1
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Ertürk ŞM, Toprak T, Cömert RG, Candemir C, Cingöz E, Akyol Sari ZN, Ercan CC, Düvek E, Ersoy B, Karapinar E, Tunaci A, Selver MA. Thorax computed tomography (CTX) guided ground truth annotation of CHEST radiographs (CXR) for improved classification and detection of COVID-19. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2024:e3823. [PMID: 38587026 DOI: 10.1002/cnm.3823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 09/20/2023] [Accepted: 03/27/2024] [Indexed: 04/09/2024]
Abstract
Several data sets have been collected and various artificial intelligence models have been developed for COVID-19 classification and detection from both chest radiography (CXR) and thorax computed tomography (CTX) images. However, the pitfalls and shortcomings of these systems significantly limit their clinical use. In this respect, improving the weaknesses of advanced models can be very effective besides developing new ones. The inability to diagnose ground-glass opacities by conventional CXR has limited the use of this modality in the diagnostic work-up of COVID-19. In our study, we investigated whether we could increase the diagnostic efficiency by collecting a novel CXR data set, which contains pneumonic regions that are not visible to the experts and can only be annotated under CTX guidance. We develop an ensemble methodology of well-established deep CXR models for this new data set and develop a machine learning-based non-maximum suppression strategy to boost the performance for challenging CXR images. CTX and CXR images of 379 patients who applied to our hospital with suspected COVID-19 were evaluated with consensus by seven radiologists. Among these, CXR images of 161 patients who also have had a CTX examination on the same day or until the day before or after and whose CTX findings are compatible with COVID-19 pneumonia, are selected for annotating. CTX images are arranged in the main section passing through the anterior, middle, and posterior according to the sagittal plane with the reformed maximum intensity projection (MIP) method in the coronal plane. Based on the analysis of coronal MIP reconstructed CTX images, the regions corresponding to the pneumonia foci are annotated manually in CXR images. Radiologically classified posterior to anterior (PA) CXR of 218 patients with negative thorax CTX imaging were classified as COVID-19 pneumonia negative group. Accordingly, we have collected a new data set using anonymized CXR (JPEG) and CT (DICOM) images, where the PA CXRs contain pneumonic regions that are hidden or not easily recognized and annotated under CTX guidance. The reference finding was the presence of pneumonic infiltration consistent with COVID-19 on chest CTX examination. COVID-Net, a specially designed convolutional neural network, was used to detect cases of COVID-19 among CXRs. Diagnostic performances were evaluated by ROC analysis by applying six COVID-Net variants (COVIDNet-CXR3-A, -B, -C/COVIDNet-CXR4-A, -B, -C) to the defined data set and combining these models in various ways via ensemble strategies. Finally, a convex optimization strategy is carried out to find the outperforming weighted ensemble of individual models. The mean age of 161 patients with pneumonia was 49.31 ± 15.12, and the median age was 48 years. The mean age of 218 patients without signs of pneumonia in thorax CTX examination was 40.04 ± 14.46, and the median was 38. When working with different combinations of COVID-Net's six variants, the area under the curve (AUC) using the ensemble COVID-Net CXR 4A-4B-3C was .78, sensitivity 67%, specificity 95%; COVID-Net CXR 4a-3b-3c was .79, sensitivity 69% and specificity 94%. When diverse and complementary COVID-Net models are used together through an ensemble, it has been determined that the AUC values are close to other studies, and the specificity is significantly higher than other studies in the literature.
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Affiliation(s)
- Şükrü Mehmet Ertürk
- Radiodiagnostics Department, Istanbul University, Istanbul Faculty of Medicine, Istanbul, Turkey
| | - Tuğçe Toprak
- Institute of Natural and Applied Sciences, Dokuz Eylul University, İzmir, Turkey
| | - Rana Günöz Cömert
- Radiodiagnostics Department, Istanbul University, Istanbul Faculty of Medicine, Istanbul, Turkey
| | - Cemre Candemir
- International Computer Institute, Ege University, Bornova, Turkey
| | - Eda Cingöz
- Radiodiagnostics Department, Istanbul University, Istanbul Faculty of Medicine, Istanbul, Turkey
| | - Zeynep Nur Akyol Sari
- Radiodiagnostics Department, Istanbul University, Istanbul Faculty of Medicine, Istanbul, Turkey
| | - Celal Caner Ercan
- Radiodiagnostics Department, Istanbul University, Istanbul Faculty of Medicine, Istanbul, Turkey
| | - Esin Düvek
- Radiodiagnostics Department, Istanbul University, Istanbul Faculty of Medicine, Istanbul, Turkey
| | - Berke Ersoy
- Radiodiagnostics Department, Istanbul University, Istanbul Faculty of Medicine, Istanbul, Turkey
| | - Edanur Karapinar
- Radiodiagnostics Department, Istanbul University, Istanbul Faculty of Medicine, Istanbul, Turkey
| | - Atadan Tunaci
- Radiodiagnostics Department, Istanbul University, Istanbul Faculty of Medicine, Istanbul, Turkey
| | - M Alper Selver
- Electrical and Electronics Engineering Department, Dokuz Eylul University, Faculty of Engineering, İzmir, Turkey
- Izmir Health Technologies Development and Accelerator (BioIzmir), Dokuz Eylul University, İzmir, Turkey
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2
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Foster M, Scheinost D. Brain states as wave-like motifs. Trends Cogn Sci 2024:S1364-6613(24)00057-3. [PMID: 38582654 DOI: 10.1016/j.tics.2024.03.004] [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: 05/27/2023] [Revised: 02/29/2024] [Accepted: 03/11/2024] [Indexed: 04/08/2024]
Abstract
There is ample evidence of wave-like activity in the brain at multiple scales and levels. This emerging literature supports the broader adoption of a wave perspective of brain activity. Specifically, a brain state can be described as a set of recurring, sequential patterns of propagating brain activity, namely a wave. We examine a collective body of experimental work investigating wave-like properties. Based on these works, we consider brain states as waves using a scale-agnostic framework across time and space. Emphasis is placed on the sequentiality and periodicity associated with brain activity. We conclude by discussing the implications, prospects, and experimental opportunities of this framework.
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Affiliation(s)
- Maya Foster
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
| | - Dustin Scheinost
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA; Department of Radiology and Biomedical Engineering, Yale School of Medicine, New Haven, CT, USA
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3
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von Wegner F, Wiemers M, Hermann G, Tödt I, Tagliazucchi E, Laufs H. Complexity Measures for EEG Microstate Sequences: Concepts and Algorithms. Brain Topogr 2024; 37:296-311. [PMID: 37751054 PMCID: PMC10884068 DOI: 10.1007/s10548-023-01006-2] [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: 04/30/2023] [Accepted: 08/31/2023] [Indexed: 09/27/2023]
Abstract
EEG microstate sequence analysis quantifies properties of ongoing brain electrical activity which is known to exhibit complex dynamics across many time scales. In this report we review recent developments in quantifying microstate sequence complexity, we classify these approaches with regard to different complexity concepts, and we evaluate excess entropy as a yet unexplored quantity in microstate research. We determined the quantities entropy rate, excess entropy, Lempel-Ziv complexity (LZC), and Hurst exponents on Potts model data, a discrete statistical mechanics model with a temperature-controlled phase transition. We then applied the same techniques to EEG microstate sequences from wakefulness and non-REM sleep stages and used first-order Markov surrogate data to determine which time scales contributed to the different complexity measures. We demonstrate that entropy rate and LZC measure the Kolmogorov complexity (randomness) of microstate sequences, whereas excess entropy and Hurst exponents describe statistical complexity which attains its maximum at intermediate levels of randomness. We confirmed the equivalence of entropy rate and LZC when the LZ-76 algorithm is used, a result previously reported for neural spike train analysis (Amigó et al., Neural Comput 16:717-736, https://doi.org/10.1162/089976604322860677 , 2004). Surrogate data analyses prove that entropy-based quantities and LZC focus on short-range temporal correlations, whereas Hurst exponents include short and long time scales. Sleep data analysis reveals that deeper sleep stages are accompanied by a decrease in Kolmogorov complexity and an increase in statistical complexity. Microstate jump sequences, where duplicate states have been removed, show higher randomness, lower statistical complexity, and no long-range correlations. Regarding the practical use of these methods, we suggest that LZC can be used as an efficient entropy rate estimator that avoids the estimation of joint entropies, whereas entropy rate estimation via joint entropies has the advantage of providing excess entropy as the second parameter of the same linear fit. We conclude that metrics of statistical complexity are a useful addition to microstate analysis and address a complexity concept that is not yet covered by existing microstate algorithms while being actively explored in other areas of brain research.
