Mind Control as a Guide for the Mind [614665]

Mind Control as a Guide for the Mind
John D. Medaglia, Perry Zurnyz, Walter Sinnott-Armstrongx, Danielle S. Bassett{k
Department of Psychology, University of Pennsylvania, Philadelphia, PA, 19104 USA,yCenter for Curiousity, University of Pennsylvania, Philadelphia, PA, 19104 USA,zDepartment
of Philosophy, American University, Washington, DC, 20016 USA,xDepartment of Philosophy and Kenan Institute for Ethics, Duke University, Durham, NC, 27708 USA,{Department
of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104 USA, andkDepartment of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia,
PA, 19104 USA
The human brain is a complex network that supports mental func-
tion. The nascent eld of network neuroscience applies tools from
mathematics to neuroimaging data in the hopes of shedding light
on cognitive function. A critical question arising from these em-
pirical studies is how to modulate a human brain network to treat
cognitive decits or enhance mental abilities. While historically a
number of tools have been employed to modulate mental states
(such as cognitive behavioral therapy and brain stimulation), the-
oretical frameworks to guide these interventions { and to optimize
them for clinical use { are fundamentally lacking. One promising and
as-yet under-explored approach lies in a sub-discipline of engineering
known as network control theory . Here, we posit that network con-
trol fundamentally relates to mind control, and that this relationship
highlights important areas for future empirical research and oppor-
tunities to translate knowledge into practical domains. We clarify
the conceptual intersection between neuroanatomy, cognition, and
control engineering in the context of network neuroscience. Finally,
we discuss the challenges, ethics, and promises of mind control.
brain network jcontrollability jnetwork science jdiusion tractography j
cognitive control
Introduction
Mind control is a common plot device in many genres of c-
tion. Its ubiquity is perhaps unsurprising: the prospect of the
explicit, full control of the mind evokes alluring and startling
possibilities. Fictional mind control often takes implausi-
ble forms: telepathy, magical interventions, and nefarious
schemes of authoritarian organizations. In more biologically-
inspired plot lines, mind control is delivered by devices im-
planted in the subject's brain, as depicted in The Matrix :
these devices manipulate neurophysiological processes result-
ing in a change of mental state.
In reality, mind control encompasses numerous means for
in
uencing the mind. This includes eects mediated through
the senses. Sense-mediated eects can include positive in
u-
ences, such as updating one's beliefs based on presented ev-
idence, or \nudging" someone to make healthy decisions via
environmental manipulation [1]. They can also be more insid-
ious, as in the case of propaganda or brain washing. Beyond
social and environmental means, mind control can also result
from direct neural stimulation. Neural stimulation can include
subtle modulation via pharmacological agents or more direct
manipulations with brain stimulation that result in neural dis-
charges. The last few decades have seen a steady increase in
the use of implanted devices to assist individuals with major
mental disorders. The ubiquitous nature of social forms of
control and increasing prevalence of neural devices motivates
important questions about the control of brain processes and,
by extension, mental functions.
Developing a true science of mind control could benet
from the engineering discipline known as \control theory",
which addresses the question of how to guide complex systems
from one state to another [2]. As a commonplace example,
control systems in a modern airliner ensure that the aircraft
stays aloft by automatically adjusting the plane's pitch, roll,
and yaw to compensate for the turbulence in the air [3]. Likea plane, the brain is a physical system that is characterized
by specic states: in this case, patterns of neural activity.
The control or guidance of the brain from one state to an-
other can either be intrinsic (the brain controls itself [4]) or
extrinsic (the brain is guided externally, for example by brain
stimulation [5]).
Beyond these naturalistic forms of mind control,
stimulation-based interventions have been developed for clin-
ical cohorts, oering a powerful link between engineering and
neuroscience. The use of control theoretic approaches are
particularly prevalent in the booming eld of neuroprosthet-
ics [6, 7], which can be used to treat motor disorders such
as Parkinson's disease [8]. Here, control theory dictates e-
cient strategies for deep brain stimulation to regulate motor
functions mediated by subcortical areas. Similar eorts have
been developed to treat obsessive compulsive disorder [9] and
depression [10], suggesting their utility across functional do-
mains. Moreover, neural control is administered through non-
invasive yet powerful techniques such as transcranial magnetic
stimulation [11].
