*Production (imperative or declarative).* Twenty-four pictures were presented, showing a character – the same person who performed subject training – looking at the subject and sitting at a table on which two take-away trays were located. Both trays were marked with a label, one showing the name of a specific salty food (e.g., "hamburger," "potato-chips") and the other showing a generic word, i.e., "dolce" (sweet). Subjects were instructed to point only to the salty food trays, after which the feedback picture followed. The salty food was used as a target stimulus and the sweet food as a control stimulus. Salty food was located on either the left or right side in a counterbalanced random order (**Figure 1A**).

*Comprehension (imperative or declarative).* Twenty-four pictures were presented. They were identical to the production pictures except for the labels on the two trays, both of them showing the generic word "cibo" (food), and for the character's posture, showing the right index finger pointing toward either the right or left tray (**Figure 1B**). Subjects were instructed to observe the character's pointing, after which the feedback picture followed.

## **Feedback pictures**

*Production (imperative or declarative).* Twenty-four (12 negative and 12 positive feedback) pictures were presented. To give the subject a feedback after producing an imperative pointing, the picture showed two hands holding either a tray containing the salty food just pointed by the subject or an empty tray, meaning positive or negative feedback, respectively. To give the subject a feedback after a DP, the figure displayed the character, sitting at a table with a tray containing the salty food just pointed by the subject, and showing a smiling or a disgusted expression, meaning that it did or did not appreciate the pointed food, respectively.

*Comprehension (imperative or declarative).* Twenty-four (12 negative and 12 positive feedback) pictures were used. Feedback pictures to IC depicted either a tray containing the salty food pointed by the character in the previous picture, or an empty tray, meaning positive or negative feedback, respectively. Feedback pictures to DC depicted a tray containing the salty food pointed by the character in the previous picture and the character showing a neutral face expression.

All pictures were presented with Gaglab (Galati et al., 2008), an in-house software implemented in Matlab (The MathWorks, Inc., Natick,MA, USA) using Cogent Graphics toolbox (developed by John Romaya at the LON, Wellcome Department of Imaging Neuroscience, UCL, London, UK), and allowing time-locked presentation of visual and auditory stimuli with millisecond timing accuracy.

Pictures were projected by means of an LCD projector positioned outside of the shielded room. Two response boxes (Lumina, Cedrus Corporation), one for each hand, were used. Subjects' pointing response was provided by means of the right box. At the beginning of the trial, the subject's right index was continuously pressing a key of the right box. Pointing timing was monitored by the release of the key at the onset of the pointing movement. Pointing direction was controlled by the position (right or left) of the object that the subject had to point trial by trial. Two buttons on left box were used to interact with character during feedback.

#### **EXPERIMENTAL DESIGN**

All subjects performed a training phase before the MEG session. Training was given by a female operator (the same person appearing in the stimuli/feedback pictures) and included a simulation of the experiment, which lasted until the subject felt confident in her/his ability to perform the task. Soon after, MEG measure was performed. Two main sessions, declarative and imperative, each including two conditions, production and comprehension, were used. The communicative goal of each session was conveyed to the subject at the beginning of the session and maintained throughout the session.

#### **Declarative session**

In this session, we investigated pointing as a gesture for sharing intention with another person (**Figure 2** – top). Therefore, the subjects were told that the task was devised either to let the character be informed about their own taste about food (production condition) or to let them informed about the character's taste (comprehension condition).

*Pointing production.* After the presentation of a fixation cross, a picture for pointing production was shown to the subject for 1500 ms. According to previous instructions, subjects had to point to the tray marked with the label showing the name of a salty food. After a time interval of variable duration (1500/2500/3500 ms), either positive or negative feedback was given to the subject with the character showing a smiling or disgusted expression, respectively.

**FIGURE 2 | Experimental design**. Top: declarative session. First part: after a fixation cross, a picture for pointing production was shown for 1500 ms. Subjects were asked to declaratively point with their right hand toward the tray containing salty food. After a variable time interval (1500/2500/3500 ms), either positive or negative feedback was provided (the character showed a smiling or disgusted expression). Second part: after a fixation cross, a picture for pointing comprehension was shown for 1500 ms. Subjects had to carefully observe the side with the food the character was pointing to, in order to provide feedback. After a time interval of variable duration, a picture displaying the food previously pointed to by the character was delivered. If the subject liked that food, she/he had to press the button at the left or the

*Pointing comprehension.* After the presentation of a fixation cross, a picture for pointing comprehension was shown to the subject for 1500 ms. According to previous instructions, subjects had to carefully observe what tray the character was pointing to, i.e., whether it was at the right or left side. After a time interval of variable duration (1500/2500/3500 ms), a feedback picture displaying the character showing a neutral face and the food which the character pointed to in the previous picture was delivered. To have the interaction fulfilled, subjects were asked to press the left or right button of the left box, based on the side of the food previously pointed by the character, if they liked that food. Otherwise, if they did not like the pointed food, no button had to be pressed.

## **Imperative session**

The session investigated pointing as a gesture for requesting something from another person (**Figure 2** – bottom). Therefore, the subjects were told that the task was devised either to obtain the right of the panel, based on the side of the pointed food; if subject did not, no button had to be pressed. Bottom: imperative session. First part: after a fixation cross, a picture for pointing production was shown for 1500 ms. Subjects were asked to make an imperative pointing toward the tray containing the salty food. Then, a positive or negative feedback was given to the subject (two hands offering a tray with the pointed food or empty, respectively). Second part: after a fixation cross, a picture for pointing comprehension was shown and subjects were asked to carefully observe which tray the character was pointing to. If the food was available (full tray), the subject had to press the button corresponding to food side (left or right); otherwise (empty tray) no button had to be pressed.

food by the character (production condition) or to give the food to the character (comprehension condition).

*Pointing production.* After the presentation of a fixation cross, a picture for pointing production was shown to the subject for 1500 ms. According to previous instructions, subjects had to point to the tray marked with the label showing the name of a salty food. After a time interval of variable duration (1500/2500/3500 ms), a positive or negative feedback was given to the subject by showing a picture with two hands holding either a tray with the pointed food or an empty tray, respectively.

*Pointing comprehension.* After the presentation of a fixation cross, a picture for pointing comprehension was shown to the subject for 1500 ms. According to previous instructions, subjects had to carefully observe what tray the character was pointing to, i.e., whether it was at the right or left side. After a time interval of variable duration (1500/2500/3500 ms), a picture displaying either a

tray with the food pointed by the character in the previous picture or an empty tray was delivered. To have the interaction fulfilled, the subject had to press the button corresponding to the side (left or right) of the tray just pointed by the character, if the food was available (full tray). Otherwise (empty tray) no button had to be pressed.

To summarize, our paradigm included two sessions, imperative and declarative, each of them comprising 24 trials, i.e., 12 "pointing production" trials (one half displaying the target on the right of the display, and the other half on the left) and 12 "pointing comprehension" trials (one half displayed the character pointing to the tray on the right of the display and the other half on the left). Trials were randomly presented at a variable interval (a central white fixation cross on a black screen) of 1500/2500/3500 ms and were balanced with respect to condition order, target location, direction of pointing hand, and positive/negative feedback. Two runs were recorded for the declarative session and other two runs for the imperative session. In summary, four runs, each lasting about 8 min, were recorded for each MEG session, yielding 24 trials for each of the four experimental conditions (total 96 trials) for each subject. Furthermore, a set of 24 pictures was used to provide the character's feedback (12 positive and 12 negative) in declarative as well as in imperative session.

## **MEG RECORDINGS**

Magnetoencephalographic signals were recorded with the 165 channel MEG system installed at the University of Chieti (Pizzella et al., 2001;Chella et al., 2012). This system includes 153 dcSQUID integrated magnetometers arranged on a helmet covering the whole head plus 12 reference channels. Two simultaneous electrical channels [electrocardiogram (ECG) and electro-oculogram (EOG)] were recorded for artifact rejection. All signals were band-pass filtered at 0.16–250 Hz and digitized at 1025 Hz.

An high-resolution MRI structural volume was acquired with a 3-T Philips Achieva MRI scanner (Philips Medical Systems, Best, The Netherlands) via a 3D fast field echo T1-weighted sequence (MP-RAGE; voxel size 1 mm isotropic, TR = 8.1 ms, echo time TE = 3.7 ms; flip angle 8°, and SENSE factor 2).

In order to coregister the head to the MRI volume,four anatomical landmarks (left and right preauricular points and nasion) were identified. Moreover, five coils were placed on the subject's scalp and their position was recovered before and after each MEG run in order to define the subject's head position with respect to the MEG helmet. The positions of the five coils and of the four anatomical landmarks were digitized by means of a 3D digitizer (3Space Fastrak; Polhemus).

#### **MEG DATA ANALYSIS**

#### **MEG source-space signal estimation**

Magnetoencephalographic data were down-sampled to 341 Hz and analyzed by using an independent components analysis (ICA) approach detailed elsewhere (Mantini et al., 2011). Briefly, the algorithm automatically classifies the ICs and identifies artifactual components and components of brain origin.

The number of artifactual ICs depends on the quality of each recording. On average, the algorithm identified 12 ± 4 artifact related components. Artifact components typically included hardware or environmental-injected noise, bad channels, contamination from high noise levels, and physiologic artifacts such as magnetocardiogram, eye blinks, and movements. To determine which ICs represented artifact, a classification procedure based on: (i) IC spectral properties; (ii) IC statistical properties; and (iii) comparison of the IC time courses with the corresponding time courses of the ECG, EOG, is adopted. See Supplemental Information in de Pasquale et al. (2010) for details. A particularly important advantage of ICA based artifact rejection is that all of the 24 recorded trials for each condition and each subject are preserved, thus a reliable number of trials is maintained for the next step of the analysis. Usually, ICA based pipelines rely on the subtraction of artifactual ICs to increase the signal-to-noise ratio. Here, an alternative approach also used in other works by our group (de Pasquale et al., 2010; Betti et al., 2013; Marzetti et al., 2013) is pursued. The approach is based on reconstructing MEG signals by recombining only the ICs of brain origin. On average, the algorithm identified 15 ± 5 ICs of brain origin, each contributed by the activity of one or more dipole sources or patches (e.g., two sources with no time lag typically contribute to the same IC). Of course, in ICA based approaches a trade-off between including unwanted and excluding wanted signals has to be faced and selecting approximately 15 brain ICs is a reasonable compromise. This strategy has been shown to improve SNR in Mantini et al. (2011).

After the decomposition through the fastICA algorithm with deflation approach and the classification steps, non-artifactual IC topographies were input to the weighted minimum-norm least squares (WMNLSs) linear inverse algorithm (Fuchs et al., 1999) implemented in Curry 6.0 (Neuroscan) and the corresponding source topography was localized. In this step, the volume conductor model was given by an individual boundary element method (BEM) (Fuchs et al., 1998) and the source space was modeled by a Cartesian 3D grid bounded by the subject anatomy as derived from individual MRI.

Single subject source-space topographies were thus mapped onto a standard Montreal Neurological Institute (MNI) stereotaxic space by an affine transformation to allow spatial comparison across subjects. For each grid voxel the activity along each direction, and for each time sample, was obtained as a linear combination of non-artifactual IC time courses weighted by their related source-space topographies.

Source activity magnitude was finally derived from the Cartesian components at each voxel; i.e., square root of the sum of the squared components.

#### **Regions of interest**

Band limited MEG power in the whole brain was estimated by using a standard Fast Fourier Transform (FFT) approach after linear detrending and Hanning windowing. The frequency boundaries defining the physiological band ranges, i.e., theta (4–7 Hz), alpha (8–13 Hz), beta (13–30 Hz), and gamma (31–80 Hz) were based on the EEG/MEG literature (Klimesch, 1996; Neuper and Pfurtscheller,2001;Engel and Fries,2010;Uhlhaas et al.,2011), and confirmed by visual inspection of the averaged spectra. Stimulus locked maps for each frequency band were estimated for declarative and imperative conditions in a 500-ms time window (from 300 to 800 ms) after the stimulus image presentation. For each

**Table 1 | Montreal Neurological Institute coordinates of the maximum activity peak for regions of interest showing significant modulations of band power with respect to baseline (FDR corrected).**

ACC, anterior cingulate cortex; lOFc/rOFc, left/right orbitofrontal cortex; rSFg, right superior frontal gyrus; lPMc, left premotor cortex; rMFg, right middle frontal gyrus; rSMa, right supplementary motor area; lpIPS/rpIPS, left/right posterior intraparietal sulcus; lIPS, left intraparietal sulcus; rPCC, right posterior cingulate cortex; lAg, left angular gyrus; rTPJ, right temporo-parietal junction; rpSTS, right posterior superior temporal sulcus; lpIC, left posterior insular cortex; rCn, right cuneus; rPCn, right precuneus.

condition, stimulus locked power maps have been contrasted, at a group level, with the power in a baseline period (from −500 to 0 ms) prior to the stimulus image presentation. This contrast was implemented by using a paired *t*-test and the resulting maps were corrected for multiple comparisons by using the false discovery rate (Benjamini and Hochberg, 1995).

