Selective cellular and regional vulnerability in frontotemporal lobar degeneration: a scoping review
Bibliographic record
Abstract
The three main types of frontotemporal lobar degeneration (FTLD) are characterized by the accumulation of abnormal proteins, namely tau, TDP-43 and FUS. The distribution of these proteins within different human brain regions is well known, as is the range of morphological variability of the cellular inclusions they form. Compared to the extensive knowledge that exists about distinct protein aggregates in FTLD, surprisingly little is known about the specific cell (sub)types that these inclusions affect. Even less is known about disease-specific abnormalities other than protein inclusions in affected and unaffected areas. These are non-trivial knowledge gaps. First, knowing which cell subtypes are vulnerable or resilient to the development of pathological protein inclusions is crucial to understand the cellular disease mechanisms. Second, mounting evidence suggests that non-cell autonomous mechanisms may play important roles in neurodegenerative conditions. For example, astrocytic tau pathology is associated with synaptic loss in corticobasal degeneration but not in progressive supranuclear palsy. Furthermore, changes that are more difficult and time-consuming to quantify, for example loss of a specific neuronal subtype that does not develop pathological inclusions, remain virtually unexplored and their relevance for disease progression are unknown. This scoping review is an attempt to collate all histological evidence from human studies that address the question of cell-specific vulnerability in the most common FTLD subtypes. By taking a systematic approach including various brain cell types such as neurons and their subtypes as well as astrocytes, microglia and oligodendrocytes and the entire central nervous system with its affected and unaffected regions, this review summarizes the current status in the field and highlights important knowledge gaps.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".