bluuebunny/crossref_metadata_2025 huggingface dataset:
Deep learning‐based estimation of future brain atrophy using baseline MRI and PET
Authors: Le Linh N. N. , Fletcher Evan , Qi Jinyi , Fan Audrey P.
Published: January 2025

Background

Cortical brain atrophy is an excellent marker of clinical decline and can support future clinical course prediction in cognitive impairment. We used a U‐Net image‐generation deep learning network to predict future cortical atrophy rates in elderly populations, with initial T1‐weighted (T1w‐) MRI or baseline amyloid‐PET serving as inputs to the model.

Method

MRI and PET data were retrospectively collected from Alzheimer’s Disease Imaging Initiative (ADNI) and all participants had two serial T1w‐MRI scans (Figure 1A,B). The ground truth was an atrophy map (in log‐Jacobian values) generated from Tensor Based Morphometry (TBM) of the two longitudinal MRIs (Figure 1E). A U‐Net architecture was used in this study to predict maps of cortical atrophy. We used mean squared error (MSE) for loss function and five‐fold cross‐validation (Figure 1C,D). The testing set was randomly chosen (n = 30). Separate models were trained for T1w‐MRI data as input (T1‐model) and amyloid‐PET as input (PET‐model). The predicted atrophy rate was evaluated across diagnosis groups in the test set using the whole‐brain structural similarity index measure (SSIM) to the TBM ground truth. Each model’s average atrophy at selected brain regions was compared with TBM using paired t‐tests.

Result

Figure 2 shows average predicted atrophy maps and corresponding error for each diagnosis group. In the whole brain, for cognitively normal (CN) and mild‐cognitive‐impaired (MCI) groups, T1 model (with SSIMs of 0.67±0.07 and 0.64±0.06, respectively) slightly outperformed PET model (with SSIMs of 0.70±0.06 and 0.69±0.06, respectively). In selected regions, especially gray matter, we observed a larger rate of atrophy in Alzheimer’s Disease (AD) compared to MCI and in MCI compared to CN groups in both deep learning models, which was consistent with the ground truth. There was no statistical difference in average atrophy predicted by either model and TBM values for all diagnostic groups in all selected regions, with relatively small absolute error and MSE in both models (Table 1).

Conclusion

A U‐Net deep learning model trained using only baseline T1 weighted MRI or amyloid PET data could be used to predict future atrophy rates. Both models predicted cortical volume loss and ventricle expansion that differed between AD, MCI, and CN.
Harmonized Tractometry Visualizes Alzheimer’s Disease Effects on the Brain’s White Matter Tract Microstructure
Authors: Chandio Bramsh Q , Reina Julio E Villalon , Nir Talia M , Thomopoulos Sophia I , Feng Yixue , Benavidez Sebastian M , Jahanshad Neda , Harezlak Jaroslaw , Garyfallidis Eleftherios , Thompson Paul M.
Published: January 2025

Background

Diffusion MRI (dMRI) metrics of brain microstructure offer valuable insight into Alzheimer’s disease (AD) pathology; recent reports have identified dMRI metrics that (1) tightly link with CSF or PET measures of amyloid and tau burden; and (2) mediate the relationship between CSF markers of AD and delayed logical memory performance, commonly impaired in early AD [1,2]. To better localize white matter tract disruption in AD, our BUndle ANalytic (BUAN) [3] tractometry pipeline allows principled use of statistical methods to map factors affecting microstructural metrics along the 3D length of the brain’s fiber tracts. Here, we extended BUAN to pool data from multiple scanning protocols/sites ‐ using a new harmonized tractometry approach, based on ComBat [4,5], a widely‐used harmonization method modeling variations in multi‐site datasets due to site‐ and scanner‐specific effects.

Method

We illustrate the effects of integrating ComBat into our BUAN tractometry pipeline. Analyzing Alzheimer’s Disease Neuroimaging Initiative (ADNI3) [6] data, we examined the impact of mild cognitive impairment (MCI) and AD on 38 white matter tracts, comparing results with and without harmonization. We analyzed data from 730 ADNI3 participants, scanned with seven dMRI protocols. After data preprocessing with the ADNI3 protocol [7,8], BUAN extracted 38 bundles and created along‐tract bundle profiles for microstructural metrics: FA, MD, AD, and RD (see Fig. 1 for full names). ComBat was applied to each point in bundle profiles to correct for scanner protocol effects and linear mixed models (LMM) were used to study the group differences in MCI and AD versus cognitively healthy controls (CN).

Result

Harmonized BUAN maps reveal AD and MCI effects throughout specific tracts (e.g., the left cingulum and right arcuate fasciculus; Fig. 1, bottom panel). ComBat harmonization enhanced the sensitivity to detect group differences compared to LMM without scanner correction. Adding data ‐ even from different scanner protocols ‐ boosted power (Fig. 2f‐g).

Conclusion

In this application of harmonization in neurodegenerative tractometry analysis, we integrated ComBat into BUAN tractometry to merge dMRI data from diverse scanning protocols and sites. Future research will examine various ComBat versions and deep‐learning approaches to track AD pathology effects on the brain’s neural circuitry.
Using a Bayesian workflow approach to define the optimal level of anatomy/functionality voxels clustering for the prediction of tau propagation and cortical loss in Alzheimer's disease
Authors: Soucy Jean‐Paul , Belasso Clyde J , Cai Zhengchen , Bezgin Gleb , Stevenson Jenna , Rahmouni Nesrine , Tissot Cécile , Lussier Firoza Z , Rosa‐Neto Pedro , Rivaz Hassan J , Benali Habib
Published: January 2025

Background

Tau aggregates in Alzheimer’s disease (AD) induce loss of synapses and neurons, leading to cognitive impairment. Predicting tau and neurodegeneration temporal evolution could be used for prognostication and for assessing results of therapeutic trials. Tau PET and MRI volumetry are reliable markers of disease stage, but cost and radiation protection considerations limit research measurement frequency, lowering the accuracy of disease progression modeling. Here, we evaluate, using Bayesian analysis, whether models based on limited numbers of observations can be refined to better predict the temporal trajectory of pathology.