This procedure led to the identification of 17 ROIs showing a significant modulation with respect to the baseline in either the declarative or imperative condition in beta and gamma bands (see **Table 1**; **Figure 3**).

### **TIME–FREQUENCY ANALYSIS**

Time–frequency analysis allowed us to investigate whether the four different experimental conditions induced changes of band power in the identified ROIs. To this end, time–frequency representations (TFRs) were estimated in a 1500-ms time window starting 250 ms prior to the image presentation. Specifically, TFRs for each ROI were calculated by Morlet wavelets (Tallon-Baudry et al., 1997; Jensen et al., 2002). The power at a given time *t* and frequency *f*0 is thus provided by the squared value of the convolution of the signal to a Morlet's wavelet:

$$\Psi\left(t, f\_0\right) = A e^{-\frac{t^2}{2s\_t^2}e^{2nf\_0t}}\tag{1}$$

where *A* = (σ*t* √ π) −1/2. The wavelet width was set to *f*02πσ*t* = 7 as a balance between temporal andfrequency resolution (Jensen et al., 2002). Accordingly, power modulation was evaluated by calculating relative changes in TFR of power after the stimulus image presentation (Pow) with respect to mean power in a baseline period of 200 ms (from −250 to −50 ms) prior to the stimulus image presentation (Powbas) for each experimental condition separately. To this aim, event-related synchronization (ERS) or event-related desynchronization (ERD) (Pfurtscheller and Lopes da Silva, 1999; Neuper and Pfurtscheller, 2001) values were obtained according to:

$$\text{ERD/ERS} \, = 100 \frac{(\text{Power} - \text{Power}\_{\text{bas}})}{\text{Power}\_{\text{bas}}} \tag{2}$$

Since a high variability in reaction times (RTs) of pointing response across subjects was observed (RTs ranging from 737 to 1332 ms), a normalization of time epochs was performed in order to realign the timing of brain processes underlying the pointing production task. To this aim,for each subject the TFR plot between 100 ms after the stimulus and the individual RT was referenced to a standard time interval between 100 and 900 ms (mean value of the RTs across subjects in the different conditions), by means of a cubic spline interpolation. Consequently, epochs shorter than 900 ms were stretched and those longer than 900 ms were shrunk to fit the standard time epoch. Henceforth, in the TFR plots the motor response occurs exactly at 900 ms. Furthermore, concerning the pointing comprehension task (in which RTs were absent since no pointing response but only pointing observation was required), an analog time window analysis (between 100 and 900 ms after "pointing comprehension stimulus" onset) was applied to analyze the pointing observation processing.

To test the effect of the different conditions on rhythmic activity modulation over time, the whole normalized time epoch was partitioned into five-time intervals. Mean relative power values were estimated by averaging TFR values in a given time interval (*t* 1 = 101–260 ms; *t* 2 = 261–420 ms; *t* 3 = 421–580 ms; *t* 4 = 581– 740 ms; *t* 5 = 741–900 ms) and physiological frequency band for each ROI and condition. The frequency boundaries defining the physiological bands were chosen according to the EEG/MEG literature (Klimesch, 1996; Neuper and Pfurtscheller, 2001; Engel and Fries, 2010; Uhlhaas et al., 2011): theta (4–7 Hz), alpha (8– 13 Hz), low beta (13–20 Hz), high beta (21–30 Hz), low gamma (31–50 Hz), and high gamma (51–80 Hz).

#### **STATISTICAL ANALYSIS**

#### **Behavioral data**

Reaction time of pointing motor response was analyzed by a oneway ANOVA to identify possible statistical differences within the two communicative goals (declarative vs. imperative). Statistical analysis was performed using Statistica 6.1 software (Statsoft Italia Srl 2003).

#### **MEG data**

To assess specificity of ROI reactivity differences between declarative and imperative conditions, we performed a repeated measures ANOVA with *Goal* (declarative, imperative) × *Band* (beta,

gamma) × *ROI* (listed in **Table 1**). A triple interaction was found [*F*(16,208) = 1.948, *p* < 0.02], indicating specific regional differences between conditions. To further test the effect of conditions on rhythmic activity modulation over time, a repeated measures ANOVA was performed separately for each frequency band and ROI on the time interval averaged TFR values, with *Role* (production, comprehension), *Goal* (declarative, imperative), and *Time* (*t* 1, *t* 2, *t* 3, *t* 4, *t* 5) as within subject factors. Bonferroni *post hoc* test was applied when significant interactions were found. Statistical analysis was performed using Statistica 6.1 software (Statsoft Italia Srl 2003).

## **RESULTS**

## **BEHAVIORAL DATA**

No significant differences were found for RTs between goals (one-way ANOVA with *Goal* – declarative and imperative – as within subject factor); mean ± SD: declarative RT: 889 ± 113 ms, imperative RT: 924 ± 163 ms.

## **MEG DATA**

The assessment of the differences between rhythmic activity modulations across conditions in the ROIs by means of ANOVA showed significant effects only for the medial anterior cingulate cortex (ACC), the rTPJ, and the right posterior superior temporal sulcus (rpSTS). These effects were observed in specific frequency bands.

In particular, the repeated measures ANOVA revealed a significant *Goal* effect in low beta for ACC, with the activity being higher in the declarative rather than the imperative session [*F*(1,13) = 8.10, *p* < 0.02]. Furthermore, a significant *Goal* ×*Time* interaction was observed [*F*(4,52) = 3.81, *p* < 0.01], indicating a time specific difference of power modulation between the declarative and the imperative sessions. Bonferroni *post hoc* test revealed that this difference was significant at the *t* 3 (*p* < 0.01), *t* 4 (*p* < 0.001) and *t* 5 (*p* < 0.001) (421–580, 580–740, 740–900 ms, respectively; **Figure 4**, right panel) time intervals. Indeed, **Figure 4** (left panel) shows a sustained low beta power modulation after the stimulus onset lasting until the motor response. Low beta power increased during the declarative and decreased during the imperative condition, regardless of the subject role. Furthermore, the difference between declarative and imperative low beta power increased with time and was maximal in the last time interval.

**Figure 5** (top left panel) shows power modulations for rTPJ during both comprehension and production conditions. Repeated measures ANOVA revealed a *Role* main effect [*F*(1,13) = 9.87, *p* < 0.05] in high gamma, the power increase being higher during pointing production than comprehension. Furthermore, a *Role* ×*Time* interaction was observed [*F*(4,52) = 3.52, *p* < 0.05], indicating a time-specific difference of power modulation between

onset (left). Gray boxes indicate the frequency bands and time intervals where a significant effect of Goal was found in Bonferroni post hoc test. Bonferroni post hoc test were found in t 3 (p < 0.01), t 4 (p < 0.001) and t 5 (p < 0.001).

pointing comprehension and production: dotted line indicates the stimulus onset (left). In the frequency and time interval marked by superimposed gray boxes (740–900 ms, high gamma), a time specific difference of power modulation between the production and the comprehension sessions was found. Bar plots show results for Bonferroni post hoc test: p < 0.01 (right).

declarative and imperative sessions (right). Significant increased high beta power during the declarative condition as well as a decreased high beta power during the imperative condition were observed in the frequency and time intervals marked by superimposed gray boxes. Bonferroni post hoc evidenced a significant difference during t 3 (p < 0.05) and t 4 (p < 0.01) (left). the production and the comprehension sessions. In fact, Bonferroni *post hoc* revealed a significant difference during *t* 5 (*p* < 0.01) (**Figure 5**, top right panel).

Furthermore, the bottom right panel of **Figure 5** shows TFR plots for rpSTS during both declarative and imperative conditions. High beta power showed an increase during the declarative condition and a decrease during the imperative condition. This effect was specific for the *t* 3 and *t* 4 time intervals. Indeed, the repeated measures ANOVA revealed a *Goal* ×*Time* significant interaction in high beta [*F*(4,52) = 2.96, *p* < 0.05] and the Bonferroni *post hoc* resulted in a significant difference during *t* 3 (*p* < 0.05) and *t* 4 (*p* < 0.01); (**Figure 5**, bottom left panel).

In addition to these significant results, a tendency to a significant *Goal* effect (*p* = 0.084) was found in the low gamma band (31–50 Hz) for ACC.

## **DISCUSSION**

This study used the MEG technique to analyze brain rhythms underlying comprehension and production of pointing as a function of communicative intention, either imperative or declarative. We adapted an interactive task previously used in developmental research (Camaioni et al., 2004; Aureli et al., 2009) and arranged for the fMRI environment (Committeri et al., 2012), with the subject playing alternatively the role of the sender or the addressee of the gesture in two different communicative contexts. The aim was to bring in the subject in a more ecological situation than that usually provided by neuroimaging studies and more similar to that used in social cognition research (Hari and Kujala, 2009). Furthermore, for the first time as far as we are aware, we analyzed the induced oscillatory activity on data elicited by a social cognition task. This strategy allows for the observation of rhythmic modulations of the activity of specific ROIs in a time range which includes late latencies, possibly related to the highlevel processes involved in social cognition (Wang et al., 2010). Our results showed different frequency-specific modulations of power based on both the goal of the gesture and the role of the subject.

### **DECLARATIVE VS. IMPERATIVE GOAL OF POINTING**

With respect to the goal of the gesture, in low beta, the declarative pointing elicited higher activity than imperative pointing in dorsal ACC, starting from 421 to 900 ms after the stimulus onset (i.e., the last three periods before motor response). This region is included in the medial frontal cortex (MFC), which was consistently found to play a core role in social cognition. In particular, Amodio and Frith (2006) argued for a functional subdivision of rostral MFC in an anterior and a more posterior section: the former associated with self-knowledge and emotional processing (Lieberman et al., 2004; Steele and Lawrie, 2004; Stern et al., 2009), attribution of mental states (Mitchell et al., 2005), and mentalizing (Brunet et al., 2000; Gallagher et al., 2000; Frith and Frith, 2003; Uchiyama et al., 2012); the latter associated to cognitive processing such as internal action monitoring and decision making (Botvinick et al., 2004; Walton et al., 2004). In this framework, the region identified by our study was localized along this anterior/posterior border of ACC and was activated in the declarative condition, regardless of the subject role (comprehension or production).

A greater beta band functional connectivity between the frontal and right temporal areas during unfamiliar compared to familiar display was showed (Calmels et al., 2012). Furthermore, beta band activity in this cortical area was also recognized as being involved in several cognitive activities, such as learning after positive feedback (van de Vijver et al., 2011), decision making (Cohen et al., 2007), and reward processing (Marco-Pallares et al., 2008). More specifically, the last two studies observed by means of EEG, a reward-related oscillatory activity between 20 and 30 Hz, highlighting the advantage of the use of time–frequency analysis to observe high cognitive processing dynamics. Results from these studies demonstrate that reward processing in the preceding trials could affect oscillatory activity on the next trials. These results were obtained by adequately manipulating positive and negative feedback preceding each trial, and by assigning them a "winner/looser" value. On the contrary, feedback used in our study was devoid of a right/wrong value, since it had the purpose to support the joint attention, over than to prevent the subject from being bored during the session, rather than functioning as a penalty/reward. Consequently, an interpretation of our data in terms of rewards could be speculative.

All these findings suggest a top-down role of beta oscillatory activity in MFC. In particular, Engel and Fries (2010) hypothesize that beta band activity is related to the maintenance of a given cognitive status and that the enhancement of that activity reflects the endogenous vs. exogenous components of subject performance. Looking at our results,we suggest that the enhanced late latency beta band activity,found in ACC-arMFC when the subjects were presented with declarative condition, reflects the endogenous components required by the mentalizing attitude involved in that condition. Conversely, the decreased beta band power, presumed to be involved in causal processes, could reflect exogenous components that interrupted the current cognitive setting during imperative condition.

A second finding concerned the right posterior STS activation in the 20- to 30-Hz frequency range during declarative pointing, in the time interval from 421 to 740 ms post-stimulus. Previous fMRI study demonstrated the role of pSTS in passive viewing of biological motion (Pelphrey et al., 2003), in the extraction of social cues like directional eye gaze (Materna et al., 2008a,b), and in intentional action understanding (Pelphrey et al., 2004). Furthermore, a MEG study by Vistoli et al. (2011) observed a right posterior STS activity 200–600 ms after the stimulus presentation during an intention attribution task. Finally, de Langavant et al. (2011) showed, by means of PET data, the right posterior STS involvement in communicative vs. non-communicative pointing. Our data confirmed the involvement of this region in social interaction when attention is shared with another person as it happens in the declarative condition. Moreover, the observed beta ERS at later latency extends to this region, the above hypothesis on beta band, as reflecting the activity of an endogenous, top-down mechanism.

