Association of 3.0-T Brain Magnetic Resonance Imaging Biomarkers With Cognitive Function in the Dallas Heart Study
Bibliographic record
Abstract
IMPORTANCE: Understanding the relationships between age-related changes in brain structure and cognitive function has been limited by inconsistent methods for assessing brain imaging, small sample sizes, and racially/ethnically homogeneous cohorts with biased selection based on risk factors. These limitations have prevented the generalizability of results from brain morphology studies. OBJECTIVE: To determine the association of 3.0-T structural brain magnetic resonance (MR) imaging measurements with cognitive function in the multiracial/multiethnic, population-based Dallas Heart Study. DESIGN, SETTING, AND PARTICIPANTS: Whole-brain, 2-dimensional, fluid-attenuated inversion recovery and 3-dimensional, magnetization-prepared, rapid acquisition with gradient echo MR imaging at 3.0 T was performed in 1645 Dallas Heart Study participants (mean [SD] age, 49.9 [10.5] years; age range, 19-85 years) who received both brain MR imaging and cognitive screening with the Montreal Cognitive Assessment between September 18, 2007, and December 28, 2009. Measurements were obtained for white matter hyperintensity volume, total brain volume, gray matter volume, white matter volume, cerebrospinal fluid volume, and hippocampal volume. Linear regression and a best predictive model were developed to determine the association of MR imaging biomarkers with the Montreal Cognitive Assessment total score and domain-specific questions. MAIN OUTCOMES AND MEASURES: High-resolution anatomical MR imaging was used to quantify brain volumes. Scores on the screening Montreal Cognitive Assessment were used for cognitive assessment in participants. RESULTS: After adjustment for demographic variables, total brain volume (P < .0001, standardized estimate [SE] = .1069), gray matter volume (P < .0001, SE = .1156), white matter volume (P = .008, SE = .0687), cerebrospinal fluid volume (P = .012, SE = -.0667), and hippocampal volume (P < .0001) were significantly associated with cognitive performance. A best predictive model identified gray matter volume (P < .001, SE = .0021), cerebrospinal fluid volume (P = .01, SE = .0024), and hippocampal volume (P = .004, SE = .1017) as 3 brain MR imaging biomarkers significantly associated with the Montreal Cognitive Assessment total score. Questions specific to the visuospatial domain were associated with the most brain MR imaging biomarkers (total brain volume, gray matter volume, white matter volume, cerebrospinal fluid volume, and hippocampal volume), while questions specific to the orientation domain were associated with the least brain MR imaging biomarkers (only hippocampal volume). CONCLUSIONS AND RELEVANCE: Brain MR imaging volumes, including total brain volume, gray matter volume, cerebrospinal fluid volume, and hippocampal volume, were independently associated with cognitive function and may be important early biomarkers of risk for cognitive insult in a young multiracial/multiethnic population. A best predictive model indicated that a combination of multiple neuroimaging biomarkers may be more effective than a single brain MR imaging volume measurement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".