bluuebunny/crossref_metadata_2025 huggingface dataset:
Digital biomarkers of sleep and nocturnal behaviour derived from contactless longitudinal sleep monitoring predict cognitive decline and deterioration in daily function in people living with dementia
Authors: Kolanko Magdalena A. , Soreq Eyal , Golemme Mara , Walsh Chloe , Giovane Martina Del , Sharp David J
Published: January 2025

Background

Growing evidence suggests sleep disturbance may promote Alzheimer’s disease (AD) pathology. However, the relationship between sleep and AD progression remains poorly defined due to lack of longitudinal sleep monitoring methods. We previously presented a novel approach for contactless sleep monitoring in people living with dementia (PLWD)1. Here, we examine the association between the Dementia Research Institute Sleep Index (DRI‐SI) ‐ an interpretable measure of sleep disturbances derived from contactless sensor data ‐ and clinical disease progression.

Method

Withing’s Sleep Analyser was used to monitor 120PLWD taking part in a home‐monitoring study between 2019‐2023 (46k total nights). Participants were assessed 6‐monthly with ADAS‐Cog and Pittsburgh Sleep Quality Index (PSQI), and 3‐monthly with Neuropsychiatric Inventory (NPI) and Bristol Activities of Daily Living Scale (BADL). We used mixed linear models to examine the association between each assessment and the DRI‐SI score and variability. Additionally, a predictive model using an explainable boosting regressor was constructed to assess the DRI‐SI’s ability to predict ADAS‐Cog, PSQI, NPI and BADL scores.

Result

We included 107 participants (69 AD, 38 non‐AD dementia) with sufficient contemporaneous questionnaire and sleep‐mat data. Minute‐to‐minute timeseries were extracted from the sleep‐mats to calculate nightly DRI‐SI scores (Figure 1). Longitudinally, there was a significant gradual decline in ADAS‐Cog and BADL scores over the 18‐month follow‐up, while the NPI and PSQI showed little change (Figure 2). The DRI‐SI score and variability were significantly associated with BADL (Coef.=2.0±0.6, Z=3.3, P=0.001; Coef.=3.9±1.7, Z=2.3, P=0.02). ADAS‐Cog and NPI were also significantly associated with the DRI‐SI score, but not the DRI‐SI variability (Coef.=3.4±1.1, Z=2.95, P=0.003; Coef.=3.1±0.9, Z=3.3, P=0.001). Interestingly, no association was seen between the DRI‐SI and PSQI. The predictive model demonstrated significant predictive power for ADAS‐Cog and BADL scores, indicating the potential of DRI‐SI as a digital biomarker for cognitive and daily function in dementia (Figure 3c,d).

Conclusion

Our results suggest that DRI‐SI, a novel digital biomarker of sleep disturbance and night‐time behaviour in dementia derived from contactless remote sleep‐monitoring, associates with measures of cognition and function in dementia. As such, DR‐SI shows great promise for better predicting dementia progression, personalizing treatments, and augmenting outcomes in clinical trials.
White matter abnormalities in association with amyloid and tau deposition in a diverse cohort with increased risk for Alzheimer’s disease
Authors: Blujus Jenna K , Rego Eliza , Bica Sarah , Demos Kathryn , Lee Athene KW , Riddle Meghan C. , Wing Rena R. , Salloway Stephen , Maillard Pauline , Fan Audrey P. , Decarli Charles , Vemuri Prashanthi , Lockhart Samuel N. , Baker Laura D , Landau Susan M. , Oh Hwamee ,   for the U.S. POINTER Study Group and the Alzheimer’s Disease Neuroimaging Initiative
Published: January 2025

Background

Neuritic plaques with fibrillar beta‐amyloid (Aβ) peptides and tau‐protein neurofibrillary tangles, hallmark features of Alzheimer’s disease (AD) pathology, have been concomitantly associated with white matter (WM) integrity loss, while a unique effect of each pathology on WM integrity in a more demographically diverse population remains unknown.

Method

To examine the degree to which each pathology affects WM integrity in a more diverse non‐demented cohort, Aβ and tau PET, diffusion‐weighted imaging (DWI), and cognition (memory and executive function composites) were examined from the U.S. POINTER neuroimaging participants (N=608; age=68.34±5.2; 61.8% females; 29% Aβ+) and a subset of Alzheimer’s Disease Neuroimaging Initiative (ADNI) participants (N=291; age=72.85±7.3; 50% females; 39% Aβ+) (Table). Aβ‐positivity was determined using tracer‐specific standardized cut‐offs. Tau SUVR was estimated for each Braak stage. For WM integrity, voxel‐based GLMs were applied to FSL‐processed fractional anisotropy (FA) maps using Braak stage tau SUVR and Aβ‐positivity status, along with covariates of age, sex, and b‐value profiles.

Result

In U.S. POINTER, independent from Aβ‐positivity status, greater tau burden in Braak stages I and II was related to lower FA across the tracts including the superior longitudinal fasciculus, fornix (cres), corpus callosum, and anterior thalamic radiation (Figure). Average FA from supra‐threshold clusters for Braak I and II significantly predicted executive function but not episodic memory. No association between FA and tau burden in Braak stages III/IV or V/VI was found. In the ADNI sample, greater tau burden in Braak I was related to lower FA mostly in left inferior longitudinal fasciculus and left uncinate fasciculus, while negative associations with FA became more diffuse with tau load in Braak III/IV and V/VI. Average FA from supra‐threshold clusters significantly predicted both episodic memory and executive function scores. Across both samples, after accounting for tau burden in each Braak stage, there were no association between Aβ‐positivity status and WM integrity.

