ArticlePDF AvailableLiterature Review

Abstract

Use of non-invasive brain stimulation methods (NIBS) has become a common approach to study social processing in addition to behavioural, imaging and lesion studies. However, research using NIBS to investigate social processing faces challenges. Overcoming these is important to allow valid and reliable interpretation of findings in neurotypical cohorts, but also to allow us to tailor NIBS protocols to atypical groups with social difficulties. In this review, we consider the utility of brain stimulation as a technique to study and modulate social processing. We also discuss challenges that face researchers using NIBS to study social processing in neurotypical adults with a view to highlighting potential solutions. Finally, we discuss additional challenges that face researchers using NIBS to study and modulate social processing in atypical groups. These are important to consider given that NIBS protocols are rarely tailored to atypical groups before use. Instead, many rely on protocols designed for neurotypical adults despite differences in brain function that are likely to impact response to NIBS.
NIBS in social processing research
Non-invasive stimulation of the social brain: the methodological challenges
Tegan Pentona,b, Caroline Catmurc, Michael J. Banissya, Geoffrey Birdb,d & Vincent Walshe
a. Department of Psychology, Goldsmiths, University of London, London, SE14 6NW, UK
b. MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry,
Psychology and Neuroscience, Kings College London, Denmark Hill, London, SE5 8AF
c. Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King’s
College London, Denmark Hill, London, SE5 8AF
d. Department of Experimental Psychology, University of Oxford, Oxford, OX1 3PH
e. Institute of Cognitive Neuroscience, University College London, London WC1N
3AR, UK
Correspondence concerning this article should be addressed to:
Tegan Penton
Department of Psychology
Goldsmiths, University of London
London, UK
SE14 6NW
tegan.penton@gold.ac.uk
Funding:
This work was supported by a doctoral studentship from the Medical Research Council
[MR/M50175X/1 to T.P.]; the Leverhulme Trust [grant number PLP-2015-019 to C.C.]; the
Economic and Social Research Council [ES/R007527/1 to G.B. & M. J. B.] and the Baily
Thomas Charitable Trust [to GB].
Declarations of interest: none
NIBS in social processing research
Abstract
Use of non-invasive brain stimulation methods (NIBS) has become a common approach to
study social processing in addition to behavioural, imaging and lesion studies. However,
research using NIBS to investigate social processing faces challenges. Overcoming these is
important to allow valid and reliable interpretation of findings in neurotypical cohorts, but
also to allow us to tailor NIBS protocols to atypical groups with social difficulties. In this
review, we consider the utility of brain stimulation as a technique to study and modulate
social processing. We also discuss challenges that face researchers using NIBS to study social
processing in neurotypical adults with a view to highlighting potential solutions. Finally, we
discuss additional challenges that face researchers using NIBS to study and modulate social
processing in atypical groups. These are important to consider given that NIBS protocols are
rarely tailored to atypical groups before use. Instead, many rely on protocols designed for
neurotypical adults despite differences in brain function that are likely to impact response to
NIBS.
Keywords: Non-invasive brain stimulation, social perception, social cognition, State-
dependent TMS, Autism Spectrum Disorder
Word Count: 5999
NIBS in social processing research
Introduction
Non-invasive brain stimulation (NIBS) refers to a range of techniques including
transcranial magnetic stimulation (TMS), transcranial electric stimulation (tES) and focused
ultrasound stimulation (tFUS), used to modulate brain excitability. Use of NIBS has increased
significantly in recent years. This has enhanced our understanding of cognitive and perceptual
processes (Miniussi, Paulus & Rossini, 2012; Miniussi & Ruzzoli, 2013; Parkin, Ekhtiari &
Walsh, 2015; Taylor, 2018), and enabled a new stream of intervention research (Rossi,
Hallett, Rossini, Pascual-Leone, 2009; Miniussi & Vallar, 2011, Miniussi et al., 2012; Perera
et al., 2016). Whilst of clear utility, this increasing experimental and applied research focus
has been accompanied by questions regarding study design and generalisability of findings
(Parkin et al., 2015). In response, the field of brain stimulation has made efforts to strengthen
experimental design. For example, several recent articles provide guidance on how to conduct
well-controlled brain stimulation experiments (transcranial direct current stimulation [tDCS]
- Woods et al., 2016; Ferrucci, Cortese & Priori, 2015; TMS - Sandrini, Umiltà & Rusconi,
2011; TMS-Electroencephalography [TMS-EEG] - Ilmoniemi & Kičić, 2010; Miniussi &
Thut, 2010). Additionally, there is increasing interest in understanding null results in NIBS
studies and the mechanisms underlying NIBS effects (Thut et al., 2018; de Graaf & Sack,
2018). One area of research that has benefitted from the use of brain stimulation techniques is
social processing. Here, we review examples of the application of NIBS in this area of
research and outline several key contributions of NIBS research to our understanding of
social processing and its neural correlates; specifically, face processing, mirror responses and
self-other processing. Whilst this review is not exhaustive, it highlights the utility of NIBS
methods to study social processing.
