*Hum Brain Mapp ; 41(4): 906-916, 2020 Mar.*

**| MEDLINE**| ID: mdl-32026600

##### RESUMO

Studies of the neural basis of intelligence have focused on comparing brain imaging variables with global scales instead of the cognitive domains integrating these scales or quotients. Here, the relation between mean tract-based fractional anisotropy (mTBFA) and intelligence indices was explored. Deterministic tractography was performed using a regions of interest approach for 10 white-matter fascicles along which the mTBFA was calculated. The study sample included 83 healthy individuals from the second wave of the Cuban Human Brain Mapping Project, whose WAIS-III intelligence quotients and indices were obtained. Inspired by the "Watershed model" of intelligence, we employed a regularized hierarchical Multiple Indicator, Multiple Causes model (MIMIC), to assess the association of mTBFA with intelligence scores, as mediated by latent variables summarizing the indices. Regularized MIMIC, used due to the limited sample size, selected relevant mTBFA by means of an elastic net penalty and achieved good fits to the data. Two latent variables were necessary to describe the indices: Fluid intelligence (Perceptual Organization and Processing Speed indices) and Crystallized Intelligence (Verbal Comprehension and Working Memory indices). Regularized MIMIC revealed effects of the forceps minor tract on crystallized intelligence and of the superior longitudinal fasciculus on fluid intelligence. The model also detected the significant effect of age on both latent variables.

*IEEE Trans Neural Syst Rehabil Eng ; 28(1): 181-190, 2020 01.*

**| MEDLINE**| ID: mdl-31751278

##### RESUMO

Analysis of joint motion data (AJMD) by Kinect, such as velocity, has been widely used in many research fields, many of which focused on how one joint moves with another, namely bivariate AJMD. However, these studies might not accurately reflect the motor symptoms in patients. The human body can be divided into six widely accepted parts (head, trunk and four limbs), which are interrelated and interact with each other. Therefore, in this study we attempted to investigate how the major joints of one body part move with the ones in another body part, namely multivariate AJMD. For method illustration, the motion data of sit-to-stand-to-sit for healthy participants and people with Parkinson disease (PD) were employed. Four types of multivariate AJMD were investigated by eigenspace-maximal-information-canonical-correlation-analysis, which obtained maximal- information-eigen-coefficients (MIECes), the parameters for quantifying the correlation between two sets of joints located in two different body parts. The results show that healthy participants have significantly higher MIECes than the PD patients (p-value < 0.0001). Furthermore, the area under the receiver operating characteristic curve value for the classification between healthy participants and PD patients reaches up to 1.00. In conclusion, we demonstrated the possibility of using multivariate AJMD for motion feature extraction, which may be helpful for medical research and engineering.

*Nat Neurosci ; 22(11): 1751-1760, 2019 11.*

**| MEDLINE**| ID: mdl-31611705

##### RESUMO

Cognition and behavior emerge from brain network interactions, such that investigating causal interactions should be central to the study of brain function. Approaches that characterize statistical associations among neural time series-functional connectivity (FC) methods-are likely a good starting point for estimating brain network interactions. Yet only a subset of FC methods ('effective connectivity') is explicitly designed to infer causal interactions from statistical associations. Here we incorporate best practices from diverse areas of FC research to illustrate how FC methods can be refined to improve inferences about neural mechanisms, with properties of causal neural interactions as a common ontology to facilitate cumulative progress across FC approaches. We further demonstrate how the most common FC measures (correlation and coherence) reduce the set of likely causal models, facilitating causal inferences despite major limitations. Alternative FC measures are suggested to immediately start improving causal inferences beyond these common FC measures.

##### Assuntos

Encéfalo/fisiologia , Neuroimagem Funcional/métodos , Modelos Neurológicos , Vias Neurais/fisiologia , Animais , Humanos , Estudos de Validação como Assunto*IEEE Trans Biomed Eng ; 2019 May 07.*

**| MEDLINE**| ID: mdl-31071012

##### RESUMO

We review the theory and algorithms of electrophysiological brain connectivity analysis. This tutorial is aimed at providing an introduction to brain functional connectivity from electrophysiological signals, including electroencephalography (EEG), magnetoencephalography (MEG), electrocorticography (ECoG), stereoelectroencephalography (SEEG). Various connectivity estimators are discussed, and algorithms introduced. Important issues for estimating and mapping brain functional connectivity with electrophysiology are discussed.

