Trajectories of Physiological Brain Aging and Related Factors in People Aged from 20 to over-80

Deep learning-based quantification of brain atrophy using 2D T1-weighted MRI for Alzheimer's disease classification

Background

Determining brain atrophy is crucial for the diagnosis of neurodegenerative diseases. Despite detailed brain atrophy assessments using three-dimensional (3D) T1-weighted magnetic resonance imaging, their practical utility is limited by cost and time. This study introduces deep learning algorithms for quantifying brain atrophy using a more accessible two-dimensional (2D) T1, aiming to achieve cost-effective differentiation of dementia of the Alzheimer's type (DAT) from cognitively unimpaired (CU), while maintaining or exceeding the performance obtained with T1-3D individuals and to accurately predict AD-specific atrophy similarity and atrophic changes [W-scores and Brain Age Index (BAI)].

Methods

Involving 924 participants (478 CU and 446 DAT), our deep learning models were trained on cerebrospinal fluid (CSF) volumes from 2D T1 images and compared with 3D T1 images. The performance of the models in differentiating DAT from CU was assessed using receiver operating characteristic analysis. Pearson's correlation analyses were used to evaluate the relations between 3D T1 and 2D T1 measurements of cortical thickness and CSF volumes, AD-specific atrophy similarity, W-scores, and BAIs.

Results

Our deep learning models demonstrated strong correlations between 2D and 3D T1-derived CSF volumes, with correlation coefficients r ranging from 0.805 to 0.971. The algorithms based on 2D T1 accurately distinguished DAT from CU with high accuracy (area under the curve values of 0.873), which were comparable to those of algorithms based on 3D T1. Algorithms based on 2D T1 image-derived CSF volumes showed high correlations in AD-specific atrophy similarity (r = 0.915), W-scores for brain atrophy (0.732 ≤ r ≤ 0.976), and BAIs (r = 0.821) compared with those based on 3D T1 images.

Conclusion

Deep learning-based analysis of 2D T1 images is a feasible and accurate alternative for assessing brain atrophy, offering diagnostic precision comparable to that of 3D T1 imaging. This approach offers the advantage of the availability of T1-2D imaging, as well as reduced time and cost, while maintaining diagnostic precision comparable to T1-3D.

Lower cortical thickness and increased brain aging in adults with cocaine use disorder

Background

Cocaine use disorder (CUD) is a global health issue with severe behavioral and cognitive sequelae. While previous evidence suggests a variety of structural and age-related brain changes in CUD, the impact on both, cortical thickness and brain age measures remains unclear.

Methods

Derived from a publicly available data set (SUDMEX_CONN), 74 CUD patients and 62 matched healthy controls underwent brain MRI and behavioral-clinical assessment. We determined cortical thickness by surface-based morphometry using CAT12 and Brain Age Gap Estimate (BrainAGE) via relevance vector regression. Associations between structural brain changes and behavioral-clinical variables of patients with CUD were investigated by correlation analyses.

Results

We found significantly lower cortical thickness in bilateral prefrontal cortices, posterior cingulate cortices, and the temporoparietal junction and significantly increased BrainAGE in patients with CUD [mean (SD) = 1.97 (±3.53)] compared to healthy controls (p < 0.001, Cohen's d = 0.58). Increased BrainAGE was associated with longer cocaine abuse duration.

Conclusion

Results demonstrate structural brain abnormalities in CUD, particularly lower cortical thickness in association cortices and dose-dependent, increased brain age.

Data-driven multivariate identification of gyrification patterns in a transdiagnostic patient cohort: A cluster analysis approach

BACKGROUND

Multivariate data-driven statistical approaches offer the opportunity to study multi-dimensional interdependences between a large set of biological parameters, such as high-dimensional brain imaging data. For gyrification, a putative marker of early neurodevelopment, direct comparisons of patterns among multiple psychiatric disorders and investigations of potential heterogeneity of gyrification within one disorder and a transdiagnostic characterization of neuroanatomical features are lacking.

