bluuebunny/crossref_metadata_2025 huggingface dataset:
The role of neurofilaments light chain in discriminating behavioral variant frontotemporal dementia from non‐neurodegenerative mimics
Authors: Gallingani Chiara , Vinceti Giulia , Carbone Chiara , Tondelli Manuela , Salvatori Davide , Salemme Simone , Bedin Roberta , Urbano Teresa , Luchetti Ludovico , Gentile Giordano , Battaglini Marco , Chiari Annalisa , Zamboni Giovanna
Published: January 2025

Background

The measurement of serum and cerebrospinal fluid (CSF) neurofilaments light chain (NfLs) has been proven promising in differentiating the behavioral variant frontotemporal dementia (bvFTD) from non‐neurodegenerative mimics, including primary psychiatric disorders and non‐progressive cognitive/behavioral changes. However, studies on this topic are based on clinical diagnosis, which remains challenging and potentially confounded by the overlapping clinical phenotypes. We investigated the role of NfLs in this field by classifying patients based on the presence/absence of pathological longitudinal brain volume changes.

Method

We prospectively recruited subjects with early‐onset (< 65 yo) mild cognitive/behavioral impairment suggestive of potential bvFTD. At baseline they underwent neuropsychological assessment, magnetic resonance imaging (MRI) brain imaging, and CSF and serum collection for NfLs measurement. They were followed‐up clinically and repeated the MRI scan at 18 months. Single‐subject yearly percentage brain volume change (PBVC/y) was calculated using the FMRIB Software Library tool SIENA and compared to normative values. Subjects with a PBVC/y exceeding the 95th normative percentile were classified as Progressive, in line with a bvFTD diagnosis. Otherwise, they were indicated as Non‐Progressive, suggestive of a non‐neurodegenerative condition. Differences in NfLs levels between Progressive and Non‐Progressive individuals and their predictive value were evaluated through non‐parametric tests and univariate logistic regression models.

Result

30 patients were recruited: 19 were classified as Non‐Progressive and 11 as Progressive. Both CSF and serum NfLs values were significantly higher in patients with atrophy progression compared to the stable ones (2992,27 vs 558,53 pg/dl, p < 0.001; 55,91 vs 13,47 pg/dl, p < 0.001). Higher serum NfLs levels at baseline were predictors of atrophy progression (p = 0.041, OR = 1.301), whereas only a trend of significance was seen for CSF NfLs (p = 0.055, OR = 1.006). A cut‐off of 21.5 pg/dl for serum NfLs differentiated the two groups with 81.82% sensitivity and 94.74% specificity (AUC = 0.949); a cut‐off of 972 pg/dl for CSF NfLs yielded discriminative sensitivity of 81.82% and specificity of 100% (AUC = 0.928).

Conclusion

We confirmed that NfLs measurement in biological fluids at baseline is a reliable biomarker to discriminate between bvFTD patients and non‐neurodegenerative mimics, objectively distinguished based on longitudinal atrophy progression.
Biological Aging and the Motoric Cognitive Risk Syndrome
Authors: Sathyan Sanish , Oh Hamilton Se‐Hwee , Emmeline Ayers , Rutledge Jarod , Adhikari Dristi , Gao Tina , Milman Sofiya , Wyss‐Coray Tony , Barzilai Nir , Verghese Joe
Published: January 2025

Background

Motoric Cognitive Risk (MCR) syndrome is a predementia syndrome characterized by slow gait and subjective cognitive concerns. Individuals with MCR are at high risk of transitioning to both Alzheimer's disease (AD) and vascular dementia. With chronological age, the incidence of MCR increases and MCR cases exhibit a higher prevalence of age‐associated diseases. Biological age reflects an individual's accumulated physiological condition captured by changes at the epigenetic (DNA methylation) as well as proteomic levels (protein expression). Aging varies between individuals and at organ‐specific level within an individual. Our objectives were to (1) examine associations between biological age acceleration and MCR, (2) to identify organs that are aging at faster rate in MCR using novel organ‐specific clocks, and (3) impact of biological aging with mortality in MCR.

Method

We measured 4,265 proteins in plasma collected at baseline in the LonGenity cohort (n=1,025, Age ≥65 years) – utilizing the SomaScan proteomic assay. Machine learning models were used to derive biological age predictors at the individual level from all the proteins. Organ‐specific ages for 11 organs were also computed from the organ‐specific plasma proteome. Participants were characterized as fast and slow agers based on these clocks. Age acceleration measures were regressed against prevalent and incident MCR using logistic and cox models, respectively. Associations with biological aging were validated using an epigenetic clock (DNAm GrimAge) in HRS (n=1,043).

Result

Faster biological aging derived from the proteomic clock were associated with prevalent MCR as well as incident MCR. Prevalent MCR was most strongly associated with accelerated aging in muscles (p<0.001) and the heart (p<0.01). Brain, liver, kidney, and immune age acceleration was also nominally associated with prevalent MCR (p<0.05). Participants with both MCR and accelerated aging had the highest risk of mortality, as indicated by proteomic and epigenetic clocks in LonGenity (HR = 3.38) and HRS (HR = 2.47), respectively.

