Introduction
Somatosensory impairment is highly associated with stroke severity (Connel et al.
2008; Meyer et al.
2016). More specifically, regaining individual finger function is considered a good predictor for recovery of upper limb function post stroke (Nijland et al.
2010). Whether this marks recovery of efferent or afferent connections from motor or somatosensory areas is a matter of dispute. In the current study, we focused on the latter and asked whether electric stimulation of the fingers may yield reliable responses in sensory areas as assessed by electro-encephalography (EEG). We tested for the candidate capacity of responses to finger stimulation as a clinical biomarker in general and more specifically for stroke recovery.
Somatosensory evoked potentials and fields (SEPs and SEFs, respectively) induced by electrical or mechanical stimulation on the median nerve is a well-established approach to investigate the electrophysiological phenomena linked to impaired somatosensation occurring, for example, while recovering from a stroke (Keren et al.
1993; Péréon et al.
1995; Timmerhuis et al.
1996; Rossini et al.
1998a,
2001; Wikström et al.
1999,
2000; Hari and Forss
1999; Feys et al.
2000; Tzvetanov and Rousseff
2003; Huang et al.
2004; Oliviero et al.
2004; Tecchio et al.
2007a,
b,
2006; Al-Rawi et al.
2009). Early components of median nerve SEPs may indicate whether afferent connections arrive at the contralateral primary somatosensory cortex (S1). By stimulating directly at the median nerve, however, both cutaneous muscle and joint afferents are stimulated and potentially efferent fibers intervening (muscles) (Dawson
1956; Mauguiere
1999; Kuiken et al.
2007). Dependent on the intensity of the stimulus, finger stimulation will excite primarily Αβ fibers (Dowman
1997), followed by Aδ, followed by C fibers (McAllister et al.
1995; Kandel et al.
2000) while median nerve stimulation includes additionally sensory and motor fibers of larger diameter and partially the ulnar nerve. In view of our interest on hand representation, we hence focused on activity induced by stimulation of the digits, as assuming this to elicit responses at a more specified area at the somatosensory cortex.
Somatotopic arrangement and discrete representation of the fingers in the human cortex is well studied in the literature; Penfield and Boldrey (
1937) already showed a systematic arrangement of representation of the human fingers on the cortex using intraoperative electrocorticography (ECoG), which was later confirmed by Penfield and Rasmussen (Penfield and Rasmussen
1950). Studies using local field potential recordings in animals revealed the refined spatial representation differentiating the input from different fingers (Kaas
1983), in particular in area 3b of SI. Over the last decade or so, high-resolution fMRI studies confirmed the somatotopic arrangement in area BA 3b reporting inter-digit distances that varied from 3.7 to 15.5 mm (van Westen et al.
2004; Martuzzi et al.
2014; Pfannmöller et al.
2015). M/EEG studies concentrated mainly on the representation of 1st and 5th digit. Using EEG, Baumgartner et al. (
1993) revealed a distance of 12.5 mm between representations of thumb and little finger. Buchner et al. (
1994) reported a somatotopic arrangement for two of three subjects tested. Barbati et al. (
2006) found statistically significantly different representations for 1st and 5th finger with MEG that Houzé et al. (
2011) confirmed with EEG, and the differences between ulnar and median nerve representation were found more significant. In the MEG studies of Rossini et al. (
2001) and Rossini et al. (
1998a) discrimination of the 1st and 5th digit was shown possible both for healthy controls and stroke patients, where enlargement of the hand area occurred.
Although the somatotopy of evoked responses has been addressed in various studies, it is still unclear how reliable and reproducible those responses are within and across subjects, both at sensor and at source level. In particular, it is unknown whether somatotopy of all the fingers can be demonstrated with EEG in individual subjects and in a reproducible way. The heterogeneity in stimulus protocols and the lack of datasets where all fingers are stimulated render the findings described in the literature difficult to judge. Using a pneumatic stimulation protocol, Schaefer et al. (
2002) sketched test-rest reliability and reported a mean Euclidean distance of 7.42 mm between sources activations revealed by EEG measurements separated 1 month in time. However, this study did not address the possibility to discriminate non-overlapping representation of all the fingers in the somatosensory cortex with the use of EEG. We consider such a discrimination crucial when interested in using finger SEPs as potential biomarker.
