A review of structural magnetic resonance neuroimaging

Unraveling the gut-brain connection: The association of microbiota-linked structural brain biomarkers with behavior and mental health

AIM

The gut microbiota can influence human behavior. However, due to the massive multiple-testing problem, research into the relationship between microbiome ecosystems and the human brain faces drawbacks. This problem arises when attempting to correlate thousands of gut bacteria with thousands of brain voxels.

METHODS

We performed brain magnetic resonance imaging (MRI) scans on 133 participants and applied machine-learning algorithms (Ridge regressions) combined with permutation tests. Using this approach, we were able to correlate specific gut bacterial families with brain MRI signals, circumventing the difficulties of massive multiple testing while considering sex, age, and body mass index as confounding factors.

RESULTS

The relative abundance (RA) of the Selenomonadaceae, Clostridiaceae, and Veillonellaceae families in the gut was associated with altered cerebellar, visual, and frontal T2-mapping and diffusion tensor imaging measures. Conversely, decreased relative abundance of the Eubacteriaceae family was also linked to T2-mapping values in the cerebellum. Significantly, the brain regions associated with the gut microbiome were also correlated with depressive symptoms and attentional deficits.

CONCLUSIONS

Our analytical strategy offers a promising approach for identifying potential brain biomarkers influenced by gut microbiota. By gathering a deeper understanding of the microbiota-brain connection, we can gain insights into the underlying mechanisms and potentially develop targeted interventions to mitigate the detrimental effects of dysbiosis on brain function and mental health.

Revealing the MRI-Contrast in Optically Cleared Brains

The current consensus holds that optically-cleared specimens are unsuitable for Magnetic Resonance Imaging (MRI); exhibiting absence of contrast. Prior studies combined MRI with tissue-clearing techniques relying on the latter's ability to eliminate lipids, thereby fostering the assumption that lipids constitute the primary source of ex vivo MRI-contrast. Nevertheless, these findings contradict an extensive body of literature that underscores the contribution of other features to contrast. Furthermore, it remains unknown whether non-delipidating clearing methods can produce MRI-compatible specimens or whether MRI-contrast can be re-established. These limitations hinder the development of multimodal MRI-light-microscopy (LM) imaging approaches. This study assesses the relation between MRI-contrast, and delipidation in optically-cleared whole brains following different tissue-clearing approaches. It is demonstrated that uDISCO and ECi-brains are MRI-compatible upon tissue rehydration, despite both methods' substantial delipidating-nature. It is also demonstrated that, whereas Scale-clearing preserves most lipids, Scale-cleared brain lack MRI-contrast. Furthermore, MRI-contrast is restored to lipid-free CLARITY-brains without introducing lipids. Our results thereby dissociate between the essentiality of lipids to MRI-contrast. A tight association is found between tissue expansion, hyperhydration and loss of MRI-contrast. These findings then enabled us to develop a multimodal MRI-LM-imaging approach, opening new avenues to bridge between the micro- and mesoscale for biomedical research and clinical applications.

Deep learning-based automated scan plane positioning for brain magnetic resonance imaging

Background

Manual planning of scans in clinical magnetic resonance imaging (MRI) exhibits poor accuracy, lacks consistency, and is time-consuming. Meanwhile, classical automated scan plane positioning methods that rely on certain assumptions are not accurate or stable enough, and are computationally inefficient for practical application scenarios. This study aims to develop and evaluate an effective, reliable, and accurate deep learning-based framework that incorporates prior physical knowledge for automatic head scan plane positioning in MRI.

Methods

A deep learning-based end-to-end automated scan plane positioning framework has been developed for MRI head scans. Our model takes a three-dimensional (3D) pre-scan image input, utilizing a cascaded 3D convolutional neural network to detect anatomical landmarks from coarse to fine. And then, with the determined landmarks, accurate scan plane localization can be achieved. A multi-scale spatial information fusion module was employed to aggregate high- and low-resolution features, combined with physically meaningful point regression loss (PRL) function and direction regression loss (DRL) function. Meanwhile, we simulate complex clinical scenarios to design data augmentation strategies.

Results

Our proposed approach shows good performance on a clinically wide range of 229 MRI head scans, with a point-to-point absolute error (PAE) of 0.872 mm, a point-to-point relative error (PRE) of 0.10%, and an average angular error (AAE) of 0.502°, 0.381°, and 0.675° for the sagittal, transverse, and coronal planes, respectively.

Conclusions

The proposed deep learning-based automated scan plane positioning shows high efficiency, accuracy and robustness when evaluated on varied clinical head MRI scans with differences in positioning, contrast, noise levels and pathologies.

Delineating abnormal individual structural covariance brain network organization in pediatric epilepsy with unilateral resection of visual cortex

Although several previous studies have used resting-state functional magnetic resonance imaging and diffusion tensor imaging to report topological changes in the brain in epilepsy, it remains unclear whether the individual structural covariance network (SCN) changes in epilepsy, especially in pediatric epilepsy with visual cortex resection but with normal functions. Herein, individual SCNs were mapped and analyzed for seven pediatric patients with epilepsy after surgery and 15 age-matched healthy controls. A whole-brain individual SCN was constructed based on an automated anatomical labeling template, and global and nodal network metrics were calculated for statistical analyses. Small-world properties were exhibited by pediatric patients after brain surgery and by healthy controls. After brain surgery, pediatric patients with epilepsy exhibited a higher shortest path length, lower global efficiency, and higher nodal efficiency in the cuneus than those in healthy controls. These results revealed that pediatric epilepsy after brain surgery, even with normal functions, showed altered topological organization of the individual SCNs, which revealed residual network topological abnormalities and may provide initial evidence for the underlying functional impairments in the brain of pediatric patients with epilepsy after surgery that can occur in the future.

Brain connectivity changes underlying depression and fatigue in relapsing-remitting multiple sclerosis: A systematic review

Multiple Sclerosis (MS) is an autoimmune disease affecting the central nervous system, characterised by neuroinflammation and neurodegeneration. Fatigue and depression are common, debilitating, and intertwined symptoms in people with relapsing-remitting MS (pwRRMS). An increased understanding of brain changes and mechanisms underlying fatigue and depression in RRMS could lead to more effective interventions and enhancement of quality of life. To elucidate the relationship between depression and fatigue and brain connectivity in pwRRMS we conducted a systematic review. Searched databases were PubMed, Web-of-Science and Scopus. Inclusion criteria were: studied participants with RRMS (n ≥ 20; ≥ 18 years old) and differentiated between MS subtypes; published between 2001-01-01 and 2023-01-18; used fatigue and depression assessments validated for MS; included brain structural, functional magnetic resonance imaging (fMRI) or diffusion MRI (dMRI). Sixty studies met the criteria: 18 dMRI (15 fatigue, 5 depression) and 22 fMRI (20 fatigue, 5 depression) studies. The literature was heterogeneous; half of studies reported no correlation between brain connectivity measures and fatigue or depression. Positive findings showed that abnormal cortico-limbic structural and functional connectivity was associated with depression. Fatigue was linked to connectivity measures in cortico-thalamic-basal-ganglial networks. Additionally, both depression and fatigue were related to altered cingulum structural connectivity, and functional connectivity involving thalamus, cerebellum, frontal lobe, ventral tegmental area, striatum, default mode and attention networks, and supramarginal, precentral, and postcentral gyri. Qualitative analysis suggests structural and functional connectivity changes, possibly due to axonal and/or myelin loss, in the cortico-thalamic-basal-ganglial and cortico-limbic network may underlie fatigue and depression in pwRRMS, respectively, but the overall results were inconclusive, possibly explained by heterogeneity and limited number of studies. This highlights the need for further studies including advanced MRI to detect more subtle brain changes in association with depression and fatigue. Future studies using optimised imaging protocols and validated depression and fatigue measures are required to clarify the substrates underlying these symptoms in pwRRMS.

Structural and Functional Alterations of Hippocampal Subfields in Patients with Adult-onset Primary Hypothyroidism

CONTEXT

Hypothyroidism is often associated with cognitive and emotional dysregulation; however, the underlying neuropathological mechanisms remain elusive.

OBJECTIVE

The study aimed to characterize abnormal alterations in hippocampal subfield volumes and functional connectivity (FC) in patients with subclinical hypothyroidism (SCH) and overt hypothyroidism (OH).