METHODS

In this study we used a data-driven, multivariate statistical approach to analyze cortical gyrification in a large cohort of N = 1028 patients with major psychiatric disorders (Major depressive disorder: n = 783, bipolar disorder: n = 129, schizoaffective disorder: n = 44, schizophrenia: n = 72) to identify cluster patterns of gyrification beyond diagnostic categories.

RESULTS

Cluster analysis applied on gyrification data of 68 brain regions (DK-40 atlas) identified three clusters showing difference in overall (global) gyrification and minor regional variation (regions). Newly, data-driven subgroups are further discriminative in cognition and transdiagnostic disease risk factors.

CONCLUSIONS

Results indicate that gyrification is associated with transdiagnostic risk factors rather than diagnostic categories and further imply a more global role of gyrification related to mental health than a disorder specific one. Our findings support previous studies highlighting the importance of association cortices involved in psychopathology. Explorative, data-driven approaches like ours can help to elucidate if the brain imaging data on hand and its a priori applied grouping actually has the potential to find meaningful effects or if previous hypotheses about the phenotype as well as its grouping have to be revisited.

Sex-specific relationship between non-alcoholic fatty liver disease and amyloid-β in cognitively unimpaired individuals

Background

Non-alcoholic fatty liver disease (NAFLD) is known to be associated with a high risk of clinically diagnosed Alzheimer's disease (AD). Additionally, the prevalence of NAFLD and AD is higher in elderly females than in males. However, a sex-specific association between NAFLD and amyloid-beta (Aβ) deposition remains unclear. Therefore, we investigated the sex-specific relationship between NAFLD and Aβ deposition in a large-sized cohort of cognitively unimpaired (CU) individuals.

Methods

We enrolled 673 (410 [60.9%] females and 263 [39.1%] males) CU individuals aged ≥45 years who underwent Aβ positron emission tomography (PET). The presence of NAFLD, assessed using the hepatic steatosis index, and the severity of NAFLD, assessed using the Fibrosis-4 index, were considered predictors. Aβ deposition on PET was considered as an outcome.

Results

Females had a higher frequency of NAFLD than males (48 and 23.2%, p < 0.001). Among females, the presence of NAFLD (β = 0.216, p < 0.001) was predictive of increased Aβ deposition, whereas among males, the presence of NAFLD (β = 0.191, p = 0.064) was not associated with Aβ deposition. Among females, the presence of NAFLD with low (β = 0.254, p = 0.039), intermediate (β = 0.201, p = 0.006), and high fibrosis (β = 0.257, p = 0.027) was predictive of increased Aβ deposition. Aβ deposition also increased as the severity of NAFLD increased in females (p for trend = 0.001).

Conclusion

We highlight the marked influence of NAFLD and its severity on the risk of Aβ deposition in relation to sex. Furthermore, our findings suggest that sex-specific strategies regarding the management of NAFLD are necessary for the prevention of Aβ deposition.

Cross-sectional associations between cortical thickness and physical activity in older adults with spontaneous memory complaints: The MAPT Study

BACKGROUND

Age-related changes in brain structure may constitute the starting point for cerebral function alteration. Physical activity (PA) demonstrated favorable associations with total brain volume, but its relationship with cortical thickness (CT) remains unclear. We investigated the cross-sectional associations between PA level and CT in community-dwelling people aged 70 years and older.

METHODS

A total of 403 older adults aged 74.8 ± 4.0 years (mean ± SD) who underwent a baseline magnetic resonance imaging examination and who had data on PA and confounders were included. PA was assessed with a questionnaire. Participants were categorized according to PA levels. Multiple linear regressions were used to compare the brain CT (mm) of the inactive group (no PA at all) with 6 active groups (growing PA levels) in 34 regions of interest.

