A Method for Automated Cortical Surface Registration and Labeling

BrainSuite BIDS App: Containerized Workflows for MRI Analysis

There has been a concerted effort by the neuroimaging community to establish standards for computational methods for data analysis that promote reproducibility and portability. In particular, the Brain Imaging Data Structure (BIDS) specifies a standard for storing imaging data, and the related BIDS App methodology provides a standard for implementing containerized processing environments that include all necessary dependencies to process BIDS datasets using image processing workflows. We present the BrainSuite BIDS App, which encapsulates the core MRI processing functionality of BrainSuite within the BIDS App framework. Specifically, the BrainSuite BIDS App implements a participant-level workflow comprising three pipelines and a corresponding set of group-level analysis workflows for processing the participant-level outputs. The BrainSuite Anatomical Pipeline (BAP) extracts cortical surface models from a T1-weighted (T1w) MRI. It then performs surface-constrained volumetric registration to align the T1w MRI to a labeled anatomical atlas, which is used to delineate anatomical regions of interest in the MRI brain volume and on the cortical surface models. The BrainSuite Diffusion Pipeline (BDP) processes diffusion-weighted imaging (DWI) data, with steps that include coregistering the DWI data to the T1w scan, correcting for geometric image distortion, and fitting diffusion models to the DWI data. The BrainSuite Functional Pipeline (BFP) performs fMRI processing using a combination of FSL, AFNI, and BrainSuite tools. BFP coregisters the fMRI data to the T1w image, then transforms the data to the anatomical atlas space and to the Human Connectome Project's grayordinate space. Each of these outputs can then be processed during group-level analysis. The outputs of BAP and BDP are analyzed using the BrainSuite Statistics in R (bssr) toolbox, which provides functionality for hypothesis testing and statistical modeling. The outputs of BFP can be analyzed using atlas-based or atlas-free statistical methods during group-level processing. These analyses include the application of BrainSync, which synchronizes the time-series data temporally and enables comparison of resting-state or task-based fMRI data across scans. We also present the BrainSuite Dashboard quality control system, which provides a browser-based interface for reviewing the outputs of individual modules of the participant-level pipelines across a study in real-time as they are generated. BrainSuite Dashboard facilitates rapid review of intermediate results, enabling users to identify processing errors and make adjustments to processing parameters if necessary. The comprehensive functionality included in the BrainSuite BIDS App provides a mechanism for rapidly deploying the BrainSuite workflows into new environments to perform large-scale studies. We demonstrate the capabilities of the BrainSuite BIDS App using structural, diffusion, and functional MRI data from the Amsterdam Open MRI Collection's Population Imaging of Psychology dataset.

An automatic extraction method on medical feature points based on PointNet++ for robot-assisted knee arthroplasty

BACKGROUND

Image registration is a crucial technology in robot-assisted knee arthroplasty, which provides real-time patient information by registering the pre-operative image data with data acquired during the operation. The existing registration method requires surgeons to manually pick up medical feature points (i.e. anatomical points) in pre-operative images, which is time-consuming and relied on surgeons experience. Moreover, different doctors have different preferences in preoperative planning, which may influence the consistency of surgical results.

METHODS

A medical feature points automatic extraction method based on PointNet++ named Point_RegNet is proposed to improve the efficiency of preoperative preparation and ensure the consistency of surgical results. The proposed method replaces the classification and segmentation layer of PointNet++ with a regression layer to predict the position of feature points. The comparative experiment is adopted to determine the optimal set of abstraction layers in PointNet++.

RESULTS

The proposed network with three set abstraction layers is more suitable for extracting feature points. The feature points predictions mean error of our method is less than 5 mm, which is 1 mm less than the manual marking method. Ultimately, our method only requires less than 3 s to extract all medical feature points in practical application. It is much faster than the manual extraction way which usually requires more than half an hour to mark all necessary feature points.

CONCLUSION

Our deep learning-based method can improve the surgery accuracy and reduce the preoperative preparation time. Moreover, this method can also be applied to other surgical navigation systems.

Deep learning-based automated segmentation of eight brain anatomical regions using head CT images in PET/CT

Objective

We aim to propose a deep learning-based method of automated segmentation of eight brain anatomical regions in head computed tomography (CT) images obtained during positron emission tomography/computed tomography (PET/CT) scans. The brain regions include basal ganglia, cerebellum, hemisphere, and hippocampus, all split into left and right.

Materials and methods

We enrolled patients who underwent both PET/CT imaging (with an extra head CT scan) and magnetic resonance imaging (MRI). The segmentation of eight brain regions in CT was achieved by using convolutional neural networks (CNNs): DenseVNet and 3D U-Net. The same segmentation task in MRI was performed by using BrainSuite13, which was a public atlas label method. The mean Dice scores were used to assess the performance of the CNNs. Then, the agreement and correlation of the volumes of the eight segmented brain regions between CT and MRI methods were analyzed.

Results

18 patients were enrolled. Four of the eight brain regions obtained high mean Dice scores (> 0.90): left (0.978) and right (0.912) basal ganglia and left (0.945) and right (0.960) hemisphere. Regarding the agreement and correlation of the brain region volumes between two methods, moderate agreements were observed on the left (ICC: 0.618, 95% CI 0.242, 0.835) and right (ICC: 0.654, 95% CI 0.298, 0.853) hemisphere. Poor agreements were observed on the other regions. A moderate correlation was observed on the right hemisphere (Spearman’s rho 0.68, p = 0.0019). Lower correlations were observed on the other regions.

Conclusions

The proposed deep learning-based method performed automated segmentation of eight brain anatomical regions on head CT imaging in PET/CT. Some regions obtained high mean Dice scores and the agreement and correlation results of the segmented region volumes between two methods were moderate to poor.

