Editorial: Neuroimaging of non-motor deficits in movement disorders
Notice bibliographique
Résumé
Parkinson's disease (PD) is a complex neurodegenerative disorder that manifests not only in motor symptoms but also in non-motor features such as cognitive impairments and neuropsychiatric issues. The heterogeneity of PD and other movement disorders has long posed challenges in diagnosis, prognosis, and treatment. Recently, advances in neuroimaging and machine learning have provided new insights into the pathophysiology of PD and its subtypes, paving the way for personalized therapeutic approaches. In this editorial, we discuss four recent studies that have utilized these cutting-edge techniques to unravel the complexities of motor and non-motor features in movement disorders.Cao et al. employed hierarchical clustering analysis based on multimodal MRI indices, amplitude of low-frequency fluctuation (ALFF), and gray matter volume (GMV) to identify two distinct PD subtypes with unique neurodegenerative patterns. The "diffuse malignant subtype" exhibited reduced ALFF in the visual-related cortex and extensive GMV reduction, coupled with severe motor and cognitive impairments. In contrast, the "mild subtype" displayed increased ALFF in the frontal lobe, temporal lobe, and sensorimotor cortex, and a slight decrease in GMV, with milder motor and cognitive impairments. Cao et al. highlight the potential of unsupervised cluster analysis based on multimodal MRI indices to inform accurate prognosis and personalized treatment plans for PD patients.Zhang et al. investigated the efficacy of subthalamic nucleus deep brain stimulation (STN-DBS) in treating both motor and depressive symptoms in PD patients with treatment-resistant depression (PD-TRD). The results indicated that STN-DBS significantly improved motor and depressive symptoms, reduced the need for antiparkinsonian and antidepressant medications, and enhanced daily living activities and health-related quality of life after one year of treatment. Moreover, the study revealed widespread abnormalities in cerebral regional glucose metabolism in PD-TRD patients, which were partially recovered following STN-DBS treatment. These findings support the potential of STN-DBS as a promising treatment option for managing motor and depressive symptoms in PD-TRD patients.Buard et al. examined the relationship between decreased prefrontal glutamate levels and cognitive impairment in PD patients using proton magnetic resonance spectroscopy. The researchers observed significant differences in prefrontal glutamate levels between PD patients with normal cognition and those with dementia. These findings suggest that fluctuations in prefrontal glutamate may serve as a biomarker for the progression of cognitive impairments in PD patients, warranting further investigation through larger magnetic resonance spectroscopy studies.Li et al. aimed to identify patients with depressed essential tremor (ET) by utilizing machine learning multivariate pattern analysis (MVPA) combined with global brain connectivity (GBC) mapping from resting-state fMRI. The study successfully distinguished depressed ET patient participants from those with non-depressed ET, primary depression, and healthy controls (HCs) using multiclass Gaussian process classification (GPC) and binary support vector machine (SVM) algorithms. The significant discriminative features were mainly located in cerebellarmotor-prefrontal cortex circuits, with GBC changes in these circuits correlating with clinical depression severity in patients with depressed ET. The Li group demonstrate the potential of combining machine learning and neuroimaging to identify patients with comorbid depressed ET and shed light on the network pathogenesis underlying depression in these patients.The studies discussed in this editorial highlight the powerful potential of neuroimaging and machine learning techniques in disentangling the heterogeneity of motor and non-motor features of Parkinson's disease and essential tremor. These state-of-the-art approaches have shown success in identifying clinically relevant subtypes, differentiating patients with specific comorbidities from those without, and uncovering the neural mechanisms underlying these conditions. As our understanding of movement disorders continues to evolve, these findings will undoubtedly inform more accurate diagnoses, refined prognoses, and personalized treatment plans, ultimately improving the quality of life for patients living with these complex disorders.
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 | 0,003 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,002 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,004 | 0,001 |
| Intégrité de la recherche | 0,012 | 0,012 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,007 |
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 ».