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Wayne K. Goodman, M.D.D. C. and Irene Ellwood Professor and ChairMenninger Department of Psychiatry and Behavioral SciencesAdjunct Professor, Electrical & Computer Engineering, Rice University

Deep Brain Stimulation for Intractable OCD

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Disclosures

• Research Funding: NIH; Simons Foundation; Biohaven Pharmaceuticals; McNair Medical Institute

• Consulting: Biohaven Pharmaceuticals• Other: Donated Devices from Medtronic and

Boston Scientific

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• Common, persistent, oftentimes disabling

• 30-40% of patients fail first line treatments of CBT or SRIs

• Adjunctive antipsychotics are only established second line approach

Obsessive Compulsive Disorder

• Ventral Striatum (VS) DBS, which has FDA HDE approval, shows benefit in about 60% of cases according to a recent meta-analysis

• There is room for improvement in both clinical benefits and reduction of DBS-induced behavioral side effects, especially hypomania.

DBS IN OCD: 1999

● Bilateral stimulation of Anterior Limb of Internal Capsule (ALIC) in severe, chronic OCD

● 3 of 4 cases showed improvement

● Follow-up in 3 cases showed:● ON/OFF blinded testing confirmed superiority of stimulation

condition

● Lasting improvement for 6-12 months

Nuttin B, et al. Lancet 1999;354:1526.

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ANATOMICAL TARGETS FOR DBSIN OCD

Goodman WK, Alterman RL. Annu Rev Med. 2012;63:511-524. PMID: 22034866.

● Effects of DBS have been reported in ∼120 cases of OCD for 5 anatomic targets● Overall response rate appears to exceed 50% during bilateral electrical stimulation.● DBS was generally well tolerated, but some unique, target- and stimulation-specific

adverse effects were observed (e.g., hypomania).

• No objective measure like tremor

• Induction of “mirth” is a guidepost to programming and

may be predictive of good response to DBS

• Programming adjustments are made largely on the

basis of acute beneficial effects on “anxiety”, “mood”

and “energy”

• Takes months to optimize settings and multiple visits

Limitations of Current Open-Loop DBS for OCD (1)

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• DBS-induced mirth also represents

a potential risk: development of

hypomania or mania

• Need for adaptive DBS (aDBS)

system that can correctly classify

hypomania and distinguish it from a

euthymic mood state and

automatically adjust stimulation

Limitations of Current Open-Loop DBS for OCD (2)

• Fluctuations in obsessive-compulsive symptoms might benefit from real

time adjustments in stimulation not possible with a continuous system

Mirth Response with VS DBS. Haq et al, NeuroImage 2011

THE BRAIN INITIATIVE®Adaptive Deep Brain Stimulation for

Intractable Obsessive Compulsive Disorder

• 2017 Baylor Awarded UH3 Grant for developing adaptive DBS (aDBS) for Intractable OCD PI: Goodman; Co-Is: Borton (Brown Engineering) & Cohn (Pitt/CMU).

• In contrast to current devices, new generation adaptive DBS (aDBS) systems can stimulate and record, and use signals from the brain to adjust stimulation.

• Goal: develop a prototype DBS system that would adjust stimulation automatically in response to the patient's changing clinical needs.

• The clinical backbone to this project is a two-stage Early Feasibility Study of aDBS in 10 adults with intractable OCD using Medtronic Activa PC/RC+S devices.

• Two subjects implanted on since October 2018 by Drs. Sheth and Viswanathan

• First subject to be implanted October 19th

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NIH UH3 for Adaptive DBS in OCDEmotional State Measure and Ground Truth

• One of the challenges was to identify a label for the classifiers that isobjective, reliable and on same time scale as brain recordings.

• Facial affect represents the motor output of emotional state – not astractable as tremor – superior to clinical ratings.

• Then looked for a collaborator who had developed an automated facialaffect recognition (AFAR) platform that could be time locked with inputsfrom LFPs, scalp EEG, ECoG, activity, physiology and changes in DBSprogramming.

