Deep Gray Matter Structures Research Articles

Decreased Activation of Deep Grey Matter Structures in Patients with Multiple Sclerosis Without Cognitive Deficits (987)

Decreased Activation of Deep Grey Matter Structures in Patients with Multiple Sclerosis Without Cognitive Deficits (987)

On the value of QSM from MPRAGE for segmenting and quantifying iron-rich deep gray matter.

Quantitative susceptibility mapping (QSM) has been employed for both iron evaluation and segmentation of deep gray matter (DGM), but QSM sequences are not typically used in standard brain volumetric studies, which use T1-weighted magnetization-prepared rapid acquisition with gradient echo (MPRAGE) with short TE. Here, QSM produced directly from standard MPRAGE phase ( ) is evaluated for segmentation and quantification of highly iron-rich DGM regions. Simulations were used to explore quality and possible limitations. In addition, QSM from a standard multi-echo gradient-echo ( ) was compared to in 40 healthy adults at 3T. DGM structures with weak contrast on MPRAGE magnitude were evaluated for improving segmentation with , with focus on the iron-rich globus pallidus (GP). Furthermore, susceptibility quantification was assessed on six DGM nuclei and compared to standard . Limited by TE and signal-to-noise ratio, only iron-rich regions like GP and dentate nucleus produced adequate contrast on , confining applications to such regions. improved GP segmentation with mean Dice scores raised by 9.0%, and mean volumetric differences reduced by 9.7%. Simulations suggested that phase contrast-to-noise ratio (CNR) should be above 3.0 to attain segmentation improvement. For quantification purposes, higher CNR is required, and typical provided comparable estimates to in large iron-rich DGM nuclei. Despite the short TE of standard MPRAGE, can improve GP segmentation over the use of MPRAGE magnitude alone and roughly quantify high-iron regions in DGM. Thus, reconstructing can be a useful addition to volumetric studies that rarely include standard .

Lithium treatment impacts nucleus accumbens shape in bipolar disorder.

The effects of lithium treatment duration on deep grey matter structures in bipolar disorder are not well known. In this cross-sectional neuroimaging case-control study, we tested the hypothesis that shape characteristics of deep grey matter structures in bipolar disorder are associated with the duration of lithium treatment and with clinical phenomenology. In a setting of neuropsychiatry outpatient clinic, we included 74 patients with bipolar disorder (BD) and 74 matched healthy control subjects (HC). Both groups underwent a Magnetic Resonance Imaging acquisition and an exhaustive assessment of clinical and psychiatrics dimensions. Shape measures of seven deep grey matter structures (hippocampus, amygdala, caudate, nucleus accumbens, putamen, globus pallidus and thalamus) were obtained from T1 weighted images in both groups, using FSL FIRST segmentation tool. The segmented structures were then analysed vertex-by-vertex with FSL Randomise tool. First, we investigated the presence of significant associations between the duration of lithium treatment and shape measures in BD sample. Then, for structures that resulted significantly associated with the duration of lithium treatment, comparisons between BD and HC were performed either considering the BD group as a whole or dividing it in three groups based on the duration of treatment (lithium drug-naïve, short and long treated). Any deformation uncovered by group comparisons was subsequently associated with depressive and hypomanic/manic symptoms. The relationship between structures shape and the duration of lithium treatment in BD sample was significant for bilateral nucleus accumbens. Specifically, significant bilateral extroflection effects, related to longer duration of lithium treatment, were found bilaterally over the surface shape of core accumbens nuclei (r2R-Accu-Core=0.12, p=0.016, r2L-Accu-Core=0.1, p=0.031). Moreover, introflection effect related to longer duration of treatment resulted over the shell of right accumbens (r2R-Accu-Shell=0.17, p=0.002). Nucleus accumbens shape did not differ between BD and HC considering BD group as a whole. By contrast, categorizing BD in subgroups as a function of the duration of lithium treatment revealed significant inward deformation on the core of left accumbens nucleus and outward deformation on the shell of the right accumbens nucleus in lithium-naive patients, compared to both patients with long duration of lithium treatment (pL-Accu-Core=0.016, pR-Accu-Shell=0.005) and HC (pL-Accu-Core=0.002; pR-Accu-Shell=0.005). Moreover, compared to HC, inward deformation on the core of the left accumbens surface was found for patients with short duration of treatment (pLAccu-Core=0.027). Finally, measures of surface deformation on the core of left accumbens observed in the group comparison showed significant positive correlations with depressive symptoms severity, as assessed by the Hamilton Depression Rating Scale (total score: r2L-AccuCore=0.07, p=0.02, somatic score: r2L-Accu-Core=0.1, p=0.005) and Beck Hopelessness Scale (r2LAccu-Core=0.05, p=0.03). Findings demonstrate that lithium untreated BD patients are characterised by localized shape abnormalities in the nucleus accumbens. Lithium treatment could act modulating these morphometric features as part of its mechanism of action in mood stabilizing.

