Resting-State Functional Connectivity in Autism Spectrum Disorders: A Review

Resting state brain networks in the prairie vole

Resting state functional magnetic resonance imaging (rsfMRI) has shown the hierarchical organization of the human brain into large-scale complex networks, referred as resting state networks. This technique has turned into a promising translational research tool after the finding of similar resting state networks in non-human primates, rodents and other animal models of great value for neuroscience. Here, we demonstrate and characterize the presence of resting states networks in Microtus ochrogaster, the prairie vole, an extraordinary animal model to study complex human-like social behavior, with potential implications for the research of normal social development, addiction and neuropsychiatric disorders. Independent component analysis of rsfMRI data from isoflurane-anestethized prairie voles resulted in cortical and subcortical networks, including primary motor and sensory networks, but also included putative salience and default mode networks. We further discuss how future research could help to close the gap between the properties of the large scale functional organization and the underlying neurobiology of several aspects of social cognition. These results contribute to the evidence of preserved resting state brain networks across species and provide the foundations to explore the use of rsfMRI in the prairie vole for basic and translational research.

Multi-modal imaging investigation of anterior cingulate cortex cytoarchitecture in neurodevelopment

Multi-modal imaging may improve our understanding of the relationship between cortical morphology and cytoarchitecture. To this end we integrated the analyses of several magnetic resonance imaging (MRI) and spectroscopy (MRS) metrics within the anterior cingulate cortex (ACC). Considering the ACCs role in neurodevelopmental disorders, we also investigated the association between neuropsychiatric symptoms and the various metrics. T1 and diffusion-weighted MRI and ¹H-MRS (ACC voxel) data along with phenotypic information were acquired from children (8-12 years) with various neurodevelopmental disorders (n=95) and healthy controls (n=50). From within the MRS voxel mean diffusivity (MD) of the grey matter fraction, intrinsic curvature (IC) of the surface and concentrations of creatine, choline, glutamate, N-acetylaspartate and myo-inositol were extracted. Linear models were used to investigate if the neurochemicals predicted MD and IC or if MD predicted IC. Finally, measures of various symptom severities were included to determine the influence of symptoms of neurodevelopmental disorders. All five neurochemicals inversely predicted MD (all p_uncorrected<0.04, β=0.23-0.36). There was no association between IC and MD or IC and the neurochemicals (all p>0.05). Severity of autism symptoms related positively to MD (p_uncorrected=0.002, β=0.39). Our findings support the notion that the neurochemicals relate to cytoarchitecture within the cortex. Additionally, we showed that autism symptoms across participants relate to the ACC cytoarchitecture.

Neural Correlates of Sensory Abnormalities Across Developmental Disabilities

Abnormalities in sensory processing are a common feature of many developmental disabilities (DDs). Sensory dysfunction can contribute to deficits in brain maturation, as well as many vital functions. Unfortunately, while some patients with DD benefit from the currently available treatments for sensory dysfunction, many do not. Deficiencies in clinical practice surrounding sensory dysfunction may be related to lack of understanding of the neural mechanisms that underlie sensory abnormalities. Evidence of overlap in sensory symptoms between diagnoses suggests that there may be common neural mechanisms that mediate many aspects of sensory dysfunction. Thus, the current manuscript aims to review the extant literature regarding the neural correlates of sensory dysfunction across DD in order to identify patterns of abnormality that span diagnostic categories. Such anomalies in brain structure, function, and connectivity may eventually serve as targets for treatment.

Using Pattern Classification to Identify Brain Imaging Markers in Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by deficits in social interaction and communication, as well as repetitive and restrictive behaviours. The etiological and phenotypic complexity of ASD has so far hindered the development of clinically useful biomarkers for the condition. Neuroimaging studies have been valuable in establishing a biological basis for ASD. Increasingly, neuroimaging has been combined with 'machine learning'-based pattern classification methods to make individual diagnostic predictions. Moving forward, the hope is that these techniques may not only facilitate the diagnostic process but may also aid in fractionating the ASD phenotype into more biologically homogeneous sub-groups, with defined pathophysiology, predictable outcomes and/or responses to targeted treatments and/or interventions. This review chapter will first introduce 'machine learning' and pattern recognition methods in general, with a focus on their application to diagnostic classification. It will highlight why such approaches to biomarker discovery may have advantages over more conventional analytical methods. Magnetic resonance imaging (MRI) findings of atypical brain structure, function and connectivity in ASD will be briefly reviewed before we describe how pattern recognition has been applied to generate predictive models for ASD. Last, we will discuss some limitations and pitfalls of pattern recognition analyses in ASD and consider how the field can advance beyond the prediction of binary outcomes.

