Biology subjects

Song, Y.

Publications and source records attributed to Song, Y..

At least 109 records · Page 6Linked to original sources

bioRxiv2025

Mechanosensitive channel PIEZO1 enhances endometrial decidualization via BECN1-dependent autophagy

The mechanosensitive ion channel PIEZO1 plays critical roles in physiological and pathological processes in response to various types of mechanical forces, including shear stress, stretch, and extracellular matrix (ECM) stiffness. Decidualization is crucial for a successful pregnancy, characterized by the differentiation of fibroblastic endometrial stromal cells into round, secretory decidual cells, along with the rapid remodeling of the ECM. Herein, we report that PIEZO1 plays a crucial role in enhancing decidualization in response to extracellular matrix (ECM) stiffness and cell contraction. Uterine-specific knockout of Piezo1 using Pgr-Cre in mice results in subfertility due to decidualization impairment in mid-late pregnancy. Silencing of PIEZO1 in human endometrial stromal cells also results in impaired decidualization. Treatment with the PIEZO1 agonist Yoda1 enhances decidualization in both in vivo and in vitro models. Stromal cells growing on ECM with 25 kPa stiffness display a better decidualization response than cells seeded on softer 2 kPa surface or harder surface of the regulator petri dish, and this difference is abolished by null of Piezo1. Consistent with PIEZO1 as a Ca2+ modulator, blocking of intracellular Ca2+ or pCaMKII significantly inhibits Yoda1-enhanced decidualization. Further investigation reveals that BECN1-dependent autophagy acts as the downstream of PIEZO1. Silencing of Beclin1 abolishes Yoda1-induced decidualization, while Tat-BECN1 fully rescues impaired decidualization caused by the lack of PIEZO1. Finally, the lower expression of PIEZO1 is associated with impaired decidualization in the endometrium of endometriotic baboons. In conclusion, we have uncovered a novel mechanism of decidualization that is regulated by PIEZO1-mediated mechanotransduction, providing further insight into decidualization studies. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=187 HEIGHT=200 SRC="FIGDIR/small/668790v1_ufig1.gif" ALT="Figure 1"> View larger version (68K): org.highwire.dtl.DTLVardef@2d25faorg.highwire.dtl.DTLVardef@186e56org.highwire.dtl.DTLVardef@1f3f61borg.highwire.dtl.DTLVardef@1a26bb7_HPS_FORMAT_FIGEXP M_FIG C_FIG

physiology↗

bioRxiv2025

Localisation of Apicomplexa motor Myosin A with axial nanometric precision using graphene energy transfer

Migration in Apicomplexan parasites, a phylum that includes Plasmodium spp. responsible for malaria and the zoonotic Toxoplasma, is explained by the linear migration motor model. This model predicts that Myosin A is confined beneath the plasma membrane and above the inner membrane complex (IMC), a membranous barrier separating the cell membrane and the cytoplasm. Cumulative data, using mutant cell lines and biochemical data support this model. Paradoxically, a proof of the precise localization of Myosin A is still lacking due to limitations in resolving the motor with nanometric precision within the space comprising the membrane and the IMC. Here, we implement graphene energy transfer (GET), a novel axial nanometric ruler with a resolution of [~]1 nm, to determine the relative axial position of Myosin A and IMC1 (a reference protein defining IMC position). Using GET, we estimate the IMC dimensions with precision matching electron microscopy (EM) data and the added advantage of identifying specific bauplan proteins. We complement these measurements with 2D STED microscopy in MIRA confiners, uExMIC, and cryo-immunolabeling. Our data present the first direct localization of Myosin A populations with nanometric resolution in the third dimension, which is compatible with the linear motor model. One sentence summaryUsing the novel GET method, this study presents the first direct localization of Myosin A populations in Toxoplasma gondii with 1-4 nm axial precision, supporting the linear model.

biophysics↗

bioRxiv2025

FlyTomo: A streamlined software for on-the-fly cryo-ET data processing and diagnosis

