Task-evoked metabolic demands of the posteromedial default mode network are shaped by dorsal attention and frontoparietal control networks

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1. 

   Version published to 10.7554/elife.84683 on eLife

   May 25, 2023
2. 

   eLife

   Apr 23, 2023

   Author Response

   Reviewer #1 (Public review):

   1.0) This paper investigates the metabolic basis of a node, posterior cingulate cortex (PCC), in the default node network (DMN). They employed sophisticated MRI-PET methods to measure both BOLD and CMRglc changes (both magnitude and dynamics) during attention-demanding and working memory tasks. They found uncoupling of BOLD and CMRglc in PCC with these different tasks. The implications of these findings are poorly interpreted, with a conclusion that is purely based on other work independent of this study. Various suggestions could allow them to place some speculations in line with a stronger interpretation of their results.

   This is one of several papers in recent years investigating the metabolic underpinnings of activated (or task-positive) and deactivated (or task-negative) cortical …

   Author Response

   Reviewer #1 (Public review):

   1.0) This paper investigates the metabolic basis of a node, posterior cingulate cortex (PCC), in the default node network (DMN). They employed sophisticated MRI-PET methods to measure both BOLD and CMRglc changes (both magnitude and dynamics) during attention-demanding and working memory tasks. They found uncoupling of BOLD and CMRglc in PCC with these different tasks. The implications of these findings are poorly interpreted, with a conclusion that is purely based on other work independent of this study. Various suggestions could allow them to place some speculations in line with a stronger interpretation of their results.

   This is one of several papers in recent years investigating the metabolic underpinnings of activated (or task-positive) and deactivated (or task-negative) cortical areas in the human brain. In this study, they used BOLD fMRI and glucose PET scan to examine the metabolic distinction of the default node network (DMN), which is known to be deactivated during attention-demanding tasks, with different types of cognitively demanding tasks. Unlike the BOLD response in posteromedial DMN which is consistently negative, they found that CMRglc of the posteromedial DMN (a task-negative network) is dependent on the metabolic demands of adjacent task-positive networks like the dorsal attention network (DAN) and frontoparietal network (FPN). With attention-demanding tasks (like Tetris) the BOLD and CMRglc are both downregulated in DMN (specifically the posterior cingulate cortex, PCC, a task-negative node of DMN), but working memory induces CMRglc increase in PCC and which is decoupled from the negative BOLD response in PCC.

   We thank the reviewer for the constructive feedback and the possibility to improve our manuscript. We agree that the interpretation of the results should be strengthened to provide a stronger focus on our data. Regarding the uncoupling of BOLD and CMRGlu during working memory, we acknowledge the need to further elaborate on this topic in our discussion. These suggestions and comments have been incorporated into the revised manuscript as outlined below.

   1.1) These complicated results are the main findings, and to provide a biological basis to these data they rather surprisingly, but without their own experimental evidence, conclude that the negative BOLD and negative CMRglc in PCC during attention-demanding tasks is due to decreased glutamate signaling (which was not measured in this study) and the negative BOLD and positive CMRglc in PCC during working memory is due to increased GABAergic activity (which was not measured in this study). It is rather surprising that without measurement, a conclusion is made which would at best be considered a hypothesis to be tested. Thus, independent of these hypothesized mechanisms, they need to summarize their results based on their own measurements in this study (see 3 for a hint).

   Thank you for bringing up this point and for the insightful suggestion concerning point 3. We have now explicitly stated that the interpretation regarding glutamate and GABAergic signaling is of speculative nature as theses were not measured in the current work, moreover, we have substantially reduced this section. As such, we agree with the reviewer that this represents an interesting hypothesis to be tested in future work. For further details please see response to comments 1.3 and 1.4.

