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Background: The combination of structural and functional analyses is a biologically valid approach that offers methodological advantages in autism spectrum disorder (ASD) neuroimaging science. The paucity of studies combining these methods constitutes an important knowledge gap. In this study, we investigate structural abnormalities and their associated functional differences in a developmentally homogeneous ASD cohort. Methods: Whole-brain voxel-based morphometry (VBM) analyses were performed on 28 ASD participants and 38 age-matched typically developing healthy controls (HC) to derive gray matter (GM) volume differences. The anatomically relevant clusters identified by VBM served as seed regions of interest (ROI) for resting-state functional-connectivity (RsFc) analysis. Results: Whole-brain VBM analyses revealed significant right lateralized GM volume abnormality in the ASD group, with lower GM volumes in cerebellar lobules VIIb/VIIIa (cluster 1) and significantly higher GM volumes in posterior middle/superior temporal gyri (Brodmann area [BA] 21/22, cluster 2) compared with HC. Whole-brain RsFc analysis in high-functioning ASD (HF-ASD) revealed significant hypoconnectivity of the cerebellar VBM cluster with the right cerebral cortical regions of superior parietal lobule (BA 7) and occipital pole (BA 19) (overlapping with dorsal attention and visual networks, respectively). Cerebral cortical VBM cluster (cluster 2) revealed significant hypoconnectivity in HF-ASD with other task-positive cerebral cortical including the left lateral prefrontal cortex (frontoparietal network) and some aspects of the insula (ventral attention network) and ectopic positive connectivity (lack of anticorrelations) with posterior cingulate cortex and medial prefrontal cortex (default mode network). Conclusions: The cerebro-cerebellar intrinsic functional dysconnectivity based on the whole-brain VBM-derived ROIs may advance our understanding of the compensatory mechanisms associated with ASD and offer cerebellum as a potential target for diagnostic, predictive, prognostic, and therapeutic interventions in ASD. Our findings also provide additional support indicating that functional abnormalities as indexed by RsFc exist in ASD, and highlight that there is likely a relationship between structural and functional abnormalities in this disorder. Impact statement Our findings indicate that functional differences as indexed by resting-state functional connectivity exist in autism spectrum disorder (ASD), and highlight that there is likely a relationship between structural and functional abnormalities in this disorder. Future developments in neuroimaging research should continue investigating structural and associated functional differences in ASD, and in this way complement the behavioral characterization of this disorder, potentially improving diagnosis, prognosis, and prediction.

Cerebellar abnormalities are commonly reported in autism spectrum disorder (ASD). Dentate nuclei (DNs) are key structures in the anatomical circuits linking the cerebellum to the extracerebellum. Previous resting-state functional connectivity (RsFc) analyses reported DN abnormalities in high-functioning ASD (HF-ASD). This study examined the RsFc of the DN in young adults with HF-ASD compared with healthy controls (HCs) with the aim to expand upon previous findings of DNs in a dataset using advanced, imaging acquisition methods that optimize spatiotemporal resolution and statistical power. Additional seed-to-voxel analyses were carried out using motor and nonmotor DN coordinates reported in previous studies as seeds. We report abnormal dentato-cerebral and dentato-cerebellar functional connectivity in ASD. Our results expand and, in part, replicate previous descriptions of DN RsFc abnormalities in this disorder and reveal correlations between DN-cerebral RsFc and ASD symptom severity.

Children with familial risk for major depressive disorder (MDD) have elevated risk for developing depression as adolescents. Here, we investigated longitudinally whether resting-state functional connectivity (RSFC) could predict the onset of MDD. In this pilot study, we followed a group of never-depressed children with familial risk for MDD and a group of age-matched controls without familial risk who had undergone an MRI study at 8-14 years of age. Participants were reassessed 3-4 years later with diagnostic interviews. We first investigated group differences in RSFC from regions in the emotion regulation, cognitive control, and default mode networks in the children who later developed MDD (converted), the children who did not develop MDD (nonconverted), and the control group. We then built a prediction model based on baseline RSFC that was independent of the group differences to classify the individuals who later developed MDD. Compared with the nonconverted group, the converted group exhibited hypoconnectivity between subgenual anterior cingulate cortex (sgACC) and inferior parietal lobule (IPL) and between left and right dorsolateral prefrontal cortices. The nonconverted group exhibited higher sgACC-IPL connectivity than did both the converted and control groups, suggesting a possible resilience factor to MDD. Classification between converted and nonconverted individuals based on baseline RSFC yielded high predictive accuracy with high sensitivity and specificity that was superior to classification based on baseline clinical rating scales. Intrinsic brain connectivity measured in healthy children with familial risk for depression has the potential to predict MDD onset, and it can be a useful neuromarker in early identification of children for preventive treatment.

