The Treatment of Insomnia Secondary to Generalized Anxiety Disorder: Evaluating Escitalopram Use With Concomitant High-Resolution, Relational, Resonance-Based, Electroencephalic Mirroring (HIRREM)

Patient autonomy is important. However, what if a patient seeks out poorly studied treatment options? In this case report, we describe a patient with insomnia secondary to generalized anxiety disorder (GAD) who was prescribed escitalopram but additionally used High-Resolution, Relational, Resonance-Based, Electroencephalic Mirroring (HIRREM) therapy. HIRREM is an electroencephalogram (EEG)-based therapy that has been evaluated for use in the treatment of various conditions including insomnia. However, there has only been one randomized clinical trial supporting the use of HIRREM for insomnia, and the Food and Drug Administration (FDA) has not approved HIRREM for insomnia. A few months after the patient initiated HIRREM therapy and escitalopram cessation, the patient's insomnia did not recur. We propose a case for how we approached educating a patient who was seeking out an alternative poorly tested therapy by helping him perform a cost-benefit analysis composed of treatment efficacy, cost, and side effects.

Using Neurofeedback from Steady-State Visual Evoked Potentials to Target Affect-Biased Attention in Augmented Reality

Biases in attention to emotional stimuli (i.e., affect-biased attention) contribute to the development and mainte-nance of depression and anxiety and may be a promising target for intervention. Past attempts to therapeutically modify affect-biased attention have been unsatisfactory due to issues with reliability and precision. Electroencephalogram (EEG)-derived steady-state visual evoked potentials (SSVEPS) provide a temporally-sensitive biological index of attention to competing visual stimuli at the level of neuronal populations in the visual cortex. SSVEPS can potentially be used to quantify whether affective distractors vs. task-relevant stimuli have "won" the competition for attention at a trial-by-trial level during neuro-feedback sessions. This study piloted a protocol for a SSVEP-based neurofeedback training to modify affect-biased attention using a portable augmented-reality (AR) EEG interface. During neurofeedback sessions with five healthy participants, signifi-cantly greater attention was given to the task-relevant stimulus (a Gabor patch) than to affective distractors (negative emotional expressions) across SSVEP indices (p<0.000l). SSVEP indices exhibited excellent internal consistency as evidenced by a maximum Guttman split-half coefficient of 0.97 when comparing even to odd trials. Further testing is required, but findings suggest several SSVEP neurofeedback calculation methods most deserving of additional investigation and support ongoing efforts to develop and implement a SSVEP-guided AR-based neurofeedback training to modify affect-biased attention in adolescent girls at high risk for depression.

Seizure tracking of epileptic EEGs using a model-driven approach

OBJECTIVE

As a chronic neurological disorder, epilepsy is characterized by recurrent and unprovoked epileptic seizures that can disrupt the normal neuro-biologic, cognitive, psychological conditions of patients. Therefore, it is worthwhile to give a detailed account of how the epileptic EEG evolves during a period of seizure so that an effective control can be guided for epileptic patients in clinics.

APPROACH

Considering the successful application of the neural mass model (NMM) in exploring the insights into brain activities for epilepsy, in this paper, we aim to construct a model-driven approach to track the development of seizures using epileptic EEGs. We first propose a new time-delay Wendling model with sub-populations (TD-W-SP model) with respect to three aspects of improvements. Then we introduce a model-driven seizure tracking approach, where a model training method is designed based on extracted features from epileptic EEGs and a tracking index is defined as a function of the trained model parameters.

MAIN RESULTS

Numerical results on eight patients on CHB-MIT database demonstrate that our proposed method performs well in simulating epileptic-like EEGs as well as tracking the evolution of three stages (that is, from pre-ictal to ictal and from ictal to post-ictal) during a period of epileptic seizure.

SIGNIFICANCE

A useful attempt to track epileptic seizures by combining the NMM with the data analysis.

