bluuebunny/crossref_metadata_2025 huggingface dataset:
Association of 18F‐flortaucipir PET with tau neuropathology in AD and other neurodegenerative disorders
Authors: Vrillon Agathe , Mejía‐Perez Jhony Alejandro , Spina Salvatore , Schonhaut Daniel R. , Yballa Claire , soleimani‐meigooni David N. , Boxer Adam L. , Dage Jeffrey L. , Rojas Julio C. , VandeVrede Lawren , Lago Argentina Lario , Jagust William J. , Miller Bruce L. , Rosen Howard J. , Seeley William W. , Grinberg Lea T. , Rabinovici Gil D. , Joie Renaud La
Published: January 2025

Background

Tau‐PET with [18F]Flortaucipir is FDA‐approved for the identification of AD tau neuropathology in the differential diagnosis of patients with cognitive impairment. However, its performance in detecting early AD stages requires further assessment. We aimed to i) examine the relationships between Flortaucipir‐PET and AD neuropathology, and ii) characterize the relationship between Flortaucipir‐PET and emerging plasma ptau217 biomarker in autopsy cases.

Method

We analyzed Flortaucipir‐PET consecutively acquired from 59 patients with a clinical diagnosis of various neurodegenerative diseases who underwent post‐mortem examination at the UCSF Alzheimer’s Disease Research Center (age=66±12, 32% female, median PET‐to‐autopsy=45 months, range 25‐60). Flortaucipir‐PET was acquired 80‐100 min post‐injection and processed using an MRI‐based pipeline to create Standardized Uptake Value Ratio (SUVR, reference: inferior cerebellar cortex). Amyloid‐PET‐negative cognitively unimpaired participants (n=103) without autopsy data were included as a reference. We examined the associations between i) Flortaucipir‐SUVR in two regions of interest (ROI): the temporal meta‐ROI (i.e. validated AD signature measure) and entorhinal cortex (i.e. early tau region), and ii) neuropathological diagnosis and neurofibrillary tangle Braak stages. Plasma p‐tau217 measurement on the Meso Scale Discovery platform was available for a sample of n=31 (median PET‐to‐plasma=3.5 months, range 1‐9).

Results

Our cohort included 30 patients with a primary neuropathological diagnosis of AD, 23 with various non‐AD tauopathies and 4 patients with non‐tau diseases. Flortaucipir‐SUVR was elevated in patients with primary AD neuropathology (Figure 1a,c) and differentiated them from non‐AD cases with high accuracy (areas under the curve>0.98 for both regions). When examining associations between Braak stage and Flortaucipir‐SUVR, we only detected consistent PET signal in patients with Braak VI (Figure 1b,d). Patients with both non‐AD tauopathies and non‐tau diseases displayed variable patterns of mild tracer uptake (Figure 2). Plasma p‐tau217 showed strong association with Flortaucipir‐SUVR (r≥0.73), fully driven by cases with Braak stages V‐VI (Figure 3).

Conclusion

[18F]flortaucipir‐PET can identify advanced AD‐tau neuropathology and is highly concordant with plasma p‐tau217. The lack of evidence for early‐stage detection should be interpreted with caution due to the low number of cases with Braak stages III‐V, and the delay between PET and autopsy.
Investigating Reactive Astrocytosis in Alzheimer's Disease Using [18F]Fluorodeprenyl‐D2 PET Imaging
Authors: Rauchmann Boris‐Stephan , Guersel Selim Üstün , Kurz Carolin Isabella , Lindner Simon , Franzmeier Nicolai , Buerger Katharina , Gnörich Johannes , Zaganjori Mirlind , Vogler Letitia , Sanzo Laura , Seelos Klaus , Perneczky Robert , Brendel Matthias
Published: January 2025

Background

Alzheimer's Disease (AD) is often accompanied by neuroinflammation, which manifests prior to significant cognitive decline. Reactive astrocytosis is a hallmark of such inflammation, potentially serving as an early biomarker for AD pathology. Our study employs [18F]fluorodeprenyl‐D2 ([18F]F‐DED) positron emission tomography (PET) imaging to in vivo quantify astrocytosis comparing AD with healthy controls and examines its assocciation with cognitive deterioration in AD.

Method

Our cross‐sectional study involved 12 early‐stage Alzheimer's Disease (AD) patients and 11 healthy controls, all undergoing [18F]F‐DED and MRI imaging. AD diagnosis was based on CSF amyloid‐β42/40 ratios under 5.5% and MMSE scores below 27, while controls had MMSE scores above 27 with no amyloid‐β pathology. We assessed differences in [18F]F‐DED uptake using ROI‐based analyses, processing data in PMOD software. This involved calculating the radioligand's volume of distribution in tissues and image‐derived plasma at equilibrium. For ROI analysis, we adapted the Automated Anatomical Labeling (AAL) atlas to each participant's gray matter masks and [18F]F‐DED maps, computing median uptake values per ROI. Cortical amyloid deposition was measured using [18F]flutemetamol β‐amyloid‐PET imaging. CSF biomarkers, including Aβ42, Aβ40, and phosphorylated tau, were analyzed using the Lumipulse G1200 platform. Cognitive performance was evaluated with the MMSE. We used ANCOVA and linear regression in R to explore correlations between cognitive performance and [18F]F‐DED uptake, adjusting for age and sex.

Results

Increased [18F]F‐DED uptake was observed in AD‐affected areas comparing AD with controls, particularly in the bilateral occipital (Left: T:2.28, p=0.03; Right: T:2.51, p=0.02), temporal (Left: T:2.21, p=0.03; Right: T‐Stat 2.22, p=0.03), and frontal lobes (Left: T:2.46, p<0.02; Right: T:2.45, p<0.02). This increased uptake was significantly correlated with cognitive decline, especially in temporal regions, associated with lower cerebrospinal fluid amyloid‐β ratios and reduced MMSE scores (p<0.01), consistent after adjusting for age and gender.

