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. Author manuscript; available in PMC: 2023 Jan 1.
Published in final edited form as: J Autism Dev Disord. 2021 Feb 25;52(1):103–112. doi: 10.1007/s10803-021-04926-9

Peak alpha frequency and thalamic structure in children with typical development and autism spectrum disorder

Heather L Green a,*, Marissa Dipiero a, Simon Koppers e, Jeffrey I Berman a,b, Luke Bloy a, Song Liu a, Emma McBride a, Matthew Ku a, Lisa Blaskey a,b,c, Emily Kuschner a,c,d, Megan Airey a, Mina Kim a, Kimberly Konka a, Timothy PL Roberts a,b, J Christopher Edgar a,b
PMCID: PMC8384980  NIHMSID: NIHMS1696777  PMID: 33629214

Abstract

Associations between age, resting-state (RS) peak-alpha-frequency (PAF=frequency showing largest amplitude alpha activity), and thalamic volume (thalamus thought to modulate alpha activity) were examined to understand differences in RS alpha activity between ASD children and typically-developing children (TDC) noted in prior studies. RS MEG and structural-MRI data were obtained from 51 ASD and 70 TDC 6- to 18-year-old males. PAF and thalamic volume maturation were observed in TDC but not ASD. Although PAF was associated with right thalamic volume in TDC (R2=0.12, p=0.01) but not ASD (R2=0.01, p =0.35), this group difference was not large enough to reach significance. Findings thus showed unusual maturation of brain function and structure in ASD as well as an across-group thalamic contribution to alpha rhythms.

Keywords: autism spectrum disorder, alpha, resting-state, magnetoencephalography, magnetic resonance imaging, maturation, thalamic volume

The alpha rhythm (8 to 12 Hz in adults) is the dominant brain oscillation in an eyes-closed resting state (Berger, 1929; Edgar et al., 2015; Haegens et al., 2014; Hari and Salmelin, 1997), with resting-state (RS) alpha activity most prominent over parietal-occipital regions (Ciulla et al., 1999; Edgar et al., 2015; Hari and Salmelin, 1997; E. Niedermeyer, 1999; Salmelin and Hari, 1994).1 The alpha frequency showing the greatest alpha power at rest, often labeled the peak alpha frequency (PAF), increases as a function of age in typically developing children (TDC) (Alvarez Amador et al., 1989; Chiang et al., 2011; Cragg et al., 2011; Dustman et al., 1999; Epstein, 1980; Gibbs and Knott, 1949; J R Hughes, 1987; John et al., 1980; Klimesch, 1999; Miskovic et al., 2015; E. Niedermeyer, 1993; Somsen et al., 1997; Stroganova et al., 1999). Studies have shown maturational differences in RS alpha activity in children with autism spectrum disorder (ASD) and TDC (Dickinson et al., 2018; Edgar et al., 2019). As an example, RS PAF does not increase as a function of age in children with ASD (Dickinson et al., 2018; Edgar et al., 2019; Lefebvre et al., 2018).

The study of RS alpha activity in ASD is of clinical interest given that the alpha rhythm plays a fundamental role in cognition. For example, PAF is associated with working memory and speed of information processing (Klimesch, 1999; 1997; Klimesch et al., 1996), and alpha rhythms provide the timing for communication within and between brain regions (Klimesch, 2012; Klimesch et al., 2007; Palva and Palva, 2007; Port et al., 2019; Berman et al., 2015). With respect to considering RS alpha activity as a potential at-risk diagnostic marker, it is of note that PAF is one of the most heritable brain measures (Van Baal et al., 1996; van Beijsterveldt and van Baal, 2002); in a large sample of adolescent twins, Smit et al. (2005) obtained a heritability estimate of 0.81 for PAF, with all non-genetic variance attributed to measurement unreliability rather than unique environmental factors.