Addressing more nuanced challenges facing social processing research using NIBS methods
is important to allow for reliable interpretation of findings in neurotypical cohorts. It also
allows us to tailor NIBS protocols to atypical groups with social difficulties. Therefore, we
highlight several methods and techniques that may help to support the use of NIBS in both
typical and atypical groups. Note, we assume that the reader has a working knowledge of
commonly used NIBS techniques, but there are several useful reviews for a more detailed
introduction (Walsh & Cowey, 2000; Walsh & Pascual-Leone, 2003; Wassermann et al.,
2008; Reed & Kadosh, 2018; Parkin et al., 2015).
NIBS in social processing research
How have NIBS studies contributed to understanding of social processing?
Facial Identity Processing
One domain where NIBS has been used to explore social perception is the study of
facial identity processing. Here, work has utilised both TMS and tES to explore this ability
(e.g. Barbieri, Negrini, Nitsche & Rivolta, 2016; Lafontaine, Théoret, Gosselin, & Lippé,
2013; Renzi et al., 2013; Romanska, Rezlescu, Susilo, Duchaine & Banissy, 2015). We
specifically highlight the work elucidating the role of the occipital face area (OFA) in facial
identity processing as a clear example of how using TMS can extend and support previous
findings in facial identity research. Whilst beyond the scope of the current review, we also
acknowledge the extensive body of work using NIBS to investigate processing of facial
expressions (see Pitcher, 2019; Atkinson & Adolphs, 2011 for reviews in this area).
Influential models of face processing suggest the OFA contributes to early visual
processing of faces (Haxby, Hoffman & Gobbini, 2000; Calder & Young, 2005), with further
processing relying on a distributed network of brain regions (Rossion, 2014). This model is
supported by a combination of fMRI, lesion, and animal work (Atkinson & Adolphs, 2011,
Rossion, 2014), but has been extended and tested through the use of NIBS methods (Pitcher,
2019; Pitcher, Walsh & Duchaine, 2011; Atkinson & Adolphs, 2011). Work by Pitcher, Walsh,
Yovel and Duchaine (2007) demonstrated the importance of the right occipital face area
(rOFA) in processing facial features (see Figure 1). Disruption of face discrimination abilities
was observed after stimulation to the rOFA when facial features were varied, but not when
the spacing between features was varied, suggesting a role for the rOFA in featural but not
holistic face processing (see also Pitcher, Charles, Devlin, Walsh & Duchaine, 2009;
Solomon-Harris, Mullin & Steeves, 2013). Furthermore, using double-pulse TMS (two single
pulses of TMS applied close together in time), the authors demonstrated the time course of
rOFA involvement. Specifically, rOFA TMS reduced face discrimination accuracy only when
delivered 60 and 100ms after stimulus onset. Ambrus, Windel, Burton and Kovács (2017a)
extended these findings using TMS to explore the role of the rOFA in recognising different
images of the same identity (see also Ambrus, Dotzer, Schweinberger, Kovács, 2017b).
Collectively, these findings validate and extend models of face processing implicating
the OFA in early face processing (Haxby et al., 2000; Calder &Young, 2005). The work
NIBS in social processing research
builds on fMRI studies by demonstrating a causal relationship between OFA activity and face
processing (Rossion, 2014). It also supports findings from lesion studies that disruption to the
OFA can impair face processing, whilst overcoming limitations of such studies (such as non-
localised lesions making it difficult to infer site-specific effects, or cortical reorganisation
following trauma limiting generalisability to a healthy brain). This work also builds on fMRI
and lesion studies by demonstrating the time course of OFA involvement in face processing.
Finally, the work demonstrates task, site and temporal specificity of brain stimulation effects.
It is clear therefore, that the use of NIBS has provided an important contribution to our
understanding of the role of the OFA in face processing.
[INSERT FIGURE 1 HERE]
Mirror Responses
In the action domain, mirror neurons fire both when performing an action, and when
observing another agent performing the same, or a similar, action (Gallese, Fadiga, Fogassi &
Rizzolatti, 1996). It has been suggested that this ability to map observed movements onto the
observer’s own motor representations may assist in understanding another’s actions
(Rizzolatti & Sinigaglia, 2010), although a re-analysis of available data suggests mirror
neurons instead respond to socially contingent actions (e.g., imitation; Cook, Bird, Catmur,
Press, & Heyes, 2014), with a potential role in action perception (Thompson, Bird & Catmur,
2019).
