[Show abstract][Hide abstract]ABSTRACT: The Empathizing-Systemizing (E-S) theory describes a profile of traits that have been linked to autism spectrum disorders, and are thought to encompass a continuum that includes typically developing (TD) individuals. Although systemizing is hypothesized to be related to mathematical abilities, empirical support for this relationship is lacking. We examine the link between empathizing and systemizing tendencies and mathematical achievement in 112 TD children (57 girls) to elucidate how socio-cognitive constructs influence early development of mathematical skills. Assessment of mathematical achievement included standardized tests designed to examine calculation skills and conceptual mathematical reasoning. Empathizing and systemizing were assessed using the Combined Empathy Quotient-Child (EQ-C) and Systemizing Quotient-Child (SQ-C). Contrary to our hypothesis, we found that mathematical achievement was not related to systemizing or the discrepancy between systemizing and empathizing. Surprisingly, children with higher empathy demonstrated lower calculation skills. Further analysis using the Social Responsiveness Scale (SRS) revealed that the relationship between EQ-C and mathematical achievement was mediated by social ability rather than autistic behaviors. Finally, social awareness was found to play a differential role in mediating the relationship between EQ-C and mathematical achievement in girls. These results identify empathy, and social skills more generally, as previously unknown predictors of mathematical achievement.
[Show abstract][Hide abstract]ABSTRACT: Background:
Causal estimation methods are increasingly being used to investigate functional brain networks in fMRI, but there are continuing concerns about the validity of these methods.
Multivariate Dynamical Systems (MDS) is a state-space method for estimating dynamic causal interactions in fMRI data. Here we validate MDS using benchmark simulations as well as simulations from a more realistic stochastic neurophysiological model. Finally, we applied MDS to investigate dynamic casual interactions in a fronto-cingulate-parietal control network using Human Connectome Project (HCP) data acquired during performance of a working memory task. Crucially, since the ground truth in experimental data is unknown, we conducted novel stability analysis to determine robust causal interactions within this network.
MDS accurately recovered dynamic causal interactions with an area under receiver operating characteristic (AUC) above 0.7 for benchmark datasets and AUC above 0.9 for datasets generated using the neurophysiological model. In experimental fMRI data, bootstrap procedures revealed a stable pattern of causal influences from the anterior insula to other nodes of the fronto-cingulate-parietal network.
Comparison with existing methods:
MDS is effective in estimating dynamic causal interactions in both the benchmark and neurophysiological model based datasets in terms of AUC, sensitivity and false positive rates.
Our findings demonstrate that MDS can accurately estimate causal interactions in fMRI data. Neurophysiological models and stability analysis provide a general framework for validating computational methods designed to estimate causal interactions in fMRI. The right anterior insula functions as a causal hub during working memory.
No preview · Article · Mar 2016 · Journal of Neuroscience Methods
[Show abstract][Hide abstract]ABSTRACT: State-space multivariate dynamical systems (MDS) (Ryali et al. 2011) and other causal estimation models are being increasingly used to identify directed functional interactions between brain regions. However, the validity and accuracy of such methods are poorly understood. Performance evaluation based on computer simulations of small artificial causal networks can address this problem to some extent, but they often involve simplifying assumptions that reduce biological validity of the resulting data. Here, we use a novel approach taking advantage of recently developed optogenetic fMRI (ofMRI) techniques to selectively stimulate brain regions while simultaneously recording high-resolution whole-brain fMRI data. ofMRI allows for a more direct investigation of causal influences from the stimulated site to brain regions activated downstream and is therefore ideal for evaluating causal estimation methods in vivo. We used ofMRI to investigate whether MDS models for fMRI can accurately estimate causal functional interactions between brain regions. Two cohorts of ofMRI data were acquired, one at Stanford University and the University of California Los Angeles (Cohort 1) and the other at the University of North Carolina Chapel Hill (Cohort 2). In each cohort optical stimulation was delivered to the right primary motor cortex (M1). General linear model analysis revealed prominent downstream thalamic activation in Cohort 1, and caudate-putamen (CPu) activation in Cohort 2. MDS accurately estimated causal interactions from M1 to thalamus and from M1 to CPu in Cohort 1 and Cohort 2, respectively. As predicted, no causal influences were found in the reverse direction. Additional control analyses demonstrated the specificity of causal interactions between stimulated and target sites. Our findings suggest that MDS state-space models can accurately and reliably estimate causal interactions in ofMRI data and further validate their use for estimating causal interactions in fMRI. More generally, our study demonstrates that the combined use of optogenetics and fMRI provides a powerful new tool for evaluating computational methods designed to estimate causal interactions between distributed brain regions.
