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INTRODUCTION: Anti‐amyloid antibody therapies such as lecanemab are increasingly being used to treat Alzheimer's disease (AD). These therapies are associated with a high rate of amyloid‐related imaging abnormalities (ARIA). METHODS: We review the case history of a patient who developed ARIA associated with lecanemab treatment. RESULTS: In addition to microhemorrhages and cerebral edema that are recognized features of ARIA, the patient developed several ischemic strokes. The patient also experienced frequent electrographic seizures without overt clinical seizures. The patient demonstrated clinical and radiographic improvement after steroid treatment. DISCUSSION: Our case suggests that ischemic strokes may be a feature of ARIA and highlights the importance of having a high clinical suspicion for seizures in ARIA. As anti‐amyloid therapies are likely going to be increasingly used to treat AD, it is important to appreciate the spectrum of clinical and radiographic findings that can result as side effects from this class of therapies. Highlights: We report a patient who developed severe amyloid‐related imaging abnormalities (ARIA) after treatment with lecanemab.Our report suggests that ischemic strokes may be a novel imaging feature of ARIA.Our report highlights the need for high clinical suspicion for seizures in ARIA. [ABSTRACT FROM AUTHOR]

This paper for the 20th anniversary of the Alzheimer's Disease Neuroimaging Initiative (ADNI) provides an overview of magnetic resonance imaging (MRI) of medial temporal lobe (MTL) subregions in ADNI using a dedicated high‐resolution T2‐weighted sequence. A review of the work that supported the inclusion of this imaging modality into ADNI Phase 3 is followed by a brief description of the ADNI MTL imaging and analysis protocols and a summary of studies that have used these data. This review is supplemented by a new study that uses novel surface‐based tools to characterize MTL neurodegeneration across biomarker‐defined AD stages. This analysis reveals a pattern of spreading cortical thinning associated with increasing levels of tau pathology in the presence of elevated amyloid beta, with apparent epicenters in the transentorhinal region and inferior hippocampal subfields. The paper concludes with an outlook for high‐resolution imaging of the MTL in ADNI Phase 4. Highlights: As of Phase 3, the Alzheimer's Disease Neuroimaging Initiative (ADNI) magnetic resonance imaging (MRI) protocol includes a high‐resolution T2‐weighted MRI scan optimized for imaging hippocampal subfields and medial temporal lobe (MTL) subregions.These scans are processed by the ADNI core to obtain automatic segmentations of MTL subregions and to derive morphologic measurements.More detailed granular examination of MTL neurodegeneration in response to disease progression is achieved by applying surface‐based modeling techniques.Surface‐based analysis of gray matter loss in MTL subregions reveals increasing and spatially expanding patterns of neurodegeneration with advancing stages of Alzheimer's disease (AD), as defined based on amyloid and tau positron emission tomography biomarkers in accordance with recently proposed criteria.These patterns closely align with post mortem literature on spread of pathological tau in AD, supporting the role of tau pathology in the presence of elevated levels of amyloid beta as the driver of neurodegeneration. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Regional glucose hypometabolism resulting in glutamate loss has been shown as one of the characteristics of Alzheimer's disease (AD). Because the impact of AD varies between the sexes, we utilized glutamate‐weighted chemical exchange saturation transfer (GluCEST) magnetic resonance imaging (MRI) for high‐resolution spatial mapping of cerebral glutamate and investigated subregional changes in a sex‐specific manner. METHODS: Eight‐month‐old male and female AD mice harboring mutant amyloid precursor protein (APPNL‐F/NL‐F: n = 36) and wild‐type (WT: n = 39) mice underwent GluCEST MRI, followed by proton magnetic resonance spectroscopy (1H‐MRS) in hippocampus and thalamus/hypothalamus using 9.4T preclinical MR scanner. RESULTS: GluCEST measurements revealed significant (p ≤ 0.02) glutamate loss in the entorhinal cortex (% change ± standard error: 8.73 ± 2.12%), hippocampus (11.29 ± 2.41%), and hippocampal fimbriae (19.15 ± 2.95%) of male AD mice. A similar loss of hippocampal glutamate in male AD mice (11.22 ± 2.33%; p = 0.01) was also observed in 1H‐MRS. DISCUSSIONS: GluCEST MRI detected glutamate reductions in the fimbria and entorhinal cortex of male AD mice, which was not reported previously. Resilience in female AD mice against these changes indicates an intact status of cerebral energy metabolism. Highlights: Glutamate levels were monitored in different brain regions of early‐stage Alzheimer's disease (AD) and wild‐type male and female mice using glutamate‐weighted chemical exchange saturation transfer (GluCEST) magnetic resonance imaging (MRI). Male AD mice exhibited significant glutamate loss in the hippocampus, entorhinal cortex, and the fimbriae of the hippocampus. Interestingly, female AD mice did not have any glutamate loss in any brain region and should be investigated further to find the probable cause. These findings demonstrate previously unreported sex‐specific glutamate changes in hippocampal sub‐regions using high‐resolution GluCEST MRI. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Typical MRI measures of neurodegeneration have limited sensitivity in early disease stages. Diffusion MRI (dMRI) microstructural measures may allow for detection in preclinical stages. METHODS: Participants had dMRI and either beta‐amyloid PET or plasma biomarkers of Alzheimer's pathology within 18 months of MRI. Microstructure was measured in portions of the medial temporal lobe (MTL) with high neurofibrillary tangle (NFT) burden based on a previously developed post mortem 3D‐map. Regressions examined relationships between microstructure and markers of Alzheimer's pathology in preclinical disease and then across disease stages. RESULTS: There was higher isometric volume fraction in amyloid‐positive compared to amyloid‐negative cognitively unimpaired individuals in high tangle MTL regions. Similarly, plasma biomarkers and 18F‐flortaucipir were associated with microstructural changes in preclinical disease. Additional microstructural effects were seen across disease stages. DISCUSSION: Combining a post mortem atlas of NFT pathology with microstructural measures allows for detection of neurodegeneration in preclinical Alzheimer's disease. Highlights: Typical markers of neurodegeneration are not sensitive in preclinical Alzheimer's.dMRI measured microstructure in regions with high NFT.Microstructural changes occur in medial temporal regions in preclinical disease.Microstructural changes occur in other typical Alzheimer's regions in later stages.Combining post mortem pathology atlases with in vivo MRI is a powerful framework. [ABSTRACT FROM AUTHOR]

INTRODUCTION: We investigate pathological correlates of plasma phosphorylated tau 181 (p‐tau181), glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL) across a clinically diverse spectrum of neurodegenerative disease, including normal cognition (NormCog) and impaired cognition (ImpCog). METHODS: Participants were NormCog (n = 132) and ImpCog (n = 461), with confirmed β‐amyloid (Aβ+/‐) status (cerebrospinal fluid, positron emission tomography, autopsy) and single molecule array plasma measurements. Logistic regression and receiver operating characteristic (ROC) area under the curve (AUC) tested how combining plasma analytes discriminated Aβ+ from Aβ‐. Survival analyses tested time to clinical dementia rating (global CDR) progression. RESULTS: Multivariable models (p‐tau+GFAP+NfL) had the best performance to detect Aβ+ in NormCog (ROCAUC = 0.87) and ImpCog (ROCAUC = 0.87). Survival analyses demonstrated that higher NfL best predicted faster CDR progression for both Aβ+ (hazard ratio [HR] = 2.94; p = 8.1e‐06) and Aβ‐ individuals (HR = 3.11; p = 2.6e‐09). DISCUSSION: Combining plasma biomarkers can optimize detection of Alzheimer's disease (AD) pathology across cognitively normal and clinically diverse neurodegenerative disease. Highlights: Participants were clinically heterogeneous, with autopsy‐ or biomarker‐confirmed Aβ.Combining plasma p‐tau181, GFAP, and NfL improved diagnostic accuracy for Aβ status.Diagnosis by plasma biomarkers is more accurate in amnestic AD than nonamnestic AD.Plasma analytes show independent associations with tau PET and post mortem Aβ/tau.Plasma NfL predicted longitudinal cognitive decline in both Aβ+ and Aβ‐ individuals. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Individuals in socioeconomically disadvantaged neighborhoods exhibit increased risk for impaired cognitive function. Whether this association relates to the major dementia‐related neuropathologies is unknown. METHODS: This cross‐sectional study included 469 autopsy cases from 2011 to 2023. The relationships between neighborhood disadvantage measured by Area Deprivation Index (ADI) percentiles categorized into tertiles, cognition evaluated by the last Mini‐Mental State Examination (MMSE) scores before death, and 10 dementia‐associated proteinopathies and cerebrovascular disease were assessed using regression analyses. RESULTS: Higher ADI was significantly associated with lower MMSE score. This was mitigated by increasing years of education. ADI was not associated with an increase in dementia‐associated neuropathologic change. Moreover, the significant association between ADI and cognition remained even after controlling for changes in major dementia‐associated proteinopathies or cerebrovascular disease. DISCUSSION: Neighborhood disadvantage appears to be associated with decreased cognitive reserve. This association is modified by education but is independent of the major dementia‐associated neuropathologies. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Variability in relationship of tau‐based neurofibrillary tangles (T) and neurodegeneration (N) in Alzheimer's disease (AD) arises from non‐specific nature of N, modulated by non‐AD co‐pathologies, age‐related changes, and resilience factors. METHODS: We used regional T‐N residual patterns to partition 184 patients within the Alzheimer's continuum into data‐driven groups. These were compared with groups from 159 non‐AD (amyloid "negative") patients partitioned using cortical thickness, and groups in 98 patients with ante mortem MRI and post mortem tissue for measuring N and T, respectively. We applied the initial T‐N residual model to classify 71 patients in an independent cohort into predefined groups. RESULTS: AD groups displayed spatial T‐N mismatch patterns resembling neurodegeneration patterns in non‐AD groups, similarly associated with non‐AD factors and diverging cognitive outcomes. In the autopsy cohort, limbic T‐N mismatch correlated with TDP‐43 co‐pathology. DISCUSSION: T‐N mismatch may provide a personalized approach for determining non‐AD factors associated with resilience/vulnerability in AD. [ABSTRACT FROM AUTHOR]