Conclusion

Our findings indicate that [18F]F‐DED PET imaging can detect reactive astrocytosis in AD‐sensitive areas, potentially preceding severe cognitive symptoms. The correlation between increased tracer uptake and cognitive impairment underscores the value of [18F]F‐DED as an AD progression biomarker, offering promising prospects for early diagnosis and intervention in AD's initial stages.
Diagnostic performance of online cognitive assessment and plasma biomarkers in Alzheimer’s Disease and Subjective Cognitive Impairment using the Oxford Cognitive Testing Portal
Authors: Toniolo Sofia , Scholcz Anna , Amein Benazir , Ganse‐Dumrath Akke , Thompson Sian , Manohar Sanjay , Zetterberg Henrik , Husain Masud , Zhao Sijia
Published: January 2025

Background

Plasma biomarkers have emerged as a promising tool to detect the presence of Alzheimer’s disease (AD) when cognitive symptoms have not yet emerged. However, there is also a pressing need to detect and track subtle cognitive change at the preclinical stage of AD for population screening purposes and to monitor disease progression at scale. A potential solution is remote cognitive assessment, yet it is still not extensively employed.

Method

114 participants (37 AD, 22 subjective cognitive impairment (SCI) patients and 55 age‐matched controls) were recruited from the Oxford Centre for Cognitive Disorders. They were tested on a newly developed, fully remote online cognitive assessment tool, Oxford Cognitive Testing Portal (OCTAL), which hosts a wide range of validated cognitive tasks, such as the Rey‐Osterrieth Complex Figure (ROCF), Trail Making Task (TMT), Digital Symbol Substitution task (DSST)), Corsi Block Task (CORSI), as well as novel visual short‐term memory (Oxford Memory task) and visual long‐term memory (Object‐in‐Scene task) tasks (Figure 1). All participants underwent standard in‐person cognitive testing, i.e. Addenbrooke's Cognitive Examination‐III (ACE). Plasma p‐tau181, GFAP, NFL, Aβ42/40 ratio were also measured. Logistic regression was used for group classification.

Result

Performance on OCTAL was able to discriminate between SCI from healthy controls with an AUC of 0.78 (Figure 2a), statistically outperforming standard neuropsychological testing (ACE), which had an AUC of 0.65. Combining plasma biomarkers to OCTAL further increased diagnostic accuracy, reaching an AUC of 0.82 in group classification. OCTAL also outperformed ACE in distinguishing individuals with AD from SCI (Figure 2b), where adding plasma biomarkers to performance at OCTAL achieved a perfect separation (AUC of 1) between the groups.

Conclusion

These findings demonstrate the potential of OCTAL for widespread, cost‐effective cognitive testing. They also emphasise the utility of combining plasma biomarkers and digital cognitive tests to improve diagnostic accuracy across different stages of the disease. These accessible tools could pave the way to more scalable protocols for screening, stratification and monitoring of patients with preclinical AD.
A longitudinal real‐world study in patients with Alzheimer’s Disease dementia using frequent multi‐domain digital measurements at‐home performed on the Cumulus NeuLogiqTM Platform: usability and feasibility findings
Authors: Diggin Shannon , Alexander‐Sefre Azar , Murphy Brian , Nolan Hugh , Rowe James B , Rueda‐Delgado Laura M , Buick Alison R
Published: January 2025

Background

Many outcome measures used in AD clinical trials require clinic visits and are paper based, making them infrequent and burdensome ‘snapshots’, subject to rater bias. A consortium of 10 pharma companies came together with Cumulus Neuroscience to design a solution for frequent, objective, real‐world measurement across a range of domains. We present a study that examined the feasibility of asking patients with mild dementia to use the neuroassessment platform repeatedly at home for one year.

Method

Seven UK sites recruited Alzheimer’s type mild dementia patients (n=59, ACE‐III scores >60 and ≤88) and a matched cohort of controls (n=60). Participants had 2 weeks of at‐home familiarisation to become comfortable with the 8 assessments presented on a mobile tablet and EEG headsets, one for wake and another for sleep. A staggered longitudinal protocol followed, with burst sampling tapering to periodic sampling over the year. Benchmark paper‐based assessments (including ADAS‐Cog) and self‐reported usability were collected at months 0, 6 and 12. Additionally, plasma was collected at months 6 and 12 for later biomarker analysis.

Result

Feedback collected at baseline showed differences in technology usage. Patients felt they required more technology support than controls (49% vs 17%). Nonetheless, they were successful in using the study technology at home on a regular basis and maintained a high level of adherence to the protocol. Overall compliance was 76.3% for dementia, 88.4% for controls and was highest in the latter stages of the study during periodic sampling (Stage 4 during wake EEG sessions: 80.8% dementia, 93.9% controls). System Usability Scale results indicated controls rated usability higher (63.8) than dementia participants (54.5) at baseline, increasing for both groups by Week 52 (74.4 controls; 58.3 dementia).

Conclusion

With appropriate technology design, and provision of training and support, patients with Alzheimer’s disease dementia are capable and willing to provide repeated, real‐world samples of a broad range of objective digital endpoints for clinical research.
Associations of plasma biomarkers of neurodegeneration with characteristics related to type 1 diabetes
Authors: Delgado Nikki , Chaytor Naomi Sage , Germine Laura Thi , Hawks Zoë W , Jung Laneé , Cleveland Michael , Bulger Jane , Grinspoon Elizabeth , Janess Kamille , Sliwinski Martin J. , Weinstock Ruth S , Chhatwal Jasmeer P. , Kivisäkk Pia , Beeri Michal S , Fonseca Luciana Mascarenhas
Published: January 2025

Background

Cognitive dysfunction is more common in individuals with type 1 diabetes (T1D) compared to the general population. Blood‐based biomarkers are accurate in identifying early signs of neurodegeneration. However, studies using these biomarkers in T1D are lacking. We examined the associations of plasma biomarkers and diabetes characteristics in adults with T1D.

