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作者： Comsa, Iulia-Maria University of Cambridge

学位级别：博士

How do we lose and regain consciousness? The space between healthy wakefulness and unconsciousness encompasses a series of gradual and rapid changes in brain activity. In this thesis, I investigate computational measures applicable to the electroencephalogram to quantify the loss and recovery of consciousness from the perspective of modern theoretical frameworks. I examine three different transitions of consciousness caused by natural, pharmacological and pathological factors: sleep, sedation and coma. First, I investigate the neural dynamics of falling asleep. By combining the established methods of phase-lag brain connectivity and EEG microstates in a group of healthy subjects, a unique microstate is identified, whose increased duration predicts behavioural unresponsiveness to auditory stimuli during drowsiness. This microstate also uniquely captures an increase in frontoparietal theta connectivity, a putative marker of the loss of consciousness prior to sleep onset. I next examine the loss of behavioural responsiveness in healthy subjects undergoing mild and moderate sedation. The Lempel-Ziv compression algorithm is employed to compute signal complexity and symbolic mutual information to assess information integration. An intriguing dissociation between responsiveness and drug level in blood during sedation is revealed: responsiveness is best predicted by the temporal complexity of the signal at single- channel and low-frequency integration, whereas drug level is best predicted by the complexity of spatial patterns and high-frequency integration. Finally, I investigate brain connectivity in the overnight EEG recordings of a group of patients in acute coma. Graph theory is applied on alpha, theta and delta networks to find that increased variability in delta network integration early after injury predicts the eventual coma recovery score. A case study is also described where the re-emergence of frontoparietal connectivity predicted a full recovery long before beha

关键词： consciousness neuroscience of consciousness states of consciousness levels of consciousness impaired consciousness onset of sleep sedation coma EEG EEG microstates brain connectivity frontoparietal connectivity temporal brain dynamics graph theory Lempel-Ziv complexity neural complexity neural integration brain networks spectral power spectral connectivity weighted phase lag index

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Evaluating Movement skills from Extended neural complexity

Evaluating Movement skills from Extended Neural Complexity

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Kwon, Woo Young Suh, Il Hong You, Bum-Jae Oh, Sang-Rok Hanyang Univ Dept Elect & Comp Engn Seoul 133791 South Korea Korea Inst Sci & Technol Seoul South Korea

ISBN: (纸本)9781467317146

For a robot to learn complex movement skills, programming by demonstration and/or learning by trial and error is necessary. Measuring the complexity of such movement skills is important to decide the appropriate learning model, and the required size of dataset and additional prior knowledge. To deal with measuring the complexity of movement skills for robots, we propose an information-theoretic complexity measure. By modeling proprioceptive as well as exteroceptive sensory data as a multivariate Gaussian distribution, movement skills can be modeled as a probabilistic model. Next, complexity of the movement skills is measured by using neural complexity. In addition to the original neural complexity measure, endogeneous changes in time of the movement skills are modeled by sampling in time and modeling as individual random variables. To evaluate our proposed complexity measure, several experiments are performed on real robotic movement skills.

关键词： complexity measure neural complexity Time-sliced neural complexity Movement skills

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Evaluating Movement skills from Extended neural complexity

Evaluating Movement skills from Extended Neural Complexity

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Woo Young Kwon Il Hong Suh Bum-Jae You Sang-Rok Oh Department of Electronics and Computer Engineering Hanyang University Korea Institute of Science and Technology

ISBN: (纸本)9781467317139

关键词： complexity measure neural complexity Time- sliced neural complexity Movement skills Movements complexity Robot

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Multisensory temporal function and EEG complexity in patients with epilepsy and psychogenic nonepileptic events

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EPILEPSY & BEHAVIOR 2017年第PartA期70卷 166-172页

作者： Noel, Jean-Paul Kurela, LeAnne Baum, Sarah H. Yu, Hong Neimat, Joseph S. Gallagher, Martin J. Wallace, Mark Vanderbilt Univ Med Sch Vanderbilt Brain Inst Neurosci Grad Program 221 Kirkland Hall Nashville TN 37235 USA Vanderbilt Univ Med Sch Vanderbilt Brain Inst 221 Kirkland Hall Nashville TN 37235 USA Vanderbilt Univ Med Ctr Dept Neurosurg 221 Kirkland Hall Nashville TN 37235 USA Vanderbilt Univ Med Ctr Dept Neurol 221 Kirkland Hall Nashville TN 37235 USA Vanderbilt Univ Med Ctr Dept Hearing & Speech Sci 221 Kirkland Hall Nashville TN 37235 USA Vanderbilt Univ Dept Psychol 221 Kirkland Hall Nashville TN 37235 USA

