Autism is a brain disorder that affects how people interact with others. It occupies a spectrum, with severe autism at one end and high-functioning autism at the other. People with severe autism usually have intellectual impairments and little spoken language. Those with high-functioning autism have average or above average IQ, but struggle with more subtle aspects of communication, such as body language. As well as social difficulties, many individuals with autism show repetitive behaviors and have narrow interests.

The brains of people with autism process information differently to those of people without autism. The brain as a whole shows less coordinated activity in autism, for example. But whether individual brain regions themselves also work differently in autism is unclear. Watanabe et al. set out to answer this question by using a brain scanner to compare the resting brain activity of high-functioning people with autism to that of people without autism.

In both groups, networks of brain regions increased and decreased their activity in predictable patterns. But in individuals with autism, sensory areas of the brain showed more random activity than in individuals without autism. The most random activity occurred in those with the most severe autism. This suggests that the brains of people with autism cannot hold onto and process sensory input for as long as those of neurotypical people. By contrast, a brain region called the caudate showed the opposite pattern, being more predictable in individuals with autism. The most predictable caudate activity occurred in those individuals with the most inflexible, repetitive behaviors. These differences in this neural randomness appear to result from changes in the structure of the individual brain regions.

The findings of Watanabe et al. suggest that changes in the structure and activity of small brain regions give rise to complex symptoms in autism. If these differences also exist in young children, they could help doctors diagnose autism earlier. Future studies should investigate whether the differences in brain activity cause the symptoms of autism. If so, it may be possible to treat the symptoms by changing brain activity, for example, by applying magnetic stimulation to the scalp.

How long neural information is likely to be stored in a neural area is a fundamental functional property of the local brain region (Chen et al., 2015; Hasson et al., 2015; Himberger et al., 2018), and has been quantified as temporal receptive window (Hasson et al., 2008; Chaudhuri et al., 2015; Honey et al., 2012; Lerner et al., 2011; Stephens et al., 2013; Yeshurun et al., 2017), temporal receptive field (Cavanagh et al., 2016), or intrinsic neural timescale (Murray et al., 2014; Kiebel et al., 2008; Gollo et al., 2015; Cocchi et al., 2016). Computational studies propose that such neural timescales should show a rostrocaudal gradient in the brains (Kiebel et al., 2008) and that densely interconnected central regions, such as prefrontal and parietal areas, should have slower timescales compared to peripheral sensory areas (Chaudhuri et al., 2015; Gollo et al., 2015). These proposals are supported by empirical observations. Human neuroimaging and macaque electrophysiology studies show that neural timescales tend to be longer in frontal and parietal areas compared to sensory-related regions (Honey et al., 2012; Stephens et al., 2013; Murray et al., 2014; Ogawa and Komatsu, 2010), and suggested that such a prolonged neural timescale enables these higher-order brain cortices to integrate diverse information for robust sensory perception (Hasson et al., 2008; Lerner et al., 2011; Stephens et al., 2013; Yeshurun et al., 2017; Ogawa and Komatsu, 2010; Gauthier et al., 2012), stable memory processing (Hasson et al., 2015; Murray et al., 2014; Bernacchia et al., 2011), and accurate decision making (Cavanagh et al., 2016; Runyan et al., 2017). A recent brain stimulation study directly demonstrates that such a hierarchy of the intrinsic timescale is closely related to functional interactions between lower and higher brain regions (Cocchi et al., 2016). The heterogeneity of the neural timescale is considered to be a basis of the functional hierarchy in the brain (Chen et al., 2015; Hasson et al., 2015; Himberger et al., 2018; Chaudhuri et al., 2015; Gollo et al., 2015; Cocchi et al., 2016; Kukushkin and Carew, 2017; Friston and Kiebel, 2009).

