• Structural and functional abnormalities of the motor system in developmental stuttering.

    5 April 2018

    Though stuttering is manifest in its motor characteristics, the cause of stuttering may not relate purely to impairments in the motor system as stuttering frequency is increased by linguistic factors, such as syntactic complexity and length of utterance, and decreased by changes in perception, such as masking or altering auditory feedback. Using functional and diffusion imaging, we examined brain structure and function in the motor and language areas in a group of young people who stutter. During speech production, irrespective of fluency or auditory feedback, the people who stuttered showed overactivity relative to controls in the anterior insula, cerebellum and midbrain bilaterally and underactivity in the ventral premotor, Rolandic opercular and sensorimotor cortex bilaterally and Heschl's gyrus on the left. These results are consistent with a recent meta-analysis of functional imaging studies in developmental stuttering. Two additional findings emerged from our study. First, we found overactivity in the midbrain, which was at the level of the substantia nigra and extended to the pedunculopontine nucleus, red nucleus and subthalamic nucleus. This overactivity is consistent with suggestions in previous studies of abnormal function of the basal ganglia or excessive dopamine in people who stutter. Second, we found underactivity of the cortical motor and premotor areas associated with articulation and speech production. Analysis of the diffusion data revealed that the integrity of the white matter underlying the underactive areas in ventral premotor cortex was reduced in people who stutter. The white matter tracts in this area via connections with posterior superior temporal and inferior parietal cortex provide a substrate for the integration of articulatory planning and sensory feedback, and via connections with primary motor cortex, a substrate for execution of articulatory movements. Our data support the conclusion that stuttering is a disorder related primarily to disruption in the cortical and subcortical neural systems supporting the selection, initiation and execution of motor sequences necessary for fluent speech production.

  • Manifestations of early brain recovery associated with abstinence from alcoholism.

    30 March 2018

    Chronic alcohol abuse results in morphological, metabolic, and functional brain damage which may, to some extent, be reversible with early effects upon abstinence. Although morphometric, spectroscopic, and neuropsychological indicators of cerebral regeneration have been described previously, the overall amount and spatial preference of early brain recovery attained by abstinence and its associations with other indicators of regeneration are not well established. We investigated global and local brain volume changes in a longitudinal two-timepoint study with T1-weighted MRI at admission and after short-term (6-7 weeks) sobriety follow-up in 15 uncomplicated, recently detoxified alcoholics. Volumetric brain gain was related to metabolic and neuropsychological recovery. On admission and after short-term abstinence, structural image evaluation using normalization of atrophy (SIENA), its voxelwise statistical extension to multiple subjects, proton MR spectroscopy (1H-MRS), and neuropsychological tests were applied. Upon short-term sobriety, 1H-MRS levels of cerebellar choline and frontomesial N-acetylaspartate (NAA) were significantly augmented. Automatically detected global brain volume gain amounted to nearly two per cent on average and was spatially significant around the superior vermis, perimesencephalic, periventricular and frontal brain edges. It correlated positively with the percentages of cerebellar and frontomesial choline increase, as detected by 1H-MRS. Moreover, frontomesial NAA gains were associated with improved performance on the d2-test of attention. In 10 age- and gender-matched healthy control subjects, no significant brain volume or metabolite changes were observed. Although cerebral osmotic regulations may occur initially upon sobriety, significant increases of cerebellar choline and frontomesial NAA levels detected at stable brain water integrals and creatine concentrations, serum electrolytes and red blood cell indices in our patient sample suggest that early brain recovery through abstinence does not simply reflect rehydration. Instead, even the adult human brain and particularly its white matter seems to possess genuine capabilities for regrowth. Our findings emphasize metabolic as well as regionally distinct morphological capacities for partial brain recovery from toxic insults of chronic alcoholism and substantiate early measurable benefits of therapeutic sobriety. Further understanding of the precise mechanisms of this recovery may become a valuable model of brain regeneration with relevance for other disorders.

