bluuebunny/crossref_metadata_2025 huggingface dataset:
APOEε4 potentiates the effects of Aβ pathology on the deposition of neurofibrillary tangles via tau phosphorylation
Authors: Ferrari‐Souza João Pedro , Bellaver Bruna , Ferreira Pamela C.L. , Benedet Andrea Lessa , Povala Guilherme , Lussier Firoza Z , Leffa Douglas Teixeira , Therriault Joseph , Tissot Cécile , Soares Carolina , Wang Yi‐Ting , Chamoun Mira , Servaes Stijn , Macedo Arthur C. , Vermeiren Marie , Bezgin Gleb , Kang Min Su , Stevenson Jenna , Rahmouni Nesrine , Pallen Vanessa , Poltronetti Nina Margherita , Cohen Annie , Lopez Oscar L. , Klunk William E , Soucy Jean‐Paul , Gauthier Serge , Souza Diogo O. , Triana‐Baltzer Gallen , Saad Ziad S. , Kolb Hartmuth C. , Karikari Thomas K , Villemagne Victor L , Tudorascu Dana , Ashton Nicholas J. , Zetterberg Henrik , Blennow Kaj , Zimmer Eduardo R , Rosa‐Neto Pedro , Pascoal Tharick A.
Published: December 2023

Background

The mechanisms by which the apolipoprotein E e4 (APOEe4) allele influences Alzheimer’s disease (AD) pathophysiological progression are poorly understood. Here, we tested the association of APOEe4 carriership and amyloid‐ß (Aß) burden with longitudinal tau pathology progression.

Method

We studied 104 individuals across the aging and AD clinical spectrum from the McGill TRIAD cohort. Study participants underwent clinical assessments, APOE genotyping, magnetic resonance imaging, positron emission tomography (PET) for Aß ([18F]AZD4694) and tau ([18F]MK6240) at baseline, as well as an additional follow‐up tau‐PET scan (mean follow‐up, 2.4 years). We further assessed longitudinal changes in tau phosphorylation (plasma phosphorylated tau at threonine 217 [p‐tau217+]), brain atrophy (gray matter density), and clinical function (clinical dementia rating scale sum of boxes).

Result

We found that APOEe4 carriership potentiates Aß effects on longitudinal tau tangle accumulation over two years (Figure 1). Interestingly, the APOEe4‐potentiated Aß effects on tangles were mediated by longitudinal plasma p‐tau217+ increase (Figure 2). In addition, this longitudinal tau accumulation as measured by PET was accompanied by brain atrophy and clinical decline during the follow‐up period (Figure 3).

Conclusion

Our results support a model in which the APOEe4 allele plays a key role in Aß downstream effects on the aggregation of phosphorylated tau in the form of neurofibrillary tangles in the living human brain, which is a key factor in the development of dementia. These observations have important implications for the design of future trials by suggesting that the combination of therapies targeting both ApoeE4 and Aß pathology might have the potential to synergistically halt tau progression in AD.
Trajectory of MRI and FDG‐PET biomarkers in Alzheimer’s disease
Authors: Heeman Fiona , Moscoso Alexis , Dunås Tora , Schöll Michael
Published: December 2023

Background

Alzheimer’s disease (AD) has a long preclinical phase, which is hypothesised to start with the accumulation of ß‐amyloid (Aß) plaques, followed by tau neurofibrillary tangles and neurodegeneration. Despite presenting quite different types of information, MRI‐derived volumetric measures and FDG‐PET‐derived metabolism are both commonly used as proxy of neurodegeneration in AD. The aim of this work is to assess the trajectory of both of these biomarkers in AD, relative to time to amyloid‐positivity.

Method

3289 MRI scans (682 participants) and 2472 FDG‐PET scans (1296 participants) were included from the ADNIGO, 1,2 and 3 cohorts. For MRI scans, total volume was calculated for a meta‐ROI consisting of entorhinal, inferior, middle temporal and fusiform gyri. For FDG‐PET, standardized uptake value ratios (SUVR) were calculated from 30‐60 min post injection for a meta‐ROI consisting of the angular, posterior cingulate and inferior temporal gyri, normalised to the pons. All metrices were transformed into z‐scores using the references values of the Aß‐negative subjects. Age of amyloid‐positivity was determined by fitting a nonlinear mixed effects model to 3607 Centloid values corresponding to 1690 participants. Next, time to amyloid‐positivity was calculated for each scan. The relationship between time to amyloid‐positivity and (z‐scored) SUVR or cortical volume values was modelled using a non‐linear mixed effects model with spline function and trajectories from both modalities were compared.

Result

Baseline demographics can be found in Table 1. Figure 1 shows the trajectory of amyloid deposition that was used to determine age of amyloid‐positivity. Cortical volume and SUVR showed similar trajectories over time, with steeper decline in both measures after the event of amyloid‐positivity (Figure 2). The overlay of both biomarker trajectories shows that for the meta‐ROI, there is no difference in when these biomarkers become abnormal.

Conclusion

The present results suggest that measures of neurodegeneration from MRI and FDG‐PET scans corresponding to a meta‐ROI, follow very similar trajectories. Future work will investigate if this also holds true for smaller regions of interest.
Investigation of neighborhood disadvantage in the context of APOE‐associated amyloid onset age risk
Authors: Irizarry‐Pagan Erica E , Teague Jordan P , Buckingham William R. , Cody Karly Alex , Langhough Rebecca E , De Chavez Elena Ruiz , Navaratna Ruvini , Okonkwo Ozioma , Asthana Sanjay , Johnson Sterling C. , Kind Amy J. , Bendlin Barbara B , Powell W. Ryan , Zuelsdorff Megan , Betthauser Tobey J
Published: December 2023

Background

Exposome social determinants of health (SDoH), such as neighborhood disadvantage, have shown associations with accelerated cognitive decline, brain volume loss, and postmortem AD pathology. However, associations between neighborhood disadvantage and amyloid biomarker positivity (A+) onset remain understudied. This study uses estimated A+ onset age (EAOA) and the Area Deprivation Index (ADI; an exposome SDoH) to investigate associations between neighborhood disadvantage and A+ risk.

