Abnormal Functional Connectivity In Patients With Mental Disease:A Resting State FMRI Study

Magnetic resonance imaging(MRI)technique has the significant advantages in spatial resolution and computability,and has been widely used in the early diagnosis and intervention of mental diseases.Utilizing MRI data,we can not only analyze the brain function and morphology from the local and network aspects of brain,but also explore the pathological features of certain mental diseases from multiple scales and modalities.Based on resting state functional MRI,the present paper utilizes the time-varying functional connectivity model to analyze the power of temporal features of dynamic functional connectivity in identifying adolescent generalized anxiety disorder(GAD).GAD is one of common anxiety disorders in adolescents.Although adolescents with GAD are thought to be at high risk for other mental diseases,the disease-specific alterations have not been adequately explored.Based on resting functional MRI,recent studies have revealed the abnormal functional connectivity(FC)in adolescents with GAD.Most previous researches have investigated the static FC which ignores the fluctuations of FC over time and focused on the structures of “fear circuit”.To figure out the alterations of dynamic FC caused by GAD and the possibilities of dynamic FC as biomarkers,we propose an effective approach to identify adolescent GAD using temporal features derived from dynamic FC.In our study,the instantaneous synchronization of pairwise signals is estimated as dynamic FC.The Hurst exponent(H)and variance,indicating regularity and variable degree of a time series respectively,are calculated as temporal features of dynamic FC.By leave-one-out cross-validation,a relatively high accuracy of 88.5% can be achieved when H and variance of dynamic FC were combined as features.The results suggest that temporal features of dynamic FC could achieve a clinically acceptable diagnostic power and serve as biomarkers of adolescent GAD.Furthermore,our work could be helpful in understanding the pathophysiological mechanism of adolescent GAD.Besides,we introduce the hyper-network model to investigate the impact of autism spectrum disorders(ASD)on higher-order functional networks.Most of the mentioned studies using fMRI data have investigated connectivity of brain regions by the correlation-based method which can only capture pairwise(second-order)information,thus being not effective enough to describe the relationship among the brain regions working together.To our best knowledge,high-order connectivity of cortical activity has been identified in recent neuroscience studies,and a previous study has demonstrated that direct interactions among a few of brain regions exist in neurological processes,which reminds us of the possibility of the altered high-order interactions in specific diseases.Using the sparse representation based methods,a hyper-connection(high-order connectivity)is defined as a sparse linear regression model in which a target brain region is represented by a linear combination of signals of other brain regions,and the regularization term is included to avoid the insignificantly or spurious connections.To explore the high-order alterations of functional network in ASD,we construct the hyper-network in normal controls and ASD patients by the sparse representation algorithm.We hypothesize that the ASD has an impact on the high-order information of the functional network in patients,and this impact could be reflected by the local abnormalities of hyper-graph.To verify our hypnosis,statistical test is then used to seek group difference between ASD patients and normal controls.The results show that there are abnormal hyper-network properties in the specific nodes of the language and default mode networks in ASD patients,and the abnormalities may be related to the imbalance development between the left and right hemispheres in ASD patients.