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For our final episode of the season, Neurosalience goes live from OHBM 2026 in Bordeaux! Recorded in front of a live audience on the final day of the conference, host Peter Bandettini is joined by Giulia Baracchini, Vince Calhoun, Satrajit Ghosh, and Mario Senden to explore one of neuroscience’s biggest challenges: how can we bring together the field’s rapidly expanding knowledge across different domains, methods, and scales? The panel discusses the fragmentation of neuroscience, the overwhelming volume of new research, and the tools, models, and infrastructure needed to connect findings and make them more accessible and meaningful. From mapping the evolving landscape of neuroscience and preserving the richness of high-dimensional data to building intelligent, AI-supported scientific infrastructure, the conversation considers how researchers might better share knowledge, identify gaps, foster collaboration, and accelerate discovery. Read Peter’s accompanying blog post for more background on the discussion and our guests: https://www.ohbm-com.com/blog/neurosaliencelive-audience-podcast-at-ohbm-2026 We hope you enjoy this special live episode! Episode producers: Xuqian Michelle Li

We sit down with four of the people who made OHBM 2026 happen to get the inside scoop on what to expect! Joining us are Christian Beckman (Program Chair-Elect), Nicola Palomero-Gallagher (Program Chair), Rosanna Olsen (Education Chair), and Michel Thiebaut de Schotten (Local Organizing Committee Chair). With 2,500+ neuroscientists descending on Bordeaux, France from June 14–18, this year's meeting is shaping up to be a big one. We dig into the sessions, keynotes, awards, and why Bordeaux is such a perfect backdrop for the conference — plus great tips for getting the most out of your time there and the newly improved meeting app to help you plan your schedule. Whether you're heading to Bordeaux or just curious about what's in store, this one's a great listen! We hope you enjoy this episode! Chapters: 00:00 - Introduction to OHBM 2026 05:21 - The Success of OHBM: Community and Content 10:05 - Methods and Applications in Neuroimaging 15:28 - Educational Courses: Structure and Selection 18:37 - Keynote Speakers and Program Design 23:56 - Symposia vs. Roundtable Discussions 30:50 - Brain Hacks: Collaborative Innovation 32:40 - Choosing the Right Venue for Conferences 37:17 - Exploring Bordeaux: A City of Wine and Culture 38:48 - Challenges in Conference Planning 46:35 - The Future of Poster Presentations 54:24 - Awards and Recognition in the Community 58:28 - Enhancing Conference Value and Engagement 01:02:02 - Social Aspects of the Conference Resources: https://www.humanbrainmapping.org/OHBM2026/ Episode producers: Xuqian Michelle Li

“The Transmitter is trying to cover the whole waterfront…” Kristin Ozelli is the Executive Editor of The Transmitter, a neuroscience publication supported by the Simons Foundation that launched in 2023. In this conversation, Peter and Kristin explore what makes The Transmitter tick. At its heart, the publication is driven by a conviction that neuroscience is most powerful when its many scales, modalities, and subfields are in conversation with each other, and that good science journalism can provide the intellectual scaffolding to make that happen. They discuss the editorial process behind the scenes, from morning story meetings and house style guides to multiple rounds of editing, and how scientist-written essays are helping fill the void left by the decline of science Twitter. The conversation also touches on The Transmitter's ambitious State of Neuroscience mapping project, the ongoing tension between a fragmenting field and the hunger for greater integration, and what it means to portray science as a genuinely human endeavor. They close with thoughts on AI in science writing, exciting new tools and resources coming to the site, and how publications like The Transmitter might help rebuild trust between scientists and a skeptical public. We hope you enjoy this episode! Chapters: 00:00 - Introduction to the Transmitter and Its Mission 05:49 - Kristin Ozelli’s Journey to Neuroscience Journalism 08:29 - The Transmitter's History and Evolution 11:03 - Finding the Sweet Spot in Neuroscience Communication 16:19 - Mapping Neuroscience: The State of Neuroscience Project 23:22 - Engaging the OHBM Community and Beyond 30:07 - The Evolution of Scientific Communication 33:45 - Public Perception of Science 40:22 - Future Directions for Transmitter 47:15 - The Role of AI in Journalism Resources: 14:20 - The State of Neuroscience https://www.thetransmitter.org/state-of-neuroscience-report-2025/ Episode producers: Karthik Sama, Xuqian Michelle Li

