The primary somatosensory cortex (SI) exhibits a detailed topographic organization of the hand and fingers, which has been found to undergo plastic changes following modifications of the sensory input. Although the spatial properties of these changes have been extensively investigated, little is known about their temporal dynamics. In this study, we adapted the paradigm of finger webbing, in which 4 fingers are temporarily webbed together, hence modifying their sensory feedback. We used magnetoencephalography, to measure changes in the hand representation in SI, before, during, and after finger webbing for about 5 h. Our results showed a decrease in the Euclidean distance (ED) between cortical sources activated by electrical stimuli to the index and small finger 30 min after webbing, followed by an increase lasting for about 2 h after webbing, which was followed by a return toward baseline values. These results provide a unique frame in which the different representational changes occur, merging previous findings that were only apparently controversial, in which either increases or decreases in ED were reported after sensory manipulation for relatively long or short duration, respectively. Moreover, these observations further confirm that the mechanisms that underlie cortical reorganization are extremely rapid in their expression and, for the first time, show how brain reorganization occurs over time.
The human brain is capable of plastic reorganization in different sensory modalities not only during development but also during adulthood following training and transient or permanent manipulation of sensory inputs. Representational plasticity has been studied for the auditory (Pantev et al. 2001), visual (Calford 2002; Sur and Rubenstein 2005), as well as somatosensory system (Elbert and Rockstroh 2004). In the latter, a vast number of studies have been conducted with magnetoencephalography (MEG) and other neuroimaging techniques: in musicians (Elbert et al. 1995), in blind Braille readers (Rockstroh et al. 1998; Sterr et al. 1998), following peripheral nerve lesions (Tecchio et al. 2002), and in patients recovering after stroke (Rossini and Pauri 2000; Rossini et al. 2003). The results of these studies indicate that the adult human brain retains some ability to reorganize itself following substantial modifications of the amount and type of sensory inputs or following a brain lesion. Within the somatosensory system, increased or decreased use of a body part or temporally correlated repeated stimuli can cause substantial cortical reorganization (Elbert et al. 1995; Sterr et al. 1998; Braun et al. 2000; Schwenkreis et al. 2001).
Despite previous studies, little is known about the temporal dynamics of these plastic changes, which is the goal of our study. The large majority of previous research deals with changes induced over a long time interval—at least a few days. Evidence from nonhuman studies on short-term plasticity shows that the initial modifications in the cortical maps occur rather rapidly (within minutes) (Calford and Tweedale 1988; Recanzone et al. 1990; Calford and Tweedale 1991; Faggin et al. 1997). In humans, rapid modifications (within 30 min) in the primary somatosensory cortex (SI) were first reported by Rossini et al. (1994) using MEG. More recent studies of SI functional organization, mostly using tactile stimulation of the fingers, have reported contradictory results. The Euclidean distance (ED) of the source location of the different fingers was found to decrease after continuous stimulation of individual digits (D) D1, D2, and D3 (Braun et al. 2000) and after passive tactile coactivation of D1, D2, D4, and D5 (Ziemus et al. 2000). Conversely, other authors reported an increase of the D2–D5 ED after more prolonged tactile coactivation of D2 (Godde et al. 2003) or increase of the D1–D5 ED after tool use with D1 and D2 (Schaefer et al. 2004). The aim of this study was to merge these apparently conflicting results by finding the precise timing of short-term plasticity within the SI.
The paradigm used is derived from the one originally employed in monkeys, where it involved the creation of an artificial syndactyly of 2 fingers (Allard et al. 1991). In this pioneering study, progressively more common finger receptive fields of the webbed fingers were observed during the long time interval (months after the surgery). In humans using MEG, complementary results were observed before and after corrective surgery in 2 cases of congenital syndactyly (Mogilner et al. 1993). In our protocol, a temporarily webbed condition of 4 fingers (index to little finger) was used to simulate an artificial syndactyly and was maintained for about 5 h during which finger somatotopy in the SI was monitored.
Materials and Methods
Eleven healthy volunteers participated in this study (4 males) aged 19–31 years (mean age 25 years). All subjects were strongly right-handed according to the Edinburgh Inventory (Oldfield 1971). None of them had a previous history of neurological disease and had normal brain magnetic resonance images (MRI). All subjects gave their written informed consent and received an honorarium. The protocol and experimental procedures were approved by the local ethics committee and were in compliance with the Declaration of Helsinki.
