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Meyer-Baese L, Anumba N, Bolt T, Daley L, LaGrow TJ, Zhang X, Xu N, Pan WJ, Schumacher EH, Keilholz S. Variation in the distribution of large-scale spatiotemporal patterns of activity across brain states. Front Syst Neurosci 2024; 18:1425491. [PMID: 39157289 PMCID: PMC11327057 DOI: 10.3389/fnsys.2024.1425491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Accepted: 07/22/2024] [Indexed: 08/20/2024] Open
Abstract
A few large-scale spatiotemporal patterns of brain activity (quasiperiodic patterns or QPPs) account for most of the spatial structure observed in resting state functional magnetic resonance imaging (rs-fMRI). The QPPs capture well-known features such as the evolution of the global signal and the alternating dominance of the default mode and task positive networks. These widespread patterns of activity have plausible ties to neuromodulatory input that mediates changes in nonlocalized processes, including arousal and attention. To determine whether QPPs exhibit variations across brain conditions, the relative magnitude and distribution of the three strongest QPPs were examined in two scenarios. First, in data from the Human Connectome Project, the relative incidence and magnitude of the QPPs was examined over the course of the scan, under the hypothesis that increasing drowsiness would shift the expression of the QPPs over time. Second, using rs-fMRI in rats obtained with a novel approach that minimizes noise, the relative incidence and magnitude of the QPPs was examined under three different anesthetic conditions expected to create distinct types of brain activity. The results indicate that both the distribution of QPPs and their magnitude changes with brain state, evidence of the sensitivity of these large-scale patterns to widespread changes linked to alterations in brain conditions.
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Affiliation(s)
- Lisa Meyer-Baese
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Nmachi Anumba
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - T. Bolt
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - L. Daley
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - T. J. LaGrow
- Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - Xiaodi Zhang
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Nan Xu
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Wen-Ju Pan
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - E. H. Schumacher
- Psychology, Georgia Institute of Technology, Atlanta, GA, United States
| | - Shella Keilholz
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
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2
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Anumba N, Kelberman MA, Pan W, Marriott A, Zhang X, Xu N, Weinshenker D, Keilholz S. The Effects of Locus Coeruleus Optogenetic Stimulation on Global Spatiotemporal Patterns in Rats. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.23.595327. [PMID: 38826205 PMCID: PMC11142206 DOI: 10.1101/2024.05.23.595327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Whole-brain intrinsic activity as detected by resting-state fMRI can be summarized by three primary spatiotemporal patterns. These patterns have been shown to change with different brain states, especially arousal. The noradrenergic locus coeruleus (LC) is a key node in arousal circuits and has extensive projections throughout the brain, giving it neuromodulatory influence over the coordinated activity of structurally separated regions. In this study, we used optogenetic-fMRI in rats to investigate the impact of LC stimulation on the global signal and three primary spatiotemporal patterns. We report small, spatially specific changes in global signal distribution as a result of tonic LC stimulation, as well as regional changes in spatiotemporal patterns of activity at 5 Hz tonic and 15 Hz phasic stimulation. We also found that LC stimulation had little to no effect on the spatiotemporal patterns detected by complex principal component analysis. These results show that the effects of LC activity on the BOLD signal in rats may be small and regionally concentrated, as opposed to widespread and globally acting.
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Affiliation(s)
- Nmachi Anumba
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
| | - Michael A Kelberman
- Department of Human Genetics, Emory University, Atlanta, GA, United States
- Molecular Cellular and Developmental Biology Department, University of Colorado Boulder, Boulder, CO, United States
| | - Wenju Pan
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
| | - Alexia Marriott
- Department of Human Genetics, Emory University, Atlanta, GA, United States
| | - Xiaodi Zhang
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
| | - Nan Xu
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
| | - David Weinshenker
- Department of Human Genetics, Emory University, Atlanta, GA, United States
| | - Shella Keilholz
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
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3
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Meyer-Baese L, Anumba N, Bolt T, Daley L, LaGrow TJ, Zhang X, Xu N, Pan WJ, Schumacher E, Keilholz S. Variation in the Distribution of Large-scale Spatiotemporal Patterns of Activity Across Brain States. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.26.591295. [PMID: 38746246 PMCID: PMC11092498 DOI: 10.1101/2024.04.26.591295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
A few large-scale spatiotemporal patterns of brain activity (quasiperiodic patterns or QPPs) account for most of the spatial structure observed in resting state functional magnetic resonance imaging (rs-fMRI). The QPPs capture well-known features such as the evolution of the global signal and the alternating dominance of the default mode and task positive networks. These widespread patterns of activity have plausible ties to neuromodulatory input that mediates changes in nonlocalized processes, including arousal and attention. To determine whether QPPs exhibit variations across brain conditions, the relative magnitude and distribution of the three strongest QPPs were examined in two scenarios. First, in data from the Human Connectome Project, the relative incidence and magnitude of the QPPs was examined over the course of the scan, under the hypothesis that increasing drowsiness would shift the expression of the QPPs over time. Second, using rs-fMRI in rats obtained with a novel approach that minimizes noise, the relative incidence and magnitude of the QPPs was examined under three different anesthetic conditions expected to create distinct types of brain activity. The results indicate that both the distribution of QPPs and their magnitude changes with brain state, evidence of the sensitivity of these large-scale patterns to widespread changes linked to alterations in brain conditions.
