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Beyond Motor Control: Diffusion MRI Reveals Associations between the Cerebello-VTA Pathway and Socio-affective Behaviors in Humans

2025· article· en· W4410142627 on OpenAlexaff
Violet Liu

Bibliographic record

VenueJournal of Neuroscience · 2025
Typearticle
Languageen
FieldMedicine
TopicAdvanced Neuroimaging Techniques and Applications
Canadian institutionsWestern University
Fundersnot available
KeywordsDiffusion MRIPsychologyNeuroscienceControl (management)DiffusionCognitive psychologyMedicinePhysicsComputer scienceMagnetic resonance imagingArtificial intelligence

Abstract

fetched live from OpenAlex

The cerebellum, a structure best known for its role in motor control, sends extensive projections throughout the cerebral cortex and subcortex. At the gross anatomical level, the cerebellum can be divided into the anterior, posterior, and flocculonodular lobes, which are separated by fissures. It can also be subdivided medial-laterally into distinct zones based on afferent and efferent patterns: the vermis (spinocerebellum) and the paravermis (intermediate zone) receive direct inputs from the spinal cord and are responsible for movement error correction; the lateral hemispheres (cerebrocerebellum) receive extensive projections throughout the cerebral cortex and facilitate motor movement planning with the motor cortex; and the flocculonodular lobe (vestibulocerebellum) receives afferents from the vestibular system and is responsible for eye movement and equilibrium (De Benedictis et al., 2022). At the cellular level, the cerebellum can be divided into the superficial gray matter and deep white matter, which surrounds the deep cerebellar nuclei (DCN): the dentate, interposed, and fastigial nuclei. These nuclei are central points for efferent fibers to converge before exiting the cerebellum via the cerebellar peduncles. Finally, the gray matter of the cerebellar cortex is divided into an outermost molecular layer, a middle Purkinje layer, and an innermost granular layer, and it can be further subdivided into parasagittal stripes based on whether or not the Purkinje cells express the glycolytic enzyme zebrin (De Benedictis et al., 2022; Fig. 1). Figure 1. Cerebellar anatomy illustrated in folded and unfolded space. Left panel, Folded cerebellar anatomy based on the open-source cerebellum atlas (Diedrichsen et al., 2009, distributed under a Creative Common license CC BY-ND). Right panel, Schematic of the cerebellum in unfolded space. Red dotted lines demarcate sagittal segments used as seeding regions in Hoffman et al. (2024) for probabilistic tractography, focusing on Lobule VI, Crus I, and Crus II of the posterior cerebellum. … Correspondence should be addressed to Violet Liu at mliu2025{at}meds.uwo.ca.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.032
GPT teacher head0.352
Teacher spread0.320 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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