The sleep architecture and microstructures alter with aging. We proposed a sleep electroencephalogram (EEG)-based brain age prediction model using the convolutional neural network method and investigated the association between brain aging acceleration and sleep disorders. The correlation between chronological age and expected brain age through our model was 80% and the mean absolute error was 5.4 years. Our model also showed higher brain age index (BAI), the difference between predicted brain age and chronological age, is associated with cortical thinning (brain structural aging). We found increase of brain age in relation to the presence of sleep disorders, as well as significant differences in BAI and the spectral pattern of EEG waves among different sleep disorders. In this study, we demonstrate that the brain age estimated using the proposed model can be a biomarker that reflects changes in sleep and brain health due to various sleep disorders.

White matter hyperintensities (WMHs) are abnormal signals within the white matter region on the human brain MRI and have been associated with aging processes, cognitive decline, and dementia. In the current study, we proposed a U-Net with multi-scale highlighting foregrounds (HF) for WMHs segmentation. Our method, U-Net with HF, is designed to improve the detection of the WMH voxels with partial volume effects. We evaluated the segmentation performance of the proposed approach using the Challenge training dataset. Up to date, the proposed method has achieved the best overall evaluation scores, the highest dice similarity index, and the best F1-score among 39 methods submitted on the WMH Segmentation Challenge that was initially hosted by MICCAI 2017 and is continuously accepting new challengers. The evaluation of the clinical utility showed that the WMH volume that was automatically computed using U-Net with HF was significantly associated with cognitive performance and improves the classification between cognitive normal and Alzheimer's disease subjects and between patients with mild cognitive impairment and those with Alzheimer's disease. The implementation of our proposed method is publicly available using Dockerhub (https://hub.docker.com/r/wmhchallenge/pgs).

Changes in brain morphology, such as cortical thinning are of great value for understanding the trajectory of brain aging and various neurodegenerative diseases. In this work, we employ a generative neural network variational autoencoder (VAE) that is conditional on age and is able to generate cortical thickness maps at various ages given an input cortical thickness map. To take into account the mesh topology in the model, we propose a loss function based on weighted adjacency to integrate the surface topography defined as edge connections with the cortical thickness mapped as vertices. Compared to traditional conditional VAE that did not use the surface topological information, our method better predict “future” cortical thickness maps, especially when the age gap become wider. Our model has the potential to predict the distinctive temporo-spatial pattern of individual cortical morphology in relation to aging and neurodegenerative diseases. (ISBI 2021)

This research aims to predict biological brain age for healthy individuals or individuals with accelerated aging due to neurodegenerative diseases by leverage deep learning-based modeling with large size multicontrast MRI datasets. Our new deep-learning approach can map the brain predicted age (BPA) on brain anatomy at voxel level for heathy controls and for patients with Alzheimer disease, mild cognitive impairment, or Parkinson disease, and can subsequently classify each patient group from healthy or other disease groups using the disease specific pattern of the BPA map. Finally, we apply an event-based model to the BPA map in order to implement a clinical tool to be used in routine patient care by determining the sequence in which regional BPAs become abnormal regarding an age-related disease, and allowing for probabilistically staging and disease-specific risk scoring of a given patient.

This research aims to use innovate MR Imaging techniques to quantitatively characterize normal fetal / neonatal brain development as well as atypical development associated with prematurity. This will be accomplished using a large database of MR scans which have been obtained twice per baby, as well as using various deep learning methods and imaging processing techniques, which correct head motion artifacts, accurately segment brain structures, and quantify tissue characteristics and brain structural and functional network properties. Next, development of an online platform integrated with a big data-driven deep learning algorithm predicts the neurodevelopmental outcome of preterm babies and will ultimately personalize early diagnoses of motor, language or cognitive impairment, playing a significant role in planning customized rehabilitation for each survivor.

BACKGROUND:

Dramatic brain morphological changes occur throughout the third trimester of gestation. In this study, we investigated whether the graph convolutional network (GCN) that account for cortical morphometrics and cortical surface topology as a sparse graph can predict preterm neonatal brain age. Moreover, we evaluated whether the predicted brain age (PBA) is associated with postnatal abnormalities and neurodevelopmental outcome.

