bluuebunny/crossref_metadata_2025 huggingface dataset:
Diabetes and brain MRI measures: Results from SOL‐INCA MRI study
Authors: Gonzalez Kevin A , Tarraf Wassim , Banks Sarah , Bangen Katherine J. , Pa Judy , Gallo Linda C , Stickel Ariana M , Filigrana Paola , Isasi Carmen R , Daviglus Martha L , Testai Fernando Daniel , Decarli Charles , Gonzalez Hector M
Published: January 2025

Background

Hispanic/Latino communities in the US are rapidly growing and aging and are at two‐fold risk of Alzheimer’s Disease and Related Dementia’s (ADRD) compared to non‐Hispanic Whites. This additional risk could be, in part, due to increased risk of cardiovascular disease. Hispanics/Latinos also have higher rate of diabetes compared to non‐Hispanic Whites and nearly 2 out of 5 individuals with diabetes go undiagnosed. While diabetes has been linked to white matter hyperintensities (WMHs), less is known about the magnitude in diverse Hispanics/Latinos and even fewer studies have considered links to other neurobiological endpoints in this population. This work aims to clarify these associations in a deeply characterized diverse middle‐aged and older Latino cohort.

Method

We used data from the Study of Latinos‐Investigation of Neurocognitive Aging (SOL‐INCA) MRI study which is an ancillary study of the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). The goal of SOL‐INCA MRI is to understand cerebrovascular pathology and ADRD etiology using MRI. A total of N=2400 middle aged and older (50+ years) and N=400 younger (35‐49 years) participants were recruited from the parent HCHS/SOL study. Diabetes was assessed using both continuous HbA1c % and recommended ADA cutoffs to assess prediabetes and diabetes status. Outcomes included structural (i.e., volumes; WMHs, total brain, regional and total gray matter volumes, lateral ventricles, hippocampus) and diffusion MRI (free water=FW, fractional anisotropy=FA). Survey weighted linear models (covariate adjusted by important confounders) were used to test the associations between our exposures and outcomes of interest.

Result

Compared to no diabetes, diabetes was linked to smaller total brain and occipital volumes, larger WMHs, larger lateral ventricles, lower FA, and higher FW (Figure 1). Higher levels of HbA1c % (treated continuously) were additionally associated with smaller occipital and larger hippocampal volumes (Figure 2). These findings were consistent across men and women. We did not find significant differences with the pre‐diabetes group.

Conclusion

Our findings suggest that elevated HbA1c and diabetes status are linked to cerebrovascular pathology and brain atrophy. Findings with hippocampal volume could be due to maladaptive process or hypertrophy. Further research is needed to understand different neurobiological mechanisms in understudied communities.
Gender differences in the associations of circadian function with cognition among older persons with sleep disturbances
Authors: Pan Yulu , Cho Gawon , Doyle Margaret , Polisetty Lakshmi , Burzynski Chase , Iannone Lynne , Knauert Melissa , Yaggi Henry , Gill Thomas M. , Miner Brienne
Published: January 2025

Background

Circadian function, characterized by circadian strength, timing, and fragmentation, has been shown to correlate with cognitive function; however, little is known about in these associations, particularly among older adults who may have more variability in their sleep‐wake schedules.

Method

Among 60 community‐living adults aged 60‐90 years with self‐reported sleep complaints ≥once/ week, we investigated gender differences in the associations between circadian and cognitive function. Participants completed seven days of actigraphy and one night of polysomnography. Circadian function variables were calculated using activity counts from actigraphy and included: circadian strength (amplitude, interdaily stability [IS; degree of consistency of activity patterns from day‐to‐day], mesor); timing (activity peak time, sleep midpoint [midpoint between sleep onset and offset]); and fragmentation (intradaily variability [IV: fragmentation of the rhythm relative to its 24‐h amplitude]). Cognitive function was assessed by the Montreal Cognitive Assessment (MoCA; higher scores indicate better cognitive function). Pearson correlation coefficients examined the associations between circadian variables and cognitive function in men, women, and all participants, adjusting for age, race, and total sleep time (from polysomnography).

Result

The mean age was 74 (±6.4) years, with 65% women and 33.3% minority race. Among women, higher IS (r=0.62, p<0.001) and lower IV (r=‐0.44, p=0.006) significantly correlated with higher MoCA score. Among men, earlier sleep midpoint (r=‐0.53, p=0.02) and earlier activity peak time (r=‐0.53, p=0.02) significantly correlated with higher MoCA score. The amplitude‐MoCA score association was non‐significant in women (r=0.31, p=0.07) and men (r=0.30, p=0.21) but significant in all participants (r=0.32, p=0.01). No significant associations were found between mesor and MoCA score in any group.

Conclusion

Among community‐dwelling older adults with sleep disturbances, circadian strength and fragmentation [IS, IV] correlated with cognitive function only in women, while circadian timing [activity peak time, sleep midpoint] correlated with cognitive function only in men. These differences may be related to sex differences in other sleep‐related factors, such as time in slow‐wave‐sleep and melatonin amplitude, which are higher in women than men. Further investigation is needed to understand biological differences that may explain the differential association of circadian variables with cognition in older men and women.
Susceptibility to smartwatch based interventions in patients with MCI or dementia in a controlled environment
Authors: Goerss Doreen , Köhler Stefanie , Teipel Stefan
Published: January 2025

Background

Promising elements of assistive technologies are available to help people with cognitive impairment in their daily lives. However, there has been limited research on how smartwatches can directly interact with persons who have cognitive impairments. We looked at the factors that affect the effectiveness of interventions provided via a smartwatch.