Invited Review: Factors associated with atypical brain development in preterm infants: insights from magnetic resonance imaging.

Preterm birth (PTB) is a leading cause of neurodevelopmental and neurocognitive impairment in childhood and is closely associated with psychiatric disease. The biological and environmental factors that confer risk and resilience for healthy brain development and long‐term outcome after PTB are uncertain, which presents challenges for risk stratification and for the discovery and evaluation of neuroprotective strategies. Neonatal magnetic resonance imaging reveals a signature of PTB that includes dysconnectivity of neural networks and atypical development of cortical and deep grey matter structures. Here we provide a brief review of perinatal factors that are associated with the MRI signature of PTB. We consider maternal and foetal factors including chorioamnionitis, foetal growth restriction, socioeconomic deprivation and prenatal alcohol, drug and stress exposures; and neonatal factors including co‐morbidities of PTB, nutrition, pain and medication during neonatal intensive care and variation conferred by the genome/epigenome. Association studies offer the first insights into pathways to adversity and resilience after PTB. Future challenges are to analyse quantitative brain MRI data with collateral biological and environmental data in study designs that support causal inference, and ultimately to use the output of such analyses to stratify infants for clinical trials of therapies designed to improve outcome.

Using deep learning for a diffusion-based segmentation of the dentate nucleus and its benefits over atlas-based methods.

The dentate nucleus (DN) is a gray matter structure deep in the cerebellum involved in motor coordination, sensory input integration, executive planning, language, and visuospatial function. The DN is an emerging biomarker of disease, informing studies that advance pathophysiologic understanding of neurodegenerative and related disorders. The main challenge in defining the DN radiologically is that, like many deep gray matter structures, it has poor contrast in T1-weighted magnetic resonance (MR) images and therefore requires specialized MR acquisitions for visualization. Manual tracing of the DN across multiple acquisitions is resource-intensive and does not scale well to large datasets. We describe a technique that automatically segments the DN using deep learning (DL) on common imaging sequences, such as T1-weighted, T2-weighted, and diffusion MR imaging. We trained a DL algorithm that can automatically delineate the DN and provide an estimate of its volume. The automatic segmentation achieved higher agreement to the manual labels compared to template registration, which is the current common practice in DN segmentation or multiatlas segmentation of manual labels. Across all sequences, the FA maps achieved the highest mean Dice similarity coefficient (DSC) of 0.83 compared to T1 imaging ( ), T2 imaging ( ), or a multisequence approach ( ). A single atlas registration approach using the spatially unbiased atlas template of the cerebellum and brainstem template achieved a DSC of 0.23, and multi-atlas segmentation achieved a DSC of 0.33. Overall, we propose a method of delineating the DN on clinical imaging that can reproduce manual labels with higher accuracy than current atlas-based tools.

Morphological Relationship Between the Superficial Cortical and Deep Grey Matter Structures in Adult Human Brains: A Cadaveric Study

Morphological Relationship Between the Superficial Cortical and Deep Grey Matter Structures in Adult Human Brains: A Cadaveric Study

Gray matter integrity predicts white matter network reorganization in multiple sclerosis.

