Diffusion tensor imaging in children with unilateral hearing loss: A pilot study

Objective: Language acquisition was assumed to proceed normally in children with unilateral hearing loss (UHL) since they have one functioning ear. However, children with UHL score poorly on speech-language tests and have higher rates of educational problems compared to normal hearing (NH) peers. Diffusion tensor imaging (DTI) is an imaging modality used to measure microstructural integrity of brain white matter. The purpose of this pilot study was to investigate differences in fractional anisotropy (FA) and mean diffusivity (MD) in hearing- and non-hearing-related structures in the brain between children with UHL and their NH siblings. Study Design: Prospective observational cohortSetting: Academic medical center.Subjects and Methods: 61 children were recruited, tested and imaged. 29 children with severe-to-profound UHL were compared to 20 siblings with NH using IQ and oral language testing, and MRI with DTI. 12 children had inadequate MRI data. Parents provided demographic data and indicated whether children had a need for an individualized educational program (IEP) or speech therapy (ST). DTI parameters were measured in auditory and non-auditory regions of interest (ROIs). Between-group comparisons were evaluated with non-parametric tests. Results: Lower FA of left lateral lemniscus was observed for children with UHL compared to their NH siblings, as well as trends towards differences in other auditory and nonauditory regions. Correlation analyses showed associations between several DTI parameters and outcomes in children with UHL. Regression analyses revealed relationships between educational outcome variables and several DTI parameters, which may provide clinically useful information for guidance of speech therapy. Discussion/Conclusion: White matter microstructural patterns in several brain regions are preserved despite unilateral rather than bilateral auditory input which contrasts with findings in patients with bilateral hearing loss

MRI-based classification of IDH mutation and 1p/19q codeletion status of gliomas using a 2.5D hybrid multi-task convolutional neural network

Isocitrate dehydrogenase (IDH) mutation and 1p/19q codeletion status are important prognostic markers for glioma. Currently, they are determined using invasive procedures. Our goal was to develop artificial intelligence-based methods to non-invasively determine these molecular alterations from MRI. For this purpose, pre-operative MRI scans of 2648 patients with gliomas (grade II-IV) were collected from Washington University School of Medicine (WUSM; n = 835) and publicly available datasets viz. Brain Tumor Segmentation (BraTS; n = 378), LGG 1p/19q (n = 159), Ivy Glioblastoma Atlas Project (Ivy GAP; n = 41), The Cancer Genome Atlas (TCGA; n = 461), and the Erasmus Glioma Database (EGD; n = 774). A 2.5D hybrid convolutional neural network was proposed to simultaneously localize the tumor and classify its molecular status by leveraging imaging features from MR scans and prior knowledge features from clinical records and tumor location. The models were tested on one internal (TCGA) and two external (WUSM and EGD) test sets. For IDH, the best-performing model achieved areas under the receiver operating characteristic (AUROC) of 0.925, 0.874, 0.933 and areas under the precision-recall curves (AUPRC) of 0.899, 0.702, 0.853 on the internal, WUSM, and EGD test sets, respectively. For 1p/19q, the best model achieved AUROCs of 0.782, 0.754, 0.842, and AUPRCs of 0.588, 0.713, 0.782, on those three data-splits, respectively. The high accuracy of the model on unseen data showcases its generalization capabilities and suggests its potential to perform a 'virtual biopsy' for tailoring treatment planning and overall clinical management of gliomas

Stronger prediction of motor recovery and outcome post-stroke by cortico-spinal tract integrity than functional connectivity

<div><p>Objectives</p><p>To examine longitudinal changes in structural and functional connectivity post-stroke in patients with motor impairment, and define their importance for recovery and outcome at 12 months.</p><p>Methods</p><p>First-time stroke patients (N = 31) were studied at 1–2 weeks, 3 months, and 12 months post-injury with a validated motor battery and resting-state fMRI to measure inter-hemispheric functional connectivity (FC). Fractional anisotropy (FA) of the cortico-spinal tract (CST) was derived from diffusion tensor imaging as a measure of white matter organization. ANOVAs were used to test for changes in FC, FA, and motor performance scores over time, and regression analysis related motor outcome to clinical and neuroimaging variables.</p><p>Results</p><p>FA of the ipsilesional CST improved significantly from 3 to 12 months and was strongly correlated with motor performance. FA improved even in the absence of direct damage to the CST. Inter-hemispheric FC also improved over time, but did not correlate with motor performance at 12 months. Clinical variables (early motor score, education level, and age) predicted 80.4% of the variation of motor outcome, and FA increased the predictability to 84.6%. FC did not contribute to the prediction of motor outcome.</p><p>Conclusions</p><p>Stroke causes changes to the CST microstructure that can account for behavioral variability even in the absence of demonstrable lesion. Ipsilesional CST undergoes remodeling post-stroke, even past the three-month window when most of the motor recovery happens. FA of the CST, but not inter-hemispheric FC, can improve to the prediction of motor outcome based on early motor scores.</p></div

