bluuebunny/crossref_metadata_2025_split huggingface dataset:
The effects of self‐efficacy training on associative memory and brain function in older adults with mild cognitive impairment
Authors: Gabiazon Raphael , Marshall Samantha , Swayze Amy , Riesen James Van , Jeyarajan Gianna , Al‐Shaikh Jennifer Hanna , Nagamatsu Lindsay S 
Published: January 2025

AbstractBackgroundOlder adults with mild cognitive impairment (MCI) are known to have declines in cognition greater than that expected for an individual’s age and education level but does not significantly interfere with daily functioning. Memory impairments during the early stages of MCI may predict conversion to Alzheimer’s disease (AD). This impairment can be especially prominent in associative memory – the ability to learn and remember the relationship between two unrelated items. While age‐related memory impairments are often attributed to biological and physiological changes, psychosocial factors could also play a role. A notable psychosocial factor that may affect memory is self‐efficacy – an individual’s confidence in their ability to carry out actions to meet situational or domain‐specific demands. Despite previous research showing a relationship between enhanced self‐efficacy and improved memory performance in older adults, the neural correlates that may explain this relationship remain unclear. Therefore, we developed a six‐week self‐efficacy training intervention to examine its effects on memory and corresponding changes in functional brain activation among older adults with MCI.MethodTwenty‐eight participants were randomized and allocated into either a memory self‐efficacy intervention (n = 14) or general education control group (n = 14). Over six weekly 1.5‐hour group sessions, participants attended either classes that focused on building memory self‐efficacy or general education covering factual and world knowledge. At baseline and endpoint measurements, memory performance and functional brain activation were assessed using an associative memory task during a functional magnetic resonance imaging scan, while memory self‐efficacy was measured with the Multifactorial Memory Questionnaire. Post‐intervention group differences for each outcome measure were analyzed using multiple linear regression models.ResultWe found changes in functional activation patterns during the associative memory task in the memory self‐efficacy training group as a result of the intervention. Specifically, key regions in the brain that are critical for associative memory performance were implicated, suggesting that memory self‐efficacy training can change the way we process and remember information.ConclusionThis study provides insight into how psychosocial factors like self‐efficacy may influence age‐related memory impairments and AD progression, demonstrating self‐efficacy training's potential to enhance memory in at‐risk populations.
South Asian Aging Brain Initiative: Neuroimaging
Authors: Willette Auriel A. , Kota Karthik , Hu William T. 
Published: January 2025

AbstractBackgroundSouth Asian (SA) older adults are one of the fastest growing US populations developing Alzheimer’s disease (AD) and related dementias (ADRD). Compared to non‐Hispanic white (NHW) Americans, SA are hesitant to enroll in neuropsychological and MRI research. This status complicates accurate assessment and diagnosis. Yet, SA may show differences in neurodegeneration patterns that elucidate novel brain‐behavioral relationships or network dysfunctions in multilingual speakers.MethodAs part of the South Asian Aging Brain (SAAB) study, we recruited 62 non‐demented SA participants without subjective cognitive concerns to undergo English‐based neuropsychological testing and brain MRI. Freesurfer 7.4.1 was used to derive bilateral volume or cortical thickness in cortical and subcortical regions. After calculating Z‐scores based on National Alzheimer’s Coordinating Center (NAAC) calculators, stepwise linear regression was used to examine adjusted SAAB z‐scores with imaging outcomes. Covariates included age, sex, years of education, age at immigration, and years spent in the United States.ResultAmong 29 women and 33 men, mean age was 64.9 years (range 24‐86y) with an average of 17.6 years of education (range 12‐20y) and US residency lasting 32.6 years (range 4‐59y). For Factor 1 (Semantic Memory), superior parietal gyrus, inferior parietal gyrus, and precentral gyrus loaded (adjusted R2=0.261; p<.01). For Factor 2 (Word Recall), only the bank of the superior temporal sulcus loaded (adjusted R2=0.231, p<.01). For Factor 3 (Story Recall), several areas explained recall including entorhinal cortex, precuneus, caudal anterior cingulate gyrus, medial orbital gyrus, and others (adjusted R2=0.640, p<.001). For Factor 4 (Attention), the inferior and superior parietal lobules loaded (R2=0.207, p<.01). For Factor 5 (Executive Function), postcentral gyrus and the inferior lateral ventricle loaded (adjusted R2=0.142, p<.05). Finally, for Factor 6 (Letter Fluency), areas included inferior parietal gyrus, lateral occipital gyrus, and bilateral cerebellar white matter (adjusted R2=0.311, p<.001).ConclusionThe results suggest that word and especially story recall are substantially explained by regions implicated in AD, such as frontal, temporal, and parietal areas. Tests associated with frontal brain regions (executive function, fluency) in NHW adults are more associated with parietal volume or cortical thickness in SA adults. Future work will need to explore this difference.
Deep Survival Analysis for Alzheimer’s Disease Based on Longitudinal Neuroimaging Data
Authors: Ma Da , Kyriacou Sophia 
Published: January 2025

