1289
Symposium
The field of neuroimaging is at a critical juncture where advanced MRSI techniques are moving beyond conventional boundaries and forging new links with connectomics, molecular neuroscience, and clinical diagnostics. Advances in whole-brain, high-resolution MRSI now enable the detailed mapping of biochemical activity across large-scale neural networks, providing unprecedented insights into the metabolite profiles that underlie both typical brain function and complex brain disorders. This topic is timely because it aligns with the growing recognition that understanding neurological and psychiatric conditions—from subtle neuroinflammatory states to challenging disorders like schizophrenia—requires more than traditional structural, diffusion and functional MR imaging. Moreover, the scientific community increasingly recognizes that common neurodegenerative and psychiatric conditions cannot be fully understood as localized brain changes, but rather as complex, distributed network disturbances. This shift toward connectomics, combined with the biochemical insights enabled by advanced MRSI, will likely offer a timely and transformative perspective into the etiology of these diseases. Also by integrating traditional structural and functional imaging with these emerging metabolic and network-based frameworks, this symposium is timely placed as it intends to highlight the potential for MRSI to bridge the gap between basic neuroscience research and clinical applications.
1. Application of Advanced MRSI Techniques: Attendees will gain the ability to critically evaluate the use of whole-brain, voxel-based MRSI approaches to detect subtle neuroinflammatory changes and diverse metabolic alterations. This includes appreciating the emerging role of phosphorus-based MRSI, which enables mapping region-specific distributions of high-energy phosphates and membrane metabolites, understanding how these biochemical patterns correlate with cognitive measures, and recognizing the potential for such insights to inform future clinical research and interventions.
2. Clinical and Translational Insights: Participants will be equipped to contextualize MRSI findings in clinical practice, using these insights to improve diagnostic approaches, inform treatment strategies, and enhance the understanding of complex, distributed pathologies across various neurological and psychiatric disorders
3. Interpretation of Metabolic Connectomics: After attending this symposium, participants will be able to integrate metabolite data obtained via MRSI into a connectomics framework, understanding how metabolic patterns relate to large-scale brain networks and their relevance to neurodegenerative and psychiatric conditions.
This symposium is primarily targeted at neuroimaging researchers, clinicians, and scientists interested in integrating advanced MRSI techniques with other MRI modalities, including MRI physicists, neurobiologists, neuroscientists, neurologists, psychiatrists, radiologists, computational modelers, and data scientists. It also aims to benefit trainees, postdoctoral fellows, and graduate students who seek to broaden their technical skill sets and apply novel metabolic imaging strategies in both basic and clinical neuroscience contexts.
Presentations
Neuroinflammation, an inflammatory response located within the central nervous system, is observed in a variety of psychiatric and neurodegenerative conditions. Understanding of neuroinflammation is currently limited by the lack of non-invasive techniques. Brain temperature, measured using magnetic resonance spectroscopic imaging (MRSI), may be a useful measure of neuroinflammation. Brain temperature is expected to increase in cases of neuroinflammation as microglial activation increases metabolic demands, leading to production of excess heat. In addition to temperature measurements, MRSI is useful for detecting inflammation-related alterations in the concentrations of metabolites such as choline-containing compounds (CHO), N-acetylaspartate (NAA), and myo-inositol (MI). We conducted a study to investigate the feasibility of MRSI for detection of low-level neuroinflammation using an experimental model of neuroinflammation in humans (intramuscular administration of the typhoid vaccine). Twenty healthy volunteers participated in a double-blind, placebo-controlled crossover study including MRSI scans before and 3 h after vaccine/placebo administration. Results showed that a significant proportion of brain regions (44/47) increased in temperature post-vaccine compared to post-placebo. Changes in metabolite ratios were also observed but did not survive correction for multiple comparisons. Our findings suggest that that regional brain temperature may be a more sensitive measure of low-level neuroinflammation than whole-brain temperature. This technique is also currently being used to investigate neuroinflammation cancer-related cognitive impairment.
