Neuroimaging Techniques for Assessing Brain Function

What if you could fly through someone’s brain — and actually watch it think in real time? 🧠 This stunning 3D visualization makes that possible. It shows live brain activity mapped from EEG (electroencephalography) signals onto a realistic 3D model of the human brain. Each color represents a different brainwave frequency — from calm alpha and focused beta, to fast, high-energy gamma rhythms. The golden lines trace the brain’s white matter pathways, and the moving light pulses represent information flowing between regions — the brain communicating with itself in real time. How it’s built The process begins with MRI scans to create a high-resolution 3D model of the brain, skull, and scalp. Then, DTI (Diffusion Tensor Imaging) maps the brain’s wiring — the white matter tracts that connect its regions. Next comes EEG recording, captured using a 64-channel mobile EEG cap. Advanced software pipelines like BCILAB and SIFT clean the data, remove noise, and use mathematical modeling to “source-localize” brain activity — estimating where in the brain each signal originates. They also analyze information flow using a technique called Granger causality, revealing which brain regions are influencing others at any given moment. From Data to Experience All of this is brought to life in Unity, a 3D engine usually used for games. Here, the brain becomes a fully navigable world — you can literally fly through it using a controller and watch live signals flicker and flow. It’s data turned into experience — a fusion of neuroscience, art, and technology that lets us see the living mind at work. Why it matters By merging EEG, MRI, and DTI, researchers can study how the brain’s networks communicate, and how this connectivity changes in conditions like epilepsy, depression, or neurodegenerative diseases. This work also pushes forward brain-computer interface research — paving the way for future technologies that help restore movement, communication, or sensation through brain signals alone. Every flicker of light here represents a thought, a signal, a decision — the brain in motion. 🎥 Video Credits: Dr. Gary Hatlen

Can Brain Activity Patterns Replace Structure as a Biomarker for Depression? 🧠 A growing body of research suggests how the brain functions may be a more reliable indicator of major depressive disorder (MDD) than how it looks. In a new study published in JAMA Psychiatry, researchers compared functional vs structural brain biomarkers in MDD, and functional patterns came out ahead. 🔬 Key finding: Lower regional homogeneity (ReHo), a measure of how synchronized brain activity is in neighboring areas, was strongly correlated with decreased cerebral blood flow (RCBF) in individuals with MDD. This pattern was particularly clear in the: - Cingulum - Superior temporal lobe - Frontal cortex 📉 These functional deficits were significantly more predictive of symptom severity than traditional structural measures like cortical thickness, which showed little correlation. ReHo values, derived from resting-state fMRI, may act as a proxy for blood flow, which is consistently reduced in MDD. When these values are aggregated into a functional Regional Variability Index (RVI), they closely track with how severe a patient’s depression is. Structural RVIs? Not so much. This work not only introduces a reproducible functional biomarker for depression but challenges the dominance of structural imaging in neuropsychiatric diagnostics. As brain imaging shifts toward function-first metrics, tools like ReHo may transform how we diagnose and monitor mental health disorders like MDD. Ref: Kochunov et al., JAMA Psychiatry, 2025 and Cooper et al., Molecular Psychiatry, 2020 Link to article: https://lnkd.in/dtMe626i #MDD #Psychiatry #Depression

