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22q11.2 deletion syndrome (22q11DS) is the most frequently occurring microdeletion in humans. It is associated with a significant impact on brain structure, including prominent reductions in gray matter volume (GMV), and neuropsychiatric manifestations, including cognitive impairment and psychosis. It is unclear whether GMV alterations in 22q11DS occur according to distinct structural patterns. Then, 783 participants (470 with 22q11DS: 51% females, mean age [SD] 18.2 [9.2]; and 313 typically developing [TD] controls: 46% females, mean age 18.0 [8.6]) from 13 datasets were included in the present study. We segmented structural T1-weighted brain MRI scans and extracted GMV images, which were then utilized in a novel source-based morphometry (SBM) pipeline (SS-Detect) to generate structural brain patterns (SBPs) that capture co-varying GMV. We investigated the impact of the 22q11.2 deletion, deletion size, intelligence quotient, and psychosis on the SBPs. Seventeen GMV-SBPs were derived, which provided spatial patterns of GMV covariance associated with a quantitative metric (i.e., loading score) for analysis. Patterns of topographically widespread differences in GMV covariance, including the cerebellum, discriminated individuals with 22q11DS from healthy controls. The spatial extents of the SBPs that revealed disparities between individuals with 22q11DS and controls were consistent with the findings of the univariate voxel-based morphometry analysis. Larger deletion size was associated with significantly lower GMV in frontal and occipital SBPs; however, history of psychosis did not show a strong relationship with these covariance patterns. 22q11DS is associated with distinct structural abnormalities captured by topographical GMV covariance patterns that include the cerebellum. Findings indicate that structural anomalies in 22q11DS manifest in a nonrandom manner and in distinct covarying anatomical patterns, rather than a diffuse global process. These SBP abnormalities converge with previously reported cortical surface area abnormalities, suggesting disturbances of early neurodevelopment as the most likely underlying mechanism.

The Enhancing NeuroImaging Genetics through Meta-Analysis copy number variant (ENIGMA-CNV) and 22q11.2 Deletion Syndrome Working Groups (22q-ENIGMA WGs) were created to gain insight into the involvement of genetic factors in human brain development and related cognitive, psychiatric and behavioral manifestations. To that end, the ENIGMA-CNV WG has collated CNV and magnetic resonance imaging (MRI) data from ~49,000 individuals across 38 global research sites, yielding one of the largest studies to date on the effects of CNVs on brain structures in the general population. The 22q-ENIGMA WG includes 12 international research centers that assessed over 533 individuals with a confirmed 22q11.2 deletion syndrome, 40 with 22q11.2 duplications, and 333 typically developing controls, creating the largest-ever 22q11.2 CNV neuroimaging data set. In this review, we outline the ENIGMA infrastructure and procedures for multi-site analysis of CNVs and MRI data. So far, ENIGMA has identified effects of the 22q11.2, 16p11.2 distal, 15q11.2, and 1q21.1 distal CNVs on subcortical and cortical brain structures. Each CNV is associated with differences in cognitive, neurodevelopmental and neuropsychiatric traits, with characteristic patterns of brain structural abnormalities. Evidence of gene-dosage effects on distinct brain regions also emerged, providing further insight into genotype-phenotype relationships. Taken together, these results offer a more comprehensive picture of molecular mechanisms involved in typical and atypical brain development. This "genotype-first" approach also contributes to our understanding of the etiopathogenesis of brain disorders. Finally, we outline future directions to better understand effects of CNVs on brain structure and behavior.

22q11.2 deletion syndrome (22q11DS) results from a hemizygous deletion that typically spans 46 protein-coding genes and is associated with widespread alterations in brain morphology. The specific genetic mechanisms underlying these alterations remain unclear. In the 22q11.2 ENIGMA Working Group, we characterized cortical alterations in individuals with 22q11DS (n = 232) versus healthy individuals (n = 290) and conducted spatial convergence analyses using gene expression data from the Allen Human Brain Atlas to prioritize individual genes that may contribute to altered surface area (SA) and cortical thickness (CT) in 22q11DS. Total SA was reduced in 22q11DS (Z-score deviance = -1.04), with prominent reductions in midline posterior and lateral association regions. Mean CT was thicker in 22q11DS (Z-score deviance = +0.64), with focal thinning in a subset of regions. Regional expression of DGCR8 was robustly associated with regional severity of SA deviance in 22q11DS; AIFM3 was also associated with SA deviance. Conversely, P2RX6 was associated with CT deviance. Exploratory analysis of gene targets of microRNAs previously identified as down-regulated due to DGCR8 deficiency suggested that DGCR8 haploinsufficiency may contribute to altered corticogenesis in 22q11DS by disrupting cell cycle modulation. These findings demonstrate the utility of combining neuroanatomic and transcriptomic datasets to derive molecular insights into complex, multigene copy number variants.

