Activation Of Network Research Articles

Multiple constraint network classification reveals functional brain networks distinguishing 0-back and 2-back task.

Working memory is associated with general intelligence and is crucial for performing complex cognitive tasks. Neuroimaging investigations have recognized that working memory is supported by a distribution of activity in regions across the entire brain. Identification of these regions has come primarily from general linear model analyses of statistical parametric maps to reveal brain regions whose activation is linearly related to working memory task conditions. This approach can fail to detect nonlinear task differences or differences reflected in distributed patterns of activity. In this study, we take advantage of the increased sensitivity of multivariate pattern analysis in a multiple-constraint deep learning classifier to analyze patterns of whole-brain blood oxygen level dependent (BOLD) activity in children performing two different conditions of the emotional n-back task. Regional (supervoxel) whole-brain activation patterns from functional imaging runs of 20 children were used to train a set of neural network classifiers to identify task category (0-back vs. 2-back) and activation co-occurrence probability, which encoded functional connectivity. These simultaneous constraints promote the discovery of coherent networks that contribute towards task performance in each memory load condition. Permutation analyses discovered the global activation patterns and interregional coactivations that distinguish memory load. Examination of model weights identified the brain regions most predictive of memory load and the functional networks integrating these regions. Community detection analyses identified functional networks integrating task-predictive regions and found distinct patterns of network activation for each task type. Comparisons to functional network literature suggest more focused attentional network activation during the 2-back task. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

IFNγ activates an immune-like regulatory network in the cardiac vascular endothelium.

The regulatory mechanisms underlying the response to pro-inflammatory cytokines in cardiac diseases are poorly understood. Here, we use iPSC-derived cardiovascular progenitor cells (CVPCs) to model the response to interferon gamma (IFNγ) in human cardiac tissue. We generate RNA-seq and ATAC-seq for four CVPCs that were treated with IFNγ and compare them with paired untreated controls. Transcriptional differences after treatment show that IFNγ initiates an innate immune cell-like response, shifts the CVPC transcriptome towards coronary artery and aorta profiles, and stimulates expression of endothelial cell-specific genes. Analysis of the accessible chromatin shows that IFNγ is a potent chromatin remodeler and establishes an IRF-STAT immune-cell like regulatory network. Finally, we show that 11 GWAS risk variants for 8 common cardiac diseases overlap IFNγ-upregulated ATAC-seq peaks. Our findings reveal insights into IFNγ-induced activation of an immune-like regulatory network in the cardiac vascular endothelium and the potential role that regulatory elements in this pathway play in common cardiac diseases.

Effects of Mental Workload Manipulation on Electroencephalography Spectrum Oscillation and Microstates in Multitasking Environments.

Multitasking during flights leads to a high mental workload, which is detrimental for maintaining task performance. Electroencephalography (EEG) power spectral analysis based on frequency-band oscillations and microstate analysis based on global brain network activation can be used to evaluate mental workload. This study explored the effects of a high mental workload during simulated flight multitasking on EEG frequency-band power and microstate parameters. Thirty-six participants performed multitasking with low and high mental workloads after 4 consecutive days of training. Two levels of mental workload were set by varying the number of subtasks. EEG signals were acquired during the task. Power spectral and microstate analyses were performed on the EEG. The indices of four frequency bands (delta, theta, alpha, and beta) and four microstate classes (A-D) were calculated, changes in the frequency-band power and microstate parameters under different mental workloads were compared, and the relationships between the two types of EEG indices were analyzed. The theta-, alpha-, and beta-band powers were higher under the high than under the low mental workload condition. Compared with the low mental workload condition, the high mental workload condition had a lower global explained variance and time parameters of microstate B but higher time parameters of microstate D. Less frequent transitions between microstates A and B and more frequent transitions between microstates C and D were observed during high mental workload conditions. The time parameters of microstate B were positively correlated with the delta-, theta-, and beta-band powers, whereas the duration of microstate C was negatively correlated with the beta-band power. EEG frequency-band power and microstate parameters can be used to detect a high mental workload. Power spectral analyses based on frequency-band oscillations and microstate analyses based on global brain network activation were not completely isolated during multitasking.