   Discussion, page 16, line 341:

   On the neurotransmitter level, one of the current hypotheses regarding BOLD deactivations proposes that CMRO2 and CBF are affected by the balance of the excitatory and inhibitory neurotransmitters, specifically GABA and glutamate (Buzsáki et al., 2007; Lauritzen et al., 2012; Sten et al., 2017). In the PCC, glutamate release prevents negative BOLD responses (Hu et al., 2013), whereas a lower glutamate/GABA ratio is associated with greater deactivation (Gu et al., 2019). As glutamate elicits proportional glucose consumption (Lundgaard et al., 2015; Zimmer et al., 2017), decreases in glutamate signaling in the pmDMN could indeed explain both, the decreased BOLD response and decreased CMRGlu during the Tetris® task. Conversely, increased GABA supports a negative BOLD response in the PCC (Hu et al., 2013), as do working memory tasks (Koush et al., 2021) and pharmacological stimulation with GABAergic benzodiazepines (Walter et al., 2016). In consequence, the observed dissociation between BOLD changes and CMRGlu during working memory could indeed result from metabolically expensive (Harris et al., 2012) GABAergic suppression of the BOLD signal (Stiernman et al., 2021). However, we need to emphasize that glutamate and GABAergic signaling was not measured in the current study, thus, the above interpretations are of speculative nature. Nonetheless, future work may test this promising hypothesis, e.g., using pharmacological alteration of GABAergic and glutamatergic signaling or optogenetic approaches modulating GABAergic interneuron activity.

   Furthermore, to maintain a more concise discussion that is closer aligned with the measured results, we have removed the following paragraph:

   Discussion, page 15, line 309:

   The associations of these metabolic demands between the DMN and task-positive networks is also reflected in their distance along a connectivity gradient, which is hierarchically organized from unimodal sensory/motor to complex associative functions and the DMN being at the end of the processing stream (Margulies et al., 2016; Smallwood et al., 2021). A corresponding decrease in pmDMN glucose metabolism was observed for tasks that activate unimodal networks and the DAN, but not for the FPN. The inverse influence of attention and control networks on the pmDMN may therefore suggest that connectivity gradients are supported by the underlying energy metabolism.

   1.2) It is mentioned that the FDG-PET scans allow quantitative CMRglc, both in terms of units of glucose use but also with high time resolution. Based on the method described, it isn't clear how this is possible. Important details of either prior work or their own work have been excluded that show how the time course of CMRglc (regardless of whether it's absolute or relative) can be compared with the BOLD time course. Furthermore, it is extremely difficult to conceive that quantitative CMRglc can be estimated without additional measurements (e.g., blood samples, etc). Significant methodological details have to be provided, which even should make their way to results given the importance of their BOLD-CMRglc coupling and decoupling in the same region.

   We thank the reviewer for this important comment and apologize for the lack of clarity. We would like to emphasize that in the current work only spatial patterns of CMRGlu and BOLD signal changes were compared, but not the time course of these signals. The manuscript was edited throughout to clarify this point.

   Introduction, page 5, line 110:

   Studies using simultaneous fPET/fMRI have shown a strong spatial correspondence between the BOLD signal changes and glucose metabolism in several task-positive networks and across various tasks requiring different levels of cognitive engagement (Hahn et al., 2020, 2016; Jamadar et al., 2019; Rischka et al., 2018; Stiernman et al., 2021; Villien et al., 2014).

   Introduction, page 5, line 123

   Specifically, it is unknown whether the observed dissociation between patterns of metabolism and BOLD changes in the DMN generalizes for complex cognitive tasks, and whether this in turn depends on the brain networks supporting the task performance and their interaction with the DMN.

   Results, page 7, line 143:

   From this dataset (DS1) we evaluated the spatial overlap of negative task responses in the cerebral metabolic rate of glucose (CMRGlu quantified with the Patlak plot) and the BOLD signal specifically in the pmDMN. […] After that, the distinct spatial activation patterns across different tasks were used to quantitatively characterize the CMRGlu response of the pmDMN in DS1.

   The method of functional PET (fPET) imaging indeed enables the evaluation of changes in glucose metabolism with a relatively high temporal resolution. That is, a conventional bolus application and subsequent quantification yield a single CMRGlu image per scan of about 60 min (typical frame length ~1-5 min) or a single SUV image from a static scan. In contrast, the constant infusion employed in fPET allows to assess baseline metabolism and changes induced by different tasks in a single scan by using a frame length currently down to 6-30 s (Rischka et al., 2018), where the latter was also used in the current study. A general description of the fPET approach is now also included in the manuscript.