Method

Participants (n = 599, Table 1) from the Wisconsin Registry for Alzheimer’s Prevention (WRAP) and the Wisconsin Alzheimer’s Disease Research Center (WADRC) with available amyloid PET, address, and APOE genotype were included. EAOA was estimated using sampled iterative local approximation. Neighborhood disadvantage was categorized by national‐rank 2020 ADI (most disadvantaged = 80th percentile; least disadvantaged <80th percentile). APOE groups were established based on the number of e4 alleles. Cox proportional hazard models assessed A+ risk associated with APOE, ADI, and their interaction. Secondary Cox models used continuous ADI and a lower 70th ADI percentile for neighborhood groups. Kaplan‐Meier curves were generated to depict A‐ probability vs. age for combinations of APOE and ADI groups. A‐ were right censored.

Result

A significant interaction (p = 0.015) between ADI and APOE groups was observed such that APOE‐e4 homozygotes who resided within a highly disadvantaged neighborhood had higher risk of becoming A+, but this group was limited to two participants. Secondary analyses indicated no significant effect of ADI or interaction with APOE on A+ risk (p>0.49). Plots of A+/‐ and EAOA by ADI and APOE group (Figure 1) and Kaplan‐Meier EAOA curves indicated EAOA overlap of ADI groups within APOE strata. A+ risk was significantly higher for APOE‐e4 homozygotes, followed by APOE‐e4 heterozygotes, and then APOE‐e4 non‐carriers.

Conclusion

In this mostly unimpaired, highly educated sample, higher number of APOE‐e4 alleles was associated with higher A+ risk and earlier A+ onset. APOE‐associated A+ risk was similar for all ADI groups, but representation across APOE genotypes for individuals residing within highly disadvantaged neighborhoods was limited. Further work is needed in more diverse samples to investigate the possible influence of ADI and other exposome SDoH factors on AD biomarker timing and trajectories.
Relationship of Atrophy and Tau Imaging Subtypes in Prodromal and Clinical AD
Authors: Risacher Shannon L , Deardorff Rachael , Saykin Andrew J.
Published: December 2023

Background

Amyloid‐positive MCI and AD participants show heterogeneity in patterns of atrophy and tau‐deposition. We sought to directly compare the previously‐defined subtypes (hippocampal sparing (HpSp), limbic predominate (LP), typical AD (tAD), and minimal atrophy) derived from [18F]flortaucipir PET (T) and structural MRI (N) in the context of positive amyloid (A). In addition, we examined assessed associations between demographic factors and subtype group for atrophy or tau.

Method

243 amyloid‐positive participants from ADNI with MRI and tau PET scans at the same visit were included (160 MCI, 83 AD). Brain volumes were extracted using Freesurfer version 6 and tau PET SUVR values were extracted using standard techniques. Hippocampal to cortical ratios for grey matter volume and tau SUVR were calculated as previously described (Risacher et al. 2017 Neurology). Minimal pathology subjects showed similar levels of atrophy relative to cognitive normal participants. Relationships between atrophy‐ and tau‐based subtypes, as well as their relationship to sex and race/ethnicity were assessed using a Chi‐square test. An ANOVA model was used to compare age and education between atrophy‐ and tau‐based subtypes.

Result

A significant association between tau‐based and atrophy‐based subtypes was observed, driven by consistency in minimal atrophy, HpSp, and tAD groupsp = 0.024). In addition, differences by sex and race/ethnicity were also observed for atrophy but not tau subtypes with non‐Hispanic Whites most likely to be either minimal atrophy or tAD, while African Americans were more likely to be HpSp. Finally, significant differences in age and education were observed across the atrophy subtypes, with the HpSp group being younger and less educated, while the LP were older and more educated.

Conclusion

Overall, we observed consistency between atrophy‐based and tau‐based subtypes, especially minimal pathology, tAD, and HpSp forms. Interesting associations between demographics and atrophy but not tau subtypes were also observed. Future studies in larger and more diverse samples are needed. These results underscore the importance of examining heterogeneity in amyloid‐positive MCI and AD across multiple modalities to better understand patterns of A/T/N pathophysiology that may have implications for risk of progression and personalized therapeutic strategies.
Females Derive Greater Neuroprotective Benefits from Social Connectedness than Males
Authors: Manchella Mohit K. , Logan Paige E. , Perry Brea L , Peng Siyun , Risacher Shannon L , Saykin Andrew J. , Apostolova Liana G.
Published: December 2023

Background

Social connectedness is associated with better cognitive performance among older adults. Previously, we found that bridging (access to an expansive network of diverse and loosely connected individuals) and bonding (close and supportive relationships to familiar others) associated with preserved gray matter density (GMD). We sought to examine whether sex modulates these relationships in the Social Networks and Alzheimer’s Disease study (SNAD).

Method

We analyzed baseline data of 176 SNAD participants (58 male, 118 female; 117 cognitively‐unimpaired, 59 cognitively‐impaired; average age 68.57+/‐12.26 years). Bridging and bonding metrics were collected via in‐person interviews. We ran analyses in the pooled sample split by sex. Demographics and biomarkers between sexes were compared. Correlations between MRI regions of interest and bridging and bonding in males and females were performed. Voxel‐wise covaried for age, education, and intracranial volume in SPM12 yielded statistical maps for males and females which were corrected for family‐wise errors at p<0.01.

