Multimodal Magnetic Resonance Research Articles

Reproducible routes: reliably navigating the connectome to enrich personalized brain stimulation strategies

Non-invasive brain stimulation (NIBS) technologies, such as repetitive transcranial magnetic stimulation (rTMS), offer significant therapeutic potential for a growing number of neuropsychiatric conditions. Concurrent with the expansion of this field is the swift evolution of rTMS methodologies, including approaches to optimize stimulation site planning. Traditional targeting methods, foundational to early successes in the field and still widely employed today, include using scalp-based heuristics or integrating structural MRI co-registration to align the transcranial magnetic stimulation (TMS) coil with anatomical landmarks. Recent evidence, however, supports refining and personalizing stimulation sites based on the target's structural and/or functional connectivity profile. These connectomic approaches harness the network-wide neuromodulatory effects of rTMS to reach deeper brain structures while also enabling a greater degree of personalization by accounting for heterogenous network topology. In this study, we acquired baseline multimodal magnetic resonance (MRI) at two time points to evaluate the reliability and reproducibility of distinct connectome-based strategies for stimulation site planning. Specifically, we compared the intra-individual difference between the optimal stimulation sites generated at each time point for (1) functional connectivity (FC) guided targets derived from resting-state functional MRI and (2) structural connectivity (SC) guided targets derived from diffusion tensor imaging. Our findings suggest superior reproducibility of SC-guided targets. We emphasize the necessity for further research to validate these findings across diverse patient populations, thereby advancing the personalization of rTMS treatments.

Structural-informed functional MRI analysis of patients with empathy impairment following stroke.

The underlying functional alterations of brain structural changes among patients with empathy impairment following stroke remain unclear. We sought to investigate functional connectivity changes informed by brain structural abnormalities in multimodal magnetic resonance imaging (MRI) among patients with empathy impairment following stroke. We enrolled people who had experienced their first ischemic stroke, along with healthy controls. We assessed empathy 3 months after stroke using the Chinese version of the Empathy Quotient (EQ). During the acute phase, all patients underwent basic magnetic resonance imaging (MRI), followed by multimodal MRI during follow-up. Our MRI analyses encompassed acute infarction segmentation, volumetric brain measurements, regional quantification of diffusion parameters, and both region-of-interest-based and seed-based functional connectivity assessments. We grouped patients based on the severity of their empathy impairment for comparative analysis. We included 84 patients who had stroke and 22 healthy controls. Patients had lower EQ scores than controls. Patients with low empathy had larger left cortical infarcts (odds ratio [OR] 4.082, 95% confidence interval [CI] 1.183-14.088), more pronounced atrophy in the right cingulate cortex (OR 1.248, 95% CI 1.038-1.502), and lower scores on the Montreal Cognitive Assessment (OR 0.873, 95% CI 0.74-0.947). In addition, the cingulate cortex served as the seed in the seed-based analysis, which showed heightened functional connectivity between the anterior cingulate gyrus and the right superior parietal lobule, specifically in the low-empathy group. We did not evaluate the relationship between specific network involvement and empathy impairment among patients following stroke. Among patients with subacute ischemic stroke, reduced empathy was strongly associated with a more severe cognitive profile and atrophy of the right cingulate cortex. Our subsequent structural-informed functional MRI analysis suggests that the enhanced connectivity between the anterior cingulate gyrus and the superior parietal lobule may function as a compensatory mechanism for this atrophy.