   Introduction, page 5, line 99:

   In this context, functional PET (fPET) imaging represents a promising approach to investigate the dynamics of brain metabolism. fPET refers to the assessment of stimulation-induced changes in physiological processes such as glucose metabolism (Villien et al., 2014; Hahn et al., 2016) and neurotransmitter synthesis (Hahn et al., 2021) in a single scan. The temporal resolution of this approach of 6-30 s (Rischka et al., 2018) is considerably higher than that of a conventional bolus administration. This is achieved through the constant infusion of the radioligand, thereby providing free radioligand throughout the scan that is available to bind according to the actual task demands. Here, the term “functional” is used in analogy to fMRI, where paradigms are often presented in repeated blocks of stimulation, which can subsequently be assessed by the general linear model.

   Regarding the absolute quantification of CMRGlu, arterial blood samples were obtained from all subjects of DS1. These were used for absolution quantification of CMRGlu with the Patlak plot. Full details were already provided in the methods section and are now also mentioned in the results.

   Results, page 7, line 140:

   Simultaneous fPET/fMRI data and arterial blood samples were acquired from 50 healthy participants during the performance of the video game Tetris®, a challenging cognitive task requiring rapid visuo spatial processing and motor coordination (Hahn et al., 2020; Klug et al., 2022). From this dataset (DS1) we evaluated the spatial overlap of negative task responses in the cerebral metabolic rate of glucose (CMRGlu quantified with the Patlak plot) and the BOLD signal specifically in the pmDMN.

   Methods, page 19, line 399:

   For glucose metabolism, these changes are absolutely quantified in μmol/100g/min with the arterial input function and the Patlak plot.

   Methods, blood sampling, page 24, line 536:

   Before the PET/MRI scan blood glucose levels were assessed as triplicate (Gluplasma). During the PET/MRI acquisitions manual arterial blood samples were drawn at 3, 4, 5, 14, 25, 36 and 47 min after the start of the radiotracer administration (Rischka et al., 2018). From these samples whole-blood and plasma activity were measured in a gamma counter (Wizard2, Perkin Elmer). The arterial input function was obtained by linear interpolation of the manual samples to match PET frames and multiplication with the average plasma-to-whole-blood ratio.

   Methods, cerebral metabolic rate of glucose metabolism, page 25, line 561:

   Quantification was carried out with the Patlak plot (t* fixed to 15 min) and the influx constant Ki was converted to CMRGlu as CMRGlu = Ki * Gluplasma / LC * 100 with LC being the lumped constant = 0.89 (Graham et al. 2002, Wienhard 2002).

   1.3) It is surmised that the glutamatergic/GABAergic involvement of these metabolic differences in PCC is from another study, but what mechanism causes the BOLD signal to decrease in both stimuli? This is where the authors have to divulge the biophysical basis of the BOLD response. At the most basic level, the BOLD signal change (dS) can be positive or negative depending on the degree of coupling with changed blood flow (dCBF) and oxidative metabolism (dCMRO2) from resting condition. Unfortunately, neither CBF nor CMRO2 was measured in this study. In the absence of these additional measurements, the authors should at least discuss the basis of the BOLD response with regard to CBF and CMRO2. If we assume that both attention-demanding and working memory tasks decreased BOLD response in PCC in the same way, we have identical dCBF/dCMRO2 in PCC with both tasks, i.e., their results seem to suggest an alteration in aerobic glycolysis with different tasks. With attention-demanding tasks, CMRglc decreases similarly to CMRO2 decreases in PCC, whereas with working memory tasks, CMRglc increases differently from CMRO2 decreases. This suggests PCC may the oxygen to glucose index (OGI=CMRO2/CMRglc) would rise in PCC attention-demanding tasks, but fall in PCC with working memory tasks. This is obviously an implication rather than a conclusion as CBF or CMRO2 were not measured.