[Show abstract][Hide abstract]ABSTRACT: Mathematical disabilities (MD) have a negative life-long impact on professional success, employment, and health outcomes. Yet little is known about the intrinsic functional brain organization that contributes to poor math skills in affected children. It is now increasingly recognized that math cognition requires coordinated interaction within a large-scale fronto-parietal network anchored in the intraparietal sulcus (IPS). Here we characterize intrinsic functional connectivity within this IPS-network in children with MD, relative to a group of typically developing (TD) children who were matched on age, gender, IQ, working memory, and reading abilities. Compared to TD children, children with MD showed hyper-connectivity of the IPS with a bilateral fronto-parietal network. Importantly, aberrant IPS connectivity patterns accurately discriminated children with MD and TD children, highlighting the possibility for using IPS connectivity as a brain-based biomarker of MD. To further investigate regional abnormalities contributing to network-level deficits in children with MD, we performed whole-brain analyses of intrinsic low-frequency fluctuations. Notably, children with MD showed higher low-frequency fluctuations in multiple fronto-parietal areas that overlapped with brain regions that exhibited hyper-connectivity with the IPS. Taken together, our findings suggest that MD in children is characterized by robust network-level aberrations, and is not an isolated dysfunction of the IPS. We hypothesize that intrinsic hyper-connectivity and enhanced low-frequency fluctuations may limit flexible resource allocation, and contribute to aberrant recruitment of task-related brain regions during numerical problem solving in children with MD.
No preview · Article · Feb 2016 · Developmental Science
[Show abstract][Hide abstract]ABSTRACT: Despite reports of mathematical talent in autism spectrum disorders (ASD), little is known about basic number processing abilities in affected children. We investigated number sense, the ability to rapidly assess quantity information, in 36 children with ASD and 61 typically developing controls. Numerical acuity was assessed using symbolic (Arabic numerals) as well as non-symbolic (dot array) formats. We found significant impairments in non-symbolic acuity in children with ASD, but symbolic acuity was intact. Symbolic acuity mediated the relationship between non-symbolic acuity and mathematical abilities only in children with ASD, indicating a distinctive role for symbolic number sense in the acquisition of mathematical proficiency in this group. Our findings suggest that symbolic systems may help children with ASD organize imprecise information.
Full-text · Article · Dec 2015 · Journal of Autism and Developmental Disorders
[Show abstract][Hide abstract]ABSTRACT: Human cognitive problem solving skills undergo complex experience-dependent changes from childhood to adulthood, yet most neurodevelopmental research has focused on linear changes with age. Here we challenge this limited view, and investigate spatially heterogeneous and nonlinear neurodevelopmental profiles between childhood, adolescence, and young adulthood, focusing on three cytoarchitectonically distinct posterior parietal cortex (PPC) regions implicated in numerical problem solving: intraparietal sulcus (IPS), angular gyrus (AG), and supramarginal gyrus (SMG). Adolescents demonstrated better behavioral performance relative to children, but their performance was equivalent to that of adults. However, all three groups differed significantly in their profile of activation and connectivity across the PPC subdivisions. Activation in bilateral ventral IPS subdivision IPS-hIP1, along with adjoining anterior AG subdivision, AG-PGa, and the posterior SMG subdivision, SMG-PFm, increased linearly with age whereas the posterior AG subdivision, AG-PGp, was equally deactivated in all three groups. In contrast, the left anterior SMG subdivision, SMG-PF showed an inverted U-shaped profile across age groups such that adolescents exhibited greater activation than both children and young adults. Critically, greater SMG-PF activation was correlated with task performance only in adolescents. Furthermore, adolescents showed greater task-related functional connectivity of the SMG-PF with ventro-temporal, anterior temporal and prefrontal cortices, relative to both children and adults. These results suggest that nonlinear up-regulation of SMG-PF and its interconnected functional circuits facilitate adult-level performance in adolescents. Our study provides novel insights into heterogeneous age-related maturation of the PPC underlying cognitive skill acquisition, and further demonstrates how anatomically precise analysis of both linear and nonlinear neurofunctional changes with age is necessary for more fully characterizing cognitive development.
[Show abstract][Hide abstract]ABSTRACT: Objective:
Cognitive impairments in Parkinson's disease (PD) are thought to be caused in part by dopamine dysregulation. However, even when nigrostriatal dopamine neuron loss is severe enough to cause motor symptoms, many patients remain cognitively unimpaired. It is unclear what brain mechanisms allow these patients to remain cognitively unimpaired despite substantial dopamine dysregulation.
