Functional and structural neural contributions to skilled word reading
Notice bibliographique
Résumé
Reading is an essential skill in our everyday lives and individuals are required to process, understand, and respond to textual information at an increasingly rapid rate in order to be active participants in society. The role of spatial attention in reading has recently been emphasized, whereby better spatial attentional skills are associated with stronger reading skills, and spatial attentional training has a large impact on improving reading ability. However, the neuroanatomical correlates of reading and attention have primarily been studied in isolation. Further, there has recently been a shift to understanding how underlying white matter connectivity networks contribute to cognitive processes. However, much of the research focusing on the intersection of reading and spatial attention, as well as underlying white matter connectivity, has focused primarily on individuals with reading impairments. This thesis will focus on unraveling the neural relationship between spatial attention and reading, and how structural connectivity accounts for functional activation in reading tasks. In Chapter 2, we examine the neural relationship between lexical and sublexical reading with voluntary and reflexive spatial attention. In Experiments 1 and 2, participants performed overt reading of both lexical exception word (EW; words with inconsistent spelling-to-sound correspondences, e.g., ‘pint’) and sublexical pseudohomophone (PH; non-words that when decoded phonetically sound like real words, e.g., ‘pynt’) reading tasks, as well as tasks involving either voluntary attention (Experiment 1) or reflexive attention (Experiment 2) during functional magnetic resonance imaging (fMRI). Experiment 3 used hybrid combined reading attention tasks during fMRI, whereby the spatial attentional cue preceded presentation of the EW or PH stimulus. Overall, the results from these experiments showed that sublexical reading was more strongly associated with brain regions involved in voluntary attention, whereas lexical reading was more strongly associated with brain regions involved in reflexive attention. Thus, Experiments 1, 2 and 3 lend support to the idea that lexical and sublexical reading strategies are differentially associated with these two types of attention. In Chapter 3, we examined the extent to which fine-grained underlying white matter connectivity is able to predict fMRI activation during both lexical reading and phonetic decoding in skilled readers. Experiment 4 employed EW and PH reading and a computational modeling technique to model the relationship between whole-brain structural DTI connectivity and task-based fMRI activation during lexical and sublexical reading. Results from this study showed that brain activation during both lexical and sublexical reading in skilled readers can be accurately predicted using DTI connectivity, specifically in known reading and language areas, as well as important spatial attentional areas. Thus, this research suggests that there is a fine-grained relationship between skilled reading and extrinsic brain connectivity, showing that functional organization of reading and language can be determined (at least in part) by structural connectivity patterns. Together, the studies presented in this thesis provide valuable insight into functional and structural contributions to word reading that may serve as biomarkers of skilled reading, which in turn may have important implications for understanding and remediating reading impairments.
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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 | 0,000 | 0,001 |
| 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 | 0,000 | 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) | 0,001 | 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 ».