A densely interconnected network for deep learning accelerated MRI.

OBJECTIVE: To improve accelerated MRI reconstruction through a densely connected cascading deep learning reconstruction framework. MATERIALS AND METHODS: A cascading deep learning reconstruction framework (reference model) was modified by applying three architectural modifications: input-level dense connections between cascade inputs and outputs, an improved deep learning sub-network, and long-range skip-connections between subsequent deep learning networks. An ablation study was performed, where five model configurations were trained on the NYU fastMRI neuro dataset with an end-to-end scheme conjunct on four- and eightfold acceleration. The trained models were evaluated by comparing their respective structural similarity index measure (SSIM), normalized mean square error (NMSE), and peak signal to noise ratio (PSNR). RESULTS: The proposed densely interconnected residual cascading network (DIRCN), utilizing all three suggested modifications achieved a SSIM improvement of 8% and 11%, a NMSE improvement of 14% and 23%, and a PSNR improvement of 2% and 3% for four- and eightfold acceleration, respectively. In an ablation study, the individual architectural modifications all contributed to this improvement for both acceleration factors, by improving the SSIM, NMSE, and PSNR with approximately 2-4%, 4-9%, and 0.5-1%, respectively. CONCLUSION: The proposed architectural modifications allow for simple adjustments on an already existing cascading framework to further improve the resulting reconstructions.

Operator dependency of arterial input function in dynamic contrast-enhanced MRI.

OBJECTIVE: To investigate the effect of inter-operator variability in arterial input function (AIF) definition on kinetic parameter estimates (KPEs) from dynamic contrast-enhanced (DCE) MRI in patients with high-grade gliomas. METHODS: The study included 118 DCE series from 23 patients. AIFs were measured by three domain experts (DEs), and a population AIF (pop-AIF) was constructed from the measured AIFs. The DE-AIFs, pop-AIF and AUC-normalized DE-AIFs were used for pharmacokinetic analysis with the extended Tofts model. AIF-dependence of KPEs was assessed by intraclass correlation coefficient (ICC) analysis, and the impact on relative longitudinal change in Ktrans was assessed by Fleiss' kappa (κ). RESULTS: There was a moderate to substantial agreement (ICC 0.51-0.76) between KPEs when using DE-AIFs, while AUC-normalized AIFs yielded ICC 0.77-0.95 for Ktrans, kep and ve and ICC 0.70 for vp. Inclusion of the pop-AIF did not reduce agreement. Agreement in relative longitudinal change in Ktrans was moderate (κ = 0.591) using DE-AIFs, while AUC-normalized AIFs gave substantial (κ = 0.809) agreement. DISCUSSION: AUC-normalized AIFs can reduce the variation in kinetic parameter results originating from operator input. The pop-AIF presented in this work may be applied in absence of a satisfactory measurement.

Sleep and sleep deprivation differentially alter white matter microstructure: A mixed model design utilising advanced diffusion modelling.

Sleep deprivation influences several critical functions, yet how it affects human brain white matter (WM) is not well understood. The aim of the present work was to investigate the effect of 32 hours of sleep deprivation on WM microstructure compared to changes observed in a normal sleep-wake cycle (SWC). To this end, we utilised diffusion weighted imaging (DWI) including the diffusion tensor model, diffusion kurtosis imaging and the spherical mean technique, a novel biophysical diffusion model. 46 healthy adults (23 sleep deprived vs 23 with normal SWC) underwent DWI across four time points (morning, evening, next day morning and next day afternoon, after a total of 32 hours). Linear mixed models revealed significant group × time interaction effects, indicating that sleep deprivation and normal SWC differentially affect WM microstructure. Voxel-wise comparisons showed that these effects spanned large, bilateral WM regions. These findings provide important insight into how sleep deprivation affects the human brain.

Evidence for wakefulness-related changes to extracellular space in human brain white matter from diffusion-weighted MRI.

