Improved cognition and preserved hippocampal fractional anisotropy in subjects undergoing carotid endarterectomy "CEA preserves cognition & hippocampal structure"

OBJECTIVES

A growing body of data indicates that extracranial carotid artery disease (ECAD) can contribute to cognitive impairment. However, there have been mixed reports regarding the benefit of carotid endarterectomy (CEA) as it relates to preserving cognitive function. In this work, diffusion magnetic resonance imaging (dMRI) and neurocognitive testing are used to provide insight into structural and functional brain changes that occur in subjects with significant carotid artery stenosis, as well as changes that occur in response to CEA.

MATERIALS AND METHODS

The study design was a prospective, non-randomized, controlled study that enrolled patients with asymptomatic carotid stenosis. Thirteen subjects had severe ECAD (≥70% stenosis in at least one carotid artery) and were scheduled to undergo surgery. Thirteen had asymptomatic ECAD with <70% stenosis, therefore not requiring surgery. All subjects underwent neurocognitive testing using the Montreal Cognitive Assessment test (MoCA) and high angular resolution, multi-shell diffusion magnetic resonance imaging (dMRI) of the brain at baseline and at four-six months follow-up. Changes in MoCA scores as well as in Fractional anisotropy (FA) along the hippocampus were compared at baseline and follow-up.

RESULTS

At baseline, FA was significantly lower along the ipsilateral hippocampus in subjects with severe ECAD compared to subjects without severe ECAD. MoCA scores were lower in these individuals, but this did not reach statistical significance. At follow-up, MoCA scores increased significantly in subjects who underwent CEA and remained statistically equal in control subjects that did not have CEA. FA remained unchanged in the CEA group and decreased in the control group.

CONCLUSIONS

This study suggests that CEA improves cognition and preserves hippocampal white matter structure compared to control subjects not undergoing CEA.

Clinical characteristics analysis of pediatric spinal cord injury without radiological abnormality in China: a retrospective study

PURPOSE

This study aims to analyze the clinical characteristics of Chinese children with spinal cord injury (SCI) without radiographic abnormality (SCIWORA) and explore their contributing factors and mechanisms of occurrence.

METHODS

A retrospective analysis was conducted on the clinical data of pediatric patients diagnosed with SCIWORA from January 2005 to May 2020. Epidemiological, etiological, mechanistic, therapeutic, and outcome aspects were analyzed.

RESULTS

A total of 47 patients with SCIWORA were included in this study, comprising 16 males and 31 females. The age range was 4 to 12 years, with an average age of 7.49 ± 2.04 years, and 70% of the patients were below eight. Sports-related injuries constituted 66%, with 70% attributed to dance backbend practice. Thoracic segment injuries accounted for 77%. In the American Spinal Injury Association (ASIA) classification, the combined proportion of A and B grades accounted for 88%. Conservative treatment was chosen by 98% of the patients, with muscle atrophy, spinal scoliosis, hip joint abnormalities, and urinary system infections being the most common complications.

CONCLUSION

SCIWORA in Chinese children is more prevalent in those under eight years old, with a higher incidence in females than males. Thoracic spinal cord injuries are predominant, dance backbend as a primary contributing factor, and the social environment of "neijuan" is a critical potential inducing factor. Furthermore, the initial severity of the injury plays a decisive role in determining the prognosis of SCIWORA.

High resolution 0.5mm isotropic T1-weighted and diffusion tensor templates of the brain of non-demented older adults in a common space for the MIITRA atlas

High quality, high resolution T1-weighted (T1w) and diffusion tensor imaging (DTI) brain templates located in a common space can enhance the sensitivity and precision of template-based neuroimaging studies. However, such multimodal templates have not been constructed for the older adult brain. The purpose of this work which is part of the MIITRA atlas project was twofold: (A) to develop 0.5 mm isotropic resolution T1w and DTI templates that are representative of the brain of non-demented older adults and are located in the same space, using advanced multimodal template construction techniques and principles of super resolution on data from a large, diverse, community cohort of 400 non-demented older adults, and (B) to systematically compare the new templates to other standardized templates. It was demonstrated that the new MIITRA-0.5mm T1w and DTI templates are well-matched in space, exhibit good definition of brain structures, including fine structures, exhibit higher image sharpness than other standardized templates, and are free of artifacts. The MIITRA-0.5mm T1w and DTI templates allowed higher intra-modality inter-subject spatial normalization precision as well as higher inter-modality intra-subject spatial matching of older adult T1w and DTI data compared to other available templates. Consequently, MIITRA-0.5mm templates allowed detection of smaller inter-group differences for older adult data compared to other templates. The MIITRA-0.5mm templates were also shown to be most representative of the brain of non-demented older adults compared to other templates with submillimeter resolution. The new templates constructed in this work constitute two of the final products of the MIITRA atlas project and are anticipated to have important implications for the sensitivity and precision of studies on older adults.

Geometric Deep Learning for Unsupervised Registration of Diffusion Magnetic Resonance Images

Deep learning based models for registration predict a transformation directly from moving and fixed image appearances. These models have revolutionized the field of medical image registration, achieving accuracy on-par with classical registration methods at a fraction of the computation time. Unfortunately, most deep learning based registration methods have focused on scalar imaging modalities such as T1/T2 MRI and CT, with less attention given to more complex modalities such as diffusion MRI. In this paper, to the best of our knowledge, we present the first end-to-end geometric deep learning based model for the non-rigid registration of fiber orientation distribution fields (fODF) derived from diffusion MRI (dMRI). Our method can be trained in a fully-unsupervised fashion using only input fODF image pairs, i.e. without ground truth deformation fields. Our model introduces several novel differentiable layers for local Jacobian estimation and reorientation that can be seamlessly integrated into the recently introduced manifold-valued convolutional network in literature. The results of this work are accurate deformable registration algorithms for dMRI data that can execute in the order of seconds, as opposed to dozens of minutes to hours consumed by their classical counterparts.

A multi-scale probabilistic atlas of the human connectome

The human brain is a complex system that can be efficiently represented as a network of structural connectivity. Many imaging studies would benefit from such network information, which is not always available. In this work, we present a whole-brain multi-scale structural connectome atlas. This tool has been derived from a cohort of 66 healthy subjects imaged with optimal technology in the setting of the Human Connectome Project. From these data we created, using extensively validated diffusion-data processing, tractography and gray-matter parcellation tools, a multi-scale probabilistic atlas of the human connectome. In addition, we provide user-friendly and accessible code to match this atlas to individual brain imaging data to extract connection-specific quantitative information. This can be used to associate individual imaging findings, such as focal white-matter lesions or regional alterations, to specific connections and brain circuits. Accordingly, network-level consequences of regional changes can be analyzed even in absence of diffusion and tractography data. This method is expected to broaden the accessibility and lower the yield for connectome research.

Multimodal data fusion of cortical-subcortical morphology and functional network connectivity in psychotic spectrum disorder

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Overlap has been noted disorders which fall on the psychotic spectrum.

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Univariate studies may miss joint brain features across diagnostic categories.

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mCCA with jICA is paired with features across the psychotic spectrum to produce joint components.

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One joint component displayed a significant relationship with cognitive scores.

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The replicate trends of cortical-subcortical irregularity in psychotic spectrum disorders.