Methods

This study included 114 adults with T1D from the Glycemic Variability and Fluctuations in Cognitive Status in Adults With Type 1 Diabetes (GluCog) study. Plasma biomarkers were: ratio of amyloid‐β (Aβ) 42/40, neurofilament light chain (NfL), glial fibrillary acidic protein (GFAP), phosphorylated Tau181 (pTau181) using Quanterix assays, and phosphorylated Tau 217 (pTau217) using ALZpath. The coefficient of variation (%) from continuous glucose monitor readings was used to define glycemic variability (GV). Relationships between biomarkers, demographics, and diabetes characteristics were evaluated using Spearman rank correlation, Mann‐Whitney U, and Kruskal‐Wallis tests, as appropriate. Linear regression models were used to examine the associations between plasma biomarkers (dependent variable) and diabetes characteristics (that were significant in bivariate analyses) as predictors in separate models adjusted for sex, age, and education. We added the adjustment for nephropathy in models in which the biomarker was associated with this variable.

Results

The sample [53% female, mean(SD) age of T1D diagnosis 20.3(12.7) years, 37% with hypertension, 11% with neuropathy, 8% with nephropathy)] had a baseline age of 49.0(14.5) years, HbA1c 7.6%(1.3), GV 36.6%(7.3). Adjusted linear regression models revealed significant associations with four biomarkers. Presence of nephropathy was associated with higher concentrations of NfL, pTau181, and pTau217 and was included in the models with these biomarkers. A lower Aβ42/40 ratio was associated with older age at diagnosis. A higher concentration of NfL was associated with higher HbA1c. A higher concentration of pTau181 was associated with higher GV. Lastly, higher pTau217 was associated with neuropathy. GFAP was not associated with any of the diabetes measures.

Conclusion

These data show that plasma biomarkers of neurodegeneration are associated with key diabetes characteristics in adults with T1D, particularly HbA1c, GV, neuropathy, and nephropathy. Further research is needed to understand whether T1D characteristics mediate biomarker associations with cognitive decline.
A polymorphism in RELN protects against amyloid‐driven tau pathology and cognitive decline in sporadic Alzheimer's disease
Authors: Carello‐Collar Giovanna , Schlickmann Thomas Hugentobler , Ferrari‐Souza João Pedro , de Bastiani Marco Antônio , Cristino Alexandre Santos , Pascoal Tharick A. , Rosa‐Neto Pedro , Souza Diogo O. , Zimmer Eduardo R.
Published: January 2025

Background

A rare reelin gene variant (RELN‐COLBOS mutation) delayed dementia onset in almost 30 years in an autosomal dominant Alzheimer’s disease (ADAD) carrier. This patient presented with high amyloid‐β (Aβ) plaque load, but low tau accumulation, suggesting that this single‐nucleotide polymorphism (SNP) in RELN conferred a resilience not only to cognitive decline but also to tauopathy in ADAD. However, whether RELN SNPs are also protective in sporadic Alzheimer’s disease (AD) is yet to be determined. Thus, we sought to examine the impact of RELN SNPs on AD pathophysiology and cognitive deterioration in sporadic AD.

Method

We assessed 198 individuals [105 cognitively unimpaired (CU) and 85 cognitively impaired (CI)] from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) with available data on RELN SNPs and amyloid‐ and tau‐PET measures ([18F]‐florbetaben/florbetapir and [18F]‐flortaucipir, respectively), Aβ1‐42 and ptau181 in the CSF, and neuropsychological testing (Clinical Dementia Rating Sum of Boxes). We analyzed the effect of RELN SNPs carriership in the association between amyloid and tau burden through linear regression analysis and on cognitive decline according to CSF AT status through linear mixed‐effect model correcting for age, sex, and ApoEε4 status (Bonferroni’s adjusted p‐value < 0.05).

Result

We performed linear regression analysis in all the 235 RELN SNPs available on ADNI (Figure 1). We found RELN rs802787 protected against amyloid‐driven tau pathology (adj. p‐value < 0.001, Figure 2). Dividing individuals according to the CSF AT status, we observed that RELN rs802787 did not impact the rate of decline in cognition in A‐T‐ and A‐T+ individuals (Figure 3A‐B). By contrast, RELN rs802787 CSF A+T‐ individuals presented a slower cognitive decline (Figure 3C), which was not observed in A+T+ individuals (Figure 3D).

Conclusion

Here, we show RELN rs802787 carriers presenting high amyloid load have lower tau accumulation than non‐carriers. In addition, CSF A+T‐ RELN carriers presented a slower cognitive decline compared to non‐carriers. Our results suggest that RELN rs802787 carriership protects against amyloid‐driven tau pathology and cognitive deterioration in sporadic AD individuals. To the best of our knowledge, this is the first RELN SNP found to be protective against non‐familial AD.
Alpha‐internexin is detectable in cerebrospinal fluid – an analogue to neurofilament light?
Authors: Meda Francisco J , Johansson Camilla , Palm Karin , Brinkmalm Gunnar , Andreasson Ulf , Blennow Kaj , Kvartsberg Hlin , Zetterberg Henrik
Published: January 2025

Background

Alpha‐internexin (AINX) is a type IV intermediate filament alongside the neurofilament triplet proteins. Although AINX is mostly expressed during the development of the brain, it is still present in mature neurons and reported to be involved in axonal outgrowth. Because of a lack of previously published studies quantifying AINX in cerebrospinal fluid (CSF), and its high sequence similarity with other neurofilaments, the objective of this project was to specifically detect AINX via antibody‐based techniques and assess its biomarker capabilities.

