Analysis of functional connectivity and oscillatory power using DICS

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en van Vliet, Marijn Liljeström, Mia Aro, Susanna Salmelin, Riitta Kujala, Jan 2018-10-16T08:54:51Z 2018-10-16T08:54:51Z 2018-08-11
dc.identifier.citation van Vliet , M , Liljeström , M , Aro , S , Salmelin , R & Kujala , J 2018 , ' Analysis of functional connectivity and oscillatory power using DICS : From raw MEG data to group-level statistics in Python ' FRONTIERS IN NEUROSCIENCE , vol 12 , 586 , pp. 1-17 . DOI: 10.3389/fnins.2018.00586 en
dc.identifier.issn 1662-453X
dc.identifier.issn 1662-4548
dc.identifier.other PURE UUID: 6b5e457f-9b38-4e74-933c-b6513006fbe7
dc.identifier.other PURE ITEMURL:
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dc.description.abstract Communication between brain regions is thought to be facilitated by the synchronization of oscillatory activity. Hence, large-scale functional networks within the brain may be estimated by measuring synchronicity between regions. Neurophysiological recordings, such as magnetoencephalography (MEG) and electroencephalography (EEG), provide a direct measure of oscillatory neural activity with millisecond temporal resolution. In this paper, we describe a full data analysis pipeline for functional connectivity analysis based on dynamic imaging of coherent sources (DICS) of MEG data. DICS is a beamforming technique in the frequency-domain that allows the study of the cortical sources of oscillatory activity and synchronization between brain regions. All the analysis steps, starting from the raw MEG data up to publication-ready group-level statistics and visualization, are discussed in depth, including methodological considerations, rules of thumb and tradeoffs. We start by computing cross-spectral density (CSD) matrices using a wavelet approach in several frequency bands (alpha, theta, beta, gamma). We then provide a way to create comparable source spaces across subjects and discuss the cortical mapping of spectral power. For connectivity analysis, we present a canonical computation of coherence that facilitates a stable estimation of all-to-all connectivity. Finally, we use group-level statistics to limit the network to cortical regions for which significant differences between experimental conditions are detected and produce vertex- and parcel-level visualizations of the different brain networks. Code examples using the MNE-Python package are provided at each step, guiding the reader through a complete analysis of the freely available openfMRI ds000117 "familiar vs. unfamiliar vs. scrambled faces" dataset. The goal is to educate both novice and experienced data analysts with the "tricks of the trade" necessary to successfully perform this type of analysis on their own data. en
dc.format.extent 17
dc.format.extent 1-17
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation.ispartofseries FRONTIERS IN NEUROSCIENCE en
dc.relation.ispartofseries Volume 12 en
dc.rights openAccess en
dc.subject.other Neuroscience(all) en
dc.subject.other 3112 Neurosciences en
dc.subject.other Systemic and cognitive neuroscience en
dc.title Analysis of functional connectivity and oscillatory power using DICS en
dc.type A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.contributor.department Department of Neuroscience and Biomedical Engineering
dc.subject.keyword DICS
dc.subject.keyword MEG
dc.subject.keyword coherence
dc.subject.keyword brain rhythms
dc.subject.keyword workflow
dc.subject.keyword tutorial
dc.subject.keyword Neuroscience(all)
dc.subject.keyword 3112 Neurosciences
dc.subject.keyword Systemic and cognitive neuroscience
dc.identifier.urn URN:NBN:fi:aalto-201810165362
dc.identifier.doi 10.3389/fnins.2018.00586
dc.type.version publishedVersion

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