About

Sepideh Sadaghiani is an Associate Professor in the Department of Bioengineering at the University of Illinois Urbana-Champaign. She is affiliated with the Grainger College of Engineering and is based at the Everitt Lab. Her research focuses on changes in brain infrastructure, their inheritance, and their impact on cognitive function. She is involved in bioengineering research that explores neural engineering, bioimaging at multi-scale, and systems biology. Her work aims to understand the biological mechanisms underlying brain function and to develop bioengineering solutions for health issues.

Research topics

• Computer Science
• Biology
• Data science
• World Wide Web

Selected publications

• Imaging of Adolescent Emotion Processing

  OpenNeuro · 2026-05-01

  datasetOpen access
  PublisherDOI

• Author response: A multiplex of connectome trajectories enables several connectivity patterns in parallel

  2025-01-06

  peer-reviewOpen accessSenior author

  Complex brain function comprises a multitude of neural operations in parallel and often at different speeds. Each of these operations is carried out across a network of distributed brain regions. How multiple distributed processes are facilitated in parallel is largely unknown. We postulate that such processing relies on a multiplex of dynamic network patterns emerging in parallel but from different functional connectivity (FC) timescales. Given the dominance of inherently slow fMRI in network science, it is unknown whether the brain leverages such multi-timescale network dynamics.We studied FC dynamics concurrently across a breadth of timescales (from infraslow to γ-range) in rare, simultaneously recorded intracranial EEG and fMRI in humans, and source-localized scalp EEG-fMRI data. We examined spatial and temporal convergence of connectome trajectories across timescales. ‘Spatial convergence’ refers to spatially similar EEG and fMRI connectome patterns, while ‘temporal convergence’ signifies the more specific case of spatial convergence at corresponding timepoints in EEG and fMRI.We observed spatial convergence but temporal divergence across FC timescales; connectome states (recurrent FC patterns) with partial spatial similarity were found in fMRI and all EEG frequency bands, but these occurred asynchronously across FC timescales. Our findings suggest that hemodynamic and frequency-specific electrophysiological signals, while involving similar large-scale networks, represent functionally distinct connectome trajectories that operate at different FC speeds and in parallel. This multiplex is poised to enable concurrent connectivity across multiple sets of brain regions independently.

  PublisherDOI

• Modulatory Neurotransmitter Genotypes Shape Dynamic Functional Connectome Reconfigurations

  Journal of Neuroscience · 2025-01-22 · 2 citations

  articleOpen accessSenior author

  Dynamic reconfigurations of the functional connectome across different connectivity states are highly heritable, predictive of cognitive abilities, and linked to mental health. Despite their established heritability, the specific polymorphisms that shape connectome dynamics are largely unknown. Given the widespread regulatory impact of modulatory neurotransmitters on functional connectivity, we comprehensively investigated a large set of single nucleotide polymorphisms (SNPs) of their receptors, metabolic enzymes, and transporters in 674 healthy adult subjects (347 females) from the Human Connectome Project. Preregistered modulatory neurotransmitter SNPs and dynamic connectome features entered a Stability Selection procedure with resampling. We found that specific subsets of these SNPs explain individual differences in temporal phenotypes of fMRI-derived connectome dynamics for which we previously established heritability. Specifically, noradrenergic polymorphisms explained Fractional Occupancy, i.e., the proportion of time spent in each connectome state, and cholinergic polymorphisms explained Transition Probability, i.e., the probability to transition between state pairs, respectively. This work identifies specific genetic effects on connectome dynamics via the regulatory impact of modulatory neurotransmitter systems. Our observations highlight the potential of dynamic connectome features as endophenotypes for neurotransmitter-focused precision psychiatry.

  PublisherOA PDFDOI

• A multiplex of connectome trajectories enables several connectivity patterns in parallel

