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.     

A comparative evaluation of quantitative neuroimaging measurements of brain status in HIV infection

Diffusion tensor imaging (DTI), magnetization transfer imaging (MT) and automated brain volumetry were used to summarize brain involvement in human immunodeficiency virus (HIV) infection. A multiparametric neuroimaging protocol was implemented at 1.5T in 10 HIV+ and 24 controls. Various summary parameters were calculated based on DTI, MT, and automated brain volumetry. The magnitude of the difference, as well as the between-group discrimination, was determined for each measure. Bivariate correlations were computed and redundancy among imaging parameters was examined by principal factor analysis. Significant or nearly significant differences were found for most measures. Large Cohen's d effect sizes were indicated for mean diffusivity (MD), fractional anisotropy (FA), magnetization transfer ratio (MTR) and gray matter volume fraction (GM). Between-group discrimination was excellent for FA and MTR and acceptable for MD. Correlations among all imaging parameters could be explained by three factors, possibly reflecting general atrophy, neuronal loss, and alterations. This investigation supports the utility of summary measurements of brain involvement in HIV infection. The findings also support assumptions concerning the enhanced sensitivity of DTI and MT to atrophic as well as alterations in the brain. These findings are broadly generalizable to brain imaging studies of physiological and pathological processes.     

Brain development of infant and MRI by diffusion tensor imaging

Studying how the brain develops and becomes functional is important to understand how the man has been able to develop specific cognitive abilities, and to comprehend the complexity of some developmental pathologies. Thanks to magnetic resonance imaging (MRI), it is now possible to image the baby's immature brain and to consider subtle correlations between the brain anatomical development and the early acquisition of cognitive functions. Dedicated methodologies for image acquisition and post-treatment must then be used because the size of cerebral structures and the image contrast are very different in comparison with the adult brain, and because the examination length is a major constraint. Two recent studies have evaluated the developing brain under an original perspective. The first one has focused on cortical folding in preterm newborns, from 6?to 8?months of gestational age, assessed with T2-weighted conventional MRI. The second study has mapped the organization and maturation of white matter fiber bundles in 1- to 4-month-old healthy infants with diffusion tensor imaging (DTI). Both studies have enabled to highlight spatio-temporal differences in the brain regions' maturation, as well as early anatomical asymmetries between cerebral hemispheres. These studies emphasize the potential of MRI to evaluate brain development compared with the infant's psychomotor acquisitions after birth.     

Quantitative evaluation of brain development using anatomical MRI and diffusion tensor imaging

The development of the brain is structure-specific, and the growth rate of each structure differs depending on the age of the subject. Magnetic resonance imaging (MRI) is often used to evaluate brain development because of the high spatial resolution and contrast that enable the observation of structure-specific developmental status. Currently, most clinical MRIs are evaluated qualitatively to assist in the clinical decision-making and diagnosis. The clinical MRI report usually does not provide quantitative values that can be used to monitor developmental status. Recently, the importance of image quantification to detect and evaluate mild-to-moderate anatomical abnormalities has been emphasized because these alterations are possibly related to several psychiatric disorders and learning disabilities. In the research arena, structural MRI and diffusion tensor imaging (DTI) have been widely applied to quantify brain development of the pediatric population. To interpret the values from these MR modalities, a “growth percentile chart,” which describes the mean and standard deviation of the normal developmental curve for each anatomical structure, is required. Although efforts have been made to create such a growth percentile chart based on MRI and DTI, one of the greatest challenges is to standardize the anatomical boundaries of the measured anatomical structures. To avoid inter- and intra-reader variability about the anatomical boundary definition, and hence, to increase the precision of quantitative measurements, an automated structure parcellation method, customized for the neonatal and pediatric population, has been developed. This method enables quantification of multiple MR modalities using a common analytic framework. In this paper, the attempt to create an MRI- and a DTI-based growth percentile chart, followed by an application to investigate developmental abnormalities related to cerebral palsy, Williams syndrome, and Rett syndrome, have been introduced. Future directions include multimodal image analysis and personalization for clinical application.     

