Independent component analysis P22

22 Brain Imaging Applications With the advent of new anatomical and functional brain imaging methods, it is now possible to collect vast amounts of data from the living human brain.. 22.

22 Brain Imaging Applications

With the advent of new anatomical and functional brain imaging methods, it is now possible to collect vast amounts of data from the living human brain It has thus become very important to extract the essential features from the data to allow an easier representation or interpretation of their properties This is a very promising area of application for independent component analysis (ICA) Not only is this an area of rapid growth and great importance; some kinds of brain imaging data also seem to be quite well described by the ICA model This is especially the case with electroencephalograms (EEG) and magnetoencephalograms (MEG), which are recordings of electric and magnetic fields of signals emerging from neural currents within the brain In this chapter, we review some of these brain imaging applications, concentrating on EEG and MEG

22.1 ELECTRO- AND MAGNETOENCEPHALOGRAPHY

22.1.1 Classes of brain imaging techniques

Several anatomical and functional imaging methods have been developed to study the living human brain noninvasively, that is, without any surgical procedures One class

of methods gives anatomical (structural) images of the brain with a high spatial res-olution, and include computerized X-ray tomography (CT) and magnetic resonance

imaging (MRI) Another class of methods gives functional information on which

parts of the brain are activated at a given time Such brain imaging methods can help

in answering the question: What parts of the brain are needed for a given task?

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ISBNs: 0-471-40540-X (Hardback); 0-471-22131-7 (Electronic)

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Well-known functional brain mapping methods include positron emission tomog-raphy (PET) and functional MRI (fMRI), which are based on probing the changes

in metabolic activity The time resolution of PET and fMRI is limited, due to the slowness of the metabolic response in the brain, which is in the range of a few seconds

Here we concentrate on another type of functional brain imaging methods that are

characterized by a high time resolution This is possible by measuring the electrical

activity within the brain Electrical activity is the fundamental means by which information is transmitted and processed in the nervous system These methods are the only noninvasive ones that provide direct information about the neural dynamics

on a millisecond scale As a trade-off, the spatial resolution is worse than in fMRI, being about 5 mm, under favorable conditions The basic methods in this class are electroencephalography (EEG) and magnetoencephalography (MEG) [317, 165], which we describe next Our exposition is based on the one in [447]; see also [162]

22.1.2 Measuring electric activity in the brain

Neurons and potentials The human brain consists of approximately10

10 to 10

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neurons [230] These cells are the basic information-processing units Signals between neurons are transmitted by means of action potentials, which are very short bursts of electrical activity The action potential is transformed in the receiving neuron to what is called a postsynaptic potential that is longer in duration, though also weaker Single action potentials and postsynaptic potentials are very weak and cannot be detected as such by present noninvasive measurement devices

Fortunately, however, neurons that have relatively strong postsynaptic potentials

at any given time tend to be clustered in the brain Thus, the total electric current produced in such a cluster may be large enough to be detected This can be done by measuring the potential distribution on the scalp by placing electrodes on it, which

is the method used in EEG A more sophisticated method is to measure the magnetic fields associated with the current, as is done in MEG

EEG and MEG The total electric current in an activated region is often modeled

as a dipole It can be assumed that in many situations, the electric activity of the brain

at any given point of time can be modeled by only a very small number of dipoles These dipoles produce an electric potential as well as a magnetic field distribution that can be measured outside the head The magnetic field is more local, as it does not suffer from the smearing caused by the different electric conductivities of the several layers between the brain and the measuring devices seen in EEG This is one

of the main advantages of MEG, as it leads to a much higher spatial resolution.