METHODS

This cross-sectional observational study comprised 47 and 40 patients with newly diagnosed adult-onset primary SCH and OH, respectively, and 53 well-matched healthy controls (HCs). The demographics, clinical variables, and neuropsychological scale scores were collected. Next, the hippocampal subfield volumes and seed-based FC were compared between the groups. Finally, correlation analyses were performed.

RESULTS

SCH and OH exhibited significant alterations in cognitive and emotional scale scores. Specifically, the volumes of the right granule cell molecular layer of dentate gyrus (GC-ML-DG) head, CA4 and CA3 head were reduced in SCH and OH groups. Moreover, the volumes of the right molecular layer (ML) head, CA1 body, left GC-ML-DG head, and CA4 head were lower in SCH. In addition, the hippocampal subfield volumes decreased more significantly in SCH than OH. The seed-based FC decreased in SCH but increased in OH compared with HCs. Correlation analyses revealed thyroid hormone (TH) was negatively correlated with FC values in hypothyroidism.

CONCLUSIONS

Patients with SCH and OH might be at risk of cognitive decline, anxiety, or depression, and exhibited alterations in the volume and FC in specific hippocampal subfields. Furthermore, the reduction in volume was more pronounced in SCH. This study provides novel insights into the neuropathological mechanisms of brain impairment in hypothyroidism.

Brain MRI changes in degenerative cervical myelopathy: a systematic review

BACKGROUND

Degenerative cervical myelopathy (DCM) is the most common cause of adult spinal cord dysfunction globally. Associated neurological symptoms and signs have historically been explained by pathobiology within the cervical spine. However, recent advances in imaging have shed light on numerous brain changes in patients with DCM, and it is hypothesised that these changes contribute to DCM pathogenesis. The aetiology, significance, and distribution of these supraspinal changes is currently unknown. The objective was therefore to synthesise all current evidence on brain changes in DCM.

METHODS

A systematic review was performed. Cross-sectional and longitudinal studies with magnetic resonance imaging on a cohort of patients with DCM were eligible. PRISMA guidelines were followed. MEDLINE and Embase were searched to 28th August 2023. Duplicate title/abstract screening, data extraction and risk of bias assessments were conducted. A qualitative synthesis of the literature is presented as per the Synthesis Without Meta-Analysis (SWiM) reporting guideline. The review was registered with PROSPERO (ID: CRD42022298538).

FINDINGS

Of the 2014 studies that were screened, 47 studies were identified that used MRI to investigate brain changes in DCM. In total, 1500 patients with DCM were included in the synthesis, with a mean age of 53 years. Brain alterations on MRI were associated with DCM both before and after surgery, particularly within the sensorimotor network, visual network, default mode network, thalamus and cerebellum. Associations were commonly reported between brain MRI alterations and clinical measures, particularly the Japanese orthopaedic association (JOA) score. Risk of bias of included studies was low to moderate.

INTERPRETATION

The rapidly expanding literature provides mounting evidence for brain changes in DCM. We have identified key structures and pathways that are altered, although there remains uncertainty regarding the directionality and clinical significance of these changes. Future studies with greater sample sizes, more detailed phenotyping and longer follow-up are now needed.

FUNDING

ODM is supported by an Academic Clinical Fellowship at the University of Cambridge. BMD is supported by an NIHR Clinical Doctoral Fellowship at the University of Cambridge (NIHR300696). VFJN is supported by an NIHR Rosetrees Trust Advanced Fellowship (NIHR302544). This project was supported by an award from the Rosetrees Foundation with the Storygate Trust (A2844).

The applied principles of EEG analysis methods in neuroscience and clinical neurology

Electroencephalography (EEG) is a non-invasive measurement method for brain activity. Due to its safety, high resolution, and hypersensitivity to dynamic changes in brain neural signals, EEG has aroused much interest in scientific research and medical fields. This article reviews the types of EEG signals, multiple EEG signal analysis methods, and the application of relevant methods in the neuroscience field and for diagnosing neurological diseases. First, three types of EEG signals, including time-invariant EEG, accurate event-related EEG, and random event-related EEG, are introduced. Second, five main directions for the methods of EEG analysis, including power spectrum analysis, time-frequency analysis, connectivity analysis, source localization methods, and machine learning methods, are described in the main section, along with different sub-methods and effect evaluations for solving the same problem. Finally, the application scenarios of different EEG analysis methods are emphasized, and the advantages and disadvantages of similar methods are distinguished. This article is expected to assist researchers in selecting suitable EEG analysis methods based on their research objectives, provide references for subsequent research, and summarize current issues and prospects for the future.

Real-time cell metabolism assessed repeatedly on the same cells via para-hydrogen induced polarization

Signal-enhanced or hyperpolarized nuclear magnetic resonance (NMR) spectroscopy stands out as a unique tool to monitor real-time enzymatic reactions in living cells. The singlet state of para-hydrogen is thereby one source of spin order that can be converted into largely enhanced signals of e.g. metabolites. Here, we have investigated a parahydrogen-induced polarization (PHIP) approach as a biological assay for in vitro cellular metabolic characterization. Here, we demonstrate the possibility to perform consecutive measurements yielding metabolic information on the same sample. We observed a strongly reduced pyruvate-to-lactate conversion rate (flux) of a Hodgkin's lymphoma cancer cell line L1236 treated with FK866, an inhibitor of nicotinamide phosphoribosyltransferase (NAMPT) affecting the amount of NAD⁺ and thus NADH in cells. In the consecutive measurement the flux was recovered by NADH to the same amount as in the single-measurement-per-sample and provides a promising new analytical tool for continuous real-time studies combinable with bioreactors and lab-on-a-chip devices in the future.

Current and Emerging Techniques in Neuroimaging of Sport-Related Concussion

SUMMARY

Sport-related concussion (SRC) affects an estimated 1.6 to 3.8 million Americans each year. Sport-related concussion results from biomechanical forces to the head or neck that lead to a broad range of neurologic symptoms and impaired cognitive function. Although most individuals recover within weeks, some develop chronic symptoms. The heterogeneity of both the clinical presentation and the underlying brain injury profile make SRC a challenging condition. Adding to this challenge, there is also a lack of objective and reliable biomarkers to support diagnosis, to inform clinical decision making, and to monitor recovery after SRC. In this review, the authors provide an overview of advanced neuroimaging techniques that provide the sensitivity needed to capture subtle changes in brain structure, metabolism, function, and perfusion after SRC. This is followed by a discussion of emerging neuroimaging techniques, as well as current efforts of international research consortia committed to the study of SRC. Finally, the authors emphasize the need for advanced multimodal neuroimaging to develop objective biomarkers that will inform targeted treatment strategies after SRC.

A Deep Learning Approach for Psychosis Spectrum Label Noise Detection from Multimodal Neuroimaging Data

Understanding the structural and functional mechanisms of the brain is challenging for mood and mental disorders. Many neuroimaging techniques are widely used to reveal hidden patterns from different brain imaging modalities. However, these findings are bounded by the limitation of each modality. In addition, the lack of validity of current psychosis nosology created more complications in understanding biomarkers. In this study, we introduced a deep convolutional framework to classify and identify label noises using structural and functional magnetic resonance imaging data. We applied our method to functional and structural MRI data from a schizophrenia dataset and evaluated the model's performance in a cross-validated form. In addition, we introduced a noise criterion to distinguish a potentially noisy subject for each modality. Our results show the learned model using resting-state functional MRI data is more informative and has higher performance in comparison with structural MRI data. Lastly, based on the noise level, we investigated potential borderline subjects as possible subtypes and made a statistical analysis to distinguish differences between resting-state static functional connectivity features.Clinical Relevance- Results show schizophrenia patients are separable from the healthy control group based on their neuroimaging data and resting-state functional MRI data is more informative than structural MRI data and hence contains less label noise.

Functional magnetic resonance imaging of headache: Issues, best-practices, and new directions, a narrative review

OBJECTIVE

To ensure readers are informed consumers of functional magnetic resonance imaging (fMRI) research in headache, to outline ongoing challenges in this area of research, and to describe potential considerations when asked to collaborate on fMRI research in headache, as well as to suggest future directions for improvement in the field.

BACKGROUND

Functional MRI has played a key role in understanding headache pathophysiology, and mapping networks involved with headache-related brain activity have the potential to identify intervention targets. Some investigators have also begun to explore its use for diagnosis.

METHODS/RESULTS

The manuscript is a narrative review of the current best practices in fMRI in headache research, including guidelines on transparency and reproducibility. It also contains an outline of the fundamentals of MRI theory, task-related study design, resting-state functional connectivity, relevant statistics and power analysis, image preprocessing, and other considerations essential to the field.

