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Hunter MD, Fisher ZF, Geier CF. What ergodicity means for you. Dev Cogn Neurosci 2024; 68:101406. [PMID: 38909566 PMCID: PMC11254532 DOI: 10.1016/j.dcn.2024.101406] [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: 12/05/2023] [Revised: 06/06/2024] [Accepted: 06/09/2024] [Indexed: 06/25/2024] Open
Abstract
This paper explores the relation between within-person and between-person research designs using the concept of ergodicity from statistical mechanics in physics. We demonstrate the consequences of ergodicity using several real data examples from previously published studies. We then create several simulated examples that illustrate the independence of within-person processes from between-person differences, and pair these examples with analytic results that reinforce our conclusions. Finally, we discuss the plausibility of ergodicity being the general rule rather than the exception for social and behavioral processes, address common arguments against heeding the implications of ergodicity for behavioral research, and offer several possible solutions.
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Affiliation(s)
- Michael D Hunter
- Department of Human Development and Family Studies, The Pennsylvania State University, University Park, PA 16802, USA.
| | - Zachary F Fisher
- Department of Human Development and Family Studies, The Pennsylvania State University, University Park, PA 16802, USA
| | - Charles F Geier
- Department of Human Development and Family Science, University of Georgia, Athens, GA 30602, USA
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2
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Mingming Z, Wenhong C, Xiaoying M, Yang J, Liu HH, Lingli S, Hongwu M, Zhirong J. Abnormal prefrontal functional network in adult obstructive sleep apnea: A resting-state fNIRS study. J Sleep Res 2024; 33:e14033. [PMID: 37723923 DOI: 10.1111/jsr.14033] [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: 04/16/2023] [Revised: 08/13/2023] [Accepted: 08/16/2023] [Indexed: 09/20/2023]
Abstract
To assess prefrontal brain network abnormality in adults with obstructive sleep apnea (OSA), resting-state functional near infrared spectroscopy (rs-fNIRS) was used to evaluate 52 subjects, including 27 with OSA and 25 healthy controls (HC). The study found that patients with OSA had a decreased connection edge number, particularly in the connection between the right medial frontal cortex (MFG-R) and other right-hemisphere regions. Graph-based analysis also revealed that patients with OSA had a lower global efficiency, local efficiency, and clustering coefficient than the HC group. Additionally, the study found a significant positive correlation between the Montreal Cognitive Assessment (MoCA) score and both the connection edge number and the graph-based indicators in patients with OSA. These preliminary results suggest that prefrontal rs-fNIRS could be a useful tool for objectively and quantitatively assessing cognitive function impairment in patients with OSA.
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Affiliation(s)
- Zhao Mingming
- Department of Sleep Medicine, People's Hospital of Guangxi Zhuang Autonomous Region, Nan Ning, China
| | - Chen Wenhong
- Department of Sleep Medicine, People's Hospital of Guangxi Zhuang Autonomous Region, Nan Ning, China
| | - Mo Xiaoying
- Department of Sleep Medicine, People's Hospital of Guangxi Zhuang Autonomous Region, Nan Ning, China
| | - Jianrong Yang
- Department of Sleep Medicine, People's Hospital of Guangxi Zhuang Autonomous Region, Nan Ning, China
| | - Howe Hao Liu
- Physical Therapy Department, Allen College, Waterloo, Lowa, USA
| | - Shi Lingli
- Department of Sleep Medicine, People's Hospital of Guangxi Zhuang Autonomous Region, Nan Ning, China
| | - Ma Hongwu
- Department of Sleep Medicine, People's Hospital of Guangxi Zhuang Autonomous Region, Nan Ning, China
| | - Jiang Zhirong
- Department of Sleep Medicine, People's Hospital of Guangxi Zhuang Autonomous Region, Nan Ning, China
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3
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Wenhong C, Xiaoying M, Lingli S, Binyun T, Yining W, Mingming Z, Yian L, Lixia Q, Wenyu H, Fengjin P. Assessing resting-state brain functional connectivity in adolescents and young adults with narcolepsy using functional near-infrared spectroscopy. Front Hum Neurosci 2024; 18:1373043. [PMID: 38606200 PMCID: PMC11007108 DOI: 10.3389/fnhum.2024.1373043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 03/13/2024] [Indexed: 04/13/2024] Open
Abstract
This study aimed to elucidate the alterations in the prefrontal cortex's functional connectivity and network topology in narcolepsy patients using functional near-infrared spectroscopy (fNIRS). Twelve narcolepsy-diagnosed patients from Guangxi Zhuang Autonomous Region's People's Hospital Sleep Medicine Department and 11 matched healthy controls underwent resting fNIRS scans. Functional connectivity and graph theory analyses were employed to assess the prefrontal cortex network's properties and their correlation with clinical features. Results indicated increased functional connectivity in these adolescent and young adult patients with narcolepsy, with significant variations in metrics like average degree centrality and node efficiency, particularly in the left middle frontal gyrus. These alterations showed correlations with clinical symptoms, including depression and sleep efficiency. However, the significance of these findings was reduced post False Discovery Rate adjustment, suggesting a larger sample size is needed for validation. In conclusion, the study offers initial observations that alterations in the prefrontal cortex's functional connectivity may potentially act as a neurobiological indicator of narcolepsy, warranting further investigation with a larger cohort to substantiate these findings and understand the underlying mechanisms.
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Affiliation(s)
- Chen Wenhong
- Department of Sleep Medicine, The People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China
| | - Mo Xiaoying
- Department of Sleep Medicine, The People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China
| | - Shi Lingli
- Department of Sleep Medicine, The People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China
| | - Tang Binyun
- Department of Sleep Medicine, The People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China
| | - Wen Yining
- Department of Sleep Medicine, The People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China
| | - Zhao Mingming
- Department of Sleep Medicine, The People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China
| | - Lu Yian
- Department of Sleep Medicine, The People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China
| | - Qin Lixia
- Guangxi Clinical Reserch Center for Sleep Medicine, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China
| | - Hu Wenyu
- Department of Sleep Medicine, The People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China
| | - Pan Fengjin
- Department of Sleep Medicine, The People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China
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Kinsey S, Kazimierczak K, Camazón PA, Chen J, Adali T, Kochunov P, Adhikari B, Ford J, van Erp TGM, Dhamala M, Calhoun VD, Iraji A. Networks extracted from nonlinear fMRI connectivity exhibit unique spatial variation and enhanced sensitivity to differences between individuals with schizophrenia and controls. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.16.566292. [PMID: 38014169 PMCID: PMC10680735 DOI: 10.1101/2023.11.16.566292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Functional magnetic resonance imaging (fMRI) studies often estimate brain intrinsic connectivity networks (ICNs) from temporal relationships between hemodynamic signals using approaches such as independent component analysis (ICA). While ICNs are thought to represent functional sources that play important roles in various psychological phenomena, current approaches have been tailored to identify ICNs that mainly reflect linear statistical relationships. However, the elements comprising neural systems often exhibit remarkably complex nonlinear interactions that may be involved in cognitive operations and altered in psychiatric conditions such as schizophrenia. Consequently, there is a need to develop methods capable of effectively capturing ICNs from measures that are sensitive to nonlinear relationships. Here, we advance a novel approach to estimate ICNs from explicitly nonlinear whole-brain functional connectivity (ENL-wFC) by transforming resting-state fMRI (rsfMRI) data into the connectivity domain, allowing us to capture unique information from distance correlation patterns that would be missed by linear whole-brain functional connectivity (LIN-wFC) analysis. Our findings provide evidence that ICNs commonly extracted from linear (LIN) relationships are also reflected in explicitly nonlinear (ENL) connectivity patterns. ENL ICN estimates exhibit higher reliability and stability, highlighting our approach's ability to effectively quantify ICNs from rsfMRI data. Additionally, we observed a consistent spatial gradient pattern between LIN and ENL ICNs with higher ENL weight in core ICN regions, suggesting that ICN function may be subserved by nonlinear processes concentrated within network centers. We also found that a uniquely identified ENL ICN distinguished individuals with schizophrenia from healthy controls while a uniquely identified LIN ICN did not, emphasizing the valuable complementary information that can be gained by incorporating measures that are sensitive to nonlinearity in future analyses. Moreover, the ENL estimates of ICNs associated with auditory, linguistic, sensorimotor, and self-referential processes exhibit heightened sensitivity towards differentiating between individuals with schizophrenia and controls compared to LIN counterparts, demonstrating the translational value of our approach and of the ENL estimates of ICNs that are frequently reported as disrupted in schizophrenia. In summary, our findings underscore the tremendous potential of connectivity domain ICA and nonlinear information in resolving complex brain phenomena and revolutionizing the landscape of clinical FC analysis.
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Affiliation(s)
- Spencer Kinsey
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA
- Neuroscience Institute, Georgia State University, Atlanta, GA, USA
| | | | - Pablo Andrés Camazón
- Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, IiSGM, Madrid, Spain
| | - Jiayu Chen
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA
| | - Tülay Adali
- Department of CSEE, University of Maryland, Baltimore County, Baltimore, MD, USA
| | - Peter Kochunov
- Department of Psychiatry and Behavioral Science, University of Texas Health Science Center Houston, Houston, TX
| | - Bhim Adhikari
- Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Judith Ford
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA
- San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA
| | - Theo G M van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California, Irvine, CA, USA
| | - Mukesh Dhamala
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA
- Department of Physics and Astronomy, Georgia State University, Atlanta, GA, USA
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA
| | - Armin Iraji
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
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Wiseman N, Iraji A, Haacke EM, Calhoun V, Kou Z. Extracting functional connectivity brain networks at the resting state from pulsed arterial spin labeling data. META-RADIOLOGY 2023; 1:100023. [PMID: 38298860 PMCID: PMC10830167 DOI: 10.1016/j.metrad.2023.100023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/02/2024]
Abstract
Introduction Functional connectivity in the brain is often studied with blood oxygenation level dependent (BOLD) resting state functional magnetic resonance imaging (rsfMRI), but the BOLD signal is several steps removed from neuronal activity. Arterial spin labeling (ASL), particularly pulsed ASL (PASL), has also the capacity to measure the blood-flow changes in response to activity. In this paper, we investigated the feasibility of extracting major brain networks from PASL data, in contrast with rsfMRI analsyis. Materials and methods In this retrospective study, we analyzed a cohort dataset that consists of 21 mild traumatic brain injury (mTBI) patients and 29 healthy controls, which was collected in a previous study. By extracting 10 major brain networks from the data of both PASL and rsfMRI, we contrasted their similarities and differences in the 10 networks extracted from both modalities. Results Our data demonstrated that PASL could be used to extract all 10 major brain networks. Eight out of 10 networks demonstrated over 60 % similarity to rsfMRI data. Meanwhile, there are similar but not identical changes in networks detected between mTBI patients and healthy controls with both modalities. Notably, the PASL-extracted default mode network (DMN), other than the rsfMRI-extracted DMN, includes some regions known to be associated with the DMN in other studies. It demonstrated that PASL data can be analyzed to identify resting state networks with reasonable reliability, even without rsfMRI data. Conclusion Our analysis provides an opportunity to extract functional connectivity information in heritage datasets in which ASL but not BOLD was collected.