A hybrid high-resolution anatomical MRI atlas with sub-parcellation of cortical gyri using resting fMRI

We present a new high-quality, single-subject atlas with sub-millimeter voxel resolution, high SNR, and excellent gray-white tissue contrast to resolve fine anatomical details. The atlas is labeled into two parcellation schemes: 1) the anatomical BCI-DNI atlas, which is manually labeled based on known morphological and anatomical features, and 2) the hybrid USCBrain atlas, which incorporates functional information to guide the sub-parcellation of cerebral cortex. In both cases, we provide consistent volumetric and cortical surface-based parcellation and labeling. The intended use of the atlas is as a reference template for structural coregistration and labeling of individual brains. A single-subject T1-weighted image was acquired five times at a resolution of 0.547 mm × 0.547 mm × 0.800 mm and averaged. Images were processed by an expert neuroanatomist using semi-automated methods in BrainSuite to extract the brain, classify tissue-types, and render anatomical surfaces. Sixty-six cortical and 29 noncortical regions were manually labeled to generate the BCI-DNI atlas. The cortical regions were further sub-parcellated into 130 cortical regions based on multi-subject connectivity analysis using resting fMRI (rfMRI) data from the Human Connectome Project (HCP) database to produce the USCBrain atlas. In addition, we provide a delineation between sulcal valleys and gyral crowns, which offer an additional set of 26 sulcal subregions per hemisphere. Lastly, a probabilistic map is provided to give users a quantitative measure of reliability for each gyral subdivision. Utility of the atlas was assessed by computing Adjusted Rand Indices (ARIs) between individual sub-parcellations obtained through structural-only coregistration to the USCBrain atlas and sub-parcellations obtained directly from each subject's resting fMRI data. Both atlas parcellations can be used with the BrainSuite, FreeSurfer, and FSL software packages.

Labeling lateral prefrontal sulci using spherical data augmentation and context-aware training

The inference of cortical sulcal labels often focuses on deep (primary and secondary) sulcal regions, whereas shallow (tertiary) sulcal regions are largely overlooked in the literature due to the scarcity of manual/well-defined annotations and their large neuroanatomical variability. In this paper, we present an automated framework for regional labeling of both primary/secondary and tertiary sulci of the dorsal portion of lateral prefrontal cortex (LPFC) using spherical convolutional neural networks. We propose two core components that enhance the inference of sulcal labels to overcome such large neuroanatomical variability: (1) surface data augmentation and (2) context-aware training. (1) To take into account neuroanatomical variability, we synthesize training data from the proposed feature space that embeds intermediate deformation trajectories of spherical data in a rigid to non-rigid fashion, which bridges an augmentation gap in conventional rotation data augmentation. (2) Moreover, we design a two-stage training process to improve labeling accuracy of tertiary sulci by informing the biological associations in neuroanatomy: inference of primary/secondary sulci and then their spatial likelihood to guide the definition of tertiary sulci. In the experiments, we evaluate our method on 13 deep and shallow sulci of human LPFC in two independent data sets with different age ranges: pediatric (N=60) and adult (N=36) cohorts. We compare the proposed method with a conventional multi-atlas approach and spherical convolutional neural networks without/with rotation data augmentation. In both cohorts, the proposed data augmentation improves labeling accuracy of deep and shallow sulci over the baselines, and the proposed context-aware training offers further improvement in the labeling of shallow sulci over the proposed data augmentation. We share our tools with the field and discuss applications of our results for understanding neuroanatomical-functional organization of LPFC and the rest of cortex (https://github.com/ilwoolyu/SphericalLabeling).

Differences in brain structure and theta burst stimulation-induced plasticity implicate the corticomotor system in loss of function after musculoskeletal injury

Traumatic musculoskeletal injury (MSI) may involve changes in corticomotor structure and function, but direct evidence is needed. To determine the corticomotor basis of MSI, we examined interactions among skeletomotor function, corticospinal excitability, corticomotor structure (cortical thickness and white matter microstructure), and intermittent theta burst stimulation (iTBS)-induced plasticity. Nine women with unilateral anterior cruciate ligament rupture (ACL) 3.2 ± 1.1 yr prior to the study and 11 matched controls (CON) completed an MRI session followed by an offline plasticity-probing protocol using a randomized, sham-controlled, double-blind, cross-over study design. iTBS was applied to the injured (ACL) or nondominant (CON) motor cortex leg representation (M1_LEG) with plasticity assessed based on changes in skeletomotor function and corticospinal excitability compared with sham iTBS. The results showed persistent loss of function in the injured quadriceps, compensatory adaptations in the uninjured quadriceps and both hamstrings, and injury-specific increases in corticospinal excitability. Injury was associated with lateralized reductions in paracentral lobule thickness, greater centrality of nonleg corticomotor regions, and increased primary somatosensory cortex leg area inefficiency and eccentricity. Individual responses to iTBS were consistent with the principles of homeostatic metaplasticity; corresponded to injury-related differences in skeletomotor function, corticospinal excitability, and corticomotor structure; and suggested that corticomotor adaptations involve both hemispheres. Moreover, iTBS normalized skeletomotor function and corticospinal excitability in ACL. The results of this investigation directly confirm corticomotor involvement in chronic loss of function after traumatic MSI, emphasize the sensitivity of the corticomotor system to skeletomotor events and behaviors, and raise the possibility that brain-targeted therapies could improve recovery.NEW & NOTEWORTHY Traumatic musculoskeletal injuries may involve adaptive changes in the brain that contribute to loss of function. Our combination of neuroimaging and theta burst transcranial magnetic stimulation (iTBS) revealed distinct patterns of iTBS-induced plasticity that normalized differences in muscle and brain function evident years after unilateral knee ligament rupture. Individual responses to iTBS corresponded to injury-specific differences in brain structure and physiological activity, depended on skeletomotor deficit severity, and suggested that corticomotor adaptations involve both hemispheres.