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Affiliation(s)
- Lisa Meyer-Baese
- Wallace H. Coulter Department of Biomedical Engineering, Emory University/Georgia Institute of Technology
| | - Nmachi Anumba
- Wallace H. Coulter Department of Biomedical Engineering, Emory University/Georgia Institute of Technology
| | - T Bolt
- Wallace H. Coulter Department of Biomedical Engineering, Emory University/Georgia Institute of Technology
| | - L Daley
- Wallace H. Coulter Department of Biomedical Engineering, Emory University/Georgia Institute of Technology
| | - T J LaGrow
- Electrical and Computer Engineering, Georgia Institute of Technology
| | - Xiaodi Zhang
- Wallace H. Coulter Department of Biomedical Engineering, Emory University/Georgia Institute of Technology
| | - Nan Xu
- Wallace H. Coulter Department of Biomedical Engineering, Emory University/Georgia Institute of Technology
| | - Wen-Ju Pan
- Wallace H. Coulter Department of Biomedical Engineering, Emory University/Georgia Institute of Technology
| | | | - Shella Keilholz
- Wallace H. Coulter Department of Biomedical Engineering, Emory University/Georgia Institute of Technology
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4
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Watters H, Davis A, Fazili A, Daley L, LaGrow TJ, Schumacher EH, Keilholz S. Infraslow dynamic patterns in human cortical networks track a spectrum of external to internal attention. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.22.590625. [PMID: 38712098 PMCID: PMC11071428 DOI: 10.1101/2024.04.22.590625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Early efforts to understand the human cerebral cortex focused on localization of function, assigning functional roles to specific brain regions. More recent evidence depicts the cortex as a dynamic system, organized into flexible networks with patterns of spatiotemporal activity corresponding to attentional demands. In functional MRI (fMRI), dynamic analysis of such spatiotemporal patterns is highly promising for providing non-invasive biomarkers of neurodegenerative diseases and neural disorders. However, there is no established neurotypical spectrum to interpret the burgeoning literature of dynamic functional connectivity from fMRI across attentional states. In the present study, we apply dynamic analysis of network-scale spatiotemporal patterns in a range of fMRI datasets across numerous tasks including a left-right moving dot task, visual working memory tasks, congruence tasks, multiple resting state datasets, mindfulness meditators, and subjects watching TV. We find that cortical networks show shifts in dynamic functional connectivity across a spectrum that tracks the level of external to internal attention demanded by these tasks. Dynamics of networks often grouped into a single task positive network show divergent responses along this axis of attention, consistent with evidence that definitions of a single task positive network are misleading. Additionally, somatosensory and visual networks exhibit strong phase shifting along this spectrum of attention. Results were robust on a group and individual level, further establishing network dynamics as a potential individual biomarker. To our knowledge, this represents the first study of its kind to generate a spectrum of dynamic network relationships across such an axis of attention.
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Affiliation(s)
| | - Aleah Davis
- Agnes Scott College
- Georgia Institute of Technology School of Psychology
| | | | - Lauren Daley
- Department of Biomedical Engineering, Emory University/Georgia Institute of Technology
| | - TJ LaGrow
- Georgia Institute of Technology School of Electrical and Computer Engineering
| | | | - Shella Keilholz
- Department of Biomedical Engineering, Emory University/Georgia Institute of Technology
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5
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Watters H, Fazili A, Daley L, Belden A, LaGrow TJ, Bolt T, Loui P, Keilholz S. Creative tempo: Spatiotemporal dynamics of the default mode network in improvisational musicians. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.07.588391. [PMID: 38645080 PMCID: PMC11030431 DOI: 10.1101/2024.04.07.588391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
The intrinsic dynamics of human brain activity display a recurring pattern of anti-correlated activity between the default mode network (DMN), associated with internal processing and mentation, and task positive regions, associated with externally directed attention. In human functional magnetic resonance imaging (fMRI) data, this anti-correlated pattern is detectable on the infraslow timescale (<0.1 Hz) as a quasi-periodic pattern (QPP). While the DMN is implicated in creativity and musicality in traditional time-averaged functional connectivity studies, no one has yet explored how creative training may alter dynamic spatiotemporal patterns involving the DMN such as QPPs. In the present study, we compare the outputs of two QPP detection approaches, sliding window algorithm and complex principal components analysis (cPCA). We apply both methods to an existing dataset of musicians captured with resting state fMRI, grouped as either classical, improvisational, or minimally trained non-musicians. The original time-averaged functional connectivity (FC) analysis of this dataset used improvisation as a proxy for creative thinking and found that the DMN and visual networks (VIS) display higher connectivity in improvisational musicians. We expand upon this dataset's original study and find that QPP analysis detects convergent results at the group level with both methods. In improvisational musicians, dynamic functional correlation in the group-averaged QPP was found to be increased between the DMN-VIS and DMN-FPN for both the QPP algorithm and complex principal components analysis (cPCA) methods. Additionally, we found an unexpected increase in FC in the group-averaged QPP between the dorsal attention network and amygdala in improvisational musicians; this result was not reported in the original seed-based study of this dataset. The current study represents a novel application of two dynamic FC detection methods with results that replicate and expand upon previous seed-based FC findings. The results show the robustness of both the QPP phenomenon and its detection methods. This study also demonstrates the value of dynamic FC methods in reproducing seed-based findings and their promise in detecting group-wise or individual differences that may be missed by traditional seed-based resting state fMRI studies.
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Affiliation(s)
| | | | - Lauren Daley
- Department of Biomedical Engineering, Emory University/Georgia Institute of Technology
| | | | - T J LaGrow
- Georgia Institute of Technology School of Electrical and Computer Engineering
| | - Taylor Bolt
- Department of Biomedical Engineering, Emory University/Georgia Institute of Technology
| | | | - Shella Keilholz
- Department of Biomedical Engineering, Emory University/Georgia Institute of Technology
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6
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Seeburger DT, Xu N, Ma M, Larson S, Godwin C, Keilholz SD, Schumacher EH. Time-varying functional connectivity predicts fluctuations in sustained attention in a serial tapping task. COGNITIVE, AFFECTIVE & BEHAVIORAL NEUROSCIENCE 2024; 24:111-125. [PMID: 38253775 PMCID: PMC10979291 DOI: 10.3758/s13415-024-01156-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 01/08/2024] [Indexed: 01/24/2024]
Abstract
The mechanisms for how large-scale brain networks contribute to sustained attention are unknown. Attention fluctuates from moment to moment, and this continuous change is consistent with dynamic changes in functional connectivity between brain networks involved in the internal and external allocation of attention. In this study, we investigated how brain network activity varied across different levels of attentional focus (i.e., "zones"). Participants performed a finger-tapping task, and guided by previous research, in-the-zone performance or state was identified by low reaction time variability and out-of-the-zone as the inverse. In-the-zone sessions tended to occur earlier in the session than out-of-the-zone blocks. This is unsurprising given the way attention fluctuates over time. Employing a novel method of time-varying functional connectivity, called the quasi-periodic pattern analysis (i.e., reliable, network-level low-frequency fluctuations), we found that the activity between the default mode network (DMN) and task positive network (TPN) is significantly more anti-correlated during in-the-zone states versus out-of-the-zone states. Furthermore, it is the frontoparietal control network (FPCN) switch that differentiates the two zone states. Activity in the dorsal attention network (DAN) and DMN were desynchronized across both zone states. During out-of-the-zone periods, FPCN synchronized with DMN, while during in-the-zone periods, FPCN switched to synchronized with DAN. In contrast, the ventral attention network (VAN) synchronized more closely with DMN during in-the-zone periods compared with out-of-the-zone periods. These findings demonstrate that time-varying functional connectivity of low frequency fluctuations across different brain networks varies with fluctuations in sustained attention or other processes that change over time.
