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Enregistrement W4389678071 · doi:10.3389/fpsyt.2023.1341182

Editorial: Impact of apathy on aging and age-related neuropsychiatric disorders

2023· editorial· en· W4389678071 sur OpenAlexaffabout
Amer M. Burhan

Notice bibliographique

RevueFrontiers in Psychiatry · 2023
Typeeditorial
Langueen
DomaineMedicine
ThématiqueDementia and Cognitive Impairment Research
Établissements canadiensUniversity of TorontoOntario Shores Centre for Mental Health Sciences
Organismes subventionnairesnon disponible
Mots-clésApathyPsychiatryPsychologyMedicineGerontologyCognition

Résumé

récupéré en direct d'OpenAlex

In this special issue focusing on the impact of apathy in older adults with neuropsychiatric disorders, we invited colleagues from around the world to share their perspectives on this understudied and under-reported syndrome. We got contributions from several countries around the world and in different areas of work. Manera et al from Université Côte d'Azur, CobTeK, Nice, France, published an application to assess apathy as a novel alternative to the clinical rating scale and showed that in their cohort of 227 older adults with mild and major neurocognitive disorders, the "interest game" has the ability to detect significant apathy with a sensitivity and specificity of 0.68 and 0.65, respectively. Innovative ways to assess apathy are critically needed to get to the core symptom of diminished interest and work of this sort is an important step in this direction. Yan et al from several universities in China conducted a thorough meta-analysis of structural neuroimaging studies in apathy across healthy and brain disorders including neurodegenerative illnesses and traumatic brain injury. They identified several brain areas that are likely involved in the mechanism of apathy. This work will likely pave the way for more work to further elucidate the basic brain mechanism of apathy and potential therapeutic targets for interventions including therapeutic brain stimulation. Indeed, the paper by Espiritu et al, with contributions from the Philippines, Japan, and Canada, presented a systematic review of the potential therapeutic benefit of repetitive transcranial magnetic stimulation in apathy across different brain disorders that are common in old age. The paper reports limited evidence but potential benefits of rTMS in Alzheimer disease, primary progressive aphasia, mild cognitive impairment, and chronic stroke. More work is being done to confirm the role of brain stimulation for apathy in neurocognitive and other brain disorders and hopefully will be reported in the near future. While there was no contribution to this special edition on the role of pharmacological interventions in apathy, some work has been published elsewhere on that topic and showed promise from several pharmacological agents as monotherapy or in combination like acetylcholine esterase inhibitors and methylphenidate, a stimulant (7) Apathy is a final common pathway of different pathologies that affect the cognitive-motivational networks in the brain. Like many illness manifestations, apathy is the product of the abnormal balance between cognitive-motivational resources, and demands of internal and external factors. Cognitive-motivational network involvement has been found to be the common underlying mechanism of many brain disorders manifesting as apathy (8). Like any other illness manifestation, it is not "all or none" but rather a spectrum of severity that is considered "illness" when the level of impairment significantly impacts the quality of life and/or function. It is important, on the other hand, to consider processes that contribute to this impairment. These factors can be external factors, like lack of opportunity to participate due to physical disability or limited support, or internal factors like competing brain network activation such as negative emotional or pain networks. The paper by Zhong et al from Chengdu University in China while didn't specifically focus on apathy, it highlighted the link between depression, which is commonly co-morbid with apathy, and sarcopenia, the hallmark of frailty in older adults. The authors utilized large cohort data and performed a Mendelian randomization methodology demonstrating a causal connection between depression and sarcopenia. This work contributes to our understanding of genetic and environmental factors that can affect the ability of older adults to engage in activities and is essential to consider when assessing and providing holistic treatment to older adults.Figure 1 outlines a model of what could be operating in the process from wanting to do something new, plan/prepare, initiate, persist in doing, getting a reward, and then wanting to do it again due to the positive reinforcement from the rewarding experience.This research topic issue is a step towards understanding the impact of apathy on the aging population worldwide, much more work is needed and we look forward to seeing more being published on the results of current and future studies including studies that improve measurements and leverage technology and data modeling (including deep learning and consumer devices), better define therapeutic targets based on better understanding of underlying brain mechanisms, and combine different interventions to address the complexity of factors that contribute to this syndrome.Figure 1: the cycle of motivation-reinforcement is depicted whereby the motivation to start an activity leads to preparation and planning, initiation, persistent in the activity, feel the reward, which ultimately result in positive reinforcement to do the activity or other activities again.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,006
score de la tête « metaresearch » (Gemma)0,025
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,015
Score d'incertitude au seuil0,050

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0060,025
Méta-épidémiologie (sens strict)0,0050,002
Méta-épidémiologie (sens large)0,0050,005
Bibliométrie0,0040,002
Études des sciences et des technologies0,0030,002
Communication savante0,0070,005
Science ouverte0,0050,002
Intégrité de la recherche0,0140,018
Charge utile insuffisante (le modèle a refusé de juger)0,0150,008

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,005
Tête enseignante GPT0,305
Écart entre enseignants0,300 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission2
Résumé présentoui

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