This study sought to better understand RS alpha activity in TDC and ASD by examining associations between brain structure and RS PAF. Several brain structures are hypothesized to be involved in the generation of RS alpha oscillations, with thalamic contributions to the cortical RS alpha rhythm thought to be prominent (for review see Valdes-Hernandez et al., 2010). For example, associations between thalamic activation and RS alpha activity are supported by animal and human studies (Danos et al., 2001; Edgar et al., 2019; Feige et al., 2005; Goldman et al., 2002; S. W. Hughes et al., 2004; S. W. Hughes et al., 2011; Lindgren et al., 1999; Lopes Da Silva et al., 1973; Lorincz et al., 2009; Nicolelis and Fanselow, 2002; Sadato et al., 1998; Schmid et al., 2012; Schreckenberger et al., 2004; Valdes et al., 1992; Valdes-Hernandez et al., 2010), including human studies using simultaneous EEG and fMRI (Feige et al., 2005; Goldman et al., 2002) or PET (Danos et al., 2001; Larson et al., 1998; Lindgren et al., 1999; Schreckenberger et al., 2004; Sadato et al., 1998).

In the present study, cross-sectional analyses were expected to indicate abnormal development of both RS PAF and thalamic volume in ASD. It was also hypothesized that weaker associations between thalamic volume and PAF would be observed in ASD versus TDC, given atypical thalamic structure in ASD (Hardan et al., 2006; Hardan et al., 2008; Lin et al., 2015; Nair et al., 2013; Schuetze et al., 2016; Tsatsanis et al., 2003), as well as given a previous finding of atypical associations between thalamic volume and alpha activity in ASD (Edgar et al., 2015).

2. Methods

This study was approved by the local Institutional Review Board and all families gave written informed consent. When competent to do so, children over 7 years of age gave verbal assent to participate.

2.1. Subjects

The present study reports on the subset of participants in Edgar et al. (2019) who had evaluable RS MEG as well as evaluable T1-weighted MRI data. Given sex differences in RS alpha activity in children (Clarke et al., 2001; Edgar et al., 2019; Gasser et al., 1988; Matsuura et al., 1985; Matousek and Petersén, 1973; Petersén and Eeg-Olofsson, 1971), and given a small number of female participants, only male subjects were included. In addition, due to extensive research establishing structural brain differences between left- and right-handed individuals (e.g., Galaburda et al., 1978), as well as a relatively small number of left-handed individuals (TDC = 7; ASD = 5), left-handed participants were excluded. Of the sample of participants included in Edgar et al. (2019), 50% of the TDC and 43% of the ASD children met the above criteria (TDC = 51; ASD = 70)2. Across all the studies represented in this dataset, ~65% of the participants had evaluable eyes-closed resting-state MEG data.

Detailed participant inclusion and exclusion criteria are outlined in Edgar et al. (2019). In brief, TDC and ASD participants: 1) were between the ages of 6 and 17 years, 2) had no history of traumatic brain injury or other significant neurological or other medical abnormality, 3) had no active psychosis, 4) had no magnetic resonance imaging (MRI) contraindications, 5) had no sensory impairments (somatosensory, hearing, visual), and 6) were native English speakers. TDC did not have a history of developmental delay, psychiatric or neurodevelopmental disorders, or a first-degree family member with ASD. The participants with ASD had a prior diagnosis, made by an expert clinician in the Children’s Hospital of Philadelphia Regional Autism Center or by community providers according to DSM criteria. Given the extensive clinical evaluations upon which original ASD diagnosis was made, an abbreviated diagnostic battery confirmed the original diagnosis in the ASD group and ruled out ASD in the TDC group. ASD diagnosis was confirmed by exceeding established cut-offs on the Autism Diagnostic Observation Schedule (ADOS/ADOS-2) and parent report on the Social Communication Questionnaire (SCQ, Lord et al., 2000; Lord et al., 1994). In combination with the ADOS, exceeding empirically established cut-offs by parent report on both the Social Responsiveness Scale and Autism Spectrum Rating Scale also led to ASD diagnostic confirmation if the SCQ did not corroborate diagnosis. Dimensional symptom severity indices were obtained by parent report on the Social Responsiveness Scale (SRS-2, Constantino and Gruber, 2012) and from the ADOS Calibrated Severity Score metric (Gotham et al., 2009).