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Affiliation(s)
- Natalie Wiseman
- Department of Psychiatry and Behavioral Sciences, Wayne State University, Detroit, MI, USA
| | - Armin Iraji
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | - E Mark Haacke
- Departments of Biomedical Engineering and Radiology, Wayne State University, Detroit, MI, USA
| | - Vince Calhoun
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | - Zhifeng Kou
- Departments of Biomedical Engineering and Radiology, Wayne State University, Detroit, MI, USA
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Sala A, Lizarraga A, Caminiti SP, Calhoun VD, Eickhoff SB, Habeck C, Jamadar SD, Perani D, Pereira JB, Veronese M, Yakushev I. Brain connectomics: time for a molecular imaging perspective? Trends Cogn Sci 2023; 27:353-366. [PMID: 36621368 PMCID: PMC10432882 DOI: 10.1016/j.tics.2022.11.015] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 11/19/2022] [Accepted: 11/30/2022] [Indexed: 01/09/2023]
Abstract
In the past two decades brain connectomics has evolved into a major concept in neuroscience. However, the current perspective on brain connectivity and how it underpins brain function relies mainly on the hemodynamic signal of functional magnetic resonance imaging (MRI). Molecular imaging provides unique information inaccessible to MRI-based and electrophysiological techniques. Thus, positron emission tomography (PET) has been successfully applied to measure neural activity, neurotransmission, and proteinopathies in normal and pathological cognition. Here, we position molecular imaging within the brain connectivity framework from the perspective of timeliness, validity, reproducibility, and resolution. We encourage the neuroscientific community to take an integrative approach whereby MRI-based, electrophysiological techniques, and molecular imaging contribute to our understanding of the brain connectome.
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Affiliation(s)
- Arianna Sala
- Department of Nuclear Medicine, Klinikum Rechts der Isar, Technical University of Munich, School of Medicine, 81675 Munich, Germany; Coma Science Group, GIGA-Consciousness, University of Liege, 4000 Liege, Belgium; Centre du Cerveau(2), University Hospital of Liege, 4000 Liege, Belgium
| | - Aldana Lizarraga
- Department of Nuclear Medicine, Klinikum Rechts der Isar, Technical University of Munich, School of Medicine, 81675 Munich, Germany
| | - Silvia Paola Caminiti
- Vita-Salute San Raffaele University, 20132 Milan, Italy; In Vivo Human Molecular and Structural Neuroimaging Unit, Division of Neuroscience, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele Scientific Institute, 20132 Milan, Italy
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain, and Behaviour (INM-7), Research Centre Jülich, 52428 Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-University Düsseldorf, 40225 Düsseldorf, Germany
| | - Christian Habeck
- Cognitive Neuroscience Division, Department of Neurology, Columbia University, New York, NY 10032, USA
| | - Sharna D Jamadar
- Turner Institute for Brain and Mental Health, Monash University, 3800 Melbourne, Australia; Monash Biomedical Imaging, Monash University, 3800 Melbourne, Australia
| | - Daniela Perani
- Vita-Salute San Raffaele University, 20132 Milan, Italy; In Vivo Human Molecular and Structural Neuroimaging Unit, Division of Neuroscience, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele Scientific Institute, 20132 Milan, Italy; Nuclear Medicine Unit, San Raffaele Hospital, 20132 Milan, Italy
| | - Joana B Pereira
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, 14152 Stockholm, Sweden; Memory Research Unit, Department of Clinical Sciences, Malmö Lund University, 20502 Lund, Sweden
| | - Mattia Veronese
- Department of Neuroimaging, King's College London, London SE5 8AF, UK; Department of Information Engineering, University of Padua, 35131 Padua, Italy
| | - Igor Yakushev
- Department of Nuclear Medicine, Klinikum Rechts der Isar, Technical University of Munich, School of Medicine, 81675 Munich, Germany.
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7
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Chen J, Zhao M, Huang L, Liu Y, Li X, Jia X, Ding Q, Wang C, Liang P. Amplitude of low-frequency fluctuation after taste exposure revealed by resting-state fMRI. Physiol Behav 2023; 261:114091. [PMID: 36669692 DOI: 10.1016/j.physbeh.2023.114091] [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: 07/10/2022] [Revised: 01/14/2023] [Accepted: 01/16/2023] [Indexed: 01/19/2023]
Abstract
Taste perception has been deeply explored from the behavioural level to delineating neural mechanisms. However, most previous studies about the neural underpinnings of taste perception have focused on task-related brain activation. Notably, evidence indicates that task-induced brain activation often involves interference from irrelevant task materials and only accounts for a small fraction of the brain's energy consumption. Investigation of the resting-state spontaneous brain activity would bring us a comprehensive understanding of the neural mechanism of taste perception. Here we acquired resting-state functional magnetic resonance imaging (rs-fMRI) data from twenty-two participants immediately after they received sweet, sour and tasteless gustatory stimulation. Our results showed that, in contrast to the tasteless condition, the sour exposure induced decreased amplitude of low-frequency fluctuation (ALFF) in the somatosensory cortex in the left post-central gyrus, and the sweet exposure led to increased ALFF in the bilateral putamen involved in reward processing. Moreover, in contrast to the sweet stimulation condition, the sour stimulation condition showed increased ALFF in the right superior frontal gyrus, which has been linked to functioning in high-order cognitive control. Altogether, our data indicate that taste exposure may affect the spontaneous functional activity in brain regions, including the somatosensory areas, reward processing areas and high-order cognitive functioning areas. Our findings may contribute to a further understanding the neural network and mechanisms after taste exposure.
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Affiliation(s)
- Jie Chen
- Department of Psychology, Faculty of Education, Hubei University, Wuhan, China; Brain and Cognition Research Center, Faculty of Education, Hubei University, Wuhan, China
| | - Mengqi Zhao
- School of Teacher Education, Zhejiang Normal University, Jinhua, China; Key Laboratory of Intelligent, Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, China
| | - Lina Huang
- Imaging Department, Changshu No. 2 People's Hospital (the Clinical Medical College Affiliated to Xuzhou Medical University), Changshu, China
| | - Yuansheng Liu
- Department of Psychology, Faculty of Education, Hubei University, Wuhan, China; Brain and Cognition Research Center, Faculty of Education, Hubei University, Wuhan, China
| | - Xueying Li
- Department of Psychology, Faculty of Education, Hubei University, Wuhan, China; Brain and Cognition Research Center, Faculty of Education, Hubei University, Wuhan, China
| | - Xize Jia
- School of Teacher Education, Zhejiang Normal University, Jinhua, China; Key Laboratory of Intelligent, Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, China
| | - Qingguo Ding
- Imaging Department, Changshu No. 2 People's Hospital (the Clinical Medical College Affiliated to Xuzhou Medical University), Changshu, China.
| | - Chunjie Wang
- Institute of Brain Science and Department of Physiology, School of Basic Medical Sciences, Hangzhou Normal University, Hangzhou, China.
| | - Pei Liang
- Department of Psychology, Faculty of Education, Hubei University, Wuhan, China; Brain and Cognition Research Center, Faculty of Education, Hubei University, Wuhan, China.
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8
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Wu L, Calhoun V. Joint connectivity matrix independent component analysis: Auto-linking of structural and functional connectivities. Hum Brain Mapp 2023; 44:1533-1547. [PMID: 36420833 PMCID: PMC9921228 DOI: 10.1002/hbm.26155] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 09/25/2022] [Accepted: 11/04/2022] [Indexed: 11/25/2022] Open
Abstract
The study of human brain connectivity, including structural connectivity (SC) and functional connectivity (FC), provides insights into the neurophysiological mechanism of brain function and its relationship to human behavior and cognition. Both types of connectivity measurements provide crucial yet complementary information. However, integrating these two modalities into a single framework remains a challenge, because of the differences in their quantitative interdependencies as well as their anatomical representations due to distinctive imaging mechanisms. In this study, we introduced a new method, joint connectivity matrix independent component analysis (cmICA), which provides a data-driven parcellation and automated-linking of SC and FC information simultaneously using a joint analysis of functional magnetic resonance imaging (MRI) and diffusion-weighted MRI data. We showed that these two connectivity modalities produce common cortical segregation, though with various degrees of (dis)similarity. Moreover, we show conjoint FC networks and structural white matter tracts that directly link these cortical parcellations/sources, within one analysis. Overall, data-driven joint cmICA provides a new approach for integrating or fusing structural connectivity and FC systematically and conveniently, and provides an effective tool for connectivity-based multimodal data fusion in brain.
