Influence of 5-HTTLPR genotypes on structural and functional connectivity within amygdala-prefrontal cortex circuitry

Abstract

Anxiety and depression are highly heritable disorders that lead to significant impairment in affected individuals across gender, ethnicity and race; these disorders are frequently the leading causes of disability and suicide in many countries. One could assume that the same pathophysiological mechanisms underlie anxiety and depression in all affected individuals; however, the observed clinical heterogeneity within these disorders raises doubts and questions as to the validity of this assumption. Moreover, no single gene is thought to underlie anxiety and depression. Instead, it is thought that a convergence of multiple genetic factors contributes to the clinical manifestations of these disorders, adding yet another layer of complexity to understanding the pathophysiology of anxiety and depression.Imaging genetics was developed to examine the effects of genetic variation on neuroimaging measurements that reflect neurobiological functioning and structure in the hope that it can help bridge the mechanistic gap between genes and phenotypes in psychiatric disorders. The study by Long et al. in this issue highlights the importance of imaging genetic studies in anxiety and depression.Deficits within the neural circuitry subserving emotional processing are strongly implicated in anxiety and depression, including key regions such as the prefrontal cortex (PFC) and amygdala [1][2][3][4] . Multi-modal neuroimaging studies provide considerable support for abnormalities in the structure, function, and functional connectivity of the PFC and amygdala in these disorders [5][6][7][8][9] . In addition, recent studies have also implicated abnormalities in the white matter connections between the PFC and amygdala in anxiety and depression, particularly in the ventral prefrontal white matter [10,11] . An important candidate gene for the development and pathophysiology of anxiety and depression is the gene encoding the serotonin transporter promoter protein (locus, SLC6A4; variant, 5-HTTLPR). The serotonin transporter (5-HTT) is central to the regulation of serotonergic activity.As the PFC-amygdala neural circuitry is rich in serotonin, 5-HTT likely plays important modulatory roles within this circuitry. A functional polymorphism in humans in 5-HTTLPR is associated with effects on transcription and the level of serotonin transporter function [12] . The presence of the short allele (S) has been associated with anxiety-related traits [13] and a vulnerability to depression in individuals exposed to stressful life events [14] in Caucasian populations. In addition, 5-HTTLPR affects the development of corticolimbic circuitry and alters the functional connectivity during emotional processing between the amygdala and medial PFC in Caucasian populations [15] . However, these associations are less clear in Asian populations.In contrast to the findings in Caucasian populations, Long et al. [16] found reduced functional coupling between the PFC and amygdala during the resting state in ChineseHan subjects carrying the long allele (L) for 5-HTTLPR...

Beyond reward prediction errors: the role of dopamine in movement kinematics

“…The reward prediction error hypothesis cannot explain the selectivity of DA neurons for different directions of motion (Figure 3 ), the linear relationship between DA activity and kinematic variables independent of reward (Figure 6 ), the high correlation between DA activity and movement on appetitive and aversive trials (Figure 7 ), the lateralization of leftward and rightward selective neurons on the two sides of the brain (Figure 9 ), and the observation that DA activity reflects the mathematical derivative of the GABAergic SNr output (Figure 13 ). Nor can it explain previous observations on locomotion (Wang et al, 2013 ), posture control (Barter et al, 2014 ), motivational modulation (Rossi et al, 2013a ), or instrumental actions (Jin and Costa, 2010 ; Fan et al, 2012 ). Yet, as we shall see below, all these results can be explained by the crucial role of DA in shaping movement kinematics.…”

“…Although we cannot rule out this possibility, classic studies from monkeys concluded that DA neurons from both SNc and VTA have similar phasic responses in relation to behavioral events (Schultz, 1998a ). In addition, recent studies also found a strong correlation between VTA activity and movement (Puryear et al, 2010 ; Wang and Tsien, 2011b ; Wang et al, 2013 ). It remains to be seen whether VTA DA neurons also represent movement kinematics.…”

Post-training activation of Rac1 in the basolateral amygdala is required for the formation of both short-term and long-term auditory fear memory

“…Infusion of actin cytoskeleton assembly inhibitors into the amygdala impaired the consolidation and reconsolidation of fear memory (Rehberg et al, 2010 ), and blocked the reacquisition when re-exposed to the US at the last extinction session (Motanis and Maroun, 2012 ). In addition, the synaptic plasticity is modulated by actin cytoskeleton, including the orchestration of the pre-synaptic vesicle cycle and the organization and trafficking of postsynaptic receptors (Kuriu et al, 2006 ; Xu and Wang, 2013 ; Gordon-Weeks and Fournier, 2014 ). Rac1 regulates dendritic spine morphogenesis, which is required for synaptic plasticity, through the modulation of actin cycling (Kennedy et al, 2005 ).…”

“…Studies have shown fear conditioning involves integration of CS and US inputs in the lateral amygdala (LA) and fear memory is stored in the basal amygdala (BA; Long et al, 2013 ) . The alterations in signaling and synaptic plasticity in the basolateral amygdala (BLA, composed of LA and BA) encode key aspects of the fear learning (Schafe et al, 2001 ; Long et al, 2013 ; Xu and Wang, 2013 ). Fear conditioning involves different stages of memory.…”

The Basal Ganglia in Action

“…This is also true of dedicated body configuration (gait) controllers for stereotyped behaviors like locomotion. Striatal activity and DA modulation are critical for regulating the speed of locomotion (Wang and Tsien, 2011; Wang et al, 2013; Kim et al, 2015; Rueda-Orozco and Robbe, 2015; Roseberry et al, 2016).…”