CONCLUSION

Best practices to increase reproducibility include methods transparency, eliminating error, using a priori hypotheses and power calculations, using standardized instruments and diagnostic criteria, and developing large-scale, publicly available datasets.

Diffusion Imaging of Sport-related Repetitive Head Impacts-A Systematic Review

Repetitive head impacts (RHI) are commonly observed in athletes participating in contact sports such as American football, ice hockey, and soccer. RHI usually do not result in acute symptoms and are therefore often referred to as subclinical or "subconcussive" head impacts. Epidemiological studies report an association between exposure to RHI and an increased risk for the development of neurodegenerative diseases. Diffusion magnetic resonance imaging (dMRI) has emerged as particularly promising for the detection of subtle alterations in brain microstructure following exposure to sport-related RHI. The purpose of this study was to perform a systematic review of studies investigating the effects of exposure to RHI on brain microstructure using dMRI. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to determine studies that met inclusion and exclusion criteria across three databases. Seventeen studies were identified and critically evaluated. Results from these studies suggest an association between white matter alterations and RHI exposure in youth and young adult athletes. The most consistent finding across studies was lower or decreased fractional anisotropy (FA), a measure of the directionality of the diffusion of water molecules, associated with greater exposure to sport-related RHI. Whether decreased FA is associated with functional outcome (e.g., cognition) in those exposed to RHI is yet to be determined. This review further identified areas of importance for future research to increase the diagnostic and prognostic value of dMRI in RHI and to improve our understanding of the effects of RHI on brain physiology and microstructure.

Multimodal 3D Mouse Brain Atlas Framework with the Skull-Derived Coordinate System

Magnetic resonance imaging (MRI) and light-sheet fluorescence microscopy (LSFM) are technologies that enable non-disruptive 3-dimensional imaging of whole mouse brains. A combination of complementary information from both modalities is desirable for studying neuroscience in general, disease progression and drug efficacy. Although both technologies rely on atlas mapping for quantitative analyses, the translation of LSFM recorded data to MRI templates has been complicated by the morphological changes inflicted by tissue clearing and the enormous size of the raw data sets. Consequently, there is an unmet need for tools that will facilitate fast and accurate translation of LSFM recorded brains to in vivo, non-distorted templates. In this study, we have developed a bidirectional multimodal atlas framework that includes brain templates based on both imaging modalities, region delineations from the Allen's Common Coordinate Framework, and a skull-derived stereotaxic coordinate system. The framework also provides algorithms for bidirectional transformation of results obtained using either MR or LSFM (iDISCO cleared) mouse brain imaging while the coordinate system enables users to easily assign in vivo coordinates across the different brain templates.

A Review of Fusion Methods for Omics and Imaging Data

The development of omics data and biomedical images has greatly advanced the progress of precision medicine in diagnosis, treatment, and prognosis. The fusion of omics and imaging data, i.e., omics-imaging fusion, offers a new strategy for understanding complex diseases. However, due to a variety of issues such as the limited number of samples, high dimensionality of features, and heterogeneity of different data types, efficiently learning complementary or associated discriminative fusion information from omics and imaging data remains a challenge. Recently, numerous machine learning methods have been proposed to alleviate these problems. In this review, from the perspective of fusion levels and fusion methods, we first provide an overview of preprocessing and feature extraction methods for omics and imaging data, and comprehensively analyze and summarize the basic forms and variations of commonly used and newly emerging fusion methods, along with their advantages, disadvantages and the applicable scope. We then describe public datasets and compare experimental results of various fusion methods on the ADNI and TCGA datasets. Finally, we discuss future prospects and highlight remaining challenges in the field.

State-of-the-art imaging of neuromodulatory subcortical systems in aging and Alzheimer's disease: Challenges and opportunities

Primary prevention trials have shifted their focus to the earliest stages of Alzheimer's disease (AD). Autopsy data indicates that the neuromodulatory subcortical systems' (NSS) nuclei are specifically vulnerable to initial tau pathology, indicating that these nuclei hold great promise for early detection of AD in the context of the aging brain. The increasing availability of new imaging methods, ultra-high field scanners, new radioligands, and routine deep brain stimulation implants has led to a growing number of NSS neuroimaging studies on aging and neurodegeneration. Here, we review findings of current state-of-the-art imaging studies assessing the structure, function, and molecular changes of these nuclei during aging and AD. Furthermore, we identify the challenges associated with these imaging methods, important pathophysiologic gaps to fill for the AD NSS neuroimaging field, and provide future directions to improve our assessment, understanding, and clinical use of in vivo imaging of the NSS.

Imputing Brain Measurements Across Data Sets via Graph Neural Networks

Publicly available data sets of structural MRIs might not contain specific measurements of brain Regions of Interests (ROIs) that are important for training machine learning models. For example, the curvature scores computed by Freesurfer are not released by the Adolescent Brain Cognitive Development (ABCD) Study. One can address this issue by simply reapplying Freesurfer to the data set. However, this approach is generally computationally and labor intensive (e.g., requiring quality control). An alternative is to impute the missing measurements via a deep learning approach. However, the state-of-the-art is designed to estimate randomly missing values rather than entire measurements. We therefore propose to re-frame the imputation problem as a prediction task on another (public) data set that contains the missing measurements and shares some ROI measurements with the data sets of interest. A deep learning model is then trained to predict the missing measurements from the shared ones and afterwards is applied to the other data sets. Our proposed algorithm models the dependencies between ROI measurements via a graph neural network (GNN) and accounts for demographic differences in brain measurements (e.g. sex) by feeding the graph encoding into a parallel architecture. The architecture simultaneously optimizes a graph decoder to impute values and a classifier in predicting demographic factors. We test the approach, called Demographic Aware Graph-based Imputation (DAGI), on imputing those missing Freesurfer measurements of ABCD (N=3760; minimum age 12 years) by training the predictor on those publicly released by the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA, N=540). 5-fold cross-validation on NCANDA reveals that the imputed scores are more accurate than those generated by linear regressors and deep learning models. Adding them also to a classifier trained in identifying sex results in higher accuracy than only using those Freesurfer scores provided by ABCD.

The neurobiological reward system and binge eating: A critical systematic review of neuroimaging studies

OBJECTIVE

Changes in reward processing are hypothesized to play a role in the onset and maintenance of binge eating (BE). However, despite an increasing number of studies investigating the neurobiological reward system in individuals who binge eat, no comprehensive systematic review exists on this topic. Therefore, this review has the following objectives: (1) identify structural and functional changes in the brain reward system, either during rest or while performing a task; and (2) formulate directions for future research.

METHODS

A search was conducted of articles published until March 31, 2022. Neuroimaging studies were eligible if they wanted to study the reward system and included a group of individuals who binge eat together with a comparator group. Their results were summarized in a narrative synthesis.

RESULTS

A total of 58 articles were included. At rest, individuals who binge eat displayed a lower striatal dopamine release, a change in the volume of the striatum, frontal cortex, and insula, as well as a lower frontostriatal connectivity. While performing a task, there was a higher activity of the brain reward system when anticipating or receiving food, more model-free reinforcement learning, and more habitual behavior. Most studies only included one patient group, used general reward-related measures, and did not evaluate the impact of comorbidities, illness duration, race, or sex.

DISCUSSION

Confirming previous hypotheses, this review finds structural and functional changes in the neurobiological reward system in BE. Future studies should compare disorders, use measures that are specific to BE, and investigate the impact of confounding factors.

PUBLIC SIGNIFICANCE STATEMENT

This systematic review finds that individuals who binge eat display structural and functional changes in the brain reward system. These changes could be related to a higher sensitivity to food, relying more on previous experiences when making decisions, and more habitual behavior. Future studies should use a task that is specific to binge eating, look across different patient groups, and investigate the impact of comorbidities, illness duration, race, and sex.

A Comparison of Hypothesis-Driven and Data-Driven Research: A Case Study in Multimodal Data Science in Gut-Brain Axis Research

Data science, bioinformatics, and machine learning are the advent and progression of the fourth paradigm of exploratory science. The need for human-supported algorithms to capture patterns in big data is at the center of personalized healthcare and directly related to translational research. This paper argues that hypothesis-driven and data-driven research work together to inform the research process. At the core of these approaches are theoretical underpinnings that drive progress in the field. Here, we present several exemplars of research on the gut-brain axis that outline the innate values and challenges of these approaches. As nurses are trained to integrate multiple body systems to inform holistic human health promotion and disease prevention, nurses and nurse scientists serve an important role as mediators between this advancing technology and the patients. At the center of person-knowing, nurses need to be aware of the data revolution and use their unique skills to supplement the data science cycle from data to knowledge to insight.

