Bipolar oscillations between positive and negative mood states in a computational model of Basal Ganglia

Abstract

Bipolar disorder is characterized by mood swings -oscillations between manic and depressive states. The swings (oscillations) mark the length of an episode in a patient's mood cycle (period), and can vary from hours to years. The proposed modeling study uses decision making framework to investigate the role of basal ganglia network in generating bipolar oscillations. In this model, the basal ganglia system performs a two-arm bandit task in which one of the arms leads to a positive outcome, while the other leads to a negative outcome. In healthy conditions, the model chooses positive action and avoids negative one, whereas under bipolar conditions, the model exhibits slow oscillations in its choice of positive or negative outcomes, reminiscent of bipolar oscillations. The model is cast at three levels of abstraction: 1) a two-dimensional dynamical system model, 2) a phenomenological basal ganglia model, 3) a detailed network model of basal ganglia. Phase-plane analyses on the simple reduced dynamical system with two variables reveal the essential parameters that generate pathological 'bipolar-like' oscillations. Phenomenological and network models of the basal ganglia extend that logic, and interpret bipolar oscillations in terms of the activity of dopaminergic and serotonergic projections on the cortico-basal ganglia network dynamics. The network's dysfunction, specifically in terms of reward and risk sensitivity, is shown to be responsible for the pathological bipolar oscillations. The study proposes a computational model that explores the effects of impaired serotonergic neuromodulation on the dynamics of the cortico basal ganglia network, and relates this impairment to abstract mood states (manic and depressive episodes) and oscillations of bipolar disorder.

Rostral Anterior Cingulate Activations inversely relate to Reward Payoff Maximation & predict Depressed Mood

“…Reinforcement learning models suggest healthy human choices ideally tend to maximize long-term beneficial outcomes (Balasubramani, Chakravarthy, Ali, et al, 2015a; Balasubramani et al, 2014; Balasubramani, Moreno-Bote, et al, 2018; Balasubramani & Chakravarthy, 2019; A. Gupta et al, 2013).…”

“…Translational neuroscience studies show that reward based decision processing deficits are found in depression and in attention disorders, leading to difficulty in reward integration and foresighted choice-behaviors (Balasubramani, Chakravarthy, Ali, et al, 2015b; Balasubramani & Chakravarthy, 2019; Gradin et al, 2011; Groen et al, 2013; Miller & Seligman, 1973; Silvetti et al, 2013; Ziegler et al, 2016). Such individuals then focus on the immediate reward outcome in the short-term, characterized by a prolonged attenuation of temporal discounting of rewards (Eshel & Roiser, 2010; Pizzagalli et al, 2005).…”

“…Second, we capitalize on computational reinforcement learning (RL) models to understand the basis of individual differences in reward and risk based learning across subjects (Balasubramani et al, 2014; Balasubramani & Chakravarthy, 2019; Chakravarthy et al, 2018; A. Gupta et al, 2013; Muralidharan et al, 2014).…”

Interaction of Indirect and Hyperdirect Pathways on Synchrony and Tremor-Related Oscillation in the Basal Ganglia

“…Parkinson's disease (PD) is a neurodegenetative disorder resulting from a loss of dopaminergic neurons, which leads to alterations of neural activity in the basal ganglia (BG), thalamus (TH), and cortex [1][2][3]. The BG is the main pathologic area of PD, which is known to be implicated in choice selection, timing, working memory, and motor sequencing [4][5][6]. Parkinsonian tremor is a well-recognized cardinal symptom affecting~70% of PD patients, which is primarily a rest tremor with the frequency in 3-9 Hz range.…”