Background and Objectives Early-onset Alzheimer disease (EOAD) is associated with substantial variability in clinical progression, and reliable biomarkers to predict the transition from mild cognitive impairment (MCI) to dementia remain limited. Structural MRI measures have demonstrated prognostic value in late-onset Alzheimer disease, but their utility for predicting progression in EOAD is less well understood. The goal was to examine whether baseline cortical atrophy predicts progression to dementia in patients with MCI because of EOAD. Methods This study included a well-characterized cohort of patients with EOAD enrolled in the large multisite natural history Longitudinal Early-Onset Alzheimer's Disease Study. Participants underwent standardized clinical assessments and structural MRI at baseline. Participants were aged between 40 and 64 years with biomarker-supported sporadic EOAD at the MCI stage. Cortical atrophy was measured within the EOAD-signature, a set of predominantly parieto-temporal regions showing greater atrophy in EOAD than in controls. Clinical severity was measured with the global Clinical Dementia Rating. Cox proportional hazards models estimated the association between baseline EOAD-signature atrophy burden and the hazard of progression to dementia over time. We evaluated whether EOAD-signature atrophy improved prognostic performance beyond baseline clinical severity using likelihood ratio tests, Akaike Information Criterion (AIC), and Harrell concordance index. Results A total of 130 patients with MCI due to EOAD (mean age 59.6 ± 4.1 years; 49% female) and 97 cognitively normal controls (mean age 56.9 ± 6.0 years; 64% female) were included. Greater baseline atrophy within the EOAD-signature predicted faster progression to dementia (hazard ratio [HR] = 1.24 per 1-SD increase in atrophy; 95% CI 1.13–1.37; p < 0.002). Adding EOAD-signature atrophy burden to a model including baseline clinical severity significantly improved model fit (ΔAIC = −4.5; likelihood ratio test p = 0.011). Discussion Baseline cortical atrophy within the EOAD-signature predicts progression from MCI to dementia in EOAD and provides prognostic information beyond baseline clinical severity. These findings support the potential value of EOAD-signature atrophy as an MRI-based biomarker for individualized prognostication and clinical trial stratification.
T. Paranhos, Yuta Katsumi, M. Brickhouse et al.· Neurology· 0 citations
Abstract Repetitive transcranial magnetic stimulation (rTMS) has been explored as an intervention in typical amnestic Alzheimer’s Disease (AD). However, its effects on episodic and associative memory in atypical forms of AD are less known. Posterior cortical atrophy (PCA) is primarily characterized by visuospatial and visuoperceptual symptoms, although significant episodic memory deficits are often observed. To investigate whether intermittent theta burst (iTBS, a form of rTMS), guided by individualized functional imaging, can modulate functional networks and improve associative memory in a patient with PCA presenting with memory impairments. A stimulation target in the left caudal middle frontal gyrus (cMFG) was selected based on peak functional connectivity with the default mode network (DMN). iTBS was administered to this target in a randomized placebo-controlled paradigm. Primary outcomes were changes in functional connectivity of the stimulation target and associative memory performance. Visuospatial working memory and global cognition were secondary outcomes. Consistent with our hypothesis, active iTBS increased connectivity between the cMFG target and distributed DMN regions. Contrary to our hypothesis, associative memory performance did not improve, but we did observe improvements in visuospatial working memory. This case report supports further controlled clinical trials using functional connectivity guided rTMS in PCA.
T. Paranhos, Anna Du, Ryan Eckbo et al.· International Review of Psyc...· 1 citation
Abstract Brain age gap estimation (BrainAGE) is a promising imaging-derived biomarker of neurobiological ageing and disease risk, yet current approaches rely predominantly on T1-weighted structural MRI, overlooking functional vascular changes that may precede tissue damage and cognitive decline. Deep learning-derived cerebral blood volume (DeepCBV) maps, synthesized from non-contrast MRI, offer a scalable alternative to contrast-enhanced perfusion imaging by capturing vascular information relevant to early neurodegeneration. We developed a multimodal BrainAGE framework that combines predictions from two separate three-dimensional convolutional neural networks: one trained only on structural MRI scans and another trained only on DeepCBV maps generated by a pre-trained three-dimensional patch-based deep learning model. Each model was trained and validated on 2851 scans (1507 females) from 13 open-source datasets and was evaluated for concordance with mild cognitive impairment (MCI) and Alzheimer’s disease (AD) using 1233 subjects. The combined model achieved the most accurate brain age gap for cognitively normal (CN) controls, with a mean absolute error of 3.95 years (R2 = 0.943), outperforming models trained on MRI (mean absolute error = 4.10) or DeepCBV alone (mean absolute error = 4.49). Saliency maps revealed complementary modality contributions: MRI emphasized white matter and cortical atrophy, while DeepCBV highlighted vascular-rich and periventricular regions implicated in hypoperfusion and early cerebrovascular dysfunction, consistent with known patterns of normal ageing. Next, we observed that BrainAGE increased stepwise across diagnostic strata (CN < MCI < AD) and correlated with cognitive impairment (Clinical Dementia Rating Sum of Boxes ⍴ = 0.403; Mini-Mental State Examination ⍴ = −0.310). DeepCBV-based BrainAGE showed a particularly strong separation between stable versus progressive MCI (Mann–Whitney U = 2.177 × 104, P = 4.43 × 10−8), suggesting sensitivity to prodromal vascular changes that precede overt atrophy. Integrating structural MRI with deep learning-derived vascular measures substantially enhances BrainAGE estimation and improves sensitivity to MCI and Alzheimer’s disease progression, supporting its potential role in risk stratification, early detection and monitoring of therapeutic response. By enabling a functional-like assessment from routine MRI, this approach lowers barriers to multimodal evaluation and provides a clinically actionable biomarker for large-scale ageing and dementia studies.
Jordan Jomsky, Zongyu Li, Kay C. Igwe et al.· Brain Communications· 1 citation
A double-cutoff analysis suggest that scans in the 11 to 26 Centiloid range should be interpreted with caution depending on the context of use, and positivity cutoffs converged around 18 Centiloids (data-driven) and 27 Centiloids (visual reads).
G. Blazhenets, D. Soleimani-Meigooni, Konstantinos Chiotis et al.· Journal of the American Medi...· 5 citations
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