Decoding stimulus identity from multi-unit activity and local field potentials along the ventral auditory stream in the awake primate: implications for cortical neural prostheses

Objective. Hierarchical processing of auditory sensory information is believed to occur in two streams: a ventral stream responsible for stimulus identity and a dorsal stream responsible for processing spatial elements of a stimulus. The objective of the current study is to examine neural coding in this processing stream in the context of understanding the possibility for an auditory cortical neural prosthesis. Approach. We examined the selectivity for species-specific primate vocalizations in the ventral auditory processing stream by applying a statistical classifier to neural data recorded from microelectrode arrays. Multi-unit activity (MUA) and local field potential (LFP) data recorded simultaneously from primary auditory complex (AI) and rostral parabelt (PBr) were decoded on a trial-by-trial basis. Main results. While decode performance in AI was well above chance, mean performance in PBr did not deviate >15% from chance level. Mean performance levels were similar for MUA and LFP decodes. Increasing the spectral and temporal resolution improved decode performance; while inter-electrode spacing could be as large as 1.14 mm without degrading decode performance. Significance. These results serve as preliminary guidance for a human auditory cortical neural prosthesis; instructing interface implementation, microstimulation patterns and anatomical placement

Decoding stimulus identity from multi-unit activity and local field potentials along the ventral auditory stream in the awake primate: implications for cortical neural prostheses

Objective. Hierarchical processing of auditory sensory information is believed to occur in two streams: a ventral stream responsible for stimulus identity and a dorsal stream responsible for processing spatial elements of a stimulus. The objective of the current study is to examine neural coding in this processing stream in the context of understanding the possibility for an auditory cortical neural prosthesis. Approach. We examined the selectivity for species-specific primate vocalizations in the ventral auditory processing stream by applying a statistical classifier to neural data recorded from microelectrode arrays. Multi-unit activity (MUA) and local field potential (LFP) data recorded simultaneously from primary auditory complex (AI) and rostral parabelt (PBr) were decoded on a trial-by-trial basis. Main results. While decode performance in AI was well above chance, mean performance in PBr did not deviate >15% from chance level. Mean performance levels were similar for MUA and LFP decodes. Increasing the spectral and temporal resolution improved decode performance; while inter-electrode spacing could be as large as 1.14 mm without degrading decode performance. Significance. These results serve as preliminary guidance for a human auditory cortical neural prosthesis; instructing interface implementation, microstimulation patterns and anatomical placement

A chronically implantable, hybrid cannula–electrode device for assessing the effects of molecules on electrophysiological signals in freely behaving animals

We describe a device for assessing the effects of diffusible molecules on electrophysiological recordings from multiple neurons. This device allows for the infusion of reagents through a cannula located among an array of micro-electrodes. The device can easily be customized to target specific neural structures. It is designed to be chronically implanted so that isolated neural units and local field potentials are recorded over the course of several weeks or months. Multivariate statistical and spectral analysis of electrophysiological signals acquired using this system could quantitatively identify electrical “signatures” of therapeutically useful drugs

Avalanche analysis from multi-electrode ensemble recordings in cat, monkey and human cerebral cortex during wakefulness and sleep

Self-organized critical states are found in many natural systems, from earthquakes to forest fires, they have also been observed in neural systems, particularly, in neuronal cultures. However, the presence of critical states in the awake brain remains controversial. Here, we compared avalanche analyses performed on different in vivo preparations during wakefulness, slow-wave sleep and REM sleep, using high-density electrode arrays in cat motor cortex (96 electrodes), monkey motor cortex and premotor cortex and human temporal cortex (96 electrodes) in epileptic patients. In neuronal avalanches defined from units (up to 160 single units), the size of avalanches never clearly scaled as power-law, but rather scaled exponentially or displayed intermediate scaling. We also analyzed the dynamics of local field potentials (LFPs) and in particular LFP negative peaks (nLFPs) among the different electrodes (up to 96 sites in temporal cortex or up to 128 sites in adjacent motor and pre-motor cortices). In this case, the avalanches defined from nLFPs displayed power-law scaling in double log representations, as reported previously in monkey. However, avalanche defined as positive LFP (pLFP) peaks, which are less directly related to neuronal firing, also displayed apparent power-law scaling. Closer examination of this scaling using more reliable cumulative distribution functions (CDF) and other rigorous statistical measures, did not confirm power-law scaling. The same pattern was seen for cats, monkey and human, as well as for different brain states of wakefulness and sleep. We also tested other alternative distributions. Multiple exponential fitting yielded optimal fits of the avalanche dynamics with bi-exponential distributions. Collectively, these results show no clear evidence for power-law scaling or self-organized critical states in the awake and sleeping brain of mammals, from cat to man.Comment: In press in: Frontiers in Physiology, 2012, special issue "Critical Brain Dynamics" (Edited by He BY, Daffertshofer A, Boonstra TW); 33 pages, 13 figures. 3 table