Method

We designed two tasks delivered by smartwatch: A) drinking water and B) circling bells on a worksheet. We created two modes of intervention‐intensity affecting vibration, alarm sounds, text sizes and images. In case of failure, interventions were repeated up to three times. We observed n = 40 patients’ reactions to interventions via cameras and rated success on a 5‐step scale from 0 to 2. Patients were assigned (n = 20/20) either to mode “regular” or mode “intensive”. All patients received both tasks in the mode corresponding to the group. In both groups, half received the tasks in order AB, the other half in BA. Participants answered questionnaires and underwent neuropsychological testing. People were diagnosed with MCI (n = 12) or dementia (n = 28). We hypothesized that the mode, the sequence of tasks, age, affinity for technology (ATI) and cognitive status (MMSE) would influence the success of the interventions. We expected that intensive interventions would be more successful than regular interventions.

Results

The groups statistically did not differ with respect to mean age, MMSE or ATI. The intensive group was more successful than regular group, for each task and global success score (mean 1.7, SD 0.3 vs. 1.2, SD 0.7., Welch t‐test p = 0.03). Testing of linear regression models showed that “mode” significantly increases model fit: H1 age + sex + MMSE + ATI + task sequence + mode p = 0.045, adjusted R² = 19%, compared to H0 age + sex + MMSE + ATI + task sequence p = 0.399, adjusted R² = 0.8%. Controlling for covariance (ANCOVA) showed only for “mode” a significant influence on success (p = 0.003) not of MMSE, sex or diagnosis (p<0.05, respectively).

Conclusion

Tasks delivered from a smartwatch as intensive interventions are more likely to be solved by patients than regularly delivered ones. Intensity is more critical than the patient’s baseline status with respect to cognition and technology‐affinity.
Novel biomarkers in LBD ‐ Implications for clinical trials
Authors: Vermunt Lisa
Published: January 2025

The lack of an in‐vivo pathology marker for synuclein pathology has been a long standing challenge for dementia for Lewy bodies (DLB) research. This issue is critically important for phase II trials, which are often small, requiring the precise measurement of the biological effects, whether disease modifying or symptomatic. Recent advances have enabled the determination of alpha‐synuclein pathology status with CSF measurements, using aggregation assays [RT‐QUIC]. Additionally, several proteomic studies have identified candidate markers for disease progression. This presentation focusses on these breakthroughs, and how they will influence selection of clinical trial participants and detection of treatment response. In clinical trial research, these markers could be instrumental in understanding the treatment effects in early stage trials (phase I‐II) and generate novel read‐outs for later stage trials (phase II‐III).

This talk will provide overview of novel CSF biomarkers, including Dopa Decarboxylase [DDC] and several others, exploring their potential implementation in clinical trials. The presentation will summarize converging evidence, offer forward‐looking perspectives, and present novel data on longitudinal cohort measurements for these markers, including DDC, to provide a comprehensive understanding of the potential applications of these biomarkers in the context of dementia research.
Network‐based prediction and real‐world patient data observation identifies Doxycycline and Irbesartan as candidate medicines for Alzheimer’s disease and Traumatic Brain Injury
Authors: Bykova Marina , Danziger Michael , Rosen‐Zvi Michal , Pieper Andrew A. , Cheng Feixiong
Published: January 2025

Background

Traumatic Brain Injury (TBI) is one of the most common nonheritable causes of Alzheimer’s disease (AD). However, there is lack of effective treatment for both AD and TBI. We posit that network‐based integration of multi‐omics and endophenotype disease module coupled with large real‐world patient data analysis of electronic health records (EHR) can help identify repurposable drug candidates for the treatment of TBI and AD.

Method

We have collected a total of 69 TBI‐related genes and 144 AD‐related genes derived from human genetic and multi‐omics findings. We conducted network proximity analysis to prioritize likely drug candidates under the human protein‐protein interactome model. Using the MarketScan patient database (∼170 million individuals), the drugs were further investigated for association with a reduced incidence of AD against two patient cohorts: A cohort comprised of all patients over 70, and a TBI cohort comprised of patients who have sustained a traumatic brain, head, or facial injury.

Result

Using network proximity‐based analysis of drug‐target networks and disease‐associated genes under the human protein‐protein interactome network, we identified 66 and 53 candidate drugs for AD and TBI, respectively (z‐score ≤ ‐3, p‐value ≤ 0.01). Drugs present within or related to drugs within both candidate lists, such as irbesartan and doxycycline, were especially prioritized for further real‐world patient data validation. In the subsequent Marketscan analysis, we found that doxycycline is significantly associated with a reduced incidence of AD in the over‐70 cohort as compared to cohort patients taking other anti‐infective and antibacterial drugs (OR = 0.92, adjusted 95% CI 0.88‐0.97, p‐value = 0.002). In addition, prescription of irbesartan was significantly associated with a reduced incidence of AD as compared to all cohort patients taking cardiovascular (OR = 0.82, adjusted 95% CI 0.71‐0.95, p‐value = 0.002) and renin‐angiotensin agents (OR = 0.82, adjusted 95% CI 0.71‐0.97, p‐value = 0.002).

