Lídice Galán-García

Wisconsin card sorting test synchronizes the prefrontal, temporal and posterior association cortex in different frequency ranges and extensions

Current findings show some brain regions consistently related to performance of the Wisconsin Card Sorting Test (WCST). An increase of local cerebral blood flow or metabolic demands has been detected in those regions. Functional integration of the neuronal circuits that subserve the task performance, based upon the identification of the oscillations and their distributed cerebral sources, has not been accomplished previously. The event‐related tonic oscillations within a period of 2,000 msec after the stimulus onset and the probable neural substrate were evaluated in healthy volunteers by variable‐resolution brain electrical tomography (VARETA). The WCST induced a significant increase of δ, θ, β‐2, and γ oscillations, but decrease of α. Areas such as the frontal subregions, temporal, cingulate, parahippocampal, parietal, occipitotemporal cortex, and occipital poles showed modified activity during the task, with EEG spectral band selectivity as well as some overlapping among them. Frontal and temporal regions generated the δ/θ oscillations. Additionally, the occipitotemporal and parietal regions were the source of the δ activity, lacking θ activation. The parietal region also showed tonic α, β‐2 and γ changes. These data imply that different processes have been simultaneously mediated during task performance. Relationships among the individual bands, the neural substrata and the specific cognitive process that support the task were established. The selectively distributed δ, θ, α, β‐2 and γ oscillations reflect communication networks through variable populations of neurons, with functional relations to the working memory functions and the information processing that subserve the WCST performance. Hum. Brain Mapping 17:37–47, 2002.

Multimodal Quantitative Neuroimaging Databases and Methods: the Cuban Human Brain Mapping Project

This article reviews the contributions of the Cuban Neuroscience Center to the evolution of the statistical parametric mapping (SPM) of quantitative Multimodal Neuroimages (qMN), from its inception to more recent work. Attention is limited to methods that compare individual qMN to normative databases (n/qMN). This evolution is described in three successive stages: (a) the development of one variant of normative topographical quantitative EEG (n/qEEG-top) which carries out statistical comparison of individual EEG spectral topographies with regard to a normative database — as part of the now popular SPM of brain descriptive parameters; (b) the development of n/qEEG tomography (n/qEEG-TOM), which employs brain electrical tomography (BET) to calculate voxelwise SPM maps of source spectral features with respect to a norm; (c) the development of a more general n/qMN by substituting EEG parameters with other neuroimaging descriptive parameters to obtain SPM maps. The study also describes the creation of Cuban normative databases, starting with the Cuban EEG database obtained in the early 90s, and more recently, the Cuban Human Brain Mapping Project (CHBMP). This project has created a 240 subject database of the normal Cuban population, obtained from a population-based random sample, comprising clinical, neuropsychological, EEG, MRI and SPECT data for the same subjects. Examples of clinical studies using qMN are given and, more importantly, receiver operator characteristics (ROC) analyses of the different developments document a sustained effort to assess the clinical usefulness of the techniques.

Electroencephalographic abnormalities in antisocial personality disorder

The presence of brain dysfunction in violent offenders has been frequently examined with inconsistent results. The aim of the study was to assess the EEG of 84 violent offenders by visual inspection and frequency-domain quantitative analysis in 84 violent prisoners. Low-resolution electromagnetic tomography (LORETA) was also employed for theta band of the EEG spectra. Antisocial personality disorder (ASPD) was present in 50 of the offenders and it was absent in the remaining 34. The prevalence of EEG abnormalities, by visual inspection, was similar for both the ASPD group (82%) and non-ASPD group (79%). The brain topography of these anomalies also did not differ between groups, in contrast to results of the EEG quantitative analysis (QEEG) and LORETA that showed remarkable regional differences between both groups. QEEG analysis showed a pattern of excess of theta-delta activities and decrease of alpha band on the right fronto-temporal and left temporo-parietal regions in the ASPD group. LORETA signified an increase of theta activity (5.08 Hz) in ASPD group relative to non-ASPD group within left temporal and parietal regions. Findings indicate that QEEG analysis and techniques of source localization may reveal differences in brain electrical activity among offenders with ASPD, which was not obvious to visual inspection.