Background: Longitudinal measurement of medial temporal lobe (MTL) atrophy is shown to be effective in measuring Alzheimer's disease (AD) progression and has been extensively studied on T1‐weighted MRI. However, there has been little work analyzing atrophy on high‐resolution T2‐weighted MRI of the hippocampal region, and it is unknown which MRI modality would yield a more effective biomarker of disease progression. We compare atrophy measurements derived from T1 and T2‐weighted MRI using a deep learning framework DeepAtrophy and a deformation‐based morphometry method ALOHA. Method: T2‐weighted MRI of 481 participants from ADNI2/3 are included in the study. DeepAtrophy was trained on 74 participants and evaluated on 407. During training, DeepAtrophy explicitly infers temporal information, such as the scan temporal order for a single scan pair and which image pair has a longer scan time interval for multiple scan pairs. During inference, DeepAtrophy predicts a "total progression score" for each scan pair. This score is expected to be higher (more progression) in individuals with more advanced AD. A similar experiment was conducted using T1‐weighted MRI from 481 ADNI2/GO participants (partially overlapping with T2‐weighted experiment). ALOHA pipelines tailored for T1‐weighted and hippocampal T2‐weighted MRI were evaluated on the same subjects and scan pairs as DeepAtrophy. Result: For both T1 and T2‐weighted MRI, DeepAtrophy is more accurate, compared with ALOHA, in inferring the temporal order of a pair of longitudinal scans. In hypothetical one‐year and two‐years clinical trials scenarios simulated using ADNI data, DeepAtrophy has an improvement over ALOHA in detecting differences in progression between disease groups in both imaging modalities. The strongest effect for differentiating between Amyloid‐positive and Amyloid‐negative cognitively unimpaired group was obtained using DeepAtrophy progression measures on T2‐weighted MRI. Conclusion: This study supports the potential of high‐resolution focal T2‐weighted MRI of the hippocampus as a longitudinal biomarker of early AD progression suitable for future clinical trials. [ABSTRACT FROM AUTHOR]

Background: Few prior studies have investigated whether genetic risk for Alzheimer's disease (AD) can manifest through altered neurodevelopment of AD‐vulnerable brain regions. This pre‐registered study tests the effects of AD polygenic risk on morphometric neurodevelopment across 3 large datasets totaling 8,364 youths aged 3‐22 (Table 1) Method: Polygenic risk score (PRS) was calculated using PRS‐CS software, with summary scores from Kunkle et al. (2021) for African ancestry individuals (AA) and Jansen et al. (2019) for European ancestry individuals (EA). For EA only, PRS were calculated with (PRS‐APOE+) and without (PRS‐APOE‐) the APOE locus included. All individuals had T1‐weighted MRI processed with FreeSurfer that passed quality assessment checks. ComBAT was used to harmonize sites within‐cohort. Primary analyses investigated effects of PRS, age and their interaction on bilateral hippocampal volume and AD MetaROI cortical thickness using generalized additive models. Age interactions were not evaluated in ABCD due to limited age range. Exploratory analysis examined effects of age and PRS on cortical thickness, volume, surface area and subcortical volume across Desikan‐Killiany regions, adjusting for multiple comparisons (FDR). For exploratory analyses only, ABCD was split into training and test sets. All analyses were conducted for EA and AA separately; sex, total intracranial volume, image quality (Euler number) and ten genetic principal components were included as model covariates. Result: Table 1 summarizes primary results. Hippocampal volume increased with age in 3/6 datasets, while MetaROI thickness decreased with age in all datasets. Surprisingly, increased PRS was associated with larger hippocampi in three datasets. In one dataset only, greater PRS was associated with increased metaROI thickness (Fig 1). 2/3 AA datasets showed age*PRS interactions on hippocampal volume, but in opposite directions. An age*PRS interaction on metaROI thickness was found in one dataset (Fig. 2A‐C). For exploratory analyses, no regions survived multiple comparisons for any contrast in 5/6 datasets; APOE only PRS was associated with larger supramarginal surface area in one dataset (Fig 2E). Effects were not correlated across studies (Fig 2D) Conclusion: This large, pre‐registered study provides little evidence that AD genetic risk manifests during pediatric brain morphometric development. [ABSTRACT FROM AUTHOR]

Background: We sought to validate two structural MRI‐based brain health models: Spatial Pattern of Atrophy for REcognition of Alzheimer's disease (SPARE‐AD) and SPARE‐Brain Age Gap (SPARE‐BAG), which estimates the discrepancy between biological brain age and chronological age. These models' generalization to non‐amnestic AD (naAD) syndromes and associations with positron emission tomography (PET) and plasma biomarkers remain underinvestigated. We hypothesized that SPARE‐AD scores in naAD would be lower (less abnormal) than in amnestic AD (aAD) but elevated (more abnormal) relative to controls; and that SPARE‐AD but not SPARE‐BAG scores would be associated with PET and plasma biomarkers of AD. Method: SPARE‐AD and SPARE‐BAG scores were estimated for each of 1607 MRI scans from 277 cognitively normal adults (62.8% female; mean age = 69.5 years) and 902 clinical participants (54.2% female; mean age = 73.1 years), including 585 with amnestic mild cognitive impairment (aMCI) or aAD; 56 with naAD syndromes; 28 with vascular disease; 28 with Lewy body disorders (LBD); 18 with suspected frontotemporal lobar degeneration (FTLD); 56 with non‐amnestic MCI of uncertain etiology; and 131 with other clinical diagnoses. We used mixed effects models adjusting for age, sex, and global cognition to test group differences in SPARE‐AD and SPARE‐BAG. Additionally, we assessed associations with tau (n = 122) and amyloid‐beta (n = 199) PET imaging and plasma‐based phosphorylated tau‐181 (p‐tau181) and glial fibrillary acidic protein (GFAP) (n = 358). Result: SPARE‐AD scores were elevated vs. controls in aMCI/aAD, naAD, vascular disease, FTLD, and naMCI (all p<0.003) but not in LBD or other diagnoses (p>0.1). NaAD patients had lower SPARE‐AD (p<0.02) but higher SPARE‐BAG scores (p<0.02) than aMCI/aAD patients. SPARE‐AD scores were positively associated with mean cortical tau PET uptake, amyloid PET positivity, and plasma p‐tau181 and GFAP (all p<0.001). In contrast, SPARE‐BAG scores were not associated with PET or plasma biomarkers (all p>0.05). Conclusion: SPARE‐AD values for naAD syndromes were intermediate between those of controls and aMCI/AD patients, likely reflecting divergent, partially overlapping cortical atrophy relative to aAD. PET and plasma biomarkers of AD neuropathologic change were associated with SPARE‐AD but not with SPARE‐BAG scores, suggesting SPARE‐BAG reflects age‐related atrophy that is largely independent of AD. [ABSTRACT FROM AUTHOR]

Background: Aging is among the largest risk factors for neurodegeneration which can be accelerated by age‐related pathologies. Hence, individuals may age at different rates reflected by a gap in biological estimates of age and actual chronological age. Graph neural networks (GNN) are deep learning models that leverage spatial information inherent in the dataset for inference. Here, we develop an MRI cortical thickness (CT) based brain age prediction framework using GNNs and illustrate that it provides a feasible mechanism to identify contributing regions leading to elevated brain age gap in Alzheimer's disease (AD). Method: We used ANTs to calculate CT in 100 parcels from 3T MRI of 170 healthy controls (HC; age = 64.26±8.27y, 101 females), 53 mild cognitive impairment (MCI; age = 68.56±8.58y, 22 females), and 62 AD (age = 67.25±8.83y, 30 females) individuals. A GNN model based on the anatomical covariance matrix from HC cohort was trained to predict chronological age from associated CT for HC cohort. GNN output was evaluated as an unweighted mean of entities associated with the brain regions in the final layer. Hence, individual contributions to the GNN output by each brain region could be quantified. Final brain age prediction was obtained after removing age‐bias from GNN outputs using linear regression model. For all subjects, we evaluated residuals at regional level (regional age‐gap) by comparing the contribution of each parcel with corresponding GNN output. The residual between predicted brain age and chronological age (age‐gap) was also evaluated. See Figure 1 for an overview. Result: ANCOVA (with correction for age and gender) with post‐hoc analyses revealed that regional age‐gap was greater for AD relative to HC in inferior parietal, temporal pole, inferior temporal, medial temporal, and superior frontal regions (Bonferroni corrected p‐value<0.05). Also, regional age‐gap was greater for MCI relative to HC for inferior parietal and temporal pole regions (Figure 2). Subsequently, age‐gap was elevated in AD (mean = 5.64y) and MCI (mean = 2.66y) relative to HC (mean = 0y) reflecting a linear increase in age‐gap with disease stage (Figure 3). Conclusion: Our novel framework identified regional CT associations with age‐gap in AD and MCI, suggesting that this GNN approach may be used to further our understanding between aging and neurodegeneration. [ABSTRACT FROM AUTHOR]

