Biology subjects

Rahimi, S.

Publications and source records attributed to Rahimi, S..

4 recordsLinked to original sources

bioRxiv2023

Identifying nonlinear Functional Connectivity with EEG/MEG using Nonlinear Time-Lagged Multidimensional Pattern Connectivity (nTL-MDPC)

Investigating task- and stimulus-dependent connectivity is key to understanding how brain regions interact to perform complex cognitive processes. Most existing connectivity analysis methods reduce activity within brain regions to unidimensional measures, resulting in a loss of information. While recent studies have introduced new functional connectivity methods that exploit multidimensional information, i.e., pattern-to-pattern relationships across regions, they have so far mostly been applied to fMRI data and therefore lack temporal information. We recently developed Time-Lagged Multidimensional Pattern Connectivity for EEG/MEG data, which detects linear dependencies between patterns for pairs of brain regions and latencies in event-related experimental designs (Rahimi et al., 2022b). Due to the linearity of this method, it may miss important nonlinear relationships between activity patterns. Thus, we here introduce nonlinear Time-Lagged Multidimensional Pattern Connectivity (nTL-MDPC) as a novel bivariate functional connectivity metric for event-related EEG/MEG applications. nTL-MDPC describes how well patterns in ROI X at time point tx can predict patterns of ROI Y at time point ty using artificial neural networks (ANNs). We evaluated this method on simulated data as well as on an existing EEG/MEG dataset of semantic word processing, and compared it to its linear counterpart (TL-MDPC). We found that nTL-MDPC indeed detected nonlinear relationships more reliably than TL-MDPC in simulations with moderate to high numbers of trials. However, in real brain data the differences were subtle, with identification of some connections over greater time lags but no change in the connections identified. The simulations and EEG/MEG results demonstrate that differences between the two methods are not dramatic, i.e. the linear method can approximate linear and nonlinear dependencies well. HighlightsO_LInTL-MDPC is a bivariate functional connectivity method for event-related EEG/MEG C_LIO_LInTL-MDPC detects linear and nonlinear connectivity at zero and non-zero lags C_LIO_LInTL-MDPC revealed connectivity between ATL hub and semantic control regions C_LIO_LIDifferences between linear and nonlinear TL-MDPC were small C_LI

neuroscience↗

bioRxiv2022

Carcinogen induced expansion of atypical B cells and pre-treatment tumor adjacent tertiary lymphoid structures associate with poor response to BCG in non-muscle invasive bladder cancer

Poor response to Bacillus Calmette-Guerin (BCG) immunotherapy remains a major barrier in the management of patients with non-muscle-invasive bladder cancer (NMIBC). Among the multiple factors contributing to poor outcomes, a B cell infiltrated pre-treatment immune microenvironment of NMIBC tumors has emerged as a key determinant of response to BCG. The mechanisms underlying the paradoxical roles of B cells in NMIBC are poorly understood. Here, we show that B cell dominant tertiary lymphoid structures (TLSs), a hallmark feature of chronic mucosal immune response, are abundant and located close to the epithelial compartment in pre-treatment tumors from BCG non-responders. Digital spatial proteomic profiling of whole tumor sections revealed higher expression of immune exhaustion-associated proteins within the TLSs from both responders and non-responders. Chronic local inflammation, induced by the N-butyl- N-(4-hydroxybutyl) nitrosamine (BBN) carcinogen, led to TLS formation with recruitment and differentiation of the immunosuppressive atypical B cell (ABCs) subset within the bladder microenvironment, predominantly in aging female mice compared to their male counterparts. Depletion of ABCs simultaneous to BCG treatment delayed cancer progression in female mice. Our findings provide the first evidence indicating the role of ABCs in BCG response and will inform future development of therapies targeting the B cell exhaustion axis.

cancer biology↗

bioRxiv2022

Time Lagged Multidimensional Pattern Connectivity (TL MDPC): An EEG/MEG Pattern Transformation Based Functional Connectivity Metric

