14 Prevalence of Mid-Range Visual Functions and their Relationship to Higher-order Visual Functions after Stroke
Notice bibliographique
Résumé
Objective: Visual deficits are common after stroke and are powerful predictors for the chronic functional outcome. However, while basic visual field and recognition deficits are relatively easy to assess with standardized methods, selective deficits in visual primitives, such as shape or motion, are harder to identify, as they often require a symmetrical bilateral posterior lesion in order to provoke full field deficits. We aimed to investigate the prevalence and co-occurrence of hemifield “mid-range” visual deficits. In addition, we looked at the repercussions of these mid-range deficits on higher-order visual cognitive functions, such as visuoconstruction and memory. At a more theoretical level, we investigated whether associations between deficits in 'mid-range’ visual functions and deficits in higher-order visual cognitive functions are in line with a hierarchical, two-pathway model of the visual brain. Participants and Methods: In 220 stroke patients and a healthy control group (N=49), we assessed the perception of colour (isoluminant stimuli in the red-green range), shape (Efron shapes), location (dot in a circle), orientation (lines at different angles), contrast (bars with converging grey-level differences), texture (from Brodatz grayscale texture album) and correlated motion (different percentages of dots moving in the same direction). All tasks started with a fixation dot presented at the centre of the screen. After one second, a target stimulus was presented on the horizontal midline at either 5° to the left or at 5° to the right side of the fixation. Then, after 1.5 seconds, two response items appeared in addition to the target stimulus for three seconds. To control for eye movements, we used an eye-tracker to present the target in a gaze contingent fashion. Thus, the target always remained in the correct retinal position independent of eye movements. In a subset of 182 ischemic stroke patients, we also assessed visuoconstruction (Copy Rey-Complex Figure Test), visual emotion recognition (FEEST test) and visual memory (Doors-test). Results: The results showed that deficits in motion-perception were most prevalent (26%), followed by colour (22%), texture (22%), location (21%), orientation (18%), contrast (14%), shape (14%) and glossiness (13%). 63% of the stroke patients showed one or more mid-range visual deficits. Overlap of deficits was small; they mostly occurred in isolation or co-occurred with only one or two other deficits. Impairments in mid-range visual functions could not predict performance on higher-order visual cognitive tasks. Impaired visuoconstruction and visual memory were only modestly predicted by a worse location perception. Impaired emotion perception was modestly predicted by a worse orientation perception. In addition, double dissociations were found: there were patients with selective deficits in 'mid-range’ visual functions without higher-order visual deficits and vice versa. Conclusions: First, deficits in “mid-range” visual functions are very prevalent. Since we found no strong patterns of co-occurrences, we suggest that an assessment of deficits at this level of visual processing requires screening the full range of visual functions. Second, the relationship between mid-range visual tasks and higher-order visual cognitive tasks is weak. Finally, our findings are not supportive of the hierarchical, two-pathway model but more in line with an alternative patchwork model.
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 | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 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 ».