Sara J. Hanrahan

Seeing is believing: neural representations of visual stimuli in human auditory cortex correlate with illusory auditory perceptions.

In interpersonal communication, the listener can often see as well as hear the speaker. Visual stimuli can subtly change a listener’s auditory perception, as in the McGurk illusion, in which perception of a phoneme’s auditory identity is changed by a concurrent video of a mouth articulating a different phoneme. Studies have yet to link visual influences on the neural representation of language with subjective language perception. Here we show that vision influences the electrophysiological representation of phonemes in human auditory cortex prior to the presentation of the auditory stimulus. We used the McGurk effect to dissociate the subjective perception of phonemes from the auditory stimuli. With this paradigm we demonstrate that neural representations in auditory cortex are more closely correlated with the visual stimuli of mouth articulation, which drive the illusory subjective auditory perception, than the actual auditory stimuli. Additionally, information about visual and auditory stimuli transfer in the caudal–rostral direction along the superior temporal gyrus during phoneme perception as would be expected of visual information flowing from the occipital cortex into the ventral auditory processing stream. These results show that visual stimuli influence the neural representation in auditory cortex early in sensory processing and may override the subjective auditory perceptions normally generated by auditory stimuli. These findings depict a marked influence of vision on the neural processing of audition in tertiary auditory cortex and suggest a mechanistic underpinning for the McGurk effect.

The effects of propofol ]on local field potential spectra, action potential firing rate, and their temporal relationship in humans and felines

Propofol is an intravenous sedative hypnotic, which, acting as a GABAA agonist, results in neocortical inhibition. While propofol has been well studied at the molecular and clinical level, less is known about the effects of propofol at the level of individual neurons and local neocortical networks. We used Utah Electrode Arrays (UEAs) to investigate the effects of propofol anesthesia on action potentials (APs) and local field potentials (LFPs). UEAs were implanted into the neocortex of two humans and three felines. The two human patients and one feline received propofol by bolus injection, while the other two felines received target-controlled infusions. We examined the changes in LFP power spectra and AP firing at different levels of anesthesia. Increased propofol concentration correlated with decreased high-frequency power in LFP spectra and decreased AP firing rates, and the generation of large-amplitude spike-like LFP activity; however, the temporal relationship between APs and LFPs remained relatively consistent at all levels of propofol. The probability that an AP would fire at this local minimum of the LFP increased with propofol administration. The propofol-induced suppression of neocortical network activity allowed LFPs to be dominated by low-frequency spike-like activity, and correlated with sedation and unconsciousness. As the low-frequency spike-like activity increased and the AP–LFP relationship became more predictable firing rate encoding capacity is impaired. This suggests a mechanism for decreased information processing in the neocortex that accounts for propofol-induced unconsciousness.

Decoding hand trajectories from micro-electrocorticography in human patients

A Kalman filter was used to decode hand trajectories from micro-electrocorticography recorded over motor cortex in human patients. In two cases, signals were recorded during stereotyped tasks, and the trajectories were decoded offline, with maximum correlation coefficients between actual and predicted trajectories of 0.51 (x-direction position) and 0.54 (y-direction position). In a third setting, a human patient with full neural control of a computer cursor acquired onscreen targets within 6.24 sec on average, with no algorithmic constraints on the output trajectory. These practical results illustrate the potential utility of signals recorded at the cortical surface with high spatial resolution, demonstrating that surface potentials contain relevant and sufficient information to drive sophisticated brain-computer interface systems.

A Multiple Kernel Learning approach for human behavioral task classification using STN-LFP signal

Deep Brain Stimulation (DBS) has gained increasing attention as an effective method to mitigate Parkinsons disease (PD) disorders. Existing DBS systems are open-loop such that the system parameters are not adjusted automatically based on patients behavior. Classification of human behavior is an important step in the design of the next generation of DBS systems that are closed-loop. This paper presents a classification approach to recognize such behavioral tasks using the subthalamic nucleus (STN) Local Field Potential (LFP) signals. In our approach, we use the time-frequency representation (spectrogram) of the raw LFP signals recorded from left and right STNs as the feature vectors. Then these features are combined together via Support Vector Machines (SVM) with Multiple Kernel Learning (MKL) formulation. The MKL-based classification method is utilized to classify different tasks: button press, mouth movement, speech, and arm movement. Our experiments show that the lp-norm MKL significantly outperforms single kernel SVM-based classifiers in classifying behavioral tasks of five subjects even using signals acquired with a low sampling rate of 10 Hz. This leads to a lower computational cost.Deep Brain Stimulation (DBS) has gained increasing attention as an effective method to mitigate Parkinsons disease (PD) disorders. Existing DBS systems are open-loop such that the system parameters are not adjusted automatically based on patients behavior. Classification of human behavior is an important step in the design of the next generation of DBS systems that are closed-loop. This paper presents a classification approach to recognize such behavioral tasks using the subthalamic nucleus (STN) Local Field Potential (LFP) signals. In our approach, we use the time-frequency representation (spectrogram) of the raw LFP signals recorded from left and right STNs as the feature vectors. Then these features are combined together via Support Vector Machines (SVM) with Multiple Kernel Learning (MKL) formulation. The MKL-based classification method is utilized to classify different tasks: button press, mouth movement, speech, and arm movement. Our experiments show that the lp-norm MKL significantly outperforms single kernel SVM-based classifiers in classifying behavioral tasks of five subjects even using signals acquired with a low sampling rate of 10 Hz. This leads to a lower computational cost.

