Mapping correlated neurological deficits after stroke to distributed brain networks

• Published in: Brain Structure and Function 2022-12, Vol.227 (9), p.3173-3187
• Main Authors: Siegel, Joshua S., Shulman, Gordon L., Corbetta, Maurizio
• Format: Article
• Language: English
• Subjects: Behavior; Biomedical and Life Sciences; Biomedicine; Brain architecture; Brain injury; Cell Biology; Cerebrum; Cognitive ability; Disease; Entropy; Mental disorders; Nervous system; Neural networks; Neurological diseases; Neurology; Neurosciences; Review; Stroke; Structure-function relationships
• ISSN: 1863-2653; 1863-2661; 0340-2061
• DOI: 10.1007/s00429-022-02525-7

Understanding the relationships between brain organization and behavior is a central goal of neuroscience. Traditional teaching emphasizes that the human cerebrum includes many distinct areas for which damage or dysfunction would lead to a unique and specific behavioral syndrome. This teaching implies that brain areas correspond to encapsulated modules that are specialized for specific cognitive operations. However, empirically, local damage from stroke more often produces one of a small number of clusters of deficits and disrupts brain-wide connectivity in a small number of predictable ways (relative to the vast complexity of behavior and brain connectivity). Behaviors that involve shared operations show correlated deficits following a stroke, consistent with a low-dimensional behavioral space. Because of the networked organization of the brain, local damage from a stroke can result in widespread functional abnormalities, matching the low dimensionality of behavioral deficit. In alignment with this, neurological disease, psychiatric disease, and altered brain states produce behavioral changes that are highly correlated across a range of behaviors. We discuss how known structural and functional network priors in addition to graph theoretical concepts such as modularity and entropy have provided inroads to understanding this more complex relationship between brain and behavior. This model for brain disease has important implications for normal brain-behavior relationships and the treatment of neurological and psychiatric diseases.