Quantitative susceptibility atlas construction in Montreal Neurological Institute space: towards histological-consistent iron-rich deep brain nucleus subregion identification

Increased iron deposition in subcortical nuclear mass in treatment naïve transgender women: An exploratory quantitative susceptibility mapping study

OBJECTIVE

The aim of this study was to investigate altered iron deposition in deep brain regions of transgender women (TW) population using quantitative susceptibility mapping (QSM).

MATERIAL & METHOD

45 TW, 28 cisgender men (CM) and 18 cisgender women (CW) were prospectively recruited. All participants underwent a 3.0T magnetic resonance imaging of the brain. QSM post-processing technique was applied to obtain susceptibility value for regions of the caudate, putamen, internal globus pallidus, external globus pallidus, ventral pallidum, nucleus accumbens, substantia nigra pars compacta, substantia nigra pars reticulata, red nucleus, subthalamic nucleus and dentate nucleus. The analysis of covariance was used to investigate the iron deposition differences between three groups controlling for age and education. The false discovery rate (FDR) was used for multiple comparison correction.

RESULTS

After correcting for FDR, the TW group showed increased susceptibility value in the left internal globus pallidus compared to the CM group. There were no significantly different susceptibility value between the TW and the CW groups after correcting for FDR.

CONCLUSION

The iron deposition in the internal globus pallidus region of TW was higher than that of CM, and there was no significant difference between TW and CW, Further investigation is warranted to gain a more comprehensive understanding of cerebral iron dysregulation in transgender people and its associated physiological mechanisms.

High resolution multi-parametric probabilistic in vivo atlas of dorsolateral nigral hyperintensity via 7 T MRI

The role of Nigrosome 1 (N1) in neurodegeneration and motor disorders, particularly in Parkinson's disease (PD), is increasingly recognized. The study of this region using quantitative measures, such as iron quantification through Quantitative Susceptibility Mapping (QSM), can provide enlightening insights into some pathological features of these diseases representing important biomarkers. However, the small size and the vanishing contrast with respect to the surrounding substantia nigra in PD patients make the segmentation of N1 challenging. For this reason, we provide a probabilistic atlas of the N1 portion corresponding to the swallow-tail hyperintensity, hereafter referred to as the Dorsolateral Nigral Hyperintensity (DNH), created on a high-resolution multi-parametric template from T1-weighted, T2*-weighted, and QSM images acquired in vivo at 7 T. The atlas also includes quantitative T2* and R2* templates and is provided in the MNI standard space. It aims to facilitate the study of N1, avoiding operator-dependent biases in segmentations, and allowing the standardisation of the quantitative assessment.

In-vivo high-resolution χ-separation at 7T

A recently introduced quantitative susceptibility mapping (QSM) technique, χ-separation, offers the capability to separate paramagnetic (χpara) and diamagnetic (χdia) susceptibility distribution within the brain. In-vivo high-resolution mapping of iron and myelin distribution, estimated by χ-separation, could provide a deeper understanding of brain substructures, assisting the investigation of their functions and alterations. This can be achieved using 7T MRI, which benefits from a high signal-to-noise ratio and susceptibility effects. However, applying χ-separation at 7T presents difficulties due to the requirement of an R2 map, coupled with issues such as high specific absorption rate (SAR), large B1 transmit field inhomogeneities, and prolonged scan time. To address these challenges, we developed a novel deep neural network, R2PRIMEnet7T, designed to convert a 7T R2* map into a 3T R2' map. Building on this development, we present a new pipeline for χ-separation at 7T, enabling us to generate high-resolution χ-separation maps from multi-echo gradient-echo data. The proposed method is compared with alternative pipelines, such as an end-to-end network and linearly-scaled R2', and is validated against χ-separation maps at 3T, demonstrating its accuracy. The 7T χ-separation maps generated by the proposed method exhibit similar contrasts to those from 3T, while 7T high-resolution maps offer enhanced clarity and detail. Quantitative analysis confirms that the proposed method surpasses the alternative pipelines. The proposed method results well delineate the detailed brain structures associated with iron and myelin. This new pipeline holds promise for analyzing iron and myelin concentration changes in various neurodegenerative diseases through precise structural examination.