Research into mirror responses provides another example where NIBS studies have
complemented animal, imaging and lesion studies to further understanding of the neural basis
of social processing (Keysers, Paracampo & Gazzola, 2018). Research in non-human
primates identified mirror neurons in area F5 (homologue of ventral premotor cortex in
humans) and inferior parietal regions (Casile, 2013). In humans, fMRI revealed increased
activation in these regions during action observation and execution (Caspers, Zilles, Laird &
Eickhoff, 2010). In NIBS studies, mirror responses are indexed by measuring muscle
responses to single-pulse TMS delivered over the primary motor cortex (motor evoked
potentials; MEPs; see Figure 1). Changes in MEP amplitudes are thought to index motor
cortex excitability, with larger amplitudes indicative of greater excitability (Fadiga, Fogassi,
Pavesi, & Rizzolatti, 1995). Strafella and Paus (2000) demonstrated a muscle-specific
increase in excitability to observation of different actions, coupled with a muscle-specific
reduction in cortical inhibition and facilitation (indexed by reduced response to short
NIBS in social processing research
intracortical inhibition and intracortical facilitation, respectively). By demonstrating the
muscle-specific nature of mirror responses, these findings go beyond what had previously
been demonstrated using fMRI. Subsequently, extensive NIBS work perturbing different
brain regions has demonstrated the anatomical specificity and functional role of brain regions
involved in producing mirror responses (Keysers et al. 2018).
NIBS studies have also shed light on connectivity patterns between regions involved
in mirror responses, and their likely origin. For example, Catmur, Mars, Rushworth and
Heyes (2011) showed that connectivity between mirror response regions can be altered
through associative learning. Initially, a conditioning pulse applied to either the dorsal or
ventral premotor cortex facilitated MEP response from M1 representations of index and little
finger muscles after observation of index or little finger actions, respectively. After counter-
mirror training to alter learned associations between observed and executed actions (where
participants move their index finger in response to observed little finger movements and vice
versa; Catmur, Walsh & Heyes, 2007), mirror responses were significantly reduced. This
reduction was amplified following conditioning pulses to the premotor cortex, supporting the
idea that the mirror system can adapt through associative learning (Cook et al., 2014) and
demonstrating the role of premotor-M1 connections in such associations.
Collectively, these NIBS studies demonstrate the causal role of a group of brain
regions, and connectivity between these regions, in mirror responses, and lend support to key
theories such as associative learning accounts of mirror neuron origin. These studies also
demonstrate muscle-specific responses to action observation, and hence mirror responses,
more directly than is possible using neuroimaging.
Self-other Processing
During social interaction it can be important to enhance representation of another
person and suppress representation of the self (e.g. in order to represent another's beliefs
when they differ from your own). Conversely, it can also be beneficial to suppress
representation of another and enhance representation of the self (e.g. to inhibit imitation of
another). This ability to selectively modulate representations of the self and the other is
known as self-other control and is thought to play a key role in several social processes
including empathy, perspective taking and theory of mind (de Guzman, Bird, Banissy &
Catmur, 2016; Ward & Banissy, 2015). The medial prefrontal cortex (mPFC) and the
temporoparietal junction (TPJ) have been linked to this process through a body of fMRI work
NIBS in social processing research
(e.g. Brass, Ruby & Spengler, 2009). The use of NIBS has allowed the causal link between
the TPJ and self-other control to be established. For example, Costa, Torriero, Oliveri and
Caltagirone (2008) and Young, Camprodon, Hauser, Pascual-Leone, and Saxe (2010) both
showed that 1Hz TMS to the right TPJ (rTPJ; see Figure 1) disrupts performance on theory of
mind tasks. Similarly, Wang, Callaghan, Gooding-Williams, McAllister and Kessler (2016)
showed that double-pulse TMS to the right posterior TPJ also disrupted performance on a
perspective-taking task. Further, TMS delivered at a theta frequency (6Hz) relative to Alpha
(10Hz) facilitated embodied perspective taking, highlighting the role of Theta oscillations in
this process (Gooding-Williams, Wang & Kessler, 2017).
Studies have also employed tDCS to investigate the role of the TPJ in self-other
control. For example, Santiesteban, Banissy, Catmur and Bird (2012) demonstrated that
anodal tDCS to the rTPJ selectively improved performance on tasks requiring self-other
control (imitation-inhibition and perspective taking) relative to a task requiring self-
referential processing. No differences in task performance were found between cathodal
stimulation and sham. This effect of improved self-other control following anodal tDCS to
the rTPJ was subsequently replicated by Santiesteban, Banissy, Catmur and Bird (2015), who
also showed a similar pattern of results for left TPJ stimulation (see also Hogeveen et al.
2014). Collectively, these findings highlight the role of the TPJ in self-other control.
Additionally, they demonstrate that modulation of social processing can be achieved, and
replicated, using tDCS methods (see Sellaro, Nitsche & Colzato, 2016, for review on tDCS in
social processing research).
Whilst NIBS research has clearly enhanced understanding of the role of the TPJ,
further research is needed to understand the role of the mPFC. It is commonly thought that
ventral regions of the mPFC are involved in self-referential processing whereas dorsal
regions are involved in representing others (see van der Meer, Costafreda, Aleman & David,
2010; Denny, Kober, Wager & Ochsner, 2012, for meta-analyses). However, Nicolle et al.