Multiple sclerosis (MS) is a chronic inflammatory and neurodegenerative disease leading to gray matter atrophy and brain network reconfiguration as a response to increasing tissue damage. We evaluated whether white matter network reconfiguration appears subsequently to gray matter damage, or whether the gray matter degenerates following alterations in white matter networks. MRI data from 83 patients with clinically isolated syndrome and early relapsing–remitting MS were acquired at two time points with a follow‐up after 1 year. White matter network integrity was assessed based on probabilistic tractography performed on diffusion‐weighted data using graph theoretical analyses. We evaluated gray matter integrity by computing cortical thickness and deep gray matter volume in 94 regions at both time points. The thickness of middle temporal cortex and the volume of deep gray matter regions including thalamus, caudate, putamen, and brain stem showed significant atrophy between baseline and follow‐up. White matter network dynamics, as defined by modularity and distance measure changes over time, were predicted by deep gray matter volume of the atrophying anatomical structures. Initial white matter network properties, on the other hand, did not predict atrophy. Furthermore, gray matter integrity at baseline significantly predicted physical disability at 1‐year follow‐up. In a sub‐analysis, deep gray matter volume was significantly related to cognitive performance at baseline. Hence, we postulate that atrophy of deep gray matter structures drives the adaptation of white matter networks. Moreover, deep gray matter volumes are highly predictive for disability progression and cognitive performance.

Cognitive impairment and structural brain damage in multiple system atrophy-parkinsonian variant.

In this multiparametric, cross-sectional study, we aimed to investigate cognitive impairment and brain structural changes in patients with multiple system atrophy (MSA)-parkinsonian variant (MSA-p). Twenty-six MSA-p patients and 19 controls underwent clinical and neuropsychological evaluation and 1.5T brain MRI scan. Cortical thickness measures and volumes of deep grey matter structures were obtained. A regression analysis correlated MRI metrics with clinical features in MSA-p patients. Almost 46% of MSA-p patients showed a mild cognitive impairment involving mainly attentive-executive and memory domains. Apathy and depression were found in half of MSA-p patients. MSA-p patients showed significant cortical thinning of fronto-temporal-parietal regions and atrophy of periaqueductal grey matter, left cerebellar hemisphere, left pallidum and bilateral putamen, compared to controls. Cortical thinning in temporal regions correlated with global cognitive status and memory impairment. Grey matter cerebellar atrophy correlated with motor deficits. MSA-p patients showed a multidomain cognitive impairment with a prominent cortical damage in anterior more than posterior brain regions and grey matter volume reduction in subcortical structures. Cortical and subcortical structural changes might lead to cognitive dysfunction in MSA-p.

Brain iron and metabolic abnormalities in C19orf12 mutation carriers: A 7.0 tesla MRI study in mitochondrial membrane protein-associated neurodegeneration.

Mitochondrial membrane protein-associated neurodegeneration is an autosomal-recessive disorder caused by C19orf12 mutations and characterized by iron deposits in the basal ganglia. The aim of this study was to quantify iron concentrations in deep gray matter structures using quantitative susceptibility mapping MRI and to characterize metabolic abnormalities in the pyramidal pathway using 1 H MR spectroscopy in clinically manifesting membrane protein-associated neurodegeneration patients and asymptomatic C19orf12 gene mutation heterozygous carriers. We present data of 4 clinically affected membrane protein-associated neurodegeneration patients (mean age: 21.0 ± 2.9 years) and 9 heterozygous gene mutation carriers (mean age: 50.4 ± 9.8 years), compared to age-matched healthy controls. MRI assessments were performed on a 7.0 Tesla whole-body system, consisting of whole-brain gradient-echo scans and short echo time, single-volume MR spectroscopy in the white matter of the precentral/postcentral gyrus. Quantitative susceptibility mapping, a surrogate marker for iron concentration, was performed using a state-of-the-art multiscale dipole inversion approach with focus on the globus pallidus, thalamus, putamen, caudate nucleus, and SN. In membrane protein-associated neurodegeneration patients, magnetic susceptibilities were 2 to 3 times higher in the globus pallidus (P = 0.02) and SN (P = 0.02) compared to controls. In addition, significantly higher magnetic susceptibility was observed in the caudate nucleus (P = 0.02). Non-manifesting heterozygous mutation carriers exhibited significantly increased magnetic susceptibility (relative to controls) in the putamen (P = 0.003) and caudate nucleus (P = 0.001), which may be an endophenotypic marker of genetic heterozygosity. MR spectroscopy revealed significantly increased levels of glutamate, taurine, and the combined concentration of glutamate and glutamine in membrane protein-associated neurodegeneration, which may be a correlate of corticospinal pathway dysfunction frequently observed in membrane protein-associated neurodegeneration patients. © 2019 International Parkinson and Movement Disorder Society.

