bluuebunny/crossref_metadata_2025 huggingface dataset:
The impact of subjective cognitive decline on aging effects in the brain as measured by diffusion tensor imaging
Authors: Flaherty Ryn , Sui Yu Veronica , Masurkar Arjun V. , Rusinek Henry , Lazar Mariana
Published: December 2023

Background

Diffusion tensor imaging (DTI) can separate subjective cognitive decline (SCD) participants who progress to mild cognitive impairment and those who do not 1. We explored the effect age and memory performance has on DTI measures in SCD and controls in the hippocampal cingulum bundle, which is typically affected early in the course of Alzheimer’s disease2.

Method

325 participants Cam‐CAN 3 (127 SCD) older than 55 were included in the analysis. Diffusion imaging data was collected on a 3T Siemens TIM Trio scanner with a 32‐channel head coil for 0, 1000 and 2000 s/mm2 b‐values and 30 gradient directions. Preprocessing and calculation of the DTI metrics fractional anisotropy (FA, mean diffusivity (MD), radial diffusivity (RD) and axial diffusivity (AxD) were completed using MRTrix3, FSL and MATLAB. The hippocampal cingulum bundle regions of interest (ROIs) were extracted from the ICBM‐DTI‐81 atlas. Memory was assessed using the delayed story recall (DSR) task from WMS‐III. Linear regression models were completed in R. Gender was included as a nuisance variable.

Result

There were no differences in gender or age distribution between cohorts, however participants with SCD had a 14% lower DSR score (Table 1). There was a significant interaction effect between age and cohort in MD, AxD, and RD (Figure 1), as well as between memory and age in AxD (Figure 2). In post‐hoc analyses, compared to controls, the SCD group showed a stronger correlation with age in MD, AxD, and RD (Figure 1) while participants with worse memory showed a stronger correlation with age in MD (Figure 2), indicating an accelerated aging effect.

Conclusion

Participants with SCD and worse memory present with an accelerated aging effect, showing steeper variations in diffusion microstructure with age. Since SCD is likely to be a preclinical stage of Alzheimer’s disease, future work will analyze this effect with more advanced diffusion microstructure models to further elucidate the underlying pathophysiology. • Selnes P, Aarsland D, Bjornerud A, et al. J Alzheimers Dis. 2013;33(3):723‐36. • Jack Jr CR, Knopman DS, Jagust WJ, et al. The Lancet Neurology. 2013;12(2):207‐16. • Shafto MA, Tyler LK, Dixon M, et al. BMC neurology. 2014;14(1):1‐25.
Sequencing cortical microstructural changes along the Alzheimer’s disease continuum
Authors: Nir Talia M , Javid Shayan , Villalon‐Reina Julio E , Thomopoulos Sophia I , Salminen Lauren , Thompson Paul M , Jahanshad Neda
Published: December 2023

Background

Microstructural abnormalities likely precede macrostructural changes in the Alzheimer’s disease (AD) cascade. Diffusion MRI (dMRI) is sensitive to microstructural properties of brain tissue but few studies have evaluated dMRI measures in cortical gray matter where many early AD histopathological changes occur. Event‐based modeling (EBM) is a data‐driven approach for probabilistically sequencing cross‐sectional biomarkers in the order that they likely become abnormal. Here, we used EBM to examine the sequence of changes in cortical dMRI measures relative to more widely used amyloid, tau, brain volume, and cognitive biomarkers in two independent AD studies.

Method

T1w, dMRI, and amyloid‐PET (FBB/FBP) data were analyzed in 461 ADNI3 participants and 188 OASIS3 participants (Figure 1A). Some ADNI3 participants also had tau‐PET (AV‐1451) and CSF pTAU‐181 and Aß1‐42 data. In addition to DTI, novel single‐shell adaptations of multi‐shell models, NODDI‐DTI and MAP‐AMURA, were fit to the dMRI data. 10 mean cortical dMRI measures were extracted using FreeSurfer parcellated T1w‐images (Figure 1B). T1w cortical and hippocampal volumes were also extracted, and all MRI measures harmonized using ComBat. Mean cortical amyloid‐PET SUVRs were converted to centiloids. Associations between each of 17 biomarkers (Figure 1C) and cognitive impairment (CI –MCI+Dementia– vs CN) were tested in ADNI3. Significant biomarkers were included in an ADNI3 EBM to determine event ordering.The EBM was validated in OASIS3 and used to classify clinical diagnosis.

Result

All biomarkers except DTI FA showed significant differences between CN and CI ADNI3 participants (Figure 2A). ADNI3 EBM confirmed existing AD models: CSF amyloid abnormalities preceded PET, followed by CSF pTau, while cognitive and volumetric abnormalities occurred at later stages. Changes in all dMRI biomarkers preceded CSF or PET Tau, cognitive, and volumetric abnormalities (Figure 2B). Estimated disease stages in ADNI3 distinguish dementia from CN with an AUC = 0.96 (10‐fold cross‐validation). OASIS3 disease stages, estimated using ADNI3 biomarker distributions excluding CSF measures and tau‐PET, classified diagnosis with an AUC = 0.85 (Figure 3).

Conclusion

Cortical microstructural measure abnormalities, including greater diffusivity, lower restriction and lower dispersion potentially due to neurite loss and lower dendritic arborization complexity, may precede those detectable with conventional T1w biomarkers.
Microglial activation interacts with amyloid‐β to drive longitudinal tau tangle accumulation and cognitive decline
Authors: Povala Guilherme , Bellaver Bruna , Lussier Firoza Z , Ferreira Pamela C.L. , Ferrari‐Souza João Pedro , Leffa Douglas Teixeira , Aguzzoli Cristiano Schaffer , Zalzale Hussein , Soares Carolina , Abbas Sarah , Rohden Francieli , Tissot Cécile , Therriault Joseph , Bezgin Gleb , Servaes Stijn , Benedet Andrea Lessa , Stevenson Jenna , Rahmouni Nesrine , Cohen Annie , Lopez Oscar L. , Tudorascu Dana , Klunk William E , Villemagne Victor L , Karikari Thomas K , Zimmer Eduardo R , Rosa‐Neto Pedro , Pascoal Tharick Ali
Published: December 2023

Background

Activation of microglial cells in the brain, more commonly known as neuroinflammation, has often been linked to the pathophysiology of Alzheimer’s disease (AD). However, how microglial activation is associated with longitudinal tau tangle accumulation and consequent cognitive decline is poorly understood. Here, we aimed to investigate whether baseline microglial activation impacts tau tangle deposition and cognitive decline in individuals across the AD continuum.