Conclusion

Accelerated aging is a hallmark of MCR and have an impact on aging of multiple organs. Older individuals with MCR and accelerated aging are at highest risk of mortality. Biological age acceleration may be an important modifiable dementia risk factor.
Association of sleep network functional connectivity with hyperexcitability and cognition in Alzheimer’s disease
Authors: Moguilner Sebastian , Berezuk Courtney , Bender Alex C , Pellerin Kyle R , Gomperts Stephen , Cash Sydney , Sarkis Rani A , Lam Alice D
Published: January 2025

Background

Sleep disturbances are common in Alzheimer's disease (AD) and occur at early stages. Hyperexcitability also arises during sleep and can lead to epileptiform activity and seizures that impact memory consolidation. The underlying mechanisms of sleep disturbances and hyperexcitability in AD pathology remain unclear but are likely associated with changes in brain networks and altered functional connectivity (FC).

Method

Using ambulatory scalp EEG recordings, we examined 33 cognitively normal healthy older adult controls (HC), 36 individuals with early clinical stages of AD without history or risk factors for epilepsy (AD‐NoEp), and 14 individuals with early clinical stages of AD who developed epilepsy related to Alzheimer's (AD‐Ep). The analysis involved analyzing FC measures such as amplitude envelope correlation with leakage correction, coherence‐based measures, and graph theory to examine FC across sleep‐wake stages, frequency bands, and its associations with cognitive performance.

Result

We found specific FC changes in Alzheimer's during sleep stages and frequency bands, including increased gamma connectivity during N2 sleep (N2‐gamma), increased delta connectivity during REM‐delta, and decreased alpha connectivity in N2‐alpha and REM‐alpha. AD‐Ep showed a distinct network signature compared to AD‐NoEp, with changes in sleep‐stage and frequency band combinations not observed in AD‐NoEp and reversal of FC changes associated with AD‐NoEp. Specific measures (N2‐gamma, REM‐alpha, Awake‐theta, and Awake‐delta connectivity) were linked to longitudinal global cognitive decline in AD. Machine learning algorithms using combinations of awake and asleep FC features accurately distinguished AD‐NoEp from HC (AUCroc = 0.905) and AD‐Ep from AD‐NoEp (AUCroc = 0.881). Moroever, we identified "fast cognitive decliners" among AD participants with high classification performance (AUCroc = 0.865).

Conclusion

The results highlight specific changes in sleep network functional connectivity in AD, offering insights for disease monitoring and finding therapeutic targets.
Harmonized Tractometry Visualizes Alzheimer’s Disease Effects on the Brain’s White Matter Tract Microstructure
Authors: Chandio Bramsh Q , Reina Julio E Villalon , Nir Talia M , Thomopoulos Sophia I , Feng Yixue , Benavidez Sebastian M , Jahanshad Neda , Harezlak Jaroslaw , Garyfallidis Eleftherios , Thompson Paul M.
Published: January 2025

Background

Diffusion MRI (dMRI) metrics of brain microstructure offer valuable insight into Alzheimer’s disease (AD) pathology; recent reports have identified dMRI metrics that (1) tightly link with CSF or PET measures of amyloid and tau burden; and (2) mediate the relationship between CSF markers of AD and delayed logical memory performance, commonly impaired in early AD [1,2].

To better localize white matter tract disruption in AD, our BUndle ANalytic (BUAN) [3] tractometry pipeline allows principled use of statistical methods to map factors affecting microstructural metrics along the 3D length of the brain’s fiber tracts. Here, we extended BUAN to pool data from multiple scanning protocols/sites ‐ using a new harmonized tractometry approach, based on ComBat [4,5], a widely‐used harmonization method modeling variations in multi‐site datasets due to site‐ and scanner‐specific effects.

Method

We illustrate the effects of integrating ComBat into our BUAN tractometry pipeline. Analyzing Alzheimer’s Disease Neuroimaging Initiative (ADNI3) [6] data, we examined the impact of mild cognitive impairment (MCI) and AD on 38 white matter tracts, comparing results with and without harmonization.

We analyzed data from 730 ADNI3 participants, scanned with seven dMRI protocols. After data preprocessing with the ADNI3 protocol [7,8], BUAN extracted 38 bundles and created along‐tract bundle profiles for microstructural metrics: FA, MD, AD, and RD (see Figure 1 for full names). ComBat was applied to each point in bundle profiles to correct for scanner protocol effects and linear mixed models (LMM) were used to study the group differences in MCI and AD versus cognitively healthy controls (CN).

Result

Harmonized BUAN maps reveal AD and MCI effects throughout specific tracts (e.g., the left cingulum and right arcuate fasciculus; Figure 1, bottom panel). ComBat harmonization enhanced the sensitivity to detect group differences compared to LMM without scanner correction. Adding data ‐ even from different scanner protocols ‐ boosted power (Figure 2f‐g).

Conclusion

In this application of harmonization in neurodegenerative tractometry analysis, we integrated ComBat into BUAN tractometry to merge dMRI data from diverse scanning protocols and sites. Future research will examine various ComBat versions and deep‐learning approaches to track AD pathology effects on the brain’s neural circuitry.
Universal tau PET scale (Uniτ) – The HEAD Study
Authors: Povala Guilherme , Bauer‐Negrini Guilherme , Bellaver Bruna , Lussier Firoza Z , Amaral Livia , Ferreira Pamela C.L. , Zatt Bruno , Tudorascu Dana , Lopresti Brian J , Jagust William J. , Klunk William E , Lowe Val J. , soleimani‐meigooni David N. , Oh Hwamee , Pascual Belen , Gordon Brian A. , Rosa‐Neto Pedro , Pascoal Tharick A.
Published: January 2025

Background

The HEAD study aims to collect a large dataset of multiple tau‐PET tracers to provide robust anchor values for tau‐PET harmonization. Here, we tested the hypothesis that anchoring two tau tracer uptake values using head‐to‐head measurements has the potential to generate an accurate universal tau‐PET scale, named Uniτ(tau).

