Matti Kajola

Functional Segregation of Movement-Related Rhythmic Activity in the Human Brain

Multiple synaptic interconnections in the human brain support concerted rhythmic activity of a large number of cortical neurons, typically close to 10 and 20 Hz. Our present neuromagnetic data provide evidence for distinct functional roles of these spectral components in the somatomotor cortex. The sites of suppression during movement and the subsequent rebound of the 20-Hz rhythm followed, along the motor cortex, the representation of fingers, toes, and mouth, as opposed to the stable origin of the 10-Hz rhythms close to the hand somatosensory cortex. The 20-Hz activity appears to be a signature of active immobilization following movement, whereas the reactive 10-Hz signals likely reflect lack of relevant sensory input from the important upper limbs.

122-channel squid instrument for investigating the magnetic signals from the human brain

A 122-channel d.c. SQUID magnetometer with a helmet-shaped detector array covering the subjects head has been operational in the Low Temperature Laboratory of the Helsinki University of Technology since June 1992. The new system allows simultaneous recording of magnetic activity all over the head. The probe employs 122 planar first-order thin-film gradiometers in dual units with two exactly orthogonal channels at 61 measurement sites. The performance of the device is analyzed and compared with more conventional axial gradiometer arrays by considering signal-to-noise ratios, spatial sampling theory, confidence intervals for the estimated equivalent current dipole positions, and information-theoretical channel capacity. The signal-to-noise ratio and the resolution of the planar and axial arrays with the same number of channels are found practically equal. The number of channels and their spacing in our new Neuromag-122 system are found fully adequate for neuromagnetic measurements. An example of whole cortex recordings of auditory evoked brain activity is presented and analyzed.

Suppression of Interference and Artifacts by the Signal Space Separation Method

Multichannel measurement with hundreds of channels oversamples a curl-free vector field, like the magnetic field in a volume free of sources. This is based on the constraint caused by the Laplaces equation for the magnetic scalar potential; outside of the source volume the signals are spatially band limited. A functional solution of Laplaces equation enables one to separate the signals arising from the sphere enclosing the interesting sources, e.g. the currents in the brain, from the magnetic interference. Signal space separation (SSS) is accomplished by calculating individual basis vectors for each term of the functional expansion to create a signal basis covering all measurable signal vectors. Because the SSS basis is linearly independent for all practical sensor arrangements, any signal vector has a unique SSS decomposition with separate coefficients for the interesting signals and signals coming from outside the interesting volume. Thus, SSS basis provides an elegant method to remove external disturbances. The device-independent SSS coefficients can be used in transforming the interesting signals to virtual sensor configurations. This can also be used in compensating for distortions caused by movement of the object by modeling it as movement of the sensor array around a static object. The device-independence of the decomposition also enables physiological DC phenomena to be recorded using voluntary head movements. When used with properly designed sensor array, SSS does not affect the morphology or the signal-to-noise ratio of the interesting signals.

Applications of the signal space separation method

The reliability of biomagnetic measurements is traditionally challenged by external interference signals, movement artifacts, and comparison problems caused by different positions of the subjects or different sensor configurations. The Signal Space Separation method (SSS) idealizes magnetic multichannel signals by transforming them into device-independent idealized channels representing the measured data in uncorrelated form. The transformation has separate components for the biomagnetic and external interference signals, and thus, the biomagnetic signals can be reconstructed simply by leaving out the contribution of the external interference. The foundation of SSS is a basis spanning all multichannel signals of magnetic origin. It is based on Maxwells equations and the geometry of the sensor array only, with the assumption that the sensors are located in a current free volume. SSS is demonstrated to provide suppression of external interference signals, standardization of different positions of the subject, standardization of different sensor configurations, compensation for distortions caused by movement of the subject (even a subject containing magnetic impurities), suppression of sporadic sensor artifacts, a tool for fine calibration of the device, extraction of biomagnetic DC fields, and an aid for realizing an active compensation system. Thus, SSS removes many limitations of traditional biomagnetic measurements.

Signal-space projections of MEG data characterize both distributed and well-localized neuronal sources

We describe the use of signal-space projection (SSP) for the detection and characterization of simultaneous and/or sequential activation of neuronal source distributions. In this analysis, a common signal space is used to represent both the signals measured by an array of detectors and the underlying brain sources. This presents distinct advantages for the analysis of EEG and MEG data. Both highly localized and distributed sources are characterized by the components of the field patterns which are measured by the detectors. As a result, a unified description of arbitrary source configurations is obtained which permits the consistent implementation of a variety of analysis techniques. The method is illustrated by the application of SSP to auditory, visual and somatosensory evoked-response MEG data. Single-trace evoked responses obtained by SSP of spontaneous activity demonstrate that a considerable discrimination against both system noise and uncorrelated brain activity may be achieved. Application of signal-space projections determined in the frequency domain to spontaneous activity illustrates the possibility of including temporal relationships into the analysis. Finally, we demonstrate that SSP is particularly useful for the description of multiple sources of distributed activity and for the comparison of the strengths of specific neuronal sources under a variety of different paradigms or subject conditions.

