bluuebunny/crossref_metadata_2025 huggingface dataset:
Which cortical regions affect the prediction of cerebrospinal fluid amyloid beta from PET images by novel residual neural network (ArcheD)
Authors: Tagmazian Arina , Vuoksimaa Eero , Pitkänen Esa , Schwarz Claudia
Published: December 2023

Background

Amyloid‐ß (Aß) aggregation, as a biological hallmark of Alzheimer’s disease (AD), is a key component of biological classification of AD in the AT(N) framework. This research aimed to develop a new deep learning model for predicting Aß cerebrospinal fluid (CSF) measures from amyloid PET scans and explore which cortical regions are most important contributors in the model’s prediction.

Method

We developed a Residual Network model (ArcheD) to predict Aß CSF from amyloid PET images, and trained it on 1,870 PET scans from the Alzheimer’s Disease Neuroimaging Initiative. We registered PET scans to MNI152 template and used the Neuromorphometrics atlas to derive cortical gray matter (GM) regions. We extracted brain regions’ contributions to model decision‐making by guided backpropagation, and visualized these contributions as relevance heatmaps. We used bootstrapping, Welch’s t‐test and FDR correction in testing significance of brain regions’ relevance to predictions in the whole cohort, and separately in clinical diagnostic classes of AD, mild cognitive impairment (MCI), and cognitively normal (CN) cases.

Result

ArcheD showed high accuracy in predicting Aß CSF (r = 0.81; p<0.00001). According to the relevance analysis for all samples (n = 1,390) temporal (mean 0.106; 95%CI [0.105, 0.108]) and parietal (0.105; 95%CI [0.104, 0.107]) lobes were the most important GM regions for the model decision‐making. Occipital (0.098; 95%CI [0.098, 0.100]) and frontal lobes (0.091; 95%CI [0.089, 0.92]) showed noticeably fewer relevance values. Based on between‐classes comparison, these regions contribute significantly more to CSF prediction in AD and MCI individuals compared to CN (p<0.05). Analysis of 49 smaller regions showed an almost 7‐fold difference between the most and least contributing regions.

Conclusion

We developed a deep residual neural network model that predicted Aß CSF from amyloid PET scans with high accuracy, and found that temporal and parietal lobes have the highest influence on Aß CSF predictions. In the next step, we will validate the model in independent data and aim to develop the model further for clinical use.
Telomeres are associated with markers and regions sensitive to aging and not Alzheimer’s disease in cognitively unimpaired older adults
Authors: Lehodey Asrar , de Flores Robin , Palix Cassandre , Touron Edelweiss , Mézenge Florence , Landeau Brigitte , Chocat Anne , Kaliman Perla , la Sayette Vincent De , Vivien Denis , Chetelat Gael , Poisnel Géraldine
Published: December 2023

Background

Telomeres (protective chromosomal ends) gradually shorten over the lifespan and have been associated with the development of age‐related diseases such as Alzheimer’s disease (AD). Most studies use median telomere length as a biomarker of cellular aging, but their dysfunction is rather reflected by the load of short telomere. No study to date has assessed the links between the percentage of short telomeres (ST) and blood, neuroimaging markers of aging, neurodegeneration, amyloid and tau pathologies in healthy elderly.

Method

Baseline data from 125 cognitively unimpaired older adults (65 to 83 years old; mean ± SD: 68.9 ± 2.8) from the Age‐Well trial (NCT02977819), who completed multimodal neuroimaging and blood sampling, were analyzed. Neuroimaging data included structural T1‐weighted MRI, T2‐weighted high‐resolution scan of the hippocampus, DKI, FDG‐PET and AV45‐PET (perfusion and amyloid load). ST, as well as neurodegeneration, amyloid, and tau biomarkers (NFL, GFAP, Aß42/40 ratio and pTau181) were measured in blood samples. Multiple linear regressions between ST and each marker were conducted corrected for age, sex, education and body mass index. Additional adjustments for APOE and amyloid status were made. Statistical significance was set to p<0.05, except for voxelwise analyses (puncorrected<0.005, kuncorrected = 500 voxels).

Result

ST was associated with lower white matter integrity (measured by kurtosis fractional anisotropy (kFA)) in prefrontal and temporal regions (Fig.1), and with the hippocampal subiculum volume, but not with total gray matter volume, mean kurtosis, glucose metabolism, brain perfusion or amyloid load; nor with blood markers of neurodegeneration, amyloid, or tau. Additional adjustments for APOE/amyloid status gave similar results.

Conclusion

Telomere dysfunctions were associated with lower white matter integrity in regions vulnerable to aging, but not to AD, nor with neurodegeneration, amyloid or tau biomarkers, and neither with AD risk factors (APOE or amyloid status). When we focused on the hippocampus, a specific link was found with the subiculum volume, an hippocampal subfield known to be the most sensitive to aging, which is not the case in AD. These results give further evidence for the tight connection between cellular and cerebral aging, but not with AD in cognitively unimpaired older adults.
Estimated Age of Amyloid Onset in Participants with Early‐ versus Late‐Onset Alzheimer’s Disease: Results from the LEADS & ADNI Studies
Authors: Blazhenets Ganna , Mundada Nidhi , Lee JiaQie , Landau Susan M. , Koeppe Robert A. , Carrillo Maria C. , Dickerson Brad C. , Jagust William J. , Apostolova Liana G. , Rabinovici Gil D. , La Joie Renaud
Published: December 2023

Background

Aß deposition is thought to start decades prior to symptom onset. Previous studies have shown that mathematical modeling enables reconstruction of Aß‐PET trajectory and age estimation at Aß‐PET positivity in Aß‐positive patients. We aimed to apply this approach to determine Aß duration and estimate age at Aß‐PET positivity in sporadic early‐ (<65yo) and late‐ ( = 65yo) onset AD (EOAD/LOAD).