Method

AINX in‐house antibodies (referred to as B15 and Ina1), alongside commercial antibodies (PA5‐95368 and MA5‐32809) were used to detect AINX in CSF and brain using ELISA and Simoa platforms. A recombinant AINX protein (AINX 89‐499) produced in‐house was used as positive control and calibrator. All antibodies were conjugated on beads, coated on microtiter plates, or biotinylated following standard protocols for usage on both platforms. Immunoprecipitation followed by mass spectrometry (IP‐MS), was also performed to evaluate antibody binding and specificity.

Result

In ELISA, all antibody combinations (capture/detector) were able to detect recombinant protein, with B15, Ina1 and PA5‐95368 providing the most effective combinations. While we were able to detect AINX in SDS brain extracts, only a few CSF samples generated any signal using ELISA. The low signals in CSF indicated that the sensitivity of ELISA might be a limiting factor. Therefore, we moved to the ultrasensitive Simoa platform. Here, we were able to show that AINX is detectable in CSF at low concentrations (1‐10pg/mL), with its increase correlating with CSF neurofilament light levels. IP‐MS results on SDS brain extracts revealed high sequence coverage of AINX for all three antibodies (90% for PA5‐95368, 56% for B15 and 85% for Ina1) thus confirming antibody specificity.

Conclusion

All antibodies tested seem to be very suitable for immunoassay development and AINX is present in CSF at low abundance, with the Simoa platform being able to detect it due to its high sensitivity. Future work will focus on evaluating the biomarker potential of AINX in different neurodegenerative diseases and acute conditions such as stroke or traumatic brain injury.
Qypredict®: A promising prognostic tool for anti‐amyloid therapy patient prioritization
Authors: Villa Luca , Jubault Thomas , Guizard Nicolas , Gordon Elizabeth
Published: January 2025

Background

The approval of anti‐amyloid therapies has led to a breakthrough in the treatment of Alzheimer’s disease. However, given these treatments remain expensive and are associated with significant side effects, identifying patients that would, and importantly would not, benefit from treatment is crucial in optimizing costs and minimizing health risks. QyPredict®, an advanced machine learning‐based predictive platform, has the potential to identify patients most likely to show cognitive decline, and may prove beneficial at prioritizing patient selection for therapies. Given limited healthcare resources, predictive tools need to perform well using only the most accessible and widely available clinical and demographic data and be trained on a broad and generalizable population.

Method

Using data from ADNI, OASIS, and NACC, QyPredict® was applied to 1784 older individuals. To represent all individuals that may require future cognitive assessment, a variety of patient groups were included. The sample (mean and standard deviation (SD) age: 73.3(7.0), 42.2% female; baseline MMSE: 27.5(2.5)) was comprised of healthy controls (n=512), Alzheimer’s Disease patients (n=382), Mild Cognitive Impairment patients (n=660), and a mixture of other dementias, Parkinson’s disease, and psychiatric conditions (n=230). Cognitive decline was defined as an increase of at least 0.5 points on the CDR‐SB over 12 months. Model features included baseline MMSE, baseline CDR‐SB, sex, and regional grey matter volumes automatically derived using QyScore®, an FDA‐cleared and CE‐marked advanced neuroimaging platform.

Result

Predicted decliners showed 5.8x greater cognitive decline on the CDR‐SB over 12 months compared to individuals predicted at baseline to remain stable (Predicted‐Decliner: 1.0(1.8) vs Predicted‐Stable: 0.17(0.85): Figure 1), which was statistically significant (t‐test: t(1782)=31.2, p<0.001). QyPredict® performed well, achieving the following: specificity: 0.81; sensitivity: 0.56; balanced accuracy: 0.69; negative predictive value: 0.76; ROC AUC: 0.77; positive predictive value: 0.64; F1 score: 0.6; precision: 0.62 (Figure 2). Baseline CDR‐SB, MMSE and QyScore®’s MRI markers of hippocampal and insular volumes demonstrated the greatest predictive value (Figure 3.)

Conclusion

Using minimal clinical and demographic data, QyPredict® was able to accurately predict cognitive decline over a 12‐month period – regardless of diagnostic status. This may be invaluable when trying to prioritize which patients should receive anti‐amyloid therapies.
Head‐to‐Head Comparison of Plasma vs. Saliva Extracellular Vesicle Subpopulations Derived Normal Controls and Cognitively Impaired Subjects
Authors: Winston Charisse N. , Sarsoza Floyd
Published: January 2025

Background

The ability to quantify pathogenic proteins related to Alzheimer’s disease (AD) in plasma neuronal extracellular vesicles (NEVs) was vital to their development as diagnostic biomarkers. A similar approach can be employed to assess the biomarker potential of saliva EVs. A head‐to‐head comparison was conducted to characterize plasma and saliva EVs derived patients enrolled in the Nathan Shock Healthy Aging Study, where participants range in age from 30–70+ years, representing the full breadth of the healthy adult human age span.

Method

Matched plasma and saliva EVs were precipitated from two cohorts, normal controls (NC, 55+ years of age) and patients with mild cognitive impairment (MCI; 55+ years of age) as defined by the Modified Mini‐Mental State Test (3MS). EVs were precipitated and enriched against neuronal‐origin, L1CAM using magnetic immunocapture and fluorescence‐activated cell sorting. EVs and NEVs were characterized for size and shape using NanoSight and marker profiling using Western Blot. AD‐related pathogenic proteins Aβ40–42, p‐tau, and Nf‐L will be quantified in EVs using SIMOA assays.

