Biology subjects

Castrillon, G.

Publications and source records attributed to Castrillon, G..

9 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

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

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

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↗

bioRxiv2018

Contribution of common and rare variants to bipolar disorder susceptibility in extended pedigrees from population isolates

Current evidence from case/control studies indicates that genetic risk for psychiatric disorders derives primarily from numerous common variants, each with a small phenotypic impact. The literature describing apparent segregation of bipolar disorder (BP) in numerous multigenerational pedigrees suggests that, in such families, large-effect inherited variants might play a greater role. To evaluate this hypothesis, we conducted genetic analyses in 26 Colombian (CO) and Costa Rican (CR) pedigrees ascertained for BP1, the most severe and heritable form of BP. In these pedigrees, we performed microarray SNP genotyping of 856 individuals and high-coverage whole-genome sequencing of 454 individuals. Compared to their unaffected relatives, BP1 individuals had higher polygenic risk scores estimated from SNPs associated with BP discovered in independent genome-wide association studies, and also displayed a higher burden of rare deleterious single nucleotide variants (SNVs) and rare copy number variants (CNVs) in genes likely to be relevant to BP1. Parametric and non-parametric linkage analyses identified 15 BP1 linkage peaks, encompassing about 100 genes, although we observed no significant segregation pattern for any particular rare SNVs and CNVs. These results suggest that even in extended pedigrees, genetic risk for BP appears to derive mainly from small to moderate effect rare and common variants.

genetics↗