Multimodal approaches to functional connectivity in autism spectrum disorders: An integrative perspective

Atypical functional connectivity has been implicated in autism spectrum disorders (ASDs). However, the literature to date has been largely inconsistent, with mixed and conflicting reports of hypo- and hyper-connectivity. These discrepancies are partly due to differences between various neuroimaging modalities. Functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and magnetoencephalography (MEG) measure distinct indices of functional connectivity (e.g., blood-oxygenation level-dependent [BOLD] signal vs. electrical activity). Furthermore, each method has unique benefits and disadvantages with respect to spatial and temporal resolution, vulnerability to specific artifacts, and practical implementation. Thus far, functional connectivity research on ASDs has remained almost exclusively unimodal; therefore, interpreting findings across modalities remains a challenge. Multimodal integration of fMRI, EEG, and MEG data is critical in resolving discrepancies in the literature, and working toward a unifying framework for interpreting past and future findings. This review aims to provide a theoretical foundation for future multimodal research on ASDs. First, we will discuss the merits and shortcomings of several popular theories in ASD functional connectivity research, using examples from the literature to date. Next, the neurophysiological relationships between imaging modalities, including their relationship with invasive neural recordings, will be reviewed. Finally, methodological approaches to multimodal data integration will be presented, and their future application to ASDs will be discussed. Analyses relating transient patterns of neural activity ("states") are particularly promising. This strategy provides a comparable measure across modalities, captures complex spatiotemporal patterns, and is a natural extension of recent dynamic fMRI research in ASDs. © 2017 Wiley Periodicals, Inc. Develop Neurobiol 78: 456-473, 2018.

Changing brain connectivity dynamics: From early childhood to adulthood

Brain maturation through adolescence has been the topic of recent studies. Previous works have evaluated changes in morphometry and also changes in functional connectivity. However, most resting-state fMRI studies have focused on static connectivity. Here we examine the relationship between age/maturity and the dynamics of brain functional connectivity. Utilizing a resting fMRI dataset comprised 421 subjects ages 3-22 from the PING study, we first performed group ICA to extract independent components and their time courses. Next, dynamic functional network connectivity (dFNC) was calculated via a sliding window followed by clustering of connectivity patterns into 5 states. Finally, we evaluated the relationship between age and the amount of time each participant spent in each state as well as the transitions among different states. Results showed that older participants tend to spend more time in states which reflect overall stronger connectivity patterns throughout the brain. In addition, the relationship between age and state transition is symmetric. This can mean individuals change functional connectivity through time within a specific set of states. On the whole, results indicated that dynamic functional connectivity is an important factor to consider when examining brain development across childhood.

Structural Covariance of Sensory Networks, the Cerebellum, and Amygdala in Autism Spectrum Disorder

Sensory dysfunction is a core symptom of autism spectrum disorder (ASD), and abnormalities with sensory responsivity and processing can be extremely debilitating to ASD patients and their families. However, relatively little is known about the underlying neuroanatomical and neurophysiological factors that lead to sensory abnormalities in ASD. Investigation into these aspects of ASD could lead to significant advancements in our general knowledge about ASD, as well as provide targets for treatment and inform diagnostic procedures. Thus, the current study aimed to measure the covariation of volumes of brain structures (i.e., structural magnetic resonance imaging) that may be involved in abnormal sensory processing, in order to infer connectivity of these brain regions. Specifically, we quantified the structural covariation of sensory-related cerebral cortical structures, in addition to the cerebellum and amygdala by computing partial correlations between the structural volumes of these structures. These analyses were performed in participants with ASD (n = 36), as well as typically developing peers (n = 32). Results showed decreased structural covariation between sensory-related cortical structures, especially between the left and right cerebral hemispheres, in participants with ASD. In contrast, these same participants presented with increased structural covariation of structures in the right cerebral hemisphere. Additionally, sensory-related cerebral structures exhibited decreased structural covariation with functionally identified cerebellar networks. Also, the left amygdala showed significantly increased structural covariation with cerebral structures related to visual processing. Taken together, these results may suggest several patterns of altered connectivity both within and between cerebral cortices and other brain structures that may be related to sensory processing.

Age and Gender Effects on Intrinsic Connectivity in Autism Using Functional Integration and Segregation

BACKGROUND

The objective of this study was to examine intrinsic whole-brain functional connectivity in autism spectrum disorder (ASD) using the framework of functional segregation and integration. Emphasis was given to potential gender and developmental effects as well as identification of specific networks that may contribute to the global results.

METHODS

We leveraged an open data resource (N = 1587) of resting-state functional magnetic resonance imaging data in the Autism Brain Imaging Data Exchange (ABIDE) initiative, combining data from more than 2100 unique cross-sectional datasets in ABIDE I and ABIDE II collected at different sites. Modularity and global efficiency were utilized to assess functional segregation and integration, respectively. A meta-analytic approach for handling site differences was used. The effects of age, gender, and diagnostic category on segregation and integration were assessed using linear regression.

RESULTS

Modularity decreased nonlinearly in the ASD group with age, as evidenced by an increase and then decrease over development. Global efficiency had an opposite relationship with age by first decreasing and then increasing in the ASD group. Both modularity and global efficiency remained largely stable in the typically developing control group during development, representing a significantly different effect than seen in the ASD group. Age effects on modularity were localized to the somatosensory network. Finally, a marginally significant interaction between age, gender, and diagnostic category was found for modularity.

