Brain Sources Research Articles

M/EEG source localization for both subcortical and cortical sources using a convolutional neural network with a realistic head conductivity model.

While electroencephalography (EEG) and magnetoencephalography (MEG) are well-established noninvasive methods in neuroscience and clinical medicine, they suffer from low spatial resolution. Electrophysiological source imaging (ESI) addresses this by noninvasively exploring the neuronal origins of M/EEG signals. Although subcortical structures are crucial to many brain functions and neuronal diseases, accurately localizing subcortical sources of M/EEG remains particularly challenging, and the feasibility is still a subject of debate. Traditional ESIs, which depend on explicitly defined regularization priors, have struggled to set optimal priors and accurately localize brain sources. To overcome this, we introduced a data-driven, deep learning-based ESI approach without the need for these priors. We proposed a four-layered convolutional neural network (4LCNN) designed to locate both subcortical and cortical sources underlying M/EEG signals. We also employed a sophisticated realistic head conductivity model using the state-of-the-art segmentation method of ten different head tissues from individual MRI data to generate realistic training data. This is the first attempt at deep learning-based ESI targeting subcortical regions. Our method showed excellent accuracy in source localization, particularly in subcortical areas compared to other methods. This was validated through M/EEG simulations, evoked responses, and invasive recordings. The potential for accurate source localization of the 4LCNNs demonstrated in this study suggests future contributions to various research endeavors such as the clinical diagnosis, understanding of the pathophysiology of various neuronal diseases, and basic brain functions.

Using deep learning and pretreatment EEG to predict response to sertraline, bupropion, and placebo

ObjectivePredicting an individual’s response to antidepressant medication remains one of the most challenging tasks in the treatment of major depressive disorder (MDD). Our objective was to use the large EMBARC study database to develop an electroencephalography (EEG)-based method to predict response to antidepressant treatment. MethodsPre-treatment EEG data were collected from study participants treated with either sertraline (N = 105), placebo (N = 119), or bupropion (N = 35). After preprocessing, the robust exact low-resolution electromagnetic tomography (ReLORETA) brain source localization method was used to reconstruct the source signals in 54 brain regions. Connectivity between regions was determined using symbolic transfer entropy (STE). A convolutional neural network (CNN) classified participants as responders or non-responders to each treatment. ResultsClassification accuracy was 91.0%, 95.4%, and 86.8% for sertraline, placebo, and bupropion, respectively. The most highly predictive features were connectivity between i) the anterior cingulate cortex and superior parietal lobule (alpha frequency), ii) the anterior cingulate cortex and orbitofrontal area (beta frequency), and iii) the orbitofrontal area and anterior cingulate cortex (gamma frequency). ConclusionCNN analysis of EEG connectivity may accurately predict response to sertraline, bupropion, and placebo. SignificanceThe suggested method may offer clinicians an accessible and cost-effective tool for speedy treatment and helps pharmaceutical firms to test new antidepressants efficiently.

Exploring age-related functional brain changes during audio-visual integration tasks in early to mid-adulthood

The seamless integration of visual and auditory information is a fundamental aspect of human cognition. Although age-related functional changes in Audio-Visual Integration (AVI) have been extensively explored in the past, thorough studies across various age groups remain insufficient. Previous studies have provided valuable insights into age-related AVI using EEG-based sensor data. However, these studies have been limited in their ability to capture spatial information related to brain source activation and their connectivity. To address these gaps, our study conducted a comprehensive audio-visual integration task with a specific focus on assessing the brain maturation effects in various age groups, particularly in early-mid adulthood. We presented visual, auditory, and audio-visual stimuli and recorded EEG data from Young (18–25 years), Transition (26–33 years), and Middle (34–50 years) age cohort healthy participants. We utilized source-based features for the classification of these age groups. We aimed to understand how aging affects brain activation and functional connectivity among hubs during audio-visual tasks. Our findings unveiled diminished levels of brain activation among middle-aged individuals, which escalate when exposed to AVI stimuli. Lower frequency bands showed substantial changes with increasing age during AVI. Our results demonstrated that implementing the k-means elbow method during the AVI task successfully categorized brain regions into five distinct brain networks. Additionally, we observed increased functional connectivity in middle age, particularly in the frontal, temporal, and occipital regions. These results highlight the compensatory neural mechanisms involved in aging during cognitive tasks.