Method

We assessed 92 individuals from the TRIAD cohort (57 cognitively unimpaired and 35 cognitively impaired) with available baseline [11C]PBR28‐PET, a measure of microglial activation and [18F]NAV4694 Aß‐PET, and longitudinal [18F]MK6240 Tau‐PET (mean follow‐up time = 1.93 years) and Mini‐Mental State Exam (MMSE) (mean follow‐up time = 1.84 years). We performed voxel‐wise associations using linear regressions accounting for age and sex and adjusted for multiple comparisons using Random Field Theory (RFT) (p < 0.05). We used the cuneus and superior temporal cortex as a composite ROI for [11C]PBR28‐PET and Aß‐PET since these regions showed a higher association with longitudinal tau accumulation in the temporal meta‐ROI.

Result

Voxel‐wise analysis showed that baseline levels of [11C]PBR28‐PET alone are not sufficient to predict longitudinal tau tangle accumulation (Fig. 1a). However, the interaction between [11C]PBR28‐PET levels and Aß burden predicted an increased accumulation of tau tangle, mainly in the cuneus, inferior frontal and lateral occipital regions (Fig. 1b).Individuals with higher baseline [11C]PBR28‐PET and Aß‐PET levels present higher rates of longitudinal tau accumulation in the temporal meta‐ROI (ß = 0.36, t = 3.46, p = 0.0009; Fig. 1c). Similarly, while [11C]PBR28‐PET levels alone did not correlate with longitudinal changes in MMSE score (ß = ‐0.17, t = ‐1.69, p = 0.10), a significant interaction between [11C]PBR28‐PET and Aß‐PET levels on MMSE annual decline was observed (ß = ‐0.24, t = ‐2.25, p = 0.028; Fig. 1d).

Conclusion

We found that baseline levels of microglial activation was associated with longitudinal tau tangle accumulation and cognitive decline in individuals across the AD continuum in the presence of Aß burden. Our results indicate that microglial activation might act potentiating the deleterious effects of Aß on forthcoming tau tangle deposition.
Comparison of Event‐Related Potential Markers in Amyloid Positive vs. Amyloid Negative Patients with Mild Cognitive Impairment (MCI) and Dementia in a general neurological clinic
Authors: Levy Igor
Published: December 2023

Background

We used an automated Event Related Potentials (ERP) technique to compare ERP responses in amyloid positive and negative group of 75 patients with Mild Cognitive Impairment (MCI) and Dementia in a general neurological outpatient clinic. Over the years, research labs have repeatedly reported abnormal ERP markers in patients with MCI and dementia. Recent advances in technology have made ERP testing safe, easy to use and low cost, thus rendering it suitable for clinical use. Indeed, ERPs have become more routinely used in clinical assessment of neurological patients with cognitive changes. The possibility that ERP could help identify amyloid status in MCI and dementia patients adds to the potential clinical utility of these biomarkers.

Method

Patients with a history of cognitive complains underwent an auditory oddball ERP paradigm in conjunction with routine neurological evaluation and imaging studies. Standard neuropsychological testing were available in most cases. Amyloid PET imaging was done as part of the IDEAS (Imaging Dementia ‐Evidence for Amyloid Scanning) study and provided the readings for amyloid load. ERP responses were analyzed in the context of beta amyloid readings, and in relation to the patients’ degree of cognitive deficits.

Result

Final data analysis will be presented at the time of the conference

Conclusion

The possibility that ERP could help identify amyloid status in MCI and dementia patients adds to the potential clinical utility of these biomarkers. possibility that ERP could help identify amyloid status in MCI and dementia patients adds to the potential clinical utility of these biomarkers.
Functional connectivity trajectories in genetic frontotemporal dementia
Authors: Zhang Liwen , Flagan Taru M. , Staffaroni Adam M. , Häkkinen Suvi , Brown Jesse A. , Mandelli Maria Luisa , Rosen Howard J. , Kantarci Kejal , Ramos Eliana Marisa , Tempini Maria Luisa Gorno , Miller Bruce L , Seeley William W. , Lee Suzee E.
Published: December 2023

Background

Characterizing disease trajectories in frontotemporal lobar degeneration (FTLD) is essential to determine the optimal timing for initiating therapeutic interventions, and there is an urgent need for novel and sensitive disease biomarkers. Intrinsic connectivity network (ICN) mapping using task‐free fMRI is a promising biomarker which reveals early abnormalities even before symptomatic onset (Dopper et al. 2014; Lee et al. 2016). Yet, ICN trajectories from the presymptomatic to symptomatic stages in FTLD mutation carriers remain unclear.

Method

In this cross‐sectional study, we used UCSF and ALLFTD Consortia data to study 129 carriers with variants in MAPT (presymptomatic/prodromal/symptomatic: 34/4/7), C9orf72 (30/9/12) and GRN (22/4/7). Using cross‐sectional data to construct ICN trajectories by genotype, we built separate generalized additive models for each genetic group to estimate non‐linear relationships between standardized connectivity changes and disease age across clinical stages. ICN connectivity was based on seed‐based analyses for networks associated with common clinical syndromes for each genetic group (i.e., salience and default mode [MAPT/C9orf72/GRN], corticobasal syndrome [MAPT/GRN], progressive supranuclear palsy syndrome [MAPT], sensorimotor and medial pulvinar‐related [C9orf72], and non‐fluent variant primary progressive aphasia (nfvPPA) syndrome networks [GRN]). We included 127 healthy controls as a reference to calculate ICN z‐scores for each carrier. Each carrier’s disease age was defined as the difference between the carrier’s chronological age and estimated age of onset based on multimodal measures of structural imaging, fluid biomarker and clinical assessments (Staffaroni et al. 2022).

