Magnetic resonance imaging as an approach towards identifying neuropathological biomarkers for Huntington's disease

Magnetic Resonance Imaging (MRI), functional MRI (fMRI) and Diffusion Tensor Imaging (DTI) have been central to characterisation of abnormalities in brain structure and function in both clinical and preclinical Huntington's disease (HD). One current challenge in clinical HD research is the identification of sensitive and reliable biomarkers to detect progressive neurodegeneration and neural dysfunction, which could be used to assess the effect of therapeutic intervention on brain structure and function in a HD clinical trial. To this end, both established and novel neuroimaging approaches could potentially provide sensitive, reliable and non-invasive tools to assess long-term and dynamic effects of treatment on specific brain regions, including their microstructure and connectivity. This review examines contributions from structural MRI, fMRI and DTI studies to our current understanding of preclinical and clinical HD, and critically appraises MRI methods potentially suitable for both scientific characterisation and for use as biomarkers in HD clinical trials. A combined neuroimaging approach incorporating structural MRI, fMRI and DTI is yet to be realised in HD clinical trials, however if proven to be sensitive and reliable, these methods could potentially serve as biomarkers for use in future clinical drug trials in HD.     

部分多模态MRI在重度抑郁症中的研究进展

重度抑郁症是最常见的高致残性的精神疾病之一,其发病机制尚不清楚。MRI技术作为非侵入性的神经影像技术,可揭示重度抑郁症患者大脑功能状态。与健康对照者相比,重度抑郁症患者额叶、颞叶、海马、扣带回、基底节、小脑等脑区功能改变,可能提示重度抑郁症的病理生理异常。现就多模态MRI,包括弥散张量成像(DTI)、弥散峰度成像(DKI)、磁共振波谱成像(MRS)、功能MRI(fMRI)、神经突方向分散度和密度成像(NODDI)在重度抑郁症中的最新研究成果进行综述,以期对其神经生物学机制有更充分的理解。     

Parallel group ICA+ICA: Joint estimation of linked functional network variability and structural covariation with application to schizophrenia

There is growing evidence that rather than using a single brain imaging modality to study its association with physiological or symptomatic features, the field is paying more attention to fusion of multimodal information. However, most current multimodal fusion approaches that incorporate functional magnetic resonance imaging (fMRI) are restricted to second‐level 3D features, rather than the original 4D fMRI data. This trade‐off is that the valuable temporal information is not utilized during the fusion step. Here we are motivated to propose a novel approach called “parallel group ICA+ICA” that incorporates temporal fMRI information from group independent component analysis (GICA) into a parallel independent component analysis (ICA) framework, aiming to enable direct fusion of first‐level fMRI features with other modalities (e.g., structural MRI), which thus can detect linked functional network variability and structural covariations. Simulation results show that the proposed method yields accurate intermodality linkage detection regardless of whether it is strong or weak. When applied to real data, we identified one pair of significantly associated fMRI‐sMRI components that show group difference between schizophrenia and controls in both modalities, and this linkage can be replicated in an independent cohort. Finally, multiple cognitive domain scores can be predicted by the features identified in the linked component pair by our proposed method. We also show these multimodal brain features can predict multiple cognitive scores in an independent cohort. Overall, results demonstrate the ability of parallel GICA+ICA to estimate joint information from 4D and 3D data without discarding much of the available information up front, and the potential for using this approach to identify imaging biomarkers to study brain disorders.     

Seeking Brain Biomarkers for Preventive Therapy in Huntington Disease

Huntington disease (HD) is a severe incurable nervous system disease that generally has an onset age of around 35–50, and is caused by a dominantly transmitted expansion mutation. A genetic test allows persons at risk, i.e., offspring or siblings of affected individuals, to discover their genetic status. Unaffected mutation‐positive subjects will manifest HD sometime during life. Despite major advances in research on pathogenic mechanisms, no studies have yet fully validated preventive therapy or biomarkers for use before the symptoms become clinically manifest. Seeking brain and peripheral biomarkers is a requisite to develop a cure for HD. Changes in the brain can be observed in vivo using methods such as structural magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), functional MRI (fMRI), and positron emission tomography (PET), detecting volumetric changes, microstructural and connectivity alterations, abnormalities in brain activity in response to specific tasks, and abnormalities in metabolism and receptor distribution. Although all these imaging techniques can detect early markers in asymptomatic HD gene carriers for premanifest screening and pharmacological responses to therapeutic interventions no single modality has yet provided and validated an optimal marker probably because this task requires an integrative multimodal imaging approach. In this article, we review the findings from imaging procedures in the attempt to identify potential brain markers, so‐called dry biomarkers, for possible application to further, yet unavailable, neuroprotective preventive therapies for HD manifestations.     