A Multi-Hierarchical Complementary Feature Interaction Network for Accelerated Multi-Modal MR Imaging

Magnetic resonance (MR) imaging is widely used in the clinical field due to its non-invasiveness, but the long scanning time is still a bottleneck for its popularization. Using the complementary information between multi-modal imaging to accelerate imaging provides a novel and effective MR fast imaging solution. However, previous technologies mostly use simple fusion methods and fail to fully utilize their potential sharable knowledge. In this study, we introduced a novel multi-hierarchical complementary feature interaction network (MHCFIN) to realize joint reconstruction of multi-modal MR images with undersampled data and thus accelerate multi-modal imaging. Firstly, multiple attention mechanisms are integrated with a dual-branch encoder–decoder network to represent shared features and complementary features of different modalities. In the decoding stage, the multi-modal feature interaction module (MMFIM) acts as a bridge between the two branches, realizing complementary knowledge transfer between different modalities through cross-level fusion. The single-modal feature fusion module (SMFFM) carries out multi-scale feature representation and optimization of the single modality, preserving better anatomical details. Extensive experiments are conducted under different sampling patterns and acceleration factors. The results show that this proposed method achieves obvious improvement compared with existing state-of-the-art reconstruction methods in both visual quality and quantity.

Prediction of epileptogenicity in patients with tuberous sclerosis complex using multimodal cerebral MRI

ObjectiveEpilepsy is the most common complication and cause of morbidity and mortality in tuberous sclerosis complex (TSC). Surgery is associated with an increased probability of achieving seizure-freedom. However, the preoperative noninvasive localisation of epileptogenic tubers remains challenging. This study aimed to identify multimodal magnetic resonance imaging (MRI) biomarkers of epilepsy in patients with TSC and develop a prediction model of epileptogenicity in these patients. MethodsPatients with TSC, with or without epilepsy, were recruited. All patients underwent MRI scanning, including T1WI, T2WI, T2W-FLAIR, DTI, and multi-parametric MR with a flexible design (MULTIPLEX). We compared the multimodal cerebral MRI characteristics of the cortical tubers, subependymal nodules, and perilesional tissue between patients with TSC with or without epilepsy and developed a prediction model for epileptogenicity. ResultsAmong the patients with TSC, 32 with and 16 without epilepsy underwent MRI. Higher proton-density mapping (PD) of cortical tubers and decreased fractional anisotropy (FA), increased mean diffusivity (MD), and increased radial diffusivity (RD) of subependymal nodules were associated with epileptogenicity in both the centre and perilesional tissue, independent of TSC gene variation. Based on the above findings, we developed a prediction model for epileptogenicity with an area under the curve of 0.973, specificity of 0.909, and sensitivity of 0.963 (P < 0.001). ConclusionIn patients with TSC, high PD of the cortical tubers, decreased FA, and elevated MD/RD of the subependymal nodules were significantly associated with epileptogenicity. A prediction model based on multimodal cerebral MRI characteristics has the potential to evaluate the likelihood of epilepsy in patients with TSC.

Identification of multimodal brain imaging biomarkers in first-episode drugs-naive major depressive disorder through a multi-site large-scale MRI consortium data

BackgroundMajor depressive disorder (MDD) is a severe and common mental illness. The first-episode drugs-naive MDD (FEDN-MDD) patients, who have not undergone medication intervention, contribute to understanding the biological basis of MDD. Multimodal Magnetic Resonance Imaging can provide a comprehensive understanding of brain functional and structural abnormalities in MDD. However, most MDD studies use single-modal, small-scale MRI data. And several multimodal studies of MDD are limited to simple linear combinations of functional and structural features. MethodsWe screened a large sample of FEDN-MDD patients and healthy controlsmultimodal MRI data. Extracting the fractional amplitude of low-frequency fluctuations (fALFF) feature from functional magnetic resonance imaging and the gray matter volume (GMV) feature from structural magnetic resonance imaging. The mCCA-jICA method was used to integrate these two modal features to investigate the functional-structural co-variation abnormalities in MDD. To validate the stability of the extracted functional-structural covariant abnormalities features, we apply them to identify FEDN-MDD patients. ResultsThe results show that compared to healthy controls, FEDN-MDD patients exhibit joint group-discriminative independent component and modality-specific group-discriminative independent component, suggesting functional-structural covariant abnormalities in MDD patients. Using lightGBM classifier, we achieve a classification accuracy of 99.84 %. LimitationWe use GMV and fALFF for multimodal fusion shows promise, but requires further validation with other datasets and exploration of additional multimodal features. ConclusionsThis may indicate that multimodal fusion features can effectively explore information between different modalities and can accurately identify FEDN-MDD patients, suggesting their potential as multimodal brain imaging biomarkers for MDD.

