Electroencephalographic characterization of subgroups of children with learning disorders.
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ABSTRACT:
SUBMITTER: Roca-Stappung M
PROVIDER: S-EPMC5510811 | biostudies-other | 2017
REPOSITORIES: biostudies-other
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Project description:IntroductionA large number of studies have investigated neural correlates of consciousness in adults. However, knowledge about brain function in children with disorders of consciousness (DOC) is very limited. We suggest that EEG recordings during sleep are a promising approach. In healthy adults as well as in children, it has been shown that the activity of sleep slow waves (EEG spectral power 1-4.5 Hz), the primary characteristic of deep sleep, is dependent on use during previous wakefulness. Thus the regulation of slow wave activity (SWA) provides indirect insights into brain function during wakefulness.MethodsIn the present study, we investigated high-density EEG recordings during sleep in ten healthy children and in ten children with acquired brain injury, including five children with DOC and five children with acquired brain injury without DOC. We used the build-up of SWA to quantify SWA regulation.ResultsChildren with DOC showed a global reduction in the SWA build-up when compared to both, healthy children and children with acquired brain injury without DOC. This reduction was most pronounced over parietal brain areas. Comparisons within the group of children with DOC revealed that the parietal SWA build-up was the lowest in patients showing poor outcome. Longitudinal measurements during the recovery period showed an increase in parietal SWA build-up from the first to the second sleep recording.ConclusionsOur results suggest that the reduced parietal SWA regulation may represent a characteristic topographical marker for brain network dysfunction in children with DOC. In the future, the regulation of SWA might be used as a complementary assessment in adult and paediatric patients with DOC.
| S-EPMC4827803 | biostudies-literature
Project description:BackgroundAffective lability is elevated and associated with increased clinical burden in psychosis spectrum disorders. The extent to which the level, structure and dispersion of affective lability varies between the specific disorders included in the psychosis spectrum is however unclear. To have potential value as a treatment target, further characterization of affective lability in these populations is necessary. The main aim of our study was to investigate differences in the architecture of affective lability in different psychosis spectrum disorders, and if putative differences remained when we controlled for current symptom status.MethodsAffective lability was measured with The Affective Lability Scale Short Form (ALS-SF) in participants with schizophrenia (SZ, n = 76), bipolar I disorder (BD-I, n = 105), bipolar II disorder (BD-II, n = 68) and a mixed psychosis-affective group (MP, n = 48). Multiple analyses of covariance were conducted to compare the ALS-SF total and subdimension scores of the diagnostic groups, correcting for current psychotic, affective and anxiety symptoms, substance use and sex. Double generalized linear models were performed to compare the dispersion of affective lability in the different groups.ResultsOverall group differences in affective lability remained significant after adjusting for covariates (p = .001). BD-II had higher affective lability compared to SZ and BD-I (p = .004), with no significant differences between SZ and BD-I. There were no significant differences in the contributions of ALS-SF dimensions to the total affective lability or in dispersion of affective lability between the groups.ConclusionsThis study provides the construct of affective lability in psychosis spectrum disorders with more granular details that may have implications for research and clinical care. It demonstrates that despite overlap in core symptom profiles, BD-I is more similar to SZ than it is to BD-II concerning affective lability and the BD groups should consequently be studied apart. Further, affective lability appears to be characterized by fluctuations between depressive- and other affective states across different psychosis spectrum disorders, indicating that affective lability may be related to internalizing problems in these disorders. Finally, although the level varies between groups, affective lability is evenly spread and not driven by extremes across psychosis spectrum disorders and should be assessed irrespective of diagnosis.
