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Semenova U, Dzhalagoniya I, Gamaleya A, Tomskiy A, Shaikh AG, Sedov A. Pallidal multifractal complexity is a new potential physiomarker of dystonia. Clin Neurophysiol 2024; 162:31-40. [PMID: 38555665 DOI: 10.1016/j.clinph.2024.03.012] [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: 05/30/2023] [Revised: 01/11/2024] [Accepted: 03/12/2024] [Indexed: 04/02/2024]
Abstract
OBJECTIVE Low-frequency 4-12 Hz pallidal oscillations are being considered as potential physiomarkers for dystonia. We suggest investigating the multifractal properties of pallidal activity as an additional marker. METHODS We employed local field potentials (LFP) recordings from 23 patients with dystonia who were undergoing deep brain stimulation (DBS) surgery to explore the connection between disease severity and the multifractal characteristics of pallidal activity. Furthermore, we performed an analysis of LFP recordings from four patients, following the externalization of DBS lead electrodes, to investigate the impact of DBS and neck muscle vibration on multifractal parameters. RESULTS Greater dystonia severity exhibited a correlation with a narrower multifractal spectrum width but higher multifractal spectral asymmetry. Both GPi DBS and muscle vibration in dystonia patients expanded the multifractal spectrum width while restoring multifractal spectral symmetry. Notably, the threshold peak intensities for an increase in multifractal spectrum width substantially overlapped with the optimal volume of tissue activated. A broader multifractal spectrum during DBS corresponded to more favorable clinical outcomes. CONCLUSIONS Multifractal properties of pallidal neuronal activity serve as indicators of neural dysfunction in dystonia. SIGNIFICANCE These findings suggest the potential of utilizing multifractal characteristics as predictive factors for the DBS outcome in dystonia.
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Affiliation(s)
- Ulia Semenova
- N.N.Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, Moscow, Russian Federation.
| | - Indiko Dzhalagoniya
- N.N.Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, Moscow, Russian Federation
| | - Anna Gamaleya
- N.N. Burdenko National Scientific and Practical Center for Neurosurgery, Moscow, Russian Federation
| | - Alexey Tomskiy
- N.N. Burdenko National Scientific and Practical Center for Neurosurgery, Moscow, Russian Federation
| | - Aasef G Shaikh
- Departments of Neurology and Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA; Neurological Institute, University Hospitals, Cleveland, OH, USA; Neurology Service, Louis Stokes Cleveland VA Medical Center, Cleveland, OH, USA
| | - Alexey Sedov
- N.N.Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, Moscow, Russian Federation; Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, Russian Federation
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Mangalam M, Seleznov I, Kolosova E, Popov A, Kelty-Stephen DG, Kiyono K. Postural control in gymnasts: anisotropic fractal scaling reveals proprioceptive reintegration in vestibular perturbation. FRONTIERS IN NETWORK PHYSIOLOGY 2024; 4:1393171. [PMID: 38699200 PMCID: PMC11063314 DOI: 10.3389/fnetp.2024.1393171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Accepted: 04/05/2024] [Indexed: 05/05/2024]
Abstract
Dexterous postural control subtly complements movement variability with sensory correlations at many scales. The expressive poise of gymnasts exemplifies this lyrical punctuation of release with constraint, from coarse grain to fine scales. Dexterous postural control upon a 2D support surface might collapse the variation of center of pressure (CoP) to a relatively 1D orientation-a direction often oriented towards the focal point of a visual task. Sensory corrections in dexterous postural control might manifest in temporal correlations, specifically as fractional Brownian motions whose differences are more and less correlated with fractional Gaussian noises (fGns) with progressively larger and smaller Hurst exponent H. Traditional empirical work examines this arrangement of lower-dimensional compression of CoP along two orthogonal axes, anteroposterior (AP) and mediolateral (ML). Eyes-open and face-forward orientations cultivate greater variability along AP than ML axes, and the orthogonal distribution of spatial variability has so far gone hand in hand with an orthogonal distribution of H, for example, larger in AP and lower in ML. However, perturbing the orientation of task focus might destabilize the postural synergy away from its 1D distribution and homogenize the temporal correlations across the 2D support surface, resulting in narrower angles between the directions of the largest and smallest H. We used oriented fractal scaling component analysis (OFSCA) to investigate whether sensory corrections in postural control might thus become suborthogonal. OFSCA models raw 2D CoP trajectory by decomposing it in all directions along the 2D support surface and fits the directions with the largest and smallest H. We studied a sample of gymnasts in eyes-open and face-forward quiet posture, and results from OFSCA confirm that such posture exhibits the classic orthogonal distribution of temporal correlations. Head-turning resulted in a simultaneous decrease in this angle Δθ, which promptly reversed once gymnasts reoriented their heads forward. However, when vision was absent, there was only a discernible negative trend in Δθ, indicating a shift in the angle's direction but not a statistically significant one. Thus, the narrowing of Δθ may signify an adaptive strategy in postural control. The swift recovery of Δθ upon returning to a forward-facing posture suggests that the temporary reduction is specific to head-turning and does not impose a lasting burden on postural control. Turning the head reduced the angle between these two orientations, facilitating the release of postural degrees of freedom towards a more uniform spread of the CoP across both dimensions of the support surface. The innovative aspect of this work is that it shows how fractality might serve as a control parameter of adaptive mechanisms of dexterous postural control.
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Affiliation(s)
- Madhur Mangalam
- Division of Biomechanics and Research Development, Department of Biomechanics, and Center for Research in Human Movement Variability, University of Nebraska at Omaha, Omaha, NE, United States
| | - Ivan Seleznov
- Graduate School of Engineering Science, Osaka University, Osaka, Japan
| | - Elena Kolosova
- National University of Ukraine on Physical Education and Sport, Scientific Research Institute, Kyiv, Ukraine
- Department of Movement Physiology, Bogomoletz Institute of Physiology, Kyiv, Ukraine
| | - Anton Popov
- Department of Electronic Engineering, Igor Sikorsky Kyiv Polytechnic Institute, Kyiv, Ukraine
- Faculty of Applied Sciences, Ukrainian Catholic University, Lviv, Ukraine
| | - Damian G. Kelty-Stephen
- Department of Psychology, State University of New York at New Paltz, New Paltz, NY, United States
| | - Ken Kiyono
- Graduate School of Engineering Science, Osaka University, Osaka, Japan
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3
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Seckler H, Metzler R, Kelty-Stephen DG, Mangalam M. Multifractal spectral features enhance classification of anomalous diffusion. Phys Rev E 2024; 109:044133. [PMID: 38755826 DOI: 10.1103/physreve.109.044133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 03/19/2024] [Indexed: 05/18/2024]
Abstract
Anomalous diffusion processes, characterized by their nonstandard scaling of the mean-squared displacement, pose a unique challenge in classification and characterization. In a previous study [Mangalam et al., Phys. Rev. Res. 5, 023144 (2023)2643-156410.1103/PhysRevResearch.5.023144], we established a comprehensive framework for understanding anomalous diffusion using multifractal formalism. The present study delves into the potential of multifractal spectral features for effectively distinguishing anomalous diffusion trajectories from five widely used models: fractional Brownian motion, scaled Brownian motion, continuous-time random walk, annealed transient time motion, and Lévy walk. We generate extensive datasets comprising 10^{6} trajectories from these five anomalous diffusion models and extract multiple multifractal spectra from each trajectory to accomplish this. Our investigation entails a thorough analysis of neural network performance, encompassing features derived from varying numbers of spectra. We also explore the integration of multifractal spectra into traditional feature datasets, enabling us to assess their impact comprehensively. To ensure a statistically meaningful comparison, we categorize features into concept groups and train neural networks using features from each designated group. Notably, several feature groups demonstrate similar levels of accuracy, with the highest performance observed in groups utilizing moving-window characteristics and p varation features. Multifractal spectral features, particularly those derived from three spectra involving different timescales and cutoffs, closely follow, highlighting their robust discriminatory potential. Remarkably, a neural network exclusively trained on features from a single multifractal spectrum exhibits commendable performance, surpassing other feature groups. In summary, our findings underscore the diverse and potent efficacy of multifractal spectral features in enhancing the predictive capacity of machine learning to classify anomalous diffusion processes.
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Affiliation(s)
- Henrik Seckler
- Institute for Physics & Astronomy, University of Potsdam, 14476 Potsdam-Golm, Germany
| | - Ralf Metzler
- Institute for Physics & Astronomy, University of Potsdam, 14476 Potsdam-Golm, Germany
- Asia Pacific Center for Theoretical Physics, Pohang 37673, Republic of Korea
| | - Damian G Kelty-Stephen
- Department of Psychology, State University of New York at New Paltz, New Paltz, New York 12561, USA
| | - Madhur Mangalam
- Department of Biomechanics and Center for Research in Human Movement Variability, University of Nebraska at Omaha, Omaha, Nebraska 68182, USA
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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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Guidolin D, Tortorella C, De Caro R, Agnati LF. A Self-Similarity Logic May Shape the Organization of the Nervous System. ADVANCES IN NEUROBIOLOGY 2024; 36:203-225. [PMID: 38468034 DOI: 10.1007/978-3-031-47606-8_10] [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
From the morphological point of view, the nervous system exhibits a fractal, self-similar geometry at various levels of observations, from single cells up to cell networks. From the functional point of view, it is characterized by a hierarchical organization in which self-similar structures (networks) of different miniaturizations are nested within each other. In particular, neuronal networks, interconnected to form neuronal systems, are formed by neurons, which operate thanks to their molecular networks, mainly having proteins as components that via protein-protein interactions can be assembled in multimeric complexes working as micro-devices. On this basis, the term "self-similarity logic" was introduced to describe a nested organization where, at the various levels, almost the same rules (logic) to perform operations are used. Self-similarity and self-similarity logic both appear to be intimately linked to the biophysical evidence for the nervous system being a pattern-forming system that can flexibly switch from one coherent state to another. Thus, they can represent the key concepts to describe its complexity and its concerted, holistic behavior.