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Affiliation(s)
- Frederic von Wegner
- School of Biomedical Sciences, University of New South Wales (UNSW), Wallace Wurth, Kensington, NSW, 2052, Australia.
| | - Milena Wiemers
- Department of Neurology and Clinical Neurophysiology, Lüneburg Hospital, Bögelstrasse 1, 21339, Lüneburg, Germany
| | - Gesine Hermann
- Department of Neurology, Christian-Albrechts University, Arnold-Heller-Strasse 3, 24105, Kiel, Germany
| | - Inken Tödt
- Institute of Sexual Medicine & Forensic Psychiatry and Psychotherapy, Christian-Albrechts University, Schwanenweg 24, 24105, Kiel, Germany
| | - Enzo Tagliazucchi
- Department of Physics, University of Buenos Aires, 1428, Buenos Aires, Argentina
| | - Helmut Laufs
- Department of Neurology, Christian-Albrechts University, Arnold-Heller-Strasse 3, 24105, Kiel, Germany
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4
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Jäger AP, Bailey A, Huntenburg JM, Tardif CL, Villringer A, Gauthier CJ, Nikulin V, Bazin P, Steele CJ. Decreased long-range temporal correlations in the resting-state functional magnetic resonance imaging blood-oxygen-level-dependent signal reflect motor sequence learning up to 2 weeks following training. Hum Brain Mapp 2024; 45:e26539. [PMID: 38124341 PMCID: PMC10915743 DOI: 10.1002/hbm.26539] [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: 05/10/2023] [Revised: 11/03/2023] [Accepted: 11/07/2023] [Indexed: 12/23/2023] Open
Abstract
Decreased long-range temporal correlations (LRTC) in brain signals can be used to measure cognitive effort during task execution. Here, we examined how learning a motor sequence affects long-range temporal memory within resting-state functional magnetic resonance imaging signal. Using the Hurst exponent (HE), we estimated voxel-wise LRTC and assessed changes over 5 consecutive days of training, followed by a retention scan 12 days later. The experimental group learned a complex visuomotor sequence while a complementary control group performed tightly matched movements. An interaction analysis revealed that HE decreases were specific to the complex sequence and occurred in well-known motor sequence learning associated regions including left supplementary motor area, left premotor cortex, left M1, left pars opercularis, bilateral thalamus, and right striatum. Five regions exhibited moderate to strong negative correlations with overall behavioral performance improvements. Following learning, HE values returned to pretraining levels in some regions, whereas in others, they remained decreased even 2 weeks after training. Our study presents new evidence of HE's possible relevance for functional plasticity during the resting-state and suggests that a cortical subset of sequence-specific regions may continue to represent a functional signature of learning reflected in decreased long-range temporal dependence after a period of inactivity.
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Affiliation(s)
- Anna‐Thekla P. Jäger
- Department of NeurologyMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- Center for Stroke Research Berlin (CSB)Charité—Universitätsmedizin BerlinBerlinGermany
- Brain Language LabFreie Universität BerlinBerlinGermany
| | - Alexander Bailey
- Temerty Faculty of MedicineUniversity of TorontoTorontoOntarioCanada
| | - Julia M. Huntenburg
- Department of NeurologyMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- Max Planck Institute for Biological CyberneticsTuebingenGermany
| | - Christine L. Tardif
- Department of Biomedical EngineeringMcGill UniversityMontrealQuébecCanada
- Montreal Neurological InstituteMontrealQuébecCanada
| | - Arno Villringer
- Department of NeurologyMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- Center for Stroke Research Berlin (CSB)Charité—Universitätsmedizin BerlinBerlinGermany
- Clinic for Cognitive NeurologyLeipzigGermany
- Leipzig University Medical Centre, IFB Adiposity DiseasesLeipzigGermany
- Collaborative Research Centre 1052‐A5University of LeipzigLeipzigGermany
| | - Claudine J. Gauthier
- Department of Physics/School of HealthConcordia UniversityMontrealQuébecCanada
- Montreal Heart InstituteMontrealQuébecCanada
| | - Vadim Nikulin
- Department of NeurologyMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Pierre‐Louis Bazin
- Department of NeurologyMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- Faculty of Social and Behavioral SciencesUniversity of AmsterdamAmsterdamNetherlands
| | - Christopher J. Steele
- Department of NeurologyMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- Department of Psychology/School of HealthConcordia UniversityMontrealQuébecCanada
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5
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Guo YB, Jiao Q, Zhang XT, Xiao Q, Wu Z, Cao WF, Cui D, Yu GH, Dou RH, Su LY, Lu GM. Increased regional Hurst exponent reflects response inhibition related neural complexity alterations in pediatric bipolar disorder patients during an emotional Go-Nogo task. Cereb Cortex 2024; 34:bhad442. [PMID: 38031362 PMCID: PMC10793568 DOI: 10.1093/cercor/bhad442] [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: 10/05/2023] [Revised: 10/28/2023] [Accepted: 10/30/2023] [Indexed: 12/01/2023] Open
Abstract
Fractal patterns have been shown to change in resting- and task-state blood oxygen level-dependent signals in bipolar disorder patients. However, fractal characteristics of brain blood oxygen level-dependent signals when responding to external emotional stimuli in pediatric bipolar disorder remain unclear. Blood oxygen level-dependent signals of 20 PBD-I patients and 17 age- and sex-matched healthy controls were extracted while performing an emotional Go-Nogo task. Neural responses relevant to the task and Hurst exponent of the blood oxygen level-dependent signals were assessed. Correlations between clinical indices and Hurst exponent were estimated. Significantly increased activations were found in regions covering the frontal lobe, parietal lobe, temporal lobe, insula, and subcortical nuclei in PBD-I patients compared to healthy controls in contrast of emotional versus neutral distractors. PBD-I patients exhibited higher Hurst exponent in regions that involved in action control, such as superior frontal gyrus, inferior frontal gyrus, inferior temporal gyrus, and insula, with Hurst exponent of frontal orbital gyrus correlated with onset age. The present study exhibited overactivation, increased self-similarity and decreased complexity in cortical regions during emotional Go-Nogo task in patients relative to healthy controls, which provides evidence of an altered emotional modulation of cognitive control in pediatric bipolar disorder patients. Hurst exponent may be a fractal biomarker of neural activity in pediatric bipolar disorder.
