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Khanra P, Nakuci J, Muldoon S, Watanabe T, Masuda N. Reliability of energy landscape analysis of resting-state functional MRI data. Eur J Neurosci 2024; 60:4265-4290. [PMID: 38837814 DOI: 10.1111/ejn.16390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 04/05/2024] [Accepted: 04/25/2024] [Indexed: 06/07/2024]
Abstract
Energy landscape analysis is a data-driven method to analyse multidimensional time series, including functional magnetic resonance imaging (fMRI) data. It has been shown to be a useful characterization of fMRI data in health and disease. It fits an Ising model to the data and captures the dynamics of the data as movement of a noisy ball constrained on the energy landscape derived from the estimated Ising model. In the present study, we examine test-retest reliability of the energy landscape analysis. To this end, we construct a permutation test that assesses whether or not indices characterizing the energy landscape are more consistent across different sets of scanning sessions from the same participant (i.e. within-participant reliability) than across different sets of sessions from different participants (i.e. between-participant reliability). We show that the energy landscape analysis has significantly higher within-participant than between-participant test-retest reliability with respect to four commonly used indices. We also show that a variational Bayesian method, which enables us to estimate energy landscapes tailored to each participant, displays comparable test-retest reliability to that using the conventional likelihood maximization method. The proposed methodology paves the way to perform individual-level energy landscape analysis for given data sets with a statistically controlled reliability.
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Affiliation(s)
- Pitambar Khanra
- Department of Mathematics, State University of New York at Buffalo, Buffalo, New York, USA
| | - Johan Nakuci
- School of Psychology, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Sarah Muldoon
- Department of Mathematics, State University of New York at Buffalo, Buffalo, New York, USA
- Institute for Artificial Intelligence and Data Science, State University of New York at Buffalo, Buffalo, New York, USA
| | - Takamitsu Watanabe
- International Research Centre for Neurointelligence, The University of Tokyo, Tokyo, Japan
| | - Naoki Masuda
- Department of Mathematics, State University of New York at Buffalo, Buffalo, New York, USA
- Institute for Artificial Intelligence and Data Science, State University of New York at Buffalo, Buffalo, New York, USA
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Theis N, Bahuguna J, Rubin JE, Cape J, Iyengar S, Prasad KM. Diagnostically distinct resting state fMRI energy distributions: A subject-specific maximum entropy modeling study. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.23.576937. [PMID: 38328170 PMCID: PMC10849576 DOI: 10.1101/2024.01.23.576937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Objective Existing neuroimaging studies of psychotic and mood disorders have reported brain activation differences (first-order properties) and altered pairwise correlation-based functional connectivity (second-order properties). However, both approaches have certain limitations that can be overcome by integrating them in a pairwise maximum entropy model (MEM) that better represents a comprehensive picture of fMRI signal patterns and provides a system-wide summary measure called energy. This study examines the applicability of individual-level MEM for psychiatry and identifies image-derived model coefficients related to model parameters. Method MEMs are fit to resting state fMRI data from each individual with schizophrenia/schizoaffective disorder, bipolar disorder, and major depression (n=132) and demographically matched healthy controls (n=132) from the UK Biobank to different subsets of the default mode network (DMN) regions. Results The model satisfactorily explained observed brain energy state occurrence probabilities across all participants, and model parameters were significantly correlated with image-derived coefficients for all groups. Within clinical groups, averaged energy level distributions were higher in schizophrenia/schizoaffective disorder but lower in bipolar disorder compared to controls for both bilateral and unilateral DMN. Major depression energy distributions were higher compared to controls only in the right hemisphere DMN. Conclusions Diagnostically distinct energy states suggest that probability distributions of temporal changes in synchronously active nodes may underlie each diagnostic entity. Subject-specific MEMs allow for factoring in the individual variations compared to traditional group-level inferences, offering an improved measure of biologically meaningful correlates of brain activity that may have potential clinical utility.
