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Cerasa A. Fractals in Neuropsychology and Cognitive Neuroscience. ADVANCES IN NEUROBIOLOGY 2024; 36:761-778. [PMID: 38468062 DOI: 10.1007/978-3-031-47606-8_38] [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 fractal dimension of cognition refers to the idea that the cognitive processes of the human brain exhibit fractal properties. This means that certain patterns of cognitive activity, such as visual perception, memory, language, or problem-solving, can be described using the mathematical concept of fractal dimension.The idea that cognition is fractal has been proposed by some researchers as a way to understand the complex, self-similar nature of the human brain. However, it's a relatively new idea and is still under investigation, so it's not yet clear to what extent cognitive processes exhibit fractal properties or what implications this might have for our understanding of the brain and clinical practice. Indeed, the mission of the "fractal neuroscience" field is to define the characteristics of fractality in human cognition in order to differently characterize the emergence of brain disorders.
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Affiliation(s)
- Antonio Cerasa
- Institute for Biomedical Research and Innovation, National Research Council, IRIB-CNR, Messina, Italy
- S. Anna Institute, Crotone, Italy
- Pharmacotechnology Documentation and Transfer Unit, Preclinical and Translational Pharmacology, Department of Pharmacy, Health Science and Nutrition, University of Calabria, Arcavacata, Italy
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2
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Karperien AL, Jelinek HF. Box-Counting Fractal Analysis: A Primer for the Clinician. ADVANCES IN NEUROBIOLOGY 2024; 36:15-55. [PMID: 38468026 DOI: 10.1007/978-3-031-47606-8_2] [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
This chapter lays out the elementary principles of fractal geometry underpinning much of the rest of this book. It assumes a minimal mathematical background, defines the key principles and terms in context, and outlines the basics of a fractal analysis method known as box counting and how it is used to perform fractal, lacunarity, and multifractal analyses. As a standalone reference, this chapter grounds the reader to be able to understand, evaluate, and apply essential methods to appreciate and heal the exquisitely detailed fractal geometry of the brain.
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Affiliation(s)
| | - Herbert F Jelinek
- Department of Medical Sciences and Biotechnology Center, Khalifa University, Abu Dhabi, UAE
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3
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Guan S, Jiang R, Chen DY, Michael A, Meng C, Biswal B. Multifractal long-range dependence pattern of functional magnetic resonance imaging in the human brain at rest. Cereb Cortex 2023; 33:11594-11608. [PMID: 37851793 DOI: 10.1093/cercor/bhad393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 10/01/2023] [Accepted: 10/02/2023] [Indexed: 10/20/2023] Open
Abstract
Long-range dependence is a prevalent phenomenon in various biological systems that characterizes the long-memory effect of temporal fluctuations. While recent research suggests that functional magnetic resonance imaging signal has fractal property, it remains unknown about the multifractal long-range dependence pattern of resting-state functional magnetic resonance imaging signals. The current study adopted the multifractal detrended fluctuation analysis on highly sampled resting-state functional magnetic resonance imaging scans to investigate long-range dependence profile associated with the whole-brain voxels as specific functional networks. Our findings revealed the long-range dependence's multifractal properties. Moreover, long-term persistent fluctuations are found for all stations with stronger persistency in whole-brain regions. Subsets with large fluctuations contribute more to the multifractal spectrum in the whole brain. Additionally, we found that the preprocessing with band-pass filtering provided significantly higher reliability for estimating long-range dependence. Our validation analysis confirmed that the optimal pipeline of long-range dependence analysis should include band-pass filtering and removal of daily temporal dependence. Furthermore, multifractal long-range dependence characteristics in healthy control and schizophrenia are different significantly. This work has provided an analytical pipeline for the multifractal long-range dependence in the resting-state functional magnetic resonance imaging signal. The findings suggest differential long-memory effects in the intrinsic functional networks, which may offer a neural marker finding for understanding brain function and pathology.
