Justin A. Blanco

Dissolvable films of silk fibroin for ultrathin conformal bio-integrated electronics

Electronics that are capable of intimate, non-invasive integration with the soft, curvilinear surfaces of biological tissues offer important opportunities for diagnosing and treating disease and for improving brain/machine interfaces. This article describes a material strategy for a type of bio-interfaced system that relies on ultrathin electronics supported by bioresorbable substrates of silk fibroin. Mounting such devices on tissue and then allowing the silk to dissolve and resorb initiates a spontaneous, conformal wrapping process driven by capillary forces at the biotic/abiotic interface. Specialized mesh designs and ultrathin forms for the electronics ensure minimal stresses on the tissue and highly conformal coverage, even for complex curvilinear surfaces, as confirmed by experimental and theoretical studies. In vivo, neural mapping experiments on feline animal models illustrate one mode of use for this class of technology. These concepts provide new capabilities for implantable and surgical devices.

Flexible, foldable, actively multiplexed, high-density electrode array for mapping brain activity in vivo

Arrays of electrodes for recording and stimulating the brain are used throughout clinical medicine and basic neuroscience research, yet are unable to sample large areas of the brain while maintaining high spatial resolution because of the need to individually wire each passive sensor at the electrode-tissue interface. To overcome this constraint, we developed new devices that integrate ultrathin and flexible silicon nanomembrane transistors into the electrode array, enabling new dense arrays of thousands of amplified and multiplexed sensors that are connected using fewer wires. We used this system to record spatial properties of cat brain activity in vivo, including sleep spindles, single-trial visual evoked responses and electrographic seizures. We found that seizures may manifest as recurrent spiral waves that propagate in the neocortex. The developments reported here herald a new generation of diagnostic and therapeutic brain-machine interface devices.

A conformal, bio-interfaced class of silicon electronics for mapping cardiac electrophysiology.

Flexible electronics and sensors that adhere to the surfaces of living, moving tissues allow detailed mapping of cardiac electrical activity in a porcine animal model. My Beating Heart The heart is tricky to work with. Usually in constant motion, it has to be stopped for most cardiac surgery and its health is most often checked by EKG measurements of net electrical activity from outside the body. When damage to the heart causes life-threatening arrhythmias, physicians can only get a get a rough idea about where the problem is located by painstakingly recording from one part of the heart after another. Improvements in electronic circuit design and fabrication, as reported here by Viventi et al., can enable sophisticated, multiunit electrodes to stay in close contact with biological tissue, making monitoring and stimulation of the living, moving heart a realistic goal. The new type of device is a multilayer circuit fabricated on a 25-μm-thick, plastic sheet of polyimide, with a built-in array of 288 gold electrodes. It is flexible but the design keeps the sensitive electronics in the neutral plane so that it still functions, even when bent. Each electrode has its own amplifier, which magnifies the tiny biological currents, and multiplexer, which allows the output of all 288 electrodes to be conveyed by only 36 wires. Electrically active devices inside the wet interior of the body can easily leak current, so the authors guarded against this by encapsulating the device in a trilayer coating of polyimide, silicon nitride, and epoxy. Most (75%) of the devices they made leaked less than 10 μA, an industry standard, and maintained this performance for at least 3 hours. To map cardiac function with their flexible electrode array, the researchers applied it to the exposed epicardial surface of the beating porcine heart. Functional for more than 10,000 bending cycles, the electrodes could record normal heart beats or beats driven by a second pacing electrode at high resolution. With a high signal-to-noise ratio of about 34 dB, conduction of a moving wave of cardiac activation was readily apparent as it swept across the array of electrodes with each contraction. The authors constructed an isochronal map of heart activation, determining that the conduction velocity was 0.9 mm per millisecond. Heart physiology is not the only possible application for these flexible electrodes. The brain is also a curved, wet organ that can only be accessed by individually wired electrodes at present. Muscles are electrically active moving tissues, found both within internal organs and as effectors for the limbs. The ability to house electrodes, amplifiers, and multiplexers in a flexible, biocompatible plastic sheet that can snuggle up right against the organ of interest will improve our ability to stimulate and monitor living tissues. In all current implantable medical devices such as pacemakers, deep brain stimulators, and epilepsy treatment devices, each electrode is independently connected to separate control systems. The ability of these devices to sample and stimulate tissues is hindered by this configuration and by the rigid, planar nature of the electronics and the electrode-tissue interfaces. Here, we report the development of a class of mechanically flexible silicon electronics for multiplexed measurement of signals in an intimate, conformal integrated mode on the dynamic, three-dimensional surfaces of soft tissues in the human body. We demonstrate this technology in sensor systems composed of 2016 silicon nanomembrane transistors configured to record electrical activity directly from the curved, wet surface of a beating porcine heart in vivo. The devices sample with simultaneous submillimeter and submillisecond resolution through 288 amplified and multiplexed channels. We use this system to map the spread of spontaneous and paced ventricular depolarization in real time, at high resolution, on the epicardial surface in a porcine animal model. This demonstration is one example of many possible uses of this technology in minimally invasive medical devices.

