Biology subjects

Riedl, V.

Publications and source records attributed to Riedl, V..

11 recordsLinked to original sources

bioRxiv2026

Directed cortical connectivity inferred from neural energy metabolism

Complex cognition depends on directed communication between brain regions. Yet, resting functional MRI resolves only undirected associations, and inferring the direction of signalling has proven difficult across the whole brain. Because synaptic transmission is metabolically asymmetric, with the postsynaptic neuron bearing most of the energetic cost, regional energy demand carries a physical signature of the direction of communication. Here, we show that this signature organizes the human cortex into a reproducible directed hierarchy, in which sensory and attentional systems preferentially drive higher-order networks, while the default mode and control networks act as principal receivers. Critically, the inferred directionality is independently predicted by two cellular markers of neuronal energy demand, regional mitochondrial density and laminar cytoarchitecture, linking macroscale information flow to its cellular substrate. The directed architecture recapitulates the known feedforward and feedback organization of visual pathways and, using an average CMRGlc template, extends to functional MRI acquired without PET. By grounding communication direction in energy metabolism, Metabolic Connectivity Mapping bridges correlative and effective connectivity and offers a scalable, biologically interpretable framework for mapping directed communication across the human brain.

neuroscience↗

bioRxiv2025

Metabolism-weighted brain connectome reveals synaptic integration and vulnerability to neurodegeneration

The brains capacity for integration arises from both its structural wiring and energetically demanding electrochemical signaling. Yet current connectome analyses treat network nodes as functionally homogeneous, ignoring that neural communication is constrained by metabolic cost. Here we introduce a metabolism-weighted connectome, a fully weighted brain graph in which both connections and the metabolic activity of each node describe the networks capacity for integration. Using three datasets of simultaneous fMRI and FDG-PET acquisitions, we define metabolism-weighted centrality (MwC), a biologically grounded index of each regions signaling dominance that integrates functional connectivity (FC) with local energy metabolism. MwC provides a more accurate representation of cortical activity flow than classical edge-based metrics and reveals that metabolically active hubs align with higher-order cognitive networks. Transcriptomic and synaptic imaging data demonstrate that these hubs exhibit increased synaptic energy turnover, linking activity-driven centrality to the molecular architecture of signaling. Notably, the same high-MwC regions show greater susceptibility to neurodegenerative pathology, suggesting that lifelong metabolic demand influences both integrative function and disease vulnerability. By linking neuronal metabolism to network organization, our framework bridges cellular energetics and system-level computation, opening new avenues for interpreting brain vulnerability and performance. Significance StatementThe various regions of the brain exhibit unequal energy consumption. Specifically, areas associated with cognitive functions such as memory and attention are metabolic hotspots of intense neural activity. Yet standard connectivity analyses ignore this energetic dimension, treating all regions as functionally equivalent. We introduce the metabolism-weighted connectome (MwC), a framework that maps brain connectivity weighted by each regions energy expenditure. Using simultaneous PET-MRI in healthy volunteers, we show that energetically most active hubs anchor networks for complex cognition, exhibit molecular signatures of intense synaptic activity, and are disproportionately vulnerable to Alzheimers disease. These findings establish a unifying principle: the brain regions most essential for cognition bear the greatest metabolic burden, and this cost may underlie their susceptibility to neurodegeneration.

neuroscience↗

bioRxiv2024

The selfish yet forgetful brain: Stable cerebral oxygen metabolism during hypoglycemia but impaired memory consolidation

The continuous supply of glucose and oxygen is essential for healthy brain function. Accordingly, the Selfish Brain Theory proposes that the human brain prioritizes its own energy demands, making it less vulnerable to fluctuations in systemic energy availability. Although studies have reported decreases in cerebral glucose metabolism, alternative energy sources other than glucose might be oxidized for ATP production. However, cerebral oxygen metabolism (CMRO2) has never been quantified across the human brain. In this study, we investigated the influence of insulin-induced hypoglycemia on CMRO2 in healthy male participants. Additionally, we explored the prolonged effects of hypoglycemia on cognitive function following the restoration of euglycemia. We found that CMRO2 remained stable under hypoglycemia, even at blood glucose levels below 49 mg/dL. Interestingly, we detected a significant increase in cerebral blood flow (CBF) of up to 11%, particularly in regions involved in higher cognitive processing. Despite stable rates of oxygen metabolism, we identified a selective impairment in memory consolidation following hypoglycemia, even after normal glucose levels were restored, with no effects observed in memory encoding or attention. In favor of the Selfish Brain Theory, the stability in CMRO2 suggests that the brain efficiently shifts to alternate energy pathways under hypoglycemia, potentially using astrocytic glycogen. Despite this metabolic flexibility, our results indicate that prior hypoglycemia imposes long-lasting effects on memory consolidation, possibly linked to glycogen depletion and impaired glutamate synthesis. In summary, our study suggests that clinical states of hypoglycemia pose a critical impact on patient brain health and functioning.