304P Multi-modal magnetic resonance imaging protocols in the multi-site brain involvement in dystrophinopathies (BIND) study

304P Multi-modal magnetic resonance imaging protocols in the multi-site brain involvement in dystrophinopathies (BIND) study

Multimodal magnetic resonance imaging (MRI) of juvenile and adult diffuse hemispheric gliomas with H3 G34-mutation: a case description.

Multimodal magnetic resonance imaging (MRI) of juvenile and adult diffuse hemispheric gliomas with H3 G34-mutation: a case description.

Comparison of different group-level templates in gradient-based multimodal connectivity analysis

Abstract The study of large-scale brain connectivity is increasingly adopting unsupervised approaches that derive low-dimensional spatial representations from high-dimensional connectomes, referred to as gradient analysis. When translating this approach to study interindividual variations in connectivity, one technical issue pertains to the selection of an appropriate group-level template to which individual gradients are aligned. Here, we compared different group-level template construction strategies using functional and structural connectome data from neurotypical controls and individuals with autism spectrum disorder (ASD) to identify between-group differences. We studied multimodal magnetic resonance imaging data obtained from the Autism Brain Imaging Data Exchange (ABIDE) Initiative II and the Human Connectome Project (HCP). We designed six template construction strategies that varied in whether (1) they included typical controls in addition to ASD; or (2) they mapped from one dataset onto another. We found that aligning a combined subject template of the ASD and control subjects from the ABIDE Initiative onto the HCP template exhibited the most pronounced effect size. This strategy showed robust identification of ASD-related brain regions for both functional and structural gradients across different study settings. Replicating the findings on focal epilepsy demonstrated the generalizability of our approach. Our findings will contribute to improving gradient-based connectivity research.

Frontoparietal network topology as a neural marker of musical perceptual abilities

Why are some individuals more musical than others? Neither cognitive testing nor classical localizationist neuroscience alone can provide a complete answer. Here, we test how the interplay of brain network organization and cognitive function delivers graded perceptual abilities in a distinctively human capacity. We analyze multimodal magnetic resonance imaging, cognitive, and behavioral data from 200+ participants, focusing on a canonical working memory network encompassing prefrontal and posterior parietal regions. Using graph theory, we examine structural and functional frontoparietal network organization in relation to assessments of musical aptitude and experience. Results reveal a positive correlation between perceptual abilities and the integration efficiency of key frontoparietal regions. The linkage between functional networks and musical abilities is mediated by working memory processes, whereas structural networks influence these abilities through sensory integration. Our work lays the foundation for future investigations into the neurobiological roots of individual differences in musicality.

Brain tumor segmentation by combining MultiEncoder UNet with wavelet fusion.

Accurate segmentation of brain tumors from multimodal magnetic resonance imaging (MRI) holds significant importance in clinical diagnosis and surgical intervention, while current deep learning methods cope with situations of multimodal MRI by an early fusion strategy that implicitly assumes that the modal relationships are linear, which tends to ignore the complementary information between modalities, negatively impacting the model's performance. Meanwhile, long-range relationships between voxels cannot be captured due to the localized character of the convolution procedure. Aiming at this problem, we propose a multimodal segmentation network based on a late fusion strategy that employs multiple encoders and a decoder for the segmentation of brain tumors. Each encoder is specialized for processing distinct modalities. Notably, our framework includes a feature fusion module based on a 3D discrete wavelet transform aimed at extracting complementary features among the encoders. Additionally, a 3D global context-aware module was introduced to capture the long-range dependencies of tumor voxels at a high level of features. The decoder combines fused and global features to enhance the network's segmentation performance. Our proposed model is experimented on the publicly available BraTS2018 and BraTS2021 datasets. The experimental results show competitiveness with state-of-the-art methods. The results demonstrate that our approach applies a novel concept for multimodal fusion within deep neural networks and delivers more accurate and promising brain tumor segmentation, with the potential to assist physicians in diagnosis.

