Anthony Randal McIntosh

The paper examines how chemoarchitecture influences frequency-resolved functional connectivity and how aging affects this relationship, finding that neurochemical-functional alignment varies by frequency and impacts cognitive performance.

This paper introduces a modular neural mass model incorporating neuromodulation, specifically dopamine dynamics, and analyzes its effects on oscillatory states.

The paper examines age and sex interactions on multiscale entropy and spectral power across the lifespan using MEG data from a large cohort.

This study shows that acute sleep restriction affects functional connectivity networks differently in young and old adults, with older adults showing weakened connections and younger adults showing strengthened ones, supporting the compensation theory of aging.

This study compares a dopamine-modulated mean-field model with its corresponding spiking network to evaluate the model's accuracy in capturing network dynamics.

This study examines how structural and functional brain network architecture, age, and attention skills develop in early childhood using graph theory and longitudinal neuroimaging data.

This study examines how structural and functional brain network dynamics relate to visual working memory across the lifespan, finding age-related changes in network states and transitions that support memory capacity.

The paper presents a workflow using HMM and PLS to analyze multi-modal dynamic data from a music listening study, discussing limitations and future directions.

This study examines brain network dynamics during music listening in healthy older adults before and after a music-based intervention, finding increased temporal-mesolimbic network occupancy post-intervention.

The paper investigates how structural brain connectivity changes with age and cognitive ability, finding both detrimental and beneficial reorganizations in older adults.

The paper introduces an automated pipeline for generating connectome-based inputs for TheVirtualBrain using UK Biobank neuroimaging data.

The paper introduces a deep learning approach to estimate parameters in connectome-based neural mass models using fMRI data and demonstrates its effectiveness on synthetic and real resting-state fMRI data.

The paper combines brain simulations with machine learning to improve dementia classification by integrating PET and MRI data with simulated electrophysiological features.

This study examines multiscale entropy (MSE) in clinical-EEG data to assess its ability to distinguish health status across neurological disorders and non-brain comorbidities.

The paper introduces virtual connectomic datasets by simulating missing connectivity features from existing neuroimaging data, enabling machine learning classification comparable to empirical data.

This paper develops a connectome-based neural mass model to simulate state- and stimulation-dependent changes in rhythmic neural activity and connectivity, revealing how corticothalamic circuits modulate oscillatory patterns.

The paper analyzes dynamic functional connectivity (dFC) in fMRI data from healthy elderly, finding that dFC slows down and becomes less complex with aging, correlating with cognitive performance.

The paper demonstrates that each individual's brain has a converging optimal working point in The Virtual Brain model that predicts fMRI and EEG features, with inter-individual differences driven by structural connectivity.

The paper models how hippocampal and medial temporal lobe activity influences oculomotor systems in macaques, showing subfield-specific effects and lesion impacts on signal propagation.

The paper measures brain signal complexity during music listening and reward tasks, finding that perceptual tasks show better complexity matching with music complexity than emotional tasks.

The paper uses computational modeling to link molecular pathways with neural dynamics in Alzheimer’s disease, showing how Abeta burden affects brain activity and potential reversibility with memantine.

The paper constructs a macaque connectome for use in TheVirtualBrain simulations to model brain network dynamics and validate against empirical resting-state fMRI data.

This study examines fMRI signal variability and complexity in children and adolescents with and without ASD, finding no group differences but significant associations with age, global efficiency, and behavioral severity.

This study compares diffusion-based tractography and invasive tracer data to assess the accuracy of topological properties in macaque cortical connectomes.

This paper introduces a framework called structured flows on manifolds (SFM) to model neural system dynamics and applies it to understand brain disorders like epilepsy and dementia.

The paper investigates how structural and functional brain connectivity relate to cognition, finding distinct patterns in each modality that uniquely contribute to cognitive functions.

The paper identifies two functional connectivity subtypes in ASD and controls, showing distinct brain-behavior relationships and a continuum from ASD to controls within one subtype.

The paper examines the subject-specificity of the correlation between structural and functional connectivity across six human neuroimaging datasets, finding limited subject-specificity except for one dataset.

The paper examines changes in brain signal complexity across development and aging, finding increased complexity with maturation and a shift in local/global balance in aging associated with cognitive performance.