Brain Struct Funct (2016) 221:421–431
DOI 10.1007/s00429-014-0915-5
ORIGINAL ARTICLE
Adaptation in human somatosensory cortex as a model of sensory
memory construction: a study using high-density EEG
Claire Bradley • Niamh Joyce • Luis Garcia-Larrea
Received: 26 June 2014 / Accepted: 14 October 2014 / Published online: 29 October 2014
Ó Springer-Verlag Berlin Heidelberg 2014
Abstract Adaptation in sensory cortices has been seen as
a mechanism allowing the creation of transient memory
representations. Here we tested the adapting properties of
early responses in human somatosensory areas SI and SII
by analysing somatosensory-evoked potentials over the
very first repetitions of a stimulus. SI and SII generators
were identified by well-defined scalp potentials and source
localisation from high-density 128-channel EEG. Earliest
responses (*20 ms) from area 3b in the depth of the postcentral gyrus did not show significant adaptation to stimuli
repeated at 300 ms intervals. In contrast, responses around
45 ms from the crown of the gyrus (areas 1 and 2) rapidly
lessened to a plateau and abated at the 20th stimulation,
and activities from SII in the parietal operculum at *100
ms displayed strong adaptation with a steady amplitude
decrease from the first repetition. Although responses in
both SI (1–2) and SII areas showed adapting properties and
hence sensory memory capacities, evidence of sensory
mismatch detection has been demonstrated only for
responses reflecting SII activation. This may index the
passage from an early form of sensory storage in SI to more
operational memory codes in SII, allowing the prediction
of forthcoming input and the triggering of a specific signal
when such input differs from the previous sequence. This is
consistent with a model whereby the length of temporal
receptive windows increases with progression in the
C. Bradley (&) N. Joyce L. Garcia-Larrea
NEUROPAIN team, Centre de Recherche en Neurosciences de
Lyon, CNRS UMR5292, INSERM U1028, Université Claude
Bernard Lyon 1, Lyon, France
e-mail: [email protected]
N. Joyce
Faculty of Life Sciences, The University of Manchester,
Manchester, UK
cortical hierarchy, in parallel with the complexity and
abstraction of neural representations.
Keywords Sensory memory Cortical hierarchy Somatosensory evoked potentials Adaptation Repetition Mismatch negativity
Introduction
Decrease in activity to repeating stimuli is a ubiquitous
feature of the nervous system that occurs over a range of
time-scales and structures, from the refractory period of
axons to the habituation of BOLD responses over an
experimental session. When applied to cortical sensory
systems this phenomenon has been most commonly
labelled ‘‘adaptation’’, ‘‘habituation’’ or ‘‘repetition suppression’’ (for reviews, see Grill-Spector et al. 2006;
McLaughlin and Kelly 1993; Näätänen and Picton 1987;
Thompson 2009). This property seems to be intimately
linked to the hierarchical organisation of the brain, inasmuch as adaptation in cortical sensory systems increases
with hierarchical order. On functional grounds, adaptation
reflects a change in the processing of a repeated stimulus
and as such has been considered a sign of memory at its
most basic level (Jääskeläinen et al. 2011). Indeed, sensory
cortical networks are thought to retain information about
recent stimuli for a short time (‘iconic’, ‘echoic’ or ‘haptic’
memories, e.g., Coltheart 1980; Lu et al. 1992; Maule et al.
2013). The decreased response to new stimuli with identical sensory features indicates that such memory traces do
influence the processing of subsequent information (Pasternak and Greenlee 2005). Adaptation to a repeated
stimulus can therefore indirectly tell us about the organisation of the brain, both on a structural (hierarchy) and on a
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functional (memory) level. Here, we aim to apply this tool
to the organisation of the somatosensory system in humans.
