Bjune CK, Marinis TF, Sriram TS, Brady JM, Moran J, Parks PD, Widge AS, Dougherty DD, Eskandar EN. Packaging architecture for an implanted system that monitors brain activity and applies therapeutic stimulation. International Symposium on MicroelectronicsInternational Symposium on Microelectronics. 2015;2015 (1) :000548-000554. Publisher's VersionAbstract
Deep brain stimulation therapies for Parkinson's disease utilize hardware, which from a packaging perspective, resembles that used in cardiac pacemakers. A hermetic package that contains stimulation electronics and a primary battery supply is implanted under the scalp in a recess cut into the skull. Stimulation probes, each with up to four electrodes, are inserted into the brain and connected to the electronics package via a plug and cable system. By contrast, the closed loop neural stimulator being developed under the DARPA SUBNETS program utilizes probes, which each carry up to 64 electrodes that can be switched between recording and stimulation functions. This capability necessitates locating low noise amplifiers, switching and communication electronics in close proximity to each probe. Each of these satellite electronics packages requires ten electrical connections to the hub package, which significantly increases the complexity of the interconnect system relative to current practice. The power requirements of this system preclude the use of a primary battery supply so instead, a large lithium ion battery is used with a recharging coil and electronics. The hub system is fabricated as a separate connector header, electronics package and battery pack that are interconnected by a flex circuit to allow it to conform to the skull for implanting. In this paper, we will describe the various packaging components of the system and the design considerations that drove our technology choices.
Faghih RT, Stokes PA, Marin M-F, Zsido RG, Zorowitz S, Rosenbaum BL, Song H, Milad MR, Dougherty DD, Eskandar EN, et al. Characterization of fear conditioning and fear extinction by analysis of electrodermal activity. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2015 :7814-7818.Abstract

Electrodermal activity (EDA) is a measure of physical arousal, which is frequently measured during psychophysical tasks relevant for anxiety disorders. Recently, specific protocols and procedures have been devised in order to examine the neural mechanisms of fear conditioning and extinction. EDA reflects important responses associated with stimuli specifically administrated during these procedures. Although several previous studies have demonstrated the reproducibility of measures estimated from EDA, a mathematical framework associated with the stimulus-response experiments in question and, at the same time, including the underlying emotional state of the subject during fear conditioning and/or extinction experiments is not well studied. We here propose an ordinary differential equation model based on sudomotor nerve activity, and estimate the fear eliciting stimulus using a compressed sensing algorithm. Our results show that we are able to recover the underlying stimulus (visual cue or mild electrical shock). Moreover, relating the time-delay in the estimated stimulation to the visual cue during extinction period shows that fear level decreases as visual cues are presented without shock, suggesting that this feature might be used to estimate the fear state. These findings indicate that a mathematical model based on electrodermal responses might be critical in defining a low-dimensional representation of essential cognitive features in order to describe dynamic behavioral states.

Yousefi A, Paulk AC, Deckersbach T, Dougherty DD, Eskandar EN, Widge AS, Eden UT. Cognitive state prediction using an EM algorithm applied to Gamma distributed data. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2015 :7819-7824.Abstract

Behavioral tests are widely used to quantify features of cognitive processing. For a large class of behavioral signals, the observed variables are non-Gaussian and dynamic; classical estimation algorithms are ill-suited to modeling such data. In this research, we propose a mathematical framework to predict a cognitive state variable related to behavioral signals, which are best modeled using a Gamma distribution. The proposed algorithm combines a Gamma Smoother and EM algorithm in the prediction process. The algorithm is applied to reaction time recorded from subjects performing a Multi-Source Interference Task (MSIT) to dynamically quantify their cognitive flexibility through the course of the experiment.