RESULTS

Compared with inactive persons, people who achieved PA at a level of 1500-1999 metabolic equivalent task-min/week (i.e., about 6-7 h of brisk walking for exercise and those who achieved it at 2000-2999 metabolic equivalent task-min/week (i.e., 8-11 h of brisk walking for exercise) had higher CT in the fusiform gyrus and the temporal pole. Additionally, dose-response associations between PA and CT were found in the fusiform gyrus (B = 0.011, SE = 0.004, adj. p = 0.035), the temporal pole (B = 0.026, SE = 0.009, adj. p = 0.048), and the caudal middle frontal gyrus, the entorhinal, medial orbitofrontal, lateral occipital, and insular cortices.

CONCLUSION

This study demonstrates a positive association between PA level and CT in temporal areas such as the fusiform gyrus, a brain region often associated to Alzheimer's disease in people aged 70 years and older. Future investigations focusing on PA type may help to fulfil remaining knowledge gaps in this field.

Different effects of cardiometabolic syndrome on brain age in relation to gender and ethnicity

BACKGROUND

A growing body of evidence shows differences in the prevalence of cardiometabolic syndrome (CMS) and dementia based on gender and ethnicity. However, there is a paucity of information about ethnic- and gender-specific CMS effects on brain age. We investigated the different effects of CMS on brain age by gender in Korean and British cognitively unimpaired (CU) populations. We also determined whether the gender-specific difference in the effects of CMS on brain age changes depending on ethnicity.

METHODS

These analyses used de-identified, cross-sectional data on CU populations from Korea and United Kingdom (UK) that underwent brain MRI. After propensity score matching to balance the age and gender between the Korean and UK populations, 5759 Korean individuals (3042 males and 2717 females) and 9903 individuals from the UK (4736 males and 5167 females) were included in this study. Brain age index (BAI), calculated by the difference between the predicted brain age by the algorithm and the chronological age, was considered as main outcome and presence of CMS, including type 2 diabetes mellitus (T2DM), hypertension, obesity, and underweight was considered as a predictor. Gender (males and females) and ethnicity (Korean and UK) were considered as effect modifiers.

RESULTS

The presence of T2DM and hypertension was associated with a higher BAI regardless of gender and ethnicity (p < 0.001), except for hypertension in Korean males (p = 0.309). Among Koreans, there were interaction effects of gender and the presence of T2DM (p for T2DM*gender = 0.035) and hypertension (p for hypertension*gender = 0.046) on BAI in Koreans, suggesting that T2DM and hypertension are each associated with a higher BAI in females than in males. In contrast, among individuals from the UK, there were no differences in the effects of T2DM (p for T2DM*gender = 0.098) and hypertension (p for hypertension*gender = 0.203) on BAI between males and females.

CONCLUSIONS

Our results highlight gender and ethnic differences as important factors in mediating the effects of CMS on brain age. Furthermore, these results suggest that ethnic- and gender-specific prevention strategies may be needed to protect against accelerated brain aging.

A novel method for estimating connectivity-based parcellation of the human brain from diffusion MRI: Application to an aging cohort

Connectivity‐based parcellation (CBP) methods are used to define homogenous and biologically meaningful parcels or nodes—the foundations of brain network fingerprinting—by grouping voxels with similar patterns of brain connectivity. However, we still lack a gold standard method and the use of CBPs to study the aging brain remains scarce. Our study proposes a novel CBP method from diffusion MRI data and shows its potential to produce a more accurate characterization of the longitudinal alterations in brain network topology occurring in aging. For this, we constructed whole‐brain connectivity maps from diffusion MRI data of two datasets: an aging cohort evaluated at two timepoints (mean interval time: 52.8 ± 7.24 months) and a normative adult cohort—MGH‐HCP. State‐of‐the‐art clustering techniques were used to identify the best performing technique. Furthermore, we developed a new metric (connectivity homogeneity fingerprint [CHF]) to evaluate the success of the final CBP in improving regional/global structural connectivity homogeneity. Our results show that our method successfully generates highly homogeneous parcels, as described by the significantly larger CHF score of the resulting parcellation, when compared to the original. Additionally, we demonstrated that the developed parcellation provides a robust anatomical framework to assess longitudinal changes in the aging brain. Our results reveal that aging is characterized by a reorganization of the brain's structural network involving the decrease of intra‐hemispheric, increase of inter‐hemispheric connectivity, and topological rearrangement. Overall, this study proposes a new methodology to perform accurate and robust evaluations of CBP of the human brain.