Method

We combined PET data from the Longitudinal Early‐Onset AD Study (LEADS, n = 288) and ADNeuroimaging Initiative (ADNI, n = 915). First, we selected Aß+ (baseline Centiloids = 24) cognitively impaired patients with = 2 Florbetaben‐PET scans to compare baseline and longitudinal amyloid‐PET in EOAD versus LOAD using linear mixed effects regression (Figure 1). Then, we used ADNI participants with = 2 Florbetapir‐PET scans to assess the relationship between Aß‐PET Centiloids and time (Figure 2). We estimated the Aß temporal trajectory by integrating a reciprocal of the spline model from the accumulation rate and Centiloidmid‐point. Finally, we used the resulting Aß trajectory to estimate Aß duration and age at Aß‐PET positivity onset for patients with EOAD and LOAD (Figure 3).

Result

EOAD patients had higher baseline Aß burden than LOAD patients. Amyloid‐PET increased at similar rates in the two groups (Figure 1), allowing us to operate under the assumption that EOAD and LOAD follow the same Aß trajectory, with a temporal shift. Aß trajectory was estimated in ADNI Florbetapir‐PET, showing an almost linear dependency between Centiloids and Aß duration (Figure 2). The model suggests that Aß‐PET positivity (CL = 24) is reached after 7 years, and an additional 26 years are needed to reach 100 CL. When applied to the Florbetaben sample, we determined that 75yo LOAD patients became Aß‐PET positive around 52yo, while 60yo patients with EOAD dementia were Aß‐PET positive around age 31 (Figure 3).

Conclusion

Based on available data, the dynamic of Aß accumulation seems similar in EOAD and LOAD, with a temporal shift indicating that EOAD patients might transition to Aß‐PET‐positive in their early 30s. Prospective studies on early phases of Aß accumulation in EOAD patients will be important for validation of these findings since existing data is limited.
Detecting cortical microstructural alterations secondary to white matter hyperintensity in amyloid positive cognitively normal subjects
Authors: Torso Mario , Ridgway Gerard R , Valotti Michele , Hardingham Ian , Chance Steven A
Published: December 2023

Background

Previous research showed that cortical diffusivity is sensitive to primary cortical microstructural changes due to amyloid deposition (1). The present study aimed to investigate the cortical microstructural alterations secondary to white matter damage in cognitively normal participants, amyloid positive and negative.

Method

Three hundred and thirty‐five cognitively normal (MMSE > 24) participants with baseline CSF Aß42 and MRI scans were obtained from the European Prevention of Alzheimer’s Dementia (EPAD) cohort. Participants were classified as Aß42‐ (N = 231) or Aß42+ (N = 104), based on a published cut‐off (2). The Fazekas scale deep white matter (FSDWM) score was used to further classify participants as LOW (0 and 1; N = 281) or HIGH (2 and 3; N = 54) FSDWM. Structural and diffusion MRI (dMRI) were used to calculate whole brain cortical volume fraction, cortical thickness and three neuropathology‐inspired cortical diffusivity measures: the angle between the radial minicolumnar direction and the principal diffusion direction (AngleR); the principal diffusion component parallel with the minicolumns (ParlPD), and the diffusion components perpendicular to the minicolumns (PerpPD+). Group differences in structural and diffusion metrics were tested using GLM adjusting for scanner manufacturer, number of dMRI volumes, age and sex, with Bonferroni’s correction (p<0.05/5).

Result

The interaction between Aß42 status and FSDWM revealed significant differences in cortical microstructural measures (GLM: AngleR F17,317 = 7.065, p = 0.008; PerpPD+ F17,317 = 7.976, p = 0.005). The pairwise comparisons showed significantly lower AngleR values in Aß42‐ HIGH compared to Aß42‐ LOW and significantly higher PerpPD+ values in Aß42+ HIGH compared to Aß42+ LOW (Figure). No significant differences in structural measures (thickness or volume) were detected.

Conclusion

Cortical diffusivity measures can detect differences in cortical microstructure of amyloid positive and negative participants with different WM damage severity. The reduced AngleR value in Aß42‐ HIGH participants was potentially due to neuroinflammatory processes, consistent with previous findings(1). The higher PerpPD+ values in Aß42+ HIGH may indicate the secondary cortical microstructural damage. References: (1) Torso et al. 2022, PMID:36281682 (2) Ingala et al. 2021 PMID:33811742
Associations between tau‐PET and neurodegeneration with cognitive and behavioural changes over time in amyloid‐positive individuals
Authors: Singleton Ellen Hanna , Binette Alexa Pichet , Strandberg Olof , Stomrud Erik , Mattsson‐Carlgren Niklas , Ossenkoppele Rik , Hansson Oskar
Published: December 2023

Background

Tau pathology is strongly associated with clinical symptoms, possibly (partly) through the manifestation of neurodegeneration. However, independent pathways between tau pathology and clinical symptoms have been suggested. Longitudinal studies investigating the role of tau vs neurodegeneration on cognitive and behavioral changes across the AD clinical spectrum are relatively lacking.

Method

We included n = 482 individuals from the Swedish‐BioFINDER‐2‐study who had undergone longitudinal neuropsychological assessments and [18F]RO948‐tau‐PET and 3T‐MRI, of whom n = 157 were amyloid‐beta positive (A+) cognitively unimpaired (CU), and n = 326 A+ cognitively impaired (CI) individuals, and calculated SUVRs and cortical thickness in multiple regions(i.e. medial‐temporal, lateral‐temporal, medial‐parietal, lateral‐parietal, frontal, occipital, whole‐brain). Mean follow‐up time was 1.63(1.29) years. Linear‐Mixed‐Effects‐models with random‐intercepts and fixed‐slopes were performed, adjusting for age, sex, and education, applying FDR‐correction, with both baseline and rate‐of‐change in tau‐PET or MRI (the annualized difference between baseline and 2 year follow‐up) as predictors and MMSE, mPACC5 or the Mild Behavioral Inventory (MBI) as outcomes. Post‐hoc analyses included corrections for the other modality and serial mediation analyses (n = 1000 bootstrap iterations).