*Brain Topogr ; 32(4): 530-549, 2019 07.*

**| MEDLINE**| ID: mdl-31037477

##### RESUMO

Which reference is appropriate for the scalp ERP and EEG studies? This unsettled problem still inspires unceasing debate. The ideal reference should be the one with zero or constant potential but unfortunately it is well known that no point on the body fulfills this condition. Consequently, more than ten references are used in the present EEG-ERP studies. This diversity seriously undermines the reproducibility and comparability of results across laboratories. A comprehensive review accompanied by a brief communication with rigorous derivations and notable properties (Hu et al. Brain Topogr, 2019. https://doi.org/10.1007/s10548-019-00706-y ) is thus necessary to provide application-oriented principled recommendations. In this paper current popular references are classified into two categories: (1) unipolar references that construct a neutral reference, including both online unipolar references and offline re-references. Examples of unipolar references are the reference electrode standardization technique (REST), average reference (AR), and linked-mastoids/ears reference (LM); (2) non-unipolar references that include the bipolar reference and the Laplacian reference. We show that each reference is derived with a different assumption and serves different aims. We also note from (Hu et al. 2019) that there is a general form for the reference problem, the 'no memory' property of the unipolar references, and a unified estimator for the potentials at infinity termed as the regularized REST (rREST) which has more advantageous statistical evidence than AR. A thorough discussion of the advantages and limitations of references is provided with recommendations in the hope to clarify the role of each reference in the ERP and EEG practice.

##### Assuntos

Eletroencefalografia/métodos , Humanos , Reprodutibilidade dos Testes , Couro Cabeludo*Brain Topogr ; 32(4): 696-703, 2019 07.*

**| MEDLINE**| ID: mdl-30972605

##### RESUMO

In this brief communication, which complements the EEG reference review (Yao et al. in Brain Topogr, 2019), we provide the mathematical derivations that show: (1) any EEG reference admits the general form of a linear transformation of the ideal multichannel EEG potentials with reference to infinity; (2) the average reference (AR), the reference electrode standardization technique (REST), and its regularized version (rREST) are solving the linear inverse problems that can be derived from both the maximum likelihood estimate (MLE) and the Bayesian theory; however, REST is based on more informative prior/constraint of volume conduction than that of AR; (3) we show for the first time that REST is also a unipolar reference (UR), allowing us to define a general family of URs with unified notations; (4) some notable properties of URs are 'no memory', 'rank deficient by 1', and 'orthogonal projector centering'; (5) we also point out here, for the first time, that rREST provides the optimal interpolating function that can be used when the reference channel is missing or the 'bad' channels are rejected. The derivations and properties imply that: (a) any two URs can transform to each other and referencing with URs multiple times will not accumulate artifacts; (b) whatever URs the EEG data was previously transformed with, the minimum norm solution to the reference problem will be REST and AR with and without modeling volume conduction, respectively; (c) the MLE and the Bayesian theory show the theoretical optimality of REST. The advantages and limitations of AR and REST are discussed to guide readers for their proper use.

##### Assuntos

Algoritmos , Eletroencefalografia/métodos , Artefatos , Teorema de Bayes , Humanos , Funções Verossimilhança*IEEE Trans Biomed Eng ; 66(1): 225-236, 2019 01.*

**| MEDLINE**| ID: mdl-29993408

##### RESUMO

OBJECTIVE: A new approach, named bilateral motion data fusion, was proposed for the analysis of movement symmetry, which takes advantage of cross-information between both sides of the body and processes the unilateral motion data at the same time. METHODS: This was accomplished using canonical correlation analysis and joint independent component analysis. It should be noted that human movements include many categories, which cannot be enumerated one by one. Therefore, the gait rhythm fluctuations of the healthy subjects and patients with neurodegenerative diseases were employed as an example for method illustration. In addition, our model explains the movement data by latent parameters in the time and frequency domains, respectively, which were both based on bilateral motion data fusion. RESULTS: They show that our method not only reflects the physiological correlates of movement but also obtains the differential signatures of movement asymmetry in diverse neurodegenerative diseases. Furthermore, the latent variables also exhibit the potentials for sharper disease distinctions. CONCLUSION: We have provided a new perspective on movement analysis, which may prove to be a promising approach. SIGNIFICANCE: This method exhibits the potentials for effective movement feature extractions, which might contribute to many research fields such as rehabilitation, neuroscience, biomechanics, and kinesiology.

