A longitudinal neuroimaging dataset on multisensory lexical processing in school-aged children

Single-subject cortical morphological brain networks: Phenotypic associations and neurobiological substrates

Although single-subject morphological brain networks provide an important way for human connectome studies, their roles and origins are poorly understood. Combining cross-sectional and repeated structural magnetic resonance imaging scans from adults, children and twins with behavioral and cognitive measures and brain-wide transcriptomic, cytoarchitectonic and chemoarchitectonic data, this study examined phenotypic associations and neurobiological substrates of single-subject morphological brain networks. We found that single-subject morphological brain networks explained inter-individual variance and predicted individual outcomes in Motor and Cognition domains, and distinguished individuals from each other. The performance can be further improved by integrating different morphological indices for network construction. Low-moderate heritability was observed for single-subject morphological brain networks with the highest heritability for sulcal depth-derived networks and higher heritability for inter-module connections. Furthermore, differential roles of genetic, cytoarchitectonic and chemoarchitectonic factors were observed for single-subject morphological brain networks. Cortical thickness-derived networks were related to the three factors with contributions from genes enriched in membrane and transport related functions, genes preferentially located in supragranular and granular layers, overall thickness in the molecular layer and thickness of wall in the infragranular layers, and metabotropic glutamate receptor 5 and dopamine transporter; fractal dimension-, gyrification index- and sulcal depth-derived networks were only associated with the chemoarchitectonic factor with contributions from different sets of neurotransmitter receptors. Most results were reproducible across different parcellation schemes and datasets. Altogether, this study demonstrates phenotypic associations and neurobiological substrates of single-subject morphological brain networks, which provide intermediate endophenotypes to link molecular and cellular architecture and behavior and cognition.

The Queensland Twin Adolescent Brain Project, a longitudinal study of adolescent brain development

We describe the Queensland Twin Adolescent Brain (QTAB) dataset and provide a detailed methodology and technical validation to facilitate data usage. The QTAB dataset comprises multimodal neuroimaging, as well as cognitive and mental health data collected in adolescent twins over two sessions (session 1: N = 422, age 9-14 years; session 2: N = 304, 10-16 years). The MRI protocol consisted of T1-weighted (MP2RAGE), T2-weighted, FLAIR, high-resolution TSE, SWI, resting-state fMRI, DWI, and ASL scans. Two fMRI tasks were added in session 2: an emotional conflict task and a passive movie-watching task. Outside of the scanner, we assessed cognitive function using standardised tests. We also obtained self-reports of symptoms for anxiety and depression, perceived stress, sleepiness, pubertal development measures, and risk and protective factors. We additionally collected several biological samples for genomic and metagenomic analysis. The QTAB project was established to promote health-related research in adolescence.

Functional MRI research involving healthy children: Ethics, safety and recommended procedures

AIM

This specific review aims to expose clinicians, researchers and administrators in hospitals to the importance, procedures and safety of fMRI studies to promote the increased utilisation of such studies in different geographical places worldwide. The child's brain is developing rapidly, both structurally and functionally. These functional changes can only be detected using functional scans generated from an MRI machine and referred to as a functional MRI (fMRI). This method may be used clinically in complex medical and surgical conditions (e.g., epilepsy surgery), but these days are often used for research purposes. However, due to ethical and logistical considerations, fMRI in the paediatric population is not widely and equally used in different geographical places.

CONCLUSIONS

The benefits of using this method to define the functional changes occurring in the developing brain are discussed in this review, along with desensitisation methods recommended when working with this vulnerable population in research and even in a clinical setting.

A longitudinal neuroimaging dataset on language processing in children ages 5, 7, and 9 years old

This dataset examines language development with a longitudinal design and includes diffusion- and T1-weighted structural magnetic resonance imaging (MRI), task-based functional MRI (fMRI), and a battery of psycho-educational assessments and parental questionnaires. We collected data from 5.5-6.5-year-old children (ses-5) and followed them up when they were 7-8 years old (ses-7) and then again at 8.5-10 years old (ses-9). To increase the sample size at the older time points, another cohort of 7-8-year-old children (ses-7) were recruited and followed up when they were 8.5-10 years old (ses-9). In total, 322 children who completed at least one structural and functional scan were included. Children performed four fMRI tasks consisting of two word-level tasks examining phonological and semantic processing and two sentence-level tasks investigating semantic and syntactic processing. The MRI data is valuable for examining changes over time in interactive specialization due to the use of multiple imaging modalities and tasks in this longitudinal design. In addition, the extensive psycho-educational assessments and questionnaires provide opportunities to explore brain-behavior and brain-environment associations.