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Affiliation(s)
- Sihai Guan
- College of Electronic and Information, Southwest Minzu University, Chengdu 610041, China
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission, Chengdu 610041, China
| | - Runzhou Jiang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
- Medical Equipment Department, Xiangyang No.1 People's Hospital, Xiangyang 441000, China
| | - Donna Y Chen
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07102, United States
| | - Andrew Michael
- Duke Institute for Brain Sciences, Duke University, Durham, NC 27708, United States
| | - Chun Meng
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Bharat Biswal
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07102, United States
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Ardelean ER, Bârzan H, Ichim AM, Mureşan RC, Moca VV. Sharp detection of oscillation packets in rich time-frequency representations of neural signals. Front Hum Neurosci 2023; 17:1112415. [PMID: 38144896 PMCID: PMC10748759 DOI: 10.3389/fnhum.2023.1112415] [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: 11/30/2022] [Accepted: 11/13/2023] [Indexed: 12/26/2023] Open
Abstract
Brain oscillations most often occur in bursts, called oscillation packets, which span a finite extent in time and frequency. Recent studies have shown that these packets portray a much more dynamic picture of synchronization and transient communication between sites than previously thought. To understand their nature and statistical properties, techniques are needed to objectively detect oscillation packets and to quantify their temporal and frequency extent, as well as their magnitude. There are various methods to detect bursts of oscillations. The simplest ones divide the signal into band limited sub-components, quantifying the strength of the resulting components. These methods cannot by themselves cope with broadband transients that look like genuine oscillations when restricted to a narrow band. The most successful detection methods rely on time-frequency representations, which can readily show broadband transients and harmonics. However, the performance of such methods is conditioned by the ability of the representation to localize packets simultaneously in time and frequency, and by the capabilities of packet detection techniques, whose current state of the art is limited to extraction of bounding boxes. Here, we focus on the second problem, introducing two detection methods that use concepts derived from clustering and topographic prominence. These methods are able to delineate the packets' precise contour in the time-frequency plane. We validate the new approaches using both synthetic and real data recorded in humans and animals and rely on a super-resolution time-frequency representation, namely the superlets, as input to the detection algorithms. In addition, we define robust tests for benchmarking and compare the new methods to previous techniques. Results indicate that the two methods we introduce shine in low signal-to-noise ratio conditions, where they only miss a fraction of packets undetected by previous methods. Finally, algorithms that delineate precisely the border of spectral features and their subcomponents offer far more valuable information than simple rectangular bounding boxes (time and frequency span) and can provide a solid foundation to investigate neural oscillations' dynamics.
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Affiliation(s)
- Eugen-Richard Ardelean
- Experimental and Theoretical Neuroscience Laboratory, Transylvanian Institute of Neuroscience, Cluj-Napoca, Romania
- Computer Science Department, Technical University of Cluj-Napoca, Cluj-Napoca, Romania
| | - Harald Bârzan
- Experimental and Theoretical Neuroscience Laboratory, Transylvanian Institute of Neuroscience, Cluj-Napoca, Romania
| | - Ana-Maria Ichim
- Experimental and Theoretical Neuroscience Laboratory, Transylvanian Institute of Neuroscience, Cluj-Napoca, Romania
| | - Raul Cristian Mureşan
- Experimental and Theoretical Neuroscience Laboratory, Transylvanian Institute of Neuroscience, Cluj-Napoca, Romania
- STAR-UBB Institute, Babeş-Bolyai University, Cluj-Napoca, Romania
| | - Vasile Vlad Moca
- Experimental and Theoretical Neuroscience Laboratory, Transylvanian Institute of Neuroscience, Cluj-Napoca, Romania
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5
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Iseki C, Suzuki S, Fukami T, Yamada S, Hayasaka T, Kondo T, Hoshi M, Ueda S, Kobayashi Y, Ishikawa M, Kanno S, Suzuki K, Aoyagi Y, Ohta Y. Fluctuations in Upper and Lower Body Movement during Walking in Normal Pressure Hydrocephalus and Parkinson's Disease Assessed by Motion Capture with a Smartphone Application, TDPT-GT. SENSORS (BASEL, SWITZERLAND) 2023; 23:9263. [PMID: 38005649 PMCID: PMC10674367 DOI: 10.3390/s23229263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 11/17/2023] [Accepted: 11/17/2023] [Indexed: 11/26/2023]
Abstract
We aimed to capture the fluctuations in the dynamics of body positions and find the characteristics of them in patients with idiopathic normal pressure hydrocephalus (iNPH) and Parkinson's disease (PD). With the motion-capture application (TDPT-GT) generating 30 Hz coordinates at 27 points on the body, walking in a circle 1 m in diameter was recorded for 23 of iNPH, 23 of PD, and 92 controls. For 128 frames of calculated distances from the navel to the other points, after the Fourier transforms, the slopes (the representatives of fractality) were obtained from the graph plotting the power spectral density against the frequency in log-log coordinates. Differences in the average slopes were tested by one-way ANOVA and multiple comparisons between every two groups. A decrease in the absolute slope value indicates a departure from the 1/f noise characteristic observed in healthy variations. Significant differences in the patient groups and controls were found in all body positions, where patients always showed smaller absolute values. Our system could measure the whole body's movement and temporal variations during walking. The impaired fluctuations of body movement in the upper and lower body may contribute to gait and balance disorders in patients.
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Affiliation(s)
- Chifumi Iseki
- Department of Behavioral Neurology and Cognitive Neuroscience, Tohoku University Graduate School of Medicine, Sendai 980-8575, Japan; (S.K.); (K.S.)
