Characterization of the Sensorimotor Rhythm in 4-Month-Old Infants Born at Term and Premature.

The sensorimotor rhythm (SMR) is an electroencephalographic rhythm associated with motor and cognitive development observed in the central brain regions during wakefulness in the absence of movement, and it reacts contralaterally to generalized and hemibody movements. The purpose of this work was to characterize the SMR of 4-month-old infants, born either healthy at term or prematurely with periventricular leukomalacia (PVL). Two groups of infants were formed: healthy and premature with PVL. Their electroencephalograms (EEGs) were recorded in four conditions: rest, free movement, right-hand grasping and left-hand grasping, in order to explore general reactivity to free movement and contralateral reactivity in hand-grasping conditions. Associations between SMR, and cognitive and motor performance were analyzed. The healthy infants showed a SMR between 5.47 and 7.03 Hz, with clear contralateral reactivity to free movement and right-hand grasping. However, the premature infants with PVL did not show enough electroencephalographic characteristics to evidence the presence of SMR. Poor performance, characteristic of children with PVL, was related to low-frequency SMR, while good performance was associated with a higher frequency rhythm in the left hemisphere. The presence of SMR in the group of healthy infants could be considered a sign of health at this age. Thus, poor SMR evidence in the EEG of infants with PVL is probably a sign of brain immaturity or brain dysfunction. Our results provide data on infant SMR development that is needed to design neurofeedback protocols for infants with PVL.

Morphology-based hypothesis testing in discrete random fields: a non-parametric method to address the multiple-comparison problem in neuroimaging.

A new method for detecting activations in random fields, which may be useful for addressing the issue of multiple comparisons in neuroimaging, is presented. This method is based on some constructs of mathematical morphology--specifically, morphological erosions and dilations--that enable the detection of active regions in random fields possessing moderate activation levels and relatively large spatial extension, which may not be detected by the standard methods that control the family-wise error rate. The method presented here permits an appropriate control of the false positive errors, without having to adjust any threshold values, other than the significance level. The method is easily adapted to permutation-based procedures (with the usual restrictions), and therefore does not require strong assumptions about the distribution and spatio-temporal correlation structure of the data. Some examples of applications to synthetic data, including realistic fMRI simulations, as well as to real fMRI and electroencephalographic data are presented, illustrating the power of the presented technique. Comparisons with other methods that combine voxel intensity and cluster size, as well as some extensions of the method presented here based on their basic ideas are presented as well.

Morphology-based hypothesis testing in discrete random fields: a non-parametric method to address the multiple-comparison problem in neuroimaging.

A new method for detecting activations in random fields, which may be useful for addressing the issue of multiple comparisons in neuroimaging, is presented. This method is based on some constructs of mathematical morphology - specifically, morphological erosions and dilations - that enable the detection of active regions in random fields possessing moderate activation levels and relatively large spatial extension, which may not be detected by the standard methods that control the family-wise error rate. The method presented here permits an appropriate control of the false positive errors, without having to adjust any threshold values, other than the significance level. The method is easily adapted to permutation-based procedures (with the usual restrictions), and therefore does not require strong assumptions about the distribution and spatio-temporal correlation structure of the data. Some examples of applications to synthetic data, including realistic fMRI simulations, as well as to real fMRI and electroencephalographic data are presented, illustrating the power of the presented technique. Comparisons with other methods that combine voxel intensity and cluster size, as well as some extensions of the method presented here based on their basic ideas are presented as well.

Exploring the Links Between Biophilic and Restorative Qualities of Exterior and Interior Spaces in Leon, Guanajuato, Mexico.

The interactive role of the ecological, architectural, biophilic, and sensory qualities of outdoor and indoor spaces in the restorative experiences of urban inhabitants is little known. We analyzed the restorative influence on mood states and situational stress related to exposure to vegetation proportion, spatial extension, landmark salience, biophilic architecture, people density, street visual access, olfactory pleasantness, and noise of 65 public spaces in a Mexican city. The environmental qualities of these places were analyzed with multidimensional scaling (MDS), leading to eight space categories (e.g., historic squares with biophilic architecture, large parks, street scenes, and interiors with non-biophilic architecture). Ratings of the restorative potential, mood states, situational stress, olfactory pleasantness, and noise annoyance were evaluated on such places and modeled through a structural equation modeling (SEM). The model shows that the restorative influence of the environmental qualities on moods and stress was related to a decrease in experiences of negative moods and perceived stress, and an increase of positive mood states. Based on our findings, we discuss design guidelines, emphasizing the relevance of including vegetation and built elements with biophilic qualities to create restorative environments.