In conclusion, both right posterior STS and MFC seemed to work in social cognition as high-level processing sites, since they seem to be selectively involved in intentions underlying the declarative pointing. This function was carried out according to different temporal dynamics, since the prefrontal area maintained a sustained activity, whereas right posterior temporal area showed a time-limited modulation. This suggested that a partial overlap in time, at a middle stage, between these two regions underpins social cognition.

## **SENDER VS. RECEIVER ROLE IN POINTING**

The second goal of our study was to analyze the brain oscillatory activity based on the subject role; i.e., as sender vs. receiver of the pointing gesture. The most important result was provided by the time–frequency modulation in the rTPJ, which showed significant high gamma band ERS during pointing production and not during pointing comprehension, in the 160–0 ms time interval before movement.

Several studies have suggested rTPJ as a critical core for comparing information coming from a self-produced action with those from the environment. Specifically, TPJ has been considered the region which was mostly engaged when the individuals had to distinguish between themselves and the others (Decety and Sommerville, 2003; Uddin et al., 2006), thus playing a predominant role in the sense of agency (Ruby and Decety, 2001; Farrer et al., 2003; Sperduti et al., 2011). The TPJ role was also addressed by Corbetta et al. (2008) with reference to the theory of mind (ToM) ability. According to the authors, TPJ activity could work as an important tool for switching between internal and external signals in the comparison of the self and the other. Our results, showing a gamma band TPJ ERS at a late latency during pointing production regardless of the intention of communication, could suggest a TPJ involvement in agency function. Moreover, since TPJ activation started after the accomplishment of other high-level mental activities localized in posterior STS, such as social information and communicative intention processing (see also Materna et al., 2008a), our data are in accordance with the Decety and Lamm's (2007) quantitative meta-analysis, revealing that the TPJ activation overlaps in different cognitive domains both at high and low level. These data support the evolutionary hypothesis proposed by these authors, according to which high-level mechanisms operate on functionally more primitive levels.

As an additional result, we found that the involvement of TPJ during pointing production was expressed by an enhanced gamma band power. Previous research showed that this frequency range is associated with attention (Jensen et al., 2007), movement preparation (Schoffelen et al., 2011), and conscious awareness (Meador, 2002). Furthermore, this rhythm was observed in the monkey lateral intraparietal area (the putative homologous of human IPS/SPL) during the delay of a delayed saccade task (Pesaran et al., 2002). Finally, gamma band synchronization in parietal cortex was considered to represent the planned direction of the saccade (Van Der Werf et al., 2008). These data, together with the lack of motor responses during pointing comprehension – that prevents from excluding that TPJ activation can be ascribed to motor planning – could drive a possible confound in the TPJ role interpretation. Nevertheless, a previous PET study provided evidence for an increased rCBF in the posterior part of the right STS, close to the TPJ, during communicative pointing contrasted to uncommunicative pointing (de Langavant et al., 2011). The absence of activation in this region during a motor response devoid of communicative intention strongly supports a TPJ involvement in agency rather than

in a mere motor process. Interestingly, induced gamma band bursts were observed in 4-month-old infants in right posterior areas (occipital, temporal and parietal EEG channels; Grossmann et al., 2007). This oscillation was elicited during averted vs. directed gaze observation. Averted gaze assumes a central role during social communication by directing the perceiver's attention toward a location. The authors suggest that their finding of gamma burst in response to averted gaze might reflects a shift in spatial attention, highlighting the potential role played by gamma band oscillations in examining the development of social perception.

Taken together, these studies on adults and infants seem to support the interpretation of enhanced rTPJ gamma oscillations observed in our study during pointing production as a sign of the involvement of this region in switching between internal and external aspects and between different spatial locations. In other words, pointing production would require the individual to decide between objects sited in two spatial locations and at the same time to plan a communicative action (pointing) in order to direct the other's attention toward the pointed location.

An alternative view comes from the recent meta-analysis by Kubit and Jack (2013). The authors hypothesize that the overlap of attention reorienting and social cognition on the same cortical region (rTPJ) could be viewed in a new light. They consider the presence of two distinct regions (angular gyrus and supramarginal gyrus) close to rTPJ that could mutually inhibit their activity in response to non-social vs. social tasks. Nevertheless, the comparison between social and non-social tasks is beyond the scope of this work. An *ad hoc* paradigm should be designed to address this issue.

In conclusion,we found that the core of the ToM circuit is active during declarative but not during imperative pointing. This finding suggests that declarative pointing reflects mentalizing skills, thus confirming in human adults the difference between imperative and declarative pointing hypothesized in developmental research. Furthermore, our results suggest that a complex process, such as a communicative interaction, involves a distributed neural circuit in which the modulation of oscillatory activity of different regions is partially overlapped in time. This process may run in parallel with a functional, effect-specific, differentiation between right temporal region and frontal areas, ending in the parietal and medial frontal regions. Our results contribute to the understanding of the roles of brain rhythm dynamics in social cognition, potentially opening the way for a targeted investigation of social interaction and language precursors during development, as well as of alteration of normal social abilities such as those related to autism spectrum disorder. The identification of relevant brain areas and, more importantly, of the frequency bands and the timing of the activation of these regions may greatly contribute to a future work on development, by suggesting to monitor the neural changes occurring in the same regions before the mature form is achieved.

## **ACKNOWLEDGMENTS**

The authors would like to thank Giorgia Committeri and Gaspare Galati for helpful contribution to adaptation of the paradigm to neuroimaging environment.

## **REFERENCES**

resonance imaging study. *J. Cogn. Neurosci.* 20, 108–119. doi:10.1162/jocn.2008. 20008

**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: 18 September 2013; accepted: 17 January 2014; published online: 04 February 2014.*

*Citation: Brunetti M, Zappasodi F, Marzetti L, Perrucci MG, Cirillo S, Romani GL, Pizzella V and Aureli T (2014) Do you know what I mean? Brain oscillations and the understanding of communicative intentions. Front. Hum. Neurosci. 8:36. doi: 10.3389/fnhum.2014.00036*

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

*Copyright © 2014 Brunetti, Zappasodi, Marzetti, Perrucci, Cirillo, Romani, Pizzella and Aureli. 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.*

## Sensitivity to auditory spectral width in the fetus and infant – an fMEG study

#### **Jana Muenssinger 1\* † ,Tamara Matuz 1,2† , Franziska Schleger 1 , Rossitza Draganova3 , MagdaleneWeiss 1,4 , Isabelle Kiefer-Schmidt 4 , AnnetteWacker-Gussmann5 , Rathinaswamy B. Govindan6 , Curtis L. Lowery 7 , Hari Eswaran7 and Hubert Preissl 1,2**

1 fMEG Center, University of Tuebingen, Tuebingen, Germany

#### **Edited by:**

Christos Papadelis, Harvard Medical School, USA

#### **Reviewed by:**

Minna Huotilainen, University of Helsinki, Finland Silvia Comani, Università degli Studi "G. d'Annunzio", Italy

#### **\*Correspondence:**

Jana Muenssinger, fMEG Center, University of Tuebingen, Otfried-Müller-Strasse 47, Tuebingen 72076, Germany e-mail: jana.muenssinger@ uni-tuebingen.de

†Jana Muenssinger and Tamara Matuz have contributed equally to this work.

Auditory change detection is crucial for the development of the auditory system and a prerequisite for language development. In neonates, stimuli with broad spectral width like white noise (WN) elicit the highest response compared to pure tone and combined tone stimuli. In the current study we addressed for the first time the question how fetuses react to "WN" stimulation.Twenty-five fetuses (Mage = 34.59 weeks GA, SD ± 2.35) and 28 healthy neonates and infants (Mage = 37.18 days, SD ± 15.52) were tested with the first paradigm, wherein 500 Hz tones, 750 Hz tones, and WN segments were randomly presented and auditory evoked responses (AERs) were measured using fetal magnetoencephalography (fMEG). In the second paradigm, 12 fetuses (Mage = 25.7 weeks GA, SD ± 2.4) and 6 healthy neonates (Mage = 23 days and SD ± 6.2) were presented with two auditory oddball conditions: condition 1 consisted of attenuated WN as standard and 500 Hz tones and WN as deviants. In condition 2, standard 500 Hz tones were intermixed with WN and attenuated WN. AERs to volume change and change in spectral width were evaluated. In both paradigms, significantly higher AER amplitudes to WN than to pure tones replicated prior findings in neonates and infants. In fetuses, no significant differences were found between the auditory evoked response amplitudes of WN segments and pure tones (both paradigms). A trend toward significance was reached when comparing the auditory evoked response amplitudes elicited by attenuated WN with those elicited by WN (loudness change, second paradigm). As expected, we observed high sensibility to spectral width in newborns and infants. However, in the group of fetuses, no sensibility to spectral width was observed. This negative finding may be caused by different attenuation levels of the maternal tissue for different frequency components.

**Keywords:WN, spectral width, auditory change detection, magnetoencephalography, auditory evoked responses**

## **INTRODUCTION**

Auditory change detection is an important prerequisite for a functional auditory system as well as for the development of language perception and can serve as an indicator for healthy cognitive functioning and development. It has been repeatedly shown that humans possess already at birth the capacity to process acoustic regularities and react to violations of such regularities, meaning that they are able to detect and discriminate different patterns of sound (Carral et al., 2005). Prior studies showed atypical auditory evoked responses (AERs) to auditory changes in pre-maturely born infants (Fellman et al., 2004) as well as in infants with genetic risk for dyslexia (Leppänen et al., 1999). However, the neurophysiological mechanisms underlying early sound perception and discrimination within both, typical and untypical development, are still not fully understood.

Most studies investigating change detection used oddball paradigms and evaluated AERs elicited by individual stimuli and mismatch negativity (MMN) responses determined by the difference between AERs elicited by standard and deviant stimuli. AERs are neurophysiologic indices of sensory functioning and their different components reflect basic cognitive functions. MMN (Näätänen, 2001) – a negative component which in adults peaks at around 150 ms after change onset – is considered as an indicator of automatic change detection reflecting discriminatory capacity. Assessing healthy infants, several studies showed that they were able to distinguish between pure tones of different frequencies (Kushnerenko et al., 2002; Huotilainen et al., 2003; Draganova et al., 2005, 2007). Kushnerenko et al. (2002) studied the development of change detection in neonates over the first 12 months and did not find statistically significant differences between the

2 Institute for Medical Psychology and Behavioral Neurobiology, University of Tuebingen, Tuebingen, Germany

responses at different ages on the group level. They also showed that changes between pure tones and novel sounds (clicks, chirps, vowels, syllables) at the age of 2 years elicited significantly higher AER responses to novel sounds compared to pure tones. This effect might be explained by the "novelty" of the stimulus itself or could be caused by the change in spectral width between stimuli, because the novel sounds were composed of different frequencies. To test both hypotheses, Kushnerenko et al. (2007) investigated responses to pure tones, novel sounds, and white noise (WN) segments. WN segments were chosen to match the novel sounds in the broad frequency range but not in the novelty. Results revealed the highest responses for WN segments. This indicates that – unlike adults – neonates are highly sensitive to the effective amount of stimulation, with broader widths eliciting higher AERs. This finding is also supported by behavioral data showing that infants are more sensitive to the amount of stimulation than to the modality of stimulation (Lewkowicz and Turkewitz, 1980; Turkewitz et al., 1983).

However, as shown in several studies using fetal magnetoencephalography, auditory change detection occurs already in the fetal stage (Draganova et al., 2005, 2007; Huotilainen et al., 2005). MMN responses were observed as early as 28 weeks of gestation. In these studies, pure tones with a frequency of 500 Hz were used as standard stimuli, which were intermixed with rarely presented deviants with a frequency of 750 Hz. However, to our best knowledge, fetal responses to more complex stimuli have not been investigated so far. One potential difficulty in using more complex stimuli in fetuses is the sound attenuation caused by maternal tissue. In general, it is known that high frequencies are more attenuated than lower frequencies [for review see Hepper and Shahidullah (1994)]. When using complex stimuli composed of multiple frequencies, the broad difference in attenuation levels between frequencies might result in the fact that fetuses perceive only parts of the whole frequency spectrum. This involves a decrement in the perceived differences between pure tones and complex stimuli by the fetus.

However, studies investigating fetal learning support the assumption that a broad spectrum of sounds reach the fetus *in utero*. Starting at the 29th week of gestational age, music as well as speech sounds were repeatedly presented to fetuses (Partanen et al., 2013a,b). Electroencephalographic (EEG) measurements directly after birth as well as 4 months after birth (Partanen et al., 2013a) showed enhanced AERs to the "learned" stimulus. AERs were significantly higher than in a control group of age-matched infants who were not presented with specific auditory stimulation before birth. This indicates that fetuses are able to perceive and learn a wide range of auditory stimuli *in utero*.

The aim of the first part of the current study (performed in Tuebingen) was to use a simplified paradigm to replicate the findings of Kushnerenko et al. (2007) in neonates and infants and to evaluate if the human fetal brain is able to process and react to differences in spectral width. Similar to newborns and infants, it was expected that fetuses will be sensitive to stimulus energy and will show increased AER amplitudes in reaction to the high spectral width of WN segments compared to those elicited by pure tones. In the second part of the study (performed in Little Rock), a within group oddball paradigm including both changes in volume and spectral width was used to further explore fetal cortical responses to different auditory changes. Higher AERs were expected for changes in frequency than for changes in volume.

