Role of standardized and study-specific human brain diffusion tensor templates in inter-subject spatial normalization

White matter abnormalities in first-episode patients with depersonalization/derealization disorder: A tract-based spatial statistics study

BACKGROUND

Neuroimaging studies on depersonalization / derealization disorder (DPD) have revealed that there are structural and functional alterations across numerous brain regions. However, to date, the exact white matter abnormalities that are associated with different clinical symptoms and cognitive impairments in first-episode, drug-naïve patients with DPD remain unclear.

METHODS

Overall, 25 first-episode, drug-naïve patients with DPD and 23 healthy controls were recruited and underwent DTI scans. The tract-based spatial statistics analysis was conducted in order to determine white matter microstructural changes between the two groups. Correlation analysis was conducted between the fractional anisotropy (FA) of abnormal WM fibers and the total score of the 30-item Cambridge Depersonalization Scale (CDS-30), cognitive assessments.

RESULTS

Patients with DPD demonstrated higher FA in the right corpus callosum (CC), and posterior corona radiate (CR), compared to healthy controls. The FA in the right CC demonstrated a positive correlation with total score of CDS-30, numbing, unreality of self, perceptual alterations, and temporal disintegration, respectively. FA in the right CR region indicated a positive correlation with the total score of CDS-30, unreality of self, perceptual alterations, and temporal disintegration, respectively. Furthermore, FA in the right CR region was found to be negatively correlated with the Continuous Performance Test and the Stroop color-word test.

CONCLUSION

The altered white matter microstructure and cognitive impairments of medication naïve DPD patients were observed. Abnormalities in the integrity of CC and CR were associated with severity of symptoms and cognitive impairments, which may provide a potential biomarker for clinical studies on DPD.

(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 ).

Development of brain atlases for early-to-middle adolescent collision-sport athletes

Human brains develop across the life span and largely vary in morphology. Adolescent collision-sport athletes undergo repetitive head impacts over years of practices and competitions, and therefore may exhibit a neuroanatomical trajectory different from healthy adolescents in general. However, an unbiased brain atlas targeting these individuals does not exist. Although standardized brain atlases facilitate spatial normalization and voxel-wise analysis at the group level, when the underlying neuroanatomy does not represent the study population, greater biases and errors can be introduced during spatial normalization, confounding subsequent voxel-wise analysis and statistical findings. In this work, targeting early-to-middle adolescent (EMA, ages 13-19) collision-sport athletes, we developed population-specific brain atlases that include templates (T1-weighted and diffusion tensor magnetic resonance imaging) and semantic labels (cortical and white matter parcellations). Compared to standardized adult or age-appropriate templates, our templates better characterized the neuroanatomy of the EMA collision-sport athletes, reduced biases introduced during spatial normalization, and exhibited higher sensitivity in diffusion tensor imaging analysis. In summary, these results suggest the population-specific brain atlases are more appropriate towards reproducible and meaningful statistical results, which better clarify mechanisms of traumatic brain injury and monitor brain health for EMA collision-sport athletes.

Quantitative brain relaxation atlases for personalized detection and characterization of brain pathology

PURPOSE

To exploit the improved comparability and hardware independency of quantitative MRI, databases of MR physical parameters in healthy tissue are required, to which tissue properties of patients can be compared. In this work, normative values for longitudinal and transverse relaxation times in the brain were established and tested in single-subject comparisons for detection of abnormal relaxation times.

METHODS

Relaxometry maps of the brain were acquired from 52 healthy volunteers. After spatially normalizing the volumes into a common space, T₁ and T₂ inter-subject variability within the healthy cohort was modeled voxel-wise. A method for a single-subject comparison against the atlases was developed by computing z-scores with respect to the established healthy norms. The comparison was applied to two multiple sclerosis and one clinically isolated syndrome cases for a proof of concept.

