1
|
Déli É, Peters JF, Kisvárday Z. How the Brain Becomes the Mind: Can Thermodynamics Explain the Emergence and Nature of Emotions? ENTROPY (BASEL, SWITZERLAND) 2022; 24:1498. [PMID: 37420518 DOI: 10.3390/e24101498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 10/07/2022] [Accepted: 10/11/2022] [Indexed: 07/09/2023]
Abstract
The neural systems' electric activities are fundamental for the phenomenology of consciousness. Sensory perception triggers an information/energy exchange with the environment, but the brain's recurrent activations maintain a resting state with constant parameters. Therefore, perception forms a closed thermodynamic cycle. In physics, the Carnot engine is an ideal thermodynamic cycle that converts heat from a hot reservoir into work, or inversely, requires work to transfer heat from a low- to a high-temperature reservoir (the reversed Carnot cycle). We analyze the high entropy brain by the endothermic reversed Carnot cycle. Its irreversible activations provide temporal directionality for future orientation. A flexible transfer between neural states inspires openness and creativity. In contrast, the low entropy resting state parallels reversible activations, which impose past focus via repetitive thinking, remorse, and regret. The exothermic Carnot cycle degrades mental energy. Therefore, the brain's energy/information balance formulates motivation, sensed as position or negative emotions. Our work provides an analytical perspective of positive and negative emotions and spontaneous behavior from the free energy principle. Furthermore, electrical activities, thoughts, and beliefs lend themselves to a temporal organization, an orthogonal condition to physical systems. Here, we suggest that an experimental validation of the thermodynamic origin of emotions might inspire better treatment options for mental diseases.
Collapse
Affiliation(s)
- Éva Déli
- Department of Anatomy, Histology, and Embryology, University of Debrecen, 4032 Debrecen, Hungary
| | - James F Peters
- Department of Electrical & Computer Engineering, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
- Department of Mathematics, Adiyaman University, Adiyaman 02040, Turkey
| | - Zoltán Kisvárday
- Department of Anatomy, Histology, and Embryology, University of Debrecen, 4032 Debrecen, Hungary
- ELKH Neuroscience Research Group, University of Debrecen, 4032 Debrecen, Hungary
| |
Collapse
|
2
|
Wolff A, Berberian N, Golesorkhi M, Gomez-Pilar J, Zilio F, Northoff G. Intrinsic neural timescales: temporal integration and segregation. Trends Cogn Sci 2022; 26:159-173. [PMID: 34991988 DOI: 10.1016/j.tics.2021.11.007] [Citation(s) in RCA: 65] [Impact Index Per Article: 32.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 11/19/2021] [Accepted: 11/23/2021] [Indexed: 12/11/2022]
Abstract
We are continuously bombarded by external inputs of various timescales from the environment. How does the brain process this multitude of timescales? Recent resting state studies show a hierarchy of intrinsic neural timescales (INT) with a shorter duration in unimodal regions (e.g., visual cortex and auditory cortex) and a longer duration in transmodal regions (e.g., default mode network). This unimodal-transmodal hierarchy is present across acquisition modalities [electroencephalogram (EEG)/magnetoencephalogram (MEG) and fMRI] and can be found in different species and during a variety of different task states. Together, this suggests that the hierarchy of INT is central to the temporal integration (combining successive stimuli) and segregation (separating successive stimuli) of external inputs from the environment, leading to temporal segmentation and prediction in perception and cognition.
