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Enregistrement W2088948301 · doi:10.1111/j.1528-1167.2010.02717.x

Sensitive and specific neuropsychological assessments of the behavioral effects of epilepsy and its treatment are essential

2010· letter· en· W2088948301 sur OpenAlexaboutno aff
Christian Hoppe, Christoph Helmstaedter

Notice bibliographique

RevueEpilepsia · 2010
Typeletter
Langueen
DomaineNeuroscience
ThématiqueEEG and Brain-Computer Interfaces
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésNeuropsychologyNeuroimagingPsychologyCognitionNeuroscienceNeuropsychological assessmentBrain activity and meditationFunctional neuroimagingDeep brain stimulationCognitive neuropsychologyCognitive psychologyElectroencephalographyMedicinePathology

Résumé

récupéré en direct d'OpenAlex

We appreciate the discussion raised by Baxendale and Thompson on the changing role of neuropsychology in view of modern neuroimaging (Baxendale & Thompson, 2010). It is not coincidental that this issue will soon be the subject of an upcoming international meeting in Toronto and that the International League Against Epilepsy (ILAE) assigned a neuropsychology task force to address this topic. In our view, neuroimaging is not a threat to the existence of neuropsychology, but rather a source to stratify clinical neuropsychology and to improve methods and measures. Psychology examines behavior and models the underlying cognitive mechanisms. Most neuroscientists agree that cognition (not behavior) is identical to brain physiology. However, in times of brain-centrism, we should remind ourselves that all clinical practice refers to the patient (i.e., the behavioral level and not the brain level). Neuropsychology provides an unrivalled portfolio of objective, reliable, valid, and cost-effective measures for evaluating multidimensional psychological alterations (performance, well-being/quality of life, and daily activities) caused by brain diseases, transient brain dysfunction, experimental brain manipulations, or any brain-related therapy (drugs, resective and radiosurgery, deep brain and peripheral nerve stimulation, psychotherapy/training). Back when neuroimaging was unavailable, it was considered reasonable to derive as much brain-related information as possible from the neuropsychological data. Today, imaging provides reliable information on structural brain lesions. However, given the complex relationship between brain and behavior one can neither have the complete picture with regard to structure from function nor vice versa [e.g., cognitive impairment in magnetic resonance imaging (MRI)–negative patients]. In a complementary approach, functional neuroimaging reveals task-related brain-activation patterns ranging somewhere between brain and behavior, but it too faces the same complexity. The obtained data may be regarded as a multivariate response from the brain in addition to the recorded behavioral data (e.g., error rates). Functional MRI-based measures will not replace neuropsychological assessments because to provide additional information they must be uncorrelated to overt behavior (e.g. laterality indices indicating hemispheric language shifts in behaviorally inconspicuous patients) (Duncan, 2009). Furthermore, to provide clinically useful information that enriches the neuropsychological assessment, fMRI-based measures must prove their objectivity, reliability, diagnostic/prognostic validity (sensitivity/specificity), and cost-effectiveness. This might be difficult, since brain-activation patterns are strongly affected by factors like task properties, practice/repetition, cognitive capability level, and endocrinologic status (e.g., Fliessbach et al., 2010). In our view, the future lies in the intelligent combination of neuropsychological and psychophysiologic approaches. Neurocognitive studies already refer to established neuropsychological paradigms when defining the experimental tasks; conversely, it is reasonable to relocate (modified parts of the) behavioral testing into the MRI scanner to obtain further information about the addressed functions from the brain level. Given the continuous therapeutic innovations in the treatment of epilepsy (tailored surgical approaches, radiosurgery, deep brain stimulation, vagal nerve stimulation, new drugs/agents, and antiinflammatory treatments), neuropsychologists face ever-increasing demands to scientifically evaluate the behavioral effects of these interventions with sensitive and specific measures. Overlooking amnesia after bilateral amygdalohippocampectomy due to the use of unspecific IQ tests should be a thing of the past (Scoville & Milner, 2000), but overlooking the unexpected cognitive adverse effects of a new drug, topiramate, despite applying a consented battery of cognitive tests provides a more recent example of how difficult this evaluation might be (Aldenkamp et al., 2000). Neuropsychological evaluation comprises behavioral monitoring during the course of a brain-related disease with regard to the underlying dynamic and sometimes progressive pathology. In addition, it allows the follow-up of developmental changes during childhood and older age with regard to the maturing and aging brain in healthy subjects. In conclusion, neuropsychologists must not fear neuroimaging, but rather that medical research relapses to “evaluate” the behavioral effects of diseases and therapies based on unscientific, insensitive, and highly biased “measures” (e.g., global change ratings). In our eyes, the further development of evidence-based brain-related and patient-centered clinical research and practice can simply not afford to ignore neuropsychology. We confirm that we have read the Journal’s position on issues involved in ethical publication and affirm that this report is consistent with those guidelines. Neither of the authors has any conflict of interest to disclose.

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)0,007
score de la tête « metaresearch » (Gemma)0,014
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: aucune
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,007
Score d'incertitude au seuil0,039

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0070,014
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0030,001
Études des sciences et des technologies0,0010,002
Communication savante0,0020,005
Science ouverte0,0020,001
Intégrité de la recherche0,0020,005
Charge utile insuffisante (le modèle a refusé de juger)0,0040,002

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 Opus0,035
Tête enseignante GPT0,313
Écart entre enseignants0,279 · 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

Citations9
Publié2010
Routes d'admission1
Résumé présentoui

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