bluuebunny/crossref_metadata_2025_split huggingface dataset:
Multimodal characterization of Alzheimer's Disease using speech, eye movement, and handwriting
Authors: Moro‐Velazquez Laureano , Favaro Anna , Thebaud Thomas , Wang Yuzhe , Butala Ankur , Oh Esther S , Dehak Najim 
Published: January 2025

AbstractBackgroundPhenotyping Alzheimer’s Disease (AD) can be crucial to providing personalized treatment. Several studies have analyzed the use of digital biomarkers to characterize a subject’s behavior, usually obtained from a single modality, such as speech. However, combining several modalities in a single study has not been deeply studied. We aim to characterize people with AD and differentiate them from a cognitively normal control group (CN), employing metrics obtained through from three different modalities: speech, handwriting, and eye movement.MethodWe collected data from a group of 11 participants in the early stages of AD or with, mild cognitive impairment due to AD, and an age‐matched CN group of 13 participants. Each participant read out loud the Rainbow Passage, while their speech was recorded with a headset microphone and their eye movement with a high precision eye‐tracker, simultaneously. Then, the participants were asked to draw a spiral on a digital pad. We obtained 57 speech metrics, related to articulation, prosody, and hesitation; 90 handwriting metrics, characterizing drawing entropy, writing speed, and acceleration; 32 eye movement metrics related to saccade, fixation, and blink statistics. Several multilayer perceptron (MLP) classifiers were trained per modality and using all modalities together, following a cross‐validation strategy. SHAP values were calculated to rank metric impact in the differentiation between AD and CN.ResultCross‐validation accuracy in the binary classification of AD/CN ranged between 75.3% and 77.4% for single modalities, whereas the combination of the three modalities provided a 77.9% accuracy. The drawing velocity skewness metric provided the highest area under the receiver operating characteristic curve (AUC) from all the handwriting features (0.83), whereas the pause variability was the speech metric with the highest AUC (0.79). The number of fixations provided the best differentiation between groups in the eye movement modality, with AUC=0.69.ConclusionCombining the three proposed modalities provides a higher differentiation power than using each modality separately. The combination can provide a more insightful and complementary characterization of patients. Handwriting metrics have the most impact on the prediction of the multimodal MLP model, according to the SHAP values on the multimodal classifier.
Increased Brain Conductivities in Patients with Alzheimer’s Disease
Authors: Jahng Geon‐Ho , Rhee Hak Young , Park Soonchan , Ryu Chang‐Woo 
Published: January 2025

AbstractBackgroundThe levels of conductivity in the extracellular and intracellular space of the brain are still unknown, especially in the brains of Alzheimer’s disease (AD) patients. The objectives of this study were to investigate how the extra‐neurite conductivity (EC) and intra‐neurite conductivity (IC) were reflected in AD patients compared with elderly CN people and patients with amnestic mild cognitive impairment (MCI) and to evaluate the association between EC and cognitive decline.MethodA total of 66 patients were included including 20 AD patients, 25 amnestic mild cognitive impairment (MCI) patients, and 21 cognitively normal (CN) old people. For the brain magnetic resonance electrical property tomography (MREPT) images, a multi‐echo turbo spin‐echo pulse sequence was used. High‐frequency conductivity (HFC) at the Larmor frequency was calculated and decomposed into extra‐ (EC) and intra‐neurite (IC) conductivities, respectively. Each parameter map was compared among the three participant groups using the full factorial design of one‐way analysis of covariance (ANCOVA). Furthermore, the association between the value of each map and either age or the MMSE score, with age as a covariate, was evaluated using the multiple regression analysis.ResultThe conductivities of both HFC and EC were increased in the AD group in the large brain areas. The IC was increased in AD compared with CN but decreased in AD compared with MCI. Compared with the CN group, all three conductivity indices were increased in the MCI group. EC was higher in the AD group in the hippocampus (p=0.009) and insular (p=0.020). Both HFC and EC were significantly negatively associated with the MMSE in the insular and MTG, but IC were significantly positively associated with the MMSE in the corpus callosum.ConclusionThe EC value was higher in patients with AD than CN elderly people and patients with amnestic MCI. The EC value decreased with increasing MMSE scores after adjusting for age in the insula and middle temporal gyrus. This study showed the possibility that the EC value might be used as an imaging biomarker for helping to monitor cognitive function.
Regional Amyloid Load Predicts Tau and Atrophy Dynamics
Authors: Chaggar Pavan , Vogel Jacob W. , Binette Alexa Pichet , Thompson Travis B , Strandberg Olof , Mattsson‐Carlgren Niklas , Karlsson Linda , Stomrud Erik , Jbabdi Saad , Magon Stefano , Klein Gregory , Hansson Oskar , Goriely Alain 
Published: January 2025