To rule out global cognitive delay, all subjects recruited had to score at or above the second percentile (SS > 70) on at least one index of verbal or nonverbal intellectual functioning from the Wechsler Intelligence Scale for Children––fourth or fifth editions (WISC-IV/WISC-V; Wechsler, 2003; 2014), the Wechsler Abbreviated Scale of Intelligence‐2nd Edition (WASI; Wechsler, 2011), or the Differential Ability Scales––Second Edition (DAS; Elliot, 2007).

As detailed in Edgar et al. (2019), RS data were obtained from children participating in different studies. For some of the studies, the children with ASD withheld stimulant medication at least 24 hours prior to testing. In the present study sample, 25% of the children with ASD were prescribed stimulant medications, with 15.8% of these children withholding medication prior to testing. In addition to stimulant medication, 15.8% of children were taking some other class of psychotropic medication, with 5.3% taking nonstimulant medication for ADHD (e.g., alpha agonists), 6.6% taking SSRI’s, and 6.6% taking atypical antipsychotics.

2.2. MEG and MRI data acquisition

RS eyes-closed data were collected using a 275-channel MEG system (VSM MedTech Inc., Coquitlam, BC). Electro-oculogram (EOG) (vertical EOG on the upper and lower left sides) and electrocardiogram (ECG) were also obtained. During the RS recording the participants’ head position was monitored using head position indicator (HPI) coils attached to the scalp. After applying a band-pass filter (0.03–150Hz), EOG and MEG signals were digitized at 600Hz or 1,200Hz (depending on study) with 3rd-order gradiometer environmental noise reduction. Participants were instructed to rest with their “eyes gently closed, like when you are sleeping” during a 2-minute or 5-minute RS exam, with length of recording depending on study. During the recording the EOG channel was monitored and if participants opened their eyes they were reminded to close them. The majority of children were scanned in a supine position.

After the MEG session, structural magnetic resonance imaging using a 3T Siemens Verio scanner with 32 channel head coil provided T1-weighted, 3-D anatomical images (voxel size 0.8 × 0.8 × 0.9 mm3). To coregister individuals’ MEG data to age-matched MRI templates, three anatomical landmarks (nasion and right and left preauriculars) as well as an additional 200+ points on the scalp and face were digitized for each subject using the Probe Position Identification (PPI) System (Polhemus, Colchester, VT). Left and right thalamic volume measures were obtained from FreeSurfer software version 5 (Fischl, 2012) with visual inspection of each parcellation map indicating that the left and right thalamic masks were correctly placed.

2.3. Assessment of PAF

MEG data were processed using BESA Research 6.1 (MEGIS Software GmbH, Grafelfing, Germany). Data were analyzed blind to group status. A two-step process was employed for removal of muscle and movement artifact. First, participants’ raw EOG data were visually examined and MEG data contaminated by blinks, saccades, or other significant EOG activity were removed. Second, participants’ MEG data were visually inspected for muscle-related activity (focusing especially on data from sensors close to the temporalis muscles), and data containing muscle activity were removed. To be included, participants were required to have at least 80 seconds of artifact-free data. In the present study, the TDC participants had a mean of 250.31 seconds (SD=56.09) and the ASD participants a mean of 211.54 seconds (SD=77.21) of evaluable data (data often lost in subjects with the shorter 2-minute exam). Although groups differed slightly on the amount of evaluable data (t(119) = 3.28, p = 0.001), analyses reported in Edgar et al. (2019) showed that such differences in the amount of artifact-free data are unlikely to impact PAF estimates.

To decompose the 275 channel data into a smaller number of measures, a model with 15 regional sources was applied to project each individual’s raw MEG surface data into brain source space where the waveforms are the modeled source activities (given two orthogonal dipoles per regional source, there are two time series at each location). These regional sources are not intended to correspond to precise neuroanatomical structures but rather to represent neural activity at coarsely defined regions as well as to provide measures of brain activity with better signal separation and with a greater signal-to-noise ratio than would be afforded at the sensor level (Scherg and Ebersole, 1993; Scherg and Berg, 1996). The locations of the regional sources in the model are such that there is an approximately equal distance between sources (3cm), helping to separate signals originating from different brain regions (see Figure 1 in Edgar et al., 2019).

