White Matter Integrity and Core Cognitive Function in Children Diagnosed with Sickle Cell Disease.
Bibliographic record
Abstract
Abstract Abstract 2589 Poster Board II-565 Introduction: Cerebral damage to white matter by overt or silent stroke presents as regions of high intensity on a diffusion-weighted (DW) magnetic resonance (MR) image. Evidence is mounting that such damage is directly linked to decreased cognitive function in children diagnosed with Sickle Cell (SC) Disease. While insult caused by infarct is visible on a DW MR scan, the degree to which normal-appearing white matter is compromised in SC patients remains unclear. Furthermore, the extent of correlation between damage in normal-appearing white matter and cognitive function has yet to be investigated. Patients and Methods: 16 children diagnosed with SC and 10 control patients were included in this retrospective study. DW MR scans were clinically acquired from all participants. Post-processing of DW images yielded measures of relative water diffusion within the brain, represented by voxels of varying intensity on apparent diffusion coefficient (ADC) maps. A template of anatomically divided white matter was registered to each ADC map to collect regional measures of diffusion. Specifically, increased diffusion (measured as increased ADC relative to controls) suggested white matter damage. Within 6 months of the scan, children from each cohort underwent a battery of neuropsychological tests. Processing speed and working memory were assessed by administering the Wechsler Intelligence Scale for Children (WISC) and sustained visual attention was assessed by administering Conners' Continuous Performance Test (CPT). Measures of regional ADC were correlated with neuropsychological test scores. Results: Approximately half of the SC patients presented with at least one lesion embedded within normal-appearing white matter. Average ADC in the frontal, parietal, temporal and cerebellar lobes was significantly higher in children with SC Disease than in control subjects (p < 0.05) when examining ADC across regions carrying both normal-appearing and infarct-containing white matter. For example, average ADC in the left frontal lobes was 1014.67 × 10−6 mm2/s in SC patients and 895.18 × 10−6 mm2/s in control subjects. Findings to date show that excluding the lesions (measuring only diffusion in normal-appearing white matter) does not substantially change average ADC. Moreover, scores from Letter/Number Sequencing and Symbol Search tests (derived from the WISC) were significantly lower (p < 0.05) in SC patients as compared to control scores. For example, while controls obtained a mean scaled score of 12.8 on the Symbol Search task, SC patients obtained a mean scaled score of 7.1. After performing multiple correlations, a significant negative correlation (p < 0.05) was detected between ADC of the frontal, parietal and cerebellar lobes, and tests of processing speed. Interestingly, SC patients showed a significantly higher standard error for Reaction Time (p < 0.05) during the CPT than did the control children. Conclusions: In this study, we present an imaging approach to identify compromised white matter earlier in SC patients. We show that while some damage is visible as focal lesions on a DW image, changes to tissue architecture exist in what otherwise appears as normal white matter. We also show that children diagnosed with SC exhibit deficits in working memory and processing speed, and are less consistent with respect to tests requiring sustained visual attention than are control children. Furthermore, as damage to normal-appearing white matter increases, proficiency in processing speed decreases. This approach can be used to detect compromised white matter prior to the appearance of lesions, and in turn, will help to pinpoint and address potential cognitive impairments in this population sooner. Disclosures: No relevant conflicts of interest to declare.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".