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Researchers are collaborating to build integrative neuro-metabolic models that can predict brain health across different species and life stages. Credit: Neuroscience News<\/p>\nSummary: Every thought has a price tag. Neurons require an immediate burst of energy to fire, but as we age, the brain\u2019s \u201cmetabolic efficiency\u201d begins to decline. Researchers have launched a five-year, $3.3M NIH-funded project to build the first whole-brain theory on how this metabolic cost impacts cognitive decline.<\/p>\n
By looking beyond traditional \u201camyloid plaques,\u201d the team is using advanced multiscale modeling\u2014from single-cell mouse data to human MRIs\u2014to identify how metabolites like glucose, lactate, and creatine predict the onset of Alzheimer\u2019s Disease years before symptoms appear.<\/p>\n
Key Facts<\/p>\n
The \u201cMetabolic Cost\u201d Theory: Neuronal activity is inextricably linked to energy consumption. When the brain can no longer adapt its metabolic processing to compensate for aging, cognitive decline begins.Beyond Amyloid: While most research focuses on protein plaques, this study targets the \u201cneurometabolic coupling\u201d\u2014the relationship between blood flow, oxygen, and nutrient consumption in brain networks.Multiscale Modeling: The project spans three biological levels:Microscopic: Red blood cell velocity and lactate transients in mice.Mesoscopic: Mitochondrial activity rippling across cortical networks.Macroscopic: Whole-brain functional connectivity in human cohorts via MRI.The Translation Challenge: A major goal is to bridge the \u201cmouse-to-human\u201d gap. Scientists have \u201ccured\u201d Alzheimer\u2019s in mice many times; this model aims to identify common metabolic vulnerabilities that actually apply to human patients.Predictive Screening: The ultimate aim is to create a screening tool that identifies at-risk individuals based on metabolic signals decades before memory loss occurs.<\/p>\n
Source: University of Pittsburgh<\/p>\n
Like a lightbulb illuminating the moment you flip a switch, the brain pays an immediate energy cost every time a neuron fires. Bistra Iordanova has built her career studying brain function, but over time, she kept returning to a question her field hadn\u2019t fully investigated: how does this \u201ccost\u201d of the brain\u2019s metabolism impact how we age?\u00a0<\/p>\n
\u201cI\u2019ve collected lots of data about blood flow and the brain\u2019s neuronal activity,\u201d Iordanova said. \u201cI eventually included data on glucose, lactate, creatine, and other brain metabolites in relation to aging, and then one morning, I found myself with so much information that I really had no clue what was going on. Simple linear models no longer worked, and dimensionality reduction approaches were not as useful as I hoped.\u201d<\/p>\n
Researchers are collaborating to build integrative neuro-metabolic models that can predict brain health across different species and life stages. Credit: Neuroscience News<\/p>\n
For\u00a0Iordanova, assistant professor of bioengineering at the University of Pittsburgh\u2019s Swanson School of Engineering, that influx of data set the stage for an interdisciplinary collaboration with\u00a0Liang Zhan, associate professor of electrical and computer engineering, to build integrative neuro-metabolic models capable of making predictions about brain health.<\/p>\n
Now, the duo is co-investigating a five-year, $3.3M R01\u00a0NIH project, \u201cMultiscale Models of Age-Specific Neurometabolic Coupling,\u201d to create a whole-brain theory on how the brain\u2019s metabolic processes affect cognition in aging.\u00a0<\/p>\n
Looking beyond blood flow<\/p>\n
While most research has typically focused on amyloid plaques and hemodynamics as early warning signs of Alzheimer\u2019s Disease (AD), Iordanova and Zhan are focused on the metabolic changes that happen in the brain\u2019s networks by looking at the impact of specific metabolites like glucose, lactate, and creatine on brain activity.<\/p>\n
\u201cThe brain requires large amounts of glucose and oxygen to function as billions of interconnected cells work together,\u201d Iordanova said. \u201cBut old age brings decline in metabolic efficiency, and our brain cells have to maintain networks by adapting their metabolic processing.\u201d\u00a0<\/p>\n
When that metabolic adaptation fails, it can lead to cognitive decline and dementia, and some individuals may be especially vulnerable due to genetics, lifestyle, or other factors. Ultimately, the goal of this work is to support the development of metabolic screening and therapies for at-risk individuals years before energy metabolism begins to affect cognition. First, however, the team must use advanced modeling strategies to make sense of the vast amount of metabolic and neural brain data they will collect.<\/p>\n
A multi-scale, multi-species model<\/p>\n
This approach will span three distinct levels of biological scale. At the smallest level, they will use two-photon microscopy to quantify the relationship between red blood cell velocity, neural activity, and lactate transients in late-onset AD mouse models.<\/p>\n
