bluuebunny/crossref_metadata_2025_split huggingface dataset:
The association between electrophysiological and neuropsychological measures and cardiovascular disease in patients with Alzheimer’s Disease dementia
Authors: Marin Anna , Schiloski Kylie A , DeCaro Renee , Vives‐Rodriguez Ana , Budson Andrew E , Turk Katherine W 
Published: June 2023

AbstractBackgroundElectrophysiological and neuropsychological measures are useful markers of Alzheimer’s Disease (AD); however, it is still unclear how these measures relate to the cardiovascular profile of AD patients, and their influence on disease progression and management. Our study examined how electrophysiological and neuropsychological measures are associated with cardiovascular risk factors and the severity of small vessel disease among patients with AD dementia.MethodData were collected using a seven‐electrode COGNISION™ rig in 185 older veterans from the VA Boston Healthcare system memory disorders clinic. EEG/ERP data were recorded while participants completed a three‐tone auditory oddball task. All subjects were administered a neuropsychological battery composed of 8 subtests. Through review of medical history, we selected a subset of patients (N = 46) that met the following criteria: core clinical criteria for probable AD dementia, ≥ 65 years of age, and MMSE score between 20 and 26. We categorized these patients based on their number of cardiovascular risk factors—among hypertension, type 2 diabetes or prediabetes, hypercholesterolemia, and body mass index (BMI) ≥ 25 kg/m2 —and their small vessel disease severity (absent, mild, moderate, or severe). Spearman correlations were computed to assess the association of electrophysiological and neuropsychological measures with the number of cardiovascular risk factors and the severity of small vessel disease.ResultAlpha peak frequency was positively correlated with the number of cardiovascular risk factors (p = .011) and was inversely correlated with small vessel disease severity (p = .029). N100 standard latency was inversely correlated with small vessel disease severity (p = .018). Scores on the verbal phonemic fluency test were inversely correlated with the number of cardiovascular risk factors (p = .014), while scores on the verbal category fluency test were inversely correlated with small vessel disease severity (p = .023).ConclusionAlpha peak frequency, N100, and verbal fluency scores are associated with the number of cardiovascular risk factors and small vessel disease severity in AD patients. Overall, these electrophysiological and neuropsychological measures represent a clinical tool to help characterize the cardiovascular profile of patients with AD dementia, and its contribution to overall disease progression.
A longitudinal study of the task‐related activation trajectory in people with mild cognitive impairment and subjective cognitive decline
Authors: Correa‐Jaraba Kenia Shaily , Mellah Samira , Dialahy Isaora Zefania , Group CIMA‐Q , Belleville Sylvie 
Published: June 2023

AbstractBackgroundBrain hyperactivation — defined as higher level of activation compared to controls — was suggested as a very early signature of prodromal Alzheimer’s disease (AD), which would gradually decrease with progression to dementia. Longitudinal studies with people who have mild cognitive impairment (MCI) and subjective cognitive decline (SCD) can be used to capture the temporal dynamics and inter‐individual differences of these very early activation changes. Here, we aimed to identify the temporal trajectory of task‐related activation in participants with SCD and MCI from the CIMA‐Q cohort, which has data collected at multiple time‐points. We thus identified subgroups based on their common activation trajectory and characterized subgroups defined from the activation trajectory.MethodThe study included 53 older participants (40 SCD; 13 MCI) from the CIMA‐Q cohort with neuroimaging data collected over at least two time‐points (66‐85 years old; 36 women, 17 men). An fMRI examination was done every two years (2‐4 time‐points; average follow‐up: 3.2 years). Task‐related activation was measured during an associative memory encoding task. Group‐based trajectory models were estimated to identify homogeneous groups of participants based on activation trajectories in the hippocampus and in regions from the cortical signature of AD. Groups defined based on activation trajectories were then compared using Apolipoprotein‐ε4 (ApoE4), baseline cognition and hippocampal volume.ResultTwo different trajectories of activation were identified: Trajectory 1 was found in several cortical regions and was characterized by a high level of initial activation, which decreased over time. Trajectory 2 was characterized by a lower activation level, which remained stable over time or increased slightly on time‐point 4. Smaller hippocampal volume and ApoE4 were associated with Trajectory 1 for the left angular gyrus, and left hippocampal and right middle temporal gyrus activation, respectively.ConclusionAn inverted U‐shape trajectory was found with high activation followed by gradually decreasing activation in AD‐signature regions. This trajectory was associated with smaller hippocampal volume and/or the presence of ApoE4 allele, both of which are biomarkers that increase the likelihood of developing AD. This finding supports the hypothesis that the inverted U‐shape trajectory of hyperactivation could be an index of prodromal AD.
Transcription‐based drug repurposing in Alzheimer disease psychosis
Authors: Kouhsar Morteza P , Creese Byron , Weymouth Luke Stephen , Smith Adam R. , Bergh Sverre , Wedatilake Yehani , Selbæk Geir , Torkamani Ali , Mill Jonathan , Ballard Clive G , Sweet Robert , Kofler Julia , Pishva Ehsan , Lunnon Katie 
Published: June 2023