Method

Imaging data comes from subjects (113; 68 females; 18 AD dementia, 23 MCI and 72 cognitively normal) of the TRIAD cohort (McGill University) who have been evaluated at least twice ( 1 year interval) with both tau PET ([18F]MK‐6240) and structural MRI. Four probability models were evaluated: 1‐ a basic one, assuming that all data points come from 1 data distribution; 2‐ one where subjects' observations are clustered within anatomical ROIs, where an independent distribution is hypothesized; 3‐ data is clustered within known physiological networks, each networks’ distribution parameters having their own specific values ; 4‐ a model assuming that subjects’ observations are described by a distribution of voxel parameters dictated by both the ROI and network(s) in which they lay. Bayesian data analysis was used to compare the predictive accuracy of those models for progression at 1 year from baseline of tau PET and MRI data.

Result

Model 4 was the most accurate model for both tau and cortical thickness prediction. We therefore used it to perform posterior predictions across hemispheres, showing that the prediction curves of the left and right hemispheres for the pericalcarine cortex differ. We also noticed a decreasing trend in the CN tau curve for the left hemisphere as the rate of cortical thinning increases. In contrast, there is an increasing trend in the AD tau curve as the rate of cortical thinning increases.

Conclusion

The model that incorporated both ROI‐level and network‐level information was the best predictor of progression, and such an approach can reveal underappreciated properties of the disease (i.e., laterality).
Longitudinal stability of extra‐cerebral off‐target binding in [F‐18]MK6240 PET images
Authors: McVea Andrew K , DiFilippo Alexandra H , McLachlan Max , Bettcher Brecca , Johnson Sterling C. , Betthauser Tobey J. , Christian Bradley T.
Published: January 2025

Background

The spread of tau in Alzheimer’s Disease (AD) can be tracked in vivo using [F‐18]MK6240, a PET radioligand that binds to tau aggregates in AD with high affinity. However, significant MK6240 signal is also observed in the meninges and sinus and the extra cerebral binding (ECB) signal from these regions can spill into exterior brain regions complicating evaluation of early stage AD tauopathy. This study evaluates the magnitude and variability of ECB in a large imaging cohort to identify trends in this signal.

Method

Participants included in this study were imaged at the University of Wisconsin–Madison on a Biograph mCT (n = 488) or ECAT HR+ (n = 475) PET scanner including 208 with longitudinal data taken an average of 2.0±1.0 years apart. Reconstructed PET images were smoothed using a 6mm3 gaussian kernel then processed using a standardized pipeline to generate SUVR images using the inferior cerebellar grey matter reference region. Meninges and sinus SUVR were compared across demographic groups of interest within the cohort and longitudinal changes in signal were quantified using the absolute change from baseline to follow up scans.

Results

While there were not significant differences in ECB signal seen with age (pmeninges= 0.89, psinus= 0.98), amyloid (pmeninges= 0.43, psinus= 0.95) or tau statuses (pmeninges= 0.18, psinus= 0.35) a significantly higher average SUVR in the meninges and sinus for female (pmeninges< 0.001, psinus< 0.001) and APOE4+ participants (pmeninges= 0.007, psinus= 0.05) was observed. Longitudinally there was an average absolute SUVR difference between scans of 0.21±0.34 (12.1±10.9%) in the meninges and 0.24±0.33 (12.9±10.4%) in the sinus. This is more than triple the average longitudinal SUVR variability of 0.06±0.06 (6.5±6.0%) in the entorhinal region in our amyloid negative participants. APOE4+ participants additionally showed a significantly higher absolute change in the meninges (p = 0.05), however, the increase in the sinus was not significant (p = 0.59).

Conclusions

MK6240 ECB can be highly variable and has the potential to bias tracer uptake outcomes. While we observed trends related to sex and genetic risk factors, further work to identify the underlying sources and correct for ECB is ongoing.
Integrating Connectomics Constraints in Event‐Based Modeling of Alzheimer's Disease Progression: An Analysis of ADNI PET Data
Authors: Vahdatian Mahdieh , Zhao Yuji , Kurmukov Anvar , Gutman Boris A
Published: January 2025

Background

Alzheimer's Disease (AD) presents a major health challenge, with complex and variable neurodegenerative progression. Traditional neuroimaging falls short in fully capturing this heterogeneity. Our study addresses this gap by applying an Event‐Based Model (EBM) to Alzheimer's Disease Neuroimaging Initiative (ADNI) Positron Emission Tomography (PET) data, enriched with connectomics data. This innovative approach promises a more individualized understanding of AD progression, combining PET imaging with insights into brain connectivity from connectomics. Our work aims to enhance early diagnosis and personalized treatment strategies in AD research.

