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Electroencephalography-based Brain Connectivity Analysis for Alzheimer's Disease and Diffuse Axonal Lesion Diagnosis


The main cause of acquired brain injury (ABI), according to the literature, is traumatic brain injury (TBI). TBI is non-degenerative and non-congenital damage caused by external mechanical force. A permanent or temporary impairment of cognitive, physical and psychosocial functions is expected, with a decrease or alteration of the state of consciousness. The most frequent causes of TBI are vehicular accidents, falls, robberies and accidents during leisure activities. Acceleration-deceleration mechanisms, responsible for diffuse axonal injury, often damage the ventral and lateral regions of the frontal and temporal lobes. Deficits in attention and memory, difficulty in learning new information, in solving problems and in planning, and problems associated with impulsiveness and self-control are common sequels. On the other hand, among the ABIs caused by degenerative processes, Alzheimer's disease stands out. Alzheimer's disease (AD), characterized by the presence of neurofibrillary tangles and senile plaques, is a dementia that affects a large portion of the elderly population, with an incidence that has increased significantly in the last decades. Thus, early detection of AD becomes a public health issue, as it allows the initiation of a treatment that can significantly delay the progression of the disease. Therefore, the development of methods to support the early diagnosis of AD is of paramount importance. In addition, in the search for very early diagnosis, mild cognitive impairment (MCI) has been shown to be an important risk factor in the development of AD. Over the last decade, quantitative electroencephalography (qEEG) has emerged as a reliable and low-cost tool for the diagnosis of cortical disorders such as AD and MCI due to its wide availability, low cost and use of non-invasive procedures, which make it possible to perform serial exams and to monitor neurological state evolution. In this research project we intend to develop, improve and validate biological markers based on the analysis of EEG brain connectivity tools to make an accurate and early diagnosis of MCI and AD, as well as understand the differences between connectivity changes in AD and TBI. (AU)

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Scientific publications (4)
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
ROSSINI, P. M.; DI IORIO, R.; VECCHIO, F.; ANFOSSI, M.; BABILONI, C.; BOZZALI, M.; BRUNI, A. C.; CAPPA, S. F.; ESCUDERO, J.; FRAGA, F. J.; GIANNAKOPOULOS, P.; GUNTEKIN, B.; LOGROSCINO, G.; MARRA, C.; MIRAGLIA, F.; PANZA, F.; TECCHIO, F.; PASCUAL-LEONE, A.; DUBOIS, B. Early diagnosis of Alzheimer's disease: the role of biomarkers including advanced EEG signal analysis. Report from the IFCN-sponsored panel of experts. CLINICAL NEUROPHYSIOLOGY, v. 131, n. 6, p. 1287-1310, JUN 2020. Web of Science Citations: 1.
DOS SANTOS, ELIANA M.; CASSANI, RAYMUNDO; FALK, TIAGO H.; FRAGA, FRANCISCO J. Improved motor imagery brain-computer interface performance via adaptive modulation filtering and two-stage classification. Biomedical Signal Processing and Control, v. 57, n. SI MAR 2020. Web of Science Citations: 0.
FRAGA, FRANCISCO J.; MAMANI, GODOFREDO QUISPE; JOHNS, ERIN; TAVARES, GUILHERME; FALK, TIAGO H.; PHILLIPS, NATALIE A. Early diagnosis of mild cognitive impairment and Alzheimer's with event-related potentials and event-related desynchronization in N-back working memory tasks. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, v. 164, p. 1-13, OCT 2018. Web of Science Citations: 4.
CASSANI, RAYMUNDO; ESTARELLAS, MAR; SAN-MARTIN, RODRIGO; FRAGA, FRANCISCO J.; FALK, TIAGO H. Systematic Review on Resting-State EEG for Alzheimer's Disease Diagnosis and Progression Assessment. DISEASE MARKERS, 2018. Web of Science Citations: 3.

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