CMOS Ultralow Power Brain Signal Acquisition Front-Ends: Design and Human Testing

Two brain signal acquisition (BSA) front-ends incorporating two CMOS ultralow power, low-noise amplifier arrays and serializers operating in mosfet weak inversion region are presented. To boost the amplifier's gain for a given current budget, cross-coupled-pair active load topology is used in the first stages of these two amplifiers. These two BSA front-ends are fabricated in 130 and 180 nm CMOS processes, occupying 5.45 mm 2 and 0.352 mm 2 of die areas, respectively (excluding pad rings). The CMOS 130-nm amplifier array is comprised of 64 elements, where each amplifier element consumes 0.216 μW from 0.4 V supply, has input-referred noise voltage (IRNoise) of 2.19 μV[Formula: see text] corresponding to a power efficiency factor (PEF) of 11.7, and occupies 0.044 mm 2 of die area. The CMOS 180 nm amplifier array employs 4 elements, where each element consumes 0.69 μW from 0.6 V supply with IRNoise of 2.3 μV[Formula: see text] (corresponding to a PEF of 31.3) and 0.051 mm 2 of die area. Noninvasive electroencephalographic and invasive electrocorticographic signals were recorded real time directly on able-bodied human subjects, showing feasibility of using these analog front-ends for future fully implantable BSA and brain- computer interface systems.

Adaptive Laplacian filtering for sensorimotor rhythm-based brain-computer interfaces

OBJECTIVE

Sensorimotor rhythms (SMRs) are 8-30 Hz oscillations in the electroencephalogram (EEG) recorded from the scalp over sensorimotor cortex that change with movement and/or movement imagery. Many brain-computer interface (BCI) studies have shown that people can learn to control SMR amplitudes and can use that control to move cursors and other objects in one, two or three dimensions. At the same time, if SMR-based BCIs are to be useful for people with neuromuscular disabilities, their accuracy and reliability must be improved substantially. These BCIs often use spatial filtering methods such as common average reference (CAR), Laplacian (LAP) filter or common spatial pattern (CSP) filter to enhance the signal-to-noise ratio of EEG. Here, we test the hypothesis that a new filter design, called an 'adaptive Laplacian (ALAP) filter', can provide better performance for SMR-based BCIs.

APPROACH

An ALAP filter employs a Gaussian kernel to construct a smooth spatial gradient of channel weights and then simultaneously seeks the optimal kernel radius of this spatial filter and the regularization parameter of linear ridge regression. This optimization is based on minimizing the leave-one-out cross-validation error through a gradient descent method and is computationally feasible.

MAIN RESULTS

Using a variety of kinds of BCI data from a total of 22 individuals, we compare the performances of ALAP filter to CAR, small LAP, large LAP and CSP filters. With a large number of channels and limited data, ALAP performs significantly better than CSP, CAR, small LAP and large LAP both in classification accuracy and in mean-squared error. Using fewer channels restricted to motor areas, ALAP is still superior to CAR, small LAP and large LAP, but equally matched to CSP.

SIGNIFICANCE

Thus, ALAP may help to improve the accuracy and robustness of SMR-based BCIs.

Analysis of grey matter in thalamus and basal ganglia based on EEG α3/α2 frequency ratio reveals specific changes in subjects with mild cognitive impairment

GM (grey matter) changes of thalamus and basal ganglia have been demonstrated to be involved in AD (Alzheimer's disease). Moreover, the increase of a specific EEG (electroencephalogram) marker, α3/α2, have been associated with AD-converters subjects with MCI (mild cognitive impairment). To study the association of prognostic EEG markers with specific GM changes of thalamus and basal ganglia in subjects with MCI to detect biomarkers (morpho-physiological) early predictive of AD and non-AD dementia. Seventy-four adult subjects with MCI underwent EEG recording and high-resolution 3D MRI (three-dimensional magnetic resonance imaging). The α3/α2 ratio was computed for each subject. Three groups were obtained according to increasing tertile values of α3/α2 ratio. GM density differences between groups were investigated using a VBM (voxel-based morphometry) technique. Subjects with higher α3/α2 ratios when compared with subjects with lower and middle α3/α2 ratios showed minor atrophy in the ventral stream of basal ganglia (head of caudate nuclei and accumbens nuclei bilaterally) and of the pulvinar nuclei in the thalamus; The integrated analysis of EEG and morpho-structural markers could be useful in the comprehension of anatomo-physiological underpinning of the MCI entity.