3D Statistical Parametric Mapping of quiet sleep EEG in the first year of life

This paper extends previously developed 3D SPM for Electrophysiological Source Imaging (Bosch et al., 2001) for neonate EEG. It builds on a prior paper by our group that established age dependent means and standard deviations for the scalp EEG Broad Band Spectral Parameters of children in the first year of life. We now present developmental equations for the narrow band log spectral power of EEG sources, obtained from a sample of 93 normal neonates from age 1 to 10 months in quiet sleep. The main finding from these regressions is that EEG power from 0.78 to 7.5 Hz decreases with age and also for 45-50 Hz. By contrast, there is an increase with age in the frequency band of 19-32 Hz localized to parietal, temporal and occipital areas. Deviations from the norm were analyzed for normal neonates and 17 with brain damage. The diagnostic accuracy (measured by the area under the ROC curve) of EEG source SPM is 0.80, 0.69 for average reference scalp EEG SPM, and 0.48 for Laplacian EEG SPM. This superior performance of 3D SPM over scalp qEEG suggests that it might be a promising approach for the evaluation of brain damage in the first year of life.

QEEG and LORETA in Teenagers With Conduct Disorder and Psychopathic Traits

Background. Few studies have investigated the impact of the psychopathic traits on the EEG of teenagers with conduct disorder (CD). To date, there is no other research studying low-resolution brain electromagnetic tomography (LORETA) technique using quantitative EEG (QEEG) analysis in adolescents with CD and psychopathic traits. Objective. To find electrophysiological differences specifically related to the psychopathic traits. The current investigation compares the QEEG and the current source density measures between adolescents with CD and psychopathic traits and adolescents with CD without psychopathic traits. Methods. The resting EEG activity and LORETA for the EEG fast spectral bands were evaluated in 42 teenagers with CD, 25 with and 17 without psychopathic traits according to the Antisocial Process Screening Device. All adolescents were assessed using the DSM-IV-TR criteria. The EEG visual inspection characteristics and the use of frequency domain quantitative analysis techniques (narrow band spectral parameters) are described. Results. QEEG analysis showed a pattern of beta activity excess on the bilateral frontal-temporal regions and decreases of alpha band power on the left central-temporal and right frontal-central-temporal regions in the psychopathic traits group. Current source density calculated at 17.18 Hz showed an increase within fronto-temporo-striatal regions in the psychopathic relative to the nonpsychopathic traits group. Conclusions. These findings indicate that QEEG analysis and techniques of source localization may reveal differences in brain electrical activity among teenagers with CD and psychopathic traits, which was not obvious to visual inspection. Taken together, these results suggest that abnormalities in a fronto-temporo-striatal network play a relevant role in the neurobiological basis of psychopathic behavior.

Stable Sparse Classifiers Identify qEEG Signatures that Predict Learning Disabilities (NOS) Severity

In this paper, we present a novel methodology to solve the classification problem, based on sparse (data-driven) regressions, combined with techniques for ensuring stability, especially useful for high-dimensional datasets and small samples number. The sensitivity and specificity of the classifiers are assessed by a stable ROC procedure, which uses a non-parametric algorithm for estimating the area under the ROC curve. This method allows assessing the performance of the classification by the ROC technique, when more than two groups are involved in the classification problem, i.e., when the gold standard is not binary. We apply this methodology to the EEG spectral signatures to find biomarkers that allow discriminating between (and predicting pertinence to) different subgroups of children diagnosed as Not Otherwise Specified Learning Disabilities (LD-NOS) disorder. Children with LD-NOS have notable learning difficulties, which affect education but are not able to be put into some specific category as reading (Dyslexia), Mathematics (Dyscalculia), or Writing (Dysgraphia). By using the EEG spectra, we aim to identify EEG patterns that may be related to specific learning disabilities in an individual case. This could be useful to develop subject-based methods of therapy, based on information provided by the EEG. Here we study 85 LD-NOS children, divided in three subgroups previously selected by a clustering technique over the scores of cognitive tests. The classification equation produced stable marginal areas under the ROC of 0.71 for discrimination between Group 1 vs. Group 2; 0.91 for Group 1 vs. Group 3; and 0.75 for Group 2 vs. Group1. A discussion of the EEG characteristics of each group related to the cognitive scores is also presented.