"Registered reports foster clarity upfront…" Dr. Oscar Esteban is a professor at Lausanne University Hospital whose lab develops open neuroimaging infrastructure, best known for tools like fMRIPrep and MRIQC. Dr. Mallar Chakravarty is Director of the Brain Imaging Center at the Douglas Research Centre at McGill University and co-editor-in-chief of Aperture Neuro. In this conversation, Peter, Mallar, and Oscar unpack what registered reports are and why they emerged as a response to the file drawer problem and publication bias toward positive results. Drawing on Oscar’s firsthand experience using registered reports in his own lab, the discussion explores how front-loading peer review can sharpen experimental design, protect researchers pursuing high-risk science, and give trainees meaningful early career credit. But the conversation doesn’t shy away from harder questions: Can any publication mechanism truly fix an incentive-driven culture? Where does the natural, iterative cycle of exploratory science fit in? And are registered reports the right tool for every type of science or just some of it? We hope you enjoy this episode! Chapters: 06:12 - Understanding Registered Reports 10:48 - The Origin and Motivation Behind Registered Reports 15:03 - Concerns and Challenges of Registered Reports 21:08 - Exploratory vs. Confirmatory Research 30:50 - The Role of Registered Reports in Training Researchers 42:15 - The Role of Pre-registration in Scientific Rigor 53:59 - The Systemic Challenges in Scientific Publishing 01:06:29 - Concluding Thoughts on Registered Reports Resources: 01:02:37 - About PCI Registered Reports - Peer Community In https://rr.peercommunityin.org/about Episode producers: Xuqian Michelle Li

"We inhibited a brain region and connectivity went up. I thought it was an artifact..." Dr. Alessandro Gozzi is a systems neuroscientist investigating how the brain functions as an integrated network and how disruptions in that network relate to behavior and mental health. He is Senior Scientist and Group Leader of the Functional Neuroimaging Laboratory at the Italian Institute of Technology in Rovereto, Italy. His research combines fMRI, functional ultrasound imaging, optogenetics, chemogenetics, electrophysiology, and computational modeling to decode the neural underpinnings of brain connectivity in rodent models, with the goal of bridging circuit-level findings to human psychopathology. In this conversation, Dr. Gozzi unpacks what resting-state fMRI connectivity actually reflects and why the answer may be more surprising than the field assumes. Drawing on a series of elegant chemogenetic and pharmacological manipulations in mice, he reveals how regional excitability, rather than direct synaptic communication, may be a dominant driver of the connectivity patterns we observe. Within this context, the conversation explores the paradoxical relationship between neural silencing and hyperconnectivity, the evolutionary conservation of brain networks across species, and what rodent models of autism can and cannot tell us about human psychiatric disorders. Join the conversation to discover how mechanistic animal studies are reshaping our understanding of human brain connectivity. We hope you enjoy this episode! Chapters: 00:00 - Introduction to Dr. Alessandro Gozzi 05:12 - Gozzi’s Unconventional Journey into Neuroscience 13:17 - Transitioning from Industry to Academia 20:49 - The Relevance of Rodent Models in Understanding Autism 32:04 - Exploring the Complexities of Brain Connectivity 38:57 - Excitability and Its Role in Connectivity Patterns 42:27 - Exploring fMRI Connectivity and Local Computation 45:28 - The Role of the Hearst Index in Brain Activity 54:00 - Implications for Treatment in Psychiatric Disorders 58:42 - The Intersection of Biology and Neuroscience Research 01:07:08 - Balancing Life and Science: Personal Reflections Works mentioned: 00:12:48 - Gutierrez-Barragan, D. et al. (2024). Evolutionarily conserved fMRI network dynamics in the mouse, macaque, and human brain. https://doi.org/10.1038/s41467-024-49245-6 00:17:40 - Zerbi, V., Pagani, M. et al. (2021). Brain mapping across 16 autism mouse models reveals a spectrum of functional connectivity subtypes. https://doi.org/10.1038/s41380-021-01245-4 00:18:00 - Pagani, M. et al. (2021). mTOR-related synaptic pathology causes autism spectrum disorder-associated functional hyperconnectivity. https://doi.org/10.1038/s41467-021-26520-8 00:29:50 - Pagani, M. et al. (2025). Biological subtyping of autism via cross-species fMRI. https://doi.org/10.1101/2025.03.04.641400 00:40:40 - Rocchi, F. et al. (2022). Increased fMRI connectivity upon chemogenetic inhibition of the mouse prefrontal cortex. https://doi.org/10.1038/s41467-022-28591-3 00:43:30 - Trakoshis, S., Martínez-Cañada, P. et al. (2020). Intrinsic excitation-inhibition imbalance affects medial prefrontal cortex differently in autistic men versus women. https://doi.org/10.7554/eLife.55684 00:45:10 - Newbold, D.J. et al. (2020). Plasticity and spontaneous activity pulses in disused human brain circuits. https://doi.org/10.1016/j.neuron.2020.05.007 Episode producer: Xuqian Michelle Li