Methods

We assessed 200 individuals across the aging and AD spectrum (Training: HEAD data freeze 2.0, n=185; Testing: UPitt dataset (Gogola et al.), n=15) with [18F]Flortaucipir and [18F]MK‐6240 tau‐PET. SUVRs were processed to a common 8mm FWHM, with inferior cerebellar gray matter as the reference region (Pascoal et al.). Uniτ explored two anchoring/harmonization methods. First, we examined within‐tracer anchoring by creating anchor values based on the mean SUVR of Youngs (<25 years) and 95th percentile voxels from cognitively impaired individuals. Second, we explored within‐ plus between‐tracer anchoring, employing linear (e.g., piecewise) and non‐linear regressions. To address the inherent problem of discontinuity of piecewise transformations, we implemented two smoothing methods at the inflection point between equations, transforming them into a continuous function.

Results

Uniτ scale anchoring within‐tracer resulted in similar estimates for high values, but less accurate in the lower range (Figure 1,2). Anchoring within‐ plus between‐tracer improved estimate consistency, with the piecewise transformation generating the best results. The piecewise smoothing equation yielded estimates comparable to those obtained from the piecewise method without smoothing. This allowed for the use of a single formula. In addition, this leads to more robust results when the goal is to study longitudinal changes in the scale (data not shown). UPitt testing dataset showed similar results to the training set (Figure 3).

Conclusion

Our preliminary findings suggest that anchoring tau‐PET values both within and between tracers has the potential to harmonize tau‐PET tracers, while preserving their characteristics. Currently, piecewise smoothing is the preferred method for Uniτ, but we are continuously fine‐tuning scale parameters as we acquire more data. The final scale parameters will be determined based on extensive training and testing data from multiple tracers. This cautious methodology holds the promise of delivering reliable, robust, and reproducible results, ensuring safe usage of the scale in clinical trials, and potentially paving the way for future use in clinical practice.
Feasibility of the Linus Health’s life and health questionnaire in primary‐care settings
Authors: Ciesla Marissa C , Haas Brendan , Gomes‐Osman Joyce Rios , Banks Russel , Jannati Ali , Showalter John , Pascual‐Leone Alvaro
Published: January 2025

Introduction

Compelling evidence from longitudinal trials demonstrates that adopting certain lifestyles, especially in midlife to early late life, can reduce the risk of dementia, reduce the severity of the associated disability, and decrease progression to dementia in individuals with mild cognitive impairment by as much as 40%. The Life and Health Questionnaire (LHQ) is a 32‐item questionnaire – written at a 6th‐grade reading level – that captures information related to potentially modifiable lifestyle and psychosocial risk factors, and protective factors for cognitive decline and dementia. The objective of this study was to gain insights into the feasibility of the LHQ in primary care settings by evaluating the influence of cognition, age, and education on the LHQ completion times.

Methods

We analyzed 1826 LHQ surveys between June and October 2023. Mann‐Whitney U tests were used to compare average LHQ completion times in relation to cognitive status as measured by the Digital Clock and Recall (DCR™), age, and education.

Results

The average time to complete the LHQ among all patients was 4.8 minutes (median = 4.2 ± 2.4). Average LHQ completion times showed a gradual increase as DCR scores decreased (increasing cognitive impairment; mean = 3.5 and 6 minutes for DCR scores 5 and 0, respectively; p<0.05). There were no statistically significant differences in LHQ completion times as patient age increased. Those with higher education levels had significantly shorter average LHQ completion times (p<0.01).

Conclusion

The LHQ is a brief survey capturing dementia risk and protective factors that takes an average of 4.8 minutes to complete. Completion time is lengthened with lower education levels and greater cognitive impairment to an average of 6 min. These results indicate the feasibility of the LHQ to be performed in primary‐care settings given its brief completion time.
Immunoassay and Immunohistochemistry of TDP‐43 and the association with annual rate of hippocampal atrophy in Alzheimer's disease
Authors: Youssef Hossam , Gatto Rodolfo G. , Pham Nha Trang Thu , Petersen Ronald C. , Dickson Dennis W. , Petrucelli Leonard , Whitwell Jennifer L. , Prudencio Mercedes , Josephs Keith A.
Published: January 2025

Background

Transactive response DNA‐binding protein of 43 kDa (TDP‐43) is implicated in numerous neurodegenerative diseases, including Alzheimer’s disease (AD). Immunohistochemically stained TDP‐43 is linked to faster rates of hippocampal atrophy (HA) in AD. However, it is unclear whether phosphorylated TDP‐43 (pTDP‐43) measured using an immunoassay is also associated with HA. We explored associations between pTDP‐43 levels, TDP‐43 immunohistochemistry, and annual rate of HA in AD. We hypothesized that higher pTDP‐43 levels would be associated with positive TDP‐43 immunohistochemistry, and faster rates of HA.

Method

Thirty‐seven cognitively impaired participants from the Mayo Clinic Alzheimer’s Disease Research Center underwent serial brain MRIs and brain autopsy. Hippocampal TDP‐43 was detected histochemically, and pTDP‐43 was quantified using biochemical assay. Hippocampal volumes were measured using the longitudinal pipeline in FreeSurfer‐7 and annualized HA rates were calculated. Linear regression analyzed the relationship between HA and pTDP‐43 biochemical assay levels. pTDP‐43 was categorized as positive (≥400) or negative (<400), and logistic regression tested the association of these levels with HA rates. The correlation between TDP‐43 immunohistochemistry status and biochemical assay levels was evaluated using descriptive statistics. All analyses were adjusted for potential confounders.