AbstractBackgroundThere are at least 50 million people with Alzheimer’s Disease (AD) or other dementias worldwide. About 50% of people with AD may develop psychotic symptoms (delusions and hallucinations). However, there is no effective pharmacological therapy for psychosis in AD and all current treatments are based on antipsychotics originally designed for other disorders like schizophrenia. In this study, we identify the molecular signature of psychosis in AD in post‐mortem brain samples and then use the connectivity map (CMAP) and library of network‐based cellular signatures (LINC) to predict candidate drugs based on the disease transcriptional signature.MethodWe generated RNA‐Seq data (Illumina Stranded mRNA Prep) and compared the gene expression levels across 4 groups: Control, AD with no psychosis (AD), AD with Delusions (ADD), and AD with both Delusions and Hallucinations (ADDH). The final identified differentially expressed up and down‐regulated genes (DEGs) were used as query signatures in the CMAP and LINC databases to identify drugs that could potentially reverse the gene expression signatures in disease samples. To investigate the disease mechanisms and the effect of repurposed drugs on it, a drug‐target interaction network was reconstructed and combined with the human interactome. Enrichment analysis of this network identifies AD and psychosis‐related pathways and the potential effect the nominated drugs have on them.ResultAfter filtering out the common DEGs, 357, 171, and 66 unique genes remained in the AD, ADD, and ADDH signature sets, respectively. The signatureSearch package was used to search transcriptome signatures against CMAP and LINC. The top ranked drugs, based on negative connectivity scores, were selected as final results. Gene set enrichment analysis of the drug target genes presented some key pathways and activities related to psychiatric disorders. For example, serotonin receptor signalling pathway is one of the most over‐represented pathway of target genes in both ADD and ADDH groups.ConclusionThis study can improve our understanding of the molecular mechanisms underlying AD psychosis. The candidate drugs we have identified could be repurposed for clinical trials of psychosis in AD and provide valuable knowledge for future pharmacological research.
High CSF cortisol levels are associated with frontal lobe and hippocampal atrophy across patients on the Alzheimer’s disease spectrum
Authors: Souza Laura Willers , Bieger Andrei , Borelli Wyllians Vendramini , De Bastiani Marco Antônio , Negrini Guilherme Bauer , DuBois Jonathan M , Zimmer Eduardo R. , Lab Zimmer Neuroimaging 
Published: June 2023

AbstractBackgroundMultiple studies have linked high cerebrospinal fluid (CSF) cortisol levels, a frequently used biomarker of stress, with the Alzheimer’s disease (AD) pathophysiology. However, the relationship between CSF cortisol levels and AD‐related brain atrophy is not fully understood. Thus, this study sought to determine the association between CSF cortisol levels and neuroimaging biomarkers of brain structure indexed by magnetic resonance imaging (MRI) in patients across the AD spectrum.MethodsParticipants with baseline measures of CSF cortisol and structural (high‐resolution T1‐weighted) MRI (n = 300) were selected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The MRI scans were processed with FreeSurfer (v. 7.1.1), and the statistical analyses were performed using R (v. 4.1.1; R Core Team, 2021). We performed a region‐of‐interest (ROI) analysis using a generalized linear model (GLM), to assess the association between CSF cortisol levels and (1) volume of hippocampus, amygdala, subcortical and cortical segmentation, (2) cortical thickness and (3) surface area of cortical structures (Fig. 1). The ROI analysis was corrected by Bonferroni (p < 0.0005).ResultsCSF cortisol levels did not correlate with left hippocampal volume (p = 0.2; Fig. 2A) and mean global brain thickness (p = 0.002; Fig. 2C). However, we found a negative correlation with the volume of left fimbria (r = ‐0.20, p = 0.0003; Fig. 2B) and right superior frontal thickness (r = ‐0.21, p = 0.0003; Fig. 2D). In the regression model, only right superior frontal thickness showed significant association with CSF cortisol levels after adjusting for age (corrected R2 = 0.11, p < 0.0001). The other regions and parameters analyzed did not present significant correlations with CSF cortisol levels.ConclusionsThis cross‐sectional study found an association between elevated CSF cortisol levels and atrophy of left fimbria and right superior frontal in patients on the AD spectrum. Our findings suggest a particular vulnerability to cortisol in selected regions. To the best of our knowledge, this is the first study to assess the associations between CSF cortisol levels and ROIs from hippocampus and amygdala segmentation. Our results have potential implications for understating AD‐related brain atrophy and developing innovative therapeutic strategies.
Are there measurement invariance in subjective cognitive decline instruments across diverse ethnoracial groups?
Authors: Lee Athene KW , De Vito Alyssa N. , Brick Leslie Ann D , Coutinho Maria Teresa , Correia Stephen 
Published: June 2023