Super‐resolution track‐density imaging of thalamic substructures: Comparison with high‐resolution anatomical magnetic resonance imaging at 7.0T

The thalamus is one of the most important brain structures, with strong connections between subcortical and cortical areas of the brain. Most of the incoming information to the cortex passes through the thalamus. Accurate identification of substructures of the thalamus is therefore of great importance for the understanding of human brain connectivity. Direct visualization of thalamic substructures, however, is not easily achieved with currently available magnetic resonance imaging (MRI), including ultra‐high field MRI such as 7.0T, mainly due to the limited contrast between the relevant structures. Recently, improvements in ultra‐high field 7.0T MRI have opened the possibility of observing thalamic substructures by well‐adjusted high‐resolution T₁‐weighted imaging. Moreover, the recently developed super‐resolution track‐density imaging (TDI) technique, based on results from whole‐brain fiber‐tracking, produces images with sub‐millimeter resolution. These two methods enable us to show markedly improved anatomical detail of the substructures of the thalamus, including their detailed locations and directionality. In this study, we demonstrate the role of TDI for the visualization of the substructures of the thalamic nuclei, and relate these images to T₁‐weighted imaging at 7.0T MRI. Hum Brain Mapp 34:2538–2548, 2013. © 2012 Wiley Periodicals, Inc.     

Clinical application of high and ultra high-field MRI

The 21st century ushered in the century of human beings. The previous era characterized by the chase after super technology has been replaced by a new era which focuses on the meaning of human existence and quality of life. Clinical practice has accordingly also undergone rapid change. Amongst the many competing technologies, clearly magnetic resonance technology, especially ultra high-field magnetic resonance imaging, plays a major role in defining current clinical practice. Elimination of all invasive aspects from diagnostic imaging, including intravenous infusion or use of ionizing radiation, is one of the final goals of the new generation of clinical imaging. This goal is especially worthwhile when one consider the welfare of children. Technological MRI advancements are steadily bridging the gap towards this goal. With T2 reversed and three-dimensional anisotropy (3DAC) contrast imaging on a 3.0T system, the anatomical resolution of routine clinical images has reached a level of resolution equivalent to general pathology. Realistic imaging microscopy application is also on the horizon with the establishment of clinical 7.0T systems. Individual brain activation maps can now be readily obtained under clinical settings thanks to high-field functional MRI (fMRI). Nevertheless, because active self-organizing processes of cortical functionalities are under active development in the pediatric population, fMRI has only limited, if any, clinical usage in children. Similarly, whereas connectivity analysis in the individual patient using diffusion tensor imaging (DTI) has little clinical usage in the pediatric population, DTI can be successfully applied to multiple subject analysis for exploring unknown connectivity abnormalities in this age group. Magnetic resonance spectroscopy (MRS) and its pictorial display (spectroscopic imaging) is now finding more and more clinical applications across the age spectrum of patients.     

Multimodal Magnetic Resonance Imaging for Brain Disorders: Advances and Perspectives

Modern brain imaging technologies play essential roles in our understanding of brain information processing and the mechanisms of brain disorders. Magnetic Resonance Imaging (MRI) and Diffusion Tensor Imaging (DTI) can image the anatomy and structure of the brain. In addition, functional MRI (fMRI) can identify active regions, patterns of functional connectivities and functional networks during either tasks that are specifically related to various aspects of brain function or during the resting state. The merging of such structural and functional information obtained from brain imaging may be able to enhance our understanding of how the brain works and how its diseases can occur. In this paper, we will review advances in both methodologies and clinical applications of multimodal MRI technologies, including MRI, DTI, and fMRI. We will also give our perspectives for the future in these fields. The ultimate goal of our study is to find early biomarkers based on multimodal neuroimages and genome datasets for brain disorders. More importantly, future studies should focus on detecting exactly where and how these brain disorders affect the human brain. It would also be also very interesting to identify the genetic basis of the anatomical and functional abnormalities in the brains of people who have neurological and psychiatric disorders. We believe that we can use brain images to obtain effective biomarkers for various brain disorders with the aid of developing computational methods and models.     

