bluuebunny/crossref_metadata_2025 huggingface dataset:
Automated Brain Volumetry Analysis for Differential Diagnosis of Frontotemporal Dementia Subtypes
Authors: Cho Hyeonwoo , Park Mina , Lee Seung Hyun , Jung Wooseok , Kim Dong‐Hee , Kim Jinyoung , Lee Yeha
Published: January 2025

Background

Clinical diagnosis of frontotemporal dementia (FTD) can be challenging, requiring an accurate tool dedicated to this diagnostic hurdle. Since FTD exhibits distinct regional atrophy patterns on magnetic resonance imaging (MRI), AI‐aided automated brain volume analysis could enhance the clinical diagnostic assessment of FTD, including the detection of the disease and the classification of subtypes, which encompass behavioral variant (BV), semantic variant (SV), and progressive non‐fluent aphasia (PNFA). In this study, we leverage automated brain volumetry software to approach both FTD detection and the differential diagnosis among its subtypes.

Method

A total 258 patients with FTD (BV = 57, SV = 40, PNFA = 34) and 127 Normal Control (NC) were identified from the frontotemporal lobar degeneration neuroimaging initiative (NIFD) data sets with available MRI. We obtained 104 areas of patient’s normalized regional brain volumes using VUNO‐Med DeepBrain. Comparative analyses were conducted to explore differences in regional volume distribution between FTD and NC, as well as among FTD subtype based on their known atrophy patterns. Additionally, regional normative percentiles were utilized as features for classification using the XGBoost algorithm to distinguish FTD vs NC, BV vs SV vs PNFA, and BV vs SV vs PNFA vs NC.

Result

In BV, significant differences in atrophy patterns were observed in the frontal lobe, demonstrating an anterior‐posterior gradient compared to SV, PNFA and NC (P<0.01). SV exhibited left side dominant asymmetric atrophy, particularly in the inferior temporal lobe, compared to BV, PNFA, and NC (P<0.01). Additionally, the inferior horn of the lateral ventricle in SV had higher volume than other subtypes, attributable to atrophy of surrounding brain regions. In contrast, the differences in atrophy patterns for PNFA were statistically less significant when compared to other FTD subtypes. Automated brain volumetry software‐based classifications demonstrated high accuracy of FTD vs NC (Accuracy 95% CI = [0.88, 0.89]), BV vs SV vs PNFA ([0.79, 0.81]), and BV vs SV vs PNFA vs NC ([0.82, 0.83]).

Conclusion

Our study demonstrates the effectiveness of automated brain volumetry and machine learning in identifying FTD and differentiating frontotemporal dementia subtypes, contributing to an enhanced understanding of subtypes and improved diagnostic precision in neurodegenerative disorders.
Associations of load and progression of imaging markers of microvascular lesions and neurodegeneration with odor identification decline: a population‐based study
Authors: Oltra Javier , Kalpouzos Grégoria , Ekström Ingrid , Larsson Maria , Qiu Chengxuan , Laukka Erika J
Published: January 2025

Background

Olfactory deficits are predictive of cognitive decline and dementia. Previous studies have linked brain magnetic resonance imaging markers of neurodegeneration to olfactory deficits in aging; however, these studies analyzed cross‐sectional data for markers, olfaction, or both. Furthermore, potential cerebrovascular contributions to understanding why olfactory deficits predict dementia remain unexplored. We aimed to examine whether markers of microvascular lesions and neurodegeneration and their progression are associated with the rate of olfactory decline in community‐dwelling older adults.

Method

Participants were 405 individuals (mean age=70.2 years; 60% females) from the Swedish National study on Aging and Care in Kungsholmen (SNAC‐K) who were free from dementia at baseline. We analyzed microvascular lesions (presence of lacunes, white matter hyperintensities volume, global perivascular spaces [PVS] count) and neurodegeneration (lateral ventricular, hippocampal, and total gray matter volumes) markers, available up to 6 years and an olfactory measure (Sniffin’ Sticks odor identification task), available up to 15 years. Additionally, we assessed summary scores for microvascular lesions and neurodegeneration load (range 0–3), accounting for all markers of each category (1 point for its presence or for being in the worst quartile, as appropriate). Data were analyzed with linear mixed‐effects models.

Result

After adjustment for sociodemographic (age, sex, and education) and extra covariates (smoking status and semantic memory), a higher global PVS count (ß =‐0.041; 95% CI, ‐0.077 to ‐0.005) and a high load (=2 points) of neurodegeneration (ß=‐0.112; 95% CI, ‐0.221 to ‐0.002) at baseline were associated with a faster olfactory decline. Longitudinally (n=228), faster accumulation of PVS (ß=‐1.812; 95% CI, ‐3.474 to ‐0.150), faster hippocampal atrophy (ß=1.348; 95% CI, 0.413 to 2.283), and an increment from low (=1 point) to high load for microvascular lesions (ß=‐0.167; 95% CI, ‐0.327 to ‐0.007) and neurodegeneration (ß=‐0.206; 95% CI, ‐0.321 to ‐0.092) were associated with an accelerated olfactory decline.

Conclusion

Both microvascular lesions and neurodegeneration are longitudinally associated with olfactory decline in aging. This relationship supports the notion that olfactory deficits could be valuable as an early marker for dementia. More longitudinal studies are needed to untangle the mechanisms in play between cerebral vascular burden, atrophy, and olfaction.
Age‐Related Structural and Vascular Changes in the Choroid Plexus: Insights from the HCP Dataset
Authors: Sun Zhe , Li Chenyang , Wisniewski Thomas , Ge Yulin
Published: January 2025

Background

The choroid plexus (ChP) plays a vital role in CSF production and waste clearance. While existing imaging studies have established connections between ChP volume changes and age‐related neurodegenerative diseases, a comprehensive investigation into the microstructural and vascular changes associated with aging remains insufficient. This study aims to explore ChP changes in normal aging using diffusion and perfusion MRI in the HCP‐Aging dataset to enhance our understanding of age‐related microstructural and vascular changes in the ChP.

