In the FG, evoked power revealed an increase in late high-beta/lowgamma band activity in the PASD group compared to controls. Similarly, we recently published evidence of gamma-band overactivation in the FG in autism first-degree relatives using language stimuli delivered in the auditory modality (McFadden et al., 2012). This increase also extended to the beta-band. Indeed, parents of children with autism show evidence of several areas of difference in common with persons with ASD, such as face memory and object recognition (Wallace et al., 2010), social cognition and working memory (Gokcen et al., 2009), and executive function, the latter being also shared by unaffected siblings (Wong et al., 2006). At the functional level, our group has recently published the evidence of differences in neural patterns associated with phonological processing in first-degree relatives (Wilson et al., 2012).

In the STG, increased activation in the high-gamma band was found in the left hemisphere of parents. Here again, we have already reported similar STG activation trends within autism relatives (McFadden et al., 2012) albeit at a lower gamma-band frequency range of 30–50 Hz. This finding is opposite to what we found in the ASD group. Such differential findings have been reported previously in autism subjects and relatives, such as intact verbal IQ in relatives while probands generally exhibit lower levels relative to performance IQ (Schmidt et al., 2008). In this context, it is possible that the observed over-activity in language-related regions is either a compensatory or protective mechanism.

## **CONCLUSION**

Our findings of altered beta and gamma oscillations in people with ASD is consistent with a change in neural synchrony, which adds to a growing literature on gamma-band deficits across a number of simple sensory and complex cognitive tasks. The findings suggest that such oscillatory changes may also be relevant to higher order visual object processing and possibly to some language functions, at least in the context of object naming. The lack of similarity between the probands and the parents represents a challenge to the endophenotype interpretation. Alternative explanations include a compensatory or protective mechanism in the first-degree relatives. Impaired connections between posterior and anterior regions of the brain may be a marker of language and/or visual processing differences in autism, but future studies of language impaired individuals with autism will be needed to clarify a specific role, if any, for altered intra-hemispheric connectivity in the language processing deficits observed in the disorder.

## **ACKNOWLEDGMENTS**

This work was funded by NIH RO1 MH082820 and Cure Autism Now (currently Autism Speaks). The authors of the manuscript declare that they have no conflict of interest to report regarding this manuscript.

## **REFERENCES**

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 15 June 2013; accepted: 17 October 2013; published online: 08 November 2013.*

*Citation: Buard I, Rogers SJ, Hepburn S, Kronberg E and Rojas DC (2013) Altered oscillation patterns and connectivity during picture naming in autism. Front. Hum. Neurosci. 7:742. doi: 10.3389/fnhum.2013.00742*

*This article was submitted to the journal Frontiers in Human Neuroscience.*

*Copyright © 2013 Buard, Rogers, Hepburn, Kronberg and Rojas. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

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## Identification of neural connectivity signatures of autism using machine learning

#### *Gopikrishna Deshpande1,2, Lauren E. Libero3, Karthik R. Sreenivasan1, Hrishikesh D. Deshpande3 and Rajesh K. Kana3 \**

*1 AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA*

*2 Department of Psychology, Auburn University, Auburn, AL, USA*

*3 Department of Psychology, University of Alabama at Birmingham, Birmingham, AL, USA*

*Edited by: Lucina Q. Uddin, Stanford University, USA*

### *Reviewed by:*

*Baxter P. Rogers, Vanderbilt University, USA Joao Sato, Universidade Federal do ABC, Brazil*

#### *\*Correspondence:*

*Rajesh K. Kana, Department of Psychology, University of Alabama, Birmingham, CIRC 235G, 1719 6th Ave South, Birmingham, AL 35294-0021, USA e-mail<EMAIL_ADDRESS>
Alterations in interregional neural connectivity have been suggested as a signature of the pathobiology of autism. There have been many reports of functional and anatomical connectivity being altered while individuals with autism are engaged in complex cognitive and social tasks. Although disrupted instantaneous correlation between cortical regions observed from functional MRI is considered to be an explanatory model for autism, the causal influence of a brain area on another (effective connectivity) is a vital link missing in these studies. The current study focuses on addressing this in an fMRI study of Theory-of-Mind (ToM) in 15 high-functioning adolescents and adults with autism and 15 typically developing control participants. Participants viewed a series of comic strip vignettes in the MRI scanner and were asked to choose the most logical end to the story from three alternatives, separately for trials involving physical and intentional causality. The mean time series, extracted from 18 activated regions of interest, were processed using a multivariate autoregressive model (MVAR) to obtain the causality matrices for each of the 30 participants. These causal connectivity weights, along with assessment scores, functional connectivity values, and fractional anisotropy obtained from DTI data for each participant, were submitted to a recursive cluster elimination based support vector machine classifier to determine the accuracy with which the classifier can predict a novel participant's group membership (autism or control). We found a maximum classification accuracy of 95.9% with 19 features which had the highest discriminative ability between the groups. All of the 19 features were effective connectivity paths, indicating that causal information may be critical in discriminating between autism and control groups. These effective connectivity paths were also found to be significantly greater in controls as compared to ASD participants and consisted predominantly of outputs from the fusiform face area and middle temporal gyrus indicating impaired connectivity in ASD participants, particularly in the social brain areas. These findings collectively point toward the fact that alterations in causal connectivity in the brain in ASD could serve as a potential non-invasive neuroimaging signature for autism.

#### **Keywords: autism, effective connectivity, fMRI, classification, machine learning, theory-of-mind**

## **INTRODUCTION**

A biological origin for autism spectrum disorders (ASD) had been proposed even in the earliest published accounts of the disorder (Kanner, 1943; Asperger, 1944). Despite several decades of research since then, a focal neurobiological marker for autism has been rather elusive. Brain imaging techniques in the last decade, particularly functional and structural MRI, have pointed to disrupted cortical connectivity as a defining neural feature of ASD (Kana et al., 2011; Just et al., 2012).  Similar findings have also been reported during task-free resting state in autism (Cherkassky et al., 2006; Assaf et al., 2010; Murdaugh et al., 2012). Furthermore, diffusion tensor imaging (DTI) studies have reported disruptions in anatomical connectivity in ASD (Barnea-Goraly et al., 2004, 2010; Alexander et al., 2007; Keller et al., 2007; Jou et al., 2011; see Travers et al., 2012 for a review). Although there is converging evidence for connection abnormalities, the neural connectivity model of ASD is based primarily on functional connectivity, with some contributing evidence from white matter integrity. While the insights gained from these models are valuable, functional connectivity is a method for assessing zero-lag correlations, and does not provide insight into the time-lagged relationships and direction of such causal influence.

Effective connectivity, on the other hand, refers to the influence one neural system exerts over another with respect to a given experimental context (Buchel and Friston, 2000), thus helping uncover more information about how brain areas communicate. Effective connectivity can provide information about the transfer of information from one node to another, and differentiate between top-down vs. bottom-up effects. Thus, effective connectivity findings have enriched models of cognitive function by emphasizing the dynamic and interactive nature of neural instantiations (McIntosh et al., 2010). Studying such interactions is important not only for understanding typical brain functioning, but also is critical in learning more about diseases. Considering relatively consistent reports of disruptions in functional connectivity in ASD, it is perhaps a logical and valuable next step to study how information transfer is accomplished in ASD brains. Of particular interest is to explore the information transfer among brain areas that are part of a team to perform higher-order cognitive and social functions, which people with ASD particularly struggle with.

Understanding the information transfer, or the lack of it, between specific nodes in the brain may help uncover the neural bases of behavioral and social problems in ASD. It should be noted that only four previous studies have examined effective connectivity between brain regions in ASD (Bird et al., 2006; Wicker et al., 2008; Shih et al., 2010; Shen et al., 2012). These studies only permit limited inferences as they used a small number of regions and made prior assumptions about the underlying connectional architecture. This is because they used confirmatory methods such as dynamic causal modeling (Friston et al., 2003) and structural equation modeling (McIntosh and Gozales-Lima, 1994) in their studies. In contrast, the present study applies multivariate autoregressive (MVAR) modeling for obtaining Granger causality between a large number of brain regions. This is an exploratory technique which does not make any prior assumptions about the underlying connectional architecture.  According to the principle of Granger causality, the directional causal influence from time series *X* to time series *Y* can be inferred if past values of time series *X* help predict the present and future values of the time series *Y* (Granger, 1969). MVAR models have been used to characterize the predictive relationship between the time series from different brain regions in many previous studies (Roebroeck et al., 2005; Abler et al., 2006; Deshpande et al., 2008, 2009b; Sathian et al., 2011). But according to many recent studies, the spatial variability of the hemodynamic response is considered to be of vascular origin, and hence confounding the Granger causal estimates obtained from raw fMRI time series (David et al., 2008; Deshpande et al., 2010b). Removing the smoothing effect of the hemodynamic response function (HRF) will increase the effective temporal resolution of the signal in addition to accounting for the intersubject and inter-regional variability of the HRF (Handwerker et al., 2004). This can be accomplished using blind hemodynamic deconvolution methods where in the underlying hidden neuronal variable for the fMRI time series can be estimated. We employed this approach in this study by deconvolving the hemodynamic response from fMRI time series using a Cubature Kalman filter (CKF) (Havlicek et al., 2011). Subsequently, these hidden neuronal variables were input into the MVAR model to obtain directional connectivity measures.

Investigating the directional interactions among brain areas in ASD could supplement functional connectivity findings, and potentially may serve as a neural signature for the disorder. Thus, connection abnormalities at anatomical, functional, and causal levels may be considered for potential diagnosis of ASD and/or to supplement the behavior-based diagnosis. However, such attempts will need to test and validate the diagnostic utility of connection abnormalities in ASD. Questions pertaining to diagnostic utility may be best answered through pattern classification analyses using sophisticated machine learning algorithms (Deshpande et al., 2010a; Weygandt et al., 2011; Shinkareva et al., 2013). In this regard, earlier studies have used pattern recognition and machine learning algorithms reliably in classification. Craddock et al. (2009) showed that by using resting state functional connectivity metrics as features in SVM based machine learning classifier, Major Depressive Disorder (MDD) patients were successfully distinguished from healthy controls. In another study, the treatment type provided to patients with MDD was accurately identified using SVM classifier based on the effective connectivity measures (Deshpande et al., 2009a). A pattern recognition approach using structural networks as biomarkers was proposed (Marquand et al., 2013) for classification of Parkinson's Disorder. This method of analysis accurately predicted the diagnosis in patients with Parkinson disorders. A study by Mirowski and colleagues (2009) showed that machine learning classifiers can be successfully used in prediction of seizures in patients with epilepsy. Given the success of pattern recognition and classification methods based on machine learning techniques in other fields and contexts, they could potentially prove to be useful to correctly identify participants with ASD after replication and fine tuning. In these lines, diagnostic information (although preliminary) has been obtained from even short fMRI BOLD sequences, such as characterization of subject age (Dosenbach et al., 2010), classification of dementia (Chen et al., 2011), and autism (Anderson et al., 2011; Murdaugh et al., 2012; Wang et al., 2012). For a neurodevelopmental disorder such as ASD, which is currently diagnosed solely by behavioral observation and inperson interviews by clinicians, classification by brain imaging signatures could be applied to gain more accurate (and perhaps earlier) diagnosis of the disorder. Classification studies have utilized a wide range of data sources to differentiate participants into ASD and TD groups, including functional connectivity (Anderson et al., 2011; Murdaugh et al., 2012; Wang et al., 2012), voxel based morphometry (Uddin et al., 2011; Calderoni et al., 2012), fMRI activation patterns (Coutanche et al., 2011), EEG (Duffy and Als, 2012), and DTI (Ingalhalikar et al., 2011). Yet, none of these methods are currently employed to diagnose the disorder. Issues remain regarding generalizability, such as whether the classification techniques can still be accurately applied to younger children. When other disorders also show functional connectivity and resting state abnormalities, such as schizophrenia (Lawrie et al., 2002; Meyer-Lindenberg et al., 2005; Garrity et al., 2007) and ADHD (Tian et al., 2006; Cubillo et al., 2010), it begs the question about the specificity of these metrics to ASD. However, notably, effective connectivity markers have not been used in classification of ASD individuals. In this regard, effective connectivity could be an additional data source utilized to add to classification of ASD participants, potentially providing sufficient information to serve as a biomarker for the disorder. In other words, effective connectivity could contribute significantly to the global connectivity-based neural characterization of ASD. Also, whereas traditional statistical analyses can uncover significant group differences in brain activation and connectivity, classification analyses can serve to identify brain imaging signatures which are not only able to separate or distinguish the groups, but also predict the group membership of a new subject.

In the current study we explored the causal influences between brain regions that may underlie the processing of theory-of-mind (ToM) in young adults with ASD and typically developing (TD) control participants. The original fMRI study on ToM was published earlier (Kana et al., 2012), reporting findings of brain activity, functional connectivity and white matter integrity. In the current study, we obtained causal connectivity between 18 brain regions activated in the ToM task in our previous publication (Kana et al., 2012). We used these causal connectivity weights along with the following metrics from our previous study assessment scores, functional connectivity values and fractional anisotropy (FA) obtained from DTI data—as features for classification. We employed recursive cluster elimination to select important features and a support vector machine (SVM) classifier to classify participants into ASD and TD based on the entire feature set. This paper is novel in that it takes into consideration different aspects of brain connectivity, instead of a single index, to characterize the nature of brain functioning in individuals with ASD for classification purposes.

