Multivariate Statistical Analysis in Neuroscience E-Book

Giovanni Cugliari

• Herausgeber: GRIN Verlag
• Sprache: Englisch

Veröffentlichungsjahr: 2015

Beschreibung

Research Paper (postgraduate) from the year 2015 in the subject Medicine - Other, grade: II Level Master, University of Pavia (Unit of Medical and Genomic Statistics), course: Medical and Genomic Statistics, language: English, abstract: Electroencephalography, commonly called 'EEG', estimates through the application of electrodes, the electrical activity of the brain (which is the sum of the electrical activity of each neuron). In recent years, with the goal of making more reliable the EEG, many researchers have turned their interest in the development of tools, methods and software. This thesis describes some best procedures for the experimental design, data visualization and descriptive or inferential statistical analysis. The application of statistical models to single or multiple subjects study-design are also described, including parametric and non-parametric approaches. Methods for processing multivariate data (PCA, ICA, clustering) were described. Re-sampling methods (bootstrap) using many randomly software-generated samples were also described. The aim of this work is to provide, with statistical concepts and examples, information on the qualitative and quantitative approaches related to the electroencephalographic signals. The work consists into three parts: INTRODUTION TO ELECTROENCEPHALOGRAPHY (GENERAL CHARACTERISTICS); DATA MINING AND STATISTICAL ANALYSIS; EXPERIMENTAL STUDY DESIGNS. The six works included in the section called “EXPERIMENTAL STUDY DESIGNS” analyze EEG alterations in the protocols: Electrocortical activity in dancers and non-dancers listening to different music genre and during imaginative dance motor activity; Electrocortical activity during monosynaptic reflex in athletes; Monitoring of electrocortical activity for evaluation of seasickness; Electrocortical activity in different body positions; Electrocortical activity in athletes and non-athletes during body balance tasks; Electrocortical responses in volunteers with and without specific experience watching movies including the execution of complex motor gestures. In the section called “OTHER INTERESTING THINGS” were included one work that analyze EMG (electromyography) alterations in pathological and healthy subjects in the protocol: Comparison between clinical diagnostic criteria of sleep bruxism and those provided by a validated portable holter. The described procedures can be used for clinical trials, although the studies proposed in this work do not refer to samples from pathological subjects. With its multi-specialist approach, through many theoretical and practical feedback, this work will be useful for specializing in neuroscience, statistics, engineering or physiology.

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Contents

Preface

Acknowledgements

1. INTRODUTION TO ELECTROENCEPHALOGRAPHY (GENERAL ASPECTS)

1.1 FUNDAMENTALS OF EEG MEASUREMENT

1.1.1 Activity of the brain

1.1.2 Electroencephalography (EEG)

1.1.3 Quantitative electroencephalography (qEEG)

1.1.4 Frequency and amplitude of the signal

1.1.5 International 10-20 system

2. DATA MINING AND STATISTICAL ANALYSIS

2.1 PRE-PROCESSING PROCEDURES

2.1.1 EEGLAB: statistical software for electro-physiological data analysis

2.1.2 Importing channel location: information about the electrodes placement

2.1.3 Filtering data to minimizing the introduction of artifacts

2.1.4 Extracting data epochs and removing baseline values

2.2 CHANNEL DATA ANALYSIS

2.2.1 Channel data scroll: visualization, normalization and channel rejection procedure

2.2.2 Channel spectra and associated topographical maps

2.2.3 ERP and associated topographical maps

2.2.4 Time/frequency decomposition

2.2.5 Cross-coherences computation

2.2.6 Channel summary

2.2.7 Rejecting artifacts in continuous and epoch data

2.3 COMPONENT DATA ANALYSIS

2.3.1 Independent Component Analysis

2.3.2 ICA Algorithms

2.3.3 Component data scroll

2.3.4 Component spectra and associated topographical maps

2.3.5 Time/frequency decomposition

2.3.6 Computing cross-coherences

2.3.7 Component summary

2.3.8 Rejecting based on independent data components

2.4 MULTIPLE SUBJECT DATA PROCESSING

2.4.1 Channel statistics

2.4.2 Component statistics

2.4.3 Clustering procedure

2.4.4 Preparing to cluster with PCA method

2.4.5 Clustering

2.4.6 Component clusters visualization

2.5 STATISTICAL PROCEDURES

2.5.1 Parametric and non-parametric statistics

2.5.2 Paired/unpaired samples

2.5.3 Re-sampling methods

2.5.4 Multivariate methods (PCA vs ICA)

2.5.5 Correcting for multiple comparisons

3. EXPERIMENTAL STUDY DESIGNS

3.1 ELECTROCORTICAL ACTIVITY IN DANCERS AND NON-DANCERS LISTENING TO DIFFERENT MUSIC GENRE AND DURING IMAGINATIVE DANCE MOTOR ACTIVITY