Causal associations between iron levels in subcortical brain regions and psychiatric disorders: a Mendelian randomization study

Despite observational studies linking brain iron levels to psychiatric disorders, the exact causal relationship remains poorly understood. This study aims to examine the relationship between iron levels in specific subcortical brain regions and the risk of psychiatric disorders. Utilizing two-sample Mendelian randomization (MR) analysis, this study investigates the causal associations between iron level changes in 16 subcortical nuclei and eight major psychiatric disorders, including schizophrenia (SCZ), major depressive disorder (MDD), autism spectrum disorders (ASD), attention-deficit/hyperactivity disorder, bipolar disorder, anxiety disorders, obsessive-compulsive disorder, and insomnia. The genetic instrumental variables linked to iron levels and psychiatric disorders were derived from the genome-wide association studies data of the UK Biobank Brain Imaging and Psychiatric Genomics Consortium. Bidirectional causal estimation was primarily obtained using the inverse variance weighting (IVW) method. Iron levels in the left substantia nigra showed a negative association with the risk of MDD (ORIVW = 0.94, 95% CI = 0.91-0.97, p < 0.001) and trends with risk of SCZ (ORIVW = 0.90, 95% CI = 0.82-0.98, p = 0.020). Conversely, iron levels in the left putamen were positively associated with the risk of ASD (ORIVW = 1.11, 95% CI = 1.04-1.19, p = 0.002). Additionally, several bidirectional trends were observed between subcortical iron levels and the risk for psychiatric disorders. Lower iron levels in the left substantia nigra may increase the risk of MDD, and potentially increase the risk of SCZ, indicating a potential shared pathogenic mechanism. Higher iron levels in the left putamen may lead to the development of ASD. The observed bidirectional trends between subcortical iron levels and psychiatric disorders, indicate the importance of the underlying biomechanical interactions between brain iron regulation and these disorders.

Spatiotemporal neurodegeneration of the substantia nigra and its connecting cortex and subcortex in Parkinson's disease

BACKGROUND AND PURPOSE

Neurodegeneration is uneven in Parkinson's disease (PD). This study aimed to investigate spatiotemporal neurodegeneration in functional subregions of the substantia nigra (SN) and their connected cortex and subcortex in people with PD.

METHODS

A total of 120 patients with early-stage PD, 45 patients with advanced PD, and 120 healthy controls (HCs) were enrolled. The SN, cortex, and subcortex were divided into sensorimotor, associative, and limbic regions, respectively. Iron deposition in the SN was assessed by quantitative susceptibility mapping (QSM). Cortex and subcortex volumes were calculated based on T1-weighted imaging. Region of interest (ROI) analysis and voxel-based analysis (VBA) were performed to explore spatiotemporal neurodegeneration in patients with PD. p values were corrected for false discovery rate.

RESULTS

In the ROI analysis, the QSM values for the limbic (p = 0.018) and sensorimotor SN subregions (p = 0.018) were higher in PD patients than in HCs, but were not higher in the associative SN subregion (p = 0.295). In VBA, all SN functional subregions had clusters with higher QSM values in PD patients than in HCs (p < 0.001). The limbic SN subregion was the only one in which iron deposition increased from early-stage to advanced PD (p = 0.023). The QSM values of VBA_limbic, sensorimotor, and associative SN had subregion-specific correlations with disease severity (p = 0.001 for the limbic and sensorimotor subregions, p = 0.003 for the associative subregion), motor symptoms (p = 0.057 for the limbic and sensorimotor subregion), and depression scores (p = 0.036 for the limbic subregion).

CONCLUSION

Iron deposition in SN functional subregions and atrophy of cortical and subcortical structures connected with the SN showed spatiotemporal selectivity. These findings reveal the potential pathogenesis of clinical heterogeneity in PD.