Background: Brain functional connectivity (FC) measures derived from resting‐state fMRI (rsfMRI) data have advanced our understanding of the brain organization. However, most existing studies investigate the brain functional networks (FNs) at a specific scale, ignoring potentially complementary FC information at different levels of spatial granularity as well as the cross‐scale information. Given that the brain is a multi‐scale, hierarchical system, FCs between FNs at different spatial scales may provide complementary information to those measured within‐scales for characterizing the aging brain. Method: We computed multiscale FNs to capture their FC measures across multiple scales and investigated the associations between cross‐scale FCs and the brain aging based on rsfMRI data of a diverse cohort (n = 4186, aged 22 to 97 years) from seven sites of the iSTAGING study [1]. For each subject, we computed the FNs at seven scales, yielding seven sets of k (k = 17, 25, 50, 75, 100, 125 and 150) FNs, same as our previous study [2‐3]. We estimated FC measures between the 542 FNs using Pearson's correlation, yielding the cross‐scale FC matrices that were harmonized across sites in the tangent space using ComBat‐GAM [4] with sex and age as covariates. The harmonized FC measures were used as features to build an age prediction model using Ridge regression as illustrated in Figure 1. The brain age prediction model was compared with that built on harmonized FC measures between FNs of the same scales, with the performance estimated with a nested five‐fold cross‐validation. Result: The cross‐scale FC measures performed significantly better than the within‐scale FC measures for the brain age prediction, as summarized in Table 1. Figure 2 shows the most informative FC measures for the brain age prediction. Specifically, FC measures computed both cross‐ and within‐scales between the visual, somatomotor, and frontoparietal networks provided relatively more useful information for the brain age prediction than FC measures between other FNs. Conclusion: Our study demonstrated that the cross‐scale FC measures provided complementary information to the within‐scale FC measures for predicting the brain age, facilitating enhanced understanding of aging related changes of the brain functional organization. [ABSTRACT FROM AUTHOR]

Background: While α‐synuclein (αSyn) is the primary pathology associated with Lewy body spectrum disorders (LBSD), concomitant Alzheimer's disease (AD) pathology is common and is predictive of clinical outcomes, including worse cognitive impairment and shorter survival. Thus, AD plasma biomarkers may help identify clinically relevant co‐pathology in LBSD to improve prognosis and clinical management. Plasma phosphorylated tau 181 (p‐tau181) and glial fibrillary acidic protein (GFAP) are two biomarkers that have been well‐studied in canonical AD, showing reliable increases with AD neuropathological change (ADNC). However, their utility in autopsy‐confirmed LBSD is relatively untested. Method: In autopsy‐confirmed LBSD with a neuropathological diagnosis of αSyn, we tested how plasma p‐tau181 and GFAP differed across αSyn with concomitant ADNC (αSyn+AD; n = 19) and αSyn without AD (αSyn; n = 30). αSyn+AD was defined by intermediate/high ADNC and αSyn had no/low ADNC; cognitively normal controls and AD without αSyn were included as reference groups (Table 1). Severity of burden was scored on a semi‐quantitative scale for β‐amyloid and tau, and scores were averaged across sampled brainstem, limbic, and neocortical regions. Plasma analytes were log‐transformed in linear models, and models covaried for plasma‐to‐death, age at plasma, and sex. Receiver operating characteristic (ROC) analyses and area under the curve (AUC) tested diagnostic utility of plasma analytes. Result: Plasma GFAP was significantly higher in αSyn+AD compared to αSyn (Figure 1), confirmed by linear models (β = 0.31, 95%CI = 0.065 – 0.56, p = 0.015); plasma p‐tau181 was not (p = 0.37). Next, linear models tested associations of AD pathological hallmarks with both plasma analytes. GFAP was significantly associated with brain β‐amyloid (β = 15, 95%CI = 6.1 – 25, p = 0.0018) and tau burden (β = 12, 95%CI = 2.5 – 22, p = 0.015); in the same model plasma p‐tau181 was not associated with either (both p>0.34). ROC analyses showed that neither analyte had an AUC>0.80 (Figure 2); GFAP had somewhat higher AUC of ADNC in LBSD (0.71; 90%CI = 0.58 – 0.83) than plasma p‐tau181 (AUC = 0.64; 90%CI = 0.50 – 0.77). Conclusion: Results demonstrate elevated plasma GFAP in LBSD with concomitant AD pathology, in association with accumulation of β‐amyloid plaques. Findings highlight the importance of validating utility of AD biomarkers in LBSD, and the need for plasma biomarker strategies to detect concomitant AD in LBSD. [ABSTRACT FROM AUTHOR]

Background: Developing effective Alzheimer's disease (AD) biomarkers in cognitively normal (CN) individuals is crucial. Prior studies have identified neuroimaging and cognitive measures that are sensitive to preclinical AD. However, the generalizability of these biomarkers in large, multi‐site cohorts and their power to predict disease progression remain unclear. Structural MRI, tau PET and standardized cognitive tests of 2819 CN adults were pooled from three studies to investigate the power of these measures in discriminating β‐amyloid positivity (A+/‐) and predicting disease progression. Methods: T1‐weighted MRI and cognitive data of 2819 CN (A‐/A+: 1180/1540, Table 2) individuals from ADNI, HABS and A4 were included. Baseline medial temporal lobe structural measures were extracted from MRI (complete list in Table 1). For participants with prospective longitudinal MRI or cognitive measures (4.5 years), annualized change rate of each measurement was estimated. ANCOVA and receiver operating characteristic analyses were used to test biomarker differences between A+/A‐. In 563 CN with cross‐sectional tau PET available, stepwise linear mixed effect modeling was performed to identify the subset of baseline cross‐sectional biomarkers (tau, MRI and cognition) that yields the optimal model (smallest Akaike information criterion) in predicting longitudinal atrophy and cognitive decline. The area under the curve (AUC) of each model in discriminating the first (fast) and last (slow progressors) tertiles of each longitudinal measurement was reported. Results: Significant differences between A+/A‐ (Table 2) were observed in cross‐sectional tau (most significant), cross‐sectional and longitudinal cognitive, as well as longitudinal structural MRI measures. When predicting disease progression with baseline measures (Figure 1‐a), tau (selected in all the models) and cognitive biomarkers were included in the most predictive models while MRI biomarkers did not provide additional information. The selected biomarkers can identify fast versus slow progressors with AUCs ranging from 0.63‐0.78 (Figure 1‐b). Conclusions: The results demonstrated that both cross‐sectional (tau PET and cognition) and longitudinal biomarkers (MRI and cognition) are sensitive to amyloid status in CN. In addition, baseline tau PET and cognitive measures provide complementary information in predicting disease progression regardless of amyloid status. This indicates that these biomarkers may have important utility in preclinical AD clinical trials and normal aging studies. [ABSTRACT FROM AUTHOR]

Background: It is assumed quantitative digital neuropathology methods will provide more information than semi‐quantitative ratings, but this presumption lacks formal assessment. Here, we compare quantitative, whole‐slide measurements of phosphorylated tau tangles and threads with semi‐quantitative tau pathology ratings. We evaluate whether quantitative measurements provide additional prediction beyond Braak stage for variables associated with tau pathology, examining the association of quantitative tau measurements in high Braak stages with age at death and measures of severity of TDP‐43 and alpha‐synuclein co‐pathologies. We hypothesized that even within the same high Braak stage, quantitative tau measurements would decrease with age at death, as younger patients often require more tau pathology for disease severity comparable to older patients, and increase with higher co‐pathology ratings, since prior work suggests co‐pathologies increase with disease severity. Method: We scanned 6µm tissue sections stained with phosphorylated tau PHF1 in 14 brain regions from 215 patients with Alzheimer's Disease continuum neuropathological diagnoses (53% male, age = 74.37 ± 11.51). A machine learning method (Figure 1) generated summary measures of tangle and thread pathology for each slide (Yushkevich et al., 2021, Sadaghiani et al., 2022). Semi‐quantitative tau ratings (0‐3) were also available in nine regions and compared to quantitative measurements in matching regions. In 150 patients with high Braak stages (V/VI), correlation analyses were performed using summary scores averaging semi‐quantitative ratings of TDP‐43 in medial temporal lobe regions ("TDP‐43 severity"), and alpha‐synuclein in commonly affected regions ("alpha‐synuclein severity"). Result: Figure 2 shows high overall agreement between quantitative tau measures and corresponding semi‐quantitative ratings. When evaluating quantitative tau measurements in high Braak stage patients, after Bonferroni correction we found significant negative associations between age and tangles in middle frontal cortex, orbital frontal cortex (OFC), and thalamus, and between age and threads in OFC. We found significant positive association between TDP‐43 severity and tangles in substantia nigra, but no associations with alpha‐synuclein severity (Figure 3). Conclusion: Quantitative tau measurements largely agree with established semi‐quantitative ratings. Significant associations of quantitative tau measurements with TDP43 pathology and age were found within high Braak stage patients, suggesting quantitative pathology measurements can offer additional information beyond stage alone in these patients. [ABSTRACT FROM AUTHOR]

