Complexity of brain activity and connectivity in functional neuroimaging

Abstract

Abbreviations: CFC, cross frequency coupling; CI, complexity index; DFBC, dynamic functional brain connectivity; DICM, dominant intrinsic coupling modes; EEG, electroencephalography; FI, flexibility index; FMRI, functional magnetic resonance imaging; iPLV, imaginary part of phase locking value; MEG, magnetoencephalography; ROI, regions of interest;ROI, Regions of Interests; WC, wavelet coherence. AbstractUnderstanding the complexity of human brain dynamics and brain connectivity across the repertoire of functional neuroimaging and various conditions, is of paramount importance. Novel measures should be designed tailored to the input focusing on multichannel activity and dynamic functional brain connectivity (DFBC). Here, we defined a novel complexity index (CI) from the field of symbolic dynamics that quantifies patterns of different words up to a length from a symbolic sequence. The CI characterizes the complexity of the brain activity. We analysed DFBC by adopting the sliding window approach using imaginary part of phase locking value (iPLV) for EEG/ECoG/MEG and wavelet coherence (WC) for fMRI. Both intra and cross-frequency couplings (CFC) namely phase-to-amplitude were estimated using iPLV/WC at every snapshot of the DFBC. Using proper surrogate analysis, we defined the dominant intrinsic coupling mode (DICM) per pair of regions-of-interest (ROI). The spatiotemporal probability distribution of DICM were reported to reveal the most prominent coupling modes per condition and modality. Finally, a novel flexibility index is defined that quantifies the transition of DICM per pair of ROIs between consecutive time windows. The whole methodology was demonstrated using four neuroimaging datasets (EEG/ECoG/MEG/fMRI). Finally, we succeeded to totally discriminate healthy controls from schizophrenic using FI and dynamic reconfiguration of DICM. Anaesthesia independently of the drug caused a global decreased of complexity in all frequency bands with the exception in δ and alters the dynamic reconfiguration of DICM. CI and DICM of MEG/fMRI resting-state recordings in two spatial scales were high reliable. K E Y W O R D S complexity, dominant coupling modes, dynamic functional connectivity, flexibility index, multiplexity, reliability, symbolic dynamics

Graph Signal Processing of Low and High-Order Dynamic Functional Connectivity Networks Using EEG Resting-State for Schizophrenia: A Whole Brain Breakdown

“…In this manner, a series of iPLV-based graph estimates were computed per subject, for each of the 7 possible intra frequency coupling refers to the studying frequencies and the 21 possible cross frequency pairs. This procedure which is described in detail in our previous papers (Dimitriadis and Salis, 2017b ;Dimitriadis et al, 2018c), resulted in 7 DFCG iPLV per participant for within frequency bands and 21 DFCG Iplv per participant for each possible cross frequency pair. DFCG iPLV tabulates iPLV estimates between every possible pair of sensors.…”

“…The adopted surrogate scheme was applied to the original whole time series and used at every signal segment at every temporal window. This procedure preserves phase dynamics and the non-stationarity of EEG brain time -series (see Dimitriadis andSalis, 2017b,Dimitriadis,2018c).…”

“…Cortical disconnectivity theory for schizophrenia has been explored also in functional neuroimaging. The analysis of multichannel electro (EEG) and magneto-encephalography (MEG) signals provides unique information for functional interaction between neural oscillations characterizing brain activity of specific areas within the same frequency (intrafrequency coupling) and between frequencies (cross-frequency coupling ; Dimitriadis et al, 2017bDimitriadis et al, ,2018c in high temporal resolution (Canolty and Knight, 2010 ;von Stein and Sarnthein, 2000 ;Lisman and Buzsaki, 2008 ;Dimitriadis et al, 2017bDimitriadis et al, , 2018c. These functional interactions have been proved to be altered in schizophrenia (Allen et al, 2011).…”

“…In the present study, we propose a novel approach by first built an integrated dynamic FCG (IDFCG) by incorporating into a single DFCG both the functional coupling strength and also the dominant intrinsic coupling mode (DICM) between every EEG sensor and also across every temporal segment following a sliding-window approach (Dimitriadis et al, ,2018c. Afterward, we built a subject-specific LO and HO-IDFCG for both the healthy control and schizophrenic group.…”

Modeling the Switching behavior of Functional Connectivity Microstates (FCμstates) as a Novel Biomarker for Mild Cognitive Impairment

“…For this aim, we analysed the MEG activity of healthy controls and MCI patients at resting-state (eyes-open) via DFC analysis. Based on a previous approach Dimitriadis et al, 2017b), we detected the dominant type of interaction per pair of MEG sources and temporal segment (Dimitriadis et al, 2018c). This approach produced a subject-specific dynamic functional connectivity graph (DFCG).…”