Result

In MAPT+, presymptomatic carriers showed declining salience network connectivity starting approximately 30 years before estimated onset, which plateaued about 20 years before onset. In C9orf72+, salience network hyperconnectivity emerged about 30 years before onset and declined throughout the presymptomatic and symptomatic stages. For GRN+, connectivity changes as a function of disease age were found within the salience and nfvPPA networks, with connectivity stable until around 10 years before estimated onset, followed by decline starting in the late presymptomatic phase and continuing through the prodromal and symptomatic stages.

Conclusion

These findings demonstrate that FTLD genes show distinct ICN trajectories throughout the clinical spectrum. These results help pave the way for determining the optimal timing to initiate therapeutic interventions.
Amyloidosis at Putamen Predicts Vulnerability to Alzheimer’s Disease
Authors: Yang Zhengshi , Cummings Jeffrey L. , Cordes Dietmar
Published: December 2023

Background

Using merely cortical amyloid burden in a binary approach was commonly adopted for determining if an individual is under AD pathology in both clinical trials and basic research. Subcortical amyloid, compared to cortical amyloid, showed a larger inter‐subject heterogeneity, which may provide valuable insights to their vulnerability to Alzheimer’s disease beyond cortical amyloid.

Method

A total of 1344 individuals from ADNI database had 18F‐AV45 amyloid PET scans available. Regional AV45 signal was computed by averaging the signal from gray matter voxels in each region and then normalized to the mean signal in the composite reference region [1, 2]. The cortical AV45 was averaged over extensive regions consisting of frontal, anterior/posterior cingulate, lateral parietal, and lateral temporal regions. The AV45 signal in the subcortex, including putamen and caudate, were also computed. Only the participants under AD pathology, determined based on cortical amyloid positivity, were included in the analysis. The association analysis between subcortical AV45 and cognition was conducted before and after adjusting cortical AV45. A linear fitting of cortical AV45 and AV45 at putamen was conducted and participants were categorized into high (HighP), middle (MidP) and low (LowP) putamen amyloid group thresholded with ±0.5 standardized residuals. Statistical analysis was carried out to evaluate the difference of clinical diagnosis, hippocampal volume and APOE e4 genotype. The relevance of amyloid at putamen with the rate of cognitive decline was examined.

Result

Putamen, compared to caudate, had a stronger association with ADAS‐cog13 and MoCA, such an association remained exist after adjusting cortical AV45. Despite distinct AV45 at putamen, participants in LowP, MidP and HighP groups had no cortical AV45 difference. The AV45 at putamen was significantly higher in individuals with more e4 alleles (e4 homozygous > e4 heterozygous > non‐ e4) and more advance clinical stages (Dementia > MCI > CN). The individuals with higher AV45 at putamen had smaller hippocampal volume and faster cognitive decline.

Conclusion

Our finding suggested that amyloidosis at putamen is a valuable imaging marker beyond cortical amyloid in predicting if a participant is more vulnerable to Alzheimer’s dementia.
The role of biofluid markers in predicting near‐term cognitive impairment
Authors: Yakoub Yara , Ashton Nicholas J. , Karikari Thomas K , Montoliu‐Gaya Laia , Schöll Michael , Spreng R. Nathan , Rosa‐Neto Pedro , Soucy Jean‐Paul , Breitner John C.S. , Zetterberg Henrik , Blennow Kaj , Poirier Judes , Villeneuve Sylvia
Published: December 2023

Background

Alzheimer’s disease (AD) has been defined as a biological construct that can be determined using in vivo biomarkers. Recent studies confirmed the clinical value of the AT(N) framework when using Aß and tau positron emission tomography (PET) scans in predicting progression from cognitively unimpaired to MCI. Our study aims to investigate if plasma biomarkers can yield similar predictive performance to PET biomarkers, and to also compare against cerebrospinal fluid (CSF) biomarkers

Method

Individuals from the PREVENT‐AD cohort were included in the study. Participants had Aß and tau plasma measurements and at least one year of clinical follow‐up thereafter (n = 278). A subset of participants had CSF measurements (n = 102) and/or PET scans (n = 133) also with 1‐year clinical follow‐up. Participants were cognitively unimpaired at the time of all biomarker measurements. MCI diagnoses were made by clinical consensus among expert clinical and research staff blind to plasma, CSF, PET, MRI and APOE genotype. We established thresholds of plasma and CSF Aß and tau positivity based on >80% specificity in identifying individuals with significant levels of Aß‐PET (plasma Aß42/40 = 0.057; plasma pTau181 = 7.53 pg/mL; CSF Aß42 = 961.37; CSF pTau181 = 66.59 pg/mL). Aß ([18F]NAV4694) and tau ([18F]AV1451)PET thresholds were defined by the mean plus 2 standard deviations from young participants (Aß SUVR cut‐off = 1.17; tau SUVR cut‐off = 1.24).

Result

36.8% (7/19) of the A+T+ plasma group progressed to MCI, 50% (4/8) of the A+T+ CSF group progressed to MCI, and 46.6% (7/15) A+T+ PET group progressed to MCI (Fig. 1).We found differences in MCI progression status between the A+T+ group compared to the A‐T‐ and A+T‐ groups across all biomarkers’ measurements, with the A+T+ group always showing a higher rate of progression than the other two groups (post‐hoc chi‐square analyses; p<0.05).

