Patric Hagmann

The paper evaluates how well population-level connectome harmonics represent individual brains across different dimensions and finds they can be useful despite not perfectly matching individual data.

MRSIPrep provides a standardized post-quantification framework for whole-brain MRSI to improve reproducibility across datasets and applications.

The paper uses longitudinal spectroscopic imaging to show sex-specific hormone-metabolite coupling in adolescent brains, revealing age and sex-dependent neurochemical changes.

This study evaluates microstructure-weighted connectomes using diffusion MRI data and finds that DePerp provides the most consistent structure-function coupling across spatial scales.

This paper shows that individualized connectome harmonics reveal a hierarchical organization linking brain structure, function, and cognition across 875 participants.

The paper uses magnetic resonance spectroscopic imaging to identify sex differences in brain metabolites across multiple cohorts, finding a consistent male higher tNAA/tCr ratio in gray and white matter.

This paper introduces directed connectomics using bicommunities to reveal asymmetric pathways in brain networks and validates them with electrophysiological data.

This study uses high-resolution 3D-MRSI to assess neurometabolite differences in youth at risk for psychosis compared to controls, finding elevated tNAA and Ins in specific brain regions.

This paper develops a whole-brain model to predict conduction velocity from MRI-derived microstructural features, explaining 29% of variance in neurophysiological conduction velocity.

This paper identifies optimal graph signal processing features for subject identification through large-scale comparative analysis of parameter choices and feature types.

This study uses whole-brain models to analyze brain connectivity differences between remitting and non-remitting early psychosis patients, finding altered functional connectivity and potential compensatory mechanisms.

The paper constructs the first human brain metabolic connectome using MR spectroscopic imaging, revealing distinct topological features and alignment with cytoarchitectonic and genetic patterns.

This study assesses how defacing structural MRI images affects quality assessments by humans and automated tools, finding significant human bias and minimal impact on MRIQC metrics.

This paper introduces CSET, a method that reconstructs electrical brain source-networks with high spatial resolution using diffusion MRI and EEG data.

This paper introduces a framework to analyze brain activity's spectral content in time and space, revealing how integration and segregation mechanisms operate at different frequency bands and relate to behavior.

The paper introduces a multimodal neuroimaging dataset containing EEG, MRI, and behavioral data for studying visual evoked potentials and brain connectivity.

The paper introduces a linear adaptive filter that uses structural connectivity priors to improve dynamic and directed functional connectivity estimation in electrophysiological data.

This paper introduces the connectome spectrum as a method to represent brain activity using structural connectivity, showing it provides a more compact and informative representation of fast brain dynamics.

The paper examines how cortico-cortical networks mediate localized and global communication by analyzing the functional connectivity of unimodal and multimodal brain regions across multiple scales.

The paper identifies functional harmonic modes as a governing principle for brain organization, showing they can reconstruct task-related activity and delineate cortical areas from resting-state fMRI data.

The paper quantifies spatio-temporal brain dynamics in autism using two novel measures and finds atypical connectivity patterns, with differences based on symptom severity and domain.

This paper introduces a method using network harmonics to track fast EEG dynamics during a face detection task with subsecond resolution.

This study investigates how the cortical hierarchy influences signal transmission in brain networks by analyzing resting state functional MRI and structural networks.

This study identifies stable biomarkers for schizophrenia using machine learning on multi-modal connectome data, showing improved stability and accuracy through recursive feature elimination and support vector machines.

The paper uses white matter structural connectivity to improve EEG functional connectivity by smoothing signals in a graph space derived from structural data, increasing resemblance to fMRI connectivity.

This study links tissue volume loss in schizophrenia to structural and functional connectivity, finding that 25-35% of regional deformation variance is explained by these factors.

The paper examines structure-function relationships in the neocortex, finding that structural and functional networks diverge in higher-order areas.

The paper introduces a method to generate group-representative structural brain networks that better preserve subject-level properties by adjusting for distance-dependent consistency thresholds.

This study identifies a distinct functional connectivity mode during movie-watching that reflects individual differences and is stable over time.

The paper shows that the right auditory cortex is more integrated in the brain network, enabling more efficient communication, with this asymmetry explained by diffusive communication mechanisms.