Background: Minimally invasive diagnostic tools are needed to better characterize heterogeneous groups of patients with mild cognitive impairment (MCI), pre‐MCI, and dementia. At DiamiR, we developed an approach based on targeted selection and quantitative analysis of microRNAs enriched in specific brain regions, present in synapses, and detectable in blood plasma. microRNAs are short, non‐coding regulatory molecules whose levels change in disease. Method: Concentrations of 24 brain‐enriched and inflammation‐associated microRNAs (Table 1) were analyzed in plasma samples of 200 study participants with the following clinical diagnoses at the time of blood draw: 76 cognitively unimpaired (CU), 85 MCI, and 39 Alzheimer's disease (AD). The demographic characteristics and clinical data of the study participants are summarized in Table 2. The plasma samples were collected at the Penn Alzheimer's Disease Research Center (ADRC). Based on amyloid status (Abeta+/‐), APOE genotype (APOE4 +/‐), and sex, each sample was assigned to a specific subgroup. RNA from plasma samples was isolated using the MagMAX mirVana kit (ThermoFisher). microRNA concentrations were measured by qPCR using custom plates (Qiagen) pre‐plated with lyophilized miRCURY LNA‐based primers for a 24‐microRNA panel. Differentiation of subgroups by microRNA pairs and their combinations (classifiers) was evaluated using a proprietary software program developed at DiamiR. Result: Using the ratio between two microRNAs as a biomarker ("pair" approach) reduces variability and enhances specificity and sensitivity. Effective microRNA pairs were combined into classifiers. Data on the differentiation of subgroups using one, two, and three microRNA pairs are presented in Table 3. In line with our prior results, sex‐stratified analysis yielded higher AUC values for females and males as compared to the combined group. Interestingly, microRNAs differ in effective pairs for female and male subgroups. For example, levels of inflammation‐associated miR‐146a were higher in female MCI/AD study participants. However, sex‐stratified groups were relatively small and further assessment is required. Conclusion: These data support the development of assays based on the analysis of circulating brain‐enriched and inflammation‐associated microRNAs for better characterization of MCI/AD patients and understanding of AD‐associated processes. Further, combining these microRNAs with other factors, such as amyloid and APOE, may enhance a biomarker algorithm. [ABSTRACT FROM AUTHOR]

Background: Mobile, valid and engaging cognitive assessments are essential for detecting and tracking change in research participants and patients at risk for Alzheimer's Disease and Related Dementias (ADRDs). This pilot study aims to determine the feasibility and generalizability of at‐home, app‐based cognitive assessments included in the mobile cognitive app performance platform (mCAPP), to detect cognitive changes associated with aging and preclinical AD. Method: The mCAPP includes three gamified tasks (Figure 1): (1) a "concentration" memory task that includes learning and matching hidden card pairs with increasing memory load, pattern separation features (lure vs. non‐lure), and spatial memory (2) a stroop‐like task ("brick drop") with speeded word and color identification and response inhibition components and (3) a digit‐symbol coding‐like task ("space imposters") with increasing pairs and incidental learning components. Participants completed the NACC UDS3 and additional paper and pencil tests. Participants used the mCAPP at home for two weeks. Participants included sixty older adults (73% female; age = 71.9±4.6, education = 16.6±2.4; 50% White, 48% Black/African American, 2% Multiracial) without cognitive impairment enrolled in the Penn ADRC cohort. Result: Participants played 12±5.1 sessions over two weeks for 11.5±2.8 min/session, with 68% playing more than the assigned sessions. Almost all participants (98%) used a smartphone and 62% played games on their phone. Usability rating was 6.3±0.8 (1‐7 scale) and most participants reported task difficulty was just right (70%‐95%). 68% reported preferring mobile device based cognitive assessment to standard in‐person cognitive batteries. All tasks showed lower performance with increasing cognitive load (p's<.05). Age and education correlated with both mCAPP and traditional cognitive measures. Concentration performance correlated with UDS3 memory measures and the PACC overall (p's<.05), however when examined by self‐identified racial group, relationships remained significant in White participants, but not in Black/African American participants (Figure 2). Brick drop performance correlated with the Stroop task (p<.05) and space imposters performance correlated with the Digit Symbol Substitution Test (p<.001) within all groups. Conclusion: This pilot study shows app usability for at‐home use in a diverse cohort of older adults. Performance across measures indicate initial reliability and validity of mCAPP with attention needed to differences in performance across participants with diverse sociodemographic backgrounds. [ABSTRACT FROM AUTHOR]

Background: Gross atrophy observed on MRI provides a clinically useful marker of possible neurodegenerative disease. However, autopsy studies of polypathology suggest atrophy is likely driven not by a single proteinopathy but also co‐pathologies. We hypothesized that in a clinically heterogeneous autopsy sample, amyloid‐β, tau, α‐synuclein, and TDP‐43, would co‐occur within regions, and that co‐pathologies would have independent associations with grey matter atrophy. Method: T1‐weighted antemortem MRI was available in 121 patients with postmortem ordinal pathology ratings (0 = none; 1 = mild; 2 = intermediate; 3 = severe). Primary neuropathologic diagnoses included Alzheimer's disease (AD; n = 52), amyotrophic lateral sclerosis (n = 2), frontotemporal lobar degeneration due to tau (n = 29) and TDP‐43 (n = 22), and Lewy body disease (16). Regional volumes were expressed as W‐scores adjusting for age, sex, and intracranial volume for 10 regions spanning the limbic system, neocortex, and subcortex. We used mixed effects models with a random intercept per patient to regress W‐scores on ordinal pathology ratings. Result: Amyloid‐β and tau co‐occurred in all regions, reflecting the prevalence of AD neuropathologic change (Fig. 2, top). Alpha‐synuclein and TDP‐43 pathology were highest in amygdala, anterior cingulate, entorhinal cortex, and hippocampus, where they frequently co‐occurred with amyloid‐β and tau. Neocortical regions including middle frontal, angular, and superior/middle temporal cortex had intermediate rates of co‐pathology, while thalamus, striatum, and globus pallidus had the lowest polypathologic burden (Fig. 2, bottom). Ordinal measures of tau [F(3,1073) = 24.1, p<0.001], TDP‐43 [F(3,1073) = 13.7, p<0.001], and ‐synuclein [F(3,1073) = 4.3, p<0.005] burden were inversely associated with regional volumes, but amyloid‐ [F(3,1073) = 1.8, p>0.15] was not. An increase in ordinal tau scoring by one unit was associated with a mean reduction of ‐0.29 in regional W‐score; for TDP‐43, a decrease of ‐0.51; and for α‐synuclein, a decrease of ‐0.16. Conclusion: Mixed proteinopathies were common at the regional level in limbic and neocortical structures. Using blinded ordinal pathology scoring, TDP‐43 was associated with greater atrophy than tau; α‐synuclein had a smaller but significant association. Amyloid‐β was not associated with atrophy when adjusting for tau. Results suggest co‐pathologies have a measurable effect on observed brain atrophy. [ABSTRACT FROM AUTHOR]

Background: The medial temporal lobe (MTL) is a site of early tau pathology and neurodegeneration in AD. In recent work [1], we reported that rates of change in MTL subregion volumes on longitudinal structural T1‐MRI differ significantly between cognitively unimpaired individuals with and without amyloid pathology (A+CU vs A‐CU), suggesting that these measures are effective biomarkers for tracking disease progression in preclinical AD. Here, we develop a specialized pipeline that quantifies pointwise longitudinal change across the MTL, revealing more informative spatial patterns of early neurodegeneration than summary volumetric longitudinal measures. Methods: Our pipeline, SkelDBM, combines skeletonization and deformation‐based morphometry to generate maps of annualized rate of atrophy on the skeleton of an MTL template (Figure 1). SkelDBM samples subjects' atrophy maps along the skeleton of an MTL template, increasing robustness to subject‐to‐template registration errors. We evaluate SkelDBM using a subset of ADNI data studied in [1] (Table 1). Two preclinical AD groups (A+CU and tau‐positive A+CU) are compared to a control group (A‐CU) using general linear models (GLM) with age as nuisance covariate; permutation testing is used to derive statistically significant clusters. Results: The full A+CU vs A‐CU comparison shows significant differences in rates of change in right anterior hippocampus, right entorhinal cortex, right transentorhinal cortex, and left posterior hippocampus (Figure 2). Limiting the A+CU group to tau‐positive individuals yields more widespread differences, including most of the right anterior MTL cortex and both hippocampi. These patterns align with volumetric longitudinal data reported in [1] but provide a richer characterization of spatial atrophy patterns. Conclusion: SkelDBM is sensitive to longitudinal atrophy occurring over relatively short periods (∼2 years) in preclinical AD and characterizes patterns of atrophy more comprehensively than summary volumetric measures. Summary measures are also subject to biases, since larger structures are typically associated with less measurement error and hence may show exaggerated statistical effects compared to smaller structures even when the underlying neurodegeneration rates are the same. Regional maps of longitudinal change may be more robust to this bias and may help discovery of "signature regions" associated with specific pathological drivers of neurodegeneration. [1] Xie et al., Human Brain Mapping, 2020. [ABSTRACT FROM AUTHOR]

