Structural Brain Abnormalities and Cardiac Dysfunction in Patients with Chronic Heart Failure
Bibliographic record
Abstract
Structural brain abnormalities and cardiac dysfunction in patients with chronic heart failureChronic heart failure (CHF) induces brain structural abnormalities that are associated with depressive symptoms and cognitive impairment. 1 -3 We have previously shown that CHF rats have histological abnormalities of the hippocampus characterized by reduction in neurogenesis and neurite outgrowth and increase in the number of astrocytes. 1 Our experimental findings are consistent with clinical studies with brain magnetic resonance imaging (MRI) that demonstrated reduced grey matter volume in several cortical and subcortical regions including the hippocampus in CHF patients.2,3 A possible mechanism for brain damage in CHF patients is cerebral hypoperfusion due to cardiac dysfunction; decreasing cardiac index (CI), even at normal levels (CI <2.92 L/min/m 2 ), is associated with reduction in total brain volume.4 However, it remains unclear which brain regions are susceptible to haemodynamic impairment in CHF patients.In this study, therefore, we examined whether there is a correlation between brain damage (e.g. the hippocampus) and cardiac dysfunction using cardiac and brain MRI recordings that had been acquired in the Brain Assessment and Investigation in Heart Failure Trial (B-HeFT).5 We enrolled 40 asymptomatic Stage B and 40 symptomatic Stage C CHF patients aged 45-90 years as described previously.5 The study protocol was approved by the ethics committee of the Tohoku University Graduate School of Medicine (no.2012-2-31) and was registered in the University Hospital Medical Information Network (UMIN000008584).We performed cine cardiac (Intera Achiva 1.5 T Nova Dual, Philips Medical Systems, Best, the Netherlands) and structural T1 brain (Signa HDxt 1.5 T GE Medical Systems, Milwaukee, WI, USA) MRI within 6 months in each patient.Left ventricular stroke volume index (LVSVI) and CI were calculated using the Simpson's method and expressed as mean ± standard deviation.We performed pre-processing for brain MRI analyses as described previously.1,5 Briefly, grey matter maps were segmented from structural brain MRI scans and normalized to the standard Montreal Neurological Institute space to perform voxel-wise statistical analyses.The normalized grey matter maps were unmodulated with Jacobian determinants and were then smoothed with an isotropic Gaussian kernel by convolving a 16 mm full width at half maximum to produce grey matter concentration (GMC) maps.Only voxels with GMC value >0.05 were included.6 To explore brain regions associated with cardiac dysfunction, we first performed a voxel-wise regression analysis of the whole brain, which examined correlations between
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".