The "central" importance of loneliness in mental health: A network psychometric study.

Loneliness is linked to negative mental health outcomes like depression and social anxiety. However, it is unclear how loneliness and these conditions are connected. This study aims to address two questions: (a) Are there pathways connecting loneliness to these conditions? (b) What symptoms play a role in these pathways? Using network analyses, the study examined a representative sample of 962 adults from the UK (mean age = 46, females = 492). The network analysis revealed four dimensions: depression, social anxiety and two dimensions for loneliness (isolation and social connectedness). Two distinct pathways were identified for the transmission of symptoms between loneliness, social anxiety and depression. The depression-isolation pathway involved a node representing the perceived loss of social connections. The social anxiety-social connectedness pathway was characterised by intimate interaction contexts. These findings suggest that loneliness is associated with different symptom pathways, potentially contributing to comorbidity between loneliness, social anxiety and depression.

Multilayer network associations between the exposome and childhood brain development.

Growing up in a high poverty neighborhood is associated with elevated risk for academic challenges and health problems. Here, we take a data-driven approach to exploring how measures of children's environments relate to the development of their brain structure and function in a community sample of children between the ages of 4 and 10 years. We constructed exposomes including measures of family socioeconomic status, children's exposure to adversity, and geocoded measures of neighborhood socioeconomic status, crime, and environmental toxins. We connected the exposome to two structural measures (cortical thickness and surface area, n = 170) and two functional measures (participation coefficient and clustering coefficient, n = 130). We found dense connections within exposome and brain layers and sparse connections between exposome and brain layers. Lower family income was associated with thinner visual cortex, consistent with the theory that accelerated development is detectable in early-developing regions. Greater neighborhood incidence of high blood lead levels was associated with greater segregation of the default mode network, consistent with evidence that toxins are deposited into the brain along the midline. Our study demonstrates the utility of multilayer network analysis to bridge environmental and neural explanatory levels to better understand the complexity of child development.

Clinical subtyping using community detection: Limited utility?

OBJECTIVES: To discover psychiatric subtypes, researchers are adopting a method called community detection. This method was not subjected to the same scrutiny in the psychiatric literature as traditional clustering methods. Furthermore, many community detection algorithms have been developed without psychiatric sample sizes and variable numbers in mind. We aim to provide clarity to researchers on the utility of this method. METHODS: We provide an introduction to community detection algorithms, specifically describing the crucial differences between correlation-based and distance-based community detection. We compare community detection results to results of traditional methods in a simulation study representing typical psychiatry settings, using three conceptualizations of how subtypes might differ. RESULTS: We discovered that the number of recovered subgroups was often incorrect with several community detection algorithms. Correlation-based community detection fared better than distance-based community detection, and performed relatively well with smaller sample sizes. Latent profile analysis was more consistent in recovering subtypes. Whether methods were successful depended on how differences were introduced. CONCLUSIONS: Traditional methods like latent profile analysis remain reasonable choices. Furthermore, results depend on assumptions and theoretical choices underlying subtyping analyses, which researchers need to consider before drawing conclusions on subtypes. Employing multiple subtyping methods to establish method dependency is recommended.

More than the sum of its parts: Merging network psychometrics and network neuroscience with application in autism.

Network approaches that investigate the interaction between symptoms and behaviours have opened new ways of understanding psychological phenomena in health and disorder in recent years. In parallel, network approaches that characterise the interaction between brain regions have become the dominant approach in neuroimaging research. In this paper, we introduce a methodology for combining network psychometrics and network neuroscience. This approach utilises the information from the psychometric network to obtain neural correlates that are associated with each node in the psychometric network (network-based regression). Moreover, we combine the behavioural variables and their neural correlates in a joint network to characterise their interactions. We illustrate the approach by highlighting the interaction between the triad of autistic traits and their resting-state functional connectivity associations. To this end, we utilise data from 172 male autistic participants (10-21 years) from the autism brain data exchange (ABIDE, ABIDE-II) that completed resting-state fMRI and were assessed using the autism diagnostic interview (ADI-R). Our results indicate that the network-based regression approach can uncover both unique and shared neural correlates of behavioural measures. For instance, our example analysis indicates that the overlap between communication and social difficulties is not reflected in the overlap between their functional brain correlates.

