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 g rowth 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? 407 Independent Component Analysis. Aapo Hyv ¨ arinen, Juha Karhunen, Erkki Oja Copyright  2001 John Wiley & Sons, Inc. ISBNs: 0-471-40540-X (Hardback); 0-471-22131-7 (Electronic) 408 BRAIN IMAGING APPLICATIONS 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 approximately to 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 ELECTRO- AND MAGNETOENCEPHALOGRAPHY 409 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, bu t 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 . The experiments is this chapter were conducted using a Neuromag-122 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]. 410 BRAIN IMAGING APPLICATIONS 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 n ew 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 ANALYSIS OF EVOKED MAGNETIC FIELDS 411 1 1 2 2 3 3 4 4 5 5 6 6 MEG 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 o f 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 p roduce 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 412 BRAIN IMAGING APPLICATIONS IC1 IC2 IC3 IC4 IC5 IC6 IC7 IC8 IC9 10 s Fig. 22.2 Artifacts found from MEG data, using the FastICA algorithm. Three vie ws of the field patterns generated by each independent component are plotted on top of the respective signal. Full lines correspond to magnetic flu x exiting the head, whereas the dashed lines correspond to the flux inwards. Zoomed portions of some of the signals a re shown as well. (Reprinted from [446], reprint permission and cop yright by the MIT Press.) ICA APPLIED ON OTHER MEASUREMENT TECHNIQUES 413 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 p atterns [50]. A comparison o f the two modes 414 BRAIN IMAGING APPLICATIONS Left side Right side MEG25 MEG83 MEG sample MEG60 (MEG-L) MEG10 (MEG-R) 1 0 1 2 3 4 5 - 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 M EG 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 n euroscientific 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. CONCLUDING REMARKS 415 1 0 1 2 3 4 5 1 0 1 2 3 4 5 - PC1 PC2 PC3 PC4 PC5 IC1 IC2 IC3 IC4 - a) b) Fig. 22.4 Principal (a) and independent (b) components found from the auditory evoked field study. For each component, both the activation signal and t hree views of the corresponding field pattern are p lotted. (Adapted from [449].) 416 BRAIN IMAGING APPLICATIONS Second, ICA can be used to decompose evoked fields or potentials. For example, ICA was able to differentiate between somatosensory and auditory brain r esponses 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. . (Hardback); 0-4 7 1-2 213 1-7 (Electronic) 408 BRAIN IMAGING APPLICATIONS Well-known functional brain mapping methods include positron emission tomog- raphy (PET). task? 407 Independent Component Analysis. Aapo Hyv ¨ arinen, Juha Karhunen, Erkki Oja Copyright  2001 John Wiley & Sons, Inc. ISBNs: 0-4 7 1-4 0540-X (Hardback);