The authors have declared that no competing interests exist.
Conceived and designed the experiments: BZ. Performed the experiments: BZ JA MBDP JN JC AC JL. Analyzed the data: BZ JA MBDP JN. Contributed reagents/materials/analysis tools: BZ JA AF MBDP JN JC AC TF AA NL EB JL. Wrote the paper: BZ JL.
Autism is a complex neurological condition characterized by childhood onset of dysfunction in multiple cognitive domains including socio-emotional function, speech and language, and processing of internally versus externally directed stimuli. Although gross brain anatomic differences in autism are well established, recent studies investigating regional differences in brain structure and function have yielded divergent and seemingly contradictory results. How regional abnormalities relate to the autistic phenotype remains unclear. We hypothesized that autism exhibits distinct perturbations in network-level brain architecture, and that cognitive dysfunction may be reflected by abnormal network structure. Network-level anatomic abnormalities in autism have not been previously described. We used structural covariance MRI to investigate network-level differences in gray matter structure within two large-scale networks strongly implicated in autism, the salience network and the default mode network, in autistic subjects and age-, gender-, and IQ-matched controls. We report specific perturbations in brain network architecture in the salience and default-mode networks consistent with clinical manifestations of autism. Extent and distribution of the salience network, involved in social-emotional regulation of environmental stimuli, is restricted in autism. In contrast, posterior elements of the default mode network have increased spatial distribution, suggesting a ‘posteriorization’ of this network. These findings are consistent with a network-based model of autism, and suggest a unifying interpretation of previous work. Moreover, we provide evidence of specific abnormalities in brain network architecture underlying autism that are quantifiable using standard clinical MRI.
The neurobiology of autism has been studied since the initial description of the disorder
Functional connectivity MRI (fcMRI) has begun to reveal distinct abnormalities in autism. fcMRI depicts synchronous blood-oxygen-level-dependent (BOLD) signal covariance of spatially discrete brain regions, implying ‘connectedness’ of correlated regions
Although inconsistent reports of over- versus under-growth and connectivity may result from technical or methodological differences
Emerging evidence supports abnormal connectivity of subregions within the DMN in autism. This network, comprised of medial and ventral prefrontal cortex, retrosplenial cortex, bilateral angular gyrus, and anchored by posterior cingulate cortex (PCC) is active during “rest”, in the absence of goal-oriented or externally-directed stimuli, and normally deactivates during performance of cognitively demanding tasks
Social functioning relies upon frontoinsula, anterior cingulate, amygdala, fusiform gyrus, and ventral prefrontal cortex
In addition, the anterior frontoinsula is uniquely positioned to mediate interactions between two counterposed large-scale networks, the DMN and the executive-control network (ECN)
Despite considerable efforts delineating regional abnormalities in autism, little is known regarding large-scale network-level architecture that may underlie the cognitive and behavioral manifestations of the disease. Numerous studies report functional and structural abnormalities in regions containing canonical network nodes, suggesting collectively that network-level abnormalities may be a fundamental characteristic of autistic neurobiology. The most prominent clinical features, namely marked deficits in social and emotional functioning as well as dysregulation of internally- versus externally-directed behaviors, predict network-level abnormalities in the SN and DMN respectively. We examined whether structural abnormalities in large-scale distributed brain networks could be detected in autism, and hypothesized that autism would demonstrate altered network-level structural architecture consistent with poor development or restricted topology of the SN, as well as overgrowth of the DMN. We employed structural covariance MRI (scMRI) techniques
Forty-nine male subjects with autism aged 3–22 years were compared to forty-nine age- and IQ-matched normal male control subjects. Diagnosis of autism was established using the Autism Diagnostic Interview-Revised (ADI-R)
Mean Age (yrs.) | S.D. Age (yrs.) | Age Range (yrs.) | ADOS-SI | ADOS-C | ADOS tot | VIQ | PIQ | |
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13.27 (5.07) | 5.07 | 3.49–22.33 | 9.47 | 4.9 | 14.37 | 98.15 | 95.55 |
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13.67 (5.53) | 5.53 | 3.47–22.44 | 0.72 | 0.49 | 1.21 | 110.7 | 106.77 |
ADOS-SI, ADOS Social Impairment score; ADOS-C, ADOS Communication score; VIQ, Verbal IQ; PIQ, Performance IQ.
