Browsing by Author "Aro, Susanna"

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  • Analysis of functional connectivity and oscillatory power using DICS: From raw MEG data to group-level statistics in Python
    (2018-08-11) van Vliet, Marijn; Liljeström, Mia; Aro, Susanna; Salmelin, Riitta; Kujala, Jan
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    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.
  • Large-scale brain networks using MEG: pipeline and application to real data
    (2019-01-28) Aro, Susanna
     Perustieteiden korkeakoulu | Master's thesis
    Research has shown that functional connectivity is a powerful tool in the study of the complex processes of the human brain. Functional connectivity is generally defined as the synchronisation of anatomically distant areas and it can be inspected for example through coherent oscillations. Magnetoencephalography (MEG) is well suited for functional connectivity studies as it has a good time resolution that allows us to observe the changes in magnetic field in real time. Dynamic Imaging of Coherent Sources (DICS) uses spatial filters to estimate the oscillatory activity in the human brain. In my master's thesis, I introduce a python-based pipeline and code library that estimates functional connectivity from MEG data using DICS. I will then demonstrate the application with a real MEG dataset. This pipeline also implements the use of canonical coherence, which provides a fast and stable way of calculating coherence between a large number of signal sources. The pipeline presented here consists of seven steps: First the data is preprocessed and the cross-spectral density (CSD) matrices are computed. Then the source space is computed and used with the CSD matrices to compute both oscillatory power and connectivity. These results are then analysed at the group-level and visualised. The results show that the pipeline is easy to apply to a real world dataset. Selection of the parameters in different steps should be made based on the dataset at hand and the results should be interpreted carefully. Further research on the stability of this pipeline is suggested.
  • Lukemisen käsittely aivoissa: parametrisen mallin käyttö fMRI-analyysissä
    (2015-12-14) Aro, Susanna
     Sähkötekniikan korkeakoulu | Bachelor's thesis