Given such fundamental roles of local neural dynamics in highly-organised information processing in the brain, we hypothesised that atypical intrinsic neural timescales should be observed in autism. In fact, the core symptoms of this prevalent neurodevelopmental disorder — challenges in socio-communicational skills and repetitive, restricted behaviours (RRB) — are often linked to atypical information processing (Happé and Frith, 2006; Palmer et al., 2017; Booth and Happé, 2018): weak coherence theory suggests that autism spectral disorder (ASD) is associated with impairments of the global integration of diverse information and over-enhancement of individual inputs (Happé and Frith, 2006; Booth and Happé, 2018); a recent Bayesian view also attributes autism to overweighing of local sensory information (Palmer et al., 2017; Lawson et al., 2017). These theories suggest that measures of local neural dynamics — such as intrinsic neural timescales — should be linked to the symptomatology of individuals with ASD.

Despite such theoretical implications, no study has investigated intrinsic timescales of neural signals in autism. Here, we aimed at exploring this local neural property in the brains of high-functioning individuals with ASD and examining its associations with the core symptoms of this condition.

First, we introduced a measurement of the intrinsic neural timescales for resting-state fMRI (rsfMRI) signals, and validated it using simultaneous EEG-fMRI data (Deligianni et al., 2016; Deligianni et al., 2014). We then applied the index to a rsfMRI dataset recorded from high-functioning adults with ASD and its demographically-matched controls, and searched for brain regions whose atypical neural timescales were associated with the ASD symptoms. Next, using a longitudinal rsfMRI dataset collected from adolescent children, we examined developmental trajectories of the local neural dynamics of these regions. Finally, we explored neuroanatomical bases of the intrinsic neural timescales. The reproducibility of our findings was tested using two independent MRI datasets.

Timescales of resting-state fMRI signals

Based on previous macaque studies (Chaudhuri et al., 2015; Cavanagh et al., 2016; Murray et al., 2014; Bernacchia et al., 2011), we calculated an intrinsic neural timescale by assessing the magnitude of autocorrelation of the resting-state brain activity. First, we estimated the sum of autocorrelation function (ACF) values in the initial positive period of the ACF (i.e., the sum of the area of the green bars in Figure 1a). The upper limit of this period was set at the discrete time lag value just before the one where the ACF became non-positive for the first time. To adjust for differences in the temporal resolution of the neural data, we then multiplied the obtained sum of ACF values by the repetition time (TR) of the fMRI recording. This product was used as an index for intrinsic neural timescales.

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This definition was validated by comparing the fMRI-based timescale index to that based on neural data with a higher temporal resolution (here, simultaneously recorded EEG data (Deligianni et al., 2016; Deligianni et al., 2014); Figure 1—figure supplement 1). The fMRI-based timescales were strongly correlated with those based on the gamma-band EEG signals (adjusted R² = 0.71; Figure 1b; see Figure 1—figure supplement 2 for other EEG bands). In addition, when the EEG signals were convolved with the hemodynamic response function (HRF), the intrinsic timescales based on the HRF-convolved EEG signals became closer to those based on fMRI signals (adjusted R² = 0.61; Figure 1c).

Intrinsic neural timescales in adults with ASD

Based on this formulation, we compared the intrinsic neural timescale between 25 high-functioning adults with ASD and 26 age-/sex-/IQ-matched typically developing (TD) individuals (Table 1) (Di Martino et al., 2014).

Table 1

	Typically developing (TD)	Autism spectrum disorder (ASD)	P value
Number of participants	26	25	-
Age	25.3 ± 6.3 (18.1–39.4)	27.3 ± 7.9 (18.4–50)	0.4
Sex	Male	Male	-
Laterality	Right-handed	Right-handed	-
Full IQ	112.6 ± 12.0 (89–131)	109.4 ± 13.6 (90–132)	0.4
Verbal IQ	112.1 ± 12.1 (88–130)	106.6 ± 13.8 (83–130)	0.2
Performance IQ	110.3 ± 10.4 (90–129)	110.9 ± 15.9 (83–133)	0.9
ADOS Social	-	4.3 ± 1.4 (1–8)	-
ADOS Communication	-	7.6 ± 2.3 (4–11_	-
ADOS RRB	-	1.1 ± 1.2 (0–3)	-
Mean head motion (mm)	1.1 ± 0.6 (0.21–2.4)	1.5 ± 0.8 (0.25–2.5)	0.1

1. Mean ±SD (min–max)

Both ASD and TD groups showed a similar whole-brain pattern of intrinsic neural timescales: longer timescales in frontal and parietal cortices and shorter timescales in sensorimotor, visual, and auditory areas (Figure 2a). This observation is consistent with previous reports about a hierarchal topography of timescales of local neural activity in brains of mice (Runyan et al., 2017), monkeys (Chaudhuri et al., 2015; Murray et al., 2014; Ogawa and Komatsu, 2010), and humans (Hasson et al., 2008; Honey et al., 2012; Lerner et al., 2011; Stephens et al., 2013; Yeshurun et al., 2017).