Collapse
Affiliation(s)
- Annemarie Wolff
- Mind, Brain Imaging, and Neuroethics Research Unit, Institute of Mental Health Research, The Royal Ottawa Mental Health Centre and University of Ottawa, Ottawa, Canada
| | - Nareg Berberian
- Mind, Brain Imaging, and Neuroethics Research Unit, Institute of Mental Health Research, The Royal Ottawa Mental Health Centre and University of Ottawa, Ottawa, Canada
| | - Mehrshad Golesorkhi
- Mind, Brain Imaging, and Neuroethics Research Unit, Institute of Mental Health Research, The Royal Ottawa Mental Health Centre and University of Ottawa, Ottawa, Canada
| | - Javier Gomez-Pilar
- Biomedical Engineering Group, University of Valladolid, Paseo de Belén, 15, 47011 Valladolid, Spain; Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicia, (CIBER-BBN), Madrid, Spain
| | - Federico Zilio
- Department of Philosophy, Sociology, Education, and Applied Psychology, University of Padova, Padua, Italy
| | - Georg Northoff
- Mind, Brain Imaging, and Neuroethics Research Unit, Institute of Mental Health Research, The Royal Ottawa Mental Health Centre and University of Ottawa, Ottawa, Canada; Centre for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, China; Mental Health Centre, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
| |
Collapse
|
3
|
Golesorkhi M, Gomez-Pilar J, Zilio F, Berberian N, Wolff A, Yagoub MCE, Northoff G. The brain and its time: intrinsic neural timescales are key for input processing. Commun Biol 2021; 4:970. [PMID: 34400800 PMCID: PMC8368044 DOI: 10.1038/s42003-021-02483-6] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 07/19/2021] [Indexed: 02/07/2023] Open
Abstract
We process and integrate multiple timescales into one meaningful whole. Recent evidence suggests that the brain displays a complex multiscale temporal organization. Different regions exhibit different timescales as described by the concept of intrinsic neural timescales (INT); however, their function and neural mechanisms remains unclear. We review recent literature on INT and propose that they are key for input processing. Specifically, they are shared across different species, i.e., input sharing. This suggests a role of INT in encoding inputs through matching the inputs' stochastics with the ongoing temporal statistics of the brain's neural activity, i.e., input encoding. Following simulation and empirical data, we point out input integration versus segregation and input sampling as key temporal mechanisms of input processing. This deeply grounds the brain within its environmental and evolutionary context. It carries major implications in understanding mental features and psychiatric disorders, as well as going beyond the brain in integrating timescales into artificial intelligence.
Collapse
Affiliation(s)
- Mehrshad Golesorkhi
- grid.28046.380000 0001 2182 2255School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada ,grid.28046.380000 0001 2182 2255Mind, Brain Imaging and Neuroethics Research Unit, Institute of Mental Health, Royal Ottawa Mental Health Centre and University of Ottawa, Ottawa, Canada
| | - Javier Gomez-Pilar
- grid.5239.d0000 0001 2286 5329Biomedical Engineering Group, University of Valladolid, Valladolid, Spain ,grid.413448.e0000 0000 9314 1427Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Madrid, Spain
| | - Federico Zilio
- grid.5608.b0000 0004 1757 3470Department of Philosophy, Sociology, Education and Applied Psychology, University of Padova, Padua, Italy
| | - Nareg Berberian
- grid.28046.380000 0001 2182 2255Mind, Brain Imaging and Neuroethics Research Unit, Institute of Mental Health, Royal Ottawa Mental Health Centre and University of Ottawa, Ottawa, Canada
| | - Annemarie Wolff
- grid.28046.380000 0001 2182 2255Mind, Brain Imaging and Neuroethics Research Unit, Institute of Mental Health, Royal Ottawa Mental Health Centre and University of Ottawa, Ottawa, Canada
| | - Mustapha C. E. Yagoub
- grid.28046.380000 0001 2182 2255School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada
| | - Georg Northoff
- grid.28046.380000 0001 2182 2255Mind, Brain Imaging and Neuroethics Research Unit, Institute of Mental Health, Royal Ottawa Mental Health Centre and University of Ottawa, Ottawa, Canada ,grid.410595.c0000 0001 2230 9154Centre for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, China ,grid.13402.340000 0004 1759 700XMental Health Centre, Zhejiang University School of Medicine, Hangzhou, Zhejiang China
| |
Collapse
|
4
|
Voxel-Wise Brain-Wide Functional Connectivity Abnormalities in Patients with Primary Blepharospasm at Rest. Neural Plast 2021; 2021:6611703. [PMID: 33505457 PMCID: PMC7808842 DOI: 10.1155/2021/6611703] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Revised: 12/22/2020] [Accepted: 12/24/2020] [Indexed: 12/12/2022] Open
Abstract
Background Primary blepharospasm (BSP) is one of the most common focal dystonia and its pathophysiological mechanism remains unclear. An unbiased method was used in patients with BSP at rest to observe voxel-wise brain-wide functional connectivity (FC) changes. Method A total of 48 subjects, including 24 untreated patients with BSP and 24 healthy controls, were recruited to undergo functional magnetic resonance imaging (fMRI). The method of global-brain FC (GFC) was adopted to analyze the resting-state fMRI data. We designed the support vector machine (SVM) method to determine whether GFC abnormalities could be utilized to distinguish the patients from the controls. Results Relative to healthy controls, patients with BSP showed significantly decreased GFC in the bilateral superior medial prefrontal cortex/anterior cingulate cortex (MPFC/ACC) and increased GFC in the right postcentral gyrus/precentral gyrus/paracentral lobule, right superior frontal gyrus (SFG), and left paracentral lobule/supplement motor area (SMA), which were included in the default mode network (DMN) and sensorimotor network. SVM analysis showed that increased GFC values in the right postcentral gyrus/precentral gyrus/paracentral lobule could discriminate patients from controls with optimal accuracy, specificity, and sensitivity of 83.33%, 83.33%, and 83.33%, respectively. Conclusion This study suggested that abnormal GFC in the brain areas associated with sensorimotor network and DMN might underlie the pathophysiology of BSP, which provided a new perspective to understand BSP. GFC in the right postcentral gyrus/precentral gyrus/paracentral lobule might be utilized as a latent biomarker to differentiate patients with BSP from controls.