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Affiliation(s)
- Nicholas Theis
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Jyotika Bahuguna
- Department of Neuroscience, Laboratoire de Neurosciences Cognitive et Adaptive, University of Strasbourg, France
| | | | - Joshua Cape
- Department of Statistics, University of Wisconsin-Madison, WI, USA
| | - Satish Iyengar
- Department of Statistics, University of Pittsburgh, PA, USA
| | - Konasale M. Prasad
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, PA, USA
- Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA, USA
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Fan L, Li Y, Huang ZG, Zhang W, Wu X, Liu T, Wang J. Low-frequency repetitive transcranial magnetic stimulation alters the individual functional dynamical landscape. Cereb Cortex 2023; 33:9583-9598. [PMID: 37376783 DOI: 10.1093/cercor/bhad228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 06/05/2023] [Accepted: 06/06/2023] [Indexed: 06/29/2023] Open
Abstract
Repetitive transcranial magnetic stimulation (rTMS) is a noninvasive approach to modulate brain activity and behavior in humans. Still, how individual resting-state brain dynamics after rTMS evolves across different functional configurations is rarely studied. Here, using resting state fMRI data from healthy subjects, we aimed to examine the effects of rTMS to individual large-scale brain dynamics. Using Topological Data Analysis based Mapper approach, we construct the precise dynamic mapping (PDM) for each participant. To reveal the relationship between PDM and canonical functional representation of the resting brain, we annotated the graph using relative activation proportion of a set of large-scale resting-state networks (RSNs) and assigned the single brain volume to corresponding RSN-dominant or a hub state (not any RSN was dominant). Our results show that (i) low-frequency rTMS could induce changed temporal evolution of brain states; (ii) rTMS didn't alter the hub-periphery configurations underlined resting-state brain dynamics; and (iii) the rTMS effects on brain dynamics differ across the left frontal and occipital lobe. In conclusion, low-frequency rTMS significantly alters the individual temporo-spatial dynamics, and our finding further suggested a potential target-dependent alteration of brain dynamics. This work provides a new perspective to comprehend the heterogeneous effect of rTMS.
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Affiliation(s)
- Liming Fan
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
- National Engineering Research Center of Health Care and Medical Devices, Guangzhou, Guangdong 510500, China
| | - Youjun Li
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
- National Engineering Research Center of Health Care and Medical Devices, Guangzhou, Guangdong 510500, China
| | - Zi-Gang Huang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
- National Engineering Research Center of Health Care and Medical Devices, Guangzhou, Guangdong 510500, China
| | - Wenlong Zhang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
- National Engineering Research Center of Health Care and Medical Devices, Guangzhou, Guangdong 510500, China
| | - Xiaofeng Wu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
- National Engineering Research Center of Health Care and Medical Devices, Guangzhou, Guangdong 510500, China
| | - Tian Liu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
- National Engineering Research Center of Health Care and Medical Devices, Guangzhou, Guangdong 510500, China
| | - Jue Wang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
- National Engineering Research Center of Health Care and Medical Devices, Guangzhou, Guangdong 510500, China
- The Key Laboratory of Neuro-Informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi 710049, China
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Multivariate Gaussian Copula Mutual Information to Estimate Functional Connectivity with Less Random Architecture. ENTROPY 2022; 24:e24050631. [PMID: 35626516 PMCID: PMC9141633 DOI: 10.3390/e24050631] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 04/13/2022] [Accepted: 04/26/2022] [Indexed: 01/27/2023]
Abstract
Recognition of a brain region’s interaction is an important field in neuroscience. Most studies use the Pearson correlation to find the interaction between the regions. According to the experimental evidence, there is a nonlinear dependence between the activities of different brain regions that is ignored by Pearson correlation as a linear measure. Typically, the average activity of each region is used as input because it is a univariate measure. This dimensional reduction, i.e., averaging, leads to a loss of spatial information across voxels within the region. In this study, we propose using an information-theoretic measure, multivariate mutual information (mvMI), as a nonlinear dependence to find the interaction between regions. This measure, which has been recently proposed, simplifies the mutual information calculation complexity using the Gaussian copula. Using simulated data, we show that the using this measure overcomes the mentioned limitations. Additionally using the real resting-state fMRI data, we compare the level of significance and randomness of graphs constructed using different methods. Our results indicate that the proposed method estimates the functional connectivity more significantly and leads to a smaller number of random connections than the common measure, Pearson correlation. Moreover, we find that the similarity of the estimated functional networks of the individuals is higher when the proposed method is used.
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