Altered white matter microstructure is associated with social cognition and psychotic symptoms in 22q11.2 microdeletion syndrome.

22q11.2 Microdeletion Syndrome (22q11DS) is a highly penetrant genetic mutation associated with a significantly increased risk for psychosis. Aberrant neurodevelopment may lead to inappropriate neural circuit formation and cerebral dysconnectivity in 22q11DS, which may contribute to symptom development. Here we examined: (1) differences between 22q11DS participants and typically developing controls in diffusion tensor imaging (DTI) measures within white matter tracts; (2) whether there is an altered age-related trajectory of white matter pathways in 22q11DS; and (3) relationships between DTI measures, social cognition task performance, and positive symptoms of psychosis in 22q11DS and typically developing controls. Sixty-four direction diffusion weighted imaging data were acquired on 65 participants (36 22q11DS, 29 controls). We examined differences between 22q11DS vs. controls in measures of fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD), using both a voxel-based and region of interest approach. Social cognition domains assessed were: Theory of Mind and emotion recognition. Positive symptoms were assessed using the Structured Interview for Prodromal Syndromes. Compared to typically developing controls, 22q11DS participants showed significantly lower AD and RD in multiple white matter tracts, with effects of greatest magnitude for AD in the superior longitudinal fasciculus. Additionally, 22q11DS participants failed to show typical age-associated changes in FA and RD in the left inferior longitudinal fasciculus. Higher AD in the left inferior fronto-occipital fasciculus (IFO) and left uncinate fasciculus was associated with better social cognition in 22q11DS and controls. In contrast, greater severity of positive symptoms was associated with lower AD in bilateral regions of the IFO in 22q11DS. White matter microstructure in tracts relevant to social cognition is disrupted in 22q11DS, and may contribute to psychosis risk

An ontology-based segmentation scheme for tracking postnatal changes in the developing rodent brain with MRI

The postnatal period of neurodevelopment has been implicated in a number of brain disorders including autism and schizophrenia. Rodent models have proven to be invaluable in advancing our understanding of the human brain, and will almost certainly play a pivotal role in future studies on postnatal neurodevelopment. The growing field of magnetic resonance microscopy has the potential to revolutionize our understanding of neurodevelopment, if it can be successfully and appropriately assimilated into the vast body of existing neuroscience research. In this study, we demonstrate the utility of a developmental neuro-ontology designed specifically for tracking regional changes in MR biomarkers throughout postnatal neurodevelopment. Using this ontological classification as a segmentation guide, we track regional changes in brain volume in rats between postnatal day zero and postnatal day 80 and demonstrate differential growth rates in axial versus paraxial brain regions. Both the ontology and the associated label volumes are provided as a foundation for future MR-based studies of postnatal neurodevelopment in normal and disease states

White Matter Structural Connectivity is Associated with Sensorimotor Function in Stroke Survivors

Purpose Diffusion tensor imaging (DTI) provides functionally relevant information about white matter structure. Local anatomical connectivity information combined with fractional anisotropy (FA) and mean diffusivity (MD) may predict functional outcomes in stroke survivors. Imaging methods for predicting functional outcomes in stroke survivors are not well established. This work uses DTI to objectively assess the effects of a stroke lesion on white matter structure and sensorimotor function. Methods A voxel-based approach is introduced to assess a stroke lesion\u27s global impact on motor function. Anatomical T1-weighted and diffusion tensor images of the brain were acquired for nineteen subjects (10 post-stroke and 9 age-matched controls). A manually selected volume of interest was used to alleviate the effects of stroke lesions on image registration. Images from all subjects were registered to the images of the control subject that was anatomically closest to Talairach space. Each subject\u27s transformed image was uniformly seeded for DTI tractography. Each seed was inversely transformed into the individual subject space, where DTI tractography was conducted and then the results were transformed back to the reference space. A voxel-wise connectivity matrix was constructed from the fibers, which was then used to calculate the number of directly and indirectly connected neighbors of each voxel. A novel voxel-wise indirect structural connectivity (VISC) index was computed as the average number of direct connections to a voxel\u27s indirect neighbors. Voxel-based analyses (VBA) were performed to compare VISC, FA, and MD for the detection of lesion-induced changes in sensorimotor function. For each voxel, a t-value was computed from the differences between each stroke brain and the 9 controls. A series of linear regressions was performed between Fugl-Meyer (FM) assessment scores of sensorimotor impairment and each DTI metric\u27s log number of voxels that differed from the control group. Results Correlation between the logarithm of the number of significant voxels in the ipsilesional hemisphere and total Fugl-Meyer score was moderate for MD (R2 = 0.512), and greater for VISC (R2 = 0.796) and FA (R2 = 0.674). The slopes of FA (p = 0.0036), VISC (p = 0.0005), and MD (p = 0.0199) versus the total FM score were significant. However, these correlations were driven by the upper extremity motor component of the FM score (VISC: R2 = 0.879) with little influence of the lower extremity motor component (FA: R2 = 0.177). Conclusion The results suggest that a voxel-wise metric based on DTI tractography can predict upper extremity sensorimotor function of stroke survivors, and that supraspinal intraconnectivity may have a less dominant role in lower extremity function

