The human face plays a vital role in human social interactions. Faces convey a tremendous amount of information, and the ability to differentiate and recognize faces/individuals and their emotional content has an extended developmental course through adulthood [see Kolb et al. (1992) for review]. Although posterior brain areas are crucial for face processing, frontal cortices play a critical role in deciphering the social significance of facial expressions and in allocating appropriate attention. Thus, emotional facial perception involves a distributed network that includes the amygdala, frontal lobes, anterior cingulate, STS, and fusiform gyri (McCarthy et al., 1999; Allison et al., 2000; Haxby et al., 2000; Adolphs et al., 2002; Kilts et al., 2003).

Numerous neuroimaging studies have investigated face processing in adults; however, knowledge regarding the developmental course of such abilities remains scant. Differences in frontal activation between adolescents and adults have been reported in fMRI in emotional regulation tasks (Burnett et al., 2009; Passarotti et al., 2009), as well as in tasks of emotional self-regulation and empathy (Lamm and Lewis, 2010). The timing of brain processing in the development of emotional face perception throughout childhood and adolescence has been determined with event-related potentials (ERPs), with the early emotion-specific responses emerging only in adolescence (Batty and Taylor, 2006; Miki et al., 2011). Due to its uniquely good combination of spatial and temporal resolution, MEG has been able to contribute tremendously to our understanding of the spatiotemporal dynamics underlying the processing of faces (Taylor et al., 2008, 2010, 2011a,b, 2012). With emotional faces in an explicit recognition task, early frontal activation that reflected implicit emotional processing was observed, whereas later insula and fusiform activity was found to be related to explicit emotional recognition (Bayle and Taylor, 2010).

Spatiotemporal dynamics of implicit brain processing of happy and fearful facial emotions has been established in adults using MEG (Hung et al., 2010). In this study, faces were presented rapidly and concurrently with a scrambled pattern, one on each side of a central fixation cross. Participants were instructed to respond to the scrambled image, thus not orienting attention to the face stimuli. This implicit emotional processing task revealed rapid activation of left amygdala at 100 ms for fearful compared with emotionally neutral faces. Increases in activity were observed concurrently in the dorsal ACC. The fast amygdala–ACC activation suggested a specialized frontal–limbic network, which may be responsible for facilitating responses to a potential threat. This study also confirmed that MEG source analyses could accurately measure both the location and time course of neurocognitive events in deep brain structures (e.g., Moses et al., 2007, 2009), as validated with simulated and real data analyses (Quraan et al., 2011; Mills et al., 2012).

This MEG protocol has also been used to investigate the development of neural activations associated with the implicit processing of fearful and happy facial emotions using school-aged children (7–10 years) and young adolescents (12–15 years) (Hung et al., 2012). Right lateralized amygdala activation to both happy and fearful faces was observed for the school-age children, whereas ACC activation did not reach statistical significance. In the young adolescent group, the pattern of neural activation similar to that observed in adults, left amygdala and ACC activation, was detected only during the perception of fearful faces. The results indicate that the processing of emotions first engaged the earlier-maturing amygdala, but was non-specific with respect to the emotion, and by the teenage years implicated the later-developing ACC. The maturational shift in the lateralization of amygdala responses sensitive to the fearful faces was intriguing, suggesting a shift from the more reflexive processing of the right amygdala to the elaborative processing typical of the left amygdala (e.g., Costafreda et al., 2008) with age. These results also inform our present views regarding the development of functional specialization of fear perception throughout childhood. Specifically, these findings indicate that this is a late-developing process involving the frontal–limbic emotion system, consistent with behavioral data on later maturation of recognition of negative emotions (De Sonneville et al., 2002; Herba and Phillips, 2004; Thomas et al., 2007).

Inhibition plays a vital role in social cognition, as inhibition of context-inappropriate behavior is critical for successful social functioning. Behavioral studies of inhibition indicate improvements in inhibitory control throughout childhood and adolescence (Luna et al., 2004). Presently, it is understood that inhibitory control is supported by a distributed network of brain areas, in which frontal cortex plays a pivotal role (Rubia et al., 2007).

