About

Vinod Menon is the Rachel L. and Walter F. Nichols, MD, Professor of Psychiatry & Behavioral Sciences at Stanford University, with courtesy appointments in Neurology & Neurological Sciences and Education. He is the director of the Stanford Cognitive and Systems Neuroscience Laboratory, which aims to advance fundamental knowledge of human brain function and dysfunction, and to apply this knowledge to help children and adults with psychiatric and neurological disorders. His research emphasizes a highly interdisciplinary approach, integrating cognitive, behavioral, neuroscience, and computational methodologies, with students and researchers coming from diverse disciplines including psychiatry, neurology, psychology, neuroscience, electrical and biomedical engineering, and computer science. Dr. Menon received his BSc (Honors) in physics from the Indian Institute of Technology and his PhD in computer science from the University of Texas at Austin. He completed a postdoctoral fellowship in neurophysiology at the University of California, Berkeley. He joined Stanford University as a Sinclair Foundation Research Fellow and has been on the faculty since 2000. Over the past two decades, his research has led to major breakthroughs in understanding the architecture, function, and development of large-scale distributed human brain networks. He and his team were among the first to discover that the human brain is organized into specialized and interacting networks of brain regions, which has resulted in a paradigm shift in how human brain function and cognition are investigated. His work has probed virtually every psychiatric and neurological disorder using this scientific framework, including the discovery of the default mode, frontoparietal, and salience networks, and their functions. These findings have elucidated how deficits in access, engagement, and disengagement of large-scale brain networks play a prominent role in psychopathology, providing novel insights into brain mechanisms underlying cognitive, affective, and social functions and dysfunctions across multiple disorders. Dr. Menon’s research has been cited over 101,000 times, with an h-index of 130, and he has been recognized as an ISI Highly Cited Researcher in Neuroscience and Cross-Field Impact in recent years.

Research topics

• Neuroscience
• Psychology
• Psychiatry
• Computer Science
• Cognitive psychology
• Artificial Intelligence
• Cognitive science
• Genetics
• Linguistics
• Clinical psychology
• Biology

Selected publications

• Temporally-resolved deep learning reveals autism symptom-specific neural signatures during naturalistic social experiences

  Research Square · 2026-04-22

  preprintOpen access1st authorCorresponding
  PublisherOA PDFDOI

• Latent brain state dynamics predict early amyloid accumulation and cognitive impairment

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-04-03

  articleOpen access

  Amyloid-β (Aβ) accumulation is a continuous process central to pathological aging that begins decades before cognitive impairment emerges. While subthreshold Aβ levels have been linked to future decline in cognitive control, the neural mechanisms connecting this early accumulation to its neurocognitive impact are poorly understood. Brain circuit dynamics, which are essential for cognitive function, may offer a sensitive lens into these initial pathological changes. Here, we tested whether brain state dynamics could serve as sensitive markers for cognitive impairment at an early stage of Aβ burden. Using the Bayesian Switching Dynamic System (BSDS) model, we identified 4 distinct latent brain states from high-temporal-resolution (800 ms) fMRI data acquired from 116 older adults, including 72 cognitively normal (CN) individuals and 44 with mild cognitive impairment (MCI), during an N-back working-memory task. Adopting a dimensional approach, we examined how latent brain state dynamics relate to early amyloid burden, cognitive performance, and clinical symptoms. While Aβ levels failed to differentiate clinical groups or predict clinical symptoms and task performance, the dynamics of latent brain states proved highly sensitive to both early Aβ accumulation and cognition. Canonical correlation analysis revealed a significant relationship between brain state dynamics and early Aβ burden. Furthermore, the temporal properties of brain states were significantly predictive of working memory performance in CN individuals, a relationship that was selectively disrupted in the MCI group. The features of brain dynamics can also successfully predict cognitive impairment. Our findings establish brain state dynamics as sensitive neural markers of initial Aβ accumulation and early cognitive impairment, offering a new framework for developing predictive models to identify individuals at risk for future cognitive decline.