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Affiliation(s)
- Diego Guidolin
- Department of Neuroscience, University of Padova, Padova, Italy.
| | | | | | - Luigi F Agnati
- Department of Biomedical Sciences, University of Modena and Reggio Emilia, Modena, Italy
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6
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Karperien AL, Jelinek HF. Morphology and Fractal-Based Classifications of Neurons and Microglia in Two and Three Dimensions. ADVANCES IN NEUROBIOLOGY 2024; 36:149-172. [PMID: 38468031 DOI: 10.1007/978-3-031-47606-8_7] [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
Microglia and neurons live physically intertwined, intimately related structurally and functionally in a dynamic relationship in which microglia change continuously over a much shorter timescale than do neurons. Although microglia may unwind and depart from the neurons they attend under certain circumstances, in general, together both contribute to the fractal topology of the brain that defines its computational capabilities. Both neuronal and microglial morphologies are well-described using fractal analysis complementary to more traditional measures. For neurons, the fractal dimension has proved valuable for classifying dendritic branching and other neuronal features relevant to pathology and development. For microglia, fractal geometry has substantially contributed to classifying functional categories, where, in general, the more pathological the biological status, the lower the fractal dimension for individual cells, with some exceptions, including hyper-ramification. This chapter provides a review of the intimate relationships between neurons and microglia, by introducing 2D and 3D fractal analysis methodology and its applications in neuron-microglia function in health and disease.
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Affiliation(s)
- Audrey L Karperien
- School of Community Health, Charles Sturt University, Albury, NSW, Australia
| | - Herbert F Jelinek
- Department of Medical Sciences and Biotechnology Center, Khalifa University, Abu Dhabi, UAE
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7
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Di Ieva A. Fractals in Neuroanatomy and Basic Neurosciences: An Overview. ADVANCES IN NEUROBIOLOGY 2024; 36:141-147. [PMID: 38468030 DOI: 10.1007/978-3-031-47606-8_6] [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
The introduction of fractal geometry to the neurosciences has been a major paradigm shift over the last decades as it has helped overcome approximations and limitations that occur when Euclidean and reductionist approaches are used to analyze neurons or the entire brain. Fractal geometry allows for quantitative analysis and description of the geometric complexity of the brain, from its single units to the neuronal networks.As illustrated in the second section of this book, fractal analysis provides a quantitative tool for the study of the morphology of brain cells (i.e., neurons and microglia) and its components (e.g., dendritic trees, synapses), as well as the brain structure itself (cortex, functional modules, neuronal networks). The self-similar logic which generates and shapes the different hierarchical systems of the brain and even some structures related to its "container," that is, the cranial sutures on the skull, is widely discussed in the following chapters, with a link between the applications of fractal analysis to the neuroanatomy and basic neurosciences to the clinical applications discussed in the third section.
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Affiliation(s)
- Antonio Di Ieva
- 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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8
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Kardan O, Stier AJ, Layden EA, Choe KW, Lyu M, Zhang X, Beilock SL, Rosenberg MD, Berman MG. Improvements in task performance after practice are associated with scale-free dynamics of brain activity. Netw Neurosci 2023; 7:1129-1152. [PMID: 37781143 PMCID: PMC10473260 DOI: 10.1162/netn_a_00319] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 04/11/2023] [Indexed: 10/03/2023] Open
Abstract
Although practicing a task generally benefits later performance on that same task, there are individual differences in practice effects. One avenue to model such differences comes from research showing that brain networks extract functional advantages from operating in the vicinity of criticality, a state in which brain network activity is more scale-free. We hypothesized that higher scale-free signal from fMRI data, measured with the Hurst exponent (H), indicates closer proximity to critical states. We tested whether individuals with higher H during repeated task performance would show greater practice effects. In Study 1, participants performed a dual-n-back task (DNB) twice during MRI (n = 56). In Study 2, we used two runs of n-back task (NBK) data from the Human Connectome Project sample (n = 599). In Study 3, participants performed a word completion task (CAST) across six runs (n = 44). In all three studies, multivariate analysis was used to test whether higher H was related to greater practice-related performance improvement. Supporting our hypothesis, we found patterns of higher H that reliably correlated with greater performance improvement across participants in all three studies. However, the predictive brain regions were distinct, suggesting that the specific spatial H↑ patterns are not task-general.
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Affiliation(s)
- Omid Kardan
- Department of Psychology, University of Chicago, Chicago, IL, USA
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Andrew J. Stier
- Department of Psychology, University of Chicago, Chicago, IL, USA
| | - Elliot A. Layden
- Department of Psychology, University of Chicago, Chicago, IL, USA
| | - Kyoung Whan Choe
- Department of Psychology, University of Chicago, Chicago, IL, USA
| | - Muxuan Lyu
- Department of Psychology, University of Chicago, Chicago, IL, USA
- Department of Management and Marketing, The Hong Kong Polytechnic University, Hong Kong
| | - Xihan Zhang
- Department of Psychology, University of Chicago, Chicago, IL, USA
| | - Sian L. Beilock
- Department of Psychology, University of Chicago, Chicago, IL, USA
- Barnard College, Columbia University, New York, NY, USA
| | | | - Marc G. Berman
- Department of Psychology, University of Chicago, Chicago, IL, USA
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Stier AJ, Cardenas-Iniguez C, Kardan O, Moore TM, Meyer FAC, Rosenberg MD, Kaczkurkin AN, Lahey BB, Berman MG. A pattern of cognitive resource disruptions in childhood psychopathology. Netw Neurosci 2023; 7:1153-1180. [PMID: 37781141 PMCID: PMC10473262 DOI: 10.1162/netn_a_00322] [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: 09/02/2022] [Accepted: 05/01/2023] [Indexed: 10/03/2023] Open
Abstract
The Hurst exponent (H) isolated in fractal analyses of neuroimaging time series is implicated broadly in cognition. Within this literature, H is associated with multiple mental disorders, suggesting that H is transdimensionally associated with psychopathology. Here, we unify these results and demonstrate a pattern of decreased H with increased general psychopathology and attention-deficit/hyperactivity factor scores during a working memory task in 1,839 children. This pattern predicts current and future cognitive performance in children and some psychopathology in 703 adults. This pattern also defines psychological and functional axes associating psychopathology with an imbalance in resource allocation between fronto-parietal and sensorimotor regions, driven by reduced resource allocation to fronto-parietal regions. This suggests the hypothesis that impaired working memory function in psychopathology follows from a reduced cognitive resource pool and a reduction in resources allocated to the task at hand.
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Affiliation(s)
| | | | - Omid Kardan
- Department of Psychology, University of Chicago
| | | | | | - Monica D. Rosenberg
- Department of Psychology, University of Chicago
- The Neuroscience Institute, University of Chicago
| | | | | | - Marc G. Berman
- Department of Psychology, University of Chicago
- The Neuroscience Institute, University of Chicago
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Papo D, Bucolo M, Dimitriadis SI, Onton JA, Philippu A, Shannahoff-Khalsa D. Editorial: Advances in brain dynamics in the healthy and psychiatric disorders. Front Psychiatry 2023; 14:1284670. [PMID: 37779613 PMCID: PMC10539585 DOI: 10.3389/fpsyt.2023.1284670] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 08/31/2023] [Indexed: 10/03/2023] Open
Affiliation(s)
- David Papo
- Center for Translational Neurophysiology of Speech and Communication, Fondazione Istituto Italiano di Tecnologia, Ferrara, Italy
- Department of Neuroscience and Rehabilitation, Section of Physiology, University of Ferrara, Ferrara, Italy
| | - Maide Bucolo
- Department of Electrical, Electronic and Informatics, University of Catania, Catania, Italy
| | - Stavros I. Dimitriadis
- Department of Clinical Psychology and Psychobiology, Faculty of Psychology, University of Barcelona, Barcelona, Spain
| | - Julie A. Onton
- Institute of Neural Computation, University of California, San Diego, La Jolla, CA, United States
| | - Athineos Philippu
- Department of Pharmacology and Toxicology, University of Innsbruck, Innsbruck, Austria
| | - David Shannahoff-Khalsa
- BioCircuits Institute, University of California San Diego, La Jolla, CA, United States
- Center for Integrative Medicine, University of California San Diego, La Jolla, CA, United States
- The Khalsa Foundation for Medical Science, Del Mar, CA, United States
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Bernardi D, Shannahoff-Khalsa D, Sale J, Wright JA, Fadiga L, Papo D. The time scales of irreversibility in spontaneous brain activity are altered in obsessive compulsive disorder. Front Psychiatry 2023; 14:1158404. [PMID: 37234212 PMCID: PMC10208430 DOI: 10.3389/fpsyt.2023.1158404] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 03/30/2023] [Indexed: 05/27/2023] Open
Abstract
We study how obsessive-compulsive disorder (OCD) affects the complexity and time-reversal symmetry-breaking (irreversibility) of the brain resting-state activity as measured by magnetoencephalography (MEG). Comparing MEG recordings from OCD patients and age/sex matched control subjects, we find that irreversibility is more concentrated at faster time scales and more uniformly distributed across different channels of the same hemisphere in OCD patients than in control subjects. Furthermore, the interhemispheric asymmetry between homologous areas of OCD patients and controls is also markedly different. Some of these differences were reduced by 1-year of Kundalini Yoga meditation treatment. Taken together, these results suggest that OCD alters the dynamic attractor of the brain's resting state and hint at a possible novel neurophysiological characterization of this psychiatric disorder and how this therapy can possibly modulate brain function.
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Affiliation(s)
- Davide Bernardi
- Center for Translational Neurophysiology of Speech and Communication, Fondazione Istituto Italiano di Tecnologia, Ferrara, Italy
| | - David Shannahoff-Khalsa
- BioCircuits Institute, University of California, San Diego, La Jolla, CA, United States
- Center for Integrative Medicine, University of California, San Diego, La Jolla, CA, United States
- The Khalsa Foundation for Medical Science, Del Mar, CA, United States
| | - Jeff Sale
- San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA, United States
| | - Jon A. Wright
- BioCircuits Institute, University of California, San Diego, La Jolla, CA, United States
| | - Luciano Fadiga
- Center for Translational Neurophysiology of Speech and Communication, Fondazione Istituto Italiano di Tecnologia, Ferrara, Italy
- Department of Neuroscience and Rehabilitation, Section of Physiology, University of Ferrara, Ferrara, Italy
| | - David Papo
- Center for Translational Neurophysiology of Speech and Communication, Fondazione Istituto Italiano di Tecnologia, Ferrara, Italy
- Department of Neuroscience and Rehabilitation, Section of Physiology, University of Ferrara, Ferrara, Italy
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12
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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: 40] [Impact Index Per Article: 20.0] [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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13
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Kelty-Stephen DG, Mangalam M. Turing's cascade instability supports the coordination of the mind, brain, and behavior. Neurosci Biobehav Rev 2022; 141:104810. [PMID: 35932950 DOI: 10.1016/j.neubiorev.2022.104810] [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: 04/15/2022] [Revised: 06/09/2022] [Accepted: 08/01/2022] [Indexed: 10/16/2022]
Abstract
Turing inspired a computer metaphor of the mind and brain that has been handy and has spawned decades of empirical investigation, but he did much more and offered behavioral and cognitive sciences another metaphor-that of the cascade. The time has come to confront Turing's cascading instability, which suggests a geometrical framework driven by power laws and can be studied using multifractal formalism and multiscale probability density function analysis. Here, we review a rapidly growing body of scientific investigations revealing signatures of cascade instability and their consequences for a perceiving, acting, and thinking organism. We review work related to executive functioning (planning to act), postural control (bodily poise for turning plans into action), and effortful perception (action to gather information in a single modality and action to blend multimodal information). We also review findings on neuronal avalanches in the brain, specifically about neural participation in body-wide cascades. Turing's cascade instability blends the mind, brain, and behavior across space and time scales and provides an alternative to the dominant computer metaphor.
