EEG-LLAMAS: A low-latency neurofeedback platform for artifact reduction in EEG-fMRI.

Simultaneous EEG-fMRI is a powerful multimodal technique for imaging the brain, but its use in neurofeedback experiments has been limited by EEG noise caused by the MRI environment. Neurofeedback studies typically require analysis of EEG in real time, but EEG acquired inside the scanner is heavily contaminated with ballistocardiogram (BCG) artifact, a high-amplitude artifact locked to the cardiac cycle. Although techniques for removing BCG artifacts do exist, they are either not suited to real-time, low-latency applications, such as neurofeedback, or have limited efficacy. We propose and validate a new open-source artifact removal software called EEG-LLAMAS (Low Latency Artifact Mitigation Acquisition Software), which adapts and advances existing artifact removal techniques for low-latency experiments. We first used simulations to validate LLAMAS in data with known ground truth. We found that LLAMAS performed better than the best publicly-available real-time BCG removal technique, optimal basis sets (OBS), in terms of its ability to recover EEG waveforms, power spectra, and slow wave phase. To determine whether LLAMAS would be effective in practice, we then used it to conduct real-time EEG-fMRI recordings in healthy adults, using a steady state visual evoked potential (SSVEP) task. We found that LLAMAS was able to recover the SSVEP in real time, and recovered the power spectra collected outside the scanner better than OBS. We also measured the latency of LLAMAS during live recordings, and found that it introduced a lag of less than 50 ms on average. The low latency of LLAMAS, coupled with its improved artifact reduction, can thus be effectively used for EEG-fMRI neurofeedback. A limitation of the method is its use of a reference layer, a piece of EEG equipment which is not commercially available, but can be assembled in-house. This platform enables closed-loop experiments which previously would have been prohibitively difficult, such as those that target short-duration EEG events, and is shared openly with the neuroscience community.

The MotoNet: A 3 Tesla MRI-Conditional EEG Net with Embedded Motion Sensors.

We introduce a new electroencephalogram (EEG) net, which will allow clinicians to monitor EEG while tracking head motion. Motion during MRI limits patient scans, especially of children with epilepsy. EEG is also severely affected by motion-induced noise, predominantly ballistocardiogram (BCG) noise due to the heartbeat. METHODS: The MotoNet was built using polymer thick film (PTF) EEG leads and motion sensors on opposite sides in the same flex circuit. EEG/motion measurements were made with a standard commercial EEG acquisition system in a 3 Tesla (T) MRI. A Kalman filtering-based BCG correction tool was used to clean the EEG in healthy volunteers. RESULTS: MRI safety studies in 3 T confirmed the maximum heating below 1 °C. Using an MRI sequence with spatial localization gradients only, the position of the head was linearly correlated with the average motion sensor output. Kalman filtering was shown to reduce the BCG noise and recover artifact-clean EEG. CONCLUSIONS: The MotoNet is an innovative EEG net design that co-locates 32 EEG electrodes with 32 motion sensors to improve both EEG and MRI signal quality. In combination with custom gradients, the position of the net can, in principle, be determined. In addition, the motion sensors can help reduce BCG noise.

Pain phenotypes classified by machine learning using electroencephalography features.

Pain is a multidimensional experience mediated by distributed neural networks in the brain. To study this phenomenon, EEGs were collected from 20 subjects with chronic lumbar radiculopathy, 20 age and gender matched healthy subjects, and 17 subjects with chronic lumbar pain scheduled to receive an implanted spinal cord stimulator. Analysis of power spectral density, coherence, and phase-amplitude coupling using conventional statistics showed that there were no significant differences between the radiculopathy and control groups after correcting for multiple comparisons. However, analysis of transient spectral events showed that there were differences between these two groups in terms of the number, power, and frequency-span of events in a low gamma band. Finally, we trained a binary support vector machine to classify radiculopathy versus healthy subjects, as well as a 3-way classifier for subjects in the 3 groups. Both classifiers performed significantly better than chance, indicating that EEG features contain relevant information pertaining to sensory states, and may be used to help distinguish between pain states when other clinical signs are inconclusive.

Complementary and Alternative Medicine for Functional Gastrointestinal Disorders.

The relevance of functional gastrointestinal (GI) disorders and their impact on quality of life for many patients has become an increasingly important topic in gastroenterology. A gastroenterologist can expect to see 40% of patients for motility and functional GI disorders, thus highlighting the necessity for physicians to have a strong foundation of knowledge in treatment strategies for these patients with complex disorders. A significant number of patients who suffer with functional GI disorders turn to complementary and alternative therapies to maintain control over their symptoms and often are happy with therapeutic results. This narrative presents information and treatment algorithms for the gastroenterologist to better understand and use some of the most common complementary and alternative therapies for patients with functional dyspepsia, nausea and vomiting, and irritable bowel syndrome.

What does a pain 'biomarker' mean and can a machine be taught to measure pain?

Artificial intelligence allows machines to predict human faculties such as image and voice recognition. Can machines be taught to measure pain? We argue that the two fundamental requirements for a device with 'pain biomarker' capabilities are hardware and software. We discuss the merits and limitations of electroencephalography (EEG) as the hardware component of a putative embodiment of the device, and advances in the application of machine learning approaches to EEG for predicting pain.

Automated detection of electroencephalography artifacts in human, rodent and canine subjects using machine learning.

