Magnetic Resonance Imaging Based Multimodal Brain Connectomics In Schizophrenia

Schizophrenia is a chronic,severe mental disorder.Patients with schizophrenia had a collection of symptoms of unknown etiology,which mainly involve paranoid delusions,auditory hallucinations and multiple cognitive function deficits.Clinical questions including genetic risks,developmental abnormality,early stage diagnose and medication treatment in schizophrenia remain as big challenges today.Magnetic Resonance Image(MRI)is a non-invasive technology for mapping the pathophysiology of schizophrenia.Human brain is the complex system with multiple regions which connected to sever as a complicate network.Resting state functional magnetic resonance imaging and Diffusion tensor imaging are used to characterize functional connectivity and structural connectivity in brain network.Studies of information integration and segregation and local modulations in brain network were widely proposed in psychiatric disorder research.In this current thesis,we used brain network analysis based on multi-model neuroimaging to explore the pathogenesis of schizophrenia and to identify the genetic related,symptom associated brain network level biomarker in schizophrenia.The current work will consist of the following parts:1.We used brain structural volume data and covariant connectivity between regions measurement,to attempt subcortical brain structure abnormality in first episode patients with adolescent-onset schizophrenia.We found patients had smaller left thalamus volume and left hippocampus volume comparing to healthy controls.We also found patients had significantly decreased covariant connectivity between left pallidus and left putamen,between left hippocampus and left amygdala,between right amygdala and right thalamus,between right amygdala and right putamen,between left putamen and right thalamus.These findings suggested that patients had subcortical limbic regions and basal ganglia system structural connectivity deficits during brain maturation.2.We performed voxel-wise amplitude of low-frequency(0.01–0.08 Hz)fluctuations(ALFF)analysis to investigate neural oscillations and synchrony in patients with adolescent-onset schizophrenia.We investigated seed based functional connectivity between significantly disturbed ALFF regions and whole brain voxels in all participants.We found patients exhibited significantly increased ALFF values in the orbitofrontal cortex and decreased ALFF in the ventral precuneus compared with controls.Decreased ALFF values in the precuneus of patients showed a significant negative correlation with negative symptom scores.Disturbed functional connectivity mainly occurred between the orbitofrontal cortex and the temporal cortex in patients.These findings demonstrate patients had abnormal spontaneous neuronal activity and functional connectivity in the frontal and parietal cortex.Aberrant ALFF in the precuneus might be a biomarker of schizophrenia.3.The voxel-wise functional connectivity of six striatal regions(three regions in the caudate and three in the putamen)per hemisphere was examined using a seed based approach.Patients showed significantly altered functional connectivities between the striatum and prefrontal and parietal regions involving the default mode network,the executive system,and the visual cortex.These altered corticostriatal connectivities were predictive of the positive symptom in patients.The functional corticostriatal connectivity in the dorsolateral prefrontal cortex and interior parietal lobule showed aberrant age associations in the patients during the adolescent stage.Corticostriatal connectivity could serve as a specific potential biomarker for psychotic symptoms in patients with adolescent-onset schizophrenia at the neurodevelopmental stage.4.We combined functional MRI with a graph theoretical approach to examine functional network topology and its age-related development in medication na?ve,first episode patients with adolescent-onset schizophrenia and matched controls.Patients demonstrated impaired large scale integration as reflected by reduced global efficiency as well as decreased regional nodal strength and efficiency in highly integrative network hubs,most consistently the hippocampal formation and the precuneus.In terms of regional strength and efficiency,the left hippocampus showed opposite age-associations in healthy controls and patients,indicating dysregulated maturational trajectories in adolescent schizophrenia and a particular vulnerability of this region during early pathological attack.Together the findings suggested that dyregulated maturation of the functional connectome during adolescence might reflect an early marker for the disorder.5.We utilized a tractography of the white matter connectivity to construct brain network and compared the topological characteristics.Our findings showed that both the first episode patients(FES)patients and siblings shared an abnormal tree hierarchy compared with the Controls and that the FES patients had the most impaired backbone connectivity strength and the lowest backbone consistency.Both FES patients and their siblings had a decreased nodal strength and regions of reduced betweenness in the default mode network,visual system,and sensory motor areas.These findings suggest that the shared abnormal brain networks topology in both patients with FES and the unaffected siblings would be regarded as endophentypes for schizophrenia in Chinese population.6.We proposed a ―randomized structural sparsity‖ method as feature selection model.Numerical experiments with synthetic data showed that our method can be superior in controlling for false negatives while also keeping the control of false positives.We also used the anatomical fiber network constructed by diffusion tenor image as potential features and used linear SVM pattern classifier to categorize the schizophrenia and healthy controls.We compared two kinds of feature selection methods 1)Univariate t-test based filtering;2)sparse regression based filtering.The sparse regression selected structural connectivities were consistent in 90% individuals 10 percent more than the t-test filtered features.