• Brain microstructure reveals early abnormalities more than two years prior to clinical progression from mild cognitive impairment to Alzheimer's disease.

    3 April 2018

    Diffusion imaging is a promising marker of microstructural damage in neurodegenerative disorders, but interpretation of its relationship with underlying neuropathology can be complex. Here, we examined both volumetric and brain microstructure abnormalities in 13 amnestic patients with mild cognitive impairment (MCI), who progressed to probable Alzheimer's disease (AD) no earlier than 2 years after baseline scanning, in order to focus on early, and hence more sensitive, imaging markers. We compared them to 22 stable amnestic MCI patients with similar cognitive performance and episodic memory impairment but who did not show progression of symptoms for at least 3 years. Significant group differences were mainly found in the volume and microstructure of the left hippocampus, while white matter group differences were also found in the body of the fornix, left fimbria, and superior longitudinal fasciculus (SLF). Diffusion index abnormalities in the SLF were the sign of a subtle microstructural injury not detected by standard atrophy measures in the corresponding gray matter regions. The microstructural measure obtained in the left hippocampus using diffusion imaging showed the most substantial differences between the two groups and was the best single predictor of future progression to AD. An optimal prediction model (91% accuracy, 85% sensitivity, 96% specificity) was obtained by combining MRI measures and CSF protein biomarkers. These results highlight the benefit of using the information of brain microstructural damage, in addition to traditional gray matter volume, to detect early, subtle abnormalities in MCI prior to clinical progression to probable AD and, in combination with CSF markers, to accurately predict such progression.

  • Resting State Correlates of Subdimensions of Anxious Affect

    3 April 2018

    Resting state fMRI may help identify markers of risk for affective disorder. Given the comorbidity of anxiety and depressive disorders and the heterogeneity of these disorders as defined by DSM, an important challenge is to identify alterations in resting state brain connectivity uniquely associated with distinct profiles of negative affect. The current study aimed to address this by identifying differences in brain connectivity specifically linked to cognitive and physiological profiles of anxiety, controlling for depressed affect. We adopted a two-stage multivariate approach. Hierarchical clustering was used to independently identify dimensions of negative affective style and resting state brain networks. Combining the clustering results, we examined individual differences in resting state connectivity uniquely associated with subdimensions of anxious affect, controlling for depressed affect. Physiological and cognitive subdimensions of anxious affect were identified. Physiological anxiety was associated with widespread alterations in insula connectivity, including decreased connectivity between insula subregions and between the insula and other medial frontal and subcortical networks. This is consistent with the insula facilitating communication between medial frontal and subcortical regions to enable control of physiological affective states. Meanwhile, increased connectivity within a frontoparietal–posterior cingulate cortex–precunous network was specifically associated with cognitive anxiety, potentially reflecting increased spontaneous negative cognition (e.g., worry). These findings suggest that physiological and cognitive anxiety comprise subdimensions of anxiety-related affect and reveal associated alterations in brain connectivity.

  • Investigations into resting-state connectivity using independent component analysis.

    4 April 2018

    Inferring resting-state connectivity patterns from functional magnetic resonance imaging (fMRI) data is a challenging task for any analytical technique. In this paper, we review a probabilistic independent component analysis (PICA) approach, optimized for the analysis of fMRI data, and discuss the role which this exploratory technique can take in scientific investigations into the structure of these effects. We apply PICA to fMRI data acquired at rest, in order to characterize the spatio-temporal structure of such data, and demonstrate that this is an effective and robust tool for the identification of low-frequency resting-state patterns from data acquired at various different spatial and temporal resolutions. We show that these networks exhibit high spatial consistency across subjects and closely resemble discrete cortical functional networks such as visual cortical areas or sensory-motor cortex.

  • Distinct patterns of brain activity in young carriers of the APOE-epsilon4 allele.

    3 April 2018

    The APOE epsilon4 allele is a risk factor for late-life pathological changes that is also associated with anatomical and functional brain changes in middle-aged and elderly healthy subjects. We investigated structural and functional effects of the APOE polymorphism in 18 young healthy APOE epsilon4-carriers and 18 matched noncarriers (age range: 20-35 years). Brain activity was studied both at rest and during an encoding memory paradigm using blood oxygen level-dependent fMRI. Resting fMRI revealed increased "default mode network" (involving retrosplenial, medial temporal, and medial-prefrontal cortical areas) coactivation in epsilon4-carriers relative to noncarriers. The encoding task produced greater hippocampal activation in epsilon4-carriers relative to noncarriers. Neither result could be explained by differences in memory performance, brain morphology, or resting cerebral blood flow. The APOE epsilon4 allele modulates brain function decades before any clinical or neurophysiological expression of neurodegenerative processes.

  • A comparison of the tissue classification and the segmentation propagation techniques in MRI brain image segmentation

    30 March 2018

    Tissue classifications of the MRI brain images can either be obtained by segmenting the images or propagating the segmentations of the atlas to the target image. This paper compares the classification results of the direct segmentation method using FAST with those of the segmentation propagation method using nreg and the MNI Brainweb phantom images. The direct segmentation is carried out by extracting the brain and classifying the tissues by FAST. The segmentation propagation is carried out by registering the Brainweb atlas image to the target images by affine registration, followed by non-rigid registration at different control spacing, then transforming the PVE (partial volume effect) fuzzy membership images of cerebrospinal fluid (CSF), grey matter (GM) and white matter (WM) of the atlas image into the target space respectively. We have compared the running time, reproducibility, global and local differences between the two methods. Direct segmentation is much faster. There is no significant difference in reproducibility between the two techniques. There are significant global volume differences on some tissue types between them. Visual inspection was used to localize these differences. This study had no gold standard segmentations with which to compare the automatic segmentation solutions, but the global and local volume differences suggest that the most appropriate algorithm is likely to be application dependent.

