Due to the dynamic, condition-dependent nature of brain activity, interest in estimating rapid functional connectivity (FC) changes that occur during resting-state functional magnetic resonance imaging (rs-fMRI) has recently soared. However, studying dynamic FC is methodologically challenging, due to the low signal-to-noise ratio of the blood oxygen level dependent (BOLD) signal in fMRI and the massive number of data points generated during the analysis. Thus, it is important to establish methods and summary measures that maximize reliability and the utility of dynamic FC to provide insight into brain function. In this study, we investigated the reliability of dynamic FC summary measures derived using three commonly used estimation methods - sliding window (SW), tapered sliding window (TSW), and dynamic conditional correlations (DCC) methods. We applied each of these techniques to two publicly available rs-fMRI test-retest data sets - the Multi-Modal MRI Reproducibility Resource (Kirby Data) and the Human Connectome Project (HCP Data). The reliability of two categories of dynamic FC summary measures were assessed, specifically basic summary statistics of the dynamic correlations and summary measures derived from recurring whole-brain patterns of FC (“brain states”). The results provide evidence that dynamic correlations are reliably detected in both test-retest data sets, and the DCC method outperforms SW methods in terms of the reliability of summary statistics. However, across all estimation methods, reliability of the brain state-derived measures was low. Notably, the results also show that the DCC-derived dynamic correlation variances are significantly more reliable than those derived using the non-parametric estimation methods. This is important, as the fluctuations of dynamic FC (i.e., its variance) has a strong potential to provide summary measures that can be used to find meaningful individual differences in dynamic FC. We therefore conclude that utilizing the variance of the dynamic connectivity is an important component in any dynamic FC-derived summary measure.

Background Imitation, which is impaired in children with Autism Spectrum Disorder (ASD) and critically depends on the integration of visual input with motor output, likely impacts both motor and social skill acquisition in children with ASD; however it is unclear what brain mechanisms contribute to this impairment. Children with ASD also exhibit what appears to be an ASD-specific bias against using visual feedback during motor learning. Does the temporal congruity of intrinsic activity, or functional connectivity, between motor and visual brain regions contribute to ASD-associated deficits in imitation, motor and social skills? Methods We acquired resting state functional Magnetic Resonance Imaging scans from 100, 8-12 year-old children (50 ASD). Group independent component analysis was used to estimate functional connectivity between visual and motor systems. Brain-behavior relationships were assessed by regressing functional connectivity measures with social deficit severity, imitation and gesture performance scores. Results We observed increased intrinsic asynchrony between visual and motor systems in children with ASD and replicated this finding in an independent sample from the Autism Brain Imaging Data Exchange. Moreover, children with more out-of-sync intrinsic visual-motor activity displayed more severe autistic traits while children with greater intrinsic visual-motor synchrony were better imitators. Conclusions Our twice replicated findings confirm that visual-motor functional connectivity is disrupted in ASD. Furthermore, the observed temporal incongruity between visual and motor systems, which may reflect diminished integration of visual consequences with motor output, was predictive of the severity of social deficits and may contribute to impaired social-communicative skill development in children with ASD.

Resting-state functional MRI (rs-fMRI) permits study of the brain’s functional networks without requiring participants to perform tasks. Robust changes in such resting state networks (RSNs) have been observed in neurologic disorders, and rs-fMRI outcome measures are candidate biomarkers for monitoring clinical trials, including trials of extended therapeutic interventions for rehabilitation of patients with chronic conditions. In this study, we aim to present a unique longitudinal dataset reporting on a healthy adult subject scanned weekly over 3.5 years and identify rs-fMRI outcome measures appropriate for clinical trials. Accordingly, we assessed the reproducibility, and characterized the temporal structure of, rs-fMRI outcome measures derived using independent component analysis (ICA). Data was compared to a 21-person dataset acquired on the same scanner in order to confirm that the values of the single-subject RSN measures were within the expected range as assessed from the multi-participant dataset. Fourteen RSNs were identified, and the inter-session reproducibility of outcome measures—network spatial map, temporal signal fluctuation magnitude, and between-network connectivity (BNC)–was high, with executive RSNs showing the highest reproducibility. Analysis of the weekly outcome measures also showed that many rs-fMRI outcome measures had a significant linear trend, annual periodicity, and persistence. Such temporal structure was most prominent in spatial map similarity, and least prominent in BNC. High reproducibility supports the candidacy of rs-fMRI outcome measures as biomarkers, but the presence of significant temporal structure needs to be taken into account when such outcome measures are considered as biomarkers for rehabilitation-style therapeutic interventions in chronic conditions.