Method

Our study applied an Event‐Based Model (EBM) to Alzheimer's Disease Neuroimaging Initiative (ADNI) datasets across ADNI1, ADNI2, and ADNI3. We analyzed a diverse cohort including Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI), Late MCI (LMCI), Early MCI (EMCI), and cognitively normal (CN) groups, using Amyloid (AMY), Fluorodeoxyglucose (FDG), and Tau PET scans. The methodology integrated connectomics data and employed Monte Carlo Markov Chain (MCMC) techniques for enhanced modeling precision. This approach provided an in‐depth understanding of AD progression, combining advanced statistical analysis with diverse neuroimaging data.

Result

Optimal order of neurodegenerative events ‐ stages of disease progression as measured by changes in metabolic signature (FDG) or Amyloid accumulation ‐ largely followed previously published results. Earliest regions of Amyloid accumulation mirrored the default mode network (Figure 1). Metabolic changes, notably reduction in FDG SUVR, occurred earliest in the same top 10 regions. Also Z‐test statistic (‐4.78): Indicates a significant difference between the average stages of AD and MCI patients, with AD stages being lower than those of MCI. P‐value (∼0.00000177): Implies an extremely low probability that the observed difference in stages occurred by chance, strongly suggesting a true difference between the groups.

Conclusion

We have presented a novel connectome‐informed progression model of amyloid‐beta accumulation and metabolic changes in the brain. The model discriminates well between stages of cognitive decline and suggests that amyloid accumulation and metabolic changes both follow a similar early of pattern conditioned on white‐matter connectivity
Plasma and neuroimaging biomarkers of small vessel disease and Alzheimer’s disease in a diverse cohort
Authors: Lockhart Samuel N. , Sutphen Courtney L. , Tanley Jordan E. , Gonzalez‐Ortiz Fernando , Kac Przemyslaw Radoslaw , Habes Mohamad , Heckbert Susan R. , Ashton Nicholas J. , Mielke Michelle M. , Whitlow Christopher T , Hiatt Kevin , Craft Suzanne , Register Thomas C. , Hayden Kathleen M. , Rapp Stephen R. , Zetterberg Henrik , Blennow Kaj , Karikari Thomas K , Hughes Timothy M.
Published: January 2025

Background

Little is known about how plasma Alzheimer’s disease (AD) biomarkers relate to neuroimaging biomarkers of cerebral small vessel disease (cSVD) in the context of neurodegeneration and AD pathology in late life.

Method

This cross‐sectional study included 251 Multi‐Ethnic Study of Atherosclerosis (MESA) Exam 6 participants with plasma AD biomarkers (Aß42/Aß40, GFAP, NfL, p‐tau181, p‐tau217, p‐tau231; Quanterix SIMOA), MRI (neurodegeneration and cSVD), PiB (amyloid) PET, and UDSv3‐based adjudicated cognitive status (69% cognitively normal, 27% MCI, 4% probable dementia) data at the Wake Forest site. Multivariable models examined relationships among cognitive status, plasma, and neuroimaging biomarkers (covariates: age, education, race, gender, smoking status, kidney function [eGFR], APOE‐e4, BMI; significance at p<.05 uncorrected).

Result

Cognitive status associated with imaging biomarkers and with plasma biomarkers of Aß42/Aß40, GFAP, p‐tau217 and p‐tau231. No NfL or p‐tau181 group differences were observed (Table 1). Lower plasma Aß42/Aß40 (Table 2) was associated with lower gray matter (GM) volume (GMV) and hippocampal volume (HCV); higher GFAP levels were associated with lower HCV. Higher NfL levels were associated with lower GMV and HCV, and with higher GM Free Water (FW). Higher p‐tau217 levels were associated with lower GMV. Plasma p‐tau231 was not associated with neurodegeneration imaging biomarkers. Higher NfL levels were associated with higher white matter (WM) hyperintensity (WMH) volume and WM FW. All plasma tau measurements were positively associated with WM FW but not other cSVD biomarkers. Lower Aß42/Aß40 ratio was significantly associated with greater prevalence of microbleeds. Higher GFAP was associated with higher WMH volume. As anticipated, lower plasma Aß42/Aß40 ratio and higher p‐tau217 and p‐tau231 were associated with higher Aß deposition and odds of Aß positivity (Table 3); a doubling in the level of p‐tau217 or p‐tau231 was associated with a 2‐3 fold increase in the odds of Aß PET positivity.

Conclusion

We found that plasma GFAP, NfL, p‐tau181, p‐tau217, and to a lesser extent p‐tau231 are associated with cognitive status, vascular comorbidities and imaging biomarkers of cSVD, in addition to measures of AD‐related pathology. Importantly, we demonstrated novel associations between plasma AD biomarkers of p‐tau and NfL with cSVD measures of WM structural health.
Spatial Similarity of Person‐Specific Resting State Topography in Cognitive SuperAgers
Authors: Diamond Bram R , Martersteck Adam , Rogalski Emily J
Published: January 2025

Background

Memory decline in late life is a common hallmark of aging, yet SuperAgers are individuals age 80+ with episodic memory performances at least as good as cognitively average 50‐to‐60‐year‐olds. Recent work, combining anatomical and functional MRI, has shown the precise boundaries of large‐scale resting state networks vary at the individual level. Further, the use of person‐specific rather than standard parcellations has led to more behaviorally meaningful associations, and has not been explored in SuperAgers. The current project examines whether the topography of person‐specific resting state networks differ between SuperAgers and older‐aged controls (OACs).