Collapse
|
5
|
Imperatori C, Panno A, Giacchini M, Massullo C, Carbone GA, Clerici M, Farina B, Dakanalis A. Electroencephalographic correlates of body shape concerns: an eLORETA functional connectivity study. Cogn Neurodyn 2020; 14:723-729. [PMID: 33014184 DOI: 10.1007/s11571-020-09618-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 06/23/2020] [Accepted: 07/16/2020] [Indexed: 12/31/2022] Open
Abstract
The main aim of the present study was to investigate the association between body shape concerns and electroencephalography (EEG) functional connectivity within body image network in a sample of university students (N = 68). EEG was recorded during 5 min of resting state. All participants were asked to complete self-report measures assessing certain psychopathological dimensions (i.e., body shape concerns, depression, anxiety, obsessive-compulsive symptoms). EEG analyses were conducted by means of the exact low-resolution electromagnetic tomography software (eLORETA). Our results showed that body shape concerns were positively associated with increased gamma functional connectivity between the left and right prefrontal cortex (PFC). Furthermore, our data revealed that this EEG pattern was independently associated with body shape concerns after controlling for potential socio-demographic and clinical confounding variables. This finding seems to suggest that increased EEG gamma connectivity between the left and right PFC might be a relevant neurophysiological alteration involved in the development and/or maintenance of dysfunctional concerns about one's body.
Collapse
Affiliation(s)
- Claudio Imperatori
- Cognitive and Clinical Psychology Laboratory, Department of Human Science, European University of Rome, Via degli Aldobrandeschi 190, 00163 Rome, Italy
| | - Angelo Panno
- Cognitive and Clinical Psychology Laboratory, Department of Human Science, European University of Rome, Via degli Aldobrandeschi 190, 00163 Rome, Italy
| | - Marta Giacchini
- Cognitive and Clinical Psychology Laboratory, Department of Human Science, European University of Rome, Via degli Aldobrandeschi 190, 00163 Rome, Italy
| | - Chiara Massullo
- Cognitive and Clinical Psychology Laboratory, Department of Human Science, European University of Rome, Via degli Aldobrandeschi 190, 00163 Rome, Italy
| | - Giuseppe Alessio Carbone
- Cognitive and Clinical Psychology Laboratory, Department of Human Science, European University of Rome, Via degli Aldobrandeschi 190, 00163 Rome, Italy
| | - Massimo Clerici
- Department of Psychiatry, San Gerardo Hospital, ASST Monza, Via G. B. Pergolesi 33, 20900 Monza, Italy
- Department of Medicine and Surgery, University of Milano Bicocca, Cadore 48, 20900 Monza, Italy
| | - Benedetto Farina
- Cognitive and Clinical Psychology Laboratory, Department of Human Science, European University of Rome, Via degli Aldobrandeschi 190, 00163 Rome, Italy
| | - Antonios Dakanalis
- Department of Medicine and Surgery, University of Milano Bicocca, Cadore 48, 20900 Monza, Italy
| |
Collapse
|
6
|
Kang Y, Liu R, Mao X. Aperiodic stochastic resonance in neural information processing with Gaussian colored noise. Cogn Neurodyn 2020; 15:517-532. [PMID: 34040675 DOI: 10.1007/s11571-020-09632-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Revised: 08/22/2020] [Accepted: 09/01/2020] [Indexed: 11/24/2022] Open
Abstract
The aim of this paper is to explore the phenomenon of aperiodic stochastic resonance in neural systems with colored noise. For nonlinear dynamical systems driven by Gaussian colored noise, we prove that the stochastic sample trajectory can converge to the corresponding deterministic trajectory as noise intensity tends to zero in mean square, under global and local Lipschitz conditions, respectively. Then, following forbidden interval theorem we predict the phenomenon of aperiodic stochastic resonance in bistable and excitable neural systems. Two neuron models are further used to verify the theoretical prediction. Moreover, we disclose the phenomenon of aperiodic stochastic resonance induced by correlation time and this finding suggests that adjusting noise correlation might be a biologically more plausible mechanism in neural signal processing.