   1.4) Given the missing attention that gives rise to the BOLD contrast mechanism, it is almost necessary to discuss the biophysical basis of BOLD contrast and specifically how metabolic changes have been linked to both increases and decreases in neuronal activity in the past. Although this type of work has largely been conducted in animal models, it seems that this topic needs to be discussed as well.

   We would like to thank the reviewer for sharing these insightful ideas and for bringing up these aspects that indeed appear to be essential for the manuscript. Since the points 1.3. and 1.4 complement each other, we have combined them and created a shared response. To fully address the points, the following paragraphs were added to the manuscript.

   Discussion, page 15, line 310:

   Metabolic and neurophysiological considerations effects

   The distinct relationships between BOLD and CMRGlu signals that emerge during specific tasks highlight the different physiological processes contributing to neuronal activation of cognitive processing (Goyal and Snyder, 2021; Singh, 2012). While CMRGlu measured by fPET provides an absolute indicator for glucose consumption, the BOLD signal reflects deoxyhemoglobin concentration, which depends on various factors, such as cerebral blood flow (CBF), cerebral blood volume (CBV) and the cerebral metabolic rate of oxygen (CMRO2) (Goense et al., 2016). In simple terms, the BOLD signal relates to the ratio of ∆CBF/∆CMRO2. Assuming that the observed BOLD decreases during Tetris® and WM emerge from the same mechanisms, this would result in a comparable ∆CBF/∆CMRO2 in the pmDMN for both tasks. Given that these types of tasks (external attention and cognitive control) elicit a reduction in CBF in the pmDMN (Shulman 97, Zou 2011), CMRO2 also decreases albeit to a lesser extent (Raichle 2001). Therefore, the respective metabolic processes can be described by their oxygen-to-glucose index (OGI), the ratio of CMRO2/CMRGlu. Accordingly, our results suggest two distinct pathways underlying BOLD deactivations in the pmDMN that differ regarding their OGI. During Tetris® there is a BOLD deactivation with a high OGI, resulting from a larger decrease in CMRGlu than CMRO2. This metabolically inactive state is in line with electrophysiological recordings in humans (Fox et al., 2018) and in non-human primates showing a decrease of neuronal activity in the pmDMN that covaries with the degree of exteroceptive vigilance (Shmuel et al., 2006; Bentley et al., 2016; Hayden et al., 2009). Therefore, we suggest that the negative BOLD response during external tasks reflects a reduction of neuronal activity and their respective metabolic demands. On the other hand, the relatively increased CMRGlu without the corresponding surge in CMRO2 hints at another kind of BOLD deactivation with a low OGI in the pmDMN during working memory, indicating energy supply by aerobic glycolysis (Vaishnavi et al., 2010; Blazey et al., 2019). Previous work in non-human primates has indeed suggested a differential coupling of neuronal activity to hemodynamic oxygen supply in this region (Bentley et al., 2016). Furthermore, tonic suppression of PCC neuronal spiking during task performance was punctuated by positive phasic responses (Hayden et al., 2009), which could indicate differences between both tasks also at the level of electrophysiologically measured activity.

   Reviewer #2 (Public Review):

   2.0) This paper provides an important and insightful investigation into patterns of activations that emerge in external task states. The authors use state-of-the-art methods and novel analytic approaches to establish that deactivations in the default mode network during external tasks are driven by activity in brain regions that are important in the current tasks (such as the visual or dorsal attention networks). It will be important in the future to understand whether this is a symmetrical phenomenon by studying this behaviour in states that maximize activity within the default mode network and also drive reductions in networks that are not relevant to these situations.

   We thank the reviewer for the encouraging feedback and the constructive comments on our manuscript. We particularly appreciate the interest in the research and the insightful suggestions for future work.