Multimodal imaging study elucidates link between neurophysiology & neurochemistry of cognitive & motor deficits in Parkinson’s disease Using magnetoencephalography (MEG) and neuromelanin-sensitive magnetic resonance imaging (MRI), researchers at The Neuro (Montreal Neurological Institute-Hospital) showed that alpha- and beta-band cortical neurophysiology (i.e. brain waves in the alpha and beta frequency range) are differentially associated with the degeneration of neuromelanin-rich cells in the brainstem nuclei of patients with Parkinson’s disease (PD): ALPHA WAVES (8-12 Hz): Increased alpha activity is related to the depletion of noradrenergic cells in the locus coeruleus (LC), with the most pronounced effect in fronto-motor cortices and in patients with stronger attentional impairments. BETA WAVES (13-30 Hz): Decreased beta activity is related to the loss of dopaminergic neurons in the substantia nigra (SN) and the severity of motor impairment, with the effect localized to the left lateral frontal-parietal and temporal cortices. These findings suggest two complementary disease pathways in PD: (1) norepinephrine–alpha activity–cognitive; and (2) dopamine–beta activity–motor The study suggests that neurophysiological (MEG / EEG) measures can potentially serve as translational biomarkers of treatment response in PD, with applications in clinical trials and patient care. 📍RESEARCH ARTICLE (Link in Comments): Wiesman et al. Associations between neuromelanin depletion and cortical rhythmic activity in Parkinson’s disease, Brain, 2024; awae295. AUTHORS: Alex Wiesman, Victoria Madge, Edward Fon, Alain Dagher, D. Louis Collins, Sylvain Baillet, PREVENT-AD Research Group, Quebec Parkinson Network -- ⭐️➡️ FOLLOW for Neuroscience & Digital Health #neuroscience #health #mentalhealth #biotechnology #psychology #digitalhealth

INSIGHTS FROM 11.7 TESLA MAGNETIC RESONANCE Even though very far from clinical implementation 11.7 Tesla MR is opening another field of research and providing tools for the understanding of human physiology and pathology. The article “In vivo imaging of the human brain with the Iseult 11.7-T MRI” discusses the technological advancements enabling high-resolution imaging of the human brain using an ultra-high magnetic field MRI system. Key technological elements include: • 11.7-Tesla Magnetic Field: The Iseult MRI system operates at an ultra-high magnetic field strength of 11.7 Tesla, significantly higher than conventional clinical MRI systems, allowing for enhanced image resolution and contrast. • Parallel Transmission Technology: To address radiofrequency (RF) field inhomogeneities inherent at such high magnetic fields, the system employs parallel transmission techniques. This approach utilizes multiple RF transmit channels to achieve more uniform excitation across the brain, improving image quality. • Specific Absorption Rate (SAR) Management: Operating at ultra-high fields increases the specific absorption rate, which is the rate at which energy is absorbed by the body. The system incorporates advanced SAR management strategies to ensure patient safety by controlling and minimizing RF energy deposition. • High-Performance Gradient System: The MRI system is equipped with a high-performance gradient system capable of rapid and precise spatial encoding, essential for acquiring high-resolution images within clinically acceptable scan times. • Advanced Reconstruction Algorithms: To process the complex data generated by the system, advanced image reconstruction algorithms are utilized. These algorithms correct for artifacts and enhance image clarity, facilitating detailed visualization of brain structures. These technological innovations collectively enable the Iseult 11.7-T MRI system to produce unprecedented in vivo images of the human brain, offering new opportunities for neurological research and clinical diagnostics. #MRI #UltraHighField #11T #MedicalImaging #HealthcareInnovation #BrainImaging #Neurology #Cardiology #Oncology #PrecisionMedicine #Neurodegeneration #AdvancedDiagnostics #MedicalResearch #ImagingTechnology #HealthcareRevolution Open access PDF at: https://lnkd.in/dkydB2E8