Quantitative EEG Tomography of Early Childhood Malnutrition

The goal of this study is to identify the quantitative electroencephalographic (qEEG) signature of early childhood malnutrition [protein-energy malnutrition (PEM)]. To this end, archival digital EEG recordings of 108 participants in the Barbados Nutrition Study (BNS) were recovered and cleaned of artifacts (46 children who suffered an episode of PEM limited to the first year of life) and 62 healthy controls). The participants of the still ongoing BNS were initially enrolled in 1973, and EEGs for both groups were recorded in 1977–1978 (at 5–11 years). Scalp and source EEG Z-spectra (to correct for age effects) were obtained by comparison with the normative Cuban Human Brain Mapping database. Differences between both groups in the z spectra (for all electrode locations and frequency bins) were assessed by t-tests with thresholds corrected for multiple comparisons by permutation tests. Four clusters of differences were found: (a) increased theta activity (3.91–5.86 Hz) in electrodes T4, O2, Pz and in the sources of the supplementary motor area (SMA); b) decreased alpha1 (8.59–8.98 Hz) in Fronto-central electrodes and sources of widespread bilateral prefrontal are; (c) increased alpha2 (11.33–12.50 Hz) in Temporo-parietal electrodes as well as in sources in Central-parietal areas of the right hemisphere; and (d) increased beta (13.67–18.36 Hz), in T4, T5 and P4 electrodes and decreased in the sources of bilateral occipital-temporal areas. Multivariate Item Response Theory of EEGs scored visually by experts revealed a neurophysiological latent variable which indicated excessive paroxysmal and focal abnormality activity in the PEM group. A robust biomarker construction procedure based on elastic-net regressions and 1000-cross-validations was used to: (i) select stable variables and (ii) calculate the area under ROC curves (AUC). Thus, qEEG differentiate between the two nutrition groups (PEM vs Control) performing as well as visual inspection of the EEG scored by experts (AUC = 0.83). Since PEM is a global public health problem with lifelong neurodevelopmental consequences, our finding of consistent differences between PEM and controls, both in qualitative and quantitative EEG analysis, suggest that this technology may be a source of scalable and affordable biomarkers for assessing the long-term brain impact of early PEM.

F168. An EEG fingerprint of early protein-energy malnutrition

Introduction Early childhood Protein Energy Malnutrition (PEM) is an increasing worldwide phenomenon with lifelong neurodevelopmental consequences. There is thus a need for inexpensive imaging technologies to objectively identify and follow up the neural impact of malnutrition—Electroencephalography being an obvious choice. But EEG studies of PEM are scarce, performed on subjects with multiple stressors, only in the acute phase. A unique opportunity to improve these enquiries is the still ongoing Barbados Nutrition Study (BNS) which enrolled (1967–72) children with PEM during their first year of life. Under the direction of Frank Ramsey and E. Roy John, 248 digital EEG recordings were obtained (children 5–11 years) at the time that the Brain Research Lab and the Cuban Neuroscience Centre were developing quantitative EEG (qEEG; John et al., 1977). Recently, a large subsample of these digital EEGs was recovered. A unique opportunity to identify a qEEG fingerprint of early PEM has thus arisen, and which we here report. Methods The final sample comprised 46 PEM and 62 control recordings (1 min resting state, eyes-closed,19 electrodes 10/20 system, sampling 100 Hz). Qualitative EEG was evaluated using a Likert-type scale. Multivariate Item Response Theory identified a neurophysiological state (NS) as a single latent variable explaining 0.88 of sample variance. qEEG evaluation at the electrodes (topography) consisted in calculating the log-power spectrum both at the scalp electrodes and sources and computing the z transform with regard to the Cuban normative database. Quantitative tomographic EEG (qEEGt) was carried out with CNEURO’s VARETA source analysis procedure based upon an MNI probabilistic template—necessary since MRIs where not available at that time. Multivariate permutation tests ( N  = 1000) were applied to t -tests in order to assess differences between groups. Results Qualitative analysis revealed highly significant changes in the latent variable (NS) with the PEM group showing excessive slow-wave, paroxysmal and focal abnormality activity, with a statistically significant effect for groups ( p 15.2 Hz. qEEGT analysis: the PEM group, showed a significant increment in source power at lower frequencies ( Conclusion The consistent differences in qEEG and qEEGt values between PEM and controls suggest they may be affordable biomarkers for the long-term actual brain impact of early childhood PEM. Excess slow-waves activity and decreased alpha activity in PEM children, may be a qEEG fingerprint of early PEM predicting which is correlated with many types of neuropathology, learning and performance difficulties.

REMOVED: Temporal polar and anterior cingulate cortical thinning in violent psychopath offenders

This article has been removed: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our‐business/policies/article‐withdrawal)Ana Calzada-Reyes a,∗, Alfredo Alvarez-Amador b, Lídice Galán-García c, Mitchell Valdés-Sosa d a Department of Clinical Neurophysiology, Institute of Legal Medicine, Independence Avenue, Plaza, Havana City, Cuba b Department of Clinical Neurophysiology, Cuban Center of Neuroscience, 15202 25th Avenue, Playa, Havana City, Cuba c Department of Neurostatistic, Cuban Center of Neuroscience, 15202 25th Avenue, Playa, Havana City, Cuba d Department of Cognitive Neuroscience, Cuban Center of Neuroscience, 15202 25th Avenue, Playa, Havana City, Cuba