| S-EPMC8566621 | biostudies-literature
Project description:Background:Some children appear to not hear well in class despite normal hearing sensitivity. These children may be referred for auditory processing disorder (APD) assessment but can also have attention, language, and/or reading disorders. Despite presenting with similar concerns regarding hearing difficulties in difficult listening conditions, the overall profile of deficits can vary in children with suspected or confirmed APD. The current study used cluster analysis to determine whether subprofiles of difficulties could be identified within a cohort of children presenting for auditory processing assessment. Methods:Ninety school-aged children (7-13 years old) with suspected APDs were included in a cluster analysis. All children had their reading, language, cognition and auditory processing assessed. Parents also completed the Children's Auditory Performance Scale (CHAPS). Cluster analysis was based on tasks where age-norms were available, including word reading (Castles and Coltheart irregular and non-words test), phonological awareness (Queensland University Inventory of Literacy), language [Comprehensive Language of Assessment-4, Comprehensive Assessment of Spoken Language (CASL)], sustained attention (Continuous Performance Test), working memory (digits forward and backward), and auditory processing [Frequency Pattern Test (FPT), Dichotic Digits Test (DDT)]. Hierarchical cluster analysis was undertaken to determine the optimal number of clusters for the data, followed by a k-means cluster analysis. Results:Hierarchical cluster analysis suggested a four-group solution. The four subgroups can be summarized as follows: children with (1) global deficits, n = 35; (2) poor auditory processing with good word reading and phonological awareness skills, n = 22; (3) poor auditory processing with poor attention and memory but good language skills, n = 15; and (4) poor auditory processing and attention with good memory skills, n = 18. Conclusion:The cluster analysis identified distinct subgroups of children. These subgroups display the variation in areas of difficulty observed across different studies in the literature (e.g., not every child with APD has an attention deficit), highlighting the heterogeneous nature of APD and the need to assess a range of skills in children with suspected APD. It would be valuable for future studies to independently verify these subgroups and to determine whether interventions can be optimized based on these subgroups.
| S-EPMC6872645 | biostudies-literature
Project description:BackgroundElectroencephalography (EEG) is best suited for long-term monitoring of brain functions in patients with disorders of consciousness (DOC). Mathematical tools are needed to facilitate efficient interpretation of long-duration sleep-wake EEG recordings.MethodsStarting with matching pursuit (MP) decomposition, we automatically detect and parametrize sleep spindles, slow wave activity, K-complexes and alpha, beta and theta waves present in EEG recordings, and automatically construct profiles of their time evolution, relevant to the assessment of residual brain function in patients with DOC.ResultsAbove proposed EEG profiles were computed for 32 patients diagnosed as minimally conscious state (MCS, 20 patients), vegetative state/unresponsive wakefulness syndrome (VS/UWS, 11 patients) and Locked-in Syndrome (LiS, 1 patient). Their interpretation revealed significant correlations between patients' behavioral diagnosis and: (a) occurrence of sleep EEG patterns including sleep spindles, slow wave activity and light/deep sleep cycles, (b) appearance and variability across time of alpha, beta, and theta rhythms. Discrimination between MCS and VS/UWS based upon prominent features of these profiles classified correctly 87% of cases.ConclusionsProposed EEG profiles offer user-independent, repeatable, comprehensive and continuous representation of relevant EEG characteristics, intended as an aid in differentiation between VS/UWS and MCS states and diagnostic prognosis. To enable further development of this methodology into clinically usable tests, we share user-friendly software for MP decomposition of EEG (http://braintech.pl/svarog) and scripts used for creation of the presented profiles (attached to this article).
| S-EPMC3819687 | biostudies-literature
Project description:INTRODUCTION:Previous reports in European populations demonstrated the existence of five data-driven adult-onset diabetes subgroups. Here, we use self-normalizing neural networks (SNNN) to improve reproducibility of these data-driven diabetes subgroups in Mexican cohorts to extend its application to more diverse settings. RESEARCH DESIGN AND METHODS:We trained SNNN and compared it with k-means clustering to classify diabetes subgroups in a multiethnic and representative population-based National Health and Nutrition Examination Survey (NHANES) datasets with all available measures (training sample: NHANES-III, n=1132; validation sample: NHANES 1999-2006, n=626). SNNN models were then applied to four Mexican cohorts (SIGMA-UIEM, n=1521; Metabolic Syndrome cohort, n=6144; ENSANUT 2016, n=614?and CAIPaDi, n=1608) to characterize diabetes subgroups in Mexicans according to treatment response, risk for chronic complications and risk factors for the incidence of each subgroup. RESULTS:SNNN yielded four reproducible clinical profiles (obesity related, insulin deficient, insulin resistant, age related) in NHANES and Mexican cohorts even without C-peptide measurements. We observed in a population-based survey a high prevalence of the insulin-deficient form (41.25%, 95%?CI 41.02% to 41.48%), followed by obesity-related (33.60%, 95%?CI 33.40% to 33.79%), age-related (14.72%, 95%?CI 14.63% to 14.82%) and severe insulin-resistant groups. A significant association was found between the SLC16A11 diabetes risk variant and the obesity-related subgroup (OR 1.42, 95%?CI 1.10 to 1.83, p=0.008). Among incident cases, we observed a greater incidence of mild obesity-related diabetes (n=149, 45.0%). In a diabetes outpatient clinic cohort, we observed increased 1-year risk (HR 1.59, 95%?CI 1.01 to 2.51) and 2-year risk (HR 1.94, 95%?CI 1.13 to 3.31) for incident retinopathy in the insulin-deficient group and decreased 2-year diabetic retinopathy risk for the obesity-related subgroup (HR 0.49, 95%?CI 0.27 to 0.89). CONCLUSIONS:Diabetes subgroup phenotypes are reproducible using SNNN; our algorithm is available as web-based tool. Application of these models allowed for better characterization of diabetes subgroups and risk factors in Mexicans that could have clinical applications.
