U.S. patent application number 16/531862 was filed with the patent office on 2019-12-12 for hybrid system for treating mental and emotional disorders with responsive brain stimulation.
The applicant listed for this patent is The General Hospital Corporation, MASSACHUSETTS INSTITUTE OF TECHNOLOGY, UNIVERSITY OF WASHINGTON. Invention is credited to Darin Dougherty, Chet Moritz, Alik S. Widge.
Application Number | 20190374159 16/531862 |
Document ID | / |
Family ID | 54333124 |
Filed Date | 2019-12-12 |
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United States Patent
Application |
20190374159 |
Kind Code |
A1 |
Widge; Alik S. ; et
al. |
December 12, 2019 |
HYBRID SYSTEM FOR TREATING MENTAL AND EMOTIONAL DISORDERS WITH
RESPONSIVE BRAIN STIMULATION
Abstract
A closed-loop brain computer interface (BCI) system for treating
mental or emotional disorders with responsive brain stimulation is
disclosed. The system includes an implanted module including a
processor configured to process neural data acquired from one or
more electrodes in communication with one or more brain regions of
a patient. The implanted module is configured to deliver
stimulation to electrodes in contact with the brain regions. An
interface is in wireless communication with the implanted module
and configured to receive the neural data from the implanted
module. A controller processes the patient's brain and body signals
to provide patient intentional control over the stimulation applied
to the one or more electrodes and to control the stimulation.
Inventors: |
Widge; Alik S.; (Somerville,
MA) ; Moritz; Chet; (Seattle, WA) ; Dougherty;
Darin; (Wellesley, MA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
The General Hospital Corporation
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
UNIVERSITY OF WASHINGTON |
Boston
Cambridge
Seattle |
MA
MA
WA |
US
US
US |
|
|
Family ID: |
54333124 |
Appl. No.: |
16/531862 |
Filed: |
August 5, 2019 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15305752 |
Oct 21, 2016 |
10413235 |
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PCT/US15/27042 |
Apr 22, 2015 |
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16531862 |
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61984416 |
Apr 25, 2014 |
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61984466 |
Apr 25, 2014 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61B 5/055 20130101;
A61B 5/0008 20130101; A61B 5/0816 20130101; A61B 5/4836 20130101;
A61B 5/4064 20130101; A61B 5/0488 20130101; A61N 1/36096 20130101;
A61N 1/36132 20130101; G06F 3/015 20130101; A61B 5/165 20130101;
A61B 5/04008 20130101; A61N 1/0531 20130101; A61N 1/0534 20130101;
A61B 5/0205 20130101; A61B 5/0075 20130101; A61B 8/0808 20130101;
A61B 3/112 20130101; A61B 3/113 20130101; A61B 5/024 20130101; A61B
5/053 20130101; A61N 1/36139 20130101; A61N 1/3787 20130101; A61B
6/037 20130101; G16H 20/70 20180101; A61B 5/0402 20130101; A61N
1/36135 20130101; G16H 20/40 20180101 |
International
Class: |
A61B 5/00 20060101
A61B005/00; A61N 1/36 20060101 A61N001/36; A61B 8/08 20060101
A61B008/08; A61B 6/03 20060101 A61B006/03; A61B 5/0488 20060101
A61B005/0488; A61B 5/0402 20060101 A61B005/0402; A61B 5/04 20060101
A61B005/04; A61B 5/0205 20060101 A61B005/0205; A61B 3/113 20060101
A61B003/113; A61B 3/11 20060101 A61B003/11; G06F 3/01 20060101
G06F003/01; A61B 5/16 20060101 A61B005/16; A61B 5/053 20060101
A61B005/053; A61B 5/024 20060101 A61B005/024; A61N 1/05 20060101
A61N001/05; A61N 1/378 20060101 A61N001/378 |
Goverment Interests
STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH
[0002] This invention was made with government support under
EEC-1028725 awarded by the National Science Foundation Engineering
Research Center. The government has certain rights in the
invention.
Claims
1. A closed-loop brain computer interface (BCI) system for treating
a patient suspected of having a mental or emotional disorder with
on-demand responsive brain stimulation, the system comprising: a
processor configured to process neural data acquired from at least
one brain region of the patient suspected of having a mental or
emotional disorder, the processor configured to control delivery of
stimulation to the at least one brain region; and a controller to
control the stimulation based on intentional control feedback from
the patient to provide therapy for the mental or emotional
disorder, wherein the intentional control feedback causes the
controller to alter the applied stimulation if desired by the
patient.
2. The system of claim 1, wherein the neural data includes at least
one of spike, local field potential, or electrocorticography data
acquired from the at least one electrode.
3. The system of claim 1, further comprising an implanted module
configured to effectuate delivery of the stimulation.
4. The system of claim 3 wherein the implanted module includes a
controlling hub and a plurality of satellite modules coupled to the
controlling hub.
5. The system of claim 4, further comprising an interface in
communication with the implanted module and wherein the interface
is a head or body mounted interface positioned between the
controlling hub and a base station to provide wireless
communication therebetween.
6. The system of claim 5, wherein the base station includes a hand
held or wearable patient controller configured for at least one of
self-reporting, triggering recordings, or monitoring at least one
of heart rate or skin conductance.
7. The system of claim 4, wherein each of the plurality of
satellite modules include a unique ID to enable the controlling hub
to interrogate the plurality of satellite modules and configure the
controlling hub based upon returned information.
8. The system of claim 1, further comprising a non-invasive
stimulation system configured to deliver the stimulation.
9. The system of claim 8, wherein the non-invasive stimulation
system is configured to perform at least one of transcranial
magnetic stimulation, transcranial direct- or alternative-current
stimulation, transcranial focused ultrasound, infrared modulation,
or optical through-skull modulation to deliver the stimulation.
10. The system of claim 1 further comprising at least one implanted
module including at least one electrode configured to deliver the
stimulation to the at least one brain region.
11. The system of claim 10 wherein the neural data comprises brain
and body signals that are acquired by the implanted module.
12. The system of claim 11 wherein the controller processes the
patient's brain and body signals to provide the patient intentional
control over the stimulation applied to the at least one electrode
based on the intentional control feedback from the patient to
provide therapy for the mental or emotional disorder.
13. The system of claim 1, wherein the processor is configured to
execute emotional decoding algorithms to merge the neural data to
estimate a psychiatric state of the patient, deliver therapeutic
stimulation, or provide the patient intentional control of the
stimulation.
14. The system of claim 1, wherein the processor is configured to
execute at least one of a training or calibration protocol to help
the patient learn to use the controller.
15. The system of claim 1, wherein the neural data comprises brain
and body signals that are acquired by an implanted module and
wherein the controller processes the brain and body signals to
provide the patient intentional control over the stimulation.
16. A method for treating a patient suspected of having a mental or
emotional disorder with on-demand responsive brain stimulation
using a closed-loop brain computer interface (BCI), the method
comprising the steps of: acquiring neural data from at least one
brain region of the patient suspected of having a mental or
emotional disorder; processing the neural data using a processor;
providing the patient intentional control as initiated by the
patient over stimulation delivered to the patient to provide
therapy for the mental or emotional disorder, wherein the
intentional control comprises the patient being permitted to alter
neural data if an alteration of the stimulation is selected by the
patient.
17. The method of claim 16, wherein the neural data includes brain
and body signals acquired as at least one of spike, local field
potential, electrocorticography, or bio-magnetic data.
18. The method of claim 16, wherein acquiring the neural data
includes non-invasively acquiring data from at least one scalp
electrode disposed on the patient.
19. The method of claim 16, further comprising an implanted module
including a controlling hub and a plurality of satellite modules
coupled to the controlling hub.
20. The method of claim 19, further comprising an interface
positioned between the controlling hub and a base station to
provide wireless communication therebetween.
21. The method of claim 20, wherein the base station includes a
hand-held patient controller configured for at least one of
self-reporting, triggering recordings, or monitoring at least one
of heart rate or skin conductance.
22. The method of claim 19, further comprising the step of
providing a unique ID for each of the plurality of satellite
modules to enable the controlling hub to interrogate the plurality
of satellite modules and configure the controlling hub based upon
returned information.
23. The method of claim 16, further comprising the step of
monitoring, using at least one sensor a set of health parameters
related to the BCI.
24. The method of claim 23, wherein monitoring the set of health
parameters includes monitoring at least one of electrode impedance,
temperature, humidity, supply voltage, or a lead line integrity of
the implanted module.
25. The method of claim 19, further comprising the step of applying
stimulation therapies through at least one electrode disposed in
the patient using at least one current pulse generator coupled to
the controlling hub.
26. The method of claim 16, wherein the neural data comprises brain
and body signals that are processed to provide the patient
intentional control over the stimulation.
27. The method of claim 26 wherein the processor receives
indications of the patient intentional control and adjusts the
stimulation based on the patient intentional control to provide the
therapy for the mental or emotional disorder.
28. The method of claim 16, wherein the stimulation is delivered
non-invasively.
29. The method of claim 28, wherein the stimulation includes at
least one of transcranial magnetic stimulation, transcranial
direct- or alternative-current stimulation, transcranial focused
ultrasound, infrared modulation, or optical through-skull
modulation to deliver the stimulation.
Description
CROSS-REFERENCES TO RELATED APPLICATIONS
[0001] This application is a continuation of U.S. patent
application Ser. No. 15/305,752 filed Oct. 21, 2016 which is a U.S.
National Phase of PCT Application No. PCT/US2015/027042 filed Apr.
22, 2015 which claims priority from U.S. Patent Application No.
61/984,466 filed Apr. 25, 2014 and U.S. Patent Application No.
61/984,416 filed Apr. 25, 2014, each of which is incorporated
herein by referenced and replied upon in its entirety.
BACKGROUND OF THE INVENTION
[0003] This invention relates to brain computer interface (BCI)
controllers. More particularly, this invention relates to an
affective BCI controller for decoding a patient's emotional
experience and a regular BCI that acquires signals from the
patient's brain regions to determine the patient's intention to
experience a specific emotion.
[0004] Mental illnesses, for example, post traumatic stress
disorder (PTSD), depression, and addiction, impair war fighters and
civilians, and are a leading cause of disability and lost
productivity. These illnesses can be conceptualized as brain
disorders of malfunctioning neural circuits. Often, psychiatric
treatments fail to cure a substantial fraction of patients, who are
then declared resistant to approved therapeutic interventions. At
the core of the problem is the focus on historical diagnostic
categories. The National Institute of Mental Health's (NIMH)
Research Domain Criteria (RDoC) project aims to develop
neuroscience-based classification schemes for diagnosis and
treatment of neural circuitry dysfunction. Diagnostic and
Statistical Manual (DSM) diagnoses are not neurobiologic entities,
but are a historical checklist-based approach of clustering
symptoms used to define hypothetical constructs or syndromes. Those
syndromes may not align with underlying neurobiological dysfunction
in neural circuitry and corresponding behavioral (functional)
domains.