- Division of Neurology and Clinical Neuroscience, Department of Internal Medicine III, Yamagata University School of Medicine, Yamagata 990-2331, Japan; (T.K.); (Y.O.)
| | - Shou Suzuki
- Department of Informatics, Faculty of Engineering, Yamagata University, Yonezawa 992-8510, Japan; (S.S.); (T.F.)
| | - Tadanori Fukami
- Department of Informatics, Faculty of Engineering, Yamagata University, Yonezawa 992-8510, Japan; (S.S.); (T.F.)
| | - Shigeki Yamada
- Department of Neurosurgery, Nagoya City University Graduate School of Medical Sciences, Nagoya 467-8601, Japan;
- Interfaculty Initiative in Information Studies, Institute of Industrial Science, The University of Tokyo, Tokyo 113-8654, Japan
- Normal Pressure Hydrocephalus Center, Rakuwakai Otowa Hospital, Kyoto 607-8062, Japan;
| | - Tatsuya Hayasaka
- Department of Anesthesiology, Yamagata University School of Medicine, Yamagata 990-2331, Japan;
| | - Toshiyuki Kondo
- Division of Neurology and Clinical Neuroscience, Department of Internal Medicine III, Yamagata University School of Medicine, Yamagata 990-2331, Japan; (T.K.); (Y.O.)
| | - Masayuki Hoshi
- Department of Physical Therapy, Fukushima Medical University School of Health Sciences, 10-6 Sakaemachi, Fukushima 960-8516, Japan;
| | - Shigeo Ueda
- Shin-Aikai Spine Center, Katano Hospital, Katano 576-0043, Japan;
| | - Yoshiyuki Kobayashi
- Human Augmentation Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Kashiwa II Campus, University of Tokyo, Kashiwa 277-0882, Japan;
| | - Masatsune Ishikawa
- Normal Pressure Hydrocephalus Center, Rakuwakai Otowa Hospital, Kyoto 607-8062, Japan;
- Rakuwa Villa Ilios, Rakuwakai Healthcare System, Kyoto 607-8062, Japan
| | - Shigenori Kanno
- Department of Behavioral Neurology and Cognitive Neuroscience, Tohoku University Graduate School of Medicine, Sendai 980-8575, Japan; (S.K.); (K.S.)
| | - Kyoko Suzuki
- Department of Behavioral Neurology and Cognitive Neuroscience, Tohoku University Graduate School of Medicine, Sendai 980-8575, Japan; (S.K.); (K.S.)
| | | | - Yasuyuki Ohta
- Division of Neurology and Clinical Neuroscience, Department of Internal Medicine III, Yamagata University School of Medicine, Yamagata 990-2331, Japan; (T.K.); (Y.O.)
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Trejo DH, Ciuparu A, da Silva PG, Velasquez CM, Rebouillat B, Gross MD, Davis MB, Muresan RC, Albeanu DF. Fast updating feedback from piriform cortex to the olfactory bulb relays multimodal reward contingency signals during rule-reversal. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.12.557267. [PMID: 37745564 PMCID: PMC10515864 DOI: 10.1101/2023.09.12.557267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
While animals readily adjust their behavior to adapt to relevant changes in the environment, the neural pathways enabling these changes remain largely unknown. Here, using multiphoton imaging, we investigated whether feedback from the piriform cortex to the olfactory bulb supports such behavioral flexibility. To this end, we engaged head-fixed mice in a multimodal rule-reversal task guided by olfactory and auditory cues. Both odor and, surprisingly, the sound cues triggered cortical bulbar feedback responses which preceded the behavioral report. Responses to the same sensory cue were strongly modulated upon changes in stimulus-reward contingency (rule reversals). The re-shaping of individual bouton responses occurred within seconds of the rule-reversal events and was correlated with changes in the behavior. Optogenetic perturbation of cortical feedback within the bulb disrupted the behavioral performance. Our results indicate that the piriform-to-olfactory bulb feedback carries reward contingency signals and is rapidly re-formatted according to changes in the behavioral context.
