The Insula: An Underestimated Brain Area in Clinical Neuroscience, Psychiatry, and Neurology

Abstract

Supported by recent human neuroimaging studies, the insula is re-emerging as an important brain area not only in the physiological understanding of the brain, but also in pathological contexts in clinical research. Here, we briefly introduce the anatomical and histological features of the human insula. We then summarize the physiological functions of the insula and underscore its pathological roles in psychiatric and neurological disorders that have long been underestimated. We finally propose possible strategies through which the role of the insula may be further understood for both basic and clinical neuroscience.

Development and evaluation of a multimodal marker of major depressive disorder

“…The insula is involved in integrating sensory interoception signals, cognition, and motivation (Namkung, Kim, & Sawa, 2017). As such, insula dysfunction (including structural and functional abnormalities) has been implicated in MDD (Namkung et al, 2017). The insula also has extensive connections to the DMN, and differences in connectivity between the insula and the DMN network as well as the amygdala may result in pathological inward focus in MDD (Sliz & Hayley, 2012).…”

Lurasidone inhibits NMDA receptor antagonist‐induced functional abnormality of thalamocortical glutamatergic transmission via 5‐HT7 receptor blockade

“…In addition, dysfunction of the ventrolateral frontal cortex, including the insula, has been linked to cognitive, learning, emotional, and other behavioural impairments in both rats and humans (Izquierdo, ; Nelson & Trainor, ; Peters, Dunlop, & Downar, ). Indeed, the insula is a functional integration hub with broad connectivity to both subcortical and cortical regions serving sensory, emotional, motivational, and cognitive functions (Namkung, Kim, & Sawa, ), and insular dysfunction contributes to the positive symptoms and cognitive impairments of schizophrenia (Wylie & Tregellas, ). The insula demonstrates higher dopamine utilization than the mPFC (Jones, Kilpatrick, & Phillipson, ), and D 2 and 5‐HT 1A receptors, both targets of lurasidone, are enriched in the insula compared with other cortical regions (Rabiner et al, ; Woodward et al, ).…”

“…The insula demonstrates higher dopamine utilization than the mPFC (Jones, Kilpatrick, & Phillipson, ), and D 2 and 5‐HT 1A receptors, both targets of lurasidone, are enriched in the insula compared with other cortical regions (Rabiner et al, ; Woodward et al, ). Furthermore, the thalamus includes regions with the highest forebrain expression levels of the 5‐HT 7 receptor (Bonaventure et al, ), and MDTN sends projections to the granular insula (Gogolla, ; Namkung et al, ). These findings suggest that 5‐HT acting via 5‐HT 7 receptors regulates thalamo‐insular glutamatergic transmission, and this pathway is an important target for the clinical actions of lurasidone.…”

Social decision‐making in the brain: Input‐state‐output modelling reveals patterns of effective connectivity underlying reciprocal choices

“…In this way, the AI is capable of exerting strong influences on cognition; by associating stimuli with internal feeling states it determines their relative salience and the cognitive resources allocated for their processing. By extension, the AI has the capacity to influence motivational processes; by associating stimuli with positive or negative feeling states it encodes their incentive value, motivating approach or avoidance behaviour (Namkung, Kim, & Sawa, 2017). Our observation of EU-modulated brain responses in the AI, and their influence on behavioural outputs via the aMCC, might therefore suggest that the AI provides the emotional motivation behind reciprocal choices.…”

“…Such connectivity places AI in an ideal position to integrate the sensory, cognitive and affective signals necessary for subjective feeling states (Craig, 2009). By extension, the AI has the capacity to influence motivational processes; by associating stimuli with positive or negative feeling states it encodes their incentive value, motivating approach or avoidance behaviour (Namkung, Kim, & Sawa, 2017). By extension, the AI has the capacity to influence motivational processes; by associating stimuli with positive or negative feeling states it encodes their incentive value, motivating approach or avoidance behaviour (Namkung, Kim, & Sawa, 2017).…”