Bressler, S. | Search bioRxiv

bioRxiv2019

Non-spatial features reduce the reliance on sustained spatial auditory attention

Top-down spatial attention is effective at selecting a target sound from a mixture. However, non-spatial features often distinguish sources in addition to location. This study explores whether redundant non-spatial features are used to maintain selective auditory attention for a spatially defined target. We recorded electroencephalography (EEG) while subjects focused attention on one of three simultaneous melodies. In one experiment, subjects (n = 17) were given an auditory cue indicating both the location and pitch of the target melody. In a second experiment (n = 17 subjects), the cue only indicated target location, and we compared two conditions: one in which the pitch separation of competing melodies was large, and one in which this separation was small. In both experiments, responses evoked by onsets of events in sound streams were modulated equally as strong by attention, suggesting that the target stimuli were correctly selected regardless of the cue or pitch information available. In all cases, parietal alpha was lateralized following the cue, but prior to melody onset, indicating that subjects always initially focused attention in space. During the stimulus presentation, however, this lateralization weakened when pitch cues were strong, suggesting that strong pitch cues reduced reliance on sustained spatial attention. These results demonstrate that once a well-defined target stream at a known location is selected, top-down spatial attention is unnecessary to filter out a segregated competing stream.

neuroscience↗

bioRxiv2019

Measuring directed functional connectivity in neural data using Non-Parametric Directionality Analysis: validation and comparison with Non-Parametric Granger Causality

Background Non-parametric directionality (NPD) is a novel method for estimation of directed functional connectivity (dFC) in neural data. The method has previously been verified in its ability to recover causal interactions in simulated spiking networks in Halliday et al. (2015) MethodsThis work presents a validation of NPD in continuous neural recordings (e.g. local field potentials). Specifically, we use autoregressive model to simulate time delayed correlations between neural signals. We then test for the accurate recovery of networks in the face of several confounds typically encountered in empirical data. We examine the effects of NPD under varying: a) signal-to-noise ratios, b) asymmetries in signal strength, c) instantaneous mixing, d) common drive, e) and parallel/convergent signal routing. We also apply NPD to data from a patient who underwent simultaneous magnetoencephalography and deep brain recording. ResultsWe demonstrate that NPD can accurately recover directed functional connectivity from simulations with known patterns of connectivity. The performance of the NPD metric is compared with non-parametric Granger causality (NPG), a well-established methodology for model free estimation of dFC. A series of simulations investigating synthetically imposed confounds demonstrate that NPD provides estimates of connectivity that are equivalent to NPG. However, we provide evidence that: i) NPD is less sensitive than NPG to degradation by noise; ii) NPD is more robust to the generation of false positive identification of connectivity resulting from SNR asymmetries; iii) NPD is more robust to corruption via moderate degrees of instantaneous signal mixing. ConclusionsThe results in this paper highlight that to be practically applied to neural data, connectivity metrics should not only be accurate in their recovery of causal networks but also resistant to the confounding effects often encountered in experimental recordings of multimodal data. Taken together, these findings position NPD at the state-of-the-art with respect to the estimation of directed functional connectivity in neuroimaging. HighlightsO_LINon-parametric directionality (NPD) is a novel directed connectivity metric. C_LIO_LINPD estimates are equivalent to Granger causality but more robust to signal confounds. C_LIO_LIMultivariate extensions of NPD can correctly identify signal routing. C_LI Abbreviations

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