Search bioRxiv⌕ Search

bioRxiv · 10.1101/2021.07.21.452832

Cerebral coherence in task-based fMRI hyperscanning: a new trick to an old dog

Abstract

This study features an fMRI hyperscanning experiment, mapping the brains of the dyads from two fMRI sites, 305 km apart. There are two conditions: in half of the trials (the cooperation condition), the dyad had to collaborate to win and then split the reward, whereas in the other half (the competition condition), the winner took all the reward, thereby resulting in dynamic strategic interactions. Each subject took alternating turns as senders and receivers. To calculate the cerebral coherence in such jittered event-related fMRI tasks, we first estimated the feedback-related BOLD responses of each trial, using 8 finite impulse response functions (16 seconds), and then concatenated the beta volume series. With the right temporal-parietal junction (rTPJ) as the seed, the interpersonal connected brain areas in the cooperation and competition conditions were separately identified: the former condition with the right superior temporal gyrus (rSTG) and the latter with the left precuneus (lPrecuneus) (as well as some other regions of interest), both peaking at the designated frequency bin (1/16 s = 0.0625 Hz), but not in permuted pairs. In addition, the extended coherence analyses on shorter (12 s, or .083 Hz) and longer (20 s, or .05 Hz) concatenated volumes verified that only approximately in the trial length were the rTPJ-rSTG and rTPJ-lPrecuneus couplings found. In sum, our approach both showcases a flexible analysis method that widens the applicability of interpersonal coherence in the rapid event-related fMRI hyperscanning, and reveals a context-based interpersonal coupling between pairs in cooperation vs. competition. Author summarySocial neuroscience is gaining momentum, while coherence analysis as one of the interpersonal connectivity measures is rarely applied to the rapid event-related fMRI. The reason could be that the inherent task design (such as the periodicity constraint for Fourier transformation), among others, limits its applicability and usage. In this fMRI hyperscanning study of a two-person strategic interactions, we independently estimated the feedback-related BOLD responses of each trial, and concatenated the beta time series for interpersonal coherence. The main advantage of this method is in its flexibility against the constraints of jittered experimental trials intermixing several task conditions in most task-based fMRI runs. In addition, our coherence results, which highlight two inter-brain couplings (e.g., rTPJ-rSTG between collaborating, and rTPJ-lPrecuneus for competing dyads) among other brain regions, plus its temporal specificity of such seed-brain couplings only between pairs, both replicate previous run-wide fMRI coherence results, and hold great promise in extending its applicability in task-based fMRI hyperscanning.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Wang, L.-S., Cheng, J.-T., Hsu, I.-J., Liou, S., Kung, C.-C., Chen, D.-Y., Weng, M.-H.. 2021-07-23. Cerebral coherence in task-based fMRI hyperscanning: a new trick to an old dog. https://doi.org/10.1101/2021.07.21.452832

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

The Unreasonable Effectiveness of Cell Types in Describing Neuronal Physiological Features

Single-cell RNA sequencing (scRNA-seq) captures detailed gene expression profiles at scale, while patch-clamp recordings measure intrinsic neuronal electrophysiological properties. Modeling the relations between these two modalities remains a challenge. Here, we compare how well electrophysiological features can be predicted by traditional transcriptomic cell type classification, representations derived from a foundational model (scGPT) pretrained on large-scale scRNA-seq datasets, ion channel-coding genes, and highly variable genes. Using paired transcriptomic and electrophysiological patch-sequencing data from 495 human neurons from neurosurgical tissue, we find that cluster-level cell type representations consistently outperform highly variable gene selection, ion channel gene selection, and context-enriched scGPT embeddings. Notably, performance varies across model architectures and initializations, and the best results are obtained by combining the outputs of separate cell type and scGPT-based models. Together, these findings suggest that traditional discrete cellular classification is highly effective in predicting physiological features. For maximum performance it can be complemented by pretrained transformer models.

neuroscience↗

A nonlinear inhibition pathway underlying cortical responses to tuned holographic optogenetic perturbations

Optogenetics enables causal manipulation of cortical activity. Perturbation responses can be counterintuitive due to network interactions, making theory essential for predicting them. Existing approaches often rely on linear approximations, which fail for many biologically relevant perturbations. Here we develop a nonlinear theory of responses to holographic perturbations in cell-type-specific recurrent networks with structured connectivity. We fit a nonlinear model to mouse V1 data, which shows cotuned-ensemble suppression: perturbing spatially clustered neurons with similar preferred orientations yields markedly stronger short-range suppression than perturbing untuned ensembles. We show that cotuned-ensemble suppression arises from a feature-tuned, nonlinear inhibition pathway implicating somatostatin-positive (SST) interneurons. The theory predicts that cotuned ensembles suppress parvalbumin-positive (PV) neurons but facilitate SST neurons, and links the degree of cotuned-ensemble suppression or facilitation to the variance of the SST response. This framework identifies mechanisms by which nonlinear inhibition sculpts cortical dynamics and establishes a predictive basis for targeted optogenetic interventions.

neuroscience↗

Proteomic signatures of APOE ε4 across human tissues and cell types in Alzheimers disease

The apolipoprotein E {varepsilon}4 (APOE {varepsilon}4) allele is the strongest genetic risk factor for late-onset Alzheimers disease (AD). However, the underlying molecular mechanisms remain unclear. This study included 1691 participants from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP), 1226 participants from the Accelerating Medicines Partnership - Alzheimers Disease (AMP-AD) Diverse Cohorts Study, and 735 participants from the Alzheimers Disease Neuroimaging Initiative (ADNI). To characterise APOE {varepsilon}4 molecular effects, we analysed proteomic data from plasma, cerebrospinal fluid (CSF), and induced pluripotent stem cell (iPSC)-derived astrocytes and neurons, as well as transcriptomic and proteomic data from multiple brain regions. The association of APOE {varepsilon}4 with AD neuropathology was also examined. APOE {varepsilon}4 carriers shared a plasma proteomic signature enriched for immune processes, irrespective of AD diagnosis. A machine learning classifier trained on this signature discriminated APOE {varepsilon}4 carriers from non-carriers in an independent cohort using CSF proteomics. APOE {varepsilon}4 carriage was associated with higher Braak stages and Consortium to Establish a Registry for Alzheimers Disease (CERAD) score. However, only limited APOE {varepsilon}4-associated transcriptomic and proteomic changes were observed in bulk brain tissue, with poor cross-layer concordance. Proteomic analyses of iPSC-derived astrocytes and neurons further revealed cell-type-specific APOE {varepsilon}4-associated changes. APOE {varepsilon}4 is associated with a consistent proteomic signature across plasma and CSF. Its molecular effects in the brain differ across cell types, brain regions and molecular layers. These findings support the need for cell-type-resolved multi-omic studies to elucidate how APOE {varepsilon}4 confers AD risk.

neuroscience↗