bluuebunny/crossref_metadata_2025 huggingface dataset:
Isolated Amyloid‐β biomarker positivity: Alzheimer's disease in a mixed pathologies context?
Authors: Ioannou Konstantinos , Abdullaieva Khadidzha , Bluma Marina , Religa Dorota , Rodriguez‐Vieitez Elena , Chiotis Konstantinos ,   Alzheimer's Disease Neuroimaging Initiative
Published: January 2025

Background

The in vivo amyloid‐β (A) and tau (T) biomarkers have been validated against the respective neuropathological burden of amyloid‐β plaques and neurofibrillary tangles. We aimed to assess the impact of mixed pathologies on the interpretation of AT biomarker system.

Method

A subset of 71 ADNI participants with available neuropathological data and ante‐mortem cerebrospinal fluid (CSF) sampling was analyzed. The median delay between CSF sampling and death was 3.12 years (interquartile range 1.44‐5.11). AT status was determined based on CSF positivity for Aβ42 (A+; ≤981 pg/mL) and p‐tau181 (T+; >24.3 pg/mL). Linear mixed‐effects models were employed to assess the interaction of AT status with longitudinal cognitive scores. A medically trained investigator evaluated the medical history and medication use records. The neuropathological burden was assessed by the ADNI Neuropathology Core according to standard procedures. Chi‐squared statistics (α=0.05) were used to compare the frequency of clinical and neuropathological diagnoses across AT profile groups.

Result

Four AT profile groups were identified: n=37 A+T+, 14 A+T‐, 10 A‐T+, and 10 A‐T‐. Both A+ groups exhibited significantly lower cognitive scores and declined significantly faster in episodic memory and executive domains over time than A‐ groups. The frequencies of psychiatric/neurological diagnoses and cerebrovascular disease risk factors were similar across groups. A+T+ and A+T‐ individuals were significantly more likely to be prescribed Alzheimer’s disease medications than A‐ individuals. The frequency of Alzheimer’s disease neuropathological change (ADNC) declined in a stepwise fashion across AT groups (A+T+ 100%; A+T‐ 71%; A‐T+ 40%; A‐T‐ 30%), and significant differences were detected between A+ and A‐ groups and within the A+ subgroups. In case of ADNC evidence, A+T‐ compared to A+T+ individuals exhibited significantly higher frequency of mixed pathologies (70% vs. 28%). Neocortical Lewy body pathology, extensive substantia nigra neurodegeneration, and argyrophilic grain disease were significantly more frequent in the A+T‐ than A+T+ individuals.

Conclusion

Despite indistinguishable clinical phenotypes, A+T‐ and A+T+ groups showed distinct differences at autopsy, with higher mixed pathology burden in the A+T‐ group. Cognitive decline in A+T‐ individuals may be associated with the additional impact of mixed pathologies rather than being exclusively linked to AD neuropathology.
Diffusion Kurtosis Imaging provide complementary information over structural MRI in the assessment of hippocampal subfields in patients with cognitive decline
Authors: Pavan Tommaso , Pievani Michela , Ribaldi Federica , Jovicich Jorge , Garibotto Valentina , Frisoni Giovanni B. , Jelescu Ileana O
Published: January 2025

Background

Hippocampal atrophy is an established biomarker of neurodegeneration in Alzheimer's disease, affecting specific subfields (De Flores, La Joie and Chételat, 2015). In this study, we used 7T MRI and advanced diffusion MRI (dMRI) to investigate the relationship between hippocampal subfield volumes and microstructure and assess their sensitivity to cognitive impairment.

Method

Seventeen cognitively impaired (CI; age: 69±8, M/F: 12/5, MMSE: 28) and 22 cognitively unimpaired subjects (CU; age: 62±10, M/F: 6/16) were recruited in the context of the COSCODE project (Ribaldi et al., 2021). MRI was performed at 7 Tesla, including a high resolution T2w FSE sequence for hippocampal segmentation and multi‐shell diffusion MRI sequence for kurtosis tensor imaging (DKI, Jensen and Helpern, 2010) fitting. Hippocampal subfields were segmented using Automated Segmentation of Hippocampal Subfields (ASHS) package. Microstructure analysis included DTI and DKI (Figure 1). Mean values for each diffusion metric were calculated in subfields of the ASHS atlas (Xie et al. 2016). Subfield volumes were normalised by the total intracranial volume while dMRI metrics were residualized by age via a linear regression trained only on CU and then correlated to the volumetric data across within each group (statistical significance: p<0.05). Lasso logistic regression was used to identify the metrics that better discriminated CI from CU.

Result

Few hippocampal subfields’ volumes correlated with dMRI metrics (Figure 2). In the CU group, DG volume showed negative associations with diffusion metrics, primarily RD (r:‐0.59, p=0.007), MD(r:‐0.56, p=0.012), MK(r:‐0.46, p=0.043) and AK(r:‐0.62, p=0.005). In the CI group, negative correlations between BA35 (r:‐0.70, p=0.008) and CA3 (r:‐0.57, p=0.041) volumes were found with AK. When discriminating CI from CU, the logistic regression found the ERC volume as the most important metric, followed by the MK of the DG, and AK of CA2 (Figure 3).

