Periodic Reporting for period 4 - SUGARCODING (The neuroenergetics of memory consolidation – hybrid PET/MR imaging of the default mode network)
Okres sprawozdawczy: 2023-01-01 do 2024-06-30
Project Summary: Understanding the Brain's Energy Demands for Memory
The human brain has one of the highest energy demands relative to overall body metabolism. This project investigates how the brain manages these crucial energy resources, particularly during processes like memory consolidation. We focused on a specific brain network known as the Default Mode Network (DMN). This network includes brain regions that are more active when individuals are resting and is believed to be essential for memory processing and consolidation. Our research combined advanced brain imaging techniques, such as Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET), to explore the relationship between brain-wide energy consumption and task-related brain activity, specifically during learning and memory tasks. We developed novel methods that allow us to quantify the metabolism of oxygen and glucose, which are the brain’s primary energy substrates.
The Problem: Understanding how the brain balances its high energy demands is critical. We need to know how energy is distributed within the brain, which regions have the highest demands, and how these demands fluctuate during various activities, such as learning and memory processing.
Why It Matters: This research is important because it enhances our understanding of healthy brain function and the limitations that arise in certain conditions, such as neurological disorders. Our findings provide insight into energy-demanding processes and how the brain operates under limited energy resources, such as low glucose levels, which can be associated with vascular issues, neurodegenerative disorders, or healthy aging. By understanding how the brain utilizes energy for memory, we may develop new strategies for treating cognitive disorders and improving brain health. Additionally, gaining insight into the aging process and its effect on memory could lead to new approaches for maintaining cognitive function as we age.
Overall Objectives:
1. Establish methods to quantify energy metabolism and demand of the human brain during different conditions: Current methods are either highly invasive or only allow relative signal changes to be measured. An absolute measure of energy metabolism in different task states was not available at the beginning of the funding period.
2. Quantify the baseline of brain energy metabolism: We aimed to define a baseline for glucose metabolism in the DMN to interpret fMRI signals better.
3. Track and compare the brain’s energy demand during different stages of learning and memory.
In conclusion, this project provides valuable insights into the brain's energy economy and its relationship to cognitive functions. Our findings challenge existing models of brain function and pave the way for future research into the metabolic underpinnings of cognition and brain disorders.
The human brain has one of the highest energy demands relative to overall body metabolism. This project investigates how the brain manages these crucial energy resources, particularly during processes like memory consolidation. We focused on a specific brain network known as the Default Mode Network (DMN). This network includes brain regions that are more active when individuals are resting and is believed to be essential for memory processing and consolidation. Our research combined advanced brain imaging techniques, such as Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET), to explore the relationship between brain-wide energy consumption and task-related brain activity, specifically during learning and memory tasks. We developed novel methods that allow us to quantify the metabolism of oxygen and glucose, which are the brain’s primary energy substrates.
The Problem: Understanding how the brain balances its high energy demands is critical. We need to know how energy is distributed within the brain, which regions have the highest demands, and how these demands fluctuate during various activities, such as learning and memory processing.
Why It Matters: This research is important because it enhances our understanding of healthy brain function and the limitations that arise in certain conditions, such as neurological disorders. Our findings provide insight into energy-demanding processes and how the brain operates under limited energy resources, such as low glucose levels, which can be associated with vascular issues, neurodegenerative disorders, or healthy aging. By understanding how the brain utilizes energy for memory, we may develop new strategies for treating cognitive disorders and improving brain health. Additionally, gaining insight into the aging process and its effect on memory could lead to new approaches for maintaining cognitive function as we age.
Overall Objectives:
1. Establish methods to quantify energy metabolism and demand of the human brain during different conditions: Current methods are either highly invasive or only allow relative signal changes to be measured. An absolute measure of energy metabolism in different task states was not available at the beginning of the funding period.
2. Quantify the baseline of brain energy metabolism: We aimed to define a baseline for glucose metabolism in the DMN to interpret fMRI signals better.
3. Track and compare the brain’s energy demand during different stages of learning and memory.
In conclusion, this project provides valuable insights into the brain's energy economy and its relationship to cognitive functions. Our findings challenge existing models of brain function and pave the way for future research into the metabolic underpinnings of cognition and brain disorders.