“Shortening scientific loops accelerates discovery” Dr. Satrajit Ghosh is a senior research scientist at the McGovern Institute for Brain Research at MIT and an assistant professor at Harvard Medical School. He has helped advance neuroinformatics, open science, and reproducible neuroimaging through both his research and the development of widely used community tools. His work spans machine learning for neuroimaging, the neural mechanisms of speech, and the use of speech features to inform psychiatric diagnosis and treatment. He earned his bachelor’s degree with honors in computer science from the National University of Singapore and his PhD in cognitive and neural systems from Boston University. He has contributed to influential projects including Nipype, fMRIPrep, and NeuroVault. More recently, he has focused on how shared scientific infrastructure can connect domains, modalities, and scales across neuroscience and help address the field’s growing fragmentation. In this episode, Peter and Satrajit discuss the origins of tools like Nipype and the broader push for reproducible neuroimaging, showing how practical research challenges can inspire infrastructure that benefits the entire field. They also explore functional gradients in the brain and cerebellum, the promise of speech as a scalable biomarker for mental health, and the cautious role AI may play in diagnosis and scientific discovery. A major theme in their conversation is the fragmentation in neuroscience, with knowledge often siloed across methods, scales, and communities. Ghosh argues for a more intelligent scientific infrastructure that connects data, tools, theory, and expertise. He closes with advice to young scientists: experiment often, make mistakes, and learn by discovering where systems fail. We hope you enjoy this episode! Chapters 00:00 Introduction to Satra Ghosh and His Work 06:46 The Intersection of Control Theory and Speech 11:18 Satra’s Academic Journey into Neuroscience 20:58 Neuroinformatics and Tool Development 34:42 Individual Differences in Brain Structure 39:21 Developing tools to augment Experimental Design 44:25 Building an Intelligent Infrastructure for Neuroscience 58:45 The Role of Theory in Neuroscience 01:00:26 Access to Scientific Research Expediting Progress 01:06:40 Experience Inherent to Learning  01:09:33 Mapping the Brain’s Functional Gradient 01:16:31 AI and Speech Analysis in Mental Health 01:29:31 Advice, Fail More, Learn More Works mentioned: 34:59 - Marek, S. et al. (2022). Reproducible brain-wide association studies require thousands of individuals. https://doi.org/10.1038/s41586-022-04492-9 43:44 - Ghosh, Satrajit (2025). An Intelligent Infrastructure as a Foundation for Modern Science. https://doi.org/10.48550/arXiv.2508.10051 01:09:33  - Margulies, Daniel S., et al. (2016).  Situating the default-mode network along a principal gradient of macroscale cortical organization. https://doi.org/10.1073/pnas.1608282113 01:10:13 - Xavier Guell, Jeremy D Schmahmann, John DE Gabrieli, Satrajit S Ghosh (2018). Functional gradients of the cerebellum. https://doi.org/10.7554/eLife.36652 Tools and resources mentioned: Nipype : an open-source Python framework for building reproducible neuroimaging workflows. https://nipype.readthedocs.io/en/latest/index.html fMRIPrep : a robust, analysis-agnostic preprocessing pipeline for functional MRI. https://fmriprep.org/en/stable/ OpenScope : an open-science effort for large-scale neuroscience data sharing and analysis. https://www.allenneuraldynamics.org/projects/openscope DANDI : a platform for publishing, sharing, and processing neurophysiology data.  https://about.dandiarchive.org/ NeuroVault : A public repository of unthresholded statistical maps, parcellations, and atlases of the brain. https://neurovault.org/ Episode producers:Ömer Faruk Gülban, Karthik Sama