Studies using fMRI in adults have identified brain regions underlying inhibition, which include striatal and thalamic structures, motor areas, anterior cingulate, parietal lobes, and the inferior and dorsolateral frontal gyri (Rubia et al., 2001; Watanabe et al., 2002; Mostofsky and Simmonds, 2008). Frontal cortex has been demonstrated to be critical for inhibitory control in studies that used Go/No-go tasks where comparisons were made between the activations during No-go (successful response inhibition) and Go trials (response execution) [see review in Dillon and Pizzagalli (2007)]. Brain imaging findings from Go/No-go paradigms in typical development have yielded variable results, yet have demonstrated a role for dorsolateral and inferior frontal regions in inhibition, although activation of this region is not reliably reported in children (e.g., Durston et al., 2002; Tamm et al., 2004; Rubia et al., 2007).

Fewer MEG studies of inhibitory control have been conducted. Our research group used MEG to investigate the maturation of spatiotemporal brain dynamics of inhibition in adolescence and early adulthood. We employed a visual Go/No-go task that included a baseline condition with many more No-go than Go trials, allowing us to compare the No-go trials between the two conditions, thereby avoiding contamination of MEG activity from motor responses (Vidal et al., 2012), seen in studies that contrast Go with No-go trials. The stimuli and sample series of stimuli from this paradigm are presented in Figure 1. Right-frontal activity beginning as early as 200 ms was observed in adults in the inhibition condition. Similar results were also reported in a prior ERP study (Bokura et al., 2001). Relative to adults, adolescents exhibited delayed frontal responses, which were also bilateral (Vidal et al., 2012). The low percentage (7%) of Go trials in the control condition, however, raised the prospect that the observed pattern of results may be attributable to an oddball effect. To address this potential confound, a follow-up study was run which included two variants of this paradigm: the frequencies of Go to No-go trials were reversed for the experimental (67:33%) and control (33:66%) conditions.

We examined spatiotemporal MEG dynamics in 15 adolescents and 15 adults using this more classic Go/No-go task. Comparison of brain activation during No-go trials using vector event-related beamformer revealed increased recruitment of right inferior frontal gyrus in adults (BA 45, at 200–250 ms), but bilateral and delayed activation of similar brain regions in adolescents (BA 45/9, at 250–300 ms) (Figure 2). Activation in the adolescent group was also observed in the right temporal (BA 21) and inferior parietal (BA 40) regions on inhibition trials. These additional activations may reflect increased recruitment of attentional processes (Durston et al., 2002; Hampshire et al., 2010). Delayed activation of frontal cortex, in concert with additional brain activation indicative of increased attentional demands (Vidal et al., 2012), indicate that brain networks mediating inhibitory control are not yet fully mature in adolescence.

Speech production is a complex human behavior that requires the integration of articulatory control and oromotor control structures. Both non-speech mouth movements and speech-related mouth movements require the coordination of similar muscles and, presumably, similar neural pathways; however, the intentions behind the movements are different. Neuroimaging studies, primarily using fMRI, have compared the frontal cortical activation patterns during speech and non-speech oromotor tasks and report that the former was associated with more activation in left primary motor cortex, and the latter with bilateral and symmetric cortical activations (Wildgruber et al., 1996). It has been suggested that the motor cortex provides a common bilateral structural network for basic vocal motor tasks on which a left-lateralized functional network involved in the production of complex vocal behaviors, including speech and language production, is overlaid (Simonyan et al., 2009).

The spatiotemporal dynamics of speech production in humans can be studied with MEG. Thus far, MEG applications have been limited primarily to the study of language comprehension [for a review see Salmelin (2007)] as speech production generates high-magnitude artifacts that overwhelm the MEG signal. Various approaches have been used to address this challenge including the development of silent, covert, or imagined speech and language tasks (Dhond et al., 2001; Nishitani and Hari, 2002; Ihara et al., 2003; Vihla et al., 2006; Kato et al., 2007; Breier and Papanicolaou, 2008; Liljeström et al., 2009; Wheat et al., 2010; Pang et al., 2011). While silent tasks have addressed the significant artifact problem, they have unfortunately, also limited the study of the oromotor planning and motor control involved in speech and language production.

