bluuebunny/crossref_metadata_2025 huggingface dataset:
CSF biomarkers of immune activation and Alzheimer's disease for predicting cognitive impairment risk in the elderly
Authors: Shue Francis , White Launia J , Hendrix Rachel , Ulrich Jason D , Henson Rachel L. , Knight William C. , Martens Yuka A , Wang Ni , Roy Bhaskar , Starling Skylar C , Ren Yingxue , Xiong Chengjie , Asmann Yan W. , Syrjanen Jeremy A. , Vassilaki Maria , Mielke Michelle M. , Cruchaga Carlos , Holtzman David M. , Bu Guojun , Petersen Ronald C. , Heckman Michael G. , Kanekiyo Takahisa
Published: January 2025

Background

The immune system is substantially involved in the development and progression of age‐related cognitive decline and Alzheimer’s disease (AD).

Method

As genetic and environmental factors interactively impact these conditions, we investigated how risk factors such as APOE genotype, age, and sex influence immune activation markers and AD biomarkers in cerebrospinal fluid (CSF) in elderly individuals enrolled in the Mayo Clinic Study of Aging cohort. Among cognitively unimpaired individuals aged over 65 at the baseline visit (N=298), we measured 365 CSF immune activation markers using the proximity extension assay.

Result

We found that age, sex, and diabetes status are associated with altered CSF levels of immune activation markers independently of other factors. For CSF AD biomarkers, we observed significant positive correlations between age and total tau, phosphorylated tau‐181 (p‐tau181), neurofilament light (NfL), and YKL40. APOE4 was also associated with lower Aß42 and higher SNAP25 in CSF. We further examined whether baseline visit variables can predict cognitive decline, represented by the conversion from CDR=0 to CDR>0. We found that age, Aß42, NfL, and REG4 were independently correlated with CDR conversion risk. When the cohort was dichotomized by their median values, older participants with lower Aß42, higher NfL, and higher REG4 at baseline developed cognitive impairment during the follow up with a c‐index of 0.762 while age alone had a c‐index of 0.699.

Conclusion

Together, our results suggest that assessing CSF immune activation markers and AD biomarkers can improve the prediction of cognitive impairment risk in the elderly.
Alzheimer's disease subjects have smaller non‐jugular venous drainage system compared to cognitive normal population
Authors: Prado Keshet , Khasminsky Vadim , Keret Ophir , Beninger Felix , Goldberg Ilan , Auriel Eitan , Glik Amir
Published: January 2025

Background

The Internal Jugular Veins (IJVs) and the non‐jugular veins (NJVs) are two pathways responsible for intracranial blood drainage. The NJVs are usually the less prominent drainage system and have been demonstrated to become smaller during aging. This phenomenon may indicate less affective venues drainage and hence less CNS’s waste products clearance as we age. One of the pathological hallmarks in Alzheimer’s disease (AD) is amyloid betta42 (AB42) accumulation. AB42 accumulation may be the result of overproduction or clearance impairment. NJVs narrowing during aging may cause AB42 accumulation due to clearance impairment and hence plays a role in AD pathology. Our aim was to compare the venous cross‐sectional area (CSA) in AD vs. cognitive normal (CN) population.

Method

Cognitive impaired patients included AD and mild cognitive impairment (MCI) patients that were recruited prospectively as part of the DIASPORA Horizon‐2020 MesCobrad Project. All patients performed comprehensive neurocognitive evaluation and Brain MRI including Time‐of‐flight sequence for venous demonstration. Venous systems CSA were measured at the jugular foramen level and were done by senior neuroradiologist. CN were defined as subjects without CNS’s known pathology. CN Measurements were taken from a previous study of our group. T‐test was used to compare between groups.

Result

A total of 39 Cognitive impaired patients and 20 CN were included in the study. Mean ages of 68 years (SD, 5.4) and 66 years (SD, 5.5), respectively. The CSA of the IJVs was similar between groups. However, NJVs CSA was significantly smaller in cognitive impaired patients (mean CSA=41.21 mm2 (SD,21.52) vs. CSA=54.5 mm2 (SD,27.3) P=0.045). Table 1.

Conclusion

NJVs are significantly smaller in cognitive impaired patients compared to CN population. NJVs narrowing in AD patients may cause impaired venous drainage hence causing AB42 accumulation in this population. New treatment options for AD patients may rise if larger studies replicate our findings.
Whole‐brain Cellome‐Wide Association Study (CWAS) Unveils Ultra‐Early Neurodegeneration Phenomena in a 3D Spatial Cell Atlas
Authors: Mitani Tomoki T , Yamaura Kosei , Matsumoto Katsuhiko , Yamada Rikuhiro , Susaki Etsuo A , Ueda Hiroki R
Published: January 2025

Background

In aging societies, neurodegenerative diseases, such as Alzheimer's disease, are receiving attention. These diseases are primary targets for preemptive medicine, emphasizing the importance of early detection and preventive treatment before the onset of severe, treatment‐resistant damages. However, there is a lack of comprehensive investigation of lesions and molecular targets in the entire organ, whereas spatial identification of early‐stage lesions is potentially overlooked at the single‐cell level.

Method

Here, we propose a novel approach, CWAS (whole body/organ cellome‐wide association studies), which integrates and analyzes spatio‐temporal cellular information across the entire mouse organ into a comprehensive organ cell atlas using tissue‐clearing imaging technologies [1,2]. [1] Murakami TC, et al. Nat Neurosci. 2018;21(4):625‐637. [2] Matsumoto K*, Mitani TT*, Horiguchi SA, et al*. (*equal contribution) Nat Protoc. 2019;14(12):3506‐3537.

