There is a dynamic and delicate balance between top-down vs bottom-up control. We all know that sometimes we are more on task and other times we are less focused and more distractible. The balance is critical: A relative weakening of top-down attention is readily apparent in ADHD. More severe and global losses of top-down control can explain the weak central coherence of autism and the disordered thought of schizophrenia (e.g., Uhlhaas & Singer, 2012). Our previous Aims employed many-electrode recording to trace top-down and bottom-up functional circuits across wide stretches of cortex for the first time at the neural level. The results suggest that oscillatory coherence between cortical areas regulates top-down and bottom-up communication. Now, we aim to leverage this understanding to determine how oscillatory dynamics fine-tunes these circuits for different balances of top-down vs bottom-up control. Our previous tasks switched between different types of top-down and bottom-up signals. We will now employ a task that varies the relative degree of top-down vs bottom-up control. Corresponding changes in neural dynamics across will powerful support for a role of oscillatory coherence in regulating top-down/bottom-up balance. Further support will come from non-invasive stimulation that modifies these neural dynamics and changes the balance of cognition.

Public Health Relevance

There is mounting evidence for a role in oscillatory coherence in regulating neural communication and for its dysfunction in neuropsychiatric disorders like ADHD, autism, and schizophrenia. This project will shed new light on the role of oscillatory dyanmics in balancing cortical processing and can directly point to theraputic interventions.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Method to Extend Research in Time (MERIT) Award (R37)
Project #
5R37MH087027-09
Application #
9440451
Study Section
Special Emphasis Panel (NSS)
Program Officer
Rossi, Andrew
Project Start
2010-03-11
Project End
2020-02-28
Budget Start
2018-03-01
Budget End
2019-02-28
Support Year
9
Fiscal Year
2018
Total Cost
Indirect Cost
Name
Massachusetts Institute of Technology
Department
Type
DUNS #
001425594
City
Cambridge
State
MA
Country
United States
Zip Code
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Lindsay, Grace W; Rigotti, Mattia; Warden, Melissa R et al. (2017) Hebbian Learning in a Random Network Captures Selectivity Properties of the Prefrontal Cortex. J Neurosci 37:11021-11036

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