Neuroimaging of Parkinson's disease by quantitative susceptibility mapping

Parkinson's disease (PD) is a common neurodegenerative disease, and apart from a few rare genetic causes, its pathogenesis remains largely unclear. Recent scientific interest has been captured by the involvement of iron biochemistry and the disruption of iron homeostasis, particularly within the brain regions specifically affected in PD. The advent of Quantitative Susceptibility Mapping (QSM) has enabled non-invasive quantification of brain iron in vivo by MRI, which has contributed to the understanding of iron-associated pathogenesis and has the potential for the development of iron-based biomarkers in PD. This review elucidates the biochemical underpinnings of brain iron accumulation, details advancements in iron-sensitive MRI technologies, and discusses the role of QSM as a biomarker of iron deposition in PD. Despite considerable progress, several challenges impede its clinical application after a decade of QSM studies. The initiation of multi-site research is warranted for developing robust, interpretable, and disease-specific biomarkers for monitoring PD disease progression.

Advanced magnetic resonance neuroimaging techniques: feasibility and applications in long or post-COVID-19 syndrome - a review

Long-term or post-COVID-19 syndrome (PCS) is a condition that affects people infected with SARS‑CoV‑2, the virus that causes COVID-19. PCS is characterized by a wide range of persistent or new symptoms that last months after the initial infection, such as fatigue, shortness of breath, cognitive dysfunction, and pain. Advanced magnetic resonance (MR) neuroimaging techniques can provide valuable information on the structural and functional changes in the brain associated with PCS as well as potential biomarkers for diagnosis and prognosis. In this review, we discuss the feasibility and applications of various advanced MR neuroimaging techniques in PCS, including perfusion-weighted imaging (PWI), diffusion-weighted imaging (DWI), susceptibility-weighted imaging (SWI), functional MR imaging (fMRI), diffusion tensor imaging (DTI), and tractography. We summarize the current evidence on neuroimaging findings in PCS, the challenges and limitations of these techniques, and the future directions for research and clinical practice. Although still uncertain, advanced MRI techniques show promise for gaining insight into the pathophysiology and guiding the management of COVID-19 syndrome, pending larger validation studies.

Connectome-based schizophrenia prediction using structural connectivity - Deep Graph Neural Network(sc-DGNN)

BACKGROUND

Connectome is understanding the complex organization of the human brain's structural and functional connectivity is essential for gaining insights into cognitive processes and disorders.

OBJECTIVE

To improve the prediction accuracy of brain disorder issues, the current study investigates dysconnected subnetworks and graph structures associated with schizophrenia.

METHOD

By using the proposed structural connectivity-deep graph neural network (sc-DGNN) model and compared with machine learning (ML) and deep learning (DL) models.This work attempts to focus on eighty-eight subjects of diffusion magnetic resonance imaging (dMRI), three classical ML, and five DL models.

RESULT

The structural connectivity-deep graph neural network (sc-DGNN) model is proposed to effectively predict dysconnectedness associated with schizophrenia and exhibits superior performance compared to traditional ML and DL (GNNs) methods in terms of accuracy, sensitivity, specificity, precision, F1-score, and Area under receiver operating characteristic (AUC).

CONCLUSION

The classification task on schizophrenia using structural connectivity matrices and experimental results showed that linear discriminant analysis (LDA) performed 72% accuracy rate in ML models and sc-DGNN performed at a 93% accuracy rate in DL models to distinguish between schizophrenia and healthy patients.

Sub-voxel quantitative susceptibility mapping for assessing whole-brain magnetic susceptibility from ages 4 to 80

The evolution of magnetic susceptibility of the brain is mainly determined by myelin in white matter (WM) and iron deposition in deep gray matter (DGM). However, existing imaging techniques have limited abilities to simultaneously quantify the myelination and iron deposition within a voxel throughout brain development and aging. For instance, the temporal trajectories of iron in the brain WM and myelination in DGM have not been investigated during the aging process. This study aimed to map the age-related iron and myelin changes in the whole brain, encompassing myelin in DGM and iron deposition in WM, using a novel sub-voxel quantitative susceptibility mapping (QSM) method. To achieve this, a cohort of 494 healthy adults (18-80 years old) was studied. The sub-voxel QSM method was employed to obtain the paramagnetic and diamagnetic susceptibility based on the approximatedR 2 ' map from acquiredR 2 * map. The linear relationship betweenR 2 * andR 2 ' maps was established from the regression coefficients on a small cohort data acquired with both 3D gradient recalled echo data andR 2 mapping. Large cohort sub-voxel susceptibility maps were used to create longitudinal and age-specific atlases via group-wise registration. To explore the differential developmental trajectories in the DGM and WM, we employed nonlinear models including exponential and Poisson functions, along with generalized additive models. The constructed atlases reveal the iron accumulation in the posterior part of the putamen and the gradual myelination process in the globus pallidus with aging. Interestingly, the developmental trajectories show that the rate of myelination differs among various DGM regions. Furthermore, the process of myelin synthesis is paralleled by an associated pattern of iron accumulation in the primary WM fiber bundles. In summary, our study offers significant insights into the distinctive developmental trajectories of iron in the brain's WM and myelination/demyelination in the DGM in vivo. These findings highlight the potential of using sub-voxel QSM to uncover new perspectives in neuroscience and improve our understanding of whole-brain myelination and iron deposit processes across the lifespan.