  • High-resolution FMRI at 1.5T using balanced SSFP.

    4 April 2018

    The resolution in conventional BOLD FMRI is considerably lower than can be achieved with other MRI methods, and is insufficient for many important applications. One major difficulty in robustly improving spatial resolution is the poor image quality in BOLD FMRI, which suffers from distortions, blurring, and signal dropout. This work considers the potential for increased resolution with a new FMRI method based on balanced SSFP. This method establishes a blood oxygenation sensitive steady-state (BOSS) signal, in which the frequency sensitivity of balanced SSFP is used to detect the frequency shift of deoxyhemoglobin. BOSS FMRI is highly SNR efficient and does not suffer from image distortions or signal dropout, making this method an excellent candidate for high-resolution FMRI. This study presents the first demonstration of high-resolution BOSS FMRI, using an efficient 3D stack-of-segmented EPI readout and combined acquisition at multiple center frequencies. BOSS FMRI is shown to enable high-resolution FMRI data (1 x 1 x 2 mm(3)) in both visual and motor systems using standard hardware at 1.5 T. Currently, the major limitation of BOSS FMRI is its sensitivity to temporal and spatial field drift.

  • Functional-anatomical validation and individual variation of diffusion tractography-based segmentation of the human thalamus.

    4 April 2018

    Parcellation of the human thalamus based on cortical connectivity information inferred from non-invasive diffusion-weighted images identifies sub-regions that we have proposed correspond to nuclei. Here we test the functional and anatomical validity of this proposal by comparing data from diffusion tractography, cytoarchitecture and functional imaging. We acquired diffusion imaging data in eleven healthy subjects and performed probabilistic tractography from voxels within the thalamus. Cortical connectivity information was used to divide the thalamus into sub-regions with highest probability of connectivity to distinct cortical areas. The relative volumes of these connectivity-defined sub-regions correlate well with volumetric predictions based on a histological atlas. Previously reported centres of functional activation within the thalamus during motor or executive tasks co-localize within atlas regions showing high probabilities of connection to motor or prefrontal cortices, respectively. This work provides a powerful validation of quantitative grey matter segmentation using diffusion tractography in humans. Co-registering thalamic sub-regions from 11 healthy individuals characterizes inter-individual variation in segmentation and results in a population-based atlas of the human thalamus that can be used to assign likely anatomical labels to thalamic locations in standard brain space. This provides a tool for specific localization of functional activations or lesions to putative thalamic nuclei.

  • Mixture models with adaptive spatial regularization for segmentation with an application to FMRI data.

    3 April 2018

    Mixture models are often used in the statistical segmentation of medical images. For example, they can be used for the segmentation of structural images into different matter types or of functional statistical parametric maps (SPMs) into activations and nonactivations. Nonspatial mixture models segment using models of just the histogram of intensity values. Spatial mixture models have also been developed which augment this histogram information with spatial regularization using Markov random fields. However, these techniques have control parameters, such as the strength of spatial regularization, which need to be tuned heuristically to particular datasets. We present a novel spatial mixture model within a fully Bayesian framework with the ability to perform fully adaptive spatial regularization using Markov random fields. This means that the amount of spatial regularization does not have to be tuned heuristically but is adaptively determined from the data. We examine the behavior of this model when applied to artificial data with different spatial characteristics, and to functional magnetic resonance imaging SPMs.

  • Prioritizing new over old: an fMRI study of the preview search task.

    30 March 2018

    In visual search, observers can successfully ignore temporally separated distractors that are presented as a preview before onset of the search display. Previous behavioral studies have demonstrated the involvement of top-down selection mechanisms in preview search, biasing attention against the old set in favor of the more relevant new set. Using functional magnetic resonance imaging, we replicate and extend findings showing the involvement of superior and inferior parietal areas in the preview task when compared to both a relatively easy single-set search task and a more effortful full-set search task. In contrast, the effortful full-set search showed activation in the dorsolateral prefrontal cortex when compared to the single-set search, suggesting that this area is involved in rejecting additional distractors that could not be separated in time.