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Affiliation(s)
- Lei Wu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Center, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA.,Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico, USA
| | - Vince Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Center, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA.,Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico, USA
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9
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Iraji A, Faghiri A, Fu Z, Kochunov P, Adhikari BM, Belger A, Ford JM, McEwen S, Mathalon DH, Pearlson GD, Potkin SG, Preda A, Turner JA, Van Erp TGM, Chang C, Calhoun VD. Moving beyond the 'CAP' of the Iceberg: Intrinsic connectivity networks in fMRI are continuously engaging and overlapping. Neuroimage 2022; 251:119013. [PMID: 35189361 PMCID: PMC9107614 DOI: 10.1016/j.neuroimage.2022.119013] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 02/11/2022] [Accepted: 02/17/2022] [Indexed: 11/05/2022] Open
Abstract
Resting-state functional magnetic resonance imaging is currently the mainstay of functional neuroimaging and has allowed researchers to identify intrinsic connectivity networks (aka functional networks) at different spatial scales. However, little is known about the temporal profiles of these networks and whether it is best to model them as continuous phenomena in both space and time or, rather, as a set of temporally discrete events. Both categories have been supported by series of studies with promising findings. However, a critical question is whether focusing only on time points presumed to contain isolated neural events and disregarding the rest of the data is missing important information, potentially leading to misleading conclusions. In this work, we argue that brain networks identified within the spontaneous blood oxygenation level-dependent (BOLD) signal are not limited to temporally sparse burst moments and that these event present time points (EPTs) contain valuable but incomplete information about the underlying functional patterns. We focus on the default mode and show evidence that is consistent with its continuous presence in the BOLD signal, including during the event absent time points (EATs), i.e., time points that exhibit minimum activity and are the least likely to contain an event. Moreover, our findings suggest that EPTs may not contain all the available information about their corresponding networks. We observe distinct default mode connectivity patterns obtained from all time points (AllTPs), EPTs, and EATs. We show evidence of robust relationships with schizophrenia symptoms that are both common and unique to each of the sets of time points (AllTPs, EPTs, EATs), likely related to transient patterns of connectivity. Together, these findings indicate the importance of leveraging the full temporal data in functional studies, including those using event-detection approaches.
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Affiliation(s)
- A Iraji
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, United States of America.
| | - A Faghiri
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, United States of America
| | - Z Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, United States of America
| | - P Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD, United States of America
| | - B M Adhikari
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD, United States of America
| | - A Belger
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, United States of America
| | - J M Ford
- Department of Psychiatry, University of California San Francisco, San Francisco, CA, United States of America; San Francisco VA Medical Center, San Francisco, CA, United States of America
| | - S McEwen
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, United States of America
| | - D H Mathalon
- Department of Psychiatry, University of California San Francisco, San Francisco, CA, United States of America; San Francisco VA Medical Center, San Francisco, CA, United States of America
| | - G D Pearlson
- Departments of Psychiatry and Neuroscience, Yale University, School of Medicine, New Haven, CT, United States of America
| | - S G Potkin
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, United States of America
| | - A Preda
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, United States of America
| | - J A Turner
- Department of Psychology, Georgia State University, Atlanta, GA, United States of America
| | - T G M Van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, United States of America
| | - C Chang
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States of America
| | - V D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, United States of America.
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10
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White T, Blok E, Calhoun VD. Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed. Hum Brain Mapp 2022; 43:278-291. [PMID: 32621651 PMCID: PMC8675413 DOI: 10.1002/hbm.25120] [Citation(s) in RCA: 44] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 05/12/2020] [Accepted: 06/22/2020] [Indexed: 12/19/2022] Open
Abstract
Collaborative networks and data sharing initiatives are broadening the opportunities for the advancement of science. These initiatives offer greater transparency in science, with the opportunity for external research groups to reproduce, replicate, and extend research findings. Further, larger datasets offer the opportunity to identify homogeneous patterns within subgroups of individuals, where these patterns may be obscured by the heterogeneity of the neurobiological measure in smaller samples. However, data sharing and data pooling initiatives are not without their challenges, especially with new laws that may at first glance appear quite restrictive for open science initiatives. Interestingly, what is key to some of these new laws (i.e, the European Union's general data protection regulation) is that they provide greater control of data to those who "give" their data for research purposes. Thus, the most important element in data sharing is allowing the participants to make informed decisions about how they want their data to be used, and, within the law of the specific country, to follow the participants' wishes. This framework encompasses obtaining thorough informed consent and allowing the participant to determine the extent that they want their data shared, many of the ethical and legal obstacles are reduced to just monsters under the bed. In this manuscript we discuss the many options and obstacles for data sharing, from fully open, to federated learning, to fully closed. Importantly, we highlight the intersection of data sharing, privacy, and data ownership and highlight specific examples that we believe are informative to the neuroimaging community.
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Affiliation(s)
- Tonya White
- Department of Child and Adolescent Psychiatry/PsychologyErasmus University Medical CenterRotterdamThe Netherlands
- Department of RadiologyErasmus University Medical CenterRotterdamThe Netherlands
| | - Elisabet Blok
- Department of Child and Adolescent Psychiatry/PsychologyErasmus University Medical CenterRotterdamThe Netherlands
| | - Vince D. Calhoun
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
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11
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Verhallen AM, Renken RJ, Marsman JBC, Ter Horst GJ. Working Memory Alterations After a Romantic Relationship Breakup. Front Behav Neurosci 2021; 15:657264. [PMID: 33897388 PMCID: PMC8062740 DOI: 10.3389/fnbeh.2021.657264] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 03/15/2021] [Indexed: 11/27/2022] Open
Abstract
Experiencing stress can have a disadvantageous effect on mental well-being. Additional to the relation between suffering from chronic stress and depression, both stress (acute and chronic) and depression are associated with cognitive alterations, including working memory. The breakup of a relationship is considered to be a stressful event that can lead to symptoms of depression in otherwise healthy people. Additional to elevated depression scores, stress-related cognitive alterations may occur in this population as well. Therefore, in the present fMRI study we investigated whether experiencing a relationship breakup is associated with working memory alterations and whether this is related to depressive symptom severity. A three workload version of the n-back task (0-back, 1-back, 2-back) was used to measure working memory in subjects who experienced a breakup in the preceding 6 months (“heartbreak group”, n = 70) and subjects in a romantic relationship (“relationship group”, n = 46). Behavioral task performance was compared between the two groups. Functional MRI scans were analyzed using General Linear Model (GLM) activation analyses. Workload conditions were contrasted to each other and to baseline and group differences were assessed. To investigate whether brain networks are associated with depressive symptom severity within the heartbreak group specifically, a post hoc feature-based Independent Component Analysis was performed on the 2-back > 0-back contrast images to identify brain regions that covaried across subjects. Behaviorally, the heartbreak group performed similar at high workload (i.e., 2-back) and better at moderate workload (i.e., 1-back) than the relationship group. GLM analysis revealed an interaction between group and 2-back > 0-back, 2-back > 1-back and 2-back > baseline; the heartbreak group showed less precuneus activation compared to the relationship group. Furthermore, within the heartbreak group, we found a negative association between depressive symptom severity and a brain network representing mostly the precuneus, anterior cingulate gyrus and supplementary motor cortex. Our findings suggest that the effect of a breakup is accompanied by workload-dependent working memory alterations. Therefore, we propose that this population can potentially be used to investigate the interplay between stress, cognitive functioning and depression.
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Affiliation(s)
- Anne M Verhallen
- Cognitive Neuroscience Center, Department of Biomedical Sciences of Cells and Systems, University Medical Center Groningen, Groningen, Netherlands
| | - Remco J Renken
- Cognitive Neuroscience Center, Department of Biomedical Sciences of Cells and Systems, University Medical Center Groningen, Groningen, Netherlands
| | - Jan-Bernard C Marsman
- Cognitive Neuroscience Center, Department of Biomedical Sciences of Cells and Systems, University Medical Center Groningen, Groningen, Netherlands
| | - Gert J Ter Horst
- Cognitive Neuroscience Center, Department of Biomedical Sciences of Cells and Systems, University Medical Center Groningen, Groningen, Netherlands
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12
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Chen Z, Zhou Q, Zhang Y, Calhoun V. A brain task state only arouses a few number of resting-state intrinsic modes. Biomed Phys Eng Express 2019. [DOI: 10.1088/2057-1976/ab0390] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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13
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Levin-Schwartz Y, Calhoun VD, Adalı T. A method to compare the discriminatory power of data-driven methods: Application to ICA and IVA. J Neurosci Methods 2019; 311:267-276. [PMID: 30389489 PMCID: PMC6258321 DOI: 10.1016/j.jneumeth.2018.10.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2018] [Revised: 08/24/2018] [Accepted: 10/08/2018] [Indexed: 11/20/2022]
Abstract
BACKGROUND The widespread application of data-driven factorization-based methods, such as independent component analysis (ICA), to functional magnetic resonance imaging data facilitates the study of neural function and how it is disrupted by psychiatric disorders such as schizophrenia. While the increasing number of these methods motivates a comparison of their relative performance, such a comparison is difficult to perform on real fMRI data, since the ground truth is, relatively, unknown and the alignment of factors across different methods is impractical and imprecise. NEW METHOD We present a novel method, global difference maps (GDMs), to compare the results of different fMRI analysis techniques on real fMRI data, quantify their relative performances, and highlight the differences between the decompositions visually. COMPARISON WITH EXISTING METHODS We apply this method to compare the performances of two different factorization-based methods, ICA and its multiset extension independent vector analysis (IVA), for the analysis of fMRI data from 109 patients with schizophrenia and 138 healthy controls during the performance of three tasks. RESULTS Through this application of GDMs, we find that IVA can determine regions that are more discriminatory between patients and controls than ICA, though IVA is less effective at emphasizing regions found in only a subset of the tasks. CONCLUSIONS These results demonstrate that GDMs are an effective way to compare the performances of different factorization-based methods as well as regression-based analyses.
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Affiliation(s)
- Yuri Levin-Schwartz
- Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, United States.