To better describe how control theory from engineering
can inform our understanding of mind control, it is useful to
invoke a network approach [4]. In a network perspective, neu-
ral components (such as individual neurons or entire brain
regions) are treated as nodes and connections between these
components are treated as network edges [12, 13, 14]. By us-
ing this conceptual framework, as well as the mathematical
formalism that accompanies it, we can explicitly study how
control input applied to one brain component can impact the
rest of the network [4, 15]. Indeed, the network formalism
allows us to capitalize on recent advances in network control
[16], which may prove useful in developing principled strate-
gies that aect the mind. In particular, these strategies can
theoretically guide neural systems, and thus cognition, toward
target states.
In this paper, we aim to brie
y review recently developed
concepts and bodies of literature pertinent to a formal sci-
ence of mind control. First, we introduce the general notion
of control in the engineer's sense. Then, we discuss recent ex-
tensions of this notion to network control. We highlight what
a control theoretic approach to the brain and mind implies
more broadly. We close with a discussion of social and ethi-
cal considerations that will emerge as mind control develops.
While we leave mathematical details to other excellent texts,
we seek throughout to provide the reader with intuitions that
are sucient to consider the state of the art, promises, and
challenges that lie ahead at the frontiers of mind control.
Control for networked systems
In the broadest sense, \control" is any form of physical in-

uence from one entity to another; in an engineering sense,
control indicates using input to move a system from an initial
state to a target state. Typically, control is exerted by a con-
troller Cvia control input, u, to change the state of a system
(or \plant" Pin engineering) (Fig. 1). The control strat-
egy may minimize the distance between the observed state of
the system and the target state, often while also maximizing
1arXiv:1610.04134v2 [q-bio.NC] 25 Apr 2017

technical simplicity and minimizing input [17]. For example,
the plant could be an aircraft, the controller could be the air-
craft's turbine engines, and the control input induces thrust.
The aircraft has sensors that monitor its state and continu-
ously report that information to the controller, which in turn
adapts its strategy to minimize the distance between the air-
craft's current trajectory and the target trajectory.
rt e utC P+
-st
FuC Psta
b
Fig. 1: Control Theory . Generic notions of control. (a): A
classic open-loop control scheme. Traditionally, a controller
(e.g., a system in the environment or designed device) delivers
an input uto the system under control (usually termed the
\plant" P) to in
uence the system state st. (b): A classic
closed-loop feedback control scheme. The goal is to guide the
system ( P) to a reference value rt. The system state stis
fed back through a sensor measurement Fto compare to the
reference value rt. The controller Cthen takes the error e
(dierence) between the reference and the output to change
the control inputs uto the system under control ( P).
A challenge in control theory is to design strategies that
interact naturally with a system's structure and dynamics. A
simple example is a child on a swing. In this system, an input
(a push) should be applied from the apex of the swing. A
small push at the apex is more eective in accelerating the
child than a large push at the bottom of the swing. This fact
illustrates an important principle for control: we should seek
solutions that involve low physical costs with relatively high
accuracy and precision in achieving our control goals [2]. In
more complex networked systems, we must understand the
structure and dynamics of the system as precisely as possible
to identify eective control strategies.
In the context of human cognition, the plant (system) is
the neural tissue that supports cognition { e.g., a single neu-
ron, an ensemble of neurons, a collection of brain regions, or
the whole brain [18, 8, 4, 19]. In deep brain stimulation in
Parkinson's disease, the controller is the stimulation device,
the input is an applied current, and the system is a portion of
the basal ganglia (often the globus pallidus or putamen). The
neural reference state is often represented in the frequency do-
main, and the neural control goal is to achieve a target state of
basal ganglia activity { and the rest of the motor system with
which it interacts { that facilitates unimpaired motor move-
ments [8, 20]. Beyond simple motor function, neural control
can be used to treat obsessive compulsive disorder, where pa-
tients engage in repetitive behaviors following overwhelming
urges to do so. Deep brain stimulation to subcortical circuitryeectively reduces symptoms of the disorder and substantially
improves quality of life [21], putatively by normalizing neural
activity in a fronto-striatal circuit [9].
One essential advantage in applying control theory to nat-
ural systems is that real stimuli tend to aect many parts of
systems, not just those directly targeted. Indeed, one chal-
lenge in neural stimulation is that input or stimulation to
one area aects others [5]. Progress in understanding this
complexity requires theory, mathematics, and nely resolved
network data to represent the interconnections between brain
areas that propagate input [22]. Control theory provides us
with principled approaches to identify strategies that in
u-
ence states in the context of indirect interactions within the
system [23]. Thus, in conjunction with brain connectomics
[24], control theory is particularly promising in our eorts to
guide the mind. Indeed, as we will discuss, control theory
may oer a much-needed framework for the use of many neu-
ral stimulation techniques to in
uence cognitive function in
health [25] and disease [26, 27, 28].