Separate electric stimulation of the fingers D2 and D5 was performed during each MEG recording session in order to localize each finger's somatotopical “hot spot” in the contralateral cortical primary sensory area. We studied the response of the fingers D2 and D5, which have analogous functional characteristics because the spatial resolution of MEG is adequate enough to monitor changes of localization for these 2 digits. Stimulus duration was set at 0.2 ms, and an interstimulus interval of 1.5 s was used. The stimulus was delivered with bar electrodes with 2.5 cm interelectrode distance (cathode proximal) that were taped on the subject's first and second interphalangeal joints. After measuring the subjective sensory threshold for D2 and D5, stimulus intensity was adjusted to twice this value; care was taken in order to ensure that the stimulation constantly elicited nearly the same level of painless sensation for both fingers throughout the experiment.
Recordings were performed with a whole-head MEG system available at the Institute of Advanced Biomedical Technologies consisting of 165 dc SQUID integrated magnetometers: 153 magnetometers are placed over a whole-head helmet surface and 12 magnetometers are used for software rejection of background noise, developed by ATB (ATB, Pescara, Italy) (Della Penna et al. 2000). Signals from 4 indicator coils attached to the subject's head were used in order to define the exact position of the head inside the sensor helmet. A head coordinate system was defined by a digitization procedure using a 3-dimensional (3D) digitizer (Polhemus, 3D space Fastrack, Colchester, VT); this included the anatomical landmarks (nasion, inion, preauricular points, and vertex), the indicator coils, and about 40 surface head points. For each subject, the individual head MRI scan was acquired using a Siemens Magnetom Vision 1.5-T scanner (Siemens, Erlangen, Germany) according to a magnetization-prepared rapid gradient-echo sequence (256 × 256, field of view = 256, time repetition = 9.7 ms, echo time = 4 ms, flip angle = 12°, thickness = 1 mm). MEG and MRI coregistration was obtained by applying 7-mm diameter spherical coils placed on the same anatomical landmarks that were used during the digitization procedure.
Somatosensory evoked fields (SEFs) were recorded at a 2050-Hz sampling rate, and 350 responses were acquired for each finger and each block. Sensory nerve action potentials (SNAPs) from the median and the ulnar nerve were recorded at the wrist via surface disk Ag/AgCl electrodes with 4 cm interelectrode distance (Eduardo and Burke 1988; Evanoff and Buschbacher 2004), in order to monitor the amount and stability of sensory input dispatched toward the central nervous system from the stimulating electrodes throughout the various experimental stages. Care was taken to maintain a stable temperature of hand and fingers.
A MEG recording session consisted of 7 recording blocks (B0–B6); each containing a separate electric stimulation of D2 and D5 of the dominant hand of the subject (Reilly and Hammond 2004). The experimental protocol consisted of the following steps (Fig. 1):The total duration of the experiment was approximately 5 h and 30 min.
One MEG block took place before the webbing of the fingers and served as a control—baseline condition (block B0). Mean duration of this step was 15 min.
Digits from D2–D5 of the dominant hand of the subject were banded together on a flat plane with medical cloth and lightly fastened with medical adhesive tape. To ensure proper electric stimulation, fingers D3 and D4 were electrically isolated with rubber finger covers. About 30 min after webbing, block B1 was recorded.
Next, 4 MEG measurements (blocks 2–5, B2–B5), were acquired at 1-h intervals. During the intervals, which lasted approximately 30 min, the subjects exited the MEG chamber and were supervised, while having a task-free condition for hand use (including the one with webbed fingers). Prior to every entrance in the MEG room, the subject was demagnetized using a strong oscillating magnetic field, in order to lower the field of magnetic particles possibly present on the subject's body.
After the fifth block, the fingers were released and a final MEG measure (B6) was performed 30 min after B5.
Furthermore, 2 subjects underwent a control experiment—few months after their first recording session—in which the same protocol was performed but without having the fingers webbed. This control experiment was carried out in order to observe whether spontaneous fluctuations of the finger locations or errors in the experimental and recording procedures might produce results similar those with the webbed fingers.