This study examines the resting-state functional-connectivity (RsFc) in young adults with high-functioning autism spectrum disorder (HF-ASD) using state-of-the-art fMRI data acquisition and analysis techniques. High temporal resolution fMRI using simultaneous multi-slice acquisition aided unbiased whole-brain connectome-wide multivariate pattern analysis (MVPA) techniques for assessing RsFc. MVPA revealed two clusters (Crus I/II and lobule IX) of abnormal connectivity in the cerebellum that are consistent with the notion of a triple representation of nonmotor processing in the cerebellum. Whole-brain seed-based RsFc analyses informed by these clusters showed significant under connectivity between the cerebellar and social, emotional, and language brain regions in the HF-ASD group compared to healthy controls. The results we report are coherent with existing structural, functional, and RsFc literature in autism, extend previous literature reporting cerebellar abnormalities in the neuropathology of autism, and highlight the cerebellum as a potential target for therapeutic, diagnostic, predictive, and prognostic developments in HF-ASD. The description of functional connectivity abnormalities reported in this study using whole-brain, data-driven analyses has the potential to crucially advance the development of ASD biomarkers, targets for therapeutic interventions, and neural predictors for measuring treatment response.

The subthalamic nucleus (STN) is a small structure situated deep in the midbrain that exhibits wide-ranging functionality. In addition to its role in motor control, the STN is considered a hub for synchronizing aspects of emotion and cognition including attention, inhibitory control, motivation, and working memory. Evidence from neuroanatomical tracer studies suggests that the medial, ventromedial, and dorsolateral parts of the STN correspond to limbic, associative, and motor subdivisions, respectively. Although the extent of STN functional anatomical overlap remains unclear, blood oxygenation level dependent imaging of the STN may provide complementary information about the diverse functions of this structure. Methodological limitations in spatial and temporal resolutions, however, have prevented a comprehensive exploration of temporal correlations from the STN to the whole brain. In this study, we optimize spatial (2 mm isotropic) and temporal (TR = 1 s) resolutions to take full advantage of the time series signal-to-noise ratio capabilities of multichannel array coils and simultaneous multislice imaging. We interrogated STN seed-to-voxel resting-state functional MRI connectivity in a group of 30 healthy participants that included the whole brain at high-temporal and spatial resolutions. This analysis revealed STN functional connectivity to limbic, associative, and motor networks. Our findings contribute to the understanding of STN functional neuroanatomy in humans and are clinically relevant for ongoing research in deep brain stimulation.

The aim of this study is to assess the resting-state functional connectivity (RsFc) profile of the default mode network (DMN) in transition-age males with autism spectrum disorder (ASD). Resting-state blood oxygen level-dependent functional magnetic resonance imaging data were acquired from adolescent and young adult males with high-functioning ASD (n = 15) and from age-, sex-, and intelligence quotient-matched healthy controls (HCs; n = 16). The DMN was examined by assessing the positive and negative RsFc correlations of an average of the literature-based conceptualized major DMN nodes (medial prefrontal cortex [mPFC], posterior cingulate cortex, bilateral angular, and inferior temporal gyrus regions). RsFc data analysis was performed using a seed-driven approach. ASD was characterized by an altered pattern of RsFc in the DMN. The ASD group exhibited a weaker pattern of intra- and extra-DMN-positive and -negative RsFc correlations, respectively. In ASD, the strength of intra-DMN coupling was significantly reduced with the mPFC and the bilateral angular gyrus regions. In addition, the polarity of the extra-DMN correlation with the right hemispheric task-positive regions of fusiform gyrus and supramarginal gyrus was reversed from typically negative to positive in the ASD group. A wide variability was observed in the presentation of the RsFc profile of the DMN in both HC and ASD groups that revealed a distinct pattern of subgrouping using pattern recognition analyses. These findings imply that the functional architecture profile of the DMN is altered in ASD with weaker than expected integration and segregation of DMN RsFc. Future studies with larger sample sizes are warranted.

