A new science of emotion: implications for functional neurological disorder

Abstract

Functional neurological disorder reflects impairments in brain networks leading to distressing motor, sensory and/or cognitive symptoms that demonstrate positive clinical signs on examination incongruent with other conditions. A central issue in historical and contemporary formulations of functional neurological disorder has been the mechanistic and aetiological role of emotions. However, the debate has mostly omitted fundamental questions about the nature of emotions in the first place. In this perspective article, we first outline a set of relevant working principles of the brain (e.g. allostasis, predictive processing, interoception and affect), followed by a focused review of the theory of constructed emotion to introduce a new understanding of what emotions are. Building on this theoretical framework, we formulate how altered emotion category construction can be an integral component of the pathophysiology of functional neurological disorder and related functional somatic symptoms. In doing so, we address several themes for the functional neurological disorder field including: (i) how energy regulation and the process of emotion category construction relate to symptom generation, including revisiting alexithymia, 'panic attack without panic', dissociation, insecure attachment and the influential role of life experiences; (ii) re-interpret select neurobiological research findings in functional neurological disorder cohorts through the lens of the theory of constructed emotion to illustrate its potential mechanistic relevance; and (iii) discuss therapeutic implications. While we continue to support that functional neurological disorder is mechanistically and aetiologically heterogenous, consideration of how the theory of constructed emotion relates to the generation and maintenance of functional neurological and functional somatic symptoms offers an integrated viewpoint that cuts across neurology, psychiatry, psychology and cognitive-affective neuroscience.

Keywords: emotion; functional neurological disorder; interoception; neurobiology; theory of constructed emotion.

Figure 1

Examples of conceptual theories on FND in the modern medical literature. While not a comprehensive list, timeline depicts select historical and prevailing mechanistic theories for the development of FND.

Figure 2

Classical (conventional) theories argue that internal emotional responses consistently relate to outwardly presenting facial and bodily expressions. (A) Photographs displaying stereotyped Ekman faces for emotion categories (replicated with permission). (B) Individual diagnosed with functional neurological disorder with a melancholic appearance (Delire Melancolique). Photograph in the public domain: commons.wikimedia.org.

Figure 3

Relationships between cytoarchitectural complexity and predictive processing. Schematic representation of cortical laminar gradient in relation to the directionality of predictions and prediction error detection. According to their cytoarchitecture (lamination profiles), neuronal collections can be divided into networks (from least to most complex) as follows: corticoid [e.g. part of the amygdala, substantia innominata (not shown)]; allocortex (e.g. hippocampus, olfactory cortex, part of the amygdala); limbic cortices (e.g. cingulate cortex, ventral anterior insula, posterior orbitofrontal/ventromedial prefrontal cortex, parahippocampus and the temporal pole, with projections to the hypothalamus, PAG area, etc.); primary motor/premotor/supplementary motor areas; eulaminate areas (eulaminate I represent multimodal association areas, e.g. dorsolateral prefrontal cortex, lateral temporal areas, posterior parietal areas; eulaminate II areas are unimodal association cortices, e.g. superior parietal lobule of the somatosensory system) and the konicortex (primary somatosensory, auditory and visual areas). Predictions arise from lower-level structures and gain granularity as they are processed towards more complex areas. Prediction errors are received from sensory input in more laminated cortical structures and compressed towards simpler structures. Note, similar predictive processing approaches are described in the cerebellum and hippocampus. Thus, abstract brain-based concepts are used to predictively organize internal (bodily) and external (environmental) signals, with the goal of supporting allostasis. In this setting, areas defined as visceromotor regions (e.g. anterior cingulate cortex, orbitofrontal cortex, insula, amygdala, ventral striatum) implement allostasis by issuing predictions about the energetic state of the body and the relevance/salience of anticipated sensations. A subset of visceromotor areas overlaps with the salience network. In their role of guiding the re-allocation of attentional resources for personally and/or environmentally relevant information, salience network (cinguloinsular) areas are involved in interoception. More broadly, visceromotor regions are interconnected to brainstem structures coordinating autonomic, immune and endocrine systems in the service of allostasis. Additionally, this display only shows the predictive processing of somatosensory and interoceptive signals; other sensory modalities (e.g. visual) have similar processes. Note that there is debate on how to characterize the lamination profiles of the motor cortices, with conflicting evidence supporting an agranular/dysgranular (‘limbic like’) versus rudimentary layer IV (‘Eulaminate I like’) profile. Thus, we display motor cortices in chequered colours representative of this intersection. I1 = interoceptive cortex; S1 = primary somatosensory cortex.

