Development of preschool social emotional inventory for preschoolers: a preliminary study

Social-emotional competencies have been established as important predictors in children’s mental health, school readiness and academic success. Age and developmentally appropriate screening for preschool children is important for early detection of developmental delays and early intervention. The purpose of this study is to measure preschool children’s social emotional competencies (SEC) based on the Malaysian context using the Preschool Social Emotional Competency Inventory (P-SECI) developed to provide parents, teachers and childcare professionals with a standardized, norm-referenced, reliable and valid instrument.  A pool of 50 items were created for P-SECI, representing eight clusters of social emotional competencies in children: self-awareness, social awareness, self-management, relationship management, attachment, communication, responsible decision making and pro-social skills. This pilot study involved two public Annex preschools in Johor Bahru, purposely selected with 49 preschool children as respondents. Results showed that P-SECI has a high reliability index (Alpha Cronbach-Teachers .98 and Parents .95). Initial findings also showed that Teachers and Parents differ in their evaluation of their children’s SEC according to age and gender. But for the age criteria, the mean difference is slightly wider in comparison to gender. Therefore, P-SECI is relevant in predicting children’s mental health, school readiness and academic achievement. Results from the study also showed that the inventory holds promise as a reliable and valid instrument to evaluate SEC in young children according to the Malaysian context

Item Analysis for the Adapted Motivation Scale Using Rasch Model

The aim of this study was to measure the validity and reliability of an adapted motivation scale used in second language learning.  The Integrative and Instrumental motivation (IIM)  was developed based on two orientations of motivation, which were integrative motivation and instrumental motivation.  IIM was used to identify students’ level of motivation towards learning Chinese language as a second language (CLSL) in national school.  The study presented empirical evidence of validity and reliability using the Rasch Model.   Pilot study was conducted in a national school which has students enrolled for the CLSL class.   There were 29 students in this pilot study.  Data was  analyzed using WINSTEPS version 3.72.3.  The result showed the reliability of the motivation scale is 0.87 based on Cronbach’s alpha.  The construct validity was determined by PT-measure correlation value (PMC) range from 0.71 to 0.86, infit and outfit MNSQ  between 0.53 to 1.43 and ZSTD range from -1.1 to 1.2. In unidimensional measure, raw variance explained was at 66.4%  with the unexplanined variance in the first factor was at 13.6%.  Finally the fit statistics showed that person separation index, 2.79 was considered good and item separation index, 1.81was in acceptable range (Linacre,2012).  Person and item reliability were at 0.89 and 0.77 respectively.  The result indicated that the new instrument with 15 items-IIM after eliminated unfit items was a reliable and valid instrument to measure the motivation in learning CLSL in the context of national school

Time of day is associated with paradoxical reductions in global signal fluctuation and functional connectivity.

The brain exhibits substantial diurnal variation in physiology and function, but neuroscience studies rarely report or consider the effects of time of day. Here, we examined variation in resting-state functional MRI (fMRI) in around 900 individuals scanned between 8 AM and 10 PM on two different days. Multiple studies across animals and humans have demonstrated that the brain's global signal (GS) amplitude (henceforth referred to as "fluctuation") increases with decreased arousal. Thus, in accord with known circadian variation in arousal, we hypothesised that GS fluctuation would be lowest in the morning, increase in the midafternoon, and dip in the early evening. Instead, we observed a cumulative decrease in GS fluctuation as the day progressed. Although respiratory variation also decreased with time of day, control analyses suggested that this did not account for the reduction in GS fluctuation. Finally, time of day was associated with marked decreases in resting-state functional connectivity across the whole brain. The magnitude of decrease was significantly stronger than associations between functional connectivity and behaviour (e.g., fluid intelligence). These findings reveal time of day effects on global brain activity that are not easily explained by expected arousal state or physiological artefacts. We conclude by discussing potential mechanisms for the observed diurnal variation in resting brain activity and the importance of accounting for time of day in future studies

Dictionary Learning and Sparse Coding-based Denoising for High-Resolution Task Functional Connectivity MRI Analysis

We propose a novel denoising framework for task functional Magnetic Resonance Imaging (tfMRI) data to delineate the high-resolution spatial pattern of the brain functional connectivity via dictionary learning and sparse coding (DLSC). In order to address the limitations of the unsupervised DLSC-based fMRI studies, we utilize the prior knowledge of task paradigm in the learning step to train a data-driven dictionary and to model the sparse representation. We apply the proposed DLSC-based method to Human Connectome Project (HCP) motor tfMRI dataset. Studies on the functional connectivity of cerebrocerebellar circuits in somatomotor networks show that the DLSC-based denoising framework can significantly improve the prominent connectivity patterns, in comparison to the temporal non-local means (tNLM)-based denoising method as well as the case without denoising, which is consistent and neuroscientifically meaningful within motor area. The promising results show that the proposed method can provide an important foundation for the high-resolution functional connectivity analysis, and provide a better approach for fMRI preprocessing.Comment: 8 pages, 3 figures, MLMI201

Implementation of genomics in medical practice to deliver precision medicine for an Asian population

