bluuebunny/crossref_metadata_2025 huggingface dataset:
A‐T+ PET participants in preclinical AD: Clinical progression and concordance with fluid markers
Authors: Yakoub Yara , St‐Onge Frédéric , Fajardo Alfonso , Mohammediyan Bery , Dery Christine , Sylvain Elisabeth , Tremblay‐Mercier Jennifer , Remz Jordana , Gonneaud Julie , Vogel Jacob W. , Soucy Jean‐Paul , Binette Alexa Pichet , Villeneuve Sylvia
Published: January 2025

Background

Amyloid‐negative tau‐positive PET (A‐T+) participants have been reported in several studies. We assessed the prevalence and characteristics of A‐T+ participants in a cohort of cognitively unimpaired individuals with a first‐degree family history of Alzheimer’s disease (AD) dementia.

Method

We studied 252 participants from the longitudinal PREVENT‐AD cohort (mean cognitive follow‐up = 3.42 years, SD = 2.86) who received an amyloid (18F‐NAV4694) and a tau (18F‐Flortaucipir) PET‐scan. The amyloid‐positivity threshold was 18 centiloids (SUVR=1.27) and tau positive threshold was set as 2SD above the mean of A‐ participants in a temporal meta‐ROI (SUVR=1.29). Follow‐up PET scans were available for 109 participants. We characterized A‐T+ participants on clinical (age, sex, APOE4 status, education, depression, apathy, anxiety, sleep), cognitive (RBANS, MMSE, ECoG) and fluid (CSF, plasma) measurements.

Result

In the PREVENT‐AD cohort, only 3 (1.2 %) of all studied participants were classified as A‐T+ (see Table 1 for participants characteristics). Participants 1 and 2 were positive based on plasma Aß42/40 and they had relatively low levels of tau binding that could represent early pathology. Participant 3 was also positive based on CSF and plasma, her Aß‐PET scan was classified as positive on visual read and became quantitatively positive at follow‐up. Participant 3 had extensive unilateral tau binding at baseline that became bilateral at follow‐up (Figure 1). Figure 2 details the longitudinal cognitive trajectory of Participant 3. During the cognitive follow‐up, participants 1 developed mild cognitive impairment and participant 3 developed dementia.

Conclusion

A‐T+ individuals are rare in the PREVENT‐AD cohort and the 3 A‐T+ were classified as Aß positive based on fluid biomarkers. One of the 3 A‐T+ individuals showed a very fast clinical progression and a tau uptake pattern atypical of AD.
Astrocyte reactivity and cerebral glucose hypermetabolism in early Alzheimer's: insights from a longitudinal study in the TgF344‐AD rat model
Authors: de Lima Amanda Muliterno Domingues Lourenço , da Rocha Andreia Silva , Machado Luiza Santos , Soares Carolina , Ferreira Pamela C.L. , Bellaver Bruna , da Silva Patrícia Eduarda Carvalho , Venturin Gianina Teribele , Greggio Samuel , Lazzarotto Gabriela , Vidor Pedro , Kunach Peter , Green Kim N. , da Costa Jaderson Costa , Souza Diogo O. , Rosa‐Neto Pedro , Zimmer Eduardo R. , AD MODEL
Published: January 2025

Background

Recent studies have suggested a transient glucose hypermetabolism in early phases of Alzheimer's Disease (AD), which is followed by a characteristic glucose hypometabolism in dementia stages. This phenomenon desveres further investigation and it is suggested to be associated to glial/inflammatory or compensatory neuronal responses. Here, we aimed to longitudinally investigate brain glucose metabolism in an AD animal model and explore associated cellular and inflammatory changes.

Method

Longitudinal assessments, including cerebral glucose metabolism (18F]FDG‐PET), behavioral tasks and cerebrospinal fluid (CSF) sample collection were performed at 3, 6, 9, and 12 months of age (mo) in wild type (WT) and TgF344‐AD rats (Tg). Glial and inflammatory markers were evaluated in the CSF via ELISA multiplex. A cross‐sectional cohort was used to follow the spatial distribution of Aß plaques (IHC), to assess the brain content of glial and neuronal proteins (western blot), and to analyze the cortical glutamate uptake (ex‐vivo slices) at the same time points.

Result

At 9mo, three months after the initial appearance of Aß plaque deposits, Tg animals exhibited cortical glucose hypermetabolism (Figure 1A‐C). At the same age, astrocytic glutamate uptake was increased in the cortex and hippocampus (Figure 2). Declines in performance on the Y‐maze and Novel Object Recognition tasks were observed at 9 and 12mo (Figure 1D). CSF analysis revealed elevated GFAP levels at 6, 9 and 12mo, and reduced S100B at 9mo (Figure 1E e F). Tissue GFAP immunocontent increased in the temporoparietal cortex at 6 and 9mo, in the hippocampus at 9mo, and was reduced in the frontal and temporoparietal cortices at 12mo (Figure 3).

Conclusion

Our findings suggest the presence of an early, transient phase of brain glucose hypermetabolism in TgF344‐AD rats, consistent with observations in recent animal and human studies. This phenomenon seems to be closely linked to the astrocyte response, reflected in variations of crucial astrocyte proteins such as GFAP and S100B, along with an increase in the glutamate uptake by these cells. In contrast, neuronal, microglial and inflammatory markers did not exhibit changes during this timeframe.
Estimating age at Aβ onset in the 1946 British birth cohort: exploring the influence of APOE ɛ4 carriership and sex
Authors: Coath William , Betthauser Tobey J. , Teague Jordan P , Scott Catherine J , Thomas David L , Dickson John , Markiewicz Pawel J , Barkhof Frederik , Schöll Michael , Richards Marcus , Fox Nick C , Cash David M , Schott Jonathan M
Published: January 2025

Background

Cerebral Aβ accumulates decades before symptom onset in AD. Sampled iterative local approximation (SILA, Betthauser et al. 2022) is a technique for estimating time from Aβ positivity (Aβ+) using Aβ‐PET. Here we explore the influence of APOE‐ɛ4 carriership and sex on estimated age of Aβ+ (EAAβ) in primarily cognitively normal individuals from the longest‐running British birth cohort.