[0005] Thus, attempts have been made using responsive brain
stimulation systems to treat mental and emotional disorders
previously treated by psychiatrists. Responsive brain stimulation
is stimulation applied to the brain that responds directly to a
patient's electrical brain activity or clinical features. One
realization of a responsive brain stimulation system is
implantable, with electrodes placed inside a patient's brain. There
are a number of sites in the brain where stimulation may be applied
in attempts to change a patient's emotional experiences. However,
these responsive brain stimulation systems often have no proven
biomarker. A biomarker may be a measurable indicator or signal from
the brain or body representative of the symptoms of the illness
being treated that indicates whether the symptoms have gotten
better or worse. Without something reliable to sense, it is
difficult for the responsive stimulator to respond accurately.
[0006] Other attempts to treat mental and emotional disorders have
moved away from trying to find biomarkers for specific mental
disorders, and instead have tried to find biomarkers for emotions
utilizing an affective brain-computer interface (aBCI). An aBCI in
combination with a brain scanner or electroencephalography (EEG)
system, for example, can look at signals in real-time and determine
whether the subject is having a positive-valence (e.g., happy,
pleasant, etc.) or a negative-valence (e.g., angry, afraid,
unpleasant, etc.) emotion. In more advanced systems, the specific
emotion (e.g., anger, fear, disgust, pleasure, etc.) can be
classified.
[0007] However, an aBCI alone may not be a useful clinical tool, as
it cannot determine whether the emotion is a healthy emotion (e.g.,
anger that was justifiably provoked, fear because the patient is in
a dangerous situation, etc.) from an unhealthy emotion (e.g.,
violent anger in response to a mild insult, fear of an ordinarily
safe situation such as driving on a freeway, etc.). Therefore,
there it is difficult for a controller to decide whether the
emotion should be corrected or altered by stimulating the
brain.
[0008] Thus, there is a clinical need for responsive
neurostimulators, which sense a patient's brain activity and
deliver targeted electrical stimulation to suppress unwanted
symptoms. This is particularly true in psychiatric illness, where
symptoms can fluctuate throughout the day. Affective BCIs, which
decode emotional experience from neural activity, are a candidate
control signal for responsive stimulators targeting the limbic
circuit. Present affective decoders, however, cannot yet
distinguish pathologic from healthy emotional extremes.
Indiscriminate stimulus delivery would reduce quality of life and
may be actively harmful.
[0009] The need for affective BCI monitoring and decoding is
clearest in deep brain stimulation (DBS). Psychiatric DBS has been
used at multiple targets, with preliminary success in treating
depression and obsessive-compulsive disorder (OCD), for example.
Progress in psychiatric DBS, however, has been limited by its
inherent open-loop nature. Present open-loop DBS systems deliver
energy continuously at a pre-programmed frequency and amplitude,
with parameter adjustments only during infrequent clinician visits.
This has led to more rapid depletion of device batteries which
requires battery replacement surgeries and introduces the patient
to associated pain and/or infection. The continuous delivery of
energy also leads to an increased side-effect burden. Side effects
in particular derive from present devices' inability to match
stimulation to a patient's current affective state, brain activity,
and therapeutic need. Atop this, many disorders have symptoms that
rapidly flare and remit, on a timescale of minutes to hours. This
is particularly common in the anxiety and trauma related clusters.
Existing open-loop DBS strategies have been unable to effectively
treat such fluctuations, because the fluctuations occur on shorter
timescales than the infrequent clinical visits.
[0010] However, development of closed-loop emotional DBS systems
has been blocked by a lack of accurate or feasible biomarkers.
Three major challenges arise when considering existing affective
BCIs as the sensing component of closed-loop DBS control. First,
many identified neural correlates of affective disorders cannot be
continuously monitored in the community. Functional magnetic
resonance imaging (fMRI) can provide deep insights into activity
across the whole brain, and has been demonstrated for partial
affective classification in real time. Similar results have been
seen with near-infrared spectroscopy (NIRS), which also measures
blood-oxygenation signals. The former, however, requires bulky
machines and is not compatible with implanted devices, and the
latter has not yet been demonstrated in an online-decoding
paradigm. Moreover, although NIRS can be reduced to a
wearable/portable device, it requires an externally worn headset.
Given the unfortunate persistence of stigma attached to patients
with mental disorders, few would wear a visible display of their
illness, even if it did control symptoms.
[0011] Another challenge with existing affective BCIs is that
affective decoding modalities that support continuous recording may
not function properly in the presence of psychiatric illness.
Electrocorticography (ECOG) is a promising approach, as it can be
implanted, and thus hidden, with relatively minimally invasive
surgery. ECOG signals offer temporal resolution and may be able to
use decoders originally developed for electroencephalography (EEG).
Non-invasive EEG has been a successful approach in affective BCI,
with some real-time decoding of emotional information. Uncertainty
arises because all successful EEG affective decoding has been
demonstrated in healthy patients. Patients with mental illness,
particularly those with treatment-resistant disorders, by
definition do not have normal or healthy neurologic function.
Furthermore, recent experiences with EEG in psychiatry suggest that
measures that accurately decode healthy controls may not transfer
to patients. EEG biomarkers that initially appeared to correlate
with psychiatric symptoms and treatment response have often not
held up under replication studies. This is at least in part because
psychiatric diagnosis focuses on syndromes and symptom clusters,
not etiologies. There is a wide consensus that clinical diagnoses
generally contain multiple neurologic entities, and that the same
clinical phenotype might arise from diametrically opposite changes
in the brain. This may present a challenge for clinical translation
of existing affective decoders.
[0012] Yet another challenge with existing affective BCIs is that
even if affective BCIs can function in the presence of clinical
symptoms, they may not be able to adequately distinguish pathologic
states. Newer affective BCI algorithms may yet be shown to
accurately classify emotion even in the presence of abnormal neural
circuit activity, but this is only part of the need. Psychiatric
disorders are marked by extremes of the same emotions that occur in
everyday normal life. The difference is not the degree or type of
affect, but its appropriateness to the context. PTSD is one clear
example where patients with this disorder over generalize from a
fearful event and experience high arousal and vigilance in contexts
that are objectively safe. It is likely possible for an affective
BCI to detect high arousal in a patient with PTSD in uncontrolled
real-world environments. It is less clear whether any algorithm
could distinguish pathologic arousal (e.g., a `flashback` in a
grocery store, confrontation with trauma cues, etc.) from healthy
variance (e.g., riding a roller coaster, watching an exciting
movie, etc.). These emotions would be very difficult to
differentiate solely on the basis of experienced affect, and yet
the use of brain stimulation to neutralize the latter set of
experiences would negatively impact the patient's quality of
life.
[0013] The above described challenges combine to reveal a final
complication. In a fully implanted system, onboard storage and
computational resources are limited, and therefore it may not be
possible to perform decoding and tracking over long periods of
time. Thus, affective decoders are caught in a dilemma of temporal
resolution. If the affective decoders are tuned to respond to brief
but intense events, the decoders may over-react to natural and
healthy emotional variation. If the decoders instead focus only on
detecting and compensating for long-term trends, sharp but short
exacerbations will go uncorrected, decreasing patients' quality of
life and continuing the problems of existing open-loop DBS. In the
very long run, these problems may be ameliorated by improvements in
battery and processing technology. However, regulatory agencies
require extensive review of all new technology components, meaning
that a new battery could take a decade to reach clinical use even
after being successfully demonstrated for non-implantable
applications. Processors might be more easily upgraded, but
increased processing power means increased heat, which cannot be
readily dissipated inside the body.
[0014] Thus, there is a need for systems and methods for responsive
decoding and stimulation capable of operating within the limits of
current clinical technology. An affective BCI usable as the sensing
component of a responsive brain stimulator and capable of inferring
emotional state from neural signals to enable a responsive,
closed-loop stimulator is desirable. It is also desirable for
continuous monitoring capable of indicating that the system is
moving into a pathological state so that the controller can adjust
parameters of an implanted DBS to counteract that trajectory, as
well as reduce the side effects of over-stimulation, alleviate
residual symptoms that may relate to under-stimulation, and improve
power consumption for a longer battery life.
SUMMARY OF THE INVENTION
[0015] The invention overcomes the aforementioned drawback by
providing a system and method for treating patients with mental or
emotional disorders utilizing an affective BCI component in a
closed-loop, symptom-responsive psychiatric DBS system. Plasticity
and volition components are incorporated into the affective BCI for
control of neurostimulation. The systems and methods may be useful
for patients with symptoms in the mood (e.g., depression, bipolar
disorder, etc.), anxiety (e.g., generalized anxiety, panic
disorder, etc.), obsessive-compulsive (e.g., obsessive-compulsive
disorder, Tourette syndrome, etc.), and trauma/stress-related
(e.g., post-traumatic stress disorder, etc.) clusters, as well as
patients with certain types of brain damage, such as white matter
injury.
[0016] Regarding plasticity, in some embodiments, a BCI may be
built by having participants perform a `predicate task`, such as
hand movement, motor imagery, or emotional imagery in the case of
affective BCI. The transdiagnostic tasks described below may also
be considered as predicate tasks. From neural activity during this
training period, a decoder is built, then deployed to classify new
brain activity as it arises. As the BCI learns the mapping between
brain signals and task variables, the brain changes to better match
the decoder. The brain's inherent capacity for plasticity will
remap cortical signals to produce improved information for the BCI.
It is also possible to create a BCI where there is no predicate
task, and where the decoder is simply initialized with arbitrary
parameters. This is sometimes referred to as a `direct control`
system, and has been shown to be an effective way of building a BCI
that is computationally efficient. The brain still changes and
learns to use this type of decoder effectively.
[0017] The volition component of affective BCI in closed-loop
psychiatric DBS includes a patient sensing that present stimulation
parameters are not well matched to his/her clinical needs, then
choosing to alter the stimulation parameters by deliberately
modulating specific aspects of brain activity (e.g., firing rates
of specific neurons, power in certain bands of local field
potential (LFP), EEG, ECOG, and the like). The list is not
exhaustive; a wide range of standard signal transforms could
feasibly and reasonably be applied to the signals and could be
under a patient's volitional control. The volitionally controlled
component may resolve some of the limitations identified above,
such that affective decoding would not need to directly classify an
emotion as pathologic vs. healthy-extreme. Rather, the patient can
express his/her desire directly and efficiently through a BCI, and
the question of whether to optimize response to fast or slow
time-scales becomes moot. Thus, stimulation can be adjusted when
the patient explicitly requests the affective controller to do so.
Heterogeneity of biomarkers within clinical disorders may also be
controlled for, because the primary decoded variable is the
patient's own intention to receive mood altering neurostimulation.
That said, with adequate application of the transdiagnostic methods
detailed below, it may be less necessary to control for
heterogeneity, because the clinical diagnosis will not be the
principal method for selecting an intervention.