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Affiliation(s)
- Damian G Kelty-Stephen
- Department of Psychology, State University of New York at New Paltz, New Paltz, NY, USA.
| | - Madhur Mangalam
- Department of Physical Therapy, Movement and Rehabilitation Sciences, Northeastern University, Boston, MA, USA.
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14
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E DO, V MS, S LV, E SY. Fractal Structure of Brain Electrical Activity of Patients With Mental Disorders. Front Physiol 2022; 13:905318. [PMID: 35923231 PMCID: PMC9340582 DOI: 10.3389/fphys.2022.905318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Accepted: 06/23/2022] [Indexed: 11/19/2022] Open
Abstract
This work was aimed at a comparative analysis of the degree of multifractality of electroencephalographic time series obtained from a group of healthy subjects and from patients with mental disorders. We analyzed long-term records of patients with paranoid schizophrenia and patients with depression. To evaluate the properties of multifractal scaling of various electroencephalographic time series, the method of maximum modulus of the wavelet transform and multifractal analysis of fluctuations without a trend were used. The stability of the width and position of the singularity spectrum for each of the test groups was revealed, and a relationship was established between the correlation and anticorrelation dynamics of successive values of the electroencephalographic time series and the type of mental disorders. It was shown that the main differences between the multifractal properties of brain activity in normal and pathological conditions lie in the different width of the multifractality spectrum and its location associated with the correlated or anticorrelated dynamics of the values of successive time series. It was found that the schizophrenia group is characterized by a greater degree of multifractality compared to the depression group. Thus, the degree of multifractality can be included in a set of tests for differential diagnosis and research of mental disorders.
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Affiliation(s)
- Dick O. E
- Laboratory of Physiology of Reception, Pavlov Institute of Physiology of Russian Academy of Science, St. Petersburg, Russia
- *Correspondence: Dick O. E,
| | - Murav’eva S. V
- Laboratory of Vision Physiology, Pavlov Institute of Physiology of Russian Academy of Science, St. Petersburg, Russia
| | - Lebedev V. S
- Laboratory of Vision Physiology, Pavlov Institute of Physiology of Russian Academy of Science, St. Petersburg, Russia
| | - Shelepin Yu. E
- Laboratory of Vision Physiology, Pavlov Institute of Physiology of Russian Academy of Science, St. Petersburg, Russia
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15
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Roy S, Banerjee A, Roy C, Nag S, Sanyal S, Sengupta R, Ghosh D. Brain response to color stimuli: an EEG study with nonlinear approach. Cogn Neurodyn 2021; 15:1023-1053. [PMID: 34790269 DOI: 10.1007/s11571-021-09692-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 05/22/2021] [Accepted: 06/11/2021] [Indexed: 11/26/2022] Open
Abstract
Color perception is a major guiding factor in the evolutionary process of human civilization, but most of the neurological background of the same are yet unknown. This work attempts to address this area with an EEG based neuro-cognitive study on response of brain to different color stimuli. With respect to a Grey baseline seven colors of the VIBGYOR were shown to 16 participants with normal color vision and corresponding EEG signals from different lobes (Frontal, Occipital & Parietal) were recorded. In an attempt to quantify the brain response while watching these colors, the corresponding EEG signals were analysed using two of the latest state of the art non-linear techniques (MFDFA and MFDXA) of dealing complex time series. MFDFA revealed that for all the participants the spectral width, and hence the complexity of the EEG signals, reaches a maximum while viewing color Blue, followed by colors Red and Green in all the brain lobes. MFDXA, on the other hand, suggests a lower degree of inter and intra lobe correlation while watching the VIBGYOR colors compared to baseline Grey, hinting towards a post processing of visual information. We hope that along with the novelty of methodologies, the unique outcomes of this study may leave a long term impact in the domain of color perception research.
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Affiliation(s)
- Souparno Roy
- Department of Physics, Jadavpur University, Kolkata, India
- Sir C.V. Raman Centre for Physics and Music, Jadavpur University, Kolkata, India
| | - Archi Banerjee
- Sir C.V. Raman Centre for Physics and Music, Jadavpur University, Kolkata, India
- Rekhi Centre of Excellence for the Science of Happiness, IIT Kharagpur, Kharagpur, India
| | - Chandrima Roy
- Sir C.V. Raman Centre for Physics and Music, Jadavpur University, Kolkata, India
- Department of Electronics & Communication Engineering, Heritage Institute of Technology, Kolkata, India
| | - Sayan Nag
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Shankha Sanyal
- Sir C.V. Raman Centre for Physics and Music, Jadavpur University, Kolkata, India
- School of Languages and Linguistics, Jadavpur University, Kolkata, India
| | - Ranjan Sengupta
- Sir C.V. Raman Centre for Physics and Music, Jadavpur University, Kolkata, India
| | - Dipak Ghosh
- Sir C.V. Raman Centre for Physics and Music, Jadavpur University, Kolkata, India
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16
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Favela LH, Amon MJ, Lobo L, Chemero A. Empirical Evidence for Extended Cognitive Systems. Cogn Sci 2021; 45:e13060. [PMID: 34762738 PMCID: PMC9285798 DOI: 10.1111/cogs.13060] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Revised: 08/28/2021] [Accepted: 09/30/2021] [Indexed: 11/27/2022]
Abstract
We present an empirically supported theoretical and methodological framework for quantifying the system‐level properties of person‐plus‐tool interactions in order to answer the question: “Are person‐plus‐tool‐systems extended cognitive systems?” Nineteen participants provided perceptual judgments regarding their ability to pass through apertures of various widths while using visual information, blindfolded wielding a rod, or blindfolded wielding an Enactive Torch—a vibrotactile sensory‐substitution device for detecting distance. Monofractal, multifractal, and recurrence quantification analyses were conducted to assess features of person‐plus‐tool movement dynamics. Trials where people utilized the rod or Enactive Torch demonstrated stable “self‐similarity,” or indices of healthy and adaptive single systems, regardless of aperture width, trial order, features of the participants’ judgments, and participant characteristics. Enactive Torch trials exhibited a somewhat greater range of dynamic fluctuations than the rod trials, as well as less movement recurrence, suggesting that the Enactive Torch allowed for more exploratory movements. Findings provide support for the notion that person‐plus‐tool systems can be classified as extended cognitive systems and a framework for quantifying system‐level properties of these systems. Implications concerning future research on extended cognition are discussed.
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Affiliation(s)
- Luis H Favela
- Department of Philosophy, University of Central Florida.,Cognitive Sciences Program, University of Central Florida
| | - Mary Jean Amon
- School of Modeling, Simulation, and Training, University of Central Florida
| | - Lorena Lobo
- Departamento de Psicología, Universidad a Distancia de Madrid
| | - Anthony Chemero
- Department of Philosophy, University of Cincinnati.,Department of Psychology, University of Cincinnati
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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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Zimmern V. Why Brain Criticality Is Clinically Relevant: A Scoping Review. Front Neural Circuits 2020; 14:54. [PMID: 32982698 PMCID: PMC7479292 DOI: 10.3389/fncir.2020.00054] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2020] [Accepted: 07/23/2020] [Indexed: 12/13/2022] Open
Abstract
The past 25 years have seen a strong increase in the number of publications related to criticality in different areas of neuroscience. The potential of criticality to explain various brain properties, including optimal information processing, has made it an increasingly exciting area of investigation for neuroscientists. Recent reviews on this topic, sometimes termed brain criticality, make brief mention of clinical applications of these findings to several neurological disorders such as epilepsy, neurodegenerative disease, and neonatal hypoxia. Other clinicallyrelevant domains - including anesthesia, sleep medicine, developmental-behavioral pediatrics, and psychiatry - are seldom discussed in review papers of brain criticality. Thorough assessments of these application areas and their relevance for clinicians have also yet to be published. In this scoping review, studies of brain criticality involving human data of all ages are evaluated for their current and future clinical relevance. To make the results of these studies understandable to a more clinical audience, a review of the key concepts behind criticality (e.g., phase transitions, long-range temporal correlation, self-organized criticality, power laws, branching processes) precedes the discussion of human clinical studies. Open questions and forthcoming areas of investigation are also considered.
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Affiliation(s)
- Vincent Zimmern
- Division of Child Neurology, The University of Texas Southwestern Medical Center, Dallas, TX, United States
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19
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Abstract
Arrhythmic fluctuations in neural activity occur at many levels of the nervous system. Such activity does not have a characteristic temporal periodicity but can exhibit statistical similarities, most commonly power-law scaling behavior, which is indicative of scale-free dynamics. The recurrence of scaling laws across many different systems and its manifestation in behavior has prompted a search for unifying principles in human brain function. With this in mind, a focused search for abnormities in scale-free dynamics is of considerable clinical relevance to migraine and other clinical pain disorders. Here, we examined the scale-free properties of the resting-state functional magnetic resonance imaging (fMRI) signal in the broadband frequency range known to be related to spontaneous neural activity (0.01-0.1 Hz). In a large cohort of episodic migraine patients (N = 40), we observed that the strength of long-range temporal correlations in the fMRI signal (captured by the scaling exponent α) was significantly higher in the sensorimotor network compared with healthy controls. Increases in the scaling exponent were positively correlated with fMRI signal variance and negatively correlated with the patient's self-reported headache intensity. These changes in the fMRI signal suggest that the temporal structure of amplitude fluctuations carries valuable information about the dynamic state of the underlying neuronal networks and ensuing sensory impairments in migraine. The demonstrated scaling laws pose a novel quantitative approach for examining clinically relevant interindividual variability in migraine and other pain disorders.
