The frontal aslant tract (FAT) and its role in speech, language and executive function
Bibliographic record
Abstract
Abstract In this review, we examine the structural connectivity of a recently-identified fiber pathway, the frontal aslant tract (FAT), and explore its function. We first review structural connectivity studies using tract-tracing methods in non-human primates, and diffusion-weighted imaging and electrostimulation in humans. These studies suggest a monosynaptic connection exists between the lateral inferior frontal gyrus and the pre-supplementary and supplementary motor areas of the medial superior frontal gyrus. This connection is termed the FAT. We then review research on the left FAT’s putative role in supporting speech and language function, with particular focus on speech initiation, stuttering and verbal fluency. Next, we review research on the right FAT’s putative role supporting executive function, namely inhibitory control and conflict monitoring for action. We summarize the extant body of empirical work by suggesting that the FAT plays a domain general role in the planning, timing, and coordination of sequential motor movements through the resolution of competition among potential motor plans. However, we also propose some domain specialization across the hemispheres. On the left hemisphere, the circuit is proposed to be specialized for speech actions. On the right hemisphere, the circuit is proposed to be specialized for general action control of the organism, especially in the visuo-spatial domain. We close the review with a discussion of the clinical significance of the FAT, and suggestions for further research on the pathway. Highlights The frontal aslant tract (FAT) is a recently identified fiber pathway It connects inferior frontal gyrus with medial frontal motor areas The left FAT has been associated with speech and language function The right FAT has been associated with inhibitory control Both FAT pathways may function in sequential motor planning
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".