RESEARCH ARTICLE

Offline impact of transcranial focusedultrasound on cortical activation inprimates
Lennart Verhagen1,2†*, Cécile Gallea3†, Davide Folloni1,2, Charlotte Constans4,
Daria EA Jensen1,2, Harry Ahnine3, Léa Roumazeilles1,2, Mathieu Santin3,
Bashir Ahmed5, Stéphane Lehericy3, Miriam C Klein-Flügge1,2, Kristine Krug5,
Rogier B Mars2,6, Matthew FS Rushworth1,2‡, Pierre Pouget7‡,
Jean-François Aubry8‡, Jerome Sallet1,2‡
1*For correspondence:lennart.verhagen@psy.ox.ac.uk

Wellcome Centre for Integrative Neuroimaging (WIN), Department ofExperimental Psychology, University of Oxford, Oxford, United Kingdom;2
Wellcome Centre for Integrative Neuroimaging (WIN), Centre for Functional MRIof the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe
Hospital, University of Oxford, Oxford, United Kingdom; 3Institute du Cerveau etde la Moelle épinière (ICM), Centre for NeuroImaging Research (CENIR), Inserm U
1127, CNRS UMR 7225, Sorbonne Université, Paris, France; 4Physics for Medicine
Paris, Inserm, ESPCI Paris, CNRS, PSL Research University, Université Paris Diderot,
Sorbonne Paris Cité, Paris, France; 5Department of Physiology, Anatomy andGenetics, University of Oxford, Oxford, United Kingdom; 6Donders Institute forBrain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The
Netherlands; 7Institute du Cerveau et de la Moelle épinière (ICM), UMRS 975
INSERM, CNRS 7225, UMPC, Paris, France; 8Physics for Medicine Paris, Inserm,
ESPCI Paris, CNRS, PSL Research University, Paris, France†

These authors contributed
equally to this work‡
These authors also contributed
equally to this work
Competing interests: The
authors declare that nocompeting interests exist.
Funding: See page 22
Received: 28 July 2018
Accepted: 26 January 2019
Published: 12 February 2019
Reviewing editor: SvenBestmann, University College
London, United Kingdom
Copyright Verhagen et al. This
article is distributed under theterms of the Creative Commons
Attribution License, which
permits unrestricted use andredistribution provided that theoriginal author and source arecredited.

Abstract To understand brain circuits it is necessary both to record and manipulate theiractivity. Transcranial ultrasound stimulation (TUS) is a promising non-invasive brain stimulation
technique. To date, investigations report short-lived neuromodulatory effects, but to deliver on its
full potential for research and therapy, ultrasound protocols are required that induce longer-lasting‘offline’ changes. Here, we present a TUS protocol that modulates brain activation in macaques formore than one hour after 40 s of stimulation, while circumventing auditory confounds. Normally
activity in brain areas reflects activity in interconnected regions but TUS caused stimulated areas tointeract more selectively with the rest of the brain. In a within-subject design, we observe
regionally specific TUS effects for two medial frontal brain regions – supplementary motor area andfrontal polar cortex. Independently of these site-specific effects, TUS also induced signal changes in
the meningeal compartment. TUS effects were temporary and not associated with microstructuralchanges.
DOI: https://doi.org/10.7554/eLife.40541.001

Introduction
In neuroscience, to understand brain circuits a two-pronged approach, entailing both recording andmanipulating brain activity, is essential. In recent years, there has been extensive progress in thisfield, which was in part made possible by the availability of new technologies (Bestmann and Walsh,
2017; Dayan et al., 2013; Polanı́a et al., 2018). While techniques for transiently manipulating

Verhagen et al. eLife 2019;8:e40541. DOI: https://doi.org/10.7554/eLife.40541

1 of 28

Research article

NeuroscienceeLife digest Ultrasound is well known for making visible what is hidden, for example, whengiving parents a glimpse of their child before birth. But researchers are now using these highfrequency sound waves – beyond the range of human hearing – for a wholly different purpose: tomanipulate the activity of the brain. Conventional brain stimulation techniques use electric currents
or magnetic fields to alter brain activity. These techniques, however, have limitations. They can only
reach the surface of the brain and are not particularly precise. By contrast, beams of ultrasound canbe focused at a millimetre scale, even deep within the brain. Ultrasound thus has the potential toprovide new insights into how the brain works.
Most studies of ultrasound stimulation have looked at what happens to the brain during thestimulation itself. But could ultrasound also induce longer-lasting changes in brain activity? Changes
that persist after the stimulation has ended would be valuable for research. They would also make it
more likely that we could use ultrasound to treat brain disorders by changing brain activity.
Verhagen, Gallea et al. used a brain scanner to measure brain activity in macaque monkeys after
ultrasound stimulation. The results showed that 40 seconds of repetitive ultrasound changed brain
activity for up to two hours. Ultrasound caused the stimulated brain area to interact more selectively
with the rest of the brain. Notably, only the stimulated area changed its activity in this way. This
helps rule out the possibility that the changes reflect non-specific effects. If the monkeys had beenable to hear the ultrasound, for example, it would have changed the activity of the parts of the brain
related to hearing. Most important of all, the changes were reversible and did not harm the brain.
The results of Verhagen, Gallea et al. show that repetitive ultrasound can induce long-lastingalterations in brain activity. It can target areas deep within the brain, including those that are out ofreach with other techniques. If this procedure also shows longer-lasting effects in people, it could
yield valuable insights into the links between brain and behaviour. It could also help us develop newtreatments for neurological and psychiatric disorders.
DOI: https://doi.org/10.7554/eLife.40541.002

activity in rodents, such as microstimulation, optogenetics, and chemogenetics (Sternson and Roth,2014; Vanduffel, 2016), are increasingly accessible and applied, techniques for manipulating activity
in the primate brain are less widely available and remain accessible to comparatively few researchers
in a limited number of research centres worldwide (Galvan et al., 2018; Krug et al., 2015; Vanduffel, 2016). A prominent limitation for many brain stimulation tools for research and therapeutic interventions is the duration of the induced neuromodulatory effects, often not outlasting the stimulation
by more than a few seconds or minutes.
Here we report on a particular protocol of low intensity pulsed transcranial focused ultrasoundstimulation (TUS) that we show induces a sustained period of neuromodulation in primates without
inducing structural damage. The TUS approach in general is a relatively new one (Dallapiazza et al.,2018; Tufail et al., 2011; Tyler et al., 2018; Yoo et al., 2011; Younan et al., 2013) and like transcranial magnetic stimulation (Dayan et al., 2013) and transcranial electric stimulation (Polanı́a et al.,2018) it can be applied in the absence of a craniotomy. In vitro recordings have identified several
mechanisms by which ultrasound stimulation could affect neurons. It has been proposed that thesound pressure wave exerts a mechanical effect on neuronal activity through ion channel gating andchanges to the membrane capacitance (Blackmore et al., 2018; Kubanek et al., 2018;
Kubanek et al., 2016; Prieto et al., 2013). While the precise mechanism is being determined, early
applications, including the current results, suggest TUS may be suitable as a tool for focal manipulation of activity in many brain areas in primates (Fomenko et al., 2018; Lee et al., 2016a;
Legon et al., 2018; Munoz et al., 2018; Naor et al., 2016; Tufail et al., 2011; Tyler et al., 2018;
Yoo et al., 2011). In the macaque, its application over the frontal eye field (FEF) affects the same
aspects of oculomotor behaviour that are compromised by FEF lesion, whilst leaving intact those
aspects of oculomotor behaviour that are unaffected by FEF lesion (Deffieux et al., 2013). Pioneering work in humans has focused on modulating and eliciting evoked responses by focused ultrasound in both cortical and subcortical sensorimotor regions (Lee et al., 2016a; Lee et al., 2015;
Legon et al., 2018; Legon et al., 2014). To date ultrasonic applications are primarily focused on

Verhagen et al. eLife 2019;8:e40541. DOI: https://doi.org/10.7554/eLife.40541

2 of 28

Research article

Neuroscience

direct ‘online’ effects. Nonetheless, some studies have observed neuromodulatory effects outlasting
the sonication by several minutes before returning to baseline, with more pronounced effects for
protocols delivered at higher intensities (Isppa >5 W/cm2) and higher duty cycles (>5%; Kim et al.,2015; Yoo et al., 2011). For example, following ultrasound protocols with a relatively high rate ofacoustic energy deposition, characterized by pulses tens of milliseconds long, repeated at 10 Hz, theneuromodulatory effects outlast the sonication period: for up to 10 min following 43.7 ms long
1.14MHz pulses repeated at 10 Hz for 40 s (Dallapiazza et al., 2018), or for up to 20 min following
30 ms long 350 kHz pulses repeated at 10 Hz for 40 s (Ahnine et al., 2018). We have built on suchprotocols when designing the current experiment.
Recent work has highlighted the possibility that in rodents some TUS protocols can evoke a startle response when the stimulation is modulated at audible frequencies (Guo et al., 2018;
Sato et al., 2018). Importantly, this auditory effect is limited to the stimulation period and dissipates
within 75 ms to 4 s. This work has also emphasized that in more deeply anaesthetized animals, when
the intrinsic neural activity of a system is suppressed, some TUS protocols might fail to evoke action
potentials at the site of stimulation. This suggests TUS’ actions might be primarily neuromodulatory
in nature and/or that they are most prominently observable when they interact with ongoing physiological activity. However, as has been noted (Airan and Butts Pauly, 2018) the impact that any stimulation protocol might have, will be a function of the animal model being used and the precise
details of the ultrasound frequencies, pulse shape, protocol, and ongoing brain activity. With the offline protocol and anaesthesia regime we used we control for such potential artefacts and show that
TUS has an effect that cannot be attributed to them.
Here we focused on the effects of TUS outlasting the stimulation period, investigating the impactof 40 s trains of TUS on measurements of neural activity in three macaque monkeys provided byfunctional magnetic resonance imaging (fMRI) up to 2 hr after stimulation (Figure 1, top panel).
FMRI is one of the most widely used methods for estimating neural activity. Despite limitations in its
spatial and temporal resolution, fMRI remains important because it is non-invasive and can often be
used to provide information about activity throughout the whole brain. Rather than providing a
direct measure of neural activity, however, it provides an estimate of how activity changes in tandemwith sensory, cognitive, or motor events or with activity in another brain region. Typically, fMRI-measured activity in any given brain area is a function of activity in other brain areas, especially thosewith which it is closely interconnected (Neubert et al., 2015; O’Reilly et al., 2013). Although many
brain areas may share any given individual connection (for example both areas A and B may projectto C), the overall pattern of connections of each area is unique (Passingham et al., 2002); as such,the overall pattern of connections therefore constitutes a ‘connectional fingerprint’. As a result it ispossible to use fMRI measurements of correlations in the blood oxygen level dependent (BOLD) signal across brain regions to estimate the connectivity fingerprints of a given brain area (Figure 1, bottom right panel; Caspari et al., 2018; Ghahremani et al., 2017; Margulies et al., 2016,
Margulies et al., 2009; Mars et al., 2013; Sallet et al., 2013; Shen, 2015; Shen et al., 2015). Thisimplies that activity in any given brain area is a function of the activity in the areas with which it isinterconnected.
We exploited this feature of activity to examine the impact of TUS application to two brain
regions in the frontal cortex: supplementary motor area (SMA; experiment 1) and frontal polar cortex (FPC; experiments 2 and 3). Simulations showed we were able to selectively target these regions(Figure 2; see Acoustic and thermal modelling for more details). These regions have distinct anatomical and functional connections; SMA is most strongly coupled with the sensorimotor system, while
the FPC interacts primarily with the prefrontal cortex and only interacts indirectly with the sensorimotor system via SMA (Petrides and Pandya, 2007; Sallet et al., 2013). This allows us to test the
spatial and connectional specificity of TUS effects. In the control state, each area’s activity is normallya function of the activity in the areas that constitute its connectional fingerprint. If this pattern isaltered by TUS in a manner that is dependent on the location of the stimulation, then this will constitute evidence that TUS exerts a spatially selective effect on neural activity. In a within-subject design,
the same three animals participated in experiments 1 and 2, and a control experiment conducted in
the absence of TUS (Figure 1). As such, fMRI was acquired in three conditions for all animals: following SMA TUS, following FPC TUS, and in a control state. In turn, each of the MRI sessions consistedof three consecutive runs. We also validated the results of experiment two in a new set of three different animals (experiment 3).

Verhagen et al. eLife 2019;8:e40541. DOI: https://doi.org/10.7554/eLife.40541

3 of 28

Research article

Neuroscience

experimental design and timing
subjects

anaesthesiat ~ -1.45h
ontrolc

exp 1

MK1, MK2, MK3

TUS (40s)

resting fMRI (26min / run)t = 0ht ~ 40mint ~ 2hcontrol
SMA TUS

run 1

run 2

run 3

exp 2

exp 3

MK4, MK5, MK6

FPC TUSvalidation

seed-based connectivity analyses

pre-processing

fingerprint
remove artefacts
in spatial, temporal,
and spectral domains
steady RF state‘motion/B0’ correction
artifact detection
spike interpolation
spatial processing- brain extraction- RF bias correction- registration to T1w
high-pass filteringband-stop filtering

pipeline 1:
correcting
non-neuronal
confounds

pipeline 2:
retaining
non-neuronal
confounds

surface projection
and improving SNR forconnectivity analyses
low-pass filtering
volume segmentation
cortical surface projection
spatial smoothingdemeaning
aggregate timeseries- individual sessions- group PCA

stimulated area

SPL
M1

MCC
0.4
0.2

9m

PCC

(SMA after SMA TUS
FPC after FPC TUS)

0
-0.2
-0.4

FPC

IPLc

9-46d

midSTS

8A

aSTG

non-stimulated area
(FPC after SMA TUS
SMA after FPC TUS)

11m

auditory cortex

whole-brain

duration of TUS effect
exploratory analyses
impact of TUS on
non-neuronal signal

Figure 1. Overview of the experimental design, time-line, pre-processing pipeline, and analysis strategy. Top panel: The same three macaque monkeys(MK1, MK2, MK3) participated in experiments 1 and 2, and a control experiment conducted in the absence of TUS. Three other monkeys (MK4, MK5,
MK6) participated in experiment 3. In all experiments anaesthesia was induced ~1.45 hr before the TUS intervention or no-TUS control. In experiment 1
the 40 s TUS protocol was delivered over the supplementary motor area (SMA), while in experiments 2 and 3, the protocol was targeted at frontal polar
cortex (FPC). In all sessions, three consecutive runs of resting fMRI (26 min per run) were acquired starting ~40 min after the TUS or control protocol.
Bottom left panel: Data from the resting fMRI runs were pre-processed following a standardized pipeline to address artefacts, improve image quality
and signal-to-noise ratio, and prepare for connectivity analyses. By default, non-neuronal confounds were removed from the timeseries, but to allow ananalysis of the effect of TUS on non-neuronal signal the data was also processed in parallel in a second pipeline, distinct from the default by omitting
the non-neuronal confound regression procedure. Bottom right panel: The effect of TUS on the coupling patterns of the stimulated regions were
quantified as ‘connectional fingerprints’ employing pre-defined targets (here illustrated for the SMA in the control state). These fingerprint analyses
allowed us to test for the presence and duration of TUS effects in the stimulated areas (SMA after SMA TUS; FPC after FPC TUS), and in control areas,
including non-stimulated areas (FPC after SMA TUS; SMA after FPC TUS) and the auditory cortex. Whole brain connectivity maps supported exploratory
analyses and an assessment of the impact of TUS on non-neuronal signal (here illustrated for SMA in the control state).
DOI: https://doi.org/10.7554/eLife.40541.003

Ultrasound modulation of SMA and FPC led to focal, area-specific changes in each stimulated
region’s connectivity profile. In each area, a region’s activity pattern after TUS application was morea function of its own activity and that of strongly connected regions, but less a function of activity inmore remote and weakly interconnected areas. Independent of these specific grey matter signal
changes, TUS also interacted with non-neuronal structures, as evidenced by more widespread signal
changes observed in the meningeal compartment (including cerebral spinal fluid and vasculature).
Finally, in experiment four we demonstrate that TUS application had no observable impact on cortical microstructure apparent on histological examination. In summary, TUS over dorsomedial frontal
cortex causes spatially specific sharpening of the stimulated region’s connectivity profile with highefficacy and reproducibility, independent of non-neuronal signal changes.