The study of how to control a complex network is com-
monly referred to as network control theory [3, 29]. A net-
worked system is represented as a \graph" of interconnected
elements, where nodes represent components of a system and
edges represent connections or interactions between nodes. In
the brain, nodes typically represent neural elements (e.g., neu-
rons or brain regions), and edges represent connections be-
tween nodes (e.g., axons, information, or white matter tracts
[30]). Network control theory is the study of how to design
control inputs to a network that can be used to guide the sys-
tem from an initial state to a target state (see Fig. 2) [15, 22].
Network control is conceptually appropriate for studying
how to aect the mind using time-varying input in specic
parts of the brain, thereby inducing trajectories of neural dy-
namics via anatomical connections to achieve precise goals at
a low cost. Here, the goal is to induce changes in neural states
over time that support cognitive functions. This highlights
an important dual nature to mind control: the neural control
goal is to induce movement from one neural state to another.
Thepsychological control goal presumably depends on neural
states, but can be sensibly discussed in its own terms. Indeed,
if no psychological, physical, or social consequences were as-
sociated with depression, there would be no such thing as a
mind control goal for depression.
2

b1
b2v1
v2
v3v4u1
u2
A14
A34A12
A24v1
v2
v3 v4s0sta bFig. 2: Network control. (a): Network nodes vare con-
nected through edges Aij. Control input ucan be adminis-
tered to nodes via controllers bin time-varying patterns to
obtain the best control strategy given knowledge of the sys-
tem and the nature of the controller. (b) Control input can
be designed to guide a network from an initial state ( s0) to a
target state later in time ( st). Figure adapted with permission
from [3].
Brain controllability and guiding the mind
In network control theory applied to the brain, we can con-
sider any case involving structural pathways in the brain (e.g.,
individual axons or white matter bundles) that connect neural
elements (e.g., single neurons or groups of neurons) to one an-
other. Initial applications of network control theory to the hu-
man connectome suggest that the brain may employ distinct
control strategies to guide mental processes [4]. Three well-
known cognitive systems display dierent patterns of struc-
tural connectivity estimated by white matter tractography,
and those patterns facilitate dierent types of control. For ex-
ample, the fronto-parietal system is a set of regions known to
facilitate the human's ability to switch between dierent tasks
[31, 32]. Interestingly, this system exhibits relatively sparse
anatomical connectivity with the rest of the brain. Results
from network control theory suggest that the fronto-parietal
system is optimized for moving the brain into dicult-to-
reach states [4] along an \energy landscape", which denes
the possible states and transitions of the network [33]. Dor-
sal and ventral attention systems [34, 35] have neither dense
nor sparse connectivity and may be optimized for integrating
or segregating other parts of the brain. Finally, the brain's
baseline system { the so-called \default mode" [36, 37, 38]
{ is strongly structurally connected with the brain and may
drive the brain to many easy-to-reach states. These ndings
suggest that the brain is organized into anatomically distinct
control systems [4].
Based on this previous work, it is plausible that distinct
brain regions could be candidate targets for interventions that
in
uence brain dynamics into distinct dynamic trajectories.
Supporting this notion, evidence from numerical simulations
suggests that the structural connections emanating from a
brain region directly impact the transmission of stimulation
from the targeted area to the rest of the brain [5]. In that
study, stimulation to regions in the default mode imparted
large global change in brain activity, suggesting the impor-
tance of considering individual dierences in white matter
tracts in brain stimulation protocols. In addition to being
a potential target for brain stimulation, regions in the default
mode may also play a role in homeostasis following stimula-tion, as evidence suggests that they are the least energetically
costly target state [15].
While these initial studies have focused on the large-scale
connectome of the human, network control theory can be ap-
plied more broadly. Presumably, cognition { and arguably its
control { occurs over multiple spatial resolutions [39], oer-
ing distinct targets for network control in support of mental
function (see Fig. 3). Neuroimaging continues to clarify the
macro scale organization of networks that supports cognitive
processes [30]. However, to gain increasing control over cog-
nitive function, a more fundamental characterization of cog-
nition will be required. Specically, in emerging theoretical
work on neural control across the structural connectome, the
mapping between brain dynamics and specic cognitive pro-
cesses will be critical to inform psychological (i.e., \mind")
control. At the level of organization associated with many
cognitive functions, neural control goals can focus on aect-
ing single neurons [40] to { in all likelihood, subtly { in
uence
cognition. In addition, control goals could involve neuronal
ensembles [41, 42, 43], and large-scale distributed neural cir-
cuits [44, 45, 30].