## **MATERIALS AND METHODS DATA ACQUISITION**

Data was recorded using SQUID Array for Reproductive Assessment (SARA) systems installed in two different locations, one at the Department of Obstetrics and Gynecology, University of Arkansas for Medical Sciences, AR, USA and a second one at the fMEG Center, University of Tuebingen. Both systems were built by VSM, Medical Technology Ltd., Canada. To avoid magnetic influences from the environment, the systems are installed in shielded rooms (Vakuumschmelze, Germany). The system in Little Rock contains 151 primary sensors and the system in Tuebingen 156 primary sensors. The primary sensors measure the biomagnetic signals from the physiological sources. Little Rock data were recorded with a sampling rate of 312.5 Hz and the Tuebingen data with 610.352 Hz. The sensor arrangement in both systems is designed to fit the shape of the pregnant women's abdomen. Before each fetal measurement, an ultrasound is performed to determine fetal head position. Afterward, the mothers are seated on the fMEG device in the shielded room. Prior to the recording localization coils are placed at the left side, the right side and the spine of the mother as well as at the position on the abdomen where the fetal head is located. These coils indicate the maternal as well as the fetal position relative to the sensor array which are used in later data analysis.

## **PARTICIPANTS FROM TUEBINGEN STUDY (FIRST PART)**

Twenty-five healthy pregnant women with uncomplicated pregnancies and normally developing fetuses (*M*age = 34.59 weeks, SD ± 2.35 weeks) between the gestational age of 30 and 38 weeks and 28 healthy newborns and infants without complications during pregnancy and birth between the age of 14 and 67 days (*M*age = 37.18 days SD ± 15.52) participated in the study. The local ethical committee approved the study and a written consent was obtained.

## **PROTOCOL USED IN TUEBINGEN STUDY (FIRST PART)**

For neonatal/infant measurements, a cradle was attached to the fMEG devise and neonates/infants were lying on their right side with their right hemisphere over the sensor array. Auditory stimulation was generated outside the shielded room and transmitted through flexible, air-filled tubes into the shielded room. Stimulation was presented with a sound pressure level of 65 dB to the left ear using earphones especially developed for neonatal measurements (bio-logic, USA). Stimulation consisted of the random presentation of a 500 Hz tone (*n* = 70), a 750 Hz tone (*n* = 70), and WN (*n* = 70). The stimulus duration was 0.5 s for the 500 Hz tone and the 750 Hz tone and 1 s for the "WN" (80–1000 Hz) segments. The inter-stimulus interval (ISI) between stimulus presentations varied randomly between 2 and 2.5 s. The pure stimulation duration was 8 min (**Figure 1**).

All neonates/infants were measured while sleeping or lying quietly. During the whole measurement, one parent was inside the shielded room with the baby.

For the fetal measurements, the same paradigm was applied. Stimulation was presented using an air-filled balloon, which was placed directly on the maternal abdomen. Stimulation intensity was 95 dB, which is attenuated by maternal tissue and amniotic fluid and which the fetus may perceive at a level of approximately 65–70 dB (Querleu et al., 1988).

#### **PARTICIPANTS FROM LITTLE ROCK STUDY (SECOND PART)**

Twelve healthy pregnant women with uncomplicated pregnancies and normally developing fetuses (*M*age = 25.7 weeks GA, SD ± 2.4) and six healthy newborns and infants without complications during pregnancy and birth (*M*age = 23 days and SD ± 6.2) participated in the study. The local ethical committee approved the study and a written consent was obtained.

#### **PROTOCOL USED IN LITTLE ROCK STUDY (SECOND PART)**

Auditory stimulation was generated outside the shielded room and the stimuli were delivered to the inside of the shielded room through Tygon tubing that ended with an air-filled bag. Similar to the first paradigm, for the neonatal measurements a cradle was attached to the fMEG device and neonates/infants were lying on their side with one hemisphere over the sensor array. Stimulation was presented with a sound pressure level of 65 dB at the end of the acoustic emitter, which was attached to the ceiling at a distance of approximately 75 cm from the newborn's head. The stimulation paradigm consisted of two oddball conditions in which a 500 Hz tone, attenuated WN and WN were either standards (probability of 0.8, presented for 560 times) or deviant sounds (each type with a probability of 0.1, presented for 70 times) (see **Figure 2**). Total number of presented stimuli was 700. The stimulus duration was 0.1 s. The ISI was 1 s. The total stimulation duration was about 17 min per condition. All neonates were measured while sleeping or lying quietly. During the measurement, one parent was inside the shielded room with the baby.

For fetal measurements, the same paradigm was used. Sound intensity measured at the end of the tube in the air was 115 dB for the 500 Hz tones as well as for the WN segments. For the attenuated WN segments the sound intensity at the end of the tube was 95 dB.

#### **DATA ANALYSIS**

All fMEG recordings were filtered offline using a bandpass filter between 1 and 10 Hz for fetal recordings and between 1 and 15 Hz for neonatal recordings using Butterworth filter with zero phase distortion. Interfering maternal and fetal cardiac signals were attenuated using an orthogonal signal space projection technique (Vrba et al., 2004; McCubbin et al., 2006) or a Hilbert transform method (Wilson et al., 2008). Data was cut in segments according to the test stimulus; the length of the segments was 200 ms before the stimulus (baseline) and 800 ms after the stimulus. All segments including artifacts due to maternal or fetal movement or muscle contractions higher than 2 pT were excluded from further analysis. Evoked responses were determined by visual inspection and quantified by calculating the root-mean squares (RMS) of the five channels with the highest amplitudes. These RMS were used for statistical analysis.

#### **STATISTICAL ANALYSIS**

For the Tuebingen Study, the RMSs of all conditions were tested for normal distribution. For normally distributed data, a repeatedmeasures ANOVA with"tones"as within-subject factor was used to test for main effects and paired *t*-tests were used for *post hoc* comparisons. For non-normally distributed data, the non-parametric Friedman test was used to test for main effects. Significance levels were adjusted for multiple comparisons using Bonferroni correction.

Due to the explorative nature and the small sample size of the Little Rock study, the statistical analysis of this part had to be limited to descriptive indices and non-parametric comparative tests.

### **RESULTS**

### **RESULTS FROM TUEBINGEN STUDY (FIRST PART) Neonatal/infant recordings**

Fifteen neonates/infants were excluded from further analysis because either no measurement was possible due to movement/crying or because measurements had to be aborted due to agitation of the neonate/infant. Statistical analysis has been done on the remaining 13 datasets (*M*age = 34.08 days, SD ± 15.18).

A significant main effect of tones was observed (*F* = 11.45, *p* < 0.05). *Post hoc* analysis revealed significant differences between the 500 Hz tone and WN (*T*12 = −3.82, *p* < 0.0167) and the 750 Hz tone and WN (*T*12 = −3.22, *p* < 0.0167). No significant differences between the 500 Hz tone and the 750 Hz tone were observed (*T*12 = −1.05, *p* = 0.317, see **Figure 3**).

#### **Fetal recordings**

Eleven fetuses were excluded from further analysis because they did not show visible evoked responses for all three conditions. All further analysis is based on the remaining 14 fetuses (*M*age = 34.57, SD ≤ 2.34).

No significant main effect of tones was found for fetal recordings (χ 2 = 2.71, *p* = 0.26, see **Figure 4**).

## **RESULTS FROM LITTLE ROCK STUDY (SECOND PART) Neonatal/infant recordings**

One out of six neonatal/infant recordings was excluded from the analysis because it was stopped within the first 5 min of the measurement due to newborn crying. The RMS values of the amplitudes of the AERs for the neonates are shown in **Table 1**.

Mean RMS value for the WN segments in the first oddball condition (*M* = 37.7, SD ± 5.6) was higher than the mean of the RMS values for the 500 Hz tones (*M* = 28.7, SD ± 9.2). The same was true for the second condition between the amplitudes of the WN segments (*M* = 40.6, SD ± 18.3) and those of the attenuated WN segments (*M* = 27.8, SD ± 0.04). Wilcoxon signed ranked tests showed significant differences (*Z* = 3.7, *p* < 0.05) between

the median amplitudes of the AERs to the WN and those to the attenuated WN segments as well as between the amplitudes of the cortical responses to the WN and those to the 500 Hz tones of the second condition (*Z* = 2.02, *p* < 0.05).

## **Fetal recordings**

Two out of 12 fetal recordings were excluded from further analysis based on maternal movements and large maternal and fetal heart residuals. AERs on the deviants were detected in 8 out of 10 fetuses in the first oddball condition and in 5 out of 10 fetuses in the second one. Amplitudes of the AERs elicited by infrequent WN segments were not significantly different than those elicited by the infrequent 500 Hz tones (*Z* = −0.70, *p* = 0.48). The difference between RMS values of AERs elicited by infrequent WN segments

**Table 1 | RMS values of the neonatal AERs for both oddball conditions applied in the second stimulation paradigm.**

and those elicited by infrequent attenuated WN segments showed a tendency toward significance (*Z* = −1.8, *p* = 0.08).

## **DISCUSSION**

The current study aimed to use a simplified paradigm to replicate the increased neonatal reaction to WN stimuli which was already found by Kushnerenko et al. (2007). Moreover, WN segments were used for the first time in fetal recordings to investigate if the human brain is able to detect changes in the spectral width of auditory stimulation during fetal period. An oddball paradigm including changes in volume and spectral width was used to further investigate fetal change detection.

Results of the first paradigm showed, that even when using a simple paradigm wherein pure tones and WN segments were presented in random order with the same probability, infants showed significantly higher reactions to the WN segments than to the pure tones. This replicates prior results showing that infants are sensitive to the effective amount of stimulation with higher reactions to high-energy stimulation (in this case high spectral width) and lower reactions to low-energy stimulation (Kushnerenko et al., 2007). Also earlier behavioral studies support these findings (Lewkowicz and Turkewitz, 1980). Even though our results are in line with other studies, it has to be taken into account that the duration of the WN segments was longer than that of pure tones in the current study. Therefore, the higher reaction to the WN segments might partly be explained by the change in duration rather than in spectral width. However, when studying change detection using a multiple deviant paradigm, Sambeth et al. (2009) found no significant differences between amplitudes of the reaction to standards and duration deviants using magnetoencephalography. This indicates that a change in tone duration plays only a minor role concerning the increment of AER amplitudes. Our results from the second paradigm further support this finding. Neonatal/infant cortical responses to WN segments showed significantly higher amplitudes when compared to pure tones and attenuated WN segments of the same duration. Nevertheless, additional data is needed to thoroughly address this question.

Different from neonates and infants, in both fetal paradigms stimuli consisting of multiple frequencies did not elicit AERs with higher amplitudes than pure tones. Special requirements for fetal measurements might be one important factor concerning the evaluated group differences between neonates and fetuses. While neonates can be stimulated directly at the ear with sounds transmitted through the air, fetal stimulation takes place outside the maternal abdomen. Since the fetus is surrounded by amniotic fluid, the characteristics of sound transmission are different than sound transmission in air. Different studies have been performed to evaluate sound attenuation in fetal measurements [Hepper and Shahidullah (1994) and references therein]. Using a hydrophone near the fetal head at the beginning of labor and after amniotomy, Querleu et al. (1988) recorded an attenuation of 2 dB at 250 Hz, 14 dB at 500 Hz, 20 dB at 1000 Hz, and 26 dB at 2000 Hz. Generally, higher frequencies are more attenuated than lower frequencies, which in some works are even reported to be slightly enhanced in volume (Richards et al., 1992). Since the WN segments in the current study were composed of frequencies in the range between 50 and 1000 Hz, which were all presented with the same sound pressure level, we assume that the higher frequencies were attenuated much more than the lower frequencies before they reached the fetal ear. Compared to neonates/infants who heard the full spectral width of WN segments, fetuses might have perceived only parts of the frequency range, narrowed down to lower and middle frequencies. In this case, due to a narrowed perceived spectral width, also the total energy of the stimulation would have been weakened, leading to AER amplitudes comparable with those of pure tones. Additionally to the differences in sound transmission between fetuses and neonates, the background noise differs between those groups. While neonates are stimulated in a quiet environment, fetuses are surrounded by background noises caused by the maternal heart beat, bowel movements or the maternal voice. Different studies reported background noise intensities of 72 dB measured during labor in the uterus (Bench, 1968), 85 dB measured before labor, and even 95 dB after the R-wave of the maternal heart (Walker et al., 1971). Since these background noises are of low frequencies, we believe that they might have superimposed and masked the lower frequency ranges of WN segments, leaving the higher frequencies nearly unaffected. Since the low frequencies are less attenuated but covered by the background noise, the perceived frequency range is additionally narrowed down. Even though learning studies in fetuses (Partanen et al., 2013a,b) clearly showed that fetuses are able to perceive and learn melodies and speech sounds, these studies did not focus on the effective amount of stimulation. Even if a high range of stimuli can be perceived when presented separately, no clear conclusion can be drawn concerning the frequencies perceived by the fetus when a broad range of frequencies are presented at the same time with the same intensity. Further research is needed to entirely solve this question.