Results
Experiment 1, TUS modulation of SMA connectivity
Transcranial focused ultrasound stimulation of SMA induced spatially specific changes to the connectivity profile of SMA. At rest, in the control state, SMA’s activity is coupled with activity throughout
sensorimotor regions in frontal and parietal cortex, inferior parietal, prefrontal, and parts of cingulate cortex (Figure 3a). Many of these regions are anatomically connected with SMA (Dum andVerhagen et al. eLife 2019;8:e40541. DOI: https://doi.org/10.7554/eLife.40541

4 of 28

Research article

Neuroscience

Figure 2. Stimulation targets and thermal modelling. (a) Stimulation target positions in SMA are shown for each of the three individual animals in threedifferent colours on sagittal and coronal views. (b) Estimates of the focused ultrasound peak intensities and spatial distribution when targeting SMA are
derived from numerical simulations using a high-resolution macaque whole-head CT scan, here displayed on a midline sagittal section. (c) FPC targets
in three animals are shown on sagittal and coronal sections. (d) Estimated peak intensities and spatial distribution when targeting FPC shown on asagittal section. (e) Whole-head simulations of the acoustic wave and thermal dynamics provided estimates of the maximum pressure amplitude (leftpanel) and the temperature after 40 s TUS (right panel). The data depict a cropped plane of the whole-head simulations with the sonic coupling cone at
the top and the brain at the bottom; the skull is outlined in black. Pressure and temperature are maximal in the skull, which is more absorbing than soft
tissue. (f) Temperature dynamics for the maximum temperature in the skull (blue), maximum temperature in the brain (red) and at the geometrical focal
point in the cortex (yellow).
DOI: https://doi.org/10.7554/eLife.40541.004

Verhagen et al. eLife 2019;8:e40541. DOI: https://doi.org/10.7554/eLife.40541

5 of 28

Research article

a

d
ps

medialwall

cs

FPC seed

ips

*

ls

cal

* cgs

as

SMA seedposcontrol
state

Neuroscience

sts

M1

SMA TUSb

SMA TUSe

SPL*

TUS over
SMA

*
11m
aSTG

c

TUS over
FPC

*

FPC
TUS

FPC 9/46d
TUS

f

*
25
coupling (Fisher’s z)-0.5

0midSTS
0.5

Figure 3. Coupling of activity between stimulated areas and the rest of the brain in experiments 1 (SMA) and 2 (FPC). The left panels show activity
coupling between SMA and the rest of the brain in the control state (a), after SMA TUS (b), and after FPC TUS (c). The right panels show activity
coupling between FPC and the rest of the brain in the control state (d), after SMA TUS (e), and after FPC TUS (f). Functional connectivity from TUStargeted regions is therefore summarized in panels (b) and (f) (i.e. SMA connectivity after SMA TUS and FPC connectivity after FPC TUS). Each type of
TUS had a relatively selective effect on the stimulated area: SMA coupling was changed by SMA TUS (b) and FPC coupling was changed by FPC TUS(f). Positive correlations are represented in warm colours from red to yellow, negative correlations are represented in cool colours from blue to green.
Key regions of change are highlighted by black dashed ovals. TUS target sites are indicated with arrows. Connectivity seed regions are indicated withblack asterisks. Key anatomical features are labelled in panel (a): pos, parieto-occipital sulcus; cal, calcarine sulcus; cgs, cingulate sulcus; ps, principal
sulcus; as, arcuate sulcus; cs, central sulcus; ips, intraparietal sulcus; sts, superior temporal sulcus; ls, lunate sulcus.
DOI: https://doi.org/10.7554/eLife.40541.005
The following figure supplement is available for figure 3:
Figure supplement 1. Specific patterns of change in the coupling of activity between stimulated areas and the rest of the brain were replicated inexperiments 2 and 3.
DOI: https://doi.org/10.7554/eLife.40541.006

Strick, 2005; Geyer et al., 2000; Strick et al., 1998). If we change the responsiveness of SMA neurons to such activity in interconnected regions by artificially modulating SMA activity with TUS, thenwe should see a change in the coupling between SMA activity and activity in other regions. Following ultrasound stimulation, SMA changed its coupling with the sensorimotor system, anterior andposterior cingulate, anterior temporal, inferior parietal, and prefrontal cortex (Figure 3b). This canbe seen on the whole brain functional connectivity maps for the SMA region (Figure 3, compare
panels a and b, representative changes highlighted by dashed black circles) and on the whole brain
differential connectivity maps in Figure 4 (panels b and c).
It is also apparent in the illustration of SMA’s connectional fingerprint (Figure 5a). The distance ofeach coloured line from the centre of the figure (and hence its proximity to the circumference of the
figure) indicates the strength of activity coupling between SMA and each of the other brain areas
indicated on the circumference. Compared to the control state (blue line), after TUS over SMA (redline), SMA’s positive coupling is enhanced with proximal areas in the sensorimotor system but
reduced in many long-range connections (non-parametric permutation test, p=0.017). The primary
motor cortex (M1), superior parietal lobe (SPL), and middle cingulate cortex (MCC) in the dorsomedial sensorimotor network have been reported to be closely connected with the SMA, whereas prefrontal regions on the dorsomedial (area 9m and FPC), dorsolateral (areas 9-46d and 8A), andventromedial (area 11m) surface, and those in the temporal lobe (anterior superior temporal gyrus,aSTG; middle superior temporal sulcus, midSTS), and parietal cortex (caudal inferior parietal lobule,
IPLc; posterior parietal cortex, PCC) have been reported to be less closely connected with the SMA(Dum and Strick, 2005; Geyer et al., 2000; Strick et al., 1998). TUS increased positive coupling
between the stimulated area and proximal areas normally closely connected with it, while, at thesame time, decreasing coupling between the stimulated area and many areas normally less closely

Verhagen et al. eLife 2019;8:e40541. DOI: https://doi.org/10.7554/eLife.40541

6 of 28

Research article

Neuroscience

SMA TUS effectacontrol
state

PCC

MCC

SMA

*

9m

FPC

9-46d 8AFPC TUS effect
M1 SPL

d

IPLc

PCC

MCC

SMA

9m

FPC

9-46d

F4

PF

IPLc

*

11m
coupling(Fisher’s z)

aSTG
midSTS

-0.5

0

0.5

b

e

c

f

14m
25
47-12omidSTS

TUS effect on
positivecoupling

TUS effect on
negativecoupling

Δ- coupling(Fisher’s z)
-0.2

0

0.2

Figure 4. Differential effect of TUS on coupling of activity between stimulated areas and the rest of the brain in experiments 1 (SMA) and 2 (FPC). The
left panels show activity coupling between SMA and the rest of the brain in the control state (a), the differential effect of SMA TUS for areas positively
coupled (z > 0.1) with SMA in the control state (b), and for areas negatively coupled (z < 0.1) with SMA in the control state (c). The right panels show
activity coupling between FPC and the rest of the brain in the control state (d), the differential effect of FPC TUS for areas positively coupled (z > 0.1)
with FPC in the control state (e), and for areas negatively coupled (z < 0.1) with FPC in the control state (f). Panels (a) and (b) are reproduced here
from Figure 3 for reference and to illustrate the location and extent of the ROIs used in the fingerprint analyses (Figure 5). Hot colours in (b) and (e)indicate enhanced coupling following TUS compared to the control state, while cool colours indicate reduced coupling. In (c) and (f) hot colours
indicate reduced negative coupling, while cool colours indicate further negative coupling. All other conventions as in Figure 3. The TUS inducedchanges to the coupling of the stimulated regions were not limited to the a-priori defined ROIs but extended across many regions according to theconnectional topography of the stimulated region.
DOI: https://doi.org/10.7554/eLife.40541.007

connected with it. This pattern not only emerges from the fingerprint analyses, but constitutes a
principle evident across the brain, as illustrated by whole-brain differential SMA-connectivity maps ofthe effect of SMA TUS (Figure 4).
These specific effects of TUS were sustained over the duration of our experiment, lasting up to 2
hr (Figure 5d–f). Disruptive effects of TUS on long-range coupling were especially prominent immediately following the end of TUS application (Figure 5d), and gradually reduced towards the end of
our recording session (Figure 5f). The enhancing effects that TUS exerted on SMA’s coupling withadjacent and strongly connected areas had a relatively delayed appearance, arising well after the
TUS had ended (more than 1 hr, Figure 5e), but again decreasing towards the end of the recording
session. In summary, the most important finding was of a protracted period of connectivity change
after TUS. While we are cautious about overinterpreting precise timing differences between longrange connectivity reductions and local connectivity increments, we note that the observed pattern
could signify distinct time courses for TUS-induced long-term depression and long-term potentiation. However, the observed pattern is also consistent with the notion that early disruption of longrange input to a network (Figure 5d) leaves relatively more signal variance in this network to be
explained by remaining local input, driving the observation of subsequent within-network couplingincrements (Figure 5e). Both these mechanisms would lead to a sharpening of the stimulated
region’s connectivity profile, as observed here.

Experiment 2, TUS modulation of FPC connectivity
Like SMA, FPC’s activity is coupled with that in interconnected brain regions even when animals areat rest in the control state (Figure 3d). FPC’s activity is positively correlated with activity in a numberof adjacent dorsomedial and lateral prefrontal areas and in the central portion of the superior temporal sulcus (midSTS) and posterior cingulate cortex with which it is monosynaptically interconnected

Verhagen et al. eLife 2019;8:e40541. DOI: https://doi.org/10.7554/eLife.40541

7 of 28

Research article

Neuroscienceb. FPC fingerprint

dorsomedial
sensorimotor

TUS
controlSMA
dorsal
FPC
PFC

SPL
M1

MCC
0.4

9m
0.4
0.2

PCC
0

9-46d

PCC
0

default
mode
network-0.2-0.4

default
mode
network

-0.2

IPLc

-0.4

14m

IPLc

ventromedial
PFC
9-46d

dorsolateral
PFC

midSTS

8A

aSTG

25

temporallobe

midSTS47-12o11m

orbitalPFC

ventromedial
PFC

TUS

SPL
M1

9m

PCC

SMA
FPC
connectivity seedf. ~1.50h after TUS
MCC

M1

MCC

0.4

0.4

0.2

0.2

9m

PCC

9m

PCC

0

0

0

-0.2

-0.2

IPLc

9-46d

8A

-0.4

FPC

midSTS

IPLc

9-46d

aSTG

8A

M1

8A

aSTG

0.2

9m

PCC

9m

MCC
0.4
0.2

9-46d

PCC

0

0

-0.2

-0.2

-0.4

IPLc

9-46d

midSTS

aSTG

11m

SMA

control
SMA
FPC

MCC
0.4

11m

midSTSaSTG

h. A1 coupling with FPC fingerprint

TUS

SPL

-0.4

14m

IPLc

midSTS25

47-12o

IPLc

9-46d

11m

g. A1 coupling with SMA fingerprint

8A

-0.4

FPC

midSTS

11m

FPC

1.4

-0.2
-0.4

FPC

1.5

SPL

M1

0.2

*

1.6

SPL

control
SMA

MCC
0.4

control
SMA
FPC

sensorimotore. ~1.25h after TUS

d. ~1h after TUS

TUS

1.3

PF
F4 ventrolateralPFi. SMA / FPC coupling with A1DXGLWRU\FRUWH[FRXSOLQJ )LVKHU V]

FPC

1.7

MCC

0.2

9m

dorsomedial
PFC

c. self-connection strength

dorsomedial
SMA sensorimotor

VHOIFRQQHFWLRQ )LVKHU V]a. SMA fingerprint

0.4
n.s.

n.s.