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20
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Hart MG, Romero-Garcia R, Price SJ, Suckling J. Global Effects of Focal Brain Tumors on Functional Complexity and Network Robustness: A Prospective Cohort Study. Neurosurgery 2020; 84:1201-1213. [PMID: 30137556 PMCID: PMC6520100 DOI: 10.1093/neuros/nyy378] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2017] [Accepted: 07/19/2018] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Neurosurgical management of brain tumors has entered a paradigm of supramarginal resections that demands thorough understanding of peritumoral functional effects. Historically, the effects of tumors have been believed to be local, and long-range effects have not been considered. OBJECTIVE To test the hypothesis that tumors affect the brain globally, producing long-range gradients in cortical function. METHODS Resting-state functional magnetic resonance imaging (fMRI) data were acquired from 11 participants with glioblastoma and split into discovery and validation datasets in a single-center prospective cohort study. Fractal complexity was computed with a wavelet-based estimator of the Hurst exponent. Distance-related effects of the tumors were tested with a tumor mask-dilation technique and parcellation of the underlying Hurst maps. RESULTS Fractal complexity demonstrates a penumbra of suppression in the peritumoral region. At a global level, as distance from the tumor increases, this initial suppression is balanced by a subsequent overactivity before finally normalizing. These effects were best fit by a quadratic model and were consistent across different network construction pipelines. The Hurst exponent was correlated with graph theory measures of centrality including network robustness, but graph theory measures did not demonstrate distance-dependent effects. CONCLUSION This work provides evidence supporting the theory that focal brain tumors produce long-range gradients in function. Consequently, the effects of focal lesions need to be interpreted in terms of the global changes on functional complexity and network architecture rather than purely in terms of functional localization. Determining whether peritumoral changes represent potential plasticity may facilitate extended resection of tumors without functional cost.
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Affiliation(s)
- Michael G Hart
- Brain Mapping Unit, Department of Psychiatry, Sir William Hardy Building, University of Cambridge, Cambridge, United Kingdom.,Academic Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Rafael Romero-Garcia
- Brain Mapping Unit, Department of Psychiatry, Sir William Hardy Building, University of Cambridge, Cambridge, United Kingdom.,Academic Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Stephen J Price
- Academic Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - John Suckling
- Brain Mapping Unit, Department of Psychiatry, Herchel Smith Building for Brain and Mind Sciences, University of Cambridge, Cambridge, United Kingdom
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21
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Churchill NW, Hutchison MG, Graham SJ, Schweizer TA. Scale-free functional brain dynamics during recovery from sport-related concussion. Hum Brain Mapp 2020; 41:2567-2582. [PMID: 32348019 PMCID: PMC7294069 DOI: 10.1002/hbm.24962] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 02/06/2020] [Accepted: 02/12/2020] [Indexed: 11/24/2022] Open
Abstract
Studies using blood‐oxygenation‐level‐dependent functional magnetic resonance imaging (BOLD fMRI) have characterized how the resting brain is affected by concussion. The literature to date, however, has largely focused on measuring changes in the spatial organization of functional brain networks. In the present study, changes in the temporal dynamics of BOLD signals are examined throughout concussion recovery using scaling (or fractal) analysis. Imaging data were collected for 228 university‐level athletes, 61 with concussion and 167 athletic controls. Concussed athletes were scanned at the acute phase of injury (1–7 days postinjury), the subacute phase (8–14 days postinjury), medical clearance to return to sport (RTS), 1 month post‐RTS and 1 year post‐RTS. The wavelet leader multifractal approach was used to assess scaling (c1) and multifractal (c2) behavior. Significant longitudinal changes were identified for c1, which was lowest at acute injury, became significantly elevated at RTS, and returned near control levels by 1 year post‐RTS. No longitudinal changes were identified for c2. Secondary analyses showed that clinical measures of acute symptom severity and time to RTP were related to longitudinal changes in c1. Athletes with both higher symptoms and prolonged recovery had elevated c1 values at RTS, while athletes with higher symptoms but rapid recovery had reduced c1 at acute injury. This study provides the first evidence for long‐term recovery of BOLD scale‐free brain dynamics after a concussion.
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Affiliation(s)
- Nathan W Churchill
- Neuroscience Research Program, St. Michael's Hospital, Toronto, Canada.,Keenan Research Centre for Biomedical Science of St. Michael's Hospital, Toronto, Canada
| | - Michael G Hutchison
- Neuroscience Research Program, St. Michael's Hospital, Toronto, Canada.,Keenan Research Centre for Biomedical Science of St. Michael's Hospital, Toronto, Canada.,Faculty of Kinesiology and Physical Education, University of Toronto, Toronto, Canada
| | - Simon J Graham
- Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Canada.,Department of Medical Biophysics, University of Toronto Faculty of Medicine, Toronto, Canada
| | - Tom A Schweizer
- Neuroscience Research Program, St. Michael's Hospital, Toronto, Canada.,Keenan Research Centre for Biomedical Science of St. Michael's Hospital, Toronto, Canada.,Faculty of Medicine (Neurosurgery), University of Toronto, Toronto, Canada
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22
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Connections, Tracts, Fractals, and the Rest: A Working Guide to Network and Connectivity Studies in Neurosurgery. World Neurosurg 2020; 140:389-400. [PMID: 32247795 DOI: 10.1016/j.wneu.2020.03.116] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Revised: 03/19/2020] [Accepted: 03/20/2020] [Indexed: 12/26/2022]
Abstract
Brain mapping and connectomics can probe networks that span the entire brain, producing a diverse range of outputs for probing specific clinically relevant questions. The potential for understanding the effect of focal lesions on brain function, cognition, and plasticity abounds, any one of which would likely yield more effective and safer neurosurgical strategies. However, the possibilities of advanced magnetic resonance imaging and connectomics have been somewhat underused in neurosurgery, arising from actual or perceived difficulties in either application or analysis. The present review builds on previous work describing the theoretical attractions of connectomics to deliberate on the practical details of performing high-quality connectomics studies in neurosurgery. First, the data and methods involved in deriving connectomics models will be considered, specifically for the purpose of determining the nature of inferences that can be made subsequently. Next, a selection of key analysis methods will be explored using practical examples that illustrate their effective implementation and the insights that can be gleaned. The principles of study design will be introduced, including analysis tips and methods for making efficient use of available resources. Finally, a review of the best research practices for neuroimaging studies will be discussed, including principles of open access data sharing, study preregistration, and methods for improving replicability. Ultimately, we hope readers will be better placed to appraise the current connectomics studies in neurosurgery and empowered to develop their own high-quality studies, both of which are key steps in realizing the true potential of connectomics and advanced neuroimaging analyses in general.
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23
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França LGS, Miranda JGV, Leite M, Sharma NK, Walker MC, Lemieux L, Wang Y. Fractal and Multifractal Properties of Electrographic Recordings of Human Brain Activity: Toward Its Use as a Signal Feature for Machine Learning in Clinical Applications. Front Physiol 2018; 9:1767. [PMID: 30618789 PMCID: PMC6295567 DOI: 10.3389/fphys.2018.01767] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2018] [Accepted: 11/22/2018] [Indexed: 01/08/2023] Open
Abstract
The quantification of brain dynamics is essential to its understanding. However, the brain is a system operating on multiple time scales, and characterization of dynamics across time scales remains a challenge. One framework to study such dynamics is that of fractal geometry; and currently there exist several methods for the study of brain dynamics using fractal geometry. We aim to highlight some of the practical challenges of applying fractal geometry to brain dynamics—and as a putative feature for machine learning applications, and propose solutions to enable its wider use in neuroscience. Using intracranially recorded electroencephalogram (EEG) and simulated data, we compared monofractal and multifractal methods with regards to their sensitivity to signal variance. We found that both monofractal and multifractal properties correlate closely with signal variance, thus not being a useful feature of the signal. However, after applying an epoch-wise standardization procedure to the signal, we found that multifractal measures could offer non-redundant information compared to signal variance, power (in different frequency bands) and other established EEG signal measures. We also compared different multifractal estimation methods to each other in terms of reliability, and we found that the Chhabra-Jensen algorithm performed best. Finally, we investigated the impact of sampling frequency and epoch length on the estimation of multifractal properties. Using epileptic seizures as an example event in the EEG, we show that there may be an optimal time scale (i.e., combination of sampling frequency and epoch length) for detecting temporal changes in multifractal properties around seizures. The practical issues we highlighted and our suggested solutions should help in developing robust methods for the application of fractal geometry in EEG signals. Our analyses and observations also aid the theoretical understanding of the multifractal properties of the brain and might provide grounds for new discoveries in the study of brain signals. These could be crucial for the understanding of neurological function and for the developments of new treatments.
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Affiliation(s)
- Lucas G Souza França
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | | | - Marco Leite
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Niraj K Sharma
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Matthew C Walker
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Louis Lemieux
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Yujiang Wang
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom.,Interdisciplinary Computing and Complex BioSystems (ICOS) Research Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom.,Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, United Kingdom
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24
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La Rocca D, Zilber N, Abry P, van Wassenhove V, Ciuciu P. Self-similarity and multifractality in human brain activity: A wavelet-based analysis of scale-free brain dynamics. J Neurosci Methods 2018; 309:175-187. [DOI: 10.1016/j.jneumeth.2018.09.010] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2018] [Revised: 09/06/2018] [Accepted: 09/06/2018] [Indexed: 12/16/2022]
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25
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Akhrif A, Romanos M, Domschke K, Schmitt-Boehrer A, Neufang S. Fractal Analysis of BOLD Time Series in a Network Associated With Waiting Impulsivity. Front Physiol 2018; 9:1378. [PMID: 30337880 PMCID: PMC6180197 DOI: 10.3389/fphys.2018.01378] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2018] [Accepted: 09/11/2018] [Indexed: 02/05/2023] Open
Abstract
Fractal phenomena can be found in numerous scientific areas including neuroscience. Fractals are structures, in which the whole has the same shape as its parts. A specific structure known as pink noise (also called fractal or 1/f noise) is one key fractal manifestation, exhibits both stability and adaptability, and can be addressed via the Hurst exponent (H). FMRI studies using H on regional fMRI time courses used fractality as an important characteristic to unravel neural networks from artificial noise. In this fMRI-study, we examined 103 healthy male students at rest and while performing the 5-choice serial reaction time task. We addressed fractality in a network associated with waiting impulsivity using the adaptive fractal analysis (AFA) approach to determine H. We revealed the fractal nature of the impulsivity network. Furthermore, fractality was influenced by individual impulsivity in terms of decreasing fractality with higher impulsivity in regions of top-down control (left middle frontal gyrus) as well as reward processing (nucleus accumbens and anterior cingulate cortex). We conclude that fractality as determined via H is a promising marker to quantify deviations in network functions at an early stage and, thus, to be able to inform preventive interventions before the manifestation of a disorder.