Temperament and psychopathology: The "community" to which you belong matters.

We utilized a community detection approach to longitudinally (a) identify distinct groups of children with common temperament profiles in infancy and at 2 and 3 years of age and (b) determine whether co-occurrence of certain temperament traits may be early predictors of internalizing problems at 5 years of age. Seven hundred and seventy-four infants (360 girls; 88.6% White, 9.8% Hispanic, and 1.6% other races) were recruited from the Boston area. Data collection spanned from 2012 to 2021. The analysis yielded three distinct groups of children with different temperament traits and was associated with significant variation in levels of internalizing symptoms and anxiety diagnosis rate. Our findings suggest that stable temperament "communities" can be detected in early childhood and may predict risk for psychopathology later in life.

Atypically slow processing of faces and non-faces in older autistic adults.

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.

Robust BOLD Responses to Faces But Not to Conditioned Threat: Challenging the Amygdala's Reputation in Human Fear and Extinction Learning.

Most of our knowledge about human emotional memory comes from animal research. Based on this work, the amygdala is often labeled the brain's "fear center", but it is unclear to what degree neural circuitries underlying fear and extinction learning are conserved across species. Neuroimaging studies in humans yield conflicting findings, with many studies failing to show amygdala activation in response to learned threat. Such null findings are often treated as resulting from MRI-specific problems related to measuring deep brain structures. Here we test this assumption in a mega-analysis of three studies on fear acquisition (n = 98; 68 female) and extinction learning (n = 79; 53 female). The conditioning procedure involved the presentation of two pictures of faces and two pictures of houses: one of each pair was followed by an electric shock [a conditioned stimulus (CS+)], the other one was never followed by a shock (CS-), and participants were instructed to learn these contingencies. Results revealed widespread responses to the CS+ compared with the CS- in the fear network, including anterior insula, midcingulate cortex, thalamus, and bed nucleus of the stria terminalis, but not the amygdala, which actually responded stronger to the CS- Results were independent of spatial smoothing, and of individual differences in trait anxiety and conditioned pupil responses. In contrast, robust amygdala activation distinguished faces from houses, refuting the idea that a poor signal could account for the absence of effects. Moving forward, we suggest that, apart from imaging larger samples at higher resolution, alternative statistical approaches may be used to identify cross-species similarities in fear and extinction learning.SIGNIFICANCE STATEMENT The science of emotional memory provides the foundation of numerous theories on psychopathology, including stress and anxiety disorders. This field relies heavily on animal research, which suggests a central role of the amygdala in fear learning and memory. However, this finding is not strongly corroborated by neuroimaging evidence in humans, and null findings are too easily explained away by methodological limitations inherent to imaging deep brain structures. In a large nonclinical sample, we find widespread BOLD activation in response to learned fear, but not in the amygdala. A poor signal could not account for the absence of effects. While these findings do not disprove the involvement of the amygdala in human fear learning, they challenge its typical portrayals and illustrate the complexities of translational science.

Connecting brain and behavior in clinical neuroscience: A network approach.

In recent years, there has been an increase in applications of network science in many different fields. In clinical neuroscience and psychopathology, the developments and applications of network science have occurred mostly simultaneously, but without much collaboration between the two fields. The promise of integrating these network applications lies in a united framework to tackle one of the fundamental questions of our time: how to understand the link between brain and behavior. In the current overview, we bridge this gap by introducing conventions in both fields, highlighting similarities, and creating a common language that enables the exploitation of synergies. We provide research examples in autism research, as it accurately represents research lines in both network neuroscience and psychological networks. We integrate brain and behavior not only semantically, but also practically, by showcasing three methodological avenues that allow to combine networks of brain and behavioral data. As such, the current paper offers a stepping stone to further develop multi-modal networks and to integrate brain and behavior.

Just a phase? Mapping the transition of behavioural problems from childhood to adolescence.

