Alexander Cerquera

Non-linear EEG analyses predict non-response to rTMS treatment in major depressive disorder

OBJECTIVE Several linear electroencephalographic (EEG) measures at baseline have been demonstrated to be associated with treatment outcome after antidepressant treatment. In this study we investigated the added value of non-linear EEG metrics in the alpha band in predicting treatment outcome to repetitive transcranial magnetic stimulation (rTMS). METHODS Subjects were 90 patients with major depressive disorder (MDD) and a group of 17 healthy controls (HC). MDD patients were treated with rTMS and psychotherapy for on average 21 sessions. Three non-linear EEG metrics (Lempel-Ziv Complexity (LZC); False Nearest Neighbors and Largest Lyapunov Exponent) were applied to the alpha band (7-13 Hz) for two 1-min epochs EEG and the association with treatment outcome was investigated. RESULTS No differences were found between a subgroup of unmedicated MDD patients and the HC. Non-responders showed a significant decrease in LZC from minute 1 to minute 2, whereas the responders and HC showed an increase in LZC. CONCLUSIONS There is no difference in EEG complexity between MDD and HC and the change in LZC across time demonstrated value in predicting outcome to rTMS. SIGNIFICANCE This is the first study demonstrating utility of non-linear EEG metrics in predicting treatment outcome in MDD.

Letters: How spurious correlations affect a correlation-based measure of spike timing reliability

The spike timing reliability of a neuron can be assessed via measuring the similarity of spike trains obtained in trials with repeated presentations of the same stimulus. Using a correlation-based measure of spike timing reliability we show that spurious correlations between independent Poisson spike trains can lead to a systematic misinterpretation to an extent that scales with the neural spike rate. Therefore, a correction is essential before comparing neurons with distinctly different spike rates. Such a comparison may, for instance, guide the choice of stimulus selective sensory neurons that are pooled for optimal stimulus reconstruction. We propose straightforward methods to abstract from these spurious correlations and demonstrate effects in an application to recorded spike trains of a retinal ganglion cell population.

Computer-Aided Detection of Microcalcifications in Digital Mammograms to Support Early Diagnosis of Breast Cancer

Early detection of microcalcifications in mammograms is considered one of the best tools to prevent breast cancer. Although traditionally this task have been performed with analog mammograms, digital mammograms are currently an alternative for examination of breast to detect microcalcifications and any other kind of breast abnormalities. Digital mammography presents some advantages in comparison to its analog counterpart, such as lower radiation dosage for acquisition and possibility to storage for telemedicine purposes. Nevertheless, digitalization entails loss of resolution and difficulties to detect microcalcifications. Therefore, several methods based on digital image processing have been proposed to perform detection of microcalcifications in digital mammograms, to support the early detection and prognosis of breast cancer. However, sometimes computer-aided methods fail due to the characteristics of certain microcalcifications that are hard to detect either by visual examination and by computerized analysis. For this reason, this work presents a method based on contrast enhancement and wavelet reconstruction oriented to increase the rate of computer-aided detected microcalcifications. The images correspond to the mini-MIAS database, which provides mammograms of healthy women and with breast microcalcifications, including the respective coordinates of their locations. The work includes also the application of the method in resolution-enhanced mammograms via sparse representation, with the aim to determine the role of resolution enhancement for a possible improvement in the performance of the method.

Nonlinear dynamics measures applied to EEG recordings of patients with Attention Deficit/Hyperactivity Disorder: Quantifying the effects of a neurofeedback treatment

This work presents the application of nonlinear dynamics measures to electroencephalograms (EEG) acquired from patients with Attention Deficit/Hyperactivity Disorder (ADHD) before and after a neurofeedback therapy, with the aim to assess the effects of the neurofeedback in a quantitative way. The database contains EEG registers of seven patients acquired in eyes-closed and eyes-opened conditions, in pre-and post-treatment phases. Five measures were applied: largest Lyapunov exponent, Lempel-Ziv complexity, Hurst exponent, and multiscale entropy on two different scales. The purpose is to test whether these measures are apt to detect and quantify differences from EEG registers between pre- and post-treatment. The results indicate that these measures could have a potential utility for detection of quantitative changes in specific EEG channels. In addition, the performance of some of these measures improved when the bandwidth was reduced to 3-30 Hz.

