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. 2017 Jan 17;38(5):2333–2344. doi: 10.1002/hbm.23520

Network over‐connectivity differentiates autism spectrum disorder from other developmental disorders in toddlers: A diffusion MRI study

E Conti 1,✉, J Mitra 3, S Calderoni 1, K Pannek 3, K K Shen 3, A Pagnozzi 3, S Rose 3, S Mazzotti 1, D Scelfo 1, M Tosetti 1, F Muratori 1,2, G Cioni 1,2, A Guzzetta 1,2
PMCID: PMC6866938  PMID: 28094463

Abstract

Advanced connectivity studies in toddlers with Autism Spectrum Disorder (ASD) are increasing and consistently reporting a disruption of brain connectivity. However, most of these studies compare ASD and typically developing subjects, thus providing little information on the specificity of the abnormalities detected in comparison with other developmental disorders (other‐DD). We recruited subjects aged below 36 months who received a clinical diagnosis of Neurodevelopmental Disorder (32 ASD and 16 other‐DD including intellectual disability and language disorder) according to DSM‐IV TR. Structural and diffusion MRI were acquired to perform whole brain probabilistic and anatomically constrained tractography. Network connectivity matrices were built encoding the number of streamlines (DNUM) and the tract‐averaged fractional anisotropy (DFA) values connecting each pair of cortical and subcortical regions. Network Based Statistics (NBS) was finally applied on the connectivity matrices to evaluate the network differences between the ASD and other‐DD groups. The network differences resulted in an over‐connectivity pattern (i.e., higher DNUM and DFA values) in the ASD group with a significance of P < 0.05. No contra‐comparison results were found. The over‐connectivity pattern in ASD occurred in networks primarily involving the fronto‐temporal nodes, known to be crucial for social‐skill development and basal ganglia, related to restricted and repetitive behaviours in ASD. To our knowledge, this is the first network‐based diffusion study comparing toddlers with ASD and those with other‐DD. Results indicate the detection of different connectivity patterns in ASD and other‐DD at an age when clinical differential diagnosis is often challenging. Hum Brain Mapp 38:2333–2344, 2017. © 2017 Wiley Periodicals, Inc.

Keywords: autism spectrum disorders, brain connectivity, overconnectivity, toddlers, developmental disorders

INTRODUCTION

Autism Spectrum Disorders (ASD) are a heterogeneous group of neurodevelopmental diseases affecting 1 in 68 children in the United States [CDC, 2014], characterized by impairment in socio‐communicative abilities, as well as restricted and stereotyped behaviours [American Psychiatric Association, 2013]. Although a wide consensus exists from both retrospective and prospective studies about appearance of ASD symptoms in the first 2 years of life, the age at which clinical diagnosis is generally made ranges between 3 and 4 years in industrialized countries [Chawarska et al., 2014; Salomone et al., 2015; Zwaigenbaum et al., 2013]. The rapid development of cognitive, social and communication skills in children during their first years of life reflects on dynamic changes in symptoms of ASD [Chawarska and Shic, 2009; Turner et al., 2006] and requires deep knowledge of the earliest and most predictive markers of ASD [Barbaro and Dissanayake, 2009; Bedford et al., 2016], with the final aim of identifying children at the earliest possible opportunity, given the importance of early specific tailored intervention [Barbaro and Dissanayake, 2012; Dawson, 2008; Pickles et al., 2016].

Advanced connectivity studies are emerging in the literature reporting disrupted connectivity in toddlers with ASD, detectable even before the full phenotypic expression of the disorder [Conti et al., 2015]. At such a young age, head circumference has been consistently reported to show abnormal growth patterns in ASD [Muratori et al., 2012] Specifically, normal brain size at birth is probably followed by a rapid rate of growth in the first 2–4 years of life with a later normalization of head circumference [Courchesne et al., 2011a; Sacco et al., 2015]. Brain dysmaturation occurring in this early phase of development can lead to abnormal brain connectivity, as widely reported in ASD adolescents and young adults, both in terms of functional and structural connections [Lewis and Elman, 2008]. Articles assessing structural connectivity in toddlers with ASD [Ben Bashat et al., 2007; Weinstein et al., 2011] reported a distinct connectivity pattern, that is, over‐connectivity, in ASD subjects in comparison with typical subjects, in contrast to under‐connectivity patterns widely reported in ASD adolescents and young adults [Ameis and Catani, 2015]. Recently, DTI studies assessing infants at risk for autism (having an older sibling with ASD), reported altered connectivity in subjects even younger than 24 months when the clinical picture of the disorder was not fully expressed [Elison et al., 2013; Wolff et al., 2012].