The power of neuroimaging biomarkers for screening frontotemporal dementia

Frontotemporal dementia (FTD) is a clinically and pathologically heterogeneous neurodegenerative disease that can result from either frontotemporal lobar degeneration (FTLD) or Alzheimer's disease (AD) pathology. It is critical to establish statistically powerful biomarkers that can achieve substantial cost‐savings and increase the feasibility of clinical trials. We assessed three broad categories of neuroimaging methods to screen underlying FTLD and AD pathology in a clinical FTD series: global measures (e.g., ventricular volume), anatomical volumes of interest (VOIs) (e.g., hippocampus) using a standard atlas, and data‐driven VOIs using Eigenanatomy. We evaluated clinical FTD patients (N = 93) with cerebrospinal fluid, gray matter (GM) magnetic resonance imaging (MRI), and diffusion tensor imaging (DTI) to assess whether they had underlying FTLD or AD pathology. Linear regression was performed to identify the optimal VOIs for each method in a training dataset and then we evaluated classification sensitivity and specificity in an independent test cohort. Power was evaluated by calculating minimum sample sizes required in the test classification analyses for each model. The data‐driven VOI analysis using a multimodal combination of GM MRI and DTI achieved the greatest classification accuracy (89% sensitive and 89% specific) and required a lower minimum sample size (N = 26) relative to anatomical VOI and global measures. We conclude that a data‐driven VOI approach using Eigenanatomy provides more accurate classification, benefits from increased statistical power in unseen datasets, and therefore provides a robust method for screening underlying pathology in FTD patients for entry into clinical trials. Hum Brain Mapp 35:4827–4840, 2014. © 2014 Wiley Periodicals, Inc .     

Diffusion tensor and perfusion imaging of brain tumors in high-field MR imaging

Diffusion tensor imaging (DTI) and perfusion-weighted imaging (PWI) are essential tools for diagnosing, differentiating, and monitoring brain tumors. High-field MRI provides higher signal-to-noise ratio, shorter scan time, and better image quality. One-stop multiparametric study, including DTI and PWI, is possible with high-field MRI in brain tumors. DTI can be used for assessing spatial relationship between major white matter tract and tumor, differentiating gliomas from nonglial tumors, and postoperative evaluation. PWI provides reliable biomarkers for glioma grading, therapeutic responses, and differential diagnosis of various brain tumors. With higher field strength, better-quality DTI and PWI can raise the diagnostic accuracy in brain tumors.     

Quantitative white matter analysis by diffusion tensor imaging and potential functional correlation

Diffusion tensor imaging (DTI) is an MRI modality used to measure the thermal motion of water molecules by combining the measured water diffusion with a simple tensor model of a 3 × 3 symmetric matrix. Since there are many structures that restrict the free motion of water molecules in the brain, we can use the diffusion property of water to study the brain anatomy. Because DTI can provide directional information about axonal fiber bundles, this technique may be one of the most effective MR tools for the investigation of the human white matter anatomy in vivo. Along with the qualitative analysis of fiber pathways using tractography, the quantitative analysis using DTI enables researchers to investigate relationships between white matter anatomy and brain functions as well as to identify tract-specific developmental patterns or disease-specific alterations of the fiber tracts. Several methods have been proposed for whole-brain DTI analysis without an a priori hypothesis. Voxel-based analysis (VBA) is one of the most widely used approaches, although it has concerning limitations, especially when isotropic spatial smoothing is applied. Alternative methods such as tract-based spatial statistics and atlas-based analysis have been introduced to overcome the limitations of VBA. Future studies combining the anatomical connectivity illustrated by using DTI and the functional connectivity illustrated by using resting-state fMRI will provide an emerging landscape of human brain connectivity.     

