An enhanced voxel-based morphometry method to investigate structural changes: application to Alzheimer’s disease

Neural Mechanisms of Paroxysmal Kinesigenic Dyskinesia: Insights from Neuroimaging

Paroxysmal kinesigenic dyskinesia (PKD) is a rare movement disorder of the nervous system, and little is known about its pathogenesis. Currently, the diagnosis of PKD is primarily based on clinical manifestations, with little objective evidence. Neuroimaging has been used to explore the pathological changes in cerebral structure and function associated with PKD. The current review highlights recent advances in neuroimaging to provide a better understanding of the neural mechanisms and early diagnosis of this disorder. Several studies utilizing single-photon emission computed tomography (CT), positron emission tomography, and structural and functional magnetic resonance imaging have found significant localized abnormalities in the caudate nucleus, putamen, pallidum, thalamus, and frontoparietal cortex in PKD patients. These studies have also revealed alterations in interhemispheric functional connectivity between the brain regions of bilateral cerebral hemispheres such as the putamen, primary motor cortex, supplementary motor area, dorsal lateral prefrontal cortex, and primary somatosensory cortex in these patients. In addition, proline-rich transmembrane protein 2 gene mutations can affect the functional organization of the brain in PKD. These results suggest that the neural mechanisms of PKD are associated with the disruption of both structural and/or functional properties in basal ganglia-thalamo-cortical circuitry and interhemispheric functional connectivity. PKD can be considered a circuitry/network disorder and is not restricted to localized structural and/or functional abnormalities. Multimodal neuroimaging combined with gene analysis can provide additional valuable information for a better understanding of the pathogenesis and early diagnosis of this disorder.

Influence of Mild White Matter Lesions on Voxel-based Morphometry

Purpose:

The aim of this study was to investigate whether the detectability of brain volume change in voxel-based morphometry (VBM) with gray matter images is affected by mild white matter lesions (MWLs).

Methods:

Three-dimensional T₁-weighted images (3D-T₁WIs) of 11 healthy subjects were obtained using a 3T MR scanner. We initially created 3D-T₁WIs with focal cortical atrophy simulated cortical atrophy in left amygdala (type A) and the left medial frontal lobe (type B) from control 3D-T₁WIs. Next, the following three types of MWL images were created: type A + 1L and type B + 1L images, only one white matter lesion; type A + 4L and type B + 4L images, four white matter lesions at distant positions; and type A + 4L^* and type B + 4L^* images, four white matter lesions at clustered positions. Comparisons between the control group and the other groups were performed with VBM using segmented gray matter images.

Results:

The gray matter volume was significantly lower in the type A group than in the control group, and similar results were observed in the type A + 1L, type A + 4L, and type A + 4L^* groups. Additionally, the gray matter volume was significantly lower in the type B group than in the control group, and similar results were observed in the type B + 1L, type B + 4L, and type B + 4L^* groups, but the cluster size in type B + 4L^* was smaller than that in type B.

Conclusion:

Our study showed that the detectability of brain volume change in VBM with gray matter images was not decreased by MWLs as lacunar infarctions. Therefore, we think that group comparisons with VBM should be analyzed by groups including and excluding subjects with MWLs, respectively.

Detecting brain structural changes as biomarker from magnetic resonance images using a local feature based SVM approach

Detecting brain structural changes from magnetic resonance (MR) images can facilitate early diagnosis and treatment of neurological and psychiatric diseases. Many existing methods require an accurate deformation registration, which is difficult to achieve and therefore prevents them from obtaining high accuracy. We develop a novel local feature based support vector machine (SVM) approach to detect brain structural changes as potential biomarkers. This approach does not require deformation registration and thus is less influenced by artifacts such as image distortion. We represent the anatomical structures based on scale invariant feature transform (SIFT). Likelihood scores calculated using feature-based morphometry is used as the criterion to categorize image features into three classes (healthy, patient and noise). Regional SVMs are trained to classify the three types of image features in different brain regions. Only healthy and patient features are used to predict the disease status of new brain images. An ensemble classifier is built from the regional SVMs to obtain better prediction accuracy. We apply this approach to 3D MR images of Alzheimer's disease, Parkinson's disease and bipolar disorder. The classification accuracy ranges between 70% and 87%. The highly predictive disease-related regions, which represent significant anatomical differences between the healthy and diseased, are shown in heat maps. The common and disease-specific brain regions are identified by comparing the highly predictive regions in each disease. All of the top-ranked regions are supported by literature. Thus, this approach will be a promising tool for assisting automatic diagnosis and advancing mechanism studies of neurological and psychiatric diseases.