Although an increasing number of reports are consistently confirming evidence supporting an early over‐connectivity in ASD before the age of 3 years, little is known on the specificity of this finding. This information is of great value since, at such an early age, a great challenge for clinicians dealing with neurodevelopmental disorders is to differentiate infants with ASD from those with other developmental disorders (other‐DD), such as developmental delay or language disorder, in order to allow for a better tailoring of early intervention strategies [Zwaigenbaum et al., 2015]. Indeed, in the absence of univocal predictive biomarkers, early detection is informed by the presence of a constellation of behavioural signs that show significant overlap between different neurodevelopmental disorders, which makes the discriminative value of early neuropsychological assessments very heterogeneous across studies [Mitchell et al., 2011]. To date, few case‐control studies compared brain structural connectivity in pre‐school children with ASD to those with other‐DD, reporting ASD white matter abnormalities in temporal lobes [Cascio et al., 2013], increased length, volume and density of right uncinate fasciculus, right arcuate fasciculus and corpus callosum [Kumar et al., 2010]. To the best of our knowledge, only one study compared brain diffusion between toddlers with ASD and those with developmental delay, showing higher fractional anisotropy in the corpus callosum, posterior cingulate cortex and limbic lobes in the former [Xiao et al., 2014], while no studies explored differences in structural connectivity.

In the present study we applied an advanced connectome approach to investigate structural connectivity in a group of toddlers with ASD and in an age‐matched group of toddlers with other‐DD to determine the specific networks differentiating the two clinical groups.

METHODS

Participants

As part of an on‐going prospective study started in January 2012, we enrolled all children referred to our tertiary care centre for early neurodevelopmental disorders. All patients were tested by a multidisciplinary team including a senior child neuropsychiatrist, an experienced clinically trained child psychologist and a speech‐language pathologist during 5–7 days of intensive evaluation. All patients underwent brain MRI, EEG and a number of tests including the Griffiths' Mental Developmental Scale, audiometry, DNA analysis of FRA‐X, array CGH and screening tests for inborn errors of metabolism. Subjects diagnosed with ASD, according to DSM‐IV TR, also were evaluated with the Autism Diagnostic Observation Schedule‐Generic (ADOS‐G) [Lord et al., 2000] or the Autism Diagnostic Observation Schedule‐Toddler Module (ADOS‐T) [Luyster et al., 2009].

For the present study, subjects were included if they (i) received a clinical diagnosis of ASD, or other‐DD, including either intellectual disabilities or language disorders (as from the DSM‐IV‐TR), (ii) were aged 36 months or less and (iii) had no neurometabolic or genetic disorders.

The study was approved by the local Ethical Committee and written informed consent was obtained from all parents or caregivers. The research was carried out in accordance with the Code of Ethics of the World Medical Association (Declaration of Helsinki) for experiments involving humans.

MRI Protocol

Acquisition

All children received inhalational anaesthesia with a halogenated agent while spontaneously breathing. No side effects were reported. Data were acquired on a GE 1.5 T scanner. At the beginning of each experimental session, whole‐brain T1‐weighted Spoiled Gradient (SPGR) echo volumes of size 256 × 256 × 144; voxel size of 0.9766 mm × 0.9766 mm × 1 mm; TR/TE/TI, 12.28/5.14/700 ms were acquired for each subject. High angular resolution diffusion imaging (HARDI) scans were acquired using either 64 or 30 diffusion‐weighted directions. Acquisition parameters were as follows: field of view, 24 cm; TR/TE, 10,000/92 ms; slice thickness, 3 mm; acquisition matrix, 80 × 80 and a diffusion encoding gradient strength, b = 1,000 s/mm2. One low (b = 0) diffusion weighted image was acquired.