3.1.1 Abstract

3.1.2 Introduction

3.1.3 Materials and Methods

3.1.4 Statistical analysis

3.1.5 Results

3.1.6 Discussions and conclusions

3.2 ELECTROCORTICAL ACTIVITY DURING MONOSYNAPTIC REFLEX IN ATHLETES

3.2.1 Abstract

3.2.2 Introdution

3.2.3 Materials and methods

3.2.4 Statistical analysis

3.2.5 Results

3.2.6 Discussion and conclusions

3.3 MONITORING OF ELECTROCORTICAL ACTIVITY FOR EVALUATION OF SEASICKNESS

3.3.1 Abstract

3.3.2 Introdution

3.3.3 Materials and methods

3.3.4 Statistical analysis

3.3.5 Results

3.3.6 Discussions and conclusions

3.4 ELECTROCORTICAL ACTIVITY IN DIFFERENT BODY POSITIONS

3.4.1   Abstract

3.4.2 Introdution

3.4.3 Materials and methods

3.4.4 Statistical analysis

3.4.5 Results

3.4.6 Discussions and conclusions

3.5 ELECTROCORTICAL ACTIVITY IN ATHLETES AND NON-ATHLETES DURING BODY BALANCE TASKS

3.5.1 Abstract

3.5.2 Introdution

3.5.3 Materials and methods

3.5.4 Statistical analysis

3.5.5 Results

3.6 ELECTROCORTICAL RESPONSES IN VOLUNTEERS WITH AND WITHOUT SPECIFIC EXPERIENCE WATCHING MOVIES INCLUDING THE EXECUTION OF COMPLEX MOTOR GESTURES

3.6.1 Abstract

3.6.2 Introduction

3.6.3 Materials and methods

3.6.4 Statistical Analysis

3.6.5 Results

3.6.6 Discussions and conclusions

4. OTHER INTERESTING THINGS

4.1 COMPARISON BETWEEN CLINICAL DIAGNOSTIC CRITERIA OF SLEEP BRUXISM AND THOSE PROVIDED BY A  VALIDATED PORTABLE HOLTER

4.1.1 Abstract

4.1.2 Introduction

4.1.3 Materials and Methods

4.1.4 Statistical Analysis

4.1.5 Results

4.1.6 Discussion and conclusions

REFERENCES

Preface

Electroencephalography, commonly called 'EEG', estimates through the application of electrodes, the electrical activity of the brain (which is the sum of the electrical activity of each neuron). In recent years, with the goal of making more reliable the EEG, many researchers have turned their interest in the development of tools, methods and software. This thesis describes some best procedures for the experimental design, data visualization and descriptive or inferential statistical analysis. The application of statistical models to single or multiple subjects study-design are also described, including parametric and non-parametric approaches. Methods for processing multivariate data (PCA, ICA, clustering) were described. Re-sampling methods (bootstrap) using many randomly software-generated samples were also described. The aim of this work is to provide, with statistical concepts and examples, information on the qualitative and quantitative approaches related to the electroencephalographic signals. The work consists into three parts: INTRODUTION TO ELECTROENCEPHALOGRAPHY (GENERAL CHARACTERISTICS); DATA MINING AND STATISTICAL ANALYSIS; EXPERIMENTAL STUDY DESIGNS. The six works included in the section called “EXPERIMENTAL STUDY DESIGNS” analyze EEG alterations in the protocols: Electrocortical activity in dancers and non-dancers listening to different music genre and during imaginative dance motor activity; Electrocortical activity during monosynaptic reflex in athletes; Monitoring of electrocortical activity for evaluation of seasickness; Electrocortical activity in different body positions; Electrocortical activity in athletes and non-athletes during body balance tasks; Electrocortical responses in volunteers with and without specific experience watching movies including the execution of complex motor gestures. In the section called “OTHER INTERESTING THINGS” were included one work that analyze EMG (electromyography) alterations in pathological and healthy subjects in the protocol: Comparison between clinical diagnostic criteria of sleep bruxism and those provided by a validated portable holter. The described procedures can be used for clinical trials, although the studies proposed in this work do not refer to samples from pathological subjects. With its multi-specialist approach, through many theoretical and practical feedback, this work will be useful for specializing in neuroscience, statistics, engineering or physiology.

