bluuebunny/crossref_metadata_2025 huggingface dataset:
Right temporal variant frontotemporal dementia: a multimodal MRI analysis
Authors: Gallingani Chiara , Carbone Chiara , Salvatori Davide , Tondelli Manuela , Vinceti Giulia , Chiari Annalisa , Zamboni Giovanna
Published: January 2025

Background

Right temporal variant frontotemporal dementia (rtvFTD), a new recognized entity among the FTD‐spectrum, is characterized by right anterior temporal lobe (rATL) atrophy and a peculiar clinical presentation, involving face and emotions recognition, memory, and naming deficits and behavioral disturbances. Clinical diagnosis is challenging, since rtvFTD shares features with both the behavioral variant FTD (bvFTD) and the semantic variant primary progressive aphasia (svPPA), and there is no consensus yet on its designation and characterization. Although rATL neurodegeneration is a hallmark of this syndrome, only a few studies investigated patterns of gray matter (GM) atrophy. Even less is known about white matter (WM) involvement. We conducted a preliminary study on the rtvFTD neuroimaging features using multimodal magnetic resonance imaging (MRI).

Method

We compared GM volume and WM microstructural integrity in rtvFTD (n=3) and svPPA (n=3) patients, and healthy controls (HC, n=27), using voxel‐based morphometry (VBM) and tract‐based spatial statistics (TBSS). Age and disease duration were considered as covariates of no interest.

Result

SvPPA patients showed GM atrophy in the left temporal structures relative to HC. rtvFTD patients compared to HC showed not only GM atrophy in the right frontal and temporal structures, but also in the insula bilaterally, and in the left temporal and orbitofrontal cortices. Direct comparison between rtvFTD and svPPA showed that rtvFTD were more atrophic than svPPA patients in the bilateral temporal and frontal regions. Moreover, rtvFTD patients had less WM integrity than controls in the corpus callosum and the right inferior fronto‐occipital, inferior longitudinal, and uncinate fasciculi, while svPPA patients had decreased WM integrity in the corpus callosum and the left inferior fronto‐occipital fasciculus.

Conclusion

We found that rtvFTD patients show greater atrophy compared to svPPA patients both in the right and left hemisphere, independently of disease duration. This suggests that rtvFTD and svPPA do not mirror each other in GM loss, and that a neurodegenerative process starting in the right hemisphere must be more widespread to become clinically evident. We also demonstrate that rtvFTD patients show WM disruption in fasciculi which have been implicated in face recognition, emotion processing, and language functions, in line with the clinical picture.
Modeling the temporal evolution of mass spectrometry plasma biomarkers
Authors: Cogswell Petrice M , Lundt Emily S. , Therneau Terry M. , Griswold Michael E. , Graff‐Radford Jonathan , Algeciras‐Schimnich Alicia , Lowe Val J. , Schwarz Christopher G. , Senjem Matthew L. , Gunter Jeffrey L. , Knopman David S. , Vemuri Prashanthi , Petersen Ronald C. , Jack Clifford R.
Published: January 2025

Background

Plasma Aβ42/40 and p‐tau217 can predict amyloid positivity in cross‐sectional studies. However, it is unclear how plasma biomarkers perform longitudinally, which is important to inform their utility in tracking disease progression. The goal of this study is to describe temporal evolutions of plasma Aβ42/40 and ptau217 ratio (p‐tau217r) measured via mass spectrometry, p‐tau217 measured via an immunoassay, and amyloid PET.

Methods

We included participants in the Mayo Clinic Study of Aging with amyloid PET (PiB) and plasma Aβ42/40 + ptau217r (mass spectrometry, C2N) and/or p‐tau217 (Lilly, MSD). We fit a joint non‐linear mixed effects model with Aβ42/40, p‐tau217r, p‐tau217 (Lilly), and amyloid PET as outcomes and age, sex, APOE ε4 carriership and education as covariates. The model included a per‐participant adjustment (time‐shift) for each biomarker indicating how much earlier or later in time the biomarker was estimated to progress relative to the average participant, a correlation (R) between adjustments for each pair of biomarkers, and the impact of covariates on each biomarker’s timing.

Results

The study included 1459 participants, age 77 (10) years, 44% female, 82% cognitively unimpaired. Mean (SD) years of followup was 5.6(3.0) years for mass spectrometry plasma measures, 2.5(0.7) years for p‐tau217 (Lilly), and 4.6(3.3) for amyloid PET. Model fits are shown for each biomarker, with greater variability in serial measurements for plasma than PET (Figure 1). Individual adjustments between each of the plasma biomarkers and amyloid PET were highly correlated: p‐tau217r, R=0.73 (95%CI: 0.67, 0.78); p‐tau217 (Lilly), R=0.66 (0.62, 0.7); Aβ42/40, R=0.6 (0.52, 0.68) (Figure 2). The p‐tau217r and Aβ42/40 individual adjustments were modestly correlated, R=0.39 (0.28, 0.49). APOE ε4 carriership had the strongest covariate effect on all biomarkers, e.g. p‐tau217r progression 9.4 (6.7, 12) years earlier (Figure 3).

Conclusions

Plasma Aβ42/40 and p‐tau217 measured with currently available assays may both be amenable to longitudinal modeling, although the degree of variability in Aβ42/40 over time may pose challenges. Findings suggest that both plasma Aβ42/40 and p‐tau217r may provide information regarding an individual’s timing of progression on amyloid PET; lower correlations with each other than amyloid PET suggests that each analyte may provide complimentary information.
Automated analysis of story recall tasks produced by AD and MCI patients
Authors: Kim Jin‐Seo , Nevler Naomi , Irwin David J , Liberman Mark Y , Cho Sunghye
Published: January 2025

Background

Story recall tasks are often employed in clinical settings to measure verbal episodic memory in individuals with mild cognitive impairment (MCI) and Alzheimer’s disease (AD). Traditional analyses (e.g. total number of recalled words) provide useful but limited information compared to detailed analyses of lexical performance. In this study, we analyzed story recall data produced by patients with AD or MCI using an automated method.