Conclusion

The disconnection between dMRI metrics and volumetric measures and the selection of DKI metrics in discriminating CI from CU participants suggest that DKI metrics may act as complementary biomarkers to subfield volumes for early detection of cognitive impairment.
ATNPD to improve detection of concomitant Alzheimer’s pathology in autopsy‐confirmed Parkinson’s disease
Authors: Cousins Katheryn A Q , Bayram Ece , Galasko Douglas R. , Hwang Kristy S , Shaw Leslie M. , Irwin David J , Coughlin David G
Published: January 2025

Background

In Parkinson’s disease (PD), concomitant Alzheimer’s disease (AD) pathologic change (ADNC) is common and results in altered motor and cognitive phenotypes. However, detection of PD with AD (PD+AD) using biofluid markers is challenging. While decreased cerebrospinal fluid (CSF) β‐amyloid 1‐42 (Aβ42) strongly reflects β‐amyloid burden, PD subjects typically harbor lower CSF phosphorylated tau 181 (p‐tau181) and total tau (t‐tau) levels than healthy controls, which complicates detection of tau tangles and neurodegeneration. We previously tested PD‐specific application of the β‐amyloid/tau/neurodegeneration framework (ATNPD); combining CSF Aβ42, CSF p‐tau181, and serum neurofilament light (NfL) in a living PD cohort. ATNPD, using a lower CSF p‐tau181 cutpoint, predicted cognitive decline. However, ATNPD cutpoints still must be validated against autopsy assessments of ADNC as gold‐standard. Here, we compare biomarker strategies in all available autopsy‐confirmed PD from the Parkinson’s Progression Markers Initiative (PPMI).

Methods

Eighteen PD participants with autopsy‐confirmed Lewy body disease and antemortem biofluid were available for analysis (Table 1). PD+AD included high/intermediate ADNC (n=9); PD without AD (PD; n=9) included not/low ADNC. Cerebral cortical atrophy determined neurodegeneration (mild/moderate vs. none). CSF was assayed for Aβ42 (n=14), p‐tau181 (n=17), and t‐tau (n=17) using Roche cobas e 601; p‐tau181/Aβ42 and t‐tau181/Aβ42 ratios were calculated. Serum NfL was assayed using Simoa Quanterix (n=18). Biofluid measurements closest to autopsy were selected. Receiver operating characteristic (ROC) analyses with bootstrapping tested discrimination of PD+AD from PD using CSF biomarkers, and of neurodegeneration from not using CSF t‐tau and serum NfL.

Results

ROC cutpoints for CSF Aβ42, p‐tau181, and serum NfL were equivalent to ATNPD cutpoints, while p‐tau181 and t‐tau were lower than published AD‐cutpoints (Table 2). CSF p‐tau181/Aβ42, t‐tau181/Aβ42, Aβ42 and serum NfL had high area under the curve (AUC>0.80; Table 2A,2B). In contrast, CSF p‐tau181 and t‐tau demonstrated poor discrimination (Table 2A) and no difference between groups (Table 1), potentially due in part to low sample size. A chi‐square test confirmed classification is improved using ATNPD and AD‐cutpoints (χ2=14, p=0.0015; Figure 1).

Conclusions

PD‐specific biomarker strategies/cutpoints are needed to maximize detection of concomitant ADNC, but must be validated in larger autopsy cohorts.
Unlocking the benefits of higher educational attainment on cognitive performance in Alzheimer’s disease
Authors: Cai Yue , Fang Lili , Li Anqi , Zhou Xin , Yang Jie , Guo Tengfei ,   Alzheimer’s Disease Neuroimaging Initiative
Published: January 2025

Background

Previous cross‐sectional studies suggest that higher educational attainment (EA) may mitigate cognitive decline in Alzheimer’s disease (AD). This study systematically investigated the association between EA and longitudinal cognitive decline across various domains and explored its connections with β‐amyloid (Aβ) plaque, tau tangle, gray matter volume (GMV) and glucose metabolism in AD.

Method

We analyzed Aβ‐PET (A), tau‐PET (T), and 3D T1‐MRI images (N) from the ADNI cohort to identify 58 A+/T‐, 77 A+/T+, and 84 cognitively unimpaired (CU) participants without evidence of AD pathology and neurodegeneration (A‐/T‐/N‐/CU). All participants had longitudinal cognitive assessments covering the Preclinical Alzheimer Cognitive Composite (PACC), memory, executive function, language and visuospatial function. Additionally, a subset of 67 individuals had FDG‐PET images. Participants were divided into High‐Edu and Low‐Edu subgroups based on the median years of education of the whole cohort (16 years). Baseline and follow‐up cognitive measurements in various domains were compared between High‐Edu and Low‐Edu individuals. Furthermore, the association of EA, GMV, FDG, Aβ plaque, tau tangle, and cognitive changes were investigated.

Result

Slower cognitive decline was specifically observed in PACC and language domains of the A+/T+ group among High‐Edu individuals compared to Low‐Edu individuals (Figure 1). The mediation analysis demonstrated that higher EA attenuated PACC decline through greater GMV in the middle temporal gyrus (MTG) and higher glucose metabolism in the left parahippocampal gyrus. Additionally, it mitigated language decline through greater GMV and glucose metabolism in the left inferior temporal gyrus. Furthermore, slower PACC and language decline in High‐Edu individuals was fully mediated through lower tau tangles in the MTG and left superior temporal gyrus (STG), rather than through an Aβ‐associated pathway (Figure 2). A four‐variable mediation analysis further confirmed that tau tangles and GMV in the MTG independently mediated the association between EA and PACC decline (Figure 3).

