Somatosensory dynamic gamma-band synchrony: A neural code of sensorimotor dexterity

Abstract

To investigate neural coding characteristics in the human primary somatosensory cortex, two fingers with different levels of functional skill were studied. Their dexterity was scored by the Fingertip writing test. Each finger was separately provided by a passive simple sensory stimulation and the responsiveness of each finger cortical representation was studied by a novel source extraction method applied to magnetoencephalographic signals recorded in a 14 healthy right handed subject cohort. In the two hemispheres, neural oscillatory activity synchronization was analysed in the three characteristic alpha, beta and gamma frequency bands by two dynamic measures, one isolating the phase locking between neural network components, the other reflecting the total number of synchronous recruited neurons. In the dominant hemisphere, the gamma band phase locking was higher for the thumb than for the little finger and it correlated with the contra-lateral finger dexterity. Neither in the dominant nor in the non-dominant hemisphere, any effect was observed in the alpha and beta bands. In the gamma band, the amplitude showed similar tendency to the phase locking, without reaching statistical significance. These findings suggest the dynamic gamma band phase locking as a code for finger dexterity, in addition to the magnification of somatotopic central maps.

Introduction

Robust and mutually coherent findings support the theory that the synchronization between recruited – also distant – neuronal pools activities can dynamically form functionally coherent assemblies in sensorimotor contexts, with spatially distributed set of cells activated in a coherent fashion becoming part of the same representation (Roskies, 1999, Singer, 1999, Engel et al., 2001, Engel et al., 2005). In the motor system, motor and premotor neurons engage in synchronous firing during preparatory delay periods (Sanes and Donoghue, 1993, Riehle et al., 1997, Riehle et al., 2000), with growing stimulus expectancy paralleling growing synchronization of network activity; as well, the level of synchronization clearly predicts the performance and the reaction times. Data obtained in sensory cortex areas indicate that synchronicity occurs also in parietal areas during reaching movements (MacKay and Mendonca, 1995) and ongoing oscillations contribute to sensory processing by biasing the coherence of stimulus-induced neuronal discharges (Herculano-Houzel et al., 1999, Roy and Alloway, 1999, Roy et al., 2001). Together, the data indicate that ongoing neural synchrony and oscillatory patterning can modulate the functional performance of sensorimotor networks. In the case of motor preparation, they seem to reflect the dynamic organization of distributed populations of motor and premotor neurons. On the sensory side, the establishment of selective temporal relations before stimulus onset could lead to the priming of particular stimulus constellations, to which the organism will then respond faster and with greater reliability.

Intra-cortical single and multiunit recordings from multiple areas, as well as extra-cephalic electrophysiological studies, have documented the functional relevance of oscillations and synchronization phenomena in the gamma frequency band. They have shown the enhancement of high-frequency synchrony components, resulting from intrinsic dynamic interactions in the involved sensorimotor loops, during attention to stimuli (Womelsdorf et al., 2006), visual search (Tallon-Baudry et al., 1997), object recognition (Rodriguez et al., 1999, Tallon-Baudry and Bertrand, 1999), attentive listening (Tiitinen et al., 1993), learning (Lutzenberger et al., 2002, Kaiser and Lutzenberger, 2005). Gamma band synchronization not only drives cognitive processing arising in widespread neural networks, but takes place locally in somatosensory areas clearly enhancing perception acuity (Crone et al., 1998a, Meador et al., 2002, Palva et al., 2005a).

In electro- and magneto-encephalographic (EEG and MEG) studies, signals from recording channels are often used to estimate synchronization phenomena: if synchronization across relatively distant brain areas is studied, this method is reliable (Gerloff et al., 1998, Simoes et al., 2003, Palva et al., 2005b); but investigating the primary hand sensorimotor cortex, all the recording channels are significantly sensitive to the activity from the same neuronal pools. Here, it is mandatory to identify activities of involved neuronal sources to accurately estimate synchronization levels. Recently, we proposed a modified blind source separation (BSS) algorithm (Functional Source Separation, FSS, Barbati et al., 2006), providing cerebral sources activity along different cerebral processing phases. BSS techniques (for a review see Cichocki and Amari, 2002) model the given observation (sensor data in our case) as a linear mixture of source activities and estimate complete source time courses on the basis of the statistical properties of the generated signal, without taking into account the physical nature of the generating phenomenon.

In the present MEG work, by assessing intra-cerebral activity using FSS, the dynamics of rhythmic activity synchronization phenomena was estimated within the primary finger cortical representations within the first 50 ms after simple sensory stimulation. The objective was to investigate whether specific synchronization phenomena differentiated representation of districts with different levels of functional skill. The thumb, considered as highly dexterous, and the little finger, considered as having low dexterity, were studied. A higher level of synchronization in the more dexterous finger was expected (Tecchio et al., 2003, Tecchio et al., 2004). Possible inter-hemispheric differences owing to hand dexterity were considered, thus both the dominant and non-dominant hemispheres were examined. Possible synchronization band-related specificity was taken into account by observing the whole range of reactive frequencies, characteristic of the sensorimotor areas activity (Crone et al., 1998a, Crone et al., 1998b). Both the extracted sources amplitude – increasing with the number of recruited neurons and their degree of synchronization (Pfurtscheller and Lopes da Silva, 1999) – and phase locking – isolating the similarity of involved signal phases (Quiroga et al., 2002) – were investigated, to identify the most relevant synchronization feature.

To quantify the level of differential dexterity of thumb and little finger of both two hands, the ‘Fingertip writing’ test (Lezak, 1976) was used. Although we would prefer a test based on a pure sensorimotor task, independent of any cognitive component, ‘Fingertip writing’ was to our knowledge the only neuropsychological standardized test differentiating different fingers (Russell et al., 1970, Lezak, 1976, Harley and Grafman, 1983).