Abstract 53: Frontal Functional Connectivity Networks and Executive Function Following Perinatal Stroke
Bibliographic record
Abstract
Perinatal stroke causes most hemiparetic cerebral palsy and a lifetime of disability with no known prevention strategies. Two types of perinatal stroke predominate, arterial ischemic stroke (AIS) and periventricular venous infarction (PVI), dictating lesion-specific differences in outcomes. Executive functioning challenges and attention deficit hyperactivity disorder (ADHD) are more common in children with perinatal stroke (19-35%) than peers (5-7%). Resting state (RS) functional magnetic resonance imaging (fMRI) measures fluctuations in the blood-oxygen level dependent (BOLD) signal that may estimate network functional connectivity (FC). We evaluated relationships between FC in relevant frontal circuits, ADHD and executive function in children with perinatal stroke compared to typically developing controls (TDC). Participant recruitment was from a population-based research cohort (AIS N=32; PVI N=30; TDC N=59). MRI imaging included T1-weighted anatomical and resting state fMRI sequences. Subsequent seed-to-seed analyses quantified FC within frontoparietal (FPN), dorsal attention (DAN) and default mode networks (DMN). Parent questionnaires quantified executive function (Behavior Rating Inventory of Executive Function (BRIEF)) and ADHD symptoms (ADHD Rating Scale-5). Large group FC differences were observed within FPN, DAN and DMN networks where AIS had lower FC compared to both PVI and TDC. For stroke participants, higher FC within the DAN and FPN was associated with poorer cognitive function (BRIEF). By contrast, higher FC within the DMN was associated with better ADHD ratings. Differences within frontal functional networks appear to be related to poorer cognitive function such that increased FC between the lesioned and nonlesioned hemisphere is associated with symptoms of executive dysfunction and ADHD suggesting that developmental plasticity leads to complex network changes following early unilateral brain injury.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".