NeuroImage
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There has been an increasing use of functional magnetic resonance imaging (fMRI) by the neuroscience community to examine differences in functional connectivity between normal control groups and populations of interest. Understanding the reliability of these functional connections is essential to the study of neurological development and degenerate neuropathological conditions. To date, most research assessing the reliability with which resting-state functional connectivity characterizes the brain's functional networks has been on scans between 3 and 11 min in length. ⋯ This improvement in reliability due to scan length is much greater for scans acquired during the same session. Gains in intersession reliability began to diminish after 9-12 min, while improvements in intrasession reliability plateaued around 12-16 min. Consequently, new techniques that improve reliability across sessions will be important for the interpretation of longitudinal fMRI studies.
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Anodal and cathodal transcranial direct current stimulations (tDCS) are both established techniques to induce cortical excitability changes. Typically, in the human motor system, such cortical modulations are inferred through changes in the amplitude of the motor evoked potentials (MEPs). However, it is now possible to directly evaluate tDCS-induced changes at the cortical level by recording the transcranial magnetic stimulation evoked potentials (TEPs) using electroencephalography (EEG). ⋯ No polarity-specific effect was found either on behavioral measures or on oscillatory brain activity. The latter showed a general increase in the power density of low frequency oscillations (theta and alpha) at both stimulation polarities. Our results suggest that tDCS is able to modulate motor cortical reactivity in a polarity-specific manner, inducing a complex pattern of direct and indirect cortical activations or inhibitions of the motor system-related network, which might be related to changes in synaptic efficacy of the motor cortex.
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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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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.
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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.