal, and middle temporal cortices2. We used linear mixed models to estimate exposure effects on standardized regional cortical thickness and the AD signature, adjusting for sociodemographic, lifestyle, and clinical characteristics, and a random effect for scanner manufacturer.

Result

After adjusting for covariates and multiple comparisons using the False discovery rate (FDR) method, higher exposures to PM2.5 and NO2 were associated with thinner cortices in at least 31 brain regions (FDR‐adjusted p’s<0.05). There were also significant adverse effects of PM2.5 (β = ‐0.055, 95%CI = [‐0.07,‐0.04] per 1µg/m3) and NO2 (β = ‐0.022, 95%CI = [‐0.03,‐0.01] per 1ppb) on the AD signature, equivalent to 1.1 and 0.5 years of aging, respectively. Across all analyses the strongest effects were observed in the frontal lobe, especially the motor cortex.

Conclusion

In cognitively unimpaired older women, higher exposures to PM2.5 and NO2 were associated with widespread cortical thinning, including regions sensitive to AD. However, the strongest associations in the frontal lobe suggest a greater impact of air pollution neurotoxicity on brain aging rather than AD risk.
Parietal‐temporal structural network reorganization is associated with pathological load and axonal damage in Alzheimer’s disease: a post‐mortem MRI and pathology study.
Authors: Jonkman Laura E. , Reijner Niels , Frigerio Irene , Bouwman Maud M.A. , Lin Chen‐Pei , Rozemuller Annemieke J.M. , Bouwman Femke H. , Douw Linda , Berg Wilma D.J.
Published: December 2023

Background

The characteristic spatial pattern of Aβ and p‐tau distribution suggests that pathology spreads from brain regions exhibiting pathology to interconnected regions through large‐scale axonal networks. Advances in MRI and graph theory have made it possible to study brain network organization. The current study aims to assess whether local proteinopathy and/or axonal degeneration contributes to regionally altered network measures of integration and segregation in clinically‐defined and pathologically‐confirmed Alzheimer’s disease (AD) and control brain donors.

Method

Postmortem In‐situ 3T‐MRI (3DT1 and DTI) data were collected for 25 AD (6F/19M, 66y±11y) and 14 control (7F/7M, 72y±8y) brain donors. Following probabilistic tractography and structural network reconstruction, graph theoretical measures of integration (eigenvector centrality) and segregation (clustering coefficient) were calculated. After autopsy, the middle frontal gyrus (MFG), middle temporal gyrus (MTG), precuneus and middle hippocampus were immunostained for amyloid‐beta (Aβ), phosphorylated tau (p‐tau) and neurofilament light (NfL), and quantitative area% load was determined. Clinical Dementia Rating (CDR) scores were obtained from clinical records. Group differences were assessed with linear models and regional associations with Spearman and Pearson correlations, corrected for age, gender, and postmortem delay.

Result

In AD, no differences were observed in Aβ, p‐tau and NfL load among the selected neocortical regions, the hippocampus had lower Aβ and NfL load. Compared to controls, the AD group showed lower integration (p = 0.078) of the MTG, associated with higher CDR scores (r = ‐0.50;p = 0.035), more p‐tau pathology (r = ‐0.61;p = 0.002) and axonal damage (r = ‐0.64;p = 0.001). Furthermore, higher segregation (p = 0.006) of the MTG was observed and associated with higher p‐tau burden (r = 0.51;p = 0.015). In contrast, the precuneus showed higher integration (p = 0.038) and lower segregation (p = 0.023) in AD compared to controls, associated with less axonal damage (r = ‐0.44;p = 0.036) and less p‐tau pathology (r = 0.46;r = 0.033) respectively. No effects were found for the MFG and hippocampus.

Conclusion

In AD decedents, a reorganization of the structural network is observed, predominantly in the parietal‐temporal region; while the temporal region is more disconnected, the parietal region is more strongly connected. Our work furthermore shows that this network (dis)integration is driven by p‐tau pathology and axonal damage, and associates with cognitive outcome.
Differences in the pattern of influencing factors on instrumental activities of daily living in persons at different stages of mildly impaired cognition
Authors: Bruderer‐Hofstetter Marina , Gorus Ellen , Cornelis Elise , Vriendt Patricia De
Published: December 2023

Background

Instrumental Activities of Daily Living (IADL) are crucial for a person’s autonomy. A better understanding of potentially influencing factors at different levels of cognitive impairment is relevant for diagnostic and therapeutic purposes. Therefore, we aimed to explore whether the pattern of influencing factors varies between persons with mild cognitive impairment (MCI) and mild dementia.

Method

We exploratively extended the secondary analysis of a cohort, including persons with amnestic (a‐)MCI and mild Alzheimer’s dementia (MD). We fitted linear regression models to the data within the two subgroups, with IADL as the response variable measured using the Brussels Integrated Activities of Daily Living Inventory (BIA) [1]. Selection of the predictors followed a multi‐step approach, including criterion‐based model selection and model fit. We used multivariate imputation by chained equations to estimate the coefficients of the final models and standardized coefficient estimates to relate and compare the effects of the predictors.

Result

Persons with a‐MCI (n = 64, mean age 81.7 years, 69% females) and with MD (n = 41, mean age 82.3 years, 73% females) differed in their mean BIA scores (p<0.001). In persons with MCI, the final model (R2 = 0.46) included memory, attention, executive function, mobility, balance, vision, hearing, education, and living situation. Mobility was the only significant predictor and the predictor with the most considerable effect, followed by memory and attention. The final model (R2 = 0.21) in persons with MD included the same predictors; however, executive function and attention were derived from other assessments. The predictors with the most considerable effect were the living situation, mobility, and executive function, yet none was significant.

Conclusion

The pattern of potentially influencing factors on IADL functioning differs between persons with aMCI and MD. Our results suggest that physical function plays an essential role in IADL functioning in persons with aMCI, whereas in MD, no significant predictor was detected. Furthermore, our results indicate that IADL functioning may be used to differentiate between diagnostic groups.

[1] De Vriendt P, Cornelis E, Cools W, Gorus E. The Usefulness of Evaluating Performance of Activities in Daily Living in the Diagnosis of Mild Cognitive Disorders. International Journal of Environmental Research and Public Health. 2021;18(21):11623.