Evidence accumulated in non-human primates shows
that a sequential flow of somatosensory information initially takes place from thalamus to SI and then to SII (Pons
et al. 1992) but also within SI subareas (Brodmann’s areas
3a, 3b, 1 and 2) (Garraghty et al. 1990). In humans, scalp
and intracranial recordings have supported such hierarchical processing by showing that cutaneous non-noxious
stimulation triggers activity first in SI and then in SII, after
a delay of tens of milliseconds (e.g., Allison et al. 1989a, b,
1991, 1992; Hari et al. 1984, 1993; Inui et al. 2004;
Mauguière et al. 1997; Thees et al. 2003; although see
Karhu and Tesche 1999). At a finer scale, serial processing
from area 3b to areas 1–2 (i.e., within SI) is also suggested
by the quick succession of somatosensory evoked potentials (SEPs) and fields (SEFs). Responses generated by
these brain areas after peripheral electrical stimuli in upper
limbs proceed from a first cortical complex N20-P25,
generated in area 3b in the posterior bank of the central
sulcus, to middle-latency potentials such as P45 in the
crown of the sulcus where area 1 lies (e.g., Allison et al.
1991, 1992; Inui et al. 2004; Papadelis et al. 2011), and to
SII-generated responses, with onset around 60 ms and
culmination at 100–120 ms, commonly labelled ‘‘N120’’.
This early bottom-up hierarchy has its counter-part in
functional top-down modulations. For instance, voluntary
attention to the stimulated hand increases the amplitude of
somatosensory responses from SII (Desmedt and Robertson 1977; Garcia-Larrea et al. 1995; Schubert et al. 2008),
and also, to a lesser extent, from area SI-1/2 (Josiassen
et al. 1982; Garcia-Larrea et al. 1991), while leaving area
3b’s earliest responses generally unmodulated (Desmedt
et al. 1983; Garcia-Larrea et al. 1991).
Although a number of studies have investigated
somatosensory adaptation through SEP recovery curves,
the vast majority of such studies measured the mean
adaptation to long trains of stimuli averaged over hundreds
of trials (e.g., Garcia-Larrea et al. 1992; Klingner et al.
2011; McLaughlin et al. 1993; Nagamine et al. 1998;
Tomberg et al. 1989; Wikström et al. 1996). While providing valuable information, this approach ignores the
dynamic properties of the system, which can only be
assessed by investigating changes over the first repetitions
of a stimulus. Some recent SEP/SEF studies have indeed
followed this approach; in doing so, however, they have
either used pairs of stimuli, which provide limited information about the dynamics of adaptation (Höffken et al.
2013; Huttunen et al. 2008; Lenz et al. 2012; Weiland et al.
2008) or employed very fast repetition rates (up to 100 Hz)
where response overlap prevents full assessment of midand long-latency responses (Hoshiyama and Kakigi 2003;
Romani et al. 1995; Lenz et al. 2012), or still compared
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responses between SI and SII, but not within SI (Kekoni
et al. 1992). There has not been—to the best of our
knowledge—a comprehensive comparison of adaptation to
a short series of repeated stimuli both within SI sub-areas
and between SI and SII regions. Taking into account the
intimate relationship between adaptation and memoryencoding properties, obtaining such information in humans
appears of importance to understand the hierarchy and
dynamics of somatosensory memory processes. Here we
addressed this question by studying SEPs evoked separately by the first, second, third and twentieth presentation
of a recurrent somatosensory stimulus delivered to the hand
at 300–500 ms intervals. This stimulation rate, combined
with source modelling of high-density EEG recordings,
allowed us to compare responses both within SI (areas 3b
and 1) and between SI and SII.
Materials and methods
Participants
Twenty-four participants took part in the experiment (13
women and 11 men, aged 35 ± 14 years). None had any
history of neurological or psychiatric diseases. All gave
written informed consent and were remunerated for their
participation. The study was approved by the local ethics
committee (CPP Léon Bérard-Lyon, 2008-A01437-48).