AbstractBackgroundA generative model of tau PET was applied to multiple cohorts across the Alzheimer's disease (AD) spectrum, revealing longitudinal changes in tau production and transport. A generalisation of the model accounts for amyloid, tau and neurodegeneration (ATN) interactions and accurately explains longitudinal ATN biomarker data, adding potential for region specific and individualized tracking of ATN biomarkers.MethodA model of tau spreading and production as measured through PET was developed and applied to longitudinal data from amyloid negative (A‐), amyloid positive tau negative (A+T‐) and amyloid positive tau positive (A+T+) cohorts from the Alzheimer’s disease neuroimaging iniative (ADNI; N = 159) and BioFINDER‐2 (BF2; N = 135) datasets. The model was extended to include ATN biomarkers and their interactions, explaining regional tau and atrophy dynamics as a by‐product of regional amyloid load. This model was applied to longitudinal amyloid PET, tau PET and atrophy data from A+T+ subjects in ADNI (N = 39).ResultThe tau PET model faithfully reproduces Braak staging (Figure 1a). In A+T‐ subjects (N = 39 ADNI; N = 18 BF2) inference reveals faster tau spreading through the brain network and slower tau production, compared to A+T+ subjects (N=57 ADNI; N = 54 BF2) who show a higher production rate with slower transport (Figure 1b). This implies greater movement of tau during early AD before an acceleration in production when participants have high levels of both amyloid and tau. This is supported by comparison with spatially shuffled null models (Figure 1c). The generalization to the dynamical ATN model shows that an amyloid‐induced acceleration in tau production explains regional variations in tau load (as measured through PET), Braak staging and downstream neurodegeneration (Figure 2). The ATN model calibrated to longitudinal data from ADNI can accurately describe regional longitudinal ATN biomarkers (Figure 3).ConclusionTau production and transport dynamics vary across the AD spectrum, characterised by an early period of fast tau spreading through the brain network followed by accelerated production of tau aggregates. A dynamical ATN model explains variations in tau and neurodegeneration through regional amyloid load and allows for accurate simulation of personalised ATN biomarkers.
Addressing the gaps of the functional neuroimaging in young APOEe4 carriers: A systematic review and Graph Theoretical analysis of brain connectivity
Authors: Kucikova Ludmila , Brass Adam , Xiong Xing , Zeng Jianmin , Ritchie Craig , Muniz‐Terrera Graciela , O'Brien John T , Su Li 
Published: January 2025

AbstractBackgroundIn the last decade, extensive research has emerged into understanding the impact of risk factors for Alzheimer’s Disease (AD) on brain function in pre‐symptomatic stages. Here, we focused on the apolipoprotein e4 (APOEe4) gene, the major genetic risk factor for sporadic AD, and its effect on brain function in early adulthood.MethodIn the first part of the study, we systematically reviewed the multimodal functional neuroimaging literature, exploring its relationship with cognition, and the potential effects of other variables including the demographics, other risk factors, and methodological and analytical choices. While the studies demonstrated consistent alterations of APOEe4 carriers in brain connectivity and activity; the results of fMRI studies covered mostly the differences in the directionality using standard connectivity and activity measures. In the second part of this study, we aimed to address this gap by using the graph theory analysis to explore the dynamic behaviour of the six resting‐state networks of interest in young APOEe4 carriers versus non‐carriers (n=129, aged 17‐22).ResultAverage Path Length and Closeness Centrality were consistently disrupted, pointing to network reorganisation in multiple resting‐state networks, albeit using different mechanisms.ConclusionThis study is the first to demonstrate the restructuring of multiple resting‐state networks in young adults modulated by the APOE genotype.
Linking White Matter Hyperintensities to Regional Cortical Thinning, Amyloid Deposition, and Synaptic Density Loss in Alzheimer's Disease
Authors: Zhang Junfang , Li Binyin 
Published: January 2025