Background: Despite the strengths conventional structural imaging measures to detect atrophy, cross‐sectional relationships between neurodegeneration and Alzheimer's disease (AD) biomarkers in early stages is limited. Neurite orientation dispersion and density imaging (NODDI) measures microstructural alterations that are unique from conventional structural metrics. Alterations in microstructure have been detected in individuals with AD compared to cognitively normal individuals, but there is sparse data regarding the relationship between these metrics and AD biomarkers. Here we sought to characterize how NODDI metrics in the medial temporal lobe (MTL) are associated with plasma AD biomarkers. Method: One‐hundred and sixty‐seven cognitively normal (CN) individuals, 47 individuals with mild cognitive impairment, and 16 individuals with AD underwent diffusion and T1w imaging. T1w images were segmented using the ASHS‐T1 protocol to form anterior and posterior MTL ROIs. Multi‐shell DTI data were collected in 113 non‐collinear directions with 1.5mm isotropic voxels, were pre‐processed using a custom pipeline, and fit with voxel‐wise NODDI models. ANTs was used to linearly register NODDI and T1w images to allow transformation of MTL ROIs to diffusion space. Stepwise‐regression models examined relationships between anterior and posterior MTL NODDI metrics with plasma biomarkers in CN and impaired (MCI/AD) groups after accounting for MTL volume. Result: NODDI metrics of intracellular volume fraction (icVf) and orientation dispersion index (ODI) were decreased, and isometric volume fraction (isoVf) was increased in cognitively impaired compared to CN. Amyloid positivity was associated with decreased ODI and increased isoVf in cognitively impaired individuals. While posterior MTL volume predicted plasma p‐Tau‐181, inclusion of NODDI metrics subsumed this effect (F‐change>7.54, p<0.0067), and reduced ODI was the best predictor of plasma pTau‐181 within cognitively impaired individuals, while isoVf best predicted pTau‐181 across all individuals. Posterior MTL volume did not predict plasma GFAP, but inclusion of ODI and icVf both significantly predicted plasma GFAP in both CN and impaired individuals (F‐change>12.68, p<.001). Anterior MTL icVf predicted both pTau‐181 and GFAP. Conclusion: Microstructural alterations measured by NODDI appear more closely linked to AD‐related molecular measures and, thus, may provide a more sensitive cross‐sectional measure of neurodegeneration of the MTL compared to conventional macrostructural metrics. [ABSTRACT FROM AUTHOR]

Background: The relationship between tau neurofibrillary tangles (T) and neurodegeneration (N) may offer clues to potential presence of comorbidities, as well as relative resilience and vulnerability to Alzheimer's disease (AD) pathology. We have previously developed measures of tau PET (T) and cortical thickness (N) mismatch based on linear model residuals. However, the underlying relationships between T and N are likely complex and non‐linear. Moreover, predicting cortical thickness based on local tau level may miss non‐local contributions of tauopathy. Here we investigate T‐N mismatch using a deep‐learning 3D image translation neural network to estimate synthetic maps of cortical thickness based on tau PET images. Deviation between the synthetic and actual cortical thickness serves as a metric of T‐N mismatch. Method: We derived 3D cortical thickness from T1‐MRI and SUVR from 18F‐flortaucipir tracer uptake to represent N and T maps respectively. We predicted cortical thickness maps from tau SUVR by training a 3D U‐Net (Ronneberger et al. 2015) to learn the relationship between paired cortical tau SUVR and thickness maps from 70 symptomatic patients from ADNI. Approximately 70% of the training set was A+. We used regional standardized mean absolute error between predicted and actual thickness across 104 bilateral gray matter regions of interest as T‐N mismatch for clustering in a separate independent sample of 194 A+ symptomatic patients. Result: The voxel‐wise mean absolute error of thickness translation on 194 tested patients was 0.54 mm. We obtained six T‐N data‐driven clusters (Figure 2). The group with lowest reconstruction error was defined as canonical – meaning that the degree of neurodegeneration was accurately predicted by tau PET. There were three groups with lower thickness than predicted, which were denoted as temporal‐limbic (TL) vulnerable, posterior vulnerable and diffuse vulnerable based on their spatial patterns. Additionally, two groups with greater thickness than predicted were defined as anterior resilient and diffuse resilient. These groups differed in subsequent longitudinal cognitive decline measured by CDR‐SB (Figure 3). The vulnerable groups displayed greater cognitive decline than the canonical group, which may be attributable to associations with comorbidities. Conclusion: Our findings support a deep‐learning T‐N mismatch approach for disentangling AD heterogeneity. [ABSTRACT FROM AUTHOR]

Background: Lewy body disorders (LBD) include Parkinson disease (PD), Parkinson disease dementia (PDD), and dementia with Lewy bodies (DLB). LBD are the second leading cause of neurodegenerative dementia after Alzheimer's disease (AD). An estimated 50% of LBD patients have coexisting AD pathology (LBD+AD), which correlates with shorter time to dementia and death. Autopsy evidence suggests that verbal memory impairment correlates with the severity of hippocampal tau pathology in LBD. However, it is unknown whether greater hippocampal tau burden in LBD+AD causes neurodegeneration detectable by in vivo structural MRI, and whether hippocampal atrophy explains verbal memory impairment. Method: Using a case‐control design, we studied 87 patients with clinical diagnoses of LBD (PD, n = 52; PDD, n = 12; DLB, n = 23) with available T1 MRI and AD biomarkers or, and cognitively‐normal controls (n = 131). AD copathology was defined hierarchically by: a) AD "intermediate" or "high" by ABC neuropathologic criteria; 2) positive amyloid PET; and 3) CSF total tau:β‐amyloid1‐42 ratio > 0.3 (autopsy‐validated cutpoint). The Automated Segmentation of Hippocampal Subfields (ASHS) pipeline was applied to subjects' T1 MRIs. Hippocampal volume (HV) was compared between controls, LBD‐AD, and LBD+AD, and correlated with verbal memory scores. Linear regression was used to test the association of AD copathology (independent variable) and HV (dependent variable), covarying for age, sex, education, and clinical diagnosis. Result: Statistically significant differences (ANOVA p = 0.002) in bilateral HV (expressed as percentage of intracranial volume) were seen between groups (mean 0.456for controls, 0.444±0.045 for LBD‐AD, and 0.416±0.063 for LBD+AD). Tukey's test showed significant differences between LBD+AD vs controls (p = 0.001) and LBD+AD vs. LBD‐AD (p = 0.047). Linear regression showed statistically significant association of LBD+AD with bilateral HV (p = 0.034). Among all LBD patients, hippocampal volume positively correlated with verbal memory (immediate recall: r2 = 0.24, p = 0.033, delayed recall: r2 = 0.22, p = 0.042, recognition discrimination r2 = 0.58, p<0.001). Conclusion: In patients with LBD, AD copathology is associated with increased hippocampal atrophy as measured by in vivo T1 MRI, which correlates with greater verbal memory deficits. This work supports the that AD copathology impairs cognition in LBD via hippocampal neurodegeneration. [ABSTRACT FROM AUTHOR]

Background: The extent to which pathological processes in aging and Alzheimer's disease (AD) relate to functional disruption of the medial temporal lobe (MTL)‐dependent brain networks is poorly understood. To address this knowledge gap, we examined functional connectivity (FC) alterations between anterior and posterior regions of the MTL and in MTL‐associated functional communities – the Anterior‐Temporal (AT) and Posterior‐Medial (PM) networks – in normal agers, individuals with preclinical AD, and patients with Mild Cognitive Impairment or mild dementia due to AD. Method: In this cross‐sectional study, we analyzed data from 179 individuals from the Aging Brain Cohort study of the Penn ADRC. Detailed information about participants is provided in Table 1. For intra‐MTL FC comparisons, the MTL subregions were segmented using the automated segmentation of hippocampal subfields‐T1 (ASHS‐T1) pipeline (Fig. 1a). When modeling the MTL's interactions with the rest of the cortex, we employed four MTL ROIs (left/right × anterior/posterior) derived from an ex vivo atlas of tau accumulation in the MTL. Our functional datasets were preprocessed using a customized fMRIprep pipeline. Sparse network estimations and modularity‐based consensus clustering were used to reconstruct the AT and PM network systems (Fig. 1b). Age effect analyses and group comparisons along the AD continuum were performed using the General Linear Model within the network‐based statistical framework. Results: The preclinical stage of AD was characterized by increased FC between the perirhinal cortex and other regions of the MTL, as well as between the anterior MTL and its direct neighbors in the AT network (Fig. 1c‐d). This effect was not present in symptomatic AD. Instead, symptomatic patients displayed reduced hippocampal and intra‐PM connectivity. For normal aging, our results led to three main conclusions (for visuals, see Fig. 2). First, intra‐network connectivity of both the AT and PM networks decreases with age. Second, FC between the anterior and posterior segments of the MTL declines with age. Finally, within the MTL, we observed greater vulnerability of the posterior MTL subregions, particularly the parahippocampal cortex, to age‐associated FC decline. Conclusion: Together, the current results provide evidence for aberrant connectivity in the preclinical stage of AD that may have implications for early AD pathophysiology. [ABSTRACT FROM AUTHOR]

Background: Hypertension (HTN) is associated with gray matter (GM) atrophy and increased white matter hyperintensity (WMH) burden, increasing their susceptibility to Alzheimer's disease and related dementias. We developed a machine learning based model to quantify spatial patterns of abnormality recognizing HTN‐related brain changes (SPARE‐HTN) from structural magnetic resonance images (sMRI). We performed a genome wide association study (GWAS) to identify the genetic variants and associated biological traits that underlie the "expression" of HTN‐related brain changes, i.e., the individualized SPARE‐HTN index. Method: SPARE‐HTN model was trained on the large multi‐study iSTAGING dataset, with regional GM volumes from T1‐weighted images and lobar WMH volumes from T2‐weighted FLAIR images used as imaging features. GWAS was carried out in a subset of N = 7490 (46% Female, Age = 65.5±7 years) hypertensive participants from UK‐Biobank. A GWAS of SPARE‐HTN scores was run using linear regression with Plink, correcting for the top 40 genetic principal components, age, sex, and intracranial volume. Significant SNPs were identified and mapped to functional genes using the Functional Mapping and Annotation (FUMA) platform; and candidate SNPs were mapped to the GWAS catalog. Result: SPARE‐HTN was associated with regional atrophy most prominent in perisylvian and temporal cortex and increased WMH volumes, particularly in frontal white matter. SPARE‐HTN scores showed better discriminative power between normal and stage 1 participants (Cohen's‐d effect size, d = 0.2) and stage 2 (d = 0.71) hypertensive participants when compared to total WMH volume (d = 0.04 and 0.38, respectively). GWAS identified N = 321 independently significant SNPs, of which a subset N = 46 candidate SNPs were mapped to 6 genes (ICA1L, CARF, WDR12, NBEAL1, KRT8P15, CYP20A1) in Chromosome 2 (Figure 2). These genes were previously reported to be associated with several cardiovascular conditions including coronary artery disease and stroke, and with increased WM damage on sMRI and diffusion MRI (Figure 3). Conclusion: The individualized imaging marker, SPARE‐HTN, was associated with genes relevant to HTN with traits including cardiovascular conditions and higher prevalence of WMH, and was more sensitive to HTN than total WMH volume. SPARE‐HTN is a useful tool to characterize HTN‐related brain changes and monitor treatment effects at an individual level. [ABSTRACT FROM AUTHOR]