Method

Participants in Insight 46 were drawn from the 1946 British birth cohort and underwent combined PET/MR scanning with [18F]florbetapir at two timepoints. Centiloid (CL) values were calculated using a whole cerebellum reference region and partial volume correction. We used Gaussian mixture modelling (99th percentile of lower distribution) to define an Aβ+ threshold of 12 CL. We constructed a CL vs Aβ time curve with SILA from available longitudinal Aβ‐PET scans (n = 324, interval = 2.4±0.2yrs), which was applied to the whole sample (n = 463) to calculate EAAβ. Some scans were below the CL range that could be modelled, and the estimated EAAβ was truncated to the furthest time before onset on the CL vs time curve. Kaplan‐Meyer plots and Cox regression were used to investigate EAAβ and risk of estimated Aβ+ by APOE‐ɛ4 and sex, right censoring participants where EAAβ was truncated.

Result

Mean (SD) chronological age was 70.6±0.7yrs at baseline and 73.0±0.6yrs at follow‐up scan and 30.5% were Aβ+ve by follow‐up (n = 141, 55.3% APOE‐ɛ4, 51.8% female). SILA results and distribution of EAAβ are shown in figures 1 and 2. EAAβ were truncated due to low Aβ binding in 35.7% (n = 166, 15.7% APOE‐ɛ4, 50% female). APOE ɛ4 carriers reached median EAAβ 8.6 years earlier than non‐carriers (figure 3A). Median EAAβ for females was marginally earlier than males with overlapping CIs (figure 3B). APOE‐ɛ4 was strongly associated with risk of estimated Aβ+ (HR[95%CI] = 2.4[1.9,3.0]), adjusted for sex. Female sex (1.1[0.8,1.3]) and the interaction between APOE‐ɛ4 and sex (1.2[0.8,1.9]) were non‐significant.

Conclusion

Estimated Aβ positivity was reached on average 8.6 years earlier for APOE ɛ4 carriers. Consistent with results from other cohorts, APOE‐ɛ4, not sex, significantly heightened the risk of estimated Aβ positivity. Future research will explore life course and downstream events related to Aβ timing.
MCI Diagnosis and Neighborhood Disadvantage Predict Differences in White Matter Microstructure in a Rural Aging Cohort
Authors: Bender Andrew R. , Giriprakash Pavithran Pattiam , Cordes Dietmar , Zhuang Xiaowei , Caldwell Jessica ZK , Miller Justin B
Published: January 2025

Background

Diffusion magnetic resonance imaging (dMRI) studies show cerebral white matter alterations are associated with both mild cognitive impairment (MCI) and contextual psychosocial factors. However, these reports are almost exclusively based on older adults residing in metropolitan areas. Individuals living in rural communities face increased risk for Alzheimer’s disease and related dementias, but little is known regarding the sensitivity of dMRI markers to incident MCI or psychosocial stressors in this population. In a rural‐dwelling community cohort of older adults, this study evaluated associations between dMRI measures of white matter microstructure and both clinical MCI diagnosis and neighborhood disadvantage.

Method

The study sample included rural‐dwelling older adults (n = 69) from the Nevada Exploratory Alzheimer’s Disease Research Center. Participants were characterized as cognitively unimpaired (CU; n = 42) or diagnosed with MCI (n = 27) based on clinical expert consensus, and all had dMRI data and Area Deprivation Index (ADI) measures of neighborhood disadvantage. dMRI data processing leveraged the MRtrix3 fixel‐based analysis framework and free water mapping to estimate voxel‐based measures of white matter organization, transformed to a sample template. Data analysis utilized voxel‐based analysis (VBA) to model differences in clinical diagnosis and ADI scores as predictors of fiber density, crossing fiber entropy, and extracellular free water, and free water‐corrected diffusion tensor parameters, accounting for differences in age, sex, and handedness.

Results

The VBA models showed both MCI diagnosis and elevated neighborhood disadvantage predicted significantly greater (familywise error corrected p<.05) free water and reduced crossing fiber entropy in centrum semiovale and internal capsule (Figure 1). Uncorrected models for both diagnosis and higher ADI level revealed widespread associations with dMRI measures of free water, tensor parameters, and fiber density that were nonsignificant following permutation‐based corrections.

Conclusion

These preliminary findings reveal white matter alterations associated with consensus MCI diagnosis and to socioeconomic differences in a clinical cohort of rural‐dwelling older adults. The present results highlight the value of modeling extracellular cerebral spinal fluid or ‘free water’ as a marker of microstructural changes associated with both cognitive impairment and contextual psychosocial factors. Future efforts are needed to compare these effects between rural‐ and metropolitan‐dwelling older adults at risk for dementia.
Voxel‐wise Florzolotau (APN‐1607) Tau PET Covariance Patterns in Alzheimer’s Disease and Progressive Supranuclear Palsy
Authors: Blazhenets Ganna , Brumberg Joachim , Endo Hironobu , Hsu Jung‐Lung , Frings Lars , Schroeter Nils , Hellwig Sabine , Chang Chiung‐Chih , Higuchi Makoto , Meyer Philipp Tobias
Published: January 2025

Background

Florzolotau (APN‐1607) tau‐PET has shown distinct patterns of binding in patients with AD and 4‐repeat tauopathies. We aimed to establish disease‐specific tau covariance patterns in AD and PSP/CBS and validate them as user‐independent quantitative biomarkers for reference‐region‐free evaluation of tau‐PET in an independent clinical cohort.