Conclusion

We demonstrated that combining network‐based predictions and large real‐world patient data observations has identified doxycycline and irbesartan as candidate treatments for AD. Further functional and clinical validation is warranted to examine a causal relationship between doxycycline and irbesartan with a future risk of developing AD.
NV23non‐verbal communication
Authors: Scharler‐Plotnik Pia Moana ,   Demedarts
Published: January 2025

Imagine a visit in the care taking home by the grandchild. The communication might be challanging. What if there was an option that offers a different way to interact without language? The NV23 is a table designed for non‐verbal and cross generational communication. Sensoric play without the possibility of doing it wrong, as there are no rules of usage. The NV23 has a surface of a landscape and can be filled with various objects. Objects should be selected by the haptic memory, to trigger positiv associations, but could simply be chosen by the appealing shape or color. Everyone who sits at the NV23 can choose or bring objects to the table. The curation of the object depends on the people using it, as well as the use of it. We have experienced ball games, sound checks of materials, storytelling to the different characteristics of the objects in the landscape, and many more. The artistic table have been designed and tested that encourage people with very advanced dementia to interact. The NV23 object has designed in different versions of appearances to test if this also has influence and make it appealing. It is still in development, so the session is an open conversation, a testing and a showing of findings. Pia Scharler is member of the DEMEDARTS project team, which is a artistic‐research team in Vienna.
Differential item functioning of patient‐reported outcomes measures of anxiety and depression by severity of cognitive impairment
Authors: Russell Andrea , Lovett Rebecca , Vogeley Abigail , Batio Stephanie , Opsasnick Lauren , Graham Eileen , Mroczek Daniel , Bonham Morgan , Yoshino‐Benavente Julia , O'Conor Rachel , Wolf Michael S
Published: January 2025

Introduction

Patient‐reported outcomes are increasingly being utilized in clinical settings to identify psychological symptoms and track fluctuations over time. Some clinicians and researchers have expressed concerns about the validity of symptom questionnaires cognitively impaired populations. We sought to determine if differential item functioning (DIF) is present based on cognitive impairment using the patient reported outcomes measurement information system (PROMIS) inventories of anxiety and depression.

Methods

Data from a longitudinal aging cohort study in Chicago, IL. Mild impairment was defined as a z‐score between ‐1 and ‐1.49 and moderate/severe impairment was a z‐score of ≤‐1.5 on 2+ tests in one of 5 cognitive domains. PROMIS Anxiety and Depression measures were utsed. Due to sample size and response variation, the anxiety model examined DIF with participants categorized using three categories (none, mild, or moderate/severe cognitive impairment). The depression model used 2 categories (none/mild vs moderate/severe impairment). Analyses were conducted in R (lordif package with Monte Carlo simulations). Effect sizes for DIF were calculated by test item. Test characteristic curves (TCCs) examined measure performance by cognitive impairment.

Results

Data was available for 396 participants, who were 71 years old, mostly female (n = 285) and about half were non‐Hispanic White (n = 214). Approximately 72% had no cognitive impairment, 14% had mild cognitive impairment and 13% had moderate/severe impairment. There was evidence of DIF in 4 of 7 anxiety items and 3 of 8 depression items. The direction of DIF varied, suggesting the response patterns of moderately/severely impaired participants resulted in psychometric overestimation of anxiety and underestimation of depression. Effect sizes of DIF were relatively small for anxiety (0.016‐0.034) and depression (0.014‐0.043) and TCC curves were similar by cognitive impairment category for both measures.

Conclusion

There is psychometric measurement bias on PROMIS anxiety and depression measures by severity of cognitive impairment. Researchers and clinicians should be aware that cognitive impairment may impact responses when using assessments, however, this study showed this bias is small and results in negligible change when examining the measures overall. These findings support the conclusion that PROMIS Anxiety and Depression are reliable assessments when used among individuals with different levels of cognitive impairment.
Microhemorrhages and Siderosis in Cognitive Impairment
Authors: Alotibi Hessah A
Published: January 2025

Background

Microhemorrhages and superficial siderosis (SS) have been reported in patients with mild cognitive impairment (MCI) and dementia, especially in the context of Alzheimer’s disease (AD) and cerebrovascular disease. Cerebral amyloid angiopathy and small vessel disease (SVD) have both been implicated in microhemorrhages and SS but their prevalence in those with MCI and dementia and their relationship to SVD is unknown.

Method

We conducted a retrospective chart review of patients with MCI or dementia that had undergone MRI scans from 2014 To 2023. We calculated prevalence of microhemorrhages and SS, analyzed the distribution patterns, and assessed small vessel ischemic changes using the Fazekas scale. Exclusions included traumatic brain injury, large stroke, and subjective cognitive impairment.

Result

2852 individuals underwent underwent an MRI for CI and 120 (4.2%, 95% CI: 3.5‐5%) patients had microhemorrhages and/or SS and fulfilled inclusion criteria (mean age 80.52 (9.1) years; 55.8% male). Of the patients with hemorrhagic findings, 73% had microhemorrhages (mean 7.51 microhemorrhages patient), 7.5% had SS and 19.2% exhibited both. The microhemorrhages were deep (25.8%), lobar (35.0%), and mixed (39.2%). Fazekas rating scale was 0 in 0.8% of patients with microhemorrhages, 1 in 34.2%, 2 in 44.2%, and 3 in 20.8%. The most prevalent diagnoses in patients with hemorrhagic findings were mixed vascular and AD dementia (34.2%), vascular CI (25.0 %), AD (18.3%), vascular MCI(7.5%), MCI due to possible AD (4.2%), Parkinson/Lewy Body dementia (5.0%), dementia NYD (4.2%), frontotemporal dementia (0.8%), and progressive supranuclear palsy (0.8%). Common comorbidities included hypertension (70.0%), dyslipidemia (50.0%), and ischemic heart disease (25.8%).

Conclusion

4% of patients investigated for cognitive impairment have microhemorrhages and/or SS. Most of the patients had CI due to AD and/or vascular disease. Mild or moderate SVD as indicated by Fazekas scale was common as was hypertension. The etiology of microhemorrhages and SS is likely multi‐factorial and SVD may contribute to their presence, but hypertension may be an independent risk factor that warrants further investigation.
Advanced brain age prediction using 3D convolutional neural network on structural MRI
Authors: Ahmadi Babak , Morshedizad Zohreh , Gahrooei Mostafa Reisi , Babajani‐Feremi Abbas
Published: January 2025

Background

Predicting brain age from neuroimaging data is an emerging field. The age gap (AG), the difference between chronological age (CA) and brain age (BA), is crucial for indicating individual neuroanatomical aging. Previous deep learning models faced challenges in generalizability and neuroanatomical interpretability. We incorporated patients with different dementia types, including dementia with Lewy bodies (DLB) and Alzheimer’s disease (AD), alongside mild cognitive impairment (MCI) and cognitive normal (CN) individuals. This inclusive strategy is essential for comprehensive mapping of neurocognitive trajectories and understanding distinct aging patterns across various cognitive conditions.