Recently, several magnetic resonance imaging (MRI) studies have reported time-of-day effects on brain structure and function. Due to the possibility that time-of-day effects reflect mechanisms of circadian regulation, the aim of this prospective study was to assess these effects while under strict experimental control of variables that might influence biological clocks, such as caffeine intake and exposure to blue-emitting light. In addition, the current study assessed whether time-of-day effects were driven by changes to extracellular space, by including estimations of non-Gaussian diffusion metrics obtained from diffusion kurtosis imaging, white matter tract integrity and the spherical mean technique, in addition to conventional diffusion tensor imaging -derived parameters. Participants were 47 healthy adults who underwent diffusion-weighted imaging in the morning and evening of the same day. Morning and evening scans were compared using voxel-wise tract based spatial statistics and permutation testing. A day of wakefulness was associated with widespread increases in fractional anisotropy, indices of kurtosis and indices of the axonal water fraction. In addition, wakefulness was associated with widespread decreases in radial diffusivity, both in the single compartment and in extra-axonal space. These results suggest that an increase in the intra-axonal space relative to the extra-axonal volume underlies time-of-day effects in human white matter, which is in line with activity-induced reductions to the extracellular volume. These findings provide important insight into possible mechanisms driving time-of-day effects in MRI.

ExploreASL: An image processing pipeline for multi-center ASL perfusion MRI studies.

Arterial spin labeling (ASL) has undergone significant development since its inception, with a focus on improving standardization and reproducibility of its acquisition and quantification. In a community-wide effort towards robust and reproducible clinical ASL image processing, we developed the software package ExploreASL, allowing standardized analyses across centers and scanners. The procedures used in ExploreASL capitalize on published image processing advancements and address the challenges of multi-center datasets with scanner-specific processing and artifact reduction to limit patient exclusion. ExploreASL is self-contained, written in MATLAB and based on Statistical Parameter Mapping (SPM) and runs on multiple operating systems. To facilitate collaboration and data-exchange, the toolbox follows several standards and recommendations for data structure, provenance, and best analysis practice. ExploreASL was iteratively refined and tested in the analysis of >10,000 ASL scans using different pulse-sequences in a variety of clinical populations, resulting in four processing modules: Import, Structural, ASL, and Population that perform tasks, respectively, for data curation, structural and ASL image processing and quality control, and finally preparing the results for statistical analyses on both single-subject and group level. We illustrate ExploreASL processing results from three cohorts: perinatally HIV-infected children, healthy adults, and elderly at risk for neurodegenerative disease. We show the reproducibility for each cohort when processed at different centers with different operating systems and MATLAB versions, and its effects on the quantification of gray matter cerebral blood flow. ExploreASL facilitates the standardization of image processing and quality control, allowing the pooling of cohorts which may increase statistical power and discover between-group perfusion differences. Ultimately, this workflow may advance ASL for wider adoption in clinical studies, trials, and practice.

Cerebral blood flow changes after a day of wake, sleep, and sleep deprivation.

Elucidating the neurobiological effects of sleep and wake is an important goal of the neurosciences. Whether and how human cerebral blood flow (CBF) changes during the sleep-wake cycle remain to be clarified. Based on the synaptic homeostasis hypothesis of sleep and wake, we hypothesized that a day of wake and a night of sleep deprivation would be associated with gray matter resting CBF (rCBF) increases and that sleep would be associated with rCBF decreases. Thirty-eight healthy adult males (age 22.1 ±â€¯2.5 years) underwent arterial spin labeling perfusion magnetic resonance imaging at three time points: in the morning after a regular night's sleep, the evening of the same day, and the next morning, either after total sleep deprivation (n = 19) or a night of sleep (n = 19). All analyses were adjusted for hematocrit and head motion. rCBF increased from morning to evening and decreased after a night of sleep. These effects were most prominent in bilateral hippocampus, amygdala, thalamus, and in the occipital and sensorimotor cortices. Group × time interaction analyses for evening versus next morning revealed significant interaction in bilateral lateral and medial occipital cortices and in bilateral insula, driven by rCBF increases in the sleep deprived individuals and decreases in the sleepers, respectively. Furthermore, group × time interaction analyses for first morning versus next morning showed significant effects in medial and lateral occipital cortices, in anterior cingulate gyrus, and in the insula, in both hemispheres. These effects were mainly driven by CBF increases from TP1 to TP3 in the sleep deprived individuals. There were no associations between the rCBF changes and sleep characteristics, vigilant attention, or subjective sleepiness that remained significant after adjustments for multiple analyses. Altogether, these results encourage future studies to clarify mechanisms underlying sleep-related rCBF changes.