Acknowledgements

I like to express my thanks to: Department of Brain and Behavioral Sciences, Unit of Medical and Genomic Statistics, University of Pavia, Strada Nuova, 65, 27100, Pavia, Italy; Department of Medical Sciences Motor Science Research Center SUISM University of Turin P.za Bernini 12 10143 Torino, Italy; Department of Surgical Sciences, Specialization School of Orthodontics, Dental School, University of Torino, Via Nizza 230, 10126, Torino, Italy; Department of Surgical Sciences, Gnathology Unit, Dental School, University of Torino, Via Nizza 230, 10126, Torino, Italy.

I like to express my thanks to: OT BioLab (version 1.8, OT Bioelettronica, Turin, Italy) for raw signal recording; MATLAB and Statistics Toolbox Release 2012b (the MathWorks, Inc., Natick, Massachusetts, United States) for data processing; EEGLAB software (Swartz Center for Computational Neuroscience, University of San Diego, California) for data visualization and statistical analysis; The R statistical package (version 3.0.1, R Core Team, Foundation for Statistical Computing, Vienna, Austria) for statistical analysis.

I would like to express my deepest gratitude to my advisor, Prof. Alberto Rainoldi, for his excellent guidance and providing me with an excellent atmosphere for doing research. He has inspired my scientific research interest.

I extend my genuine thanks with gratitude to my advisor, Prof. Luisa Bernardinelli. She has inspired my scientific research interest.

I would like to express my special appreciation and thanks to my advisor Dr. Marco Ivaldi, for his  motivation, enthusiasm, knowledge and for encouraging my research, allowing me to grow as a research scientist. “You have been a mentor for me”.

Thanks also to all the trainees Eleonora Fiorenti, Francesca Pretari, Valentina Frison, Sara Peracchione, Valentina Verzoletto and Michela Carlucci for providing a good atmosphere in our department.

I would like to thank affectionately the friends of this adventure Simona De Summa, Serena Martire,  Alessandra Dentamaro and all teachers.

I would also like to thank my parents, and two brothers. They were always supporting me and encouraging me with their best wishes.

Last but not the least, I would like to thank my girlfriend, Dr. Daniela Donatiello. She was always there cheering me up and stood by me through the good times and bad. “Your support has been fantastic”.

Giovanni Cugliari

1. INTRODUTION TO ELECTROENCEPHALOGRAPHY (GENERAL ASPECTS)

1.1 FUNDAMENTALS OF EEG MEASUREMENT

The aim of this section is not to explain all of the features referred to electroencephalographic signals, but to provide the needed inputs to address to mining and analysis procedures.

However, in order to proceed with the second section, you must have some information about the characteristics of the EEG signals (frequency, amplitude) and importing procedures (electrodes placement on the scalp).

1.1.1 Activity of the brain

Bioelectrical phenomena, that occurs in the cerebral cortex, determines the generation of the electric potential that can be recorded via electrodes placed to the scalp (electroencephalography, EEG) or directly on the cortical surface (electrocorticography, ECOG). The same phenomena also give rise to very weak magnetic fields registered by sensors (magnetoencephalography, MEG).

1.1.2 Electroencephalography (EEG)

Electroencephalography is the recording of brain electrical activity by sensors. The electrodes are arranged on the surface of the head using a suitable amplifying equipment. The EEG signal reflects the electrical events of the skull. These events include cerebral post-synaptic potentials, action potentials, electrical signals of the skin, muscles, blood vessels and eyes.