| S-EPMC7380860 | biostudies-literature
Project description:Children with specific learning disorders (SLDs) face a unique set of socio-emotional challenges as a result of their academic difficulties. Although a higher prevalence of anxiety in children with SLD is often reported, there is currently no research on cognitive mechanisms underlying this anxiety. One way to elucidate these mechanisms is to investigate attentional bias to threatening stimuli using a dot-probe paradigm. Our study compared children ages 9-16 with SLD (n = 48) to typically-developing (TD) controls (n = 33) on their attentional biases to stimuli related to general threats, reading, and stereotypes of SLD. We found a significant threat bias away from reading-related stimuli in the SLD, but not TD group. This attentional bias was not observed with the general threat and stereotype stimuli. Further, children with SLD reported greater anxiety compared to TD children. These results suggest that children with SLD experience greater anxiety, which may partially stem from reading specifically. The finding of avoidance rather than vigilance to reading stimuli indicates the use of more top-down attentional control. This work has important implications for therapeutic approaches to anxiety in children with SLD and highlights the need for attention to socio-emotional difficulties in this population. Future research is needed to further investigate the cognitive aspects of socio-emotional difficulties in children with SLD, as well as how this may impact academic outcomes.
| S-EPMC6639079 | biostudies-literature
Project description:Learning is assumed to induce specific changes in neuronal activity during sleep that serve the consolidation of newly acquired memories. To specify such changes, we measured electroencephalographic (EEG) coherence during performance on a declarative learning task (word pair associations) and subsequent sleep. Compared with a nonlearning control condition, learning performance was accompanied with a strong increase in coherence in several EEG frequency bands. During subsequent non-rapid eye movement sleep, coherence only marginally increased in a global analysis of EEG recordings. However, a striking and robust increase in learning-dependent coherence was found when analyses were performed time-locked to the occurrence of slow oscillations (<1 Hz). Specifically, the surface-positive half-waves of the slow oscillation resulting from widespread cortical depolarization were associated with distinctly enhanced coherence after learning in the slow-oscillatory, delta, slow-spindle, and gamma bands. The findings identify the depolarizing phase of the slow oscillations in humans as a time period particularly relevant for a reprocessing of memories in sleep.