Data pre‐processing

An extensive pre‐processing procedure was performed on the diffusion‐weighted images [Pannek et al., 2012a]. Signal intensity outlier voxels (caused by cardiac pulsation, bulk head motion and other artefacts) were detected and replaced using DROP‐R [Morris et al., 2011]. DROP‐R was modified from the originally proposed method to employ a higher order model of the diffusion signal suitable for the detection and replacement of outliers in high b‐value diffusion data (HOMOR) [Pannek et al., 2012b]. Between‐volume registration to account for head movement during the scan time was performed using Fit Model to All Measurements (FMAM) [Bai and Alexander, 2008] with adjustment of the b‐matrix [Leemans and Jones, 2009; Rohde et al., 2004]. Finally, intensity inhomogeneities were removed using N3 correction [Sled et al., 1998] and the FA was estimated from the corrected diffusion data. Constrained spherical deconvolution [Tournier et al., 2007] (http://nitrc.org/projects/mrtrix) was employed to estimate the fibre orientation distribution for tractography at maximum harmonic order 6. The T1‐weighted volumes were resampled isotropically to 1 mm resolution and corrected for bias due to magnetic field inhomogeneities.

Diffusion tractography and connectivity matrices

Diffusion datasets acquired along 64 non‐collinear directions were reduced to 30‐direction datasets by extracting an optimal subset of directions using tools implemented in MRtrix3 [Tournier et al., 2012].

Diffusion weighted images were corrected for head motion and eddy current distortions using FSL eddy [Andersson and Sotiropoulos, 2016] and signal intensity inhomogeneities were removed [Zhang et al., 2001]. The single fibre response function was estimated using the “tournier” method implemented in MRtrix3 [Tournier et al., 2012]. Constrained spherical deconvolution [Tournier et al., 2007, 2012] was performed to obtain fibre orientation distributions. Diffusion and structural images were co‐registered using boundary‐based registration [Greve and Fischl, 2009]. The resulting transformation matrix was applied to the header information of structural images, with no need to resample structural images to diffusion space. Brain extraction from structural images was performed using volbrain [Manjon and Coupe, 2016]. A five‐tissue‐type (white matter, cortical grey matter, deep grey matter, CSF, other) mask was calculated from the structural images using methods implemented in MRtrix3, which make use of FSL FAST [Zhang et al., 2001] and FIRST [Patenaude et al., 2011]. Anatomically constrained tractography (ACT, [Smith et al., 2012]) was used to generate 10 million probabilistic streamlines, which were subsequently filtered to 5 million streamlines using SIFT [Smith et al., 2013].

Cortical parcellation of the structural images into 90 regions excluding the cerebellum was performed by registering [Avants et al., 2011] to an infant AAL (anatomical atlas labelling) atlas [Feng et al., 2005] (from UNC Chapel Hill, North Carolina). Connectivity matrices of size 90 × 90 were generated by encoding streamline number and average FA of connections between each pair of regions in the AAL atlas (See Fig. 1).

Figure 1.

Figure 1

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Example of whole brain tractography (left column), mean DFA connectivity matrix (central column) and mean DNUM connectivity matrix normalised between 0 and 1 using a row‐wise normalisation (right column). [Color figure can be viewed at http://wileyonlinelibrary.com]

Statistical Analysis

Only connections that were present in all participants, and that contained, on average, at least 250 streamlines were included in the analysis.

Between‐group differences

The connectivity matrices of the ASD and other‐DD groups were contrasted for a group‐wise comparison using the NBS toolbox (https://sites.google.com/site/bctnet/ comparison/nbs) for Matlab [Zalesky et al., 2010]. NBS performs a graph‐based clustering involving generalized linear model to find clusters of network connections that are significantly different between the groups. It controls the family‐wise error (FWE) rate in a weak‐sense by isolating the graph network components that significantly differ between the two groups, while performing mass‐univariate testing at every connection comprising the graph. A t‐test was used for the univariate testing at every connection of the graph to identify differences in DFA and DNUM between participant groups. A t‐statistic threshold between 2.7 and 3.8 was chosen empirically to define a set of suprathreshold edges with P < 0.05. Any connected component defined by the suprathreshold edges was identified. Five‐thousand permutations were performed, each time permuting the members of the two groups and storing the largest component at each permutation. This provided an estimate of the null distribution of the maximal network component. Finally, the P‐value was estimated for each observed component by finding the number of permutations for which the observed component was larger than the maximal component, and then normalizing by the total number of permutations. In this manner, FWE was controlled for each observed component based on its size instead of computationally intensive multiple comparisons being performed on each connection as in generic false‐discovery rate methods. In our experiments, the chosen t‐statistic thresholds were t = 3.7 for DFA, and t = 3.2 for DNUM and components with corrected P < 0.05 were retained.