By combining and establishing advanced brain imaging techniques (fMRI and PET), the project aimed to understand how the brain manages its high energy demands during cognitive and memory processing and identify the metabolic processes involved in brain connectivity and brain work.
The first subproject (Ref 1 below) explored how the brain’s energy is distributed in its functional connectome. By using multimodal brain imaging, we found that evolutionarily expanded brain regions, particularly those involved in cognitive functions like reading and memory processing, have up to 67% higher energetic costs than sensory-motor regions. Additionally, we found evidence that increased brain size alone does not account for the human brain's heightened energy requirements; rather, the nature of signaling mechanisms in specific regions is key.
In a related subproject (Ref 2), we introduced the concept of "time-averaged control energy" (TCE) to quantify the energy costs of controlling brain dynamics at rest. Using functional and diffusion MRI, TCE was found to correlate spatially with oxygen metabolism, providing a bioenergetic perspective on how the brain manages energy consumption during resting states.
In a third subproject (Ref 3), we examined the relationship between fMRI signals and the brain's oxygen metabolism, challenging the traditional interpretation of BOLD signals in terms of neural activity. We found that changes in oxygen extraction fraction (OEF), rather than cerebral blood flow (CBF), were the primary drivers of oxygen supply in a large portion of voxels, especially during task states. This finding suggests that the interpretation of BOLD signals requires a more nuanced understanding of neurovascular coupling and that quantitative fMRI or additional CBF measurements are necessary for a valid assessment of regional brain activity.
In a fourth subproject (Ref 4), we explored how the brain balances its energy demands during visual perception under varying levels of predictability and subjective uncertainty. The findings revealed that predictable visual input led to reduced oxygen metabolism, particularly when participants were confident in their predictions. This resulted in cortical energy savings of up to 12%, suggesting that predictive processing enhances both behavioral performance and energy efficiency. This has significant implications for understanding how the brain optimizes energy use during cognitive tasks, such as memory consolidation.
In a final subproject (Ref 5), we examined the brain's response to insulin-induced hypoglycemia, focusing on the impact on cerebral oxygen metabolism (CMRO2) and memory consolidation. We found that despite a drop in blood glucose levels, CMRO2 remained stable, indicating that the brain efficiently shifts to alternative energy pathways, such as astrocytic glycogen, during hypoglycemia. However, we also found that hypoglycemia had long-lasting effects on memory consolidation, even after glucose levels were restored. This study underscores the brain's metabolic flexibility but also highlights the vulnerability of memory processes to metabolic disturbances.
Ref 1: An Energy Costly Architecture of Neuromodulators for Human Brain Evolution and Cognition.
Castrillon G, Epp S, Bose A, Fraticelli L, Hechler A, Belenya R, Ranft A, Yakushev I, Utz L, Sundar L, Rauschecker JP, Preibisch C, Kurcyus K, Riedl V.
Science Advances. 2023 Dec 13;9(50): eadi7632. DOI: 10.1126/sciadv.adi7632
Ref 2: The control costs of human brain dynamics.
Ceballos EG, Luppi AI, Castrillon G, Saggar M, Misic B, Riedl V.
Network Neuroscience. 2024 1-23; DOI: 10.1162/netn_a_00425
Ref 3: Two distinct modes of hemodynamic responses in the human brain
Epp SM, Castrillón G, Yuan B, Andrews-Hanna J, Preibisch C, Riedl V
bioRxiv 2023.12.08.570806; DOI: 10.1101/2023.12.08.570806
Ref 4: The energy metabolic footprint of predictive processing in the human brain
Hechler A, de Lange FP, Riedl V
bioRxiv 2023.12.08.570804; DOI: 10.1101/2023.12.08.570804
Ref 5: The selfish yet forgetful brain: Stable cerebral oxygen metabolism during hypoglycemia but impaired memory consolidation.
Bose A, Haschka SJ, Koehler J, Hesse F, Martin S, Steinberg L, Iakoubov R, Riedl V
bioRxiv 2024.12.12.628178; DOI: 10.1101/2024.12.12.628178
The first subproject (Ref 1 below) explored how the brain’s energy is distributed in its functional connectome. By using multimodal brain imaging, we found that evolutionarily expanded brain regions, particularly those involved in cognitive functions like reading and memory processing, have up to 67% higher energetic costs than sensory-motor regions. Additionally, we found evidence that increased brain size alone does not account for the human brain's heightened energy requirements; rather, the nature of signaling mechanisms in specific regions is key.