Method

We analyzed Florzolotau PET data from four different cohorts. The derivation dataset included 30 Aß‐ healthy controls (HC) and 30 patients each with Aß+ AD and PSP with Richardson’s syndrome (PSP‐RS). The validation dataset included 44, 51, 6, 15, and 17 patients with AD, PSP/CBS, MSA, LBD, and FTD, respectively. First, we applied scaled sub‐profile modelling principal components analysis to the derivation dataset to define the AD‐ and PSP‐RS‐related covariance patterns. Second, the topographic profile rating algorithm was applied to each scan to calculate AD‐ and PSP‐RS‐related pattern scores.

Result

The AD‐ and PSP‐RS‐related patterns are described in Figures 1 and 2. In the derivation dataset, the AD‐ and PSP‐RS‐scores showed strong increases compared to HC (both p<0.001,AUC‐ROC = 0.994 and 0.898, respectively). AD‐scores showed a significant negative association with age (r = ‐0.66,p<0.001) and MMSE (r = ‐0.64,p<0.001) in AD, while PSP‐RS‐scores correlated with the PSP rating scale (r = 0.43,p = 0.018) in PSP‐RS. In the validation dataset (Figure 3), AD‐scores were highest in AD (p<0.001 vs. all other, AUC‐ROC[AD vs. all other] = 0.946) and were negatively associated with age and MMSE in AD (r = ‐0.62,p<0.001 and r = ‐0.49,p = 0.002, respectively). PSP‐RS‐scores were highest in the PSP/CBS (p<0.001 vs. all other [at least p<0.05 vs. each group], AUC‐ROC[PSP/CBS vs. all other] = 0.734), though also relatively increased in FTD and MSA. UPDRS‐III scores (off‐state) were available in 23 PSP/CBS patients and significantly associated with PSP‐RS‐scores (r = 0.60,p = 0.002).

Conclusion

Modelling with principal components analysis allows for reference‐region‐free quantification of tau imaging data by constructing disease‐specific covariance patterns. When applied to Florzolotau, AD‐ and PSP‐RS‐related patterns showed high discrimination power in both the derivation and the clinically heterogeneous validation cohorts. PSP‐RS‐related pattern expression in FTD and MSA may be related to PSP pathology and known striatal off‐target binding, respectively. Strong association with respective disease severity scores further advocates for use of AD‐ and PSP‐RS‐related patterns as potential quantitative biomarkers of tau pathology.
MTL networks are differentially affected by tau pathology
Authors: Hrybouski Stanislau , Das Sandhitsu R. , Xie Long , Wisse Laura E.M. , Kelley Melissa , Lane Jacqueline , Sherin Monica , DiCalogero Michael , Nasrallah Ilya M. , Detre John A. , Yushkevich Paul A. , Wolk David A
Published: January 2025

Background

The extent to which pathological processes in aging and Alzheimer’s disease (AD) relate to functional alterations in the medial temporal lobe (MTL)‐dependent brain networks is poorly understood. Here, we examined the relationship between tau accumulation in the (trans)entorhinal cortex and functional connectivity (FC) in two MTL‐affiliated brain networks — the Anterior‐Temporal (AT) and Posterior‐Medial (PM) — in normal agers, individuals with preclinical AD, and patients with symptomatic AD.

Method

In this cross‐sectional study, we analyzed data from 125 individuals from the Penn ADRC (Table 1). All participants underwent structural and functional MRI on a Siemens 3 T Prisma system, as well as 18F‐Florbetaben and 18F‐Flortaucipir (FTP) PET imaging. We used the AT/PM network architecture from our previous work (Fig. 1; PMID37767219) and analyzed the effects of (trans)entorhinal tau accumulation on the AT/PM FC as a function of distance to the MTL (Fig. 1). Guided by our previous findings of an inverted U‐shaped FC pattern in the AT network over the course of the disease, we performed separate analyses of the AT FC in cognitively normal and symptomatic individuals.

Results

FC between the anterior MTL and its direct neighbors in the AT network was positively correlated with tau accumulation in the MTL (r = 0.201, p = 0.031; Fig. 2a). Associations with tau were not present in other MTL‐AT connections (Figs. 2b‐c) or in individuals with symptomatic AD. In contrast, the PM FC was broadly affected by tau pathology with both direct MTL‐PM and more distant connections showing a negative relationship to (trans)entorhinal tau (Fig. 2d‐e). Excluding amyloid‐negative controls, the relationship between MTL tau and AT/PM FC remained statistically significant in the PM [one‐hop connections: r = ‐0.323, p = 0.0479; distant connections: r = ‐0.324, p = 0.047] but not the AT network.

