bluuebunny/crossref_metadata_2025 huggingface dataset:
Relationship and impact of hand segmentation in IADLs for predicting Alzheimer
Authors: Babatope Eyitomilayo Yemisi , García‐Vázquez Mireya Saraí , Ramírez‐Acosta Alejandro Álvaro
Published: January 2025

Background

A decline in Instrumental Activities of Daily Living (IADLs) indicates cognitive impairment, a marker of early detection of Alzheimer’s disease (AD). Obtaining hand information within the assessment of IADLs may be an innovative approach to predicting cognitive decline. Hands play a vital role while performing IADL and can be used in assessing human visuomotor skills. This is a non‐invasive approach to early detection and intervention. Hand segmentation techniques can help obtain hand information necessary for cognitive function assessment. This research evaluates the visuomotor skills in humans by segmenting hands in egocentric videos to obtain hand information as a feature to predict cognitive decline.

Method

In this work, visual information with motor actions was measured based on hand coordination and movement. Analysis of hand information obtained from hand interaction is measured to predict Alzheimer's disease. A convolutional neural network was used to build an improved segmentation model (RefineNet‐Pix) for hand segmentation. This model was trained with a public dataset that captures hand interaction and movements using an egocentric camera (egoHands). To validate its accuracy, we tested the model on egohands test data, GTEA (Georgia Tech Egocentric Activities) datasets, and random images from the internet. We made use of less expensive kernels and more optimization techniques in performing the segmentation task as compared to existing models.

Result

The result obtained from the semantic segmentation task yielded a better performance in terms of speed and accuracy when compared with the baseline model.

Conclusion

Details of hand movements can point to precise changes in cognitive decline. An improved performance of the hand segmentation process with our RefineNet‐Pix can help in extracting hand information. This is a useful feature in identifying a decline in cognitive status. This is a promising, non‐invasive, and cost‐effective approach that can help clinicians in the early detection of Alzheimer's disease.
Design and Validation of the ADNI MR Protocol
Authors: Arani Arvin , Borowski Bret J. , Felmlee John , Reid Robert I. , Thomas David L , Gunter Jeffrey L. , Buckner Randy L. , Jung Youngkyoo , Tosun Duygu , Weiner Michael S. W. , Jack Clifford R.
Published: January 2025

Background

Phase four of the Alzheimer’s Disease Neuroimaging Initiative (ADNI4) began in 2023. This time‐period corresponded to MRI vendors introducing product sequences with compressed sensing (CS), cross‐vendor adoption of arterial spin‐labelling (ASL) and multi‐band slice excitation, and hardware improvements (head‐coils, increased gradient amplitudes). These advances enabled the acquisition of new imaging measures and reduced scan times. The ADNI4 MRI protocol aims to maintain longitudinal consistency across two decades of data acquisition, while adopting new technologies. Here we describe the design and justification behind the ADNI4 protocol and list the target imaging biomarkers.

Methods

The ADNI4 MRI protocol includes 9 MRI sequences; i) T1‐weighted (T1w) MP‐RAGE, ii) a compressed sensing T1w MP‐RAGE, iii) 3D T2w FLAIR, iv) structural isotropic 3D T2w v) T2*‐weighted susceptibility imaging vi) multi‐post labelling delay (PLD) 3D ASL, vii) 2D multi‐band‐multiple‐shell diffusion (dMRI), viii) 2D multi‐band EPI BOLD ix) high resolution, limited field of view, imaging centered on the hippocampus. Table 1 gives a detailed summary of the key protocol parameters and lists some of the imaging biomarkers that can be obtained from each series.

Results

Structural imaging was kept consistent across imaging sites. Variations in vendor specific sequences, software upgrades, and hardware performance capabilities, resulted in variations in advanced imaging strategies. Table 2 highlights the advanced sequences introduced in ADNI4, which will gradually be implemented as the required upgrades and licenses are obtained at each site. The main advanced sequences that are new in ADNI4 from ADNI 3 are i) CS for T1w images, ii) pseudo‐continuous‐ASL (pCASL) being implemented on all 3 vendors (GE, Siemens, Philips), iii) multi‐PLD ASL, which allows for the generation of quantitative maps of cerebral blood‐flow (CBF) and arterial transit times (ATTs), iv) and isotropic 1mm3 resolution for 3D FLAIR. When ADNI4 began, product sequences that utilized artificial intelligence were not widely available and could not be adequately tested before the rollout of ADNI4.

Conclusions

The ADNI4 database will continue to help the neuroimaging community, not only extract valuable imaging biomarkers, but provide the data necessary to test the impact of advanced imaging strategies on diagnostic accuracy and disease sensitivity.
Identification of stage‐specific blood biomarkers for early diagnosis of Alzheimer’s disease through RNA sequencing analysis
Authors: Yamakawa Akiko , Suganuma Mutsumi , Mitsumori Risa , Niida Shumpei , Ozaki Kouichi , Shigemizu Daichi
Published: January 2025

Background

The detailed mechanism of Alzheimer’s disease (AD) has not been fully elucidated, and a comprehensive gene expression analysis of the entire process leading up to the onset of AD has never been conducted on a large scale.

Method

We performed RNA‐seq analysis of 1227 blood samples consisting of 424 AD, 543 mild cognitive impairment (MCI), and 260 cognitive normal (CN) subjects, and examined differentially expressed genes (DEGs) between CN and MCI (CN‐MCI), and between MCI and AD (MCI‐AD). Pathway analysis of DEGs were performed and subsequently retrospective prediction models were constructed using the enriched genes within these pathways.