High t‐thresholds ensured more confidence in the network differences and provided localised networks. In our experiments, we found that lower thresholds provided dense networks with high spatial coverage that was difficult to interpret clinically, while a threshold higher than t = 3.7 or t = 3.1 resulted in isolated connections.

Intra‐group correlations

Intra‐group correlations were done in the ASD group by relating MRI data with ADOS severity scores, calculated according to Gotham et al. [2009]. Using the list of AAL cortical regions (outlined from the network connections obtained from NBS), Mrtrix3 was used to mask the diffusion parameters (DNUM and DFA) by these AAL regions. Mrtrix3 was then used find the mean diffusion measure in that mask, leading to a mean DNUM and DFA for each of the 33 patients in each AAL region. Using the 26 ADOS severity scores, correlations between these mean diffusion measures and severity scores were performed in R statistical software for each AAL region.

RESULTS

Out of the 93 subjects enrolled from January 2012 to March 2014, 67 fulfilled our inclusion criteria and were recruited for the study. Nineteen patients were later excluded due to either low quality or incomplete MRI scans. The high incidence of incomplete MRI scans was due to the fact that the diffusion acquisitions required for the study were added at the end of the clinical scan and the decision to proceed was independently taken by the anaesthesiologist. The final cohort consisted of 48 subjects of whom 32 were diagnosed with ASD and 16 with other‐DD. All ASD subjects were diagnosed based on clinical observation, as from DSM‐IV TR criteria. Diagnosis was further supported by the results of ADOS‐G in 26/32 patients and ADOS‐toddler in 3 patients. The other‐DD group included subjects with intellectual disability (n = 10) or language disorder (n = 6) according to DSM‐IV‐TR criteria [American Psychiatric Association, 2000], in whom a co‐occurrent diagnosis of ASD had been ruled out. In all children, both in the ASD and in the other‐DD group, diagnosis was confirmed at an age of 36 months or higher (i.e., no subjects grew out of the original diagnosis).

Clinical characteristics of the participants are reported in Table 1.

Table 1.

Demographical and clinical characteristics of participants

Characteristics Measure ASD (n = 32) Other‐DD (n = 16) P value
Gender (Male/Female) Frequencies 28/4 12/4 ns
Age mean (SD) 26.1 (5.4) 29.8 (5.8) ns
Age range months 16‐36 20‐38
Non‐verbal DQ mean (SD) 77.1 (14.1) 76.5 (28.0) ns
ADOS score tot mean (SD) 14.9 (3.0) n/a
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ASD: Autism Spectrum Disorder; Other‐DD: other Developmental Disorder; DQ: Developmental Quotient; ADOS: Autism Diagnostic Observation Schedule; SD: Standard Deviation.

Between‐Group Differences in Network Connections: Streamline Analysis

The group‐wise comparison of the network connections (NBS statistics) in terms of number of streamlines (DNUM) revealed significant differences between the groups.

Number of streamlines was increased in the ASD group, compared with the other‐DD group, in three distinct network components (Table 2 and Fig. 2). Network component 1 (P = 0.002) comprised 7 edges between 8 nodes in the left hemisphere and was centred on the left caudate (connections with left precentral gyrus and left superior, middle, inferior frontal gyri within the frontal lobe; left precuneus and middle occipital gyri within the occipital lobe; left putamen within the basal ganglia).

Table 2.

DNUM connections

Network 1 (P = 0.002)
Precentral_L Caudate_L
Frontal_Sup_L Caudate_L
Frontal_Mid_L Caudate_L
Frontal_Inf_Tri_L Caudate_L
Precuneus_L Caudate_L
Occipital_Mid_L Putamen_L
Caudate_L Putamen_L
Network 2 (P = 0.005)
Precentral_R Caudate_R
Frontal_Sup_R Caudate_R
Frontal_Mid_R Caudate_R
Frontal_Inf_Tri_R Caudate_R
Frontal_Inf_Orb_R Caudate_R
Network 3 (P = 0.020)
Parietal_Inf_L Thalamus_L
SupraMarginal_R Thalamus_L
Angular_L Thalamus_L
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Figure 2.