Result

Table 1 shows demographic and biomarker comparisons between males and females. In females greater social bridging was significantly associated with (left p = 0.004, right p<0.001), as well as left frontal lobe thickness (p = 0.002), putamen (p = 0.011), and thalamus volumes (p = 0.002). No significant associations were observed for social bridging in males. Greater social bonding was significantly associated with preserved right frontal pole (p = 0.018) and caudal anterior cingulate thickness (p = 0.017) in females and greater right orbitofrontal thickness (p<0.001) in males (Figure 1). Our 3D voxel‐wise regression maps showed that in females greater social bridging was associated with preserved GMD of the frontal, temporal and parietooccipital lobes, while males showed no significant associations (Figure 2). In females greater social bonding was associated with preserved GMD in the lateral temporal and parietal lobes, while in males the associations were localized to the medial frontal and right orbitofrontal lobe (Figure 2).

Conclusion

Women compared to men showed stronger effects of social enrichment on GMD and subcortical structures. Social bridging and bonding were associated to different brain regions in males and females. These results suggest that females may have a greater social‐based protection against neurodegeneration compared to their male counterparts.
Differentiation of frontotemporal dementia subtypes using neuroimaging‐based multi‐type parallel feature embedding
Authors: Ma Da , Stocks Jane K , Lockhart Samuel N. , Craft Suzanne , Rosen Howard J. , Gurcan Metin Nafi , Popuri Karteek , Beg Mirza Faisal , Wang Lei
Published: December 2023

Background

Frontotemporal dementia (FTD) represents a collection of neurocognitive syndromes with frontotemporal lobar degeneration (FTLD) neuropathology, and is associated with significant clinical, pathological, and genetic heterogeneity. We trained a deep neural network (DNN) classifier to differentiate behavioral‐variant FTD (bvFTD), semantic variant primary progressive aphasia (svPPA), and non‐fluent variant (nfvPPA) patients using MRI scans drawn from two multi‐site neuroimaging consortiums.

Method

BvFTD (N = 173), nfvPPA (N = 63), and svPPA (N = 41) patients with T1‐MRI were extracted from the FTLD Neuroimaging Initiative (FTLDNI) and the ARTFL‐LEFFTDS Longitudinal Frontotemporal Lobar Degeneration (ALLFTD) databases. MRI data were preprocessed with FreeSurfer, with cortical thickness, cortical volume, and subcortical volumes extracted. Cortical measures were parcellated into 360 patches (i.e., ROIs) according to the HCP‐MMP1 atlas. Both the patch‐based cortical thickness and volume features were harmonized to control confounding effects of sex, age, total intracranial volume (TIV), cohort, and scanner. Multi‐type features were parallelly fed into a multi‐layer‐perceptron (MLP)‐based classifier. Weighted cross‐entropy loss function was used to account for unbalanced sample sizes across FTD subtypes. 10‐fold nested cross‐validation was used to evaluate the robustness of the classification model, with data split into 80/10/10 of training/validation/test data sets.

Result

Visual evaluation z‐scores of cortical features among FTLDNI and ALLFTD groups revealed site‐specific differences significantly reduced by feature harmonization, especially for volume (Figure 1). The balanced accuracy of the ensembled DNN classifier of each FTD subtype on test sets across all ten folds reached 0.76 ± 0.09 for bvFTD, 0.79 ± 0.07 for nfvPPA, and 0.88 ± 0.07 for svPPA (Figure 2). Figure 3 shows confusion matrices for classification performance in the test set across ten cross‐validation folds.

Conclusion

We developed a deep‐learning‐based framework to classify three FTD subtypes: bvFTD, nfvPPA, and svPPA. The combination of feature harmonization and parallel multi‐type feature embedding framework showed promising differentiation power. This work can be used to recognize at‐risk populations for early and precise diagnosis, to aid intervention planning. Future studies will compare classification based on different input features and use visualization methods to identify the most discriminative regions and explore their clinical relevance.
Longitudinal changes of self‐ and informant‐reported cognitive decline in elderly with regard to clinical progression and amyloid‐status
Authors: Kuhn Elizabeth , Stark Melina , Kleineidam Luca , Wolfsgruber Steffen , Boecker Henning , Bürger Katharina , Düzel Emrah , Fliessbach Klaus , Gaertner Florian , Heneka Michael T. , Laske Christoph , Perneczky Robert , Peters Oliver , Priller Josef , Schmid Matthias , Schneider Anja , Spottke Annika , Teipel Stefan , Wiltfang Jens , Jessen Frank , Wagner Michael
Published: December 2023

Background

Self‐ and informant‐reports of cognitive decline have been previously associated with a higher risk of cognitive decline in elderly. Yet, few studies assessed their longitudinal changes over time with regard to both incident mild cognitive impairment (iMCI) and Alzheimer’s disease (AD) pathology. This study aims at describing these links to better understand which trajectories are indicative of higher AD‐risk.

Method

We used data from 501 participants followed‐up annually (2.51±1.35 years) in the DELCODE project. All were cognitively unimpaired, and had an amyloid measurement available, at inclusion. Among them, 145 were amyloid‐positive, and 65 progressed to iMCI during the follow‐up period (31 iMCI‐Aß+). The SCD‐Interview was used to measure (i) the number of cognitive domains in which the participants or its relative perceived a decline (i.e., self‐ and informant‐SCD‐I), and (ii) the number of fulfilled SCD‐plus criteria. Linear mixed‐effect models were used to assess SCD trajectories over time, according to four groups defined by amyloid status and clinical progression (Stable‐Aß‐, Stable‐Aß+, iMCI‐Aß‐ and iMCI‐Aß+). Analyses were adjusted for age, sex and education; and their interaction with time.