Advances and challenges in serine in the central nervous system: physicochemistry, physiology, and pharmacology.

Neurological disorders are the primary cause of human disability and mortality globally, however, current medications slightly alleviate some symptoms of degenerative diseases. Serine is an important amino acid for the brain function and involved in a variety of biosynthetic pathways and signal transduction processes. The imbalance of serine metabolism is associated with neurodegeneration, including neuroinflammation, oxidative stress and apoptosis. Altered activities of serine metabolizing enzymes and accumulation of serine metabolites affect the survival and function of nerve cells. Abnormal serine levels are observed in animal models with neurological diseases, but not all human studies, therefore, the maintenance of serine homeostasis is a potentially therapeutic strategy for neurological disorders. To date, physiological and pharmacological roles of serine in neurological diseases have not been systemically recapitulated, and the association between serine and neurological diseases is controversial. In this review, we summarize physicochemical properties of serine, biological processes of serine in the brain (source, biotransformation, and transport), and the application of serine in neurological diseases including Alzheimer's disease, schizophrenia, and depression. Here, we highlight physicochemistry, physiology, pharmacology, and therapeutic potentials of serine in the prevention and treatment of neurological dysfunction. Our work provides valuable hints for future investigation that will lead to a comprehensive understanding of serine and its metabolism in cellular physiology and pharmacology. Although broad by necessity, the review helps researchers to understand great potentials of serine in the prevention and treatment of neurological dysfunction.

XDL-ESI: Electrophysiological Sources Imaging via explainable deep learning framework with validation on simultaneous EEG and iEEG

Electroencephalography (EEG) or Magnetoencephalography (MEG) source imaging aims to estimate the underlying activated brain sources to explain the observed EEG/MEG recordings. Solving the inverse problem of EEG/MEG Source Imaging (ESI) is challenging due to its ill-posed nature. To achieve a unique solution, it is essential to apply sophisticated regularization constraints to restrict the solution space. Traditionally, the design of regularization terms is based on assumptions about the spatiotemporal structure of the underlying source dynamics. In this paper, we propose a novel paradigm for ESI via an Explainable Deep Learning framework, termed as XDL-ESI, which connects the iterative optimization algorithm with deep learning architecture by unfolding the iterative updates with neural network modules. The proposed framework has the advantages of (1) establishing a data-driven approach to model the source solution structure instead of using hand-crafted regularization terms; (2) improving the robustness of source solutions by introducing a topological loss that leverages the geometric spatial information applying varying penalties on distinct localization errors; (3) improving the reconstruction efficiency and interpretability as it inherits the advantages from both the iterative optimization algorithms (interpretability) and deep learning approaches (function approximation). The proposed XDL-ESI framework provides an efficient, accurate, and interpretable paradigm to solve the ESI inverse problem with satisfactory performance in both simulated data and real clinical data. Specially, this approach is further validated using simultaneous EEG and intracranial EEG (iEEG).

A human brain atlas of χ-separation for normative iron and myelin distributions.

Iron and myelin are primary susceptibility sources in the human brain. These substances are essential for a healthy brain, and their abnormalities are often related to various neurological disorders. Recently, an advanced susceptibility mapping technique, which is referred to as χ-separation (pronounced as "chi"-separation), has been proposed, successfully disentangling paramagnetic iron from diamagnetic myelin. This method provided a new opportunity for generating high-resolution iron and myelin maps of the brain. Utilizing this technique, this study constructs a normative χ-separation atlas from 106 healthy human brains. The resulting atlas provides detailed anatomical structures associated with the distributions of iron and myelin, clearly delineating subcortical nuclei, thalamic nuclei, and white matter fiber bundles. Additionally, susceptibility values in a number of regions of interest are reported along with age-dependent changes. This atlas may have direct applications such as localization of subcortical structures for deep brain stimulation or high-intensity focused ultrasound and also serve as a valuable resource for future research.