Conclusion

Abnormal levels of Aß and tau plasma biomarkers can help predict near‐ term progression to MCI. When plasma thresholds are derived based on PET data, however, plasma biomarkers have a slightly lower specificity than PET and CSF biomarkers, and both plasma and CSF biomarkers are less sensitive than PET biomarkers.
The cortical glucose metabolism signatures of cognitive resilience in amyloid positive cognitively unimpaired elderly adults
Authors: Liu Lin , Sun Pan , Lan Guoyu , Cai Yue , Li Anqi , Zhu Yalin , Ma Shaohua , Guo Tengfei
Published: December 2023

Background

ß‐amyloid (Aß) positive (A+) cognitively unimpaired (CU) individuals who maintain normal cognitive function at follow‐up may have good cognitive resilience (CR). A+/CU Individuals with high CR may have hypermetabolism. In this study, we explored the cortical glucose metabolism features of CR in A+/CU elderly adults.

Method

We analyzed 855 ADNI participants with Aß PET (18F‐florbetapir (FBP) or 18F‐florbetaben (FBB)), CSF p‐Tau181, 18F‐fluorodeoxyglucose (FDG) PET and longitudinal residual hippocampal volume (rHCV) and cognitive assessments. A+/T+/N+ were defined as AD summary cortical standardized uptake value ratio (SUVR) for FBP = 1.11 or FBB = 1.08, CSF p‐Tau = 23pg/ml, and rHCV = ‐0.67 respectively. We compared intensity‐ and spatial‐normalized FDG SUVR images between 96 A‐/T‐/N‐/CU individuals (Reference group) and 68 A+/CU individuals who maintained CU at follow‐up regardless of T+ or N+ (CR group). Cortical regions with higher FDG SUVR in the CR group were identified as the CR_metaROI. Baseline CR_metaROI FDG‐SUVRs were compared between different stages of AD. Finally, we determined whether CR_metaROI FDG‐SUVR can modulate the association of Aß PET and CSF p‐Tau with longitudinal changes of cognition, controlling for age, sex, and education.

Result

CR_metaROI was primarily located in the precentral and postcentral regions (Fig. 1). AD dementia patients had lower CR_metaROI FDG SUVRs than the CU (standardized ß (ßstd) = ‐0.326[95% confidence interval (ci): ‐0.534, ‐0.119], p = 0.002) and MCI (ßstd = ‐0.303[95%ci: ‐0.496, ‐0.111], p = 0.002) groups (Fig. 2A), and A+/CU group had higher (ßstd = 0.605[95%ci: 0.352, 0.859], p<0.001) CR_metaROI FDG SUVRs than the A‐/CU group (Fig. 2B). A‐/T+ and A+/T‐ individuals had lower (ßstd = ‐0.248[95%ci: ‐0.4474, ‐0.022], p = 0.031) and higher (ßstd = 0.305[95%ci: 0.093, 0.516], p = 0.005) CR_metaROI FDG SUVRs than the A‐/T‐ group respectively (Fig. 2C). The A+/T‐/N+ group had higher (ßstd = 0.466[95%ci: 0.153, 0.779], p = 0.004) CR_metaROI FDG SUVRs than the A‐/T‐/N‐group (Fig. 2D). Moreover, higher baseline metaROI FDG‐SUVRs were associated with slower rates of decline in memory, executive function and preclinical Alzheimer cognitive composite scores upon the same levels of Aß PET (Fig. 3A‐C) and CSF p‐Tau (Fig. 3D‐F).

Conclusion

We identified a metabolism signature of CR in A+/CU elderly adults. These findings may provide novel insights into understanding the hypermetabolism patterns of CR in aging people.
Middle Age Prevention of Future Alzheimer’s Disease and Related Dementias with Intensive Treatment of Hypertension: Middle PATH Clinical Trial Proposal
Authors: Pruzin Jeremy J.
Published: December 2023

Strong epidemiological evidence from multiple studies suggests that elevated systolic blood pressure (SBP) in midlife is a risk factor for Alzheimer’s disease and Related Dementias (ADRD) later in life. The most common neuropathology of biomarker‐confirmed AD is combined AD neuropathological changes and cerebrovascular disease. Hypertension is a leading cause of cerebrovascular disease. The SPRINT MIND trial demonstrated that a goal SBP<120 mm Hg reduced risk for combined mild cognitive impairment (MCI) and dementia by 15% in older individuals compared to a goal SBP<140 mm Hg. SBP can be safely treated with inexpensive medications. Despite being potentially safe and cost‐effective, with strong supporting evidence for benefit, intensive SBP lowering in midlife to reduce future ADRD risk has never been studied, likely because clinical outcomes of MCI and dementia are not practical in this age range in a trial that is usually limited to about 5 years. Elevated SBP is a major risk factor for white matter hyperintensity volume (WMHV) accumulation, an MRI marker with extensive evidence demonstrating this to be associated with and predictive of incident ADRD, making WMHV a suitable surrogate outcome for the initial trial period. While we acknowledge the challenges of using a surrogate outcome, there is no suitable alternative when testing interventions to lower future ADRD risk starting in middle age, a strategy that we believe is essential. The Alzheimer’s Prevention Initiative and Wake Forest have formed a collaboration to investigate the effect of goal SBP<120 mm Hg compared to the SBP achieved by enhanced usual care (SBP = 135 mm Hg) on annualized rate of WMHV accumulation in persons ages 45‐65 who have some existing vascular risk at baseline. Secondary outcomes of changes in AD blood biomarkers and relevant cognitive test scores will help elucidate whether and how SBP and AD are interactive or independent deleterious pathophysiological processes. The intention, should the initial phase prove successful, is to continue and expand the trial until a clinical benefit can definitively be assessed.
Thalamic Contributions to Functional Circuitry in Preclinical Alzheimer’s Disease
Authors: Li Joyce S , Tun Samantha M , Lacadie Cheryl , Fredricks Carolyn
Published: December 2023

Background

Thalamic nuclei are heterogeneously impacted by Alzheimer’s disease (AD) pathology. The anterior thalamus shows tau deposition early on in AD, while other nuclei, such as the mediodorsal (MD) thalamus are spared by tau until much later in the disease process (Braak and Braak, 1991). Furthermore, anterior and MD thalamus have dissociable roles in memory performance – the anterior has a greater role in recollective memory, while the MD plays a role in familiarity. Additionally, the anterior thalamus has direct anatomical links to brain regions implicated in poor short‐term memory performance in AD, while MD thalamus connectivity is primarily to regions spared of AD pathology until late. We sought to identify differences in functional connectivity between the anterior and MD thalamus in amyloid negative and positive individuals.

