Vinod Menon

The paper uses fMRI and representational similarity analysis to show that forward reactivation in hippocampal CA1 supports repetition learning and is preserved in aging.

The paper investigates excitation-inhibition balance dynamics in brain networks during naturalistic behavior in rats, finding coordinated E-I dynamics and transient imbalances linked to behavioral changes.

This paper introduces MDSI-AVI, a framework for modeling whole-brain causal interactions in fMRI data, revealing new insights into working memory mechanisms.

The paper uses longitudinal fMRI data to show that proactive and reactive control networks stabilize after mid-adolescence, predicting cognitive stability and psychopathology.

The paper investigates nonergodicity and Simpson's paradox in cognitive control dynamics using brain imaging and behavioral data from over 4,000 children, revealing brain-behavior associations that reverse between between-subjects and within-subjects analyses.

This study uses brain state dynamics to predict early amyloid accumulation and cognitive impairment in older adults.

The paper uses fMRI and a Bayesian Switching Dynamic Systems model to analyze brain states during encoding and post-encoding rest in children, showing how dynamic neural states predict episodic memory.

The paper evaluates an Integrated Number Sense tutoring program for children with mathematical disabilities, showing it improves cross-format number processing and arithmetic skills by normalizing neural representations to typically developing levels.

This paper uses computational modeling to show how excitatory-inhibitory balance and cholinergic modulation affect default mode network (DMN) function and dysfunction in mice.

This study identifies increased temporal variability and reduced spatial stability in neural activity patterns in children with ADHD, correlating with cognitive control deficits and symptom severity.

This paper validates an explainable AI framework to identify brain regions affected by excitation/inhibition imbalance using simulations and empirical data from autism.

This paper develops a Bayesian model to uncover latent cognitive and affective processes in children's arithmetic problem-solving and shows it predicts academic outcomes and mediates the effects of anxiety.

This paper introduces a computational model to analyze inhibitory control mechanisms using data from a large cohort study, revealing individual variability and nonergodicity in cognitive processes.

The paper examines how age and parental caregiving influence social brain development and Theory of Mind in children through naturalistic fMRI studies of mother-child dyads.

This study replicates findings of hippocampus-parietal cortex causal directed connectivity during memory encoding and recall across three experiments.

The paper develops personalized deep neural networks to model learning disabilities in children, revealing how neuronal hyperexcitability affects learning performance and neural representation structures.

This study investigates the default mode network (DMN) in freely exploring rats, finding it dynamically encodes spatial exploration behaviors and challenges the notion of the DMN being task-negative.

The paper replicates electrophysiological interactions between salience, default mode, and frontoparietal networks during memory tasks using iEEG data from four experiments.

This study examines cognitive learning and brain plasticity in children with autism, finding that learning strategies and neural plasticity differ and are influenced by insistence on sameness.

The paper investigates the neuronal dynamics of the rodent default mode network and its interaction with the anterior insular cortex, revealing how salient stimuli modulate these networks.

This study uses optogenetic stimulation to show that anterior insular cortex neurons suppress default mode network activity and reduce its connectivity with the salience network in rats.

This study uses intracranial EEG to reveal dynamic electrophysiological features of the human default-mode network during rest and cognition, showing increased slow-wave synchronization and causal interactions during memory tasks.

The paper links mTOR-related synaptic pathology to functional hyperconnectivity in autism using mouse models and human fMRI data.

The paper uses diffusion MRI to analyze microstructural and macrofunctional organization of the human and macaque insula, linking these features to cognitive control abilities.