Result

Linear‐Mixed‐Effects‐models with baseline tau‐PET or MRI as predictors showed stronger associations between tau‐PET and cognitive and behavioral changes over time than cortical thickness, which were more pronounced in CI than CU individuals (Figure 1). Analyses with rate‐of‐change in tau‐PET or MRI as predictors showed stronger associations for tau‐PET in CU individuals, but superiority of MRI in CI individuals, which was confirmed when correcting for the other modality, i.e. assessing independent effects (Figure 2). No significant associations were found between rate‐of‐change in tau‐PET or MRI and longitudinal MBI (all p>0.05; data not shown). Serial mediation analyses revealed no mediating effect of rate‐of‐change in tau‐PET or MRI between baseline tau‐PET and cognition over time in CU, while in CI, rate‐of‐change in MRI did show a significant mediating effect (p = 0.002; Figure 3).

Conclusion

Tau‐PET showed stronger associations with cognitive and behavioral changes over time than cortical thickness at baseline across the AD clinical spectrum. However, assessing rate‐of‐change, MRI showed superiority over tau‐PET in CI individuals, suggesting differential contributions of tau pathology vs neurodegeneration in different disease stages.
Age‐related Modularity Changes of the Structural Brain Network: The Framingham Study
Authors: Seiler Stephan , Fletcher Evan , Beiser Alexa S. , Himali Jayandra Jung , Satizabal Claudia L. , Seshadri Sudha , Maillard Pauline , Decarli Charles
Published: December 2023

Background

While studies have consistently shown that brain network efficiency decreases with age, data on age‐related reconfiguration of network modules are scarce. We assessed structural brain networks from healthy adults of the Framingham Study to explore age‐related modularity differences.

Method

Structural brain networks of 2,903 healthy adults (mean age 55, SD 0.96, range 25‐95, 53% female) were obtained from DTI probabilistic tractography. Connectivity matrices were constructed from 72 cortical and subcortical regions. First, we calculated efficiency to check the validity of our data. Second, we constructed a multilayer network with each layer representing one age decile and calculated modularity for each layer using a robust implementation of the Louvain community‐detection algorithm (Blondel et al., 2008). Third, we compared modular structures between each layer and a reference group (youngest) using the variation of information (VI), an index measure of distance between two modular patterns (e.g. modularity at different ages). Finally, we calculated the participation coefficient, a measure of modular node integration, and the node flexibility, a measure of module allegiance change and assessed their relationships with age.

Result

Efficiency decreased with age (b = ‐0.34, SE 0.02, p<0.001), being stable until approximately 50 and declining linearly thereafter (see Figure 1, panel a). Modularity showed a u‐shaped trajectory with most integration at the fifth decile of age (see Figure1, panel b). VI index revealed that modular reconfiguration was greatest between the youngest and the oldest age decile (0.43). Participation coefficient was highest at the fifth decile (see Figure2, panel c) and in frontal nodes (see Figure2, panel a), where it also exhibited high variability. Frontal variability followed a complex pattern (Figure2, panel b). Conversely, central and temporal‐occipital nodes showed less participation and variability (see Figure2, panels a and b), and the lowest flexibility (see Figure3).

Conclusion

Brain network modules reconfigure with aging. This reconfiguration is non‐uniform. While midline‐near modules, including the cingulate and parieto‐occipital cortices, show relative stability, frontal modules seemed to reconfigure considerably, as seen by increased average flexibility (change in modular allegiance) and less integration with advancing age, as seen by declining participation coefficients.
Regional correlation analysis between neuronal metabolism and synaptic density using [18F]FDG and [11C]UCB‐J
Authors: DiFilippo Alexandra H , McLachlan Max , Jonaitis Erin M. , Ennis Gilda E. , McVea Andrew K , Bettcher Brecca , Pasquesi Mary‐Elizabeth , Thor Yer , Davenport Nancy J , Grover Ethan , Hudson Sarah , Barnhart Todd E , Betthauser Tobey J , Engle Jonathan W , Johnson Sterling C , Bendlin Barbara B , Christian Bradley T.
Published: December 2023

Background

Neuronal hypometabolism measured with [18F]FDG PET scans can be used to identify neurodegeneration as part of the AT(N) (amyloid, tau, neurodegeneration) framework in AD. PET imaging of synaptic vesicle glycoprotein SV2A using [11C]UCB‐J provides an index of synaptic density, which has been shown to decrease during neurodegeneration. The purpose of this study was to examine the regional relationship between neuronal metabolism and synaptic density in participants across the continuum of AD‐related pathology.

Method

Participants were recruited from the Wisconsin ADRC and the Wisconsin Registry for Alzheimer’s Prevention (WRAP) study. A subset of 13 participants (Table 1) were selected who had completed cognitive testing, structural MR imaging, and PET imaging with the following: [11C]PiB centiloid (CL) for amyloid +/‐ status, [18F]MK‐6240 SUVR for tau +/‐ status, [11C]UCB‐J DVR for regional synaptic density burden, and [18F]FDG SUVR for regional glucose metabolism. UCB‐J and FDG analysis used a whole cerebellum reference region; PiB and MK‐6240 analysis used cerebellar GM reference regions. Inter‐tracer (UCB‐J/FDG) correlations were performed in Freesurfer‐defined bilateral Braak‐associated ROIs (Medial Temporal Lobe [MTL]: entorhinal cortex, hippocampus, and amygdala; Ventral Temporal Lobe [VTL]: fusiform, lingual, and inferior temporal gyri), signature regions of hypometabolism in AD (posterior cingulate, parietal, and frontal cortices) and a region with only marginal hypometabolism in AD (occipital cortex). Associations were determined using Pearson correlations (r).