Presenter
Joanne Lin, University of Auckland
School of Pharmacy
Auckland, Auckland
New Zealand
Studying brain metabolism can provide unique insight into the processes that underlie changes in brain structure and function, which occur in normal and pathological states. Phosphorus (31P) magnetic resonance spectroscopy (MRS) allows for non-invasive visualization of metabolic content for crucial high-energy phosphates and membrane phospholipids. These compounds provide the basis for the normal functioning of the brain on a cellular level, being key players in energy production and utilization, as well as membrane synthesis and breakdown. Changes in their content are associated with various pathological states, occur in healthy aging and correlate with cognitive changes. However, only a select number of studies have been able to assess the distribution of high-energy and membrane phosphates across the brain in healthy people, with the vast majority of research having focused on one brain area. To paint a comprehensive picture of how phosphorus metabolism impacts and is impacted in health and disease, whole-brain assessment is crucial. In this present study, we have utilized 31P-MRS to examine the regional disparity in phosphorus-containing metabolites in healthy young adults. Additionally, considering the known alterations of energy metabolism in various types of cognitive impairment, we evaluated the link between metabolite concentrations in different brain regions with cognitive measures.
Whole-brain 31P-MRS spectra (10x10x8 voxels) were collected from 74 healthy volunteers (32 male, 42 female) aged 18 to 40 years (mean age 26.2 years). 3D maps of phosphocreatine (PCr), adenosine triphosphate (ATP), nicotinamide adenine dinucleotide (tNAD), phosphomonoesters (PME) and phosphodiesters (PDE) were quantified using an established convolutional neural network a (Songeon et al., 2022) (31P-SPAWNN)). All maps were registered to the standard MNI space using each participant’s structural scan. From this data, metabolites ATP, tNAD, PCr, PME and PDE were referenced to total phosphorus and assessed for voxel-wise whole brain distribution, as well as ratios ATP/PCr and PME/PDE. 8 tests from the MATRICS Consensus Cognitive Battery were administered to obtain a global cognitive score and 5 cognitive domain scores: executive function, processing speed, working memory, verbal and visual learning. We tested for possible correlations of metabolic content with age, sex and cognitive measures at the voxel level using cluster-based permutation testing in PALM, FSL. We used Friedman’s test with post hoc Wilcoxon’s tests for within-subject differences between regions.
Whole-brain maps of energy metabolism (ATP, tNAD, PCr, ATP/PCr) and membrane metabolism (PME, PDE, PME/PDE) were estimated. Regional analysis demonstrated significant heterogeneity in metabolite distribution across brain regions for all assessed metabolites (p<0.0001). Voxel-wise analysis showed a significant negative correlation of tNAD with age, as well as higher PME content in females (p value range <0.05). Negative correlations of phosphocreatine in parietal lobe voxels were found with global cognition and verbal learning (p value range <0.05). ATP/PCr and tNAD were found to correlate positively with verbal learning in parts of the parietal and frontal cortex (p value range <0.05).
To sum up, firstly, we have demonstrated that in young healthy adults, energy and membrane metabolism significantly vary across brain regions, which advances on findings from several previous 31P MRS studies and data from FDG-PET. Secondly, for the first time, we assess the link between cognitive measures and energy and membrane metabolism markers at voxel resolution. Our findings highlight the potential link between phosphate-containing energy metabolites and cognitive performance, particularly verbal learning. Overall, this study significantly advances our understanding of phosphate metabolite distribution across the brain in healthy individuals and its potential impact on cognitive function.
Presenter
Mariia Ptukha, University of Melbourne Melbourne, Victoria
Australia
Background:
Glutamatergic dysfunction is implicated in schizophrenia pathoaetiology, but this may vary in extent between patients. Studies report both increased and reduced levels of brain glutamate in schizophrenia relative to controls, and it has been proposed that glutamate subtypes in schizophrenia may exist in relation to treatment response. To address this, we examine whether patients exhibit greater variability in glutamate measures compared to controls and conduct an updated meta-analysis of glutamate differences.
Methods:
MEDLINE and EMBASE databases were searched from inception to September 2022 for proton magnetic resonance spectroscopy (1H-MRS) studies reporting glutamate, glutamine or Glx values in schizophrenia patients in comparison to controls. We requested individual patient data from authors as part of a previous mega-analysis.
Outcomes were (1) variability of glutamate measures in patients relative to controls, indexed by coefficient of variation ratio (CVR); (2) mean differences quantified using Hedges g; (3) modal distribution of individual-level glutamate data using Hartigan’s unimodality dip test. Analyses were carried out in R using the “metafor” package and “weights” package.