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Affiliation(s)
- Dolly T Seeburger
- School of Psychology, Georgia Institute of Technology, Atlanta, GA, USA.
| | - Nan Xu
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Marcus Ma
- College of Computing, Georgia Institute of Technology, Atlanta, GA, USA
| | - Sam Larson
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Christine Godwin
- School of Psychology, Georgia Institute of Technology, Atlanta, GA, USA
| | - Shella D Keilholz
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Eric H Schumacher
- School of Psychology, Georgia Institute of Technology, Atlanta, GA, USA.
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7
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Xiang J, Sun Y, Wu X, Guo Y, Xue J, Niu Y, Cui X. Abnormal Spatial and Temporal Overlap of Time-Varying Brain Functional Networks in Patients with Schizophrenia. Brain Sci 2023; 14:40. [PMID: 38248255 PMCID: PMC10813230 DOI: 10.3390/brainsci14010040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 12/25/2023] [Accepted: 12/27/2023] [Indexed: 01/23/2024] Open
Abstract
Schizophrenia (SZ) is a complex psychiatric disorder with unclear etiology and pathological features. Neuroscientists are increasingly proposing that schizophrenia is an abnormality in the dynamic organization of brain networks. Previous studies have found that the dynamic brain networks of people with SZ are abnormal in both space and time. However, little is known about the interactions and overlaps between hubs of the brain underlying spatiotemporal dynamics. In this study, we aimed to investigate different patterns of spatial and temporal overlap of hubs between SZ patients and healthy individuals. Specifically, we obtained resting-state functional magnetic resonance imaging data from the public dataset for 43 SZ patients and 49 healthy individuals. We derived a representation of time-varying functional connectivity using the Jackknife Correlation (JC) method. We employed the Betweenness Centrality (BC) method to identify the hubs of the brain's functional connectivity network. We then applied measures of temporal overlap, spatial overlap, and hierarchical clustering to investigate differences in the organization of brain hubs between SZ patients and healthy controls. Our findings suggest significant differences between SZ patients and healthy controls at the whole-brain and subnetwork levels. Furthermore, spatial overlap and hierarchical clustering analysis showed that quasi-periodic patterns were disrupted in SZ patients. Analyses of temporal overlap revealed abnormal pairwise engagement preferences in the hubs of SZ patients. These results provide new insights into the dynamic characteristics of the network organization of the SZ brain.
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Affiliation(s)
- Jie Xiang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (J.X.); (Y.S.); (X.W.); (J.X.); (Y.N.)
| | - Yumeng Sun
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (J.X.); (Y.S.); (X.W.); (J.X.); (Y.N.)
| | - Xubin Wu
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (J.X.); (Y.S.); (X.W.); (J.X.); (Y.N.)
| | - Yuxiang Guo
- School of Software, Taiyuan University of Technology, Taiyuan 030024, China;
| | - Jiayue Xue
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (J.X.); (Y.S.); (X.W.); (J.X.); (Y.N.)
| | - Yan Niu
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (J.X.); (Y.S.); (X.W.); (J.X.); (Y.N.)
| | - Xiaohong Cui
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (J.X.); (Y.S.); (X.W.); (J.X.); (Y.N.)
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8
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Xu N, Yousefi B, Anumba N, LaGrow TJ, Zhang X, Keilholz S. QPPLab: A generally applicable software package for detecting, analyzing, and visualizing large-scale quasiperiodic spatiotemporal patterns (QPPs) of brain activity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.25.559086. [PMID: 37808706 PMCID: PMC10557593 DOI: 10.1101/2023.09.25.559086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
One prominent brain dynamic process detected in functional neuroimaging data is large-scale quasi-periodic patterns (QPPs) which display spatiotemporal propagations along brain cortical gradients. QPP associates with the infraslow neural activity related to attention and arousal fluctuations and has been identified in both resting and task-evoked brains across various species. Several QPP detection and analysis tools were developed for distinct applications with study-specific parameter methods. This MATLAB package provides a simplified and user-friendly generally applicable toolbox for detecting, analyzing, and visualizing QPPs from fMRI timeseries of the brain. This paper describes the software functions and presents its ease of use on any brain datasets. Metadata [Table: see text].
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Affiliation(s)
- Nan Xu
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
| | - Behnaz Yousefi
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
| | - Nmachi Anumba
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
| | - Theodore J LaGrow
- Electrical and Computer Engineering, Georgia Tech, Atlanta, GA, United States
| | - Xiaodi Zhang
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
| | - Shella Keilholz
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
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9
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Xu N, Smith DM, Jeno G, Seeburger DT, Schumacher EH, Keilholz SD. The interaction between random and systematic visual stimulation and infraslow quasiperiodic spatiotemporal patterns of whole brain activity. IMAGING NEUROSCIENCE (CAMBRIDGE, MASS.) 2023; 1:1-19. [PMID: 37701786 PMCID: PMC10494556 DOI: 10.1162/imag_a_00002] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Accepted: 05/14/2023] [Indexed: 09/14/2023]
Abstract
One prominent feature of the infraslow BOLD signal during rest or task is quasi-periodic spatiotemporal pattern (QPP) of signal changes that involves an alternation of activity in key functional networks and propagation of activity across brain areas, and that is known to tie to the infraslow neural activity involved in attention and arousal fluctuations. This ongoing whole-brain pattern of activity might potentially modify the response to incoming stimuli or be modified itself by the induced neural activity. To investigate this, we presented checkerboard sequences flashing at 6Hz to subjects. This is a salient visual stimulus that is known to produce a strong response in visual processing regions. Two different visual stimulation sequences were employed, a systematic stimulation sequence in which the visual stimulus appeared every 20.3 secs and a random stimulation sequence in which the visual stimulus occurred randomly every 14~62.3 secs. Three central observations emerged. First, the two different stimulation conditions affect the QPP waveform in different aspects, i.e., systematic stimulation has greater effects on its phase and random stimulation has greater effects on its magnitude. Second, the QPP was more frequent in the systematic condition with significantly shorter intervals between consecutive QPPs compared to the random condition. Third, the BOLD signal response to the visual stimulus across both conditions was swamped by the QPP at the stimulus onset. These results provide novel insights into the relationship between intrinsic patterns and stimulated brain activity.
