Brain Network Evaluation by Functional-Guided Effective Connectivity Reinforcement Learning Method Indicates Therapeutic Effect for Tinnitus.

Using functional connectivity (FC) or effective connectivity (EC) alone cannot effectively delineate brain networks based on functional magnetic resonance imaging (fMRI) data, limiting the understanding of the mechanism of tinnitus and its treatment. Investigating brain FC is a foundational step in exploring EC. This study proposed a functionally guided EC (FGEC) method based on reinforcement learning (FGECRL) to enhance the precision of identifying EC between distinct brain regions. An actor-critic framework with an encoder-decoder model was adopted as the actor network. The encoder utilizes a transformer model; the decoder employs a bidirectional long short-term memory network with attention. An FGEC network was constructed for the enrolled participants per fMRI scan, including 65 patients with tinnitus and 28 control participants healthy at the enrollment time. After 6 months of sound therapy for tinnitus and prospective follow-up, fMRI data were acquired again and retrospectively categorized into an effective group (EG) and an ineffective group (IG) according to the treatment effect. Compared with FC and EC, the FGECRL method demonstrated better accuracy in discriminating between different groups, highlighting the advantage of FGECRL in identifying brain network features. For the FGEC network of the EG and IG per state (before and after treatment) and healthy controls, effective therapy is characterized by a similar pattern of FGEC network between patients with tinnitus after treatment and healthy controls. Deactivated information output in the motor network, somatosensory network, and medioventral occipital cortex may biologically indicate effective treatment. The maintenance of decreased EC in the primary auditory cortex may represent a failure of sound therapy, further supporting the Bayesian inference theory for tinnitus perception. The FGEC network can provide direct evidence for the mechanism of sound therapy in patients with tinnitus with distinct outcomes.

Learning Causal Biological Networks with Parallel Ant Colony Optimization Algorithm.

A wealth of causal relationships exists in biological systems, both causal brain networks and causal protein signaling networks are very classical causal biological networks (CBNs). Learning CBNs from biological signal data reliably is a critical problem today. However, most of the existing methods are not excellent enough in terms of accuracy and time performance, and tend to fall into local optima because they do not take full advantage of global information. In this paper, we propose a parallel ant colony optimization algorithm to learn causal biological networks from biological signal data, called PACO. Specifically, PACO first maps the construction of CBNs to ants, then searches for CBNs in parallel by simulating multiple groups of ants foraging, and finally obtains the optimal CBN through pheromone fusion and CBNs fusion between different ant colonies. Extensive experimental results on simulation data sets as well as two real-world data sets, the fMRI signal data set and the Single-cell data set, show that PACO can accurately and efficiently learn CBNs from biological signal data.