Figure 2

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However, we also identified significant differences between the two groups (Table 2; P_FDR <0.05). Individuals with ASD had a significantly shorter intrinsic timescale than TD individuals in bilateral postcentral gyri, right inferior parietal lobule (IPL), right middle insula, bilateral middle temporal gyri (MTG), and right inferior occipital gyrus (IOG) (Figure 2b and d), whereas the intrinsic timescale in the right caudate was significantly larger in the ASD group (Figure 2c and e).

Table 2

		Coordinates
Right/Left	Anatomical label	X	Y	Z	Cluster size	T value
TD > ASD
Right	Post-central gyrus	58	–14	44	470	5.2
Left	Post-central gyrus	–58	–14	40	309	4.3
Right	Middle temporal gyrus	60	2	–26	170	4.8
Left	Middle temporal gyrus	–70	–26	–6	321	4.3
Right	Inferior occipital gyrus	52	–74	–6	168	4.2
Right	Inferior parietal lobule	50	–44	32	121	4.7
Right	Middle insula	50	10	–4	228	4.3
ASD > TD
Right	Caudate	14	20	12	41	3.7
Threshold: PFDR < 0.05.

Associations between intrinsic timescales and core symptoms of ASD

We then tested for any associations between the observed atypical intrinsic neural timescales and the severity of autism, as measured by the Autism Diagnostic Observation Schedule (ADOS) (Lord et al., 1989). Because previous studies indicate that atypical neural information processing is a common basis for various ASD symptoms (Happé and Frith, 2006; Belmonte et al., 2004; Watanabe and Rees, 2017), we first examined associations between the neural timescales and the overall severity of this disorder (ADOS total scores). When no significant link was found in this analysis, we then calculated associations between the neural timescales and specific core symptoms.

Of the seven brain regions of interest (ROIs) whose intrinsic timescales were shorter in the ASD group (Figure 2b and d, Table 2), the bilateral postcentral gyri and right IOG showed negative correlations between the intrinsic timescale and overall severity of autism (rho ≤ –0.49, P_uncorrected <0.01, P_FDR <0.05; Figure 3a).

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The right caudate, a single ROI whose intrinsic timescale was significantly longer in the ASD group (Figure 2e), did not show such an association with the overall ADOS score (rho = 0.19, p=0.34). However, its intrinsic timescale was longer in individuals with more severe repetitive, restricted behaviours (RRB), as measured by ADOS RRB scores (F_3,21 = 9.9, p<0.001, main effect of ADOS RRB in a one-way ANOVA; Spearman’s rho = 0.57, p=0.002; Figure 3b).

These brain-symptom associations were preserved even when we conducted this association analysis in a more statistically rigorous manner (Figure 3—figure supplement 1). That is, we applied the same ROI sets to two independent fMRI datasets (ETH Zürich and Indiana University datasets; Supplementary Table 1 in Supplementary file 1) that were not used in the ROI search, and found negative correlations between the intrinsic timescales and the ADOS total scores in the bilateral postcentral gyri and right IOG (rho ≤ –0.60) and a significant association between the intrinsic timescale and the ADOS RRB scores in the right caudate (F ≥ 6.0, p≤0.03 in one-way ANOVAs).

