A Pilot Investigation of Visual Pathways in Patients with Mild Traumatic Brain Injury.

In this study, we examined visual processing within primary visual areas (V1) in normal and visually impaired individuals who exhibit significant visual symptomology due to sports-related mild traumatic brain injury (mTBI). Five spatial frequency stimuli were applied to the right, left and both eyes in order to assess the visual processing of patients with sports-related mild traumatic brain injuries who exhibited visual abnormalities, i.e., photophobia, blurriness, etc., and controls. The measurement of the left/right eye and binocular integration was accomplished via the quantification of the spectral power and visual event-related potentials. The principal results have shown that the power spectral density (PSD) measurements display a distinct loss in the alpha band-width range, which corresponded to more instances of medium-sized receptive field loss. Medium-size receptive field loss may correspond to parvocellular (p-cell) processing deprecation. Our major conclusion provides a new measurement, using PSD analysis to assess mTBI conditions from primary V1 areas. The statistical analysis demonstrated significant differences between the mTBI and control cohort in the Visual Evoked Potentials (VEP) amplitude responses and PSD measurements. Additionally, the PSD measurements were able to assess the improvement in the mTBI primary visual areas over time through rehabilitation.

Auditory and olfactory findings in patients with USH2A-related retinal degeneration-Findings at baseline from the rate of progression in USH2A-related retinal degeneration natural history study (RUSH2A).

Sensorineural hearing loss (SNHL) is characteristic of Usher syndrome type 2 (USH2), but less is known about SNHL in nonsyndromic autosomal recessive retinitis pigmentosa (ARRP) and olfaction in USH2A-associated retinal degeneration. The Rate of Progression of USH2A-related Retinal Degeneration (RUSH2A) is a natural history study that enrolled 127 participants, 80 with USH2 and 47 with ARRP. Hearing was measured by pure-tone thresholds and word recognition scores, and olfaction by the University of Pennsylvania Smell Identification Test (UPSIT). SNHL was moderate in 72% of USH2 participants and severe or profound in 25%, while 9% of ARRP participants had moderate adult-onset SNHL. Pure-tone thresholds worsened with age in ARRP but not in USH2 participants. The degree of SNHL was not associated with other participant characteristics in either USH2 or ARRP. Median pure-tone thresholds in ARRP participants were significantly higher than the normative population (p < 0.001). Among 14 USH2 participants reporting newborn hearing screening results, 7 reported passing. Among RUSH2A participants, 7% had mild microsmia and 5% had moderate or severe microsmia. Their mean (±SD) UPSIT score was 35 (±3), similar to healthy controls (34 [±3]; p = 0.39). Olfaction differed by country (p = 0.02), but was not significantly associated with clinical diagnosis, age, gender, race/ethnicity, smoking status, visual measures, or hearing. Hearing loss in USH2A-related USH2 did not progress with age. ARRP patients had higher pure-tone thresholds than normal. Newborn hearing screening did not identify all USH2A-related hearing loss. Olfaction was not significantly worse than normal in participants with USH2A-related retinal degeneration.

Automatic Detection of a Student's Affective States for Intelligent Teaching Systems.

AutoTutor is an automated computer tutor that simulates human tutors and holds conversations with students in natural language. Using data collected from AutoTutor, the following determinations were sought: Can we automatically classify affect states from intelligent teaching systems to aid in the detection of a learner's emotional state? Using frequency patterns of AutoTutor feedback and assigned user emotion in a series of pairs, can the next pair of feedback/emotion series be predicted? Through a priori data mining approaches, we found dominant frequent item sets that predict the next set of responses. Thirty-four participants provided 200 turns between the student and the AutoTutor. Two series of attributes and emotions were concatenated into one row to create a record of previous and next set of emotions. Feature extraction techniques, such as multilayer-perceptron and naive Bayes, were performed on the dataset to perform classification for affective state labeling. The emotions 'Flow' and 'Frustration' had the highest classification of all the other emotions when measured against other emotions and their respective attributes. The most common frequent item sets were 'Flow' and 'Confusion'.

Seizure localization using EEG analytical signals.