Conclusions

Together, the current results implicate functional abnormalities in the AT network during the preclinical stage, while those in the PM network are closely related to disease severity. This dissociation likely represents distinct pathophysiology in AD and has potential implications for FC‐based metrics as a surrogate measure for assessing functional response to disease‐modifying immunotherapies.
Older adults at‐risk for type 2 diabetes exhibit decreased performance on spatial and working memory tasks using classic and novel cognitive testing
Authors: Ghosh‐Swaby Olivia R , Al‐Shaikh Jennifer Hanna , Palmer Daniel , Khan Ali R. , Thornton Jane , Bussey Timothy J , Saksida Lisa M , Liu‐Ambrose Teresa , Nagamatsu Lindsay
Published: January 2025

Background

Type 2 diabetes (T2D) and older age are well‐known risk factors for dementia. Indeed, there is evidence that older adults not diagnosed, but at‐risk for T2D can show early signs of cognitive decline, further exacerbated by excessive body weight or high blood glucose levels. Such a finding would have implications for early treatment strategies; however, the evidence is still sparse. We examined the correlation of risk factors for diabetes with cognitive function in older adults at‐risk for T2D using a battery of touchscreen tasks translated from their rodent versions, as well as traditional pen‐to‐paper cognitive tests.

Method

Sixty‐five older adults (69.39 ± 10.31 years old, 68% female) at‐risk for T2D (BMI = 25 kg/m2, hemoglobin A1c = 6.0%, CANRISK score = 21) completed 3 novel touchscreen tasks: paired associative learning (PAL) (learning and object‐in‐location memory), progressive ratio (motivation), and trial unique, non‐matching to location (TUNL) (spatial pattern separation and working memory). They were also tested on pen‐to‐paper cognitive tests: trail‐making (task switching), Stroop (selective inhibition), and digit span (working memory). A correlation analysis was performed between BMI or HbA1c and cognitive performance. Performance on touchscreen tasks was analyzed using a repeated measures one‐way ANOVA.

Result

Higher HbA1c levels were correlated with lower digit span scores (r2 = 0.12, p = 0.011). There was no correlation between breakpoint (motivation level) and BMI or HbA1c during the progressive ratio task (r2 = 0.0, p = 0.38). Interestingly, this population performed at chance level (59.7 ± 5.3% accuracy) on the PAL task, indicating they were unable to learn object‐location paired associates. When manipulating the spatial similarity in distance between stimuli during the TUNL task, older adults at risk for diabetes were 10% lower in accuracy when stimuli were close together compared to further apart (p<0001). Participants also responded more slowly to stimuli at choice during the TUNL task during the heaviest working memory load condition (p = 0.003).

Conclusion

Older adults at‐risk for T2D exhibit decreased performance on tasks with higher demands on spatial and working memory. Future research will compare performance on all tasks to healthy age‐matched controls and reassess performance after a six‐month exercise intervention.
Functional connectivity changes through Alzheimer's disease continuum: disease onset, progression, and response to therapy
Authors: Zimmer Aline R. , Bourourou Miled , Lesage Frédéric , Hamel Edith
Published: January 2025

Background

Hemodynamic signals are the basis of functional brain imaging techniques, such as fMRI and NIRS, and are often used to infer changes in resting‐state functional connectivity (RSFC) in Alzheimer’s disease (AD) and other dementias. Increasing evidence suggests that disruption of neuronal circuits has been associated with the AD continuum and may precede changes in Ab and tau biomarkers, neurodegeneration, and cognitive impairment. To better understand the changes in brain RSFC through the AD spectrum, we use hemodynamic signals to detect disease onset, progression, and response to therapy in a mouse model of AD.

Method

Hemodynamic signals were recorded longitudinally (3‐8 months) in anesthetized (ketamine/xylazine) and awake WT and APP (J20) mice, implanted with full cranial window, using optical imaging of intrinsic signals (OIS), together with cognitive testing. Seed‐based functional connectivity maps were generated for the bilateral connectivity (BC) and RSFC analyses. The effects of simvastatin (SV), a cardiovascular medicine that has shown promise in preventing dementia, were evaluated after 2.5 and 5 months.

Result

Alterations in RSFC in brain regions associated with the sensory‐motor (SM) and default‐mode (DMN) networks were detected before the appearance of cognitive impairment. 3‐month‐old APP mice showed consistent decrease in bilateral functional connectivity (BC) in motor (M) and cingulate (C) cortical regions and a severe hypoconnectivity within the SM network in the RSFC analysis. Throughout the course of the disease, RSFC analysis in APP mice uncovered an early hyperconnectivity within the DMN, mainly driven by the frontal (F) cortex, followed by a later hypoconnectivity stage. At late stage of the disease (8‐months‐old), a decreased BC in somatosensory (S) cortex was detected. SV treatment prevented the aberrant increases in DMN FC in midlife APP mice, improved the BC of S cortex, while concurrently sparing cognitive function.

Conclusion

Our results demonstrated that hemodynamic signals measured by OIS at the cortical level successfully detected RSFC disruptions preceding dementia in APP mice and allowed to capture SV therapeutic benefits. These findings suggest that OIS, or its human equivalent NIRS, could contribute to effectively diagnosing the early stages of AD, leading to promising intervention opportunities.
Task fMRI networks predict memory in cognitively unimpaired older adults
Authors: Ziontz Jacob , Chen Xi , Jagust William J.
Published: January 2025

Background

Episodic memory decline is a hallmark feature of aging and Alzheimer’s disease, but the mechanisms underlying its earliest stages are unknown.