Results:
123 studies reporting on 8,256 patients and 7,532 controls were included. Compared with controls, patients demonstrated greater variability in glutamatergic metabolites in the medial frontal cortex ( glutamate: CVR = 0.15, p < 0.001; glutamine: CVR = 0.15, p = 0.003; Glx: CVR = 0.11, p = 0.002), dorsolateral prefrontal cortex (DLPFC, glutamine: CVR = 0.14, p = 0.05; Glx: CVR = 0.25, p < 0.001) and thalamus (glutamate: CVR = 0.16, p = 0.008; Glx: CVR = 0.19, p = 0.008). For individual patient data, most studies showed a unimodal distribution of glutamatergic metabolites. Meta-analysis of mean differences found lower medial frontal cortex glutamate (g = -0.15, p = 0.03), higher thalamic glutamine (g = 0.53, p < 0.001) and higher basal ganglia Glx in patients relative to controls (g = 0.28, p < 0.001).
Conclusions:
The finding of greater variability in patients in the medial frontal cortex, DLPFC and thalamus are consistent with the hypothesis of glutamate subtypes in schizophrenia. Future research focused on these brain regions may inform the identification of patient subgroups for personalised medicine approaches to treatment.
Presenter
Kate Merritt, University College London London
United Kingdom
The emergence of network science in neurobiology has significantly advanced our understanding of brain organization, uncovering the self-organizing, scale-invariant principles underlying its structural and functional connectivity. However, existing neuroimaging modalities, such as diffusion MRI and fMRI, fail to capture the biochemical processes sustaining these networks, leaving a critical gap in connectomics. Since brain function is fundamentally sustained by oxidative glucose metabolism, metabolic imaging offers a transformative opportunity to explore the biochemical underpinnings of brain network organization.
Inter-individual variation in brain network organization has been linked to behavioral differences, including general intelligence, working memory, and personality traits, and is increasingly recognized as critical to understanding neurological and psychiatric disorders. Many such disorders, often termed connectopathies, arise from disruptions in distributed brain networks rather than isolated regions. Central to these networks are highly connected hub nodes, which play critical roles in maintaining global network integrity. These nodes, characterized by high centrality, exhibit proportionally higher metabolic demands, making them vulnerable to pathologies that induce oxidative stress. Energy deprivation in these nodes can lead to global network failure, emphasizing the need for imaging methods capable of assessing metabolic activity across the brain.
In this presentation, we introduce the first human brain metabolic connectome, constructed using advanced 3D proton magnetic resonance spectroscopic imaging (MRSI). This state-of-the-art technique enables whole-brain, multi-metabolite imaging at high spatial resolution (5mm isotropic) with rapid acquisition times (<12 minutes), making it practical for large-scale and clinical studies. Using data from 68 healthy adolescents, validated on an independent cohort (N=13), we developed a robust pipeline for constructing metabolic similarity matrices (MeSiMs). These matrices demonstrate reproducibility across individuals, datasets, MRI scanners and anatomical parcellations and robustness to uncertainties in MRSI reconstruction.
Our analysis reveals that metabolic networks exhibit natural network properties, including modular organization and robust homotopic patterns, reflecting functionally integrated yet spatially distinct systems. Central to these findings is the identification of the metabolic fiber: a smooth homotopic gradient of metabolic similarity that traverses major brain regions from the occipital lobe through the parietal lobe, prefrontal cortex, cingulate cortex, and subcortical regions. This gradient underscores the biochemical integration of the brain’s network architecture. Furthermore, we demonstrate that structural hubs align with metabolically active nodes, reinforcing the relationship between network centrality and biochemical demand.
Importantly, we show that this metabolic network organization is not merely a byproduct of structural connectivity but is rooted in neurodevelopmental mechanisms, including genetic co-expression and cytoarchitectonic patterns. These results suggest that the topology of the metabolic connectome reflects underlying biological frameworks and developmental trajectories.
This work bridges the gap between metabolism and connectomics, offering a robust and replicable framework for constructing metabolic connectomes. By introducing the concept of the metabolic fiber and providing evidence for the biochemical basis of network organization, we lay the foundation for integrating MRSI into studies of brain health, disease, and neurodevelopment.
Presenter
Federico Lucchetti, Lausanne University Hospital and University of Lausanne Lausanne, Vaud
Switzerland