Conclusion

These findings suggest that higher EA has a prominent protective effect in mitigating PACC and language decline among A+/T+ individuals. The protective effects of higher EA are mediated through the maintenance of greater GMV (or FDG) and lower tau tangles, and these pathways independently contribute to the attenuation of PACC and language decline.
A Model for Implementing Psychotherapy for Individuals with Cognitive Impairments
Authors: Johnson Adrienne L , Wyman Mary F. , Chin Nathaniel A.
Published: January 2025

Individuals with Mild Cognitive Impairment (MCI) and dementia are at greater risk for mental health conditions including depression and anxiety than those without cognitive impairments.1,2 Non‐pharmacological interventions are considered first‐line to address these problems, but access to psychotherapy services for these groups remains limited. Adapted evidence‐based therapy approaches, including Cognitive Behavioral Therapy (CBT)3‐5 and Acceptance and Commitment Therapy (ACT)6 are effective for treating comorbid mental health conditions in populations with cognitive impairments. We report on the implementation of psychotherapy services for patients with MCI and Mild Dementia within the Memory Assessment Clinic of a large academic healthcare system in Midwest U.S. Pre‐implementation process included identifying a clinic champion to support adoption and observing current clinic flow by the practitioner (a licensed Clinical Health Psychologist with expertise treating patients with neurological issues) to determine logistics. Referrals were initially solely provided by the clinic champion, then opened to the entire clinic with pre‐determined eligibility guidelines: recent MCI or Mild Dementia diagnosis, comorbid anxiety or depressive symptoms, and lack of rapid forgetting in during medical examination. Therapy patients were seen on a short‐term basis (6‐8, 45‐minute sessions) to maximize access for as many patients as possible, using CBT or ACT‐based therapy modalities. Therapy content was tailored and person‐centered but included (in chronological order): evaluation of cognitive capacities to leverage during therapy, identification of treatment goals, identification of problematic cognitions/behaviors, introduction of new treatment content/coping skills (one topic/session), assign homework assignments using learned skills, evaluation of homework effectiveness and roadblocks. The initial session was in‐person to facilitate comprehensive evaluation and build rapport, and family caregivers were included as indicated. Follow‐up sessions were provided in‐person or via telehealth. Barriers to implementation included incorrect use of referral process, lack of provider knowledge of new services, and limited provider availability. Facilitators included developing a wait list for referred patients and regular education to providers with written details of the service (e.g., referral process and eligibility; waitlist duration). Similar strategies have been used in other health systems and suggest the feasibility of incorporating psychological treatment for cognitively impaired patients as part of a multi‐component treatment model.
Contrasting the Association of episodic memory using traditional neuropsychological test and acoustic features with PET imaging markers
Authors: Sunderaraman Preeti , Lyu Chenglin , Young Christina B. , Karjadi Cody , Mormino Elizabeth , Sidelinger Lauren , Devine Sherral A. , Kolachalama Vijaya B. , Lin Honghuang , Au Rhoda
Published: January 2025

Background

Preclinical measures of Alzheimer’s disease (AD) risk include markers of amyloid and tau measured via PET scans and decline in verbal memory. Audio features from recorded speech collected during neuropsychological testing offer an alternative for monitoring preclinical AD symptoms that is nonintrusive, economical, and scalable but warrants further validation. This study aims to examine whether there is a stronger association between acoustic metrics and PET imaging biomarkers compared to a traditional verbal memory test.

Methods

This study involved 196 Framingham Heart Study dementia‐free participants with available acoustic, as well as PET amyloid and tau imaging data (refer to Table 1 for demographics). For each participant, 65 acoustic features were extracted from speech samples recorded during the Wechsler's Memory Scale Logical Memory immediate (LMI) and delayed (LMD) recall tests, utilizing OpenSMILE for feature extraction. Multiple regression analyses were performed with a global composite score for PET amyloid, or PET tau for five regions (entorhinal, amygdala, parahippocampal, inferiortemporal, putamen) that were examined individually as outcomes. LMI, LMD, acoustic features derived from LMI and from LMD recorded voice segments were related to AD PET biomarkers in each regression model. Age, sex, education level, and APOE status were used as covariates for all analysis. Models using tau as outcome were adjusted for global amyloid.

Results

After adjusting for multiple comparisons using FDR (significance set at 0.5), among the 65 acoustic features, the Mel‐frequency cepstral coefficient (p=0.01) and audio spectral features of the acoustic features derived from LMD (p=0.02) were most strongly associated with inferiotemporal tau region (see Table 2). No traditional measure of LMI or LMD was significantly related to any AD PET biomarker.

Conclusions

In a group that was clinically asymptomatic for dementia/AD, only digital measures were associated with AD tau biomarker. These findings suggest that digital voice features have increased sensitivity to detect those at AD risk compared to traditional cognitive tests and have potential to serve as a biomarker of AD. Future studies in diverse populations are needed to test the robustness of these findings.
Utility of CSF biomarkers in assessing neurodegeneration in Early‐Onset Alzheimer’s disease
Authors: Eldaief Mark C. , Touroutoglou Alexandra , Brickhouse Michael , Katsumi Yuta , Eloyan Ani , Nudelman Kelly N. , Foroud Tatiana M. , Carrillo Maria C. , Rabinovici Gil D. , Apostolova Liana G. , Schindler Suzanne E. , Herries Elizabeth M. , Henson Rachel L. , Dage Jeffrey L. , Dickerson Bradford C.
Published: January 2025

Background

There is a significant need for biomarkers of neurodegenerative burden in Early‐onset Alzheimer’s disease (EOAD). Evidence suggests that levels of specific CSF biomarkers (e.g., Neurofilament light (NfL), Synaptosomal‐Associated Protein (SNAP‐25), neurogranin, Visinin‐like protein 1 (VILIP‐1), Aß 42/40, phospho‐tau and total tau) index the extent of neurodegeneration in dementing illnesses. However, it remains unclear whether these biomarkers correlate to cortical atrophy patterns in EOAD. Based on prior work demonstrating correlations between NfL, SNAP‐25, neurogranin and Aß 42/40 CSF levels and cognitive impairment in EOAD (Dage et al. 2023), we hypothesized that these biomarkers (and not VILIP‐1, phospho‐tau or total tau) would variably predict cortical atrophy within our recently described EOAD signature (Touroutoglou et al. 2023).

