NeuroImage
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We investigated the neural underpinnings of timbral, tonal, and rhythmic features of a naturalistic musical stimulus. Participants were scanned with functional Magnetic Resonance Imaging (fMRI) while listening to a stimulus with a rich musical structure, a modern tango. We correlated temporal evolutions of timbral, tonal, and rhythmic features of the stimulus, extracted using acoustic feature extraction procedures, with the fMRI time series. ⋯ While timbral feature processing was associated with activations in cognitive areas of the cerebellum, and sensory and default mode network cerebrocortical areas, musical pulse and tonality processing recruited cortical and subcortical cognitive, motor and emotion-related circuits. In sum, by combining neuroimaging, acoustic feature extraction and behavioral methods, we revealed the large-scale cognitive, motor and limbic brain circuitry dedicated to acoustic feature processing during listening to a naturalistic stimulus. In addition to these novel findings, our study has practical relevance as it provides a powerful means to localize neural processing of individual acoustical features, be it those of music, speech, or soundscapes, in ecological settings.
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In Multiple Sclerosis (MS), detection of T2-hyperintense white matter (WM) lesions on magnetic resonance imaging (MRI) has become a crucial criterion for diagnosis and predicting prognosis in early disease. Automated lesion detection is not only desirable with regard to time and cost effectiveness but also constitutes a prerequisite to minimize user bias. Here, we developed and evaluated an algorithm for automated lesion detection requiring a three-dimensional (3D) gradient echo (GRE) T1-weighted and a FLAIR image at 3 Tesla (T). ⋯ We found good agreement with lesions determined by manual tracing (R2 values of over 0.93 independent of FLAIR slice thickness up to 6mm). These results require validation with data from other protocols based on a conventional FLAIR sequence and a 3D GRE T1-weighted sequence. Yet, we believe that our tool allows fast and reliable segmentation of FLAIR-hyperintense lesions, which might simplify the quantification of lesions in basic research and even clinical trials.
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Structural connectivity research in the human brain in vivo relies heavily on fiber tractography in diffusion-weighted MRI (DWI). The accurate mapping of white matter pathways would gain from images with a higher resolution than the typical ~2mm isotropic DWI voxel size. Recently, high field gradient echo MRI (GE) has attracted considerable attention for its detailed anatomical contrast even within the white and gray matter. ⋯ The STIFT method improves the anatomical accuracy of tractography of various fiber tracts, such as the optic radiation and cingulum. Furthermore, it has been demonstrated that STIFT can differentiate between kissing and crossing fiber configurations. Future investigations are required to establish the applicability in more white matter pathways.
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MRI estimates of brain iron concentration in normal aging using quantitative susceptibility mapping.
Quantifying tissue iron concentration in vivo is instrumental for understanding the role of iron in physiology and in neurological diseases associated with abnormal iron distribution. Herein, we use recently-developed Quantitative Susceptibility Mapping (QSM) methodology to estimate the tissue magnetic susceptibility based on MRI signal phase. To investigate the effect of different regularization choices, we implement and compare ℓ1 and ℓ2 norm regularized QSM algorithms. ⋯ However, QSM values are influenced by factors such as the myelin content, whereas FDRI is a more specific indicator of iron content. Hence, FDRI demonstrated higher specificity to iron yet yielded noisier data despite longer scan times and lower spatial resolution than QSM. The robustness, practicality, and demonstrated ability of predicting the change in iron deposition in adult aging suggest that regularized QSM algorithms using single-field-strength data are possible alternatives to tissue iron estimation requiring two field strengths.
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Although the pathogenesis of amyotrophic lateral sclerosis (ALS) is uncertain, there is mounting neuroimaging evidence to suggest a mechanism involving the degeneration of multiple white matter (WM) motor and extramotor neural networks. This insight has been achieved, in part, by using MRI Diffusion Tensor Imaging (DTI) and the voxelwise analysis of anisotropy indices, along with DTI tractography to determine which specific motor pathways are involved with ALS pathology. Automated MRI structural connectivity analyses, which probe WM connections linking various functionally discrete cortical regions, have the potential to provide novel information about degenerative processes within multiple white matter (WM) pathways. ⋯ Maps of the outlier rejection voxels, revealed clusters within the corpus callosum and corticospinal projections. This finding highlights the importance of correcting for motion artefacts and physiological noise when studying clinical populations. Our novel findings, many of which are consistent with known pathology, show extensive involvement and degeneration of multiple corticomotor pathways in patients with upper and lower motor neuron signs and provide support for the use of automated structural connectivity techniques for studying neurodegenerative disease processes.