Cognitive Neuroscience Developments

New discovery from our lab, just out in a preprint: https://lnkd.in/eu-NTr7X Information we learn is often embedded in complex networks of inter-related information. Networks have structure, some nodes are highlight connected, some have locally dense connections, others globally dense connections. Which regions of the brain represent that information? We suspected that brain regions that evolved to help us navigate - hippocampus, retrospenial complex (RSC) - might be key to this and tested this with a novel paradigm Using a graph-learning paradigm and fMRI to examine stimulus response voxel similarity/dissimlimarity we found this was the case. While hippocampus represented the distance between nodes in the network (nice replication of our prior spatial navigation work), RSC represented the global connectivity of a given node, e.g. its betweenness centrality in the network. This is research was led by Gordon Feld when he was on his personal DFG Post-doctoral fellowship, with crucial input from: Pablo Fernandez Velasco Christoffer Gahnstrom Matthieu Bernard Mona Garvert Qasim Mian Martin Gerchen Global structure of new relational knowledge networks is represented in retrosplenial complex, and node-distance in hippocampus

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

3D BRAIN MODELS UNLOCK NEW INSIGHTS INTO MEMORY & CONNECTIVITY Researchers have developed the most detailed 3D computational models of key brain regions, including the hippocampus and sensory cortices, to better understand their roles in memory formation and connectivity. These models integrate anatomical and physiological data, capturing synaptic plasticity and long-range interactions. By simulating brain activity, the models enable predictions about cortical processing and provide tools for future experimental validation. They are openly accessible to the scientific community for further research and refinement. Insights from the models reveal how connectivity shapes complex brain networks and how learning occurs through synaptic plasticity in realistic conditions. This work paves the way for studying phenomena ranging from neural coding to the impacts of specific neurotransmitters. Key Facts: 1. Researchers created 3D models integrating data on anatomy, connectivity, and physiology of the hippocampus and sensory cortices. 2. The models reveal how connectivity patterns form structured brain networks and enable learning through synaptic plasticity. 3. Accessible on a public platform, the models support global research and experimental validation. Source: https://lnkd.in/gfsKe94d

We finally have the first "Brain Atlas"!!! The first comprehensive atlas showing how the human brain’s functional organization evolves from birth to over 100 years old. Published in Nature Magazine in March 2026, this groundbreaking study analyzed functional MRI scans from 3,556 healthy individuals, ranging from newborns just 16 days old to centenarians. Researchers mapped functional connectivity gradients, patterns that reveal how different brain regions communicate and coordinate with each other, rather than just looking at physical structure. They mapped distinct developmental trajectories for how brain networks mature and specialize over a lifetime, found clear milestones in functional hierarchy and integration, and explained how coordinated activity changes across infancy, childhood, adolescence, adulthood, and advanced aging. This atlas provides a powerful new reference for understanding normal brain development, aging, and what goes wrong in neurological and psychiatric conditions. It bridges a major gap in neuroscience by offering a continuous, lifespan-wide view of brain function. As one of the study’s authors noted, this work could eventually help identify when and how interventions might be most effective for brain health across all stages of life. #Neuroscience #BrainAtlas #BrainDevelopment #FunctionalConnectivity #LifespanNeuroscience #BrainHealth #Neuroimaging #Aging