Stimuli and experimental design
Participants were comfortably seated in a quiet, semi-darkened room. They were instructed to shut their eyes and relax
while remaining awake. No other explicit task was given
to the subjects. Sensory thresholds to electrical stimuli were
estimated in each subject by the method of limits. Then,
stimulation intensity was set to elicit a clear, non-painful
sensation at threefold the individual sensory threshold
(mean ± SEM 7.9 ± 0.4 mA) (Cruccu et al. 2008). Electrical stimuli were square wave pulses of 0.2 ms, generated
by an IRES-600 isolated, constant-current stimulator
(MicromedÒ). They were delivered, cathode proximal, to the
participant’s left hand (in equal proportion to the ulnar nerve
and the thumb). Trains of 200 stimuli were delivered at a
rate of 2 and 3 Hz1 (28 and 72 % of data, respectively),
each train separated from the next by at least 6 s. Only the
cortical responses to the 1st, 2nd, 3rd and 20th stimuli in a
train are reported here, analyzed at the single trial level.
1
Initially, stimulation rate was 2 Hz; it was increased to 3 Hz during
the course of the study for time reasons. Therefore, participants
received one of the two stimulation rates and never both. In our data,
there was no significant or systematic difference in responses
recorded using these two rates; the data were therefore pooled.
Brain Struct Funct (2016) 221:421–431
Somatosensory-evoked potentials recording
Somatosensory evoked potentials (SEPs) were recorded
using a 128-channel EEG cap (Waveguard Cap, ANT). All
electrodes were referred to the left mastoid (M1), ipsilateral to the stimulated hand. A ground electrode was
incorporated in the cap between AFz and Fz, on the nasioninion line. Electrode impedances were kept under 5 kX
using a conductive gel (ElectroCap). The signal was
amplified, digitized and filtered using a sampling rate of
2,048 Hz and a 0.263–1,024 Hz band-pass filter (ASA
software and amplifier, ANT).
Somatosensory-evoked potentials analysis
The analysis was performed using BrainVision Analyzer
(BrainProducts). Continuous, raw 128-channel EEG
recordings were filtered off-line to remove mains power
contamination (50 Hz notch filter) and direct current drift
artefacts (low-pass Butterworth filter: 0.3 Hz, 12 dB/
octave). The recordings were then segmented in 250 mslong epochs (including 50 ms pre-stimulus interval). These
series of epochs had two separate fates:
Determination of regions of interest and optimal timewindows on the grand-average (Fig. 1)
This first step aimed at constructing a grand-average SEP
across all epochs and subjects. This served to identify
optimal scalp topographies and latency windows for three
SEP components of interest, known to reflect respectively
activation of parietal area 3b (N20), of areas 1–2 in the
crown of the post-central gyrus (P45) and of SII in the
parietal operculum (N120) (Allison et al. 1991, 1992; Hari
et al. 1993; Mauguière et al. 1997). To this aim, an optimal
‘SEP template’ was computed by grand-averaging all
epochs across all subjects, after semi-automatic artefact
rejection (discarding segments with activity exceeding ± 200 lV) and baseline correction using pre-stimulus
interval (Fig. 1a). The components of interest were identified on the basis of their latency, polarity and topography
(Mauguière et al. 2004; Cruccu et al. 2008 for N20 and
P45, Garcia-Larrea et al. 1995 for N120). The four electrodes displaying the highest activity for each component
were selected and their signals averaged to create regions
of interest (ROI) (CP6, CPP6 h, P4, P6 for N20; C4,
CCP4h, CCP6h, CP4 for P45; and C6, FTT8h, T8, TTP8h
for N120; Fig. 1b). The time-window for amplitude measurements was defined as the time interval including each
component peak and during which the signal exceeded
60 % of its maximal peak amplitude. It was 19–23 ms for
N20, 41–52 ms for P45, and 115–148 ms for N120
(Fig. 1c). After a time frequency analysis allowing the
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determination of the response frequency contents, an
adapted band-pass filter of 5–100 Hz for N20 and P45, and
1–30 Hz for N120 was applied. These settings were used in
the second step of analysis.