Method

Utilizing T1‐weighted MRI images of n = 3,859 subjects (Table 1) from the CamCAN, NACC, and ADNI databases, this study aimed to predict brain age in four groups (CN, MCI, AD, and DLB). Structural MRI data were spatial normalized and skull‐striped. Then a 3D Convolutional Neural Network (CNN) based on the skull‐striped data was used for age prediction. The model’s architecture includes three convolutional layers with ReLU activation, max‐pooling, batch normalization, and dropout for regularization, ending with global average pooling and dense layers. The model was trained and validated on CN subjects. The trained model was used to predict age in MCI, DLB, and AD patients as well as the test set of CN subjects.

Result

The 3D CNN model accurately predicted brain age in the CN test set with an AG of 0.64 ± 2.74 years and an absolute AG of 1.86 ± 2.11 years (Figure 1 and Table 1). In DLB and AD patients, the average AG was 3.81 and 2.90 years, respectively, and significantly larger than 0 (P < 10‐5), indicating accelerated aging patterns in these groups. The average AG of MCI was 0.09 years which was significantly smaller than that of both DLB and AD (P < 10‐3), indicating the early stage of impairment in MCI patients.

Conclusion

Our 3D CNN model accurately predicted brain age in cognitively normal individuals and identified accelerated aging in DLB and AD patients. The model's precision highlights its potential for early detection and understanding of neurocognitive trajectories, contributing to advancements in neurological research and clinical diagnostics.
A Convolutional Neural Network to Identify Parkinson’s Disease using Multimodal Retinal Images
Authors: Richardson Alexander , Kundu Anita , Henao Ricardo , Lee Terry , Scott Burton L. , Grewal Dilraj S , Fekrat Sharon
Published: January 2025

Background

Retinal structure and microvasculature may be used as a surrogate for parallel processes in the brain. Previous studies have revealed differences in retinal structure and microvasculature in patients with Parkinson’s disease (PD) compared to cognitively normal controls[1]. Previous work developed a convolutional neural network (CNN) trained on multimodal retinal images that was able to identify Alzheimer’s dementia with an area under the curve (AUC) of 0.841[2]. Herein, we developed a CNN to identify PD.

Methods

Building upon a pretrained VGG19 architecture, we trained a CNN to receive inputs of ganglion cell‐inner plexiform layer (GC‐IPL) thickness color maps, optical coherence tomography angiography (OCTA) superficial capillary plexus 6x6mm en face images centered on the fovea obtained using the Zeiss Cirrus HD‐5000 with AngioPlex (v11.0.0.29946), ultra‐widefield (UWF) fundus color and autofluorescence (FAF) scanning laser ophthalmoscopy (Optos California) images from patients with a clinical diagnosis of PD and from controls without PD or cognitive impairment. The model consisted of a shared convolutional encoder, parallel image modality specific feature transformations, and prediction heads for all modalities that converge into a single output.

Results

1,784 images from 371 eyes of 249 control subjects and 75 eyes of 52 PD subjects were used for development and testing. 296 (80%) control eyes were used for training, 36 (10%) for validation, and 39 (10%) for testing. 59 (79%) PD eyes were used for training, 6 for validation (8%), and 10 (14%) for testing. Our best performing model had an AUC of 0.918. UWF color images proved to be the most effective imaging input (highest AUC 0.838), and GCL‐IPL color maps were the least effective (highest AUC 0.630).

Conclusions

This CNN was able to successfully identify individuals with a clinical diagnosis of PD. Further work is ongoing to expand the number of images and the diversity of the population to improve translatability.

Ma, J.P., et al., Retinal vascular changes in Parkinson’s disease on ultra‐widefield retinal imaging. Investigative Ophthalmology & Visual Science, 2021. 62(8): p. 1779‐1779.

Wisely, C.E., et al., Convolutional neural network to identify symptomatic Alzheimer’s disease using multimodal retinal imaging. British Journal of Ophthalmology, 2020. 106: p. 388 ‐ 395.
Detecting Undue Harm Arising From Elder Abuse Screening: Innovative Methods from the EASI‐ltc© Pilot Study
Authors: Wilchesky Machelle , Ballard Stephanie A.
Published: January 2025

Background

Screening for elder abuse can cause victims to experience feelings of unpleasantness and/or relive painful memories which can be an ethical concern. Ensuring the safety of all participants/users, in our case long‐term care (LTC) residents, is of the utmost importance.

Method

Drawing from approaches used in the intimate partner violence and clinical trials literature, we developed a novel typology of harm and a series of procedures to evaluate any negative consequences that might be incurred as a result of participating in the Piloting the Elder Abuse Suspicion Index‐long term care©: A Mixed Methods Feasibility Study. Our typology includes subjective and objective reporting from a multitude of sources, including: 1) Interviews with LTC residents using a modified Consequences of Screening Tool, adapted from MacMillan et al, 2009; Opinions of the 2) Study social worker conducting validation assessments and 3) LTC residents and institutional stakeholders; 4) Feedback from study research assistants who administer the tool using a specialized Adverse Events Form; and 5) Chart reviews. Procedures include the creation of an independent interdisciplinary pilot study safety committee, chaired by a geriatric psychiatrist and convened to conduct interim evaluations after the first 5, 10, and 20 residents have participated, the liberation of social work teams, who are on standby and available to intervene as necessary, and the creation of participant resource cards displaying pertinent phone numbers for immediate assistance.

