# MRS validation of computational metabolic modeling of human brain function to determine energetic disruptions underlying fMRI-derived functional connectivity in degenerative or psychiatric disorders > **NIH NIH R01** · YALE UNIVERSITY · 2020 · $188,017 ## Abstract Resting-state fMRI has implicated hubs of high functional connectivity in the resting human brain, e.g., the default mode network (DMN), compared to other regions. In this proposal we will directly assess the role of metabolism in supporting high connectivity and assess whether metabolic dysfunction leads to connectivity reduction in DMN hubs seen in neurodegenrative (e.g., aging, Alzheimer's) and neuropsychiatric disorders. The complex nature of functional connectivity in healthy human brain has very high-energy demands, a need that is met by high ATP yielded from glucose oxidation (CMRglc(ox)). Using H[ C] MRS, we found that 1 13 neuronal CMRglc(ox) changes linearly with glutamatergic neurotransmission and that at rest most of the cerebral cortex's energy production is devoted to signaling. However there is a significant fraction of energy devoted to housekeeping needs such as synaptogenesis and maintaining membrane potentials. Given the tight link of energetics and signaling we found surprisingly, using quantitative PET imaging, that total CMRglc(ox) in the DMN is similar to regions with lower fMRI-derived connectivity. A potential explanation for this paradox is that the hubs in DMN vs. other cortical regions have a greater fraction of their total energy devoted to signaling than to nonsignaling, thus making the DMN hubs more vulnerable to functional energy failure. Alternatively it has been proposed that higher nonsignaling needs (e.g., synaptic remodeling) is present in DMN. To answer this novel question with large implications for interpreting resting-state fMRI data and to study how dysfunction of energy metabolism and tissue composition impacts function, there is a need for novel measurement and computational tools. To address this challenge we will develop a computational model to calculate signaling and nonsignaling energy costs. The model uses data from individual subjects on tissue composition obtained from high- resolution MRI. The model will be validated in both a rodent model and humans by comparison with 1H[13C] MRS, which can uniquely measure the signaling and nonsignaling components of neuroenergetics. The relative ratios of signaling to nonsignaling will be measured and calculated in high functional connectivity regions of the DMN and control low connectivity cortical regions in healthy young and elderly adults. We hypothesize that regions of high functional connectivity will have a greater fraction of energy production devoted to signaling and that this fraction will decline with age. Once the computational budget model is developed, and validated, it will provide a powerful noninvasive tool for studying alterations in cortical energetics and tissue composition that lead to loss of fMRI-derived connectivity as well as potentially as a novel clinical biomarker for assessing prognosis and treatment. ## Key facts - **NIH application ID:** 9842571 - **Project number:** 5R01NS100106-04 - **Recipient organization:** YALE UNIVERSITY - **Principal Investigator:** Dewan Syed Fahmeed Hyder - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2020 - **Award amount:** $188,017 - **Award type:** 5 - **Project period:** 2017-01-01 → 2021-12-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/9842571 ## Citation > US National Institutes of Health, RePORTER application 9842571, MRS validation of computational metabolic modeling of human brain function to determine energetic disruptions underlying fMRI-derived functional connectivity in degenerative or psychiatric disorders (5R01NS100106-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9842571. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*