Altered development of structural MRI connectome hubs at near-term age in very and moderately preterm infants.

Preterm infants may exhibit altered developmental patterns of the brain structural network by endogenous and exogenous stimuli, which are quantifiable through hub and modular network topologies that develop in the third trimester. Although preterm brain networks can compensate for white matter microstructural abnormalities of core connections, less is known about how the network developmental characteristics of preterm infants differ from those of full-term infants. We identified 13 hubs and 4 modules and revealed subtle differences in edgewise connectivity and local network properties between 134 preterm and 76 full-term infants, identifying specific developmental patterns of the brain structural network in preterm infants. The modules of preterm infants showed an imbalanced composition. The edgewise connectivity in preterm infants showed significantly decreased long- and short-range connections and local network properties in the dorsal superior frontal gyrus. In contrast, the fusiform gyrus and several nonhub regions showed significantly increased wiring of short-range connections and local network properties. Our results suggested that decreased local network in the frontal lobe and excessive development in the occipital lobe may contribute to the understanding of brain developmental deviances in preterm infants.

Neuropsychiatric disease classification using functional connectomics - results of the connectomics in neuroimaging transfer learning challenge.

Large, open-source datasets, such as the Human Connectome Project and the Autism Brain Imaging Data Exchange, have spurred the development of new and increasingly powerful machine learning approaches for brain connectomics. However, one key question remains: are we capturing biologically relevant and generalizable information about the brain, or are we simply overfitting to the data? To answer this, we organized a scientific challenge, the Connectomics in NeuroImaging Transfer Learning Challenge (CNI-TLC), held in conjunction with MICCAI 2019. CNI-TLC included two classification tasks: (1) diagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD) within a pre-adolescent cohort; and (2) transference of the ADHD model to a related cohort of Autism Spectrum Disorder (ASD) patients with an ADHD comorbidity. In total, 240 resting-state fMRI (rsfMRI) time series averaged according to three standard parcellation atlases, along with clinical diagnosis, were released for training and validation (120 neurotypical controls and 120 ADHD). We also provided Challenge participants with demographic information of age, sex, IQ, and handedness. The second set of 100 subjects (50 neurotypical controls, 25 ADHD, and 25 ASD with ADHD comorbidity) was used for testing. Classification methodologies were submitted in a standardized format as containerized Docker images through ChRIS, an open-source image analysis platform. Utilizing an inclusive approach, we ranked the methods based on 16 metrics: accuracy, area under the curve, F1-score, false discovery rate, false negative rate, false omission rate, false positive rate, geometric mean, informedness, markedness, Matthew's correlation coefficient, negative predictive value, optimized precision, precision, sensitivity, and specificity. The final rank was calculated using the rank product for each participant across all measures. Furthermore, we assessed the calibration curves of each methodology. Five participants submitted their method for evaluation, with one outperforming all other methods in both ADHD and ASD classification. However, further improvements are still needed to reach the clinical translation of functional connectomics. We have kept the CNI-TLC open as a publicly available resource for developing and validating new classification methodologies in the field of connectomics.

Longitudinal structural connectomic and rich-club analysis in adolescent mTBI reveals persistent, distributed brain alterations acutely through to one year post-injury.

The diffuse nature of mild traumatic brain injury (mTBI) impacts brain white-matter pathways with potentially long-term consequences, even after initial symptoms have resolved. To understand post-mTBI recovery in adolescents, longitudinal studies are needed to determine the interplay between highly individualised recovery trajectories and ongoing development. To capture the distributed nature of mTBI and recovery, we employ connectomes to probe the brain's structural organisation. We present a diffusion MRI study on adolescent mTBI subjects scanned one day, two weeks and one year after injury with controls. Longitudinal global network changes over time suggests an altered and more 'diffuse' network topology post-injury (specifically lower transitivity and global efficiency). Stratifying the connectome by its back-bone, known as the 'rich-club', these network changes were driven by the 'peripheral' local subnetwork by way of increased network density, fractional anisotropy and decreased diffusivities. This increased structural integrity of the local subnetwork may be to compensate for an injured network, or it may be robust to mTBI and is exhibiting a normal developmental trend. The rich-club also revealed lower diffusivities over time with controls, potentially indicative of longer-term structural ramifications. Our results show evolving, diffuse alterations in adolescent mTBI connectomes beginning acutely and continuing to one year.

Network structural dependency in the human connectome across the life-span.