Result

Both quantitative and qualitative analyses of harms detected among the first 50 participating LTC residents of this ongoing study will be presented.

Conclusion

To our knowledge, this is the first elder abuse study to conduct a thorough analysis of assessment‐induced harm.
Improved detection of cerebral microbleeds in Alzheimer’s Disease using 7T susceptibility weighted imaging
Authors: Ashouri Laya
Published: January 2025

Background

Cerebral microbleeds (CMBs) are small, round aggregations of hemosiderin‐laden macrophages that indicate leakage of blood products from cerebral vessels damaged by β‐amyloid‐40 (Aβ), which is the basis of cerebral amyloid angiopathy (CAA). Pathology studies have demonstrated a high correlation between CAA and AD, both amyloid‐related pathologies. CMBs can be visualized commonly using T2*‐weighted MRI, as small foci of decreased signal intensity. The increased magnetic field strength at 7 Tesla (7T) MRI results in substantially enhanced sensitivity to variations in magnetic susceptibility. The more robust SWI at 7T MRI proves especially advantageous in the detection of CMBs, which are small hemorrhagic lesions distinguished by subtle alterations in magnetic susceptibility. In this study, we used the CLEAR SWI technique at 7T MRI to improve the visualization of CMBs that might be missed or undetectable with lower field strengths to better understand the role of CAA in mild cognitive impairment (MCI) and AD patients.

Method

Imaging with four MCI, two AD, and six healthy subjects (73 ± 6 years old) was performed using a 7T MRI scanner. CMBs were defined as small hypointense lesions within the brain parenchyma on the SWI images. The microbleeds were categorized into two groups according to their locations: non‐lobar region and lobar. Calcifications of the basal ganglia and deep cerebellar nuclei were considered as normal findings. The analysis was performed to evaluate the associations between the number of CMBs with the patient’s Aβ PET SUVR levels.

Result

Our results showed that out of 51 observed CMBs in six MCI/AD patients 48 (94%) CMBs were lobar and 3 (6%) non‐lobar. A total of four CMBs were found in three AD/MCI patients. A total of three CMBs (one lobar and 2 non‐lobar) were observed in six healthy participants. A significant correlation was found between the number of lobar CMBs and the Aβ PET SUVR levels in AD/MCI patients.

Conclusion

CMBs have attracted attention in Amyloid immunization therapy trials in AD, therefore improved visualization of CMBs with CLEAR SWI sequences at 7T MRI may contribute to more accurate identification of CMBs, and better understanding of their role in MCI and AD.
Improving detection of concomitant Alzheimer’s disease pathology in autopsied Lewy body disorders using CSF biomarkers
Authors: Albuja Isabela , Irwin David J , Chen‐Plotkin Alice , Shaw Leslie M. , Lee Eddie B , Wolk David A , Tropea Thomas F. , Cousins Katheryn A Q
Published: January 2025

Background

While the cerebrospinal fluid (CSF) biomarkers β‐amyloid 1‐42 (Aβ42), β‐amyloid 1‐40 (Aβ40), total tau (t‐tau), and phosphorylated tau 181 (p‐tau181) can detect Alzheimer’s disease (AD) pathology during life, there is a need to evaluate CSF cutpoints for specific application in Lewy body spectrum disorders (LBSD) in order to maximize detection to AD co‐pathology. Using the Fujirebio LUMIPULSE G, we developed LBSD‐derived cutpoints for CSF biomarkers and their ratios (Aβ42/Aβ40, p‐tau181/Aβ42, t‐tau/Aβ42) to determine which best discriminates αSyn with AD (αSyn+AD) from αSyn without (αSyn). We compared our LBSD‐derived cutpoints to the established AD‐derived cutpoints.

Method

Participants were neuropathology‐confirmed LBSD, with (αSyn+AD; n=24) or without AD co‐pathology (αSyn; n=26). Amnestic AD without αSyn were included as a reference group (n=11). Receiver operating characteristic (ROC) and area under the curve (AUC) tested how accurately biomarkers discriminated between αSyn+AD and αSyn. Optimal cutpoints were identified by Youden's Index using bootstrapping (2000 iterations). Sensitivity, specificity, and accuracy were calculated at best cutpoints. Confidence intervals (95%CI) tested for differences between empirically‐defined LBSD‐specific cutpoints and previously established AD‐derived cutpoints. Chi‐square tests compared classification accuracy between LBSD‐ and AD‐derived cutpoints. Survival analyses tested if biomarker status predicted time to death.

Result

Discriminating between αSyn and αSyn+AD, the CSF Aβ42/Aβ40 ratio had the highest accuracy (0.90), followed by p‐tau181/Aβ42 ratio (0.87). We found that LBSD‐derived cutpoints were higher than established AD‐derived cutpoints for CSF Aβ42/Aβ40 ratio (0.080 [95%CI=0.069 – 0.089] vs. 0.058 pg/mL), and lower for CSF p‐tau181 (32.7 [95%CI=26.84 – 38.76] vs. 50.2 pg/mL) and t‐tau (227 [95%CI=203.51‐ 278.66] vs. 409 pg/mL). Furthermore, LBSD‐derived cutpoints identified individuals with AD co‐pathology more accurately than the AD‐derived cutpoints for CSF Aβ42/Aβ40 (X2= 23.26, P<0.001), p‐tau181 (X2=22.82, P<0.001) and t‐tau (X2= 15.09, P<0.001). Survival analysis show that AD‐positive individuals identified by the LBSD‐derived CSF Aβ42/Aβ40 ratio cutpoint associate with earlier death (HR = 2.98, 95%CI = 0.41 – 1.77, P = 0.0017).