Figure 2

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(a–c) NBS connectivity graph for DNUM in axial, coronal and sagittal views, identifying the 3 different networks (see text). (d) Color‐coded DNUM disconnectivity maps. The colours indicate the degree of disconnection of cortical/subcortical areas, “yellow” means a high degree and “black” means zero degree association. [Color figure can be viewed at http://wileyonlinelibrary.com]

Network component 2 (P = 0.005) comprised 5 edges between 6 nodes in the right hemisphere and was centred on right caudate (connections with right precentral gyrus and right middle, superior, inferior frontal gyri within the frontal lobe).

Network component 3 (P = 0.020) comprised 3 edges between 4 nodes, centred on left thalamus (connections with left inferior parietal, angular gyri and right supramarginal gyrus within the parietal lobe).

Between‐Group Differences in Network Connections: Fractional Anisotropy Analysis

The group‐wise comparison of the network connections (NBS statistics), in terms of FA values (DFA), revealed significant differences between ASD and other‐DD in three distinct network components, with higher values in ASD compared with other‐DD (Table 3 and Fig. 3). Network component 1 (P = 0.001) comprised 26 edges between 19 nodes and was centred on the left putamen (connection with left and right middle, superior, inferior frontal gyri, left anterior and middle cingulum gyri, right anterior cingulum gyrus and left olfactory gyrus within the frontal lobe; left rectus, precuneus, cuneus, middle occipital gyri and right rectus gyrus within the occipital lobe; left putamen, caudate, thalamus and right putamen within the basal ganglia; left and right parietal inferior gyri, left angular gyrus and right supramarginal gyrus within the parietal lobe; left and right insula, left middle, superior temporal gyri and right superior temporal gyrus within the temporal lobe).

Table 3.

DFA connections

Network 1 (P = 0.001)
Frontal_Mid_L Frontal_Inf_Orb_L
Frontal_Mid_R Frontal_Inf_Orb_R
Frontal_Sup_L Frontal_Sup_Medial_L
Frontal_Inf_Orb_L Frontal_Sup_Medial_L
Frontal_Sup_R Frontal_Sup_Medial_R
Frontal_Mid_Orb_R Frontal_Sup_Medial_R
Frontal_Inf_Orb_R Frontal_Sup_Medial_R
Frontal_Sup_Orb_L Frontal_Mid_Orb_L
Frontal_Inf_Orb_L Frontal_Mid_Orb_L
Frontal_Sup_L Rectus_L
Frontal_Sup_Orb_L Rectus_L
Frontal_Sup_R Rectus_R
Frontal_Mid_Orb_R Rectus_R
Frontal_Mid_Orb_L Insula_L
Frontal_Mid_Orb_L Insula_L
Rectus_L Insula_L
Frontal_Mid_Orb_R Insula_R
Frontal_Mid_Orb_R Cingulum_Ant_R
Frontal_Inf_Orb_R Cingulum_Ant_R
Frontal_Sup_Medial_L Cingulum_Mid_L
Cingulum_Mid_L Cuneus_L
Precentral_L Caudate_L
Frontal_Sup_L Caudate_L
Frontal_Mid_L Caudate_L
Frontal_Inf_Tri_L Caudate_L
Frontal_Inf_Orb_L Caudate_L
Olfactory_L Caudate_L
Frontal_Mid_Orb_L Caudate_L
Precuneus_L Caudate_L
Precentral_R Caudate_R
Frontal_Sup_R Caudate_R
Frontal_Mid_R Caudate_R
Frontal_Mid_Orb_R Caudate_R
Frontal_Inf_Tri_R Caudate_R
Frontal_Inf_Orb_R Caudate_R
Rectus_R Caudate_R
Frontal_Sup_Orb_L Putamen_L
Frontal_Mid_Orb_L Putamen_L
Frontal_Inf_Orb_L Putamen_L
Olfactory_L Putamen_L
Frontal_Mid_Orb_L Putamen_L
Rectus_L Putamen_L
Cingulum_Ant_L Putamen_L
Occipital_Mid_L Putamen_L
Caudate_L Putamen_L
Frontal_Mid_Orb_R Putamen_R
Rectus_R Putamen_R
Parietal_Inf_L Thalamus_L
SupraMarginal_R Thalamus_L
Angular_L Thalamus_L
Frontal_Inf_Oper_R Temporal_Sup_R
Parietal_Inf_R Temporal_Sup_R
Cingulum_Mid_L Temporal_Mid_L
Angular_L Temporal_Mid_L
Temporal_Sup_L Temporal_Mid_L
Temporal_Pole_Sup_L Temporal_Mid_L
Network 2 (P = 0.002)
Frontal_Sup_Orb_R Frontal_Mid_Orb_R
Frontal_Mid_R Frontal_Mid_Orb_R
Frontal_Sup_Orb_R Frontal_Inf_Tri_R
Frontal_Mid_Orb_R Frontal_Inf_Tri_R
Frontal_Mid_R Frontal_Inf_Orb_R
Frontal_Sup_R Frontal_Sup_Medial_R
Frontal_Mid_Orb_R Frontal_Sup_Medial_R
Frontal_Inf_Orb_R Frontal_Sup_Medial_R
Frontal_Sup_R Rectus_R
Frontal_Mid_Orb_R Rectus_R
Frontal_Mid_Orb_R Insula_R
Frontal_Mid_Orb_R Cingulum_Ant_R
Frontal_Inf_Orb_R Cingulum_Ant_R
Frontal_Mid_Orb_R Caudate_R
Frontal_Inf_Orb_R Caudate_R
Rectus_R Caudate_R
Frontal_Mid_Orb_R Putamen_R
Rectus_R Putamen_R
Network 3 (P = 0.007)
Frontal_Inf_Oper_R Temporal_Sup_R
Parietal_Inf_R Temporal_Sup_R
Parietal_Inf_R Temporal_Mid_R
SupraMarginal_R Temporal_Mid_R
Temporal_Sup_R Temporal_Mid_R
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Figure 3.