“It’s not a depression prevention plan, it’s a life improvement plan. It’s a whole…” Dr. John Allen is a Distinguished Professor of Psychology, Cognitive Science, and Neuroscience at the University of Arizona. He received his PhD in 1991 from the University of Minnesota, specializing in psychophysiology and biological measurement, and joined the Arizona faculty in 1992. A leading figure in psychophysiology and mood and anxiety disorders, John is known for his pioneering work on frontal EEG alpha asymmetry as a biomarker for emotional processing and depression risk. His research spans the etiology and treatment of depression, the integration of fMRI with autonomic nervous system measures to study brain-body interactions, and the development of novel interventions grounded in the neurobiology of emotional disorders—including transcranial ultrasound, EEG biofeedback, and transcranial stimulation techniques. In this episode, Peter and John trace John's path into psychology and his focus on mood and anxiety disorders. They discuss the significance of EEG asymmetry as an indicator of depression and explore the need for transdiagnostic approaches to mental health. The conversation delves into the potential of neuromodulation techniques—including psilocybin therapy and focused ultrasound—for treating depression, and examines the broader intersection of neuroscience, physiology, psychology, and technology in mental health treatment. They also touch on the challenges of translating research into clinical practice and the emerging role of AI in mental health assessment. We hope you enjoy this episode! Chapters 00:00 - Introduction to John Allen and His Work 05:26 - John's Journey into Psychology 16:44 - Understanding EEG Asymmetry and Its Depression 23:08 - Transdiagnostic Approaches to Mental Health 26:32 - Exploring Neuromodulation and Psilocybin 30:34 - Focused Ultrasound for Depression Treatment 42:25 - The Future of Mental Health Interventions 46:39 - Translating Research into Clinical Practice 51:14 - The Role of Technology in Mental Health Interventions 58:14 - AI’s Potential in Mental Health Assessment 01:03:40 - Advice for Aspiring Neuropsychologists Works mentioned: 16:30 - Stewart et al. (2010). Resting frontal EEG asymmetry as an endophenotype for depression risk: Sex-specific patterns of frontal brain asymmetry. https://doi.org/10.1037/a0019196 18:00 - Coan et al. (2006). A capability model of individual differences in frontal EEG asymmetry. https://doi.org/10.1016/j.biopsycho.2005.10.003 29:00 - Moreno et al. (2006). Safety, tolerability, and efficacy of psilocybin in 9 patients with obsessive-compulsive disorder. https://doi.org/10.4088/jcp.v67n1110 31:00 - Schachtner et al. (2025). An open-label trial of stereotactic, non-invasive transcranial focused ultrasound targeting the default mode network for the treatment of depression. https://doi.org/10.3389/fpsyt.2025.1451828 54:07 - Lord et al. (2024). Transcranial focused ultrasound to the posterior cingulate cortex modulates default mode network and subjective experience: an fMRI pilot study. https://doi.org/10.3389/fnhum.2024.1392199 01:01:17 - Kaplan et al. (2025). AI and the coming mental health zombie apocalypse. https://doi.org/10.1038/s41380-025-03323-3 Producer’s note:  We ran into some technical issues with John's video, so you'll see captions in place of his footage throughout the episode. Audio quality is all good though! Thanks for understanding, and enjoy the conversation. Episode producers: Xuqian Michelle Li

Dr. Mario Senden is an assistant professor in the Department of Cognitive Neuroscience at Maastricht University in the Netherlands, where he has spent his entire academic career. He received his bachelor's in psychology in 2009 and his PhD in cognitive computational neuroscience in 2016, both from Maastricht. A pioneer in biophysics-aware deep learning, Mario is known for his work on how large-scale brain networks support communication, integration, and perception. His research spans mesoscale laminar microcircuits to the macro-scale connectome, and his functional whole-brain modeling framework combines large-scale anatomical structure with local dynamics and goal-driven computation — asking not just whether a dynamical regime is biologically plausible, but whether it actually supports perceptual and cognitive function. In this episode, Peter and Mario explore the cutting edge of computational neuroscience and whole-brain modeling. They discuss Mario's influential work on rich club networks, which showed how highly connected cortical hubs dynamically gate information flow during tasks, as well as the principles behind oscillatory behavior in neural systems. A central focus of the conversation is Mario's most recent paper, "The Evolving Landscape of Neuroscience," submitted to Aperture Neuro — a sweeping meta-scientific analysis of roughly half a million neuroscience articles published between 1999 and 2023. Using text embeddings, semantic clustering, and large language models, Mario mapped the