In a related subproject (Ref 2), we introduced the concept of "time-averaged control energy" (TCE) to quantify the energy costs of controlling brain dynamics at rest. Using functional and diffusion MRI, TCE was found to correlate spatially with oxygen metabolism, providing a bioenergetic perspective on how the brain manages energy consumption during resting states.
In a third subproject (Ref 3), we examined the relationship between fMRI signals and the brain's oxygen metabolism, challenging the traditional interpretation of BOLD signals in terms of neural activity. We found that changes in oxygen extraction fraction (OEF), rather than cerebral blood flow (CBF), were the primary drivers of oxygen supply in a large portion of voxels, especially during task states. This finding suggests that the interpretation of BOLD signals requires a more nuanced understanding of neurovascular coupling and that quantitative fMRI or additional CBF measurements are necessary for a valid assessment of regional brain activity.
In a fourth subproject (Ref 4), we explored how the brain balances its energy demands during visual perception under varying levels of predictability and subjective uncertainty. The findings revealed that predictable visual input led to reduced oxygen metabolism, particularly when participants were confident in their predictions. This resulted in cortical energy savings of up to 12%, suggesting that predictive processing enhances both behavioral performance and energy efficiency. This has significant implications for understanding how the brain optimizes energy use during cognitive tasks, such as memory consolidation.
In a final subproject (Ref 5), we examined the brain's response to insulin-induced hypoglycemia, focusing on the impact on cerebral oxygen metabolism (CMRO2) and memory consolidation. We found that despite a drop in blood glucose levels, CMRO2 remained stable, indicating that the brain efficiently shifts to alternative energy pathways, such as astrocytic glycogen, during hypoglycemia. However, we also found that hypoglycemia had long-lasting effects on memory consolidation, even after glucose levels were restored. This study underscores the brain's metabolic flexibility but also highlights the vulnerability of memory processes to metabolic disturbances.
Ref 1: An Energy Costly Architecture of Neuromodulators for Human Brain Evolution and Cognition.
Castrillon G, Epp S, Bose A, Fraticelli L, Hechler A, Belenya R, Ranft A, Yakushev I, Utz L, Sundar L, Rauschecker JP, Preibisch C, Kurcyus K, Riedl V.
Science Advances. 2023 Dec 13;9(50): eadi7632. DOI: 10.1126/sciadv.adi7632
Ref 2: The control costs of human brain dynamics.
Ceballos EG, Luppi AI, Castrillon G, Saggar M, Misic B, Riedl V.
Network Neuroscience. 2024 1-23; DOI: 10.1162/netn_a_00425
Ref 3: Two distinct modes of hemodynamic responses in the human brain
Epp SM, Castrillón G, Yuan B, Andrews-Hanna J, Preibisch C, Riedl V
bioRxiv 2023.12.08.570806; DOI: 10.1101/2023.12.08.570806
Ref 4: The energy metabolic footprint of predictive processing in the human brain
Hechler A, de Lange FP, Riedl V
bioRxiv 2023.12.08.570804; DOI: 10.1101/2023.12.08.570804
Ref 5: The selfish yet forgetful brain: Stable cerebral oxygen metabolism during hypoglycemia but impaired memory consolidation.
Bose A, Haschka SJ, Koehler J, Hesse F, Martin S, Steinberg L, Iakoubov R, Riedl V
bioRxiv 2024.12.12.628178; DOI: 10.1101/2024.12.12.628178
One major breakthrough and unexpected result is the identification of two distinct modes of hemodynamic responses across the cortex, which challenges the conventional interpretation of BOLD fMRI signals. The discovery that negative BOLD signal changes do not necessarily reflect reduced oxygen metabolism in a significant portion of voxels is a substantial advancement beyond the state of the art. This finding suggests that the traditional neurovascular coupling model, widely used in interpreting fMRI results, needs revision, especially in relation to oxygen metabolism and brain activity.