Integrating direct electrical stimulation with brain connectivity predicts lesion-induced language impairment and recovery
Notice bibliographique
Résumé
Neurological conditions account for millions of deaths per year and induce long-lasting cognitive impairments. The disruption of structural brain networks predicts the emergence of cognitive impairments in stroke cases, but the role of the white matter in modeling longitudinal behavioral trajectories in glioma patients is understudied. We analyzed 486 intracranial brain stimulations from 297 patients (age range 37–40, male ratio 53–64% depending on the functional categories) along with functional and structural brain connectivity data from over 1750 healthy individuals, to create a network mapping method able to identify the neural substrate causally involved in language production. We tested the validity of our procedure by (i) quantifying the spatial correspondence between white matter metabolic and hemodynamic spontaneous activity, measured via resting-state functional Magnetic Resonance Imaging and [18 F]-fluorodeoxyglucose functional Positron Emission Tomography (respectively); (ii) predicting unseen intracranial stimulations points; (iii) modeling the severity of stroke-induced aphasia (n = 105) and the longitudinal recovery of language abilities in glioma patients (n = 42, 3 timepoints). We show that spontaneous white matter hemodynamic oscillations map into metabolic fluctuations. We also demonstrate that the integration of patient-specific intracranial stimulation points and normative human connectivity data (i) is predictive of unseen stimulation points; (ii) provides better estimates than total lesion volume in predicting the severity of stroke-induced aphasia symptoms; (iii) models post-operative language recovery trajectories better than state-of-the-art clinical measures in glioma patients. This work presents a data-driven and neurobiologically grounded tool for modeling cognitive and neurological impairments in terms of network disruption, demonstrating improved precision over existing approaches. Coletta et al. integrate patient-specific intracranial stimulations in the white matter with normative brain connectivity data. Findings reveal that intracranial driven network mapping is an accurate predictor of both neurological impairments in stroke patients and post-operative language recovery trajectories in glioma patients. Understanding how connections in the brain can affect the recovery of language skills after stroke or surgery for brain tumors is of critical importance. Here, we developed a method that combines non-invasive brain scans with direct brain stimulation to identify the exact brain connections important for language production. Our findings reveal a brain map that could predict the severity of language problems after a stroke and how well people recovered their language skills following surgery for brain tumors, better than traditional ways of predicting language problems. We created an effective way to predict and track language difficulties caused by damage in the brain. This could help doctors plan better treatments and support for people recovering from brain injuries or surgery.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,024 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».