Increased Excursions to Functional Networks in Schizophrenia in the Absence of Task

Abstract

Schizophrenia is a chronic psychotic disorder characterized by the disruption of thought processes, perception, cognition, and behaviors, for which there is still a lack of objective and quantitative biomarkers in brain activity. Using functional magnetic resonance imaging (fMRI) data from an open-source database, this study investigated differences between the dynamic exploration of resting-state networks in 71 schizophrenia patients and 74 healthy controls. Focusing on recurrent states of phase coherence in fMRI signals, brain activity was examined for intergroup differences through the lens of dynamical systems theory. Results showed reduced fractional occupancy and dwell time of a globally synchronized state in schizophrenia. Conversely, patients exhibited increased fractional occupancy, dwell time and limiting probability of being in states during which canonical functional networks—i.e., Limbic, Dorsal Attention and Somatomotor—synchronized in anti-phase with respect to the rest of the brain. In terms of state-to-state transitions, patients exhibited increased probability of switching to Limbic, Somatomotor and Visual networks, and reduced probability of remaining in states related to the Default Mode network, the Orbitofrontal network and the globally synchronized state. All results revealed medium to large effect sizes. Combined, these findings expose pronounced differences in the temporal expression of resting-state networks in schizophrenia patients, which may relate to the pathophysiology of this disorder. Overall, these results reinforce the utility of dynamical systems theory to extend current knowledge regarding disrupted brain dynamics in psychiatric disorders.

Frequency-Specific Analysis of the Dynamic Reconfiguration of the Brain in Patients with Schizophrenia

“…In functional magnetic resonance imaging (fMRI) studies, traditional brain network construction methods are generally during the resting-state. Current dynamic network analyses have confirmed that fluctuations in functional connections exist, which has attracted increasing attention in the academic world [ 5 , 6 ]. Studies have shown that dynamic network analysis can better detect fluctuations in the brain’s functional connections.…”

Metastability as a candidate neuromechanistic biomarker of schizophrenia pathology

“…In order to identify recurrent spatiotemporal patterns of phase-locking–henceforth called ‘LEiDA modes’–we performed k-means clustering on the phase-locked time-series of each of the datasets that were analyzed (HCPEP CONx4, HCPEP NAPx4, Cobre CONx1, Cobre SCHZx1, see Materials and methods ). This is similar to a previous study [ 47 ], but different from other studies that used LEiDA where k-means clustering was either performed on concatenated datasets across groups [ 54 – 56 ] or where the centroids extracted from one group were used to seed the clustering of other groups [ 57 – 59 ]. The approach in this study considers each dataset as a unique observation of brain activity with associated variability in the spatiotemporal modes and avoids data leakage which occurs when dimensionality reduction is performed on the dataset as a whole [ 60 ].…”

“…Effect sizes were negligible to small ( d = -0.11 to d = 0.36) for early disorder subjects (NAP group) and moderate to large ( d = -0.58 to d = -0.82) for subjects with established schizophrenia (SCHZ group) (see S2 Table ). Previous discrimination analysis on the same Cobre dataset using a distance measure between patterns of instantaneous phase synchrony reported a moderate effect size ( d = 0.67) [ 33 ], as did another study using the mean probability of dwell time in a global state (Hedge’s g = 0.73) [ 56 ]. In contrast, one study reported significantly lower effect sizes ( d = 0.06 to d = 0.31) using measures of metastability in its original form, and using measures of between-network FC ( d = 0.04 to d = 0.52) [ 68 ].…”

Alteration of resting-state network dynamics in autism spectrum disorder based on leading eigenvector dynamics analysis

“…Previous evidence has suggested that the LEiDA framework is highly flexible, robust, and precise ( Glerean et al, 2012 ; Ponce-Alvarez et al, 2015 ; Cabral et al, 2017 ), allowing for recurrent states that were detected and characterized in resting state and task conditions in the healthy brain. It can also distinguish the abnormal brain states in psychiatric diseases, such as schizophrenia ( Farinha et al, 2022 ), major depressive disorders ( Figueroa et al, 2019 ; Alonso Martínez et al, 2020 ; Wang et al, 2022 ), and trait self-reflectiveness ( Larabi et al, 2020 ), and the alteration of brain states in psilocybin ( Lord et al, 2019 ) and sleep ( Deco et al, 2019 ). The fundamental framework of LEiDA is shown in Figure 1 , and the detailed steps are mentioned later.…”

“…A previous study indicated that the dynamic properties of recurrent FC states are related to cognitive performance in healthy participants ( Cabral et al, 2017 ). Meanwhile, the PL patterns obtained from the LEiDA approach have shown particular sensitivity to alterations in psychiatric symptoms, such as schizophrenia ( Farinha et al, 2022 ) and major depressive disorders ( Figueroa et al, 2019 ; Alonso Martínez et al, 2020 ; Wang et al, 2022 ). However, the recurrent PL patterns identified by LEiDA in ASD have not yet been qualitatively probed.…”