SEARCHING NEUROIMAGING BIOMARKERS IN MENTAL DISORDERS WITH GRAPH AND MULTIMODAL FUSION ANALYSIS OF FUNCTIONAL CONNECTIVITY

Mental disorders such as schizophrenia (SZ), bipolar (BD), and major depression disorders (MDD) can cause severe symptoms and life disruption. They share some symptoms, which can pose a major clinical challenge to their differentiation. Objective biomarkers based on neuroimaging may help to improve diagnostic accuracy and facilitate optimal treatment for patients. Over the last decades, non-invasive in-vivo neuroimaging techniques such as magnetic resonance imaging (MRI) have been increasingly applied to measure structure and function in human brains. With functional MRI (fMRI) or structural MRI (sMRI), studies have identified neurophysiological deficits in patients’ brain from different perspective. Functional connectivity (FC) analysis is an approach that measures functional integration in brains. By assessing the temporal coherence of the hemodynamic activity among brain regions, FC is considered capable of characterizing the large-scale integrity of neural activity. In this work, we present two data analysis frameworks for biomarker detection on brain imaging with FC, 1) graph analysis of FC and 2) multimodal fusion analysis, to better understand the human brain. Graph analysis reveals the interaction among brain regions based on graph theory, while the multimodal fusion framework enables us to utilize the strength of different imaging modalities through joint analysis. Four applications related to FC using these frameworks were developed. First, FC was estimated using a model-based approach, and revealed altered the small-world network structure in SZ. Secondly, we applied graph analysis on functional network connectivity (FNC) to differentiate BD and MDD during resting-state. Thirdly, two functional measures, FNC and fractional amplitude of low frequency fluctuations (fALFF), were spatially overlaid to compare the FC and spatial alterations in SZ. And finally, we utilized a multimodal fusion analysis framework, multi-set canonical correlation analysis + joint independent component analysis (mCCA+jICA) to link functional and structural abnormalities in BD and MDD. We also evaluated the accuracy of predictive diagnosis through classifiers generated on the selected features. In summary, via the two frameworks, our work has made several contributions to advance FC analysis, which improves our understanding of underlying brain function and structure, and our findings may be ultimately useful for the development of biomarkers of mental disease

Brain network signatures of depressive symptoms

Depressive symptoms are common in the general population. Even in individuals who do not meet the criteria for a Major Depression Disorder (MDD), their symptoms are of clinical relevance because they increase the likelihood of progressing into a full-blown depressive episode, which in turn increases the prevalence of future episodes. The studies in this thesis apply advanced computational methods to functional magnetic resonance imaging (fMRI) data to investigate the dynamics of network connectivity, with the aim of understanding what brain mechanisms make a person more vulnerable to depression. Our results suggest that imbalances in whole-brain connectivity can already be linked to higher levels of depressive symptoms in healthy (undiagnosed) individuals. These imbalances correspond to a reduced dynamism in the overall functional organization of the brain, suggesting a link between a ‘rigid brain’ and rigid behavior, such as the lack of flexibility in cognitive and emotional responses that often accompanies depressive symptoms. Additionally, individual differences in the repertoire of brain states indicate that people with more depressive symptoms engage more in states related to self-referential thinking. This tendency was also observed in remitted patients during the transition into a depressive episode. This emphasizes that the present experience of depressive symptoms, whether in healthy individuals or MDD patients, is associated with changes in brain communication. The findings of this thesis lead to a deeper understanding of the complex orchestration of brain communication and its changes concerning depressive symptomatology in clinical and nonclinical populations

Disturbed resting state EEG synchronization in bipolar disorder: A graph-theoretic analysis

AbstractDisruption of functional connectivity may be a key feature of bipolar disorder (BD) which reflects disturbances of synchronization and oscillations within brain networks. We investigated whether the resting electroencephalogram (EEG) in patients with BD showed altered synchronization or network properties. Resting-state EEG was recorded in 57 BD type-I patients and 87 healthy control subjects. Functional connectivity between pairs of EEG channels was measured using synchronization likelihood (SL) for 5 frequency bands (δ, θ, α, β, and γ). Graph-theoretic analysis was applied to SL over the electrode array to assess network properties. BD patients showed a decrease of mean synchronization in the alpha band, and the decreases were greatest in fronto-central and centro-parietal connections. In addition, the clustering coefficient and global efficiency were decreased in BD patients, whereas the characteristic path length increased. We also found that the normalized characteristic path length and small-worldness were significantly correlated with depression scores in BD patients. These results suggest that BD patients show impaired neural synchronization at rest and a disruption of resting-state functional connectivity