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Affiliation(s)
- Yi-Bing Guo
- School of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai’an 271000, China
- Brain and Mind Center, The University of Sydney, Sydney, NSW 2008, Australia
| | - Qing Jiao
- School of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai’an 271000, China
| | - Xiao-Tong Zhang
- School of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai’an 271000, China
| | - Qian Xiao
- Mental Health Centre of Xiangya Hospital, Central South University, Changsha 410083, China
| | - Zhou Wu
- School of Psychology, Nanjing Normal University, Nanjing 210097, China
| | - Wei-Fang Cao
- School of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai’an 271000, China
| | - Dong Cui
- School of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai’an 271000, China
| | - Guang-Hui Yu
- School of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai’an 271000, China
| | - Ru-Hai Dou
- School of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai’an 271000, China
| | - Lin-Yan Su
- Mental Health Institute of the Second Xiangya Hospital, Central South University, Changsha 410083, China
| | - Guang-Ming Lu
- Department of Medical Imaging, Jinling Hospital, Clinical School of Medical College, Nanjing University, Nanjing 210023, China
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6
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Porcaro C, Moaveninejad S, D'Onofrio V, DiIeva A. Fractal Time Series: Background, Estimation Methods, and Performances. ADVANCES IN NEUROBIOLOGY 2024; 36:95-137. [PMID: 38468029 DOI: 10.1007/978-3-031-47606-8_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
Over the past 40 years, from its classical application in the characterization of geometrical objects, fractal analysis has been progressively applied to study time series in several different disciplines. In neuroscience, starting from identifying the fractal properties of neuronal and brain architecture, attention has shifted to evaluating brain signals in the time domain. Classical linear methods applied to analyzing neurophysiological signals can lead to classifying irregular components as noise, with a potential loss of information. Thus, characterizing fractal properties, namely, self-similarity, scale invariance, and fractal dimension (FD), can provide relevant information on these signals in physiological and pathological conditions. Several methods have been proposed to estimate the fractal properties of these neurophysiological signals. However, the effects of signal characteristics (e.g., its stationarity) and other signal parameters, such as sampling frequency, amplitude, and noise level, have partially been tested. In this chapter, we first outline the main properties of fractals in the domain of space (fractal geometry) and time (fractal time series). Then, after providing an overview of the available methods to estimate the FD, we test them on synthetic time series (STS) with different sampling frequencies, signal amplitudes, and noise levels. Finally, we describe and discuss the performances of each method and the effect of signal parameters on the accuracy of FD estimation.
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Affiliation(s)
- Camillo Porcaro
- Department of Neuroscience (DNS) and Padova Neuroscience Center (PNC), University of Padova, Padua, Italy.
- Institute of Cognitive Sciences and Technologies (ISTC) National Research Council (CNR), Rome, Italy.
- Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, UK.
| | | | | | - Antonio DiIeva
- Computational NeuroSurgery (CNS) Lab & Macquarie Neurosurgery, Macquarie Medical School, Faculty of Medicine, Human and Health Sciences, Macquarie University, Sydney, NSW, Australia
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7
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Guan S, Jiang R, Chen DY, Michael A, Meng C, Biswal B. Multifractal long-range dependence pattern of functional magnetic resonance imaging in the human brain at rest. Cereb Cortex 2023; 33:11594-11608. [PMID: 37851793 DOI: 10.1093/cercor/bhad393] [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: 09/12/2023] [Revised: 10/01/2023] [Accepted: 10/02/2023] [Indexed: 10/20/2023] Open
Abstract
Long-range dependence is a prevalent phenomenon in various biological systems that characterizes the long-memory effect of temporal fluctuations. While recent research suggests that functional magnetic resonance imaging signal has fractal property, it remains unknown about the multifractal long-range dependence pattern of resting-state functional magnetic resonance imaging signals. The current study adopted the multifractal detrended fluctuation analysis on highly sampled resting-state functional magnetic resonance imaging scans to investigate long-range dependence profile associated with the whole-brain voxels as specific functional networks. Our findings revealed the long-range dependence's multifractal properties. Moreover, long-term persistent fluctuations are found for all stations with stronger persistency in whole-brain regions. Subsets with large fluctuations contribute more to the multifractal spectrum in the whole brain. Additionally, we found that the preprocessing with band-pass filtering provided significantly higher reliability for estimating long-range dependence. Our validation analysis confirmed that the optimal pipeline of long-range dependence analysis should include band-pass filtering and removal of daily temporal dependence. Furthermore, multifractal long-range dependence characteristics in healthy control and schizophrenia are different significantly. This work has provided an analytical pipeline for the multifractal long-range dependence in the resting-state functional magnetic resonance imaging signal. The findings suggest differential long-memory effects in the intrinsic functional networks, which may offer a neural marker finding for understanding brain function and pathology.
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Affiliation(s)
- Sihai Guan
- College of Electronic and Information, Southwest Minzu University, Chengdu 610041, China
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission, Chengdu 610041, China
| | - Runzhou Jiang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
- Medical Equipment Department, Xiangyang No.1 People's Hospital, Xiangyang 441000, China
| | - Donna Y Chen
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07102, United States
| | - Andrew Michael
- Duke Institute for Brain Sciences, Duke University, Durham, NC 27708, United States
| | - Chun Meng
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Bharat Biswal
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07102, United States
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8
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Gao W, Biswal B, Yang J, Li S, Wang Y, Chen S, Yuan J. Temporal dynamic patterns of the ventromedial prefrontal cortex underlie the association between rumination and depression. Cereb Cortex 2023; 33:969-982. [PMID: 35462398 DOI: 10.1093/cercor/bhac115] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 02/21/2022] [Accepted: 02/22/2022] [Indexed: 11/14/2022] Open
Abstract
As a major contributor to the development of depression, rumination has proven linked with aberrant default-mode network (DMN) activity. However, it remains unclear how the spontaneous spatial and temporal activity of DMN underlie the association between rumination and depression. To illustrate this issue, behavioral measures and resting-state functional magnetic resonance images were connected in 2 independent samples (NSample1 = 100, NSample2 = 95). Fractional amplitude of low-frequency fluctuations (fALFF) and regional homogeneity (ReHo) were used to assess spatial characteristic patterns, while voxel-wise functional concordance (across time windows) (VC) and Hurst exponent (HE) were used to assess temporal dynamic patterns of brain activity. Results from both samples consistently show that temporal dynamics but not spatial patterns of DMN are associated with rumination. Specifically, rumination is positively correlated with HE and VC (but not fALFF and ReHo) values, reflecting more consistent and regular temporal dynamic patterns in DMN. Moreover, subregion analyses indicate that temporal dynamics of the ventromedial prefrontal cortex (VMPFC) reliably predict rumination scores. Furthermore, mediation analyses show that HE and VC of VMPFC mediate the association between rumination and depression. These findings shed light on neural mechanisms of individual differences in rumination and corresponding risk for depression.
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Affiliation(s)
- Wei Gao
- Institute of Brain and Psychological Science, Sichuan Normal University, Chengdu, Sichuan, China.,Faculty of Psychology, Southwest University, Chongqing, China
| | - Bharat Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, United States
| | - Jiemin Yang
- Institute of Brain and Psychological Science, Sichuan Normal University, Chengdu, Sichuan, China
| | - Songlin Li
- School of Educational Science, Sichuan Normal University, Chengdu, Sichuan, China
| | - YanQing Wang
- Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Shengdong Chen
- School of Psychology, Qufu Normal University, Qufu, Shandong, China
| | - JiaJin Yuan
- Institute of Brain and Psychological Science, Sichuan Normal University, Chengdu, Sichuan, China
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9
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Klar P, Çatal Y, Langner R, Huang Z, Northoff G. Scale-free dynamics of core-periphery topography. Hum Brain Mapp 2022; 44:1997-2017. [PMID: 36579661 PMCID: PMC9980897 DOI: 10.1002/hbm.26187] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Revised: 11/15/2022] [Accepted: 12/11/2022] [Indexed: 12/30/2022] Open
Abstract
The human brain's cerebral cortex exhibits a topographic division into higher-order transmodal core and lower-order unimodal periphery regions. While timescales between the core and periphery region diverge, features of their power spectra, especially scale-free dynamics during resting-state and their mdulation in task states, remain unclear. To answer this question, we investigated the ~1/f-like pink noise manifestation of scale-free dynamics in the core-periphery topography during rest and task states applying infra-slow inter-trial intervals up to 1 min falling inside the BOLD's infra-slow frequency band. The results demonstrate (1) higher resting-state power-law exponent (PLE) in the core compared to the periphery region; (2) significant PLE increases in task across the core and periphery regions; and (3) task-related PLE increases likely followed the task's atypically low event rates, namely the task's periodicity (inter-trial interval = 52-60 s; 0.016-0.019 Hz). A computational model and a replication dataset that used similar infra-slow inter-trial intervals provide further support for our main findings. Altogether, the results show that scale-free dynamics differentiate core and periphery regions in the resting-state and mediate task-related effects.