Evidence for cortical structural plasticity in humans after a day of waking and sleep deprivation.

Sleep is an evolutionarily conserved process required for human health and functioning. Insufficient sleep causes impairments across cognitive domains, and sleep deprivation can have rapid antidepressive effects in mood disorders. However, the neurobiological effects of waking and sleep are not well understood. Recently, animal studies indicated that waking and sleep are associated with substantial cortical structural plasticity. Here, we hypothesized that structural plasticity can be observed after a day of waking and sleep deprivation in the human cerebral cortex. To test this hypothesis, 61 healthy adult males underwent structural magnetic resonance imaging (MRI) at three time points: in the morning after a regular night's sleep, the evening of the same day, and the next morning, either after total sleep deprivation (N=41) or a night of sleep (N=20). We found significantly increased right prefrontal cortical thickness from morning to evening across all participants. In addition, pairwise comparisons in the deprived group between the two morning scans showed significant thinning of mainly bilateral medial parietal cortices after 23h of sleep deprivation, including the precuneus and posterior cingulate cortex. However, there were no significant group (sleep vs. sleep deprived group) by time interactions and we can therefore not rule out that other mechanisms than sleep deprivation per se underlie the bilateral medial parietal cortical thinning observed in the deprived group. Nonetheless, these cortices are thought to subserve wakefulness, are among the brain regions with highest metabolic rate during wake, and are considered some of the most sensitive cortical regions to a variety of insults. Furthermore, greater thinning within the left medial parietal cluster was associated with increased sleepiness after sleep deprivation. Together, these findings add to a growing body of data showing rapid structural plasticity within the human cerebral cortex detectable with MRI. Further studies are needed to clarify whether cortical thinning is one neural substrate of sleepiness after sleep deprivation.

Signal Enhancement of the Dentate Nucleus at Unenhanced MR Imaging after Very High Cumulative Doses of the Macrocyclic Gadolinium-based Contrast Agent Gadobutrol: An Observational Study.

Purpose To test for measurable visual enhancement of the dentate nucleus (DN) on unenhanced T1-weighted magnetic resonance (MR) images in a cohort of patients with a primary brain tumor who had not received linear gadolinium-based contrast agents (GBCAs) but had received many injections of macrocyclic GBCAs. Materials and Methods Seventeen patients with high-grade gliomas who had received 10-44 administrations of the macrocyclic GBCA gadobutrol (0.1 mmol/kg of body weight) were retrospectively included in this regional ethics committee-approved study. Two neuroradiologists inspected T1-weighted MR images with optimized window settings to visualize small differences in contrast at the baseline and at the last examination for the presence of visual DN signal enhancement. Signal intensity (SI) in the DN was normalized to the SI of the pons, and a one-sample t test was used to test for differences between baseline normalized SI (nSI) in the DN (nSIDN) and the average change in nSIDN of all postbaseline MR imaging sessions (ΔnSIDNavg) or the change in nSIDN from baseline to the last MR imaging session (ΔnSIDN). Linear and quadratic correlation analyses were used to examine the association between the number of macrocyclic GBCA administrations and ΔnSIDN or ΔnSIDNavg. Results The mean ± standard deviation number of macrocyclic GBCA administrations was 22.2 ± 10.6 administered throughout 706 days ± 454. Visually appreciable signal enhancement was observed in two patients who had received 37 and 44 macrocyclic GBCA injections. Mean ΔnSIDN was greater than zero (0.03 ± 0.05; P = .016), and there was a significant linear association between the number of macrocyclic GBCA injections and ΔnSIDN (r = 0.69, P = .002) and ΔnSIDNavg (r = 0.77, P < .001). Conclusion A small but statistically significant dose-dependent T1-weighted signal enhancement was observed in the DN after multiple macrocyclic GBCA injections. Visually appreciable enhancement in the DN was observed on contrast-optimized images in two patients who had received 37 and 44 standard doses of macrocyclic GBCAs. © RSNA, 2017 Online supplemental material is available for this article.

The brain functional connectome is robustly altered by lack of sleep.