Method

Cognitively unimpaired older adults from the Berkeley Aging Cohort Study (n=49) completed an fMRI memory encoding task preceded by 15 minutes rsfMRI and standard neuropsychological testing. Participants viewed object, scene, and object‐in‐scene (pair) stimuli in the scanner for 40 minutes. They then completed a recognition test to distinguish repeated from novel stimuli, and their performance was quantified as d’ (false alarm rate – hit rate; Figure 1). Functional connectivity (FC) was computed between all regions of the Schaefer 400 ROI atlas during presentation of each stimulus type and during rest. An elastic net filter model approach identified a sparse subset of connections highly related to successful performance in each task component. Model weights for these connections were applied to each participant’s rsfMRI connectivity to predict performance on each task component. Network strength was computed by summing FC values for each connection in the network.

Result

Task fMRI networks comprised unique sets of connections positively and negatively related to performance in each task component: the object network included 27 positive and 37 negative connections, the scene network included 29 positive and 29 negative connections, and the pair network include 28 positive and 10 negative connections (Figure 2). Applied to rsfMRI data collected prior to task, respective networks predicted object (r2 = 0.28, p < 0.001), scene (r2 = 0.52, p < 0.001), and pair (r2 = 0.29, p < 0.001) recognition performance (Figure 3a). Adjusting for age, sex, and education, strength of object (b = 0.36, p = 0.013) and scene networks (b = 0.33, p = 0.005) were correlated with a composite measure of episodic memory performance generated from a neuropsychological test battery (Figure 3b).

Conclusion

Functional networks that support successful memory encoding in older adults can be applied to rsfMRI to predict performance, and the strength of these networks is further associated with independent measures of episodic memory. Future work will investigate the potential for these networks to predict the earliest stages of cognitive decline.
Harmonization of amyloid PET radiotracers using ComBat and its influence on detecting treatment effects in a simulated clinical trial
Authors: Yang Braden , Earnest Tom , Kumar Sayantan , Gordon Brian A. , Sotiras Aristeidis
Published: January 2025

Background

Differences in amyloid PET radiotracer pharmacokinetics and binding properties lead to discrepancies in amyloid uptake measurements, which may adversely affect the statistical power of clinical trials that utilize multiple tracers to track brain amyloid deposition. To address this, Centiloid was developed for standardizing global amyloid SUVRs across tracers to a common scale. Alternatively, ComBat is a technique for harmonizing batch effects while preserving variations from biologically‐relevant covariates. Unlike Centiloid, ComBat is entirely data‐driven, does not require a calibration process, and can be applied to regional SUVRs. However, it has not been validated for amyloid PET. Here, we evaluate whether ComBat improves detection of treatment effects in a simulated clinical trial.

Method

365 amyloid‐positive subjects were identified from the OASIS‐3 dataset, from which 322 Pittsburgh compound B (PiB) and 260 18F‐Florbetapir (FBP) PET scans were selected. SUVRs of the global cortical region and 82 FreeSurfer regions were computed from each scan using the PET Unified Pipeline. Linear mixed effects (LME) models were fitted on each tracer separately and used to simulate longitudinal SUVRs of placebo and treatment groups in a hypothetical clinical trial (Figure 1). A negative rate‐of‐change was introduced to the treatment group to mimic a treatment effect. Additionally, tracer mixing proportions were varied within each group. Centiloid and ComBat were applied to simulated data to harmonize across tracers. A separate LME model was fitted to test for significant differences in SUVR rate‐of‐change between groups. Power was estimated as the proportion of significant findings across 1000 simulations.

Result

After harmonization with either Centiloid or ComBat with no covariates, an increase in power was observed in the presence of a treatment effect, and a decrease in Type‐I error was observed for no treatment effect (Figure 2). These changes were most prominent in cases where groups exhibited differing tracer mixing proportions. Similar patterns were observed for regional SUVRs (Figure 3).

Conclusion

We demonstrated that tracer harmonization is important for improving power in the presence of a treatment effect and reducing Type‐I error in its absence. ComBat performs comparably to Centiloid in harmonizing amyloid radiotracers in the context of a clinical trial.
Diffuse juxtacortical white matter texture changes in pre‐clinical Alzheimer’s disease
Authors: ILESANMI ADEMOLA E , Lyu Xueying , McGrew Emily , Yushkevich Paul A. , Das Sandhitsu R. , Wolk David A
Published: January 2025

Background

Detection of neurodegeneration during preclinical stages of Alzheimer’s disease (AD) is critical for disease prediction and monitoring. Macrostructural brain changes (volume/thickness) during preclinical AD are often not detectable in cross‐sectional studies. Microstructural changes manifested as alterations in texture features may capture subtle neurodegeneration at this stage. Here we investigated the discriminative ability of texture‐based radiomics features in both gray and juxtacortical white matter in preclinical AD.

Method

The dataset included amyloid negative (CN) and amyloid positive (preclinical AD, pAD) cognitively normal individuals from two independent cohorts (ADRC: 122 CN, 35 pAD; ADNI: 183 CN, 95 pAD). T1‐weighted MRI was segmented. Subsequently, we extracted thirty texture features each from 100 gray matter (GM) regions of interest (ROI) and corresponding adjacent white matter (WM) ROIs extending 2mm from the GM/WM interface. Principal Component Analysis was employed to reduce these features to eight: total energy (TE), sum variance, skewness, sum average, entropy, contrast, kurtosis, and inverse difference moment (IDM).

Result

Initial analysis in the ADRC cohort demonstrated group differences in TE and IDM in juxtacortical white matter regions broadly throughout the brain (Figure 1C, D), but not in the grey matter (Figure 1A, B). Given the exploratory nature of the analysis in the ADRC, we then examined TE and IDM in ADNI in groups defined in the same manner. These findings were largely replicated in the ADNI dataset (Figure 2). Conversely, gray matter thickness measures in both cohorts displayed minimal group discrimination (Figures 1E and 2E).