Background: In Alzheimer's disease neuropathologic change (ADNC), maturation of neurofibrillary tangles and tau pathology are characterized by posttranslational modifications of tau. ANDC and Lewy body disease (LBD) commonly co‐exist in aging brains and there are overlapping clinical syndromes and heterogeneity in clinical features among cases with mixed ADNC and LBD pathology. Given this clinical and pathological overlap, we examined the postmortem expression of tau conformational epitopes that mark various stages of tangle maturation in clinicopathologic spectrum of Alzheimer's disease (AD) and LBD. Method: Autopsy‐confirmed cases with diffuse neocortical LBD with/without ADNC (n = 59) were selected (Table1). We used digital histology methods to measure percent area occupied (%AO) with pathology in three regions (middle frontal, anterior cingulate, and superior temporal cortices) immunohistochemically stained with MJF‐R13 (α‐synuclein), NAB228 (beta‐amyloid), and tau monoclonal antibodies marking phosphorylated (AT8), early conformational (MC1, preferentially reactive in pre‐tangles), and C‐terminally truncated tau epitopes (TauC3, preferentially reactive in mature intracellular and ghost tangles). Linear models tested the group comparisons, and correlation between α‐synuclein or beta‐amyloid with outcome of average neocortical tau. In models, %AO were log‐transformed, and age and sex were included as covariates. Result: α‐synuclein was strongly correlated with early tau epitopes labelled with AT8 (β = 1.37, p<0.001) and MC1 (β = 1.20, p<0.001), but not with mature tau epitopes detected by TauC3 (p = 0.38). There were significant positive correlations between all tau epitopes and beta‐amyloid, including TauC3 immunoreactive tau (p≤0.013) (Figure1). In the mixed pathology group, those with clinical LBD had a positive association between α‐synuclein and MC1 (β = 1.61, p = 0.014) and AT8 (β = 1.67, p = 0.005) positive tau, while those with clinical AD had no correlation between α‐synuclein and tau epitopes. There was greater MC1 (β = 3.40, p<0.001), AT8 (β = 3.59, p<0.001), and TauC3 (β = 1.43, p = 0.005) pathology in patients with clinical AD compared to clinical LBD. Conclusion: In LBD, higher α‐synuclein is associated with greater phosphorylated and early tau conformations, but not later conformation of mature tau, which was selectively associated with beta‐amyloid pathology. Our findings suggest that tau pathology may modify the clinical expression of synucleinopathies and C‐terminal truncation of tau may be more closely related to AD pathophysiology than synucleinopathy. [ABSTRACT FROM AUTHOR]

Background: Neurofibrillary tangle pathology is thought to drive neurodegeneration in beta‐amyloid positive (A+) cognitively normal (CN) individuals, i.e., preclinical Alzheimer's disease (AD). However, in beta‐amyloid negative (A‐) CN, the contribution of tau pathology [primary age‐related tauopathy (PART)] to neurodegeneration remains uncertain. We investigate the correlation between tau burden measured by PET in the medial temporal lobe (MTL) and MRI‐derived cross‐sectional and longitudinal structural atrophy in these cohorts. Methods: 420 CN (A‐/A+: 294/101, Table 1) individuals from ADNI with AV1451 PET and T1‐weighted MRI acquired within one year were included. Bilateral anterior/posterior hippocampal volume and thickness of entorhinal cortex (ERC), Brodmann areas 35/36 (BA35/BA36) and parahipocampal cortex (PHC) were obtained from baseline MRI scans. Bilateral MTL tau burden was computed as AV1451 uptake across ERC and BA35. Beta‐amyloid status was determined with PET by standard cut‐offs (Florbetapir: 1.11; Florbetaben: 1.08). In a subset of participants with prospective longitudinal MRI scans (up to 4.5 years), annualized volume change rate of each MTL subregion was estimated. Partial Pearson's correlation, controlling for age, sex, and MRI‐PET date difference, was performed between MTL tau burden and structural atrophy measurements. Intracranial volume and MRI follow‐up time were additional covariates for cross‐sectional and longitudinal analysis respectively. We performed the analysis separately for each hemisphere in the whole CN cohort and its A+ and A‐ subgroups. Results: Tau burden was significantly associated with cross‐sectional left BA35/36 thickness in the whole cohort and bilateral posterior hippocampus volume in both A+ CN and the whole cohort (Table 2, Figure 1), but not in in A‐ CN. Stronger correlations between MTL tau burden and longitudinal atrophy, despite smaller sample size, was observed in almost all the MTL subregions regardless of amyloid status (Table 3, Figure 1). In general, effects from the left hemisphere were stronger than those from the right hemisphere. All significant correlations were maintained when corrected for beta‐amyloid PET SUVR. Conclusions: The results demonstrated that elevated tau predicts subsequent neurodegeneration in early Braak regions in CN subjects regardless of amyloid status. This indicates that PART may be an important driver of neurodegeneration already during normal ageing in cognitively normal individuals. [ABSTRACT FROM AUTHOR]

Background: Recent models of Alzheimer's disease (AD) posit that pathogenic tau propagates through interconnected structural networks, suggesting amnestic and non‐amnestic AD (aAD & naAD) phenotypes could reflect differential involvement of functional networks. We tested this hypothesis by contrasting longitudinal grey matter (GM) atrophy and tau accumulation across AD clinical phenotypes in 7 canonical functional networks. Methods: We analyzed longitudinal 3‐Tesla T1‐weighted MRI from 122 patients [aAD=49; logopenic primary progressive aphasia (lvPPA)=31; posterior cortical atrophy (PCA)=23; behavioral/dysexecutive AD (bvAD)=6; corticobasal syndrome (CBS)=6; mixed naAD=4] with autopsy or CSF evidence of AD pathology and 91 cognitively normal controls (Table 1). Thirty‐one patients (aAD=9; lvPPA=7; PCA=9; bvAD=2; CBS=3; naAD=1) had longitudinal flortaucipir PET (FTP‐PET) data. We computed baseline values and annualized change for GM volume and FTP‐PET standardized uptake value ratios (SUVRs) in the 100‐label, 7‐network parcellation of Schaefer et al. (2018). Linear mixed effects models (α=0.05, corrected) adjusting for MMSE assessed group differences in atrophy and tau accumulation. Results: All groups exhibited significant baseline and longitudinal atrophy vs. controls in multiple networks (Figs. 1‐2). Differences in longitudinal atrophy rate (Fig. 2) were observed in the dorsal attention network [PCA>lvPPA, t(371)=3.1, p<0.04], salience network [PCA>aAD, t(371)=3.1, p<0.04], and limbic network [CBS>aAD, t(371)=3.3, p<0.02; CBS>bvAD, t(371)=3.7, p<0.006]. PCA patients exhibited FTP‐PET SUVR increases in all networks (all p<0.02), while aAD patients displayed increases in all networks except visual and limbic (all others p<0.0001). LvPPA patients exhibited net SUVR increases only in the somatomotor network [t(97)=3.5, p<0.001]. At baseline, lvPPA patients had left > right atrophy in all networks [all t>3.7, p<0.0002]; right > left atrophy was observed in the visual network for PCA and CBS; the salience network for PCA and bvAD; and in the dorsal attention, frontoparietal control, and default mode networks for bvAD [all t>3.0, p<0.03]. In longitudinal analyses, no hemispheric differences were found in rates of GM volume loss or FTP‐PET change. Conclusion: AD clinical variants exhibited GM atrophy and tau accumulation in partially overlapping networks. Investigating early‐stage disease will be crucial for assessing whether disease spread through canonical functional networks can explain anatomic differences in aAD and naAD phenotypes. [ABSTRACT FROM AUTHOR]