Advances in longitudinal MRI diagnostic tests

INTRODUCTION

The past decade has seen an explosion of functional magnetic resonance imaging (MRI) studies in neuroscience. As the technology progresses, it is now possible to carry out longitudinal studies using functional MRI. Such studies can be used to understand the progression of mental and neurological disorders and the effectiveness of different treatments by obtaining direct measures of brain activity as well as markers of tissue health and connectivity.

AREAS COVERED

We review six popular neuroimaging tools that can be used for longitudinal studies: blood oxygen level-dependent (BOLD)-weighted imaging, BOLD-based functional connectivity, arterial spin labeling, dynamic R2* imaging, voxel-based morphometry, and diffusion tensor imaging.

EXPERT OPINION

Each of these techniques is targeted to probe a specific feature of brain function or brain structure and can reveal important information about the progression of a pathological condition. We anticipate that in the near future, the MRI techniques discussed here may become standard tools in clinical use and will not be used for research purposes only.

Gray matter concentration and effective connectivity changes in Alzheimer's disease: a longitudinal structural MRI study

INTRODUCTION

Understanding disease progression in Alzheimer's disease (AD) awaits the resolution of three fundamental questions: first, can we identify the location of "seed" regions where neuropathology is first present? Some studies have suggested the medial temporal lobe while others have suggested the hippocampus. Second, are there similar atrophy rates within affected regions in AD? Third, is there evidence of causality relationships between different affected regions in AD progression?

METHODS

To address these questions, we conducted a longitudinal MRI study to investigate the gray matter (GM) changes in AD progression. Abnormal brain regions were localized by a standard voxel-based morphometry method, and the absolute atrophy rate in these regions was calculated using a robust regression method. Primary foci of atrophy were identified in the hippocampus and middle temporal gyrus (MTG). A model based upon the Granger causality approach was developed to investigate the cause-effect relationship over time between these regions based on GM concentration.

RESULTS

Results show that in the earlier stages of AD, primary pathological foci are in the hippocampus and entorhinal cortex. Subsequently, atrophy appears to subsume the MTG.

CONCLUSION

The causality results show that there is in fact little difference between AD and age-matched healthy control in terms of hippocampus atrophy, but there are larger differences in MTG, suggesting that local pathology in MTG is the predominant progressive abnormality during intermediate stages of AD development.

Diffusion tensor imaging and white matter lesions at the subacute stage in mild traumatic brain injury with persistent neurobehavioral impairment

Mild traumatic brain injury (mTBI) can induce long-term behavioral and cognitive disorders. Although the exact origin of these mTBI-related disorders is not known, they may be the consequence of diffuse axonal injury (DAI). Here, we investigated whether MRI at the subacute stage can detect lesions that are associated with poor functional outcome in mTBI by using anatomical images (T(1) ) and diffusion tensor imaging (DTI). Twenty-three patients with mTBI were investigated and compared with 23 healthy volunteers. All patients underwent an MRI investigation and clinical tests between 7 and 28 days (D15) and between 3 and 4 months (M3) after injury. Patients were divided in two groups of poor outcome (PO) and good outcome (GO), based on their complaints at M3. Groupwise differences in gray matter partial volume between PO patients, GO patients and controls were analyzed using Voxel-Based Morphometry (VBM) from T(1) data at D15. Differences in microstructural architecture were investigated using Tract-Based Spatial Statistics (TBSS) and the diffusion images obtained from DTI data at D15. Permutation-based non-parametric testing was used to assess cluster significance at p < 0.05, corrected for multiple comparisons. Twelve GO patients and 11 PO patients were identified on the basis of their complaints. In PO patients, gray matter partial volume was significantly lower in several cortical and subcortical regions compared with controls, but did not differ from that of GO patients. No difference in diffusion variables was found between GO and controls. PO patients showed significantly higher mean diffusivity values than both controls and GO patients in the corpus callosum, the right anterior thalamic radiations and the superior longitudinal fasciculus, the inferior longitudinal fasciculus and the fronto-occipital fasciculus bilaterally. In conclusion, PO patients differed from GO patients by the presence of diffusion changes in long association white matter fiber tracts but not by gray matter partial volume. These results suggest that DTI at the subacute stage may be a predictive marker of poor outcome in mTBI.