TEST-RETEST RELIABILITY OF FRACTIONAL ANISOTROPY IN 5-YEAR-OLDS

Diffusion tensor imaging (DTI) has provided great insights to the microstructural features of developing brain and has been shown to be reliable in infants. However, the repeatability of the DTI scalars for older pediatric age groups has not been thoroughly addressed. In this study, DTI scans of 5-year-olds were used to investigate the test-retest reliability of three different measurements with both voxel-wise and region of interest (ROI) analysis. Out of 96 diffusion encoding directions, divided into three parts, 20 unique diffusion encoding directions were chosen per measurement from 48 subjects. Tract based spatial analysis (TBSS) was used to extract fractional anisotropy (FA) values from those images and using the FA values the repeatability of the measurements was assessed by intraclass correlation coefficient (ICC) and standard error of measurement (SEM). Overall, FA values had high repeatability both in voxel-based analysis (ICC>0.73) and ROI analysis (for non-skeletonized ROI type 88% of the ROI labels: ICC>0.75, for skeletonized ROI type 87% of the ROI labels: ICC>0.75). Using a skeleton in the ROI analysis did not contribute to the repeatability and the volume size was found to be a contributing factor for repeatability. Interscanner reliability as well as reliability measured by using different atlases are yet to be investigated in 5-year-old data

Using diffusion imaging to explore the anatomical nature of early course schizophrenia

Schizophrenia (SCZ) is a serious brain disorder that affects around 1% of the world population. Despite a long history of research in diagnosis and treatment of SCZ, we are still far from being able to explain the origin of the disease and the interindividual differences in the trajectory of the disease. The neurodevelopmental hypothesis states that SCZ is caused by early maturational abnormalities, which interact with later brain development. Neuroimaging provides a noninvasive opportunity to study this theory in vivo. Traditionally, Magnetic resonance imaging (MRI) has been used to examine macrostructural gray matter features such as gray matter volume or cortical thickness and SCZ has been established as a brain disorder hereinafter. Diffusion tensor imaging (DTI) allows to investigate the microstructure of brain tissue. It measures the magnitude and direction of water molecule`s diffusion and is highly sensitive to alterations of gray and white matter organization. Gray matter contains the neurons and the white matter contains myelinated axons and provides long and middle range connectivity between cortical neurons. White matter alterations observed in SCZ, therefore, support the disconnection theory stating that SCZ is a brain disorder with disrupted integration of different brain systems. Finally, while early imaging research focused on chronic states of SCZ a shift of the field towards studying early stages can be observed in more recent years. Understanding early course SCZ raises the hope to improve diagnosis and subsequently prevention and intervention. In line with this research the aim of the presented studies is to characterize microstructural white and gray matter alterations in early course SCZ using diffusion MRI combined with advanced post-processing techniques, which are sufficiently sensitive to detect subtle brain conspicuities. Implications of and associations with neuropsychological and clinical symptoms and diagnosis of SCZ will be discussed subsequently. Paper 1 The purpose of the first project is to characterize white matter organization in patients with early course SCZ. To my knowledge this is the first study investigating five main intra-hemispheric corti-cocortical white matter tracts using manual guided tractography in early course SCZ. The tracts were selected based on previous findings: uncinate fasciculus (UF), cingulum bundle (CB), inferior longitudinal fasciculus (ILF), superior longitudinal fasciculus (SLF) and arcuate fasciculus (AF). Diffusion parameters (fractional anisotropy [FA], trace, axial diffusivity [AD] and radial diffusivity [RD]) were computed for each tract and compared between patients with early course SCZ (number [n]=30) and healthy controls (HC) (n=30). The association of the diffusion parameters of the tracts with clinical symptoms, memory performance, and processing speed was examined afterwards. A significant group effect, represented by reduced FA and increased RD and trace in the patients’ group compared to HC was observed for the right AF (FA [F=5.94, df=1, p=.016]; RD [F=5.60, df=1, p=.020]), CB (FA [F=9.35, df=1, p=.003]; RD [F=11.55, df=1, p=.0010] and ILF (FA [F=14.77, df=1, p=.004]; RD [F=13.25, df=1, p<.0001]). The pattern of lower FA and higher RD is indicative for myelin abnormalities. Structural alterations were correlated with positive symptoms (ILF, AF), and cognitive performance (CB), which points to the clinical relevance of the observed white matter conspicuities. Paper 2 In the past, DTI has mainly been used to study white mater, because technical challenges limited the use of DTI for the characterization of gray matter organization. However, as an extension of the classical disconnection theory one would not only expect dysconnectivity in white matter, but also a disruption of gray matter organization. The aim of this study therefore is to use novel DTI method- heterogeneity- to study the microstructural gray matter organization over the course of SCZ. In comparison to traditional diffusion indices, which focus on intra-voxel diffusion properties, heterogeneity captures the microstructural organization of a larger cortical area. After applying a free water correc-tion to control for partial volume effects, T1 and diffusion images were registered to each other and the variability (=heterogeneity) of diffusion parameters within the four brain lobes defined by auto-matic parcellation method was calculated. Patients with chronic SCZ (n=27) did not show differences of cortical organization when compared to HC (n=22). However, patients with early course SCZ (n=19) showed increased heterogeneity in the frontal lobe when compared to HC (n=15) (F=10.68, df=1, p<.0030). This indicates a lower grade of cortical organization in patients than in HC. It is suggested that this can be explained by neurodevelopmental abnormalities, plausibly caused by abnormal synaptic reorganization and pruning during adolescence and early adulthood in SCZ