## **METHOD**

## **PARTICIPANTS**

Fifteen adolescents and young adults with high-functioning ASD (mean age: 21.14 years) and 15 age-and-IQ-matched individuals with typical development (TD) (mean age: 22.18 years) participated in this fMRI study. Functional connectivity, structural connectivity, behavioral data, and brain activation measures from the same participants were reported elsewhere (Kana et al., 2012). All participants were required to have an IQ of 80 or above measured by the Wechsler Abbreviated Scale of Intelligence (WASI). The participants with ASD were recruited from the University of Alabama ASD Clinic and surrounding service providers. The study was approved by the Institutional Review Board of the University of Alabama at Birmingham, and all participants provided informed consent for their participation in the study. Participants with ASD had received a previous diagnosis of an ASD based on Autism Diagnostic Interview (ADI-R) symptoms, and Autism Diagnostic Observation Schedule (ADOS). Eight of the 15 ASD participants in this study had received a diagnosis of Asperger's Disorder. The TD participants were recruited through newspaper advertisements and through the University of Alabama at Birmingham's Psychology 101 course subject pool. They were screened through a parent-report (for participants younger than 18 years) or self-report history questionnaire to rule out neurological disorders, such as ASD, ADHD, or Tourette's Disorder, that could potentially confound the results. All participants completed the Autism Spectrum Quotient (AQ) questionnaire (Baron-Cohen et al., 2001b), and the Reading the Mind in the Eyes (RME) test (Baron-Cohen et al., 2001a). Demographic information about the participants is shown in **Table 1.**

## **EXPERIMENTAL PARADIGM AND IMAGING PARAMETERS**

The stimuli consisted of a series of black and white comic strip vignettes (adapted from Brunet et al., 2000) depicting scenarios that demand either a physical causal attribution or an intentional causal attribution to arrive at a logical ending. The first part of the vignette was presented for 5 s and the participants' task was to choose a logical ending to the story from the three choices in the second panel presented for 6 s. The entire vignette remained on the screen for a total of 11 s. The experiment was designed in an event-related format. All data were collected using a Siemens 3.0 Tesla Allegra head-only scanner (*Siemens* Medical Inc., Erlangen, Germany). For functional imaging, a single-shot gradient-recalled echo-planar pulse sequence was used for rapid image acquisition (*TR* = 1000 ms, *TE* = 30 ms, flip angle = 60 degrees). Seventeen adjacent oblique-axial slices were acquired in an interleaved sequence with 5 mm slice thickness, 1 mm slice gap, a 24 × 24 cm2 field of view (FOV), and a 64 × 64 matrix, resulting in an in-plane resolution of 3.75 × 3.75 × 5 mm3. More information on the experimental paradigm and imaging parameters for the 3D MPRAGE structural MRI data and diffusion weighted echo-planar imaging data can be found in Appendix A (for further details, please refer to Kana et al., 2012).

## **DATA ANALYSES**

## *Head motion correction and regions of interest (ROI) definition*

Within-group brain activation was examined for the whole group (ASD + TD) of participants (see Kana et al., 2012). Functional ROIs were defined on the group activation map for the whole group (ASD + TD) for the contrast (Intentional Causality + Physical Causality) vs. Fixation, so that it best represented the study. Because head motion can impact connectivity analyses (Satterthwaite et al., 2012; Van Dijk et al., 2012), a conservative threshold of 0.5 mm was set for head motion in any direction. In addition, the root mean square (RMS) values of head motion were measured in three translational directions (*x*, *y*, and *z*) and three rotations (pitch, roll, and yaw) for each individual participant in the study (see Appendix B **Table B1**). We examined group differences in head motion on this data using a Mann-Whitney *U* Test, which is a non-parametric test and may be more appropriate in case assumptions about normality of sample distributions are not met.

Eighteen ROIs were identified: supplementary motor area (SMA), left and right inferior frontal gyrus (LIFG, RIFG), left and right precentral cortex (LPRCN, RPRCN), left and right middle temporal gyrus (LMTG, RMTG), right superior temporal gyrus (RSTG), left and right inferior parietal lobule (LIPL, RIPL), left and right fusiform gyrus (LFFG, RFFG), left and right superior parietal lobule (LSPL, RSPL), left and right middle occipital gyrus (LMOG, RMOG), and left and right temporal parietal junction (LTPJ, RTPJ). A sphere was defined for each cluster (with a radius ranging from 8 to 12 mm) that best captured the cluster of activation in the contrast map for each group. The radius was selected to specifically encompass as much of the activation cluster as possible, without including surrounding (not significantly activated) areas. Selecting ROIs of the same radius or utilizing anatomically defined ROIs may entail those ROIs not encompassing the entire cluster of activation, or may include tissue that is not significantly active for the task. As a result, extracting time courses from ROIs defined in these ways may result in time series variability that does not reflect the cognitive task being processed.

## *The effective connectivity model*

Let *l* fMRI time series be represented as *X*(*t*) = [*x*1(*t*)*x*2(*t*)... *xl*(*t*)]. Below, we present a model linking observed fMRI time series to underlying latent neuronal variables. A dynamic state-space model can be described as follows.

$$
\tilde{h}\_T^l = \begin{bmatrix} h\_T^l \\ u\_T^l \\ \theta\_T^l \end{bmatrix} = \begin{bmatrix} f(h\_{T-1}^l, u\_{T-1}^l, \theta\_{T-1}^l) \\ u\_{T-1}^l \\ \theta\_{T-1}^l \end{bmatrix} + \begin{bmatrix} P\_{T-1}^l \\ Q\_{T-1}^l \\ R\_{T-1}^l \end{bmatrix} \tag{1}
$$

Where *h* is the hidden neuronal state variable, *u* is the exogenous input and θ represents the HRF parameter variables. *f* is the function which links the current neuronal state to the previous neuronal states, exogenous inputs and parameters. The subscript *T* indicates continuous time and the superscript *l* indicates the number of time series in the model. *P*, *Q*, and *R* are the zero mean Gaussian state noise vectors. The observation equation links the state to observation variables as given below.

$$\alpha\_l(t) = \mathfrak{m}(\tilde{h}\_t^l) + \mathfrak{e}\_{t-1} \tag{2}$$

where ε is the measurement noise, *t* is discrete time and *m* is the measurement function which links the state variables to measurement variables. The exogenous inputs *u*, which is the experimental boxcar function, and *xl*(*t*) are the inputs to the model. As demonstrated before, using the CKF (Havlicek et al., 2011), the hidden neuronal variables can be estimated successfully. The CKF performs very efficient joint estimation of the hidden neuronal state variables and parameters. In addition, since Eq. 1 represents a continuous time model, the neuronal variables can be estimated with a highly improved temporal resolution up to 10 times smaller than the TR. When the hidden neuronal state variables *hl*(*t*) are input into the MVAR model, we get the following equation.

$$
\begin{aligned}
\begin{bmatrix} h\_1(t) \\ h\_2(t) \\ \vdots \\ \vdots \\ h\_l(t) \end{bmatrix} &= \begin{bmatrix} 0 & a\_{12}(0) & \cdots & a\_{1l}(0) \\ a\_{21}(0) & 0 & & a\_{2l}(0) \\ \cdot & \cdot & 0 & \cdot \\ \cdot & \cdot & 0 & \cdot \\ a\_{l1}(0) & a\_{l2}(0) & \cdots & 0 \end{bmatrix} \times \begin{bmatrix} h\_1(t) \\ h\_2(t) \\ \vdots \\ \cdot \\ \cdot \\ h\_l(t) \end{bmatrix} \\ &+ \sum\_{j=1}^{p} \begin{bmatrix} a\_{11}(j) \ a\_{12}(j) \ \ldots \ a\_{1l}(j) \\ a\_{21}(j) \ a\_{22}(j) \ \ldots \ a\_{2l}(j) \\ \cdot \\ \cdot \\ a\_{l1}(j) \ a\_{2l}(j) \ \ldots \ a\_{ll}(j) \end{bmatrix} \times \begin{bmatrix} h\_1(t-j) \\ h\_2(t-j) \\ \vdots \\ \cdot \\ h\_l(t-j) \end{bmatrix} \\ &+ \begin{bmatrix} c\_1(t) \\ c\_2(t) \\ \vdots \\ c\_l(t) \end{bmatrix} \end{aligned} \tag{3}$$

where *p* is the model order estimated by the Akaike/Bayesian information criterion (Deshpande et al., 2009b), *a* represent the model coefficients and *e* represents the error of the MVAR model. From the above equation it can be observed that *a*(0) represents the instantaneous influences between the time series, and the Granger causal influences between them is indicated by *a*(*j*), *j* = 1 .... *p*. Both terms are used in the model because including both instantaneous and causal terms in the model minimizes the "leakage" of instantaneous correlation into causality (Deshpande et al., 2010c). The multivariate model we have used is less sensitive to the effects of missing variables than the traditionally used pairwise bivariate models (Kus et al., 2004 ´ ). Also, since we included all 18 regions which were activated in the effect of interest, it guaranteed to a certain level that all regions involved in the task were indeed included in the model.

## *Effective connectivity analysis*

Mean time series from 18 activated regions were obtained for each of the 15 participants with ASD and the 15 typical control participants. Using the boxcar function corresponding to "intentional causality" as the exogenous input, hidden neuronal variables corresponding to normalized mean fMRI time series were obtained and input into the MVAR model. The Granger causal relationships between the 18 regions for each participant (ASD and TD) were obtained. The number of coefficients in the MVAR model is equal to *k*2*p* (*k* is the number of time series and *p* is the model order) (Kus et al., 2004 ´ ). This must be smaller than the number of time points in each time series. We had 18 ROI time series, each of length 460. Since we used a first order model, *k*2*p* = 324 which is less than 460. Therefore, we were able to estimate the model.

## *Classification using support vector machine*

The statistical separation of neural signatures (e.g., *t*-test) does not guarantee generalizability or predictive power of those signatures for diagnosis. Therefore, in this study, we also used machine learning approaches for identification of metrics which can accurately classify individuals with ASD from individuals with typical development. A Recursive Cluster Elimination based Support Vector Machine (RCE-SVM) (Deshpande et al., 2010a) was used in this study to classify the participants based on granger causal path weights between the 18 ROIs, functional connectivity z-scores for all pairs of the 18 ROIs, assessment scores (AQ and RME scores) and FA values for the white matter tract extending into the temporal lobe as the input features. The functional connectivity, assessment and DTI FA values were obtained from our prior study (Kana et al., 2012).

Our choice of SVM for classification was motivated by its wide applicability as a machine learning approach (Vapnik, 1995) for classification in many different fields (Wang, 2005). Previous studies have demonstrated that using discriminatory features enhances SVM classification (Craddock et al., 2009). Therefore, to enhance the performance of the SVM classifier, filtering and wrapper methods for feature selection have been used. Filtering methods are based on extraction of features that are statistically different between classes. They can be extracted using statistical tests such as a *t*-test. The wrapper approach is based on iteratively eliminating features to minimize the prediction error. RCE is one of the wrapper methods that is an iterative process were the feature selection and classification steps are embedded with each other. The main steps of the RCE-SVM algorithm, shown in the flowchart in **Figure 1**, are the cluster step, the SVM scoring step and the RCE step. Initially, the features that were input into the classifier were divided into training and testing data sets. Fifty such splits were carried out in order to ensure the generalizability of the results. In the clustering step, k-means algorithm (Yang et al., 2003) was used to cluster the training data into *n* clusters. The number of clusters was first set to the number of features, and was progressively decreased by one until there were no empty clusters. The *n* obtained by this iteration served as the initial *n* for the RCE-SVM loop.

In the SVM scoring step, each cluster was scored based on its ability to differentiate the two categories by applying linear SVM. In order to rate the clusters, the training data was randomly partitioned into 10 non-overlapping subsets of equal sizes (10 folds). Using 9 subsets, the linear SVM was trained and performance was calculated using the remaining subset. Different

possible partitions were taken into account by repeating the clustering and cross validation procedure 50 times. For each of these 50 repetitions, the classification accuracy of SVM was ascertained using the test data. The average value of this accuracy, taking into account the repetitions and all the folds was assigned as the score of the cluster. The bottom 10% of low score clusters were eliminated in the RCE step. The remaining features were merged and the value of *n* was decreased by 10% and the cluster step, the SVM scoring step and the RCE step were repeated again in an iterative manner. After each iteration, the performance of the classifier was assessed using the testing data and lesser number of features compared to the earlier iteration. When the number of clusters was equal to two, the procedure was stopped. Complete separation of testing and training data in this algorithm eliminates bias in performance accuracy (Kriegeskorte et al., 2009). The accuracy at every RCE-SVM loop was calculated as a mean value of accuracy obtained over 50 repetitions of each loop and each train-test split, using the feature clusters of test data available at the corresponding loop and split. The statistical significance of mean accuracies was calculated by estimating the *p*-values of a binomial null distribution B(η,ρ), η being the number of participants and ρ is the probability of accurate classification as in previous studies (Pereira et al., 2009). Only accuracies whose *p*-values were less than 0.05 after correcting for multiple comparisons using Bonferroni method were considered as statistically significant.