Conclusion

Texture features in juxtacortical white matter were sensitive to pAD in two independent cohorts, whereas GM thickness, a macrostructural measure, was not. These data suggest that texture features, a possible surrogate of microstructural changes, may be particularly sensitive to early downstream effects of AD. However, the diffuse nature of these findings across the brain suggests the possibility that they are related to the presence of cerebral amyloid rather than the more focal effects that would be expected with tau‐related neurodegeneration. Future work will test the longitudinal nature of these texture findings and their relationship to prediction of future cognitive decline.
BOLD fMRI signal changes in preclinical Alzheimer’s disease
Authors: Hrybouski Stanislau , Das Sandhitsu R. , Xie Long , Lyu Xueying , Wisse Laura E.M. , Kelley Melissa , Lane Jacqueline , Sherin Monica , DiCalogero Michael , Nasrallah Ilya M. , Detre John A. , Yushkevich Paul A. , Wolk David A
Published: January 2025

Background

Previous work suggests functional abnormalities in the human brain in preclinical Alzheimer’s disease. However, little has been explored about the relationship between BOLD fMRI signal amplitude/energy over time and AD pathology. In this work we analyzed the effects of AD progression on amplitude of low‐frequency fluctuations (ALFF) during resting‐state fMRI scans both at the whole‐brain level and at a more granular level, focused on regions of the medial temporal lobe (MTL) that are most vulnerable to AD pathology.

Method

In this cross‐sectional study, we analyzed data from 224 individuals from the Penn ADRC cohort (Table 1). All participants underwent structural and functional MRI on a Siemens 3T Prisma system, and 18F‐Florbetaben or 18F‐Florbetapir amyloid‐PET imaging. 125 participants also underwent 18F‐Flortaucipir tau‐PET scans. Functional images were preprocessed using a custom implementation of fMRIprep and ALFF was extracted using Conn software. In the whole‐brain analyses we performed voxelwise GLMs with age and sex as covariates.

Results

We observed reduced ALFF in both preclinical AD (Amyloid‐positive (Aß+) cognitively unimpaired, CU) and Aß+ cognitively impaired (CI) individuals. Relative to Aß‐ controls, individuals with preclinical AD displayed lower ALFF in frontal, parietal and temporal association cortices (Fig 1, top left). CI individuals displayed lower ALFF in most of the brain, except in inferior temporal cortex, temporal pole, and MTL. The effect of AD progression on ALFF was characterized by a progressive reduction primarily in frontal and parietal regions that roughly align with the anatomy of the default mode network. In contrast, transentorhinal tau pathology was negatively associated with ALFF in frontal and anterior temporal lobes, as well as insula and MTL (Figure 1 bottom right). Negative association between tau burden and MTL ALFF was observed in all main MTL subregions, and strongest in the transetorhinal cortex (Figure 2).

Conclusion

We conclude that: (1) ALFF might be a promising biomarker for studying functional abnormalities in preclinical AD, and (2) based on the spatial topography of amyloid and tau effects on ALFF (Figure 1 bottom), there is likely a differential effect of of the two on ALFF that needs to be further explored.
A Neuroimaging Perspective on Familial Alzheimer’s Functional Data through Bayesian Eyes
Authors: Grazia Alice , Dyrba Martin , Nunzio Pomara , Temp Anna G.M. , Grothe Michel J. , Teipel Stefan
Published: January 2025

Background

Familial Alzheimer’s disease research necessitates innovative methodologies to disentangle the intricate relationships between genetic factors and neuroimaging measures. Traditional frequentist approaches, often hampered by small sample sizes in this population and challenges in incorporating prior knowledge transparently, may limit the robustness of findings.

Methods

We analyzed neuroimaging data of preclinical PSNE1 single mutation carriers, utilizing the software JASP to test effects of carrier status on measures of basal forebrain functional connectivity using both frequentist and Bayesian approach. Bayesian Analysis of Covariance was first implemented to test the hypothesis of a basal forebrain functional connectivity reduction in mutation carriers compared to non‐mutation carriers. To get more precise information, Informative priors, derived from the existing literature on preclinical sporadic Alzheimer’s disease, were integrated into post‐hoc Independent samples t‐tests. Student t‐tests were utilized for parameter estimation, using an Informed Cauchy specification of prior’s location (‐0.7) and scale (0.707) allowing the derivation of posterior distributions.

Results

Our findings demonstrated that preclinical mutation carriers exhibited no significant alterations in basal forebrain functional connectivity compared to non‐carriers. However, the Bayesian analysis revealed distinct advantages over the traditional frequentist approach (anterior basal forebrain: t = ‐1.126 (213), p = 0.131, Cohen’s d = ‐0.165, 95% CI for Cohen’s d: [‐Inf., 0.077]; posterior basal forebrain: t = ‐1.337 (213), p = 0.091, Cohen’s d = ‐0.196, 95% CI for Cohen’s d: [‐Inf., 0.046]. Incorporating informative priors significantly improved the precision of parameter estimates, allowing us to reject our hypothesis with a relatively high degree of confidence, from anecdotal (anterior basal forebrain: BF10 = 1.167, d = ‐ 0.319, 95% CI: [‐0.599, ‐0.061]) to strong evidence (posterior basal forebrain: BF10 = 0.033, d = ‐0.067, 95% CI: [‐0.260, ‐0.003]).