Activation of the human posterior parietal cortex by median nerve stimulation

We recorded somatosensory evoked magnetic fields from ten healthy, right-handed subjects with a 122-channel whole-scalp SQUID magnetometer. The stimuli, exceeding the motor threshold, were delivered alternately to the left and right median nerves at the wrists, with interstimulus intervals of 1, 3, and 5 s. The first responses, peaking around 20 and 35 ms, were explained by activation of the contralateral primary somatosensory cortex (SI) hand area. All subjects showed additional deflections which peaked after 85 ms; the source locations agreed with the sites of the secondary somatosensory cortices (SII) in both hemispheres. The SII responses were typically stronger in the left than the right hemisphere. All subjects had an additional source, not previously reported in human evoked response data, in the contralateral parietal cortex. This source was posterior and medial to the SI hand area, and evidently in the wall of the postcentral sulcus. It was most active at 70–110 ms.

Presentation of electromagnetic multichannel data: The signal space separation method

Measurement of external magnetic fields provides information on electric current distribution inside an object. For example, in magnetoencephalography modern measurement devices sample the magnetic field produced by the brain in several hundred distinct locations around the head. The signal space separation (SSS) method creates a fundamental linear basis for all measurable multichannel signal vectors of magnetic origin. The SSS basis is based on the fact that the magnetic field can be expressed as a combination of two separate and rapidly converging expansions of harmonic functions with one expansion for signals arising from inside of the measurement volume of the sensor array and another for signals arising from outside of this volume. The separation is based on the different convergence volumes of the two expansions and on the fact that the sensors are located in a source current-free volume between the interesting and interfering sources. Individual terms of the expansions are shown to contain uncorrel...

Visual cortex activation in blind humans during sound discrimination

We used a whole-scalp magnetometer with 122 planar gradiometers to study the activity of the visual cortex of five blind humans deprived of visual input since early infancy. Magnetic responses were recorded to pitch changes in a sound sequence when the subjects were either counting these changes or ignoring the stimuli. In two of the blind subjects, magnetic resonance images were also obtained, showing normal visual cortex macroanatomy. In these subjects, the magnetic responses to counted pitch changes were located at visual and temporal cortices whereas ignored pitch changes activated the temporal cortices almost exclusively. Also in two of the other three blind, the visual-cortex activation was detectable in the auditory counting task. Our results suggest that the visual cortex of blind humans can participate in auditory discrimination.

Magnetic mu rhythm in man

We report detection of magnetic mu rhythm in four subjects using a large-area seven-channel first-order superconducting quantum interference device gradiometer. The polarity of this activity was opposite at the upper and lower ends of the rolandic fissure, and during the sharp transients the field patterns could be satisfactorily explained by a current dipole model. The equivalent dipoles were located close to the sources of the early somatosensory evoked field component N20m, which suggests that the mu rhythm is generated mainly at the primary somatosensory hand projection area. The frequency spectrum of the mu had major peaks around 10 and 21 Hz in all subjects. The high-frequency activity was blocked by clenching of the fist, but not by opening of the eyes, in agreement with characteristics of the electric mu rhythm.

Movement-related slow cortical magnetic fields and changes of spontaneous MEG- and EEG-brain rhythms

Cortical activity was recorded from 5 healthy adults with a 122-channel whole-head magnetometer while the subjects performed during unilateral finger movements at self-paced intervals exceeding 6 s. The readiness field (RF) started over the contralateral somatomotor area 0.3-1 s prior to the movement onset in subjects (Ss) 1, 2, and 4, and culminated in the motor field (MF) 30 ms after it (Ss 1-4). These signals were followed by movement evoked fields MEFI (Ss 1-5) and MEFII (Ss 1-4) at 100-150 ms and 200-250 ms after the movement onset, respectively. One subject showed clear RF over the ipsilateral hemisphere as well. The contralateral dominance of the RF contrasted the more symmetric distribution of the simultaneously recorded electric Bereitschaftspotential (BP). The RF onset never preceded the BP onset. We suggest that BP receives contribution from the early bilateral activation of the crown of the precentral gyrus, whereas RF reflects later activity of the fissural motor cortex. Spontaneous oscillations in the background activity (spontaneous activity) of approximately 10 Hz started to dampen 2-3 s prior to the movement onset in the somatomotor areas of both hemispheres with contralateral predominance (S1 and S3), and returned to a steady level 0.8-2 s after the movement onset in all subjects. Higher frequency bands in the same area displayed a prominent rebound about 1 s after the movement onset in 4 subjects. Execution of self-paced movements is evidently expressed differently in the slow movement-related fields and in the cortical spontaneous activity.