Taken together, we assume that fetuses probably did not perceive the full frequency range of the WN stimulus, which weakened stimulus energy and made it less distinguishable from the pure tone stimuli. The results of the second study suggest that the neonates are sensitive to both spectral and volume changes, whereas the fetuses are rather sensitive to loudness change.

It has to be stressed that the two studies were performed with two separate systems for fMEG recordings and the results were comparable, providing further support for the fMEG as a reliable method to assess fetal brain development.

In summary, the current study showed that newborns are able to differentiate between pure tones and WN segments even in simple paradigms, where tones are randomly presented with the same probability. This enables shorter measurement times, which is an important point in neonatal measurements since movement artifacts are highly probable especially in long measurements. To thoroughly evaluate fetal ability to distinguish between high- and low-energy stimulation, stimulation methods need to be developed which are not attenuated by the maternal abdomen and amniotic fluid and therefore ensure that the fetus receives the complete spectrum of the WN.

## **AUTHORS CONTRIBUTION**

Jana Muenssinger was involved in the development of the design, data acquisition and analysis, statistical analysis, and wrote the manuscript. Tamara Matuz was involved in the development of the design, data acquisition and analysis, statistical analysis, and writing of the manuscript. Franziska Schleger was involved in data acquisition and analysis and revised the manuscript. Rossitza Draganova was involved in the data analysis and revised the manuscript. Magdalene Weiss was involved in the recruitment of subjects, data acquisition, and revised the manuscript. Isabelle Kiefer-Schmidt and Annette Wacker-Gussmann were involved in the recruitment of subjects and revised the manuscript. Rathinaswamy B. Govindan was involved in the development of the paradigm, data analysis, and revised the manuscript. Curtis L. Lowery was involved in the conceptual design of the study and revised the manuscript. Hari Eswaran was involved in designing the study, analyzing and interpreting results, and revised the manuscript. Hubert Preissl was involved in the development of the design, data analysis and revised the manuscript.

### **ACKNOWLEDGMENTS**

We thank all families for their participation. The fetal MEG and the authors in Tuebingen were supported by the Deutsche Forschungsgemeinschaft (DFG BI 195-50), the Ministry of Science, Baden-Württemberg, the Werner Reichardt Center for Integrative Neuroscience (CIN, Pool-Project 2009–2013) and the Open Access Publishing Fund of Tuebingen University (provided by the "Deutsche Forschungsgemeinschaft"). The Little Rock study was partially supported by grants from the National Institutes of Health (NIH) R01EB007826 and 5R01NS036277-03, USA.

### **REFERENCES**

**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: 01 October 2013; paper pending published: 13 October 2013; accepted: 15 December 2013; published online: 31 December 2013.*

*Citation: Muenssinger J, Matuz T, Schleger F, Draganova R, Weiss M, Kiefer-Schmidt I, Wacker-Gussmann A, Govindan RB, Lowery CL, Eswaran H and Preissl H (2013) Sensitivity to auditory spectral width in the fetus and infant – an fMEG study. Front. Hum. Neurosci. 7:917. doi: 10.3389/fnhum.2013.00917*

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

*Copyright © 2013 Muenssinger, Matuz, Schleger, Draganova, Weiss, Kiefer-Schmidt, Wacker-Gussmann, Govindan, Lowery, Eswaran and Preissl. 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.*

## Region-specific slowing of alpha oscillations is associated with visual-perceptual abilities in children born very preterm

#### **Sam M. Doesburg1,2,3,4\*, Alexander Moiseev 5 , Anthony T. Herdman5,6, Urs Ribary 5,7,8,9 and Ruth E. Grunau8,9**

#### **Edited by:**

Christos Papadelis, Boston Children's Hospital, USA; Harvard Medical School, USA

#### **Reviewed by:**

USA

Paul Sauseng, University of Surrey, UK Stephanie Jones, Brown University,

## **\*Correspondence:**

Sam M. Doesburg, Department of Diagnostic Imaging, The Hospital for Sick Children, 555 University Avenue, Toronto, ON M5G 1X8, Canada e-mail<EMAIL_ADDRESS>
Children born very preterm (≤32 weeks gestational age) without major intellectual or neurological impairments often express selective deficits in visual-perceptual abilities. The alterations in neurophysiological development underlying these problems, however, remain poorly understood. Recent research has indicated that spontaneous alpha oscillations are slowed in children born very preterm, and that atypical alpha-mediated functional network connectivity may underlie selective developmental difficulties in visual-perceptual ability in this group. The present study provides the first source-resolved analysis of slowing of spontaneous alpha oscillations in very preterm children, indicating alterations in a distributed set of brain regions concentrated in areas of posterior parietal and inferior temporal regions associated with visual perception, as well as prefrontal cortical regions and thalamus. We also uniquely demonstrate that slowing of alpha oscillations is associated with selective difficulties in visual-perceptual ability in very preterm children. These results indicate that region-specific slowing of alpha oscillations contribute to selective developmental difficulties prevalent in this population.

#### **Keywords: preterm, magnetoencephalography, neural oscillation, development, resting state, alpha-band, perception, cognition**

## **INTRODUCTION**

Children born very prematurely, even in the absence of brain injury and when intelligence is broadly normal, often experience selective developmental difficulties including problems with visual-perceptual abilities (Rickards et al., 2001; Grunau et al., 2002; Atkinson and Braddick, 2007). The biological basis of these issues remains poorly understood. MR imaging has identified numerous structural and functional atypicalities in very preterm children, many of which have been associated with problems in cognitive and perceptual development (Hart et al., 2008; Ment et al., 2009; Miller and Ferriero, 2009). An approach to understanding preterm child brain development which has received somewhat less attention is the mapping of neural oscillations using magnetoencephalography (MEG). MEG is a neurophysiological imaging modality that is particularly effective for characterizing the development of functional brain systems due to its unique combination of spatial and temporal resolution (Hari and Salmelin, 1997), and has been successfully employed to image brain activation in specific cortical systems in preterm infants and children (Nevalainen et al., 2008; Frye et al., 2010).  Both spontaneous neural oscillations and their test-dependent dynamics develop throughout childhood and infancy (Clarke et al., 2001; Uhlhaas et al., 2009b; Xiang et al., 2009), including during the developmental epoch corresponding to very premature birth (Okumura et al., 2006; Gonzalez et al., 2011), and are relevant for the maturation of functional brain networks (Uhlhaas et al., 2010). Spontaneous cortical oscillations are characterized by a distinct peak in the alpha-band, and progressive increases in the frequency of spontaneous brain oscillations have been identified as a reliable marker of childhood neurodevelopment (John et al., 1980). Such developmental acceleration in alpha-band oscillations can be reliably measured using MEG. For example, maturational increases in the peak frequency of the mu rhythm have been reported in infants and school-age children (Berchicci et al., 2011) and deviations from typical patterns of spontaneous oscillations often indicate learning disabilities or increased risk for neurological disorders (Ahn et al., 1980).

Using MEG, we previously demonstrated that spontaneous alpha oscillations (8–14 Hz) in school-age children born very preterm are slowed (Doesburg et al., 2011a). Atypical spontaneous alpha oscillations were also found to be associated with poor visual-perceptual abilities, and linked to extensive neonatal procedural pain, in children born at extremely low gestational age (Doesburg et al., 2013a). Such persistent alterations in the spectral structure of spontaneous brain oscillations may contribute to life-long cognitive difficulties in this group, as adults born at extremely low birth weight express altered ratio of high-frequency to low-frequency oscillations (Miskovic et al., 2009). Pronounced differences in long-range alpha-band MEG connectivity have also been identified in very preterm children during the performance of a visual short-term memory task, and these alterations were associated with selective visuospatial difficulties in this group (Doesburg et al., 2011b). Together, these findings indicate that spontaneous oscillations are slowed in very preterm children, and cortical alphaband connectivity dynamics supporting task processing are not typically expressed in this group. Our previous research has indicated that altered alpha oscillations are strongly related to selective difficulties with visual-perceptual abilities in very preterm children, rather than to general intellectual ability (Doesburg et al., 2011b, 2013a).

Previous studies of altered MEG alpha oscillations in very preterm children and their relation to cognitive development have analyzed data at the sensor level (Doesburg et al., 2011a,b, 2013a), which does not reveal the contribution of specific brain regions, thus limiting interpretation of the underlying functional systems involved. In the present study we investigated slowing of alpha oscillations in very preterm children within specific brain regions and examined their relation to difficulties in visual-perceptual abilities. To this end, we recorded spontaneous-eyes-open MEG activity from a group of school-age children born very preterm and full-term control children. Beamformer source analysis was employed to reconstruct brain activity from 72 locations distributed throughout the brain. These locations were predicated on an anatomical brain parcellation scheme in order to estimate activity within multiple functionally distinct brain regions. Peak oscillatory frequency was obtained for each analyzed brain region and compared with neuropsychological assessments.

## **MATERIALS AND METHODS SUBJECTS**

Groups of 27 very preterm (≤32 weeks GA) children and 27 fullterm controls were tested as part of a longitudinal study investigating the long-term impact of neonatal procedural pain on the neurocognitive development of very preterm children (i.e., Grunau et al., 2007, 2009). Full-term control children were recruited either through pediatricians in infancy or from the community at schoolage. The groups were matched on age, sex, and handedness. Both groups comprised 17 girls and 8 boys. Children had been excluded if they were diagnosed with a major sensory (hearing, vision), motor, or cognitive impairment, or had periventricular leukomalacia (PVL) or grade III–IV intraventricular hemorrhage (IVH) on neonatal ultrasound according to Papile's classification (Papile et al., 1978). Following inspection of movement during the MEG scan, one very preterm child, and one full-term control child

were excluded due to excessive motion. The resulting group of 26 preterm children (mean age 7.76 years; SD = 0.46 years) consisted of 8 boys and 16 girls; the group of 26 full-term controls (mean age 7.66 years; SD = 0.28 years) was comprised of 9 boys and 17 girls. Both groups contained 24 right handed children and 2 left handed children. The neonatal characteristics of the preterm group are presented in **Table 1**.

## **MEG RECORDING**

Two minutes of spontaneous-eyes-open MEG activity was recorded using a 151-channel whole-head CTF Omega system (CTF Systems, Coquitlam, BC, Canada). Subjects were supine during recording and were instructed to maintain visual fixation on a "happy face" stimulus which was presented 40 cm above their eyes. A research assistant accompanied each subject within the magnetically shielded room to monitor the subjects. Data were digitized continuously at 1200 Hz, stored offline for analysis, and subsequently downsampled to 600 Hz. Fiducial coils were attached at the nasion as well as the right and left preauricular points, and each was energized at a distinct high narrow-band frequency. T1 weighted volumetric MRI images were also collected (1.5 T). Due to practical limitations imposed by multimodal neuroimaging in special child populations, MRI images were not available for all subjects. For those subjects without usable MRIs, a substitute matching MRI was found using the NIH database. MRIs for 4 very preterm children and 22 typically developing controls were taken from the NIH pediatric database. To obtain matching MRIs for individual subjects several candidate volumetric MRIs were selected from the database based on small differences between MEG and candidate MRI fiducial points. A best-matching structural MRI was then fitted to the subject's digitized head surface manually.

#### **MEG ANALYSIS**

Head motion was corrected for by obtaining a dipole source solution for each fiducial coil 30 times/s throughout the recording of spontaneous activity. The MEG data were then transformed to a common position by performing an inverse solution, data rotation, and forward solution 30 times/s (Wilson et al., 2007). To investigate slowing and reduction in the magnitude of spontaneous alpha oscillations in preterm children, we reconstructed the activity of multiple brain regions. The regions of interest for the source reconstruction were based on an anatomical brain parcellation scheme (see Kötter and Wanke, 2005; Bezgin et al., 2008).

\*One very preterm child was recruited from outside the longitudinal cohort, excluding scores for certain neonatal variables.

This approach was chosen because it provides multiple functionally distinct regions which are distributed throughout the brain, and because this approach was successfully employed by a MEG study of functional brain activity and its relation to cognition,from which the set of source locations used in the present study were obtained (Diaconescu et al., 2011). A list of each source location is provided in **Table 2**, and a depiction of each seed location in brain space is available in Doesburg et al. (2013b). Each subject's MRI was warped into a common Talairach space using SPM2. The 72 locations were then warped back into each individual's brain space.