AbstractBackgroundSubjective cognitive decline (SCD) indicates a likelihood of the presence of cerebral beta‐amyloid burden associated with preclinical Alzheimer’s disease (AD), future objective cognitive decline, and clinical progression to dementia. The applicability of SCD as a preclinical AD marker in diverse ethnoracial groups has not been validated. In the few studies with diverse samples, participants from under‐represented populations such as Latinx and Black, tend to endorse differing levels of SCD than Non‐Hispanic White (NHW) individuals. Better understanding of ethnoracial differences in SCD and addressing potential cultural biases in existing measures are critical to validate its use as a preclinical AD marker in diverse samples. In this study, we examined two SCD instruments to evaluate measurement invariance across ethnoracial groups.Method807 participants aged ≥55 who reside in the United States (298 Latinx, 270 Black, 239 NHW) were recruited through Amazon’s Mechanical Turk and Qualtrics Panel. We examined the psychometric properties of the Cognitive Change Index (CCI) and Cognitive Function Index (CFI) across ethnoracial groups. First, we evaluated the factor structure of each measure using split‐half exploratory and confirmatory factor analyses (EFA/CFA). Next, we used multiple‐group CFA to evaluate configural, metric, and scalar invariance via a step‐wise approach.ResultsParticipants (mean age of 65), were mostly women (63%) and received education beyond high school (83%). Split‐half EFA/CFA on the whole sample supported a one‐factor solution for both measures. Multiple‐group CFA revealed that when race/ethnicity was considered, model fit was negatively impacted. Neither measure achieved metric invariance, indicating that factor loadings were not equivalent across groups. For example, the CFI item “memory has declined substantially” had the highest loading for NHW and Latinx, but was low for the Black group. Conversely, “trouble following the news” loaded high for Latinx and Black, but low for NHW.ConclusionMeasurement invariance was not supported for either instrument, indicating that it cannot be assumed that the same construct is being measured across NHW, Latinx and Black individuals. Scale refinement, such as abbreviating SCD measures to include only items that perform well across all groups, may minimize biases and facilitate cross‐cultural comparison in preclinical AD research.
Physical activity engagement, memory function, and hippocampal volume in adults with down syndrome
Authors: Peven Jamie C , Handen Benjamin L. , Laymon Charles M , Fleming Victoria L , Piro‐Gambetti Brianna , Christian Bradley T. , Klunk William E , Cohen Ann D. , Hartley Sigan L 
Published: June 2023

AbstractBackgroundHigher engagement in moderate‐to‐vigorous physical activity (PA) is related to better cognitive functioning in neurotypical adults; however, little is known about the impact of PA on cognitive aging in adults with Down syndrome (DS). Individuals with DS have three copies of the chromosome 21, which includes the gene for the amyloid precursor protein, resulting in an increased risk for/earlier onset of Alzheimer’s disease. The goal of this study was to understand the relationship between moderate PA engagement and memory functioning in non‐demented adults with DS.MethodAdults with DS participated in an ancillary Lifestyle study linked to the Alzheimer’s Biomarkers Consortium for Down Syndrome (N = 54). Participants completed the Cued Recall Test (CRT) to measure episodic memory. The total free + cued CRT score was used in analyses. Participants wore a wrist‐worn accelerometer (GT9X) for 7 days to measure PA. Moderate PA was classified using the Freedson cutpoints in ActiLife software and was defined as the daily average minutes. Structural MRI data were acquired within 18 months of actigraphy/cognitive data collection. Hippocampal volume was extracted using Freesurfer v5.3. Associations between moderate PA engagement, memory, and hippocampal volume were evaluated with hierarchical linear regressions controlling for relevant covariates (age, body mass index (BMI), intracranial volume (ICV)).ResultParticipants were 37.81 years old (SD = 8.15 years) and 55.6% female. They spent ∼10.67% of their time engaged in moderate PA (SD = 7.48%). Participants recalled an average of 32.09 items on the CRT (SD = 6.09, range = 7‐36). After controlling for age and BMI, greater moderate PA engagement was associated with higher total recall score (b = 0.279, p = 0.044). After controlling for age and ICV, moderate PA engagement was not significantly associated with hippocampal volume (p = 0.48). Greater hippocampal volume was associated with higher total recall score after controlling for age and ICV (b = 0.496, p = 0.004).ConclusionA higher proportion of time spent engaging in moderate PA was related to better memory function in adults with DS. While greater hippocampal volume was related to better memory performance, it was not associated with moderate PA engagement. Greater moderate PA engagement may be a promising lifestyle behavior to preserve memory in aging adults with DS.
Federated Deep Learning for Detecting Alzheimer’s Disease in Multi‐Cohort Brain MRI
Authors: Stripelis Dimitris , Dhinagar Nikhil J , Romero Rafael V. Sanchez , Thomopoulos Sophia I , Thompson Paul M , Ambite Jose Luis 
Published: June 2023