Some EEG waves are associated with certain states of consciousness and brain-specific pathological conditions (like epilepsy). In some cases, researchers are more interested in the EEG analysis related to certain psychological events. These EEG associated with events (external or internal) are called “event related potentials” (ERPs). Since the EEG amplitude of signal decreases as it spreads from its point of origin, a comparison of the signals recorded from different points of the scalp can sometimes indicate the origin of any particular waves. This explains how the EEG activity is recorded in many points of the scalp. One of the most common potential event related is the sensory evoked potential, the modification of the EEG caused by momentary presence of a sensorial stimulus.

The EEG signal has two components: the response to stimulus and the contemporary background activity. Obviously, the signal is an important part of each trace recorded, while the background activity isn’t so important. The problem in sensory evoked potentials recording of is that the background is usually great as to obscure the event related signal. The analysis of evoked potentials takes into account the peaks or waves present in the average EEG. Each wave is characterized by direction, positive or negative, and latency. These waves are called stem-encephalic potential (far-field potentials) because, although recorded on the scalp, they originate in the sensory nuclei of the brain stem.

Even if the electroencephalography has an excellent temporal resolution, in a first time showed very modest results as to the spatial resolution. With the electroencephalographic conventional procedures, we can estimate the signal source only approximately. The latest techniques, that using sophisticated software, can locate precisely the signals source.

1.1.3 Quantitative electroencephalography (qEEG)

The quantitative electroencephalography (qEEG) is different from clinical EEG due to the mathematical analysis of the brain waves also in not pathological conditions. This procedure is not invasive and allows better performance compared to other neuroimaging techniques, such as magnetoencephalography (MEG), as regards of the time resolution of the recorded signal. Another advantage of this device is that it reduced the required spatial, no larger than a personal computer, and much less expensive than other instruments.

QEEG is accurate, functional, fast and reliable (2 minutes are reliable to 96%). The normal brain activity includes electrical activity that, although attenuated, is still measurable on surface of the scalp and his magnitude is some dozens of microvolt. The continuous oscillation of these electric currents creates the phenomenon of brain waves. The track recorded is the representation of different signals recorded from electrodes and analyzed in a differential way with reference medially of the skull. This type of representation is defined monopolar, since all the signals refer to the signal of a single electrode (therefore defined reference).

The EEG is the representation of the postsynaptic potentials that born in the cortex of the brain by synchronous activity of about 105 neurons; the number is so high because the signal must pass through several layers of non-neutral tissue including the meninges, the intermediate liquids, the bones of the skull and skin before being picked up by the electrode.

The reference electrode should be positioned at a certain distance from active electrodes, it can be placed on the scalp, cephalic reference, or in other body regions (mastoid, earlobes, backs of hands) electrically inert or anyway without electrical activity named non-cephalic reference. The electrical activity of the brain is the sum of several waves at different frequencies and generated by specific signals related to particular tasks (sensorial, movement related or cognitive) in which the subject is involved during the recording.

From the comparison between the spontaneous activity and its variation during the induced activity is possible to identify in real time the areas of change electrical activity. The strength of this type of analysis is the time resolution, in fact is possible to obtain the information every millisecond of electrical activity at the intra-extra cortical level of recordings; its major limitation is the spatial resolution, the electrodes pick up only the current that reaches the surface of the skull and just due reconstruction signal algorithms is possible to locate the source of signal.

The elementary functional units of the cerebral cortex are composed of columnar clusters of neurons organized with perpendicular orientation  to the surface of the cerebral cortex. The potentials measured by EEG are associated to the flow of electrical current from the brain to the scalp.

1.1.4 Frequency and amplitude of the signal

Alpha waves (8-12 Hz) are characteristics in the wakefulness and rest condition, but absent in sleep (except for the REM stadium). When  subjects show a brain activity increased, the presence of beta rhythm is possible to recognize.

Beta (16-32 Hz) presents an average voltage of 19 uV (8-30 uV). Beta waves are dominant in subjects with open eyes, but also in states of alertness and REM sleep.

The theta rhythm is dominant in newborns and in people in emotional stress (4-8 Hz), has an average voltage of 100 uV.