Collapse
Affiliation(s)
- Yanmei Kang
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, 710049 China
| | - Ruonan Liu
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, 710049 China
| | - Xuerong Mao
- Department of Mathematics and Statistics, University of Strathclyde, Livingstone Tower, 26 Richmond Street, Glasgow, G1 1XT Scotland, UK
| |
Collapse
|
7
|
Wang Y, Xu X, Wang R. Energy features in spontaneous up and down oscillations. Cogn Neurodyn 2020; 15:65-75. [PMID: 33786080 DOI: 10.1007/s11571-020-09597-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Revised: 03/25/2020] [Accepted: 05/04/2020] [Indexed: 12/22/2022] Open
Abstract
Spontaneous brain activities consume most of the brain's energy. So if we want to understand how the brain operates, we must take into account these spontaneous activities. Up and down transitions of membrane potentials are considered to be one of significant spontaneous activities. This kind of oscillation always shows bistable and bimodal distribution of membrane potentials. Our previous theoretical studies on up and down oscillations mainly looked at the ion channel dynamics. In this paper, we focus on energy feature of spontaneous up and down transitions based on a network model and its simulation. The simulated results indicate that the energy is a robust index and distinguishable of excitatory and inhibitory neurons. Meanwhile, one the whole, energy consumption of neurons shows bistable feature and bimodal distribution as well as the membrane potential, which turns out that the indicator of energy consumption encodes up and down states in this spontaneous activity. In detail, energy consumption mainly occurs during up states temporally, and mostly concentrates inside neurons rather than synapses spatially. The stimulation related energy is small, indicating that energy consumption is not driven by external stimulus, but internal spontaneous activity. This point of view is also consistent with brain imaging results. Through the observation and analysis of the findings, we prove the validity of the model again, and we can further explore the energy mechanism of more spontaneous activities.
Collapse
Affiliation(s)
- Yihong Wang
- Institute for Cognitive Neurodynamics, East China University of Science and Technology, 130 Meilong Road, Shanghai, China
| | - Xuying Xu
- Institute for Cognitive Neurodynamics, East China University of Science and Technology, 130 Meilong Road, Shanghai, China
| | - Rubin Wang
- Institute for Cognitive Neurodynamics, East China University of Science and Technology, 130 Meilong Road, Shanghai, China.,School of Computer Science, Hangzhou Dianzi University, Hangzhou, China
| |
Collapse
|
8
|
Tozzi A, Peters JF. Removing uncertainty in neural networks. Cogn Neurodyn 2020; 14:339-345. [PMID: 32399075 DOI: 10.1007/s11571-020-09574-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Revised: 01/24/2020] [Accepted: 02/19/2020] [Indexed: 01/26/2023] Open
Abstract
Neuroscientists draw lines of separation among structures and functions that they judge different, arbitrarily excluding or including issues in our description, to achieve positive demarcations that permits a pragmatic treatment of the nervous activity based on regularity and uniformity. However, uncertainty due to disconnectedness, lack of information and absence of objects' sharp boundaries is a troubling issue that prevents these scientists to select the required proper sets/subsets during their experimental assessment of natural and artificial neural networks. Starting from the detection of metamorphoses of shapes inside a Euclidean manifold, we propose a technique to detect the topological changes that occur during their reciprocal interactions and shape morphing. This method, that allows the detection of topological holes development and disappearance, makes it possible to solve the problem of uncertainty in the assessment of countless dynamical phenomena, such as cognitive processes, protein homeostasis deterioration, fire propagation, wireless sensor networks, migration flows, and cosmic bodies analysis.