Methods

Twenty‐four SuperAgers and 16 OACs were included in the study. SuperAger/OAC phenotype was determined based on measures of episodic memory, executive functioning, verbal fluency, and picture naming across two visits, separated by at least 18 months. Person‐specific network parcellations were derived for each participant using their first visit resting state fMRI connectivity, constrained by anatomical priors, with a Multi‐Session Hierarchical Bayesian Model (MS‐HBM; Kong et al., 2021). Sørensen‐Dice spatial similarity coefficients (Dice) were calculated to determine the within‐group spatial similarity of each network. Dice was defined as the number of overlapping vertices between two participants’ network segmentation, multiplied by two, and divided by the sum of vertices in both network segmentations. Dice was calculated for each participant compared to all other within‐group participants and averaged to create a Dice score per participant per network. Dice scores for each of the 17 person‐specific networks were compared between SuperAgers and OACs using Mann‐Whitney tests, Bonferroni‐corrected.

Results

Demographics did not differ significantly between groups. Person‐specific network topography varied between participants (Figure 1). SuperAgers had significantly lower Dice scores than OACs in six of 17 networks (Figure 2; p‐value = 0.0029).

Conclusion

Compared to OACs, SuperAgers demonstrated greater within‐group variability in resting state network topography. Greater spatial variability may reflect unique patterns of functional or structural topography that support SuperAgers’ exceptional memory abilities. Future investigations will include further examination of the functional connectome of SuperAgers, leveraging the precision approach employed here.
Clinical utility of 123I‐Ioflupane SPECT striatal binding for evaluating parkinsonism: a retrospective study of a dementia with Lewy bodies cohort
Authors: Maly Emily F , Leverenz James B , Lapin Brittany , Li Yadi , DiFilippo Frank A
Published: January 2025

Background

This study set to determine if 123I‐ioflupane SPECT striatal binding ratio (SBR) correlated with parkinsonian symptoms measured on Movement Disorder Society – Unified Parkinson’s Disease Rating Scale Part III (UPDRS‐III) in a dementia with Lewy bodies (DLB) cohort and if SBR measured at baseline could predict progression of parkinsonian symptoms over 2 years.

Methods

This is a retrospective cohort study using the U.S. Dementia with Lewy Bodies Consortium (DLBC) dataset. Sample group included 54 individuals with probable DLB or mild cognitive impairment with Lewy bodies (MCI‐LB). Participants had baseline 123I‐ioflupane SPECT scan analyzed with DaTQUANT and baseline UPDRS‐III examination; a subset of 25 participants had a 24‐month UPDRS‐III examination. Mean of the left and right SBR Z‐scores for the striatum, putamen, posterior putamen, and caudate were analyzed. A subset of 45 participants had CSF tested with [Amprion] a‐synulcein seed amplification assay (SAA).

Results

Multivariable linear regression models were used to predict 54 participants’ UPDRS‐III at baseline; this showed baseline SBRs are significant predictors of baseline UPDRS‐III scores with the caudate SBR having the strongest correlation (p=0.005). Multivariable linear regression models were used to predict 25 participants’ UPDRS‐III score at 24‐months; this showed baseline SBRs are significant predictors of 24‐month UPDRS‐III scores with the caudate SBR having the strongest correlation (p=0.009). When adjusting for baseline UPDRS‐III scores, SBRs are no longer significant predictors of 24‐month UPDRS‐III scores (p=0.36). When a multivariable logistic regression model was used to predict meaningful worsening in UPDRS‐III from baseline to 24‐months (defined as worsening by = +5 points) the relationship was not significant (AUC=0.53). A sub‐analysis of the 45 participants with a‐synulcein SAA showed that presence a‐synulcein was associated with higher UPDRS‐III scores and lower SBRs.

Conclusion

Findings reveal a significant relationship between 123I‐ioflupane SPECT SBRs and motor parkinsonism in DLB as measured on UPDRS‐III. In DLB, 123I‐ioflupane SPECT scan, at time of diagnosis, could be used to confirm underlying dopamine deficiency in the context of clinical parkinsonism. Findings suggest baseline 123I‐ioflupane SPECT SBRs do not add value beyond baseline UPDRS‐III in predicting clinical progression of parkinsonism in 2 years.
Joint Contribution of NfL and PSMD for Improved Risk Stratification of VCID
Authors: Luckey Alison M. , Beiser Alexa S , Himali Jayandra J. , Gaona Carlos A. , Zeynoun Joy , Rodriguez Luis A. Mendez , Habes Mohamad , Kautz Tiffany F. , Seshadri Sudha , Aparicio Hugo J. , Satizabal Claudia L
Published: January 2025

Background

Advancing therapeutic and prevention strategies for vascular contributions to cognitive impairment and dementia (VCID) warrants identifying novel biomarkers. However, due to the high heterogeneity underlying dementia pathology, a single marker may not fully risk‐stratify for VCID. A blood‐based biomarker of neuroaxonal injury, neurofilament light chain (NfL), and a neuroimaging‐based biomarker of white matter microstructural damage on diffusion weighted imaging, peak width of skeletonized mean diffusivity (PSMD), have been related to worse general cognition and proposed as robust biomarkers for cerebral small vessel disease (cSVD). We investigated the joint contribution of NfL and PSMD for improved specificity/sensitivity to identify persons at risk for VCID.

Method

Dementia‐free participants from the Framingham Offspring Study with cognitive, neuroimaging, and NfL data were included (N = 969). NfL was measured in plasma, and PSMD was derived from MRI diffusion weighted imaging. Executive function and general cognitive function were assessed from a neuropsychological battery. NfL and PSMD were dichotomized by the top quartile and combined to indicate a high cSVD burden. The high NfL‐PSMD risk category was related to cognitive function using linear regression adjusting for age, age‐squared, sex, education, renal function (eGFR), and total intracranial volume. Additional analyses incorporating amyloid PET uptake in 64 participants were performed to discern Alzheimer’s disease pathology from VCID.

