Connectivity analysis with structural equation modelling: an example of the effects of voxel selection.

Structural equation modelling (SEM) of neuroimaging data is commonly applied to a network of distributed brain regions. We applied SEM to an fMRI dataset to identify condition-specific effects in a simple experiment composed of visual stimulation and baseline conditions. The visual network was composed of three well-defined anatomical regions (V1, V2, and V5) and three path connections (V1 --> V2, V1 --> V5, and V2 --> V5). This network was used to test four hypotheses: (1) whether the condition-specific effects for all three connections vary according to the data selected for modelling; (2) whether the "summary" measures that are often used are indeed appropriate; (3) whether measures taken from the voxel timecourse can reliably predict the condition-specific effects for each one of the three path connections, and (4) whether all voxels within an anatomical region yield equivalent SEM outcomes. There was some variability in the significance of the condition-specific effects across randomly selected voxels within regions. However, the SEM outcome from the "summary" measures was comparable to the most frequent pattern of condition-specific effects. Magnitude, delay, spread, and goodness-of-fit measures taken from a gamma fit to the voxel time courses predicted reliably the significance of the SEM condition-specific effects for each connection. This result enabled us to identify spatially coherent regions at the boundaries of V2 that displayed different condition-specific effects from those seen in the majority of the voxels. Although the generality of these results awaits further investigation, this example highlights a number of important issues for SEM. We have provided further evidence that the SEM outcome does vary somewhat according to the voxels selected and that, although the use of summary measures can give a generalised view of the connectivity pattern, they could fail to capture functional differences within specialised areas.

Binaural specialisation in human auditory cortex: an fMRI investigation of interaural correlation sensitivity.

A listener's sensitivity to the interaural correlation (IAC) of sound plays an important role in several phenomena in binaural hearing. Although IAC has been examined humans, little is known about the neural basis of sensitivity to IAC in humans. The present study employed functional magnetic resonance imaging to measure blood oxygen level-dependent (BOLD) activity in auditory brainstem and cortical structures in human listeners during presentation of band-pass noise stimuli between which IAC was varied systematically. The stimuli evoked significant bilateral activation in the inferior colliculus, medial geniculate body, and auditory cortex. There was a significant positive relationship between BOLD activity and IAC which was confined to a distinct subregion of primary auditory cortex located bilaterally at the lateral extent of Heschl's gyrus. Comparison with published anatomical data indicated that this area may also be cytoarchitecturally distinct. Larger differences in activation were found between levels of IAC near unity than between levels near zero. This response pattern is qualitatively compatible with previous measures of psychophysical and neurophysiological sensitivity to IAC. extensively in neurophysiological studies in animals and in psychophysical studies in

A method for determining venous contribution to BOLD contrast sensory activation.

While BOLD contrast reflects hemodynamic changes within capillaries serving neural tissue, it also has a venous component. Studies that have determined the relation of large blood vessels to the activation map indicate that veins are the source of the largest response, and the most delayed in time. It would be informative if the location of these large veins could be extracted from the properties of the functional responses, since vessels are not visible in BOLD contrast images. The present study describes a method for investigating whether measures taken from the functional response can reliably predict vein location, or at least be useful in down-weighting the venous contribution to the activation response, and illustrates this method using data from one subject. We combined fMRI at 3 Tesla with high-resolution anatomic imaging and MR venography to test whether the intrinsic properties of activation time courses corresponded to tissue type. Measures were taken from a gamma fit to the functional response. Mean magnitude showed a significant effect of tissue type (p < 0.001) where CSF > veins approximately gray matter > white matter. Mean delays displayed the same ranking across tissue types (p < 0.001), except that veins > gray matter. However, measures for all tissue types were distributed across an overlapping range. A logistic regression model correctly discriminated 72% of the veins from gray matter in the absence of independent information of macroscopic vessels (ROC = 0.72). While tissue classification was not perfect for this subject, weighting the T contrast by the predicted probabilities materially reduced the venous component to the activation map.