Figure 3

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(a–c) NBS connectivity graph for DFA in axial, coronal and sagittal views, identifying the three different networks (see text). (d) Color‐coded DFA disconnectivity maps. The colours indicate the degree of disconnection of cortical/subcortical areas, “yellow” means a high degree and “black” means zero degree association. [Color figure can be viewed at http://wileyonlinelibrary.com]

Network component 2 (P = 0.002) comprised 18 edges between 13 nodes primarily distributed in the right frontal lobe and was centred on right frontal middle orbital gyrus (connections with left, right superior, middle, inferior frontal gyri and right anterior cingulum gyrus within the frontal lobe; right rectus gyrus within the occipital lobe; right insula in the temporal lobe; right putamen and caudate within the basal ganglia).

Network component 3 (P = 0.007) comprised 5 edges between 5 nodes and was centred on right middle and superior temporal (connections with right inferior frontal gyrus in the frontal lobe; right inferior parietal and supramarginal gyri within the parietal lobe; right middle, superior temporal gyri in the temporal lobe).

Intra‐Group Correlations

ADOS severity score was available in all subjects who performed the ADOS (n = 26), and were used for the analysis. Three significant correlations (P < 0.05) between diffusion indexes and severity scores were found: one positive correlation in tract connecting left superior medial frontal gyrus to left middle cingulum gyrus (P 0.023) and one negative correlation in the tract connecting right inferior frontal gyrus to right caudate (P 0.042) in DNUM analysis; one negative correlation in the tract connecting left olfactory gyrus to left caudate (P 0.034) in DFA analysis. None of the above correlation remained significant after correcting for multiple comparisons.

DISCUSSION

To our knowledge, this study is the first to employ a network‐based structural connectivity approach to assess brain connectivity differences in toddlers with ASD compared with peers with other developmental disorders (other‐DD), as the only previous study using diffusion imaging [Xiao et al., 2014] employed voxel‐based analysis, thus focussing on brain regions rather than connections.