The causal connectivity weights obtained from the MVAR model, the behavioral assessment scores, the functional connectivity z-scores for each ROI pair, and DTI FA metrics for each of the 30 subjects (15 ASD and 15 TD) were input into the RCE-SVM classifier to determine the accuracy with which the classifier can predict a novel subject's group membership (autism or control).

## **RESULTS**

The main results of this study are summarized as follows: (1) The effective connectivity path weights were able to successfully classify participants by diagnosis with 95.9% accuracy. These path weights were the most discriminative features among all the different metrics used in classification; (2) Effective connectivity paths most important for classification were significantly reduced (*p* < 0.05) in ASD participants compared to typical control participants; and (3) The paths that were among the top ranked features in the classification analysis were found to be negatively correlated with the AQ and positively correlated with the RME test scores.

The first set of results pertains to a pattern classification analysis involving several indices of connectivity (functional connectivity, effective connectivity, white matter integrity) and performance accuracy in this ToM task. In this analysis, utilizing 2 feature clusters comprised of 19 metrics, the classification accuracy reached a maximum accuracy of 95.9% (specificity 94.8%, and sensitivity 96.9%). It should be noted that all of the 19 features were effective connectivity paths. **Figure 2** demonstrates the increase in performance of classification with decreasing number of features (and removal of uninformative features). The *p*-values for all the accuracy values shown in **Figure 2** can be seen in **Table 2.**

Second, the causal connectivity weights of the 19 paths which led to maximum accuracy of 95.9% showed clear separation between participants with autism (blue) and typical control participants (green) as shown in **Figure 3**, with these paths showing significantly (*p* < 0.05 corrected using Bonferroni method for 18 paths; for one of the paths *p* < 0.05 uncorrected) weaker connectivity in participants with ASD compared to TD controls. Many of these connections are between regions that are part of the social brain network (LTPJ, RTPJ, LFFG, RFFG, LMTG, RMTG, RIFG) which may prove critical in accomplishing the ToM task used in this study. It is noteworthy that there may be other paths which

**Table 2 | Classification accuracy values and the corresponding** *p***-values obtained at each step of the RCE algorithm.**

are significantly different between the groups. Here, we restrict ourselves to finding the statistical separation of features which have the highest ability for predicting the diagnosis of a given subject. We do so primarily because we are interested in features with predictive ability rather than those which just "differ" between the groups. Please refer to Appendix B **Figures B1**, **B2** in order to gain a qualitative understanding of the functional and effective

**FIGURE 3 | Mean of nineteen paths which was most important for giving maximum classification accuracy for autism and control groups.** All paths had significantly decreased connectivity (*p* < 0.05 corrected using

Bonferroni method for 18 paths; for one of the paths *p* < 0.05 uncorrected) in the Autism group as compared to controls. The bars represent standard errors.

being the least significant.

connectivity paths, respectively, between all 18 ROIs in both ASD and TD groups.

**(Left panel: participants with autism; and right panel: control**

The 19 effective connectivity paths which were most important in classification are shown in **Figure 4**. The left panel shows these paths in ASD participants and the right panel in control participants. The width of the arrows illustrates the path weight in the corresponding group and the color represents the rank of the path obtained during classification.

Third, a correlation analysis was also performed between the features that were ranked highest in classification and gave rise to maximum accuracy, and assessment scores (AQ and RME). Given that the top-ranked features are not guaranteed to have normal distribution, we used Spearman's non-parametric correlation method to determine whether the top-ranked features were correlated with behavior. This analysis (including all participants in the study) revealed a significant negative correlation between several effective connectivity paths and the AQ scores as well as a significant positive correlation between effective connectivity paths and RME scores (see **Table 3** for specific paths, correlation and *p*-values). These results suggest that as autism symptom severity increased, the effective connectivity of the top-ranked paths decreased; and as the theory-of-mind ability increased, effective connectivity of the top-ranked paths also increased. This provides a second-level test of the behavioral relevance of the topranked paths, which is to be expected given the fact that diagnosis was based on behavioral symptoms. As a cautionary note, these results should not be construed as a general discovery regarding brain connectivity features in autism which correlate with behavioral symptoms.

 We conducted several different measures to make sure that our data

**Table 3 | Paths correlated with Autism Quotient (AQ) scores and Reading Mind in Eye (RME) scores.**

*The paths in red were correlated with both AQ and RME.*

and the reported results were not influenced by quality related issues. First, the root mean square (RMS) values for each subject and each head motion parameter were obtained (see Appendix B **Table B1**). The RMS values were then submitted to a nonparametric Mann-Whitney *U* test, which also revealed no significant difference in motion in x [*U*(28) = 66, *Z* = −1.929, *p* = 0.06], y [*U*(28) = 93, *Z* = −0.809, *p* = 0.42], and z [*U*(28) = 96, *Z* = −0.684, *p* = 0.49] translational directions. Nor was there a significant group difference in rotation in pitch [*U*(28) = 68, *Z* = −1.846, *p* = 0.06], roll [*U*(28) = 107, *Z* = −0.228, *p* = 0.82], and yaw [*U*(28) = 93, *Z* = −0.809, *p* = 0.42]. These results indicate that there were no statistical differences in head motion between the two groups, assuming a *p*-value threshold of 0.05. However, there was a non-significant trend (*p* = 0.06) for translation in x direction and the degree of rotation in pitch to differ between the groups.

Further, we obtained the mean value of frame wise displacement (FD) for each subject as a quality control (QC) metric and investigated whether they correlated with any of the 19 top-ranked paths obtained from classification across the entire sample. The instantaneous motion of the head was expressed as a scalar quantity using the formula, *FDi* = |*dix*|+|*diy*| + |*diz*|+|α*i*|+|β*i*|+|γ*i*|, where *dik* = *d*(*j* − 1)*k* − *dik* and k is any of the 3 translational parameters (*x*, *y*, *z*) or rotational parameters (α, β, γ). We converted the rotational displacements from degrees to millimeters by calculating displacement on the surface of a sphere of radius 50 mm, assuming that the approximate mean distance from the center of the head to the cerebral cortex is 50 mm. The above procedure of calculating FD and correlating its mean with connectivity metrics obtained from individual subjects has been recommended recently for either confirming or ruling out the influence of head motion on connectivity measures (Power et al., 2012; Satterthwaite et al., 2012; Van Dijk et al., 2012; Satterthwaite et al., 2013; Yan et al., 2013). The QC-connectivity Spearman's correlations and corresponding *p*-values indicating their statistical significance are shown in **Table 4**. It is evident that none of the QC-connectivity correlations were statistically significant (*p* > 0.05). Given these

**Table 4 | The Spearman's correlation between mean frame wise displacement (our quality control metric) and the Granger causality weights for the top ranked 19 paths.**

*The paths shown in the table are ordered according to the rank obtained during classification with 1 being the most significant and 19 being the least significant (first path is Rank* −*1 and the last path is Rank* −*19).*

evidence, any significant group differences for imaging metrics was probably not due to head motion. We did not use the scrubbing method described in Power et al. (2012), where removal of certain parts of the time series (scrubbing) creates an artificial discontinuity in the data. This may not be a problem while using Pearson's correlation coefficient as zero-lag synchronization in the data does not depend on the temporal ordering in the data as long as the correspondence between the variables being examined is preserved. However, other methods which are sensitive to temporal ordering in the data cannot use scrubbing. Granger causality is one such method which is indeed sensitive to temporal ordering in the data and hence we did not use scrubbing.

Differences in signal to noise ratio (SNR) can also impact Granger causality estimates (Nalatore et al., 2007) when the SNR is low. On the other hand, when SNR = 2, which is typically the case for task-based fMRI, we have previously showed using simulations that Granger causality estimates are accurate in the absence of hemodynamic variability (which is the case here since we deconvolved the hemodynamic response) (Deshpande et al., 2010b). We calculated effective SNR of the deconvolved fMRI time series by estimating the variance of the entire deconvolved signal, i.e., the hidden neuronal variable, and divided it by the variance of the deconvolved signal during non-stimulation phases. We then populated the SNRs of each ROI in autism and control groups to two different samples and performed a nonparametric Wilcoxon ranksum to find statistical differences. The SNR was significantly higher (*p* < 0.05, *z*-value = 20.1) in the ASD group (SNR = 4.13 ± 0.01) as compared to the control group (SNR = 3.2 ± 0.03). The SNRs for both groups were high enough so that SNR differences between the groups will not impact Granger causality. SNR has an impact on Granger causality only when the SNR is low.

## **DISCUSSION**

The goals of this study were: (1) to investigate effective connectivity among brain areas during intentional causal attribution in ASD and (2) to utilize machine learning techniques to classify participants based on effective connectivity weights from this study, and behavior assessment scores, functional connectivity, and fractional anisotropy obtained from DTI data from our previous study (Kana et al., 2012). Using SVM based classification, we found that the causal connectivity path weights had the highest discriminative power to separate groups by diagnosis with high accuracy. It was uncovered that the top-ranked causal connectivity paths were also significantly weaker between social brain regions in young adults with ASD as compared to their TD peers and correlated with the ASD symptom severity (AQ) scores and theory-of-mind ability as measured by the RME test.

An application of characterizing brain connectivity patterns is to test whether such patterns can differentiate individuals with ASD from typically developing control participants such that the diagnostic label of a new participant can be determined based on imaging data. Thus, in this study we conducted a classification analysis using the effective connectivity measures, functional connectivity values, fractional anisotropy obtained from DTI data and the causal attribution task performance scores to get a fair assessment of which metric possesses the highest discriminative power. A maximum classification accuracy of 95.9% was obtained with 2 clusters and 19 features, all of these being effective connectivity paths. These results suggest that significantly weaker causal influence between brain regions during ToM processing in ASD is sufficient to separate adults with ASD from typical control participants. The discriminative patterns found in this study using SVM may have clinical applications in the long-run. Accurate separation of ASD adults from TD peers may provide potential value for clinicians, particularly in cases when behavioral observation and clinical interviews are not sufficient enough to determine a diagnosis. The key finding of differences in the causal influence of brain regions for ToM in ASD in this study adds to the relatively limited literature on effective connectivity in ASD. In addition, while previous studies explored effective connectivity in ASD during language processing, facial and emotional processing, and imitation, the current study examined effective connectivity in the context of a ToM task, which has not been studied in ASD to date. The current study expands what we know about inferring mental states in ASD, and provides insight into the causal relationships of brain regions during ToM processing. In addition, this study, to our knowledge, is the first to use effective connectivity measures for classification purposes in ASD. While this method will require some fine tuning, validation in a larger sample, and replication through multiple studies to be applied within clinical settings, the causal relationships between brain areas related to ToM holds promise for separating individuals with ASD from typical controls or from other disorders. Nevertheless, the current study marks the first attempt at using effective connectivity measures as inputs for a classification analysis of ASD subjects, therefore marking the first step in the direction of more accurate classification of the disorder.

Weaker effective connectivity of the 19 top-ranked paths found in participants with ASD in this study involved paths and regions that are found to be part of the social brain network. Several nodes, such as the TPJ, MTG, RIFG, IPL, FFG, and SMA have been associated with processing theory-of-mind, face processing, and the mirror neuron system. These findings are in line with previous studies of effective connectivity in ASD (Wicker et al., 2008; Shih et al., 2010). Our results also include significant functional alterations in social brain and visuospatial brain regions (e.g., TPJ, IFG, IPL, FFG, etc.) seen previously in functional connectivity findings (Kana et al., 2006, 2009, 2012; Just et al., 2007; Koshino et al., 2008; Mason et al., 2008), suggesting some consistency in disrupted connectivity across different modalities of connectivity and providing further support for disrupted connectivity accounts of ASD (Just et al., 2004; Kana and Just, 2011; Schipul et al., 2011; Kana et al., 2012). The findings here supplement the functional connectivity results in our previous study utilizing the same ToM stimuli, where ASD participants displayed significantly reduced functional connectivity between temporal and frontal regions, and weaker connectivity between networks made up of ventral premotor regions and TPJ (Kana et al., 2012). Our results in the current study further these previous findings by illustrating the directionality of connectivity. We found that, for ToM processing in TD participants, significantly stronger (compared to ASD group) causal connections existed among the 19 top-ranked paths which included the nodes that are associated with social cognition. So, here we find that the critical regions of the social brain are not as well coordinated with others, that they should be sharing information with, in participants with ASD. This lack of synchrony and reduced flow of information may represent a critical problem of bandwidth (maximal rate of data transfer supported by a communication channel) in ASD, where some information is getting by, but at a much lower rate than what would be needed for complex ToM connections (Just et al., 2012).

