POLITECNICO DI TORINO MASTER THESIS Disorders of Consciousness: using the Perturbational Complexity Index to distinguish between voluntary and involuntary movements Author: Supervisors: Federica Sgrò Gabriella Olmo Vito De Feo ICT for Smart Societies 16 April, 2019 iii POLITECNICO DI TORINO Abstract Collegio di Ingegneria Elettronica, delle Telecomunicazioni e Fisica (ETF) ICT for Smart Societies Disorders of Consciousness: using the Perturbational Complexity Index to distinguish between voluntary and involuntary movements by Federica Sgrò The debate behind the word "consciousness" has been vivid for centuries among ethicists, philosophers and neurologists. Distinguishing between different Disorders of Consciousness (DOCs) in after-coma patients, in particular, is still difficult, and the diagnosis is based on scales whose assignement may be subjective and thus mistaken. Finding an accurate mathematical way to interpret a patient’s movements and understand if they are completely voluntary or just involuntary reflexes may be a more objective way to classify their level of consciousness. This thesis work proposes to find a way to distinguish among voluntary and involuntary movements in healthy subjects by analysing the Event Related Potentials (ERPs) behaviour obtained from EEG recordings. For each subject and experiment (voluntary, semi-voluntary and involuntary actions), after recording the EEG signals while performing the movements, the ERPs were extracted and a measure of the Integrated Information Theory firstly proposed by Giulio Tononi, called Perturbational Complexity Index (PCI), was calculated on the basis of the state transitions before and after the on-set of the movement. The values obtained from the algorithm were finally used to validate two different classifiers, one based on the Minimum Distance Criterion and the other using Support Vector Machines (SVM). In both cases, significant results were obtained in the case of the binary classifier (considering only voluntary and involuntary movements), and in particular 88.9% specificity and 77.8% sensitivity were achieved with the minimum distance and 66.7% specificity and 83.3% sensitivity with SVM. The multi-class classifiers, however, led to many errors due to the ambiguous nature of the semi-voluntary experiment. v Contents Abstract iii List of Figures vii List of Tables ix 1 Introduction1 1.1 Consciousness............................1 1.1.1 What is consciousness?..................1 1.1.2 Wakefulness and Awareness...............2 1.1.3 Disorders of Consciousness (DOCs)...........2 1.1.4 Between Mind and Matter.................4 1.2 The Human Brain..........................4 1.2.1 Anatomy of the Human Brain...............5 1.2.2 The role of neurons in consciousness...........6 1.2.3 The Electroencephalogram (EEG).............6 2 The Road so far9 2.1 Readiness Potentials (Kornhuber and Deecke, 1964)......9 2.1.1 Event Related Potentials (ERPs).............9 2.1.2 Movements and Readiness Potentials.......... 10 2.1.3 Libet’s experiment..................... 11 2.1.4 Contingent Negative Variance.............. 11 2.1.5 Readiness Potential and "Free will"............ 11 2.2 Information Integration Theory (Tononi, 2004)......... 12 2.2.1 Integrated Information................... 12 2.2.2 The F measure....................... 13 2.2.3 Complexes.......................... 15 2.2.4 Integrated information theory and thalamocortical system 16 2.2.5 For the future........................ 17 2.3 Decoding Algorithms and Information Theory (Quiroga and Panzeri, 2009)............................ 17 2.3.1 The Multiple-neuron Single-trial approach....... 17 2.3.2 Decoding Algorithms................... 18 2.3.3 Information theory..................... 18 2.3.4 Merging the two approaches............... 20 2.4 Approximate Entropy (Sarà, Pistoia et al., 2011)......... 20 2.4.1 Approximate Entropy calculation............ 20 2.4.2 Results............................ 21 2.5 Connectivity Biomarkers (Höller et al., 2014).......... 22 vi 2.5.1 Classification........................ 22 2.5.2 Results............................ 23 3 An Integrated Information measure: the Perturbational Complexity Index 25 3.1 PCI as Lempel-Ziv compression algorithm complexity..... 25 3.1.1 Recordings of the brain response............. 25 3.1.2 Source Modelling...................... 27 3.1.3 Lempel-Ziv algorithm................... 28 3.1.4 Results............................ 28 3.1.5 Limitations......................... 32 3.2 PCI as State Transitions....................... 32 3.2.1 Recordings and Principal Component Analysis (PCA) 32 3.2.2 Distance and Transition Matrices............. 33 3.2.3 State Transitions...................... 33 3.2.4 Results............................ 