Association Between Plasma Amyloid-β and Neuropsychological Performance in Patients With Cognitive Decline

Objective: To investigate the association between plasma amyloid-β (Aβ) levels and neuropsychological performance in patients with cognitive decline using a highly sensitive nano-biosensing platform.

Methods: We prospectively recruited 44 patients with cognitive decline who underwent plasma Aβ analysis, amyloid positron emission tomography (PET) scanning, and detailed neuropsychological tests. Patients were classified into a normal control (NC, n = 25) or Alzheimer’s disease (AD, n = 19) group based on amyloid PET positivity. Multiple linear regression was performed to determine whether plasma Aβ (Aβ₄₀, Aβ₄₂, and Aβ_42/40) levels were associated with neuropsychological test results.

Results: The plasma levels of Aβ_42/40 were significantly different between the NC and AD groups and were the best predictor of amyloid PET positivity by receiver operating characteristic curve analysis [area under the curve of 0.952 (95% confidence interval, 0.892–1.000)]. Although there were significant differences in the neuropsychological performance of cognitive domains (language, visuospatial, verbal/visual memory, and frontal/executive functions) between the NC and AD groups, higher levels of plasma Aβ_42/40 were negatively correlated only with verbal and visual memory performance.

Conclusion: Our results demonstrated that plasma Aβ analysis using a nano-biosensing platform could be a useful tool for diagnosing AD and assessing memory performance in patients with cognitive decline.

New assessment for residential greenness and the association with cortical thickness in cognitively healthy adults

BACKGROUND

Recent evidence suggests that neurological health could be improved with the intervention of local green space. A few studies adopted cortical thickness, as an effective biomarker for neurodegenerative disorder, to investigate the association with residential greenness. However, they relied on limited data sources, definitions or applications to assess residential greenness. Our cross-sectional study assessed individual residential greenness using an alternative measure, which provides a more realistic definition of local impact and application based on the type of area, and investigated the association with cortical thickness.

METHODS

The study population included 2542 subjects who participated in the medical check-up program at the Health Promotion Center of the Samsung Medical Center in Seoul, Korea, from 2008 to 2014. The cortical thickness was calculated by each of the four and global lobes from brain MRI. For greenness, we used the enhanced vegetation index (EVI) that detects canopy structural variation by adjusting background noise based on satellite imagery data. To assess individual exposure to residential greenness, we computed the maximum annual EVI before the date of a medical check-up and averaged it within 750 m from subjects' homes to represent an average walking distance. Finally, we assessed the association with cortical thickness by urban and non-urban populations using multiple linear regression adjusting for individual characteristics.

RESULTS

The average global cortical thickness and EVI were 3.05 mm (standard deviation = 0.1 mm) and 0.31 (0.1), respectively. An interquartile range increase in EVI was associated with 11 μm (95% confidence interval = 3-20) and 9 μm (1-16) increases in cortical thickness of the parietal and occipital regions among the urban population. We did not find associations in non-urban subjects.

CONCLUSIONS

Our findings confirm the association between residential greenness and neurological health using alternative exposure assessments, indicating that high exposure to residential greenness can prevent neurological disorders.