0.2

0

TUS
-0.2

-0.4

F4

control
SMA
FPC
SMA

FPC

connectivity seed

Figure 5. Connectional fingerprints of the stimulated areas. Fingerprint target regions were drawn from the literature and chosen based on their known
and distinct connectivity, either strong or weak, with the seed region. Activity coupling in the control state is indicated in blue, following SMA TUS in
red, and FPC TUS in yellow. (a) SMA connectional fingerprint. In the control state (blue) SMA is strongly coupled to areas in the dorsomedial
sensorimotor system (M1, SPL, MCC). Coupling with each of these areas is enhanced after SMA TUS (red). In contrast, coupling with regions that SMA
is weakly connected with in the control state are even further reduced after SMA TUS (red). However, SMA’s fingerprint is relatively unaffected after FPC
TUS (yellow), but some effects are visible for regions that are strongly coupled with FPC (compare to panel b). (b) FPC connectional fingerprint issharpened following FPC TUS, but not following SMA TUS. The latter only affected coupling with SMA itself and regions strongly connected to it(MCC). (c) TUS induced more homogenous activity within the stimulated area (SMA in red on the left, FPC in yellow on the right). (d–f) Temporal
evolution of the effect of SMA TUS on the SMA connectional fingerprint. The left panel depicts effects observed in the first fMRI run, followed by thesecond run in the middle, and the third and last run on the right. For each run, the time after TUS refers to the duration between the end of TUS andthe midpoint of the run, averaged across the three animals. (g–h) TUS-induced effects on the connectional fingerprints of SMA and FPC could not be
explained by auditory activity. (i) There were no changes in the coupling between the stimulation sites and auditory cortex. In all plots, thick lines andhistograms indicate mean activity coupling; lighter coloured error bands and error bars depict standard-error of the mean; the asterisk denotes theinteraction between TUS and connectivity seed (p<0.001).
DOI: https://doi.org/10.7554/eLife.40541.008
The following figure supplement is available for figure 5:
Figure supplement 1. TUS did not affect the temporal variability of the BOLD signal.
DOI: https://doi.org/10.7554/eLife.40541.009

Verhagen et al. eLife 2019;8:e40541. DOI: https://doi.org/10.7554/eLife.40541

8 of 28

Research article

Neuroscience(Petrides and Pandya, 2007). By contrast there was, at a rest, a negative relationship with the activity in sensorimotor areas, with which FPC is indirectly connected via regions such as SMA.
In comparison to the control state, FPC stimulation induced an enhancement in the normal shortrange connectivity between FPC and adjacent dorsomedial (area 9m) and lateral prefrontal cortex(area 9-46d) with which it is particularly strongly connected. In addition, a similar effect was seen in
more distant regions with which it is also strongly connected – the midSTS, IPLc and PCC, together
comprising temporal and parietal segments of the primate ‘default mode network’ (Petrides andPandya, 2007). By contrast, there was reduced coupling with other areas in prefrontal cortex, including ventromedial (area 14m), subgenual cingulate (area 25), and lateral orbitofrontal cortex (area 4712o). These are all areas that FPC is connected to but less strongly (Petrides and Pandya, 2007).
Finally, TUS applied to FPC also led to a change in long-range connectivity between FPC and several
motor association regions with which it is not directly connected, especially those in the ventrolateralparieto-frontal sensorimotor network (areas PF and F4 Petrides and Pandya, 2007). As noted, inthe control state, the activity in FPC and these sensorimotor association areas is negatively or anticorrelated, but this anti-correlation was reduced by FPC TUS. These results are apparent in thewhole brain functional connectivity maps for the FPC region (Figure 3, compare panels d and f, representative changes highlighted by dashed black circles) and on the whole brain differential connectivity maps in Figure 4 (panels d and f). It is also apparent in the illustration of FPC’s connectional
fingerprint (Figure 5b). Here the blue line indicates the strength of activity coupling between FPC
and each of the other brain areas indicated on the circumference in the control state. The yellow line
shows that FPC’s coupling with each area is changed after FPC TUS (non-parametric permutation
test, p=0.027).

Comparing experiments 1 (SMA) and 2 (FPC)
It is important to test the claim that TUS induces effects that are spatially specific to each sonicatedarea by directly comparing effects between stimulation sites. Although FPC TUS significantly altered
FPC functional connectivity, it had comparatively little impact on SMA’s pattern of functional connectivity; there was no difference in SMA’s functional connectivity between the control state and after
FPC TUS (non-parametric permutation test, p=0.231; whole-brain map in Figure 3c and yellow line
in connectivity fingerprint in Figure 5a). Importantly, the effects of TUS over SMA on SMA’s connectivity were significantly dissociable from the effects of FPC TUS (non-parametric permutation test,
p=0.041). Similarly, SMA TUS had some but comparatively little impact on FPC’s pattern of functional connectivity (non-parametric permutation tests, SMA versus control, p=0.047; SMA versus
FPC, p=0.028; whole-brain map in Figure 3e and red line in connectivity fingerprint in Figure 5b). In
fact, the most prominent changes in each area’s connectional fingerprint that were induced by stimulation of the other area were the disruption of functional connectivity between FPC and sensorimotor areas when SMA was stimulated (Figure 3e, encircled). This particular result may have occurredbecause, as already noted, FPC has no direct monosynaptic connections with these sensorimotor
areas (Petrides and Pandya, 2007) and so its functional coupling with these areas is likely to be
mediated by areas such as SMA and the areas that surround it such as the pre-supplementary motor
area and the cingulate motor areas (Bates and Goldman-Rakic, 1993; Lu et al., 1994). In fact, thesecircumscribed exceptions may well confirm the rule that a region’s connectivity pattern is only
affected by stimulation of the region itself. Namely, SMA TUS only affects FPC coupling with SMA
itself and the regions which are coupled with FPC only through SMA. In conclusion, the effects of
TUS in different frontal regions were clearly dissociable.

Experiment 3, replication of TUS effects on FPC connectivity
We investigated the reproducibility of TUS effects by examining the impact of TUS to FPC in three
additional individuals in a biological replication experiment (experiment 3, Figure 3—figure supplement 1). TUS had the same effects as seen in experiment 2: a site-specific sharpening of the stimulated region’s connectional profile. While experiments 1 and 2 followed a within-subject design, forexperiment three we conducted a between-subject analysis where the subjects in the two experiments differed in age. We are therefore careful not to draw too strong conclusions on any main
effect of subject group but focus on the interaction of the TUS effect with the fingerprint shape. Notwithstanding, when reviewing the simple effects driving the well-matched interaction, we note that

Verhagen et al. eLife 2019;8:e40541. DOI: https://doi.org/10.7554/eLife.40541

9 of 28

Research article

Neuroscience

the observed sharpening was less prominently related to activity coupling decreases in experiment 3
than in experiment 2. Putatively, parameters of the general anaesthesia could impact on the effectof TUS. However, subjects used in experiment 2 and 3 did not differ regarding depth of anaesthesia
or duration between sedation and fMRI data collection.

Effects of TUS on activation in the stimulated regions and on theauditory system
It has recently been suggested that certain TUS protocols might have a limited efficacy in evoking
spiking activity at the stimulation site, but rather exert their influence on the brain through the auditory system, not unlike an auditory startle response (Guo et al., 2018; Sato et al., 2018). Althoughthese online observations in rodents should perhaps not be extrapolated far outside the tested conditions (for example to our measurements taken tens of minutes after the stimulation ended), theseobservations do argue in favour of performing controlled experiments that address and exclude
such confounds (Airan and Butts Pauly, 2018). Here we consider, first, why neural effects of TUS
might be evident in the current study when they were not clear previously. Second, we consider
whether neural effects may be due to an auditory artefact.
First, it is possible that the efficacy of TUS is a function of both the specifics of the stimulation
protocol and of ongoing neural activity. A neuromodulatory technique may fail to elicit spiking activity in deeply anaesthetized rodents (Guo et al., 2018; Sato et al., 2018). However, in this study wespecifically test whether it simply modulates ongoing activity, while adopting lighter anaesthesia levels. Importantly, it is known that whole-brain functional connectivity, as measured with the BOLD signal, is preserved at these levels (Mars et al., 2013; Neubert et al., 2015; Neubert et al., 2014;
O’Reilly et al., 2013; Sallet et al., 2013; Vincent et al., 2007). Finally, the experiments were conducted in a primate model as opposed to a rodent model; the importance of species-specific effects
in TUS models are currently unknown. Under these distinct conditions, we observed that TUS modulated the activity coupling of each stimulated area in a regionally specific manner.
Nevertheless, following these investigations of the effect of TUS on whole-brain connectivity patterns of the stimulated regions, we carried out a second line of investigation and examined theeffect of TUS on the signal in the stimulated regions themselves (Figure 5c). While BOLD fMRI cannot provide an absolute measure of neural activity, we can characterize how homogeneous the activation signal is within the stimulated region, as quantified by the coupling strength of the signal ateach point in the stimulated region of interest to all other points in that region. This analysis revealed
that TUS induced more spatially homogenous activation within the stimulated area, but not in the
non-stimulated region (interaction of TUS x connectivity seed: F(1,8)=1571.2, p=1.8044e-10,
d = 11.4426, CI=[1.2733 1.1333]). This effect on spatial homogeneity of the signal was not accompanied by changes to the temporal variance of the BOLD signal fluctuations in the stimulated or otherregions (Figure 5—figure supplement 1). In fact, the standard deviation of the BOLD signal fluctuations over time were strikingly similar between the two stimulation sites (SMA and FPC, highlighted
in Figure 5—figure supplement 1) and across the different experimental conditions (control, SMA
TUS, and FPC TUS). This suggests that TUS leaves intact basic haemodynamics and neurophysiology
and instead has a circumscribed and specific impact on the coupling of the stimulated region withthe rest of the brain.
Third, the presence of auditory and somatosensory confounds is likely to be a function of the specifics of the TUS protocol. In sonication protocols the ultrasound wave is often pulse modulated
at ~1 kHz, well within the audible range of many rodents and primates. At these modulation frequencies auditory stimulation is perhaps not unexpected (Guo et al., 2018; Sato et al., 2018). This issomething that we have avoided in our work with macaques: we pulse modulated the 250 kHz ultrasound wave at 10 Hz: as such we ensured that the frequency of both the ultrasound wave and its
modulating envelope are well outside of the macaque hearing range. Moreover, here we adopted
an offline experimental design where any potential audible stimulation associated with the TUS
application was limited to the 40 s sonication period, while the neural activation measures were initiated tens of minutes later. Furthermore, the specificity of our results strengthens the suggestion thatit might not be possible to explain away the current findings as the result of an auditory artefact having occurred up to two hours earlier.
Nevertheless, we also carried out a fourth line of inquiry and examined the activation in the primary auditory cortex (A1) and its relationship with activity in the rest of the brain (Figure 5g–i). The

Verhagen et al. eLife 2019;8:e40541. DOI: https://doi.org/10.7554/eLife.40541

10 of 28

Research article

Neuroscience

effects of TUS on SMA and FPC coupling patterns, as quantified in their connectional fingerprints,
could not be explained by a potential impact of TUS on A1 activity coupling. In fact, TUS did notaffect A1 activity coupling with SMA fingerprint targets (Figure 5g; non-parametric permutation
tests, SMA TUS: p=0.8234, FPC TUS p=0.3452), nor its coupling with FPC fingerprint targets(Figure 5h; non-parametric permutation tests, SMA TUS: p=0.5411, FPC TUS p=0.2667). Moreover,neither SMA nor FPC changed its coupling with A1 as a function of TUS (Figure 5i; main effect of
TUS: F(1,8)=0.015445, p=0.90416, d = 0.03583, CI=[ 0.41483 0.46209]; interaction of TUS x connectivity seed: F(1,8)=0.18284, p=0.68022, d = 0.1234, CI=[ 0.25058 0.36466]).

Non-neuronal signal changes
The ability of ultrasound to reversibly interact with biological tissue is not limited to grey matter. We
were aware that our ultrasonic beam, placed over the central midline to target SMA or FPC in bothhemispheres simultaneously, was also likely to reach the meningeal compartment in the interhemispheric fissure. In fMRI analyses this region is sometimes referred to as ‘cerebral spinal fluid’,
although in reality it contains the cortical membranes (dura, arachnoid, and pia mater), some cerebral spinal fluid, and important vascular structures, such as the superior sagittal sinus. Ultrasoundprotocols designed to induce vasodilation or to temporally open the blood-brain-barrier
are conventionally markedly distinct from those employed here, for example using higher intensities
or supplemented with intravenously injected microbubbles. Nonetheless, we set out to test the influence of ultrasound on what we shall continue to refer to as the ‘meningeal’ signal and the grey matter signal. To do this it was obviously necessary to take a somewhat unconventional rs-fMRI analysis
approach that did not remove the meningeal signal that is typically regarded as a confound.
A principal component analysis of the signal in the meningeal and grey matter compartments
revealed the main components in either compartment explained significantly more variance following TUS compared to control (Figure 6c; main effect of TUS: F(1,9)=30.6, p=0.00036, d = 0.67031,
CI=[2.1916 5.3343]; in grey matter: F(1,4)=10.743, p=0.0306, d = 1.3381, CI=[0.47735 5.7655]; inmeningeal compartment: F(1,4)=16.263, p=0.0157, d = 1.6464, CI=[1.3379 7.2512]). The fact that thiseffect is present in both compartments could reflect the tight vascular coupling between grey matterand meningeal signal or be driven by partial-voluming effects (these are more pronounced when thesize of the brain is relatively small, as for monkey fMRI). This observation suggests that after TUS the
BOLD signal became more homogenous. In a seed-based connectivity analysis, as performed here,
this would be reflected in a stronger contribution of global signal coupling. However, the impact of
TUS presented above does not seem to exhibit this effect, as illustrated by the specificity of the TUS
effects for SMA and FPC (Figures 3,5), and further underpinned by the absence of TUS effects inregions remote from the stimulation sites (for example, Figure 6a,d illustrates the case of the posterior parietal operculum, POp). Importantly, these coupling estimates are obtained when following
the conventional rs-fMRI analysis approach to account for global signal confounds by removing WM
and meningeal signal contributions before estimating grey matter coupling indices.
We hypothesized that if TUS leads to more homogenous global signal, its contribution to thegrey matter signal might have been accounted for when removing meningeal signal components ina linear regression framework (Verhagen, 2012). Accordingly, we have repeated the seed-based
connectivity analyses after accounting for global signal confounds based on the white matter compartment alone, excluding the meningeal compartment. In these data, global signal contributions
were indeed preserved as evidenced by anatomically implausible global connectivity patterns present in the control state (compare panels a and b in Figure 6, e.g. the prefrontal cortex, encircled).
As such, this procedure allowed us to interrogate the global effects of TUS over dorsomedial frontal
regions on BOLD signal (see Materials and Methods for full details). Following this procedure, we
observed that SMA stimulation appeared to induce widespread increases in signal coupling compared to the control state. This effect was not limited to the stimulation site but also present inremote regions (e.g. when seeded in POp, compare panels b and e in Figure 6). This effect can bequantified by considering the strength of local connections for every point in the cortex. It is thenapparent that the changes induced by stimulation are global in nature (Figure 6f). In general, when
not fully accounting for global confounds, a region’s connectivity profile after TUS could be predicted by considering its profile in the control state and adding a spatially flat constant. This suggests an additive non-neuronal source, captured by signal components in the meningeal
compartment, may explain the presence and enhancement of global signal observed following TUS

