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Brain clocks, which quantify discrepancies between brain age and chronological age, hold promise for understanding brain health and disease. However, the impact of multimodal diversity (geographical, socioeconomic, sociodemographic, sex, neurodegeneration) on the brain age gap (BAG) is unknown. Here, we analyzed datasets from 5,306 participants across 15 countries (7 Latin American countries -LAC, 8 non-LAC). Based on higher-order interactions in brain signals, we developed a BAG deep learning architecture for functional magnetic resonance imaging (fMRI=2,953) and electroencephalography (EEG=2,353). The datasets comprised healthy controls, and individuals with mild cognitive impairment, Alzheimer's disease, and behavioral variant frontotemporal dementia. LAC models evidenced older brain ages (fMRI: MDE=5.60, RMSE=11.91; EEG: MDE=5.34, RMSE=9.82) compared to non-LAC, associated with frontoposterior networks. Structural socioeconomic inequality and other disparity-related factors (pollution, health disparities) were influential predictors of increased brain age gaps, especially in LAC (R2=0.37, F2=0.59, RMSE=6.9). A gradient of increasing BAG from controls to mild cognitive impairment to Alzheimer's disease was found. In LAC, we observed larger BAGs in females in control and Alzheimer's disease groups compared to respective males. Results were not explained by variations in signal quality, demographics, or acquisition methods. Findings provide a quantitative framework capturing the multimodal diversity of accelerated brain aging.

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Diversity in brain health is influenced by individual differences in demographics and cognition. However, most studies on brain health and diseases have typically controlled for these factors rather than explored their potential to predict brain signals. Here, we assessed the role of individual differences in demographics (age, sex, and education; n = 1298) and cognition (n = 725) as predictors of different metrics usually used in case-control studies. These included power spectrum and aperiodic (1/f slope, knee, offset) metrics, as well as complexity (fractal dimension estimation, permutation entropy, Wiener entropy, spectral structure variability) and connectivity (graph-theoretic mutual information, conditional mutual information, organizational information) from the source space resting-state EEG activity in a diverse sample from the global south and north populations. Brain-phenotype models were computed using EEG metrics reflecting local activity (power spectrum and aperiodic components) and brain dynamics and interactions (complexity and graph-theoretic measures). Electrophysiological brain dynamics were modulated by individual differences despite the varied methods of data acquisition and assessments across multiple centers, indicating that results were unlikely to be accounted for by methodological discrepancies. Variations in brain signals were mainly influenced by age and cognition, while education and sex exhibited less importance. Power spectrum activity and graph-theoretic measures were the most sensitive in capturing individual differences. Older age, poorer cognition, and being male were associated with reduced alpha power, whereas older age and less education were associated with reduced network integration and segregation. Findings suggest that basic individual differences impact core metrics of brain function that are used in standard case-control studies. Considering individual variability and diversity in global settings would contribute to a more tailored understanding of 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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Reading is essentially a two-channel function, requiring the integration of intact visual and auditory processes both peripheral and central. It is essential for normal reading that these component processes go forward automatically. Based on this model, Boder described three main subtypes of dyslexia: dysphonetic dyslexia (DD), dyseidetic, mixed and besides a fourth group defined non-specific reading delay (NSRD). The subtypes are identified by an algorithm that considers the reading quotient and the % of errors in the spelling test. Chiarenza and Bindelli have developed the Direct Test of Reading and Spelling (DTRS), a computerized, modified and validated version to the Italian language of the Boder test. The sample consisted of 169 subjects with DD and 36 children with NSRD. The diagnosis of dyslexia was made according to the DSM-V criteria. The DTRS was used to identify the dyslexia subtypes and the NSRD group. 2⁻5 min of artefact-free EEG (electroencephalogram), recorded at rest with eyes closed, according to 10⁻20 system were analyzed. Stability based Biomarkers identification methodology was applied to the DTRS and the quantitative EEG (QEEG). The reading quotients and the errors of the reading and spelling test were significantly different in the two groups. The DD group had significantly higher activity in delta and theta bands compared to NSRD group in the frontal, central and parietal areas bilaterally. The classification equation for the QEEG, both at the scalp and the sources levels, obtained an area under the robust Receiver Operating Curve (ROC) of 0.73. However, we obtained a discrimination equation for the DTRS items which did not participate in the Boder classification algorithm, with a specificity and sensitivity of 0.94 to discriminate DD from NSRD. These results demonstrate for the first time the existence of different neuropsychological and neurophysiological patterns between children with DD and children with NSRD. They may also provide clinicians and therapists warning signals deriving from the anamnesis and the results of the DTRS that should lead to an earlier diagnosis of reading delay, which is usually very late diagnosed and therefore, untreated until the secondary school level.

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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.

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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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BACKGROUND: Voxel-based morphometric (VBM) studies in neuromyelitis optica (NMO) have shown limited reproducibility. A previous study suggests that the number of optic neuritis (ON) attacks may be a confounding factor when comparing NMO patients with controls if it is not taken into account during VBM analysis. PURPOSE: To investigate the potential confounding effect of the number of ON attacks, for both tissue volumes and perfusion by voxel-based statistical analysis. MATERIAL AND METHODS: Volumetric magnetic resonance imaging (MRI) and perfusion SPECT were obtained from 15 controls and two patient subgroups: subgroup I was composed of nine patients with one or two ON attacks; and subgroup II of six patients with three or four ON attacks. We performed non-parametric voxel-based comparison of tissue volumes and perfusion between controls versus the two patient subgroups and for the whole patient group. RESULTS: Subgroup I presented no volume reductions, contrary to subgroup II that showed unequivocal reduction. We also found hypoperfusion in different brain regions in different subgroups. The results were quite different for the whole patient group. CONCLUSION: These findings highlight the confounding effect of the number of ON attacks, providing a new methodological insight that could explain the limited reproducibility of previous VBM studies in NMO.