Method

We analyzed digitized recordings of the Craft story recall task produced by 15 individuals with MCI (age=65.8±10.14, females=40%, education=16.07±2.94), 27 individuals with AD (age=65.19±6.82, females=37%, education=15.42±3.01) and 14 healthy age‐matched individuals (HC; age=58.86±16.16, females=50%, education=17.29±2.16). The groups did not differ in any demographic characteristics. Our automated pipeline identified the number of verbatim and paraphrase recalls, calculated semantic similarities between the original story and recalled immediate and delayed stories using word embeddings and rated several lexical characteristics, including word frequency, concreteness, and word length. Two‐way ANOVA tests examined the effect of Group (HC vs. MCI vs. AD), Task (immediate vs. delayed), and their interactions. As the interactions of Group and Task were not significant, post‐hoc analyses examined significant pairwise group differences in immediate and delayed recalls, separately.

Result

Participants with AD produced the fewest verbatim and total recalled words, followed by MCI patients and HC in both immediate and delayed recalls (Figure 1A–B, Table 1). AD patients produced fewer paraphrase recalls compared to the other groups in the delayed recall only (Figure 1C). AD group’s recalled story was also the least similar to the original story, followed by MCI and HC (Figure 1D). Also, individuals with AD produced words that were more frequent and less concrete during immediate and delayed recalls compared to MCI patients and HC (Figure 1E–F). Words produced by AD patients were also shorter than those produced by the MCI and HC groups during the immediate recall (Figure 1G).

Conclusion

Our method highlights that digitized language‐based tests of verbal episodic memory can be automatically analyzed. These capture traditional language features that cannot be extracted manually without massive effort and promise to provide novel, granular, digitized biomarkers that traditional assessment methods of neuropsychological tests cannot show.
Management of Neuropsychiatric and Sleep Symptoms in LBD
Authors: Onyike Chiadi U.
Published: January 2025

The presentation of neuropsychiatric and sleep features in Lewy body dementia (LBD) are challenging to identify and manage. The US Based Diamond Lewy Neuropsychiatric Symptoms management guidelines provide expert consensus recommendations for the assessment and treatment of the neuropsychiatric features of LBD. These guidelines address the management of delusions, hallucinations, agitation and aggression, apathy, depression and anxiety. Pharmacologic interventions will be discussed in detail, in order to describe recommended agents, prescribing strategies, and potential adverse effects and safety aspects. The parallel Diamond Lewy Sleep Disturbances management expert guidelines will also be discussed, to bring to light best practices in the assessment and management of excessive daytime sleepiness, restless legs syndrome (RLS), motor‐related sleep disturbances, sleep apnea, REM sleep behavior disorder, insomnia and sleep fragmentation.
Bayesian changepoint models for amyloid‐centric synchronisation of biomarker trajectories in AIBL
Authors: Saint‐Jalmes Martin , Beck Daniel , Bourgeat Pierrick , Masters Colin L , Goudey Benjamin
Published: January 2025

Background

The modelling of biomarker dynamics in Alzheimer’s Disease from cohort studies faces challenges due to the lack of clear temporal points of references and the natural variability across individuals. Mixed‐effects models are often used to account for individual differences, but a disease timescale can enable better population‐level modelling than age or time since enrolment. Previous literature explored the temporal synchronisation of patients through observed time of conversion to MCI or AD, amyloid positivity, or aligning cognitive trajectories. We propose an amyloid‐centric synchronisation approach that estimates patient‐specific time‐shifts to best align them on a population‐level piecewise linear trajectory.

Method

We rely on 521 patients of the AIBL study with at least 3 measurements of PET amyloid, and model a 3‐segment, piecewise linear trajectory of centiloid over time. Our Bayesian model assumes that observed amyloid values are noisy realisations of the piecewise linear trajectory, and that the time of measurements is shifted by some individual‐specific lag. The first segment is constrained to have a zero slope, and individuals who were not observed to reach the first changepoint were excluded from subsequent analysis, as they may never develop Alzheimer’s Disease.

Result

We identified a changepoint in the amyloid trajectory at a median of (89% credible interval: [14.45, 23.64]) after initial accumulation in shifted time. Patients with measurements falling within this credible interval have a median of 18.9 centiloid (range of 2.0 to 30.1). On average, this changepoint describes an acceleration in amyloid accumulation, switching from a median 0.75 centiloid/year [0.65, 0.87] rate to 6.38 centiloid/year [6.20, 6.56] (Figure 1).

Conclusion

Synchronisation approaches allow for better group‐level assessments (such as the relative timing of biomarkers) rather than attributing variability to patient‐specific effects. Our approach was able to realign individuals on a common trajectory and amyloid‐centric timescale. Markov chain Monte Carlo sampling allows us to estimate uncertainties for all parameters: slopes and intercepts, changepoints, time‐shifts and the fit residual. Further work could explore the timing of other biomarker changes in this amyloid‐synchronised time.
Head‐to‐Head Comparison of Four Plasma Phosphorylated Tau 217 Biomarkers
Authors: Warmenhoven Noëlle , Salvadó Gemma , Janelidze Shorena , Mattsson‐Carlgren Niklas , Kolb Hartmuth C. , Triana‐Baltzer Gallen , Barthélemy Nicolas R. , Bateman Randall J. , Binette Alexa Pichet , Hansson Oskar
Published: January 2025

Background

We assessed the efficacy of four plasma phospho‐tau217 (p‐tau217) biomarkers in a head‐to‐head comparison, and against two clinically available CSF biomarkers for Alzheimer’s disease (AD).