Figure 1.

Figure 1.

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In TDC (blue) but not ASD (red), positive associations were observed between age and PAF (top panel), age and left thalamic volume, (middle panel), and age and right thalamic volume (lower panel). Age is shown on the x axis and PAF or thalamic volume on the y axis. *p < 0.05; **p < 0.01

To transform MEG data from the time domain into the frequency domain, a Fast Fourier Transform (FFT) was applied to artifact-free 3.41 second epochs of continuous data for each of the two orthogonally oriented time series at each regional source. The mean power spectra for the two orthogonally oriented time series at each regional source were summed to yield the power at a given frequency at that source. The PAF for each participant was determined via identifying the source with the most alpha power within a 7–13 Hz range from the 15 power spectra. Chi-square analyses showed no group differences in the location of the PAF. In the vast majority of participants PAF was observed at midline Parietal (78%), midline Occipital (12%) and midline Central (7%) sources, with the remaining participants showing a PAF at other lateral posterior locations (3.3%).

2.4. Statistical Analyses

Group differences in demographic measures were assessed via t-tests. Pearson’s correlations assessed associations between age and brain measures of interest for each group (i.e., PAF, left thalamic volume, right thalamic volume), and with a Fisher’s r-to-z transformation testing group differences in correlation values (http://vassarstats.net/rdiff.html). This approach was also used to assess associations, as well as group differences, between left and right thalamic volume and PAF. A t-test examined group differences in PAF, and an ANOVA, with hemisphere as a repeated measure, examined group differences in thalamic volume. Finally, exploratory analyses explored associations between thalamic volume and full scale intelligence quotient (FSIQ), verbal and nonverbal IQ, processing speed, and SRS scores (analyses reporting associations with PAF and clinical measures in a larger overlapping sample are provided in Edgar et al. (2019) and see discussion this paper). Given group differences in FSIQ (see Table 1), PAF and thalamic analyses were re-run excluding participants with FSIQ scores < 90 (removing 1 TDC and 12 ASD). The findings reported below did not change when these participants were excluded; as such, all following results are reported using the full sample.

Table 1.

Demographics

TDC Mean (SD) ASD Mean (SD) p-value
Age (years) 12.9 (2.9) Range: (6.4–18.2) 12.2 (2.6) Range: (7.5–17.6) .19
Estimated Full Scale IQ 112 (14) 105 (15) .01*
Verbal IQ 109 (14) 103 (15) .01*
Nonverbal IQ 110 (14) 105 (14) .07
SRS T-Score 42 (4) 74 (14) <.001*
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*

significant group differences

3. Results

3.1. Demographics

Table 1 provides demographic information for each group.

As shown in Table 1, the distribution of age in the two groups was similar (t(119) = 1.31, p > 0.05). As expected, FSIQ was higher in TDC than ASD (t (118) = 2.63, p = 0.01).

3.2. Age associations with PAF and thalamic volume

Figure 1 scatterplots show age associations with PAF and thalamic volume for each group. A significant association between age and PAF was observed for TDC ( = 0.41, R2 = 0.17, F(1, 49) = 10.08, p < 0.01) but not ASD ( = 0.06, R2 = 0.00, F(1, 68) = 0.28, p > 0.05), a difference that reached significance (z = 1.99; p = 0.05). An association between age and left thalamic volume was also observed in TDC ( = 0.62, R2 = 0.38, F(1, 49) = 30.38, p < 0.01) but not ASD ( = 0.20, R2 = 0.04, F(1, 68) = 2.83, p > 0.05), with this group difference also reaching significance (z = 2.76, p = 0.001). This same pattern was observed for age and right thalamic volume, although here the group difference in correlations was not large enough to reach significance (TDC: = 0.38, R2 = 0.15, F(1, 49) = 8.45, p < 0.01; ASD: = 0.12, R2 = 0.02, F(1, 68) = 1.03, p > 0.05; group difference: z = 1.48, p > 0.05). No group differences in PAF or thalamic volume were observed (ps > 0.05).