structural organization of the field and identified emerging trends and future directions. The conversation also touches on the promise of interdisciplinary approaches, the growing role of AI tools in neuroscience research, and the broader challenge of integrating theories and data across scales and domains to truly understand the brain. We hope you enjoy this episode! Chapters: 00:00 - Introduction to Dr. Mario Senden 05:11 - Journey from Psychology to Computational Neuroscience 10:01 - Understanding Cognitive Computational Neuroscience 14:09 - Limits of Current Models in Cognitive Computational Neuroscience 20:44 - Exploring the Rich Club Concept in Brain Networks 29:22 - The Interplay of Cortex and Subcortex 42:44 - Oscillatory Behavior and Network Coordination 48:41 - Multi-Scale Modeling in Neuroscience 57:49 - Exploring the Evolving Landscape of Neuroscience 01:21:08 - Advice for Young Scientists Works mentioned: 42:19 - Senden et al. (2017). Cortical rich club regions can organize state-dependent functional network formation by engaging in oscillatory behavior. https://doi.org/10.1016/j.neuroimage.2016.10.044 48:27 - Pronold et al. (2024). Multi-scale spiking network model of human cerebral cortex. https://doi.org/10.1093/cercor/bhae409 48:27 - Senden et al. (2024). Modular-integrative modeling: a new framework for building brain models that blend biological realism and functional performance. https://doi.org/10.1093/nsr/nwad318 57:50 - Senden, M. (2025). The Evolving Landscape of Neuroscience. https://doi.org/10.1101/2025.02.13.638094 Episode producers: Ömer Faruk Gülban, Xuqian Michelle Li

“Predictive coding offers a powerful lens for understanding psychosis…” Dr. Marta Garrido is a professor at the Melbourne School of Psychological Sciences, where she leads the Cognitive Neuroscience and Computational Psychiatry Laboratory and directs the Cognitive Neuroscience Hub. She is also a research program lead at the Graeme Clark Institute. With a background in engineering physics from the University of Lisbon and a PhD in neuroscience from University College London under the mentorship of Professor Karl Friston, Marta has become a leading figure in understanding how the brain processes predictions and surprise. Her research spans mismatch negativity, predictive coding theory, dynamic causal modeling, and the development of cutting-edge neuroimaging technologies, including Australia’s first optically pumped MEG system. In this episode, Peter and Marta explore the elegant framework of predictive coding and its implications for understanding psychiatric conditions like psychosis. They discuss how the brain generates predictions about sensory input and how disruptions in these mechanisms may contribute to symptoms of mental illness. Marta shares her journey from engineering to neuroscience, her transformative PhD experience, and the challenges of building a new MEG system from the ground up. The conversation covers fascinating topics including mismatch negativity as a prediction error signal, subcortical shortcuts for processing threatening stimuli, the phenomenon of blindsight, and the critical importance of mentorship in academic careers. Marta also offers candid reflections on being a woman in neuroscience and her vision for the future of computational psychiatry. We hope you enjoy this episode! Chapters: 00:00 - Introduction to Dr. Marta Guerrero 04:46 - Journey from Engineering to Neuroscience 10:39 - Understanding Predictive Coding and Bayesian Inference 18:34 - Implications of Predictive Coding in Schizophrenia 27:08 - Advancements in Brain Imaging Techniques 36:31 - Exploring Blindsight and Subcortical Shortcuts 44:14 - Reverse Engineering the Brain: Challenges and Ambitions 51:23 - The Journey of Developing Optically Pumped Magnetometers 01:00:29 - Promoting Women in Neuroscience and Leadership Challenges Works mentioned: 15:59 - Randeniya et al. (2018). Sensory prediction errors in the continuum of psychosis. https://doi.org/10.1016/j.schres.2017.04.019 18:36 - Goodwin et al. (2026). Predictive processing accounts of psychosis: Bottom-up or top-down disruptions. https://doi.org/10.1038/s44220-025-00558-5 26:02 - Larsen et al. (2019). 22q11.2 deletion syndrome: intact prediction but reduced adaptation. https://doi.org/10.1016/j.nicl.2019.101721 29:40 - Garvert et al. (2014). Subcortical amygdala pathways enable rapid face processing. https://doi.org/10.1016/j.neuroimage.2014.07.047 29:40 - McFadyen et al. (2017). A rapid subcortical amygdala route for faces. https://doi.org/10.1523/JNEUROSCI.3525-16.2017 Episode producers: Karthik Sama, Xuqian Michelle Li