Functional and effective connectivity methods are essential to study the complex information flow in brain networks underlying human cognition. Only recently have connectivity methods begun to emerge that make use of the full multidimensional information contained in patterns of brain activation, rather than univariate summary measures of these patterns. To date, these methods have mostly been applied to fMRI data, and no method allows vertex-vertex transformation with the temporal specificity of EEG/MEG data. Here, we introduce time-lagged multidimensional pattern connectivity (TL-MDPC) as a novel bivariate functional connectivity metric for EEG/MEG research. TL-MDPC estimates the vertex-to-vertex transformations among multiple brain regions and across different latency ranges. It determines how well patterns in ROI X at time point tx can linearly predict patterns of ROI Y at time point ty. In the present study, we use simulations to demonstrate TL-MDPCs increased sensitivity to multidimensional effects compared to a univariate approach across realistic choices of number of trials and signal-to-noise ratio. We applied TL-MDPC, as well as its univariate counterpart, to an existing dataset varying the depth of semantic processing of visually presented words by contrasting a semantic decision and a lexical decision task. TL-MDPC detected significant effects beginning very early on, and showed stronger task modulations than the univariate approach, suggesting that it is capable of capturing more information. With TL-MDPC only, we observed rich connectivity between core semantic representation (left and right anterior temporal lobes) and semantic control (inferior frontal gyrus and posterior temporal cortex) areas with greater semantic demands. TL-MDPC is a promising approach to identify multidimensional connectivity patterns, typically missed by univariate approaches. HighlightsO_LITL-MDPC is a multidimensional functional connectivity method for event-related EMEG C_LIO_LITL-MDPC captures both univariate and multidimensional connectivity C_LIO_LITL-MDPC yields both zero-lag and time-lagged dependencies C_LIO_LITL-MDPC produced richer connectivity than univariate approaches in a semantic task C_LIO_LITL-MDPC identified connectivity between the ATL hubs and semantic control regions C_LI

neuroscience↗

bioRxiv2021

Task modulation of spatiotemporal dynamics in semantic brain networks: an EEG/MEG study

How does brain activity in distributed semantic brain networks evolve over time, and how do these regions interact to retrieve the meaning of words? We compared spatiotemporal brain dynamics between visual lexical and semantic decision tasks (LD and SD), analysing whole-cortex evoked responses and spectral functional connectivity (coherence) in source-estimated electroencephalography and magnetoencephalography (EEG and MEG) recordings. Our evoked analysis revealed generally larger activation for SD compared to LD, starting in primary visual area (PVA) and angular gyrus (AG), followed by left posterior temporal cortex (PTC) and left anterior temporal lobe (ATL). The earliest activation effects in ATL were significantly left-lateralised. Our functional connectivity results showed significant connectivity between left and right ATLs and PTC and right ATL in an early time window, as well as between left ATL and IFG in a later time window. The connectivity of AG was comparatively sparse. We quantified the limited spatial resolution of our source estimates via a leakage index for careful interpretation of our results. Our findings suggest that semantic task demands modulate visual and attentional processes early-on, followed by modulation of multimodal semantic information retrieval in ATLs and then control regions (PTC and IFG) in order to extract task-relevant semantic features for response selection. Whilst our evoked analysis suggests a dominance of left ATL for semantic processing, our functional connectivity analysis also revealed significant involvement of right ATL in the more demanding semantic task. Our findings demonstrate the complementarity of evoked and functional connectivity analysis, as well as the importance of dynamic information for both types of analyses. HighlightsO_LISemantic task demands affect activity and connectivity at different processing stages C_LIO_LIEarliest task modulations occurred in posterior visual brain regions C_LIO_LIATL, PTC and IFG effects reflect task-relevant retrieval of multimodal information C_LIO_LIATL effects left-lateralised for activation but bilateral for functional connectivity C_LIO_LIDynamic evoked and connectivity data are essential to study semantic networks C_LI

neuroscience↗