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Physical and cognitive decline at an older age is preceded by changes that accumulate over time until they become clinically evident difficulties. These changes, frequently overlooked by patients and health professionals, may respond better than fully established conditions to strategies designed to prevent disabilities and dependence in later life. The objective of this study was twofold; to provide further support for the need to screen for early functional changes in older adults and to look for an early association between decline in mobility and cognition. A cross-sectional cohort study was conducted on 95 active functionally independent community-dwelling older adults in Havana, Cuba. We measured their gait speed at the usual pace and the cognitive status using the MMSE. A value of 0.8 m/s was used as the cut-off point to decide whether they presented a decline in gait speed. A quantitative analysis of their EEG at rest was also performed to look for an associated subclinical decline in brain function. Results show that 70% of the sample had a gait speed deterioration (i.e., lower than 0.8 m/s), of which 80% also had an abnormal EEG frequency composition for their age. While there was no statistically significant difference in the MMSE score between participants with a gait speed above and below the selected cut-off, individuals with MMSE scores below 25 also had a gait speed<0.8 m/s and an abnormal EEG frequency composition. Our results provide further evidence of early decline in older adults-even if still independent and active-and point to the need for clinical pathways that incorporate screening and early intervention targeted at early deterioration to prolong the years of functional life in older age.

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PURPOSE: This study aimed to identify the most effective summary cognitive index predicted from spatio-temporal gait features (STGF) extracted from gait patterns. METHODS: The study involved 125 participants, including 40 young (mean age: 27.65 years, 50% women), and 85 older adults (mean age: 73.25 years, 62.35% women). The group of older adults included both healthy adults and those with Mild Cognitive Impairment (MCI). Participant´s performance in various cognitive domains was evaluated using 12 cognitive measures from five neuropsychological tests. Four summary cognitive indexes were calculated for each case: 1) the z-score of Mini-Mental State Examination (MMSE) from a population norm (MMSE z-score); 2) the sum of the absolute z-scores of the patients' neuropsychological measures from a population norm (ZSum); 3) the first principal component scores obtained from the individual cognitive variables z-scores (PCCog); and 4) the Mahalanobis distance between the vector that represents the subject's cognitive state (defined by the 12 cognitive variables) and the vector corresponding to a population norm (MDCog). The gait patterns were recorded using a body-fixed Inertial Measurement Unit while participants executed four walking tasks (normal, fast, easy- and hard-dual tasks). Sixteen STGF for each walking task, and the dual-task costs for the dual tasks (when a subject performs an attention-demanding task and walks at the same time) were computed. After applied Principal Component Analysis to gait measures (96 features), a robust regression was used to predict each cognitive index and individual cognitive variable. The adjusted proportion of variance (adjusted-R2) coefficients were reported, and confidence intervals were estimated using the bootstrap procedure. RESULTS: The mean values of adjusted-R2 for the summary cognitive indexes were as follows: 0.0248 for MMSE z-score, 0.0080 for ZSum, 0.0033 for PCCog, and 0.4445 for MDCog. The mean adjusted-R2 values for the z-scores of individual cognitive variables ranged between 0.0009 and 0.0693. Multiple linear regression was only statistically significant for MDCog, with the highest estimated adjusted-R2 value. CONCLUSIONS: The association between individual cognitive variables and most of the summary cognitive indexes with gait parameters was weak. However, the MDCog index showed a stronger and significant association with the STGF, exhibiting the highest value of the proportion of the variance that can be explained by the predictor variables. These findings suggest that the MDCog index may be a useful tool in studying the relationship between gait patterns and cognition.