3.3. Associations between thalamic volume and PAF

Figure 2 scatterplots show associations between PAF and left and right thalamic volume for each group. No associations were observed between PAF and left thalamic volume in either group (ps > 0.05). Although Figure 2 shows a significant association between PAF and right thalamic volume in TDC ( = 0.35, R2 = 0.12, F(1,49) = 6.89, p = 0.01) but not ASD ( = 0.11, R2 = 0.01, F(1, 68) = 0.89, p > 0.05), this group difference was not statistically significant (z = 1.35, p > 0.05).

Figure 2.

Figure 2.

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Scatterplots showing associations between PAF (y axis) and thalamic volume (x axis) for TDC (blue) and ASD (red). Left: no associations were observed between PAF and left thalamic volume in TDC or ASD. Right: a positive association was observed between PAF and right thalamic volume in TDC but not ASD. *p < 0.05; **p < 0.01

3.4. Associations between clinical and imaging measures

No associations were observed between thalamic volume (left or right hemisphere) and FSIQ, nonverbal IQ, verbal IQ, processing speed, or SRS (ps > 0.05).

4. Discussion

Findings showed unusual maturation of brain function and structure in ASD as well as an across-group thalamic contribution to alpha rhythms. In particular, and as hypothesized, unusual maturation of RS PAF and the thalamus was observed in children with ASD. In addition, analyses supported the hypothesis of a contribution of thalamic volume to PAF, although only weakly supporting the hypothesis that alpha and thalamic volume associations were abnormal in children with ASD (see Figure 2, right thalamic volume).

With respect to brain function, findings from a non-overlapping sample from our laboratory as well as findings from another laboratory have reported abnormal age and PAF associations in ASD as compared to than TDC (Edgar et al., 2015; Dickinson et al., 2018). For example, in Edgar et al. (2015), age was associated with PAF (obtained from a Calcarine region of interest) in TDC but not ASD. With respect to thalamic volume, in the present study, age was associated with increased thalamic volume in both hemispheres in TDC but not ASD, a group difference that reached significance for left thalamic volume. In Edgar et al. (2015), abnormal maturation of the thalamus in ASD was also suggested. Present and previous findings thus indicate group differences in PAF and thalamus maturation.

Regarding brain function and structure associations, although the Figure 2 right thalamic volume and PAF scatterplots suggest an association only in TDC, the samples were not large enough to detect differences in the PAF and right thalamic associations between TDC (R2 = 0.12, p = 0.01) and ASD (R2 = 0.01, p = 0.35). A similar pattern of findings in a different cohort was observed in Edgar et al. (2015), with right thalamic volume and alpha power associations observed in TDC (R2 = 0.14, p < 0.05) and not ASD (R2 = 0.00, p > 0.05), but with the group correlations again not different enough to reach statistical significance. Present and past findings thus suggest a lack of a PAF and thalamic association in ASD, but also indicate that this effect is medium to weak and so requires larger samples (at least 100 per group) to statistically detect.

Putting aside group differences, present findings provide support for the theory that the thalamus is a key component of a RS alpha network (Edgar et al., 2015; Feige et al., 2005; Goldman et al., 2002; S. W. Hughes et al., 2004; S. W. Hughes et al., 2011; Lindgren et al., 1999; Lopes Da Silva et al., 1973; Lorincz et al., 2009; Nicolelis and Fanselow, 2002; Sadato et al., 1998; Schmid et al., 2012; Schreckenberger et al., 2004; Danos et al., 2001; Valdés et al., 1992; Whitford et al., 2007). As noted in the Introduction, findings from other non-invasive brain imaging studies support this finding. As example, using simultaneous fMRI and EEG, Goldman et al. (2002) found that RS alpha was correlated with the thalamic bold signal. Similarly, research using PET and EEG have shown a negative correlation between thalamic metabolism and alpha power (Danos et al., 2001; Lindgren et al., 1999; Schreckenberger et al., 2004). An unanswered question is why, in this study, were the thalamic and PAF associations specific to the right thalamus? A priori, to our knowledge, there is no reason to suspect a lateralized finding. Future research is needed to replicate the present right but not left association.