RESULTS

The established atlases exhibit a low variation in white matter structures (median RMSE of models equal to 32 ms for T₁ and 4 ms for T₂ ), indicating that relaxation times are in a narrow range for normal tissues. The proposed method for single-subject comparison detected relaxation time deviations from healthy norms in the example patient data sets. Relaxation times were found to be increased in brain lesions (mean z-scores >5). Moreover, subtle and confluent differences (z-scores ~2-4) were observed in clinically plausible regions (between lesions, corpus callosum).

CONCLUSIONS

Brain T₁ and T₂ quantitative norms were derived voxel-wise with low variability in healthy tissue. Example patient deviation maps demonstrated good sensitivity of the atlases for detecting relaxation time alterations.

A framework for multi-component analysis of diffusion MRI data over the neonatal period

We describe a framework for creating a time-resolved group average template of the developing brain using advanced multi-shell high angular resolution diffusion imaging data, for use in group voxel or fixel-wise analysis, atlas-building, and related applications. This relies on the recently proposed multi-shell multi-tissue constrained spherical deconvolution (MSMT-CSD) technique. We decompose the signal into one isotropic component and two anisotropic components, with response functions estimated from cerebrospinal fluid and white matter in the youngest and oldest participant groups, respectively. We build an orientationally-resolved template of those tissue components from data acquired from 113 babies between 33 and 44 weeks postmenstrual age, imaged as part of the Developing Human Connectome Project. These data were split into weekly groups, and registered to the corresponding group average templates using a previously-proposed non-linear diffeomorphic registration framework, designed to align orientation density functions (ODF). This framework was extended to allow the use of the multiple contrasts provided by the multi-tissue decomposition, and shown to provide superior alignment. Finally, the weekly templates were registered to the same common template to facilitate investigations into the evolution of the different components as a function of age. The resulting multi-tissue atlas provides insights into brain development and accompanying changes in microstructure, and forms the basis for future longitudinal investigations into healthy and pathological white matter maturation.

White matter abnormalities in the corpus callosum with cognitive impairment in Parkinson disease

OBJECTIVE

To evaluate microstructural characteristics of the corpus callosum using diffusion tensor imaging (DTI) and their relationships to cognitive impairment in Parkinson disease (PD).

METHODS

Seventy-five participants with PD and 24 healthy control (HC) participants underwent structural MRI brain scans including DTI sequences and clinical and neuropsychological evaluations. Using Movement Disorder Society criteria, PD participants were classified as having normal cognition (PD-NC, n = 23), mild cognitive impairment (PD-MCI, n = 35), or dementia (PDD, n = 17). Cognitive domain (attention/working memory, executive function, language, memory, visuospatial function) z scores were calculated. DTI scalar values, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD), were established for 5 callosal segments on a midsagittal plane, single slice using a topographically derived parcellation method. Scalar values were compared among participant groups. Regression analyses were performed on cognitive domain z scores and DTI metrics.

RESULTS

Participants with PD showed increased AD values in the anterior 3 callosal segments compared to healthy controls. Participants with PDD had significantly increased AD, MD, and RD in the anterior 2 segments compared to participants with PD-NC and most anterior segment compared to participants with PD-MCI. FA values did not differ significantly between participants with PD and participants with HC or among PD cognitive groups. The strongest associations for the DTI metrics and cognitive performance occurred in the most anterior and most posterior callosal segments, and also reflected fronto-striatal and posterior cortical type cognitive deficits, respectively.

CONCLUSIONS

Microstructural white matter abnormalities of the corpus callosum, as measured by DTI, may contribute to PD cognitive impairment by disrupting information transfer across interhemispheric and callosal-cortical projections.

Evaluation of standardized and study-specific diffusion tensor imaging templates of the adult human brain: Template characteristics, spatial normalization accuracy, and detection of small inter-group FA differences