AbstractBackgroundWhite matter hyperintensities (WMH) were reported to contribute to the thinning of regional cortex connected to WMH in cerebral small vessel disease. However, the relationship between WMH and regional changes in WMH‐connected cortex in Alzheimer’s disease (AD) remains unclear. The objective of this study is to investigate the association between WMH and regional cortical thickness, amyloid and tau deposition, and synaptic density changes in the WMH‐connected cortex.MethodIn the cross‐sectional analysis, we included 107 participants (59 AD, 27 mild cognitive impairment, and 21 cognitively normal controls) who tested positive for beta‐amyloid (Aβ) using amyloid‐PET. All the participants had T1, fluid‐attenuated inversion recovery (FLAIR), multi‐shell diffusion MRI, tau‐PET, and cognitive assessment. A subset (n=33) had synaptic vesicle glycoprotein 2 A (SV2A) PET to assess synaptic density. We segmented WMH on FLAIR images. The cortex connected to WMH was identified using probabilistic tractography. We measured the cortical thickness, amyloid and tau deposition, and synaptic density based on T1 images, amyloid‐PET, tau‐PET, and SV2A‐PET. A subset of participants (n=23) underwent a follow‐up multimodal MRI after 12 months. Changes in cortical thickness were measured using the longitudinal stream within FreeSurfer. (Figure 1)ResultWe found that WMH‐connected cortex showed significantly lower thickness and synaptic density than WMH‐unconnected cortex (all p‐corrected<0.001). Besides, higher levels of Aβ deposition in frontal (ratio = 1.045, p<0.001), temporal (ratio=1.045, p=0.002), and parietal (ratio=1.054, p<0.001) deep WMH (dWMH)‐connected cortex, and higher levels of tau deposition in parietal dWMH‐connected cortex (ratio=1.050, p<0.001) were found, compared to the corresponding WMH‐unconnected cortex. (Figure 2) After adjusting for age, education years, and volume of WMH, higher Aβ ratio in the total dWMH‐connected cortex was significantly related to lower cognitive score (linear regression: β=‐37.8, p‐corrected=0.040). Lastly, the cortical thickness of WMH‐connected cortex reduced more than WMH‐unconnected cortex over 12 months (all p‐corrected <0.001, Figure 3).ConclusionWe revealed that WMH are related to more severe regional cortical degeneration as measured by thickness, Aβ, tau, and synaptic density in the WMH‐connected cortex in AD. Our results suggest that WMH are likely to be associated with AD‐intrinsic processes of degeneration, in addition to vascular mechanism.
Analysis of Macular thickness and retinal nerve fiber layer by using of spectrum domain‐optical coherence tomography in patients with Alzheimer’s disease and amnestic mild cognitive impairment
Authors: Ku Bon D , Shin Kyung‐Hoon , Kim Do‐Gyun 
Published: January 2025

AbstractBackgroundSpectrum Domain‐Optical Coherence Tomography (SD‐OCT) is a non‐invasive technology that acquires cross‐sectional images of retinal structures allowing neural fundus integrity assessment. Macular thickness and retinal nerve fiber layer (RNFL) thickness measured by an SD‐OCT have been used as a indicator of Alzheimer's disease (AD) and amnestic mild cognitive impairment (aMCI). However which portion of retinal RNFL is the most sensitive area among normal control, aMCI and AD is not clear yet. The purpose of this study is to demonstrate that RNFL thickness is a useful indicator and which portion of reninal RNFL is the most sensitive area among normal control.MethodIn a cross‐sectional study we consecutively recruited 53 patients with AD, 58 with aMCI, and 54 normal controls. AD‐OCT was performed in all of them to measure circumpapillary macular thickness in the 9 sectors (fovea, temporal outer superior outer, nasal outer, inferior outer, temporal inner, superior inner, nasal inner, inferior inner). We made 4 RNFL quadrant area as following: superior (superior outer+superior inner), inferior (inferior outer+ inferior inner), nasal (nasal outer + nasal inner), temporal (temporal outer + temporal inner). We also evaluated the correlation of the RNFL thickness and MMSE score and disease duration of the patientsResultAverage macular thickness and 9 sectors of RFNL thickness were not significant among the group in our patients. In quadrant analysis, however superior quadrant RNFL thickness showed significant differences among groups (109.98±12.01 um, 106.83±10.05 um 101.25±11.90 um in normal control, aMCI and AD respectively, p<0.01). The RNFL thinning of the superior quadrant showed a significant correlation with MMSE score (r=0.555, p<0.01) and AD duration (r=‐0.528, p<0.01).ConclusionThe superior quadrant RNFL thickness is the most sensitive among the group and as well as we know this is not reported before. This finding could suggest superior quadrant retinal RNFL by SD‐OCT could be a useful marker of AD and a MCI for early detection and monitoring of disease progression. Also distinct correlation of RNFL thinning in superior quadrant and MMSE score and disease duration can imply that the severity of dementia can be inferred from RNFL thickness in superior quadrant.
Use of plasma p‐tau217 as a pre‐screening method for detecting amyloid‐PET positivity in cognitively unimpaired participants: A multicenter study
Authors: Salvadó Gemma , Janelidze Shorena , Therriault Joseph , Brum Wagner Scheeren , Binette Alexa Pichet , Stomrud Erik , Mattsson‐Carlgren Niklas , Palmqvist Sebastian , Benzinger Tammie L.S. , Gispert Juan Domingo , Blennow Kaj , Villeneuve Sylvia , Johnson Sterling C. , Rosa‐Neto Pedro , Schindler Suzanne E. , Suarez‐Calvet Marc , Ossenkoppele Rik , Hansson Oskar 
Published: January 2025