Analysis of SEP responses to the 1st, 2nd, 3rd and 20th
stimulus of a train
This second step focused on SEP responses to the 1st, 2nd,
3rd and 20th stimulus in each train. If an artefact (i.e., a
signal exceeding ±100 lV) was present in response to any
of the 1st, 2nd, 3rd or 20th stimuli, the whole train was
rejected. These strict artefact rejection criteria led to
rejection of so many trials in six subjects that they finally
contributed too few trains and had to be excluded from
further analysis. The 18 remaining subjects contributed on
average 11 ± 1 trains (mean ± SEM). Overall, 206 trains
out of 431 could be retained. In each single trial, the mean
amplitude of N20, P45 and N120 was measured across
their respective time-window and in their respective
regions of interest, as defined in the grand-average (see
above). This allowed investigating changes in component
amplitude with repetition. Statistical analysis was performed using R and GraphPad Prism 6. A two-way repeated-measures ANOVA was used to examine the effects of
the two main factors: ‘‘component’’ (N20, P45, N120) and
‘‘stimulus order’’ (1st, 2nd, 3rd and 20th stimulus in the
trains) on mean amplitude of SEPs in single trials. Maulchy’s test was used to examine sphericity of the data and
Huynh–Feldt epsilon correction was applied to the rmANOVA when the sphericity assumption was violated. In
the latter case, the epsilon was reported together with the
corrected p value and original degrees of freedom. Significant effects of the rm-ANOVA were investigated by
post hoc Tukey’s multiple comparisons tests. Statistical
significance level was set to p \ 0.05 after correction.
Source reconstruction of SEPs
Source reconstruction of the grand-average SEP was done
using dipolar modelling in BESAÒ (BESA GmbH, Germany). To estimate the location and orientation of intracranial sources best explaining the potentials recorded at
the scalp surface, BESA iteratively correlates the scalp
potential distributions generated by theoretical dipoles
within the brain with the actual scalp distribution obtained
experimentally. It outputs an estimate of source localisation—the model—which then has to be judged according
to the amount of data it explains (goodness of fit, or GoF,
which is the explained variance of the data) but also to
current anatomic and functional knowledge. We used nonmoving equivalent current dipoles in 4-layer ellipsoidal
head model, with standard conductance values in BESA
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Brain Struct Funct (2016) 221:421–431
Fig. 1 a Superimposed waveforms from the 128 recording electrodes
of grand-averaged SEPs. Dashed black lines identify the peaks of
activity corresponding to the N20, P45 and N120 components. b The
four most active electrodes for each of these peaks were selected
(black dots on 3D activity maps, see ‘‘Materials and methods’’). c The
activity of these groups of electrodes was pooled (bold waveforms);
the shaded time-window was selected for later amplitude measurements (see ‘‘Materials and methods’’)
Research 6.0. Dipoles were allowed to change position and
orientation freely, except for one dipole located in the
brainstem whose location was fixed; dipoles previously
fitted were subsequently left active. They were fitted to
major global field power peaks corresponding to the
components of interest (GFP being a measure of spatial
variance) and were kept if they explained more than 80 %
of the variance of their fit interval. Dipoles were added one
at a time until the overall residual variance was of less than
10 %, indicating that at least 90 % of the data variance was
accounted for by the proposed model. The solutions were
overlaid on the standard average anatomical MRI (magnetic resonance image) in BESA.
dipolar sources spatially distributed but partially overlapping in time (Fig. 2a). The model achieved a goodness of
fit of 91 %, leaving less than 9 % of the data variance
unexplained. Over a post-stimulus interval (i.e., at the time
of the physiological responses), it explained 94 % of data
variance (Fig. 2c). A noise dipole captured the variance of
the stimulation artefact and was inactive thereafter
(Fig. 2c). Another dipole, whose location was fixed in the
brainstem, displayed a peak of activity at 16 ms, followed
by an inversion of polarity at 23 ms. The three remaining
dipolar sources were cortical and located in the parietal
cortex, contralateral to the stimulation (Fig. 2b). One was
located in anterior parietal cortex, tangential to the scalp;
its first peak of activity was a sharp deflection with onset
and culmination, respectively, at 18 and 21 ms (Fig. 2c).