Aberrant dentato-rubro-thalamic pathway in action tremor but not rest tremor: A multi-modality magnetic resonance imaging study

AIMS

The purpose of this study was to clarify the dentato-rubro-thalamic (DRT) pathway in action tremor in comparison to normal controls (NC) and disease controls (i.e., rest tremor) by using multi-modality magnetic resonance imaging (MRI).

METHODS

This study included 40 essential tremor (ET) patients, 57 Parkinson's disease (PD) patients (29 with rest tremor, 28 without rest tremor), and 41 NC. We used multi-modality MRI to comprehensively assess major nuclei and fiber tracts of the DRT pathway, which included decussating DRT tract (d-DRTT) and non-decussating DRT tract (nd-DRTT), and compared the differences in DRT pathway components between action and rest tremor.

RESULTS

Bilateral dentate nucleus (DN) in the ET group had excessive iron deposition compared with the NC group. Compared with the NC group, significantly decreased mean diffusivity and radial diffusivity were observed in the left nd-DRTT in the ET group, which were negatively correlated with tremor severity. No significant difference in each component of the DRT pathway was observed between the PD subgroup or the PD and NC.

CONCLUSION

Aberrant changes in the DRT pathway may be specific to action tremor and were indicating that action tremor may be related to pathological overactivation of the DRT pathway.

Dysfunctional Coupling of Cerebral Blood Flow and Susceptibility Value in the Bilateral Hippocampus is Associated with Cognitive Decline in Nondialysis Patients with CKD

SIGNIFICANCE STATEMENT

Patients with end stage CKD often develop cognitive decline, but whether this is related to the underlying disease or to hemodialysis remains unclear. We performed three-dimensional pseudocontinuous arterial spin labeling and quantitative susceptibility mapping prospectively in 40 patients with stage 1-4 CKD, 47 nondialysis patients with stage 5 CKD, and 44 healthy controls. Our magnetic resonance imaging data demonstrate that changes in cerebral blood flow-susceptibility coupling might underlie this cognitive decline, perhaps in the hippocampus and thalamus. These results suggest that magnetic resonance imaging parameters are potential biomarkers of cognitive decline in patients with CKD. Moreover, our findings may lead to discovery of novel therapeutic targets to prevent cognitive decline in patients with CKD.

BACKGROUND

Cerebral blood flow (CBF) and susceptibility values reflect vascular and iron metabolism, providing mechanistic insights into conditions of health and disease. Nondialysis patients with CKD show a cognitive decline, but the pathophysiological mechanisms underlying this remain unclear.

METHODS

Three-dimensional pseudocontinuous arterial spin labeling and quantitative susceptibility mapping were prospectively performed in 40 patients with stage 1-4 CKD (CKD 1-4), 47 nondialysis patients with stage 5 CKD (CKD 5ND), and 44 healthy controls (HCs). Voxel-based global and regional analyses of CBF, susceptibility values, and vascular-susceptibility coupling were performed. Furthermore, the association between clinical performance and cerebral perfusion and iron deposition was analyzed.

RESULTS

For CBF, patients with CKD 5ND had higher normalized CBF in the hippocampus and thalamus than HCs. Patients with CKD 5ND had higher normalized CBF in the hippocampus and thalamus than those with CKD 1-4. The susceptibility values in the hippocampus and thalamus were lower in patients with CKD 5ND than in HCs. Patients with CKD 5ND had higher susceptibility value in the caudate nucleus than those with CKD 1-4. More importantly, patients with CKD 5ND had lower CBF-susceptibility coupling than HCs. In addition, CBF and susceptibility values were significantly associated with clinical performance.