Method

Samples were analyzed from 1009 individuals from the Swedish BioFINDER‐2 cohort (Table 1). We included the following biomarkers: %p‐tau217WashU, p‐tau217WashU (both mass‐spectrometry), p‐tau217Lilly, p‐tau217Janssen (both immunoassays), CSF p‐tau181 and p‐tau181/Aβ42 (Elecsys). Biomarker correlations were assessed using linear regression models. Their discriminative accuracy for global Aβ‐ and temporal meta‐ROI tau‐PET status was evaluated with receiver operating characteristic (ROC) curves. Area under the curve (AUC) values from two ROC curves were compared with DeLong tests. Linear regression models with continuous Aβ‐ and tau‐PET measures were performed. Participants were grouped into PET‐positive quartiles, which were compared with t‐tests. Effect sizes (Cohen’s D (CD)) were calculated between PET‐positive/negative groups, and between neighboring quantiles.

Result

All plasma biomarkers were correlated (0.62≥Radj2≥0.92, Figure 1). %p‐tau217WashU showed the significantly largest effect size for both Aβ‐PET status and tau‐PET status (CDAβ‐PET=1.635; CDTau‐PET=1.828) compared to the other biomarkers (all pFDR <0.05). p‐tau217Janssen had a lower plasma effect size (CDAβ‐PET=1.313; CDTau‐PET=1.590), but not significantly different from p‐tau217Lilly. Although all plasma biomarkers showed high AUCs (0.90‐0.95) for Aβ‐PET positivity, %p‐tau217WashU was the highest, performing significantly better than all other biomarkers including CSF p‐tau181/Aβ42Elecsys (all pFDR<0.01) (Figure 2A). A similar pattern was observed for tau‐PET where %p‐tau217WashU also performed significantly better than all other biomarkers except for p‐tau217WashU (all pFDR<0.01) (Figure 2A). With continuous PET measures, %p‐tau217WashU showed the highest Radj2 compared to the other biomarkers for Aβ‐PET and tau‐PET (Figure 2B). In this context, all plasma ptau217 markers performed better that CSF ptau181Elecsys. Compared to CSF p‐tau181/Aβ42Elecsys, p‐tau217Lilly and p‐tau217WashU performed similarly whereas %p‐tau217WashU performed significantly better. Quantile grouping revealed that all biomarkers showed significant differences when distinguishing between negatives and early‐stage positives for both Aβ‐PET and tau‐PET, with %p‐tau217WashU consistently having the significantly largest effect size (Figure 2C). For tau‐PET, plasma biomarkers distinguished better between disease stages compared to CSF.

Conclusion

When predicting Aβ‐ and tau‐PET load, both mass‐spectrometry and immunoassay methods detecting plasma p‐tau217 perform similarly to an FDA‐approved CSF test, with %p‐tau217WashU performing even better.
Associations between cognitive function and long‐term cognitive enhancement therapy: Insights from a 5‐year follow‐up, case study
Authors: Zarouna Stalo , Tziannarou Antonia , Chrysostomou Savvina , Moza Sotiria
Published: January 2025

Background

A 69‐year‐old retired businessman, born in 1954, with 12 years of education, had been participating in cognitive enhancement sessions for the past 5 years. His medical history included two ischemic strokes, left hemiplegia, as well as disturbances in the left visual field. This study aimed to examine the individual’s cognitive performance over the course of these 5 years, including the COVID‐19 pandemic period.

Methods

Bi‐weekly, 45‐minute cognitive remediation sessions were performed for 1.5 years, transitioning to once weekly later on. Exercises included orientation (e.g., use of calendar, news, articles), language (e.g., repetition, fluency, fill the gaps), visuospatial (e.g., pattern completion, object localisation, tracking of moving objects), memory (e.g., stories/words/images, learning, consolidation and recall of information, reminiscence and old songs), executive (e.g., dual tasks, categorisation) and motor tasks (e.g., ball games).

Results

At baseline (July 2018), the person scored 11/30 in the Montreal Cognitive Assessment (MoCA) with significant visuospatial, memory recall, and orientation difficulties. In October 2019 (after 59 sessions), the MoCA score improved to 15/30, with better performance in visuospatial tasks, calculation, delayed recall, and orientation. However, a re‐evaluation in May 2020, following the first wave of COVID‐19, during which sessions were paused, resulted in a score of 11/30. The lowest points were noted in orientation and delayed recall. Sessions continued at a reduced frequency due to COVID‐19 restrictions and medical complications (flu, serious respiratory and urinary tract infections, and hospitalisation) which also affected the fine motor skills of the right hand. Last evaluation, in September 2023, yielded a score of 12/30 points, showing a regain of orientation and calculation scores, but losses in visuospatial skills, naming and delayed recall compared to the 2019 evaluation.