As it was consistent with a generator in area 3b of SI, it was
labelled ‘‘SI-3b’’. Another dipole, more radial and superficial, was located slightly anterior to SI-3b. Its activity
displayed a first peak at 26 ms followed by a more
important one at 46 ms. This source lumped together
neural activities in the crown of the gyrus (areas 1 and 2),
Results
Source modelling of somatosensory evoked-potentials
Grand-average brain activity in the first 250 ms following
somatosensory stimulation could be modelled using five
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Fig. 2 Source reconstruction of grand-average SEPs. a Five dipoles
were required to reach a goodness of fit (GoF) of 91 %. The arte
dipole accounted for the stimulus artefact. The earliest activity was
located in the brainstem (bs), while the two following sources were
located in parietal cortex (SI-3b and SI-1/2). Finally the latest source
was located in the parietal operculum (SII). b Overlay of the early
parietal dipoles (upper row) and opercular dipole (lower row) on
sagittal, coronal and axial slices of a standard structural MRI. c Timecourses of the dipole moments. The shaded areas represent the timewindows of the three scalp components. Source activity is expressed
in nA m. Goodness of fit (GoF, in black) and global field power (GFP,
in grey) are displayed at the very bottom. Please note the two different
non-linear scales, expressed as % of maximum value during the
interval
and was accordingly labelled ‘‘SI-1/2’’. The last dipole was
mostly radial in orientation, clearly located in opercular
cortex and displayed a slow rising waveform starting at
70 ms and culminating at *130 ms; it was therefore
labelled ‘‘SII’’. Importantly, at the respective times when
the components of interest (N20, P45 and N120) were
measured on the scalp, their cortical generators were active
one at a time (Fig. 2c, shaded areas). These findings suggest that the evoked activities N20, P45 and N120 are a
reasonable approximation for somatosensory responses in
areas SI(3b), SI(1–2) and SII, respectively.
order’, [F(6, 1230) = 4.24, p \ 0.001, eHF = 0.96], indicating that stimulus repetition affected the component
amplitude differently depending on the components
(Fig. 3).
Post-hoc tests comparing component amplitudes at
different stimulus positions within a train showed no
significant differences for N20 (SI-3b) as shown in Fig. 4.
For P45 (SI-1/2), a non-significant amplitude drop
between the 1st and 3rd stimulus was followed by a higher
amplitude decline of the responses to the 20th stimulus
(uncorrected p = 0.042; adjusted p = 0.058). In contrast,
for the SII response (N120 component) mean amplitude
was significantly higher for the 1st stimulus than for both
the 3rd (p \ 0.05) and 20th stimuli (p \ 0.0001), while
other comparisons did not yield significant differences.
Comparison of 2nd stimulus amplitude to the others was
never significant. In summary, across repetitions, SI-3b
response amplitude remained constant; SI-1/2 amplitudes
decreased near-significantly for the 20th response only,
Somatosensory evoked-potentials to the 1st, 2nd, 3rd
and 20th stimulus of a train
An ANOVA on N20, P45 and N120 response amplitude
demonstrated a significant main effect of ‘component’
[F(2, 410) = 103.1, p \ 0.0001, eHF = 0.98] as well as a
significant interaction between ‘component’ and ‘stimulus
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Brain Struct Funct (2016) 221:421–431
Fig. 4 Mean amplitude change of SEP components across stimulus
repetition. Mean amplitude was measured across specific timewindows for each component in specific regions of interest (see
‘‘Materials and methods’’). Absolute values in lV, error bars denote
standard error of the mean, * denotes p \ 0.05, *** denotes
p \ 0.001, N = 206 trains
Fig. 3 Effect of stimulus repetition on SEP waveforms. Activity in
the N20, P45 and N120 regions of interest (see ‘‘Materials and
methods’’) for the first stimulus of a train (black), the second (red),
the third (blue) and the twentieth (green); average waveforms
computed from 206 trains. The grey area represents the time-window
in which the mean amplitude of the component was measured
while SII responses were progressively and significantly
reduced.