Results

The mean age at death was 83.5 (±9.9) years with 49% females. Median scan interval was 3‐years. A negative relationship was observed between age at death and annual HA rate (β=‐3.1; 95% CI [‐5.3,‐0.9]; p=0.007), and a positive relationship was observed between pTDP‐43 level and annual HA rate (β=0.02; 95% CI [0.003,0.03]; p=0.015). A one‐unit increase in HA rate was associated with a 3% increase in the odds of being pTDP‐43 positive (≥400) (odds ratio=1.03; CI [1.007,1.05]; p=0.014). Cases that tested positive for Immunohistochemistry TDP‐43 had significantly higher mean pTDP‐43 immunoassay levels (1508 ± 2072 vs. 202 ± 131; p=0.008). More cases with positive IHC TDP‐43 surpassed the pTDP‐43 immunoassay cut‐off level of 400 vs the negative participants (p=0.003).

Conclusion

Higher pTDP‐43 levels in the hippocampus are linked to faster HA in AD and correlated with immunohistochemistry TDP‐43 positivity. These findings underscore the role of TDP‐43 in AD neurodegeneration. The biological and clinical implications of these observations necessitate further exploration in subsequent studies.
Intracellular adhesion molecule 1 in prodromal Alzheimer’s disease
Authors: Behfar Qumars , Behfar Stefan Kambiz , Onur Oezguer A.
Published: January 2025

Background

Age represents the predominant risk factor for Alzheimer's disease (AD) dementia. Nevertheless, not every elderly individual undergoes age‐related processes that inevitably lead to dementia. The aging process is characterized by cellular senescence, manifesting as morphological changes and the secretion of immune signaling mediators linked to systemic low‐grade inflammation. Consequently, our investigation aimed to assess the correlation between cerebrospinal fluid (CSF) levels of specific senescence‐associated secretory phenotype (SASP) proteins and accelerated biological aging. Furthermore, we sought to determine whether this acceleration has an impact on the progression along the Alzheimer's disease continuum.

Method

Biological age was computed utilizing established epigenetic clocks, employing genome‐wide DNA methylation (DNAm) data derived from blood samples collected in the Alzheimer’s Disease Neuroimaging Initiative (ADNI). In addition, BrainAge, a neuroimaging‐based aging clock, was calculated. Age acceleration was ascertained through residuals obtained by regressing predicted biological age against chronological age. Linear and Cox regressions were employed to evaluate the correlation between SASP and the acceleration of biological age, as well as the associated risk of progression from prodromal mild cognitive impairment (MCI) to AD.

Result

In our SASP panel, intercellular adhesion molecule 1 (ICAM‐1), Interleukin 8 (IL‐8), matrix metalloproteinase 3 (MMP‐3), plasminogen activator inhibitor 1 (PAI‐1), tissue inhibitor of metalloproteinases 1(TIMP‐1), vascular endothelial growth factor (VEGF) and stem cell factor (SCF) associated significantly with chronological age. Next, we observed that DNAm acceleration was associated with higher risk of progression to AD, and ICAM‐1 was the only SASP factor which was found associated with DNAm age acceleration, and explained part of the effect of age‐acceleration on progression from MCI to AD.

Conclusion

Our findings provide supporting evidence on the contribution of biological age‐acceleration to the progression of AD. Herein, ICAM‐1 might serve as a standalone biomarker determining how biological age‐acceleration affects the progression along the AD continuum.
Seed amplification assay‐determined alpha‐synuclein associates with Alzheimer’s pathology, clinical symptoms, and cognitive change
Authors: Jonaitis Erin M. , MacLeod Karen R , Jennifer Lamoureux , Jeffers Beckie , Studer Rachel L , Middleton John , Wilson Rachael E , Chin Nathaniel A. , Hermann Bruce P , McEvoy Sean , Zetterberg Henrik , Johnson Sterling C. , Lebovitz Russ , Langhough Rebecca E.
Published: January 2025

Background

The high prevalence of mixed‐pathology dementias suggests that multi‐drug treatments may improve clinical outcomes; thus, in‐vivo biomarkers for co‐pathologies are needed. We investigated a novel assay for detecting seeds of misfolded alpha synuclein (αSyn) and explored its relationship to outcomes including Alzheimer’s disease (AD) biomarkers, clinical features, and cognitive trajectories, in two community‐based cohorts enriched for AD risk.

Method

Cerebrospinal fluid (CSF) obtained from participants in the Wisconsin Registry for Alzheimer’s Prevention and the Wisconsin Alzheimer’s Disease Research Center (N=418 participants; 515 LPs; Table 1) was assayed using a clinically validated, qualitative Syn seed amplification assay (SAA; Amprion). Co‐occurrence of αSyn pathology (αSyn‐SAA+), and AD pathology (CSF amyloid, Aβ42/40 and pTau181/Aβ42, and tau, pTau181, positivity), and clinical symptoms (neurological finding; Parkinsonian signs; etc) were examined with chi‐square tests. Cognitive trajectories were modeled retrospectively using separate mixed effects models of Trail‐Making Test B, Backward Digit Span, Digit Symbol Substitution, and a three‐test Preclinical Alzheimer’s Cognitive Composite (PACC‐3). Models included subject‐level random intercepts and age slopes and standard covariates (sex, education, and practice with the battery). For each outcome, we examined whether αSyn‐SAA+ modified age trajectories (centered; linear and quadratic terms; models 1A‐4A). In models 1B‐4B, we further examined effects of αSyn‐SAA+ after adjusting for age× and age× interactions. Nonsignificant quadratic interaction terms were removed (p>0.1).