  eLife · 2025-01-06

  preprintOpen accessSenior author

  Abstract Complex brain function comprises a multitude of neural operations in parallel and often at different speeds. Each of these operations is carried out across a network of distributed brain regions. How multiple distributed processes are facilitated in parallel is largely unknown. We postulate that such processing relies on a multiplex of dynamic network patterns emerging in parallel but from different functional connectivity (FC) timescales. Given the dominance of inherently slow fMRI in network science, it is unknown whether the brain leverages such multi-timescale network dynamics. We studied FC dynamics concurrently across a breadth of timescales (from infraslow to γ-range) in rare, simultaneously recorded intracranial EEG and fMRI in humans, and source-localized scalp EEG-fMRI data. We examined spatial and temporal convergence of connectome trajectories across timescales. ‘Spatial convergence’ refers to spatially similar EEG and fMRI connectome patterns, while ‘temporal convergence’ signifies the more specific case of spatial convergence at corresponding timepoints in EEG and fMRI. We observed spatial convergence but temporal divergence across FC timescales; connectome states (recurrent FC patterns) with partial spatial similarity were found in fMRI and all EEG frequency bands, but these occurred asynchronously across FC timescales. Our findings suggest that hemodynamic and frequency-specific electrophysiological signals, while involving similar large-scale networks, represent functionally distinct connectome trajectories that operate at different FC speeds and in parallel. This multiplex is poised to enable concurrent connectivity across multiple sets of brain regions independently.

  PublisherOA PDFDOI

• Functional Connectome Dynamics and Brain Plasticity

  Oxford University Press eBooks · 2025-08-21

  book-chapter1st authorCorresponding

  Abstract This chapter explores how connectivity between distributed brain regions contributes to cognitive enhancement, with a focus on connectivity dynamics. These dynamics comprise fluctuations in the strength and spatial pattern of connectivity in the order of several seconds to minutes, primarily measured using functional magnetic resonance imaging (fMRI). The chapter starts with an introduction to the brain’s connectivity architecture (functional connectome) as observed in fMRI, its dynamic nature, and the features of these dynamics that can inform personalized interventions. The chapter then discusses the cognitive significance of connectivity dynamics, essential for their use as intervention targets to enhance cognition. Lastly, the chapter reviews cross-sectional and longitudinal research into the neuroplasticity of connectivity dynamics, noting that such studies are rare due to the field’s methodological infancy. The text focuses on meditation and mindfulness studies, because these interventions occupy a particularly large proportion of the current research at the intersection of connectome dynamics and cognitive enhancement. Most of connectome dynamics research conducted to date and discussed in this chapter has been performed in neurotypical young adults. However, the chapter also covers work in older adults, where available, as an example of a population that may benefit particularly strongly from cognitive enhancement. The chapter concludes with a brief outlook on methods with real-time resolution, extending beyond fMRI. In summary, despite methodological challenges, functional connectivity dynamics is a rapidly developing field with great potential to inform effective cognitive interventions.

  PublisherDOI

• A network correspondence toolbox for quantitative evaluation of novel neuroimaging results

  Nature Communications · 2025-03-25 · 46 citations

  articleOpen access

  The brain can be decomposed into large-scale functional networks, but the specific spatial topographies of these networks and the names used to describe them vary across studies. Such discordance has hampered interpretation and convergence of research findings across the field. We have developed the Network Correspondence Toolbox (NCT) to permit researchers to examine and report spatial correspondence between their novel neuroimaging results and multiple widely used functional brain atlases. We provide several exemplar demonstrations to illustrate how researchers can use the NCT to report their own findings. The NCT provides a convenient means for computing Dice coefficients with spin test permutations to determine the magnitude and statistical significance of correspondence among user-defined maps and existing atlas labels. The adoption of the NCT will make it easier for network neuroscience researchers to report their findings in a standardized manner, thus aiding reproducibility and facilitating comparisons between studies to produce interdisciplinary insights.

  PublisherOA PDFDOI

• Author response: A multiplex of connectome trajectories enables several connectivity patterns in parallel

  2025-04-29

  peer-reviewOpen accessSenior author

  Complex brain function comprises a multitude of neural operations in parallel and often at different speeds. Each of these operations is carried out across a network of distributed brain regions. How multiple distributed processes are facilitated in parallel is largely unknown. We postulate that such processing relies on a multiplex of dynamic network patterns emerging in parallel but from different functional connectivity (FC) timescales. Given the dominance of inherently slow fMRI in network science, it is unknown whether the brain leverages such multi-timescale network dynamics.We studied FC dynamics concurrently across a breadth of timescales (from infraslow to γ-range) in rare, simultaneously recorded intracranial EEG and fMRI in humans, and source-localized scalp EEG-fMRI data. We examined spatial and temporal convergence of connectome trajectories across timescales. ‘Spatial convergence’ refers to spatially similar EEG and fMRI connectome patterns, while ‘temporal convergence’ signifies the more specific case of spatial convergence at corresponding timepoints in EEG and fMRI.We observed spatial convergence but temporal divergence across FC timescales; connectome states (recurrent FC patterns) with partial spatial similarity were found in fMRI and all EEG frequency bands, but these occurred asynchronously across FC timescales. Our findings suggest that hemodynamic and frequency-specific electrophysiological signals, while involving similar large-scale networks, represent functionally distinct connectome trajectories that operate at different FC speeds and in parallel. This multiplex is poised to enable concurrent connectivity across multiple sets of brain regions independently.