Deletions and duplications at the 22q11.2 locus are associated with significant neurodevelopmental and psychiatric morbidity. Previous diffusion-weighted magnetic resonance imaging (MRI) studies in 22q11.2 deletion carriers (22q-del) found nonspecific white matter (WM) abnormalities, characterized by higher fractional anisotropy. Here, utilizing novel imaging and processing methods that allow separation of signal contribution from different tissue properties, we investigate whether higher anisotropy is driven by (1) extracellular changes, (2) selective degeneration of secondary fibers, or (3) volumetric differences. We further, for the first time, investigate WM microstructure in 22q11.2 duplication carriers (22q-dup). Multi-shell diffusion-weighted images were acquired from 26 22q-del, 19 22q-dup, and 18 healthy individuals (HC). Images were fitted with the free-water model to estimate anisotropy following extracellular free-water elimination and with the novel BedpostX model to estimate fractional volumes of primary and secondary fiber populations. Outcome measures were compared between groups, with and without correction for WM and cerebrospinal fluid (CSF) volumes. In 22q-del, anisotropy following free-water elimination remained significantly higher compared with controls. BedpostX did not identify selective secondary fiber degeneration. Higher anisotropy diminished when correcting for the higher CSF and lower WM volumes. In contrast, 22q-dup had lower anisotropy and greater extracellular space than HC, not influenced by macrostructural volumes. Our findings demonstrate opposing effects of reciprocal 22q11.2 copy-number variation on WM, which may arise from distinct pathologies. In 22q-del, microstructural abnormalities may be secondary to enlarged CSF space and more densely packed WM. In 22q-dup, we see evidence for demyelination similar to what is commonly observed in neuropsychiatric disorders.

The 22q11.2 deletion (22q11DS) is a common chromosomal microdeletion and a potent risk factor for psychotic illness. Prior studies reported widespread cortical changes in 22q11DS, but were generally underpowered to characterize neuroanatomic abnormalities associated with psychosis in 22q11DS, and/or neuroanatomic effects of variability in deletion size. To address these issues, we developed the ENIGMA (Enhancing Neuro Imaging Genetics Through Meta-Analysis) 22q11.2 Working Group, representing the largest analysis of brain structural alterations in 22q11DS to date. The imaging data were collected from 10 centers worldwide, including 474 subjects with 22q11DS (age = 18.2 ± 8.6; 46.9% female) and 315 typically developing, matched controls (age = 18.0 ± 9.2; 45.9% female). Compared to controls, 22q11DS individuals showed thicker cortical gray matter overall (left/right hemispheres: Cohen's d = 0.61/0.65), but focal thickness reduction in temporal and cingulate cortex. Cortical surface area (SA), however, showed pervasive reductions in 22q11DS (left/right hemispheres: d = -1.01/-1.02). 22q11DS cases vs. controls were classified with 93.8% accuracy based on these neuroanatomic patterns. Comparison of 22q11DS-psychosis to idiopathic schizophrenia (ENIGMA-Schizophrenia Working Group) revealed significant convergence of affected brain regions, particularly in fronto-temporal cortex. Finally, cortical SA was significantly greater in 22q11DS cases with smaller 1.5 Mb deletions, relative to those with typical 3 Mb deletions. We found a robust neuroanatomic signature of 22q11DS, and the first evidence that deletion size impacts brain structure. Psychotic illness in this highly penetrant deletion was associated with similar neuroanatomic abnormalities to idiopathic schizophrenia. These consistent cross-site findings highlight the homogeneity of this single genetic etiology, and support the suitability of 22q11DS as a biological model of schizophrenia.