[0018] In one aspect, the invention provides a closed-loop brain
computer interface (BCI) system for treating mental or emotional
disorders with responsive brain stimulation. The system includes an
implanted module including a processor configured to process neural
data acquired from at least one electrode in communication with at
least one brain region of a patient. The implanted module is
configured to deliver stimulation to the at least one electrode in
contact with the at least one brain region. An interface is in
wireless communication (which need not be continuous) with the
implanted module and is configured to receive the neural data from
the implanted module. A controller processes the patient's brain
and body signals to provide patient intentional control over the
stimulation applied to the at least one electrode and to control
the stimulation in concert with the patient's intentional control.
In some embodiments, the controller may reside on any aspect of the
hardware, or be partitioned between multiple parts of the
system.
[0019] In another aspect, the invention provides a method for
diagnosing mental or emotional disorders with responsive brain
stimulation using a closed-loop brain computer interface (BCI). The
method includes acquiring neural data from at least one electrode
in communication with at least one brain region of a patient. The
neural data is processed using an implanted module including a
processor. The processed neural data is received at an interface in
wireless communication with the implanted module. Stimulation is
delivered, using the implanted module, to the at least one
electrode in contact with the at least one brain region. The method
further includes providing patient intentional control over the
applied stimulation using a controller that processes the patient's
brain and body signals initiated by the patient and in
communication with the at least one electrode to control the
stimulation.
[0020] These and other features, aspects, and advantages of the
present invention will become better understood upon consideration
of the following detailed description, drawings and appended
claims.
BRIEF DESCRIPTION OF THE DRAWINGS
[0021] FIG. 1 is a schematic illustration of a system for
diagnosing and treating psychiatric patients using a
transdiagnostic assessment and activation of one or more brain
regions in accordance with the present invention.
[0022] FIG. 2 is a block diagram of the system illustrated in FIG.
1.
[0023] FIG. 3 is an exploded view of a satellite module
incorporated into the system of FIG. 2 for delivering stimulation
to brain regions.
[0024] FIG. 4A is a schematic illustration of a controller hub
incorporated into the system of FIG. 2 to be implanted under the
scalp of a patient for estimating the patient's psychiatric state
and delivering therapeutic stimulation.
[0025] FIG. 4B is an exploded view of a housing for the controller
hub of FIG. 4A.
[0026] FIG. 4C is an exploded view of a battery and battery package
for powering the controller hub of FIG. 4A.
[0027] FIG. 5 is a schematic illustration of a head mounted
interface incorporated into the system of FIG. 2.
[0028] FIG. 6 is a flow chart setting forth the steps of a method
for diagnosing and treating mental and emotional disorders by
analyzing an individuals brain with both imaging and behavioral
testing in accordance with the present invention.
[0029] FIG. 7 is a table of example functional domains for
transdiagnostic assessment, corresponding functional tasks for
probing each functional domain, and brain regions known to be
involved in performance/impairment for each functional domain.
[0030] FIG. 8A is an image of an example transdiagnostic assessment
task for measuring the functional domain of fear extinction.
[0031] FIG. 8B is a graph illustrating a percentage of fear over
various phases of the transdiagnostic assessment task of FIG. 8A
for a PTSD patient and a population average.
[0032] FIG. 9 is schematic illustration of an example
transdiagnostic assessment task for measuring the functional domain
of reward motivation.
[0033] FIG. 10 is schematic illustration of an example
transdiagnostic assessment task for measuring the functional domain
of emotion regulation.
[0034] FIG. 11A is a schematic illustration of an example
transdiagnostic assessment task for measuring the functional domain
of decision making and impulsivity.
[0035] FIG. 11B is a graph illustrating a reaction time over
various card values assessed during the transdiagnostic assessment
task of FIG. 11A.
[0036] FIG. 11C is a graph illustrating a percentage of high bets
over various stimulation categories during the transdiagnostic
assessment task of FIG. 11A.
[0037] FIG. 12A is schematic illustration of an example
transdiagnostic assessment task for measuring the functional domain
of attention and perseveration.
[0038] FIG. 12B is a graph illustrating a normalized reaction time
over various trails including DBS stimulation during the
transdiagnostic assessment task of FIG. 12A.
[0039] FIG. 13A is a schematic illustration of an example
transdiagnostic assessment task for measuring the functional domain
of cognitive capacity.
[0040] FIG. 13B is a graph illustrating a percentage of correct
trials for patients receiving DBS stimulation in various brain
regions over several trials while performing the transdiagnostic
assessment task of FIG. 13A.
[0041] FIG. 14 is a schematic illustration of an analysis and
interpretation of a transdiagnostic assessment for two patients
within a transdiagnostic dimensional space.
[0042] FIG. 15 is an image illustrating an example of
patient-specific brain mapping relative to functional domains.
[0043] FIG. 16 is a schematic illustration of example implant sites
for a patient with deficits in the functional domains of emotion
regulation and fear extinction.
[0044] FIG. 17 is a schematic illustration of an example
intention-decoding, BCI-based closed loop neurostimulator in
accordance with the present invention.
[0045] FIG. 18A is a block diagram illustrating an example tethered
recording, decoding, and stimulating system in accordance with
another aspect of the present invention.
[0046] FIG. 18B is a schematic diagram illustrating a BCI algorithm
to be incorporated into the system of FIG. 18A.
[0047] FIG. 18C is a graph illustrating a spike rate over time for
successful use of the intentional control system compared to
non-use of the intentional control system.
[0048] FIG. 19 is a graph illustrating success rates for BCI
control over time for an actual target acquisition rate, an on-line
estimate of chance-level performance, and an offline estimate of
chance-level performance.
[0049] FIG. 20 is a schematic illustration of two examples of
peri-stimulus discharge rates on single channels involved in the
prefrontal cortex (PFC) BCI.
[0050] Like reference numerals will be used to refer to like parts
from Figure to Figure in the following description of the
drawings.
DETAILED DESCRIPTION OF THE INVENTION
[0051] FIGS. 1 and 2 show an affective BCI component in a
closed-loop, symptom-responsive psychiatric DBS system 10. The
system 10 may be capable of diagnosing patients not by clinical,
symptom-focused interview, but by transdiagnostic assessment of
objective measurement of the patient's performance on quantitative
tests of psychological function. The system is configured to link
the transdiagnostic symptom assessment to a treatment that
specifically activates or de-activates one or more brain areas via
DBS. More specifically, the system 10 is designed to record and
decode neural information from specific dysfunctional networks
associated with specific symptoms, behaviors and then deliver
stimulation to these networks to afford symptom relief and
measurable improvements in dysfunctional behaviors. The patient may
be given control over the stimulation system's actions through a
hybrid BCI algorithm that monitors the patient's intentions.
Closed-loop algorithms further focus the treatment not only in
space (i.e., region of the brain) but in time, so that stimulation
occurs only when the patient needs it.
[0052] The system 10 generally includes a central decoding and
controlling hub 12, connected satellite modules 14 that deliver
stimulation and recording through electrodes 16, for example,
existing commercial electrodes and innovative electrodes offering
high channel counts with integrated low-power signal conditioning.
In some embodiments, the hub 12 and satellite modules 14 may be a
single implanted module. Either part may also exist outside the
body and communicate wirelessly to the electrodes 16. The hub 12,
or implanted module, may be implanted under the scalp of a patient
and can wirelessly communicate with an external base station 18 for
data streaming, reprogramming, wireless recharge, and coordinating
intervention across sites to enhance treatment of neuropsychiatric
dysfunction. Alternatively, the hub 12 may itself be wearable or
otherwise non-implanted, or the hub 12 and base station 18 may be
merged as a single component. The base station 18 is in electrical
communication with the hub 12 via a head mounted interface 20 and,
in some embodiments, may interface with an offline processor 22
having a user interface 24, such as a clinician interface. The head
mounted interface 20 may be a wearable processing unit that
communicates, configures, and can control the implanted system 10.
The head mounted interface 20 might also mount or implant to some
other body part (e.g., chest) depending on the surgical clinician's
preference. In some embodiments, a hand held patient controller
(e.g., a watch) may be provided for self-reporting and triggering
recordings, as well as monitoring heart rate wirelessly, skin
conductance, and the like.
[0053] The implantable system 10 may be designed to record and
stimulate brain circuits. The configuration shown in FIGS. 1 and 2
is an example of bilateral electrode satellite placement. In one
non-limiting example, some of the electrodes 16 may be recording
electrodes that consist of cortical ECoG arrays recording from the
dorsolateral prefrontal cortex (dlPFC) and subcortical DBS leads
recording from the ventromedial prefrontal cortex (vmPFC).
Stimulation may be performed with subcortical DBS leads placed in
the nucleus accumbens (NAcc) and ventral capsule/ventral striatum,
for example. One of more of the electrodes 16 may be a recording or
stimulating electrode in this configuration. Additionally, or
alternatively, any of the electrodes 16 may be replaced with one of
several commercial designs capable of recording single units.
[0054] Algorithms stored on the hub 12 may enable the system 10 to
merge spike and field-potential data to estimate the patient's
psychiatric state and deliver therapeutic stimulation. The
frequency of stimulation delivery may depend upon how frequently
neural signatures that trigger stimulation occur. Those signatures
may be fully or partly under a patient's direct intentional
control. Real-time telemetry may enable a clinician, for example,
to tune algorithm parameters as required by the patient. Thus, the
system 10 will also provide neuroscientists an unprecedented view
of real-time brain activity in fully conscious patients interacting
with real-world environments.
[0055] The system 10 may be configured to operate in one or more
modes. For example, in autonomous mode, the hub 12 may be
controlled by an internal processor 26 and powered by an internal
battery 28 that can be recharged periodically, as shown in FIG. 2.
Low-bandwidth (e.g., 2 MBps) telemetry 30 may be used to report on
the hub's state of health and provide the subject comfort in the
ability to both wake up and put the system 10 to sleep when needed.
In a continuous recharge mode, the head-mounted interface 20 can be
attached to the base station 18 to wirelessly power the implanted
device via a charger 32. The continuous recharge mode may be
desirable when operating the system 10 in modes that consume more
power. In a base station control mode, a high-bandwidth telemetry
link 34 can be used to stream live neural data to be processed
within the base station 18, which can then control stimulation
therapies over the low-bandwidth telemetry 30. The base station
control mode permits higher-power algorithms to be implemented
without burden on the hub 12 and satellite modules 14. In a
computer control mode, the offline processor 22 can be connected
through the base station 18 to further increase processing power
and provide additional interfaces and resources to researchers and
clinicians. The computer control mode may be used during the
initial configuration period, where the implanted device may be
running sub-optimally with high-channel counts and high processing
power. As the device and algorithms are iteratively matched to the
patients needs, operation may become less dependent upon external
modes and move toward autonomous control.