Background: Mild Cognitive Impairment with Suspected non‐Alzheimer's Pathophysiology (SNAP‐MCI) represents a group of patients with Alzheimer's disease (AD) like neurodegeneration without beta‐amyloid pathology. While this group likely has a heterogeneous etiology, tau pathology, as in Primary Age‐Related Tauopathy (PART), has been hypothesized to play a major role. We investigate tau positron emission tomography (PET) uptake in the medial temporal lobe (MTL) in SNAP‐MCI and the association of MTL Tau‐PET uptake with structural measures and delayed recall in beta‐amyloid negative (A‐) MCI patients. Method: 237 MCI patients and 301 A‐ controls with available beta‐amyloid and tau PET and magnetic resonance images (within 200 days) were included. Baseline hippocampal volume and entorhinal cortex (ERC) and Brodmann areas (BA)35 thickness were obtained using an in‐house developed pipeline and annualized atrophy rates were estimated in an unbiased fashion using follow‐up MRIs within 4.5 years. Βeta‐amyloid status (A+/‐) was determined by a standard cut‐off (Florbetapir: 1.11; Florbetaben: 1.08) and neurodegeneration status (N+/‐) by hippocampal volumes, corrected for intracranial volume, using the 90th percentile of A+ AD patients as the cut‐off. Tau‐PET standardized uptake value ratio (SUVR) in the ERC/BA35 was calculated. A composite z‐score of delayed recall at baseline and change over 2 years was obtained. Result: SNAP‐MCI had significantly higher ERC/BA35 Tau‐PET SUVR than A‐ controls (Table 1, corrected for age) and qualitatively higher than A‐N‐ MCI (p=0.10). ERC/BA35 Tau‐PET SUVR in A‐N‐ MCI was not significantly different from A‐ controls. In A‐ MCI patients, ERC/BA35 Tau‐PET SUVR was significantly associated with BA35 thickness and hippocampal, ERC and BA35 atrophy rates, corrected for beta‐amyloid PET SUVR (Table 2; Figure 1). MTL structural measures, but not ERC/BA35 Tau‐PET SUVR, were associated with cross‐sectional and longitudinal delayed recall measures (Table 3; Figure 2). Conclusion: SNAP‐MCI had elevated ERC/BA35 Tau‐PET SUVR and ERC/BA35 Tau‐PET SUVR was associated with MTL structural measures in A‐ MCI patients. This indicates that tau pathology might be an important driver of neurodegeneration in the absence of beta‐amyloid pathology, supporting the notion that PART contributes to SNAP‐MCI. Additionally, MTL structure was associated with memory performance, consistent with SNAP not being a benign condition. [ABSTRACT FROM AUTHOR]

Background: Tau pathology is a hallmark of Alzheimer's disease, but measuring this pathology in vivo with PET is expensive. MRI represents a low‐cost alternative for measuring in vivo disease progression, but is non‐specific. We present an MR‐based marker of tau‐specific pathology using machine learning (ML). Method: 1851 pairs of RO948 tau‐PET and T1‐weighted MRI scans were aggregated from 1262 BioFINDER II study participants. The Braak I‐IV ROI from Cho et al., 2016 was used to represent tau‐pathology. A tau‐positive threshold (SUVR=1.41) was defined using Gaussian mixture modeling, and Braak I‐IV SUVR was z‐scored using the mean and SD of the tau‐negative group. A grid search of 80 ML pipelines with 100 rounds of nested 5‐fold cross‐validation was performed while varying estimator, dimension reduction strategy, preregression of intracranial volume (ICV), and one vs multiple scans‐per‐subject. For all models, 294 features from Freesurfer were used. The best‐performing model was then applied to 1225 participants from the ROSMAP cohort, yielding MR‐predicted tau. A subset (n=159) of these participants had postmortem AT8‐immunostained tau pathology, Braak staging, TDP‐43 and lewy body pathology. Result: The best pipeline involved random forest regression with FDR‐based feature selection (RMSE[95%CI]=3.67[3.6‐3.8]; R2[95%CI]=0.42[0.40‐0.45]; Figure1; Figure2A). (Lack of) ICV pre‐regression had the biggest positive impact across pipelines. This model significantly predicted zTau in both tau‐postive and tau‐negative individuals (Figure2B), and showed fair weighted precision (0.84) and recall (0.80) in tau‐positivity classification (Figure2C). Temporoparietal and medial temporal regions contributed most to predictions (Figure2D). When applied to ROSMAP, antemoretem MR‐predicted tau predicted postmortem tau pathology (p=0.001) and Braak stage (p=0.005; Figure3A) but not other pathologies (Figure3B), predicted clinical diagnosis and progression (Figure3C), and outperformed a common neurodegeneration marker (Figure3D‐F). Conclusion: We create an easily deployable tau‐specific MRI biomarker, which was validated using postmortem tau pathology in a separate dataset. MR‐predicted tau provides a more specific non‐invasive biomarker for researchers without access to PET. [ABSTRACT FROM AUTHOR]

Background: Structural changes in Alzheimer's disease (AD) can be detected as atrophy in the medial temporal lobe (MTL), which is normally studied by applying volumetric or deformation‐based morphometry (DBM) to longitudinal scans. However, conventional DMB methods are computationally intensive and may not estimate the atrophy accurately due to the noise in the longitudinal images, e.g., head motion or MRI artifacts. We hypothesize that deep learning models can be trained to estimate atrophy from longitudinal MRI scans and are more sensitive to biological changes rather than artifactual. Method: Two models were trained on longitudinal T1 MRI from 155 ADNI participants and tested on 326 participants. First, a deep learning‐based registration model Voxelmorph (Balakrishnan et al., 2019) was applied to generate a deformation field to calculate MTL atrophy rate for each pair of images. Hypothesizing that noise is independent of time while neurodegeneration is time dependent, we then extended Voxelmorph to incorporate a loss that encourages the model to correctly infer the input scan temporal order (STO). In addition, in order to preserve the deformable registration nature of Voxelmorph, it's weights were held fixed during network training, and instead a U‐Net reconstruction network was introduced, to synthesize inputs to Voxelmorph. Models were compared with a DBM method ALOHA (Das et al., 2012). Result: Voxelmorph generated atrophy measurements comparable to ALOHA in terms of differentiating AD stages (Figure 2) and STO consistency (Table 2). Voxelmorph + STO model has increased STO consistency and can more significantly differentiate disease stages (in absolute terms), especially between the normal controls and preclinical AD. Conclusion: Deep learning‐based registration method Voxelmorph achieves comparable performance in longitudinal analysis of subtle changes as in DBM method ALOHA. With the introduction of a scan temporal order loss and reconstruction network, the model achieves higher sensitivity to disease stage, potentially because it learns to discount longitudinal changes that are related to noise and artifact. This may lead to more sensitive biomarkers for disease monitoring in clinical trials of early AD. [ABSTRACT FROM AUTHOR]

Background: Pathological aggregation of tar DNA‐binding protein 43 (TDP‐43) in the brain is the primary cause of many cases of frontotemporal lobar degeneration (FTLD), amyotrophic lateral sclerosis (ALS) and limbic‐predominant age‐related TDP‐43 encephalopathy (LATE). It is therefore imperative to establish empirical staging systems to characterize and distinguish stereotypical patterns and commonplace deviations of different TDP‐43 proteinopathies. Method: We use ordinal ratings of TDP‐43 burden from 19 brain regions to perform data‐driven disease progression modeling (SuStaIn) to find the most likely trajectories for FTLD‐TDP (n = 108), ALS (n = 137) and LATE (n = 283) from the CNDR Brain Bank at the University of Pennsylvania. Subtype number was defined using cross‐validated information criterion. Each individual was assigned a subtype and stage. Multivariate OLS models tested differences between subtypes. Stages were compared to age and existing staging schemes. Cross‐validated logistic regression was used for 3‐way classification using SuStaIn information only. Result: SuStaIn provided data‐driven staging of TDP‐43 proteinopathies complementing previously described human‐defined staging schema, further providing additional detail (Fig1A‐C; Fig3A‐C). SuStaIn also identified two distinct subtypes within FTLD‐TDP and a further two within ALS (Fig1D). FTLD‐TDP subtypes differed in TDP‐43 type and Alzheimer's disease pathology (Table1); ALS subtypes were differentiated by age (Table 2) and by antemortem clinical characteristics. No subtypes were observed for the LATE group. Progression along data‐driven stages was positively associated with age in LATE individuals, but negatively associated with age in individuals with FTLD‐TDP (Fig2). Using only regional TDP‐43 severity, our data driven model could distinguish individuals diagnosed with ALS, FTD or LATE with a cross‐validated balanced precision of 0.93 and balanced recall of 0.92, and these metrics improved to 0.95 and 0.96 when combined with a logistic regression model (Fig3). Very little stage overlap was found between FTLD‐TDP and LATE, but stages that did overlap showed subtly different patterns (Fig4) Conclusion: We provide an empirical pathological staging system for ALS, FTLD‐TDP and LATE, which is sufficient for staging and accurate classification. We demonstrate that there is substantial heterogeneity amongst ALS and FTLD‐TDP progression patterns, whilst LATE exhibits a homogeneous progression pattern. [ABSTRACT FROM AUTHOR]

Background: Neurofibrillary tangle pathology is thought to drive neurodegeneration in beta‐amyloid positive (A+) cognitively normal (CN) individuals, i.e., preclinical Alzheimer's disease (AD).However, in beta‐amyloid negative (A‐) CN, the contribution of tau pathology [primary age‐related tauopathy (PART)] to neurodegeneration remains uncertain. We investigate the correlation between tau burden measured by PET in the medial temporal lobe (MTL) and MRI‐derived cross‐sectional and longitudinal structural atrophy in these cohorts. Methods: 420 CN (A‐/A+: 294/101, Table 1) individuals from ADNI with AV1451 PET and T1‐weighted MRI acquired within one year were included. Bilateral anterior/posterior hippocampal volume and thickness of entorhinal cortex (ERC), Brodmann areas 35/36 (BA35/BA36) and parahipocampal cortex (PHC) were obtained from baseline MRI scans. Bilateral MTL tau burden was computed as AV1451 uptake across ERC and BA35. Beta‐amyloid status was determined with PET by standard cut‐offs (Florbetapir: 1.11; Florbetaben: 1.08). In a subset of participants with prospective longitudinal MRI scans (up to 4.5 years), annualized volume change rate of each MTL subregion was estimated. Intracranial volume and MRI follow‐up time were additional covariates for cross‐sectional and longitudinal analysis respectively. We performed the analysis separately for each hemisphere in the whole CN cohort and its A+ and A‐ subgroups. Results: Tau burden was significantly associated with cross‐sectional left BA35/36 thickness in the whole cohort and bilateral volume in both A+ CN and the whole cohort (Table 2, Figure 1), but not in in A‐ CN. Stronger correlations between MTL tau burden and longitudinal atrophy, despite smaller sample size, was observed in almost all the MTL subregions regardless of amyloid status (Table 3, Figure 1). In general, effects from the left hemisphere were stronger than those from the right hemisphere. All significant correlations were maintained when corrected for beta‐amyloid PET SUVR. Conclusions: The results demonstrated that elevated tau predicts subsequent neurodegeneration in early Braak regions in CN subjects regardless of amyloid status. This indicates that PART may be an important driver of neurodegeneration already during normal ageing in cognitively normal individuals. [ABSTRACT FROM AUTHOR]