Relationship between age and white matter integrity in children with phenylketonuria

Diffusion tensor imaging (DTI) has shown poorer microstructural white matter integrity in children with phenylketonuria (PKU), specifically decreases in mean diffusivity (MD), in comparison with healthy children. However, little research has been conducted to investigate the relationship between age and white matter integrity in this population. The present study examined group differences in the relationship between age and MD across a range of brain regions in 31 children with early- and continuously-treated PKU and 51 healthy control children. Relationships among MD, age, and group were explored using hierarchical linear regression and Pearson correlation. Results indicated a stronger age-related decrease in MD for children with PKU in comparison with healthy children in 4 of the 10 brain regions examined, suggesting that the trajectory of white matter development is abnormal in children with PKU. Further research using longitudinal methodology is needed to fully elucidate our understanding of white matter development in children with PKU

A comparison of resting state functional magnetic resonance imaging to invasive electrocortical stimulation for sensorimotor mapping in pediatric patients

Localizing neurologic function within the brain remains a significant challenge in clinical neurosurgery. Invasive mapping with direct electrocortical stimulation currently is the clinical gold standard but is impractical in young or cognitively delayed patients who are unable to reliably perform tasks. Resting state functional magnetic resonance imaging non-invasively identifies resting state networks without the need for task performance, hence, is well suited to pediatric patients. We compared sensorimotor network localization by resting state fMRI to cortical stimulation sensory and motor mapping in 16 pediatric patients aged 3.1 to 18.6 years. All had medically refractory epilepsy that required invasive electrographic monitoring and stimulation mapping. The resting state fMRI data were analyzed using a previously trained machine learning classifier that has previously been evaluated in adults. We report comparable functional localization by resting state fMRI compared to stimulation mapping. These results provide strong evidence for the utility of resting state functional imaging in the localization of sensorimotor cortex across a wide range of pediatric patients

White matter integrity of contralesional and transcallosal tracts may predict response to upper limb task-specific training in chronic stroke

OBJECTIVE: To investigate white matter (WM) plasticity induced by intensive upper limb (UL) task specific training (TST) in chronic stroke. METHODS: Diffusion tensor imaging data and UL function measured by the Action Research Arm Test (ARAT) were collected in 30 individuals with chronic stroke prior to and after intensive TST. ANOVAs tested the effects of training on the entire sample and on the Responders [ΔARAT ≥ 5.8, N = 13] and Non-Responders [ΔARAT \u3c 5.8, N = 17] groups. Baseline fractional anisotropy (FA) values were correlated with ARATpost TST controlling for baseline ARAT and age to identify voxels predictive of response to TST. RESULTS: While ARAT scores increased following training (p \u3c 0.0001), FA changes within major WM tracts were not significant at p \u3c 0.05. In the Responder group, larger baseline FA of both contralesional (CL) and transcallosal tracts predicted larger ARAT scores post-TST. Subcortical lesions and more severe damage to transcallosal tracts were more pronounced in the Non-Responder than in the Responder group. CONCLUSIONS: The motor improvements post-TST in the Responder group may reflect the engagement of interhemispheric processes not available to the Non-Responder group. Future studies should clarify differences in the role of CL and transcallosal pathways as biomarkers of recovery in response to training for individuals with cortical and subcortical stroke. This knowledge may help to identify sources of heterogeneity in stroke recovery, which is necessary for the development of customized rehabilitation interventions

Machine learning analytics of resting-state functional connectivity predicts survival outcomes of glioblastoma multiforme patients