Conclusion

This study helps to establish AD CSF biomarkers for specific application in LBSD using the Fuijirebio LUMIPULSE G platform to maximize detection of AD co‐pathology. Validation in an independent cohort is needed.
Dynamic contrast enhanced MRI reveals impaired blood brain barrier in basal forebrain region in patients with Alzheimers disease: A pilot study
Authors: Lerch Ondrej , Kala David , Nedelska Zuzana , Hadzic Haris , Otahal Jakub , Hort Jakub
Published: January 2025

Background

Blood brain barrier (BBB) is a protective layer of cells that separates the circulatory system from the brain. Its dysfunction is one of the possible mechanisms leading to onset of Alzheimer's disease (AD), a progressive neurodegenerative disease and a leading cause of dementia worldwide. Dynamic contrast enhanced magnetic resonance imaging (DCE‐MRI) is a imaging technique allowing regional assessment of BBB breakdown by estimating local metrics of capillary permeability such as K‐trans (volume transfer constant). We used DCE‐MRI to examine BBB dysfunction in regions affected early in the course of AD ‐ hippocampus, entorhinal cortex (EC) and basal forebrain (BF) nuclei.

Method

A pilot group of 29 participants ‐ 17 cognitively normal, 12 with mild cognitive impairment (MCI) due to AD from Czech Brain Aging Study underwent DCE‐MRI. K‐trans maps were estimated using ROCKETSHIP software implemented within MATLAB 2023b. Segmentations of hippocampal head, body and tail, anterolateral and posteromedial EC and BF nuclei were obtained using in house developed pipeline. Average K‐trans values were extracted for each region. Regional differences in BBB permeability between groups were assessed using ANOVA.

Result

Participants in the MCI group had increased mean K‐trans in anterior (K‐transCN= 0.752 x10‐3min‐1; K‐transMCI= 0.991 x10‐3min‐1, p = 0.023) and posterior (K‐transCN = 0.656 x10‐3min‐1; K‐transMCI = 0.984 x10‐3min‐1, p = 0.018) part of nucleus basalis Meynerti (NBM). There were no other significant differences between regional BBB permeability in measures structures (p>0.05)

Conclusion

Our pilot data show regional BBB dysfunction in NBM region in patients with MCI due to AD. This may imply regional BBB vulnerability of BF or another ongoing process such as hyperactivation or inflammation, suggesting a link between BBB dysfunction and early pathological changes in the BF area. Verification of these results on larger dataset is necessary before any further conclusions can be made.
Assessments for Prodromal Alzheimer’s Disease Having Positive Amyloid Pathology Using Plasma Biomarkers And APOE Genotype
Authors: Wang Pei‐Ning , Chiu Ming‐Jang , Sabbagh Marwan N. , Yang Charles S.Y.
Published: January 2025

Background

With the success in developing drugs able to slow cognitive decline in Alzheimer’s disease (AD), the disease managements of AD become aggressive and positive. The target patients of drug are the prodromal AD, i.e. clinical dementia rating being 0.5 and mini‐mental status examination being between 20 and 28, having positive amyloid pathology. In practical, it seems to have big burdens in finding prodromal AD patients have positive amyloid pathology at neurological departments in hospitals. The impact of drug in managing AD is intensively suppressed. A pre‐screening tool for finding target patients is needed. By integrating the reported assessments of prodromal AD and positive amyloid positron emission tomography (PET) using plasma biomarkers, a pre‐screening flow chart is proposed.

Method

A multi‐centered study was done to validate assessments of amnestic mild cognitive impairment (aMCI) and Alzheimer’s disease dementia (ADD) using plasm concentration ratio of amyloid β 1‐42 (Aβ1‐42) to Aβ1‐40 (Aβ1‐42/Aβ1‐40) and the concentration product of Aβ1‐42 and total tau protein (T‐Tau) (Aβ1‐42xT‐Tau), which were assayed with immunomagnetic reduction (IMR). Explorations of pre‐screening model of amyloid pathology in prodromal using plasma Aβ1‐42 and APOE genotype in prodromal AD was investigated in the multi‐centered study.

Result

The levels of Aβ1‐42/Aβ1‐40 or Aβ1‐42xT‐Tau in plasma show high accuracy (> 85%) in assessing prodromal AD. The combination of plasma Aβ1‐42 and APOE genotypes show the high prediction (> 84%) in positive amyloid PET in prodromal AD. These results demonstrate the feasibilities of assessing prodromal AD using plasma Aβ1‐42/Aβ1‐40 or Aβ1‐42xT‐Tau, followed by combing plasma Aβ1‐42 with APOE genotypes to predict the positive amyloid PET.

Conclusion

Such pre‐screening flow chart could breakdown the bottleneck of finding prodromal AD having positive amyloid PET in clinic and promote the impact of drug to the managements of AD.
Diagnostic Utility of Plasma Biomarkers to Differentiate Controls, SCD, and MCI Subjects
Authors: Boutajangout Allal , Masurkar Arjun V. , Osorio Ricardo S. , Ahmed Wajiha , Debure Ludovic , Vedvyas Alok , Links Jon , Marsh Karyn , Wisniewski Thomas
Published: January 2025

Background

Plasma biomarkers of Alzheimer’s disease (AD) are less invasive and have lower costs than cerebrospinal fluid (CSF) biomarkers. Blood biomarkers are potential instruments for diagnosis, prognosis, and monitoring of disease progression. We assessed the diagnostic potential of several plasma biomarkers for the detection of early stages of AD.

