Editorial: Advanced analytic techniques in developmental neuroscience
Notice bibliographique
Résumé
Modern developmental neuroscience research is fundamentally multidisciplinary and includes the extensive use of advanced analytic techniques. These analyses typically employ computationally demanding methods, inclusive of advanced techniques, such as machine learning, combined with animal models as well as imaging of human patient populations, to further our understanding of brain development. In this special topic, we have published inter-related manuscripts focused on a number of important developmental conditions, each involving the use of advanced analytic techniques. Machine learning is emerging as a powerful technique for analyzing neurological data and our collection has included a manuscript on the application of machine learning to neurological magnetic resonance imaging (MRI) examinations in a schizophrenia population (link), inclusive of public domain software to assist researchers in applying machine learning to their analyses. Additionally, a neural network model was developed to reveal motoric effects associated with exposure to nicotine in an approach for movement disorder diagnoses (link). Advanced methods for quantitatively analyzing brain development are also available, including automated software for extracting biomarkers of potential interest. An advanced computational analytic technique known as connectomics, which maps major fiber tracts across the brain has been used to identify a potential association between hyperconnectivity and symptom severity in autism (link). A review article covering the quantitative analysis of Rett syndrome was included (link), and an additional study focused on using advanced tools in support of a comprehensive volumetric analysis of a mouse model of Rett syndrome was also included in our article collection (link). Finally, an analysis was performed using perfusion contrast for spatial normalization of arterial spin labeling MRI examinations in a pediatric craniosynostosis population (link).The use of advanced analytic techniques has the potential to assist in improving our understanding of neurological development. For example, the analysis of a schizophrenia population (link) revealed correlations between patient depression and the thickness of the right medial orbitofrontal cortex, and the machine learning analysis reported on a variety of brain regions with potentially abnormal development. The application of machine learning in studying nicotine exposure (link) has resulted in new technology capable of identifying movement alterations in posture, movement initiation and repeated movements. The application of advanced analytics in autism (link) has reported that patient symptom severity is correlated with aberrant hyperconnectivity. Advanced analysis of a mouse model of Rett syndrome has revealed hemispheric asymmetry in several brain regions (link), and a related review article on imaging in Rett syndrome provides an overview of advanced methods being actively used to characterize neurological data in this domain (link). Finally, the perfusion contrast study demonstrates the potential for the use of advanced analytic techniques for the characterization of patients with pediatric craniosynostosis (link).In summary, this research topic demonstrates the use of advanced analytical techniques in a wide range of fields. The techniques used in each of the studies presented here are not limited to the field of that particular paper, but can be used in a wide range of applications. As analytic techniques for studying the brain continue to improve in terms of image acquisition, post processing analyses, animal models and machine learning, we expect our ability to evaluate and monitor brain development to continue to improve.
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 | 0,010 | 0,046 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,005 |
| Bibliométrie | 0,006 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,004 |
| Communication savante | 0,008 | 0,005 |
| Science ouverte | 0,005 | 0,002 |
| Intégrité de la recherche | 0,012 | 0,017 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,018 | 0,015 |
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 ».