Principles of network topology have been widely studied in the human connectome. Of particular interest is the modularity of the human brain, where the connectome is divided into subnetworks from which changes with development, aging or disease can be investigated. We present a weighted network measure, the Network Dependency Index (NDI), to identify an individual region's importance to the global functioning of the network. Importantly, we utilize NDI to differentiate four subnetworks (Tiers) in the human connectome following Gaussian mixture model fitting. We analyze the topological aspects of each subnetwork with respect to age and compare it to rich club-based subnetworks (rich club, feeder, and seeder). Our results first demonstrate the efficacy of NDI to identify more consistent, central nodes of the connectome across age groups, when compared with the rich club framework. Stratifying the connectome by NDI led to consistent subnetworks across the life-span, revealing distinct patterns associated with age where, for example, the key relay nuclei and cortical regions are contained in a subnetwork with highest NDI. The divisions of the human connectome derived from our data-driven NDI framework have the potential to reveal topological alterations described by network measures through the life-span.

Longitudinal Changes in Magnetic Resonance Spectroscopy in Pediatric Concussion: A Pilot Study.

Background: Nearly 20% of US adolescents report at least one lifetime concussion. Pathophysiologic models suggest that traumatic biomechanical forces caused by rotational deceleration lead to shear stress, which triggers a neurometabolic cascade beginning with excitotoxicity and leading to significant energy demands and a period of metabolic crisis for the injured brain. Proton magnetic resonance spectroscopy (1H MRS) offers a means for non-invasive measurement of neurometabolic changes after concussion. Objective: Describe longitudinal changes in metabolites measured in vivo in the brains of adolescent patients with concussion. Methods: We prospectively recruited 9 patients ages 11 to 20 who presented to a pediatric Emergency Department within 24 h of concussion. Patients underwent MRI scanning within 72 h (acute, n = 8), 2 weeks (subacute, n = 7), and at approximately 1 year (chronic, n = 7). Healthy, age and sex-matched controls were recruited and scanned once (n = 9). 1H MRS was used to measure N-acetyl-aspartate, choline, creatine, glutamate + glutamine, and myo-inositol concentrations in six regions of interest: left and right frontal white matter, posterior white matter and thalamus. Results: There was a significant increase in total thalamus glutamate+glutamine/choline at the subacute (p = 0.010) and chronic (p = 0.010) time points, and a significant decrease in total white matter myo-inositol/choline (p = 0.030) at the chronic time point as compared to controls. Conclusion: There are no differences in 1H MRS measurements in the acute concussive period; however, changes in glutamate+glutamine and myo-inositol concentrations detectable by 1H MRS may develop beyond the acute period.

Automatic labeling of cortical sulci for the human fetal brain based on spatio-temporal information of gyrification.

Accurate parcellation and labeling of primary cortical sulci in the human fetal brain is useful for regional analysis of brain development. However, human fetal brains show large spatio-temporal changes in brain size, cortical folding patterns, and relative position/size of cortical regions, making accurate automatic sulcal labeling challenging. Here, we introduce a novel sulcal labeling method for the fetal brain using spatio-temporal gyrification information from multiple fetal templates. First, spatial probability maps of primary sulci are generated on the templates from 23 to 33 gestational weeks and registered to an individual brain. Second, temporal weights, which determine the level of contribution to the labeling for each template, are defined by similarity of gyrification between the individual and the template brains. We combine the weighted sulcal probability maps from the multiple templates and adopt sulcal basin-wise approach to assign sulcal labels to each basin. Our labeling method was applied to 25 fetuses (22.9-29.6 gestational weeks), and the labeling accuracy was compared to manually assigned sulcal labels using the Dice coefficient. Moreover, our multi-template basin-wise approach was compared to a single-template approach, which does not consider the temporal dynamics of gyrification, and a fully-vertex-wise approach. The mean accuracy of our approach was 0.958 across subjects, significantly higher than the accuracies of the other approaches. This novel approach shows highly accurate sulcal labeling and provides a reliable means to examine characteristics of cortical regions in the fetal brain.

NODDI reproducibility and variability with magnetic field strength: A comparison between 1.5 T and 3 T.