Method

We recruited 92 EOAD patients. In each patient, atrophy within the EOAD cortical signature were calculated as W‐scores (i.e., Z‐scores adjusted for age and sex relative to a sample of healthy controls). We first ran a simple regression analysis of each of the 7 CSF biomarkers and W‐scores in the EOAD signature across EOAD patients. We then entered the biomarkers with significant correlations to the EOAD signature into a stepwise regression analysis with backward elimination to ascertain the most parsimonious model predicting atrophy in the EOAD signature. As a control region not expected to be related to these biomarkers, we assessed correlations to calcarine fissure cortical atrophy.

Result

As predicted, we observed a significant correlation between CSF levels of NfL, SNAP‐25, neurogranin and Aß 42/40 and atrophy within the EOAD signature. After entering these four biomarkers into the stepwise regression analysis, the most parsimonious model identified complementary contributions of NfL, SNAP‐25, and Aß 42/40, in predicting atrophy in the EOAD signature. There were no correlations between any biomarker and calcarine atrophy.

Conclusion

Selected CSF biomarkers in EOAD patients predict the degree of atrophy within the EOAD cortical signature. Ongoing work includes correlating these biomarkers with topographical atrophy patterns on the whole‐brain voxel‐wise level. Our results suggest that certain CSF biomarkers could assess neurodegenerative burden within EOAD individuals. This would provide valuable information regarding disease progression for clinical care and clinical trials involving disease‐modifying therapies.
Investigating the ATN (Amyloid, Tau, Neurodegeneration) framework in Alzheimer’s Disease and its causal genetic‐drivers using Digital‐Twins
Authors: Sanjay Apoorva Bharthur , Sun Li , Shokeen Deepanshi , Isacson Ole , Shin So‐Youn , Latourelle Jeanne
Published: January 2025

Background

Amyloid, Tau and neurodegeneration (ATN), the hallmark pathologies of Alzheimer’s Disease (AD) translating to measurable biomarkers are important for disease modifying therapeutics.

Method

AD Digital‐Twins were built using AITIA’s patented A.I. platform REFSTM [aitiabio.com], based on a Bayesian network model of ADNI data which reverse‐engineered the connectivity of ∼59K multi‐modal variables and AD‐related outcomes profiled from 317 subjects (Control: MCI: Dementia=97:191:29). The average causal effect of each upstream‐downstream variables was estimated through in‐silico counterfactual experiments to evaluate the temporal relationship between the ATN outcomes, to identify the causal gene‐drivers (at blood RNA‐expression level) of “ATN” outcomes, and to investigate the known AD genotypic variants strongly driving ATN gene‐drivers. Age‐gene interaction was additionally explored through “double‐intervention” experiments, to evaluate age‐specific effects of gene‐drivers on ATN outcomes.

Result

AD Digital‐Twins evaluated the ATN temporal relationship, and recapitulated some known relationships such that CSF‐abeta and Tau measures drive neurodegeneration measures, although it also showed CSF‐Tau measures to be upstream of amyloid PET, suggesting Tau changes may interact with other outcome changes more dynamically over the course of disease progression (Figure 1). Next, in‐silico experiments identified a total of 228 ATN gene‐drivers, including 8 common to all outcome‐categories, which affect cognitive outcomes as well (Figure 2) and are strongly related to immune‐response and inflammation pathways. Many of these genes were causally driven by multiple AD‐associated genotypic variants reported in GWAS, especially in the “NECTIN2” and “APOE” region. Furthermore, in‐silico experiments showed some Tau‐driving genes are likely to be causally driven by Amyloid and Neurodegeneration driving genes. Lastly, age‐specific effects were observed for a portion of each A‐, T‐ and N‐ gene‐drivers, especially with Tau‐driving genes having a stronger effect with opposite directionality for younger vs older age groups (Figure 3).

Conclusion

Tau related abnormalities are likely early events in AD progression and more strongly linked to disease pathophysiology. Aitia’s AI platform allows powerful and systematic evaluation of multiple modalities and outcomes, accelerating precision medicine efforts in AD.
Using a blood‐based biomarker panel for Alzheimer’s disease to determine eligibility for disease modifying treatment in a memory clinic setting: three scenarios
Authors: Vigneswaran Sinthujah , Verberk Inge M.W. , Gouda Mariam , van de Giessen Elsmarieke , Wilson David H , Vijverberg Everard G.B. , Pijnenburg Yolande A.L. , van der Flier Wiesje M. , Teunissen Charlotte , van Harten Argonde C.
Published: January 2025

Background

Blood‐based biomarkers (BBM) for Alzheimer’s disease (AD) may be able to identify individuals eligible for emerging anti‐amyloid treatments (DMT). We aimed to evaluate how to use BBM as (pre‐) selection tools for DMTs by simulating different triaging scenarios in a memory clinic setting.

Methods

We included 1199 participants from the Amsterdam Dementia Cohort with measured BBM (plasma pTau181, GFAP and NfL) and used a predefined Youden‐index based cut point for amyloid positivity (method: https://doi.org/10.1002/alz.078748) (Table 1). We created three scenarios by changing at which step in the triaging process BBM were used. Other steps in the triaging process included seeing the medical doctor who determined clinical eligibility and an MRI (Table 1). A traditional screening approach including determination of amyloid positivity by CSF/amyloid‐PET after the MRI was taken as reference scenario A. Scenarios B, C and D all placed BBM determination at different triaging steps (Figure 1). In addition, we tested a scenario E with a high–intermediate–low likelihood categorisation using pre‐defined 90%‐sensitivity and 90%‐specificity levels.