Result

In our initial application to neurodegenerative models, we have been able to identify previously unreported neural degenerations at specific times and locations at the single‐cell level. In addition, our time‐series analysis in a mouse model of Alzheimer's disease revealed the possible simultaneous onset of amyloid deposition and neurodegeneration, challenging the traditional amyloid hypothesis. Furthermore, we were able to identify unique multicellular inflammation networks surrounding neurodegenerative lesions in several disease models.

Conclusion

Our findings establish the concept of "spatial cellomics" realized through spatially precise organ mapping and integrated multiomics analysis at the single‐cell level. This approach shifts pathology, providing deeper insight into disease mechanisms and preventive strategies.
Demonstration of an open‐source toolbox for network spreading models: regional amyloid burden promotes tau production in Alzheimer's disease
Authors: Thompson Elinor , Schroder Anna , He Tiantian , Legouhy Antoine , Zhao Xin , Cole James H. , Oxtoby Neil P , Alexander Daniel C
Published: January 2025

Background

Connectome‐based models of disease propagation are used to probe mechanisms of pathology spread in neurodegenerative disease. We present our network spreading model toolbox that allows the user to compare model fits across different models and parameters. We apply the toolbox to assess whether local amyloid levels affect production of pathological tau.

Methods

Data: Regional tau‐PET and amyloid‐PET SUVRs from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study were downloaded in September 2023 (demographics in Figure 1). Subjects were separated into groups based on AT disease stage: 1) amyloid‐negative, tau‐negative; 2) amyloid‐positive, tau‐negative; 3) amyloid‐positive, tau‐positive. Positivity thresholds were defined using Gaussian mixture modelling. Data were averaged within groups to produce stage‐specific maps. Spreading models: We compared three models: the network diffusion model, Fisher‐Kolmogorov‐Petrovsky‐Piscounov (FKPP) and weighted‐FKPP models (Figure 2). Previously we showed that weighting the production rate of FKPP with regional vectors improves the model fit by accounting for different regional characteristics (He et al, MICCAI 2023). Here, we demonstrate this new “weighted‐FKPP” model, using regional amyloid as the fixed production weight. Models were initialised with unity pathology in a bilateral seed region, zero elsewhere. Parameter optimisation: Free parameters were fitted using Gaussian processes optimisation (α and seed region). NDM toolbox: All models are available in our toolbox: https://github.com/ethompson93/network_spreading_models. Null Modelling: we repeated the weighted‐FKPP model optimisation with a distribution of 100 randomly shuffled amyloid maps to demonstrate the spatial significance of the amyloid weighting.

Result

Adding amyloid weighting to the production term in the FKPP model improved the model fit to the data for each group. The reduction in squared model residuals with the weighted FKPP model was significant only in the amyloid‐positive tau‐positive group (Figure 3). For all groups, the results from the FKPP weighted with amyloid had significantly improved model fit (Pearson’s R) compared to the null model (all p‐values<1×10‐10).

Conclusion

Using our toolbox, we found that incorporating regional amyloid levels improves pathology‐spreading model fit to measured tau‐PET data, particularly at later stages of AD. This supports the hypothesis that amyloid promotes tau aggregation.
Postmortem MRI signature of Hippocampal Sclerosis of Aging
Authors: Rådman Gustaf , Denning Amanda E , Ravikumar Sadhana , Spotorno Nicola , Ittyerah Ranjit , Levorse Lisa M , Bedard Madigan , Chung Eunice , Robinson John L. , Schuck Theresa , Lee Eddie B , Lim Sydney A , Trotman Winifred , Bahena Alejandra , Olsen Rosanna K , Irwin David J , Jimenez Maria del Mar Arroyo , Vela Alicia , Buendia Esther , Martin Maria Mercedes Iniguez de Onzono , Rabal Maria del Pilar Marcos , Munoz Monica , Insausti Ricardo , Yushkevich Paul A. , Wisse Laura E.M.
Published: January 2025

Background

Hippocampal Sclerosis of aging (HS) refers to age‐related selective neuronal loss and gliosis in hippocampal cornu ammonis 1 (CA1) and subiculum that is out of proportion to tau pathology in Alzheimer’s Disease (AD). HS is related to cognitive decline and memory impairments separately from other neurodegenerative pathologies. To date, in vivo imaging biomarkers of HS of aging are non‐existent, and their development would greatly improve diagnosis and prognosis in memory clinics. We therefore aimed to characterize the magnetic resonance imaging (MRI) signature of HS among autopsy confirmed cases of HS.

Method

Data consisted of same‐subject histological Nissl‐stained slices and postmortem MRI (0.2mm isotropic) acquired from 27 subjects at the University of Pennsylvania and University Castilla‐La Mancha. Based on neuropathological assessment, cases were grouped into HS (N = 5), intermediate to high AD neuropathologic change without HS (N = 10) and limited or no neuropathological burden (N = 12). We manually measured grey matter thickness and GM/WM intensity ratio in select locations across the hippocampal head, body, and tail. We also obtained more global structural measures from hippocampal subfields using an automated postmortem atlas‐based segmentation algorithm.

Result

Qualitatively, HS is clearly noticeable as cortical thinning and hypointense signal (approximating white matter) on postmortem MRI across the CA1 and subiculum, particularly anteriorly closer to the border between CA1/subiculum (Figure 1). Manual thickness measures showed complete separation between the HS group and the two reference groups across both the hippocampal head and body, and a less pronounced difference in the tail. Global structural measures from automated segmentations corroborated these group differences showing complete separation in CA1 thickness and significant differences in subiculum and the white matter layer of CA1 (Stratum Radiatum Lacunosum Moleculare, SRLM).