(2012) suggest that the mPFC is organised with respect to task-relevance, thus challenging
prevailing accounts of mPFC organisation (Cook, 2014). They argue that ventral regions of
the mPFC keep track of task-relevant information (e.g. information about the self during a
self-relevant trial), whereas more dorsal regions of the mPFC keep track of task-irrelevant
information (e.g. information about the self during an other-relevant trial). Use of more focal
NIBS techniques (such as TMS) is one way to test contrasting accounts of brain function in
social processing. However, we are generally limited to stimulating areas near the cortical
NIBS in social processing research
surface. Targeting deeper regions often requires higher-intensity stimulation which impacts
focality of the electric field. Thus, in order to test accounts regarding the role of deeper or less
accessible brain structures in social processing (e.g. mPFC), we must first overcome several
challenges associated with using NIBS in social processing research.
Challenges using NIBS to study social processing
Whilst the above examples highlight successes of using NIBS to modulate social
processing, there are also a number of challenges. The remainder of this paper will discuss
key challenges facing researchers using NIBS to study social processing in neurotypical and
atypical populations. This section is not an exhaustive list of limitations, but rather highlights
several challenges that are particularly problematic.
Depth of Regions of Interest
With most brain stimulation methods, we are only able to target shallow cortical
regions (Kammer, 1998; Roth, Amir, Levkovitz & Zangen, 2007). This can be problematic
for many areas of study, but is particularly challenging when investigating social processing
that relies on networks encompassing subcortical regions. For example, processing of facial
emotions requires a distributed network including cortical regions such as the ventromedial
prefrontal cortex and somatosensory cortex; less accessible structures such as the fusiform
gyrus; and subcortical regions such as the amygdala and insula (Adolphs, 2002; Fairhall &
Ishai, 2006). If we could reliably target deeper regions, we may be able to further understand
the role of, and connectivity between, different regions within networks responsible for social
processing. In TMS, it is possible to stimulate subcortically using alternative coil types to the
commonly used figure-of-eight coil. However, the increased current spread makes
approaches like this unsuitable for most studies as it reduces the focality of stimulation.
Unintended cortical surface stimulation is also a problem with such techniques. Collectively,
these issues make it difficult to make inferences regarding the function of more specific,
deeper brain regions (for comparison of induced electric field see Deng, Lisanby &
Peterchev, 2013; Lu & Ueno, 2017). Therefore, methods that allow focal stimulation of
deeper regions would be very useful in social processing research.
Currently, it may be possible to overcome this issue using an indirect stimulation
protocol (see Wang et al., 2014 Kim et al., 2018; for examples of network stimulation effects
in associative and episodic memory). Many studies have shown that the effects of TMS can
NIBS in social processing research
alter activity in non-targeted areas of a network activated during a given task (see Ruff,
Driver & Bestmann, 2009, for review). This approach has been used to modulate
interoceptive processing through direct stimulation of cortical regions implicated in the
interoception network (dorsolateral prefrontal cortex; DLPFC) that results in indirect
activation of subcortical regions in the network (anterior insula; Mai, Braun, Probst, Kammer
& Pollatos, 2019). Similar network effects have been shown in face processing whereby
stimulating the rOFA alters fusiform face area activity, and stimulating the pSTS alters
amygdala activity (Pitcher, Duchaine & Walsh, 2014; Pitcher et al., 2017). Thus, it may be
possible to exploit such effects to modulate activity in less accessible brain areas (i.e.
targeting cortical sites to indirectly modulate less accessible regions). Whilst useful, this
potential for indirect effects of NIBS can also make it difficult to interpret regional
involvement in a given process (Coll, Penton & Hobson, 2017).
One thing that several of these studies have in common, is the use of imaging methods
to verify change in subcortical network activation. Use of imaging methods is important to
ensure that indirect stimulation protocols are indeed modulating these less accessible regions.
This may not always be the case when targeting cortical regions that are implicated in several
networks. The flexible hub theory (Cole et al., 2013) posits that brain areas are involved in
multiple networks and that brain state will determine whether interaction with one network is
privileged over another. Regions can flexibly interact with different brain networks
depending on the nature of a participant’s task. Thus, if a brain region is part of more than
one functional network (e.g. involved in both perception and memory networks), caution is
required to ensure that tasks used capture the role of the region in the specific functional
process of interest. In such cases, confirmation of network effects with neuroimaging would
permit stronger inferences to be drawn.
In addition to indirect effects of NIBS, it may be possible to target deeper regions in
the future using two emerging techniques. First, low-intensity transcranial focused ultrasound
stimulation (tFUS) is a form of non-invasive brain stimulation relying on pressure produced
by ultrasound waves to modulate brain activity (see Tyler, Lani & Hwang, 2018; di Biase,
Falato & Di Lazzaro, 2019; Darrow, 2019). Importantly, this technique is thought to be able
to stimulate subcortically whilst preserving spatial focality. This is because the acoustic focus
(where the acoustic energy is greatest) can be steered towards deep sites whilst keeping
the size of the stimulated area as small as possible (Folloni et al., 2019; Legon, Bansal,
Tyshynsky, Ai & Mueller, 2018). Accordingly, this also reduces the degree of unintended
NIBS in social processing research
cortical stimulation (i.e. stimulation of superficial sites when targeting less accessible
regions). Thus, tFUS provides a useful alternative to other deep NIBS methods (e.g. deep
TMS using H- or double-cone coils), which suffer from a depth-focality trade off (Deng et
al., 2013; Lu & Ueno, 2017). Preliminary data in humans has shown that tFUS can alter
unilateral thalamic activity (Legon et al., 2018). Additionally, tFUS over the primary
somatosensory cortex modulates somatosensory evoked potentials and behavioural
performance on a sensory discrimination task (Legon et al., 2014), thus highlighting the
potential of tFUS techniques to modulate behaviour in humans. However, tFUS is still in its
infancy and more research into safety thresholds and mechanisms of action is needed prior to
use in social processing research (Pasquinelli, Hanson, Siebner, Lee & Thielscher, 2019).