Multiple authors have noted overlapping symptoms and alterations across clinical, anatomical, and functional brain features in schizophrenia (SZ), schizoaffective disorder (SZA), and bipolar disorder (BPI). However, regarding brain features, few studies have approached this line of inquiry using analytical techniques optimally designed to extract the shared features across anatomical and functional information in a simultaneous manner. Univariate studies of anatomical or functional alterations across these disorders can be limited and run the risk of omitting small but potentially crucial overlapping or joint neuroanatomical (e.g., structural images) and functional features (e.g., fMRI-based features) which may serve as informative clinical indicators of across multiple diagnostic categories. To address this limitation, we paired an unsupervised multimodal canonical correlation analysis (mCCA) together with joint independent component analysis (jICA) to identify linked spatial gray matter (GM), resting-state functional network connectivity (FNC), and white matter fractional anisotropy (FA) features across these diagnostic categories. We then calculated associations between the identified linked features and trans-diagnostic behavioral measures (MATRICs Consensus Cognitive Battery, MCCB).

Component number 4 of the 13 identified displayed a statistically significant relationship with overall MCCB scores across GM, resting-state FNC, and FA. These linked modalities of component 4 consisted primarily of positive correlations within subcortical structures including the caudate and putamen in the GM maps with overall MCCB, sparse negative correlations within subcortical and cortical connection tracts (e.g., corticospinal tract, superior longitudinal fasciculus) in the FA maps with overall MCCB, and negative relationships with MCCB values and loading parameters with FNC matrices displaying increased FNC in subcortical-cortical regions with auditory, somatomotor, and visual regions.

(Un)common space in infant neuroimaging studies: A systematic review of infant templates

In neuroimaging, spatial normalization is an important step that maps an individual's brain onto a template brain permitting downstream statistical analyses. Yet, in infant neuroimaging, there remain several technical challenges that have prevented the establishment of a standardized template for spatial normalization. Thus, many different approaches are used in the literature. To quantify the popularity and variability of these approaches in infant neuroimaging studies, we performed a systematic review of infant magnetic resonance imaging (MRI) studies from 2000 to 2020. Here, we present results from 834 studies meeting inclusion criteria. Studies were classified into (a) processing data in single subject space, (b) using an off the shelf, or "off the shelf," template, (c) creating a study specific template, or (d) using a hybrid of these methods. We found that across the studies in the systematic review, single subject space was the most used (no common space). This was the most used common space for diffusion-weighted imaging and structural MRI studies while functional MRI studies preferred off the shelf atlases. We found a pattern such that more recently published studies are more commonly using off the shelf atlases. When considering special populations, preterm studies most used single subject space while, when no special populations were being analyzed, an off the shelf template was most common. The most used off the shelf templates were the UNC Infant Atlases (24%). Using a systematic review of infant neuroimaging studies, we highlight a lack of an established "standard" template brain in these studies.

The SACT Template: A Human Brain Diffusion Tensor Template for School-age Children

School-age children are in a specific development stage corresponding to juvenility, when the white matter of the brain experiences ongoing maturation. Diffusion-weighted magnetic resonance imaging (DWI), especially diffusion tensor imaging (DTI), is extensively used to characterize the maturation by assessing white matter properties in vivo. In the analysis of DWI data, spatial normalization is crucial for conducting inter-subject analyses or linking the individual space with the reference space. Using tensor-based registration with an appropriate diffusion tensor template presents high accuracy regarding spatial normalization. However, there is a lack of a standardized diffusion tensor template dedicated to school-age children with ongoing brain development. Here, we established the school-age children diffusion tensor (SACT) template by optimizing tensor reorientation on high-quality DTI data from a large sample of cognitively normal participants aged 6-12 years. With an age-balanced design, the SACT template represented the entire age range well by showing high similarity to the age-specific templates. Compared with the tensor template of adults, the SACT template revealed significantly higher spatial normalization accuracy and inter-subject coherence upon evaluation of subjects in two different datasets of school-age children. A practical application regarding the age associations with the normalized DTI-derived data was conducted to further compare the SACT template and the adult template. Although similar spatial patterns were found, the SACT template showed significant effects on the distributions of the statistical results, which may be related to the performance of spatial normalization. Looking forward, the SACT template could contribute to future studies of white matter development in both healthy and clinical populations. The SACT template is publicly available now ( https://figshare.com/articles/dataset/SACT_template/14071283 ).

Group characterization of impact-induced, in vivo human brain kinematics

Brain movement during an impact can elicit a traumatic brain injury, but tissue kinematics vary from person to person and knowledge regarding this variability is limited. This study examines spatio-temporal brain-skull displacement and brain tissue deformation across groups of subjects during a mild impact in vivo. The heads of two groups of participants were imaged while subjected to a mild (less than 350 rad s^-2) impact during neck extension (NE, n = 10) and neck rotation (NR, n = 9). A kinematic atlas of displacement and strain fields averaged across all participants was constructed and compared against individual participant data. The atlas-derived mean displacement magnitude was 0.26 ± 0.13 mm for NE and 0.40 ± 0.26 mm for NR, which is comparable to the displacement magnitudes from individual participants. The strain tensor from the atlas displacement field exhibited maximum shear strain (MSS) of 0.011 ± 0.006 for NE and 0.017 ± 0.009 for NR and was lower than the individual MSS averaged across participants. The atlas illustrates common patterns, containing some blurring but visible relationships between anatomy and kinematics. Conversely, the direction of the impact, brain size, and fluid motion appear to underlie kinematic variability. These findings demonstrate the biomechanical roles of key anatomical features and illustrate common features of brain response for model evaluation.

Disrupted Topological Organization of the Brain Structural Network in Patients With Thyroid-Associated Ophthalmopathy

Purpose

Increasing evidence indicated that thyroid-associated ophthalmopathy (TAO) might be a neural related disease more than an ocular disease. In this study, we aimed to investigate the alterations of structural brain connectome in patients with TAO.

Methods

Twenty-seven patients with TAO and 27 well-matched healthy controls underwent diffusion tensor imaging. Graph theoretical analyses, including global (shortest path length, clustering coefficient, small-worldness, global efficiency, and local efficiency) and nodal (nodal betweenness, nodal degree, and nodal efficiency) topological properties and network-based statistics were performed to evaluate TAO-related changes in brain network pattern. Correlations were assessed between the network properties and clinical variables, including disease duration, visual acuity, neuropsychiatric measurements, and serum thyroid function indexes.

Results

Compared with healthy controls, patients with TAO exhibited preserved global network parameters but altered nodal properties. We found decreased nodal betweenness and nodal degree in right anterior cingulate and paracingulate gyri, decreased nodal degree and nodal efficiency in the right orbital part of middle frontal gyrus (ORBmid), whereas increased nodal degree and nodal efficiency in the left cuneus. Decrease of structural connectivity strength was found involving the right ORBmid, right putamen, left caudate nucleus, and left medial superior frontal gyrus. Significant correlations were also found between nodal properties and neuropsychological performances as well as visual acuity.

Conclusions

Patients with TAO developed disruption of structural brain network connectome. Disrupted topological organization of the brain structural network may be associated with the clinical-psychiatric dysfunction of patients with TAO.

Relationships of Language Lateralization with Diffusion Tensor Imaging Metrics of Corpus Callosum, Tumor Grade, and Tumors Distance to Language-Eloquent Areas in Glial Neoplasms

PURPOSE

The aim of the study was to search relationships between language lateralization and corpus callosum (CC) connectivity, tumor grade, and tumors distance to language-eloquent areas in glial neoplasms.