   Reviewer #3 (Public Review):

   3.0) The authors report a study where, using multiple datasets with [18F]FDG PET bolus + continuous infusion ("functional PET") and BOLD fMRI data, they re-evaluate the metabolic and hemodynamic properties of the default mode network (DMN) in a task-evoked context, with a focus on posteromedial DMN due to its relevance for across-network integration. They show how posterior DMN is differently engaged depending on the chosen task: while visual and motor tasks lead to BOLD deactivations and glucose metabolic decrease, specifically in the dorsal posterior cingulate cortex (PCC) area, working memory tasks produce BOLD deactivations but metabolic increases, specifically in ventral PCC, as shown in their previous paper (Stiernman et al. 2021, https://doi.org/10.1073/pnas.2021913118). This aims to solve the controversies elicited by findings of both increased and decreased glucose consumption in the presence of BOLD deactivation in the DMN.

   Additionally, they show how task-evoked glucose metabolism in posterior DMN seems to be shaped by that of the corresponding task-positive networks, with a positive link with dorsal attention and a negative link with frontoparietal network metabolism. This is explored using a type of directional connectivity analysis called "metabolic connectivity mapping", drawn from their previous work (Riedl et al. 2016, https://doi.org/10.1073/pnas.1513752113; Hahn et al. 2020, https://doi.org/10.7554/eLife.52443). They go on to speculate that concomitant BOLD deactivation and reductions in glucose expense might relate to decreased glutamatergic signaling, while BOLD deactivations accompanied by increased glucose consumption might depend on increased GABAergic neuronal activity.

   This is a relevant topic because it not only shows how the DMN is flexibly engaged in different tasks but also allows us to better understand the complex relationships between BOLD fMRI and [18F]FDG PET signals, which are still not fully characterized to this day. Of course, while in resting state the situation is further complicated by the more uncertain physiological meaning of the resting BOLD signal, task-evoked states are expected to provide a more interpretable intermodal link between metabolism and hemodynamics, due to the known major changes in blood flow, blood volume, and glucose metabolism - which underlie BOLD and [18F]FDG signal changes - in response to neural activation. However, even in task states, there is not always a strong association between the two responses, as previously shown by the authors themselves (Rischka et al. 2018, https://doi.org/10.1016/j.neuroimage.2018.06.079). This is something I think the authors should stress out a little more, as they have previously done (Rischka et al. 2018, https://doi.org/10.1016/j.neuroimage.2018.06.079), both in the introduction and in reference to Figure 1, which shows clear differences between BOLD and [18F]FDG activations/deactivations (e.g., widespread negative responses in the cerebellum for [18F]FDG).

   Overall, the analyses reported in the manuscript are simple and seem mostly sound, drawing from well-established methods in PET and fMRI activation studies, with additional approaches previously developed by some of the authors themselves (e.g., "metabolic connectivity mapping", Riedl et al. 2016, https://doi.org/10.1073/pnas.1513752113). Moreover, a clear strength of the paper is the high number of subjects, at least from a PET perspective, i.e., n = 50 for the Tetris task, plus group averages of previously published data for working memory (Stiernman et al. 2021, https://doi.org/10.1073/pnas.2021913118) and motor tasks (Hahn et al. 2018, https://doi.org/10.1007/s00429-017-1558-0).

   The conclusions are in line with the results, and, though a little speculative, are potentially relevant for further exploration aimed at characterizing the neurotransmitter pathways underlying positive and negative BOLD and [18F]FDG responses. Moreover, the language is sufficiently clear to allow a proper understanding of the aims and the results, as well as the details of the analyses. As a side note, the title should probably be adjusted to "Task-evoked metabolic demands of the posteromedial default mode network are shaped by dorsal attention and frontoparietal control networks", to emphasize that the findings do not necessarily generalize to the resting state.

   In conclusion, I am overall quite positive about this manuscript, which seems to nicely position itself within the existing literature, making some additional contributions.

   We thank the reviewer for the thorough evaluation and the positive feedback on our manuscript, we appreciate the constructive and insightful suggestions. We agree that the differential spatial patterns of activation between the BOLD signal and CMRGlu response require further attention. To address this point in more detail, we have added the following information to the manuscript.