Result

Plasma and saliva EVs derived from NC and MCI subjects demonstrated similar size distributions and shape characteristics as previously reported. EV marker profiling revealed differential expression of well characterized EV and cellular markers in saliva and plasma EVs. We determined that the rabbit monoclonal antibody raised against tetraspannin CD81 (Abcam) did not identify populations of plasma and saliva EVs while mouse monoclonal antibody against CD81 (Santa Cruz) identified separate populations of plasma and saliva EVs. CD9, TSG101 and ALIX markers positively identified subpopulations of EVs in plasma and saliva while CD63+ EVs were only observed in plasma EVs derived from NC and MCI subjects. MAP2 confirmed neuronal origin of NEVs derived from the plasma of NC and MCI subjects.

Conclusion

The presence of different subpopulations of EVs in plasma and saliva may impact their AD biomarker potential and requires further investigation. Additional analyses will be employed to confirm neuronal origin of EVs from saliva. EV cargo analyses will be performed to assess the biomarker potential of plasma and saliva EVs derived from NC and MCI subjects.
Validation study of the effectiveness of a stepwise care program for dementia family caregivers
Authors: Suh Jeewon , Kang Mi Kyoung , Lee Kyung‐hae , Park Myoung Hwa , Koh Im‐Seok
Published: January 2025

Objective

This study aims to validate the effectiveness of the education program for dementia family caregivers (so‐called Value Care program), and to assess the applicability of the program at local dementia centers in the community.

Method

The Value Care program is an educational program designed for family caregivers of dementia, conducted once a week for 90 minutes over a total duration of 8 weeks. This program aims to enhance understanding of the characteristics and symptoms of dementia at various stages and to provide specific response strategies for caregivers.

In this case‐control study, family caregivers of dementia patients were recruited from 38 local dementia centers in Korea. Each participants were randomly assigned to either the experimental group or the control group in a 1:1 ratio. After 8 weeks, assessment was conducted on measure such as depression, care‐burden scales, caregiver self‐efficacy and satisfaction surveys.

Results

A total of 226 family caregivers were recruited in the experimental group, and 210 participants in the control group. The experimental group showed significant reductions in depression (P<0.001) and care‐burden scores (P<0.001) and significant increases in caregiver self‐efficacy scores (P<0.001) after participating in the Value Care program. The overall satisfaction score averaged 4.47 over 5.

Conclusion

The “Value Care Program” demonstrated effectiveness in reducing depression, care burden, and enhancing the efficacy of care for family caregivers of dementia patients. Participants expressed a high likelihood of rejoining the program and recommending it to others. Therefore, the Value Care Program could be a crucial initiative for alleviating caregiver burden, stress, and feelings of depression among dementia family caregivers.
Dissociable effect of different measures of Vascular Metrics on Cognitive Function: The Mediating role of the Thalamic Subregions
Authors: Konwar Srijan , Bocchetta Martina , Marco Matteo De , Venneri Annalena
Published: January 2025

Background

The impact of white matter hyperintensities (WMH) on cognitive performance is uncertain due to inconsistent findings. The thalamus is of particular interest given its susceptibility to vascular damage. To test how different vascular risks (Deep/Periventricular WMH and Vascular Scores (VS)) affect thalamic subregional volumes and whether that has any mediating effect on different cognitive domains in healthy controls, mild cognitive impairment (MCI) and AD.

Method

A sub‐sample of 93 participants (age = 64.96 ± 10.847, sex = 49 males/44 females) included controls (n = 40), MCI (n = 28) and AD (n = 25) were selected from the larger multi‐site VPH‐DARE cohort. 3T1‐weighted MRI images were pre‐processed using Freesurfer and its module to segment into 6 thalamic regions and its subcomponents. MD values of the bilateral posterior thalamic radiation (PTR) were extracted. Lexical semantic, episodic and executive functions were assessed. A non‐parametric mediation analysis was run using a bias‐corrected percentile bootstrapped with 5000 permutations controlling for covariates. A complementary analysis was conducted using the mean diffusivity (MD) values of the left and right PTR as a mediator.

Result

A indirect effect of VS on the midline regions led to poor performance across all three cognitive domains: lexical (β = –0.077, p = 0.045), episodic (β = –0.097, p = 0.028) and executive (β = –0.079, p = 0.048). VS had a indirect effect on lexical (β = –0.097, p = 0.040) and episodic (β = –0.153, p = 0.005) tests through MD of the left PTR. Similarly, both deep (β = –0.249, p = 0.016) and periventricular (β = –0.309, p = 0.002) WMH resulted in a indirect effect on episodic memory. Only VS resulted in a negative indirect effect on episodic memory (β = –0.121, p = 0.020) through the MD of the right PTR.

Conclusion

Higher VS resulted in poor cognitive performance by impacting subregional volumes of the thalamus and white matter integrity of tracts. The effect of both deep and periventricular WMH on cognitive performance appears to be mediated by the white matter integrity of the left thalamic tracts more than thalamic sub‐regional volumes possibly reflecting different vascular mechanisms.
A combination of plasma phospho‐tau217 and Aβ42/40 detects early development of amyloid pathology in cognitively unimpaired individuals
Authors: Janelidze Shorena , Barthélemy Nicolas R. , Salvadó Gemma , Palmqvist Sebastian , Mattsson‐Carlgren Niklas , Ovod Vitaliy , Bollinger James G. , He Yingxin , Schindler Suzanne E. , Blennow Kaj , Stomrud Erik , Bateman Randall J. , Hansson Oskar
Published: January 2025

Background

Phase 3 trials of successful anti‐amyloid therapies in Alzheimer's disease (AD) have indicated better clinical efficacy in people with less severe disease. Plasma biomarkers will be essential for efficient screening of participants in future primary prevention clinical trials testing anti‐amyloid therapies in cognitively unimpaired (CU) people with normal brain β‐amyloid (Aβ) levels by PET who are at high risk of accumulating Aβ. Here we investigated if combining plasma phospho‐tau (p‐tau)217 and Aβ42/40 could be useful to predict subsequent development of Aβ pathology in CU with normal brain Aβ by PET at baseline.

