Search

Benefits of listening to music with emotional attachment while recovering from a cerebral ischemic event have been reported. To develop a better understanding of the effects of music listening on the human brain, an algorithm for the graph-theoretical analysis of functional magnetic resonance imaging (fMRI) data was developed. From BOLD data of two paradigms (block-design, first piece: music without emotional attachment, additional visual guidance by a moving cursor in the score sheet; second piece: music with emotional attachment), network graphs were constructed with correlations between signal time courses as edge weights. Functional subunits in these graphs were identified with the MCODE clustering algorithm and mapped back into anatomical space using AFNI. Emotional centers including the right amygdala and bilateral insula were activated by the second piece (emotional attachment) but not by the first piece. Network clustering analysis revealed two separate networks of small-world property corresponding to task-oriented and resting state conditions, respectively. Functional subunits with highest interactions were bilateral precuneus for the first piece and left middle frontal gyrus and right amygdala, bilateral insula, left middle temporal gyrus for the second piece. Our results indicate that fMRI in connection with graph theoretical network analysis is capable of identifying and differentiating functional subunits in the human brain when listening to music with and without emotional attachment.

Graph-theoretical analysis algorithms have been used for identifying subnetworks in the human brain during the Default Mode State. Here, these methods are expanded to determine the interaction of the sensory and the motor subnetworks during the performance of an approach-avoidance paradigm utilizing the correlation strength between the signal intensity time courses as measure of synchrony. From functional magnetic resonance imaging (fMRI) data of 9 healthy volunteers, two signal time courses, one from the primary visual cortex (sensory input) and one from the motor cortex (motor output) were identified and a correlation difference map was calculated. Graph networks were created from this map and visualized with spring-embedded layouts and 3D layouts in the original anatomical space. Functional clusters in these networks were identified with the MCODE clustering algorithm. Interactions between the sensory sub-network and the motor sub-network were quantified through the interaction strengths of these clusters. The percentages of interactions involving the visual cortex ranged from 85 % to 18 % and the motor cortex ranged from 40 % to 9 %. Other regions with high interactions were: frontal cortex (19 ± 18 %), insula (17 ± 22 %), cuneus (16 ± 15 %), supplementary motor area (SMA, 11 ± 18 %) and subcortical regions (11 ± 10 %). Interactions between motor cortex, SMA and visual cortex accounted for 12 %, between visual cortex and cuneus for 8 % and between motor cortex, SMA and cuneus for 6 % of all interactions. These quantitative findings are supported by the visual impressions from the 2D and 3D network layouts.

The integrity of functional brain networks inpatients (n=12) diagnosed with traumatic brain injury (TBI) was compared to age-matched subjects (n=12) with orthopedic injury (OI) during a working memory task. A graph-theoretical analysis algorithm was developed and integrated into the AFNI software. Functional networks with correlations between time courses as edge-weights were automatically created and their integrity was quantified by determining the statistical significance of the following network parameters: diameter, density, clustering coefficient, average path length, two largest eigenvalues, spectral density, and minimum eccentricity. Network graphs using a spring-embedded layout (Cytoscape) and a 3D layout integrated into the anatomical space (Paraview) were created. Functional images were composed by color-coding the degree of each voxel (network node) and transformed into Talairach space. Using the AFNI Talairach atlas, degrees of distinct brain regions were quantified. Reduced averaged BOLD responses were found for the TBI group with a higher network integrity potentially as a compensatory mechanism. Regions of high functional connectivity varied in between groups with largest differences in the cerebellum, the temporal lobes and deep brain structures including the lentiform nucleus, caudate and thalamus.