Abstract In computational models of reading, written words can be read using print-to-sound and/or print-to-meaning pathways. Neuroimaging data associate dorsal stream regions (left posterior ...occipitotemporal cortex, intraparietal cortex, dorsal inferior frontal gyrus dIFG) with the print-to-sound pathway and ventral stream regions (left anterior fusiform gyrus, middle temporal gyrus) with the print-to-meaning pathway. In 69 typical adults, we investigated whether resting state functional connectivity (RSFC) between the visual word form area (VWFA) and dorsal and ventral regions correlated with phonological (nonword reading, nonword repetition, spoonerisms), lexical-semantic (vocabulary, sensitivity to morpheme units in reading), and general literacy (word reading, spelling) skills. VWFA activity was temporally correlated with activity in both dorsal and ventral reading regions. In pre-registered whole-brain analyses, spoonerisms performance was positively correlated with RSFC between the VWFA and left dorsal regions (dIFG, superior parietal and intraparietal cortex). In exploratory region-of-interest analyses, VWFA-dIFG connectivity was also positively correlated with nonword repetition, spelling, and vocabulary. Connectivity between the VWFA and ventral stream regions was not associated with performance on any behavioural measure, either in whole-brain or region-of-interest analyses. Our results suggest that tasks such as spoonerisms and spellings, which are both complex (i.e., involve multiple subprocesses) and have high between-subject variability, provide greater opportunity for observing resting-state brain-behaviour associations. However, the complexity of these tasks limits the conclusions we can draw about the specific mechanisms that drive these associations. Future research would benefit from constructing latent variables from multiple tasks tapping the same reading subprocess.
The formation of large-scale brain networks, and their continual refinement, represent crucial developmental processes that can drive individual differences in cognition and which are associated with ...multiple neurodevelopmental conditions. But how does this organization arise, and what mechanisms drive diversity in organization? We use generative network modeling to provide a computational framework for understanding neurodevelopmental diversity. Within this framework macroscopic brain organization, complete with spatial embedding of its organization, is an emergent property of a generative wiring equation that optimizes its connectivity by renegotiating its biological costs and topological values continuously over time. The rules that govern these iterative wiring properties are controlled by a set of tightly framed parameters, with subtle differences in these parameters steering network growth towards different neurodiverse outcomes. Regional expression of genes associated with the simulations converge on biological processes and cellular components predominantly involved in synaptic signaling, neuronal projection, catabolic intracellular processes and protein transport. Together, this provides a unifying computational framework for conceptualizing the mechanisms and diversity in neurodevelopment, capable of integrating different levels of analysis-from genes to cognition.
Face recognition is a fundamental function that requires holistic processing. Differences in face processing have been consistently identified in autistic children, but it is unknown whether these ...differences persist across the adult lifespan. Using event-related functional magnetic resonance imaging, we measured holistic face processing with a rapid Mooney faces task in 50 autistic and 49 non-autistic participants (30–74 years). Behavioral tasks included a self-paced version of the same paradigm and a global–local processing task (Navon). Reduced detection rates for faces, but not non-faces, were found in autistic adults, including slower responses on all conditions. Without time constraints, differences in accuracy disappeared between groups, although reaction times in correctly identifying faces remained higher in autistic adults. The functional magnetic resonance imaging results showed lower activation in the left and right superior frontal gyrus in the autism group but no age-related differences. Overall, our findings point toward slower information processing speed rather than a face recognition deficit in autistic adults. This suggests that face-processing differences are not a core feature of autism across the adult lifespan.
Lay abstract
Some theories suggested that social difficulties in autism arise from differences in the processing of faces. If face-processing difficulties are central to autism, then they should be as persistent as social difficulties across the lifespan. We tested this by asking autistic and neurotypical participants between 30 and 75 years to complete face detection tasks. Both autistic and neurotypical adults responded more slowly with age. When participants had to respond quickly, autistic adults made more errors in face detection regardless of their age. However, when the time constraint was removed, autistic adults performed as well as the neurotypical group. Across tasks, autistic adults responded more slowly when asked to detect both face and non-face stimuli. We also investigated brain activation differences in the face detection task with functional magnetic resonance imaging. The results indicated lower activation in the autism group in the left and right superior frontal gyrus. The superior frontal gyrus is not typically implicated in face processing but in more general processing, for example, keeping instructions in mind and following them. Together with the behavioral results, this suggests that there is no specific deficit in face processing in autistic adults between 30 and 75 years. Instead, the results suggest differences in general processing, particularly in the speed of processing. However, this needs to be investigated further with methods that are more sensitive to the timing of brain activation.
