Description and classification of normal and pathological aging processes based on brain magnetic resonance imaging morphology measures

M/EEG Dynamics Underlying Reserve, Resilience, and Maintenance in Aging: A Review

Cognitive reserve and resilience refer to the set of processes allowing the preservation of cognitive performance in the presence of structural and functional brain changes. Investigations of these concepts have provided unique insights into the heterogeneity of cognitive and brain changes associated with aging. Previous work mainly relied on methods benefiting from a high spatial precision but a low temporal resolution, and thus the temporal brain dynamics underlying these concepts remains poorly known. Moreover, while spontaneous fluctuations of neural activity have long been considered as noise, recent work highlights its critical contribution to brain functions. In this study, we synthesized the current state of knowledge from magnetoencephalography (MEG) and electroencephalography (EEG) studies that investigated the contribution of maintenance of neural synchrony, and variability of brain dynamics, to cognitive changes associated with healthy aging and the progression of neurodegenerative disease (such as Alzheimer's disease). The reviewed findings highlight that compensations could be associated with increased synchrony of higher (>10 Hz) frequency bands. Maintenance of young-like synchrony patterns was also observed in healthy older individuals. Both maintenance and compensation appear to be highly related to preserved structural integrity (brain reserve). However, increased synchrony was also found to be deleterious in some cases and reflects neurodegenerative processes. These results provide major elements on the stability or variability of functional networks as well as maintenance of neural synchrony over time, and their association with individual cognitive changes with aging. These findings could provide new and interesting considerations about cognitive reserve, maintenance, and resilience of brain functions and cognition.

Mild cognitive impairment classification using combined structural and diffusion imaging biomarkers

Alzheimer's disease is a multifactorial neurodegenerative disorder preceded by a prodromal stage called mild cognitive impairment (MCI). Early diagnosis of MCI is crucial for delaying the progression and optimizing the treatment. In this study we propose a random forest (RF) classifier to distinguish between MCI and healthy control subjects (HC), identifying the most relevant features computed from structural T1-weighted and diffusion-weighted magnetic resonance images (sMRI and DWI), combined with neuro-psychological scores. To train the RF we used a set of 60 subjects (HC = 30, MCI = 30) drawn from the Alzheimer's disease neuroimaging initiative database, while testing with unseen data was carried out on a 23-subjects Mexican cohort (HC = 12, MCI = 11). Features from hippocampus, thalamus and amygdala, for left and right hemispheres were fed to the RF, with the most relevant being previously selected by applying extra trees classifier and the mean decrease in impurity index. All the analyzed brain structures presented changes in sMRI and DWI features for MCI, but those computed from sMRI contribute the most to distinguish from HC. However, sMRI+DWI improves classification performance in training area under the receiver operating characteristic curve (AUROC = 93.5 ± 8%, accuracy = 88.8 ± 9%) and testing with unseen data (AUROC = 93.79%, accuracy = 91.3%), having a better performance when neuro-psychological scores were included. Compared to other classifiers the proposed RF provide the best performance for HC/MCI discrimination and the application of a feature selection step improves its performance. These findings imply that multimodal analysis gives better results than unimodal analysis and hence may be a useful tool to assist in early MCI diagnosis.

Evaluation of Brain Tortuosity Measurement for the Automatic Multimodal Classification of Subjects with Alzheimer's Disease

The 3D tortuosity determined in several brain areas is proposed as a new morphological biomarker (BM) to be considered in early detection of Alzheimer's disease (AD). It is measured using the sum of angles method and it has proven to be sensitive to anatomical changes that appear in gray and white matter and temporal and parietal lobes during mild cognitive impairment (MCI). Statistical analysis showed significant differences (p < 0.05) between tortuosity indices determined for healthy controls (HC) vs. MCI and HC vs. AD in most of the analyzed structures. Other clinically used BMs have also been incorporated in the analysis: beta-amyloid and tau protein CSF and plasma concentrations, as well as other image-extracted parameters. A classification strategy using random forest (RF) algorithms was implemented to discriminate between three samples of the studied populations, selected from the ADNI database. Classification rates considering only image-extracted parameters show an increase of 9.17%, when tortuosity is incorporated. An enhancement of 1.67% is obtained when BMs measured from several modalities are combined with tortuosity.

Automatic Classification of Alzheimer's Disease based on CPD Brain Point Cloud Registration for Feature Extraction

In this paper, a new scheme of feature extraction based on the registration of two point clouds that model the brain surface area and a cerebral baseline, is presented. The aim is to design an automatic Random Forest classifier based on brain-shape pattern extraction, in order to discriminate among Control subjects, patients with Mild Cognitive Impairment, and patients with Alzheimer's disease using their MRI data. The features used were rotation and displacement parameters obtained from the rigid-register transformation matrix between each subject's point cloud model and the brain baseline cloud. Additional features consisted of distance metrics performed over the aligned clouds and the baseline; those metrics attempt to estimate the cerebral cortex deformations. The training set utilized 60 MRI grouped into subsets of 20 studies a class; while the test consisted of 30 not seen data (10 per class). The proposed method achieved up to 87.2% accuracy in the final validation stage. These results suggest that our proposed scheme could be useful to discriminate among several pathologies that affect brain gray matter morphology. Moreover, the proposed features might be used as brain deformation quantifiers.

The role of cofilin in age-related neuroinflammation

Aging brain becomes susceptible to neurodegenerative diseases due to the shifting of microglia and astrocyte phenotypes to an active “pro-inflammatory” state, causing chronic low-grade neuroinflammation. Despite the fact that the role of neuroinflammation during aging has been extensively studied in recent years, the underlying causes remain unclear. The identification of relevant proteins and understanding their potential roles in neuroinflammation can help explain their potential of becoming biomarkers in the aging brain and as drug targets for prevention and treatment. This will eventually reduce the chances of developing neurodegenerative diseases and promote healthier lives in the elderly. In this review, we have summarized the morphological and cellular changes in the aging brain, the effects of age-related neuroinflammation, and the potential role of cofilin-1 during neuroinflammation. We also discuss other factors contributing to brain aging and neuroinflammation.