Result

αSyn‐SAA+ co‐occurred with binary pTau181/Aβ42 (p=0.048) and pTau181 (p=0.0045), but not Aβ42/40 (p=0.14). Among pTau181/Aβ42+ participants, 20/119 (17%) were also αSyn SAA+, compared to 27/287 (9%) pTau181/Aβ42‐ participants. For pTau181+ and pTau181‐, the proportions were 18/89 (20%) and 30/322 (9%), respectively. αSyn‐SAA+ participants had greater cognitive impairment (p=0.00050) and additional neurological findings (p=0.026), but did not differ (p>=.17) on Parkinsonian signs, REM behavior disorder, or depression. αSyn‐SAA+ participants declined faster in Digit Symbol performance after accounting for Alzheimer’s pathology (model 3B: = ‐0.20, p=0.045; Table 2; Figure 1).

Conclusion

Results suggest significant co‐pathology in an AD‐risk enriched cohort. Relationships with cognitive impairment provide evidence for the clinical utility of αSyn SAA in this population. Steeper age‐related decreases in Digit Symbol performance among αSyn SAA+ individuals suggest possible association with preclinical cognitive change.
Memory Consolidation, Brain Atrophy, and Metabolism in Amyloid Positive and Negative Amnestic Mild Cognitive Impairment
Authors: Teixeira Camila Vieira de Ligo , Keith Cierra M , Haut Mark W , Mehta Rashi , Phelps Holly , Ward Melanie , Miller Mark , Navia R. Osvaldo , Coleman Michelle M , Marano Gary D , Wang Xiaofei , Pockl Stephanie , Rajabalee Nafiisah , McCuddy William T , D’Haese Pierre‐Francois , Rezai Ali R , Wilhelmson Kirk
Published: January 2025

Background

The existing literature has established that Alzheimer's disease (AD) is typically characterized by changes in memory‐associated temporal and parietal lobe atrophy and hypometabolism. However, some individuals clinically diagnosed with AD do not have biomarkers consistent with AD pathology. In this cross‐sectional study, we aimed to investigate differences in memory consolidation, temporal and parietal lobe atrophy, as well as temporal and parietal lobe metabolism within a clinically diagnosed cohort of individuals with amnestic Mild Cognitive Impairment (aMCI) who were either positive or negative for amyloid.

Method

We retrospectively analyzed clinical data from 149 participants: 78 aMCI amyloid positive (A+), 48 aMCI amyloid negative (A‐), and 41 healthy subjects (HP). To analyze memory performance, we used the CVLT 1. % retained (CVLT%), 2. discrimination (CVLT‐D), 3. false positive (CVLT‐FP), and 4. intrusions errors (CVLT‐I). Entorhinal, supramarginal and angular cortical thickness were computed using CAT 12 and DKT atlas, and hippocampus volume using SLANT. A subset of the aMCI participants had an FDG‐PET, and we extracted the metabolism in the brain regions of interest for analysis. ANOVAs explored differences between groups on memory, brain atrophy, and metabolism (adjusted p<.002). Pearson’s correlations examined relationships between CVLT, MRI values, FDG‐PET measures, and biomarker data (adjusted p<0.0009).

Result

A+ exhibited worse performance on CVLT%, CVLT‐D, and CVLT‐FP compared to A‐ and HP, and A‐ was worse on those tests compared to HP. There were no significant differences in the hippocampal volume or entorhinal thickness between biomarker status; A+ showed more atrophy in supramarginal and angular thickness than A‐ and HP. A+ showed hypometabolism in the entorhinal cortex compared to A‐. While hippocampus volume was related to CVLT%, CVLT‐D and CVLT‐FP, CVLT% was positively related to all the metabolism in all areas analyzed except hippocampus.

Conclusion

In our memory clinic cohort characterized by biomarker status, our findings indicated that factors beyond AD might contribute to memory consolidation, temporal lobe atrophy, and temporal lobe. In addition, by inference, adding parietal atrophy to mesial temporal atrophy worsens memory consolidation.
Anti‐Amyloid Treatments for Alzheimer’s Disease: Patients’ and Families’ Perception and Experience with Drug Consultation
Authors: Lapins Allison , Lucca Kate , Morhardt Darby , Welch Sarah
Published: January 2025

Background

Drug consultations for the new anti‐amyloid therapies for Alzheimer’s Disease are currently taking place at the Northwestern Neurobehavior and Memory Clinic with potentially eligible patients and their families. Study aims are to: 1) measure patient and family perception, barriers and concerns of the drug treatment following a formal consultation or informal discussion with a neurologist, and 2) gather data on the patient and family experience with the drug consultation as a method for making an informed decision.

Methods

Participants who are potentially eligible for anti‐amyloid therapies and have a drug consultation with their behavioral neurologist will be asked to participate in our study survey. Patients who are potentially eligible for anti‐amyloid therapies must be in the early stages of the disease and have biomarker evidence of Alzheimer’s Disease pathology. Interested participants complete the survey over the phone with a study team member. The survey asks questions aimed to measure interest in starting this treatment (before and after consultation), concerns and barriers surrounding the treatment, as well as overall experience with the drug consultation.

Result

This study is underway with data collection and will have preliminary results to share in the coming months.