Method

Our analysis included 641 healthy participants (age: 36‐90 years; mean±SD = 60±16 years; 282 males) with the following MRI parameters: (1) T1 MPRAGE for ChP segmentation[1]; (2) Diffusion MRI for quantification of mean diffusivity (MD); (3) PCASL MRI for quantification of cerebral blood flow (CBF) and arterial transit time (ATT). The ChP mask was co‐registered with MD, CBF, and ATT maps to extract corresponding values. Pearson's correlation and one‐way ANOVA were used for age effects and perfusion property comparisons among gray matter (GM), white matter (WM), and ChP, respectively.

Result

ChP imaging revealed increased volume, increased MD, and decreased CBF across different age groups (Figure 1). Figure 2 demonstrated a positive correlation between ChP volume and age (r = 0.31, p<0.0001), while ChP CBF declined with age (r = ‐0.41, p<0.0001) and MD positively correlated with age (r = 0.56, p<0.0001). Adjusting for age and sex, ChP CBF negatively correlated with volume (r = ‐0.26, p < 0.0001). One‐way ANOVA analyses indicated significant differences in CBF and ATT among ChP, GM, and WM (p<0.0001 for all comparisons), with ChP exihbiting the highest CBF and shortest ATT (Figure 3).

Conclusion

In addition to volume changes, we found significant age‐related diffusion and perfusion changes in the ChP in this large dataset. The decline in CBF and the increase in MD with aging suggest vascular degeneration and compensatory dystrophic hyperplasia of surrounding tissues of ChP. These findings in normal aging set a crucial normative reference for future studies on cognitive impairment in abnormal aging processes References: 1. Tadayon, E., et al. J Alzheimers Dis (2020).
Clinical Phenotypes of Alzheimer's Disease: Atrophy Patterns and their Pathological Correlates
Authors: Reijner Niels , Frigerio Irene , Bouwman Maud M.A. , Boon Baayla D.C. , Guizard Nicolas , Jubault Thomas , Hoozemans Jeroen J.M. , Rozemuller Annemieke J.M. , Bouwman Femke H. , Barkhof Frederik , Gordon Elizabeth , van de Berg Wilma D.J. , Jonkman Laura E.
Published: January 2025

Background

Recent studies highlight distinct patterns of cortical atrophy between amnestic (typical) and non‐amnestic (atypical, with subtypes: behavioural, dysexecutive, logopenic and visuospatial) clinical phenotypes of Alzheimer’s disease (AD). The current study aimed to assess regional MRI patterns of cortical atrophy across AD phenotypes, and their association with amyloid‐beta (Aß), phosphorylated tau (pTau), axonal degeneration (NfL) and microvascular deterioration (COLIV).

Method

Postmortem In‐situ 3DT1 3T‐MRI data was collected for 33 AD (17 typical, 16 atypical) and 16 control brain donors. Images were segmented and AAL3 atlas regional volumes were obtained using QyScore®. At subsequent autopsy, eight brain regions were selected, immunostained for Aß (4G8), pTau (AT8), Neurofilament‐light (NFL), and Collagen‐IV (COLIV), and quantified using qupath. Group comparisons and volume‐pathology associations were analyzed using linear models and partial correlations with covariates age, sex, postmortem delay, and intracranial volume.

Results

Compared to controls, AD phenotype groups showed overall lower cortical volume, while only minor volume differences were observed between AD phenotype groups, observed primarily in limbic regions (Figure 1). Across pathological markers, AD phenotype groups showed consistently higher immunoreactivity than controls, while atypical AD showed consistently higher immunoreactivity than typical AD (Figure 2). Moreover, different patterns of pathology could be observed between atypical subtypes (e.g. distinctly higher pTau load in the occipital gyrus of the visuospatial subtype). In typical AD, global volume loss was associated with lower Aß and higher pTau, NFL and COLIV immunoreactivity, while in atypical AD, global volume loss was primarily associated with higher NFL immunoreactivity (Figure 3a). Regionally, AD phenotype differences in atrophy‐pathology association were most pronounced in the (para)hippocampal regions. This distinction was mainly characterized by negative associations for NFL and COLIV, which was only observed in typical AD (Figure 3b‐c).

Conclusion

Atrophy patterns between AD phenotypes showed only minor differences, potentially attributable to the on average later disease stage of the study cohort. The higher immunoreactivity for pathological markers found in atypical AD might suggest a more severe disease burden. (Para)hippocampal volume decline was associated with axonal and microvascular deterioration in typical AD, but not in the higher pathologically burdened atypical AD group, suggesting a differential susceptibility between AD phenotypes.
Lower amygdala and hippocampal volume correlates with TDP‐43 load in limbic‐predominant age‐related TDP‐43 encephalopathy (LATE)
Authors: Wesseling Alex J. , Calandri Ismael Luis , Ossenkoppele Rik , Bouwman Maud M.A. , Deshayes Natasja A. C. , van de Berg Wilma D.J. , Rozemuller Annemieke J.M. , Pijnenburg Yolande A.L. , Hoozemans Jeroen J.M. , Jonkman Laura E.
Published: January 2025

Background

Limbic‐predominant age‐related TDP‐43 encephalopathy (LATE) is often described as occurring in older individuals, either with or without co‐occurring neurodegenerative disease. Because the presence of LATE can only be determined post mortem, little is known about the clinical and neuroimaging features of LATE. The current study aims to assess the correlation between LATE and MRI‐measured amygdala and hippocampal volume in Alzheimer’s disease (AD) and Lewy Body Diseases (LBD), including Parkinson’s disease (PD), PD dementia, and dementia with Lewy Bodies