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Affiliation(s)
- Nan Xu
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
| | - Derek M. Smith
- School of Psychology, Georgia Institute of Technology, Atlanta, GA, United States
- Department of Neurology, Division of Cognitive Neurology/Neuropsychology, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - George Jeno
- School of Computer Science, Georgia Institute of Technology, Atlanta, GA, United States
| | - Dolly T. Seeburger
- School of Psychology, Georgia Institute of Technology, Atlanta, GA, United States
| | - Eric H. Schumacher
- School of Psychology, Georgia Institute of Technology, Atlanta, GA, United States
| | - Shella D. Keilholz
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
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10
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Brahmi H, Ammar B, Ksibi A, Cherif F, Aldehim G, Alimi AM. Finite-time complete periodic synchronization of memristive neural networks with mixed delays. Sci Rep 2023; 13:12545. [PMID: 37532702 PMCID: PMC10397264 DOI: 10.1038/s41598-023-37737-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 06/27/2023] [Indexed: 08/04/2023] Open
Abstract
In this paper we study the oscillatory behavior of a new class of memristor based neural networks with mixed delays and we prove the existence and uniqueness of the periodic solution of the system based on the concept of Filippov solutions of the differential equation with discontinuous right-hand side. In addition, some assumptions are determined to guarantee the globally exponentially stability of the solution. Then, we study the adaptive finite-time complete periodic synchronization problem and by applying Lyapunov-Krasovskii functional approach, a new adaptive controller and adaptive update rule have been developed. A useful finite-time complete synchronization condition is established in terms of linear matrix inequalities. Finally, an illustrative simulation is given to substantiate the main results.
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Affiliation(s)
- Hajer Brahmi
- Research Groups on Intelligent Machines, National Engineering School of Sfax, University of Sfax, 3038, Sfax, Tunisia
| | - Boudour Ammar
- Research Groups on Intelligent Machines, National Engineering School of Sfax, University of Sfax, 3038, Sfax, Tunisia.
| | - Amel Ksibi
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
| | - Farouk Cherif
- Laboratory of Math Physics, Specials Functions and Applications LR11ES35, Department of Mathematics, ESSTHS, University of Sousse, Tunisia
| | - Ghadah Aldehim
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
| | - Adel M Alimi
- Research Groups on Intelligent Machines, National Engineering School of Sfax, University of Sfax, 3038, Sfax, Tunisia
- Department of Electrical and Electronic Engineering Science, Faculty of Engineering and the Built Environment, University of Johannesburg, South Africa
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11
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Henry TR, Fogleman ND, Nugiel T, Cohen JR. Effect of methylphenidate on functional controllability: a preliminary study in medication-naïve children with ADHD. Transl Psychiatry 2022; 12:518. [PMID: 36528602 PMCID: PMC9759578 DOI: 10.1038/s41398-022-02283-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 11/18/2022] [Accepted: 12/05/2022] [Indexed: 12/23/2022] Open
Abstract
Methylphenidate (MPH) is the recommended first-line treatment for attention-deficit/hyperactivity disorder (ADHD). While MPH's mechanism of action as a dopamine and noradrenaline transporter blocker is well known, how this translates to ADHD-related symptom mitigation is still unclear. As functional connectivity is reliably altered in ADHD, with recent literature indicating dysfunctional connectivity dynamics as well, one possible mechanism is through altering brain network dynamics. In a double-blind, placebo-controlled MPH crossover trial, 19 medication-naïve children with ADHD underwent two functional MRI scanning sessions (one on MPH and one on placebo) that included a resting state scan and two inhibitory control tasks; 27 typically developing (TD) children completed the same protocol without medication. Network control theory, which quantifies how brain activity reacts to system inputs based on underlying connectivity, was used to assess differences in average and modal functional controllability during rest and both tasks between TD children and children with ADHD (on and off MPH) and between children with ADHD on and off MPH. Children with ADHD on placebo exhibited higher average controllability and lower modal controllability of attention, reward, and somatomotor networks than TD children. Children with ADHD on MPH were statistically indistinguishable from TD children on almost all controllability metrics. These findings suggest that MPH may stabilize functional network dynamics in children with ADHD, both reducing reactivity of brain organization and making it easier to achieve brain states necessary for cognitively demanding tasks.
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Affiliation(s)
- Teague R Henry
- Department of Psychology and School of Data Science, University of Virginia, Charlottesville, VA, USA.
| | - Nicholas D Fogleman
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Tehila Nugiel
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jessica R Cohen
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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12
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Northoff G. Spatiotemporal Psychopathology - A Novel Approach to Brain and Symptoms. Noro Psikiyatr Ars 2022; 59:S3-S9. [PMID: 36578984 PMCID: PMC9767129 DOI: 10.29399/npa.28146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Accepted: 03/13/2022] [Indexed: 12/31/2022] Open
Abstract
How can we characterize psychopathological symptoms and connect them to the brain? Current psychopathological symptoms only focus on either the symptoms themselves or predominantly on the brain. This leaves open their intimate connection. A novel approach, Spatiotemporal Psychopathology, proposes that the brain inner spatiotemporal organisation of its neural activity provides the spatiotemporal organization of the psychopathological symptoms. Specifically, the brains' neuronal topography and dynamic is manifest in a more or less analogous spatiotemporal organisation on the mental level, i.e., mental topography and dynamic. This is strongly supported by various examples including major depressive disorder, bipolar disorder, schizophrenia, and autism. We therefore conclude that Spatiotemporal Psychopathology provides a promising approach to intimately connect brain and symptoms.