31 cognitively unimpaired PD participants OFF dopaminergic-medications were scanned using fMRI while they performed a working memory task, along with 23 controls. We first compared the PD_OFF medication group with controls to determine whether PD participants engage compensatory frontostriatal mechanisms during working memory. We then studied the same PD participants ON dopaminergic-medications to determine whether these compensatory brain changes are altered with dopamine.
Controls and PD showed working memory load-dependent activation in the bilateral putamen, anterior-dorsal insula, supplementary motor area, and anterior cingulate cortex. Compared to controls, PD_OFF showed compensatory hyper-activation of bilateral putamen and posterior insula, and machine learning algorithms identified robust differences in putamen activation patterns. Compared to PD_OFF, PD_ON showed reduced compensatory activation in the putamen. Loss of compensatory hyper-activation ON dopaminergic-medication correlated with slower performance on the working memory task and slower cognitive speed on the Symbol Digit Modality Test.
Our results provide novel evidence that PD patients maintain normal cognitive performance through compensatory hyper-activation of the putamen. Dopaminergic-medication down-regulates this hyper-activation and the degree of down-regulation predicts behavior. Identifying cognitive compensatory mechanisms in PD is important for understanding how some patients maintain intact cognitive performance, despite nigrostriatal dopamine loss. This article is protected by copyright. All rights reserved.
Full-text · Article · Dec 2015 · Annals of Neurology
[Show abstract][Hide abstract]ABSTRACT: Background:
Attention-deficit/hyperactivity disorder (ADHD) is increasingly viewed as a disorder stemming from disturbances in large-scale brain networks, yet the exact nature of these impairments in affected children is poorly understood. We investigated a saliency-based triple-network model and tested the hypothesis that cross-network interactions between the salience network (SN), central executive network, and default mode network are dysregulated in children with ADHD. We also determined whether network dysregulation measures can differentiate children with ADHD from control subjects across multisite datasets and predict clinical symptoms.
Functional magnetic resonance imaging data from 180 children with ADHD and control subjects from three sites in the ADHD-200 database were selected using case-control design. We investigated between-group differences in resource allocation index (RAI) (a measure of SN-centered triple network interactions), relation between RAI and ADHD symptoms, and performance of multivariate classifiers built to differentiate children with ADHD from control subjects.
RAI was significantly lower in children with ADHD than in control subjects. Severity of inattention symptoms was correlated with RAI. Remarkably, these findings were replicated in three independent datasets. Multivariate classifiers based on cross-network coupling measures differentiated children with ADHD from control subjects with high classification rates (72% to 83%) for each dataset. A novel cross-site classifier based on training data from one site accurately (62% to 82%) differentiated children with ADHD on test data from the two other sites.
Aberrant cross-network interactions between SN, central executive network, and default mode network are a reproducible feature of childhood ADHD. The triple-network model provides a novel, replicable, and parsimonious systems neuroscience framework for characterizing childhood ADHD and predicting clinical symptoms in affected children.
No preview · Article · Nov 2015 · Biological psychiatry
[Show abstract][Hide abstract]ABSTRACT: Autism spectrum disorder (ASD) is characterized by reduced attention to salient social stimuli. Here we use two visual oddball
tasks to investigate brain systems engaged during attention to social (face) and non-social (scene) stimuli. We focused on
the dorsal and ventral subdivisions of the anterior insula (dAI and vAI, respectively), anatomically distinct regions contributing
to a ‘salience network’ that is known to regulate attention to behaviorally meaningful stimuli. Children with ASD performed
comparably to their typically developing (TD) peers, but they engaged the right dAI and vAI differently in response to deviant
faces compared with deviant scenes. Multivariate activation patterns in the dAI reliably discriminated between children with
ASD and TD children with 85% classification accuracy, and children with ASD activated the vAI more than their TD peers. Children
with ASD and their TD peers also differed in dAI connectivity patterns to deviant faces, with stronger within-salience network
interactions in the ASD group and stronger cross-network interactions in the TD group. Our findings point to atypical patterns
of right anterior insula activation and connectivity in ASD and suggest that multiple functions subserved by the insula, including
attention and affective processing of salient social stimuli, are aberrant in children with the disorder.
No preview · Article · Oct 2015 · Social Cognitive and Affective Neuroscience
[Show abstract][Hide abstract]ABSTRACT: Competency with numbers is essential in today's society; yet, up to 20% of children exhibit moderate to severe mathematical learning disabilities (MLD). Behavioural intervention can be effective, but the neurobiological mechanisms underlying successful intervention are unknown. Here we demonstrate that eight weeks of 1:1 cognitive tutoring not only remediates poor performance in children with MLD, but also induces widespread changes in brain activity. Neuroplasticity manifests as normalization of aberrant functional responses in a distributed network of parietal, prefrontal and ventral temporal-occipital areas that support successful numerical problem solving, and is correlated with performance gains. Remarkably, machine learning algorithms show that brain activity patterns in children with MLD are significantly discriminable from neurotypical peers before, but not after, tutoring, suggesting that behavioural gains are not due to compensatory mechanisms. Our study identifies functional brain mechanisms underlying effective intervention in children with MLD and provides novel metrics for assessing response to intervention.
