Neurosalience

“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