Association between Deep Gray Matter Changes and Neurocognitive Function in Mild Cognitive Impairment and Alzheimer’s Disease: A Tensor-Based Morphometric MRI Study

Background: Atrophy of the deep gray matter (DGM) has been associated with a risk of conversion from mild cognitive impairment (MCI) to Alzheimer’s disease (AD) and the degree of cognitive impairment. However, specific knowledge of the associations between degenerative DGM changes and neurocognitive functions remains limited. Objective: To examine degenerative DGM changes and evaluate their association with neurocognitive functions. Method: We examined DGM volume changes with tensor-based morphometry (TBM) and analyzed the relationships between DGM changes and neurocognitive functions in control (n = 58), MCI (n = 38), and AD (n = 58) groups with multiple linear regression analyses. Results: In all DGM areas, the AD group had the largest changes in TBM volume. The differences in TBM volume changes were larger between the control group and the AD group than between the other pairs of groups. In the AD group, volume changes of the right thalamus were significantly associated with episodic memory, learning, and semantic processing. Significant or trend-level associations were identified between bilateral caudate nucleus changes and episodic memory as well as semantic processing. In the control and MCI groups, very few significant associations emerged. Conclusions: Atrophy of the DGM structures, especially the thalamus and caudate nucleus, is related to cognitive impairment in AD. DGM atrophy is associated with tests reflecting both subcortical and cortical cognitive functions.

Combining quantitative susceptibility mapping to the morphometric index in differentiating between progressive supranuclear palsy and Parkinson's disease

PurposeTo determine whether the susceptibility value in the deep gray matter obtained by quantitative susceptibility mapping (QSM) provides additive value to the morphometric index for differentiating progressive supranuclear palsy (PSP) from Parkinson's disease (PD). Materials and methodsPSP- (n = 8) and PD patients (n = 18) and 18 age-matched healthy controls who underwent QSM and 3D magnetization-prepared rapid gradient echo (MPRAGE) sequences. The mean susceptibility values (MSVs) of the deep gray matter structures on QSM- and areas of the midbrain (morphometric index, MI) on 3D MPRAGE images were measured by two neuroradiologists. Analysis of variance, the Scheffe test and receiver operating characteristic (ROC) analysis were conducted to assess differences and discriminate among PSP, PD and controls by the MSVs and the MI. Using the MSV of a structure with the best area under the curve (AUC) and the MI, we created a decision tree to differentiate between PSP and PD. ResultsThe MSVs of the globus pallidus (GP) and substantia nigra (SN) were significantly higher in PSP than PD and the controls (p < .05). By ROC analysis (PSP vs PD), AUC was greatest (0.903) for the GP. The MI was significantly smaller in PSP than PD and the controls (p < .05); AUC (PSP vs PD) was 0.917. The decision tree using cutoff values of 244 parts per billion for MSV of the GP and 74.0 mm2 for MI served to completely differentiate between PSP and PD. ConclusionThe MSV in the GP on QSM images adds value to the MI for differentiating PSP from PD.

Quantitative susceptibility map reconstruction using annihilating filter-based low-rank Hankel matrix approach.

Quantitative susceptibility mapping (QSM) inevitably suffers from streaking artifacts caused by zeros on the conical surface of the dipole kernel in k-space. This work proposes a novel and accurate QSM reconstruction method based on k-space low-rank Hankel matrix constraint, avoiding the over-smoothing problem and streaking artifacts. Based on the recent theory of annihilating filter-based low-rank Hankel matrix approach (ALOHA), QSM is formulated as deconvolution under low-rank Hankel matrix constraint in the k-space. The computational complexity and the high memory burden were reduced by successive reconstruction of 2-D planes along 3 independent axes of the 3-D phase image in Fourier domain. Feasibility of the proposed method was tested on a simulated phantom and human data and were compared with existing QSM reconstruction methods. The proposed ALOHA-QSM effectively reduced streaking artifacts and accurately estimated susceptibility values in deep gray matter structures, compared to the existing QSM methods. The suggested ALOHA-QSM algorithm successfully solves the 3-dimensional QSM dipole inversion problem using k-space low rank property with no anatomical constraint. ALOHA-QSM can provide detailed brain structures and accurate susceptibility values with no streaking artifacts.

Imaging appearances of toxic and acquired metabolic encephalopathic disorders.