BACKGROUND: Electroencephalography (EEG) invariably contains extra-cranial artifacts that are commonly dealt with based on qualitative and subjective criteria. Failure to account for EEG artifacts compromises data interpretation. NEW METHOD: We have developed a quantitative and automated support vector machine (SVM)-based algorithm to accurately classify artifactual EEG epochs in awake rodent, canine and humans subjects. An embodiment of this method also enables the determination of 'eyes open/closed' states in human subjects. RESULTS: The levels of SVM accuracy for artifact classification in humans, Sprague Dawley rats and beagle dogs were 94.17%, 83.68%, and 85.37%, respectively, whereas 'eyes open/closed' states in humans were labeled with 88.60% accuracy. Each of these results was significantly higher than chance. COMPARISON WITH EXISTING METHODS: Other existing methods, like those dependent on Independent Component Analysis, have not been tested in non-human subjects, and require full EEG montages, instead of only single channels, as this method does. CONCLUSIONS: We conclude that our EEG artifact detection algorithm provides a valid and practical solution to a common problem in the quantitative analysis and assessment of EEG in pre-clinical research settings across evolutionary spectra.

An Electroencephalography Bioassay for Preclinical Testing of Analgesic Efficacy.

We present a multimodal method combining quantitative electroencephalography (EEG), behavior and pharmacology for pre-clinical screening of analgesic efficacy in vivo. The method consists of an objective and non-invasive approach for realtime assessment of spontaneous nociceptive states based on EEG recordings of theta power over primary somatosensory cortex in awake rats. Three drugs were chosen: (1) pregabalin, a CNS-acting calcium channel inhibitor; (2) EMA 401, a PNS-acting angiotensin II type 2 receptor inhibitor; and (3) minocycline, a CNS-acting glial inhibitor. Optimal doses were determined based on pharmacokinetic studies and/or published data. The effects of these drugs at single or multiple doses were tested on the attenuation of theta power and paw withdrawal latency (PWL) in a rat model of neuropathic pain. We report mostly parallel trends in the reversal of theta power and PWL in response to administration of pregabalin and EMA 401, but not minocycline. We also note divergent trends at non-optimal doses and following prolonged drug administration, suggesting that EEG theta power can be used to detect false positive and false negative outcomes of the withdrawal reflex behavior, and yielding novel insights into the analgesic effects of these drugs on spontaneous nociceptive states in rats.

Electroencephalographic frontal synchrony and caudal asynchrony during painful hand immersion in cold water.

Recent studies in our laboratory showed that cortical theta oscillations correlate with pain in rodent models. In this study, we sought to validate our pre-clinical data using EEG recordings in humans during immersion of the hand in ice cold water, a moderately noxious stimulus. Power spectral analysis shows that an increase in pain score is associated with an increase in power amplitude within a frequency range of 6-7Hz at the frontal (Fz) electrode. These results are consistent with our previous pre-clinical animal studies and the clinical literature. We also report a novel reduction in power at the caudal (O1) electrode within a broader 3-30Hz rand and decreased coherence between Fz and C3, C4 electrodes within the theta (4-8Hz) and low beta (13-21Hz) bands, while coherence (an indirect measure of functional connectivity) between Fz and O1 increased within the theta and alpha (8-12Hz) bands. We argue that pain is associated with EEG frontal synchrony and caudal asynchrony, leading to the disruption of cortico-cortical connectivity.

Thalamic Bursts Down-regulate Cortical Theta and Nociceptive Behavior.

We tested the relation between pain behavior, theta (4-8 Hz) oscillations in somatosensory cortex and burst firing in thalamic neurons in vivo. Optically-induced thalamic bursts attenuated cortical theta and mechanical allodynia. It is proposed that thalamic bursts are an adaptive response to pain that de-synchronizes cortical theta and decreases sensory salience.

Obesity and age at diagnosis of endometrial cancer.

OBJECTIVE: Obesity is an established risk factor for development of endometrial cancer. We hypothesized that obesity might also be associated with an earlier age at endometrial cancer diagnosis, because mechanisms that drive the obesity-endometrial cancer association might also accelerate tumorigenesis. METHODS: A retrospective chart review was conducted of all cases of endometrial cancer diagnosed from 1999 to 2009 at a large medical center in New York City. The association of body mass index (BMI) with age at endometrial cancer diagnosis, comorbidities, stage, grade, and radiation treatment was examined using analysis of variance and linear regression. Overall survival by BMI category was assessed using Kaplan-Meier method and the log-rank test. RESULTS: A total of 985 cases of endometrial cancer were identified. The mean age at endometrial cancer diagnosis was 67.1 years (±11.9 standard deviation) in women with a normal BMI, whereas it was 56.3 years (±10.3 standard deviation) in women with a BMI greater than 50. Age at diagnosis of endometrioid-type cancer decreased linearly with increasing BMI (y=67.89-1.86x, R=0.049, P<.001). This association persisted after multivariable adjustment (R=0.181, P<.02). A linear association between BMI and age of nonendometrioid cancers was not found (P=.12). There were no differences in overall survival by BMI category. CONCLUSIONS: Obesity is associated with earlier age at diagnosis of endometrioid-type endometrial cancers. Similar associations were not, however, observed with nonendometrioid cancers, consistent with different pathways of tumorigenesis. LEVEL OF EVIDENCE: II.