We created customized image analysis templates by normalizing, segmenting, and averaging MPRAGE images using SPM5 (
Age effects were modeled as covariates-of-no-interest but resulted in minimal appreciable topological differences in the resulting maps, likely due to sample size, group matching, and statistical constraints of the scMRI technique (see
This research was approved and conducted according to the policies, procedures, and regulatory oversight of the University of Utah IRB. All consent, scanning, data management, and analytical procedures were performed in accordance with these policies and procedures. Written informed consent and/or assent was obtained for each participant and/or guardian according to IRB-approved procedures. Capacity to consent was assessed by a trained study staff member present at time of consent. Guardians were asked to give consent on behalf of participants deemed unable or<18 years of age. Signed consent documentation is maintained in the laboratory and a copy was provided to the patient and/or guardian. The University of Utah IRB complies with all U.S regulatory requirements related to the protection of human subjects research participants.
Seed-based scMRI revealed expected structural covariance maps in normal controls
Statistical parametric maps depict brain regions in which gray matter intensity covaried with that of the seed ROI (right FI) in each group. (A) Structural covariance patterns appear substantially spatially restricted in autism (hot colors; see also
In our control group, the right FI anchored covariance maps (
In controls, the right PCC seed covaried with bilateral precuneus/retrosplenial cortex, lateral parietal regions including angular gyrus, right medial prefrontal cortex, and discrete nodes within right superior frontal gyrus and medial frontal cortex (
Statistical parametric maps depict brain regions in which gray matter intensity covaried with that of the seed ROI (right PCC) in each group. (A) Structural covariance patterns appear robust in posterior brain regions, but restricted in frontal areas in autism (hot colors; see also
Plots of voxel counts by group indicate substantially restricted network extent in the SN of autistic subjects, whereas the DMN is more spatially extensive in the autistic group. Y-axis scale is voxel number from associated statistical maps. AUT, autism group; CTRL, control group; DMN, default mode network; L, left; R, right; SN, salience network.
x | y | z | p (FWE) | T height | Peak Region | Secondary Regions | |||
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38 | 26 | −10 | 0.000 | 94.87 | R FI | |||
−37 | 27 | −12 | 0.000 | 9.16 | L FI | ||||
−30 | 21 | −19 | 0.000 | 6.68 | L FI | L Insula | |||
10 | −6 | 49 | 0.000 | 6.69 | R SMA | R Mid Cingulum | |||
10 | 14 | 50 | 0.000 | 5.99 | R SMA | R Sup Frontal | |||
10 | 27 | 49 | 0.001 | 5.9 | Medial R Sup Frontal | R SMA | R Sup Frontal | ||
−12 | −9 | 60 | 0.000 | 6.6 | L SMA | ||||