Results

In scenario A, 268 out of 1199 (22%) patients would undergo CSF/amyloid‐PET to select 183 individuals for DMT (Figure 1). When only patients with an abnormal BBM panel were selected to undergo CSF/amyloid‐PET, the number of CSF/amyloid‐PET was reduced with 47% to 141, at the cost of missing 59 (22%) eligible patients. Not performing CSF/amyloid‐PET after BBM measurement in scenario B‐D, resulted in 17 patients receiving treatment without being amyloid positive (12% of all patients receiving DMT) and missing 59 (22% of total eligible) eligible patients. Further consequences for the number of MRIs and medical consultations needed are summarized in Table 2. Only offering CSF/amyloid‐PET to patients with an intermediate chance of being amyloid positive in scenario E would result in 90 CSF/amyloid‐PET evaluations (49%) at the cost of missing 51 (19%) eligible patients and offering DMT to 11 (4%) amyloid negative patients.

Conclusions

Using BBM to preselect patients for DMT may reduce triaging costs, but it comes at the risk of under‐ or over‐treating patients. These unwanted effects can only be reduced by using blood‐based biomarkers with excellent diagnostic value.
Sex and amyloid burden modify tau spatial progression following entorhinal tau onset
Authors: Cody Karly , Heston Margo B. , Teague Jordan P , Alberson Sara Frost , Wintlend Madilynn , Bahr Melissa , Langhough Rebecca E. , Johnson Sterling C. , Betthauser Tobey J.
Published: January 2025

Background

Characterizing factors that impact longitudinal tau progression is important for understanding tau heterogeneity observed in clinical trials and research. This work examined factors associated with longitudinal tau PET (18F‐MK‐6240) accumulation relative to onset of entorhinal tau (EC) to better understand neurofibrillary tangle (NFT) spatial accumulation in Alzheimer’s disease.

Method

Participants (N=595; Table 1) from the Wisconsin Registry for Alzheimer’s Prevention and WADRC underwent serial amyloid and tau PET imaging. Amyloid burden was quantified from global cortical 11C‐PiB DVR(0‐70). Tau burden was quantified in regions of interest (ROIs) corresponding to Braak NFT1‐6 using 18F‐MK‐6240 SUVR(70‐90). Sampled iterative local approximation (SILA) was used to model EC tau trajectories and estimate EC tau‐time and onset age and, separately, to estimate amyloid level at tau scan. Regional tau accumulation was modeled with mixed effects models separately for Braak NFT2‐6 as a function of EC tau‐time (i.e. years of EC T+) starting with cubic polynomials. Models also included EC tau‐time*estimated CL at baseline tau (due to known associations between amyloid and tau) and additionally examined EC tau‐time interactions with age at baseline tau, self‐reported sex, and APOEe4.

Result

In this largely unimpaired cohort, there were no significant differences observed in APOEe4 carriage, baseline clinical status or amyloid level, or estimated age of A+ or EC T+ onset across males and females (Table 1). Regional T+ followed the expected spatiotemporal pattern (NFT2‐6) as time from EC T+ onset increased (Figure 1A). Across NFT3‐6, ROIs outside the medial temporal lobe (MTL), female sex and higher baseline amyloid were each associated with significantly faster tau accumulation rates relative to EC tau‐time (Figure 1A‐B). Post‐hoc comparisons indicated that rates of tau accumulation relative to EC tau‐time increased sequentially with increasing levels of amyloid, and that in NFT3‐6, the average rate of tau accumulation was significantly faster for females compared to males (Table 2).

Conclusion

These findings provide evidence that tau accumulation and spread outside the MTL is accelerated by higher levels of amyloid and female sex. These data suggest that while people follow a similar spatial progression, the rate of progression varies, which may inform tau‐related dementia prediction.
ED care and Amyloid‐Related Imaging Abnormalities ‐ Impact and Future research
Authors: Aggarwal Neelum T. , Carpenter Christopher R
Published: January 2025

With the approval of disease modifying treatments for Alzheimer’s Disease, the practice of dementia care and treatment has changed. Amyloid related imaging changes, are the most common adverse effects of these types of drugs. There is limited guidance across multiple specialties on how to recognize and manage these changes in a dementia patient population. The use of these types of drugs for treatment, impacts multiple stakeholders regarding how they manage and treat dementia patients. ns. Thus the use of these types of drugs, involve multiple stakeholders with different perspectives on how they perceive their role in care and overall treatment plans. In order to optimize collaborative care in the ED setting, recognizing how ARIA may present in a patient who seeks care in the ED, and what types of treatment options are available are needed.
Clinical evaluation of a plasma Neurofilament Light chain electrochemiluminescence immunoassay in Alzheimer’s disease
Authors: Fatima Hadia A , Padmanabhan Nikhil , Cohen Rachel , Romero Daniel , Demos Catherine , Wohlstadter Jacob N , Sigal George , Arnold Steven E. , Kivisäkk Pia
Published: January 2025

Background

Neurofilament light chain (NfL) is a key biomarker for axonal injury providing diagnostic and prognostic value in many neurodegenerative diseases. The sensitivity of electrochemiluminescence (ECL) immunoassays has previously not been adequate to quantify plasma NfL levels in Alzheimer’s disease (AD). Here we performed clinical evaluation of a recently developed ultra‐sensitive plasma NfL ECL S‐PLEX® immunoassay in individuals with AD and other neurodegenerative diseases.