Development of the intrinsic neural timescale in autism

We then examined whether these observations from adults with ASD could be seen in children with ASD. To this end, we analysed a longitudinal fMRI dataset recorded from adolescent children (two MRI scans for each participant, interval of the two scans = 2.8 ± 0.4 years for ASD children, 3.0 ± 0.4 years for TD children; Supplementary Table 2 in Supplementary file 1) (Di Martino et al., 2014). We traced the developmental trajectories of the intrinsic neural timescales of the four ROIs whose intrinsic timescales were atypical in the ASD group and associated with the severity of the symptoms. These four ROIs were defined as clusters found in the whole-brain analysis using the adult fMRI data (Figure 2, Table 2).

In adolescence, we found that the intrinsic neural timescale in bilateral postcentral gyri and right IOG was consistently shorter in individuals with ASD compared to TD individuals (F_1,31 > 9.0, P_uncorrected <0.005, P_FDR <0.05, main effect of diagnosis in repeated-measures two-way ANOVAs with a diagnosis [ASD/TD] × scan order [1 st/2nd] structure; Figure 4a). In addition, the decreases in the intrinsic timescale in these areas during this period were predictive of the increases in the overall severity of autism (rho ≤ –0.68, P_uncorrected <0.003, P_FDR <0.05; Figure 4b).

Figure 4

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In contrast, the intrinsic timescale in the right caudate was consistently longer in the group with ASD during adolescence (F_1,31 = 18.2, P_uncorrected <0.001, main effect of diagnosis in a repeated-measures two-way ANOVA; Figure 4c), and the increase in the intrinsic timescale in this region was associated with progression of RRB symptoms (F_2,8 = 10.3, p=0.006, main effect of ADOS RRB changes in a one-way ANOVA; Figure 4d).

These longitudinal observations are consistent with the cross-sectional findings (Figures 2 and 3) and suggest that autistic atypicality of temporal neural processing in local brain areas may already occur before adolescence.

Associations between intrinsic neural timescale and local grey matter volume

Finally, we explored possible neuroanatomical bases for (or consequences of) intrinsic neural timescales by examining relationship with local grey matter volumes (GMVs). We focused on GMV because theoretically, an increase in neuronal density, which is measured by GMV (Kanai and Rees, 2011), would enhance recurrent neural network activity, and then enlarge the autocorrelation strength in the neural signals.

This theoretical assumption was validated by comparisons between intrinsic timescales and GMV across 360 brain areas (Glasser et al., 2016): at a group level, these functional and anatomical properties were positively correlated with each other (TD: r = 0.40, ASD: r = 0.38, p<10^–5; Figure 5a). Furthermore, the significant correlations were robustly observed at a single-participant level as well (TD: r ≥ 0.29, ASD: r ≥ 0.28, p<10^–5; Figure 5b). This association was also seen in the four brain regions whose atypical intrinsic neural timescale was associated with symptoms of autism (r > 0.52, p≤0.005; Figure 5c).

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Given these findings together with atypical GMVs in the four brain regions in individuals with ASD (t₄₉ >3.0, P_uncorrected ≤0.004 in two-sample t-tests, P_FDR <0.05; Figure 5d), we can infer that intrinsic neural timescale is a mediator linking atypical GMV and ASD symptoms. This inference was, in fact, consistent with results of mediation analyses (Figure 5e).

We investigated the intrinsic neural timescale, whose length is closely related to the functional hierarchy in the brain (Hasson et al., 2015; Murray et al., 2014; Cocchi et al., 2016), in high-functioning individuals with autism. By calculating the time-dependent magnitude of autocorrelation function in resting-state fMRI (rsfMRI) signals, we found that in adults with ASD, the intrinsic timescale was significantly shorter in the bilateral postcentral gyri and right inferior occipital gyrus, and longer in the right caudate. The shorter intrinsic timescale in these primary sensory/visual areas in autism was correlated with the overall severity of autism. The longer intrinsic timescale in the caudate in autism was associated with the severity of repetitive, restricted behaviours. Moreover, this temporal property in local neural signals was linked to local grey matter volumes (GMVs). These findings indicate the possibility that functional and structural properties in local brain areas could have a critical influence on higher-order cognitive symptoms in autism.