Discussion

Our findings suggest a connection between cognitive functionality and cognitive enhancement therapy, particularly in orientation, memory recall, and calculation tasks. Achieving impact appears to require sustained and long‐term engagement in personalised enhancement sessions, which can be significantly hindered by external obstacles, such as COVID‐19 restrictions and other health issues.
Integrated Disease Chronicity Modeling to Map Longitudinal Biomarker Relationships in Alzheimer’s Disease
Authors: Robey Seth , Yee Ka Lai , Stone Julie A
Published: January 2025

Background

A lesson of the recent progress in Alzheimer’s Disease therapy is that biomarker‐driven trials will be crucial to demonstrating efficacy in the clinic. Many studies have demonstrated the potential predictive power of fluid and imaging biomarkers in guiding patient selection1,2 and continued progress of precision medicine approaches will demand development of multi‐dimensional biomarker arrays. However, correlations between candidate biomarkers change non‐linearly with time, requiring methodologies to align biomarkers across a common disease timescale (time from amyloid positivity; TFAP). We present a disease chronicity analysis using latent time joint mixed effects modeling3 (LTJM) which enables prediction of progression from cross‐sectional data.

Method

A model of amyloid PET, tau PET, ADAS‐COG13, MMSE, and CDRSB was estimated with training data from amyloid positive subjects in the ADNI database (N=831). Longitudinal profiles for each endpoint were modeled with a 4‐parameter logistic curve. TFAP was estimated for each subject informed jointly by all biomarkers/endpoints. An external validation set consisted of a random sampling of 76 subjects with longitudinal tau PET and cognitive assessments collected within 3 months prior to the first PET scan (‘baseline data’). Longitudinal predictive power was assessed by projecting disease trajectories from subjects in the validation set, given only the observed baseline data and comparing with the observed post‐baseline data.

Result

Without prior guidance, the model correctly estimated the sequencing of clinical diagnoses (CN, MCI, AD) and the timing of tau and clinical progression are consistent with previous empirical studies; acceleration of clinical progression is closely associated with the onset of tau positivity. When applied to baseline data from the validation set, the model accurately captured the data 2‐7 years post‐baseline across biomarkers, covering the span of a typical trial.

Conclusion

LTJM enables bridging across biomarkers to extract a full progression profile aligned to a common time scale. The model provides unbiased predictions of progression from cross‐sectional data, highlighting its utility for guiding clinical trial patient selection and its potential for refining subject‐level prognoses. Further modeling efforts will incorporate emerging fluid biomarkers and imaging modalities.

Ref: 1) Mattsson‐Carlgren, JAMA Neurol. 2023 Dec; and 2) 2023 Apr; 3) Li, Alzheimers Dement. 2018 Aug
Microbiome‐Immune Interplay in Aging Brains: A Pilot Investigation from the MiaGB Cohort
Authors: Prajapati Santosh K , Kumar Vivek , Shukla Rohit , Szekeres Charles , Ma Yonggang , Jain Shalini , Yadav Hariom
Published: January 2025

Background

The microbiota‐immune‐brain axis is implicated in Alzheimer’s disease. Alterations in gut microbiota and immune functions in mild cognitive impairment (MCI) are inconsistent and remain to be understood. This study aims to investigate immune cell phenotyping and its link with gut microbial composition associated with cognitive function.

Method

In this pilot study, we used data and samples from MCI (n=6) and cognitively healthy (n=22) older adults (≥60 years of age) from a large, multi‐site, community‐based prospective clinical study called MiaGB (Microbiome in Aging Gut and Brain) consortium. We recorded measures of cognitive function (using the MoCA score), immunophenotyping (using flow cytometry), and stool microbiome (using whole‐genome metagenomics and bioinformatics pipelines). Differences in immune cell populations and the microbiome were found in the two groups and correlated with cognitive function.

Result

The abundance of immune cells such as granulocytes, neutrophils, T‐cells (CD4, CD8, and NK T‐cells), B‐cells, NK cells, and total monocytes were not significantly distinct. However, the levels of neutrophils and classical monocytes increased, while CD8+ T cells decreased in trend in the blood of MCI participants compared to controls. These immune cells were positively correlated with increased detrimental microbial species while negatively correlated with beneficial microbes in the MCI gut. Altered immune and microbial species discriminated MCI patients well from controls and were associated with MoCA scores.

Conclusion

This study provides insights into the role of gut microbiota‐immune interactions in the process of cognitive decline in older adults and indicates the potential to be used as biomarkers to diagnose the risk of cognitive decline‐ a debilitating public health problem in older adults.
Plasma biomarker trajectories: Relationship to AD genetic risk factors among cognitively unimpaired individuals
Authors: Pettigrew Corinne , Wang Jiangxia , Soldan Anja , Hohman Timothy J. , Dumitrescu Logan C. , Wang Mei‐Cheng , Albert Marilyn S. , Blennow Kaj , Bittner Tobias , Moghekar Abhay
Published: January 2025

Background

Few studies have examined how a range of potential modifiers may influence the trajectories of Alzheimer’s disease (AD) blood biomarkers in those who were cognitively unimpaired when first assessed. This study examined potential modifiers of longitudinal changes in plasma biomarkers, including genetic factors (i.e., APOE e4 allele and AD‐polygenic risk scores (PRS), demographics (age and sex), and follow‐up diagnoses (i.e., progression to MCI or dementia) among participants who were cognitively unimpaired at baseline.

Method

Analyses included a subset of BIOCARD Study participants who had all of the relevant measures of interest, including AD‐PRS (N = 133, M baseline age = 64.7y; 7.3 biomarker measures over 8.3 years of follow‐up; Table 1); 19 (14%) had progressed to MCI or dementia over time. These analyses focused on log‐transformed and standardized values of AB42/AB40, p‐tau181, NfL, GFAP, YKL‐40, and sTREM2 in plasma, measured using cobas Elecsys assays in the NeuroToolKit panel (Roche Diagnostics).