Result

Both self‐SCD‐I levels and SCD‐plus scores decreased significantly over time, without significant differences between groups (Figure 1A, 1B). Conversely, informant‐SCD‐I levels did not significantly change over time in the whole sample, although slopes significantly differed between groups. Compared to the Stable‐Aß‐ group, there was a greater increase over time of informant‐SCD‐I report only in iMCI‐Aß+ [est 0.16, SE 0.05, p<.001] (Figure 1C). The resulting discrepancy score (Self‐SCD‐I minus Informant‐SCD‐I) tended to decrease over time with significant differences between groups. Compared to the Stable‐Aß‐ group, the discrepancy score showed a faster decrease over time in iMCI‐Aß+ [est ‐0.21, SE 0.056 p<.001] (Figure 1D).

Conclusion

Longitudinal patterns in subjective reports of cognitive decline might help identify preclinical AD subjects at higher risk of clinical progression. Specifically, an increase of informant‐reported cognitive decline over time may indicate objective cognitive decline in amyloid‐positive subjects. Conversely, the decrease of self‐reported cognitive decline over time, regardless of the group, may reflect generally reduced concerns after inclusion, or result from current limitations in longitudinal SCD assessment.
Associations among plasma, MRI and amyloid PET biomarkers of dementia and the impact of health‐related comorbidities
Authors: Rudolph Marc D. , Sutphen Courtney L. , Register Thomas C. , Whitlow Christopher T. , Sai Kiran K. Solingapuram , Hughes Timothy M. , Bateman James R. , Dage Jeff L. , Russ Kristen A. , Mielke Michelle M. , Craft Suzanne , Lockhart Samuel N.
Published: December 2023

Background

Knowledge regarding associations between plasma and neuroimaging biomarkers indexing neurodegeneration and neuropathology observed in dementia is limited. Further, it is uncertain how comorbid health complications (e.g., kidney function) may alter plasma levels and impact associations with neuroimaging biomarkers.

Method

We examined associations between plasma and neuroimaging biomarkers in cognitively normal participants (NC; N = 300) and individuals with consensus diagnosis (Dx) of mild cognitive impairment (MCI; N = 192) or dementia (DEM; N = 64) enrolled in the Wake Forest ADRC (Table 1). We examined plasma biomarkers (Quanterix SIMOA HD‐X: Aß42/40, GFAP, NfL, p‐tau181) and neuroimaging measures of amyloid deposition (global PiB PET SUVr; Aß‐PET), total brain volume (BVOL), global white matter hyperintensity volume (WMH), diffusion‐weighted fractional anisotropy (FA) and NODDI freewater (FW; white matter). Linear models adjusted for APOE‐e4 carrier status, demographics (age, sex, race, education), and cardiometabolic factors (estimated glomerular filtration rate (eGFR); BMI).

Result

Plasma biomarkers were moderately correlated with each other (absolute r = .22‐.64; all p<.001) and significantly elevated (Aß42/40 lower) in DEM and MCI (Figure 1) versus NC, and Aß‐PET‐positive (SUVr >1.21) versus negative individuals (all p<.001). Plasma and neuroimaging markers were significantly associated in both unadjusted models and models including eGFR and BMI (all p<.05; attenuation effect <10% with and without Dx; Figure 2a & 2b). In fully adjusted models (Figure 2b: Dx and all covariates), age, sex, and race differentially impacted associations of Aß42/40, p‐tau181, and NfL with neuroimaging biomarkers (coefficients p <.05). APOE‐e4 status exclusively impacted associations with Aß‐PET SUVr/status. GFAP remained significantly associated with all neuroimaging biomarkers after covariate adjustment; no Aß42/40 associations survived adjustment. P‐tau181 remained significantly associated with Aß‐PET and BVOL, while associations with NfL were reduced in models stratified by Dx.

Conclusion

Among aging community‐dwelling participants, plasma biomarkers significantly differed between diagnostic groups (DEM>MCI>NC), were elevated in Aß‐PET positive individuals, and associated with poorer brain health. Except for GFAP, and to an extent p‐tau181, associations between plasma and neuroimaging biomarkers were differentially impacted by inclusion of comorbidities and covariates when stratified by diagnosis. Future work will examine high‐dimensional interactions among comorbidities, demographic information, and plasma and neuroimaging biomarkers in individuals with or at‐risk of dementia.
BrainAGE methods: influence of field strength, voxel size, race, and ethnicity
Authors: Dempsey Desarae A , Deardorff Rachael , Saykin Andrew J. , Risacher Shannon L
Published: December 2023

Background

The brain‐age algorithm utilizes a regression model of the typical aging trajectory, trained on structural MRIs and chronological ages of a large population of healthy individuals spanning a wide age range (Cole et al. 2017, Neuroimage). MRIs from new populations are then compared to this regression for estimating brain age, a biomarker of brain health. Few studies have investigated stability of the brain‐age model across field strength and the influence of voxel size and race is understudied.

Method

We analyzed stability of the brain‐age model within and across field strength in the ADNI cohort (n = 75: 25 CN/25 MCI/25 AD) using intra‐class correlation coefficients (ICC) and multiple R2 values. Impact of voxel size (0.8mm, 1mm, and ADNI2 sequence 1.1×1.1×1.2mm) was compared in a sample from the Indiana ADRC (n = 48: 19 CN/16 SCD/10 MCI/3 AD). We then calculated brain age gap (BAG; estimated– chronological age) and compared results across self‐reported racial and ethnic groups matched on diagnosis, sex, age, education, and handedness using independent t‐tests. BAG results of African Americans (AAs) from the IADRC (n = 71: 30 CN/19 SCD/15 MCI/7 AD) and ADNI (n = 147: 41 CN/20 EMCI/27 LMCI/15 AD/44 SMC), and Hispanics (n = 76: 24 CN/16 EMCI/11 LMCI/11 AD/14 SMC) and Asians (n = 47: 12 CN/7 EMCI/4 LMCI/9 AD/15 SMC) from ADNI only, were compared to matched non‐Hispanic Whites (NHWs).

