Age-related differences in the structural complexity of subcortical and ventricular structures

It has been well established that the volume of several subcortical structures decreases in relation to age. Different metrics of cortical structure (e.g., volume, thickness, surface area, and gyrification) have been shown to index distinct characteristics of interindividual differences; thus, it is important to consider the relation of age to multiple structural measures. Here, we compare age-related differences in subcortical and ventricular volume to those differences revealed with a measure of structural complexity, quantified as fractal dimensionality. Across 3 large data sets, totaling nearly 900 individuals across the adult lifespan (aged 18–94 years), we found greater age-related differences in complexity than volume for the subcortical structures, particularly in the caudate and thalamus. The structural complexity of ventricular structures was not more strongly related to age than volume. These results demonstrate that considering shape-related characteristics improves sensitivity to detect age-related differences in subcortical structures

Cortical complexity as a measure of age-related brain atrophy

The structure of the human brain changes in a variety of ways as we age. While a sizeable literature has examined age-related differences in cortical thickness, and to a lesser degree, gyrification, here we examined differences in cortical complexity, as indexed by fractal dimensionality in a sample of over 400 individuals across the adult lifespan. While prior studies have shown differences in fractal dimensionality between patient populations and age-matched, healthy controls, it is unclear how well this measure would relate to age-related cortical atrophy. Initially computing a single measure for the entire cortical ribbon, i.e., unparcellated gray matter, we found fractal dimensionality to be more sensitive to age-related differences than either cortical thickness or gyrification index. We additionally observed regional differences in age-related atrophy between the three measures, suggesting that they may index distinct differences in cortical structure. We also provide a freely available MATLAB toolbox for calculating fractal dimensionality

Fractal dimension of the cortical gray matter outweighs other brain MRI features as a predictor of transition to dementia in patients with mild cognitive impairment and leukoaraiosis

BackgroundThe relative contribution of changes in the cerebral white matter (WM) and cortical gray matter (GM) to the transition to dementia in patients with mild cognitive impairment (MCI) is not yet established. In this longitudinal study, we aimed to analyze MRI features that may predict the transition to dementia in patients with MCI and T2 hyperintensities in the cerebral WM, also known as leukoaraiosis.MethodsSixty-four participants with MCI and moderate to severe leukoaraiosis underwent baseline MRI examinations and annual neuropsychological testing over a 2 year period. The diagnosis of dementia was based on established criteria. We evaluated demographic, neuropsychological, and several MRI features at baseline as predictors of the clinical transition. The MRI features included visually assessed MRI features, such as the number of lacunes, microbleeds, and dilated perivascular spaces, and quantitative MRI features, such as volumes of the cortical GM, hippocampus, T2 hyperintensities, and diffusion indices of the cerebral WM. Additionally, we examined advanced quantitative features such as the fractal dimension (FD) of cortical GM and WM, which represents an index of tissue structural complexity derived from 3D-T1 weighted images. To assess the prediction of transition to dementia, we employed an XGBoost-based machine learning system using SHapley Additive exPlanations (SHAP) values to provide explainability to the machine learning model.ResultsAfter 2 years, 18 (28.1%) participants had transitioned from MCI to dementia. The area under the receiving operator characteristic curve was 0.69 (0.53, 0.85) [mean (90% confidence interval)]. The cortical GM-FD emerged as the top-ranking predictive feature of transition. Furthermore, aggregated quantitative neuroimaging features outperformed visually assessed MRI features in predicting conversion to dementia.DiscussionOur findings confirm the complementary roles of cortical GM and WM changes as underlying factors in the development of dementia in subjects with MCI and leukoaraiosis. FD appears to be a biomarker potentially more sensitive than other brain features

Predicting age from cortical structure across the lifespan

Despite inter-individual differences in cortical structure, cross-sectional and longitudinal studies have demonstrated a large degree of population-level consistency in age-related differences in brain morphology. The present study assessed how accurately an individual’s age could be predicted by estimates of cortical morphology, comparing a variety of structural measures, including thickness, gyrification, and fractal dimensionality. Structural measures were calculated across up to seven different parcellation approaches, ranging from 1 region to 1000 regions. The age-prediction framework was trained using morphological measures obtained from T1-weighted MRI volumes collected from multiple sites, yielding a training dataset of 1056 healthy adults, aged 18-97. Age predictions were calculated using a machine-learning approach that incorporated non-linear differences over the lifespan. In two independent, held-out test samples, age predictions had a median error of 6-7 years. Age predictions were best when using a combination of cortical metrics, both thickness and fractal dimensionality. Overall, the results reveal that age-related differences in brain structure are systematic enough to enable reliable age prediction based on metrics of cortical morphology

The Use of EEG-fMRI Features for Characterizing Mental Disorders

Determining clinically relevant biomarkers of mental disorders for reliably indicating pathophysiological processes or predicting therapeutic responses remains a major challenge, despite decades of research. Identifying such biomarkers can help patients significantly improve their quality of life and alleviate their suffering. Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are non-invasive tools to investigate neurobiological mechanisms underlying mental disorders. Extracting and leveraging informative features from the high temporal resolution EEG and high spatial resolution fMRI may offer a more comprehensive understanding of brain spatial and temporal activities in health and disease. More importantly, this information can lead to a better understanding of the neurobiology of mental illness. This dissertation investigates the analyses and applications of extracting and combining informative features from EEG and fMRI, along with applying machine learning (ML) and computational methods for building biomarkers of mental illnesses. Several methodological challenges in the extraction of informative and reproducible features are also addressed. First, two types of EEG features obtained from resting state EEG-fMRI measurements were extracted: 1) broadband-multichannel EEG dynamical features, called EEG microstates (EEG-ms); and 2) heterogeneous, static EEG features. Using EEG features only, results elucidate that: 1) EEG-ms characteristics and information theoretical properties can successfully differentiate individuals with mood and anxiety disorders from healthy comparison subjects with potential applications for other clinical groups; and 2) heterogeneous static EEG features can successfully predict “brain aging,” noted here as BrainAGE from 468 EEG datasets, achieving a correlation of r=0.61 between predicted age and chronological age. Next, extracted EEG features were leveraged with fMRI to enhance the predictivity of BrainAGE and localizing the associated EEG-ms brain regions. More specifically, static EEG features were combined with resting state fMRI features to construct a multimodal BrainAGE predictor as a case study. Notably, it was found that EEG and fMRI contain a large portion of shared information about age, although each modality has its fingerprint of the aging process. The developed approach is a general purpose and be applied to predict other outcomes from brain imaging data. Similarly, EEG-ms features were integrated with fMRI to localize associated brain regions within fMRI space, revealing functional brain connectivity changes in individuals with mood and anxiety disorders as a case study. As a result, harnessing combined EEG-fMRI methods have enriched our knowledge some mental disorders and broadened our understanding of them with potential applications for other clinical groups and outcomes. Finally, this work evaluated the reproducibility and replication of EEG-ms analysis to address technical issues that have thus far been overlooked in the literature. In conclusion, the presented work describes technical methods developed to study and discover several clinically translatable biomarkers that can be reliably used to characterize various mental disorders