  PublisherOA PDFDOI

• Biophysical validation of explainable AI for functional brain imaging: bridging cellular mechanisms and network dynamics

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-02-25

  preprintOpen accessSenior author

  Abstract Deep neural networks have revolutionized functional neuroimaging analysis but remain “black boxes,” concealing which brain mechanisms and regions drive their predictions—a critical limitation for clinical neuroscience. Here we develop and validate an explainable AI (xAI) framework to test whether feature attribution techniques can reliably recover brain regions affected by excitation/inhibition (E/I) imbalance—a fundamental dysregulation implicated in autism, schizophrenia, and other neuropsychiatric disorders. We employed complementary simulation approaches: recurrent neural networks for controlled parameter exploration, and The Virtual Brain simulator incorporating empirically-derived human and mouse connectomes to model E/I balance alterations with unprecedented biological realism. Through systematic validation, we demonstrate that Integrated Gradients and DeepLIFT methods reliably identify brain regions affected by E/I imbalance across challenging conditions, including high noise, low prevalence, and subtle neurophysiological alterations. This performance remains robust across species and anatomical scales, from 68-region human to 426-region mouse connectomes. Application to the ABIDE autism dataset (N=834) reveal convergence between our biophysically grounded simulations and empirical findings, providing computational support for E/I imbalance mechanisms in autism. This work establishes essential tools and data for interpretation of deep learning models in functional neuroimaging, and enables hypothesis-driven analysis of cellular mechanisms across neuropsychiatric disorders.

  PublisherOA PDFDOI

• Reply to Lockhart et al.: Advancing the understanding of sex differences in functional brain organization with innovative AI tools

  Proceedings of the National Academy of Sciences · 2025-01-02

  articleOpen accessSenior authorCorresponding
  PublisherOA PDFDOI

• Dynamic brain states during encoding and their post-encoding reinstatement predicts episodic memory in children

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-11-26

  preprintOpen accessSenior authorCorresponding

  Episodic memory, the ability to rapidly learn and explicitly remember past events and experiences, plays a critical role in children's academic learning and knowledge acquisition. The formation of lasting memories relies on the brain's ability to dynamically organize its activity. How these neural configurations unfold moment-by-moment across encoding and offline phases remains poorly understood. To probe these dynamics, we applied a novel Bayesian Switching Dynamic Systems approach, a hidden Markov model with automatic state detection, to fMRI data from children performing scene encoding followed by an offline post-encoding rest. We identified four distinct brain states during encoding with unique activation modes between visual, medial temporal lobe, and frontoparietal and default mode network nodes. An "active-encoding" state with integrated visual-hippocampal and frontoparietal activity dominated encoding and predicted individual memory performance, while an inactive state negatively predicted performance. State transition dynamics revealed that flexible shifts into the active-encoding state enhanced memory formation, whereas transitions toward inactive states impaired it, demonstrating that memory success depends on dynamic neural flexibility. Critically, encoding states spontaneously reemerged during post-encoding rest. A "default-mode" state characterized by enhanced default mode network activity showed sustained maintenance during rest and robustly predicted memory outcomes, an effect specific to post-encoding, not pre-encoding rest. These findings establish that episodic memory emerges from coordinated brain state sequences bridging online encoding with offline consolidation, providing a computational framework for how moment-to-moment neural dynamics support memory formation in children. This work has broad implications for optimizing educational interventions and understanding developmental disorders affecting learning and memory.

  PublisherOA PDFDOI

• Author response: Mother-child dyadic interactions shape the developing social brain and Theory of Mind in young children

  2025-11-18

  peer-reviewOpen access
  PublisherDOI

• Personalized deep neural networks reveal mechanisms of math learning disabilities in children

  Science Advances · 2025-06-06 · 9 citations

  articleOpen accessSenior authorCorresponding

  Learning disabilities affect a substantial proportion of children worldwide, with far-reaching consequences for their academic, professional, and personal lives. Here we develop digital twins-biologically plausible personalized deep neural networks (pDNNs)-to investigate the neurophysiological mechanisms underlying learning disabilities in children. Our pDNN reproduces behavioral and neural activity patterns observed in affected children, including lower performance accuracy, slower learning rates, neural hyperexcitability, and reduced neural differentiation of numerical problems. Crucially, pDNN models reveal aberrancies in the geometry of manifold structure, providing a comprehensive view of how neural excitability influences both learning performance and the internal structure of neural representations. Our findings not only advance knowledge of the neurophysiological underpinnings of learning differences but also open avenues for targeted, personalized strategies designed to bridge cognitive gaps in affected children. This work reveals the power of digital twins integrating artificial intelligence and neuroscience to uncover mechanisms underlying neurodevelopmental disorders.

  PublisherDOI

• Reduced temporal and spatial stability of neural activity patterns predict cognitive control deficits in children with ADHD

  Nature Communications · 2025-03-08 · 6 citations

  articleOpen access

  This study investigates the neural underpinnings of cognitive control deficits in attention-deficit/hyperactivity disorder (ADHD), focusing on trial-level variability of neural coding. Using fMRI, we apply a computational approach to single-trial neural decoding on a cued stop-signal task, probing proactive and reactive control within the dual control model. Reactive control involves suppressing an automatic response when interference is detected, and proactive control involves implementing preparatory strategies based on prior information. In contrast to typically developing children (TD), children with ADHD show disrupted neural coding during both proactive and reactive control, characterized by increased temporal variability and diminished spatial stability in neural responses in salience and frontal-parietal network regions. This variability correlates with fluctuating task performance and ADHD symptoms. Additionally, children with ADHD exhibit more heterogeneous neural response patterns across individuals compared to TD children. Our findings underscore the significance of modeling trial-wise neural variability in understanding cognitive control deficits in ADHD.