To reconstruct the activity from each analyzed source location, a minimum variance beamformer method was employed (Sekihara et al., 2001). This method has been shown to be very effective in estimating activity of a given source while maximally attenuating signal contributions from all other sources, as well as removing ocular and non-ocular artifacts (Cheyne et al., 2006, 2007). Note that on the sensor level volume currents result in mixing of brain signals from various locations. Source space analysis mostly eliminates the effects of volume currents, because the latter are already taken into account in the beamformer solution. Theoretically, minimum variance beamformers assume that correlations between sources are small, which might pose a problem when synchronous activity of different brain regions is expected. Extensive study of this question has shown that in practice the adverse effects of source correlations on the reconstructed power are significant only if correlations are very strong (Sekihara et al., 2002). Moreover, the signal-to-noise ratio (SNR) also needs to be relatively high for the correlations to introduce significant issues, such as SNRs observed in averaged evoked responses in event-related experimental paradigms. More advanced methods which take correlations into account show that low SNRs (∼0.1), even when strong correlations exist (∼0.8), do not alter reconstructed power significantly (Moiseev et al., 2011; Moiseev and Herdman, 2013). In the case of oscillatory resting state activity, correlations are moderate and SNRs are relatively small. It is for these regions that beamformer techniques have been successfully applied to resting state MEG data in recent years, including in functional connectivity analyses (Gross et al., 2001; Brookes et al., 2011; Hillebrand et al., 2012).

Data reconstructed from each of the 72 source locations were then filtered at 1 Hz intervals from 1 to 60 Hz (passband = *f* ± 0.05*f*, where f is the filter frequency), using the eegfilt function from the EEGLAB toolbox, a two-way least-squares FIR filter (Delorme and Makeig, 2004). These methods which has previously been effective for estimating the spectral content of oscillatory signals in M/EEG data (Doesburg et al., 2008, 2011a,b). Power was calculated at each frequency for each data point and sensor, and was subsequently averaged across all time points in the 120 s recording session. Peak oscillatory frequency for each brain region for each subject was defined as the frequency expressing maximum power between 5 and 20 Hz, which included the alphaband (8–14 Hz), as well as other nearby frequencies. This wider frequency range was selected because peak frequencies of MEG oscillations in specific brain regions in atypically developing children have not been extensively studied. Given this, we did not wish to make strong *a priori* assumptions about frequency content in specific brain regions in very preterm children. Furthermore, slowing of alpha rhythms into the upper theta range has been reported in several other clinical populations (Llinás et al., 1999; Sarnthein et al., 2006; Sarnthein and Jeanmonod, 2007, 2008; Boord et al., 2008). These methods for determining peak oscillatory frequency in MEG data have been previously established (Doesburg et al., 2011a).

To test the hypothesis that peak oscillatory frequency is slowed in very preterm children without statistical challenges imposed by multiple comparisons, peak frequency was averaged across all 72 analyzed regions for each subject to obtain a global measure for each subject. Permutation testing was then used to test for global differences between the very preterm and full-term control groups (see Blair and Karniski, 1993). Permutation tests were one-tailed as the direction these effects were predicted from previous results (Doesburg et al., 2011a). Once global group differences were established we tested whether oscillatory slowing was present in each of the 72 analyzed brain regions. Since our goal at this stage of the analysis was to determine which set of regions contributed to the established global pattern of oscillatory slowing, and type II errors are as misleading as type I errors in determining which brain regions are involved in the global pattern and which are not, these tests were not corrected for multiple comparisons. The rationale for this is that the conservative nature of such corrections would likely underestimate the number of areas expressing atypical alpha oscillations and provide a distorted account of the location and extent of altered brain oscillations in very preterm children. Permutation tests were also used to evaluate regional between-group comparisons.

## **ASSOCIATIONS BETWEEN MEG ACTIVITY AND PSYCHOMETRIC DATA**

Following MEG recording, all children underwent psychometric assessment using the Wechsler Intelligence Scale for Children, 4th Edn. (WISC-IV; Wechsler, 2003) and the Beery–Buktenica Developmental Test of Visual-Motor Integration, 5th Edn. (Beery et al., 2004), administered by a psychometrician. For the present study, we elected to focus on visual-perceptual abilities as previous studies have indicated that altered alpha oscillations in preterm children are strongly associated with visual-perceptual difficulties (Doesburg et al., 2011b, 2013a). The visual perception subscale of the Beery VMI was chosen as an index of visual-perceptual ability, as it has proven to be a sensitive measure in previous studies (Doesburg et al., 2011b, 2013a). The Motor Coordination and Visual-Motor Integration Subscales of the Beery VMI and Full-Scale IQ (FSIQ) from the WISC-IV were also evaluated in relation to the MEG data in order to assess the specificity of relations between altered alpha oscillations and functional abilities in very preterm children, consistent with methods employed in previous research (Doesburg et al., 2011a).

Correlations were examined between each of these psychometric measures, within both the very preterm and full-term groups, and peak oscillatory frequency for each brain region that exhibited slowing. Statistical analysis of correlations between oscillatory slowing and psychometric data were controlled for multiple comparisons using the False Discovery Rate (FDR; see Storey, 2002). The approach taken to multiple comparisons here differs from that in the regional analysis of oscillatory slowing because correlations among oscillatory brain activity and psychometric measures aim

**Table 2 | Each source location used in the analysis of spontaneous MEG activity, together with associated Brodmann area and Talairach coordinates.**

#### **RESULTS**

### **OSCILLATORY SLOWING IN CHILDREN BORN VERY PRETERM**

## **OSCILLATORY SLOWING ASSOCIATED WITH REDUCED VISUAL-PERCEPTUAL ABILITY IN VERY PRETERM CHILDREN**

(Continued)

Visual-perceptual ability, indexed by the visual perception subscale of the Beery VMI, was associated with oscillatory slowing

**FIGURE 1 | Oscillatory slowing in very preterm children**. Peak oscillatory frequency in each of the 72 analyzed brain regions for the full-term control and very preterm groups, as well as statistically significant regional slowing in children born very preterm. The 7- to 10-Hz scale bar corresponds to the peak oscillatory frequency for each analyzed region for the very preterm and typically developing groups. The 0.4- to 1.8-Hz scale bar indicates the difference in peak oscillatory frequency between the very preterm and typically developing groups for regions where statistically significant differences were observed (regions for which no significant group differences were observed are presented in dark blue).

in multiple regions, including several known to be involved in the processing of visual information including inferior parietal cortex, ventral temporal cortex, and thalamus. Regional slowing was not significantly associated with FSIQ or the Motor or Visual-Motor scores on the Beery VMI for the very preterm children. Peak oscillatory frequency was not associated with any of the psychometric tests for full-term children for any region. Complete results of the analysis of associations between regional oscillatory slowing and visual-perceptual ability in preterm children are provided in **Table 3**.

## **DISCUSSION**

We provide the first source-resolved analysis of slowing of alpha oscillations in children born very preterm. Identification of the specific brain regions across thalamocortical systems involved in slowed alpha oscillations in very preterm children represents an important step, as altered alpha oscillations may mediate relations between neonatal procedural pain and cognitive outcome in children born at extremely low gestational age (Doesburg et al.,2013a), and alpha oscillations are particularly relevant for altered network connectivity underlying problems in visual-perceptual abilities in very preterm children (Doesburg et al., 2011b). We observed oscillatory slowing within multiple cortical regions encompassing bilateral temporal, parietal, and prefrontal cortical areas. Many regions expressing oscillatory slowing in preterm children are important for visual processing,including regions involved in both the dorsal visual system (i.e., inferior parietal cortex), and the ventral visual system (i.e., inferior temporal cortex, ventral temporal

**Table 3 | Associations between slowing of peak oscillatory frequency and visual-perceptual ability, indexed by the visual perception subscore of the Beery VMI, in children born very preterm.**

Significant correlations are presented in bold.

cortex). Slowing of alpha oscillations in preterm children was also prevalent in areas of prefrontal cortex involved in executive function (i.e., dorsolateral prefrontal cortex and orbitofrontal cortex).

The present study provides the first evidence that slowing of peak oscillatory frequency is associated with cognitive outcome in very preterm children. Oscillatory slowing was found to be selectively related to visual-perceptual ability, an area of selective developmental vulnerability in very preterm children (Rickards et al., 2001; Grunau et al., 2002; Taylor et al., 2004), and not associated with overall intellectual function, indexed by FSIQ. These results confirmed associations between slowing of alpha oscillations and visual-perceptual ability were typically, but not exclusively, found in regions involved in visual processing including inferior parietal cortex, angular gyrus, ventral temporal, cortex, and thalamus. There were no associations found between peak oscillatory frequency and neuropsychological ability in the fullterm children, indicating that this phenomenon corresponds to neural mechanisms underlying selective developmental difficulties prevalent in very preterm children, rather than normal variance in childhood neurocognitive development.

Although not as prevalent as was observed for visual cortical regions, oscillatory slowing was also present in prefrontal cortical areas (i.e., ventrolateral prefrontal cortex and orbitofrontal cortex), congruent with earlier reports of slowed alpha oscillations over frontal MEG sensors (Doesburg et al., 2011a). This may be significant as executive functions are also an area of selective developmental difficulty prevalent in children born very prematurely (Anderson et al., 2004; Marlow et al., 2007; Mulder et al., 2009). Together with our results implicating region-specific slowing of alpha oscillations with selective difficulties in visual-perceptual abilities, the presence of atypical alpha rhythmicity in prefrontal brain regions in very preterm children suggests that region-specific slowing may be associated with developmental difficulties in executive function as well. Stated more generally, alpha slowing within a specific cortical region may be associated with reduced function in the corresponding psychological domain. In this view, specific cortical systems situated in posterior parietal, inferior temporal, and prefrontal regions may be selectively vulnerable to aspects of adverse neonatal experience, impacting the development of brain oscillations underlying the maturation of cognitive and perceptual functions associated with these areas (Benasich et al., 2008; Uhlhaas et al., 2009b, 2010) and causing selective developmental difficulties in the corresponding domains of executive function and visual-perceptual ability.

The perspective that oscillatory slowing is associated with impairment of normal brain function is consistent with observations of MEG slowing in association with neurological insults in other contexts. For example, regional analysis of excessive lowfrequency oscillations has been introduced as a method for mapping abnormal functional brain activity following mild traumatic brain injury (Huang et al., 2012), and the anatomical focus of such excessive slow wave activity appears related to the location of white matter injury, measured using diffusion tensor imaging, in this population (Huang et al., 2009). Furthermore, slowing of peak alpha oscillatory frequency toward the theta range in resting state MEG recordings has been reported in diverse pathological conditions including Parkinson's disease, neurogenic pain, tinnitus, and major depression (Llinás et al., 1999). Subsequent combination of methods for the analysis of oscillatory slowing with MEG source analysis techniques have indicated that neuroanatomical systems involved in slowing depend on the pathological condition in question, and likely correspond to the nature of symptoms expressed (Schulman et al., 2011). This finding is consistent with the results of the present study which demonstrates that regionspecific slowing of alpha oscillations in visual cortical regions are associated with visual-perceptual difficulties in children born preterm. Slowing of alpha oscillations in neuropathic pain has been confirmed using scalp EEG (Sarnthein et al., 2006; Boord et al., 2008). Slowing of alpha oscillations toward the theta range has also been reported using intracranial EEG in patients with neuropathic pain and Parkinson's disease (Sarnthein and Jeanmonod, 2007, 2008). Oscillatory alpha slowing in M/EEG recordings in numerous pathological conditions is thought to result from deafferentation, particularly in thalamocortical systems (Llinás et al., 2005), and it has been demonstrated that the parameters of alpha oscillations are related to white matter properties revealed using diffusion tensor imaging (Valdés-Hernández et al., 2010).

Slowing of alpha oscillations, together with the purported association of this phenomenon with the loss of integrity in structural brain connectivity, raises questions regarding relations between alpha oscillatory slowing and communication in brain networks. In particular, evidence increasingly indicates that coherence of alpha-band oscillations plays a critical role in communication among brain regions supporting cognition and perception (see Palva and Palva, 2007, 2012 for reviews). Recently, it has been reported that alpha-band oscillatory connectivity is reduced in preterm neonates with brain lesions (Tokariev et al., 2012). Alphaband oscillatory connectivity during cognitive processing has been found to be slowed and reduced, and associated with selective difficulties in visual-perceptual ability, in very preterm children (Doesburg et al., 2011b). Moreover, this reduced alpha-band phase synchronization was observed in concert with increased interhemispheric theta-band synchronization in the very preterm children, suggesting that task-dependent interactions among brain regions may also be slowed. In light of the prevalence of white matter injury and atypical white matter development in this population (see Khwaja and Volpe, 2008; Miller and Ferriero, 2009), this raises the prospect that increased conduction delays among cortical regions may adversely impact the ability to recruit inter-regional oscillatory network coherence to support task performance.