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, NM 87106, United States; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131, United States
| | - Tülay Adalı
- Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, United States
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14
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Müller-Oehring EM, Kwon D, Nagel BJ, Sullivan EV, Chu W, Rohlfing T, Prouty D, Nichols BN, Poline JB, Tapert SF, Brown SA, Cummins K, Brumback T, Colrain IM, Baker FC, De Bellis MD, Voyvodic JT, Clark DB, Pfefferbaum A, Pohl KM. Influences of Age, Sex, and Moderate Alcohol Drinking on the Intrinsic Functional Architecture of Adolescent Brains. Cereb Cortex 2018; 28:1049-1063. [PMID: 28168274 PMCID: PMC6059181 DOI: 10.1093/cercor/bhx014] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2016] [Revised: 12/16/2016] [Indexed: 12/11/2022] Open
Abstract
The transition from adolescent to adult cognition and emotional control requires neurodevelopmental maturation likely involving intrinsic functional networks (IFNs). Normal neurodevelopment may be vulnerable to disruption from environmental insult such as alcohol consumption commonly initiated during adolescence. To test potential disruption to IFN maturation, we used resting-state functional magnetic resonance imaging (rs-fMRI) in 581 no-to-low alcohol-consuming and 117 moderate-to-high-drinking youth. Functional seed-to-voxel connectivity analysis assessed age, sex, and moderate alcohol drinking on default-mode, executive-control, salience, reward, and emotion networks and tested cognitive and motor coordination correlates of network connectivity. Among no-to-low alcohol-consuming adolescents, executive-control frontolimbicstriatal connectivity was stronger in older than younger adolescents, particularly boys, and predicted better ability in balance, memory, and impulse control. Connectivity patterns in moderate-to-high-drinking youth were tested mainly in late adolescence when drinking was initiated. Implicated was the emotion network with attenuated connectivity to default-mode network regions. Our cross-sectional rs-fMRI findings from this large cohort of adolescents show sexual dimorphism in connectivity and suggest neurodevelopmental rewiring toward stronger and spatially more distributed executive-control networking in older than younger adolescents. Functional network rewiring in moderate-to-high-drinking adolescents may impede maturation of affective and self-reflection systems and obscure maturation of complex social and emotional behaviors.
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Affiliation(s)
- Eva M Müller-Oehring
- Center for Health Sciences, SRI International, Menlo Park, CA 94025, USA
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Dongjin Kwon
- Center for Health Sciences, SRI International, Menlo Park, CA 94025, USA
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Bonnie J Nagel
- Departments of Psychiatry and Behavioral Neuroscience, Oregon Health & Sciences University, Portland, OR 97239, USA
| | - Edith V Sullivan
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Weiwei Chu
- Center for Health Sciences, SRI International, Menlo Park, CA 94025, USA
| | - Torsten Rohlfing
- Center for Health Sciences, SRI International, Menlo Park, CA 94025, USA
| | - Devin Prouty
- Center for Health Sciences, SRI International, Menlo Park, CA 94025, USA
| | - B Nolan Nichols
- Center for Health Sciences, SRI International, Menlo Park, CA 94025, USA
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Jean-Baptiste Poline
- Henry H. Wheeler, Jr. Brain Imaging Center, Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA 94720, USA
| | - Susan F Tapert
- Department of Psychiatry, University of California, La Jolla, San Diego, CA 92093, USA
| | - Sandra A Brown
- Department of Psychiatry, University of California, La Jolla, San Diego, CA 92093, USA
| | - Kevin Cummins
- Department of Psychiatry, University of California, La Jolla, San Diego, CA 92093, USA
| | - Ty Brumback
- Department of Psychiatry, University of California, La Jolla, San Diego, CA 92093, USA
| | - Ian M Colrain
- Center for Health Sciences, SRI International, Menlo Park, CA 94025, USA
| | - Fiona C Baker
- Center for Health Sciences, SRI International, Menlo Park, CA 94025, USA
| | - Michael D De Bellis
- Department of Psychiatry & Behavioral Sciences, Duke University School of Medicine, Durham, NC 27710, USA
| | - James T Voyvodic
- Department of Radiology, Duke University School of Medicine, Durham, NC 27710, USA
| | - Duncan B Clark
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Adolf Pfefferbaum
- Center for Health Sciences, SRI International, Menlo Park, CA 94025, USA
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Kilian M Pohl
- Center for Health Sciences, SRI International, Menlo Park, CA 94025, USA
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15
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Liu P, Li R, Bao C, Wei Y, Fan Y, Liu Y, Wang G, Wu H, Qin W. Altered topological patterns of brain functional networks in Crohn’s disease. Brain Imaging Behav 2018; 12:1466-1478. [DOI: 10.1007/s11682-017-9814-8] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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16
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Kyathanahally SP, Wang Y, Calhoun VD, Deshpande G. Investigation of True High Frequency Electrical Substrates of fMRI-Based Resting State Networks Using Parallel Independent Component Analysis of Simultaneous EEG/fMRI Data. Front Neuroinform 2017; 11:74. [PMID: 29311887 PMCID: PMC5743737 DOI: 10.3389/fninf.2017.00074] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2017] [Accepted: 12/07/2017] [Indexed: 11/13/2022] Open
Abstract
Previous work using simultaneously acquired electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data has shown that the slow temporal dynamics of resting state brain networks (RSNs), e.g., default mode network (DMN), visual network (VN), obtained from fMRI are correlated with smoothed and down sampled versions of various EEG features such as microstates and band-limited power envelopes. Therefore, even though the down sampled and smoothed envelope of EEG gamma band power is correlated with fMRI fluctuations in the RSNs, it does not mean that the electrical substrates of the RSNs fluctuate with periods <100 ms. Based on the scale free properties of EEG microstates and their correlation with resting state fMRI fluctuations in the RSNs, researchers have speculated that truly high frequency electrical substrates may exist for the RSNs, which would make resting fluctuations obtained from fMRI more meaningful to typically occurring fast neuronal processes in the sub-100 ms time scale. In this study, we test this critical hypothesis using an integrated framework involving simultaneous EEG/fMRI acquisition, fast fMRI sampling (TR = 200 ms) using multiband EPI (MB EPI), and EEG/fMRI fusion using parallel independent component analysis (pICA) which does not require the down sampling of EEG to fMRI temporal resolution. Our results demonstrate that with faster sampling, high frequency electrical substrates (fluctuating with periods <100 ms time scale) of the RSNs can be observed. This provides a sounder neurophysiological basis for the RSNs.
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Affiliation(s)
- Sreenath P Kyathanahally
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States.,Department of Clinical Research/AMSM, University of Bern, Bern, Switzerland
| | - Yun Wang
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States.,Department of Psychiatry, Columbia University, New York, NY, United States
| | - Vince D Calhoun
- Mind Research Network and Lovelace Biomedical, Environmental Research Institute, Albuquerque, NM, United States.,Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, United States
| | - Gopikrishna Deshpande
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States.,Department of Psychology, Auburn University, Auburn, AL, United States.,Alabama Advanced Imaging Consortium, Auburn University and University of Alabama Birmingham, Birmingham, AL, United States
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17
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Abrol A, Damaraju E, Miller RL, Stephen JM, Claus ED, Mayer AR, Calhoun VD. Replicability of time-varying connectivity patterns in large resting state fMRI samples. Neuroimage 2017; 163:160-176. [PMID: 28916181 PMCID: PMC5775892 DOI: 10.1016/j.neuroimage.2017.09.020] [Citation(s) in RCA: 125] [Impact Index Per Article: 17.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2017] [Revised: 09/07/2017] [Accepted: 09/09/2017] [Indexed: 12/12/2022] Open
Abstract
The past few years have seen an emergence of approaches that leverage temporal changes in whole-brain patterns of functional connectivity (the chronnectome). In this chronnectome study, we investigate the replicability of the human brain’s inter-regional coupling dynamics during rest by evaluating two different dynamic functional network connectivity (dFNC) analysis frameworks using 7 500 functional magnetic resonance imaging (fMRI) datasets. To quantify the extent to which the emergent functional connectivity (FC) patterns are reproducible, we characterize the temporal dynamics by deriving several summary measures across multiple large, independent age-matched samples. Reproducibility was demonstrated through the existence of basic connectivity patterns (FC states) amidst an ensemble of inter-regional connections. Furthermore, application of the methods to conservatively configured (statistically stationary, linear and Gaussian) surrogate datasets revealed that some of the studied state summary measures were indeed statistically significant and also suggested that this class of null model did not explain the fMRI data fully. This extensive testing of reproducibility of similarity statistics also suggests that the estimated FC states are robust against variation in data quality, analysis, grouping, and decomposition methods. We conclude that future investigations probing the functional and neurophysiological relevance of time-varying connectivity assume critical importance.
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Affiliation(s)
- Anees Abrol
- The Mind Research Network, Albuquerque, NM, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA.
| | - Eswar Damaraju
- The Mind Research Network, Albuquerque, NM, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA
| | | | | | | | | | - Vince D Calhoun
- The Mind Research Network, Albuquerque, NM, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA
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18
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Wang N, Chang C, Zeng W, Shi Y, Yan H. A Novel Feature-Map Based ICA Model for Identifying the Individual, Intra/Inter-Group Brain Networks across Multiple fMRI Datasets. Front Neurosci 2017; 11:510. [PMID: 28943838 PMCID: PMC5596109 DOI: 10.3389/fnins.2017.00510] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2017] [Accepted: 08/28/2017] [Indexed: 11/17/2022] Open
Abstract
Independent component analysis (ICA) has been widely used in functional magnetic resonance imaging (fMRI) data analysis to evaluate functional connectivity of the brain; however, there are still some limitations on ICA simultaneously handling neuroimaging datasets with diverse acquisition parameters, e.g., different repetition time, different scanner, etc. Therefore, it is difficult for the traditional ICA framework to effectively handle ever-increasingly big neuroimaging datasets. In this research, a novel feature-map based ICA framework (FMICA) was proposed to address the aforementioned deficiencies, which aimed at exploring brain functional networks (BFNs) at different scales, e.g., the first level (individual subject level), second level (intragroup level of subjects within a certain dataset) and third level (intergroup level of subjects across different datasets), based only on the feature maps extracted from the fMRI datasets. The FMICA was presented as a hierarchical framework, which effectively made ICA and constrained ICA as a whole to identify the BFNs from the feature maps. The simulated and real experimental results demonstrated that FMICA had the excellent ability to identify the intergroup BFNs and to characterize subject-specific and group-specific difference of BFNs from the independent component feature maps, which sharply reduced the size of fMRI datasets. Compared with traditional ICAs, FMICA as a more generalized framework could efficiently and simultaneously identify the variant BFNs at the subject-specific, intragroup, intragroup-specific and intergroup levels, implying that FMICA was able to handle big neuroimaging datasets in neuroscience research.