−7 | 17 | 48 | 0.000 | 6.15 | L SMA | L Sup Frontal | |||
−20 | 8 | 55 | 0.000 | 6.1 | L Sup Frontal | L Mid Frontal | |||
19 | 53 | −17 | 0.001 | 5.86 | R Sup Frontal Orb | R Mid Frontal Orb | |||
11 | 55 | −24 | 0.003 | 5.53 | R Sup Frontal Orb | Rectus | |||
−21 | 48 | −15 | 0.001 | 5.86 | L Mid Frontal Orb | L Sup Frontal Orb | |||
20 | 56 | 1 | 0.002 | 5.65 | R Sup Frontal | R Sup Frontal Orb | R Med Sup Frontal | ||
−49 | −1 | 39 | 0.004 | 5.41 | L Precentral | L Postcentral | |||
7 | 18 | −19 | 0.006 | 5.34 | R Rectus | R Olfactory | |||
−5 | 23 | −23 | 0.007 | 5.29 | L Rectus | L Sup Frontal Orb | |||
−53 | −14 | 23 | 0.007 | 5.29 | L Postcentral | ||||
1 | 14 | −1 | 0.008 | 5.27 | L Caudate | R Caudate | B Olfactory | ||
52 | 6 | 23 | 0.008 | 5.26 | R Inf Frontal Oper | R Precentral | |||
26 | 41 | 41 | 0.008 | 5.25 | R Sup Frontal | R Mid Frontal | |||
11 | 51 | −9 | 0.008 | 5.24 | R Med Frontal Orb | R Sup Frontal Orb | |||
−53 | 1 | 11 | 0.009 | 5.23 | L Rolandic Oper | L Inf Frontal Oper | L Postcentral | ||
|
38 | 26 | −10 | 0.000 | 116.57 | R FI | R Insula | ||
23 | 59 | 2 | 0.000 | 12.19 | R Sup Frontal | R Sup Frontal Orb | |||
−24 | 58 | −3 | 0.000 | 11.3 | L Sup Frontal Orb | L Sup Frontal | |||
−37 | −28 | −27 | 0.001 | 5.93 | L Fusiform | L Inf Temporal | |||
−15 | −11 | 58 | 0.001 | 5.74 | L SMA | ||||
−53 | 10 | 9 | 0.005 | 5.36 | L Inf Frontal Oper | L Rolandic Oper | |||
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4 | −40 | 36 | 0.000 | 252.96 | R Mid Cingulum | L Mid Cingulum | B Precuneus | ||
11 | −70 | 42 | 0.000 | 9.81 | R Precuneus | R Cuneus | |||
−33 | −76 | 37 | 0.000 | 8.21 | L Mid Occipital | L Inf Parietal | |||
−3 | −16 | −16 | 0.000 | 7.79 | L Ventral Tegmental | ||||
−7 | −30 | 12 | 0.000 | 7.57 | L Thalamus | ||||
−17 | −29 | 23 | 0.000 | 6.27 | L Caudate | ||||
−16 | −38 | −2 | 0.009 | 5.23 | L Lingual | L Hippocampus | |||
11 | −21 | −14 | 0.000 | 6.64 | R Parahippo | ||||
−53 | 9 | −14 | 0.000 | 6.49 | L Sup Temporal Pole | ||||
−19 | 15 | −23 | 0.000 | 6.38 | L Inf Frontal Orb | L Sup Frontal Orb | |||
21 | −29 | 70 | 0.000 | 6.28 | R Precentral | R Postcentral | |||
11 | −22 | 72 | 0.000 | 6.16 | R Paracentral Lobule | R SMA | R Precentral | ||
0 | −25 | 15 | 0.000 | 6.25 | L Thalamus | ||||
10 | −31 | 12 | 0.000 | 6.06 | R Thalamus | ||||
−58 | −2 | 8 | 0.000 | 6.03 | L Rolandic Oper | L Sup Temporal | L Postcentral | ||
17 | −26 | 23 | 0.001 | 5.98 | R Caudate | ||||
17 | 64 | 26 | 0.001 | 5.77 | R Sup Frontal | R Med Sup Frontal | |||
−13 | −26 | −22 | 0.002 | 5.7 | L Parahippo | ||||
−58 | 6 | 27 | 0.003 | 5.57 | L Precentral | L Inf Frontal Oper | |||
−18 | 54 | 37 | 0.003 | 5.5 | L Sup Frontal | ||||
−19 | 63 | 24 | 0.005 | 5.38 | L Sup Frontal | ||||
−52 | 38 | 8 | 0.005 | 5.38 | L Inf Frontal Tri | ||||
−43 | 13 | 0 | 0.005 | 5.38 | L Insula | L Inf Frontal Oper | L Inf Frontal Tri | ||
−20 | 16 | 60 | 0.005 | 5.38 | L Sup Frontal | L Mid Frontal | |||
−21 | −37 | 67 | 0.006 | 5.36 | L Postcentral | ||||