Estrogenic regulation of hippocampal inhibitory system across lifespan.

Estrogens produced in peripheral tissues and locally in the brain are potent neuromodulators. The function of the hippocampus, a brain region essential for episodic memory and spatial navigation, relies on the activity of ensembles of excitatory neurons whose activity is temporally and spatially coordinated by a wide diversity of inhibitory neurons (INs) types. Over the last years, we have accumulated evidence that indicates that estrogens regulate the function of hippocampal INs through different mechanisms, including transcriptional regulation and rapid nongenomic signaling. Here, we argue that the well-documented influence of estrogens on episodic memory may be related to the actions of local and peripheral estrogens on the heterogenous populations of hippocampal INs. We discuss how physiological changes in peripheral sex hormone levels throughout lifespan may interact with local brain sources to regulate IN function at different stages of life, from early hippocampal development to the aging brain. We conclude that considering INs as mediators of sex hormone actions in the hippocampus across the healthy life span will benefit our understanding of sex-biased neurodevelopmental disorders and physiological aging.

MEG Microstates: An Investigation of Underlying Brain Sources and Potential Neurophysiological Processes

Microstates are transient scalp configurations of brain activity measured by electroencephalography (EEG). The application of microstate analysis in magnetoencephalography (MEG) data remains challenging. In one MEG dataset (N = 113), we aimed to identify MEG microstates at rest, explore their brain sources, and relate them to changes in brain activity during open-eyes (ROE) or closed-eyes resting state (RCE) and an auditory Mismatch Negativity (MMN) task. In another dataset of simultaneously recorded EEG-MEG data (N = 21), we investigated the association between MEG and EEG microstates. Six MEG microstates (mMS) provided the best clustering of resting-state activity, each linked to different brain sources: mMS 1–2: left/right occipito-parietal; mMS 3: fronto-temporal; mMS 4: centro-medial; mMS 5–6: left/right fronto-parietal. Increases in occipital alpha power in RCE relative to ROE correlated with greater mMS 1–2 time coverage (τbs < 0.20, ps > .002), while the lateralization of deviance detection in MMN was associated with mMS 5–6 time coverage (τbs < 0.16, ps > .012). No temporal correlation was found between EEG and MEG microstates (ps > .05), despite some overlap in brain sources and global explained variance between mMS 2–3 and EEG microstates B-C (rs > 0.60, ps < .002). Hence, the MEG signal can be decomposed into microstates, but mMS brain activity clustering captures phenomena different from EEG microstates. Source reconstruction and task-related modulations link mMS to large-scale networks and localized activities. Thus, mMSs offer insights into brain dynamics and task-specific processes, complementing EEG microstates in studying physiological and dysfunctional brain activity.

Plasminogen activator inhibitor-1 mediates cerebral ischemia-induced astrocytic reactivity.

Although ischemia increases the abundance of plasminogen activator inhibitor-1 (PAI-1), its source and role in the ischemic brain remain unclear. We detected PAI-1-immunoreactive cells with morphological features of reactive astrocytes in the peri-ischemic cortex of mice after an experimentally-induced ischemic lesion, and of a chimpanzee that suffered a naturally-occurring stroke. We found that although the abundance of PAI-1 increases 24 hours after the onset of the ischemic injury in a non-reperfusion murine model of ischemic stroke, at that time-point there is no difference in astrocytic reactivity and the volume of the ischemic lesion between wild-type (Wt) animals and in mice either genetically deficient (PAI-1-/-) or overexpressing PAI-1 (PAI-1Tg). In contrast, 72 hours later astrocytic reactivity and the volume of the ischemic lesion were decreased in PAI-1-/- mice and increased in PAI-1Tg animals. Our immunoblottings and fractal analysis studies show that the abundance of astrocytic PAI-1 rises during the recovery phase from a hypoxic injury, which in turn increases the abundance of glial fibrillary acidic protein (GFAP) and triggers morphological features of reactive astrocytes. These studies indicate that cerebral ischemia-induced release of astrocytic PAI-1 triggers astrocytic reactivity associated with enlargement of the necrotic core.