In a correlation analysis using assessment measures and effective connectivity paths for the entire sample of participants, we found the paths that were among the top ranked features in the classification analysis were correlated with AQ and RME scores. While the AQ showed significant negative correlation, the RME showed significant positive correlation with connectivity paths. Similarly, participants with better ToM skills had stronger effective connectivity during this causal attribution task. It should be noted that most of these connection paths involved information transfer to different regions mainly from the temporal lobe (LMTG, and bilateral FFG). While FFG has been associated with face processing and processing socially salient stimuli (Schultz, 2005), middle and superior temporal areas have been found to be involved in social cognition, especially in taking intentional stance, as seen in the current study, on social scenarios (Mosconi et al., 2005). The correlations found in our study reveal how social abilities such as ToM skill can influence information transfer in the brain. In addition, it also points out that severe autism symptoms may have a neural basis in reduced causal brain connectivity from the temporal lobe. As noted earlier, the correlation analysis was performed across the entire sample and we restricted it to the top-ranked 19 paths because we feel that the covariance of a brain imaging based metric with a behavioral assessment

## **REFERENCES**

in autism: preliminary evidence from diffusion tensor imaging. *Biol. Psychiatry* 55, 323–326. doi: 10.1016/j.biopsych.2003.10.022

score is clinically meaningful only if the imaging metric under consideration has the power to predict the diagnostic label of a new subject. Therefore, there may very well be other connectivity paths in the brain which may be correlated with behavior (but which lack the discriminative ability) which we have not discussed here.

In conclusion, this study provides preliminary evidence to support a hypothesis that metrics based on directional brain connectivity obtained from a task engaging social brain areas may provide highly discriminative features for predicting whether a given subject has ASD or not. Studies involving larger sample size, and replication of these findings across multiple studies would be required to fully test the extent of this hypothesis and investigate its clinical implications.

## **ACKNOWLEDGMENTS**

The authors would like to thank the UAB department of Psychology and the Civitan-McNulty Scientist Award to Rajesh K. Kana as well as support from Auburn University MRI Research Center to Gopikrishna Deshpande as sources of funding support for this study.

task. *Neuroimage* 11, 157–166. doi: 10.1006/nimg.1999.0525

*Reson. Med*. 62, 1619–1628. doi: 10.1002/mrm.22159

using granger-based connectivity and support vector machines," in *Proceedings of ISMRM 17th Scientific Meeting 2009* (Honolulu, HI), 3362.

*Econometrica* 37, 424–438. doi: 10.2307/1912791

connectivity. *Brain* 129, 2484–2493. doi: 10.1093/brain/awl164

stance" on eye-gaze shifts: a functional neuroimaging study of social perception in children. *Neuroimage* 27, 247–252. doi: 10.1016/j.neuroimage.2005.03.027

*Neuroimage* 64, 240–256. doi: 10.1016/j.neuroimage.2012.08.052

Altered resting-state functional connectivity patterns of anterior cingulate cortex in adolescents with attention deficit hyperactivity disorder. *Neurosci. Lett*. 400, 39–43. doi:

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 15 June 2013; accepted: 25 September 2013; published online: 17 October 2013.*

*Citation: Deshpande G, Libero LE, Sreenivasan KR, Deshpande HD and Kana RK (2013) Identification of neural connectivity signatures of autism using machine learning. Front. Hum. Neurosci. 7:670. doi: 10.3389/fnhum.2013.00670 This article was submitted to the journal*

*Frontiers in Human Neuroscience. Copyright © 2013 Deshpande, Libero, Sreenivasan, Deshpande and Kana. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

## **APPENDIX A**

## **IMAGE ACQUISITION**

Structural Imaging: Acquisition of initial high-resolution T1 weighted scans was done using a 160-slice 3D MPRAGE volume scan with *TR* = 200 ms, *TE* = 3.34 ms, flip angle = 78, field of view (FOV) = 25.6 cm, matrix size = 256 × 256 and slice thickness = 1 mm.

Diffusion tensor imaging: A diffusion-weighted, single-shot, spin-echo, echo-planar imaging (EPI) sequence (*TR* = 4400 ms, *TE* = 85 ms, bandwidth = 1860 Hz/voxel, FOV = 240 mm and 128 × 128 matrix size, resulting in an in-plane resolution of 1.87 × 1.87 × 3 mm) was used to collect the images. Thirty-two 3 mm thick slices were imaged (no slice gap) with no diffusion weighting (*b* = 0 s/mm2) and with diffusion weighting (*b* = 1000 s/mm2) gradients applied in 12 orthogonal directions. Twenty-four images of each slice by gradient direction combination were acquired and averaged to produce the final diffusion imaging data set.

## **DATA ANALYSIS**

The brain activation data were analyzed using Statistical Parametric Mapping (SPM8) software (Wellcome Department of Cognitive Neurology, London, UK). Images were corrected for slice acquisition timing, motion-corrected, normalized to the Montreal Neurological Institute (MNI) template, resampled to 2 × 2 × 2 mm voxels, and smoothed with an 8-mm Gaussian kernel to decrease spatial noise. We performed statistical analysis on individual and group data by using SPM8's implementation of the general linear model (Friston et al., 1995). Within-group activation was analyzed for the ASD group, TD group, and for the whole group (ASD + TD) of participants. Activation data was analyzed for all trials with separate regressors defined for intentional causality, physical causality, and fixation baseline conditions. The within-group analyses used a cluster size of 80 mm3 determined by 10,000 Monte Carlo simulations at an uncorrected *p* value of 0.001. According to Lieberman and Cunningham (2009), simulations can implicate cluster size thresholds that produce the best balance between Type I and Type II error. The between-group analyses used a cluster threshold of 10 contiguous voxels at an uncorrected *p* value of 0.005, as the effects did not survive a more conservative statistical threshold.

ROIs were defined on the group activation map for the whole group (ASD + TD) for the contrast Intention + Physical vs. Fixation, so that it best represents the study. Eighteen ROIs were identified: supplementary motor area (SMA), left and right inferior frontal gyrus (LIFG, RIFG), left and right ventral premotor cortex (LPMv, RPMv), left and right middle temporal gyrus (LMTG, RMTG), right superior temporal gyrus (RSTG), left and right inferior parietal lobule (LIPL, RIPL), left and right fusiform gyrus (LFFG, RFFG), left and right superior parietal lobule (LSPL, RSPL), left and right middle occipital gyrus (LMOG, RMOG), and left and right temporal parietal junction (LTPJ, RTPJ). A sphere was defined for each cluster (with a radius ranging from 8 to 12 mm) that best captured the cluster of activation in the contrast map for each group. The activation time-course extracted for each participant over the activated voxels within the ROI originated from the normalized and smoothed images that were low-pass filtered and had the linear trend removed.

## **APPENDIX B**

**Table B1 | Root mean square values of head motion.**

## Age related changes in striatal resting state functional connectivity in autism

## *Aarthi Padmanabhan\*, Andrew Lynn , William Foran , Beatriz Luna and Kirsten O'Hearn*

*Laboratory of Neurocognitive Development, Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA*

#### *Edited by:*

*Rajesh K. Kana, University of Alabama at Birmingham, USA*

#### *Reviewed by:*

*Chandan J. Vaidya, Georgetown University, USA Adriana Di Martino, New York University Langone Medical Center, USA*

#### *\*Correspondence:*

*Aarthi Padmanabhan, Laboratory of Neurocognitive Development, Department of Psychiatry, University of Pittsburgh, 121 Meyran Avenue, Loeffler Building 108, Pittsburgh, PA 15206, USA*

*e-mail<EMAIL_ADDRESS>
Characterizing the nature of developmental change is critical to understanding the mechanisms that are impaired in complex neurodevelopment disorders such as autism spectrum disorder (ASD) and, pragmatically, may allow us to pinpoint periods of plasticity when interventions are particularly useful. Although aberrant brain development has long been theorized as a characteristic feature of ASD, the neural substrates have been difficult to characterize, in part due to a lack of developmental data and to performance confounds. To address these issues, we examined the development of intrinsic functional connectivity, with resting state fMRI from late childhood to early adulthood (8–36 years), using a seed based functional connectivity method with the striatal regions. Overall, we found that both groups show decreases in cortico-striatal circuits over age. However, when controlling for age, ASD participants showed increased connectivity with parietal cortex and decreased connectivity with prefrontal cortex relative to typically developed (TD) participants. In addition, ASD participants showed aberrant age-related connectivity with anterior aspects of cerebellum, and posterior temporal regions (e.g., fusiform gyrus, inferior and superior temporal gyri). In sum, we found prominent differences in the development of striatal connectivity in ASD, most notably, atypical development of connectivity in striatal networks that may underlie cognitive and social reward processing. Our findings highlight the need to identify the biological mechanisms of perturbations in brain reorganization over development, which may also help clarify discrepant findings in the literature.

**Keywords: autism, fMRI, resting state, functional connectivity, striatum, development**

## **INTRODUCTION**

A recent focus in autism research is the differences in functional connectivity in autism spectrum disorder (ASD).  These findings have led to the strong hypothesis that ASD is a "disorder of abnormal brain connectivity" (Belmonte et al., 2004; Frith, 2004; Just et al., 2004; Geschwind and Levitt, 2007; Hughes, 2007; Minshew and Williams, 2007; Ecker et al., 2013), with the predominant theory being that hypo-connectivity, was core to the pathophysiology of the disorder. However, results from task-related functional connectivity studies have been mixed as reports indicate both hypoand hyper- functional connectivity in ASD relative to typically developing (TD) individuals (Muller et al., 2011).

Examining resting state functional connectivity may help address some of the discrepant findings, as it provides a measure of intrinsic functional connectivity without the task-related differences that confound comparisons across different age and clinical groups.  Taken together, these findings suggest that the nature of connectivity differences in ASD is not yet fully characterized. Inconsistent findings in functional connectivity might be due to many important differences between various studies (e.g., data acquisition and analysis, small sample sizes, diagnostic criteria). It is especially likely that differences in age ranges and a lack of examination of developmental changes contribute to conflicting reports in the literature (for review, see Uddin et al., 2013b). In general, developmental work in ASD is limited, with a few prior studies examining age-related change in gray matter (Langen et al., 2009; Greimel et al., 2013), structural connectivity (Kleinhans et al., 2012), behavior (Luna et al., 2007), task modulated brain function (Schulte-Ruther et al., 2013), and resting state connectivity with the default mode network (Wiggins et al., 2011). The majority of these studies report atypical trajectories in ASD. Given that (1) typical connectivity develops substantially, with connectivity patterns changing well into adulthood (Fair et al., 2009; Hwang and Luna, 2011), (2) group differences may manifest differently at different time points over the lifespan, and (3) ASD is characterized as a disorder of abnormal and delayed development in the brain, there is a strong possibility that functional connectivity matures differently in ASD. Thus, examining age-related changes in brain connectivity is crucial to understanding the neural basis of ASD. Importantly, there is a need to clarify the nature of developmental change (**Figure 1**). For example, developmental abnormalities can be classified as arrested (a lack of development) (**Figure 1C**), or atypical (a deviating developmental trajectory) (**Figures 1D–F**). In addition, it is important to know if regions show intact development in ASD (**Figure 1A**), or no developmental changes, but show persistent disorder effects (**Figure 1B**). Characterizing these patterns is essential to understanding the progression of the disorder and for identifying time points of plasticity and/or vulnerabilities.

One particularly understudied topic in the ASD literature is differences in connectivity with the striatum, an important subcortical region associated with a number of core cognitive and affective functions (Postuma and Dagher, 2006) that are atypical in ASD-including social reward processing. The striatum, which includes the caudate, putamen, and nucleus accumbens bilaterally, has extensive connections to cortex and cerebellum via the thalamus. Prior studies of functional connectivity during rest in typical adults have revealed a number of functionally distinct but overlapping cortico-striatal circuits (Di Martino et al., 2008; Kelly et al., 2009b; Furman et al., 2011) that underlie core motor, cognitive, affective, and reward processes. Important for the current study, neuroimaging research has found structural and functional differences in ASD in the striatum. Structural neuroimaging studies have shown differences in striatal volume in both children and adults with ASD, specifically in the caudate nucleus (Stanfield et al., 2008; Langen et al., 2009; Qiu et al., 2010; Estes et al., 2011).  Diffusion tensor imaging studies have previously shown decreased white matter connectivity between striatum and prefrontal cortex ASD relative to TD (Langen et al., 2011) or no group differences in adults (Delmonte et al., 2013). With regards to functional connectivity with striatum, most prior research has suggested hyper-connectivity. For example, Turner et al. (2006) found increased and more diffuse functional connectivity between the caudate nucleus and cortical areas such as prefrontal cortex, premotor areas, and parietal cortex in adults with ASD during a task of visuomotor coordination. Di Martino et al. (2011) reported increased cortico-striatal

lines. **(A)** Both groups display age-related change, but do not differ in trajectories suggesting intact development in ASD. **(B)** Stable disorder effects that persist over development, with no age-related changes. **(C)** TD group

developmental trajectories that converge into adulthood (Equifinality). **(E,F)** Both groups display differential developmental trajectories that diverge in adulthood (Multifinality).

resting state connectivity in children with ASD relative to TD children and TD adults, and Delmonte et al. (2013) showed increased resting state connectivity between striatum (specifically caudate and nucleus accumbens) and prefrontal cortex in adults during rest. Di Martino et al. (2011) also reported aberrant as well as increased cortico-striatal connectivity in ASD children relative to TD children and TD adults. These differences were widespread but included limbic regions such as the insula and face processing regions such as the superior temporal cortex. Another prior resting state study reported hypo-connectivity between the ventral striatum and the temporal lobe in children with ASD (Abrams et al., 2013). Taken together, these findings suggest that striatal connectivity is atypical in ASD, and may be predominantly characterized by hyperconnectivity with cortical areas. To date, no study has examined the development of striatal functional connectivity in ASD from childhood to adulthood.