Method

We included 495 CU individuals and 339 patients with cognitive impairment (CI) from the BioFINDER‐2 study (Table). Plasma Aβ42/40 (mass spectrometry), the ratio of p‐tau217 to non‐phosphorylated tau (%p‐tau217; mass spectrometry), p‐tau231 and GFAP (immuno‐assays) were measured at baseline. Among CU participants, 224 underwent longitudinal Aβ‐PET assessments. Threshold of 40 centiloids and previously established CSF Aβ42/40 cut‐offs were applied to define Aβ‐PET and CSF Aβ status, respectively. For replication we included 249 CU individuals from BioFINDER‐1.

Result

When detecting abnormal CSF Aβ‐status, a combination of plasma %p‐tau217 and Aβ42/40 showed better performance than individual biomarkers in CU (AUCrange=0.949, pdiff<0.024) but not in CI participants (Figure 1). Further, in CU participants with normal baseline Aβ‐PET, baseline plasma %p‐tau217 and Aβ42/40 were associated with continuous baseline Aβ‐PET measures (Figure 2) and increases in Aβ‐PET signal over time (Figure 3). Associations of plasma %p‐tau217 and Aβ42/40 with baseline Aβ‐PET (%p‐tau217: β=4.23, p<0.001; Aβ42/40: β=‐2.61, p<0.001) and longitudinal Aβ‐PET (%p‐tau217: β=0.527, p<0.001; Aβ42/40: β=‐0.259, p=0.002) were also significant in the models combining the two baseline biomarkers as predictors (Figures 2‐3). Similarly, in BioFINDER‐1, baseline plasma p‐tau217 (immuno‐assay) and Aβ42/40 were independently associated with longitudinal changes in CSF Aβ42/40 in CU participants with normal CSF Aβ42/40 at baseline. Plasma p‐tau231 and GFAP did not provide any independent value when combined with %p‐tau217and Aβ42/40.

Conclusion

Combining plasma measures of p‐tau217 and Aβ42/40 could be useful for predicting development of Aβ pathology in people with early stages of subthreshold Aβ accumulation. These biomarkers might thus facilitate screening of participants for future primary prevention trials.
Interpretable Deep Clustering Survival Machines for Alzheimer's Disease Risk Groups Discovery
Authors: Hou Bojian , Wen Zixuan , Bao Jingxuan , Zhang Richard , Tong Boning , Yang Shu , Wen Junhao , Cui Yuhan , Moore Jason H. , Saykin Andrew J. , Huang Heng , Thompson Paul M. , Ritchie Marylyn D. , Davatzikos Christos , Shen Li ,   Alzheimer's Disease Neuroimaging Initiative
Published: January 2025

Background

Alzheimer's disease (AD) is a complex neurodegenerative disorder that has impacted millions of people worldwide. Identifying different risk groups converting to AD during the mild cognitive impairment (MCI) stage and determining their genetic basis would be immensely valuable for drug discovery and subsequent clinical treatment. Previous studies typically clustered subgroups by unsupervised learning techniques, neglecting the survival information. To address this problem, we propose an interpretable survival analysis method called Deep Clustering Survival Machines (DCSM), which performs clustering and risk prediction simultaneously.

Method

Our proposed DCSM is a hybrid survival analysis method that integrates the advantages of the discriminative and generative ideas and employ the mixture of expert distributions to fit the final survival function by maximum likelihood estimation. For the real data analysis, we use positron emission tomography (PET) from the ADNI dataset as feature. The survival information includes whether the patients convert from MCI to AD and the corresponding time. Our DCSM model predicts the risk of each patient and clusters the patients into two groups based on their risk simultaneously.

Result

We first evaluate time‐to‐event prediction performance by Concordance Index which is shown in Table 1 where our model outperforms the other baselines. Besides, we use the LogRank to evaluate the clustering performance. The difference in survival between two risk groups as shown in Kaplan‐Meier plots in Figure 2(h) is significantly larger than other baselines. Furthermore, to biologically validate our clustering findings, we carry out targeted genetic association analyses (Figure 3(a)). There are numerous targeted SNPs discovered when comparing high‐risk group to low‐risk group and normal group. Figure 3(b) illustrates the important brain regions for the high‐risk group. We highlight the top ten important regions which is consistent with the conventional brain‐AD relation findings.

Conclusion

Our DCSM model builds a good bridge between the imaging modality and survival information. We demonstrate the superiority of the DCSM by applying this approach to cluster patients with MCI into subgroups with different risks of converting to AD. Conventional clustering measurements for survival analysis along with genetic association studies successfully validate the effectiveness of our method and characterize our clustering findings.
Associations between neuropsychological tests, plasma biomarkers and PET amyloid scans in a memory clinic cohort
Authors: Bucci Marco , Almkvist Ove , Bluma Marina , Savitcheva Irina , Zapater‐Fajari Mariola , Chiotis Konstantinos , Matton Anna , Kivipelto Miia , Jeromin Andreas , Molfetta Guglielmo Di , Ashton Nicholas J. , Blennow Kaj , Zetterberg Henrik , Nordberg Agneta K
Published: January 2025

Background

Studies on plasma biomarkers have shown promising results for Alzheimer’s disease(AD) neuropathology in a research setting, but their associations to neuropsychological(NP) tests have not been extensively studied in clinical cohorts. The main aim was to investigate associations between NP, plasma biomarkers and amyloid accumulation in the brain(Aβ‐PET).

