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Golestani, A.

Publications and source records attributed to Golestani, A..

2 recordsLinked to original sources

Multimodal Object Representations Rely on Integrative Coding

Combining information from multiple senses is essential to object recognition. Yet how the mind combines sensory input into coherent multimodal representations - the multimodal binding problem - remains poorly understood. Here, we applied multi-echo fMRI across a four-day paradigm, in which participants learned 3-dimensional multimodal object representations created from well-characterized visual shape and sound features. Our novel paradigm decoupled the learned multimodal object representations from their baseline unimodal shape and sound features, thus tracking the emergence of multimodal concepts as they were learned by healthy adults. Critically, the representation for the whole object was different from the combined representation of its individual parts, with evidence of an integrative object code in anterior temporal lobe structures. Intriguingly, the perirhinal cortex - an anterior temporal lobe structure - was by default biased towards visual shape, but this initial shape bias was attenuated with learning. Pattern similarity analyses suggest that after learning the perirhinal cortex orthogonalized combinations of visual shape and sound features, transforming overlapping feature input into distinct multimodal object representations. These results provide evidence of integrative coding in the anterior temporal lobes that is distinct from the distributed sensory features, advancing the age-old question of how the mind constructs multimodal objects from their component features.

neuroscience↗

Performance of temporal and spatial ICA in identifying and removing low-frequency physiological and motion effects in resting-state fMRI

Effective separation of signal from noise (including physiological processes and head motion) is one of the chief challenges for improving the sensitivity and specificity of resting-state fMRI (rs-fMRI) measurements and has a profound impact when these noise sources vary between populations. Independent component analysis (ICA) is an approach for addressing these challenges. Conventionally, due to the lower amount of temporal than spatial information in rs-fMRI data, spatial ICA (sICA) is the method of choice. However, with recent developments in accelerated fMRI acquisitions, the temporal information is becoming enriched to the point that the temporal ICA (tICA) has become more feasible. This is particularly relevant as physiological processes and motion exhibit very different spatial and temporal characteristics when it comes to rs-fMRI applications, leading us to conduct a comparison of the performance of sICA and tICA in addressing these types of noise. In this study, we embrace the novel practice of using theory (simulations) to guide our interpretation of empirical data. We find empirically that sICA can identify more noise-related signal components than tICA. However, on the merit of functional-connectivity results, we find that while sICA is more adept at reducing whole-brain motion effects, tICA performs better in dealing with physiological effects. These interpretations are corroborated by our simulation results. The overall message of this study is that if ICA denoising is to be used for rs-fMRI, there is merit in considering a hybrid approach in which physiological and motion-related noise are each corrected for using their respective best-suited ICA approach. Impact StatementResting-state fMRI is influenced by low-frequency physiological noise and head motion. Independent component analysis (ICA) is becoming increasingly relied on for reducing these influences, but the utility of spatial and temporal ICA remains unclear. We conducted a comparison of the performance of these two ICA types, using physiological-noise and motion time courses as reference. We found that spatial ICA is more adept at reducing motion effects, while temporal ICA performs better in dealing with physiological effects. We believe these findings provide much-needed clarity on the role of ICA, and recommend using a hybrid of tICA and sICA as a paradigm shift in resting-state fMRI.

neuroscience↗