Conclusion

In MRI, HS is marked by hypointense voxels and cortical thinning across CA1 and subiculum. Both local manual thickness measurements and thickness measures of whole subfields derived from automatic segmentations provided promising results. As a next step, we plan to perform similar measurements on antemortem MRI of the same subjects, with the hopes of making progress toward an in vivo MRI biomarker of HS.
Detecting amyloid positivity from MRI scans using comprehensible convolutional neural networks
Authors: Levin Fedor , Li Qingyue , Singh Devesh , Buerger Katharina , Fliessbach Klaus , Heneka Michael T. , Kleineidam Luca , Laske Christoph , Perneczky Robert , Peters Oliver , Priller Josef , Ramirez Alfredo , Schneider Anja , Spottke Annika , Wagner Michael , Wiltfang Jens , Düzel Emrah , Jessen Frank , Teipel Stefan , Dyrba Martin
Published: January 2025

Background

Analysis of neuroimaging data based on convolutional neural networks (CNNs) can improve detection of clinically relevant characteristics of patients with Alzheimer’s disease (AD). Previously, our group developed a CNN‐based approach for detecting AD via magnetic resonance imaging (MRI) scans and for identifying features that are relevant to the decision of the network. In the current study, we aimed to evaluate the potential utility of applying this approach to MRI scans to assist in the identification of individuals at high risk for amyloid positivity to aid in the selection of study samples and case finding for treatment.

Method

In the current analysis, we have trained a CNN to detect amyloid positivity using MRI scans from 1461 Alzheimer’s Disease Neuroimaging Initiative (ADNI) participants (498 cognitively normal participants, 103 participants with significant memory concern, 640 participants with mild cognitive impairment, and 220 participants with AD dementia). Amyloid positivity was assessed via amyloid PET scans obtained with [18F]florbetapir or [18F]florbetaben and quantified on a Centiloid scale. A threshold of 24.1 CL categorized 46% of participants as amyloid‐positive. The modeling approach was evaluated using 10‐fold cross‐validation, the number of epochs in training was set to 10.

Result

For each of 10 cross‐validation folds, we selected a model state corresponding to an epoch showing best performance in the validation partition. Balanced accuracy across these models ranged from 0.62 to 0.72 with an average of 0.68 (SD = 0.03).

Conclusion

We used a previously established approach to train CNNs for detecting amyloid positivity using MRI scans. Such models, particularly when tuned to have low rates of false negatives, have a potential to enhance identification of patients who would benefit from more in‐depth assessments, which could then inform antibody treatment. We are conducting ongoing work to improve and characterize the modeling approach, including evaluation of relevance maps which indicate importance of brain regions for detecting amyloid positivity. Future work will evaluate the role of amyloid positivity threshold selection. Planned analyses also include validation in independent data such as the German DZNE ‐ Longitudinal Cognitive Impairment and Dementia Study (DELCODE) dataset.
Exposure Severity and Cognitive Dysfunction are Mediated by Low‐Grade Cerebral Amyloidosis: A PET/MRI study of World Trade Center responders
Authors: Kritikos Minos , Zhou Juin W. , Huang Chuan , Vaska Paul , Clouston Sean A.P. , Luft Benjamin J
Published: January 2025

Background

World Trade Center (WTC) responders endured exposures to neurotoxic dust particulate matter. This neuroimaging study examined the presence of amyloidosis in Alzheimer’s disease (AD) regions of interest (ROIs) and associations with exposure duration.

Method

Simultaneous positron‐emission tomography with [18F]‐florbetaben and magnetic resonance neuroimaging was acquired on 34 middle aged WTC responders. Centiloid scale used mean standardized uptake value ratio (SUVR) with between subjects comparisons of cognitively unimpaired (n = 17) and early‐onset dementia (n = 17) responders. Pathway analyses investigated potential neuropathological cascades.

Result

Only one subject was Centiloid positive. However, considerable SUVRs in non‐AD ROIs associated with changes in cortical mean diffusivity, cortical thickness, cognition, and exposure duration, mediated by the olfactory bulb.

Conclusion

Fibrillar amyloidosis may have originated in, and spread from the olfactory cortex because of prolonged exposures to WTC neurotoxic dust. Our study also identifies a non‐AD neuropathological topology of amyloidosis, which associated with lower measures of brain health.
Relationship of Mito‐Nuclear Genetics to FDG‐PET Alzheimer’s Disease Biomarkers in Aging
Authors: Honea Robyn A , Vidoni Eric D , Lepping Rebecca J , Morris Jill K , Wilkins Heather M , Burns Jeffrey M , Swerdlow Russell H
Published: January 2025

Background

A growing amount of data has implicated the various roles of nuclear genes involved in mitochondrial function, and mitochondrial genes, on risk for Alzheimer’s disease (AD) and AD neuroimaging biomarkers. To date, no studies have investigated the relationship of mitochondrial haplogroups or the APOE and TOMM40 genes on brain glucose metabolism, a sensitive early marker of metabolic decline and possible mitochondrial dysfunction in AD.

Method

We analyzed regional standard uptake value ratio (SUVR) differences in 18F‐fluorodeoxyglucose positron emission tomography (FDG‐PET) using SPM12 and CAT12 software between nondemented (ND, n = 69), mild cognitively impaired (MCI, n = 19) and AD (n = 18) groups in a sample of individuals from the University of Kansas Alzheimer’s Disease Research Center Cohort, controlling for age, sex, and education. We then tested whether genetic variation in APOE (e4‐carriers vs. non‐carriers), TOMM40 rs2075650 (‘650 G‐allele carriers vs. A‐Allele) or mitochondrial haplogroup significantly influenced regional differences in glucose metabolism in nondemented individuals, as they made up the bulk of our sample of individuals with these scans.

