On the brain networks organization of individuals with high versus average fluid intelligence: a combined DTI and MEG study

The neural underpinning of human fluid intelligence (Gf) has gathered a large interest in the scientific community. Nonetheless, previous research did not provide a full understanding of such intriguing topic. Here, we studied the structural (from diffusion tensor imaging, DTI) and functional (from magnetoencephalography (MEG) resting state) connectivity in individuals with high versus average Gf scores. Our findings showed greater values in the brain areas degree distribution and higher proportion of long-range anatomical connections for high versus average Gfs. Further, the two groups presented different community structures, highlighting the structural and functional integration of the cingulate within frontal subnetworks of the brain in high Gfs. These results were consistently observed for structural connectivity and functional connectivity of delta, theta and alpha. Notably, gamma presented an opposite pattern, showing more segregation and lower degree distribution and connectivity in high versus average Gfs. Our study confirmed and expanded previous perspectives and knowledge on the small-worldness of the brain. Further, it complemented the widely investigated structural brain network of highly intelligent individuals with analyses on fast-scale functional networks in five frequency bands, highlighting key differences in the integration and segregation of information flow between slow and fast oscillations in groups with different Gf.

Functional Brain Connectivity Hyper-Network Embedded with Structural Information for Epilepsy Diagnosis

Automatic diagnosis of brain diseases based on brain connectivity network (BCN) classification is one of the hot research fields in medical image analysis. The functional brain network reflects the brain functional activities and structural brain network reflects the neural connections of the main brain regions. It is of great significance to explore and explain the inner mechanism of the brain and to understand and treat brain diseases. In this paper, based on the graph structure characteristics of brain network, the fusion model of functional brain network and structural brain network is designed to classify the diagnosis of brain mental diseases. Specifically, the main work of this paper is to use the Laplacian graph embed the information of diffusion tensor imaging, which contains the characteristics of structural brain networks, into the functional brain network with hyper-order functional connectivity information built based on functional magnetic resonance data using the sparse representation method, to obtain brain network with both functional and structural characteristics. Projection of the brain network and the two original modes data to the kernel space respectively and then classified by the multi-task learning method. Experiments on the epilepsy dataset show that our method has better performance than several state-of-the-art methods. In addition, brain regions and connections that are highly correlated with disease revealed by our method are discussed.

Modular-Level Alterations of Single-Subject Gray Matter Networks in Schizophrenia

Schizophrenia is often regarded as a psychiatric disorder caused by disrupted connections in the brain. Evidence suggests that the gray matter of schizophrenia patients is damaged in a modular pattern. Recently, abnormal topological organization was observed in the gray matter networks of patients with schizophrenia. However, the modular-level alteration of gray matter networks in schizophrenia remains unclear. In this study, single-subject gray matter networks were constructed for a total of 217 subjects (116 patients with schizophrenia and 101 controls). We analyzed the topological characteristics of the brain network and the strengths of connections between and within modules. Compared with the outcomes in the control group, the global efficiency and participation coefficient values of the single-subject gray matter networks in schizophrenic patients were significantly reduced. The nodal participation coefficient of the regions involving the FPN, DMN and SCN were significantly decreased in subjects with schizophrenia. The intermodule connections between the FPN and VIS and between the DMN and SCN, in the FPN were significantly reduced in the patient group. In the FPN, the intramodule nodal connection strength of the left orbital inferior frontal gyrus and right inferior parietal gyrus was significantly decreased in schizophrenia patients. Reduced intermodule nodal connection strength between the FPN and VIS was associated with the severity of schizophrenia symptoms. These findings suggest that abnormal intramodule and intermodule connections in the structural brain network may a biomarker of schizophrenia symptoms.

Laryngeal Dystonia: Consensus on terminology, pathophysiology, and research priorities

Objective.To delineate research priorities for improving clinical management of laryngeal dystonia, the NIH convened a multi-disciplinary panel of experts for a one-day workshop to examine the current progress in understanding its etiopathophysiology and clinical care.Methods.The participants reviewed the current terminology of disorder and discussed advances in understanding its pathophysiology since a similar workshop was held in 2005. Clinical and research gaps were identified, and recommendations for future directions were delineated.Results.The panel unanimously agreed to adopt the term “laryngeal dystonia” instead of “spasmodic dysphonia” to reflect the current progress in characterizations of this disorder. Laryngeal dystonia was recognized as a multifactorial, phenotypically heterogeneous form of isolated dystonia. Its etiology remains unknown, whereas the pathophysiology likely involves large-scale functional and structural brain network disorganization. Current challenges include the lack of clinically validated diagnostic markers and outcome measures and the paucity of therapies that address the disorder pathophysiology.Conclusion.Research priorities should be guided by challenges in clinical management of laryngeal dystonia. Identification of disorder-specific biomarkers would allow the development of novel diagnostic tools and unified measures of treatment outcome. Elucidation of the critical nodes within neural networks that cause or modulate symptoms would allow the development of targeted therapies that address the underlying pathophysiology. Given the rarity of laryngeal dystonia, future rapid research progress may be facilitated by multi-center, national and international collaborations.