Result

The brain‐age method was stable across repeated scans within field strength, but less stable across field strengths (Fig.1). 3T scans consistently estimated individuals to be ∼4.43 years younger than the 1.5T scans. The method was relatively stable within and across voxel sizes (Fig.2). There was a significant difference in BAG between races (NHWs and AAs, and NHWs and Asians), but not between ethnicities (NHWs and Hispanics) (Fig.3).

Conclusion

Due to the model’s variability across field strength, we propose a linear offset equation for data harmonization (Fig.1): ((3T estimate/0.92)+9.55). We are also investigating ComBat harmonization to eliminate effects of field strength. While other studies show structural differences across race, it will be important to determine whether the self‐reported racial differences in BAG are due to inherent model bias or true biological differences.
Microglial Activation Contributes to Neuropsychiatric Dysfunction in Alzheimer’s Disease
Authors: Aguzzoli Cristiano Schaffer , Ferreira Pamela C.L. , Povala Guilherme , Soares Carolina , Ferrari‐Souza João Pedro , Bellaver Bruna , Zalzale Hussein , Lussier Firoza Z , Rohden Francieli , Abbas Sarah , Lemaire Peter Charles , Leffa Douglas Teixeira , Cabrera Arlec , Therriault Joseph , Benedet Andrea Lessa , Tissot Cécile , Servaes Stijn , Macedo Arthur C. , Vermeiren Marie , Bezgin Gleb , Kang Min Su , Stevenson Jenna , Rahmouni Nesrine , Pallen Vanessa , Cohen Annie , Lopez Oscar L. , Tudorascu Dana , Klunk William E , Villemagne Victor L , Poltronetti Nina Margherita , Soucy Jean‐Paul , Wang Yi‐Ting , Gauthier Serge , Souza Diogo O. , Ashton Nicholas J. , Schilling Lucas Porcello , Karikari Thomas K , Blennow Kaj , Zetterberg Henrik , Zimmer Eduardo R , Rosa‐Neto Pedro , Pascoal Tharick A.
Published: December 2023

Background

Previous studies have shown that microglial activation (MA) plays a key role in the pathophysiological and clinical progressions of Alzheimer’s disease (AD). However, little is known whether MA is also associated with the development of neuropsychiatric symptoms typically found in patients with AD. Thus, we aim to investigate here the association of MA with neuropsychiatric symptoms (NPS) of individuals across the AD continuum.

Method

We assessed 132 individuals (86 cognitively unimpaired (CU), 28 MCI, and 18 AD dementia) from the TRIAD cohort who underwent clinical assessments with the Neuropsychiatry Inventory Questionnaire (NPI‐Q), and had positron emission tomography (PET) for amyloid‐ß (Aß) ([18F]AZD4694), tau tangles ([18F]MK6240) and MA ([11C]PBR28) at the same visit. Regions were tailored using Desikan‐Killiany (DK) atlas. SUVRs were calculated using the cerebellum gray matter as a reference. Linear regression tested the association between biomarkers accounting for age, sex, and cognitive status.

Result

NPI‐Q total score was significantly associated with [11C]PBR28 in the cingulate, inferior temporal, and precuneus accounting for age, sex, and after false discovery rate (FDR) correction for multiple comparisons (Figures 1A, 1B, 1C). This association was independent of Aß and tau levels (Table 1). Notably, MA predicted neuropsychiatric dysfunction with higher magnitude than Aß or tau using PET values from overlap region (regional SUVR) and if we use a global measure for all tracers (global SUVR) (Figure 2A). When we stratify NPI‐Q domains (agitation, irritability, motor disturbance, disinhibition, elation, delusion, hallucinations, nighttime disturbance, depression, anxiety, apathy, and appetite disturbance) severity score, we found that the hyperactivity subdomain (agitation, irritability, motor disturbance, disinhibition, and elation) showed the larger contribution to the results (Figure 2B).

Conclusion

Our results suggest MA as a key element associated with neuropsychiatric dysfunction in AD independent of Aß and tau pathologies. These findings provide additional rationale for the therapeutics targeting glial cells activation in AD patients.
Systematic Comparison of Reproducibility of Diffusion MRI‐Derived Free‐Water Measures Across Two Multi‐Shell Protocols
Authors: Mishra Virendra R , Sreenivasan Karthik , Cordes Dietmar
Published: December 2023

Background

Free‐water (FW) measures have been widely used to understand and predict the progression of various neurodegenerative disorders1,2. However, the estimation of FW in a brain voxel needs a multi‐shell acquisition to ensure reproducible fit3,4. With the availability of multi‐shell diffusion MRI (dMRI) protocol using simultaneous multislice technique from the Human Connectome Project (HCP) and Alzheimer’s Disease Neuroimaging Initiative (ADNI‐3), such protocols can now be harmonized across various centers and MRI vendors. However, limited knowledge exists on whether the results obtained for FW measures across HCP protocols are generalizable across ADNI‐3 protocols along with which protocol is more reproducible that will assist us in understanding the variability of FW measure in an individual participant longitudinally. Hence, in this study, we compared the reproducibility of FW across major white matter (WM) tracts across both HCP and ADNI‐3 protocols.

Method

A 32‐year‐old healthy male participant was scanned over five weeks, both with Basic (2mm3, b‐value = 1000s/mm2) and Advanced ADNI‐3 protocol and 2 variants of the HCP sequence (1.5mm3 and 2mm3) both with 3 b‐values (500s/mm2, 1000s/mm2, and 2500s/mm2) and 213 directions on a clinical 3T Skyra scanner. FW dMRI measures were obtained using the original Matlab implementation (Matlab)5 and DiPY implementation6 of multi‐shell dMRI acquisition (DiPY). WM atlas from Johns Hopkins University (JHU) of 20 major WM tracts pre‐registered to MNI152 was then used as a mask to obtain FW at each WM tract, and the coefficient of variance (CoV) was computed across each protocol. The total acquisition scan time was 75 minutes.