All data sets were visually checked for noisy epochs, and approximately 300 artifact-free trials were selected for each block and each subject for further analysis. Before averaging, a 1- to 150-Hz bandpass filter was applied and heart artifact was removed. A time window of 50-ms prestimulus and 300-ms poststimulus was used for averaging, and a 10- to 15-ms poststimulus time interval was chosen for baseline correction. The averaged SEF waveforms were then analyzed with the BESA 5 software program (MEGIS Software GmbH, Munich, Germany), and the equivalent current dipole (ECD) inside a homogeneously conducting sphere was used as the source model. The dipole was fitted on a time interval starting from the onset until the peak of the P30m wave using the information recorded by all MEG channels. The ECD was accepted as a solution only if it accounted for at least 90% of the explained variance. For each subject, the ECD was then projected on the individual MRI for visualization of the solution (Brain Voyager, Brain Innovation B.V., Maastricht, Netherlands) using the common digitized anatomical head landmarks for coregistration.
For every session and finger, the dipole explaining the P30m component was identified. Dipole positions were expressed in Cartesian and in polar (BESA) head coordinate systems. This later system as defined by BESA states the following (Fig. 2): eccentricity (r) is the distance from the center of the coordinate system, theta angle is positive on the right side, from 0° to 180°, upward to downward, and negative on the left side, and phi is taking values from 0° to 90°, being positive on the upper right quadrant and alternating sign for every quadrant.
The ED between the cortical representations of fingers D2 and D5 was calculated from their dipole locations in the Cartesian coordinates.
Statistical analysis was performed using analysis of variance (ANOVA) and post hoc Duncan's test.
MEG has been repeatedly demonstrated to noninvasively assess the hand and finger somatotopy (Hari et al. 1993; Nakamura et al. 1998; Pizzella et al. 1999). The recorded scalp SEFs from our experiment displayed the classic response waves elicited by the electric stimulation of the fingers. Peripheral nerve SNAPs at the wrist were found stable, both in latency and amplitude, throughout the recordings. For the present study, all analysis was related to P30m SEF response elicited by fingers electrical stimulation, because this deflection is assumed to be generated in the posterior bank of the central sulcus (SI), it is stable within and across subjects and well explained by a single equivalent source (Hari et al. 1993; Rossini et al. 1994; Hashimoto et al. 2004), it is not influenced by attention or cognition, and it represented the largest wave of the early cortical response. The results that follow regard 9 subjects from whom a stable and reproducible localization of the stimulated fingers could be extracted. For the remaining 2 cases, the ECD could not be reliably located during the whole session because of a low signal-to-noise ratio; therefore, they were excluded from further analysis.
When the locations of the ECDs were integrated with individual MRI, ECDs always fell in the posterior wall of the central sulcus, in the omega region that is known to contain the sensorimotor hand representation. The localization procedure confirmed the “traditional” somatotopy projection, with the little finger being located more superior and medially than the index finger. In fact, although the ECD model is a point-like source, it identifies the center of the neuronal pools firing within the activated cortical area, and therefore it can establish a spatial relationship between active cortical patches. The ECD location for both fingers changed over time during the whole experiment, but remained inside the SI hand representational area during all experimental blocks (Fig. 3).
Analysis of Dipoles
Individual ECD positions were analyzed in their polar BESA coordinate system with 2-way ANOVA using Time and Finger as factors. Concerning the eccentricity r, the Finger factor was significant (P < 0.03), (mean for D2, r = 62.73 mm, and for D5, r = 64.98 mm), but no significant effect was observed for the factor Time. For the theta polar angle, both the Finger factor (P < 0.0001), with D5 theta value significantly smaller (more upward) than D2, and the Time factor (P < 0.03) were significant. There was also a significant interaction of Finger × Time (P < 0.03) (Fig. 4), showing that theta values of D2 and D5 change following different trends over time. Concerning the phi polar angle, the interaction of Finger × Time (D2–D5) was significant (P < 0.05). From post hoc analysis we saw that during block B3 (i.e., the block of mean maximal ED), D2 shifted anteriorly with respect to all other blocks of D2 and with respect to D5 (Fig. 5).
A 2-way statistical analysis of the dipole strength showed that the strengths of the dipoles representing D2 and D5 were not significantly altered throughout the blocks of the experiment. The only statistically significant effect was represented by a stronger D2 dipole (P < 0.02), as already found in previous studies (Hari et al. 1993). No statistically significant changes were found for their latency either.