Methods

Post‐mortem in‐situ 3DT1 3T‐MRI data were collected for 51 cases (27 AD and 24 LBD) of which 17 had post‐mortem confirmed LATE neuropathological change (9 AD and 8 LBD), as well as 34 non‐LATE (18 AD and 16 PD) donors (matched on age, sex, and Braak stage). Right hemisphere amygdala and hippocampal volumes were calculated using Freesurfer. Pathological load of TDP‐43, p‐tau and a‐synuclein from the same regions were obtained using IHC and quantified using QuPath. Group differences were assessed with univariate analyses and correlations using linear mixed models, both using age, sex, post‐mortem delay and intracranial volume as covariates.

Results

Two AD cases with a disease onset below 65 showed LATE pathology. In the combined cohort, brain donors with LATE neuropathological change showed significantly lower amygdala (p=.003) and hippocampal (p=.002) volumes, even when corrected for p‐tau and Lewy body load (Figure 1a). In AD, this difference remained (amygdala p=.001, hippocampus p=.007, corrected for p‐tau), but not in the LBD group. In donors with LATE, TDP‐43 load correlated negatively with MRI measured amygdala and hippocampal volume when corrected for p‐tau and Lewy body load (r= .70; p=.005) (Figure 1b). The same association was found in AD (r=.70; p=.015 corrected for tau load) but not in LBD donors.

Conclusion

These results suggest that TDP‐43 inclusions plays a role in hippocampal and amygdala atrophy on MRI, even when correcting for effects of primary pathology. Moreover, a salient detail is that LATE also seems to play a role in the disease presentation of young‐onset dementia patients.
Mapping local and distal effects of different neuropathologies on amygdala volume
Authors: Annettesdotter Amanda , Selezneva Katja , Denning Amanda E , Wuestefeld Anika , Ravikumar Sadhana , Ittyerah Ranjit , Levorse Lisa M , Bedard Madigan , Chung Eunice , Robinson John L. , Schuck Theresa , Lee Eddie B , Lim Sydney A , Trotman Winifred , Bahena Alejandra , Irwin David J , Jimenez Maria del Mar Arroyo , Vela Alicia , Buendia Esther , Martin Maria Mercedes Iniguez de Onzono , Rabal Maria del Pilar Marcos , Munoz Monica , Spotorno Nicola , Hansson Oskar , Insausti Ricardo , Yushkevich Paul A. , Wisse Laura E.M.
Published: January 2025

Background

The amygdala is a hotspot for neuropathologies; however, it is unclear 1) which neuropathologies lead to amygdala neurodegeneration, 2) what specific amygdala subnuclei are affected, and 3) if the neuropathologies related to amygdala volume are local (inside the amygdala), or distal (in other regions). We investigate the relationships between different neuropathologies (tau, amyloid‐ß [Aß], a‐synuclein [a‐syn], and transactive response DNA‐binding protein 43 [TDP‐43]) and amygdala volumes.

Method

We analyzed postmortem data from 73 individuals with and without neurodegenerative diseases (age: 77±11 [45–101] years; 26 [36%] females; 51 [70%] cognitively impaired). Volumes of the whole amygdala and three amygdala subregions (lateral, B/Ab [basal/accessory basal], Co/Me/Ce [cortical/medial/central] nuclei: for n = 24 individuals) were manually segmented on 0.2×0.2×0.2 mm3 postmortem magnetic resonance images (Figure 1). We used semiquantitative scores (0‐3) of Aß, tau, a‐syn, and TDP‐43 in the amygdala, the dentate gyrus (DG), the Cornu Ammonis 1 and subiculum (CA1/SUB), the entorhinal cortex (ERC), and averaged between the medial temporal lobe (MTL) regions (excluding the amygdala). Partial Spearman correlations were performed to investigate the relationships between neuropathologies and amygdala volumes, adjusting for age, sex, and other pathologies.

Results

Higher ratings of amygdala TDP‐43, MTL TDP‐43, and MTL tau were associated with smaller whole amygdala volume (Figure 2A–B, Figure 3A). However, MTL TDP‐43 was no longer significant when adjusting for amygdala TDP‐43 (Figure 3B). As MTL tau was associated with amygdala volume, we looked into which MTL subregions could be responsible for this association. Here, tau in DG, CA1/SUB, and ERC were all negatively associated with whole amygdala volume (Figure 2B). For exploratory subregional analyses, amygdala a‐syn was initially associated with B/Ab volume (Figure 2C, Figure 3C), but this correlation did not survive FDR‐correction.

Conclusions

These preliminary results indicate that TDP‐43 has a local effect on whole amygdala volume, whereas tau seems to affect amygdala volume distantly through other MTL regions, with the largest effect seen in DG tau, followed by CA1/SUB and ERC. Subregional analyses will be updated once a larger sample is available, as well as further analyses on non‐MTL regions.
Predicting Antipsychotic Drug Doses for BPSD: A Transfer Learning Approach Using Neuroimaging Data
Authors: Yue Ling , Hong Bo , Tao Tianli , Li Xia , Xiao Shifu , Zhang Han
Published: January 2025

Background

About 50‐90% people with dementia would develop behavioral disturbances, namely, behavioral and psychological symptoms of dementia (BPSD). Antipsychotic medications are widely used to control severe BPSD symptoms which suffers serious safety risks. It is challenge for individualized precise prediction of antipsychotic drug doses. Neuroimaging, particularly MRI, reveals brain structure associated with aging, cognitive decline, and psychiatric symptoms, making it a potential tool for predicting the drug doses. This study employs transfer learning to predict drug dose and offer neuroanatomical interpretation of BPSD from the perspective of deep learning.