Result

All ROIs demonstrate a positive association between FDG SUVR and UCB‐J DVR, with r > 0.7 and p < 0.01 (Figure 1). The occipital, frontal, and posterior cingulate cortices demonstrate the strongest correlations (r = 0.84, 0.83, and 0.81, respectively).

Conclusion

In cognitively impaired and unimpaired participants showing varying degrees of ß‐amyloid and neurofibrillary tau burden, strong positive correlations are observed in regions associated with AD tau pathology and hypometabolism. Correlations are also observed in regions not typically associated with hypometabolism. This suggests that a relationship may exist between synaptic density and neuronal metabolism in the presence of AD neuropathology.
Neuroinflammatory biomarkers in neurodegenerative disease: Insights from the ONDRI Cohort
Authors: Sumra Vishaal , Dilliott Allison Ann , Frank Andrew R , Lang Anthony E , Roberts Angela C , Troyer Angela , Levine Brian , Arnott Stephen R. , Tan Brian , Fischer Corinne E. , Marras Connie , Kwan Donna , Munoz Douglas , Tang‐Wai David F. , Finger Elizabeth , Rogaeva Ekaterina , Orange Joseph B , Ramirez Joel , Sunderland Kelly M , Zinman Lorne , Binns Malcolm , Borrie Michael , Masellis Mario , Freedman Morris , Montero‐Odasso Manuel , Ozzoude Miracle , Bartha Robert , Swartz Richard H. , Tartaglia Carmela
Published: December 2023

Background

Neuroinflammation (NI) has been implicated in both the pathogenesis of and neuroprotection against neurodegenerative diseases (NDs)(Psenicka et al., 2021). Plasma glial fibrillary acidic protein (GFAP), and Neurofilament light (NFL) are measures of astrogliosis and neurodegeneration, respectively. Amyloid beta (Aß)42/40 ratio (Aß42 concentration to total Aß concentration) below 0.068 is associated with AD pathology (Baldeiras et al., 2018). Neuroimaging‐based inflammatory biomarkers have been proposed, including free‐water diffusion (FWD)(Pasternak et al., 2009). Here we investigated FWD as a candidate biomarker for NI in AD compared to non‐AD dementia using Aß42/40 ratio in a subset of data from the Ontario Neurodegenerative Disease Research Initiative (ONDRI).

Method

FWD maps were generated in 370 subjects (126 non‐AD and 244 AD). MRI processing included ICVmapp3r for brain extraction and bias field correction, Synb0, Topup and Eddy for dMRI preprocessing and MATLAB for freewater mapping. Plasma Aß42, Aß40, GFAP and NFL were measured using the Simoa Human Neurology 4‐Plex E assay, and cognition was estimated using the Montreal Cognitive Assessment (MoCA). Linear regression was used to estimate the ability for FWD in the left (LcGM) and right (RcGM) cortical grey matter to predict GFAP, NFL and MoCA score in AD and nonAD based on Aß42/40 threshold of 0.068.

Result

FWD correlated with GFAP (LcGM; R = 0.4, p = 0.0013 and RcGM; R = 0.37, p = 0.0069), and MoCA total score (LcGM; R = ‐0.29, p = 0.001 and RcGM; R = ‐0.27, p = 0.001), but not with NFL across the whole group. This relationship was largely driven by the AD group wherein FWD predicted GFAP (LcGM: R = 0.12, p = 0.02, RcGM approaching significance R = 0.1, p = 0.06), and MoCA Total score (LcGM: R = ‐0.33, p<0.0001), RcGM: R = ‐0.25, p<0.0001). The nonAD group did not show this relationship. FWD did not predict NFL in the AD and nonAD group.

Conclusion

In patients with Aß42/40<0.068, suggestive of AD, FWD in cGM was more strongly related to GFAP than NFL, and predicted cognition, a pattern that was not observed in nonAD patients. Our results suggest distinct patterns of NI in AD compared with nonAD that can be detected with FWD and with a multi‐modal approach could further understanding of differences in pathophysiology across NDs.
Effects of cerebrovascular disease on tau progression and cognitive decline in Alzheimer’s disease
Authors: Dewenter Anna , Steward Anna , Dehsarvi Amir , Biel Davina , Roemer Sebastian Niclas , Wanger Fabian , Franzmeier Nicolai
Published: December 2023

Background

Recent post‐mortem studies suggest that cerebrovascular disease contributes to Alzheimer’s disease (AD) pathophysiology and clinical progression. In particular, cerebral amyloid angiopathy is highly common in AD and has been linked to faster cognitive decline. Yet, in‐vivo evidence on the contribution of cerebrovascular disease to pathobiological and/or clinical AD progression is rare. Thus, we assessed in‐vivo, whether i) higher cerebrovascular burden is associated with accelerated progression of primary AD pathology and ii) whether cerebrovascular burden synergistically accelerates AD‐related cognitive decline.

Method

We included n = 256 participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) across the AD spectrum with longitudinal 18F‐flortaucipir tau‐PET (follow‐up∼2.30±1.32 years), 18F‐florbetapir or 18F‐florbetaben Aß‐PET transformed to centiloid (A7: n = 85 Cognitively normal (CN); Aß+: n = 83 CN, n = 57 Mild Cognitive Impairment, n = 31 Dementia). We focused on global white matter hyperintensity (WMH) volume adjusted to intracranial volume and microhemorrhage (MCH) count as well‐established cerebrovascular disease markers. Cognition was probed through the AD Assessment Scale Cognition 13‐item scale (ADAS‐13). Subject‐specific annual changes in tau‐PET and cognition were calculated via linear mixed models, including random slope and intercept. To enhance reproducibility of our findings, statistical analyses were pre‐specified at AsPredicted.org. All statistical models were controlled for age, sex and APOE.