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This study was a two-armed parallel group design aimed at testing real world effectiveness of a music therapy (MT) intervention for children with severe neurological disorders. The control group received only the standard neurorestoration program and the experimental group received an additional MT "Auditory Attention plus Communication protocol" just before the usual occupational and speech therapy. Multivariate Item Response Theory (MIRT) identified a neuropsychological status-latent variable manifested in all children and which exhibited highly significant changes only in the experimental group. Changes in brain plasticity also occurred in the experimental group, as evidenced using a Mismatch Event Related paradigm which revealed significant post intervention positive responses in the latency range between 308 and 400 ms in frontal regions. LORETA EEG source analysis identified prefrontal and midcingulate regions as differentially activated by the MT in the experimental group. Taken together, our results showing improved attention and communication as well as changes in brain plasticity in children with severe neurological impairments, confirm the importance of MT for the rehabilitation of patients across a wide range of dysfunctions.

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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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The aim of this experiment was to investigate the influence of musical expertise on the automatic perception of foreign syllables and harmonic sounds. Participants were Cuban students with high level of expertise in music or in visual arts and with the same level of general education and socio-economic background. We used a multi-feature Mismatch Negativity (MMN) design with sequences of either syllables in Mandarin Chinese or harmonic sounds, both comprising deviants in pitch contour, duration and Voice Onset Time (VOT) or equivalent that were either far from (Large deviants) or close to (Small deviants) the standard. For both Mandarin syllables and harmonic sounds, results were clear-cut in showing larger MMNs to pitch contour deviants in musicians than in visual artists. Results were less clear for duration and VOT deviants, possibly because of the specific characteristics of the stimuli. Results are interpreted as reflecting similar processing of pitch contour in speech and non-speech sounds. The implications of these results for understanding the influence of intense musical training from childhood to adulthood and of genetic predispositions for music on foreign language perception are discussed.

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Recent neuroimaging studies show that brain abnormalities in neuromyelitis optica (NMO) are more frequent than earlier described. Yet, more research considering multiple aspects of NMO is necessary to better understand these abnormalities. A clinical feature of relapsing NMO (RNMO) is that the incremental disability is attack-related. Therefore, association between the attack-related process and neuroimaging might be expected. On the other hand, the immunopathological analysis of NMO lesions has suggested that CNS microvasculature could be an early disease target, which could alter brain perfusion. Brain tissue volume changes accompanying perfusion alteration could also be expected throughout the attack-related process. The aim of this study was to investigate in RNMO patients, by voxel-based correlation analysis, the assumed associations between regional brain white (WMV) and grey matter volumes (GMV) and/or perfusion on one side, and the number of optic neuritis (ON) attacks, myelitis attacks and/or total attacks on the other side. For this purpose, high resolution T1-weighted MRI and perfusion SPECT imaging were obtained in 15 RNMO patients. The results showed negative regional correlations of WMV, GMV and perfusion with the number of ON attacks, involving important components of the visual system, which could be relevant for the comprehension of incremental visual disability in RNMO. We also found positive regional correlation of perfusion with the number of ON attacks, mostly overlapping the brain area where the WMV showed negative correlation. This provides evidence that brain microvasculature is an early disease target and suggests that perfusion alteration could be important in the development of brain structural abnormalities in RNMO.

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The Cuban Twin Registry is a nation-wide, prospective, population-based twin registry comprising all zygosity types and ages. It was initiated in 2004 to study genetic and environmental contributions to complex diseases with high morbidity and mortality in the Cuban population. The database contains extensive information from 55,400 twin pairs enrolled in the period 2004-2006. Additionally, 2,600 new multiple births have been included from 2007 to date. In the past 4 years, more than 130 studies have been carried out using the registry with a classical genetic epidemiological approach in which concordance rates for monozygotic and dizygotic twins and heritability of various disease traits were estimated. This article summarizes the history, registry's methodology, recent research findings, and future directions of work.

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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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This is the final paper in a Comments and Controversies series dedicated to "The identification of interacting networks in the brain using fMRI: Model selection, causality and deconvolution". We argue that discovering effective connectivity depends critically on state-space models with biophysically informed observation and state equations. These models have to be endowed with priors on unknown parameters and afford checks for model Identifiability. We consider the similarities and differences among Dynamic Causal Modeling, Granger Causal Modeling and other approaches. We establish links between past and current statistical causal modeling, in terms of Bayesian dependency graphs and Wiener-Akaike-Granger-Schweder influence measures. We show that some of the challenges faced in this field have promising solutions and speculate on future developments.

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Recently, a related morphometry-based connection concept has been introduced using local mean cortical thickness and volume to study the underlying complex architecture of the brain networks. In this article, the surface area is employed as a morphometric descriptor to study the concurrent changes between brain structures and to build binarized connectivity graphs. The statistical similarity in surface area between pair of regions was measured by computing the partial correlation coefficient across 186 normal subjects of the Cuban Human Brain Mapping Project. We demonstrated that connectivity matrices obtained follow a small-world behavior for two different parcellations of the brain gray matter. The properties of the connectivity matrices were compared to the matrices obtained using the mean cortical thickness for the same cortical parcellations. The topology of the cortical thickness and surface area networks were statistically different, demonstrating that both capture distinct properties of the interaction or different aspects of the same interaction (mechanical, anatomical, chemical, etc.) between brain structures. This finding could be explained by the fact that each descriptor is driven by distinct cellular mechanisms as result of a distinct genetic origin. To our knowledge, this is the first time that surface area is used to study the morphological connectivity of brain networks.

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