CHAPTER 42 - Dynamic causal models for EEG
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Conductance-based dynamic causal modeling: A mathematical review of its application to cross-power spectral densities
2021, NeuroImageCitation Excerpt :Finally, this paper will not cover the topics of Bayesian inference or model inversion in detail. More in-depth accounts of these concepts can be found elsewhere (Bishop, 2006; Friston et al., 2006). Generally, a DCM for electrophysiological data comprises two parts (Fig. 1): the neuronal model, which delineates the intra- and inter-neuronal source2 dynamics, and the observation model, which describes how source activity propagates through surrounding tissues (brain, skull, scalp) in order to generate the data registered at the level of the sensors (Kiebel et al., 2008c).
Multilevel growth curve analyses of behavioral activation for anhedonia (BATA) and mindfulness-based cognitive therapy effects on anhedonia and resting-state functional connectivity: Interim results of a randomized trial<sup>✰</sup>
2021, Journal of Affective DisordersCitation Excerpt :T1 images were bias-corrected with FSL v5.0.9 to enhance FreeSurfer v6.0 removal of non-brain tissue. Remaining steps proceeded in CONN toolbox v19c (Whitfield-Gabrieli and Nieto-Castanon, 2012) and SPM12 (Friston et al., 2007) including 1) realignment and motion estimation, 2) slice-timing correction, 3) volume outlier detection (framewise displacement > 0.5 mm or global signal intensity change > 3 z-scores) 4) direct normalization to MNI space, and 5) smoothing 4 mm FWHM (only applicable for seed-to-voxel analyses). Brain masks were segmented into white matter (WM) and cerebrospinal fluid (CSF), normalized, and eroded to minimize partial volume effects.
Bayesian Model Selection Maps for Group Studies Using M/EEG Data
2018, Frontiers in NeuroscienceClassifying dynamic transitions in high dimensional neural mass models: A random forest approach
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