Multimodal frontostriatal connectivity underlies individual differences in self-esteem

A heightened sense of self-esteem is associated with a reduced risk for several types of affective and psychiatric disorders, including depression, anxiety and eating disorders. However, little is known about how brain systems integrate self-referential processing and positive evaluation to give rise to these feelings. To address this, we combined diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) to test how frontostriatal connectivity reflects long-term trait and short-term state aspects of self-esteem. Using DTI, we found individual variability in white matter structural integrity between the medial prefrontal cortex and the ventral striatum was related to trait measures of self-esteem, reflecting long-term stability of self-esteem maintenance. Using fMRI, we found that functional connectivity of these regions during positive self-evaluation was related to current feelings of self-esteem, reflecting short-term state self-esteem. These results provide convergent anatomical and functional evidence that self-esteem is related to the connectivity of frontostriatal circuits and suggest that feelings of self-worth may emerge from neural systems integrating information about the self with positive affect and reward. This information could potentially inform the etiology of diminished self-esteem underlying multiple psychiatric conditions and inform future studies of evaluative self-referential processing.     

Multimodal functional and structural imaging investigations in psychosis research

Substantial pathophysiological questions about the relationship of brain pathologies in psychosis can only be answered by multimodal neuroimaging approaches combining different imaging modalities such as structural MRI (sMRI), functional MRI (fMRI), diffusion tensor imaging (DTI), positron emission tomography (PET) and magnetic-resonance spectroscopy. In particular, the multimodal imaging approach has the potential to shed light on the neuronal mechanisms underlying the major brain structural and functional pathophysiological features of schizophrenia and high-risk states such as prefronto-temporal gray matter reduction, altered higher-order cognitive processing, or disturbed dopaminergic and glutamatergic neurotransmission. In recent years, valuable new findings have been revealed in these fields by multimodal imaging studies mostly reflecting a direct and aligned correlation of brain pathologies in psychosis. However, the amount of multimodal studies is still limited, and further efforts have to be made to consolidate previous findings and to extend the scope to other pathophysiological parameters contributing to the pathogenesis of psychosis. Here, investigating the genetic foundations of brain pathology relationships is a major challenge for future multimodal imaging applications in psychosis research.

     

Multimodal analysis of functional and structural disconnection in Alzheimer's disease using multiple kernel SVM

Alzheimer's disease (AD) patients exhibit alterations in the functional connectivity between spatially segregated brain regions which may be related to both local gray matter (GM) atrophy as well as a decline in the fiber integrity of the underlying white matter tracts. Machine learning algorithms are able to automatically detect the patterns of the disease in image data, and therefore, constitute a suitable basis for automated image diagnostic systems. The question of which magnetic resonance imaging (MRI) modalities are most useful in a clinical context is as yet unresolved. We examined multimodal MRI data acquired from 28 subjects with clinically probable AD and 25 healthy controls. Specifically, we used fiber tract integrity as measured by diffusion tensor imaging (DTI), GM volume derived from structural MRI, and the graph‐theoretical measures ‘local clustering coefficient’ and ‘shortest path length’ derived from resting‐state functional MRI (rs‐fMRI) to evaluate the utility of the three imaging methods in automated multimodal image diagnostics, to assess their individual performance, and the level of concordance between them. We ran the support vector machine (SVM) algorithm and validated the results using leave‐one‐out cross‐validation. For the single imaging modalities, we obtained an area under the curve (AUC) of 80% for rs‐fMRI, 87% for DTI, and 86% for GM volume. When it came to the multimodal SVM, we obtained an AUC of 82% using all three modalities, and 89% using only DTI measures and GM volume. Combined multimodal imaging data did not significantly improve classification accuracy compared to the best single measures alone. Hum Brain Mapp 36:2118–2131, 2015. © 2015 Wiley Periodicals, Inc.     

Toward dysfunctional connectivity: a review of neuroimaging findings in pediatric major depressive disorder

Child and adolescent psychiatric neuroimaging research typically lags behind similar advances in adult disorders. While the pediatric depression imaging literature is less developed, a recent surge in interest has created the need for a synthetic review of this work. Major findings from pediatric volumetric and functional magnetic resonance imaging (fMRI), magnetic resonance spectroscopy (MRS), diffusion tensor imaging (DTI) and resting state functional connectivity studies converge to implicate a corticolimbic network of key areas that work together to mediate the task of emotion regulation. Imaging the brain of children and adolescents with unipolar depression began with volumetric studies of isolated brain regions that served to identify key prefrontal, cingulate and limbic nodes of depression-related circuitry elucidated from more recent advances in DTI and functional connectivity imaging. Systematic review of these studies preliminarily suggests developmental differences between findings in youth and adults, including prodromal neurobiological features, along with some continuity across development.     