Result

Using linear regression, we found no main effect of cerebrovascular disease markers on baseline amyloid‐PET or tau‐PET (ps>0.05). Further, higher cerebrovascular burden was not associated with faster Aß‐related tau‐PET SUVR increase (Fig. 1A+B). In contrast, higher cerebrovascular burden was associated with accelerated cognitive decline. Specifically, interaction models revealed that a higher MCH count was associated with faster cognitive decline at higher tau levels (Fig. 1C), while higher white matter hyperintensity burden was associated with faster cognitive decline at lower tau levels (Fig. 1D).

Conclusion

We found no effect of cerebrovascular disease on primary AD pathology. In contrast, cerebrovascular disease was linked to accelerated tau‐associated cognitive decline. This suggests that cerebrovascular disease may not modulate pathobiological AD trajectories but rather add synergistically to AD symptom severity. This has important therapeutic implications, as treating cerebrovascular disease in addition to primary AD pathology (e.g. amyloid) may yield largest clinical efficacy.
Cognitively healthy Indians with higher Framingham Cardiovascular Risk Scores have higher WMH burden with lesser grey matter, white matter and hippocampal volumes
Authors: Sundarakumar Jonas S. , Lingegodwa Abhishek Mensegere , Issac Thomas Gregor
Published: December 2023

Background

Increasing evidence indicates the important role of cardiovascular risk factors in dementia risk, particularly in Indians. It is known that cardiovascular risk factors tend to cluster together, and a widely used multivariable risk scoring is the Framingham Cardiovascular Risk Score (FRS). However, very little is known about the association between FRS and structural brain MRI parameters in cognitively healthy Indians.

Method

Baseline clinical and brain MRI data of 487 cognitively healthy Indians (Clinical Dementia Rating score of ‘0’), who are part of two ongoing, large, aging cohort studies in India – Srinivaspura Aging, Neuro Senescence and COGnition study (SANSCOG) study (n = 200) and Tata Longitudinal Study of Aging (TLSA, n = 289) were utilized. FRS was calculated using clinical and blood biochemistry data, whereas brain MRI scans were performed on 3T MRI scanners. White matter hyperintensity (WMH) lesion segmentation was performed using Lesion Segmentation Toolbox (LST) and brain volumes (total grey matter, total white matter, 3rd ventricle, right and left hippocampus) were derived using FreeSurfer software and normalized according to total intracranial volumes of the respective subjects. Linear regression analysis was performed with FRS as independent variable and various structural MRI parameters as dependent variables. A p value of <0.05 was considered significant.

Result

Urban subjects with higher FRS had significantly higher WMH volume (ß = 0.00011, p<0.001, R2 = 0.130), lower grey matter (ß = ‐0.00285, p<0.001, R2 = 0.206), white matter (ß = ‐0.00180, p<0.001, R2 = 0.106), right hippocampal (ß = ‐0.00004, p<0.001, R2 = 0.180), left hippocampal (ß = ‐0.00003, p<0.001, R2 = 0.156) volumes and higher 3rd ventricle volumes (ß = 0.00003, p<0.001, R2 = 0.150). Rural subjects higher FRS had lower grey matter (ß = ‐0.00166, p<0.001, R2 = 0.118, white matter (ß = ‐0.0011, p<0.001, R2 = 0.060), right hippocampal (ß = ‐0.00002, p<0.001, R2 = 0.087), left hippocampal (ß = ‐0.00002, p<0.001, R2 = 0.084) volumes and higher 3rd ventricle volumes (ß = 0.00004, p<0.001, R2 = 0.264) but no significant association was seen with WMH volume.

Conclusion

These findings indicate that cardiovascular risk is associated with structural brain changes in cognitively healthy Indians. Since cardiovascular risk can be reduced through cost‐effective community level strategies, such as lifestyle‐based interventions, promptly addressing this risk in Indians could be an important part of dementia prevention strategies.
Cerebellar‐cortical functional connectivity is associated with Alzheimer’s disease pathology and cerebellar atrophy
Authors: Kim Soyun , Adams Jenna N. , Stith Lea , Taylor Lisa M. , Harris Alyssa L. , McMillan Liv , Yassa Michael A.
Published: December 2023

Background

Brain atrophy has been associated with Alzheimer’s disease (AD) pathology. Previous research also suggests that functional connectivity (FC) among distributed neural networks is disrupted in preclinical AD. The cerebellum has been thought to play a crucial role in motor control, yet recent evidence points to its additional role in non‐motor domains as well as cognitive decline in AD. Cerebellar volume has been shown to decline along the course of AD progression. Moreover, neuroimaging studies have delineated intrinsic cerebellar‐cortical functional networks that presumably support cognitive functions. Despite the accumulating findings on neurodegeneration or FC, how cerebellar FC and atrophy relate to AD pathology in the early stage has not been extensively investigated.

Method

Data from 83 cognitively normal older adults (mean 71.0 yrs, 66% females) in the Alzheimer’s Disease Neuroimaging Initiative were analyzed. Volumetric data were obtained from FreeSurfer 6.0 segmentation. Resting state fMRI scans were preprocessed using the CONN Toolbox. Aß and tau standardized uptake value ratio (SUVR) data were obtained from [18F]‐Florbetapir or [18F]‐Florbetaben (Aß) and [18F]‐Flortaucipir (tau) PET scans. For Aß, SUVR values were transformed to the Centiloid scale, and a global summary measure was used. For tau, SUVR values in the temporal meta regions of interest were used. Seed‐based functional connectivity metrics were computed using the 7 cerebellar parcellations (Buckner et al., 2011) that are functionally coupled to different cortical networks (Yeo et al., 2011).