3

109
46
45
47
1144864
3
1
25
7
4039
4241 43
52
3822
21
203719 1817ab
cb1
b2v1
v2
v3v4u1
u2
A14
A34A12
A24d
v1
v2
v3 v4s0st
Fig. 3: Brain control. (a): The gross anatomical organization of the brain can be described by cytoarchitectonic regimes that
serve distinct roles in neural computation [46]. (b): Techniques such as diusion weighted imaging can provide information
about the macro-scale connectivity among brain regions (the \connectome" [24]). (c): Low level cellular organization facilitates
information processing and is embedded within the macro-scale connectome. (d): The structural and dynamic organization
of the brain at multiple scales can be represented as a networked system that can be guided using control input targeted to
specic brain regions.
4

Controlling specic mental functions
In reality, controlling mental functions would require a mar-
riage between network control theory and the neural processes
that enable cognition: namely, neural codes. Neural codes oc-
cur in several forms. These are typically thought to involve
temporal characteristics of neural ring within a certain popu-
lation of neurons [47]. The population may include relatively
few neurons to increase the information processing capacity ofa neural system [48]. Temporal rate codes can be represented
in the frequency domain, where information is represented in
how quickly neurons re [49, 50]. Temporal codes based on
spike timing encode information in the delay between a stim-
ulus and neural discharge [51]. Temporal codes can be mul-
tiplexed to increase encoding capacity, disambiguate stimuli,
and stabilize representations [40] (see Fig. 4 for a schematic
example in visual perception).
a b c
!
!!
!
s0
sts1 s2 s3
Stimuli & Spike Trains Neural Codes Controlling Codes
!
Fig. 4: Neural codes and cognition. A speculative schematic for the control of object perception. (a): Neurons transmit
information in the form of neural discharges or \spikes" associated with stimuli. Here, neural spike trains in distinct colors
represent dierent stimuli. (b): Neural spike trains can be analyzed to determine the nature of coding that supports distinct
representations and processes that constitute stimuli. The green exclamation point represents an arbitrary stimulus that can
(in principle) be represented by a number of possible codes. Top to bottom: a stimulus representation is maintained by a
population of neurons, which may use frequency or \rate" coding, latency coding, interspike interval codes, or the phase of
ring. (c): Top: the wine glass is represented by the initial neural state s0of two neurons in the four-neuron system. Middle to
bottom: control input is applied to dierent neurons in varying quantities over time to induce a transition to a state representing
the bicycle. The variable strepresents the state of the neural network at time t. Realizing the control strategy requires the
right neural population, code, and manipulating apparatus. While we select a visual perception example for clarity, a similar
intuition can apply to any neurocognitive process that involves temporal codes in populations of neurons.
5

Dening control goals for neural applications is challeng-
ing because the mapping from neural states and state transi-
tions to cognition is presently incomplete. There is tremen-
dous spatiotemporal detail we could attempt to specify within
neural systems. In addition, it is not known whether and to
what extent the neural states underlying any particular cog-
nitive process are memoryless [52] or are achieved by passing
through other states. Thus, a major challenge for any mind
control goal is to identify what is important and what can be
ignored. In clinical contexts, we often know that we want to
guide behavior away from a particular pattern. Even if we
can identify the neural state we want to avoid, however, we
may not necessarily know the neural state we want to achieve
[8]. Moreover, dierent individuals may use dierent cogni-
tive strategies to approach the same tasks [53], suggesting that
optimizing to individuals will require knowledge of neural rep-
resentations within a single individual. Indeed, we must care-
fully evaluate our models of neural health, neuropathology,
and neural repair with respect to our goals and the success of
our interventions.
While the nature and relevance of cognitive \represen-
tations" remains an open issue [54], a well-formed control
strategy includes a clearly dened neural control goal and a
mapping from neural state to cognitive representations and
processes. This fact highlights a potentially important role
for control theoretic approaches to drive fundamental discov-
ery in cognitive neuroscience. To move cognitive neuroscience
from correlation to causative models, a combination of direct
manipulation and observation is required. Correlative neu-
roscience can help us identify specic neural patterns that
may be associated with specic functions. Then, the pos-
sible causal nature of these relationships can be tested and
validated with experimental perturbations using stimulation.