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Affiliation(s)
- Philipp Klar
- Medical Faculty, C. & O. Vogt‐Institute for Brain ResearchHeinrich Heine University of DüsseldorfDüsseldorfGermany
| | - Yasir Çatal
- The Royal's Institute of Mental Health Research & University of Ottawa. Brain and Mind Research Institute, Centre for Neural Dynamics, Faculty of MedicineUniversity of OttawaOttawaOntarioCanada
| | - Robert Langner
- Institute of Systems NeuroscienceHeinrich Heine University DusseldorfDusseldorfGermany,Institute of Neuroscience and MedicineBrain & Behaviour (INM‐7), Research Centre JülichJülichGermany
| | - Zirui Huang
- Department of AnesthesiologyUniversity of Michigan Medical SchoolAnn ArborMichiganUSA,Center for Consciousness ScienceUniversity of Michigan Medical SchoolAnn ArborMichiganUSA
| | - Georg Northoff
- The Royal's Institute of Mental Health Research & University of Ottawa. Brain and Mind Research Institute, Centre for Neural Dynamics, Faculty of MedicineUniversity of OttawaOttawaOntarioCanada,Centre for Cognition and Brain DisordersHangzhou Normal UniversityHangzhouZhejiangChina
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10
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Lau ZJ, Pham T, Chen SHA, Makowski D. Brain entropy, fractal dimensions and predictability: A review of complexity measures for EEG in healthy and neuropsychiatric populations. Eur J Neurosci 2022; 56:5047-5069. [PMID: 35985344 PMCID: PMC9826422 DOI: 10.1111/ejn.15800] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 07/20/2022] [Accepted: 08/10/2022] [Indexed: 01/11/2023]
Abstract
There has been an increasing trend towards the use of complexity analysis in quantifying neural activity measured by electroencephalography (EEG) signals. On top of revealing complex neuronal processes of the brain that may not be possible with linear approaches, EEG complexity measures have also demonstrated their potential as biomarkers of psychopathology such as depression and schizophrenia. Unfortunately, the opacity of algorithms and descriptions originating from mathematical concepts have made it difficult to understand what complexity is and how to draw consistent conclusions when applied within psychology and neuropsychiatry research. In this review, we provide an overview and entry-level explanation of existing EEG complexity measures, which can be broadly categorized as measures of predictability and regularity. We then synthesize complexity findings across different areas of psychological science, namely, in consciousness research, mood and anxiety disorders, schizophrenia, neurodevelopmental and neurodegenerative disorders, as well as changes across the lifespan, while addressing some theoretical and methodological issues underlying the discrepancies in the data. Finally, we present important considerations when choosing and interpreting these metrics.
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Affiliation(s)
- Zen J. Lau
- School of Social SciencesNanyang Technological UniversitySingapore
| | - Tam Pham
- School of Social SciencesNanyang Technological UniversitySingapore
| | - S. H. Annabel Chen
- School of Social SciencesNanyang Technological UniversitySingapore,Centre for Research and Development in LearningNanyang Technological UniversitySingapore,Lee Kong Chian School of MedicineNanyang Technological UniversitySingapore,National Institute of EducationNanyang Technological UniversitySingapore
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11
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Omidvarnia A, Liégeois R, Amico E, Preti MG, Zalesky A, Van De Ville D. On the Spatial Distribution of Temporal Complexity in Resting State and Task Functional MRI. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1148. [PMID: 36010812 PMCID: PMC9407401 DOI: 10.3390/e24081148] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 08/08/2022] [Accepted: 08/10/2022] [Indexed: 06/15/2023]
Abstract
Measuring the temporal complexity of functional MRI (fMRI) time series is one approach to assess how brain activity changes over time. In fact, hemodynamic response of the brain is known to exhibit critical behaviour at the edge between order and disorder. In this study, we aimed to revisit the spatial distribution of temporal complexity in resting state and task fMRI of 100 unrelated subjects from the Human Connectome Project (HCP). First, we compared two common choices of complexity measures, i.e., Hurst exponent and multiscale entropy, and observed a high spatial similarity between them. Second, we considered four tasks in the HCP dataset (Language, Motor, Social, and Working Memory) and found high task-specific complexity, even when the task design was regressed out. For the significance thresholding of brain complexity maps, we used a statistical framework based on graph signal processing that incorporates the structural connectome to develop the null distributions of fMRI complexity. The results suggest that the frontoparietal, dorsal attention, visual, and default mode networks represent stronger complex behaviour than the rest of the brain, irrespective of the task engagement. In sum, the findings support the hypothesis of fMRI temporal complexity as a marker of cognition.
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Affiliation(s)
- Amir Omidvarnia
- Applied Machine Learning Group, Institute of Neuroscience and Medicine, Forschungszentrum Juelich, 52428 Juelich, Germany
- Institute of Systems Neuroscience, Heinrich Heine University Duesseldorf, 40225 Duesseldorf, Germany
- Neuro-X Institute, École Polytechnique Fédérale de Lausanne, 1202 Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, 1211 Geneva, Switzerland
| | - Raphaël Liégeois
- Neuro-X Institute, École Polytechnique Fédérale de Lausanne, 1202 Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, 1211 Geneva, Switzerland
| | - Enrico Amico
- Neuro-X Institute, École Polytechnique Fédérale de Lausanne, 1202 Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, 1211 Geneva, Switzerland
| | - Maria Giulia Preti
- Neuro-X Institute, École Polytechnique Fédérale de Lausanne, 1202 Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, 1211 Geneva, Switzerland
- CIBM Center for Biomedical Imaging, 1015 Lausanne, Switzerland
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Melbourne, VIC 3010, Australia
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC 3010, Australia
| | - Dimitri Van De Ville
- Neuro-X Institute, École Polytechnique Fédérale de Lausanne, 1202 Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, 1211 Geneva, Switzerland
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12
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Ferraz MSA, Kihara AH. Beyond randomness: Evaluating measures of information entropy in binary series. Phys Rev E 2022; 105:044101. [PMID: 35590660 DOI: 10.1103/physreve.105.044101] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 03/09/2022] [Indexed: 06/15/2023]
Abstract
The enormous amount of currently available data demands efforts to extract meaningful information. For this purpose, different measurements are applied, including Shannon's entropy, permutation entropy, and the Lempel-Ziv complexity. These methods have been used in many applications, such as pattern recognition, series classification, and several other areas (e.g., physical, financial, and biomedical). Data in these applications are often presented in binary series with temporal correlations. Herein, we compare the measures of information entropy in binary series conveying short- and long-range temporal correlations characterized by the Hurst exponent H. Combining numerical and analytical approaches, we scrutinize different methods that were not efficient in detecting temporal correlations. To surpass this limitation, we propose a measure called the binary permutation index (BPI). We will demonstrate that BPI efficiently discriminates patterns embedded in the series, offering advantages over previous methods. Subsequently, we collect stock market time series and rain precipitation data as well as perform in vivo electrophysiological recordings in the hippocampus of an experimental animal model of temporal lobe epilepsy, in which the BPI application in both public open source and experimental data is demonstrated. An index is proposed to evaluate information entropy, allowing the ability to discriminate randomness and extract meaningful information in binary time series.
