Periodic Reporting for period 3 - SUGARCODING (The neuroenergetics of memory consolidation – hybrid PET/MR imaging of the default mode network)
Reporting period: 2021-07-01 to 2022-12-31
The default mode network (DMN) is among the most studied networks in the human brain. The reason for this broad interest is a special behavior of the network’s activity that has been observed in hundreds of studies so far: During functional magnetic resonance imaging (fMRI) of the brain, the DMN deactivates as soon as a participant engages with any cognitively demanding task during scanning.
Despite this consistent finding, several aspects about this network’s activity are still unclear.
First, DMN deactivations are usually interpreted in relation to a different (baseline) state. However, the absolute level of activity or energy metabolism in the DMN is largely unknown as fMRI only measures qualitative signal changes. Second, it is hypothesized that the DMN is engaged with self-related memory processing and therefore deactivates once a participant engages with the outside world. However, the energy demands of memory processing have never been quantified in the human brain and particularly the DMN.
The DMN is not only a brain network with a particular activity pattern, it has also been often studied and reported in the context of several neuropsychiatric disorders such as Alzheimer’s disease. As an example, the regions of the DMN show most prominent decline in energy metabolism in patients with Alzheimer’s disease. Understanding the baseline metabolism of the DMN in relation to other cortical areas and the neuroenergetic demands during memory processing is therefore relevant to our understanding of healthy and diseased brain states.
In this project, we therefore aim at addressing the following objectives:
- We establish quantitative measurements of energy metabolism by tracking the cerebral metabolic rates of glucose (using FDG-PET) and oxygen (using quantitative BOLD).
- We characterize the metabolic baseline and task profile of the DMN in healthy human subjects.
Despite this consistent finding, several aspects about this network’s activity are still unclear.
First, DMN deactivations are usually interpreted in relation to a different (baseline) state. However, the absolute level of activity or energy metabolism in the DMN is largely unknown as fMRI only measures qualitative signal changes. Second, it is hypothesized that the DMN is engaged with self-related memory processing and therefore deactivates once a participant engages with the outside world. However, the energy demands of memory processing have never been quantified in the human brain and particularly the DMN.
The DMN is not only a brain network with a particular activity pattern, it has also been often studied and reported in the context of several neuropsychiatric disorders such as Alzheimer’s disease. As an example, the regions of the DMN show most prominent decline in energy metabolism in patients with Alzheimer’s disease. Understanding the baseline metabolism of the DMN in relation to other cortical areas and the neuroenergetic demands during memory processing is therefore relevant to our understanding of healthy and diseased brain states.
In this project, we therefore aim at addressing the following objectives:
- We establish quantitative measurements of energy metabolism by tracking the cerebral metabolic rates of glucose (using FDG-PET) and oxygen (using quantitative BOLD).
- We characterize the metabolic baseline and task profile of the DMN in healthy human subjects.
Until the first reporting period, we achieved the following technological and research goals:
- We established, tested and piloted quantitative metabolic imaging simultaneously measuring cerebral metabolic rates of oxygen and glucose on an integrated PET/MR-scanner.
- We setup, tested and continuously improve an automatized brain imaging analysis pipeline fully based on open-source database (XNAT, Jupyter, python) and analytical tools (AFNI, FSL, fMRIPrep), and fully compliant with the open-science framework for neuroimaging data (such as BIDS and open repositories). We share our experience with this pipeline with the brain imaging community in an online manual and video tutorials.
- We finished data acquisition for the first study (WP1) where we quantified oxygen metabolism of the DMN during different external and internal, self-related task conditions.
- We prepared two successive studies where we aim to modulate energy metabolism in the DMN with different internal and external parameters.
- We established, tested and piloted quantitative metabolic imaging simultaneously measuring cerebral metabolic rates of oxygen and glucose on an integrated PET/MR-scanner.
- We setup, tested and continuously improve an automatized brain imaging analysis pipeline fully based on open-source database (XNAT, Jupyter, python) and analytical tools (AFNI, FSL, fMRIPrep), and fully compliant with the open-science framework for neuroimaging data (such as BIDS and open repositories). We share our experience with this pipeline with the brain imaging community in an online manual and video tutorials.
- We finished data acquisition for the first study (WP1) where we quantified oxygen metabolism of the DMN during different external and internal, self-related task conditions.
- We prepared two successive studies where we aim to modulate energy metabolism in the DMN with different internal and external parameters.
Until the end of the project period, we aim to characterize and quantify energy metabolism of the human cortex and particularly the DMN during different baseline states and its deviation during cognitive demands. We expect that the quantitative approach to study brain function will reveal important characteristics about brain function and organization that are so far hidden when measuring relative signal changes as with classical brain imaging approaches.