If we tune control inputs to elicit desired cognitive eects,
we can rule out some control strategies, allowing us to iden-
tify cognition-relevant ranges of neural responses. Once these
ranges are identied, we can aim to understand the neural
dynamics responsible for specic cognitive changes, then iter-
atively attempt better control strategies.
Another overarching challenge is the fact that the brain's
anatomy and neural activity change over the lifespan [55], dur-
ing learning [56], and in response to exogenous stimulation
[57]. Thus, mind control goals are enacted in dynamic mul-
tiplexed neural networks [58], meaning that we should mon-
itor not only changes in neural state due to stimulation, but
also changes in anatomy and the basic neural organization of
cognition in our eorts to build robust control strategies for
cognitive function.
Furthermore, to enact practical mind control strategies,
the time scale of eects is an important consideration. If a
control strategy only aects cognition for moments, it may
have no practical utility. Encouragingly, control strategies
can be designed to administer continuous stimulation to treat
motor [8] and cognitive symptoms [59] over months and years.
Crucially, it may not always be necessary to administer contin-
uous control to achieve long-lasting cognitive eects [60, 61].
Thus, the use of control should be evaluated against the dif-
culty and cost of the design and administration of control
in the face of alternative strategies. Some cognitive prob-
lems may require a few open-loop sessions to achieve durable
eects, whereas others might require continuous and fast-
timescale stimulation. Speculatively, this may depend on dif-
ferent degrees and types of neuroplasticity in dierent cogni-
tive systems. Whether we can further optimize long-lastingeects with interventions based on network control theory re-
mains to be seen.
While conjectural at this point in scientic history, it is
possible that one could combine an understanding of neural
codes, experimental manipulation via brain stimulation, and
network control theory to design control strategies to achieve
certain mental states. This denes the frontier of mind con-
trol.
Technologies for control
Some opportunities for mind control exist in nascent stages of
development that could benet from analysis within network
control theory. In clinical contexts, network control theory
could inform cognitive repair strategies. It could also inform
cognitive enhancement. At present, a number of technolo-
gies for neural stimulation are routinely used in experimental
and clinical neuroscience (Fig. 4). Stimulation techniques
typically capitalize on dierent spatiotemporal properties of
electromagnetism. Microstimulation can in
uence the activ-
ity of single neurons. Most techniques used in cognitive neu-
roscience and clinics operate at a scale much coarser than a
single neuron. For example, transcranial magnetic stimula-
tion non-invasively induces current in cortical tissue, where
it causes action potentials in large populations of neurons.
With much less spatial precision, approaches such as tran-
scranial direct current stimulation use current administered
to the scalp that modulates neural ring thresholds in the
cortex. Invasive approaches such as deep brain stimulation
use implanted electrodes to in
uence local eld potentials in
groups of neurons. Techniques such as optogenetics allow us
to deliver spatiotemporally precise control sequences in tissues
engineered to express light-sensitive ion channels [62]. The
dynamics supporting cognitive function can be in
uenced by
these techniques at a low energy and risk cost [63].
6

a b
Fig. 5: Forms of control. (a): While not the primary
emphasis of the current discussion, we note that one form
of neural control is constantly mediated through sensation,
which in
uences immediate experience and learning, and the
basis of psychological treatments such as cognitive behavioral
therapy. (b): Neural control can be administered via direct
noninvasive or invasive neural stimulation devices. Clockwise
from top left: MagStim transcranial magnetic stimulation coil,
NeuroPace implanted stimulator, transcranial direct current
stimulator, Triangle Biosystems optogenetic stimulator, and
Activa deep brain stimulator.
Most applications of noninvasive electrical and magnetic
stimulation are a form of open-loop control without feedback
about internal neural states. This stands in contrast to closed-
loop control, which involves feedback about internal neural
states [8]. In open-loop control, control strategies are eval-
uated based on their success in in
uencing specic cognitive
and behavioral functions [64] or their ecacy in in
uencing
clinical outcomes [65]. Here, the goal or \reference" state
is the cognitive status of the subject rather than the internal
neural states. Studies in this area have revealed putative local-
izations for specic brain functions and led to FDA-approved
therapies for depression, which involves signicant emotional
and cognitive disturbances [66]. Open-loop control research
continues to provide substantial scientic and clinical benets
in behavioral neuroscience. It also illustrates that a psycho-
logical control goal can be approached even when knowledge
of specic neural codes is limited or completely absent. In
open-loop control, it may be critical to calibrate methods for
patients based on individualized neural patterns [67], using
advanced measurements available at the hospital to optimize
its use by the patient outside the hospital.
