Structure–Function Connectomics Reveals Aberrant Developmental Trajectory Occurring at Preadolescence in the Autistic Brain

Abstract

Accumulating neuroimaging evidence shows that age estimation obtained from brain connectomics reflects the level of brain maturation along with neural development. It is well known that autism spectrum disorder (ASD) alters neurodevelopmental trajectories of brain connectomics, but the precise relationship between chronological age (ChA) and brain connectome age (BCA) during development in ASD has not been addressed. This study uses neuroimaging data collected from 50 individuals with ASD and 47 age- and gender-matched typically developing controls (TDCs; age range: 5–18 years). Both functional and structural connectomics were assessed using resting-state functional magnetic resonance imaging and diffusion tensor imaging data from the Autism Brain Imaging Data Exchange repository. For each participant, BCA was estimated from structure–function connectomics through linear support vector regression. We found that BCA matched well with ChA in TDC children and adolescents, but not in ASD. In particular, our findings revealed that individuals with ASD exhibited accelerated brain maturation in youth, followed by a delay of brain development starting at preadolescence. Our results highlight the critical role of BCA in understanding aberrant developmental trajectories in ASD and provide the new insights into the pathophysiological mechanisms of this disorder.

The stage‐specifically accelerated brain aging in never‐treated first‐episode patients with depression

“…These evidences include association between depression and decline in cognitive state (John, Patel, & Rusted, 2019), elevated risk of metabolic syndrome (Vancampfort et al, 2014) and cellular aging (Verhoeven et al, 2014) in patients with depression. Recent years, researchers began to use brain images combined with machine learning method to predict brain age (Gaser, Franke, Klöppel, Koutsouleris, & Sauer, 2013;Habes & Janowitz, 2016;Hajek et al, 2019;He et al, 2020) to explore disease such as schizophrenia, mild cognitive impairment (MCI).Exploring whether and how brain aging patterns are altered using machine learning could deepened our understanding of physiological mechanism of these disease.…”

“…Recently, studies employ machine learning method combined with structural brain MRI images accurately predict individual brain age (Cole, Franke, & Cherbuin, 2019). Then, the prediction model is successfully applied to the study of several neurological diseases and reveals accelerated brain aging in disease such as schizophrenia, MCI, Alzheimer's disease, and autism (Gaser et al, 2013;Habes & Janowitz, 2016;Hajek et al, 2019;He et al, 2020). However, there is a paucity of studies exploring whether and how brain aging patterns is disturbed in patients with depression.…”

Individual‐based morphological brain network organization and its association with autistic symptoms in young children with autism spectrum disorder

“…This theory assumes an inefficient interregional brain connectivity across the cerebral cortex that results in abnormal information integration at psychological and neural levels in autistic brains and may also explain diverse impairments in social symptoms (Just et al, 2004; Schipul, Keller, & Just, 2011). Other studies also revealed that the overall connectivity class in ASD is highly heterogeneous, and a combination of hyper‐ and hypo‐connectivity seems to coexist across different subtypes of ASD (Maximo & Kana, 2019; Rasero, Jimenez‐Marin, Diez, Hasan, & Cortes, 2020; Yerys et al, 2017) and trajectories of neural development (C. He et al, 2020).…”

“…Autism spectrum disorder (ASD) is a childhood‐onset atypical neurodevelopmental condition causing deficits in social communication, social reciprocity, and restricted and repetitive behaviors (RRBs) (Association American Psychiatric, 2013). Accumulating evidence suggests that ASD is accompanied by atypical structural brain connectivity within the neural systems related to social deficits (Billeci, Calderoni, Tosetti, Catani, & Muratori, 2012; Im et al, 2018; Noriuchi et al, 2010) that is majorly referred to connectivity based on DTI (Chung, Adluru, Dalton, Alexander, & Davidson, 2010; Chung, Adluru, Dalton, Alexander, & Davidson, 2011; Dennis et al, 2011; He et al, 2020). One study found that the decreased fractional anisotropy (FA), a metric of white matter integrity, of the inferior longitudinal fasciculus is negatively correlated with social interaction in ASD (Im et al, 2018).…”

Genes involved in cholesterol cascades are linked to brain connectivity in one third of autistic patients

“…When considering brain networks, the frontal, default mode and salience have been implicated in ASD (23,(26)(27)(28)(29)(30). Moreover, the neuroanatomical structures implicated in ASD are not static but they undergo changes throughout development (31)(32)(33), and this also happens in social functioning and communication (34).…”