Method

We examined resting‐state functional MRI data from the A4 study (n = 569), including 254 healthy controls and 315 individuals with preclinical AD based on amyloid SUVR>1.14 (Table 1). We assessed functional connectivity of anterior and MD thalamus to the whole brain using 2mm radius spherical seeds at the center of mass of Morel atlas nuclei. All results were covaried for age, sex, handedness, and years of education.

Result

Anterior thalamic activity was positively correlated with parahippocampal gyrus, fusiform, and insula, and negatively with the striatum and thalamus. MD thalamic activity was positively correlated with the amygdala, ventral striatum, and anterior cingulate, and negatively correlated to the superior temporal gyrus and regions of the parieto‐occipital lobe. In preclinical AD, compared with HCs, the positive correlations in either thalamic nuclei were lost.

Conclusion

The thalamic functional connectivity observed agrees with preexisting literature dissociating structures related to the anterior and MD thalamus. Our results provide a basis for further exploration of thalamic nuclei to AD‐involved functional networks, as well as future analysis of the observed connectivity measures to cognition.
Blood transcriptomics differentiate tau positive discordant and concordant individuals
Authors: Souza Lara Angi , Schlickmann Thomas , Machado Luiza Santos , Pascoal Tharick A. , De Bastiani Marco Antônio , Zimmer Eduardo R
Published: December 2023

Background

Levels of soluble hyperphosphorylated tau (p‐tau) in the cerebrospinal fluid (CSF) and tau tangles measured by positron emission tomography (PET) are used to determine tau positivity in Alzheimer’s disease (AD). However, a mismatch between CSF and PET positivity occurs in some cases. In fact, it is thought that CSF tau biomarker abnormalities may precede changes in tau‐PET imaging. With this in mind, the aim of our study was to examine the blood transcriptomic phenotypes in individuals presenting tau positivity biomarker discordance.

Method

Blood transcriptomics data from 64 individuals (cognitively unimpaired = 31 and cognitively impaired = 33) were obtained from the ADNI database. We divided these individuals into negative (CSF‐PET‐), discordant (CSF‐PET+ and CSF+PET‐) and positive group (CSF+PET+) to tau biomarkers. We also performed differential expression analyses (DEA) using the limma package. Additionally, we conducted a KEGG pathway enrichment analysis using altered genes obtained from DEA (p‐value < 0.05). Amyloid B42, total tau, Mini Mental State Examination, Clinical Dementia Rating Sum of Boxes, APOE4 status, education, and gender difference in the groups were described in Fig1. Data were analyzed with Pearson’s Chi‐squared test and Linear Model ANOVA. All statistical analyses were performed in R.

Result

Our analysis revealed that discordant and positive individuals had, respectively, 1191 and 314 DEGs if compared to negative individuals. Interestingly, we found 324 DEGs in common between them (Fig2A‐C). In the KEGG functional enrichment analysis (Fig3), we observed that the main terms were enriched in the tau discordant individuals were related to aminoacids metabolism and inflammation (Fig3A). In the CSF+/PET+ individuals pathways related to calcium signaling and viral response were the most altered compared to CSF‐/PET‐ (Fig3B).

Conclusion

Our results indicate that blood transcriptome differs among tau negative, discordant and positive individuals. This suggests that multiple biological pathways in the blood are dependent on tau changes. Further investigation of DEGs clusters possibly could demonstrate new insights about tau mechanisms involved in the disease.
Antemortem MRI detects atrophy related to hippocampal sclerosis pathology in the oldest‐old but side matters: The 90+ Study
Authors: Woodworth Davis C. , Li Janice X. , Faramarzi Sina , Greenia Dana E. , Bukhari Syed A. , Montine Thomas J. , Kawas Claudia H. , Corrada María M. M. , Sajjadi S. Ahmad
Published: December 2023

Background

Hippocampal sclerosis of aging (HS) is the neuropathological finding of neuronal death and gliosis in the hippocampus. HS is more prevalent, and associated with dementia, in the oldest‐old (those = 90 years), but cannot be diagnosed during life. HS is often unilateral and can be missed if only one brain hemisphere is assessed, which is standard neuropathological practice but may hamper the development of biomarkers for HS. Here we examined hippocampal volumes from antemortem MRI in relation to bilaterally‐assessed HS at autopsy, accounting for side, in oldest‐old participants from The 90+ Study.

Method

We included participants with both autopsy and antemortem MRI data. All participants had neuropathological assessment of HS for both hemispheres. We performed hippocampal segmentation of antemortem 3D‐T1w MRIs using FreeSurfer, adjusting by intracranial volume. We compared total average (average of left and right), left, and right, hippocampal volumes between those with and those without HS. For HS participants we combined the hippocampal volumes from each hemisphere with HS into a single group and from each hemisphere without HS into a separate group, and compared these to the average hippocampal volumes from those without HS. We used Welch’s T‐tests for comparisons and report Cohen’s d effect sizes.

Result

Participant characteristics (N = 89) are shown in Table‐1. All HS was unilateral (Right = 6, Left = 4). Average total hippocampal volume was not significantly different between those with and those without HS (d = 0.51, P = 0.11, Figure‐1a). However, right hippocampal volumes were significantly related to right‐sided HS with a large effect size (P = 0.002, d = 1.02) and left hippocampal volumes had a large effect size for left‐sided HS (P = 0.17, d = 0.90, Figure‐1c), whereas this was not the case when HS was on the opposite side (P>0.3, d<0.4, Figure‐1b,c). Combined HS participant hippocampal volumes for the side with HS (d = 0.98, P = 0.003), but not the side without HS (d = ‐0.14, P = 0.7), were different from volumes in participants without HS (Figure‐1d).