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Affiliation(s)
- Nizhuan Wang
- Neuroimaging Lab, School of Biomedical Engineering, Health Science Center, Shenzhen UniversityShenzhen, China
- Guangdong Key Laboratory of Biomedical Information Detection and Ultrasound ImagingShenzhen, China
| | - Chunqi Chang
- Neuroimaging Lab, School of Biomedical Engineering, Health Science Center, Shenzhen UniversityShenzhen, China
- Guangdong Key Laboratory of Biomedical Information Detection and Ultrasound ImagingShenzhen, China
- Center for Neuroimaging, Shenzhen Institute of NeuroscienceShenzhen, China
| | - Weiming Zeng
- Lab of Digital Image and Intelligent Computation, Shanghai Maritime UniversityShanghai, China
| | - Yuhu Shi
- Lab of Digital Image and Intelligent Computation, Shanghai Maritime UniversityShanghai, China
| | - Hongjie Yan
- Department of Neurology, Affiliated Lianyungang Hospital of Xuzhou Medical UniversityLianyungang, China
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19
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Calhoun VD, de Lacy N. Ten Key Observations on the Analysis of Resting-state Functional MR Imaging Data Using Independent Component Analysis. Neuroimaging Clin N Am 2017; 27:561-579. [PMID: 28985929 DOI: 10.1016/j.nic.2017.06.012] [Citation(s) in RCA: 67] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
For more than 20 years, the powerful, flexible family of independent component analysis (ICA) techniques has been used to examine spatial, temporal, and subject variation in functional magnetic resonance (fMR) imaging data. This article provides an overview of 10 key principles in the basic and advanced application of ICA to resting-state fMR imaging. ICA's core advantages include robustness to artifact; false-positives and autocorrelation; adaptability to variant study designs; agnosticism to the temporal evolution of fMR imaging signals; and ability to extract, identify, and analyze neural networks. ICA remains in the vanguard of fMRI methods development.
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Affiliation(s)
- Vince D Calhoun
- The Mind Research Network, 1101 Yale Boulevard Northeast, Albuquerque, NM 87106, USA; Department of ECE, University of New Mexico, 1 University of New Mexico, Albuquerque, NM 87131, USA.
| | - Nina de Lacy
- Department of Psychiatry and Behavioral Science, University of Washington, Seattle, WA 98195, USA
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20
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Savio A, Fünger S, Tahmasian M, Rachakonda S, Manoliu A, Sorg C, Grimmer T, Calhoun V, Drzezga A, Riedl V, Yakushev I. Resting-State Networks as Simultaneously Measured with Functional MRI and PET. J Nucl Med 2017; 58:1314-1317. [PMID: 28254868 DOI: 10.2967/jnumed.116.185835] [Citation(s) in RCA: 61] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2016] [Accepted: 01/31/2017] [Indexed: 12/13/2022] Open
Abstract
Functional MRI (fMRI) studies reported disruption of resting-state networks (RSNs) in several neuropsychiatric disorders. PET with 18F-FDG captures neuronal activity that is in steady state at a longer time span and is less dependent on neurovascular coupling. Methods: In the present study, we aimed to identify RSNs in 18F-FDG PET data and compare their spatial pattern with those obtained from simultaneously acquired resting-state fMRI data in 22 middle-aged healthy subjects. Results: Thirteen and 17 meaningful RSNs could be identified in PET and fMRI data, respectively. Spatial overlap was fair to moderate for the default mode, left central executive, primary and secondary visual, sensorimotor, cerebellar, and auditory networks. Despite recording different aspects of neural activity, similar RSNs were detected by both imaging modalities. Conclusion: The results argue for the common neural substrate of RSNs and encourage testing of the clinical utility of resting-state connectivity in PET data.
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Affiliation(s)
- Alexandre Savio
- Department of Nuclear Medicine, Klinikum Rechts der Isar, Technische Universität München, Munich, Germany
| | - Sarah Fünger
- Department of Nuclear Medicine, Klinikum Rechts der Isar, Technische Universität München, Munich, Germany
| | - Masoud Tahmasian
- Department of Neuroradiology, Klinikum Rechts der Isar, Technische Universität München, Munich, Germany.,Department of Nuclear Medicine, Universität zu Köln, Cologne, Germany.,Institute of Medical Science and Technology at Shahid Beheshti University and School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
| | | | - Andrei Manoliu
- Department of Neuroradiology, Klinikum Rechts der Isar, Technische Universität München, Munich, Germany.,Department of Psychiatry, University of Zürich, Zurich, Switzerland
| | - Christian Sorg
- Department of Neuroradiology, Klinikum Rechts der Isar, Technische Universität München, Munich, Germany.,Neuroimaging Center (TUM-NIC), Klinikum Rechts der Isar, Technische Universität München, Munich, Germany.,Department of Psychiatry and Psychotherapy, Klinikum Rechts der Isar, Technische Universität München, Munich, Germany; and
| | - Timo Grimmer
- Department of Psychiatry and Psychotherapy, Klinikum Rechts der Isar, Technische Universität München, Munich, Germany; and
| | - Vince Calhoun
- The Mind Research Network and LBERI, Albuquerque, New Mexico.,Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico
| | - Alexander Drzezga
- Department of Nuclear Medicine, Klinikum Rechts der Isar, Technische Universität München, Munich, Germany.,Department of Nuclear Medicine, Universität zu Köln, Cologne, Germany
| | - Valentin Riedl
- Department of Nuclear Medicine, Klinikum Rechts der Isar, Technische Universität München, Munich, Germany.,Department of Neuroradiology, Klinikum Rechts der Isar, Technische Universität München, Munich, Germany.,Neuroimaging Center (TUM-NIC), Klinikum Rechts der Isar, Technische Universität München, Munich, Germany
| | - Igor Yakushev
- Department of Nuclear Medicine, Klinikum Rechts der Isar, Technische Universität München, Munich, Germany .,Neuroimaging Center (TUM-NIC), Klinikum Rechts der Isar, Technische Universität München, Munich, Germany
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21
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Dalenberg JR, Weitkamp L, Renken RJ, Nanetti L, ter Horst GJ. Flavor pleasantness processing in the ventral emotion network. PLoS One 2017; 12:e0170310. [PMID: 28207751 PMCID: PMC5312947 DOI: 10.1371/journal.pone.0170310] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2016] [Accepted: 01/02/2017] [Indexed: 11/28/2022] Open
Abstract
The ventral emotion network-encompassing the amygdala, insula, ventral striatum, and ventral regions of the prefrontal cortex-has been associated with the identification of emotional significance of perceived external stimuli and the production of affective states. Functional magnetic resonance imaging (fMRI) studies investigating chemosensory stimuli have associated parts of this network with pleasantness coding. In the current study, we independently analyzed two datasets in which we measured brain responses to flavor stimuli in young adult men. In the first dataset, participants evaluated eight regular off the shelf drinking products while participants evaluated six less familiar oral nutritional supplements (ONS) in the second dataset. Participants provided pleasantness ratings 20 seconds after tasting. Using independent component analysis (ICA) and mixed effect models, we identified one brain network in the regular products dataset that was associated with flavor pleasantness. This network was very similar to the ventral emotion network. Although we identified an identical network in the ONS dataset using ICA, we found no linear relation between activation of any network and pleasantness scores within this dataset. Our results indicate that flavor pleasantness is processed in a network encompassing amygdala, ventral prefrontal, insular, striatal and parahippocampal regions for familiar drinking products. For more unfamiliar ONS products the association is not obvious, which could be related to the unfamiliarity of these products.
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Affiliation(s)
- Jelle R. Dalenberg
- Top Institute Food and Nutrition, Wageningen, The Netherlands
- Neuroimaging Center Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Liselore Weitkamp
- Top Institute Food and Nutrition, Wageningen, The Netherlands
- Neuroimaging Center Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Remco J. Renken
- Top Institute Food and Nutrition, Wageningen, The Netherlands
- Neuroimaging Center Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Luca Nanetti
- Neuroimaging Center Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Gert J. ter Horst
- Top Institute Food and Nutrition, Wageningen, The Netherlands
- Neuroimaging Center Groningen, University Medical Center Groningen, Groningen, The Netherlands
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22
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Meng X, Jiang R, Lin D, Bustillo J, Jones T, Chen J, Yu Q, Du Y, Zhang Y, Jiang T, Sui J, Calhoun VD. Predicting individualized clinical measures by a generalized prediction framework and multimodal fusion of MRI data. Neuroimage 2016; 145:218-229. [PMID: 27177764 DOI: 10.1016/j.neuroimage.2016.05.026] [Citation(s) in RCA: 70] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2015] [Revised: 04/13/2016] [Accepted: 05/07/2016] [Indexed: 12/24/2022] Open
Abstract
Neuroimaging techniques have greatly enhanced the understanding of neurodiversity (human brain variation across individuals) in both health and disease. The ultimate goal of using brain imaging biomarkers is to perform individualized predictions. Here we proposed a generalized framework that can predict explicit values of the targeted measures by taking advantage of joint information from multiple modalities. This framework also enables whole brain voxel-wise searching by combining multivariate techniques such as ReliefF, clustering, correlation-based feature selection and multiple regression models, which is more flexible and can achieve better prediction performance than alternative atlas-based methods. For 50 healthy controls and 47 schizophrenia patients, three kinds of features derived from resting-state fMRI (fALFF), sMRI (gray matter) and DTI (fractional anisotropy) were extracted and fed into a regression model, achieving high prediction for both cognitive scores (MCCB composite r=0.7033, MCCB social cognition r=0.7084) and symptomatic scores (positive and negative syndrome scale [PANSS] positive r=0.7785, PANSS negative r=0.7804). Moreover, the brain areas likely responsible for cognitive deficits of schizophrenia, including middle temporal gyrus, dorsolateral prefrontal cortex, striatum, cuneus and cerebellum, were located with different weights, as well as regions predicting PANSS symptoms, including thalamus, striatum and inferior parietal lobule, pinpointing the potential neuromarkers. Finally, compared to a single modality, multimodal combination achieves higher prediction accuracy and enables individualized prediction on multiple clinical measures. There is more work to be done, but the current results highlight the potential utility of multimodal brain imaging biomarkers to eventually inform clinical decision-making.