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Affiliation(s)
- Mariana Sacrini Ayres Ferraz
- Centro de Matemática, Computação e Cognição (CMCC), Universidade Federal do ABC (UFABC), São Bernardo do Campo, São Paulo, Brazil
| | - Alexandre Hiroaki Kihara
- Centro de Matemática, Computação e Cognição (CMCC), Universidade Federal do ABC (UFABC), São Bernardo do Campo, São Paulo, Brazil
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13
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Campbell OL, Weber AM. Monofractal analysis of functional magnetic resonance imaging: An introductory review. Hum Brain Mapp 2022; 43:2693-2706. [PMID: 35266236 PMCID: PMC9057087 DOI: 10.1002/hbm.25801] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 01/24/2022] [Accepted: 01/26/2022] [Indexed: 11/11/2022] Open
Abstract
The following review will aid readers in providing an overview of scale-free dynamics and monofractal analysis, as well as its applications and potential in functional magnetic resonance imaging (fMRI) neuroscience and clinical research. Like natural phenomena such as the growth of a tree or crashing ocean waves, the brain expresses scale-invariant, or fractal, patterns in neural signals that can be measured. While neural phenomena may represent both monofractal and multifractal processes and can be quantified with many different interrelated parameters, this review will focus on monofractal analysis using the Hurst exponent (H). Monofractal analysis of fMRI data is an advanced analysis technique that measures the complexity of brain signaling by quantifying its degree of scale-invariance. As such, the H value of the blood oxygenation level-dependent (BOLD) signal specifies how the degree of correlation in the signal may mediate brain functions. This review presents a brief overview of the theory of fMRI monofractal analysis followed by notable findings in the field. Through highlighting the advantages and challenges of the technique, the article provides insight into how to best conduct fMRI fractal analysis and properly interpret the findings with physiological relevance. Furthermore, we identify the future directions necessary for its progression towards impactful functional neuroscience discoveries and widespread clinical use. Ultimately, this presenting review aims to build a foundation of knowledge among readers to facilitate greater understanding, discussion, and use of this unique yet powerful imaging analysis technique.
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Affiliation(s)
- Olivia Lauren Campbell
- School of Biomedical Engineering, University of British Columbia, Vancouver, British Columbia, Canada
| | - Alexander Mark Weber
- School of Biomedical Engineering, University of British Columbia, Vancouver, British Columbia, Canada.,Division of Neurology, Department of Pediatrics, University of British Columbia, Vancouver, British Columbia, Canada.,Department of Neuroscience, University of British Columbia, Vancouver, British Columbia, Canada.,BC Children's Hospital Research Institute, Vancouver, British Columbia, Canada
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14
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Campbell O, Vanderwal T, Weber AM. Fractal-Based Analysis of fMRI BOLD Signal During Naturalistic Viewing Conditions. Front Physiol 2022; 12:809943. [PMID: 35087421 PMCID: PMC8787275 DOI: 10.3389/fphys.2021.809943] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 12/14/2021] [Indexed: 01/04/2023] Open
Abstract
Background: Temporal fractals are characterized by prominent scale-invariance and self-similarity across time scales. Monofractal analysis quantifies this scaling behavior in a single parameter, the Hurst exponent (H). Higher H reflects greater correlation in the signal structure, which is taken as being more fractal. Previous fMRI studies have observed lower H during conventional tasks relative to resting state conditions, and shown that H is negatively correlated with task difficulty and novelty. To date, no study has investigated the fractal dynamics of BOLD signal during naturalistic conditions. Methods: We performed fractal analysis on Human Connectome Project 7T fMRI data (n = 72, 41 females, mean age 29.46 ± 3.76 years) to compare H across movie-watching and rest. Results: In contrast to previous work using conventional tasks, we found higher H values for movie relative to rest (mean difference = 0.014; p = 5.279 × 10-7; 95% CI [0.009, 0.019]). H was significantly higher in movie than rest in the visual, somatomotor and dorsal attention networks, but was significantly lower during movie in the frontoparietal and default networks. We found no cross-condition differences in test-retest reliability of H. Finally, we found that H of movie-derived stimulus properties (e.g., luminance changes) were fractal whereas H of head motion estimates were non-fractal. Conclusions: Overall, our findings suggest that movie-watching induces fractal signal dynamics. In line with recent work characterizing connectivity-based brain state dynamics during movie-watching, we speculate that these fractal dynamics reflect the configuring and reconfiguring of brain states that occurs during naturalistic processing, and are markedly different than dynamics observed during conventional tasks.
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Affiliation(s)
- Olivia Campbell
- School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Tamara Vanderwal
- British Columbia (BC) Children's Hospital Research Institute, UBC, Vancouver, BC, Canada.,Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
| | - Alexander Mark Weber
- School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada.,British Columbia (BC) Children's Hospital Research Institute, UBC, Vancouver, BC, Canada.,Division of Neurology, Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada.,Department of Neuroscience, University of British Columbia, Vancouver, BC, Canada
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15
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Fagerholm ED, Foulkes WMC, Gallero-Salas Y, Helmchen F, Friston KJ, Leech R, Moran RJ. Neural Systems Under Change of Scale. Front Comput Neurosci 2021; 15:643148. [PMID: 33967728 PMCID: PMC8099030 DOI: 10.3389/fncom.2021.643148] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 03/26/2021] [Indexed: 11/30/2022] Open
Abstract
We derive a theoretical construct that allows for the characterisation of both scalable and scale free systems within the dynamic causal modelling (DCM) framework. We define a dynamical system to be "scalable" if the same equation of motion continues to apply as the system changes in size. As an example of such a system, we simulate planetary orbits varying in size and show that our proposed methodology can be used to recover Kepler's third law from the timeseries. In contrast, a "scale free" system is one in which there is no characteristic length scale, meaning that images of such a system are statistically unchanged at different levels of magnification. As an example of such a system, we use calcium imaging collected in murine cortex and show that the dynamical critical exponent, as defined in renormalization group theory, can be estimated in an empirical biological setting. We find that a task-relevant region of the cortex is associated with higher dynamical critical exponents in task vs. spontaneous states and vice versa for a task-irrelevant region.
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Affiliation(s)
- Erik D. Fagerholm
- Department of Neuroimaging, King’s College London, London, United Kingdom
| | - W. M. C. Foulkes
- Department of Physics, Imperial College London, London, United Kingdom
| | - Yasir Gallero-Salas
- Brain Research Institute, University of Zürich, Zurich, Switzerland
- Neuroscience Center Zurich, Zurich, Switzerland
| | - Fritjof Helmchen
- Brain Research Institute, University of Zürich, Zurich, Switzerland
- Neuroscience Center Zurich, Zurich, Switzerland
| | - Karl J. Friston
- Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
| | - Robert Leech
- Department of Neuroimaging, King’s College London, London, United Kingdom
| | - Rosalyn J. Moran
- Department of Neuroimaging, King’s College London, London, United Kingdom
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16
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Cieri F, Zhuang X, Caldwell JZK, Cordes D. Brain Entropy During Aging Through a Free Energy Principle Approach. Front Hum Neurosci 2021; 15:647513. [PMID: 33828471 PMCID: PMC8019811 DOI: 10.3389/fnhum.2021.647513] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Accepted: 02/25/2021] [Indexed: 02/01/2023] Open
Abstract
Neural complexity and brain entropy (BEN) have gained greater interest in recent years. The dynamics of neural signals and their relations with information processing continue to be investigated through different measures in a variety of noteworthy studies. The BEN of spontaneous neural activity decreases during states of reduced consciousness. This evidence has been showed in primary consciousness states, such as psychedelic states, under the name of "the entropic brain hypothesis." In this manuscript we propose an extension of this hypothesis to physiological and pathological aging. We review this particular facet of the complexity of the brain, mentioning studies that have investigated BEN in primary consciousness states, and extending this view to the field of neuroaging with a focus on resting-state functional Magnetic Resonance Imaging. We first introduce historic and conceptual ideas about entropy and neural complexity, treating the mindbrain as a complex nonlinear dynamic adaptive system, in light of the free energy principle. Then, we review the studies in this field, analyzing the idea that the aim of the neurocognitive system is to maintain a dynamic state of balance between order and chaos, both in terms of dynamics of neural signals and functional connectivity. In our exploration we will review studies both on acute psychedelic states and more chronic psychotic states and traits, such as those in schizophrenia, in order to show the increase of entropy in those states. Then we extend our exploration to physiological and pathological aging, where BEN is reduced. Finally, we propose an interpretation of these results, defining a general trend of BEN in primary states and cognitive aging.