OBJECTIVE: Localization of epileptic seizures, usually characterized by abnormal hypersynchronous wave patterns from the cortex, remains elusive. We present a novel, robust method for automatic localization of seizures on the scalp from clinical electroencephalogram (EEG) data. METHODS: Seizure patient EEG data was decomposed via the Hilbert Transform and processed through the following methodology: sorting the analytic amplitude (AA) in the time instance, locating the maximum amplitude within the vector of channels, cross-correlating amplitude values in the time index with the channel vector. The channel with highest AA value in time was located. RESULTS: Our approach provides an automated way to isolate the epi-genesis of seizure events with 93.3% precision and 100% sensitivity. The method differentiates seizure-related neural activity from other common EEG noise artifacts (e.g., blinks, myogenic noise). CONCLUSIONS: We evaluated performance characteristics of our source location methodology utilizing both phase and energy of EEG signals from patients who exhibited seizure events. Feasibility of the new algorithm is demonstrated and confirmed. SIGNIFICANCE: The proposed method contributes to high-performance scalp localization for seizure events that is more straightforward and less computationally intensive than other methods (e.g., inverse source modeling). Ultimately, it may aid clinicians in providing improved patient diagnosis.

Frontal cortex selectively overrides auditory processing to bias perception for looming sonic motion.

Rising intensity sounds signal approaching objects traveling toward an observer. A variety of species preferentially respond to looming over receding auditory motion, reflecting an evolutionary perceptual bias for recognizing approaching threats. We probed the neural origins of this stark perceptual anisotropy to reveal how the brain creates privilege for auditory looming events. While recording neural activity via electroencephalography (EEG), human listeners rapidly judged whether dynamic (intensity varying) tones were looming or receding in percept. Behaviorally, listeners responded faster to auditory looms confirming a perceptual bias for approaching signals. EEG source analysis revealed sensory activation localized to primary auditory cortex (PAC) and decision-related activity in prefrontal cortex (PFC) within 200 ms after sound onset followed by additional expansive PFC activation by 500 ms. Notably, early PFC (but not PAC) activity rapidly differentiated looming and receding stimuli and this effect roughly co-occurred with sound arrival in auditory cortex. Brain-behavior correlations revealed an association between PFC neural latencies and listeners' speed of sonic motion judgments. Directed functional connectivity revealed stronger information flow from PFC → PAC during looming vs. receding sounds. Our electrophysiological data reveal a critical, previously undocumented role of prefrontal cortex in judging dynamic sonic motion. Both faster neural bias and a functional override of obligatory sensory processing via selective, directional PFC signaling toward auditory system establish the perceptual privilege for approaching looming sounds.

Spatial Directionality Found in Frontal-Parietal Attentional Networks.

Research in last few years on neurophysiology focused on several areas across the cortex during cognitive processing to determine the dominant direction of electrical activity. However, information about the frequency and direction of episodic synchronization related to higher cognitive functions remain unclear. Our aim was to determine whether neural oscillations carry perceptual information as spatial patterns across the cortex, which could be found in the scalp EEG of human subjects while being engaged in visual sensory stimulation. Magnitude squared coherence of neural activity during task states that "finger movement with Eyes Open (EO) or Eyes Wandering (EW)" among all electrode combinations has the smallest standard deviation and variations. Additionally, the highest coherence among the electrode pairs occurred between alpha (8-12 Hz) and beta (12-16 Hz) ranges. Our results indicate that alpha rhythms seem to be regulated during activities when an individual is focused on a given task. Beta activity, which has also been implicated in cognitive processing to neural oscillations, is seen in our work as a manner to integrate external stimuli to higher cognitive activation. We have found spatial network organization which served to classify the EEG epochs in time with respect to the stimuli class. Our findings suggest that cortical neural signaling utilizes alpha-beta phase coupling during cognitive processing states, where beta activity has been implicated in shifting cognitive states. Significance. Our approach has found frontoparietal attentional mechanisms in shifting brain states which could provide new insights into understanding the global cerebral dynamics of intentional activity and reflect how the brain allocates resources during tasking and cognitive processing states.

Mesoscopic neuron population modeling of normal/epileptic brain dynamics.