[Show abstract][Hide abstract]ABSTRACT: Math anxiety is a negative emotional reaction that is characterized by feelings of stress and anxiety in situations involving mathematical problem solving. High math-anxious individuals tend to avoid situations involving mathematics and are less likely to pursue science, technology, engineering, and math-related careers than those with low math anxiety. Math anxiety during childhood, in particular, has adverse long-term consequences for academic and professional success. Identifying cognitive interventions and brain mechanisms by which math anxiety can be ameliorated in children is therefore critical. Here we investigate whether an intensive 8 week one-to-one cognitive tutoring program designed to improve mathematical skills reduces childhood math anxiety, and we identify the neurobiological mechanisms by which math anxiety can be reduced in affected children. Forty-six children in grade 3, a critical early-onset period for math anxiety, participated in the cognitive tutoring program. High math-anxious children showed a significant reduction in math anxiety after tutoring. Remarkably, tutoring remediated aberrant functional responses and connectivity in emotion-related circuits anchored in the basolateral amygdala. Crucially, children with greater tutoring-induced decreases in amygdala reactivity had larger reductions in math anxiety. Our study demonstrates that sustained exposure to mathematical stimuli can reduce math anxiety and highlights the key role of the amygdala in this process. Our findings are consistent with models of exposure-based therapy for anxiety disorders and have the potential to inform the early treatment of a disability that, if left untreated in childhood, can lead to significant lifelong educational and socioeconomic consequences in affected individuals.
Full-text · Article · Sep 2015 · The Journal of Neuroscience : The Official Journal of the Society for Neuroscience
[Show abstract][Hide abstract]ABSTRACT: Background:
Autism spectrum disorder (ASD) is diagnosed much less often in females than males. Emerging behavioral accounts suggest that the clinical presentation of autism is different in females and males, yet research examining sex differences in core symptoms of autism in affected children has been limited. Additionally, to date, there have been no systematic attempts to characterize neuroanatomical differences underlying the distinct behavioral profiles observed in girls and boys with ASD. This is in part because extant ASD studies have included a small number of girls.
Leveraging the National Database for Autism Research (NDAR), we first analyzed symptom severity in a large sample consisting of 128 ASD girls and 614 age- and IQ-matched ASD boys. We then examined symptom severity and structural imaging data using novel multivariate pattern analysis in a well-matched group of 25 ASD girls, 25 ASD boys, 19 typically developing (TD) girls, and 19 TD boys, obtained from the Autism Brain Imaging Data Exchange (ABIDE).
In both the NDAR and ABIDE datasets, girls, compared to boys, with ASD showed less severe repetitive/restricted behaviors (RRBs) and comparable deficits in the social and communication domains. In the ABIDE imaging dataset, gray matter (GM) patterns in the motor cortex, supplementary motor area (SMA), cerebellum, fusiform gyrus, and amygdala accurately discriminated girls and boys with ASD. This sex difference pattern was specific to ASD as the GM in these brain regions did not discriminate TD girls and boys. Moreover, GM in the motor cortex, SMA, and crus 1 subdivision of the cerebellum was correlated with RRB in girls whereas GM in the right putamen-the region that discriminated TD girls and boys-was correlated with RRB in boys.
We found robust evidence for reduced levels of RRB in girls, compared to boys, with ASD, providing the strongest evidence to date for sex differences in a core phenotypic feature of childhood ASD. Sex differences in brain morphometry are prominent in the motor system and in areas that comprise the "social brain." Notably, RRB severity is associated with sex differences in GM morphometry in distinct motor regions. Our findings provide novel insights into the neurobiology of sex differences in childhood autism.
