Connectivity Analysis of Electroencephalograms in Epilepsy

This dissertation introduces a novel approach at gauging patterns of informa- tion flow using brain connectivity analysis and partial directed coherence (PDC) in epilepsy. The main objective of this dissertation is to assess the key characteristics that delineate neural activities obtained from patients with epilepsy, considering both focal and generalized seizures. The use of PDC analysis is noteworthy as it es- timates the intensity and direction of propagation from neural activities generated in the cerebral cortex, and it ascertains the coefficients as weighted measures in formulating the multivariate autoregressive model (MVAR). The PDC is used here as a feature extraction method for recorded scalp electroencephalograms (EEG) as means to examine the interictal epileptiform discharges (IEDs) and reflect the phys- iological changes of brain activity during interictal periods. Two experiments were set up to investigate the epileptic data by using the PDC concept. For the investigation of IEDs data (interictal spike (IS), spike and slow wave com- plex (SSC), and repetitive spikes and slow wave complex (RSS)), the PDC analysis estimates the intensity and direction of propagation from neural activities gener- ated in the cerebral cortex, and analyzes the coefficients obtained from employing MVAR. Features extracted by using PDC were transformed into adjacency matrices using surrogate data analysis and were classified by using the multilayer Perceptron (MLP) neural network. The classification results yielded a high accuracy and pre- cision number. The second experiment introduces the investigation of intensity (or strength) of information flow. The inflow activity deemed significant and flowing from other regions into a specific region together with the outflow activity emanating from one region and spreading into other regions were calculated based on the PDC results and were quantified by the defined regions of interest. Three groups were considered for this study, the control population, patients with focal epilepsy, and patients with generalized epilepsy. A significant difference in inflow and outflow validated by the nonparametric Kruskal-Wallis test was observed for these groups. By taking advantage of directionality of brain connectivity and by extracting the intensity of information flow, specific patterns in different brain regions of interest between each data group can be revealed. This is rather important as researchers could then associate such patterns in context to the 3D source localization where seizures are thought to emanate in focal epilepsy. This research endeavor, given its generalized construct, can extend for the study of other neurological and neurode- generative disorders such as Parkinson, depression, Alzheimers disease, and mental illness

Computerized neuropsychological assessment in mild cognitive impairment based on natural language processing-oriented feature extraction

Typically, neuropsychological testing helps medical experts situate a given patient in continuum of the Alzheimer's disease (AD) spectrum, especially in the continuum between cognitively normal controls (CN) and the prodromal stage of mild cognitive impairment (MCI). As a well-known early symptom, some linguistic complexity changes of language have been associated with the progression of AD. Currently, the investigations of linguistic manifestations of MCI do mostly rely on manual analysis, yet linguistic complexity-based neuropsychological assessment cannot be effectively attained by using manual approaches. Taking advantage of the existing natural language processing (NLP) techniques to extract key features in linguistic complexity changes associated with the progression of MCI, this study develops an NLP-oriented computerized neuropsychological assessment to automatically analyze the distinguishing characteristics of data in the MCI group versus those in cognitively normal control (CN) group. By using the transcripts of news conferences from President Ronald Reagan, who was diagnosed with Alzheimer's disease in 1994, and President Dwight D. Eisenhower (DE), and President George H. W. Bush (GB), who have no known diagnosis of AD, this study found the significantly different patterns of the linguistic complexity changes between CN and MCI subjects, which can be applied into the diagnosis of MCI