Yakovlev's Basolateral Limbic Circuit in Multiple Sclerosis Related Cognitive Impairment
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: In 1948, Paul Yakovlev described an additional limbic circuit located basolateral to James Papez's circuit (1937) and included orbitofrontal cortex, amygdala, and dorsomedial nucleus of thalamus. This circuit is shown to be an important component of subcortical cognitive abilities. We aimed to demonstrate this circuit in a multiple sclerosis (MS) cohort using diffusion tensor imaging (DTI) and evaluate its role in MS-related cognitive impairment (CI). METHODS: We enrolled cognitively intact (n = 10) and impaired (n = 36) MS patients who underwent a comprehensive cognitive assessment; the minimal assessment of cognitive function in MS (MACFIMS) and structural magnetic resonance imaging. Correlation analyses between volumetric and DTI-derived values of the orbitofrontothalamic (OFT), amygdalothalamic tracts (ATTs), and dorsomedial nucleus of thalamus and CI index derived from MACFIMS were computed after adjustment for age, education, and lesion load. RESULTS: We observed a consistent trend between CI index and bilateral dorsomedial nucleus' mean diffusivity (MD) (r = .316; P = .02), left OFT Fractional anisotropy (FA) (r = -.302; P = .02), MD (r = .380; .006), and radial diffusivities (RDs) (r = .432; P = .002), also with right ATT FA (r = -.475; P = .0006) and left ATT FA ( = -.487; P = .0005). After Bonferroni correction, correlations of left OFT RD and right and left ATT FA with CI were found to be significant. CONCLUSIONS: Our study provides in vivo DTI delineation of Yakovlev's historical basolateral limbic circuit and establishes a role in MS-related CI. These findings may potentially pave the way for future clinical studies using targeted invasive and noninvasive neurostimulation modalities for CI in MS.
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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 | 0.000 | 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) | 0.001 | 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".