EEG is used extensively for monitoring the electrical activity within the human brain, both for research and clinical purposes It is in fact one of the most widespread brain mapping techniques to date EEG is used both for the measurement of sponta-neous activity and for the study of evoked potentials Evoked potentials are activity triggered by a particular stimulus that may be, for example, auditory or somatosen-sory Typical clinical EEG systems use around 20 electrodes, evenly distributed

over the head State-of-the-art EEGs may consist of a couple hundred sensors The signal-to noise ratio is typically quite low: the background potential distribution is

of the order of 100 microvolts, whereas the evoked potentials may be two orders of magnitude weaker

MEG measurements give basically very similar information to EEG, but with a higher spatial resolution MEG is mainly used for basic cognitive brain research

To measure the weak magnetic fields of the brain, superconducting quantum inter-ference devices (SQUIDs) are needed The measurements are carried out inside a magnetically shielded room The superconducting characteristics of the device are guaranteed through its immersion in liquid helium, at a temperature of
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C The experiments is this chapter were conducted using a Neuromag-122TM

device, manufactured by Neuromag Ltd., and located at the Low Temperature Laboratory of the Helsinki University of Technology The whole-scalp sensor array in this device

is composed of 122 sensors (planar gradiometers), organized in pairs at 61 locations around the head, measuring simultaneously the tangential derivatives of the magnetic field component normal to the helmet-shaped bottom of the dewar The sensors are mainly sensitive to currents that are directly below them, and tangential to the scalp

22.1.3 Validity of the basic ICA model

The application of ICA to the study of EEG and MEG signals assumes that several conditions are verified, at least approximately: the existence of statistically inde-pendent source signals, their instantaneous linear mixing at the sensors, and the stationarity of the mixing and the independent components (ICs)

The independence criterion considers solely the statistical relations between the amplitude distributions of the signals involved, and not the morphology or physiology

of neural structures Thus, its validity depends on the experimental situation, and cannot be considered in general

Because most of the energy in EEG and MEG signals lies below 1 kHz, the so-called quasistatic approximation of Maxwell equations holds, and each time instance can be considered separately [162] Therefore, the propagation of the signals is immediate, there is no need for introducing any time-delays, and the instantaneous mixing is valid

The nonstationarity of EEG and MEG signals is well documented [51] When considering the underlying source signals as stochastic processes, the requirement

of stationarity is in theory necessary to guarantee the existence of a representative distribution of the ICs Yet, in the implementation of batch ICA algorithms, the data are considered as random variables, and their distributions are estimated from the whole data set Thus, the nonstationarity of the signals is not really a violation of the

assumptions of the model On the other hand, the stationarity of the mixing matrix A

is crucial Fortunately, this assumption agrees with widely accepted neuronal source models [394, 309]

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22.2 ARTIFACT IDENTIFICATION FROM EEG AND MEG

As a first application of ICA on EEG and MEG signals, we consider separation of artifacts Artifacts mean signals not generated by brain activity, but by some external disturbances, such as muscle activity A typical example is ocular artifacts, generated

by eye muscle activity

A review on artifact identification and removal, with special emphasis on the ocular ones, can be found in [56, 445] The simplest, and most widely used method consists

of discarding the portions of the recordings containing attributes (e.g., amplitude peak, frequency contents, variance and slope) that are typical of artifacts and exceed

a determined threshold This may lead to significant loss of data, and to complete inability of studying interesting brain activity occuring near or during strong eye activity, such as in visual tracking experiments

Other methods include the subtraction of a regression portion of one or more additional inputs (e.g., from electrooculograms, electrocardiograms, or electromyo-grams) from the measured signals This technique is more likely to be used in EEG recordings, but may, in some situations, be applied to MEG It should be noted that this technique may lead to the insertion of undesirable new artifacts into the brain recordings [221] Further methods include the signal-space projection [190], and subtracting the contributions of modeled dipoles accounting for the artifact [45] In both of these latter methods we need either a good model of the artifactual source

or a considerable amount of data where the amplitude of the artifact is much higher than that of the EEG or MEG

ICA gives a method for artifact removal where we do not need an accurate model

of the process that generated the artifacts; this is the blind aspect of the method.

