| There are huge individual differences in the human brain function network,and accurate acquisition of functional information of the individual brain is a prerequisite for the successful use of imaging data to study the neurobiological mechanisms of the brain.Functional magnetic resonance imaging(fMRI)can quantitatively analyze the functional connections between brain regions,and has been widely used in neuroscience research.However,most of the current studies are based on group-level analysis,which underestimate individual differences in imaging studies and make these experimental results more representative of the general trend of brain function networks in the population,limiting the guiding reference of clinical personalized treatment.Studying the brain functional activities according to the individualized brain imaging characteristics to reveal the abnormal biomaker of patients,to assist to make the personalized treatment plan truly become the determinant of precision medicine.However,individual-level imaging studies have only just begun,to understand the brain cognitive activities from an individual level is still an issue.This dissertation maps the brain function at the individual level,defines the functionally homologous brain regions across individuals,which provides an important functional ‘localier' technique for functional analysis at the individual level.Secondly,through the study of schizophrenia and bipolar disorder with psychosis,we initiated an individualized connectivity method to explore the abnormal function of mental illness.The abnormal regions provide an effective imaging biomarker,providing a new means for understanding the neuropathophysiological mechanisms of the mental illness.The work of this dissertation mainly includes the following three parts.(1)Structural and functional brain network topology in left-and right-handersThe human brain is a complex network that efficiently integrates information,and brain size is an important factor in shaping the nervous system and explaining the variability of behavior and cognition between individuals.Based on the regions from group-level automated anatomical labeling(AAL)template,we examined the relationship between the topological efficiency of functional network and brain size.Secondly,we analyzed the lateralization of the brain affected by handedness based on structural connectivity network.We found that the smaller the brain size is,the higher the local efficiency and the larger the brain is,the higher the global efficiency,indicating that the brain adjusts the local and global efficiency to achieve efficient information transmission while brain volume changes.On the other hand,the "small-world" of the right-handers is significantly asymmetrical,while that of the left-handers is relatively symmetrical.Specifially,the left-handers have an atypical connection in the language network,in which the superior temporal gyrus plays an important role.Our findings help to understand the brain structure associated with handedness and the lateralization from the perspective of large-scale brain network,indidicating brain volume and handedness were important factors in inter-subject variability.(2)Performing group-level functional image analyses based on homologous functional regions mapped in individualsFunctional MRI studies have traditionally relied on inter-subject normalization based on global brain morphology,which cannot establish proper functional correspondence between subjects due to substantial inter-subject variability in functional organization.Here we reliably identified a set of discrete,homologous functional regions in individuals to improve inter-subject alignment of fMRI data.These functional regions demonstrated marked inter-subject variability in size,position and connectivity.We found that previously reported inter-subject variability in functional connectivity maps was largely explained by variability in position of the functional regions,but was less related to the variability in size of these regions.Importantly,individual differences in network topography is associated with individual differences in task-evoked activations,suggesting that these individually-specified regions may serve as the “localizer” to improve the alignment of task-fMRI data.We demonstrated that aligning task-fMRI data using the regions derived from resting-state fMRI may lead to increased statistical power of task-fMRI analyses.In addition,resting state functional connectivity among these homologous regions is able to capture the idiosyncrasies of subjects and better predict fluid intelligence than connectivity measures derived from group-level brain atlases.Critically,we showed that not only the connectivity,but also the size and position of functional regions,are related to human behavior.Collectively,these findings suggest that identifying homologous functional regions across individuals can benefit a wide range of studies in the investigation of connectivity,task activation,and brain-behavior associations.(3)Individual-specific functional connectivity markers track dimensional and categorical features of psychotic illnessNeuroimaging studies of psychotic disorders have demonstrated abnormalities in structural and functional connectivity involving widespread brain networks.However,these group-level observations have failed to yield any biomarkers that can provide confirmatory evidence of a patient's current symptoms,predict future symptoms,or predict a treatment response.Lack of precision in both neuroanatomical and clinical boundaries have likely contributed to the inability of even well-powered studies to resolve these key relationships.Here,we employed a novel approach to defining individualspecific functional connectivity in 158 patients diagnosed with schizophrenia(n = 49),schizoaffective disorder(n = 37)or bipolar disorder with psychosis(n = 72),and identified neuroimaging features that track psychotic symptoms in a dimension-or disorder-specific fashion.Using individually-specified functional connectivity,we were able to estimate positive,negative,and manic symptoms that showed correlations ranging from r = 0.35 to r = 0.51 with the observed symptom scores.Comparing optimized estimation models among schizophrenia spectrum patients,positive and negative symptoms were associated with largely non-overlapping sets of cortical connections.Comparing between schizophrenia spectrum and bipolar disorder patients,the models for positive symptoms were largely non-overlapping between the two disorder classes.Finally,models derived using conventional region definition strategies performed at chance levels for most symptom domains.Individual-specific functional connectivity analyses revealed important new distinctions among cortical circuits responsible for the positive and negative symptoms,as well as key new information about how circuits underlying symptom expressions may vary depending on the underlying etiology and illness syndrome from which they manifest... |
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