Method

We quantified plasma biomarkers (tau phosphorylated at 217, 181 and 231, glial fibrillary acidic protein(GFAP) and neurofilament‐light(NFL)) by single molecular array in 126 patients (age=65±8) who were admitted to the Clinic for Cognitive Disorders, Karolinska University Hospital. Patients underwent an extensive initial clinical assessment encompassing MRI/CT, CSF sampling, NP evaluation and, within a year, a [18F]flutemetamol‐PET(Aβ‐PET) scan. NP assessment consisted of the following tests: RAVL(learning&retention), Rey‐Osterrieth(copy&retention), Trail making test(TMT) A&B and Digit Symbol. Test results were transformed in Z‐scores. At this point in the data collection, 47 patients were initially diagnosed as mild cognitive impaired(MCI) and had available >=1 of the NP tests. To refine the diagnosis, Aβ‐PET images were acquired and visually rated for positivity/negativity(VR) and quantified in Centiloid(Aβ‐PET‐CL) and after reassessment of the cohort, 28 subjects with available NP scores became MCI‐Aβ+(n=12) or Aβ‐(n=16). Significant(p<0.05) associations with Spearman’s coefficients(rho) are reported. Z‐scores were used to predict Aβ‐PET+ with receiver‐operating‐characteristic curves and area‐under‐curves(AUC).

Result

Cognitive performance(Z‐scores) of the MCI cohort(after PET) was on average 1‐2 standard deviations below the reference database and Rey‐Osterrieth‐copy correlated with Rey‐Osterrieth‐retention(rho=0.44) and Digit Symbol(rho=0.43) tests. MCI‐Aβ+ and Aβ‐ groups did not statistically differ in cognitive performance. TMT‐A only presented an association with Aβ‐PET‐CL(rho=0.64, n=11)(Figure 1) and RAVL retention correlated negatively with plasma NFL(rho=0.46, n=26)(Figure 2); these correlations were significant in the Aβ+ group but not in the Aβ‐ group. In the MCI cohort before Aβ‐PET, TMT‐A correlated with plasma pTau231(rho=0.59, n=17)(Figure 3). From preliminary analyses, Z‐Scores seem to poorly predict VR(AUC<=0.65).

Conclusion

The most promising results from TMT‐A need to be confirmed when all the data are available, TMT‐A was associated with plasma NFL and pTau231 and Aβ‐PET, but not with pTau217 and GFAP, previously found to be associated to Aβ‐PET. Poor performance of NP tests alone in predicting amyloid accumulation in the brain might require integration with other biomarkers.
Effect of brain network scale on Persistence Cycles: An ADNI comparative study
Authors: Garai Sumita , Liu Mintao , Xu Fredericks , Goñi Joaquín , Duong‐Tran Duy , Zhao Yize , Shen Li ,   for the ADNI
Published: January 2025

Background

Our study aims to understand how network resolution scale impacts the association between topological features in the brain network and Alzheimer’s disease (AD) outcomes. In particular, we examine persistence homological cycles, derived from DTI and fMRI neuroimaging. We study subjects in various stages of AD progression. We utilize structural and functional connectomes extracted from aforementioned modalities and assess between group differences in average persistence of homological cycles, across various scales.

Method

This study is conducted on data obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database and utilizes two different modalities, with two independent datasets. First dataset involves structural connectomes obtained from diffusion tensor imaging (DTI) based on Lausanne parcellation on five scales: 33, 60,125, 250 and 500. The second dataset consists of functional connectome obtain from fMRI scans, measuring Pearson correlations between BOLD signal fluctuations of various regions of interests in the brain, based on Shaefer atlas and ten scales, starting from 100 up to 1000. We use topological data analysis tools, specifically persistence homology and computational package ripser to compute birth and death time for homological cycles for the underlying topological network obtained from both structural and functional connectomes, for all scales. Life of a cycle is defined to be birth time subtracted from death time, also equal to the length of a bar in the persistence barcode. Average Persistence (AP) is average length of a bar in the persistence barcode for each subject’s brain network. We perform a comparative study among different stages of disease progression across all scales of resolution.

Result

We find that the t‐test for the means of average persistence for different stages of disease progression reveals statistically significant between‐group difference in scale‐33 (HC vs MCI and MCI vs AD) and in scale‐125 (MCI vs AD) for structural connectomes (Figure 1) and in scale‐100 (MCI vs AD and HC vs AD) for functional connectomes (Figure 2). We also note that average persistence tends to stabilize as we increase image resolution.

Conclusion

From topological point of view, lower resolution images surprisingly perform better at capturing between group differences in various degrees of disease progression.
Usability assessment in an E‐Health App for Cognitive Stimulation (LABPSI) in Elderly population
Authors: Sarmiento Elsa Araceli Revollo , Sarmiento Gisela Noelia Revollo , Sarmiento Natalia Veronica Revollo , Moreno Maria Celeste López , Vivas Leticia
Published: January 2025

Background

Advances in the development of e‐health applications enhance the availability of preventive tools and the adherence to certain treatments in old age (Roberts et al., 2015; Granath et al., 2023). Using these apps provides benefits for cognitive stimulation, offering a technologically accessible option that promotes autonomy in the elderly. In this study, we present the app version of a web platform for cognitive stimulation in the elderly called LABPSI (https://labpsi.mdp.edu.ar). This lite version can be downloaded for android mobile phones and used offline. The app was developed based on user‐centered design, focusing on the elderly. The aim of this study is to assess the usability of the app version of LABPSI in this population.

Methods

The sample consisted of 16 elderly people (81% women) with a mean age of 71.5 (DS = 8.72) and an average of 10.9 years of education.

Initially, users were introduced to the app providing the necessary time for use and exploration. Subsequently, they answered the MAUQ (Zhou L. et al., 2019) e‐health application usability questionnaire to assess participants' perceived ease of use, satisfaction, and utility when using LABPSI. The LABPSI final prototype was developed using App Inventor software (MIT APP INVENTOR, 2012).

Results

The descriptive analysis of the MAUQ usability questionnaire variables were: a) ease of use; b) interface and satisfaction; c) utility. The first variable had a median value of 22 with a maximum questionnaire score of 25 and a range of responses of [6‐25]. The second variable had a median value of 30, with a maximum questionnaire score of 35, and a range of responses of [21, 35]. Finally, the utility variable scored a median of 26 with respect to the maximum questionnaire score of 30, with a range of [16‐30].

