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Investigating brain connectivity from a signal processing perspective

Year: 2025

Type: article

Abstract: Understanding brain connectivity is crucial for deciphering both neural function and dysfunction. This review highlights the key signal-processing methods used to analyze brain connectivity, including techniques such as Fourier transforms, wavelet analysis, and graph-theoretical approaches. Applications of these techniques across neuroimaging and electrophysiological modalities such as electroencephalography (EEG), magnetoencephalography (MEG), and functional magnetic resonance imaging (fMRI) are examined. Additionally, challenges such as noise reduction, signal non-stationarity, and computational complexity are addressed. By bridging neuroscience and signal processing, this review aims to provide insights into the strengths and limitations of both traditional and cutting-edge signal-processing methods for studying brain connectivity while also highlighting potential future research directions.

Source: Journal of Multiscale Neuroscience

DOI: 10.56280/1702827275

Authors: Alexandra Tsipourakis, Marco Agostino Deriu

Language: English

Topic: Functional Brain Connectivity Studies

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

Open Access status: hybrid

Funders: European Commission, HORIZON EUROPE Framework Programme

Awards: PremAtuRe nEwborn motor and cogNitive impairmenTs: Early diagnosis, G7263021398

OpenAlex ID: W4411814300

FWCI:

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