bluuebunny/crossref_metadata_2025_split huggingface dataset:
Within‐network Functional Connectivity of Intrinsic Networks in SuperAgers
Authors: Diamond Bram Ryder , Martersteck Adam , Sridhar Jaiashre , Wood Jessica , Gill Nathan Pruneau , Weintraub Sandra , Mesulam Marsel , Rogalski Emily J 
Published: December 2023

AbstractBackgroundMemory decline in late life is a hallmark of aging, yet SuperAgers are individuals age 80 or older with episodic memory performance at least as good as cognitively average 50‐to‐60‐year‐olds. Neuroimaging analyses have found SuperAgers to have thicker cortices and slower atrophy rates compared to similarly aged controls (Controls). This project extended the anatomical findings by examining the functional integrity of seven intrinsic networks spanning the entire cortex and two memory‐specific systems identified from task‐based and resting state studies.MethodsWithin‐network functional connectivity (FC) of seven large‐scale networks and two memory systems were compared between 25 SuperAgers and 16 Controls using resting state functional MRI (rs‐fMRI) and T1‐weighted structural imaging from a single visit. Classification of SuperAgers/Controls was determined based on measures of episodic memory, executive functioning, verbal fluency, and picture naming (Harrison et al., 2012). Inclusion criteria required stable cognitive status across two visits. In the first analysis, we included all seven networks from a common resting‐state atlas (Schaefer et al., 2018). Within‐network FCs for all networks were defined as the average FCs between all within‐network regions (Figure 1A). In the second analysis, we investigated two memory‐specific networks: the anterior temporal (AT) and posterior medial (PM) systems. AT and PM cortical maps (Figure 1B) were identified from prior research (de Flores et al., 2022). Within‐network FC of the AT and PM systems were defined as the FC between AT‐to‐anterior‐hippocampus and PM‐to‐posterior‐hippocampus, respectively. For both analyses, FCs were compared across groups using two‐sample independent t‐tests and corrected for multiple comparisons (Benjamini‐Hochberg false discovery rate).ResultsWe found no significant between‐group differences in demographic characteristics including age, sex, and education. Performance on neuropsychological measures was significantly different for episodic memory, verbal fluency, and picture naming. Group differences in within‐network FCs for large‐scale networks and memory systems were nonsignificant.ConclusionAt the group‐level, within‐network FC of large‐scale networks and memory‐specific systems were not a primary differentiator between cognitively average aging and SuperAging phenotypes. Person‐specific functional connectome approaches may be important for understanding network organizational features of SuperAgers.
Selective vulnerability of subcortical nuclei in atypical Alzheimer’s Disease
Authors: Craig Miranda C , Grinberg Lea T. , Ehrenberg Alexander J. , Li Song Hua , Seeley William W. , Satpati Abhijit ,   Memory and Aging Center
Published: December 2023

AbstractBackgroundRecent evidence suggests that sleep‐wake dysregulation in Alzheimer’s disease (AD) is an early event and parallels the tau accumulation in the arousal system spefically noted in the locus coeruleus (LC) as well as other subcortical nuclei as the Pedunculopontine Tegmental Nucleus (PPTg), Laterodorsal Tegmental Nucleus (LDTg), and the Dorsal Raphe (DR). We have previously demonstrated that the pattern of sleep‐wake dysfunction is different among AD clinical variants. This existence of specific sleep‐wake profiles suggest differential degeneration patterns of the arousal system within differing AD presentations. This study continues this investigation at the cellular/molecular level to uncover the selective vulnerability of subcortical structures to AD specific tau accumulation. That remains elusive to‐date.MethodHuman postmortem brainstem samples were sourced from the Neurodegenerative Disease Brain Bank (NDBB) at UCSF. 24 subjects were selected based on primary diagnosis, including 6 amnestic AD, 17 non‐amnestic AD (5 EOAD, 3 PCA, 5 lvPPA, 4 CBS) and 1 control. Paraffin blocks containing the areas of interest were cut into 8‐lm‐thick sections that underwent double‐labeled immunohistochemistry (IHC) and brightfield imaging. The images were then overlayed to created mutlidimentaional maps and neurons were classified in 4 subgroups: (1) positive for the neurotransmitter of interest (NEU‐positive), (2) bearing hyperphosphorylated tau neuronal cytoplasmic inclusions (tau‐positive), (3) positive for the neurotransmitter of interest and bearing tau‐positive (NEU‐positive/tau‐positive colocalized neurons), and (4) negative for NEU and tau.ResultIn the LC, lvPPA had the highest had the highest percentage of tau postive neurons (40%), followed by amnestic AD (31.1%), PCA (29.3%), EOAD (27.1%), and finally CBS (24.3%). EOAD had the highest percentage of colocalized neurons (34.7%) as well as LOAD (31%) compared to PCA (0.02%), lvPPA (0.18%), CBS (0.09%).ConclusionMultidimensional quantitative analysis suggests differences in vulnerabliblity of sleep‐wake subcortical nuclei to AD, EOAD, PCA, lvPPA, and CBS. For instance the neuronal loss in EOAD was so extreme in the LC that few neurons remained to develop aggregates whereas in PCA, CBS, and lvPPA the high percentage of tau accumulation presents a neuropathological framework underlying atypical clinical manifestations and possible insight into therapeutic targets.