Simulations of EEG data provide the understanding of how the limbic system exhibits normal and abnormal states of the electrical activity of the brain. While brain activity exhibits a type of homeostasis of excitatory and inhibitory mesoscopic neuron behavior, abnormal neural firings found in the seizure state exhibits brain instability due to runaway oscillatory entrained neural behavior. We utilize a model of mesoscopic brain activity, the KIV model, where each network represents the areas of the limbic system, i.e., hippocampus, sensory cortex, and the amygdala. Our model initially demonstrates oscillatory entrained neural behavior as the epileptogenesis, and then by increasing the external weights that join the three networks that represent the areas of the limbic system, seizure activity entrains the entire system. By introducing an external signal into the model, simulating external electrical titration therapy, the modeled seizure behavior can be 'rebalanced' back to its normal state.

A pilot investigation of audiovisual processing and multisensory integration in patients with inherited retinal dystrophies.

BACKGROUND: In this study, we examined audiovisual (AV) processing in normal and visually impaired individuals who exhibit partial loss of vision due to inherited retinal dystrophies (IRDs). METHODS: Two groups were analyzed for this pilot study: Group 1 was composed of IRD participants: two with autosomal dominant retinitis pigmentosa (RP), two with autosomal recessive cone-rod dystrophy (CORD), and two with the related complex disorder, Bardet-Biedl syndrome (BBS); Group 2 was composed of 15 non-IRD participants (controls). Audiovisual looming and receding stimuli (conveying perceptual motion) were used to assess the cortical processing and integration of unimodal (A or V) and multimodal (AV) sensory cues. Electroencephalography (EEG) was used to simultaneously resolve the temporal and spatial characteristics of AV processing and assess differences in neural responses between groups. Measurement of AV integration was accomplished via quantification of the EEG's spectral power and event-related brain potentials (ERPs). RESULTS: Results show that IRD individuals exhibit reduced AV integration for concurrent audio and visual (AV) stimuli but increased brain activity during the unimodal A (but not V) presentation. This was corroborated in behavioral responses, where IRD patients showed slower and less accurate judgments of AV and V stimuli but more accurate responses in the A-alone condition. CONCLUSIONS: Collectively, our findings imply a neural compensation from auditory sensory brain areas due to visual deprivation.

Quantitative EEG Signatures through Amplitude and Phase Modulation Patterns.

Cortical spatiotemporal signal patterns based on object recognition can be discerned from visual stimulation. These are in the form of amplitude modulation (AM) and phase modulation (PM) patterns, which contain perceptual information gathered from sensory input. A high-density Electroencephalograph (EEG) device consisting of 48 electrodes with a spacing of 5 mm was utilized to measure frontal lobe activity in order to capture event-related potentials from visual stimuli. Four randomized stimuli representing different levels of salient responsiveness were measured to determine if mild stimuli can be discerned from more extreme stimuli. AM/PM response patterns were detected between mild and more salient stimuli across participants. AM patterns presented distinct signatures for each stimulus. AM patterns had the highest number of incidents detected in the middle of the frontal lobe. Through this work, we can expand our encyclopedia of neural signatures to object recognition, and provide a broader understanding of quantitative neural responses to external stimuli. The results provide a quantitative approach utilizing spatiotemporal patterns to analyze where distinct AM patterns can be linked to object perception.

Power Spectral Density Analysis of Electrocorticogram Recordings during Cerebral Hypothermia in Neonatal Seizures.

BACKGROUND: Neonatal seizures (NS) are the most common form of neurological dysfunction observed in newborns. PURPOSE: The purpose of this study in newborn piglets was to determine the effect of cerebral hypothermia (CH) on neural activity during pharmacologically induced NS. We hypothesized that the neuroprotective effects of CH would preserve higher frequencies observed in electrocorticogram (ECoG) recordings. METHODS: Power spectral density was employed to determine the levels of brain activity in ECoGs to quantitatively assess the power of each frequency observed in neurological brain states of delta, theta, alpha, and beta-gamma frequencies. RESULT: The most significant reduction of power occurs in the lower frequency band of delta-theta-alpha of CH cohorts, while t score probabilities imply that high-frequency brain activity in the beta-gamma range is preserved in the CH population. CONCLUSION: While the overall power density decreases over time in both groups, the decrease is to a lesser degree in the CH population.

Ambulatory Seizure Monitoring: From Concept to Prototype Device.