Verhagen et al. eLife 2019;8:e40541. DOI: https://doi.org/10.7554/eLife.40541

11 of 28

Research articlebc
POp connectivity(confounded by non-neuronal signal) 20

POp connectivity(unconfounded)variance explained (%)

a

Neuroscience

prefrontal cortex

controlstate* POp seed*

compartment PCA
control
TUS

*

15

*

*

10

5

0

GM

d

TUS overSMAe

SMA TUSSMA TUS*somatomotor
cortexf

TUS-induced widespreadmeningeal signal coupling*
coupling (Fisher’s z)-0.5gmeningeal
compartment0

Δ -coupling (Fisher’s z)
0.5

-0.4

0

0.4

temporal evolution of TUS-induced meningeal signal coupling~1.25h after TUS~1h after TUS~1.50h after TUS

control vs.
SMA TUSΔ -coupling (Fisher’s z)
-0.4

0

0.4

Figure 6. Non-neuronal signal changes driving widespread coupling. In fully processed fMRI data, unconfounded by global nuisance sources, effects of
TUS were not immediately apparent outside the stimulated areas. (a,d) For example, the whole-brain connectivity pattern of the parietal operculum
area (POp, spatially removed from either stimulation site) was unaffected by TUS over SMA. (b) When nuisance contributions from the meningeal
compartment are not removed (but those from WM are), the presence of globally shared signal is evident: POp activity is now coupled with that inmany other areas. (e) POp’s global nuisance confounded functional connectivity pattern became stronger after SMA TUS. This was because SMA TUS
induced broad changes in BOLD signal even in the meningeal compartment, captured in a principal component analysis (c). Error bars denotestandard-error of the mean; asterisks denote effects of TUS and WM/meningeal compartment (p<0.05). (f) This led to many points in the cortex
exhibiting stronger coupling with other brain areas; all areas shown in red are points that have stronger coupling with the rest of the brain after TUS.
Note that this effect is present far beyond the stimulation site. (g) This widespread TUS-induced meningeal signal coupling persisted over time. Allconventions as in Figures 3,5.
DOI: https://doi.org/10.7554/eLife.40541.010
The following figure supplement is available for figure 6:
Figure supplement 1. Widespread non-neuronal signal changes were replicated in experiments 2 and 3.
DOI: https://doi.org/10.7554/eLife.40541.011

over SMA (Figure 6e,f, e.g. primary sensorimotor cortex). These effects of TUS on widespread coupling mediated by meningeal signal persisted over time for more than 1 hr after stimulation had
ended (Figure 6g).
Similar effects of TUS on meningeal signal were also observed after FPC TUS (Figure 6—figure
supplement 1). We note that in unconfounded fMRI data FPC TUS did not have a strong impact outside the stimulated region (illustrated for POp in Figure 6—figure supplement 1). In contrast, in
fMRI data confounded by meningeal-driven global nuisance, FPC stimulation led to widespreadenhanced signal coupling compared to the control state. This effect of FPC TUS was replicated in a
new set of animals in experiment 3 (Figure 6—figure supplement 1). Although the effect was perhaps not as strong as that seen after SMA TUS, this finding again suggests that TUS over the medial
meningeal compartment may produce widespread changes that are non-neuronal in origin. Differences in morphology of the sagittal sinus along the rostro-caudal axis might explain this weakened
global effect.

Verhagen et al. eLife 2019;8:e40541. DOI: https://doi.org/10.7554/eLife.40541

12 of 28

Research article

Neuroscience

Experiment 4, meso- and micro-structural analyses
Some higher intensity ultrasound stimulation protocols, distinct from those used here, have beenshown to induce thermal lesions or haemorrhage following cavitation (Elias et al., 2013). Despite
the fact that 40 s trains of TUS induced sustained changes in the post-stimulation period in experiments 1 and 2, no structural changes remotely resembling those seen with higher intensity ultrasound protocols were observed. First of all, we did not observe any indication of TUS-induced
oedema when comparing T1w MRI structural scans collected in baseline sessions with T1w scans collected after TUS (Figure 7). We also did not observe tissue alteration (e.g. tissue burn) at the postmortem examination. Neither were any signs of neuronal alteration or haemorrhage observed in histological analyses of three macaques following pre-SMA TUS (Figure 8).

Acoustic and thermal modelling
To quantify the pressure amplitude, peak intensities, spatial distribution, and potential temperaturechanges in the monkey brain associated with the TUS protocol used in this study we simulated theacoustic wave propagation and its thermal effect in a whole head finite element model based on ahigh-resolution monkey CT scan. As estimated by these numerical simulations, the maximum spatialpeak pulse-averaged intensity (Isppa) at the acoustic focus point was 24.1 W/cm2 for the SMA target
and 31.7 W/cm2 for the FPC target (spatial peak temporal average intensities, Ispta: 7.2 W/cm2 and9.5 W/cm2 for SMA and FPC, respectively). Given that the skull is more acoustically absorbing thansoft tissue, the highest thermal increase is located in the skull itself, estimated by the simulation to
be 2.9˚C. Given an approximate 0.5 mm thickness of the dura (Galashan et al., 2011) the maximum
temperature below the dura was 38.0˚C. The maximal thermal increase at the geometrical focus of
the sonic transducer was less than 0.5˚C (Figure 2).

control

TUS

MK1

MK2

Figure 7. No effect of TUS was apparent on T1w structural MRI images. Examination of T1-weighted structural MRI images did not reveal any evidence
for oedema after TUS. For two monkeys (top and bottom row, respectively), T1w images were acquired immediately following the resting-state fMRI
runs in the control state (left column) and ~2 hr after TUS over FPC (right column). The sonication target regions are highlighted with dashed blueboxes.
DOI: https://doi.org/10.7554/eLife.40541.012

Verhagen et al. eLife 2019;8:e40541. DOI: https://doi.org/10.7554/eLife.40541

13 of 28

Research article

Neuroscience

Figure 8. No effect of TUS was apparent on histological examination. (a) Dorsal view of perfused macaque brain. A post-mortem examination of thebrain did not reveal macroscopic damage to the brain. (b) Histological assessment of 50-micron thick sections obtained at the level of the stimulationsite with Cresyl Violet Nissl staining and H and E staining did not reveal any evidence of thermal lesions or haemorrhage after the TUS protocol used
here. Magnified images are centred at the focal point of the stimulation.
DOI: https://doi.org/10.7554/eLife.40541.013

Discussion
In this study we demonstrate a protocol for transcranial focused ultrasound stimulation (TUS) that
can induce a sustained yet reversible change in neural activity. We focused our investigation on modulations of brain connectivity, following the notion that each brain area’s unique contribution to cognition and behaviour is shaped by how activity in each area is a function of a unique fingerprint ofinterconnected areas (Passingham et al., 2002). We found that each area’s connectional fingerprint
was significantly changed by TUS, but only when it was applied to that area itself (Figures 3,5). Thechanges observed might be summarized as more uniform activation in the stimulated region combined with a sharpening of the normal coupling pattern that each area has even at rest. Activity coupling with strongly interconnected areas, which are often nearby, was increased but activity couplingwith less strongly connected regions was reduced. Such changes in connectional fingerprints might
constitute the mechanism by which TUS is able to induce regionally specific patterns of behaviouralchange when applied in awake behaving animals (Deffieux et al., 2013; Fouragnan et al., 2019).

Verhagen et al. eLife 2019;8:e40541. DOI: https://doi.org/10.7554/eLife.40541

14 of 28

Research article

Neuroscience

The pattern of inputs each area receives from other areas and the influence it wields over other areas
are a major determinant of its function and here we have shown that this pattern is altered by TUS.
TUS may therefore provide a relatively straightforward method for sustained but reversible manipulation of specific components of neural circuits in the primate brain (Wattiez et al., 2017). This may
be important for investigating primate brain areas when homologues in non-primate species, such as
rodents, are non-existent or disputed (Preuss, 1995; Wise, 2008). This work paves the way for thedevelopment and use of offline TUS protocols in primates, including humans, both as a research tool
and as potential clinical intervention.
In experiments 1 and 2, adopting a within-subject design with three animals, we found that TUS
application produced different effects when applied to different brain regions: the SMA, a part of
motor association cortex, and FPC a part of granular prefrontal cortex (Figures 3,5). However, ineach case the TUS effects were prominent within the connectional fingerprint of the area stimulated.
The connectional fingerprints of SMA and FPC are distinct (Johansen-Berg et al., 2004;
Neubert et al., 2014; Sallet et al., 2013). The effects of TUS are thus regionally specific.
Our results confirm that TUS can be used as a neuromodulatory technique that allows one tonon-surgically target cortical and subcortical brain areas with superior spatial specificity and depthof stimulation (Folloni et al., 2019) compared to other transcranial stimulation approaches (e.g.
TMS and TCS; Bestmann and Walsh, 2017; Dayan et al., 2013; Polanı́a et al., 2018). While successes have been achieved with some invasive techniques, such as electrical microstimulation(Krug et al., 2015; Vanduffel, 2016), it is not easy to use them to disrupt activity in all areas especially when they are not somatotopically mapped. Recently it has been reported that some online
TUS protocols in rodents induce neural changes as an indirect consequence of the auditory stimulation they entail (Guo et al., 2018; Sato et al., 2018). The spatially specific effects that we observed
after TUS cannot, however, be attributed to any common auditory impact that occurs at the time ofstimulation. Moreover, in order to avoid both the confounding effect associated with the sound of
TUS and interference of the ultrasonic wave field with fMRI measurement, we opted for an ‘offline’
stimulation protocol. Stimulation was a 40 s train that ended at least 20 min before the fMRI dataacquisition period. The sustained nature of the train and other features of the stimulation pulses may
make the protocol used here more effective for neuromodulation, while ensuring the thermal modulation of the cortex remains limited (<1˚C, Figure 2). Such limited thermal changes are not associated with neuromodulatory effects observed more than 30 min after the stimulation: the thermal riseis short-lived (Dallapiazza et al., 2018), not accompanied by tissue damage and below the thermal
effects observed with some protocols in rodents (for a review, see Constans et al., 2018).
In experiment 3 we found that TUS had reproducible effects. When TUS was applied to FPC in
three other individuals, it induced spatially specific effects similar to those seen in experiment 2.
The effects of the non-invasive 40 s stimulation protocol used here are sustained and lasted overmuch of the two-hour period we investigated. These effects are more extended than those produced by other techniques commonly used for offline disruption of cortical activity such as TMS(Huang et al., 2005; O’Shea et al., 2007). Care should therefore be taken in using the technique inhuman cognitive neuroscience experiments; TUS effects may continue beyond the short periods thatparticipants typically spend within the laboratory. It may therefore be important to carefully characterize the time course of TUS effects in animal models before their use with human participants.
Some caution might also be warranted in relation to the potential of ultrasound to cause microstructural damage, especially when stimulating at higher intensities, longer durations, or for more repetitions. While our structural MRI and histological analyses did not reveal any evidence of damage andwere comparable to previous studies (Dallapiazza et al., 2018; Lee et al., 2016b), it would be ofinterest in the future to include additional histological indices of apoptotic or inflammatory processes(Tufail et al., 2010) to further assess the safety of TUS. Similarly, the thermal modelling approach
we adopted here was designed to estimate an informed upper-bound on potential thermal effects,
but when further developing this protocol additional simulation validations – for example based onphantom measurements – might be informative.
In addition to the spatially specific effects of TUS we also observed changes in the BOLD signal
originating from the meningeal compartment that were not specific to the area stimulated. Althoughsimilar effects were seen each time either the SMA or FPC was stimulated, the effect was generally
more pronounced after SMA stimulation than after FPC stimulation. Our preliminary results from
TUS of other brain areas suggest that these spatially non-specific effects may be even smaller when

Verhagen et al. eLife 2019;8:e40541. DOI: https://doi.org/10.7554/eLife.40541

15 of 28

Research article

Neuroscience

TUS is applied elsewhere (Folloni et al., 2019). While the precise origin of the non-specific effects
was difficult to determine it is possible that they may result from a direct vascular effect of TUS; thesagittal sinus is directly above the midline frontal regions that we targeted and is included in themeningeal compartment during MRI analysis protocols. The presence of such non-specific effects
again underlines the need for care in translating the technique to humans; especially when the targeted region is near venous sinuses or cerebral arteries. In addition, they underline the need forcomparing the behavioural effects of TUS not just with a non-stimulation sham condition but with
TUS application to another control brain region. The absence of marked histological changes inexperiment 4, however, provides one important safety benchmark and confirms previous histologicalresults in lagomorphs (Yoo et al., 2011).
Combining TUS and fMRI is a promising approach to overcome the restrictions of each of theindividual techniques. Here we have shown that TUS has a detectable offline and sustained impact
on the distinctive network of connectivity associated with the stimulated brain region – the connectional fingerprint. A brain region’s interactions with other regions – its unique connectional fingerprint or specific pattern of inputs and outputs – are an important determinant of its functional role.
The current results are therefore consistent with TUS application exerting regionally specific effects
on behaviour (Deffieux et al., 2013; Fouragnan et al., 2019). The fact that fMRI allows the effects
of TUS to be studied with a high spatial resolution suggests the TUS-fMRI combination has thepotential to become a powerful neuroscientific tool.

Materials and methods
Subject details
For this study, six healthy male macaques (Macaca mulatta, NCBITaxon:9544) were stimulated withtranscranial focused ultrasound and scanned to acquire resting state functional magnetic resonanceimages (rs-fMRI) and anatomical MR images. Three animals participated in experiment 1 [SMA TUS]
(all males, mean age and weight at time of scan: 5.6 years, 10.7 kg). The same three animals participated in experiment 2 [FPC TUS] (at time of scan: 6.1 years, 11.8 kg), and the control condition (attime of scan: 5.5 years, 10.2 kg). Three different animals participated in experiment 3 [FPC TUS validation] (all males, mean age and weight at time of scan: 10.3 years, 13 kg). In addition to this set ofanimals, six animals were included in the histology analysis (experiment 4): three control animals who
did not receive TUS (two females; mean age and weight at time of perfusion: 9.3 years, 9.1 kg) andthree pre-SMA TUS animals post stimulation (all males; mean age and weight at time of perfusion:
8.4 years, 13.1 kg).
All procedures were conducted under licenses from the United Kingdom (UK) Home Office inaccordance with The Animals (Scientific Procedures) Act 1986. In all cases they complied with theEuropean Union guidelines (EU Directive 2010/63/EU).