Discussion
Rather than averaging several hundreds of responses to
estimate response decrement, as it is common in ERP literature, the present experiments concentrated on the very
initial stages of stimulus sequences. This, along with a
moderate repetition rate which prevented component
overlap, allowed the simultaneous study of three somatosensory areas in SI and SII showing differential dynamics
to stimulus repetition. Initial activity from SI-3b in the
depth of the post-central gyrus sustained repetition at 3 Hz
without decrement.2 Responses from the crown of the
2
Amplitude of initial SI-3b responses is known to decline with
repetition rates around 10 Hz and higher (e.g. McLaughlin et al 1993
for a review).
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gyrus at 45 ms (Brodmann areas 1–2) became depressed
(p = 0.042–0.058) at the 20th stimulation only, while
responses from the second somatosensory region around
100 ms (SII) displayed a steady and significant decrease in
amplitude with successive repetitions (Fig. 4). The stronger
adaptation to repetitive input in SII relative to SI initial
responses, as well as the more subtle differences between
SI areas 1–2 and 3b, appear to reflect disparities in the way
somatosensory areas extract and maintain information.
Adaptation to repetitive stimuli and sensory memory
in SII
Response adaptation implies retaining information from a
preceding input, and in that sense sensory adaptation can
be seen as the index of a memory trace (Jääskeläinen
et al. 2011); hence, increasing adapting properties in SII
relative to SI should also parallel increasing sensory
memory capacities in this area. This is consistent with
single-unit recordings in macaque monkeys during a
comparison task of consecutive flutter stimuli. While
neurons in area SI-3b were easily driven by the flutter
with high spike-train periodicity, units in SII were seldom
entrained by the repetitive stimuli (Salinas et al. 2000;
Hernández et al. 2000), and instead showed evidence of
memory encoding in form of activity persisting beyond
the end of the stimulus train (Hernández et al. 2002).
Such prolongation of activity into the period separating
two consecutive stimuli was interpreted as reflecting interstimulus comparison processes active in SII neurons
(Romo et al. 2002a, b). In our experiments with human
subjects, the responses recorded from SII also displayed
stronger adaptation properties than those recorded from
SI, thus lending substance to their greater role in sensory
Brain Struct Funct (2016) 221:421–431
memory processes. Objective indices of sensory memory
traces in human cortical responses are indeed associated
with activations in second sensory areas. For instance, any
discernible change in a repetitive sensory stream is
reflected by an EEG response called ‘‘mismatch negativity’’ or MMN (Näätänen and Picton 1987; Näätänen and
Alho 1995; Kujala and Näätänen 2010; Kimura et al.
2010), whose neural generators have been identified to
comprise second sensory areas (Csépe et al. 1987; Maess
et al. 2007). The human somatosensory MMN develops in
the same latency window as the SII responses recorded in
this study (Kekoni et al. 1997; Akatsuka et al. 2007) and
has a similar topography to the SII N120 potential (Hu
et al. 2013; their Figs. 2, 3). In keeping with these results,
focal lesions in second sensory areas impair both sensory
memory processes and the MMN (Alain et al. 1998;
Kojima et al. 2014), and virtual lesions induced by TMS
in SII modulate grasping actions that rely on haptic sensory memory (Maule et al. 2013).
Enhanced adaptation in a network is defined as an
increase in its time constant (i.e., the time needed for
network activity to decay to a level that allows responding
again to an identical stimulus). Although the persistent
nature of network responses is at odds with the shorter time
constants of synaptic currents in single neurons (Goldman
2009), mathematical models suggest that both recurrent
feedback (Seung 1996; Seung et al. 2000) and feed-forward
spread (Goldman 2009) can increase the time constant of
networks well beyond that of their unit constituents, and
generate persistent neural activity between stimuli. A
common model of short-term memory posits that networks
tend to stay on specific patterns of activity (‘‘attractors’’)
which are self-sustained due to positive feedback loops and
feed-forward transmission (Seung et al. 2000; Brody et al.
2003; Goldman 2009). Increasing network size and connectivity is therefore linked to an increase of the network’s
time constant. Increasing size also appears to enhance the
network’s adaptation properties. Both phenomena might be
linked and converge into providing the network with
memory capacities.