Conclusion

We expect that drug consultations with patients and families on the new Alzheimer Disease therapies will help to identify concerns, understanding of the risks and benefits and overall impact of taking these newly available treatments.
Developing a novel brain‐based biomarker of mild cognitive impairment using time‐domain functional near‐infrared spectroscopy
Authors: Dubois Julien , Field Ryan M , Jawhar Sami , Koch Erin M , Aghajan Zahra M. , Miller Naomi , Perdue Katherine L , Taylor Moriah
Published: January 2025

Background

Mild cognitive impairment (MCI) is largely under‐diagnosed by primary care physicians. There is an urgent need to develop new objective screening tools to assist with early detection of MCI. Time‐domain functional near‐infrared spectroscopy (TD‐fNIRS) can be used to measure brain function in clinical settings and may fill this need.

Method

MCI patients (n=21) and control older adults (HC; n=24) participated in brain recordings using TD‐fNIRS during rest and cognitive tasks implicated in MCI: 1) Verbal fluency task (VFT) consisting of interleaved blocks reciting words (control: weekdays; semantic: words from a given category; phonological: words starting with a given letter); 2) N‐back task targeting working memory by testing memory for patterns with specific memory loads (0‐, 1‐ and 2‐backs) in a block‐design manner.

Raw data, i.e. the distributions of the times of flight (DToFs) of photons at two wavelengths (690 and 905nm), underwent standard preprocessing to obtain moments of DToFs and corresponding changes in the concentrations of oxygenated/deoxygenated hemoglobin. Features related to brain function, systemic physiology, and head optical properties were extracted. For tasks, behavioral metrics were also computed.

Result

First, we looked for features that significantly differentiated between MCI and HC groups during a resting state session (watching a 7‐minute audiovisual segment; Figure 1A). Multiple features, including head optical properties and prefrontal fractional Amplitude of Low Frequency Fluctuations (fALFF) were statistically different between MCI and HC (Figure 1B). Next, we explored the tasks both in terms of behavior and neural features. In VFT, behavior was significantly different between groups (Figure 2A), which is expected from prior literature. Additionally, whole‐brain hemodynamic activation maps, as computed using Generalized Linear Models (GLM) revealed areas with significant differences between the two groups (Figure 2B). Similarly, for the N‐back task, multiple behavioral metrics were separated between the groups (Figure 3A) and group‐level GLM showed brain regions with significantly different activations between groups, specifically medial prefrontal cortex (Figure 3B).

Conclusion

We demonstrated significant group‐level differences between MCI vs healthy controls using TD‐fNIRS. As such, with ongoing data collection, we seek to validate these findings and employ classification techniques for MCI detection.
Impacts of a Comprehensive Dementia Care Management Program on Patient and Caregiver Health and Wellbeing
Authors: Tan Zaldy S , Qureshi Nabeel , Spivack Erica , Rhinehart Deana , Gatmaitan Dyane , Guinto Augustine , Kremen Sarah , Sicotte Nancy
Published: January 2025

Background

The United States faces a growing challenge with over 6.5 million people living with dementia (PLwD). PLwD and their caregivers struggle with cognitive, functional, behavioral, and psychosocial issues. As dementia care shifts to home settings, caregivers bear increasing responsibilities, leading to increase strain. This study assesses the initial impacts of a pragmatic dementia care model, the Cedars‐Sinai C.A.R.E.S. Program, on patient and caregiver health and wellbeing.

Methods

The Cedars‐Sinai C.A.R.E.S. Program is an integrated care management program that enrolled eligible patients aged 65 and older with a dementia diagnosis or dementia medication within the health system. Enrolled patients underwent comprehensive assessments of patient and caregiver health and wellbeing using the Neuropsychiatric Inventory‐Questionnaire (NPI‐Q) (patient), Patient Health Questionnaire (PHQ) 2 and 9 (caregiver), and Modified Caregiver Strain Index (MCSI) (caregiver) at baseline and follow up one‐year after enrollment. We calculated overall and sub‐assessment scores for each measure and compared the scores using t‐tests to test for statistical significance.

Results

To date, of the 431 were enrolled in the C.A.R.E.S. Program, 80 completed their baseline and follow‐up health and wellbeing assessment. Assessment scores were higher (worse health and lower wellbeing) at baseline than at follow up for all measures. For the NPI‐Q, the overall score decreased from 23.54 (SD = 15.68) to 20.74 (17.71) (p = 0.29), the distress sub‐assessment score decreased from 10.7 (7.94) to 9.42 (8.85) (p = 0.34), and the severity sub‐assessment score decreased from 8.3 (5.6) to 7.36 (6.48) (p = 0.33), though none were statistically significant. Similarly, caregiver depression decreased from 1.12 (1.41) to 0.8 (1.19) using the PHQ‐2 (p‐0.13) and decreased from 4.73 (5.32) to 4.22 (4.61) using the PHQ‐9 (0.52). Finally, caregiver strain decreased from 9.84 (6.25) to 8.47 (5.84) (p = 0.15).

Conclusions

The Cedars‐Sinai C.A.R.E.S. Program offers a promising approach to dementia care. While there were differences in assessment scores at baseline and follow‐up, differences were not statistically significant. As the program continues to enroll patients and they complete their assessments, we expect the increase in statistical power will help determine whether differences are real or due to chance.
Longitudinal associations of microstructural changes, amyloid, and cognition in preclinical Alzheimer's disease
Authors: Vannini Patrizia , Bertazzoli Giacomo , Rodriguez‐Vieitez Elena , Ramseyer Max , Diez Ibai , Sepulcre Jorge , Hanseeuw Bernard J , Lois Cristina , Gagliardi Geoffroy Pierre , Westin Carl‐Fredrik , Johnson Keith A , Sperling Reisa A , Marshall Gad A
Published: January 2025

Background

A novel method using diffusion‐weighted imaging (DWI) allows for assessing microstructural injury in the gray matter by measuring cortical mean diffusivity (cMD). Previous studies have shown that altered cMD is related to amyloid (Aβ) cross‐sectionally and can predict longitudinal cognitive decline and clinical progression, suggesting utility in clinical trials. However, the longitudinal associations between these measures are unknown. This study aimed to investigate the longitudinal associations among cMD, Aβ, and cognition, in individuals who were cognitively normal (CN) at baseline.

