Lithium alters brain activation in bipolar disorder in a task- and state-dependent manner: an fMRI study

BACKGROUND: It is unknown if medications used to treat bipolar disorder have effects on brain activation, and whether or not any such changes are mood-independent. METHODS: Patients with bipolar disorder who were depressed (n = 5) or euthymic (n = 5) were examined using fMRI before, and 14 days after, being started on lithium (as monotherapy in 6 of these patients). Patients were examined using a word generation task and verbal memory task, both of which have been shown to be sensitive to change in previous fMRI studies. Differences in blood oxygenated level dependent (BOLD) magnitude between the pre- and post-lithium results were determined in previously defined regions of interest. Severity of mood was determined by the Hamilton Depression Scale for Depression (HAM-D) and the Young mania rating scale (YMRS). RESULTS: The mean HAM-D score at baseline in the depressed group was 15.4 ± 0.7, and after 2 weeks of lithium it was 11.0 ± 2.6. In the euthymic group it was 7.6 ± 1.4 and 3.2 ± 1.3 respectively. At baseline mean BOLD signal magnitude in the regions of interest for the euthymic and depressed patients were similar in both the word generation task (1.56 ± 0.10 and 1.49 ± 0.10 respectively) and working memory task (1.02 ± 0.04 and 1.12 ± 0.06 respectively). However, after lithium the mean BOLD signal decreased significantly in the euthymic group in the word generation task only (1.56 ± 0.10 to 1.00 ± 0.07, p < 0.001). Post-hoc analysis showed that these differences were statistically significant in Broca's area, the left pre-central gyrus, and the supplemental motor area. CONCLUSION: This is the first study to examine the effects of lithium on brain activation in bipolar patients. The results suggest that lithium has an effect on euthymic patients very similar to that seen in healthy volunteers. The same effects are not seen in depressed bipolar patients, although it is uncertain if this lack of change is linked to the lack of major improvements in mood in this group of patients. In conclusion, this study suggests that lithium may have effects on brain activation that are task- and state-dependent. Given the small study size and the mildness of the patient's depression these results require replication

xQSM: Quantitative Susceptibility Mapping with Octave Convolutional and Noise Regularized Neural Networks

Quantitative susceptibility mapping (QSM) is a valuable magnetic resonance imaging (MRI) contrast mechanism that has demonstrated broad clinical applications. However, the image reconstruction of QSM is challenging due to its ill-posed dipole inversion process. In this study, a new deep learning method for QSM reconstruction, namely xQSM, was designed by introducing modified state-of-the-art octave convolutional layers into the U-net backbone. The xQSM method was compared with recentlyproposed U-net-based and conventional regularizationbased methods, using peak signal to noise ratio (PSNR), structural similarity (SSIM), and region-of-interest measurements. The results from a numerical phantom, a simulated human brain, four in vivo healthy human subjects, a multiple sclerosis patient, a glioblastoma patient, as well as a healthy mouse brain showed that the xQSM led to suppressed artifacts than the conventional methods, and enhanced susceptibility contrast, particularly in the ironrich deep grey matter region, than the original U-net, consistently. The xQSM method also substantially shortened the reconstruction time from minutes using conventional iterative methods to only a few seconds.Comment: 37 pages, 10 figures, 3 tabl

Plug-and-Play Latent Feature Editing for Orientation-Adaptive Quantitative Susceptibility Mapping Neural Networks