Whilst the underlying principles of precision medicine are comparable across the globe, genomic references, health practices, costs and discrimination policies differ in Asian settings compared to the reported initiatives involving European-derived populations. We have addressed these variables by developing an evolving reference base of genomic and phenotypic data and a framework to return medically significant variants to consenting research participants applicable for the Asian context. Targeting 10,000 participants, over 2000 Singaporeans, with no known pre-existing health conditions, have consented to an extensive clinical health screen, family health history collection, genome sequencing and ongoing follow-up. Genomic variants in a subset of genes associated with Mendelian disorders and drug responses are analysed using an in-house bioinformatics pipeline. A multidisciplinary team reviews the classification of variants and a research report is generated. Medically significant variants are returned to consenting participants through a bespoke return-of-result genomics clinic. Variant validation and subsequent clinical referral are advised as appropriate. The design and implementation of this flexible learning framework enables a cohort of detailed phenotyping and genotyping of healthy Singaporeans to be established and the frequency of disease-causing variants in this population to be determined. Our findings will contribute to international precision medicine initiatives, bridging gaps with ethnic-specific data and insights from this understudied population

Comparison between gradients and parcellations for functional connectivity prediction of behavior

Resting-state functional connectivity (RSFC) is widely used to predict behavioral measures. To predict behavioral measures, representing RSFC with parcellations and gradients are the two most popular approaches. Here, we compare parcellation and gradient approaches for RSFC-based prediction of a broad range of behavioral measures in the Human Connectome Project (HCP) and Adolescent Brain Cognitive Development (ABCD) datasets. Among the parcellation approaches, we consider group-average “hard” parcellations (Schaefer et al., 2018), individual-specific “hard” parcellations (Kong et al., 2021a), and an individual-specific “soft” parcellation (spatial independent component analysis with dual regression; Beckmann et al., 2009). For gradient approaches, we consider the well-known principal gradients (Margulies et al., 2016) and the local gradient approach that detects local RSFC changes (Laumann et al., 2015). Across two regression algorithms, individual-specific hard-parcellation performs the best in the HCP dataset, while the principal gradients, spatial independent component analysis and group-average “hard” parcellations exhibit similar performance. On the other hand, principal gradients and all parcellation approaches perform similarly in the ABCD dataset. Across both datasets, local gradients perform the worst. Finally, we find that the principal gradient approach requires at least 40 to 60 gradients to perform as well as parcellation approaches. While most principal gradient studies utilize a single gradient, our results suggest that incorporating higher order gradients can provide significant behaviorally relevant information. Future work will consider the inclusion of additional parcellation and gradient approaches for comparison

Evaluating Acquisition Time of rfMRI in the Human Connectome Project for Early Psychosis. How Much Is Enough?

Resting-state functional MRI (rfMRI) correlates activity across brain regions to identify functional connectivity networks. The Human Connectome Project (HCP) for Early Psychosis has adopted the protocol of the HCP Lifespan Project, which collects 20 min of rfMRI data. However, because it is difficult for psychotic patients to remain in the scanner for long durations, we investigate here the reliability of collecting less than 20 min of rfMRI data. Varying durations of data were taken from the full datasets of 11 subjects. Correlation matrices derived from varying amounts of data were compared using the Bhattacharyya distance, and the reliability of functional network ranks was assessed using the Friedman test. We found that correlation matrix reliability improves steeply with longer windows of data up to 11–12 min, and ≥14 min of data produces correlation matrices within the variability of those produced by 18 min of data. The reliability of network connectivity rank increases with increasing durations of data, and qualitatively similar connectivity ranks for ≥10 min of data indicates that 10 min of data can still capture robust information about network connectivities

3D time series analysis of cell shape using Laplacian approaches

Background: Fundamental cellular processes such as cell movement, division or food uptake critically depend on cells being able to change shape. Fast acquisition of three-dimensional image time series has now become possible, but we lack efficient tools for analysing shape deformations in order to understand the real three-dimensional nature of shape changes. Results: We present a framework for 3D+time cell shape analysis. The main contribution is three-fold: First, we develop a fast, automatic random walker method for cell segmentation. Second, a novel topology fixing method is proposed to fix segmented binary volumes without spherical topology. Third, we show that algorithms used for each individual step of the analysis pipeline (cell segmentation, topology fixing, spherical parameterization, and shape representation) are closely related to the Laplacian operator. The framework is applied to the shape analysis of neutrophil cells. Conclusions: The method we propose for cell segmentation is faster than the traditional random walker method or the level set method, and performs better on 3D time-series of neutrophil cells, which are comparatively noisy as stacks have to be acquired fast enough to account for cell motion. Our method for topology fixing outperforms the tools provided by SPHARM-MAT and SPHARM-PDM in terms of their successful fixing rates. The different tasks in the presented pipeline for 3D+time shape analysis of cells can be solved using Laplacian approaches, opening the possibility of eventually combining individual steps in order to speed up computations

Heritability Estimation of Reliable Connectomic Features*

Brain imaging genetics is an emerging research field to explore the underlying genetic architecture of brain structure and function measured by different imaging modalities. However, not all the changes in the brain are a consequential result of genetic effect and it is usually unknown which imaging phenotypes are promising for genetic analyses. In this paper, we focus on identifying highly heritable measures of structural brain networks derived from diffusion weighted imaging data. Using the twin data from the Human Connectome Project (HCP), we evaluated the reliability of fractional anisotropy measure, fiber length and fiber number of each edge in the structural connectome and seven network level measures using intraclass correlation coefficients. We then estimated the heritability of those reliable network measures using SOLAR-Eclipse software. Across all 64,620 network edges between 360 brain regions in the Glasser parcellation, we observed ~5% of them with significantly high heritability in fractional anisotropy, fiber length or fiber number. All the tested network level measures, capturing the network integrality, segregation or resilience, are highly heritable, with variance explained by the additive genetic effect ranging from 59% to 77%