Neurosalience

"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