Cryo-electron tomography (Cryo-ET) combined with subtomogram averaging (STA) enables the structural elucidation of macromolecular assemblies in their native environments. However, the widespread adoption of cryo-ET has been limited by its labor-intensive, expertise-dependent data processing workflow. Here we present FlyTomo, a software that streamlines data processing from frame alignment to STA with high-throughput for authentic cryo-ET scenarios. During data acquisition, FlyTomo performs real-time diagnosis, enabling prompt feedback on sample quality, microscope performance and structural features. After acquisition, it aggregates diagnostic metrics into an interactive overview, guiding users through data review and refinement. FlyTomo also curates raw and processed data into structured directories to simplify data management and archiving. We validated FlyTomo across a diverse set of authentic cryo-ET samples, including purified enveloped viruses and cryo-lamellae, on multiple microscopes and cameras, achieving structures at resolutions ranging from 3.4 to 5.7 [A]. Collectively, by integrating accuracy, scalability and usability, FlyTomo reduces the technical barrier for in situ structural biology using cryo-ET.

biophysics↗

bioRxiv2025

Functional screening of TCR-like antibodies for targeted cancer immunotherapy

Although hybridomas and display technologies have long been used in antibody discovery, identifying TCR-like antibodies that recognize major histocompatibility (MHC)-presented neoantigens remains an inefficient process. Here, we present a functional screening approach for antibody discovery based on T cell-specific functionality. By constructing Synthetic T cell receptor and Antigen Receptor (STAR)-T cells expressing antibody libraries and co-culturing them with target cells, we observed distinct antigen-specific STAR endocytosis and T cell activation. These two indicators are combined into an E-A functional index, which we demonstrate as an effective screening strategy for identifying antibodies with strict antigen sensitivity and specificity. Using this E-A functional screening method, we successfully discovered antibodies or nanobodies targeting cell-surface tumor antigens and neoantigens presented by human leukocyte antigen (HLA). These antibodies exhibited potent anti-tumor efficacy both in vitro and in vivo. Moreover, converting these antibodies into chimeric antigen receptors (CARs) and bispecific antibodies also induced T cell activation and tumor killing. In summary, our study establishes a robust functional screening strategy for identifying antibodies or TCR-like nanobodies targeting tumor-specific neoantigens, offering significant potential for advancing targeted cancer therapies.

immunology↗

bioRxiv2025

RUNX2 inhibition disrupts a PAX3::FOXO1-RUNX2 feed-forward loop and dismantles oncogenic gene programs in fusion-positive rhabdomyosarcoma

Fusion-positive rhabdomyosarcoma is an aggressive pediatric cancer of skeletal muscle lineage, with a 5-year overall survival of <30% for high-risk disease, and <8% when metastatic. The PAX3::FOXO1 fusion gene, resulting from t(2:13), is a signature driver of fusion-positive rhabdomyosarcoma, but similar to other transcription-factor based fusion genes in other cancers, not currently pharmacologically tractable. To identify novel druggable proteins in fusion-positive rhabdomyosarcoma tumor tissue and cell lines, we performed mRNA-seq of RMS patient tumors and utilizing the human FP-RMS cell lines Rh30 and Rh4, found that the RUNX2 transcription factor was the top druggable dependency. In vitro loss of function studies using genetic (RNAi) or pharmacologic (small molecule CADD522) inhibition showed that RUNX2 suppression inhibited FP-RMS cell growth, induced myogenic differentiation and apoptosis, and phenocopied PAX3::FOXO1 suppression. In vivo loss of function studies using conditional (dox-inducible) or pharmacologic (small molecule CADD522) blockade of tumor growth in a xenograft model system showed that RUNX2 suppression inhibited tumor growth. Mechanistically, we identify a PAX3::FOXO1 feed-forward loop whereby PAX3::FOXO1 binds a RUNX2 enhancer to upregulate gene expression alongside MYOD1, while RUNX2 expression supports the expression of PAX3::FOXO1 at the mRNA and protein level. SignificanceRUNX2 inhibition reduces PAX3::FOXO1 expression and signaling, which impairs fusion-positive rhabdomyosarcoma oncogenic phenotypes. In vivo treatment with CADD522 decreased tumor growth and increased survival, indicating that RUNX2 is a promising therapeutic target.