Health Sentinel: A mobile crowdsourcing platform for self-reported surveys provides early detection of COVID-19 clusters in San Luis Potosí, Mexico.

BACKGROUND: The Health Sentinel (Centinela de la Salud, CDS), a mobile crowdsourcing platform that includes the CDS app, was deployed to assess its utility as a tool for COVID-19 surveillance in San Luis Potosí, Mexico. METHODS: The CDS app allowed anonymized individual surveys of demographic features and COVID-19 risk of transmission and exacerbation factors from users of the San Luis Potosí Metropolitan Area (SLPMA). The platform's data processing pipeline computed and geolocalized the risk index of each user and enabled the analysis of the variables and their association. Point process analysis identified geographic clustering patterns of users at risk and these were compared with the patterns of COVID-19 cases confirmed by the State Health Services. RESULTS: A total of 1554 COVID-19 surveys were administered through the CDS app. Among the respondents, 50.4 % were men and 49.6 % women, with an average age of 33.5 years. Overall risk index frequencies were, in descending order: no-risk 77.8 %, low risk 10.6 %, respiratory symptoms 6.7 %, medium risk 1.4 %, high risk 2.0 %, very high risk 1.5 %. Comorbidity was the most frequent vulnerability category (32.4 %), followed by the inability to keep home lockdown (19.2 %). Statistically significant risk clusters identified at a spatial scale between 5 and 730 m coincided with those in neighborhoods containing substantial numbers of confirmed COVID-19 cases. CONCLUSIONS: The CDS platform enables the analysis of the sociodemographic features and spatial distribution of individual risk indexes of COVID-19 transmission and exacerbation. It is a useful epidemiological surveillance and early detection tool because it identifies statistically significant and consistent risk clusters in neighborhoods with a substantial number of confirmed COVID-19 cases.

Symbolic signatures for deformable shapes.

Recognizing classes of objects from their shape is an unsolved problem in machine vision that entails the ability of a computer system to represent and generalize complex geometrical information on the basis of a finite amount of prior data. A practical approach to this problem is particularly difficult to implement, not only because the shape variability of relevant object classes is generally large, but also because standard sensing devices used to capture the real world only provide a partial view of a scene, so there is partial information pertaining to the objects of interest. In this work, we develop an algorithmic framework for recognizing classes of deformable shapes from range data. The basic idea of our component-based approach is to generalize existing surface representations that have proven effective in recognizing specific 3D objects to the problem of object classes using our newly introduced symbolic-signature representation that is robust to deformations, as opposed to a numeric representation that is often tied to a specific shape. Based on this approach, we present a system that is capable of recognizing and classifying a variety of object shape classes from range data. We demonstrate our system in a series of large-scale experiments that were motivated by specific applications in scene analysis and medical diagnosis.

A maximum linear separation criterion for the analysis of neurophysiological data.

In this paper we propose an approach for the extraction of features that differentiate two populations or two experimental conditions in a neurophysiological experiment. These features consist of summarizing variables defined as total activity (e.g., total normalized log-power), computed over sets of sites in a discrete domain, such as the time-frequency-topography space. These sets are obtained as those that maximize the linear separation between the two populations, and the corresponding maps provide information that may complement that obtained by standard procedures, such as statistical parametric mapping. It is shown experimentally, using both simulated and real data, that the proposed approach may provide useful information even when the standard procedures fail, due to the conservative nature of the multiple comparison correction that must be applied in the later case.

Little evidence of association between severity of trigonocephaly and cognitive development in infants with single-suture metopic synostosis.

OBJECTIVES: To measure severity of trigonocephaly among infants with single-suture metopic craniosynostosis by using a novel shape descriptor, the trigonocephaly severity index (TSI), and to evaluate whether degree of trigonocephaly correlates with their neurodevelopmental test scores. METHODS: We conducted a multicenter cross-sectional and longitudinal study, identifying and recruiting 65 infants with metopic synostosis before their corrective surgery. We obtained computed tomography images for 49 infants and measured the presurgical TSI, a 3-dimensional outline-based cranial shape descriptor. We evaluated neurodevelopment by administering the Bayley Scales of Infant Development, Second Edition, and the Preschool Language Scale, Third Edition, before surgery and at 18 and 36 months of age. We fit linear regression models to estimate associations between test scores and TSI values adjusted for age at testing and race/ethnicity. We fit logistic regression models to estimate whether the odds of developmental delay were increased among children with more severe trigonocephaly. RESULTS: We observed little adjusted association between neurodevelopmental test scores and TSI values, and no associations that persisted at 3 years. Trigonocephaly was less severe among children referred at older ages. CONCLUSION: We observed little evidence of an association between the severity of trigonocephaly among metopic synostosis patients and their neurodevelopmental test scores. Detecting such a relationship with precision may require larger sample sizes or alternative phenotypic quantifiers. Until studies are conducted to explore these possibilities, it appears that although associated with the presence of metopic synostosis, the risk of developmental delays in young children is unrelated to further variation in trigonocephalic shape.