Result

In longitudinal mixed‐effects models, NfL, GFAP, YKL‐40, and TREM2 increased over time, whereas AB42/AB40 decreased over time (Table 2). YKL‐40 increases were greater among individuals with higher AD‐PRS and rates of sTREM2 increase were attenuated among APOE e4 carriers. Additionally, baseline AB42/AB40 levels were lower among APOE e4 carriers and baseline sTREM2 levels were lower in individuals with higher AD‐PRS. Older participants had more abnormal baseline biomarker levels, as well as greater rates of change in p‐tau181. Baseline GFAP and sTREM2 levels were higher among females, and rates of change in YKL‐40 were greater among males. In a separate set of models, with AD genetic risk factors covaried, those who progressed to MCI/dementia had more abnormal ptau181 at baseline (Table 3) compared to those who remained cognitively unimpaired, but biomarker rates of change did not differ by follow‐up diagnosis.

Conclusion

Among initially cognitively unimpaired individuals, blood biomarkers were differentially related to APOE e4 genetic risk and AD‐PRS. Only YKL‐40 and sTREM2, each markers of neuroinflammation, demonstrated relationships with an AD‐PRS. Additional work is underway to explore whether lower sTREM2 values among cognitively unimpaired participants with higher AD genetic risk reflect reduced microglial activation during preclinical AD.
Lessons Learned from a Global, Multisite Early Detection Program in Primary Care Implementation Program
Authors: MacLeod Tim
Published: January 2025

Davos Alzheimer’s Collaborative Healthcare System Preparedness (DAC‐SP) aims to catalyze global healthcare system transformation, providing patients with quicker access to life‐changing innovations and therapies. Utilizing implementation science, the DAC‐SP Early Detection flagship program launched in 2021, engaging seven healthcare systems across six countries (Brazil, Jamaica, Japan, Mexico, Scotland, and the United States). The program's primary aim was to increase the rate of early detection of cognitive impairment by integrating commercially available digital cognitive assessments (DCAs) into primary care and other non‐specialty care settings. Patients with suspected cognitive decline based on the DCA were then offered a blood‐based biomarker (BBM) to determine the likelihood of amyloid presence in addition to customary follow‐up testing.

Over the past two years, health system leaders from the early detection program were convened into a Community of Practice (CoP) that met monthly to discuss operational and clinical challenges, collaborate on solutions, and exchange learnings. As part of the CoP meetings, site leaders identified common implementation challenges for DCAs and BBMs in real‐world primary care settings. These challenges span various levels of health systems, including policy (e.g., reimbursement and procurement mechanisms, lack of guidance and consensus on workflow), the outer setting (e.g., availability/capacity of brain health expertise, lab infrastructure, fit with administrative systems), the inner setting (e.g., training and equipment required for blood draws, shipping logistics, administrative support) and understanding clinician knowledge, attitudes and behavior (e.g., level of comfort or experience with cognitive assessments, interpretation and disclosure of BBM results, access to training, task shifting resources, support from peers). Education about BBM specifications, use cases, and integration into clinical routines was essential in understanding the adoptability and scalability of BBMs in primary care settings.
Quantification of tau pathology in the basal forebrain: Associations with age, amyloid, brain structure and cognition
Authors: Chadwick Trevor , Ward Tyler J. , Baker Suzanne L. , Jagust William J. , Landau Susan M. , Harrison Theresa M. ,   the U.S. POINTER Study Group, the Alzheimer's Disease Neuroimaging Initiative
Published: January 2025

Background

Tau pathology accumulates early in the basal forebrain (BF) in Alzheimer’s disease (AD). The feasibility of measuring in vivo BF tau is unclear given PET resolution and possible partial volume effects of off‐target signal (OTS) which varies by tracer.

Method

We compared measurements of tau in cognitively unimpaired older adults with either an FTP or MK6240 scan: 93 FTP scans from the Berkeley Aging Cohort Study (BACS), 424 FTP scans from ADNI (N=517 FTP scans; 72.5±7.1 years, 61% F) and 888 MK6240 scans (68.4±5.2 years, 62% F) from the U.S. POINTER imaging study were processed identically to derive SUVRs in native space FreeSurfer regions and template space BF. Using linear regression we explored associations between tau‐PET ROIs across the brain focusing on the temporal lobe (entorhinal cortex [ERC], temporal MetaROI), BF (Ch1‐3, Ch4) and common regions of OTS (e.g., pallidum, thalamus). We examined relationships between tau in these ROIs and age, Aβ, hippocampal volume, BF volume, and cognition.

Result

The MK6240 cohort was significantly younger, less educated, more racially diverse, and had higher hippocampal and BF Ch1‐3 volume (Table. 1). For both tracers, BF and temporal lobe ROI SUVRs were positively correlated with Aβ, while OTS ROI SUVRs were not related to Aβ (Figure 1). In the BF, the effect size (Cohen’s d) of t‐tests comparing tau SUVR by Aβ status was higher using MK6240 compared to FTP. BF and ERC SUVR positively correlated with age and negatively correlated with hippocampal volume and episodic memory (Figure 2). BF Ch4 SUVR negatively correlated with BF Ch4 volume in both cohorts.