[0056] With continued reference to FIG. 2, each satellite module 14
may interface with or be fully integrated into an electrode 16. In
one example, the electrodes 16 may be multi-channel
macro-electrodes or micro-electrodes. A cross-point switch (CPS)
matrix (Neural CPS) 36 may be used to re-configure the electrodes
16 for recording and stimulation. Additionally, the neural CPS 36
may enable multiple electrodes 16 to be simultaneously connected to
one of two analog stimulus inputs to create lower impedance
larger-area electrode clusters for stimulation. Neural
amplification and digitization within the satellite module 14 may
provide a higher level of signal to noise ratio (SNR) and reduce
the wire-count burden to communicate with the hub 12 through
multiplexed data. A field-programmable gate array (FPGA) 38 may
reduce wire-counts and provide control over the Neural CPS 36 and
amplifier. The FPGA 38 may also provide charge-balanced
communication with the hub 12, which mitigates risks of tissue
damage caused by DC leakage currents. Power may be provided by an
AC power supply 40 that will convert an AC supply 42 provided by
the hub 12 into DC voltages required by the satellite modules
16.
[0057] As previously described, the system 10 may be capable of
recording several types of neural signals (e.g., Spikes, LFPs,
ECoG, etc.) from different types of electrodes 16 (e.g., Micro-,
DBS, ECoG arrays, etc.). This requires the satellite 14 to include
a neural amplifier 44, such as a multi-purpose re-configurable
neural amplifier that is both low-noise and low-power. Low noise
may be necessary to capture small LFP and ECoG features in high
gamma frequency bands, while low power may be necessary to reduce
heat dissipation and extend the operational lifetime.
[0058] Each satellite module 14 may include a unique ID 46 that is
linked to its specifications, internal components, manufacturing
data, and attached electrodes 16. The satellite's 14 unique ID 46
may allow the hub 12 to interrogate the connected satellite modules
14 and configure itself based upon returned information. This
information may then be transmitted to the base station 18 to be
verified and logged. The satellite module 14 may also be configured
to monitor and report a set of health parameters that includes
electrode impedance and may also include temperature, humidity,
supply voltage, and hub-to-satellite lead line integrity, for
example. In the case of malfunction, actions may be taken by the
hub 12 to modify or shut-down satellite functions.
[0059] Turning now to FIG. 3, an exploded view of one of the
satellite modules 14 of FIG. 2 is shown. The components of the
satellite module 14 may be enclosed within a hermetically sealed
housing 48 that is hard-wired to the electrodes 16 and a lead body
with a distal connector for inter-operative attachment to the hub
12 (see FIG. 2). The housing 48 may be a titanium package measuring
about 14 mm in diameter and about 7 mm in thickness. This package
size is targeted so that the satellite module 14 can fit within a
standard neurosurgical craniectomy window. A lid 50 made from a
ceramic material, for example, may include a plurality of hermetic
apertures 52 and may be coupled to the housing 48 to form a
hermetic seal. Electronics of the satellite modules 14 may be
positioned on two rigid flex boards 55 in stacked configuration. In
some embodiments, wires to the electrode arrays 16 and lead cables
to the hub 12 may be permanently attached to the satellite module
14.
[0060] Returning to FIG. 2, the hub 12 may configure the satellite
modules 14, process neural data, decode neuropsychiatric states,
control closed-loop neurostimulation therapies, and transmit data
to the external base station 18. The hub 12 may include the
processor 26, which may be a low-power processing core configured
to execute algorithms. The hub 12 may further include one or more
current pulse generators 54 for applying stimulation therapies
through the electrodes 16. In addition, the hub 12 may include a
memory 56, such as dynamic random-access memory, accessible by the
processor 26 for stored data. The hub 12 may also include the
high-bandwidth telemetry link 34 for neural data exfiltration, and
the battery 28, which may be wirelessly charged, for extended
operational life.
[0061] The current pulse generators 54 for neural stimulation may
be positioned in the hub 12 and may be responsible for generating
the programmable current-controlled stimulation pulses for
neuromodulation. Analog stimulation waveforms may be sent to the
satellite modules 14 and routed to stimulation electrodes 16 via
the neural CPS 36. In order to effectively modulate stimulation
therapies based upon closed-loop neural activity, stimulus
waveforms may be dynamically re-programmable by hub algorithms.
Waveforms may be biphasic and charge balanced, and voltages across
the electrode-tissue interface may be limited to the water window
to inhibit chronic tissue damage. For monopolar stimulation,
currents may be returned to the conductive hub 12, and for bipolar
stimulation, currents may return through adjacent electrodes
16.
[0062] With continued reference to FIG. 2, the hub 12 may further
include one or more sensors 58 including, for example, humidity,
temperature, and accelerometer sensors. Humidity and temperature
sensors may be a useful part of health monitoring in the hub 12.
For example, an increase in moisture may be detected by the sensor
58 and a warning of failure due to ingress of moisture from the
body, or possible accumulation of moisture from parts may be
provided. In another non-limiting example, accelerometer data
gathered from one of the sensors 58 may be useful for determining
subject activity, including sleep, which may be used as a signal
for closed-loop control. Measurements acquired from the sensors 58
may be logged and reported to the base station 18. Similar to the
satellite modules 14, the hub 12 may include a unique ID, for
example a 16 bit unique identifier, that can be read through the
low-bandwidth telemetry 30.
[0063] As previously described, the hub 12 may include the
processor 26 and a control unit 62, such as a programmable logic
controller (PLC) to manage system-level functions and execute
closed-loop algorithms for adaptive neuromodulation therapy. The
processor 26 and control unit 62 may be adaptable and
re-programmable in order for closed-loop algorithms to be
developed, tested, and tuned for enhanced therapeutic benefit to
each patient. The processor 26 and control unit 62 may be capable
of configuring satellite modules 14 and receiving neural data,
extracting signal features from raw neural data, decoding
neuropsychiatric states, modulating stimulation therapy, monitoring
and logging system health data, detecting and recording neural
data, and managing wireless communication to the base station
18.
[0064] The hub 12 may further include a nonvolatile storage module
64. A non-limiting example would be a single-level cell (SLC)
flash. SLC flash may have favorable power characteristics and
sufficient bandwidth for high channel count raw spike recording.
Peak power consumption is below 30 milliwatts, with spike scenarios
expected around 17 milliwatts. As requisite data rate decreases
(fewer channels recorded or LFPs vs. spikes or features vs. raw),
power consumption decreases. Recording 320 channels of LFP (Fs=2000
Hz) is expected to take less than 2 milliwatts. These numbers
assume a pre-erased device and page-size writes.
[0065] Collection of data may be necessary to understanding brain
function in relation to neuropsychiatric diseases and in assessing
the effectiveness of the device in treating symptoms. In addition
to being able to stream data via the high-bandwidth telemetry link
34, data may be stored internally and later uploaded to the base
station 18. Depending upon the type of data desired, varying
lengths of recordings may be saved. For example, storing only spike
times requires less memory than storing raw spike data.
[0066] The hub 12 may further include a wireless power link 60 for
inductive recharge. Thus, the hub 12 may include three antennas for
inductive recharge (wireless power link 60), high-bandwidth
telemetry 34, and low-bandwidth telemetry 30. As shown in FIGS. 4A
and 4B the antennas 30, 34, 60 may be constructed from PCB micro
strip traces, for example, and positioned on an antenna board 66
near the top of a housing 68 where the antennas can be in close
proximity to the scalp. A magnet 70 may be positioned in the center
of the antenna board 66 to help with alignment with the head
mounted interface 20.
[0067] A connector 72, such as a high-density connector that may
include 64 or more sockets, may be utilized for connection to the
satellite modules 14 and may also be connected to the hub housing
68. The connector 72, the battery 28 and the hub housing 68 may be
attached by a thin film flex cable 74 and over-molded with
medical-grade silicone (not shown), for example. The thinness of
the hub 12 components may be desirable for comfortable and
unobtrusive implantation on top of the skull, and the flexibility
of the hub 12 components may provide a better fit to varying skull
curvature. As shown in FIG. 4C, the battery 28 may be contained in
a package 76, such as a hermetically-sealed titanium package, and
may power the hub 12 components via a multi-layer flex cable, for
example. The housing 68 of the hub 12 components may be constructed
of low temperature co-fired ceramic (LTCC) which may allow improved
performance of the wireless power link 60, high-bandwidth telemetry
34, and low-bandwidth telemetry 30.
[0068] Returning to FIG. 2, the base station 18 is coupled to the
processor 22 and user interface 24. The user interface 24 may be a
display including, for example, LEDs and buttons for basic
functions, such as putting the system 10 to sleep, waking the
system 10 up, performing a check status, and the like. The base
station 18 may also be coupled to the head mounted interface 20
that includes antennas for the high-bandwidth telemetry 34 and the
wireless power charger 32. Without the head mounted interface 20
attached, the base station 18 may communicate and control the
implanted unit via the low-bandwidth telemetry 34. When the head
mounted interface 20 is attached, the high-bandwidth telemetry 34
link may be used to stream real-time neural data to the base
station 18 where closed-loop algorithms can be executed at higher
power than can be used inside the implant. Closed-loop control may
then be achieved by controlling the system 10 over the
low-bandwidth telemetry 34 link. This closed-loop control may
include a component of directly detecting/decoding the patient's
intentions regarding neurostimulation. The head mounted interface
20 may also be used to wirelessly recharge or continuously power
the implant via the charger 32.
[0069] As shown in FIG. 5, the head mounted interface 20 may
include a first antenna 78 for the wireless power charger 32, a
second antenna 80, such as a transmit antenna, for the
high-bandwidth telemetry 34, and a third antenna 82, such as an
receive antenna, for the high-bandwidth telemetry 34. The antennas
78, 80, 82 may be mounted on a PCB 84 which also includes matching
networks 86, a wireless power amplifier 88, and an alignment magnet
90. The alignment magnet 90 may help to align and retain the head
mounted interface 20 with the magnet 70 of the hub 12 to ensure
power transfer efficiency and communication. The head mounted
interface 20 may further include a cable bundle 92 for connection
to the base station 18 that may be soldered directly to the PCB 84,
for example. In one non-limiting example, the cable bundle 92 may
be a 1 m highly flexible cable bundle including two RF coax cables,
as well as DC power and clock signals for the wireless power
transmitter. A plastic enclosure (not shown) may protect the PCB 84
from contact by the user. The head module may connect to the base
station via a like lm highly flexible cable bundle.
[0070] In some embodiments, the system 10 may be a non-invasive
system for stimulating the brain. That non-invasive realization may
collapse components such as the satellites 14, base station 18, and
hub 12 into a single part. The non-invasive system may enable not
only treatment via induction of brain plasticity, but also pre
surgical planning of DBS targets. The non-invasive system may be
combined with EEG, for example, to produce a closed-loop system.
The non-invasive system may include a plurality of scalp or
non-contact electrodes and/or neuro-stimulation coils in
communication with a software-controlled helmet, cap, or set of
electrodes that can stimulate areas of the brain. The embedded
software may steer the amount and polarity of energy sent to each
electrode, thus shaping the E-fields and allowing accurate
targeting of specific cortical areas. In other embodiments, the
non-invasive system may be combined with non-electrical modalities,
such as magnetic or ultrasonic stimulation, for stimulating the
brain.