Collapse
Affiliation(s)
- Arturo Tozzi
- 1Center for Nonlinear Science, Department of Physics, University of North Texas, 1155 Union Circle, #311427, Denton, TX 76203-5017 USA
| | - James F Peters
- 2Department of Electrical and Computer Engineering, University of Manitoba, Winnpeg, MB R3T 5V6 Canada.,3Department of Mathematics, Adıyaman University, 02040 Adıyaman, Turkey
| |
Collapse
|
9
|
Moraczewski D, Nketia J, Redcay E. Cortical temporal hierarchy is immature in middle childhood. Neuroimage 2020; 216:116616. [PMID: 32058003 DOI: 10.1016/j.neuroimage.2020.116616] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Revised: 01/21/2020] [Accepted: 02/05/2020] [Indexed: 12/24/2022] Open
Abstract
The development of successful social-cognitive abilities requires one to track, accumulate, and integrate knowledge of other people's mental states across time. Regions of the brain differ in their temporal scale (i.e., a cortical temporal hierarchy) and those receptive to long temporal windows may facilitate social-cognitive abilities; however, the cortical development of long timescale processing remains to be investigated. The current study utilized naturalistic viewing to examine cortical development of long timescale processing and its relation to social-cognitive abilities in middle childhood - a time of expanding social spheres and increasing social-cognitive abilities. We found that, compared to adults, children exhibited reduced low-frequency power in the temporo-parietal junction (TPJ) and reduced specialization for long timescale processing within the TPJ and other regions broadly implicated in the default mode network and higher-order visual processing. Further, specialization for long timescales within the right dorsal medial prefrontal cortex became more 'adult-like' as a function of children's comprehension of character mental states. These results suggest that cortical temporal hierarchy in middle childhood is immature and may be important for an accurate representation of complex naturalistic social stimuli during this age.
Collapse
Affiliation(s)
- Dustin Moraczewski
- Neuroscience and Cognitive Science Program, University of Maryland, College Park, MD, USA; Computation and Mathematics for Biological Networks, University of Maryland, College Park, MD, USA; Department of Psychology, University of Maryland, College Park, MD, USA.
| | - Jazlyn Nketia
- Department of Psychology, University of Maryland, College Park, MD, USA; Department of Cognitive, Linguistics, And Psychological Sciences, Brown University, RI, USA
| | - Elizabeth Redcay
- Neuroscience and Cognitive Science Program, University of Maryland, College Park, MD, USA; Department of Psychology, University of Maryland, College Park, MD, USA
| |
Collapse
|
10
|
Liu C, Li Y, Song S, Zhang J. Decoding disparity categories in 3-dimensional images from fMRI data using functional connectivity patterns. Cogn Neurodyn 2019; 14:169-179. [PMID: 32226560 DOI: 10.1007/s11571-019-09557-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2018] [Revised: 09/05/2019] [Accepted: 09/29/2019] [Indexed: 02/02/2023] Open
Abstract
Humans use binocular disparity to extract depth information from two-dimensional retinal images in a process called stereopsis. Previous studies usually introduce the standard univariate analysis to describe the correlation between disparity level and brain activity within a given brain region based on functional magnetic resonance imaging (fMRI) data. Recently, multivariate pattern analysis has been developed to extract activity patterns across multiple voxels for deciphering categories of binocular disparity. However, the functional connectivity (FC) of patterns based on regions of interest or voxels and their mapping onto disparity category perception remain unknown. The present study extracted functional connectivity patterns for three disparity conditions (crossed disparity, uncrossed disparity, and zero disparity) at distinct spatial scales to decode the binocular disparity. Results of 27 subjects' fMRI data demonstrate that FC features are more discriminatory than traditional voxel activity features in binocular disparity classification. The average binary classification of the whole brain and visual areas are respectively 87% and 79% at single subject level, and thus above the chance level (50%). Our research highlights the importance of exploring functional connectivity patterns to achieve a novel understanding of 3D image processing.
