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Riitta Hari

Publications and source records attributed to Riitta Hari.

2 recordsLinked to original sources
bioRxiv2016

Cortical dynamics of saccade-target selection during free-viewing of natural scenes

Natural visual behaviour entails explorative eye movements, saccades, that bring different parts of a visual scene into the central vision. The neural processes guiding the selection of saccade targets are still largely unknown. Therefore, in this study, we tracked with magnetoencephalography (MEG) cortical dynamics of viewers who were freely exploring novel natural scenes. Overall, the viewers were largely consistent in their gaze behaviour, especially if the scene contained any persons. We took a fresh approach to relate the eye-gaze data to the MEG signals by characterizing dynamic cortical representations by means of representational distance matrices. Specifically, we compared the representational distances between the stimuli in the evoked MEG responses with predictions based (1) on the low-level visual similarity of the stimuli (as visually more similar stimuli evoke more similar responses in early visual areas) and (2) on the eye-gaze data. At 50-75 ms after the scene onset, the similarity of the occipital MEG patterns correlated with the low-level visual similarity of the scenes, and already at 75-100 ms the visual features attracting the first saccades predicted the similarity of the right parieto-occipital MEG responses. Thereafter, at 100-125 ms, the landing positions of the upcoming saccades explained MEG responses. These results indicate that MEG signals contain signatures of the rapid processing of natural visual scenes as well as of the initiation of the first saccades, with the processing of the saccade target preceding the processing of the landing position of the upcoming saccade.\n\nSIGNIFICANCE STATEMENTHumans naturally make eye movements to bring different parts of a visual scene to the fovea where our visual acuity is the best. Tracking of eye gaze can reveal how we make inferences about the content of a scene by looking at different objects, or which visual cues automatically attract our attention and gaze. The brain dynamics governing natural gaze behaviour is still largely unknown. Here we suggest a novel approach to relate eye-tracking results with brain activity, as measured with magnetoencephalography (MEG), and demonstrate signatures of natural gaze behaviour in the MEG data already before the eye movements occur.

Neuroscience↗
bioRxiv2016

Functional brain segmentation using inter-subject correlation in fMRI

The human brain continuously processes massive amounts of rich sensory information. To better understand such highly complex brain processes, modern neuroimaging studies are increasingly utilizing experimental setups that better mimic daily-life situations. We propose a new exploratory data-analysis approach, functional segmentation intersubject correlation analysis (FuSeISC), to facilitate the analysis of functional magnetic resonance (fMRI) data sets collected in these experiments. The method provides a new type of functional segmentation of brain areas, not only characterizing areas that display similar processing across subjects but also areas in which processing across subjects is highly variable.\n\nWe tested FuSeISC using fMRI data sets collected during traditional block-design stimuli (37 subjects) as well as naturalistic auditory narratives (19 subjects). The method identified spatially local and/or bilaterally symmetric clusters in several cortical areas, many of which are known to be processing the types of stimuli used in the experiments. The method is not only prominent for spatial exploration of large fMRI data sets obtained using naturalistic stimuli, but has other potential applications such as generation of a functional brain atlases including both lower-and higher-order processing areas.\n\nFinally, as a part of FuSeISC, we propose a criterion-based sparsification of the shared nearest-neighbor graph for detecting clusters in noisy data. In our tests with synthetic data, this technique was superior to well-known clustering methods, such as Ward's method, affinity propagation and K-means++.

Neuroscience↗