Neuroanatomical correlates of autism spectrum disorders: A meta-analysis of structural magnetic resonance imaging (MRI) studies

Casale, Antonio Del; Ferracuti, Stefano; Alcibiade, Alessandro; Simone, Sara De; Modesti, Martina Nicole; Pompili, Maurizio

doi:10.1016/j.pscychresns.2022.111516

Cited by 15 publications

(7 citation statements)

References 50 publications

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“…Both autistic males and females had thinner parahippocampal gyri across childhood. This region is important for visuospatial processing and was found to be smaller in autistic individuals across multiple studies in a recent metanalysis [69,70]. We also observed that the right fusiform gyrus was thicker across sexes in autism across childhood.…”

Section: Discussionsupporting

confidence: 63%

Sex differences in trajectories of cortical development in autistic children from 2–13 years of age

Andrews,

Diers,

Lee

et al. 2024

Mol Psychiatry

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Previous studies have reported alterations in cortical thickness in autism. However, few have included enough autistic females to determine if there are sex specific differences in cortical structure in autism. This longitudinal study aimed to investigate autistic sex differences in cortical thickness and trajectory of cortical thinning across childhood. Participants included 290 autistic (88 females) and 139 nonautistic (60 females) individuals assessed at up to 4 timepoints spanning ~2–13 years of age (918 total MRI timepoints). Estimates of cortical thickness in early and late childhood as well as the trajectory of cortical thinning were modeled using spatiotemporal linear mixed effects models of age-by-sex-by-diagnosis. Additionally, the spatial correspondence between cortical maps of sex-by-diagnosis differences and neurotypical sex differences were evaluated. Relative to their nonautistic peers, autistic females had more extensive cortical differences than autistic males. These differences involved multiple functional networks, and were mainly characterized by thicker cortex at ~3 years of age and faster cortical thinning in autistic females. Cortical regions in which autistic alterations were different between the sexes significantly overlapped with regions that differed by sex in neurotypical development. Autistic females and males demonstrated some shared differences in cortical thickness and rate of cortical thinning across childhood relative to their nonautistic peers, however these areas were relatively small compared to the widespread differences observed across the sexes. These results support evidence of sex-specific neurobiology in autism and suggest that processes that regulate sex differentiation in the neurotypical brain contribute to sex differences in the etiology of autism.

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Section: Discussionsupporting

confidence: 63%

Sex differences in trajectories of cortical development in autistic children from 2–13 years of age

Andrews,

Diers,

Lee

et al. 2024

Mol Psychiatry

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“…In line with previous studies (Del Casale et al, 2022;Fairchild et al, 2019;Noppari, 2022;Rogers & De Brito, 2016;Sebastian et al, 2016), GMV was used as brain structural measure as it takes into account influences of cortical volume subcomponents such as cortical thickness or surface areas (Vijayakumar et al, 2018) including their different developmental trajectories (Mills et al, 2016). Thus, GMV allows us to relate the findings with the available evidence in the literature.…”

Section: Structural Mri Acquisition and Analysesmentioning

confidence: 96%

“…The evidence is however inconsistent (Klapwijk et al, 2016; Noppari, 2022). In a recent meta‐analysis, ASD youth displayed gray matter volume (GMV) decreases in limbic regions including temporal cortex and amygdala, linked to processes underlying AE (Marsh, 2018), compared with typically developing youth (TD) (Del Casale et al, 2022). Youth with CD, have meanwhile shown reduced GMV across cortical regions including the anterior cingulate cortex (ACC), ventromedial prefrontal cortex (vmPFC), temporal cortex and anterior Insula (AI), as well as subcortical structures (Fairchild et al, 2019; Rogers & De Brito, 2016; Sebastian et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Empathy deficits, callous‐unemotional traits and structural underpinnings in autism spectrum disorder and conduct disorder youth

