Aim: Evaluate whether sonicating the Nucleus Accumbens (NAc) with transcranial focused ultrasound modifies functional connectivity between the NAc and the prefrontal cortex (PFC). In this single visit, open-label pilot trial, we plan to evaluate whether transcranial focused ultrasound (tFUS), delivered to the nucleus accumbens (NAc) within the magnetic resonance imaging (MRI) scanner will impact resting state functional connectivity between the NAc and functionally connected brain regions like the prefrontal cortex (PFC) and the anterior cingulate cortex (ACC) in up to 10 healthy individuals. HYPOTHESIS : tFUS will reduce prefrontal cortex (PFC)-NAc functional connectivity, in healthy individuals. We will investigate this hypothesis by administering tFUS within to MRI scanner to healthy individuals and conduct resting state functional neuroimaging before- and after the tFUS stimulation.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
BASIC_SCIENCE
Masking
SINGLE
Enrollment
10
Transcranial focused ultrasound (tFUS) is a promising new technology that is both noninvasive and may be focally applied to deep brain targets. tFUS utilizes transducers which contain piezoelectric elements to produce pulses of ultrasonic waves that summate deep in the brain. Transcranial focused ultrasound (tFUS) uses a single transducer fixed in a head-worn apparatus on the scalp to produce ultrasonic waves deep into the brain.
Medical University of South Carolina Institute of Psychiatry
Charleston, South Carolina, United States
Resting State Functional Connectivity
The main outcomes of this study are brain imaging related. Using a neuroimaging technique called resting state functional connectivity, which is a statistical dependence between time series of electro-physiological activity and (de)oxygenated blood levels in distinct regions of the brain. Modularity is measured on a -1 to 1 scale, with higher scores indicating stronger community structure, or a stronger tendency of clusters of brain regions to separate into distinct, highly interconnected networks with sparse connections across networks. The optimal modularity value depends on the context. For example, during complex tasks lower modularity is better, while during basic, automatic tasks higher modularity is better.
Time frame: 24 minutes
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