The current study utilized resting state fMRI, expanding on previous work (Di Martino et al., 2011), to explore the development of the functional connectivity of the striatum in ASD and TD. We predicted that TD individuals would show decreasing cortico-striatal connectivity over development, consistent with previous literature (Supekar et al., 2009; Dosenbach et al., 2010). Given prior evidence of relative hyper-connectivity with striatum in both children and adults (Di Martino et al., 2011; Delmonte et al., 2013), we predicted that ASD individuals would overall show increased connectivity relative to typical individuals when controlling for age. We also predicted that ASD individuals would show both deviant and arrested development in connectivity with areas that change typically, especially with regions of the striatum known to support cognitive (dorsal caudate) and affective (ventral striatum) circuits, which may contribute to known behavioral impairments of the disorder. We did not have any directional hypotheses for age related differences in striatal connectivity between ASD and TD given the lack of prior developmental research in this area.

## **MATERIALS AND METHODS**

## **PARTICIPANTS**

Forty-two ASD and 48 TD participants between the ages of 8 and 36 were tested. There were no differences in age (*p* = 0*.*88) and IQ (above 80; *p* = 0*.*81) between groups. Participants were recruited through the University of Pittsburgh Autism Center of Excellence (ACE) subject core (HD#055748). Participants were diagnosed with ASD using the Autism Diagnostic Interview (ADI; Lord et al., 1994) and Autism Diagnostic Observation Schedule-G (ADOS; Lord et al., 2000). Participants and/or their legal guardians provided consent and assent prior to being enrolled in the study, which was approved by the Internal Review Board at the University of Pittsburgh. The ASD group met cut-offs for autism on the ADI (except one individual in section D) and cut-offs for either autism or spectrum disorder on the ADOS, and an expert clinician confirmed diagnosis. Individuals were excluded from the ASD group if they reported concussions, vision problems, drug abuse, epilepsy, meningitis, and/or encephalitis. There were no effects of age on ADI scores or the ADOS social or final scores (*p*'s *>* 0.05, Bonferroni corrected for multiple comparisons) though there was a significant effect of age on the ADOS communication score (*t* = 3*.*422, *p* = 0*.*002), increasing in severity with age. TD participants were recruited through the Autism Center for Excellence (Pittsburgh, PA) subject core. Exclusion criteria included learning disabilities and psychiatric disorders (individual and first-degree relative). All participants were screened for MR safety (absence of any metal and claustrophobia). See **Table 1** for full description of participant demographics.

## **PROCEDURE**

All scans were conducted at the Neuroscience Imaging Center at the McGowan Institute for Regenerative Medicine at the University of Pittsburgh on a Siemens Allegra 3T MRI scanner. Participants first completed six runs of face and car memorization and recognition tasks. Next, we acquired Magnetization-prepared rapid gradient echo (MPRAGE) and DTI sequences prior to the final resting state scan. The participants watched a movie of their choice during structural scans in order to reduce the potential for head movement. During the resting state scan, participants were instructed to lie in the scanner with their eyes closed but to remain awake. Functional images were obtained using a gradient echo, echo-planar imaging (EPI) sequence sensitive to bloodoxygen-level-dependent (BOLD) contrast (*TR* = 1500 ms, *TE* = 25 ms, flip angle = 70◦, 29 4 mm slices axial slices, voxel size = 3*.*1 × 3*.*1 × 4, 200 volumes per run, *FOV* = 200 mm), interleaved slices MPRAGE sequences (*TR* = 2100 ms, *TE* = 3*.*93 ms, flip angle = 7◦, 176 1 mm axial slices, voxel size = 1*.*1 × 1*.*1 × 1*.*1) were used to obtain structural images, prior to functional imaging.

## **fMRI PREPROCESSING**

Each participant's resting scan data were motion corrected using AFNI (Cox, 1996), using the first volume as a reference. To correct for signal corrupted by physiological noise, Physiologic EStimation by Temporal Independent Component Analysis (PESTICA v2.0) (Beall and Lowe, 2007) was used to create respiration and cardiac estimators, and apply impulse response function retrospective correction of physiological motion effects (IRF-RETROICOR) (Beall, 2010). These estimates were then filtered temporally based on the empirically derived default windows of 48–85 bpm for cardiac and 10–24 bpm for respiration and adjusted for dithering. Resulting images were then slice time corrected, aligned to the MPRAGE using FLIRT in FSL (Smith et al., 2004) and, scaled to the mean of each voxel. We used Freesurfer's automated segmentation program (Fischl et al., 2002) to segment gray matter, white matter, ventricles and non-brain tissue (NBT) in each participant's MPRAGE scan. These anatomical parcellations were used to extract signal from white matter, ventricles and NBT in the resting state fMRI scans. Using measures of head movement obtained from motion correction, we averaged translation and rotation values in the x, y, and z directions to calculate root mean square (RMS) of linear and angular precision. Next, using the ANATICOR program in AFNI (Jo et al., 2010), we reduced noise and artifacts from hardware, the draining vessel effect, and motion in each gray matter voxel by regressing out the following nuisance variables: (1) motion regressors for **ADOS**

**ADI**

## **Table 1 | Demographic information.**

Verbal IQ 108 (11) 83 132 108 (11) 88 132 0.91 Performance IQ 112 (12) 86 128 110 (10) 86 132 0.65

*ADOS, Autism Diagnosis Observation Schedule; ADI, Autism Diagnostic Interview; SD, Standard Deviation; ASD, Autism Spectrum Disorder; TD, Typical Development. IQ, Intelligence Quotient; RRB, Restricted Repetitive Behaviors.*

the standard 6 parameters, (2) local white matter regressors averaged from white matter voxels within a spherical mask (radius = 30 mm) centered at each gray matter voxel of interest, (3) ventricle signal regressors, and (4) NBT regressors. There were no significant differences in head motion across age (*P* = 0*.*15) or between groups (*P* = 0*.*25). Data were subsequently bandpass filtered at 0.009 Hz *<* f *<* 0.08 Hz and voxels were spatially smoothed using a 5 mm full width at half maximum Gaussian kernel. Structural scans (MPRAGE) were warped to a standard template space using a template brain from the Montreal Neurological Institute (MNI, Montreal, Canada) using FSL's non-linear registration procedure (FLIRT and FNIRT), and resulting warp coefficients were saved. Preprocessed fMRI data were spatially aligned and normalized to each participant's warped MPRAGE scan using FSL's non-linear registration procedure (FNIRT). Further, using the methods proposed by Power et al. (2012), we calculated frame-wise displacement (FD) and RMS variance of the temporal derivative of the time-series (DVARS). FD and DVARS values were used to identify volumes in the fMRI time series to remove from data analysis. There were no differences in FD between groups or across age (**Figure S1**) (*p*'s *>* 0.05). Using the same threshold as Power et al. (2012) we removed volumes where FD exceeded 0.5 mm and DVARS exceeded 0.5% signal change. There were no significant differences between groups (*t* = 0*.*656, *p* = 0*.*513) or across age (*t* = −1*.*449, *p* = 0*.*151) for the number of volumes removed (TD: mean 26*.*08 ± 10*.*02, ASD: mean 30*.*66 ± 9*.*38).

Communication 4 (1) 2 8 – Social 8 (2) 5 12 – Total 12 (3) 7 19 –

Social 21 (5) 8 28 – Communication 16 (4) 9 25 – RRB 6 (2) 2 12 – Abnormal 3 (1) 0 5 –

## **STRIATAL VOLUME ANALYSIS**

Given prior research suggesting that the structural development of the striatum differs in ASD relative to TD, we obtained volumetric measurements from the caudate, putamen, and nucleus accumbens, and the whole brain using Freesurfer's automated segmentation tool (Fischl et al., 2002). We entered these values into linear regression models with age and age2 as continuous variables, and diagnosis as a categorical variable. We chose to model both linear and quadratic functions of age given prior evidence that striatal structures show both linear and quadratic changes over development (Langen et al., 2009). We also examined whether striatal volume differed as a function of age or diagnosis therefore potentially introducing a confound. We found no age or diagnosis, group main effects or interactions on striatal volume for any of the six striatal structures even at an uncorrected threshold of *p <* 0*.*05.

## **fMRI DATA ANALYSIS**

We used functionally distinct seed regions of interest (ROI) in striatum, bilaterally, including the dorsal caudate (DC), inferior and superior ventral striatum (VSi, VSs), dorsal-caudal and dorsal-rostral putamen (dcP, drP), and ventral rostral putamen (vrP) as previously defined in the literature (Di Martino et al., 2008) (**Table 2**). Prior research using these seed regions has demonstrated increased connectivity in ASD children relative to TD children and adults, suggesting that developmental differences might exist (Di Martino et al., 2011). Striatal ROI's were manually inspected against each participant's warped MPRAGE to ensure that they all fell within the boundaries of the striatal regions that they represented. In each participant's resting scan images, using single value decomposition in AFNI (1dSVD), we extracted the first principal component vector in the time series for each of the 12 ROIs using AFNI (Cox, 1996). In order to ensure that the first component time series was the most representative of all of the voxels in the seed, we correlated this time

### **Table 2 | Coordinates for striatal seed regions in left and right hemisphere in MNI space.**

*DC, Dorsal Caudate; dcP, dorsal caudal Putamen; drP, dorsal rostral Putamen; vrP, ventral rostral Putamen; VSi, Ventral Striatum inferior; VSs, Ventral Striatum superior.*

series with the average time series of each seed. Across participants and across all seed ROI's, the correlation values were above *r* = 0*.*99. We then correlated each first component vector with time series in every voxel in the brain. Resulting whole brain correlation maps were Z-transformed (Fisher r to z transformation) and entered into group-level regression models with diagnosis (ASD, TD) as a categorical factor and age as a continuous factor, controlling for sex. We also ran regression analyses to examine any non-linear relationships with age, including a quadratic model of age (age2), given prior research suggesting that striatal volume in ASD showed both quadratic and linear change with age (Langen et al., 2009). We generated maps of regions that exhibited positive and negative connectivity for each seed as a function of age within group (ASD, TD) as well as age by group interactions. We used a group inclusive mask, masking for regions that overlapped across all participants (**Figure S2**). To correct for multiple comparisons (family-wise error correction), we ran a Monte-Carlo simulation to determine cluster size at a voxel threshold of *p* = 0*.*005, and a cluster threshold of *p* = 0*.*004. We chose the value for the cluster threshold based on a Bonferroni correction for multiple seed regions (*p* = 0*.*05/12 seeds = 0.004).

Furthermore, as a secondary analysis, we excluded all participants over the age of 25, as we had fewer participants who exceeded this age, and reran the whole brain group analysis. We thus report resulting significant clusters that showed main effects of age or age2, main effect of diagnosis group (controlling for age), and age by diagnosis group interaction, across all participants in regions that remained significant when we excluded participants over the age of 25 (in order to ensure that our age effects were not driven by the older participants). For the regions that showed an age by diagnosis interaction, average beta values were extracted for each individual and entered into linear regression models using the lm function in R (R Core Team, 2012) to determine the direction of the developmental slope of each group. To assess the effect of autism diagnosis on connectivity, we ran separate regression models on the ASD group with age and ADI scores as independent variables (controlling for sex) in each of our resulting significant clusters. Lastly, given the recent discussions in the literature regarding the optimal methods of motion correction (e.g., Power et al., 2013; Satterthwaite et al., 2013), we reran our analyses without the motion censoring procedure and covaried FD at the group level.

## **RESULTS**

For the analyses that included the quadratic function of age, we found no significant clusters. Therefore, all results reported below are based on regression models that included only linear relationships with age. We also did not find any significant effects of ADI scores on connectivity (all *p*'s *>* 0.05). Lastly, we did not find differences in which clusters were significant when covarying FD at the group level. Therefore, we report our findings when employing the scrubbing procedure described previously.

Our results were consistent with previous work on striatal connectivity and development. Collapsed across groups and age, we found patterns of positive correlations between the striatal seeds and a distributed set of cortical areas. Overall, connectivity patterns were similar to previously published research in TD adults (Di Martino et al., 2008) (**Figures S3**, **S4**). Independent of diagnostic group, we found decreases with age in connectivity between striatal seeds and a wide set of striatal and cortical regions including prefrontal, temporal and parietal cortices, and cerebellum (**Figures 2**, **3**). Below, for each striatal seed, we first report clusters that showed a main effect of diagnosis group when controlling for age, to establish regions that show differences in ASD relative to TD overall (**Figure 4**, **Table 3**). We then report clusters that showed significant group by age interactions (**Figures 5**, **6**, and **Figure S5**, **Table 4**).

## **DORSAL CAUDATE (DC)**

The DC has extensive connections with dorsal and lateral aspects of cortex involved in inhibitory control, working memory, and task switching.

Group differences: We found no group differences between the DC when controlling for age.