Result

We found that, as expected, individuals with AD had significantly decreased glucose metabolism across all regions except the motor cortex and the cerebellum compared to ND individuals (Figure 1). Our results across various gene groups, controlling for age, sex, education, and with and without APOE e4 showed that there were no significant differences in FDG‐PET SUVR between ND TOMM40 ‘650 G‐allele carriers, or mitochondrial haplogroups (H, JT, and UK groups combined), however APOE e4‐carriers had significantly lower glucose metabolism in the anterior cingulate, superior medial frontal, posterior cingulate, medial frontal, right middle frontal, and bilateral superior frontal cortices than e4‐non carriers (Figure 2). There were no significant effects of APOE e4 on glucose metabolism in limbic regions.

Conclusion

The APOE e4 allele may have more impact on FDG‐PET in default mode regions (posterior and anterior cingulate, medial frontal cortex) than TOMM40 '650, a larger study in cognitively healthy individuals will be needed to confirm these results.
Lower slow wave sleep and rapid eye‐movement sleep are associated with brain atrophy of AD‐vulnerable regions
Authors: Cho Gawon , Mecca Adam P , Buxton Orfeu M. , Liu Xiao , Miner Brienne
Published: January 2025

Background

Sleep deficiency is associated with an increased risk of Alzheimer’s disease (AD), warranting research on underlying mechanisms. This study examined the association of sleep architecture with anatomical features frequently observed in AD: (1) atrophy of cuneus, hippocampus, entorhinal, inferior parietal, parahippocampal, and precuneus regions (henceforth referred to as “AD‐vulnerable regions”) and (2) the presence of cerebral microbleeds.

Method

In 271 participants of the Atherosclerosis Risk in the Communities Study, we examined the prospective association of baseline sleep architecture with anatomical features of the brain identified on MRI conducted ∼17 years later. Sleep architecture was quantified as the proportion of time spent in slow wave sleep (SWS), proportion of time spent in rapid eye‐movement sleep (REM), and the number of arousals per hour of total sleep time using polysomnography. Outcomes included (1) volumetric measurements of each AD‐vulnerable region and (2) the presence of any cerebral microbleeds (CMBs) and that of lobar CMBs, which are more specifically associated with AD. We analyzed the association of each sleep predictor with each brain outcome, adjusting for demographics, baseline cognitive function, APOE genotype, intracranial volume, other covariates, and false discovery rate (FDR; family‐wise p<.05).

Result

Having less SWS was associated with smaller volumes of the inferior parietal region (β = ‐41.56 mm3 per percentage point (PP) [95%CI = ‐73.97, ‐9.15]) and cuneus (β = ‐11.56 mm3 per PP [‐20.51,‐2.62]) after adjustments for covariates. Having less REM was associated with smaller volumes of the inferior parietal region (β = ‐73.13 mm3 per PP [‐126.95, ‐19.30]) and precuneus (β = ‐32.46 mm3 per PP [‐64.48,‐0.44]). After FDR adjustments, lower SWS and REM, respectively, were associated with lower volumes of the inferior parietal region only. Arousal index was not associated with the volumes of AD‐vulnerable regions. None of the sleep architecture variables were associated with the presence of CMBs or lobar CMBs.

Conclusion

Sleep deficiency is associated with the atrophy of the inferior parietal region, which is often observed in early AD. Sleep architecture may be a modifiable risk factor for AD pathogenesis.
Impact of Grey Matter Volume and White Matter Hyperintensities on Functional Capacity for Activities of Daily Living: A Study in the Alzheimer’s Disease Continuum
Authors: Campo Cecilia Gonzalez , Henriquez Fernando , Contreras Patricio Riquelme , Lillo Patricia , Martínez‐Pernía David , Thumala Daniela , Aboitiz Francisco , Chonchol Andrea Slachevsky , Henriquez Rodrigo
Published: January 2025

Background

The Alzheimer’s disease (AD) continuum is composed of the stages of Subjective Cognitive Decline (SCD), Mild Cognitive Impairment (MCI), and Alzheimer’s Disease Dementia (ADD). The decrease in gray matter volume (GMV) secondary to cortical atrophy, commonly seen in this continuum, is related to cognitive and activities of daily living (ADL) impairment. Additionally, White Matter Hyperintensities (WMH), MRI abnormalities frequently observed in older adults and patients with dementia, are also associated with cognitive and ADL performance. This study aimed to evaluate the relationship of GMV and WMH on ADL performance across the AD continuum.

Method

We conducted a cross‐sectional study of 77 SCD, 30 MCI, and 23 ADD subjects, matched for age, sex, and education. ADL was assessed using the Technology‐Activities of Daily Living Questionnaire (T‐ADLQ). Total GMV was calculated via voxel‐based morphometry analysis in SPM12. Total (tWMH), periventricular (pWMH), and subcortical (sWMH) WMH lesions were calculated with the lesion prediction algorithm (LST) and the ALVIN mask (Figure 1a). Spearman correlation analysis was used to test associations among GMV, WMH, and ADL. A linear regression model was employed to assess the relative effect of GMV and WMH on ADL scores.

Result

ADD patients performed worse on ADL and showed decreased GMV and higher pWMH burden in comparison with all the other AD continuum groups (Table 1). Within the whole sample, worse ADL scores correlated significantly with decreased GMV and higher pWMH load (Figure 1b and 1c, respectively). In addition, lower GMV was correlated with greater pWMH load (Figure 1d). The linear regression model (R2 = 0.183, F = 13.890, p = 0.000) showed that only GMV had a significant impact (Beta = ‐0.338, p = 0.000), while pWMH load did not significantly influence ADL performance (Beta = 0.164, p = 0.064).