[Show abstract][Hide abstract]ABSTRACT: The medial temporal lobe (MTL), encompassing the hippocampus and parahippocampal gyrus (PHG), is a heterogeneous structure which plays a critical role in memory and cognition. Here, we investigate functional architecture of the human MTL along the long axis of the hippocampus and PHG. The hippocampus showed stronger connectivity with striatum, ventral tegmental area and amygdala-regions important for integrating reward and affective signals, whereas the PHG showed stronger connectivity with unimodal and polymodal association cortices. In the hippocampus, the anterior node showed stronger connectivity with the anterior medial temporal lobe and the posterior node showed stronger connectivity with widely distributed cortical and subcortical regions including those involved in sensory and reward processing. In the PHG, differences were characterized by a gradient of increasing anterior-to-posterior connectivity with core nodes of the default mode network. Left and right MTL connectivity patterns were remarkably similar, except for stronger left than right MTL connectivity with regions in the left MTL, the ventral striatum and default mode network. Graph theoretical analysis of MTL-based networks revealed higher node centrality of the posterior, compared to anterior and middle hippocampus. The PHG showed prominent gradients in both node degree and centrality along its anterior-to-posterior axis. Our findings highlight several novel aspects of functional heterogeneity in connectivity along the long axis of the human MTL and provide new insights into how its network organization supports integration and segregation of signals from distributed brain areas. The implications of our findings for a principledunderstanding of distributed pathways that support memory and cognition are discussed.
Full-text · Article · Sep 2015 · Brain Structure and Function
[Show abstract][Hide abstract]ABSTRACT: Early numerical proficiency lays the foundation for acquiring quantitative skills essential in today’s technological society. Identification of cognitive and brain markers associated with long-term growth of children’s basic numerical computation abilities is therefore of utmost importance. Previous attempts to relate brain structure and function to numerical competency have focused on behavioral measures from a single time point. Thus, little is known about the brain predictors of individual differences in growth trajectories of numerical abilities. Using a longitudinal design, with multimodal imaging and machine-learning algorithms, we investigated whether brain structure and intrinsic connectivity in early childhood are predictive of 6 year outcomes in numerical abilities spanning childhood and adolescence. Gray matter volume at age 8 in distributed brain regions, including the ventrotemporal occipital cortex (VTOC), the posterior parietal cortex, and the prefrontal cortex, predicted longitudinal gains in numerical, but not reading, abilities. Remarkably, intrinsic connectivity analysis revealed that the strength of functional coupling among these regions also predicted gains in numerical abilities, providing novel evidence for a network of brain regions that works in concert to promote numerical skill acquisition. VTOC connectivity with posterior parietal, anterior temporal, and dorsolateral prefrontal cortices emerged as the most extensive network predicting individual gains in numerical abilities. Crucially, behavioral measures of mathematics, IQ, working memory, and reading did not predict children’s gains in numerical abilities. Our study identifies, for the first time, functional circuits in the human brain that scaffold the development of numerical skills, and highlights potential biomarkers for identifying children at risk for learning difficulties.
Full-text · Article · Aug 2015 · The Journal of Neuroscience : The Official Journal of the Society for Neuroscience
[Show abstract][Hide abstract]ABSTRACT: Male predominance is a prominent feature of autism spectrum disorders (ASD), with a reported male to female ratio of 4:1. Because of the overwhelming focus on males, little is known about the neuroanatomical basis of sex differences in ASD. Investigations of sex differences with adequate sample sizes are critical for improving our understanding of the biological mechanisms underlying ASD in females.
We leveraged the open-access autism brain imaging data exchange (ABIDE) dataset to obtain structural brain imaging data from 53 females with ASD, who were matched with equivalent samples of males with ASD, and their typically developing (TD) male and female peers. Brain images were processed with FreeSurfer to assess three key features of local cortical morphometry: volume, thickness, and gyrification. A whole-brain approach was used to identify significant effects of sex, diagnosis, and sex-by-diagnosis interaction, using a stringent threshold of p < 0.01 to control for false positives. Stability and power analyses were conducted to guide future research on sex differences in ASD.
We detected a main effect of sex in the bilateral superior temporal cortex, driven by greater cortical volume in females compared to males in both the ASD and TD groups. Sex-by-diagnosis interaction was detected in the gyrification of the ventromedial/orbitofrontal prefrontal cortex (vmPFC/OFC). Post-hoc analyses revealed that sex-by-diagnosis interaction was driven by reduced vmPFC/OFC gyrification in males with ASD, compared to females with ASD as well as TD males and females. Finally, stability analyses demonstrated a dramatic drop in the likelihood of observing significant clusters as the sample size decreased, suggesting that previous studies have been largely underpowered. For instance, with a sample of 30 females with ASD (total n = 120), a significant sex-by-diagnosis interaction was only detected in 50 % of the simulated subsamples.
Our results demonstrate that some features of typical sex differences are preserved in the brain of individuals with ASD, while others are not. Sex differences in ASD are associated with cortical regions involved in language and social function, two domains of deficits in the disorder. Stability analyses provide novel quantitative insights into why smaller samples may have previously failed to detect sex differences.