Conclusion

Our results suggest HS is detectable by atrophy on antemortem MRI, but most strongly when using hippocampal volumes from the same side as HS. Researchers should consider assessing and documenting the presence of HS in both hemispheres to accelerate development of neuroimaging biomarkers for HS.
Computational progression subtypes of hypometabolism due to Alzheimer’s disease: towards linking antemortem and postmortem diagnoses
Authors: Oxtoby Neil P , Shand Cameron , Bourgeat Pierrick
Published: December 2023

Background

Postmortem diagnosis of Alzheimer’s and related pathologies remains the gold standard. Advanced neuroimaging techniques enable assessment of some pathologies in living subjects, but antemortem signatures are often defined post hoc in postmortem‐defined groups. Here we flip this and estimate data‐driven antemortem signatures of pathology using Subtype and Stage Inference (SuStaIn), then predict postmortem classification. SuStaIn is a machine learning algorithm that jointly estimates subtype clusters of data‐driven disease progression signatures to uniquely unravel spatiotemporal heterogeneity using cross‐sectional neuroimaging data. We evaluate SuStaIn on FDG‐PET scans to produce the first AD subtypes of hypometabolism and explore links with LATE‐NC.

Method

ADNI data was downloaded 06/2022. Model inputs were baseline FDG‐PET SUVR (Desikan‐Killiany parcellation) extracted using FreeSurfer from the N = 1181 (286/791/104 AD/MCI/Ab+CN) available ADNI participants having both T1w MRI and FDG‐PET at baseline. Biomarkers were converted into w‐scores relative to 203 controls (Ab–CN), adjusted for control trends in age and sex using robust linear regression. Postmortem diagnoses (N = 43) of AD/LATE‐NC/mixed were predicted. Modeling: Hypometabolism subtypes were estimated using SuStaIn. A bespoke “disease signal” approach selected informative biomarkers for w‐score events of interest: 1 (mild), 1.5 (moderate), 3 (severe). Average out‐of‐sample log likelihood (10‐fold cross‐validation) selected the final model (#subtypes, max 5). Statistical Analysis: Subtype group comparisons of cross‐sectional outcomes and postmortem pathology were performed using common statistical tests.

Result

Figure 1 shows the four subtypes of hypometabolism resulting from our analysis: Typical (limbic+temporal, N = 283: 21/190/72 CN/MCI/AD), Limbic Sparing (N = 261: 19/157/85), Aggressive Typical (early high w‐scores; N = 90: 2/40/48), and Lateral Cortical (N = 62: 1/33/28), with N = 485 (41%) not subtyped. Cognition was worse in the Aggressive Typical/Lateral Cortical subtypes than the Typical/Limbic Sparing (MMSE 25 vs 27; ADAS‐Cog13 19 vs 27) Kruskal‐Wallis test p<10‐3. Figure 2 shows subtyping and staging results (59% subtypeable: N = 696), with AD cases distributed evenly within each subtype and MCI at earlier stages, as expected. Figure 3 is a confusion matrix showing that our model imperfectly predicted postmortem LATE‐NC diagnosis.

Conclusion

Data‐driven antemortem spatiotemporal subtypes of hypometabolism in Alzheimer’s disease were imperfectly predictive of postmortem LATE‐NC/AD classification in a small sample. Validation on larger histopathology samples is necessary to make general conclusions.
Longitudinal morphometry of medial temporal lobe subregions in earlyAlzheimer’s disease
Authors: Baumeister Hannah , Gellersen Helena M , Kleineidam Luca , Wolfsgruber Steffen , Bürger Katharina , Laske Christoph , Perneczky Robert , Peters Oliver , Priller Josef , Schneider Anja , Spottke Annika , Teipel Stefan , Wiltfang Jens , Heneka Michael T. , Düzel Emrah , Jessen Frank , Berron David
Published: December 2023

Background

Structural MRI is frequently used to track Alzheimer’s disease (AD) progression in clinic, research, and pharmacological trials. It has been shown that established MRI‐derived measures lack diagnostic sensitivity particularly in early stages of AD. Here, we investigate if the utility of structural MRI for tracking early AD progression can be increased when focusing on subregions of the medial temporal lobe (MTL) which are known to exhibit earliest AD‐related neuropathological change.

Method

We used data from N = 143 participants enrolled in the DZNE Longitudinal Cognitive Impairment and Dementia Study (DELCODE). This included cognitively unimpaired participants who were either Aß‐negative (CU Aß‐; n = 67) or Aß‐positive (CU Aß+; n = 20), Aß+ participants with subjective cognitive decline (SCD Aß+; n = 38), and Aß+ participants with MCI (MCI Aß+; n = 18). Participants underwent annual assessments up to three years after baseline, including MRI (2.19 ± 0.86 follow‐ups) and a neuropsychological testing (2.18 ± 0.83 follow‐ups). Segmentations of MTL subregions were generated using a longiudinal, template‐based implementation of the Automated Segmentation of Hippocampal Subfields (ASHS; Yushkevich et al., 2015, Hum Brain Mapp.) algorithm (Figure 1). Linear mixed models were fitted to derive volumetric slopes and to test their relationship with longitudinal changes in Preclinical Alzheimer Cognitive Composite (PACC‐5) scores.

Result

We observed significant volume losses across diagnostic groups with the MCI Aß+ group exhibiting the fastest rates. Compared with the CU Aß‐ group, there were accelerated volume losses in the subiculum and dentate gyrus in the SCD Aß+ group and, additionally, in the entorhinal cortex and Brodmann areas 35 and 36 in the MCI Aß+ group. There was also a trend towards faster volume losses in the entorhinal cortex in the CU Aß+ group. Higher rates of MTL subregional volume losses, particularly in the cornu ammonis 1 subfield, were related to a faster decline on the PACC‐5 in the MCI Aß+ group. This effect remained significant when controlling for baseline volumes.