White matter abnormalities and brain activation in schizophrenia: a combined DTI and fMRI study

Diffusion tensor imaging (DTI) studies of schizophrenia have revealed white matter abnormalities in several areas of the brain. The functional impact on either psychopathology or cognition remains, however, poorly understood. Here we analysed both functional MRI (during a working memory task) and DTI data sets in 18 patients with schizophrenia and 18 controls. Firstly, DTI analyses revealed reductions of fractional anisotropy (FA) in the right medial temporal lobe adjacent to the right parahippocampal gyrus, likely to contain fibres of the inferior cingulum bundle, and in the right frontal lobe. Secondly, functional MRI revealed prefrontal, superior parietal and occipital relative hypoactivation in patients with the main effect of task. This was accounted for by reduced prefrontal activation during the encoding phase of the task, but not during maintenance or retrieval phases. Thirdly, we found a direct correlation in patients between the frontal FA reduction (but not medial temporal reductions) and fMRI activation in regions in the prefrontal and occipital cortex. Our study combining fMRI and DTI thus demonstrates altered structure-function relationships in schizophrenia. It highlights a potential relationship between anatomical changes in a frontal-temporal anatomical circuit and functional alterations in the prefrontal cortex.     

Magnetic Resonance Imaging of Functional Anatomy: Use for Small Animal Epilepsy Models

Summary:  Neuroimaging has greatly assisted the diagnosis and treatment of epilepsy. Volumetric analysis, diffusion-weighted imaging, and other magnetic resonance imaging (MRI) modalities provide a clear picture of altered anatomical structures in both focal and nonfocal disease. More recently, advances in novel imaging methodologies have provided unique insights into this disease. Two examples include manganese-enhanced MRI (MEMRI) and diffusion tensor imaging (DTI). MEMRI involves injection of MnCl₂ to evaluate neuronal activity where it is actively transported. Areas of neuronal hyperactivity are expected to have altered uptake and transport. Mapping of activation along preferential uptake pathways can be confirmed by T₁-weighted imaging. DTI uses the intrinsic preferential mobility of water movement along axonal pathways to map anatomical regions. DTI has been used to investigate white matter disease and is now being applied to clinical and, to a lesser extent, animal investigations of seizure disorders. These two diverse MRI methods can be applied to animal models to provide important information about the functional status of anatomical regions that may be altered by epilepsy.     

A review of diffusion tensor imaging studies in schizophrenia

Both post-mortem and neuroimaging studies have contributed significantly to what we know about the brain and schizophrenia. MRI studies of volumetric reduction in several brain regions in schizophrenia have confirmed early speculations that the brain is disordered in schizophrenia. There is also a growing body of evidence suggesting that a disturbance in connectivity between different brain regions, rather than abnormalities within the separate regions themselves, are responsible for the clinical symptoms and cognitive dysfunctions observed in this disorder. Thus an interest in white matter fiber tracts, subserving anatomical connections between distant, as well as proximal, brain regions, is emerging. This interest coincides with the recent advent of diffusion tensor imaging (DTI), which makes it possible to evaluate the organization and coherence of white matter fiber tracts. This is an important advance as conventional MRI techniques are insensitive to fiber tract direction and organization, and have not consistently demonstrated white matter abnormalities. DTI may, therefore, provide important new information about neural circuitry, and it is increasingly being used in neuroimaging studies of psychopathological disorders. Of note, in the past five years 18 DTI studies in schizophrenia have been published, most describing white matter abnormalities. Questions still remain, however, regarding what we are measuring that is abnormal in this disease, and how measures obtained using one method correspond to those obtained using other methods? Below we review the basic principles involved in MR-DTI, followed by a review of the different methods used to evaluate diffusion. Finally, we review MR-DTI findings in schizophrenia.     