PURPOSE: Young people change substantially between childhood and adolescence. Yet, the current description of behavioural problems does not incorporate any reference to the developmental context. In the current analysis, we aimed to identify common transitions of behavioural problems between childhood and adolescence. METHOD: We followed 6744 individuals over 6 years as they transitioned from childhood (age 10) into adolescence (age 16). At each stage, we used a data-driven hierarchical clustering method to identify common profiles of behavioural problems, map transitions between profiles and identify factors that predict specific transitions. RESULTS: Common profiles of behavioural problems matched known comorbidity patterns but crucially showed that the presentation of behavioural problems changes markedly between childhood and adolescence. While problems with hyperactivity/impulsivity, motor control and conduct were prominent in childhood, adolescents showed profiles of problems related to emotional control, anxiety and inattention. Transitions were associated with socio-economic status and cognitive performance in childhood CONCLUSION: We show that understanding behavioural difficulties and mental ill-health must take into account the developmental context in which the problems occur, and we establish key risk factors for specific negative transitions as children become adolescents.

Far and wide: Associations between childhood socio-economic status and brain connectomics.

Previous studies have identified localized associations between childhood environment - namely their socio-economic status (SES) - and particular neural structures. The primary aim of the current study was to test whether associations between SES and brain structure are widespread or limited to specific neural pathways. We employed advances in whole-brain structural connectomics to address this. Diffusion tensor imaging was used to construct whole-brain connectomes in 113 6-12 year olds. We then applied an adapted multi-block partial-least squares (PLS) regression to explore how connectome organisation is associated with childhood SES (parental income, education levels, and neighbourhood deprivation). The Fractional Anisotropy (FA) connectome was significantly associated with childhood SES and this effect was widespread. We then pursued a secondary aim, and demonstrated that the connectome mediated the relationship between SES and cognitive ability (matrix reasoning and vocabulary). However, the connectome did not significantly mediate SES relationships with academic ability (maths and reading) or internalising and externalising behavior. This multivariate approach is important for advancing our theoretical understanding of how brain development may be shaped by childhood environment, and the role that it plays in predicting key outcomes. We also discuss the limitations with this new methodological approach.

Difference in default mode network subsystems in autism across childhood and adolescence.

LAY ABSTRACT: Neuroimaging research has identified a network of brain regions that are more active when we daydream compared to when we are engaged in a task. This network has been named the default mode network. Furthermore, differences in the default mode network are the most consistent findings in neuroimaging research in autism. Recent studies suggest that the default mode network is composed of subnetworks that are tied to different functions, namely memory and understanding others' minds. In this study, we investigated if default mode network differences in autism are related to specific subnetworks of the default mode network and if these differences change across childhood and adolescence. Our results suggest that the subnetworks of the default mode network are less differentiated in autism in middle childhood compared to neurotypicals. By late adolescence, the default mode network subnetwork organisation was similar in the autistic and neurotypical groups. These findings provide a foundation for future studies to investigate if this developmental pattern relates to improvements in the integration of memory and social understanding as autistic children grow up.

Transdiagnostic Brain Mapping in Developmental Disorders.

Childhood learning difficulties and developmental disorders are common, but progress toward understanding their underlying brain mechanisms has been slow. Structural neuroimaging, cognitive, and learning data were collected from 479 children (299 boys, ranging in age from 62 to 223 months), 337 of whom had been referred to the study on the basis of learning-related cognitive problems. Machine learning identified different cognitive profiles within the sample, and hold-out cross-validation showed that these profiles were significantly associated with children's learning ability. The same machine learning approach was applied to cortical morphology data to identify different brain profiles. Hold-out cross-validation demonstrated that these were significantly associated with children's cognitive profiles. Crucially, these mappings were not one-to-one. The same neural profile could be associated with different cognitive impairments across different children. One possibility is that the organization of some children's brains is less susceptible to local deficits. This was tested by using diffusion-weighted imaging (DWI) to construct whole-brain white-matter connectomes. A simulated attack on each child's connectome revealed that some brain networks were strongly organized around highly connected hubs. Children with these networks had only selective cognitive impairments or no cognitive impairments at all. By contrast, the same attacks had a significantly different impact on some children's networks, because their brain efficiency was less critically dependent on hubs. These children had the most widespread and severe cognitive impairments. On this basis, we propose a new framework in which the nature and mechanisms of brain-to-cognition relationships are moderated by the organizational context of the overall network.

Age-variant and age-invariant features of functional brain organization in middle-aged and older autistic adults.