Conclusion

Lower GMV and higher pWMH load are associated with worse ADL performance. These features differentially contribute to explain ADL performance across the AD continuum, with a more pronounced effect of GMV over pWMH. Widespread GM atrophy and pWMH burden are distinctive features of ADD, strongly suggesting a fundamental detrimental role of these structural brain abnormalities along the AD continuum.
Comprehensive Multi‐Neuron Imaging During Aging and Degeneration in C. elegans
Authors: Yadav Dilip , Wirak Gregory , Connor Christopher , Gabel Chritopher
Published: January 2025

Background

Despite the prevalence of Alzheimer’s Disease in the aged human population and advancements in our understanding of the disease at the molecular level, we still lack a fundamental understanding of how cognitive dysfunction stems from alterations in cellular neuron dynamics and how it relates to the normal aging process. To address this gap in knowledge, we measured the breakdown of nervous system function with cellular resolution during normal aging and in an Alzheimer’s Disease model.

Method

Employing comprehensive multi‐neuron florescence microscopy in the nematode worm, C. elegans, a simple yet powerful model animal, we can measure neuron activity across the majority of the animal’s nervous system with single cell resolution. To measure how system‐state dynamics and neuronal connectivity breakdown during normal aging and in the disease model, imaging was performed throughout the animal’s lifespan in wild‐type as well as a transgenic strain in which human amyloid‐ß (Aß) peptide (1‐42) is pan‐neuronally expressed.

Results

During normal aging we measure a breakdown in system‐state dynamics that accompany shifts in activity to higher frequencies and a global loss of inhibitory signaling. In Aß expressing animals this process is accelerated and includes loss of both inhibitory and excitatory signaling.

Conclusion

Our work is defining the functional degradation of a complete nervous system from single neurons to system‐wide dynamics during normal aging and in the disease model. In doing so we are revealing the specific changes in neuron activity at the cellular level that cause the breakdown of global nervous system dynamics. These results will enable a knowledge‐based approach to novel neurotherapeutic strategies in the treatment and prevention of dementia.
Mapping alterations in the hippocampal subfields in the ARTE10 mouse model of Alzheimer's disease using dMRI derived soma and neurite density maps
Authors: Shahid Syed Salman , Jarvis Erin E , Butch Elizabeth R , Snyder Scott E , Wu Yu‐Chien
Published: January 2025

Background

The hippocampus, a region vital for memory and cognition, is prone to abnormal deposition of beta‐amyloid (Aß) during the early stages of the Alzheimer’s disease. Aß‐associated pathophysiological mechanisms instigate dendritic deficit, neuronal loss, and neuroinflammation, leading to abnormal functional and behavioral changes. These factors directly impact tissue microstructures. Histological studies suggest subfield specific Aß accumulation. The aim of the current study is to map the changes in soma‐ and neurite‐density in the hippocampal‐subfields due to Aß load in the ARTE10 mouse‐model of AD using a compartment‐specific diffusion model.

Method

Ten‐month‐old male ARTE10 (N=8) and age‐matched littermate controls underwent MRI on a 9.4T scanner (Bruker BioSpin, GmbH, Germany) equipped with an 1H cryogenic surface coil. DWIs were acquired using a 2D multi‐shot spin‐echo echo planar imaging sequence using the following parameters: TE/TR=26/4000 ms; voxel size=125x125x400 µm3, 20 slices, 4 segments, b values= (500, 1000, 2500, 4000, 5500, 7000) s/mm2 and 5 b0 per b‐shell. The averaged b0 image was non‐linearly registered to Badhwar hippocampus atlas. The hippocampal‐subfields (CA1, CA2, CA3, CA4‐DG, subiculum, and stratum‐granulosum of hippocampus (SGH)) in atlas space were inverse transformed to diffusion space. Multi‐compartment microstructural estimation was performed using soma and neurite density imaging (SANDI) model. This model provided volume fractions for extra‐cellular (fec), intra‐neurite (fin), and intra‐soma (fsoma) compartments (Figure 1). The regional mean values of microstructural parameters of individual subfields were extracted and used in a general linear model for group comparisons.

Result

The group‐level differences (WT vs. ARTE10) in fin were observed in CA1 (p=0.02; d=‐1.25), CA2 (p=0.03; d=‐1.20), CA4‐DG (0.01; d=‐1.34), and SGH (p=0.03; d=‐1.22) Figure 2. Group differences in fsoma were significant in CA1 (p=0.04; d=1.08), CA2 (p=0.02; d=1.22), CA4DG (p=0.04; d=1.10), and SGH (p=0.03; d=0.99). In general, the ARTE10 AD mice had reduced neurite density and elevated soma density in the hippocampal subfields.

Conclusion

The results demonstrated that the SANDI model is sensitive to the microstructural alterations in the hippocampal subfields of the animal model with profound AD pathologies.
Local disruptions in non‐rapid eye movement sleep expression are associated with cerebrovascular pathology and worse memory performance in older adults
Authors: Berisha Destiny E , Rizvi Batool , Chappel‐Farley Miranda G. , Dave Abhishek , Meza Novelle J , Sattari Negin , Lui Kitty K. , Chen Ivy Y. , Tustison Nicholas J. , Neikrug Ariel , Benca Ruth M , Yassa Michael A. , Mander Bryce A
Published: January 2025

Background

Aging is associated with disruptions in non‐rapid eye movement (NREM) sleep and memory decline. Cerebral small vessel disease (CSVD) increases with age and is associated with clinical sleep disturbance, but little is known about its relationship with local expression of NREM sleep. Here, we explore associations between CSVD burden, memory, and local electroencephalography (EEG) measures during NREM sleep in older adults.

