Neurosalience #S6E10 with Satrajit Ghosh - How Better Tools Can Transform Brain Science

“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!Chapters00:00 Introduction to Satra Ghosh and His Work06:46 The Intersection of Control Theory and Speech11:18 Satra’s Academic Journey into Neuroscience20:58 Neuroinformatics and Tool Development34:42 Individual Differences in Brain Structure39:21 Developing tools to augment Experimental Design44:25 Building an Intelligent Infrastructure for Neuroscience58:45 The Role of Theory in Neuroscience01:00:26 Access to Scientific Research Expediting Progress01:06:40 Experience Inherent to Learning 01:09:33 Mapping the Brain’s Functional Gradient01:16:31 AI and Speech Analysis in Mental Health01:29:31 Advice, Fail More, Learn MoreWorks mentioned:34:59 - Marek, S. et al. (2022). Reproducible brain-wide association studies require thousands of individuals. https://doi.org/10.1038/s41586-022-04492-943:44 - Ghosh, Satrajit (2025). An Intelligent Infrastructure as a Foundation for Modern Science.https://doi.org/10.48550/arXiv.2508.1005101:09:33  - 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.160828211301:10:13 - Xavier Guell, Jeremy D Schmahmann, John DE Gabrieli, Satrajit S Ghosh (2018). Functional gradients of the cerebellum. https://doi.org/10.7554/eLife.36652Tools and resources mentioned:Nipype : an open-source Python framework for building reproducible neuroimaging workflows.https://nipype.readthedocs.io/en/latest/index.htmlfMRIPrep : 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/openscopeDANDI : 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!Chapters00:00 - Introduction to John Allen and His Work05:26 - John's Journey into Psychology16:44 - Understanding EEG Asymmetry and Its Depression23:08 - Transdiagnostic Approaches to Mental Health26:32 - Exploring Neuromodulation and Psilocybin30:34 - Focused Ultrasound for Depression Treatment42:25 - The Future of Mental Health Interventions46:39 - Translating Research into Clinical Practice51:14 - The Role of Technology in Mental Health Interventions58:14 - AI’s Potential in Mental Health Assessment01:03:40 - Advice for Aspiring NeuropsychologistsWorks 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/a001919618:00 - Coan et al. (2006). A capability model of individual differences in frontal EEG asymmetry. https://doi.org/10.1016/j.biopsycho.2005.10.00329:00 - Moreno et al. (2006). Safety, tolerability, and efficacy of psilocybin in 9 patients with obsessive-compulsive disorder. https://doi.org/10.4088/jcp.v67n111031:00 - 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.2025.145182854:07 - 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.2024.139219901:01:17 - Kaplan et al. (2025). AI and the coming mental health zombie apocalypse. https://doi.org/10.1038/s41380-025-03323-3Producer’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 Senden05:11 - Journey from Psychology to Computational Neuroscience10:01 - Understanding Cognitive Computational Neuroscience14:09 - Limits of Current Models in Cognitive Computational Neuroscience20:44 - Exploring the Rich Club Concept in Brain Networks29:22 - The Interplay of Cortex and Subcortex42:44 - Oscillatory Behavior and Network Coordination48:41 - Multi-Scale Modeling in Neuroscience57:49 - Exploring the Evolving Landscape of Neuroscience01:21:08 - Advice for Young ScientistsWorks 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.2016.10.04448:27 - Pronold et al. (2024). Multi-scale spiking network model of human cerebral cortex. https://doi.org/10.1093/cercor/bhae40948:27 - 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/nwad31857:50 - Senden, M. (2025). The Evolving Landscape of Neuroscience. https://doi.org/10.1101/2025.02.13.638094Episode 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 Guerrero04:46 - Journey from Engineering to Neuroscience10:39 - Understanding Predictive Coding and Bayesian Inference18:34 - Implications of Predictive Coding in Schizophrenia27:08 - Advancements in Brain Imaging Techniques36:31 - Exploring Blindsight and Subcortical Shortcuts44:14 - Reverse Engineering the Brain: Challenges and Ambitions51:23 - The Journey of Developing Optically Pumped Magnetometers01:00:29 - Promoting Women in Neuroscience and Leadership ChallengesWorks mentioned:15:59 - Randeniya et al. (2018). Sensory prediction errors in the continuum of psychosis. https://doi.org/10.1016/j.schres.2017.04.01918:36 - Goodwin et al. (2026). Predictive processing accounts of psychosis: Bottom-up or top-down disruptions. https://doi.org/10.1038/s44220-025-00558-526:02 - Larsen et al. (2019). 