Once better understood, tFUS may provide a useful tool to modulate deeper regions in social
brain networks.
Transcranial temporal interference stimulation (tTIS; Grossman et al., 2017) may also
overcome unintended cortical stimulation whilst being able to target less accessible regions.
This method applies two different high-frequency electrical fields to the brain via surface
electrodes. Applying current at such high frequencies (in the kHz range) is not thought to
modulate neural oscillations (Hutcheon & Yarom, 2000). However, at the point where the
frequencies overlap, an amplitude-modulated field is created. This waveform oscillates at a
slower frequency – the rate of which is equal to the difference between the frequencies
generated by the two surface electrode pairs. Depending on surface electrode placement, it
may be possible for this overlap to occur in deeper brain regions, thus modulating activity of
deeper areas. Importantly, because the waveforms are not overlapping on the cortical surface,
activity of more superficial areas is unaffected. This method may therefore be useful for
modulating deeper areas of social brain networks. tTIS has been shown to modulate focal
cortical and subcortical regions in rats (Grossman et al., 2017) and feasibility of this
technique in humans has recently been addressed using computational modelling approaches
(Rampersad et al., 2019; Grossman, Okun & Boyden, 2018). However, more work is needed
to understand the mechanisms of action, feasibility and safety of this approach in humans.
Overlapping and Neighbouring Brain Regions
It can be difficult when using NIBS, to dissociate the role of a region of interest in
task performance from the role of other neighbouring regions. This is due to both network
activation and current spread to other neighbouring regions. For example, different regions of
NIBS in social processing research
the TPJ are involved in different cortical networks. The anterior TPJ shows connectivity with
the ventral attention network (Corbetta & Shulman, 2002) and is implicated in both social
and non-social processing, whilst the posterior TPJ shows connectivity with the social
cognition network and is primarily implicated in social processing (Mars et al., 2011; see
Krall et al., 2015, for meta-analysis). Whilst associated with different processes, these regions
are topographically close. Thus, targeting just one with NIBS techniques becomes
challenging. As such, it is important to ensure that when investigating the effects of brain
stimulation on regions involved in social processing, we do not use tasks that also rely on
alternative networks that include anatomically close regions. Conversely, it is also possible to
use control tasks that may differentially activate these alternative networks. For example,
Santiesteban, Kaur, Bird and Catmur (2017) demonstrated that domain-general attentional
processes, rather than implicit mentalising, were modulated by rTMS to the rTPJ. By
investigating both domain-general and domain-specific effects of rTPJ stimulation, the
authors were able to shed light on rTPJ involvement in social processing. It can be difficult to
design tasks that allow for this dissociation, but it is essential if we are to understand how
modulation to an area affects social processing specifically, rather than more general
processing.
It may also be possible to account for anatomical specificity of an effect by
stimulating the region of interest and other anatomically close control regions. If task
behaviour is modulated by stimulation to one site but not another nearby site, this would
provide stronger evidence that modulation of the region of interest, rather than neighbouring
regions, is driving the effect (subtractive inference, Walsh & Cowey, 2000). Coupling such
protocols with imaging methods would further enhance our knowledge of anatomical
specificity. It is also possible to record network activation following plasticity-inducing NIBS
protocols (e.g. network activation recorded prior to and following a theta-burst TMS
protocol). Whilst this does not overcome the issue of stimulating overlapping or neighbouring
regions, it does allow for regional and network changes in activity to be detected.
One way to potentially overcome this issue is to exploit state-dependent effects of
NIBS. Brain stimulation effects are influenced by the state of the brain at the time of
stimulation (Silvanto, Muggleton, Cowey & Walsh, 2007). For example, in visual perception,
researchers have been able to selectively influence the behavioural outcomes of brain
stimulation by altering the brain state at the time of stimulation (e.g. Silvanto, Cattaneo,
Battelli and Pascual-Leone, 2008; Cattaneo & Silvanto, 2008; Silvanto & Muggleton, 2008).