Collapse
Affiliation(s)
- Chunyu Liu
- 1College of Information Science and Technology, Beijing Normal University, Beijing, China
| | - Yuan Li
- 2School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Sutao Song
- 3School of Education and Psychology, University of Jinan, Jinan, China
| | - Jiacai Zhang
- 1College of Information Science and Technology, Beijing Normal University, Beijing, China
| |
Collapse
|
11
|
Fellner M, Varga B, Grolmusz V. The frequent subgraphs of the connectome of the human brain. Cogn Neurodyn 2019; 13:453-460. [PMID: 31565090 PMCID: PMC6746900 DOI: 10.1007/s11571-019-09535-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2018] [Revised: 04/14/2019] [Accepted: 04/25/2019] [Indexed: 01/30/2023] Open
Abstract
In mapping the human structural connectome, we are in a very fortunate situation: one can compute and compare graphs, describing the cerebral connections between the very same, anatomically identified small regions of the gray matter among hundreds of human subjects. The comparison of these graphs has led to numerous recent results, as the (1) discovery that women's connectomes have deeper and richer connectivity-related graph parameters like those of men, or (2) the description of more and less conservatively connected lobes and cerebral regions, and (3) the discovery of the phenomenon of the consensus connectome dynamics. Today one of the greatest challenges of brain science is the description and modeling of the circuitry of the human brain. For this goal, we need to identify sub-circuits that are present in almost all human subjects and those, which are much less frequent: the former sub-circuits most probably have functions with general importance, the latter sub-circuits are probably related to the individual variability of the brain structure and function. The present contribution describes the frequent connected subgraphs of at most six edges in the human brain. We analyze these frequent graphs and also examine sex differences in these graphs: we demonstrate numerous connected subgraphs that are more frequent in female or male connectomes. While there is no difference in the number of k edge connected subgraphs in males or females for k = 1 , and for k = 2 males have slightly more frequent subgraphs, for k = 6 there is a very strong advantage in the case of female braingraphs. Our data source is the public release of the Human Connectome Project, and we are applying the data of 426 human subjects in this study.
Collapse
Affiliation(s)
- Máté Fellner
- PIT Bioinformatics Group, Eötvös University, Budapest, 1117 Hungary
| | - Bálint Varga
- PIT Bioinformatics Group, Eötvös University, Budapest, 1117 Hungary
| | - Vince Grolmusz
- PIT Bioinformatics Group, Eötvös University, Budapest, 1117 Hungary
- Uratim Ltd., Budapest, 1118 Hungary
| |
Collapse
|
12
|
Tafreshi TF, Daliri MR, Ghodousi M. Functional and effective connectivity based features of EEG signals for object recognition. Cogn Neurodyn 2019; 13:555-566. [PMID: 31741692 DOI: 10.1007/s11571-019-09556-7] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2018] [Revised: 09/17/2019] [Accepted: 09/24/2019] [Indexed: 01/06/2023] Open
Abstract
Classifying different object categories is one of the most important aims of brain-computer interface researches. Recently, interactions between brain regions were studied using different methods, such as functional and effective connectivity techniques. Functional and effective connectivity techniques are applied to estimate human brain areas connectivity. The main purpose of this study is to compare classification accuracy of the most advanced functional and effective methods in order to classify 12 basic object categories using Electroencephalography (EEG) signals. In this paper, 19 channels EEG signals were collected from 10 healthy subjects; when they were visiting color images and instructed to select the target images among others. Correlation, magnitude square coherence, wavelet coherence (WC), phase synchronization and mutual information were applied to estimate functional cortical connectivity. On the other hand, directed transfer function, partial directed coherence, generalized partial directed coherence (GPDC) were used to obtain effective cortical connectivity. After feature extraction, the scalar feature selection methods including T-test and one-sided-anova were applied to rank and select the most informative features. The selected features were classified by a one-against-one support vector machine classifier. The results indicated that the use of different techniques led to different classifying accuracy and brain lobes analysis. WC and GPDC are the most accurate methods with performances of 80.15% and 64.43%, respectively.