Methods

Fifteen cognitively intact older adults (mean age 71.8±6.3 years, 10 female, Apnea Hypopnea Index (AHI) = 7.82±7.95) underwent T2‐weighted fluid‐attenuated inversion recovery magnetic resonance imaging, an in‐lab overnight polysomnography with 128‐channel EEG, and a sleep‐dependent mnemonic discrimination task was administered. Lobar white matter hyperintensity (WMH) volumes were calculated using a validated semi‐automated toolbox. Topographical correlations were run with 5000‐permutation threshold free cluster enhancement.

Results

Older age was associated with lower absolute posterior total (all p<0.016, r>‐0.61) and slow sigma power (all p<0.018, r>‐0.60) and slow wave activity (SWA) (all p<0.017, r>‐0.61) over centro‐posterior EEG derivations. Age was also positively associated with frontal (t = 0.51, p<0.01) and parietal (t = 0.49, p<0.01) but not occiptal (t = 0.33, p = 0.092) or temporal (t = 0.36, p = 0.064) WMH burden. Occipital WMH burden was also not associated with AHI (t = 0.038, p = 0.843). Occipital WMH burden was associated with global reductions in alpha activity (all p<0.043, t>‐0.78), centro‐posterior reductions in SWA (all p<0.043, t>‐0.63) and Delta (all p<0.009, t>‐0.63), frontal reductions in total (all p<0.043, t>‐0.71) and fast sigma power (all p<0.004, t>‐0.57), global reductions in slow sigma power (all p<0.043, t>‐0.71), and central reductions in theta activity (all p<0.042, t>‐0.57). Better lure discrimination for low similarity lures was significantly positively associated with higher relative slow oscillation expression over fronto‐parietal regions, though this did not survive TFCE correction (all p<0.033, r>0.55).

Conclusions

Collectively, these findings indicate that WMH burden in older adults is associated with local disruptions in NREM sleep expression in multiple frequency bands, of which lower frequencies were particularly vulnerable. This in turn may explain weakened memory performance. Future studies should examine the role of NREM sleep expression in cognitive decline associated with CSVD burden.
Mechanistic links of amyloid, atrophy, and cognition profiles for gait disturbance in Alzheimer’s disease
Authors: Baek Min Seok , Seong Joon‐Kyung , Kim Sung‐Woo , Kim Dong Ho , Hong Jin Yong
Published: January 2025

Background

This study delves into the relationship between gait disturbances and the progression of Alzheimer’s disease (AD), focusing on how these changes correlate with cognitive impairments and key neuropathological indicators.

Method

We prospectively enrolled 48 patients with AD dementia (ADD), 27 patients with prodromal AD (proAD), and 41 cognitively unimpaired (CU) individuals between January 2022 and May 2023. Participants underwent brain T1‐weighted MRI, 18F‐florbetaben PET, neuropsychiatric tests, and APOE genotyping, and quantified gait analysis was assessed using a 5.79‐m long pressure sensor embed walkway. To evaluate the relationships between gait parameters, cognitive scores, regional amyloid deposition, and regional cortical thickness, Pearson partial correlation coefficients were computed, adjusting for age, sex, education duration, and leg length. Path analysis was employed to test hypothetical models comprising cortical amyloid deposition, cortical thickness, cognition, and gait parameters. Our models hypothesized five main pathways: from amyloid deposition to cortical thickness, cognition, and gait parameters; and from cortical thickness to cognition and gait parameters.

Result

As AD progresses, a significant decline in velocity (p = 0.0144 for comparing CU and proAD; p = 0.0010 for comparing CU and ADD) and step length (p = 0.0033 for comparing CU and proAD; p = 0.0002 for comparing CU and ADD) was observed, indicative of a slower gait pace. The swing time percentage was reduced, and the double support time percentage was increased, showing a significant difference only in ADD patients (p = 0.0015 and p = 0.0007 respectively). Gait variability, reflected in the standard deviations of step length and double support time, escalated with AD progression (p = 0.0173 and p = 0.0143 for comparing CU and ADD, respectively) (Fig. 1). Changes in gait pace, variability, and rhythm were found to be linked to neuroimaging markers of AD, including Aß deposition in widespread areas and cortical atrophy in the inferior parietal and middle temporal gyri, precuneus, and insula (Fig. 2). Path analysis revealed directed relationships between Aß deposition and cognitive impairment, as well as cortical atrophy, collectively impacting gait disturbance in AD (Fig. 3).

Conclusion

Our analysis indicates directed relationships between amyloid deposition and cognitive impairment, as well as cortical atrophy in these regions, collectively impacting gait disturbance in AD
Cross‐cohort epigenome‐wide association study in Alzheimer's disease risk and related traits
Authors: Milicic Lidija , Vacher Michael , Porter Tenielle , Dore Vincent , Bourgeat Pierrick , Doecke James D. , Shishegar Rosita , Maruff Paul , Masters Colin L. , Rowe Christopher C. , Villemagne Victor L , Laws Simon M.
Published: January 2025

Background

Epigenome‐wide association studies (EWAS) have identified multiple loci that are differentially methylated in Alzheimer’s disease (AD). However, for complex diseases such as AD, single methylation sites associated with disease and disease‐related traits have relatively low effect sizes. At the genetic level, measures of cumulative genetic risk, such as polygenetic risk scores, have yielded success in risk prediction as well as in association and interaction studies. Such approaches have the potential to be directly transferred to DNA methylation data.

