dmt preprint

research.html

@@ -37,6 +37,13 @@ <h2 class="h2-spacing">Postdoctoral Researcher in Computational Neuroscience</h2
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             <h1>Research</h1>
             <h2>Publications</h2>
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+                    <img src="images/biorxiv2023b.jpg" alt="abstract image">
+                    <figcaption><b>Time-resolved network control analysis of the human brain during a pharmacologically-induced alteration of consciousness.</b> <b>(a)</b> Fourteen individuals were scanned over two days in which they received either DMT and saline placebo in separate visits (two-weeks apart, single-blind, counterbalanced design). On each day, a 28-minute long eyes-closed resting-state EEG-fMRI scan was performed with DMT/placebo intravenously administered at the end of the 8th minute. On the same day, identical scanning sessions were performed where participants were asked to rate the subjective intensity of drug effects at the end of every minute. <b>(b)</b> Here, we deploy a time-resolved network control analysis of the brain's trajectory through its activational landscape. The position in the landscape is illustrated here as a 3D vector containing regional BOLD signal amplitude at a given time t. We compute a control energy time-series from the regional activity vector time series by modeling transitions between adjacent regional activity vectors (x0 and xf, respectively) using a linear time-invariant model within a network control theory framework. In this framework, the state of the network x(t), here a vector of regional  BOLD activations at time t, evolves over time via diffusion through the brain’s weighted structural connectome A, the adjacency matrix. In order to complete the desired transition from the initial (x0) to the target state (xf), input (u) is injected into each region in the network. Varying control strategies (reflected in the matrix B) may be deployed wherein different regions are assigned varied amounts of control within the system. Integrating input u(t) at each node over the length of the trajectory from x0 to xf yields region-wise control energy, and summing over all regions yields a global value of control energy required to complete the transition. <b>(c)</b> We find regional metrics related to control energy are correlated with the serotonin 2a receptor distribution. <b>(d)</b> Using pharmacological information and only the placebo fMRI, we are able to simulate DMT's impacts on control energy trajectories in the brain.</figcaption>
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+              <p>[PREPRINT] <b>S. P. Singleton</b>, C. Timmermann, A. I. Luppi, E. Eckernäs, L. Roseman, R. L. Carhart-Harris, A. Kuceyeski. (2023). "Time-resolved network control analysis links reduced control energy under DMT with the serotonin 2a receptor, signal diversity, and subjective experience”. <em>bioRxiv</em>, DOI: <a href="https://www.biorxiv.org/content/10.1101/2023.05.11.540409v1" target="_blank">10.1101/2023.05.11.540409</a>. Open Access.</p>
+            </section>
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                     <img src="images/biorxiv2023a.jpg" alt="abstract image">