Collapse
Affiliation(s)
| | - Mohammad Reza Daliri
- 2Neuroscience and Neuroengineering Research Lab., Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran
| | - Mahrad Ghodousi
- 3Department of Neuroscience, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
| |
Collapse
|
13
|
Wang R, Fan Y, Wu Y. Spontaneous electromagnetic induction promotes the formation of economical neuronal network structure via self-organization process. Sci Rep 2019; 9:9698. [PMID: 31273270 PMCID: PMC6609776 DOI: 10.1038/s41598-019-46104-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Accepted: 06/24/2019] [Indexed: 12/16/2022] Open
Abstract
Developed through evolution, brain neural system self-organizes into an economical and dynamic network structure with the modulation of repetitive neuronal firing activities through synaptic plasticity. These highly variable electric activities inevitably produce a spontaneous magnetic field, which also significantly modulates the dynamic neuronal behaviors in the brain. However, how this spontaneous electromagnetic induction affects the self-organization process and what is its role in the formation of an economical neuronal network still have not been reported. Here, we investigate the effects of spontaneous electromagnetic induction on the self-organization process and the topological properties of the self-organized neuronal network. We first find that spontaneous electromagnetic induction slows down the self-organization process of the neuronal network by decreasing the neuronal excitability. In addition, spontaneous electromagnetic induction can result in a more homogeneous directed-weighted network structure with lower causal relationship and less modularity which supports weaker neuronal synchronization. Furthermore, we show that spontaneous electromagnetic induction can reconfigure synaptic connections to optimize the economical connectivity pattern of self-organized neuronal networks, endowing it with enhanced local and global efficiency from the perspective of graph theory. Our results reveal the critical role of spontaneous electromagnetic induction in the formation of an economical self-organized neuronal network and are also helpful for understanding the evolution of the brain neural system.
Collapse
Affiliation(s)
- Rong Wang
- College of Science, Xi'an University of Science and Technology, Xi'an, 710054, China.
| | - Yongchen Fan
- State Key Laboratory for Strength and Vibration of Mechanical Structures, Shaanxi Engineering Laboratory for Vibration Control of Aerospace Structures, School of Aerospace, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Ying Wu
- State Key Laboratory for Strength and Vibration of Mechanical Structures, Shaanxi Engineering Laboratory for Vibration Control of Aerospace Structures, School of Aerospace, Xi'an Jiaotong University, Xi'an, 710049, China
| |
Collapse
|
14
|
Hejazi M, Motie Nasrabadi A. Prediction of epilepsy seizure from multi-channel electroencephalogram by effective connectivity analysis using Granger causality and directed transfer function methods. Cogn Neurodyn 2019; 13:461-473. [PMID: 31565091 DOI: 10.1007/s11571-019-09534-z] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2018] [Revised: 02/07/2019] [Accepted: 04/25/2019] [Indexed: 01/09/2023] Open
Abstract
Epilepsy is a chronic disorder, which causes strange perceptions, muscle spasms, sometimes seizures, and loss of awareness, associated with abnormal neuronal activity in the brain. The goal of this study is to investigate how effective connectivity (EC) changes effect on unexpected seizures prediction, as this will authorize the patients to play it safe and avoid risk. We approve the hypothesis that EC variables near seizure change significantly so seizure can be predicted in accordance with this variation. We introduce two time-variant coefficients based on standard deviation of EC on Freiburg EEG dataset by using directed transfer function and Granger causality methods and compare index changes over the course of time in five different frequency bands. Comparison of the multivariate and bivariate analysis of factors is implemented in this investigation. The performance based on the suggested methods shows the seizure occurrence period is approximately 50 min that is expected onset stated in, the maximum value of sensitivity approaching ~ 80%, and 0.33 FP/h is the false prediction rate. The findings revealed that greater accuracy and sensitivity are obtained by the designed system in comparison with the results of other works in the same condition. Even though these results still are not sufficient for clinical applications. Based on the conclusions, it can generally be observed that the greater results by DTF method are in the gamma and beta frequency bands.