Method

We measured NfL in banked plasma samples from 541 participants in the Massachusetts ADRC’s longitudinal cohort. Of these, 262 had a diagnosis of AD, 121 non‐AD neurodegenerative diseases (Ndgen), 46 other neurological diseases (OND), and 112 were controls (CON). The clinical diagnoses (AD/no AD) were verified by AD biomarkers (neuropathology, amyloid PET, CSF amyloid‐β 42/40 ratio, and/or plasma pTau181). Longitudinal analysis identified 223 participants with mild cognitive impairment (CDR 0.5) at baseline and 4 years of follow up. Of these, 131 remained stable with unchanged CDR, and 92 declined with worsening cognitive impairment (CDR³1.0). NfL was measured in all participants using an ECL immunoassays from Meso Scale Discovery (MSD) and in 72 participants using a SIMOA assay from Quanterix.

Result

NfL levels measured using the two assays were highly correlated. Cross‐sectional analysis using the MSD assay showed the expected differences between the diagnostic groups with highest NfL levels in the Ndgen group, followed by AD, and the two control groups. NfL levels correlated with disease severity measured by cognitive testing (MoCA, global CDR, CDR SOB, and FAQ) at the time of blood collection. NfL levels at baseline were furthermore higher in participants who declined during follow‐up compared to stable participants both when studying the whole cohort and when limiting the analysis to participants with a final diagnosis of AD.

Conclusion

The ultrasensitive ECL plasma NfL assay demonstrated utility for detection of AD and correlated with disease severity. Moreover, baseline plasma levels of NfL reflect cognitive decline over the next 4 years, providing prognostic information that may have utility in both clinical practice and clinical trial populations.
Hearing Loss and Default‐Mode Network Connectivity in Older Adults with Subjective Cognitive Decline and Mild Cognitive Impairment
Authors: Grant Nicole , Phillips Natalie
Published: January 2025

Background

Hearing loss (HL) is a risk factor for dementia that is prevalent in older adults. Altered brain connectivity is present in individuals with HL, individuals with subjective cognitive decline (SCD), and individuals with mild cognitive impairment (MCI). In individuals with HL, altered brain connectivity has been associated with impaired scores on cognitive tests. In individuals with MCI, altered brain connectivity has been associated with degree of cognitive impairment and progression to dementia. Therefore, altered brain connectivity is a potential mechanism by which HL is associated with increased dementia risk.

Methods

Data are from the Comprehensive Assessment of Neurodegeneration in Dementia study (Data Release 6, May 2023). Participants include cognitively unimpaired (CU, n=78, age=69.5), %female=77%), SCD (n=85, age=70.0, %female=74%), and MCI (n=159, age=72.2), %female=40%) individuals. Independent component analysis was used to identify the default‐mode network (DMN), a resting‐state network that is altered in individuals at risk for dementia. Hearing data include a pure‐tone hearing screening protocol and speech‐in‐noise perception. Based on the pure‐tone hearing screening, participants were classified as having normal hearing (NH) or HL. In each diagnostic group (CU, SCD, MCI), analyses of covariance compared DMN connectivity between the NH and HL participants and linear regressions evaluated the relationship between speech‐in‐noise perception and DMN connectivity.

Results

Preliminary results are available for 94 older adults with MCI (NH = 60, pure‐tone HL=34). Compared to individuals with MCI and NH, those with MCI and HL had decreased connectivity between the DMN and the caudate and thalamus. There was no relationship between DMN connectivity and speech‐in‐noise perception. The CU and SCI groups are expected to have a greater capacity for compensatory changes in functional connectivity. Therefore, it is expected that in the CU and SCI groups, pure‐tone HL and speech‐in‐noise perception will be associated with increased DMN connectivity.

Conclusion

Current results suggest that the increased dementia risk associated with HL may be due to decreased DMN connectivity and that pure‐tone HL and speech‐in‐noise perception are differentially related to DMN connectivity. Future research will determine whether the relationship between hearing and DMN connectivity differs between older adults with varying risk of cognitive impairment.
AT(N), neuronal and glial pathologies underlying neuropsychiatric symptoms and cognition in young‐onset dementia
Authors: Chiu Wei‐Hsuan , Goh Anita M Y , Malpas Charles B , Eratne Dhamidhu , Kelso Wendy , Walterfang Mark , Velakoulis Dennis , Loi Samantha M
Published: January 2025

Background

Young‐onset dementia (YOD) refers to the occurrence of dementia symptoms in people under the age of 65. Neuropsychiatric symptoms (NPS) are increasingly considered as the preclinical manifestations of YOD, posing a challenge to differentiate from psychiatric conditions with overlapping symptoms. The aim of this study is to investigate the AT(N), neuronal and glial pathologies underlying NPS and cognition in YOD.

Method

This study used clinical data from the Neuropsychiatry Centre, the Royal Melbourne Hospital, Melbourne, Australia. Sixty‐nine people with YOD were identified and assessed for biomarkers, NPS and cognition (mean age [SD] = 57.6 [7.3], female N = 23 [33%]). Their cerebrospinal fluid and plasma levels of amyloid‐beta, total‐tau (t‐tau), phosphorylated‐tau (p‐tau), neurofilament light chain protein (NfL) and glial fibrillary acidic protein (GFAP) were measured. Common NPS such as depressive and behavioural symptoms were also measured. Cognitive functions were measured by a comprehensive battery of neuropsychological assessments. General linear regressions were used to investigate the associations between biomarkers, NPS and cognition.