Multimodal magnetic resonance imaging: The coordinated use of multiple,mutually informative probes to understand brain structure and function

Differing imaging modalities provide unique channels of information to probe differing aspects of the brain's structural or functional organization. In combination, differing modalities provide complementary and mutually informative data about tissue organization that is more than their sum. We acquired and spatially coregistered data in four MRI modalities—anatomical MRI, functional MRI, diffusion tensor imaging (DTI), and magnetic resonance spectroscopy (MRS)—from 20 healthy adults to understand how interindividual variability in measures from one modality account for variability in measures from other modalities at each voxel of the brain. We detected significant correlations of local volumes with the magnitude of functional activation, suggesting that underlying variation in local volumes contributes to individual variability in functional activation. We also detected significant inverse correlations of NAA (a putative measure of neuronal density and viability) with volumes of white matter in the frontal cortex, with DTI‐based measures of tissue organization within the superior longitudinal fasciculus, and with the magnitude of functional activation and default‐mode activity during simple visual and motor tasks, indicating that substantial variance in local volumes, white matter organization, and functional activation derives from an underlying variability in the number or density of neurons in those regions. Many of these imaging measures correlated with measures of intellectual ability within differing brain tissues and differing neural systems, demonstrating that the neural determinants of intellectual capacity involve numerous and disparate features of brain tissue organization, a conclusion that could be made with confidence only when imaging the same individuals with multiple MRI modalities. Hum Brain Mapp, 2013. © 2011 Wiley Periodicals, Inc.     

Characterization of excitatory synaptic transmission in the anterior cingulate cortex of adult tree shrew

The tree shrew, as a primate-like animal model, has been used for studying high brain functions such as social emotion and spatial learning memory. However, little is known about the excitatory synaptic transmission in cortical brain areas of the tree shrew. In the present study, we have characterized the excitatory synaptic transmission and intrinsic properties of pyramidal neurons in the anterior cingulate cortex (ACC) of the adult tree shrew, a key cortical region for pain perception and emotion. We found that glutamate is the major excitatory transmitter for fast synaptic transmission. Excitatory synaptic responses induced by local stimulation were mediated by AMPA and kainate (KA) receptors. As compared with mice, AMPA and KA receptor mediated responses were significantly greater. Interestingly, the frequency of spontaneous excitatory postsynaptic currents (sEPSCs) and miniature excitatory postsynaptic currents (mEPSCs) in tree shrews was significantly less than that of mice. Moreover, both the ratio of paired-pulse facilitation (PPF) and the time of 50% decay for fast blockade of NMDA receptor mediated EPSCs were greater in the tree shrew. Finally, tree shrew neurons showed higher initial firing frequency and neuronal excitability with a cell type-specific manner in the ACC. Our studies provide the first report of the basal synaptic transmission in the ACC of adult tree shrew.     

Whole mouse brain connectomics

Methods have been developed to allow quantitative connectivity of the whole fixed mouse brain by means of magnetic resonance imaging (MRI). We have translated what we have learned in clinical imaging to the very special domain of the mouse brain. Diffusion tensor imaging (DTI) of perfusion fixed specimens can now be performed with spatial resolution of 45 μm³, that is, voxels that are 21,000 times smaller than the human connectome protocol. Specimen preparation has been optimized through an active staining protocol using a Gd chelate. Compressed sensing has been integrated into high performance reconstruction and post processing pipelines allowing acquisition of a whole mouse brain connectome in <12 hr. The methods have been validated against retroviral tracer studies. False positive tracts, which are especially problematic in clinical studies, have been reduced substantially to ~28%. The methods have been streamlined to provide high-fidelity, whole mouse brain connectomes as a routine study. The data package provides holistic insight into the mouse brain with anatomic definition at the meso-scale, quantitative volumes of subfields, scalar DTI metrics, and quantitative tractography.     