Comprehensive MRI assessment reveals subtle brain findings in non-hospitalized post-COVID patients with cognitive impairment.

Impaired cognitive ability is one of the most frequently reported neuropsychiatric symptoms in the post-COVID phase among patients. It is unclear whether this condition is related to structural or functional brain changes. In this study, we present a multimodal magnetic resonance imaging study of 36 post-COVID patients and 36 individually matched controls who had a mild form of severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) infection from March 2020 to February 2022. This study aimed to investigate structural and functional brain alterations and their correlation with post-COVID symptoms and neurocognitive functions. The study protocol comprised an assessment of physical fatigue [Fatigue Severity Scale (FSS)], mental fatigue (Mental Fatigue Scale (MFS)], depression [Montgomery Asberg Depression Rating Scale (MADRS)], anxiety [Hospital Anxiety and Depression Scale (HAD)], post-COVID Symptoms Severity Score, and neurocognitive status [Repeatable Battery for the Assessment of Neuropsychological Status Update (RBANS)]. The magnetic resonance imaging protocol included morphological sequences, arterial spin labeling (ASL) and dynamic susceptibility contrast-enhanced (DSC) perfusion, diffusion tensor imaging (DTI), and resting-state functional magnetic resonance imaging (fMRI) sequences. Using these protocols, the assessments of macrostructural abnormalities, perfusion, gray matter density, white matter integrity, and brain connectivity were performed. Post-COVID patients had higher levels of physical fatigue, mental fatigue, depression, and anxiety than controls and showed cognitive impairment in all the RBANS domains except in Visuospatial/Construction. The subjective mental fatigue correlated with objective impaired cognitive ability in the RBANS test, particularly in the Attention domain. There were no differences between patients and controls regarding macrostructural abnormalities, regional volumes, regional perfusion metrics, gray matter density, or DTI parameters. We observed a significant positive correlation between RBANS Total Scale Index score and gray matter volume in the right superior/middle-temporal gyrus (p < 0.05) and a significant negative correlation between the white matter integrity and post-COVID symptoms (p < 0.05) in the same area. The connectivity differences were observed between patients and controls in a few regions, including the right middle frontal gyrus, an important area of convergence of the dorsal and ventral attention networks. We also noted a positive correlation between post-COVID symptoms and increased connectivity in the right temporoparietal junction, which is part of the ventral attention system. In non-hospitalized subjects with post-COVID, we did not find any structural brain changes or changes in perfusion, compared to controls. However, we noted differences in connectivity within an important area for attention processes, which may be associated with post-COVID brain fog.

Multimodal magnetic resonance longitudinal study on the deep gray matter in multiple sclerosis patients with teriflunomide