Result

Cerebellar volume showed no significant relationship with amyloid or tau SUVR. By contrast, hippocampal atrophy was positively associated with amyloid or tau SUVR and showed trending significance (ps < 0.1). Increased Aß burden was associated with increased cerebellar‐cortical FC in the attention, frontoparietal, and default mode networks. Increased tau load was associated with increased cerebellar‐cortical FC in the visual, somatomotor, and attention networks. Cerebellar volume was associated with cerebellar‐cortical FC in the visual, somatomotor, attention networks, but not with the limbic, frontoparietal, or default mode networks.

Conclusion

Our findings suggest that functional cerebellar‐cortical connectivity varies with amyloid and tau pathology in different networks, and such functional changes may precede cerebellar atrophy in preclinical AD.
Increased regional brain inflammation predicts longitudinal brain atrophy in individuals in the Alzheimer’s disease continuum
Authors: Povala Guilherme , Bellaver Bruna , Ferreira Pamela C.L. , Lussier Firoza Z , Ferrari‐Souza João Pedro , Leffa Douglas Teixeira , Tissot Cécile , Therriault Joseph , Bezgin Gleb , Servaes Stijn , Benedet Andrea Lessa , Stevenson Jenna , Rahmouni Nesrine , Cohen Ann D. , 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

Neuroinflammation typically involves the activation of microglial cells in the brain and has been linked to Alzheimer’s disease (AD) pathology. However, how microglial activation influences brain neurodegeneration in individuals across the AD continuum is still poorly understood. Here, we aimed to investigate the influence of microglial activation in longitudinal brain atrophy in individuals across the AD continuum. We hypothesize that high levels of regional brain inflammation predict widespread brain atrophy.

Method

We assessed 95 individuals from the TRIAD cohort (60 cognitively unimpaired and 35 cognitively impaired) with available [11C]PBR28‐PET, a measure of microglial activation, and a 2‐year longitudinal MRI (mean = 2.07 years). We generated grey matter voxel‐based morphometry (VBM) images using SPM12 and DARTEL, smoothed with a Gaussian kernel of full‐width half maximum of 8mm. We built the uncorrected (p < 0.05) association matrix between the [11C]PBR28‐PET SUVR and longitudinal VBM ROIs (z‐score) with the ß‐estimates from linear regressions accounting for age, sex, and diagnosis. We divided the [11C]PBR28‐PET levels into terciles (low, intermediate, and high) to generate the averages of longitudinal VBM changes.

Result

Baseline ROI‐based [11C]PBR28‐PET levels associate with longitudinal brain atrophy not only locally but also in distinct brain regions (Fig 1a). After FDR‐correction, the inferior temporal cortex was the region where [11C]PBR28‐PET levels were better associated with widespread longitudinal brain atrophy (Fig. 1b). Brain atrophy was observed in AD‐related regions, including the amygdala, insula, and the superior temporal cortex, independently of global amyloid load and tau (Fig. 1b). Accordingly, individuals with higher [11C]PBR28‐PET levels in the inferior temporal cortex presented increased longitudinal brain atrophy compared to individuals with lower [11C]PBR28‐PET (Fig. 1c).

Conclusion

We identified increased baseline [11C]PBR28‐PET levels in the inferior temporal cortex that were highly associated with longitudinal brain atrophy in individuals across the AD continuum. Our results demonstrated that higher levels of inflammation in key brain regions could predict widespread longitudinal brain atrophy, suggesting that microglial activation has a detrimental impact on AD‐related neurodegeneration progression.
Discovering correlates of off‐target 18F‐MK6240 signal in community‐dwelling adults
Authors: Johnson Aubrey S. , Zou James , Smith Anna C. , Okafor Amarachukwu , Guzmán Diana S. , Kreisl William Charles , Brickman Adam M. , Luchsinger José A. , Lao Patrick J.
Published: December 2023

Background

18F‐MK6240 binds hyperphosphorylated tau protein in neurofibrillary tangles, a hallmark of Alzheimer’s disease(AD); however, the tracer may also bind to other similarly structured molecules, known as off‐target binding. Off‐target binding is inconsistent across individuals and can lead to biased quantitative assessment, making it difficult to accurately identify neurofibrillary tangle pathology. We examined variable off‐target binding by performing a correlation analysis with demographic factors, vascular risk, lifestyle factors, and co‐morbidities.

Method

Ninety‐five participants (65±3yrs; 67% women; 42/58% White/Black; 66% Hispanic; 12±4yrs education) in the Northern Manhattan Study of Metabolism and Mind(NOMEM) underwent 18F‐MK6240(90‐110min) and 18F‐Florbetaben(90‐110min) PET imaging and completed surveys, including age, sex, education, exercise levels, and medical history. Only visually‐determined amyloid‐negative participants were included to increase the likelihood that elevated tau PET signal was off‐target. Tau SUVR (cerebellar gray matter reference region) was calculated for composite Braak stages (I/II, III/IV, V/VI) and two known off‐target regions, the choroid plexus and the extra‐cerebral space around the brain. The extra‐cerebral mask was generated by dilating the gray/CSF boundary of the ANTs probability map and then subtracting the brain. Percent of variance explained (r2) from general linear models are reported between off‐target regions and factors of interest.