MATERIALS AND METHODS

The functional magnetic resonance imaging and CC diffusion tensor imaging (DTI) metrics of 42 patients with glial neoplasm were evaluated for relationships of language lateralization (left, right, and bilateral) with CC DTI metrics (tracts number, voxel, volume, length, fractional anisotropy [FA], and apparent diffusion coefficient), tumor grade, and tumors distance to language-eloquent areas and relationships of CC DTI metrics with tumor grade. Kruskal-Wallis, Mann-Whitney U, and χ tests were used. All were repeated in 26 patients with left hemispheric masses.

RESULTS

In glial masses, language bilateralism was more common than normal population and more pronounced in low grade than high grade. In right lateralism and bilateralism, tumor settlement nearby language-eloquent areas was more common. In the left lateralism, highest CC tract number, higher tumor grade, and more remote tumor settlements were noted. There was no relationship between CC DTI metrics and tumor grade but increase in CC tracts number and FA with increasing tumor grade.

CONCLUSIONS

Increased bilateralism in glial masses than normal population and in low grade tumors than high grade and increased nearby tumor settlement in right lateralism and bilateralism support interhemispheric reorganization and plasticity. This is more pronounced in low grade because of higher life span. Highest CC tract number, higher tumor grade, and more remote tumor settlement in left lateralized group suggest intact CC integrity with limited hemispheric destruction. Increasing CC tracts number and FA with increasing tumor grade support preserved CC integrity in the shorter life span of high-grade tumors.

3D Deep Learning on Medical Images: A Review

The rapid advancements in machine learning, graphics processing technologies and the availability of medical imaging data have led to a rapid increase in the use of deep learning models in the medical domain. This was exacerbated by the rapid advancements in convolutional neural network (CNN) based architectures, which were adopted by the medical imaging community to assist clinicians in disease diagnosis. Since the grand success of AlexNet in 2012, CNNs have been increasingly used in medical image analysis to improve the efficiency of human clinicians. In recent years, three-dimensional (3D) CNNs have been employed for the analysis of medical images. In this paper, we trace the history of how the 3D CNN was developed from its machine learning roots, we provide a brief mathematical description of 3D CNN and provide the preprocessing steps required for medical images before feeding them to 3D CNNs. We review the significant research in the field of 3D medical imaging analysis using 3D CNNs (and its variants) in different medical areas such as classification, segmentation, detection and localization. We conclude by discussing the challenges associated with the use of 3D CNNs in the medical imaging domain (and the use of deep learning models in general) and possible future trends in the field.

Diffusion tensor imaging in schizophrenia

Diffusion tensor imaging (DTI) is a magnetic resonance imaging technique that is increasingly being used for the non-invasive evaluation of brain white matter abnormalities. In this review, we discuss the basic principles of DTI, its roots and the contribution of European centres in its development, and we review the findings from DTI studies in schizophrenia. We searched EMBASE, PubMed, PsychInfo, and Medline from February 1998 to December 2006 using as keywords ‘schizophrenia’, ‘diffusion’, ‘tensor’, and ‘DTI’. Forty studies fulfilling the inclusion criteria of this review were included and systematically reviewed. White matter abnormalities in many diverse brain regions were identified in schizophrenia. Although the findings are not completely consistent, frontal and temporal white matter seems to be more commonly affected. Limitations and future directions of this method are discussed.

Optimization of energy state transition trajectory supports the development of executive function during youth

Executive function develops during adolescence, yet it remains unknown how structural brain networks mature to facilitate activation of the fronto-parietal system, which is critical for executive function. In a sample of 946 human youths (ages 8-23y) who completed diffusion imaging, we capitalized upon recent advances in linear dynamical network control theory to calculate the energetic cost necessary to activate the fronto-parietal system through the control of multiple brain regions given existing structural network topology. We found that the energy required to activate the fronto-parietal system declined with development, and the pattern of regional energetic cost predicts unseen individuals' brain maturity. Finally, energetic requirements of the cingulate cortex were negatively correlated with executive performance, and partially mediated the development of executive performance with age. Our results reveal a mechanism by which structural networks develop during adolescence to reduce the theoretical energetic costs of transitions to activation states necessary for executive function.

Structural Variability Across the Primate Brain: A Cross-Species Comparison

A large amount of variability exists across human brains; revealed initially on a small scale by postmortem studies and, more recently, on a larger scale with the advent of neuroimaging. Here we compared structural variability between human and macaque monkey brains using grey and white matter magnetic resonance imaging measures. The monkey brain was overall structurally as variable as the human brain, but variability had a distinct distribution pattern, with some key areas showing high variability. We also report the first evidence of a relationship between anatomical variability and evolutionary expansion in the primate brain. This suggests a relationship between variability and stability, where areas of low variability may have evolved less recently and have more stability, while areas of high variability may have evolved more recently and be less similar across individuals. We showed specific differences between the species in key areas, including the amount of hemispheric asymmetry in variability, which was left-lateralized in the human brain across several phylogenetically recent regions. This suggests that cerebral variability may be another useful measure for comparison between species and may add another dimension to our understanding of evolutionary mechanisms.

Hierarchical complexity of the adult human structural connectome

The structural network of the human brain has a rich topology which many have sought to characterise using standard network science measures and concepts. However, this characterisation remains incomplete and the non-obvious features of this topology have largely confounded attempts towards comprehensive constructive modelling. This calls for new perspectives. Hierarchical complexity is an emerging paradigm of complex network topology based on the observation that complex systems are composed of hierarchies within which the roles of hierarchically equivalent nodes display highly variable connectivity patterns. Here we test the hierarchical complexity of the human structural connectomes of a group of seventy-nine healthy adults. Binary connectomes are found to be more hierarchically complex than three benchmark random network models. This provides a new key description of brain structure, revealing a rich diversity of connectivity patterns within hierarchically equivalent nodes. Dividing the connectomes into four tiers based on degree magnitudes indicates that the most complex nodes are neither those with the highest nor lowest degrees but are instead found in the middle tiers. Spatial mapping of the brain regions in each hierarchical tier reveals consistency with the current anatomical, functional and neuropsychological knowledge of the human brain. The most complex tier (Tier 3) involves regions believed to bridge high-order cognitive (Tier 1) and low-order sensorimotor processing (Tier 2). We then show that such diversity of connectivity patterns aligns with the diversity of functional roles played out across the brain, demonstrating that hierarchical complexity can characterise functional diversity strictly from the network topology.

Diffusion MRI visualization

Modern diffusion magnetic resonance imaging (dMRI) acquires intricate volume datasets and biological meaning can only be found in the relationship between its different measurements. Suitable strategies for visualizing these complicated data have been key to interpretation by physicians and neuroscientists, for drawing conclusions on brain connectivity and for quality control. This article provides an overview of visualization solutions that have been proposed to date, ranging from basic grayscale and color encodings to glyph representations and renderings of fiber tractography. A particular focus is on ongoing and possible future developments in dMRI visualization, including comparative, uncertainty, interactive and dense visualizations.

Advanced lesion symptom mapping analyses and implementation as BCBtoolkit

Background

Patients with brain lesions provide a unique opportunity to understand the functioning of the human mind. However, even when focal, brain lesions have local and remote effects that impact functionally and structurally connected circuits. Similarly, function emerges from the interaction between brain areas rather than their sole activity. For instance, category fluency requires the associations between executive, semantic, and language production functions.

Findings

Here, we provide, for the first time, a set of complementary solutions for measuring the impact of a given lesion on the neuronal circuits. Our methods, which were applied to 37 patients with a focal frontal brain lesions, revealed a large set of directly and indirectly disconnected brain regions that had significantly impacted category fluency performance. The directly disconnected regions corresponded to areas that are classically considered as functionally engaged in verbal fluency and categorization tasks. These regions were also organized into larger directly and indirectly disconnected functional networks, including the left ventral fronto-parietal network, whose cortical thickness correlated with performance on category fluency.

