[7] S. P. Singleton, C. Timmermann, A. I. Luppi, E. Eckernäs, L. Roseman, R. L. Carhart-Harris, A.
Kuceyeski. (2025). "Network control energy reductions under DMT relate to serotonin receptors, signal
diversity, and subjective experience”. Nature Communications Biology, DOI: 10.1038/s42003-025-08078-9.
Open Access.
Time-resolved network control analysis of the human brain during a pharmacologically-induced
alteration of consciousness.(a) 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) 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.
(c) We find regional control energy and its correlation with EEG signal entropy and subjective
experience are associated with the serotonin 2a receptor spatial pattern. (d) Using pharmacological
information and only the placebo fMRI, we are able to simulate DMT's impacts on control energy trajectories
in the brain.
[COMMENTARY] S. P. Singleton, B. L. Sevchik, S. N. Vandekar, E. C. Strain, S. M. Nayak, R. H. Dworkin,
J. C. Scott, T. D. Satterthwaite. (2025). "An initiative for living evidence synthesis in clinical psychedelic
research”. Nature Mental Health, DOI: 10.1038/s44220-024-00373-4. Pre-proofs here.
[6] A. I. Luppi, S. P. Singleton, J. Y. Hansen, K. W. Jamison, D. Bzdok, A. Kuceyeski, R. F. Betzel,
B. Misic. (2024). "Contributions of network structure, chemoarchitecture and diagnostic categories to
transitions between cognitive topographies”. Nature Biomedical Engineering, DOI: 10.1038/s41551-024-01242-2.
Open Access.
Network control with cognitive topographies.(a) Functional brain activity (coloured
nodes are active, grey nodes are inactive) evolves through time over a fixed network structure (displayed
below the brains). From a given starting configuration of activity (green), some alternative configurations
are relatively easy to reach in the space of possible configurations (valley, in blue), whereas others are
relatively difficult to achieve (peak, in yellow). To reach a desired target configuration of activity,
input energy (represented by the lightning bolt icons) can be injected locally into the system, and this
energy will spread to the rest of the system based on its network organisation. (b) We define states
as 123 meta-analytic activation maps from the NeuroSynth database. We then use network control theory to
quantify the cost of transitioning between these cognitive topographies. (c) Systematic
quantification of transition cost between each pair of cognitive topographies results in a look-up table
mapping the energy required for each transition.
[COMMENTARY] S. P. Singleton, A. Kuceyeski. (2024). "Bridging Psilocybin-Induced Changes in the
Brain’s Dynamic Functional Connectome With an Individual’s Subjective Experience”. Biological Psychiatry:
Cognitive Neuroscience and Neuroimaging, DOI: 10.1016/j.bpsc.2024.05.003. Full text here.
[5] S. P. Singleton, P. Velidi, L. Schilling, A. I. Luppi, K. Jamison, L. Parkes, A. Kuceyeski.
(2024). "Altered structural connectivity and functional brain dynamics in individuals with heavy alcohol use
elucidated via network control theory”. Biological Psychiatry: Cognitive Neuroscience and
Neuroimaging, DOI: 10.1016/j.bpsc.2024.05.006. Proofs available here.
[4] S. P. Singleton, J. B. Wang, M. Mithoefer, C. Hanlon, M. S. George, A. Mithoefer, O. Mithoefer, A.
R Coker, B. Yazar-Klosinski, A. Emerson, R. Doblin, A. Kuceyeski. (2023). "Altered brain activity and
functional connectivity after MDMA-assisted therapy for post-traumatic stress disorder”. Frontiers in
Psychiatry, DOI: 10.3389/fpsyt.2022.947622. Open Access.
Simplified study design. Subjects were assessed and imaged at the start of the study
(baseline). All subjects [low dose (LD; 30 mg MDMA), medium dose (MD; 75 mg MDMA), and high dose (HD; 125 mg
MDMA)] underwent three non-drug preparatory therapy sessions prior to their first MDMA dosing session. Each
MDMA session was followed by three non-drug integration therapy sessions. After MDMA session 2 and the
subsequent integration sessions, subjects were assessed and the dosing blind was broken. HD subjects
completed their final set of drug and non-drug therapy sessions unblinded, and LD/MD subjects crossed over
into the HD arm where they completed three sets of drug and non-drug sessions, now with the higher dose and
unblinded. All subjects were assessed and underwent MRI approximately two months following their last HD
MDMA session.
[3] S. P. Singleton, A. I. Luppi, R. L. Carhart-Harris, J. Cruzat, L. Roseman, D.J. Nutt, G. Deco, M.
L. Kringelbach, E. A. Stamatakis, A. Kuceyeski. (2022). "Receptor-informed network control theory links LSD
and psilocybin to a flattening of the brain’s control energy landscape”. Nature Communications, DOI:
10.1038/s41467-022-33578-1.
Open Access.
Mapping the energy landscape of the human brain with network control theory.a We
concatenated all fMRI time series together (all subjects, all conditions) and employed the k-means
clustering algorithm to identify common activation patterns, or states. b Using network control
theory and a representative structural connectome, we calculated the minimum energy required to transition
between states (or maintain the same state) using each individual’s brain states derived from the
psychedelic and placebo conditions separately. Our calculations reveal an energy landscape that is flattened
by LSD and psilocybin. c Weighting the energy calculations of the placebo brain states with inputs
from PET-derived receptor density maps of the serotonin 2a receptor also resulted in a flattened energy
landscape, providing a mechanistic explanation for these drug’s flattening effects.
[2] M. Nadgorny, D. T. Gentekos, Z. Xiao, S. P. Singleton, B. P. Fors, L. A. Connal. (2017).
"Manipulation of Molecular Weight Distribution Shape as a New Strategy to Control Processing Parameters”.
Macromolecular Rapid Communications, DOI: 10.1002/marc.201700352. Full text here.
[1] M. S. Ganewatta, K. P. Miller, S. P. Singleton, P. Mehrpouya-Bahrami, Y. P. Chen, Y. Yan, M.
Nagarkatti, P. Nagarkatti, A. W. Decho, C. Tang. (2015). "Antibacterial and Biofilm-Disrupting Coatings from
Resin Acid-Derived Materials”. Biomacromolecules, DOI: 10.1021/acs.biomac.5b01005.
Full text here.