Result

A total of 883 and 1,169 statistically significant DEGs were identified in CN‐MCI and MCI‐AD, respectively. Pathway analyses using these DEGs and subsequent construction of risk prediction models led to the identification of 6 and 29 representative genes, achieving area under the curves of 0.792 (95% confidence interval: 0.768‐0.853) for CN‐MCI and 0.781 (95% confidence interval: 0.712‐0.799) for MCI‐AD. Furthermore, protein–protein interaction network analysis using the representative genes revealed key roles of ribosomal genes and phagosomes in the progress of MCI, while immune‐related genes were involved in the progression of AD.

Conclusion

Our findings suggest that the onset of AD may be associated with gene expression changes in the immune system, protein processing and cell cycle, following observed alterations in gene expression within ribosomes and phagosomes during the MCI stage. Given the effectiveness of delaying MCI progression in preventing AD, the regulation of ribosomal expression and phagocytosis emerges as potential biomarkers.
Test‐retest reliability of FreeSurfer measures of neurodegeneration
Authors: Chen Haiyun , Bokacheva Louisa , Vedvyas Alok , Osorio Ricardo S. , Masurkar Arjun V. , Wisniewski Thomas , Rusinek Henry
Published: January 2025

Background

FreeSurfer is widely used for monitoring neurodegeneration by quantifying MRI brain regions vulnerable to atrophic changes associated with Alzheimer’s disease (AD). FreeSurfer test‐retest reliability has been explored mainly in younger adults, but the reliability of AD imaging biomarkers is less well studied. We used two closely spaced T1‐weighted MRIs in 100 older adults to assess FreeSurfer 7.1 test‐retest reliability of AD imaging biomarkers of neurodegeneration.

Method

Consecutive participants (n=100, age ≥60 years) with two brain MRI exams between Jan 2021 and Oct 2023 and less than 180 days between exams were selected from NYU Langone’s Alzheimer Disease Research Center database. Images were acquired on the same Siemens 3T PET‐MR system using 32‐channel head coil and sagittal T1‐weighted MPRAGE sequence (TR=2100ms; TE=2.98ms; TI=900ms; FA=8°; bandwidth, 260Hz/pixel; GRAPPA, 2; matrix, 256x240x176; voxel, 1x1x1 mm3; acquisition time, 4:35min). FreeSurfer 7.1 was used to segment the brain and determine regional volumes, cortical thickness, and intracranial volume (ICV). We compared bilateral and total hippocampal volume, entorhinal cortex thickness, AD signature (after Dickerson et al. Cereb Cortex 2009; PMID: 18632739), and ICV between two exams. Test‐retest reliability was assessed using mean absolute difference, root mean square difference, and intraclass correlation coefficient (ICC). Inter‐exam differences in AD measures were tested for association with demographic, cognitive (MOCA), and imaging features using linear regression.

Result

The 100 participants (age, 73.5±6.1 years, 64% women, 33% Black, 9‐20 years education) had MRIs within an interval of 5.5±5.2 weeks (Figure 1). FreeSurfer estimates of MRI‐based biomarkers of AD were consistent between exams (Table 1). There were no associations between the differences in any of the FreeSurfer measures and inter‐MRI interval or participants’ demographic and cognitive/biological features (Figure 2). Detecting within‐individual 1% change in AD signature, whole hippocampus, or entorhinal cortex (using paired t‐test) at p=0.05 and 80% power, would require an estimated sample of 38, 69, or 347 subjects, respectively.

Conclusion

Our test‐retest results reflect random errors due to image acquisition and post‐processing precision of FreeSurfer 7.1. The test‐retest reliability of most measures is excellent, except entorhinal cortex thickness. Detecting small neurodegenerative changes appears to be feasible with relatively small sample sizes.
OVERTURE Phase 2 MRI Analysis Demonstrates Reduced Corpus Callosum Atrophy in Alzheimer's Disease
Authors: Da Xiao , Hempel Evan , Hajos Mihaly , Kern Ralph , Cimenser Aylin
Published: January 2025

Background

Recent research by Da et al. (2023) has demonstrated that non‐invasive gamma sensory stimulation can reduce brain white matter atrophy and myelin content loss. The impact on the Corpus Callosum (CC), the brain's largest commissural white matter tract essential for hemispheric connectivity, remains unexplored. Diffusion Tensor Imaging (DTI) studies (Delvenne et al. 2023), have identified variations and alterations in the CC's tract bundles in Alzheimer's disease (AD). We conducted an analysis of CC changes in patients along the Alzheimer’s continuum targeting both the overall and regional structural alterations.

Method

In this study, we used neuroimaging data from the OVERTURE clinical trial (NCT03556280). Participants were randomized 2:1 (Active: Sham) to receive daily, 1‐hour, audio and visual stimulation at gamma frequency or sham stimulation for 6 months. Magnetic Resonance Imaging (MRI) assessments were performed at baseline, month 3, and month 6. T1w MRI images were processed using the Yuki module within the Automated Registration Toolbox (ART) (Ardekani et al., 2014) with CC segmentation in the midsagittal plane (MSP) and its five subregions (Hampel et al., 1998). The analysis was conducted on participants with valid data for the total MSP CC and its subregions (N=50). Using Bayesian linear mixed‐effects modeling, we examined the changes in the area of the total MSP CC and its subregions.

Result

The active group demonstrated preservation of the total CC area and its subregions after six months of treatment. Differences in percentage changes between active and sham groups were: Total CC area (2.28±0.87%, p<.02), genu/rostrum (2.36±0.90%, p<.02), anterior‐body (2.64±1.26%, p<.04), mid‐body (2.79±1.18%, p<.03), posterior‐body (2.87±1.41%, p<.05) and splenium (1.58±0.73%, p<.04). Significant differences between the two groups were also observed in total CC area, genu/rostrum, and splenium at month 3.

