Progress and roadblocks in the search for brain-based biomarkers of autism and attention-deficit/hyperactivity disorder

Abstract

Children with neurodevelopmental disorders benefit most from early interventions and treatments. The development and validation of brain-based biomarkers to aid in objective diagnosis can facilitate this important clinical aim. The objective of this review is to provide an overview of current progress in the use of neuroimaging to identify brain-based biomarkers for autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD), two prevalent neurodevelopmental disorders. We summarize empirical work that has laid the foundation for using neuroimaging to objectively quantify brain structure and function in ways that are beginning to be used in biomarker development, noting limitations of the data currently available. The most successful machine learning methods that have been developed and applied to date are discussed. Overall, there is increasing evidence that specific features (for example, functional connectivity, gray matter volume) of brain regions comprising the salience and default mode networks can be used to discriminate ASD from typical development. Brain regions contributing to successful discrimination of ADHD from typical development appear to be more widespread, however there is initial evidence that features derived from frontal and cerebellar regions are most informative for classification. The identification of brain-based biomarkers for ASD and ADHD could potentially assist in objective diagnosis, monitoring of treatment response and prediction of outcomes for children with these neurodevelopmental disorders. At present, however, the field has yet to identify reliable and reproducible biomarkers for these disorders, and must address issues related to clinical heterogeneity, methodological standardization and cross-site validation before further progress can be achieved.

Oxytocin modulates the intrinsic dynamics between attention-related large scale networks

“…Finally, a disrupted interplay between the DAN-SN-DN has been consistently observed in autism (e.g. Uddin et al, 2017) and considerable efforts aim at evaluating the therapeutic potential of intranasal OXT for alleviating symptoms of autism (Guastella and Hickie, 2016;Keech et al, 2018). Thus, in line with previous studies (Bethlehem et al, 2017;Scheele et al, 2014) associations between OXT effects and individual variations in autistic traits were additionally explored.…”

“…In line with prior work reporting that OXT's effect on intrinsic connectivity (Bethlehem et al, 2017) and social processing related neural activity vary as a function of autistic traits, exploratory analysis revealed positive associations between levels of autistic traits and SN-DN (i.e., accSN-pvDN, accSN-pdDN, inSN-pdDN) connectivity following OXT, but not PLC. Both, ACC and AI core regions of the SN have repeatedly been associated with ASD (Anderson et al, 2011;Uddin et al, 2013) and overconnectivity between these networks, possibly reflecting deficient segregation (Rudie et al, 2012), has been considered as promising diagnostic and predictive ASD marker (Hogeveen et al, 2018;Uddin et al, 2017). Initial clinical trials in ASD demonstrated attenuated social-cognitive deficits in ASD following OXT that were mediated by modulatory effects on AI and ACC activity (Andari et al, 2016;Aoki et al, 2014;Watanabe et al, 2015).…”

Investigating functional brain network integrity using a traditional and novel diagnostic system for neurodevelopmental disorders

“…Specifically, studies of individuals with ADHD report reduced segregation of DMN-FPN networks in children, adolescents, and adults 54,55 , but results from withinnetwork connectivity studies are less coherent. For example, both hyperconnectivity 16 and hypoconnectivity 18 techniques, which suggested that heterogeneity within and between disorders may be limiting researchers' (and machines') ability to distinguish disorders based on neurobiology 57 . Two factors may be contributing to the limited neurobiological separability of ASD and ADHD categories: high within-category heterogeneity and high between-category similarity.…”

Predictive Modelling of The Dynamic Patterns of Thinking in Attention-Deficit/Hyperactivity Disorder: Diagnostic Accuracy of Spatiotemporal Fractal Measures

“…This review included other existing methods proposed to diagnose ADHD, that were not covered in the former systematic review. To make the indirect comparison possible, this in-house scoping review included only diagnostic accuracy studies that reported the chosen metrics of diagnostic for methods based on; electroencephalography and eventrelated potentials (Marcano et al, 2017;Loo et al, 2016;Snyder et al, 2015;Mohammadi et al, 2016;Gloss et al, 2016;Biederman et al, 2017;Gamma and Kara, 2016;Marcano et al, 2018;Manouilenko et al, 2017), structural and functional neuroimaging (Iannaccone et al, 2015;Rangarajan et al, 2014;de Celis Alonso et al, 2017;Qureshi et al, 2017;Serrallach et al, 2016;Hasaneen et al, 2017;Tan et al, 2017b;Uddin et al, 2017), simulated virtual reality and computer games (Negut et al, 2017(Negut et al, , 2016Berger et al, 2017;Faraone et al, 2016), and peripheral biochemical markers (Faraone et al, 2014;Scassellati and Bonvicini, 2015;Scassellati et al, 2012;Thome et al, 2012). According with this complementary "scoping review", the method presented in this research, seems to outperforms all the diagnostic accuracy metrics reported in the trials scrutinised in the aforementioned review.…”