Method

This study utilised data from two well‐characterised longitudinal cohort studies, the Australian Imaging, Biomarker and Lifestyle (AIBL) study (n=707) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI; n=620) and aimed to investigate the association between peripheral blood DNA methylation, at CpG sites across the epigenome, and eight AD‐related phenotypes: AD risk, MRI‐derived brain volumetric measures (hippocampal, ventricle, grey and white matter volumes), PET derived brain Aß‐amyloid burden (as a continuous and binary (high/low) variable) and cognition (Pre‐Alzheimer’s Cognitive Composite (PACC)). Each trait‐specific EWAS was first undertaken in the AIBL cohort, and eight trait‐specific methylation risk scores (MRS) were then developed using summary statistics. An additional MRS was also developed using an elastic net machine learning method. MRSs were then validated within and across traits in the ADNI cohort.

Result

Nominally significant associations were observed between CpG sites within several of the EWAS that were performed; however, no individual CpG sites remained significant after FDR correction. Several MRSs were associated with AD risk after correction for the false discovery rate.

Conclusion

While this study adds to the growing literature supporting the role of MRSs in AD‐related traits, it suggests that there are limited associations of individual epigenome‐wide CpG sites with cognitive and neuroimaging traits using the methods undertaken in this study. Future studies with larger cohort sizes and phenotypically rich datasets will help to confirm these findings and potentially elucidate further associations.
Associations between imaging biomarkers and sleep quality in the U.S. POINTER study baseline cohort
Authors: Winer Joseph R. , Ward Tyler J. , Scales Margaret , Taggett Jacinda , Howard Marjorie , Stone Katie L , Lockhart Samuel N. , Baker Laura D , Hayden Kathleen M. , Landau Susan M. , Harrison Theresa M.
Published: January 2025

Background

Worse sleep is associated with a greater risk of Alzheimer’s disease (AD). However, few studies have linked objective sleep with neuroimaging outcomes, limiting our understanding of how poor sleep impacts the brain. The U.S. POINTER trial, which has enrolled a heterogeneous cohort of older adults at elevated risk for cognitive decline, is a unique sample differing from prior investigations of sleep, aging, and brain health. Here we investigate how actigraphy‐assessed sleep duration and efficiency are cross‐sectionally associated with baseline neuroimaging measures of brain structure and AD pathology.

Method

Participants underwent neuroimaging (MRI, 18F‐florbetaben amyloid PET, and 18F‐MK6240 tau PET), cognitive testing, and wristwatch actigraphy, which assessed sleep duration and sleep efficiency averaged across six nights. Imaging analyses focused on bilateral entorhinal tau PET, cortical amyloid PET, hippocampal volume, white matter hyperintensity (WMH) volume, and diffusion free water. Based on prior literature suggesting short and long sleep duration are disadvantageous, quadratic models were fit for sleep duration predicting neuroimaging and cognitive measures, with age, sex, years of education, body mass index, and self‐identified racial ethnic category as covariates. Linear models were fit for sleep efficiency with the same covariates.

Result

Across 454 participants (Table 1), sleep measures were not related to cognition. Hippocampal volume, WMH volume, and free water showed U‐shaped relationships with sleep duration such that average sleep durations shorter and longer than approximately 6‐7 hours were associated with worse brain health outcomes (Figure 1). There were no associations between tau or amyloid PET and sleep duration. When stratified by amyloid status, associations between sleep duration and brain structure were significant in only the amyloid negative group. Unexpectedly, there were no associations between neuroimaging measures and sleep efficiency except for a significant positive relationship with entorhinal tau PET uptake.

Conclusion

In the largest and most heterogeneous sample to date of older adults with actigraphy and neuroimaging measures, sleep duration was associated with multiple measures of brain structure but not AD pathology. These relationships may reflect heterogeneity and selection characteristics of the at‐risk POINTER cohort, which differ from previous studies of sleep and AD biomarkers.
Identifying transition points in AD biomarkers using machine learning: A supplement to the reliable worsening method
Authors: Gebre Robel K , Jack Clifford R. , Vemuri Prashanthi
Published: January 2025

Background

Biomarker cutpoints have received significant importance in AD research. Reliable worsening method based on serial biomarker data has been proposed (Jack et al., 2017 Alz & Dem). Advanced machine learning models (ML) and explainable AI provide an opportunity to enhance existing techniques. We aimed to estimate biomarker cutpoints on cross‐sectional data using ML by identifying important transitions in the biomarker values.

Method

We first trained an ML model with the biomarker of interest as an input including other available biomarkers, demographics, and diagnosis, and predicted global cognition z‐score. We then obtained SHAP values (Shapley Additive exPlanations), fit a curve between the SHAP values and biomarker values, and calculated the first derivatives (slope) of the curve. Then, a CUSUM (Cumulative Sum) analysis was used to identify SHAP change points and cross‐referenced to values where the slopes were steepest near the zero‐crossing location of the curve. A zero SHAP is an important indicator of a feature’s progressive importance for model prediction because a feature’s influence on prediction significantly shifts when its SHAP values go from positive to negative or vice versa. However, relying on zero‐crossing alone may be insufficient when the relationship is non‐linear. Consequently, we calculated the second derivatives to identify inflection points (peaks and troughs) along the curve. Lastly, we compiled all the potential cutpoints and performed post‐processing as shown in Fig. 1.

Result

The algorithm identified cutpoints at 1.44 SUVR for all and at 1.42 SUVR for CU participants (Fig. 2). At age 70, both cutpoints estimated the percentage of abnormals at less than 40% (Fig. 3).

