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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 enables 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. [ABSTRACT FROM AUTHOR]

Objective The aim of our study was to investigate the neuronal networks underlying background oscillations of epileptic encephalopathy with continuous spikes and waves during slow sleep (CSWS). Methods Sleep electroencephalography ( EEG) studies before and after the treatment were investigated in 15 patients with CSWS. To investigate functional and effective connectivity within the network generating the delta activity in the background sleep EEG, the methods of dynamic imaging of coherent sources ( DICS) and renormalized partial directed coherence ( RPDC) were applied. Results Independent of etiology and severity of epilepsy, background EEG pattern in patients with CSWS before treatment is associated with the complex network of coherent sources in medial prefrontal cortex, somatosensory association cortex/posterior cingulate cortex, medial prefrontal cortex, middle temporal gyrus/parahippocampal gyrus/insular cortex, thalamus, and cerebellum. The analysis of information flow within this network revealed that the medial parietal cortex, the precuneus, and the thalamus act as central hubs, driving the information flow to other areas, especially to the temporal and frontal cortex. The described CSWS-specific pattern was no longer observed in patients with normalized sleep EEG. In addition, frequency of spiking showed a strong linear correlations with absolute source power, source coherence strength, and source RPDC strength at both time points: (1) Spike and wave index (SWI) versus absolute source power at EEG1 (r = 0.56; p = 0.008) and at EEG2 (r = 0.45; p = 0.009); (2) SWI versus source coherence strength at EEG1 (r = 0.71; p = 0.005) and at EEG2 (r = 0.52; p = 0.006); and (3) SWI versus source RPDC strength at EEG1 (r = 0.65; p = 0.003) and at EEG2 (r = 0.47; p = 0.009). Significance The leading role of the precuneus and thalamus in the hierarchical organization of the network underlying the background EEG points toward the significance of fluctuations of vigilance in the generation of CSWS. This hierarchical network organization appears to be specific for CSWS as it is resolved after successful treatment. [ABSTRACT FROM AUTHOR]

Although functional imaging studies described networks associated with generalized epileptic activity, propagation patterns within these networks are not clear. In this study, electroencephalogram (EEG)-based coherent source imaging dynamic imaging of coherent sources (DICS) was applied to different types of generalized epileptiform discharges, namely absence seizures (10 patients) and photoparoxysmal responses (PPR) (eight patients) to describe the representation and propagation of these discharges in the brain. The results of electrical source imaging were compared to EEG-functional magnetic resonance imaging (fMRI) which had been obtained from the same data sets of simultaneous EEG and fMRI recordings. Similar networks were described by DICS and fMRI: (1) absence seizures were associated with thalamic involvement in all patients. Concordant results were also found for brain areas of the default mode network and the occipital cortex. (2) Both DICS and fMRI identified the occipital, parietal, and the frontal cortex in a network associated with PPR. (3) However, only when PPR preceded a generalized tonic-clonic seizure, the thalamus was involved in the generation of PPR as shown by both imaging techniques. Partial directed coherence suggested that during absences, the thalamus acts as a pacemaker while PPR could be explained by a cortical propagation from the occipital cortex via the parietal cortex to the frontal cortex. In conclusion, the electrical source imaging is not only able to describe similar neuronal networks as revealed by fMRI, including deep sources of neuronal activity such as the thalamus, but also demonstrates interactions interactions within these networks and sheds light on pathogenetic mechanisms of absence seizures and PPR. Hum Brain Mapp, 2013. © 2012 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

West syndrome is a severe epileptic encephalopathy of infancy with a poor developmental outcome. This syndrome is associated with the pathognomonic EEG feature of hypsarrhythmia. The aim of the study was to describe neuronal networks underlying hypsarrhythmia using the source analysis method (dynamic imaging of coherent sources or DICS) which represents an inverse solution algorithm in the frequency domain. In order to investigate the interaction within the detected network, a renormalized partial directed coherence (RPDC) method was also applied as a measure of the directionality of information flow between the source signals. Both DICS and RPDC were performed for EEG delta activity (1-4 Hz) in eight patients with West syndrome and in eight patients with partial epilepsies (control group). The brain area with the strongest power in the given frequency range was defined as the reference region. The coherence between this reference region and the entire brain was computed using DICS. After that, the RPDC was applied to the source signals estimated by DICS. The results of electrical source imaging were compared to results of a previous EEG-fMRI study which had been carried out using the same cohort of patients. As revealed by DICS, delta activity in hypsarrhythmia was associated with coherent sources in the occipital cortex (main source) as well as the parietal cortex, putamen, caudate nucleus and brainstem. In patients with partial epilepsies, delta activity could be attributed to sources in the occipital, parietal and sensory-motor cortex. In West syndrome, RPDC showed the strongest and most significant direction of ascending information flow from the brainstem towards the putamen and cerebral cortex. The neuronal network underlying hypsarrhythmia in this study resembles the network which was described in previous EEG-fMRI and PET studies with involvement of the brainstem, putamen and cortical regions in the generation of hypsarrhythmia. The RPDC suggests that brainstem could have a key role in the pathogenesis of West syndrome. This study supports the theory that hypsarrhythmia results from ascending brainstem pathways that project widely to basal ganglia and cerebral cortex. [ABSTRACT FROM AUTHOR]