AbstractBackgroundDeep learning shows promise in detecting Alzheimer’s Disease (AD) based on brain MRI1. Models trained on limited samples may fail when applied to new datasets (other cohorts, scanners), but privacy concerns make it difficult to centralize large amounts of data2. To address this, we developed a general federated learning architecture3, which we applied to train a 3D convolutional neural network (CNN) to detect AD from T1‐weighted brain MRI data from the three independently collected phases of the Alzheimer’s Disease Neuroimaging Initiative (ADNI).MethodWe trained a 3D‐CNN over a federated learning environment of 3 sites corresponding to each ADNI phases (ADNI1, ADNI2/GO, ADNI3). No subject data are shared across sites, only model parameters, thus satisfying privacy/regulatory requirements. Each site trains the model on its own local dataset for several epochs and shares only the locally‐trained parameters with a central server. The server aggregates the parameters and computes the global model, which is sent back to each learner, and the process repeats.We trained each learner for 4 local epochs in between federation rounds for a total number of 25 rounds. We divided each ADNI dataset into training, validation and test sets with distribution 70/20/10% (Figure 1). We also compared the performance of training our CNN models with random initialization versus using pre‐trained weights from a UK Biobank MRI sex classification task.ResultTable 2 shows the accuracy, precision, recall and F1 scores for our federated CNN, compared to a centralized model that was trained over all ADNI data. All models achieved excellent performance for classifying AD (96‐98%). Overall, the federated model with pretrained weights performed best. Pre‐training boosted accuracy for the federated, but not the centralized, case.ConclusionFederated learning achieves high performance in AD prediction from MRI and promises new applications in neuroimaging.[1] Lu et al. A Practical Alzheimer Disease Classifier via Brain Imaging‐Based Deep Learning on 85,721 Samples. bioRxiv 2020_08_18_256594. 2021[2] Ming et al. COINSTAC: Decentralizing the future of brain imaging analysis. F1000Research 6:1512, 2017.[3] Stripelis et al. Scaling Neuroscience Research using Federated Learning. ISBI 2021.
Feasibility of frequent self‐administered mobile cognitive assessments in adults with and without cognitive impairment
Authors: Christianson Kieffer , Prabhu Meha , Popp Zachary T , Rahman Md Salman , Drane James , Lee Marissa , Lathan Corinna , Lin Honghuang , Au Rhoda , Sunderaraman Preeti , Hwang Phillip H 
Published: June 2023

AbstractBackgroundTraditional paper and pencil approaches measure cognitive performance at one time point. Early indications of cognitive impairment often appear irregularly, suggesting diagnoses derived from a single session can fail to characterize normal fluctuations and lead to inaccurate conclusions. Mobile applications enable brief cognitive assessments to be completed frequently and remotely. However, the feasibility of older adults adhering to a schedule of repeated self‐administered cognitive assessments is unclear. We tested the repeated use of a mobile assessment among older adults as a potential method for collecting cognitive performance data to detect early signs of cognitive impairment.MethodThe current study included 33 participants (n = 23 cognitively unimpaired, n = 10 cognitively impaired) recruited from the Boston University Alzheimer’s Disease Center. A remote practice session using the Digital Automated Neurobehavioral Assessment (DANA) mobile application was completed prior to beginning an assessment schedule consisting of three assessments within the first week (segment 1) and one assessment per week during the following three weeks (segment 2). Feasibility was measured via schedule adherence across segments, stratified by age and cognitive status. Cognitive efficiency, a derived measure of speed and accuracy, was compared for the Procedural Reaction Time (PRT) task at the first assessment and longitudinally across segments 1 and 2. Demographics including age, sex, race, education, and mobile device type were also collected (Table 1).ResultAcross all segments, 90.9% of participants adhered to the assessment schedule with a ±1 day grace period applied. Adherence was similar between each segment, and by cognitive status and age group (Table 2). No significant differences in cognitive efficiency were observed on the PRT task between cognitive groups (Figures 1‐2, Table 3).ConclusionOur findings suggest that older adults, including those with cognitive impairment, are capable of completing self‐administered remote smartphone assessments at a high‐frequency. Traditional analytic methods failed to differentiate cognitive groups based on mean cognitive performance; however, between‐group comparisons could mask within‐person fluctuations characteristic of cognitive impairment. Therefore, further study with a longer longitudinal assessment period and larger sample size will be required to determine whether more advanced analyses can distinguish impairment status based on cognitive performance patterns.