Conclusion

TPE amyloid‐PET SUVR cutpoints for CUs equal the reported RW values (1.42 SUVR). A strength of the TPE is its application on cross‐sectional data making it versatile for various biomarkers across different cohorts.
The association between the age and the rate of tau accumulation and spreading in different sexes
Authors: Zhu Zeyu , Dehsarvi Amir , Roemer Sebastian Niclas , Steward Anna , Dewenter Anna , Gross Mattes , Wagner Fabian , Ewers Michael , Brendel Matthias , Franzmeier Nicolai
Published: January 2025

Background

Neuroimaging studies have revealed age and sex‐specific differences in Alzheimer’s disease (AD) trajectories. However, how age and sex modulate tau spreading remains unclear. Thus, we investigated how age and sex modulate the amyloid‐beta (Aß)‐induced accumulation and spreading of tau pathology from local epicenters across connected brain regions.

Method

We included 313 ADNI participants (female/male, n=167/146), i.e. 110 cognitively normal (CN) Aß‐negative, and 203 Aß‐positive subjects across the AD spectrum (i.e. CN/MCI/Dementia, n=98/70/35) with baseline amyloid‐PET and longitudinal Flortaucipir tau‐PET. Annual tau‐PET change rates for 200 cortical regions of the Schaefer atlas were calculated. Sex‐specific resting‐state fMRI‐connectivity templates across the 200 Schaefer regions were determined in independent Aß‐negative controls (female/male, n=118/82) to determine the connectivity of tau epicenters to the rest of the brain. Using linear regression, we investigated interactions between age, sex and Aß on tau accumulation and spread, controlling for APOE4‐status and diagnosis.

Result

Higher Aß (i.e. centiloid) predicted faster tau accumulation, where this association was pronounced in younger individuals (i.e. age x centiloid interaction, b=‐3.64, p<0.001, Figure 1A). This age x centiloid interaction was stronger in men (b=‐4.82, p<0.001, Figure 1B) vs. women (b=‐1.67, p=0.029, Figure 1C), suggesting that younger age promotes Aß‐related tau accumulation predominantly in men. Bootstrapping analysis further confirmed this effect (Figure 1D). In Aß+, epicenters with highest baseline tau‐PET showed a similar temporal‐lobe distribution in men and women (Figure 2A&B), yet epicenter connectivity to the rest of the brain was stronger in men vs. women (Figure 2C). Stronger connectivity of tau epicenters to the rest of the brain was linked to faster tau accumulation especially in younger Aß+ subjects (i.e. interaction age x epicenter connectivity, b= 4.41, p<0.001, Figure 3A). However, this effect was clearly driven by men (b=6.13, p<0.001, Figure 3B) and not observed when tested in women only (b=1.55, p=0.252, Figure 3C).

Conclusion

Aß drives faster tau accumulation and this effect is particularly strong at younger age and even further pronounced in men, whose tau epicenters are more densely interconnected with the rest of the brain. Together, age and sex have clear modulating effects on tau spreading, and heterogeneous AD trajectories may be partly arisen due to sex‐specific differences in brain network architecture.
Distinct Spatial Patterns for White Matter Microstructure Changes in an Aging and Alzheimer's Disease Cohort
Authors: Tian Jianqiao , Raghavan Sheelakumari , Cogswell Petrice M , Reid Robert I. , Przybelski Scott A. , Schwarz Christopher G. , Graff‐Radford Jonathan , Lowe Val J. , Kantarci Kejal , Knopman David S. , Petersen Ronald C. , Jack Clifford R. , Vemuri Prashanthi
Published: January 2025

Background

White matter (WM) damage is seen with neurodegenerative and cerebrovascular pathologies and contributes to cognitive dysfunction. We hypothesized that there are multiple disease progression patterns in WM microstructural changes related to aging and dementia, and these can be identified using SuStaIn (data‐driven clustering algorithm for disease subtype and stage discovery) on multishell diffusion MRI (NODDI) data. NODDI provides information on cellular tissue architecture – neurite density index (NDI) measures packing density of axons and dendrites, and free water fraction (FWF) measures unrestricted water, e.g., interstitial and cerebrospinal fluid.

Method

We identified 1136 participants (mean age=70 years, 51% male, 40% Aß‐positive, 19% MCI/AD dementia) from the Mayo ADRC and Mayo Clinic Study of Aging. We analyzed NDI and FWF in 55 WM regions. We selected a subset of WM features that monotonically changed with clinical disease progression and then applied the Z‐score SuStaIn algorithm.

Result

NDI in seven regions and FWF in six regions survived selection criteria (Figure 1). Noteworthy, the NDI regions were predominantly located in the temporal lobe, while FWF regions were primarily distributed in the frontal and subcortical regions. The SuStaIn algorithm discovered two stable clusters: the NDI‐First (Temporal) and FWF‐First (Frontal) groups (Figure 2). For the NDI‐First group, temporal lobe NDI changed early followed by frontal FWF. The FWF‐First group had the reverse order. There was clear phase separation based on which measure changed first. Stages of the NDI‐First group correlated better with meta‐ROI Tau‐PET SUVR and with MMSE than the FWF‐First group (p<0.01), suggesting that NDI‐First may be indicative of a primarily AD pathway.

Conclusion

Given NDI changes are likely degenerative and FWF are suggested to be other processes such as neurodegeneration or inflammatory, the observed changes in the temporal and frontal regions may be due to distinct etiologies, i.e., neurodegenerative in the temporal lobes vs. vascular dysfunction often observed in the frontal lobes. The SuStaIn algorithm showed promise in discovering disease subtypes of WM microstructural changes. Further work is needed to understand the mechanistic basis of these observed changes and integrate WM changes for differential etiology identification and disease staging.