Development of a new toolbox for mouse PET-CT brain image analysis fully based on CT images and validation in a PD mouse model

Automatic analysis toolboxes are popular in brain image analysis, both in clinical and in preclinical practices. In this regard, we proposed a new toolbox for mouse PET–CT brain image analysis including a new Statistical Parametric Mapping-based template and a pipeline for image registration of PET–CT images based on CT images. The new templates is compatible with the common coordinate framework (CCFv3) of the Allen Reference Atlas (ARA) while the CT based registration step allows to facilitate the analysis of mouse PET–CT brain images. From the ARA template, we identified 27 volumes of interest that are relevant for in vivo imaging studies and provided binary atlas to describe them. We acquired 20 C57BL/6 mice with [¹⁸F]FDG PET–CT, and 12 of them underwent 3D T2-weighted high-resolution MR scans. All images were elastically registered to the ARA atlas and then averaged. High-resolution MR images were used to validate a CT-based registration pipeline. The resulting method was applied to a mouse model of Parkinson’s disease subjected to a test–retest study (n = 6) with the TSPO-specific radioligand [¹⁸F]VC701. The identification of regions of microglia/macrophage activation was performed in comparison to the Ma and Mirrione template. The new toolbox identified 11 (6 after false discovery rate adjustment, FDR) brain sub-areas of significant [¹⁸F]VC701 uptake increase versus the 4 (3 after FDR) macro-regions identified by the Ma and Mirrione template. Moreover, these 11 areas are functionally connected as found by applying the Mouse Connectivity tool of ARA. In conclusion, we developed a mouse brain atlas tool optimized for PET–CT imaging analysis that does not require MR. This tool conforms to the CCFv3 of ARA and could be applied to the analysis of mouse brain disease models.

Evaluation of a novel 8-channel RX coil for speech production MRI at 0.55 T

OBJECTIVE

Speech production MRI benefits from lower magnetic fields due to reduced off-resonance effects at air-tissue interfaces and from the use of dedicated receiver coils due to higher SNR and parallel imaging capability. Here we present a custom designed upper airway coil for ¹H imaging at 0.55 Tesla and evaluate its performance in comparison with a vendor-provided prototype 16-channel head/neck coil.

MATERIALS AND METHODS

Four adult volunteers were scanned with both custom speech and prototype head-neck coils. We evaluated SNR gains of each of the coils over eleven upper airway volumes-of-interest measured relative to the integrated body coil. We evaluated parallel imaging performance of both coils by computing g-factors for SENSE reconstruction of uniform and variable density Cartesian sampling schemes with R = 2, 3, and 4.

RESULTS

The dedicated coil shows approximately 3.5-fold SNR efficiency compared to the head-neck coil. For R = 2 and 3, both uniform and variable density samplings have g-factor values below 1.1 in the upper airway region. For R = 4, g-factor values are higher for both trajectories.

DISCUSSION

The dedicated coil configuration allows for a significant SNR gain over the head-neck coil in the articulators. This, along with favorable g values, makes the coil useful in speech production MRI.

DBB - A Distorted Brain Benchmark for Automatic Tissue Segmentation in Paediatric Patients

T1-weighted magnetic resonance images provide a comprehensive view of the morphology of the human brain at the macro scale. These images are usually the input of a segmentation process that aims detecting the anatomical structures labeling them according to a predefined set of target tissues. Automated methods for brain tissue segmentation rely on anatomical priors of the human brain structures. This is the reason why their performance is quite accurate on healthy individuals. Nevertheless model-based tools become less accurate in clinical practice, specifically in the cases of severe lesions or highly distorted cerebral anatomy. More recently there are empirical evidences that a data-driven approach can be more robust in presence of alterations of brain structures, even though the learning model is trained on healthy brains. Our contribution is a benchmark to support an open investigation on how the tissue segmentation of distorted brains can be improved by adopting a supervised learning approach. We formulate a precise definition of the task and propose an evaluation metric for a fair and quantitative comparison. The training sample is composed of almost one thousand healthy individuals. Data include both T1-weighted MR images and their labeling of brain tissues. The test sample is a collection of several tens of individuals with severe brain distortions. Data and code are openly published on BrainLife, an open science platform for reproducible neuroscience data analysis.

Congenital Adrenal Hyperplasia and Brain Health: A Systematic Review of Structural, Functional, and Diffusion Magnetic Resonance Imaging (MRI) Investigations

BACKGROUND

Congenital adrenal hyperplasia (CAH) is a group of genetic disorders that affects the adrenal glands and is the most common cause of primary adrenal insufficiency in children. In the past few decades, magnetic resonance imaging (MRI) has been implemented to investigate how the brain may be affected by CAH. A systematic review was conducted to evaluate and synthesize the reported evidence of brain findings related to CAH using structural, functional, and diffusion-weighted MRI.

METHODS

We searched bibliographical databases through July 2021 for brain MRI studies in individuals with CAH.

RESULTS

Twenty-eight studies were identified, including 13 case reports or series, 10 studies that recruited and studied CAH patients vs unaffected controls, and 5 studies without a matched control group. Eleven studies used structural MRI to identify structural abnormalities or quantify brain volumes, whereas 3 studies implemented functional MRI to investigate brain activity, and 3 reported diffusion MRI findings to assess white matter microstructure. Some commonly reported findings across studies included cortical atrophy and differences in gray matter volumes, as well as white matter hyperintensities, altered white matter microstructure, and distinct patterns of emotion and reward-related brain activity.

CONCLUSIONS

These findings suggest differences in brain structure and function in patients with CAH. Limitations of these studies highlight the need for CAH neuroimaging studies to incorporate larger sample sizes and follow best study design and MRI analytic practices, as well as clarify potential neurologic effects seen across the lifespan and in relation to clinical and behavioral CAH phenotypes.

Cerebellar Abnormalities on Proton MR Spectroscopy and Imaging in Patients With Gluten Ataxia: A Pilot Study

Gluten ataxia is a rare immune-mediated neurological disorder caused by the ingestion of gluten. The diagnosis is not straightforward as antibodies are present in only up to 38% of patients, but often at lower titers. The symptoms of ataxia may be mild at the onset but lead to permanent damage if remain untreated. It is characterized by damage to the cerebellum however, the pathophysiology of the disease is not clearly understood. The present study investigated the neurochemical profile of vermis and right cerebellum and structural changes in various brain regions of patients with gluten ataxia (n = 6, age range 40–65 years) and compared it with healthy controls (n = 10, 40–55 years). Volumetric 3-D T1 and T1-weighted magnetic resonance imaging (MRI) in the three planes (axial, coronal, and sagittal) of the whole brain and single-voxel ¹H- magnetic resonance spectroscopy (MRS) of the vermis and right cerebellum were acquired on 3 T human MR scanner. The metabolite concentrations were estimated using LC Model (6.1–4A) while brain volumes were estimated using the online tool volBrain pipeline and CERES and corrected for partial volumes. The levels of neuro-metabolites (N-acetyl aspartate + N-acetyl aspartate glutamate, glycerophosphocholine + phosphocholine, and total creatine) were found to be significantly lower in vermis, while N-acetyl aspartate + N-acetyl aspartate glutamate and glycerophosphocholine + phosphocholine was lower in cerebellum regions in the patients with gluten ataxia compared to healthy controls. A significant reduction in the white matter of (total brain, cerebellum, and cerebrum); reduction in the volumes of cerebellum lobe (X) and thalamus while lateral ventricles were increased in the patients with gluten ataxia compared to healthy controls. The reduced neuronal metabolites along with structural changes in the brain suggested neuronal degeneration in the patients with gluten ataxia. Our preliminary findings may be useful in understanding the gluten-induced cerebral damage and indicated that MRI and MRS may serve as a non-invasive useful tool in the early diagnosis, thereby enabling better management of these patients.