Activity states driven by external stimuli are neural
representations of these stimuli; as such their persistence
becomes a memory trace. Longer time constants in sensory
networks could therefore be seen as a causal element for
memory traces to develop, rather than their simple corollary. Sensory memory may then be considered as an
intrinsic property of networks whose time constant
lengthens due to increasingly complex processing loops.
Since higher neural complexity in cortical networks also
tends to increase sensory adaptation, persistent activity and
sensory adaptation might be seen as two sides of the same
coin, which may be a general frame of the development of
memory systems (Jääskeläinen et al. 2011).
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Functionally distinct networks within SI: area 3b
versus areas 1–2
The adaptation properties of responses recorded in the
crown of the post-central gyrus occupied an intermediate
position between those from the depth of the gyrus (area
3b) and from SII. Thus, unlike the N20 potential, P45 (SI1/2) was unable to sustain 3 Hz repetition without some
amplitude drop, but its level of adaptation was smaller
than that of N120 (Fig. 4). Such intermediate properties
relative to 3b and SII have also been described in single
neuron data from different mammals. For instance, neurons in areas 1–2 of flying foxes habituate to repetitive
stimulation more rapidly than neurons in area 3b (Krubitzer and Calford 1992). Also in macaques, the periodicity of spike trains to repetitive stimuli diminishes from
area 3b to area 1, before vanishing in SII (Salinas et al.
2000). Although possible intermediate steps between the
responses analysed in SI areas 3b and 1–2 might exist (see
‘Limitations’ below), the changes in their adaptation
properties are consistent with a hierarchical progression
between these sub-areas, which is supported by multiple
anatomical and functional data in animal models. The
main thalamo-cortical projections from the ventrobasal
thalamus reach areas 3a and 3b (deep and cutaneous
inputs, respectively). Serial processing from these to other
SI areas is thought to have become dominant with
anthropoid primates, as neurons in areas 1–2 became
dependent on area 3b for activation (reviews Iwamura
1998, Kaas 2004). Direct thalamic input to area 1 does
exist (Mountcastle and Powell 1959), but is apparently
unable to activate this area in the absence of input from
area 3b. For instance, ablation of the hand representations
in area 3b immediately deactivated the corresponding
parts in area 1, whereas the reverse was not true (Garraghty et al. 1990). On behavioural grounds, selective
lesions of areas 1 or 2 impair high-order aspects of tactile
processing such as roughness, weight or angle discrimination, rather than simple touch detection (Randolf and
Semmes 1974, Kutoku et al. 2007), whereas lesions in 3b
with N20 abolition impair all types of lemniscal sensation
(Knecht et al. 1996). Receptive fields (RFs) in the crown
of the postcentral gyrus diversify and increase in size
(Hyvarinen and Poranen 1978, Iwamura et al. 1985), and
digit representations of human area 1 are larger than those
observed in area 3b (Overduin and Servos 2004). These
data suggest a progression in hierarchy within SI,
whereby the capacity to extract increasingly complex
stimulus features is paralleled by a decrease in spatial and
temporal resolution. Of notice, a similar progression has
been established within the primary auditory cortex of
cats (A1), where stimulus adaptation also shows a continuum of time constants (Ulanovsky et al. 2003, 2004).
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A hierarchical continuum of representations
in somatosensory cortices
Despite the adaptation properties of P45 showed here,
human experiments have failed so far to demonstrate significant changes of this potential during tasks manipulating
sensory memory. Some ‘memory cells’ have been reported
in monkey SI, which changed activity in the period
between two consecutive stimuli (Zhou and Fuster 1996).
Such units were, however, equally prevalent in all SI subareas (3a, 3b, 1 and 2) and their activity was seldom specific to the type of stimulus received. We may speculate
that a threshold in sensory adaptation exists, whereby only
if the network time constant is sufficiently long may shortterm memories be co-localized with sensory representations. In this view, SI networks would be operational in
memory processes only at later stages (e.g., Harris et al.
2002), when they receive re-entrant afferences.