White matter connectome correlates of auditory over-responsivity: edge density imaging and machine-learning classifiers

Sensory over-responsivity (SOR) commonly involves auditory and/or tactile domains, and can affect children with or without additional neurodevelopmental challenges. In this study, we examined white matter microstructural and connectome correlates of auditory over-responsivity (AOR), analyzing prospectively collected data from 39 boys, aged 8–12 years. In addition to conventional diffusion tensor imaging (DTI) maps – including fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD); we used DTI and high-resolution T1 scans to develop connectome Edge Density (ED) maps. The tract-based spatial statistics was used for voxel-wise comparison of diffusion and ED maps. Then, stepwise penalized logistic regression was applied to identify independent variable (s) predicting AOR, as potential imaging biomarker (s) for AOR. Finally, we compared different combinations of machine learning algorithms (i.e., naïve Bayes, random forest, and support vector machine (SVM) and tract-based DTI/connectome metrics for classification of children with AOR. In direct sensory phenotype assessment, 15 (out of 39) boys exhibited AOR (with or without neurodevelopmental concerns). Voxel-wise analysis demonstrates extensive impairment of white matter microstructural integrity in children with AOR on DTI maps – evidenced by lower FA and higher MD and RD; moreover, there was lower connectome ED in anterior-superior corona radiata, genu and body of corpus callosum. In stepwise logistic regression, the average FA of left superior longitudinal fasciculus (SLF) was the single independent variable distinguishing children with AOR (p = 0.007). Subsequently, the left SLF average FA yielded an area under the curve of 0.756 in receiver operating characteristic analysis for prediction of AOR (p = 0.008) as a region-of-interest (ROI)-based imaging biomarker. In comparative study of different combinations of machine-learning models and DTI/ED metrics, random forest algorithms using ED had higher accuracy for AOR classification. Our results demonstrate extensive white matter microstructural impairment in children with AOR, with specifically lower connectomic ED in anterior-superior tracts and associated commissural pathways. Also, average FA of left SLF can be applied as ROI-based imaging biomarker for prediction of SOR. Finally, machine-learning models can provide accurate and objective image-based classifiers for identification of children with AOR based on white matter tracts connectome ED

Automated voxel-wise brain DTI analysis of fitness and aging

Diffusion Tensor Imaging (DTI) has become a widely used MR modality to investigate white matter integrity in the brain. This paper presents the application of an automated method for voxel-wise group comparisons of DTI images in a study of fitness and aging. The automated processing method consists of 3 steps: 1) preprocessing including image format converting, image quality control, eddy-current and motion artifact correction, skull stripping and tensor image estimation, 2) study-specific unbiased DTI atlas computation via diffeomorphic fluid-based and demons deformable registration and 3) voxel-wise statistical analysis via heterogeneous linear regression and a wild bootstrap technique for correcting for multiple comparisons. Our results show that this fully automated method is suitable for voxel-wise group DTI analysis. Furthermore, in older adults, the results suggest a strong link between reduced fractional anisotropy (FA) values, fitness and aging

UNC-Utah NA-MIC framework for DTI fiber tract analysis

pre-printDiffusion tensor imaging has become an important modality in field of neuroimaging to capture changes in micro-organization and to assess white matter integrity or development While there exists a number of tractography toolsets, these usually lack tools for preprocessing or to analyze diffusion properties along the fiber tracts. Currently, the field is in critical need of a coherent end-to-end toolset for performing an along-fiber tract analysis, accessible to non-technical neuroimaging researchers. The UNC-Utah NA-MIC DTI framework represents a coherent, open source, end-to-end toolset for atlas building, fiber tractography, fiber parameterization, and statistical analysis of diffusion properties. Most steps utilize graphical user interfaces (GUI) to simplify interaction and provide an extensive DTI analysis framework for non-tecnical researchers/investigators. We illustrate the use of our framework on a small sample, cross sectional neuroimaging study of eight healthy 1-year-old children from the Infant Brain Imaging Study (IBIS) Network. In This limited test study, we illustrate the power of our method by quantifying the diffusion properties at 1 year of age on the genu and splenium fiber tracts