Conclusions

The combination of structural and functional connectivity together with cortical thickness estimates reveal the remote effects of brain lesions, provide for the identification of the affected networks, and strengthen our understanding of their relationship with cognitive and behavioral measures. The methods presented are available and freely accessible in the BCBtoolkit as supplementary software [1].

White matter neuro-plasticity in mental arithmetic: Changes in hippocampal connectivity following arithmetic drill training

Verbally-mediated arithmetic fact retrieval has been suggested to be subserved by a left-lateralized network including angular gyrus and hippocampus. However, the contribution of these areas to retrieval of arithmetic facts has been under debate lately, challenging the prominent role of the angular gyrus in arithmetic fact retrieval. In the present study, we evaluated changes in structural connectivity of left hippocampus and left angular gyrus in 32 participants following a short extensive drill training of complex multiplication. We observed a significant increase of structural connectivity in fibers encompassing the left hippocampus but not the left angular gyrus. As such, our findings substantiate that the left hippocampus plays a central role in arithmetic fact retrieval. While both structures, left angular gyrus and left hippocampus seem to be parts of the network processing arithmetic facts, hippocampus actually seems to subserve encoding and retrieval of arithmetic facts. In turn, the role of the left angular gyrus might rather be to mediate the fact retrieval network as to whether or not processes of fact retrieval are referred to.

The impact of in-scanner head motion on structural connectivity derived from diffusion MRI

Multiple studies have shown that data quality is a critical confound in the construction of brain networks derived from functional MRI. This problem is particularly relevant for studies of human brain development where important variables (such as participant age) are correlated with data quality. Nevertheless, the impact of head motion on estimates of structural connectivity derived from diffusion tractography methods remains poorly characterized. Here, we evaluated the impact of in-scanner head motion on structural connectivity using a sample of 949 participants (ages 8-23 years old) who passed a rigorous quality assessment protocol for diffusion magnetic resonance imaging (dMRI) acquired as part of the Philadelphia Neurodevelopmental Cohort. Structural brain networks were constructed for each participant using both deterministic and probabilistic tractography. We hypothesized that subtle variation in head motion would systematically bias estimates of structural connectivity and confound developmental inference, as observed in previous studies of functional connectivity. Even following quality assurance and retrospective correction for head motion, eddy currents, and field distortions, in-scanner head motion significantly impacted the strength of structural connectivity in a consistency- and length-dependent manner. Specifically, increased head motion was associated with reduced estimates of structural connectivity for network edges with high inter-subject consistency, which included both short- and long-range connections. In contrast, motion inflated estimates of structural connectivity for low-consistency network edges that were primarily shorter-range. Finally, we demonstrate that age-related differences in head motion can both inflate and obscure developmental inferences on structural connectivity. Taken together, these data delineate the systematic impact of head motion on structural connectivity, and provide a critical context for identifying motion-related confounds in studies of structural brain network development.

Coupled changes in hippocampal structure and cognitive ability in later life

Introduction

The hippocampus plays an important role in cognitive abilities which often decline with advancing age.

Methods

In a longitudinal study of community-dwelling adults, we investigated whether there were coupled changes in hippocampal structure and verbal memory, working memory, and processing speed between the ages of 73 (N = 655) and 76 years (N = 469). Hippocampal structure was indexed by hippocampal volume, hippocampal volume as a percentage of intracranial volume (H_ICV), fractional anisotropy (FA), mean diffusivity (MD), and longitudinal relaxation time (T1).

Results

Mean levels of hippocampal volume, H_ICV, FA, T1, and all three cognitive abilities domains decreased, whereas MD increased, from age 73 to 76. At baseline, higher hippocampal volume was associated with better working memory and verbal memory, but none of these correlations survived correction for multiple comparisons. Higher FA, lower MD, and lower T1 at baseline were associated with better cognitive abilities in all three domains; only the correlation between baseline hippocampal MD and T1, and change in the three cognitive domains, survived correction for multiple comparisons. Individuals with higher hippocampal MD at age 73 experienced a greater decline in all three cognitive abilities between ages 73 and 76. However, no significant associations with changes in cognitive abilities were found with hippocampal volume, FA, and T1 measures at baseline. Similarly, no significant associations were found between cognitive abilities at age 73 and changes in the hippocampal MRI biomarkers between ages 73 and 76.

Conclusion

Our results provide evidence to better understand how the hippocampus ages in healthy adults in relation to the cognitive domains in which it is involved, suggesting that better hippocampal MD at age 73 predicts less relative decline in three important cognitive domains across the next 3 years. It can potentially assist in diagnosing early stages of aging-related neuropathologies, because in some cases, accelerated decline could predict pathologies.

White Matter Microstructural Changes Following Quadrato Motor Training: A Longitudinal Study

Diffusion tensor imaging (DTI) is an important way to characterize white matter (WM) microstructural changes. While several cross-sectional DTI studies investigated possible links between mindfulness practices and WM, only few longitudinal investigations focused on the effects of these practices on WM architecture, behavioral change, and the relationship between them. To this aim, in the current study, we chose to conduct an unbiased tract-based spatial statistics (TBSS) analysis (n = 35 healthy participants) to identify longitudinal changes in WM diffusion parameters following 6 and 12 weeks of daily Quadrato Motor Training (QMT), a whole-body mindful movement practice aimed at improving well-being by enhancing attention, coordination, and creativity. We also investigated the possible relationship between training-induced WM changes and concomitant changes in creativity, self-efficacy, and motivation. Our results indicate that following 6 weeks of daily QMT, there was a bilateral increase of fractional anisotropy (FA) in tracts related to sensorimotor and cognitive functions, including the corticospinal tracts, anterior thalamic radiations, and uncinate fasciculi, as well as in the left inferior fronto-occipital, superior and inferior longitudinal fasciculi. Interestingly, significant FA increments were still present after 12 weeks of QMT in most of the above WM tracts, but only in the left hemisphere. FA increase was accompanied by a significant decrease of radial diffusivity (RD), supporting the leading role of myelination processes in training-related FA changes. Finally, significant correlations were found between training-induced diffusion changes and increased self-efficacy as well as creativity. Together, these findings suggest that QMT can improve WM integrity and support the existence of possible relationships between training-related WM microstructural changes and behavioral change.

User-independent diffusion tensor imaging analysis pipelines in a rat model presenting ventriculomegalia: A comparison study

Automated analysis of diffusion tensor imaging (DTI) data is an appealing way to process large datasets in an unbiased manner. However, automation can sometimes be linked to a lack of interpretability. Two whole-brain, automated and voxelwise methods exist: voxel-based analysis (VBA) and tract-based spatial statistics (TBSS). In VBA, the amount of smoothing has been shown to influence the results. TBSS is free of this step, but a projection procedure is introduced to correct for residual misalignments. This projection assigns the local highest fractional anisotropy (FA) value to the mean FA skeleton, which represents white matter tract centers. For both methods, the normalization procedure has a major impact. These issues are well documented in humans but, to our knowledge, not in rodents. In this study, we assessed the quality of three different registration algorithms (ANTs SyN, DTI-TK and FNIRT) using study-specific templates and their impact on automated analysis methods (VBA and TBSS) in a rat pup model of diffuse white matter injury presenting large unilateral deformations. VBA and TBSS results were stable and anatomically coherent across the three pipelines. For VBA, in regions around the large deformations, interpretability was limited because of the increased partial volume effect. With TBSS, two of the three pipelines found a significant decrease in axial diffusivity (AD) at the known injury site. These results demonstrate that automated voxelwise analyses can be used in an animal model with large deformations.