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Background: Although gait patterns disturbances are known to be related to cognitive decline, there is no consensus on the possibility of predicting one from the other. It is necessary to find the optimal gait features, experimental protocols, and computational algorithms to achieve this purpose. Purposes: To assess the efficacy of the Stable Sparse Classifiers procedure (SSC) for discriminating young and healthy older adults (YA vs. HE), as well as healthy and cognitively impaired elderly groups (HE vs. MCI-E) from their gait patterns. To identify the walking tasks or combinations of tasks and specific spatio-temporal gait features (STGF) that allow the best prediction with SSC. Methods: A sample of 125 participants (40 young- and 85 older-adults) was studied. They underwent assessment with five neuropsychological tests that explore different cognitive domains. A summarized cognitive index (MDCog), based on the Mahalanobis distance from normative data, was calculated. The sample was divided into three groups (young adults, healthy and cognitively impaired elderly adults) using k-means clustering of MDCog in addition to Age. The participants executed four walking tasks (normal, fast, easy- and hard-dual tasks) and their gait patterns, measured with a body-fixed Inertial Measurement Unit, were used to calculate 16 STGF and dual-task costs. SSC was then employed to predict which group the participants belonged to. The classification's performance was assessed using the area under the receiver operating curves (AUC) and the stable biomarkers were identified. Results: The discrimination HE vs. MCI-E revealed that the combination of the easy dual-task and the fast walking task had the best prediction performance (AUC = 0.86, sensitivity: 90.1%, specificity: 96.9%, accuracy: 95.8%). The features related to gait variability and to the amplitude of vertical acceleration had the largest predictive power. SSC prediction accuracy was better than the accuracies obtained with linear discriminant analysis and support vector machine classifiers. Conclusions: The study corroborated that the changes in gait patterns can be used to discriminate between young and healthy older adults and more importantly between healthy and cognitively impaired adults. A subset of gait tasks and STGF optimal for achieving this goal with SSC were identified, with the latter method superior to other classification techniques.

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Purpose: To identify clinical phenotypes and biomarkers for best mortality prediction considering age, symptoms and comorbidities in COVID-19 patients with chronic neurological diseases in intensive care units (ICUs). Subjects and Methods: Data included 1252 COVID-19 patients admitted to ICUs in Cuba between January and August 2021. A k-means algorithm based on unsupervised learning was used to identify clinical patterns related to symptoms, comorbidities and age. The Stable Sparse Classifiers procedure (SSC) was employed for predicting mortality. The classification performance was assessed using the area under the receiver operating curve (AUC). Results: Six phenotypes using a modified v-fold cross validation for the k-means algorithm were identified: phenotype class 1, mean age 72.3 years (ys)-hypertension and coronary artery disease, alongside typical COVID-19 symptoms; class 2, mean age 63 ys-asthma, cough and fever; class 3, mean age 74.5 ys-hypertension, diabetes and cough; class 4, mean age 67.8 ys-hypertension and no symptoms; class 5, mean age 53 ys-cough and no comorbidities; class 6, mean age 60 ys-without symptoms or comorbidities. The chronic neurological disease (CND) percentage was distributed in the six phenotypes, predominantly in phenotypes of classes 3 (24.72%) and 4 (35,39%); χ² (5) 11.0129 p = 0.051134. The cerebrovascular disease was concentrated in classes 3 and 4; χ² (5) = 36.63, p = 0.000001. The mortality rate totaled 325 (25.79%), of which 56 (17.23%) had chronic neurological diseases. The highest in-hospital mortality rates were found in phenotypes 1 (37.22%) and 3 (33.98%). The SSC revealed that a neurological symptom (ageusia), together with two neurological diseases (cerebrovascular disease and Parkinson's disease), and in addition to ICU days, age and specific symptoms (fever, cough, dyspnea and chilliness) as well as particular comorbidities (hypertension, diabetes and asthma) indicated the best prediction performance (AUC = 0.67). Conclusions: The identification of clinical phenotypes and mortality biomarkers using practical variables and robust statistical methodologies make several noteworthy contributions to basic and experimental investigations for distinguishing the COVID-19 clinical spectrum and predicting mortality.

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Ictal semiology and brain single-photon emission computed tomography have been performed in approaching the epileptogenic zone in drug-resistant focal epilepsies. The authors aim to describe the brain structures involved in the ictal and interictal epileptogenic network from sequential semiology and brain perfusion quantitative patterns analysis. A sequential representation of seizures was performed (n = 15). A two-level analysis (individual and global) was carried out for the analysis of brain perfusion quantification and estimating network structures from the perfusion indexes. Most of the subjects started with focal seizures without impaired consciousness, followed by staring, automatisms, language impairments and evolution to a bilateral tonic-clonic seizure (temporal lobe and posterior quadrant epilepsy). Frontal lobe epilepsy seizures continued with upper limb clonus and evolution to bilateral tonic-clonic. The perfusion index of the epileptogenic zone ranged between 0.439-1.362 (mesial and lateral structures), 0.826-1.266 in dorsolateral frontal structures and 0.678-1.507 in the occipital gyrus. The interictal epileptogenic network proposed involved the brainstem and other subcortical structures. For the ictal state, it included the rectus gyrus, putamen and cuneus. The proposed methodology provides information about the brain structures in the neural networks in patients with drug-resistant focal epilepsies.