Ultrasound stimulation
A single element ultrasound transducer (H115-MR, diameter 64 mm, Sonic Concept, Bothell, WA,USA) with 51.74 mm focal depth was used with a coupling cone filled with degassed water andsealed with a latex membrane (Durex). The resonance frequency of the ultrasonic wave was set at
250 kHz with 30 ms bursts of ultrasound generated every 100 ms, controlled through a digital function generator (Handyscope HS5, TiePie engineering, Sneek, The Netherlands). The stimulation
lasted for 40 s. A 75-Watt amplifier (75A250A, Amplifier Research, Souderton, PA) was used todeliver the required power to the transducer. A TiePie probe (Handyscope HS5, TiePie engineering,
Sneek, The Netherlands) connected to an oscilloscope was used to monitor the voltage delivered.
The recorded peak-to-peak voltage was kept constant throughout the stimulation. Voltage values
per session ranged from 130 to 142 V, corresponding to 1.17 to 1.35 MPa as measured in waterwith an in house heterodyne interferometer (Constans et al., 2017). Based on numerical simulations(see Acoustic and thermal modelling below for more details), the maximum peak pressure (Pmax) andIsppa at the acoustic focus point were estimated to be 0.88 MPa and 24.1 W/cm2 for the SMA target,and 1.01 MPa and 31.7 W/cm2 for the FPC target (Ispta: 7.2 W/cm2 and 9.5 W/cm2 for SMA and
FPC, respectively). Each of the areas targeted in experiments 1–4 lie close to the midline. Therefore,

Verhagen et al. eLife 2019;8:e40541. DOI: https://doi.org/10.7554/eLife.40541

16 of 28

Research article

Neuroscience

we applied a single train over the midline stimulating the target region in both hemispheres
simultaneously.
In order to direct TUS to the target region, we guided the stimulation using a frameless stereotaxic neuronavigation system (Rogue Research, Montreal, CA; RRID:SCR_009539) set up for eachanimal individually by registering a T1-weighted MR image to the animal’s head. Positions of boththe ultrasound transducer and the head of the animal were tracked continuously with infrared reflectors to inform online and accurate positioning of the transducer over the targeted brain region: SMA
in experiment 1, (Montreal Neurological Institute (MNI) X, Y, and Z coordinates in mm [0.1 2 19]);
FPC in experiment 2 [0.6 24 10]; FPC in experiment 3 [-0.7 24 11]; pre-SMA in experiment 4 [0.2 1117]. The ultrasound transducer/coupling cone montage was placed directly onto previously shaved
skin prepared with conductive gel (SignaGel Electrode; Parker Laboratories Inc.) to ensure ultrasoniccoupling between the transducer and the animal’s scalp. In the non-stimulation condition (control),all procedures (anaesthesia, pre-scan preparation, fMRI scan acquisition and timing), with the exception of actual TUS, matched the TUS sessions.

Acoustic and thermal modelling
The acoustic wave propagation of our focused ultrasound protocol (at 130 V peak-to-peak voltage)
was simulated using a k-space pseudospectral method-based solver, k-Wave (Cox et al., 2007) toobtain estimates for the pressure amplitude, peak intensity, spatial distribution, and thermal impact
at steady state. 3D maps of the skull were extracted from a monkey CT scan (Kyoto University online
database, ID 1478, 0.26 mm isotropic resolution). Soft tissues were assumed to be homogeneous,with acoustic values of water (tissue =1000 kg/m3 and ctissue =1500 m/s). In the bone, a linear relationship between the Hounsfield Units (HU) from the CT scan and the sound speed, as well as thedensity, was used. The power law model for attenuation is aatt ¼ a1  fb where the porosity f ismax 
defined by f ¼ max
water in the skull (Aubry et al., 2003). The attenuation coefficient for the acoustic
propagation

depends

a1

on

the

frequency:

a1 ¼ a0 f b .

3

We

set

the

parameterstobmax ¼ 2200 kg=m ; cmax ¼ 3100 m=s; b ¼ 0:5; a0 ¼ 8 dB=cm=MHz ; b ¼ 1:1 (Constans et al., 2018).
The attenuation coefficient in bone accounts for both absorption and scattering.
The propagation simulation was performed at 250 kHz with a 150ms-long pulse signal (enough toreach a steady state). The transducer was modelled as a spherical section (63 mm radius of curvatureand 64 mm active diameter). The simulated pulses were spatially apodized (r = 0.35) on the sphericalsection. Ultrasound propagates first through water before entering the skull cavity with the geometrical focal point located below the surface, inside the brain. Simulations were performed in freewater, and the maximum amplitude obtained was used to rescale the results in skull (the transducer
calibration indicates that the maximum amplitude in water at 130V is 1.2 MPa). The thermal modelling is based on the bio-heat equation (Pennes, 1948):
CqT¼ kr2 T þ q þ wb Cb ðT
qt

Ta Þ

where T, , C, k and q are the temperature, density, specific heat, thermal conductivity and rate
P2
of heat production respectively. Heat production is defined as q ¼ aabs 2C- 1, aabs - 1 being theabsorption coefficient and P the peak negative pressure. k is set to 0.528 W.m 1.K 1 in soft tissue
and 0.4 W.m 1.K 1 in the skull; C is set to 3600 J.kg 1.K 1 in soft tissue and 1300 J.kg 1.K 1 in theskull(Duck,
2013).
Inthetissue,theabsorption
coefficientwasset
to aabs tissue ¼ 0:21 dB=cm=MHzb - 1 (Goss et al., 1979). In the skull the longitudinal absorption coefficient is proportional to the density with aabs max ¼ a0 =3 ¼ 2:7dB=cm=MHzb - 1(Pinton et al., 2012).
The last term corresponds to the perfusion process: w, b, Cb, and Ta correspond to the blood perfusion rate, blood density, blood specific heat and blood ambient temperature respectively. These
parameters are assumed homogeneous over the brain, although a more detailed description of thebrain cooling processes can be found in the literature (Wang et al., 2015). The perfusion parameters
are based on previous reports (Pulkkinen et al., 2011): w=0.008s 1; b= 1030 kg.m 3; Cb = 3620 J.
kg 1.K 1 and Ta = 37˚C.
The bioheat equation is solved by using a 3D finite-difference scheme in MATLAB (Mathworks,
Natick, USA) with Dirichlet boundary conditions. Initial temperature conditions were 37˚C in the

Verhagen et al. eLife 2019;8:e40541. DOI: https://doi.org/10.7554/eLife.40541

17 of 28

Research article

Neuroscience

brain, skull and tissue, and 24˚C in the water coupling cone. Simulations were run over 1 min presonication, followed by 40 s of sonication, and 5 min post-sonication, closely following the experimental procedure.

Macaque MRI acquisition
We acquired one MRI session per monkey per condition: in total we performed three sessions per
animal across experiments 1 and 2, and one session per animal in experiment 3. The ultrasound sonication and subsequent MRI scans were performed under inhalational isoflurane gas anaesthesia
using a protocol which has previously proven successful in preserving whole-brain functional connectivity as measured with BOLD signal (Mars et al., 2013; Mars et al., 2011; Neubert et al., 2015;
Neubert et al., 2014; O’Reilly et al., 2013; Sallet et al., 2013; Vincent et al., 2007). In the case of
the TUS conditions, fMRI data collection began only after completion of the TUS train. Anaesthesia
was induced using intramuscular injection of ketamine (10 mg/kg), xylazine (0.125–0.25 mg/kg), andmidazolam (0.1 mg/kg). Macaques also received injections of atropine (0.05 mg/kg, intramuscularly),
meloxicam (0.2 mg/kg, intravenously), and ranitidine (0.05 mg/kg, intravenously). The anaesthetizedanimals were placed in an MRI-compatible frame (Crist Instruments) in a sphinx position and placedin a horizontal 3 T MRI scanner with a full-size bore. Scanning commenced ~2 hr after induction,
when the clinical peak of ketamine had passed. Anaesthesia was maintained, in accordance with veterinary recommendation, using the lowest possible concentration of isoflurane to ensure that macaques were anaesthetized. The depth of anaesthesia was assessed and monitored using physiologicalparameters (heart rate and blood pressure, as well as clinical checks before the scan for muscle
relaxation). During the acquisition of the functional data the expired isoflurane concentration was in
the range 0.6–0.8%. Isoflurane was selected for the scans as it was previously demonstrated to preserve rs-fMRI networks (Mars et al., 2013; Mars et al., 2011; Neubert et al., 2015; Neubert et al.,2014; O’Reilly et al., 2013; Sallet et al., 2013; Vincent et al., 2007). Macaques were maintainedwith intermittent positive pressure ventilation to ensure a constant respiration rate during the functional scan, and respiration rate, inspired and expired CO2, and inspired and expired isoflurane concentration were monitored and recorded using VitalMonitor software (Vetronic Services Ltd.). Core
temperature and SpO2 were also constantly monitored throughout the scan. A four-channel phasedarray coil was used for data acquisition (Dr. H. Kolster, Windmiller Kolster Scientific, Fresno, CA,USA).
FMRI data were collected in experiments 1–3. In each session whole-brain BOLD fMRI data werecollectedfor 3 runs of approximately 26 min each, using the following parameters: 36 axial slices; inplane resolution, 2  2 mm; slice thickness, 2 mm; no slice gap; TR, 2000 ms; TE, 19 ms; 800 volumes per run. A minimum period of 10 days elapsed between sessions.
T1-weighted structural MRI scans were collected in experiments 1–3. A structural scan (averageover up to three T1w images acquired in the same session) was acquired for each macaque in thesame session, using a T1 weighted magnetization-prepared rapid-acquisition gradient echo
sequence (0.5  0.50.5 mm voxel resolution).

Macaque anatomical MRI pre-processing
The pre-processing and analysis of the MRI data was designed to follow the HCP Minimal Processing
Pipeline (Glasser et al., 2013), using tools of FSL (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki; RRID:SCR_
002823), HCP Workbench (https://www.humanconnectome.org/software/connectome-workbench;
RRID:SCR_008750), and the Magnetic Resonance Comparative Anatomy Toolbox (MrCat; https://github.com/neuroecology/MrCat; copy archived at https://github.com/elifesciences-publications/
MrCat; Verhagen, 2019). The T1w images were processed in an iterative fashion cycling throughbrain-extraction, RF bias-field correction, and linear and non-linear template registration to the
Macaca mulatta F99 atlas (Van Essen and Dierker, 2007). The initial skull stripping was performed
using a multi-seeded implementation of BET (Smith, 2002) optimized for macaque brains, while subsequent brain extraction was based on a high-fidelity template registered to the F99 macaquespace. The RF bias-field was estimated and corrected using a robust implementation of FAST
(Zhang et al., 2001). Linear and non-linear registration to F99 space was achieved using FLIRT(Jenkinson et al., 2002; Jenkinson and Smith, 2001) and FNIRT (Andersson et al., 2007;
Jenkinson et al., 2012) with configurations adjusted to reflect macaque rather than human brain

Verhagen et al. eLife 2019;8:e40541. DOI: https://doi.org/10.7554/eLife.40541

18 of 28

Research article

Neurosciencecharacteristics. The application of a robust and macaque-optimised version of FAST also resulted
inestimated compartments for grey matter, white matter, and meninges with cerebral spinal fluid.
For each compartment a posterior-probability map was created by integrating a set of prior probability maps based on 112 Macaca mullata individuals (McLaren et al., 2009) with the dataset-specificevidence provided by FAST. Segmentation of subcortical structures was achieved by registration to
the D99 atlas (Reveley et al., 2017).

Macaque rs-fMRI pre-processing
The first 5 volumes of the functional EPI datasets were discarded to ensure a steady RF excitation
state. EPI timeseries were motion corrected using MCFLIRT. Given that the animals were anaesthetized and their heads were held in a steady position, any apparent image motion, if present at all, iscaused by changes to the B0 field and temperature, rather than by head motion. Accordingly, theparameter estimates from MCFLIRT can be considered to be ‘B0-confound parameters’ instead.
Each timeseries was checked rigorously for spikes and other artefacts, both visually and using automated algorithms; where applicable slices with spikes were linearly interpolated based on temporally
neighbouring slices. Brain extraction, bias-correction, and registration was achieved for the functional EPI datasets in an iterative manner, similar to the pre-processing of the structural images withthe only difference that the mean of each functional dataset was registered to its corresponding
T1w image using rigid-body boundary-based registration (FLIRT). EPI signal noise was reduced bothin the frequency and temporal domain. The functional timeseries were high-pass filtered with a frequency cut-off at 2000 s. Temporally cyclical noise, for example originating from the respiration
apparatus, was removed using band-stop filters set dynamically to noise peaks in the frequency
domain of the first three principal components of the timeseries.
To account for remaining global signal confounds we considered the signal timeseries in whitematter (WM) and meningeal compartments. Specifically, the WM + meningeal confound timeseries
was described by the mean time course and the first five subsequent principal components of the
combined WM and meningeal compartment (considering only voxels with a high posterior probability of belonging to the WM or meningeal compartment, obtained in the T1w image using FAST).
The principal components of the WM + meningeal signal were estimated using a singular value
decomposition approach. The B0 confound parameter estimates obtained from MCFLIRT wereexpanded as a second degree Volterra series to capture both linear and non-linear B0 effects.
Together the WM + meningeal and expanded B0 confound parameters were regressed out of the
BOLD signal for each voxel.
In a separate analysis (Figure 6 and Figure 6—figure supplement 1), to assess the contributionof the meningeal compartment signal we repeated the identical procedure as above, with the only
difference that the mean and principal components were extracted from signal in the WM compartment alone, excluding the meningeal compartment.
Following this confound cleaning step, the timeseries were low-pass filtered with a cut-off at 10 s.
The cleaned and filtered signal was projected from the conventional volumetric representation (2mm voxels) to the F99 cortical surface (~1.4 mm spaced vertices), while maintaining the subcorticalvolumetric structures. The data were spatially smoothed using a 3 mm FWHM gaussian kernel, whileconsidering the folding of the cortex and the anatomical boundaries of the subcortical structures.
Lastly, the data timeseries were demeaned to prepare for functional connectivity analyses.