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Affiliation(s)
- Atae Akhrif
- Center of Mental Health, Department of Child and Adolescent Psychiatry, University of Wuerzburg, Wuerzburg, Germany
| | - Marcel Romanos
- Center of Mental Health, Department of Child and Adolescent Psychiatry, University of Wuerzburg, Wuerzburg, Germany
| | - Katharina Domschke
- Department of Psychiatry and Psychotherapy, Faculty of Medicine, Medical Centre - University of Freiburg, Freiburg, Germany
| | - Angelika Schmitt-Boehrer
- Center of Mental Health, Department of Psychiatry, Psychosomatics and Psychotherapy, University of Wuerzburg, Wuerzburg, Germany
| | - Susanne Neufang
- Center of Mental Health, Department of Child and Adolescent Psychiatry, University of Wuerzburg, Wuerzburg, Germany
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26
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Muthukumaraswamy SD, Liley DT. 1/f electrophysiological spectra in resting and drug-induced states can be explained by the dynamics of multiple oscillatory relaxation processes. Neuroimage 2018; 179:582-595. [PMID: 29959047 DOI: 10.1016/j.neuroimage.2018.06.068] [Citation(s) in RCA: 58] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2017] [Revised: 06/20/2018] [Accepted: 06/25/2018] [Indexed: 02/01/2023] Open
Abstract
Neurophysiological recordings are dominated by arhythmical activity whose spectra can be characterised by power-law functions, and on this basis are often referred to as reflecting scale-free brain dynamics (1/fβ). Relatively little is known regarding the neural generators and temporal dynamics of this arhythmical behaviour compared to rhythmical behaviour. Here we used Irregularly Resampled AutoSpectral Analysis (IRASA) to quantify β, in both the high (5-100 Hz, βhf) and low frequency bands (0.1-2.5 Hz, βlf) in MEG/EEG/ECoG recordings and to separate arhythmical from rhythmical modes of activity, such as, alpha rhythms. In MEG/EEG/ECoG data, we demonstrate that oscillatory alpha power dynamically correlates over time with βhf and similarly, participants with higher rhythmical alpha power have higher βhf. In a series of pharmaco-MEG investigations using the GABA reuptake inhibitor tiagabine, the glutamatergic AMPA receptor antagonist perampanel, the NMDA receptor antagonist ketamine and the mixed partial serotonergic agonist LSD, a variety of effects on both βhf and βlf were observed. Additionally, strong modulations of βhf were seen in monkey ECoG data during general anaesthesia using propofol and ketamine. We develop and test a unifying model which can explain, the 1/f nature of electrophysiological spectra, their dynamic interaction with oscillatory rhythms as well as the sensitivity of 1/f activity to drug interventions by considering electrophysiological spectra as being generated by a collection of stochastically perturbed damped oscillators having a distribution of relaxation rates.
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Affiliation(s)
- Suresh D Muthukumaraswamy
- School of Pharmacy and Centre for Brain Research, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand.
| | - David Tj Liley
- Centre for Human Psychopharmacology, School of Health Sciences, Swinburne University of Technology, Melbourne, Australia
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27
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Tozzi A, Peters JF, Çankaya MN. The informational entropy endowed in cortical oscillations. Cogn Neurodyn 2018; 12:501-507. [PMID: 30250628 DOI: 10.1007/s11571-018-9491-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2018] [Revised: 05/31/2018] [Accepted: 06/14/2018] [Indexed: 12/20/2022] Open
Abstract
A two-dimensional shadow may encompass more information than its corresponding three-dimensional object. Indeed, if we rotate the object, we achieve a pool of observed shadows from different angulations, gradients, shapes and variable length contours that make it possible for us to increase our available information. Starting from this simple observation, we show how informational entropies might turn out to be useful in the evaluation of scale-free dynamics in the brain. Indeed, brain activity exhibits a scale-free distribution that leads to the variations in the power law exponent typical of different functional neurophysiological states. Here we show that modifications in scaling slope are associated with variations in Rényi entropy, a generalization of Shannon informational entropy. From a three-dimensional object's perspective, by changing its orientation (standing for the cortical scale-free exponent), we detect different two-dimensional shadows from different perception angles (standing for Rényi entropy in different brain areas). We show how, starting from known values of Rényi entropy (easily detectable in brain fMRIs or EEG traces), it is feasible to calculate the scaling slope in a given moment and in a given brain area. Because changes in scale-free cortical dynamics modify brain activity, this issue points towards novel approaches to mind reading and description of the forces required for transcranial stimulation.
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Affiliation(s)
- Arturo Tozzi
- 1Computational Intelligence Laboratory, University of Manitoba, Winnipeg, MB R3T 5V6 Canada
| | - James F Peters
- 2Department of Electrical and Computer Engineering, University of Manitoba, 75A Chancellor's Circle, Winnipeg, MB R3T 5V6 Canada
- 3Department of Mathematics, Faculty of Arts and Sciences, Adıyaman University, 02040 Adıyaman, Turkey
| | - Mehmet Niyazi Çankaya
- 4Applied Sciences School, Department of International Trading, Department of Statistics, Faculty of Arts and Science, Usak University, Usak, Turkey
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Beyond the anatomy-based representation of brain function: Comment on "Topodynamics of metastable brains" by Arturo Tozzi et al. Phys Life Rev 2017; 21:42-45. [PMID: 28460822 DOI: 10.1016/j.plrev.2017.04.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2017] [Accepted: 04/25/2017] [Indexed: 11/21/2022]
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29
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Papo D, Goñi J, Buldú JM. Editorial: On the relation of dynamics and structure in brain networks. CHAOS (WOODBURY, N.Y.) 2017; 27:047201. [PMID: 28456177 DOI: 10.1063/1.4981391] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Affiliation(s)
- David Papo
- SCALab, CNRS, Université Lille 3, Villeneuve d'Ascq, France
| | - Joaquin Goñi
- School of Engineering, Purdue University, West-Lafayette, Indiana 47907-2023, USA
| | - Javier M Buldú
- Complex, Systems Group, Universidad Rey Juan Carlos, Madrid, Spain
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30
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Rădulescu AR, Hannon ER. Applying fMRI complexity analyses to the single subject: a case study for proposed neurodiagnostics. Neurocase 2017; 23:120-137. [PMID: 28562172 DOI: 10.1080/13554794.2017.1316410] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Nonlinear dynamic tools have been statistically validated at the group level to identify subtle differences in system wide regulation of brain meso-circuits, often increasing clinical sensitivity over conventional analyses alone. We explored the feasibility of extracting information at the single-subject level, illustrating two pairs of healthy individuals with psychological differences in stress reactivity. We applied statistical and nonlinear dynamic tools to capture key characteristics of the prefrontal-limbic loop. We compared single subject results with statistical results for the larger group. We concluded that complexity analyses may identify important differences at the single-subject level, supporting their potential towards neurodiagnostic applications.
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Affiliation(s)
| | - Emily R Hannon
- b Department of Ecology and Evolutionary Biology , University of Colorado at Boulder , Boulder , CO , USA
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31
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Khalili-Mahani N, Rombouts SARB, van Osch MJP, Duff EP, Carbonell F, Nickerson LD, Becerra L, Dahan A, Evans AC, Soucy JP, Wise R, Zijdenbos AP, van Gerven JM. Biomarkers, designs, and interpretations of resting-state fMRI in translational pharmacological research: A review of state-of-the-Art, challenges, and opportunities for studying brain chemistry. Hum Brain Mapp 2017; 38:2276-2325. [PMID: 28145075 DOI: 10.1002/hbm.23516] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2016] [Revised: 11/21/2016] [Accepted: 01/04/2017] [Indexed: 12/11/2022] Open
Abstract
A decade of research and development in resting-state functional MRI (RSfMRI) has opened new translational and clinical research frontiers. This review aims to bridge between technical and clinical researchers who seek reliable neuroimaging biomarkers for studying drug interactions with the brain. About 85 pharma-RSfMRI studies using BOLD signal (75% of all) or arterial spin labeling (ASL) were surveyed to investigate the acute effects of psychoactive drugs. Experimental designs and objectives include drug fingerprinting dose-response evaluation, biomarker validation and calibration, and translational studies. Common biomarkers in these studies include functional connectivity, graph metrics, cerebral blood flow and the amplitude and spectrum of BOLD fluctuations. Overall, RSfMRI-derived biomarkers seem to be sensitive to spatiotemporal dynamics of drug interactions with the brain. However, drugs cause both central and peripheral effects, thus exacerbate difficulties related to biological confounds, structured noise from motion and physiological confounds, as well as modeling and inference testing. Currently, these issues are not well explored, and heterogeneities in experimental design, data acquisition and preprocessing make comparative or meta-analysis of existing reports impossible. A unifying collaborative framework for data-sharing and data-mining is thus necessary for investigating the commonalities and differences in biomarker sensitivity and specificity, and establishing guidelines. Multimodal datasets including sham-placebo or active control sessions and repeated measurements of various psychometric, physiological, metabolic and neuroimaging phenotypes are essential for pharmacokinetic/pharmacodynamic modeling and interpretation of the findings. We provide a list of basic minimum and advanced options that can be considered in design and analyses of future pharma-RSfMRI studies. Hum Brain Mapp 38:2276-2325, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Najmeh Khalili-Mahani
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montreal, Canada.,PERFORM Centre, Concordia University, Montreal, Canada
| | - Serge A R B Rombouts
- Department of Radiology, Leiden University Medical Centre, Leiden, The Netherlands.,Institute of Psychology and Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | | | - Eugene P Duff
- Institute of Psychology and Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands.,Oxford Centre for Functional MRI of the Brain, Oxford University, Oxford, United Kingdom
| | | | - Lisa D Nickerson
- McLean Hospital, Belmont, Massachusetts.,Harvard Medical School, Boston, Massachusetts
| | - Lino Becerra
- Center for Pain and the Brain, Harvard Medical School & Boston Children's Hospital, Boston, Massachusetts
| | - Albert Dahan
- Department of Anesthesiology, Leiden University Medical Centre, Leiden, The Netherlands
| | - Alan C Evans
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montreal, Canada.,McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Jean-Paul Soucy
- PERFORM Centre, Concordia University, Montreal, Canada.,McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Richard Wise
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Alex P Zijdenbos
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montreal, Canada.,Biospective Inc, Montreal, Quebec, Canada
| | - Joop M van Gerven
- Centre for Human Drug Research, Leiden University Medical Centre, Leiden, The Netherlands
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The suppression of scale-free fMRI brain dynamics across three different sources of effort: aging, task novelty and task difficulty. Sci Rep 2016; 6:30895. [PMID: 27498696 PMCID: PMC4976369 DOI: 10.1038/srep30895] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2015] [Accepted: 07/10/2016] [Indexed: 12/02/2022] Open
Abstract
There is growing evidence that fluctuations in brain activity may exhibit scale-free (“fractal”) dynamics. Scale-free signals follow a spectral-power curve of the form P(f ) ∝ f−β, where spectral power decreases in a power-law fashion with increasing frequency. In this study, we demonstrated that fractal scaling of BOLD fMRI signal is consistently suppressed for different sources of cognitive effort. Decreases in the Hurst exponent (H), which quantifies scale-free signal, was related to three different sources of cognitive effort/task engagement: 1) task difficulty, 2) task novelty, and 3) aging effects. These results were consistently observed across multiple datasets and task paradigms. We also demonstrated that estimates of H are robust across a range of time-window sizes. H was also compared to alternative metrics of BOLD variability (SDBOLD) and global connectivity (Gconn), with effort-related decreases in H producing similar decreases in SDBOLD and Gconn. These results indicate a potential global brain phenomenon that unites research from different fields and indicates that fractal scaling may be a highly sensitive metric for indexing cognitive effort/task engagement.