Cognitive and perceptual comorbidities frequently accompany epilepsy and psychogenic nonepileptic events (PNEE). However, and despite the fact that perceptual function is built upon a multisensory foundation, little knowledge exists concerning multisensory function in these populations. Here, we characterized facets of multi sensory processing abilities in patients with epilepsy and PNEE, and probed the relationship between individual resting-state EEG complexity and these psychophysical measures in each patient. We prospectively studied a cohort of patients with epilepsy (N = 18) and PNEE (N = 20) patients who were admitted to Vanderbilt's Epilepsy Monitoring Unit (EMU) and weaned off of anticonvulsant drugs. Unaffected age-matched persons staying with the patients in the EMU (N = 15) were also recruited as controls. All participants performed two tests of multisensory function: an audio-visual simultaneity judgment and an audio-visual redundant target task. Further, in the cohort of patients with epilepsy and PNEE we quantified resting state EEG gamma power and complexity. Compared with both patients with epilepsy and control subjects, patients with PNEE exhibited significantly poorer acuity in audiovisual temporal function as evidenced in significantly larger temporal binding windows (i.e., they perceived larger stimulus asynchronies as being presented simultaneously). These differences appeared to be specific for temporal function, as there was no difference among the three groups in a non temporally based measure of multisensory function the redundant target task. Further, patients with PNEE exhibited more complex resting state EEG patterns as compared to their patients with epilepsy, and EEG complexity correlated with multisensory temporal performance on a subject-by-subject manner. Taken together, findings seem to indicate that patients with PNEE bind information from audition and vision over larger temporal intervals when compared with control subjects as we

关键词： Multisensory Temporal Epilepsy Psychogenic EEG neural complexity

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Charting moment-to-moment brain signal variability from early to late childhood

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CORTEX 2016年第0期83卷 51-61页

作者： Miskovic, Vladimir Owens, Max Kuntzelman, Karl Gibb, Brandon E. SUNY Binghamton Ctr Affect Sci Binghamton NY 13902 USA

Large-scale brain signals exhibit rich intermittent patterning, reflecting the fact that the cortex actively eschews fixed points in favor of itinerant wandering with frequent state transitions. Fluctuations in endogenous cortical activity occur at multiple time scales and index a dynamic repertoire of network states that are continuously explored, even in the absence of external sensory inputs. Here, we quantified such moment-to-moment brain signal variability at rest in a large, cross-sectional sample of children ranging in age from seven to eleven years. Our findings revealed a monotonic rise in the complexity of electroencephalogram (EEG) signals as measured by sample entropy, from the youngest to the oldest age cohort, across a range of time scales and spatial regions. From year to year, the greatest changes in intraindividual brain signal variability were recorded at electrodes covering the anterior cortical zones. These results provide converging evidence concerning the age-dependent expansion of functional cortical network states during a critical developmental period ranging from early to late childhood. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： neural complexity Multiscale entropy Electroencephalogram (EEG) Resting state Development

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neural complexity and structural connectivity

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Physical Review E 2009年第5期79卷 051914-051914页

作者： L. Barnett C. L. Buckley S. Bullock []Department of Informatics Centre for Computational Neuroscience and Robotics School of Science and Technology University of Sussex Brighton BN1 9QH United Kingdom

Tononi et al. [Proc. Natl. Acad. Sci. U.S.A. 91, 5033 (1994)] proposed a measure of neural complexity based on mutual information between complementary subsystems of a given neural network, which has attracted much interest in the neuroscience community and beyond. We develop an approximation of the measure for a popular Gaussian model which, applied to a continuous-time process, elucidates the relationship between the complexity of a neural system and its structural connectivity. Moreover, the approximation is accurate for weakly coupled systems and computationally cheap, scaling polynomially with system size in contrast to the full complexity measure, which scales exponentially. We also discuss connectivity normalization and resolve some issues stemming from an ambiguity in the original Gaussian model.

关键词： .Gaussian PHYSICAL REVIEW approximation Normalization neural network Proc Natl subsystems cheap continuous-time neural systems system size neural complexity structural connectivity complexity measure

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PRUNING ARTIFICIAL neural NETWORKS USING neural complexity MEASURES

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INTERNATIONAL JOURNAL OF neural SYSTEMS 2008年第5期18卷 389-403页

作者： Jorgensen, Thomas D. Haynes, Barry P. Norlund, Charlotte C. F. Univ Portsmouth Dept Elect & Comp Engn Portsmouth Hants England Univ Southampton Sch Elect & Comp Sci Southampton Hants England

This paper describes a new method for pruning artificial neural networks, using a measure of the neural complexity of the neural network. This measure is used to determine the connections that should be pruned. The measure computes the information-theoretic complexity of a neural network, which is similar to, yet different from previous research on pruning. The method proposed here shows how overly large and complex networks can be reduced in size, whilst retaining learnt behaviour and fitness. The technique proposed here helps to discover a network topology that matches the complexity of the problem it is meant to solve. This novel pruning technique is tested in a robot control domain, simulating a racecar. It is shown, that the proposed pruning method is a significant improvement over the most commonly used pruning method Magnitude Based Pruning. Furthermore, some of the pruned networks prove to be faster learners than the benchmark network that they originate from. This means that this pruning method can also help to unleash hidden potential in a network, because the learning time decreases substantially for a pruned a network, due to the reduction of dimensionality of the network.