Neither do we need specified observation intervals that contain mainly the artifact, nor additional inputs; this is the unsupervised aspect of the method Thus ICA gives a promising method for artifact identification and removal It was shown in [445, 446] and [225] that artifacts can indeed be estimated by ICA alone It turns out that the artifacts are quite independent from the rest of the signal, and thus even this requirement of the model is reasonably well fulfilled

In the experiments on MEG artifact removal [446], the MEG signals were recorded

in a magnetically shielded room with the 122-channel whole-scalp magnetometer described above The test person was asked to blink and make horizontal saccades,

in order to produce typical ocular (eye) artifacts Moreover, to produce myographic (muscle) artifacts, the subject was asked to bite his teeth for as long as 20 seconds Yet another artifact was created by placing a digital watch one meter away from the helmet into the shielded room Figure 22.1 presents a subset of 12 artifact-contaminated MEG signals, from a total of 122 used in the experiment Several artifact structures are evident from this figure, such as eye and muscle activity The results of artifact extraction using ICA are shown in Fig 22.2 Components IC1 and IC2 are clearly the activations of two different muscle sets, whereas IC3 and IC5 are, respectively, horizontal eye movements and blinks Furthermore, other disturbances with weaker signal-to-noise ratio, such as the heart beat and a digital watch, are extracted as well (IC4 and IC8, respectively) IC9 is probably a faulty

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saccades blinking biting

Fig 22.1 A subset of 12 spontaneous MEG signals from the frontal, temporal and occipital areas The data contains several types of artifacts, including ocular and muscle activity, the cardiac cycle, and environmental magnetic disturbances (Adapted from [446].)

sensor ICs 6 and 7 may be breathing artifacts, or alternatively artificial bumps caused by overlearning (Section 13.2.2) For each component the left, back and right views of the field patterns are shown These field patterns can be computed from the columns of the mixing matrix

22.3 ANALYSIS OF EVOKED MAGNETIC FIELDS

Evoked magnetic fields, i.e., the magnetic fields triggered by an external stimulus, are one of the fundamental research methods in cognitive brain research State-of-the-art approaches for processing magnetic evoked fields are often based on a careful expert scrutiny of the complete data, which can be either in raw format or averaged over several responses to repeating stimuli At each time instance, one or several neural sources are modeled, often as dipoles, so as to produce as good a fit to the data as possible [238] The choice of the time instances where this fitting should be made,

as well as the type of source models employed, are therefore crucial Using ICA, we can again obtain a blind decomposition without imposing any a priori structure on the measurements

The application of ICA in event related studies was first introduced in the blind separation of auditory evoked potentials in [288] This method has been further developed using magnetic auditory and somatosensory evoked fields in [449, 448] Interestingly, the most significant independent components that were found in these studies seem to be of dipolar nature Using a dipole model to calculate the source

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IC1

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Fig 22.2 Artifacts found from MEG data, using the FastICA algorithm Three views of the field patterns generated by each independent component are plotted on top of the respective signal Full lines correspond to magnetic flux exiting the head, whereas the dashed lines correspond to the flux inwards Zoomed portions of some of the signals are shown as well (Reprinted from [446], reprint permission and copyright by the MIT Press.)

locations, we have found them to fall on very plausible brain areas Thus, ICA validates the conventional dipole modeling assumption in these studies Future studies, though, will probably find cases where the dipole model is too restrictive

In [448], ICA was shown to be able to differentiate between somatosensory and auditory brain responses in the case of vibrotactile stimulation, which, in addition to tactile stimulation, also produced a concomitant sound Principal component analysis (PCA) has often been used to decompose signals of this kind, but as we have seen

in Chapter 7, it cannot really separate independent signals In fact, computing the principal components of these signals, we see that most of the principal components still represent combined somatosensory and auditory responses [448] In contrast, computing the ICs, the locations of the equivalent current sources fall on the expected brain regions for the particular stimulus, showing separation by modality