Conclusion

Our findings highlight the viability of subregional MTL morphometry for tracking earliest neurodegeneration and related cognitive decline due to AD.
Brain and Neural Reserve Mediate Associations Between Lifestyle and Cognition: The Maastricht Study
Authors: De Jong Nathan R. , Jansen Jacobus F.A. , van Boxtel Martin P.J. , Schram Miranda T. , Stehouwer Coen D.A. , van Greevenbroek Marleen M.J. , van der Kallen Carla J.H. , Koster Annemarie , Eussen Simone J.P.M. , de Galan Bastiaan B.E. , Backes Walter H. , Köhler Sebastian
Published: December 2023

Background

Life‐course exposure to risk and protective factors impacts brain macro‐ and micro‐structure, which in turn affects cognition. Brain Age Gap (BAG) directly measures brain reserve by comparing an individual’s neuroimaging‐based predicted age with their calendar age. Higher BAG implies accelerated brain aging and lower brain reserve. In this study, we comprehensively modeled mutual associations between brain risk and protective factors, reserve, and cognition in a large, middle‐aged population using structural equation modelling (SEM).

Method

Cognitive test scores, lifestyle, and 3T MRI data for n = 4881 participants (mean age(±SD) = 59.2(±8.6), 50.1% male) were available from The Maastricht Study, a population‐based cohort study with extensive phenotyping. Whole brain volumes (gray matter (GM), cerebrospinal fluid (CSF), white matter hyperintensity (WMH)), cerebral microbleeds (CMB), and structural white matter connectivity were calculated. Lifestyle factors were combined into the Lifestyle for BRAin health (LIBRA) weighted sum score, with higher score indicating greater dementia risk (Figure 1). Cognition was calculated by averaging z‐scores across three cognitive domains (executive function and attention, memory, information processing speed). BAG was calculated by comparing calendar age to predictions from a neuroimaging‐based multivariable regression model. Paths between LIBRA, BAG and cognitive function were tested using linear regression and SEM, adjusting for confounders. LIBRA was split into tertiles for SEM analysis.

Result

CSF, GM, WMH and CMB best predicted BAG (R2 = 0.455, RMSE = 6.44). In regression analysis higher LIBRA scores (greater dementia risk) were associated with higher BAG (standardized regression coefficient ß = 0.127, p<0.001), lower connectivity (ß = ‐0.071, p = 0.002) and worse cognition (ßlinear = ‐0.044, p = 0.017, ßquadratic = ‐0.047, p = 0.018). Higher BAG was associated with lower connectivity (ß = ‐0.418, p<0.001) and worse cognition (ß = ‐0.165, p<0.001). Lower connectivity was associated with worse cognition (ß = 0.086, p<0.001). In mediation analysis (Figure 2), 21% of the total differences between the highest and lowest LIBRA tertile on cognition was mediated by BAG (ßindirect = ‐0.019, p<0.001; ßtotal = ‐0.092, p<0.001), and an additional 4.1% was mediated via connectivity (ßindirect = ‐0.003, p<0.001; ßtotal = ‐0.073, p<0.001).

Conclusion

Associations between health‐ and lifestyle‐based risk/protective factors (LIBRA) and cognition is partially explained by maintenance of brain reserve (BAG) and neural reserve (connectivity). Lifestyle interventions promoting brain and neural reserve may help maintain cognitive function into older age.
The effect of local versus remote Beta‐Amyloid on functional connectivity mediated Tau‐Spread in Alzheimer’s Disease
Authors: Wanger Fabian , Steward Anna , Biel Davina , Dewenter Anna , Roemer Sebastian Niclas , Ewers Michael , Dehsarvi Amir , Franzmeier Nicolai
Published: December 2023

Background

In Alzheimer’s disease (AD), Amyloid‐beta deposition (Ab) is associated with tau spreading from epicenters to connected regions. Yet, Ab and tau accumulate in strikingly different patterns, and it is unclear how Abdrives tau spreading. We envision two mechanisms, i.e. i) that cortical Ab exerts remote effects and “pulls” tau out of connected epicenters or ii) that tau is pushed out of epicenters, once Ab has reached the tau epicenters (Fig.1A). We tested both mechanisms in AD spectrum patients and determined the predominant mechanism by which Ab drives tau spreading.

Method

We included 313 ADNI participants (n = 110 CN Ab‐, n = 98 CN‐Ab+, n = 70 MCI Ab+, n = 35 Dementia Ab+), with baseline amyloid‐ and longitudinal tau‐PET, based on which we calculated tau‐PET rates of change (ROC) for 200 cortical ROIs (Schaefer‐atlas; Fig.2). Using the same atlas, a resting‐state fMRI‐connectivity template was obtained in 42 healthy controls. For each subject, we determined tau epicenters as 5% of ROIs with highest baseline tau‐PET and assessed epicenter connectivity. Using ROI‐wise regression, we tested whether faster tau‐PET increase was explained by the simultaneous occurrence of i) higher tau‐PET in the epicenter, ii) stronger connectivity to the tau epicenter and iii) higher Ab in the target region (i.e. pull‐effect) or the tau epicenter (i.e. push‐effect). Lastly, we assessed ROI‐wise main effects of local Ab vs. Ab in the tau epicenter on tau‐PET changes, to determine which mechanism predominates.

Result

Higher tau and Ab in the epicenter explained faster tau‐PET increase in connected temporo‐parietal and frontal regions (Ab‐push‐effect, Fig.1B). In contrast, Ab‐pull‐effects were found also for late‐Braak regions (Fig.1C). Congruently, we found that Ab push effects dominated over Ab‐pull effects in temporo‐parietal brain regions, whereas Ab‐pull effects dominated in late‐Braak regions (Fig.1D‐F). Lastly, patients in which Ab and tau converged locally showed fastest subsequent tau accumulation (Fig.3).