Method

We employed a two‐step process to train our model (Figure 1). Initially, a large dataset from the Chinese Brain Molecular and Functional Mapping (CBMFM) project (n=646,334 females and 312males, age 18‐82) was used to pretrain the model with a brain age prediction task. Subsequently, the pretrained model was fine‐tuned for drug dose prediction from the Alzheimer's Disease and Related Disorders Center in Shanghai Jiao Tong University (ADRDC) dataset (83 BPSD patients,27 males and 56 females, age 55‐80). Finally, we utilized gradient‐weighted class activation mapping to generate attention maps and conducted statistical analyses on the attention maps to identify critical brain regions for drug dose prediction. To determine the individual usage of different antipsychotic drugs, the concept of defined daily dose (DDD) was used. The DDD, which individualized control the BPSD, was calculated, ranging from 0 to 1.5 mg/day, serving as the label for fine‐tuning.

Result

Our pretrained Cas‐ResNet exhibited enhanced performance with fewer training epochs, achieving a competitive Pearson correlation of 0.59 between estimated and real DDD (Figure 2). Through feature interpretability analysis, we identified five significant brain regions crucial for BPSD drug dose prediction, mainly located in the temporal lobe, including the parahippocampal area and the striatum (putamen and caudate)(Figure 3). These findings indicate that the antipsychotic dosage to control the BPSD is linked to brain structural alterations, involving both dementia‐related and emotion‐regulating areas.

Conclusion

For the first time, we showed a promising result of using a lightweight deep learning model to predict drug dose prescribed for controlling BPSD. The work promotes the discussion toward appropriate use of antipsychotics in patients with dementia.
Widespread neurite degeneration in mild cognitive impairment and Alzheimer’s disease revealed using advanced diffusion MRI
Authors: Laporte John P , Gong Zhaoyuan , Guo Alex , Bae Jonghyun , Faulkner Mary E , Bouhrara Mustapha
Published: January 2025

Background

Neurite degeneration is increasingly suspected to represent a causal feature of mild cognitive impairment (MCI) and Alzheimer’s disease (AD). Therefore, sensitive and specific imaging biomarkers of neuronal degeneration are needed to elucidate the mechanisms underlying cognitive impairment in MCI and AD. However, the recently developed Neurite Orientation Dispersion and Density Imaging (NODDI) MRI technique, used to measure the neurite density index (NDI), has some limitations. To address these, we have introduced the constrained NODDI (C‐NODDI) analysis providing more accurate determination of NDI (PMC10435293). Here, we investigated neurite degeneration in MCI and AD using C‐NODDI and compared the results to those derived using NODDI.

Methods

We obtained Multi‐shell diffusion MR images from the ADNI study (Table 1) and calculated NDI using NODDI or C‐NODDI. We conducted a voxelwise two‐sample t‐test to compare NDI of control vs. AD, control vs. MCI, and MCI vs. AD groups. Further, we assessed the regional associations between NDI and overall cognition as measured using the Mini‐Mental State Examination (MMSE) score (dependent variable), controlling for age and sex. All continuous variables were Z‐scored.

Results

The AD group exhibited significantly lower NDI values as compared to control, with C‐NODDI exhibiting greater sensitivity in capturing regional NDI variations as compared to NODDI (Figure.1). Further, unlike NODDI, C‐NODDI was capable of detecting subtitle differences in NDI between control and MCI and between MCI and AD (Figure.1). Interestingly, NODDI exhibited higher NDI values in MCI or AD as compared to control in several brain regions (Figure.1), a physiologically implausible result that C‐NODDI corrected. Additionally, the associations between MMSE and NDI derived using C‐NODDI were significant in several white matter regions, with higher NDI values corresponding to expected higher MMSE scores (Figure.2). NODDI has failed to capture this anticipated association.

Conclusions

This study provides evidence of widespread neuronal degeneration in MCI and AD. Importantly, our results highlight the superior sensitivity of C‐NODDI analysis in detecting these differences in neurite density as compared to NODDI, making it a potent MRI method for clinical investigations and trials studying neuronal degeneration in aging and age‐related diseases.
Cognitive reserve is associated with the recruitment of compensatory brain networks in individuals at risk for Alzheimer’s disease
Authors: Lizarraga Aldana , Kassinopoulos Michalis , González‐de‐Echávarri José María , Huguet Jordi , Sánchez‐Benavides Gonzalo , Brugulat‐Serrat Anna , Suarez‐Calvet Marc , Milà‐Alomà Marta , Blennow Kaj , Zetterberg Henrik , Kollmorgen Gwendlyn , Quijano‐Rubio Clara , Molinuevo Jose Luis , Gispert Juan Domingo , Cacciaglia Raffaele
Published: January 2025

Background

Cognitive Reserve (CR) refers to the brain’s ability to maintain optimal cognitive function despite damage or pathology. The neural implementation of CR is a major research focus, and resting‐state functional connectivity (RSFC) has emerged as a promising imaging correlate of CR. We assessed RSFC as a function of two different proxy measures of CR and further assessed the impact of these brain networks on longitudinal cognitive performance in a sample of cognitively unimpaired (CU) individuals at risk of Alzheimer’s disease (AD).