27 | −30 | −41 | 0.006 | 5.35 | cerebellum | ||||
−30 | −29 | −33 | 0.007 | 5.31 | L Fusiform | Cerebellum | |||
15 | −20 | 25 | 0.007 | 5.3 | R Caudate | ||||
3 | 34 | −33 | 0.008 | 5.26 | Rectus (bilat) | ||||
−5 | −24 | 70 | 0.010 | 5.21 | L paracentral lobule | ||||
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4 | −40 | 36 | 0.000 | 227.54 | R Mid Cingulum | L Mid Cingulum | B Precuneus | |
−9 | −52 | 64 | 0.000 | 7.64 | L Precuneus | ||||
38 | −55 | 51 | 0.000 | 7.36 | R Inf Parietal | R Sup Parietal | R Angular | ||
−18 | 54 | 37 | 0.000 | 6.14 | L Sup Frontal | ||||
15 | 59 | 33 | 0.001 | 5.84 | R Sup Frontal | R Sup Medial Frontal | |||
−57 | 5 | 30 | 0.001 | 5.74 | L Precentral | ||||
−61 | −6 | 34 | 0.002 | 5.59 | L postcentral | L Precentral | |||
−8 | −27 | 16 | 0.003 | 5.54 | L Thalamus | ||||
−26 | 13 | 61 | 0.004 | 5.47 | L Mid Frontal | L Sup Frontal | |||
14 | 70 | 9 | 0.005 | 5.42 | R Sup Medial Frontal | R Sup Frontal | |||
21 | 25 | 55 | 0.005 | 5.4 | R Sup Frontal | ||||
21 | 35 | 49 | 0.007 | 5.3 | R Sup Frontal | ||||
−58 | −6 | 3 | 0.008 | 5.27 | L Sup Temporal | L Rolandic Oper | |||
−7 | −23 | 69 | 0.008 | 5.26 | L Paracentral Lobule | ||||
−41 | −79 | 20 | 0.010 | 5.21 | L Mid Occipital |
FWE, family-wise error.
Direct contrasts between autism and control groups revealed characteristic regional differences, confirming the above groupwise analyses (
Statistical parametric maps depict brain regions in which gray matter intensity covaried with that of the seed ROI (right FI or PCC) differently between groups. (A) Structural covariance with right FI is greater in bilateral SMA in autistic subjects (hot colors; see also
x | y | z | p (UC) | T height | Peak region | Secondary Regions | |||
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−14 | −15 | 62 | 0.014 | 3.55 | L SMA | L Paracentral Lobule | L Precentral | |
−10 | −6 | 58 | 0.02 | 2.07 | L SMA | L Paracentral Lobule | |||
−20 | −6 | 58 | 0.027 | 1.95 | L Sup Frontal | L Mid Frontal | |||
36 | 27 | −10 | 0.029 | 1.92 | R Inf Fronal Orb (FI) | R Insula | |||
12 | −3 | 55 | 0.033 | 1.86 | R SMA | R Mid Cingulum | |||
−49 | 1 | 40 | 0.036 | 1.81 | L Precentral | ||||
2 | 16 | −2 | 0.043 | 1.73 | R olfactory | R Caudate | L Caudate | L Olfactory | |
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42 | 39 | −14 | 0 | 4.86 | R Mid Frontal Orb | |||
−28 | 55 | 7 | 0 | 4.2 | L Mid Frontal | L Sup Frontal | |||
27 | 55 | 12 | 0 | 4.03 | R Sup Frontal | R Mid Frontal | |||
−7 | 51 | 14 | 0 | 3.61 | L Medial Sup Frontal | L Ant Cingulate | |||
−11 | 56 | 5 | 0 | 2.87 | L Medial Sup Frontal | L Sup Frontal | |||
−5 | 30 | 40 | 0 | 2.36 | L Medial Sup Frontal | ||||
45 | 9 | −32 | 0 | 3.49 | R Mid Temporal Pole | R Sup Temporal Pole | |||
−37 | 46 | 17 | 0.001 | 3.19 | L Mid Frontal | L Inf Frontal Tri | |||
−31 | 44 | 26 | 0.001 | 3.05 | L Mid Frontal | ||||
−18 | 47 | 34 | 0.013 | 2.24 | L Sup Frontal | L Mid Frontal | |||