Decoding spoken words using local field potentials recorded from the cortical surface

Pathological conditions such as amyotrophic lateral sclerosis or damage to the brainstem can leave patients severely paralyzed but fully aware, in a condition known as 'locked-in syndrome'. Communication in this state is often reduced to selecting individual letters or words by arduous residual movements. More intuitive and rapid communication may be restored by directly interfacing with language areas of the cerebral cortex. We used a grid of closely spaced, nonpenetrating micro-electrodes to record local field potentials (LFPs) from the surface of face motor cortex and Wernicke's area. From these LFPs we were successful in classifying a small set of words on a trial-by-trial basis at levels well above chance. We found that the pattern of electrodes with the highest accuracy changed for each word, which supports the idea that closely spaced micro-electrodes are capable of capturing neural signals from independent neural processing assemblies. These results further support using cortical surface potentials (electrocorticography) in brain–computer interfaces. These results also show that LFPs recorded from the cortical surface (micro-electrocorticography) of language areas can be used to classify speech-related cortical rhythms and potentially restore communication to locked-in patients

Classification of spoken words using surface local field potentials

Cortical surface potentials recorded by electrocorticography (ECoG) have enabled robust motor classification algorithms in large part because of the close proximity of the electrodes to the cortical surface. However, standard clinical ECoG electrodes are large in both diameter and spacing relative to the underlying cortical column architecture in which groups of neurons process similar types of stimuli. The potential for surface micro-electrodes closely spaced together to provide even higher fidelity in recording surface field potentials has been a topic of recent interest in the neural prosthetic community. This study describes the classification of spoken words from surface local field potentials (LFPs) recorded using grids of subdural, nonpenetrating high impedance micro-electrodes. Data recorded from these micro-ECoG electrodes supported accurate and rapid classification. Furthermore, electrodes spaced millimeters apart demonstrated varying classification characteristics, suggesting that cortical surface LFPs may be recorded with high temporal and spatial resolution to enable even more robust algorithms for motor classification

Multi-scale recordings for neuroprosthetic control of finger movements

We trained a rhesus monkey to perform individuated and combined finger flexions and extensions of the thumb, index, and middle finger. A Utah Electrode Array (UEA) was implanted into the hand region of the motor cortex contralateral to the monkey's trained hand. We also implanted a microwire electrocorticography grid (µECoG) epidurally so that it covered the UEA. The µECoG grid spanned the arm and hand regions of both the primary motor and somatosensory cortices. Previously this monkey had Implantable MyoElectric Sensors (IMES) surgically implanted into the finger muscles of the monkey's forearm. Action potentials (APs), local field potentials (LFPs), and µECoG signals were recorded from wired head-stage connectors for the UEA and µECoG grids, while EMG was recorded wirelessly. The monkey performed a finger flexion/extension task while neural and EMG data were acquired. We wrote an algorithm that uses the spike data from the UEA to perform a real-time decode of the monkey's finger movements. Also, analyses of the LFP and µECoG data indicate that these data show trial-averaged differences between different finger movements, indicating the data are potentially decodeable

Multi-scale recordings for neuroprosthetic control of finger movements

We trained a rhesus monkey to perform individuated and combined finger flexions and extensions of the thumb, index, and middle finger. A Utah Electrode Array (UEA) was implanted into the hand region of the motor cortex contralateral to the monkey's trained hand. We also implanted a microwire electrocorticography grid (µECoG) epidurally so that it covered the UEA. The µECoG grid spanned the arm and hand regions of both the primary motor and somatosensory cortices. Previously this monkey had Implantable MyoElectric Sensors (IMES) surgically implanted into the finger muscles of the monkey's forearm. Action potentials (APs), local field potentials (LFPs), and µECoG signals were recorded from wired head-stage connectors for the UEA and µECoG grids, while EMG was recorded wirelessly. The monkey performed a finger flexion/extension task while neural and EMG data were acquired. We wrote an algorithm that uses the spike data from the UEA to perform a real-time decode of the monkey's finger movements. Also, analyses of the LFP and µECoG data indicate that these data show trial-averaged differences between different finger movements, indicating the data are potentially decodeable