Overall there is moderate group level rs-vs-task fMRI language network concordance, however substantial subject-level variability exists; iRMS may be used to determine reliability of rs-fMRI derived language networks.

Diffusion tensor imaging (DTI) provides an indirect measure of tissue structure on a microscopic scale. To date, DTI is the only imaging method that provides such information in vivo, and has proven to be a valuable tool in both research and clinical settings. In this study, we investigated the relationship between white matter structure and diffusion parameters measured by DTI. We used micrographs from light microscopy of fixed, myelin-stained brain sections as a gold standard for direct comparison with data from DTI. Relationships between microscopic tissue properties observed with light microscopy (fiber orientation, density and coherence) and fiber properties observed by DTI (tensor orientation, diffusivities and fractional anisotropy) were investigated. Agreement between the major eigenvector of the tensor and myelinated fibers was excellent in voxels with high fiber coherence. In addition, increased fiber spread was strongly associated with increased radial diffusivity (p = 6 × 10−6) and decreased fractional anisotropy (p = 5 × 10−8), and was weakly associated with decreased axial diffusivity (p = 0.07). Increased fiber density was associated with increased fractional anisotropy (p = 0.03), and weakly associated with decreased radial diffusivity (p < 0.06), but not with axial diffusivity (p = 0.97). The mean diffusivity was largely independent of fiber spread (p = 0.24) and fiber density (p = 0.34).

A multi-step procedure was developed to register magnetic resonance imaging (MRI) and histological data from the same sample in the light microscopy image space, with the ultimate goal of allowing quantitative comparisons of the two datasets. The fixed brain of an owl monkey was used to develop and test the procedure. In addition to the MRI and histological data, photographic images of the brain tissue block acquired during sectioning were assembled into a blockface volume to provide an intermediate step for the overall registration process. The MR volume was first registered to the blockface volume using a combination of linear and nonlinear registration, and two dimensional (2D) blockface sections were registered to corresponding myelin stained sections using a combination of linear and nonlinear registration. Before this 2D registration, two major types of tissue distortions were corrected: tissue tearing and independent movement of different parts of the brain, both introduced during histological processing of the sections. The correction procedure utilized a 2D method to close tissue tears and a multiple iterative closest point (ICP) algorithm to reposition separate pieces of tissue in the image. The accuracy of the overall MR to micrograph registration procedure was assessed by measuring the distance between registered landmarks chosen in the MR image space and the corresponding landmarks chosen in the micrograph space. The average error distance of the MR data registered to micrograph data was 0.324 ± 0.277 mm, only 8% larger than the width of the MRI voxel (0.3 mm).

Diffusion tensor imaging (DTI) tractography provides noninvasive measures of structural cortico-cortical connectivity of the brain. However, the agreement between DTI-tractography-based measures and histological ‘ground truth’ has not been quantified. In this study, we reconstructed the 3D density distribution maps (DDM) of fibers labeled with an anatomical tracer, biotinylated dextran amine (BDA), as well as DTI tractography-derived streamlines connecting the primary motor (M1) cortex to other cortical regions in the squirrel monkey brain. We evaluated the agreement in M1-cortical connectivity between the fibers labeled in the brain tissue and DTI streamlines on a regional and voxel-by-voxel basis. We found that DTI tractography is capable of providing inter-regional connectivity comparable to the neuroanatomical connectivity, but is less reliable measuring voxel-to-voxel variations within regions.

Neurological recovery in patients with severe spinal cord injury (SCI) is extremely rare. We have identified a patient with chronic cervical traumatic SCI, who suffered a complete loss of motor and sensory function below the injury for 6 weeks after the injury, but experienced a progressive neurological recovery that continued for 17 years. The extent of the patient's recovery from the severe trauma-induced paralysis is rare and remarkable. A detailed study of this patient using diffusion tensor imaging (DTI), magnetization transfer imaging (MTI), and resting state fMRI (rs-fMRI) revealed structural and functional changes in the central nervous system that may be associated with the neurological recovery. Sixty-two percent cervical cord white matter atrophy was observed. DTI-derived quantities, more sensitive to axons, demonstrated focal changes, while MTI-derived quantity, more sensitive to myelin, showed a diffuse change. No significant cortical structural changes were observed, while rs-fMRI revealed increased brain functional connectivity between sensorimotor and visual networks. The study provides comprehensive description of the structural and functional changes in the patient using advanced MR imaging technique. This multimodal MR imaging study also shows the potential of rs-fMRI to measure the extent of cortical plasticity.