Method

Analyses were conducted in 328 CU individuals from the ALFA cohort (mean age = 60.8, SD = 4.74) with available CSF Aß, p‐tau, resting‐state fMRI and longitudinal cognitive assessment (average follow‐up time = 3.35 years, SD = 0.53). CSF Aß42 and Aß40 were assessed with the exploratory NeuroToolKit, while p‐tau181 was measured with the Elecsys® Phospho‐Tau (181P) CSF immunoassay (both Roche Diagnostics International Ltd). We examined the impact of years of education (YOE) and global score of the Cognitive Reserve Questionnaire (CRQ) on the RSFC amongst 246 brain regions of the Brainnetome atlas using the CONN toolbox, selecting a cluster threshold of p<0.005, and adjusting or the effects of age, sex, and APOE status.

Result

Of the entire sample, 38.4% had positive CSF Aß42/40 markers. YOE was related to an increased RSFC between regions of the salience network and the anterior default‐mode network (DMN). Increased negative RSFC was found as a function of YOE between primary visual areas and regions of the executive control as well as dorsal attention (Fig. 1). In models adjusted by CSF biomarkers, the increased RSFC between the anterior DMN and salience regions predicted better PACC score over time and further modulated the association between CSF Aß42/40 and longitudinal PACC (Fig. 2). CRQ score was associated with a decreased RSFC between the posterior DMN and inferior temporal areas. These patterns of reduced RSFC significantly mediated the impact of CSF Aß42/40 on longitudinal memory performance (Fig.3).

Conclusion

RSFC may provide insights into the mechanisms relating CR and cognitive resilience in preclinical AD. Further, the expression of RSFC patterns may serve as an outcome in intervention studies aiming to boost CR.
Reproducibility Challenges in Brain PET Data Analysis
Authors: Naseri Maryam , Carmichael Owen T. , Pillai Sreekrishna R.
Published: January 2025

Background

While a great deal of recent effort has focused on addressing a perceived reproducibility crisis within brain structural magnetic resonance imaging (MRI) and functional MRI research communities, less attention has been paid to reproducibility of brain positron emission tomography (PET) research.

Methods

We examined the current landscape of factors that contribute to reproducible neuroimaging data analysis, comparing brain MRI to brain PET. The factors included scientific standards, analytic plan pre‐registration, data and code sharing, containerized workflows, and standardized processing pipelines. To demonstrate the positive impact that further developing such reproducibility factors would have on brain PET research, we conducted one case study in which the brain PET processing pipeline developed for one flagship study (Alzheimer’s Disease Neuroimaging Initiative, ADNI) for brain 18F‐florbetapir and 18F‐fluorodeoxyglucose (FDG) analysis was reproduced in our laboratory.

Results

Compared to fMRI, PET research encounters substantial challenges in adopting essential reproducibility practices. While 85% of fMRI studies adhere to the standardized reporting practices, PET is in the early stages of establishing reporting practices, lacking a standardized checklist. Pre‐registration is prevalent for fMRI (57.6%) but notably absent in PET, with no pre‐registered PET studies found on platforms such as the Center for Open Science. The number of shared datasets is higher for fMRI compared to PET, with 92 fMRI datasets and only 9 PET datasets on repositories like OpenNeuro. Moreover, there are very few data repositories for newer PET radiotracers. Containerized software availability also favor fMRI, with platforms like (Brain Imaging Data Structure, BIDS) apps hosting 66% dedicated fMRI tools, while no PET‐specific software is available. Standardized processing pipelines follow the same trend, with fMRI boasting optimized workflows for specific protocols, while PET has few dedicated pipelines and lacks validation studies for existing options. Furthermore, while our case study demonstrated excellent correlation results for both the 18F‐FDG and 18F‐florbetapir methods (Figure 2), the replication of the ADNI PET pipeline posed substantial challenges and implementation delays. These obstacles and delays in execution were due to incomplete documentation and ambiguities in application details.

Conclusion

PET neuroimaging lags behind its MRI‐related counterparts in achieving robust reproducibility.
Longitudinal cortical atrophy in cases of AT concordance and discordance
Authors: Ioannou Konstantinos , Mohanty Rosaleena , Poulakis Konstantinos , Leuzy Antoine , Muehlboeck J‐Sebastian , Rodriguez‐Vieitez Elena , Westman Eric , Chiotis Konstantinos
Published: January 2025

Background

Although ß‐amyloid and tau PET positivity (A+T+) has been related to neurodegeneration and cognitive decline in Alzheimer’s disease (AD), the driving force of neurodegeneration in discordant AT cases remains controversial. We investigated the impact of AT status on longitudinal rates of cortical atrophy and cognitive decline.

Method

A subset of 349 individuals (cognitively unimpaired [CU; n = 230], cognitively impaired [CI; n = 119]) with ß‐amyloid and tau PET (a priori baseline), longitudinal MRI (interval; Mean = 4.5±3.3 years, scans/individual; Median = 3, IQR = [2, 8]), and a cognitive follow‐up = 2 years, was selected from the ADNI dataset. We classified the individuals based on their AT PET status (A+; global ß‐amyloid = 20 CL, T+; composite temporal region = 1.34 SUVR) and clustered them based on their annual rates of cognitive decline on the ADAS‐Cog13 (fast vs. slow decliners [FDs vs. SDs]). Baseline AD‐signature cortical thickness was compared across AT groups. We employed a linear mixed‐effects model to assess the interaction between AT status and longitudinal cortical thickness across all cortical regions.

