Risto J. Ilmoniemi

Interpreting magnetic fields of the brain : minimum norm estimates

The authors have applied estimation theory to the problem of determining primary current distributions from measured neuromagnetic fields. In this procedure, essentially nothing is assumed about the source currents, except that they are spatially restricted to a certain region. Simulation experiments show that the results can describe the structure of the current flow fairly well. By increasing the number of measurements, the estimate can be made more localised. The current distributions may be also used as an interpolation and an extrapolation for the measured field patterns.

Long-Range Temporal Correlations and Scaling Behavior in Human Brain Oscillations

The human brain spontaneously generates neural oscillations with a large variability in frequency, amplitude, duration, and recurrence. Little, however, is known about the long-term spatiotemporal structure of the complex patterns of ongoing activity. A central unresolved issue is whether fluctuations in oscillatory activity reflect a memory of the dynamics of the system for more than a few seconds. We investigated the temporal correlations of network oscillations in the normal human brain at time scales ranging from a few seconds to several minutes. Ongoing activity during eyes-open and eyes-closed conditions was recorded with simultaneous magnetoencephalography and electroencephalography. Here we show that amplitude fluctuations of 10 and 20 Hz oscillations are correlated over thousands of oscillation cycles. Our analyses also indicated that these amplitude fluctuations obey power-law scaling behavior. The scaling exponents were highly invariant across subjects. We propose that the large variability, the long-range correlations, and the power-law scaling behavior of spontaneous oscillations find a unifying explanation within the theory of self-organized criticality, which offers a general mechanism for the emergence of correlations and complex dynamics in stochastic multiunit systems. The demonstrated scaling laws pose novel quantitative constraints on computational models of network oscillations. We argue that critical-state dynamics of spontaneous oscillations may lend neural networks capable of quick reorganization during processing demands.

Neuronal responses to magnetic stimulation reveal cortical reactivity and connectivity

MOTOR and visual cortices of normal volunteers were activated by transcranial magnetic stimulation. The electrical brain activity resulting from the brief electromagnetic pulse was recorded with high-resolution electroencephalography (HR-EEG) and located using inversion algorithms. The stimulation of the left sensorimotor hand area elicited an immediate response at the stimulated site. The activation had spread to adjacent ipsilateral motor areas within 5–10 ms and to homologous regions in the opposite hemisphere within 20 ms. Similar activation patterns were generated by magnetic stimulation of the visual cortex. This new non-invasive method provides direct information about cortical reactivity and area-to-area neuronal connections.

Functional links between motor and language systems

Transcranial magnetic stimulation (TMS) was applied to motor areas in the left language‐dominant hemisphere while right‐handed human subjects made lexical decisions on words related to actions. Response times to words referring to leg actions (e.g. kick) were compared with those to words referring to movements involving the arms and hands (e.g. pick). TMS of hand and leg areas influenced the processing of arm and leg words differentially, as documented by a significant interaction of the factors Stimulation site and Word category. Arm area TMS led to faster arm than leg word responses and the reverse effect, faster lexical decisions on leg than arm words, was present when TMS was applied to leg areas. TMS‐related differences between word categories were not seen in control conditions, when TMS was applied to hand and leg areas in the right hemisphere and during sham stimulation. Our results show that the left hemispheric cortical systems for language and action are linked to each other in a category‐specific manner and that activation in motor and premotor areas can influence the processing of specific kinds of words semantically related to arm or leg actions. By demonstrating specific functional links between action and language systems during lexical processing, these results call into question modular theories of language and motor functions and provide evidence that the two systems interact in the processing of meaningful information about language and action.

Responses of the primary auditory cortex to pitch changes in a sequence of tone pips: Neuromagnetic recordings in man

Auditory evoked magnetic fields of the human brain were recorded with a four-channel 1st order gradiometer. Pitch deviance in a sequence of repetitive tone pips elicited magnetic evoked-response changes with a topography suggesting that a neuronal mismatch process to the deviant tones activates the primary auditory cortex.

Separate Time Behaviors of the Temporal and Frontal Mismatch Negativity Sources

It has been proposed that mismatch negativity (MMN) is generated by temporal and frontal lobe sources, the former being associated with change detection and the latter with involuntary switching of attention to sound change. If this switching of attention is triggered by the temporal cortex change-detection mechanism, one would expect that the frontal component of MMN is activated later than the temporal one. This was studied by using 64-channel electroencephalography (EEG) and 122-channel magnetoencephalography (MEG) with realistically shaped head models to determine the source current distribution in different lobes as a function of time. Minimum-norm estimation (MNE) was performed, constraining the solution to the reconstructed cortical sheet. The results support the hypothesis that the frontal MMN generator is activated later than the auditory cortex generator.