关键词： Pruning neural networks neuroevolution robot controllers neural complexity

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Differences in eeg between gifted and average students: neural complexity and functional cluster analysis

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INTERNATIONAL JOURNAL OF NEUROSCIENCE 2007年第8期117卷 1167-1184页

作者： Jin, Seung-Hyun Kim, Soo Yong Park, Kyung Hee Lee, Kil-Jae NIH NINDS Human Motor Control Sect Bethesda MD 20892 USA Korea Adv Inst Sci & Technol Dept Phys Taejon 305701 South Korea Jeonju Dukil Middle Sch Jeonju South Korea Korea Natl Univ Educ Dept Sci Educ Chungbuk South Korea

The main aim of the present study was to assess the differences in EEG between gifted and average students. Another aim of the present study was to investigate which brain areas are related to a Rey-Osterrieth complex figure (ROCF) memorizing using a functional cluster (FC) analysis and how the complexity of cortical activities changes in both gifted and average students. The EEG was recorded from 16 electrodes in both 18 right-handed healthy gifted and age-matched average students before and during ROCF memorizing. FC was estimated to characterize the joint interactions among many brain regions and neural complexity. The study assessed the visuo-spatial memory abilities through examining EEG profiles using the measure of FC, and planning and executive function using recall score. The gifted students made a significantly high score compared to the average students during ROCF memorizing. Both groups showed very different FC patterns. ROCF memorizing is related to the visual mental process, thus simultaneous neuronal activities appears on the right central, temporal, occipital, and bilateral prefrontal regions. One of the notable characteristics of gifted students' FC map is the dominance of the right hemisphere compared with that of average students, and it is accordance with the characteristics of gifted brain.

关键词： functional cluster analysis gifted students neural complexity Rey-Osterrieth complex figure test

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How evolution guides complexity

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HFSP JOURNAL 2009年第5期3卷 328-339页

作者： Yaeger, Larry S. Indiana Univ Sch Informat Bloomington IN 47408 USA

Long-standing debates about the role of natural selection in the growth of biological complexity over geological time scales are difficult to resolve from the paleobiological record. Using an evolutionary model-a computational ecosystem subjected to natural selection-we investigate evolutionary trends in an information-theoretic measure of the complexity of the neural dynamics of artificial agents inhabiting the model. Our results suggest that evolution always guides complexity change, just not in a single direction. We also demonstrate that neural complexity correlates well with behavioral adaptation but only when complexity increases are achieved through natural selection, as opposed to increases generated randomly or optimized via a genetic algorithm.. We conclude with a suggested research direction that might be able to use the artificial neural data generated in these experiments to determine which aspects of network structure give rise to evolutionarily meaningful neural complexity. [DOI: 10.2976/1.3233712]

关键词： .time scales cial evolution guides computational ecosystem evolutionary trends neural complexity behavioral adaptation complexity increases

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A neural complexity measure applied to MEG data in Alzheimer's disease

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CLINICAL NEUROPHYSIOLOGY 2003年第6期114卷 1034-1040页

作者： van Walsum, AMV Pijnenburg, YAL Berendse, HW van Dijk, BW Knol, DL Scheltens, P Stam, CJ VU Univ Med Ctr Dept Clin Neurophysiol NL-1007 MB Amsterdam Netherlands VU Univ Med Ctr Dept Neurol Alzheimer Ctr NL-1007 MB Amsterdam Netherlands VU Univ Med Ctr MEG Ctr Amsterdam Netherlands VU Univ Med Ctr Dept Clin Epidemiol & Biostat NL-1007 MB Amsterdam Netherlands

Objective: A measure of neural complexity (C-N) (Proc. Natl Acad. Sci. USA 91 (1994) 5033) was applied to magnetoencephalography (MEG) data to test the hypothesis that C-N decreases when information processing in the brain is impaired, as is the case in patients with Alzheimer's Disease (AD). Methods: One hundred and fifty-one channel MEGs were recorded in 20 AD patients and 20 healthy age-matched controls in a resting condition with eyes open (EO) and eyes closed (EC). Artifact-free epochs of 117 channels were selected for analysis. C-N and D-2 were computed in different frequency bands, and correlated with the MMSE. Results: The Group X Frequency band interaction was significant for both C-N and D-2. C-N was higher in AD, as compared with controls, in the 2-4 and 4-8 Hz bands, and D-2 was higher in AD patients in the 14-20 and 20-30 Hz bands. The C-N was higher in the EC condition compared to the EO condition, whereas the D-2 was higher in the EO condition. Conclusions: The hypothesis of Tononi et al. (Proc. Natl Acad. Sci. USA 91 (1994) 5033) that the neural complexity decreases in AD patients has to be rejected. However, both neural complexity and the correlation dimension did show differences between controls and AD patients which depended on frequency band. (C) 2003 International Federation of Clinical Neurophysiology. Published by Elsevier Science Ireland Ltd. All rights reserved.

关键词： Alzheimer's disease cognition correlation dimension frequency bands magnetoencephalography neural complexity

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