Figure 4

Concepts, predictions and the construction of categories. Concepts are abstract mental representations of features or instances that repeatedly occur together; the more granular a concept is, the more detailed and better defined the representation (e.g. knowing the difference between a Granny Smith, Golden Delicious, MacIntosh or Red Delicious apple, versus only knowing green and red apples). Individuals engage in allostasis by issuing brain-based situation-specific predictions based on available concepts. If the prediction matches the incoming sensory input, a category is constructed to reflect that instance (e.g. a Granny Smith apple). The incoming sensory input will then help improve future predictions through learning of prediction errors. If the instances that are grouped together share physical features, they are called concrete or perceptual categories (e.g. apples), otherwise they share mental features and are referred to as abstract, conceptual or functional categories (e.g. food). Moreover, the same object or event can be categorized as abstract or concrete depending on the situation: a grasshopper can be categorized as food in certain cultures, and as only an insect in other cultures. Thus, the category depends on the individual’s goal in a particular situation. Abstract, conceptual categories are easily mistaken for concrete ones. For example, a facial movement, such as smiling, is abstract because movements that look identical to the naked eye are variable under the skin, and the same action can vary at the muscular level within one individual. Similarly, scents where different chemicals produce the same smell, or phonemes where the sound of a letter changes in each word but serves the same phonematic function, are all abstract categories. In fact, categories that are assumed to be concrete or perceptual, such as dogs, flowers or weeds, can be understood as abstract, conceptual categories.^, The ability to construct abstract categories is determined by the degree of compression in the features that the brain can support. The expansion of the human brain allows for compression and dimensionality reduction, suggesting that we can assemble multimodal summaries (i.e. features) in early infancy and later in life.^,, The pictures are in the public domain and taken from: https://www.pexels.com.

Figure 5

The theory of constructed emotion. Schematic representation of predictive processing and emotion category construction. (1) Interoception is the brain’s modelling of the physiological state of the body with the goal of efficient regulation through allostasis. (2) This model of the bodily state is experienced as affect, which is a quality of consciousness that ‘colours’ predictions, concepts, perceptions etc., regarding the allostatic needs of the body. (3) The available repertoire and detailed granularity of emotion concepts depends on lived experiences. (4) The concepts that were relevant in similar past experiences will be used to issue predictions by preparing a motor action plan and a sensory experience. Concepts may differ in their granularity. (5) Incoming sensory input will be compared to the predictions: (6) if there is a match, a category that represents that instance can be constructed; (7) if there is not a match, the sensory input can be used to improve future predictions through learning prediction errors. Precision signals (not depicted here) tune predictions and prediction errors. (8) Biopsychosocial factors are relevant across the emotion construction process, and predictive processing more broadly. The picture used is in the public domain and taken from: https://www.pexels.com.

Figure 6

The theory of constructed emotion applied to FND. Illustration of posited points for aberrant emotion construction in individuals with FND. (A) Chronic energy mismanagement is present in some people with FND, leading to chronic fatigue and hyperarousal states among other symptoms. (B) Adverse life experiences may lead to a limited or lack of granular emotion concepts. Additionally, childhood maltreatment may aid the development of more bodily and health/illness related non-emotion concepts. (C) Incoming sensory input can match a prediction that does not have emotion content, and a bodily/illness category is constructed (e.g. ‘shaking’). (D) Deficits in sensory processing, interoceptive accuracy, biased attention and impairments in motor learning among other constructs limit the use of precision signals and predictive errors to improve future predictions. (E) The momentary uncoupling of the brain’s interoceptive and allostatic models may play a role in dissociative mechanisms. (F) Biopsychosocial factors can predispose, precipitate and/or perpetuate the deficits at each stage of the predictive processing stream. The picture used is in the public domain and taken from: https://www.pexels.com.