Background: The majority of research effort into autism has been dedicated to understanding mechanisms during early development. As a consequence, research on the broader life course of an autism spectrum condition (ASC) has largely been neglected and almost nothing is known about ASC beyond middle age. Differences in brain connectivity that arise during early development may be maintained across the lifespan and may play protective or detrimental roles in older age. Method: This study explored age-related differences in functional connectivity across middle and older age in clinically diagnosed autistic adults (n = 44, 30-73 years) and in an age-matched typical comparison group (n = 45). Results: The results indicated parallel age-related associations in ASC and typical aging for the local efficiency and connection strength of the default mode network and for the segregation of the frontoparietal control network. In contrast, group differences in visual network connectivity are compatible with a safeguarding interpretation of less age-related decline in brain function in ASC. This divergence was mirrored in different associations between visual network connectivity and reaction time variability in the ASC and comparison group. Limitations: The study is cross-sectional and may be affected by cohort effects. As all participants received their autism diagnosis in adulthood, this might hinder generalizability. Conclusion: These results highlight the complexity of aging in ASC with both parallel and divergent trajectories across different aspects of functional network organization.

Neurocognitive reorganization between crystallized intelligence, fluid intelligence and white matter microstructure in two age-heterogeneous developmental cohorts.

Despite the reliability of intelligence measures in predicting important life outcomes such as educational achievement and mortality, the exact configuration and neural correlates of cognitive abilities remain poorly understood, especially in childhood and adolescence. Therefore, we sought to elucidate the factorial structure and neural substrates of child and adolescent intelligence using two cross-sectional, developmental samples (CALM: N = 551 (N = 165 imaging), age range: 5-18 years, NKI-Rockland: N = 337 (N = 65 imaging), age range: 6-18 years). In a preregistered analysis, we used structural equation modelling (SEM) to examine the neurocognitive architecture of individual differences in childhood and adolescent cognitive ability. In both samples, we found that cognitive ability in lower and typical-ability cohorts is best understood as two separable constructs, crystallized and fluid intelligence, which became more distinct across development, in line with the age differentiation hypothesis. Further analyses revealed that white matter microstructure, most prominently the superior longitudinal fasciculus, was strongly associated with crystallized (gc) and fluid (gf) abilities. Finally, we used SEM trees to demonstrate evidence for developmental reorganization of gc and gf and their white matter substrates such that the relationships among these factors dropped between 7-8 years before increasing around age 10. Together, our results suggest that shortly before puberty marks a pivotal phase of change in the neurocognitive architecture of intelligence.

A Hierarchical Watershed Model of Fluid Intelligence in Childhood and Adolescence.

Fluid intelligence is the capacity to solve novel problems in the absence of task-specific knowledge and is highly predictive of outcomes like educational attainment and psychopathology. Here, we modeled the neurocognitive architecture of fluid intelligence in two cohorts: the Centre for Attention, Leaning and Memory sample (CALM) (N = 551, aged 5-17 years) and the Enhanced Nathan Kline Institute-Rockland Sample (NKI-RS) (N = 335, aged 6-17 years). We used multivariate structural equation modeling to test a preregistered watershed model of fluid intelligence. This model predicts that white matter contributes to intermediate cognitive phenotypes, like working memory and processing speed, which, in turn, contribute to fluid intelligence. We found that this model performed well for both samples and explained large amounts of variance in fluid intelligence (R2CALM = 51.2%, R2NKI-RS = 78.3%). The relationship between cognitive abilities and white matter differed with age, showing a dip in strength around ages 7-12 years. This age effect may reflect a reorganization of the neurocognitive architecture around pre- and early puberty. Overall, these findings highlight that intelligence is part of a complex hierarchical system of partially independent effects.

Mapping differential responses to cognitive training using machine learning.

We used two simple unsupervised machine learning techniques to identify differential trajectories of change in children who undergo intensive working memory (WM) training. We used self-organizing maps (SOMs)-a type of simple artificial neural network-to represent multivariate cognitive training data, and then tested whether the way tasks are represented changed as a result of training. The patterns of change we observed in the SOM weight matrices implied that the processes drawn upon to perform WM tasks changed following training. This was then combined with K-means clustering to identify distinct groups of children who respond to the training in different ways. Firstly, the K-means clustering was applied to an independent large sample (N = 616, Mage  = 9.16 years, range = 5.16-17.91 years) to identify subgroups. We then allocated children who had been through cognitive training (N = 179, Mage  = 9.00 years, range = 7.08-11.50 years) to these same four subgroups, both before and after their training. In doing so, we were able to map their improvement trajectories. Scores on a separate measure of fluid intelligence were predictive of a child's improvement trajectory. This paper provides an alternative approach to analysing cognitive training data that go beyond considering changes in individual tasks. This proof-of-principle demonstrates a potentially powerful way of distinguishing task-specific from domain-general changes following training and of establishing different profiles of response to training.