Conclusion

Distinct associations between tau burden, WM integrity, and cognition were found across the studies, indicating greater impact of early tau burden on WM integrity in U.S. POINTER, accounting for Aβ‐positivity status. Factors contributing to the distinct associations across studies need further investigation.
GEAR 2.0 ‐ Communication and Shared Decision Making
Authors: Carpenter Christopher R
Published: January 2025

Aging adults worldwide are presenting to the emergency department with acute and subacute illness and injury confounded by often unrecognized cognitive impairment, including dementia and delirium. Conveying medical information and weighing various diagnostic and therapeutic approaches during times of emergency is difficult for all aging adults. In adult ED populations without dementia, communication is imperfect with incomplete recollection of test results, presumptive diagnoses, prescriptions, and follow‐up recommendations. Communication strategies striving to improve ED nurse/physician‐to‐patient information exchange such as TeachBack are ineffective in non‐dementia populations and untested in subsets with dementia or their care partners. Increasingly, healthcare disparities associated with dementia detection and interventions are acknowledged. For example, limited health literacy is one source of disparity, interacts with the severity of dementia, is one communication barrier, and yet is not routinely evaluated in ED settings. Shared Decision Making is an evolving approach to engage patients and families in complex medical conversations, but emergency medicine shared decision making research often excludes individuals with dementia by design. This session will explore theoretical opportunities to improve medical decision‐making during times of emergency for persons living with dementia and their care partners.
Theta‐ and alpha‐band functional connectivity are associated with longitudinal tau accumulation in asymptomatic individuals expressing Aβ pathology
Authors: Rudolf Jonathan Gallego , Wiesman Alex I , Baillet Sylvain , Villeneuve Sylvia ,   PREVENT‐AD Research group
Published: January 2025

Background

Recent studies suggest that tau pathology spreads between functionally connected (FC) brain regions. The accumulation of amyloid‐beta (Aβ) promotes neural hyper‐activity in asymptomatic older adults, which might enhance tau spreading. We assessed the relationship between band‐specific neurophysiological FC and the rate of tau accumulation over time and tested whether this association is dependent on the presence of Aβ pathology.

Method

We measured cortical Aβ deposition using positron emission tomography (PET) and resting‐state neurophysiological activity using magnetoencephalography (MEG) at baseline, as well as longitudinal cortical tau PET deposition (mean time between scans = 4.3 years), in a group of cognitively unimpaired older adults with family history of Alzheimer’s Disease (PREVENT‐AD cohort; N = 84). Region‐specific measures (i.e. Aβ, tau, MEG timeseries) were extracted from 68 cortical parcels of the Desikan‐Killiany atlas. Neurophysiological FC between cortical regions was estimated across delta, theta, alpha and beta frequency bands using the corrected amplitude envelope correlation (AEC‐c). A global Aβ standardized uptake value ratio (SUVR) was calculated by taking the average across a set of early Aβ accumulating regions. Individual‐specific tau epicenters were defined as the six cortical regions with the highest levels of baseline tau‐PET binding. The annual rate of change in tau SUVR between the first and second scan was calculated for each cortical region (Figure 1). Linear mixed‐effects models were designed to test if the neurophysiological FC connectivity of each cortical parcel with tau epicenters was related to increased longitudinal tau accumulation in that region, and whether this association was dependent on baseline levels of Aβ pathology.

Result

Tau epicenters identified across individuals were mainly localized in temporal and medial posterior brain regions. Regional increases in theta‐ and alpha‐band FC with tau epicenters at baseline related to greater longitudinal tau accumulation in individuals expressing higher levels of global Aβ pathology (Figure 2).

Conclusion

Our results suggest that tau spreads across cortical regions that are functionally connected in the theta‐ and alpha‐bands, but only in individuals with high levels of cortical Aβ. This finding supports the notion that Aβ‐related neural hyper‐activity promotes tau spreading across the cortex following individual‐specific patterns of FC in asymptomatic older adults.
Simvastatin restores neurovascular coupling and cognition in APP mice
Authors: Huth Maria Paula Faccin , Bourourou Miled , Lesage Frédéric , Hamel Edith , Zimmer Aline R.
Published: January 2025

Background

Alzheimer's disease (AD) is the leading cause of dementia worldwide and vascular dysfunction represents one of the first abnormalities in AD spectrum. Brain imaging techniques that use changes in hemodynamic signals to measure alterations in neurovascular coupling (NVC) have proven useful for early detection of cognitive deterioration. Pharmacological interventions targeting vascular risk factors, including simvastatin (SV), show promise in preventing dementia. To better understand the changes in brain NVC through the AD progression, we measured whisker‐evoked changes in hemodynamics signals and cognition longitudinally in a transgenic mouse model of AD (APP‐J20 mice) and wild‐type (WT) mice treated or not with SV.

Method

Hemodynamic signals following whisker stimulation (8Hz, 10 sec) were recorded longitudinally (3‐8 months) in anesthetized (ketamine/xylazine) and awake WT and APP mice, implanted with full cranial window, using optical imaging of intrinsic signals (OIS) (525, 590, and 625 nm), together with cognitive testing. The effects of SV treatment were evaluated after 2.5 and 5 months.

Result

APP mice displayed significant NVC deficits compared to WT littermates in both awake and anesthetized conditions. The anesthesia worsened the whisker‐evoked hemodynamic responses in APP mice. These changes were detected at early stage of the disease (3 months‐old), before the appearance of cognitive impairment (5.5 months‐old). SV treatment totally restored NVC and cognitive function after 5 months of treatment in APP mice.

