Functional connectivity within an anxiety network and associations with anxiety symptom severity in middle-aged adults with and without autism.

Anxiety is highly prevalent in autism spectrum disorders (ASDs). However, few functional magnetic resonance imaging (fMRI) studies of ASDs have focused on anxiety (and fewer still on anxiety in middle-aged adults). Thus, relationships between atypical connectivity and anxiety in this population are poorly understood. The current study contrasted functional connectivity within anxiety network regions across adults (40-64 years) with and without autism, and tested for group by functional connectivity interactions on anxiety. Twenty-two adults with ASDs (16 males) and 26 typical control (TC) adults (22 males) completed the Beck Anxiety Inventory and a resting-state fMRI scan. An anxiety network consisting of 12 regions of interest was defined, based on a meta-analysis in TC individuals and two studies on anxiety in ASDs. We tested for main effects of group and group by anxiety interactions on connectivity within this anxiety network, controlling for head motion using ANCOVA. Results are reported at an FDR adjusted threshold of q < 0.1 (corrected) and p < 0.05 (uncorrected). Adults with ASDs showed higher anxiety and underconnectivity within the anxiety network, mostly involving bilateral insula. Connectivity within the anxiety network in the ASD group showed distinct relationships with anxiety symptoms that did not relate to ASD symptom severity. Functional connectivity involving the bilateral posterior insula was positively correlated with anxiety in the ASD (but not the TC) group. Increased anxiety in middle-aged adults with ASD is associated with atypical functional connectivity, predominantly involving bilateral insula. Results were not related to ASD symptom severity suggesting independence of anxiety-related effects. LAY SUMMARY: Anxiety is very common in adults with autism but the brain basis of this difference is not well understood. We compared functional connectivity between anxiety-related brain regions in middle-aged adults with and without autism. Adults with autism were more anxious and showed weaker functional connections between these regions. Some relationships between functional connectivity and higher anxiety were specific to the autism group. Results suggest that anxiety functions differently in autism.

Performance of machine learning classification models of autism using resting-state fMRI is contingent on sample heterogeneity.

Autism spectrum disorders (ASDs) are heterogeneous neurodevelopmental conditions. In fMRI studies, including most machine learning studies seeking to distinguish ASD from typical developing (TD) samples, cohorts differing in gender and symptom severity composition are often treated statistically as one "ASD group". Using resting-state functional connectivity (FC) data, we implemented random forest to build diagnostic classifiers in 4 ASD samples including a total of 656 participants (NASD = 306, NTD = 350, ages 6-18). Groups were manipulated to titrate heterogeneity of gender and symptom severity and partially overlapped. Each sample differed on inclusionary criteria: (1) all genders, unrestricted severity range; (2) only male participants, unrestricted severity; (3) all genders, higher severity only; (4) only male participants, higher severity. Each set consisted of 200 participants per group (ASD, TD; matched on age and head motion), 160 for training and 40 for validation. FMRI time series from 237 regions of interest (ROIs) were Pearson correlated in a 237×237 FC matrix and classifiers were built using random forest in training samples. Classification accuracies in validation samples were 62.5%, 65%, 70% and 73.75%, respectively for samples 1-4. Connectivity within cingulo-opercular task control (COTC) network, and between COTC ROIs and default mode and dorsal attention network contributed overall most informative features, but features differed across sets. Findings suggest that diagnostic classifiers vary depending on ASD sample composition. Specifically, greater homogeneity of samples regarding gender and symptom severity enhances classifier performance. However, given the true heterogeneity of ASDs, performance metrics alone may not adequately reflect classifier utility.

Distinct Patterns of Atypical Functional Connectivity in Lower-Functioning Autism.