[0071] Referring now to FIG. 6, a flow chart sets forth exemplary
steps 200 for diagnosing and treating mental and emotional
disorders by analyzing an individuals brain with both imaging and
behavioral testing. The method incorporates the affective BCI
component in the closed loop, symptom-responsive psychiatric DBS
system 10 previously described. In general, the method may include
performing a series of brain scans on a patient while the patient
performs a battery of behavioral tasks. From the patient's
performance on the task battery and/or the brain's activations
during performance of the tasks, the system 10 may identify what is
abnormal for that individual patient in terms of functional domains
(e.g., fear, reward motivation, emotion regulation, decision
making/impulsivity, attention/preservation, cognition, etc.) as
shown in the table of FIG. 7. The system 10 then may link the
patient-specific behavioral measurement to patterns of activation
and de-activation across different brain regions, identifying
specific structures that are the source of the patient's individual
impairment. The system 10 may then use a range of invasive or
non-invasive brain stimulation technologies to specifically target
the brain regions and provide individualized psychiatric treatment,
or to probe the network further to better classify the
impairment.
[0072] Thus, the method to be described shifts away from treating
patients based on conventional classifications of mental diseases
and disorders (e.g., The International Classification of Diseases
(ICD) or the Diagnostic and Statistical Manual of Mental Disorders
(DSM)). Diagnosis-based treatment is the standard of care in
psychiatry. However, there is a widespread recognition that the
diagnoses do not correlate well to underlying brain circuit
dysfunction. For example, a single disorder called "major
depression" could be one or more different kinds of brain
dysfunction. The NIH has proposed a domain oriented solution, the
Research Domain Criteria (RDoC). However, RDoC is meant as a
scientific research tool, not a clinical system or diagnostic tool.
Thus, the present system and method enable a shift to
functional-domain diagnosis using behavioral testing that consider
more abnormal domains as the "problem" to be treated.
[0073] Returning to FIG. 6, the method may begin by administering a
transdiagnostic assessment at process block 202. Administering the
transdiagnostic assessment requires a patient to perform one or
more psycho-physical tasks to determine the patient's impairment
along a set of functional domains. The functional domains, as shown
in the table of FIG. 7, may be validated psychological constructs
that are transdiagnostic and occur in multiple named mental
disorders (e.g., PSTD, MDD, generalized anxiety disorder (GAD),
traumatic brain injury (TBI), substance use disorder (SUD),
borderline personality disorder (BPD), pain disorder, etc.). The
disorders shown in bold represent the primary domains for the
disorder.
[0074] The functional domains are not limited to those shown in
FIG. 7, and in one non-limiting example, the system 10 may be
configured to access the NIMH's RDoC Matrix for additional
functional domains. Each functional domain may be measured by one
or more corresponding behavioral tasks that probe each domain and
certain brain regions known to be involved in
performance/impairment for each domain. The various brain regions
may include, but are not limited to the rostral anterior cingulate
(rACC), dorsolateral prefrontal cortex (dlPFC), ventromedial
prefrontal cortex (vmPFC), orbitofrontal cortex (OFC), subgenual
anterior cingulate (cg25), amygdala, anterior insula, thalamus,
periaqueductal gray, ventral striatum, subthalamic nucleus,
hippocam pus, dorsal striatum, and the like.
[0075] The transdiagnostic assessment, optionally administered at
process block 202 of FIG. 6, may be performed by the patient on a
computer, for example, and may be one or more simple computer games
that are each designed to probe functioning in a different domain.
As a non-limiting example, the tasks outlined below will be
described with respect to a patient who has a difficulty with fear
regulation (a subset of difficulties with emotion regulation) which
may commonly be found in a patient who has PTSD, but can also be
seen in obsessive-compulsive disorder, generalized anxiety
disorder, panic disorder, and sometimes major depressive disorder.
Such a patient may have several psychiatric diagnoses, for example,
a prototypical patient may be a returning service member who had
been diagnosed with PTSD, but also with an alcohol use disorder and
sub-threshold depressive symptoms. All of the below descriptions of
task-based diagnostics and resulting stimulation protocols may be
combined with the patient-intention controller described in below
to form a hybrid brain-computer interface.
[0076] Turning to FIG. 8A, a fear learning and extinction task is
shown for measuring the functional domain of fear extinction.
Performance on this task assesses integrity of the fear and fear
suppression systems. The fear learning and extinction task may be
impaired for the example PTSD patient described above. During fear
conditioning, the patient may be presented with a conditioned
stimulus 302, such as multiple, different colored lights. Two of
the lights, for example, may be paired with the presence of an
unpleasant, but not painful electrical current applied to the
fingers of the subject, which is called an unconditioned stimulus
304. This pairing induces conditioned fear responses, such as
increased heart rate (HR) and skin conductance response to the
presentation of the conditioned stimulus 302. In some embodiments,
the fear responses may be measured through biomarkers. The
conditioning phase happens outside the imaging scanner before an
initial scan.
[0077] During an extinction phase, which happens in the imaging
scanner, the patient may be presented with the conditioned stimulus
302 in the absence of one or more of the unconditioned stimuli 304
(i.e., one of the lights is "extinguished"--repeatedly re-presented
without the shock, so the patient learns it is now safe.), which
leads to abolishment of the conditioned responses. The degree of
extinction recall (i.e., how much the extinction has been
consolidated) is then assessed by a repetition of the extinction
trials in the scanner on a second scanning day. For example, during
the recall phase, the patient may be presented with both the feared
and safe lights. Patients with deficits in fear extinction learning
either are unable to learn that one of the lights is safe on Day 1,
or will learn this but not recall it on Day 2. A patient with
difficulty learning safety may show elevated fear biomarkers (e.g.,
heart rate, heart rate variability, eye pupil diameter, skin
conductance due to sweating, etc.) compared to normal controls at
multiple phases of the task.
[0078] As shown in the graph of FIG. 8B, the PTSD subject shows
impaired recall on Day 2 compared to a population norm. In other
disorders, such as MDD, a patient may exhibit extinction deficits
(threat bias). Similar to PTSD patients, patients with GAD may show
impaired recall, while TBI patients may exhibit impaired extinction
learning. SUD patients may exhibit impaired fear acquisition
(reward bias), and BPD patients may exhibit rapid acquisition,
impaired extinction, and maybe impaired recall, while pain patients
may exhibit impaired extinction and recall.
[0079] Turning to FIG. 9, an example aversion reward conflict (ARC)
task is shown for measuring the functional domain of reward
motivation. Performance on this task assesses whether the patient
will accept a certain amount of punishment to obtain a certain
amount of reward, as well as the patient's reaction time. The ARC
task may be impaired for the example PTSD patient described
above.
[0080] During the ARC task, the patient may be trained on a simple
two-choice discrimination task. The patient's performance on each
trial may be modified by the expectation of both rewards (e.g.,
monetary rewards) and aversive stimuli. In the ARC paradigm, the
task begins with the patient fixating on a central point 402 on a
computer monitor, a shown in FIG. 9. Next, two cues 404 represented
as circles are presented on the periphery of the computer monitor.
One cue is the combined option cue that represents two distinct
stimuli: the probability of aversion and an expected reward of
varying magnitude. The probability of aversion is indicated by a
color of the cue (e.g., Blue: low, 10%; Black: medium, 50%; Red:
high, 90%) and consists of a half second shock to the fingertips.
Additionally, or alternatively, the unpleasant punishment may be an
air puff to eye or a loud noise, for example. For reward, the
magnitude is determined by a thickness of the cue (e.g., Thin:
small; Medium: medium; Thick: large). In some embodiments, the
reward is represented by pictures of money (e.g., small ($0.10),
medium ($0.25), and large ($1.00)). The other cue may be a white
"pass" circle that predicts a smaller reward ($0.01) as a safe
alternative with no aversion. The subjects must choose either the
left hand or right hand cue 404 by pressing the corresponding
button on a button box, for example. The side of the screen in
which the cues 404 are presented in each trial may be
randomized.
[0081] Thus, the ARC task can measure how often a patient takes the
risk choice overall, for example. In addition, the ARC task can
measure what level of reward, and what level of risk-reward
balance, is needed to get the patient to run the risk of
punishment, as well as the patient's reaction (i.e., how long does
the patient take to choose). The ARC task can further determine how
the patient's choice may change immediately after the patient "bet
wrong" and received punishment.
[0082] In one example, the system 10 may incorporate DBS to modify
ARC behavior. For example, the NAcc brain region may be stimulated
to increase reward sensitivity, the STN brain region may be
stimulated to decrease impulsivity, and the Amygdala brain region
may be stimulated to increase or decrease threat sensitivity.
[0083] The ARC task may show that the example PTSD patient is more
driven by threat than reward. In other disorders, such as MDD, the
ARC task may show decreased reward sensitivity, possibly with
increased threat sensitivity (dissociable). Similar to PTSD, the
ARC task may show that a GAD patient and/or a BPD patient is more
driven by treat than reward. TBI patients may show faster response
speeds and insensitivity to punishment from the ARC task. A SUD
patient may be more driven by reward than threat, and a pain
patient may be more driven by threat than reward and show an
overall lower aversion threshold.
[0084] Turning now to FIG. 10, an emotion conflict resolution (ECR)
task is shown for measuring the functional domain of emotion
regulation. Performance on this task assesses a patient's reaction
time, as a measure of conflict. The ECR task may be impaired for
the example PSTD patient described above. During the ECR task, the
patient may be shown one or more images 502 of a person showing an
emotion. A word 504 describing an emotion is displayed over the
image 502. The patient is asked to report the emotion shown in the
image 502 while attempting to ignore the word 504 by pressing a 1
(fearful) or 2 (happy) key on a button box, for example. Trials are
either congruent (facial expression and word match; e.g., happy
expression and the word "HAPPY"), or incongruent (facial expression
and word do not match, e.g. happy expression and the word "FEAR").
The task is designed to measure the ability to regulate emotions
and adapt behavior after being presented with the initial
"surprise" of incongruent stimuli. The presentation of an
incongruent image after seeing a congruent image encodes the
initial recognition of emotion conflict ("low conflict
resolution"). The presentation of an incongruent image followed by
a second incongruent image encodes the ability to regulate the
emotion conflict and continue performing the task, or behavioral
adaptation ("high conflict resolution").
[0085] Thus, the ECR task can generate affective responses, while
also evoking top-down processing and stressing that network. In
addition, the ECR task can identify both cognitive conflict and the
response of the brain to highly salient affective stimuli to
understand the interaction (i.e., how does a patient become
differentially biased to process emotions when under load?) and the
dissociation (i.e., how does X get processed when Y is controlled
for?). The ECR task can also measure how slowed down a patient is
by conflicting emotions, or by fearful instead of happy faces shown
in the images 502, for example. The ECR task may also identify
whether seeing an image 502 with a fearful emotion makes the
patient more error-prone.