In the present study, we assessed the test–retest variability of the responses. We also tested for the number of trials needed to obtain robust topographies and examined the possibility of discriminating different fingers at the cortex. Ultimate goal was to examine the possibility of using EEG and SEP on the finger as a subject-specific biomarker.
Discussion
In the present study, we evaluated the use of somatosensory evoked potentials induced by finger stimulation, as a potential biomarker tool for post-stroke recovery. We were especially interested in testing the robustness of such a biomarker as a lack of consistency would make it impossible to track longitudinal changes at a subject-specific level. The large overlap of the dipole representations for different fingers and the relatively low reproducibility of the test–retest design indicate the difficulty of separating the representations of different fingers as determined by EEG and electrical finger stimulation. Moreover, the somatotopic representation of the fingers could only be confirmed in one of the five subjects, whereas for the others it was not so consistent. We emphasize that these results were found in the ‘ideal’ condition in which an optimal stimulation protocol was used and reproducibility measure was not compromised by detaching and re-attaching the EEG cap.
Parts of these admittedly discouraging results are consistent with other studies. We found one or two peaks from 25 to 65 ms (Baumgartner et al.
1993; Buchner et al.
1994; Hari and Forss
1999; Wang et al.
2004; Schubert et al.
2008; Houzé et al.
2011; Bourguignon et al.
2013; Nierula et al.
2013) and a later peak around 100 ms (Hämäläinen et al.
1990). Note that the early peaks are often referred to as N20 and P27 in the literature in accordance with peaks found after median nerve stimulation, the latencies found after finger stimulation do not agree with 20 or 27 ms (Wang et al.
2004). In order to assess the test–retest repeatability of the responses and the number of trials needed for reproducible responses, we selected a window from 25 to 65 ms, since only a few studies referred to the later peaks. The early peak appears to correspond to activity in S1 (Forss et al.
1994). Even at the convenient setup of not removing the EEG cap, correlation coefficients were at mean (± SD) of 0.8 ± 0.16. The fairly low reproducibility was not affected by habituation effects in the test re-test design because the correlation coefficients of the random subset of trials are in the same range. Seeking to eliminate all parameters that may lead to irreproducible responses, we estimated the SNR with a similar way as Darvas et al. (
2005) and the minimum number of trials needed in order to have repeatable spatiotemporal patterns. With an SNR of 2.9 [2.8 found in a MEG study, (Darvas et al.
2005)] we believe that our pre-processed signal was accurate. We found a minimum number of 230 trials to be needed for reproducing topographies that can be achieved with the total number of 780 trials. An indication about the variations at the brain responses is given by Darvas et al. (
2005). We also observed a higher correlation coefficient for the middle finger and a lower correlation coefficient for the pinky, in accordance with small amplitudes, poor SNR and difficulty in source localization for the fifth digit reported in other studies (Baumgartner et al.
1993; Buchner et al.
1994; Houzé et al.
2011).
Although many studies applied electrical stimulation on the fingers to evoke SEPs or SEFs, as of yet there is no consensus on stimulus characteristics, nor explanation on the design of the chosen experimental protocol. To the best of our knowledge all studies used a monophasic anodic rectangular pulse of various pulse widths and intensities. MEG studies used a pulse width of 0.2 ms and an intensity of two times the sensory threshold or a pulse width of 1 ms and an intensity below the pain threshold (Kristeva-feige et al.
1995; Xiang et al.
1997; Darvas et al.
2005). Stimulation protocols among EEG studies were even more inconsistent. The width of electrical pulse found was 0.2, 0.3, 0.4 ms and the intensities varied as well as 1.5, 2, 3 times the sensory threshold, maximum comfortable level or below the pain threshold (Baumgartner et al.
