Normal Alert Consciousness: A Central Executive Model of Hippocampal Function

The relationship between brain and consciousness has been debated since Descartes in the 1500s, new theories arising in the twentieth century with the development of modern neuroscience. All are controversial due to the lack of consensus on the definition of consciousness, what cognitive properties must be explained, and how to evaluate sentience. Most theoretical explanations bear little relationship to our inner conscious experiences. In the current monograph, the normal alert state of consciousness is defined, and components to be explained are delineated. Debunking misconceptions from previous theories and presenting new evidence, a model is proposed whereby the hippocampus plays a central role in executing and coordinating cognitive functions associated with normal alert consciousness. Key elements of the model reflect recent findings that the combined effect from the left and right hippocampus influences other regions involved in performing many or all cognitive tasks while filtering out irrelevant information. Methods are described for testing the model. Finally, implications are discussed for a variety of neurological disorders and philosophophical issues, including free will and the possibility of sentience in artificial intelligence

Identification of potential biomarkers in the hippocampus region for the diagnosis of ADHD using PBL-McRBFN approach

Attention Deficiency Hyperactivity Disorder (ADHD) as a disruptive behavior disorder is receiving lots of attention because of its complexity and need for early detection. This paper presents a study on identification of potential biomarkers in the diagnosis of ADHD based on the structural-MRI of the brain obtained through ADHD-200 competition data set. The region of the brain considered here is "hippocampus". The grey matter probability of the T1 images is segmented followed by tissue alignment and inter subject normalization. Then, the voxels of the hippocampus are segregated using a region-of-interest mask, and the grey matter tissue probability values are obtained. These values are then used as features to classify ADHD patients against typically developing controls using a projection based learning algorithm for a meta-cognitive radial basis function network (PBL-McRBFN) and compared the results with that of support vector machines. Initially we take all the voxels of hippocampus for our study and then we have selected the most relevant voxels as a biomarker using Chi-square approach and developed a classifier to diagnosis ADHD. The results clearly highlight that use of hippocampus from the structural-MRI is sufficient to diagnosis ADHD to certain degree of confidence.Accepted versio