Digital diffusion tensor imaging (DTI) templates of the adult human brain are commonly used in neuroimaging research, and their characteristics influence the accuracy of the application. However, a systematic evaluation of the characteristics and performance of standardized and study-specific DTI templates has not been conducted. The purpose of this work was to compare eight available standardized DTI templates to each other (ICBM81, ENIGMA, FMRIB58, SRI24, IIT2, NTU-DSI-122-DTI, IIT v.3.0, Eve), as well as to study-specific templates, in terms of template characteristics (image sharpness, ability to identify small brain structures, artifacts, mean values, noise properties) and performance in spatial normalization and detection of small inter-group FA differences. The IIT v.3.0 template was shown to combine a number of desirable characteristics: includes full-tensor information, is population-based, has high image sharpness, shows no visible artifacts, has low noise levels, has diffusion tensor properties and spatial features representative of data from the average individual adult brain. Furthermore, the IIT v.3.0 template was shown to allow higher inter-subject DTI spatial normalization accuracy, and detection of smaller inter-group FA differences, compared to all other templates, including study-specific templates. These findings were consistent when evaluating the templates in younger as well as older adult cohorts.

The brain anatomy of attention-deficit/hyperactivity disorder in young adults - a magnetic resonance imaging study

Background

This is one of the first studies to examine the structural brain anatomy and connectivity associated with an ADHD diagnosis and child as well as adult ADHD symptoms in young adults. It was hypothesized that an adult ADHD diagnosis and in particular childhood symptoms, are associated with widespread changes in the brain macro- and microstructure, which can be used to develop a morphometric biomarker for ADHD.

Methods

Voxel-wise linear regression models were used to examine structural and diffusion-weighted MRI data in 72 participants (31 young adults with ADHD and 41 controls without ADHD) in relation to diagnosis and the number of self-reported child and adult symptoms.

Results

Findings revealed significant associations between ADHD diagnosis and widespread changes to the maturation of white matter fiber bundles and gray matter density in the brain, such as structural shape changes (incomplete maturation) of the middle and superior temporal gyrus, and fronto-basal portions of both frontal lobes. ADHD symptoms in childhood showed the strongest association with brain macro- and microstructural abnormalities. At the brain circuitry level, the superior longitudinal fasciculus (SLF) and cortico-limbic areas are dysfunctional in individuals with ADHD. The morphometric findings predicted an ADHD diagnosis correctly up to 83% of all cases.

Conclusion

An adult ADHD diagnosis and in particular childhood symptoms are associated with widespread micro- and macrostructural changes. The SLF and cortico-limbic findings suggest complex audio-visual, motivational, and emotional dysfunctions associated with ADHD in young adults. The sensitivity of the morphometric findings in predicting an ADHD diagnosis was sufficient, which indicates that MRI-based assessments are a promising strategy for the development of a biomarker.

A Comparative evaluation of voxel-based spatial mapping in diffusion tensor imaging

This paper presents a comparative evaluation of methods for automated voxel-based spatial mapping in diffusion tensor imaging studies. Such methods are an essential step in computational pipelines and provide anatomically comparable measurements across a population in atlas-based studies. To better understand their strengths and weaknesses, we tested a total of eight methods for voxel-based spatial mapping in two types of diffusion tensor templates. The methods were evaluated with respect to scan-rescan reliability and an application to normal aging. The methods included voxel-based analysis with and without smoothing, two types of region-based analysis, and combinations thereof with skeletonization. The templates included a study-specific template created with DTI-TK and the IIT template serving as a standard template. To control for other factors in the pipeline, the experiments used a common dataset, acquired at 1.5T with a single shell high angular resolution diffusion MR imaging protocol, and tensor-based spatial normalization with DTI-TK. Scan-rescan reliability was assessed using the coefficient of variation (CV) and intraclass correlation (ICC) in eight subjects with three scans each. Sensitivity to normal aging was assessed in a population of 80 subjects aged 25-65 years old, and methods were compared with respect to the anatomical agreement of significant findings and the R2 of the associated models of fractional anisotropy. The results show that reliability depended greatly on the method used for spatial mapping. The largest differences in reliability were found when adding smoothing and comparing voxel-based and region-based analyses. Skeletonization and template type were found to have either a small or negligible effect on reliability. The aging results showed agreement among the methods in nine brain areas, with some methods showing more sensitivity than others. Skeletonization and smoothing were not major factors affecting sensitivity to aging, but the standard template showed higher R2 in several conditions. A structural comparison of the templates showed that large deformations between them may be related to observed differences in patterns of significant voxels. Most areas showed significantly higher R2 with voxel-based analysis, particularly when clusters were smaller than the available regions-of-interest. Looking forward, these results can potentially help to interpret results from existing white matter imaging studies, as well as provide a resource to help in planning future studies to maximize reliability and sensitivity with regard to the scientific goals at hand.