METHODS:

577 T1 MRI scans of preterm neonates from two different datasets were analyzed; the NEOCIVET pipeline generated cortical surfaces and morphological features, which were then fed to the GCN to predict the brain age globally and regionally. The brain age index (BAI; PBA minus chronological age) was used to determine the relationships among preterm birth (i.e., birthweight and birth age), perinatal brain injuries, postnatal events/clinical conditions, BAI at postnatal scan, and neurodevelopmental scores at 30 months.

RESULTS:

GCN-based age prediction of preterm neonates without brain lesions (mean absolute error [MAE]: 0.96 weeks) outperformed conventional machine learning methods using no topological information. Structural equation models (SEM) showed that BAI mediated the influence of preterm birth and postnatal clinical factors, but not perinatal brain injuries, on neurodevelopmental outcome at 30 months of age. Regional BAI of several frontal cortices correlated with cognitive abilities at 30 months whereas BAI of left Broca’s area was associated with language functional scores.

CONCLUSIONS:

GCN provide a clinically meaningful index in measuring brain age, localizing regional growth as it relates to postnatal factors, and predicting neurodevelopmental outcome.

Head motion during MRI acquisition presents significant problems for subsequent neuroimaging analyses. In this work, we propose to use convolutional neural networks (CNNs) to correct motion-corrupted images as well as investigate a possible improvement by augmenting L1 loss with adversarial loss. For training, in order to gain access to a ground-truth, we first selected a large number of motion-free images from the ABIDE dataset. We then added simulated motion artifacts on these images to produce motion-corrupted data and a 3D regression CNN was trained to predict the motion-free volume as the output. We tested the CNN on unseen simulated data as well as real motion affected data. Quantitative evaluation was carried out using metrics such as Structural Similarity (SSIM) index, Correlation Coefficient (CC), and Tissue Contrast T-score (TCT). It was found that Gaussian smoothing as a conventional method did not significantly differ in SSIM, CC and RMSE from the uncorrected data. On the other hand, the two CNN models successfully removed the motion-related artifact as their SSIM and CC significantly increased after their correction and the error was reduced. The CNN displayed significantly larger TCT compared to the uncorrected images whereas the adversarial network, while improved, did not show a significantly increased TCT, which may be explained also by its over-enhancement of edges. Our results suggest that the proposed CNN framework enables the network to generalize well to both unseen simulated motion artifacts as well as real motion artifact-affected data. The proposed method could easily be adapted to estimate a motion severity score, which could be used as a score of quality control or as a nuisance covariate in subsequent statistical analyses.

Objective: Focal cortical dysplasias (FCDs) often cause pharmacoresistant epilepsy, and surgical resection can lead to seizure-freedom. Magnetic resonance imaging (MRI) and positron emission tomography (PET) play complementary roles in FCD identification/localization; nevertheless, many FCDs are small or subtle, and difficult to find on routine radiological inspection. We aimed to automatically detect subtle or visually-unidentifiable FCDs by building a classifier based on an optimized cortical surface sampling of combined MRI and PET features.

Methods: Cortical surfaces of 28 patients with histopathologically-proven FCDs were extracted. Morphology and intensity-based features characterizing FCD lesions were calculated vertex-wise on each cortical surface, and fed to a 2-step (Support Vector Machine and patch-based) classifier. Classifier performance was assessed compared to manual lesion labels.

Results: Our classifier, using combined feature selections from MRI and PET, outperformed both quantitative MRI and multimodal visual analysis in FCD detection (93% vs 82% vs 68%). No false positives were identified in the controls, whereas 3.4% of the vertices outside FCD lesions were also classified to be lesional (“extralesional clusters”). Patients with type I or IIa FCDs displayed a higher prevalence of extralesional clusters at an intermediate distance to the FCD lesions compared to type IIb FCDs (p < 0.05). The former had a correspondingly lower chance of positive surgical outcome (71% vs 91%).

Conclusions: Machine learning with multimodal feature sampling can improve FCD detection. The spread of extralesional clusters characterize different FCD subtypes, and may represent structurally or functionally abnormal tissue on a microscopic scale, with implications for surgical outcomes.

We are currently working on the analysis of MRI-negative temporal lobe epilepsy using PET and machine-learning approaches.