−24 | 41 | 35 | 0.013 | 2.09 | L Sup Frontal | L Mid Frontal | |||
16 | 54 | 18 | 0.017 | 2.16 | R Sup Frontal | R Medial Sup Frontal | |||
−49 | 32 | 18 | 0.036 | 1.81 | L Inf Frontal Tri | ||||
−43 | 11 | −26 | 0.041 | 1.74 | L Sup Temporal Pole | L Mid Temporal Pole | |||
−40 | 12 | −29 | 0.046 | 1.69 | L Mid Temporal Pole | L Sup Temporal Pole | |||
−44 | 27 | −15 | 0.047 | 1.68 | L Inf Frontal Orb (FI) | L Sup Temporal Pole | |||
51 | 23 | −2 | 0.047 | 1.68 | R Inf Frontal Tri | R Inf Frontal Orb (FI) | |||
−43 | 24 | −15 | 0.049 | 1.66 | L Inf Frontal Orb (FI) | L Sup Temporal Pole | |||
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14 | −73 | 40 | 0.003 | 3.61 | R Precuneus | R cuneus | ||
2 | −70 | 30 | 0.019 | 3.58 | R Precuneus | L cuneus | L Precuneus | R cuneus | |
−30 | −78 | 35 | 0.01 | 3.51 | L Mid occipital | L Inf parietal | |||
46 | −74 | 28 | 0.011 | 3.48 | R Mid Occipital | R Angular | |||
52 | −66 | 25 | 0.017 | 2.86 | R Angular | R Mid Temporal | R Mid Occipital | ||
57 | −24 | −3 | 0.017 | 2.15 | R Sup Temporal | R Mid Temporal | |||
59 | −54 | −11 | 0.024 | 2 | R Inf Temporal | R Mid Temporal | |||
57 | −58 | −3 | 0.03 | 1.9 | R Mid Temporal | R Inf temporal | |||
−48 | −76 | 16 | 0.025 | 1.98 | L Mid Occipital | L Mid Temporal | |||
48 | −13 | 1 | 0.044 | 1.71 | R Sup Temporal | R Insula | R Heschl | ||
53 | −61 | 15 | 0.048 | 1.67 | R Mid Temporal | ||||
|
−56 | −46 | 39 | 0.003 | 3.79 | L Inf Parietal | L Supramarginal | ||
−49 | −47 | 46 | 0.017 | 3.59 | L Inf Parietal | ||||
−46 | −34 | 49 | 0.018 | 3.51 | L postcentral | L Inf Parietal | |||
61 | 1 | 24 | 0.004 | 3.5 | R precentral | R postecentral | |||
49 | −36 | 53 | 0.007 | 2.5 | R Inf Parietal | R postcentral | |||
50 | −27 | 53 | 0.017 | 2.16 | R postcentral | ||||
−6 | −58 | 57 | 0.009 | 2.42 | L precuneus | ||||
−19 | −56 | 54 | 0.015 | 2.19 | L Sup Parietal | L precuneus | |||
−11 | −50 | 53 | 0.028 | 1.93 | L Precuneus | ||||
−51 | −30 | 48 | 0.012 | 2.3 | L Inf Parietal | L postcentral | |||
−57 | −56 | −6 | 0.018 | 2.12 | L mid temporal | L Inf temporal | |||
62 | −6 | 18 | 0.019 | 2.09 | R postcentral | R Rolandic Oper | |||
−62 | −8 | 33 | 0.029 | 1.91 | L postcentral | ||||
0 | −50 | −2 | 0.031 | 1.88 | Vermis | L Cerebellum | |||
−26 | −32 | 60 | 0.033 | 1.85 | L postcentral | L precentral | |||
8 | −16 | 58 | 0.036 | 1.81 | R SMA | ||||
13 | 70 | 8 | 0.039 | 1.77 | R Medial Sup Frontal | R Sup Frontal | |||
21 | 23 | 56 | 0.039 | 1.77 | R Sup Frontal | ||||
62 | −19 | 18 | 0.041 | 1.75 | R Supramarginal | R Rolandic Oper | R Postcentral | R Sup Temporal | |
−49 | −56 | 26 | 0.043 | 1.73 | L Angular | L Mid Temporal | L Supramarginal | ||
−28 | 12 | 61 | 0.045 | 1.7 | L Mid Frontal | ||||
62 | −15 | 19 | 0.047 | 1.68 | R Postcentral | R Rolandic Oper | R Supramarginal | ||
−29 | 15 | 60 | 0.047 | 1.68 | L Mid Frontal | ||||
20 | 27 | 54 | 0.048 | 1.67 | R Sup Frontal | ||||
19 | 26 | 55 | 0.049 | 1.66 | R Sup Frontal |
UC, uncorrected.