Comparing EEG Nonlinearity in Deficit and Nondeficit Schizophrenia Patients: Preliminary Data

Electroencephalogram (EEG) contains valuable information obtained noninvasively that can be used for assessment of brain’s processing capacity of patients with psychiatric disorders. The purpose of the present work was to evaluate possible differences in EEG complexity between deficit (DS) and nondeficit (NDS) subtypes of schizophrenia as a reflection of the cognitive processing capacities in these groups. A particular nonlinear metric known as Lempel-Ziv complexity (LZC) was used as a computational tool in order to determine the randomness in EEG alpha band time series from 3 groups (deficit schizophrenia [n = 9], nondeficit schizophrenia [n = 10], and healthy controls [n = 10]) according to time series randomness. There was a significant difference in frontal EEG complexity between the DS and NDS subgroups (p = .013), with DS group showing less complexity. A significant positive correlation was found between LZC values and Positive and Negative Syndrome Scale (PANSS) general psychopathology scores (ie, larger frontal EEG complexity correlated with more severe psychopathology), explained partially by the emotional component subscore of the PANSS. These findings suggest that cognitive processing occurring in the frontal networks in DS is less complex compared to NDS patients as reflected by EEG complexity measures. The data also suggest that there may be a relationship between the degree of emotionality and the complexity of the frontal EEG signal.

Transfer entropy to characterize brain-heart topology in sleep apnea patients treated with continuous positive airway pressure

Transfer entropy (TE) is a nonlinear metric employed recently in polysomnography (PSG) recordings to quantify the topological characteristics of the brain-heart physiological network. The present study applies the TE to evaluate its usefulness to identify quantitative differences in PSG registers of patients diagnosed with oclusive sleep apnea (OSA), before and after a continuous positive air pressure (CPAP) therapy. PSG recordings corresponding to 19 OSA patients were analysed under the rationale that the set of EEG subbands represents the sympathetic activity of the autonomic nervous system (ANS), and the high frequency component of the heart rate variability (HRV) represents the parasympathetic activity. The TE was computed based on a binning estimation and the results were analyzed via effect size calculation. The results showed that the sympathetic activity is increased in the presence of OSA, which is represented by the increased flow of information among brain subsystems and dropping to values close to zero during CPAP therapy. In contrast, the parasympathetic activity showed to be reduced in the presence of OSA and augmented during the CPAP therapy.

Classifying the motion of visual stimuli from the spike response of a population of retinal ganglion cells

We present an analysis of the spike response of a retinal ganglion cell ensemble. The retina of a turtle was stimulated in vitro by moving light patterns. Its non-steady motion was specified by two features: changes of direction and changes of speed. The spike response of a ganglion cell population was recorded extracellularly with a multielectrode array and responding neurons were identified through spike sorting. Restricting further analysis to a time window of greatest firing activity, we selected a subset of cells with reliable firing patterns, excluding cells that were not selective to the stimulus. The reliability of a firing pattern was assessed on the single cell level in terms of two measures: temporal precision (jitter) of the first spike and the fraction of trials in which a spike was generated. We then condensed the spike response of the extracted group by merging the multivariate spike trains into a single spike train. Finally, we compared different coding hypotheses that are based on the timing of the first and the second spike of the population or the spike count in the preselected time window. We found that the second spike of the population significantly increases the classification efficiency beyond that of the first spike. Moreover, the combination of first plus second spike is comparably efficient as the combination of the first spike plus the spike count but allows for a classification that is much faster.