CONCLUSIONS

Our results support prior work that suggested a quadratic effect of age on brain development in ASD, while providing new insights about the specific characteristics of developmental and gender effects on intrinsic connectivity in ASD.

The Default Mode Network in Autism

Autism spectrum disorder (ASD) is characterized by deficits in social communication and interaction. Since its discovery as a major functional brain system, the default mode network (DMN) has been implicated in a number of psychiatric disorders, including ASD. Here we review converging multimodal evidence for DMN dysfunction in the context of specific components of social cognitive dysfunction in ASD: 'self-referential processing' - the ability to process social information relative to oneself and 'theory of mind' or 'mentalizing' - the ability to infer the mental states such as beliefs, intentions, and emotions of others. We show that altered functional and structural organization of the DMN, and its atypical developmental trajectory, are prominent neurobiological features of ASD. We integrate findings on atypical cytoarchitectonic organization and imbalance in excitatory-inhibitory circuits, which alter local and global brain signaling, to scrutinize putative mechanisms underlying DMN dysfunction in ASD. Our synthesis of the extant literature suggests that aberrancies in key nodes of the DMN and their dynamic functional interactions contribute to atypical integration of information about the self in relation to 'other', as well as impairments in the ability to flexibly attend to socially relevant stimuli. We conclude by highlighting open questions for future research.

Altered Brain Functional Connectivity in Small-Cell Lung Cancer Patients after Chemotherapy Treatment: A Resting-State fMRI Study

Previous studies in small-cell lung cancer (SCLC) patients have mainly focused on exploring neurocognitive deficits associated with prophylactic cranial irradiation (PCI). Little is known about functional brain alterations that might occur due to chemotherapy treatment in this population before PCI is administered. For this reason, we used resting-state functional Magnetic Resonance Imaging (fMRI) to examine potential functional connectivity disruptions in brain networks, including the Default Mode Network (DMN), the Sensorimotor Network, and the Task-Positive Network (TPN). Nineteen SCLC patients after platinum-based chemotherapy treatment and thirteen controls were recruited in the current study. ROI-to-ROI and Seed-to-Voxel analyses were carried out and revealed functional connectivity deficits in patients within all the networks investigated demonstrating the possible negative effect of chemotherapy in cognitive functions in SCLC populations.

The WAGR syndrome gene PRRG4 is a functional homologue of the commissureless axon guidance gene

WAGR syndrome is characterized by Wilm's tumor, aniridia, genitourinary abnormalities and intellectual disabilities. WAGR is caused by a chromosomal deletion that includes the PAX6, WT1 and PRRG4 genes. PRRG4 is proposed to contribute to the autistic symptoms of WAGR syndrome, but the molecular function of PRRG4 genes remains unknown. The Drosophila commissureless (comm) gene encodes a short transmembrane protein characterized by PY motifs, features that are shared by the PRRG4 protein. Comm intercepts the Robo axon guidance receptor in the ER/Golgi and targets Robo for degradation, allowing commissural axons to cross the CNS midline. Expression of human Robo1 in the fly CNS increases midline crossing and this was enhanced by co-expression of PRRG4, but not CYYR, Shisa or the yeast Rcr genes. In cell culture experiments, PRRG4 could re-localize hRobo1 from the cell surface, suggesting that PRRG4 is a functional homologue of Comm. Comm is required for axon guidance and synapse formation in the fly, so PRRG4 could contribute to the autistic symptoms of WAGR by disturbing either of these processes in the developing human brain.

Disruption to control network function correlates with altered dynamic connectivity in the wider autism spectrum

Autism is a common developmental condition with a wide, variable range of co-occurring neuropsychiatric symptoms. Contrasting with most extant studies, we explored whole-brain functional organization at multiple levels simultaneously in a large subject group reflecting autism's clinical diversity, and present the first network-based analysis of transient brain states, or dynamic connectivity, in autism. Disruption to inter-network and inter-system connectivity, rather than within individual networks, predominated. We identified coupling disruption in the anterior-posterior default mode axis, and among specific control networks specialized for task start cues and the maintenance of domain-independent task positive status, specifically between the right fronto-parietal and cingulo-opercular networks and default mode network subsystems. These appear to propagate downstream in autism, with significantly dampened subject oscillations between brain states, and dynamic connectivity configuration differences. Our account proposes specific motifs that may provide candidates for neuroimaging biomarkers within heterogeneous clinical populations in this diverse condition.

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Presents the first network-based treatment of dynamic connectivity in autism

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Analyzes whole-brain functional organization at multiple levels simultaneously

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Examines motifs in a large subject group reflecting autism's clinical diversity

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Utilizes a high-order model to delineate a more complete set of brain networks

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Uncovers significant coupling differences among control networks in autism