Time-Lagged Multidimensional Pattern Connectivity (TL-MDPC): An EEG/MEG pattern transformation based functional connectivity metric

Rahimi, Setareh; Jackson, Rebecca L.; Farahibozorg, Seyedeh-Rezvan; Hauk, Olaf

doi:10.1016/j.neuroimage.2023.119958

Cited by 11 publications

(33 citation statements)

References 96 publications

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“…For every pair of ROIs, these steps are performed for all pairs of time points, and the resulting EVs are placed in a time-time matrix referred to as a Temporal Transformation Matrix (TTM; Rahimi et al, 2023a). This provides the time points of ROI 𝑋 2 in the vertical axis and that of ROI 𝑋 1 in the horizontal axis.…”

Section: Methodsmentioning

confidence: 99%

“…We employed the same dataset as in our prior studies (Rahimi, 2023;Rahimi et al, 2023aRahimi et al, , 2022) containing EEG/MEG recordings of 18 healthy native English speakers (mean age 27.00±5.13, 12 female) with normal or corrected-to-normal vision (for more detail see Farahibozorg, 2018;Rahimi et al, 2022). The experiment was approved by the Cambridge Psychology Research Ethics Committee and volunteers were paid for their time and effort.…”

Section: Participantsmentioning

confidence: 99%

“…Data acquisition and pre-processing steps are identical to previous analyses of this dataset (Farahibozorg, 2018;Rahimi et al, 2023aRahimi et al, , 2022. MEG and EEG signals were recorded simultaneously using a Neuromag Vectorview system (Elekta AB, Stockholm, Sweden) and MEG-compatible EEG cap (EasyCap GmbH, Herrsching, Germany) at the MRC Cognition and Brain Sciences Unit, University of Cambridge, UK.…”

Section: Data Acquisition and Pre-processingmentioning

confidence: 99%

“…However, most widely used connectivity methods are limited in two ways, namely by assessing only bivariate and unidimensional connectivity. As in recent studies (Basti et al, 2019;Rahimi et al, 2023bRahimi et al, , 2023a, we use the term "unidimensional" for methods that collapse signals to one time course per region, in contrast to "multidimensional" methods that use multiple time courses per ROI. We use the terms "bivariate" and "multivariate" to distinguish the computation of connectivity between only two regions at a time and when considering the effect of more than two regions simultaneously, respectively.…”

Section: Introductionmentioning

confidence: 99%

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Multivariate Time-Lagged Multidimensional Pattern Connectivity (mvTL-MDPC) for EEG/MEG Functional Connectivity Analysis

Rahimi,

Jackson,

Hauk

2024

Preprint

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Multidimensional connectivity methods are critical to reveal the full pattern of complex interactions between brain regions over time. However, to date only bivariate multidimensional methods are available for time-resolved EEG/MEG data, which may overestimate connectivity due to the confounding effects of spurious and indirect dependencies. Here, we introduce a novel functional connectivity method which is both multivariate and multidimensional, Multivariate Time-lagged Multidimensional Pattern Connectivity (mvTL-MDPC), to address this issue in time-resolved EEG/MEG applications. This novel method extends its bivariate counterpart TL-MDPC to estimate how well patterns in an ROI 1 at time pointt1can be linearly predicted from patterns of an ROI 2 at time pointt2while partialling out the multivariate contributions from other brain regions. We compared the performance of mvTL-MDPC and TL-MDPC on simulated data designed to test their ability to identify true direct connections, using the Euclidean distance to the ground truth to measure goodness-of-fit. These simulations demonstrate that mvTL-MDPC produces more reliable and accurate results than the bivariate method. We therefore applied this method to an existing EEG/MEG dataset contrasting words presented in more or less demanding semantic tasks, to identify the dynamic brain network underlying controlled semantic cognition. As expected, mvTL-MDPC was more selective than TL-MDPC, identifying fewer connections, likely due to a reduction in the detection of spurious or indirect connections. Dynamic connections were identified between bilateral anterior temporal lobes, posterior temporal cortex and inferior frontal gyrus, in line with recent neuroscientific models of semantic cognition.

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Section: Methodsmentioning

confidence: 99%

Section: Participantsmentioning

confidence: 99%

Section: Data Acquisition and Pre-processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multivariate Time-Lagged Multidimensional Pattern Connectivity (mvTL-MDPC) for EEG/MEG Functional Connectivity Analysis

Rahimi,

Jackson,

Hauk

2024

Preprint

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“…To address this, we can use a recently developed method, representational connectivity analysis (RCA; Karimi-Rouzbahani et al, 2021), which can show how activity in one region contributes to information-specific activity in another region at a later point in time. This follows the recent development of multivariate informational connectivity approaches (Anzellotti & Coutanche, 2018; Basti et al, 2020; Goddard et al, 2016; Rahimi, Jackson, et al, 2022), which seek to provide information about the timing and direction of between region connectivity, alongside the representational content of such connectivity.…”

Section: Introductionmentioning

confidence: 99%

Recurrent connectivity supports higher-level visual and semantic object representations in the brain

Seth

Nicholls

Tyler

et al. 2023

Preprint

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Visual object recognition is a highly dynamic process by which we extract meaningful information about the things we see. However, the functional relevance and informational properties of feedforward and feedback signals remains largely unspecified. Additionally, it remains unclear whether computational models of vision alone can accurately capture object-specific representations and the evolving spatiotemporal neural dynamics. Here, we probe these dynamics using a combination of representational similarity and connectivity analyses of fMRI and MEG data recorded during the recognition of familiar, unambiguous objects from a wide range of categories. Modelling the visual and semantic properties of our stimuli using an artificial neural network as well as a semantic feature model, we find that unique aspects of the neural architecture and connectivity dynamics relate to visual and semantic object properties. Critically, we show that recurrent processing between anterior and posterior ventral temporal cortex relates to higher-level visual properties prior to semantic object properties, in addition to semantic-related feedback from the frontal lobe to the ventral temporal lobe between 250 and 500ms after stimulus onset. These results demonstrate the distinct contributions semantic object properties make in explaining neural activity and connectivity, highlighting it is a core part of object recognition.

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Updating functional brain units: Insights far beyond Luria

Peña-Casanova,

Sánchez-Benavides,

Sigg-Alonso

2024

Cortex

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Time-Lagged Multidimensional Pattern Connectivity (TL-MDPC): An EEG/MEG pattern transformation based functional connectivity metric

Cited by 11 publications

References 96 publications

Multivariate Time-Lagged Multidimensional Pattern Connectivity (mvTL-MDPC) for EEG/MEG Functional Connectivity Analysis

Multivariate Time-Lagged Multidimensional Pattern Connectivity (mvTL-MDPC) for EEG/MEG Functional Connectivity Analysis

Recurrent connectivity supports higher-level visual and semantic object representations in the brain

Updating functional brain units: Insights far beyond Luria

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