Sleep is a universal phenomenon necessary for maintaining homeostasis and function across a range of organs. Lack of sleep has severe health-related consequences affecting whole-body functioning, yet no other organ is as severely affected as the brain. The neurophysiological mechanisms underlying these deficits are poorly understood. Here, we characterize the dynamic changes in brain connectivity profiles inflicted by sleep deprivation and how they deviate from regular daily variability. To this end, we obtained functional magnetic resonance imaging data from 60 young, adult male participants, scanned in the morning and evening of the same day and again the following morning. 41 participants underwent total sleep deprivation before the third scan, whereas the remainder had another night of regular sleep. Sleep deprivation strongly altered the connectivity of several resting-state networks, including dorsal attention, default mode, and hippocampal networks. Multivariate classification based on connectivity profiles predicted deprivation state with high accuracy, corroborating the robustness of the findings on an individual level. Finally, correlation analysis suggested that morning-to-evening connectivity changes were reverted by sleep (control group)-a pattern which did not occur after deprivation. We conclude that both, a day of waking and a night of sleep deprivation dynamically alter the brain functional connectome.

Multi-vendor reliability of arterial spin labeling perfusion MRI using a near-identical sequence: implications for multi-center studies.

INTRODUCTION: A main obstacle that impedes standardized clinical and research applications of arterial spin labeling (ASL), is the substantial differences between the commercial implementations of ASL from major MRI vendors. In this study, we compare a single identical 2D gradient-echo EPI pseudo-continuous ASL (PCASL) sequence implemented on 3T scanners from three vendors (General Electric Healthcare, Philips Healthcare and Siemens Healthcare) within the same center and with the same subjects. MATERIAL AND METHODS: Fourteen healthy volunteers (50% male, age 26.4±4.7years) were scanned twice on each scanner in an interleaved manner within 3h. Because of differences in gradient and coil specifications, two separate studies were performed with slightly different sequence parameters, with one scanner used across both studies for comparison. Reproducibility was evaluated by means of quantitative cerebral blood flow (CBF) agreement and inter-session variation, both on a region-of-interest (ROI) and voxel level. In addition, a qualitative similarity comparison of the CBF maps was performed by three experienced neuro-radiologists. RESULTS: There were no CBF differences between vendors in study 1 (p>0.1), but there were CBF differences of 2-19% between vendors in study 2 (p<0.001 in most gray matter ROIs) and 10-22% difference in CBF values obtained with the same vendor between studies (p<0.001 in most gray matter ROIs). The inter-vendor inter-session variation was not significantly larger than the intra-vendor variation in all (p>0.1) but one of the ROIs (p<0.001). CONCLUSION: This study demonstrates the possibility to acquire comparable cerebral CBF maps on scanners of different vendors. Small differences in sequence parameters can have a larger effect on the reproducibility of ASL than hardware or software differences between vendors. These results suggest that researchers should strive to employ identical labeling and readout strategies in multi-center ASL studies.

Serial diffusion tensor imaging for early detection of radiation-induced injuries to normal-appearing white matter in high-grade glioma patients.

PURPOSE: To study the potential of diffusion tensor imaging (DTI) to serve as a biomarker for radiation-induced brain injury during chemo-radiotherapy (RT) treatment. MATERIALS AND METHODS: Serial DTI data were collected from 18 high-grade glioma (HGG) patients undergoing RT and 7 healthy controls. Changes across time in mean, standard deviation (SD), skewness, and kurtosis of fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (λa ), and transversal diffusivity (λt ) within the normal-appearing white matter (NAWM) were modeled using a linear mixed-effects model to assess dose dependent changes of five dose bins (0-60 Gy), and global changes compared with a control group. RESULTS: Mean MD, λa and λt were all significantly increasing in >41 Gy dose regions (0.14%, 0.10%, and 0.18% per week) compared with <12 Gy regions. SD λt had significant dose dependent time evolution of 0.019*dose per week. Mean and SD MD, λa and λt in the global NAWM of the patient group significantly increased (mean; 0.06%, 0.03%, 0.09%, and SD; 0.57%, 0.34%, 0.51 per week) compared with the control group. The changes were significant at week 6 of, or immediately after RT. CONCLUSION: DTI is not sensitive to acute global NAWM changes during the treatment of HGG, but sensitive to early posttreatment changes.

T2*-correction in dynamic contrast-enhanced MRI from double-echo acquisitions.