Broad white matter impairment in multiple system atrophy

Multiple system atrophy (MSA) is a rare neurodegenerative disorder characterized by the widespread aberrant accumulation of α-synuclein (α-syn). MSA differs from other synucleinopathies such as Parkinson's disease (PD) in that α-syn accumulates primarily in oligodendrocytes, the only source of white matter myelination in the brain. Previous MSA imaging studies have uncovered focal differences in white matter. Here, we sought to build on this work by taking a global perspective on whole brain white matter. In order to do this, in vivo structural imaging and diffusion magnetic resonance imaging were acquired on 26 MSA patients, 26 healthy controls, and 23 PD patients. A refined whole brain approach encompassing the major fiber tracts and the superficial white matter located at the boundary of the cortical mantle was applied. The primary observation was that MSA but not PD patients had whole brain deep and superficial white matter diffusivity abnormalities (p < .001). In addition, in MSA patients, these abnormalities were associated with motor (Unified MSA Rating Scale, Part II) and cognitive functions (Mini-Mental State Examination). The pervasive whole brain abnormalities we observe suggest that there is widespread white matter damage in MSA patients which mirrors the widespread aggregation of α-syn in oligodendrocytes. Importantly, whole brain white matter abnormalities were associated with clinical symptoms, suggesting that white matter impairment may be more central to MSA than previously thought.

Changes in auditory cortical thickness following music training in children: converging longitudinal and cross-sectional results

Evidence is accumulating to suggest that music training is associated with structural brain differences in children and in adults. We used magnetic resonance imagining in two studies to investigate neuroanatomical correlates of music training in children. In study 1, we cross-sectionally compared a group of child musician (ages 9-11) matched to non-musicians and found that cortical thickness was greater in child musician in the posterior segment of the right-superior temporal gyrus (STG), an auditory association area that is involved in processing complex auditory stimuli, including pitch. We also found that thickness in the right posterior STG is related to music proficiency, however this relationship did not reach significance. In study 2, a longitudinal study, we investigated change in cortical thickness over a four-year period comparing a group of children involved in a systematic music training program with another group of children who did not have any music training. In this 2^nd study we assessed both groups at the beginning of the study, prior to music training for the music group, and four years later. We found that children in the music group showed a strong trend of lower rate of cortical thinning in the right posterior superior temporal gyrus. Together, our results provide evidence that music training induces structural brain changes in school-age children and that these changes are predominantly pronounced in the right auditory association areas.

Neurovascular and cortical responses to hyperoxia: enhanced cognition and electroencephalographic activity despite reduced perfusion

KEY POINTS

Extreme aviation is accompanied by ever-present risks of hypobaric hypoxia and decompression sickness. Neuroprotection against those hazards is conferred through fractional inspired oxygen ( F I , O 2 ) concentrations of 60-100% (hyperoxia). Hyperoxia reduces global cerebral perfusion (gCBF), increases reactive oxygen species within the brain and leads to cell death within the hippocampus. However, an understanding of hyperoxia's effect on cortical activity and concomitant levels of cognitive performance is lacking. This limits our understanding of whether hyperoxia could lower the brain's threshold of tolerance to physiological stressors inherent to extreme aviation, such as high gravitational forces. This study aimed to quantify the impact of hyperoxia upon global cerebral perfusion (gCBF), cognitive performance and cortical electroencephalography (EEG). Hyperoxia evoked a rapid reduction in gCBF, yet cognitive performance and vigilance were enhanced. EEG measurements revealed enhanced alpha power, suggesting less desynchrony, within the cortical temporal regions. Collectively, this work suggests hyperoxia-induced brain hypoperfusion is accompanied by enhanced cognitive processing and cortical arousal.

ABSTRACT

Extreme aviators continually inspire hyperoxic gas to mitigate risk of hypoxia and decompression injury. This neuroprotection carries a physiological cost: reduced cerebral perfusion (CBF). As reduced CBF may increase vulnerability to ever-present physiological challenges during extreme aviation, we defined the magnitude and duration of hyperoxia-induced changes in CBF, cortical electrical activity and cognition in 30 healthy males and females. Magnetic resonance imaging with pulsed arterial spin labelling provided serial measurements of global CBF (gCBF), first during exposure to 21% inspired oxygen ( F I , O 2 ) followed by a 30-min exposure to 100% F I , O 2 . High-density EEG facilitated characterization of cortical activity during assessment of cognitive performance, also measured during exposure to 21% and 100% F I , O 2 . Acid-base physiology was measured with arterial blood gases. We found that exposure to 100% F I , O 2 reduced gCBF to 63% of baseline values across all participants. Cognitive performance testing at 21% F I , O 2 was accompanied by increased theta and beta power with decreased alpha power across multiple cortical areas. During cognitive testing at 100% F I , O 2 , alpha activity was less desynchronized within the temporal regions than at 21% F I , O 2 . The collective hyperoxia-induced changes in gCBF, cognitive performance and EEG were similar across observed partial pressures of arterial oxygen ( P a O 2 ), which ranged between 276-548 mmHg, and partial pressures of arterial carbon dioxide ( P aC O 2 ), which ranged between 34-50 mmHg. Sex did not influence gCBF response to 100% F I , O 2 . Our findings suggest hyperoxia-induced reductions in gCBF evoke enhanced levels of cortical arousal and cognitive processing, similar to those occurring during a perceived threat.

Biomarkers of Seizure Activity in Patients With Intracranial Metastases and Gliomas: A Wide Range Study of Correlated Regions of Interest

Introduction: Studies quantifying cortical metrics in brain tumor patients who present with seizures are limited. The current investigation assesses morphometric/volumetric differences across a wide range of anatomical regions, including temporal and extra-temporal, in patients with gliomas and intracranial metastases (IMs) presenting with seizures that could serve as a biomarker in the identification of seizure expression and serve as a neuronal target for mitigation.

Methods: In a retrospective design, the MR sequences of ninety-two tumor patients [55% gliomas; 45% IM] and 34 controls were subjected to sophisticated morphometric and volumetric assessments using BrainSuite and MATLAB modules. We examined 103 regions of interests (ROIs) across eight distinct cortical categories of interests (COI) [gray matter, white matter; total volume, CSF; cortical areas: inner, mid, pial; cortical thickness]. The primary endpoint was quantifying and identifying ROIs with significant differences in z-scores based upon the presence of seizures. Feature selection employing neighborhood component analysis (NCA) determined the ROI within each COI having the highest significance/weight in the differentiation of seizure vs. non-seizure patients harboring brain tumor.