Modeling electroencephalography waveforms with semi-supervised deep belief nets: fast classification and anomaly measurement.

Clinical electroencephalography (EEG) records vast amounts of human complex data yet is still reviewed primarily by human readers. Deep belief nets (DBNs) are a relatively new type of multi-layer neural network commonly tested on two-dimensional image data but are rarely applied to times-series data such as EEG. We apply DBNs in a semi-supervised paradigm to model EEG waveforms for classification and anomaly detection. DBN performance was comparable to standard classifiers on our EEG dataset, and classification time was found to be 1.7-103.7 times faster than the other high-performing classifiers. We demonstrate how the unsupervised step of DBN learning produces an autoencoder that can naturally be used in anomaly measurement. We compare the use of raw, unprocessed data--a rarity in automated physiological waveform analysis--with hand-chosen features and find that raw data produce comparable classification and better anomaly measurement performance. These results indicate that DBNs and raw data inputs may be more effective for online automated EEG waveform recognition than other common techniques.

Data mining neocortical high-frequency oscillations in epilepsy and controls.

Transient high-frequency (100-500 Hz) oscillations of the local field potential have been studied extensively in human mesial temporal lobe. Previous studies report that both ripple (100-250 Hz) and fast ripple (250-500 Hz) oscillations are increased in the seizure-onset zone of patients with mesial temporal lobe epilepsy. Comparatively little is known, however, about their spatial distribution with respect to seizure-onset zone in neocortical epilepsy, or their prevalence in normal brain. We present a quantitative analysis of high-frequency oscillations and their rates of occurrence in a group of nine patients with neocortical epilepsy and two control patients with no history of seizures. Oscillations were automatically detected and classified using an unsupervised approach in a data set of unprecedented volume in epilepsy research, over 12 terabytes of continuous long-term micro- and macro-electrode intracranial recordings, without human preprocessing, enabling selection-bias-free estimates of oscillation rates. There are three main results: (i) a cluster of ripple frequency oscillations with median spectral centroid = 137 Hz is increased in the seizure-onset zone more frequently than a cluster of fast ripple frequency oscillations (median spectral centroid = 305 Hz); (ii) we found no difference in the rates of high frequency oscillations in control neocortex and the non-seizure-onset zone neocortex of patients with epilepsy, despite the possibility of different underlying mechanisms of generation; and (iii) while previous studies have demonstrated that oscillations recorded by parenchyma-penetrating micro-electrodes have higher peak 100-500 Hz frequencies than penetrating macro-electrodes, this was not found for the epipial electrodes used here to record from the neocortical surface. We conclude that the relative rate of ripple frequency oscillations is a potential biomarker for epileptic neocortex, but that larger prospective studies correlating high-frequency oscillations rates with seizure-onset zone, resected tissue and surgical outcome are required to determine the true predictive value.

Temporal changes of neocortical high-frequency oscillations in epilepsy.

High-frequency (100-500 Hz) oscillations (HFOs) recorded from intracranial electrodes are a potential biomarker for epileptogenic brain. HFOs are commonly categorized as ripples (100-250 Hz) or fast ripples (250-500 Hz), and a third class of mixed frequency events has also been identified. We hypothesize that temporal changes in HFOs may identify periods of increased the likelihood of seizure onset. HFOs (86,151) from five patients with neocortical epilepsy implanted with hybrid (micro + macro) intracranial electrodes were detected using a previously validated automated algorithm run over all channels of each patients entire recording. HFOs were characterized by extracting quantitative morphologic features and divided into four time epochs (interictal, preictal, ictal, and postictal) and three HFO clusters (ripples, fast ripples, and mixed events). We used supervised classification and nonparametric statistical tests to explore quantitative changes in HFO features before, during, and after seizures. We also analyzed temporal changes in the rates and proportions of events from each HFO cluster during these periods. We observed patient-specific changes in HFO morphology linked to fluctuation in the relative rates of ripples, fast ripples, and mixed frequency events. These changes in relative rate occurred in pre- and postictal periods up to thirty min before and after seizures. We also found evidence that the distribution of HFOs during these different time periods varied greatly between individual patients. These results suggest that temporal analysis of HFO features has potential for designing custom seizure prediction algorithms and for exploring the relationship between HFOs and seizure generation.