A few studies have examined overt speech and language production by using a subtraction method to remove the artifacts (Salmelin et al., 1994) or examining the neural responses prior to the onset of actual movements (Herdman et al., 2007; Carota et al., 2010). Another strategy has been to filter out high-frequency muscle-related activity and focus only on the 20 Hz beta-band motor responses to elucidate the neural regions involved in verbal and non-verbal lip, tongue (Salmelin et al., 1994), and mouth movements (Saarinen et al., 2006). As well, our group has used a spatial filtering approach to suppress artifacts from oromotor structures and have identified the sequence of neural activations involved in a simple oromotor task compared to a simple phoneme production task (Memarian et al., 2012). The successful application of this method in a control group allows us to use this approach in the examination of children with oromotor control and speech and language production difficulties.

The ability to perceive, recognize, and interpret emotional information in faces is critical for social interaction and communication. Impaired social interaction is considered to be a hallmark of ASD. Accordingly, studies designed to understand the bases for emotional face processing deficits in ASD play an important role in establishing the biological underpinnings of cognitive and social deficits in this group. Behavioral studies have consistently reported difficulties with face processing in children with ASD, and this population has also been shown to exhibit poor eye contact (Hobson and Lee, 1998) as well as a reduced tendency to look at the faces of others (Langdell, 1978). Prior work investigating neural responses to emotional faces in ASD have demonstrated activation of brain regions implicated in social cognition, including medial prefrontal and STS regions (Pierce et al., 2001; Pelphrey et al., 2007; Wang et al., 2007). ERP studies have also demonstrated that early responses to emotional faces in children with ASD were delayed and smaller (Wong et al., 2008; Batty et al., 2011). Such findings underscore the importance of MEG, as its uniquely good combination of spatial and temporal resolution permits accurate mapping of altered spatiotemporal dynamics in clinical child populations.

Using an implicit face processing paradigm in MEG (Figure 3), we investigated neural processing during the perception of happy and angry faces in adolescents with and without ASD. We chose angry instead of fearful faces, as this is a more commonly experienced emotion during childhood (Todd et al., 2012). In this study, emotional faces, adapted from the NimStim Face Stimulus Set (Tottenham et al., 2009), and scrambled images were presented concurrently on either side of a central fixation cross for 80 ms to adolescents with and without ASD; participants responded as quickly as possible indicating the side of the scrambled faces. Event-related beamforming analyses were performed on early MEG activation (60–200 ms). This revealed distinct brain activation patterns in response to happy and angry faces, which also differed between groups. We found significant group differences between the adolescents with and without ASD starting as early as 120–160 ms in source localized analyses, including greater left frontal activity in ASD, expressed particularly for angry faces.

We also examined connectivity with these data as inter-regional phase locking is a neurophysiological mechanism of communication among brain regions implicated in cognitive functions. When completed in task-based studies, these measures reflect the connectivity underlying task performance. Thus, we investigated inter-regional MEG phase synchronization during the perception of the emotional faces in this task. We found significant task-dependent increases in beta synchronization. However, beta-band inter-regional phase locking in the group of adolescents with ASD was reduced compared to controls during the presentation of angry faces. This decreased activity was seen in a distributed network involving the right fusiform gyrus and insula (Figure 4). This network also included activation in the right superior medial and dorsolateral frontal gyri; right fusiform and right supramarginal gyri; and right precuneus, left middle frontal, left insula, and left angular gyri.

Graph analysis of the connectivity in the beta-band, with the hub region of the right insula, revealed reduced task-dependent connectivity clustering, strength, and eigenvector centrality in adolescents with ASD compared to controls. Beta-band coherence has been suggested to be particularly important for long-range communication among brain regions, and these results suggest that reduced recruitment of this network, involving the limbic and frontal areas in the adolescents with ASD impacts their ability to integrate emotional information, particularly for angry faces.

In contrast, no significant differences were found in MEG responses to happy faces. These results are consistent with behavioral findings showing that high functioning ASD participants perform comparably to controls on tasks involving happy faces, but experience pronounced difficulties with processing angry faces (Kuusikko et al., 2009; Rump et al., 2009; Farran et al., 2011).