The observation that slowing of alpha-band oscillations in very preterm children occurred in thalamus is also significant as animal research has demonstrated that thalamocortical mechanisms underlie the generation of alpha oscillations, as well as their slowing under pathological conditions (see Hughes and Crunelli, 2005 for review). Moreover, thalamocortical systems undergo critical phases of development during the gestational epoch corresponding to very premature birth (see Kostovic and Judaš, 2010 for review). The slowing of alpha oscillations, mediated by altered thalamocortical interactions, has been proposed to underlie disruptions of function in several neurological and neuropsychiatric populations (Llinás et al., 1999, 2005). Altered connectivity and white matter development are prevalent in children born very preterm (see Miller and Ferriero, 2009 for review), including altered development of structural (Anjari et al., 2007;Dudink et al., 2007) and functional (Smyser et al., 2010) connectivity in thalamocortical systems. The importance of thalamus for understanding alterations in cortical development in preterm children is further underscored by recent findings that reduced thalamic volume is predictive of reduced cortical volume in preterm infants (Ball et al., 2012).

Our results showing significant slowing in deep brain structures such as the thalamus should be interpreted with caution. Reconstructing MEG signals from deep structures is not only difficult, but in many cases impossible because the SNR is too small (see Quraan et al., 2011). It should be noted, however, that in terms of power the alpha-band signal is the strongest in the brain, improving the likelihood of accurate source reconstruction in this frequency range (Attal et al., 2007). Moreover, prior MEG source analysis studies have reported task-dependent changes involving thalamus (i.e., Schnitzler et al., 2009). Further investigation will be needed to definitively determine the reliability of MEG measures of oscillations power from deep structures such as thalamus. Future research will also be required to examine relations between the structural and functional development of thalamocortical systems, disruption of the normative structure of cortical alpha oscillations, andfunctional outcomes in specific psychological domains in children who were born very prematurely. Involvement of specific regions identified in the source space analysis of oscillatory slowing in very preterm children should also be interpreted with some caution, as correction for multiple comparisons was not performed across all analyzed 72 regions. As such, potential false positives for individual regions may have arisen. The consistency of slowing located in regions involved in dorsal and ventral visual pathways, however, together with prefrontal cortical regions, is highly suggestive that region-specific slowing in brain structures is associated with selective developmental difficulties prevalent in children born very prematurely.

## **CONCLUSION**

We demonstrate that slowing of spontaneous alpha oscillations is evident in widespread brain regions in very preterm children. These alterations of alpha oscillations were concentrated in regions involved in visual processing including thalamus, posterior parietal cortex, and interior temporal cortex, as well as in prefrontal regions relevant for executive functions. We provide the first evidence that slowing of spontaneous alpha oscillations is associated with functional outcome in preterm children. This association between slowing of alpha oscillations in particular brain regions and function was specific for visual-perceptual abilities, and was typically found in posterior parietal and inferior temporal brain regions implicated in visual processing. These results indicate that area-specific atypicalities in alpha oscillatory brain activity are associated with selective difficulties in children born very prematurely.

### **ACKNOWLEDGMENTS**

We would like to thank Dr. Ivan Cepeda and Gisela Gosse for coordinating the study, Katia Jitlina, Amanda Degenhardt, Cecil Chau, Teresa Cheung, and Julie Unterman for their help in data collection. This work was supported by grant RO1 HD039783 from The Eunice Kennedy Shriver National Institute of Child Health and Human Development of the National Institutes of Health (NICHD/NIH) to Ruth E. Grunau, who received salary support from the Child and Family Research Institute (CFRI). Urs Ribary holds a BC LEEF Leadership Chair in Cognitive Neuroscience in Early Childhood Health and Development supported by the BC Leading Edge Endowment Fund (BC LEEF), and the MEG facility infrastructure was supported by the CFI grant to Urs Ribary and the Behavioral and Cognitive Neuroscience Institute (BCNI). Research support for Sam M. Doesburg is from the Natural Sciences and Engineering Research Council of Canada (NSERC RGPIN 435659) and the EpLink program of the Ontario Brain Institute (OBI).

## **REFERENCES**

traumatic brain injury in military and civilian patients. *J. Neurotrauma* 26, 1213–1226. doi:10.1089/neu.2008.0672

estimating synchrony in both oscillations and events. *Neuroimage* 60, 1562–1573. doi:10.1016/j.neuroimage.2011.12.080

**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: 12 June 2013; accepted: 30 October 2013; published online: 15 November 2013.*

*Citation: Doesburg SM, Moiseev A, Herdman AT, Ribary U and Grunau RE (2013) Region-specific slowing of alpha oscillations is associated with visualperceptual abilities in children born very preterm. Front. Hum. Neurosci. 7:791. doi: 10.3389/fnhum.2013.00791*

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

*Copyright © 2013 Doesburg , Moiseev, Herdman, Ribary and Grunau. 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.*

## Reduced theta connectivity during set-shifting in children with autism

## **Sam M. Doesburg1,2,3,4\*, Julie Vidal 5,6 and Margot J. Taylor 1,2,3,4**

1 Department of Diagnostic Imaging, The Hospital for Sick Children, Toronto, ON, Canada

2 Neurosciences & Mental Health Program, The Hospital for Sick Children Research Institute, Toronto, ON, Canada

3 Department of Medical Imaging, University of Toronto, Toronto, ON, Canada

4 Department of Psychology, University of Toronto, Toronto, ON, Canada

5 Paris Descartes University, Paris, France

6 UMR, Centre National de la Recherche Scientifique 3521, Paris, France

#### **Edited by:**

Christos Papadelis, Harvard Medical School, USA

#### **Reviewed by:**

Tal Kenet, Massachusetts General Hospital, USA Jeff Anderson, University of Utah, USA

#### **\*Correspondence:**

Sam M. Doesburg, Department of Diagnostic Imaging, The Hospital for Sick Children, 555 University Avenue, Toronto, ON M5G 1X8, Canada e-mail<EMAIL_ADDRESS>
Autism spectrum disorder (ASD) is a characterized by deficits in social cognition and executive function. An area of particular difficulty for children with ASD is cognitive flexibility, such as the ability to shift between attentional or response sets. The biological basis of such deficits remains poorly understood, although atypical development of structural and functional brain connectivity have been reported in ASD, suggesting that disruptions of normal patterns of inter-regional communication may contribute to cognitive problems in this group. The present magnetoencephalography study measured inter-regional phase synchronization while children with ASD and typically developing matched controls (6– 14 years of age) performed a set-shifting task. Reduced theta-band phase synchronization was observed in children with ASD during extradimensional set-shifting. This reduction in task-dependent inter-regional connectivity encompassed numerous areas including multiple frontal lobe regions, and indicates that problems with communication among brain areas may contribute to difficulties with executive function in ASD.

**Keywords: ASD, autism, neural synchrony, neural oscillation, magnetoencephalography, set-shifting, executive function, functional connectivity**

## **INTRODUCTION**

Autism spectrum disorder (ASD) is associated with difficulties in cognitive development, particularly in domains such as social cognition and executive functions (Hill and Bird, 2006; O'Hearn et al., 2008). These abilities depend heavily on frontal lobe functions, and atypical frontal lobe development has been linked with cognitive difficulties in ASD (Courchesne and Pierce, 2005; Gilbert et al., 2008; Just et al., 2012). ASD is particularly associated with difficulties in cognitive flexibility, which includes the ability to switch between attentional or response sets (Yerys et al., 2009; Maes et al., 2011). This poor mental flexibility, the tendency to get "stuck" in a set is the underlying cognitive basis of the behavioral rigidity, perseveration, and repetitive behavior symptoms that are one of the definitive hallmarks of ASD (Hill, 2004). Several tasks have been used to assess cognitive flexibility behaviorally in ASD, most commonly being the Wisconsin Card Sorting Task (WCST), and have shown impaired performance for individuals with ASD on the WCST (e.g., Lopez et al., 2005). Evidence for cognitive flexibility impairment in ASD also comes from performance on the Cambridge Neuropsychological Test Automated Battery (CANTAB) Intradimensional-Extradimensional (ID-ED) Shift Task [developed by Dias et al. (1996)]. ED attentional shifts are thought to demand greater cognitive flexibility to successfully make the switch than ID shifts. Some studies have shown that individuals with ASD were impaired on ED shifts, but performed similarly to controls on ID shifts (Hughes et al., 1994; Ozonoff et al., 2004); however, other studies have failed

to replicate this finding (e.g., Corbett et al., 2009). The neural basis of executive set-shifting abilities in ASD, however, remains unclear.

In previous neuroimaging studies atypical development of connectivity among cortical regions in ASD has been reported. Diffusion tensor imaging (DTI) studies have found that structural connections among cortical regions are atypical in ASD (see Travers et al., 2012 for review; Mak-Fan et al., 2013). Hemodynamic imaging has also demonstrated that functional connectivity among brain regions is abnormal inASD, both in resting-state conditions and during the performance of cognitive tasks (see Müller et al., 2011 for review). Such results suggest that disordered development of brain network connectivity is common in autism and authors have suggested that this may lead to atypical interactions among brain regions resulting in social and cognitive impairments in this group (Just et al., 2007, 2012; Gilbert et al., 2008; Noonan et al., 2009).

 Neural coherence is related both to the organization of resting-state brain networks (Brookes et al., 2011; de Pasquale et al., 2012; Hipp et al., 2012) as well as to cortical network dynamics underling cognition and perception (see Palva and Palva, 2012 for review). Neural oscillations and their coherence develop throughout infancy, childhood and adolescence (Clarke et al., 2001; Uhlhaas et al., 2009b; Boersma et al., 2011, 2013a) and are relevant for the maturation of cognitive abilities (Benasich et al., 2008; Gou et al., 2011). Atypical oscillatory responses, including altered expression of local power and inter-regional coherence, have been reported in ASD using electroencephalography (EEG) and magnetoencephalography (MEG) (Sun et al., 2012; Wright et al., 2012; Khan et al., 2013). In resting-state networks atypical oscillatory synchrony has also been reported in ASD (Murias et al., 2007; Maxwell et al., 2013). Accumulating evidence that maturation of oscillatory activity in functional networks underlies cognitive development (see Uhlhaas et al., 2010 for review) highlights the importance of understanding the role of oscillatory neural synchronization during cognitive processing in neurodevelopmental disorders such as ASD.

Overall, the literature shows that frontal and parietal areas are implicated in tasks of cognitive flexibility, and there is some initial evidence for abnormalities in these areas in adults with ASD (Schmitz et al., 2006; Shafritz et al., 2008). However, to determine how these atypical patterns arise, investigation is needed in children with ASD. Previous findings have also suggested that abnormal coordination among brain regions may contribute to dysfunctional network interactions leading to cognitive difficulties in children with ASD (e.g., Just et al., 2007). In the present study, we recorded MEG while children with ASD and matched controls performed a set-shifting task requiring strong engagement of executive processes. In particular, this task places strong demands on cognitive flexibility. Atlas-based source reconstruction was performed and task-dependent changes in inter-regional oscillatory synchrony were assessed to test the hypothesis that children with ASD express atypical coordination of oscillatory activity in large-scale networks in tasks requiring cognitive flexibility and executive functions.

## **MATERIALS AND METHODS**

#### **SUBJECTS**

A total of 20 children with ASD and 18 typically developing typically developing children were tested. However, due to too much movement during the MEG study, 4 in each group were excluded from analyses. Thus, 16 children with ASD (13 male; mean age 11.1 years; SD = 2.5) and 14 typically developing controls (12 male; mean age 11.5 years; SD = 2.4) were included in the present study. Intellectual ability was assessed using WASI 2-score IQ and did not differ between groups (*p* = 0.81); mean IQ was 107 (SD = 12.7) for the ASD group and 105.8 (SD = 15.6) for the controls. Subjects were excluded if they had a history of neurological disorders (other than ASD for those in the clinical group), brain injury, were using psychoactive medications, did not have normal or corrected-to-normal vision, an IQ below 75, language skill which made them unable to complete the task, or had other contraindications for MEG or MRI imaging. This research was approved by the Hospital for Sick Children Research Ethics Board. All children gave informed assent and their parents gave informed written consent.

## **TASK AND DATA ACQUISITION**

Magnetoencephalographic data were acquired on a 151 channel whole head CTF/MISL system (Coquitlam, Canada). Children were supine throughout data recording, and three coils placed at the nasion and left and right preauricular points were used to monitor head location during recording. Data were collected continuously at 625 Hz, during which subjects performed a setshifting task (**Figure 1**). In this task, the children were presented with three stimuli on each trial. Subjects were instructed to match the bottom stimulus to one of the two upper stimuli. There were two dimensions on which the stimuli could vary: shape (triangle, circle, diamond, cross, star, pentagon) and color (cyan,

sets, within which the rule for matching is held constant (i.e., green, star).

to star).

blue, pink, green, yellow, red). The rule for correctly matching remained constant throughout each "set" and there was always only one correct response. Following three or four consecutive correct trials a "set-shift" occurred, in which the rule for matching would change. In ID shifts the rule would change within a given dimension (e.g., red to green, or star to cross); in ED shifts the rule for matching would change across dimensions, e.g., green to pentagon or triangle to yellow). The order of ID and ED shifts was randomized. Stimulus duration depended on subject response time, with a maximum duration of 4000 ms. The inter-trial interval varied between 1000 and 1500 ms. Two 6 min runs were collected for each subject. To facilitate co-registration of MEG activity to brain anatomy a structural volumetric MR image was collected following MEG recording for each subject using a 1.5-T Signa Advantage system (GE Medical Systems). MEG fiducial coils were replaced by radio-opaque markers for the T1 weighted 3-D SPGR scan to maintain accurate MEG-MRI co-registration.