Results

Demographic characteristics were similar across AT groups (A‐T‐; n = 192, A‐T+; n = 5, A+T‐; n = 106, A+T+; n = 46). Most A+T+ individuals (70%) were CI, whereas most A+T‐ (71%), A‐T+ (80%), and A‐T‐ (71%) individuals were CU. Similarly, most A+T+ individuals (85%) were characterized as FDs, whereas most A+T‐ (74%), A‐T+ (60%), and A‐T‐ (88%) individuals were characterized as SDs. A+T+ individuals showed significantly lower AD‐signature cortical thickness (MeanA+T+ = 2.78±0.19 mm) compared to A+T‐ (MeanA+T‐ = 2.89±0.15 mm; P = 0.0023) and A‐T‐ (MeanA‐T‐ = 2.89±0.15 mm; P<0.001) individuals, and the highest Aß burden (Mean = 85.3±30.6 CL; P<0.0001) at baseline. AT status significantly influenced longitudinal cortical thickness. A+T+ individuals showed significantly steeper cortical thinning rates compared to A‐T‐ individuals, primarily in temporoparietal areas. Despite similar group‐level cortical thinning rates with A‐T‐ individuals, A+T‐ individuals showed heterogeneous patterns of cortical atrophy at the individual level. These findings remained consistent even when conducting separate analyses for CU and CI individuals.

Conclusion

Unlike AT discordance, concordant AT positivity exhibited homogeneous cortical atrophy patterns consistent with AD. Considering the concurrent high prevalence of mixed pathologies in neuropathological AD, we further aim to explore the underlying neurodegenerative force in discordant AT cases.
Correlations of antemortem [18F]MK‐6240 PET with ROI‐matched postmortem biochemical assessment of total tau and tau phospho‐epitopes pThr181, pSer199, pThr231, and pSer396
Authors: Betthauser Tobey J. , Abrahamson Eric E , De Chavez Elena Ruiz , Teague Jordan P , McVea Andrew K , Deming Yuetiva , Schroeder Brooke E , Barger Madeleine R , Johnson Sterling C. , Asthana Sanjay , Villemagne Victor L , Christian Bradley T. , Salamat Shahriar , Ikonomovic Milos D
Published: January 2025

Background

[18F]MK‐6240 was developed for PET imaging of AD tau pathology, but the exact molecular signature of specific binding remains unclear. This study quantified levels of four phospho‐tau forms and total tau in postmortem brain tissues from [18F]MK‐6240 imaged cases to investigate associations with antemortem [18F]MK‐6240 PET.

Methods

This study included four participants from the Wisconsin ADRC or WRAP with antemortem [18F]MK‐6240 and [11C]PiB PET imaging and postmortem brain tissue obtained on average 32‐months after imaging (Table 1). Parametric SUVR were generated using T1‐w MRI to delineate regions matched to equivalent ROIs on fresh frozen brain tissue slabs. Tissue ROI dissected from ten brain regions per case were homogenized and ELISA was used to quantify concentrations of guanidine‐extracted total tau and tau phosphorylated at epitopes pThr181, pSer199, pThr231, and pSer396.

Results

Matched PET‐autopsy ROI analysis showed significant correlations of higher [18F]MK‐6240 SUVR with higher levels of all measured p‐tau forms or their ratios to total tau across all regions and cases. Analyses of two A+T+ cases with clinical AD dementia and postmortem Thal Phase 5/Braak NFT Stage VI showed higher [18F]MK‐6240 SUVR correlated significantly with higher levels of pThr181, pSer199, pThr231, and pSer396 tau or their ratios to total tau in a 79‐yo APOE‐e4e4 case, and with higher levels of pThr231 and pSer396 tau in a 78‐yo APOE‐e3e3 case. In two T‐ cases, we observed weak correlations of [18F]MK‐6240 SUVR with pThr181 and pSer396 tau in an A‐T‐ cognitively unimpaired case (Thal Phase 2/Braak NFT Stage II; 70‐yo; APOE‐e3e3), but no correlations in A+T‐ early‐onset AD dementia case with Thal Phase 4/Braak NFT Stage V (63‐yo; APOE‐e4e4). This latter case also had high [18F]MK‐6240 uptake outside the brain and a low tangle density outside the MTL.

Conclusions

Higher [18F]MK‐6240 PET binding reflects high brain concentrations of pThr181, pSer199, pThr231, and pSer396 tau in AD dementia cases with high AD neuropathological change. Lack of associations between PET and p‐tau biochemistry in the T‐, Braak V early‐onset dementia case suggests that the T‐ PET status is more closely associated with p‐tau biochemistry than postmortem neuropathological staging, likely due to low tangle density.
Diabetes and brain MRI measures: results from SOL‐INCA MRI study
Authors: Gonzalez Kevin A , Tarraf Wassim , Banks Sarah , Bangen Katherine J. , Pa Judy , Gallo Linda C , Stickel Ariana M , Filigrana Paola , Isasi Carmen R , Daviglus Martha L , Testai Fernando Daniel , Decarli Charles , Gonzalez Hector M
Published: January 2025

Background

Hispanic/Latino communities in the US are rapidly growing and aging and are at two‐fold risk of Alzheimer’s Disease and Related Dementia’s (ADRD) compared to non‐Hispanic Whites. This additional risk could be, in part, due to increased risk of cardiovascular disease. Hispanics/Latinos also have higher rate of diabetes compared to non‐Hispanic Whites and nearly 2 out of 5 individuals with diabetes go undiagnosed. While diabetes has been linked to white matter hyperintensities (WMHs), less is known about the magnitude in diverse Hispanics/Latinos and even fewer studies have considered links to other neurobiological endpoints in this population. This work aims to clarify these associations in a deeply characterized diverse middle‐aged and older Latino cohort.

Method

We used data from the Study of Latinos‐Investigation of Neurocognitive Aging (SOL‐INCA) MRI study which is an ancillary study of the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). The goal of SOL‐INCA MRI is to understand cerebrovascular pathology and ADRD etiology using MRI. A total of N = 2400 middle aged and older (50+ years) and N = 400 younger (35‐49 years) participants were recruited from the parent HCHS/SOL study. Diabetes was assessed using both continuous HbA1c % and recommended ADA cutoffs to assess prediabetes and diabetes status. Outcomes included structural (i.e., volumes; WMHs, total brain, regional and total gray matter volumes, lateral ventricles, hippocampus) and diffusion MRI (free water = FW, fractional anisotropy = FA). Survey weighted linear models (covariate adjusted by important confounders) were used to test the associations between our exposures and outcomes of interest.