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The main goals of the present study were to replicate and extend current knowledge related to paralimbic dysfunctions associated with psychopathy. The research evaluated the quantitative electroencephalography, current density (CD) source and synchronization likelihood analysis during the rest condition and structural magnetic resonance imaging images to compare volumetric and cortical thickness, in inmates recruited from two prisons located in Havana City. The Psychopathy Checklist-Revised (PCL-R) was used as a quantitative measure of psychopathy. This study showed most beta energy and less alpha activity in male psychopath offenders. Low-resolution electromagnetic tomography signified an increase of beta activity in psychopath offender groups within paralimbic regions. The superior temporal gyrus volume was associated with the F1 factor while the fusiform, anterior cingulate and associative occipital areas were primarily associated with the F2 factor of PCL-R scale. Cortical thickness in the left dorsal anterior cingulate cortex and the temporal pole was negatively associated with PCL-R total score.

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To explore brain function using functional connectivity and network topology derived from electroencephalogram (EEG) in patients with pharmacoresistant epileptic encephalopathy with cannabidiol as adjunctive antiepileptic treatment. Sixteen epileptic patients participated in the study, six of whom had epileptic encephalopathy with a stable dose of cannabidiol Epidiolex (CBD) as adjunctive therapy. Functional connectivity derived from EEG was analyzed based on the synchronization likelihood (SL). The analysis also included reconstructing graph-theoretic measures from the synchronization matrix. Comparison of functional connectivity data between each pathological group with the control group was carried out using a nonparametric permutation test applied to SL values between pairs of electrodes for each frequency band. To compare the association patterns between graph-theoretical properties of each pathological group with the control group, Z Crawford was calculated as a measure of distance. There were differences between pairs of electrodes in all frequency bands evaluated in encephalopathy epileptic patients with CBD adjunctive therapy compared with the control (p < 0.05, permutation test). In the epileptic encephalopathy group without CBD therapy, the SL values were higher than in the control group for the beta, theta, and delta EEG frequency bands, and lower for the alpha frequency band. Interestingly, patients who had CBD as adjunctive therapy demonstrated greater synchronization for all frequency bands, showing less spatial distribution for alpha frequency compared with the control. When comparing both epileptic groups, those patients who had adjunctive CBD treatment also showed increased synchronization for all frequency bands. In epileptic encephalopathy with adjunctive CBD therapy, the pattern of differences for graph-theoretical measures according to Z Crawford indicated less segregation and greater integration suggesting a trend towards the random organization of the network principally for alpha and beta EEG bands. This exploratory study revealed a tendency to an overconnectivity with a random network topology mainly for fast EEG bands in epileptic encephalopathy patients using CBD adjunctive therapy. It can therefore be assumed that the CBD treatment could be related to inhibition of the transition of the interictal to ictal state and/or to the improvement of EEG organization and brain function.

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The Cuban Human Brain Mapping Project (CHBMP) repository is an open multimodal neuroimaging and cognitive dataset from 282 young and middle age healthy participants (31.9 ± 9.3 years, age range 18-68 years). This dataset was acquired from 2004 to 2008 as a subset of a larger stratified random sample of 2,019 participants from La Lisa municipality in La Habana, Cuba. The exclusion criteria included the presence of disease or brain dysfunctions. Participant data that is being shared comprises i) high-density (64-120 channels) resting-state electroencephalograms (EEG), ii) magnetic resonance images (MRI), iii) psychological tests (MMSE, WAIS-III, computerized go-no go reaction time), as well as iv,) demographic information (age, gender, education, ethnicity, handedness, and weight). The EEG data contains recordings with at least 30 minutes in duration including the following conditions: eyes closed, eyes open, hyperventilation, and subsequent recovery. The MRI consists of anatomical T1 as well as diffusion-weighted (DWI) images acquired on a 1.5 Tesla system. The dataset presented here is hosted by Synapse.org and available at https://chbmp-open.loris.ca .