Quantitative susceptibility mapping (QSM) is a post-processing technique for deriving tissue magnetic susceptibility distribution from MRI phase measurements. Deep learning (DL) algorithms hold great potential for solving the ill-posed QSM reconstruction problem. However, a significant challenge facing current DL-QSM approaches is their limited adaptability to magnetic dipole field orientation variations during training and testing. In this work, we propose a novel Orientation-Adaptive Latent Feature Editing (OA-LFE) module to learn the encoding of acquisition orientation vectors and seamlessly integrate them into the latent features of deep networks. Importantly, it can be directly Plug-and-Play (PnP) into various existing DL-QSM architectures, enabling reconstructions of QSM from arbitrary magnetic dipole orientations. Its effectiveness is demonstrated by combining the OA-LFE module into our previously proposed phase-to-susceptibility single-step instant QSM (iQSM) network, which was initially tailored for pure-axial acquisitions. The proposed OA-LFE-empowered iQSM, which we refer to as iQSM+, is trained in a self-supervised manner on a specially-designed simulation brain dataset. Comprehensive experiments are conducted on simulated and in vivo human brain datasets, encompassing subjects ranging from healthy individuals to those with pathological conditions. These experiments involve various MRI platforms (3T and 7T) and aim to compare our proposed iQSM+ against several established QSM reconstruction frameworks, including the original iQSM. The iQSM+ yields QSM images with significantly improved accuracies and mitigates artifacts, surpassing other state-of-the-art DL-QSM algorithms.Comment: 13pages, 9figure

Quantitative susceptibility mapping using single-shot echo-planar imaging

PurposeTo perform quantitative susceptibility mapping (QSM) in negligible acquisition time and apply it to measuring iron-rich subcortical gray matter.MethodsWhole brain QSM was performed using single-shot gradient echo-planar imaging (EPI) in under 7 seconds on a standard 1.5 T system for imaging brain iron in subcortical gray matter. The method was compared to a standard 6-minute gradient recalled echo (GRE) QSM acquisition in healthy subjects. Region-of-interest QSM measurements were compared between methods in six subcortical gray-matter nuclei and two white matter territories.ResultsEPI-QSM provided similar mean susceptibility values to standard GRE-QSM in iron-rich subcortical gray matter regions, while providing greater than 50-fold scan time reduction. Blurring from the low spatial resolution and transverse relaxation decay of EPI affected edges but had negligible effect on whole subcortical nuclei measurements, which had a high correlation (R-2=0.96) to estimated iron content.ConclusionEPI-QSM can be performed in several seconds, which enables expansion of brain iron studies of subcortical gray matter to cases where time is limited and to existing MRI studies that already uses gradient echo EPI. Magn Reson Med 73:1932-1938, 2015. (c) 2014 Wiley Periodicals, Inc

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

Bloch modelling enables robust T2 mapping using retrospective proton density and T2-weighted images from different vendors and sites

T2 quantification is commonly attempted by applying an exponential fit to proton density (PD) and transverse relaxation (T2)-weighted fast spin echo (FSE) images. However, inter-site studies have noted systematic differences between vendors in T2 maps computed via standard exponential fitting due to imperfect slice refocusing, different refocusing angles and transmit field (B1+) inhomogeneity. We examine T2 mapping at 3T across 13 sites and two vendors in healthy volunteers from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database using both a standard exponential and a Bloch modelling approach. The standard exponential approach resulted in highly variable T2 values across different sites and vendors. The two-echo fitting method based on Bloch equation modelling of the pulse sequence with prior knowledge of the nominal refocusing angles, slice profiles, and estimated B1+ maps yielded similar T2 values across sites and vendors by accounting for the effects of indirect and stimulated echoes. By modelling the actual refocusing angles used, T2 quantification from PD and T2-weighted images can be applied in studies across multiple sites and vendors

DSMRI: Domain Shift Analyzer for Multi-Center MRI Datasets

In medical research and clinical applications, the utilization of MRI datasets from multiple centers has become increasingly prevalent. However, inherent variability between these centers presents challenges due to domain shift, which can impact the quality and reliability of the analysis. Regrettably, the absence of adequate tools for domain shift analysis hinders the development and validation of domain adaptation and harmonization techniques. To address this issue, this paper presents a novel Domain Shift analyzer for MRI (DSMRI) framework designed explicitly for domain shift analysis in multi-center MRI datasets. The proposed model assesses the degree of domain shift within an MRI dataset by leveraging various MRI-quality-related metrics derived from the spatial domain. DSMRI also incorporates features from the frequency domain to capture low- and high-frequency information about the image. It further includes the wavelet domain features by effectively measuring the sparsity and energy present in the wavelet coefficients. Furthermore, DSMRI introduces several texture features, thereby enhancing the robustness of the domain shift analysis process. The proposed framework includes visualization techniques such as t-SNE and UMAP to demonstrate that similar data are grouped closely while dissimilar data are in separate clusters. Additionally, quantitative analysis is used to measure the domain shift distance, domain classification accuracy, and the ranking of significant features. The effectiveness of the proposed approach is demonstrated using experimental evaluations on seven large-scale multi-site neuroimaging datasets