The choroid plexus synergizes with immune cells during neuroinflammation

The choroid plexus (ChP) is a vital brain barrier and source of cerebrospinal fluid (CSF). Here, we use longitudinal two-photon imaging in awake mice and single-cell transcriptomics to elucidate the mechanisms of ChP regulation of brain inflammation. We used intracerebroventricular injections of lipopolysaccharides (LPS) to model meningitis in mice and observed that neutrophils and monocytes accumulated in the ChP stroma and surged across the epithelial barrier into the CSF. Bi-directional recruitment of monocytes from the periphery and, unexpectedly, macrophages from the CSF to the ChP helped eliminate neutrophils and repair the barrier. Transcriptomic analyses detailed the molecular steps accompanying this process and revealed that ChP epithelial cells transiently specialize to nurture immune cells, coordinating their recruitment, survival, and differentiation as well as regulation of the tight junctions that control the permeability of the ChP brain barrier. Collectively, we provide a mechanistic understanding and a comprehensive roadmap of neuroinflammation at the ChP brain barrier.

EEG dynamic source imaging using a regularized optimization with spatio-temporal constraints.

One of the most important needs in neuroimaging is brain dynamic source imaging with high spatial and temporal resolution. EEG source imaging estimates the underlying sources from EEG recordings, which provides enhanced spatial resolution with intrinsically high temporal resolution. To ensure identifiability in the underdetermined source reconstruction problem, constraints on EEG sources are essential. This paper introduces a novel method for estimating source activities based on spatio-temporal constraints and a dynamic source imaging algorithm. The method enhances time resolution by incorporating temporal evolution of neural activity into a regularization function. Additionally, two spatial regularization constraints based on and norms are applied in the transformed domain to address both focal and spread neural activities, achieved through spatial gradient and Laplacian transform. Performance evaluation, conducted quantitatively using synthetic datasets, discusses the influence of parameters such as source extent, number of sources, correlation level, and SNR level on temporal and spatial metrics. Results demonstrate that the proposed method provides superior spatial and temporal reconstructions compared to state-of-the-art inverse solutions including STRAPS, sLORETA, SBL, dSPM, and MxNE. This improvement is attributed to the simultaneous integration of transformed spatial and temporal constraints. When applied to a real auditory ERP dataset, our algorithm accurately reconstructs brain source time series and locations, effectively identifying the origins of auditory evoked potentials. In conclusion, our proposed method with spatio-temporal constraints outperforms the state-of-the-art algorithms in estimating source distribution and time courses.

Simultaneous MEG and EEG source imaging of electrophysiological activity in response to acute transcranial photobiomodulation.

Transcranial photobiomodulation (tPBM) is a non-invasive neuromodulation technique that improves human cognition. The effects of tPBM of the right forehead on neurophysiological activity have been previously investigated using EEG in sensor space. However, the spatial resolution of these studies is limited. Magnetoencephalography (MEG) is known to facilitate a higher spatial resolution of brain source images. This study aimed to image post-tPBM effects in brain space based on both MEG and EEG measurements across the entire human brain. MEG and EEG scans were concurrently acquired for 6 min before and after 8-min of tPBM delivered using a 1,064-nm laser on the right forehead of 25 healthy participants. Group-level changes in both the MEG and EEG power spectral density with respect to the baseline (pre-tPBM) were quantified and averaged within each frequency band in the sensor space. Constrained modeling was used to generate MEG and EEG source images of post-tPBM, followed by cluster-based permutation analysis for family wise error correction (p < 0.05). The 8-min tPBM enabled significant increases in alpha (8-12 Hz) and beta (13-30 Hz) powers across multiple cortical regions, as confirmed by MEG and EEG source images. Moreover, tPBM-enhanced oscillations in the beta band were located not only near the stimulation site but also in remote cerebral regions, including the frontal, parietal, and occipital regions, particularly on the ipsilateral side. MEG and EEG results shown in this study demonstrated that tPBM modulates neurophysiological activity locally and in distant cortical areas. The EEG topographies reported in this study were consistent with previous observations. This study is the first to present MEG and EEG evidence of the electrophysiological effects of tPBM in the brain space, supporting the potential utility of tPBM in treating neurological diseases through the modulation of brain oscillations.