##### Assuntos

Análise da Marcha/métodos , Movimento/fisiologia , Análise de Ondaletas , Adulto , Idoso , Esclerose Amiotrófica Lateral/fisiopatologia , Humanos , Doença de Huntington/fisiopatologia , Pessoa de Meia-Idade , Caminhada/fisiologia , Adulto Jovem*Brain Topogr ; 32(4): 583-598, 2019 07.*

**| MEDLINE**| ID: mdl-29362974

##### RESUMO

Electrocorticography (ECoG) is an electrophysiological technique that records brain activity directly from the cortical surface with high temporal (ms) and spatial (mm) resolution. Its major limitations are in the high invasiveness and in the restricted field-of-view of the electrode grid, which partially covers the cortex. To infer brain activity at locations different from just below the electrodes, it is necessary to solve the electromagnetic inverse problem. Limitations in the performance of source reconstruction algorithms from ECoG have been, to date, only partially addressed in the literature, and a systematic evaluation is still lacking. The main goal of this study is to provide a quantitative evaluation of resolution properties of widely used inverse methods (eLORETA and MNE) for various ECoG grid sizes, in terms of localization error, spatial dispersion, and overall amplitude. Additionally, this study aims at evaluating how the use of simultaneous electroencephalography (EEG) affects the above properties. For these purposes, we take advantage of a unique dataset in which a monkey underwent a simultaneous recording with a 128 channel ECoG grid and an 18 channel EEG grid. Our results show that, in general conditions, the reconstruction of cortical activity located more than 1 cm away from the ECoG grid is not accurate, since the localization error increases linearly with the distance from the electrodes. This problem can be partially overcome by recording simultaneously ECoG and EEG. However, this analysis enlightens the necessity to design inverse algorithms specifically targeted at taking into account the limited field-of-view of the ECoG grid.

##### Assuntos

Mapeamento Encefálico/métodos , Eletrocorticografia/métodos , Eletroencefalografia/métodos , Algoritmos , Encéfalo/fisiologia , Eletrodos , Humanos*Brain Topogr ; 32(4): 643-654, 2019 07.*

**| MEDLINE**| ID: mdl-27905073

##### RESUMO

Many different analysis techniques have been developed and applied to EEG recordings that allow one to investigate how different brain areas interact. One particular class of methods, based on the linear parametric representation of multiple interacting time series, is widely used to study causal connectivity in the brain. However, the results obtained by these methods should be interpreted with great care. The goal of this paper is to show, both theoretically and using simulations, that results obtained by applying causal connectivity measures on the sensor (scalp) time series do not allow interpretation in terms of interacting brain sources. This is because (1) the channel locations cannot be seen as an approximation of a source's anatomical location and (2) spurious connectivity can occur between sensors. Although many measures of causal connectivity derived from EEG sensor time series are affected by the latter, here we will focus on the well-known time domain index of Granger causality (GC) and on the frequency domain directed transfer function (DTF). Using the state-space framework and designing two simulation studies we show that mixing effects caused by volume conduction can lead to spurious connections, detected either by time domain GC or by DTF. Therefore, GC/DTF causal connectivity measures should be computed at the source level, or derived within analysis frameworks that model the effects of volume conduction. Since mixing effects can also occur in the source space, it is advised to combine source space analysis with connectivity measures that are robust to mixing.

##### Assuntos

Eletroencefalografia/métodos , Mapeamento Encefálico/métodos , Simulação por Computador , Humanos*Front Neurosci ; 12: 595, 2018.*

**| MEDLINE**| ID: mdl-30233291

##### RESUMO

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.