Result

FW estimated using Matlab had a mean CoV = 2.78±1.09 across all the protocols with the least CoV for Basic ADNI‐3 (CoV = 1.31±0.54) and the maximum CoV for 2mm3 HCP sequence (CoV = 4.98±1.83) (Fig.1A) while FW estimated using DiPY had a mean CoV = 6.71±2.86 across all the protocols with the least CoV for Advanced ADNI‐3 (CoV = 4.31±1.85) and the maximum CoV for 2mm3 HCP sequence (CoV = 9.66±4.15) (Fig.1A).

Conclusion

Both acquisition parameters and processing techniques significantly affect the absolute quantification of FW measures. Furthermore, our data suggest that the pooling of dMRI data across various protocols should be performed with caution.
Multivariate white matter differences links to cognition in individuals with family history of Alzheimer’s disease and APOE4 genetic risk
Authors: Tremblay Stefanie A , Spreng R. Nathan , Alasmar Zaki , Pirhadi Amir , Tardif Christine L , Chakravarty M. Mallar , Villeneuve Sylvia , Leppert Ilana R , Carbonell Felix , Medina Yasser Iturria , Steele Christopher J , Gauthier Claudine J
Published: December 2023

Background

Growing evidence suggests Alzheimer’s disease (AD) develops from a complex cascade of events that vary between individuals. Multivariate approaches have the potential to capture the complexity and heterogeneity of pathologies underlying AD in a more holistic manner compared to univariate approaches.

Method

The MRI data of 105 older adults with a family history of AD from the PREVENT‐AD dataset were analysed. Forty six of these individuals had the APOE4‐3 genotype (greater risk of developing AD), while 59 subjects had the APOE3‐3 genotype (normal risk). Diffusion‐weighted imaging and multi‐echo magnetization transfer, proton density and T1‐weighted data were used to compute several WM metrics (Fig1 b‐c). We computed the voxel‐wise Mahalanobis distance (D2) in WM between APOE4‐3 individuals and a reference group (APOE3‐3) (Fig1). D2 is a multivariate measure that combines several MRI metrics, accounting for their covariance, and yielding a score indicative of the degree of abnormality at each WM voxel (Fig2). Associations between WM D2 and cognitive performance were then investigated (Fig3). Independent component analysis (ICA) was performed on spatial dimensions and linear regression analyses were conducted between D2 in ICA components and scores on items of the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). We then extracted the contribution of each MRI feature to D2 in the regions where significant associations were found.

Result

A significant negative association (R = ‐0.36, p = 0.014) was found between D2 in a component corresponding to the splenium of the corpus callosum (CC) and scores on RBANS‐immediate memory (Fig3), indicating a link between a greater degree of WM abnormality and poorer performance. Proton density (MPM_PD) and longitudinal relaxation rate (MPM_R1), potentially indicative of WM loss, contributed most to D2 in this region.

Conclusion

Our findings suggest that microstructural alterations in the posterior part of the CC may play a role in memory deficit in older adults who are at greater risk of developing AD. This is in line with other studies that showed a link between CC degeneration and memory impairments in early AD stages.1 1. Qiu et al. (2016). Inter‐hemispheric dysconnectivity in AD and aMCI. Sci.Rep.
Synaptic dysfunction in the presence of tau tangles is not a strong predictor of atrophy
Authors: Soares Carolina , Bellaver Bruna , Ferreira Pamela C.L. , Povala Guilherme , Aguzzoli Cristiano Schaffer , Rohden Francieli , Ferrari‐Souza João Pedro , Zalzale Hussein , Abbas Sarah , Lemaire Peter Charles , Leffa Douglas Teixeira , Cabrera Arlec , Therriault Joseph , Benedet Andrea Lessa , Tissot Cécile , Servaes Stijn , Macedo Arthur C. , Vermeiren Marie , Bezgin Gleb , Kang Min Su , Stevenson Jenna , Rahmouni Nesrine , Pallen Vanessa , Cohen Annie , Lopez Oscar L. , Tudorascu Dana , Klunk William E , Villemagne Victor L , Soucy Jean‐Paul , Gauthier Serge , Souza Diogo O. , Karikari Thomas K , Ashton Nicholas J. , Zetterberg Henrik , Blennow Kaj , Rosa‐Neto Pedro , Zimmer Eduardo R , Pascoal Tharick A.
Published: December 2023

Background

Tau plays a prominent role in the synapse and has been shown to be closely related to neurodegeneration. Recent evidence show that cerebrospinal fluid (CSF) synaptic markers are related to CSF tau and degeneration. However, it is not clear whether synaptic dysfunction in combination with tau tangles is associated with neurodegeneration in the same brain regions. Our study aims to map in the brain the association of synaptic markers with tau and degeneration.

Method

We evaluated 147 individuals from the TRIAD cohort with CSF GAP43, SYT1, SNAP25, neurogranin (Ng), ptau217, neurofilament light protein (NfL) quantification, as well as tau positron emission tomography (PET), magnetic resonance imaging, and clinical assessments. One‐way ANOVA tested group differences on synaptic levels. Spearman correlation tested the association among synaptic proteins. Linear regressions tested the association between biomarkers. Voxel‐based morphometry (VBM) was considered as an index of neurodegeneration.

Result

We observed an increase in synaptic markers across age and the AD spectrum, reaching a plateau when cognition is affected (Figure 1A). Among the synaptic markers, SNAP25 showed the greatest magnitude of change, on average, between young, aged cognitively unimpaired, mild cognitive impaired, and AD individuals (mean 62.44%). (Figure 1B‐C). In addition, we found that all synaptic markers are significantly intercorrelated (Figure 1D). Moreover, CSF ptau217 correlated with all synaptic biomarkers, GAP43 (ß = ‐044), SYT1 (ß = ‐0.41), SNAP25 (ß = ‐0.59) and Ng (ß = ‐0.25). Conversely, CSF NfL was strongly but only correlated with GAP43 (ß = 0.62) (Figure 2). Voxel‐wise correlation revealed that tau tangles measured by [18F]‐MK6240 PET, was positively associated with SNAP25 in AD‐related regions, and VBM was associated with all synaptic markers, but more closely with GAP43. Furthermore, the interaction between synaptic markers and temporal meta‐ROI [18F]‐MK6240 on VBM was significant for Ng and GAP43 (Figure 3).