cancer biology↗

bioRxiv2025

RNALens: Study on 5' UTR Modeling and Cell-Specificity

Recently, the Transformer architecture has been applied to predict the structure, function, and regulatory activity of biological sequences. Predicting the cell-specific regulatory impact of 5 untranslated regions (5 UTRs) on mRNA expression and translation remains a key challenge for rational mRNA design. Existing studies such as UTR-LM, RNABERT, and RNA-FM train transformer-based models solely on 5 UTR sequences with fixed nucleotide tokenization schemes and auxiliary structural features. These models pay less attention to the integration of broader genomic context and thermodynamic objectives, which limits their ability to generalize across diverse cell types and accurately predict both mRNA expression level (EL) and translation efficiency (TE). In this paper, we propose RNALens, a foundation model pre-trained in two stages on multispecies genomic sequences and curated 5 UTR data using masked language modeling augmented with secondary structure prediction and minimum free energy regression. RNALens employs byte-pair encoding to capture variable-length nucleotide motifs. It is then fine-tuned on high-throughput reporter assay datasets from HEK293T, PC3, and muscle tissues to yield specialized predictors for EL and TE in each cellular context. Experiment results on benchmark datasets demonstrate that RNALens achieves superior performance than existing machine learning methods for both expression and translation predictions across cell-specific and cross-context tests, offering an efficient in silico platform for guiding the design of mRNA therapeutics with precise cellular targeting.1

bioinformatics↗

bioRxiv2025

Gradient scheme optimization for PRESS-localized edited MRS using weighted pathway suppression

This study aimed to design and implement an optimized gradient scheme for PRESS-localized edited magnetic resonance spectroscopy (MRS) to enhance suppression of out-of-voxel (OOV) artifacts. These artifacts, which originate from insufficient crushing of unwanted coherence transfer pathways (CTPs), are particularly challenging in editing schemes for metabolites like gamma-aminobutyric acid and glutathione. To address this, a volume-based likelihood model was developed to guide gradient scheme optimization, prioritizing suppression of CTPs based on likelihood. A volume-based likelihood model for CTP weighting was integrated into a Dephasing optimization through coherence order pathway selection (DOTCOPS) gradient optimization. Using a genetic algorithm with a new dual-penalty cost function, gradient schemes were optimized to maximize pathway-specific suppression. Hardware and sequence constraints, maximum gradient amplitudes and delay durations respectively, informed the optimization. Validation of the optimized scheme was performed with simulations by calculating the k-space crushing efficiency analytically with k-space trajectory. and in vivo using an edited MRS sequence in three brain regions (posterior cingulate cortex PCC, thalamus, and medial prefrontal cortex (mPFC)), with particular focus on OOV artifact reduction and spectral quality improvements. A three-way Analysis of Variance was used to assess the significance level of OOV artifact reduction. The optimized gradient scheme demonstrated improved k-space crushing efficiency (by an average of 197%). OOV artifacts were reduced in all brain regions, particularly in highly OOV-susceptible regions (thalamus and mPFC). Improvements were most notable around 4.3 ppm with significant OOV artifact amplitude reductions (p < 0.001). By using a volume-based likelihood model for CTP prioritization, the optimized DOTCOPS scheme ensures robust and region-agnostic performance in reducing OOV artifacts.

neuroscience↗

bioRxiv2025

Granulin+ macrophages promote lineage plasticity in prostate cancer through paracrine signaling loops

The role of tumor-infiltrating immune cells in driving phenotype switching remains unclear, despite the well-established association between lineage transition and drug resistance in prostate cancer. This study employed an integrated analysis of single-cell multiomics to investigate the dynamics of immune infiltration, transcriptional programs, and cell-cell communication in prostate cancer. Our results demonstrated that granulin (GRN) positive macrophages facilitated the transition from adenocarcinoma to a multilineage state with mesenchymal and stem-like traits by activating intra-tumoral NF-{kappa}B signaling. Subsequently, the multilineage clones induced macrophages to highly express granulin through the secretion of CSF1, forming a positive feedback cell communication loop. Next, we validated the biological function of granulin in mediating epithelial-mesenchymal transition in vitro. Additionally, organoids drug resistance assay demonstrated that granulin drove resistance to androgen receptor (AR)-targeted drug (enzalutamide). Moreover, pharmacologic blockade of the CSF-1/CSF-1R axis in TRAMP mouse models reduced the expression of GRN in macrophages and suppressed the formation of multilineage subclones in prostate malignant cells. Furthermore, multiplex immunofluorescence staining of tumor samples from TRAMP mouse models revealed the VIM lineages were spatially in close contact with macrophages. Meanwhile, Cytometry by Time-Of-Flight (CyTOF) analysis validated our findings at single-cell protein level in patients with castration-resistant prostate cancer. Besides, three distinct tumor-infiltrating subsets associated with disease relapse were identified, including DCN+ endothelial cells, CCL7+ fibroblasts, and IFIT1+ neutrophils. These results offer potential therapeutic targets to address lineage plasticity-driven resistance to AR-targeted therapy.