Result

Compared to no diabetes, diabetes was linked to smaller total brain and occipital volumes, larger WMHs, larger lateral ventricles, lower FA, and higher FW (Figure 1). Higher levels of HbA1c % (treated continuously) were additionally associated with smaller occipital and larger hippocampal volumes (Figure 2). These findings were consistent across men and women. We did not find significant differences with the pre‐diabetes group.

Conclusion

Our findings suggest that elevated HbA1c and diabetes status are linked to cerebrovascular pathology and brain atrophy. Findings with hippocampal volume could be due to maladaptive process or hypertrophy. Further research is needed to understand different neurobiological mechanisms in understudied communities.
Early Alterations of White Matter Neuroinflammation in Autosomal Dominant Alzheimer's Disease
Authors: Wang Qing , Jiang Meng , Cohen Kayle , Flores Shaney , Keefe Sarah J. , Patel Aakash , Chen Gengsheng , Millar Peter R , Schindler Suzanne E. , Cruchaga Carlos , Gordon Brian A. , Morris John C. , Bateman Randall J. , McDade Eric , Wang Yong , Benzinger Tammie L.S.
Published: January 2025

Background

Autosomal Dominant Alzheimer's Disease (ADAD) is a rare and early‐onset form of Alzheimer's disease with a familial pattern of inheritance. While the pathological features of ADAD, such as amyloid plaques and neurofibrillary tangles, have been extensively studied, the involvement of white matter (WM) neuroinflammation is not well‐explored. In sporadic AD, the hindered ratio (HR) derived from diffusion basis spectrum imaging (DBSI) has been used to study neuroinflammation in WM. This study aims to investigate the potential role of WM neuroinflammation as an early driver in the progression of ADAD using DBSI.

Method

This study included 104 participants from the Dominantly Inherited Alzheimer Network (DIAN) Observational Study. 60 participants were identified as mutation carriers of pathologic variants in PSEN1, PSEN2, or APP. Estimated years until symptom onset (EYO) were calculated for each participant based on previous methods (1). Participants’ demographics are summarized in Table 1. Diffusion MRI was acquired using a multi‐b value scheme (bmax =2000 s/mm2 and 28 directions) (2). The DBSI‐HR was quantified and obtained from the JHU white matter tracts. Non‐parametric Kruskal‐Wallis and Chi‐square tests were employed to compare demographic variables. The DBSI‐HR on each WM tract as a function of EYO was explored using generalized additive mixed models. All statistical tests were conducted in R4.2.2 (R Core Team). Age, sex, education, and APOE e4 carrier status were adjusted as covariates.

Result

Demographic data are summarized in Table 1. Mutation carriers had an elevated averaged DBSI‐HR for total WM tracts compared to non‐carriers (Fig. 1). Mutation carriers displayed divergent trends from non‐carriers prior to the expected symptom onset for the posterior thalamic radiation (‐5.87 EYO, Fig. 2), tapetum (‐2.9 EYO), and sagittal stratum (‐1.20 EYO).

Conclusion

DBSI‐HR measurements indicate increased WM neuroinflammation in the posterior thalamic radiation, tapetum, and sagittal stratum prior to symptom onset in ADAD. We have recently identified these three WM tracts as having the earliest change in DBSI‐HR in sporadic AD. Together, these findings suggest that WM neuroinflammation occurs prior to onset of AD symptoms.
Atrophy Network Mapping of Anxiety in Cognitively Unimpaired Older Adults in Preclinical AD
Authors: Lee Soyoung , Palm Stephan T , Drew William J , Zide Benjamin S , Donovan Nancy , Fox Michael D , Siddiqi Shan
Published: January 2025

Background

Anxiety is prevalent among cognitively unimpaired older adults and is associated with accelerated amyloid‐ß‐related cognitive decline and incident cognitive impairment. Investigating these mechanisms is challenging due to low pathologic burden, high individual variability, and subsyndromal level of symptoms. Recently, brain networks involved in AD were successfully localized by mapping the brain connectivity of atrophy patterns associated with memory impairment and delusions. In this study, we investigated anxiety symptoms in older adults with elevated amyloid‐ß using the novel method of atrophy network mapping.

Method

We utilized baseline data from ∼1800 cognitively unimpaired older adults, aged 65‐85, from the Anti‐Amyloid in Asymptomatic Alzheimer’s Disease (A4) trial, including clinical assessment of anxiety measured by the 6‐item State‐Trait Anxiety Inventory as well as structural brain MRI and amyloid‐ß (18F‐florbetapir) PET acquisitions. We generated a vertex‐wise general linear model of cortical thickness to create individualized atrophy maps for each participant with elevated amyloid‐ß deposition, using data from older adults with low amyloid‐ß deposition as a control group. Then, we used a normative human connectome (n = 1000) to estimate the functional connectivity of each participant’s unique atrophy pattern. From the network connectivity maps, we identified atrophy‐based brain networks associated with higher anxiety. To validate this brain network, we performed a spatial correlation with a pre‐established anxiosomatic network derived from focal brain stimulation sites that selectively modify anxiety and assessed significance using permutation testing. We hypothesized that atrophy patterns associated with greater anxiety would correspond to this anxiosomatic network.

Result

Anxiety symptoms localized to a distinct brain network based on individuals' atrophy patterns. The identified brain network was correlated to the anxiosomatic network (r = 0.65), and the correlation was stronger when controlled for depressive symptoms (r = 0.67). The network was more similar to the anxiosomatic network than expected by chance (p<0.05).