Results: Overall, the mean age of the cohort was 58.0 ± 12.8 years, and 45% were women. The prevalence of seizures in tumor patients was 28%. Forty-two ROIs across the eight pre-defined COIs had significant differences in z-scores between tumor patients presenting with and without seizures. The NCA feature selection noted the volume of pars-orbitalis and right middle temporal gyrus to have the highest weight in differentiating tumor patients based on seizures for three distinct COIs [GM, total volume, and CSF volume] and white matter, respectively. Left-sided transverse temporal gyrus, left precuneus, left transverse temporal, and left supramarginal gyrus were associated with having the highest weight in the differentiation of seizure vs. non-seizure in tumor patients for morphometrics relating to cortical areas in the pial, inner and mid regions and cortical thickness, respectively.

Conclusion: Our study elucidates potential biomarkers for seizure targeting in patients with gliomas and IMs based upon morphometric and volumetric assessments. Amongst the widespread brain regions examined in our cohort, pars orbitalis, supramarginal and temporal gyrus (middle, transverse), and the pre-cuneus contribute a maximal potential for differentiation of seizure patients from non-seizure.

Surface-constrained volumetric registration for the early developing brain

The T1-weighted and T2-weighted MRI contrasts of the infant brain evolve drastically during the first year of life. This poses significant challenges to inter- and intra-subject registration, which is key to subsequent statistical analyses. Existing registration methods that do not consider temporal contrast changes are ineffective for infant brain MRI data. To address this problem, we present in this paper a method for deformable registration of infant brain MRI. The key advantage of our method is threefold: (i) To deal with appearance changes, registration is performed based on segmented tissue maps instead of image intensity. Segmentation is performed by using an infant-centric algorithm previously developed by our group. (ii) Registration is carried out with respect to both cortical surfaces and volumetric tissue maps, thus allowing precise alignment of both cortical and subcortical structures. (iii) A dynamic elasticity model is utilized to allow large non-linear deformation. Experimental results in comparison with well-established registration methods indicate that our method yields superior accuracy in both cortical and subcortical alignment.

Improving human cortical sulcal curve labeling in large scale cross-sectional MRI using deep neural networks

BACKGROUND

Human cortical primary sulci are relatively stable landmarks and commonly observed across the population. Despite their stability, the primary sulci exhibit phenotypic variability.

NEW METHOD

We propose a fully automated pipeline that integrates both sulcal curve extraction and labeling. In this study, we use a large normal control population (n = 1424) to train neural networks for accurately labeling the primary sulci. Briefly, we use sulcal curve distance map, surface parcellation, mean curvature and spectral features to delineate their sulcal labels. We evaluate the proposed method with 8 primary sulcal curves in the left and right hemispheres compared to an established multi-atlas curve labeling method.

RESULTS

Sulcal labels by the proposed method reasonably well agree with manual labeling. The proposed method outperforms the existing multi-atlas curve labeling method.

COMPARISON WITH EXISTING METHOD

Significantly improved sulcal labeling results are achieved with over 12.5 and 20.6 percent improvement on labeling accuracy in the left and right hemispheres, respectively compared to that of a multi-atlas curve labeling method in eight curves (p≪0.001, two-sample t-test).

CONCLUSION

The proposed method offers a computationally efficient and robust labeling of major sulci.

Predicting Cognitive Scores from Resting fMRI Data and Geometric Features of the Brain

Anatomical T1 weighted Magnetic Resonance Imaging (MRI) and functional magnetic resonance imaging collected during resting (rfMRI) are promising markers that offer insight into the structure and function of the human brain. The objective of this work is to explore the use of a deep learning neural network to predict cognitive performance scores for a population of normal controls and subjects with Attention Deficit Hyperactivity Disorder (ADHD). Specifically, we predict verbal and performance IQs and ADHD index from features derived from T1 and rfMRI imaging data. First, we processed the rfMRI and MRI data of subjects using the BrainSuite fMRI Processing (BFP) pipeline to perform anatomical and functional preprocessing. This produces for each subject fMRI and geometric (anatomical) features represented in a standardized grayordinate system. The geometric and functional cortical data corresponding to the two hemispheres were then transformed to 128×128 multichannel images and input to a convolutional component of the neural network. Subcortical data were presented in a standard vector form and inputted to a input layer of the network. The neural network was implemented in Python using the Keras library with a TensorFlow backend. Training was performed on 168 images with 90 images used for testing. We observed a high correlation between predicted and actual values of the indices tested: Performance IQ: 0.47; Verbal IQ: 0.41, ADHD: 0.57. Comparing these values to those from network trained on functional-only and structural-only data, we saw that rfMRI is more informative than MRI, but the two modalities are highly complementary in terms of predicting these indices.

CACNA1C risk variant affects microstructural connectivity of the amygdala

Deficits in perception of emotional prosody have been described in patients with affective disorders at behavioral and neural level. In the current study, we use an imaging genetics approach to examine the impact of CACNA1C, one of the most promising genetic risk factors for psychiatric disorders, on prosody processing on a behavioral, functional and microstructural level. Using functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) we examined key areas involved in prosody processing, i.e. the amygdala and voice areas, in a healthy population. We found stronger activation to emotional than neutral prosody in the voice areas and the amygdala, but CACNA1C rs1006737 genotype had no influence on fMRI activity. However, significant microstructural differences (i.e. mean diffusivity) between CACNA1C rs1006737 risk allele carriers and non carriers were found in the amygdala, but not the voice areas. These modifications in brain architecture associated with CACNA1C might reflect a neurobiological marker predisposing to affective disorders and concomitant alterations in emotion perception.