Discriminative analysis of regional evolution of iron and myelin/calcium in deep gray matter of multiple sclerosis and healthy subjects

Background: Combined R2* and quantitative susceptibility (QS) has been previously used in cross-sectional multiple sclerosis (MS) studies to distinguish deep gray matter (DGM) iron accumulation and demyelination.Purpose: We propose and apply discriminative analysis of regional evolution (DARE) to define specific changes in MS and healthy DGM.Study Type: Longitudinal (baseline and 2-year follow-up) retrospective study.Subjects: Twenty-seven relapsing-remitting MS (RRMS), 17 progressive MS (PMS), and corresponding age-matched healthy subjects.Field Strength/Sequence: 4.7T 10-echo gradient-echo acquisition.Assessment: Automatically segmented caudate nucleus (CN), thalamus (TH), putamen (PU), globus pallidus, red nucleus (RN), substantia nigra, and dentate nucleus were retrospectively analyzed to quantify regional volumes, bulk mean R2*, and bulk mean QS. DARE utilized combined R2* and QS localized changes to compute spatial extent, mean intensity, and total changes of DGM iron and myelin/ calcium over 2 years.Statistical Tests: We used mixed factorial analysis for bulk analysis, nonparametric tests for DARE (a50.05), and multiple regression analysi

Progressive iron accumulation across multiple sclerosis phenotypes revealed by sparse classification of deep gray matter

Purpose: To create an automated framework for localized analysis of deep gray matter (DGM) iron accumulation and demyelination using sparse classification by combining quantitative susceptibility (QS) and transverse relaxation rate (R2*) maps, for evaluation of DGM in multiple sclerosis (MS) phenotypes relative to healthy controls. Materials and Methods: R2*/QS maps were computed using a 4.7T 10-echo gradient echo acquisition from 16 clinically isolated syndrome (CIS), 41 relapsing-remitting (RR), 40 secondary-progressive (SP), 13 primary-progressive (PP) MS patients, and 75 controls. Sparse classification for R2*/QS maps of segmented caudate nucleus (CN), putamen (PU), thalamus (TH), and globus pallidus (GP) structures produced localized maps of iron/myelin in MS patients relative to controls. Paired t-tests, with age as a covariate, were used to test for statistical significance (P ≤ 0.05). Results: In addition to DGM structures found significantly different in patients compared to controls using whole region analysis, singular sparse analysis found significant results in RRMS PU R2* (P = 0.03), TH R2* (P = 0.04), CN QS (P = 0.04); in SPMS CN R2* (P = 0.04), GP R2* (P = 0.05); and in PPMS CN R2* (P = 0.04), TH QS (P = 0.04). All sparse regions were found to conform to an iron accumulation pattern of changes in R2*/QS, while none conformed to demyelination. Intersection of sparse R2*/QS regions also resulted in RRMS CN R2* becoming significant, while RRMS R2* TH and PPMS QS TH becoming insignificant. Common iron-associated volumes in MS patients and their effect size progressively increased with advanced phenotypes. Conclusion: A localized technique for identifying sparse regions indicative of iron or myelin in the DGM was developed. Progressive iron accumulation with advanced MS phenotypes was demonstrated, as indicated by iron-associated sparsity and effect size. Level of Evidence: 1. Technical Efficacy: Stage 1. J. Magn. Reson. Imaging 2017;46:1464–1473