*MEDICC Rev ; 20(2): 43-48, 2018 Apr.*

**| MEDLINE**| ID: mdl-29773777

##### RESUMO

Protein-energy malnutrition affects one in nine people worldwide and is most prevalent among children aged less than five years in low-income countries. Early childhood malnutrition can have damaging neurodevelopmental effects, with significant increases in cognitive, neurological and mental health problems over the lifespan, outcomes which can also extend to the next generation. This article describes a research collaboration involving scientists from five centers in Barbados, China, Cuba and the USA. It builds on longer-term joint work between the Barbados Nutrition Study (which, over a 45-year span, has extensively documented nutritional, health, behavioral, social and economic outcomes of individuals who experienced protein-energy malnutrition in the first year of life and healthy controls from the same classrooms and neighborhoods) and the Cuban Neuroscience Center (which has developed low-cost brain imaging methods that can be readily used in low income settings to identify biomarkers for early detection and treatment of adverse consequences of childhood malnutrition). This collaboration, which involved Barbadian, Cuban and US scientists began in the 1970s, when quantitative EEG techniques were applied to EEG data collected in 1977-78, at which time study participants were aged 5-11 years. These EEG records were never fully analyzed but were stored in New York and made available to this project in 2016. These data have now been processed and analyzed, comparing EEG findings in previously malnourished and control children, and have led to the identification of early biomarkers of long-term effects of early childhood protein-energy malnutrition. The next stage of the project will involve extending earlier work by collecting EEG recordings in the same individuals at ages 45-51 years, 40 years later, and comparing findings to earlier data and to these individuals' behavioral and cognitive outcomes. Quantitative EEG biomarkers of the effects of protein-energy malnutrition may help identify children at greatest risk for early malnutrition's adverse neurodevelopmental effects and inform development of targeted interventions to mitigate the long-term adverse effects of protein-energy malnutrition in developing countries. KEYWORDS Protein-energy malnutrition, electroencephalography, EEG, biomarkers, neurosciences, Barbados, Cuba, USA.

##### Assuntos

Biomarcadores , Desnutrição Proteico-Calórica/diagnóstico , Criança , Cuba , Eletroencefalografia , Humanos , Pessoa de Meia-Idade , Neurociências , Estados Unidos*Neuroimage ; 178: 370-384, 2018 09.*

**| MEDLINE**| ID: mdl-29746906

##### RESUMO

A Bayesian model for sparse, hierarchical, inver-covariance estimation is presented, and applied to multi-subject functional connectivity estimation in the human brain. It enables simultaneous inference of the strength of connectivity between brain regions at both subject and population level, and is applicable to fMRI, MEG and EEG data. Two versions of the model can encourage sparse connectivity, either using continuous priors to suppress irrelevant connections, or using an explicit description of the network structure to estimate the connection probability between each pair of regions. A large evaluation of this model, and thirteen methods that represent the state of the art of inverse covariance modelling, is conducted using both simulated and resting-state functional imaging datasets. Our novel Bayesian approach has similar performance to the best extant alternative, Ng et al.'s Sparse Group Gaussian Graphical Model algorithm, which also is based on a hierarchical structure. Using data from the Human Connectome Project, we show that these hierarchical models are able to reduce the measurement error in MEG beta-band functional networks by 10%, producing concomitant increases in estimates of the genetic influence on functional connectivity.

##### Assuntos

Encéfalo/fisiologia , Conectoma/métodos , Processamento de Imagem Assistida por Computador/métodos , Modelos Neurológicos , Rede Nervosa/fisiologia , Adulto , Algoritmos , Animais , Teorema de Bayes , Gatos , Conjuntos de Dados como Assunto , Feminino , Humanos , Macaca , Masculino , Vias Neurais/fisiologia , Adulto Jovem*Front Neurosci ; 12: 297, 2018.*

**| MEDLINE**| ID: mdl-29780302

##### RESUMO

The choice of reference for the electroencephalogram (EEG) is a long-lasting unsolved issue resulting in inconsistent usages and endless debates. Currently, both the average reference (AR) and the reference electrode standardization technique (REST) are two primary, apparently irreconcilable contenders. We propose a theoretical framework to resolve this reference issue by formulating both (a) estimation of potentials at infinity, and (b) determination of the reference, as a unified Bayesian linear inverse problem, which can be solved by maximum a posterior estimation. We find that AR and REST are very particular cases of this unified framework: AR results from biophysically non-informative prior; while REST utilizes the prior based on the EEG generative model. To allow for simultaneous denoising and reference estimation, we develop the regularized versions of AR and REST, named rAR and rREST, respectively. Both depend on a regularization parameter that is the noise to signal variance ratio. Traditional and new estimators are evaluated with this framework, by both simulations and analysis of real resting EEGs. Toward this end, we leverage the MRI and EEG data from 89 subjects which participated in the Cuban Human Brain Mapping Project. Generated artificial EEGs-with a known ground truth, show that relative error in estimating the EEG potentials at infinity is lowest for rREST. It also reveals that realistic volume conductor models improve the performances of REST and rREST. Importantly, for practical applications, it is shown that an average lead field gives the results comparable to the individual lead field. Finally, it is shown that the selection of the regularization parameter with Generalized Cross-Validation (GCV) is close to the "oracle" choice based on the ground truth. When evaluated with the real 89 resting state EEGs, rREST consistently yields the lowest GCV. This study provides a novel perspective to the EEG reference problem by means of a unified inverse solution framework. It may allow additional principled theoretical formulations and numerical evaluation of performance.