Conclusion

Our results support a heterogenous role of synaptic markers in the AD continuum and suggest that synaptic dysfunction in the presence of tau pathology may not be driving atrophy.
Altered cerebrovascular reactivity along functional networks in older adults with long COVID
Authors: Pommy Jessica , Cohen Alexander D , Patel Milan , Umfleet Laura Glass , Franczak Malgorzata , Brett Benjamin L , Obarski Shawn , Agarwal Mohit , Ristow Kelly , Swanson Sara J , Wang Yang
Published: December 2023

Background

Cerebrovascular reactivity (CVR), an indicator of neurovascular function associated with cognitive decline, relies on vascular and inflammatory processes, making it a potential mechanism of interest driving neurological changes in long COVID among older adults. Given the variability in symptoms associated with long COVID, distribution corrected z‐scores (DisCo‐Z), a subject‐specific abnormalities (SSA) approach more suited for heterogenous conditions, was employed to examine CVR and long COVID in older adults. Relationships between CVR, post‐COVID subjective cognitive decline (pcSCD), and cognitive function were examined.

Method

15 older adults with long COVID (M age = 63.9, 6 males) and 24 healthy older adults (M age = 66.7, 6 males) performed a breath‐holding task while in the MR scanner to examine CVR. DisCo‐Z was used to examine the presence and size of extreme clusters of increased or decreased CVR (>100 voxels) within the 7 Yeo functional networks. Non‐parametric tests were used to examine group differences in presence and mean size of extreme clusters. Finally, relationships between: 1) extent of pcSCD and extreme cluster values in long COVID, and 2) cognitive testing scores and global “burden” (cluster size mean and sum) were examined.

Result

Groups did not differ significantly on gender (p = .323) or age (p = .324). Greater incidence of positive extreme clusters within Ventral Attention (p = .028) and Limbic (p = .049) networks were observed in long COVID (with minimal incidences of negative extreme clusters across networks). Group differences were observed for mean cluster size in the Ventral Attention network (p = .016), as well. Extent of pcSCD was positively correlated with DisCo‐Z values for the Ventral Attention network (p = .021) with a trending level of significance for the Limbic network (p = 0.053). Finally, within the full sample, global CVR burden was negatively correlated with memory test scores (p = 0.014).

Conclusion

Using the SSA approach, group differences in CVR were observed in older adults experiencing long COVID. In particular, differences were observed within functional networks thought to drive attention, executive function, and emotional control, which was correlated with pcSCD. Finally, global CVR burden was negatively correlated with memory. These findings, though preliminary, suggest CVR may represent a potential neurobiological mechanism driving pcSCD in older adults.
The investigation of hippocampal free‐water as a biomarker for cognitive impairment and decline in older adults
Authors: Sathe Aditi , Shashikumar Niranjana , Moore Elizabeth E. , Pechman Kimberly R. , Landman Bennett A. , Gifford Katherine A. , Hohman Timothy J. , Jefferson Angela L. , Archer Derek B
Published: December 2023

Background

While diffusion MRI derived free‐water (FW) metrics have shown promise in predicting cognitive impairment and decline in aging and Alzheimer’s disease (AD), it is unclear whether hippocampal FW metrics would be superior to traditional structural neuroimaging biomarkers (i.e., hippocampal volume, AD Schwarz Signature, AD McEvoy Signature). We examined the relationships between hippocampal FW metrics and traditional neuroimaging biomarkers with longitudinal memory and executive function.

Method

Vanderbilt Memory & Aging Project participants [n = 298, age at baseline: 73 ± 7 years, 40% mild cognitive impairment (MCI)] underwent longitudinal neuropsychological assessment and brain MRI over a 7 year follow‐up (mean: 3 ± 2 years) period. Structural MRI was used to quantify hippocampal volume in addition to Schwarz and McEvoy signatures. Diffusion MRI in conjunction with the FW elimination technique was used to quantify hippocampal FW and FW‐corrected fractional anisotropy (FAFWcorr). Linear regression was used to determine the association of each biomarker with baseline memory and executive function. A subsequent competitive model analysis was conducted to determine how much additional variance was provided by each biomarker compared to the covariate only model, which included age, sex, race/ethnicity, apolipoprotein‐e4 status, cognitive status, and modified Framingham Stroke Risk Profile scores. Additionally, linear mixed‐effects regression was used to determine the association between baseline biomarker values with longitudinal memory and executive function scores. All analyses were corrected for multiple comparisons using the FDR procedure.

Result

At baseline, all biomarkers (excluding hippocampal FAFWcorr) were significantly associated with cognitive performance (Table 1), with hippocampal FW exhibiting the most significant association for memory (pFDR = 3.92×10‐6) and executive function (pFDR = 5.35×10‐3). Competitive models revealed that hippocampal FW contributed the most unique variance beyond covariates for memory (ΔRadj2 = 3.87%) and executive function (ΔRadj2 = 1.59%). Longitudinal models (Table 2, Figure 1) largely mirrored cross‐sectional models by demonstrating that hippocampal FW was most significantly associated with longitudinal decline in memory (pFDR = 5.04×10‐13) and executive function (pFDR = 6.15×10‐5).

