NeuroImage
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Clinical Trial
Measurement of OEF and absolute CMRO2: MRI-based methods using interleaved and combined hypercapnia and hyperoxia.
Blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI) is most commonly used in a semi-quantitative manner to infer changes in brain activity. Despite the basis of the image contrast lying in the cerebral venous blood oxygenation level, quantification of absolute cerebral metabolic rate of oxygen consumption (CMRO2) has only recently been demonstrated. Here we examine two approaches to the calibration of fMRI signal to measure absolute CMRO2 using hypercapnic and hyperoxic respiratory challenges. ⋯ The combined approach to oxygen and carbon dioxide modulation, as well as taking less time to acquire data, yielded whole brain grey matter estimates of CMRO2 and OEF of 184±45 μmol/100 g/min and 0.42±0.12 respectively, along with additional estimates of the vascular parameters α=0.33±0.06, the exponent relating relative increases in CBF to CBV, and β=1.35±0.13, the exponent relating deoxyhaemoglobin concentration to the relaxation rate R2*. Maps of cerebrovascular and cerebral metabolic parameters were also calculated. We show that combined modulation of oxygen and carbon dioxide can offer an experimentally more efficient approach to estimating OEF and absolute CMRO2 along with the additional vascular parameters that form an important part of the commonly used calibrated fMRI signal model.
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Modern machine learning algorithms are increasingly being used in neuroimaging studies, such as the prediction of Alzheimer's disease (AD) from structural MRI. However, finding a good representation for multivariate brain MRI features in which their essential structure is revealed and easily extractable has been difficult. We report a successful application of a machine learning framework that significantly improved the use of brain MRI for predictions. ⋯ In contrast, predictions using the original features performed not better than by chance (accuracy/sensitivity/specificity: =0.56/0.65/0.46). In conclusion, LLE is a very effective tool for classification studies of AD using multivariate MRI data. The improvement in predicting conversion to AD in MCI could have important implications for health management and for powering therapeutic trials by targeting non-demented subjects who later convert to AD.
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Trial-to-trial reaction time (RT) variability is consistently higher in children and older adults than in younger adults. Converging evidence also indicates that higher RT variability is (a) associated with lower behavioral performance on complex cognitive tasks, (b) distinguishes patients with neurological deficits from healthy individuals, and also (c) predicts longitudinal cognitive decline in older adults. However, so far the processes underlying increased RT variability are poorly understood. ⋯ Importantly, this effect was strongest at high performance monitoring demands and independent of motor response execution as well as theta power. Taken together, our findings reveal that lower theta inter-trial coherence is related to greater behavioral variability within and across age groups. These results hint at the possibility that more variable MFC control may be associated with greater performance fluctuations.
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Several large imaging-genetics consortia aim to identify genetic variants influencing subcortical brain volumes. We investigated the extent to which genetic variation accounts for the variation in subcortical volumes, including thalamus, amygdala, putamen, caudate nucleus, globus pallidus and nucleus accumbens and obtained the stability of these brain volumes over a five-year period. ⋯ Five-year stability was substantial and higher for larger [e.g., thalamus (.88), putamen (.86), caudate nucleus (.87)] compared to smaller [nucleus accumbens (.45)] subcortical structures. These results provide additional evidence that subcortical structures are promising starting points for identifying genetic variants that influence brain structure.
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In this work a method is described to discern the perfusion territories in the cerebellum that are exclusively supplied by either or both vertebral arteries. In normal vascular anatomy the posterior inferior cerebellar artery (PICA) is supplied exclusively by its ipsilateral vertebral artery. The perfusion territories of the vertebral arteries were determined in 14 healthy subjects by means of a super-selective pseudo-continuous ASL sequence on a 3T MRI scanner. ⋯ The inferior part of the vermis is supplied by the PICA in all subjects. Two subjects were found with interhemispheric blood flow to both tonsils from one PICA without contribution from the contralateral PICA. With the method as presented, clinicians may in the future accurately classify cerebellar infarcts according to affected perfusion territories, which might be helpful in the decision whether a stenosis should be considered symptomatic.