Alprazolam modulates persistence energy during emotion processing in first-degree relatives of individuals with schizophrenia: a network control study

Abstract

Schizophrenia is marked by deficits in facial affect processing associated with abnormalities in GABAergic circuitry, deficits also found in first-degree relatives. Facial affect processing involves a distributed network of brain regions including limbic regions like amygdala and visual processing areas like fusiform cortex. Pharmacological modulation of GABAergic circuitry using benzodiazepines like alprazolam can be useful for studying this facial affect processing network and associated GABAergic abnormalities in schizophrenia. Here, we use pharmacological modulation and computational modeling to study the contribution of GABAergic abnormalities toward emotion processing deficits in schizophrenia. Specifically, we apply principles from network control theory to model persistence energy – the control energy required to maintain brain activation states – during emotion identification and recall tasks, with and without administration of alprazolam, in a sample of first-degree relatives and healthy controls. Here, persistence energy quantifies the magnitude of theoretical external inputs during the task. We find that alprazolam increases persistence energy in relatives but not in controls during threatening face processing, suggesting a compensatory mechanism given the relative absence of behavioral abnormalities in this sample of unaffected relatives. Further, we demonstrate that regions in the fusiform and occipital cortices are important for facilitating state transitions during facial affect processing. Finally, we uncover spatial relationships (i) between regional variation in differential control energy (alprazolam versus placebo) and (ii) both serotonin and dopamine neurotransmitter systems, indicating that alprazolam may exert its effects by altering neuromodulatory systems. Together, these findings reveal differences in emotion-processing circuitry associated with genetic vulnerability to schizophrenia.

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“…To assess the significance of each brain region in achieving the desired transitions, we defined the control impact I i as follows, which is consistent with previous literature [49]: where B 0 represents the control set including all brain regions and B i represents the control set after the exclusion of brain region i . Subsequently, we utilized Hotelling’s T to define the craving-associated structural alterations (CSA) as the multivariate group comparison of control impact in the context of craving-associated state transitions.…”

“…To assess the significance of each brain region in achieving the desired transitions, we defined the control impact 𝐼 𝑖 as follows, which is consistent with previous literature (Mahadevan et al 2023):…”

Using network control theory to study the dynamics of the structural connectome

“…We refer to these energies as persistence energy [20,73,145], which we interpret as the amount of control energy required to maintain a given brain state (see Figure S9 for a persistence energy example of Figure 4). Persistence energy is typically lower than the energy for control tasks wherein the initial and target states differ (i.e., the off-diagonal elements of energy_matrix).…”