cancer biology↗

bioRxiv2025

Epitranscriptome-wide profiling identifies RNA editing events regulated by ADAR1 that are associated with DNA repair mechanisms in human TK6 cells

Adenosine-to-inosine (A-to-I) editing is an endogenous RNA modification in eukaryotes, catalyzed by adenosine deaminases acting on RNA (ADARs). This modification modulates the gene expression by influencing splicing, RNA stability, and coding potential, depending on the site of editing. Although recent studies suggest a crosstalk between A-to-I editing and transcripts involved in DNA repair, the extent and functional significance of this interaction remain unclear. To investigate this, we applied the EpiPlex RNA assay--a method enabling epitranscriptome-wide detection of RNA modifications--in human lymphoblastoid TK6 cells. Across two biological replicates, we identified 869 transcripts bearing A-to-I-modifications. Gene Ontology analysis revealed significant enrichment in genome maintenance pathways, including "chromatin remodeling" and "DNA repair." Notably, 27 transcripts encoding DNA repair proteins--such as ATM, FANCA, BRCA1, POLH, and XPA--contained A-to-I sites within introns or 3' untranslated regions. To assess the isoform-specific contributions of ADAR enzymes--specifically ADAR1 p150 and p110--to RNA editing, we generated p150-deficient (p150 KO) and p150/p110-deficient (p150/p110 KO) TK6 cells. A-to-I editing peaks were reduced by [~]73.4% in p150 KO cells and nearly abolished (99.9%) in p150/p110 KO cells, indicating that most editing sites are p150-dependent, while a notable subset relies on p110. Importantly, a novel splice variant of XPA emerged in ADAR1-deficient cells, suggesting a role for RNA editing in alternative splicing regulation. Our epitranscriptomic analysis of A-to-I RNA editing underscores a multifaceted role for ADAR1-dependent editing in preserving genome integrity through posttranscriptional regulation of DNA repair genes, laying the groundwork for future studies into RNA-based mechanisms of genome maintenance.

genetics↗

bioRxiv2025

Low and high frequency signatures of impaired consciousness in temporal lobe seizures

Impaired consciousness is a debilitating and unpredictable outcome of mesial temporal lobe seizures whose mechanisms to date remain unclear. Moreover, questions about the relationship between impaired consciousness and lateralization, hemispheric spread and electrophysiological characteristics of seizures are yet to be answered. To address these gaps, we conducted in-depth investigation of behavioral and intracranial EEG data from 186 mesial temporal lobe seizures of 51 patients with intractable mesial temporal lobe epilepsy. We found that bilateral mesial temporal spread of seizures is not a necessary condition for impaired consciousness, although seizures with bilateral mesial temporal involvement were significantly more likely to have impaired consciousness than unilateral seizures. Contrary to prior belief, we found no relationship between the onset side (left vs. right temporal lobe or language dominant vs. non-dominant lobe) of seizures and the probability of impaired consciousness. Lastly, we established that widespread increases in slow-wave activity (delta band) in extratemporal cortical areas, as well as increases in fast activity (beta band) in the temporal lobes were both robust markers of seizures with impaired consciousness and could predict ictal impairment with up to 86% accuracy. Our findings shed new light on networks that underlie impaired consciousness in temporal lobe epilepsy and may help guide deep brain stimulation of such systems (e.g. via thalamic nuclei) as a potential intervention to improve consciousness during seizures.

neuroscience↗

bioRxiv2025

Identifying out-of-voxel echoes in edited MRS with phase cycle inversion

1.PurposeTo identify the origin of out-of-voxel (OOV) signals based on the coherence transfer pathway (CTP) formalism using signal phase conferred by the acquisition phase cycling scheme. Knowing the CTP driving OOV artifacts enables optimization of crusher gradients to improve their suppression without additional data acquisition. Theory and MethodsA phase cycle systematically changes the phase of RF pulses across the transients of an experiment, encoding phase shifts into the data that can be used to suppress unwanted CTPs. We present a new approach, phase cycle inversion (PCI), which removes the receiver phase originally applied to the stored transients, replacing it with new receiver phases, matching the phase evolutions associated with each unwanted CTP, to identify the OOV signals. We demonstrated the efficacy of PCI using the MEGA-edited PRESS sequence in simulations, phantom and in vivo experiments. Based on these findings, the crusher gradient scheme was optimized. ResultsThe simulation results demonstrated that PCI can fully separate signals originating from different CTPs using a complete phase cycling scheme. PCI effectively identified the CTP responsible for OOV signals in phantom experiments and in vivo, though with reduced specificity in vivo due to phase instabilities. Re-optimization of the gradient scheme based on the identified OOV-associated CTP to suppress these signals, resulted in cleaner spectra in six volunteers. ConclusionPCI can be broadly applied across pulse sequences and voxel locations, making it a flexible and generalizable approach for diagnosing the CTP origin of OOV signals.

