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

Abstract Network approaches that investigate the interaction between symptoms or behaviours have opened new ways of understanding psychological phenomena in health and disorder. In parallel, network approaches that characterise the interaction between brain regions have become the dominant approach in neuroimaging research. Combining these parallel approaches would enable new insights into the interaction between behaviours and their brain-level correlates. 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 for each node in the psychometric network (network-based regression). We illustrate the approach by highlighting the interaction between autistic traits and their resting-state functional associations. To this end, we utilise data from 172 male autistic participants (10–21 years) from the autism brain data exchange (ABIDE, ABIDE-II). Our results indicate that the network-based regression approach can uncover both unique and shared neural correlates of behavioural measures. In addition, the methodology enables us to isolate mechanisms at the brain-level that are unique to particular behavioural variables. For instance, our example analysis indicates that the overlap between communication and social difficulties is not reflected in the overlap between their functional correlates.

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

10.1101/2020.11.17.386276 ◽

2020 ◽

Author(s):

Joe Bathelt ◽

Hilde M. Geurts ◽

Denny Borsboom

Keyword(s):

Resting State ◽

Data Exchange ◽

Autistic Traits ◽

Neural Correlates ◽

Brain Regions ◽

Diagnostic Interview ◽

Resting State Functional Connectivity ◽

Psychological Phenomena ◽

Brain Level ◽

Network Approaches

ABSTRACTNetwork 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 to understanding brain function in neuroimaging research. Combining these parallel approaches would enable new insights into the interaction between behaviours and their brain-level correlates. In this paper, we introduce a methodology for combining network psychometrics and network neuro-science. 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. In addition, because the shared variance between behavioural measures is controlled for in the approach, the methodology enables us to isolate mechanisms at the brain-level that are unique to particular behavioural variables. 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.

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Developmental trajectories of autistic social traits in the general population

Psychological Medicine ◽

10.1017/s0033291721002166 ◽

2021 ◽

pp. 1-9

Author(s):

Richard Pender ◽

Pasco Fearon ◽

Beate St Pourcain ◽

Jon Heron ◽

Will Mandy

Keyword(s):

General Population ◽

Full Range ◽

Population Sample ◽

Autistic Traits ◽

Communication Disorders ◽

Autism Spectrum ◽

Growth Mixture Modelling ◽

Autistic People ◽

Social Difficulties ◽

Autism Trait

Abstract Background Autistic people show diverse trajectories of autistic traits over time, a phenomenon labelled ‘chronogeneity’. For example, some show a decrease in symptoms, whilst others experience an intensification of difficulties. Autism spectrum disorder (ASD) is a dimensional condition, representing one end of a trait continuum that extends throughout the population. To date, no studies have investigated chronogeneity across the full range of autistic traits. We investigated the nature and clinical significance of autism trait chronogeneity in a large, general population sample. Methods Autistic social/communication traits (ASTs) were measured in the Avon Longitudinal Study of Parents and Children using the Social and Communication Disorders Checklist (SCDC) at ages 7, 10, 13 and 16 (N = 9744). We used Growth Mixture Modelling (GMM) to identify groups defined by their AST trajectories. Measures of ASD diagnosis, sex, IQ and mental health (internalising and externalising) were used to investigate external validity of the derived trajectory groups. Results The selected GMM model identified four AST trajectory groups: (i) Persistent High (2.3% of sample), (ii) Persistent Low (83.5%), (iii) Increasing (7.3%) and (iv) Decreasing (6.9%) trajectories. The Increasing group, in which females were a slight majority (53.2%), showed dramatic increases in SCDC scores during adolescence, accompanied by escalating internalising and externalising difficulties. Two-thirds (63.6%) of the Decreasing group were male. Conclusions Clinicians should note that for some young people autism-trait-like social difficulties first emerge during adolescence accompanied by problems with mood, anxiety, conduct and attention. A converse, majority-male group shows decreasing social difficulties during adolescence.

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Sex differences in neural correlates of common psychopathological symptoms in early adolescence

Psychological Medicine ◽

10.1017/s0033291720005140 ◽

2021 ◽

pp. 1-11

Author(s):

Francesca Biondo ◽

Charlotte Nymberg Thunell ◽

Bing Xu ◽

Congying Chu ◽

Tianye Jia ◽

...