Method

152 participants (age>60) were recruited at the NYU Alzheimer's Disease Research Center. This cohort includes n=55 with normal cognition (NL), n=70 with subject cognitive decline (SCD), and n=27 with mild cognitive impairment (MCI). Comprehensive neuropsychological and magnetic resonance imaging evaluations were conducted for all patients. Plasma biomarkers assay Aβ40, Aβ42, total tau [t‐tau], neurofilament light [NfL], glial fibrillary acid protein [GFAP], ubiquitin carboxyl‐terminal hydrolase L1 [UCH‐L1], and pTau181 were measured using the SIMOA SR‐X a novel technology that employs highly sensitive immunoassays with a limit of detection (LOD) under 100 fg/ml.

Result

The levels of Aβ40, Aβ42, and t‐tau were measured using the Neurology 3‐plex A. Aβ42 levels showed a significant difference between NL and MCI (one‐tailed t‐test p=0.0262). The level of Aβ42/Aβ40 ratio was also significantly different between SCD and MCI (one‐tailed t‐test p=0.0206). pTau181 level showed a statistically significant difference between NL and SCD (one‐tailed t‐test p=0.0341). The pTau181/Aβ42 ratio was significantly different between NL and SCD; and NL and MCI (one‐tailed t‐test p=0.0190 and p=0.0037, respectively). In addition, the t‐tau/Aβ42 ratio was significantly different between NL and SCD, and NL and MCI (p=0.0359 and p=0.0091, respectively). Correlation of these biomarkers with brain imaging and cognitive measures is underway.

Conclusion

Biomarker levels of Aβ42, pTau181, pTau181/ Aβ42 ratio, and t‐tau/Aβ42 in plasma samples showed significant differences between the three groups of subjects. Plasma biomarkers can be useful for the diagnosis of AD‐related pathology at early stages.
Association between medial temporal and neocortical Tau SUVR across tau imaging agents: HEAD Study
Authors: Rahmouni Nesrine , Tissot Cécile , Hosseini Seyyed Ali , Macedo Arthur C. , Servaes Stijn , Therriault Joseph , Lussier Firoza Z , Arias Jaime Fernandez , Wang Yi‐Ting , Stevenson Jenna , Trudel Lydia , Gauthier Serge , Baker Suzanne L. , Pascoal Tharick Ali , Rosa‐Neto Pedro
Published: January 2025

Background

The association between medial temporal and neocortical SUVR depends on availability of cortical tau. However, tracer differences in affinity and off‐target binding might interfere in these associations. Here, we examined the association between medial temporal and neocortical SUVR using voxel‐based approach.

Method

We included 92 individuals (46 Cognitively Unimpaired and 46 Cognitively Impaired) from the HEAD Study recruited at McGill University. All individuals were assessed for amyloid‐β (Aβ) deposition using [18F]AZD4694‐PET and at least two tau‐PET ligands ([18F]MK6240 and [18F]Flortaucipir). A subset of 25 individuals had 2 additional tau‐PETs scans: [18F]RO948 and [18F]PI‐2620. Voxel‐based regression models evaluated the relationship between PET tracers’ uptake in the entorhinal cortex (EC) and its association with the tracer’s uptake in later Braak regions. All models include Aβ‐PET, age, and sex as covariates and RFT was used to account for multiple comparisons. Additionally, we extracted the number of significant voxels in the associations within each Braak regions.

Result

Positive associations were observed between tau‐PET SUVR in the EC (Figure 1). [18F]MK6240 EC was correlated with tracer uptake in the whole cortex. [18F] Flortaucipir was associated with tracer uptake in similar regions, albeit showing a lower t‐value. [18F]PI2620 EC uptake correlated with binding in regions Braak II‐V. Finally, [18F]RO948 was associated with uptake in regions Braak II‐IV. We further validated our results by extracting the number of significant voxels in each Braak regions. [18F]MK6240 showed the highest percentage of spatial extent of tau spreading in all Braak regions, followed by [18F]Flortaucipir and [18F]RO948. [18F]PI2620 demonstrated the lowest spatial extent.

Conclusion

[18F]MK6240 SUVR better predicted the spatial extent of tau spreading in the whole cortex. Results were concordant at the voxel and region‐on‐interested levels. [18F]Flortaucipir showed similar results, in lower magnitude. Finally, [18F]PI2620 and [18F]RO948 were further lower. Nevertheless, the later analyses were conducted in a smaller sample. Our results support the concept that MK6240 affinity possibly explain these results.
Ruminococcus‐based metagenomic‐brain signature linked to cognitive performance and Alzheimer’s disease markers
Authors: Labus Jennifer S , Heston Margo B. , Mayer Emeran A , Mohanty Ipsita , Tran Thai , Huynh Kevin , Gupta Arpana , Rey Federico E. , González Antonio , Dilmore Amanda H. , Knight Rob , Kaddurah‐Daouk Rima , Bendlin Barbara B. ,   Alzheimer's Gut Microbiome Project Consortium (AGMP)
Published: January 2025

Background

The aim of this study was to identify a gut microbial signature associated with patterns of gray matter volume in AD, and to validate the microbial signature by testing it against measures of AD pathology and cognitive performance. Prior literature suggests that microbial species involved in bile acid production and inflammation may be implicated in the microbial signature.

Method

The sample comprised 204 Microbiome in Alzheimer’s Risk Study participants (22 AD, 10 MCI, and 172 CN; 129 Females, 78 APOE+) from the Wisconsin Alzheimer’s Disease Research Center and Wisconsin Registry for Alzheimer’s Prevention. With shotgun metagenomic sequencing data and regional gray matter volumes calculated using T1‐weighted neuroimaging (Destrieux, Harvard Oxford subcortical atlases), sparse partial least squares regression was used to determine correlated sets of microbial features and gray matter regions that maximally explained the variance in the two datasets. General linear models were then used to link the derived microbial signature with an aggregated score of cognitive performance (PACC3), an estimated cognitive score (ADAS‐Cog‐13) derived using participants’ serum lipidomics, and amyloid and tau positivity status.