1993; Buchner et al.
1994; Yao and Dewald
2005; Schubert et al.
2008; Houzé et al.
2011; Nierula et al.
2013). Inconsistency of stimulation protocols led us to test several stimulation parameters and their ability to produce reproducible responses (see
Appendix). A more detailed test protocol with smaller steps between the pulse widths will be helpful in revealing the effect of the pulse width to the responses. However, this is not the purpose of the present study.
With a high SNR, a number of trials adequate for resulting in reproducible responses and a stimulation protocol optimal for finger stimulation, we further tested the representation of the fingers at the sources level. Although there is evidence for discrimination of the activation related to different fingers and mainly of thumb and pinky using EEG (Baumgartner et al.
1993; Buchner et al.
1994; Houzé et al.
2011; Nierula et al.
2013) or MEG (Buchner et al.
1994; Rossini et al.
1998a,
2001; Barbati et al.
2006) it was still unclear if this discrete representation of the fingers is prone to trial to trial variations or subject-specific differences. Darvas et al. (
2005) addressed this topic with using MEG of one subject revealing the somatotopic arrangement of the fingers. However, they found for the S1 sources, a standard deviation of the 1000 locations of the bootstrapped dipoles between 3 and 5 mm revealing an overlap for some of the fingers representation. Our results show even higher standard deviation between 2 and 28 mm that may be explained by the poorer spatial resolution of EEG in comparison to MEG (Leahy et al.
1998) and the lower SNR of EEG concerning superficial sources (de Jongh et al.
2005; Goldenholz et al.
2009). As it can be seen in Table
2 we failed to pinpoint a pattern of somatotopic arrangement of the fingers for all the subjects, let alone a clear pattern of which fingers could be disentangled in the human cortex. The use of realistic head models in the dipole calculation might have resulted in more accurate dipole positions (Schaefer et al.
2002). In the current study, however, we were interested in relative locations of fingers in the brain and their reproducibility and not in exact 3D locations of every finger on the cortex.
EEG being an affordable and accessible technique along with single nerve recruitment by electrical stimulation of the finger can serve as a tool in the clinic, for example for monitoring stroke rehabilitation. However, the variability of the responses and the absence of a reproducible pattern of the finger somatotopy imply that the finger representation estimated with EEG is not a recommendable subject-specific monitoring tool for a longitudinal stroke study. MEG or fMRI along with electrical finger stimulation are modalities with higher spatial resolution (Rossini et al.
2001; Darvas et al.
2005), but patient’s ease and longitudinal monitoring will be at stake for patients having to travel to the hospital. If not only refined finger representation is of interest, median nerve stimulation could be used as an alternative. Electrical stimulation of the median nerve is a popular experimental choice when stroke assessment is of interest (Rossini et al.
1998a,
b,
2001; Forss et al.
1999; Wikström et al.
2000,
1999; Hari and Forss
1999; Tecchio et al.
2001,
2007a; Huang et al.
2004). It induces several peaks including an early peak around 20 ms and one around 50 ms. Due to the fact that the median nerve stimulation activates both the sensory and motor areas, responses with larger amplitudes are observed when compared to the ones after electrical stimulation of the finger and with more prominent peaks. However, median nerve stimulation on stroke patients also showed limitations. For example in (Tecchio et al.
2007b) median nerve stimulation responses were identifiable at the affected hemisphere only in 56% of the patients. Yet, even when responses are identifiable we should expect an accuracy of 10 mm as indicated by Bourguignon et al. (
2013) .A potential clinical use of the SEPs induced by finger stimulation could be the study of afferent pathways and brain areas recruited in S1 and S2 time locked to the appearance of early and late peaks.
Acknowledgements
The authors would like to thank Juhani Dabek (PhD) for his help in organizing the experiment and collecting the data. The authors will also like to thank Ines de Castro Fernandes (MSc) for her contribution at the bootstrapping algorithm.