Keyword(s):

Internalizing Symptoms ◽

Grey Matter ◽

Neural Correlates ◽

Brain Regions ◽

Female Adolescents ◽

Grey Matter Volume ◽

Community Based ◽

Psychopathological Symptom ◽

Strengths And Difficulties ◽

The Brain

Abstract Background Sex-related differences in psychopathology are known phenomena, with externalizing and internalizing symptoms typically more common in boys and girls, respectively. However, the neural correlates of these sex-by-psychopathology interactions are underinvestigated, particularly in adolescence. Methods Participants were 14 years of age and part of the IMAGEN study, a large (N = 1526) community-based sample. To test for sex-by-psychopathology interactions in structural grey matter volume (GMV), we used whole-brain, voxel-wise neuroimaging analyses based on robust non-parametric methods. Psychopathological symptom data were derived from the Strengths and Difficulties Questionnaire (SDQ). Results We found a sex-by-hyperactivity/inattention interaction in four brain clusters: right temporoparietal-opercular region (p < 0.01, Cohen's d = −0.24), bilateral anterior and mid-cingulum (p < 0.05, Cohen's d = −0.18), right cerebellum and fusiform (p < 0.05, Cohen's d = −0.20) and left frontal superior and middle gyri (p < 0.05, Cohen's d = −0.26). Higher symptoms of hyperactivity/inattention were associated with lower GMV in all four brain clusters in boys, and with higher GMV in the temporoparietal-opercular and cerebellar-fusiform clusters in girls. Conclusions Using a large, sex-balanced and community-based sample, our study lends support to the idea that externalizing symptoms of hyperactivity/inattention may be associated with different neural structures in male and female adolescents. The brain regions we report have been associated with a myriad of important cognitive functions, in particular, attention, cognitive and motor control, and timing, that are potentially relevant to understand the behavioural manifestations of hyperactive and inattentive symptoms. This study highlights the importance of considering sex in our efforts to uncover mechanisms underlying psychopathology during adolescence.

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Graph Ricci Curvatures Reveal Disease-related Changes in Autism Spectrum Disorder

10.1101/2021.11.28.470231 ◽

2021 ◽

Author(s):

Pavithra Elumalai ◽

Yasharth Yadav ◽

Nitin Williams ◽

Emil Saucan ◽

Jürgen Jost ◽

...

Keyword(s):

Autism Spectrum Disorder ◽

Resting State ◽

Neurodevelopmental Disorders ◽

Data Exchange ◽

Meta Analysis ◽

Resting State Fmri ◽

Brain Regions ◽

Autism Spectrum ◽

Spectrum Disorder ◽

The Brain

Autism Spectrum Disorder (ASD) is a set of neurodevelopmental disorders that pose a significant global health burden. Measures from graph theory have been used to characterise ASD-related changes in resting-state fMRI functional connectivity networks (FCNs), but recently developed geometry-inspired measures have not been applied so far. In this study, we applied geometry-inspired graph Ricci curvatures to investigate ASD-related changes in resting-state fMRI FCNs. To do this, we applied Forman-Ricci and Ollivier-Ricci curvatures to compare networks of ASD and healthy controls (N = 1112) from the Autism Brain Imaging Data Exchange I (ABIDE-I) dataset. We performed these comparisons at the brain-wide level as well as at the level of individual brain regions, and further, determined the behavioral relevance of region-specific differences with Neurosynth meta-analysis decoding. We found brain-wide ASD-related differences for both Forman-Ricci and Ollivier-Ricci curvatures. For Forman-Ricci curvature, these differences were distributed across 83 of the 200 brain regions studied, and concentrated within the Default Mode, Somatomotor and Ventral Attention Network. Meta-analysis decoding identified the brain regions showing curvature differences as involved in social cognition, memory, language and movement. Notably, comparison with results from previous non-invasive stimulation (TMS/tDCS) experiments revealed that the set of brain regions showing curvature differences overlapped with the set of brain regions whose stimulation resulted in positive cognitive or behavioural outcomes in ASD patients. These results underscore the utility of geometry-inspired graph Ricci curvatures in characterising disease-related changes in ASD, and possibly, other neurodevelopmental disorders.