The effect of feature image on sensitivity of the statistical analysis in the pipeline of a tractography atlas-based analysis

Tractography atlas-based analysis (TABS) is a new diffusion tensor image (DTI) statistical analysis method for detecting and understanding voxel-wise white matter properties along a fiber tract. An important requisite for accurate and sensitive TABS is the availability of a deformation field that is able to register DTI in native space to standard space. Here, three different feature images including the fractional anisotropy (FA) image, T1 weighted image, and the maximum eigenvalue of the Hessian of the FA (hFA) image were used to calculate the deformation fields between individual space and population space. Our results showed that when the FA image was a feature image, the tensor template had the highest consistency with each subject for scalar and vector information. Additionally, to demonstrate the sensitivity and specificity of the TABS method with different feature images, we detected a gender difference along the corpus callosum. A significant difference between the male and female group in diffusion measurement appeared predominantly in the right corpus callosum only when FA was the feature image. Our results demonstrated that the FA image as a feature image was more accurate with respect to the underlying tensor information and had more accurate analysis results with the TABS method.

A prospective microstructure imaging study in mixed-martial artists using geometric measures and diffusion tensor imaging: methods and findings

Although diffusion magnetic resonance imaging (dMRI) has been widely used to characterize the effects of repetitive mild traumatic brain injury (rmTBI), to date no studies have investigated how novel geometric models of microstructure relate to more typical diffusion tensor imaging (DTI) sequences. Moreover, few studies have evaluated the sensitivity of different registration pipelines (non-linear, linear and tract-based spatial statistics) for detecting dMRI abnormalities in clinical populations. Results from single-subject analyses in healthy controls (HC) indicated a strong negative relationship between fractional anisotropy (FA) and orientation dispersion index (ODI) in both white and gray matter. Equally important, only moderate relationships existed between all other estimates of free/intracellular water volume fractions and more traditional DTI metrics (FA, mean, axial and radial diffusivity). These findings suggest that geometric measures provide differential information about the cellular microstructure relative to traditional DTI measures. Results also suggest greater sensitivity for non-linear registration pipelines that maximize the anatomical information available in T1-weighted images. Clinically, rmTBI resulted in a pattern of decreased FA and increased ODI, largely overlapping in space, in conjunction with increased intracellular and free water fractions, highlighting the potential role of edema following repeated head trauma. In summary, current results suggest that geometric models of diffusion can provide relatively unique information regarding potential mechanisms of pathology that contribute to long-term neurological damage.

Modular Segregation of Structural Brain Networks Supports the Development of Executive Function in Youth

The human brain is organized into large-scale functional modules that have been shown to evolve in childhood and adolescence. However, it remains unknown whether the underlying white matter architecture is similarly refined during development, potentially allowing for improvements in executive function. In a sample of 882 participants (ages 8-22) who underwent diffusion imaging as part of the Philadelphia Neurodevelopmental Cohort, we demonstrate that structural network modules become more segregated with age, with weaker connections between modules and stronger connections within modules. Evolving modular topology facilitates global network efficiency and is driven by age-related strengthening of hub edges present both within and between modules. Critically, both modular segregation and network efficiency are associated with enhanced executive performance and mediate the improvement of executive functioning with age. Together, results delineate a process of structural network maturation that supports executive function in youth.

[Diffusion tensor imaging of the visual pathway in glaucomatous optic nerve atrophy]

In Germany more than one million inhabitants suffer from glaucoma, more than 100,000 are threatened with blindness because glaucoma is often diagnosed too late or not at all. Diagnosis and monitoring is usually carried out "only" by examination of the retina and not the whole visual pathway. However, the eye is just "the tip of the iceberg" of the actual visual pathway, which extends through the brain to the visual cortex. The interdisciplinary holistic assessment of the whole visual pathway in glaucoma is of crucial importance because glaucoma is a complex neurodegenerative disease. Subtypes, such as normal tension glaucoma (NTG), seem to originate from primary damage to the intracranial visual pathway with secondary retrograde retinal degeneration. Recent studies including glaucoma patients and healthy controls could show that diffusion tensor imaging with calculation of diffusion coefficients, i.e. fractional anisotropy (FA), mean and radial diffusivity (MD and RD) as markers of axonal integrity, provide the potential to assess the intracranial visual pathway with a high correlation to established ophthalmological examinations. In particular, calculation of FA maps of the visual pathway and accompanying voxel-based approaches, can be integrated into clinical routine. Thus, detection of glaucoma-related intracranial alterations, even in early stages of the disease, as well as differentiation of different glaucoma subtypes, is made possible. Furthermore, the diagnosis of normal tension glaucoma seems to be possible much earlier with these new imaging techniques compared to established ophthalmological work-up. Moreover, holistic imaging provides new insights into the pathophysiology of this form of glaucoma. This review article gives an overview of these novel magnetic resonance imaging techniques for assessment of the visual pathway in glaucomatous optic nerve atrophy and reveals the potential of an interdisciplinary approach.

Evaluations of diffusion tensor image registration based on fiber tractography

Background

Diffusion Tensor Magnetic Resonance Imaging (DT-MRI, also known as DTI) measures the diffusion properties of water molecules in tissues and to date is one of the main techniques that can effectively study the microstructures of the brain in vivo. Presently, evaluation of DTI registration techniques is still in an initial stage of development.

Methods and results

In this paper, six well-known open source DTI registration algorithms: Elastic, Rigid, Affine, DTI-TK, FSL and SyN were applied on 11 subjects from an open-access dataset, among which one was randomly chosen as the template. Eight different fiber bundles of 10 subjects and the template were obtained by drawing regions of interest (ROIs) around various structures using deterministic streamline tractography. The performances of the registration algorithms were evaluated by computing the distances and intersection angles between fiber tracts, as well as the fractional anisotropy (FA) profiles along the fiber tracts. Also, the mean squared error (MSE) and the residual MSE (RMSE) of fibers originating from the registered subjects and the template were calculated to assess the registration algorithm. Twenty-seven different fiber bundles of the 10 subjects and template were obtained by drawing ROIs around various structures using probabilistic tractography. The performances of registration algorithms on this second tractography method were evaluated by computing the spatial correlation similarity of the fibers between subjects as well as between each subject and the template.

Conclusion

All experimental results indicated that DTI-TK performed the best under the study conditions, and SyN ranked just behind it.

On the averaging of cardiac diffusion tensor MRI data: the effect of distance function selection

Diffusion tensor magnetic resonance imaging (DT-MRI) allows a unique insight into the microstructure of highly-directional tissues. The selection of the most proper distance function for the space of diffusion tensors is crucial in enhancing the clinical application of this imaging modality. Both linear and nonlinear metrics have been proposed in the literature over the years. The debate on the most appropriate DT-MRI distance function is still ongoing. In this paper, we presented a framework to compare the Euclidean, affine-invariant Riemannian and log-Euclidean metrics using actual high-resolution DT-MRI rat heart data. We employed temporal averaging at the diffusion tensor level of three consecutive and identically-acquired DT-MRI datasets from each of five rat hearts as a means to rectify the background noise-induced loss of myocyte directional regularity. This procedure is applied here for the first time in the context of tensor distance function selection. When compared with previous studies that used a different concrete application to juxtapose the various DT-MRI distance functions, this work is unique in that it combined the following: (i) metrics were judged by quantitative-rather than qualitative-criteria, (ii) the comparison tools were non-biased, (iii) a longitudinal comparison operation was used on a same-voxel basis. The statistical analyses of the comparison showed that the three DT-MRI distance functions tend to provide equivalent results. Hence, we came to the conclusion that the tensor manifold for cardiac DT-MRI studies is a curved space of almost zero curvature. The signal to noise ratio dependence of the operations was investigated through simulations. Finally, the 'swelling effect' occurrence following Euclidean averaging was found to be too unimportant to be worth consideration.