neuroscience↗

bioRxiv2024

Simultaneous quantification of oxygen and glucose consumption during visual stimulation in the human cortex

The brain is fueled by oxidative glucose metabolism, yet activation may uncouple oxygen and glucose consumption. Because the two have never been measured simultaneously in humans across conditions, we combined multiparametric quantitative BOLD (mqBOLD) imaging with functional PET (fPET) during rest and visual stimulation. Participants (n = 12 with complete data) were scanned on an integrated PET/MR system during alternating six-minute blocks of checkerboard stimulation and fixation, with continuous arterial sampling. Cerebral metabolic rates of oxygen (CMRO2) and glucose (CMRglc), and cerebral blood flow (CBF) were quantified per condition and combined into the oxygen-to-glucose index (OGI = CMRO2/CMRglc), where six indicates fully oxidative glucose use. At rest, OGI was 4.6-5.0 in task-responsive visual regions and 4.9 elsewhere. Stimulation increased CMRglc (12-26%) and CBF (10-22%) more than CMRO2; (3-10%, not significant), lowering OGI by 12% in metabolically defined visual regions. Beyond the visual cortex, OGI fell 8%, significantly in 47% of cortical regions. The mechanisms differed, however: glycolytic upregulation in visual cortex (CBF, CMRglc up) versus oxidative downregulation elsewhere (CBF, CMRO2; down; CMRglc unchanged). Simultaneous imaging thus shows that focal activation shifts metabolism toward glycolysis across the cortex, with consequences for interpreting metabolic contrasts outside engaged regions.

neuroscience↗

bioRxiv2024

Bridging Glucose Metabolism and Intrinsic Functional Organization of the Human Cortex

The human brain requires a continuous supply of energy to function effectively. Here, we investigated how the low-dimensional organization of intrinsic functional connectivity patterns based on resting-state functional magnetic resonance imaging relates to brain energy expenditure measured by fluorodeoxyglucose positron emission tomography. By incrementally adding more dimensions of brain organization (via functional gradients), we were able to show that increasing amounts of variance in the map of brain energy expenditure could be accounted for. In particular, the brain organization dimensions that explained a large amount of the variance in intrinsic brain function also explained a large amount of the regional variance in the energy expenditure maps. This was particularly true for brain organization maps based on the strongest connections, suggesting that "weak" connections may not explain as much energy variance. Notably, our topological model was more effective than random brain organization configurations, suggesting that brain organization may be specifically associated with energy optimization. Finally, using brain asymmetry as a model for metabolic efficiency, we found that optimizing energy expenditure independently in each hemisphere outperformed non-independent optimization. This supports the concept of hemispheric competition rather than lateralization in energy allocation. Our results demonstrate how the spatial organization of functional connections is systematically linked to optimized energy expenditure in the human brain, providing new insights into the metabolic basis of brain function.

neuroscience↗

bioRxiv2024

Subjective confidence modulates individual BOLD patterns of predictive processing

Humans are adept at extracting and learning sequential patterns from sensory input. This ability enables predictions about future states, resulting in anticipation both on a behavioral and neural level. Stimuli deviating from predictions usually evoke higher neural and hemodynamic activity than predicted stimuli. This difference indicates increased surprise, or prediction error signaling in the context of predictive coding. However, interindividual differences in learning performance and uncertainty have rarely been taken into account. Under Bayesian formulations of cortical function, surprise should be strongest if a subject makes incorrect predictions with high confidence. In the present study, we studied the impact of subjective confidence on imaging markers of predictive processing. Participants viewed visual object sequences of varying predictability over multiple days. After each day, we instructed them to complete partially presented sequences and to rate their confidence in the decision. During fMRI scanning, participants saw sequences that either confirmed predictions, deviated from them, or were random. We replicated findings of increased BOLD responses to surprising input in the ventral visual stream. In line with our hypothesis, response magnitude increased with the level of confidence after the training phase. Interestingly, the activity difference between predictable and random input also scaled with confidence: In the anterior cingulate, we found tentative evidence that predictable sequences elicited higher activity for low levels of confidence, but lower activity for high levels of confidence. In summary, we showed that confidence is a crucial moderator of the link between predictive processing and BOLD activity.