NIBS in social processing research
Silvanto et al. (2008) showed that priming area V5 of the visual cortex (versus the vertex)
with 1Hz inhibitory rTMS resulted in facilitation of motion detection performance when
receiving online TMS. In contrast, online TMS to area V5 disrupted motion detection
performance when activity in this area was not suppressed (offline rTMS delivered to vertex
control site). This study shows that it is possible to change the nature of the effects of
stimulation by influencing the brain state at the time of stimulation (also see Cattaneo &
Silvanto, 2008; Silvanto & Muggleton, 2008). Endogenous baseline activity has also been
shown to partially explain variability in response to TMS (Pasley, Allen & Freeman, 2009;
for theoretical framework see Silvanto & Cattaneo, 2017). Silvanto and Pascual-Leone
(2008) describe the potential utility of exploiting state-dependent effects of NIBS in
perceptual studies to selectively target specific brain networks. It may be possible to apply a
similar approach to social processing research. In theory, this approach may provide a way to
selectively activate networks involved in social processing whilst limiting modulation of
other contiguous networks that may otherwise be influenced by NIBS (see Figure 2).
[INSERT FIGURE 2 HERE]
One example comes from Mazzoni, Jacobs, Venuti, Silvanto and Cattaneo (2017) who
exploited state-dependent effects of TMS to investigate areas involved in representing
affective body kinematics (using point light displays). Working on the premise that single-
pulse TMS facilitates less active/excitable neural populations (Silvanto & Pascual-Leone,
2008), Mazzoni et al. (2017) used an adaptation paradigm where participants were exposed to
happy or fearful adapters prior to a judgement task. During the judgement task, participants
indicated whether a target display was happy, fearful or neutral. Participants were faster to
respond to adapter-incongruent targets when receiving no TMS, TMS to an active control
site, or TMS to the posterior superior temporal sulcus. However, this effect was abolished for
fearful displays only when receiving TMS to the anterior intraparietal sulcus (aIPS). This
suggests that neural populations in the aIPS code affective (fearful) kinematic profiles and
highlights the utility of state-dependent effects of TMS in study social processing research
(also see Cattaneo, Sandrini & Schwarzbach, 2010; Cattaneo et al., 2011; Jacquet & Avenanti,
2015 for state-dependent studies of action observation; and Ambrus et al., 2017b, Ambrus,
Amado, Krohn & Kovacs, 2019 for state-dependent studies of face processing). Thus, state
manipulations may provide a useful method to understand the role of regions/networks in
social processing, and to overcome limitations associated with stimulation of
overlapping/neighbouring regions.
NIBS in social processing research
Use of NIBS in autism and other atypical groups
There is a growing body of work assessing the potential use of NIBS in clinical
disorders (for reviews see Kim, Pesiridou, & O’Reardon, 2009; Machado et al., 2008; Schulz,
Gerloff, & Hummel, 2013; Wassermann & Zimmermann, 2012). Several studies have also
shown promising results using NIBS to modulate social processing in atypical groups (see
Boggio, Asthana, Costa, Valasek & Osório, 2015, for review). However, the research in this
area in limited. It is also important to consider that, in addition to the key challenges
mentioned above, there are several additional challenges facing NIBS studies of social
processing in atypical groups. These are important to consider given that NIBS protocols are
rarely tailored to atypical groups. Instead, research in atypical cohorts often relies on
protocols shown to be effective in neurotypical groups. Below, we discuss challenges facing
studies of social processing in atypical groups using NIBS. We will use the case of Autism
Spectrum Disorder (hereafter ‘autism’) as an example throughout.
Autism is a neurodevelopmental disorder characterised by social difficulties and rigid
and repetitive behaviours (APA, 2013). In addition to these core symptoms, people with
autism often exhibit motor control difficulties (Gowen & Hamilton, 2013), and have
significantly higher rates of neuropsychiatric disorders such as depression and anxiety
(Hollocks, Lerh, Magiati, Meiser-Stedman & Brugha, 2019). Research investigating ways to
ameliorate social difficulties associated with autism or co-occurring disorders and traits (e.g.
social anxiety, alexithymia) is therefore an important area of study for researchers
investigating social processing and for the autistic community (Pellicano, Dinsmore &
Charman, 2014).
Atypical groups may also benefit greatly from social interventions in neurotypical
participants. For example, many autistic individuals find social situations challenging due to
difficulties interpreting social cues of others. However, social situations may also be
challenging due to a failure of neurotypical controls to interpret social cues of their autistic
peers (Brewer et al., 2016; Edey et al., 2016). Thus, interventions must account for both
autistic and neurotypical difficulties in order to improve social interactions across these
cohorts. NIBS techniques may provide a useful tool to understand and ameliorate social
difficulties in both typical and atypical populations. However, use of such techniques in
people with autism and other disorders should be approached with caution (Wassermann &
NIBS in social processing research
Lisanby, 2001; Bersani et al., 2013; Kuo, Paulus & Nitsche, 2014; Oberman, Rotenberg &
Pascual-Leone, 2015).
Stimulation protocols in typical and atypical populations
Network recruitment & connectivity. Multiple papers highlight high variability in
response to brain stimulation in neurotypical adults and the need to individualise or tailor
protocols to achieve maximal gain in both typical and atypical groups (for review see Krause
& Cohen Kadosh, 2014). However, in practice, many studies investigating social perception
in atypical groups are reliant on findings from the neuroptyical literature to inform protocols.
