Evaluation of functional brain connectivity abnormalities in head injured patients using fMRI image processing

The human brain consists of many different neural elements that comprise a functioning processing and control system. Though each area has its unique role, they do not operate separately. It is empirically known that during brain activity, different elements affect each other. Such neural network's activity is called Functional Brain Connectivity (FBC). It is a known phenomenon among athletes who suffer mild head injuries frequently along their career, to be more likely to suffer from neurological diseases such as Alzheimer's disease or Parkinson's at old age. This phenomenon has been studied by physicians and health researchers as early as 1971 [1]. Patients who suffered from frequent mild head injuries have no neurological appearable symptoms at young age. FBC abnormality accompanies the mentioned neurological diseases, each characterizes with a different pattern [2], [3]. These two facts led the hypothesis that frequent head injuries can, with some probability, cause FBC abnormalities. Proving this hypothesis and mapping the transformed pattern of FBC can contribute to understanding neurological diseases and by this help the search after treatment generally and preventing for frequent mild injured athletes particularly. This work offers a technology that enables brain researchers to evaluate and locate abnormalities in FBC using fMRI data of a patient. It does so using the fact that connectivity pattern, at rest state, is to a large extent symmetrical. The system evaluates connectivity between classes of voxels using a new nonlinear-Differential Filtered Coherence. It is a mathematical solution for describing connections that are not necessary linear by following the influences of changes in one signal on the changes in another. Finally, the system finds FBC asymmetry using registration of the generated connectivity map between the two hemispheres of the brain.