Furthermore, we investigated the validity of these observations on longitudinal neuroimaging data recorded from adolescents with ASD. We found atypical development of the intrinsic timescale during adolescence in autism (Figure 4a and c) and identified significant associations between such atypical neural development and progression of ASD symptoms (Figure 4b and d). These findings imply that such atypicality in the temporal characteristics of local neural activity may be one of the basic neuro-aetiologies of autism, which needs to be tested using data collected from much younger children with ASD in future studies.

The association between the intrinsic timescales and GMVs is theoretically reasonable. Large GMVs are considered to indicate a high density of neurons in local brain regions (Kanai and Rees, 2011), which is thought to be accompanied with more synapses (Cullen et al., 2010) and greater synaptic weights (Perin et al., 2011). Moreover, computational studies suggest that such a high neuronal and synaptic density should increase reciprocal connections within the areas and enhance local clustering (Perin et al., 2013). Given that spontaneous neural activity largely depends on such recurrent neural networks (Ikegaya et al., 2004), resting-state neural activities of brain regions with large GMVs would show more repetition patterns and larger autocorrelations. Although future studies have to directly examine this hypothesis, this logic accounts for the significant correlations between the local neural dynamics and local neuroanatomical structures.

Some human neuroimaging researches examined autistic local neural dynamics in the sensory-related areas and reported observations that are consistent with the current findings. For example, fMRI studies that investigated intra-participant variability of brain signals across time found atypically large signal variability in the prefrontal region (Dinstein et al., 2011), somato-sensory area (Haigh et al., 2015; Dinstein et al., 2012), auditory area (Haigh et al., 2015; Dinstein et al., 2012), and primary visual cortex (Dinstein et al., 2012; Dinstein et al., 2010; Milne, 2011) in individuals with ASD. In particular, one study found that the severity of ASD was significantly correlated with such atypical signal variability in the sensory/visual areas (Dinstein et al., 2012). If we can assume that such large variability of local brain signals indicates more random brain activity and consequently yields weak autocorrelations, these previous reports can be interpreted as being consistent with the current findings.

In contrast, local neural dynamics in the caudate in autism were poorly understood. In fact, the neuroanatomical association between the subcortical region and the RRB symptoms was reported in previous structural MRI studies (Langen et al., 2014; Langen et al., 2009; Langen et al., 2007; Hollander et al., 2005; Schuetze et al., 2016), which is consistent with the current observation about the caudate. However, to the best of our knowledge, no prior research has been conducted on intrinsic neural timescales or signal variability of the caudate in autism.

A recent review has suggested that the intrinsic timescale and TRW is not an artefact of neuroimaging signals but closely associated with an ability of local brain areas to pool, normalise, and complete information (Himberger et al., 2018). In particular, for sensory information processing, a longer neural timescale is considered to make brain responses more robust against fluctuations in sensory inputs and enable steady and consistent perception (Himberger et al., 2018; Honey et al., 2012; Murray et al., 2014). Given this, we speculate that the atypically short intrinsic timescale in the primary sensory/visual cortices observed in the ASD group (Figures 2b and 4a) might potentially be one of neural bases of perceptual hyper-sensitivity often seen in autism (American Psychiatric Association, 2013).

This rsfMRI study did not adopt a formulation that has been used to measure neural timescales in previous human fMRI studies (Hasson et al., 2015; Hasson et al., 2008; Lerner et al., 2011; Stephens et al., 2013; Yeshurun et al., 2017; Gauthier et al., 2012), because this definition of neural timescale — so-called temporal receptive window (TRW) — was designed for task-related brain activity data and cannot be directly applied to resting-state fMRI data. Instead, based on previous non-human electrophysiology studies (Cavanagh et al., 2016; Murray et al., 2014; Bernacchia et al., 2011; Runyan et al., 2017), we defined the intrinsic neural timescale as the magnitude of autocorrelation of brain activity. In addition, to reduce adverse effects of the low sampling rates of fMRI recording, we did not conduct curve fitting to the autocorrelation coefficients as electrophysiological work did (Cavanagh et al., 2016; Murray et al., 2014; Runyan et al., 2017); we simply calculated the area under the autocorrelation function (ACF) to estimate the autocorrelation strength (Figure 1a).