Conclusion

OVERTURE Phase 2 MRI outcomes demonstrate that six months of non‐invasive gamma sensory stimulation may reduce the progression of CC atrophy in individuals with mild‐moderate AD, potentially preserving the integrity of interhemispheric communication. We are currently conducting a phase 3 clinical trial titled HOPE (NCT05637801) to evaluate the efficacy and safety of Cognito’s medical device.
Variational autoencoder latent space as a robust and pragmatic clinical classification tool for dementia
Authors: Wakefield William C , Barnard Leland R , Botha Hugo , Graff‐Radford Jonathan , Kantarci Kejal , Boeve Brad F. , Petersen Ronald C. , Jack Clifford R. , Lowe Val J. , Jones David T.
Published: January 2025

Background

Many proposed clinical decision support systems (CDSS) require multiple disparate data elements as input, which makes implementation difficult, and furthermore have a black‐box nature leading to low interpretability. Fluorodeoxyglucose Positron Emission Tomography (FDG‐PET) is an established modality for the diagnosis of dementia, and a CDSS that uses only an FDG‐PET image to produce a reliable and understandable result would ease both of these challenges to clinical application.

Method

A deep variational autoencoder (VAE) was used to extract a latent representation of each image through prior training from FDG‐PET brain images (n=2000). This unsupervised VAE has a novel graph convolutional architecture that makes it applicable to masked template space images. A logistic regression model was used to classify each image. A parametric study of the latent space revealed the imaging features that were used in the logistic regression model to differentiate each class. A separate dataset of participants labeled with their clinical diagnosis (n=1239) was used to assess the logistic regression model’s diagnostic accuracy of cognitively unimpaired (CU) (n=679) and between common dementia subtypes: Alzheimer’s disease (AD) (n=310), Lewy body dementia (DLB) (n=151), and behavioral variant frontotemporal dementia (bvFTD) (n=99). The logistic regression classifier was evaluated using receiver‐operator characteristic (ROC‐AUC) curves.

Result

ROC‐AUC curves for CU and each dementia subtype are illustrated in Figure 1a. A k nearest neighbors model was used to develop a graphical representation of the latent space where nodes are images and edges are drawn between nearest neighbors, illustrated in Figure 1b. The graph of the latent space demonstrates a comparison between dementia subtypes and CU images.

Conclusion

In this study, we developed a proof‐of‐concept VAE that can achieve differential diagnosis with 84.8% balanced accuracy. The model architecture provides visual interpretability, and shows that the hippocampi, parietal lobes, and cerebellum, were useful for distinguishing AD, the frontal lobe, posterior cingulate cortex, and hippocampi were useful for distinguishing DLB, and prefrontal pole, subcortical structures, and occipital lobe were useful for distinguishing bvFTD. These regions of metabolism align with clinical pathology and metabolic patterns.
Multimodal characterization of Alzheimer's Disease using speech, eye movement, and handwriting
Authors: Moro‐Velazquez Laureano , Favaro Anna , Thebaud Thomas , Wang Yuzhe , Butala Ankur , Oh Esther S , Dehak Najim
Published: January 2025

Background

Phenotyping Alzheimer’s Disease (AD) can be crucial to providing personalized treatment. Several studies have analyzed the use of digital biomarkers to characterize a subject’s behavior, usually obtained from a single modality, such as speech. However, combining several modalities in a single study has not been deeply studied. We aim to characterize people with AD and differentiate them from a cognitively normal control group (CN), employing metrics obtained through from three different modalities: speech, handwriting, and eye movement.

Method

We collected data from a group of 11 participants in the early stages of AD or with, mild cognitive impairment due to AD, and an age‐matched CN group of 13 participants. Each participant read out loud the Rainbow Passage, while their speech was recorded with a headset microphone and their eye movement with a high precision eye‐tracker, simultaneously. Then, the participants were asked to draw a spiral on a digital pad. We obtained 57 speech metrics, related to articulation, prosody, and hesitation; 90 handwriting metrics, characterizing drawing entropy, writing speed, and acceleration; 32 eye movement metrics related to saccade, fixation, and blink statistics. Several multilayer perceptron (MLP) classifiers were trained per modality and using all modalities together, following a cross‐validation strategy. SHAP values were calculated to rank metric impact in the differentiation between AD and CN.

Result

Cross‐validation accuracy in the binary classification of AD/CN ranged between 75.3% and 77.4% for single modalities, whereas the combination of the three modalities provided a 77.9% accuracy. The drawing velocity skewness metric provided the highest area under the receiver operating characteristic curve (AUC) from all the handwriting features (0.83), whereas the pause variability was the speech metric with the highest AUC (0.79). The number of fixations provided the best differentiation between groups in the eye movement modality, with AUC=0.69.

Conclusion

Combining the three proposed modalities provides a higher differentiation power than using each modality separately. The combination can provide a more insightful and complementary characterization of patients. Handwriting metrics have the most impact on the prediction of the multimodal MLP model, according to the SHAP values on the multimodal classifier.
Increased Brain Conductivities in Patients with Alzheimer’s Disease
Authors: Jahng Geon‐Ho , Rhee Hak Young , Park Soonchan , Ryu Chang‐Woo
Published: January 2025

Background

The levels of conductivity in the extracellular and intracellular space of the brain are still unknown, especially in the brains of Alzheimer’s disease (AD) patients. The objectives of this study were to investigate how the extra‐neurite conductivity (EC) and intra‐neurite conductivity (IC) were reflected in AD patients compared with elderly CN people and patients with amnestic mild cognitive impairment (MCI) and to evaluate the association between EC and cognitive decline.