Thalamic diaschisis following perinatal stroke is associated with clinical disability

BACKGROUND

Perinatal stroke causes most hemiparetic cerebral palsy and leads to lifelong disability. Understanding developmental neuroplasticity following early stroke is increasingly translated into novel therapies. Diaschisis refers to alterations brain structures remote from, but connected to, stroke lesions. Ipsilesional thalamic diaschisis has been described following adult stroke but has not been investigated in perinatal stroke. We hypothesized that thalamic diaschisis occurs in perinatal stroke and its degree would be inversely correlated with clinical motor function.

METHODS

Population-based, controlled cohort study. Participants were children (<19 years) with unilateral perinatal stroke (arterial ischemic stroke [AIS] or periventricular venous infarction [PVI]), anatomical magnetic resonance imaging (MRI) >6 months of age, symptomatic hemiparetic cerebral palsy, and no additional neurologic disorders. Typically developing controls had comparable age and gender proportions. T1-weighted anatomical scans were parcellated into 99 regions of interest followed by generation of regional volumes. The primary outcome was thalamic volume expressed as ipsilesional (ILTV), contralesional (CLTV) and thalamic ratio (CLTV/ILTV). Standardized clinical motor assessments were correlated with thalamic volume metrics.

RESULTS

Fifty-nine participants (12.9 years old ±4.0 years, 46% female) included 20 AIS, 11 PVI, and 28 controls. ILTV was reduced in both AIS and PVI compared to controls (p < .001, p = .029, respectively). Ipsilesional thalamic diaschisis was not associated with clinical motor function. However, CLTV was significantly larger in AIS compared to both controls and PVI (p = .005, p < .001, respectively). CLTV was inversely correlated with all four clinical motor assessments (all p < .003).

CONCLUSION

Bilateral thalamic volume changes occur after perinatal stroke. Ipsilesional volume loss is not associated with clinical motor function. Contralesional volume is inversely correlated with clinical motor function, suggesting the thalamus is involved in the known developmental plasticity that occurs in the contralesional hemisphere after early unilateral injury.

Multiuser virtual reality environment for visualising neuroimaging data

The recent advent of high-performance consumer virtual reality (VR) systems has opened new possibilities for immersive visualisation of numerous types of data. Medical imaging has long made use of advanced visualisation techniques, and VR offers exciting new opportunities for data exploration. The author presents a new framework for interacting with neuroimaging data, including MRI volumes, neuroanatomical surface models, diffusion tensors, and streamline tractography, as well as text-based annotations. The system was developed for the HTC Vive using C++, OpenGL, and the OpenVR software development kit. The author developed custom GLSL shaders for each type of data to provide high-performance real-time rendering suitable for use in a VR environment. These are integrated with an interface that enables the user to manipulate the scene through the Vive controllers and perform operations such as volume slicing, fibre track selection, and structural queries. The software can read data generated by existing automated brain MRI analysis packages, enabling the rapid development of subject-specific visualisations of multimodal data or annotated atlases. The system can also support multiple simultaneous users, placing them in the same virtual space to interact with each other while visualising the same datasets, opening new possibilities for teaching and for collaborative exploration of neuroimaging data.

Superficial white matter damage in anti-NMDA receptor encephalitis

BACKGROUND

Clinical brain MRI is normal in the majority of patients with anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis. However, extensive deep white matter damage wasrecently identifiedin these patients using diffusion weighted imaging. Here, our aim was to study a particularly vulnerable brain compartment, the late myelinating superficial white matter.

METHODS

Forty-six patients with anti-NMDAR encephalitis were included. Ten out of these were considered neurologically recovered (modified Rankin scale of zero), while 36 patients were non-recovered. In addition, 30 healthy controls were studied. MRI data were collected from all subjects and superficial white matter mean diffusivity derived from diffusion tensor imaging was compared between groups in whole brain, lobar and vertex-based analyses. Patients underwent comprehensive cognitive testing, and correlation analyses were performed between cognitive performance and superficial white matter integrity.

RESULTS

Non-recovered patients showed widespread superficial white matter damage in comparison to recovered patients and healthy controls. Vertex-based analyses revealed that damage predominated in frontal and temporal lobes. In contrast, the superficial white matter was intact in recovered patients. Importantly, persistent cognitive impairments in working memory, verbal memory, visuospatial memory and attention significantly correlated with damage of the superficial white matter in patients.

CONCLUSIONS

Anti-NMDAR encephalitis is associated with extensive superficial white matter damage in patients with incomplete recovery. The strong association with impairment in several cognitive domains highlights the clinical relevance of white matter damage in this disorder and warrants investigations of the underlying pathophysiological mechanisms.

A comparison of seven different DTI-derived estimates of corticospinal tract structural characteristics in chronic stroke survivors

BACKGROUND

Different diffusion tensor imaging (DTI) has been used to estimate corticospinal tract (CST) structure in the context of stroke rehabilitation research. However, there is no gold standard for the estimate of CST structure in chronic stroke survivors. This study aims to determine the most accurate DTI-derived CST estimate that is associated with a clinical motor outcome measure.

METHODS

We obtained imaging and behavioral data from a phase-I stroke rehabilitation clinical trial. We included thirty-seven chronic stroke survivors with mild-to-moderate motor impairment. Imaging data were processed using BrainSuite16a software. We calculated mean FA for each of 7 different ROIs/VOIs that include manually drawn 2-D ROIs and 3-D VOIs of CST from individual tractography or standard atlas. We compared ipsi- and contralesional CST FA for each method. Partial correlation was conducted between each CST FA asymmetry index and a time-based motor outcome measure, controlling for age and chronicity.

RESULTS

Ipsilesional CST FA was significantly lower than contralesional CST FA for each of the 7 methods Only CST FA asymmetry from the 3-D individual CST tractography showed a significant correlation with the primary motor outcome (r = 0.46, p = .005), while CST FA from the other six methods did not.