Result

Descriptively, 18F‐MK6240 off‐target binding in choroid plexus was positively correlated with SUVR in Braak stage regions I/II (r2 = 0.37;p<0.001) and III/IV (r2 = 0.44;p<0.001) to a larger extent than V/VI (r2 = 0.21;p<0.001), while off‐target binding in the extra‐cerebral space was positively correlated with Braak stages I/II (r2 = 0.07;p<0.001) to a lesser extent than III/IV (r2 = 0.13;p<0.001) and V/VI (r2 = 0.18;p<0.001), suggesting that proximity of key AD regions to off‐target regions increases the likelihood of confounded signal. Off‐target binding in the choroid plexus was elevated amongst self‐identified Hispanic participants (r2 = 0.19;p<0.001). Extra‐cerebral off‐target binding was negatively associated with participant weight (r2 = 0.12;p<0.001).

Conclusion

Extra‐cerebral off‐target binding of 18F‐MK6240 may confound signal in key AD regions in amyloid‐negative participants. Off‐target binding should be accounted for regionally and methods for capturing extra‐cerebral signal are critical. Understanding the role of factors related to tracer distribution, including weight, and sociodemographic factors will enhance the quantification of 18F‐MK6240 for the early detection of neurofibrillary tangle pathology.
Harmonization of amyloid and tau PET tracers in a multi‐center cohort from NIA Alzheimer’s disease research centers in the United States
Authors: Harrison Theresa M. , Landau Susan M. , Baker Suzanne L. , Lee JiaQie , Ward Tyler J. , Murphy Alice , Chadwick Trevor , Mormino Elizabeth C. , Johnson Sterling C , Rogalski Emily J , Koeppe Robert A. , Decarli Charles , Toga Arthur W. , Kukull Walter A. , Biber Sarah , Jack Clifford R. , Jagust William J.
Published: December 2023

Background

The Standardized Centralized Alzheimer’s & related dementias Neuroimaging (SCAN) project harmonizes processing of prospectively acquired PET and MRI scans from Alzheimer’s disease research centers (ADRCs) across the USA. SCAN data will be integrated with other ADRC data streams within the National Alzheimer’s Coordinating Center (NACC), including the Uniform Data Set which includes rich longitudinal demographic, cognitive, and neuropathological data, resulting in a large, potentially representative cohort. The project has sought to accommodate site‐to‐site variation in image acquisition while permitting harmonization of imaging data, necessitating new analytic approaches for different PET tracers and PET scans without MRIs.

Method

PET scans underwent preprocessing for quality control and smoothing to a uniform resolution. MRI‐free processing pipelines and positivity thresholds were developed for each beta‐amyloid (Aß)‐PET tracer: [11C]PiB, [18F]florbetaben (FBB), [18F]florbetapir (FBP) and [18F]flutafuranol (NAV). A generic Aß‐PET template was used for spatial normalization and cortical summary SUVRs were calculated in template space. Aß‐positivity thresholds were determined by examining both MRI‐dependent thresholds transformed into MRI‐free units and a young control mean+2SD. Centiloid (CL) conversions were completed for each Aß‐PET tracer. Tau‐PET tracers ([18F]flortaucipir (FTP) and [18F]MK‐6240) required tracer‐specific warping templates for best performance relative to gold‐standard MRI‐dependent processing. Demographic and clinical data were obtained from NACC.

Result

15 ADRCs have contributed data to SCAN as of January 2023. 477 participants had preprocessed, SCAN‐compliant (https://scan.naccdata.org/) Aß‐PET scans and NACC data; 219 of these participants (46%) also had tau‐PET scans (Table 1). This cohort included 18% of individuals from underrepresented groups. Clinical diagnostic groups ranged from cognitively unimpaired to MCI and dementia (74% of whom had AD dementia). Using pooled Aß‐PET data across 4 tracers in CLs, there were expected differences in Aß burden by diagnosis group (Fig. 1) and positive associations with tau (Fig. 2).

Conclusion

SCAN has successfully harmonized PET data from more than a dozen US ADRCs yielding a large, clinically heterogeneous cohort of deeply phenotyped research participants. SCAN data will be compatible with ADNI data allowing for further pooling of data and greater statistical power to drive critical questions in AD and related dementias research.
Cortical and substantia nigra 18F‐PI‐2620 tau PET signal in Lewy body disease
Authors: Winer Joseph R. , Vossler Hillary , Young Christina B. , Romero America , Shahid Marian , Abdelnour Carla , Anders David , Shen Bin , Morales Aimara Pacheco , Davidzon Guido , Mormino Elizabeth C. , Poston Kathleen L.
Published: December 2023

Background

Tau PET imaging is a potential tool for measuring comorbid tau pathology in patients with Lewy body disease. Furthermore, tau PET ligands are known to bind to neuromelanin within substantia nigra (SN), with reduced binding in Parkinson’s disease relative to healthy individuals. We sought to characterize 18F‐PI‐2620, a next generation PET tracer, in patients from across the Lewy body disease spectrum.

Method

A total of 126 participants from the Stanford Alzheimer’s Disease Center (ADRC) and the Stanford Aging and Memory Study (SAMS) were recruited for PET scanning with 18F‐PI‐2620. We compared 18F‐PI‐2620 uptake within medial temporal (entorhinal cortex, amygdala) and occipital (precuneus, lingual gyrus) regions as well as SN across patients with Parkinson’s disease (PD, n = 26), dementia with Lewy bodies (DLB, n = 6), and Alzheimer’s disease spectrum (AD, n = 21), in addition to cognitively normal older adults (healthy controls, HC, n = 73). Mean bilateral signal was extracted from cortical regions of interest in native space 18F‐PI‐2620 SUVRs (inferior cerebellar gray reference). Amyloid status was determined either by 18F‐florbetaben PET or CSF. To quantify SN, SUVRs were normalized to template space and an anatomical mask was used to extract mean signal. MDS‐UPDRS‐III motor exam scores, collected in an off‐medication state for all patients, were used as a measure of motor impairment.

