| Literature DB >> 30683707 |
Zina-Mary Manjaly1,2, Neil A Harrison3,4, Hugo D Critchley3,4, Cao Tri Do5, Gabor Stefanics5,6, Nicole Wenderoth2, Andreas Lutterotti7, Alfred Müller8, Klaas Enno Stephan5,9,10.
Abstract
Fatigue is one of the most common symptoms in multiple sclerosis (MS), with a major impact on patients' quality of life. Currently, treatment proceeds by trial and error with limited success, probably due to the presence of multiple different underlying mechanisms. Recent neuroscientific advances offer the potential to develop tools for differentiating these mechanisms in individual patients and ultimately provide a principled basis for treatment selection. However, development of these tools for differential diagnosis will require guidance by pathophysiological and cognitive theories that propose mechanisms which can be assessed in individual patients. This article provides an overview of contemporary pathophysiological theories of fatigue in MS and discusses how the mechanisms they propose may become measurable with emerging technologies and thus lay a foundation for future personalised treatments. © Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.Entities:
Keywords: dopamine; dyshomeostasis; inflammation; interoception; lesion; metacognition; network
Mesh:
Year: 2019 PMID: 30683707 PMCID: PMC6581095 DOI: 10.1136/jnnp-2018-320050
Source DB: PubMed Journal: J Neurol Neurosurg Psychiatry ISSN: 0022-3050 Impact factor: 10.154
Figure 1Pathophysiological mechanisms of fatigue discussed in this article. White and grey boxes represent classes of mechanisms and specific mechanisms, respectively; directed arrows and circle-ended arrows represent direct and mediating effects, respectively. Due to space limitations, only one mechanism per arrow is shown; see main text for other mechanisms. CNS, central nervous system; DA, dopamine; GM, grey matter; NAWM, normally appearing white matter; WM, white matter.
Figure 2A coarse schematic overview of the inference–control–metacognition loop for bodily regulation (for details, see16). Interoceptive surprise as a possible computational substrate of fatigue can arise from perturbations of any components of this loop: (1) actual perturbations of bodily state that evade cerebral attempts of correction (eg, chronic inflammation, cancer); (2) altered interosensations (due to pathologies of interoceptors or afferent pathways); (3) disturbances of interoception (eg, inflammatory lesions of insula); (4) disturbances of interoactions (neuronally or endocrinologically mediated cerebral influences on bodily functions), for example, inflammatory lesions of ACC, brainstem, hypothalamus or their projections; (5) altered metacognitive processes (eg, changes in expected performance levels). The multiple failure loci offer a potential explanation for the clinical heterogeneity of fatigue and speaks to the necessity of developing tools for differential diagnostics at the circuit level.
Figure 3Neuroanatomically specific circuit model of interoception that plays a central role in theories of fatigue from computational psychiatry.14 The regions are based on anatomical investigations of interoeptive circuitry81; the network they form is thought to instantiate a predictive model of bodily states.111 in this hierarchical network, predictions are sent from higher to lower areas, while prediction errors (PEs; the difference between actual and predicted states) are signalled in the opposite direction and used to update predictions (‘predictive coding’110). Specifically, hierarchically higher visceromotor areas, such as the anterior insula (AI) and anterior cingulate cortex (ACC), are thought to tune homeostatic reflex arcs by means of allostatic predictions computed from bodily and environmental information. In turn, AI/ACC inform hierarchically lower areas, such as posterior and mid-insula, about the expected interosensory consequences (corollary discharge). The latter areas compare these predictions against actual interosensory input and return PE that serve to update the predictions by AI/ACC. At the top of the hierarchy, metacognitive areas (possibly medial prefrontal cortex, mPFC) monitor the level of PE and compute interoceptive surprise. The better the predicted bodily states can be achieved through regulatory action, the smaller PE and interoceptive surprise. Importantly, because of the closed-loop nature of brain–body interactions (figure 2), impairments of any part of the network can lead to chronic interoceptive surprise. This may lead to the metacognitive diagnosis of helplessness or low allostatic self-efficacy (lack of control over bodily states) and has been posited as the substrate for fatigue as a feeling state.14