A transition-constrained discrete hidden Markov model for automatic sleep staging

BACKGROUND

Approximately one-third of the human lifespan is spent sleeping. To diagnose sleep problems, all-night polysomnographic (PSG) recordings including electroencephalograms (EEGs), electrooculograms (EOGs) and electromyograms (EMGs), are usually acquired from the patient and scored by a well-trained expert according to Rechtschaffen & Kales (R&K) rules. Visual sleep scoring is a time-consuming and subjective process. Therefore, the development of an automatic sleep scoring method is desirable.

METHOD

The EEG, EOG and EMG signals from twenty subjects were measured. In addition to selecting sleep characteristics based on the 1968 R&K rules, features utilized in other research were collected. Thirteen features were utilized including temporal and spectrum analyses of the EEG, EOG and EMG signals, and a total of 158  hours of sleep data were recorded. Ten subjects were used to train the Discrete Hidden Markov Model (DHMM), and the remaining ten were tested by the trained DHMM for recognition. Furthermore, the 2-fold cross validation was performed during this experiment.

RESULTS

Overall agreement between the expert and the results presented is 85.29%. With the exception of S1, the sensitivities of each stage were more than 81%. The most accurate stage was SWS (94.9%), and the least-accurately classified stage was S1 (<34%). In the majority of cases, S1 was classified as Wake (21%), S2 (33%) or REM sleep (12%), consistent with previous studies. However, the total time of S1 in the 20 all-night sleep recordings was less than 4%.

CONCLUSION

The results of the experiments demonstrate that the proposed method significantly enhances the recognition rate when compared with prior studies.

Adverse rearing environments and neural development in children: the development of frontal electroencephalogram asymmetry

BACKGROUND

Children raised in institutional settings experience marked deprivation in social and environmental stimulation. This deprivation may disrupt brain development in ways that increase risk for psychopathology. Differential hemispheric activation of the frontal cortex is an established biological substrate of affective style that is associated with internalizing psychopathology. Previous research has never characterized the development of frontal electroencephalogram asymmetry in children or evaluated whether adverse rearing environments alter developmental trajectories.

METHODS

A sample of 136 children (mean age = 23 months) residing in institutions in Bucharest, Romania, and a sample of community control subjects (n = 72) participated. Half of institutionalized children were randomized to a foster care intervention. Electroencephalogram data were acquired at study entry and at ages 30, 42, and 96 months. A structured diagnostic interview of psychiatric disorders was completed at 54 months.

RESULTS

Children exhibited increases in right relative to left hemisphere frontal activation between the second and fourth years of life, followed by an increase in left relative to right hemisphere activation. Children reared in institutions experienced a prolonged period of increased right hemisphere activation and a blunted rebound in left frontal activation. Foster care placement was associated with improved developmental trajectories but only among children placed before 24 months. The development trajectory of frontal electroencephalogram asymmetry in early childhood predicted internalizing symptoms at 54 months.

CONCLUSIONS

Exposure to adverse rearing environments can alter brain development, culminating in heightened risk for psychopathology. Interventions delivered early in life have the greatest potential to mitigate the long-term effects of these environments.

Surgical decision making in temporal lobe epilepsy: a comparison of [(18)F]FDG-PET, MRI, and EEG

OBJECTIVES

The goals of this work were (1) to determine the effect of [(18)F]fluorodeoxyglucose positron emission tomography (FDG-PET), MRI, and EEG on the decision to perform temporal lobe epilepsy (TLE) surgery, and (2) to determine if FDG-PET, MRI, or EEG predicts surgical outcome.