There is increasing interest in applying connectivity analysis to brain measures (Rubinov and Sporns, 2010), but most studies have relied on fMRI, which substantially limits the participant groups ...and numbers that can be studied. High-density EEG recordings offer a comparatively inexpensive easy-to-use alternative, but require channel-level connectivity analysis which currently lacks a common analytic framework and is very limited in spatial resolution. To address this problem, we have developed a new technique for studies of network development that overcomes the spatial constraint and obtains functional networks of cortical areas by using EEG source reconstruction with age-matched average MRI templates (He et al., 1999). In contrast to previously reported channel-level analysis, this approach provides information about the cortical areas most likely to be involved in the network as well as their functional relationship (Babiloni et al., 2005; De Vico Fallani et al., 2007). In this study, we applied source reconstruction with age-matched templates to task-free high-density EEG recordings in typically-developing children between 2 and 6 years of age (O'Reilly, 2012). Graph theory was then applied to the association strengths of 68 cortical regions of interest based on the Desikan-Killiany atlas. We found linear increases of mean node degree, mean clustering coefficient and maximum betweenness centrality between 2 years and 6 years of age. Characteristic path length was negatively correlated with age. The correlation of the network measures with age indicates network development towards more closely integrated networks similar to reports from other imaging modalities (Fair et al., 2008; Power et al., 2010). We also applied eigenvalue decomposition to obtain functional modules (Clayden et al., 2013). Connection strength within these modules did not change with age, and the modules resembled hub networks previously described for MRI (Hagmann et al., 2010; Power et al., 2010). The high temporal resolution of EEG additionally allowed us to distinguish between frequency bands potentially reflecting dynamic coupling between different neural oscillators. Generally, network parameters were similar for networks based on different frequency bands, but frequency band did emerge as a significant factor for clustering coefficient and characteristic path length. In conclusion, the current analysis shows that source reconstruction of high-density EEG recordings with appropriate head models offers a valuable tool for estimating network parameters in studies of brain development. The findings replicate the pattern of closer functional integration over development described for other imaging modalities (Fair et al., 2008; Power et al., 2010).
Background
Behavioural and language difficulties co‐occur in multiple neurodevelopmental conditions. Our understanding of these problems has arguably been slowed by an overreliance on study designs ...that compare diagnostic groups and fail to capture the overlap across different neurodevelopmental disorders and the heterogeneity within them.
Methods
We recruited a large transdiagnostic cohort of children with complex needs (N = 805) to identify distinct subgroups of children with common profiles of behavioural and language strengths and difficulties. We then investigated whether and how these data‐driven groupings could be distinguished from a comparison sample (N = 158) on measures of academic and socioemotional functioning and patterns of global and local white matter connectome organisation. Academic skills were assessed via standardised measures of reading and maths. Socioemotional functioning was captured by the parent‐rated version of the Strengths and Difficulties Questionnaire.
Results
We identified three distinct subgroups of children, each with different levels of difficulties in structural language, pragmatic communication, and hot and cool executive functions. All three subgroups struggled with academic and socioemotional skills relative to the comparison sample, potentially representing three alternative but related developmental pathways to difficulties in these areas. The children with the weakest language skills had the most widespread difficulties with learning, whereas those with more pronounced difficulties with hot executive skills experienced the most severe difficulties in the socioemotional domain. Each data‐driven subgroup could be distinguished from the comparison sample based on both shared and subgroup‐unique patterns of neural white matter organisation. Children with the most pronounced deficits in language, cool executive, or hot executive function were differentiated from the comparison sample by altered connectivity in predominantly thalamocortical, temporal–parietal‐occipital, and frontostriatal circuits, respectively.
Conclusions
These findings advance our understanding of commonly co‐morbid behavioural and language problems and their relationship to behavioural outcomes and neurobiological substrates.
Executive function, an umbrella term used to describe the goal-directed regulation of thoughts, actions, and emotions, is an important dimension implicated in neurodiversity and established malleable ...predictor of multiple adult outcomes. Neurodevelopmental differences have been linked to both executive function strengths and weaknesses, but evidence for associations between specific profiles of executive function and specific neurodevelopmental conditions is mixed. In this exploratory study, we adopt an unsupervised machine learning approach (self-organising maps), combined with k-means clustering to identify data-driven profiles of executive function in a transdiagnostic sample of 566 neurodivergent children aged 8–18 years old. We include measures designed to capture two distinct aspects of executive function: performance-based tasks designed to tap the state-like efficiency of cognitive skills under optimal conditions, and behaviour ratings suited to capturing the trait-like application of cognitive control in everyday contexts. Three profiles of executive function were identified: one had consistent difficulties across both types of assessments, while the other two had inconsistent profiles of predominantly rating- or predominantly task-based difficulties. Girls and children without a formal diagnosis were more likely to have an inconsistent profile of primarily task-based difficulties. Children with these different profiles had differences in academic achievement and mental health outcomes and could further be differentiated from a comparison group of children on both shared and profile-unique patterns of neural white matter organisation. Importantly, children's executive function profiles were not directly related to diagnostic categories or to dimensions of neurodiversity associated with specific diagnoses (e.g., hyperactivity, inattention, social communication). These findings support the idea that the two types of executive function assessments provide non-redundant information related to children's neurodevelopmental differences and that they should not be used interchangeably. The findings advance our understanding of executive function profiles and their relationship to behavioural outcomes and neural variation in neurodivergent populations.