AbstractBackgroundRecent results from clinical trials in Alzheimer’s disease (AD) emphasize the importance of treating early‐stage disease. However, recruitment of preclinical AD participants is difficult due to the lack of symptoms, and the costs and/or invasiveness of established CSF and PET tests. We aimed to investigate whether plasma p‐tau217 could be used to pre‐screen cognitively unimpaired (CU) potential participants for amyloid‐β (Aβ) pathology to improve the efficiency of clinical trial recruitment.MethodWe included 1,471 CU participants from eight cohorts (Table 1) with available plasma p‐tau217, Aβ CSF biomarkers and Aβ‐PET status (served as standard‐of‐truth). Plasma p‐tau217 concentrations were z‐scored based on Aβ‐negative participants and harmonized across cohorts using neuroCombat. Cut‐offs for plasma p‐tau217 were derived in the BioFINDER‐1 cohort (n=104) based on different specificity levels (90%, 95% and 97.5%) to maximize positive predictive values (PPV). These cut‐offs were used in the other cohorts to assess the accuracy of plasma p‐tau217 for detecting Aβ‐PET positivity. Next, within plasma positive participants only, we evaluated the value of dichotomized Aβ CSF (based on established clinical thresholds) on assessing Aβ‐PET positivity. All models included age and APOE‐ε4 carriership as covariates.Result334 (24.4%) of participants were Aβ‐PET positive. Using the a priori defined cutoffs, plasma p‐tau217 categorization resulted in high PPVs (72.9%‐81.2%), negative predictive values (NPVs, 82.5%‐86.2%), and accuracy (82.4%‐83.8%), with an overall rate of positivity between 10.9%‐18.1% (Figure 1). When applying CSF biomarkers to the plasma positive participants in a second step, the PPVs increased up to 90.8%‐95.3%, with NPVs ranging between 82.8%‐86.7% and accuracies between 84.0%‐87.3%, with a slight decrease in the proportion of overall positive cases (9.3%‐14.3% from the original sample) given that the CSF positivity in the plasma positive participants ranged between 79.4%‐85.2% (Figure 2).ConclusionPlasma p‐tau217 can identify Aβ‐PET positive CU individuals with PPVs reaching 81%, which can be further improved to PPVs of up to 95% with a subsequent CSF measurement. Plasma p‐tau217 could be used, either as stand‐alone biomarker, or as an initial step before CSF biomarkers (reducing their need by ∼80‐90%), for pre‐screening in clinical trials of preclinical AD depending on the certainty needed for Aβ‐PET positivity.
White Matter Changes in Alzheimer’s and Treated Huntington’s Disease Mouse Models Detected by Diffusion MRI
Authors: Liu Andy , Simmons Danielle , Alvarez Connor , Wang Yi , Spincemaille Pascal , Longo Frank M. , Zeineh Michael , Georgiadis Marios 
Published: January 2025

