Kyle M. Jensen

NeuroMark dFNC uses reference-state templates to estimate subject-specific dynamic connectivity states across datasets, enabling cross-cohort comparison while preserving individual patterns.

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

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

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.

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.

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.

This study uses high-order ICA on PET scans to identify amyloid-beta networks in Alzheimer's Disease, revealing distinct gray and white matter patterns associated with cognitive impairment.

The paper presents a WM intrinsic connectivity network template derived from 100,000 fMRI scans, showing distinct WM and GM networks with task-related activity and differences in schizophrenia.

This paper creates a whole-brain functional atlas from resting-state fMRI data to standardize terminology and improve consistency across neuroimaging studies.