METHODS

All PET scans ordered (2000-2010) for epilepsy or seizures were tabulated. Medical records were investigated to determine eligibility and collect data. Statistical analysis included odds ratios, κ statistics, univariate analysis, and logistic regression.

RESULTS

Of the 186 patients who underwent FDG-PET, 124 had TLE, 50 were surgical candidates, and 34 had surgery with post-operative follow-up. Median length of follow-up was 24 months. MRI, FDG-PET, and EEG were significant predictors of surgical candidacy (P<0.001) with odds ratios of 42.8, 20.4, and 6.3, respectively. FDG-PET was the only significant predictor of postoperative outcome (P<0.01).

CONCLUSION

MRI showed a trend toward having the most influence on surgical candidacy, but only FDG-PET predicted surgical outcome.

Delayed maturation in brain electrical activity partially explains the association between early environmental deprivation and symptoms of attention-deficit/hyperactivity disorder

BACKGROUND

Children raised in institutional settings are exposed to social and environmental circumstances that may deprive them of expected environmental inputs during sensitive periods of brain development that are necessary to foster healthy development. This deprivation is thought to underlie the abnormalities in neurodevelopment that have been found in previously institutionalized children. It is unknown whether deviations in neurodevelopment explain the high rates of developmental problems evident in previously institutionalized children, including psychiatric disorders.

METHODS

We present data from a sample of children raised in institutions in Bucharest, Romania (n = 117) and an age- and sex-matched sample of community control subjects (n = 49). Electroencephalogram data were acquired following entry into the study at age 6 to 30 months, and a structured diagnostic interview of psychiatric disorders was completed at age 54 months.

RESULTS

Children reared in institutions evidenced greater symptoms of attention-deficit/hyperactivity disorder, anxiety, depression, and disruptive behavior disorders than community controls. Electroencephalogram revealed significant reductions in alpha relative power and increases in theta relative power among children reared in institutions in frontal, temporal, and occipital regions, suggesting a delay in cortical maturation. This pattern of brain activity predicted symptoms of hyperactivity and impulsivity at age 54 months, and significantly mediated the association between institutionalization and attention-deficit/hyperactivity disorder symptoms. Electroencephalogram power was unrelated to depression, anxiety, or disruptive behaviors.

CONCLUSIONS

These findings document a potential neurodevelopmental mechanism underlying the association between institutionalization and psychiatric morbidity. Deprivation in social and environmental conditions may perturb early patterns of neurodevelopment and manifest as psychiatric problems later in life.

Quantitative complexity analysis in multi-channel intracranial EEG recordings form epilepsy brains

Epilepsy is a brain disorder characterized clinically by temporary but recurrent disturbances of brain function that may or may not be associated with destruction or loss of consciousness and abnormal behavior. Human brain is composed of more than 10 to the power 10 neurons, each of which receives electrical impulses known as action potentials from others neurons via synapses and sends electrical impulses via a sing output line to a similar (the axon) number of neurons. When neuronal networks are active, they produced a change in voltage potential, which can be captured by an electroencephalogram (EEG). The EEG recordings represent the time series that match up to neurological activity as a function of time. By analyzing the EEG recordings, we sought to evaluate the degree of underlining dynamical complexity prior to progression of seizure onset. Through the utilization of the dynamical measurements, it is possible to classify the state of the brain according to the underlying dynamical properties of EEG recordings. The results from two patients with temporal lobe epilepsy (TLE), the degree of complexity start converging to lower value prior to the epileptic seizures was observed from epileptic regions as well as non-epileptic regions. The dynamical measurements appear to reflect the changes of EEG's dynamical structure. We suggest that the nonlinear dynamical analysis can provide a useful information for detecting relative changes in brain dynamics, which cannot be detected by conventional linear analysis.