Multimodal imaging reveals a complex pattern of dysfunction in corticolimbic pathways in major depressive disorder

Major depressive disorder (MDD) is highly prevalent and associated with considerable morbidity, yet its pathophysiology remains only partially understood. While numerous studies have investigated the neurobiological correlates of MDD, most have used only a single neuroimaging modality. In particular, diffusion tensor imaging (DTI) studies have failed to yield uniform results. In this context, examining key tracts and using information from multiple neuroimaging modalities may better characterize potential abnormalities in the MDD brain. This study analyzed data from 30 participants with MDD and 26 healthy participants who underwent DTI, magnetic resonance spectroscopy (MRS), resting‐state functional magnetic resonance imaging (fMRI), and magnetoencephalography (MEG). Tracts connecting the subgenual anterior cingulate cortex (sgACC) and the left and right amygdala, as well as connections to the left and right hippocampus and thalamus, were examined as target areas. Reduced fractional anisotropy (FA) was observed in the studied tracts. Significant differences in the correlation between medial prefrontal glutamate concentrations and FA were also observed between MDD and healthy participants along tracts connecting the sgACC and right amygdala; healthy participants exhibited a strong correlation but MDD participants showed no such relationship. In the same tract, a correlation was observed between FA and subsequent antidepressant response to ketamine infusion in MDD participants. Exploratory models also suggested group differences in the relationship between DTI, fMRI, and MEG measures. This study is the first to combine MRS, DTI, fMRI, and MEG data to obtain multimodal indices of MDD and antidepressant response and may lay the foundation for similar future analyses.     

Improved prediction of brain age using multimodal neuroimaging data

Brain age prediction based on imaging data and machine learning (ML) methods has great potential to provide insights into the development of cognition and mental disorders. Though different ML models have been proposed, a systematic comparison of ML models in combination with imaging features derived from different modalities is still needed. In this study, we evaluate the prediction performance of 36 combinations of imaging features and ML models including deep learning. We utilize single and multimodal brain imaging data including MRI, DTI, and rs‐fMRI from a large data set with 839 subjects. Our study is a follow‐up to the initial work (Liang et al., 2019. Human Brain Mapping) to investigate different analytic strategies to combine data from MRI, DTI, and rs‐fMRI with the goal to improve brain age prediction accuracy. Additionally, the traditional approach to predicting the brain age gap has been shown to have a systematic bias. The potential nonlinear relationship between the brain age gap and chronological age has not been thoroughly tested. Here we propose a new method to correct the systematic bias of brain age gap by taking gender, chronological age, and their interactions into consideration. As the true brain age is unknown and may deviate from chronological age, we further examine whether various levels of behavioral performance across subjects predict their brain age estimated from neuroimaging data. This is an important step to quantify the practical implication of brain age prediction. Our findings are helpful to advance the practice of optimizing different analytic methodologies in brain age prediction.     

Diffusion Tensor and Functional Magnetic Resonance Imaging of Diffuse Axonal Injury and Resulting Language Impairment

Diffuse axonal injury (DAI) is a common aftermath of brain trauma. The diagnosis of DAI is often difficult using conventional magnetic resonance imaging (MRI). We report a diffusion tensor imaging (DTI) study of a patient who sustained DAI presenting with language impairment. Fractional anisotropy (FA) and DTI tractography revealed a reduction of white matter integrity in the left frontal and medial temporal areas. White matter damage identified by DTI was correlated with the patient's language impairment as assessed by functional MRI (fMRI) and a neuropsychological exam. The findings demonstrate the utility of DTI for identifying white matter changes secondary to traumatic brain injury (TBI).     