Construction and application of human neonatal DTI atlases

Atlas-based MRI analysis is one of many analytical methods and is used to investigate typical as well as abnormal neurodevelopment. It has been widely applied to the adult and pediatric populations. Successful applications of atlas-based analysis (ABA) in those cohorts have motivated the creation of a neonatal atlas and parcellation map (PM). The purpose of this review is to discuss the various neonatal diffusion tensor imaging (DTI) atlases that are available for use in ABA, examine how such atlases are constructed, review their applications, and discuss future directions in DTI. Neonatal DTI atlases are created from a template, which can be study-specific or standardized, and merged with the corresponding PM. Study-specific templates can retain higher image registration accuracy, but are usually not applicable across different studies. However, standardized templates can be used to make comparisons among various studies, but may not accurately reflect the anatomies of the study population. Methods such as volume-based template estimation are being developed to overcome these limitations. The applications for ABA, including atlas-based image quantification and atlas-based connectivity analysis, vary from quantifying neurodevelopmental progress to analyzing population differences in groups of neonates. ABA can also be applied to detect pathology related to prematurity at birth or exposure to toxic substances. Future directions for this method include research designed to increase the accuracy of the image parcellation. Methods such as multi-atlas label fusion and multi-modal analysis applied to neonatal DTI currently comprise an active field of research. Moreover, ABA can be used in high-throughput analysis to efficiently process medical images and to assess longitudinal brain changes. The overarching goal of neonatal ABA is application to the clinical setting, to assist with diagnoses, monitor disease progression and, ultimately, outcome prediction.

Heritability of fractional anisotropy in human white matter: a comparison of Human Connectome Project and ENIGMA-DTI data

The degree to which genetic factors influence brain connectivity is beginning to be understood. Large-scale efforts are underway to map the profile of genetic effects in various brain regions. The NIH-funded Human Connectome Project (HCP) is providing data valuable for analyzing the degree of genetic influence underlying brain connectivity revealed by state-of-the-art neuroimaging methods. We calculated the heritability of the fractional anisotropy (FA) measure derived from diffusion tensor imaging (DTI) reconstruction in 481 HCP subjects (194/287 M/F) consisting of 57/60 pairs of mono- and dizygotic twins, and 246 siblings. FA measurements were derived using (Enhancing NeuroImaging Genetics through Meta-Analysis) ENIGMA DTI protocols and heritability estimates were calculated using the SOLAR-Eclipse imaging genetic analysis package. We compared heritability estimates derived from HCP data to those publicly available through the ENIGMA-DTI consortium, which were pooled together from five-family based studies across the US, Europe, and Australia. FA measurements from the HCP cohort for eleven major white matter tracts were highly heritable (h(2)=0.53-0.90, p<10(-5)), and were significantly correlated with the joint-analytical estimates from the ENIGMA cohort on the tract and voxel-wise levels. The similarity in regional heritability suggests that the additive genetic contribution to white matter microstructure is consistent across populations and imaging acquisition parameters. It also suggests that the overarching genetic influence provides an opportunity to define a common genetic search space for future gene-discovery studies. Uniquely, the measurements of additive genetic contribution performed in this study can be repeated using online genetic analysis tools provided by the HCP ConnectomeDB web application.

The effects of acupuncture treatment on the right frontoparietal network in migraine without aura patients

BACKGROUND

Functional and structural abnormalities in resting-state brain networks in migraine patients have been confirmed by previous functional magnetic resonance imaging (fMRI) studies. However, few studies focusing on the neural responses of therapeutic treatment on migraine have been conducted. In this study, we tried to examined the treatment-related effects of standard acupuncture treatment on the right frontoparietal network (RFPN) in migraine patients.