Reorganization of functional brain networks in healthy aging and mild cognitive impairment during the visual motion discrimination task
Authors: Požar Rok , Giordani Bruno , Kavcic Voyko 
Published: December 2023

AbstractBackgroundPreviously, we reported that there was significant decrease in spectral power for eyes‐closed resting state EEG (rsEEG) immediately following a visual motion discrimination task as compared to before task engagement in older adults – the so‐called Task Aftereffect (TA). Further, we have also shown that rsEEG TA differentiated between older adults with mild cognitive impairment (MCI) and healthy controls (HC), with the MCI participants showing more integrated topology in post‐ as compared to pre‐cognitive task rsEEG. We now report EEG topological changes during task performance with regards to: changes in functional network organization from the beginning to the end of the task and evidence for significant differences in topology between MCI patients and HC.MethodWe studied a sample of community dwelling African American older participants over age 60 years, with consensus‐based diagnosis for 51 HC and 39 MCI. EEG epochs extracted for analysis were two‐second epochs before the motion onset of each stimulus at the beginning, middle, and end of the full task. Functional connectivity between channels was assessed by the phase lag index. Minimum spanning tree (MST) was used to characterize network topology.ResultTime effect was confirmed, as network organization in all participants significantly improved with task progression. On average, brain topology became more integrated with higher leaf fraction and smaller average eccentricity. Moreover, our results show differences between groups in MST parameters over the whole task. Most differences were observed at the earliest timepoint at lower frequency range (theta and lower alpha) where average eccentricity was significantly decreased. Leaf fraction and maximal betweenness centrality were significantly increased in the MCI group as compared to HC.ConclusionThe change in network organization suggests that MCI require greater network integration across brain areas before repeated motion onset presentations. This could reflect compensatory brain mechanisms, especially as both groups did not show significant differences in task performance. These findings enhance our understanding of the functional brain network organization immediately prior to repeated responses in a visual perceptual task in MCI compared to HC. This emphasizes the potential for using graph‐theoretic measures as a valid tool for the early detection of cognitive impairment.