In human experiments, a time-window of about
100–150 ms post-stimulus appears necessary and sufficient
to establish an operational memory trace, i.e., a trace
allowing the system to ‘‘decide’’ whether a sensory stimulus pertains or not to the same class as prior input
received. Such low-level memory systems can progress to
more abstract and longer-duration representations, as
illustrated by the fact that neurons from SII, pre-motor and
pre-frontal cortices display stimulus-related activity at
different times during a working memory task in primates
(Romo et al. 2002a, b). Rather than discrete memory systems, the progression of memory representations from
sensory to more abstract, longer-duration states may be
conceptualised as a continuum (Jääskeläinen et al. 2011).
As part of such ongoing processes, the role of cortical
short-term sensory memory is not just the static storage of
information about previous stimuli but rather a step in a
dynamic process that uses stimulus information to anticipate and prepare upcoming behavioural requirements
(Brody et al. 2003). The present data show that relatively
simple experiments can help to tag such progression using
non-invasive recordings in humans.
Brain Struct Funct (2016) 221:421–431
work. For instance, while there is little doubt that the N20
reflects the earliest activity of cortical area 3b, a longlasting debate exists about the generators of a subsequent
parieto-central positivity P22. Despite a multitude of
well-conducted studies using intracortical and subdural
recordings, dipole analysis and clinical lesions, it remains
unclear whether the P22 generators lie in the post-central
gyrus (SI 1/2, e.g., Allison et al. 1989a, 1991; Buchner
et al. 1996, Baumgärtner et al. 2010), the pre-central
gyrus (M1 area 4, e.g., Mauguière et al. 1983; Mauguière
and Desmedt 1991; Inui et al. 2004; Jung et al. 2003,
2008) or the depth of the central sulcus (SI 3a, e.g., Jones
and Power 1984; Valeriani et al. 1997). In view of these
uncertainties and small signal-to-noise ratio, we did not
analyse this response, and this prevents any firm assertion
about the hierarchical position of P45 in the sequence of
SI activities. Also, neural activities earlier than those in
this study have been ascribed to SII (Karhu and Tesche
1999; Barba et al. 2002; Inui et al. 2004). Reports are,
however, scarce and not entirely convergent, since in
other studies no SII activity could be detected culminating
before 120 ms (e.g., Mauguière et al. 1997; Hinkley et al.
2007; Onishi et al. 2013).
Evoked potentials recordings can be seen as a window
on brain activity, of which they cannot provide a full picture. This intrinsic limitation has bearings on our study: for
instance, neurons within a given area may respond differently to repetition, according to cell type, laminar position,
or other functional organisation criteria (Ulanovsky et al.
2004). This diversity cannot not be captured by scalp
recordings in humans. Although we acknowledge that
activities with little representation on the scalp may have
been overlooked in the present study, the main implication
of the results remains that sensory memory and temporal
adaptation capacities may be intricately related in
somatosensory networks; in this sense our work may be
viewed from the wider perspective of memory construction
in its most elementary steps. Further work applying similar
single-trial methodology to intracranially recorded SEPs
should help in the future to complete and refine the present
results.
Limitations of the study
This study was based on response to electrical stimuli that
may be seen as relatively ‘unnatural’, but which have
been successfully used to assess and detect even subtle
sensory abnormalities in clinical settings (Mauguière
et al. 2004). Emphasis was placed upon three sequential
steps of activity, whose origins in three key sub-regions of
the somatosensory cortex are well accepted. Other activities from the same cortical regions at different points in
time were not assessed, which constitutes a limit of this
123
Acknowledgments We are most grateful to Bérengère Houzé and
Caroline Perchet for help with data collection, to Michel Magnin for
insightful discussion on the manuscript and to two anonymous
reviewers for their important comments. This work was supported by
Grants from National Agency for Research (LABEX CORTEX,
ANR-11-LABX-0042), SFETD Translational prize, Rhône-Alpes
Region, APICIL Foundation and French Ministère de l’Enseignement
Supérieur et de la Recherche (ENS Cachan), as well as the LyonManchester Erasmus Program.
Conflict of interest
of interest.
The authors declare that they have no conflict
Brain Struct Funct (2016) 221:421–431
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