We hypothesized that the marked abnormalities in SN architecture could result in poor social and emotional function, which in turn may be reflected in ADOS Social Impairment scores. To determine whether ADOS Social Impairment (ADOS-SI) scores were related to differential brain structure in autism, we analyzed whole-brain GM covariance using ADOS-SI scores as the covariate of interest. Direct contrasts between autism and control groups revealed distinct differences in structural architecture (
Statistical parametric maps depict brain regions in which gray matter intensity covaried with ADOS Social Impairment (ADOS-SI) score differently between groups. In controls, ADOS-SI scores covaried with frontal regions overlapping with SN including medial frontal wall, anterior cingulate, and frontoinsular cortex. In contrast, in autistic subjects ADOS-SI covaried with posterior brain regions including cuneus, precuneus, parieto-occipital regions, and temporoparietal cortex. scMRI data are T-statistic maps (p<0.05, inclusively masked to the network global map for both groups at p<0.01 FWE) displayed on the average anatomical template of all subjects. scMRI maps from both groups overlaid on a single anatomic volume reveal distinct between-group differences. The left side of the image corresponds to the right side of the brain. SN, salience network.
We demonstrate that specific abnormalities in brain network structure are present in autism. Moreover, the topology of network-level abnormalities is consistent with prior morphological and functional work as well as clinical hallmarks of the disease. Specifically, the SN, anchored by the right FI, appears underdeveloped in autism. In addition, distinct nodal differences are apparent. Frontal regions are underrepresented in autism, whereas SMA covariance exceeds that of controls. In contrast, the DMN, anchored by PCC, may have both ‘overconnected’ and ‘underconnected’ components. Covariance beyond normal DMN topology was evidenced in autism, and this overrepresentation of the DMN was restricted to posterior brain regions. Many of the regions outside of DMN boundaries have been previously implicated in autism, including caudate, inferotemporal/fusiform cortex, auditory and temporal association cortex, and lateral postero-occipital regions. In contrast, covariance with frontal canonical DMN nodes was nearly absent in autism. These data suggest that distinct and network-specific alterations in structural architecture may underlie autism, providing a plausible neural substrate for clinical hallmarks of the disease. Furthermore, these data suggest an anatomic substrate for abnormal functional connectivity reported in autism, and demonstrate that this abnormal architecture is observable using standard anatomic MRI.