Interhemispheric white matter integrity in young people with bipolar disorder and at high genetic risk

BACKGROUND

White matter (WM) impairments have been reported in patients with bipolar disorder (BD) and those at high familial risk of developing BD. However, the distribution of these impairments has not been well characterized. Few studies have examined WM integrity in young people early in the course of illness and in individuals at familial risk who have not yet passed the peak age of onset.

METHOD

WM integrity was examined in 63 BD subjects, 150 high-risk (HR) individuals and 111 participants with no family history of mental illness (CON). All subjects were aged 12 to 30 years.

RESULTS

This young BD group had significantly lower fractional anisotropy within the genu of the corpus callosum (CC) compared with the CON and HR groups. Moreover, the abnormality in the genu of the CC was also present in HR participants with recurrent major depressive disorder (MDD) (n = 16) compared with CON participants.

CONCLUSIONS

Our findings provide important validation of interhemispheric abnormalities in BD patients. The novel finding in HR subjects with recurrent MDD - a group at particular risk of future hypo/manic episodes - suggests that this may potentially represent a trait marker for BD, though this will need to be confirmed in longitudinal follow-up studies.

Effect of high-potency cannabis on corpus callosum microstructure

BACKGROUND

The use of cannabis with higher Δ9-tetrahydrocannabinol content has been associated with greater risk, and earlier onset, of psychosis. However, the effect of cannabis potency on brain morphology has never been explored. Here, we investigated whether cannabis potency and pattern of use are associated with changes in corpus callosum (CC) microstructural organization, in patients with first-episode psychosis (FEP) and individuals without psychosis, cannabis users and non-users.

METHOD

The CC of 56 FEP (37 cannabis users) and 43 individuals without psychosis (22 cannabis users) was virtually dissected and segmented using diffusion tensor imaging tractography. The diffusion index of fractional anisotropy, mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity was calculated for each segment.

RESULTS

Across the whole sample, users of high-potency cannabis had higher total CC MD and higher total CC AD than both low-potency users and those who never used (p = 0.005 and p = 0.004, respectively). Daily users also had higher total CC MD and higher total CC AD than both occasional users and those who never used (p = 0.001 and p < 0.001, respectively). However, there was no effect of group (patient/individuals without psychosis) or group x potency interaction for either potency or frequency of use. The within-group analysis showed in fact that the effects of potency and frequency were similar in FEP users and in users without psychosis.

CONCLUSIONS

Frequent use of high-potency cannabis is associated with disturbed callosal microstructural organization in individuals with and without psychosis. Since high-potency preparations are now replacing traditional herbal drugs in many European countries, raising awareness about the risks of high-potency cannabis is crucial.

DR-TAMAS: Diffeomorphic Registration for Tensor Accurate Alignment of Anatomical Structures

In this work, we propose DR-TAMAS (Diffeomorphic Registration for Tensor Accurate alignMent of Anatomical Structures), a novel framework for intersubject registration of Diffusion Tensor Imaging (DTI) data sets. This framework is optimized for brain data and its main goal is to achieve an accurate alignment of all brain structures, including white matter (WM), gray matter (GM), and spaces containing cerebrospinal fluid (CSF). Currently most DTI-based spatial normalization algorithms emphasize alignment of anisotropic structures. While some diffusion-derived metrics, such as diffusion anisotropy and tensor eigenvector orientation, are highly informative for proper alignment of WM, other tensor metrics such as the trace or mean diffusivity (MD) are fundamental for a proper alignment of GM and CSF boundaries. Moreover, it is desirable to include information from structural MRI data, e.g., T1-weighted or T2-weighted images, which are usually available together with the diffusion data. The fundamental property of DR-TAMAS is to achieve global anatomical accuracy by incorporating in its cost function the most informative metrics locally. Another important feature of DR-TAMAS is a symmetric time-varying velocity-based transformation model, which enables it to account for potentially large anatomical variability in healthy subjects and patients. The performance of DR-TAMAS is evaluated with several data sets and compared with other widely-used diffeomorphic image registration techniques employing both full tensor information and/or DTI-derived scalar maps. Our results show that the proposed method has excellent overall performance in the entire brain, while being equivalent to the best existing methods in WM.

Probabilistic atlases of default mode, executive control and salience network white matter tracts: an fMRI-guided diffusion tensor imaging and tractography study

Diffusion tensor imaging (DTI) is a powerful MRI technique that can be used to estimate both the microstructural integrity and the trajectories of white matter pathways throughout the central nervous system. This fiber tracking (aka, "tractography") approach is often carried out using anatomically-defined seed points to identify white matter tracts that pass through one or more structures, but can also be performed using functionally-defined regions of interest (ROIs) that have been determined using functional MRI (fMRI) or other methods. In this study, we performed fMRI-guided DTI tractography between all of the previously defined nodes within each of six common resting-state brain networks, including the: dorsal Default Mode Network (dDMN), ventral Default Mode Network (vDMN), left Executive Control Network (lECN), right Executive Control Network (rECN), anterior Salience Network (aSN), and posterior Salience Network (pSN). By normalizing the data from 32 healthy control subjects to a standard template-using high-dimensional, non-linear warping methods-we were able to create probabilistic white matter atlases for each tract in stereotaxic coordinates. By investigating all 198 ROI-to-ROI combinations within the aforementioned resting-state networks (for a total of 6336 independent DTI tractography analyses), the resulting probabilistic atlases represent a comprehensive cohort of functionally-defined white matter regions that can be used in future brain imaging studies to: (1) ascribe DTI or other white matter changes to particular functional brain networks, and (2) compliment resting state fMRI or other functional connectivity analyses.

NTU-DSI-122: A diffusion spectrum imaging template with high anatomical matching to the ICBM-152 space

A diffusion-weighted (DW) template in a standard coordinate system is often necessary for the analysis of white matter (WM) structures using DW images. Although several DW templates have been constructed in the ICBM-152 space, a template for diffusion spectrum imaging (DSI) is still lacking. In this study, we developed a DSI template in the ICBM-152 space from 122 healthy adults. This high quality template, NTU-DSI-122, was built through incorporating the macroscopic anatomical information using high-resolution T1 -weighted images and the microscopic structural information obtained from DSI datasets. Two evaluations were conducted to examine the quality of NTU-DSI-122. The first evaluation examined the anatomical consistency of NTU-DSI-122 in matching to the ICBM-152 coordinate system. The results showed that this template matched to the ICBM-152 templates very well across the whole brain, not only in the deep white matter regions as other DW templates but also in the superficial white matter regions. In the second evaluation, a large number of independent diffusion tensor imaging (DTI) datasets were registered to the DTI template derived from NTU-DSI-122. The examination was performed by quantifying the anatomical consistency among the registered DTI datasets. The results showed that using NTU-DSI-122 as the registration template the registered DTI datasets can achieve high anatomical alignment. Both evaluations demonstrate that NTU-DSI-122 is a useful high quality DW template. Therefore, NTU-DSI-122 can serve as a representative DSI dataset for a healthy adult population, and will be of potential value for brain research and clinical applications. The NTU-DSI-122 template is available at http://www.nitrc.org/projects/ntu-dsi-122/.