Diffusion models are advantageous for examining brain microstructure non-invasively and their validation is important for transference into the clinical domain. Neurite Orientation Dispersion and Density Imaging (NODDI) is a promising model for estimating multiple diffusion compartments from MRI data acquired in a clinically feasible time. As a relatively new model, it is necessary to examine NODDI under certain experimental conditions, such as change in magnetic field-strength, and assess it in relation to diffusion tensor imaging (DTI), an established model that is largely understood by the neuroimaging community. NODDI measures (intracellular volume fraction, νic , and orientation distribution, OD) were compared with DTI at 1.5 and 3 T data in healthy adults in whole-brain tissue masks and regions of white- and deep grey-matter. Within-session reproducibility and between-subject differences of NODDI with field-strength were also investigated. Field-strength had a significant effect on NODDI measures, suggesting careful interpretation of results from data acquired at 1.5 and 3 T. It was demonstrated that NODDI is feasible at 1.5 T, but with lower νic in white-matter regions compared with 3 T. Furthermore, the advantages of NODDI over DTI in a region of complex microstructure were shown. Specifically, in the centrum-semiovale where FA is typically as low as in grey-matter, νic was comparable to other white-matter regions yet accompanied by an OD similar to deep grey-matter. In terms of reproducibility, NODDI measures varied more than DTI. It may be that NODDI is more susceptible to noisier parameter estimates when compared with DTI, conversely it may have greater sensitivity to true within- and between-subject heterogeneity. Hum Brain Mapp 37:4550-4565, 2016. © 2016 Wiley Periodicals, Inc.

Structural network efficiency is associated with cognitive impairment in small-vessel disease.

OBJECTIVE: To characterize brain network connectivity impairment in cerebral small-vessel disease (SVD) and its relationship with MRI disease markers and cognitive impairment. METHODS: A cross-sectional design applied graph-based efficiency analysis to deterministic diffusion tensor tractography data from 115 patients with lacunar infarction and leukoaraiosis and 50 healthy individuals. Structural connectivity was estimated between 90 cortical and subcortical brain regions and efficiency measures of resulting graphs were analyzed. Networks were compared between SVD and control groups, and associations between efficiency measures, conventional MRI disease markers, and cognitive function were tested. RESULTS: Brain diffusion tensor tractography network connectivity was significantly reduced in SVD: networks were less dense, connection weights were lower, and measures of network efficiency were significantly disrupted. The degree of brain network disruption was associated with MRI measures of disease severity and cognitive function. In multiple regression models controlling for confounding variables, associations with cognition were stronger for network measures than other MRI measures including conventional diffusion tensor imaging measures. A total mediation effect was observed for the association between fractional anisotropy and mean diffusivity measures and executive function and processing speed. CONCLUSIONS: Brain network connectivity in SVD is disturbed, this disturbance is related to disease severity, and within a mediation framework fully or partly explains previously observed associations between MRI measures and SVD-related cognitive dysfunction. These cross-sectional results highlight the importance of network disruption in SVD and provide support for network measures as a disease marker in treatment studies.

Strategic lacunes and their relationship to cognitive impairment in cerebral small vessel disease.

OBJECTIVES: Lacunes are an important disease feature of cerebral small vessel disease (SVD) but their relationship to cognitive impairment is not fully understood. To investigate this we determined (1) the relationship between lacune count and total lacune volume with cognition, (2) the spatial distribution of lacunes and the cognitive impact of lacune location, and (3) the whole brain anatomical covariance associated with these strategically located regions of lacune damage. METHODS: One hundred and twenty one patients with symptomatic lacunar stroke and radiological leukoaraiosis were recruited and multimodal MRI and neuropsychological data acquired. Lacunes were mapped semi-automatically and their volume calculated. Lacune location was automatically determined by projection onto atlases, including an atlas which segments the thalamus based on its connectivity to the cortex. Lacune locations were correlated with neuropsychological results. Voxel based morphometry was used to create anatomical covariance maps for these 'strategic' regions. RESULTS: Lacune number and lacune volume were positively associated with worse executive function (number p < 0.001; volume p < 0.001) and processing speed (number p < 0.001; volume p < 0.001). Thalamic lacunes, particularly those in regions with connectivity to the prefrontal cortex, were associated with impaired processing speed (Bonferroni corrected p = 0.016). Regions of associated anatomical covariance included the medial prefrontal, orbitofrontal, anterior insular cortex and the striatum. CONCLUSION: Lacunes are important predictors of cognitive impairment in SVD. We highlight the importance of spatial distribution, particularly of anteromedial thalamic lacunes which are associated with impaired information processing speed and may mediate cognitive impairment via disruption of connectivity to the prefrontal cortex.

Diffusion tensor parameters and principal eigenvector coherence: relation to b-value intervals and field strength.