Although the present study focused on thalamic volume, there is growing evidence for a contribution of posterior white-matter pathways to RS alpha (Valdes-Hernandez et al., 2010; Jann et al., 2012). As discussed in Edgar et al. (2015), other mechanisms may also contribute to RS alpha activity. These include a role for cortical inhibitory interneurons in maintaining alpha oscillations (Lorincz et al., 2009), a role for thalamic “pacemaker” neurons (e.g., see Anderson and Sears, 1964; Jahnsen and Llinás, 1984; Steriade and Deschenes, 1984), and an influence of the mGluR1a subtype of the metabotropic glutamate receptor located postsynaptically to corticothalamic fibers (S. W. Hughes et al., 2002). Studies investigating the above in individuals with ASD report abnormalities in all the above systems including an imbalance of excitatory (e.g., glutamatergic) and inhibitory (e.g., GABAergic) activity in inhibitory interneuron and pyramidal cell cortical networks (Casanova et al., 2002; Levitt et al., 2004), as well as studies reporting abnormal thalamic structure and connectivity in ASD (Hardan et al., 2006; Hardan et al., 2008; Nair et al., 2013; Tsatsanis et al., 2003). Studies examining associations between other brain measures (such as diffusion measures) and PAF are needed.

The present finding of no group difference in PAF or thalamic volume might seem surprising. However, as noted in Edgar (2019), control and case group differences in rate of maturation often constrain when brain markers can distinguish the two groups. As an example, the large sample findings from Edgar et al. (2019, an overlapping sample) showed increased PAF in young children with ASD compared to TDC (6 to 10 years) with no significant group differences observed in older children (10 to 18 years). Given that the average age of participants in this sample was ~12 years old, group differences in PAF were not expected. Similar to the PAF findings, the thalamic volume findings suggested unusual developmental acceleration followed by a plateau in ASD versus TDC (see Figure 2), with such group maturation differences potentially also accounting for no group differences in thalamic volume. Failure to observe group differences in thalamic structure in the present study, however, could also be due to the fact that our non-invasive in-vivo measures of brain structure (T1 image) are limited to volumetrics and that structural brain differences in young children with ASD likely include microstructural differences in gray-matter architecture, not easily resolved using structural MRI (Nair et al., 2013).

With respect to brain maturation in ASD, there is growing evidence to support the hypothesis of an abnormal rate of brain development (structure and function) in children with ASD. As some examples different from those already cited, abnormal brain maturation in individuals with ASD has been observed in studies examining white-matter pathways (Ouyang et al., 2016) as well as cortical brain volume and surface area (Aylward et al., 2002; Courchesne et al., 2001; Gage et al., 2009; Hazlett et al., 2005; Hazlett et al., 2011; Hazlett et al., 2017; Knaus et al., 2009; Redcay and Courchesne, 2005; Rojas et al., 2005; Smith et al., 2016). Present findings provide additional evidence for this hypothesis.