Result

Memory recall was correlated with levels of amyloid‐beta (r = .52, 95% conference interval [CI] = [.32, .75]), t‐tau (r = ‐.4, 95% CI = [‐.65, ‐.16]) and p‐tau (r = ‐.34, 95% CI = [‐.58, ‐.11]). Executive function was correlated with levels of NfL (r = ‐.4, 95% CI = [‐.78, ‐.03]). Levels of NfL were further correlated with the severity of stress (r = ‐.41, 95% CI = [‐.73, ‐.11]). No evidence of associations between levels of GFAP and NPS or cognition was observed. Visuoconstruction was correlated with levels of amyloid‐beta (r = .45, 95% CI = [.24, .67]), in which this relationship was moderated by the severity of self‐care problems (F [6, 28] = 5.48, p < .05).

Conclusion

The results suggest that the changes in memory recall are associated with an intricate interplay between AT(N) pathologies in people with YOD. Executive function in YOD may be related to neuronal alterations rather than glial alternations in the brain. Combining biomarker results, the real challenge of self‐care problems in people with YOD observed by their caregivers may serve as a surrogate estimate to predict outcomes in visuoconstruction.
Multivariate Neuroimaging Analyses Reveal an Important Role for Iron Metabolism in Populations at Risk for Alzheimer’s Disease
Authors: Badea Alexandra , Moon Hae Sol , Anderson Robert J , Mahzarnia Ali , Stout Jacques Andrew , Han Zay Yar , Johnson Kim G , O'Brien Richard J
Published: January 2025

Background

Alzheimer's disease (AD) is multifactorial, thus multivariate analyses help untangle its effects. We employed multiple contrast MRI to reveal age‐related brain changes in populations at risk for AD, due to APOE4 carriage. We assessed volume and microstructure changes using diffusion weighted imaging, and quantitative magnetic susceptibility maps (QSM) reflective primarily of cerebral iron metabolism.

Method

Our study included 48 non APOE4 carriers, and 42 APOE4 carriers, with age ranging from 20.2 to 83; males and females. Imaging was done at 3T using diffusion weighted imaging (DWI) with TE 60.6 ms, TR=14725 ms, 21 diffusion directions (b=1000 s/mm2), reconstructed at 1 mm isotropic resolution using MUSE. A T1‐weighted FSPGR sequence was used for morphometry and co‐registration, TE=3.2 ms, TR=2263.7 ms, α=8°, prep time 900 ms, recovery time 700 ms, 1 mm isotropic resolution. QSM were calculated using STISuite based on fGRE images using TEeff=24.3 ms, TR 100 ms, α=15°, 8 echoes, reconstructed at 1x1x1.5 mm. Images were mapped into a common space before univariate and multivariate voxel‐based analyses (VBA).

Result

Univariate VBA (Figure 1) showed accentuated age effects in APOE4 carriers in areas involved in visual information processing (pericalcarine and lingual cortex, precuneus); as well as in cognitive and motor control processes (superior frontal cortex). FA supported the findings in visual processing areas, and added regions in the left temporal lobe (entorhinal cortex, middle and inferior‐temporal cortices). FA also revealed a role for the anterior cingulate, insula, left cerebellum; pars triangularis and pars orbitalis, involved in language processing. QSM analyses supported the findings in visual, cingulate, and cerebellar cortices, and revealed a role for the hippocampus, amygdala, and basal ganglia (caudate, putamen, pallidum). Multivariate analyses (Figure 2) supported a role for the precuneus, cuneus, and lingual cortex, the inferior temporal and cingulate cortices, and the caudate. The canonical vectors (Figure 3) for the top significant clusters revealed an important role for QSM.

Conclusion

Our results suggest accelerated changes in multiple domains besides memory in aging APOE4 carriers. Multivariate analyses supported an important role for age associated changes in cerebral iron metabolism, and oxidative stress, suggesting increased vulnerability to toxic insults.
Consistent classification of amyloid status by fasted and non‐fasted plasma Aβ42/40 and Amyloid Probability Score
Authors: Bonomi Samuele , Benzinger Tammie L.S. , Xiong Chengjie , Morris John C. , Braunstein Joel B. , Schindler Suzanne E.
Published: January 2025

Background

It is unknown whether fasting status affects classification of amyloid status by plasma Aβ42/40.

Methods

The cohort included 50 amyloid PET positive and 50 amyloid PET negative individuals enrolled in studies of memory and aging at the Knight Alzheimer Disease Research Center (ADRC). Included individuals had a non‐fasted plasma sample and an amyloid PET scan performed within 3 months of a fasted plasma sample. Non‐fasted samples were collected throughout the day. CSF was collected at approximately 8 am at the same session as the fasted plasma sample. Plasma Aβ42/40 was measured by an immunoprecipitation‐mass spectrometry (IPMS) assay at C2N Diagnostics. The classification accuracy of amyloid PET and CSF status was evaluated in the fasted and non‐fasted samples by logistic regression. Cut‐offs for amyloid positivity with the highest Youden index (combined sensitivity and specificity) were selected. The correlations of fasted and non‐fasted plasma Aβ42/40 and amyloid probability score (APS, a modeled value incorporating plasma Ab42/40, ApoE proteotype, and age) were evaluated.

Results

Participants had an average age of 69.3 ± 8.3 years (mean ± standard deviation) and 94 of 100 were cognitively unimpaired. The average interval between collection of fasted and non‐fasted plasma samples was 1.8 ± 0.7 months. Classification accuracy of amyloid status based on fasted or non‐fasted samples (either plasma Aβ42/Aβ40 or APS) was almost identical: for amyloid PET status, ROC AUC 0.88 and 0.89 respectively; for CSF Ab42/40 status, ROC AUC 0.89 and 0.91 respectively. The APS cut‐off for amyloid PET status and CSF status was 16 for both fasted and non‐fasted samples; based on this cut‐off, 8 of 100 samples had discordance between fasted and non‐fasted plasma APS. The Spearman correlation between fasted and non‐fasted plasma for Ab42/40 was ρ=0.74 (0.64‐0.82) and for APS was ρ=0.87 (0.82‐0.91).