Semi-Supervised Anomaly Detection for EEG Waveforms Using Deep Belief Nets

Clinical electroencephalography (EEG) is routinely used to monitor brain function in critically ill patients, and specific EEG waveforms are recognized by clinicians as signatures of abnormal brain. These pathologic EEG waveforms, once detected, often necessitate accute clinincal interventions, but these events are typically rare, highly variable between patients, and often hard to separate from background, making them difficult to reliably detect. We show that Deep Belief Nets (DBNs), a type of multi-layer generative neural network, can be used effectively for such EEG anomaly detection. We compare this technique to the state-of-the-art, a one-class Support Vector Machine (SVM), showing that the DBN outperforms the SVM by the F1 measure for our EEG dataset. We also show how the outputs of a DBN-based detector can be used to aid visualization of anomalies in large EEG data sets and propose a method for using DBNs to gain insight into which features of signals are characteristically anomalous. These findings show that Deep Belief Nets can facilitate human review of large amounts of clinical EEG as well as mining new EEG features that may be indicators of unusual activity.

Cognitive stress recognition

This work explores using a low-cost electroencephalography (EEG) headset to quantify the human response to stressed and non-stressed states. We used a Stroop color-word interference test to elicit a mild stress response in 18 test subjects while recording scalp EEG. EEG signals were analyzed using an algorithm that computed the root mean square voltage in the beta, alpha, and theta bands immediately following the presentation of the Stroop stimuli. These features were then used as inputs to logistic regression and k-nearest neighbor classifiers. Results showed that there was a median accuracy of 73.96% for classifying mental state using the O1 sensor on the Emotiv headset.

Millimeter-scale epileptiform spike propagation patterns and their relationship to seizures

OBJECTIVE Current mapping of epileptic networks in patients prior to epilepsy surgery utilizes electrode arrays with sparse spatial sampling (∼1.0 cm inter-electrode spacing). Recent research demonstrates that sub-millimeter, cortical-column-scale domains have a role in seizure generation that may be clinically significant. We use high-resolution, active, flexible surface electrode arrays with 500 μm inter-electrode spacing to explore epileptiform local field potential (LFP) spike propagation patterns in two dimensions recorded from subdural micro-electrocorticographic signals in vivo in cat. In this study, we aimed to develop methods to quantitatively characterize the spatiotemporal dynamics of epileptiform activity at high-resolution. APPROACH We topically administered a GABA-antagonist, picrotoxin, to induce acute neocortical epileptiform activity leading up to discrete electrographic seizures. We extracted features from LFP spikes to characterize spatiotemporal patterns in these events. We then tested the hypothesis that two-dimensional spike patterns during seizures were different from those between seizures. MAIN RESULTS We showed that spatially correlated events can be used to distinguish ictal versus interictal spikes. SIGNIFICANCE We conclude that sub-millimeter-scale spatiotemporal spike patterns reveal network dynamics that are invisible to standard clinical recordings and contain information related to seizure-state.

Development of high resolution, multiplexed electrode arrays: Opportunities and challenges

More than one third of the worlds 60 million people with epilepsy have seizures that cannot be controlled by medication. Some of these individuals may be candidates for surgical removal of brain regions that generate seizures, but the chance of being seizure free after epilepsy surgery is as low as 35% in many patients [1]. Even when surgery is successful, patients risk neurological deficits like memory loss and speech difficulties. The need for new treatments is clear. A central barrier to better treatments for epilepsy is technological: we do not have devices capable of interfacing with the brain with small enough electrodes over large enough regions to map epileptic networks in sufficient detail to enable treatment. Our collaborative group has developed new implantable brain devices to address this challenge [2]. Our devices, made from flexible silicon nanoribbons, can record from these very small brain regions, with electrodes ½ millimeter apart or less, and can be scaled up to clinically useful sizes, on the order of 64 cm2. They consist of thousands of individually controllable microelectrodes.