•Left frontal EEG alpha-power asymmetry in congenital visual impairment (VI) infants does not differ from that in sighted infants.•22.7% of the VI sample had ‘internalizing’ behavior difficulties at ...two years.•Greater left frontal asymmetry was associated with later increased internalizing behavior risk in VI infants.
Young children with congenital visual impairment (VI) are at increased risk of behavioral vulnerabilities. Studies on ‘at risk’ populations suggest that frontal EEG asymmetry may be associated with behavioral risk. We investigated frontal asymmetry at 1year (Time 1), behavior at 2years (Time 2) and their longitudinal associations within a sample of infants with VI. Frontal asymmetry in the VI sample at 1year was also compared cross-sectionally to an age-matched typically sighted (TS) group.
At Time 1, 22 infants with VI and 10 TS infants underwent 128-channel EEG recording. Frontal asymmetry ratios were calculated from power spectral density values in the alpha frequency band. At Time 2, Achenbach Child Behavior Checklist data was obtained for the VI sample.
63.6% of the VI sample and 50% of the TS sample showed left frontal asymmetry; no significant difference in frontal asymmetry was found between the two groups. 22.7% of the VI sample had subclinical to clinical range ‘internalizing’ behavior difficulties. Greater left frontal asymmetry at one year was significantly associated with greater emotionally reactive scores at two years within the VI sample (r=0.50, p=0.02).
Left frontal asymmetry correlates with later behavior risk within this vulnerable population.
These findings make an important first contribution regarding the utility of frontal EEG asymmetry as a method to investigate risk in infants with VI.
Children and adolescents with developmental problems are at increased risk of experiencing mental health problems. The Strengths and Difficulties Questionnaire (SDQ) is widely used as a screener for ...detecting mental health difficulties in these populations, but its use thus far has been restricted to groups of children with diagnosed disorders (e.g., ADHD). Transdiagnostic approaches, which focus on symptoms and soften or remove the boundaries between traditional categorical disorders, are increasingly adopted in research and practice. The aim of this study was to assess the potential of the SDQ to detect concurrent mental health problems in a transdiagnostic sample of children. The sample were referred by health and educational professionals for difficulties related to learning (
= 389). Some had one diagnosis, others had multiple, but many had no diagnoses. Parent-rated SDQ scores were significantly positively correlated with parent ratings of mental health difficulties on the Revised Child Anxiety and Depression Scale (RCADS). Ratings on the SDQ Emotion subscale significantly predicted the likelihood of having concurrent clinical anxiety and depression scores. Ratings on the Hyperactivity subscale predicted concurrent anxiety levels. These findings suggest the SDQ could be a valuable screening tool for identifying existing mental health difficulties in children recognized as struggling, as it can be in typically developing children and those with specific diagnoses.
EEG is traditionally described as a neuroimaging technique with high temporal and low spatial resolution. Recent advances in biophysical modelling and signal processing make it possible to exploit ...information from other imaging modalities like structural MRI that provide high spatial resolution to overcome this constraint. This is especially useful for investigations that require high resolution in the temporal as well as spatial domain. In addition, due to the easy application and low cost of EEG recordings, EEG is often the method of choice when working with populations, such as young children, that do not tolerate functional MRI scans well. However, in order to investigate which neural substrates are involved, anatomical information from structural MRI is still needed. Most EEG analysis packages work with standard head models that are based on adult anatomy. The accuracy of these models when used for children is limited, because the composition and spatial configuration of head tissues changes dramatically over development. In the present paper, we provide an overview of our recent work in utilizing head models based on individual structural MRI scans or age specific head models to reconstruct the cortical generators of high density EEG. This article describes how EEG recordings are acquired, processed, and analyzed with pediatric populations at the London Baby Lab, including laboratory setup, task design, EEG preprocessing, MRI processing, and EEG channel level and source analysis.
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