Editorial: Advanced analytic techniques in developmental neuroscience
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.046 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.018 | 0.015 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".