Signal-space projection method for separating MEG or EEG into components

CURRENTS INSIDE a conducting body can be estimated by measuring the magnetic and/or the electric field at multiple locations outside and then constructing a solution to the inverse problem, i.e. determining a current configuration that could have produced the measured field. Unfortunately, there is no unique solution to this problem (HELMHOLTZ, 1853) unless restricting assumptions are made. The minimum-norm estimate (HAM/~.L,~INEN and ILMONIEMI, 1994) provides a solution with the smallest expected overall error when minimum a priori information about the source distribution is available. Other methods to estimate a continuous current distribution producing the measured signals have been studied (PASCUAL-MARQUI et al., 1994; WANG et aL, 1995; GORODNITSKY, et al., 1995). A different approach is to divide the brain activity into discrete components such as current dipoles (ScHERG, 1990; MOSHER et al., 1992). Here we widen this approach into arbitrary current configurations. In our signal-space projection (SSP) method, the signals measured by d sensors are considered to form a time-varying vector in a d-dimensional signal space. The component vectors,, i.e. the signals caused by the different neuronal sources, have different and fixed orientations in the signal space. In other words, each source has a distinct and stable field pattern. All the current eonfi~marations producing the same measured field pattern are indistinguishable on the basis of the field: they have the same vector direction in the signal space and thus belong to the same equivalence class of current configurations (TESCHE et al., 1995a). The angle in the signal space between vectors representing different equivalence classes, e.g. between component vectors, is a measure of similarity of the equivalence classes in signal space and a way to characterise the separability of sources. The cosine of this angle has previously been used as a numerical charaeterisation of the difference between topographical distributions (DESMEDT and CHALK[.IN, 1989). If the direction of at least one of the component vectors forming the measured multi-channel signal can be determined from the data, or is known otherwise, SSP can be used to simplify subsequent analysis. For example, if an early deflection in an evoked response is produced by one source, and the rest of the response is a mixture of signals from this and other sources, SSP can separate the data into two parts so that the early source contributes only to one part. In general, the signals are divided into two orthogonal parts: s~, including the time-varying contribution from sources with known signalspace directions; and s~_, including the rest of the signals. Both sl~ and s j_ can then be analysed separately in more detail. By analysing s t , we can detect activity originally masked by s~. On the other hand, the sources included in stl are seen with an enhanced signal-to-noise ratio. By forward modelling of sources in selected patches of cortex, it is possible to form a spatial filter that selectively passes only the signals that may have been generated by currents in the given patches. If the subspace defined by artefacts can be determined, the artefactflee S L can be analysed. In SSP, in contrast to PCA (HARRIS, 1975; MAIER et al., 1987) and other analysis methods (GRUMMICH et al., 1991; KOLES et aL, 1990; KOLES, 1991; SOONG and KOLES, 1995; BESA*), the source decomposition does not depend on the orthogonality of source components or the availability of source or conductivity models. No conductivity or source models are needed if the component vectors are estimated directly from the measured signals. This is useful when no source estimation is needed, e.g. when artefacts or somatomotor activity in a cogrritive study must be filtered out. The angles between the components provide an easy and illustrative way to characterise the linear dependence between the components and thus the separability of sources. The concept of signal space in MEG was introduced previously ([LMONIEMI, 1981; [LMONIEMI and WILLIAMSON,

Brain Signatures of Meaning Access in Action Word Recognition

The brain basis of action words may be neuron ensembles binding language-and action-related information that are dispersed over both language-and action-related cortical areas. This predicts fast spreading of neuronal activity from language areas to specific sensorimotor areas when action words semantically related to different parts of the body are being perceived. To test this, fast neurophysiological imaging was applied to reveal spatiotemporal activity patterns elicited by words with different action-related meaning. Spoken words referring to actions involving the face or leg were presented while subjects engaged in a distraction task and their brain activity was recorded using high-density magnetoencephalography. Shortly after the words could be recognized as unique lexical items, objective source localization using minimum norm current estimates revealed activation in superior temporal (130 msec) and inferior frontocentral areas (142-146 msec). Face-word stimuli activated inferior frontocentral areas more strongly than leg words, whereas the reverse was found at superior central sites (170 msec), thus reflecting the cortical somatotopy of motor actions signified by the words. Significant correlations were found between local source strengths in the frontocentral cortex calculated for all participants and their semantic ratings of the stimulus words, thus further establishing a close relationship between word meaning access and neurophysiology. These results show that meaning access in action word recognition is an early automatic process reflected by spatiotemporal signatures of word-evoked activity. Word-related distributed neuronal assemblies with specific cortical topographies can explain the observed spatiotemporal dynamics reflecting word meaning access.

Somatosensory evoked cerebral magnetic fields from SI and SII in man.

We have recorded cerebral magnetic fields elicited by electrical stimulation of median and peroneal nerves. Field mapping indicates that the deflections at 30-80 and 150-180 msec are due to activity at SI. Additional activity at 90-125 msec is generated at SII, on the superior bank of the sylvian fissure. At SI, the source locations are in agreement with the known somatotopy. Only contralateral stimuli evoke responses at SI, whereas both ipsi- and contralateral stimuli elicit responses at SII.

Processing of novel sounds and frequency changes in the human auditory cortex: Magnetoencephalographic recordings

Whole-head magnetoencephalographic (MEG) responses to repeating standard tones and to infrequent slightly higher deviant tones and complex novel sounds were recorded together with event-related brain potentials (ERPs). Deviant tones and novel sounds elicited the mismatch negativity (MMN) component of the ERP and its MEG counterpart (MMNm) both when the auditory stimuli were attended to and when they were ignored. MMNm generators were located bilateral to the superior planes of the temporal lobes where preattentive auditory discrimination appears to occur. A subsequent positive P3a component was elicited by deviant tones and with a larger amplitude by novel sounds even when the sounds were to be ignored. Source localization for the MEG counterpart of P3a (P3am) suggested that the auditory cortex in the superior temporal plane is involved in the neural network of involuntary attention switching to changes in the acoustic environment.