neuroscience↗

bioRxiv2025

MultiAlloDriver: a multi-model method to predict and identify cancer driver mutations

A minority of driver mutations in cancer significantly alter protein structure and key functionalities, thereby driving cancer progression. Consequently, the prediction and identification of driver mutations hold critical implications for targeted cancer therapy. This study introduces MultiAlloDriver, a novel multi-modal machine learning model based on an attention mechanism, which for the first time, incorporates protein surface information. By integrating information from three dimensions-protein sequence, structure, and surface-the model achieves high-accuracy driver target prediction with an accuracy exceeding 93%. Notably, we utilized this tool to predict and identify the driver effect of the F90S mutation in the PTEN tumor suppressor gene, uncovering mutations associated with cancer signaling mechanisms. Overall, MultiAlloDriver contributes to elucidating the underlying mechanisms of cancer development and progression, providing a robust framework for the identification of driver targets.

bioinformatics↗

bioRxiv2025

Pentose Sugars Encode Sequence-Dependent DNA-RNA Segregation for Biomimetic Multiphase Condensates

The nucleus segregates DNA and RNA with high precision to safeguard genome stability and gene regulation despite their homologous structures, yet the underlying molecular principles have long evaded both understanding and synthetic translation. Here we show that a single atomic difference between the pentose sugars, a 2-OH in RNA versus a 2-H in DNA, suffices to drive their phase segregation with cationic peptides. This subtle chemical distinction makes RNA bind peptides more strongly than sequence-identical DNA, generating an asymmetry that, when modulated by sequence-encoded homotypic interactions, drives the formation of core-shell multiphase condensates. Using a supervised machine-learning approach, we distill these molecular principles into a predictive design rule and harness it to program a library of oligonucleotide pairs, termed SEGREGamers, that self-assemble into droplets with coexisting DNA- and RNA-rich domains. These synthetic condensates recapitulate nuclear compartment functions, including selective molecular partitioning and enhanced RNA catalysis. Our results establish a chemically encoded platform for engineering synthetic nuclear mimics and programmable biomolecular condensates, and suggest that sugar identity may have served as an ancient physical mechanism for organizing nucleic acids.

biophysics↗

bioRxiv2025

An insect brain-based bioelectronic neural sensor for the systemic detection and precise classification of endometriosis

Endometriosis is a chronic inflammatory disease with limited screening options and a recognized diagnostic delay. To investigate detection potentialities, a bioelectronic sensor is realized to detect endometriotic vs endometrial models via their emitted volatile organic compounds (VOCs) by leveraging an insect olfactory system combined with computational analytical techniques for classification. Our analyses of cell culture headspace-evoked neural responses show that our sensor can distinguish multiple cell lines by their scent (i.e., emitted VOC mixture). By combining neural responses across experiments, we obtained high-dimensional population neural response templates that were used to classify unknown samples with a high accuracy. We obtained an accuracy of 89% in differentiating 4 cell lines at two growth timepoints (24 hr. and 72 hr.) and obtained an accuracy of 88% in classifying epithelial co-cultures of endometriotic and endometrial cell lines cultured at 0%, 25%, 50%, 75%, and 100% in ascending/descending ratios. Our results support the hypothesis that endometriosis is detectable via the metabolic differences found in the emitted gas mixtures or scent from various cell lines and demonstrates the effectiveness of our sensor in distinguishing the subtle changes in endometriotic vs endometrial models pertaining to durations in growth and co-cultures.

bioengineering↗

bioRxiv2025

Longitudinal multi-omic profiling uncovers immune escape and predictors of response in multiple myeloma