Neuroimaging in Leber Hereditary Optic Neuropathy: State-of-the-art and future prospects

Leber Hereditary Optic Neuropathy (LHON) is an inherited mitochondrial retinal disease that causes the degeneration of retinal ganglion cells and leads to drastic loss of visual function. In the last decades, there has been a growing interest in using Magnetic Resonance Imaging (MRI) to better understand mechanisms of LHON beyond the retina. This is partially due to the emergence of gene-therapies for retinal diseases, and the accompanying expanded need for reliably quantifying and monitoring visual processing and treatment efficiency in patient populations. This paper aims to draw a current picture of key findings in this field so far, the challenges of using neuroimaging methods in patients with LHON, and important open questions that MRI can help address about LHON disease mechanisms and prognoses, including how downstream visual brain regions are affected by the disease and treatment and why, and how scope for neural plasticity in these pathways may limit or facilitate recovery.

Machine learning in neuroimaging: from research to clinical practice

Neuroimaging is critical in clinical care and research, enabling us to investigate the brain in health and disease. There is a complex link between the brain's morphological structure, physiological architecture, and the corresponding imaging characteristics. The shape, function, and relationships between various brain areas change during development and throughout life, disease, and recovery. Like few other areas, neuroimaging benefits from advanced analysis techniques to fully exploit imaging data for studying the brain and its function. Recently, machine learning has started to contribute (a) to anatomical measurements, detection, segmentation, and quantification of lesions and disease patterns, (b) to the rapid identification of acute conditions such as stroke, or (c) to the tracking of imaging changes over time. As our ability to image and analyze the brain advances, so does our understanding of its intricate relationships and their role in therapeutic decision-making. Here, we review the current state of the art in using machine learning techniques to exploit neuroimaging data for clinical care and research, providing an overview of clinical applications and their contribution to fundamental computational neuroscience.

Deep learning based pipelines for Alzheimer's disease diagnosis: A comparative study and a novel deep-ensemble method

BACKGROUND

Alzheimer's disease is a chronic neurodegenerative disease that destroys brain cells, causing irreversible degeneration of cognitive functions and dementia. Its causes are not yet fully understood, and there is no curative treatment. However, neuroimaging tools currently offer help in clinical diagnosis, and, recently, deep learning methods have rapidly become a key methodology applied to these tools. The reason is that they require little or no image preprocessing and can automatically infer an optimal representation of the data from raw images without requiring prior feature selection, resulting in a more objective and less biased process. However, training a reliable model is challenging due to the significant differences in brain image types.

METHODS

We aim to contribute to the research and study of Alzheimer's disease through computer-aided diagnosis (CAD) by comparing different deep learning models. In this work, there are three main objectives: i) to present a fully automated deep-ensemble approach for dementia-level classification from brain images, ii) to compare different deep learning architectures to obtain the most suitable one for the task, and (iii) evaluate the robustness of the proposed strategy in a deep learning framework to detect Alzheimer's disease and recognise different levels of dementia. The proposed approach is specifically designed to be potential support for clinical care based on patients' brain images.

RESULTS

Our strategy was developed and tested on three MRI and one fMRI public datasets with heterogeneous characteristics. By performing a comprehensive analysis of binary classification (Alzheimer's disease status or not) and multiclass classification (recognising different levels of dementia), the proposed approach can exceed state of the art in both tasks, reaching an accuracy of 98.51% in the binary case, and 98.67% in the multiclass case averaged over the four different data sets.

CONCLUSION

We strongly believe that integrating the proposed deep-ensemble approach will result in robust and reliable CAD systems, considering the numerous cross-dataset experiments performed. Being tested on MRIs and fMRIs, our strategy can be easily extended to other imaging techniques. In conclusion, we found that our deep-ensemble strategy could be efficiently applied for this task with a considerable potential benefit for patient management.

Clinical Outcomes of Severe Forms of Early Social Stress

Early social stress, particularly severe but nevertheless frequent forms such as abuse and neglect, are among the major risk factors for the development of mental disorders. However, we only have very limited knowledge of the psychobiological disease mechanisms underlying the influence of early life stress and stress-related disorders during this vulnerable phase of life. Early stress can have long-lasting adverse effects on the brain and other somatic systems, e.g. through influences on brain development. In adulthood, the prior experience of abuse or neglect can result in complex clinical profiles. Besides conditions such as mood and anxiety disorders as well as posttraumatic stress disorder, substance use disorders (SUD) are among the most prevalent sequelae of early social stress. Current social stress further influences the development and maintenance of these disorders, e.g., by increasing the risk of relapses. In this chapter, we will first give an overview of currently used methods to assess the phenomenology and pathophysiology of stress-related disorders and then focus on the phenomenological and neurobiological background of the interaction between early social stress and SUD. We will give an overview of important insights from neuroimaging studies and will also highlight recent findings from studies using digital tools such as ecological momentary assessment or virtual reality to capture the influence of early social stress as well as current social stress in everyday life of persons with SUD.

T2 heterogeneity as an in vivo marker of microstructural integrity in medial temporal lobe subfields in ageing and mild cognitive impairment

A better understanding of early brain changes that precede loss of independence in diseases like Alzheimer's disease (AD) is critical for development of disease-modifying therapies. Quantitative MRI, such as T2 relaxometry, can identify microstructural changes relevant to early stages of pathology. Recent evidence suggests heterogeneity of T2 may be a more informative MRI measure of early pathology than absolute T2. Here we test whether T2 markers of brain integrity precede the volume changes we know are present in established AD and whether such changes are most marked in medial temporal lobe (MTL) subfields known to be most affected early in AD. We show that T2 heterogeneity was greater in people with mild cognitive impairment (MCI; n = 49) compared to healthy older controls (n = 99) in all MTL subfields, but this increase was greatest in MTL cortices, and smallest in dentate gyrus. This reflects the spatio-temporal progression of neurodegeneration in AD. T2 heterogeneity in CA1-3 and entorhinal cortex and volume of entorhinal cortex showed some ability to predict cognitive decline, where absolute T2 could not, however further studies are required to verify this result. Increases in T2 heterogeneity in MTL cortices may reflect localised pathological change and may present as one of the earliest detectible brain changes prior to atrophy. Finally, we describe a mechanism by which memory, as measured by accuracy and reaction time on a paired associate learning task, deteriorates with age. Age-related memory deficits were explained in part by lower subfield volumes, which in turn were directly associated with greater T2 heterogeneity. We propose that tissue with high T2 heterogeneity represents extant tissue at risk of permanent damage but with the potential for therapeutic rescue. This has implications for early detection of neurodegenerative diseases and the study of brain-behaviour relationships.

Multimodal Neuroimaging Predictors of Learning Performance of Sensorimotor Rhythm Up-Regulation Neurofeedback

Electroencephalographic (EEG) neurofeedback (NFB) is a popular neuromodulation method to help one selectively enhance or inhibit his/her brain activities by means of real-time visual or auditory feedback of EEG signals. Sensory motor rhythm (SMR) NFB protocol has been applied to improve cognitive performance, but a large proportion of participants failed to self-regulate their brain activities and could not benefit from NFB training. Therefore, it is important to identify the neural predictors of SMR up-regulation NFB training performance for a better understanding the mechanisms of individual difference in SMR NFB. Twenty-seven healthy participants (12 males, age: 23.1 ± 2.36) were enrolled to complete three sessions of SMR up-regulation NFB training and collection of multimodal neuroimaging data [resting-state EEG, structural magnetic resonance imaging (MRI), and resting-state functional MRI (fMRI)]. Correlation analyses were performed between within-session NFB learning index and anatomical and functional brain features extracted from multimodal neuroimaging data, in order to identify the neuroanatomical and neurophysiological predictors for NFB learning performance. Lastly, machine learning models were trained to predict NFB learning performance using features from each modality as well as multimodal features. According to our results, most participants were able to successfully increase the SMR power and the NFB learning performance was significantly correlated with a set of neuroimaging features, including resting-state EEG powers, gray/white matter volumes from MRI, regional and functional connectivity (FC) of resting-state fMRI. Importantly, results of prediction analysis indicate that NFB learning index can be better predicted using multimodal features compared with features of single modality. In conclusion, this study highlights the importance of multimodal neuroimaging technique as a tool to explain the individual difference in within-session NFB learning performance, and could provide a theoretical framework for early identification of individuals who cannot benefit from NFB training.