[0086] The ECR task may show that the example PTSD patient, GAD
patient and/or BPD adapts more slowly to conflict when emotion
distracts. The ECR task may show that a MDD patient has faster
response time on negative-affect stimuli and more difficulty with
incongruent images 502 and words 504. TBI and SUD patients may show
primarily incongruence without affective component (except from
comorbidities). A pain patient may show similar results as a GAD
patient. In one non-limiting example, the ECR task may show
abnormal behavior in a patient with depression when compared to
controls. That patient would be expected to also endorse greater
difficulty with regulation emotions using a standardized clinical
rating scale.
[0087] Further, the patient with depression, for example, may
exhibit greater recruitment relative to the healthy controls of
brain structures identified as key regions for processing negative
affect and salience; specifically, the amygdala, insula, and
rostral anterior cingulate (rACC). By contrast, the healthy
controls may show greater recruitment of structures identified as
key regions for the regulation of emotion and attentional control;
specifically, the dorsal anterior cingulate (dACC) and the
dorsolateral prefrontal cortex (DLPFC).
[0088] Patterns of functional connectivity between brain regions
showing differential activation in the patient with depression and
healthy controls and other key regions subserving emotion
processing and cognitive control of emotion may be analyzed. This
analysis may further identify where and how the individual patient
is deviating from healthy individuals along distributed neural
networks subserving these processes.
[0089] Healthy controls may demonstrate strong cohesion in
activation between the amygdala and cognitive control regions
(DLPFC, dACC, rACC). By contrast, the patient with depression and
ADHD may demonstrate low cohesion between the amygdala and
cognitive control regions. In particular, lower amygdala-rACC
functional connectivity may be found relative to healthy controls.
Additionally, healthy controls may demonstrate strong functional
connectivity between the amygdala and dACC during High Conflict
Resolution (emotion regulation) relative to Low Conflict Resolution
(emotion conflict recognition). In contrast, the patient with
depression and ADHD may show a reduction in cohesion between
amygdala and dACC during High Conflict Resolution. Thus, specific
altered connectivity in a known network of emotion regulation, one
that is linked both to the specific task and to the general
diagnosis (depression) can be identified in the individual
patient.
[0090] The above described analyses may reveal strong correlations
between the strength of functional connectivity between the rACC
and dACC and 1) reaction time (behavior); 2) emotion regulation
measures (psychological functioning); and 3) psychiatric symptoms
and impairment. Those signatures may differ substantially between
individual patients. Therefore, a specific brain signature, at the
slow metabolic time course detectable with fMRI, that has clinical
implications may be identified.
[0091] In one example, the system 10 may incorporate DBS to modify
ECR behavior. For example, the dACC, VC/VS and dlPFC brain regions
may be stimulated to increase the patient's ability to process
conflict. Additionally, or alternatively, the Amygdala brain region
may be stimulated to lower threat bias and affective activation to
reduce distractions.
[0092] Turning now to FIG. 11, a gambling task is shown for
measuring the functional domain of impulsivity and decision making.
Performance on this task assesses an overall percentage of time the
patient bets their card, as well as reaction times and responses to
losses. For example, a "rational" person will typically bet their
card around 50% overall and show specific patterns on specific
cards. If the patient bets more or less than 50% overall the system
may identify risk/reward problems with the patient.
[0093] During the gambling task, the patient may be presented with
a reduced 5-card deck of playing cards. The patient is instructed
to play "war" against the computer and the patient bets whether
their card is higher than the card displayed by the computer. There
is an optimal bet for each card except 6. The 6 card, by
definition, is a 50% win-lose chance, "decisional equipoise". As
shown in FIG. 11B, the patients reaction time is greatest for the 6
card.
[0094] The gambling task may show that the example PTSD patient is
unimpaired and/or impulsive. In other disorders, such as MDD, the
gambling task may show loss aversion and generally slowed reaction
times. GAD patients may show loss aversion and increased
conservatism after a loss on a prior trial. TBI patients may show
impulsivity and overall broad activations, and SUD patients may
show impulsivity. BPD patients may show impulsive, suboptimal
decision making, and pain patients may show a strong loss aversion,
similar to MDD patients.
[0095] In one example, the system 10 may incorporate DBS to modify
impulsive behavior, as indexed by the gambling task. For example,
as shown in the graph of FIG. 11C, the STN brain region may be
stimulated while the patient is betting, and the result shows a
more conservative bet in comparison to no stimulation or fixed
stimulation. The NAcc, VC-VS, and/or the OFC brain regions may also
be stimulated to increase impulsivity. The subcortical brain region
may also be stimulated to alter the impulsive-conservative balance.
Additionally, or alternatively, the dACC brain region may be
stimulated to slow processing, thereby forcing the patient to slow
down and think before betting.
[0096] Turning now to FIG. 12A, a multi-source interference task is
shown for measuring the functional domain of flexibility and
perseveration. Performance on this task assesses reaction time and
measures cognitive capacity and cognitive flexibility. The
multi-source interference task may be impaired for the example PTSD
patient described above.
[0097] During the multi-source interference task, the patient may
be provided a group of numbers 602 and asked to select one number
from the group of numbers 602 that is different from the others.
The patient may be asked to perform the multi-source interference
task as quickly and accurately as possible. In general,
interference stimuli should increase the patient's reaction time
and switching rapidly between interference/non-interference trials
may impose extra load. Further, having the patient perform the
multi-source interference task alongside other emotional tasks,
allows the system 10 to dissociate out the different effects and
their networks.
[0098] The multi-source interference task may show that reaction
times for the example PTSD patient is slowed under load. In other
disorders, such as MDD and pain, the multi-source interference task
may show greater interference effects and/or wider activation under
interference. Similar to PTSD, a GAD patient and BPD patient may
show slowed reaction times under load. TBI patients, and some SUD
patients, may show poor trial-trial adaptation and greater
interference effects due to dACC and/or dlPFC impairment, for
example.
[0099] In one example, the system 10 may incorporate DBS to modify
attention and perseveration behavior. As shown in FIG. 12B, the
VC/VS brain region is chronically stimulated, but the DBS is turned
off while the patient performs a multi-source interference task. As
shown in the graph, the patient's reaction time is less when DBS is
on, whereas the reaction time is longer under cognitive load from
interference. That is, effective tuning of the DBS may modify the
patient's reaction time, representing improvements in attention and
mental flexibility. The system 10 may monitor responses in the
dlPFC and dACC brain regions and tune VC-VS stimulation to keep
overall activation adequate. In addition, frequency-domain
signatures may be uncovered in processing of acquired data and
serve as calibration to dissociate non-affect signals that should
not be closed-loop targets.
[0100] Turning now to FIG. 13A, an associative learning task is
shown for measuring the functional domain of cognitive capacity.
Performance on this task assesses the patient's speed to learn new
items, memory, number of items that the patient can perform
correctly at some criterion (e.g., 80% consistent correct), speed
to detect a reversal when it happens (i.e., to notice that what was
correct is now wrong), and perseverative thinking. The associative
learning task may be impaired for the example PTSD patient
described above.
[0101] During the associative learning task, the patient may be
presented with a stimuli 702, such as a colored shape, and be
instructed to move a joystick (not shown) to the right, for
example, when presented with the stimuli. Thus, when the patient
sees the stimuli 702, he/she should learn stimulus-response
associations. The speed of learning associations and the number of
associations that the patient can learn correctly may be a measure
of the patient's overall cognitive capacity. Overall cognitive
capacity may also be measured by adding rule-switching or reversals
into the associative learning task. For example, after the patient
learns a rule associated with the stimulus, the rule may suddenly
change. Rule-switching or reversals may also be a measure of
cognitive flexibility, top-down attention shifting, and frustration
tolerance, which is often impaired in psychiatric disorders. The
associative learning task also provides situations with prepotent
and overlearned responses, the inhibition of which is a core
psychiatric function.
[0102] The associative learning task may show that the example PTSD
patient is characterized by extinction/reversal specific deficits.
MDD patients may show more difficulty with set shifting, even when
cued. Similar to PTSD, the associative learning task may show that
a GAD patient shows extinction/reversal specific deficits. TBI
patients may show perseveration on uncued or cued reversals and may
also be impaired on association generally. SUD patients may show
possible general impairment during the associative learning task,
and BPD may show extinctions, reversals, and decreased capacity
overall. Pain patients may show impaired set-shifting and reversals
during the associative learning task.
[0103] In one example, the system 10 may incorporate DBS to modify
associative learning behavior. For example, the NAcc, caudate, STN,
OFC and/or the hippocampus brain region may be stimulated to modify
the patient's ability to learn. Stimulation may be performed during
a specific time-limited formal therapy or exposure sessions, and
biomarkers (e.g., heart rate, skin conductance, etc.) may be
incorporated to enhance safety/extinction learning.
[0104] Turning now to FIG. 13B, cumulative data is shown from over
20 sessions in animals performing an associative learning task
utilizing DBS stimulation in the Cd and NAc brain regions.
Closed-loop stimulation may be applied in different combinations.
For example, the best performance is shown when the subject
received combined stimulation of the NAc and Cd brain regions.
Thus, as shown in the graph of FIG. 13B, DBS stimulation at correct
sites and timing may enhance learning and showing an improvement in
overall performance approaching 100% correct.
[0105] Returning to FIG. 6, one or more of the psycho-physical
tasks just described may be given to the patient while
administering the transdiagnostic assessment at process block 202
in order to determine the patient's impairment along a set of
functional domains. Once the transdiagnostic assessment is
administered, the system 10 may record electrical, magnetic, or
other physiologically produced activity from the patient's brain
and/or body at process block 204 while the patient is performing
one or more of the psycho-physical tasks. The activity may be
recorded through non-invasive or invasive methodologies.
[0106] More specifically, at process block 204, while the patient
is performing the task(s) of transdiagnostic assessment, multiple
forms of electro-magnetic signals may be recorded from the patient
during a single session, for example, or during multiple sessions
of multiple modalities (i.e., imaging types) done over several
days. In some embodiments, the majority of the electro-magnetic
signals are measured from the patient's brain using one or more of
structural magnetic resonance imaging (MRI), with particular
variants such as diffusion tensor imaging (DTI), functional
magnetic resonance imaging (fMRI), positron emission tomography
(PET), single-photon emission computed tomography (SPECT),
electroencephalography (EEG), magnetoencephalography (MEG),
near-infrared spectroscopy (NIRS), reflected ultrasonic energy,
fluorescent energy emitted from molecules within certain
structures, direct recording of the brain with invasive, surgically
implanted electrodes, and the like. Alternatively, any suitable
method or combination of methods for recording the electro-magnetic
signals of the brain with sufficient spatial resolution and
temporal resolution may be used.
