Josef Klingler's Models of White Matter Tracts: Influences on Neuroanatomy, Neurosurgery, and Neuroimaging
Bibliographic record
Abstract
During the 1930s, white matter tracts began to assume relevance for neurosurgery, especially after Cajal's work. In many reviews of white matter neurobiology, the seminal contributions of Josef Klingler (1888-1963) and their neurological applications have been overlooked. In 1934 at the University of Basel under Eugen Ludwig, Klingler developed a new method of dissection based on a freezing technique for brain tissue that eloquently revealed the white matter tracts. Klingler worked with anatomists, surgeons, and other scientists, and his models and dissections of white matter tracts remain arguably the most elegant ever created. He stressed 3-dimensional anatomic relationships and laid the foundation for defining mesial temporal, limbic, insular, and thalamic fiber and functional relationships and contributed to the potential of stereotactic neurosurgery. Around 1947, Klingler was part of a Swiss-German group that independently performed the first stereotactic thalamotomies, basing their targeting and logic on Klingler's white matter studies, describing various applications of stereotaxy and showing Klingler's work integrated into a craniocerebral topographic system for targeting with external localization of eloquent brain structures and stimulation of deep thalamic nuclei. Klingler's work has received renewed interest because it is applicable for correlating the results of the fiber-mapping paradigms from diffusion tensor imaging to actual anatomic evidence. Although others have described white matter tracts, none have had as much practical impact on neuroscience as Klinger's work. More importantly, Josef Klingler was an encouraging mentor, influencing neurosurgeons, neuroscientists, and brain imaging for more than three quarters of a century.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.012 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".