Reorganization of brain structural networks in aging: A longitudinal study

Normal aging is characterized by structural and functional changes in the brain contributing to cognitive decline. Structural connectivity (SC) describes the anatomical backbone linking distinct functional subunits of the brain and disruption of this communication is thought to be one of the potential contributors for the age-related deterioration observed in cognition. Several studies already explored brain network's reorganization during aging, but most focused on average connectivity of the whole-brain or in specific networks, such as the resting-state networks. Here, we aimed to characterize longitudinal changes of white matter (WM) structural brain networks, through the identification of sub-networks with significantly altered connectivity along time. Then, we tested associations between longitudinal changes in network connectivity and cognition. We also assessed longitudinal changes in topological properties of the networks. For this, older adults were evaluated at two timepoints, with a mean interval time of 52.8 months (SD = 7.24). WM structural networks were derived from diffusion magnetic resonance imaging, and cognitive status from neurocognitive testing. Our results show age-related changes in brain SC, characterized by both decreases and increases in connectivity weight. Interestingly, decreases occur in intra-hemispheric connections formed mainly by association fibers, while increases occur mostly in inter-hemispheric connections and involve association, commissural, and projection fibers, supporting the last-in-first-out hypothesis. Regarding topology, two hubs were lost, alongside with a decrease in connector-hub inter-modular connectivity, reflecting reduced integration. Simultaneously, there was an increase in the number of provincial hubs, suggesting increased segregation. Overall, these results confirm that aging triggers a reorganization of the brain structural network.

Functional and structural correlates of working memory performance and stability in healthy older adults

Despite the well-described deleterious effects of aging on cognition, some individuals are able to show stability. Here, we aimed to describe the functional and structural brain characteristics of older individuals, particularly focusing on those with stable working memory (WM) performance, as measured with a verbal N-back task across a 2-year follow-up interval. Forty-seven subjects were categorized as stables or decliners based on their WM change. Stables were further subdivided into high performers (SHP) and low performers (SLP), based on their baseline scores. At both time points, magnetic resonance imaging (MRI) data were acquired, including task-based functional MRI (fMRI) and structural T1-MRI. Although there was no significant interaction between overall stables and decliners as regards fMRI patterns, decliners exhibited over-activation in the right superior parietal lobule at follow-up as compared to baseline, while SHP showed reduced the activity in this region. Further, at follow-up, decliners exhibited more activity than SHP but in left temporo-parietal cortex and posterior cingulate (i.e., non-task-related areas). Also, at the cross-sectional level, SLP showed lower activity than SHP at both time points and less activity than decliners at follow-up. Concerning brain structure, a generalized significant cortical thinning over time was identified for the whole sample. Notwithstanding, the decliners evidenced a greater rate of atrophy comprising the posterior middle and inferior temporal gyrus as compared to the stable group. Overall, fMRI data suggest unsuccessful compensation in the case of decliners, shown as increases in functional recruitment during the task in the context of a loss in WM performance and brain atrophy. On the other hand, among older individuals with WM cognitive stability, differences in baseline performance might determine dissimilar fMRI trajectories. In this vein, the findings in the SHP subgroup support the brain maintenance hypothesis, suggesting that stable and high WM performance in aging is sustained by functional efficiency and maintained brain structure rather than compensatory changes.

Differences in neuroimaging features of early- versus late-onset nonfluent/agrammatic primary progressive aphasia

This study investigated distinct neuroimaging features measured by cortical thickness and subcortical structural shape abnormality in early-onset (EO, onset age <65 years) and late-onset (LO, onset age ≥65 years) nonfluent/agrammatic variant of primary progressive aphasia (nfvPPA) patients. Cortical thickness and subcortical structural shape analyses were performed using a surface-based method from 38 patients with nfvPPA and 76 cognitively normal individuals. To minimize the effects of physiological aging, we used W-scores in comparisons between the groups. The EO-nfvPPA group exhibited more extensive cortical thickness reductions predominantly in the left perisylvian, lateral and medial prefrontal, temporal, posterior cingulate, and precuneus regions than the LO-nfvPPA group. The EO-nfvPPA group also exhibited significantly greater subcortical structural shape abnormality than the LO-nfvPPA group, mainly in the left striatum, hippocampus, and amygdala. Our findings suggested that there were differences in neuroimaging features between these groups by the age of symptom onset, which might be explained by underlying heterogeneous neuropathological differences or the age-related brain reserve hypothesis.