In closed-loop control, a critical component in the
controller-observer (feedback sensor) cycle is the ability to de-
tect states of the system. This is one reason that closed-loop
control is easier to achieve invasively: we can better observe
and control neural states when the sensors and controllers
have direct access to neurons [8]. Techniques such as deep
brain stimulation benet from real-time detection of neural
states, where the control strategy can adapt its input sequence
based on the current states of the system [8]. At this scale,
implanted microsensors record the local activity in neurons
[68]. Optogenetics can be paired with sensors to administer
closed-loop control in translational models [69]. Although in-
vasive strategies can generally better achieve closed-loop con-
trol, at higher spatiotemporal scales, spatially coarse states
can be measured by techniques such as EEG [70], and tem-
porally coarse states can be measured with real-time fMRI
[71]. Technologies including mobile EEG and functional near-infrared spectroscopy (fNIRS) can be useful in applied and
rehabilitative settings where more cumbersome measurement
tools are of limited use [72]. In biofeedback paradigms, the
participant can observe his or her own measured neural states,
and can adaptively learn to control these signals [73].
Good enough control
To truly understand the means to control the mind suggests
that one has suciently observed it [74, 8]. This is no simple
task. Brain stimulation strategies for mind control are limited
by knowledge of the underlying brain network architecture,
the nature of neural codes, and the limitations of technology
and computing [8]. All measurement techniques have spa-
tiotemporal and other technological limitations [72]. Given
the diculties in measuring neural states, modeling system
dynamics, and estimating signals within noise, simplifying as-
sumptions can be useful to facilitate insights into intrinsic [4]
and extrinsic [5] control of brain function. Even with sim-
plifying assumptions, studies suggest that the brain is very
dicult, if not impossible, to control [4, 75]. Thus, one can
likely only \guide" the brain rather than \control" it in its
entirety.
Encouragingly, neural control strategies can be designed
with limited information about neural systems [8]. Various
techniques in neuroscience provide information about neural
states with varying degrees of spatiotemporal precision. To
date, eorts in mapping cognitive functions within the brain
have relied on techniques ranging from invasive microstimula-
tion and recording on limited sets of neurons to non-invasive
imaging and computational techniques such as multi-voxel
pattern analysis [76]. In addition, transitions between states
at a coarse scale can be studied using dynamic network ap-
proaches [45, 77, 30]. Thus, while an ideal control strategy
would include precise mapping between neural codes and the
mind, numerous ndings in brain stimulation research across
neural scales suggest that the mind can be in
uenced in ways
that provide interpretive and practical value.
In particular, guiding the mind is perhaps most pragmatic
in clinical scenarios where internal neural states are not di-
rectly measured, sensor feedback is dicult to maintain, and
costs for comprehensive evaluation are prohibitive. For these
scenarios, engineers are developing tools to in
uence the dy-
namic trajectory of the system using control strategies with
limited access to the system and nite control input [78]. More
specically, these techniques oer guidelines to steer the net-
work into the intended state via cost-ecient strategies that
simultaneously in
uence the system and allow the system's
natural dynamics to help drive the system to the control goal
[79]. In the human brain, we could use models of neural ac-
tivity [80, 81, 82] to identify strategies that guide the brain to
desirable states at a low cost by assessing which inputs will
drive the brain's natural dynamics into an energy ecient
trajectory. One notable issue is that some structures such as
subcortical systems are dicult to access non-invasively, so
we can seek to develop indirect control strategies using non-
invasive stimulation [83] and knowledge of cortico-subcortical
connectivity [84]. Future studies utilize these and similar ap-
proaches to identify candidate strategies to guide brain net-
work dynamics in the context of missing information and sub-
optimal control.
To close this speculative discussion, we mention a few ad-
ditional limitations. First, we motivate these ideas based on
the notion of \structural controllability": the control input is
mediated through the nodes (brain regions) in the network,
and its in
uences are relayed through edges (white matter
tracts) to other nodes. In principle, other control strategies
7

can be designed [85], including (i) modifying network edges by
introducing bypasses for damaged tissue via neuroprosthetics
[86] and (ii) modifying local regional dynamics using phar-
macology [87]. Second, we note that mind control holds no
special ontological status relative to motor control. Indeed,
the distinction between motor and cognitive processes may
simply be an artifact of historical emphasis [88]. Accordingly,
trade-os between motor and cognitive function are evident
in Parkinson's disease when a single site is stimulated and
elicits improvements in motor performance at the cost of in-
creased impulsivity [89]. If both motor and cognitive function
are fundamentally computational processes [90], both can be
informed by neural control engineering. However, there may
be dierences in the quality and degree of control required
across these domains, as well as dierent stakes.
