A review of functional magnetic resonance imaging for Brainnetome

The functional brain network using blood-oxygen-level-dependent（BOLD） functional magnetic resonance imaging（fMRI） has revealed the potentials for probing brain architecture,as well as for identifying clinical biomarkers for brain diseases.In the general context of Brainnetome,this review focuses on the development of approaches for modeling and analyzing functional brain networks with BOLD fMRI.The prospects for these approaches are also discussed.

精神分裂症认知功能障碍的脑网络组研究(英文)

Impaired cognitive function,along with positive and negative symptoms,is a core clinical feature of schizophrenia.Earlier studies suggest that impaired cognitive functioning should be assessed from the perspective of brain networks.The recently developed brainnetome approach to evaluating brain networks-an approach that was initially developed by Chinese scientists-provides a new methodology for studying this issue.In this paper we first introduce the concept of brainnetome.We then review recent progress in developing a brainnetome of impaired cognitive function in people with schizophrenia.The models of the relevant brain networks considered were created using data obtained from functional and anatomical brain imaging technologies at different levels of analysis:networks centered on regions of interest,networks related to specific cognitive functions,whole brain networks,and the attributes of brain networks.Finally,we discuss the current challenges and potential new directions for research about brainnetome.

Age-related connectivity differences between attention deficit and hyperactivity disorder patients and typically developing subjects：a resting-state functional MRI study

Attention deficit and hyperactivity disorder（ADHD） is a disorder characterized by behavioral symptoms including hyperactivity/impulsivity among children,adolescents,and adults.These ADHD related symptoms are influenced by the complex interaction of brain networks which were under explored.We explored age-related brain network differences between ADHD patients and typically developing（TD） subjects using resting state f MRI（rs-f MRI） for three age groups of children,adolescents,and adults.We collected rs-f MRI data from 184 individuals（27 ADHD children and 31 TD children;32 ADHD adolescents and 32 TD adolescents;and 31 ADHD adults and 31 TD adults）.The Brainnetome Atlas was used to define nodes in the network analysis.We compared three age groups of ADHD and TD subjects to identify the distinct regions that could explain age-related brain network differences based on degree centrality,a well-known measure of nodal centrality.The left middle temporal gyrus showed significant interaction effects between disease status（i.e.,ADHD or TD） and age（i.e.,child,adolescent,or adult）（P 0.001）.Additional regions were identified at a relaxed threshold（P 0.05）.Many of the identified regions（the left inferior frontal gyrus,the left middle temporal gyrus,and the left insular gyrus） were related to cognitive function.The results of our study suggest that aberrant development in cognitive brain regions might be associated with age-related brain network changes in ADHD patients.These findings contribute to better understand how brain function influences the symptoms of ADHD.

Brain Network Studies in Chronic Disorders of Consciousness:Advances and Perspectives

Neuroimaging has opened new opportunities to study the neural correlates of consciousness, and provided additional information concerning diagnosis, prognosis, and therapeutic interventions in patients with disorders of consciousness. Here, we aim to review neuroimaging studies in chronic disorders of consciousness from the viewpoint of the brain network, focusing on positron emission tomogra- phy, functional MRI, functional near-infrared spectroscopy, electrophysiology, and diffusion MRI. To accelerate basic research on disorders of consciousness and provide a panoramic view of unconsciousness, we propose that it is urgent to integrate different techniques at various spatiotemporal scales, and to merge fragmented findings into a uniform ＂Brainnetome＂ （Brain-net-ome） research framework.