The neuroreceptors and transporters underlying spontaneous brain activity
Presented During:
Saturday, June 28, 2025: 11:30 AM - 12:45 PM
Brisbane Convention & Exhibition Centre
Room:
Great Hall
Poster No:
1419
Submission Type:
Abstract Submission
Authors:
Johan Nakuci1, Kanika Bansal2
Institutions:
1US DEVCOM Army Research Laboratory, Aberdeen Proving Ground, MD, 2US DEVCOM Army Research Labs, Aberdeen Proving Ground, MD
First Author:
Co-Author:
Introduction:
At any moment, neuromodulators bind and unbind from neurons, driving brain activity through neuroreceptors and transporters. How the coordination of neuromodulators drives brain activity is nontrivial to quantify. Traditional in vivo methods, such as fluorescent probes (Marvin et al., 2013), microdialysis (Watson et al., 2006), and electrochemistry (Bucher & Wightman, 2015), provide limited insight, measuring only a few neuromodulators or brain regions at a time. The invasive nature of these methods further limits their application in humans, highlighting the need for a non-invasive approach.
Neuroreceptor density, crucial in mediating neuromodulator effects, can be estimated non-invasively using PET imaging. Neuroreceptor maps have been linked to brain-wide communication measured by the BOLD signal, suggesting that combining PET-derived neuroreceptor maps with BOLD signals could uncover how neuromodulators drive human brain activity and elucidate mechanisms of cognition and behavior (Salvan et al., 2023).
Neuroreceptor density, crucial in mediating neuromodulator effects, can be estimated non-invasively using PET imaging. Neuroreceptor maps have been linked to brain-wide communication measured by the BOLD signal, suggesting that combining PET-derived neuroreceptor maps with BOLD signals could uncover how neuromodulators drive human brain activity and elucidate mechanisms of cognition and behavior (Salvan et al., 2023).
Methods:
Reconstructing BOLD Signals
To reveal neuromodulators driving spontaneous brain activity, we used a linear model to reconstruct BOLD signals using PET-derived neuroreceptor density maps (Fig. 1A).
Neuroreceptor Density Maps
We used PET imaging data from Hansen et al., 2022 to map 19 neuroreceptors and transporters across 9 neurotransmitter systems, including dopamine (D1, D2, DAT), norepinephrine (NET), serotonin (5-HT1A, 5-HT1B, 5-HT2A, 5-HT4, 5-HT6, 5-HTT), acetylcholine (α4β2, M1, VAChT), glutamate (mGluR5, NMDA), GABAa, histamine (H3), cannabinoid (CB1), and μ-opioid (MOR).
Functional MRI Data
1. HCP Resting-State fMRI Dataset: Pre-processed 15 mintutes of resting-state fMRI from 48 healthy participants.
2. LSD Dataset: Pre-processed 7 minutes of resting-state fMRI from 15 participants after placebo and LSD (Carhart-Harris et al., 2016).
3. Modafinil Dataset: 5 minutes of resting-state fMRI from 13 participants after receiving Modafinil(Cera et al., 2014).
4. UCLA Consortium (LA5c): Pre-processed 5 minutes of resting-state fMRI from healthy participants (N = 118), schizophrenia (N = 45), bipolar disorder (N = 25), and ADHD (N = 39) (Poldrack et al., 2016).
To reveal neuromodulators driving spontaneous brain activity, we used a linear model to reconstruct BOLD signals using PET-derived neuroreceptor density maps (Fig. 1A).
Neuroreceptor Density Maps
We used PET imaging data from Hansen et al., 2022 to map 19 neuroreceptors and transporters across 9 neurotransmitter systems, including dopamine (D1, D2, DAT), norepinephrine (NET), serotonin (5-HT1A, 5-HT1B, 5-HT2A, 5-HT4, 5-HT6, 5-HTT), acetylcholine (α4β2, M1, VAChT), glutamate (mGluR5, NMDA), GABAa, histamine (H3), cannabinoid (CB1), and μ-opioid (MOR).