Conclusion

The longitudinal analysis of NVC supports their relevance as translational measures for early diagnosis. Our data with SV indicate that preventive cardiovascular strategy for individuals at risk of developing AD may bear promise in protecting brain function.
Exploring the Effects of Ethnicity, Sex, and APOE Status on Markers of Inflammation
Authors: Contreras Joey A , Aslanyan Vahan , Ortega Nancy E , Pa Judy
Published: January 2025

Background

Alzheimer’s Disease (AD) is the most common form of dementia and is considered to disproportionately affect underserved populations like Hispanics/Latinos. Most research available with AD markers such as inflammation and APOE4 has been conducted in predominantly White cohorts, leaving a dearth of knowledge relating APOE4 and inflammation in Hispanic/Latinos. Recent research also suggests inflammation and AD risk may be sex‐specific. This study investigated the effects of race/ethnicity, sex/gender, and APOE4 on inflammatory markers.

Method

Using data from the Health and Aging Brain Study‐Health Disparities (HABS‐HD), we assessed group differences between APOE4 status, ethnicity, and sex on anti‐inflammatory marker interleukin‐10 covarying for age and education.

Result

The dataset included 817 Mexican Americans (65% women, 18% APOE4 carrier, mean age±SD=63.21±8.04 years) and 746 white participants (54% women, 30% APOE4 carrier, mean age±SD=68.59±8.70 years) (Table 1). We found two‐way interactions between sex and race/ethnicity (p=0.03) on IL‐10, driven by higher IL‐10 levels in Mexican American men; sex and APOE4 status (p=0.001), driven by higher IL‐10 among APOE4 carriers in men; and race/ethnicity and APOE4 status (p=0.001), driven by higher IL‐10 levels APOE4 carriers among Mexican Americans (Figure 1A,B and C respectively).

Conclusion

IL‐10 is an important anti‐inflammatory modulator of glial activation, preventing inflammation‐mediated neuronal degeneration under pathological conditions. It has multiple immunoregulatory effects such as in ability to inhibit the production of several inflammatory cytokines. These findings support previous studies demonstrating heightened systemic inflammation in APOE4 carriers, but little has been shown regarding their relationship with gender and ethnicity. This finding suggests the importance of examining differences that may exist between APOE4, sex, and inflammatory markers in Hispanics/Latinos. Future work will examine other inflammatory markers.
Assessing the association between cholinergic integrity and cognitive performance in adults with Down Syndrome using cholinergic anatomy and [18F]‐FEOBV PET
Authors: Russell Jason K , Conley Alexander C , Boyd Brian , Schlossberg Rachel , Stranick Allison , Rosenberg Adam J , Acosta Lealani Mae Y. , Rafii Michael , Shokouhi Sepideh , Newhouse Paul A
Published: January 2025

Background

Triplication of the amyloid precursor protein in individuals with Down Syndrome (DS) produces an increased risk for the development of Alzheimer's disease (AD). Declining cholinergic integrity plays a role in the cognitive deficits observed in late‐onset AD. In the present study, we assess the relationship between basal forebrain volume or [18F]‐FEOBV uptake and cognitive performance in adults with DS.

Method

188 individuals from the ABC‐DS cohort (U01AG051406; U01AG051412; U19AG068054) were assessed cross‐sectionally. Basal forebrain volumes were calculated from T1 MRIs using the ScLimbic FreeSurfer pipeline. Ten individuals were recruited as part of a TRC‐DS sub‐study (25‐55 years old) or de novo (18‐24 years old), where they underwent MRI imaging and [18F]‐FEOBV PET imaging. Regional [18F]‐FEOBV SUVRs were calculated using PetSurfer. Participants from both studies underwent numerous cognitive assessments. Linear regressions were performed between cognitive performance and regional [18F]‐FEOBV SUVR or basal forebrain volume correcting for intellectual disability.

Result

In the ABC‐DS cohort, the Down Syndrome Mental State Exam (DSMSE) total score was positively associated with basal forebrain volume (p<0.05), and total free recall on the cued recall task was positively associated with left basal forebrain volume (p=0.0027). In the TRC‐DS sub‐study, [18F]‐FEOBV uptake in the right amygdala, right rostral anterior cingulate cortex, and left transverse temporal cortex were positively associated with DSMSE total score (p<0.05). In the cued recall task, [18F]‐FEOBV uptake in the left cuneus and right entorhinal cortexes and the isthmus of the right cingulate cortex was negatively associated with total free recall (p<0.05).

Conclusion

In the ABC‐DS cohort, increased cholinergic integrity predicted improved performance on the DSMSE and cued recall task. Regional [18F]‐FEOBV uptake can indicate which areas of cholinergic innervation are important for these different cognitive tasks. Increased regional [18F]‐FEOBV uptake predicted improved performance on the DSMSE, with the object memory and apraxia sub‐scores displaying the largest effect sizes. Interestingly, increased [18F]‐FEOBV uptake predicted decreased free recall in the cued recall task. This unexpected result may indicate upregulation of the vesicular acetylcholine transporter in this non‐demented DS cohort or may be spurious due to the small cohort. Increased sample size will further elucidate this effect.
Clinical Performance of Plasma Aβ42/Aβ40, p‐tau217 and Neurofilament Light in Sporadic Frontotemporal Dementia Spectrum Disorders with subsequent comparison of pathology
Authors: Rajbanshi Binita , Heuer Hilary W. , VandeVrede Lawren , Ljubenkov Peter A. , Lago Argentina Lario , Araujo Igor Prufer Q C , Staffaroni Adam M. , Petrucelli Leonard , Gendron Tania F , Rosen Howard J. , Boeve Brad F. , Bateman Randall J. , Rojas Julio C. , Boxer Adam L. ,   ALLFTD Consortium
Published: January 2025