“Naturalistic stimuli open up new exploration…” Dr. Christopher Baldassano is an associate professor at Columbia University and leads the Dynamic Perception and Memory Lab. With a background in electrical engineering from Princeton and a PhD in computer science from Stanford, Chris has pioneered innovative approaches to understanding memory and cognition. Following a postdoc at Princeton with Uri Hasson and Ken Norman, he joined Columbia in 2018. His research focuses on how the brain processes, stores, and retrieves events using naturalistic stimuli, hidden Markov models, and multivariate analysis techniques. In this episode, Peter and Chris explore the fascinating world of event structures and memory. They discuss Chris’s pioneering work on event scripts, neural frameworks that act as cognitive scaffolds for autobiographical memories. The conversation covers how the brain segments continuous experience into discrete events, the role of event boundaries in memory encoding, and the critical function of the hippocampus in organizing these temporal structures. Chris explains his use of naturalistic stimuli and hidden Markov models to reveal the subtle dynamics of how we combine recurring information to respond more efficiently to future experiences. Along the way, Chris shares valuable insights on the evolution of neuroscience research and offers thoughtful advice for aspiring scientists navigating the field. We hope you enjoy this episode! Chapters: 00:00 - Introduction 07:37 - Transitioning from Computer Science to Neuroscience 13:01 - Exploring Naturalistic Stimuli in Neuroscience 18:11 - Hidden Markov Models in Narrative Perception 22:46 - Event Boundaries and Memory Encoding 27:49 - The Role of the Hippocampus in Memory 33:01 - Implications for Mental Health and Memory Disorders 38:19 - Enhancing Memory Techniques 41:11 - Contextualization in Memory 46:19 - Understanding Brain States 49:01 - AI and Contextual Knowledge 53:29 - Infant Cognition and Event Structures 01:01:31 - Future Directions in Research Works mentioned: 2:28 - https://www.youtube.com/watch?v=jPLWOBmaLkY (Baldassano talk at NIH workshop on naturalistic stimuli) 14:42 - https://pubmed.ncbi.nlm.nih.gov/28772125/ (Baldassano et al., 2017 - Neuron - "Discovering Event Structure in Continuous Narrative Perception and Memory") 15:02 - https://pubmed.ncbi.nlm.nih.gov/30249790/ (Baldassano et al., 2018 - Journal of Neuroscience - "Representation of Real-world Event Schemas During Narrative Perception") 18:24 - https://pubmed.ncbi.nlm.nih.gov/29087305/ (Vidaurre, Smith & Woolrich, 2017 - PNAS - "Brain network dynamics are hierarchically organized in time" - using Markov models in a different way) 19:41 - https://pubmed.ncbi.nlm.nih.gov/17338600/ (Zacks et al., 2007 - Psychological Bulletin - "Event perception: A mind-brain perspective" - foundational work on event boundary processes) 27:04 - https://pubmed.ncbi.nlm.nih.gov/27121839/ (Huth et al., 2016 - Nature - "Natural speech reveals the semantic maps that tile human cerebral cortex" - semantic information stored throughout the brain) 37:15 - https://pubmed.ncbi.nlm.nih.gov/22982082/ (LePort et al., 2012 - Neurobiology of Learning and Memory - Jim McGaugh's study on highly superior autobiographical memory) 53:01 - https://pubmed.ncbi.nlm.nih.gov/36252007/ (Yates et al., 2022 - PNAS - "Neural event segmentation of continuous experience in human infants") Episode producers: Xuqian Michelle Li

Dr. Ahmed Khalil is an MD-PhD currently serving his residency in radiology at the Institute of Neuroradiology at Charité University Hospital in Berlin. Originally from Sudan, he has been doing pioneering work on resting-state BOLD latency mapping, a technique that reveals flow deficits in the brain associated with stroke. His research demonstrates that this approach compares favorably with the current clinical gold standard of dynamic susceptibility contrast imaging using gadolinium, while capturing useful data in as little as two minutes. In this episode, Peter and Ahmed discuss his work translating advanced MRI techniques into clinical practice. They explore how BOLD latency mapping can detect perfusion deficits and compare with both traditional gadolinium-based methods and DTI for identifying stroke lesions. The conversation delves into the broader challenge faced by all promising research methods: what does it actually take to move from successful proof-of-concept to daily clinical practice on scanners around the world? Ahmed and Peter also talk about the cultural gap between research-level image processing and the clinical preference for minimally processed, interpretable data and how AI might help bridge that divide. Along the way, Ahmed shares valuable advice for MD-PhD students on the importance of