AbstractBackgroundAlzheimer’s Disease (AD) accounts for more than half of dementia cases and is a leading cause of death in the United States among elderly population. Early risk prediction is crucial to ensure effective and timely intervention plans. Deep learning‐based survival analysis based on cross‐sectional neuroimaging data has shown promising results in predicting the future progression of the dementia onset at an early stage. This study aims to derive deep survival analysis using multi‐timepoint longitudinal neuroimaging data to achieve a more accurate dementia onset prediction.MethodData: Longitudinal MRI of 1724 subjects (average age was 73.8) from the Alzheimer’s Disease Neuroimaging Initiative was used. To derive dementia onset event and duration information for the survival analysis, censored data (i.e. dementia conversion happened before or after the study period) was excluded (19.6% left‐censored, 63.2% right‐censored), resulting in 297 subjects. The input features included 89 FreeSurfer‐parcellated brain structural volumes, and demographic factors such as age and sex. Deep survival analysis was conducted using DeepSurv Pytorch library, using a multi‐layer perceptron (MLP) model architecture as the feature extractor. Image features from three timepoints were concatenated before feeding into the deep survival model for predicting future AD conversion. For comparison, ablation studies were conducted to include single timepoint neuroimaging features for each subject (derived from the last timepoints from the longitudinal features). The survival models’ performance was evaluated using the concordance index (C‐index) using 10‐fold cross‐validation.ResultThe accuracy of the three‐timepoint longitudinal data (C‐index=97.1%) is higher than the single timepoint study (C‐index=75.6%) (Figure 1). Figure 2 shows the histogram of the difference between the actual and predicted dementia onset time (derived by taking the 0.5 threshold cutoff point from the C‐index, Positive value means delayed prediction). Single timepoint prediction showed more false negatives (delayed prediction) while longitudinal prediction showed relatively even distribution of false positive (earlier prediction) and false negative.ConclusionIn this study, we demonstrated the of longitudinal neuroimaging data in deep survival analysis to improve the prediction the time of conversion for future dementia, with less false negative, promoting early prediction and potential early intervention prior to the dementia.
Volumetric imaging analysis using deep learning classifier for various stages of dementia
Authors: Cheung Eva YW , Mak Henry , Shea Yat Fung , Chiu Patrick Ka‐Chun 
Published: January 2025

AbstractBackgroundThis study aimed to apply deep learning for various stages of dementia classification.MethodsThe ADNI database and OASIS database were used, where ADNI 21 centers with total 406 images (69AD, 202MCI and 135HC) were used as training and validation, and ADNI 4 centers with 176 images (28AD, 91MCI and 57HC) were used as testing, and another 176 images (28AD, 91MCI and 57HC) from local memory clinic were used as local testing. The 39 brain regional volumes were segmented and calculated using Freesurfer. Ten‐fold cross validation were performed to minimize the chance of overfitting. Four classifiers, including decision tree (DT), ensemble classifier (EC), support vector machine (SVM), K‐nearest neighbors algorithm (KNN) were used to build the model in the MATLAB classifier toolbox.ResultsThe receiver operating characteristics (ROC) curves showed that the SVM achieved the best and balanced result, with AUC =1.00, 0.76 and 0.68; with 98.0%, 74.3% and 59.7% accuracy in training, testing and local testing respectively. This study showed that the training set attained the highest accuracy, while it dropped in testing data and local data. It highlighted the importance of local data testing prior to the application.ConclusionThe SVM can differentiate AD, MCI from HC with satisfactory result. The application is practical and can be fully automatic, which can be used for dementia screening. Further studies should be conducted to fine‐tune the models for improving the accuracy in local dataset, prior to the application in local application.
Volumes of specific substructures within the Amygdala and Hippocampus are impacted by brain amyloid‐β
Authors: Murali Leema , Charil Arnaud , Reilhac Anthonin , Qi Xin 
Published: January 2025