Parkinson’s as a Somato-Cognitive Action Network Disorder? A New Brain Circuit Model Emerges. Somato-cognitive refers to how the body and brain work together by integrating movement, motivation and internal body states in order to guide action. Ren, Zhang Dosenbach, Liu and colleagues describe in a new paper that just dropped in Nature how Parkinson’s disease may be better understood, not just as a movement disorder, but as a disorder of the Somato-Cognitive Action Network (SCAN). The team leveraged a massive multimodal dataset across six interventions to show how SCAN hyperconnectivity might explain both motor and non-motor symptoms and could guide future neuromodulation treatments. Key Points: – The SCAN links motor, cognitive, autonomic and emotional systems, and is more tightly connected to PD circuits than traditional motor-only areas. – Folks w/ Parkinson’s showed hyperconnectivity between the SCAN and key subcortical structures (e.g., substantia nigra, STN, GPi), a pattern not seen in other movement disorders. – Successful therapies like DBS, TMS, focused ultrasound, and levodopa reduced the SCAN to subcortex hyperconnectivity. My take: This paper opens the door to rethinking Parkinson’s as a disorder of action readiness and integration, not just movement execution. This is a big step forward for brain network science in Parkinson's. Here are 5 points that resonated w/ me: 1- Parkinson’s may be better framed as a whole-body network disorder rooted in the SCAN, not just a hand or foot movement issue. 2- Brain imaging showed that the subthalamic nucleus and other DBS targets are functionally linked to the SCAN, not just to movement effectors. 3- Reducing SCAN hyperconnectivity through neuromodulation or dopamine led to better motor outcomes. 4- Targeting cortical SCAN regions w/ repetitive transcranial magnetic stimulation (rTMS) doubled the benefit as compared to traditional motor cortex stimulation. 5- Future neuromodulation strategies have the potential to personalize therapy by matching treatment to SCAN hubs, rather than single motor targets. https://lnkd.in/e5VGq-h2 Nice write up in Scientific American today: https://lnkd.in/eJ7NUsiF Parkinson's Foundation The Michael J. Fox Foundation for Parkinson's Research Norman Fixel Institute for Neurological Diseases PD Avengers

🚨 New preprint out! 🚨 “Translating the Transcriptome: A Connectomics Approach for Gene-Network Mapping and Clinical Application” 🔗 https://lnkd.in/edC4Twm2 🧵 A short thread: Genes shape brain networks—but do genes linked to the same disorder converge on shared circuits? Clemens Neudorfer presents Gene-Network Mapping: a framework combining spatial transcriptomics with functional connectomics to uncover the molecular architecture of brain networks. Using the Allen Human Brain Atlas + normative functional connectivity, we generated gene-network maps for >20,000 genes. These maps capture distributed connectivity patterns linked to each gene’s expression. Think of it as a “connectome fingerprint” for every gene. Aggregating across genes tied to the same disorder, we built disease-network maps. Example: Parkinsonism genes—though diverse in pathways—converged on the nigrostriatal system & extended basal ganglia-thalamocerebellar circuits. Dystonia genes converged on cerebellum & basal ganglia. Validation: ✅ Maps aligned with pharmacological MRI & PET data (neurotransmitter systems). ✅ Converged with networks from brain lesions causing the same symptoms (Lesion Network Mapping). Genetic & lesional causes of movement disorders mapped to the same circuits. Clinical translation: In cohorts of DBS patients (Parkinson’s, dystonia, OCD), symptom improvement correlated with how well stimulation engaged the gene-derived disease network. Better DBS outcomes = closer match to genetic networks. This suggests gene-network mapping could: • Bridge genetics & connectomics • Provide mechanistic insight into disease networks • Guide neuromodulation & precision medicine • Open doors for drug discovery & gene therapy targeting In short: We introduce a framework that links genes → networks → clinical interventions. A step toward network-informed, gene-guided brain therapeutics. 🙌 Huge congratulations to Clemens Neudorfer – this has been his oevre magnum for the last 3-4 years – and thanks to our fantastic team of collaborators across multiple centers worldwide.

We are not just listening to the brain, we are starting to understand its language. And that understanding could revolutionize how we treat neurological and psychiatric disorders. The landmark study published in Nature Biomedical Engineering “Invasive neurophysiology and whole brain connectomics for neural decoding in patients with brain implants” introduces a novel platform that merges invasive brain recordings with MRI-based connectomics. Analyzing 123 hours of data from 73 patients with epilepsy, Parkinson’s, and depression, the researchers achieved: 🧠 Generalizable movement decoders across cohorts in the US, Europe, and China. 💡 Emotion decoding through prefrontal and cingulate networks in DBS-treated depression patients. ⚡ Enhanced seizure detection in responsive neurostimulation for epilepsy. This is more than brain-computer interfacing: it's a step toward adaptive, closed-loop neurotherapies that respond dynamically to each patient’s unique neural state. 👇 Explore the full article: https://lnkd.in/dA7AQtdH #Neurotech #BCI #BrainImplants #DeepBrainStimulation #PrecisionMedicine #Connectomics #Neuroscience #Epilepsy #Parkinsons #MentalHealth #MachineLearning #NeuralDecoding #Neuroengineering #AdaptiveTherapies #Neuropsychiatry #HealthcareInnovation