This is problematic since it assumes that what holds in a neurotypical population will directly
apply to atypical populations (Walsh & Pascual-Leone, 2003). This is important when
considering the use of NIBS in atypical groups such as those with autism. Hanson, Hanson,
Ramsey and Glymour (2013) found that autistic participants showed different connectivity
patterns during social processing tasks relative to neurotypical controls. This is consistent
with other findings suggesting general atypical connectivity in autistic cohorts (Assaf et al.,
2010; Rubenstein & Merzenich, 2003). Importantly, this difference was not uniform across
tasks. Participants with autism showed similar connectivity patterns to neurotypical controls
when face processing networks were recruited, but not when theory of mind or action
understanding networks were recruited (Hanson et al., 2013). Collectively, these findings
highlight different network recruitment and connectivity patterns in participants with autism
relative to neurotypical controls. NIBS studies investigating social processing in these groups
should, therefore, take this into account when selecting target sites or when designing
paradigms to investigate connectivity patterns in participants with autism. Importantly, we
cannot assume that stimulation to target sites shown to modulate social processing in
neurotypical adults will modulate social processing in the same way in atypical groups.
Neurotransmitters. Atypical inhibition in the brain has been proposed as a common
candidate endophenotype for a range of disorders (Marín, 2012). In autism, atypical
GABAergic activity in the brain is observed due to a multitude of factors including reduced
GABA synthesis and reduced numbers of GABAergic receptors (for reviews see Rubenstein
& Merzenich, 2003; Blatt & Fatemi, 2011). Atypical inhibition in autism may also result from
atypical NMDA receptor activity (Lee, Choi & Kim, 2015). In line with the above, atypical
plasticity profiles have been observed across a range of disorders including schizophrenia and
autism (e.g. Forrest, Parnell & Penzes, 2018; Hall, Trent, Thomas, O’Donovan & Owen,
NIBS in social processing research
2015; Bourgeron, 2015). These findings are important given that several NIBS techniques are
thought to work by influencing NMDA and GABAergic activity and increasing plasticity in
targeted regions (Liebetanz, Nitsche, Tergau & Paulus, 2002; Bachtiar, Near, Johansen-Berg
& Stagg, 2015; Stagg et al., 2009; Huang, Chen, Rothwell & Wen, 2007). Therefore,
modulating these systems in the atypical brain may not have the same outcome as in a
neurotypical brain. Indeed, atypical plasticity following rTMS in participants with autism has
been observed (Oberman et al., 2010; Oberman et al., 2012). Thus, whilst interventions
targeting these neurotransmitters in atypical groups may be useful, it is important to first
tailor such interventions to the intended cohort.
One way to achieve this is testing physiological and behavioural responses to NIBS
techniques in atypical cohorts. This can be done by borrowing protocols from studies
addressing this in neurotypical controls (Walsh & Cowey, 2000; Jacobson, Koslowsky &
Lavidor, 2012; Krause & Cohen Kadosh, 2014; Parkin et al., 2015; Reed & Kadosh, 2018).
Ideally, this should be done prior to attempts to induce long-term changes in atypical groups
using NIBS. A good example of work in atypical groups comes from Hoy, Arnold, Emonson,
Daskalakis and Fitzgerald (2014) who show dose-dependent effects of tDCS on working
memory in participants with schizophrenia. Such work is important to ensure the safety of
participants undergoing interventions and to increase the likelihood that participation is
worthwhile for these groups. NIBS interventions can span months and require regular lab
visits. Regular visits may be draining for atypical groups for many reasons (e.g. unknown
social situation, anxiety when using public transport, etc.). Therefore, the time and energy
cost to the participant must be taken into account when engaging atypical groups in
interventions. Understanding how NIBS affects these groups, prior to undertaking longer-
term interventions, is one way to address this. Thus, whilst this work does not explicitly relate
to investigating social processing in atypical groups, it is a necessary precursor.
Stimulus properties. Several studies have used NIBS methods to investigate social
processing in autism (e.g. Enticott et al., 2012, Théoret et al., 2005). For example, Théoret et
al. (2005) demonstrated a reduced MEP response to observed actions in participants with
autism relative to neurotypical controls. One explanation for these results may be that
participants with autism show a reduced mirror response to observed actions. However, this
reduced response may also be due to the type of stimuli used. Specifically, if stimuli
presented do not adequately map onto motor representations in the brains of participants with
autism, this may also present as a reduced MEP response. One reason for this may be that
NIBS in social processing research
participants with autism move differently to neurotypical controls. For example, participants
with autism have a different kinematic profile when executing intransitive movements
compared to neurotypical controls (Cook, Blakemore & Press, 2013). Considering such
differences when designing stimuli is important to allow stronger inferences to be drawn. In
the case of action observation, this could simply involve inclusion of movements made by
autistic and non-autistic individuals, as well as several movements made by the participant
themselves.