The ethics of brain control
As eorts to guide complex brain processes advance, we will
not only need new theoretical and technical tools. We will
also face new societal, legal, and ethical challenges. Our best
chance of meeting those challenges is through ongoing, rigor-
ous discussion between scientists, ethicists, and policy makers.
Even at this early stage of the science of mind control,
present and future ethical issues are important to consider.
These issues pertain to developing research and clinical appli-
cations. In both cases, it is crucial to maximize benets to
society and protect against harm. In addition, ethical restric-
tions that apply in the use of mind control to treat dysfunction
and alleviate suering may dier from those that apply where
mind control may be used to enhance typical function [91].
Insofar as mind control has and will be undertaken in ex-
perimental and clinical contexts, four basic principles of med-
ical and research ethics apply: nonmalecence, benecence,
justice, and autonomy [92]. Adhering to these principles is
a rst step toward ensuring that eorts to guide the mind
enhance human welfare without violating human rights. Here
we will take direct neural and magnetic stimulation as our pri-
mary points of emphasis. In addition, we will identify basic
ethical issues that may be important to the more complex case
of sensation-mediated stimulation, which includes the broader
social and environmental forms of control that individuals ex-
perience in daily living.
Non-malecence. The principle of nonmalecence should
supersede any implementation of control in experimental or
clinical contexts. In developing and using these techniques,
the physical and psychological safety of subjects should be
the priority. For neural control devices, deep brain stimulation
involves implanted neural electrodes and is associated with a
risk of incidental neural damage and infection [93]. Psychi-
atric side eects include depression, delusion, euphoria, and
disinhibition [94]. Transcranial magnetic stimulation, on the
other hand, is noninvasive and carries an exceptionally rare
risk of seizure [95]. As these and other technologies are re-
ned and created, improved safeguards against tissue damage
and adverse psychological eects must remain a high priority.
In current practice, none of these harms is intended either as
a goal or as a means; researchers are therefore not directly
malecent, but may nevertheless be indirectly responsible for
their subjects' suering. As the potential for mind control
is explored, therefore, eorts should be made to reduce the
potential for indirect malecence and safeguard against the
possibility of direct malecence.Benecence. In addition to harm prevention, the principle
of human welfare should guide mind control eorts. In applied
brain stimulation, clinical symptoms can be reduced and im-
provements in cognitive performance in healthy individuals
can be produced [91]. The general trade-os of mind control
strategies are as yet unknown, but several specic trade-os
are already known to exist [89, 96, 97, 98]. Concerning control
engineering, maximizing benets and protecting against harm
may be at odds. Multi-cost control optimization is harder
than single-cost optimization with constraints [99], so one ap-
proach to minimizing undesirable side-eects is to optimize
one cognitive goal while constraining other cognitive functions
to remain outside an undesirable range. This eort may also
improve our understanding of the dynamic trade-os between
cognitive systems more generally. As theoretical models of
brain function at multiple scales develop, new opportunities
for maximally benecent control should be conceptualized and
tested. Nonetheless, given early misuses of lobotomies and
electroconvulsive therapy, it is important to separate uses of
mind control to improve mental function in clinical cases from
its potential misuses for social normalization of incarcerated
or otherwise vulnerable persons.
Justice. The principle of justice demands that the applica-
tions of mind control neither participate in nor exacerbate sys-
tems of inequality or exploitation. As such, already existing
nancial barriers to neural control based treatments and en-
hancements should be reduced. In turn, access to information
about the benets and risks of such applications should be ju-
diciously increased, in concert with eorts to improve general
public health education, especially in underserved communi-
ties and countries. To date, mind control application exists
in relatively circumscribed clinical and experimental contexts.
If, however, mind control strategies for optimal cognitive func-
tion become widely available, or even marketable for public
consumption, safeguards should preclude the construction of
anenhanced class, which may result in harm to the unen-
hanced in competitive environments [91].