Result

Higher NfL‐PSMD was significantly associated with worse executive (Beta±SE, ‐0.06±0.02, p = 0.002) and general cognitive function (‐0.15±0.07, p = 0.02). Additionally, amyloid PET over‐predicted NfL levels in those with PSMD below the median (observedpredicted, 0.06 (0.33)). Although results were not significant (p = 0.15) due to the limited sample, they suggest that amyloid pathology does not explain cSVD burden measured with NfL and PSMD.

Conclusion

Our findings suggest combining NfL and PSMD can better risk‐stratify those with VCID and poorer cognitive function. This NfL‐PSMD multi‐biomarker has potential to discriminate vascular dysfunction from Alzheimer’s pathology, improving identification of persons better suited for VCID clinical trials. Utilizing a multi‐biomarker approach may improve accuracy for risk stratification of persons bearing covert cerebral vascular injury. Further studies are underway to confirm these findings in larger, diverse samples.
Unravelling the role of subcortical brain volumes in the conversion to dementia: a multi‐cohort analysis
Authors: Rosbergen Mathijs T. , van der Veere Pieter J. , Claus Jacqueline Josephine , Evans Tavia E , Venkatraghavan Vikram , Barkhof Frederik , van Harten Argonde C. , Ikram M. Arfan , van der Flier Wiesje M. , Vernooij Meike W. , Wolters Frank J.
Published: January 2025

Background

Hippocampal volume is an acknowledged biomarker of neurodegenerative disease, including Alzheimer’s disease (AD). However, the relationship between other subcortical brain structures and dementia risk is uncertain and may differ by disease stage. We aimed to assess the prognostic value of subcortical volumes for dementia risk across different disease stages by investigating memory clinic‐based populations and community‐dwelling individuals.

Method

We included 9613 dementia‐free participants from a population‐based cohort (Rotterdam Study [n=5442, MCI:4.2%]) and two memory clinic‐based cohorts (Amsterdam Dementia Cohort [n=1964, MCI:36.1%] and the Alzheimer's Disease Research Centers of NACC: National Alzheimer’s Coordinating Center, funded by NIA/NIH Grant U24 AG072122 [n=2207, MCI:32.7%]). Mean age ranged from 62 to 65 years, and 40 to 61% were women. We performed automated segmentations of the nucleus accumbens, amygdala, caudate, hippocampus, pallidum, putamen and thalamus on T1‐MRI using Freesurfer. We determined the association between volumes of each subcortical structure and 5‐year risk of dementia in each cohort, using Cox proportional hazard models adjusted for various confounders and the other subcortical volumes.

Result

During 5 years of follow‐up, dementia was diagnosed in 371 individuals in NACC, 249 in ADC, and 321 in the Rotterdam Study. Consistently across all cohorts (Figure 1), smaller hippocampal volume was significantly associated with an increased dementia risk, driven by the progression to AD dementia rather than non‐AD dementia. Smaller amygdala volume was associated with an increased dementia risk in both memory clinic cohorts, with similar effect estimates in the population‐based cohort. Smaller thalamic volume was associated with dementia risk only in the population‐based cohort, driven by the progression from normal cognition to dementia (HR: 1.73 [95%CI:1.14‐2.63]), rather than from mild cognitive impairment to dementia (HR 0.83 [0.45‐1.54]). The association between the amygdala and dementia risk was similar for AD and non‐AD dementia in both memory clinic‐based populations, while in the population‐based cohort the association was only evident in progression to AD dementia.

Conclusion

Various subcortical structures play a role in the conversion to dementia. While the hippocampus and amygdala show consistent associations across populations, variation between populations for the thalamus and accumbens suggest differences in their role with underlying pathophysiology and disease stage.
Links between cognition and multivariate brain white matter differences in individuals with family history of Alzheimer's disease
Authors: Tremblay Stefanie A , Spreng R. Nathan , Wearn Alfie , Alasmar Zaki , Pirhadi Amir , Tardif Christine L , Chakravarty M. Mallar , Villeneuve Sylvia , Leppert Ilana R , Carbonell Felix , Medina Yasser Iturria , Steele Christopher J , Gauthier Claudine J
Published: January 2025

Background

Alzheimer’s disease (AD) is thought to result from a complex cascade of events involving several pathological processes. Recent studies have reported alterations in white matter (WM) microstructure in the early phase of AD, but WM remains understudied. We used a multivariate approach to capture the complexity and heterogeneity of WM pathologies and its links to cognition and AD risk factors in a more holistic manner.

Method

The MRI data of 134 cognitively unimpaired older adults with a family history of AD from the PREVENT‐AD cohort were analysed. Diffusion‐weighted imaging and multi‐echo magnetization transfer, proton density and T1‐weighted data were used to compute several WM metrics (see Fig. 1b‐c). We used the Mahalanobis distance (D2) to summarize the MRI metrics into a single score, indicative of the degree a voxel’s microstructure differs relative to a reference. Voxel‐wise D2 was computed for each subject relative to the group average of all other subjects using the MVComp tool and D2 maps were residualized for age (Fig. 2). Partial Least Squares (PLS) analyses were then performed to relate WM D2 with cognition (RBANS) and AD risk factors, separately in each sex.