Another study was conducted in [449], using only averaged auditory evoked fields The stimuli consisted of 200 tone bursts that were presented to the subject’s right ear, using 1 s interstimulus intervals These bursts had a duration of 100 ms, and

a frequency of 1 kHz Figure 22.3 shows the 122 averages of the auditory evoked responses over the head The insert, on the left, shows a sample enlargement of such averages, for an easier comparison with the results depicted in the next figures

Again, we see from Figs 22.4 a and 22.4 b that PCA is unable to resolve the

complex brain response, whereas the ICA technique produces cleaner and sparser

response components For each component presented in Fig 22.4 a and Fig 22.4 b,

left, top and, right side views of the corresponding field pattern are shown Note that the first principal component exhibits clear dipole-like pattern both over the left and the right hemispheres, corroborating the idea of an unsuccessful segmentation of the evoked response Subsequent principal components, however, tend to have less and less structured patterns

From the field patterns associated with the independent components we see that the evoked responses of the left hemisphere are isolated in IC1 and IC4 IC2 has stronger presence over the right hemisphere, and IC3 fails to show any clear field pattern structure Furthermore, we can see that IC1 and IC2 correspond to responses typically labeled as N1m, with the characteristic latency of around 100 ms after the onset of the stimulation The shorter latency of IC1, mainly reflecting activity contralateral to the stimulated ear, agrees with the known information available for such studies

22.4 ICA APPLIED ON OTHER MEASUREMENT TECHNIQUES

In addition to the EEG/MEG results reported here, ICA has been applied to other brain imaging and biomedical signals as well:

Functional magnetic resonance images (fMRI) One can use ICA in two dif-ferent ways, separating either independent spatial activity patterns [297], or independent temporal activation patterns [50] A comparison of the two modes

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MEG25

MEG83

MEG sample

MEG60

(MEG-L)

MEG10

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-Fig 22.3 Averaged auditory evoked responses to 200 tones, using MEG Channels MEG10 and MEG60 are used in Fig 22.4 as representatives of one left-hemisphere and one right-hemisphere MEG signal Each tick in the MEG sample corresponds to 100 ms, going from

100 ms before stimulation onset to 500 ms after (Adapted from [449].)

can be found in [367] A combination of the two modes can be achieved by spatiotemporal ICA, see Section 20.1.4

Optical imaging means directly “photographing” the surface of the brain after making a hole in the skull Application of ICA can be found in [374, 396] As

in the case of fMRI signals, this is a case of separating image mixtures as in the example in Fig 12.4 Some theory for this particular case is further developed

in [164, 301]; also the innovations processes may be useful (see Section 13.1.3 and [194])

Outside the area of brain imaging, let us mention applications to the removal of artifacts from cardiographic (heart) signals [31, 459] and magnetoneurographic signals [482] The idea is very similar to the one used in MEG artifact removal Further related work is in [32, 460] Another neuroscientific application is in intracellular calcium spike analysis [375]

22.5 CONCLUDING REMARKS

In this chapter we have shown examples of ICA in the analysis of brain signals First, ICA was shown to be suitable for extracting different types of artifacts from EEG and MEG data, even in situations where these disturbances are smaller than the background brain activity

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Fig 22.4 Principal (a) and independent (b) components found from the auditory evoked field

study For each component, both the activation signal and three views of the corresponding field pattern are plotted (Adapted from [449].)

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Second, ICA can be used to decompose evoked fields or potentials For example, ICA was able to differentiate between somatosensory and auditory brain responses in the case of vibrotactile stimulation Also, it was able to discriminate between the ipsi-and contralateral principal responses in the case of auditory evoked potentials In addition, the independent components, found with no other modeling assumption than their statistical independence, exhibit field patterns that agree with the conventional current dipole models The equivalent current dipole sources corresponding to the independent components fell on the brain regions expected to be activated by the particular stimulus

Applications of ICA have been proposed for analysis of other kinds of biomedical data as well, including fMRI, optical imaging, and ECG