Method

A total of 66 patients were included including 20 AD patients, 25 amnestic mild cognitive impairment (MCI) patients, and 21 cognitively normal (CN) old people. For the brain magnetic resonance electrical property tomography (MREPT) images, a multi‐echo turbo spin‐echo pulse sequence was used. High‐frequency conductivity (HFC) at the Larmor frequency was calculated and decomposed into extra‐ (EC) and intra‐neurite (IC) conductivities, respectively. Each parameter map was compared among the three participant groups using the full factorial design of one‐way analysis of covariance (ANCOVA). Furthermore, the association between the value of each map and either age or the MMSE score, with age as a covariate, was evaluated using the multiple regression analysis.

Result

The conductivities of both HFC and EC were increased in the AD group in the large brain areas. The IC was increased in AD compared with CN but decreased in AD compared with MCI. Compared with the CN group, all three conductivity indices were increased in the MCI group. EC was higher in the AD group in the hippocampus (p=0.009) and insular (p=0.020). Both HFC and EC were significantly negatively associated with the MMSE in the insular and MTG, but IC were significantly positively associated with the MMSE in the corpus callosum.

Conclusion

The EC value was higher in patients with AD than CN elderly people and patients with amnestic MCI. The EC value decreased with increasing MMSE scores after adjusting for age in the insula and middle temporal gyrus. This study showed the possibility that the EC value might be used as an imaging biomarker for helping to monitor cognitive function.
Regional Amyloid Load Predicts Tau and Atrophy Dynamics
Authors: Chaggar Pavan , Vogel Jacob W. , Binette Alexa Pichet , Thompson Travis B , Strandberg Olof , Mattsson‐Carlgren Niklas , Karlsson Linda , Stomrud Erik , Jbabdi Saad , Magon Stefano , Klein Gregory , Hansson Oskar , Goriely Alain
Published: January 2025

Background

A generative model of tau PET was applied to multiple cohorts across the Alzheimer's disease (AD) spectrum, revealing longitudinal changes in tau production and transport. A generalisation of the model accounts for amyloid, tau and neurodegeneration (ATN) interactions and accurately explains longitudinal ATN biomarker data, adding potential for region specific and individualized tracking of ATN biomarkers.

Method

A model of tau spreading and production as measured through PET was developed and applied to longitudinal data from amyloid negative (A‐), amyloid positive tau negative (A+T‐) and amyloid positive tau positive (A+T+) cohorts from the Alzheimer’s disease neuroimaging iniative (ADNI; N = 159) and BioFINDER‐2 (BF2; N = 135) datasets. The model was extended to include ATN biomarkers and their interactions, explaining regional tau and atrophy dynamics as a by‐product of regional amyloid load. This model was applied to longitudinal amyloid PET, tau PET and atrophy data from A+T+ subjects in ADNI (N = 39).

Result

The tau PET model faithfully reproduces Braak staging (Figure 1a). In A+T‐ subjects (N = 39 ADNI; N = 18 BF2) inference reveals faster tau spreading through the brain network and slower tau production, compared to A+T+ subjects (N=57 ADNI; N = 54 BF2) who show a higher production rate with slower transport (Figure 1b). This implies greater movement of tau during early AD before an acceleration in production when participants have high levels of both amyloid and tau. This is supported by comparison with spatially shuffled null models (Figure 1c). The generalization to the dynamical ATN model shows that an amyloid‐induced acceleration in tau production explains regional variations in tau load (as measured through PET), Braak staging and downstream neurodegeneration (Figure 2). The ATN model calibrated to longitudinal data from ADNI can accurately describe regional longitudinal ATN biomarkers (Figure 3).

Conclusion

Tau production and transport dynamics vary across the AD spectrum, characterised by an early period of fast tau spreading through the brain network followed by accelerated production of tau aggregates. A dynamical ATN model explains variations in tau and neurodegeneration through regional amyloid load and allows for accurate simulation of personalised ATN biomarkers.
Addressing the gaps of the functional neuroimaging in young APOEe4 carriers: A systematic review and Graph Theoretical analysis of brain connectivity
Authors: Kucikova Ludmila , Brass Adam , Xiong Xing , Zeng Jianmin , Ritchie Craig , Muniz‐Terrera Graciela , O'Brien John T , Su Li
Published: January 2025

Background

In the last decade, extensive research has emerged into understanding the impact of risk factors for Alzheimer’s Disease (AD) on brain function in pre‐symptomatic stages. Here, we focused on the apolipoprotein e4 (APOEe4) gene, the major genetic risk factor for sporadic AD, and its effect on brain function in early adulthood.

Method

In the first part of the study, we systematically reviewed the multimodal functional neuroimaging literature, exploring its relationship with cognition, and the potential effects of other variables including the demographics, other risk factors, and methodological and analytical choices. While the studies demonstrated consistent alterations of APOEe4 carriers in brain connectivity and activity; the results of fMRI studies covered mostly the differences in the directionality using standard connectivity and activity measures. In the second part of this study, we aimed to address this gap by using the graph theory analysis to explore the dynamic behaviour of the six resting‐state networks of interest in young APOEe4 carriers versus non‐carriers (n=129, aged 17‐22).

Result

Average Path Length and Closeness Centrality were consistently disrupted, pointing to network reorganisation in multiple resting‐state networks, albeit using different mechanisms.