CONCLUSIONS

Our findings demonstrate a new neuropathological mechanism in patients with CKD, which leads to regional changes in CBF-susceptibility coupling. These changes are related to cognitive decline, providing potential imaging markers for assessing clinical disability and cognitive decline in these patients.

msQSM: Morphology-based Self-supervised Deep Learning for Quantitative Susceptibility Mapping

Quantitative susceptibility mapping (QSM) has been applied to the measurement of iron deposition and the auxiliary diagnosis of neurodegenerative disease. There still exists a dipole inversion problem in QSM reconstruction. Recently, deep learning approaches have been proposed to resolve this problem. However, most of these approaches are supervised methods that need pairs of the input phase and ground-truth. It remains a challenge to train a model for all resolutions without using the ground-truth and only using one resolution data. To address this, we proposed a self-supervised QSM deep learning method based on morphology. It consists of a morphological QSM builder to decouple the dependency of the QSM on acquisition resolution, and a morphological loss to reduce artifacts effectively and save training time efficiently. The proposed method can reconstruct arbitrary resolution QSM on both human data and animal data, regardless of whether the resolution is higher or lower than that of the training set. Our method outperforms the previous best unsupervised method with a 3.6% higher peak signal-to-noise ratio, 16.2% lower normalized root mean square error, and 22.1% lower high-frequency error norm. The morphological loss reduces training time by 22.1% with respect to the cycle gradient loss used in the previous unsupervised methods. Experimental results show that the proposed method accurately measures QSM with arbitrary resolutions, and achieves state-of-the-art results among unsupervised deep learning methods. Research on applications in neurodegenerative diseases found that our method is robust enough to measure significant increase in striatal magnetic susceptibility in patients during Alzheimer's disease progression, as well as significant increase in substantia nigra susceptibility in Parkinson's disease patients, and can be used as an auxiliary differential diagnosis tool for Alzheimer's disease and Parkinson's disease.

Direct localization and delineation of human pedunculopontine nucleus based on a self-supervised magnetic resonance image super-resolution method

The pedunculopontine nucleus (PPN) is a small brainstem structure and has attracted attention as a potentially effective deep brain stimulation (DBS) target for the treatment of Parkinson's disease (PD). However, the in vivo location of PPN remains poorly described and barely visible on conventional structural magnetic resonance (MR) images due to a lack of high spatial resolution and tissue contrast. This study aims to delineate the PPN on a high-resolution (HR) atlas and investigate the visibility of the PPN in individual quantitative susceptibility mapping (QSM) images. We combine a recently constructed Montreal Neurological Institute (MNI) space unbiased QSM atlas (MuSus-100), with an implicit representation-based self-supervised image super-resolution (SR) technique to achieve an atlas with improved spatial resolution. Then guided by a myelin staining histology human brain atlas, we localize and delineate PPN on the atlas with improved resolution. Furthermore, we examine the feasibility of directly identifying the approximate PPN location on the 3.0-T individual QSM MR images. The proposed SR network produces atlas images with four times the higher spatial resolution (from 1 to 0.25 mm isotropic) without a training dataset. The SR process also reduces artifacts and keeps superb image contrast for further delineating small deep brain nuclei, such as PPN. Using the myelin staining histological atlas as guidance, we first identify and annotate the location of PPN on the T1-weighted (T1w)-QSM hybrid MR atlas with improved resolution in the MNI space. Then, we relocate and validate that the optimal targeting site for PPN-DBS is at the middle-to-caudal part of PPN on our atlas. Furthermore, we confirm that the PPN region can be identified in a set of individual QSM images of 10 patients with PD and 10 healthy young adults. The contrast ratios of the PPN to its adjacent structure, namely the medial lemniscus, on images of different modalities indicate that QSM substantially improves the visibility of the PPN both in the atlas and individual images. Our findings indicate that the proposed SR network is an efficient tool for small-size brain nucleus identification. HR QSM is promising for improving the visibility of the PPN. The PPN can be directly identified on the individual QSM images acquired at the 3.0-T MR scanners, facilitating a direct targeting of PPN for DBS surgery.