Conclusions

Measurements of plasma Aβ42/40 with an IPMS assay and the APS had high and consistent associations with amyloid PET and CSF Aβ42/40 status in fasted and non‐fasted samples. These results suggest that fasting status, and potentially the time of day that a sample is collected, does not significantly affect classification of amyloid status by plasma Aβ42/40 or APS.
Associations between BDNF Val66Met and tau‐PET and episodic memory performance in sporadic AD
Authors: Thomson Diny , Rosenich Emily , Maruff Paul , Lim Yen Ying ,   the Alzheimer’s Disease Neuroimaging Initiative
Published: January 2025

Background

In the presence of abnormally high amyloid (Aβ+), carriage of the brain‐derived neurotrophic factor (BDNF) Val66Met polymorphism Met allele (Met66) is associated with faster clinical disease progression, greater neuronal loss and faster rate of CSF total‐tau and p‐tau181 compared to matched Val66 Aβ+ homozygotes. Aβ levels are unaffected by carriage of Met66. This suggests reduced neurotrophic support may accelerate Aβ‐related neuronal dysfunction and cognitive decline. We aimed to clarify the role of BDNF Val66Met in moderating region‐specific neurofibrillary tangle (NFT) formation in the brain in sporadic Alzheimer’s disease (AD).

Method

Data from 26 Aβ+ older adult Met66 carriers and 68 Val66 homozygotes enrolled in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) were included in the analysis if they had at least one tau‐PET scan, Aβ‐PET scan, and neuropsychological assessment available. Participants were not excluded based on disease stage. ANCOVAs were conducted to determine, cross‐sectionally, whether levels of tau‐PET tracer retention at each Braak stage, episodic memory performance (measured on ADNI’s memory composite), and Aβ levels differed between Met66 carriers and Val66 homozygotes at the time of their first tau‐PET scan. Aβ levels, APOE ε4 status, sex, age, baseline diagnosis, and years of education were included as covariates.

Result

Aβ+ Met66 carriers showed significantly greater tracer retention in the entorhinal cortex (i.e., Braak 1) compared to Val66 homozygotes (p=.03), with the magnitude of this difference moderate (d=0.39; Table 1). There were no significant differences between Met66 carriers and Val66 homozygotes in tracer retention across all other Braak stages, episodic memory performance or Aβ levels (Table 1).

Conclusion

The greater tau‐PET tracer retention in the entorhinal cortex in Met66 carriers may reflect their greater NFT pathology relative to Val66 homozygotes during early stages of AD. This is consistent with previous observations of higher levels of CSF total‐tau and p‐tau181 in Aβ+ Met66 carriers, both in sporadic AD and autosomal dominant AD. Together, these findings support the hypothesis suggesting that loss of neurotrophic support, associated with Met66 carriage, may confer greater vulnerability to Aβ‐related neurodegeneration compared to Val66 homozygosity.
Staging of tau accumulation associated with cognitive decline in AD progression using 18F‐MK‐6240 PET data
Authors: Shafiee Neda , Fonov Vladimir S , Rajabli Reza , Therriault Joseph , Rahmouni Nesrine , Servaes Stijn , Gauthier Serge , Stevenson Jenna , Poltronetti Nina Margherita , Rosa‐Neto Pedro , Collins D Louis
Published: January 2025

Background

Alzheimer's disease is characterized by the accumulation of amyloid beta and the formation of tau neurofibrillary tangles (NFTs), leading to irreversible neurodegeneration. The formation of NFTs is believed to follow a stereotypical pattern known as Braak stages. Here, using tau‐PET tracer 18F‐MK‐6240 we aim to analyze patterns of Tau accumulation associated with AD‐related cognitive decline and build an in‐vivo, data‐driven staging system based on longitudinal data, using an estimated latent time of disease onset based on cognitive scores to place all subjects on a common timeline.

Method

We used 18F‐MK‐6240 scans from the TRIAD dataset, including 194 cognitively normal, 99 mild cognitive impairment and 77 with Alzheimer’s disease dementia. We used a trajectory model (Kühnel et al. 2021) to align patients based on their longitudinal cognitive scores along a continuous latent disease timeline. ADAS‐cog‐13, CDR‐SB and MMSE were used simultaneously to estimate time‐shifts for each subject. As there were not enough longitudinal time‐points in the TRIAD dataset to directly apply this method, we first applied the method to the full ADNI dataset and then used a nearest‐neighbour technique to impute the disease offset for TRIAD subjects from the closest 38 subjects in the ADNI cohort. (n=38 was found to be optimal through cross‐validation within ADNI.) This supervised imputation model used baseline cognitive test scores (MMSE and CDR‐SB) along with the age of participants to impute their latent disease onset.

Result

We defined 5 2‐year windows on the 10‐year span of the estimated latent disease offset timeline from 4y before onset and up to 6y afterwards. Tau PET scans for subjects within each window were averaged, resulting in 5 average Tau templates, in addition to an initial template generated using amyloid negative normal participants. This staging system depicts the incremental tau accumulation along with the decline in cognition. Medial temporal regions show initial accumulation, starting in transentorhinal and entorhinal cortices, and later stages show full brain involvement.

Conclusion

Using purely data‐driven techniques, this method reveals patterns of tau accumulation associated with cognitive decline. These models will help understand the link between Tau and cognitive decline, even for those subjects that don't fit Braak framework.