COMPARISON WITH EXISTING METHODS

Compared to the six other methods, CST FA asymmetry from 3-D individual tractography is the most accurate estimate of CST structure in this cohort of stroke survivors.

CONCLUSION

We recommend this method for future research seeking to understand brain-behavior mechanisms of motor recovery in chronic stroke survivors.

Kernel Methods for Riemannian Analysis of Robust Descriptors of the Cerebral Cortex

Typical cerebral cortical analyses rely on spatial normalization and are sensitive to misregistration arising from partial homologies between subject brains and local optima in nonlinear registration. In contrast, we use a descriptor of the 3D cortical sheet (jointly modeling folding and thickness) that is robust to misregistration. Our histogram-based descriptor lies on a Riemannian manifold. We propose new regularized nonlinear methods for (i) detecting group differences, using a Mercer kernel with an implicit lifting map to a reproducing kernel Hilbert space, and (ii) regression against clinical variables, using kernel density estimation. For both methods, we employ kernels that exploit the Riemannian structure. Results on simulated and clinical data shows the improved accuracy and stability of our approach in cortical-sheet analysis.

Hemoglobin and mean platelet volume predicts diffuse T1-MRI white matter volume decrease in sickle cell disease patients

Sickle cell disease (SCD) is a life-threatening genetic condition. Patients suffer from chronic systemic and cerebral vascular disease that leads to early and cumulative neurological damage. Few studies have quantified the effects of this disease on brain morphometry and even fewer efforts have been devoted to older patients despite the progressive nature of the disease. This study quantifies global and regional brain volumes in adolescent and young adult patients with SCD and racially matched controls with the aim of distinguishing between age related changes associated with normal brain maturation and damage from sickle cell disease.

T1 weighted images were acquired on 33 clinically asymptomatic SCD patients (age = 21.3 ± 7.8; F = 18, M = 15) and 32 racially matched control subjects (age = 24.4 ± 7.5; F = 22, M = 10). Exclusion criteria included pregnancy, previous overt stroke, acute chest, or pain crisis hospitalization within one month. All brain volume comparisons were corrected for age and sex.

Globally, grey matter volume was not different but white matter volume was 8.1% lower (p = 0.0056) in the right hemisphere and 6.8% (p = 0.0068) in the left hemisphere in SCD patients compared with controls. Multivariate analysis retained hemoglobin (β = 0.33; p = 0.0036), sex (β = 0.35; p = 0.0017) and mean platelet volume (β = 0.27; p = 0.016) as significant factors in the final prediction model for white matter volume for a combined r² of 0.37 (p < 0.0001). Lower white matter volume was confined to phylogenetically younger brain regions in the anterior and middle cerebral artery distributions.

Our findings suggest that there are diffuse white matter abnormalities in SCD patients, especially in the frontal, parietal and temporal lobes, that are associated with low hemoglobin levels and mean platelet volume. The pattern of brain loss suggests chronic microvascular insufficiency and tissue hypoxia as the causal mechanism. However, longitudinal studies of global and regional brain morphometry can help us give further insights on the pathophysiology of SCD in the brain.

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Total white matter brain volume is decreased in sickle cell disease patients.

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Global white matter decrease is found to be due to anemia.

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Diffuse WM volume decrease is found especially in watershed areas.

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Diffuse WM volume decrease spatially colocalize with silent stroke in SCD patients.

On the relationship between head circumference, brain size, prenatal long-chain PUFA/5-methyltetrahydrofolate supplementation and cognitive abilities during childhood

Head circumference in infants has been reported to predict brain size, total grey matter volume (GMV) and neurocognitive development. However, it is unknown whether it has predictive value on regional and subcortical brain volumes. We aimed to explore the relationship between several head circumference measurements since birth and distributions of GMV and subcortical volumes at later childhood. We examined seventy-four, Caucasian, singleton, term-born infants born to mothers randomised to receive fish oil and/or 5-methyltetrahydrofolate or placebo prenatal supplementation. We assessed head circumference at birth and at 4 and 10 years of age and cognitive abilities at 7 years of age. We obtained brain MRI at 10 years of age, on which we performed voxel-based morphometry, cortical surface extraction and subcortical segmentation. Analyses were controlled for sex, age, height, weight, family status, laterality and total intracranial volume. Prenatal supplementation did not affect head circumference at any age, cognitive abilities or total brain volumes. Head circumference at 4 years presented the highest correlation with total GMV, white matter volume and brain surface area, and was also strongly associated with GMV of frontal, temporal and occipital areas, as well as with caudate nucleus, globus pallidus, putamen and thalamus volumes. As relationships between brain volumes in childhood and several outcomes extend into adulthood, we have found that ages between 0 and 4 years as the optimal time for brain growth; postnatal factors might have the most relevant impact on structural maturation of certain cortical areas and subcortical nuclei, independent of prenatal supplementation.

Riemannian Statistical Analysis of Cortical Geometry with Robustness to Partial Homology and Misalignment

Typical studies of the geometry of the cerebral cortical structure focus on either cortical folding or thickness. They rely on spatial normalization, but use cortical descriptors that are sensitive to misregistration arising from the well-known problems of partial homologies between subject brains and local optima in nonlinear registration. In contrast to these approaches, we propose a novel framework for studying the geometry of the entire cortical sheet, subsuming its folding and thickness characteristics. We propose a novel descriptor of local cortical geometry to increase robustness to partial homology and misregistration. The proposed descriptor lies on a Riemannian manifold, and we describe a method for hypothesis testing on manifolds for cross-sectional studies. Results on simulated and clinical data show the benefits of the proposed approach for detecting between-group differences with greater accuracy and consistency.

Automated MRI Volumetric Analysis in Patients with Rasmussen Syndrome

BACKGROUND AND PURPOSE

Rasmussen syndrome, also known as Rasmussen encephalitis, is typically associated with volume loss of the affected hemisphere of the brain. Our aim was to apply automated quantitative volumetric MR imaging analyses to patients diagnosed with Rasmussen encephalitis, to determine the predictive value of lobar volumetric measures and to assess regional atrophy differences as well as monitor disease progression by using these measures.