Most imaging findings relating to toxic and acquired metabolic disorders follow a certain pattern with affinity to a specific topographic area, which can help narrow the differential diagnosis. This is especially useful when the clinical presentation can be variable and there is diagnostic uncertainty. Usually, there is bilateral symmetrical abnormality within the deep grey matter structures and the cerebral cortex because of the high metabolic activity and raised oxygen requirements in these areas. Magnetic resonance imaging, particularly diffusion weighted imaging and fluid-attenuated inversion recovery sequences, is very important in differentiating between various aetiologies in this group. Magnetic resonance imaging can be useful in demonstrating both acute and chronic damage, in evaluating treatment response and in disease prognostication. This pictorial review discusses the computed tomography and magnetic resonance imaging appearances of a spectrum of toxic and metabolic disorders observed in a district general hospital with reference to clinical presentation and imaging features that may allow diagnosis. This includes carbon monoxide poisoning, hypoglycaemia, non-ketotic hyperglycaemia, osmotic demyelination syndrome, posterior reversible encephalopathy syndrome, hypoxic ischaemic encephalopathy, the syndrome of delayed post-hypoxic leukoencephalopathy, hepatic encephalopathy and cocaine toxicity.

Regional High Iron in the Substantia Nigra Differentiates Parkinson's Disease Patients From Healthy Controls.

Background: Iron is important in the pathophysiology of Parkinson’s disease (PD) specifically related to degeneration of the substantia nigra (SN). Magnetic resonance imaging (MRI) can be used to measure brain iron in the entire structure but this approach is insensitive to regional changes in iron content.Objective: The goal of this work was to use quantitative susceptibility mapping (QSM) and R2∗ to quantify both global and regional brain iron in PD patients and healthy controls (HC) to ascertain if regional changes correlate with clinical conditions and can be used to discriminate patients from controls.Methods: Susceptibility and R2∗ maps of 25 PD and 24 HC subjects were reconstructed from data collected on a 3T GE scanner. For the susceptibility maps, three-dimensional regions-of-interest (ROIs) were traced on eight deep gray matter (DGM) structures and an age-based threshold was applied to define regions of high iron content. The same multi-slice ROIs were duplicated on the R2∗ maps as well. Mean susceptibility values of both global and regional high iron (RII) content along with global R2∗ values were measured and compared not only between the two cohorts, but also to susceptibility and R2∗ baselines as a function of age. Finally, clinical features were compared for those PD patients lying above and below the upper 95% regional susceptibility-age prediction intervals.Results: The SN was the only structure showing significantly higher susceptibility in PD patients compared to controls globally (p < 0.01) and regionally (p < 0.001). The R2∗ values were also higher only in the SN of PD patients compared to the healthy cohort (p < 0.05). Furthermore, those patients with abnormal susceptibility values lying above the upper 95% prediction intervals had significantly higher united Parkinson’s diagnostic rating scores. R2∗ values had larger errors and showed larger dispersion as a function of age than QSM data for global analysis while the dispersion was significantly less for QSM using the RII iron content.Conclusion: Abnormal iron deposition in the SN, especially in RII areas, could serve as a biomarker to distinguish PD patients from HC and to assess disease severity.

The Impact of MRI T1 Hypointense Brain Lesions on Cerebral Deep Gray Matter Volume Measures in Multiple Sclerosis.

Deep gray matter (DGM) atrophy has been shown at early stages of multiple sclerosis (MS) and reported as an informative marker of cognitive dysfunction and clinical progression. Therefore, accurate measurement of DGM structure volume is a key priority in MS research. Findings from prior studies have shown that hypointense T1 lesions may impact the accuracy of global brain volume measures; however, literature on the effects of hypointense T1 lesions on DGM structure volumes is sparse. We explored the effects of hypointense T1 lesions on data from 54 relapsing remitting MS patients. Lesions were segmented both manually and with a freely available automatic lesion segmentation/in-painting algorithm (Lesion Segmentation Tool-LST). Volumes of 14 DGM structures were calculated from non-in-painted and in-painted images and compared via paired t-tests, intraclass correlation coefficient, and Dice similarity coefficient. There were no significant differences in DGM structural volumes between non-in-painted and in-painted images. Automatic lesion-segmentation/in-painting tool provided similar results to manual segmentation/in-painting. Our results suggest that lesion in-painting has a negligible impact on DGM structure volume measurement although some regions are more vulnerable to the impact of lesions than others. Furthermore, manual lesion segmentation/in-painting can be replaced by an automatic segmentation/in-painting process.