ED between D2 and D5
The MEG study of plasticity associated with the representation of fingers is commonly carried out using the ED between the finger ECDs. The EDs for each subject and each recording block were calculated and graphically represented in Figure 6. In the baseline condition before webbing (B0), the ED of D2–D5 was in the range of 6.0–13.7 mm. These values agreed with those previously reported in literature (Hari et al. 1993; Mogilner et al. 1993; Nakamura et al. 1998; Tecchio et al. 1998; Pizzella et al. 1999; Kurth et al. 2000). A rapid decrease in the ED was found in the first recording block (B1), 30 min after webbing, with the ratio B1/B0 ranging from 0.46 to 0.79 depending on the subject. Afterward, during blocks B2–B4 (1.5–3.5 h after webbing), their ED ratio with respect to its value before webbing (B0) ranged from 1.3 up to 3.05 times, reaching a maximum at about 2.2 h. The maximum ED reached a 3-fold increase with respect to the baseline; it is noteworthy to remind that for similar time frames, analogous enlarged activation areas have been found with functional magnetic resonance images (fMRI) (Hodzic et al. 2004). Finally, the ED started to decline and showed a progressive return toward its initial prewebbing baseline level. Interestingly, the trend of ED was similar to the trend observed in the change of D2 and D5 through the phi and theta angles (Figs 4–6).
The effect of webbing time on the ED was statistically analyzed by one-way ANOVA showing high statistical significance with time (P < 0.0001, F6,48 = 26.49, MS effect = 115.8). The post hoc analysis revealed that 1) ED during B0 is different from all the forthcoming blocks (P < 0.002) except B5 and B6 (after unwebbing); 2) ED during the first block after webbing B1 was significantly smaller than that in all the other blocks (P < 0.002); 3) during B2 and B3, the values of ED clustered together and were significantly larger compared with the other blocks (P < 0.0001 and P < 0.00006 respectively, except block B4, with were P < 0.05 and P < 0.01, respectively); (iv) B4 was statistically significant with the other blocks (P < 0.05); (v) during B5 and B6, the ED was statistically different from all the other blocks (P < 0.009 and P < 0.01, respectively) except between them and B0. The average ED values along with the post hoc analysis and P values of the various blocks, with respect to B0, are shown in Figure 7.
Finally, the ED estimated in the 2 subjects who were measured in the control experiment, using the complete stimulation sequence without webbing the fingers, did not change significantly over time (Fig. 8).
Calculation of Time Constants
In order to describe the timing of the maximum plasticity effect, a polynomial function of third order was fitted to the data set of the ED curve throughout the webbing condition (B1–B5). This set of ED points from all the subjects can be fitted by the function: f(t) = 0.56t3 – 5.94t2 + 17.8t + 0.11, where t corresponds to time (R2 = 0.63). This function reaches a first maximum at 2.16 h after webbing, thus this number can be considered as the timing of the maximum ED observed. By separating the rising and falling parts of the ED curve, we can describe the first part by an exponential time-increasing function f(t) = a × exp(1 − t/tc1) with a = 16.6 ± 1.0 mm and tc1 = 0.73 ± 0.12 h, (R2 = 0.93), thus stability was reached after about 3 × tc1 (i.e., about 2.19 h). The second part, representing the return toward baseline values was fitted by a exponential time-decreasing function f(t) = c × exp(−t/tc2) and was found that c = 22.9 ± 13.4 mm and tc2 = 2.1 ± 1.6 h (R2 = 0.96).
In this study, the time course of changes in finger cortical representation in humans was examined, by applying a reversible 4-finger webbing condition for about 5 h. Results have shown that rapid and statistically significant changes in cortical representation do occur within this time frame. We studied these changes through the ED between the ECDs representing the examined fingers.
Present observations can be seen under 2 distinct perspectives. One is that the EDs representing the stimulated areas follow a decrease–increase–decrease behavior and the second is the short-term timing of those effects. The initial effects were evident after only 30 min, followed by a subsequent increase reaching its plateau in about 2 h and a final return to baseline levels again after approximately 2 h.