Widge AS, Arulpragasam AR, Deckersbach T, Dougherty DD. Deep brain stimulation for psychiatric disorders. In: Scott RA, Kosslyn SM Emerging Trends in the Social and Behavioral SciencesEmerging Trends in the Social and Behavioral Sciences. John Wiley & Sons, Inc. ; 2015. Publisher's VersionAbstract

In this monograph, we briefly review the rationale for deep brain stimulation (DBS) for psychiatric illness, beginning with current noninvasive treatment options and progressing to the evolution and success of DBS as a therapy. This discussion will focus on obsessive-compulsive disorder (OCD) and major depressive disorder (MDD) particularly, as these are the only two diagnoses that have been subjected to adequately controlled DBS trials to date. The majority of the essay then describes the significant limitations that DBS is currently facing and emerging approaches to address them. This will lead into a discussion of new technologies such as patient-specific modeling of electric fields and closed-loop DBS systems and how we can best utilize these to increase our understanding of DBS and the overall efficacy of this novel therapy.

Widge AS, Dougherty DD. Deep brain stimulation for treatment-refractory mood and obsessive-compulsive disorders. Current Behavioral Neuroscience ReportsCurrent Behavioral Neuroscience Reports. 2015;2 (4) :187-197. Publisher's Version art3a10.10072fs40473-015-0049-y.pdf
Deng X, Faghih RT, Barbieri R, Paulk AC, Asaad WF, Brown EN, Dougherty DD, Widge AS, Eskandar EN, Eden UT. Estimating a dynamic state to relate neural spiking activity to behavioral signals during cognitive tasks. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2015 :7808-7813.Abstract

An important question in neuroscience is understanding the relationship between high-dimensional electrophysiological data and complex, dynamic behavioral data. One general strategy to address this problem is to define a low-dimensional representation of essential cognitive features describing this relationship. Here we describe a general state-space method to model and fit a low-dimensional cognitive state process that allows us to relate behavioral outcomes of various tasks to simultaneously recorded neural activity across multiple brain areas. In particular, we apply this model to data recorded in the lateral prefrontal cortex (PFC) and caudate nucleus of non-human primates as they perform learning and adaptation in a rule-switching task. First, we define a model for a cognitive state process related to learning, and estimate the progression of this learning state through the experiments. Next, we formulate a point process generalized linear model to relate the spiking activity of each PFC and caudate neuron to the stimated learning state. Then, we compute the posterior densities of the cognitive state using a recursive Bayesian decoding algorithm. We demonstrate that accurate decoding of a learning state is possible with a simple point process model of population spiking. Our analyses also allow us to compare decoding accuracy across neural populations in the PFC and caudate nucleus.

Wheeler JJ, Baldwin K, Kindle A, Guyon D, Nugent B, Segura C, Rodriguez J, Czarnecki A, Dispirito HJ, Lachapelle J, et al. An implantable 64-channel neural interface with reconfigurable recording and stimulation. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2015 :7837-7840.Abstract

Next generation implantable medical devices will have the potential to provide more precise and effective therapies through adaptive closed-loop controllers that combine sensing and stimulation across larger numbers of electrode channels. A major challenge in the design of such devices is balancing increased functionality and channel counts with the miniaturization required for implantation within small anatomical spaces. Customized therapies will require adaptive systems capable of tuning which channels are sensed and stimulated to overcome variability in patient-specific needs, surgical placement of electrodes, and chronic physiological responses. In order to address these challenges, we have designed a miniaturized implantable fully-reconfigurable front-end system that is integrated into the distal end of an 8-wire lead, enabling up to 64 electrodes to be dynamically configured for sensing and stimulation. Full reconfigurability is enabled by two custom 32×2 cross-point switch (CPS) matrix ASICs which can route any electrode to either an amplifier with reprogrammable bandwidth and integrated ADC or to one of two independent stimulation channels that can be driven through the lead. The 8-wire circuit includes a digital interface for robust communication as well as a charge-balanced powering scheme for enhanced safety. The system is encased in a hermetic package designed to fit within a 14 mm bur-hole in the skull for neuromodulation of the brain, but could easily be adapted to enhance therapies across a broad spectrum of applications.