New severity indices for quantifying single-suture metopic craniosynostosis.

OBJECTIVE: To describe novel severity indices with which to quantify severity of trigonocephaly malformation in children diagnosed with isolated metopic synostosis. METHODS: Computed tomographic scans of the cranium were obtained from 38 infants diagnosed with isolated metopic synostosis and 53 age-matched control patients. Volumetric reformations of the cranium were used to trace two-dimensional planes defined by the cranium-base plane and well-defined brain landmarks. For each patient, novel trigonocephaly severity indices (TSI) were computed from outline cranium shapes on each of these planes. The metopic severity index based on measurements of interlandmark distances was also computed and a receiver operating characteristic analysis used to compare the accuracy of classification based on TSIs versus that based on the metopic severity index. RESULTS: The proposed TSIs are a sensitive measure of trigonocephaly malformation that can provide a classification accuracy of 96% with a specificity of 95%, in contrast with 82% of the metopic severity index at the same specificity level. CONCLUSIONS: We completed exploratory analysis of outline-based severity measurements computed from computed tomographic image planes of the cranium. These TSIs enable quantitative analysis of cranium features in isolated metopic synostosis that may not be accurately detected by analytic tools derived from a sparse set of traditional interlandmark and semilandmark distances.

Severity of skull malformation is unrelated to presurgery neurobehavioral status of infants with sagittal synostosis.

OBJECTIVE: To examine the association between severity of scaphocephalic skull malformation and neurodevelopmental status prior to cranioplasty. DESIGN AND PARTICIPANTS: Seventy-five infants with single-suture sagittal craniosynostosis (median age, 4.5 months) referred to the Infant Learning Project, a prospective, multisite, longitudinal study to evaluate neurocognitive development. MAIN OUTCOME MEASURES: Scaphocephaly severity indices were used to quantify synostotic skull shape from computed tomography scans. Infants were assessed with the mental (MDI) and motor scales (PDI) of the Bayley Scales of Infant Development and the receptive (PLS-AC) and expressive (PLS-EC) language scales of the Preschool Language Scale. RESULTS: No association between skull shape and neurodevelopmental status was found. CONCLUSIONS: Lack of association between skull shape and neurodevelopment in infancy may indicate that the presurgical degree of scaphocephaly has little or no direct effect on brain development. Alternatively, such relationships, if they exist, may be evident only at older ages. Finally, it also is possible that aspects of skull malformation not measured in this study may be related to neuropsychological functioning.

New scaphocephaly severity indices of sagittal craniosynostosis: a comparative study with cranial index quantifications.

OBJECTIVE: To describe a novel set of scaphocephaly severity indices (SSIs) for predicting and quantifying head- and skull-shape deformity in children diagnosed with isolated sagittal synostosis (ISS) and compare their sensitivity and specificity with those of the traditional cranial index (CI). METHODS: Computed tomography head scans were obtained from 60 patients diagnosed with ISS and 41 age-matched control patients. Volumetric reformations of the skull and overlying skin were used to trace two-dimensional planes defined in terms of skull-base plane and internal or surface landmarks. For each patient, novel SSIs were computed as the ratio of head width and length as measured on each of these planes. A traditional CI was also calculated and a receiver operating characteristic curve analysis was applied to compare the sensitivity and specificity of the proposed indices with those of CI. RESULTS: Although the CI is a sensitive measure of scaphocephaly, it is not specific and therefore not a suitable predictor of ISS in many practical applications. The SSI-A provides a specificity of 95% at a sensitivity level of 98%, in contrast with the 68% of CI. On average, the sensitivity and specificity of all proposed indices are superior to those of CI. CONCLUSIONS: Measurements of cranial width and length derived from planes that are defined in terms of internal or surface landmarks and skull-base plane produce SSIs that outperform traditional CI measurements.

A symbolic shaped-based retrieval of skull images.

In this work, we describe a novel symbolic representation of shapes for quantifying skull abnormalities in children with craniosynostosis. We show the efficacy of our work by demonstrating an application of this representation in shape-based retrieval of skull morphologies. This tool will enable correlation with potential pathogenesis and prognosis in order to enhance medical care.