Polar Coordinates of Individual Dipoles Describe the ED Results
The ED behavior seen in our results can be elucidated from the analysis of the ECD polar coordinates. The ECDs representing D2 and D5 have similar theta angles during the shortest ED, whereas having maximally different theta angles when the longest ED is reached; at this stage, the ECD for D2 is reaching the most anterior shift (maximal phi value), whereas D5 is maximally shifted upward (maximal theta value) (Figs 4 and 5), such a mutual positioning explaining their increased distance. The anterior movement of the D2 ECD might be due to the rostrocaudal direction of the postcentral gyrus. Actually, a more anterior localization for D2 has been frequently described in literature (Nakamura et al. 1998; Maldjian et al. 1999). The D5 ECD is located at the boundary of the hand area, whereas the D2 ECD is still well within it. We thus expect D5 ECD to move differently from D2.
Trend of the ED over Time—Comparison with Other MEG Studies
Different behavior of the distance between representational areas in neocortex (either decrease or increase) has been reported in literature after various sensory manipulations. A decrease in the distance has been reported in the following previous MEG studies on short-term finger plasticity: a fixed sequence of stimuli on D1, D2, and D3 resulted in a decrease in the ED of D1–D3 in SI representation after few minutes (Braun et al. 2000); passive coactivation lasting 40 min of D1, D2, D4, and D5 induced a decrease in the ED between median and ulnar nerve (Ziemus et al. 2000); median and radial nerve anesthesia produced a decrease in the ED between ECDs representing D5 and lower lip starting 45 min after beginning of anesthesia (Weiss et al. 2004). Conversely, other MEG studies have shown an increase in the ED between fingers after prolonged use/stimulation. An increase in the D2–D5 distance after a 3-h stimulation of D2 adjacent skin receptive fields has been reported (Godde et al. 2003). A tool use of D1 and D2 for 40 min has led to an increase in the ED of D1–D5 (Schaefer et al. 2004).
The above studies show either a short-term decrease or an increase in the ED according to the specific protocol, within a single-shot measurement. Indeed, none but one of the above-mentioned studies has monitored the ED changes over a long time interval. Findings from the present paradigm suggest that the 2-fold feature of the ED we observed is not related only to a specific sensory manipulation but is mainly a result of the accurate sampling of such parameter along with a relatively expanded time scale. We therefore conclude that what we see is reflecting the inherent processing dynamics of sensory cortex.
Short-Term and Distinct Alterations of ED as the Main Outputs of Our Study—Comparison with fMRI and Animal Studies
In 2 fMRI studies, passive tactile coactivation has lead to an increase in the blood oxygen level–dependent signal and to a shift of the center of gravity (CoG) of the activated area (Pleger et al. 2003; Hodzic et al. 2004). One of those was a follow-up of a previous electroencephalography (EEG) study where the shift found for the N20 waveform was actually correlated with the movement of the CoG of the fMRI activated area (Pleger et al. 2001), thus showing that there is a clear correspondence between the magnitude of cortical representation area (and receptive field through spatial discrimination threshold), the shift in the CoG, and the strength and position of the equivalent dipole.
One interesting aspect of our results is the alternate behavior that the ED between D2 and D5 is exhibiting: the decrease–increase–decrease pattern. It has been shown in very early animal studies that during different sensory manipulations, a similar behavior has been encountered: cortical RFs have been seen to initially expand and then decrease in size (Weinberger 1995). This occurred within 39 min after anesthesia (Calford and Tweedale 1991) or within 1 day or weeks after amputation (Merzenich et al. 1983; Calford 2002; Florence 2002). These alternate modifications in RF size occurring at different time frames might as well represent a characteristic of the processing dynamics of the sensory cortex, with cortical maps and RFs being in a state of use-dependent fluctuations.
A second aspect of our results is the short timing of the ED changes. Studies in animals have shown that functional plasticity phenomena occur quite rapidly: after amputation of one digit in the flying fox, modifications within 15 min occurred on neural RFs (Calford and Tweedale 1988); after 3–5 min of upper-lip anesthesia in rats cortical and subcortical sensory reorganization was evoked (Faggin et al. 1997). It should be remembered that the contribution of subcortical, including spinal mechanisms, cannot be traced with the present method as rapid plastic modifications have been found in subcortical levels as well (Nicolelis et al. 1993; Faggin et al. 1997; Krupa et al. 1999). For these reasons, we are witnessing at this study changes occurring at the cortical level.