Conclusion

We identified a brain network associated with anxiety symptoms in cognitively unimpaired older adults with elevated amyloid‐ß. These findings help elucidate a neuropathophysiology of anxiety in preclinical AD and, possibly, its role in disease progression. The brain network identified in this project could yield new neurostimulation targets for anxiety in older adults across the AD continuum.
Astrocyte reactivity associates with tau phosphorylation and neurodegeneration before detectable amyloid‐β pathology
Authors: Saha Pampa , Povala Guilherme , Ferreira Pamela C.L. , Amaral Livia , Abbas Sarah , Lussier Firoza Z , Rodrigues Matheus Scarpatto , Oliveira Markley , Rahmouni Nesrine , Tissot Cécile , Soares Carolina , Therriault Joseph , Servaes Stijn , Stevenson Jenna , Hong Chang‐Hyung , Roh Hyun Woong , Karim Helmet T. , Benedet Andrea L. , Ashton Nicholas J. , Zetterberg Henrik , Blennow Kaj , Karikari Thomas K , Kim Sang Joon , Bellaver Bruna , Pascoal Tharick Ali
Published: January 2025

Background

Aß plaques are the first detectable signs of AD pathology. Our group recently demonstrated that the astrocyte activation marker, glial fibrillary acidic protein (GFAP), has a pivotal role in the association between Aß burden and tau phosphorylation. However, the role of astrocyte activation in individuals that do not present detectable Aß pathology using biomarkers is still underexplored. Here we sought to understand the association between plasma GFAP with tau phosphorylation and neurodegeneration in Aß negative individuals.

Method

598 study participants [cognitively unimpaired, (CU)=200, and cognitively impaired, (CI)=398] from two cohorts (TRIAD, Canada and BICWALZ, South Korea) were assessed for Aß‐PET, plasma GFAP, p‐tau217, NfL, and hippocampal volume measures. Pearson correlations between plasma GFAP and plasma p‐tau217, plasma NfL along with hippocampal volume was performed in Aß‐PET negative individuals.

Result

Remarkably, we found that plasma GFAP levels positively correlate with plasma p‐tau217 levels in Aß‐PET negative individuals in both cohorts [TRIAD cohort: r=0.23, p=0.01; BICWALZ cohort: r=0.21, p=0.0001; Figure: 1A, B]. Further, the correlation between plasma GFAP with plasma NfL has also been found to be positively correlated among the two cohorts [TRIAD cohort: r=0.45, p=0.0001; BICWALZ cohort: r=0.49, p=0.0001; Figure: 2A, B]. We also observed that hippocampal volume, as a neurodegeneration marker, reveals a significant negative Pearson correlation with plasma GFAP levels in Aß‐PET negative human individuals from both the cohorts [TRIAD cohort: r=‐0.27, p=0.0009; BICWALZ cohort: r=‐0.24, p=0.0001; Figure: 3A, B].

Conclusion

Our study provides evidence from research and clinical cohorts that astrocyte reactivity may be associated with very early tau phosphorylation and neurodegeneration in the absence of Aß pathology.
Poor sleep is adversely associated with MRI markers of AD‐specific neurodegeneration
Authors: Shi Hui , Archer Derek B. , Perry Arden , Pechman Kimberly R. , Shashikumar Niranjana , Landman Bennett A. , Hohman Timothy J. , Jefferson Angela L. , Full Kelsie M
Published: January 2025

Background

Poor sleep has emerged as a potentially modifiable risk factor for Alzheimer’s disease (AD) and related dementias (ADRD). Few previous studies have incorporated objectively‐measured sleep and structural neuroimaging to understand if poor sleep relates to changes in brain structure. In a cohort of older adults, we investigated actigraphy‐measured sleep health and associations with well‐established magnetic resonance imaging (MRI) markers of AD‐specific neurodegeneration.

Method

Participants from the Vanderbilt Memory and Aging Project (n=390, Mean age: 69.5± 9.3 year; 47.4% women) wore ActiGraph GT9X accelerometers on their wrist daily for 10 days. Data were processed to estimate sleep regularity, wake after sleep onset (WASO), average awakening length, sleep fragmentation index, sleep timing, and sleep duration. 3T brain MRI was used to quantify hippocampal volume and to estimate the McEvoy AD signature (calculated according to published guidelines). We used cross‐sectional multivariate linear regression to assess the associations between individual sleep measures and neuroimaging markers. Models adjusted for age, sex, race, education, APOE‐e4 status, cognitive status, sleep medication use, body mass, index, physical activity, depressive symptoms, and cardiovascular risk. For sleep timing and duration, quadratic terms were added in regression models. Models examining the total hippocampal volume also adjusted for intracranial volume.

Result

In the fully adjusted models, we found abnormal sleep duration (ß = ‐2.35, 95% CI = ‐4.58, ‐0.11) and greater sleep irregularity (ß = ‐0.34, 95% CI = ‐0.62, ‐0.07) were associated with lower AD signature values. Additionally, greater sleep irregularity (ß = ‐262.36; 95% CI = ‐451.71, ‐73.00), greater WASO (ß = ‐2.76; 95% CI = ‐5.30, ‐0.21) and greater average awakening length (ß = ‐74.20; 95% CI = ‐128.47, ‐19.94) were associated with lower hippocampal volumes.

Conclusion

Our results suggest that several measures of poor sleep, including greater sleep irregularity, wake time, and sleep fragmentation, are cross‐sectionally linked to smaller hippocampal volume and smaller brain volumes in regions most affected by AD neuropathology. Longitudinal studies are needed to clarify if poor sleep contributes to brain atrophy over time.