Effects of the spatial resolution of the Virtual Epileptic Patient on the identification of epileptogenic networks

Abstract Digital twins play an increasing role in clinical decision making. This study evaluates a digital brain twin approach in presurgical evaluation, the Virtual Epileptic Patient (VEP), which estimates the epileptogenic zone in patients with drug-resistant epilepsy. We built the personalized digital brain twins of 14 patients and a series of synthetic dataset by considering different spatial configurations of the epileptogenic and/or propagation zone networks (EZN and PZN, respectively). Brain source signals were simulated with a high spatial resolution neural field model (NFM) composed of 81942 nodes, embedding both long-range (between brain regions) and short-range (within brain regions) coupling. Brain signals were then projected to stereotactic electroencephalographic (SEEG) contacts with an accurate forward solution. An inversion procedure based on a low spatial resolution neural mass model (NMM) composed of 162 nodes was applied to estimate the excitability of each region in each simulation. The ensuing estimated EZN/PZN was compared to the simulated ground truth by means of classification metrics. Overall, we observed correct but degraded performance when using an NMM to estimate the EZN from data simulated with an NFM, which was significant for the simplest spatial configurations. We quantified the reduced performance and demonstrated that the oversimplification of the forward problem is its principal cause. We showed that the absence of local coupling in the NMM affects the inversion process by an overestimation of the excitability, representing a significant clinical impact when using this procedure in the context of presurgical planning. In conclusion, this study highlighted the importance to shift from an NMM towards a full NFM modeling approach for the estimation of EZN, with a particularly relevant need when considering the most complex clinical cases.

Verification of Source Activations in a 3D Brain Model Using 'CLEVER' Algorithm for Mental Arithmetic Conditions.

The focus of this study is to detect the exact location of the source which causes such damage. In this article, we analyse the mental conditions of subjects under a workload of performing mental arithmetic calculations for various frequency bands and plot the topography to understand the areas of active potentials. We propose a Novel Cluster Ensemble Verifier (CLEVER) algorithm, which combines two different techniques: clustering and source localisation. The proposed algorithm is highly efficient in identifying the exact location of the source. It is seen that the topographic plots of the independent component analysis (ICA), which has the maximum percentage of relative variance, correlates to the cluster generated. We are able to give the percentage-wise contribution of every component which is responsible for brain source activation with less time complexity. Out of 72 subjects, in 67 subjects, 299 out of 433 components originate from the occipital and parietal areas of the brain with a maximum power of 43.5 µv2. As an example, the relative variance of one component is found to be contributing up to 74.03% to source activations. Clusters show similarity across the subjects in the parietal and occipital areas of the brain. The dataset used for experimentation is EEGMAT from Physionet's repository. The computation time for the algorithms is 17.6 ± 3.2 minutes. Conclusion: Findings show that during mental arithmetic calculations, both occipital and parietal areas of the brain are involved. As the data is acquired by orally mentioning the mathematical problem, subjects tend to visualise the numbers while finding the solution, which is reflected in the occipital area of the brain. CLEVER algorithm verifies the origin of the activity in the occipital and parietal areas of the brain.

Neuroscientific results of experimental studies on the control of acute pain with hypnosis and suggested analgesia.