Disease-modifying therapies (DMTs) are used in an increasing number of patients with multiple sclerosis (MS). However, whether DMTs have intrinsic effects on deep gray matter (DGM) microstructure and atrophy is still poorly understood. In this study, we described the quantitative susceptibility values (QSV) and diffusion kurtosis imaging (DKI) metrics of DGM in relapsing–remitting MS (RRMS) patients and their association with cognitive deficits. We recruited 62 patients with RRMS receiving DMTs and 30 patients with RRMS not receiving DMTs underwent MRI on a 3T scanner. Fractional anisotropy (FA), kurtosis fractional anisotropy (KFA), mean diffusivity (MD), mean kurtosis (MK), QSV and volumes of bilateral caudate nucleus (CAU), amygdala (AMYG), putamen (PUT), hippocampus (Hipp), globus pallidus (GP) and thalamus (THA) were measured. Correlation analysis was performed between those image indexes with longitudinal significant changes and clinical neurological scores, including Expanded Disability Status Scale (EDSS), Digit Span Testand (DST), Symbol Digit Modalities Test (SDMT), Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). Significant longitudinal increases in FA, KFA and MK values were found in both groups in bilateral CAU, AMYG, PUT, Hipp, GP and THA (all p < 0.005). MD values of the right of CAU in the two groups were significant longitudinal increase (p = 0.009, p = 0.047); MD values of the right of GP (p = 0.042), the left of THA (p = 0.003), the right of THA (p = 0.001) in treated MS were significant longitudinal decrease; There were no significant longitudinal changes between treated and untreated groups in normalized deep gray matter volume. For QSV, longitudinal increase in the right of PUT (p = 0.022) in the treated MS group and in the left of Hipp (p = 0.045) in the untreated MS group. The QSV and DKI measures were highly correlated with cognitive and disability tests. The treated RRMS patients showed different longitudinal changes of MD value and QSV with untreated in several DGM regions, and these differences were correlated with cognitive and microstructural integrity.

Prediction of early recurrence of adult-type diffuse gliomas following radiotherapy using multi-modal magnetic resonance images.

Adult-type diffuse gliomas are among the central nervous system's most aggressive malignant primary neoplasms. Despite advancements in systemic therapies and technological improvements in radiation oncology treatment delivery, the survival outcome for these patients remains poor. Fast and accurate assessment of tumor response to oncologic treatments is crucial, as it can enable the early detection of recurrent or refractory gliomas, thereby allowing timely intervention with life-prolonging salvage therapies. Radiomics is a developing field with great potential to improve medical image interpretation. This study aims to apply a radiomics-based predictive model for classifying response to radiotherapy within the first 3 months post-treatment. Ninety-five patients were selected from the Burdenko Glioblastoma Progression Dataset. Tumor regions were delineated in the axial plane on contrast-enhanced T1(CE T1W) and T2 fluid-attenuated inversion recovery (T2_FLAIR) magnetic resonance imaging (MRI). Hand-crafted radiomic (HCR) features, including first- and second-order features, were extracted using PyRadiomics (3.7.6) in Python (3.10). Then, recursive feature elimination with a random forest (RF) classifier was applied for feature dimensionality reduction. RF and support vector machine (SVM) classifiers were built to predict treatment outcomes using the selected features. Leave-one-out cross-validation was employed to tune hyperparameters and evaluate the models. For each segmented target, 186 HCR features were extracted from the MRI sequence. Using the top-ranked radiomic features from a combination of CE T1W and T2_FLAIR, an optimized classifier achieved the highest averaged area under the curve (AUC) of 0.829±0.075 using the RF classifier. The HCR features of CE T1W produced the worst outcomes among all models (0.603±0.024 and 0.615±0.075 for RF and SVM classifiers, respectively). We developed and evaluated a radiomics-based predictive model for early tumor response to radiotherapy, demonstrating excellent performance supported by high AUC values. This model, harnessing radiomic features from multi-modal MRI, showed superior predictive performance compared to single-modal MRI approaches. These results underscore the potential of radiomics in clinical decision support for this disease process.

Assessment of multi-modal magnetic resonance imaging for glioma based on a deep learning reconstruction approach with the denoising method.