Objective22q11.2 deletion syndrome (22q11DS) is among the strongest known genetic risk factors for schizophrenia. Previous studies have reported variable alterations in subcortical brain structures in 22q11DS. To better characterize subcortical alterations in 22q11DS, including modulating effects of clinical and genetic heterogeneity, the authors studied a large multicenter neuroimaging cohort from the ENIGMA 22q11.2 Deletion Syndrome Working Group.MethodsSubcortical structures were measured using harmonized protocols for gross volume and subcortical shape morphometry in 533 individuals with 22q11DS and 330 matched healthy control subjects (age range, 6-56 years; 49% female).ResultsCompared with the control group, the 22q11DS group showed lower intracranial volume (ICV) and thalamus, putamen, hippocampus, and amygdala volumes and greater lateral ventricle, caudate, and accumbens volumes (Cohen's d values, -0.90 to 0.93). Shape analysis revealed complex differences in the 22q11DS group across all structures. The larger A-D deletion was associated with more extensive shape alterations compared with the smaller A-B deletion. Participants with 22q11DS with psychosis showed lower ICV and hippocampus, amygdala, and thalamus volumes (Cohen's d values, -0.91 to 0.53) compared with participants with 22q11DS without psychosis. Shape analysis revealed lower thickness and surface area across subregions of these structures. Compared with subcortical findings from other neuropsychiatric disorders studied by the ENIGMA consortium, significant convergence was observed between participants with 22q11DS with psychosis and participants with schizophrenia, bipolar disorder, major depressive disorder, and obsessive-compulsive disorder.ConclusionsIn the largest neuroimaging study of 22q11DS to date, the authors found widespread alterations to subcortical brain structures, which were affected by deletion size and psychotic illness. Findings indicate significant overlap between 22q11DS-associated psychosis, idiopathic schizophrenia, and other severe neuropsychiatric illnesses.

Traumatic brain injury can cause extensive damage to the white matter (WM) of the brain. These disruptions can be especially damaging in children, whose brains are still maturing. Diffusion magnetic resonance imaging (dMRI) is the most commonly used method to assess WM organization, but it has limited resolution to differentiate causes of WM disruption. Magnetic resonance spectroscopy (MRS) yields spectra showing the levels of neurometabolites that can indicate neuronal/axonal health, inflammation, membrane proliferation/turnover, and other cellular processes that are on-going post-injury. Previous analyses on this dataset revealed a significant division within the msTBI patient group, based on interhemispheric transfer time (IHTT); one subgroup of patients (TBI-normal) showed evidence of recovery over time, while the other showed continuing degeneration (TBI-slow). We combined dMRI with MRS to better understand WM disruptions in children with moderate-severe traumatic brain injury (msTBI). Tracts with poorer WM organization, as shown by lower FA and higher MD and RD, also showed lower N-acetylaspartate (NAA), a marker of neuronal and axonal health and myelination. We did not find lower NAA in tracts with normal WM organization. Choline, a marker of inflammation, membrane turnover, or gliosis, did not show such associations. We further show that multi-modal imaging can improve outcome prediction over a single modality, as well as over earlier cognitive function measures. Our results suggest that demyelination plays an important role in WM disruption post-injury in a subgroup of msTBI children and indicate the utility of multi-modal imaging.

PurposeIn diffusion MRI (dMRI), fractional anisotropy derived from the single-tensor model (FADTI ) is the most widely used metric to characterize white matter (WM) microarchitecture, despite known limitations in regions with crossing fibers. Due to time constraints when scanning patients in clinical settings, high angular resolution diffusion imaging acquisition protocols, often used to overcome these limitations, are still rare in clinical population studies. However, the tensor distribution function (TDF) may be used to model multiple underlying fibers by representing the diffusion profile as a probabilistic mixture of tensors.MethodsWe compared the ability of standard FADTI and TDF-derived FA (FATDF ), calculated from a range of dMRI angular resolutions (41, 30, 15, and 7 gradient directions), to profile WM deficits in 251 individuals from the Alzheimer's Disease Neuroimaging Initiative and to detect associations with 1) Alzheimer's disease diagnosis, 2) Clinical Dementia Rating scores, and 3) average hippocampal volume.ResultsAcross angular resolutions and statistical tests, FATDF showed larger effect sizes than FADTI , particularly in regions preferentially affected by Alzheimer's disease, and was less susceptible to crossing fiber anomalies.ConclusionThe TDF "corrected" form of FA may be a more sensitive and accurate alternative to the commonly used FADTI , even in clinical quality dMRI data. Magn Reson Med 78:2322-2333, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