## **MEG PREPROCESSING AND SOURCE RECONSTRUCTION**

Recording runs were excluded from analysis if more than 1 cm of movement occurred between head localization at the beginning and end of the run, consistent with movement thresholds for MEG studies in child populations (e.g., Pang, 2011; Taylor et al., 2011; Hung et al., 2012). Data epochs were extracted from −1500 to 2500 ms, relative to stimulus onset for trials immediately following extradimensional (ED1) and intradimensional (ID1) shift trials, as well as on the third trials of extradimensional (ED3) and intradimensional (ID3) sets, where task performance but no setshifting was required. MEG data were co-registered with MRI data by aligning the fiducial markers, and a multisphere head model was constructed for each subject based on their individual MRIs. MRIs were normalized into standard MRI space using SPM2. Seed locations, which were 72 cortical and sub-cortical locations for source-space MEG analysis previously used by Diaconescu et al. (2011) (see **Figure 2**), were then unwarped from standard MRI space into the corresponding location in head space for each individual. The complete list of 72 locations, each Talairach coordinate and anatomical region can be found in Diaconescu et al. (2011) as well as Doesburg et al. (2013). Broadband time-series representing the activity of each of the 72 sources were then reconstructed for each trial using beamformer analysis. Beamformer analysis implements a spatial filter, estimating the activity at each location in the brain while maximally attenuating contributions from other sources,including ocular and muscle artifacts (Robinson andVrba, 1999; Sekihara et al., 2001; Cheyne et al., 2006).

## **INTER-REGIONAL PHASE-LOCKING ANALYSIS**

Data were filtered into theta (4–7 Hz), alpha (8–14 Hz), beta (15– 30 Hz), low gamma (30–80 Hz), and high gamma (80–150 Hz) frequency ranges. Time-series of instantaneous phase values were then obtained for each source and frequency using the Hilbert transform. Inter-regional phase locking was calculated for each source pair and frequency using the phase lag index (PLI) which quantifies the stability of phase relations between source pairs across trials, while removing/attenuating phase locking occurring at zero/near-zero phase lag, thereby providing a measure of inter-regional phase synchronization that is robust against spurious synchronization originating from the activity of common sources (Stam et al., 2007). This strategy of performing phaselocking analysis on time-series of MEG activity reconstructed using beamformer analysis builds on established techniques for investigating oscillatory connectivity dynamics during cognitive processing (Doesburg and Ward, 2009; Doesburg et al., 2012b). This produced a 72-by-72 connectivity matrix for each time point and frequency, for each subject. Time-series of connectivity strengths for each region and frequency band were then calculated using the Brain Connectivity Toolbox (Rubinov and Sporns, 2010). This approach produces a graph metric for each region, for each analyzed time point. Connectivity strength accordingly indexes how functionally connected (phase-locked) a given region is to all other regions in the analyzed network. Time series of average connectivity strength were produced for each frequency by averaging across all 72 analyzed regions, and standardized relative to a −250 to 0 ms baseline interval to reveal the time courses of task-dependent changes in inter-regional phase locking for ED1, ID1, ED3, and ID3 conditions. This approach allowed the determination of dynamics in large-scale connectivity dynamics and the identification of time intervals for further statistical analysis. Time courses of mean connectivity strength averaged across 72 regions distributed across widely separated cortical and subcortical regions reflect fairly global coherence dynamics, whereas connectivity differences in children with ASD during the setshifting task likely involve more specific subsets of connections, which we evaluated using the Network Based Statistic (NBS; see Zalesky et al., 2010). To this end, time windows exhibiting peaks in networks connectivity were selected, and connectivity matrices representing this task-dependent increase in network connectivity were obtained by averaging the connectivity matrices across time points in the peak window. A baseline connectivity matrix was constructed by averaging across an equivalent number of time points in the pre-stimulus interval. Task-dependent increases in connectivity were indexed by subtracting the baseline connectivity matrix from the active window connectivity matrix for each subject. Group differences in task-dependent phase locking were assessed using the NBS toolbox (Zalesky et al., 2010, 2012). This approach evaluates network differences between groups by first applying a univariate statistical threshold to each element in the connectivity matrix. If a connectivity component (a contiguous set of node pairs which are differentially connected between the compared groups) is discovered, its statistical significance is evaluated by shuffling the group membership, and observing the largest connectivity component observed in the shuffled data. Data surrogation was thus employed to create a null distribution, and

the size of observed real connectivity components was considered relative to the surrogate data distribution to evaluate statistical confidence. As such, statistical significance is assigned at the level of the network connectivity component, rather than at the level of individual pair-wise comparisons (see Zalesky et al., 2010). As the largest components in the surrogate data are obtained considering all pair-wise comparisons, NBS controls for false positives due to multiple comparisons (Zalesky et al., 2010, 2012). NBS is a non-parametric technique that is appropriate for analysis of raw connectivity measures (see Zalesky et al., 2010), such as PLI. Seed regions and altered connectivity in children with ASD were plotted using the BrainNet Viewer toolbox (Xia et al., 2013).

## **RESULTS**

## **BEHAVIORAL PERFORMANCE ON THE SET-SHIFTING TASK**

Behavioral data for children with ASD and typically developing controls are presented in **Table 1**. Reaction times for the typically developing control children were significantly longer (*p* < 0.02) on ED1 trials than for ED3 trials, but did not differ significantly between the ID1 and ID3 trials (*p* < 0.54). The children with ASD also exhibited significantly longer reaction times (*p* < 0.05) on ED1 trials than on ED3 trials, but did not express significantly longer RTs (*p* = 0.17) for ID1 relative to ID3 trials. Interestingly, children with ASD and controls did not differ in their reaction times, for either ED (ED1; *p* = 0.5) or ID setshifting (ID1; *p* = 0.49). This indicates that the ED set-shifting task required additional cognitive resources for both the ASD and control groups, and overall performance did not differ between the groups.

## **THETA-BAND CONNECTIVITY DYNAMICS DURING SET-SHIFTING**

In the theta band, clear increases above baseline values of average network connectivity were seen for both children with ASD and typically developing controls in both the ID1 and ED1 shift conditions, as well as during ED3 and ID3 conditions, in which no set-shifting occurred. **Figure 3** displays the time course of theta-band network connectivity strength, averaged across all 72 analyzed regions, for ASD and controls for each trial condition, standardized relative to a 250 ms pre-stimulus baseline. Time courses of task-dependent theta-band connectivity appeared roughly similar between ASD children and controls, except on ED1 shift trials, where demands on executive function are expected to be greatest. This reduced engagement of network connectivity in children with ASD, relative to typically developing controls, peaked 150–400 ms following stimulus onset. No clear modulations of

**Table 1 | Means and standard deviations of reaction times (ms) for children with ASD and typically developing controls for each trial type.**

inter-regional network synchronization were observed outside the theta frequency range.

## **REDUCED TASK-DEPENDENT INTER-REGIONAL PHASE-LOCKING IN CHILDREN WITH ASD**

The Network Based Statistic was employed to investigate group differences in theta connectivity during the ED1 set-shifting condition. Connectivity matrices reflecting network connectivity 150– 400 ms post-stimulus, averaged across time points and bracketing the interval where group differences in connectivity were observed, were compared with connectivity matrices averaged across time points in a −250 to 0 ms pre-stimulus baseline, using a threshold of 2.5. This threshold is a *t*-statistic and is adapted for the data distribution being analyzed (see Zalesky et al., 2010, 2012). This revealed reduced task-dependent connectivity in children with ASD in a distributed network of brain regions which included strong involvement of frontal, temporal, and occipital brain regions, but also encompassed some sub-cortical and parietal brain areas (*p* = 0.01). **Figure 4** displays the network expressing reduced theta-band connectivity in children with ASD, as well as the magnitude of group differences in regional connectivity strength for each region in this network. A complete list of

each region in this network, together with its affiliated anatomical location and Talairach coordinates, is presented in **Table 2**. Comparison of connectivity between baseline and active windows within the ASD and typically developing groups, using NBS, did not reveal any statistically significant differences.

## **DISCUSSION**

Children with ASD expressed reduced theta-band network synchronization during ED set-shifting relative to typically developing controls. The RTs did not differ between the groups,indicating that they were matched on behavioral performance. Thus, differences seen in brain connectivity were not due to performance differences. In studies with adults with ASD, this was also found: despite no differences in behavior, fMRI results showed either increased (Schmitz et al., 2006) or decreased (Shafritz et al., 2008) activation patterns in widespread brain regions, linked to task performance. Our observation that performance differences were seen in the ED but not ID shifts is consistent with the ED task being more difficult and with the prior reports of greater difficulty in ASD with extra rather than ID shifts (e.g.,Hughes et al., 1994;Ozonoff et al., 2004). Results from the present study also indicated that ED shifting (ED1 compared with ED3) resulted in significantly longer reaction times

for both children with ASD and controls, whereas ID shifting did not. These findings are consistent with evidence indicating that clinical child populations can achieve comparable behavioral performance by recruiting different patterns of connectivity among brain areas, suggesting that brain systems associated with these functions in controls are impacted in ASD, leading to compensatory reorganization of function (e.g., Schafer et al., 2009).

Alterations in connectivity were widespread and encompassed frontal, parietal, occipital, temporal, and sub-cortical regions. These results add to a growing literature indicating that functional interactions among brain regions are atypical in ASD (Just et al., 2007; Murias et al., 2007; Müller et al., 2011; Sun et al., 2012; Boersma et al., 2013b). These alterations in functional connectivity may be related to abnormal development of underlying structural connections (see Travers et al., 2012 for review), including atypical inter-regional relations in cortical thickness (Shi et al., 2013). Altered structural brain connectivity has been associated with individual differences in cognitive ability in this group (Li et al., 2012). Recent studies, however, have shown that functional connectivity cannot be entirely predicted from brain structure (see Deco et al., 2011). This underscores the importance of understanding how functional interactions are expressed in ASD in the context

of specific cognitive tasks. Several frontal regions were implicated in abnormal connectivity during set-shifting including prefrontal cortical areas. This is significant as ASD has been associated with atypical frontal lobe development (i.e., Ecker et al., 2012) as well as atypical connectivity between the frontal lobes and other regions of the brain (see Courchesne and Pierce, 2005). Viewed through this lens, our results suggest that disordered frontal lobe connectivity may impact the ability to marshal task-dependent functional interactions among frontal regions and other areas typically used to perform executive and cognitive tasks. Accordingly, disruptions of the frontal lobes' capacity to orchestrate communication within distributed networks may contribute to selective difficulties in executive function in ASD.

There is a large body of work showing the importance of the prefrontal cortex in executive function in general and cognitive flexibility in particular. This evidence comes from lesion and neuropsychological studies, primate research, and neuroimaging studies. For example, patients with prefrontal lesions have difficulty on neuropsychological measures of flexibility such as the WCST (Stuss and Alexander, 2007). Seminal work by Dias et al. (1996) showed that damage to the dorsolateral prefrontal cortex of monkeys caused specific impairments in set-shifting. A

**Table 2 | Each region in the network showing reduced connectivity in children with ASD, together with associated Brodmann areas (BA) and Talairach coordinates.**

host of neuroimaging studies have confirmed the importance of prefrontal cortex in cognitive flexibility, although specific regions have differed and also include parietal regions (e.g., Konishi et al., 1999; Dove et al., 2000; Sohn et al., 2000; Monchi et al., 2001; Zanolie et al., 2008). In light of this literature, the results of the present study may reflect reduced ability of prefrontal regions to coordinate activity among cortical regions to support set-shifting performance in children with ASD. Right ventrolateral premotor cortex also emerged as an important region in the pattern of reduced inter-regional connectivity during set-shifting in children with ASD, which is intriguing due to the proximity of this seed location to the right insula. Given results implicating right insula in executive abilities and the spatial resolution of MEG, it is possible this is due to reduced network engagement of right insula during ED set-shifting in children with ASD. Although the insula has been less frequently identified in reviews or meta-analyses of executive function, some studies have shown that the insula (specifically the right anterior insula) is functionally connected to other regions important for executive function, and may also play a role in monitoring of task performance. The right insula is part of a network hub responsible for switching between other brain systems during task performance (e.g., Sridharan et al., 2008; Eckert et al., 2009). In addition, a study of cognitive flexibility showed insula activation in a task switch condition (Dove et al., 2000), as well as a

region in cuneus/precuneus that was more active with task switching. Accordingly, our findings suggest that reduced incorporation of right insula into distributed task-dependent networks may contribute to difficulties with cognitive flexibility and executive control in children with ASD.