“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

Early career researchers give their perspectives on being an academic today. With Arshiya  Sangchooli, Natasha L. Taylor, Ashlea Segal, Stefano Moia, Jiajia Yang, and Peter Bandettini Episode ProducersOmer Faruk GulbanXuqian Michelle Li

Peter Bandettini interviews Dr. Rosanna Olsen, a senior scientist at the Rotman Research Institute and the University of Toronto. She is pioneering what we know about human memory and its associated structures, primarily focusing on the hippocampus, the role it plays, and how it changes with age and neurological diseases. Her work has shed light on how the hippocampus facilitates the flexible binding and comparison of new and existing information. She has also shown how visual exploration reveals memory processes, and has uncovered promising early dementia biomarkers based on measures of visual exploration and hippocampus. Dr. Olsen is also a leader in education. She is co-lead of the Research Training Center in Toronto, disseminating essential knowledge and skills to younger scientists, and is chair-elect of the OHBM education committee. She is also the leader of a consortium organized to reach a consensus on hippocampus segmentation. Lastly she's an avid and accomplished runner, having run the Boston Marathon, as well as many others.We hope you enjoy the conversation! Episode ProducersOmer Faruk GulbanXuqian Michelle Li

This OHBM preview podcast offers an in-depth look at how the OHBM Annual Meeting is organized, along with reflections on the unique character of the meeting and the broader OHBM community. Peter Bandettini hosts Jean Chen, Marta Garrido, and Lena Oestreich, with Kevin Sitek serving as co-host. Michael Breakspear joins the discussion in the final 20 minutes. The conversation covers both logistical and thematic aspects of the meeting, providing valuable context for attendees and those interested in the field.

Episode ProducersXuqian Michelle LiKarthik Sama