Finally, the delta waves have a frequency between 0 and 4 Hz and an average voltage of about 150 UV; are not present in physiological conditions in the wakefulness in adulthood, although they are also prevalent in childhood and generally appear in anesthesia and in some brain or general metabolic diseases, such as azotemia. The delta waves are characteristic of REM sleep (slow wave sleep). In different stages of sleep are mainly theta and delta waves, glimpses of alpha activities and, rarely, beta activity.

The resulting signal is a mixture of all these waves, with percentages greater for beta waves and alpha (90-95%) and percentages smaller for theta and delta waves (the first 3-4% and  the second 0,5-1%).

Figure 1 – EEG rhythms show an example of signal filtered with a passband filter for the indicated frequencies (Kevin Roebuck, 2012)

Figure 2 – Example of power spectrum, after independent component analysis (ICA, )with specifics frequency bands:. Frequency bands subdivision has been highlighted

Figure 3 – Subdivision of the EEG frequency bands with the description of the related frequencies and several literature references associated to the band (Kevin Roebuck, 2012)

1.1.5 International 10-20 system

The electrodes are applied on the scalp according to the standard International 10-20 system, 10% or 20% refer to the percentage of the distance that separates the electrodes, this distance usually varies from 30 to 36 cm with great interpersonal variability between two landmarks points of cranial "inion" (prominence at the base of the occipital bone) and "nasion" (hanging above the nose).  Electrodes (16+3) and mass are placed, along five lines. For each electrode on the scalp there is a code reference. The acronyms identify the position of an electrode and they are formed by one/two letters identifying the region of the cortex (Fp: frontopolar; F: frontal, C central, P: parietal, T: temporal, O: occipital) and a number (or z) that identifies the hemisphere (odd numbers: left, even numbers: right; z: midline).

Figure 4 – Electrode placement in International 10-20 System. Azure highlighted shows the typical electrodes sequence of used in EEG recording (EEGLAB)

2. DATA MINING AND STATISTICAL ANALYSIS

2.1 PRE-PROCESSING PROCEDURES

This section has the aim to explicate data mining procedures first of all; secondly, to show the signal analysis procedures; finally, to provide the statistical methods to analyze multi-subjects dataset.

This section is divided into four parts: pre-processing procedures; channel data analysis; component data analysis; multiple subject dataset processing; statistical procedures. Procedures show a linear order list of execution.Each procedure is followed by the graph and the associated legend

2.1.1 EEGLAB: statistical software for electro-physiological data analysis

EEGLAB is a MATLAB toolbox for processing event-related and continuous electroencephalography (EEG), magnetoencephalography (MEG) and other electrophysiological data. EEGLAB provides a programming environment for accessing, visualizing, measuring, manipulating, and storing electrophysiological data. EEGLAB primary allows several modes of visualization of the single-trial and averaged data, event-related statistics, independent component analysis, time/frequency analysis and artifact rejection. EEGLAB include several plug-in such as NFT (3-D head and source location modeling), SIFT (3-D source information flow modeling), MPT (3-D source measure projection analysis), BCILAB (Brain-computer interface design & analysis), MoBILAB (Mobile brain/body imaging) and PACT (epileptic spike detection). The chief are Arnaud Delorme and Scott Makeig and now the development is possible due to Swartz Center for Computational Neuroscience (SCCN) of the Institute for Neural Computation at the University of California San Diego (UCSD) supported by the US National Institute of Neurological Disorders and Stroke (NINDS).

2.1.2 Importing channel location: information about the electrodes placement

To plot EEG scalp maps, dataset (loaded manually) must contain information about the locations of the recording electrodes. Usually all the channels use the same reference electrode for recording EEG data. Typical references (Figure 5) are one mastoid (for example, TP10 in the International 10-20 System), or vertex electrode (CZ in the International 10-20 System). There is no best common reference site. Some researches shows that non-scalp references (mastoid, earlobes, nose) introduce more noise than a scalp channel reference. If the data have been recorded with a reference, they can usually be re-referenced to any other reference channel.

Figure 5 – Electrode placement in International 10-20 System.Red highlighted show the typical references in EEG recording (EEGLAB)

2.1.3 Filtering data to minimizing the introduction of artifacts

Filtering the continuous data minimizes the introduction of artifacts (linear trends) at level of epoch end-lines. High-pass or low-pass filter may be used (applied in different call). Another common use for band-pass filtering is to remove 50-Hz noise. The filtering step uses the linear finite impulse response (FIR), forward and backward, to ensure that delays phase introduced by the filter are nullified.