Neurotransmitter-Related Functional Connectivity in Alzheimer’s Disease: Insights, Challenges, and Future Directions A recent study by Manca et al. in Brain Communications sheds light on alterations in neurotransmitter-related functional connectivity (FC) along the Alzheimer’s disease (AD) continuum. Utilizing a combination of PET atlases and functional MRI (fcMRI), the authors investigated changes in dopaminergic (DA) and cholinergic (ACh) pathways across cognitively unimpaired (CU), mild cognitive impairment due to AD (AD-MCI), and AD-dementia groups. Key Findings: - DA-Related Connectivity: The AD-dementia group exhibited reduced mesocorticolimbic connectivity in the precuneus and increased thalamic connectivity, while the AD-MCI group demonstrated diminished nigrostriatal connectivity in the left temporal regions. - ACh-Related Connectivity: Both AD-MCI and AD-dementia groups displayed significant declines in temporo-parietal connectivity. - Cognitive Correlations: Episodic memory scores were positively associated with ACh- and DA-related FC in the temporo-parietal cortex and negatively associated with DA-related FC in fronto-thalamic regions. While these findings provide important evidence for the interplay between neurotransmitter systems and cognitive decline in AD, I approach the methods with some skepticism. The cross-sectional design limits causal inferences, and the reliability of inferred connectivity warrants further validation. Replication with longitudinal data will be critical to strengthen these conclusions. Broader Implications: This study underscores the clinical relevance of neurotransmitter-related FC alterations as potential biomarkers and therapeutic targets in early AD. Furthermore, the combination of fcMRI with neurotransmitter-specific imaging modalities (e.g., PET) represents a powerful approach to elucidate the neural mechanisms underpinning neurodegenerative diseases. Such methodologies hold promise not only for AD but also for conditions like Parkinson’s disease; combining DAT imaging with fcMRI could advance our understanding of Parkinsonian syndromes by mapping the relationship between dopaminergic dysfunction and functional network integrity. Integrating functional connectivity with other biomarkers offers a robust framework for generating novel insights into pathophysiological processes and identifying new therapeutic targets. Riccardo Manca, Matteo De Marco, Hilkka Soininen, Livia Ruffini, Annalena Venneri, Changes in neurotransmitter-related functional connectivity along the Alzheimer’s disease continuum, Brain Communications, 2025;, fcaf008 #Neuroimaging #AlzheimersDisease #FunctionalConnectivity #Dopamine #TranslationalScience #BrainHealth

What’s latest in Neurotech : AI brain maps & neuroimaging platforms This series has been all about neurotech startups mastering their own niches. Each post has revolved around one core theme. The last post was about tools and communities for people building neurotech; Part 20 is about AI brain maps and neuroimaging platforms that sit underneath everything else. Omniscient Neurotechnology (o8t) : connectome-guided brain care Omniscient uses advanced MRI and connectomics to build personalized brain network maps. Their software helps neurosurgeons and psychiatrists see which networks are involved in a patient’s symptoms and plan surgery or stimulation around those circuits, turning “where should we operate or stimulate?” into a data-driven decision instead of guesswork. QMENTA : cloud platform for AI neuroimaging in trials and research QMENTA runs a cloud-based imaging platform that ingests brain scans from multi-site studies and clinical trials, then applies AI biomarkers for conditions like multiple sclerosis, dementia, and brain tumors. It gives pharma and researchers a central hub for uploading, processing, and quantifying brain MRI data at scale, instead of everyone hacking together their own pipelines. BrainKey : personal brain dashboard from MRI BrainKey analyzes brain MRI with AI to estimate brain age, measure key structures like hippocampus volume, and surface early signs of atrophy. Clinicians and individuals get a “brain health dashboard” they can track over time, making brain longevity feel more like a measurable vital sign rather than a vague idea. icometrix : quantitative MRI for MS, dementia, and brain injury icometrix builds FDA-cleared and CE-marked AI tools (icobrain) that automatically quantify lesions, atrophy, and other changes on brain MRI. Neurologists and radiologists can compare a patient’s scans over time with objective numbers instead of just eyeballing “a bit worse” or “a bit better,” improving management of multiple sclerosis, dementia, and traumatic brain injury. Piramidal (YC W24) : ICU “foundation model” for EEG Piramidal is training a large AI model on nearly a million hours of EEG data to act as a real-time co-pilot in brain ICUs. Instead of specialists manually reviewing an entire day of EEG per patient, their system flags seizures, altered consciousness, and other abnormalities in seconds, aiming to monitor hundreds of ICU beds at once and catch neurological problems much earlier. Parts 1–19 covered implants, spinal stimulators, sleep tech, haptics, wrist interfaces, digital check-ups, precision mental health, sensory restoration, exoskeletons, bioelectronic medicine, and builder tools. Part 20 is the AI brain-mapping layer the infrastructure turning raw MRI and EEG into maps, biomarkers, and decisions that most people never see, but every future neurotech product will rely on. #BCI #Neurotech #BrainHealth