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Dimitriadis SI, Linden D. Modulation of brain criticality via suppression of EEG long-range temporal correlations (LRTCs) in a closed-loop neurofeedback stimulation. Clin Neurophysiol 2016; 127:2878-2881. [PMID: 27323657 DOI: 10.1016/j.clinph.2016.05.359] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2016] [Accepted: 05/28/2016] [Indexed: 12/29/2022]
Affiliation(s)
- Stavros I Dimitriadis
- Institute of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine, Cardiff, CF24 4HQ, UK; Cardiff University Brain Research Imaging Center (CUBRIC), School of Psychology, Cardiff University, Cardiff, CF24 4HQ, UK; Artificial Intelligence and Information Analysis Laboratory, Department of Informatics, Aristotle University, 54124 Thessaloniki, Greece; NeuroInformatics Group, AUTH, Thessaloniki, Greece.
| | - David Linden
- Institute of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine, Cardiff, CF24 4HQ, UK; Cardiff University Brain Research Imaging Center (CUBRIC), School of Psychology, Cardiff University, Cardiff, CF24 4HQ, UK
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Kida T, Tanaka E, Kakigi R. Multi-Dimensional Dynamics of Human Electromagnetic Brain Activity. Front Hum Neurosci 2016; 9:713. [PMID: 26834608 PMCID: PMC4717327 DOI: 10.3389/fnhum.2015.00713] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2015] [Accepted: 12/21/2015] [Indexed: 12/21/2022] Open
Abstract
Magnetoencephalography (MEG) and electroencephalography (EEG) are invaluable neuroscientific tools for unveiling human neural dynamics in three dimensions (space, time, and frequency), which are associated with a wide variety of perceptions, cognition, and actions. MEG/EEG also provides different categories of neuronal indices including activity magnitude, connectivity, and network properties along the three dimensions. In the last 20 years, interest has increased in inter-regional connectivity and complex network properties assessed by various sophisticated scientific analyses. We herein review the definition, computation, short history, and pros and cons of connectivity and complex network (graph-theory) analyses applied to MEG/EEG signals. We briefly describe recent developments in source reconstruction algorithms essential for source-space connectivity and network analyses. Furthermore, we discuss a relatively novel approach used in MEG/EEG studies to examine the complex dynamics represented by human brain activity. The correct and effective use of these neuronal metrics provides a new insight into the multi-dimensional dynamics of the neural representations of various functions in the complex human brain.
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Affiliation(s)
- Tetsuo Kida
- Department of Integrative Physiology, National Institute for Physiological SciencesOkazaki, Japan
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35
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Fetterhoff D, Kraft RA, Sandler RA, Opris I, Sexton CA, Marmarelis VZ, Hampson RE, Deadwyler SA. Distinguishing cognitive state with multifractal complexity of hippocampal interspike interval sequences. Front Syst Neurosci 2015; 9:130. [PMID: 26441562 PMCID: PMC4585000 DOI: 10.3389/fnsys.2015.00130] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2015] [Accepted: 09/03/2015] [Indexed: 11/15/2022] Open
Abstract
Fractality, represented as self-similar repeating patterns, is ubiquitous in nature and the brain. Dynamic patterns of hippocampal spike trains are known to exhibit multifractal properties during working memory processing; however, it is unclear whether the multifractal properties inherent to hippocampal spike trains reflect active cognitive processing. To examine this possibility, hippocampal neuronal ensembles were recorded from rats before, during and after a spatial working memory task following administration of tetrahydrocannabinol (THC), a memory-impairing component of cannabis. Multifractal detrended fluctuation analysis was performed on hippocampal interspike interval sequences to determine characteristics of monofractal long-range temporal correlations (LRTCs), quantified by the Hurst exponent, and the degree/magnitude of multifractal complexity, quantified by the width of the singularity spectrum. Our results demonstrate that multifractal firing patterns of hippocampal spike trains are a marker of functional memory processing, as they are more complex during the working memory task and significantly reduced following administration of memory impairing THC doses. Conversely, LRTCs are largest during resting state recordings, therefore reflecting different information compared to multifractality. In order to deepen conceptual understanding of multifractal complexity and LRTCs, these measures were compared to classical methods using hippocampal frequency content and firing variability measures. These results showed that LRTCs, multifractality, and theta rhythm represent independent processes, while delta rhythm correlated with multifractality. Taken together, these results provide a novel perspective on memory function by demonstrating that the multifractal nature of spike trains reflects hippocampal microcircuit activity that can be used to detect and quantify cognitive, physiological, and pathological states.
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Affiliation(s)
- Dustin Fetterhoff
- Neuroscience Program, Wake Forest School of Medicine Winston-Salem, NC, USA ; Department of Physiology and Pharmacology, Wake Forest School of Medicine Winston-Salem, NC, USA
| | - Robert A Kraft
- Department of Biomedical Engineering, Wake Forest School of Medicine Winston-Salem, NC, USA
| | - Roman A Sandler
- Department of Biomedical Engineering, University of Southern California Los Angeles, CA, USA
| | - Ioan Opris
- Department of Physiology and Pharmacology, Wake Forest School of Medicine Winston-Salem, NC, USA
| | - Cheryl A Sexton
- Department of Biomedical Engineering, Wake Forest School of Medicine Winston-Salem, NC, USA
| | - Vasilis Z Marmarelis
- Department of Biomedical Engineering, University of Southern California Los Angeles, CA, USA
| | - Robert E Hampson
- Department of Physiology and Pharmacology, Wake Forest School of Medicine Winston-Salem, NC, USA
| | - Sam A Deadwyler
- Department of Physiology and Pharmacology, Wake Forest School of Medicine Winston-Salem, NC, USA
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36
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Ni H, Zhou L, Ning X, Wang L. Exploring multifractal-based features for mild Alzheimer's disease classification. Magn Reson Med 2015; 76:259-69. [PMID: 26193379 DOI: 10.1002/mrm.25853] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2014] [Revised: 06/28/2015] [Accepted: 06/29/2015] [Indexed: 11/11/2022]
Abstract
PURPOSE Multifractal applications to resting state functional MRI (rs-fMRI) time series for diagnosing Alzheimer's disease (AD) are still limited. We aim to address two issues: (I) if and what multifractal features are sufficiently discriminative to detect AD from the healthy; (II) if AD classification could be further improved by combining multifractal features with traditional features in this field. METHODS Rs-fMRI data of 25 AD patients and 38 normal controls were analyzed. A set of multifractal features were systematically investigated. Traditional features in monofractal, linear, and network-based categories were also extracted for comparison and combination. Both support vector machines and multiple kernel learning (MKL) were used to perform classification with individual and combined features. RESULTS We identified a multifractal feature, Δf, which has the strongest discriminative power among all the features in our study. Moreover, we found that the classification accuracy could be significantly improved from 69% (by Δf only) to up to 76%, when nonsparse MKL is used to combine Δf with the monofractal feature, Hurst. Finally, we showed that incorporating other multifractal features, α(0), Δα and Pc, could also improve traditional-feature-based AD classification. CONCLUSION Our work demonstrated the potential usefulness of multifractal analysis for AD research, especially when combining with the traditional rs-fMRI features. It contributes to distinguishing AD from NC subjects. Magn Reson Med 76:259-269, 2016. © 2015 Wiley Periodicals, Inc.
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Affiliation(s)
- Huangjing Ni
- School of Electronic Science and Engineering, Nanjing University, Nanjing, China.,School of Computer Science and Software Engineering, University of Wollongong, Wollongong, NSW, Australia
| | - Luping Zhou
- School of Computer Science and Software Engineering, University of Wollongong, Wollongong, NSW, Australia
| | - Xinbao Ning
- School of Electronic Science and Engineering, Nanjing University, Nanjing, China
| | - Lei Wang
- School of Computer Science and Software Engineering, University of Wollongong, Wollongong, NSW, Australia
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Abstract
The brain did not develop a dedicated device for reasoning. This fact bears dramatic consequences. While for perceptuo-motor functions neural activity is shaped by the input's statistical properties, and processing is carried out at high speed in hardwired spatially segregated modules, in reasoning, neural activity is driven by internal dynamics and processing times, stages, and functional brain geometry are largely unconstrained a priori. Here, it is shown that the complex properties of spontaneous activity, which can be ignored in a short-lived event-related world, become prominent at the long time scales of certain forms of reasoning. It is argued that the neural correlates of reasoning should in fact be defined in terms of non-trivial generic properties of spontaneous brain activity, and that this implies resorting to concepts, analytical tools, and ways of designing experiments that are as yet non-standard in cognitive neuroscience. The implications in terms of models of brain activity, shape of the neural correlates, methods of data analysis, observability of the phenomenon, and experimental designs are discussed.
