A new paper by faculty researchers Fred Travis and Keith Wallace published in the Journal of Ayurveda & Integrative Medicine lays out the possible areas of the brain that are associated with each of the three doshas: basic body types described by Maharishi AyurVeda that are fundamental to mind, behavior, physiological function — and health and well-being.
The hypotheses presented provide a basis for a research program, with doctoral student Amrita Shrivastava doing facets of the research for her dissertation.
A growing body of research on the doshas is finding that each dosha type — Vata, Pitta, and Kapha — is associated with specific blood chemistry and genetic expression, as well as specific physiological states and chronic diseases.
The authors postulate that since metabolic and growth factors are controlled by the nervous system, each dosha type should be associated with patterns of functioning of six major areas of the nervous system: the prefrontal cortex, the reticular activating system, the autonomic nervous system, the enteric nervous system, the limbic system, and the hypothalamus.
The authors give as an example the prefrontal cortex, which includes the anterior cingulate, ventral medial, and the dorsal lateral cortices. It would be expected to exhibit a high range of functioning in the Vata brain-type, leading to the possibility of being easily overstimulated. A Vata brain-type performs activity quickly, learns quickly, and forgets quickly. This gives an edge in creative problem solving.
The Pitta brain-type reacts strongly to all challenges leading to purposeful and resolute actions. Those with this brain type never give up and are dynamic and goal oriented.
The Kapha brain-type is slow and steady, leading to methodical thinking and action. Those with this brain type prefer routine and need stimulation to get going.
The authors suggest that a model of dosha brain-types could provide a physiological foundation to understand individual differences. These variables can be assessed by standard physiological and electrophysiological measures.
Such a model could also help individualize treatment modalities to address different mental and physical dysfunctions. And it could explain differences in behavior seen in clinical as well as in normal populations.
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