collaboration, learning from diverse experts, and maintaining curiosity across disciplines. We hope you enjoy this episode! Chapters: 00:00 - Introduction to Ahmed Khalil and His Work 05:02 - Journey into Medicine and Radiology 12:10 - The Challenges of Methods Development in Clinical Applications 22:15 - Research on Resting State BOLD Latency 37:27 - Clinical Implications of Perfusion Imaging in Stroke 43:52 - Challenges in Clinical Implementation of New Imaging Techniques 47:50 - The Role of AI in Radiology and Imaging Interpretation 52:42 - Future Aspirations and Research Directions in Imaging 01:01:03 - Collaborative Efforts in Physiologic MRI Book Project 01:03:25 - Advice for Aspiring MD-PhD Students Works mentioned: 22:48 - https://pubmed.ncbi.nlm.nih.gov/23378326/ (Lv et al., 2013 - First paper showing BOLD delay in stroke with Arno Villinger) 23:08 - https://www.ahajournals.org/doi/10.1161/STROKEAHA.116.015566 (Khalil et al., 2017 - Stroke paper, Relationship between BOLD delay and DSC-MRI) 23:08 - https://pubmed.ncbi.nlm.nih.gov/30334657/ (Khalil et al., 2018 - JCBFM paper, Longitudinal changes in BOLD delay) 39:00 - https://pubmed.ncbi.nlm.nih.gov/34323339/ (Hu et al., 2021 - Human Brain Mapping paper with Daniel Margulies - ICA approach) Episode producers: Ömer Faruk Gülban, Xuqian Michelle Li

“What makes certain brain networks vulnerable to disease—and can AI help us predict what comes next?” Dr. Juan Helen Zhou is a computational neuroscientist at the National University of Singapore, where she is an Associate Professor and Director of the Center for Translational Magnetic Resonance Research at the Yong Loo Lin School of Medicine. She leads the Multimodal Neuroimaging in Neuropsychiatric Disorders Laboratory, integrating multimodal brain imaging and machine learning to study network vulnerability in aging and neuropsychiatric disorders, including dementia, psychosis, and ADHD. In this episode, Peter and Helen discuss her path from computer science to neuroscience and how that background shaped her approach to brain imaging and AI. They explore her work on dementia, including the role of cerebral vascular disease, why different forms of dementia must be understood as distinct network-level disorders, and how selective brain network vulnerabilities can predict cognitive decline. The discussion also covers recent advances from Dr. Zhou’s lab in reconstructing images from brain activity using generative AI and self-supervised learning, highlighting both the promise and challenges of these approaches. Along the way, Helen reflects on the importance of collaboration in neuroscience and shares advice for early-career researchers on persistence, communication, and navigating interdisciplinary science. We hope you enjoy this episode! Chapters: 00:00 - Introduction to Helen Zhou and Her Background 03:28 - Journey from Computer Science to Neuroscience 11:13 - The Center for Translational MR Research 12:59 - Involvement with OHBM and Community Growth 23:44 - Research Focus on Dementia and Brain Networks 28:05 - Exploring Cerebral Vasculitis and Dementia Stages 44:02 - Functional Specialization and Cognitive Performance 45:34 - AI-Based Interventions for Cognitive Health 58:30 - Utilizing Large Datasets for Brain Research 01:08:53 - Advice for Aspiring Neuroscientists Works mentioned: 25:18 - https://www.cell.com/neuron/fulltext/S0896-6273(09)00249-9 25:18 - https://www.cell.com/neuron/fulltext/S0896-6273(12)00227-9 26:55 - https://www.neurology.org/doi/10.1212/WNL.0000000000008315 38:33 - https://www.neurology.org/doi/10.1212/wnl.0000000000207401 41:00 - https://www.sciencedirect.com/science/article/abs/pii/S1053811916002342 42:33 - https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0079419 47:46 - https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Seeing_Beyond_the_Brain_Conditional_Diffusion_Model_With_Sparse_Masked_CVPR_2023_paper.html 55:11 - https://www.nature.com/articles/s41586-022-04554-y Episode producers: Karthik Sama, Xuqian Michelle Li

