Biology subjects

Dimitrios Pantazis

Publications and source records attributed to Dimitrios Pantazis.

4 recordsLinked to original sources

bioRxiv2016

Hearing scenes: A neuromagnetic signature of perceived auditory spatial extent

Perceiving the geometry of surrounding space is a multisensory process, crucial to contextualizing object perception and guiding navigation behavior. Auditory cues are informative about the shape and extent of large-scale environments: humans can make judgments about surrounding spaces from reverberation cues. However, how the scale of auditory space is represented neurally is unknown. Here, by orthogonally varying the spatial extent and sound source content of auditory scenes during magnetoencephalography (MEG) recording, we report a neural signature of auditory space size perception, starting ~145 ms after stimulus onset. Importantly, this neuromagnetic response is readily dissociable in form and time into representations of the source and its reverberant enclosing space: while the source exhibits an early and transient response, the neural signature of space is sustained and independent of the original source that produced it. Further, the space size response is robust to variations in sound source, and vice versa. The MEG decoding signal was distributed primarily across bilateral temporal sensor locations, significantly correlated with behavioral responses in a separate experiment. Together, our results provide the first neuromagnetic evidence for a robust auditory space size representation in the human brain, sensitive to reverberant decay, and reveal the temporal dynamics of how such a code emerges over time from the transformation of complex naturalistic auditory signals.

Neuroscience↗

bioRxiv2016

From what we perceive to what we remember: Characterizing representational dynamics of visual memorability

Not all visual memories are equal--some endure in our minds, while others quickly disappear. Recent behavioral work shows we can reliably predict which images will be remembered. This image property is called memorability. Memorability is intrinsic to an image, robust across observers, and unexplainable by low-level visual features. However, its neural bases and relation with perception and memory remain unknown. Here we characterize the representational dynamics of memorability using magnetoencephalography (MEG). We find memorability is indexed by brain responses starting at 218ms for faces and 371ms for scenes--later than classical early face/scene discrimination perceptual signals, yet earlier than the late memory encoding signal observed at ~700ms. The results show memorability is a high-level image property whose spatio-temporal neural dynamics are different from those of memory encoding. Together, this work brings new insights into the underlying neural processes of the transformation from what we perceive to what we remember.

Neuroscience↗

bioRxiv2015

Similarity-based fusion of MEG and fMRI reveals spatio-temporal dynamics in human cortex during visual object recognition

Every human cognitive function, such as visual object recognition, is realized in a complex spatio-temporal activity pattern in the brain. Current brain imaging techniques in isolation cannot resolve the brains spatio-temporal dynamics because they provide either high spatial or temporal resolution but not both. To overcome this limitation, we developed a new integration approach that uses representational similarities to combine measurements from different imaging modalities - magnetoencephalography (MEG) and functional MRI (fMRI) - to yield a spatially and temporally integrated characterization of neuronal activation. Applying this approach to two independent MEG-fMRI data sets, we observed that neural activity first emerged in the occipital pole at 50-80ms, before spreading rapidly and progressively in the anterior direction along the ventral and dorsal visual streams. These results provide a novel and comprehensive, spatio-temporally resolved view of the rapid neural dynamics during the first few hundred milliseconds of object vision. They further demonstrate the feasibility of spatially unbiased representational similarity based fusion of MEG and fMRI, promising new insights into how the brain computes complex cognitive functions.

Neuroscience↗

bioRxiv2015

Dynamics of scene representations in the human brain revealed by magnetoencephalography and deep neural networks

Human scene recognition is a rapid multistep process evolving over time from single scene image to spatial layout processing. We used multivariate pattern analyses on magnetoencephalography (MEG) data to unravel the time course of this cortical process. Following an early signal for lower-level visual analysis of single scenes at ~100ms, we found a marker of real-world scene size, i.e. spatial layout processing, at ~250ms indexing neural representations robust to changes in unrelated scene properties and viewing conditions. For a quantitative explanation that captures the complexity of scene recognition, we compared MEG data to a deep neural network model trained on scene classification. Representations of scene size emerged intrinsically in the model, and resolved emerging neural scene size representation. Together our data provide a first description of an electrophysiological signal for layout processing in humans, and a novel quantitative model of how spatial layout representations may emerge in the human brain.

Neuroscience↗