Sex-specific relationship of cardiometabolic syndrome with lower cortical thickness

OBJECTIVE

To investigate whether cardiometabolic factors were associated with age-related differences in cortical thickness in relation to sex.

METHODS

In this cross-sectional study, we enrolled 1,322 cognitively normal elderly (≥65 years old) individuals (774 [58.5%] men, 548 [41.5%] women). We measured cortical thickness using a surface-based analysis. We analyzed the associations of cardiometabolic risk factors with cortical thickness using multivariate linear regression models after adjusting for possible confounders and interactions with age.

RESULT

Among women, hypertension (β = -1.119 to -0.024, p < 0.05) and diabetes mellitus (β = -0.920, p = 0.03) were independently associated with lower mean cortical thickness. In addition, there was an interaction effect between obesity (body mass index [BMI] ≥27.5 kg/m²) and age on cortical thickness in women (β = -0.324 to -0.010, p < 0.05), suggesting that age-related differences in cortical thickness were more prominent in obese women compared to women with normal weight. Moreover, low education level (<6 years) was correlated with lower mean cortical thickness (β = -0.053 to -0.046, p < 0.05). Conversely, among men, only being underweight (BMI ≤18.5 kg/m², β = -2.656 to -0.073, p < 0.05) was associated with lower cortical thickness.

CONCLUSIONS

Our findings suggest that cortical thickness is more vulnerable to cardiometabolic risk factors in women than in men. Therefore, sex-specific prevention strategies may be needed to protect against accelerated brain aging.

Age moderates the relationship between cortical thickness and cognitive performance

Findings from cross-sectional and longitudinal magnetic resonance imaging (MRI) studies indicate that cortical thickness declines across the adult lifespan, with regional differences in rate of decline. Global and regional thickness have also been found to co-vary with cognitive performance. Here we examined the relationships between age, mean cortical thickness, and associative recognition performance across three age groups (younger, middle-aged and older adults; total n = 133). Measures of cortical thickness were obtained using a semi-automated method. Older age was associated with decreased memory performance and a reduction in mean cortical thickness. After controlling for the potentially confounding effects of head motion, mean cortical thickness was negatively associated with associative memory performance in the younger participants, but was positively correlated with performance in older participants. A similar but weaker pattern was evident in the relationships between cortical thickness and scores on four cognitive constructs derived from a neuropsychological test battery. This pattern is consistent with prior findings indicating that the direction of the association between cortical thickness and cognitive performance reverses between early and later adulthood. In addition, head motion was independently and negatively correlated with associative recognition performance in younger and middle-aged, but not older, participants, suggesting that variance in head motion is determined by multiple factors that vary in their relative influences with age.

Distinct Brain Regions in Physiological and Pathological Brain Aging

Background

Studying structural brain aging is important to understand age-related pathologies, as well as to identify the early manifestations of the Alzheimer’s disease (AD) continuum. In this study, we investigated the long-term trajectory of physiological and pathological brain aging in a large number of participants ranging from the 50s to over 80 years of age.

Objective

To explore the distinct brain regions that distinguish pathological brain aging from physiological brain aging using sophisticated measurements of cortical thickness.

Methods

A total of 2,823 cognitively normal (CN) individuals and 2,675 patients with AD continuum [874 with subjective memory impairment (SMI), 954 with amnestic mild cognitive impairment (aMCI), and 847 with AD dementia] who underwent a high-resolution 3.0-tesla MRI were included in this study. To investigate pathological brain aging, we further classified patients with aMCI and AD according to the severity of cognitive impairment. Cortical thickness was measured using a surface-based method. Multiple linear regression analyses were performed to evaluate age, diagnostic groups, and cortical thickness.