Conclusion

Evidence that BF tau, along with temporal lobe tau, correlates with Aβ uptake suggests that AD‐relevant tau pathology can be measured in vivo in the BF. In addition, BF tau and temporal tau showed similar relationships to other key variables including age, brain structure and cognition with both tracers. MK6240 slightly better differentiated Aβ positive and negative subjects in BF SUVR measurements compared to FTP. Future research will investigate if BF tau is a critical early marker of AD progression.
DeepSUVR: Using temporal constraints to improve SUVR and Centiloid quantification
Authors: Bourgeat Pierrick , Fripp Jurgen , Gillman Ashley G , Cox Timothy , Goyal Manu S. , Tosun Duygu , LaMontagne Pamela , Benzinger Tammie L.S. , Weiner Michael S. W. , Villemagne Victor L , Masters Colin L , Rowe Christopher C. , Dore Vincent ,   for the Alzheimer’s Disease Neuroimaging Initiative, OASIS3, and the AIBL research group
Published: January 2025

Background

PET quantification using the Standardised Uptake Value Ratio (SUVR) relies on the availability of a robust reference region. Intrinsic noise, spill in, and specific binding in the reference region can impact the reliability of the resulting SUVR. We evaluate a novel deep learning method trained on longitudinal data that penalises unexpected temporal changes and learns a SUVR correction factor that compensates for any noise or bias in the reference region, resulting in an improved quantification.

Method

3098 participants with 2+ visits in AIBL/ADNI/OASIS (8826 scans) had their Amyloid PET images spatially normalised and quantified using the Centiloid SPM pipeline. A deep learning network (DeepSUVR) was trained to predict a SUVR correction factor (CF) for each spatially normalised image. For each iteration, the prediction was run on 2 random timepoints from the same participant, resulting in 2 Adjusted Centiloids computed using SUVR*CF and each tracer’s standard Centiloid transform. A loss function was defined to penalise unexpected temporal changes: Centiloid decreasing over time, Centiloid deviating from the curve defining the change in Centiloid/Year given the mean Centiloid. The loss also included a penalty for over‐correction (Slope of the linear regression between original and adjusted Centiloid should be 1). The model was trained using a subset of ADNI/AIBL (2037 participants) and evaluated in terms of longitudinal consistency on the remaining ADNI/AIBL participants and on OASIS data. The correlations between head‐to‐head tracers in the OASIS and GAAIN calibration datasets were also computed.

Result

DeepSUVR increased the correlation between each pair of 11C‐PIB/18F‐Tracers in the GAAIN calibration dataset (Figure 1) and between pairs of 11C‐PIB/18F‐Florbetapir in OASIS (from R2=0.86 to R2=0.92) (Figure 2). Using DeepSUVR, the correlation between Centiloid/Year vs mean Centiloid increased from ρ=0.39 to ρ=0.48 in AIBL/ADNI and ρ=0.47 to ρ=0.51 in OASIS (Figure 3).

Conclusion

We have proposed a novel deep learning technique to correct SUVR estimation based on noise in longitudinal trends. The longitudinal constraint led to higher agreement between tracers in the head‐to‐head GAAIN/OASIS datasets and higher longitudinal consistency. Importantly, while the model was trained using pairs of images, the prediction is run on single images. Future work will evaluate this approach on tau tracers.
Plasma metabolites associated with plasma P‐tau181 in preclinical Alzheimer's disease
Authors: Sharmin Tahmida , Doecke James D. , Chatterjee Pratishtha , Huynh Kevin , Heng Benjamin , Eslick Shaun , Garg Manohar , Martins Ralph N
Published: January 2025

Background

Alzheimer's disease (AD) pathogenesis is not restricted to amyloid‐beta, Aβ, and tau pathologies but involves dysregulation in diverse cellular and molecular processes. Numerous metabolomic studies revealed plasma metabolite alterations in AD individuals compared to healthy controls. Nevertheless, plasma P‐tau181, an established biomarker for AD diagnosis and prognosis, has been described to reflect initial multiple cortical region Aβ deposition in cognitively intact adults. The current study aims to identify plasma metabolites associated with plasma P‐tau181 at the preclinical stage and better understand the associated biochemical mechanisms for AD pathogenesis.

Method

In the current study, 100 older adults with no objective cognitive impairment, MoCA and MMSE ≥ 26, from the Kerr Anglican Retirement Village Initiative in Ageing Health (KARVIAH) cohort, comprising 65 CI Aβ‐ (cognitively intact normal brain Aβ) and 35 CI Aβ+ (cognitively intact higher brain Aβ) individuals, were assessed for plasma P‐tau181, via ultra‐sensitive Quanterix Simoa technology, and plasma metabolites, via mass spectrometry‐based BIOCRATES kit, and then investigated for associations, both before and after adjusting for confounding variables, in the study groups. Additionally, P‐tau181‐associated plasma metabolites were evaluated using the receiver operating characteristic (ROC) curves for the potential to classify brain Aβ status.

Result

In the entire cohort, significant positive associations of plasma metabolites, including acylcarnitines, amino acid citrulline, and three biogenic amines (creatinine, kynurenine and SDMA), were observed with P‐tau181 and similar associations, except for kynurenine, were detected in CI Aβ‐. In contrast, in CI Aβ+, only acylcarnitine, AC(10:3), was found to have a positive association with P‐tau181 and further, upon including AC(10:3), the AUC for P‐tau181 (AUC=70.9%) potentially outperformed (AUC=76.2%), which additionally topped to 83.9% when combined with a base model (Abstract Figure).

Conclusion

These findings suggest that the higher the plasma P‐tau181, the higher the medium chain acylcarnitine, AC(10:3), in plasma in cognitively intact older adults at risk for AD, indicating a link between early Aβ pathology and fatty acid oxidation mediated energy metabolism pathway. Additionally, associated metabolite strengthens the significance of P‐tau181 in classifying brain Aβ status in cognitively intact older adults. Therefore, plasma P‐tau181‐associated plasma metabolite may serve as potential predictive marker for preclinical AD pathogenesis.
Neuroinflammation and synaptic markers relate to trajectories of core biomarkers in Alzheimer’s Disease continuum
Authors: Ersoezlue Ersin , Hellmann‐Regen Julian ,   Alzheimer’s Disease Neuroimaging Initiative
Published: January 2025

Background

Mechanisms of cellular senescence affecting neuroimmune responses, homeostasis, and synaptic functioning associate with core Alzheimer’s disease (AD) biomarkers, i.e., pathological aggregation of β‐amyloid (Aβ) and tau protein, while their longitudinal associations remain unclear.