BACKGROUND: The brain, made up of billions of neurons and synapses, is the marvelous core of human thought, action and memory. However, if neuronal activity manifests into abnormal electrical activity across the brain, neural behavior may exhibit synchronous neural firings known as seizures. If unprovoked seizures occur repeatedly, a patient may be diagnosed with epilepsy. PURPOSE: The scope of this project is to develop an ambulatory seizure monitoring system that can be used away from a hospital, making it possible for the user to stay at home, and primary care personnel to monitor a patient's seizure activity in order to provide deeper analysis of the patient's condition and apply personalized intervention techniques. METHODS: The ambulatory seizure monitoring device is a research device that has been developed with the objective of acquiring a portable, clean electroencephalography (EEG) signal and transmitting it wirelessly to a handheld device for processing and notification. RESULT: This device is comprised of 4 phases: acquisition, transmission, processing and notification. During the acquisition stage, the EEG signal is detected using EEG electrodes; these signals are filtered and amplified before being transmitted in the second stage. The processing stage encompasses the signal processing and seizure prediction. A notification is sent to the patient and designated contacts, given an impending seizure. Each of these phases is comprised of various design components, hardware and software. The experimental findings illustrate that there may be a triggering mechanism through the phase lock value method that enables seizure prediction. CONCLUSION: The device addresses the need for long-term monitoring of the patient's seizure condition in order to provide the clinician a better understanding of the seizure's duration and frequency and ultimately provide the best remedy for the patient.

Seizure Prediction and Detection via Phase and Amplitude Lock Values.

A robust seizure prediction methodology would enable a "closed-loop" system that would only activate as impending seizure activity is detected. Such a system would eliminate ongoing stimulation to the brain, thereby eliminating such side effects as coughing, hoarseness, voice alteration, and paresthesias (Murphy et al., 1998; Ben-Menachem, 2001), while preserving overall battery life of the system. The seizure prediction and detection algorithm uses Phase/Amplitude Lock Values (PLV/ALV) which calculate the difference of phase and amplitude between electroencephalogram (EEG) electrodes local and remote to the epileptic event. PLV is used as the seizure prediction marker and signifies the emergence of abnormal neuronal activations through local neuron populations. PLV/ALVs are used as seizure detection markers to demarcate the seizure event, or when the local seizure event has propagated throughout the brain turning into a grand-mal event. We verify the performance of this methodology against the "CHB-MIT Scalp EEG Database" which features seizure attributes for testing. Through this testing, we can demonstrate a high degree of sensivity and precision of our methodology between pre-ictal and ictal events.

A Feedback Control Approach to Organic Drug Infusions Using Electrochemical Measurement.

GOAL: Target-controlled infusion of anesthesia is a closed-loop automated drug delivery method with a computer-aided control. Our goal is to design and test an automated drug infusion platform for propofol delivery in total intravenous anesthesia (TIVA) administration. METHODS: In the proposed method, a dilution chamber with first-order exponential decay characteristics was used to model the pharmacodynamics decay of a drug. The dilution chamber was connected to a flow system through an electrochemical cell containing an organic film-coated glassy carbon electrode as working electrode. To set up the feedback-controlled delivery platform and optimize its parameters, ferrocene methanol was used as a proxy of the propofol. The output signal of the sensor was connected to a PI controller, which prompted a syringe pump for feedback-controlled drug infusion. RESULTS: The result is a bench-top drug infusion platform to automate the delivery of a propofol based on the measurement of concentration with an organic film-coated voltammetric sensor. CONCLUSION: To evaluate the performance characteristics of the infusion platform, the propofol concentration in the dilution chamber was monitored with the organic film-coated glassy carbon electrode and the difference between the set and measured concentrations was assessed. The feasibility of measurement-based feedback-controlled propofol delivery is demonstrated and confirmed. SIGNIFICANCE: This platform will contribute to high-performance TIVA application of intravenous propofol anesthesia.

Amplitude suppression and chaos control in epileptic EEG signals.

In this paper we have proposed a novel amplitude suppression algorithm for EEG signals collected during epileptic seizure. Then we have proposed a measure of chaoticity for a chaotic signal, which is somewhat similar to measuring sensitive dependence on initial conditions by measuring Lyapunov exponent in a chaotic dynamical system. We have shown that with respect to this measure the amplitude suppression algorithm reduces chaoticity in a chaotic signal (EEG signal is chaotic). We have compared our measure with the estimated largest Lyapunov exponent measure by the largelyap function, which is similar to Wolf's algorithm. They fit closely for all but one of the cases. How the algorithm can help to improve patient specific dosage titration during vagus nerve stimulation therapy has been outlined.