Regarding associations with clinical measures, in a larger overlapping sample, Edgar et al. (2019) observed a positive association between processing speed and PAF in younger children with TDC (N = 121) but not ASD (N = 183). Given no associations with PAF and verbal IQ or non-verbal IQ, the association with processing speed in TDC appeared to be specific to tests assessing the ability to complete tasks quickly versus tasks that rely on fluid or crystallized intelligence (Grandy et al., 2013; Klimesch, 1997). The loss of an association between PAF and processing speed in children with ASD was hypothesized to perhaps account for the slower performance observed on processing speed tasks in some individuals with ASD (Mayes and Calhoun, 2003; 2008; Nydén et al., 2001; Oliveras-Rentas et al., 2012). Additionally, a significant positive association between PAF and nonverbal IQ was observed for older children with ASD only (p < .01; Edgar et al., 2019). Edgar et al. (2019) hypothesized that the atypically high PAF measures observed in young children with ASD suggest that alpha neural circuits mature unusually early in ASD. Given that alpha rhythms are thought to provide the basic structure for cognitive processes (Dickinson et al., 2018; Haegens et al., 2014; Klimesch, 1999; Mierau et al., 2017), premature development of these circuits may result in a brain that is not optimized for later acquisition of higher-order cognitive skills including language and social skills.

In the present study, no associations between thalamic volume and clinical scores were observed. Although failure to observe associations between thalamic structure and clinical measures could be due to sample size, the scatterplots (not shown) provided no indication of ‘missed’ associations. Failure to observe thalamic volume and clinical measure associations could again be due to the fact that our non-invasive in-vivo measures of brain structure (T1 image) are limited to volumetrics.

A few study limitations are of note. First, the cohort was limited to right-handed males and to older children with ASD. Second, this sample was biased towards higher-functioning children with ASD. Future studies should include minimally verbal/nonverbal children with ASD to determine if study results can be generalized to children with ASD at different ability levels. In order to accomplish such research, however, it will be necessary to develop strategies to help children of all language levels understand task instructions and remain still with their eyes closed during the resting-state task.

In conclusion, findings demonstrated abnormal brain maturation in children with ASD. Study findings also demonstrated a contribution of thalamic volume to RS alpha activity in children. Longitudinal brain imaging studies recruiting moderately large samples of young children (e.g., 3 to 7 years old) and obtaining structural as well as diffusion MR are needed to better understand PAF and thalamic volume maturation in TDC and ASD.

Acknowledgments

The authors gratefully acknowledge the help and support of John Dell, Peter Lam, Rachel Golembski, Na’Keisha Robinson, Erin Huppman, and Shivani Desai. This work was supported in part by NIH grant R01DC008871 (TR), R01MH107506 (JCE), R21MH098204 (JCE), R21 NS090192 (JCE), NICHD grant R01HD093776 (JCE), and the Clinical Translational Core, Biostatistics & Bioinformatics Core and the Neuroimaging Core of the Intellectual and Developmental Disabilities Research Center funded by NICHD grant 5U54HD086984 (to RTS, TR and MP; principal investigator, M. Robinson, PhD). Dr. Roberts gratefully acknowledges the Oberkircher Family for the Oberkircher Family Chair in Pediatric Radiology at CHOP. Simon Koppers was funded by the International Research Training Group 2150 of the German Research Foundation (DFG).

Footnotes

Publisher's Disclaimer: This Author Accepted Manuscript is a PDF file of an unedited peer-reviewed manuscript that has been accepted for publication but has not been copyedited or corrected. The official version of record that is published in the journal is kept up to date and so may therefore differ from this version.

Publisher's Disclaimer: DISCLAIMER: Dr. Berman reports a consultancy with McGowan Associates. Dr. Roberts declares his position on the advisory boards of 1) CTF MEG, 2) Ricoh, 3) Spago Nano Medical and 4) Prism Clinical Imaging. Dr. Roberts and Dr. Edgar also declare intellectual property relating to the potential use of electrophysiological markers for treatment planning in clinical ASD.

1

Individuals on the autism spectrum, their parents, and professionals in the field differ regarding the use of person-first (e.g., children with ASD) or identity first (e.g., autistic child) language. With respect for divided opinions, both approaches to terminology are used (Kenny, et al., 2016).

2

In the current sample, older children were more likely to have evaluable RS data. In particular, 76% of children ≥ 10 years old had evaluable RS data. The somewhat high rate of children with non-evaluable data is primarily due to the fact that in many of our early studies we collected only 2 minutes of eye-closed RS data. Increasing the length of the eyes-closed task to 5+ minutes has resulted in much higher success rates.

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