   Introduction, page 5, line 110:

   Studies using simultaneous fPET/fMRI have shown a strong spatial correspondence between the BOLD signal changes and glucose metabolism in several task-positive networks and across various tasks requiring different levels of cognitive engagement (Hahn et al., 2020, 2016; Jamadar et al., 2019; Rischka et al., 2018; Stiernman et al., 2021; Villien et al., 2014). […]. However, also regional differences in activation patterns have been observed previously between these modalities in these and previous studies (Wehrl et al., 2013). Moreover, a dissociation between BOLD changes (negative) and glucose metabolism (positive) has recently been observed even in the same region of the DMN during working memory (Stiernman et al., 2021), namely the posteromedial default mode network (pmDMN).

   Results, caption Figure 1, page 8, line 173

   White clusters represent the intersection of significant CMRGlu and BOLD signal changes, irrespective of direction. Note, that also relevant differences between both imaging parameters can be observed, such as decreased CMRGlu in the cerebellum (in both datasets), without changes in the BOLD signal.

   We appreciate the reviewer’s proposal for the title as it raises awareness that the activation patterns reflect task-specific inference.

   Title:

   Task-evoked metabolic demands of the posteromedial default mode network are shaped by dorsal attention and frontoparietal control networks

   We have limited the discussion of underlying neurotransmitter effects and explicitly mention that these are of speculative nature. For manuscript adaptation on this point, we would like to refer to points 1.1, 1.3, 1.4 that address this topic as well.

   Read the original source
3. 

   eLife

   Apr 22, 2023

   eLife assessment

   This important study advances our understanding of the metabolic and hemodynamic underpinnings of different brain networks. The evidence is convincing, drawn from multiple datasets and including simultaneous fMRI and PET, although the authors should make clear which claims are supported by their evidence and which are speculation based on the literature. The study will be of interest to neuroscientists and researchers who use functional neuroimaging tools to study brain activity.

   Read the original source
4. 

   eLife

   Apr 22, 2023

   Reviewer #1 (Public Review):

   This paper investigates the metabolic basis of a node, posterior cingulate cortex (PCC), in the default node network (DMN). They employed sophisticated MRI-PET methods to measure both BOLD and CMRglc changes (both magnitude and dynamics) during attention-demanding and working memory tasks. They found uncoupling of BOLD and CMRglc in PCC with these different tasks. The implications of these findings are poorly interpreted, with a conclusion that is purely based on other work independent of this study. Various suggestions could allow them to place some speculations in line with a stronger interpretation of their results.

   This is one of several papers in recent years investigating the metabolic underpinnings of activated (or task-positive) and deactivated (or task-negative) cortical areas in the human brain. In …

   Reviewer #1 (Public Review):

   This paper investigates the metabolic basis of a node, posterior cingulate cortex (PCC), in the default node network (DMN). They employed sophisticated MRI-PET methods to measure both BOLD and CMRglc changes (both magnitude and dynamics) during attention-demanding and working memory tasks. They found uncoupling of BOLD and CMRglc in PCC with these different tasks. The implications of these findings are poorly interpreted, with a conclusion that is purely based on other work independent of this study. Various suggestions could allow them to place some speculations in line with a stronger interpretation of their results.

   This is one of several papers in recent years investigating the metabolic underpinnings of activated (or task-positive) and deactivated (or task-negative) cortical areas in the human brain. In this study, they used BOLD fMRI and glucose PET scan to examine the metabolic distinction of the default node network (DMN), which is known to be deactivated during attention-demanding tasks, with different types of cognitively demanding tasks. Unlike the BOLD response in posteromedial DMN which is consistently negative, they found that CMRglc of the posteromedial DMN (a task-negative network) is dependent on the metabolic demands of adjacent task-positive networks like the dorsal attention network (DAN) and frontoparietal network (FPN). With attention-demanding tasks (like Tetris) the BOLD and CMRglc are both downregulated in DMN (specifically the posterior cingulate cortex, PCC, a task-negative node of DMN), but working memory induces CMRglc increase in PCC and which is decoupled from the negative BOLD response in PCC.