Background: White matter hyperintensities (WMH) on FLAIR MRI are highly prevalent in older adults and are linked to cognitive deficits as well as dementia. Likely mechanisms underlying associations between WMH and cognitive deficits include widespread partial disconnection due to the diffuse nature of progressive WMH and/or tract‐specific disconnection syndromes. We used a virtual lesion approach to evaluate cognitive deficits in domains associated with three tracts: left arcuate fasciculus (LAF; verbal working memory), inferior longitudinal fasciculus (ILF; visual processing), and fornix (memory). Method: WMH were semi‐automatically segmented from FLAIR MRI data in 481 ADNI controls and applied as regions of avoidance for probabilistic fiber tracking on DTI data in 46 healthy older subjects from the Human Connectome Lifespan database. Correlations between overall WMH burden or individual tract disruption and global or tract‐domain‐specific cognitive tests were assessed. Tract disconnection was also compared in subgroups with enriched disruption of specific tracts with respect to global WMH burden, Amyloid beta deposition status, and best (>Mean+1SD) versus worst (

Background: Data‐driven unsupervised and semi‐supervised clustering methods have parsed neuroanatomical heterogeneity of Alzheimer's disease (AD) and/or mild cognitive impairment (MCI) into multidimensional neuroimaging representations (Vogel et al., 2021; Wen et al., 2021b, 2021a; Yang et al., 2021; Young et al., 2018; Zhang et al., 2016). However, whether this neuroanatomical heterogeneity is partly underpinned by genetic heterogeneity remains unanswered. Method: Whole‐genome sequencing (WGS) data were analyzed for individuals in the Alzheimer's Disease Neuroimaging Initiative. WGS data underwent a standard genetic pipeline. This resulted in 1,487 participants (428 healthy controls, 489 MCI and 570 AD; age = 78.96 ± 7.72; 44.05% females) with 24,773,167 single nucleotide polymorphism (SNP). The deep learning Smile‐GAN model (Yang et al., 2021) generates expression scores (ES) across each of the four subtypes of MCI/AD (Fig. 2). The ES of each subtype were used as phenotypes in genome‐wide association studies (GWAS). Specifically, we performed multiple linear regressions controlling for age, sex, intracranial volume, disease diagnosis, and the first four genetic principle components using Plink (Purcell et al., 2007). We then performed clumping to define the independent significant variants (ISVs). Result: GWAS discovered 17 ISV‐ES pairwise associations. WGS data allowed us to identify four de no ISVs that were not included in dbSNP (Fig. 1). P1 and P4 detected similar AD‐related genetic variants (e.g., APOE, NECTIN2, and TOMM40, Fig. 1 and 2), but with opposite directions of effect, i.e., protective factors for P1 and risk factors for P4, consistent with respective patterns of neurodegeneration in these phenotypes. In particular, for rs429358 (APOE‐4), the P4 subtype had more C alleles (minor allele) than the P1 subtype (P‐value < 1e‐20). The four ISVs identified for P2 and P3 were not previously associated with any clinical trait. Conclusion: The current study identified genetic heterogeneity related to the expression of four MCI/AD subtypes previously defined via Smile‐GAN. Notably, our results confirmed previous findings that AD is genetically heterogeneous (Nacmias et al., 2018), identifying known genetic risk factors and several novel variants. Further research is needed to understand how these variants may affect AD pathophysiology and determine if any may provide a therapeutic target. [ABSTRACT FROM AUTHOR]

Background: In previous work (Das et al., 2021), we exploited the non‐specific nature of neurodegeneration by investigating the amount and spatial pattern of deviation from the level of neurodegeneration (N) expected for a given level of tau (T). Using a data‐driven clustering approach based on T‐N mismatch, we found phenotypes that had greater N than expected for T ("vulnerable") and phenotypes with less N than expected for T ("resilient") in specific patterns. Here we extend this work to include amyloid positive (A+) and negative (A‐) participants. The logic of inclusion of A‐ individuals is that areas of mismatch in A+ individuals is likely driven by factors outside of AD pathology, which can be present in the absence of AD pathology. Method: We employed cortical thickness to represent N and 18F‐flortaucipir tracer uptake as T for 104 bilateral cortical regions of interest (ROI) in 343 symptomatic individuals including both A+ and A‐ from ADNI. We established T‐N robust regression and partitioned participants based on thresholded residuals. Longitudinal trajectories of cognitive scores were assessed with linear mixed‐effects model effect models using CDRSB as the dependent variable. Result: We replicated prior work and obtained clusters with distinct T‐N mismatch patterns. We defined the largest group as "canonical", as it displayed typical T‐N relationships (N∼T, low residuals). The limbic vulnerable cluster displayed N>T (negative residuals) in limbic regions, while diffuse vulnerable group displayed N>T throughout the cortex. Alternatively, the resilient (N

Background: Mobile, valid and engaging cognitive assessments are essential for detecting and tracking change in research participants and patients at risk for Alzheimer's Disease and Related Dementias (ADRDs). This pilot study aims to determine the feasibility of at‐home, app‐based memory and executive functioning tasks included in the mobile cognitive app performance platform (mCAPP), to detect cognitive changes associated with aging and preclinical AD. Method: The mCAPP includes three gamified tasks (Figure 1): (1) a "concentration" memory task that includes learning and matching hidden card pairs with increasing memory load, pattern separation features (lure vs. non‐lure), and spatial memory (2) a stroop‐like task ("brick drop") with speeded word and color identification and response inhibition components and (3) a digit‐symbol coding‐like task ("space imposters") with increasing pairs and incidental learning components. Participants also completed the NACC UDS3, additional paper and pencil tests, and NIH Toolbox measures. Participants used the mCAPP at home for two weeks. Participants included sixteen older adults (56% female; age=71.4±5.0, education=16.4±3.1; 63% White, 31% Black, 6% Multiracial) without cognitive impairment enrolled in the Penn ADRC cohort. Results: Participants played 12.1±4 times over two weeks, with 15/16 playing more than the assigned sessions. Almost all participants (88%) used a smartphone, but few (31%) played games on their phone. Usability rating was 6.2±0.7 (1‐7 scale) and most participants reported task difficulty was just right (75%‐81%). All tasks showed lower performance with increasing age and brick drop inhibition and space imposters also correlated with education (p's<0.05). Concentration performance correlated with UDS3 memory measures (p's<0.05, Figure 2). Brick drop performance correlated with measures of attention and executive functioning (p's<0.05, Figure 3). Space imposters correlated with the Digit Symbol Substitution Test (p<0.001, Figure 4). Some participants performed similarly across AM and PM sessions, while others showed differential performance. Initial change scores from the first to second session and from the 2nd to the last sessions did not show significant change. Conclusion: This pilot study shows acceptability and usability of the app for at‐home use. Performance across measures indicate initial reliability and validity of mCAPP with continued data collection with a larger sample needed. [ABSTRACT FROM AUTHOR]

Background: In clinical trials targeting early Alzheimer's disease (AD), recruiting participants that are more likely to progress over the course of the trials is crucial to success. Molecular and neurodegenerative markers obtained through PET or in cerebrospinal fluid have significant prognostic value, but they are costly or invasive. In this study, we hypothesize that a combination of plasma and structural MRI biomarkers, which overcome these limitations, are predictive of longitudinal progression measured by atrophy and cognitive decline in early AD, providing a more feasible solution. Methods: Longitudinal 3 Tesla T1‐weighted MRI and cognitive measurements [logical memory, delayed recall and clinical dementia rating sum‐of‐boxes] of 662 cognitively normal (CN) and MCI participants [divided into β‐amyloid positive/negative (A+/A‐)] from ADNI were included (Table 1). Anterior/posterior hippocampus, entorhinal cortex, Brodmann areas (BA) 35 and 36, and parahippocampal cortex were segmented in baseline MRI. Annualized change rates of BA35 thickness [the first region of neurofibrillary tangle pathology] and cognitive measurements were estimated using linear modeling. In each diagnostic group and A+/A‐ subgroups, stepwise linear regression model with age, gender, education and intracranial volume as fixed variables and all baseline MRI and plasma measurements [neurofilament light chain (NfL), pTau‐181] was implemented to investigate their predictive power for each longitudinal measure. In addition, the area under the curve (AUC) of each model in discriminating the first and last terciles of each longitudinal measurement was reported. Results: Baseline plasma and structural biomarkers provided complementary information in predicting longitudinal atrophy and cognitive decline in controls and MCI (Table 2, Figure 1). These relationships were maintained (albeit with some differences in most predicted measures) when limited to A+ subgroups (preclinical and prodromal AD, Table 4) with AUCs ranging from 0.70‐0.84. Interestingly, baseline structural measures are consistently included in all the models. Conclusions: We demonstrated that baseline plasma and MRI biomarkers provide complementary information in predicting brain atrophy and cognitive decline. This indicates the combination of these biomarkers, which are relatively easy to obtain, may have important use for recruitment in clinical trials and practice. Additionally, the effect in A‐ CN indicates the potential use of our structural biomarker in predicting normal age‐related decline. [ABSTRACT FROM AUTHOR]