Collapse
Affiliation(s)
- Mona Hejazi
- 1Department of Biomedical Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran
| | - Ali Motie Nasrabadi
- 2Department of Biomedical Engineering, Faculty of Biomedical Engineering, Shahed University, Tehran, Iran
| |
Collapse
|
15
|
Placing pure experience of Eastern tradition into the neurophysiology of Western tradition. Cogn Neurodyn 2019; 13:121-123. [PMID: 30728875 DOI: 10.1007/s11571-018-9506-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Revised: 08/29/2018] [Accepted: 09/04/2018] [Indexed: 11/27/2022] Open
|
16
|
Zhang Q, Hu G, Tian L, Ristaniemi T, Wang H, Chen H, Wu J, Cong F. Examining stability of independent component analysis based on coefficient and component matrices for voxel-based morphometry of structural magnetic resonance imaging. Cogn Neurodyn 2018; 12:461-470. [PMID: 30250625 PMCID: PMC6139102 DOI: 10.1007/s11571-018-9484-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2017] [Revised: 01/18/2018] [Accepted: 03/12/2018] [Indexed: 10/17/2022] Open
Abstract
Independent component analysis (ICA) on group-level voxel-based morphometry (VBM) produces the coefficient matrix and the component matrix. The former contains variability among multiple subjects for further statistical analysis, and the latter reveals spatial maps common for all subjects. ICA algorithms converge to local optimization points in practice and the mostly applied stability investigation approach examines the stability of the extracted components. We found that the practically stable components do not guarantee to produce the practically stable coefficients of ICA decomposition for the further statistical analysis. Consequently, we proposed a novel approach including two steps: (1), the stability index for the coefficient matrix and the stability index for the component matrix were examined, respectively; (2) the two indices were multiplied to analyze the stability of ICA decomposition. The proposed approach was used to study the sMRI data of Type II diabetes mellitus group and the healthy control group (HC). Group differences in VBM were found in the superior temporal gyrus. Besides, it was revealed that the VBMs of the region of the HC group were significantly correlated with Montreal Cognitive Assessment (MoCA) describing the level of cognitive disorder. In contrast to the widely applied approach to investigating the stability of the extracted components for ICA decomposition, we proposed to examine the stability of ICA decomposition by fusion the stability of both coefficient matrix and the component matrix. Therefore, the proposed approach can examine the stability of ICA decomposition sufficiently.
Collapse
Affiliation(s)
- Qing Zhang
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
- Department of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
| | - Guoqiang Hu
- Department of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
| | - Lili Tian
- Department of Psychology, University of Jyvaskyla, Jyvaskyla, Finland
| | - Tapani Ristaniemi
- Faculty of Information Technology, University of Jyvaskyla, Jyvaskyla, Finland
| | - Huili Wang
- School of Foreign Languages, Dalian University of Technology, Dalian, China
| | - Hongjun Chen
- School of Foreign Languages, Dalian University of Technology, Dalian, China
| | - Jianlin Wu
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Fengyu Cong
- Department of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
- Faculty of Information Technology, University of Jyvaskyla, Jyvaskyla, Finland
| |
Collapse
|
17
|
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.
Collapse
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
| |
Collapse
|
18
|
Zhu Z, Wang R, Zhu F. The Energy Coding of a Structural Neural Network Based on the Hodgkin-Huxley Model. Front Neurosci 2018; 12:122. [PMID: 29545741 PMCID: PMC5838014 DOI: 10.3389/fnins.2018.00122] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2017] [Accepted: 02/15/2018] [Indexed: 11/13/2022] Open
Abstract
Based on the Hodgkin-Huxley model, the present study established a fully connected structural neural network to simulate the neural activity and energy consumption of the network by neural energy coding theory. The numerical simulation result showed that the periodicity of the network energy distribution was positively correlated to the number of neurons and coupling strength, but negatively correlated to signal transmitting delay. Moreover, a relationship was established between the energy distribution feature and the synchronous oscillation of the neural network, which showed that when the proportion of negative energy in power consumption curve was high, the synchronous oscillation of the neural network was apparent. In addition, comparison with the simulation result of structural neural network based on the Wang-Zhang biophysical model of neurons showed that both models were essentially consistent.
Collapse
Affiliation(s)
- Zhenyu Zhu
- Institute for Cognitive Neurodynamics, East China University of Science and Technology, Shanghai, China
| | - Rubin Wang
- Institute for Cognitive Neurodynamics, East China University of Science and Technology, Shanghai, China
| | - Fengyun Zhu
- Institute for Cognitive Neurodynamics, East China University of Science and Technology, Shanghai, China
| |
Collapse
|