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17
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Tetereva A, Kartashov S, Ivanitsky A, Martynova O. Variance and Scale-Free Properties of Resting-State Blood Oxygenation Level-Dependent Signal After Fear Memory Acquisition and Extinction. Front Hum Neurosci 2020; 14:509075. [PMID: 33192382 PMCID: PMC7581738 DOI: 10.3389/fnhum.2020.509075] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Accepted: 09/18/2020] [Indexed: 12/02/2022] Open
Abstract
Recently, the dynamic properties of brain activity rather than its stationary values have attracted more interest in clinical applications. It has been shown that brain signals exhibit scale-free dynamics or long-range temporal correlations (LRTC) that differ between rest and cognitive tasks in healthy controls and clinical groups. Little is known about how fear-inducing tasks may influence dispersion and the LRTC of subsequent resting-state brain activity. In this study, we aimed to explore the changes in the variance and scale-free properties of the brain’s blood oxygenation level-dependent (BOLD) signal during the resting-state sessions before and after fear learning and fear memory extinction. During a 1-h break between magnetic resonance imaging (MRI) scanning, 23 healthy, right-handed volunteers experienced a fear extinction procedure, followed by Pavlovian fear conditioning that included partial reinforcement using mild electrical stimulation. We extracted the average time course of the BOLD signal from 245 regions of interest (ROIs) taken from the resting-state functional atlas. The variance of the BOLD signal and the Hurst exponent (H), which reflects the scale-free dynamic, were compared in the resting states before and after fear learning and fear memory extinction. After fear extinction, six ROIs showed a difference in H at the uncorrected level of significance, including areas associated with fear processing. H decreased during fear extinction but then became higher than before fear learning, specifically in areas related to the fear extinction network (FEN). However, activity in the other ROIs restored the H to its initial level. The variance of the BOLD signal in six ROIs demonstrated a significant increase from initial rest to the post-task rest. A limited number of ROIs showed changes in both H and variance. Our results imply that the variability and scale-free properties of the BOLD signal might serve as additional indicators of changes in spontaneous brain activity related to recent experience.
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Affiliation(s)
- Alina Tetereva
- Institute of Higher Nervous Activity and Neurophysiology of the Russian Academy of Sciences, Moscow, Russia.,Department of Psychology, University of Otago, Dunedin, New Zealand
| | | | - Alexey Ivanitsky
- Institute of Higher Nervous Activity and Neurophysiology of the Russian Academy of Sciences, Moscow, Russia
| | - Olga Martynova
- Institute of Higher Nervous Activity and Neurophysiology of the Russian Academy of Sciences, Moscow, Russia.,Centre for Cognition and Decision Making, Institute of Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russia
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18
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Core and matrix thalamic sub-populations relate to spatio-temporal cortical connectivity gradients. Neuroimage 2020; 222:117224. [DOI: 10.1016/j.neuroimage.2020.117224] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 07/28/2020] [Accepted: 07/30/2020] [Indexed: 01/24/2023] Open
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19
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Dhamala E, Jamison KW, Sabuncu MR, Kuceyeski A. Sex classification using long-range temporal dependence of resting-state functional MRI time series. Hum Brain Mapp 2020; 41:3567-3579. [PMID: 32627300 PMCID: PMC7416025 DOI: 10.1002/hbm.25030] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Accepted: 04/27/2020] [Indexed: 12/13/2022] Open
Abstract
A thorough understanding of sex differences that exist in the brains of healthy individuals is crucial for the study of neurological illnesses that exhibit phenotypic differences between males and females. Here we evaluate sex differences in regional temporal dependence of resting-state brain activity in 195 adult male-female pairs strictly matched for total grey matter volume from the Human Connectome Project. We find that males have more persistent temporal dependence in regions within temporal, parietal, and occipital cortices. Machine learning algorithms trained on regional temporal dependence measures achieve sex classification accuracies up to 81%. Regions with the strongest feature importance in the sex classification task included cerebellum, amygdala, and frontal and occipital cortices. Secondarily, we show that even after strict matching of total gray matter volume, significant volumetric sex differences persist; males have larger absolute cerebella, hippocampi, parahippocampi, thalami, caudates, and amygdalae while females have larger absolute cingulates, precunei, and frontal and parietal cortices. Sex classification based on regional volume achieves accuracies up to 85%, highlighting the importance of strict volume-matching when studying brain-based sex differences. Differential patterns in regional temporal dependence between the sexes identifies a potential neurobiological substrate or environmental effect underlying sex differences in functional brain activation patterns.
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Affiliation(s)
- Elvisha Dhamala
- Department of RadiologyWeill Cornell MedicineNew YorkNew YorkUSA
- Brain and Mind Research InstituteWeill Cornell MedicineNew YorkNew YorkUSA
| | - Keith W. Jamison
- Department of RadiologyWeill Cornell MedicineNew YorkNew YorkUSA
| | - Mert R. Sabuncu
- School of Electrical and Computer EngineeringCornell UniversityIthacaNew YorkUSA
- Meinig School of Biomedical EngineeringCornell UniversityIthacaNew YorkUSA
| | - Amy Kuceyeski
- Department of RadiologyWeill Cornell MedicineNew YorkNew YorkUSA
- Brain and Mind Research InstituteWeill Cornell MedicineNew YorkNew YorkUSA
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20
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Neufang S, Akhrif A. Regional Hurst Exponent Reflects Impulsivity-Related Alterations in Fronto-Hippocampal Pathways Within the Waiting Impulsivity Network. Front Physiol 2020; 11:827. [PMID: 32765298 PMCID: PMC7381286 DOI: 10.3389/fphys.2020.00827] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2020] [Accepted: 06/22/2020] [Indexed: 12/01/2022] Open
Abstract
In general, the Hurst exponent. is used as a measure of long-term memory of time series. In previous neuroimaging studies, H has been introduced as one important parameter to define resting-state networks, reflecting upon global scale-free properties emerging from a network. H has been examined in the waiting impulsivity (WI) network in an earlier study. We found that alterations of H in the anterior cingulate cortex (HACC) and the nucleus accumbens (HNAcc) were lower in high impulsive (highIMP) compared to low impulsive (lowIMP) participants. Following up on those findings, we addressed the relation between altered fractality in HACC and HNAcc and brain activation and neural network connectivity. To do so, brain activation maps were calculated, and network connectivity was determined using the Dynamic Causal Modeling (DCM) approach. Finally, 1–H scores were determined to quantify the alterations of H. This way, the focus of the analyses was placed on the potential effects of alterations of H on neural network activation and connectivity. Correlation analyses between the alterations of HACC/HNAcc and activation maps and DCM estimates were performed. We found that the alterations of H predominantly correlated with fronto-hippocampal pathways and correlations were significant only in highIMP subjects. For example, alterations of HACC was associated with a decrease in neural activation in the right HC in combination with increased ACC-hippocampal connectivity. Alteration inHNAcc, in return, was related to an increase in bilateral prefrontal activation in combination with increased fronto-hippocampal connectivity. The findings, that the WI network was related to H alteration in highIMP subjects indicated that impulse control was not reduced per se but lacked consistency. Additionally, H has been used to describe long-term memory processes before, e.g., in capital markets, energy future prices, and human memory. Thus, current findings supported the relation of H toward memory processing even when further prominent cognitive functions were involved.