   1. These complicated results are the main findings, and to provide a biological basis to these data they rather surprisingly, but without their own experimental evidence, conclude that the negative BOLD and negative CMRglc in PCC during attention-demanding tasks is due to decreased glutamate signaling (which was not measured in this study) and the negative BOLD and positive CMRglc in PCC during working memory is due to increased GABAergic activity (which was not measured in this study). It is rather surprising that without measurement, a conclusion is made which would at best be considered a hypothesis to be tested. Thus, independent of these hypothesized mechanisms, they need to summarize their results based on their own measurements in this study (see 3 for a hint).

   2. It is mentioned that the FDG-PET scans allow quantitative CMRglc, both in terms of units of glucose use but also with high time resolution. Based on the method described, it isn't clear how this is possible. Important details of either prior work or their own work have been excluded that show how the time course of CMRglc (regardless of whether it's absolute or relative) can be compared with the BOLD time course. Furthermore, it is extremely difficult to conceive that quantitative CMRglc can be estimated without additional measurements (e.g., blood samples, etc). Significant methodological details have to be provided, which even should make their way to results given the importance of their BOLD-CMRglc coupling and decoupling in the same region.

   3. It is surmised that the glutamatergic/GABAergic involvement of these metabolic differences in PCC is from another study, but what mechanism causes the BOLD signal to decrease in both stimuli? This is where the authors have to divulge the biophysical basis of the BOLD response. At the most basic level, the BOLD signal change (dS) can be positive or negative depending on the degree of coupling with changed blood flow (dCBF) and oxidative metabolism (dCMRO2) from resting condition. Unfortunately, neither CBF nor CMRO2 was measured in this study. In the absence of these additional measurements, the authors should at least discuss the basis of the BOLD response with regard to CBF and CMRO2. If we assume that both attention-demanding and working memory tasks decreased BOLD response in PCC in the same way, we have identical dCBF/dCMRO2 in PCC with both tasks, i.e., their results seem to suggest an alteration in aerobic glycolysis with different tasks. With attention-demanding tasks, CMRglc decreases similarly to CMRO2 decreases in PCC, whereas with working memory tasks, CMRglc increases differently from CMRO2 decreases. This suggests PCC may the oxygen to glucose index (OGI=CMRO2/CMRglc) would rise in PCC attention-demanding tasks, but fall in PCC with working memory tasks. This is obviously an implication rather than a conclusion as CBF or CMRO2 were not measured.

   4. Given the missing attention that gives rise to the BOLD contrast mechanism, it is almost necessary to discuss the biophysical basis of BOLD contrast and specifically how metabolic changes have been linked to both increases and decreases in neuronal activity in the past. Although this type of work has largely been conducted in animal models, it seems that this topic needs to be discussed as well.

   Read the original source
5. 

   eLife

   Apr 22, 2023

   Reviewer #2 (Public Review):

   This paper provides an important and insightful investigation into patterns of activations that emerge in external task states. The authors use state-of-the-art methods and novel analytic approaches to establish that deactivations in the default mode network during external tasks are driven by activity in brain regions that are important in the current tasks (such as the visual or dorsal attention networks). It will be important in the future to understand whether this is a symmetrical phenomenon by studying this behaviour in states that maximize activity within the default mode network and also drive reductions in networks that are not relevant to these situations.

   Read the original source
6. 

   eLife

   Apr 22, 2023

   Reviewer #3 (Public Review):

   The authors report a study where, using multiple datasets with [18F]FDG PET bolus + continuous infusion ("functional PET") and BOLD fMRI data, they re-evaluate the metabolic and hemodynamic properties of the default mode network (DMN) in a task-evoked context, with a focus on posteromedial DMN due to its relevance for across-network integration.
   They show how posterior DMN is differently engaged depending on the chosen task: while visual and motor tasks lead to BOLD deactivations and glucose metabolic decrease, specifically in the dorsal posterior cingulate cortex (PCC) area, working memory tasks produce BOLD deactivations but metabolic increases, specifically in ventral PCC, as shown in their previous paper (Stiernman et al. 2021, https://doi.org/10.1073/pnas.2021913118). This aims to solve the …

