The energetic costs of signaling as a proxy of cognition in health and disease
Presented During:
Thursday, June 27, 2024: 11:30 AM - 12:45 PM
COEX
Room:
Hall D 2
Poster No:
2411
Submission Type:
Abstract Submission
Authors:
Gabriel Castrillon1,2, Katarzyna Kurcyus3, Samira Epp1, Antonia Bose3, Roman Belenya3, Andreas Ranft4, Igor Yakushev5, Christine Preibisch3, Valentin Riedl1,3
Institutions:
1Department of Neuroradiology, Uniklinikum Erlangen, Erlangen, Germany, 2Research Group in Medical Imaging, SURA Ayudas Diagnósticas, Medellin, Colombia, 3Department of Neuroradiology, Technical University of Munich, Munich, Germany, 4Department of Anesthesiology, Technical University Munich, Munich, Germany, 5Department of Nuclear Medicine, Technical University of Munich, Munich, Germany
First Author:
Gabriel Castrillon
Department of Neuroradiology, Uniklinikum Erlangen|Research Group in Medical Imaging, SURA Ayudas Diagnósticas
Erlangen, Germany|Medellin, Colombia
Department of Neuroradiology, Uniklinikum Erlangen|Research Group in Medical Imaging, SURA Ayudas Diagnósticas
Erlangen, Germany|Medellin, Colombia
Co-Author(s):
Valentin Riedl
Department of Neuroradiology, Uniklinikum Erlangen|Department of Neuroradiology, Technical University of Munich
Erlangen, Germany|Munich, Germany
Department of Neuroradiology, Uniklinikum Erlangen|Department of Neuroradiology, Technical University of Munich
Erlangen, Germany|Munich, Germany
Introduction:
During evolution, the human brain has continuously expanded and increased its energy demands relative to the body (1). The most expanded brain areas have been associated with higher cognitive functions in humans (2), increasing considerably the energy demands related to neural processing (3). Patients with mental disorders often exhibit impaired cognitive functions (4). We hypothesized that the distribution of energy metabolism along signaling pathways could reveal mechanisms of higher cognitive processing in the human brain and its deviation in disease.
Methods:
Forty-seven quantitative FDG-PET and BOLD-fMRI data from thirty healthy participants from three independent cohorts of healthy participants were included. The cerebral metabolic rate of glucose (CMRglc) was derived using the Patlak plot model on the preprocessed PET images and the arterial input function. The degree of functional connectivity (dFC) was derived from the preprocessed BOLD images. We defined the energetic costs of signaling as the residual of the linear regression between the CMRglc and the dFC. Our findings were interpreted using the cortical expansion from non-human primates to humans (5), preprocessed microarray expression data from the Allen Human Brain Atlas (AHBA) (6), 28 previously published PET neuroreceptors maps (7), meta-analytic cognitive maps from the Neurosynth database (8, 9), behavioral neural maps from psychotic disorders (10), and gene lists from genome-wide association studies (GWAS) (11-15). The association between variables was assessed using the Pearson correlation's p-value corrected for autocorrelation (p_smash) (16).
Results:
We identified a consistent cortical distribution with deviating energetic costs of signaling calculated as the residual CMRglc per dFC across all cohorts (Fig. 1A). From an evolutionary perspective, the energetic costs of signaling correlated significantly with the extent of cortex expansion from chimpanzees to humans (Fig. 1B), indicating that regions that expanded most during human evolution have a higher energy demand compared to the rest of the brain. Next, we analyzed the molecular profile associated with the distribution of energetic costs of signaling by running a gene ontology enrichment (GOE) analysis on the AHBA genes significantly correlated with the energetic costs map. We found that 95% of genes that are overexpressed in regions with high energetic costs are involved in signal transduction, mainly in metabotropic neuromodulation (Fig. 1C). This finding was replicated by entering 19 unique receptors, and transporters maps from 28 different PET studies into a partial least-squares (PLS) analysis, which explained 86% of the cortical variance in energetic costs (Fig. 1D), replicating that the cortical distribution of energetic costs of signaling is strongly related to the regional level of neuromodulator activity. Finally, we analyzed the relationship between the distribution of the energetic costs and cognition in health, observing that energetic costly regions were involved in high cognitive functions such as memory processing and reading but less involved in sensory-motor functions (Fig. 1E), whereas in disease, the distribution of the energetic costs was consistently correlated with psychotic disorders symptoms derived from behavioral (Fig. 1F, left) and genetic data in patients with schizophrenia (SCZ; Fig. 1F middle) and bipolar 1 disorder (BD; Fig. 1F right). In contrast, the energetic costs did not correlate with other non-psychotic disorders such as depression, autism spectrum disorder, or attention-deficit/hyperactivity disorder.
·Figure 1.
Conclusions:
We derived a map of the energetic costs of signaling of the human brain, showing that energetic costly regions i) were most expanded during human evolution, ii) have upregulated neuromodulatory signaling, iii) are mainly involved in higher cognitive processing, and iv) are specifically related to pheno and genotypes from psychotic disorders but not to other mental disorders.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 2
Higher Cognitive Functions:
Higher Cognitive Functions Other
Modeling and Analysis Methods:
PET Modeling and Analysis
Novel Imaging Acquisition Methods:
Multi-Modal Imaging 1
Physiology, Metabolism and Neurotransmission :
Physiology, Metabolism and Neurotransmission Other
Keywords:
Cognition
FUNCTIONAL MRI
Modeling
Neurotransmitter
Positron Emission Tomography (PET)
Psychiatric Disorders
Other - Neuromodulation
1|2Indicates the priority used for review
Provide references using author date format
2. Tomasello, M., & Call, J. (1997). Primate cognition. Oxford University Press
3. Aiello, L. C., & Wheeler, P . (1995). The expensive-tissue hypothesis: the brain and the digestive system in human and primate evolution. Current anthropology, 36(2)
4. Service, S.K., et al. (2020). Distinct and shared contributions of diagnosis and symptom domains to cognitive performance in severe mental illness in the Paisa population: a case-control study. The Lancet Psychiatry 7(5).
5. Wei, Y., et al. (2019). Genetic mapping and evolutionary analysis of human-expanded cognitive networks. Nature communications, 10(1).
6. Hawrylycz, M.J., et al. (2012). An anatomically comprehensive atlas of the adult human brain transcriptome. Nature, 489(7416).
7. Hansen, J. Y. (2022). Mapping neurotransmitter systems to the structural and functional organization of the human neocortex. Nature Neuroscience, 22.
8. Yarkoni, T., et al. (2011). Large-scale automated synthesis of human functional neuroimaging data. Nature methods, 8(8).
9. Margulies, et al.(2016). Situating the default-mode network along a principal gradient of macroscale cortical organization. Proceedings of the National Academy of Sciences, 113(44).
10. Ji, J.L., et al. (2021). Mapping brain-behavior space relationships along the psychosis spectrum. Elife, 10.
11. Stahl, E. A., et al. (2019). Genome-wide association study identifies 30 loci associated with bipolar disorder. Nature genetics, 51(5).
12. Pardiñas, A.F., et al. (2018). Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection. Nature genetics, 50(3).
13. Howard, D.M., et al. (2019). Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nature neuroscience, 22(3).
14. Grove, J., et al. (2019). Identification of common genetic risk variants for autism spectrum disorder. Nature genetics, 51(3).
15. Demontis, D., et al. (2019). Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nature genetics, 51(1).
16. Burt, J.B., et al. (2020). Generative modeling of brain maps with spatial autocorrelation. NeuroImage, 117038.