Neuroimaging meta-analysis in neurodevelopmental disorders

Abstract



Background and Objectives: ‘Neurodevelopmental disorders’ is often synonymously used with childhood developmental disorders such as autism spectrum disorder (ASD), however, increasingly new lines of evidence from genetics and epidemiology suggests having schizophrenia and bipolar disorder to be included as well. For example, there is a strong tendency for schizophrenia and bipolar disorder to occur in people with ASD and shared aetiological factors such as prenatal infection and maternal vitamin D deficiency during pregnancy have all been linked with increased risks in all three conditions. To investigate into this, I have turned to brain imaging, a technique which has opened up a new horizon for neurobiologists. Typically, neuroimaging studies focus on one disorder, matching patients with healthy volunteers and compare their brain structures volumetric differences. On the other hand, such studies are limited by various factors including small ample size, low power, no psychiatric control group, and sample or design heterogeneity.

Methods: To summarize all the data into a more meaningful biological representation, Anatomical Likelihood Estimation (ALE), a cutting edge meta-analytic approach was applied. The rationale behind ALE is that it identifies brain differences most consistently reported across studies, while filtering away differences that are least documented. In this thesis, a novel application of ALE known as “dual disorder ALE” is introduced, which serves to estimate the extent of brain regional differences implicated in either disorder – in other words, a method to quantify which areas of the brain are more likely to be affected by ASD, schizophrenia or bipolar disorder.

Findings: The analysis is separated into two parts. First, dual disorder ALE technique was applied to investigate the relationship between ASD and first-episode schizophrenia. Data from 25 MRI studies was extracted comprising 660 participants (308 ASD, 352 schizophrenia) and 801 healthy controls. In ASD and FE schizophrenia, there were similar brain differences near the limbic-striato-thalamic circuitry, and distinctive brain differences including amygdala, caudate, frontal and medial gyrus for schizophrenia and putamen for ASD. In the second part comparing bipolar disorder and schizophrenia, data from 651 schizophrenic patients, 540 bipolar patients, and 1438 healthy controls was used, and matched one-to-one by pairing up bipolar disorder studies with corresponding schizophrenia studies to minimize confounders. The ALE result indicated that there are substantial overlaps across the two disorders, with schizophrenia having more extensive brain differences than bipolar disorder.

Conclusions: Both parts of the analysis suggest that there are similar aetiological pressures affecting neurodevelopmental disorders including ASD, schizophrenia and bipolar disorder.