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Enregistrement W2148100143 · doi:10.1093/brain/awu253

Reply: Probabilistic map of language regions: challenge and implication

2014· letter· en· W2148100143 sur OpenAlexaff
Matthew C. Tate, Guillaume Herbet, Sylvie Moritz‐Gasser, Joseph Euzebe Tate, Hugues Duffau

Notice bibliographique

RevueBrain · 2014
Typeletter
Langueen
DomaineNeuroscience
ThématiqueNeurobiology of Language and Bilingualism
Établissements canadiensUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésProbabilistic logicNatural language processingPsychologyComputer scienceArtificial intelligence

Résumé

récupéré en direct d'OpenAlex

Sir, We thank Dr Wu and colleagues for their interest in our recent probabilistic map of critical functional regions of the human cerebral cortex (Tate et al., 2014). In particular, we are pleased to note that in their experience with 69 Chinese-speaking patients the authors confirmed a 79% probability of anarthria/speech arrest with stimulation of the left ventral premotor cortex (PMC), similar to our results (83%). These data support the crucial role of the ventral PMC for speech output, as suggested in previous reports (Duffau et al., 2003; van Geemen et al., 2014). Nonetheless, they also found a probability of ‘speech arrest’ in 32% of patients within the left pars opercularis (i.e. Broca’s area), which they stated is similar to the 26.7% rate observed by Sanai et al. (2008). However, if one looks at the primary data from the report by Sanai et al. (2008), which uses a relatively arbitrary 1 × 1 cm grid system, which does not specifically respect anatomic (sulci, gyri) or cytoarchitectural (Brodmann area) cortical features, there are three grids that include a portion of pars opercularis: for the two grids that are completely contained within pars opercularis, one has 0% speech arrest and another has 4.9%—these data are similar to our reported 4%. A third grid location has a reported 26.7% speech arrest rate (referred to in the letter by Wu and colleagues), but this region spans both pars opercularis and ventral PMC, so it is possible that many of the speech arrest sites are within the ventral PMC and not pars opercularis. In fact, in our study, there is a high density of speech arrest sites just posterior to the precentral sulcus, immediately behind the superior pars opercularis, but within ventral PMC (Tate et al., 2014). In addition, a recent study examining language deficits in glioma patients demonstrated that gliomas involving the ventral PMC were 5-fold more likely to cause aphasia compared to gliomas involving the inferior frontal gyrus (Bizzi et al., 2012), which also points to the ventral PMC as the primary speech output region. Another potential source for the discrepancy among studies evaluating speech arrest and anomias is related to the specifics of the chosen intraoperative tasks. For example, in our study, during the picture naming paradigm, patients are asked to proceed the object name with ‘This is a … .’, enabling distinction of true anomia (where the patient will say ‘This is a … ..’ and cannot produce the object name) from speech arrest (patient is shown the picture and has zero verbal output). If one simply asked the patient to name pictures, it would be difficult to discern speech arrest from anomia. Thus, when reporting anomia, speech arrest, and articulation outcomes, it is important to precisely specify the intraoperative tasks employed to enable direct comparisons. Finally, as stated by Wu et al. (2014) alphabetic languages (such as French and English) differ from pictographic languages (such as Chinese) in many aspects. However, it is nonetheless remarkable to note that, despite this difference, the actual area of speech output was the ventral PMC, independently of the language class, which strongly supports our theory. Regarding the subcortical connectivity underlying speech articulation, it is crucial to distinguish the arcuate fascicle and the lateral part of the superior longitudinal fascicle (SLF). Indeed, by combining anatomic dissection and tractography, we have recently demonstrated that the arcuate fascicle primarily connected the posterior portion of the inferior and middle frontal gyri (and partly the ventral PMC) with the middle and inferior temporal gyri, whereas the anterior segment of the longitudinal SLF connected the ventral PMC with the supramarginal gyrus and superior temporal gyrus (Martino et al., 2013). First, this means that Broca’s area in not directly connected with Wernicke’s area, a concept breaking with the traditional view of language processing, and allowing the proposal of new models of language connectivity (Duffau et al., 2014). Second, stimulation of longitudinal SLF generates articulatory disturbances, confirming the crucial role of the ventral PMC (i.e. the anterior cortical epicentre of this articulatory loop) in speech output (Maldonado et al., 2011; van Geemen et al., 2014). Third, stimulation of the arcuate fascicle does not elicit articulatory disorders, but phonemic disturbances (possibly associated with repetition errors) (Maldonado et al., 2011; Moritz-Gasser and Duffau, 2013), supporting the role of the pars opercularis (i.e. the anterior cortical epicentre of this dorsal pathway) in phonology (Duffau et al., 2002). In the same vein, recent data about the connectivity underpinning the left inferior frontal gyrus suggest a role for this area in high-order cognitive functions, such as verbal and non-verbal semantic processing (Broca’s area being one of the anterior cortical terminations of the ventral semantic stream subserved by the inferior fronto-occipital fascicle) (Duffau et al., 2005; Moritz-Gasser et al., 2013) as well as speech control (Broca’s area being a cortical termination of the frontal aslant tract) (Kinoshita et al., 2014) or even mentalizing (partly subserved by the arcuate fascicle) (Herbet et al., 2014a, b). Finally, we acknowledge that slow-growing lesions such as low-grade gliomas may induce functional reorganization (Duffau, 2005). However, if we admit that neuroplasticity enabled the compensation of all areas involved by the tumour, it would not be possible to identify crucial epicentres in a probabilistic map based on glioma patients. Thus, the ability to detect critical epicentres with a high-rate of probability by inducing a reproducible deficit during intraoperative stimulation (such as anarthria during stimulation of the ventral PMC), despite cerebral reshaping, has significant implications for understanding the normal functional anatomy of the brain (Duffau, 2011). In fact, the crucial cortical language (phonemic, semantic) epicentres detected by intraoperative electrical mapping in the current study involving glioma patients (Tate et al., 2014) correlate well with results provided by functional MRI, extensively described in a meta-analysis extracted from 129 scientific reports (with 730 activation peaks) that investigated language using functional neuroimaging in healthy volunteers (Vigneau et al., 2006). In the same vein, we have recently demonstrated that specific critical structures, both at cortical level (e.g. the ventral PMC) and at subcortical level (e.g. the longitudinal SLF) (van Geemen et al., 2014) have a very low plastic potential (Duffau, 2013), leading to the concept of ‘minimal common brain’ (Ius et al., 2012).

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)0,004
score de la tête « metaresearch » (Gemma)0,038
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: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,043
Score d'incertitude au seuil0,034

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,038
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0030,005
Communication savante0,0040,004
Science ouverte0,0030,002
Intégrité de la recherche0,0430,046
Charge utile insuffisante (le modèle a refusé de juger)0,0100,007

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.

Tête enseignante Opus0,037
Tête enseignante GPT0,291
Écart entre enseignants0,254 · 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
GenreCommentaire

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

En bref

Citations13
Publié2014
Routes d'admission1
Résumé présentoui

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