| S-EPMC518860 | biostudies-literature
Project description:OBJECTIVE:Guidelines recommend that encephalopathic critically ill children undergo continuous electroencephalographic (CEEG) monitoring for electrographic seizure (ES) identification and management. However, limited data exist on antiseizure medication (ASM) safety for ES treatment in critically ill children. METHODS:We performed a single-center prospective observational study of encephalopathic critically ill children undergoing CEEG. Clinical and EEG features and ASM utilization patterns were evaluated. We determined the incidence, types, and risk factors for adverse events associated with ASM administration. RESULTS:A total of 472 consecutive critically ill children undergoing CEEG were enrolled. ES occurred in 131 children (28%). Clinicians administered ASM to 108 children with ES (82%). ES terminated after the initial ASM in 38% of patients who received one ASM, after the second ASM in 35% of patients who received two ASMs, after the third ASM in 50% of patients who received three ASMs, and after the fourth ASM in 53% of patients who received four ASMs. Thirty patients (28%) received anesthetic infusions for ES management. Adverse events occurred in 18 patients (17%). Adverse effects were expected and resolved in all patients, and they were generally serious (in 15 patients) and definitely related (in 12 patients). Adverse events were rare in patients with acute symptomatic seizures requiring only one to two ASMs for treatment, but were more common in children with epilepsy, ictal-interictal continuum EEG patterns, or patients requiring more extensive ASM management. SIGNIFICANCE:ES ceased after one ASM in only 38% of critically ill children but ceased after two ASMs in 73% of critically ill children. Thus, ES management was often accomplished with readily available medications, but optimization of multistep ES management strategies might be beneficial. Adverse events were rare and manageable in children with acute symptomatic seizures requiring only one to two ASMs for treatment. Future studies are needed to determine whether management of acute symptomatic ES improves neurobehavioral outcomes.
| S-EPMC6786930 | biostudies-literature
Project description:BackgroundDiscontinuous electroencephalographic activity in children is thought to reflect brain inactivation. Discontinuity has been observed in states of pathology, where it is predictive of adverse neurological outcome, as well as under general anesthesia. Though in preterm-infants discontinuity reflects normal brain development, less is known regarding its role in term children, particularly in the setting of general anesthesia. Here, we conduct a post-hoc exploratory analysis to investigate the spectral features of discontinuous activity in children under general anesthesia.MethodsWe previously recorded electroencephalography in children less than forty months of age under general anesthesia (n = 65). We characterized the relationship between age, anesthetic depth, and discontinuous activity, and used multitaper spectral methods to compare the power spectra of subjects with (n = 35) and without (n = 30) discontinuous activity. In the subjects with discontinuous activity, we examined the amplitude and power spectra associated with the discontinuities and analyzed how these variables varied with age.ResultsCumulative time of discontinuity was associated with increased anesthetic depth and younger age. In particular, age-matched children with discontinuity received higher doses of propofol during induction as compared with children without discontinuity. In the tens of seconds preceding the onset of discontinuous activity, there was a decrease in high-frequency power in children four months and older that could be visually observed with spectrograms. During discontinuous activity, there were distinctive patterns of amplitude, spectral edge, and power in canonical frequency bands that varied with age. Notably, there was a decline in spectral edge in the seconds immediately following each discontinuity.ConclusionDiscontinuous activity in children reflects a state of a younger or more deeply anesthetized brain, and characteristic features of discontinuous activity evolve with age and may reflect neurodevelopment.
| S-EPMC6776336 | biostudies-literature
Project description:Oppositional defiant disorder and conduct disorder, collectively referred to as disruptive behavior disorders (DBDs), are prevalent psychiatric disorders in children. Early diagnosis of DBDs is crucial because they can increase the risks of other mental health and substance use disorders without appropriate psychosocial interventions and treatment. However, diagnosing DBDs is challenging as they are often comorbid with other disorders, such as attention-deficit/hyperactivity disorder, anxiety, and depression. In this study, a multimodal ensemble three-dimensional convolutional neural network (3D CNN) deep learning model was used to classify children with DBDs and typically developing children. The study participants included 419 females and 681 males, aged 108-131 months who were enrolled in the Adolescent Brain Cognitive Development Study. Children were grouped based on the presence of DBDs (n = 550) and typically developing (n = 550); assessments were based on the scores from the Child Behavior Checklist and on the Schedule for Affective Disorders and Schizophrenia for School-age Children-Present and Lifetime version for DSM-5. The diffusion, structural, and resting-state functional magnetic resonance imaging (rs-fMRI) data were used as input data to the 3D CNN. The model achieved 72% accuracy in classifying children with DBDs with 70% sensitivity, 72% specificity, and an F1-score of 70. In addition, the discriminative power of the classifier was investigated by identifying the cortical and subcortical regions primarily involved in the prediction of DBDs using a gradient-weighted class activation mapping method. The classification results were compared with those obtained using the three neuroimaging modalities individually, and a connectome-based graph CNN and a multi-scale recurrent neural network using only the rs-fMRI data.
| S-EPMC8652047 | biostudies-literature