Autonomy. In the process of guiding the mind, it is im-
perative that the individual's autonomy, or power of self-
determination, remains intact. Autonomy is violated by any
involuntary use of mind control on anyone, whether by ex-
plicit or implicit coercion. Explicit coercion means forcing
individuals to undergo mind control against their conscious
will, whether for the perceived greater good of society or the
advancement of some constituency. The strongest safeguard
against explicit coercion to date has been informed consent
[100]; however, the informed consent process still needs to
be improved [101] and additional methods to supplement it
should be developed. This is especially relevant to potential
applications of mind control in vulnerable populations. In
the future, moreover, should a group of enhanced individuals
gain dominance, unenhanced individuals could conceivably be
at risk of involuntary medicalization [91].
Implicit coercion means manipulating individuals through
the activation of external or internal pressure rather than
force. While implicit coercion has sullied the history of re-
search ethics [92], the distinction between implicit coercion
and participatory incentive today still needs further clarica-
tion. For mind control, implicit coercion may include social
pressures to increase productivity or eciency in competitive
environments [102, 103, 104, 61]. What is more, the real-
ity of structural constraints and mediated freedoms makes
appropriate levels of adult autonomy diculty to calibrate.
Nevertheless, research clinicians should make an eort not to
capitalize on social conditions that might introduce implicit
8

coercion. For example, poverty may predispose subjects to
enter paid research trials, leading to higher rates of experi-
mentation on lower class bodies. As such, the potential for
subjects to enroll in research and clinical designs under con-
ditions of implicit coercion should be the focus of redoubled
ethical review.
While our discussion here bears on humans with a typical
adult capacity for self-determination, further ethical consid-
erations would come into play were mind control to become
available for people lacking such capacity { for example, chil-
dren, some elderly individuals, and adults with certain forms
of mental or physical disabilities.
Rethinking human persons
As mind control develops, the ability to interact intelligently
with human nature may bring certain stakes into sharper fo-
cus. Humans privilege the notion of a \mind" and perceived
internal locus of control as central to their identities [105].
Further, within minds, humans privilege some traits, such
as social comfort, honesty, kindness, empathy, and fairness,
as more fundamental than functions, such as concentration,
wakefulness, and memory [106]. These dierent values depend
on the notion of conscious identity and are often at the core of
common ethical distinctions applied to humans versus other
animals [107]. Importantly, modern notions of human per-
sons, in
uenced by continuing advances in the cognitive and
brain sciences, erode the classical boundary between the eth-
ical treatment of humans and animals [108]. These theories
suggest that tolerance for mind control may scale with ethical
issues identied across all animals. In kind, this implies that
there exists a need to evaluate the practice of mind control
on both human and non-human animals and its permissible
scope in principle.
As the science of mind control advances, it will be im-
portant to clarify acceptable control practices with respect to
our fundamental nature and self-identity. In addition, the po-
tential for mind control to undermine responsibility connects
to our fundamental intuitions about whether we really con-
trol what we do [109]. Implicit within this new technology,
then, is the call to redene ourselves. For this reason, scien-
tists, clinicians, ethicists, and philosophers will need to work
together.
Conclusions
The study of mind control in human brains has been devel-
oping over several years without any particular name. Here,
we have described mind control to be fundamentally a prob-
lem of network control in the human brain. New frontiers at
the intersection of network neuroscience and cognitive science
can provide striking new questions and possibilities in under-
standing and controlling cognitive function. Our success will
be predicated on the development of robust theories of neural
coding and advances in technologies for recording and manip-
ulating neural activity. In reclaiming a term long relegated to
science ction, new opportunities within and between compu-
tational, cognitive, clinical, ethical, and control neuroscience
may produce a new era in the science of guiding the mind.
9

Acknowledgments
The authors thank Joshua Gold, Elisabeth Karuza, Richard
Betzel, and the anonymous reviewers for helpful comments
and discussion regarding this work. JDM acknowledges sup-
port from the Oce of the Director at the National Insti-
tutes of Health through grant number 1-DP5-OD-021352-01.
DSB acknowledges support from the John D. and Cather-
ine T. MacArthur Foundation, the Alfred P. Sloan Foun-
dation, the Army Research Laboratory and the Army Re-
search Oce through contract numbers W911NF-10-2-0022
and W911NF-14-1-0679, the National Institute of Health (2-
R01-DC-009209-11, 1R01HD086888-01, R01-MH107235, R01-
MH107703, R01MH109520, 1R01NS099348 and R21-M MH-
106799), the Oce of Naval Research, and the National
Science Foundation (BCS-1441502, CAREER PHY-1554488,
BCS-1631550, and CNS-1626008). WS-A acknowledges sup-
port from the John Templeton Foundation. The content is
solely the responsibility of the authors and does not necessar-
ily represent the ocial views of any of the funding agencies.
10

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