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Affiliation(s)
- Susanne Neufang
- Department of Psychiatry and Psychotherapy, Medical Faculty Heinrich-Heine University, Düsseldorf, Germany.,Comparative Psychology, Institute of Experimental Psychology, Heinrich-Heine University, Düsseldorf, Germany
| | - Atae Akhrif
- Comparative Psychology, Institute of Experimental Psychology, Heinrich-Heine University, Düsseldorf, Germany.,Center of Mental Health, Department of Child and Adolescent Psychiatry, University of Würzburg, Würzburg, Germany
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21
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Battaglia D, Boudou T, Hansen ECA, Lombardo D, Chettouf S, Daffertshofer A, McIntosh AR, Zimmermann J, Ritter P, Jirsa V. Dynamic Functional Connectivity between order and randomness and its evolution across the human adult lifespan. Neuroimage 2020; 222:117156. [PMID: 32698027 DOI: 10.1016/j.neuroimage.2020.117156] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2020] [Revised: 05/25/2020] [Accepted: 07/07/2020] [Indexed: 12/14/2022] Open
Abstract
Functional Connectivity (FC) during resting-state or task conditions is not static but inherently dynamic. Yet, there is no consensus on whether fluctuations in FC may resemble isolated transitions between discrete FC states rather than continuous changes. This quarrel hampers advancing the study of dynamic FC. This is unfortunate as the structure of fluctuations in FC can certainly provide more information about developmental changes, aging, and progression of pathologies. We merge the two perspectives and consider dynamic FC as an ongoing network reconfiguration, including a stochastic exploration of the space of possible steady FC states. The statistical properties of this random walk deviate both from a purely "order-driven" dynamics, in which the mean FC is preserved, and from a purely "randomness-driven" scenario, in which fluctuations of FC remain uncorrelated over time. Instead, dynamic FC has a complex structure endowed with long-range sequential correlations that give rise to transient slowing and acceleration epochs in the continuous flow of reconfiguration. Our analysis for fMRI data in healthy elderly revealed that dynamic FC tends to slow down and becomes less complex as well as more random with increasing age. These effects appear to be strongly associated with age-related changes in behavioural and cognitive performance.
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Affiliation(s)
- Demian Battaglia
- Université Aix-Marseille, INSERM UMR 1106, Institut de Neurosciences des Systèmes, F-13005, Marseille, France.
| | - Thomas Boudou
- Université Aix-Marseille, INSERM UMR 1106, Institut de Neurosciences des Systèmes, F-13005, Marseille, France; ENSTA ParisTech, F-91762, Palaiseau, France.
| | - Enrique C A Hansen
- Université Aix-Marseille, INSERM UMR 1106, Institut de Neurosciences des Systèmes, F-13005, Marseille, France; Institut de biologie de l'Ecole normale supérieure (IBENS), École Normale Supérieure, CNRS, INSERM, PSL Université Paris, F-75005, Paris, France.
| | - Diego Lombardo
- Université Aix-Marseille, INSERM UMR 1106, Institut de Neurosciences des Systèmes, F-13005, Marseille, France.
| | - Sabrina Chettouf
- Brain Simulation Section, Department of Neurology, Charité Universitätsmedizin and Berlin Institute of Health, D-10117, Berlin, Germany; Bernstein Center for Computational Neuroscience, D-10117, Berlin, Germany; Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, 1081 BT, Amsterdam, the Netherlands.
| | - Andreas Daffertshofer
- Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, 1081 BT, Amsterdam, the Netherlands.
| | - Anthony R McIntosh
- Rotman Research Institute, Baycrest Centre, Toronto, Ontario, M6A 2E1, Canada.
| | - Joelle Zimmermann
- Brain Simulation Section, Department of Neurology, Charité Universitätsmedizin and Berlin Institute of Health, D-10117, Berlin, Germany; Rotman Research Institute, Baycrest Centre, Toronto, Ontario, M6A 2E1, Canada.
| | - Petra Ritter
- Brain Simulation Section, Department of Neurology, Charité Universitätsmedizin and Berlin Institute of Health, D-10117, Berlin, Germany; Bernstein Center for Computational Neuroscience, D-10117, Berlin, Germany.
| | - Viktor Jirsa
- Université Aix-Marseille, INSERM UMR 1106, Institut de Neurosciences des Systèmes, F-13005, Marseille, France.
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22
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Consciousness & Brain Functional Complexity in Propofol Anaesthesia. Sci Rep 2020; 10:1018. [PMID: 31974390 PMCID: PMC6978464 DOI: 10.1038/s41598-020-57695-3] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Accepted: 12/31/2019] [Indexed: 12/04/2022] Open
Abstract
The brain is possibly the most complex system known to mankind, and its complexity has been called upon to explain the emergence of consciousness. However, complexity has been defined in many ways by multiple different fields: here, we investigate measures of algorithmic and process complexity in both the temporal and topological domains, testing them on functional MRI BOLD signal data obtained from individuals undergoing various levels of sedation with the anaesthetic agent propofol, replicating our results in two separate datasets. We demonstrate that the various measures are differently able to discriminate between levels of sedation, with temporal measures showing higher sensitivity. Further, we show that all measures are strongly related to a single underlying construct explaining most of the variance, as assessed by Principal Component Analysis, which we interpret as a measure of “overall complexity” of our data. This overall complexity was also able to discriminate between levels of sedation and serum concentrations of propofol, supporting the hypothesis that consciousness is related to complexity - independent of how the latter is measured.
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23
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Naro A, Marra A, Billeri L, Portaro S, De Luca R, Maresca G, La Rosa G, Lauria P, Bramanti P, Calabrò RS. New Horizons in Early Dementia Diagnosis: Can Cerebellar Stimulation Untangle the Knot? J Clin Med 2019; 8:E1470. [PMID: 31527392 PMCID: PMC6780127 DOI: 10.3390/jcm8091470] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Revised: 08/28/2019] [Accepted: 09/12/2019] [Indexed: 12/26/2022] Open
Abstract
Differentiating Mild Cognitive Impairment (MCI) from dementia and estimating the risk of MCI-to-dementia conversion (MDC) are challenging tasks. Thus, objective tools are mandatory to get early diagnosis and prognosis. About that, there is a growing interest on the role of cerebellum-cerebrum connectivity (CCC). The aim of this study was to differentiate patients with an early diagnosis of dementia and MCI depending on the effects of a transcranial magnetic stimulation protocol (intermittent theta-burst stimulation -iTBS) delivered on the cerebellum able to modify cortico-cortical connectivity. Indeed, the risk of MDC is related to the response to iTBS, being higher in non-responder individuals. All patients with MCI, but eight (labelled as MCI-), showed preserved iTBS aftereffect. Contrariwise, none of the patients with dementia showed iTBS aftereffects. None of the patients showed EEG aftereffects following a sham TBS protocol. Five among the MCI- patients converted to dementia at 6-month follow-up. Our data suggest that cerebellar stimulation by means of iTBS may support the differential diagnosis between MCI and dementia and potentially identify the individuals with MCI who may be at risk of MDC. These findings may help clinicians to adopt a better prevention/follow-up strategy in such patients.
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Affiliation(s)
- Antonino Naro
- IRCCS Centro Neurolesi Bonino Pulejo, via Palermo, S.S. 113, C.da Casazza, 98124 Messina, Italy.
| | - Angela Marra
- IRCCS Centro Neurolesi Bonino Pulejo, via Palermo, S.S. 113, C.da Casazza, 98124 Messina, Italy.
| | - Luana Billeri
- IRCCS Centro Neurolesi Bonino Pulejo, via Palermo, S.S. 113, C.da Casazza, 98124 Messina, Italy.
| | - Simona Portaro
- IRCCS Centro Neurolesi Bonino Pulejo, via Palermo, S.S. 113, C.da Casazza, 98124 Messina, Italy.
| | - Rosaria De Luca
- IRCCS Centro Neurolesi Bonino Pulejo, via Palermo, S.S. 113, C.da Casazza, 98124 Messina, Italy.
| | - Giuseppa Maresca
- IRCCS Centro Neurolesi Bonino Pulejo, via Palermo, S.S. 113, C.da Casazza, 98124 Messina, Italy.
| | - Gianluca La Rosa
- IRCCS Centro Neurolesi Bonino Pulejo, via Palermo, S.S. 113, C.da Casazza, 98124 Messina, Italy.
| | - Paola Lauria
- IRCCS Centro Neurolesi Bonino Pulejo, via Palermo, S.S. 113, C.da Casazza, 98124 Messina, Italy.
| | - Placido Bramanti
- IRCCS Centro Neurolesi Bonino Pulejo, via Palermo, S.S. 113, C.da Casazza, 98124 Messina, Italy.
| | - Rocco Salvatore Calabrò
- IRCCS Centro Neurolesi Bonino Pulejo, via Palermo, S.S. 113, C.da Casazza, 98124 Messina, Italy.