Physiological Mechanisms of Plasticity
Findings of the present report can be ascribed to short-term reorganization of synaptic connections within the cortical finger topography, mainly due to long-term potentiation or long-term depression (LTP/LTD) mechanisms. Synapses in the somatosensory system exhibit N-methyl-D-aspartate receptor–dependent LTP and LTD forms of plasticity. LTP and LTD, as well as short-term potentiation and depression and spike-timing–dependent plasticity (STDP) are induced in time frames of some minutes (Castro-Alamancos et al. 1995; Bi and Rubin 2005). LTP or LTD needs tens of minutes in order to be established. It seems also that reversibility of those mechanisms is possible and can occur quite rapidly (Abraham et al. 2006). Remarkably, both animal and human studies have revealed a correlation between LTP/LTD and the output (expansion/shrinkage) of cortical representations (Hess and Donoghue 1994; Allen et al. 2003; Schwenkreis et al. 2005). In addition, Wolters et al. (2005) using a protocol of paired associative stimulation have shown that induced excitability changes measured by EEG share a common timing with STDP in animals.
Another plausible explanation for our system-level observations could arise from STDP modeling studies for adult plasticity. It has been shown that STDP strengthens synapses that receive correlated inputs, providing a model for describing the time course of plastic changes (Song and Abbott 2001; Fox and Wong 2005). In the absence of competition from a feedforward source of input, STDP will potentiate the most correlated set of intracortical inputs to a given neuron. However, as this model predicts, once these inputs are strengthened, they can act as a training signal allowing feedforward synapses to strengthen. The shorter latency of the feedforward over recurrent inputs leads to their ultimate dominance (Song and Abbott 2001).
Recent studies have revealed that dendritic spines in adult brain have dynamics that are regulated by sensory experience. Their motility has been found to change after some hours of sensory experience (Engert and Bonhoeffer 1999; Knott et al. 2002; Feldman and Brecht 2005). They seem to appear or outgrow after 30 min of potentiation in vitro and increase in number after 2 h (Engert and Bonhoeffer 1999; Kirov et al. 1999; Sheng 2001).
Systems Level—Neural Circuitry Changes
As Hebb has initially suggested, individual neurons can participate in different cell assemblies and be involved in multiple functions and representations. Connectivity between assemblies is enhanced by increase of neuronal excitability between them due to mutual interactions. In the existing and above-mentioned literature, on animals and humans, short-term changes are attributed to a change in the balance of excitation and inhibition. This concept has been referred to as “pseudoplasticity” because it does not require morphological modifications. Inhibitory interneurons are assumed to reshape and refine receptive fields (Florence 2002; Feldman and Brecht 2005; Foeller et al. 2005). Moreover, horizontal excitatory intracortical connections within the supragranular cortex are supposed to play a major role (Finnerty et al. 1999; Foeller and Feldman 2004).
A number of theoretical studies verified the existence of functionally neural segregates serving plasticity representational changes and being activated when required and according to the sensory input (Pearson et al. 1987; Xing and Gerstein 1996). A selective “disinhibition” and enhancement of local intracortical connections could enhance the function of those cell assemblies that functionally best represent any new sensory condition (Hess and Donoghue 1994; Huntley 1997; Calford 2002; Florence 2002; Ungerleider et al. 2002).
But what is a possible explanation for the mechanism accounting for the observed ED? Within this general theoretical frame, one can argue that the early postwebbing changes (block B1) are due to weakening of synapses at the periphery of the RF of the webbed fingers and due to activation of intermediate neurons having the net and cumulative effect of restriction of the “sensory hand” cortical representation and of the D2–D5 approximation (Zarzecki et al. 1993; Godde et al. 1996; Calford 2002; Florence 2002). Two and a half hours later (blocks B2–B4), a progressive resetting of the excitatory/inhibitory (or “selective disinhibition”) mechanisms regulating the potency of the synaptic contacts within the sensory hand territory might take place allocating the dedicated networks within the previous dimensions and exploring even more toward the periphery of the hand representations. The final return of the ED toward baseline values might rely on the optimization of the unified representation through a use-dependent selection of the functionally more useful neuronal aggregates. Alternatively, it might be due to a confinement from the underlying local anatomy (Calford 2002; Sur and Rubenstein 2005) or a homeostatic response to reverse and stabilize the previous enlargement or shift of the corresponding areas (Turrigiano and Nelson 2000).