AbstractBackgroundMyelin integrity is central to healthy brains and is increasingly shown to be compromised in neurodegenerative diseases. Diffusion‐ and susceptibility‐based MRI metrics can detect myelin changes. We show advanced diffusion and susceptibility metrics can detect degenerative myelin changes in ex vivo AD and HD mouse models.MethodAD mice included 2 groups: 8‐month‐old male hAPP (Lond/Swe mutations, n=4) and wild‐type (WT, n=4). HD mice included 3 groups: 12‐week‐old vehicle‐treated controls (WT, n=4), R6/2 HD treated for 7 weeks with vehicle (HD, n=4) or LM11A‐31 (HD‐treated, n=4). After perfusion‐fixation and brain extraction, 4‐shell diffusion (b=1,2,5,10ms/μm2, 100 directions) and multi‐echo gradient echo (MGE) MRI (10 echoes, GRE=4‐40ms) were performed on a Bruker 7T scanner.We used DESIGNER and MEDI to generate diffusion‐ and susceptibility‐based myelin‐sensitive metrics, respectively (diffusion: mean/radial/axial diffusivities ‐MD/RD/AD, kurtoses ‐MK/RK/AK, axonal water fraction ‐AWF; susceptibility: R2*, quantitative susceptibility mapping‐QSM) (Figure 1A). Datasets were registered to the Allen Atlas through linear/non‐linear transformations using FSL flirt/ANTs (Figure 1B).Median values were derived for two Allen Atlas white matter regions‐of‐interest ‐ROIs (corpus callosum and fornix). For AD, scan timing effects were included in a 2‐way ANOVA analysis. For HD, two‐sample t‐tests were performed between the 3 groups.For histological validation, a second set of R6/2 HD brains (WT, HD, HD‐treated n=10‐13 mice/group) were myelin basic protein (MBP)‐stained, and the percent area of striatum immunostaining determined by ImageJ thresholding.ResultsAD mice showed increased RD and reduced AWF and RK compared to controls in both ROIs, suggesting a loss of myelinated axons. Susceptibility‐based metrics show no clear pattern.HD mice showed an increasing trend in AWF and RK and decreases in RD in both ROIs vs WT, suggesting increased myelination. Interestingly, LM11A‐31‐treated HD mice showed a significant reversal in RD and AWF. Histologically, myelin immunostaining was increased in the striatum of HD mice, reversed by LM11A‐31.ConclusionDiffusion MRI myelin‐sensitive metrics detected possibly compromised myelin in AD mouse white matter (corpus callosum and fornix). In HD mice, both diffusion MRI and histology showed a trend towards increased myelin, which was reversed by LM11A‐31 treatment.
Gut microbiome features associate with cognitive scores in individuals at risk for Alzheimer’s disease
Authors: Kang Jea Woo , Khatib Lora A. , Dilmore Amanda H. , Heston Margo B. , Ulland Tyler K. , Johnson Sterling C. , Asthana Sanjay , Carlsson Cynthia M. , Chin Nathaniel A. , Jonaitis Erin M. , Blennow Kaj , Zetterberg Henrik , Knight Rob , Kaddurah‐Daouk Rima , Rey Federico E. , Bendlin Barbara B. ,   Alzheimer's Gut Microbiome Project Consortium (AGMP)
Published: January 2025

AbstractBackgroundGut microbiome features have been linked with many diseases including Alzheimer’s disease (AD). Evidence suggests that the gut microbiota may impact cognition of AD patients. We explored the association of gut microbiota and three PACC3 cognitive scores in individuals at risk for AD.MethodParticipants from the Microbiome in AD Risk Study with clinical diagnosis (N=239) and amyloid status (N=93) underwent cognitive testing and collection of microbiome samples. Three different types (Animal‐Naming Test PACC3 (AN), Category Fluency Test PACC3 (CFL), and Trail‐Making Test B PACC3 (TRLB)) of PACC scores (PACC3) were utilized followed by z‐score transformation. Fecal samples were analyzed by 16S sequencing and a taxonomic count table was generated. Kruskal‐Wallis test with Dunn's post‐hoc test was performed to find associations between PACC3 scores and group differences in diagnosis or amyloid status. Negative binomial regression (BIRDMAn, R) was performed to identify associations between microbial genus abundance and AD diagnosis, amyloid status, and PACC3 scores.ResultSeveral genera were differentially abundant between diagnosis groups (Normal vs. AD) and across amyloid status (A− vs. A+) (Figure 1). For example, in AD genera Clostridium_A, Bacteroides_H, and Blautia_A_141781 were more abundant, and genera Turicibacter and Prevotella were less abundant. Similarly in A+, Clostridium_A was more abundant and Prevotella was less abundant. All PACC3 scores significantly differed across diagnosis groups; however, no associations were found between PACC3 scores and amyloid status (Figure 2). Abundance of Blautia_A_141781, a genus previously linked to AD, was negatively associated with all three PACC3 scores in the A+ group (AN: β=−0.2, p=0.02; CFL: β=−0.3, p<0.001; TRLB: β=−0.2, p=0.04). Abundance of Prevotella was positively associated with PACC3 scores derived using AN and TRLB in the A+ group (AN: β=0.3, p=0.03; TRLB: β=0.3, p=0.04) (Figure 3).ConclusionIncreases in genera Blautia_A_141781 and Prevotella were associated with higher and lower cognitive performance scores, respectively. Certain microbes associate with cognitive performance in amyloid‐positive individuals. Validation of these associations in larger cohorts and diverse populations is needed.