Background

Identification of useful fluid biomarkers is expected to advance frontotemporal dementia spectrum disorders (FTD) care and therapeutic development. The clinical utility of emerging plasma Amyloid, Tau, and Neurodegeneration (ATN) biomarkers in FTD remains unexplored. This study analyzed ATN patterns by sporadic FTD phenotype, based on amyloid beta1‐42 (Aβ1‐42), amyloid beta1‐40 (Aβ1‐40), phospho‐tau217 (p‐tau217), and neurofilament (NfL) plasma concentrations.

Method

The present study included 625 participants enrolled in ALLFTD (46% female, median age 61 ± 12 years) for whom biomarker data were available. Plasma Aβ1‐42/Aβ1‐40 measures were determined by immunoprecipitation‐mass spectrometry whereas plasma p‐tau217 and NfL concentrations were respectively determined by electrochemiluminescence‐ and fluorescence‐based immunoassays. Biomarker concentrations were compared by phenotype and disease severity with ANOVA. Their diagnostic performance was tested with Receiver Operating Characteristic (ROC) curves, and biomarker relationships with measures of clinical severity were tested through linear regressions controlling for age, sex, and APOE genotype.

Result

Higher plasma p‐tau217 and p‐tau217/Aβ1‐42, levels were observed in logopenic primary progressive aphasia (lvPPA) and amnestic dementia (AmD) cohorts, compared to other FTD groups. These biomarkers were not associated with disease severity but were affected by APOE4 genotype. High plasma NfL level was observed in fully symptomatic patients compared to controls, and as a function of disease severity. Plasma Aβ1‐42/Aβ1‐40 level was not different between phenotypes and was not affected by clinical severity. P‐tau217 level, but not Aβ1‐42/Aβ1‐40 or NfL levels showed high discrimination of lvPPA and AmD from other phenotypes (AUC = 0.87 (95% CI 0.77 – 0.96, p < 0.0001, 73% sensitivity, 92% specificity). The p‐tau217/Aβ1‐42 ratio improved this discrimination (AUC = 0.9, 95% CI 0.83 – 1, p < 0.0001, 77% sensitivity, 94% specificity). Plasma Aβ1‐42/Aβ1‐40, p‐tau217, NfL, p‐tau217/Aβ1‐42, p‐tau217/NfL, and (Aβ1‐42 x p‐tau217)/NfL were associated with cognitive (MoCA, CVLT), motor (UPDRS), and social behavior (RSMS) performances at baseline.

Conclusion

Plasma p‐tau217/Aβ1‐42 identifies, with high specificity, two phenotypes associated with Alzheimer’s disease: lvPPA and AmD. Pending molecular neuroimaging and neuropathological confirmation, this biomarker could be of value to identify primary Alzheimer’s disease pathology or co‐pathology associated with Alzheimer’s disease when sporadic FTD is suspected.
Discovery of region‐specific lipid biomarkers characterized by distinct changes over time in the brains of APPsw mice
Authors: McIntire Laura Beth , Wartchow Krista Minéia , Costa Ana Paula , Krumsiek Jan , Mares Jason , Menon Vilas , Lazarian Artur , Bartelo Nicholas , Dartora William
Published: January 2025

Background

Alzheimer's disease (AD) is associated with impaired lipid metabolism in the brain. To identify the specific regions where pathological change to cell functionality occurs, a spatial investigation of regional lipid dysregulation is needed.

Method

We measured untargeted spatial lipidomics using Desorption Electrospray Ionization (DESI) mass spectrometry in the brains of mice from two genotypes, wild type (WT) and APPsw, an AD mouse model overexpression amyloid precursor protein (APP). We generated a longitudinal profile of these mice at ages 6 months, 12 months, and 22 months. We computationally segmented brain regions defined by the Allen Mouse Brain Atlas to discover region‐specific differential lipid biomarkers across age and genotype. We developed “Spatial Lipidomics Analysis Tool” (SLAT), a computational framework which identifies statistically differential ions between age and genotype in a region‐specific manner, and applied this to our data. Independently of SLAT, we applied K‐means clustering to lipidomics as an unbiased approach to find regions in whole brains that may drive the development of disease. We applied K‐means clustering to log2 fold changes (log2FC) to find groups of significant ions reported by SLAT characterized by similar changes in abundance in APPsw mice over time compared to 6 months WT mice.

Result

We identified the globus pallidus (GP) as having multiple significantly differential lipids between WT and APPsw samples. However, whole brain analysis was unable to identify significant differential ions between genotypes, demonstrating the advantage of region‐specific‐analysis. Additionally, the GP was found to drive clustering for specific ages and genotypes. Longitudinal analysis of significant GP ions revealed multiple groups of ions with coordinated changes in ion intensity over time. The group of ions with the greatest temporal changes had a 0.50 log2FC increase from 6 to 12 months, followed by a 0.63 log2FC decrease from 12 to 22 months, on average.