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Affiliation(s)
- Georg Northoff
- University of Ottawa, Institute of Mental Health Research, Ontario, Canada,Correspondence Address: Georg Northoff, 1145 Carling Avenue, Ottawa, K1L 8K9 Ontario, Canada • E-mail:
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13
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Yin W, Li T, Mucha PJ, Cohen JR, Zhu H, Zhu Z, Lin W. Altered neural flexibility in children with attention-deficit/hyperactivity disorder. Mol Psychiatry 2022; 27:4673-4679. [PMID: 35869272 PMCID: PMC9734048 DOI: 10.1038/s41380-022-01706-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 07/05/2022] [Accepted: 07/07/2022] [Indexed: 12/14/2022]
Abstract
Attention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders of childhood, and is often characterized by altered executive functioning. Executive function has been found to be supported by flexibility in dynamic brain reconfiguration. Thus, we applied multilayer community detection to resting-state fMRI data in 180 children with ADHD and 180 typically developing children (TDC) to identify alterations in dynamic brain reconfiguration in children with ADHD. We specifically evaluated MR derived neural flexibility, which is thought to underlie cognitive flexibility, or the ability to selectively switch between mental processes. Significantly decreased neural flexibility was observed in the ADHD group at both the whole brain (raw p = 0.0005) and sub-network levels (p < 0.05, FDR corrected), particularly for the default mode network, attention-related networks, executive function-related networks, and primary networks. Furthermore, the subjects with ADHD who received medication exhibited significantly increased neural flexibility (p = 0.025, FDR corrected) when compared to subjects with ADHD who were medication naïve, and their neural flexibility was not statistically different from the TDC group (p = 0.74, FDR corrected). Finally, regional neural flexibility was capable of differentiating ADHD from TDC (Accuracy: 77% for tenfold cross-validation, 74.46% for independent test) and of predicting ADHD severity using clinical measures of symptom severity (R2: 0.2794 for tenfold cross-validation, 0.156 for independent test). In conclusion, the present study found that neural flexibility is altered in children with ADHD and demonstrated the potential clinical utility of neural flexibility to identify children with ADHD, as well as to monitor treatment responses and disease severity.
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Affiliation(s)
- Weiyan Yin
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Tengfei Li
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Peter J Mucha
- Department of Mathematics, Dartmouth College, Hanover, NH, USA
- Department of Mathematics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jessica R Cohen
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Hongtu Zhu
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Ziliang Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Weili Lin
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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14
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Meyer-Baese L, Watters H, Keilholz S. Spatiotemporal patterns of spontaneous brain activity: a mini-review. NEUROPHOTONICS 2022; 9:032209. [PMID: 35434180 PMCID: PMC9005199 DOI: 10.1117/1.nph.9.3.032209] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 03/04/2022] [Indexed: 06/14/2023]
Abstract
The brain exists in a state of constant activity in the absence of any external sensory input. The spatiotemporal patterns of this spontaneous brain activity have been studied using various recording and imaging techniques. This has enabled considerable progress to be made in elucidating the cellular and network mechanisms that are involved in the observed spatiotemporal dynamics. This mini-review outlines different spatiotemporal dynamic patterns that have been identified in four commonly used modalities: electrophysiological recordings, optical imaging, functional magnetic resonance imaging, and electroencephalography. Signal sources for each modality, possible sources of the observed dynamics, and future directions are also discussed.
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Affiliation(s)
- Lisa Meyer-Baese
- Emory University, Georgia Institute of Technology, Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia, United States
| | | | - Shella Keilholz
- Emory University, Georgia Institute of Technology, Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia, United States
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15
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Idesis S, Faskowitz J, Betzel RF, Corbetta M, Sporns O, Deco G. Edge-centric analysis of stroke patients: An alternative approach for biomarkers of lesion recovery. Neuroimage Clin 2022; 35:103055. [PMID: 35661469 PMCID: PMC9163596 DOI: 10.1016/j.nicl.2022.103055] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 05/19/2022] [Accepted: 05/21/2022] [Indexed: 11/17/2022]
Abstract
Most neuroimaging studies of post-stroke recovery rely on analyses derived from standard node-centric functional connectivity to map the distributed effects in stroke patients. Here, given the importance of nonlocal and diffuse damage, we use an edge-centric approach to functional connectivity in order to provide an alternative description of the effects of this disorder. These techniques allow for the rendering of metrics such as normalized entropy, which describes the diversity of edge communities at each node. Moreover, the approach enables the identification of high amplitude co-fluctuations in fMRI time series. We found that normalized entropy is associated with stroke lesion severity and continually increases across the time of patients' recovery. Furthermore, high amplitude co-fluctuations not only relate to the lesion severity but are also associated with patients' level of recovery. The current study is the first edge-centric application for a clinical population in a longitudinal dataset and demonstrates how a different perspective for functional data analysis can further characterize topographic modulations of brain dynamics.
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Affiliation(s)
- Sebastian Idesis
- Center for Brain and Cognition (CBC), Department of Information Technologies and Communications (DTIC), Pompeu Fabra University, Edifici Mercè Rodoreda, Carrer Trias i Fargas 25-27, 08005 Barcelona, Catalonia, Spain.