The reported changes in the ED that we record cannot be ascribed to alterations from the electric stimulation, as its level was kept constant. Moreover, the amount of sensory input, monitored via nerve recordings, remained stable (Fig. 1). Changes in dipole location due to altered positioning of the subject's head inside the MEG helmet during the consecutive recordings can also be excluded; both fingers were stimulated within the same block, thus the ECD locations for the 2 fingers would have been similarly affected, leaving their distance unaltered. Furthermore, localization of head position was checked at the beginning and conclusion of each block ensuring the stability of the head position throughout the recording. The increase in the ED could not be attributed to the repeated electric stimulations: if this were the reason, the increase should have continued during the rest of the blocks and this was not the case. More important, no ED increase was detected when the 2 subjects were studied again without finger webbing (Fig. 8).
Previous works have declared that attention can modify the representation of the fingers (Braun et al. 2000, 2002; Iguchi et al. 2005). However in these studies, an active task was involved in parallel with the stimulation, and attention is supposed to play an important role when an active task is involved rather than a passive one (Nicolelis 2005). In our case, the fact that all our subjects have shown a similar result without any of them to be asked to follow a particular attentional task, lead us to think that the observed changes of the ED are not dependent on conscious attentional effects. In addition, the initial deflections of the somatosensory response are not affected by attentional effects (Mima et al. 1998; Eimer and Foster 2003). Finally, the intensity of electric stimulation plays a role in segregating the representation of fingers because an attention effect has been found only for stimulation at low intensity; but this effect was not detected for stronger well perceivable stimuli as those employed by us (Iguchi et al. 2002).
Finally, behavioral alterations potentially induced by the webbing should be considered in interpreting the precise factors that induce time-dependent somatosensory plasticity. One factor concerns passive stimulation of the digits directly related to the webbing procedure. However, this factor remains constant throughout the experiment. A second factor concerns the coactivation of D2–D5 skin surfaces due to “active” cojoint movements of the digits. Without a certain task for the subjects to perform during the intersession intervals, this factor may vary from intersession to intersession and possibly could contribute to the nonmonotonic behavior of the ED. However, we tend to believe that because the ED trend is similar among all subjects, that it is not likely to be ascribable to any specific intersession behavioral (i.e., motor) activity.
The aim of our study was to examine the time course of cortical reorganization in SI that occurs in the first hours after reversible finger webbing. The results indicate that the mechanisms that underlie plastic changes in SI are extremely rapid while adapting to an altered sensory input, and the brain's response is not found to be following a simple linear trend. Characteristic times have been extracted. Understanding how the human brain is functioning after an altered condition in the short-time frame could help to “taylor” rehabilitation strategies for a better efficacy. Long-term experience or perception is not necessary to induce cortical reorganization but only to establish it.
On the basis of the present demonstration of dynamic modifications of the sensory hand extension at the cortical level, future studies utilizing psychophysical measurements should aim to demonstrate the eventual impact of such modifications on sensory function. Moreover, it would be interesting to study the effect of this sensory manipulation for time epochs longer than in the current experiment in order to investigate whether the trend we see is a part of the processing dynamics or of some circadian rhythms connected with sensory manipulation.
MLS was supported by a Marie Curie Fellowship (FP5) of the European Commission under contract number HPMT-GH-01-00254-02 (“Functional Imaging of the Human Body”) and in part by a “Karatheodoris” Research Grant (number 2459), Research Committee of University of Patras, Greece. RF was supported by a grant from the Italian Ministry of Research to the Center of Excellence on Aging of University of Chieti. The authors would like to thank Fabio D' Avorgna (ATB, Italy) for technical assistance, Massimo Caulo, Davide S. Rossi, Hugues Fontenelle and Laura Cimponeriu; Nicoletta Savini, Stefano Sensi, Ada Mitsacos, Roni Angelatou, and Costa Papatheodoropoulos for helpful insights on synaptic mechanisms; Cosimo Del Gratta and Maurizio Corbetta for critical review of the above work. Conflict of Interest: None declared.