33 4 Project Development 37 4.1 EEG Recordings and ERP extraction............... 37 4.1.1 Protocol definition..................... 37 4.1.2 Materials........................... 38 4.1.3 ERP extraction....................... 39 4.1.4 Chosen subjects....................... 39 4.2 PCI calculation........................... 40 4.2.1 Data cleaning........................ 40 4.2.2 Principal Component Analysis.............. 42 4.2.3 Distance Matrix....................... 43 4.2.4 Transition Matrix...................... 43 4.2.5 Number of State Transitions and PCI calculation.... 44 4.3 Collected results and classification methods........... 46 4.3.1 Obtained PCI measures.................. 46 4.3.2 Obtained Number of Principal Components...... 47 4.3.3 Classification with Minimum Distance Criterion.... 48 4.3.4 Classification with Support Vector Machines (SVM).. 52 5 Conclusions 55 5.1 Issues and limitations of the approach.............. 55 5.1.1 The dataset......................... 56 5.1.2 The classification...................... 56 5.1.3 Actions and consciousness................ 56 5.2 Ideas for the future......................... 57 5.2.1 Enlarging the dataset.................... 57 5.2.2 Studying the semi-voluntary actions........... 58 5.2.3 Testing on brain-injured patients............. 58 Bibliography 59 vii List of Figures 1.1 Awareness and Wakefulness in different states of consciousness.3 1.2 Anatomy of the human brain....................5 1.3 The 10-20 Electrode Location System...............7 2.1 Readiness Potential (RP)....................... 10 2.2 Libet’s Experiment.......................... 11 2.3 Contingent Negative Variation (CNV)............... 12 2.4 Effective Information, Minimum Information Bipartition and Complexes............................... 14 2.5 Two complexes with the same F.................. 15 2.6 Information integration for a thalamocortical-like system.... 16 2.7 Spike-sorting algorithm....................... 18 2.8 Decoding algorithm......................... 19 2.9 Mean Approximate Entropy for healty subjects and patients.. 22 2.10 Relationship between Approximate Entropy and Coma Scales. 23 3.1 PCI calculation based on Lempel-Ziv algorithm complexity.. 26 3.2 PCI and loss of integration/differentiation............ 27 3.3 Parameters of cortical responses to TMS during wakefulness and sleep/anesthesia......................... 29 3.4 PCI and graded changes in consciousness............. 30 3.5 PCI in brain injured patients.................... 30 3.6 PCI in healthy patients under ansthesia.............. 31 3.7 PCI calculation based on state transitions............. 35 3.8 PCIST in healthy subjects in consciousness and unconsciousness. 36 3.9 States of consciousness in brain-injured patients using PCIST in different EEG setups....................... 36 4.1 Channel locations of the EEG recordings............. 38 4.2 Grand Average of ERP on Fc3 for the experiments........ 39 4.3 ERPs of subject 3 after filtering and data cleaning........ 41 4.4 Principal Components of subject 3................. 42 4.5 Distance Matrices of subject 3 for Principal Component 1.... 44 4.6 Transition Matrices of subject 3 for Principal Component 1... 45 4.7 Normalized Number of State Transitions of subject 3 for Principal Component 1....................... 46 4.8 Obtained PCI for each experiment................. 48 4.9 Obtained number of principal components for each experiment. 50 4.10 Confusion matrix in binary classification using the minimum distance criterion........................... 51 viii 4.11 Confusion matrix in non-binary classification using the minimum distance criterion..................... 52 4.12 Confusion matrix in binary classification using Support Vector Machines............................... 53 4.13 Confusion matrix in non-binary classification using Support Vector Machines........................... 54 ix List of Tables 4.1 Table of the obtained Perturbational Complexity Index (PCI) for each subject and experiment.................. 47 4.2 Table of the obtained number of extracted principal components for each subject and experiment........... 49 1 Chapter 1 Introduction The debate behind consciousness has been vivid for centuries, and it still is largely discussed in many disciplines, such as philosophy, ethics, and neurology. This introductory chapter aims at clarifying the difference between conscious and unconscious state and at coupling the most abstract aspects of the subject with a more scientific approach from which we can start our work. 1.1 Consciousness 1.1.1 What is consciousness? The first problem that arises in writing this work is understanding what consciousness is, and especially finding a uniform way to define it and calculate it. Yet, whoever tries to look up for the term on a dictionary or on the internet will probably