Apathy: a neurocircuitry model based on frontotemporal dementia
Bibliographic record
Abstract
Apathy is a symptom shared among many neurological and psychiatric disorders. However, the underlying neurocircuitry remains incompletely understood. Apathy is one of the core features of behavioural variant frontotemporal dementia (bvFTD), a neurodegenerative disease presenting with heterogeneous combinations of socioaffective symptoms and executive dysfunction. We reviewed all neuroimaging studies of apathy in frontotemporal dementia (FTD) attempting to refine a neurocircuitry model and inform clinical definitions. Levels of apathy have been consistently shown to correlate with the severity of executive dysfunctions across a wide range of diseases, including FTD. Some authors view 'energisation'-the loss of which is central in apathy-as a core executive function. Apathy in FTD is most robustly associated with atrophy, hypometabolism and/or hypoperfusion in the dorsolateral prefrontal cortex, the anterior and middle cingulate cortex, the orbitofrontal cortex and the medial and ventromedial superior frontal gyri. Data also suggest that abnormalities in connecting white matter pathways and functionally connected more posterior cortical areas could contribute to apathy. There is a lack of consistency across studies due to small samples, lenient statistical thresholds, variable measurement scales and the focus on apathy as a unitary concept. Integrating findings across studies, we revise a neurocircuitry model of apathy divided along three subcomponents (cognition/planning, initiation, emotional-affective/motivation) with specific neuroanatomical and cognitive substrates. To increase consistency in clinical practice, a recommendation is made to modify the bvFTD diagnostic criteria of apathy/inertia. More generally, we argue that bvFTD constitutes a disease model to study the neurocircuitry of complex behaviours as a 'lesion-based' approach to neuropsychiatric symptoms observed across diagnostic categories.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".