Conclusion

Our results illustrate lipid biomarkers specific to the GP which may have potential use for the diagnosis and prognosis of AD. The development of SLAT as a partially automated pipeline to analyze longitudinal data with different phenotypes can be utilized as a tool for discovery of disease‐specific mechanisms from spatial lipidomics and metabolomics data.
The association between plasma amyloid β and a risk of conversion from mild cognitive impairment to Alzheimer’s disease dementia
Authors: Kaneko Naoki , Kimura Noriyuki , Masuda Teruaki , Ataka Takuya , Takeuchi Tsukasa , Ito Temmei , Tasai Hideaki , Miyagawa Takehiko , Iwamoto Shinichi , Matsubara Etsuro
Published: January 2025

Background

Abnormal amyloid‐β (Aβ) deposition in the brain has been defined as the earliest pathological change of Alzheimer disease (AD). Immunoprecipitation coupled with MALDI‐TOF MS (IP‐MS) has previously revealed that APP669–711/Aβ1–42 ratio, Aβ1–40/Aβ1–42 ratio, and a combination of these two biomarkers (a composite biomarker) in human plasma correlates highly with SUVR obtained from amyloid PET. In this work, we investigate the usefulness of plasma Aβ biomarkers at baseline in predicting the conversion to AD dementia.

Method

Plasma samples were obtained from participants with mild cognitive impairment (MCI) in community‐based cohort. One‐hundred seven samples measured with IP‐MS were analyzed to evaluate a risk of conversion from MCI to AD dementia. Amyloid positivity was assessed using 11C‐PiB PET based on a threshold of a standardized uptake value ratio of 1.2.

Result

Among 107 MCI participants, 28 converted to AD dementia within seven years and 61 remained stable at the MCI state for seven years or longer. Others converted to non‐AD dementia or dropped out within seven years. The MCI participants who converted to AD dementia (MCI‐AD) showed higher baseline plasma Aβ biomarker values than stable MCI (p < 0.001). ROC analysis demonstrated high AUC (0.860, for the composite biomarker; 0.825 for the Aβ1‐40/Aβ1‐42; 0.828 for the APP669‐711/Aβ1‐42) in discriminating between MCI‐AD and stable MCI. The risk of AD conversion was significantly differed among low, intermediate and high groups categorized by the composite biomarker level (p<0.0001, Log‐rank test). In Cox regression analysis, the high and intermediate groups were associated with increased risk of AD dementia compared to the low group (the hazard ratio (HR) = 6.51 for the intermediate group and the HR = 10.33 for the high group).

Conclusion

Our results suggested that the Aβ composite biomarker was associated with the risk of the conversion from MCI to AD dementia. In addition, the time from MCI to AD conversion was differed dependent on the level of the Aβ composite biomarker. The Aβ composite biomarker might be useful in predicting the conversion from MCI to AD dementia.
Individual variation in proteomic cascades along the Alzheimer’s disease spectrum
Authors: Vogel Jacob W. , Vilkaite Gabriele , Binette Alexa Pichet , Hristovska Ines , Xiao Yu , Mastenbroek Sophie E. , Rittmo Jonathan , Janelidze Shorena , Palmqvist Sebastian , Salvadó Gemma , Smith Ruben , Stomrud Erik , Strandberg Olof , Young Alexandra L. , Ossenkoppele Rik , Mattsson‐Carlgren Niklas , Hansson Oskar
Published: January 2025

Background

Cerebrospinal fluid (CSF) proteomics allows for characterization of multiple disease‐related biological processes in vivo. These processes likely occur along temporal cascades mirroring disease evolution. This study describes interindividual variation in these cascades, in the context of Alzheimer’s disease.

Method

Participants were recruited from the Swedish BioFINDER‐2 study, diagnosed as cognitively normal (n=251; CN), subjective cognitive decline (SCD; n=129), mild cognitive impairment (MCI; n=134) or Alzheimer’s dementia (AD; n=159). All MCI and AD were β‐amyloid (Aβ)‐positive via CSF Aβ42/40 ratio. CSF was sequenced with the Olink Explore 3072 panel, resulting in 1331 proteins after quality control. All participants also underwent [18F]RO948 tau‐PET scans. Age, sex, and the first non‐disease‐related principal components (n=3) were regressed from the proteomic data. A bootstrapped hierarchical clustering approach extracted and annotated ten networks of co‐expressed proteins (Figure 1A). Networks with univariate relationships to Aβ, tau or diagnosis (n=7) were submitted to the Subtype and Stage Inference (SuStaIn) algorithm (Figure 1B). SuStaIn assigned participants to a subtype and stage based on sequential abnormality of proteomic network expression. Regression models assessed SubtypeXStage interactions on regional tau‐PET or MMSE, adjusting for age, sex and diagnosis.

Result

A two‐subtype model best fit the data (Table 1). N=244 showed normal protein network levels (S0). Subtype‐1 (S1) showed early and sustained abnormality in vascular (P3) and chemokine (P0) networks, followed by increasing abnormality of a nucleotide‐metabolism network (P9). Subtype‐2 (S2) showed increasing abnormality in lysosomal (P1) and immune (P2) networks (Figure 1C). CN were more likely to be S0 (𝛘2=23.2, p<0.001) and a lower stage (F=20.5, p<0.001), but S1 and S2 did not differ by diagnosis (Figure 2A). S0 and S2 showed decreased tau‐PET load compared to other groups (Figure 2C). Subtype*stage interactions showed S2 participants had less tau‐PET load and higher MMSE scores at more advanced stages compared to S1 (Figure 2C‐E).