Anatomical brain connectivity and positive symptoms of schizophrenia: A diffusion tensor imaging study

Structural brain changes in schizophrenia are well documented in the neuroimaging literature. The classical morphometric analyses of magnetic resonance imaging (MRI) data have recently been supplemented by diffusion tensor imaging (DTI), which mainly assesses changes in white matter (WM). DTI increasingly provides evidence for abnormal anatomical connectivity in schizophrenia, most often using fractional anisotropy (FA) as an indicator of the integrity of WM tracts. To better understand the clinical significance of such anatomical changes, we studied FA values in a whole-brain analysis comparing paranoid schizophrenic patients with a history of auditory hallucinations and matched healthy controls. The relationship of WM changes to psychopathology was assessed by correlating FA values with PANSS scores (positive symptoms and severity of auditory hallucinations) and with illness duration. Schizophrenic patients showed FA reductions indicating WM integrity disturbance in the prefrontal regions, external capsule, pyramidal tract, occipitofrontal fasciculus, superior and inferior longitudinal fasciculi, and corpus callosum. The arcuate fasciculus was the only tract which showed increased FA values in patients. Increased FA values in this region correlated with increased severity of auditory hallucinations and length of illness. Our results suggest that local changes in anatomical integrity of WM tracts in schizophrenia may be related to patients' clinical presentation.     

Quantification of age- and gender-related changes in diffusion tensor imaging indices in deep grey matter of the normal human brain

This study aimed to demonstrate age-related and gender-related changes in diffusion tensor imaging (DTI) indices of deep grey matter (GM) nuclei of the normal human brain. DTI was performed on 142 subjects (age: 10-52 years). Regions of interest were placed on the caudate nucleus (CN), putamen, globus pallidus, frontal white matter (WM), occipital WM, anterior and posterior limb of internal capsule, genu of the corpus callosum and splenium in all participants. The quadratic regression model was used to describe age-related and gender-related changes in DTI indices for GM and WM. We observed increased fractional anisotropy (FA) values with age up to adulthood in GM, and a rise up to the third decade of life followed by a decrease in FA for WM. We observed higher FA values in males compared to females in CN and all WM regions. Decreased mean diffusivity with age was observed in GM and WM irrespective of gender. This normative data may be valuable in the diagnosis of neurodegenerative diseases.     

Gray Matter Abnormalities in Idiopathic Parkinson's Disease: Evaluation by Diffusional Kurtosis Imaging and Neurite Orientation Dispersion and Density Imaging

Mapping gray matter (GM) pathology in Parkinson's disease (PD) with conventional MRI is challenging, and the need for more sensitive brain imaging techniques is essential to facilitate early diagnosis and assessment of disease severity. GM microstructure was assessed with GM‐based spatial statistics applied to diffusion kurtosis imaging (DKI) and neurite orientation dispersion imaging (NODDI) in 30 participants with PD and 28 age‐ and gender‐matched controls. These were compared with currently used assessment methods such as diffusion tensor imaging (DTI), voxel‐based morphometry (VBM), and surface‐based cortical thickness analysis. Linear discriminant analysis (LDA) was also used to test whether subject diagnosis could be predicted based on a linear combination of regional diffusion metrics. Significant differences in GM microstructure were observed in the striatum and the frontal, temporal, limbic, and paralimbic areas in PD patients using DKI and NODDI. Significant correlations between motor deficits and GM microstructure were also noted in these areas. Traditional VBM and surface‐based cortical thickness analyses failed to detect any GM differences. LDA indicated that mean kurtosis (MK) and intra cellular volume fraction (ICVF) were the most accurate predictors of diagnostic status. In conclusion, DKI and NODDI can detect cerebral GM abnormalities in PD in a more sensitive manner when compared with conventional methods. Hence, these methods may be useful for the diagnosis of PD and assessment of motor deficits. Hum Brain Mapp 38:3704–3722, 2017. © 2017 Wiley Periodicals, Inc.