| | - Joshua Faskowitz
- Department of Psychological and Brain Science, Indiana University, Bloomington, IN 47405, United States; Program in Neuroscience, Indiana University, Bloomington, IN 47405, United States
| | - Richard F Betzel
- Department of Psychological and Brain Science, Indiana University, Bloomington, IN 47405, United States; Program in Neuroscience, Indiana University, Bloomington, IN 47405, United States; Cognitive Science Program, Indiana University, Bloomington, IN 47405, United States; Network Science Institute, Indiana University, Bloomington, IN 47405, United States
| | - Maurizio Corbetta
- Padova Neuroscience Center (PNC), University of Padova, via Orus 2/B, 35129 Padova, Italy; Department of Neuroscience (DNS), University of Padova, via Giustiniani 2, 35128 Padova, Italy; Department of Neurology, Washington University School of Medicine, 660 S. Euclid Ave, St. Louis, MO 63110, United States; Department of Radiology, Washington University School of Medicine, 660 S. Euclid Ave, St. Louis, MO 63110, United States; VIMM, Venetian Institute of Molecular Medicine (VIMM), Biomedical Foundation, via Orus 2, 35129 Padova, Italy
| | - Olaf Sporns
- Department of Psychological and Brain Science, Indiana University, Bloomington, IN 47405, United States; Program in Neuroscience, Indiana University, Bloomington, IN 47405, United States; Cognitive Science Program, Indiana University, Bloomington, IN 47405, United States; Network Science Institute, Indiana University, Bloomington, IN 47405, United States
| | - Gustavo Deco
- Center for Brain and Cognition (CBC), Department of Information Technologies and Communications (DTIC), Pompeu Fabra University, Edifici Mercè Rodoreda, Carrer Trias i Fargas 25-27, 08005 Barcelona, Catalonia, Spain; Institució Catalana de Recerca I Estudis Avançats (ICREA), Passeig Lluis Companys 23, 08010 Barcelona, Catalonia, Spain
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16
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Maltbie E, Yousefi B, Zhang X, Kashyap A, Keilholz S. Comparison of Resting-State Functional MRI Methods for Characterizing Brain Dynamics. Front Neural Circuits 2022; 16:681544. [PMID: 35444518 PMCID: PMC9013751 DOI: 10.3389/fncir.2022.681544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 03/11/2022] [Indexed: 11/13/2022] Open
Abstract
Resting-state functional MRI (fMRI) exhibits time-varying patterns of functional connectivity. Several different analysis approaches have been developed for examining these resting-state dynamics including sliding window connectivity (SWC), phase synchrony (PS), co-activation pattern (CAP), and quasi-periodic patterns (QPP). Each of these approaches can be used to generate patterns of activity or inter-areal coordination which vary across time. The individual frames can then be clustered to produce temporal groupings commonly referred to as “brain states.” Several recent publications have investigated brain state alterations in clinical populations, typically using a single method for quantifying frame-wise functional connectivity. This study directly compares the results of k-means clustering in conjunction with three of these resting-state dynamics methods (SWC, CAP, and PS) and quantifies the brain state dynamics across several metrics using high resolution data from the human connectome project. Additionally, these three dynamics methods are compared by examining how the brain state characterizations vary during the repeated sequences of brain states identified by a fourth dynamic analysis method, QPP. The results indicate that the SWC, PS, and CAP methods differ in the clusters and trajectories they produce. A clear illustration of these differences is given by how each one results in a very different clustering profile for the 24s sequences explicitly identified by the QPP algorithm. PS clustering is sensitive to QPPs with the mid-point of most QPP sequences grouped into the same single cluster. CAPs are also highly sensitive to QPPs, separating each phase of the QPP sequences into different sets of clusters. SWC (60s window) is less sensitive to QPPs. While the QPPs are slightly more likely to occur during specific SWC clusters, the SWC clustering does not vary during the 24s QPP sequences, the goal of this work is to improve both the practical and theoretical understanding of different resting-state dynamics methods, thereby enabling investigators to better conceptualize and implement these tools for characterizing functional brain networks.
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17
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Xu N, LaGrow TJ, Anumba N, Lee A, Zhang X, Yousefi B, Bassil Y, Clavijo GP, Khalilzad Sharghi V, Maltbie E, Meyer-Baese L, Nezafati M, Pan WJ, Keilholz S. Functional Connectivity of the Brain Across Rodents and Humans. Front Neurosci 2022; 16:816331. [PMID: 35350561 PMCID: PMC8957796 DOI: 10.3389/fnins.2022.816331] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 02/14/2022] [Indexed: 12/15/2022] Open
Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI), which measures the spontaneous fluctuations in the blood oxygen level-dependent (BOLD) signal, is increasingly utilized for the investigation of the brain's physiological and pathological functional activity. Rodents, as a typical animal model in neuroscience, play an important role in the studies that examine the neuronal processes that underpin the spontaneous fluctuations in the BOLD signal and the functional connectivity that results. Translating this knowledge from rodents to humans requires a basic knowledge of the similarities and differences across species in terms of both the BOLD signal fluctuations and the resulting functional connectivity. This review begins by examining similarities and differences in anatomical features, acquisition parameters, and preprocessing techniques, as factors that contribute to functional connectivity. Homologous functional networks are compared across species, and aspects of the BOLD fluctuations such as the topography of the global signal and the relationship between structural and functional connectivity are examined. Time-varying features of functional connectivity, obtained by sliding windowed approaches, quasi-periodic patterns, and coactivation patterns, are compared across species. Applications demonstrating the use of rs-fMRI as a translational tool for cross-species analysis are discussed, with an emphasis on neurological and psychiatric disorders. Finally, open questions are presented to encapsulate the future direction of the field.
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Affiliation(s)
- Nan Xu
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Theodore J. LaGrow
- Electrical and Computer Engineering, Georgia Tech, Atlanta, GA, United States
| | - Nmachi Anumba
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Azalea Lee
- Neuroscience Graduate Program, Emory University, Atlanta, GA, United States
- Emory University School of Medicine, Atlanta, GA, United States
| | - Xiaodi Zhang
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Behnaz Yousefi
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Yasmine Bassil
- Neuroscience Graduate Program, Emory University, Atlanta, GA, United States
| | - Gloria P. Clavijo
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | | | - Eric Maltbie
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Lisa Meyer-Baese
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Maysam Nezafati
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Wen-Ju Pan
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Shella Keilholz
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
- Neuroscience Graduate Program, Emory University, Atlanta, GA, United States
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18
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Contribution of animal models toward understanding resting state functional connectivity. Neuroimage 2021; 245:118630. [PMID: 34644593 DOI: 10.1016/j.neuroimage.2021.118630] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 08/06/2021] [Accepted: 09/29/2021] [Indexed: 12/27/2022] Open
Abstract
Functional connectivity, which reflects the spatial and temporal organization of intrinsic activity throughout the brain, is one of the most studied measures in human neuroimaging research. The noninvasive acquisition of resting state functional magnetic resonance imaging (rs-fMRI) allows the characterization of features designated as functional networks, functional connectivity gradients, and time-varying activity patterns that provide insight into the intrinsic functional organization of the brain and potential alterations related to brain dysfunction. Functional connectivity, hence, captures dimensions of the brain's activity that have enormous potential for both clinical and preclinical research. However, the mechanisms underlying functional connectivity have yet to be fully characterized, hindering interpretation of rs-fMRI studies. As in other branches of neuroscience, the identification of the neurophysiological processes that contribute to functional connectivity largely depends on research conducted on laboratory animals, which provide a platform where specific, multi-dimensional investigations that involve invasive measurements can be carried out. These highly controlled experiments facilitate the interpretation of the temporal correlations observed across the brain. Indeed, information obtained from animal experimentation to date is the basis for our current understanding of the underlying basis for functional brain connectivity. This review presents a compendium of some of the most critical advances in the field based on the efforts made by the animal neuroimaging community.