ObjectiveTo examine longitudinal trajectories of white matter organization in pediatric moderate/severe traumatic brain injury (msTBI) over a 12-month period.MethodsWe studied 21 children (16 M/5 F) with msTBI, assessed 2-5 months postinjury and again 13-19 months postinjury, as well as 20 well-matched healthy control children. We assessed corpus callosum function through interhemispheric transfer time (IHTT), measured using event-related potentials, and related this to diffusion-weighted MRI measures of white matter (WM) microstructure. At the first time point, half of the patients with TBI had significantly slower IHTT (TBI-slow-IHTT, n = 11) and half were in the normal range (TBI-normal-IHTT, n = 10).ResultsThe TBI-normal-IHTT group did not differ significantly from healthy controls, either in WM organization in the chronic phase or in the longitudinal trajectory of WM organization between the 2 evaluations. In contrast, the WM organization of the TBI-slow-IHTT group was significantly lower than in healthy controls across a large portion of the WM. Longitudinal analyses showed that the TBI-slow-IHTT group experienced a progressive decline between the 2 evaluations in WM organization throughout the brain.ConclusionsWe present preliminary evidence suggesting a potential biomarker that identifies a subset of patients with impaired callosal organization in the first months postinjury who subsequently experience widespread continuing and progressive degeneration in the first year postinjury.

Traumatic brain injury (TBI) can disrupt the white matter (WM) integrity in the brain, leading to functional and cognitive disruptions that may persist for years. There is considerable heterogeneity within the patient group, which complicates group analyses. Here we present improvements to a tract identification workflow, automated multi-atlas tract extraction (autoMATE), evaluating the effects of improved registration. Use of study-specific template improved group classification accuracy over the standard workflow. The addition of a multi-modal registration that includes information from diffusion weighted imaging (DWI), T1-weighted, and Fluid-Attenuated Inversion Recovery (FLAIR) further improved classification accuracy. We also examined whether particular tracts contribute more to group classification than others. Parts of the corpus callosum contributed most, and there were unexpected asymmetries between bilateral tracts.

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

Traumatic brain injury (TBI) is the leading cause of death and disability in children and can lead to a wide range of impairments. Brain imaging methods such as DTI (diffusion tensor imaging) are uniquely sensitive to the white matter (WM) damage that is common in TBI. However, higher-level analyses using tractography are complicated by the damage and decreased FA (fractional anisotropy) characteristic of TBI, which can result in premature tract endings. We used the newly developed autoMATE (automated multi-atlas tract extraction) method to identify differences in WM integrity. 63 pediatric patients aged 8-19 years with moderate/severe TBI were examined with cross sectional scanning at one or two time points after injury: a post-acute assessment 1-5 months post-injury and a chronic assessment 13-19 months post-injury. A battery of cognitive function tests was performed in the same time periods. 56 children were examined in the first phase, 28 TBI patients and 28 healthy controls. In the second phase 34 children were studied, 17 TBI patients and 17 controls (27 participants completed both post-acute and chronic phases). We did not find any significant group differences in the post-acute phase. Chronically, we found extensive group differences, mainly for mean and radial diffusivity (MD and RD). In the chronic phase, we found higher MD and RD across a wide range of WM. Additionally, we found correlations between these WM integrity measures and cognitive deficits. This suggests a distributed pattern of WM disruption that continues over the first year following a TBI in children.

Characterizing brain changes in Alzheimer's disease (AD) is important for patient prognosis and for assessing brain deterioration in clinical trials. In this diffusion weighted imaging study, we used a new fiber-tract modeling method to investigate white matter integrity in 50 elderly controls (CTL), 113 people with mild cognitive impairment, and 37 AD patients. After clustering tractography using a region-of-interest atlas, we used a shortest path graph search through each bundle's fiber density map to derive maximum density paths (MDPs), which we registered across subjects. We calculated the fractional anisotropy (FA) and mean diffusivity (MD) along all MDPs and found significant MD and FA differences between AD patients and CTL subjects, as well as MD differences between CTL and late mild cognitive impairment subjects. MD and FA were also associated with widely used clinical scores. As an MDP is a compact low-dimensional representation of white matter organization, we tested the utility of diffusion tensor imaging measures along these MDPs as features for support vector machine based classification of AD.