AbstractBackgroundReductions in medial temporal lobe (MTL) volume, particularly in the amygdala and hippocampus, are present in early Alzheimer's disease (AD). We explore the correlations between hippocampal and amygdalar subfield volumes and brain amyloid‐β (Aβ) accumulation using T1‐weighted structural MRI and amyloid PET data from ADNI and Eisai clinical trials.MethodWe used FreeSurfer (v7.2.0) and a high‐resolution MRI‐based probabilistic atlas using Bayesian inference to measure volumes of hippocampal and amygdalar sub‐structures in one of Eisai’s clinical trial cohorts and ADNI (3113 and 1443 subjects), mostly with MCI due to AD (>80% and 45%, respectively). In Eisai’s clinical cohort, Aβ positivity was established through a concordance between PET visual assessment and Centiloids (CL), using a threshold of 32.21. In ADNI, Aβ positivity was defined by CL using a threshold of 20. We first evaluated correlations between baseline CL and volumes of hippocampus and amygdala substructures. For predicting brain amyloid burden, machine learning models, including stochastic gradient boosting, were applied in conjunction with cognitive and demographic information.ResultIn Eisai’s clinical cohort, amygdalar subfields were more strongly correlated (p<0.05) with global amyloid levels than hippocampal subfields. Global amyloid is inversely associated with amygdala and hippocampal volumes, with the strongest correlations being with the amygdala accessory basal nucleus (left ‐0.41. right ‐0.40). This regional association with global amyloid is more significant than with the entire amygdala (left ‐0.36, right ‐0.35, p<0.05). Both the whole hippocampus (‐0.36) and its subfields (hippocampal GC‐ML‐DG (Granule Cell (GC) and Molecular Layer (ML) of the Dentate Gyrus (DG))‐body, subiculum‐body, and tail) showed equivalent correlations with global amyloid levels.Machine learning models indicated that hippocampal/amygdalar subfields are better predictors of amyloid positivity than MTL volumes (increase in AUC from 66% to 70% (p<0.05)), which improved to 78% when adding ApoE4. Consistent with the results of the correlational analyses, the accessory‐basal nucleus, and hippocampal tail and fissure were among the top predictors in this model.ConclusionThis analysis emphasizes the importance of specific hippocampal and amygdalar substructures that are impacted by amyloid in early AD, with implications for staging and disease progression monitoring.
Longitudinal changes in neuroimaging markers of small vessel disease: Implications for clinical trials
Authors: Lao Patrick J. , Edwards Natalie C. , Flores‐Aguilar Lisi , Rizvi Batool , Smith Anna C. , Tudorascu Dana , Rosas H. Diana , Yassa Michael A. , Handen Benjamin L , Christian Bradley T. , Gutierrez Jose , Wilcock Donna M. , Head Elizabeth , Brickman Adam M. 
Published: January 2025

