A systematic review of MRI studies of language development from birth to 2 years of age
Bibliographic record
Abstract
Neurocognitive functions supporting language development start to develop well before first words are spoken during the first years of life. This process coincides with the initial growth spurt of the brain. While the core components of the language network are well characterized in adults and children, the initial neural correlates of language skills are still relatively unknown. We reviewed 10 studies identified via a systematic search that combined magnetic resonance imaging and language-related measures in healthy infants from birth to 2 years of age. We aimed to describe the current knowledge as well as point out viable future directions for similar studies. Expectedly, the implicated cerebral areas included many established components of the language networks, including frontal and temporal regions. A volumetric leftward asymmetry of the brain was suggested as a determinant of language skills, yet with marked interindividual variation. Overall, temporal and frontal brain volumes associated positively with language skills. Positive associations were described between the maturation of language related white matter tracts and language skills. The language networks showed adult-like structural similarities already in neonates, with weaker asymmetry compared to adults. In summary, we found some evidence that the language circuit described in older age groups is also associated to language skills during the first 2 years of life. However, across the reviewed studies there were no systematic neural correlates of language skills, which is partly explained by a modest number of studies, scattered representation of ages in measurements and the variance in the used methods.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".