Ongoing debate exists within the resting-state functional MRI (fMRI) literature over how intrinsic connectivity is altered in the autistic brain, with reports of general over-connectivity, under-connectivity, and/or a combination of both. Classifying autism using brain connectivity is complicated by the heterogeneous nature of the condition, allowing for the possibility of widely variable connectivity patterns among individuals with the disorder. Further differences in reported results may be attributable to the age and sex of participants included, designs of the resting-state scan, and to the analysis technique used to evaluate the data. This review systematically examines the resting-state fMRI autism literature to date and compares studies in an attempt to draw overall conclusions that are presently challenging. We also propose future direction for rs-fMRI use to categorize individuals with autism spectrum disorder, serve as a possible diagnostic tool, and best utilize data-sharing initiatives.
Perinatal care advances emerging over the past twenty years have helped to diminish the mortality and severe neurological morbidity of extremely and very preterm neonates (e.g., cystic Periventricular Leukomalacia [c-PVL] and Germinal Matrix Hemorrhage – Intraventricular Hemorrhage [GMH-IVH grade 3–4/4]; 22 to < 32 weeks of gestational age, GA). However, motor and/or cognitive disabilities associated with mild-to-moderate white and gray matter injury are frequently present in this population (e.g., non-cystic Periventricular Leukomalacia [non-cystic PVL], neuronal–axonal injury and GMH-IVH grade 1–2/4). Brain research studies using magnetic resonance imaging (MRI) report that 50% to 80% of extremely and very preterm neonates have diffuse white matter abnormalities (WMA) which correspond to only the minimum grade of severity. Nevertheless, mild-to-moderate diffuse WMA has also been associated with significant affectations of motor and cognitive activities. Due to increased neonatal survival and the intrinsic characteristics of diffuse WMA, there is a growing need to study the brain of the premature infant using non-invasive neuroimaging techniques sensitive to microscopic and/or diffuse lesions. This emerging need has led the scientific community to try to bridge the gap between concepts or ideas from different methodologies and approaches; for instance, neuropathology, neuroimaging and clinical findings. This is evident from the combination of intense pre-clinical and clinicopathologic research along with neonatal neurology and quantitative neuroimaging research. In the following review, we explore literature relating the most frequently observed neuropathological patterns with the recent neuroimaging findings in preterm newborns and infants with perinatal brain injury. Specifically, we focus our discussions on the use of neuroimaging to aid diagnosis, measure morphometric brain damage, and track long-term neurodevelopmental outcomes.
Autism spectrum disorder (ASD) encompasses a set of neurodevelopmental conditions whose striking sex-related disparity (with an estimated male-to-female ratio of 4:1) remains unknown. Here we use magnetic resonance imaging (MRI) and diffusion weighted imaging (DWI) to identify the brain structure correlates of the sex-by-ASD diagnosis interaction in a carefully selected cohort of 110 ASD patients (55 females) and 83 typically-developing (TD) subjects (40 females). The interaction was found to be predicated primarily upon white matter connectivity density innervating, bilaterally, the lateral aspect of the temporal lobe, the temporo-parieto-occipital junction and the medial parietal lobe. By contrast, regional gray matter (GM) thickness and volume are not found to modulate this interaction significantly. When interpreted in the context of previous studies, our findings add considerable weight to three long-standing hypotheses according to which the sex disparity of ASD incidence is (A) due to WM connectivity rather than to GM differences, (B) modulated to a large extent by temporoparietal connectivity, and (C) accompanied by brain function differences driven by these effects. Our results contribute substantially to the task of unraveling the biological mechanisms giving rise to the sex disparity in ASD incidence, whose clinical implications are significant.
Despite substantial efforts, it remains difficult to identify reliable neuroanatomic biomarkers of autism spectrum disorder (ASD) based on magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI). Studies which use standard statistical methods to approach this task have been hampered by numerous challenges, many of which are innate to the mathematical formulation and assumptions of general linear models (GLM). Although the potential of alternative approaches such as machine learning (ML) to identify robust neuroanatomic correlates of psychiatric disease has long been acknowledged, few studies have attempted to evaluate the abilities of ML to identify structural brain abnormalities associated with ASD. Here we use a sample of 110 ASD patients and 83 typically developing (TD) volunteers (95 females) to assess the suitability of support vector machines (SVMs, a robust type of ML) as an alternative to standard statistical inference for identifying structural brain features which can reliably distinguish ASD patients from TD subjects of either sex, thereby facilitating the study of the interaction between ASD diagnosis and sex. We find that SVMs can perform these tasks with high accuracy and that the neuroanatomic correlates of ASD identified using SVMs overlap substantially with those found using conventional statistical methods. Our results confirm and establish SVMs as powerful ML tools for the study of ASD-related structural brain abnormalities. Additionally, they provide novel insights into the volumetric, morphometric, and connectomic correlates of this epidemiologically significant disorder.
Females versus males are less frequently diagnosed with autism spectrum disorder (ASD), and while understanding sex differences is critical to delineating the systems biology of the condition, female ASD is understudied. We integrated functional MRI and genetic data in a sex-balanced sample of ASD and typically developing youth (8–17 years old) to characterize female-specific pathways of ASD risk. Our primary objectives were to: (i) characterize female ASD (n = 45) brain response to human motion, relative to matched typically developing female youth (n = 45); and (ii) evaluate whether genetic data could provide further insight into the potential relevance of these brain functional differences. For our first objective we found that ASD females showed markedly reduced response versus typically developing females, particularly in sensorimotor, striatal, and frontal regions. This difference between ASD and typically developing females does not resemble differences between ASD (n = 47) and typically developing males (n = 47), even though neural response did not significantly differ between female and male ASD. For our second objective, we found that ASD females (n = 61), versus males (n = 66), showed larger median size of rare copy number variants containing gene(s) expressed in early life (10 postconceptual weeks to 2 years) in regions implicated by the typically developing female > female functional MRI contrast. Post hoc analyses suggested this difference was primarily driven by copy number variants containing gene(s) expressed in striatum. This striatal finding was reproducible among n = 2075 probands (291 female) from an independent cohort. Together, our findings suggest that striatal impacts may contribute to pathways of risk in female ASD and advocate caution in drawing conclusions regarding female ASD based on male-predominant cohorts.
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