Tkalcec,

Bierlein,

Seeger‐Schneider

et al. 2023

Autism Research

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Distinct empathy deficits are often described in patients with conduct disorder (CD) and autism spectrum disorder (ASD) yet their neural underpinnings and the influence of comorbid Callous‐Unemotional (CU) traits are unclear. This study compares the cognitive (CE) and affective empathy (AE) abilities of youth with CD and ASD, their potential neuroanatomical correlates, and the influence of CU traits on empathy. Adolescents and parents/caregivers completed empathy questionnaires (N = 148 adolescents, mean age = 15.16 years) and T1 weighted images were obtained from a subsample (N = 130). Group differences in empathy and the influence of CU traits were investigated using Bayesian analyses and Voxel‐Based Morphometry with Threshold‐Free Cluster Enhancement focusing on regions involved in AE (insula, amygdala, inferior frontal gyrus and cingulate cortex) and CE processes (ventromedial prefrontal cortex, temporoparietal junction, superior temporal gyrus, and precuneus). The ASD group showed lower parent‐reported AE and CE scores and lower self‐reported CE scores while the CD group showed lower parent‐reported CE scores than controls. When accounting for the influence of CU traits no AE deficits in ASD and CE deficits in CD were found, but CE deficits in ASD remained. Across all participants, CU traits were negatively associated with gray matter volumes in anterior cingulate which extends into the mid cingulate, ventromedial prefrontal cortex, and precuneus. Thus, although co‐occurring CU traits have been linked to global empathy deficits in reports and underlying brain structures, its influence on empathy aspects might be disorder‐specific. Investigating the subdimensions of empathy may therefore help to identify disorder‐specific empathy deficits.

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“…In recent years, there has been significant progress in the development of multimodal neuroimaging techniques for studying brain diseases, including ESRD-related neurological complications. These techniques include functional magnetic resonance imaging (fMRI) [ 6 ], structural magnetic resonance imaging [ 7 ], magnetic resonance spectroscopy [ 8 ], fluorodeoxyglucose positron emission tomography [ 9 ], single photon emission tomography [ 10 ], arterial spin labeling [ 11 ], diffusion tensor imaging [ 12 ], and diffusion kurtosis imaging (DKI) [ 13 ]. DKI has gained rapid development and has provided valuable insights into the pathophysiological mechanisms of brain diseases from various perspectives such as deposition of pathological markers, brain structural connectivity, and functional connectivity.…”

Section: Introductionmentioning

confidence: 99%

End-stage renal disease accompanied by mild cognitive impairment: A study and analysis of trimodal brain network fusion

Chen,

Liu,

Shi

2024

PLoS ONE

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The function and structure of brain networks (BN) may undergo changes in patients with end-stage renal disease (ESRD), particularly in those accompanied by mild cognitive impairment (ESRDaMCI). Many existing methods for fusing BN focus on extracting interaction features between pairs of network nodes from each mode and combining them. This approach overlooks the correlation between different modal features during feature extraction and the potentially valuable information that may exist between more than two brain regions. To address this issue, we propose a model using a multi-head self-attention mechanism to fuse brain functional networks, white matter structural networks, and gray matter structural networks, which results in the construction of brain fusion networks (FBN). Initially, three networks are constructed: the brain function network, the white matter structure network, and the individual-based gray matter structure network. The multi-head self-attention mechanism is then applied to fuse the three types of networks, generating attention weights that are transformed into an optimized model. The optimized model introduces hypergraph popular regular term and L1 norm regular term, leading to the formation of FBN. Finally, FBN is employed in the diagnosis and prediction of ESRDaMCI to evaluate its classification performance and investigate the correlation between discriminative brain regions and cognitive dysfunction. Experimental results demonstrate that the optimal classification accuracy achieved is 92.80%, which is at least 3.63% higher than the accuracy attained using other methods. This outcome confirms the effectiveness of our proposed method. Additionally, the identification of brain regions significantly associated with scores on the Montreal cognitive assessment scale may shed light on the underlying pathogenesis of ESRDaMCI.

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Neuroanatomical correlates of autism spectrum disorders: A meta-analysis of structural magnetic resonance imaging (MRI) studies

Cited by 15 publications

References 50 publications

Sex differences in trajectories of cortical development in autistic children from 2–13 years of age

Sex differences in trajectories of cortical development in autistic children from 2–13 years of age

Empathy deficits, callous‐unemotional traits and structural underpinnings in autism spectrum disorder and conduct disorder youth

End-stage renal disease accompanied by mild cognitive impairment: A study and analysis of trimodal brain network fusion

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