TY - JOUR
T1 - Endless Fluctuations
T2 - Temporal Dynamics of the Amplitude of Low Frequency Fluctuations
AU - Liao, Wei
AU - Chen, Huafu
AU - Li, Jiao
AU - Ji, Gong Jun
AU - Wu, Guo Rong
AU - Long, Zhiliang
AU - Xu, Qiang
AU - Duan, Xujun
AU - Cui, Qian
AU - Biswal, Bharat B.
N1 - Funding Information:
imaging(EEG-fMRI)data,andrepetitivetranscranialelectroencephalogram-functionalmagneticresonance I NTRINSIC (spontaneous) neural activity ubiquitously magnetic stimulation (rTMS) fMRI data using sliding- occurs in the mammalian brains [1], [2]; and this intrinsic window analysis. The temporal variability (dynamics) of brain activity (iBA) has been mapped using resting-state iBA were quantified using the variance of the amplitude functional magnetic resonance imaging (rs-fMRI). Biswal usedsimulatedfMRIdatatoexaminetheeffectsofvariousof low-frequencyfluctuations(ALFF)overtime.Wefirst et al. first revealed that large-amplitude spontaneous low-parameters including window length, and step size on frequency fluctuations (<0.1 Hz) of blood-oxygen-level dynamic ALFF. Second, using EEG-fMRI data, we found that dependent (BOLD) signals were temporally synchronous theheteromodalassociationcortexhadthemostvariable across the somatomotor system [3]. Intrinsic functional dynamicswhilethelimbicregionshadtheleast,consistent connectivity has since been extended to multiple functional ofdynamic ALFFdependedon EEGpower fluctuations.withpreviousfindings.Inaddition,thetemporalvariability systems, such as default mode and frontoparietal networks, Moreover,usingrTMSfMRIdata,wefoundthatthetemporal and also to the entire brain connectomes [4]–[6]. variability of dynamic ALFF could be modulated by rTMS. Intrinsic functional connectivity has been shown to be time Takentogether,theseresultsprovideevidenceaboutthe varying and dynamic, and to be related to ongoing behav-theory,relevance,andadjustabilityofiBAdynamics. ior [7]–[9], rather than remaining stationary over time. The Manuscript received January 21, 2019; revised March 8, 2019; connectivity repertoire is continually revisited and rehearsed accepted March 8, 2019. Date of publication March 12, 2019; date of in endogenous neural activity [10]. Emerging dynamics of con-currentversionOctober25,2019.Thisworkwassupportedinpartbythe nectivity exhibit variability in the strength of spatial organiza-Grant61533006,GrantU1808204,Grant61876156,Grant81771919,NationalNaturalScienceFoundationofChinaunderGrant61871077, tions [11]–[19], and these dynamics can be captured simply by and Grant 61673089, in part by the Sichuan Science and Technology analyzing the timing and location of a few spontaneous points ProgramunderGrant2018TJPT0016,inpartbytheChinaPostdoctoral (at a smaller temporal scale), suggesting a potential functional ProjectunderGrantB12027.(Correspondingauthor:HuafuChen.)ScienceFoundationunderGrant2013M532229,andinpartbythe“111” relevance [20], [21]. Thus, the brain’s dynome elaborates the W. Liao, J. Li, X. Duan, Q. Cui, and H. Chen are with the MOE Key temporal variability across cognitive adaptation [22], [23], Laboratory for Neuroinformation, Clinical Hospital of Chengdu Brain learning [24], as well as the evolution of development [25] and China,Chengdu610054,China,andalsowithCenterforInformationScienceInstitute,UniversityofElectronicScienceandTechnologyof diseased brain [26]–[29]. Such collective studies offer support in BioMedicine, School of Life Science and Technology, University of for the hypothesis that the dynamics of brain networks are ElectronicScienceandTechnologyofChina,Chengdu610054,China critical to gaining a more thorough understanding of the brain’s G.-J.JiiswiththeLaboratoryofCognitiveNeuropsychology,Depart-(e-mail:chenhf@uestc.edu.cn). biological details [30], [31]. ment of Medical Psychology, Anhui Medical University, Hefei 230000, Compared with functional connectivity, which mainly tar-China. gets the spatial dimension, the amplitude of low-frequency andCognition,MinistryofEducation,FacultyofPsychology,SouthwestG.-R.WuandZ.LongarewiththeKeyLaboratoryofPersonality fluctuations (ALFF) [32] characterizes the BOLD signal in University, Chongqing 400715, China. the temporal dimension [33], [34]. The ALFF may represent Q.XuiswiththeDepartmentofMedicalImaging,JinlingHospital, a potentially meaningful relationship to the brain’s energy [35] B.B.BiswaliswiththeMOEKeyLaboratoryforNeuroinformation,NanjingUniversitySchoolofMedicine,Nanjing210002,China. and morphology [36]. In summary, the ALFF summarizes Clinical Hospital of Chengdu Brain Science Institute, University of Elec- the amplitude characteristics and reliable properties of the tronicScienceandTechnologyofChina,Chengdu610054,China,and local brain activity in the time domain [37], [38], and pro-Technology,Newark,NJ07102USA.alsowithDepartmentofBiomedicalEngineering,NewJerseyInstituteof vides a neuromarker that can be used to highlight brain This article has supplementary downloadable material available at regions with altered low frequency amplitudes in pathological http://ieeexplore.ieee.org,providedbytheauthor. states [39], [40].
Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant 61871077, Grant 61533006, Grant U1808204, Grant 61876156, Grant 81771919, and Grant 61673089, in part by the Sichuan Science and Technology Program under Grant 2018TJPT0016, in part by the China Postdoctoral Science Foundation under Grant 2013M532229, and in part by the "111" Project under Grant B12027.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - Intrinsic neural activity ubiquitously persists in all physiological states. However, how intrinsic brain activity (iBA) changes over a short time remains unknown. To uncover the brain dynamics' theoretic underpinning, electrophysiological relevance, and neuromodulation, we identified iBA dynamics on simulated data, electroencephalogram-functional magnetic resonance imaging (EEG-fMRI) data, and repetitive transcranial magnetic stimulation (rTMS) fMRI data using sliding-window analysis. The temporal variability (dynamics) of iBA were quantified using the variance of the amplitude of low-frequency fluctuations (ALFF) over time. We first used simulated fMRI data to examine the effects of various parameters including window length, and step size on dynamic ALFF. Second, using EEG-fMRI data, we found that the heteromodal association cortex had the most variable dynamics while the limbic regions had the least, consistent with previous findings. In addition, the temporal variability of dynamic ALFF depended on EEG power fluctuations. Moreover, using rTMS fMRI data, we found that the temporal variability of dynamic ALFF could be modulated by rTMS. Taken together, these results provide evidence about the theory, relevance, and adjustability of iBA dynamics.
AB - Intrinsic neural activity ubiquitously persists in all physiological states. However, how intrinsic brain activity (iBA) changes over a short time remains unknown. To uncover the brain dynamics' theoretic underpinning, electrophysiological relevance, and neuromodulation, we identified iBA dynamics on simulated data, electroencephalogram-functional magnetic resonance imaging (EEG-fMRI) data, and repetitive transcranial magnetic stimulation (rTMS) fMRI data using sliding-window analysis. The temporal variability (dynamics) of iBA were quantified using the variance of the amplitude of low-frequency fluctuations (ALFF) over time. We first used simulated fMRI data to examine the effects of various parameters including window length, and step size on dynamic ALFF. Second, using EEG-fMRI data, we found that the heteromodal association cortex had the most variable dynamics while the limbic regions had the least, consistent with previous findings. In addition, the temporal variability of dynamic ALFF depended on EEG power fluctuations. Moreover, using rTMS fMRI data, we found that the temporal variability of dynamic ALFF could be modulated by rTMS. Taken together, these results provide evidence about the theory, relevance, and adjustability of iBA dynamics.
KW - Amplitude of low-frequency fluctuations
KW - electrophysiological relevance
KW - intrinsic brain activity
KW - neuromodulation
KW - temporal dynamics
UR - http://www.scopus.com/inward/record.url?scp=85065026208&partnerID=8YFLogxK
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U2 - 10.1109/TMI.2019.2904555
DO - 10.1109/TMI.2019.2904555
M3 - Article
C2 - 30872224
AN - SCOPUS:85065026208
SN - 0278-0062
VL - 38
SP - 2523
EP - 2532
JO - IEEE Transactions on Medical Imaging
JF - IEEE Transactions on Medical Imaging
IS - 11
M1 - 8666168
ER -