Method

The study cohort included participants from the Alzheimer’s Disease Neuroimaging Initiative study, which consisted of n=65 healthy controls (HC, mean years of every follow‐up visit 4±3), n=41 suspected non‐AD pathology (SNAP, mean years of every follow‐up visit 4±3), and n=198 AD continuum (ADC, mean years of every follow‐up visit 3±3) who underwent lumbar puncture at the baseline and available data for multiplex based immunoassay panel for the a priori selected analytes in cerebrospinal fluid (CSF). We derived adjusted annual change rates as slopes from separately conducted linear mixed models with Aβ42 or phosphorylated‐tau181 (p‐Tau) dependent variables, including age, sex, APOE‐ε4 status and year from baseline.

We tested the correlations between annual change rates and nine CSF analytes (listed by category in Figure 1). Significance level was defined as alpha=0.05, and the Bonferroni‐correction method was used for multiple testing. We also explored the associations of an incident abnormality of p‐Tau/Aβ42‐ratio with CSF markers using Cox regression models and subsequently tested interactions.

Result

The demographic, genetic, clinical and biomarker levels are presented in Table 1 for each study group, showing that only FABP‐3 levels were altered in ADC compared to HC and SNAP. In both HC and ADC groups, markers for glia and cell adhesion were negatively correlated with annual change of Aβ42 and positively with p‐Tau, respectively (Figure 1A‐C). Specifically in ADC, higher FABP‐3 was associated with an increasing trend in p‐Tau, while FRTN with a decline in Aβ42 (Figure 1C). In the exploratory cox regressions, higher FABP‐3 levels were associated with conversion to abnormal p‐Tau/Aβ42‐ratio status over the up to ∼15 years follow‐up period (Figure 2).

Conclusion

Our results suggests that CSF biomarkers based on cell adhesion, glial activity and synaptic function can contribute to a more precise identification of individuals at‐risk for AD and prediction of trajectories of established AD biomarkers. Moreover, identifying related biological stages of AD can guide the development of preventive approaches targeting these pathways.
Plasma GFAP and pTau‐181 Moderate the Relationship between Cognition and White Matter Hyperintensities in a Southeast Asian Non‐Demented‐at‐Risk Cohort
Authors: Sandhu Gurveen Kaur , Vipin Ashwati , Zailan Fatin Zahra , Tanoto Pricilia , Lee Faith Phemie Hui En , Soo See Ann , Ghildiyal Smriti , Liew Shan Yao , Tan Isabelle Yu Zhen , Leow Yi Jin , Kumar Dilip , Azam Mohammed Adnan , Chong Wayne Freeman , Kandiah Nagaendran
Published: January 2025

Background

Astrocyte reactivity marked by elevations in Glial Fibrillary Acidic Protein(GFAP), was found to increase Phosphorylated Tau181(pTau181) induced neurodegenerative effects in Alzheimer’s Disease (Bellaver et al., 2023). Nevertheless, the effect of the GFAP‐ pTau181 axis in vascular/mixed dementias has yet to be completely elucidated. In this study, we investigated the effect of GFAP‐pTau181 on cognition in a Southeast Asian cohort having high burden of cerebrovascular disease.

Methods

A cross‐sectional study was conducted in 190 non‐demented but at risk, Southeast Asian community participants recruited into the Biomarker and Cognition Study. Neuropsychological Assessments of global cognition [Visual Cognitive Assessment Test(VCAT)] and processing speed were measured (Kumar, 2018; Nagaendran et al., 2016; Tian et al., 2022; Wiseman et al., 2018). The burden of white matter hyperintensities (WMH), a surrogate measure for cerebral small vessel disease was quantified using the automated lesion growth algorithm (Lesion Segmentation Tool) (Vipin et al., 2021). Plasma biomarkers (GFAP, Aβ40, Aβ42, pTau181) were quantified using the Single Molecule Array(Simoa) platform (Wilson et al., 2016). A partial correlation analysis was used to demonstrate associations between variables. A moderation analysis was conducted to determine if pTau/GFAP influenced the relationship between cognition and WMH, via linear regression analysis with the inclusion of interaction terms (WMH*pTau/GFAP).

Results

Findings indicate that, WMH volume positively correlated to GFAP and pTau181(Figure 1A and 1B). Information processing speed and VCAT negatively correlated to WMH volume. GFAP negatively correlated to Aβ42/Aβ40 ratio (Figure 2A). pTau181 demonstrated a negative correlative trend with Aβ42/Aβ40 ratio (Figure 2B) and a positive correlative trend with GFAP (Figure 2C). Moderation analysis revealed that GFAP and pTau181 independently influenced the relationship between cognitive measures and WMH volume, such that increasing WMH volume and GFAP/pTau181 levels related to deteriorating processing speed and VCAT scores (Figure 3A and 3B).