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Mapping the common and distinct neural correlates of visual, rule and motor conflict

10.1101/2020.11.19.388231 ◽

2020 ◽

Author(s):

Bryony Goulding Mew ◽

Darije Custovic ◽

Eyal Soreq ◽

Romy Lorenz ◽

Ines Violante ◽

...

Keyword(s):

Region Of Interest ◽

Neural Correlates ◽

Brain Regions ◽

Control Mechanisms ◽

Activation Patterns ◽

The Common ◽

Full Factorial ◽

The Brain ◽

Categorical Scale ◽

Conflict Types

AbstractFlexible behaviour requires cognitive-control mechanisms to efficiently resolve conflict between competing information and alternative actions. Whether a global neural resource mediates all forms of conflict or this is achieved within domainspecific systems remains debated. We use a novel fMRI paradigm to orthogonally manipulate rule, response and stimulus-based conflict within a full-factorial design. Whole-brain voxelwise analyses show that activation patterns associated with these conflict types are distinct but partially overlapping within Multiple Demand Cortex (MDC), the brain regions that are most commonly active during cognitive tasks. Region of interest analysis shows that most MDC sub-regions are activated for all conflict types, but to significantly varying levels. We propose that conflict resolution is an emergent property of distributed brain networks, the functional-anatomical components of which place on a continuous, not categorical, scale from domain-specialised to domain general. MDC brain regions place towards one end of that scale but display considerable functional heterogeneity.

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The social brain in female autism: a structural imaging study of twins

Social Cognitive and Affective Neuroscience ◽

10.1093/scan/nsaa064 ◽

2020 ◽

Vol 15 (4) ◽

pp. 423-436

Author(s):

Élodie Cauvet ◽

Annelies van’t Westeinde ◽

Roberto Toro ◽

Ralf Kuja-Halkola ◽

Janina Neufeld ◽

...

Keyword(s):

Sex Differences ◽

Social Cognition ◽

Brain Structure ◽

Imaging Study ◽

Autistic Traits ◽

Brain Regions ◽

Autism Spectrum ◽

Social Brain ◽

Twin Design ◽

The Social

Abstract A female advantage in social cognition (SoC) might contribute to women’s underrepresentation in autism spectrum disorder (ASD). The latter could be underpinned by sex differences in social brain structure. This study investigated the relationship between structural social brain networks and SoC in females and males in relation to ASD and autistic traits in twins. We used a co-twin design in 77 twin pairs (39 female) aged 12.5 to 31.0 years. Twin pairs were discordant or concordant for ASD or autistic traits, discordant or concordant for other neurodevelopmental disorders or concordant for neurotypical development. They underwent structural magnetic resonance imaging and were assessed for SoC using the naturalistic Movie for the Assessment of Social Cognition. Autistic traits predicted reduced SoC capacities predominantly in male twins, despite a comparable extent of autistic traits in each sex, although the association between SoC and autistic traits did not differ significantly between the sexes. Consistently, within-pair associations between SoC and social brain structure revealed that lower SoC ability was associated with increased cortical thickness of several brain regions, particularly in males. Our findings confirm the notion that sex differences in SoC in association with ASD are underpinned by sex differences in brain structure.

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Tau PET Distributional Pattern in AD Patients with Visuospatial Dysfunction

Current Alzheimer Research ◽

10.2174/1567205016666191113152434 ◽

2019 ◽

Vol 16 (11) ◽

pp. 1055-1062

Author(s):

Xi Sun ◽

Binbin Nie ◽

Shujun Zhao ◽

Qian Chen ◽

Panlong Li ◽

...

Keyword(s):