Deckersbach T, Widge A, Franklin R, Zorowitz S, Corse A. Lurasidone for the treatment of bipolar depression: an evidence-based review. Neuropsychiatric Disease and TreatmentNeuropsychiatric Disease and Treatment. 2015 :2143. Publisher's Version franklin_et_al_2015_lurasidone_for_the_treatment_of_bipolar_depression.pdf
Hamilton L, McConley M, Angermueller K, Goldberg D, Corba M, Kim L, Moran J, Parks PD, Chin S, Widge AS, et al. Neural signal processing and closed-loop control algorithm design for an implanted neural recording and stimulation system. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2015 :7831-7836.Abstract

A fully autonomous intracranial device is built to continually record neural activities in different parts of the brain, process these sampled signals, decode features that correlate to behaviors and neuropsychiatric states, and use these features to deliver brain stimulation in a closed-loop fashion. In this paper, we describe the sampling and stimulation aspects of such a device. We first describe the signal processing algorithms of two unsupervised spike sorting methods. Next, we describe the LFP time-frequency analysis and feature derivation from the two spike sorting methods. Spike sorting includes a novel approach to constructing a dictionary learning algorithm in a Compressed Sensing (CS) framework. We present a joint prediction scheme to determine the class of neural spikes in the dictionary learning framework; and, the second approach is a modified OSort algorithm which is implemented in a distributed system optimized for power efficiency. Furthermore, sorted spikes and time-frequency analysis of LFP signals can be used to generate derived features (including cross-frequency coupling, spike-field coupling). We then show how these derived features can be used in the design and development of novel decode and closed-loop control algorithms that are optimized to apply deep brain stimulation based on a patient's neuropsychiatric state. For the control algorithm, we define the state vector as representative of a patient's impulsivity, avoidance, inhibition, etc. Controller parameters are optimized to apply stimulation based on the state vector's current state as well as its historical values. The overall algorithm and software design for our implantable neural recording and stimulation system uses an innovative, adaptable, and reprogrammable architecture that enables advancement of the state-of-the-art in closed-loop neural control while also meeting the challenges of system power constraints and concurrent development with ongoing scientific research design- d to define brain network connectivity and neural network dynamics that vary at the individual patient level and vary over time.

Bjune CK, Marinis TF, Brady JM, Moran J, Wheeler J, Sriram TS, Parks PD, Widge AS, Dougherty DD, Eskandar EN. Package architecture and component design for an implanted neural stimulator with closed loop control. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2015 :7825-7830.Abstract

An implanted neural stimulator with closed loop control requires electrodes for stimulation pulses and recording neuron activity. Our system features arrays of 64 electrodes. Each electrode can be addressed through a cross bar switch, to enable it to be used for stimulation or recording. This electrode switch, a bank of low noise amplifiers with an integrated analog to digital converter, power conditioning electronics, and a communications and control gate array are co-located with the electrode array in a 14 millimeter diameter satellite package that is designed to be flush mounted in a skull burr hole. Our system features five satellite packages connected to a central hub processor-controller via ten conductor cables that terminate in a custom designed, miniaturized connector. The connector incorporates features of high reliability, military grade devices and utilizes three distinct seals to isolate the contacts from fluid permeation. The hub system is comprised of a connector header, hermetic electronics package, and rechargeable battery pack, which are mounted on and electrically interconnected by a flexible circuit board. The assembly is over molded with a compliant silicone rubber. The electronics package contains two antennas, a large coil, used for recharging the battery and a high bandwidth antenna that is used to download data and update software. The package is assembled from two machined alumina pieces, a flat base with brazed in, electrical feed through pins and a rectangular cover with rounded corners. Titanium seal rings are brazed onto these two pieces so that they can be sealed by laser welding. A third system antenna is incorporated in the flexible circuit board. It is used to communicate with an externally worn control package, which monitors the health of the system and allows both the user and clinician to control or modify various system function parameters.