We validated this definition of the intrinsic neural timescales using the simultaneously recorded EEG-fMRI data. Although the fMRI-based neural timescale was different from the EEG-based one by two orders of magnitude, the two measures were significantly correlated with each other (Figure 1b and Figure 1—figure supplement 2). Moreover, such a difference would be reasonable because an EEG signal is likely to peak ~100 ms after a stimulus onset and an fMRI signal — a product of neurovascular coupling (Hillman, 2014; Martindale et al., 2003) — tends to take 5 ~ 10 s to peak (Logothetis et al., 2001; Yeşilyurt et al., 2008). In fact, when we convolved the EEG signals with the hemodynamic response function (HRF) to take into account such neurovascular coupling, the resultant intrinsic timescales based on HRF-convolved EEG signals were similar in the magnitude to those based on the fMRI data (Figure 1c).

These EEG-fMRI comparisons indicate that the fMRI-based neural timescales represent an aspect of local neuronal activity. However, it is beyond the scope of this study to conclude that such an fMRI-based index reflects the same neuronal phenomena as those seen in previous electrophysiology work that calculated neural timescales from spike activity data (Cavanagh et al., 2016; Murray et al., 2014; Ogawa and Komatsu, 2010; Runyan et al., 2017). To clarify this issue, future studies have to directly compare the fMRI-based neural timescales with those based on neuronal spike activities that are collected simultaneously with fMRI data.

Our exploratory study identified significant associations between local brain dynamics and behavioural tendencies in autism; however, biological mechanisms underlying this brain-behaviour link remain unknown. Previous work proposed that such an atypical neural timescale may represent atypical functional hierarchy in information processing in brains (Himberger et al., 2018; Chaudhuri et al., 2015; Gjorgjieva et al., 2016). However, it is necessary to directly examine this hypothesis by analysing task-related whole-brain activity in ASD populations. Future work also needs to investigate mechanisms linking these local neural dynamics to atypical large-scale brain dynamics seen in autism (Watanabe and Rees, 2017).

Another limitation of this study is the relative homogeneity of the participants with ASD that we have studied. To improve detectability, we limited the ASD group to high-functioning right-handed adult males. Although we confirmed the main findings in an adolescent dataset (Figure 4), future studies have to examine the current observations in different subsets of ASD cohorts.

This resting-state fMRI study investigated how long local brain areas can store information in individuals with autism and identified a shorter intrinsic timescale in the bilateral primary sensory/visual cortices and a longer intrinsic timescale in the right caudate. This atypicality in local neural dynamics was associated with the severity of autism and also correlated with local grey matter volumes. Although these findings should be examined in larger and more diverse cohorts of individuals with ASD, our work highlights the importance of investigating neural dynamics in neuro-psychiatric disorders.

All the data used here were shared in ABIDE (http://fcon_1000.projects.nitrc.org/indi/abide/) or Open Science Framework (https://osf.io/94c5t/). From the ABIDE site, the data submitted by the University of Utah (ABIDE I), ETH Zürich (ABIDE II), Indiana University (ABIDE II), and UCLA (longitudinal, ABIDE II) were used. To access these datasets, users first need to register with the NITRC and 1000 Functional Connectomes Project (further information here http://fcon_1000.projects.nitrc.org/indi/req_access.html).

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Author details

1. Takamitsu Watanabe

   1. Institute of Cognitive Neuroscience, University College London, London, United Kingdom
   2. RIKEN Centre for Brain Science, Wako, Japan

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing

   For correspondence

   takamitsu.watanabe@ucl.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8104-6873

2. Geraint Rees

   1. Institute of Cognitive Neuroscience, University College London, London, United Kingdom
   2. Wellcome Trust Centre for Human Neuroimaging, University College London, London, United Kingdom

   Contribution

   Supervision, Funding acquisition, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

3. Naoki Masuda

   Department of Engineering Mathematics, University of Bristol, Bristol, United Kingdom

   Contribution

   Conceptualization, Funding acquisition, Writing—review and editing

   For correspondence

   naoki.masuda@bristol.ac.uk

   Competing interests

   No competing interests declared

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