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Affiliation(s)
- Xing Meng
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Rongtao Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Dongdong Lin
- The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM 87106, USA
| | - Juan Bustillo
- Dept. of Psychiatry and Neuroscience, University of New Mexico, Albuquerque, NM 87131, USA
| | - Thomas Jones
- Dept. of Psychiatry and Neuroscience, University of New Mexico, Albuquerque, NM 87131, USA
| | - Jiayu Chen
- The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM 87106, USA
| | - Qingbao Yu
- The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM 87106, USA
| | - Yuhui Du
- The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM 87106, USA
| | - Yu Zhang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Tianzi Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; CAS Center for Excellence in Brain Science, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Jing Sui
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM 87106, USA; CAS Center for Excellence in Brain Science, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
| | - Vince D Calhoun
- The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM 87106, USA; Dept. of Psychiatry and Neuroscience, University of New Mexico, Albuquerque, NM 87131, USA; Dept. of Electronic and Computer Engineering, University of New Mexico, Albuquerque, NM 87131, USA
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Calhoun VD, Sui J. Multimodal fusion of brain imaging data: A key to finding the missing link(s) in complex mental illness. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2016; 1:230-244. [PMID: 27347565 PMCID: PMC4917230 DOI: 10.1016/j.bpsc.2015.12.005] [Citation(s) in RCA: 165] [Impact Index Per Article: 20.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
It is becoming increasingly clear that combining multi-modal brain imaging data is able to provide more information for individual subjects by exploiting the rich multimodal information that exists. However, the number of studies that do true multimodal fusion (i.e. capitalizing on joint information among modalities) is still remarkably small given the known benefits. In part, this is because multi-modal studies require broader expertise in collecting, analyzing, and interpreting the results than do unimodal studies. In this paper, we start by introducing the basic reasons why multimodal data fusion is important and what it can do, and importantly how it can help us avoid wrong conclusions and help compensate for imperfect brain imaging studies. We also discuss the challenges that need to be confronted for such approaches to be more widely applied by the community. We then provide a review of the diverse studies that have used multimodal data fusion (primarily focused on psychosis) as well as provide an introduction to some of the existing analytic approaches. Finally, we discuss some up-and-coming approaches to multi-modal fusion including deep learning and multimodal classification which show considerable promise. Our conclusion is that multimodal data fusion is rapidly growing, but it is still underutilized. The complexity of the human brain coupled with the incomplete measurement provided by existing imaging technology makes multimodal fusion essential in order to mitigate against misdirection and hopefully provide a key to finding the missing link(s) in complex mental illness.
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Affiliation(s)
- Vince D Calhoun
- The Mind Research Network & LBERI, Albuquerque, New Mexico.; Dept. of ECE, University of New Mexico, Albuquerque, New Mexico
| | - Jing Sui
- The Mind Research Network & LBERI, Albuquerque, New Mexico.; Brainnetome Center and National Laboratory of Pattern Recognition, Beijing, China; CAS Center for Excellence in Brain Science, Institute of Automation, Chinese Academy of Sciences, Beijing, China
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Iraji A, Calhoun VD, Wiseman NM, Davoodi-Bojd E, Avanaki MRN, Haacke EM, Kou Z. The connectivity domain: Analyzing resting state fMRI data using feature-based data-driven and model-based methods. Neuroimage 2016; 134:494-507. [PMID: 27079528 PMCID: PMC4957565 DOI: 10.1016/j.neuroimage.2016.04.006] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2016] [Accepted: 04/04/2016] [Indexed: 12/25/2022] Open
Abstract
Spontaneous fluctuations of resting state functional MRI (rsfMRI) have been widely used to understand the macro-connectome of the human brain. However, these fluctuations are not synchronized among subjects, which leads to limitations and makes utilization of first-level model-based methods challenging. Considering this limitation of rsfMRI data in the time domain, we propose to transfer the spatiotemporal information of the rsfMRI data to another domain, the connectivity domain, in which each value represents the same effect across subjects. Using a set of seed networks and a connectivity index to calculate the functional connectivity for each seed network, we transform data into the connectivity domain by generating connectivity weights for each subject. Comparison of the two domains using a data-driven method suggests several advantages in analyzing data using data-driven methods in the connectivity domain over the time domain. We also demonstrate the feasibility of applying model-based methods in the connectivity domain, which offers a new pathway for the use of first-level model-based methods on rsfMRI data. The connectivity domain, furthermore, demonstrates a unique opportunity to perform first-level feature-based data-driven and model-based analyses. The connectivity domain can be constructed from any technique that identifies sets of features that are similar across subjects and can greatly help researchers in the study of macro-connectome brain function by enabling us to perform a wide range of model-based and data-driven approaches on rsfMRI data, decreasing susceptibility of analysis techniques to parameters that are not related to brain connectivity information, and evaluating both static and dynamic functional connectivity of the brain from a new perspective.
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Affiliation(s)
- Armin Iraji
- Department of Biomedical Engineering, Wayne State University, Detroit, MI, USA.
| | - Vince D Calhoun
- The Mind Research Network & LBERI, Albuquerque, NM, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA
| | - Natalie M Wiseman
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University, Detroit, MI, USA
| | - Esmaeil Davoodi-Bojd
- Radiology and Research Administration Department, Henry Ford Health System, Detroit, MI, USA
| | - Mohammad R N Avanaki
- Department of Biomedical Engineering, Wayne State University, Detroit, MI, USA; Department of Neurology, Wayne State University, Detroit, MI, USA
| | - E Mark Haacke
- Department of Biomedical Engineering, Wayne State University, Detroit, MI, USA; Department of Radiology, Wayne State University, Detroit, MI, USA
| | - Zhifeng Kou
- Department of Biomedical Engineering, Wayne State University, Detroit, MI, USA; Department of Radiology, Wayne State University, Detroit, MI, USA.
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25
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Levin-Schwartz Y, Song Y, Schreier PJ, Calhoun VD, Adalı T. Sample-poor estimation of order and common signal subspace with application to fusion of medical imaging data. Neuroimage 2016; 134:486-493. [PMID: 27039696 DOI: 10.1016/j.neuroimage.2016.03.058] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2015] [Revised: 03/08/2016] [Accepted: 03/23/2016] [Indexed: 11/29/2022] Open
Abstract
Due to their data-driven nature, multivariate methods such as canonical correlation analysis (CCA) have proven very useful for fusion of multimodal neurological data. However, being able to determine the degree of similarity between datasets and appropriate order selection are crucial to the success of such techniques. The standard methods for calculating the order of multimodal data focus only on sources with the greatest individual energy and ignore relations across datasets. Additionally, these techniques as well as the most widely-used methods for determining the degree of similarity between datasets assume sufficient sample support and are not effective in the sample-poor regime. In this paper, we propose to jointly estimate the degree of similarity between datasets and their order when few samples are present using principal component analysis and canonical correlation analysis (PCA-CCA). By considering these two problems simultaneously, we are able to minimize the assumptions placed on the data and achieve superior performance in the sample-poor regime compared to traditional techniques. We apply PCA-CCA to the pairwise combinations of functional magnetic resonance imaging (fMRI), structural magnetic resonance imaging (sMRI), and electroencephalogram (EEG) data drawn from patients with schizophrenia and healthy controls while performing an auditory oddball task. The PCA-CCA results indicate that the fMRI and sMRI datasets are the most similar, whereas the sMRI and EEG datasets share the least similarity. We also demonstrate that the degree of similarity obtained by PCA-CCA is highly predictive of the degree of significance found for components generated using CCA.
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Affiliation(s)
- Yuri Levin-Schwartz
- Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USA.
| | - Yang Song
- Signal and System Theory Group, Universität Paderborn, Germany
| | | | - Vince D Calhoun
- The Mind Research Network, Albuquerque, NM 87106, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131, USA
| | - Tülay Adalı
- Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USA
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Lowe MJ, Sakaie KE, Beall EB, Calhoun VD, Bridwell DA, Rubinov M, Rao SM. Modern Methods for Interrogating the Human Connectome. J Int Neuropsychol Soc 2016; 22:105-19. [PMID: 26888611 PMCID: PMC4827018 DOI: 10.1017/s1355617716000060] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
OBJECTIVES Connectionist theories of brain function took hold with the seminal contributions of Norman Geschwind a half century ago. Modern neuroimaging techniques have expanded the scientific interest in the study of brain connectivity to include the intact as well as disordered brain. METHODS In this review, we describe the most common techniques used to measure functional and structural connectivity, including resting state functional MRI, diffusion MRI, and electroencephalography and magnetoencephalography coherence. We also review the most common analytical approaches used for examining brain interconnectivity associated with these various imaging methods. RESULTS This review presents a critical analysis of the assumptions, as well as methodological limitations, of each imaging and analysis approach. CONCLUSIONS The overall goal of this review is to provide the reader with an introduction to evaluating the scientific methods underlying investigations that probe the human connectome.
