Biology subjects

Rahimi, S.

Publications and source records attributed to Rahimi, S..

7 recordsLinked to original sources

bioRxiv2024

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

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 point t1 can be linearly predicted from patterns of an ROI 2 at time point t2 while 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.

neuroscience↗

bioRxiv2023

Increased stromal densities of B cells, CD103+ cells, and CD163+ M2-like macrophages associate with poor clinical outcomes in BCG treated non-muscle invasive bladder cancer

Non-muscle invasive bladder cancer (NMIBC) constitutes a significant clinical challenge, with over 50% of patients experiencing poor clinical outcomes in the form of early recurrence or progression following treatment with Bacillus Calmette-Guerin (BCG) immunotherapy. The pre-treatment tumor immune microenvironment (TIME) is an established determinant of response to BCG. This study explores the spatial profiles of CD79a+ B cells, CD163+ M2-like macrophages, proliferating and tissue-resident phenotypes of T cells, along with PD-1/PD-L1 checkpoint expression in pre-BCG treatment tumors of 173 patients (139 males, 34 females). Multiplex immunofluorescence staining of a tumor tissue microarray, revealed elevated infiltration of CD79a+ B cells, CD163+ M2-like macrophages, CD103+ cells, and CD8+ T cells at the tumor invasive margins. Increased epithelial PD-L1 immune-checkpoint expression in tumors was observed in female and male patients who exhibited significantly shorter recurrence-free survival (RFS). Importantly, high CD79a+ B cell density in BCG-treated females in both stromal and epithelial compartments exhibited significantly shorter RFS and progression-free survival compared to males. Stromal CD79a+ B cell density was positively correlated with M2-like macrophages, CD8+ T cells, CD103+ cells and PD-1 expressing cells. CD79a+ B cells, CD103+ cells, and M2-like macrophage density were associated with higher grade and enriched in basal subtype tumor. This study highlights the significance of an understudied role of B cells and their cellular neighborhoods in the pre-treatment TIME and BCG-therapy response. Overall, findings from this study underscore the importance of considering sex-related immunobiological differences in the stromal compartments of bladder tumors towards the development of optimal therapeutic targeting strategies.

cancer biology↗

bioRxiv2023

The Impact of subicular VIP-expressing interneurons on seizure dynamics in temporal lobe epilepsy: Insights from preclinical models

The subiculum, a key output region of the hippocampus, is increasingly recognized as playing a crucial role in seizure initiation and spread. The subiculum consists of glutamatergic pyramidal cells, which show alterations in intrinsic excitability in the course of epilepsy, and multiple types of GABAergic interneurons, which exhibit varying characteristics in epilepsy. In this study, we aimed to assess the role of the vasoactive intestinal peptide interneurons (VIP-INs) of the ventral subiculum in the pathophysiology of temporal lobe epilepsy. We observed that an anatomically restricted inhibition of VIP-INs of the ventral subiculum was sufficient to reduce seizures in the intrahippocampal kainic acid model of epilepsy, changing the circadian rhythm of seizures, emphasizing the critical role of this small cell population in modulating TLE. As we expected, permanent unilateral or bilateral silencing of VIP-INs of the ventral subiculum in non-epileptic animals did not induce seizures or epileptiform activity. Interestingly, transient activation of VIP-INs of the ventral subiculum was enough to increase the frequency of seizures in the acute seizure model. Our results offer new perspectives on the crucial involvement of VIP-INs of the ventral subiculum in the pathophysiology of TLE. Given the observed predominant disinhibitory role of the VIP-INs input in subicular microcircuits, modifications of this input could be considered in the development of therapeutic strategies to improve seizure control.

neuroscience↗

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↗