Combining fMRI and SNP data to investigate connections between brain function and genetics using parallel ICA

There is current interest in understanding genetic influences on both healthy and disordered brain function. We assessed brain function with functional magnetic resonance imaging (fMRI) data collected during an auditory oddball task--detecting an infrequent sound within a series of frequent sounds. Then, task-related imaging findings were utilized as potential intermediate phenotypes (endophenotypes) to investigate genomic factors derived from a single nucleotide polymorphism (SNP) array. Our target is the linkage of these genomic factors to normal/abnormal brain functionality. We explored parallel independent component analysis (paraICA) as a new method for analyzing multimodal data. The method was aimed to identify simultaneously independent components of each modality and the relationships between them. When 43 healthy controls and 20 schizophrenia patients, all Caucasian, were studied, we found a correlation of 0.38 between one fMRI component and one SNP component. This fMRI component consisted mainly of parietal lobe activations. The relevant SNP component was contributed to significantly by 10 SNPs located in genes, including those coding for the nicotinic alpha-7 cholinergic receptor, aromatic amino acid decarboxylase, disrupted in schizophrenia 1, among others. Both fMRI and SNP components showed significant differences in loading parameters between the schizophrenia and control groups (P = 0.0006 for the fMRI component; P = 0.001 for the SNP component). In summary, we constructed a framework to identify interactions between brain functional and genetic information; our findings provide a proof-of-concept that genomic SNP factors can be investigated by using endophenotypic imaging findings in a multivariate format.     

Resting State fMRI-guided Fiber Clustering: Methods and Applications

Clustering streamline fibers derived from diffusion tensor imaging (DTI) data into functionally meaningful bundles with group-wise correspondences across individuals and populations has been a fundamental step for tract-based analysis of white matter integrity and brain connectivity modeling. Many approaches of fiber clustering reported in the literature so far used geometric and/or anatomic information derived from structural MRI and/or DTI data only. In this paper, we take a novel, alternative multimodal approach of combining resting state fMRI (rsfMRI) and DTI data, and propose to use functional coherence as the criterion to guide the clustering of fibers derived from DTI tractography. Specifically, the functional coherence between two streamline fibers is defined as their rsfMRI time series’ correlations, and the affinity propagation (AP) algorithm is used to cluster DTI-derived streamline fibers into bundles. Currently, we use the corpus callosum (CC) fibers, which are the largest fiber bundle in the brain, as a test-bed for methodology development and validation. Our experimental results have shown that the proposed rsfMRI-guided fiber clustering method can achieve functionally homogeneous bundles that are reasonably consistent across individuals and populations, suggesting the close relationship between structural connectivity and brain function. The clustered fiber bundles were evaluated and validated via the benchmark data provided by task-based fMRI, via reproducibility studies, and via comparison with other methods. Finally, we have applied the proposed framework on a multimodal rsfMRI/DTI dataset of schizophrenia (SZ) and reproducible results were obtained.     

Imaging resting state brain function in multiple sclerosis

In multiple sclerosis (MS), physical and cognitive deficits not only reflect structural damage, but also functional imbalance in and between brain networks. Resting-state functional magnetic resonance imaging (fMRI) allows one to investigate intrinsic, synchronized brain activity across the whole brain, and to measure the degree of functional correlation between different cortical regions. This review describes the major findings obtained in MS patients at different clinical stages using resting state fMRI, and discusses how the use of fMRI techniques may improve our ability to identify novel biomarkers useful in the context of the diagnostic work-up, establishing prognosis and monitoring treatment.     

Diagnostic approaches to predict persistent post-traumatic symptoms after mild traumatic brain injury – a literature review

Mild traumatic brain injury (mTBI) is one of the most frequently diagnosed neurological disorders in emergency departments. Although there are established recommendations for the diagnosis and treatment in the acute stage, there is an on-going debate in which diagnostic methods and risk factors predict unfavourable long-term outcome after mTBI. This literature review addresses the question, which diagnostic approaches may best predict persistent post-traumatic symptoms (pPTS). A literature search for experimental studies from January 2000 to September 2014 evaluating the following diagnostic approaches (1) susceptibility weighted imaging (SWI), (2) diffusion tensor imaging (DTI), (3) magnetic resonance spectroscopy (MRS), (4) functional magnetic resonance imaging (fMRI), as predictive factors of pPTS or unfavourable cognitive outcome in adult populations with mTBI was performed. DTI has been proved to be a valuable tool to identify diffuse axonal injury (DAI) after mTBI. Additionally, some studies showed associations between DAI and unfavourable cognitive outcome. SWI has shown to be a highly sensitive imaging method to identify microbleeds. The presence and quantity of microbleeds in this imaging technique can further provide aetiological evidence for pPTS. MRS provides information about local neurons metabolism and preliminary data show that creatine–phosphocreatine levels measured after mTBI are predictive of cognitive outcome and emotional distress. The results of one study have shown fMRI as a useful tool to differentiate mTBI patients with pPTS from controls and mTBI patients without pPTS in a resting-state condition. From the evaluated diagnostic approaches to predict pPTS after mTBI, DTI, SWI, MRS, and fMRI seem to have adequate sensitivity and specificity as predictive diagnostic tools for pPTS. Large longitudinal clinical trials are warranted to validate the prognostic applicability and practicability in daily clinical practice.     