neuroscience↗

bioRxiv2024

The control costs of human brain dynamics

The human brain is a complex system with high metabolic demands and extensive connectivity that requires control to balance energy consumption and functional efficiency over time. How this control is manifested on a whole-brain scale is largely unexplored, particularly what the associated costs are. Using network control theory, here we introduce a novel concept, time-averaged control energy (TCE), to quantify the cost of controlling human brain dynamics at rest, as measured from functional and diffusion MRI. Importantly, TCE spatially correlates with oxygen metabolism measures from positron emission tomography, providing insight into the bioenergetic footing of resting state control. Examining the temporal dimension of control costs, we find that brain state transitions along a hierarchical axis from sensory to association areas are more efficient in terms of control costs and more frequent within hierarchical groups than between. This inverse correlation between temporal control costs and state visits suggests a mechanism for maintaining functional diversity while minimizing energy expenditure. By unpacking the temporal dimension of control costs, we contribute to the neuroscientific understanding of how the brain governs its functionality while managing energy expenses.

neuroscience↗

bioRxiv2023

The energy metabolic footprint of predictive processing in the human brain

Our expectations about the world influence how we interpret visual information, improving the speed and accuracy of perception. However, the underlying neural activity requires energy which is strictly limited in the brain. While predictive processing is a prevalent framework to explain perception, it remains unclear whether it also serves energy-efficient processing. Here, we employed metabolic brain imaging to quantify oxygen consumption during visual perception under varying levels of input predictability and subjective uncertainty. For three days, we presented participants with object sequences that were either predictable or unpredictable, and assessed their performance and confidence in predicting follow-up objects from partial sequences. On the fourth day, we first tested for behavioral consequences of predictability. We found that subjects detected predictable objects quicker than unpredictable ones. We then quantified cortical oxygen consumption during passive viewing of predictable, unpredictable or surprising sequences. Despite highly similar sensory load, predictable visual input elicited reduced oxygen metabolism when subjects were confident, across both sensory and higher cognitive areas. Crucially, this summed up to cortical energy savings of up to 12%, or 118 mol oxygen per minute, given average brain size. In contrast, cost increases due to surprising input were restricted to a network of fronto-parietal areas. In summary, we found that predictive processing enhances behavioral performance and notably reduces signaling costs, moderated by subjective confidence. This suggests that examining energy efficiency alongside behavioral performance may uncover novel computational strategies of human cognition and behavior.

neuroscience↗

bioRxiv2023

Two distinct modes of hemodynamic responses in the human brain

Functional MRI measures brain activity by monitoring changes in blood oxygen levels, known as the blood-oxygen-level-dependent (BOLD) signal, rather than measuring neuronal activity directly. This approach crucially relies on neurovascular coupling, the mechanism that links neuronal activity to changes in cerebral blood flow (CBF). However, it remains unclear whether this relationship is consistent for both positive and negative BOLD responses across the human cortex. Here we found that about 40% of voxels with significant BOLD signal changes during various tasks showed reversed oxygen metabolism, particularly in the default mode network. These "discordant" voxels differed in baseline oxygen extraction fraction (OEF) and regulated oxygen demand via OEF changes, while "concordant" voxels depended mainly on CBF changes. Our findings challenge the canonical interpretation of the BOLD signal, indicating that quantitative fMRI provides a more reliable assessment of both absolute and relative changes in neuronal activity.

neuroscience↗

bioRxiv2023

An energy costly architecture of neuromodulators for human brain evolution and cognition

Humans spend more energy on the brain than any other species. However, the high energy demand cannot be fully explained by brain size scaling alone. We hypothesized that energy-demanding signaling strategies may have contributed to human cognitive development. We measured the energy distribution along signaling pathways using multimodal brain imaging and found that evolutionarily novel connections have up to 67% higher energetic costs of signaling than sensory-motor pathways. Additionally, histology, transcriptomic data, and molecular imaging independently reveal an upregulation of signaling at G-protein coupled receptors in energy-demanding regions. We found that neuromodulators are predominantly involved in complex cognition such as reading or memory processing. Our study suggests that the upregulation of neuromodulator activity, alongside increased brain size, is a crucial aspect of human brain evolution.

neuroscience↗

bioRxiv2019

The physiological effects of non-invasive brain stimulation fundamentally differ across the human cortex

Non-invasive brain stimulation reliably modulates brain activity and symptoms of neuropsychiatric disorders. However, stimulation effects substantially vary across individuals and brain regions. We combined transcranial magnetic stimulation (TMS) and functional magnetic resonance imaging (fMRI) to investigate the neuronal basis of inter-individual and inter-areal differences after TMS. We found that stimulating sensory and cognitive areas yielded fundamentally heterogeneous effects. Stimulation of occipital cortex enhanced brain-wide functional connectivity and biophysical modeling identified increased local inhibition and enhanced forward-signaling after TMS. Conversely, frontal stimulation decreased functional connectivity, associated with local disinhibition and disruptions of both feedforward and feedback connections. Finally, we identified brain-wide functional integration as a predictive marker for these heterogeneous stimulation effects in individual subjects. Together, our study suggests that modeling of local and global signaling parameters of a target area will improve the specificity of non-invasive brain stimulation for research and clinical applications.

neuroscience↗