Discussion

Our results underscore the importance of adopting Bayesian frameworks in familial Alzheimer’s disease research. The explicit integration of prior knowledge, as facilitated by Bayesian analysis, enhances the reliability of conclusions drawn from neuroimaging data. These methodological advantages are crucial in advancing the field and encouraging replication efforts.
Subject Harmonization of Multi‐Modal Digital Markers: Improved Detection of Mild Cognitive Impairment Using Language and Facial Expression
Authors: Hoang Bao , Pang Yijiang , Dodge Hiroko H , Zhou Jiayu
Published: January 2025

Background

Mild Cognitive Impairment (MCI) is the prodromal stage of dementia, including Alzheimer’s Disease (AD). Early identification and accurate assessment of MCI are critical for clinical trial enrichment as well as the early intervention of AD. Digital makers offered a unique opportunity for ecologically valid and affordable early detection approaches. Language markers extracted from verbal communications have shown diagnostic efficacy in detecting early MCI. Recent studies have shown that in addition to semantic and syntactic information in dialogues, emotions in communication can also be helpful in early MCI detection. A joint analysis of language markers and emotion indicative of facial expression is thus of great interest. Features from emotion could have additional predictive benefits to language markers. One general challenge of digital biomarkers is that feature distributions are very distinct. We hereby conduct a multi‐modal analysis of language and facial expression, combined with different harmonization.

Method

We used 3,501 conversations from 69 participants from the Internet‐Based Conversational Engagement Clinical Trial (I‐CONECT) (NCT02871921) and extracted language and emotion variables. For language variables, we used 99‐dimensional features from four types: Linguistic Inquiry and Word Count (LIWC) (64), Syntactic Complexity (23), Lexical Diversity (10), and Response Length (2). For emotion features, we extracted a 7‐dimensional emotion feature (angry, disgust, fear, happy, sad, surprise, neutral) from a facial frame using the DeepFace library. For analysis, we used the subject harmonization approach to remove information that is predictive of subjects. The harmonized features are then used to build a neural network for MCI detection.

Result

We evaluated Area‐under‐Curve (AUC) on subject classification. Our results show that applying harmonization will increase detection performance when only using language markers. If we harmonize both language and emotion features, the performance decreases. However, if we do harmonization on language and without harmonization on emotion, then the performance of subject classification increases to 0.701 AUC.

Conclusion

Our study has shown the additional benefits of emotion variables to augment language variables in the early detection of MCI. Multimodality analyses need careful selection of harmonization: harmonization strategy should be chosen for individual modality rather than the concatenated feature variables.
BreatheBot Buddy: Fostering Cognitive Well‐being with a Cuddly Companion for Regulated Breathing
Authors: Khan Arshia A , Sawchuk Matthew , Saheel Sabir , Quigley Jack , Heck Detlef
Published: January 2025

Background

Regulated breathing is increasingly recognized as a vital component in enhancing cognition. Scientific studies suggest that intentional and controlled breathing techniques, such as deep and rhythmic breathing, can promote relaxation, reduce stress, and improve oxygen flow to the brain [1, 2]. Consequently, this may contribute to heightened cognitive function, better concentration, and increased mental clarity, accentuating the importance of incorporating mindful breathing practices as a potential avenue for cognitive improvement [3]. The increasing recognition of the cognitive benefits of regulated breathing is leading to the development of innovative technologies, such as robots, for better breathing practices. These technologies can be used to potentially enhance the cognitive well‐being and mindfulness of people in their daily routines.

Methods

In order to accomplish a breathing robot, the entire design consists of two linear actuators controlled by an L298N Motor Driver Interface and a Raspberry Pi 3B. The Raspberry Pi coordinates both linear actuators to extend, hold, and contract in opposite directions for 4 seconds at each stage. Additionally, the mechanical design utilizes two “push caps” that push at the sides of a contained space, such as a stuffed animal, simulating breathing (see Fig. 1). This design simulates a breathing pattern in any stuffed animal (see Fig. 2).

Results

The developed robot underwent rigorous testing in both a lab and poster presentation setting. Various case studies were examined during the poster presentation, revealing the effectiveness of a tool designed to aid in regulated breathing. The present robot demonstrated increased participant engagement in mindful breathing practices, which suggests a promising avenue for leveraging robot‐assisted therapies to enhance cognitive well‐being in people.

Conclusion

The present project aimed to build a robot that fosters cognitive well‐being through regulated breathing. The case study findings of this project highlight the effectiveness of a cuddly bear‐shaped robot as a valuable tool for promoting regulated breathing, offering positive implications for integrating such robotic assistance in enhancing cognitive well‐being.
Reciprocating the Future: Exploring Natural Human Behaviors in Humanoid Social Robot Interaction
Authors: Moberg Reilly , Khan Arshia A
Published: January 2025

Background

When designing cutting‐edge technology, particularly humanoid social robots, an essential consideration is understanding how individuals naturally engage in social interactions, shaping their relationships with technology and media.

Method

In pursuit of insights into the application of natural human behavior, specifically reciprocation, in human‐robot interaction, an experiment involving 72 participants, involving facial electromyography, focusing on zygomatic and corrugator muscles, served as a tool to gauge users' emotional valence during interactions. The study assessed users' willingness to reciprocate a favor and measured compliance by tracking the number of raffle tickets purchased by users at the robot’s request. Events were recorded during the participant’s interaction with Pepper the humanoid robot, analyzing facial EMG data in five‐second intervals to capture changes in muscle activity before and after key events. The study comprised three scripted events, including the participant’s first sighting of Pepper, Pepper offering water (favor condition only), and a simulated glitch on Pepper’s tablet. Post‐interaction, participants filled out a final survey, contributing to the comprehensive examination of their experiences. The research involved 72 undergraduate students from diverse majors. The study aimed to assess initial impressions of humanoid robots, utilizing a phasic study design with a five‐second response time, aligning with previous research practices. The participants, largely lacking prior experience with social, service, or humanoid robots, provided valuable insights into human‐robot interaction dynamics.