Brain structure in children with congenital visual disorders and visual impairment.

AIM: To examine if congenital visual impairment is associated with differences in brain anatomy in children. METHOD: Ten children (8-12y) with congenital disorders of the peripheral visual system with severe visual impairment (SVI; >0.8 logMAR) or mild-to-moderate visual impairment (MVI; 0.6-0.8 logMAR) were compared to 21 typically sighted comparison (TSC) children. Thalamus volume, grey matter density, white matter microstructure, and integrity of visual tracts were investigated in SVI, MVI, and TSC groups with anatomical and diffusion-weighted magnetic resonance imaging. RESULTS: Compared to the TSC group, the SVI group had lower white matter integrity in tracts of the visual system (optic radiations: SVI 0.35±0.015, TSC 0.39±0.007 [p=0.022]; posterior corpus callosum: SVI 0.37±0.019; TSC 0.42±0.009 [p=0.033]) and lower left thalamus volume (SVI 4.37±0.087; TSC 4.99±0.339 [p=0.015]). Neuroanatomical differences were greater in the SVI group, while no consistent differences between the MVI and TSC group were observed. INTERPRETATION: Posterior tracts of the visual system are compromised in children with congenital visual impairment versus those who are typically sighted. The severity of visual input appears to have affected neuroanatomical development as significant reductions were only found in the SVI group. WHAT THIS PAPER ADDS: Severe visual impairment in mid-childhood is associated with reduced integrity of visual pathways and reduced thalamus volume.

Active touch facilitates object size perception in children but not adults: A multisensory event related potential study.

In order to increase perceptual precision the adult brain dynamically combines redundant information from different senses depending on their reliability. During object size estimation, for example, visual, auditory and haptic information can be integrated to increase the precision of the final size estimate. Young children, however, do not integrate sensory information optimally and instead rely on active touch. Whether this early haptic dominance is reflected in age-related differences in neural mechanisms and whether it is driven by changes in bottom-up perceptual or top-down attentional processes has not yet been investigated. Here, we recorded event-related-potentials from a group of adults and children aged 5-7 years during an object size perception task using auditory, visual and haptic information. Multisensory information was presented either congruently (conveying the same information) or incongruently (conflicting information). No behavioral responses were required from participants. When haptic size information was available via actively tapping the objects, response amplitudes in the mid-parietal area were significantly reduced by information congruency in children but not in adults between 190 ms-250 ms and 310 ms-370 ms. These findings indicate that during object size perception only children's brain activity is modulated by active touch supporting a neural maturational shift from sensory dominance in early childhood to optimal multisensory benefit in adulthood.

The cingulum as a marker of individual differences in neurocognitive development.

The canonical approach to exploring brain-behaviour relationships is to group individuals according to a phenotype of interest, and then explore the neural correlates of this grouping. A limitation of this approach is that multiple aetiological pathways could result in a similar phenotype, so the role of any one brain mechanism may be substantially underestimated. Building on advances in network analysis, we used a data-driven community-clustering algorithm to identify robust subgroups based on white-matter microstructure in childhood and adolescence (total N = 313, mean age: 11.24 years). The algorithm indicated the presence of two equal-size groups that show a critical difference in fractional anisotropy (FA) of the left and right cingulum. Applying the brain-based grouping in independent samples, we find that these different 'brain types' had profoundly different cognitive abilities with higher performance in the higher FA group. Further, a connectomics analysis indicated reduced structural connectivity in the low FA subgroup that was strongly related to reduced functional activation of the default mode network. These results provide a proof-of-concept that bottom-up brain-based groupings can be identified that relate to cognitive performance. This provides a first demonstration of a complimentary approach for investigating individual differences in brain structure and function, particularly for neurodevelopmental disorders where researchers are often faced with phenotypes that are difficult to define at the cognitive or behavioural level.