Current Research Areas
Current Research Areas
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Analysis of large-scale brain activity
Analysis of large-scale brain activity
We develop techniques for analyzing and visualizing the rich spatiotemporal structure and network organization of brain activity. In one avenue, we are studying time-varying changes in whole-brain fMRI signal characteristics across scales of seconds to minutes. These "dynamic" imaging features may reveal new information about brain function and provide more sensitive disease biomarkers.
Selected publications:
A Said, R Bayrak, T Derr, M Shabbir, D Moyer, C Chang, X Koutsoukos. Neurograph: Benchmarks for graph machine learning in brain connectomics. Advances in Neural Information Processing Systems 36, 6509-6531 (2023).
TS Bolt, JS Nomi, D Bzdok, JA Salas, C Chang, BTT Yeo, LQ Uddin, SD Keilholz. A parsimonious description of global functional brain organization in three spatiotemporal patterns. Nature Neuroscience 25, 1093-1103 (2022).
S Zhang, LJ Spoletini, BP Gold, VL Morgan, BP Rogers, C Chang. Inter-individual signatures of fMRI temporal fluctuations. Cerebral Cortex, doi:10.1093/cercor/bhab099 (2021).
RG Bayrak*, N Hoang*, CB Hansen, C Chang, M Berger. PRAGMA: Interactively Constructing Functional Brain Parcellations. IEEE Transactions on Visualization and Computer Graphics (2020).
RM Hutchison, T Womelsdorf, EA Allen, PA Bandettini, VD Calhoun, M Corbetta, S Della Penna, JH Duyn, GH Glover, J Gonzalez-Castillo, DA Handwerker, S Keilholz, V Kiviniemi, DA Leopold, F de Pasquale, O Sporns, M Walter, C Chang. Dynamic functional connectivity: Promise, issues, and interpretations. Neuroimage 80:360-78 (2013).
C Chang, GH Glover. Time-frequency dynamics of resting-state brain connectivity measured with fMRI. Neuroimage. Mar; 50(1):81-98 (2010).
AI and signal processing techniques for multimodal integration and analysis
AI and signal processing techniques for multimodal integration and analysis
We develop methods for modeling and integrating data from complementary brain imaging modalities, such as simultaneous EEG and fMRI, along with physiological signals from the body. Combining multiple modalities can advance our ability to capture spatiotemporal information about brain-body function, and deepen our understanding of individual differences and disease. We are also developing corresponding open-source software tools.
Selected publications:
Y Li, A Lou, Z Xu, S Wang, C Chang. Leveraging sinusoidal representation networks to predict fMRI signals from EEG. Medical Imaging 2024: Image Processing 12926, 795-800 (2024).
RG Bayrak, I Lyu, C Chang. Learning Subject-Specific Functional Parcellations from Cortical Surface Measures. in: Predictive Intelligence in Medicine: 5th International Workshop in Conjunction with MICCAI 2022, Singapore: Springer-Verlag; 2022;172–180 (2022).
RG Bayrak, CB Hansen, JA Salas, N Ahmed, I Lyu, Y Huo, C Chang. From Brain to Body: Learning Low-Frequency Respiration and Cardiac Signals from fMRI Dynamics. MICCAI (2021).
C Chang, JE Chen. Multimodal EEG-fMRI: advancing insight into large-scale human brain dynamics. Current Opinion in Biomedical Engineering, 18:100279 (2021).
JA Salas, RG Bayrak, Y Huo, C Chang. Reconstruction of respiratory variation signals from fMRI data. Neuroimage 225:117459 (2020).
C Chang, JP Cunningham, GH Glover. Influence of heart rate on the BOLD signal: the cardiac response function. Neuroimage 44:857-69 (2009).
State-dependent brain activity
State-dependent brain activity
Internal states, such as levels of vigilance, are closely intertwined with behavior, cognition, and numerous disorders. By integrating fMRI with complementary neural, physiological, and behavioral measures, we are studying brain-wide signatures of vigilance and autonomic states, and developing computational methods for detecting state changes from fMRI signals. This research also aims to increase the sensitivity of fMRI studies more broadly by modeling state-related variability in fMRI datasets.
Selected publications:
S Zhang, SE Goodale, BP Gold, VL Morgan, DJ Englot, C Chang. Vigilance associates with the low-dimensional structure of fMRI data. Neuroimage 267:119818 (2023).
SE Goodale, N Ahmed, C Zhao, JA de Zwart, PS Özbay, D Picchioni, JH Duyn, DJ Englot, VL Morgan, C Chang. fMRI-based detection of alertness predicts behavioral response variability. eLife, 10:e62376 (2021).
CG Martin, BJ He, C Chang. State-related neural influences on fMRI connectivity estimation. Neuroimage, 244:118590 (2021).
C Chang, DA Leopold, ML Scholvinck, H Mandelkow, D Picchioni, X Liu, FQ Ye, J N Turchi, JH Duyn. Tracking brain arousal fluctuations with fMRI. PNAS (2016)
C Chang*, CD Metzger*, GH Glover, JH Duyn, HJ Heinze, M Walter. Association between heart rate variability and fluctuations in resting-state functional connectivity. Neuroimage 68:93-104 (2013).
C Chang, Z Liu, MC Chen, X Liu, JH Duyn. EEG correlates of time-varying BOLD functional connectivity. Neuroimage 72:227-36 (2013).
Mechanisms and origins of fMRI signals/networks
Mechanisms and origins of fMRI signals/networks
fMRI signals have complex neural and physiological underpinnings. In one avenue, we work on parsing the variability of fMRI signals and their long-range correlation patterns, translating this knowledge into tools for improving the sensitivity and interpretation of fMRI studies. Secondly, to probe the mechanisms supporting large-scale network organization and function, we integrate fMRI with complementary biological measures and targeted manipulations of neural circuits.
Selected publications:
NL Taylor, CJ Whyte, RR Munn, C Chang, JT Lizier, DA Leopold, JN Turchi, L Zaborszky, EJ Müller, JM Shine. Causal evidence for cholinergic stabilization of attractor landscape dynamics. Cell Reports 43(6), 2024.
J Turchi*, C Chang*, FQ Ye, BE Russ, DK Yu, CR Cortes, IE Monosov, JH Duyn, DA Leopold. The Basal Forebrain Regulates Global Resting-State fMRI Fluctuations. Neuron 2018
Richiardi J*, Altmann A*, Milazzo AC, Chang C, Chakravarty MM, [et al.], Greicius MD; IMAGEN consortium. Correlated gene expression supports synchronous activity in brain networks. Science 2015
AC Chen, DJ Oathes, C Chang, T Bradley, ZW Zhou, LM Williams, GH Glover, K Deisseroth, A Etkin. Causal interactions between fronto-parietal central executive and default-mode networks in humans. PNAS 2013
C Chang, ME Thomason, GH Glover. Mapping and correction of vascular hemodynamic latency in the BOLD signal. Neuroimage 2008 43:90-102
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