An Examination of the Long-Term Neurodevelopmental Impact of Prenatal Zika Virus Infection in a Rat Model Using a High Resolution, Longitudinal MRI Approach

Since Zika virus (ZIKV) first emerged as a public health concern in 2015, our ability to identify and track the long-term neurological sequelae of prenatal Zika virus (ZIKV) infection in humans has been limited. Our lab has developed a rat model of maternal ZIKV infection with associated vertical transmission to the fetus that results in significant brain malformations in the neonatal offspring. Here, we use this model in conjunction with longitudinal magnetic resonance imaging (MRI) to expand our understanding of the long-term neurological consequences of prenatal ZIKV infection in order to identify characteristic neurodevelopmental changes and track them across time. We exploited both manual and automated atlas-based segmentation of MR images in order to identify long-term structural changes within the developing rat brain following inoculation. The paradigm involved scanning three cohorts of male and female rats that were prenatally inoculated with 10⁷ PFU ZIKV, 10⁷ UV-inactivated ZIKV (iZIKV), or diluent medium (mock), at 4 different postnatal day (P) age points: P2, P16, P24, and P60. Analysis of tracked brain structures revealed significantly altered development in both the ZIKV and iZIKV rats. Moreover, we demonstrate that prenatal ZIKV infection alters the growth of brain regions throughout the neonatal and juvenile ages. Our findings also suggest that maternal immune activation caused by inactive viral proteins may play a role in altered brain growth throughout development. For the very first time, we introduce manual and automated atlas-based segmentation of neonatal and juvenile rat brains longitudinally. Experimental results demonstrate the effectiveness of our novel approach for detecting significant changes in neurodevelopment in models of early-life infections.

Brain-based sex differences in autism spectrum disorder across the lifespan: A systematic review of structural MRI, fMRI, and DTI findings

Females with autism spectrum disorder (ASD) have been long overlooked in neuroscience research, but emerging evidence suggests they show distinct phenotypic trajectories and age-related brain differences. Sex-related biological factors (e.g., hormones, genes) may play a role in ASD etiology and have been shown to influence neurodevelopmental trajectories. Thus, a lifespan approach is warranted to understand brain-based sex differences in ASD. This systematic review on MRI-based sex differences in ASD was conducted to elucidate variations across the lifespan and inform biomarker discovery of ASD in females We identified articles through two database searches. Fifty studies met criteria and underwent integrative review. We found that regions expressing replicable sex-by-diagnosis differences across studies overlapped with regions showing sex differences in neurotypical cohorts. Furthermore, studies investigating age-related brain differences across a broad age-span suggest distinct neurodevelopmental patterns in females with ASD. Qualitative comparison across youth and adult studies also supported this hypothesis. However, many studies collapsed across age, which may mask differences. Furthermore, accumulating evidence supports the female protective effect in ASD, although only one study examined brain circuits implicated in "protection." When synthesized with the broader literature, brain-based sex differences in ASD may come from various sources, including genetic and endocrine processes involved in brain "masculinization" and "feminization" across early development, puberty, and other lifespan windows of hormonal transition. Furthermore, sex-related biology may interact with peripheral processes, in particular the stress axis and brain arousal system, to produce distinct neurodevelopmental patterns in males and females with ASD. Future research on neuroimaging-based sex differences in ASD would benefit from a lifespan approach in well-controlled and multivariate studies. Possible relationships between behavior, sex hormones, and brain development in ASD remain largely unexamined.

Quantification of T1, T2 relaxation times from Magnetic Resonance Fingerprinting radially undersampled data using analytical transformations

Quantitative magnetic resonance imaging (MRI) estimates magnetic parameters related to tissue, such as T1, T2 relaxation times and proton density. MR fingerprinting (MRF) is a new concept that uses pseudo-random, incoherent measurements to create a unique fingerprint for each tissue type to quantify magnet parameters. This paper aims to enhance MRF performance by investigating (i) the most suitable acquisition trajectory, and (ii) analytical transformations, suitable for radial acquisitions. Highly undersampled MRF brain (k, t)-space data have been simulated and non-linearly reconstructed to exploit the low-rank property of dynamic imaging. Based on our findings, the radial trajectory is the most suitable for MRF compared to Cartesian and spiral acquisitions. Perhaps this is due to the fact that its aliasing artifacts are more noise-like, and that unlike spiral trajectories, it can use analytical transformations that do not require re-gridding. One such analytical algorithm is the spline reconstruction technique (SRT) that is based on a novel numerical implementation of an analytic representation of the inverse Radon transform. Here, for the first time, this algorithm is applied to MR radial data. Reconstructions using SRT were compared to the ones using filtered back-projection. SRT provided images of higher contrast, lower bias, which resulted in more accurate T1, T2 values.

Strategies to solve the reverse inference fallacy in future MRI studies of schizophrenia: a review

Few advances in schizophrenia research have been translated into clinical practice, despite 60 years of serum biomarkers studies and 50 years of genetic studies. During the last 30 years, neuroimaging studies on schizophrenia have gradually increased, partly due to the beautiful prospect that the pathophysiology of schizophrenia could be explained entirely by the Human Connectome Project (HCP). However, the fallacy of reverse inference has been a critical problem of the HCP. For this reason, there is a dire need for new strategies or research "bridges" to further schizophrenia at the biological level. To understand the importance of research "bridges," it is vital to examine the strengths and weaknesses of the recent literature. Hence, in this review, our team has summarized the recent literature (1995-2018) about magnetic resonance imaging (MRI) of schizophrenia in terms of regional and global structural and functional alterations. We have also provided a new proposal that may supplement the HCP for studying schizophrenia. As postulated, despite the vast number of MRI studies in schizophrenia, the lack of homogeneity between the studies, along with the relatedness of schizophrenia with other neurological disorders, has hindered the study of schizophrenia. In addition, the reverse inference cannot be used to diagnose schizophrenia, further limiting the clinical impact of findings from medical imaging studies. We believe that multidisciplinary technologies may be used to develop research "bridges" to further investigate schizophrenia at the single neuron or neuron cluster levels. We have postulated about future strategies for overcoming the current limitations and establishing the research "bridges," with an emphasis on multimodality imaging, molecular imaging, neuron cluster signals, single transmitter biomarkers, and nanotechnology. These research "bridges" may help solve the reverse inference fallacy and improve our understanding of schizophrenia for future studies.

Translational neuroimaging in mild traumatic brain injury

Traumatic brain injuries (TBIs) are common with an estimated 27.1 million cases per year. Approximately 80% of TBIs are categorized as mild TBI (mTBI) based on initial symptom presentation. While in most individuals, symptoms resolve within days to weeks, in some, symptoms become chronic. Advanced neuroimaging has the potential to characterize brain morphometric, microstructural, biochemical, and metabolic abnormalities following mTBI. However, translational studies are needed for the interpretation of neuroimaging findings in humans with respect to the underlying pathophysiological processes, and, ultimately, for developing novel and more targeted treatment options. In this review, we introduce the most commonly used animal models for the study of mTBI. We then summarize the neuroimaging findings in humans and animals after mTBI and, wherever applicable, the translational aspects of studies available today. Finally, we highlight the importance of translational approaches and outline future perspectives in the field of translational neuroimaging in mTBI.

Healthy individuals vs patients with bipolar or unipolar depression in gray matter volume

BACKGROUND

Previous studies using voxel-based morphometry (VBM) revealed changes in gray matter volume (GMV) of patients with depression, but the differences between patients with bipolar disorder (BD) and unipolar depression (UD) are less known.

AIM

To analyze the whole-brain GMV data of patients with untreated UD and BD compared with healthy controls.

METHODS

Fourteen patients with BD and 20 with UD were recruited from the Mental Health Center of Shantou University between August 2014 and July 2015, and 20 non-depressive controls were recruited. After routine three-plane positioning, axial T2WI scanning was performed. The connecting line between the anterior and posterior commissures was used as the scanning baseline. The scanning range extended from the cranial apex to the foramen magnum. Categorical data are presented as frequencies and were analyzed using the Fisher exact test.

RESULTS

There were no significant intergroup differences in gender, age, or years of education. Disease course, age at the first episode, and Hamilton depression rating scale scores were similar between patients with UD and those with BD. Compared with the non-depressive controls, patients with BD showed smaller GMVs in the right inferior temporal gyrus, left middle temporal gyrus, right middle occipital gyrus, and right superior parietal gyrus and larger GMVs in the midbrain, left superior frontal gyrus, and right cerebellum. In contrast, UD patients showed smaller GMVs than the controls in the right fusiform gyrus, left inferior occipital gyrus, left paracentral lobule, right superior and inferior temporal gyri, and the right posterior lobe of the cerebellum, and larger GMVs than the controls in the left posterior central gyrus and left middle frontal gyrus. There was no difference in GMV between patients with BD and UD.

CONCLUSION

Using VBM, the present study revealed that patients with UD and BD have different patterns of changes in GMV when compared with healthy controls.