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Affiliation(s)
- David Papo
- GISC and Laboratory of Biological Networks, Center for Biomedical Technology, Universidad Politécnica de MadridMadrid, Spain
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38
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Mujica-Parodi L, Carlson JM, Cha (차지욱) J, Rubin D. The fine line between ‘brave’ and ‘reckless’: Amygdala reactivity and regulation predict recognition of risk. Neuroimage 2014; 103:1-9. [DOI: 10.1016/j.neuroimage.2014.08.038] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2014] [Revised: 07/22/2014] [Accepted: 08/20/2014] [Indexed: 12/30/2022] Open
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Ni H, Huang X, Ning X, Huo C, Liu T, Ben D. Multifractal analysis of resting state fMRI series in default mode network: age and gender effects. CHINESE SCIENCE BULLETIN-CHINESE 2014. [DOI: 10.1007/s11434-014-0355-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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40
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Stan C, Astefanoaei C, Pretegiani E, Optican L, Creanga D, Rufa A, Cristescu CP. Nonlinear analysis of saccade speed fluctuations during combined action and perception tasks. J Neurosci Methods 2014; 232:102-9. [PMID: 24854830 DOI: 10.1016/j.jneumeth.2014.05.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2014] [Revised: 05/09/2014] [Accepted: 05/12/2014] [Indexed: 11/29/2022]
Abstract
BACKGROUND Saccades are rapid eye movements used to gather information about a scene which requires both action and perception. These are usually studied separately, so that how perception influences action is not well understood. In a dual task, where the subject looks at a target and reports a decision, subtle changes in the saccades might be caused by action-perception interactions. Studying saccades might provide insight into how brain pathways for action and for perception interact. NEW METHOD We applied two complementary methods, multifractal detrended fluctuation analysis and Lempel-Ziv complexity index to eye peak speed recorded in two experiments, a pure action task and a combined action-perception task. RESULTS Multifractality strength is significantly different in the two experiments, showing smaller values for dual decision task saccades compared to simple-task saccades. The normalized Lempel-Ziv complexity index behaves similarly i.e. is significantly smaller in the decision saccade task than in the simple task. COMPARISON WITH EXISTING METHODS Compared to the usual statistical and linear approaches, these analyses emphasize the character of the dynamics involved in the fluctuations and offer a sensitive tool for quantitative evaluation of the multifractal features and of the complexity measure in the saccades peak speeds when different brain circuits are involved. CONCLUSION Our results prove that the peak speed fluctuations have multifractal characteristics with lower magnitude for the multifractality strength and for the complexity index when two neural pathways are simultaneously activated, demonstrating the nonlinear interaction in the brain pathways for action and perception.
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Affiliation(s)
- C Stan
- Department of Physics, Politehnica University of Bucharest, 313 Spl. Independentei, RO 060042, Romania.
| | - C Astefanoaei
- Physics Department, University Alexandru Ioan Cuza, 11 Blvd. Carol I., Iasi, Romania.
| | - E Pretegiani
- Eye-tracking & Visual Application Lab EVALab, Department of Medicine Surgery and Neuroscience, University of Siena, Siena 53100, Italy.
| | - L Optican
- Laboratory of Sensorimotor Research, IRP, National Eye Institute, DHHS, Bethesda, MD 20892, USA.
| | - D Creanga
- Physics Department, University Alexandru Ioan Cuza, 11 Blvd. Carol I., Iasi, Romania.
| | - A Rufa
- Eye-tracking & Visual Application Lab EVALab, Department of Medicine Surgery and Neuroscience, University of Siena, Siena 53100, Italy.
| | - C P Cristescu
- Department of Physics, Politehnica University of Bucharest, 313 Spl. Independentei, RO 060042, Romania.
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Ciuciu P, Abry P, He BJ. Interplay between functional connectivity and scale-free dynamics in intrinsic fMRI networks. Neuroimage 2014; 95:248-63. [PMID: 24675649 PMCID: PMC4043862 DOI: 10.1016/j.neuroimage.2014.03.047] [Citation(s) in RCA: 63] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2013] [Revised: 02/28/2014] [Accepted: 03/17/2014] [Indexed: 02/05/2023] Open
Abstract
Studies employing functional connectivity-type analyses have established that spontaneous fluctuations in functional magnetic resonance imaging (fMRI) signals are organized within large-scale brain networks. Meanwhile, fMRI signals have been shown to exhibit 1/f-type power spectra - a hallmark of scale-free dynamics. We studied the interplay between functional connectivity and scale-free dynamics in fMRI signals, utilizing the fractal connectivity framework - a multivariate extension of the univariate fractional Gaussian noise model, which relies on a wavelet formulation for robust parameter estimation. We applied this framework to fMRI data acquired from healthy young adults at rest and while performing a visual detection task. First, we found that scale-invariance existed beyond univariate dynamics, being present also in bivariate cross-temporal dynamics. Second, we observed that frequencies within the scale-free range do not contribute evenly to inter-regional connectivity, with a systematically stronger contribution of the lowest frequencies, both at rest and during task. Third, in addition to a decrease of the Hurst exponent and inter-regional correlations, task performance modified cross-temporal dynamics, inducing a larger contribution of the highest frequencies within the scale-free range to global correlation. Lastly, we found that across individuals, a weaker task modulation of the frequency contribution to inter-regional connectivity was associated with better task performance manifesting as shorter and less variable reaction times. These findings bring together two related fields that have hitherto been studied separately - resting-state networks and scale-free dynamics, and show that scale-free dynamics of human brain activity manifest in cross-regional interactions as well.
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Affiliation(s)
- Philippe Ciuciu
- CEA, NeuroSpin center, INRIA, Parietal Team, Bât. 145, F-91191 Gif-sur-Yvette, France.
| | - Patrice Abry
- CNRS, UMR 5672, Physics Department, ENS Lyon, F-69007 Lyon, France
| | - Biyu J He
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
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42
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Papo D. Functional significance of complex fluctuations in brain activity: from resting state to cognitive neuroscience. Front Syst Neurosci 2014; 8:112. [PMID: 24966818 PMCID: PMC4052734 DOI: 10.3389/fnsys.2014.00112] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2014] [Accepted: 05/26/2014] [Indexed: 11/15/2022] Open
Abstract
Behavioral studies have shown that human cognition is characterized by properties such as temporal scale invariance, heavy-tailed non-Gaussian distributions, and long-range correlations at long time scales, suggesting models of how (non observable) components of cognition interact. On the other hand, results from functional neuroimaging studies show that complex scaling and intermittency may be generic spatio-temporal properties of the brain at rest. Somehow surprisingly, though, hardly ever have the neural correlates of cognition been studied at time scales comparable to those at which cognition shows scaling properties. Here, we analyze the meanings of scaling properties and the significance of their task-related modulations for cognitive neuroscience. It is proposed that cognitive processes can be framed in terms of complex generic properties of brain activity at rest and, ultimately, of functional equations, limiting distributions, symmetries, and possibly universality classes characterizing them.
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Affiliation(s)
- David Papo
- Computational Systems Biology Group, Center for Biomedical Technology, Universidad Politécnica de Madrid Madrid, Spain
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43
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Li YC, Huang YA. Fractal analysis of spontaneous fluctuations of the BOLD signal in the human brain networks. J Magn Reson Imaging 2013; 39:1118-25. [PMID: 24027126 DOI: 10.1002/jmri.24274] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2012] [Accepted: 05/16/2013] [Indexed: 11/09/2022] Open
Abstract
PURPOSE To investigate what extent brain regions are continuously interacting during resting-state, independent component analyses (ICA) was applied to analyze resting-state functional MRI (RS-fMRI) data. According to the analyzed results, it was surprisingly found that low frequency fluctuations (LFFs), which belong to the 1/f signal (a signal with power spectrum whose power spectral density is inversely proportional to the frequency), have been classified into groups using ICA; furthermore, the spatial distributions of these groups within the brain were found to resemble the spatial distributions of different networks, which manifests that the signal characteristics of RS LFFs are distinct across networks. In our work, we applied the 1/f model in the fractal analyses to further investigate this distinction. MATERIALS AND METHODS Twenty healthy participants got involved in this study. They were scanned to acquire the RS-fMRI data. The acquired data were first processed with ICA to obtain the networks of the resting brain. Afterward, the blood-oxygenation level dependent (BOLD) signals of these networks were processed with the fractal analyses for obtaining the fractal parameter α. RESULTS α was found to significantly vary across networks, which reveals that the fractal characteristic of LFFs differs across networks. According to prior literatures, this difference could be brought by the discrepancy of hemodynamic response amplitude (HRA) between networks. Hence, in our work, we also performed the computational simulation to discover the relationship between α and HRA. Based on the simulation results, HRA is highly linear-correlated with the fractal characteristics of LFFs which is revealed by α. CONCLUSION Our results support that the origin of RS-fMRI signals contains arterial fluctuations. Hence, in addition to the commonly used method such as synchrony analysis and power spectral analysis, another approach, the fractal analysis, is suggested for acquiring the information of hemodynamic responses by means of RS-fMRI data.
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Affiliation(s)
- Yi-Chia Li
- Graduate Institute of Biomedical Engineering and Bioinformatics, National Taiwan University, Taipei, Taiwan
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Optimizing complexity measures for FMRI data: algorithm, artifact, and sensitivity. PLoS One 2013; 8:e63448. [PMID: 23700424 PMCID: PMC3660309 DOI: 10.1371/journal.pone.0063448] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2012] [Accepted: 04/02/2013] [Indexed: 01/17/2023] Open
Abstract
INTRODUCTION Complexity in the brain has been well-documented at both neuronal and hemodynamic scales, with increasing evidence supporting its use in sensitively differentiating between mental states and disorders. However, application of complexity measures to fMRI time-series, which are short, sparse, and have low signal/noise, requires careful modality-specific optimization. METHODS HERE WE USE BOTH SIMULATED AND REAL DATA TO ADDRESS TWO FUNDAMENTAL ISSUES: choice of algorithm and degree/type of signal processing. Methods were evaluated with regard to resilience to acquisition artifacts common to fMRI as well as detection sensitivity. Detection sensitivity was quantified in terms of grey-white matter contrast and overlap with activation. We additionally investigated the variation of complexity with activation and emotional content, optimal task length, and the degree to which results scaled with scanner using the same paradigm with two 3T magnets made by different manufacturers. Methods for evaluating complexity were: power spectrum, structure function, wavelet decomposition, second derivative, rescaled range, Higuchi's estimate of fractal dimension, aggregated variance, and detrended fluctuation analysis. To permit direct comparison across methods, all results were normalized to Hurst exponents. RESULTS Power-spectrum, Higuchi's fractal dimension, and generalized Hurst exponent based estimates were most successful by all criteria; the poorest-performing measures were wavelet, detrended fluctuation analysis, aggregated variance, and rescaled range. CONCLUSIONS Functional MRI data have artifacts that interact with complexity calculations in nontrivially distinct ways compared to other physiological data (such as EKG, EEG) for which these measures are typically used. Our results clearly demonstrate that decisions regarding choice of algorithm, signal processing, time-series length, and scanner have a significant impact on the reliability and sensitivity of complexity estimates.