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Introduction. Functional brain differences related to sex in psychopathic behavior represent an important field of neuroscience research; there are few studies on this area, mainly in offender samples. Objective. The aim of this study was to investigate the presence of electrophysiological differences between male and female psychopath offenders; specifically, we wanted to assess whether the results in quantitative EEG, low-resolution electromagnetic tomography (LORETA), and changes in synchronous brain activity could be related to sex influence. Sample and Methods. The study included 31 male and 12 female psychopath offenders, according to the Hare Psychopathy Checklist-Revised criteria from 2 prisons located in Havana City. The EEG visual inspection characteristics and the use of frequency domain quantitative analysis techniques are described. Results. The resting EEG visual analyses revealed a high percentage of EEG abnormalities in both studied groups. Significant statistical differences between the mean parameters of cross spectral measures between psychopathic offender groups were found in the beta band at bilateral frontal derivation and centroparietal areas. LORETA showed differences especially in the paralimbic and parieto-occipital areas Synchronization likelihood revealed a significant group effect in the 26 to 30 Hz band. These results indicate that combining quantitative EEG, LORETA analysis, and synchronization likelihood may improve the neurofunctional differentiation between psychopath offenders of both sexes.

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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.

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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.

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OBJETIVOS: en este trabajo nos propusimos identificar la relación entre las alteraciones electroencefalográficas y las lesiones vasculares cerebrales iniciales en pacientes hipertensos neurológicamente asintomáticos, con el uso de la escala GTE. MÉTODOS: se obtuvieron imágenes de resonancia magnética (0.35 Tesla, T1,T2, FLAIR) y electroencefalograma de 19 canales en 49 hipertensos asintomáticos, los cuales fueron divididos en 3 grupos: A, sin lesiones cerebrovasculares; B, con lesión vascular cerebral no clásica y C, con lesión cerebrovascular isquémica clásica. RESULTADOS: el grupo C mostró valores promedios superiores de la escala GTE (4,42) que el grupo B (3,36) y A (2,63) con una diferencia significativa de la mediana alcanzada por cada grupo (p=0,03). CONCLUSIÓN: las alteraciones electroencefalográficas en los pacientes de nuestro estudio están asociadas con los cambios cerebrovasculares inducidos por la hipertensión arterial. La escala GTE es un método simple que podría ser empleado en el pesquisaje de lesión cerebrovascular subclínica en pacientes hipertensos.

OBJECTIVES: Identify the relationship between electroencephalographic alterations and initial cerebrovascular lesions among neurologically asymptomatic hypertensive patients using the GTE scale. METHODS: Magnetic resonance (0.35 Tesla, T1, T2, FLAIR) and 19-channel electroencephalogram images were obtained from 49 asymptomatic hypertensives, who were divided into three groups: A, without cerebrovascular lesion; B, with non-classical cerebrovascular lesion; and C, with classical ischemic cerebrovascular lesion. RESULTS: Group C showed higher mean values on the GTE scale (4.42) than Groups B (3.36) and A (2.63), with a significant difference in the median for each group (p=0.03). CONCLUSION: The electroencephalographic alterations found in the patients studied are associated with cerebrovascular changes induced by arterial hypertension. The GTE scale is a simple method which could be used for the screening of subclinical cerebrovascular lesions among hypertensive patients.

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UNLABELLED: Previous EEG studies attempted to examine violent behavior as homogeneous construct. Up to date, there is no other research studying Low-Resolution Brain Electromagnetic Tomography (LORETA) technique in psychopath offenders. OBJECTIVE: To find electrophysiological differences specifically related to the psychopathy construct and independent of the violent behavior. The current investigation compares the QEEG and the current source density measures of violent psychopath offenders to a non-psychopath violent group. METHODS: The resting EEG activity and LORETA for the EEG spectral fast bands were evaluated in 58 violent offenders, 31 with and 27 without psychopathy according to the Hare Psychopathy Checklist - Revised. All subjects were assessed using the DSM IV-R 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 excess of beta activity on the left parieto-temporal regions and bilateral occipital areas and decrease of alpha band on the left centro-temporal and parieto-central derivations in the psychopath group. LORETA signified an increase of beta activity (17.18 Hz) in psychopath group relative to a non- psychopath group within fronto-temporo-limbic regions. CONCLUSIONS: These findings indicate that QEEG analysis and techniques of source localization may reveal differences in brain electrical activity among offenders with psychopathy, which was not obvious to visual inspection. Taken together, these results suggest that abnormalities in a fronto-temporo-limbic network play a relevant role in the neurobiological basis of psychopathy.