Social anxiety disorder-related alterations in basal ganglia regions, such as striatum and globus pallidus, though evident from metabolic imaging, remain to be explored using seed-based resting-state functional connectivity magnetic resonance imaging. Capitalizing on the enhanced sensitivity of a multichannel array coil, we collected high-resolution (2-mm isotropic) data from medication-naive patients and healthy control participants. Subcortical resting-state networks from structures including the striatum (caudate and putamen), globus pallidus, thalamus, amygdala, and periaqueductal gray were compared between the two groups. When compared with controls, the caudate seed revealed significantly higher functional connectivity (hyper-connectivity) in the patient group in medial frontal, prefrontal (anterior and dorsolateral), orbito-frontal, and anterior cingulate cortices, which are regions that are typically associated with emotional processing. In addition, with the putamen seed, the patient data exhibited increased connectivity in the fronto-parietal regions (executive control network) and subgenual cingulate (affective network). The globus pallidus seed showed significant increases in connectivity in the patient group, primarily in the precuneus, which is part of the default mode network. Significant hyper-connectivity in the precuneus, interior temporal, and parahippocampal cortices was also observed with the thalamus seed in the patient population, when compared with controls. With amygdala as seed region, between-group differences were primarily in supplementary motor area, inferior temporal gyrus, secondary visual cortex, angular gyrus, and cingulate gyrus. Seed from periaqueductal gray resulted in hyper-connectivity in the patient group, when compared with controls, in dorsolateral prefrontal cortex, precuneus, middle temporal gyrus, and inferior parietal lobule. In all the subcortical regions examined in this study, the control group did not have any significant enhancements in functional connectivity when compared with the patient group.

The use of multichannel array head coils in functional and structural magnetic resonance imaging (MRI) provides increased signal-to-noise ratio (SNR), higher sensitivity, and parallel imaging capabilities. However, their benefits remain to be systematically explored in the context of resting-state functional connectivity MRI (fcMRI). In this study, we compare signal detectability within and between commercially available multichannel brain coils, a 32-Channel (32Ch), and a 12-Channel (12Ch) at 3T, in a high-resolution regime to accurately map resting-state networks. We investigate whether the 32Ch coil can extract and map fcMRI more efficiently and robustly than the 12Ch coil using seed-based and graph-theory-based analyses. Our findings demonstrate that although the 12Ch coil can be used to reveal resting-state connectivity maps, the 32Ch coil provides increased detailed functional connectivity maps (using seed-based analysis) as well as increased global and local efficiency, and cost (using graph-theory-based analysis), in a number of widely reported resting-state networks. The exploration of subcortical networks, which are scarcely reported due to limitations in spatial-resolution and coil sensitivity, also proved beneficial with the 32Ch coil. Further, comparisons regarding the data acquisition time required to successfully map these networks indicated that scan time can be significantly reduced by 50% when a coil with increased number of channels (i.e., 32Ch) is used. Switching to multichannel arrays in resting-state fcMRI could, therefore, provide both detailed functional connectivity maps and acquisition time reductions, which could further benefit imaging special subject populations, such as patients or pediatrics who have less tolerance in lengthy imaging sessions.

Resting state functional connectivity reveals intrinsic, spontaneous networks that elucidate the functional architecture of the human brain. However, valid statistical analysis used to identify such networks must address sources of noise in order to avoid possible confounds such as spurious correlations based on non-neuronal sources. We have developed a functional connectivity toolbox Conn ( www.nitrc.org/projects/conn ) that implements the component-based noise correction method (CompCor) strategy for physiological and other noise source reduction, additional removal of movement, and temporal covariates, temporal filtering and windowing of the residual blood oxygen level-dependent (BOLD) contrast signal, first-level estimation of multiple standard functional connectivity magnetic resonance imaging (fcMRI) measures, and second-level random-effect analysis for resting state as well as task-related data. Compared to methods that rely on global signal regression, the CompCor noise reduction method allows for interpretation of anticorrelations as there is no regression of the global signal. The toolbox implements fcMRI measures, such as estimation of seed-to-voxel and region of interest (ROI)-to-ROI functional correlations, as well as semipartial correlation and bivariate/multivariate regression analysis for multiple ROI sources, graph theoretical analysis, and novel voxel-to-voxel analysis of functional connectivity. We describe the methods implemented in the Conn toolbox for the analysis of fcMRI data, together with examples of use and interscan reliability estimates of all the implemented fcMRI measures. The results indicate that the CompCor method increases the sensitivity and selectivity of fcMRI analysis, and show a high degree of interscan reliability for many fcMRI measures.