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 have developed new devices integrating ultrathin and flexible silicon nanomembrane transistors into the electrode array, enabling new dense arrays of thousands of amplified and multiplexed sensors connected using many fewer wires. We used this system to record novel spatial properties of brain activity in vivo, including sleep spindles, single-trial visual evoked responses, and electrographic seizures. Our electrode array allowed us to discover that seizures may manifest as recurrent spiral waves which propagate in the neocortex. The developments reported here herald a new generation of diagnostic and therapeutic brain-machine interface (BMI) devices. KeywordsMultielectrode array; electrode array; flexible electronics; multiplexed electrode; cortical surface electrode; foldable electrode; ECoG; μECoG; brain machine interface; high temporal resolution; high spatial resolution; spindle; visual neuroscience; spiral wave; epilepsy; seizure; epileptiform spike; interhemispheric fissure; silicon nanoribbonThe utility of high-resolution neural recordings from the cortical surface for basic research and clinical medicine has been shown for a wide range of applications. Spatial spectral analysis of electrocorticograms (ECoG) from the superior temporal gyrus and motor cortex demonstrate that electrode spacing should be 1.25 mm or closer in humans to sufficiently capture the rich spatial information available 1 . Motor control signals 2 and spoken words 3 can be decoded with substantially improved performance utilizing electrodes spaced 1 mm apart or less. In occipital cortex, arrays with 500 μm spacing have demonstrated micro-field evoked potentials that can distinguish ocular dominance columns 4 . The spatial scale for some pathologic signals is also submillimeter, based on observations of microseizures, microdischarges and high frequency oscillations in epileptic brain 5,6 .Yet the subdural electrodes in use clinically, for example, in the diagnosis and treatment of epilepsy, are much larger (~3 mm diameter) and have large interspacing (~10mm) because of the clinical need to record from large areas of the brain surface (80 mm × 80 mm) in order to accurately localize seizure generating brain regions. Large area electrode arrays with high spatial resolution are also needed in BMI applications to account for variability in the location of brain functions, which can vary by ~5mm across subjects [7][8][9][10] . High-resolution interface over a large area has previously been impossible due to the infeasibility of connecting thousands of wires in the small intracranial space. Author Manuscript Author ManuscriptAuthor Manuscript Author ManuscriptMuch of the existing researc...

Matrix metalloproteinase- (MMP-9) is involved in processes that occur during cutaneous wound healing such as inflammation, matrix remodeling, and epithelialization, To investigate its role in healing, full thickness skin wounds were made in the dorsal region of MMP-9-null and control mice and harvested up to 14 days post wounding. Gross examination and histological and immunohistochemical analysis indicated delayed healing in MMP-9-null mice. Specifically, MMP-9-null wounds displayed compromised reepithelialization and reduced clearance of fibrin clots. In addition, they exhibited abnormal matrix deposition, as evidenced by the irregular alignment of immature collagen fibers. Despite the presence of matrix abnormalities, MMP-9-null wounds displayed normal tensile strength. Ultrastructural analysis of wounds revealed the presence of large collagen fibrils, some with irregular shape. Keratinocyte proliferation, inflammation, and angiogenesis were found to be normal in MMP-9-null wounds. In addition, VEGF levels were similar in control and MMP-9-null wound extracts. To investigate the importance of MMP-9 in wound reepithelialization we tested human and murine keratinocytes in a wound migration assay and found that antibody-based blockade of MMP-9 function or MMP-9 deficiency retarded migration. Collectively, our observations reveal defective healing in MMP-9-null mice and suggest that MMP-9 is required for normal progression of wound closure.

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 to 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—to hand-chosen features and find that raw data produces 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.

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 patient's 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.

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