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19
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Duffy KA, Rosch KS, Nebel MB, Seymour KE, Lindquist MA, Pekar JJ, Mostofsky SH, Cohen JR. Increased integration between default mode and task-relevant networks in children with ADHD is associated with impaired response control. Dev Cogn Neurosci 2021; 50:100980. [PMID: 34252881 PMCID: PMC8278154 DOI: 10.1016/j.dcn.2021.100980] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Revised: 06/03/2021] [Accepted: 06/17/2021] [Indexed: 01/22/2023] Open
Abstract
Default mode network (DMN) dysfunction is theorized to play a role in attention lapses and task errors in children with attention-deficit/hyperactivity disorder (ADHD). In ADHD, the DMN is hyperconnected to task-relevant networks, and both increased functional connectivity and reduced activation are related to poor task performance. The current study extends existing literature by considering interactions between the DMN and task-relevant networks from a brain network perspective and by assessing how these interactions relate to response control. We characterized both static and time-varying functional brain network organization during the resting state in 43 children with ADHD and 43 age-matched typically developing (TD) children. We then related aspects of network integration to go/no-go performance. We calculated participation coefficient (PC), a measure of a region’s inter-network connections, for regions of the DMN, canonical cognitive control networks (fronto-parietal, salience/cingulo-opercular), and motor-related networks (somatomotor, subcortical). Mean PC was higher in children with ADHD as compared to TD children, indicating greater integration across networks. Further, higher and less variable PC was related to greater commission error rate in children with ADHD. Together, these results inform our understanding of the role of the DMN and its interactions with task-relevant networks in response control deficits in ADHD. The DMN is more integrated with task-relevant networks in children with ADHD. Higher and less variable DMN integration relates to poorer response control in ADHD. DMN dysfunction may play a key role in response control deficits in ADHD.
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Affiliation(s)
- Kelly A Duffy
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Keri S Rosch
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, USA; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, MD, USA; Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, USA
| | - Mary Beth Nebel
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, USA; Department of Neurology, Johns Hopkins University, Baltimore, MD, USA
| | - Karen E Seymour
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, USA; Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, USA; Department of Mental Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Martin A Lindquist
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - James J Pekar
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA; Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, MD, USA
| | - Stewart H Mostofsky
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, USA; Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, USA; Department of Neurology, Johns Hopkins University, Baltimore, MD, USA
| | - Jessica R Cohen
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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20
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Yousefi B, Keilholz S. Propagating patterns of intrinsic activity along macroscale gradients coordinate functional connections across the whole brain. Neuroimage 2021; 231:117827. [PMID: 33549755 DOI: 10.1016/j.neuroimage.2021.117827] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 01/21/2021] [Accepted: 01/28/2021] [Indexed: 11/30/2022] Open
Abstract
The intrinsic activity of the human brain, observed with resting-state fMRI (rsfMRI) and functional connectivity, exhibits macroscale spatial organization such as functional networks and gradients. Dynamic analysis techniques have shown that functional connectivity is a mere summary of time-varying patterns with distinct spatial and temporal characteristics. A better understanding of these patterns might provide insight into aspects of the brain's intrinsic activity that cannot be inferred by functional connectivity or the spatial maps derived from it, such as functional networks and gradients. Here, we describe three spatiotemporal patterns of coordinated activity across the whole brain obtained by averaging similar ~20-second-long segments of rsfMRI timeseries. In each of these patterns, activity propagates along a particular macroscale functional gradient, simultaneously across the cerebral cortex and in most other brain regions. In some regions, like the thalamus, the propagation suggests previously-undescribed gradients. The coordinated activity across areas is consistent with known tract-based connections, and nuanced differences in the timing of peak activity between regions point to plausible driving mechanisms. The magnitude of correlation within and particularly between functional networks is remarkably diminished when these patterns are regressed from the rsfMRI timeseries, a quantitative demonstration of their significant role in functional connectivity. Taken together, our results suggest that a few recurring patterns of propagating intrinsic activity along macroscale gradients give rise to and coordinate functional connections across the whole brain.
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Affiliation(s)
- Behnaz Yousefi
- Wallace H. Coulter Department of Biomedical Engineering, Emory University/Georgia Institute of Technology, Atlanta 30322, GA, United States
| | - Shella Keilholz
- Wallace H. Coulter Department of Biomedical Engineering, Emory University/Georgia Institute of Technology, Atlanta 30322, GA, United States.
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21
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Shappell HM, Duffy KA, Rosch KS, Pekar JJ, Mostofsky SH, Lindquist MA, Cohen JR. Children with attention-deficit/hyperactivity disorder spend more time in hyperconnected network states and less time in segregated network states as revealed by dynamic connectivity analysis. Neuroimage 2021; 229:117753. [PMID: 33454408 DOI: 10.1016/j.neuroimage.2021.117753] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Revised: 12/17/2020] [Accepted: 01/06/2021] [Indexed: 12/12/2022] Open
Abstract
Previous studies in children with attention-deficit/hyperactivity disorder (ADHD) have observed functional brain network disruption on a whole-brain level, as well as on a sub-network level, particularly as related to the default mode network, attention-related networks, and cognitive control-related networks. Given behavioral findings that children with ADHD have more difficulty sustaining attention and more extreme moment-to-moment fluctuations in behavior than typically developing (TD) children, recently developed methods to assess changes in connectivity over shorter time periods (i.e., "dynamic functional connectivity"), may provide unique insight into dysfunctional network organization in ADHD. Thus, we performed a dynamic functional connectivity (FC) analysis on resting state fMRI data from 38 children with ADHD and 79 TD children. We used Hidden semi-Markov models (HSMMs) to estimate six network states, as well as the most probable sequence of states for each participant. We quantified the dwell time, sojourn time, and transition probabilities across states. We found that children with ADHD spent less total time in, and switched more quickly out of, anticorrelated states involving the default mode network and task-relevant networks as compared to TD children. Moreover, children with ADHD spent more time in a hyperconnected state as compared to TD children. These results provide novel evidence that underlying dynamics may drive the differences in static FC patterns that have been observed in ADHD and imply that disrupted FC dynamics may be a mechanism underlying the behavioral symptoms and cognitive deficits commonly observed in children with ADHD.