*J Neural Eng ; 15(2): 026013, 2018 Jan 25.*

**| MEDLINE**| ID: mdl-29368697

##### RESUMO

OBJECTIVE: Human scalp electroencephalogram (EEG) is widely applied in cognitive neuroscience and clinical studies due to its non-invasiveness and ultra-high time resolution. However, the representativeness of the measured EEG potentials for the underneath neural activities is still a problem under debate. This study aims to investigate systematically how both reference montage and electrodes setup affect the accuracy of EEG potentials. APPROACH: First, the standard EEG potentials are generated by the forward calculation with a single dipole in the neural source space, for eleven channel numbers (10, 16, 21, 32, 64, 85, 96, 128, 129, 257, 335). Here, the reference is the ideal infinity implicitly determined by forward theory. Then, the standard EEG potentials are transformed to recordings with different references including five mono-polar references (Left earlobe, Fz, Pz, Oz, Cz), and three re-references (linked mastoids (LM), average reference (AR) and reference electrode standardization technique (REST)). Finally, the relative errors between the standard EEG potentials and the transformed ones are evaluated in terms of channel number, scalp regions, electrodes layout, dipole source position and orientation, as well as sensor noise and head model. MAIN RESULTS: Mono-polar reference recordings are usually of large distortions; thus, a re-reference after online mono-polar recording should be adopted in general to mitigate this effect. Among the three re-references, REST is generally superior to AR for all factors compared, and LM performs worst. REST is insensitive to head model perturbation. AR is subject to electrodes coverage and dipole orientation but no close relation with channel number. SIGNIFICANCE: These results indicate that REST would be the first choice of re-reference and AR may be an alternative option for high level sensor noise case. Our findings may provide the helpful suggestions on how to obtain the EEG potentials as accurately as possible for cognitive neuroscientists and clinicians.

*Brain Connect ; 8(2): 57-59, 2018 03.*

**| MEDLINE**| ID: mdl-29212344

##### RESUMO

We comment on a recent article published in Brain Connectivity (Hatz et al., 2016 ) that combined electroencephalography (EEG) microstate analysis with the phase-locking index (PLI) and found that the test-retest reliability of connectivity patterns as obtained by the PLI increased when the data had been previously parcellated into microstates. Although we acknowledge the need to parcellate the continuous data into periods that supposedly correspond to transiently stable patterns of connectivity, we believe that the approach chosen by the authors is seriously mistaken. In particular, their approach disregards the particular a priori assumptions contained in each of the two methods that define connectivity in specific terms. Unfortunately, for microstate analyses and the PLI, these definitions are mutually exclusive, which makes attempts to draw any coherent conclusion in terms of comprehensibly interlinked biological processes meaningless. The occurrence of this type of problems should draw the attention to the importance of the particular methodological and conceptual features and limitations that come with the specific a priori assumptions contained in any quantifier of brain functional connectivity.

##### Assuntos

Mapeamento Encefálico , Eletroencefalografia , Atenção , Encéfalo , Reprodutibilidade dos Testes*Front Neurosci ; 11: 635, 2017.*