Glioblastoma multiforme (GBM) is the most frequently occurring brain malignancy. Due to its poor prognosis with currently available treatments, there is a pressing need for easily accessible, non-invasive techniques to help inform pre-treatment planning, patient counseling, and improve outcomes. In this study we determined the feasibility of resting-state functional connectivity (rsFC) to classify GBM patients into short-term and long-term survival groups with respect to reported median survival (14.6 months). We used a support vector machine with rsFC between regions of interest as predictive features. We employed a novel hybrid feature selection method whereby features were first filtered using correlations between rsFC and OS, and then using the established method of recursive feature elimination (RFE) to select the optimal feature subset. Leave-one-subject-out cross-validation evaluated the performance of models. Classification between short- and long-term survival accuracy was 71.9%. Sensitivity and specificity were 77.1 and 65.5%, respectively. The area under the receiver operating characteristic curve was 0.752 (95% CI, 0.62-0.88). These findings suggest that highly specific features of rsFC may predict GBM survival. Taken together, the findings of this study support that resting-state fMRI and machine learning analytics could enable a radiomic biomarker for GBM, augmenting care and planning for individual patients

Semi-automated segmentation of the lateral periventricular regions using diffusion magnetic resonance imaging

The lateral ventricular perimeter (LVP) of the brain is a critical region because in addition to housing neural stem cells required for brain development, it facilitates cerebrospinal fluid (CSF) bulk flow and functions as a blood-CSF barrier to protect periventricular white matter (PVWM) and other adjacent regions from injurious toxins. LVP injury is common, particularly among preterm infants who sustain intraventricular hemorrhage or post hemorrhagic hydrocephalus and has been associated with poor neurological outcomes. Assessment of the LVP with diffusion MRI has been challenging, primarily due to issues with partial volume artifacts since the LVP region is in close proximity to CSF and other structures of varying signal intensities that may be inadvertently included in LVP segmentation. This research method presents:•A novel MATLAB-based method to segment a homogenous LVP layer using high spatial resolution parameters (voxel size 1.2 × 1.2 × 1.2 m

On the role of the corpus callosum in interhemispheric functional connectivity in humans

Resting state functional connectivity is defined in terms of temporal correlations between physiologic signals, most commonly studied using functional magnetic resonance imaging. Major features of functional connectivity correspond to structural (axonal) connectivity. However, this relation is not one-to-one. Interhemispheric functional connectivity in relation to the corpus callosum presents a case in point. Specifically, several reports have documented nearly intact interhemispheric functional connectivity in individuals in whom the corpus callosum (the major commissure between the hemispheres) never develops. To investigate this question, we assessed functional connectivity before and after surgical section of the corpus callosum in 22 patients with medically refractory epilepsy. Section of the corpus callosum markedly reduced interhemispheric functional connectivity. This effect was more profound in multimodal associative areas in the frontal and parietal lobe than primary regions of sensorimotor and visual function. Moreover, no evidence of recovery was observed in a limited sample in which multiyear, longitudinal follow-up was obtained. Comparison of partial vs. complete callosotomy revealed several effects implying the existence of polysynaptic functional connectivity between remote brain regions. Thus, our results demonstrate that callosal as well as extracallosal anatomical connections play a role in the maintenance of interhemispheric functional connectivity

Neuroinflammation and white matter alterations in obesity assessed by Diffusion Basis Spectrum Imaging

Human obesity is associated with low-grade chronic systemic inflammation, alterations in brain structure and function, and cognitive impairment. Rodent models of obesity show that high-calorie diets cause brain inflammation (neuroinflammation) in multiple regions, including the hippocampus, and impairments in hippocampal-dependent memory tasks. To determine if similar effects exist in humans with obesity, we applied Diffusion Basis Spectrum Imaging (DBSI) to evaluate neuroinflammation and axonal integrity. We examined diffusion-weighted magnetic resonance imaging (MRI) data in two independent cohorts of obese and non-obese individuals (Cohort 1: 25 obese/21 non-obese; Cohort 2: 18 obese/41 non-obese). We applied Tract-based Spatial Statistics (TBSS) to allow whole-brain white matter (WM) analyses and compare DBSI-derived isotropic and anisotropic diffusion measures between the obese and non-obese groups. In both cohorts, the obese group had significantly greater DBSI-derived restricted fraction (DBSI-RF; an indicator of neuroinflammation-related cellularity), and significantly lower DBSI-derived fiber fraction (DBSI-FF; an indicator of apparent axonal density) in several WM tracts (all correcte