Mood disturbance and tau pathology in older women at risk for Alzheimer’s disease
Authors: Dratva Melanie A , Wang Xin , Lui Kitty K , Heyworth Nadine , Sundermann Erin E. , Banks Sarah J 
Published: December 2023

AbstractBackgroundNeuropsychiatric symptoms, particularly depression, have been associated with Alzheimer’s disease (AD) biomarkers in individuals on the AD continuum. Although mood changes are a clinical manifestation of AD, research examining how the spectrum of common mood symptoms relates to AD biomarkers is limited. We examined the association between mood disturbance and tau pathology measured with positron emission tomography (PET) in older women at risk for AD and particular mood domains driving any associations.MethodParticipants included 18 women (Mage = 71.5, SDage = 3.3 years) with higher AD genetic risk (polygenetic hazard score ≥50th percentile) and mild impairment on the Montreal Cognitive Assessment who had completed the Profile of Mood States (POMS) questionnaire and a PET Scan as part of a larger research study. Those with formal psychiatric or neurodegenerative diagnoses were excluded. The POMS assessed six mood subcategories. The Total Mood Disturbance (TMD) was calculated as the sum of Tension, Depression, Anger, Fatigue, and Confusion subscores, minus the Vigor subscore. Tau standardized uptake value ratio (SUVR) were calculated for each Braak region of interest (ROI) (i.e., Braak 1‐2: transentorhinal stage, Braak 3‐4: limbic stage, and Braak 5‐6: isocortical stage). Linear regression models were conducted with TMD as the predictor and each Braak‐derived ROI as the outcome while controlling for age. Significant relationships were further probed by repeating analyses with each POMS subcategory as the predictor.ResultHigher POMS TMD scores significantly predicted higher tau‐PET SUVRs in Braak 1‐2 (β = 0.73, p<0.01), Braak 3‐4 (β = 0.88, p<0.001), and Braak 5‐6 (β = 0.72, p<0.01) (Figure 1). Further analysis showed that the Tension subscore significantly predicted tau‐PET in all three regions (Figure 2) and Confusion significantly predicted tau‐PET in Braak 1‐2 and Braak 5‐6 (Figure 3).ConclusionMood disturbance was consistently associated with higher tau across all Braak regions in older women with higher AD risk. These relationships were strongest when reflecting the combination of mood symptoms, but tension may have a stronger influence than other subcategories. Longitudinal research should be done to assess the directionality of this relationship and to explore whether treatments for mood symptoms may have protective effects on Alzheimer’s pathogenesis before diagnosis.
Exploring brain‐derived exosomal miRNA as a novel blood‐based biomarker for Alzheimer’s disease
Authors: Jiang Yanfeng , Li Jincheng , Sun Jian , Wang Yingzhe , Li Jialin , Cui Mei 
Published: December 2023

AbstractBackgroundBiomarkers of Alzheimer’s disease (AD) currently rely on invasive and time‐consuming methods such as cerebrospinal fluid analysis and neuroimaging. Non‐invasive blood‐based biomarkers are needed for early diagnosis and risk prediction. Brain‐derived exosomes are promising candidates for blood biomarkers, as they can cross the blood‐brain barrier and enter the bloodstream, and exosomal miRNAs could serve as potential biomarkers for identifying patients with AD and screening individuals at high risk. Here, we conducted a hospital‐based case‐control study to investigate exosomal miRNAs as biomarkers for AD.MethodExosomes were isolated from plasma samples of AD patients (n = 18) and healthy controls (HC; n = 18), and exosomal miRNAs were analyzed using high‐throughput sequencing. AD‐differentially expressed miRNAs were identified and their associations with Mini‐Mental State Examination (MMSE) and amyloid positron emission tomography with standardized uptake value ratio (PET‐SUVR) were evaluated by Spearman’s correlation analyses. Logistic regression was used to select miRNA biomarkers and construct a classifier to discriminate AD from HC.ResultWe identified 65 miRNAs (47 downregulated and 18 upregulated) that were differentially expressed in AD, and 20 of which were significantly associated with both MMSE and PET‐SUVR (all false discovery rate adjusted P <0.05) (Figure 1). We selected hsa‐miR‐33a‐5p and hsa‐miR‐342‐3p as potential biomarkers to identify AD from HC, with an area under the curve (AUC) reaching 100% in the internal validation, whereas a model consisting of gender, age, and apolipoprotein E ε4 only had an AUC of 0.63. Both miRNAs were significantly correlated with MMSE and PET‐SUVR after adjusting age and sex. The miRNA target analysis revealed that the target genes of hsa‐miR‐33a‐5p and hsa‐miR‐342‐3p were involved in multiple processes related to the nervous system, including axon guidance, neurotransmitter receptors and postsynaptic signal transmission, and dopaminergic synapse, among others (Figure 2).ConclusionOur results suggest that differentially expressed miRNAs are significantly correlated with the pathological process of AD and cognitive function, and that blood exosomal miRNAs have the potential to detect AD with high sensitivity and specificity. These findings may provide a basis for the development of non‐invasive blood‐based biomarkers for AD diagnosis and risk prediction.