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Affiliation(s)
- Mark J. Lowe
- Imaging Institute, Cleveland Clinic, Cleveland, OH, 44195 USA
| | - Ken E. Sakaie
- Imaging Institute, Cleveland Clinic, Cleveland, OH, 44195 USA
| | - Erik B. Beall
- Imaging Institute, Cleveland Clinic, Cleveland, OH, 44195 USA
| | - Vince D. Calhoun
- The Mind Research Network, Albuquerque, NM 87131, USA
- Department of ECE, University of New Mexico, Albuquerque, NM 87131, USA
| | - David A. Bridwell
- The Mind Research Network, Albuquerque, NM 87131, USA
- Department of ECE, University of New Mexico, Albuquerque, NM 87131, USA
| | - Mikail Rubinov
- Department of Psychiatry, University of Cambridge, Cambridge, CB3 2QQ, UK
| | - Stephen M. Rao
- Neurological Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
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Sui J, Pearlson GD, Du Y, Yu Q, Jones TR, Chen J, Jiang T, Bustillo J, Calhoun VD. In search of multimodal neuroimaging biomarkers of cognitive deficits in schizophrenia. Biol Psychiatry 2015; 78:794-804. [PMID: 25847180 PMCID: PMC4547923 DOI: 10.1016/j.biopsych.2015.02.017] [Citation(s) in RCA: 131] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/30/2014] [Revised: 12/12/2014] [Accepted: 02/02/2015] [Indexed: 01/02/2023]
Abstract
BACKGROUND The cognitive deficits of schizophrenia are largely resistant to current treatments and thus are a lifelong illness burden. The Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) Consensus Cognitive Battery (MCCB) provides a reliable and valid assessment of cognition across major cognitive domains; however, the multimodal brain alterations specifically associated with MCCB in schizophrenia have not been examined. METHODS The interrelationships between MCCB and the abnormalities seen in three types of neuroimaging-derived maps-fractional amplitude of low-frequency fluctuations (fALFF) from resting-state functional magnetic resonance imaging (MRI), gray matter (GM) density from structural MRI, and fractional anisotropy from diffusion MRI-were investigated by using multiset canonical correlation analysis in data from 47 schizophrenia patients treated with antipsychotic medications and 50 age-matched healthy control subjects. RESULTS One multimodal component (canonical variant 8) was identified as both group differentiating and significantly correlated with the MCCB composite. It demonstrated 1) increased cognitive performance associated with higher fALFF (intensity of regional spontaneous brain activity) and higher GM volumes in thalamus, striatum, hippocampus, and the mid-occipital region, with co-occurring fractional anisotropy changes in superior longitudinal fascicules, anterior thalamic radiation, and forceps major; 2) higher fALFF but lower GM volume in dorsolateral prefrontal cortex related to worse cognition in schizophrenia; and 3) distinct domains of MCCB might exhibit dissociable multimodal signatures, e.g., increased fALFF in inferior parietal lobule particularly correlated with decreased social cognition. Medication dose did not relate to these findings in schizophrenia. CONCLUSIONS Our results suggest linked functional and structural deficits in distributed cortico-striato-thalamic circuits may be closely related to MCCB-measured cognitive impairments in schizophrenia.
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Affiliation(s)
- Jing Sui
- The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, Albuquerque, New Mexico; Brainnetome Center and National Laboratory of Pattern Recognition (JS, TJ), Institute of Automation, Chinese Academy of Sciences, Beijing, China.
| | - Godfrey D. Pearlson
- Olin Neuropsychiatry Research Center, Hartford, CT, USA, 06106,Dept. of Psychiatry, Yale University, New Haven, CT, USA, 06519,Dept. of Neurobiology, Yale University, New Haven, CT, USA, 06519
| | - Yuhui Du
- The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM, USA, 87106,School of Information and Communication Engineering, North University of China, Taiyuan, China, 030051
| | - Qingbao Yu
- The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM, USA, 87106
| | - Thomas R. Jones
- Dept. of Psychiatry and Neuroscience, University of New Mexico, Albuquerque, NM, USA, 87131
| | - Jiayu Chen
- The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM, USA, 87106
| | - Tianzi Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China, 100190
| | - Juan Bustillo
- Dept. of Psychiatry and Neuroscience, University of New Mexico, Albuquerque, NM, USA, 87131
| | - Vince D. Calhoun
- The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM, USA, 87106,Dept. of Psychiatry, Yale University, New Haven, CT, USA, 06519,Dept. of Psychiatry and Neuroscience, University of New Mexico, Albuquerque, NM, USA, 87131,Dept. of Electronic and Computer Engineering, University of New Mexico, Albuquerque, NM, USA, 87131
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Dalenberg JR, Hoogeveen HR, Renken RJ, Langers DR, ter Horst GJ. Functional specialization of the male insula during taste perception. Neuroimage 2015; 119:210-20. [DOI: 10.1016/j.neuroimage.2015.06.062] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2015] [Revised: 06/22/2015] [Accepted: 06/22/2015] [Indexed: 11/28/2022] Open
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Pearlson GD, Liu J, Calhoun VD. An introductory review of parallel independent component analysis (p-ICA) and a guide to applying p-ICA to genetic data and imaging phenotypes to identify disease-associated biological pathways and systems in common complex disorders. Front Genet 2015; 6:276. [PMID: 26442095 PMCID: PMC4561364 DOI: 10.3389/fgene.2015.00276] [Citation(s) in RCA: 60] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2015] [Accepted: 08/17/2015] [Indexed: 11/26/2022] Open
Abstract
Complex inherited phenotypes, including those for many common medical and psychiatric diseases, are most likely underpinned by multiple genes contributing to interlocking molecular biological processes, along with environmental factors (Owen et al., 2010). Despite this, genotyping strategies for complex, inherited, disease-related phenotypes mostly employ univariate analyses, e.g., genome wide association. Such procedures most often identify isolated risk-related SNPs or loci, not the underlying biological pathways necessary to help guide the development of novel treatment approaches. This article focuses on the multivariate analysis strategy of parallel (i.e., simultaneous combination of SNP and neuroimage information) independent component analysis (p-ICA), which typically yields large clusters of functionally related SNPs statistically correlated with phenotype components, whose overall molecular biologic relevance is inferred subsequently using annotation software suites. Because this is a novel approach, whose details are relatively new to the field we summarize its underlying principles and address conceptual questions regarding interpretation of resulting data and provide practical illustrations of the method.
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Affiliation(s)
- Godfrey D Pearlson
- The Olin Neuropsychiatry Research Center, Institute of Living, Hartford CT, USA ; Department of Neurobiology, Yale School of Medicine, Yale University, New Haven CT, USA ; Department of Psychiatry, Yale School of Medicine, Yale University, New Haven CT, USA
| | - Jingyu Liu
- Department of Electrical and Computer Engineering, and The Mind Research Network, The University of New Mexico, Albuquerque NM, USA
| | - Vince D Calhoun
- Department of Psychiatry, Yale School of Medicine, Yale University, New Haven CT, USA ; Department of Electrical and Computer Engineering, and The Mind Research Network, The University of New Mexico, Albuquerque NM, USA
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30
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Wang N, Zeng W, Shi Y, Ren T, Jing Y, Yin J, Yang J. WASICA: An effective wavelet-shrinkage based ICA model for brain fMRI data analysis. J Neurosci Methods 2015; 246:75-96. [PMID: 25791013 DOI: 10.1016/j.jneumeth.2015.03.011] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2014] [Revised: 03/08/2015] [Accepted: 03/09/2015] [Indexed: 11/16/2022]
Abstract
BACKGROUND Researches declared that the super-Gaussian property contributed to the success of some spatial independent component analysis (ICA) algorithms in brain fMRI source separation (e.g., Infomax and FastICA), which implied that sparse approximation transforming the sources (super-Gaussian or Gaussian-like) with stronger super-Gaussian feature would possibly improve the separation performance of these algorithms. NEW METHOD This paper presented a novel wavelet-shrinkage based ICA (WASICA) model, an extension of our previous SACICA, for single-subject analysis. In contrast, two main aspects had been effectively enhanced: (1) sparse approximation coefficients set formation, made up of two sub-procedures: the wavelet-shrinkage of wavelet packet (WP) tree nodes, and the automatic nodes selection and integration based on the relative WP energy; (2) ICA-based decomposition and reconstruction, composed of temporal dynamics extraction using ICA, WP reconstruction based on the sparse approximation coefficients set and least-square-based functional networks reconstruction. RESULTS The wavelet-shrinkage and the automatic nodes selection and integration simultaneously transformed both the mixtures and underlying sources into effective sparse approximation coefficients sets, exhibiting stronger super-Gaussian distribution; WP projected-back approximation with nuisance-exclusion contributed to networks reconstruction. COMPARISON WITH EXISTING METHODS Simulation 1 revealed WASICA successfully recovered super-Gaussian and some Gaussian-like sources. Simulation 2 and hybrid data experiments showed that WASICA with good temporal performance had stronger source recovery ability and signal detection sensitivity spatially than FastICA, Infomax and SACICA did; the higher intra-consistency in task-related experiments denoted WASICA occupied stronger spatial robustness against smooth kernels. CONCLUSIONS WASICA was a promising brain signal separation model with charming spatial-temporal performance.
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Affiliation(s)
- Nizhuan Wang
- Lab of Digital Image and Intelligent Computation, Shanghai Maritime University, Shanghai 201306, China
| | - Weiming Zeng
- Lab of Digital Image and Intelligent Computation, Shanghai Maritime University, Shanghai 201306, China.
| | - Yingchao Shi
- Lab of Digital Image and Intelligent Computation, Shanghai Maritime University, Shanghai 201306, China
| | - Tianlong Ren
- Lab of Digital Image and Intelligent Computation, Shanghai Maritime University, Shanghai 201306, China
| | - Yanshan Jing
- Lab of Digital Image and Intelligent Computation, Shanghai Maritime University, Shanghai 201306, China
| | - Jun Yin
- Lab of Digital Image and Intelligent Computation, Shanghai Maritime University, Shanghai 201306, China
| | - Jiajun Yang
- Department of Neurology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai 200233, China.