  PublisherDOI

• A multiplex of connectome trajectories enables several connectivity patterns in parallel

  eLife · 2025-04-29

  preprintOpen accessSenior author

  Abstract Complex brain function comprises a multitude of neural operations in parallel and often at different speeds. Each of these operations is carried out across a network of distributed brain regions. How multiple distributed processes are facilitated in parallel is largely unknown. We postulate that such processing relies on a multiplex of dynamic network patterns emerging in parallel but from different functional connectivity (FC) timescales. Given the dominance of inherently slow fMRI in network science, it is unknown whether the brain leverages such multi-timescale network dynamics. We studied FC dynamics concurrently across a breadth of timescales (from infraslow to γ-range) in rare, simultaneously recorded intracranial EEG and fMRI in humans, and source-localized scalp EEG-fMRI data. We examined spatial and temporal convergence of connectome trajectories across timescales. ‘Spatial convergence’ refers to spatially similar EEG and fMRI connectome patterns, while ‘temporal convergence’ signifies the more specific case of spatial convergence at corresponding timepoints in EEG and fMRI. We observed spatial convergence but temporal divergence across FC timescales; connectome states (recurrent FC patterns) with partial spatial similarity were found in fMRI and all EEG frequency bands, but these occurred asynchronously across FC timescales. Our findings suggest that hemodynamic and frequency-specific electrophysiological signals, while involving similar large-scale networks, represent functionally distinct connectome trajectories that operate at different FC speeds and in parallel. This multiplex is poised to enable concurrent connectivity across multiple sets of brain regions independently.

  PublisherDOI

• Consistency of resting-state correlations between fMRI networks and EEG band power

  Imaging Neuroscience · 2025-01-01 · 6 citations

  articleOpen access

  Several simultaneous electroencephalography (EEG)-functional magnetic resonance imaging (fMRI) studies have aimed to identify the relationship between EEG band power and fMRI resting-state networks (RSNs) to elucidate their neurobiological significance. Although common patterns have emerged, inconsistent results have also been reported. This study aims to explore the consistency of these correlations across subjects and to understand how factors such as the hemodynamic response delay and the use of different EEG data spaces (source/scalp) influence them. Using three distinct EEG-fMRI datasets, acquired independently on 1.5T, 3T, and 7T MRI scanners (comprising 42 subjects in total), we evaluate the generalizability of our findings across different acquisition conditions. We found consistent correlations between fMRI RSN and EEG band power time series across subjects in the three datasets studied, with systematic variations with RSN, EEG frequency band, and hemodynamic response function (HRF) delay, but not with EEG space. Several of these correlations were consistent across the three datasets, despite important differences in field strength and resting-state conditions. These included spatially widespread patterns observed across HRF delays from 2 to 10 s, such as positive delta correlations with the visual and somatomotor networks, negative delta correlations with the default mode network, positive theta correlations with the somatomotor network, negative alpha correlations with both the visual and dorsal attention networks, positive alpha correlations with the default mode network, and negative beta correlations with the somatomotor network. Our findings support consistent correlations across specific fMRI RSNs and EEG bands and highlight the importance of methodological considerations in interpreting them that may explain conflicting reports in the existing literature.

  PublisherOA PDFDOI

• Author response: A multiplex of connectome trajectories enables several connectivity patterns in parallel

  2025-06-13

  peer-reviewOpen accessSenior author
  PublisherDOI

Frequent coauthors

• Andreas Kleinschmidt

  33 shared

• Jonathan Wirsich

  University of Geneva

  22 shared

• Uta Noppeney

  Radboud University Nijmegen

  14 shared

• Karl J. Friston

  University College London

  14 shared

• Herta Flor

  University of Mannheim

  12 shared

• Guido Hesselmann

  12 shared

• Ben Ridley

  Istituto delle Scienze Neurologiche di Bologna

  11 shared

• Anne‐Lise Giraud

  11 shared

Education

• Ph.D. Neural and Behavioral Sciences

  Max-Planck-Campus Tubingen

  2010