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Affiliation(s)
- Heather M Shappell
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
| | - Kelly A Duffy
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Keri S Rosch
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, USA; Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, MD, USA; Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, USA
| | - James J Pekar
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA; Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, MD, USA
| | - Stewart H Mostofsky
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, USA; Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, USA; Department of Neurology, Johns Hopkins University, Baltimore, MD, USA
| | - Martin A Lindquist
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Jessica R Cohen
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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22
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Szczupak D, Yen CC, Liu C, Tian X, Lent R, Tovar-Moll F, Silva AC. Dynamic Interhemispheric Desynchronization in Marmosets and Humans With Disorders of the Corpus Callosum. Front Neural Circuits 2020; 14:612595. [PMID: 33408615 PMCID: PMC7779638 DOI: 10.3389/fncir.2020.612595] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 11/24/2020] [Indexed: 11/13/2022] Open
Abstract
The corpus callosum, the principal structural avenue for interhemispheric neuronal communication, controls the brain's lateralization. Developmental malformations of the corpus callosum (CCD) can lead to learning and intellectual disabilities. Currently, there is no clear explanation for these symptoms. Here, we used resting-state functional MRI (rsfMRI) to evaluate the dynamic resting-state functional connectivity (rsFC) in both the cingulate cortex (CG) and the sensory areas (S1, S2, A1) in three marmosets (Callithrix jacchus) with spontaneous CCD. We also performed rsfMRI in 10 CCD human subjects (six hypoplasic and four agenesic). We observed no differences in the strength of rsFC between homotopic CG and sensory areas in both species when comparing them to healthy controls. However, in CCD marmosets, we found lower strength of quasi-periodic patterns (QPP) correlation in the posterior interhemispheric sensory areas. We also found a significant lag of interhemispheric communication in the medial CG, suggesting asynchrony between the two hemispheres. Correspondingly, in human subjects, we found that the CG of acallosal subjects had a higher QPP correlation than controls. In comparison, hypoplasic subjects had a lower QPP correlation and a delay of 1.6 s in the sensory regions. These results show that CCD affects the interhemispheric synchrony of both CG and sensory areas and that, in both species, its impact on cortical communication varies along the CC development gradient. Our study shines a light on how CCD misconnects homotopic regions and opens a line of research to explain the causes of the symptoms exhibited by CCD patients and how to mitigate them.
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Affiliation(s)
- Diego Szczupak
- Department of Neurobiology, University Pittsburgh Brain Institute, University of Pittsburgh, Pittsburgh, PA, United States.,Cerebral Microcirculation Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Cecil C Yen
- Cerebral Microcirculation Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Cirong Liu
- Department of Neurobiology, University Pittsburgh Brain Institute, University of Pittsburgh, Pittsburgh, PA, United States.,Cerebral Microcirculation Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States.,Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Xiaoguang Tian
- Department of Neurobiology, University Pittsburgh Brain Institute, University of Pittsburgh, Pittsburgh, PA, United States.,Cerebral Microcirculation Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Roberto Lent
- Instituto de Ciências Biomédicas, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil.,Instituto D'Or de Pesquisa e Ensino, Rio de Janeiro, Brazil
| | - Fernanda Tovar-Moll
- Instituto de Ciências Biomédicas, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil.,Instituto D'Or de Pesquisa e Ensino, Rio de Janeiro, Brazil
| | - Afonso C Silva
- Department of Neurobiology, University Pittsburgh Brain Institute, University of Pittsburgh, Pittsburgh, PA, United States.,Cerebral Microcirculation Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
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Briend F, Armstrong WP, Kraguljac NV, Keilhloz SD, Lahti AC. Aberrant static and dynamic functional patterns of frontoparietal control network in antipsychotic-naïve first-episode psychosis subjects. Hum Brain Mapp 2020; 41:2999-3008. [PMID: 32372508 PMCID: PMC7336157 DOI: 10.1002/hbm.24992] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Revised: 02/05/2020] [Accepted: 03/08/2020] [Indexed: 12/20/2022] Open
Abstract
Psychotic disorders are disabling clinical syndromes characterized by widespread alterations in cortical information processing. Disruption of frontoparietal network (FPN) connectivity has emerged as a common footprint across the psychosis spectrum. Our goal was to characterize the static and dynamic resting‐state functional connectivity (FC) of the FPN in antipsychotic‐naïve first‐episode psychosis (FEP) subjects. We compared the static FC of the FPN in 40 FEP and 40 healthy control (HC) subjects, matched on age, sex, and socioeconomic status. To study the dynamic FC, we measured quasiperiodic patterns (QPPs) that consist of infraslow spatioemporal patterns embedded in the blood oxygen level‐dependent signal that repeats over time, exhibiting alternation of high and low activity. Relative to HC, we found functional hypoconnectivity between the right middle frontal gyrus and the right middle temporal gyrus, as well as the left inferior temporal gyrus and the left inferior parietal gyrus in FEP (p < .05, false discovery rate corrected). The correlation of the QPP with all functional scans was significantly stronger for FEP compared to HC, suggesting a greater impact of the QPPs to intrinsic brain activity in psychotic population. Regressing the QPP from the functional scans erased all significant group differences in static FC, suggesting that abnormal connectivity in FEP could result from altered QPP. Our study supports that alterations of cortical information processing are not a function of psychotic chronicity or antipsychotic medication exposure and may be regarded as trait specific. In addition, static connectivity abnormality may be partly related to altered brain network temporal dynamics.
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Affiliation(s)
- Frederic Briend
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - William P Armstrong
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Nina V Kraguljac
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Shella D Keilhloz
- Department of Biomedical Engineering, Georgia Tech and Emory University, Atlanta, Georgia, USA
| | - Adrienne C Lahti
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, Alabama, USA
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24
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Dynamic mode decomposition of resting-state and task fMRI. Neuroimage 2019; 194:42-54. [DOI: 10.1016/j.neuroimage.2019.03.019] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2019] [Accepted: 03/08/2019] [Indexed: 12/19/2022] Open
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