Higher pupillary dilation during phasic transcutaneous vagus nerve stimulation (taVNS) and its relevance to the noradrenergic system of the locus coeruleus (LC‐NE)
Authors: Ludwig Mareike , Düzel Emrah , Betts Matthew , Hämmerer Dorothea 
Published: December 2023

AbstractBackgroundTranscutaneous auricular vagus nerve stimulation (taVNS) may affect the locus coeruleus‐norepinephrine system (LC‐NE system) via modulation of the vagus nerve. The LC is our main source of NE in the brainstem and declines with age and is a potential epicentre of tau pathology in Alzheimer´s diseases (AD). Besides the excessive NE release leading to the formation of amyloid plaques, the severity and duration of AD are also related to LC cell death, decreased cortical NE levels, and accelerated tau spread. An increase in NE release in the LC accompanied by reduced inflammatory markers was achieved in animal models using phasic invasive (i)VNS. In humans, phasic taVNS leads to an increase in pupil dilation, which can serve as a proxy for LC activation. Therefore, taVNS may have the potential to increase NE release and thereby impact LC function and related links to tau pathology.MethodTwenty‐four subjects (50% women; 22.96 +/‐ 2.24 years) received time‐synchronous phasic taVNS during a fixation task (∼ 18min) while recording pupillary dilation changes, which allowed systematic testing of phasic taVNS (3 sec) with low (3 mA) and high (5 mA) intensities and low (10 Hz) and high (25 Hz) frequencies at constant pulse width (250 µs).ResultDuring on stimulation pupil dilation was higher during real (0.18±0.03) as compared to sham (0.09±0.03) stimulation (χ2 = 10.29, p = 0.001) (see Fig. 1). Likewise higher (0.21±0.03) compared to lower (0.06±0.03) intensity (χ2 = 23.02, p < 0.001) led to higher pupil dilation, while there was no significant difference between higher (0.13±0.03) and lower (0.14±0.03) frequencies (χ2 = 0.11, p = 0.74).ConclusionSystematic testing of different frequencies and intensities, while keeping the pulse width constant, showed that phasic real taVNS and higher intensity lead to higher pupillary dilation, which is consistent with animal and human studies. Furthermore, the results suggest that the influence of intensity on pupil dilation might be stronger than that of frequency. The increased pupillary dilation during phasic taVNS could be an indication of LC involvement, however, more detailed studies e.g., functional magnetic resonance imaging (fMRI) during taVNS are needed to confirm this association.
Bridging the Gap: The Global CEOi Collaborative Workgroup for Adoption of Alzheimer’s disease Blood‐Based Biomarkers in Clinical Practice
Authors: Udeh‐Momoh Chi T. , Mielke Michelle M. , Schindler Suzanne E. , Hansson Oskar , Khachaturian Ara S , Weiss Joan 
Published: December 2023

AbstractBackgroundThe United States healthcare system is not prepared for disease modifying therapies (DMTs) of Alzheimer’s disease (AD). Eligibility for DMTs will be contingent on confirmation of AD pathology using biomarkers. Major difficulties are expected in providing PET and CSF testing in the population. Blood‐based biomarkers (BBMs) could greatly reduce the burden of biomarker testing by triaging patients for subsequent confirmation of AD brain pathology in specialty and primary care. The Global CEO Initiative on Alzheimer’s Disease (CEOi) has convened a Workgroup (WG) to develop guidelines and recommendations for the widespread adoption of BBMs in clinical practice to support feasible, timely, and accurate AD dementia diagnosis among symptomatic patients.MethodOver 80 stakeholders spanning academia, healthcare, advocacy, industry, non‐profit, and venture capital have joined the Workgroup, ensuring a wide range of expertise. The Workgroup consists of three active workstreams. Workstream A has defined use cases for symptomatic patients and minimum viable test specifications; Workstream B will provide recommendations on implementing BBMs in clinical practice; and Workstream C serves to educate practicing clinicians on outputs garnered from Workstreams A and B. Jointly led by 2 CEOi‐nominated experts, each Workstream has a core team, consisting of 6‐8 experts, and meet on a weekly basis to drive the WG objectives. General Workstream meetings are held periodically for the core team to present plans and outputs for full group input and feedback.ResultWorkgroup members have conceptualized the clinical care pathway for operationalizing BBM across specialty and primary care settings; and will present a feasible framework for real‐world implementation of BBMs. We have defined appropriate‐use criteria for BBMs in clinical settings, including test specifications, and will provide actionable recommendations to accelerate adoption in clinical care, while critically considering implementation challenges such as test result interpretation, and value to existing diagnostic pathway. Strategies to facilitate optimal implementation including novel educational tools will be disseminated.ConclusionThe Workgroup seeks rapid, efficient and effective utility of BBMs in clinical practice that is scientifically valid, and patient focused. Successful adoption will facilitate effectual triaging of patients for comprehensive diagnostic evaluations, streamlining patient access to appropriate and integrated care.