Result

Greater abundance of 14 microbial features dominated by Ruminococcus species was associated (r=0.39, p=7.7e‐09) with greater volume in 13 regions dominated by the hippocampus, amygdala, and temporal regions, which are implicated in AD (Figure 1). This relationship was especially strong among APOE ε4 carriers. Higher scores on the microbial signature (associated with higher gray matter volume) were also associated with pTau negative status, and with better performance on the PACC3 and estimated ADAS‐Cog‐13.

Conclusion

With roles in producing secondary bile acids and short chain fatty acids, Ruminococcus species may benefit the gut mucosal barrier and abrogate inflammatory mechanisms. This study suggests these microbes could protect against neurotoxic processes including tau hyperphosphorylation, resulting in preserved cortical volume and better cognitive performance.

Funding: Alzheimer Gut Microbiome Project, NIA U19 AG063744
Alzheimer’s disease diagnosis using gray matter of T1‐weighted sMRI data and vision transformer
Authors: Aghdam Maryam Akhavan , Bozdag Serdar , Saeed Fahad
Published: January 2025

Background

Alzheimer's Disease (AD) is a progressive neurodegenerative disorder characterized by memory loss and cognitive decline. Traditional diagnostic methods, mainly based on cognitive, memory, and behavioral tests, have limitations, particularly in the early detection of AD. Structural magnetic resonance imaging (sMRI) has emerged as a key tool in understanding the brain changes associated with AD, focusing particularly on alterations in gray matter (GM). However, the complexity of brain changes in AD requires sophisticated analysis methods. In recent years, machine learning (ML) models have shown great potential in interpreting complex neuroimaging data. These models can detect intricate patterns in neuroimaging data, making them invaluable in enhancing the diagnostic accuracy and early AD diagnosis. Therefore, combining the neuroimaging data with ML models presents a promising direction in improving the early‐diagnosis and understanding of AD.

Method

We propose a novel approach to diagnose AD using Vision Transformer (ViT) (Figure 1) [1], a cutting‐edge class of ML model, and GM of T1‐weighted sMRI data. The proposed approach leverages the power of deep‐learning (DL) to detect the GM changes that are indicative of AD. We used pretrained ViT model to extract features from the GM sagittal and coronal slices of sMRI data and classify AD from cognitively normal (CN). We employed ADNI dataset, focusing on subjects with T1‐weighed MPRAGE sMRI scans, including 70 AD patients and 85 CN individuals.

Result

The study achieved an average classification accuracy of 97.6% in sagittal slices and 97.7% in coronal slices (Figures 2 and 3). These results indicate a significantly higher accuracy in diagnosing AD using the proposed method compared to other state‐of‐the‐art models based on sMRI data. The high accuracy underscores the model's capability in effectively distinguishing between AD patients and CN individuals, demonstrating its potential utility in clinical settings.

Conclusion

The proposed approach demonstrates a significant advancement in the accurate diagnosis of AD, which might be useful for early‐diagnosis. Our proposed ML model represents a considerable improvement over existing ML methods, offering a new avenue for research and application in the field of neurodegenerative diseases.
Supervised clustering of MRI measures informed by incident cognitive impairment via aggregated survival model SHAP values
Authors: Petersen Kellen K. , Nallapu Bhargav Teja , Demirsoy Idris , Lipton Richard B. , Grober Ellen , Morris John C. , Hassenstab Jason J. , Gordon Brian A. , Ezzati Ali
Published: January 2025

Background

Alzheimer’s disease (AD) is a complex heterogeneous neurodegenerative disease. Unsupervised clustering techniques have been used to identify disease subtypes, but such approaches are limited since subtypes may not directly be related to disease progression. Herein, we implement a novel supervised clustering approach that aims to identify MRI‐derived subtypes that are likely to experience incident cognitive impairment (ICI).

Methods

We used data from 822 cognitively normal individuals (68.4 ± 9.5 years old) with Clinical Dementia Rating® (CDR®) of 0 at baseline, from the Knight ADRC. We performed supervised clustering of eight volumetric MRI regional measures at baseline. MRI measures were adjusted for age, sex, and education. Mean aggregated time‐dependent Shapley Additive exPlanation (SHAP) values were derived from random survival forest models with an ICI outcome based on time‐to‐conversion from a CDR=0 to CDR>0 over 10 years of follow‐up. K‐means clustering was then applied to SHAP values. Analysis of Variance was used to assess cluster differences and linear regression was used to understand longitudinal trajectories of MRI volumetrics and CSF biomarkers.

Results

In the entire sample, hippocampus, entorhinal, and amygdala SHAP values were determined to be the most important MRI measures in terms of estimated contribution to the model prediction with the hippocampus differentiating from other regional measure about 24 months from baseline (Figure 1). The optimal number of clusters was determined to be four. Cluster characteristics and significant differences among them are presented in Table 1 and Figure 1. Cluster 4 is characterized by the largest mean hippocampus SHAP value with approximately 89% of participants experiencing ICI. Figure 2 shows longitudinal linear trajectories for MRI and cerebrospinal measures. Cluster 1 was the healthiest across all longitudinal biomarkers. Whereas, Cluster 4 had the smallest hippocampal volume and abnormal CSF measures both at baseline and over time.

Conclusions

We implemented a novel application of SHAP values from survival models in a supervised clustering approach to identifying MRI‐subtypes with different probabilities of incident cognitive impairment. This approach could be used to elucidate the heterogeneity inherent in the relationships among AD biomarkers and longitudinal cognitive changes.