The main finding of our study is the detection of an over‐connectivity pattern in ASD toddlers expressed by the number of streamlines or by the average fractional anisotropy of the connections for several brain networks, while no significant contra‐comparison results were found. This is in accordance with the existing literature exploring brain structural connectivity in toddlers with ASD, showing an overall tendency to brain over‐connectivity in ASD versus typically developing peers, observed at around 30 months of age, when autistic features start to be clinically detected, or even before [Conti et al., 2015]. Recently Solso et al. [2016], based on diffusion studies, reported an over‐connectivity pattern in ASD toddlers within the frontal projection of the superior cortico‐striatal tract, in the inferior frontal occipital tract, in the inferior frontal superior frontal tract and inferior longitudinal fasciculus and theorized ASD as an “early over‐connectivity disorder” in opposition to the previous “under‐connectivity theory” formulated by Just et al. [2004], mainly based on functional studies. Indeed, the over‐connectivity pattern has been widely associated to early brain overgrowth, extensively reported in the literature [Courchesne and Pierce, 2005a], with prominent and lasting effects on frontal white matter structures [Herbert et al., 2003; Radua et al., 2011]. This is increasingly interpreted as a connectivity disruption process that gives rise to recurrent patterns of under‐connectivity at a later age [Just et al., 2004], consistent with chronic altered white matter development, reduced white matter organization and integrity and possibly impaired myelination [Beaulieu, 2002; Klawiter et al., 2011].

Our analysis of fractional anisotropy as well as of the number of streamlines show higher connectivity in the ASD group within networks centred on frontal and temporal gyri and basal ganglia, thus corroborating previous reports comparing ASD and other‐DD. Indeed, Xiao et al. [2014] performed a VBM and voxel based DTI analysis comparing toddlers with ASD and those with other developmental delay, with clinical characteristics highly similar to those of our samples (in terms of age range, sample size and type of other developmental concerns). They reported higher FA values within the limbic lobe and higher volumes of the temporal lobes in ASD, supporting the hypothesis of a specific early disruption of brain maturation of these regions in ASD. Indeed, networks involving the temporal lobe and the striatum have been specifically implicated in certain symptoms of ASD such as social/emotional [Adolphs et al., 2001], and stereotypic/repetitive behaviour [Langen et al., 2014], respectively. However, it has been recently hypothesized that abnormalities of striatal function are involved not only in stereotyped routines and repetitive movements, but also in a variety of clinical manifestations typical of patients with ASD, such as altered processing of sensory stimuli, abnormal social approach and reduced response to rewarding stimuli [Fuccillo, 2016]. Functional connectivity studies have also suggested that communication between these nodes may be suboptimal in autism [Minshew and Keller, 2010; Noonan et al., 2009]. Many other studies demonstrated that social cognition mediated by the temporal lobe is among the most fundamental deficits in ASD [Ameis and Catani, 2015] .

Other nodes emerging from our analysis as being important to differentiate ASD and other‐DD are the cingulate and the caudate/putamen nuclei. Higher FA values related to cingulum in ASD versus other‐DD were also found by Xiao et al. [2014] while higher FA values in the same regions in ASD versus typical subjects have been reported in other studies investigating young children [Ben Bashat et al., 2007; Billeci et al., 2012; Weinstein et al., 2011]. This is not surprising as the cingulum is considered as a key region for socio‐communicative skill development and its alteration in ASD has been widely reported also in older subjects [Ameis et al., 2013; Hoppenbrouwers et al., 2014; Ikuta et al., 2014]. Little evidence from DTI studies exists on the basal ganglia networks, even if volumetric MRI studies indicate those regions as presenting higher volumes and being related to repetitive symptoms of ASD [Calderoni et al., 2014]. Higher FA values have been detected within the putamen in adolescents [Cheng et al., 2010] and school‐age children [Brito et al., 2009], while Langen et al. [2012] found that adults with autism had a significantly smaller total brain white matter volume, lower fractional anisotropy of white matter tracts connecting putamen to frontal cortical areas, higher mean diffusivity of white matter tracts connecting accumbens to frontal cortex. These findings however are difficult to compare with ours as they are based on older children or adults.

Lots of connections emerging from our analysis, especially in the DFA results are distributed within the frontal lobes, being part of fronto‐striatal tracts or intra‐frontal tract or fronto‐occipital tracts. This is in keeping with the early overconnectivity/disruption theory of Solso et al. [2016] who reported higher FA values and volumes in frontal fibre tracts in young children (aged 1–4 years) with ASD in comparison with typical toddlers. Other authors have reported alterations in the frontal lobe [Courchesne and Pierce, 2005b; Geschwind and Levitt, 2007; Just et al., 2012], in particular considering the pars opercularis of the inferior frontal gyrus, previously reported as hypo‐connected (lower FA values) in autistic subjects of older age [Ke et al., 2009; Lo et al., 2011].