Employing transfer learning to optimize deep learning models for Alzheimer’s disease classification using two tau PET tracers
Authors: Bauer‐Negrini Guilherme , Povala Guilherme , Lucchese Luisa Vieira , Lussier Firoza Z , Bellaver Bruna , Ferreira Pamela C.L. , Aguzzoli Cristiano Schaffer , Soares Carolina , Zalzale Hussein , Rohden Francieli , Abbas Sarah , Ferrari‐Souza João Pedro , Leffa Douglas Teixeira , Lemaire Peter Charles , Cabrera Arlec , Tissot Cécile , Therriault Joseph , Bezgin Gleb , Servaes Stijn , Stevenson Jenna , Rahmouni Nesrine , Gauthier Serge , Karikari Thomas K , Zatt Bruno , Rosa‐Neto Pedro , Pascoal Tharick A. 
Published: December 2023

AbstractBackgroundDeep learning models, particularly convolutional neural networks (CNNs), have shown promise in Alzheimer’s disease (AD) classification using tau PET data. However, limited sample sizes and unharmonized tau tracers present challenges to developing an agnostic tau tracer tool to predict AD using machine learning. Transfer learning, which leverages pre‐trained models for related tasks, may address these issues. Here we evaluate the effectiveness of transfer learning in optimizing 3D CNNs for AD classification with distinct cohorts and tau tracers.MethodWe used tau PET images from ADNI ([18F]Flortaucipir, n = 437) and TRIAD ([18F]MK‐6240, n = 423) cohorts, categorizing patients into CU (cognitively unimpaired) and CI (cognitively impaired). Standardized uptake value ratios (SUVR) were used for tau PET data. Separate 3D CNNs were trained for each tracer, with SUVR volumes as input and diagnosis as output. For transfer learning, we trained a model on [18F]Flortaucipir data with a reduced learning rate, using a pre‐trained model from [18F]MK‐6240. Models underwent 5‐fold cross‐validation, and metrics were computed as the average of validation metrics across folds. To avoid data leakage, images from the same subject were assigned to the same fold.ResultThe model trained on [18F]MK‐6240 tracer demonstrated higher classification performance than [18F]Flortaucipir (AUC = 0.84 vs 0.67; Figure 1. F1‐score = 79.77% vs 64.66%; Table 1). To enhance the classification performance of [18F]Flortaucipir model, we employed a transfer learning approach by leveraging the model pre‐trained with [18F]MK‐6240. With this approach, we observed a slight improvement in all classification metrics compared to the model trained solely on [18F]Flortaucipir data (AUC = 0.71 vs 0.67; Figure 1. Accuracy = 71.39% vs 67.72, F1‐score = 67.97% vs 64.66%; Table 1).ConclusionThis finding highlights the value of transfer learning in optimizing deep learning models for Alzheimer’s disease classification, particularly when handling tau tracers with varying performance levels. Our results are consistent with previous on transfer learning’s effectiveness in this context. These preliminary findings indicate that applying this technique to larger datasets of tau tracers may further enhance model performance, potentially leading to the development of a tau tracer‐agnostic tool that overcomes the need of tracer harmonization for predicting dementia.