Deep learning reconstruction (DLR) with denoising has been reported as potentially improving the image quality of magnetic resonance imaging (MRI). Multi-modal MRI is a critical non-invasive method for tumor detection, surgery planning, and prognosis assessment; however, the DLR on multi-modal glioma imaging has not been assessed. To assess multi-modal MRI for glioma based on the DLR method. We assessed multi-modal images of 107 glioma patients (49 preoperative and 58 postoperative). All the images were reconstructed with both DLR and conventional reconstruction methods, encompassing T1-weighted (T1W), contrast-enhanced T1W (CE-T1), T2-weighted (T2W), and T2 fluid-attenuated inversion recovery (T2-FLAIR). The image quality was evaluated using signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and edge sharpness. Visual assessment and diagnostic assessment were performed blindly by neuroradiologists. In contrast with conventionally reconstructed images, (residual) tumor SNR for all modalities and tumor to white/gray matter CNR from DLR images were higher in T1W, T2W, and T2-FLAIR sequences. The visual assessment of DLR images demonstrated the superior visualization of tumor in T2W, edema in T2-FLAIR, enhanced tumor and necrosis part in CE-T1, and fewer artifacts in all modalities. Improved diagnostic efficiency and confidence were observed for preoperative cases with DLR images. DLR of multi-modal MRI reconstruction prototype for glioma has demonstrated significant improvements in image quality. Moreover, it increased diagnostic efficiency and confidence of glioma.

Cross-Modal Consistency for Single-Modal MR Image Segmentation.

Multi-modal magnetic resonance (MR) image segmentation is an important task in disease diagnosis and treatment, but it is usually difficult to obtain multiple modalities for a single patient in clinical applications. To address these issues, a cross-modal consistency framework is proposed for a single-modal MR image segmentation. To enable single-modal MR image segmentation in the inference stage, a weighted cross-entropy loss and a pixel-level feature consistency loss are proposed to train the target network with the guidance of the teacher network and the auxiliary network. To fuse dual-modal MR images in the training stage, the cross-modal consistency is measured according to Dice similarity entropy loss and Dice similarity contrastive loss, so as to maximize the prediction similarity of the teacher network and the auxiliary network. To reduce the difference in image contrast between different MR images for the same organs, a contrast alignment network is proposed to align input images with different contrasts to reference images with a good contrast. Comprehensive experiments have been performed on a publicly available prostate dataset and an in-house pancreas dataset to verify the effectiveness of the proposed method. Compared to state-of-the-art methods, the proposed method can achieve better segmentation. The proposed image segmentation method can fuse dual-modal MR images in the training stage and only need one-modal MR images in the inference stage. The proposed method can be used in routine clinical occasions when only single-modal MR image with variable contrast is available for a patient.

Multimodal Magnetic Resonance Imaging Assessments of Kidney Disease Severity in Autosomal Recessive Polycystic Kidney Disease

Multimodal Magnetic Resonance Imaging Assessments of Kidney Disease Severity in Autosomal Recessive Polycystic Kidney Disease

Deep unfolding network with spatial alignment for multi-modal MRI reconstruction

Multi-modal Magnetic Resonance Imaging (MRI) offers complementary diagnostic information, but some modalities are limited by the long scanning time. To accelerate the whole acquisition process, MRI reconstruction of one modality from highly under-sampled k-space data with another fully-sampled reference modality is an efficient solution. However, the misalignment between modalities, which is common in clinic practice, can negatively affect reconstruction quality. Existing deep learning-based methods that account for inter-modality misalignment perform better, but still share two main common limitations: (1) The spatial alignment task is not adaptively integrated with the reconstruction process, resulting in insufficient complementarity between the two tasks; (2) the entire framework has weak interpretability. In this paper, we construct a novel Deep Unfolding Network with Spatial Alignment, termed DUN-SA, to appropriately embed the spatial alignment task into the reconstruction process. Concretely, we derive a novel joint alignment-reconstruction model with a specially designed aligned cross-modal prior term. By relaxing the model into cross-modal spatial alignment and multi-modal reconstruction tasks, we propose an effective algorithm to solve this model alternatively. Then, we unfold the iterative stages of the proposed algorithm and design corresponding network modules to build DUN-SA with interpretability. Through end-to-end training, we effectively compensate for spatial misalignment using only reconstruction loss, and utilize the progressively aligned reference modality to provide inter-modality prior to improve the reconstruction of the target modality. Comprehensive experiments on four real datasets demonstrate that our method exhibits superior reconstruction performance compared to state-of-the-art methods.