[0107] In some embodiments, the electrical activity recorded from
the body at process block 204 may be accomplished by recording one
or more biomarkers. These may be signals that relate to brain
activity and psychological state, but are not directly measured
from the brain. For example, the biomarkers may include, but are
not limited to, heart rate, eye movements and blinks, eye pupil
diameter, skin conductance/galvanic skin response (i.e., measure of
autonomic arousal), respiratory rate, recorded speech (e.g.,
quantitatively analyzed for tone, amount, and prosody), and
electromyography.
[0108] Once activity from the brain and body are recorded at
process block 204, optionally, the system 10 may identify the
patient's deviation in functional domains, such as the functional
domains shown in the table of FIG. 7, that the transdiagnostic
assessment showed as compared to healthy controls or population
norms at process block 206. Transdiagnostic assessments of
population norms may be acquired from a database of patients
without evident psychiatric impairment who have performed the
various transdiagnostic tasks. The patient's performance on the
battery of transdiagnostic tasks may be compared to healthy
controls by the system 10, or alternatively a trained clinician. As
previously described, performance varies from task to task and may
include, for example, how a biomarker changed in response to
certain stimuli, how fast the patient responded to questions, what
decisions the patient made when confronted with uncertain choices,
and the like.
[0109] In one non-limiting example, the example PTSD patient may
show impairment in the functional domains of fear extinction and a
partial deficit in emotion regulation. However, the patient may not
show impairment in the functional domains of cognitive capacity or
reward motivation, whereas the patient may or may not show
impairment in the decision making/impulsivity functional
domain.
[0110] As shown in FIG. 14, analysis and interpretation of a
transdiagnostic assessment may classify patients by a distance from
an origin 802. The origin 802 represents the population mean of
healthy controls in a transdiagnostic space 804, which generally is
high-dimensional. Scaling of the transdiagnostic space may be
spherical, normalized by Z-scores, or allowed to remain elliptical.
The degree of impairment along each axis 806 (i.e., the distance
from the origin 802) may then prioritize the patient's treatment in
terms of selecting brain stimulation locations and treatment
modalities, as will be described in further detail below.
[0111] The example PTSD patient, shown in FIG. 14 as Patient A, may
have a phenotype characterized by problems with excessive anxiety
and deficient safety learning and is impaired in fear extinction
and emotion regulation in the transdiagnostic space. However,
Patient A is essentially at population normal for reward
motivation. An alternate patient, Patient B shown in FIG. 14, may
have depressive symptoms but less anxiety and shows over-regulated
(i.e., flattened) emotion regulation and low reward motivation
(i.e., anhedonia). However, Patient B may have no meaningful defect
in fear extinction. Therefore, Patient A and Patient B can be
classified differently in the transdiagnostic space 804.
[0112] Returning to FIG. 6, once the patient's deviation(s) in
functional domains is identified at process block 206, the system
10 may identify the brain regions and signal characteristics within
those brain regions that correspond to the patient's deviation from
functional domains at process block 208. More specifically, once
the patient's functional domains of maximal impairment have been
identified, brain regions, as well as specific sub-regions within
the brain regions, may be identified that correlate to that
abnormality. This is done using the recordings of blocks 210 and
212, but may additionally involve consideration of general patterns
of brain activation known to exist in the scientific
literature.
[0113] In order to identify the brain regions and signal
characteristics at process block 208, the brain imaging data
acquired during the transdiagnostic assessment at process block 202
may be analyzed with relation to the patient's behavior on
individual trials (stimulus presentations) of each behavioral task,
starting with the most-impaired functional domains. The imaging
data from the impaired functional domains may be compared to
population averages to identify the brain regions where the patient
has abnormally high or low levels of brain activity. Alternately,
the population averages of other patients with similar behavioral
performance may be substituted as a proxy for the individual
patient's brain activity.
[0114] Furthermore, because the brain imaging data, such as brain
imaging data 902 shown in FIG. 15, includes the patient's specific
brain anatomy, the system 10 may identify one or more points 904
within the individual patient's brain that are generating the
abnormal activity, as shown in FIG. 15. The point 904 of abnormal
activity may be compared to a point 906 of normal activity as
determined by behavior on a transdiagnostic task identified by a
population average. In the example shown in FIG. 15, the individual
patient with impairment on the task had a focus of maximum
activation that was shifted by about 1 centimeter from the
population average.
[0115] Once the brain regions related to the patient's impairment
(i.e., deviations in functional domains) are identified at process
block 208, additional imaging data of the brain regions may be
obtained at process block 210 in order to identify abnormal
features of neural activity. The additional imaging data may be
acquired using any imaging modality having suitable high time
resolution including, for example, EEG, MEG, invasive recording,
EMG, and the like. Abnormal features of neural activity may
include, for example, electrical signals from event-related
potentials related to features of a transdiagnostic task,
event-related synchronizations and de-synchronizations in the
frequency domain related to task features, changes in phase of
oscillatory brain activity (e.g., phase resetting), coupling and
connectivity between different brain regions (e.g., coherence,
phase-locking values, Granger causality, etc.), interactions of
functional brain rhythms (cross-frequency coupling) within and
between different structures, and the like.
[0116] Once the patient's impaired functional domain(s) have been
identified and the brain regions and signals correlated to the
impairment have been identified, the system 10 may apply
stimulation to the identified brain regions at process block 212.
In one example, stimulation may be performed with the electrodes 16
(see FIG. 1) having cortical and/or subcortical leads placed near
brain regions of the patient. The stimulation may be applied to the
brain regions identified at process block 208 in order to alter
activity in those regions.
[0117] In some embodiments, sub-regions are selected within each
brain region based on the imaging data obtained at process block
210. Stimulation modalities for applying stimulation to the
identified brain regions and sub-regions at process block 212 may
include, but are not limited to, non-invasive electro-magnetic
modalities (e.g., transcranial magnetic stimulation, transcranial
direct- or alternative-current stimulation, transcranial focused
ultrasound, infrared/optical through-skull modulation, etc.),
invasive electro-magnetic modalities, and invasive optical
modalities. In the case of invasive electro-magnetic modalities,
electrodes or other amplifying devices may be surgically implanted
into one or more brain regions and/or sub-regions. In the case of
invasive optical modalities, transfection of one or more brain
regions with proteins or other molecules may be involved that make
neurons sensitive to light. Invasive optical modalities for
stimulation may also involve implanting optical fibers into the
brain regions. Combined non-invasive and invasive realizations,
such as implantation of magnetic particles that then respond to
applied magnetic fields, would also be reasonable.
[0118] Turning to FIG. 16, the example PTSD patient's brain 1002 is
shown with implant sites in various brain regions to modulate brain
activity in the emotion regulation and fear extinction functional
domains. The example PTSD patient with impaired fear extinction may
have deficits localized in a first brain region 1004, such as the
vmPFC, a second brain region 1006, such as the dACC, and a third
brain region 1008, such as the amygdala. The vmPFC brain region
1004 and the amygdale 1008 are deep structures of the brain 1002,
and the dACC brain region 1006 is superficial. Thus, brain activity
may be modulated by placing electrodes surgically into any of the
brain regions 1004, 1006, 1008, using a grid-type electrode 1010
for the superficial structure (i.e., the dACC brain region 1006)
and deep brain stimulation probes 1012, 1014 for the vmPFC brain
region 1004 and the amygdala brain region 1008, respectively.
[0119] Returning to FIG. 6, once stimulation is applied to the
identified brain regions at process block 212, intentional control
over the neurostimulation system (e.g., the BCI system 10) and over
the application of a control algorithm may be provided to the
patient at process block 214. By providing patient intentional
control, difficulties with over-control and emotional numbing, for
example, may be circumvented. The closed-loop controller may be an
emotional/affective brain-computer interface (BCI), such as the BCI
found in the system 10 of FIGS. 1 and 2, that actively monitors the
patient's emotional experience as expressed in the patient's brain
activity.
[0120] Next, the stimulation may be adjusted in real-time to
suppress the identified abnormal signals within the target brain
regions at process block 216. The stimulation may be directly
adjusted using, for example, real-time recordings of brain
electrical signals for closed loop control. In one non-limiting
example, if the patient receives an invasive stimulation modality
including implanted electrodes, such as the electrodes 16 of system
10 shown in FIG. 2, and a neuro-stimulation pulse generator, such
as the current pulse generator 54 shown in FIG. 2, to deliver
electricity, stimulation may be delivered in a closed loop fashion.
That is, the system may directly monitor the brain's electrical
activity at the implant sites, and may alter the stimulation dose
at each site based on observations throughout the network. In some
embodiments, monitoring electrical activity may be done optically,
for example, through genetically encoded voltage reporters.
Monitoring may attempt to directly infer the patient's emotional
state, to respond to the patient's intentional commands, or to
merge both types of monitoring into a hybrid system.
[0121] In order to adequately adjust stimulation in the various
brain regions at process block 216, an emotional decoding algorithm
may be stored on the controlling hub 12 of system 10 (see FIG. 1)
that can infer the patient's current emotional/symptom state from
brain activity. The emotional decoding algorithm may utilize the
time-resolved imaging and recording data acquired at process block
210. For example, it may be known that theta-frequency (4-8 Hz)
coupling between the vmPFC brain region and the amygdala brain
region plays a role in the encoding and extinction of fear
memories. For the example PTSD patient, the emotional decoding
algorithm may "decode" the patient's current capacity for fear
extinction, either from relative theta power in the vmPFC and dACC
brain regions, or more likely from theta-band coherence between the
dACC, vmPFC, and amygdala brain regions.
[0122] In an alternative embodiment, the patient's overall level of
fear may be focused on, and the amygdala activity (likely in the
high-gamma 65-200 Hz band) may be monitored directly as a proxy for
level of emotional distress. This could then be regulated with the
help of an intention decoder, as will be described in further
detail below. Regardless of the method used to adjust stimulation,
there is a signal that the controller hub 12 (see FIG. 1) is
programmed to sense. The controller hub 12 may then adjust
stimulation (e.g., the stimulation intensity or different
parameters) until the signal returns to a pre-defined range. For
the example PTSD patient with impaired fear regulation, amygdala
activity may be directly sensed, and the controller hub 12 may be
configured to keep the amygdala activity within the pre-defined
range (e.g., a pre-measured, non-anxious baseline). When amygdala
activity exceeded the pre-defined range, the controller hub 12 may
be configured to interpret this as the patient experiencing
ungovernable fear, and would drive the implanted neurostimulator to
shut down amygdala.
[0123] This closed-loop control may not be limited to brain
electrical signals. For example, fear may be related to a number of
autonomic signals that are detectable non-invasively, including
heart rate variability, skin conductance, and pupil diameter. These
autonomic signals may be measured by a non-invasive device, such as
the sensors 58 shown in FIG. 2, and transmitted to the controller
hub 12 via a communication protocol. The controller hub 12 may then
integrate these peripheral biomarkers with its recorded neural
signals to determine adequate stimulation adjustments to suppress
the abnormal signals in the brain regions.