Multimodal MR Imaging: Functional, Diffusion Tensor, and Chemical Shift Imaging in a Patient with Localization-Related Epilepsy

PURPOSE: To demonstrate the integration of complementary functional and structural data acquired with magnetic resonance imaging (MRI) in a patient with localization-related epilepsy. METHODS: We studied a patient with partial and secondarily generalized seizures and a hemiparesis due to a malformation of cortical development (MCD) in the right hemisphere by using EEG-triggered functional MRI (fMRI), diffusion tensor imaging (DTI), and chemical shift imaging (CSI). RESULTS: fMRI revealed significant changes in regional blood oxygenation associated with interictal epileptiform discharges within the MCD. DTI showed a heterogeneous microstructure of the MCD with reduced fractional anisotropy, a high mean diffusivity, and displacement of myelinated tracts. CSI demonstrated low N-acetyl aspartate (NAA) concentrations in parts of the MCD. CONCLUSIONS: The applied MR methods described functional, microstructural, and biochemical characteristics of the epileptogenic tissue that cannot be obtained with other noninvasive means and thus improve the understanding of the pathophysiology of epilepsy.     

From research to clinical practice: implementation of functional magnetic imaging and white matter tractography in the clinical environment

In the last two decades functional magnetic resonance imaging (fMRI) has dominated research in neuroscience. However, only recently has it taken the first steps in translation to the clinical field. In this paper we describe the advantages of fMRI and DTI and the possible benefits of implementing these methods in clinical practice. We review the current clinical usages of fMRI and DTI and discuss the challenges and difficulties of translating these methods to clinical use. The most common application today is in neurosurgery. fMRI and DTI are done preoperatively for brain tumor patients who are having tumors removed and for epilepsy patients who are candidates for temporal resection. Imaging results supply the neurosurgeon with essential information regarding possible functional damage and thereby aid both in planning and performing surgery. Scientific research suggests more promising potential implementations of fMRI and DTI in improving diagnosis and rehabilitation. These advanced imaging methods can be used for pre-symptomatic diagnosis, as a differentiating biomarker in the absence of anatomical measurements, and for identification of mental response in the absence of motor-sensory abilities. These methods can aid and direct rehabilitation by predicting the success of possible interventions and rehabilitation options and by supplying a measure for biofeedback. This review opens a window to the state of the art neuroimaging methods being implemented these days into the clinical practice and provides a glance to the future clinical possibilities of fMRI and DTI.     

Resolution of homonymous visual field loss documented with functional magnetic resonance and diffusion tensor imaging.

A 68-year-old man developed right homonymous hemianopic paracentral scotomas from acute infarction of the left extrastriate area. He was studied over the ensuing 12 months with visual fields, conventional MRI, functional MRI (fMRI), and diffusion tensor imaging (DTI). As the visual field defect became smaller, fMRI demonstrated progressively larger areas of cortical activation. DTI initially showed that the lesioned posterior optic radiations were completely interrupted. This interruption lessened in time and had disappeared by one year after onset. fMRI and DTI are innovative measures to follow functional and structural recovery in the central nervous system. This is the first reported application of these imaging techniques to acute cerebral visual field disorders.     

Advanced neuroimaging applied to veterans and service personnel with traumatic brain injury: state of the art and potential benefits

Traumatic brain injury (TBI) remains one of the most prevalent forms of morbidity among Veterans and Service Members, particularly for those engaged in the conflicts in Iraq and Afghanistan. Neuroimaging has been considered a potentially useful diagnostic and prognostic tool across the spectrum of TBI generally, but may have particular importance in military populations where the diagnosis of mild TBI is particularly challenging, given the frequent lack of documentation on the nature of the injuries and mixed etiologies, and highly comorbid with other disorders such as post-traumatic stress disorder, depression, and substance misuse. Imaging has also been employed in attempts to understand better the potential late effects of trauma and to evaluate the effects of promising therapeutic interventions. This review surveys the use of structural and functional neuroimaging techniques utilized in military studies published to date, including the utilization of quantitative fluid attenuated inversion recovery (FLAIR), susceptibility weighted imaging (SWI), volumetric analysis, diffusion tensor imaging (DTI), magnetization transfer imaging (MTI), positron emission tomography (PET), magnetoencephalography (MEG), task-based and resting state functional MRI (fMRI), arterial spin labeling (ASL), and magnetic resonance spectroscopy (MRS). The importance of quality assurance testing in current and future research is also highlighted. Current challenges and limitations of each technique are outlined, and future directions are discussed.