H2020Индивидуална стипендия2020–2022

ESNECO · Estimation of Neural Code from the Electroencephalogram (EEG)

„Хоризонт 2020“ — Действия „Мария Склодовска-Кюри“

Период: 2020-10-16 → 2022-10-15
Финансиране от ЕС: 171 473 €
Участници: 1
Схема: MSCA-IF

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Накратко на български

Невронните механизми, които създават сигналите при електроенцефалограмата (ЕЕГ), се анализират чрез математически инструменти за определяне на показатели като съотношението между възбуда и инхибиране. Това помага за по-доброто разбиране на функционалните промени в мозъка при шизофрения и аутизъм.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

Estimation of Neural Code from the Electroencephalogram (EEG)

The action “Estimation of Neural Code from the Electroencephalogram (EEG)” was focused on answering the question: Where does the electroencephalogram (EEG) come from? Electroencephalography is one of the most important non-invasive brain imaging techniques in neuroscience and in the clinic; it has been widely applied to diagnose numerous brain diseases. What we know about how to use the EEG for clinical diagnose is largely based on a machine-learning perspective that employs features of the EEG signal to detect and classify the neural disease. However, the underlying brain mechanisms that produce those EEG features are largely unknown. Few labs have been interested in linking neurophysiology to the EEG. The EEG could be a much more powerful and insightful brain measurement tool if only we could understand the neural mechanisms that produce it. It is remarkable to think that EEG has been a dominant tool in studying healthy and diseased brain function, and for diagnosing medical conditions, for a century, and we still do not have answers to this fundamental question. The goal of this Marie Skłodowska Curie Action (MSCA) has been to develop rigorous mathematical tools to disambiguate the EEG and robustly interpret it in terms of specific neural features (e.g., excitation-inhibition ratio). Such features are key elements in determining the neural microcircuit configuration and have been documented to contribute to important brain disorders such as schizophrenia and autism spectrum disorders. These brain disorders result, at least in part, from anomalous changes in the functional organization and dynamics of neural circuits. However, we still do not know how to identify these atypical changes of neural dynamics in terms of the EEG signal. Ensuring healthy lives and promoting the well-being at all ages is a priority at European and global levels. Understanding the origins of EEG may increase the usability of EEG to diagnose brain disorders and predict treatment outcome success.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

The electroencephalogram (EEG) is one of the most important non-invasive brain imaging tools in neuroscience and in the clinic, but surprisingly little is known about the features of neural circuit activity that give rise to the EEG. The challenge is to explain the functional and anatomical configurations, e.g., the neural interactions among different classes of cells, that produce the diverse spatial, spectral and temporal EEG features linked to cognition and neurological diseases. By means of an interdisciplinary approach, combing advanced theoretical modeling with state-of-the-art multiscale neurophysiology and interventional techniques, I will address the above challenge. I will develop rigorous mathematical tools to disambiguate the EEG and robustly interpret it in terms of specific neural features (e.g., firing rate). Such features are key elements in determining the microcircuit configuration and have been documented to contribute to brain disorders such as schizophrenia and Autism Spectrum Disorders (ASD). First, I will develop neural network models that include the key components of cortical microcircuits. I will then turn these models into accurate EEG analysis tools by fitting them to empirical data to “invert”, or translate back, the EEG into an estimate of the neural parameters. In particular, I am interested in studying the relationship of the EEG with spiking activity and synchrony of excitatory and inhibitory populations. The experiments will record simultaneously EEG and intracortical neural activity in mice, combined with optogenetic tools that can determine the contributions of specific classes of cells and specific patterns of activity in cells to different EEG features. The analysis tools developed in this project will be used to infer neural circuit changes from EEG measures, which will produce substantial progress toward bridging the gap between EEG and neuron dynamics.

Оригинален текст от CORDIS (на английски).

Участници

FONDAZIONE ISTITUTO ITALIANO DI TECNOLOGIA · GenovaКоординаторИталия