The bulk of our efforts involve a relatively novel functional MRI (fMRI) approach known as resting-state connectivity. This approach entails measuring temporal correlations in the spontaneous activity of brain regions while a patient rests quietly in the scanner and is capable of detecting a host of distinct networks in a single 8-minute scan. To enhance our understanding of these functional networks we use multimodal approaches combining resting-state fMRI with task-activation fMRI (Seeley et al., J Neurosci 2007), diffusion tensor imaging (Greicius, Supekar, et al., Cer Cortex 2009) and structural covariance measures (Seeley et al., Neuron 2009). Multimodal approaches provide a broader perspective of how these functional networks interact during task performance, how they are supported structurally, how they drive cortical size and shape, and how they impact cognition and behavior.
Current work in the lab includes examining these brain networks in several clinical settings.
We are studying functional connectivity as a biomarker in Alzheimer's disease to determine if it is sensitive enough to diagnose Alzheimer's
in the preclinical stages (funded by the NIH). In depression we are studying whether functional connectivity measures
can predict (at two weeks) clinical response to treatment measured at 6-8 weeks (funded by the Dana Foundation).
Our study of healthy aging addresses how changes in functional and structural connectivity relate to age-related cognitive decline
(funded by the Stanford Center for Longevity).
In addition, current work also involves investigating the genetics of neurodegeneration and aging.
Specifically, we are looking for genetic variants that either increase or decrease the risk of developing Alzheimer's disease.
We are conducting this genetic study using both healthy individuals and individuals affected by Alzheimer's disease.
More information about participating in this study can be found at
Additional studies are underway applying these approaches to Parkinson's disease,
impulse control disorders, and hypnosis.
On the methods front, we have developed a novel whole-brain connectivity algorithm that can identify free-flowing, subject-driven cognitive states such as episodic memory recall, and distinguish distinct states from one another (Shirer et al., Cer Cortex 2011). Current work involves using this algorithm to identify and distinguish different traits; for example, distinguishing individuals with Alzheimer's disease from healthy older subjects. When creating this algorithm, we also created an atlas of 90 functional ROIs which is more representative of the brain's functional organization than traditional structural ROIs. These fROIs are available for anyone to use, and can be downloaded at the link below.