Conclusion

Ab exerts local and remote effects on tau spreading, yet local convergence of Ab and tau in the epicenter may predominantly drive tau spreading. Thus, local convergence of Ab and tau is a key event in AD that gives rise to accelerated tau spreading, previously referred to as cataustrophy.
ADPrep ‐ A fully automated Alzheimer’s Disease Neuroimaging Preprocessing Pipeline for MRI and multi‐tracer PET data
Authors: Dehsarvi Amir , Denecke Jannis , Biel Davina , Dewenter Anna , Roemer Sebastian Niclas , Steward Anna , Wanger Fabian , Brendel Matthias , Franzmeier Nicolai
Published: December 2023

Background

State‐of‐the‐art preprocessing of MRI and PET data is crucial for Alzheimer’s disease (AD) neuroimaging research. Therefore, standardization and harmonization of neuroimaging preprocessing across sites is key to generate comparable and sharable datasets and to reduce potential bias introduced by different preprocessing strategies. Further, neuroimaging preprocessing requires strong expertise in different programming languages (R/MATLAB/Python/Bash) and software packages (SPM/FSL/AFNI/FreeSurfer), i.e., a major barrier for non‐expert users. Therefore, we developed a docker‐based, state‐of‐the‐art, fully automated, neuroimaging toolbox that integrates robust preprocessing of structural/functional MRI, and multi‐tracer PET (amyloid/tau/FDG/TSPO), generating easy to use spreadsheet and nifti outputs. The toolbox requires no programming expertise and can facilitate neuroimaging analyses, data harmonization and sharing across the AD neuroimaging community.

Method

The pipeline works on standardized bids‐formatted data and was fully developed in nipype (see Figure 1). Preprocessing for structural MRI includes volumetric and cortical thickness assessments for widely used brain atlases (Desikan‐Killiany/Schaefer/LPBA/Hammers/Neuromorphometrics/Cobra/Destrieux), plus spatially normalized and smoothed grey/white‐matter segments for voxel‐based morphometry and generation of subject‐specific PET‐reference regions. Functional MRI processing includes slice‐timing and motion correction, nuisance regression, spatial normalization, and functional connectivity assessments for the above‐mentioned atlases. PET processing includes generation of spatially normalized SUVR images for different tracer‐specific reference regions, as well as extraction of atlas‐based SUVRs (PVE and non‐PVE‐corrected). Standardized imaging outputs in FreeSurfer space were benchmarked against available ADNI data to illustrate comparability with existing pipelines (see Figure 2). Cluster implementation is provided to ensure large‐scale data processing.

Result

The toolbox was tested successfully on large‐scale multimodal datasets, including several thousand scans from ADNI, ADNI‐DOD, and A4 with an overall processing failure rate of <4%. Using ADNI data as a benchmark, our data closely reproduce global amyloid‐PET (r = 0.99, p<0.001, Fig.2A) and temporal meta tau‐PET SUVRs (r = 0.98, p<0.001, Fig.2B), suggesting that PET‐thresholds can be applied. Runtime is ∼1h for a structural MRI and ∼5hrs for a PET including PVE‐correction.

Conclusion

We propose a user‐friendly multimodal robust neuroimaging preprocessing toolbox, that can be applied to different neuroimaging datasets by non‐expert users. The toolbox provides outputs that can be directly used for statistical analyses. Future work involves implementing other imaging modalities, e.g., diffusion tensor imaging.
GABA transporters gene expression associates with [18F]FDG‐PET signal in regions typically hypometabolic in Alzheimer’s disease
Authors: Carello‐Collar Giovanna , Ferrari‐Souza João Pedro , Machado Luiza Santos , Povala Guilherme , Limberger Christian , Pascoal Tharick A. , Zimmer Eduardo R
Published: December 2023

Background

It is suggested that the [18F]fluorodeoxyglucose(FDG)‐PET signal reflects astrocyte and neuronal activity, with the excitatory glutamate uptake by astrocytes as the trigger for brain glucose metabolism. The inhibitory neurotransmitter γ‐aminobutyric acid (GABA) is taken up by neurons and astrocytes through the GABA transporters (GAT) 1 and 3. However, its role in the [18F]FDG‐PET signal in healthy aging and Alzheimer’s disease (AD) remains poorly investigated. Thus, we aimed to explore associations between neuronal and astrocytic GABA transporters gene expression with [18F]FDG‐PET in cognitively unimpaired (CU) and AD individuals.

Method

We obtained [18F]FDG‐PET imaging data of CU and AD from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (Table 1). GAT1 and GAT3 gene expression in the brain was obtained from the Allen Brain Atlas (Table 1, Figure 1). We extracted SUVr (pons as reference region) and mRNA values using the RMINC package and performed Pearson’s correlation to test the association between [18F]FDG‐PET and GABA transporters mRNA (p‐value < 0.05).

Result

In brain regions typically hypometabolic in AD, we observed a strong positive association of [18F]FDG‐PET SUVr with GAT1 and GAT3 gene expression in CU (GAT1: r = 0.93; p < 0.0001; Figure 2A, GAT3: r = 0.94; p < 0.0001; Figure 2B) and AD individuals (GAT1: r = 0.9; p < 0.0001; Figure 2A, GAT3: r = 0.92; p < 0.0001; Figure 2B). By contrast, in brain regions typically resilient, we found a weak correlation with CU individuals (GAT1: r = 0.32; p = 0.016; Figure 2C, GAT3: r = 0.26; p = 0.04; Figure 2D) and no significant correlation in AD patients (GAT1: r = 0.24; p = 0.07; Figure 2C, GAT3: r = 0.24; p = 0.059; Figure 2D).

Conclusion

Our results demonstrate a significant strong positive association between [18F]FDG‐PET SUVr and GABA transporters gene expression in CU and AD in hypometabolic regions. These findings suggest that GABA transporters are involved with brain glucose metabolism in regions classically vulnerable to AD pathology. ”