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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 distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,141
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

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 tête enseignante, pas un consensus.

Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

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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