Registration-based image enhancement improves multi-atlas segmentation of the thalamic nuclei and hippocampal subfields

Magnetic resonance imaging (MRI) is an important tool for analysis of deep brain grey matter structures. However, analysis of these structures is limited due to low intensity contrast typically found in whole brain imaging protocols. Herein, we propose a big data registration-enhancement (BDRE) technique to augment the contrast of deep brain structures using an efficient large-scale non-rigid registration strategy. Direct validation is problematic given a lack of ground truth data. Rather, we validate the usefulness and impact of BDRE for multi-atlas (MA) segmentation on two sets of structures of clinical interest: the thalamic nuclei and hippocampal subfields. The experimental design compares algorithms using T1-weighted 3 T MRI for both structures (and additional 7 T MRI for the thalamic nuclei) with an algorithm using BDRE. As baseline comparisons, a recent denoising (DN) technique and a super-resolution (SR) method are used to preprocess the original 3 T MRI. The performance of each MA segmentation is evaluated by the Dice similarity coefficient (DSC). BDRE significantly improves mean segmentation accuracy over all methods tested for both thalamic nuclei (3 T imaging: 9.1%; 7 T imaging: 15.6%; DN: 6.9%; SR: 16.2%) and hippocampal subfields (3 T T1 only: 8.7%; DN: 8.4%; SR: 8.6%). We also present DSC performance for each thalamic nucleus and hippocampal subfield and show that BDRE can help MA segmentation for individual thalamic nuclei and hippocampal subfields. This work will enable large-scale analysis of clinically relevant deep brain structures from commonly acquired T1 images.

Longitudinal growth of the basal ganglia and thalamus in very preterm children.

The impact of very preterm (VP) birth on the development of individual basal ganglia nuclei and the thalamus during childhood remains unclear. We first aimed to compare (1a) the volumes of individual basal ganglia nuclei (nucleus accumbens, caudate nucleus, pallidum, putamen) and the thalamus at age 7years, and (1b) their volumetric change from infancy to 7years, in VP children with term-born children. Secondly, we aimed to (2a) determine whether basal ganglia and thalamic volumes at 7years, or (2b) basal ganglia and thalamic growth rates from infancy to 7years were associated with neurodevelopmental outcomes at 7years, and whether these associations differed between the VP and term-born children. One hundred and fifty-four VP (<30weeks' gestational age or birth weight < 1250g) and 35 term-born children had useable magnetic resonance imaging (MRI) scans that could be analyzed at 7years. Of these, 149 VP and 30 term-born infants also had useable MRI scans at term-equivalent age. Volumes of the individual basal ganglia nuclei and the thalamus were automatically generated from the MRI scans. Compared with the term-born group, the VP group had smaller basal ganglia and thalamic volumes at 7years and slower growth rates from birth to 7years. After controlling for overall brain size, VP children still had smaller thalamic volumes but the deep grey matter volume growth rates from birth to 7years were similar between groups. Reduced basal ganglia and thalamic volumes and slower growth rates in the VP group were associated with poorer cognition, academic achievement and motor function at 7years. After controlling for overall brain size, the nucleus accumbens and pallidum were the deep grey matter structures most strongly associated with 7-year neurodevelopmental outcomes. In conclusion, basal ganglia and thalamic growth is delayed during early childhood in VP children, with delayed development contributing to poorer functional outcomes.

SWI post processing using granularity controlled edge‐preserved denoising of multichannel GRE images

AbstractThe influence of granularities in the background suppressed phase of susceptibility‐weighted images (SWI) and susceptibility‐weighted angiogram (SWAN) becomes significant when the susceptibility based contrast is enhanced by exponential weighting of the high‐pass filtered phase. Furthermore, the effect of noise due to the inherently low signal‐to‐noise ratio resulting from high‐resolution SWI/SWAN acquisition, can be minimized by application of edge‐preserved denoising of the channel phase images without loss of venous structural details. Simultaneous reduction of granularity effects with edge‐preserved denoising is achieved using the proposed granularity controlled adaptive edge‐preserved regularization (GRADER). In this approach, the edge‐preserving cost is minimized with respect to the desired channel phase image and an unknown scale parameter that adaptively tunes the high‐pass filter. The algorithm is implemented using quasi‐Newton type iterations, with the scale parameter updated using a search procedure in each alternating minimization step. The iterations are stopped once the scale parameter converges to a steady state value. Extension of GRADER to parallel MRI (pMRI) by processing the real and imaginary components of complex channel images (IR‐GRADER) results in enhanced susceptibility‐related contrast‐to‐noise ratio of the magnitude SWI, leading to improved visualization of superficial veins and deep gray matter structures.