Next, wide-field imaging will capture how mitochondrial activity ripples across cortical networks in mice. At the largest scale, the team will explore the impact of metabolism on functional whole-brain connectivity by integrating data from brain MRI in both animal models and human cohorts.\u00a0<\/p>\n
\u201cWe want to connect what we know in mice on a cellular level to what we know from non-invasive imaging in humans,\u201d Iordanova said. \u201cAnd to build a comprehensive brain network theory, we need a lot of robust information at different scales.\u201d<\/p>\n
Once the data is collected at each step, Zhan will use his expertise in brain network modeling and graph theory to build the computational architecture that ties these complicated layers of brain data together.\u00a0<\/p>\n
\u201cThe structures of mouse and human brains differ, and of course, we know that the brain is highly complex,\u201d Zhan said. \u201cBecause many brain regions function together, diseases such as Alzheimer\u2019s affect not just a single region but the entire brain network, which we aim to characterize and understand.\u201d<\/p>\n
Toward better treatments through interdisciplinary collaboration\u00a0<\/p>\n
The ultimate goal of the project is to bridge the long-standing gap between discoveries in the laboratory and treatments that benefit patients. Although researchers have plenty of AD data from mouse models, translating those insights to humans remains a challenge.<\/p>\n
By studying how metabolic factors interact with genetics, sex-differences and aging, the team hopes to identify biological signals that could eventually guide more personalized approaches to preventing cognitive decline.<\/p>\n
\u201cI\u2019m obsessed with figuring out how to translate what we learn in mouse models of Alzheimer\u2019s into something meaningful for humans, because translation is not trivial,\u201d Iordanova said.<\/p>\n
\u201cA frequent quip on the translation from mouse to human is that scientists have cured Alzheimer\u2019s in mice many times, but patients and families are still living through the disease every day. If we can improve on the cross-species approach to identify common metabolic vulnerabilities combined with certain genetic risk factors, then perhaps we could tailor interventions at the right time to the people who would benefit most.\u201d<\/p>\n
Although they now collaborate closely, Iordanova and Zhan were not previously familiar with each other\u2019s work or the potential of their combined expertise until a chance meeting at a radiology event just a few years ago. While the research itself is promising, the team also hopes their partnership will encourage more engineers and scientists from different fields to pursue collaborative projects together.\u00a0<\/p>\n
\u201cEngineers and scientists from different fields often speak very different technical languages, and it\u2019s rare to see those perspectives truly come together,\u201d Iordanova said. \u201cThere\u2019s real value when you are actually in the room with someone from another field and working to bridge that gap, and I think that\u2019s what\u2019s really valuable about this collaboration.\u201d<\/p>\n
The interdisciplinary team also includes co-investigators\u00a0Alberto Vazquez,\u00a0Tao Jin\u00a0and\u00a0Alex Poplawsky\u00a0from the School of Medicine and\u00a0Nicholas Fitz\u00a0and\u00a0Rebecca Deek\u00a0from the School of Public Health at University of Pittsburgh.<\/p>\n
Funding: This project is supported by the National Institute on Aging (R01AG092661) for the period January 2026 through December 2030.<\/p>\n
Key Questions Answered:Q: Why focus on \u201cenergy\u201d instead of the \u201cplaques\u201d we always hear about in Alzheimer\u2019s?<\/p>\n
A: Amyloid plaques are often the result of the disease, not necessarily the first spark. Iordanova believes metabolic changes happen much earlier. If the brain\u2019s \u201cpower grid\u201d starts failing, the cells can\u2019t clean up waste or communicate properly, which may lead to those plaques forming later.<\/p>\n
Q: Does this mean we can \u201ceat\u201d our way to a younger brain?<\/p>\n
A: While diet affects glucose and lactate, this research is about how the brain processes those fuels. As we age, our cells become less efficient at turning glucose into \u201cthought.\u201d The goal is to find therapies that help aging neurons maintain their \u201cmetabolic flexibility\u201d regardless of fuel supply.<\/p>\n
Q: Why is it so hard to translate mouse research to humans?<\/p>\n
A: Mice have different brain structures and shorter lifespans. By using \u201cgraph theory\u201d and complex network modeling, Liang Zhan is building a mathematical \u201cbridge\u201d that can look at mouse cellular data and human MRI data side-by-side to find the universal rules of brain aging.<\/p>\n
Editorial Notes:This article was edited by a Neuroscience News editor.Journal paper reviewed in full.Additional context added by our staff.About this sleep and neuroscience research news<\/p>\n
Author:\u00a0Anna Ligorio<\/a>
Source:\u00a0University of Pittsburgh<\/a>
Contact:\u00a0Anna Ligorio \u2013 University of Pittsburgh
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