The Value of Neuroimaging Techniques in the Translation and Transdiagnostic Validation of Psychiatric Diagnoses - Selective Review

Psychiatric diagnosis has long been perceived as more of an art than a science since its foundations lie within the observation, and the self-report of the patients themselves and objective diagnostic biomarkers are lacking. Furthermore, the diagnostic tools in use not only stray away from the conventional medical framework but also remain invalidated with evidence-based concepts. However, neuroscience, as a source of valid objective knowledge has initiated the process of a paradigm shift underlined by the main concept of psychiatric disorders being "brain disorders". It is also a bridge closing the explanatory gap among the different fields of medicine via the translation of the knowledge within a multidisciplinary framework. The contemporary neuroimaging methods, such as fMRI provide researchers with an entirely new set of tools to reform the current status quo by creating an opportunity to define and validate objective biomarkers that can be translated into clinical practice. Combining multiple neuroimaging techniques with the knowledge of the role of genetic factors, neurochemical imbalance and neuroinflammatory processes in the etiopathophysiology of psychiatric disorders is a step towards a comprehensive biological explanation of psychiatric disorders and a final differentiation of psychiatry as a well-founded medical science. In addition, the neuroscientific knowledge gained thus far suggests a necessity for directional change to exploring multidisciplinary concepts, such as multiple causality and dimensionality of psychiatric symptoms and disorders. A concomitant viewpoint transition of the notion of validity in psychiatry with a focus on an integrative validatory approach may facilitate the building of a collaborative bridge above the wall existing between the scientific fields analyzing the mind and those studying the brain.

The Deficits of Individual Morphological Covariance Network Architecture in Schizophrenia Patients With and Without Violence

Background: Schizophrenia is associated with a significant increase in the risk of violence, which constitutes a public health concern and contributes to stigma associated with mental illness. Although previous studies revealed structural and functional abnormalities in individuals with violent schizophrenia (VSZ), the neural basis of psychotic violence remains controversial. Methods: In this study, high-resolution structural magnetic resonance imaging (MRI) data were acquired from 18 individuals with VSZ, 23 individuals with non-VSZ (NSZ), and 22 age- and sex-matched healthy controls (HCs). Whole-brain voxel-based morphology and individual morphological covariance networks were analysed to reveal differences in gray matter volume (GMV) and individual morphological covariance network topology. Relationships among abnormal GMV, network topology, and clinical assessments were examined using correlation analyses. Results: GMV in the hypothalamus gradually decreased from HCs and NSZ to VSZ and showed significant differences between all pairs of groups. Graph theory analyses revealed that morphological covariance networks of HCs, NSZ, and VSZ exhibited small worldness. Significant differences in network topology measures, including global efficiency, shortest path length, and nodal degree, were found. Furthermore, changes in GMV and network topology were closely related to clinical performance in the NSZ and VSZ groups. Conclusions: These findings revealed the important role of local structural abnormalities of the hypothalamus and global network topological impairments in the neuropathology of NSZ and VSZ, providing new insight into the neural basis of and markers for VSZ and NSZ to facilitate future accurate clinical diagnosis and targeted treatment.

Magnetic resonance imaging for chronic pain: diagnosis, manipulation, and biomarkers

Pain is a multidimensional subjective experience with biological, psychological, and social factors. Whereas acute pain can be a warning signal for the body to avoid excessive injury, long-term and ongoing pain may be developed as chronic pain. There are more than 100 million people in China living with chronic pain, which has raised a huge socioeconomic burden. Studying the mechanisms of pain and developing effective analgesia approaches are important for basic and clinical research. Recently, with the development of brain imaging and data analytical approaches, the neural mechanisms of chronic pain have been widely studied. In the first part of this review, we briefly introduced the magnetic resonance imaging and conventional analytical approaches for brain imaging data. Then, we reviewed brain alterations caused by several chronic pain disorders, including localized and widespread primary pain, primary headaches and orofacial pain, musculoskeletal pain, and neuropathic pain, and present meta-analytical results to show brain regions associated with the pathophysiology of chronic pain. Next, we reviewed brain changes induced by pain interventions, such as pharmacotherapy, neuromodulation, and acupuncture. Lastly, we reviewed emerging studies that combined advanced machine learning and neuroimaging techniques to identify diagnostic, prognostic, and predictive biomarkers in chronic pain patients.

Brain MRI findings in relation to clinical characteristics and outcome of tuberculous meningitis

Neuroradiological abnormalities in tuberculous meningitis (TBM) are common, but the exact relationship with clinical and inflammatory markers has not been well established. We performed magnetic resonance imaging (MRI) at baseline and after two months treatment to characterise neuroradiological patterns in a prospective cohort of adult TBM patients in Indonesia. We included 48 TBM patients (median age 30, 52% female, 8% HIV-infected), most of whom had grade II (90%), bacteriologically confirmed (71%) disease, without antituberculotic resistance. Most patients had more than one brain lesion (83%); baseline MRIs showed meningeal enhancement (89%), tuberculomas (77%), brain infarction (60%) and hydrocephalus (56%). We also performed an exploratory analysis associating MRI findings to clinical parameters, response to treatment, paradoxical reactions and survival. The presence of multiple brain lesion was associated with a lower Glasgow Coma Scale and more pronounced motor, lung, and CSF abnormalities (p-value <0.05). After two months, 33/37 patients (89%) showed worsening of MRI findings, mostly consisting of new or enlarged tuberculomas. Baseline and follow-up MRI findings and paradoxical responses showed no association with six-month mortality. Severe TBM is characterized by extensive MRI abnormalities at baseline, and frequent radiological worsening during treatment.

Elimination of low-inversion-efficiency induced artifacts in whole-brain MP2RAGE using multiple RF-shim configurations at 7 T

The magnetization-prepared two-rapid-gradient-echo (MP2RAGE) sequence is used for structural T₁ -weighted imaging and T₁ mapping of the human brain. In this sequence, adiabatic inversion RF pulses are commonly used, which require the B₁⁺ magnitude to be above a certain threshold. Achieving this threshold in the whole brain may not be possible at ultra-high fields because of the short RF wavelength. This results in low-inversion regions especially in the inferior brain (eg cerebellum and temporal lobes), which is reflected as regions of bright signal in MP2RAGE images. This study aims at eliminating the low-inversion-efficiency induced artifacts in MP2RAGE images at 7 T. The proposed technique takes advantage of parallel RF transmission systems by splitting the brain into two overlapping slabs and calculating the complex weights of transmit channels (ie RF shims) on these slabs for excitation and inversion independently. RF shims were calculated using fast methods implemented in the standard workflow. The excitation RF pulse was designed to obtain slabs with flat plateaus and sharp edges. These slabs were joined into a single volume during the online image reconstruction. The two-slab strategy naturally results in a signal-to-noise ratio loss; however, it allowed the use of independent shims to make the B₁⁺ field exceed the adiabatic threshold in the inferior brain, eliminating regions of low inversion efficiency. Accordingly, the normalized root-mean-square errors in the inversion were reduced to below 2%. The two-slab strategy was found to outperform subject-specific k_T -point inversion RF pulses in terms of inversion error. The proposed strategy is a simple yet effective method to eliminate low-inversion-efficiency artifacts; consequently, MP2RAGE-based, artifact-free T₁ -weighted structural images were obtained in the whole brain at 7 T.

T2 heterogeneity: a novel marker of microstructural integrity associated with cognitive decline in people with mild cognitive impairment

BACKGROUND

Early Alzheimer's disease (AD) diagnosis is vital for development of disease-modifying therapies. Prior to significant brain tissue atrophy, several microstructural changes take place as a result of Alzheimer's pathology. These include deposition of amyloid, tau and iron, as well as altered water homeostasis in tissue and some cell death. T2 relaxation time, a quantitative MRI measure, is sensitive to these changes and may be a useful non-invasive, early marker of tissue integrity which could predict conversion to dementia. We propose that different microstructural changes affect T2 in opposing ways, such that average 'midpoint' measures of T2 are less sensitive than measuring distribution width (heterogeneity). T2 heterogeneity in the brain may present a sensitive early marker of AD pathology.

METHODS

In this cohort study, we tested 97 healthy older controls, 49 people with mild cognitive impairment (MCI) and 10 with a clinical diagnosis of AD. All participants underwent structural MRI including a multi-echo sequence for quantitative T2 assessment. Cognitive change over 1 year was assessed in 20 participants with MCI. T2 distributions were modelled in the hippocampus and thalamus using log-logistic distribution giving measures of log-median value (midpoint; T2μ) and distribution width (heterogeneity; T2σ).