Result

In males, there was only one significant latent variable (LV 1). There were extensive brain WM regions associated with this LV pattern: higher white matter D2, was associated with higher BMI, lower total cholesterol (likely due to lower HDL) and worse cognitive performance in all cognitive domains except attention (Fig. 2a‐b). In females, only the first LV was significant. Higher D2 in several WM tracts, including the inferior and superior longitudinal fasciculus, and the parahippocampal cingulum, was associated with lower education, and worse cognitive performance in all cognitive domains except attention and visuospatial construction. Higher WM D2 was also associated with several metabolic risk factors in females including higher SBP, higher BMI, higher glycated hemoglobin (HbA1c) and lower cholesterol (Fig. 2c‐d).

Conclusion

The different patterns of associations observed suggest there are sex‐specific risk profiles associated with WM microstructure differences in this population of older adults at risk of AD. The WM tracts affected in each sex were also associated with specific cognitive profiles.
Left frontoparietal control network connectivity moderates the effect of amyloid on cognitive decline in preclinical Alzheimer’s disease
Authors: Boyle Rory , Shirzadi Zahra , Coughlan Gillian T , Seto Mabel , Properzi Michael J , Klinger Hannah M , Townsend Diana L , Yuan Ziwen , Scanlon Catherine E , Jutten Roos J , Papp Kathryn V , Amariglio Rebecca E , Johnson Keith A , Rentz Dorene M , Chhatwal Jasmeer P. , Sperling Reisa A , Schultz Aaron P , Buckley Rachel F
Published: January 2025

Background

Stronger default mode (DMN) and bilateral frontoparietal control network (FPCN) resting‐state functional connectivity are associated with reduced ß‐amyloid (Aß)‐related cognitive decline in cognitively unimpaired older adults, who were predominantly Aß negative. This suggests that these networks might support cognitive resilience in the face of early AD pathology but it remains unclear whether these effects are apparent in preclinical AD. We investigated whether left‐FPCN, right‐FPCN, and DMN connectivity moderated the effect of Aß on cognitive decline using a large multi‐site dataset from the Anti‐Amyloid Treatment in Asymptomatic Alzheimer’s Disease (A4) study.

Method

Cognitively unimpaired participants with elevated Aß on screening amyloid‐PET (n = 1,062, mean age = 71.9 years, 60% women, see Table1), underwent functional MRI (3T) at baseline. We obtained connectivity estimates of the left‐FPCN, right‐FPCN, and DMN using template‐based rotation (Fig.1). Cognition was measured using the Preclinical Alzheimer’s Cognitive Composite (PACC; average follow‐up including open‐label extension period = 280 weeks [SD = 115]). We examined three‐way linear and quadratic interactions between functional connectivity and Aß burden with time in mixed effects models (model 2 = DMN, 3 = left‐FPCN, 4 = right‐FPCN). 1. PACC∼time + covariates×time 2‐4A. PACC∼FC×time + covariates×time 2‐4B. PACC∼FC×Aß×time + covariates×time 2‐4C. PACC∼FC×Aß×time + FC×Aß×time2 + covariates×time Covariates included age, education, APOEe4 status, ICV‐adjusted gray matter volume, treatment group (Solanezumab vs placebo), and head motion. We modeled random effects of intercept and slope for each participant and included scanner as a random effect.

Result

The three‐way quadratic interaction of connectivity and Aß burden with time was statistically significant for the left FPCN (p‐value corrected for multiple comparisons = .047, Fig.2), with a moderate effect size. That is, stronger left FPCN connectivity was associated with less cognitive decline in those with elevated Aß. The pattern of effects were similar, but not statistically significant once all covariates were included, for the right FPCN and DMN.

Conclusion

In a very large multi‐site clinical trial dataset, individuals with stronger left FPCN connectivity showed reduced Aß‐related cognitive decline. This effect was specific to the left‐FPCN, in line with previous cross‐sectional findings. Our finding demonstrates that this protective effect of left FPCN connectivity can be observed in preclinical AD.
Longitudinal Regional Flortaucipir Profiles in Preclinical Alzheimer’s Disease: Evaluation using A4 phase 3 trial with Solanezumab
Authors: Kotari Vikas , Holdridge Karen Chilcott , Yaari Roy , Schultz Aaron P , Johnson Keith A , Aisen Paul S. S. , Sperling Reisa A , Sims John R. , Shcherbinin Sergey
Published: January 2025

Background

The A4 Study (NCT02008357) was a multicenter, randomized, double‐blind, placebo‐controlled phase 3 trial, assessing the safety and efficacy of solanezumab in preclinical Alzheimer’s Disease (AD). We previously reported on the baseline regional flortaucipir (FTP) profiles in this study1. Here, we present the longitudinal tau profiles.

Methods

FTP scans were acquired at baseline and weeks 84,168, and 240. All FTP scans were analyzed using an established image processing pipeline to evaluate global[2] and regional tau patterns in template space. Global tau level was measured using an AD‐signature weighted volume‐of‐interest (VOI) (tauSUVR)2. Regional tau uptake and accumulation were measured in 3 prespecified composite regions representing tau pathology in preclinical AD, ordered by hypothetical pathologic spreading: 1) “early” (amygdala, entorhinal cortex, parahippocampal gyrus), 2) “middle” (fusiform, inferior, and middle temporal, and inferior parietal gyri), and 3) “late” (lateral occipital, posterior cingulate, superior parietal, and supramarginal gyri). We evaluated associations between baseline amyloid burden, age, sex, APOE e4 carriage and longitudinal tau change within placebo group.