Results

In testing two hypotheses on participants' interaction with a Humanoid Social Robot (HSR), we found no significant differences in ticket purchases between favor and non‐favor conditions using statistical tests, including chi‐square and Mann‐Whitney U. However, an independent samples t‐test, after outlier removal, revealed a significant difference, supporting both hypotheses. Pearson’s correlation coefficients showed positive correlations between emotional responses, but no significant relation with self‐reported affect or ticket purchases. These findings enhance understanding of human‐robot interaction dynamics and inform future research directions.

Conclusion

This study emphasizes the importance of incorporating natural human behaviors, like reciprocation, in designing humanoid social robots, indicating that initiating positive interactions significantly impacts users' compliance in subsequent interactions, advancing the field of human‐robot interaction with theoretical implications and proposing future research avenues despite acknowledged study limitations.
Deployment of Tablet‐based Cognitive Assessment Tool (TabCAT) for detecting cognitive impairment in elderly Primary Health Care Population in Southeast Nigeria
Authors: Ogbuagu Chukwuanugo , Uwakwe Richard , Kahn James G , OGBUAGU Ekenechukwu , Emelumadu Obiageli , Okereke Uzoma , Okeke Irene , Chisom Chigbo , Javandel Shireen , Possin Katherine L , Miller Bruce L. , Valcour Victor , Allen Isabel Elaine , Goode Collette A
Published: January 2025

Background

Cognitive assessment should be actively incorporated into the clinical evaluation of patients in Primary Health Care (PHC) settings. This is because of the imminent demand with the changing demographics especially in low‐ and middle‐income countries (LMICs) concerning dementia and Alzheimer’s disease.

Method

A cross‐sectional mixed‐method descriptive study was conducted to evaluate the useability and performance of a tablet‐based cognitive assessment tool (TabCAT) for use in geriatric primary healthcare settings in southeast Nigeria.

Result

In our study involving 207 participants, with an average age of 64.69 years, educational levels varied, with 51.8% having primary education, 41.2% secondary, and 6.6% tertiary. The research found that education had a significant positive impact on associative memory, processing speed, executive function, language skills, and visuospatial skills (p<0.001). Rural residents exhibited higher scores in associative memory, processing speed, executive function, language, and visuospatial skills (p<0.000). However, gender and location did not show significant contributions to cognitive performance across the examined domains.

Conclusion

There is an urgent need to deploy and routinely conduct cognitive assessments in primary healthcare clinics for elderly patients for early detection of cognitive changes to improve healthy living.
Digitally Assessed Sleep Disturbances for Early Diagnosis of Alzheimer’s Disease
Authors: Krix Sophia , Odeh Joud , Lentzen Manuel , Val‐Guardiola Andrea , Martínez Neus Falgàs , Atreya Alankar , Conde Pauline , Doherty Aiden , Vairavan Srinivasan , Muurling Marijn , de Boer Casper , Curcic Jelena , Grammatikopoulou Margarita , Nikolopoulos Spiros , Brem Anna‐Katharine , Coello Neva , Narayan Vaibhav , Wittenberg Gayle , Van Duijn Cornelia M , Hinds Chris , Aarsland Dag , Sanchez‐Valle Raquel , Fröhlich Holger
Published: January 2025

Background

Alzheimer’s Disease (AD) is associated with sleep disturbances. Moreover, individuals with sleep disturbances have been reported to have a higher risk for developing AD. The measurement of sleep behavior therefore opens the opportunity for a potential digital biomarker of AD.

Method

We modeled sleep patterns coming from the RADAR‐AD cohort from two sleep monitoring devices (Tab. 1). We applied a stochastic modeling approach, multi‐state models, and analyzed the times spent in each sleep state before transitioning and transition probabilities in and between sleep states. We further applied statistical analysis of sleep monitoring data and sleep questionnaires (ESS, PSQI) from the RADAR‐AD study (Fitbit Charge 3, DREEM) and preliminary data from the ADIS study (MotionWatch8) (Tab. 1), using a likelihood ratio test with the aim to assess the diagnostic potential of sleep monitoring devices compared to traditional sleep questionnaires.

Result

Modeling of digital device data showed that preclinial (preAD), prodromal (proAD) and mild‐to‐moderate (mildAD) AD patients spent more time in the light sleep and awake state, and less time in the REM sleep states compared to healthy controls (HC) before transitioning to the next state, showing non‐linear associations between diagnostic stage and sojourn times (Fig. 1A). ProAD and mildAD patients had a higher probability to transit to a light sleep phase compared to HC and to subsequently wake up (Fig. 1B). Based on our current and partially still preliminary data, only digital sleep monitoring via Fitbit allowed for a separation between HC and preclinical AD at nominal significance (preAD) but findings were not significant when adjusting for multiple testing (Tab. 2A). A significant distinction between HC and proAD (p < 0.001) as well as between HC and mildAD (p < 0.01) was possible using Fitbit sleep monitoring data, whereas traditional sleep questionnaires were only able to distinguish HC from mildAD (p < 0.05) (Tab. 2).

Conclusion

Sleep patterns assessed via tested digital devices were able to separate proAD from HC – in case of Fitbit ‐ which was not possible by traditional sleep questionnaires. Digital sleep monitoring has thus the potential to support the early diagnosis of dementia.