RESULTS

We show an increase in T2 heterogeneity (T2σ; p < .0001) in MCI compared to healthy controls, which was not seen with midpoint (T2μ; p = .149) in the hippocampus and thalamus. Hippocampal T2 heterogeneity predicted cognitive decline over 1 year in MCI participants (p = .018), but midpoint (p = .132) and volume (p = .315) did not. Age affects T2, but the effects described here are significant even after correcting for age.

CONCLUSIONS

We show that T2 heterogeneity can identify subtle changes in microstructural integrity of brain tissue in MCI and predict cognitive decline over a year. We describe a new model that considers the competing effects of factors that both increase and decrease T2. These two opposing forces suggest that previous conclusions based on T2 midpoint may have obscured the true potential of T2 as a marker of subtle neuropathology. We propose that T2 heterogeneity reflects microstructural integrity with potential to be a widely used early biomarker of conditions such as AD.

LEGO-compatible modular mapping phantom for magnetic resonance imaging

Physical phantoms have been widely used for performance evaluation of magnetic resonance imaging (MRI). Although there are many kinds of physical phantoms, most MRI phantoms use fixed configurations with specific sizes that may fit one or a few different types of radio frequency (RF) coils. Therefore, it has limitations for various image quality assessments of scanning areas. In this article, we report a novel design for a truly customizable MRI phantom called the LEGO-compatible Modular Mapping (MOMA) phantom, which not only serves as a general quality assurance phantom for a wide range of RF coils, but also a flexible calibration phantom for quantitative imaging. The MOMA phantom has a modular architecture which includes individual assessment functionality of the modules and LEGO-type assembly compatibility. We demonstrated the feasibility of the MOMA phantom for quantitative evaluation of image quality using customized module assembly compatible with head, breast, spine, knee, and body coil features. This unique approach allows comprehensive image quality evaluation with wide versatility. In addition, we provide detailed MOMA phantom development and imaging characteristics of the modules.

Diagnostic quality assessment of IR-prepared 3D magnetic resonance neuroimaging accelerated using compressed sensing and k-space sampling order optimization

PURPOSE

To evaluate the clinical diagnostic efficacy of accelerated 3D magnetic resonance (MR) neuroimaging by radiological assessment for image quality and artefacts.

STUDY TYPE

Prospective healthy volunteer study.

SUBJECTS

Eight healthy subjects.

FIELD STRENGTH/SEQUENCE

Inversion Recovery (IR) prepared 3D Gradient Echo (GRE) sequence on a 1.5 T GE Signa HDx scanner.

ASSESSMENT

Independent radiological diagnostic quality assessments of accelerated 3D MR brain datasets were carried out by four experienced neuro-radiologists who were blinded to the acceleration factor and to the subject. The radiological grading was based on a previously reported radiological scoring key that was used for image quality assessment of human brains.

STATISTICAL TESTS

Bland-Altman analysis.

RESULTS

Optimization of the k-space sampling order was important for preserving contrast in accelerated scans. Despite having lower scores than fully sampled datasets, the majority of the compressed sensing (CS) accelerated brain datasets with k-space sampling order optimization (19/24 datasets by Radiologist 1, 24/24 datasets by Radiologist 2 and 16/24 datasets by Radiologist 3) were graded to be fully diagnostic indicating that there was adequate confidence for performing gross structural assessment of the brain.

CONCLUSION

Optimization of k-space acquisition order improves the clinical utility of CS accelerated 3D neuroimaging. This method may be appropriate for routine radiological assessment of the brain.

Meta-Modal Information Flow: A Method for Capturing Multimodal Modular Disconnectivity in Schizophrenia

OBJECTIVE

Multimodal measurements of the same phenomena provide complementary information and highlight different perspectives, albeit each with their own limitations. A focus on a single modality may lead to incorrect inferences, which is especially important when a studied phenomenon is a disease. In this paper, we introduce a method that takes advantage of multimodal data in addressing the hypotheses of disconnectivity and dysfunction within schizophrenia (SZ).

METHODS

We start with estimating and visualizing links within and among extracted multimodal data features using a Gaussian graphical model (GGM). We then propose a modularity-based method that can be applied to the GGM to identify links that are associated with mental illness across a multimodal data set. Through simulation and real data, we show our approach reveals important information about disease-related network disruptions that are missed with a focus on a single modality. We use functional MRI (fMRI), diffusion MRI (dMRI), and structural MRI (sMRI) to compute the fractional amplitude of low frequency fluctuations (fALFF), fractional anisotropy (FA), and gray matter (GM) concentration maps. These three modalities are analyzed using our modularity method.

RESULTS

Our results show missing links that are only captured by the cross-modal information that may play an important role in disconnectivity between the components.

CONCLUSION

We identified multimodal (fALFF, FA and GM) disconnectivity in the default mode network area in patients with SZ, which would not have been detectable in a single modality.

SIGNIFICANCE

The proposed approach provides an important new tool for capturing information that is distributed among multiple imaging modalities.

Volume of Lateral Geniculate Nucleus in Patients with Glaucoma in 7Tesla MRI

The aim of the study was to assess the volume of the lateral geniculate nucleus (LGN) in patients with open-angle glaucoma in 7Tesla MRI and to evaluate its relation to RNFL thickness and VF indices. Material and methods. The studied group consisted of 20 open-angle glaucoma patients with bilaterally the same stage of glaucoma (11 with early glaucoma and nine with advanced glaucoma) and nine healthy volunteers from the Department of Diagnostics and Microsurgery of Glaucoma, Medical University of Lublin, Poland. Circumpapillary RNFL-thickness measurements were performed using OCT in all patients and visual fields were performed in the glaucoma group. A 7Tesla MRI was performed to assess the volume of both lateral geniculate bodies. Results. The LGN volume varied significantly between groups from 122.1 ± 14.4 mm³ (right LGN) and 101.6 ± 13.3 mm³ (left LGN) in the control group to 80.2 ± 17.7 mm³ (right LGN) and 71.8 ± 14.2 mm³ (left LGN) in the advanced glaucoma group (right LGN p = 0.003, left LGN p = 0.018). However, volume values from early glaucoma: right LGN = 120.2 ± 26.5 mm³ and left LGN = 103.2 ± 28.0 mm³ differed significantly only from values from the advanced group (right LGN p = 0.006, left LGN p = 0.012), but not from controls (right LGN p = 0.998, left LGN p = 0.986). There were no significant correlations between visual field indices (MD (mean deviation) and VFI (visual field index)) and LGN volumes in both glaucoma groups. Significant correlations between mean RNFL (retinal nerve fiber layers) thickness and corresponding and contralateral LGN were observed for the control group (corresponding LGN: p = 0.064; contralateral LGN: p = 0.031) and early glaucoma (corresponding LGN: p = 0.017; contralateral LGN: p = 0.008), but not advanced glaucoma (corresponding LGN: p = 0.496; contralateral LGN: p = 0.258). Conclusions. The LGN volume decreases in the course of glaucoma. These changes are correlated with RNFL thickness in early stages of glaucoma and are not correlated with visual field indices.

Genetic and Neuroimaging Approaches to Understanding Post-Traumatic Stress Disorder

Post-traumatic stress disorder (PTSD) is a highly disabling condition, increasingly recognized as both a disorder of mental health and social burden, but also as an anxiety disorder characterized by fear, stress, and negative alterations in mood. PTSD is associated with structural, metabolic, and molecular changes in several brain regions and the neural circuitry. Brain areas implicated in the traumatic stress response include the amygdala, hippocampus, and prefrontal cortex, which play an essential role in memory function. Abnormalities in these brain areas are hypothesized to underlie symptoms of PTSD and other stress-related psychiatric disorders. Conventional methods of studying PTSD have proven to be insufficient for diagnosis, measurement of treatment efficacy, and monitoring disease progression, and currently, there is no diagnostic biomarker available for PTSD. A deep understanding of cutting-edge neuroimaging genetic approaches is necessary for the development of novel therapeutics and biomarkers to better diagnose and treat the disorder. A current goal is to understand the gene pathways that are associated with PTSD, and how those genes act on the fear/stress circuitry to mediate risk vs. resilience for PTSD. This review article explains the rationale and practical utility of neuroimaging genetics in PTSD and how the resulting information can aid the diagnosis and clinical management of patients with PTSD.