   Reviewer #3 (Public Review):

   The authors report a study where, using multiple datasets with [18F]FDG PET bolus + continuous infusion ("functional PET") and BOLD fMRI data, they re-evaluate the metabolic and hemodynamic properties of the default mode network (DMN) in a task-evoked context, with a focus on posteromedial DMN due to its relevance for across-network integration.
   They show how posterior DMN is differently engaged depending on the chosen task: while visual and motor tasks lead to BOLD deactivations and glucose metabolic decrease, specifically in the dorsal posterior cingulate cortex (PCC) area, working memory tasks produce BOLD deactivations but metabolic increases, specifically in ventral PCC, as shown in their previous paper (Stiernman et al. 2021, https://doi.org/10.1073/pnas.2021913118). This aims to solve the controversies elicited by findings of both increased and decreased glucose consumption in the presence of BOLD deactivation in the DMN.

   Additionally, they show how task-evoked glucose metabolism in posterior DMN seems to be shaped by that of the corresponding task-positive networks, with a positive link with dorsal attention and a negative link with frontoparietal network metabolism. This is explored using a type of directional connectivity analysis called "metabolic connectivity mapping", drawn from their previous work (Riedl et al. 2016, https://doi.org/10.1073/pnas.1513752113; Hahn et al. 2020, https://doi.org/10.7554/eLife.52443). They go on to speculate that concomitant BOLD deactivation and reductions in glucose expense might relate to decreased glutamatergic signaling, while BOLD deactivations accompanied by increased glucose consumption might depend on increased GABAergic neuronal activity.

   This is a relevant topic because it not only shows how the DMN is flexibly engaged in different tasks but also allows us to better understand the complex relationships between BOLD fMRI and [18F]FDG PET signals, which are still not fully characterized to this day. Of course, while in resting state the situation is further complicated by the more uncertain physiological meaning of the resting BOLD signal, task-evoked states are expected to provide a more interpretable intermodal link between metabolism and hemodynamics, due to the known major changes in blood flow, blood volume, and glucose metabolism - which underlie BOLD and [18F]FDG signal changes - in response to neural activation. However, even in task states, there is not always a strong association between the two responses, as previously shown by the authors themselves (Rischka et al. 2018, https://doi.org/10.1016/j.neuroimage.2018.06.079). This is something I think the authors should stress out a little more, as they have previously done (Rischka et al. 2018, https://doi.org/10.1016/j.neuroimage.2018.06.079), both in the introduction and in reference to Figure 1, which shows clear differences between BOLD and [18F]FDG activations/deactivations (e.g., widespread negative responses in the cerebellum for [18F]FDG).

   Overall, the analyses reported in the manuscript are simple and seem mostly sound, drawing from well-established methods in PET and fMRI activation studies, with additional approaches previously developed by some of the authors themselves (e.g., "metabolic connectivity mapping", Riedl et al. 2016, https://doi.org/10.1073/pnas.1513752113). Moreover, a clear strength of the paper is the high number of subjects, at least from a PET perspective, i.e., n = 50 for the Tetris task, plus group averages of previously published data for working memory (Stiernman et al. 2021, https://doi.org/10.1073/pnas.2021913118) and motor tasks (Hahn et al. 2018, https://doi.org/10.1007/s00429-017-1558-0).

   The conclusions are in line with the results, and, though a little speculative, are potentially relevant for further exploration aimed at characterizing the neurotransmitter pathways underlying positive and negative BOLD and [18F]FDG responses. Moreover, the language is sufficiently clear to allow a proper understanding of the aims and the results, as well as the details of the analyses. As a side note, the title should probably be adjusted to "Task-evoked metabolic demands of the posteromedial default mode network are shaped by dorsal attention and frontoparietal control networks", to emphasize that the findings do not necessarily generalize to the resting state.

   In conclusion, I am overall quite positive about this manuscript, which seems to nicely position itself within the existing literature, making some additional contributions.

   Read the original source
7. 

   Version published to 10.1101/2022.08.12.503715v1 on bioRxiv

   Aug 12, 2022