It is of interest that postmortem studies reported an early alteration in neuronal proliferation, migration, maturation and organization in prefrontal and temporal regions of children with ASD [Stoner et al., 2014; Wegiel et al., 2010]. In particular, an increase in total neuronal number within the prefrontal cortex of ASD patients (2–16 years) has been detected [Courchesne et al., 2011b] as well as a reduction in pyramidal neuron size within the anterior mid cingulate cortex of young children with ASD that may interfere with an efficient connectivity involving this region [Uppal et al., 2014].

When correlating the diffusion indexes with clinical severity within the ASD subgroup, we found few significant correlations, which however did not hold true after correcting for multiple comparisons. This might be due to the small sample size or to the sensitivity of the scale used to assess severity. Our findings are also difficult to compare to others', as there are no other network based diffusion studies exploring correlation between diffusion indexes and ASD severity at this age. Previous diffusion studies based on voxel based techniques reported negative correlations of Fractional Anisotropy (FA) and symptom expression in fronto‐striatal temporal pathways and posterior brain pathways [Cheung et al., 2009]; other studies based on DTI [Poustka et al., 2012] reported negative correlation between FA with ASD severity in fronto‐temporal pathways.

An important limitation of our study concerns the lack of gender balance between the two clinical groups, which however reflects the epidemiological distribution of the disorders in the two groups. Indeed, sexual dimorphisms in diffusion properties have been observed in some WM tracts (e.g. left cingulum bundle, right inferior fronto‐occipital fasciculus, left inferior longitudinal fasciculus and right uncinated fasciculus) of young children with typical development [Johnson et al., 2013] as well as in the corpora callosa of preschoolers with ASD [Nordahl et al., 2015]. Future DTI investigations in larger groups evaluating separately males and females with ASD are needed in order to provide new insights into possible sex differences in structural brain connectivity. Furthermore, in our study we did not include a control group of typically developing toddlers since ethical considerations did not allow us to perform MRI scans under general anaesthesia in young children for research purposes only. Indeed, in order to perform a reliable analysis and interpretation of imaging data it is important to select controls scanned under sedation and therefore comparable with ASD patients in terms of head motion [Walker et al., 2012]. Future investigations in which subjects would be acquired during natural sleep may overcome this critical aspect [Nordahl et al., 2008].

Although the absence of a control group makes it impossible to directly investigate the differences and the peculiarities of ASD and other‐DD structural connectivity with respect to typically developing infants, some conclusions can be drawn by relating our findings to the literature comparing typically developing children with other‐DD. The few studies exploring the differences in diffusion in children with neurodevelopmental disorders other than ASD, compared with typically developing ones, are consistent in reporting an overall alteration in diffusion parameters. DTI in preschool children with developmental delay showed decreases in anisotropy in several white matter fibre tracts in the centrum semiovale, corona radiata, internal capsule, corpus callosum and frontal and parieto‐occipital subcortical white matter [Filippi et al., 2003]. A reduction in FA values within the corpus callosum was also reported in children aged 1‐9 years with developmental delay, likely reflecting an overall reduction of inter‐hemispheric connectivity [Ding et al., 2009]. Consistent with these findings, tractography studies reported reduced FA in several tracts of the other‐DD brains, including the right uncinate fasciculus, the cingulum and the inferior fronto‐occipital fasciculus [Kumar et al., 2010]. A specific abnormality of the arcuate fasciculus and particularly of its temporal bundle has been also described in children with developmental delay [Sundaram et al., 2008]. In the light of these studies that consistently reported reduced connectivity in young children with developmental delay, it is not surprising that in our study we found significant differences between other‐DD and ASD only in the direction of over‐connectivity in the latter.

CONCLUSIONS

In conclusion, our findings suggest that at an early stage, when ASD symptoms are emerging and becoming apparent, an over‐connectivity pattern represents a specific finding in toddlers with ASD, differentiating them from those with other developmental delays. More specifically, ASD over‐connectivity is observed in networks centred on temporal gyri, striatal regions, frontal and cingulum gyri, all known to be involved in social‐skills development and executive functions. Further studies, with larger cohorts and healthy control groups, are needed to confirm our findings in order to clarify their translational meaning, and ascertain the complex underpinnings of ASD at the age of onset. Moreover, correlation with multifunctional assessments (e.g., adaptive functioning, receptive language abilities and repetitive behaviour) will contribute to shed light on the structural underpinnings of the different dimensional components of neurodevelopmental disabilities.

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