Conclusion

This study shows that hippocampal FW is a sensitive biomarker for cognitive impairment and decline, and provides strong evidence for further exploration of this measure in aging and AD.
White Matter Correlates of Speech in Cerebrovascular Disease
Authors: Broberg Dana N , Haddad Seyyed MH , Aveni Katharine , Havens Alexander , Orange Joseph B , McLaughlin Paula , Binns Malcolm , Roberts Angela C , Bartha Robert
Published: December 2023

Background

Analyses of spoken discourse (speech beyond single words or sentences) can detect cognitive impairment in cerebrovascular disease (CVD) [Roberts, et al. (2021). Top Lang Disord 41(1):73‐98]. However, the neurological basis for altered spoken discourse in CVD is poorly defined. This study examined the association between spoken discourse and visible white matter damage detected using structural magnetic resonance imaging (MRI) and potential early‐stage damage in normal‐appearing white matter (NAWM) detected using diffusion tensor imaging (DTI) to better define the link between CVD‐related neurodegeneration and altered speech.

Method

Spoken discourse and 3T MRI data were obtained from the CVD cohort of the Ontario Neurodegenerative Disease Research Initiative (n = 127). Spoken discourse analyses were completed previously [Roberts et al., 2021]. White matter hyperintensities (WMH) and the NAWM portions of the inferior longitudinal fasciculus (ILF), superior longitudinal fasciculus – parietal (SLFp) and temporal (SLFt) endings, and uncinate fasciculus (UNC) were segmented in each hemisphere (Figure 1). Mean values of fractional anisotropy (FA) and mean diffusivity (MD) within NAWM, and normalized WMH volume were calculated for each of the white matter tracts. Three canonical correlation analyses examined associations between 10 spoken discourse measures and the imaging metrics (WMH volume, FA, MD) in each tract.

Result

Canonical correlations were significant for FA (rc = 0.51, p<0.05) and MD (rc = 0.56, p<0.05) but not WMH volume (rc = 0.42, p = 0.69). Among the white matter tracts, the strongest canonical loadings were seen for FA and MD in the left SLFp (FA: 0.80; MD: ‐0.61) and left SLFt (FA: 0.60; MD: ‐0.40) (Figure 2). Higher FA and lower MD in the left SLFp and left SLFt were associated with fluency, information content, and syntax performance.

Conclusion

Spoken discourse performance was associated with microstructural integrity of NAWM but not visible white matter damage. These results suggest that DTI metrics provide a more relevant assessment of white matter tract function in relation to speech than a volumetric measure of tract lesion load. Of the white matter tracts investigated in this study, spoken discourse performance in CVD was most strongly linked to white matter microstructure in the parietal and temporal endings of the left superior longitudinal fasciculus.
Increased CSF GAP‐43 is associated with accelerated tau accumulation and spread in Alzheimer’s disease
Authors: Franzmeier Nicolai , Dehsarvi Amir , Steward Anna , Biel Davina , Dewenter Anna , Roemer Sebastian Niclas , Wanger Fabian , Brendel Matthias , Ewers Michael , Moscoso Alexis , Blennow Kaj , Zetterberg Henrik , Schöll Michael
Published: December 2023

Background

Animal and in vitro studies of Alzheimer’s disease (AD) found that tau spreads trans‐synaptically in an activity‐dependent manner, suggesting that synapses are key routes for tau spread. Importantly, amyloid‐beta (Ab) induces synaptic remodeling and aberrant synaptic activity, which may accelerate trans‐synaptic tau spread. In AD patients, we found previously that PET‐assessed tau spreads from circumscribed epicenters across functionally connected brain regions. Yet, it remains unclear whether connectivity‐associated spread of tau in AD is accelerated by Ab‐related synaptic changes. The presynaptic growth‐associated protein 43 (GAP‐43) is implicated in synaptic plasticity and is consistently increased in cerebrospinal fluid (CSF) in AD, suggesting that GAP‐43 captures deviations in synaptic integrity such as aberrant synaptic activity. We therefore tested whether higher GAP‐43 is associated with faster Ab‐related tau spread.

Method

We included longitudinal [18F]Flortaucipir tau‐PET (follow‐up∼3.21±1.48yrs), baseline [18F]Florbetapir amyloid‐PET and CSF GAP‐43 from 93 subjects (i.e., cognitively normal [CN] controls Ab‐, n = 54; CN/Mild Cognitive Impairment Ab+, n = 33/21). To model connectivity‐associated tau spread, we determined a connectivity template across 200 cortical regions (i.e., Schaefer atlas) using 3T multi‐band resting‐state fMRI in 42 age‐ and sex‐matched Ab‐/tau‐ controls. All statistical models were controlled for age, sex and diagnosis.

Result

Using linear regression, we found that higher CSF GAP‐43 levels were associated with faster Ab‐related tau‐PET increase in pre‐defined ROIs (centiloid x GAP‐43 interaction, global/temporal‐meta‐ROI: b = 0.0019/0.0028; p = 0.009/0.007, Fig.1) as well as in the personalized Q1 ROI (b = 0.0024; p = 0.004) that summarizes regions most closely connected to subject‐specific tau epicenters with highest baseline tau‐PET (Fig.2A). Importantly, this centiloid x GAP‐43 interaction decreased across regions less strongly connected to tau epicenters (Q2/Q3/Q4: b = 0.0017/0.0008/0.0002; p = 0.009/0.080/0.344, Fig.2B‐D, 3A). Further, we quantified subject‐specific connectivity‐associated tau spread (i.e., the regression‐derived association between epicenter connectivity and tau‐PET increase in remaining brain regions). Here, higher CSF GAP‐43 was associated with a stronger association between Ab and connectivity‐associated tau spread (centiloid x GAP‐43 interaction, b = ‐0.0031; p = 0.037, Fig.3B).

Conclusion

Higher CSF GAP‐43 is associated with faster Ab‐related tau spread across interconnected brain regions in AD, suggesting that AD‐related synaptic abnormalities are associated with accelerated tau spread rendering synaptic changes a potential treatment target to attenuate tau spreading.