Understanding NIBS-medication interactions
Many cognitive studies using NIBS typically exclude participants taking psychotropic
medications based on safety criteria from Rossi, Hallett, Rossini, Pascual-Leone and Safety
of TMS Consensus Group (2009). In neurotypical adults this is important to reduce noise in
the data and to ensure participant safety. However, this approach is less straightforward in
atypical groups. It is common to decide on exclusion based on contraindications to NIBS by
assessing the cost/benefit ratio of participant involvement in the study. Whilst this may be a
good approach for therapeutic interventions targeting treatment-resistant disorders, it does
limit inclusion of participants in research investigating atypical groups. Approximately 60%
of participants with autism are taking one or more psychotropic medications (Buck et al.,
2014). Therefore, we need to understand safety and efficacy of NIBS in combination with
these drugs to prevent sampling bias when testing atypical groups. Due to high heterogeneity,
ensuring that study findings reflect the wider cohort is essential in order to interpret cognitive
processes in atypical groups.
This is particularly important when investigating social processing, as people may be
on medication to ameliorate social deficits. Excluding such participants from studies
investigating social processing can therefore bias the sample tested. McLaren, Nissim and
Woods (2018) reviewed the interaction between medications and tDCS effects over M1 in
neurotypical adults. Among others, interactions between drugs which alter neurotransmitter
concentrations (e.g. GABA and dopamine) and the effects of tDCS were observed. The
authors highlight the use of such drugs in treating neuropsychiatric conditions (e.g. anxiety
and schizophrenia) and therefore, the importance of considering such interactions when
translating tDCS protocols to atypical cohorts. However, the authors also stress caution when
applying such findings to an atypical cohort, given differences in brain structure and function
relative to neurotypical controls as well as potential differences in response to a given drug.
NIBS in social processing research
Support for this cautious approach comes from work by Ajram et al. (2017), who showed that
participants with autism showed a different neural response to a GABA- and glutamate-acting
drug compared to neurotypical controls. Thus, it is important to consider the way in which a
drug works in an atypical group, as well as potential (differential) NIBS-medication
interactions. This will be a challenging line of research requiring data beyond that collected
in neurotypical controls, and such research is currently in its infancy.
One way to inform design of such studies is to use existing data from atypical groups
taking part in clinical trials using NIBS. An increasing number of studies are being conducted
using NIBS in participants with psychiatric disorders either as a treatment for core symptoms
or to treat co-occurring disorders (e.g. for treatment of depression in participants with
Schizophrenia or autism). Many of these participants are also on psychotropic medications,
and so, whilst not the primary aim of the research, some of these studies also include analyses
looking at NIBS-drug interactions (e.g. Hoffman et al., 2000). Using findings from this
literature, and literature assessing NIBS-drug interactions in neurotypical participants (e.g.
McLaren et al., 2018; Herwig, 2007; Rumi et al., 2005; Liu, Zhang, Zhang & Li, 2014) may
help us to begin to identify common interactions and safety limits of NIBS use in an atypical
brain. Once these are better understood, we can then use these findings to inform the design
of studies investigating other areas of cognition such as social processing.
Conclusions
It is clear that NIBS methods have improved understanding of social processing.
However, many challenges still face research into social processing in typical and atypical
groups. Promising techniques (e.g. targeting deeper structures using tFUS) are emerging, and
it may be possible to exploit existing knowledge of NIBS techniques (e.g. state-dependent
effects of TMS) to refine methodology. Research into NIBS in typical groups can also be
used to inform NIBS protocol in atypical groups when combined with advances in
understanding of brain stimulation effects in different cohorts. Along with growing
understanding of NIBS mechanisms in typical and atypical cohorts, advances in our
understanding of social processing have brought behavioural paradigms in the field to a stage
where they are accessible both conceptually and anatomically to NIBS research.
NIBS in social processing research
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Figure Captions
Figure 1. Commonly targeted stimulation sites in studies investigating face processing, self-other
control, and mirror responses. (a) right Occipital Face Area; coordinates taken from Pitcher et al.
(2007), (b) right Temporoparietal Junction; coordinates taken from Young et al. (2010) , (c) left
Primary Motor Hand Area; coordinates taken from Maegherman, Nuttall, Devlin & Adank (2019).
Figure 2. Theoretical approach to exploiting state-dependent effects of NIBS in social processing
research. Left to right: Neural activation of representations of different facial emotions is initially at a
baseline level. Activation of neurons coding for a particular facial emotion is then manipulated
through use of priming. Following subsequent TMS, activity of the primed neurons (i.e. those coding
for happy faces) may be inhibited compared to baseline, whereas activity of unprimed neurons
(coding for sad faces) may be facilitated. This theoretical pattern of results is in line with empirical
evidence in the visual perception domain whereby TMS facilitates activity of less active neural
populations (Silvanto et al., 2008).
NIBS in social processing research
Figures
Figure 1.
Figure 2.
... NIBS techniques have been widely applied in both basic neuroscience as well as translational application studies across a broad range of fields including visual function [5,57,58]. NIBS techniques include a range of neuromodulation techniques, such as transcranial magnetic stimulation (TMS) techniques, transcranial electrical stimulation (tES) techniques, and transcranial focused ultrasound stimulation (tFUS) techniques [59]. Among NIBS techniques, tES techniques are particularly noteworthy. ...
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