Methods

The study sample comprised n=156 CN older adults (73.8±6.1 y/o;92 [59%] women) from the Harvard Aging Brain Study. All participants underwent structural MRI, DWI, and 11C‐Pittsburgh compound‐B‐PET at baseline and after 4 years. Cognitive assessment using the Preclinical Alzheimer Cognitive Composite‐5 (PACC‐5) was collected at baseline and yearly for an average of 9.7±3.2 years. Generalized linear models (GLMs) were used to assess the regional (focusing on brain areas vulnerable to early Aβ accumulation) change in Aβ on change in cMD, covarying for baseline cortical thickness, change in cortical thickness, age, and sex. GLMs were used to assess the impact of change in regional cMD by Aβ group, on the rate of change in cognition covarying for baseline cMD, baseline age, sex, and education.

Results

Overall, Aβ and cMD signal increased over the 4‐year period. 44 participants (28%) exhibited an initial high Aβ burden (Aβ+, thresholded using 1.186 SUVR). An increase in Aβ pathology (over 4 years) predicted a concomitant increase in cMD in the isthmus cingulate (ßstd=6.8e‐06;p=0.019). Post hoc stratified analyses by Aβ‐status revealed that this effect was only observed in the Aβ+individuals (ßstd=1.6e‐05;p=0.023, Figure 1A). Moreover, a significant interaction between Aβ and cMD signal predicted cognition, such that an antecedent increase in cMD was related to a steeper cognitive decline in Aβ+ individuals (ßstd=‐0.061;p=0.004, Figure 1B).

Conclusion

Amyloid accumulation is associated with a concomitant increase in cMD, suggesting that it may reflect microstructural alterations in response to Aβ aggregation. Moreover, an increase in cMD over time predicted cognitive decline in at‐risk individuals. Overall, metrics of cortical diffusion may provide important outcome measures in Aβ‐modifying clinical trials for detecting drug‐induced changes in cortical microstructure.
Digital speech markers of cognitive impairment in ALS‐FTD spectrum disorders
Authors: Shellikeri Sanjana , Cho Sunghye , Ash Sharon , Gonzalez‐Recober Carmen , Cousins Katheryn A Q , McMillan Corey T , Elman Lauren , Quinn Colin , Amado Defne A , Baer Michael , Irwin David J , Massimo Lauren , Liberman Mark Y , Nevler Naomi
Published: January 2025

Background

Comorbid cognitive impairment in amyotrophic lateral sclerosis (i.e., ALS‐FTD), is associated with adverse clinical outcomes and survival. It often exhibits clinical features of behavioral variant frontotemporal dementia (bvFTD) and/or non‐fluent agrammatic primary progressive aphasia (naPPA). Traditional cognitive assessments, such as the Edinburgh Cognitive Assessment Scale (ECAS), require trained personnel for administration. An additional repeatable, objective approach involves automatic analysis of picture descriptions. Prior research identified acoustic and lexical‐semantic impairments in bvFTD, PPAs, and ALS‐FTD (acoustics). This study extends the analysis to the complete speech feature set (n=30) in ALS‐FTD spectrum disorders.

Method

ANCOVAs (FDR adjusted) compared picture descriptions in n=51 ALS, n=22 ALS‐FTD (n=10 bvFTD‐dominant, n=4 naPPA‐dominant), and n=12 ALS with ECAS‐defined mild cognitive/behavioural impairment (ALS‐MCI). Backward selection identified the best feature subset. Post‐hoc assessments examined associations with ECAS behavioral, language, and executive scores. Longitudinal analysis compared within‐individual change over time in n=11 ALS vs. n=9 ALS+cog (combining ALS‐MCI with ALS‐FTD) with available longitudinal data. All models adjusted for bulbar motor scores.

Result

ALS‐FTD significantly differed from ALS and ALS‐MCI on twelve features at first visit: slow speaking and articulatory rates, reduced word count, less percent speech time, shorter speech segments, and longer and more frequent pausing. ALS‐FTD also exhibited fewer adjectives and prepositions, more repetitions, lower lexical diversity, and words with a lower mean age of acquisition (AoA). The best model comprising three features (percent speech, prepositions, and lexical diversity) strongly distinguished ALS‐FTD from ALS (AUC=0.90). ALS‐MCI did not differ from ALS on any speech measure. Acoustic measures were all significantly associated with behavioral, language, and executive scores. Fewer prepositions related to worse behavioral and language scores, and more repetitions and lower lexical diversity related to worse language scores. Mean AoA and articulatory rate were not linked to any ECAS sub‐domains. Longitudinally, ALS+cog exhibited increased partial words and reduced word AoA, while ALS showed no significant change.

