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

L Q Uddin, D R Dajani, W Voorhies, H Bednarz, R K Kana, L Q Uddin, D R Dajani, W Voorhies, H Bednarz, R K Kana

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.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Illustration of support vector machine (SVM). SVM is one machine learning approach that is often used in classification studies. If there is a population of subjects (x=autism spectrum disorders, o=typically developing) with voxel values (v1 and v2, for example), then evaluation of one voxel at a time would not differentiate the two groups because there is a substantial amount of overlap between the two groups on each dimension (as shown by the dashed red and blue lines). A univariate analysis evaluating data one voxel at a time (e.g., either v1 alone v2) would not be able to detect group differences in such a scenario. However, if v1 and v2 are considered together, a plane separating the two groups can be constructed, thereby identifying a neighborhood where the two groups differ in spatial patterns of the anatomical or functional measures of interest.

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