⚡ First Ever Draft of the Connectome at Scale 🌟 Overview Almost 50 years after Francis Crick called it “impossible,” the MICrONS consortium has delivered the first complete functional connectome of a cubic millimeter of the mouse visual cortex. That’s 75,000 neurons recorded in vivo, 200,000 cells reconstructed in 3D, and 0.5 billion synapses—all matched with single-cell visual response data. Think of it as the neural equivalent of the Human Genome Project—except it doesn’t just show what’s there. It shows what’s happening. 🤓 Geek Mode This dataset is the first to combine high-resolution in vivo 2-photon calcium imaging with dense serial-section electron microscopy in the same brain. Not just for a few cells—but for tens of thousands of them. The team then used deep learning to generate a digital twin of the mouse’s visual cortex, capable of predicting how the neurons would respond to entirely new stimuli. Proofreading over a million edits by hand, the project reconstructed complete dendritic trees and axonal arbors for excitatory and inhibitory neurons—including long-range projections between cortical areas. Some axons spanned 32 millimeters. In a mouse. 💼 Opportunity for VCs This is a platform shift. If “scale creates structure,” then this dataset enables a new wave of startups and labs to discover, validate, and simulate brain circuits with unprecedented fidelity. Applications? - Brain-inspired AI architectures - Disease mapping at synaptic resolution - Scalable neuromorphic chips - Automated neuroscience tools - Next-gen BCIs Think of MICrONS as the foundational layer—an open scaffold where new - companies will build the neurotechnological future. 🌍 Humanity-Level Impact We are no longer guessing at how the brain works. We can see it. This connectome bridges the gap between structure and function. It allows us to simulate vision, not just measure it. And by doing so, it offers a path to understanding cognition itself—one synapse at a time. For neuroscience, it’s a Rosetta Stone. For AI, it’s a blueprint. For humanity, it’s a mirror. 📄 Original paper: https://lnkd.in/gqC-h6x9 #Connectomics #Neuroscience #DeepTech #AI #FunctionalBrainMapping #DigitalTwins #BiologicalIntelligence #OpenScience

🧠 Personalized Neuromodulation: Revolutionizing Brain Stimulation Therapies 🎯 🔍 What is Personalized Neuromodulation? Personalized neuromodulation is a tailored approach to brain stimulation therapies that takes into account individual neuroanatomy, functional connectivity, and specific neural signatures. It moves beyond the "one-size-fits-all" approach to optimize treatment efficacy for each patient. 🖼️ The Role of Advanced Neuroimaging: Recent breakthroughs in neuroimaging techniques are making personalized neuromodulation a reality: High-resolution structural MRI: Allows precise targeting of specific brain regions Functional MRI (fMRI): Reveals individual patterns of brain activity and connectivity Diffusion Tensor Imaging (DTI): Maps white matter tracts for more accurate stimulation EEG-guided targeting: Provides real-time feedback on neural responses 📊 Key Benefits: • Improved treatment outcomes • Reduced side effects • More efficient therapy protocols • Enhanced understanding of individual brain dynamics 🔬 Recent Research Highlights: A groundbreaking study by Siddiqi et al. (2021) in Nature Medicine demonstrated how personalized TMS targeting using functional connectivity MRI led to superior outcomes in treatment-resistant depression. 🔗 https://lnkd.in/gDfVfJnn Another pivotal study by Horn et al. (2019) in Brain showed how personalized DBS electrode placement using advanced imaging techniques improved outcomes in Parkinson's disease. 🔗 https://lnkd.in/gjS7Pzwm 💡 Future Directions: • Integration of AI and machine learning for optimal targeting • Development of closed-loop systems for real-time adjustments • Expansion to other neurological and psychiatric conditions 🤔 What are your thoughts on personalized neuromodulation? How might this impact your clinical practice or research? Let's discuss in the comments below! 👇 #PersonalizedNeuromodulation #BrainStimulation #Neuroimaging #PrecisionMedicine #NeurologicalInnovation #PsychiatricTreatments 🔔 Follow for more updates on cutting-edge neuroscience and psychiatry advancements! 📚 For a comprehensive review, check out this article by Medaglia et al. (2019) in Neuroscience & Biobehavioral Reviews:🔗 https://lnkd.in/gZuTuBX2