Echo... Japanese scientists mapped every neuron connection in a cubic millimeter of human brain — creating the most detailed neural wiring diagram ever produced by any country. The human brain contains approximately 86 billion neurons connected by 100 trillion synapses — a connectome of staggering complexity that neuroscientists have been attempting to map systematically since the 1970s. Previous complete connectomes covered only simple organisms: C. elegans roundworm with 302 neurons, Drosophila fruit fly with 140,000. Human brain mapping at this resolution had been considered computationally impossible. Researchers at RIKEN Center for Brain Science in Saitama used a combination of focused ion beam scanning electron microscopy — slicing tissue in 8-nanometer sections and imaging each — and AI-assisted neuron tracing to map a 1-cubic-millimeter sample of human temporal cortex containing 57,000 neurons, 150 million synapses, and 4 kilometers of axonal wiring. The dataset occupies 1.4 petabytes of storage. The map revealed previously unknown circuit motifs — recurring wiring patterns not seen in any other species studied. It identified a class of inhibitory interneuron with a unique branching pattern appearing to coordinate activity across cortical layers in a way not previously described. It showed that excitatory neurons connect to themselves in rare self-reinforcing loops — a potential substrate for memory consolidation. The map is publicly available. Every neuroscientist on Earth is now mining it. Source: RIKEN Center for Brain Science & Science Journal, 2024 #BrainTechnology #Japan #Neuroscience

🚨 New publication alert! 🚨 Thrilled to share a new paper from our Brain Stimulation and Cognition group at the Faculty of Psychology and Neuroscience | Maastricht University by Tingting Zhu, Alexander Sack , and Inge Leunissen: “Phase-Specific Dual-Site Beta Transcranial Alternating Current Stimulation Differentially Influences Functional Connectivity Associated With Motor Inhibition Performance”, now published in Human Brain Mapping 🎉 In this study researchers applied individualized beta frequency dual-site transcranial alternating current stimulation (ds-tACS) targeting the right inferior frontal gyrus (rIFG) and left primary motor cortex (lM1) to directly manipulate phase relationships (by applying either in-phase or anti-phase ds-tACS) in the beta band and assessed effects on both functional connectivity and motor inhibition. 🔑 Key highlights: ·      Both in-phase and anti-phase ds-tACS increased beta power at target sites, yet produced opposite effects on functional connectivity between rIFG and lM1. ·      In-phase ds-tACS enhances connectivity and predicts better inhibitory control. ·      Anti-phase ds-tACS reduces connectivity, linked to faster go responses. 💡 Take-home message: These findings suggest that how well brain areas “sync up” can shape motor inhibition dynamics by modulating both local excitability and long-range connectivity. The pattern hints at a functional trade-off: stronger beta synchrony within the rIFG–M1 pathway supports stopping, but may come with a cost for movement initiation, whereas reduced synchrony can facilitate motor execution. This aligns with frameworks proposing that overly synchronized beta activity can rigidify motor control, while desynchronization may restore adaptability. 🔎 Broader relevance: This mechanistic insight may inform future research exploring dual-site beta-tACS as a tool to probe or potentially normalize inhibitory network dynamics in disorders characterized by impaired inhibition (e.g., Parkinson’s disease). 📖 Read the full paper here: https://lnkd.in/emecnPu5 #BrainStimulation #tACS #Neuroscience #CognitiveEnhancement #NeuralOscillations #EEG #BetaBand #FunctionalConnectivity #MotorControl #InhibitoryControl Figure 1. Experimental design for concurrent dual-site tACS (ds-tACS) and EEG study.

⚡ 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