White matter involvement in chronic musculoskeletal pain

There is emerging evidence that chronic musculoskeletal pain is associated with anatomic and functional abnormalities in gray matter. However, little research has investigated the relationship between chronic musculoskeletal pain and white matter. In this study, we used whole-brain tract-based spatial statistics and region-of-interest analyses of diffusion tensor imaging data to demonstrate that patients with chronic musculoskeletal pain exhibit several abnormal metrics of white matter integrity compared with healthy controls. Chronic musculoskeletal pain was associated with lower fractional anisotropy in the splenium of the corpus callosum and the left cingulum adjacent to the hippocampus. Patients also had higher radial diffusivity in the splenium, right anterior and posterior limbs of the internal capsule, external capsule, superior longitudinal fasciculus, and cerebral peduncle. Patterns of axial diffusivity (AD) varied: patients exhibited lower AD in the left cingulum adjacent to the hippocampus and higher AD in the anterior limbs of the internal capsule and in the right cerebral peduncle. Several correlations between diffusion metrics and clinical variables were also significant at a P < .01 level: fractional anisotropy in the left uncinate fasciculus correlated positively with total pain experience and typical levels of pain severity. AD in the left anterior limb of the internal capsule and left uncinate fasciculus was correlated with total pain experience and typical pain level. Positive correlations were also found between AD in the right uncinate and both total pain experience and pain catastrophizing. These results demonstrate that white matter abnormalities play a role in chronic musculoskeletal pain as a cause, a predisposing factor, a consequence, or a compensatory adaptation.

PERSPECTIVE

Patients with chronic musculoskeletal pain exhibit altered metrics of diffusion in the brain's white matter compared with healthy volunteers, and some of these differences are directly related to symptom severity.

Diffusion tensor MRI of chemotherapy-induced cognitive impairment in non-CNS cancer patients: a review

Patients with non-central nervous system cancers often experience subtle cognitive deficits after treatment with cytotoxic agents. Therapy-induced structural changes to the brain could be one of the possible causes underlying these reported cognitive deficits. In this review, we evaluate the use of diffusion tensor imaging (DTI) for assessing possible therapy-induced changes in the microstructure of the cerebral white matter (WM) and provide a critical overview of the published DTI research on therapy-induced cognitive impairment. Both cross-sectional and longitudinal DTI studies have demonstrated abnormal microstructural properties in WM regions involved in cognition. These findings correlated with cognitive performance, suggesting that there is a link between reduced "WM integrity" and chemotherapy-induced impaired cognition. In this paper, we will also introduce the basics of diffusion tensor imaging and how it can be applied to evaluate effects of therapy on structural changes in cerebral WM. The review concludes with considerations and discussion regarding DTI data interpretation and possible future directions for investigating therapy-induced WM changes in cancer patients. This review article is part of a Special Issue entitled: Neuroimaging Studies of Cancer and Cancer Treatment.

Advanced diffusion MRI fiber tracking in neurosurgical and neurodegenerative disorders and neuroanatomical studies: A review

Diffusion MRI enabled in vivo microstructural imaging of the fiber tracts in the brain resulting in its application in a wide range of settings, including in neurological and neurosurgical disorders. Conventional approaches such as diffusion tensor imaging (DTI) have been shown to have limited applications due to the crossing fiber problem and the susceptibility of their quantitative indices to partial volume effects. To overcome these limitations, the recent focus has shifted to the advanced acquisition methods and their related analytical approaches. Advanced white matter imaging techniques provide superior qualitative data in terms of demonstration of multiple crossing fibers in their spatial orientation in a three dimensional manner in the brain. In this review paper, we discuss the advancements in diffusion MRI and introduce their roles. Using examples, we demonstrate the role of advanced diffusion MRI-based fiber tracking in neuroanatomical studies. Results from its preliminary application in the evaluation of intracranial space occupying lesions, including with respect to future directions for prognostication, are also presented. Building upon the previous DTI studies assessing white matter disease in Huntington's disease and Amyotrophic lateral sclerosis; we also discuss approaches which have led to encouraging preliminary results towards developing an imaging biomarker for these conditions.

A Bayesian approach to the creation of a study-customized neonatal brain atlas

Atlas-based image analysis (ABA), in which an anatomical "parcellation map" is used for parcel-by-parcel image quantification, is widely used to analyze anatomical and functional changes related to brain development, aging, and various diseases. The parcellation maps are often created based on common MRI templates, which allow users to transform the template to target images, or vice versa, to perform parcel-by-parcel statistics, and report the scientific findings based on common anatomical parcels. The use of a study-specific template, which represents the anatomical features of the study population better than common templates, is preferable for accurate anatomical labeling; however, the creation of a parcellation map for a study-specific template is extremely labor intensive, and the definitions of anatomical boundaries are not necessarily compatible with those of the common template. In this study, we employed a volume-based template estimation (VTE) method to create a neonatal brain template customized to a study population, while keeping the anatomical parcellation identical to that of a common MRI atlas. The VTE was used to morph the standardized parcellation map of the JHU-neonate-SS atlas to capture the anatomical features of a study population. The resultant "study-customized" T1-weighted and diffusion tensor imaging (DTI) template, with three-dimensional anatomical parcellation that defined 122 brain regions, was compared with the JHU-neonate-SS atlas, in terms of the registration accuracy. A pronounced increase in the accuracy of cortical parcellation and superior tensor alignment were observed when the customized template was used. With the customized atlas-based analysis, the fractional anisotropy (FA) detected closely approximated the manual measurements. This tool provides a solution for achieving normalization-based measurements with increased accuracy, while reporting scientific findings in a consistent framework.

Harnessing graphics processing units for improved neuroimaging statistics

Simple models and algorithms based on restrictive assumptions are often used in the field of neuroimaging for studies involving functional magnetic resonance imaging, voxel based morphometry, and diffusion tensor imaging. Nonparametric statistical methods or flexible Bayesian models can be applied rather easily to yield more trustworthy results. The spatial normalization step required for multisubject studies can also be improved by taking advantage of more robust algorithms for image registration. A common drawback of algorithms based on weaker assumptions, however, is the increase in computational complexity. In this short overview, we will therefore present some examples of how inexpensive PC graphics hardware, normally used for demanding computer games, can be used to enable practical use of more realistic models and accurate algorithms, such that the outcome of neuroimaging studies really can be trusted.

Multi-contrast multi-atlas parcellation of diffusion tensor imaging of the human brain

In this paper, we propose a novel method for parcellating the human brain into 193 anatomical structures based on diffusion tensor images (DTIs). This was accomplished in the setting of multi-contrast diffeomorphic likelihood fusion using multiple DTI atlases. DTI images are modeled as high dimensional fields, with each voxel exhibiting a vector valued feature comprising of mean diffusivity (MD), fractional anisotropy (FA), and fiber angle. For each structure, the probability distribution of each element in the feature vector is modeled as a mixture of Gaussians, the parameters of which are estimated from the labeled atlases. The structure-specific feature vector is then used to parcellate the test image. For each atlas, a likelihood is iteratively computed based on the structure-specific vector feature. The likelihoods from multiple atlases are then fused. The updating and fusing of the likelihoods is achieved based on the expectation-maximization (EM) algorithm for maximum a posteriori (MAP) estimation problems. We first demonstrate the performance of the algorithm by examining the parcellation accuracy of 18 structures from 25 subjects with a varying degree of structural abnormality. Dice values ranging 0.8-0.9 were obtained. In addition, strong correlation was found between the volume size of the automated and the manual parcellation. Then, we present scan-rescan reproducibility based on another dataset of 16 DTI images - an average of 3.73%, 1.91%, and 1.79% for volume, mean FA, and mean MD respectively. Finally, the range of anatomical variability in the normal population was quantified for each structure.