Whole brain group × age interaction: We found significant age by diagnosis interactions in connectivity between the left DC and right fusiform gyrus. TD individuals exhibited a significant decrease in connectivity with age, and ASD individuals showed increased connectivity with age.

## **DORSAL CAUDAL AND ROSTRAL PUTAMEN (dcP, drP)**

The dorsal putamen is involved in primary motor control including motor selection and execution.

## *dcP*

Group differences: We found a main effect of diagnosis group in connectivity between the left dcP and the left superior medial gyrus, and between the right dcP and the left middle frontal gyrus. In both clusters, TD individuals exhibited increased connectivity relative to ASD.

## *drP*

Group differences: With the left drP, the ASD group showed increased connectivity with the right superior and inferior parietal lobule, and decreased connectivity with the left superior medial, superior frontal, and inferior frontal gyri. With the right drP, the ASD group demonstrated decreased connectivity with the right parahippocampal gyrus, and increased connectivity with the superior occipital gyrus.

cluster correction (voxel-wise *p <* 0*.*005, cluster-level *p <* 0*.*004 or 105 voxels) (AFNI; 3dClustSim). Slices were generated using AFNI software. Blue

Whole brain group × age interaction: We found no whole

except the superior temporal gyrus, which showed no change over development in the ASD group and a significant decrease in connectivity in the TD group.

Putamen; drP, dorsal rostral Putamen; vrP, ventral rostral Putamen; VSi,

Ventral Striatum inferior; VSs, Ventral Striatum superior.

## **INFERIOR AND SUPERIOR VENTRAL STRIATUM (VSi AND VSs)**

The ventral striatum, which has connections to medial aspects of the prefrontal cortex, and other areas of the limbic system, is strongly implicated in reward-related processing.

## *VSi*

Group differences: We found a significant group difference between the Right VSi and the right anterior cingulate cortex.

Whole brain group × age interaction: Age by diagnosis group interactions were evident in connectivity from both VSi seeds to the bilateral cerebellum (Lobule VI and Lobule VIIa, Crus I). In addition, we found an interaction between the left VSi and the right amygdala and right fusiform gyrus, and between the right VSi and the left supplementary motor area. The ASD participants showed significant increases with age whereas TD showed

## brain age group by diagnosis interactions with either the dcP or the drP.

## **VENTRAL ROSTRAL PUTAMEN (vrP)**

The ventral rostral putamen is implicated in cognitive control and executive function, with connections to the anterior cingulate, and regions of the insula.

Group differences: Collapsed across age, the ASD group showed increased connectivity between the left vrP and right superior parietal lobule, and decreased connectivity between the right vrP and the right inferior frontal gyrus, relative to TD.

Whole brain group × age interaction: There were significant age by diagnosis interactions in connectivity between the left vrP and the left medial temporal pole, the right fusiform gyrus, and the right superior temporal gyrus. We also found an interaction between the right vrP and the right inferior temporal gyrus. The ASD group showed increased connectivity with age, and the TD group showed significant decreases with age in all clusters

**hemisphere seeds.** For all analyses, we used a Monte Carlo simulation for cluster correction (voxel-wise *p <* 0*.*005, cluster-level *p <* 0*.*004 or 105 voxels) (AFNI; 3dClustSim). Slices were generated using AFNI software.

dorsal caudal Putamen; drP, dorsal rostral Putamen; vrP, ventral rostral Putamen; VSi, Ventral Striatum inferior; VSs, Ventral Striatum superior.

decreases with age except with the right fusiform gyrus, where the ASD group showed no change with development.

## *VSs*

Whole brain group × age interaction: There were significant age by diagnosis group interactions between both VSs seeds and the cerebellum (Lobules VI and VIIa, Crus I), precuneus, and right inferior temporal gyrus. The ASD group showed increased connectivity with age, whereas the TD group showed decreased connectivity with age.

## **DISCUSSION**

We examined striatal resting state functional connectivity across the ages of 8–36 years in individuals diagnosed with ASD, relative to typically developing individuals. Using previously defined striatal seed ROIs (Di Martino et al., 2008), we identified connections associated with each seed, respectively, and examined changes in connectivity patterns across age and between diagnosis groups. To the best of our knowledge this study is the first to examine striatal functional connectivity in ASD across development, from late childhood to adulthood. Thus, our results provide a novel understanding of the development of functional connectivity with the striatum in ASD and identify connectivity patterns that parallel and deviate from typical development.

Patterns of striatal functional connectivity in both individuals with ASD and TD individuals were consistent with previous studies utilizing the same seed regions in both child and adult populations separately (Di Martino et al., 2008, 2011; Kelly et al., 2009b; Furman et al., 2011). In general, we noted a dorsal to ventral and medial to lateral gradient, where more dorsal seeds in striatum were significantly connected to dorsal and lateral aspects of cortex, and ventral areas of striatum connected to more medial and ventral cortical regions. This is consistent with purported cognitive/affective divisions previously identified in cortico-striatal circuits using the same seed regions (Di Martino et al., 2008). Collapsed across diagnostic groups, we found decreases in connectivity between all striatal

**striatal region.** For all analyses, we used Monte Carlo simulation for cluster correction (voxel-wise *p <* 0*.*005, cluster-level *p <* 0*.*004 or 105 voxels) (AFNI; 3dClustSim). Slices were generated using Analysis of Functional NeuroImages (AFNI) software. Regions showing increased connectivity in ASD relative TD are depicted in red and regions showing increased connectivity in TD relative to ASD are depicted in green. See *(Continued)*

#### **FIGURE 4 | Continued**

**Table 3** for cluster coordinates and connections to specific seed regions. **(A–E)** Regions connected with the dorsal rostral putamen (drP). **(F–G)** Regions connected with the dorsal caudal putamen (dcP). **(H–I)** Regions connected with the ventral rostral putamen (vrP). **(J)** Regions connected with the inferior ventral striatum (VSi). L, Left Hemisphere. R, Right Hemisphere.

seeds and various cortical areas across age, consistent with studies of typical development, which may reflect necessary decreases in striatal influence over cortical function, supporting the emergence of long-range cortico-cortico connectivity in adulthood (Fair et al., 2007, 2009; Kelly et al., 2009a; Supekar et al., 2009; Dosenbach et al., 2010). As developmental changes that occur in typically developing individuals are often interpreted as necessary for maturation into adulthood, regions that show similar age-related change with TD suggest intact development in ASD. Thus, deviations from typical maturation may indicate development of compensatory connections or impairments that persist into adulthood.

Stable disorder effects (i.e., connections that are atypical in ASD independent of age) were noted with only the putamen and the inferior ventral striatum seeds, and suggest that posterior connections (superior and inferior parietal lobule) are increased, whereas anterior connections (anterior cingulate and superior, middle, and inferior frontal gyri) are decreased in ASD. Previous resting state functional connectivity studies using the striatum did not find differences in the posterior parietal cortex, and differences with the prefrontal cortex have been in the opposite direction with ASD individuals (both children and adults) showing increased striatal-prefrontal connectivity relative to typicals (Turner et al., 2006; Di Martino et al., 2011; Delmonte et al., 2013) with some studies reporting no differences (Kennedy and Courchesne, 2008; Tyszka et al., 2013). Discrepant findings previously might have been specific to the age groups tested, whereas the present study controlled for age-related changes when examining group differences. We suggest that there are differences in network connectivity in ASD that is characterized by both hypoand hyper- connectivity in a region specific manner. Although the behavioral implications of these findings are unclear, these novel results highlight atypical connectivity patterns that are unchanged with development (see **Figure 1B**). One caveat to the interpretation of these group level findings in relation to prior results must be highlighted. We did not include the average global time series as a nuisance regressor (GSR), as prior studies did due to our use of the PESTICA program to estimate and remove the effects of physiological noise, as well as the recent evidence in the literature regarding the potential spurious correlations that may arise when using GSR with seed based resting state analyses in both typical (Saad et al., 2012), and ASD samples (Gotts et al., 2013). A recent resting state study in children with ASD suggested that GSR does not significantly alter resting state results (Di Martino et al., 2013), although those findings were specific to a different measure of connectivity [network centrality—which may be less affected by GSR (Yan et al., 2013)] than the seed based correlations used in the current study. Although differences in

**Table 3 | Regions showing a significant main effect of diagnosis group, controlling for age.**

*L, Left; R, Right; DC, Dorsal Caudate; dcP, dorsal caudal Putamen; drP, dorsal rostral Putamen; vrP, ventral rostral Putamen; VSi, Ventral Striatum inferior; VSs, Ventral Striatum superior.*

*Negative max intensity values indicate that TD showed increased connectivity relative to ASD. Positive max intensity values indicate that ASD showed increased connectivity relative to TD.*

preprocessing is a limitation in our ability to compare our results to previous findings, nonetheless, we estimated and removed physiological noise without the potential confounds of GSR. As it is vital that results be comparable in the literature, and future developmental work in ASD should consider both GSR and non-GSR approaches in analysis and interpretation.

The predominant goal of the current study was to identify regions that showed age-related differences from late childhood to adulthood in ASD relative to TD. Within the majority of clusters that showed age by diagnosis group interactions, the TD individuals showed significant developmental decreases, whereas the ASD group showed increases with age, suggesting deviating developmental trajectories into adulthood. Similar to the direction of results in the current study, prior research has suggested that maturation of white matter connectivity is aberrant in ASD relative to TD in a similar fashion in roughly the same age range (10–40), with ASD participants showing increased white matter integrity in subcortical to cortical projection tracts across age, whereas TD participants showed decreased white matter connectivity (Kleinhans et al., 2012). It is possible that age-related differences in structural connectivity in subcortical-cortical tracts underlie the functional differences noted in the present study.

Notably, age by diagnosis interactions revealed that the connectivity between striatum and superior aspects of the cerebellum, specifically with regions VI and VIIa (including Crus I) were decreased in TD participants but increased in ASD. These differences in the development of cerebellar connectivity are not surprising given the convergence of evidence targeting the cerebellum as a locus of abnormality in ASD (Courchesne et al., 1988; Nowinski et al., 2005; for review see Fatemi et al., 2012). The cerebellum has extensive connections with cortical and subcortical brain regions, including bidirectional connections with striatum (Habas et al., 2009; Krienen and Buckner, 2009; Strick et al., 2009; Bostan and Strick, 2010). Studies have previously shown that individuals with lesions in cerebellar areas including lobules VI and VIIa demonstrate cognitive impairments such as motor control and planning, attention, sensory integration, language, and affective processes (Habas et al., 2009; Krienen and Buckner, 2009; Stoodley et al., 2010), all of which are known to be affected in ASD. Furthermore, structural MRI research has found reduced overall cerebellar as well as reduced regional gray matter volume in children, adolescents and adults with ASD (Hashimoto et al., 1995; Bauman and Kemper, 2005; Stanfield et al., 2008; Riva et al., 2013). Functional MRI research has demonstrated atypical cerebellar activation during motor control (Muller et al., 2001; Allen et al., 2004; Mostofsky et al., 2009), and attention (Allen and Courchesne, 2003) in children, adolescent and adults with ASD separately. Finally, functional connectivity findings suggest reduced connectivity between cerebellum and motor execution areas (e.g., sensorimotor and supplementary motor cortices) in children with ASD (Mostofsky et al., 2009). These findings highlight a potential developmental deficit where initial hypoconnectivity relative to TD may be later compensated with relative hyperconnectivity in adulthood, specifically in more ventral aspects of striatum that are involved in reward processing (VSi and VSs). As we did not find any significant correlations with our cerebellar clusters and ADI scores, further work will have to explore the behavioral implications of the aberrant development of cerebellar connectivity in ASD. It is important to note one limitation with our cerebellar results; due to differences in head size between participants, we were unable to acquire complete coverage of cerebellum across all participants. Therefore, our results

**striatum (VSs and VSi).** For all analyses, we used a Monte Carlo simulation for cluster correction (voxel-wise *p <* 0*.*005, cluster-level *p <* 0*.*004 or 105 voxels) (AFNI; 3dClustSim). Z-transformed correlation coefficients are displayed on the y-axis and age in years on the x-axis squares and dashed lines are ASD participants. L, Left; R, Right; VSi, Ventral Striatum inferior; VSs, Ventral Striatum superior; TD, Typical Development; ASD, Autism Spectrum Disorder. See **Table 4** for cluster coordinates. See **Figure S5** for all graphs.

**and putamen (DC, drP, vrP).** For all analyses, we used a Monte Carlo simulation for cluster correction (voxel-wise *p <* 0*.*005, cluster-level *p <* 0*.*004 or 105 voxels) (AFNI; 3dClustSim). Z-transformed correlation coefficients are displayed on the y-axis and age in years on the x-axis of each graph. Title of each graph describes the seed region and the

solid lines are TD participants, squares and dashed lines are ASD participants. L, Left; R, Right; DC, Dorsal Caudate; drP, dorsal rostral Putamen; vrP, ventral rostral Putamen; TD, Typical Development; ASD, Autism Spectrum Disorder. See **Table 4** for cluster coordinates. See **Figure S5** for all graphs.