Conclusion

This study is the first to demonstrate the restructuring of multiple resting‐state networks in young adults modulated by the APOE genotype.
Linking White Matter Hyperintensities to Regional Cortical Thinning, Amyloid Deposition, and Synaptic Density Loss in Alzheimer's Disease
Authors: Zhang Junfang , Li Binyin
Published: January 2025

Background

White matter hyperintensities (WMH) were reported to contribute to the thinning of regional cortex connected to WMH in cerebral small vessel disease. However, the relationship between WMH and regional changes in WMH‐connected cortex in Alzheimer’s disease (AD) remains unclear. The objective of this study is to investigate the association between WMH and regional cortical thickness, amyloid and tau deposition, and synaptic density changes in the WMH‐connected cortex.

Method

In the cross‐sectional analysis, we included 107 participants (59 AD, 27 mild cognitive impairment, and 21 cognitively normal controls) who tested positive for beta‐amyloid (Aβ) using amyloid‐PET. All the participants had T1, fluid‐attenuated inversion recovery (FLAIR), multi‐shell diffusion MRI, tau‐PET, and cognitive assessment. A subset (n=33) had synaptic vesicle glycoprotein 2 A (SV2A) PET to assess synaptic density. We segmented WMH on FLAIR images. The cortex connected to WMH was identified using probabilistic tractography. We measured the cortical thickness, amyloid and tau deposition, and synaptic density based on T1 images, amyloid‐PET, tau‐PET, and SV2A‐PET. A subset of participants (n=23) underwent a follow‐up multimodal MRI after 12 months. Changes in cortical thickness were measured using the longitudinal stream within FreeSurfer. (Figure 1)

Result

We found that WMH‐connected cortex showed significantly lower thickness and synaptic density than WMH‐unconnected cortex (all p‐corrected<0.001). Besides, higher levels of Aβ deposition in frontal (ratio = 1.045, p<0.001), temporal (ratio=1.045, p=0.002), and parietal (ratio=1.054, p<0.001) deep WMH (dWMH)‐connected cortex, and higher levels of tau deposition in parietal dWMH‐connected cortex (ratio=1.050, p<0.001) were found, compared to the corresponding WMH‐unconnected cortex. (Figure 2) After adjusting for age, education years, and volume of WMH, higher Aβ ratio in the total dWMH‐connected cortex was significantly related to lower cognitive score (linear regression: β=‐37.8, p‐corrected=0.040). Lastly, the cortical thickness of WMH‐connected cortex reduced more than WMH‐unconnected cortex over 12 months (all p‐corrected <0.001, Figure 3).

Conclusion

We revealed that WMH are related to more severe regional cortical degeneration as measured by thickness, Aβ, tau, and synaptic density in the WMH‐connected cortex in AD. Our results suggest that WMH are likely to be associated with AD‐intrinsic processes of degeneration, in addition to vascular mechanism.
Analysis of Macular thickness and retinal nerve fiber layer by using of spectrum domain‐optical coherence tomography in patients with Alzheimer’s disease and amnestic mild cognitive impairment
Authors: Ku Bon D , Shin Kyung‐Hoon , Kim Do‐Gyun
Published: January 2025

Background

Spectrum Domain‐Optical Coherence Tomography (SD‐OCT) is a non‐invasive technology that acquires cross‐sectional images of retinal structures allowing neural fundus integrity assessment. Macular thickness and retinal nerve fiber layer (RNFL) thickness measured by an SD‐OCT have been used as a indicator of Alzheimer's disease (AD) and amnestic mild cognitive impairment (aMCI). However which portion of retinal RNFL is the most sensitive area among normal control, aMCI and AD is not clear yet. The purpose of this study is to demonstrate that RNFL thickness is a useful indicator and which portion of reninal RNFL is the most sensitive area among normal control.

Method

In a cross‐sectional study we consecutively recruited 53 patients with AD, 58 with aMCI, and 54 normal controls. AD‐OCT was performed in all of them to measure circumpapillary macular thickness in the 9 sectors (fovea, temporal outer superior outer, nasal outer, inferior outer, temporal inner, superior inner, nasal inner, inferior inner). We made 4 RNFL quadrant area as following: superior (superior outer+superior inner), inferior (inferior outer+ inferior inner), nasal (nasal outer + nasal inner), temporal (temporal outer + temporal inner). We also evaluated the correlation of the RNFL thickness and MMSE score and disease duration of the patients

Result

Average macular thickness and 9 sectors of RFNL thickness were not significant among the group in our patients. In quadrant analysis, however superior quadrant RNFL thickness showed significant differences among groups (109.98±12.01 um, 106.83±10.05 um 101.25±11.90 um in normal control, aMCI and AD respectively, p<0.01). The RNFL thinning of the superior quadrant showed a significant correlation with MMSE score (r=0.555, p<0.01) and AD duration (r=‐0.528, p<0.01).

Conclusion

The superior quadrant RNFL thickness is the most sensitive among the group and as well as we know this is not reported before. This finding could suggest superior quadrant retinal RNFL by SD‐OCT could be a useful marker of AD and a MCI for early detection and monitoring of disease progression. Also distinct correlation of RNFL thinning in superior quadrant and MMSE score and disease duration can imply that the severity of dementia can be inferred from RNFL thickness in superior quadrant.
Use of plasma p‐tau217 as a pre‐screening method for detecting amyloid‐PET positivity in cognitively unimpaired participants: A multicenter study
Authors: Salvadó Gemma , Janelidze Shorena , Therriault Joseph , Brum Wagner Scheeren , Binette Alexa Pichet , Stomrud Erik , Mattsson‐Carlgren Niklas , Palmqvist Sebastian , Benzinger Tammie L.S. , Gispert Juan Domingo , Blennow Kaj , Villeneuve Sylvia , Johnson Sterling C. , Rosa‐Neto Pedro , Schindler Suzanne E. , Suarez‐Calvet Marc , Ossenkoppele Rik , Hansson Oskar
Published: January 2025

Background

Recent results from clinical trials in Alzheimer’s disease (AD) emphasize the importance of treating early‐stage disease. However, recruitment of preclinical AD participants is difficult due to the lack of symptoms, and the costs and/or invasiveness of established CSF and PET tests. We aimed to investigate whether plasma p‐tau217 could be used to pre‐screen cognitively unimpaired (CU) potential participants for amyloid‐β (Aβ) pathology to improve the efficiency of clinical trial recruitment.