The moderating role of information processing speed in the relationship between brain remodeling and episodic memory in amnestic mild cognitive impairment.

The role of information processing speed (IPS) on relationships between episodic memory (EM) and central remodeling features in amnestic mild cognitive impairment (aMCI) was investigated. Neuropsychological evaluations and multimodal magnetic resonance imaging were performed on 48 patients diagnosed with aMCI and 50 healthy controls (HC). Moderation models explored the moderating effect of IPS on associations between EM and imaging features at single-region, connectivity, and network levels. IPS significantly enhanced the positive correlations between recall and cortical thickness of left inferior temporal gyrus. IPS also notably amplified negative correlations between recognition and functional connectivity (FC) of left inferior parietal lobe and right occipital, as well as between recall/recognition and nodal clustering coefficient of left anterior cingulate cortex. IPS functioned as a moderator of associations between recall and neuroimaging metrics at the "single region-connectivity-network" level, providing new insights for cognitive rehabilitation in aMCI patients. aMCI patients exhibited brain functional and structural remodeling alterations. IPS moderated relations between episodic memory and brain remodeling metrics. Therapy targeted at IPS can be considered for improving episodic memory in aMCI.

A Multimodal Magnetic Resonance Imaging Study on Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Feasibility and Clinical Correlation.

Background/Objectives: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a neurological disorder characterized by post-exertional malaise. Despite its clinical relevance, the disease mechanisms of ME/CFS are not fully understood. The previous studies targeting brain function or metabolites have been inconclusive in understanding ME/CFS complexity. We combined single-voxel magnetic resonance spectroscopy (SV-MRS) and functional magnetic resonance imaging (fMRI). Our objectives were to examine the feasibility of the multimodal MRI protocol, identify possible differences between ME/CFS and healthy controls (HCs), and relate MRI findings with clinical symptoms. Methods: We enrolled 18 female ME/CFS participants (mean age: 39.7 ± 12.0 years) and five HCs (mean age: 45.6 ± 14.5 years). SV-MRS spectra were acquired from three voxels of interest: the anterior cingulate gyrus (ACC), brainstem (BS), and left dorsolateral prefrontal cortex (L-DLPFC). Whole-brain fMRI used n-back task testing working memory and executive function. The feasibility was assessed as protocol completion rate and time. Group differences in brain metabolites and fMRI activation between ME/CFS and HCs were compared and correlated with behavioral and symptom severity measurements. Results: The completion rate was 100% regardless of participant group without causing immediate fatigue. ME/CFS appeared to show a higher N-Acetylaspartate in L-DLPFC compared to HCs (OR = 8.49, p = 0.040), correlating with poorer fatigue, pain, and sleep quality scores (p's = 0.001-0.015). An increase in brain activation involving the frontal lobe and the brainstem was observed in ME/CFS compared to HCs (Z > 3.4, p's < 0.010). Conclusions: The study demonstrates the feasibility of combining MRS and fMRI to capture neurochemical and neurophysiological features of ME/CFS in female participants. Further research with larger cohorts of more representative sampling and follow-ups is needed to validate these apparent differences between ME/CFS and HCs.

Development and Maturation of the Human Brain, from Infancy to Adolescence.

This chapter describes basic principles and key findings regarding the development and maturation of the human brain, the former referring to the pre-natal and early post-natal periods and the latter concerning childhood and adolescence. In both cases, we focus on brain structure as revealed in vivo with multi-modal magnetic resonance imaging (MRI). We begin with a few numbers about the human brain and its cellular composition and a brief overview of a number of MRI-based metrics used to characterize age-related variations in grey and white matter. We then proceed with synthesizing current knowledge about developmental and maturational changes in the cerebral cortex (its thickness, surface area, and intra-cortical myelination) and the underlying white matter (volume and structural properties). To facilitate biological interpretations of MRI-derived metrics, we introduce the concept of virtual histology. We conclude the chapter with a few notes about future directions in the study of factors shaping the human brain from conception onwards.