Conclusion

Two proteomic cascades emerged along the AD continuum, with one accompanying relatively lower tau levels and preserved cognition. Longitudinal follow‐up will reveal if this cascade is indicative of increased vulnerability or resilience of AD pathology.
Association of Plasma Protein Risk Score and Risk of Cognitive Impairment in the Framingham Heart Study
Authors: Rehman Habbiburr , Ang Ting Fang Alvin , Tao Qiushan , Au Rhoda , Farrer Lindsay A. , Qiu Wei Qiao , Zhang Xiaoling
Published: January 2025

Background

Plasma‐based biomarkers are emerging as a new diagnostic tool for Alzheimer’s disease (AD) with high accuracy and low cost. Whether aggregated plasma protein risk score (PPRS) derived from a single sample could be useful for diagnosis of incident mild cognitive impairment (MCI) and AD is uncertain. This study aims to understand the association between PPRS and future risk of AD related outcomes.

Method

We included Framingham Heart Study (FHS) Offspring cohort participants with plasma aptamer based SOMAscan proteomics assay data at exam 5. After excluding subjects who were have missing data for AD risk factors (age, sex, education and APOE ɛ4) and had other‐types of dementia, 1523 participants (cognitive normal (CN)=1258, MCI=137, AD=128) were considered for the primary analyses. The Cox model with LASSO penalty was used to build the PPRs for AD and MCI. The association of PPRS for AD risk was tested for outcomes of MCI/AD incidence (mean follow‐up=23.06 years), brain volume, and blood AD biomarkers.

Result

We identified 279 proteins for inclusion in the MCI PPRS and 103 proteins for inclusion in the AD PPRS. The MCI PPRS had a hazard ratio (HR) of 1.79 [4.99, 7.37] and the discriminating power (C‐index) was 0.86, and after including risk factors C‐index was 0.88. The AD PPRS had a HR of 1.79 [4.86,7.33] and the C‐index was 0.89 and after including the risk factors the power of the model was improved (C‐index=0.92). The chance of developing AD for a CN participant at age 80 years with in 20 years with high AD PPRS was 85% and with low AD PPRS was 15%. Brain volume distribution using 8 variables was significantly decreasing with increasing level of MCI PPRS (p‐value<0.001 for 5 variables) and AD PPRS (p‐value<0.001 for all 8 variables). The increased levels of MCI PPRS (p‐value<0.001) and AD PPRS (p‐value<0.001) were also significantly associated with increased plasma p‐tau181.

Conclusion

This study suggests that aggregated protein risk scores from plasma samples can be used to predict future risk of MCI and AD, thus serving as a potential screening tool for early intervention to prevent AD or slow its progression.
Plasma dicarboxylic acids as indicators of impaired energy metabolism and lipid oxidation associated with pre‐symptomatic Alzheimer’s disease
Authors: Jose Joby , Fonteh Alfred N.
Published: January 2025

Background

Dicarboxylic acids (DCAs) are critically important for intermediate metabolism. Since mitochondrial dysfunction and energy dysregulation are associated with AD pathology, we hypothesize that fluctuations in plasma DCAs would accompany AD pathology.

Method

In an ongoing brain‐aging study, we recruited older adults (>65 years) classified as cognitively healthy (CH) after neuropsychological testing. Based on the CSF biomarker analysis, we further categorized them as CH participants with normal biomarker levels (CH‐A‐T‐N‐) and CH participants with AD‐like pathological biomarker levels (CH‐A+T*N‐) having a high risk of cognitive decline (presymptomatic AD). We collected plasma from the participants and extracted DCAs using ethyl acetate. We derivatized the DCAs using dimethylaminophenacyl bromide (DmPABr) to reverse the DCA polarity and enhance the sensitivity of detection. DCA levels were quantified using isotope dilution liquid chromatography mass spectrometry with multiple reaction monitoring of C3‐C22 DCAs. The group differences between CH‐A‐T‐N‐ and CH‐A+T*N‐ were calculated using Mann‐Whitney comparison tests.

Result

Distribution studies revealed that most of the dicarboxylic acids in the plasma samples (n = 4) are short‐chain (≈80%) and medium‐chain DCAs (≈15%), while long‐chain DCAs make less than 5% (Figure 1). We compared the distribution of plasma DCAs in CH‐A‐T‐N‐ versus CH‐A+T*N‐ and identified three groups. The DCA composition of group 1 DCAs (C4+C5) was lower in CH‐A+T*N‐ than in CH‐A‐T‐N‐. In contrast, the DCA levels of group 2 (C7+C8+C9) and group 3 (C14+C16+C18+C22) were higher in the CH‐A+T*N‐ plasma compared to CH‐A‐T‐N‐. Using ROC, significant differences in the AUC were shown for each of the three groups (Figure 2). The group differences resulted in significantly higher group 1/group 2 and group 1/group 3 ratios in CH‐A‐T‐N‐ compared to CH‐A+T*N‐ (Figure 3).

Conclusion

We identified 14 DCA species in plasma and showed that their plasma composition varied with AD biomarker levels. DCAs associated with energy (group 1) were higher in CH‐A‐T‐N‐ than in CH‐A+T*N‐, while DCAs linked with longer‐chain fatty acid oxidation (group2 & 3) were higher in CH‐A+T*N‐ than in CH‐A‐T‐N‐. These data suggest that there is dysregulation of DCA metabolism in pre‐symptomatic AD, and plasma levels of DCAs can be used as biomarkers of early‐stage AD.
Preliminary Validation of a Structural Magnetic Resonance Imaging Metric for Differential Diagnosis of Frontotemporal Lobe Dementias
Authors: Kress Gavin T , Popa Emily S. , Thompson Paul M. , Bookheimer Susan Y. , Thomopoulos Sophia I , Ching Christopher RK , Zheng Hong , Merrill David A. , Raji Cyrus A. , Panos Stella E. , Siddarth Prabha , Bramen Jennifer E.
Published: January 2025