2.1.4 Extracting data epochs and removing baseline values

Procedures to study the event-related of continuously recorded EEG data:

 specifying the data epochs time and the baseline value in each epoch;

 extracting data epochs time to events of interest;

 removing a mean baseline value from each epoch

If the objective of the analysis is to estimate changes that occur in the data following the time-locking events, the mean value in the pre-stimulus period is effective.

2.2 CHANNEL DATA ANALYSIS

2.2.1 Channel data scroll: visualization, normalization and channel rejection procedure

Data scrolling is useful to reject epochs of data which contains artifact (if the channel acquisition data not is completely compromise) before the data processing (Figure 6). Data visualization, voltage scale (microvolts), time (seconds), and rejection of the portion of continuous data were show in the following Figures.

Figure 6 – On the left of the plot window there is a channel list ordered by name. On the right there is a vertical scale value (microvolts), which indicates the height of the vertical scale bar. In this case, that value is 0.1 microvolts. In abscissa there is the horizontal scale value (seconds), which indicates the time scale of the horizontal scale bar. In this case, that value is 5 seconds

Normalization procedure (Figure 7) of the voltage allows a better visualization of data, in this way the differences that may be highlighted between the channels are reduced.

Figure 7 – In this case, the normalized value is 5 microvolts. The horizontal scale value (seconds), which indicates the time scale of the horizontal scale bar after normalizing procedure, because not is dependent on the activation level of the channels

Channel rejection procedure (Figure 8) allows to exclude channels with outliers, after kurtosis or probability  tests.

Figure 8 – Channel rejection by kurtosis and probability tests with threshold limits (max) set at 5%. In this case one channel (P4) was excluded by dataset used for the analysis

2.2.2 Channel spectra and associated topographical maps

Spectral analysis is one method used for EEG quantification. A signal can be analyzed with power spectrum (figure 9), which provides information on the signal power at each frequency. The EEG spectrum includes frequencies from 0.1 Hz to 100 Hz. The Fourier transform decomposes the EEG time series into power spectrum, in which the power is the square of the EEG amplitude, and the amplitude is the integral average of the EEG signal during the epoch sampled. This type of analysis is a mathematical approach to quantify the EEG. The frequency resolution is given by the inverse of the time value of the epoch. For example, with one epoch of 5 s the frequency resolution is 0.20 Hz.

Figure 9 – Each colored trace represents the spectrum of the activity of one data channel

2.2.3 ERP and associated topographical maps

In ERP plot (Figure 10), EEG data epochs (trials) are before ordered and then smoothed with others trials, and finally color-coded. As opposed to the average ERP, which exists in only one form, the number of possible ERP plots of a set of single trials is nearly infinite. Not all of the sorting orders will give equal insights into the brain dynamics. By default, trials are sorted in the order of temporal occurrence.

Figure 10 – Each colored trace represents the ERP of the activity of one data channel

2.2.4 Time/frequency decomposition

Time/frequency plot (Figure 11) characterizes changes in the spectral of the data, considered as a sum of sinusoidal functions. A significant ITC indicates that the EEG activity at a given time and frequency in single trials becomes phase-locked. The time/frequency points showing significant ITC and ERSP are not necessarily identical.

Figure 11 – The top image shows mean event-related changes in spectral power from pre-stimulus (baseline =0 ms) at each time during the epoch and at each frequency (<50 Hz). The upper left panel shows the baseline mean power spectrum, and the lower part of the upper panel shows the ERSP envelope (low and high mean dB values, relative to baseline, at each time in the epoch). The lower image shows the inter-trial coherence (ITC) at all frequency.

2.2.5 Cross-coherences computation

We may plot event-related cross-coherence (Figure 12), to determine the degree of synchronization between the activations of two channels. Channels are maximally independent over the whole time range of the training data and may become transiently partially synchronized in specific frequency bands.

Figure 12 – The upper panel shows the coherence amplitude between 0 and 1 (1 representing the perfectly synchronizing among the two signals). The lower panel indicates the phase difference among the two signals at time/frequency points where cross-coherence amplitude is significant.

2.2.6 Channel summary