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Affiliation(s)
| | - Andrei Ciuparu
- Transylvanian Institute of Neuroscience, Cluj-Napoca, Romania
| | - Pedro Garcia da Silva
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
- current address – Champalimaud Neuroscience Program, Lisbon, Portugal
| | - Cristina M. Velasquez
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
- current address – University of Oxford, UK
| | - Benjamin Rebouillat
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
- current address –École Normale Supérieure, Paris, France
| | | | | | - Raul C. Muresan
- Transylvanian Institute of Neuroscience, Cluj-Napoca, Romania
- STAR-UBB Institute, Babeş-Bolyai University, Cluj-Napoca, Romania
| | - Dinu F. Albeanu
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
- School for Biological Sciences, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
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Huynh PK, Nguyen D, Binder G, Ambardar S, Le TQ, Voronine DV. Multifractality in Surface Potential for Cancer Diagnosis. J Phys Chem B 2023; 127:6867-6877. [PMID: 37525377 DOI: 10.1021/acs.jpcb.3c01733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/02/2023]
Abstract
Recent advances in high-resolution biomedical imaging have improved cancer diagnosis, focusing on morphological, electrical, and biochemical properties of cells and tissues, scaling from cell clusters down to the molecular level. Multiscale imaging revealed high complexity that requires advanced data processing methods of multifractal analysis. We performed label-free multiscale imaging of surface potential variations in human ovarian cancer cells using Kelvin probe force microscopy (KPFM). An improvement in the differentiation between nonmalignant and cancerous cells by multifractal analysis using adaptive versus median threshold for image binarization was demonstrated. The results reveal the multifractality of cancer cells as a new biomarker for cancer diagnosis.
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Affiliation(s)
- Phat K Huynh
- Department of Industrial and Management Systems Engineering, University of South Florida, Tampa, Florida 33620, United States
| | - Dang Nguyen
- Department of Medical Engineering, University of South Florida, Tampa, Florida 33620, United States
| | - Grace Binder
- Department of Chemistry, University of South Florida, Tampa, Florida 33620, United States
| | - Sharad Ambardar
- Department of Medical Engineering, University of South Florida, Tampa, Florida 33620, United States
| | - Trung Q Le
- Department of Industrial and Management Systems Engineering, University of South Florida, Tampa, Florida 33620, United States
- Department of Medical Engineering, University of South Florida, Tampa, Florida 33620, United States
| | - Dmitri V Voronine
- Department of Medical Engineering, University of South Florida, Tampa, Florida 33620, United States
- Department of Physics, University of South Florida, Tampa, Florida 33620, United States
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Davoudi S, Schwartz T, Labbe A, Trainor L, Lippé S. Inter-individual variability during neurodevelopment: an investigation of linear and nonlinear resting-state EEG features in an age-homogenous group of infants. Cereb Cortex 2023; 33:8734-8747. [PMID: 37143183 PMCID: PMC10321121 DOI: 10.1093/cercor/bhad154] [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: 11/15/2022] [Revised: 04/11/2023] [Accepted: 04/12/2023] [Indexed: 05/06/2023] Open
Abstract
Electroencephalography measures are of interest in developmental neuroscience as potentially reliable clinical markers of brain function. Features extracted from electroencephalography are most often averaged across individuals in a population with a particular condition and compared statistically to the mean of a typically developing group, or a group with a different condition, to define whether a feature is representative of the populations as a whole. However, there can be large variability within a population, and electroencephalography features often change dramatically with age, making comparisons difficult. Combined with often low numbers of trials and low signal-to-noise ratios in pediatric populations, establishing biomarkers can be difficult in practice. One approach is to identify electroencephalography features that are less variable between individuals and are relatively stable in a healthy population during development. To identify such features in resting-state electroencephalography, which can be readily measured in many populations, we introduce an innovative application of statistical measures of variance for the analysis of resting-state electroencephalography data. Using these statistical measures, we quantified electroencephalography features commonly used to measure brain development-including power, connectivity, phase-amplitude coupling, entropy, and fractal dimension-according to their intersubject variability. Results from 51 6-month-old infants revealed that the complexity measures, including fractal dimension and entropy, followed by connectivity were the least variable features across participants. This stability was found to be greatest in the right parietotemporal region for both complexity feature, but no significant region of interest was found for connectivity feature. This study deepens our understanding of physiological patterns of electroencephalography data in developing brains, provides an example of how statistical measures can be used to analyze variability in resting-state electroencephalography in a homogeneous group of healthy infants, contributes to the establishment of robust electroencephalography biomarkers of neurodevelopment through the application of variance analyses, and reveals that nonlinear measures may be most relevant biomarkers of neurodevelopment.
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Affiliation(s)
- Saeideh Davoudi
- CHU Sainte-Justine Research Center, Université de Montréal, Montréal H3T 1C5, Canada
- Department of Neuroscience, Université de Montréal, Montréal H3T 1J4, Canada
| | - Tyler Schwartz
- Department of Decision Sciences, HEC Montréal, Montréal H3T 2A7, Canada
| | - Aurélie Labbe
- Department of Decision Sciences, HEC Montréal, Montréal H3T 2A7, Canada
| | - Laurel Trainor
- Department of Psychology, Neuroscience and Behavior, McMaster University, Hamilton L8S 4K1, Canada
| | - Sarah Lippé
- CHU Sainte-Justine Research Center, Université de Montréal, Montréal H3T 1C5, Canada
- Department of Psychology, Université de Montréal, Montréal H2V 2S9, Canada
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