Our findings provide a resolution for previously reported inconsistencies in both structural and functional brain architecture in autism, as network-level effects could drive these apparent differences. Regional variability in GM and WM volume, WM integrity, cortical thickness, functional connectivity, and microscopic structure are predicted by network-level abnormalities. A framework of altered network-specific architecture unifies earlier divergent reports suggesting under- and overconnectivity within the same gross brain regions in autism and remains consistent with earlier hypotheses of abnormal connectivity
Our data suggest restricted topology of the SN in autism. These results support the notion that autistic subjects lack the cognitive mechanism to disambiguate relevant social and emotional cues within complex environmental stimulus streams
Posterior overgrowth and ‘anterior-posterior disconnection’ of the autistic DMN may reflect a perturbed ‘division of labor’ within this network. Our data support an emerging theme in the literature of anterior-posterior underconnectivity
The neural systems that support social and emotional processing appear to be underdeveloped in autism, whereas those that mediate internally- versus externally-directed processing appear to be overrepresented in posterior nodes but isolated from anterior network nodes. The neuropathological processes underlying these selective abnormalities in large-scale brain networks in autism remain unknown. Plausible factors include early neuronal excess and later neuronal loss, abnormal microstructure, excess synapse formation, excessive dentritic outgrowth or hyperconnectivity, aberrant axonal pathfinding, overgrowth, or connections, and altered myelination
How these processes relate to gray matter density is unclear. Our work suggests, however, that downstream effects of presumably disrupted molecular and cellular mechanics produce distinct and measurable alterations of normal brain network architecture within networks that underlie the core manifestations of the autistic disease state. Posterior DMN subnets may be overconnected, whereas rostro-caudal connectivity may be limited. In the SN, interconnections between critical nodes may be malformed, mature architecture not achieved, and the network left rudimentary and dysfunctional. Phenotypic features of autism could result as network ‘dysconnection’ leads to a deficit of ‘salience filtering’ and resultant inefficiencies in recruiting appropriate attentional, socio-emotional, behavioral, and higher-order cognitive resources
We describe anatomic substrates consistent with altered functional connectivity reported in autism, and demonstrate that structural network-level abnormalities are quantifiable using standard anatomic MRI. It is plausible that multiple large-scale network architectures, including ICNs and SCNs, may be affected in autism. Our work predicts abnormal fcMRI covariance within large-scale networks commensurate with specific structural abnormalities that together disrupt emergence and maintenance of complex psychological and physiological functions in autism. However, although connectivity is often assumed by techniques measuring MRI signal covariance, neither fcMRI nor scMRI techniques directly measure anatomic connectedness. Moreover, direct associations between functional synchrony and underlying anatomic structure have yet to be established in autism.
Our whole-brain seed-based approach differs from most neuroimaging studies examining pairwise correlations between
Our data suggest that discrepancies in fcMRI over- and under-connectivity, WM and GM volume, cortical thickness, and WM integrity measures may be reconciled by a model of autism as a network-based disease. Recent fcMRI studies are consistent with the concept of autism as a disease with distinctive network distribution patterns
These results could guide future studies of network abnormalities in autism. For example, our findings predict decreased fcMRI and WM connectivity between SN nodes, as well as increased connectivity between specific posterior elements within as well as outside of the canonical DMN. Moreover, it remains unknown whether other large-scale networks show similar patterns of abnormal structural architecture. Distinctive frontal, temporal, and cingulate gray matter overgrowth in young autistics, with relative sparing of occipital cortices has been reported
Our study supports a model of autistic pathophysiology affecting domain-specific large-scale brain networks. Using standard anatomic MRI, we identified network-level structural abnormalities in the autistic brain, providing the first account of whole-brain, network-specific perturbations within autistic brain architecture. Our results suggest that structural brain abnormalities in autism may affect distinct large-scale networks. The SN appears underdeveloped in volume and extent, whereas the DMN demonstrates elements of both under- as well as over-development. These network-level perturbations are consistent with the clinical manifestations of the disease, and may provide targets for further study and intervention. Moreover, FI may represent an epicenter of perturbed structure and function in the autistic brain. Our work provides a unifying model of previously discordant findings based on structural and functional assessment, reconciles recent work with classic gross morphological findings in the disease, and reveals divergent network-dependent over- and underdevelopment in the same subjects. The diffuse specificity of our findings is consistent with emerging literature identifying regional abnormalities using varying techniques on microstructural as well as macrostructural levels.
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Group age distributions.
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MNI coordinates and characteristics of peak voxels and associated clusters of groupwise scMRI maps, with age as a covariate in the model.
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MNI coordinates and characteristics of peak voxels and associated clusters of between-group scMRI maps, with age as a covariate in the model.
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The authors express their sincere gratitude to the young people and their families who participated in the study.