  PublisherOA PDFDOI

• Causal dynamics of memory circuits in mathematical development from childhood to adulthood

  Developmental Cognitive Neuroscience · 2025-10-09

  articleOpen accessSenior authorCorresponding

  Mathematical cognition engages a distributed brain network, but the causal dynamics of information flow within it, particularly how memory circuits interact with other brain regions across development, remain unknown. We examined causal dynamic interactions in typically developing children and adolescents/young adults (AYA) using fMRI during three tasks involving mental arithmetic and symbolic and non-symbolic number comparison. Using multivariate dynamic state-space identification modeling, we found that causal dynamic interactions differed between children and AYA across all three tasks, especially during arithmetic processing. The left medial temporal lobe (MTL) served as a causal signaling hub in AYA across all three tasks, but not in children. The left angular gyrus (AG) maintained consistent hub-like properties during arithmetic task across development. Compared to AYA, children exhibited heightened causal interactions in both the MTL and AG. Moreover, network hub properties of these regions correlated with individual's mathematical achievement specifically during arithmetic processing. Together, we found that the MTL transitioned from heightened, context-dependent, interactions in childhood to a stable causal hub in adulthood, while the AG maintained as a hub during arithmetic processing across development. This dissociation between memory systems, coupled with their task-specific relationship to mathematical abilities, provides novel insights into how brain networks mature to support mathematical cognition.

  PublisherDOI

• Symbolic numerical generalization through representational alignment.

  PubMed · 2025-01-01

  articleOpen accessSenior author

  The mapping between nonsymbolic quantities and symbolic numbers lays the foundation for mathematical development in children. However, the neural mechanisms underlying this crucial cognitive bridge remain unclear. Here, we investigate the computational principles governing symbolic-nonsymbolic integration using a biologically inspired neural network trained through developmentally inspired stages. Our investigation reveals that generalization from nonsymbolic to symbolic numerical processing emerges specifically when representational alignment forms between these numerical formats. Notably, this alignment appears to be stronger in cross-format comparison-based mapping compared to direct-label-based mapping. Furthermore, we demonstrate that subsequent symbolic specialization creates a representational divergence that impairs nonsymbolic performance while maintaining the ordinal structure of the mapping. These findings highlight representational alignment as a fundamental mechanism in numerical cognition and suggest that targeted cross-format comparison tasks may be particularly effective in improving mathematical learning in children with numerical processing difficulties.

  PublisherOA PDF

Recent grants

• Computational modeling of dynamic causal brain circuits underlying cognitive dysfunction in Alzheimer's disease

  NIH · $3.9M · 2014–2026

• Speaker-listener coupling and brain dynamics during naturalistic verbal communication in children with autism

  NIH · $827k · 2020–2025

• NIH Grant K25HD040761

  NIH · $718k · 2006

• NCS-FO: Integrated neurocognitive process models of individual differences in children’s math problem solving strategies, learning and development

  NSF · $500k · 2020–2024

• Interventions in math learning disabilities: cognitive and neural correlates

  NIH · $10.1M · 2008–2026

Frequent coauthors

• Allan L. Reiss

  Stanford University

  105 shared

• Kaustubh Supekar

  Stanford University

  103 shared

• Weidong Cai

  75 shared

• Srikanth Ryali

  63 shared

• Miriam Rosenberg‐Lee

  Universidad de Guadalajara

  48 shared

• Tianwen Chen

  Hangzhou First People's Hospital

  43 shared

• Gary H. Glover

  Stanford University

  39 shared

• Teresa Iuculano

  Centre National de la Recherche Scientifique

  37 shared

Labs

• Stanford Cognitive & Systems Neuroscience LaboratoryPI

Education

• Ph.D., Neuroscience

  Stanford University

  1998

• M.D., Medicine

  Stanford University

  1994

• B.A., Human Biology

  Stanford University

  1990

Awards & honors

• ISI Highly Cited Researcher - Cross-Field Impact, Thomson Re…
• ISI Highly Cited Researcher - Cross-Field Impact, Thomson Re…
• ISI Highly Cited Researcher - Neuroscience & Behavior, Thoms…
• NIH MERIT Award (R37) for Outstanding Research, NIH (2018)
• ISI Highly Cited Researcher - Neuroscience & Behavior, Thoms…