Method

We included 1,471 CU participants from eight cohorts (Table 1) with available plasma p‐tau217, Aβ CSF biomarkers and Aβ‐PET status (served as standard‐of‐truth). Plasma p‐tau217 concentrations were z‐scored based on Aβ‐negative participants and harmonized across cohorts using neuroCombat. Cut‐offs for plasma p‐tau217 were derived in the BioFINDER‐1 cohort (n=104) based on different specificity levels (90%, 95% and 97.5%) to maximize positive predictive values (PPV). These cut‐offs were used in the other cohorts to assess the accuracy of plasma p‐tau217 for detecting Aβ‐PET positivity. Next, within plasma positive participants only, we evaluated the value of dichotomized Aβ CSF (based on established clinical thresholds) on assessing Aβ‐PET positivity. All models included age and APOE‐ε4 carriership as covariates.

Result

334 (24.4%) of participants were Aβ‐PET positive. Using the a priori defined cutoffs, plasma p‐tau217 categorization resulted in high PPVs (72.9%‐81.2%), negative predictive values (NPVs, 82.5%‐86.2%), and accuracy (82.4%‐83.8%), with an overall rate of positivity between 10.9%‐18.1% (Figure 1). When applying CSF biomarkers to the plasma positive participants in a second step, the PPVs increased up to 90.8%‐95.3%, with NPVs ranging between 82.8%‐86.7% and accuracies between 84.0%‐87.3%, with a slight decrease in the proportion of overall positive cases (9.3%‐14.3% from the original sample) given that the CSF positivity in the plasma positive participants ranged between 79.4%‐85.2% (Figure 2).

Conclusion

Plasma p‐tau217 can identify Aβ‐PET positive CU individuals with PPVs reaching 81%, which can be further improved to PPVs of up to 95% with a subsequent CSF measurement. Plasma p‐tau217 could be used, either as stand‐alone biomarker, or as an initial step before CSF biomarkers (reducing their need by ∼80‐90%), for pre‐screening in clinical trials of preclinical AD depending on the certainty needed for Aβ‐PET positivity.
White Matter Changes in Alzheimer’s and Treated Huntington’s Disease Mouse Models Detected by Diffusion MRI
Authors: Liu Andy , Simmons Danielle , Alvarez Connor , Wang Yi , Spincemaille Pascal , Longo Frank M. , Zeineh Michael , Georgiadis Marios
Published: January 2025

Background

Myelin integrity is central to healthy brains and is increasingly shown to be compromised in neurodegenerative diseases. Diffusion‐ and susceptibility‐based MRI metrics can detect myelin changes. We show advanced diffusion and susceptibility metrics can detect degenerative myelin changes in ex vivo AD and HD mouse models.

Method

AD mice included 2 groups: 8‐month‐old male hAPP (Lond/Swe mutations, n=4) and wild‐type (WT, n=4). HD mice included 3 groups: 12‐week‐old vehicle‐treated controls (WT, n=4), R6/2 HD treated for 7 weeks with vehicle (HD, n=4) or LM11A‐31 (HD‐treated, n=4). After perfusion‐fixation and brain extraction, 4‐shell diffusion (b=1,2,5,10ms/μm2, 100 directions) and multi‐echo gradient echo (MGE) MRI (10 echoes, GRE=4‐40ms) were performed on a Bruker 7T scanner.

We used DESIGNER and MEDI to generate diffusion‐ and susceptibility‐based myelin‐sensitive metrics, respectively (diffusion: mean/radial/axial diffusivities ‐MD/RD/AD, kurtoses ‐MK/RK/AK, axonal water fraction ‐AWF; susceptibility: R2*, quantitative susceptibility mapping‐QSM) (Figure 1A). Datasets were registered to the Allen Atlas through linear/non‐linear transformations using FSL flirt/ANTs (Figure 1B).

Median values were derived for two Allen Atlas white matter regions‐of‐interest ‐ROIs (corpus callosum and fornix). For AD, scan timing effects were included in a 2‐way ANOVA analysis. For HD, two‐sample t‐tests were performed between the 3 groups.

For histological validation, a second set of R6/2 HD brains (WT, HD, HD‐treated n=10‐13 mice/group) were myelin basic protein (MBP)‐stained, and the percent area of striatum immunostaining determined by ImageJ thresholding.

Results

AD mice showed increased RD and reduced AWF and RK compared to controls in both ROIs, suggesting a loss of myelinated axons. Susceptibility‐based metrics show no clear pattern.

HD mice showed an increasing trend in AWF and RK and decreases in RD in both ROIs vs WT, suggesting increased myelination. Interestingly, LM11A‐31‐treated HD mice showed a significant reversal in RD and AWF. Histologically, myelin immunostaining was increased in the striatum of HD mice, reversed by LM11A‐31.

