Inbred mouse strains differ in multiple hippocampal activity traits.

A major challenge in neuroscience is to identify genes that influence specific behaviors and to understand the intermediary neuronal mechanisms. One approach is to identify so-called endophenotypes at different levels of neuronal organization from synapse to brain activity. An endophenotype is a quantitative trait that is closer to the gene action than behavior, and potentially a marker of neuronal mechanisms underlying behavior. Hippocampal activity and, in particular, hippocampal oscillations have been suggested to underlie various cognitive and motor functions. To identify quantitative traits that are potentially useful for identifying genes influencing hippocampal activity, we measured gamma oscillations and spontaneous activity in acute hippocampal slices from eight inbred mouse strains under three experimental conditions. We estimated the heritability of more than 200 quantitative traits derived from this activity. We observed significant differences between the different mouse strains, particularly in the amplitude of the activity and the correlation between activities in different hippocampal subregions. Interestingly, these traits had a low genetic correlation between the three experimental conditions, which suggests that different genetic components influence the activity in different conditions. Our findings show that several traits of hippocampal gamma oscillations and spontaneous activity are heritable and could thus be potentially useful in gene-finding strategies based on endophenotypes.

Synchronization likelihood with explicit time-frequency priors.

Cognitive processing requires integration of information processed simultaneously in spatially distinct areas of the brain. The influence that two brain areas exert on each others activity is usually governed by an unknown function, which is likely to have nonlinear terms. If the functional relationship between activities in different areas is dominated by the nonlinear terms, linear measures of correlation may not detect the statistical interdependency satisfactorily. Therefore, algorithms for detecting nonlinear dependencies may prove invaluable for characterizing the functional coupling in certain neuronal systems, conditions or pathologies. Synchronization likelihood (SL) is a method based on the concept of generalized synchronization and detects nonlinear and linear dependencies between two signals (Stam, C.J., van Dijk, B.W., 2002. Synchronization likelihood: An unbiased measure of generalized synchronization in multivariate data sets. Physica D, 163: 236-241.). SL relies on the detection of simultaneously occurring patterns, which can be complex and widely different in the two signals. Clinical studies applying SL to electro- or magnetoencephalography (EEG/MEG) signals have shown promising results. In previous implementations of the algorithm, however, a number of parameters have lacked a rigorous definition with respect to the time-frequency characteristics of the underlying physiological processes. Here we introduce a rationale for choosing these parameters as a function of the time-frequency content of the patterns of interest. The number of parameters that can be arbitrarily chosen by the user of the SL algorithm is thereby decreased from six to two. Empirical evidence for the advantages of our proposal is given by an application to EEG data of an epileptic seizure and simulations of two unidirectionally coupled Hénon systems.

Interhemispheric phase synchrony and amplitude correlation of spontaneous beta oscillations in human subjects: a magnetoencephalographic study.

Interhemispheric phase synchrony and amplitude correlation of beta oscillations were studied with MEG in a resting condition. The left and right hemisphere beta oscillations exhibited phase-locking with a phase-lag near zero degrees. The index of synchronization was strongest when these oscillations had large amplitude. Functionally, we interpret the phase synchrony on the basis of bilaterality of movement organization. A positive interhemispheric correlation was also found for the amplitude of spontaneous beta oscillations over long time intervals (> 1 s). The low-frequency correlation of spontaneous rhythmic activity may be the source of the low-frequency correlations of the hemodynamic responses in homologous areas that have been reported previously and have been interpreted as functional connectivity between these areas.

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.

Dynamics of mu-rhythm suppression caused by median nerve stimulation: a magnetoencephalographic study in human subjects.

We studied event-related desynchronization (ERD) of the 8-13 Hz rhythm (mu rhythm) of the primary somatosensory cortex (SI) caused by contra- and ipsilateral median-nerve stimulation. We used whole-head magnetoencephalography (MEG) and wavelet analysis together with our newly developed color-coded single-trial ERD display. The somatosensory stimuli suppressed mu rhythm at both contra- and ipsilateral SI, but the attenuation was clearly lateralized, being at least 20% stronger contra- than ipsilaterally. Moreover, repeated stimulation significantly reduced mu-rhythm ERD in the ipsilateral but not in the contralateral hemisphere in the course of the experiment. The observed lateralization is in agreement with the classical concept of a dominant role of the contralateral hemisphere in the processing of somatosensory information. The strong ipsilateral ERD in the beginning of the experiment may reflect the presence of non-specific arousal-like activation, which attenuates toward the end of the experiment.

Somatosensory evoked magnetic fields: relation to pre-stimulus mu rhythm.

OBJECTIVES: Brain responses to auditory and visual stimuli have been previously shown to depend on the level of spontaneous brain activity in the 8-13 Hz range. Our aim was to determine whether somatosensory evoked responses are influenced by ongoing rhythmic activity in the 8-13 Hz frequency range originating in the sensorimotor cortex (mu rhythm). METHODS: We used a whole-head 122 channel magnetoencephalography (MEG) system to record somatosensory evoked fields (SEFs) in response to median nerve stimulation in 11 subjects. Spontaneous oscillations in the 8-13 Hz band over the contralateral sensorimotor cortex were evaluated in 3 different pre-stimulus time intervals using wavelet analysis. RESULTS: The N20m SEF deflection did not depend on pre-stimulus activity, while the amplitude of the P35m deflection, and to a lesser extent that of the P60m deflection, showed a small positive correlation with the amplitude of the pre-stimulus mu rhythm. Although the amplitude of the mu rhythm varied by a factor of 2.3-5, the maximum variations in P35m and P60m amplitude were only 21 and 12%, respectively. The latencies of the peaks were not affected by the strength of the pre-stimulus mu rhythm. CONCLUSIONS: It appears that the first excitatory cortical response (N20m) is independent of the oscillatory state (8-13 Hz frequency range) of the sensorimotor cortex. Later parts of the response (P35m and P60m) are also relatively stable compared with the large variations in mu rhythm.

Face-selective processing in human extrastriate cortex around 120 ms after stimulus onset revealed by magneto- and electroencephalography.

Quick recognition of faces is crucial to a variety of human interactions, and highly specialized pathways may be involved in the processing of faces. To reveal selectivity to faces in early cortical processing, whole-scalp magnetoencephalography (MEG) and electroencephalography (EEG) were used to record event-related responses to faces and degraded faces and their inverted counterparts. We observed increases in the peak latency and amplitude of the early 120-ms component (P120) for the inverted faces. These effects were enhanced for the 1 70-ms component (N170). For the degraded counterparts, a significant effect of the inversion was observed only for the N170, which was strongly delayed. Source modelling suggested that the early response originated at the posterior occipital areas whereas the later response was generated anterior and lateral to this location. We conclude that under sufficiently good conditions face-selective activity may be taking place during the P120.