Jeff Cohn, PhDUniversity of PittsburghPsychology

LFPs=Local Field PotentialsECoG=Electrocorticography

Automated Facial Affect Recognition (AFAR)

• Anatomically based on movement of

specific muscle groups using Facial

Action Unit Coding System (FACS)

• About 40 Actions Units (AUs) have

been identified.

• Can map onto discrete emotions or

coded as dimensional (positive or

negative valence affect)

• Can be quantified as binary (present

or absent) or ordinal (e.g., according

to intensity)

• Previously measured by EMG or

manually coded videos – in last few

years advances in machine learning

enabled automatic measurement

using computer vision.

Example of AU mapping of

expression

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Ventral Striatum Adaptive DBS for Intractable OCDMachine Learning Aims

• Step 1. Build Classifiers for decoding brain neural signatures to accurately classify:

• DBS-induced hypomania;

• OCD symptom fluctuations; and

• Distinguish from states (e.g., normal anxiety or natural mirth) not needing adjustments.

• Step 2. Develop control algorithms that can automatically adjust stimulation parameters

• Step 3. Test in Clinic

• Step 4. Test in Real-World

Experimental setup for synchronized recording of behavior (affect, autonomic activity, voice, etc.) and neural data (LFPs, EEG, ECog) during: 1) DBS Programming; 2) OCD and anxiety exposures; 3) and 3) Tasks.

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Testing SessionsSynchronized recording of behavior (affect, autonomic activity, voice, etc.) and neural data (LFPs, EEG) during:–1) DBS Programming

–2) OCD and anxiety exposures

–3) Computer based tasks to interrogate cognitive/attention network control, impulsivity, etc.

NIH Admin Supplement: Multimodal measuresto label behavior time locked with neural data

• Facial expression

• Face dynamics

• Body Pose tracking and dynamics

• Gaze

• Physiology (heart rate)

• Voice quality

AU 12a AU 12C AU 0

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Summary 1: Adaptive DBS for OCD study• Aims: Develop a prototype ambulatory aDBS system that:– Automatically manages fluctuations in OCD symptoms – Automatically manages device-related side effects

• Multiple streams of time-locked neurophysiological and behavioral data are captured to build classifiers, including local field potentials (LFPs), EEG, and AFAR, which objectively measures emotional valence.

• We hypothesize that classifiers using combined LFP/EEG data will perform better than VS LFPs alone, but that direct cortical recordings using electrocorticography (ECoG) will be needed in combination with VS LFPs for a fully embedded and mobile aDBS system.

Acknowledgements

Baylor• Psychiatry: Eric Storch PhD, Liz McIngvale PhD, Ray Cho MD, Ramiro Sala PhD,

Stefan Ursu MD, PhD; Nithya Ramakrishan PhD: Greg Vogt,

• Neurosurgery: Sameer Sheth MD PhD, Ashwin Viswanathan MD, Mike Beauchamp PhD, Meghan Robinson PhD

• Neurology: Joohi Jimenez-Shahed MD, Adriana Strutt PhD

• Medical Ethics: Gabe Lazaro JD, PhD, Amy McGuire JD, PhD; Cody Brannan

Medtronic• Rob Raike PhD, Rene Molina PhD, Paul Stypulkowski PhD

• Devices: PC/RC+S

NIH• Nick Langhals PhD, Kari Ashmont PhD, David McMullen MD, Mi Hillefors MD,

PhD

NIH BRAIN Funding• UH3NS100549-01 Adaptive DBS in Non-Motor Neuropsychiatric Disorders:

Regulating Limbic Circuit Imbalance

• UH3NS100549 Measuring Automated Behavioral Observations and Vocal Expressions (ABOVE) while recording from the brain.

• R01MH114854 Neuroethics of aDBS targeting neuropsychiatric disorders

Brown Engineering• Dave Borton, PhD,

• Nicole Provenza, PhD

• Michael Frank, PhD

University of Pittsburgh Psychology• Jeff Cohn, PhD

Carnegie Mellon University• Laszlo Jeni, PhD

• Itir Ertugrul, PhD

Harvard• Suzanne Haber, PhD

Icahn Mount Sinai• Gordon Xu, PhD

Blackfynn• Joost Wagenaar, PhD