AbstractBackgroundAdults with Down syndrome (DS) overproduce amyloid precursor protein, develop amyloid plaques at an early age, and are diagnosed with Alzheimer’s disease (AD) dementia at a high frequency. There is emerging evidence that cerebrovascular disease is elevated across the AD continuum in older adults with DS, independent of age and vascular risk, around the same time as amyloid and tau, but the regional rates of accumulation within individuals are unknown.MethodAdults with DS from the multisite Alzheimer’s Biomarker Consortium‐Down Syndrome study (ABC‐DS; n=78; age=50±6; 40% women) have two timepoints of T2 FLAIR MRI (1.2±0.6 years apart) quantified as white matter hyperintensity volume (WMH), which represents ischemic small vessel disease. Participants underwent consensus diagnosis at both timepoints (59% Cognitively‐Stable at both timepoints, 9% Cognitively‐Stable to MCI‐DS, 8% MCI‐DS at both timepoints, 14% MCI‐DS to AD, 10% AD at both timepoints). The annual rate of change in frontal, temporal, parietal, and occipital WMH volume was assessed, adjusting for baseline WMH volume.ResultThe annual rate of change in frontal WMH was not significantly different by diagnosis. The annual rate of change in temporal (0.7 [0.4, 1.1], p<0.001) and in occipital WMH (1.6 [0.7, 2.5], p=0.0008) was faster in the group that remained AD at both timepoints compared to the group that remained Cognitively‐Stable at both timepoints. The annual rate of change in parietal WMH was greater in the group that progressed from MCI‐DS to AD (0.6 [0.1, 1.0], p=0.02) and in the group that remained AD at both timepoints (1.1 [0.6, 1.7], p=0.0002) compared to the group that remained Cognitively‐Stable at both timepoints.ConclusionIn adults with DS, parietal WMH accumulates fastest in those that progress to or have a diagnosis of AD, while temporal and occipital WMH accumulate fastest in those with a diagnosis of AD. Posteriorly distributed WMH may have specificity for AD progression in adults with DS with implications for anti‐amyloid therapeutics that have cerebrovascular side effects.
A simple measure of voluntary control of breathing is an effective physio‐marker for differentiating MCI from mild AD patients and MCI/AD patients from cognitively normal controls
Authors: Marmarelis Vasilis , Chui Helena C , Billinger Sandra A , Joe Elizabeth B , Shin Dae , Hashem Suhaib , Rizko Jasmin , Hazen Emily , Cardim Danilo , Zhang Rong 
Published: January 2025

AbstractBackgroundControl of breathing is known to be adversely affected by cognitive impairment, often associated with sleep apnea or disordered breathing during sleep in MCI/AD. The origin of this disorder is thought to be in the dysfunction of the respiratory control centers of the brainstem or in the impaired afferent signaling from cortical regions. Continuous breathing data were collected in a multi‐center study in Los Angeles (USC), Kansas City (KUMC) and Dallas (UT‐SWMC), and used to compute respiratory rate variability (RRV) to test the hypothesis that voluntary control of breathing is impaired in MCI and mild AD patients relative to cognitively normal controls, and whether this impairment is more severe in mild AD than MCI patients.MethodThe collected continuous breathing data were used to compute the RRV as the standard deviation of breath‐to‐breath respiratory rate variations during metronome‐guided slow‐paced breathing (6 breaths per minute) over 5 minutes under supine resting conditions for each of 61 MCI, 32 mild AD patients and 78 age/sex‐matched cognitively normal controls. The computed RRV was used to quantify and compare the voluntary control of breathing for all participants.ResultThe resulting p‐values of mean‐difference t‐tests were:• Controls vs. MCI patients: 0.304 (0.145) vs. 0.393 (0.222), p = 0.0039• Controls vs. AD patients: 0.304 (0.145) vs. 0.561 (0.258), p = 2.4E‐06• Controls vs. MCI & AD patients: 0.304 (0.145) vs. 0.451 (0.247), p = 1.7E‐06• MCI vs. AD patients: 0.393 (0.222) vs. 0.561 (0.258), p = 0.0014The results are shown in Figure 1 as box‐whisker plots. The p‐values showed significant mean‐differences in all four cases, with comparisons against mild AD patients showing greater differentiation. The observed mean RRV values showed stratification of impairment: 0.393 for MCI vs 0.561 for mild AD, relative to 0.304 for controls.ConclusionQuantitative analysis of a simple measure of voluntary control of breathing during metronome‐guided slow‐paced breathing under supine resting conditions showed significant mean‐differences between 61 MCI patients, 32 mild AD patients and 78 age/sex‐matched cognitively normal controls (see Figure 1), suggesting that the RRV physio‐marker has the potential to aid in the diagnosis of MCI and mild AD.
Validation of Clinical Decision Support Recommendations of a Digital Cognitive Assessment by Expert Cognitive Neurologists
Authors: Jannati Ali , Toro‐Serey Claudio , Gomes‐Osman Joyce Rios , Morrow William Isaiah , Ciesla Marissa C , Banks Russell , Bates David , Tobyne Sean , Showalter John , Pascual‐Leone Alvaro 
Published: January 2025

