Frontotemporal disorders: the expansive panoply of syndromes and spectrum of etiologies
Notice bibliographique
Résumé
Background: Frontotemporal lobe disorders (FTD) are amongst the most common brain neurodegenerative disorders. Their relatively covert, frequently subtle presentations and diverse etiologies, pose major challenges in diagnosis and treatments. Recent studies have yielded insights that the etiology in the majority are due to environmental and sporadic causes, rather than genetic in origin. Aims: To retrospectively examine the cognitive and behavioral impairments in the veteran population to garner the range of differing syndrome presentations and etiological subcategories with a specific focus on frontotemporal lobe disorders. Methodology: The design is a retrospective, observational registry, case series with the collection of epidemiological, clinical, cognitive, laboratory and radiological data on people with cognitive and behavioral disorders. Inclusion criteria for entry were veterans evaluated exclusively at Orlando VA Healthcare System, neurology section, receiving a diagnosis of FTD by standard criteria, during the observation period dated from July 2016 to March 2021. Frontotemporal disorders (FTD) were delineated into five clinical 5 subtypes. Demographic, cardiovascular risk factors, cognitive, behavioral neurological, neuroimaging data and presumed etiological categories, were collected for those with a diagnosis of frontotemporal disorder. Results: Of the 200 patients with FTD, further cognitive, behavioral neurological evaluation with standardized, metric testing was possible in 105 patients. Analysis of the etiological groups revealed significantly different younger age of the traumatic brain injury (TBI) and Gulf War Illness (GWI) veterans who also had higher Montreal Cognitive Assessment (MOCA) scores. The TBI group also had significantly more abnormalities of hypometabolism, noted on the PET brain scans. Behavioral neurological testing was notable for the findings that once a frontotemporal disorder had been diagnosed, the four different etiological groups consistently had abnormal FRSBE scores for the 3 principal frontal presentations of (i) abulia/apathy, (ii) disinhibition, and (iii) executive dysfunction as well as abnormal Frontal Behavioral Inventory (FBI) scores with no significant difference amongst the etiological groups. The most common sub-syndromes associated with frontotemporal syndromes were the Geschwind-Gastaut syndrome (GGS), Klüver-Bucy syndrome (KBS), involuntary emotional expression disorder (IEED), cerebellar cognitive affective syndrome (CCA), traumatic encephalopathy syndrome (TES) and prosopagnosia. Comparisons with the three principal frontal lobe syndrome clusters (abulia, disinhibition, executive dysfunction) revealed a significant association with abnormal disinhibition FRSBE T-scores with the GGS. The regression analysis supported the potential contribution of disinhibition behavior that related to this complex, relatively common behavioral syndrome in this series. The less common subsyndromes in particular, were notable, as they constituted the initial overriding, presenting symptoms and syndromes characterized into 16 separate conditions. Conclusion: By deconstructing FTD into the multiple sub-syndromes and differing etiologies, this study may provide foundational insights, enabling a more targeted precision medicine approach for future studies, both in treating the sub-syndromes as well as the underlying etiological process.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».