Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Resting state functional network connectivity (rsFNC) derived from functional magnetic resonance (fMRI) imaging is emerging as a possible biomarker to identify several brain disorders. Recently it has been pointed out that methods used to preprocess head motion variance might not fully remove all unwanted effects in the data. Proposed processing pipelines locate the treatment of head motion effects either close to the beginning or as one of the final steps. In this work,  assess several preprocessing pipelines applied in group independent component analysis (gICA) methods to study the rsFNC of the brain. The evaluation method utilizes patient/control classification performance based on linear support vector machines and leave-one-out cross validation. In addition,  explored group tests and correlation with severity measures in the patient population.  also tested the effect of removing high frequencies via filtering. Two real data cohorts re used: one consisting of 48 mTBI and one composed of 21 smokers, both with their corresponding matched controls. A simulation procedure was designed to test the classification por of each pipeline. Results show that data preprocessing can change the classification performance. In real data, regressing motion variance before gICA produced clearer group differences and stronger correlation with nicotine dependence.
['Victor M. Vergara', 'Andrew R. Mayer', 'Eswar Damaraju', 'Kent E. Hutchison', 'Vince D. Calhoun']
I am writing a research paper on a idea The effect of preprocessing pipelines in subject classification and detection of abnormal resting state functional network connectivity using group ICA.
[{"from": "human", "value": "I am writing a research paper on a idea The effect of preprocessing pipelines in subject classification and detection of abnormal resting state functional network connectivity using group ICA."}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Resting state functional network connectivity (rsFNC) derived from functional magnetic resonance (fMRI) imaging is emerging as a possible biomarker to identify several brain disorders. Recently it has been pointed out that methods used to preprocess head motion variance might not fully remove all unwanted effects in the data. Proposed processing pipelines locate the treatment of head motion effects either close to the beginning or as one of the final steps. In this work,  assess several preprocessing pipelines applied in group independent component analysis (gICA) methods to study the rsFNC of the brain. The evaluation method utilizes patient/control classification performance based on linear support vector machines and leave-one-out cross validation. In addition,  explored group tests and correlation with severity measures in the patient population.  also tested the effect of removing high frequencies via filtering. Two real data cohorts re used: one consisting of 48 mTBI and one composed of 21 smokers, both with their corresponding matched controls. A simulation procedure was designed to test the classification por of each pipeline. Results show that data preprocessing can change the classification performance. In real data, regressing motion variance before gICA produced clearer group differences and stronger correlation with nicotine dependence."}]