Results

At baseline, a lower tau signal was observed generally in regions identified later in pathologic sequences. Longitudinal change was region‐ and visit‐specific. In both treatment groups, the early and middle regions showed the largest change from baseline at Week 84; the middle region showed the largest change at Weeks 168 and 240; the global assessment changes were smaller than the early and middle regions at each time point (figure 1; tables 1&2) and a majority of participants remained global “tau negative”3(tauSUVR<1.11 figure 2) throughout the trial. Higher baseline global amyloid level was most strongly associated with increased tau PET signal in the prespecified middle composite region at all timepoints (figure 3).

Conclusion

A multi‐regional composite VOI staging scheme was developed using baseline tau profiles and prespecified for analysis of longitudinal tau PET images. Results confirmed that cognitively unimpaired, amyloid positive participants demonstrate larger tau accumulation in the early and middle than late and neocortical regions. The early composite performed better at detecting tau accumulation initially and middle composite at later timepoints. This multi‐regional staging scheme appears to be useful to characterize tau progression in this preclinical AD cohort.
Diffusion‐Weighted MRI Brain Connectivity Assessment of Healthy Aging Trajectories in Marmosets
Authors: Szczupak Diego , Bhik‐Ghanie Rebecca , Zhang Bei , Papoti Daniel , Campos Vinicius , Dubberley Lauren R , Hitchens T Kevin , Yeh Fang‐Cheng , Schaeffer Lauren K Hayrynen , Carter Gregory W , Rizzo Stacey J Sukoff , Schaeffer David J , Silva Afonso C
Published: January 2025

Background

The common marmoset (Callithrix jacchus) is an important animal model in neuroscience and neurological diseases (e.g., Alzheimer’s disease ‐ AD), as they present primate‐specific evolutionary features such as an expanded frontal cortex. We established a new consortium with funding support from the National Institute on Aging to generate, characterize, and validate MArmosets as Research MOdels of AD (MARMO‐AD). This consortium develops and studies gene‐edited marmoset models carrying genetic risk for AD, comparing them against wild‐type aging marmosets from birth throughout their lifespan, using non‐invasive longitudinal assessments. Here, we aimed to characterize the structural cortical connectivity (white matter fibers) in a population of marmosets across the lifespan to establish healthy aging trajectories against which we will compare our genetically engineered marmoset models of AD.

Method

We performed high‐resolution (500 µm isotropic) diffusion‐weighted MRI (dMRI) in a cohort of 19 marmosets (13 males, 6 females) aged 8 to 89 months using a dedicated 9.4T 30cm bore MRI scanner (Bruker BioSpin Corp, Billerica). The animals were anesthetized under isoflurane and maintained at normal physiological conditions. The brain images were aligned and registered to the Marmoset Brain Mapping (MBM) V2 template. The brain was segmented using the MBM white matter atlas, and voxel‐based morphology (VBM) was used to quantify regional white matter volume in the left and right hemispheres. Furthermore, we used DSI STUDIO to perform the whole‐brain tractogram, calculate network‐based statistics (NBS), and correlate them with age.

Result

We measured decreases in the NBS properties of Assortativity, Small Worldliness, and Hierarchy with age but an increase in Efficiency, showing the maturation processes of white matter across development. We are actively working on increasing our sample size to identify the entire marmoset age trajectory and its impact on white matter connectivity.

Conclusion

Our work is the first to thoroughly describe the healthy aging trajectories of the marmoset brain, a valuable model for age‐related neuropathologies (e.g., AD). This research will establish normative baselines for changes in marmoset brain connectivity with aging that will be used to evaluate our genetically engineered marmoset models of AD, which is the goal of the MARMO‐AD consortium.
The Relationship of Neuropsychiatric Symptoms and ADRD Pathologies
Authors: Bacci Julia R. , Rudolph Marc D. , Mielke Michelle M. , Craft Suzanne , Bateman James R. , Lockhart Samuel N.
Published: January 2025

Background

Neuropsychiatric symptoms (NPS) in the Alzheimer’s Disease (AD) clinical spectrum are common, yet the pathological underpinnings are unclear. Prior research has been inconsistent, attributed in part to concomitant co‐pathologies. Additionally, prior modeling approaches have disregarded the distributional properties of right‐skewed NPS data and utilized predominantly racially homogenous cohorts. Thus, we examined the relationship between NPS and markers of vascular injury and neurodegeneration using generalized linear models (GLM) in a diverse community‐based sample. Of additional interest was whether these relationships differed by cognitive status or race.

Methods

We evaluated cross‐sectional relationships of brain white matter hyperintensity volume (WMHv) and plasma neurofilament light chain (pNfL) to NPS (NPI‐Q total severity scores) in 588 Wake Forest ADRC Clinical Core study participants. Analyses included participants with consensus diagnoses of cognitively unimpaired (CU; N = 348) and mild cognitive impairment (MCI; N = 240). WMHv and pNfL were log‐transformed prior to model entry. GLMs included age, sex, education, race, and cognitive status as covariates and pathologies as predictor variables. Secondary models included aforementioned covariates and pathology by (a) cognitive status and (b) race interaction terms to investigate how these associations differed.

Results

WMHv was not significantly associated with NPS (N = 561; ß = 0.203; p = 0.053); further, we did not observe an interaction between cognitive status and WMHv (N = 561; ß = ‐0.055; p = 0.800). However, a significant race by WMHv interaction on NPI‐Q was demonstrated (N = 561; ß = ‐0.414; p = 0.016). In White participants, elevated WMHv was significantly related to elevated NPS (N = 434; ß = 0.325; p = 0.006); whereas in Black/African American participants elevated WMHv was significantly related to lower NPS (N = 119; ß = ‐0.239; p = 0.049). pNfL was not significantly associated with NPS (N = 506; ß = 0.149; p = 0.576). A significant pNfL by cognitive status interaction on NPI‐Q was found (N = 506; ß = ‐1.055; p = 0.029; Figure 2), where elevated pNfL was related to elevated NPS in MCI participants (N = 198; ß = 1.656; p = 0.004) but not CU participants (N = 308; ß = ‐0.316; p = 0.260). However, we did not observe an interaction between race and pNfL (N = 506; ß = 0.185; p = 0.678).