PURPOSE: To evaluate the importance of T2*-effects on the arterial input function (AIF) and on the resulting dynamic parameter estimation in dynamic contrast-enhanced (DCE) MRI of high-grade gliomas. MATERIALS AND METHODS: Seven patients with high-grade gliomas were imaged in total 50 times using a double-echo DCE sequence. Kinetic analysis using the extended Tofts model was performed using AIFs with and without correction for T2*-effects, and the resulting estimates of the transfer constant (K(trans) ), blood plasma volume (vp ), and the rate constant (kep ) were compared. Numerical simulations were done for comparison with clinical results as well as to further investigate the dependency of parameter values on the magnitude of T2*-induced errors. RESULTS: All kinetic parameters were found to be overestimated if T2*-effects in the AIF were not accounted for; with vp being most severely affected. The relative error in each parameter was dependent on the absolute parameter magnitude, resulting in incorrect parametric tumor distributions in the presence of uncorrected AIF T2*-effects. CONCLUSION: In DCE, a sufficiently short echo time should be used or corrections for T2*-effects based on double-echo acquisition should be made for correct quantification of kinetic parameters.

Correction of B0-distortions in echo-planar-imaging-based perfusion-weighted MRI.

PURPOSE: To evaluate and quantify a scheme for correcting susceptibility artifacts in spin-echo echo-planar-imaging-based dynamic susceptibility contrast (DSC) perfusion MRI of high-grade gliomas at 3 Tesla. MATERIALS AND METHODS: Sixteen patients with a total of 78 scans were studied. DSC-MRI images were corrected using a displacement map generated from opposite phase-encoding polarity images. Two methods were used for quantification in the correction: (i) linear regression of pixel-by-pixel comparisons, performed both globally and relative to the anterior and posterior commissure plane (AC-PC plane), of T2-weighted images with both corrected and uncorrected raw DSC images; and (ii) counting significant (>2.0) normalized cerebral blood volume (nCBV) pixels from perfusion maps in the tumor region of interest. RESULTS: Sixty-four of 78 datasets showed significant differences in the coefficient of correlation (r2) values. The difference between corrected and uncorrected r2 values was positive in all but one patient. Correction of B0- distortion significantly improved r2 in slices around the AC-PC plane. In 62% of the datasets, we observed an increased number of significant pixels in the corrected nCBV maps; 36% showed more significant pixels in uncorrected nCBV maps; 1% showed no difference. CONCLUSION: Distortion correction of DSC-MRI may provide improved accuracy compared with uncorrected data, especially for tumors located below the corpus callosum and near the frontal sinuses.

Treatment Response Prediction in Major Depressive Disorder Using Multimodal MRI and Clinical Data: Secondary Analysis of a Randomized Clinical Trial.

OBJECTIVE: Response to antidepressant treatment in major depressive disorder varies substantially between individuals, which lengthens the process of finding effective treatment. The authors sought to determine whether a multimodal machine learning approach could predict early sertraline response in patients with major depressive disorder. They assessed the predictive contribution of MR neuroimaging and clinical assessments at baseline and after 1 week of treatment. METHODS: This was a preregistered secondary analysis of data from the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study, a multisite double-blind, placebo-controlled randomized clinical trial that included 296 adult outpatients with unmedicated recurrent or chronic major depressive disorder. MR neuroimaging and clinical data were collected before and after 1 week of treatment. Performance in predicting response and remission, collected after 8 weeks, was quantified using balanced accuracy (bAcc) and area under the receiver operating characteristic curve (AUROC) scores. RESULTS: A total of 229 patients were included in the analyses (mean age, 38 years [SD=13]; 66% female). Internal cross-validation performance in predicting response to sertraline (bAcc=68% [SD=10], AUROC=0.73 [SD=0.03]) was significantly better than chance. External cross-validation on data from placebo nonresponders (bAcc=62%, AUROC=0.66) and placebo nonresponders who were switched to sertraline (bAcc=65%, AUROC=0.68) resulted in differences that suggest specificity for sertraline treatment compared with placebo treatment. Finally, multimodal models outperformed unimodal models. CONCLUSIONS: The study results confirm that early sertraline treatment response can be predicted; that the models are sertraline specific compared with placebo; that prediction benefits from integrating multimodal MRI data with clinical data; and that perfusion imaging contributes most to these predictions. Using this approach, a lean and effective protocol could individualize sertraline treatment planning to improve psychiatric care.