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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.

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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.

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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.

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Se estudiaron las bases neurales involucradas en la generación de los potenciales evocados auditivos de estado estable, obtenidos por estimulación con tonos modulados en amplitud a 40 hertzios de múltiples frecuencias, para lo cual se diseñó un protocolo de registro de alta resolución espacial, se aplicó el método bayesiano para estimar sus neurogeneradores y se definieron restricciones a priori para focalizar el cálculo inicial de las áreas de activación primaria al sistema auditivo. Los resultados logran activar fuentes generadoras a nivel del lóbulo temporal superior de ambos hemisferios; pero la activación cortical no es simétrica y se registra un efecto de lateralización interhemisférica, con activación del lóbulo temporal superior contralateral al oído estimulado en respuesta a tonos de 500 hertzios, mientras que para los tonos de 4 000 hertzios, el nivel de activación cortical es máximo a nivel del lóbulo temporal superior ipsilateral al oído estimulado. Los presentes resultados obtienen una nueva metodología para la evaluación del sistema auditivo, que puede ser aplicada en la caracterización de la reorganización cortical y neuroplasticidad en hipoacusias neurosensoriales severas y otras deprivaciones sensoriales.

The involved neural bases were studied in the generation of the auditory steady state response, obtained by stimulation with 40 hertz multiple frequency amplitude modulate tones, for which a high space resolution registration protocol was designed, the Bayes`method was applied to estimate their neurogenerators and restrictions were previously defined to focus the initial calculation from the primary activation areas to the auditory system. The results are able to detect generating sources at the level of the upper temporal lobe of both hemispheres; but the cortical activation is not symmetrical and an interhemispheric lateralization effect is registered, with activation of the contralateral upper temporal lobe to the stimulated ear as a response to tones of 500 hertz, while for the tones of 4 000 hertz, the cortical activation level is higher at the level of the ipsilateral upper temporal lobe to the stimulated ear. The present results obtain a new methodology for the evaluation of the auditory system that can be applied in the characterization of the cortical reorganization and neuroplasticity in severe neurosensory hearing loss and other sensory deprivations.

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INTRODUCTION AND OBJECTIVES: The aim of this study was to examine the efficiency of the averaging technique for estimating multiple auditory steady state responses in normal hearing subjects and to provide quantifiable stopping criteria at near-threshold intensities. METHODS: Multiple amplitude-modulated (89-115 Hz) tones (500, 1,000, 2,000 and 4,000 Hz) were simultaneously presented to both ears at a fixed intensity of 40 dB HL. A total of 128 data epochs were averaged (23.9 minutes). RESULTS: The results showed that "classic" ensemble averaging, although accurate and time-efficient in most cases, could not extract all near-threshold MSSR from noise, even after recording a considerable number of sweeps. The present study also proposed a different approach to evaluate the background noise based on evaluating the mean of the variance close to the signal. CONCLUSIONS: The study proposed quantitative parameters to establish stopping criteria during auditory steady-state recordings.

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Evidence for interregional structural asymmetries has been previously reported for brain anatomic regions supporting well-described functional lateralization. Here, we aimed to investigate whether the two brain hemispheres demonstrate dissimilar general structural attributes implying different principles on information flow management. Common left hemisphere/right hemisphere structural network properties are estimated and compared for right-handed healthy human subjects and a nonhuman primate, by means of 3 different diffusion-weighted magnetic resonance imaging fiber tractography algorithms and a graph theory framework. In both the human and the nonhuman primate, the data support the conclusion that, in terms of the graph framework, the right hemisphere is significantly more efficient and interconnected than the left hemisphere, whereas the left hemisphere presents more central or indispensable regions for the whole-brain structural network than the right hemisphere. From our point of view, in terms of functional principles, this pattern could be related with the fact that the left hemisphere has a leading role for highly demanding specific process, such as language and motor actions, which may require dedicated specialized networks, whereas the right hemisphere has a leading role for more general process, such as integration tasks, which may require a more general level of interconnection.

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