Conclusion

Consistent with previous work we demonstrate associations of pNfL and WMHv with NPS. Future analyses with this cohort will examine additional biomarkers, including amyloid‐beta and tau measured in CSF, plasma, and PET.
Association of APOE/TOMM40 Haplotypes and Limbic System White Matter Microstructure in Black and White Individuals
Authors: Mooney Katelyn Elizabeth , Archer Derek B. , Hohman Timothy J. , Sathe Aditi , Kadiri Ose , Barnes Lisa L. , Deters Kacie
Published: January 2025

Background

APOE is in linkage disequilibrium with the length of poly‐T repeats at the rs10524523 (‘523) locus of the TOMM40 gene. APOE‐e3 is associated with short (S) and (VL) variants of ‘523 in white and Black individuals. In white individuals, APOE‐e4 is associated with the long (L) ‘523 variant, but is associated with ‘523‐S, ‘523‐L, and ‘523‐VL variants in Black individuals. Black e3‐’523‐S carriers have faster rates of cognitive decline while Black e4‐’523‐S carriers have slower rates of cognitive decline. Little is known about the association of these haplotypes with white matter integrity. This study aims to determine the association between APOE/TOMM40 haplotypes and white matter microstructure (WMM) in tracts important for cognition and risk of AD.

Methods

Data comes from participants without cognitive impairment from the Rush Memory and Aging Project and the Minority Aging Research Study who self‐identified as non‐Hispanic white (N=500) and non‐Hispanic Black (N=144). WMM was measured using diffusion MRI (dMRI). dMRI was preprocessed using PreQual and microstructural metrics were calculated in DTIFIT and MATLAB. Data was harmonized using Longitudinal ComBat. We assessed free‐water (FW; higher values are abnormal) and FW‐corrected fractional anisotropy (FAFW‐corr; lower values are abnormal) in five limbic tracts that are associated with AD: the inferior longitudinal fasciculus, cingulum, fornix, inferior temporal gyrus, and uncinate fasciculus. The effect of 0, 1, or 2 copies of ‘523‐S on FW and FAFW‐corr was assessed using linear regression models, controlling for age, sex/gender, and education.

Results

White e3/e3 participants with two copies of ‘523‐S had lower FAFW‐corr in the cingulum (ß=‐0.006, SE=0.003, P=0.031) and inferior temporal gyrus (ß=‐0.007, SE=0.003, P=0.042) (Table). Black e4+ participants with one‐‘523‐S had lower FW in cingulum (ß=‐0.024, SE=0.01, P=0.020) and inferior longitudinal fasciculus (ß=‐0.0304, SE=0.013, P=0.026) (Table).

Conclusions

This is the first study examining APOE/‘523 variation on WMM in Black participants, and the first to examine this association using FW‐corrected metrics. These results support prior findings that ‘523‐S copy‐number is deleterious in White‐e3/e3 participants, but protective in Black‐e4+ participants. These findings suggest a differential effect by racialized background of APOE on brain microstructural integrity in older adults.
Aging, beta‐amyloid deposition, and brain functional connectivity decline
Authors: Yi Dahyun , Chumin Evgeny J. , Byun Min Soo , Cha Woo‐Jin , Ahn Hyejin , Kim Yu Kyeong , Kang Koung Mi , Sohn Chul‐Ho , Risacher Shannon L. , Sporns Olaf , Nho Kwangsik , Saykin Andrew J. , Lee Dong Young
Published: January 2025

Background

Changes in brain network organization are influenced by aging. Accumulation of amyloid‐beta (Aß) and neurodegeneration in the neocortex are also expected to alter neuronal networks. Therefore, we examined the relationship between aging and brain functional connectivity (FC), as well as the effect of brain Aß on this relationship.

Method

Resting state functional MRI (rsfMRI) from 594 participants spanning age and diagnostic severity of AD from the Korean Brain Aging Study for the Early Diagnosis and Prediction of AD (KBASE) was preprocessed as previously described in studies conducted at the Indiana AD Research Center (Chumin 2021, 2023). Cortical FC data from 200 regions (Schaefer 2018) grouped into 7 canonical resting state networks (RSN; Yeo 2011) was used to compute a network segregation measure (ratio of within‐ to between‐network connectivity (Chan 2014); here used as an index of FC) across all RSNs. Additionally, a subsample of older participants was classified as Aß positive or negative based on global amyloid in Centiloid units (Klunk 2015).

Result

Intrinsic network connectivity was reduced with increasing age beginning in young adulthood (Fig, left), resulting in a dedifferentiated, or less segregated, network architecture (t = ‐4.79, p = 0.000002). The relationship between age and network segregation was significant in the Aß negative group (t = ‐4.09, p = 0.00005); however, such relationship was not found in the Aß positive group (Fig, right). Fitted Aß values were significantly different (Welch Two Sample t‐test: p < 2.2e‐16).

Conclusion

This preliminary study elucidates age‐related decline of brain FC, quantified as network segregation, from young adulthood to late‐life, wherein RSN communication become less coherent, manifesting as a degeneration of FC structure. Such age‐related reduction pattern of brain connectivity appears disappear under the presence of pathological Aß deposition in brain.