Temporal Cortex ◽

Cingulate Cortex ◽

Normal Subjects ◽

Neural Correlates ◽

Brain Regions ◽

Posterior Cingulate Cortex ◽

Distributional Pattern ◽

Cognitively Normal ◽

Posterior Cingulate ◽

Cognitively Normal Subjects

Background: Visuospatial dysfunction is one predominant symptom in many atypical Alzheimer’s disease (AD) patients, however, until now its neural correlates still remain unclear. For the accumulation of intracellular hyperphosphorylated tau proteins is a major pathogenic factor in neurodegeneration of AD, the distributional pattern of tau could highlight the affected brain regions associated with specific cognitive deficits. Objective: We investigated the brain regions particularly affected by tau accumulation in patients with visuospatial dysfunction to explore its neural correlates. Methods: Using 18F-AV-1451 tau positron emission tomography (PET), voxel-wise two-sample t-tests were performed between AD patients with obvious visuospatial dysfunction (VS-AD) and cognitively normal subjects, AD patients with little-to-no visuospatial dysfunction (non VS-AD) and cognitively normal subjects, respectively. Results: Results showed increased tau accumulations mainly located in occipitoparietal cortex, posterior cingulate cortex, precuneus, inferior and medial temporal cortex in VS-AD patients, while increased tau accumulations mainly occurred in the inferior and medial temporal cortex in non VS-AD patients. Conclusion: These findings suggested that occipitoparietal cortex, posterior cingulate cortex and precuneus, which were particularly affected by increased tau accumulation in VS-AD patients, may associate with visuospatial dysfunction of AD.

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Neural correlates of integrated self and social processing

Social Cognitive and Affective Neuroscience ◽

10.1093/scan/nsaa121 ◽

2020 ◽

Vol 15 (9) ◽

pp. 941-949

Author(s):

Laura Finlayson-Short ◽

Christopher G Davey ◽

Ben J Harrison

Keyword(s):

Prefrontal Cortex ◽

Medial Prefrontal Cortex ◽

Processing Condition ◽

Emotional Valence ◽

Neural Correlates ◽

Brain Regions ◽

Affective Processing ◽

Social Processing ◽

Referential Processing ◽

Core Self

Abstract Self-referential and social processing are often engaged concurrently in naturalistic judgements and elicit activity in overlapping brain regions. We have termed this integrated processing ‘self-other referential processing’ and developed a task to measure its neural correlates. Ninety-eight healthy young people aged 16–25 (M = 21.5 years old, 67% female) completed our novel functional magnetic resonance imaging task. The task had two conditions, an active self-other referential processing condition in which participants rated how much they related to emotional faces and a control condition. Rating relatedness required thinking about oneself (self-referential processing) and drawing a comparison to an imagined other (social processing). Self-other referential processing elicited activity in the default mode network and social cognition system; most notably in the ‘core self’ regions of the medial prefrontal cortex and posterior cingulate cortex. Relatedness and emotional valence directly modulated activity in these core self areas, while emotional valence additionally modulated medial prefrontal cortex activity. This shows the key role of the medial prefrontal cortex in constructing the ‘social-affective self’. This may help to unify disparate models of medial prefrontal cortex function, demonstrating its role in coordinating multiple processes—self-referential, social and affective processing—to allow the self to exist in a complex social world.

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Large-scale functional coactivation patterns reflect the structural connectivity of the medial prefrontal cortex

Social Cognitive and Affective Neuroscience ◽

10.1093/scan/nsaa132 ◽

2020 ◽

Author(s):

Dale T Tovar ◽

Robert S Chavez

Keyword(s):

Prefrontal Cortex ◽

Medial Prefrontal Cortex ◽

Large Scale ◽

Spatial Scales ◽

Structural Connectivity ◽

Brain Regions ◽

Machine Learning Techniques ◽

Affective Processing ◽

Affective Neuroscience ◽

Psychological Phenomena

Abstract The medial prefrontal cortex (MPFC) is among the most consistently implicated brain regions in social and affective neuroscience. Yet, this region is also highly functionally heterogeneous across many domains and has diverse patterns of connectivity. The extent to which the communication of functional networks in this area is facilitated by its underlying structural connectivity fingerprint is critical for understanding how psychological phenomena are represented within this region. In the current study, we combined diffusion magnetic resonance imaging and probabilistic tractography with large-scale meta-analysis to investigate the degree to which the functional co-activation patterns of the MPFC is reflected in its underlying structural connectivity. Using unsupervised machine learning techniques, we compared parcellations between the two modalities and found congruence between parcellations at multiple spatial scales. Additionally, using connectivity and coactivation similarity analyses, we found high correspondence in voxel-to-voxel similarity between each modality across most, but not all, subregions of the MPFC. These results provide evidence that meta-analytic functional coactivation patterns are meaningfully constrained by underlying neuroanatomical connectivity and provide convergent evidence of distinct subregions within the MPFC involved in affective processing and social cognition.

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