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The gap between chronological age (CA) and biological brain age, as estimated from magnetic resonance images (MRIs), reflects how individual patterns of neuroanatomic aging deviate from their typical trajectories. MRI-derived brain age (BA) estimates are often obtained using deep learning models that may perform relatively poorly on new data or that lack neuroanatomic interpretability. This study introduces a convolutional neural network (CNN) to estimate BA after training on the MRIs of 4,681 cognitively normal (CN) participants and testing on 1,170 CN participants from an independent sample. BA estimation errors are notably lower than those of previous studies. At both individual and cohort levels, the CNN provides detailed anatomic maps of brain aging patterns that reveal sex dimorphisms and neurocognitive trajectories in adults with mild cognitive impairment (MCI, N  = 351) and Alzheimer’s disease (AD, N  = 359). In individuals with MCI (54% of whom were diagnosed with dementia within 10.9 y from MRI acquisition), BA is significantly better than CA in capturing dementia symptom severity, functional disability, and executive function. Profiles of sex dimorphism and lateralization in brain aging also map onto patterns of neuroanatomic change that reflect cognitive decline. Significant associations between BA and neurocognitive measures suggest that the proposed framework can map, systematically, the relationship between aging-related neuroanatomy changes in CN individuals and in participants with MCI or AD. Early identification of such neuroanatomy changes can help to screen individuals according to their AD risk. 

The aim of this study is to identify the potential cerebrospinal fluid (CSF) biomarkers for Alzheimer's disease and to evaluate these markers on independent CSF samples using parallel reaction monitoring (PRM) assays.

High‐Resolution mass spectrometry and tandem mass tag (TMT) multiplexing technology are employed to identify potential biomarkers for Alzheimer's disease. Some of the identified potential biomarkers are validated using PRM assays.

A total of 2327 proteins are identified in the CSF of which 139 are observed to be significantly altered in the CSF of AD patients. The proteins altered in AD includes a number of known AD marker such as MAPT, NPTX2, VGF, GFAP, and NCAM1 as well as novel biomarkers such as PKM and YWHAG. These findings are validated in a separate set of CSF specimens from AD dementia patients and controls. NPTX2, in combination with PKM or YWHAG, leads to the best results with AUCs of 0.935 and 0.933, respectively.

The proteins that are found to be altered in the CSF of patients with AD could be used for monitoring disease progression and therapeutic response and perhaps also for early detection once they are validated in larger studies.

 

In the Alzheimer’s disease (AD) continuum, the prodromal state of mild cognitive impairment (MCI) precedes AD dementia and identifying MCI individuals at risk of progression is important for clinical management. Our goal was to develop generalizable multivariate models that integrate high-dimensional data (multimodal neuroimaging and cerebrospinal fluid biomarkers, genetic factors, and measures of cognitive resilience) for identification of MCI individuals who progress to AD within 3 years. Our main findings were i) we were able to build generalizable models with clinically relevant accuracy (~93%) for identifying MCI individuals who progress to AD within 3 years; ii) markers of AD pathophysiology (amyloid, tau, neuronal injury) accounted for large shares of the variance in predicting progression; iii) our methodology allowed us to discover that expression ofCR1(complement receptor 1), an AD susceptibility gene involved in immune pathways, uniquely added independent predictive value. This work highlights the value of optimized machine learning approaches for analyzing multimodal patient information for making predictive assessments.