22q11.2 deletion syndrome: intact prediction but reduced adaptation. https://doi.org/10.1016/j.nicl.2019.10172129:40 - Garvert et al. (2014). Subcortical amygdala pathways enable rapid face processing. https://doi.org/10.1016/j.neuroimage.2014.07.04729:40 - McFadyen et al. (2017). A rapid subcortical amygdala route for faces. https://doi.org/10.1523/JNEUROSCI.3525-16.2017Episode 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 - Introduction07:37 - Transitioning from Computer Science to Neuroscience13:01 - Exploring Naturalistic Stimuli in Neuroscience18:11 - Hidden Markov Models in Narrative Perception22:46 - Event Boundaries and Memory Encoding27:49 - The Role of the Hippocampus in Memory33:01 - Implications for Mental Health and Memory Disorders38:19 - Enhancing Memory Techniques41:11 - Contextualization in Memory46:19 - Understanding Brain States49:01 - AI and Contextual Knowledge53:29 - Infant Cognition and Event Structures01:01:31 - Future Directions in ResearchWorks 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 Work05:02 - Journey into Medicine and Radiology12:10 - The Challenges of Methods Development in Clinical Applications22:15 - Research on Resting State BOLD Latency37:27 - Clinical Implications of Perfusion Imaging in Stroke43:52 - Challenges in Clinical Implementation of New Imaging Techniques47:50 - The Role of AI in Radiology and Imaging Interpretation52:42 - Future Aspirations and Research Directions in Imaging01:01:03 - Collaborative Efforts in Physiologic MRI Book Project01:03:25 - Advice for Aspiring MD-PhD StudentsWorks 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 Background03:28 - Journey from Computer Science to Neuroscience11:13 - The Center for Translational MR Research12:59 - Involvement with OHBM and Community Growth23:44 - Research Focus on Dementia and Brain Networks28:05 - Exploring Cerebral Vasculitis and Dementia Stages44:02 - Functional Specialization and Cognitive Performance45:34 - AI-Based Interventions for Cognitive Health58:30 - Utilizing Large Datasets for Brain Research01:08:53 - Advice for Aspiring NeuroscientistsWorks mentioned:25:18 - https://www.cell.com/neuron/fulltext/S0896-6273(09)00249-925:18 - https://www.cell.com/neuron/fulltext/S0896-6273(12)00227-926:55 - https://www.neurology.org/doi/10.1212/WNL.000000000000831538:33 - https://www.neurology.org/doi/10.1212/wnl.000000000020740141:00 - https://www.sciencedirect.com/science/article/abs/pii/S105381191600234242:33 - https://journals.plos.org/plosone/article?id=10.1371/journal.pone.007941947:46 - https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Seeing_Beyond_the_Brain_Conditional_Diffusion_Model_With_Sparse_Masked_CVPR_2023_paper.html55:11 - https://www.nature.com/articles/s41586-022-04554-yEpisode 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 Work04:51 - Philosophy’s Influence on Neuroscience17:17 - How Far Will fMRI Take Us?23:27 - Understanding Attention in Neuroscience30:00 - Science as a Process34:17 - The Role of Large Language Models (LLMs) in Scientific Progress38:29 - Why Humans Should Stay in the Equation40:30 - Creativity vs. AI in Scientific Research54:48 - Dr. Kay’s Natural Scenes Dataset (NSD)01:00:27 - Deep Sampling: Considerations and Implications01:08:00 - Accounting for biological variation in Brain Scans: Differences and Similarities01:13:00 - Separating Hemodynamic Effects from Neural Effects01:16:00 - Areas of Hope and Progress in the field01:21:00 - How Should We Benchmark Progress?01:22:59 - Advice for Aspiring ScientistsWorks mentioned:54:48 -  https://www.nature.com/articles/s41593-021-00962-x54:50 - https://www.sciencedirect.com/science/article/pii/S0166223624001838?via%3DihubEpisode 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 Neuroscience14:25 - Neural Bases of Early Childhood Theory of Mind21:58 - False Belief Task and Theory of Mind25:11 - Attention Schema for Consciousness27:14 - Primary Areas Involved in Theory of Mind31:24 - Impact of Neuro Deficits on Social Cognition33:57 - Role of Environment and Timing on Social Cognition37:11 - Implicit and Explicit Mechanisms of Social Development45:02 - Social Cognition Across Species47:37 - Connecting Neural Code to Social Cognition49:56 - Temporal Progression in Theory of Mind Tasks54:54 - Future Research Directions in Understanding Social Cognition01:00:08 - Infant Learning Inspires AI Development01:04:50 - Advice for Aspiring ScientistsWorks mentioned:14:31 -  White matter maturation is associated with the emergence of Theory of Mind in early childhood37:20 -  Two systems for thinking about others’ thoughts in the developing brain49:50 -  Timing matters: disentangling the neurocognitive sequence of mentalizingEpisode 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 601:43 - Highlights from season 507:55 - Reflections on the podcast's impact20:36 - Discussion on the DIANA paper retraction27:21 - Upcoming guests and topics32:04 - Innovations for season 6Episode producer:Xuqian Michelle Li