Conclusion

Diffusion MRI myelin‐sensitive metrics detected possibly compromised myelin in AD mouse white matter (corpus callosum and fornix). In HD mice, both diffusion MRI and histology showed a trend towards increased myelin, which was reversed by LM11A‐31 treatment.
Gut microbiome features associate with cognitive scores in individuals at risk for Alzheimer’s disease
Authors: Kang Jea Woo , Khatib Lora A. , Dilmore Amanda H. , Heston Margo B. , Ulland Tyler K. , Johnson Sterling C. , Asthana Sanjay , Carlsson Cynthia M. , Chin Nathaniel A. , Jonaitis Erin M. , Blennow Kaj , Zetterberg Henrik , Knight Rob , Kaddurah‐Daouk Rima , Rey Federico E. , Bendlin Barbara B. ,   Alzheimer's Gut Microbiome Project Consortium (AGMP)
Published: January 2025

Background

Gut microbiome features have been linked with many diseases including Alzheimer’s disease (AD). Evidence suggests that the gut microbiota may impact cognition of AD patients. We explored the association of gut microbiota and three PACC3 cognitive scores in individuals at risk for AD.

Method

Participants from the Microbiome in AD Risk Study with clinical diagnosis (N=239) and amyloid status (N=93) underwent cognitive testing and collection of microbiome samples. Three different types (Animal‐Naming Test PACC3 (AN), Category Fluency Test PACC3 (CFL), and Trail‐Making Test B PACC3 (TRLB)) of PACC scores (PACC3) were utilized followed by z‐score transformation. Fecal samples were analyzed by 16S sequencing and a taxonomic count table was generated. Kruskal‐Wallis test with Dunn's post‐hoc test was performed to find associations between PACC3 scores and group differences in diagnosis or amyloid status. Negative binomial regression (BIRDMAn, R) was performed to identify associations between microbial genus abundance and AD diagnosis, amyloid status, and PACC3 scores.

Result

Several genera were differentially abundant between diagnosis groups (Normal vs. AD) and across amyloid status (A− vs. A+) (Figure 1). For example, in AD genera Clostridium_A, Bacteroides_H, and Blautia_A_141781 were more abundant, and genera Turicibacter and Prevotella were less abundant. Similarly in A+, Clostridium_A was more abundant and Prevotella was less abundant. All PACC3 scores significantly differed across diagnosis groups; however, no associations were found between PACC3 scores and amyloid status (Figure 2). Abundance of Blautia_A_141781, a genus previously linked to AD, was negatively associated with all three PACC3 scores in the A+ group (AN: β=−0.2, p=0.02; CFL: β=−0.3, p<0.001; TRLB: β=−0.2, p=0.04). Abundance of Prevotella was positively associated with PACC3 scores derived using AN and TRLB in the A+ group (AN: β=0.3, p=0.03; TRLB: β=0.3, p=0.04) (Figure 3).

Conclusion

Increases in genera Blautia_A_141781 and Prevotella were associated with higher and lower cognitive performance scores, respectively. Certain microbes associate with cognitive performance in amyloid‐positive individuals. Validation of these associations in larger cohorts and diverse populations is needed.
Ch4p volumes mediate the relationship between plasma p‐tau and spatial navigation
Authors: Chen Qian
Published: January 2025

Background

To investigate the relationships among plasma biomarkers, basal forebrain, and spatial navigation.

Method

A total of 78 participants were enrolled, including 23 normal controls (NC), 38 subjective cognitive decline (SCD), and 17 mild cognitive impairment (MCI) patients. According to the spatial navigation distance errors in the human version of the Morris Water Maze, the whole cohort was divided into the good spatial navigation performance (gSN) group and the bad spatial navigation performance (bSN) group, with 39 cases in each group. The plasma biomarkers of p‐tau217, neurofilament light chain (NfL), glial fibrillary acidic protein (GFAP), β‐amyloid (Aβ) 42/40, and p‐tau181 were measured by single molecule array (SimoA). Basal forebrain (BF) volumes were calculated, as well as the subfields of Ch4p, Ch4a‐i, Ch3, NSP, and Ch1/2. Correlation and mediation analyses were performed to assess the relationships among plasma biomarkers, basal forebrain, and spatial navigation.

Result

The gSN and bSN groups did not differ significantly in sex, age, and education years. Compared to the gSN group, the bSN group showed increased p‐tau217 and p‐tau181. The differences in NfL, GFAP, and Aβ42/40 levels were not significant. The bSN group showed significant volume reduction in the basal forebrain, especially the Ch4p subfield. The plasma p‐tau217 and p‐tau181 levels were positively correlated with navigation errors (r = 0.340, p = 0.003; r = 0.344, p = 0.003; respectively), while negatively correlated with basal forebrain (r = ‐0.250, p = 0.032; r = ‐0.254, p = 0.029; respectively) and Ch4p volumes (r = ‐0.415, p <0.001; r = ‐0.319, p = 0.006; respectively). Mediation analysis showed that the Ch4p volumes mediated the relationships between plasma p‐tau217/181 and navigation errors.

Conclusion

These findings suggest that spatial navigation may serve as sensitive markers for the early identification of Alzheimer’s disease p‐tau pathology. The results may also indicate the relationships among “plasma p‐tau → basal forebrain atrophy → spatial navigation dysfunction”.
Recruitment for ADRD Clinical Trials Optimized through Academic and Community Partnership in Appalachia
Authors: Rhodus Elizabeth K. , Gross David , Fairchild Vickie , Henley Laura , Roberts Celeste , Jicha Gregory A , Kryscio Richard J , Gibson Allison
Published: January 2025

Introduction

The United States is undergoing a demographic shift with increasing proportions of older adults. Currently, one in three older adults pass away with a form of Alzheimer’s disease or related dementias (ADRD). This figure is higher in underrepresented and underserved groups including older adults in rural Appalachian communities. Programs that promote inclusion in clinical services and research are needed in rural, medically underserved regions. To establish and evaluate mechanisms for outreach and recruitment in clinical research for caregivers of persons with ADRD living in rural Kentucky counties.

