Mapping the living mouse brain neural architecture: strain specific patterns of brain structural and functional connectivity
Bibliographic record
Abstract
Abstract Mapping the structural and functional brain connectivity fingerprints became an essential approach in neurology and experimental neuroscience because network properties can underlie behavioral phenotypes. In mouse models, revealing strain related patterns of brain wiring have a tremendous importance, since these animals are used to answer questions related to neurological or neuropsychiatric disorders. C57BL/6 and BALB/cJ inbred strains are primary “genetic backgrounds” for brain disease modelling and for testing therapeutic approaches. Nevertheless, extensive literature describes basal differences in the behavioral, neuroanatomical and neurochemical profiles of the two strains, which raises the question whether the observed effects are pathology specific or depend on the genetic background. Here we performed a systematic comparative exploration of brain structure and function of C57BL/6 and BALB/cJ mice via Magnetic Resonance Imaging (MRI). We combined voxel-based morphometry (VBM), diffusion MRI and high resolution fiber mapping (hrFM) and resting state functional MRI (rs-fMRI) and depicted brain-wide dissimilarities in the morphology and “connectome” features in the two strains. Particularly C57BL/6 animals show bigger and denser frontal cortical areas, cortico-striatal tracts and thalamic and midbrain pathways, and higher density of fibers in the genu and splenium of the corpus callosum. These features are fairly reflected in the functional connectograms that emphasize differences in “hubness”, frontal cortical and basal forbrain connectivity. We demonstrate strongly divergent reward-aversion circuitry patterns and some variations of the default mode network features. Inter-hemispherical functional connectivity showed flexibility and adjustment regarding the structural patterns in a strain specific manner. We further provide high-resolution tractograms illustrating also inter-individual variability across inter-hemispherical callosal pathways in the BALB/cJ strain.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".