Nonlinear Recurrent Dynamics and Long-Term Nonstationarities in EEG Alpha Cortical Activity: Implications for Choosing Adequate Segment Length in Nonlinear EEG Analyses:

Nonlinear analysis of EEG recordings allows detection of characteristics that would probably be neglected by linear methods. This study aimed to determine a suitable epoch length for nonlinear analysis of EEG data based on its recurrence rate in EEG alpha activity (electrodes Fz, Oz, and Pz) from 28 healthy and 64 major depressive disorder subjects. Two nonlinear metrics, Lempel-Ziv complexity and scaling index, were applied in sliding windows of 20 seconds shifted every 1 second and in nonoverlapping windows of 1 minute. In addition, linear spectral analysis was carried out for comparison with the nonlinear results. The analysis with sliding windows showed that the cortical dynamics underlying alpha activity had a recurrence period of around 40 seconds in both groups. In the analysis with nonoverlapping windows, long-term nonstationarities entailed changes over time in the nonlinear dynamics that became significantly different between epochs across time, which was not detected with the linear spectral analysis. Findings suggest that epoch lengths shorter than 40 seconds neglect information in EEG nonlinear studies. In turn, linear analysis did not detect characteristics from long-term nonstationarities in EEG alpha waves of control subjects and patients with major depressive disorder patients. We recommend that application of nonlinear metrics in EEG time series, particularly of alpha activity, should be carried out with epochs around 60 seconds. In addition, this study aimed to demonstrate that long-term nonlinearities are inherent to the cortical brain dynamics regardless of the presence or absence of a mental disorder.

Self-organizing Maps for Motor Tasks Recognition from Electrical Brain Signals

Recently, there has been a relevant progress and interest for brain–computer interface (BCI) technology as a potential channel of communication and control for the motor disabled, including post-stroke and spinal cord injury patients. Different mental tasks, including motor imagery, generate changes in the electro-physiological signals of the brain, which could be registered in a non-invasive way using electroencephalography (EEG). The success of the mental motor imagery classification depends on the choice of features used to characterize the raw EEG signals, and of the adequate classifier. As a novel alternative to recognize motor imagery tasks for EEG-based BCI, this work proposes the use of self-organized maps (SOM) for the classification stage. To do so, it was carried out an experiment aiming to predict three-class motor tasks (rest versus left motor imagery versus right motor imagery) utilizing spectral power-based features of recorded EEG signals. Three different pattern recognition algorithms were applied, supervised SOM, SOM+k-means and k-means, to classify the data offline. Best results were obtained with the SOM trained in a supervised way, where the mean of the performance was 77% with a maximum of 85% for all classes. Results indicate potential application for the development of BCIs systems.

Decoding of Imaginary Motor Movements of Fists Applying Spatial Filtering in a BCI Simulated Application

This work presents a study that evaluates different scenarios of preprocessing and processing of EEG registers, with the aim to predict fist imaginary movements utilizing the data of the EEG Motor Movement/Imaginary Dataset. Three types of imaginary fist movements have been decoded: sustained opening and closing of right fist, sustained opening and closing of left fist and rest. Initially, the registers were band-pass filtered to separate frequency ranges given by mu rhythms (7.5-12.5 Hz), beta rhythms (12.5-30 Hz), mu&beta rhythms, and a broad range of 0.5-30 Hz. Afterward, the signals of the separated subbands were epoched in time windows of 0-0.5, 0-1, 0-1.5 and 0-2 seconds, as well as preprocessed with two techniques of spatial filtering: common spatial patterns and independent component analysis. In both cases, a set of selected channels was established for feature extraction, by calculation of the logarithms of the variance in the time series corresponding to each preprocessed and selected channel. The classification stage was based on linear discriminant analysis and support vector machines. The results showed that the combination given by common spatial patterns and support vector machines allowed to reach a mean decoding accuracy close to 99.9%, where epoching and filtering to separate subbands did not influence the results in a noticeable way.