Training causes activation increase in parietal and temporo-parietal regions in children with developmental dyscalculia
Bibliographic record
Abstract
Children with developmental dyscalculia (DD) differ from typically developing (TD) children regarding brain activation. While arithmetic training reduces fronto-temporo-parietal activation related to domain-general processes in TD children, we do not know whether these findings also hold for children with DD. Since children with DD are one main target group for intervention and remediation, it is essential to know how arithmetic training that improves arithmetic performance induces brain activation changes in these children. In a within-participant design, a group of 20 children with DD underwent two weeks of training in simple and complex multiplication. Brain activation was measured using functional near-infrared spectroscopy (fNIRS) before and after training to assess training-related changes. Two weeks of training led to increased temporo-parietal activation for trained versus untrained simple multiplication. For both trained and untrained complex multiplication, widespread increases in activation were observed in frontal, parietal, and temporo-parietal cortices. Interestingly, training-specific activation increases were observed only in the bilateral parietal cortex, but not in the other regions. These brain activation changes were more robust in younger children. We conclude that in contradiction to the training-related brain activation decreases seen in studies of TD children, children with DD showed improved behavioral performance along with increased brain activation. Therefore, using neuroimaging techniques such as fNIRS on children with DD provided valuable insights about strategy changes and the neural networks involved in mental arithmetic.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".