  • Metal artifact reduction for CT based on sinusoidal description

    30 March 2018

    Computed tomography has played a key role in bone structure imaging for over two decades. However, when a metal implant is present in the sample, the reconstructions are seriously distorted by artifact, and no method has successfully met the clinical demands. This paper presents a new method for metal artifact reduction in Computed Tomography based on sinusoidal description with the concentration of clinical applications. A piece of pig's leg with a lead nail placed inside the bone was scanned, generating 224 slices, in 177 of which the metal implant was present. The method includes detection of the correspondence of metal implants, fitting, amendment, and reconstruction based on sinusoidal description. Simulation and statistical error analysis show that the method improves PSNR (Peak Signal-to-Noise Ratio). A 3D modeling based on the reconstruction using the sinusoidal amendment method for a real case demonstrates that most of the metal artifact has been removed, which is compared with that based on the default output of the scanner. Metal artifacts in CT can be reduced effectively by the method based on the sinusoidal description, which isolates the correspondence of a metal implant from the original projection, so that a high quality reconstruction can be obtained.

  • Interferon beta-1a for brain tissue loss in patients at presentation with syndromes suggestive of multiple sclerosis: a randomised, double-blind, placebo-controlled trial.

    3 April 2018

    BACKGROUND: In patients who present with clinically isolated syndromes suggestive of multiple sclerosis, interferon beta-1a is effective in delaying evolution to clinically definite disease and in reducing MRI-measured disease activity. We aimed to assess whether this drug can also reduce the rate of brain volume decrease in such patients enrolled in the ETOMS (early treatment of multiple sclerosis) trial. METHODS: MRI data for brain volume measurements at baseline, month 12, and month 24 were available from 131, 111, and 112 patients assigned treatment (22 microg interferon beta-1a), and 132, 98, and 99 patients assigned placebo respectively. Normalised brain parenchymal volume (NBV) at baseline and percentage brain volume changes (PBVC) were measured with a fully-automated segmentation technique. The primary endpoint was conversion to clinically definite multiple sclerosis due to clinical relapse. Analysis was by intention to treat. FINDINGS: 41 (31%) of 131 patients on interferon beta-1a and 62 (47%) of 132 on placebo converted to clinically definite multiple sclerosis (odds ratio 0.52 [95% CI 0.31-0.86], p=0.0115). Mean PBVC for patients on placebo was -0.83% during the first year, -0.67% during the second year, and -1.68% during the entire study period. Respective values for treated patients were -0.62%, -0.61%, and -1.18%. The changes in brain volume were significant in both groups at all timepoints. A significant treatment effect was detected for month 24 versus baseline values (p=0.0031). The number of new T2 lesions formed during the first year correlated weakly with PBVC during the second year. INTERPRETATION: Early treatment with interferon beta-1a is effective in reducing conversion to clinically definite multiple sclerosis and in slowing progressive loss of brain tissue in patients with clinically isolated syndromes. The modest correlation between new lesion formation and brain volume decrease suggests that inflammatory and neurodegenerative processes are, at least partly, dissociated from the earliest clinical stage of multiple sclerosis onwards.

  • Preparing fMRI data for statistical analysis

    30 March 2018

    © Oxford University Press 2001. All rights reserved. This chapter describes the various preprocessing steps necessary to take raw data from the scanner and prepare it for the 'heart' of functional magnetic resonance imaging analysis, namely statistical analysis. These preprocessing steps take the raw MR data, convert it into images that actually look like brains, then reduce unwanted noise of various types and precondition the data in order to aid the later statistics. The chapter also discusses how it is much easier to 'automate' the preprocessing steps than the statistical analysis because optimal tuning of preprocessing algorithms is less dependent on the details of any particular experiment than is the case with later statistics. It discusses the reasons for applying spatial filtering as a preprocessing step and results clearly show the blurring of activation areas as spatial filtering extent increases, even causing 'activation' outside of the brain.

  • Preface

    30 March 2018

  • Functional magnetic resonance imaging: An introduction to methods

    30 March 2018

    © Oxford University Press 2001. All rights reserved. This book provides an introduction to functional magnetic resonance imaging (fMRI), the scanning technique that allows the mapping of active processes within the brain. There are six sections to the book, with chapters from an international team. Part I provides a broad overview of the field and sets the context. Part II describes the physiological and physical background to fMRI, including coverage of the hardware required and pulse-sequence selection. Practical issues involving experimental design of the paradigms, psycho-physical stimulus delivery, and subject response are covered in Part III, followed by a comprehensive treatment of data analysis in Part IV. Part V deals with practical applications of the technique in the field of neuroscience and in clinical practice. The final section describes how fMRI can be integrated with other neuro-electromagnetic functional mapping techniques.