Why musical memory can be preserved in advanced Alzheimer’s disease

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Information-based functional brain mapping.

Nikolaus Kriegeskorte, Rainer Goebel, Peter A. Bandettini (2006)

The development of high-resolution neuroimaging and multielectrode electrophysiological recording provides neuroscientists with huge amounts of multivariate data. The complexity of the data creates a need for statistical summary, but the local averaging standardly applied to this end may obscure the effects of greatest neuroscientific interest. In neuroimaging, for example, brain mapping analysis has focused on the discovery of activation, i.e., of extended brain regions whose average activity changes across experimental conditions. Here we propose to ask a more general question of the data: Where in the brain does the activity pattern contain information about the experimental condition? To address this question, we propose scanning the imaged volume with a "searchlight," whose contents are analyzed multivariately at each location in the brain.

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A whole brain fMRI atlas generated via spatially constrained spectral clustering.

R Cameron Craddock, G Andrew James, Paul E Holtzheimer … (2012)

Connectivity analyses and computational modeling of human brain function from fMRI data frequently require the specification of regions of interests (ROIs). Several analyses have relied on atlases derived from anatomical or cyto-architectonic boundaries to specify these ROIs, yet the suitability of atlases for resting state functional connectivity (FC) studies has yet to be established. This article introduces a data-driven method for generating an ROI atlas by parcellating whole brain resting-state fMRI data into spatially coherent regions of homogeneous FC. Several clustering statistics are used to compare methodological trade-offs as well as determine an adequate number of clusters. Additionally, we evaluate the suitability of the parcellation atlas against four ROI atlases (Talairach and Tournoux, Harvard-Oxford, Eickoff-Zilles, and Automatic Anatomical Labeling) and a random parcellation approach. The evaluated anatomical atlases exhibit poor ROI homogeneity and do not accurately reproduce FC patterns present at the voxel scale. In general, the proposed functional and random parcellations perform equivalently for most of the metrics evaluated. ROI size and hence the number of ROIs in a parcellation had the greatest impact on their suitability for FC analysis. With 200 or fewer ROIs, the resulting parcellations consist of ROIs with anatomic homology, and thus offer increased interpretability. Parcellation results containing higher numbers of ROIs (600 or 1,000) most accurately represent FC patterns present at the voxel scale and are preferable when interpretability can be sacrificed for accuracy. The resulting atlases and clustering software have been made publicly available at: http://www.nitrc.org/projects/cluster_roi/. Copyright © 2011 Wiley Periodicals, Inc.

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Brain correlates of music-evoked emotions.

Stefan Koelsch (2014)

Music is a universal feature of human societies, partly owing to its power to evoke strong emotions and influence moods. During the past decade, the investigation of the neural correlates of music-evoked emotions has been invaluable for the understanding of human emotion. Functional neuroimaging studies on music and emotion show that music can modulate activity in brain structures that are known to be crucially involved in emotion, such as the amygdala, nucleus accumbens, hypothalamus, hippocampus, insula, cingulate cortex and orbitofrontal cortex. The potential of music to modulate activity in these structures has important implications for the use of music in the treatment of psychiatric and neurological disorders.

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Author and article information

Journal

Title: Brain

Abbreviated Title: Brain

Publisher: Oxford University Press (OUP)

ISSN (Print): 0006-8950

ISSN (Electronic): 1460-2156

Publication date Created: July 23 2015

Publication date (Print): August 03 2015

Volume: 138

Issue: 8

Pages: 2438-2450

Article

DOI: 10.1093/brain/awv135

PubMed ID: 26041611

SO-VID: cc6ceda2-9bdc-4ba6-93ef-184abd97f6dc

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