Thrilled to share that our paper just published in PNAS, reporting a unified #EEG spatial-temporal-spectral (STSI) #sourceimaging framework that can image location, extent and dynamics of transient and oscillatory neural activation. We further analyzed 2,081 individual #spikes, #seizures, and high frequency oscillations (HFOs) in 42 drug-resistant #epilepsy patients using this novel technique, and quantitatively evaluated the performance of various epilepsy biomarkers. Compared with surgical resections in the patients, our data suggest that #HFOs riding interictal spikes (spike ripples) provide the best #sourcelocalization capability among all interictal biomarkers. The findings enable identification of important epilepsy biomarkers to assist #surgicalplanning and #neuromodulationplanning using non-invasive brain recordings, such as EEG/MEG, and suggest STSI's capability in imaging complex neural signals in various brain disorders tied to #attention, #memory, #perception, #pain, and its application to #BCI. Congratulations to the team, especially the first author, Xiyuan Jiang, my former PhD student at Carnegie Mellon University, for a job well done. Also hearty appreciations to our clinical collaborator Dr. Gregory Worrell from Mayo Clinic for collaboration. Hugh thanks to National Institute of Neurological Disorders and Stroke (NINDS) and National Institute of Biomedical Imaging and Bioengineering (NIBIB) of The National Institutes of Health for funding support. And many thanks to Sara Pecchia at Carnegie Mellon University's College of Engineering to covering our work. Read the paper at: https://lnkd.in/eDsCuqeP #EEG  #SourceLocalization #BrainComputerInterface #NeuralComputation #NeuralOscillations #EventRelatedPotentials #EvokedPotentials #BainMapping #Neuroimaging #Neuromodulation #Neurotechnology #Neuroengineering #Brain

New MRI approach maps brain metabolism, revealing disease signatures. The non-invasive, high-resolution metabolic imaging of the whole brain revealed differences in metabolic activity and neurotransmitter levels among brain regions; found metabolic alterations in brain tumors; mapped and characterized multiple sclerosis lesions — with patients only spending minutes in an MRI scanner. University of Illinois Urbana-Champaign. June 23, 2025 Key: Magnetic Resonance Spectroscopic Imaging (MRSI) Excerpt:  Led by Zhi-Pei Liang, a professor of electrical and computer engineering and a member of the Beckman Institute for Advanced Science and Technology at the U. of I., the team reported its findings in the journal Nature Biomedical Engineering (link enc). Conventional MRI provides high-resolution, detailed imaging of brain structures. Functional MRI maps brain activity by detecting changes in blood flow and blood oxygenation level, which are closely linked to neural activity. However, neither technique provides information on the metabolic activity in the brain, which is important for understanding function and disease, said postdoctoral researcher Yibo Zhao, the first author of the paper. Note: “Metabolic and physiological changes often occur before structural and functional abnormalities are visible on conventional MRI and fMRI images,” Zhao said. “Metabolic imaging, therefore, can lead to early diagnosis and intervention of brain diseases.” Both MRI and fMRI techniques are based on magnetic resonance signals from water molecules. The new technology measures signals from brain metabolites and neurotransmitters as well as water molecules, a technique known as magnetic resonance spectroscopic imaging. These MRSI images can provide significant new insights into brain function and disease processes, and could improve sensitivity and specificity for the detection and diagnosis of brain diseases, Zhao said. “Our technology overcomes several long-standing technical barriers to fast high-resolution metabolic imaging by synergistically integrating ultrafast data acquisition with physics-based machine learning methods for data processing,” Liang said. With the new MRSI technology, the Illinois team cut the time required for a whole brain scan to 12 and a half minutes. Refer to enclosed announcement for further details. https://lnkd.in/eSXHTCMm