AbstractBackgroundDisease‐modifying treatments for Alzheimer’s disease highlight the need for early detection of cognitive decline. However, most primary care providers do not currently perform routine cognitive testing, in part due to a lack of time and resources to administer and interpret the tests. Brief, self‐scoring, and sensitive digital cognitive assessments, such as the Linus Health Core Cognitive Evaluation (CCE)–which includes the Digital Clock and Recall (DCR™) and the Life and Health Questionnaire (LHQ)–can automatically provide medically‐informed recommendations that can address this need. Here we evaluate the clinical appropriateness of the recommendations generated by this clinical decision support (CDS) tool to guide the diagnosis of cognitive impairment by primary care providers (PCPs).MethodThe CDS tool uses data from the CCE to list potential medical concerns and recommend pathways toward formal diagnosis and/or care. We conducted a retrospective expert‐review study in June 2023 to evaluate the nine CDS pathways for patients aged 55 and above. Experts were five board‐certified cognitive neurologists affiliated with academic institutions. We calculated the median ratings (on a scale of 1 to 9, where 9 is high appropriateness) for the nine CDS pathways across raters. A rating of 7 or above was deemed clinically appropriate.ResultAll 7 pathways related to cognitive impairment received a clinically appropriate rating (median = 7, SD = 0.3, range=7‐8). Pathways below the appropriateness threshold included the one for Green DCR scores (i.e., cognitively unimpaired; median = 6, SD = 0.87) and a preliminary Lecanemab eligibility pathway (median = 5, SD = 1.10).ConclusionPathways and the cognition‐related recommendations generated by the CDS tool of the Linus Health CCE are clinically appropriate, as rated by this initial survey of board‐certified, academic cognitive neurologists. The findings indicate the clinical utility of digital cognitive assessments such as the CCE for guiding the PCPs’ approach to diagnosis and management of patients with cognitive impairment.
Unique gut microbiome signatures associated with physical and cognitive function
Authors: Nair Sridevi U , Lin Yi , Jain Shalini , Chaudhari Diptaraj S , Shukla Rohit , Kumar Vivek , Mankowski Robert T , Meng Hongdao , Buford Thomas W , Kuchel George A , Yadav Hariom , Consortium MiaGB 
Published: January 2025

AbstractBackgroundEmerging studies suggest the interaction of the gut microbiome with physical and cognitive function. However, it remains unknown whether the same microbiome is linked with both functions and/or if they are unique.MethodUtilizing data and samples from the Microbiome in Aging Gut and Brain (MiaGB) Consortium cohort, we assessed the whole genome gut microbiome metagenomics in 113 older adults (mean age 72 years [39 male and 74 female]) with high and low physical functions (measured using Short Physical Performance Battery [SPPB]) and cognitive function (measure using Montreal Cognitive Assessment [MoCA]). Based on the SPPB score, participants were categorized into low (score: 3‐9) and high (score: 10‐12) physical function groups, while based on MoCA score, mild cognitive impaired (MCI; MoCA:18‐25) and cognitively healthy (MoCA: 26‐30).ResultWe observed the presence of distinct and specific microbiome signatures that are uniquely associated with the low and high physical functioning groups, as opposed to those specific to cognitive functioning. Specifically, in low physical functioning group Bacteroidacea and Rikenellaceae were increased and Clostridiaceae were decreased compared to high physical functioning group. On the other hand, Streptococcaceae decreased in low cognitive function group compared to cognitively healthy people. Increase in the ratio of Firmicutes ‐Bacteroidetes was specific for physical activity, whereas the increase in the ratio of Actinobacteria‐Proteobacteria was specific for cognitive function.ConclusionDifferentiating unique and specific microbiome signatures for physical function and cognitive function represents significant progress in the field of the gut‐brain axis. Our results support the distinct impact of the microbiome to impact the gut‐muscle axis versus the gut‐brain axis; and opens the doors for further comprehensive studies to determine the causal role of the microbiome in physical and cognitive functions, as well as its potential to be a biomarker for predicting the risk of decline in these functions in older adults – two of the most debilitating public health problems in older adults.