Result

Entorhinal and amygdala SUVR were significantly elevated in the AD group relative to all other groups, and in amyloid‐positive relative to amyloid‐negative HC (Fig 1). Tau burden in all regions was low across PD and DLB groups and did not differ based on amyloid status. SN SUVR was significantly lower in the PD group compared to the HC and AD group (Fig 2A). In patients with Lewy body disease (PD & DLB), MDS‐UPDRS‐III scores were negatively associated with SN SUVR (Fig 2B).

Conclusion

These findings suggest that cortical tau is low in Lewy body disease. Neuromelanin‐related 18F‐PI‐2620 signal within SN carries information about disease severity. Cortical tau and SN binding represent two unique signals in the same tau PET image that may be informative in the context of Lewy body disease.
The association between 73 Alzheimer’s disease risk variants and white matter microstructural decline in aging
Authors: Archer Derek B , Shashikumar Niranjana , Jasodanand Varuna , Moore Elizabeth E. , Murray Sydney , Pechman Kimberly R. , Bilgel Murat , Beason‐Held Lori L , An Yang , Risacher Shannon L , Landman Bennett A. , Jefferson Angela L. , Saykin Andrew J. , Resnick Susan M. , Hohman Timothy J.
Published: December 2023

Background

Although several prior diffusion MRI studies have indicated a role of medial temporal lobe (MTL) white matter microstructural decline in Alzheimer’s disease (AD), it is unclear which AD risk genes are associated with this decline. The goal of this study is to leverage multi‐site harmonized diffusion MRI data to investigate the relationship between 73 AD risk variants and microstructural decline in MTL tracts.

Method

The dataset used in this study was collated from three longitudinal cohorts of aging [Alzheimer’s Neuroimaging Initiative (ADNI), Baltimore Longitudinal Study of Aging (BLSA), Vanderbilt Memory & Aging Project (VMAP)]. In total, this dataset included 1,308 participants (70% cognitively unimpaired, 27% MCI, 3% AD) aged 50+ who had both diffusion MRI and genotype data and includes a total of 3,442 imaging sessions (mean number of visits: 2.68 ± 1.66, interval range: 1 ‐ 12 years). Genotype data was preprocessed using a standardized approach, and the dosage of the 73 AD risk variants was extracted. Diffusion MRI data was preprocessed using the PreQual pipeline and free‐water (FW) correction was conducted to obtain FW and FW‐corrected fractional anisotropy (FAFWcorr) maps. FW and FAFWcorr were quantified within 5 MTL tracts, including the cingulum, fornix, uncinate fasciculus, inferior longitudinal fasciculus, which were subsequently harmonized using the Longitudinal ComBat package. Linear regression was conducted to investigate the association between AD risk variants and MTL microstructure at baseline, while linear mixed effects regression was used for longitudinal analysis. All models were corrected for age at baseline and sex and corrected for multiple comparisons using the FDR approach.

Result

At baseline, APOE‐e4 dosage was strongly associated with MTL FW in all tracts, with additional associations for TMEM106B and CR1 dosages with fornix FW (Table 1). Longitudinally, APOE‐e4 dosage was a strong predictor of MTL FW decline in all tracts, and KAT8, CR1, and TMEM106B dosages exhibited significant associations with fornix microstructural decline (Table 2 and Figure 1).

Conclusion

This study suggests that although APOE‐e4 is the strongest predictor of white matter microstructural decline, several other AD risk genes (i.e., CR1, SPI1, TMEM106B, KAT8) contribute to decline, particularly in the fornix.
A comparison of two novel visual read‐based methods for high tau identification from flortaucipir‐PET
Authors: Iaccarino Leonardo , Tunali Ilke , Kim Min Jung , Lu Ming , DiFabbio Kelly , Kowaleski Stacey , Ducker Courtney , Patel Sneha , Arora Anupa , Southekal Sudeepti , Kotari Vikas , Shcherbinin Sergey , Pontecorvo Michael J.
Published: December 2023

Background

To compare the performances of two novel visual read‐based methods for flortaucipir‐PET in patients with Alzheimer’s Disease (AD) to identify high tau accumulation, defined as AD‐signature weighted neocortical Standardized Uptake Value ratio (SUVr)>1.46.

Method

Baseline flortaucipir‐PET scans displaying advanced‐AD tau‐patterns (increased neocortical activity beyond posterior lateral temporal and occipital regions with bindings 1.65‐times higher [1.65x‐visual threshold] than cerebellar average) were utilized from the confirmatory phase of A05 study (N = 78, NCT02016560). Four expert human‐readers conducted reads using two novel visual read‐based methods while being blinded to each other and all quantitative results. The first approach (SUVrmax‐method) requires a reader to visually identify and hand‐draw focal regions‐of‐interest on frontal areas with the highest bindings and calculate an SUVr between the identified frontal maximum and average cerebellum binding. The second approach (Enhanced Read‐method) requires the reader to evaluate binding elevation compared to cerebellum with an enhanced visual threshold, either across gray matter (global, 3.795x‐visual threshold) or only in the frontal lobe (2.805x‐visual threshold), compared to the standard 1.65x‐visual threshold specified in the FDA‐approved Tauvid™ label. Methods were compared to the standard quantitative high tau determination as standard‐of‐truth.

Result

Based on a previously found ROC‐determined cut point using computer‐generated SUVrmax values, the SUVrmax‐method showed median 88% Positive Percent Agreement (PPA), 87% Negative Percent Agreement (NPA) and 87% overall accuracy across raters. The Enhanced Read‐method showed median 75% PPA, 88% NPA and 85% overall accuracy and median 81% PPA, 91% NPA and 88% overall accuracy for global and frontal approaches, respectively.

Conclusion

Both novel flortaucipir‐PET visual read‐based methods showed comparable and promising performances in identifying AD with high tau, do not require computationally intensive approaches and can be implemented with different imaging software.