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24
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Chen X, Wang D, Lin J, Zhang T, Deng S, Huang L, Jin Y, Chen C, Zhang Z, Zheng J, Sun B, Bogdan P, Zhang XD. Analyzing Complexity and Fractality of Glucose Dynamics in a Pregnant Woman with Type 2 Diabetes under Treatment. Int J Biol Sci 2019; 15:2373-2380. [PMID: 31595155 PMCID: PMC6775315 DOI: 10.7150/ijbs.33825] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2019] [Accepted: 05/27/2019] [Indexed: 11/05/2022] Open
Abstract
Currently, the rapid development of continuous glucose monitoring (CGM) device brings new insights into the treatment of diabetic patients including those during pregnancy. Complexity and fractality have recently under fast development for extracting information embodied in glucose dynamics measured using CGM. Although scientists have investigated the difference of complexity in glucose dynamics between diabetes and non-diabetes in order to discover better approaches for diabetes care, no one has analyzed the complexity and fractality of glucose dynamics during the process of adopting CGM to successfully treat pregnant women with type 2 diabetes. Thus, we analyzed the complexity and fractality using power spectral density (PSD), multi-scale sample entropy (MSE) and multifractal detrended fluctuation analysis (MF-DFA) in a clinical case. Our results show that (i) there exists multifractal behavior in blood glucose dynamics; (ii) the alpha stable distribution fits to the glucose increment data better than the Gaussian distribution; and (iii) the "global" complexity indicated by multiscale entropy, spectrum exponent and Hurst exponent increase and the "local" complexity indicated by multifractal spectrum decrease after the successful therapy. Our results offer findings that may bring value to health care providers for managing glucose levels of pregnant women with type 2 diabetes as well as provide scientists a reference on applying complexity and fractality in the clinical practice of treating diabetes.
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Affiliation(s)
- Xiaoyan Chen
- Department of Endocrinology, First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510000, China
| | - Dandan Wang
- Faculty of Health Sciences, University of Macau, Taipa 999078, Macau
| | - Jinxiang Lin
- Department of Endocrinology, First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510000, China
| | - Teng Zhang
- Faculty of Health Sciences, University of Macau, Taipa 999078, Macau
| | - Shunyou Deng
- Department of Endocrinology, First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510000, China
| | - Lianyi Huang
- Department of Endocrinology, First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510000, China
| | - Yu Jin
- Faculty of Health Sciences, University of Macau, Taipa 999078, Macau
| | - Chang Chen
- Faculty of Health Sciences, University of Macau, Taipa 999078, Macau
| | - Zhaozhi Zhang
- Department of Statistical Science, Duke University, Durham, NC 27708, USA
| | - Jun Zheng
- Faculty of Health Sciences, University of Macau, Taipa 999078, Macau
| | - Baoqing Sun
- Department of Allergy and Clinical Immunology, Guangzhou Institute of Respiratory Diseases, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510000, China
| | - Paul Bogdan
- Department of Electrical Engineering - Systems, University of Southern California, CA 90089, USA
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25
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Ferraz MSA, Kihara AH. Hurst entropy: A method to determine predictability in a binary series based on a fractal-related process. Phys Rev E 2019; 99:062115. [PMID: 31330637 DOI: 10.1103/physreve.99.062115] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2018] [Indexed: 11/07/2022]
Abstract
Shannon's concept of information is related to predictability. In a binary series, the value of information relies on the frequency of 0's and 1's, or how it is expected to occur. However, information entropy does not consider the bias in randomness related to autocorrelation. In fact, it is possible for a binary temporal series to carry both short- and long-term memories related to the sequential distribution of 0's and 1's. Although the Hurst exponent measures the range of autocorrelation, there is a lack of mathematical connection between information entropy and autocorrelation present in the series. To fill this important gap, we combined numerical simulations and an analytical approach to determine how information entropy changes according to the frequency of 0's and 1's and the Hurst exponent. Indeed, we were able to determine how predictability depends on both parameters. Our findings are certainly useful to several fields when binary times series are applied, such as neuroscience to econophysics.
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Affiliation(s)
- Mariana Sacrini Ayres Ferraz
- Centro de Matemática, Computação e Cognição (CMCC), Universidade Federal do ABC (UFABC), São Bernardo do Campo, São Paulo 09606-045, Brasil
| | - Alexandre Hiroaki Kihara
- Centro de Matemática, Computação e Cognição (CMCC), Universidade Federal do ABC (UFABC), São Bernardo do Campo, São Paulo 09606-045, Brasil
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26
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Gao W, Chen S, Biswal B, Lei X, Yuan J. Temporal dynamics of spontaneous default-mode network activity mediate the association between reappraisal and depression. Soc Cogn Affect Neurosci 2019; 13:1235-1247. [PMID: 30339260 PMCID: PMC6277739 DOI: 10.1093/scan/nsy092] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2018] [Accepted: 10/12/2018] [Indexed: 12/24/2022] Open
Abstract
Cognitive reappraisal is associated with major depressive disorder (MDD), while spontaneous activity patterns of the default mode network (DMN) is implicated in reappraisal and MDD. However, neural mechanisms subserving the close association of spontaneous reappraisal and depression are unclear. Spontaneous reappraisal, depression and resting-state functional magnetic resonance imaging (rsfMRI) were measured from 105 healthy subjects. We assessed the temporal complexity (Hurst exponent), Regional Homogeneity (ReHo) and fractional Amplitude of Low Frequency Fluctuation (fALFF) profiles of DMN, a network involved in both reappraisal and depression. Mediation effects of these standard measures on the relationship between reappraisal and depression, and the contributions of each DMN subregion, were assessed. Results indicated that Hurst exponent (H) of DMN, whether extracted by independent component analysis (ICA) or region of interest (ROI), was significantly associated with reappraisal scores. An individual with a higher reappraisal score has a lower Hurst value of DMN. Mediation analyses suggest that H of DMN partially mediates the association between reappraisal and the degree of depression, and this mediation effect arises from the contribution of medial prefrontal cortex. Neither ReHo nor fALFF showed a similar correlation or mediation effect. These findings suggest that temporal dynamics of DMN play an important role in emotion regulation and its association with depression. H of DMN may serve as a neural marker mediating the association between reappraisal and depression.
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Affiliation(s)
- Wei Gao
- The Laboratory for Affect Cognition and Regulation (ACRLAB), Key Laboratory of Cognition and Personality of Ministry of Education (SWU), Faculty of Psychology, Southwest University, Chongqing, China
| | - ShengDong Chen
- The Laboratory for Affect Cognition and Regulation (ACRLAB), Key Laboratory of Cognition and Personality of Ministry of Education (SWU), Faculty of Psychology, Southwest University, Chongqing, China
| | - Bharat Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, USA
| | - Xu Lei
- Sleep and Neuroimaging Center, Faculty of Psychology, Southwest University, Chongqing, China
| | - JiaJin Yuan
- The Laboratory for Affect Cognition and Regulation (ACRLAB), Key Laboratory of Cognition and Personality of Ministry of Education (SWU), Faculty of Psychology, Southwest University, Chongqing, China
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