Macaque rs-fMRI analyses
To represent subject effects, the timeseries from the three runs were concatenated to create a single
timeseries of 3  ð800 5Þ ¼ 2385 volumes per animal per intervention (control, SMA TUS, FPC TUS).
To represent group effects the run-concatenated timeseries of all animals were combined using agroup-PCA approach resulting in a series of 200 volumes representing the principal components(Smith et al., 2014).
We report on the whole-brain functional coupling of the stimulation sites in the control state andafter TUS, adopting a seed-based correlation analysis approach. Effects of TUS are quantified basedon a limited set of regions, a ‘fingerprint’, whose strength of interconnection with macaque SMA or
FPC is known from anatomical tracing studies (Bates and Goldman-Rakic, 1993; Dum and Strick,
2005; Geyer et al., 2000; Lu et al., 1994; Petrides and Pandya, 2007; Strick et al., 1998) andVerhagen et al. eLife 2019;8:e40541. DOI: https://doi.org/10.7554/eLife.40541

19 of 28

Research article

Neurosciencefrom studies of fMRI activity coupling under anaesthesia (Neubert et al., 2015; Neubert et al.,2014; Sallet et al., 2013). For example, SMA is strongly interconnected with core nodes in the sensorimotor network, including M1, SPL, and MCC. The unique profile of interconnections characterizing a functional area is defined by a pattern of both strong and limited interconnections(Passingham et al., 2002). Accordingly, we selected a set of regions based on either distinctively
strong or distinctively limited interconnection and fMRI coupling with SMA or FPC. In fact, as a resultof the complementary patterns of connections defining SMA and FPC (Petrides and Pandya, 2007),
areas that were included based on their known strong connection and coupling with FPC (9m, 946d, PCC, IPLc, midSTS) were also included because of their known limited connections and coupling with SMA. Furthermore, to characterize the effect of TUS targeted at FPC on adjacent prefrontal cortex, we also included additional ventromedial and orbital prefrontal regions with which FPC
shared less strong connections. To ensure a balanced distribution of fingerprint targets across thebrain and to ensure that both SMA and FPC fingerprints were comprised of the same number ofregions we consider areas 11m and aSTG in the SMA fingerprint, both regions with which the SMA
is weakly interconnected. Lastly, to test for specificity of the TUS effects, we included SMA as a target for the FPC fingerprint and FPC as a target for the SMA fingerprint.
To construct regions-of-interest (ROI) for SMA and FPC, circles of 4 mm radius were drawn on thecortical surface around the point closest to the average stimulation coordinate (Figure 2), in boththe left and the right hemisphere. The same procedure was used to define other bilateral corticalregions of interest, based on literature coordinates (Mars et al., 2011; Neubert et al., 2015;
Neubert et al., 2014; Sallet et al., 2013), to serve as seeds for connectivity analyses (A1,
Figure 5g–i; POp, Figure 6, Figure 6—figure supplement 1) or targets for the fingerprint analyses(Figure 5). Apart from the stimulation sites and primary auditory cortex, all ROIs were selectedbased on their known anatomical connectivity, of relevance because they are known to be distinctively strongly or distinctively weakly connected with the stimulation sites.
Coupling between the activity of each region of interest and the rest of the brain was estimatedby calculating the correlation coefficient between each point in the ROI and all other data points(Fisher’s z: inverse hyperbolic tangent of Pearson’s r, bounded at [ 2 2]). The resulting ‘connectivitymaps’ were averaged across all vertices/voxels in the ROI, and subsequently averaged across hemispheres. Accordingly, the final maps represent the average coupling of a bilateral ROI with the restof the brain. The fingerprints are obtained by extracting the average coupling with each target ROI
and averaging across the two hemispheres.
The strength of self-connections within SMA and FPC ROIs was estimated in the same way as thecoupling of either SMA or FPC with any remote ROI was determined. Namely, for each point in the
ROI the Fisher’s z-transformed correlation between this point’s timeseries and that of each of theother points in the ROI was calculated. Subsequently, for each point the resultant z-values were averaged, describing local coupling at that point, and averaged across the whole ROI to obtain a single
estimate of self-coupling per ROI (Figure 5c).
To assess the contribution of the meningeal compartment signal, we quantified the effect of TUS
for each point on the cortical surface in data that was not corrected for the meningeal signal (seeMacaque rs-fMRI pre-processing). For each point, we calculated it’s coupling with the rest of thebrain, sorted all Fisher’s z-values in ascending order, and extracted the z-value at the 98th-percentile.
Z-values at this level describe relatively strong coupling with the seed point and are often observedin the vicinity of the seed region or other strongly connected regions (depicted in bright yellow in
Figure 6), ensuring that irrelevant connections are ignored. As such, this simple statistic is wellsuited to capture any main effects of TUS on coupling strength, including those observed when comparing panels (b) and (e) in Figure 6. This statistic allows us to quantify the effect of TUS in a single
value for each point on the cortical surface, and to directly contrast the values obtained in the control state with those obtained after TUS (Figure 6f, Figure 6—figure supplement 1f,i).
We described the signal variance in the GM and meningeal compartments, or more specifically,
the variance explained by the first five principal components (Figure 6c, Figure 6—figure supplement 1 panel c), using the same approach that was used to decompose signal in WM or
WM + meningeal compartments in the EPI timeseries cleaning step, as described above. We defined
the explained variance as the sum of the first five eigenvalues of the covariance matrix of the compartment signals divided by the sum of all eigenvalues.

Verhagen et al. eLife 2019;8:e40541. DOI: https://doi.org/10.7554/eLife.40541

20 of 28

Research article

Neuroscience

Statistics
All suitable animals available at the time of experimentation took part in this study. Accordingly,
there was no pre-selection nor restriction for group allocation. No data was excluded for analysis.
Sample sizes could not be predetermined statistically in the absence of a prior literature reporting
relevant expected effect sizes; instead we adopted sample sizes similar to those reported in previous
publications detailing (interventional) macaque fMRI studies (Chau et al., 2015; O’Reilly et al.,2013; Papageorgiou et al., 2017). Data collection and analysis were not performed blind to theconditions of the experiments. We used a group PCA approach to define the centre of group rsfMRI effects (see Macaque rs-fMRI analyses). For all other effects the centre of the group is definedas the mean of the individual subject effects.
To report on the full extent of the TUS effects and the simple effects that drive any observed differences we reproduce the whole-brain functional connectivity maps unthresholded for the control,
SMA TUS, and FPC TUS conditions separately. In order to make a statistical comparison of the functional coupling of the SMA and FPC in the control and TUS conditions it is problematic to compare
coupling at each and every other point in the brain because there is a risk of false positive effects if
multiple comparisons are made. Given the limited sample sizes possible with non-human primate
experiments, however, there is a risk of false negative results if stringent correction for multiple comparisons is undertaken at the whole-brain level. Indeed, here we avoid these pitfalls and make inference on a single statistic that combines information from a planned limited set of regions defined bycoordinates from previous studies (Mars et al., 2011; Neubert et al., 2015; Neubert et al., 2014;
Sallet et al., 2013).
Importantly, rather than examining activity coupling between the seed area and each of the fingerprint ROIs in turn and risking potential false positive results, we compared the overall pattern ofcoupling using the method devised by Mars and colleagues (Mars et al., 2016); non-parametric permutation tests were performed on cosine similarity metrics summarizing pairs of fingerprints (SMA
TUS versus control and FPC TUS versus control). Each connectional fingerprint can be represented
as a multidimensional vector, with the number of dimensions corresponding to the number of target
ROIs in the fingerprint. The cosine similarity metric quantifies the angle between two of such multidimensional vectors. As such, it takes into account the shape of the fingerprint irrespective of its
mean amplitude and results in a single metric per pair of fingerprints, negating the necessity for correcting for multiple comparisons across fingerprint targets. To assess the likelihood of observing thiscosine similarity metric under the null-hypothesis of no difference between conditions, it needs to becompared against a distribution of metrics obtained from a random sample. To avoid non-compliance to the requirements for a conventional parametric approach (e.g. independence of sampling
and adherence to a Gaussian normal distribution) we applied a non-parametric permutation
approach, in which the parent distribution is empirically drawn by repeatedly (randomly) shuffling
the group-membership of data points and re-calculating the metric based on the shuffled labelling(e.g. data points acquired in the control state might be randomly assigned to the intervention condition, and vice versa). In this way the empirical distribution under the null-hypothesis does not rely on
assumptions about the shape of the distribution but will acknowledge dependencies between target
ROIs in the fingerprint; as such this approach will avoid inflation of type I error (Maris and Oostenveld, 2007; Winkler et al., 2014). The relatively small number of TUS sites, animals, and fMRI runs
allowed us to exhaustively test all possible permutations (24309) to obtain the true probability ofrejecting the null hypothesis.
All other statistical inferences were drawn in the context of generalized linear mixed-effects(GLME) models that considered the intercept and factorial design (including interactions whereappropriate) as fixed effects and the intercept and slope grouped per animal as random effects withpossible correlation between them (as implemented in MATLAB, Mathworks, Natick, USA). The
models were assumed to adhere to a normal distribution of the data (not formally tested) and werefitted using Maximum-Pseudo-Likelihood estimation methods where the covariance of the random
effects was approximated using Cholesky parameterization. Statistical significance was set at
a = 0.05, two-tailed, and estimated using conventional analyses of variance (ANOVA). For allparametric tests we report the test statistic (F), probability estimate (p), effect size (Cohen’s d), andthe lower and upper limits of the 95% confidence interval (CI). For all plots, the central tendency
across individual animals is derived directly from the group-PCA approach(Smith et al., 2014) or

Verhagen et al. eLife 2019;8:e40541. DOI: https://doi.org/10.7554/eLife.40541

21 of 28

Research article

Neurosciencedescribed as the mean across all animals (n = 3). Dispersion is described as the standard-error of themean (SE).

Histology
In experiment 4, prior to histological examination, animals were anaesthetized with sodium pentorbarbitone and perfused with 90% saline and 10% formalin. A post-mortem examination of the surface of the brain was conducted prior to the brain extraction. The brains were then removed andplaced in 10% sucrose formalin. The brains were blocked in the coronal plane at the level of the
lunate sulcus. Each brain was cut in 50 mm coronal sections. Every tenth section, two adjacent sections were retained for analysis whereby one was stained with Cresyl Violet (Nissl body staining) andthe other with Haemotoxylin and Eosin (H and E staining), in line with previous studies(Dallapiazza et al., 2018; Lee et al., 2016b).

Acknowledgements
We would like to thank Greg Daubney for his assistance with the histological preparation and analysis, and Dr. Constantin Coussios and Dr. Robin Cleveland for their technical support. Funding for thisresearch was provided by a Wellcome Trust UK Henry Dale fellowship (105651/Z/14/Z), Wellcome
Trust Senior Investigator Award (WT100973AIA), Medical Research Council UK (MRC) programme
grant (MR/P024955/1), MRC programme grant (G0902373), Wellcome Trust UK Grant (105238/Z/14/
Z), Wellcome Trust Sir Henry Wellcome fellowship (103184/Z/13/Z), Biotechnology and Biological
Sciences Research Council UK (BBSRC) grant (BB/N019814/1), The Netherlands Organisation for Scientific Research (NWO) grant (452-13-015), The Wellcome Centre for Integrative Neuroimaging(203139/Z/16/Z), Bettencourt Schueller Foundation, and the Agence Nationale de la Recherche
under the Future Investments program (ANR-10-EQPX-15).

Additional information
Funding
FunderGrant reference number

Author

Wellcome

203139/Z/16/Z

Lennart Verhagen
Davide Folloni
Daria EA JensenLéa Roumazeilles
Miriam C Klein-Flügge
Rogier B Mars
Matthew FS Rushworth
Jerome Sallet

Wellcome

WT100973AIA

Lennart Verhagen
Miriam C Klein-Flügge
Matthew FS Rushworth

Bettencourt Schueller FoundationCécile Gallea
Charlotte Constans
Harry Ahnine
Mathieu Santin
Stéphane Lehericy
Pierre Pouget
Jean-François Aubry

Agence Nationale de la Recherche

ANR-10-EQPX-15

Cécile Gallea
Charlotte Constans
Harry Ahnine
Mathieu Santin
Stéphane Lehericy
Pierre Pouget
Jean-François Aubry

Wellcome

105238/Z/14/Z

Davide Folloni

Verhagen et al. eLife 2019;8:e40541. DOI: https://doi.org/10.7554/eLife.40541

22 of 28

Research article

Neuroscience
Medical Research Council

MR/P024955/1

Davide Folloni
Miriam C Klein-Flügge
Matthew FS Rushworth
Jerome Sallet

Wellcome

103184/Z/13/Z

Miriam C Klein-Flügge

Biotechnology and Biological
Sciences Research Council

BB/N019814/1

Rogier B Mars

Nederlandse Organisatie voor 452-13-015
Wetenschappelijk Onderzoek

Rogier B Mars

Medical Research Council

G0902373

Matthew FS Rushworth
Jerome Sallet

Wellcome

105651/Z/14/Z

Jerome Sallet

The funders had no role in study design, data collection and interpretation, or thedecision to submit the work for publication.
Author contributions
Lennart Verhagen, Resources, Data curation, Software, Formal analysis, Supervision, Validation,
Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing;Cécile Gallea, Formal analysis, Investigation, Methodology, Writing—review and editing; Davide Folloni, Data curation, Formal analysis, Investigation, Methodology, Writing—review and editing; Charlotte Constans, Data curation, Software, Formal analysis, Investigation, Methodology, Writing—review and editing; Daria EA Jensen, Data curation, Formal analysis, Writing—review and editing;
Harry Ahnine, Léa Roumazeilles, Mathieu Santin, Bashir Ahmed, Investigation, Writing—review andediting; Stéphane Lehericy, Resources, Writing—review and editing; Miriam C Klein-Flügge, Rogier
B Mars, Software, Methodology, Writing—review and editing; Kristine Krug, Resources, Methodology, Writing—review and editing; Matthew FS Rushworth, Resources, Formal analysis, Supervision,
Funding acquisition, Investigation, Methodology, Writing—original draft, Project administration,
Writing—review and editing; Pierre Pouget, Conceptualization, Resources, Supervision, Investigation, Methodology, Project administration, Writing—review and editing; Jean-François Aubry, Conceptualization, Resources, Formal analysis, Supervision, Funding acquisition, Investigation,
Methodology, Project administration, Writing—review and editing; Jerome Sallet, Conceptualization, Resources, Data curation, Formal analysis, Supervision, Funding acquisition, Investigation,
Methodology, Project administration, Writing—review and editing
Author ORCIDs
Lennart Verhagen
http://orcid.org/0000-0003-3207-7929
Davide Folloni
http://orcid.org/0000-0003-1653-5969
Charlotte Constans
http://orcid.org/0000-0001-6378-9158
Miriam C Klein-Flügge
http://orcid.org/0000-0002-5156-9833
Kristine Krug
http://orcid.org/0000-0001-7119-9350
Pierre Pouget
http://orcid.org/0000-0002-4721-7376
Jean-François Aubry
http://orcid.org/0000-0003-2644-3945
Jerome Sallet
http://orcid.org/0000-0002-7878-0209
EthicsAnimal experimentation: All procedures were conducted under project licenses from the United
Kingdom (UK) Home Office in accordance with The Animals (Scientific Procedures) Act 1986. In allcases they complied with the European Union guidelines (EU Directive 2010/63/EU).

