2023 American Academy of Neurology Abstract Website

John Rhee¹, Michael Ferguson², Anna Bonkhoff³, Jay Patel⁴, Martin Bretzner⁵, Sungmin Hong⁶, Sophia Ryan⁷, Brandon Westover⁴, Michael Fox⁸, Natalia Rost⁴, Eyal Kimchi⁹
¹Mass General Brigham, Harvard Medical School, ²Neurology, Brigham and Women's Hospital, ³Massachusetts General Hospital, Harvard Medical School, ⁴Massachusetts General Hospital, ⁵Mass General Brigham, ⁶MGH, ⁷Massachusetts General Hospital, Brigham, Harvard, ⁸Brigham and Women's Hospital / Harvard Medical School, ⁹Northwestern University

Objective:

To determine whether delirium in acute ischemic stroke is localizable to specific neural networks.

Background:

Delirium is estimated to occur in one-fourth of patients with acute stroke. Despite its associated morbidity and mortality, prediction and pathophysiological understanding of delirium remains challenging. A recent meta-analysis of patients with delirium and stroke found increased risks of delirium in supratentorial, cortical, and anterior circulation strokes, raising the possibility of an underlying neural network for delirium.

Design/Methods:

We performed a retrospective cohort study of patients admitted to a comprehensive stroke center with acute ischemic stroke from January 2016 to April 2019. Patients were assessed for delirium by trained clinical nursing staff using the Confusion Assessment Method (CAM) framework. Acute stroke lesions from MRI diffusion weighted images (DWI) lesions were automatically segmented using a machine learning algorithm. Lesions were registered to a 2mm Montreal Neurological Institute template (MNI-152). We then used lesion network mapping to identify potential unifying brain networks for delirium in patients with acute stroke.

Results:

907 scans from unique patients were analyzed. 274 patients had delirium (30.2%), 47.5% were women (431), and the mean age was 69.6 years old. In comparison to a general stroke cohort, patients with delirium had lesions connected to the right and left inferior frontal gyri and left middle temporal gyrus and left temporo-parietal junction (one-sample t-test, family-wise error p<0.05). Sensitivity analysis showed that delirium was not associated with parietal, occipital, brainstem, and cerebellar lesions.

Conclusions:

Delirium in stroke localizes to a network connected to frontotemporal regions. These results have implications for prediction of delirium in patients with acute stroke, with the potential for targeting multimodal prevention. Future work will refine their specificity, however, the results suggest that frontotemporal networks may be particularly important for cardinal symptoms of delirium, a disorder of impaired attention and awareness