The Connectivity Fingerprints of Highly-Skilled and Disordered Reading Persist Across Cognitive Domains

The capacity to produce and understand written language is a uniquely human skill that exists on a continuum, and foundational to other facets of human cognition. Multivariate classifiers based on support vector machines (SVM) have provided much insight into the networks underlying reading skill beyond what traditional univariate methods can tell us. Shallow models like SVM require large amounts of data, and this problem is compounded when functional connections, which increase exponentially with network size, are predictors of interest. Data reduction using independent component analyses (ICA) mitigates this problem, but conventionally assumes linear relationships. Multilayer feedforward networks, in contrast, readily find optimal low-dimensional encodings of complex patterns that include complex nonlinear or conditional relationships. Samples of poor and highly-skilled young readers were selected from two open access data sets using rhyming and mental multiplication tasks, respectively. Functional connectivity was computed for the rhyming task within a functionally-defined reading network and used to train multilayer feedforward classifier models to simultaneously associate functional connectivity patterns with lexicality (word vs. pseudoword) and reading skill (poor vs. highly-skilled). Classifiers identified validation set lexicality with significantly better than chance accuracy, and reading skill with near-ceiling accuracy. Critically, a series of replications used pre-trained rhyming-task models to classify reading skill from mental multiplication task participants' connectivity with near-ceiling accuracy. The novel deep learning approach presented here provides the clearest demonstration to date that reading-skill dependent functional connectivity within the reading network influences brain processing dynamics across cognitive domains.

A naturalistic neuroimaging database for understanding the brain using ecological stimuli

Neuroimaging has advanced our understanding of human psychology using reductionist stimuli that often do not resemble information the brain naturally encounters. It has improved our understanding of the network organization of the brain mostly through analyses of 'resting-state' data for which the functions of networks cannot be verifiably labelled. We make a 'Naturalistic Neuroimaging Database' (NNDb v1.0) publically available to allow for a more complete understanding of the brain under more ecological conditions during which networks can be labelled. Eighty-six participants underwent behavioural testing and watched one of 10 full-length movies while functional magnetic resonance imaging was acquired. Resulting timeseries data are shown to be of high quality, with good signal-to-noise ratio, few outliers and low movement. Data-driven functional analyses provide further evidence of data quality. They also demonstrate accurate timeseries/movie alignment and how movie annotations might be used to label networks. The NNDb can be used to answer questions previously unaddressed with standard neuroimaging approaches, progressing our knowledge of how the brain works in the real world.

A neuroimaging dataset on orthographic, phonological and semantic word processing in school-aged children

Here we describe the public neuroimaging and behavioral dataset entitled "Cross-Sectional Multidomain Lexical Processing" available on the OpenNeuro project (https://openneuro.org). This dataset explores the neural mechanisms and development of lexical processing through task based functional magnetic resonance imaging (fMRI) of rhyming, spelling, and semantic judgement tasks in both the auditory and visual modalities. Each task employed varying degrees of trial difficulty, including conflicting versus non-conflicting orthography-phonology pairs (e.g. harm - warm, wall - tall) in the rhyming and spelling tasks as well as high versus low word pair association in the semantic tasks (e.g. dog - cat, dish - plate). In addition, this dataset contains scores from a battery of standardized psychoeducational assessments allowing for future analyses of brain-behavior relations. Data were collected from a cross-sectional sample of 91 typically developing children aged 8.7- to 15.5- years old. The cross-sectional design employed in this dataset as well as the inclusion of multiple measures of lexical processing in varying difficulties and modalities allows for multiple avenues of future research on reading development.