Research On Brain Functional Connectivity And Classification Model Among College Students' Depression Mood

Along with the continuous increasing of people's material requirements as well as living cost yearly,a growing number of people suffer depression,which have a negative influence on their families and society.Nowadays,the increasingly sophisticated medical imaging technology has a decisive role in promoting the study of depression.At the present time,there is a risk of misjudge of depression symptom due to the lacking of sufficient quantitative physiological parameters.This paper focuses on functional connection and analysis on fMRI data in resting state and task state.Diverse algorithms have been employed to the classification launched on the subjects after grouping.To start with the research,design experiments and collect subjects,whom will be divided into depression group and normal control group according to the score of the test.All subjects were collected fMRI two kinds of data,resting state and task-state.After that,apply single-sample t-test within the group and two-sample t-test among the groups.Secondly,another t-test is utilized between the two states,to find significant differences of the brain regions between them under the same conditions.Then,combine with the obtained prior knowledge of the statistical analysis of the two groups,those differences existed in brain regions is defined as a region of interest,the selected regions are then put into brain functional link analysis,calculate two subjects in the range of brain time-level coordination synchronization.Finally,the functional connectivity analysis is defined as the feature of the two groups,therefore the subjects were classified by various classification algorithms and then compare them by accuracy.The classification results illustrate that accurate rate of each classification algorithm reached an average of more than 80%for both the state of data after dimensionality reduction,fully demonstrates the method of the experiment feasible and can distinguish good subjects.Results of this study could enrich the definition of depression with additional quantitative physiological parameters.It could also provide a reference for the diagnosis of depression,and has a certain reference value for the exploration of depression-induced mechanism,as well as provide guidance for the brain level neurological diseases.It would contribute to Interdisciplinary work for computer science,neuroscience,brain science and other branch of courses.