NeuroImage. Clinical
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NeuroImage. Clinical · Jan 2014
White matter tract integrity metrics reflect the vulnerability of late-myelinating tracts in Alzheimer's disease.
Post-mortem and imaging studies have observed that white matter (WM) degenerates in a pattern inverse to myelin development, suggesting preferential regional vulnerabilities influencing cognitive decline in AD. This study applied novel WM tract integrity (WMTI) metrics derived from diffusional kurtosis imaging (DKI) to examine WM tissue properties in AD within this framework. ⋯ WMTI metrics in late-myelinating tracts correlated with semantic verbal fluency, a cognitive function known to decline in AD. These findings corroborate the preferential vulnerability of late-myelinating tracts, and illustrate an application of WMTI metrics to characterizing the regional course of WM changes in AD.
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NeuroImage. Clinical · Jan 2014
Comparative StudyMeasurement of brain perfusion in newborns: pulsed arterial spin labeling (PASL) versus pseudo-continuous arterial spin labeling (pCASL).
Arterial spin labeling (ASL) perfusion-weighted imaging (PWI) by magnetic resonance imaging (MRI) has been shown to be useful for identifying asphyxiated newborns at risk of developing brain injury, whether or not therapeutic hypothermia was administered. However, this technique has been only rarely used in newborns until now, because of the challenges to obtain sufficient signal-to-noise ratio (SNR) and spatial resolution in newborns. ⋯ This study demonstrates that both ASL methods are feasible to assess brain perfusion in healthy and sick newborns. However, pCASL might be a better choice over PASL in newborns, as pCASL perfusion maps had a superior image quality that allowed a more detailed identification of the different brain structures.
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NeuroImage. Clinical · Jan 2014
Meta AnalysisGray matter alterations in chronic pain: A network-oriented meta-analytic approach.
Several studies have attempted to characterize morphological brain changes due to chronic pain. Although it has repeatedly been suggested that longstanding pain induces gray matter modifications, there is still some controversy surrounding the direction of the change (increase or decrease in gray matter) and the role of psychological and psychiatric comorbidities. In this study, we propose a novel, network-oriented, meta-analytic approach to characterize morphological changes in chronic pain. ⋯ Conversely, alterations in the sensorimotor and attention circuits were differentially targeted by chronic pain pathologies. Moreover, model-based clustering revealed that chronic pain, in line with some neurodegenerative diseases, selectively targets some large-scale brain networks. Altogether these findings indicate that chronic pain can be better conceived and studied in a network perspective.
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NeuroImage. Clinical · Jan 2014
Randomized Controlled TrialResting state connectivity correlates with drug and placebo response in fibromyalgia patients.
Fibromyalgia is a chronic pain syndrome characterized by widespread pain, fatigue, and memory and mood disturbances. Despite advances in our understanding of the underlying pathophysiology, treatment is often challenging. New research indicates that changes in functional connectivity between brain regions, as can be measured by magnetic resonance imaging (fcMRI) of the resting state, may underlie the pathogenesis of this and other chronic pain states. ⋯ This pattern was not observed for the placebo period. However a more robust placebo response was associated with lower baseline functional connectivity between the ACC and the dorsolateral prefrontal cortex. This study indicates that ACC-IC connectivity might play a role in the mechanism of action of MLN, and perhaps more importantly fcMRI might be a useful tool to predict pharmacological treatment response.
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NeuroImage. Clinical · Jan 2014
Randomized Controlled Trial Comparative StudyRandom Forest ensembles for detection and prediction of Alzheimer's disease with a good between-cohort robustness.
Computer-aided diagnosis of Alzheimer's disease (AD) is a rapidly developing field of neuroimaging with strong potential to be used in practice. In this context, assessment of models' robustness to noise and imaging protocol differences together with post-processing and tuning strategies are key tasks to be addressed in order to move towards successful clinical applications. In this study, we investigated the efficacy of Random Forest classifiers trained using different structural MRI measures, with and without neuroanatomical constraints in the detection and prediction of AD in terms of accuracy and between-cohort robustness. ⋯ The models trained using parcelled and high-dimensional (HD) input demonstrated equivalent performance, but the former was more effective in terms of computation/memory and time costs. The sensitivity/specificity for detecting MCI-to-AD conversion (but not AD/HC classification performance) was further improved from 79.5%/75%-83.3%/81.3% by a combination of morphometric measurements with ApoE-genotype and demographics (age, sex, education). When applied to the independent AddNeuroMed cohort, the best ADNI models produced equivalent performance without substantial accuracy drop, suggesting good robustness sufficient for future clinical implementation.