Multiple myeloma (MM) is an incurable malignancy of clonally expanded plasma cells shaped by complex interactions with the immune microenvironment. To investigate immune factors driving treatment response and resistance, we conducted multi-omics profiling including CD138neg single-cell RNA sequencing of 243 bone marrow samples from 102 patients (631,226 cells) and CD138pos bulk RNA and whole-genome sequencing from 209 samples. Longitudinal analyses revealed that interferon gamma signaling impairs T cell memory after autologous stem cell transplant, while naive B cell abundance and immunoglobulin diversity correlated with improved progression-free survival (HR = 0.48, p = 2.3e-4). At disease progression, MM cells upregulated cancer-testis antigens and immune effector genes, with concurrent B cell depletion, enrichment of myeloid-derived suppressor cell genes in monocytes, and T cell exhaustion. These findings highlight dynamic immune-tumor interactions, identifying naive B cell reconstitution as a biomarker of durable response, and cancer-testis antigens as potential targets for high-risk disease at progression. Statement of SignificanceLongitudinal profiling of multiple myeloma and the immune microenvironment revealed dynamic immune-tumor interactions across the disease course. Dysfunctional CD8 T cells limited memory formation post-transplant, while naive B recovery associated with sustained treatment response. At progression, cancer-testis antigen expression associated with immunosuppression, revealing novel mechanisms of immune escape.

cancer biology↗

bioRxiv2025

Tolerance to Lung Infection in TWIK2 K+ Efflux Mediated Macrophage Trained Immunity

Lung macrophages such as alveolar macrophages (AM{varphi}) are essential for innate immune function in the lungs. It is now apparent that macrophages can be trained to become better at attacking infections. Although trained immunity is thought to result from metabolic and epigenetic reprogramming, the underlying mechanisms remain unclear. Here, we report that macrophages can be trained by extracellular ATP, which is ubiquitously released during inflammation. ATP ligates the canonical Purinergic Receptor 2 subtype X7 receptor (P2X7) to mediate endosomal Two-pore domain Weak Inwardly rectifying K+ channel 2 (TWIK2) translocation into the plasma membrane (PM). This enables the cells to transit to a ready state for microbial killing in two directions: first, K+ efflux via PM-TWIK2 induces NLRP3 inflammasome activation, which further activates metabolic pathways; second, upon bacterial phagocytosis, PM-TWIK2 internalizes into phagosome membrane with proper topological orientation, where TWIK2 mediates K+ influx into phagosomes to control pH and ionic strength favoring bacterial killing. Therefore, the enhanced association of TWIK2 in phagosomal and plasma membranes signaled by danger-associated molecular patterns (DAMPs), such as ATP, mediates trained immunity in macrophages and enhances microbiocidal activity.

immunology↗

bioRxiv2025

Sampling Aware Ancestral State Inference

In phylogeography, ancestral state inference methods are used to identify the geographic or host species origin of viral or bacterial lineages and reconstruct their transmission histories over time. However, differences in sampling among states can bias these inference methods. We introduce sampling-aware ancestral state inference (SAASI), a method that accounts for sampling differences. Here, we apply SAASI to the multi-host spread of the H5N1 virus in the United States in 2024 and find that the key transmission event from wild birds to cattle is estimated to occur later under lower sampling in wild birds (compared to other species) than when sampling is not accounted for. Using simulation, we find that SAASI infers past viral locations/host species considerably more accurately than standard methods when sampling bias exists, is computationally feasible for large datasets and scales to trees with 100,000 tips.

evolutionary biology↗

bioRxiv2025

Somato-Motor Network Neural Connectivity Correlates with Visuomotor Adaptation

Humans show tremendous individual differences in learning motor skills. Prediction of individual differences in motor learning is of theoretical significance and practical relevance. Brain as the central neural apparatus supporting motor control and learning has attracted considerable research efforts on examining brain functional and structural predictors of individual differences in motor learning. Many previous studies applied a resting state (RS) approach in recording brain activity, selected the contralateral primary motor cortex (M1) a priori as a seed or region of interest (ROI), and reported that functional connectivity between M1 and other brain regions predicted individual differences in tasks mostly on motor sequence learning. However, task-evoked brain neural activity and connectivity that represent different brain dynamics from spontaneous brain dynamics have not been studied in this line of research. This study presents findings on RS and finger-tapping evoked cortical somato-motor network (SMN) neural connectivity correlates with individual performances in a visuomotor adaptation (VMA) task, based on analysis of multimodal data from the Cam-CAN study. SMN nodes were initially localized through finger-tapping evoked brain magnetic fields as sampled with magnetoencephalography (MEG). It was found that SMN node specific neural connectivity strength at the contralateral primary somatosensory cortex (S1), supplementary motor area (SMA), and dorsal premotor cortex (PMd) during different stages around finger tapping but not in RS were significantly correlated with individual mean target errors in the final adaptation stage. The results imply critical roles of somatosensory and secondary motor areas in human motor learning.

neuroscience↗