Background

AD‐NeuroScore is a validated metric that summarizes Alzheimer’s disease (AD)‐specific atrophy and can detect AD early, benchmark disease severity, predict and monitor AD progression, and can aid in testing the efficacy of therapeutic interventions using a single number (Kress, 2023). It meets criteria for translatability by using clinically available regional brain volumes as input (Ahdidan, 2015; Cavedo, 2022) and having patient and model‐level interpretability (Pinto, 2022). Also, features can be reviewed by a neuroradiologist (Larson, 2019). Scores are harmonized for intracranial volume, age, sex, and scanner model, improving workflows for longitudinal patient tracking. The objective of this study is to examine whether AD‐NeuroScore can be used in the differential diagnosis of AD and FTD dementia subtypes.

Method

Cognitively normal (CN) individuals and patients with a mild cognitive impairment (MCI), AD, behavioral variant (bv)‐FTD, semantic variant (sv)‐FTD, and progressive nonfluent aphasia (pnfa)‐FTD diagnoses were drawn from the ADNI and NIFD cohort studies. Eighty‐four cortical and subcortical regional volumes were estimated from T1‐weighted MR images using FreeSurfer. AD‐NeuroScore uses a modified Euclidean‐inspired distance function to calculate differences between individuals and a CN template based on regional brain volumes associated with cognitive decline (Kress, 2023). Validation used an experimental cohort (Table 1) and tested sensitivity to differentiating CN, MCI, and AD from FTD‐subtypes (bv, sv, and pnfa) using pairwise t‐tests and an alpha=0.001, Bonferroni corrected. Area under the curve (AUC) was calculated. Results were benchmarked against adjusted hippocampal volume (AHV), an NIA‐AA diagnostic biomarker for AD (Jack, 2018), which was also harmonized for intracranial volume, age, sex, and scanner.

Result

Both AD‐NeuroScore and AHV distinguished between AD and svFTD and pnfaFTD variants. AD‐NeuroScore better differentiated svFTD from AD than AHV (Table 2; Figure 1). AHV performed as well or qualitatively better than AD‐NeuroScore at distinguishing AD from pnfaFTD. Neither AD‐NeuroScore nor AHV differentiated AD and bv‐FTD.

Conclusion

AD‐NeuroScore differentiated between AD, svFTD, and pnfaFTD and overall, performed equivalently to or better than AHV. Performance and utility in longitudinal tracking may be improved by creating FTD variant scores using with a focus on regions specific to FTD subtypes.
The effects of self‐efficacy training on associative memory and brain function in older adults with mild cognitive impairment
Authors: Gabiazon Raphael , Marshall Samantha , Swayze Amy , Riesen James Van , Jeyarajan Gianna , Al‐Shaikh Jennifer Hanna , Nagamatsu Lindsay S
Published: January 2025

Background

Older adults with mild cognitive impairment (MCI) are known to have declines in cognition greater than that expected for an individual’s age and education level but does not significantly interfere with daily functioning. Memory impairments during the early stages of MCI may predict conversion to Alzheimer’s disease (AD). This impairment can be especially prominent in associative memory – the ability to learn and remember the relationship between two unrelated items. While age‐related memory impairments are often attributed to biological and physiological changes, psychosocial factors could also play a role. A notable psychosocial factor that may affect memory is self‐efficacy – an individual’s confidence in their ability to carry out actions to meet situational or domain‐specific demands. Despite previous research showing a relationship between enhanced self‐efficacy and improved memory performance in older adults, the neural correlates that may explain this relationship remain unclear. Therefore, we developed a six‐week self‐efficacy training intervention to examine its effects on memory and corresponding changes in functional brain activation among older adults with MCI.

Method

Twenty‐eight participants were randomized and allocated into either a memory self‐efficacy intervention (n = 14) or general education control group (n = 14). Over six weekly 1.5‐hour group sessions, participants attended either classes that focused on building memory self‐efficacy or general education covering factual and world knowledge. At baseline and endpoint measurements, memory performance and functional brain activation were assessed using an associative memory task during a functional magnetic resonance imaging scan, while memory self‐efficacy was measured with the Multifactorial Memory Questionnaire. Post‐intervention group differences for each outcome measure were analyzed using multiple linear regression models.

Result

We found changes in functional activation patterns during the associative memory task in the memory self‐efficacy training group as a result of the intervention. Specifically, key regions in the brain that are critical for associative memory performance were implicated, suggesting that memory self‐efficacy training can change the way we process and remember information.

Conclusion

This study provides insight into how psychosocial factors like self‐efficacy may influence age‐related memory impairments and AD progression, demonstrating self‐efficacy training's potential to enhance memory in at‐risk populations.
South Asian Aging Brain Initiative: Neuroimaging
Authors: Willette Auriel A. , Kota Karthik , Hu William T.
Published: January 2025

Background

South Asian (SA) older adults are one of the fastest growing US populations developing Alzheimer’s disease (AD) and related dementias (ADRD). Compared to non‐Hispanic white (NHW) Americans, SA are hesitant to enroll in neuropsychological and MRI research. This status complicates accurate assessment and diagnosis. Yet, SA may show differences in neurodegeneration patterns that elucidate novel brain‐behavioral relationships or network dysfunctions in multilingual speakers.