Conclusion

GFAP, a neuroinflammatory measure moderates the relationship between WMH and cognition and may be a useful biomarker for the detection and prognostication of cognitive impairment among Southeast Asians. The pathobiology could be a result of early upstream astrocyte mediated modulation of Blood‐Brain Barrier permeability and resultant perfusion dynamics. pTau181 facilitated effects are likely downstream events of GFAP activation. Further mechanistic validation in a larger longitudinal study is underway.
Markers of atrophy progression in patients with young‐onset cognitive complaints not due to Alzheimer’s Disease
Authors: Carbone Chiara , Balboni Erica , Luchetti Ludovico , Urbano Teresa , Beltrami Daniela , Maramotti Riccardo , Tondelli Manuela , Gallingani Chiara , Gasparini Federico , Vinceti Giulia , Marti Alessandro , Chiari Annalisa , Zamboni Giovanna
Published: January 2025

Background

In clinical settings, the prognosis of patients with subtle cognitive complaints and no imaging evidence of neurodegeneration is often challenging, especially in conditions unrelated to Alzheimer’s Disease (AD). We aimed to identify which baseline indicators can help in the clinical decision‐making process of patients with Subjective Cognitive Decline (SCD) and Mild Cognitive Impairment (MCI) without AD pathology, by identifying those with faster brain atrophy for their age.

Method

Young‐onset SCD and MCI patients (symptoms ≤65yo) were recruited. At baseline we administered neuropsychological assessment and anxiety/depression questionnaires to patients, while the Neuropsychiatric Inventory for behavioural symptoms to caregivers. Patients also underwent Magnetic Resonance Imaging (MRI) and serum neurofilaments light‐chain (NfLs) measurement, other than a (18F)Flutemetamol‐PET (amy‐PET) or lumbar puncture for collection of cerebrospinal fluid (CSF) NfLs, b‐amyloid1‐42, b‐amyloid1‐42/1‐40, total‐, and phosphorylated‐tau. After 18 months, MRI was repeated. Single‐subject percentage of brain volume change was calculated using the FMRIB Software Library tool SIENA and compared to the longitudinal change observed in healthy controls (Battaglini et al., 2019). Patients exceeding the 95th percentile of normative values were categorized as having non‐physiological progressive atrophy. All variables and biomarkers were added in logistic and spline regression analyses with age, sex, education, and global cognitive status (i.e., Mini‐Mental State Examination – MMSE) as covariates, performed with Stata18.

Result

Among the 75 patients recruited, 20 were excluded because of a CSF/amy‐PET suitable for AD; of the remaining 55, atrophy progressed in 18 while 37 had physiological brain volume changes. Low baseline performance in attentional‐executive (i.e., Frontal Assessment Battery) and language (i.e., phonemic, semantic, alternate fluency) tests were predictors of progressive atrophy, over and above baseline MMSE and demographical variables. Furthermore, caregiver‐reported apathy, along with elevated levels of serum NfLs, were indicative of a greater‐than‐expected change in brain volume after 18 months.

Conclusion

In the assessment of young‐onset SCD and MCI patients, poor performance on attentional‐executive and language tests and high levels of serum NfLs, combined with apathy, should be considered markers for faster progression of brain atrophy in the short‐term future (18‐months), over and above the global cognitive status. Patients with these features should be monitored closely.
Early phase Amyloid‐PET reproduces metabolic signatures of cognitive decline in Parkinson's disease
Authors: Heron Campbell Le , Aye William , Stark Megan , Horne Kyla‐Louise , Myall Daniel J , Pitcher Toni , Almuqbel Mustafa , Keenan Ross , Dalrymple‐Alford John C , Anderson Tim J , Melzer Tracy R
Published: January 2025

Background

Establishing practical and effective diagnostic pathways for people with cognitive impairment is a crucial international research priority. Traditional (late‐phase) analysis of an amyloid‐beta (Aβ) PET scan (∼90 minutes after radiotracer injection) provides important information about the presence/absence of underlying Alzheimer’s pathology, but no information about brain metabolism/perfusion. Recent work suggests that amyloid‐beta PET tracer uptake shortly after injection (‘early‐phase’) closely reflects brain metabolism and perfusion. Parkinson’s disease (PD), the second most common neurodegenerative disease worldwide, serves as a model disorder in which cognitive impairment occurs across a spectrum, is not specifically associated with underlying brain amyloid status, and has known patterns of altered metabolism and perfusion associated with cognitive decline. We investigated the utility of early‐phase 18F‐Florbetaben Aβ PET (eFBB) uptake as a surrogate marker of cerebral perfusion/metabolism in people with PD. We hypothesised that eFBB uptake would reproduce characteristic patterns of hypometabolism and hypoperfusion associated with cognitive decline in PD, irrespective of late‐phase Aβ status, and positively correlate with a direct measure of cerebral perfusion – ASL MRI.

Method

115 closely phenotyped PD patients across the spectrum of cognitive impairment underwent dual‐phase (early and late) Aβ PET, structural and perfusion (ASL) MRI, and neuropsychological assessments. Multiple linear regression models compared eFBB uptake with 1) cognitive performance and 2) ASL‐MRI perfusion, correcting for multiple comparisons by family‐wise error correction (threshold‐free cluster enhancement).

Result

eFBB uptake significantly correlated with cerebral perfusion across widespread cortical and subcortical regions (r = 0.15‐0.49, p < 0.005, Figure 2). Reduced eFBB uptake was significantly associated with poorer cognitive performance in brain regions previously linked to hypometabolism‐associated cognitive decline in PD: parieto‐occipital regions, middle and inferior temporal gyri, prefrontal cortex and the thalamus (all p < 0.05, Figure 1). These findings were independent of amyloid status.

Conclusion

eFBB uptake is a reliable surrogate measure for cerebral perfusion/metabolism in a predominantly Aβ negative group. It provides complimentary information to the late‐phase Aβ scan, and can add value whether the late phase is positive or negative. Such dual‐phase PET imaging approaches show significant promise in streamlining diagnostic pathways for people with cognitive impairment.