Reproducibility of diffusion tensor imaging in normal subjects: an evaluation of different gradient sampling schemes and registration algorithm

INTRODUCTION

Diffusion tensor imaging (DTI) is very useful for investigating white matter integrity in ageing and neurological disorders; thus, evaluating its reproducibility under different acquisition protocols and analysis methods may assist in the design of clinical studies.

METHODS

To measure the reproducibility of DTI in normal subjects, this study include (1) depicting the reproducibility of DTI measurements in commonly used regions-of-interest analysis by intraclass correlation coefficient (ICC) and coefficient of variation (CV), (2) evaluating and comparing inter and intrasession test-retest reproducibility, and (3) illustrating the effect of the number of diffusion-encoding directions (NDED) and registration algorithms on measurement reproducibility.

RESULTS

DTI measurements exhibit high reproducibility, with overall (430/480) ICC ≥ 0.70, (478/480) within-subject CV (CVws) ≤10.00 % and between-subject CV (CVbs) ranging from 1.32 to 13.63 %. Repeated measures ANOVAs and paired t tests were conducted to compare inter and intrasession reproducibility with different diffusion sampling schemes and registration algorithms. Our results also confirmed that increasing the NDED could improve the accuracy and reproducibility of DTI measurements. In addition, we compared reproducibility indices that were derived using different registration algorithms, and a tensor-based deformable registration yielded the most reproducible results. Finally, we found that increasing the NDED could reduce the difference between the reproducibility of measurement derived using different registration algorithms and between the reproducibility of intersession and intrasession.

CONCLUSION

Our results suggest that the choice of DTI acquisition protocol and post-processing methods can influence the accurate estimation and reproducibility of DTI measurements and should be considered carefully for clinical applications.

Where numbers meet words: a common ventral network for semantic classification

Recent research has shown that both language and number processing are clear examples of distributed and connected processing in the human brain, emphasizing the importance of white matter connections between the associated cortex sites. Against this background we hypothesized joint cognitive processes and functions in a cross-domain manner to be reflected by the involvement of specific white matter tracts. Therefore, we evaluated white matter connectivity for the specific cognitive process of semantic classification, which is an integral part of tasks commonly employed to investigate the neural correlates of language and number processing. In line with our expectations, fiber tracking results clearly indicated a common ventral network for semantic classification for the domains of language and number processing. Thereby, the present data are hard to reconcile with a localizationalist view on processing characteristics of the human brain, but strongly suggest that white matter connectivity should be considered when investigating the neural underpinnings of human cognition.

Magnetic resonance imaging of a randomized controlled trial investigating predictors of recovery following psychological treatment in adolescents with moderate to severe unipolar depression: study protocol for Magnetic Resonance-Improving Mood with Psychoanalytic and Cognitive Therapies (MR-IMPACT)

BACKGROUND

Major depressive disorders (MDD) are a debilitating and pervasive group of mental illnesses afflicting many millions of people resulting in the loss of 110 million working days and more than 2,500 suicides per annum. Adolescent MDD patients attending NHS clinics show high rates of recurrence into adult life. A meta-analysis of recent research shows that psychological treatments are not as efficacious as previously thought. Modest treatment outcomes of approximately 65% of cases responding suggest that aetiological and clinical heterogeneity may hamper the better use of existing therapies and discovery of more effective treatments. Information with respect to optimal treatment choice for individuals is lacking, with no validated biomarkers to aid therapeutic decision-making.

METHODS/DESIGN

Magnetic resonance-Improving Mood with Psychoanalytic and Cognitive Therapies, the MR-IMPACT study, plans to identify brain regions implicated in the pathophysiology of depressions and examine whether there are specific behavioural or neural markers predicting remission and/or subsequent relapse in a subsample of depressed adolescents recruited to the IMPACT randomised controlled trial (Registration # ISRCTN83033550).

DISCUSSION

MR-IMPACT is an investigative biomarker component of the IMPACT pragmatic effectiveness trial. The aim of this investigation is to identify neural markers and regional indicators of the pathophysiology of and treatment response for MDD in adolescents. We anticipate that these data may enable more targeted treatment delivery by identifying those patients who may be optimal candidates for therapeutic response.

TRIAL REGISTRATION

Adjunctive study to IMPACT trial (Current Controlled Trials: ISRCTN83033550).

Risk variant of oligodendrocyte lineage transcription factor 2 is associated with reduced white matter integrity

The oligodendrocyte lineage transcription factor 2 (OLIG2) regulates the genesis of oligodendrocytes, the brain cells responsible for axonal myelination. Although it has been associated with psychiatric and neurological disorders, the impact of this gene on white matter integrity has never been investigated in humans. Using diffusion tensor imaging, we examined the effect of a single nucleotide polymorphism (rs1059004) in OLIG2 previously associated with reduced gene expression, and with psychiatric disorders on fractional anisotropy in 78 healthy subjects. We found that the risk allele (A) was associated with reduced white matter integrity in the corona radiata bilaterally. This is consistent with evidence that it is a schizophrenia susceptibility gene, and suggests that it may confer increased risk through an effect on neuroanatomical connectivity.

Magnetic resonance imaging of the newborn brain: automatic segmentation of brain images into 50 anatomical regions

We studied methods for the automatic segmentation of neonatal and developing brain images into 50 anatomical regions, utilizing a new set of manually segmented magnetic resonance (MR) images from 5 term-born and 15 preterm infants imaged at term corrected age called ALBERTs. Two methods were compared: individual registrations with label propagation and fusion; and template based registration with propagation of a maximum probability neonatal ALBERT (MPNA). In both cases we evaluated the performance of different neonatal atlases and MPNA, and the approaches were compared with the manual segmentations by means of the Dice overlap coefficient. Dice values, averaged across regions, were 0.81±0.02 using label propagation and fusion for the preterm population, and 0.81±0.02 using the single registration of a MPNA for the term population. Segmentations of 36 further unsegmented target images of developing brains yielded visibly high-quality results. This registration approach allows the rapid construction of automatically labeled age-specific brain atlases for neonates and the developing brain.

White matter integrity following traumatic brain injury: the association with severity of injury and cognitive functioning

Traumatic brain injury (TBI) frequently results in impairments of memory, speed of information processing, and executive functions that may persist over many years. Diffuse axonal injury is one of the key pathologies following TBI, causing cognitive impairments due to the disruption of cortical white matter pathways. The current study examined the association between injury severity, cognition, and fractional anisotropy (FA) following TBI. Two diffusion tensor imaging techniques-region-of-interest tractography and tract-based spatial statistics-were used to assess the FA of white matter tracts. This study examined the comparability of these two approaches as they relate to injury severity and cognitive performance. Sixty-eight participants with mild-to-severe TBI, and 25 healthy controls, underwent diffusion tensor imaging analysis. A subsample of 36 individuals with TBI also completed cognitive assessment. Results showed reduction in FA values for those with moderate and severe TBI, compared to controls and individuals with mild TBI. Although FA tended to be lower for individuals with mild TBI no significant differences were found compared to controls. Information processing speed and executive abilities were most strongly associated with the FA of white matter tracts. The results highlight similarities and differences between region-of-interest tractography and tract-based spatial statistics approaches, and suggest that they may be used together to explore pathology following TBI.