**Table 4 | Regions showing significant Age × Diagnosis Group Interactions.**

*\*p < 0.05; \*\*p < 0.01; \*\*\*p < 0.001. L, Left; R, Right; DC, Dorsal Caudate; dcP, dorsal caudal Putamen; drP, dorsal rostral Putamen; vrP, ventral rostral Putamen; VSi, Ventral Striatum inferior; VSs, Ventral Striatum superior.*

were limited to the anterior and superior portions of the cerebellum. It is possible that there are connectivity differences with inferior aspects of the cerebellum that we were unable to detect.

The inferior and superior temporal gyri (ITG and STG) and the fusiform gyrus (FG) also showed age by diagnosis group interactions, mainly demonstrating increased connectivity with age in ASD, and decreased in TD. Two clusters suggested developmental arrests or delays in ASD (showing no change with age), and significant decreases with TD; connectivity between the left vrP and the superior temporal gyrus, and the left VSi and the right fusiform gyrus. Structural abnormalities of the temporal gyri gray matter have also been reported in children, adolescents, and adults with ASD, which may contribute to differences in the development of connectivity with temporal regions (Jou et al., 2010; Toal et al., 2010). ITG and FG connectivity differences were found in connections with both dorsal and ventral aspects of the striatum. The ITG and FG, which are components of the ventral stream visual pathway with direct connections to occipital cortex, are largely implicated with face processing, face recognition, and in discrimination of facial expression, including affective interpretation, which may be affected in ASD (Sergent et al., 1992; Kanwisher et al., 1997; Apps et al., 2012; Prochnow et al., 2013). Exaggerated ITG activation and reduced FG activation during face perception in young adults with ASD has also previously been reported (Schultz et al., 2000; Coutanche et al., 2011). The STG has previously been implicated in social communication abnormalities in ASD (Frith, 2001; Wang et al., 2007; Pelphrey et al., 2009; Hubbard et al., 2012). Therefore, abnormalities in striatal connectivity with the temporal cortex may underlie social and/or social reward deficits.

We also found a significant age by group interaction between the inferior ventral striatum and the amygdala. Several prior studies have reported altered activation in and reduced connectivity with the amygdala in association with social perception deficits in ASD (e.g., Kleinhans et al., 2008; Pelphrey and Carter, 2008; Sato et al., 2012), although these findings were with the ITG and not striatum. Given the role of the ventral striatum in reward-related processing and its extensive connections to the amygdala, it is possible that aberrant functional connectivity between striatum and amygdala underlies deficits in social rewards in ASD, which may increase in a compensatory fashion over development (e.g., Delmonte et al., 2012; Sepeta et al., 2012).

This is the first study to examine age related change in functional connectivity with striatum in ASD compared to typical development. We identified a number of connections, with a range of brain regions, showing atypical development from late childhood to adulthood. Importantly, we found that social processing regions such as ITG, STG, and FG, and cerebellar regions implicated in cognitive and motor functions demonstrated a decrease in connectivity over development in TD, but an increase in ASD. As these are novel findings, replication will be necessary, especially given recent debates in the literature regarding methodological considerations related to head motion (e.g., Power et al., 2012; Satterthwaite et al., 2013; Yan et al., 2013), and the removal of nuisance variables such as physiological noise and/or the global signal e.g., when analyzing resting state data. In addition, it is likely that larger sample sizes, wider age ranges, and longitudinal data are needed to replicate these findings, and perhaps identify patterns that the current study may not have had the power to detect, including correlations with symptoms of ASD, identifying regions that show developmental delays, and non-linear trajectories. Despite these limitations, our findings were robust and highlight the important notion that examining the progression of ASD over development is crucial for identifying the neural bases of ASD and how they relate to behavioral impairments in the disorder.

## **ACKNOWLEDGMENTS**

This work was completed at the University of Pittsburgh and supported by NIMH 5 R01 MH067924 (PI Luna), NIH HD055748 (PI Minshew) from the Eunice Kennedy Shriver National Institute of Child Health & Human Development, and NIMH K01 MH081191 (PI O'Hearn). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Recruitment was supported by NICHD ACE grant HD055648 and CPEA grant HD35469. We thank the participants, their families, Jennifer Fedor, and the staff at the Autism Center for Excellence for their generous help.

### **SUPPLEMENTARY MATERIAL**

The Supplementary Material for this article can be found online at: http://www.frontiersin.org/Journal/10.3389/fnhum. 2013.00814/abstract

#### **Figure S1 | Scatter plot of mean FD values across groups.** Mean

framewise displacement (FD) values for each participant on Y-axis and age in years is depicted on the X-axis. TD participants are in open circles and ASD participants in filled circles. TD, Typical Development; ASD, Autism Spectrum Disorder. There were no significant differences in FD between groups or across age *p >* 0*.*05.

#### **Figure S2 | Mask of overlapping voxels across all participants.**

#### **Figure S3 | Statistical maps depicting connectivity with striatal seeds in the left hemisphere across all participants, controlling for age.** For all analyses, we used a Monte Carlo simulation for cluster correction

(voxel-wise *p <* 0*.*005, cluster-level *p <* 0*.*004 or 105 voxels) (AFNI; 3dClustSim). Slices were generated using AFNI software. L, Left; DC, Dorsal Caudate; dcP, dorsal caudal Putamen; drP, dorsal rostral Putamen; vrP, ventral rostral Putamen; VSi, Ventral Striatum inferior; VSs, Ventral Striatum superior.

**Figure S4 | Statistical maps depicting connectivity with striatal seeds in the right hemisphere across all participants, controlling for age.** For all analyses, we used a Monte Carlo simulation for cluster correction (voxel-wise *p <* 0*.*005, cluster-level *p <* 0*.*004 or 105 voxels) (AFNI; 3dClustSim). Slices were generated using AFNI software. R, Right; DC, Dorsal Caudate; dcP, dorsal caudal Putamen; drP, dorsal rostral Putamen; vrP, ventral rostral Putamen; VSi, Ventral Striatum inferior; VSs, Ventral Striatum superior.

**Figure S5 | All graphs showing age by group interactions.** For all analyses, we used a Monte Carlo simulation for cluster correction (voxel-wise *p <* 0*.*005, cluster-level *p <* 0*.*004 or 105 voxels) (AFNI; 3dClustSim). Z-transformed correlation coefficients are displayed on the y-axis and age in years on the x-axis of each graph. Title of each graph describes the seed region and the relevant connecting cluster. Triangles and solid lines are TD participants, squares and dashed lines are ASD participants. L, Left; R, Right; DC, Dorsal Caudate; drP, dorsal rostral Putamen; vrP, ventral rostral Putamen; TD, Typical Development; ASD, Autism Spectrum Disorder. See **Table 4** for cluster coordinates.

## **REFERENCES**

in autism spectrum disorders. *Brain Res. Brain Res. Rev.* 1479, 1–16. doi: 10.1016/j.brainres.2012.07.056

discrimination among individuals with autism and asperger syndrome. *Arch. Gen. Psychiatry* 57, 331–340. doi: 10.1001/archpsyc.57.4.331

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 16 June 2013; accepted: 10 November 2013; published online: 28 November 2013.*

*Citation: Padmanabhan A, Lynn A, Foran W, Luna B and O'Hearn K (2013) Age related changes in striatal resting state functional connectivity in autism. Front. Hum. Neurosci. 7:814. doi: 10.3389/fnhum.2013.00814*

*This article was submitted to the journal Frontiers in Human Neuroscience.*

*Copyright © 2013 Padmanabhan, Lynn, Foran, Luna and O'Hearn. This is an openaccess article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

## Intrinsic functional network organization in high-functioning adolescents with autism spectrum disorder

## *Elizabeth Redcay1\*, Joseph M. Moran2,3 , Penelope L. Mavros 4 , Helen Tager-Flusberg5 , John D. E. Gabrieli 6,7 and SusanWhitfield-Gabrieli 6,7*

7 Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA

#### *Edited by:*

Ralph-Axel Müller, San Diego State University, USA

#### *Reviewed by:*

Christian Sorg, Klinikum rechts der Isar Technische Universität München, Germany

Michal Assaf, Institute of Living, USA

#### *\*Correspondence:*

Elizabeth Redcay, Department of Psychology, University of Maryland, 1147 Biology-Psychology Building, College Park, MD 20742, USA e-mail<EMAIL_ADDRESS>
Converging theories and data suggest that atypical patterns of functional and structural connectivity are a hallmark neurobiological feature of autism. However, empirical studies of functional connectivity, or, the correlation of MRI signal between brain regions, have largely been conducted during task performance and/or focused on group differences within one network [e.g., the default mode network (DMN)]. This narrow focus on task-based connectivity and single network analyses precludes investigation of whole-brain intrinsic network organization in autism. To assess whole-brain network properties in adolescents with autism, we collected resting-state functional connectivity MRI (rs-fcMRI) data from neurotypical (NT) adolescents and adolescents with autism spectrum disorder (ASD). We used graph theory metrics on rs-fcMRI data with 34 regions of interest (i.e., nodes) that encompass four different functionally defined networks: cingulo-opercular, cerebellar, fronto-parietal, and DMN (Fair et al., 2009). Contrary to our hypotheses, network analyses revealed minimal differences between groups with one exception. Betweenness centrality, which indicates the degree to which a seed (or node) functions as a hub within and between networks, was greater for participants with autism for the right lateral parietal (RLatP) region of the DMN. Follow-up seed-based analyses demonstrated greater functional connectivity in ASD than NT groups between the RLatP seed and another region of the DMN, the anterior medial prefrontal cortex. Greater connectivity between these regions was related to lower ADOS (Autism Diagnostic Observation Schedule) scores (i.e., lower impairment) in autism. These findings do not support current theories of underconnectivity in autism, but, rather, underscore the need for future studies to systematically examine factors that can influence patterns of intrinsic connectivity such as autism severity, age, and head motion.

**Keywords: autism, resting-state functional connectivity, default mode network, intrinsic network organization, graph theory, functional MRI**

## **INTRODUCTION**

Atypical patterns of functional and structural connectivity are proposed to be a hallmark neurobiological feature of autism (Belmonte et al., 2004; Just et al., 2004; Courchesne and Pierce, 2005; Cherkassky et al., 2006). Most theories and data point to a pattern of underconnectivity, particularly for long-distance connections such as interhemispheric or anterior–posterior intrahemispheric connections (Belmonte et al., 2004; Just et al., 2004; Anderson et al., 2011; Dinstein et al., 2011). Some also suggest an increase in local connections at the expense of long-distance connections (Courchesne and Pierce, 2005; Courchesne et al., 2007; Rippon et al., 2007). Recent findings, however, offer mixed support and suggest a more complex picture of connectivity differences in autism with evidence for both hypo- and hyper-connectivity for short- and long-distance connections, depending partly on the specific experimental and analytic methods used and age of the participants (e.g., Courchesne et al., 2007; Noonan et al., 2009; Khan et al., 2013; Lynch et al., 2013; review, Müller et al., 2011).

Structural connectivity findings, indexed by measures of white matter integrity from diffusion tensor imaging (DTI) (e.g., fractional anisotropy, or FA) or white matter volumes from structural MRI, reveal atypical connectivity patterns in autism but do not support general underconnectivity in autism. Rather, findings suggest developmentally *increased* white matter volume (Courchesne et al., 2001; Hazlett et al., 2006), particularly radiate white matter bundles supporting interhemispheric and cortico-cortical connections (Herbert et al., 2004) and increased FA in infants and young children with autism (e.g., Ben Bashat et al., 2007; Wolff et al., 2012), whereas later in development (e.g., adolescents and adults), FA is decreased (e.g., Barnea-Goraly et al., 2004; Lee et al., 2007; Nair et al., 2013).

"fnhum-07-00573" — 2013/9/19 — 15:46 — page 1 — #1

1 Department of Psychology, University of Maryland, College Park, MD, USA

2 Center for Brain Science, Harvard University, Cambridge, MA, USA

3 United States Army Natick Soldier Research Development and Engineering Center, Natick, MA, USA

4 Simons Center for the Social Brain at Massachusetts Institute of Technology, Cambridge, MA, USA

5 Department of Psychology, Boston University, Boston, MA, USA

6 McGovern Institute for Brain Research at Massachusetts Institute of Technology, Cambridge, MA, USA

Studies of *functional* connectivity, or the correlation in signal between brain regions, largely have supported the underconnectivity theory when functional connectivity has been assessed in the context of a task (review, Müller et al., 2011). This pattern of reduced long-distance connectivity (e.g., between regions of different hemispheres or lobes) is seen across domains of function including tasks involving language processing (e.g., Just et al., 2004; Kana et al., 2006), executive function (e.g., Just et al., 2007), and social processing (e.g., Mason et al., 2008; Kana et al., 2012; , but see Murphy et al., 2012), but notably these tasks also resulted in reduced activation in the autism spectrum disorder (ASD) group as compared to the neurotypical (NT) group. Thus, while informative, task-based functional connectivity analyses may reflect differences in performance during a task and may not reflect differences in intrinsic functional organization of the brain.