BACKGROUND: Functional magnetic resonance imaging research on autism spectrum disorders (ASDs) has been largely limited to individuals with near-average intelligence. Although cognitive impairment is common in ASDs, functional network connectivity in this population remains poorly understood. Specifically, it remains unknown whether lower-functioning individuals exhibit exacerbated connectivity abnormalities similar to those previously detected in higher-functioning samples or specific divergent patterns of connectivity. METHODS: Resting-state functional magnetic resonance imaging data from 88 children (44 ASD, 44 typically developing; average age: 11 years) were included. Based on IQ, individuals with ASDs were assigned to either a lower-functioning group (mean IQ = 77 ± 6) or a higher-functioning group (mean IQ = 123 ± 8). Two typically developing comparison groups were matched to these groups on head motion, handedness, and age. Seeds in the medial prefrontal cortex, posterior cingulate cortex, posterior superior temporal sulcus, insula, and amygdala were used to contrast whole-brain functional connectivity across groups. RESULTS: Lower-functioning ASD participants (compared with higher-functioning ASD participants) showed significant underconnectivity within the default mode network and the ventral visual stream. Higher-functioning ASD participants (compared with matched typically developing participants) showed significantly decreased anticorrelations among default mode, salience, and task-positive regions. Effect sizes of detected differences were large (Cohen's d > 1.46). CONCLUSIONS: Lower- and higher-functioning individuals with ASDs demonstrated distinct patterns of atypical connectivity. Findings suggest a gross pattern of predominantly reduced network integration in lower-functioning ASDs (affecting default mode and visual networks) and predominantly reduced network segregation in higher-functioning ASDs. Results indicate the need for stratification by general functional level in studies of functional connectivity in ASDs.

Pain Catastrophizing and Pain Persistence in Temporomandibular Disorder Patients.

AIMS: To describe pain catastrophizing in temporomandibular disorder (TMD) patients in relation to disability and pain persistence. METHODS: A total of 163 TMD patients underwent a complete TMD evaluation according to the Diagnostic Criteria for Temporomandibular Disorders (DC/TMD), including the Pain Catastrophizing Scale (PCS). Patients were divided into subgroups according to their PCS, Graded Chronic Pain Scale (GCPS), and pain persistence scores. The GCPS and pain persistence subgroups were assigned as dependent variables in a stepwise multiple logistic regression model. The ability of the DC/TMD Axis II parameters and of the PCS to discriminate between patients of low and high disability (according to the GCPS) and low and high pain persistence were examined using area under the receiver operating characteristic (ROC) curve. α < .05 was considered to reflect statistical significance. RESULTS: Significant differences were found between high and low pain catastrophizing patients as to socioeconomic parameter, Axis I diagnoses, pain persistence, and Axis II evaluation. The parameters with significant discriminant ability for pain persistence were pain catastrophizing, depression, and nonspecific physical symptoms, with no significant differences between them. Depression increased the odds of high disability by 1.2, while pain catastrophizing increased the odds for high pain persistence more than 6-fold. Pain catastrophizing was not significantly associated with pain disability, and depression was not significantly associated with pain persistence. CONCLUSION: High-pain catastrophizing TMD patients were similar to patients with other chronic pain conditions, but differed from TMD patients as a group. The findings of this study support the addition of an assessment for pain catastrophizing to the DC/TMD for early identification of TMD patients who might be at higher risk for developing chronic pain.

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.

Using Make for Reproducible and Parallel Neuroimaging Workflow and Quality-Assurance.

The contribution of this paper is to describe how we can program neuroimaging workflow using Make, a software development tool designed for describing how to build executables from source files. A makefile (or a file of instructions for Make) consists of a set of rules that create or update target files if they have not been modified since their dependencies were last modified. These rules are processed to create a directed acyclic dependency graph that allows multiple entry points from which to execute the workflow. We show that using Make we can achieve many of the features of more sophisticated neuroimaging pipeline systems, including reproducibility, parallelization, fault tolerance, and quality assurance reports. We suggest that Make permits a large step toward these features with only a modest increase in programming demands over shell scripts. This approach reduces the technical skill and time required to write, debug, and maintain neuroimaging workflows in a dynamic environment, where pipelines are often modified to accommodate new best practices or to study the effect of alternative preprocessing steps, and where the underlying packages change frequently. This paper has a comprehensive accompanying manual with lab practicals and examples (see Supplemental Materials) and all data, scripts, and makefiles necessary to run the practicals and examples are available in the "makepipelines" project at NITRC.

