Driving brain state transitions in major depressive disorder through external stimulation

Abstract

Major depressive disorder (MDD) as a dysfunction of neural circuits and brain networks has been established in modern neuroimaging sciences. However, the brain state transitions between MDD and health through external stimulation remain unclear, which limits translation to clinical contexts and demonstrable clinical utility.We propose a framework of the large-scale whole-brain network model for MDD linking the underlying anatomical connectivity with functional dynamics obtained from diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI). Then, we further explored the optimal brain regions to promote the transition of brain states between MDD and health through external stimulation of the model.Based on the whole-brain model successfully fitting the brain state space in MDD and the health, we demonstrated that the transition from MDD to health is achieved by the excitatory activation of the limbic system and from health to MDD by the inhibitory stimulation of the reward circuit. Our finding provides novel biophysical evidence for the neural mechanism of MDD and its recovery and allows the discovery of new stimulation targets for MDD recovery.

A new causal centrality measure reveals the prominent role of subcortical structures in the causal architecture of the extended default mode network

“…The advantage of using causal centrality -as a proxy for controllability -would be in the computational efficiency of the former by virtue of Bayesian model reduction. Further research is required to formalize the relationship between causal centrality and controllability metrics in neuronal networks, and to study the implications for identifying therapeutic stimulation targets (Alosaimi et al 2022;Eraifej et al 2023;Ezzyat et al 2018;Georgiev et al 2021;Wang et al 2022;Zangen et al 2023).…”

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.…”

Virtual external stimulation promotes the transformation of the brain state from early mild cognitive impairment to health

“…The range of k for the number of clusters was 2–10, with higher k indicating that the recurrent functional connectivity states were divided more carefully. Based on the minimum p ‐value of the probability of a significant difference between the patients with EMCI and healthy individuals, [30,33] we selected the optimal number of clusters as k = 3. The clustering resulted in the generation of three cluster centers ( N × 1), representing the three substates found using the clustering algorithm, to assess the recurrent functional connectivity patterns of the brain (Figure 1C).…”

“…Leading eigenvector dynamics analysis (LEiDA), established based on dynamic functional connectivity, can capture the potential functional connectivity patterns of the brain at a quasi‐instantaneous level [29–31] . It can build out the probabilistic metastable substate (PMS) space to recognize different brain states [32,33] . This enables quantitative characterization of a whole‐brain model analyzing abnormal brain dynamics and implementing stimuli.…”