MATERIALS AND METHODS

Nineteen patients (42 scans) with diagnosed Rasmussen encephalitis were studied. We used 2 control groups: one with 42 age- and sex-matched healthy subjects and the other with 42 epileptic patients without Rasmussen encephalitis with the same disease duration as patients with Rasmussen encephalitis. Volumetric analysis was performed on T1-weighted images by using BrainSuite. Ratios of volumes from the affected hemisphere divided by those from the unaffected hemisphere were used as input to a logistic regression classifier, which was trained to discriminate patients from controls. Using the classifier, we compared the predictive accuracy of all the volumetric measures. These ratios were used to further assess regional atrophy differences and correlate with epilepsy duration.

RESULTS

Interhemispheric and frontal lobe ratios had the best prediction accuracy for separating patients with Rasmussen encephalitis from healthy controls and patient controls without Rasmussen encephalitis. The insula showed significantly more atrophy compared with all the other cortical regions. Patients with longitudinal scans showed progressive volume loss in the affected hemisphere. Atrophy of the frontal lobe and insula correlated significantly with epilepsy duration.

CONCLUSIONS

Automated quantitative volumetric analysis provides accurate separation of patients with Rasmussen encephalitis from healthy controls and epileptic patients without Rasmussen encephalitis, and thus may assist the diagnosis of Rasmussen encephalitis. Volumetric analysis could also be included as part of follow-up for patients with Rasmussen encephalitis to assess disease progression.

Large Deformation Multiresolution Diffeomorphic Metric Mapping for Multiresolution Cortical Surfaces: A Coarse-to-Fine Approach

Brain surface registration is an important tool for characterizing cortical anatomical variations and understanding their roles in normal cortical development and psychiatric diseases. However, surface registration remains challenging due to complicated cortical anatomy and its large differences across individuals. In this paper, we propose a fast coarse-to-fine algorithm for surface registration by adapting the large diffeomorphic deformation metric mapping (LDDMM) framework for surface mapping and show improvements in speed and accuracy via a multiresolution analysis of surface meshes and the construction of multiresolution diffeomorphic transformations. The proposed method constructs a family of multiresolution meshes that are used as natural sparse priors of the cortical morphology. At varying resolutions, these meshes act as anchor points where the parameterization of multiresolution deformation vector fields can be supported, allowing the construction of a bundle of multiresolution deformation fields, each originating from a different resolution. Using a coarse-to-fine approach, we show a potential reduction in computation cost along with improvements in sulcal alignment when compared with LDDMM surface mapping.

Sulcal curve extraction using Laplace Beltrami eigenfunction level sets

The complexity of the human cortex is demonstrated in the intricate pattern of gyri and sulci that arise from the cortical folding process during development. Quantitative assessment of cortical folding is important in the definition of normal brain development and provides insight into neurodevelopmental disorders. In this work, a method for sulcal curve extraction is proposed that combines the advantages of previously proposed depth based and curvature based methods. The technique, derived from Laplace Beltrami eigenfunction level sets, maps mean curvature on the level sets, and incorporates depth information using extracted sulci and gyri, a characteristic previously attributed only to depth based methods. The use of Laplace Beltrami eigenfunction level sets requires neither definition of an outer hull surface nor correspondence between the cortical surface and outer hull, both of which are required by depth based methods. The utility of the method for extracting sulcal curves is demonstrated by application to fetal sheep brain MRI data, imaged at key time points during development.

Major Superficial White Matter Abnormalities in Huntington's Disease

BACKGROUND

The late myelinating superficial white matter at the juncture of the cortical gray and white matter comprising the intracortical myelin and short-range association fibers has not received attention in Huntington's disease. It is an area of the brain that is late myelinating and is sensitive to both normal aging and neurodegenerative disease effects. Therefore, it may be sensitive to Huntington's disease processes.

METHODS

Structural MRI data from 25 Pre-symptomatic subjects, 24 Huntington's disease patients and 49 healthy controls was run through a cortical pattern-matching program. The surface corresponding to the white matter directly below the cortical gray matter was then extracted. Individual subject's Diffusion Tensor Imaging (DTI) data was aligned to their structural MRI data. Diffusivity values along the white matter surface were then sampled at each vertex point. DTI measures with high spatial resolution across the superficial white matter surface were then analyzed with the General Linear Model to test for the effects of disease.

RESULTS

There was an overall increase in the axial and radial diffusivity across much of the superficial white matter (p < 0.001) in Pre-symptomatic subjects compared to controls. In Huntington's disease patients increased diffusivity covered essentially the whole brain (p < 0.001). Changes are correlated with genotype (CAG repeat number) and disease burden (p < 0.001).

CONCLUSIONS

This study showed broad abnormalities in superficial white matter even before symptoms are present in Huntington's disease. Since, the superficial white matter has a unique microstructure and function these abnormalities suggest it plays an important role in the disease.

Interactive effects of BDNF Val66Met genotype and trauma on limbic brain anatomy in childhood

Childhood trauma is a major precipitating factor in psychiatric disease. Emerging data suggest that stress susceptibility is genetically determined, and that risk is mediated by changes in limbic brain circuitry. There is a need to identify markers of disease vulnerability, and it is critical that these markers be investigated in childhood and adolescence, a time when neural networks are particularly malleable and when psychiatric disorders frequently emerge. In this preliminary study, we evaluated whether a common variant in the brain-derived neurotrophic factor (BDNF) gene (Val66Met; rs6265) interacts with childhood trauma to predict limbic gray matter volume in a sample of 55 youth high in sociodemographic risk. We found trauma-by-BDNF interactions in the right subcallosal area and right hippocampus, wherein BDNF-related gray matter changes were evident in youth without histories of trauma. In youth without trauma exposure, lower hippocampal volume was related to higher symptoms of anxiety. These data provide preliminary evidence for a contribution of a common BDNF gene variant to the neural correlates of childhood trauma among high-risk urban youth. Altered limbic structure in early life may lay the foundation for longer term patterns of neural dysfunction, and hold implications for understanding the psychiatric and psychobiological consequences of traumatic stress on the developing brain.

