Abnormal Brain Connectome In Schizophrenia Based On Magnetic Resonance Imaging

Schizophrenia is a severe and complex mental disorder,while its pathomechanism remains unclear.Clinical diagnosis is generally based on symptom evaluation by psychiatrists.Till now,no reliable neuroimaging biomarker for this disease.As a type of neurodevelopmental disease,schizophrenia results from both effects of genetic and environmental factors during brain developing.One popular hypothesis,called“dysconnectivity hypothesis” has been proposed by researchers to elaborate on the pathological mechanism of schizophrenia.This theory indicates that structural and functional connectivity abnormalities across brain regions underlies diverse symptoms of schizophrenia.Magnetic resonance imaging(MRI)method could efficiently capture brain activity and delineate high-resolution brain structure,and is drawing more and more attention.Driven by “dysconnectivity hypothesis”,the current study used multi-modal MRI technology to characterize brain abnormalities in patients with schizophrenia,from white-matter to grey-matter,from function to structure,to reveal the pathological mechanism of the disease.Moreover,this study identified a specific set of connectome-based biomarkers that may aid in early clinical diagnosis of schizophrenia.The current study consists of the following parts:Firstly,this study investigated white-matter functional network connectome in schizophrenia.Converging evidence suggests that white-matter resting state functional MRI signals can effectively depict neuronal activity and psychopathological status.Therefore,this study examined white-matter network-level interactions in antipsychotic-naive first-episode schizophrenia to facilitate the interpretation of the psychiatric pathological mechanisms in schizophrenia.The author first identified 11white-matter functional networks,which could be further classified into deep,middle,and superficial layers of networks,and then examined network-level interactions among these 11 white-matter functional networks using coefficient Granger causality analysis.Excitatory influences from the middle superior corona radiate network to the superficial orbitofrontal and deep networks were disrupted in patients compared with healthy controls.Additionally,an extra failure of suppression within superficial networks(including the fronto-parietal network,temporofrontal network,and the orbitofrontal network)was observed in patients.An independent cohort from another center was used to examine the replicability of the findings across centers.Similar replication results further verified the white-matter functional network interaction model of schizophrenia.This chapter indicated impaired functional connectivity among white-matter functional networks in schizophrenia.Secondly,this study investigated grey-matter functional network connectome in early-onset schizophrenia.The thalamus has a coordinative role in cortical function and is key to the development of the cerebral cortex.Conversely,altered functional organization of the thalamus might relate to overarching cortical disruptions in schizophrenia,anchored in development.Employing dimensional reduction techniques on thalamocortical functional connectivity,the author derived lateral-medial and anterior-posterior thalamic functional axes.Patients showed increased segregation of macroscale thalamic functional organization,which was related to altered thalamocortical interactions both in unimodal and transmodal networks.Moreover,core cells particularly underlie the macroscale abnormalities in patients.These macroscale disruptions were associated with schizophrenia-related gene expression maps.Behavioral and disorder decoding analyses indicated that the macroscale hierarchy disturbances might perturb both perceptual and abstract cognitive functions and contribute to negative syndromes in schizophrenia,suggesting a unitary pathophysiological framework of schizophrenia.This chapter indicated abnormal grey-matter functional connectivity in the still developing brain of schizophrenic patients.Thirdly,this study investigated brain structural network connectome in early-onset schizophrenia.The anatomical profiles of changing gray matter volume deficits in patients were detected using two-way analyses of variance with diagnosis and age as factors,and its covarying structural networks were established using structural covariance network analyses.Antagonistic interaction results suggested dynamic gray matter abnormalities of the left fusiform gyrus,inferior occipital gyrus,lingual gyrus in patients.These regions comprise a dominating part of the ventral stream,a ventral occipitotemporal network engaged in early social information processing.Gray matter abnormalities were mainly located in the ventral occipitotemporal regions in childhood-onset patients,while in the rostral prefrontal cortex in adolescent-onset patients.Moreover,patients’ gray matter synchronization with the ventral stream was disrupted in widespread high-order social perception regions including the rostral prefrontal cortex and salience network.This chapter revealed brain structural network abnormalities in patients with early-onset schizophrenia.Lastly,this study used grey-matter functional network connectome to identify reliable biomarkers of clinical symptoms in early-onset schizophrenia.Youth is a period of dramatic brain maturation,with substantial inter-individual variability in brain anatomy.However,existing group-level hypotheses of youth with schizophrenia lack precision in terms of neuroanatomical boundaries.This study aimed to identify individual-specific functional connectivity biomarkers associated with schizophrenic symptom manifestation during adolescent brain maturation.A reliable individual-level cortical parcellation approach was used to map functional brain regions in each subject,that were then used to identify connectivity-based biomarkers for predicting dimension-specific psychotic symptoms in patients.Age-related changes in biomarker expression were compared between these patients and controls.Moreover,an independent cohort was recruited from another center to test the generalizability of the prediction model.Individual-specific connectivity-based biomarkers could significantly and better predict positive-dimension symptoms with a relatively stronger generalizability than at the group level.Specifically,positive symptom domains were estimated based on connections between the frontoparietal network and salience network and within frontoparietal network.Consistent with the neurodevelopmental model of schizophrenia,the frontoparietal-salience network connection exhibited aberrant age-associated alteration in patients.The final chapter reveal reproducible individualized connectivity-based biomarkers associated with positive symptom domains,which could aid in early diagnosis and personalized treatment of schizophrenia.