Comments and ControversiesObject familiarity modulates effective connectivity during haptic shape perception
Introduction
In the preceding paper (Lacey et al., 2009a), we showed that the extent to which haptic shape perception (HS) shares neural circuitry with visual imagery (VI) depends on familiarity of the objects used during the haptic shape task: For unfamiliar, meaningless objects, the extent of overlap of shape-selective activation with activation during visual shape imagery is limited, and most of the overlapping foci do not exhibit correlated activation magnitudes across subjects between the HS and VI tasks. In contrast, the activity evoked during HS perception using familiar objects overlaps much more extensively with that evoked during VI, and activation magnitudes across subjects are significantly correlated between the HS and VI tasks in a number of the overlap zones, including the lateral occipital complex (LOC) bilaterally. These findings suggest that recruitment of the LOC during HS perception reflects VI when the palpated objects are familiar, but not when they are unfamiliar.
In this paper, we present converging evidence on the role of VI during HS, using analysis of connectivity during task performance. Since VI involves top-down paths from prefrontal and posterior parietal cortex into visual cortex (Mechelli et al., 2004), imagery mediation of LOC recruitment would imply analogous top-down paths into the LOC during both the VI and HS tasks. An alternative hypothesis is that LOC recruitment during HS perception reflects engagement of a multisensory representation of shape via bottom-up projections from somatosensory cortex. This would be favored by the existence of bottom-up paths into the LOC from somatosensory cortical areas. Earlier studies of effective connectivity (EC) during HS were consistent with the idea that there are both bottom-up paths from somatosensory cortex, and top-down paths from postero-supero-medial parietal cortex, into the LOC (Peltier et al., 2007, Deshpande et al., 2008). These studies, however, were based on the use of unfamiliar objects only, and also did not analyze EC by task. Here we analyzed task-specific EC during VI, HS perception of unfamiliar objects and HS perception of familiar objects using data presented in the preceding paper to test specific predictions about paths into the LOC: that the LOC would be driven primarily top-down from prefrontal and parietal cortex during VI and HS perception of familiar objects, but by bottom-up paths from somatosensory cortex during HS perception of unfamiliar objects. Further, we tested the more general predictions that connectivity patterns during VI would be similar to those during HS for familiar, but not unfamiliar objects. A set of regions of interest (ROIs) was chosen to test these predictions, as elaborated later.
EC was analyzed using Granger causality (GC), which infers causality between two time series by means of cross-prediction: if future values of time series y(t) can be predicted from past values of time series x(t), then x(t) can be inferred to have a causal influence on y(t) (Granger, 1969). EC between a number of ROIs can be assessed by applying this approach, in a multivariate manner, to the time series of blood oxygenation level-dependent (BOLD) signal intensities from selected ROIs, as described in previous reports from our group (Stilla et al., 2007, Stilla et al., 2008, Deshpande et al., 2008). Unlike earlier, bivariate, implementations of GC analysis which could only probe inputs to and outputs from one region at a time, the multivariate approach allows simultaneous assessment of all potential interactions between the selected ROIs while filtering out indirect effects mediated through intermediate nodes in the network. While our previous studies examined EC using the entire time series data, here we employed the same approach in a task-specific manner.
Recent work from our group (Deshpande et al., 2009) has examined the relation between GC and the commonly used functional connectivity (FC), which is based on instantaneous correlations between BOLD time series in various regions (Büchel and Friston, 2001). Previous studies have likened instantaneous GC, obtained from the vector autoregressive model (VAR) used to compute GC, to zero-lag correlation (Roebroeck et al., 2005). However, since instantaneous GC and time-lagged GC are derived from a common parameter—the variance of the VAR model error—the causality measures obtained are not independent of zero-lag correlation effects. Using simulations and analyses of functional magnetic resonance imaging (fMRI) data from a verbal working memory task, we showed that zero-lag correlations can leak into time-lagged GC estimates based on the hemodynamic response, owing to temporal blurring of neuronal activity (Deshpande et al., 2009). We therefore introduced a method to eliminate the effect of such zero-lag correlations from GC-based networks by explicitly modeling the zero-lag influences in the VAR model and then excluding their contribution to causal influences; we term this analysis correlation-purged GC (CPGC). Here we apply this method to the task-specific GC analysis of VI and HS outlined above. The resulting CPGC-based network can be considered to conservatively estimate directional interactions (i.e., EC) as inferred from BOLD time series, while the original GC-based network includes the true EC as well as the FC that leaked into the causal domain and thus spuriously appeared as EC. We present the results of both GC and CPGC analyses, and also report networks based on zero-lag correlation, corresponding to the commonly computed FC. It should be noted that zero-lag correlation-based (FC-based) networks in the present work as well as previous studies may include not only true instantaneous neuronal correlations, but also, potentially, directional neuronal interactions that occur too fast to be captured by the sluggish BOLD response. Examining all three types of analysis affords a complete picture of the network interactions, as reflected in the hemodynamic response.
Section snippets
Materials and methods
Details regarding participants, tasks, image acquisition and image analysis are provided in the preceding paper (Lacey et al., 2009a). Briefly, we conducted two experiments, with eight neurologically normal participants in each experiment (one person participated in both experiments). In each experiment, there was a session comprising runs of a VI task that required discrimination of the shape of visually imaged objects in response to hearing the object names, with a control condition involving
Results
Fig. 1, Fig. 2 illustrate the significant connections, and their directions, that emerged from multivariate connectivity analyses for the task-pairs in Experiments 1 and 2, respectively, in the conventional GC analyses (left panels), FC analyses (middle panels) and CPGC analyses (right panels). Tables 1–12 show the path weights for all interactions in the connectivity matrices for each condition in each type of analysis, with the significant paths shown in bold type. All significant path
Discussion
The results of the task-specific connectivity analyses presented here are consistent with our predictions, and converge with the evidence presented in the preceding paper (Lacey et al., 2009a) on the conclusion that object VI is strongly linked to HS for familiar, but not for unfamiliar, objects. The underlying neural networks are quite similar during VI and HS perception of familiar objects, with the network underlying HS perception of unfamiliar objects being rather different from either.
Conclusions
VI, and more specifically, visual object imagery, is functionally involved in HS perception of familiar objects: both tasks activate similar networks involving top-down pathways into the LOC from prefrontal areas. By contrast, HS perception of unfamiliar objects activates a very different network involving bottom-up pathways from somatosensory areas into the LOC. These findings from connectivity analyses converge with findings from the preceding paper (Lacey et al., 2009a) that VI-evoked
Acknowledgments
This work was supported by research grants from the NIH (R01 EY12440 and K24 EY17332 to KS, and R01 EB002009 to XH) and NSF (BCS-0519417 to KS). Support to KS from the Veterans Administration is also gratefully acknowledged.
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