These findings are also in keeping with a broader literature reporting differences in functional connectivity between participants with and without ASD (e.g., Just et al., 2012; Khan et al., 2013) and suggest that these reductions in long-range task-dependent connectivity may be a factor in the social cognitive difficulties common in ASD.

Individuals with ASD often experience problems with inhibitory control, and this likely contributes to emotional outbursts and inappropriate social behavior commonly seen in this population. Inhibitory control has a protracted maturational course (Luna et al., 2010; Vidal et al., 2012). As such, investigation of these processes in adolescents with ASD is important, as this is a period when youths are adapting to increasing social demands. Moreover, it has been proposed that deficits in inhibitory control worsen with increasing age in ASD (Luna et al., 2004).

Several previous studies have investigated the neural underpinnings of inhibition difficulties ASD. For example in fMRI, adults with ASD have been shown to express greater left frontal activity (Schmitz et al., 2006), reduced anterior cingulate activity (Kana et al., 2007), together with atypical timing in the recruitment of frontal cortex. Since inhibitory control largely depends on the slowly maturing frontal lobes, the results from these studies in adults with ASD may not be generalizable to a younger population. Few neuroimaging investigations have been carried out during adolescence in ASD, when adult patterns of neural activity supporting inhibitory control are being established.

To elucidate the maturation of inhibitory control in ASD, we recorded MEG while adolescents with ASD and age- and sex-matched controls performed a Go/No-go task (Vara et al., 2014) (see Figure 1). Participants were instructed to respond to Go stimuli and withhold responses to No-go stimuli (as described above). During inhibitory control, adolescents with ASD primarily recruited frontal regions, whereas typically developing controls showed bilateral frontal activation together with activation in temporal and parietal regions (Figure 5).

Results from this study suggest that inhibitory control is atypical in adolescents with ASD, whose false alarm rate was also higher than the control group, demonstrating poorer inhibitory control behaviorally. Also, patterns of the inhibition-related MEG activity in the adolescents with ASD exhibited different spatiotemporal neural processing than their matched controls. More extensive frontal activity was found in adolescents with ASD, which may be due to reduced long-range connectivity as well as increased short-range connectivity, or local over-connectivity (Belmonte et al., 2004).

While ASD is characterized by deficits, most notably in the realm of social cognition, children with ASD have a variety of speech and language difficulties, the neurobiology of which is not well understood. We used MEG to examine a group of children with ASD as they completed several oromotor speech tasks to explore the neural regions implicated in the control and execution of these functions.

A group of children with ASD and a group of age- and sex-matched controls completed three increasingly complex oromotor and speech tasks in the MEG. These tasks included a simple motor task (open and close the mouth), a simple speech task (speak the phoneme/pa/), and a simple speech sequencing task (speak the phonemic sequence/pa-da-ka/). Beamforming analyses identified neural sources of interest which were then interrogated to generate the time courses of activation for the ASD and control groups. The peaks of activation were identified in each participant, and the latency and magnitude of prominent peaks were submitted to statistical testing.

Figures 6 and 7 show the time courses where significant differences were observed in magnitude and latency, respectively, between the ASD and control groups, in three frontal areas and one temporal region (Pang et al., 2013). In the simple oromotor task, the children with ASD showed greater activation in right hemisphere motor control areas (BA 4) and the middle frontal gyrus, a motor planning area (BA 9), together with delayed activation in right hemisphere motor planning areas (BA 6). In the phoneme production task, the children with ASD showed delayed activation in left frontal language control areas (BA 47). In the sequencing task, the ASD group showed greater activation in right hemisphere motor control (BA 4) and right hemisphere sequencing (BA 22) areas. As well, for the sequencing task, the children with ASD showed higher and delayed activations in the left insula (BA 13), an area known to be involved in sensorimotor integration (Hesling et al., 2005).

These results fit with reports of difficulties with oromotor control and challenges with more complex speech patterns in children with ASD. In summary, we observed atypical neural activation in frontal areas associated with motor control, speech production, and speech sequencing. These were characterized by an unusual pattern of laterality (mostly in the right hemisphere), with higher magnitude and delayed activations. It is likely that these neurophysiological abnormalities underlie some of the speech/language difficulties observed in this cohort and may contribute to the speech and language deficits frequently experienced in children with ASD.