Widge AS, Licon E, Zorowitz S, Corse A, Arulpragasam AR, Camprodon JA, Cusin C, Eskandar EN, Deckersbach T, Dougherty DD. Predictors of hypomania during ventral capsule/ventral striatum deep brain stimulation. Journal of Neuropsychiatry and Clinical NeurosciencesJournal of Neuropsychiatry and Clinical Neurosciences. 2015;Accepted. appi.neuropsych.pdf
Makris N, Rathi Y, Mouradian P, Bonmassar G, Papadimitriou G, Ing WI, Yeterian EH, Kubicki M, Eskandar EN, Wald LL, et al. Variability and anatomical specificity of the orbitofrontothalamic fibers of passage in the ventral capsule/ventral striatum (VC/VS): precision care for patient-specific tractography-guided targeting of deep brain stimulation (DBS) in obsessive compulsive. Brain Imaging and BehaviorBrain Imaging and Behavior. 2015 :1-14. Publisher's VersionAbstract

Deep Brain Stimulation (DBS) is a neurosurgical procedure that can reduce symptoms in medically intractable obsessive-compulsive disorder (OCD). Conceptually, DBS of the ventral capsule/ventral striatum (VC/VS) region targets reciprocal excitatory connections between the orbitofrontal cortex (OFC) and thalamus, decreasing abnormal reverberant activity within the OFC-caudate-pallidal-thalamic circuit. In this study, we investigated these connections using diffusion magnetic resonance imaging (dMRI) on human connectome datasets of twenty-nine healthy young-adult volunteers with two-tensor unscented Kalman filter based tractography. We studied the morphology of the lateral and medial orbitofrontothalamic connections and estimated their topographic variability within the VC/VS region. Our results showed that the morphology of the individual orbitofrontothalamic fibers of passage in the VC/VS region is complex and inter-individual variability in their topography is high. We applied this method to an example OCD patient case who underwent DBS surgery, formulating an initial proof of concept for a tractography-guided patient-specific approach in DBS for medically intractable OCD. This may improve on current surgical practice, which involves implanting all patients at identical stereotactic coordinates within the VC/VS region.

Fung LK, Akil M, Widge AS, Roberts LW, Etkin A. Attitudes Toward Neuroscience Education in Psychiatry: a National Multi-stakeholder Survey. Academic Psychiatry. 2015;39 (2) :139-146. Publisher's Version fung_et_al_2014_attitudes_toward_neuroscience_education_in_psychiatry.pdf
Widge AS, Schultz H. Opportunities and Challenges: Residents’ Perspectives on the Next Accreditation System in Psychiatry. Academic Psychiatry. 2014;38 (3) :303-304. Publisher's Version widge_schultz_2014_opportunities_and_challenges.pdf
Widge AS, Dougherty DD, Moritz CT. Affective brain-computer interfaces as enabling technology for responsive psychiatric stimulation. Brain-Computer Interfaces. 2014;1 (2) :126-136. Publisher's VersionAbstract

There is a pressing 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. We argue that the key to overcoming this limitation is to specifically decode volition, in particular the patient’s intention to experience emotional regulation. Those emotion-regulation signals already exist in prefrontal cortex (PFC), and could be extracted with relatively simple BCI algorithms. We describe preliminary data from an animal model of PFC-controlled limbic brain stimulation and discuss next steps for pre-clinical testing and possible translation.

Benjamin S, Widge A, Shaw K. Neuropsychiatry and Neuroscience Milestones for General Psychiatry Trainees. Academic Psychiatry. 2014 :1–8. benjamin_et_al_neuropsychiatry_and_neuroscience_milestones_for_general_psychiatry_trainees.pdf
Widge AS, Moritz CT. Pre-frontal control of closed-loop limbic neurostimulation by rodents using a brain–computer interface. Journal of Neural Engineering. 2014;11 :024001. widge_moritz_2014_pre-frontal_control_of_closed-loop_limbic_neurostimulation_by_rodents_using_a.pdf
Widge AS, Hunt J, Servis M. Systems-Based Practice and Practice-Based Learning for the General Psychiatrist: Old Competencies, New Emphasis. Academic Psychiatry. 2014 :1–6. widge_et_al_systems-based_practice_and_practice-based_learning_for_the_general_psychiatrist.pdf
Widge AS, Avery DH, Zarkowski P. Methodology and the Limits of QEEG: Reply to Olbrich & Arns. Brain stimulation. 2014;7 :148.
Fung LK, Akil M, Widge A, Roberts LW, Etkin A. Attitudes toward neuroscience education among psychiatry residents and fellows. Academic Psychiatry. 2014 :1–8. fung_et_al_2014_attitudes_toward_neuroscience_education_among_psychiatry_residents_and_fellows.pdf