METHODS

A total of 12 migraine without aura (MWoA) patients were recruited to undergo resting-state fMRI scanning and were rescanned after 4 weeks standard acupuncture treatment. Another 12 matched healthy control (HC) subjects underwent once scanning for comparison. We analyzed the functional connectivity of the RFPN between MWoA patients and HC subjects before treatment and that of the MWoA patients before and after treatment. Diffusion tensor images (DTI) data analyzing was also performed to detect fiber-related treatment responses.

RESULTS

We observed significantly decreased FC in the RFPN and that the decreased FC could be reversed by acupuncture treatment. The changes of FC in MWoA patients was negatively correlated with the decrease of visual analogue scale (VAS) scores after treatment. This study indicated that acupuncture treatment for MWoA patients was associated with normalizing effects on the intrinsic decreased FC of the RFPN.

CONCLUSIONS

Our study provided new insights into the treatment-related neural responses in MWoA patients and suggested potential functional pathways for the evaluation of treatment in MWoA patients. Future studies are still in need to confirm the current results and to elucidate the complex neural mechanisms of acupuncture treatment.

White matter segmentation based on a skeletonized atlas: effects on diffusion tensor imaging studies of regions of interest

PURPOSE

To compare the influence of conventional and skeletonized atlas-based white matter (WM) segmentation on diffusion tensor imaging (DTI) region-of-interest (ROI) investigations.

MATERIALS AND METHODS

A conventional WM atlas was skeletonized by thinning the corresponding fractional anisotropy (FA) map and labels. The conventional and skeletonized versions of the atlas were used for WM segmentation. The percentage of non-WM voxels assigned to WM labels, as well as statistical summaries of tensor-derived quantities, were compared between segmentation approaches. The ability to detect small differences in diffusion properties across groups of subjects was also compared between segmentation approaches.

RESULTS

Skeletonized segmentation resulted in significantly lower non-WM percentage (P < 0.05), higher mean FA and lower trace (P < 0.05) in most WM labels, and mainly lower standard deviation of FA and trace in labels neighboring the ventricles. In terms of maximizing the ability to detect intergroup DTI differences, skeletonized segmentation was superior in the corpus callosum, but the optimal approach varied for other WM labels.

CONCLUSION

Conventional and skeletonized atlas-based segmentation probe different portions of brain tissue and lead to different statistical summaries of diffusion characteristics in WM labels. Careful selection of segmentation approach is required for DTI investigations of WM ROIs.

General fluid-type intelligence is related to indices of white matter structure in middle-aged and old adults

General fluid-type intelligence (gF) reflects abstract reasoning and problem solving abilities, and is an important predictor for lifetime trajectories of cognition, and physical and mental health. Structural and functional neuroimaging studies have demonstrated the role of parieto-frontal gray matter, but the white matter (WM) underpinnings of gF and the contribution of individual gF components to gF-WM relationship still need to be explored. The aim of this study was to characterize, in a sample of 100 healthy middle-aged and old subjects (mean=63.8 years), the relationship between gF and indices of WM structure obtained from diffusion tensor magnetic resonance imaging (DT-MRI) (fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD)). gF was estimated by principal component analysis including measures of episodic memory, reasoning, and processing speed. Tract-based spatial statistics and permutation-based inference statistics were used to test the association between gF and WM indices, while controlling for the effect of age and sex. We hypothesized a positive relationship between gF and WM structure. Based on previous studies, we further hypothesized that this relationship was heavily influenced by the processing speed component of gF. We found a robust relationship between gF and DT-MRI measures of FA, RD and MD in all major WM tracts. Higher gF score was related to higher degree of WM integrity, in middle-aged as well as old individuals. Thus, the distributed relationship between gF and indices of WM microstructure is consistent with the notion that gF reflects efficient signaling between cortical areas. Furthermore, analysis of relationships between WM measures and gF components revealed an association with information processing speed and reasoning ability, but not with episodic memory. Thus, although all subcomponents loaded high on gF factor, the speed-related components were most strongly associated with DT-MRI-derived measures. These results suggest that DT-MRI can be used to parse gF.