Periodic Reporting for period 3 - Deep Brain (Deep Brain: Decision Mechanisms in Deep Brain Networks)
Reporting period: 2023-12-01 to 2025-05-31
Making everyday life decisions is vital to our survival and relies as much on cerebral cortex as on regions that reside Deep in our Brain, namely the subcortex. Studies on networks connecting small nuclei in the Deep Brain with cortical regions can provide key insights into learning, memory-guided, economic, and perceptual decision-making processes. However, the structural and functional organization of these processes is still incompletely understood, highlighting a knowledge gap in the field. The aim of this proposal is to reduce the gap in functional understanding by studying the mechanisms of different decision-making domains implemented in Deep Brain networks.
The human brain consists of densely connected networks of interacting areas and nuclei, and while the cerebral cortex has been mapped with great precision, research on the human subcortex has been relatively side-lined (Forstmann et al., Nat Rev Neurosci, 2017). The subcortex contains hundreds of small grey matter nuclei, which take up approximately 1/4th of the entire human brain volume. Importantly, only approximately 7% of these nuclei are currently accessible in standard human brain magnetic resonance imaging (MRI) atlases. Major ongoing efforts in my group have focused on structural imaging of the subcortex, thereby removing an important technical barrier. These efforts have paved the way for functional studies on the role of Deep Brain in decision making as described in the current proposal.
Ultra-high-field (UHF) 7Tesla (T) MRI and one of the three world-wide available Connectom MRI will be used to investigate decision mechanisms in Deep Brain networks. Three projects are proposed using a strong theory-driven and statistically high-powered model-based approach to combine beyond the-state-of-the-art techniques ranging from cognitive neuroscience, experimental psychology, to quantitative modeling. Ultimately this proposal will yield a unified deep-brain network model of decision making.
The human brain consists of densely connected networks of interacting areas and nuclei, and while the cerebral cortex has been mapped with great precision, research on the human subcortex has been relatively side-lined (Forstmann et al., Nat Rev Neurosci, 2017). The subcortex contains hundreds of small grey matter nuclei, which take up approximately 1/4th of the entire human brain volume. Importantly, only approximately 7% of these nuclei are currently accessible in standard human brain magnetic resonance imaging (MRI) atlases. Major ongoing efforts in my group have focused on structural imaging of the subcortex, thereby removing an important technical barrier. These efforts have paved the way for functional studies on the role of Deep Brain in decision making as described in the current proposal.
Ultra-high-field (UHF) 7Tesla (T) MRI and one of the three world-wide available Connectom MRI will be used to investigate decision mechanisms in Deep Brain networks. Three projects are proposed using a strong theory-driven and statistically high-powered model-based approach to combine beyond the-state-of-the-art techniques ranging from cognitive neuroscience, experimental psychology, to quantitative modeling. Ultimately this proposal will yield a unified deep-brain network model of decision making.
Project 1: Developing and testing quantitative decision-making models across multiple domains.
2019 Turner, B. M., Palestro, J., Miletic, S., & Forstmann, B. U. (2019). Advances in Techniques for Imposing Reciprocity in Brain-Behavior Relations. Neuroscience & Biobehavioral Reviews, 102, 327-336.
2021 Miletić, S., Boag, R. J., Trutti, A., Stevenson, N., Forstmann, B. U., & Heathcote, A. (2021). A new model of decision processing in instrumental learning tasks. eLife, 10:e63055.
Project 2: Refining Deep Brain networks anatomically using Connectom imaging.
In press Alkemade, A., Grossmann, R., Bazin, P.-L. & Forstmann, B. U. (in press). Mixed methodology in human brain research: Integrating MRI and histology. Brain Structure and Function.
2023 Isherwood, S. J. S., Bazin, P.-L. Miletic, S., Stevenson, N. R., Trutti, A. C., Tse, D. H. Y., Heathcote, A., Matzke, D., Innes, R. J., Habli, S., Sokotowski, D. R., Alkemade, A., Haberg, A. K., Forstmann, B. U. (in press). Investigating Intra-Individual Networks of Response Inhibition and Interference Resolution using 7T MRI. NeuroImage, 271:119988.
Project 3: Testing and joint modeling of behaviour and Deep-Brain decision-making networks using 7T fMRI.
In press Innes, R. U., Stevenson, N., Gronau, Q. F., Miletic, S., Heathcote, A., Forstmann, B. U., & Brown, S. D. (in press). Using group level factor models to resolved high dimensionality in model-based sampling. Computational Brain & Behavior.
2019 Turner, B. M., Palestro, J., Miletic, S., & Forstmann, B. U. (2019). Advances in Techniques for Imposing Reciprocity in Brain-Behavior Relations. Neuroscience & Biobehavioral Reviews, 102, 327-336.
2021 Miletić, S., Boag, R. J., Trutti, A., Stevenson, N., Forstmann, B. U., & Heathcote, A. (2021). A new model of decision processing in instrumental learning tasks. eLife, 10:e63055.
Project 2: Refining Deep Brain networks anatomically using Connectom imaging.
In press Alkemade, A., Grossmann, R., Bazin, P.-L. & Forstmann, B. U. (in press). Mixed methodology in human brain research: Integrating MRI and histology. Brain Structure and Function.
2023 Isherwood, S. J. S., Bazin, P.-L. Miletic, S., Stevenson, N. R., Trutti, A. C., Tse, D. H. Y., Heathcote, A., Matzke, D., Innes, R. J., Habli, S., Sokotowski, D. R., Alkemade, A., Haberg, A. K., Forstmann, B. U. (in press). Investigating Intra-Individual Networks of Response Inhibition and Interference Resolution using 7T MRI. NeuroImage, 271:119988.
Project 3: Testing and joint modeling of behaviour and Deep-Brain decision-making networks using 7T fMRI.
In press Innes, R. U., Stevenson, N., Gronau, Q. F., Miletic, S., Heathcote, A., Forstmann, B. U., & Brown, S. D. (in press). Using group level factor models to resolved high dimensionality in model-based sampling. Computational Brain & Behavior.
One of the achievements is the development of a multi-contrast anatomical subcortical parcellation algorithm that allows the automatic parcellation of 22 individual subcortical nuclei based on in-vivo and postmortem ultra-high field magnetic resonance imaging (MRI) data (Bazin et al., 2020, eLife). Another achievement is the unified 3D map of microscopic architecture and MRI of the human brain. This study presents a unique data set of 2 postmortem brains which are freely available and unprecedented in its multimodality and resolution (Alkemade et al., 2022, Sci Adv).