Decision letter and Author response
Decision letter https://doi.org/10.7554/eLife.40541.018
Author response https://doi.org/10.7554/eLife.40541.019

Verhagen et al. eLife 2019;8:e40541. DOI: https://doi.org/10.7554/eLife.40541

23 of 28

Research article

Neuroscience

Additional files
Supplementary files. Transparent reporting form
DOI: https://doi.org/10.7554/eLife.40541.014

Data availability
FSL can be downloaded from https://fsl.fmrib.ox.ac.uk/fsl/fslwiki. HCP Workbench can be downloaded from https://www. humanconnectome.org/software/connectome-workbench. For any information regarding MrCat please see http://www.rbmars.dds.nl/lab/toolbox.html; further inquiries canbe directed to Lennart Verhagen (lennart.verhagen@psy.ox.ac.uk). All dedicated software tools areavailable at https://github.com/neuroecology/MrCat. Data contributing to this work have been publicly deposited at the Wellcome Centre for Integrative Neuroimaging (https://git.fmrib.ox.ac.uk/lverhagen/offlinetus). For any inquiries regarding the data, please contact Jérôme Sallet (jerome.
sallet@psy.ox.ac.uk).
The following dataset was generated:
Author(s)Year

Dataset title

Dataset URL

Lennart Verhagen2019

offlineTUS

https://git.fmrib.ox.ac.
uk/lverhagen/offlinetus

Database and
Identifier
Wellcome Centre forIntegrative
Neuroimaging,offlinetus

References
Ahnine H, Constans C, Wattiez N, Tanter M, Aubry J-F, Pouget P. 2018. Long-Lasting modulation of visuomotor
activity by repetitive focused ultrasound stimulation of frontal CortexInternational society for therapeutic
ultrasound. Presented at the the 18th International Symposium on Therapeutic Ultrasound.
Airan RD, Butts Pauly K. 2018. Hearing out ultrasound neuromodulation. Neuron 98:875–877. DOI: https://doi.
org/10.1016/j.neuron.2018.05.031, PMID: 29879389
Andersson JLR, Jenkinson M, Smith S. 2007. Non-Linear Registration Aka Spatial Normalisation. In: FMRIBTechnial Report TR07JA2. Oxford: FMRIB Centre.
Aubry JF, Tanter M, Pernot M, Thomas JL, Fink M. 2003. Experimental demonstration of noninvasive transskull
adaptive focusing based on prior computed tomography scans. The Journal of the Acoustical Society ofAmerica 113:84–93. DOI: https://doi.org/10.1121/1.1529663, PMID: 12558249
Bates JF, Goldman-Rakic PS. 1993. Prefrontal connections of medial motor areas in the rhesus monkey. The
Journal of Comparative Neurology 336:211–228. DOI: https://doi.org/10.1002/cne.903360205, PMID: 7503997
Bestmann S, Walsh V. 2017. Transcranial electrical stimulation. Current Biology 27:R1258–R1262. DOI: https://doi.org/10.1016/j.cub.2017.11.001, PMID: 29207262
Blackmore J, Shrivastava S, Sallet J, Butler CR. 2018. Ultrasound neuromodulation; a review of results,
mechanisms and safety. Ultrasound in Medicine & Biology.
Caspari N, Arsenault JT, Vandenberghe R, Vanduffel W. 2018. Functional similarity of medial superior parietal
areas for Shift-Selective attention signals in humans and monkeys. Cerebral Cortex 28:2085–2099. DOI: https://doi.org/10.1093/cercor/bhx114, PMID: 28472289
Chau BK, Sallet J, Papageorgiou GK, Noonan MP, Bell AH, Walton ME, Rushworth MF. 2015. Contrasting roles
for orbitofrontal cortex and amygdala in credit assignment and learning in macaques. Neuron 87:1106–1118.
DOI: https://doi.org/10.1016/j.neuron.2015.08.018, PMID: 26335649
Constans C, Deffieux T, Pouget P, Tanter M, Aubry JF. 2017. A 200-1380-kHz quadrifrequency focusedultrasound transducer for neurostimulation in rodents and primates: transcranial in vitro calibration andnumerical study of the influence of skull cavity. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency
Control 64:717–724. DOI: https://doi.org/10.1109/TUFFC.2017.2651648, PMID: 28092531
Constans C, Mateo P, Tanter M, Aubry JF. 2018. Potential impact of thermal effects during ultrasonicneurostimulation: retrospective numerical estimation of temperature elevation in seven rodent setups. Physics
in Medicine & Biology 63:025003. DOI: https://doi.org/10.1088/1361-6560/aaa15c, PMID: 29235453
Cox BT, Kara S, Arridge SR, Beard PC. 2007. k-space propagation models for acoustically heterogeneous media:
application to biomedical photoacoustics. The Journal of the Acoustical Society of America 121:3453–3464.
DOI: https://doi.org/10.1121/1.2717409, PMID: 17552697
Dallapiazza RF, Timbie KF, Holmberg S, Gatesman J, Lopes MB, Price RJ, Miller GW, Elias WJ. 2018.
Noninvasive neuromodulation and thalamic mapping with low-intensity focused ultrasound. Journal of
Neurosurgery 128:875–884. DOI: https://doi.org/10.3171/2016.11.JNS16976, PMID: 28430035

Verhagen et al. eLife 2019;8:e40541. DOI: https://doi.org/10.7554/eLife.40541

24 of 28

Research article

Neuroscience
Dayan E, Censor N, Buch ER, Sandrini M, Cohen LG. 2013. Noninvasive brain stimulation: from physiology to
network dynamics and back. Nature Neuroscience 16:838–844. DOI: https://doi.org/10.1038/nn.3422,
PMID: 23799477
Deffieux T, Younan Y, Wattiez N, Tanter M, Pouget P, Aubry JF. 2013. Low-intensity focused ultrasoundmodulates monkey visuomotor behavior. Current Biology 23:2430–2433. DOI: https://doi.org/10.1016/j.cub.
2013.10.029, PMID: 24239121
Duck FA. 2013. Physical Properties of Tissues: A Comprehensive Reference Book. Academic Press.
Dum RP, Strick PL. 2005. Frontal lobe inputs to the digit representations of the motor areas on the lateral surfaceof the hemisphere. Journal of Neuroscience 25:1375–1386. DOI: https://doi.org/10.1523/JNEUROSCI.3902-04.
2005, PMID: 15703391
Elias WJ, Khaled M, Hilliard JD, Aubry JF, Frysinger RC, Sheehan JP, Wintermark M, Lopes MB. 2013. A
magnetic resonance imaging, histological, and dose modeling comparison of focused ultrasound,radiofrequency, and gamma knife radiosurgery lesions in swine thalamus. Journal of Neurosurgery 119:307–
317. DOI: https://doi.org/10.3171/2013.5.JNS122327, PMID: 23746105
Folloni D, Verhagen L, Mars RB, Fouragnan E, Constans C, Aubry J-F, Rushworth MFS, Sallet J. 2019.
Manipulation of subcortical and deep cortical activity in the primate brain using transcranial focused ultrasoundstimulation. Neuron. DOI: https://doi.org/10.1016/j.neuron.2019.01.019
Fomenko A, Neudorfer C, Dallapiazza RF, Kalia SK, Lozano AM. 2018. Low-intensity ultrasoundneuromodulation: an overview of mechanisms and emerging human applications. Brain Stimulation 11:1209–
1217. DOI: https://doi.org/10.1016/j.brs.2018.08.013, PMID: 30166265
Fouragnan E, Chau BKH, Folloni D, Kolling N, Verhagen L, Klein-Flügge M, Tankelevitch L, Papageorgiou GK,
Aubry J-F, Sallet J, Rushworth MFS. 2019. The macaque anterior cingulate cortex translates counterfactual
choice value into actual behavioral change. Nature Neuroscience. DOI: https://doi.org/10.1101/336917
Galashan FO, Rempel HC, Meyer A, Gruber-Dujardin E, Kreiter AK, Wegener D. 2011. A new type of recording
chamber with an easy-to-exchange microdrive array for chronic recordings in macaque monkeys. Journal of
Neurophysiology 105:3092–3105. DOI: https://doi.org/10.1152/jn.00508.2010, PMID: 21451061
Galvan A, Caiola MJ, Albaugh DL. 2018. Advances in Optogenetic and chemogenetic methods to study brain
circuits in non-human primates. Journal of Neural Transmission 125:547–563. DOI: https://doi.org/10.1007/
s00702-017-1697-8, PMID: 28238201
Geyer S, Matelli M, Luppino G, Zilles K. 2000. Functional neuroanatomy of the primate isocortical motor system.
Anatomy and Embryology 202:443–474. DOI: https://doi.org/10.1007/s004290000127, PMID: 11131014
Ghahremani M, Hutchison RM, Menon RS, Everling S. 2017. Frontoparietal functional connectivity in the common
marmoset. Cerebral Cortex 27:3890–3905. DOI: https://doi.org/10.1093/cercor/bhw198, PMID: 27405331
Glasser MF, Sotiropoulos SN, Wilson JA, Coalson TS, Fischl B, Andersson JL, Xu J, Jbabdi S, Webster M,
Polimeni JR, Van Essen DC, Jenkinson M, WU-Minn HCP Consortium. 2013. The minimal preprocessing
pipelines for the Human Connectome Project. NeuroImage 80:105–124. DOI: https://doi.org/10.1016/j.
neuroimage.2013.04.127, PMID: 23668970
Goss SA, Frizzell LA, Dunn F. 1979. Ultrasonic absorption and attenuation in mammalian tissues. Ultrasound in
Medicine & Biology 5:181–186. DOI: https://doi.org/10.1016/0301-5629(79)90086-3, PMID: 556199
Guo H, Hamilton M, Offutt SJ, Gloeckner CD, Li T, Kim Y, Legon W, Alford JK, Lim HH. 2018. Ultrasoundproduces extensive brain activation via a cochlear pathway. Neuron 98:1020–1030. DOI: https://doi.org/10.
1016/j.neuron.2018.04.036, PMID: 29804919
Huang YZ, Edwards MJ, Rounis E, Bhatia KP, Rothwell JC. 2005. Theta burst stimulation of the human motor
cortex. Neuron 45:201–206. DOI: https://doi.org/10.1016/j.neuron.2004.12.033, PMID: 15664172
Jenkinson M, Bannister P, Brady M, Smith S. 2002. Improved optimization for the robust and accurate linear
registration and motion correction of brain images. NeuroImage 17:825–841. DOI: https://doi.org/10.1006/
nimg.2002.1132, PMID: 12377157
Jenkinson M, Beckmann CF, Behrens TE, Woolrich MW, Smith SM. 2012. FSL. NeuroImage 62:782–790.
DOI: https://doi.org/10.1016/j.neuroimage.2011.09.015, PMID: 21979382
Jenkinson M, Smith S. 2001. A global optimisation method for robust affine registration of brain images. Medical
Image Analysis 5:143–156. DOI: https://doi.org/10.1016/S1361-8415(01)00036-6, PMID: 11516708
Johansen-Berg H, Behrens TE, Robson MD, Drobnjak I, Rushworth MF, Brady JM, Smith SM, Higham DJ,
Matthews PM. 2004. Changes in connectivity profiles define functionally distinct regions in human medial
frontal cortex. PNAS 101:13335–13340. DOI: https://doi.org/10.1073/pnas.0403743101, PMID: 15340158
Kim H, Park MY, Lee SD, Lee W, Chiu A, Yoo SS. 2015. Suppression of EEG visual-evoked potentials in rats
through neuromodulatory focused ultrasound. NeuroReport 26:211–215. DOI: https://doi.org/10.1097/WNR.
0000000000000330, PMID: 25646585
Krug K, Salzman CD, Waddell S. 2015. Understanding the brain by controlling neural activity. PhilosophicalTransactions of the Royal Society B: Biological Sciences 370:20140201. DOI: https://doi.org/10.1098/rstb.2014.
0201
Kubanek J, Shi J, Marsh J, Chen D, Deng C, Cui J. 2016. Ultrasound modulates ion channel currents. Scientific
Reports 6:24170. DOI: https://doi.org/10.1038/srep24170, PMID: 27112990
Kubanek J, Shukla P, Das A, Baccus SA, Goodman MB. 2018. Ultrasound elicits behavioral responses throughmechanical effects on neurons and ion channels in a simple nervous system. The Journal of Neuroscience 38:3081–3091. DOI: https://doi.org/10.1523/JNEUROSCI.1458-17.2018, PMID: 29463641