Method

As part of the South Asian Aging Brain (SAAB) study, we recruited 62 non‐demented SA participants without subjective cognitive concerns to undergo English‐based neuropsychological testing and brain MRI. Freesurfer 7.4.1 was used to derive bilateral volume or cortical thickness in cortical and subcortical regions. After calculating Z‐scores based on National Alzheimer’s Coordinating Center (NAAC) calculators, stepwise linear regression was used to examine adjusted SAAB z‐scores with imaging outcomes. Covariates included age, sex, years of education, age at immigration, and years spent in the United States.

Result

Among 29 women and 33 men, mean age was 64.9 years (range 24‐86y) with an average of 17.6 years of education (range 12‐20y) and US residency lasting 32.6 years (range 4‐59y). For Factor 1 (Semantic Memory), superior parietal gyrus, inferior parietal gyrus, and precentral gyrus loaded (adjusted R2=0.261; p<.01). For Factor 2 (Word Recall), only the bank of the superior temporal sulcus loaded (adjusted R2=0.231, p<.01). For Factor 3 (Story Recall), several areas explained recall including entorhinal cortex, precuneus, caudal anterior cingulate gyrus, medial orbital gyrus, and others (adjusted R2=0.640, p<.001). For Factor 4 (Attention), the inferior and superior parietal lobules loaded (R2=0.207, p<.01). For Factor 5 (Executive Function), postcentral gyrus and the inferior lateral ventricle loaded (adjusted R2=0.142, p<.05). Finally, for Factor 6 (Letter Fluency), areas included inferior parietal gyrus, lateral occipital gyrus, and bilateral cerebellar white matter (adjusted R2=0.311, p<.001).

Conclusion

The results suggest that word and especially story recall are substantially explained by regions implicated in AD, such as frontal, temporal, and parietal areas. Tests associated with frontal brain regions (executive function, fluency) in NHW adults are more associated with parietal volume or cortical thickness in SA adults. Future work will need to explore this difference.
Deep Survival Analysis for Alzheimer’s Disease Based on Longitudinal Neuroimaging Data
Authors: Ma Da , Kyriacou Sophia
Published: January 2025

Background

Alzheimer’s Disease (AD) accounts for more than half of dementia cases and is a leading cause of death in the United States among elderly population. Early risk prediction is crucial to ensure effective and timely intervention plans. Deep learning‐based survival analysis based on cross‐sectional neuroimaging data has shown promising results in predicting the future progression of the dementia onset at an early stage. This study aims to derive deep survival analysis using multi‐timepoint longitudinal neuroimaging data to achieve a more accurate dementia onset prediction.

Method

Data: Longitudinal MRI of 1724 subjects (average age was 73.8) from the Alzheimer’s Disease Neuroimaging Initiative was used. To derive dementia onset event and duration information for the survival analysis, censored data (i.e. dementia conversion happened before or after the study period) was excluded (19.6% left‐censored, 63.2% right‐censored), resulting in 297 subjects. The input features included 89 FreeSurfer‐parcellated brain structural volumes, and demographic factors such as age and sex. Deep survival analysis was conducted using DeepSurv Pytorch library, using a multi‐layer perceptron (MLP) model architecture as the feature extractor. Image features from three timepoints were concatenated before feeding into the deep survival model for predicting future AD conversion. For comparison, ablation studies were conducted to include single timepoint neuroimaging features for each subject (derived from the last timepoints from the longitudinal features). The survival models’ performance was evaluated using the concordance index (C‐index) using 10‐fold cross‐validation.

Result

The accuracy of the three‐timepoint longitudinal data (C‐index=97.1%) is higher than the single timepoint study (C‐index=75.6%) (Figure 1). Figure 2 shows the histogram of the difference between the actual and predicted dementia onset time (derived by taking the 0.5 threshold cutoff point from the C‐index, Positive value means delayed prediction). Single timepoint prediction showed more false negatives (delayed prediction) while longitudinal prediction showed relatively even distribution of false positive (earlier prediction) and false negative.

Conclusion

In this study, we demonstrated the of longitudinal neuroimaging data in deep survival analysis to improve the prediction the time of conversion for future dementia, with less false negative, promoting early prediction and potential early intervention prior to the dementia.
Volumetric imaging analysis using deep learning classifier for various stages of dementia
Authors: Cheung Eva YW , Mak Henry , Shea Yat Fung , Chiu Patrick Ka‐Chun
Published: January 2025

Background

This study aimed to apply deep learning for various stages of dementia classification.

Methods

The ADNI database and OASIS database were used, where ADNI 21 centers with total 406 images (69AD, 202MCI and 135HC) were used as training and validation, and ADNI 4 centers with 176 images (28AD, 91MCI and 57HC) were used as testing, and another 176 images (28AD, 91MCI and 57HC) from local memory clinic were used as local testing. The 39 brain regional volumes were segmented and calculated using Freesurfer. Ten‐fold cross validation were performed to minimize the chance of overfitting. Four classifiers, including decision tree (DT), ensemble classifier (EC), support vector machine (SVM), K‐nearest neighbors algorithm (KNN) were used to build the model in the MATLAB classifier toolbox.

Results

The receiver operating characteristics (ROC) curves showed that the SVM achieved the best and balanced result, with AUC =1.00, 0.76 and 0.68; with 98.0%, 74.3% and 59.7% accuracy in training, testing and local testing respectively. This study showed that the training set attained the highest accuracy, while it dropped in testing data and local data. It highlighted the importance of local data testing prior to the application.

Conclusion

The SVM can differentiate AD, MCI from HC with satisfactory result. The application is practical and can be fully automatic, which can be used for dementia screening. Further studies should be conducted to fine‐tune the models for improving the accuracy in local dataset, prior to the application in local application.