Conclusion

Automated speech analysis strongly distinguishes ALS‐FTD from ALS, declines with cognitive severity, and captures distinctive longitudinal changes in ALS‐FTD. These results highlight the potential of automated speech analysis as an objective tool for assessing cognitive impairment in ALS.
Generative AI improves MRI‐based Detection of Alzheimer’s Disease by using Latent Diffusion Models and Convolutional Neural Networks
Authors: Dhinagar Nikhil J , Thomopoulos Sophia I , Thompson Paul M.
Published: January 2025

Background

As new treatments (such as the anti‐amyloid vaccine, lecanamab) emerge for Alzheimer’s disease (AD) and other dementias, approaches are required to rapidly diagnose AD at the earliest possible stage, and to assess disease progression and prognosis. In January 2024, the FDA approved the first AI tool to predict AD progression based on magnetic resonance imaging (MRI) [1]. Here we train a generative AI approach based on latent diffusion models ‐ to encode disease effects on brain structures. We show how AI‐based generation of synthetic scans can complement existing datasets for AD‐related diagnostic tasks.

Method

We analyzed 4,098 3D T1‐weighted brain MRI scans (556M/632F, 55.7 ‐ 92.8 years) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). We trained a latent diffusion model (LDM) [2] as our generative AI framework. The model was conditioned on the disease status of AD versus healthy controls to generate synthetic brain MRI scans. For validation, the generated synthetic MRI scans was used to initialize a 3D DenseNet‐121 CNN (convolutional neural network) [3], and then further fine‐tuned for AD classification.

Result

The table below summarizes the AD classification performance of a 3D CNN on a test set of 1,219 scans from ADNI. The synthetic data improved performance by over 2%, i.e., receiver‐operator characteristic curve‐area under the curve (ROC‐AUC) from 0.8656 to 0.8869.

Conclusion

In this work, we trained a latent diffusion model to generate brain MRI scans. When these scans were used to train a deep neural network to detect AD, performance increased. Future work will test these generative AI models for interpretable disease detection via counterfactual image generation, and to discover factors that affect AD onset and progression via meta‐data encoding in the generative model.

References:

[1]. J. Shugart, FDA Approves BrainSee, AI Software That Purportedly Predicts AD, AlzForum News, available at: https://www.alzforum.org/news/research‐news/fda‐approves‐brainsee‐ai‐software‐purportedly‐predicts‐ad, 2024.

[2] W. H. L. Pinaya et al., “Brain Imaging Generation with Latent Diffusion Models,” MICCAI Workshop on Deep Generative Models (DGM4MICCAI) 2022, pp 117‐126.

[3] N. Dhinagar et al., “Video and Synthetic MRI Pre‐training of 3D Vision Architectures for Neuroimage Analysis,” SPIE Medical Imaging 2024.
Multi‐dimensional Feature Extraction Technology of Spontaneous Speech Based on Machine Learning: Early Identification of Alzheimer's Disease
Authors: Chen Quan , Li Yatian , Wang Ying , Li Yiming , Gao Jingyi , Chen Nan , Wu Jingnan , Li Ran
Published: January 2025

Background

Speech variations appear at early stages of Alzheimer's disease (AD) and are potential early indicators. However, the commonly used neuropsychological assessment scales as screening tools for cognitive impairments have limitations due to possible subjective bias of the assessors and the lack of sensitivity in voice detection. We propose a solution based on machine learning for multi‐dimensional feature extraction and data fusion of spontaneous speech signals.

Method

Participates with gold standard diagnosis including cognitively normal, mild cognitive impairment (MCI), and mild AD underwent a speech‐based cognitive assessment (Shanghai Cognitive Screening, SCS), including picture learning and recall tasks. Their voice data were generated and created into semantic datasets and audio datasets, respectively. Then, to obtain semantic and acoustic features, the two datasets were trained in multiple machine learning models and tested separately. Finally, to separating cognitively normal and impaired (MCI+AD) participants, multi‐dimensional features of the speech signal are combined through data fusion using voting style.

Result

The accuracy of this model is 91.5 ± 0.8%, with a specificity of 97.0%. In addition, there are significant differences between cognitively impaired patients and normal controls regarding instant recall as opposed to delayed recall tasks, with the most notable difference being the duration of silence in delayed recall tasks.

Conclusion

This method maximizes the exploration of feature information in speech signals, greatly improving the accuracy of identification for Alzheimer's disease.
Collaboration introduces an innovative practice for incontinence monitoring to provide efficiencies and higher quality of care
Authors: Chidester Amy S
Published: January 2025

Incontinence Management has long been overlooked as a normal part of aging and the national standard of care has become changing individuals every 2 hours. While this was effective for decades, it is no longer providing results and even more challenging for those suffering from Dementia. Over 50% of nursing home residents are incontinent with over 57% of each shift of a certified nursing aide being attributed to incontinence care and management.1

Studies show that incontinence is often the root cause of wounds, infections, falls, odor, depression, and behaviors.2 Senior care has long provided care in a systematic, uniform way, not allowing for an individualized experience. Instead of making briefs more absorbent, we need to focus on real‐time changes to help mitigate health risks.

Data and science behind predicting when someone will void and helping them not void in an incontinence product is the key. This is the practice of developing and implementing voiding schedules from data that are individualized to each person and providing them with the help they need and deserve. Going further into this concept, the data around patterns of voiding is also extremely powerful for care teams as over time care teams can identify outliers that can indicate to providers a brewing event or condition.

Our practice innovation is a collaboration between an assisted living memory care provider and a digital health sensor platform providing a smart brief that is disposable and remotely “talks” through readers, disguised as art to alert staff to when an individual has had an incontinent episode. Data is collected and reported via a dashboard that provides real‐time alerts for the care team to make decisions on care plans and quality of care can be elevated in ways not available before.