  • On bias in the estimation of autocorrelations for fMRI voxel time-series analysis.

    3 April 2018

    For fMRI time-series analysis to be statistically valid, it is important to deal correctly with temporal autocorrelation in the noise. Most of the approaches in the literature adopt a two-stage approach in which the autocorrelation structure is estimated using the residuals of an initial model fit. This estimate is then used to "prewhiten" the data and the model before the model is refit to obtain final activation parameter estimates. An assumption implicit in this scheme is that the residuals from the initial model fit represent a realization of the "true" noise process. In general this assumption will not be correct as certain components of the noise will be removed by the model fit. In this paper we examine (i) the form of the bias induced by the initial model fit, (ii) methods of correcting for the bias, and (iii) the impact of bias correction on the model parameter estimates. We find that while bias correction does result in more accurate estimates of the correlation structure, this does not translate into improved estimates of the model parameters. In fact estimates of the model parameters and their standard errors are seen to be so accurate that we conclude that bias correction is unnecessary.

  • Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm.

    4 April 2018

    The finite mixture (FM) model is the most commonly used model for statistical segmentation of brain magnetic resonance (MR) images because of its simple mathematical form and the piecewise constant nature of ideal brain MR images. However, being a histogram-based model, the FM has an intrinsic limitation--no spatial information is taken into account. This causes the FM model to work only on well-defined images with low levels of noise; unfortunately, this is often not the the case due to artifacts such as partial volume effect and bias field distortion. Under these conditions, FM model-based methods produce unreliable results. In this paper, we propose a novel hidden Markov random field (HMRF) model, which is a stochastic process generated by a MRF whose state sequence cannot be observed directly but which can be indirectly estimated through observations. Mathematically, it can be shown that the FM model is a degenerate version of the HMRF model. The advantage of the HMRF model derives from the way in which the spatial information is encoded through the mutual influences of neighboring sites. Although MRF modeling has been employed in MR image segmentation by other researchers, most reported methods are limited to using MRF as a general prior in an FM model-based approach. To fit the HMRF model, an EM algorithm is used. We show that by incorporating both the HMRF model and the EM algorithm into a HMRF-EM framework, an accurate and robust segmentation can be achieved. More importantly, the HMRF-EM framework can easily be combined with other techniques. As an example, we show how the bias field correction algorithm of Guillemaud and Brady (1997) can be incorporated into this framework to achieve a three-dimensional fully automated approach for brain MR image segmentation.

  • Study protocol: the Whitehall II imaging sub-study

    3 April 2018

    Background The Whitehall II (WHII) study of British civil servants provides a unique source of longitudinal data to investigate key factors hypothesized to affect brain health and cognitive ageing. This paper introduces the multi-modal magnetic resonance imaging (MRI) protocol and cognitive assessment designed to investigate brain health in a random sample of 800 members of the WHII study. Methods A total of 6035 civil servants participated in the WHII Phase 11 clinical examination in 2012-2013. A random sample of these participants was included in a sub-study comprising an MRI brain scan, a detailed clinical and cognitive assessment, and collection of blood and buccal mucosal samples for the characterisation of immune function and associated measures. Data collection for this sub-study started in 2012 and will be completed by 2016. The participants, for whom social and health records have been collected since 1985, were between 60-85 years of age at the time the MRI study started. Here, we describe the pre-specified clinical and cognitive assessment protocols, the state-of-the-art MRI sequences and latest pipelines for analyses of this sub-study. Discussion The integration of cutting-edge MRI techniques, clinical and cognitive tests in combination with retrospective data on social, behavioural and biological variables during the preceding 25 years from a well-established longitudinal epidemiological study (WHII cohort) will provide a unique opportunity to examine brain structure and function in relation to age-related diseases and the modifiable and non-modifiable factors affecting resilience against and vulnerability to adverse brain changes. PMID: 24885374