This narrative review summarizes a representative collection of electrophysiological and imaging studies on the neural processes and brain sources underlying hypnotic trance and the effects of hypnotic suggestions on the processing of experimentally induced painful events. It complements several reviews on the effect of hypnosis on brain processes and structures of chronic pain processing. Based on a summary of previous findings on the neuronal processing of experimentally applied pain stimuli and their effects on neuronal brain structures in healthy subjects, three neurophysiological methods are then presented that examine which of these neuronal processes and structures get demonstrably altered by hypnosis and can thus be interpreted as neuronal signatures of the effect of analgesic suggestions: (A) On a more global neuronal level, these are electrical processes of the brain that can be recorded from the cranial surface of the brain with magnetoencephalography (MEG) and electroencephalography (EEG). (B) On a second level, so-called evoked (EPs) or event-related potentials (ERPs) are discussed, which represent a subset of the brain electrical parameters of the EEG. (C) Thirdly, imaging procedures are summarized that focus on brain structures involved in the processing of pain states and belong to the main imaging procedures of magnetic resonance imaging (MRI/fMRI) and positron emission tomography (PET). Finally, these different approaches are summarized in a discussion, and some research and methodological suggestions are made as to how this research could be improved in the future.

Connectome spectrum electromagnetic tomography: A method to reconstruct electrical brain source networks at high-spatial resolution.

Connectome spectrum electromagnetic tomography (CSET) combines diffusion MRI-derived structural connectivity data with well-established graph signal processing tools to solve the M/EEG inverse problem. Using simulated EEG signals from fMRI responses, and two EEG datasets on visual-evoked potentials, we provide evidence supporting that (i) CSET captures realistic neurophysiological patterns with better accuracy than state-of-the-art methods, (ii) CSET can reconstruct brain responses more accurately and with more robustness to intrinsic noise in the EEG signal. These results demonstrate that CSET offers high spatio-temporal accuracy, enabling neuroscientists to extend their research beyond the current limitations of low sampling frequency in functional MRI and the poor spatial resolution of M/EEG.

Rejuvenating classical brain electrophysiology source localization methods with spatial graph Fourier filters for source extents estimation

EEG/MEG source imaging (ESI) aims to find the underlying brain sources to explain the observed EEG or MEG measurement. Multiple classical approaches have been proposed to solve the ESI problem based on different neurophysiological assumptions. To support clinical decision-making, it is important to estimate not only the exact location of the source signal but also the extended source activation regions. Existing methods may render over-diffuse or sparse solutions, which limit the source extent estimation accuracy. In this work, we leverage the graph structures defined in the 3D mesh of the brain and the spatial graph Fourier transform (GFT) to decompose the spatial graph structure into sub-spaces of low-, medium-, and high-frequency basis. We propose to use the low-frequency basis of spatial graph filters to approximate the extended areas of brain activation and embed the GFT into the classical ESI methods. We validated the classical source localization methods with the corresponding improved version using GFT in both synthetic data and real data. We found the proposed method can effectively reconstruct focal source patterns and significantly improve the performance compared to the classical algorithms.

DCT based multi-head attention-BiGRU model for EEG source location

Electroencephalogram source imaging (ESI) pertains to localize brain sources. Due to the one-to-many relationship between electroencephalogram (EEG) signals and brain sources, ESI becomes a complex inverse problem with poor conditioning, leaving a lot of research space. This article proposes an enhanced multi-head attention (MA) and discrete cosine transform (DCT) based Bidirectional Gated Recurrent Unit (BiGRU) (MA-DCT-BiGRU for short) for addressing the EEG inverse problem. Initially, EEG signal characteristics are captured employing multi-head attention with the inclusion of average hidden states. Then, the DCT is used to project brain source signals into the low, medium, and high frequency subspaces composed of spatial frequency basis vectors. The spatial low-frequency component serves as a filter for extended source reconstruction. Subsequently, BiGRU is employed to learn the mapping from the output of the attention layer to the low frequency DCT coefficients of the brain-derived signals. The simulation results undeniably establish the superiority of the MA-DCT-BiGRU configuration in comparison to other state-of-the-art (SOA) methods for source recovery, irrespective of the source modes and signal-to-noise ratio conditions. Experimental results, utilizing both synthetic and actual epilepsy data, clearly demonstrate the effectiveness of the framework presented in this article for epileptogenic area localization.