Subregional differences in intrinsic amygdala hyperconnectivity and hypoconnectivity in autism spectrum disorder.

The amygdala is a complex structure with distinct subregions and dissociable functional networks. The laterobasal subregion of the amygdala is hypothesized to mediate the presentation and severity of autism symptoms, although very little data are available regarding amygdala dysfunction at the subregional level. In this study, we investigated the relationship between abnormal amygdalar intrinsic connectivity, autism symptom severity, and anxiety and depressive symptoms. We collected resting state fMRI data on 31 high functioning adolescents and adults with autism spectrum disorder and 38 typically developing (TD) controls aged 14-45. Twenty-five participants with ASD and 28 TD participants were included in the final analyses. ASD participants were administered the Autism Diagnostic Interview-Revised and the Autism Diagnostic Observation Schedule. Adult participants were administered the Beck Depression Inventory II and the Beck Anxiety Inventory. Functional connectivity analyses were conducted from three amygdalar subregions: centromedial (CM), laterobasal (LB) and superficial (SF). In addition, correlations with the behavioral measures were tested in the adult participants. In general, the ASD group showed significantly decreased connectivity from the LB subregion and increased connectivity from the CM and SF subregions compared to the TD group. We found evidence that social symptoms are primarily associated with under-connectivity from the LB subregion whereas over-connectivity and under-connectivity from the CM, SF and LB subregions are related to co-morbid depression and anxiety in ASD, in brain regions that were distinct from those associated with social dysfunction, and in different patterns than were observed in mildly symptomatic TD participants. Our findings provide new evidence for functional subregional differences in amygdala pathophysiology in ASD. Autism Res 2016, 9: 760-772. © 2015 International Society for Autism Research, Wiley Periodicals, Inc.

The association between higher order abilities, processing speed, and age are variably mediated by white matter integrity during typical aging.

Although aging is associated with changes in brain structure and cognition it remains unclear which specific structural changes mediate individual cognitive changes. Several studies have reported that white matter (WM) integrity, as assessed by diffusion tensor imaging (DTI), mediates, in part, age-related differences in processing speed (PS). There is less evidence for WM integrity mediating age-related differences in higher order abilities (e.g., memory and executive functions). In 165 typically aging adults (age range 54-89) we show that WM integrity in select cerebral regions is associated with higher cognitive abilities and accounts variance not accounted for by PS or age. Specifically, voxel-wise analyses using tract-based spatial statistics (TBSS) revealed that WM integrity was associated with reasoning, cognitive flexibility and PS, but not memory or word fluency, after accounting for age and gender. While cerebral fractional anisotropy (FA) was only associated with PS; mean (MD), axial (AD) and radial (RD) diffusivity were associated with reasoning and flexibility. Reasoning was selectively associated with left prefrontal AD, while cognitive flexibility was associated with MD, AD and RD throughout the cerebrum. Average WM metrics within select WM regions of interest accounted for 18% and 29% of the variance in reasoning and flexibility, respectively, similar to the amount of variance accounted for by age. WM metrics mediated ~50% of the age-related variance in reasoning and flexibility and different proportions, 11% for reasoning and 44% for flexibility, of the variance accounted for by PS. In sum, (i) WM integrity is significantly, but variably, related to specific higher cognitive abilities and can account for a similar proportion of variance as age, and (ii) while FA is selectively associated with PS; while MD, AD and RD are associated with reasoning, flexibility and PS. This illustrates both the anatomical and cognitive selectivity of structure-cognition relationships in the aging brain.