Functional MRI Data
1. HCP Resting-State fMRI Dataset: Pre-processed 15 mintutes of resting-state fMRI from 48 healthy participants.
2. LSD Dataset: Pre-processed 7 minutes of resting-state fMRI from 15 participants after placebo and LSD (Carhart-Harris et al., 2016).
3. Modafinil Dataset: 5 minutes of resting-state fMRI from 13 participants after receiving Modafinil(Cera et al., 2014).
4. UCLA Consortium (LA5c): Pre-processed 5 minutes of resting-state fMRI from healthy participants (N = 118), schizophrenia (N = 45), bipolar disorder (N = 25), and ADHD (N = 39) (Poldrack et al., 2016).
Results:
Across four datasets, our framework reconstructs spontaneous brain activity and functional connectivity (FC) from BOLD signals measured by functional MRI (fMRI), achieving an average accuracy of RBOLD = 0.58 ± 0.05 and RFC = 0.62 ± 0.02, significantly greater than that obtained from null models based on spatially permuted neuroreceptor and transporter maps (Pspatial < 0.001) and from a permutation that preserved the spatial auto-correlation, spin test (Pspin < 0.001). Figure 1B-E shows the results obtained from the HCP dataset. Notably, our analysis reveals that the excitation-inhibition balance is maintained by two distinct modules of neuroreceptors and transporters exhibiting regional density differences across the cortex (Fig. 2A,B). The framework's validity is supported by its ability to recover neuroreceptors and transporters - 5-HT1a, 5-HT1b, 5-HT2a, and D2 - known to bind lysergic acid diethylamide (LSD), and D2, 5-HT1a, 5-HT2a, and NET mediating the effects of Modafinil (Fig. 2C-E). Furthermore, the framework identified mechanistic links between neuroreceptors, transporters, and altered brain activity in schizophrenia, bipolar disorder, and ADHD (Fig. 2F). Finally, neuroreceptor-based reconstruction outperformed models based on structural connectivity and cortical folding geometry.
Conclusions:
This neuroreceptor-based framework provides is non-invasive approach to understanding neuromodulatory mechanisms across brain states and conditions, advancing insights into cognition and behavior.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia)
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 1
PET Modeling and Analysis
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Transmitter Receptors 2
Physiology, Metabolism and Neurotransmission:
Pharmacology and Neurotransmission
Keywords:
Attention Deficit Disorder
FUNCTIONAL MRI
Neurotransmitter
Norpinephrine
Pharmacotherapy
Schizophrenia
Sub-Cortical
Treatment
1|2Indicates the priority used for review
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Hansen, J. Y., Shafiei, G., Markello, R. D., Smart, K., Cox, S. M. L., Nørgaard, M., Beliveau, V., Wu, Y., Gallezot, J.-D., Aumont, É., Servaes, S., Scala, S. G., DuBois, J. M., Wainstein, G., Bezgin, G., Funck, T., Schmitz, T. W., Spreng, R. N., Galovic, M., … Misic, B. (2022). Mapping neurotransmitter systems to the structural and functional organization of the human neocortex. Nature Neuroscience, 25(11), 1569–1581. https://doi.org/10.1038/s41593-022-01186-3
Marvin, J. S., Borghuis, B. G., Tian, L., Cichon, J., Harnett, M. T., Akerboom, J., Gordus, A., Renninger, S. L., Chen, T.-W., Bargmann, C. I., Orger, M. B., Schreiter, E. R., Demb, J. B., Gan, W.-B., Hires, S. A., & Looger, L. L. (2013). An optimized fluorescent probe for visualizing glutamate neurotransmission. Nature Methods, 10(2), 162–170. https://doi.org/10.1038/nmeth.2333
Salvan, P., Fonseca, M., Winkler, A. M., Beauchamp, A., Lerch, J. P., & Johansen-Berg, H. (2023). Serotonin regulation of behavior via large-scale neuromodulation of serotonin receptor networks. Nature Neuroscience, 26(1), 53–63. https://doi.org/10.1038/s41593-022-01213-3
Watson, C. J., Venton, B. J., & Kennedy, R. T. (2006). In Vivo Measurements of Neurotransmitters by Microdialysis Sampling. Analytical Chemistry, 78(5), 1391–1399. https://doi.org/10.1021/ac0693722