Ph. D. Project
Title:
Diagnosis et prognosis of Alzheimer disease with fast periodic stimulation in electrophysiology
Dates:
2021/10/04 - 2024/10/03
Student:
Supervisor(s): 
Description:
Alzheimer's disease (AD) is the first cause of neurodegenerative cognitive impairment. It is characterized by a
progressive loss of episodic memory linked to an alteration of the medial temporal lobe, and other cognitive
functions (language, gesture, executive functions ...) with dramatic consequences for the patients and their
relatives. Both an early diagnosis and an accurate prognosis of MA are essentials. The current measures are
biological markers that are not always available, costly and with a weak prognosis value, since they can precede
clinical signs of more than 10 years. As for the diagnosis of AD based on clinical signs (explicit neuropsychological
tests), it remains highly complicated since cognitive impairments can have multiple causes beyond AD, and the
explicit measures can be stressful for the patients.

In this context, the goal of the PhD will be to develop and optimize implicit measures of (visual) memory function
with fast periodic visual stimulation coupled with electroencephalography (EEG). Specifically, the objectives will be
to :

1) Identify and optimize (sensitivity, reliability, reduction of the number of channels for fast measures) novel EEG
biomarkers of AD, evaluated on groups of well-diagnosed MA patients. The EEG biomarkers will be related to the
biological markers and neuropsychological scores.
2) Identify spectral signatures of AD (combination of amplitudes to differentiate MA from other pathologies,
including other forms of dementia)
3) Identify the prognosis of EEG biomarkers on an individual basis
4) Build a new prognosis model of AD by machine learning analysis on a large cohort of neuroscientific data
5) Develop an integrated portable system of stimulation, recording and frequency analysis with Bioserenity
Keywords:
Alzheimer disease, EEG, fast periodic stimulation, diagnosis, temporal lobe, recognition
Department(s): 
Biology, Signals and Systems in Cancer and Neuroscience