Results

Aging extensively affected cortical thickness not only in CN individuals but also in AD continuum patients; however, the precuneus and inferior temporal regions were relatively preserved against age-related cortical thinning. Compared to CN individuals, AD continuum patients including those with SMI showed a decreased cortical thickness in the perisylvian region. However, widespread cortical thinning including the precuneus and inferior temporal regions were found from the late-stage aMCI to the moderate to severe AD. Unlike the other age groups, AD continuum patients aged over 80 years showed prominent cortical thinning in the medial temporal region with relative sparing of the precuneus.

Conclusion

Our findings suggested that the precuneus and inferior temporal regions are the key regions in distinguishing between physiological and pathological brain aging. Attempts to differentiate age-related pathology from physiological brain aging at a very early stage would be important in terms of establishing new strategies for preventing accelerated pathological brain aging.

Modeling grey matter atrophy as a function of time, aging or cognitive decline show different anatomical patterns in Alzheimer's disease

Background

Grey matter (GM) atrophy in Alzheimer's disease (AD) is most commonly modeled as a function of time. However, this approach does not take into account inter-individual differences in initial disease severity or changes due to aging. Here, we modeled GM atrophy within individuals across the AD clinical spectrum as a function of time, aging and MMSE, as a proxy for disease severity, and investigated how these models influence estimates of GM atrophy.

Methods

We selected 523 individuals from ADNI (100 preclinical AD, 288 prodromal AD, 135 AD dementia) with abnormal baseline amyloid PET/CSF and ≥1 year of MRI follow-up. We calculated total and 90 regional GM volumes for 2281 MRI scans (median [IQR]; 4 [3–5] scans per individual over 2 [1.6–4] years) and used linear mixed models to investigate atrophy as a function of time, aging and decline on MMSE. Analyses included clinical stage as interaction with the predictor and were corrected for baseline age, sex, education, field strength and total intracranial volume. We repeated analyses for a sample of participants with normal amyloid (n = 387) to assess whether associations were specific for amyloid pathology.

Results

Using time or aging as predictors, amyloid abnormal participants annually declined −1.29 ± 0.08 points and − 0.28 ± 0.03 points respectively on the MMSE and −12.23 ± 0.47 cm³ and −8.87 ± 0.34 respectively in total GM volume (p < .001). For the time and age models atrophy was widespread and preclinical and prodromal AD showed similar atrophy patterns. Comparing prodromal AD to AD dementia, AD dementia showed faster atrophy mostly in temporal lobes as modeled with time, while prodromal AD showed faster atrophy in mostly frontoparietal areas as modeled with age (p_FDR < 0.05). Modeling change in GM volume as a function of decline on MMSE, slopes were less steep compared to those based on time and aging (−4.1 ± 0.25 cm³ per MMSE point decline; p < .001) and showed steeper atrophy for prodromal AD compared to preclinical AD in the right inferior temporal gyrus (p < .05) and compared to AD dementia mostly in temporal areas (p_FDR < 0.05). Associations with time, aging and MMSE remained when accounting for these effects in the other models, suggesting that all measures explain part of the variance in GM atrophy. Repeating analyses in amyloid normal individuals, effects for time and aging showed similar widespread anatomical patterns, while associations with MMSE were largely reduced.

Conclusion

Effects of time, aging and MMSE all explained variance in GM atrophy slopes within individuals. Associations with MMSE were weaker than those for time or age, but specific for amyloid pathology. This suggests that at least some of the atrophy observed in time or age models may not be specific to AD.

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Modeling atrophy as a function of time or aging show similar anatomical patterns.

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GM atrophy as a function of time or aging seems nonspecific for amyloid pathology.

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GM atrophy as a function of MMSE shows involvement of different anatomical patterns.

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Atrophy modeled based on time or age was steeper than modeled based on MMSE.

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Atrophy patterns based on MMSE were specific for amyloid pathology.