Methods

The University of Kentucky (UK) Sanders‐Brown Center on Aging partnered with St. Claire Healthcare and the Northeast Kentucky Area Health Education Center to promote rural‐tailored clinical trial recruitment of caregivers of older adults with ADRD. Successful approaches and methods were tracked and documented.

Results

A model for rural recruitment and outreach for older adult engagement emerged. Over the course of seven months, 106 rural Kentucky caregivers of persons with ADRD were screened for clinical trial enrollment. This model identified successful outlets for recruitment dissemination through multiple strategies, including community agencies, healthcare providers, social media, emailing, word of mouth, and in‐person promotion of the study (health fairs, self‐initiated conversation at community events, etc.). Flexible engagement efforts were effective, such as calling at convenient times for participants and adapting to schedule changes (i.e., changes during and following historic flooding in recruitment regions).

Conclusions

Academic‐community partnerships extend the enterprise of university‐based clinical research to offer innovative programming directly in underserved and underrepresented rural communities. Recommendations from this program include 1) involving community representatives in recruitment and outreach efforts, 2) targeting communication with local organizations, and 3) including a variety in modes of contact with potential participants. This program illustrates a model that optimizes inclusion of underrepresented populations in clinical research and may have applicability to improve clinical care options in underserved areas.
Alzheimer’s and Related disorders Multicenter Archive (ARMA): a collaborative platform to advance ADRD diagnosis and treatment in Spain
Authors: Illán‐Gala Ignacio
Published: January 2025

Background

Alzheimer’s and related disorders (ADRD) represent a range of neurodegenerative conditions characterized by abnormal protein deposits in the brain. Despite advances, there is a need for enhanced diagnostic and treatment approaches that acknowledge the diversity of ADRD. This project introduces the Alzheimer’s and Related Disorders Multicenter Archive (ARMA), a collaborative platform with an advanced Electronic Data Capture (EDC) system linked to Electronic Medical Records (EMR) designed to refine ADRD diagnosis and natural history understanding, thus informing precision medicine.

Specific Aims

ARMA aims to: 1) prove the feasibility of implementing an EDC‐augmented ARMA within two memory units; 2) Establish the clinical utility of biofluid and imaging biomarkers with real world data from diverse ADRD clinical profiles; 3) Develop the ARMA Trial Platform (ARMA‐TP) for clinical trials, hypothesizing enhanced, recruitment and retentionfor of diverse ADRD populations.

Methodology

This four‐year project will recruit key personnel to integrate ARMA into clinical workflows, ensuring data integrity and GDPR compliance. The registry’s feasibility and the clinical utility of biomarkers will be evaluated, and disease progression will be tracked. Its advanced EDC system facilitates decentralized data collection, enhancing patient engagement and providing a scalable model for collaborative research. By its final year, ARMA‐TP will be launched to facilitate personalized treatment options in trials. Clinical‐biological profiles and recuittment data will be analyzed to describe biases in the recruitment process. Debiasing algorithms will be developed to ensure diverse recruitment and targeted recruitment campaigns will be designed to attract underrepresented ADRD profiles. We anticipate that the ARMA echosystem will reduce barriers to participation in both industry sponsored and industry‐independent trials, thereby improving the external validity of research findings.

Impact

The ARMA project is set to transform collaboration within the Alzheimer´s Disease Barcelona Hub, impacting clinical practice and research in Spain. The project will be instrumental in improving the recruitment of a diverse and representative populations in clinical trials.
Echinacoside improves the memory impairment of senescence accelerated mouse‐prone 8 (SAMP8) mice via inhibiting glial activation
Authors: Zhou Rong , Tian Ge , Yu Jing , Wang Rui , Guo Xingzhi , Li Rui
Published: January 2025

Background

Cognitive impairment, a common aging‐related pathology, is a risk factor for dementia. Echinacoside (ECH), derived from the traditional Chinese medicine Cistanche deserticola, shows anti‐aging properties including anti‐inflammation, oxidative stress reduction, and neuronal protection. Despite its benefits, the beneficial impact of ECH on age‐related cognitive decline remains unclear. Senescence accelerated mouse‐prone 8 (SAMP8) mice, known for rapid aging and related pathologies in the brain like glial activation, neuro‐inflammation, neuron loss, and cognitive decline, are ideal for this study. The purpose of this study is to investigate the effect of ECH effects on cognitive functions in SAMP8 mice.

Methods

Six‐month‐old male SAMP8 mice (n = 8∼9) were used as the model group, while age‐matched senescence‐accelerated mouse resistant 1 (SAMR1) mice were used as normal controls. After adaptation in the specific pathogen free (SPF) room for one week, we administered ECH intragastrically to the SAMP8 mice daily for two months, and the control group was administered with saline. Behavioral tests, including open field test and Morris water maze, were performed to assess the mood and memory function of the SAMP8 mice. After that, all mice were sacrificed by intraperitoneal perfusion to extract brain tissues for western blotting and immunofluorescence.

Results

ECH‐treated SAMP8 mice showed significantly reduced escape latency in the Morris water maze compared to controls, indicating improved cognitive abilities (P<0.05). ECH also tended to lower beta‐amyloid and phosphorylated Tau levels in the hippocampus of SAMP8 mice, though not statistically significant due to small sample sizes (n = 3). SAMP8 mice had higher microglia and astroglia activation than SAMR1 mice, but ECH notably inhibited this in SAMP8 mice (Figure 1).

Conclusions

Our study demonstrates that ECH intervention can markedly enhance the memory function in SAMP8 mice and inhibits microglial and astroglial activation. These findings suggest a beneficial role of ECH in alleviating cognitive decline in SAMP8 mice by reducing glia‐related inflammation.