[0124] Advantageously, the system 10 may have the ability to deploy
the above described algorithms and have the algorithms available as
the symptoms are experienced in the daily living of the patient.
The patient may be given a degree of control over the stimulator's
operation to gate its ability to turn stimulation on/off or to
modulate the intensity. For example, FIG. 17 shows an example of a
closed-loop affective decoder 2012 and brain stimulator 2054 for
psychiatric indications. The affective decoder 2012 may be
monitoring the vmPFC brain region 2004 and decoding the patient's
intention to activate the stimulator 2054. The stimulator 2054
shown in FIG. 17 is operating in the limbic circuit 2014. The
stimulator 2054 may be tracking and responding to activity in the
amygdala brain region 2008. Command signals recorded in the vmPFC
brain region 2004 may modulate the stimulator's 2054 behavior. When
vmPFC activity is high, the stimulator 2054 may remain active and
may be aggressive in emotional regulation. When vmPFC activity is
low, the stimulator 2054 may allow amygdala activity to vary freely
without intervening. Importantly, vmPFC activity is under the
patient's direct and intentional control, meaning that the patient
chooses what the stimulator 2054 will do.
[0125] More specifically, the system may use the patient's brain
signals as a read-out of what he/she wants the stimulator to be
doing, then use that to guide application of the closed-loop
system. This may be called a hybrid BCI because there is an
autonomous part (e.g., the emotional decoder) and a
patient-controllable part (the intention decoder), and the two are
coupled together to achieve adequate clinical performance. In some
realizations, the emotional decoder may not be needed, or may be
relatively trivial (e.g., the monitoring of a single channel in a
single brain area).
EXAMPLE
[0126] The prefrontal cortex may be a natural source of intentional
emotion regulation signals, and therefore the BCI controller may
not only infer the patient's emotional state, but may also decode
volitionally controlled brain signals. The desire to suppress or
amplify emotional experiences is already contained in PFC activity,
and that activity may correlate directly with patients' ability to
succeed in emotion regulation. Further, PFC neurons are flexible
and may regularly re-tune themselves into new ensembles, encoding
complex features of multiple tasks. Thus, given that plasticity may
be important for successful affective decoding and control, a BCI
that decodes signals from highly plastic cortex is more likely to
succeed, because the brain can more readily re-tune to communicate
a clinical need.
[0127] Unifying the themes above, one approach to affective BCI for
closed-loop DBS is to record volitionally controlled signals from
PFC, then use that activity as a reflection of the patient's desire
to adjust stimulation parameters. This would not directly decode
emotion, but instead could be seen as decoding an intention towards
emotional regulation. That signal is well known to exist in PFC
based on neuroimaging data. An affective decoder, similar to those
already demonstrated, may then classify the patient's current
emotional state and serve as a feedback signal for an adaptive
stimulation algorithm. Alternatively, the volitional PFC activity
could itself be that feedback signal. This `direct control` BCI
approach is known to be capable of decoding one or more degrees of
freedom, which should be sufficient to control the parameters of
most clinical brain stimulators. A rodent proof-of-concept study
demonstrating this PFC-based affective BCI strategy is presented
below, as well as how the strategy may be scaled and adapted to
achieve the goal of closed-loop emotional brain stimulation.
Importantly, although the examples below describe control based on
the firing of single neurons, a wide range of signals may be used
to encode and infer the patient's intention. This would include
power in a variety of frequency bands, the connectivity between
multiple brain areas, the summed firing of multiple neurons within
one or more brain areas, and the like.
[0128] Turning to FIGS. 18A and 18B, a block diagram of an
experimental setup and a schematic of an affective BCI algorithm
are shown. The experimental setup of FIG. 18A includes a tethered
recording, decoding, and stimulating system. Modular components
control each function, permitting modification of experimental
setup for alternate decoders or stimulation schemes. As
illustrated, neural data flow from the animal in the operant
chamber, through dedicated on-line spike sorting and behavioral
tagging, to a desktop PC for processing and storage. Based on
neural signals, the PC controls the behavioral system and
stimulator, which deliver neurofeedback and brain stimulation to
the animal.
[0129] Briefly, adult female Long-Evans rats were implanted with
arrays of recording electrodes in the PFC (prelimbic and
infralimbic cortices), and stimulating deep-brain electrodes.
Stimulating electrodes targeted the medial forebrain bundle (MFB),
a structure within the reward pathway where electrical stimulation
is known to be reinforcing. MFB is a target for human clinical
trials in DBS for depression, highlighting its relevance as a
stimulation site in this closed-loop testbed. For this work,
however, the MFB is used for its reinforcing properties, and not as
a candidate treatment site for human translation.
[0130] Affective dysregulation and a desire to activate or alter
brain stimulation can be decoded from volitionally controlled PFC
activity. To demonstrate that rodents can learn to use an
intention-decoding BCI to drive brain stimulation, the animals may
be trained to use an auditory BCI. As shown in FIG. 18B, activity
of a single unit recorded from the PFC may be converted to an
auditory cursor using the BCI algorithm. Raw data may be recorded
from the PFC and sorted online, after which spike rates may be
estimated and converted to an audio cursor for the animal. The
firing rate of the decoded unit may control the frequency of a tone
presented to the animal, implementing a paradigm similar to a
neurofeedback system. To model the plasticity component, the BCI
was not trained or otherwise pre-tuned to neural firing modulation.
Rather, at the start of each session a unit was selected and mapped
into the BCI with fixed parameters. Animals were thus required to
learn and adapt to the new BCI mapping each day to communicate
their desire for neurostimulation.
[0131] Referring to FIG. 18C, schematics of BCI trials are shown.
The animal may be presented with auditory cues, represented as a
target cue 3002 and corresponding cue periods 3004, initiated in a
self-paced fashion by holding PFC activity at an initial baseline
3006. Moving the audio cursor to within a window of the target cue
3002 constituted control of the BCI, and activated neurostimulation
in the medial forebrain bundle (MFB). Failure to reach the target
cue 3002 within a pre-set time may have led to a brief time-out. In
a human clinical system, subjects may deliberately avoid the
target, and thus leave the neurostimulator off, except when the
patient experiences symptoms that they wish to control. Because
stimulation was reinforcing in this paradigm, the subject learned
to use BCI to express desire for stimulation by acquiring targets
when available and communicate an affect-regulation desire.
[0132] Baseline firing 3006 of the selected PFC unit was measured
at the start of each day, and dwelling the firing rate at the
baseline 3006 initiated a new trial. For each trial, a tone was
briefly played, and the animal had 5-10 seconds to modulate the PFC
firing rate to match her audio feedback cursor to the target cue
3002. Targets were based on the standard deviation (SD) of the
baseline firing rate 3006, and required the animal to elevate
firing rate by about 1.5 SD. Successful target acquisition, as
shown in the top graph of FIG. 18C, triggered a phasic burst 3008
of MFB stimulation, whereas failure target acquisition, as shown in
the bottom graph of FIG. 18C, did not. That is, each time the
animal successfully controlled PFC neural firing, electrical
stimulation was delivered within her limbic circuit. Trials were
followed by brief time-outs, after which a new trial became
available for self-initiation. This demonstrates the concept that
volitional PFC activity can be decoded via a BCI and used as a
signal of desire to activate a neurostimulator.
[0133] In the above described system, the animals successfully
learned to control the PFC BCI to trigger MFB stimulation. For
example, FIG. 19 shows successful PFC BCI control for limbic
stimulation. A first line 4002 represents the actual target
acquisition rate over a single testing session. A second dashed
line 4004 and a horizontal solid line 4006 represent on-line
(`catch`) and off-line (`bootstrap`) estimates of chance-level
performance, respectively. BCI target acquisition, and thus
successful delivery of reinforcing neurostimulation, rises to a
peak 4008 above both measures of chance. Performance is sustained
for over 20 minutes before the performance declines, possibly due
to fatigue.
[0134] Target-acquisition rates were initially low as the animal
learned the new decoder, then rapidly increased and were sustained
for over 20 minutes, as just described. During this core
performance period, when the animal had learned the decoder and was
actively attending to the BCI, target hit rates remained well above
both on-line (catch trials) 4004 and off-line (bootstrap
replication) measures 4006 of chance. The animals generally learned
to control newly isolated PFC units after about 20-40 minutes of
practice. About eighty percent of tested sites in the PFC were
controllable, consistent with the hypothesis that arbitrary neurons
can be used for affective decoding by exploiting
neuroplasticity.
[0135] Referring to FIG. 20, control of PFC neurons in the BCI
paradigm was highly specific. FIG. 20 shows two examples of
peri-stimulus discharge rates on single channels involved in the
PFC BCI. Examples are taken from the same animal, on two successive
days, during which different units were controlled. Layout of
subfigures within each example reflects relative location of
individual electrodes within the cortex. In each panel, the channel
controlling the BCI (A, channel 8; B, channel 14) shows a sharp
rise into the middle of the target (arrowhead) followed by an
equally sharp return to baseline once success is achieved. Other
channels show little to no peri-event modulation, consistent with
specific control of PFC unit selected for the BCI.
[0136] FIG. 20 further shows the averaged firing rates of multiple
simultaneously recorded PFC units during two consecutive training
days, time-locked to successful acquisition of a BCI target and
delivery of reinforcing brain stimulation. Substantial modulation
in discharge rate occurred on the channel used for the BCI
(arrowhead). That channel, which was changed between these two
sessions, shows a sharp rise into the target and equally sharp
return to baseline after the onset of brain stimulation. The other,
non-decoded channels show no average time-locked modulation. This
provides further evidence that animals were able to specifically
remap arbitrary PFC neurons to match the BCI decoder, establishing
an `emotional communication channel` to indicate their intent to
receive neurostimulation. Intentional control of PFC units is also
demonstrated by the channel-specific modulation of FIG. 20. This is
evidence that animals were executing a learned and specific skill
to achieve control of the BCI.
[0137] Psychiatric patients frequently report that they recognize
emotional symptoms as `not my real self` (ego-dystonic) and attempt
to suppress them, clear evidence that they would be able to
recognize the need to activate a stimulator. Some are even able to
learn new cognitive skills that enable active suppression of
symptoms. A responsive BCI based stimulator, such at the one just
described, may effectively amplify those skills and achieve what
some patients are unable to do on their own.
[0138] Work using similar operant paradigms has shown that
artificial coupling of activity between two brain sites or between
brain and spinal cord can induce long-term increases in functional
connectivity. A hybrid BCI such as that described above could be
targeted not to directly control symptoms, but to train and
strengthen the user's internal regulatory circuits. This device
would then be used for a limited time period to repair an
identified brain deficit.
[0139] Although the invention has been described in considerable
detail with reference to certain embodiments, one skilled in the
art will appreciate that the present invention can be practiced by
other than the described embodiments, which have been presented for
purposes of illustration and not of limitation. Therefore, the
scope of the appended claims should not be limited to the
description of the embodiments contained herein.
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