Polygenic risk for neuropsychiatric disease and vulnerability to abnormal deep grey matter development

Neuropsychiatric disease has polygenic determinants but is often precipitated by environmental pressures, including adverse perinatal events. However, the way in which genetic vulnerability and early-life adversity interact remains obscure. We hypothesised that the extreme environmental stress of prematurity would promote neuroanatomic abnormality in individuals genetically vulnerable to psychiatric disorders. In 194 unrelated infants (104 males, 90 females), born before 33 weeks of gestation (mean gestational age 29.7 weeks), we combined Magnetic Resonance Imaging with a polygenic risk score (PRS) for five psychiatric pathologies to test the prediction that: deep grey matter abnormalities frequently seen in preterm infants are associated with increased polygenic risk for psychiatric illness. The variance explained by the PRS in the relative volumes of four deep grey matter structures (caudate nucleus, thalamus, subthalamic nucleus and lentiform nucleus) was estimated using linear regression both for the full, mixed ancestral, cohort and a subsample of European infants. Psychiatric PRS was negatively associated with lentiform volume in the full cohort (β = −0.24, p = 8 × 10−4) and a European subsample (β = −0.24, p = 8 × 10−3). Genetic variants associated with neuropsychiatric disease increase vulnerability to abnormal lentiform development after perinatal stress and are associated with neuroanatomic changes in the perinatal period.

Multi-atlas tool for automated segmentation of brain gray matter nuclei and quantification of their magnetic susceptibility

Quantification of tissue magnetic susceptibility using MRI offers a non-invasive measure of important tissue components in the brain, such as iron and myelin, potentially providing valuable information about normal and pathological conditions during aging. Despite many advances made in recent years on imaging techniques of quantitative susceptibility mapping (QSM), accurate and robust automated segmentation tools for QSM images that can help generate universal and sharable susceptibility measures in a biologically meaningful set of structures are still not widely available. In the present study, we developed an automated process to segment brain nuclei and quantify tissue susceptibility in these regions based on a susceptibility multi-atlas library, consisting of 10 atlases with T1-weighted images, gradient echo (GRE) magnitude images and QSM images of brains with different anatomic patterns. For each atlas in this library, 10 regions of interest in iron-rich deep gray matter structures that are better defined by QSM contrast were manually labeled, including caudate, putamen, globus pallidus internal/external, thalamus, pulvinar, subthalamic nucleus, substantia nigra, red nucleus and dentate nucleus in both left and right hemispheres. We then tested different pipelines using different combinations of contrast channels to bring the set of labels from the multi-atlases to each target brain and compared them with the gold standard manual delineation. The results showed that the segmentation accuracy using dual contrasts QSM/T1 pipeline outperformed other dual-contrast or single-contrast pipelines. The dice values of 0.77 ± 0.09 using the QSM/T1 multi-atlas pipeline rivaled with the segmentation reliability obtained from multiple evaluators with dice values of 0.79 ± 0.07 and gave comparable or superior performance in segmenting subcortical nuclei in comparison with standard FSL FIRST or recent multi-atlas package of volBrain. The segmentation performance of the QSM/T1 multi-atlas was further tested on QSM images acquired using different acquisition protocols and platforms and showed good reliability and reproducibility with average dice of 0.79 ± 0.08 to manual labels and 0.89 ± 0.04 in an inter-protocol manner. The extracted quantitative magnetic susceptibility values in the deep gray matter nuclei also correlated well between different protocols with inter-protocol correlation constants all larger than 0.97. Such reliability and performance was ultimately validated in an external dataset acquired at another study site with consistent susceptibility measures obtained using the QSM/T1 multi-atlas approach in comparison to those using manual delineation. In summary, we designed a susceptibility multi-atlas tool for automated and reliable segmentation of QSM images and for quantification of magnetic susceptibilities. It is publicly available through our cloud-based platform (www.mricloud.org). Further improvement on the performance of this multi-atlas tool is expected by increasing the number of atlases in the future.