**| MEDLINE**| ID: mdl-29200994

##### RESUMO

The estimation of EEG generating sources constitutes an Inverse Problem (IP) in Neuroscience. This is an ill-posed problem due to the non-uniqueness of the solution and regularization or prior information is needed to undertake Electrophysiology Source Imaging. Structured Sparsity priors can be attained through combinations of (L1 norm-based) and (L2 norm-based) constraints such as the Elastic Net (ENET) and Elitist Lasso (ELASSO) models. The former model is used to find solutions with a small number of smooth nonzero patches, while the latter imposes different degrees of sparsity simultaneously along different dimensions of the spatio-temporal matrix solutions. Both models have been addressed within the penalized regression approach, where the regularization parameters are selected heuristically, leading usually to non-optimal and computationally expensive solutions. The existing Bayesian formulation of ENET allows hyperparameter learning, but using the computationally intensive Monte Carlo/Expectation Maximization methods, which makes impractical its application to the EEG IP. While the ELASSO have not been considered before into the Bayesian context. In this work, we attempt to solve the EEG IP using a Bayesian framework for ENET and ELASSO models. We propose a Structured Sparse Bayesian Learning algorithm based on combining the Empirical Bayes and the iterative coordinate descent procedures to estimate both the parameters and hyperparameters. Using realistic simulations and avoiding the inverse crime we illustrate that our methods are able to recover complicated source setups more accurately and with a more robust estimation of the hyperparameters and behavior under different sparsity scenarios than classical LORETA, ENET and LASSO Fusion solutions. We also solve the EEG IP using data from a visual attention experiment, finding more interpretable neurophysiological patterns with our methods. The Matlab codes used in this work, including Simulations, Methods, Quality Measures and Visualization Routines are freely available in a public website.

*Front Neurosci ; 11: 93, 2017.*

**| MEDLINE**| ID: mdl-28289373

##### RESUMO

Functional MRI (fMRI) studies have demonstrated that the rodent brain shows a default mode network (DMN) activity similar to that in humans, offering a potential preclinical model both for physiological and pathophysiological studies. However, the neuronal mechanism underlying rodent DMN remains poorly understood. Here, we used electrophysiological data to analyze the power spectrum and estimate the directed phase transfer entropy (dPTE) within rat DMN across three vigilance states: wakeful rest (WR), slow-wave sleep (SWS), and rapid-eye-movement sleep (REMS). We observed decreased gamma powers during SWS compared with WR in most of the DMN regions. Increased gamma powers were found in prelimbic cortex, cingulate cortex, and hippocampus during REMS compared with WR, whereas retrosplenial cortex showed a reverse trend. These changed gamma powers are in line with the local metabolic variation of homologous brain regions in humans. In the analysis of directional interactions, we observed well-organized anterior-to-posterior patterns of information flow in the delta band, while opposite patterns of posterior-to-anterior flow were found in the theta band. These frequency-specific opposite patterns were only observed in WR and REMS. Additionally, most of the information senders in the delta band were also the receivers in the theta band, and vice versa. Our results provide electrophysiological evidence that rat DMN is similar to its human counterpart, and there is a frequency-dependent reentry loop of anterior-posterior information flow within rat DMN, which may offer a mechanism for functional integration, supporting conscious awareness.

*Front Neurosci ; 11: 749, 2017.*

**| MEDLINE**| ID: mdl-29379411

##### RESUMO

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.

*IEEE Trans Biomed Eng ; 64(1): 52-60, 2017 01.*

**| MEDLINE**| ID: mdl-26955011

##### RESUMO

Previous studies have indicated that gait rhythm fluctuations are useful for characterizing certain pathologies of neurodegenerative diseases such as Huntington's disease (HD), amyotrophic lateral sclerosis (ALS), and Parkinson's disease (PD). However, no previous study has investigated the properties of frequency range distributions of gait rhythms. Therefore, in our study, empirical mode decomposition was implemented for decomposing the time series of gait rhythms into intrinsic mode functions from the high-frequency component to the low-frequency component sequentially. Then, Kendall's coefficient of concordance and the ratio for energy change for different IMFs were calculated, which were denoted as W and RE , respectively. Results revealed that the frequency distributions of gait rhythms in patients with neurodegenerative diseases are less homogeneous than healthy subjects, and the gait rhythms of the patients contain much more high-frequency components. In addition, parameters of W and RE can significantly differentiate among the four groups of subjects (HD, ALS, PD, and healthy subjects) (with the minimum p-value of 0.0000493). Finally, five representative classifiers were utilized in order to evaluate the possible capabilities of W and RE to distinguish the patients with neurodegenerative diseases from the healthy subjects. This achieved maximum area under the curve values of 0.949, 0.900, and 0.934 for PD, HD, and ALS detection, respectively. In sum, our study suggests that gait rhythm features extracted in the frequency domain should be given consideration seriously in the future neurodegenerative disease characterization and intervention.