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Calhoun VD. A spectrum of sharing: maximization of information content for brain imaging data. Gigascience 2015; 4:2. [PMID: 25653850 PMCID: PMC4316396 DOI: 10.1186/s13742-014-0042-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2014] [Accepted: 12/17/2014] [Indexed: 12/14/2022] Open
Abstract
Efforts to expand sharing of neuroimaging data have been growing exponentially in recent years. There are several different types of data sharing which can be considered to fall along a spectrum, ranging from simpler and less informative to more complex and more informative. In this paper we consider this spectrum for three domains: data capture, data density, and data analysis. Here the focus is on the right end of the spectrum, that is, how to maximize the information content while addressing the challenges. A summary of associated challenges of and possible solutions is presented in this review and includes: 1) a discussion of tools to monitor quality of data as it is collected and encourage adoption of data mapping standards; 2) sharing of time-series data (not just summary maps or regions); and 3) the use of analytic approaches which maximize sharing potential as much as possible. Examples of existing solutions for each of these points, which we developed in our lab, are also discussed including the use of a comprehensive beginning-to-end neuroinformatics platform and the use of flexible analytic approaches, such as independent component analysis and multivariate classification approaches, such as deep learning.
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Affiliation(s)
- Vince D Calhoun
- />The Mind Research Network & LBERI, 1101 Yale Blvd NE, Albuquerque, New Mexico 87106 USA
- />Department of ECE, University of New Mexico, Albuquerque, New Mexico USA
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Salami A, Rieckmann A, Fischer H, Bäckman L. A multivariate analysis of age-related differences in functional networks supporting conflict resolution. Neuroimage 2014; 86:150-63. [DOI: 10.1016/j.neuroimage.2013.08.002] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2013] [Revised: 07/09/2013] [Accepted: 08/01/2013] [Indexed: 11/16/2022] Open
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Sui J, He H, Yu Q, Chen J, Rogers J, Pearlson GD, Mayer A, Bustillo J, Canive J, Calhoun VD. Combination of Resting State fMRI, DTI, and sMRI Data to Discriminate Schizophrenia by N-way MCCA + jICA. Front Hum Neurosci 2013; 7:235. [PMID: 23755002 PMCID: PMC3666029 DOI: 10.3389/fnhum.2013.00235] [Citation(s) in RCA: 70] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2013] [Accepted: 05/15/2013] [Indexed: 11/16/2022] Open
Abstract
Multimodal brain imaging data have shown increasing utility in answering both scientifically interesting and clinically relevant questions. Each brain imaging technique provides a different view of brain function or structure, while multimodal fusion capitalizes on the strength of each and may uncover hidden relationships that can merge findings from separate neuroimaging studies. However, most current approaches have focused on pair-wise fusion and there is still relatively little work on N-way data fusion and examination of the relationships among multiple data types. We recently developed an approach called “mCCA + jICA” as a novel multi-way fusion method which is able to investigate the disease risk factors that are either shared or distinct across multiple modalities as well as the full correspondence across modalities. In this paper, we applied this model to combine resting state fMRI (amplitude of low-frequency fluctuation, ALFF), gray matter (GM) density, and DTI (fractional anisotropy, FA) data, in order to elucidate the abnormalities underlying schizophrenia patients (SZs, n = 35) relative to healthy controls (HCs, n = 28). Both modality-common and modality-unique abnormal regions were identified in SZs, which were then used for successful classification for seven modality-combinations, showing the potential for a broad applicability of the mCCA + jICA model and its results. In addition, a pair of GM-DTI components showed significant correlation with the positive symptom subscale of Positive and Negative Syndrome Scale (PANSS), suggesting that GM density changes in default model network along with white-matter disruption in anterior thalamic radiation are associated with increased positive PANSS. Findings suggest the DTI anisotropy changes in frontal lobe may relate to the corresponding functional/structural changes in prefrontal cortex and superior temporal gyrus that are thought to play a role in the clinical expression of SZ.
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Affiliation(s)
- Jing Sui
- The Mind Research Network, Lovelace Biomedical and Environmental Research Institute , Albuquerque, NM , USA ; LIAMA Center for Computational Medicine, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences , Beijing , China
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Sui J, He H, Pearlson GD, Adali T, Kiehl KA, Yu Q, Clark VP, Castro E, White T, Mueller BA, Ho BC, Andreasen NC, Calhoun VD. Three-way (N-way) fusion of brain imaging data based on mCCA+jICA and its application to discriminating schizophrenia. Neuroimage 2012; 66:119-32. [PMID: 23108278 DOI: 10.1016/j.neuroimage.2012.10.051] [Citation(s) in RCA: 90] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2012] [Revised: 09/29/2012] [Accepted: 10/13/2012] [Indexed: 10/27/2022] Open
Abstract
Multimodal fusion is an effective approach to better understand brain diseases. However, most such instances have been limited to pair-wise fusion; because there are often more than two imaging modalities available per subject, there is a need for approaches that can combine multiple datasets optimally. In this paper, we extended our previous two-way fusion model called "multimodal CCA+joint ICA", to three or N-way fusion, that enables robust identification of correspondence among N data types and allows one to investigate the important question of whether certain disease risk factors are shared or distinct across multiple modalities. We compared "mCCA+jICA" with its alternatives in a 3-way fusion simulation and verified its advantages in both decomposition accuracy and modal linkage detection. We also applied it to real functional Magnetic Resonance Imaging (fMRI)-Diffusion Tensor Imaging (DTI) and structural MRI fusion to elucidate the abnormal architecture underlying schizophrenia (n=97) relative to healthy controls (n=116). Both modality-common and modality-unique abnormal regions were identified in schizophrenia. Specifically, the visual cortex in fMRI, the anterior thalamic radiation (ATR) and forceps minor in DTI, and the parietal lobule, cuneus and thalamus in sMRI were linked and discriminated between patients and controls. One fMRI component with regions of activity in motor cortex and superior temporal gyrus individually discriminated schizophrenia from controls. Finally, three components showed significant correlation with duration of illness (DOI), suggesting that lower gray matter volumes in parietal, frontal, and temporal lobes and cerebellum are associated with increased DOI, along with white matter disruption in ATR and cortico-spinal tracts. Findings suggest that the identified fractional anisotropy changes may relate to the corresponding functional/structural changes in the brain that are thought to play a role in the clinical expression of schizophrenia. The proposed "mCCA+jICA" method showed promise for elucidating the joint or coupled neuronal abnormalities underlying mental illnesses and improves our understanding of the disease process.
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Affiliation(s)
- Jing Sui
- The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM 87106, USA.
| | - Hao He
- The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM 87106, USA; Dept. of ECE, University of New Mexico, Albuquerque, NM 87131, USA
| | - Godfrey D Pearlson
- Olin Neuropsychiatry Research Center, Hartford, CT 06106, USA; Depts. of Psychiatry and Neurobiology, Yale University, New Haven, CT, 06519 USA
| | - Tülay Adali
- Dept. of CSEE, University of Maryland, Baltimore County, Baltimore, MD, 21250 USA
| | - Kent A Kiehl
- The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM 87106, USA; Dept. of Psychology, University of New Mexico, Albuquerque, NM, 87131 USA
| | - Qingbao Yu
- The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM 87106, USA
| | - Vince P Clark
- The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM 87106, USA; Dept. of Psychology, University of New Mexico, Albuquerque, NM, 87131 USA
| | - Eduardo Castro
- The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM 87106, USA; Dept. of ECE, University of New Mexico, Albuquerque, NM 87131, USA
| | - Tonya White
- Department of Psychiatry, University of Minnesota, Minneapolis, MN, 55454 USA; Department of Child and Adolescent Psychiatry, Erasmus University, 3000 CB Rotterdam, The Netherlands
| | - Bryon A Mueller
- Department of Psychiatry, University of Minnesota, Minneapolis, MN, 55454 USA
| | - Beng C Ho
- Department of Psychiatry, University of Iowa, Iowa City, IA, 52242 USA
| | - Nancy C Andreasen
- Department of Psychiatry, University of Iowa, Iowa City, IA, 52242 USA
| | - Vince D Calhoun
- The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM 87106, USA; Dept. of ECE, University of New Mexico, Albuquerque, NM 87131, USA; Dept. of CSEE, University of Maryland, Baltimore County, Baltimore, MD, 21250 USA
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Segall JM, Allen EA, Jung RE, Erhardt EB, Arja SK, Kiehl K, Calhoun VD. Correspondence between structure and function in the human brain at rest. Front Neuroinform 2012; 6:10. [PMID: 22470337 PMCID: PMC3313067 DOI: 10.3389/fninf.2012.00010] [Citation(s) in RCA: 124] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2011] [Accepted: 03/12/2012] [Indexed: 01/01/2023] Open
Abstract
To further understanding of basic and complex cognitive functions, previous connectome research has identified functional and structural connections of the human brain. Functional connectivity is often measured by using resting-state functional magnetic resonance imaging (rs-fMRI) and is generally interpreted as an indirect measure of neuronal activity. Gray matter (GM) primarily consists of neuronal and glia cell bodies; therefore, it is surprising that the majority of connectome research has excluded GM measures. Therefore, we propose that by exploring where GM corresponds to function would aid in the understanding of both structural and functional connectivity and in turn the human connectome. A cohort of 603 healthy participants underwent structural and functional scanning on the same 3 T scanner at the Mind Research Network. To investigate the spatial correspondence between structure and function, spatial independent component analysis (ICA) was applied separately to both GM density (GMD) maps and to rs-fMRI data. ICA of GM delineates structural components based on the covariation of GMD regions among subjects. For the rs-fMRI data, ICA identified spatial patterns with common temporal features. These decomposed structural and functional components were then compared by spatial correlation. Basal ganglia components exhibited the highest structural to resting-state functional spatial correlation (r = 0.59). Cortical components generally show correspondence between a single structural component and several resting-state functional components. We also studied relationships between the weights of different structural components and identified the precuneus as a hub in GMD structural network correlations. In addition, we analyzed relationships between component weights, age, and gender; concluding that age has a significant effect on structural components.
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