Neural Connectivity and Episodic Memory in Autism Spectrum Disorder: A Literature Review
Bibliographic record
Abstract
Introduction: There is a growing interest in the social and biological context of episodic memory in children with autism spectrum disorder (ASD). Research has previously found that episodic memory deficits are overrepresented in this population. In an attempt to learn why children with ASD are disproportionately impacted by episodic memory impairments, this paper explores literature from 1970-2020 concerning the relationship between functional connectivity (FC), effective connectivity (EC) and structural connectivity (SC) and episodic memory in children with ASD. Methods: The method of this review involved an extensive literature search in scientific databases for experimental studies and magnetic resonance imaging (MRI) data pertaining to episodic memory in children with ASD. The literature review was conducted by searching for literature in electronic databases (Google Scholar, PubMed and MEDLINE) using the following search words: “ASD and memory,” “episodic memory in ASD,” “connectivity in ASD”. Results: In the studies reviewed, children with ASD consistently underperformed on episodic memory tasks relative to typically developing children. Additionally, the MRI scans of the children with ASD showed hyper- and hypoconnectivity of brain regions across the three connectivity metrics. The results indicated that the abnormalities seen in the FC, SC, and EC of children with ASD is an area of research and intervention opportunity for clinicians. Discussion: Research has found that interventions introduced early to children with autism have the potential to reduce symptoms of ASD before adulthood. Therefore, it is important that early interventions related to improving episodic memory are introduced to children early on to increase quality of life later. Additionally, future research must explore if connectivity abnormalities contribute to ASD or if it precedes ASD diagnosis. As a result, clinicians may also consider adding episodic memory deficits to the diagnostic criteria for ASD since it is overrepresented in this population. Conclusion: Clarifying the relationship between ASD, connectivity, and episodic memory will improve the quality of life of children with ASD in the future. This understanding will have broader implications in children and adults with ASD who struggle with episodic memory in terms of improving their experience in education, work and personal life.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".