Armin Iraji

From micro-architecture to networks: canonical neurobiological architecture of functional brain organization across the lifespan and in psychopathology

The paper links functional brain network connectivity across age and psychiatric disorders to neurobiological microprofiles, showing stage-specific associations.

A Chronnectogram-ICA Framework for Tracking Continuously Evolving and Overlapping Functional Brain States

This paper introduces a chronnectogram-ICA framework to track continuously evolving and overlapping brain states in fMRI data, revealing new insights into schizophrenia's connectivity dynamics.

Mapping the Psychosis Spectrum -- Imaging Neurosubtypes from Multi-Scale Functional Network Connectivity

This study identifies three psychosis neurosubtypes using imaging data, showing distinct connectivity patterns and supporting their biological validity through relative alignment.

Concurrent Supervised-Unsupervised Representative Subspace Clustering (CSU-RSC)

CSU-RSC identifies sex-dominant subgroups in psychosis by integrating supervised and unsupervised clustering on neuroimaging data, revealing sex-specific biological heterogeneity.

Multiscale Functional Dysconnectivity Reveals Sex-Dominant Neurosubtypes and Predicts Unique Neurocognitive Pathways in Psychosis

The paper identifies sex-dominant neurosubtypes in psychosis using fMRI data and shows their unique dysconnectivity patterns and cognitive implications.

Cognition emerges from phase dynamics of intrinsic coordination

This paper introduces the intrinsic network flow framework to explain cognitive states through phase dynamics, showing that task classification accuracy is higher using phase information than activation patterns.

Dynamic Co-Modulation (DyCoM): A Unified Operator Framework for Dynamic Connectivity in Neuroimaging

This paper introduces DyCoM, a framework that unifies dynamic connectivity analysis in neuroimaging by decomposing estimators into fundamental operations, revealing distinct biological interpretations from different computational choices.

Amyloid-Tau PET Fusion Reveals Covarying Network Territories Linked to Disease Stage, Cognition, and APOE4 in Alzheimers Disease

The paper uses PET imaging data to identify spatial patterns of Amyloid and Tau covariance in Alzheimer's disease, showing distinct disease-stage and cognitive associations.

Dynamic fusion of structural and functional connectivity via joint connectivity matrix ICA

This paper introduces a dynamic fusion framework to integrate structural and functional connectivity data, revealing differences between schizophrenia patients and controls.

NeuroMark-SZ: A Holistic Resting-State-fMRI-Based Model for Divergent Functional Circuitry in Schizophrenia

This paper develops a model using resting-state fMRI to identify functional connectivity patterns in schizophrenia, finding a robust cerebellothalamic disruption signature.

Robustness of NeuroMark-Derived Functional Networks to fMRI Spatial Normalization Across the Human Lifespan

This study evaluates the robustness of NeuroMark-derived functional networks to fMRI spatial normalization across different age groups, finding high stability regardless of normalization strategy.

Dynamic Estimation of Spatially Interactive Networks (DESINE) Reveals Constrained Brain Repertoire in Schizophrenia Linked to Clinical and Cognitive Symptoms

DESINE quantifies dynamic spatial network interactions in schizophrenia, finding reduced variability and rigidity linked to cognitive and clinical symptoms.

Investigating White Matter Functional Network Connectivity Across the Alzheimers Disease Spectrum Using Resting-State fMRI

The paper examines changes in white matter functional connectivity across Alzheimer's disease stages using resting-state fMRI data from ADNI, finding early WM-GM dysconnectivity in MCI.

Dynamic fusion of genomics and functional network connectivity in UK biobank reveals static and time-varying SNP manifolds

Explicitly nonlinear fMRI networks reveal hidden trajectories of infant brain development

This study uses explicitly nonlinear fMRI connectivity to uncover developmental trajectories of brain networks in infants, revealing insights into key functional systems.

When Randomness Becomes Rigid: Dynamic Connectivity Entropy and Symptom-Linked Network Dysfunction in Schizophrenia

The paper introduces DICE, a framework analyzing dynamic functional connectivity entropy in schizophrenia, revealing distinct pathophysiological signatures linked to symptom dimensions.

Deciphering Multiway Multiscale Brain Network Connectivity:Insights from Birth to 6 Months

This study investigates multiway, multiscale brain network interactions in infants, finding that tri-FNC provide additional insights beyond pairwise interactions during early brain development.

Spatiotemporal Network Dynamics Reveal Alzheimer's Disease Progression

This study uses QPP and cPCA to analyze spatiotemporal network changes in AD progression, identifying early disruptions in specific brain networks and showing their potential as biomarkers.

Diminished spatial dynamics and maladaptive spatial complexity link resting brain network disruption to cognition in schizophrenia

This paper introduces dSNCC to analyze spatial dynamics and complexity in brain networks, finding reduced dynamic flexibility and increased complexity in schizophrenia linked to cognitive impairment.

Topologically Optimized Intrinsic Brain Networks

This paper introduces TOIBN, a method that uses topological information from group-level brain networks to improve subject-level network estimation while preserving individual variability.

Spatial Development of Brain Networks During The First Six Postnatal Months

This study examines spatial changes in infant brain networks using resting-state fMRI data from 74 infants, revealing age-related patterns in network organization and intensity distribution.

Multimodal Fusion Analysis of Florbetapir PET and Multiscale Functional Network Connectivity in Alzheimer's Disease

This study integrates Florbetapir PET and functional network connectivity to identify disease progression markers and genetic risk associations in Alzheimer's disease.

st-DenseViT: A Weakly Supervised Spatiotemporal Vision Transformer for Dense Prediction of Dynamic Brain Networks

This paper introduces st-DenseViT, a weakly supervised spatiotemporal vision transformer that generates 4D dynamic brain networks from fMRI data, showing significant differences between schizophrenia patients and healthy controls.

PhaseICA: Resolving Individual-Specific Macroscale Brain Propagations

PhaseICA extracts reoccurring spatiotemporal brain patterns by incorporating phase information, improving the analysis of nonstationary brain activity.

Building Multivariate Molecular Imaging Brain Atlases Using the NeuroMark PET Independent Component Analysis Framework

The paper introduces NeuroMark PET, an automated framework using spatially constrained ICA to identify Aβ networks in PET data, showing higher sensitivity for age-related changes compared to traditional ROIs.

Mapping the Developmental Trajectory of Functional Brain Networks in Early Infancy: Insights into Typical Maturation

The paper maps functional brain network maturation in early infancy using resting-state fMRI, showing age-related increases in network consistency and engagement.

Normalized Dynamic Time Warping Increases Sensitivity In Differentiating Functional Network Connectivity In Schizophrenia

This study introduces normalized dynamic time warping to improve sensitivity in detecting functional network connectivity differences in schizophrenia compared to standard DTW and correlation methods.

A method for estimating dynamic functional network connectivity gradients (dFNG) from ICA captures smooth inter-network modulation.

This paper introduces dFNG, a method to estimate dynamic functional network connectivity gradients from ICA, and applies it to schizophrenia data to reveal distinct connectivity patterns between patient and healthy groups.

Structural connectivity-informed dynamic estimation (STRiDE): Multimodal connectivity constrained ICA

STRiDE improves dynamic brain state estimation by integrating structural and functional connectivity, revealing disruptions in schizophrenia.

Toward Granular Brain Intrinsic Connectivity Networks and Insights into Schizophrenia

This study uses sgr-ICA with 500 components on a large rsfMRI dataset to identify fine-grained intrinsic connectivity networks (ICNs) and reveals dysconnectivity patterns in schizophrenia.