Abstract B050: Metabolic rewiring in human brain cancer

Abstract Metabolic adaptation can promote oncogenic phenotypes in glioblastoma (GBM), but the metabolic pathways utilized by GBM and how they differ from the pathways utilized in normal cortex are poorly understood. Here, we utilize in vivo stable isotope tracing in mice and patients with glioblastoma to define carbon fate and metabolic rewiring in cancer. We infused uniformly labeled 13C glucose into mice bearing orthotopic GBM patient-derived xenografts or into patients undergoing surgical resection for likely GBM. Tumor and cortex were physically separated in mice by fluorescence-guided microdissection. In humans, samples of enhancing tumor, non-enhancing tumor and normal cortex were separated intraoperatively using MRI guidance. Samples were subsequently analyzed by liquid chromatography mass spectrometry and MALDI imaging mass spectrometry to determine the downstream metabolic fates of infused 13C glucose. Of eight patients infused with 13C6-glucose, six were confirmed to have GBM while the others had non-GBM high grade gliomas. In mice and in patients, glucose-derived carbon effectively entered glycolysis and labeled glycolytic intermediates equivalently in tumor and cortex. Compared to GBM tissue, cortical tissue preferentially oxidized glucose-derived carbon in the TCA cycle and used glucose-derived carbon to synthesize the neurotransmitter GABA. Cortical tissue utilized glucose-derived carbon to synthesize the amino acid and neurotransmitter precursor serine, while GBM tissue preferentially derived serine from extracellular sources. By contrast, GBM preferentially utilized glucose-derived carbon for the synthesis of metabolites required for growth: purines, pyrimidines and NAD+/NADH. These studies are the first measurements of numerous metabolic pathways in human brain cancer and cortex and suggest that GBMs suppress the physiologic utilization of glucose carbons for ATP and neurotransmitter generation and instead use these carbons to synthesize the biomolecules they need to proliferate. Consistent with this hypothesis, eliminating dietary serine in mice slowed GBM PDX growth and forced tumors to synthesize serine from glucose, which in turn lowered tumor nucleotide and NAD+/NADH levels. Dietary serine depletion had minimal effects on cortical metabolism, consistent with glucose being the primary serine source in the normal brain. These direct measurements of metabolic pathway activity in human and mouse brain cancer reveal adaptive metabolic rewiring and new therapeutic targets. Citation Format: Andrew Scott, Anjali Mittal, Sravya Palavalasa, Baharan Meghdadi, Nathan Qi, Alexandra O'Brien, Ayesha Kothari, Nathalie Agar, Ethan Yang, Joshua Fischer, Vijay Tarnal, Wajd Al-Holou, Costas Lyssiotis, Deepak Nagrath, Daniel Wahl. Metabolic rewiring in human brain cancer [abstract]. In: Proceedings of the AACR Special Conference on Brain Cancer; 2023 Oct 19-22; Minneapolis, Minnesota. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_1):Abstract nr B050.

Modelling EEG Dynamics with Brain Sources

An electroencephalogram (EEG), recorded on the surface of the scalp, serves to characterize the distribution of electric potential during brain activity. This method finds extensive application in investigating brain functioning and diagnosing various diseases. Event-related potential (ERP) is employed to delineate visual, motor, and other activities through cross-trial averages. Despite its utility, interpreting the spatiotemporal dynamics in EEG data poses challenges, as they are inherently subject-specific and highly variable, particularly at the level of individual trials. Conventionally associated with oscillating brain sources, these dynamics raise questions regarding how these oscillations give rise to the observed dynamical regimes on the brain surface. In this study, we propose a model for spatiotemporal dynamics in EEG data using the Poisson equation, with the right-hand side corresponding to the oscillating brain sources. Through our analysis, we identify primary dynamical regimes based on factors such as the number of sources, their frequencies, and phases. Our numerical simulations, conducted in both 2D and 3D, revealed the presence of standing waves, rotating patterns, and symmetric regimes, mirroring observations in EEG data recorded during picture naming experiments. Notably, moving waves, indicative of spatial displacement in the potential distribution, manifested in the vicinity of brain sources, as was evident in both the simulations and experimental data. In summary, our findings support the conclusion that the brain source model aptly describes the spatiotemporal dynamics observed in EEG data.