A predictive coding framework of allostatic–interoceptive overload in frontotemporal dementia
Year: 2022
Type: review
Abstract: Recent allostatic-interoceptive explanations using predictive coding models propose that efficient regulation of the body's internal milieu is necessary to correctly anticipate environmental needs. We review this framework applied to understanding behavioral variant frontotemporal dementia (bvFTD) considering both allostatic overload and interoceptive deficits. First, we show how this framework could explain divergent deficits in bvFTD (cognitive impairments, behavioral maladjustment, brain atrophy, fronto-insular-temporal network atypicality, aberrant interoceptive electrophysiological activity, and autonomic disbalance). We develop a set of theory-driven predictions based on levels of allostatic interoception associated with bvFTD phenomenology and related physiopathological mechanisms. This approach may help further understand the disparate behavioral and physiopathological dysregulations of bvFTD, suggesting targeted interventions and strengthening clinical models of neurological and psychiatric disorders.
Source: Trends in Neurosciences
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $6,700
Funders: Alzheimer's Association, Universidad de Santiago de Chile, Global Brain Health Institute, Fonds De La Recherche Scientifique - FNRS, Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad del Valle, Agencia Nacional de Investigación y Desarrollo, National Institutes of Health, Medical Research Council, National Health and Medical Research Council, National Institute on Aging, Fondo para la Investigación Científica y Tecnológica, Fondo de Fomento al Desarrollo Científico y Tecnológico, Fondo de Financiamiento de Centros de Investigación en Áreas Prioritarias
Awards: G1011791342, G103627852, G152928366, G1581799926, G1689919419, Maximising quality of life in frontotemporal dementia, G2078453160, G2118524674, G2363475639, MACHINE LEARNING FOR MENTAL HEALTH NEW DEVELOPMENTS AND APPLICATIONS USING MULTIMODAL DATA, G2822108074, G314322576, G318596217, G3206389968, G346120217, G3585329184, G3626066913, G3746078142, CENTRO DE GEROCIENCIA SALUD MENTAL Y METABOLISMO (GERO), G4216404362, G4258903815, G4651165891, G497235149, G5163463781, G534379622, G546164382, G5476320740, MACHINE LEARNING PARA EL DIAGNOSTICO PRECOZ Y PERFIL MULTIMODAL DE LA ENFERMEDAD DE ALZHEIMER BASADO EN MIRNA DE EXOSOMAS CIRCULANTES., G5996283446, G6291921393, G6318961629, SENSITIVE NEUROCOGNITIVE MARKERS OF NEURODEGENERATION A MULTI-FEATURE FRAMEWORK, CONTRIBUTION OF PERIPHERAL TAU AGGREGATES TO ALZHEIMER´S DISEASE PATHOGENESIS, G6619092739, G7007719478, G734147807, G7574155280, G7616641907, NATURAL SPEECH MARKERS OF NEURODEGENERATION AN INTERDISCIPLINARY APPROACH FOR DISEASE DISCRIMINATION AND SYMPTOM TRACKING, G7953417809, G844855352, G883642430, US-South American Initiative for Genetic-Neural-Behavioral Interactions in Human Neurodegenerative Research
OpenAlex ID: W4294142652