Segmenting white matter hyperintensities on isotropic three-dimensional Fluid Attenuated Inversion Recovery magnetic resonance images: Assessing deep learning tools on a Norwegian imaging database.

An important step in the analysis of magnetic resonance imaging (MRI) data for neuroimaging is the automated segmentation of white matter hyperintensities (WMHs). Fluid Attenuated Inversion Recovery (FLAIR-weighted) is an MRI contrast that is particularly useful to visualize and quantify WMHs, a hallmark of cerebral small vessel disease and Alzheimer's disease (AD). In order to achieve high spatial resolution in each of the three voxel dimensions, clinical MRI protocols are evolving to a three-dimensional (3D) FLAIR-weighted acquisition. The current study details the deployment of deep learning tools to enable automated WMH segmentation and characterization from 3D FLAIR-weighted images acquired as part of a national AD imaging initiative. Based on data from the ongoing Norwegian Disease Dementia Initiation (DDI) multicenter study, two 3D models-one off-the-shelf from the NVIDIA nnU-Net framework and the other internally developed-were trained, validated, and tested. A third cutting-edge Deep Bayesian network model (HyperMapp3r) was implemented without any de-novo tuning to serve as a comparison architecture. The 2.5D in-house developed and 3D nnU-Net models were trained and validated in-house across five national collection sites among 441 participants from the DDI study, of whom 194 were men and whose average age was (64.91 +/- 9.32) years. Both an external dataset with 29 cases from a global collaborator and a held-out subset of the internal data from the 441 participants were used to test all three models. These test sets were evaluated independently. The ground truth human-in-the-loop segmentation was compared against five established WMH performance metrics. The 3D nnU-Net had the highest performance out of the three tested networks, outperforming both the internally developed 2.5D model and the SOTA Deep Bayesian network with an average dice similarity coefficient score of 0.76 +/- 0.16. Our findings demonstrate that WMH segmentation models can achieve high performance when trained exclusively on FLAIR input volumes that are 3D volumetric acquisitions. Single image input models are desirable for ease of deployment, as reflected in the current embedded clinical research project. The 3D nnU-Net had the highest performance, which suggests a way forward for our need to automate WMH segmentation while also evaluating performance metrics during on-going data collection and model retraining.

The role of brain white matter in depression resilience and response to sleep interventions.

Insomnia poses a high risk for depression. Brain mechanisms of sleep and mood improvement following cognitive behavioural therapy for insomnia remain elusive. This longitudinal study evaluated whether (i) individual differences in baseline brain white matter microstructure predict improvements and (ii) intervention affects brain white matter microstructure. People meeting the Diagnostic and Statistical Manual of Mental Disorders-5 criteria for Insomnia Disorder (n = 117) participated in a randomized controlled trial comparing 6 weeks of no treatment with therapist-guided digital cognitive behavioural therapy for insomnia, circadian rhythm support or their combination (cognitive behavioural therapy for insomnia + circadian rhythm support). Insomnia Severity Index and Inventory of Depressive Symptomatology-Self Report were assessed at baseline and followed up at Weeks 7, 26, 39 and 52. Diffusion-weighted magnetic resonance images were acquired at baseline and Week 7. Skeletonized white matter tracts, fractional anisotropy and mean diffusivity were quantified both tract-wise and voxel-wise using tract-based spatial statistics. Analyses used linear and mixed effect models while correcting for multiple testing using false discovery rate and Bonferroni for correlated endpoint measures. Our results show the following: (i) tract-wise lower fractional anisotropy in the left retrolenticular part of the internal capsule at baseline predicted both worse progression of depressive symptoms in untreated participants and more improvement in treated participants (fractional anisotropy × any intervention, PFDR = 0.053, Pcorr = 0.045). (ii) Only the cognitive behavioural therapy for insomnia + circadian rhythm support intervention induced a trend-level mean diffusivity decrease in the right superior corona radiata (PFDR = 0.128, Pcorr = 0.108), and individuals with a stronger mean diffusivity decrease showed a stronger alleviation of insomnia (R = 0.20, P = 0.035). In summary, individual differences in risk and treatment-supported resilience of depression involve white matter microstructure. Future studies could target the role of the left retrolenticular part of the internal capsule and right superior corona radiata and the brain areas they connect.