Ignacio Sáez

This paper presents a functional atlas of the human central nervous system using high-resolution surface electrophysiology from 70 patients, revealing conserved electrophysiological patterns and enabling neuroprosthetic applications.

This study examines slow and fast theta oscillations across the brain in a developmental cohort, revealing spatial gradients and age-related changes linked to attention and memory.

The paper examines high-frequency broadband activity in frontoparietal regions during working memory and decision-making tasks to identify distinct and overlapping neural dynamics.

The paper records single neuron activity in the substantia nigra during social learning tasks and finds dopaminergic neurons encode norm prediction error valence more strongly during human vs computer interactions.

This study links reinforcement learning computations to memory by showing hippocampal-cingulate dynamics in humans during a gambling task.

This study investigates how slow and fast theta oscillations in the hippocampal-orbitofrontal network affect working memory performance, finding opposing effects on response times and accuracy.

The paper shows prefrontal circuits coordinate to encode attention-weighted value signals during learning, shaping state representations in complex environments.

This study uses recurrent neural networks to infer human attention dynamics during learning, finding that attention reflects an interaction between value-based learning and hypothesis testing.

This paper shows that dopamine and serotonin changes during deep brain stimulation predict symptom relief in treatment-resistant depression.

This study identifies high-level visual cortex as the neural substrate for one-shot perceptual learning and develops a deep neural network model that replicates human behavior and neural coding.

This study investigates how OFC gamma oscillations and hippocampal ripples interact during rapid spatial learning in humans using iEEG recordings.

The paper uses scalp and intracranial EEG to identify network-level signatures of executive control during a working memory task, linking local spectral changes to broader network organization.

This study maps age-related changes in aperiodic neural activity across brain regions using iEEG data from children and adults, linking these changes to cognitive development and memory performance.

The paper uses dynamic systems modeling to identify distinct neural subspaces during risky decision-making, showing convergence to choice-specific subspaces as decisions approach.

The paper shows that low-frequency oscillatory coherence in the orbitofrontal cortex correlates with risk preferences and that targeted stimulation reduces risk-taking behavior.

The paper investigates meditation-induced neuromodulation in the amygdala and hippocampus using invasive electrophysiology recordings from epilepsy patients during loving-kindness meditation.

This study examines how high-frequency broadband activity and theta oscillations coordinate during memory processing, showing that specific network connectivity patterns predict memory performance.

This study shows human substantia nigra neurons encode reward expectations through firing rates modulated by trial outcomes and linked to reaction times.

This study uses intracranial recordings to show that risky choices are represented in distributed prefrontal and subcortical regions, while reward computations are more modular and localized.

The paper investigates how the human orbitofrontal cortex encodes self- and other-rewards during social decision-making, finding state-dependent changes in reward encoding based on inequity type.

The paper investigates how different oscillatory networks in the human orbitofrontal cortex support processing of monetary gains and losses during a gambling task.

The paper investigates neural mechanisms of context-dependent inequity aversion using a combination of behavioral games and neuroimaging, revealing dissociable insula activity patterns for advantageous and disadvantageous inequity.

The paper describes a protocol for integrating anatomical and electrophysiological human intracranial EEG data using an open-source toolbox, enabling rapid and reproducible analysis.