Diffusion-weighted MRI images acquired at b-value greater than 1000 s mm(-2) measure the diffusion of a restricted pool of water molecules. High b-value images are accompanied by a reduction in signal-to-noise ratio (SNR) due to the application of large diffusion gradients. By fitting the diffusion tensor model to data acquired at incremental b-value intervals, we determined the effect of SNR on tensor parameters in normal human brains, in vivo. In addition, we also investigated the impact of field strength on the diffusion tensor model. Data were acquired at 1.5 and 3T, at b-values 0, 1000, 2000 and 3000 s mm(-2) in twenty diffusion-sensitised directions. Fractional anisotropy (FA), mean diffusivity (MD) and principal eigenvector coherence (κ) were calculated from diffusion tensors fitted between datasets with b-values 0-1000, 0-2000, 0-3000, 1000-2000 and 2000-3000 s mm(-2). Field strength and b-value effects on diffusion parameters were analysed in white and grey matter regions of interest. Decreases in FA, κ and MD were found with increasing b-value in white matter. Univariate analysis showed a significant increase in FA with increasing field strength in highly organised white matter. These results suggest there are significant differences in diffusion parameters at 1.5 and 3T and that the optimal results, in terms of the highest values of FA in white matter, are obtained at 3T with a maximum b=1000 s mm(-2).

Cerebral microbleeds and cognition in patients with symptomatic small vessel disease.

BACKGROUND AND PURPOSE: Cerebral microbleeds (CMBs) are common in cerebral small vessel disease. They may cause cognitive impairment, possibly via white matter tract disruption but previous studies have produced inconsistent results. We determined whether CMB number and location are associated with impaired cognition in symptomatic small vessel disease and whether any association was independent of other magnetic resonance imaging markers of small vessel disease. METHODS: One hundred sixteen patients with lacunar stroke and radiological leukoaraiosis were studied. Neuropsychological assessment was performed. CMBs on gradient echo images were assessed using the Brain Observer Microbleed Rating Scale criteria. Magnetic resonance imaging measures, including diffusion tensor imaging, were also analyzed. Associations between cognitive function and the presence, number, and location of CMBs were determined. RESULTS: CMBs were present in 46 (39.7%) patients. CMB number correlated weakly with executive function (r=0.22; P=0.022) but not with other cognitive indices. CMBs count in the top decile (≥ 9 CMB, N=12) was more strongly associated with poor executive function; this association remained significant after controlling for T2-lesion load, brain volume, lacune count, and mean diffusivity (b=-0.51; P=0.043). CONCLUSIONS: In symptomatic small vessel disease, CMB number was weakly associated with executive dysfunction. There seemed to be a threshold effect with the association being largely accounted for by an association of impaired executive function with high CMB count. No association of CMBs with other cognitive domains, including processing speed, was found.

Brain structure and function in chronic obstructive pulmonary disease: a multimodal cranial magnetic resonance imaging study.

RATIONALE: Brain pathology is a poorly understood systemic manifestation of chronic obstructive pulmonary disease (COPD). Imaging techniques using magnetic resonance (MR) diffusion tensor imaging (DTI) and resting state functional MR imaging (rfMRI) provide measures of white matter microstructure and gray functional activation, respectively. OBJECTIVES: We hypothesized that patients with COPD would have reduced white matter integrity and that functional communication between gray matter resting-state networks would be significantly different to control subjects. In addition, we tested whether observed differences related to disease severity, cerebrovascular comorbidity, and cognitive dysfunction. METHODS: DTI and rfMRI were acquired in stable nonhypoxemic patients with COPD (n = 25) and compared with age-matched control subjects (n = 25). Demographic, disease severity, stroke risk, and neuropsychologic assessments were made. MEASUREMENTS AND MAIN RESULTS: Patients with COPD (mean age, 68; FEV(1) 53 ± 21% predicted) had widespread reduction in white matter integrity (46% of white matter tracts; P < 0.01). Six of the seven resting-state networks showed increased functional gray matter activation in COPD (P < 0.01). Differences in DTI, but not rfMRI, remained significant after controlling for stroke risk and smoking (P < 0.05). White matter integrity and gray matter activation seemed to account for difference in cognitive performance between patients with COPD and control subjects. CONCLUSIONS: In stable nonhypoxemic COPD there is reduced white matter integrity throughout the brain and widespread disturbance in functional activation of gray matter, which may contribute to cognitive dysfunction. White matter microstructural integrity but not gray matter functional activation is independent of smoking and cerebrovascular comorbidity. The mechanisms remain unclear, but may include cerebral small vessel disease caused by COPD.