Conclusions

This usability study on the new LABPSI cognitive stimulation app shows high levels of ease of use, satisfaction, and perceived utility by participants. The results demonstrate a good interaction between the elderly and the system, defining the app as a usable, appealing, and easy to learn software product for end‐users
Goldilocks Framework: semi‐automated model configuration for democratising data‐driven analyses of Alzheimer’s disease progression
Authors: Oxtoby Neil P
Published: January 2025

Background

Data‐driven models are at the mercy of the data upon which they are built. Poorly configured models can lead to misleading conclusions. Here we introduce a framework called Goldilocks for carefully configuring data‐driven modelling analyses to remain in the Goldilocks Zone — features that are not too weak (in terms of disease signal), and model (hyper)parameters that are not too strong (with respect to available training data).

Method

Goldilocks has three key steps: 1. Normalise data. Transform data from cases (e.g., patients) into “disease signal”, e.g., normative z‐scores relative to controls. 2. Feature selection. Identify candidate input features having the necessary minimum signal. 3. Model configuration (hyperparameters). Remain within the available maximum signal.

Experiments: we synthesize ADNI‐like biomarker data, define the Goldilocks Zone to lie between two event horizons (minimum and maximum disease signal), and demonstrate Goldilocks for configuring a Subtype and Stage Inference (SuStaIn) analysis. Minimum disease signal zmin is determined by the tipping point between case/control data, where the half‐normal cumulative density function HN‐CDF(zmin)==0.5. Maximum disease signal zmax is defined where the empirical distribution function EDF(zmax)==0.9, such that 10% of the biomarker’s data (from cases, not controls) exceeds zmax.

Result

We synthesized z‐scored ADNI‐like synthetic CSF biomarkers (ABETA, TAU), cognitive test scores (ADAS13, MMSE), and a brain volume (Ventricles normalised by intracranial volume). Figure 1 shows biomarker empirical distributions as a function of z‐score, and the Goldilocks Zone between event horizons at zmin==0.67 and EDF(zmax)==0.9. Since EDF(zmin) < 0.5 for all biomarkers, they each contain sufficient disease signal to be included in a data‐driven modelling analysis like SuStaIn. The maximum disease signal for each biomarker ranged between approximately 2 to 6. Figure 2 compares default parameters in a Z‐Score SuStaIn analysis (using the official pySuStaIn software) to Goldilocks‐informed parameters.

Conclusion

Goldilocks has the potential to improve validity and interpretability of data‐driven discoveries in general while also making data‐driven modelling accessible to non‐experts. This promotes democratization of data‐driven analyses, unlocking previously untapped data responsibly. This is particularly important in fields such as computational medicine, where data‐driven decisions can relate to life‐or‐death outcomes.
Plasma proteomic profiling of mild cognitive impairment in two cohorts using the NULISAseq CNS Disease panel
Authors: Molfetta Guglielmo Di , Benedet Andrea L. , Zhang Bingqing , Rahmouni Nesrine , Servaes Stijn , Stevenson Jenna , Pola Ilaria , Blennow Kaj , Zetterberg Henrik , Rosa‐Neto Pedro , Ashton Nicholas J.
Published: January 2025

Background

Recently, the development of ultra‐sensitive immunoassays has allowed for the detection, in blood, of proteins related to the pathophysiology of Alzheimer’s disease (AD), with phosphorylated tau (p‐tau) being the most promising. However, current methods are often limited by their ability to measure one analyte, lacking the potential for discovery and inclusion of additional biomarkers with supplemental value. In this pilot study, we explored proteomic changes using the novel NUcleic acid Linked Immuno‐Sandwich Assay (NULISA™) platform, focusing on patients with mild cognitive impairment (MCI).

Method

In this study, MCI patients with a confirmed Aβ status were recruited from two independent cohorts. A discovery cohort (mean[SD] age, 73.5 [5.5] years; 25 females [62.5%]) was classified by cerebrospinal fluid Aβ42/40 (Aβ+ =28; Aβ‐ =12). For the second cohort (mean[SD] age, 70.7 [7.31] years; 39 females[42.9%]; CDR 0.5), from the TRIAD study, amyloid positron emission tomography was utilized instead (Aβ+ =47; Aβ‐ =44). We performed the NULISAseq CNS Disease Panel measurements on the plasma samples (n=131) in two separate batch analyses. LIMMA models were used to evaluate differential expression of protein NPQ values between the two MCI (Aβ+ Vs. Aβ‐) patient groups, with a total of 121 proteins included in the analysis.

Result

In the discovery cohort (n=40), only p‐tau217 survived multiple comparison (Log2FC, 1.49; Padj <0.001). Further targets were present with a significant unadjusted p‐value (P <0.05) (Figure 1b). In the larger TRIAD cohort (n=91), p‐tau217 (Log2FC, 1.29; Padj <0.001), p‐tau231 (Log2FC, 0.76; Padj <0.05), p‐tau181 (Log2FC, 0.42; Padj <0.05) and GFAP (Log2FC, 0.78; Padj <0.05) remained significant after adjusting for multiple testing. Similarly to the discovery cohort, additional targets were identified as significantly changed with an unadjusted p‐value (P<0.05) (Figure 1A). This included PARK7, which was the only target present in both.

Conclusion

In this study, consisting of two independent groups of MCI patients characterized by Aβ burden, we utilized the NULISAseq CNS Disease Panel to identify dysregulated proteins of the prodromal stage of AD. This novel multiplex technology continues to demonstrate the importance of p‐tau217, p‐tau231, p‐tau181 and GFAP as indicators of cerebral amyloid deposition while offering additional markers that may increase their utility.