“What does it actually mean to understand the brain?” Dr. Kendrick Kay is a computational neuroscientist and neuroimaging expert at the University of Minnesota’s Center for Magnetic Resonance Research, where he is an Associate Professor in the Department of Radiology. With training spanning philosophy and neuroscience, from a bachelor’s degree in philosophy at Harvard University to a PhD in neuroscience from UC Berkeley, Dr. Kay’s work bridges deep theoretical questions with cutting-edge neuroimaging methods. In this conversation, Peter Bandettini and Kendrick Kay explore the evolving landscape of neuroscience at the intersection of fMRI, philosophy, and artificial intelligence. They reflect on the limits of current neuroimaging methodologies, what fMRI can and cannot tell us about brain mechanisms, and why creativity and human judgment remain central to scientific progress. The discussion also dives into Dr. Kay’s landmark contributions to fMRI decoding and the Natural Scenes Dataset, a high-resolution resource that has become foundational for computational neuroscience and neuro AI research. Along the way, they examine deep sampling in neuroimaging, individual variability in brain data, and the challenges of separating neural signals from hemodynamic effects. Framed by broader questions about understanding benchmarking progress, and the growing role of LLM’s in neuroscience, this wide-ranging conversation offers a thoughtful look at where the field has been and where it may be headed. We hope you enjoy this episode! Chapters: 00:00 - Introduction to Kendrick Kay and His Work 04:51 - Philosophy’s Influence on Neuroscience 17:17 - How Far Will fMRI Take Us? 23:27 - Understanding Attention in Neuroscience 30:00 - Science as a Process 34:17 - The Role of Large Language Models (LLMs) in Scientific Progress 38:29 - Why Humans Should Stay in the Equation 40:30 - Creativity vs. AI in Scientific Research 54:48 - Dr. Kay’s Natural Scenes Dataset (NSD) 01:00:27 - Deep Sampling: Considerations and Implications 01:08:00 - Accounting for biological variation in Brain Scans: Differences and Similarities 01:13:00 - Separating Hemodynamic Effects from Neural Effects 01:16:00 - Areas of Hope and Progress in the field 01:21:00 - How Should We Benchmark Progress? 01:22:59 - Advice for Aspiring Scientists Works mentioned: 54:48 -  https://www.nature.com/articles/s41593-021-00962-x 54:50 - https://www.sciencedirect.com/science/article/pii/S0166223624001838?via%3Dihub Episode producers: Xuqian Michelle Li, Naga Thovinakere

"AI is really bad at perspective taking…" Dr. Charlotte Grosse Wiesmann is a cognitive neuroscientist exploring how the human social brain takes shape in early life. She is a Professor at the University of Technology Nuremberg and directs the Research Group on Social Brain Development at the Max Planck Institute in Leipzig. Her research blends developmental psychology, brain imaging, and computational modeling to uncover how infants begin to infer other people’s beliefs, intentions and mental states.  In this conversation, Dr. Wiesmann unpacks how children’s brains develop the capacity for social understanding and theory of mind. Drawing on developmental psychology and neuroimaging, she reveals how the brain transforms as children first succeed on false-belief tasks, a fleeting yet powerful window into the emergence of the social mind. Within this context, the conversation explores white matter maturation, environmental influences, and brain plasticity, offering fresh insights into how studying infant development can inform the future of AI. Join the conversation to discover how early brain development is reshaping our understanding of our social minds. We hope you enjoy this episode! Chapters: 00:00 - A Journey from Physics to Neuroscience 14:25 - Neural Bases of Early Childhood Theory of Mind 21:58 - False Belief Task and Theory of Mind 25:11 - Attention Schema for Consciousness 27:14 - Primary Areas Involved in Theory of Mind 31:24 - Impact of Neuro Deficits on Social Cognition 33:57 - Role of Environment and Timing on Social Cognition 37:11 - Implicit and Explicit Mechanisms of Social Development 45:02 - Social Cognition Across Species 47:37 - Connecting Neural Code to Social Cognition 49:56 - Temporal Progression in Theory of Mind Tasks 54:54 - Future Research Directions in Understanding Social Cognition 01:00:08 - Infant Learning Inspires AI Development 01:04:50 - Advice for Aspiring Scientists Works mentioned: 14:31 -  White matter maturation is associated with the emergence of Theory of Mind in early childhood 37:20 -  Two systems for thinking about others’ thoughts in the developing brain 49:50 -  Timing matters: disentangling the neurocognitive sequence of mentalizing Episode producers: Xuqian Michelle Li, Karthik Sama

"I really enjoy good conversations..." In this episode, we look back at the highlights from last season and share some fun insights from our audience metrics. We’re celebrating six years of Neurosalience, and we’re excited for the incredible guests and topics coming up this season. We hope you enjoy this episode! Chapters: 00:00 - Introduction to season 6 01:43 - Highlights from season 5 07:55 - Reflections on the podcast's impact 20:36 - Discussion on the DIANA paper retraction 27:21 - Upcoming guests and topics 32:04 - Innovations for season 6 Episode producer: Xuqian Michelle Li