We introduce a framework for population analysis of white matter tracts based on diffusion-weighted images of the brain. The framework enables extraction of fibers from high angular resolution diffusion images (HARDI); clustering of the fibers based partly on prior knowledge from an atlas; representation of the fiber bundles compactly using a path following points of highest density (maximum density path; MDP); and registration of these paths together using geodesic curve matching to find local correspondences across a population. We demonstrate our method on 4-Tesla HARDI scans from 565 young adults to compute localized statistics across 50 white matter tracts based on fractional anisotropy (FA). Experimental results show increased sensitivity in the determination of genetic influences on principal fiber tracts compared to the tract-based spatial statistics (TBSS) method. Our results show that the MDP representation reveals important parts of the white matter structure and considerably reduces the dimensionality over comparable fiber matching approaches.

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.

Children with chromosome 22q11.2 deletion syndrome (22q11.2DS), Fragile X syndrome (FXS), or Turner syndrome (TS) are considered to belong to distinct genetic groups, as each disorder is caused by separate genetic alterations. Even so, they have similar cognitive and behavioral dysfunctions, particularly in visuospatial and numerical abilities. To assess evidence for common underlying neural microstructural alterations, we set out to determine whether these groups have partially overlapping white matter abnormalities, relative to typically developing controls. We scanned 101 female children between 7 and 14years old: 25 with 22q11.2DS, 18 with FXS, 17 with TS, and 41 aged-matched controls using diffusion tensor imaging (DTI). Anisotropy and diffusivity measures were calculated and all brain scans were nonlinearly aligned to population and site-specific templates. We performed voxel-based statistical comparisons of the DTI-derived metrics between each disease group and the controls, while adjusting for age. Girls with 22q11.2DS showed lower fractional anisotropy (FA) than controls in the association fibers of the superior and inferior longitudinal fasciculi, the splenium of the corpus callosum, and the corticospinal tract. FA was abnormally lower in girls with FXS in the posterior limbs of the internal capsule, posterior thalami, and precentral gyrus. Girls with TS had lower FA in the inferior longitudinal fasciculus, right internal capsule and left cerebellar peduncle. Partially overlapping neurodevelopmental anomalies were detected in all three neurogenetic disorders. Altered white matter integrity in the superior and inferior longitudinal fasciculi and thalamic to frontal tracts may contribute to the behavioral characteristics of all of these disorders.

The Alzheimer's Disease Neuroimaging Initiative (ADNI) recently added diffusion tensor imaging (DTI), among several other new imaging modalities, in an effort to identify sensitive biomarkers of Alzheimer's disease (AD). While anatomical MRI is the main structural neuroimaging method used in most AD studies and clinical trials, DTI is sensitive to microscopic white matter (WM) changes not detectable with standard MRI, offering additional markers of neurodegeneration. Prior DTI studies of AD report lower fractional anisotropy (FA), and increased mean, axial, and radial diffusivity (MD, AxD, RD) throughout WM. Here we assessed which DTI measures may best identify differences among AD, mild cognitive impairment (MCI), and cognitively healthy elderly control (NC) groups, in region of interest (ROI) and voxel-based analyses of 155 ADNI participants (mean age: 73.5 ± 7.4; 90 M/65 F; 44 NC, 88 MCI, 23 AD). Both VBA and ROI analyses revealed widespread group differences in FA and all diffusivity measures. DTI maps were strongly correlated with widely-used clinical ratings (MMSE, CDR-sob, and ADAS-cog). When effect sizes were ranked, FA analyses were least sensitive for picking up group differences. Diffusivity measures could detect more subtle MCI differences, where FA could not. ROIs showing strongest group differentiation (lowest p-values) included tracts that pass through the temporal lobe, and posterior brain regions. The left hippocampal component of the cingulum showed consistently high effect sizes for distinguishing groups, across all diffusivity and anisotropy measures, and in correlations with cognitive scores.