Task-independent studies of the "resting" brain provide a window with which to examine intrinsic functional network organization. As first noted by Biswal et al. (1995), even in the absence of a specific task, fluctuations in brain signal are temporally correlated within regions that are part of the same functional network. These large-scale functional networks can be identified using data-driven ICA (independent component analysis) analyses (e.g., Damoiseaux et al., 2006) or seed-based analyses (e.g., Fox et al., 2005) and are thought to reflect regions that have a history of co-activation. Indeed, differences in the organization or connection strength within these regions are related to developmental changes (e.g., Fair et al., 2009), training (Lewis et al., 2009), and individual differences, for example in memory (Wang et al., 2010), math abilities (Emerson and Cantlon, 2012), and face processing (Zhu et al., 2011), suggesting intrinsic network connectivity is behaviorally relevant.

There has been considerable divergence across studies in regards to the status of resting-brain functional connectivity in ASD. Like task-based studies, many studies of the resting brain in ASD (or those in which the task is used as a regressor of no interest) have revealed reduced functional connectivity in ASD, particularly for long-range connections (Cherkassky et al., 2006; Kennedy and Courchesne, 2008; Ebisch et al., 2011; Tsiaras et al., 2011; Murdaugh et al., 2012; Rudie et al., 2012; Washington et al., 2013). However, unlike task-based studies, a number of studies report findings that are inconsistent with a general theory of underconnectivity (e.g., Monk et al., 2009; Müller et al., 2011; Tyszka et al., 2013), and in some cases hyper-connectivity in ASD groups has been reported (Mizuno et al., 2006; Turner et al., 2006; Noonan et al., 2009; Di Martino et al., 2011; Shih et al., 2011; Lynch et al., 2013).

In sum, extant data suggest a general underconnectivity theory in autism is likely not the full story. Possibly, the age of the participant, the context in which connectivity is assessed (e.g., resting vs. task), and the specific networks examined may result in different findings between groups. Further, recent studies suggest that head motion may lead to systematic, spurious correlations which could mimic some of the same patterns of connectivity differences reported between autism and NT groups (Power et al., 2011). An incomplete picture of how each of these factors contributes to functional connectivity in autism still remains. One additional contributing factor is that most previous studies only focused on the strength of correlations within a single network rather than examining network organization with graph theoretical metrics. Recent advances in graph theory (or complex network) analyses for resting-state functional connectivity MRI (rs-fcMRI) data allow for characterization of whole-brain intrinsic network organization (e.g., review, Rubinov and Sporns, 2010; Bullmore and Bassett, 2011). Specifically, rather than focusing on the strength of region–region correlations, graph theory methods can examine the topological properties of each region within the context of all other regions of interest. For example, graph theory metrics can include measures of the integration (global efficiency, average path length), segregation (local efficiency, clustering coefficient), and centrality (betweenness centrality) of networks. Thus, these metrics can provide a more robust test of the theory of reduced long-distance and increased local connectivity by testing differences in measures of whole-brain network integration and segregation.

In the current study, we assessed whole-brain network properties in a group of adolescents with and without autism by using graph theory and seed-based analyses on rs-fcMRI data with functionally defined regions of interest. The functional regions of interest included 34 regions identified from previous metaanalyses (Dosenbach et al., 2006; Fair et al., 2009) that encompass four different functionally defined networks: cingulo-opercular (CO), cerebellar (C), fronto-parietal (FP), and default mode (DMN; Fair et al., 2009). These networks were chosen because previous research with these same networks has demonstrated a developmental pattern of progressive increases in long-distance connectivity between nodes of the same network and concurrent decreases in connectivity between anatomically proximal nodes of distinct networks (Fair et al., 2008, 2009). Furthermore, functions associated with these networks have all been implicated in autism (e.g., reviews, Di Martino et al., 2009; Minshew and Keller, 2010). Thus, examining these networks allows for a more rigorous test of the hypothesis of reduced long-distance and increased local connectivity in autism, across multiple networks that support varied functions.

## **MATERIALS AND METHODS PARTICIPANTS**

All participants gave written, informed consent and parental consent was obtained for participants under 18 years of age as approved by the Committee on the Use of Humans as Experimental Subjects (COUHES) at the Massachusetts Institute of Technology. Participants were compensated monetarily for their time. Participants were part of a multi-site study involving three visits for TD adolescents and four for the ASD group but only the resting-state functional MRI data are presented in the current study. Participant IQ was measured using the Kaufman Brief Intelligence Test (KBIT-2).

## **AUTISM SPECTRUM DISORDER PARTICIPANTS**

We collected resting-state functional MRI data from 22 male adolescents and young adults (14–20 years; mean 17.3 ± 2.2 years; all male) with a clinical diagnosis of ASD or Asperger's disorder. Diagnosis was confirmed using a combination of the

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**Table 1 | Demographic and head motion information for NT and ASD groups and those ASD participants excluded due to excessive head motion.**

Note: Data are mean (SD). Age is in years. IQ was measured using the Kaufman Brief Intelligence Test-2. ADOS Comm is the communication subscale. p-Value is based on a t-test comparing groups. ◦This difference is circular because these groups were created based on differences in motion outliers.

Autism Diagnostic Observation Schedule (ADOS) Module 3 or 4 (administered to the participant; Lord et al., 2000) and the Social Communication Questionnaire (SCQ; completed by the parent of the participant; Corsello et al., 2007). The SCQ is a questionnaire designed to screen for autism and all included ASD participants received an SCQ score greater than the suggested cut-off for ASD of 15 (mean 21.6; 16–28). All participants reached criteria for Autism or spectrum from the ADOS except 1 who was subsequently removed from the analyses. Seven participants were excluded from the analyses because of excessive movement artifact (see below for description) resulting in a final sample of 14 participants with ASD (**Table 1**). Information about co-morbid diagnoses and current medications were obtained through a phone screen with either the participant or parent if the participant was a minor. This information was not available for 2 of the 14 ASD participants. Six of the 12 participants reported use of medications associated with symptoms of neuropsychiatric disorders [ADHD (4), depression/anxiety (3), psychosis (2)]. Only two participants, however, reported any co-morbid neurological disorders and these were obsessive–compulsive disorder (1) and attention deficit hyperactivity disorder (2).

## **NEUROTYPICAL PARTICIPANTS**

Twenty-three NT participants (14–20 years; all male) performed a resting-state scan. Participants were excluded if they reported any psychiatric or neurological disorders on a selfreport screening questionnaire, which was filled out either by the participant or the parent. To screen for the presence of autism or autistic-like traits in the typical population, the participant's parents completed the SCQ screening described above. One participant who was no longer a minor completed the Autism Spectrum Quotient (AQ; Woodbury-Smith et al., 2005). No included participants received scores above the suggested threshold for autism screening. One was excluded due to excessive movement. Of the 22 remaining participants, 14 were matched as closely as possible to the ASD group on age. IQ scores did not differ significantly between groups (see **Table 1**).

## **MRI DATA ACQUISITION**

Participants came to the Athinoula A. Martinos Imaging Center at the McGovern Institute for Brain Research at MIT for MRI data collection on a 3T Siemens Magnetom Tim Trio Scanner. We collected a structural MPRAGE image (128 sagittal slices, TE = 3.39 ms, TR = 25 ms, voxel size 1.3 mm × 1 mm × 1.3 mm) and a resting-state functional MRI scan (67 sagittal slices, TE = 30 ms, TR = 6000 ms, # of TRs = 64, voxel size = 2.0 mm isotropic) as part of a 90-min battery of tasks examining social processing that are not presented here. The last scan of the battery was the resting-state scan for which we asked participants to remain still with eyes open and fixated on a cross in the center of the screen. We chose a 6 s TR for the resting-state scan in order to achieve high spatial resolution with whole-brain coverage because previous work has demonstrated that array coils provide the biggest increases in temporal signal to noise ratio (tSNR) at high spatial resolutions (Triantafyllou et al., 2011). While this TR is unusually long, a study by Van Dijk et al. (2010), showed that there was no significant difference in the correlation strengths between the resting-state networks when compared between a TR of 2.5 and 5 s.

## **FUNCTIONAL MRI PREPROCESSING**

All data were analyzed using SPM81, Nipype (Gorgolewski et al., 2011), the CONN functional connectivity toolbox ver 13e2 (Whitfield-Gabrieli and Nieto-Castanon, 2012), and in-house Matlab (The Mathworks, Natick, MA, USA) scripts. All restingstate volumes were corrected for differences in the timing of slice acquisition. Functional data were realigned to the mean of all functional volumes in the timeseries using a 6◦ rigid spatial transformation, which provided the spatial deviationfor each timepoint for translational (x, y, z) and rotational (roll, pitch, yaw) directions of movement. Functional data were then smoothed with a Gaussian smoothing kernel of 6 mm full-width half maximum, and normalized into standard Montreal Neurological Institute (MNI) space using non-linear transformations.

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1www.fil.ion.ucl.ac.uk/spm

2http://www.nitrc.org/projects/conn/

## **ANALYSES OF HEAD MOTION**

The artifact detection toolbox (ART)3 was used to examine outliers in global signal and movement for each participant. Timepoints were marked as outliers if global signal exceeded three standard deviations of the mean or if movement exceeded 1 mm (across translational and rotational directions) of scan-to-scan deviation. Participants for whom greater than 20% of the run was marked as an outlier were removed from the analyses (seven ASD; one NT). Head motion has been shown to result in spurious patterns of correlations (both increased and decreased; e.g., Power et al., 2011). Thus to examine whether groups differed as a function of head motion we used between-group *t*-tests to test for differences in (1) the total number of outliers and (2) the sum across all volumes of the absolute value of the deviation (in mm) from the reference volume (i.e., the realignment parameters) for each of the six possible motion directions (i.e., x, y, z, roll, pitch, yaw). Using between group *t*-tests, we also examined whether those participants who were excluded from the analyses due to excessive head motion were systematically different from those included in terms of age, IQ, or autism severity (**Table 1**). No significant differences in head motion between groups were present for either the number of outliers (see **Table 1**) or realignment parameters in any of the six directions [x: *t*(24) = −0.56, *p* < 0.58; y: *t*(24) = −0.58, *p* < 0.57; z: *t*(24) = 1.1, *p* < 0.28; roll: *t*(24) = 0.85, *p* < 0.41; pitch: *t*(24) = 0.18, *p* < 0.86, yaw: *t*(24) = 1.7, *p* < 0.11). However, the ASD participants who were excluded due to excessive head motion had significantly lower Verbal Composite IQ scores, and higher (worse) social impairments as measured by the ADOS Reciprocal Social Interaction subscale and autism severity as measured by the Combined ADOS Communication and Reciprocal Social Interaction subscales. Excluded participants also showed a trend toward significantly younger ages (**Table 1**).

## **FUNCTIONAL CONNECTIVITY ANALYSES**

To minimize the effects of head motion, whole-brain voxel-wise regression analyses were run for each seed region of interest with the six motion parameters from realignment and their temporal derivatives and each outlier timepoint entered separately as noise covariates. Additionally, using the aCompCor method (Behzadi et al., 2007) to account for physiological noise, covariates were included with a principal components analysis (PCA)-reduction (three dimensions) of the signal from white matter and CSF voxels based on each individual's unique segmented white matter and CSF masks. The residual datasets were then temporally filtered (0.01<*f* <0.08) to focus analyses to the low-frequency oscillations characteristic of resting-state networks.

Whole-brain regression analyses were computed for each of the 34 seed regions of interest (Fair et al., 2009; **Table 2**) on the preprocessed, "clean" datasets for each participant. These analyses resulted in a correlation value in each voxel for each of the 34 seed regions. Normalized correlation values were created by a Fishers r-to-z transform and used in subsequent analyses. Averaging the normalized correlation coefficients within each group for each region pair created correlation matrices

for each of the 34 regions of interest (ROI). Two-way between group (ASD vs. NT) *t*-tests were run for each of the 561 ROI–ROI pairs to examine whether differences in connectivity strength between groups were present and specific to particular networks. False discovery rate (FDR; *q* < 0.05) was used to correct for multiple comparisons for the ROI–ROI comparisons.

Graph theory analyses were computed using the CONN functional connectivity toolbox. The unweighted ROI-to-ROI correlation matrices were first thresholded at a cost value of *k* = 0.15. Cost is a measure of the proportion of connections for each ROI in relation to all connections in the network. Rather than determining a fixed correlation value as a threshold (e.g.,*r* = 0.1), using a cost threshold allows for roughly the same number of connections across participants by varying the correlation threshold for each participant to achieve the fixed cost threshold. When cost is equated across participants, direct comparisons across groups of network property differences can be made. Small world properties are observed in the range of costs 0.05 < *k* < 0.34, where global efficiency is greater than that of a lattice graph and local efficiency is greater than that of a random graph (Achard and Bullmore, 2007). A cost threshold of .15 has also been demonstrated to provide a high degree of reliability when comparing session-specific estimates of graph theoretical measures across repeated runs or sessions (e.g., global efficiency *r* = 0.95, local efficiency *r* = 0.9; Whitfield-Gabrieli and Nieto-Castanon, 2012). We employed both one- and two-sided cost thresholds. In a one-sided cost threshold only positive correlations are considered, whereas two-sided includes both positive and negative correlations. To confirm that our findings generalize beyond these specific parameters, data were examined at a cost threshold of 0.05, 0.1, 0.2, and 0.25 and compared to the findings with our *a priori* cost threshold of 0.15.