Verhagen et al. eLife 2019;8:e40541. DOI: https://doi.org/10.7554/eLife.40541

25 of 28

Research article

Neuroscience
Lee W, Kim H, Jung Y, Song IU, Chung YA, Yoo SS. 2015. Image-guided transcranial focused ultrasoundstimulates human primary somatosensory cortex. Scientific Reports 5:8743. DOI: https://doi.org/10.1038/
srep08743, PMID: 25735418
Lee W, Kim HC, Jung Y, Chung YA, Song IU, Lee JH, Yoo SS. 2016a. Transcranial focused ultrasound stimulationof human primary visual cortex. Scientific Reports 6:34026. DOI: https://doi.org/10.1038/srep34026,
PMID: 27658372
Lee W, Lee SD, Park MY, Foley L, Purcell-Estabrook E, Kim H, Fischer K, Maeng LS, Yoo SS. 2016b. ImageGuided focused Ultrasound-Mediated regional brain stimulation in sheep. Ultrasound in Medicine & Biology
42:459–470. DOI: https://doi.org/10.1016/j.ultrasmedbio.2015.10.001, PMID: 26525652
Legon W, Sato TF, Opitz A, Mueller J, Barbour A, Williams A, Tyler WJ. 2014. Transcranial focused ultrasoundmodulates the activity of primary somatosensory cortex in humans. Nature Neuroscience 17:322–329.
DOI: https://doi.org/10.1038/nn.3620, PMID: 24413698
Legon W, Ai L, Bansal P, Mueller JK. 2018. Neuromodulation with single-element transcranial focused ultrasoundin human thalamus. Human Brain Mapping 39:1995–2006. DOI: https://doi.org/10.1002/hbm.23981, PMID: 293
80485
Lu MT, Preston JB, Strick PL. 1994. Interconnections between the prefrontal cortex and the premotor areas in
the frontal lobe. The Journal of Comparative Neurology 341:375–392. DOI: https://doi.org/10.1002/cne.
903410308, PMID: 7515081
Margulies DS, Vincent JL, Kelly C, Lohmann G, Uddin LQ, Biswal BB, Villringer A, Castellanos FX, Milham MP,
Petrides M. 2009. Precuneus shares intrinsic functional architecture in humans and monkeys. PNAS 106:20069–
20074. DOI: https://doi.org/10.1073/pnas.0905314106, PMID: 19903877
Margulies DS, Ghosh SS, Goulas A, Falkiewicz M, Huntenburg JM, Langs G, Bezgin G, Eickhoff SB, Castellanos
FX, Petrides M, Jefferies E, Smallwood J. 2016. Situating the default-mode network along a principal gradientof macroscale cortical organization. PNAS 113:12574–12579. DOI: https://doi.org/10.1073/pnas.1608282113,
PMID: 27791099
Maris E, Oostenveld R. 2007. Nonparametric statistical testing of EEG- and MEG-data. Journal of Neuroscience
Methods 164:177–190. DOI: https://doi.org/10.1016/j.jneumeth.2007.03.024, PMID: 17517438
Mars RB, Jbabdi S, Sallet J, O’Reilly JX, Croxson PL, Olivier E, Noonan MP, Bergmann C, Mitchell AS, Baxter
MG, Behrens TE, Johansen-Berg H, Tomassini V, Miller KL, Rushworth MF. 2011. Diffusion-weighted imaging
tractography-based parcellation of the human parietal cortex and comparison with human and macaqueresting-state functional connectivity. Journal of Neuroscience 31:4087–4100. DOI: https://doi.org/10.1523/
JNEUROSCI.5102-10.2011, PMID: 21411650
Mars RB, Sallet J, Neubert FX, Rushworth MF. 2013. Connectivity profiles reveal the relationship between brain
areas for social cognition in human and monkey temporoparietal cortex. PNAS 110:10806–10811. DOI: https://doi.org/10.1073/pnas.1302956110, PMID: 23754406
Mars RB, Verhagen L, Gladwin TE, Neubert FX, Sallet J, Rushworth MF. 2016. Comparing brains by matchingconnectivity profiles. Neuroscience & Biobehavioral Reviews 60:90–97. DOI: https://doi.org/10.1016/j.
neubiorev.2015.10.008, PMID: 26627865
McLaren DG, Kosmatka KJ, Oakes TR, Kroenke CD, Kohama SG, Matochik JA, Ingram DK, Johnson SC. 2009. A
population-average MRI-based atlas collection of the rhesus macaque. NeuroImage 45:52–59. DOI: https://doi.
org/10.1016/j.neuroimage.2008.10.058, PMID: 19059346
Munoz F, Aurup C, Konofagou EE, Ferrera VP. 2018. Modulation of brain function and behavior by focusedultrasound. Current Behavioral Neuroscience Reports 5:153–164. DOI: https://doi.org/10.1007/s40473-0180156-7, PMID: 30393592
Naor O, Krupa S, Shoham S. 2016. Ultrasonic neuromodulation. Journal of Neural Engineering 13:031003.
DOI: https://doi.org/10.1088/1741-2560/13/3/031003, PMID: 27153566
Neubert FX, Mars RB, Thomas AG, Sallet J, Rushworth MF. 2014. Comparison of human ventral frontal cortex
areas for cognitive control and language with areas in monkey frontal cortex. Neuron 81:700–713. DOI: https://doi.org/10.1016/j.neuron.2013.11.012, PMID: 24485097
Neubert FX, Mars RB, Sallet J, Rushworth MF. 2015. Connectivity reveals relationship of brain areas for rewardguided learning and decision making in human and monkey frontal cortex. PNAS 112:E2695–E2704.
DOI: https://doi.org/10.1073/pnas.1410767112, PMID: 25947150
O’Reilly JX, Croxson PL, Jbabdi S, Sallet J, Noonan MP, Mars RB, Browning PG, Wilson CR, Mitchell AS, Miller
KL, Rushworth MF, Baxter MG. 2013. Causal effect of disconnection lesions on interhemispheric functional
connectivity in rhesus monkeys. PNAS 110:13982–13987. DOI: https://doi.org/10.1073/pnas.1305062110,
PMID: 23924609
O’Shea J, Johansen-Berg H, Trief D, Göbel S, Rushworth MF. 2007. Functionally specific reorganization in human
premotor cortex. Neuron 54:479–490. DOI: https://doi.org/10.1016/j.neuron.2007.04.021, PMID: 17481399
Papageorgiou GK, Sallet J, Wittmann MK, Chau BKH, Schüffelgen U, Buckley MJ, Rushworth MFS. 2017.
Inverted activity patterns in ventromedial prefrontal cortex during value-guided decision-making in a less-ismore task. Nature Communications 8:1886. DOI: https://doi.org/10.1038/s41467-017-01833-5, PMID: 291921
86
Passingham RE, Stephan KE, Kötter R. 2002. The anatomical basis of functional localization in the cortex. Nature
Reviews Neuroscience 3:606–616. DOI: https://doi.org/10.1038/nrn893, PMID: 12154362
Pennes HH. 1948. Analysis of tissue and arterial blood temperatures in the resting human forearm. Journal ofApplied Physiology 1:93–122. DOI: https://doi.org/10.1152/jappl.1948.1.2.93, PMID: 18887578

Verhagen et al. eLife 2019;8:e40541. DOI: https://doi.org/10.7554/eLife.40541

26 of 28

Research article

Neuroscience
Petrides M, Pandya DN. 2007. Efferent association pathways from the rostral prefrontal cortex in the macaquemonkey. Journal of Neuroscience 27:11573–11586. DOI: https://doi.org/10.1523/JNEUROSCI.2419-07.2007,
PMID: 17959800
Pinton G, Aubry JF, Bossy E, Muller M, Pernot M, Tanter M. 2012. Attenuation, scattering, and absorption ofultrasound in the skull bone. Medical Physics 39:299–307. DOI: https://doi.org/10.1118/1.3668316,
PMID: 22225300
Polanı́a R, Nitsche MA, Ruff CC. 2018. Studying and modifying brain function with non-invasive brain stimulation.
Nature Neuroscience 21:174–187. DOI: https://doi.org/10.1038/s41593-017-0054-4, PMID: 29311747
Preuss TM. 1995. Do rats have prefrontal cortex? the rose-woolsey-akert program reconsidered. Journal of
Cognitive Neuroscience 7:1–24. DOI: https://doi.org/10.1162/jocn.1995.7.1.1, PMID: 23961750
Prieto ML, Ömer O, Khuri-Yakub BT, Maduke MC. 2013. Dynamic response of model lipid membranes toultrasonic radiation force. PLoS ONE 8:e77115. DOI: https://doi.org/10.1371/journal.pone.0077115, PMID: 24194863
Pulkkinen A, Huang Y, Song J, Hynynen K. 2011. Simulations and measurements of transcranial low-frequency
ultrasound therapy: skull-base heating and effective area of treatment. Physics in Medicine and Biology 56:
4661–4683. DOI: https://doi.org/10.1088/0031-9155/56/15/003, PMID: 21734333
Reveley C, Gruslys A, Ye FQ, Glen D, Samaha J, E Russ B, Saad Z, K Seth A, Leopold DA, Saleem KS. 2017.
Three-Dimensional digital template atlas of the macaque brain. Cerebral Cortex 27:4463–4477. DOI: https://doi.org/10.1093/cercor/bhw248, PMID: 27566980
Sallet J, Mars RB, Noonan MP, Neubert FX, Jbabdi S, O’Reilly JX, Filippini N, Thomas AG, Rushworth MF. 2013.
The organization of dorsal frontal cortex in humans and macaques. Journal of Neuroscience 33:12255–12274.
DOI: https://doi.org/10.1523/JNEUROSCI.5108-12.2013, PMID: 23884933
Sato T, Shapiro MG, Tsao DY. 2018. Ultrasonic Neuromodulation Causes Widespread Cortical Activation via an
Indirect Auditory Mechanism. Neuron 98:1031–1041. DOI: https://doi.org/10.1016/j.neuron.2018.05.009,
PMID: 29804920
Shen HH. 2015. Core concept: resting-state connectivity. PNAS 112:14115–14116. DOI: https://doi.org/10.1073/
pnas.1518785112, PMID: 26578753
Shen K, Hutchison RM, Bezgin G, Everling S, McIntosh AR. 2015. Network structure shapes spontaneousfunctional connectivity dynamics. Journal of Neuroscience 35:5579–5588. DOI: https://doi.org/10.1523/
JNEUROSCI.4903-14.2015, PMID: 25855174
Smith SM. 2002. Fast robust automated brain extraction. Human Brain Mapping 17:143–155. DOI: https://doi.
org/10.1002/hbm.10062, PMID: 12391568
Smith SM, Hyvärinen A, Varoquaux G, Miller KL, Beckmann CF. 2014. Group-PCA for very large fMRI datasets.
NeuroImage 101:738–749. DOI: https://doi.org/10.1016/j.neuroimage.2014.07.051, PMID: 25094018
Sternson SM, Roth BL. 2014. Chemogenetic tools to interrogate brain functions. Annual Review of Neuroscience
37:387–407. DOI: https://doi.org/10.1146/annurev-neuro-071013-014048, PMID: 25002280
Strick PL, Dum RP, Picard N. 1998. Motor areas on the medial wall of the hemisphere. Novartis Foundation
Symposium 218:64–75. PMID: 9949816
Tufail Y, Matyushov A, Baldwin N, Tauchmann ML, Georges J, Yoshihiro A, Tillery SI, Tyler WJ. 2010. Transcranial
pulsed ultrasound stimulates intact brain circuits. Neuron 66:681–694. DOI: https://doi.org/10.1016/j.neuron.
2010.05.008, PMID: 20547127
Tufail Y, Yoshihiro A, Pati S, Li MM, Tyler WJ. 2011. Ultrasonic neuromodulation by brain stimulation withtranscranial ultrasound. Nature Protocols 6:1453–1470. DOI: https://doi.org/10.1038/nprot.2011.371, PMID: 21886108Tyler WJ, Lani SW, Hwang GM. 2018. Ultrasonic modulation of neural circuit activity. Current Opinion in
Neurobiology 50:222–231. DOI: https://doi.org/10.1016/j.conb.2018.04.011, PMID: 29674264
Van Essen DC, Dierker DL. 2007. Surface-based and probabilistic atlases of primate cerebral cortex. Neuron 56:
209–225. DOI: https://doi.org/10.1016/j.neuron.2007.10.015, PMID: 17964241
Vanduffel W. 2016. In-Vivo Connectivity in MonkeysMicro-, Meso- and Macro-Connectomics of the Brain,
Research and Perspectives in Neurosciences. In: Micro-, Meso- and Macro-Connectomics of the Brain. Springer.
Verhagen L. 2012. How to Grasp a Ripe Tomato. Utrecht University; Donders Institute, Radboud University.
Verhagen L. 2019. The MR Comparative Anatomy Toolbox (Mr Cat). 15f8a00. Github. https://github.com/
neuroecology/MrCat
Vincent JL, Patel GH, Fox MD, Snyder AZ, Baker JT, Van Essen DC, Zempel JM, Snyder LH, Corbetta M, Raichle
ME. 2007. Intrinsic functional architecture in the anaesthetized monkey brain. Nature 447:83–86. DOI: https://doi.org/10.1038/nature05758
Wang H, Kim M, Normoyle KP, Llano D. 2015. Thermal regulation of the Brain-An anatomical and physiologicalreview for clinical neuroscientists. Frontiers in Neuroscience 9.
Wattiez N, Constans C, Deffieux T, Daye PM, Tanter M, Aubry JF, Pouget P. 2017. Transcranial ultrasonicstimulation modulates single-neuron discharge in macaques performing an antisaccade task. Brain Stimulation
10:1024–1031. DOI: https://doi.org/10.1016/j.brs.2017.07.007, PMID: 28789857
Winkler AM, Ridgway GR, Webster MA, Smith SM, Nichols TE. 2014. Permutation inference for the generallinear model. NeuroImage 92:381–397. DOI: https://doi.org/10.1016/j.neuroimage.2014.01.060, PMID: 24530
839
Wise SP. 2008. Forward frontal fields: phylogeny and fundamental function. Trends in Neurosciences 31:599–
608. DOI: https://doi.org/10.1016/j.tins.2008.08.008, PMID: 18835649

Verhagen et al. eLife 2019;8:e40541. DOI: https://doi.org/10.7554/eLife.40541

27 of 28

Research article

Neuroscience
Yoo SS, Bystritsky A, Lee JH, Zhang Y, Fischer K, Min BK, McDannold NJ, Pascual-Leone A, Jolesz FA. 2011.
Focused ultrasound modulates region-specific brain activity. NeuroImage 56:1267–1275. DOI: https://doi.org/
10.1016/j.neuroimage.2011.02.058, PMID: 21354315
Younan Y, Deffieux T, Larrat B, Fink M, Tanter M, Aubry JF. 2013. Influence of the pressure field distribution intranscranial ultrasonic neurostimulation. Medical Physics 40:082902. DOI: https://doi.org/10.1118/1.4812423,
PMID: 23927357
Zhang Y, Brady M, Smith S. 2001. Segmentation of brain MR images through a hidden markov random fieldmodel and the expectation-maximization algorithm. IEEE Transactions on Medical Imaging 20:45–57.
DOI: https://doi.org/10.1109/42.906424, PMID: 11293691

Verhagen et al. eLife 2019;8:e40541. DOI: https://doi.org/10.7554/eLife.40541

28 of 28