Mania secondary to focal brain lesions: implications for understanding the functional neuroanatomy of bipolar disorder
Bibliographic record
Abstract
OBJECTIVES: Approximately 3.5 million Americans will experience a manic episode during their lifetimes. The most common causes are psychiatric illnesses such as bipolar I disorder and schizoaffective disorder, but mania can also occur secondary to neurological illnesses, brain injury, or neurosurgical procedures. METHODS: For this narrative review, we searched Medline for articles on the association of mania with stroke, brain tumors, traumatic brain injury, multiple sclerosis, neurodegenerative disorders, epilepsy, and neurosurgical interventions. We discuss the epidemiology, features, and treatment of these cases. We also review the anatomy of the lesions, in light of what is known about the neurobiology of bipolar disorder. RESULTS: The prevalence of mania in patients with brain lesions varies widely by condition, from <2% in stroke to 31% in basal ganglia calcification. Mania occurs most commonly with lesions affecting frontal, temporal, and subcortical limbic brain areas. Right-sided lesions causing hypo-functionality or disconnection (e.g., stroke; neoplasms) and left-sided excitatory lesions (e.g., epileptogenic foci) are frequently observed. CONCLUSIONS: Secondary mania should be suspected in patients with neurological deficits, histories atypical for classic bipolar disorder, and first manic episodes after the age of 40 years. Treatment with antimanic medications, along with specific treatment for the underlying neurologic condition, is typically required. Typical lesion locations fit with current models of bipolar disorder, which implicate hyperactivity of left-hemisphere reward-processing brain areas and hypoactivity of bilateral prefrontal emotion-modulating regions. Lesion studies complement these models by suggesting that right-hemisphere limbic-brain hypoactivity, or a left/right imbalance, may be relevant to the pathophysiology of mania.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".