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Garrett DD, Samanez-Larkin GR, MacDonald SWS, Lindenberger U, McIntosh AR, Grady CL. Moment-to-moment brain signal variability: a next frontier in human brain mapping? Neurosci Biobehav Rev 2013; 37:610-24. [PMID: 23458776 PMCID: PMC3732213 DOI: 10.1016/j.neubiorev.2013.02.015] [Citation(s) in RCA: 370] [Impact Index Per Article: 33.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2012] [Revised: 02/13/2013] [Accepted: 02/19/2013] [Indexed: 11/26/2022]
Abstract
Neuroscientists have long observed that brain activity is naturally variable from moment-to-moment, but neuroimaging research has largely ignored the potential importance of this phenomenon. An emerging research focus on within-person brain signal variability is providing novel insights, and offering highly predictive, complementary, and even orthogonal views of brain function in relation to human lifespan development, cognitive performance, and various clinical conditions. As a result, brain signal variability is evolving as a bona fide signal of interest, and should no longer be dismissed as meaningless noise when mapping the human brain.
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Affiliation(s)
- Douglas D Garrett
- Max Planck Society-University College London Initiative: Computational Psychiatry and Aging Research (ICPAR); Center for Lifespan Psychology, Max Planck Institute for Human Development, Lentzeallee 94, 14195 Berlin, Germany.
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Neuronal long-range temporal correlations and avalanche dynamics are correlated with behavioral scaling laws. Proc Natl Acad Sci U S A 2013; 110:3585-90. [PMID: 23401536 DOI: 10.1073/pnas.1216855110] [Citation(s) in RCA: 273] [Impact Index Per Article: 24.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Scale-free fluctuations are ubiquitous in behavioral performance and neuronal activity. In time scales from seconds to hundreds of seconds, psychophysical dynamics and the amplitude fluctuations of neuronal oscillations are governed by power-law-form long-range temporal correlations (LRTCs). In millisecond time scales, neuronal activity comprises cascade-like neuronal avalanches that exhibit power-law size and lifetime distributions. However, it remains unknown whether these neuronal scaling laws are correlated with those characterizing behavioral performance or whether neuronal LRTCs and avalanches are related. Here, we show that the neuronal scaling laws are strongly correlated both with each other and with behavioral scaling laws. We used source reconstructed magneto- and electroencephalographic recordings to characterize the dynamics of ongoing cortical activity. We found robust power-law scaling in neuronal LRTCs and avalanches in resting-state data and during the performance of audiovisual threshold stimulus detection tasks. The LRTC scaling exponents of the behavioral performance fluctuations were correlated with those of concurrent neuronal avalanches and LRTCs in anatomically identified brain systems. The behavioral exponents also were correlated with neuronal scaling laws derived from a resting-state condition and with a similar anatomical topography. Finally, despite the difference in time scales, the scaling exponents of neuronal LRTCs and avalanches were strongly correlated during both rest and task performance. Thus, long and short time-scale neuronal dynamics are related and functionally significant at the behavioral level. These data suggest that the temporal structures of human cognitive fluctuations and behavioral variability stem from the scaling laws of individual and intrinsic brain dynamics.
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Ihlen EAF, Vereijken B. Identifying multiplicative interactions between temporal scales of human movement variability. Ann Biomed Eng 2012; 41:1635-45. [PMID: 23247986 DOI: 10.1007/s10439-012-0724-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2012] [Accepted: 12/08/2012] [Indexed: 11/30/2022]
Abstract
Conventional scaling analyses of human movement variability such as detrended fluctuation analyses assume that the movement variable can be decomposed into scale-dependent variation. However, these conventional scaling analyses are insensitive to multiplicative interactions within the movement variable. Multiplicative interactions refer to couplings between the scale-dependent variations across multiple scales that generate intermittent changes in the human movement variable. The mathematical concept for intermittent variability generated by multiplicative interactions is called multifractal variability. Multifractal variability is numerically defined by a spectrum of scaling exponents (i.e., a multifractal spectrum) that can be an important feature of coordinated movements and, consequently, relevant when aiming to identify movement disorders. In the current study, a new method is introduced based on detrended fluctuation analysis that can identify the multifractal spectrum from the temporal variation of local scaling exponents. The influence of multiplicative interactions on the local scaling exponents is tested by a Monte Carlo surrogate test. The methods are validated on multiplicative cascading processes with known multiplicative interactions. The application of the new methods is subsequently illustrated on an example of centre of pressure variations during quiet and relaxed standing. The results show that multiplicative interactions are present during periods with large movements of the center of gravity, where the movements of the centre of gravity and centre of pressure couple into coordinative structures. Further application and interpretation of the developed method for the study of human movement variability are discussed.
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Affiliation(s)
- Espen A F Ihlen
- Department of Neuroscience, Norwegian University of Science and Technology, Trondheim, Norway
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48
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Ihlen EAF. Introduction to multifractal detrended fluctuation analysis in matlab. Front Physiol 2012; 3:141. [PMID: 22675302 PMCID: PMC3366552 DOI: 10.3389/fphys.2012.00141] [Citation(s) in RCA: 240] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2012] [Accepted: 04/26/2012] [Indexed: 11/30/2022] Open
Abstract
Fractal structures are found in biomedical time series from a wide range of physiological phenomena. The multifractal spectrum identifies the deviations in fractal structure within time periods with large and small fluctuations. The present tutorial is an introduction to multifractal detrended fluctuation analysis (MFDFA) that estimates the multifractal spectrum of biomedical time series. The tutorial presents MFDFA step-by-step in an interactive Matlab session. All Matlab tools needed are available in Introduction to MFDFA folder at the website www.ntnu.edu/inm/geri/software. MFDFA are introduced in Matlab code boxes where the reader can employ pieces of, or the entire MFDFA to example time series. After introducing MFDFA, the tutorial discusses the best practice of MFDFA in biomedical signal processing. The main aim of the tutorial is to give the reader a simple self-sustained guide to the implementation of MFDFA and interpretation of the resulting multifractal spectra.
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Affiliation(s)
- Espen A F Ihlen
- Department of Neuroscience, Norwegian University of Science and Technology Trondheim, Norway
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Warsi MA, Molloy W, Noseworthy MD. Correlating brain blood oxygenation level dependent (BOLD) fractal dimension mapping with magnetic resonance spectroscopy (MRS) in Alzheimer's disease. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2012; 25:335-44. [PMID: 22446877 DOI: 10.1007/s10334-012-0312-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2011] [Revised: 02/15/2012] [Accepted: 03/02/2012] [Indexed: 11/29/2022]
Abstract
OBJECTIVES To correlate temporal fractal structure of resting state blood oxygen level dependent (rsBOLD) functional magnetic resonance imaging (fMRI) with in vivo proton magnetic resonance spectroscopy ((1)H-MRS), in Alzheimer's disease (AD) and healthy age-matched normal controls (NC). MATERIALS AND METHODS High temporal resolution (4 Hz) rsBOLD signal and single voxel (left putamen) magnetic resonance spectroscopy data was acquired in 33 AD patients and 13 NC. The rsBOLD data was analyzed using two types of fractal dimension (FD) analysis based on relative dispersion and frequency power spectrum. Comparisons in FD were performed between AD and NC, and FD measures were correlated with (1)H-MRS findings. RESULTS Temporal fractal analysis of rsBOLD, was able to differentiate AD from NC subjects (P = 0.03). Low FD correlated with markers of AD severity including decreased concentrations of N-acetyl aspartate (R = 0.44, P = 0.015) and increased myoinositol (mI) (R = -0.45, P = 0.012). CONCLUSION Based on these results we suggest fractal analysis of rsBOLD could provide an early marker of AD.
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Affiliation(s)
- Mohammed A Warsi
- School of Biomedical Engineering, McMaster University, Hamilton, ON, Canada
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Palva JM, Palva S. Infra-slow fluctuations in electrophysiological recordings, blood-oxygenation-level-dependent signals, and psychophysical time series. Neuroimage 2012; 62:2201-11. [PMID: 22401756 DOI: 10.1016/j.neuroimage.2012.02.060] [Citation(s) in RCA: 90] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2011] [Revised: 02/16/2012] [Accepted: 02/20/2012] [Indexed: 10/28/2022] Open
Abstract
Converging electrophysiological and neuroimaging data show that mammalian brain dynamics are governed by spontaneous modulations of neuronal activity levels in cortical and subcortical structures. The time scales of these fluctuations form a continuum from seconds to tens and hundreds of seconds corresponding to slow (0.1-1Hz), infra-slow (0.01-0.1Hz), and "ultradian" (<0.01Hz) frequency bands, respectively. We focus here on the spontaneous neuronal dynamics in the infra-slow frequency band, infra-slow fluctuations (ISFs), and explore their electrophysiological substrates and behavioral correlates. Although electrophysiological ISFs and the associated infra-slow modulations of fast (here, >1Hz) neuronal activities have been recognized on numerous occasions since late 50's, a resurgence in interest towards this frequency band has been driven by a discovery that ISFs in blood-oxygenation-level dependent (BOLD) signals are correlated among specific constellations of brain regions, which constitute intrinsic connectivity networks and define the dynamic architecture of spontaneous brain activity at large. Importantly, electrophysiological and BOLD signal ISFs are directly correlated both with ISFs in amplitudes of fast neuronal activities and with ISFs in behavioral performance. Moreover, both electrophysiological and neuroimaging data suggest that the apparently scale-free ISFs may arise from more local quasi-periodic infra-slow oscillations with a contribution of time-scale-specific cellular-level mechanisms. We conclude that ISFs in electrophysiological recordings, BOLD signals, neuronal activity levels, and behavioral time series are likely to reflect the same underlying phenomenon; a superstructure of interacting and transiently oscillatory ISFs that regulate both the integration within and decoupling between concurrently active neuronal communities.
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