How age of acquisition influences brain architecture in bilinguals

In the present study, we explored how Age of Acquisition (AoA) of L2 affected brain structures in bilingual individuals. Thirty-six native English speakers who were bilingual were scanned with high resolution MRI. After MRI signal intensity inhomogeneity correction, we applied both voxel-based morphometry (VBM) and surface-based morphometry (SBM) approaches to the data. VBM analysis was performed using FSL's standard VBM processing pipeline. For the SBM analysis, we utilized a semi-automated sulci delineation procedure, registered the brains to an atlas, and extracted measures of twenty four pre-selected regions of interest. We addressed three questions: (1) Which areas are more susceptible to differences in AoA? (2) How do AoA, proficiency and current level of exposure work together in predicting structural differences in the brain? And (3) What is the direction of the effect of AoA on regional volumetric and surface measures? Both VBM and SBM results suggested that earlier second language exposure was associated with larger volumes in the right parietal cortex. Consistently, SBM showed that the cortical area of the right superior parietal lobule increased as AoA decreased. In contrast, in the right pars orbitalis of the inferior frontal gyrus, AoA, proficiency, and current level of exposure are equally important in accounting for the structural differences. We interpret our results in terms of current theory and research on the effects of L2 learning on brain structures and functions.

Reproducibility of brain-cognition relationships using three cortical surface-based protocols: An exhaustive analysis based on cortical thickness

People differ in their cognitive functioning. This variability has been exhaustively examined at the behavioral, neural and genetic level to uncover the mechanisms by which some individuals are more cognitively efficient than others. Studies investigating the neural underpinnings of interindividual differences in cognition aim to establish a reliable nexus between functional/structural properties of a given brain network and higher order cognitive performance. However, these studies have produced inconsistent results, which might be partly attributed to methodological variations. In the current study, 82 healthy young participants underwent MRI scanning and completed a comprehensive cognitive battery including measurements of fluid, crystallized, and spatial intelligence, along with working memory capacity/executive updating, controlled attention, and processing speed. The cognitive scores were obtained by confirmatory factor analyses. T1 -weighted images were processed using three different surface-based morphometry (SBM) pipelines, varying in their degree of user intervention, for obtaining measures of cortical thickness (CT) across the brain surface. Distribution and variability of CT and CT-cognition relationships were systematically compared across pipelines and between two cognitively/demographically matched samples to overcome potential sources of variability affecting the reproducibility of findings. We demonstrated that estimation of CT was not consistent across methods. In addition, among SBM methods, there was considerable variation in the spatial pattern of CT-cognition relationships. Finally, within each SBM method, results did not replicate in matched subsamples.

Simultaneous and consistent labeling of longitudinal dynamic developing cortical surfaces in infants

The human cerebral cortex develops extremely dynamically in the first 2years of life. Accurate and consistent parcellation of longitudinal dynamic cortical surfaces during this critical stage is essential to understand the early development of cortical structure and function in both normal and high-risk infant brains. However, directly applying the existing methods developed for the cross-sectional studies often generates longitudinally-inconsistent results, thus leading to inaccurate measurements of the cortex development. In this paper, we propose a new method for accurate, consistent, and simultaneous labeling of longitudinal cortical surfaces in the serial infant brain MR images. The proposed method is explicitly formulated as a minimization problem with an energy function that includes a data fitting term, a spatial smoothness term, and a temporal consistency term. Specifically, inspired by multi-atlas based label fusion, the data fitting term is designed to integrate the contributions from multi-atlas surfaces adaptively, according to the similarities of their local cortical folding with that of the subject cortical surface. The spatial smoothness term is then designed to adaptively encourage label smoothness based on the local cortical folding geometries, i.e., allowing label discontinuity at sulcal bottoms (which often are the boundaries of cytoarchitecturally and functionally distinct regions). The temporal consistency term is to adaptively encourage the label consistency among the temporally-corresponding vertices, based on their similarity of local cortical folding. Finally, the entire energy function is efficiently minimized by a graph cuts method. The proposed method has been applied to the parcellation of longitudinal cortical surfaces of 13 healthy infants, each with 6 serial MRI scans acquired at 0, 3, 6, 9, 12 and 18months of age. Qualitative and quantitative evaluations demonstrated both accuracy and longitudinal consistency of the proposed method. By using our method, for the first time, we reveal several hitherto unseen properties of the dynamic and regionally heterogeneous development of the cortical surface area in the first 18months of life.

An equal start: absence of group differences in cognitive, social, and neural measures prior to music or sports training in children

Several studies comparing adult musicians and non-musicians have provided compelling evidence for functional and anatomical differences in the brain systems engaged by musical training. It is not known, however, whether those differences result from long-term musical training or from pre-existing traits favoring musicality. In an attempt to begin addressing this question, we have launched a longitudinal investigation of the effects of childhood music training on cognitive, social and neural development. We compared a group of 6- to 7-year old children at the start of intense after-school musical training, with two groups of children: one involved in high intensity sports training but not musical training, another not involved in any systematic training. All children were tested with a comprehensive battery of cognitive, motor, musical, emotional, and social assessments and underwent magnetic resonance imaging and electroencephalography. Our first objective was to determine whether children who participate in musical training were different, prior to training, from children in the control groups in terms of cognitive, motor, musical, emotional, and social behavior measures as well as in structural and functional brain measures. Our second objective was to determine whether musical skills, as measured by a music perception assessment prior to training, correlates with emotional and social outcome measures that have been shown to be associated with musical training. We found no neural, cognitive, motor, emotional, or social differences among the three groups. In addition, there was no correlation between music perception skills and any of the social or emotional measures. These results provide a baseline for an ongoing longitudinal investigation of the effects of music training.