Single trial behavioral task classification using subthalamic nucleus local field potential signals

Deep Brain Stimulation (DBS) has been a successful technique for alleviating Parkinsons disease (PD) symptoms especially for whom drug therapy is no longer efficient. Existing DBS therapy is open-loop, providing a time invariant stimulation pulse train that is not customized to the patients current behavioral task. By customizing this pulse train to the patients current task the side effects may be suppressed. This paper introduces a method for single trial recognition of the patients current task using the local field potential (LFP) signals. This method utilizes wavelet coefficients as features and support vector machine (SVM) as the classifier for recognition of a selection of behaviors: speech, motor, and random. The proposed method is 82.4% accurate for the binary classification and 73.2% for classifying three tasks. These algorithms will be applied in a closed loop feedback control system to optimize DBS parameters to the patients real time behavioral goals.

Adaptive learning of behavioral tasks for patients with Parkinson's disease using signals from deep brain stimulation

We propose adaptive learning methods for identifying different behavioral tasks of patients with Parkinsons disease (PD). The methods use local field potential (LFP) signals that were collected during Deep Brain Stimulation (DBS) implantation surgeries. Using time-frequency signal processing methods, features are first extracted and then clustered in the feature space using two different methods. The first method requires training and uses a hybrid model that combines support vector machines and hidden Markov models. The second method does not require any a priori information and uses Dirichlet process Gaussian mixture models. Using the DBS acquired signals, we demonstrate the performance of both methods in clustering different behavioral tasks of PD patients and discuss the advantages of each method under different conditions.

Motor task event detection using Subthalamic Nucleus Local Field Potentials

Deep Brain Stimulation (DBS) provides significant therapeutic benefit for movement disorders such as Parkinsons disease. Current DBS devices lack real-time feedback (thus are open loop) and stimulation parameters are adjusted during scheduled visits with a clinician. A closed-loop DBS system may reduce power consumption and DBS side effects. In such systems, DBS parameters are adjusted based on patients behavior, which means that behavior detection is a major step in designing such systems. Various physiological signals can be used to recognize the behaviors. Subthalamic Nucleus (STN) Local Field Potential (LFP) is a great candidate signal for the neural feedback, because it can be recorded from the stimulation lead and does not require additional sensors. A practical behavior detection method should be able to detect behaviors asynchronously meaning that it should not use any prior knowledge of behavior onsets. In this paper, we introduce a behavior detection method that is able to asynchronously detect the finger movements of Parkinson patients. As a result of this study, we learned that there is a motor-modulated inter-hemispheric connectivity between LFP signals recorded bilaterally from STN. We used non-linear regression method to measure this connectivity and use it to detect the finger movements. Performance of this method is evaluated using Receiver Operating Characteristic (ROC).

Platinum microwire for subdural electrocorticography over human neocortex: Millimeter-scale spatiotemporal dynamics

Platinum microwires, terminated at regular intervals to form a grid of contacts, were used to record electric potentials at the surface of the cerebral cortex in human subjects. The microwire grids were manufactured commercially with 75 μm platinum wire and 1 mm grid spacing, and are FDA approved. Because of their small size and spacing, these grids could be used to explore the scale of spatiotemporal dynamics in cortical surface potentials. Electrochemical impedance spectroscopy was used to characterize their recording properties and develop a frequency-dependent electrical model of the micro-electrodes. Data recorded from multiple sites in human cortex were analyzed to explore the relationship between linear correlation and separation distance. A model was developed to explore the impact of cerebrospinal fluid on signal spread among electrodes. Spatial variation in the per-electrode performance decoding articulated speech from face-motor and Wernickes areas of cortex was explored to understand the scale of information processing at the cortex. We conclude that there are important dynamics at the millimeter scale in human subdural electrocorticography which may be important in maximizing the performance of neural prosthetic applications.

Sensing millimeter-scale dynamics in cortical surface potentials for neural prosthetics

A brain signal sensor consisting of an array of millimeter-spaced platinum microwire electrodes was characterized as a recording medium for a brain-machine interface. The small physical size of the electrodes and tight grid spacing constitute a novel approach for applications which depend on the ability to accurately capture the spatiotemporal dynamics of neural activity. Because their geometry approaches the scale of the underlying structures of cortical information processing, microwire grids offer high signal fidelity for the inherent implantation risks. Characterization of the recording properties of these electrodes and of data recorded from multiple functional areas of human neocortex support the claim that millimeter-scale dynamics are present in cortical surface potentials and may be important to the performance of brain-computer interface applications.

Long-Term Task- and Dopamine-Dependent Dynamics of Subthalamic Local Field Potentials in Parkinson’s Disease

Subthalamic nucleus (STN) local field potentials (LFP) are neural signals that have been shown to reveal motor and language behavior, as well as pathological parkinsonian states. We use a research-grade implantable neurostimulator (INS) with data collection capabilities to record STN-LFP outside the operating room to determine the reliability of the signals over time and assess their dynamics with respect to behavior and dopaminergic medication. Seven subjects were implanted with the recording augmented deep brain stimulation (DBS) system, and bilateral STN-LFP recordings were collected in the clinic over twelve months. Subjects were cued to perform voluntary motor and language behaviors in on and off medication states. The STN-LFP recorded with the INS demonstrated behavior-modulated desynchronization of beta frequency (13–30 Hz) and synchronization of low gamma frequency (35–70 Hz) oscillations. Dopaminergic medication did not diminish the relative beta frequency oscillatory desynchronization with movement. However, movement-related gamma frequency oscillatory synchronization was only observed in the medication on state. We observed significant inter-subject variability, but observed consistent STN-LFP activity across recording systems and over a one-year period for each subject. These findings demonstrate that an INS system can provide robust STN-LFP recordings in ambulatory patients, allowing for these signals to be recorded in settings that better represent natural environments in which patients are in a variety of medication states.