Distinct Neural Bases of Visual Art- and Music-Induced Aesthetic Experiences

Aesthetic experiences are characterized by a conscious, emotionally and hedonically rewarding perceptions of a stimulus's aesthetic qualities and are thought to arise from a unique combination of cognitive and affective processes. To pinpoint neural correlates of aesthetic experiences, in the present study, we performed a series of meta-analyses based on the existing functional Magnetic Resonance Imaging (fMRI) studies of art appreciation in visual art (34 experiments, 692 participants) and music (34 experiments, 718 participants). The Activation Likelihood Estimation (ALE) analyses showed that the frontal pole (FP), ventromedial prefrontal cortex (vmPFC), and inferior frontal gyrus (IFG) were commonly activated in visual-art-induced aesthetic experiences, whilst bilateral superior temporal gyrus (STG) and striatal areas were commonly activated in music appreciation. Additionally, task-independent Resting-state Functional Connectivity (RSFC), task-dependent Meta-analytical Connectivity Modelling (MACM) analyses, as well as Activation Network Modeling (ANM) further showed that visual art and music engaged quite distinct brain networks. Our findings support the domain-specific view of aesthetic appreciation and challenge the notion that there is a general “common neural currency” for aesthetic experiences across domains.

The feasibility and modification mechanism studies of healing-sensing asphalt modified by polyurea polyurethane-polyaniline double network structure

ABSTRACT The future city is no longer a fantasy but a reality through the integration of emerging technologies with urban development. The smart pavement, the basis of the future city, is also facing challenges and development. This paper added self-healing and self-sensing performances to asphalt through Polyurethane and polyaniline to meet the requirements of future smart pavements. The performances of polyurea polyurethane and polyaniline were analysed and it was feasible that healing-sensing asphalt could be prepared by polyurea polyurethane and polyaniline. The modification mechanism of polyurea polyurethane and polyaniline-modified asphalt was analysed, and it was found that the polyurea polyurethane and polyaniline formed a double network structure with healing-sensing performance in the asphalt. The effect of the double network structure on the basic performances and healing-sensing performance of asphalt was verified. The results showed that the double network structure not only had a better influence on the basic performances but also improved the self-healing and self-sensing performances of asphalt. The sensitivity of the self-sensing performance to the self-healing performance of asphalt was enhanced by the double network structure. The self-healing performance was enhanced by the accurate activation of the healing network by the double network structure.

Interplay of Transcriptomic Regulation, Microbiota, and Signaling Pathways in Lung and Gut Inflammation-Induced Tumorigenesis.

Inflammation can positively and negatively affect tumorigenesis based on the duration, scope, and sequence of related events through the regulation of signaling pathways. A transcriptomic analysis of five pulmonary arterial hypertension, twelve Crohn's disease, and twelve ulcerative colitis high throughput sequencing datasets using R language specialized libraries and gene enrichment analyses identified a regulatory network in each inflammatory disease. IRF9 and LINC01089 in pulmonary arterial hypertension are related to the regulation of signaling pathways like MAPK, NOTCH, human papillomavirus, and hepatitis c infection. ZNF91 and TP53TG1 in Crohn's disease are related to the regulation of PPAR, MAPK, and metabolic signaling pathways. ZNF91, VDR, DLEU1, SATB2-AS1, and TP53TG1 in ulcerative colitis are related to the regulation of PPAR, AMPK, and metabolic signaling pathways. The activation of the transcriptomic network and signaling pathways might be related to the interaction of the characteristic microbiota of the inflammatory disease, with the lung and gut cell receptors present in membrane rafts and complexes. The transcriptomic analysis highlights the impact of several coding and non-coding RNAs, suggesting their relationship with the unlocking of cell phenotypic plasticity for the acquisition of the hallmarks of cancer during lung and gut cell adaptation to inflammatory phenotypes.

Disrupted Lipid Metabolism, Cytokine Signaling, and Dormancy: Hallmarks of Doxorubicin-Resistant Triple-Negative Breast Cancer Models.

Chemoresistance in triple-negative breast cancer (TNBC) presents a significant clinical hurdle, limiting the efficacy of treatments like doxorubicin. This study aimed to explore the molecular changes associated with doxorubicin resistance and identify potential therapeutic targets to overcome this resistance, thereby improving treatment outcomes for TNBC patients. Doxorubicin-resistant (DoxR) TNBC models (MDA-MB-231 and BT-549) were generated by exposing cells to increasing concentrations of doxorubicin. RNA sequencing (RNA-Seq) was performed using the Illumina platform, followed by bioinformatics analysis with CLC Genomics Workbench and iDEP. Functional assays assessed proliferation, sphere formation, migration, and cell cycle changes. Protein expression and phosphorylation were confirmed via Western blotting. Pathway and network analyses were conducted using Ingenuity Pathway Analysis (IPA) and STRING, while survival analysis was performed using Kaplan-Meier Plotter database. DoxR cells exhibited reduced proliferation, sphere formation, and migration, but showed enhanced tolerance to doxorubicin. Increased CHK2 and p53 phosphorylation indicated cellular dormancy as a resistance mechanism. RNA-Seq analysis revealed upregulation of cytokine signaling and stress-response pathways, while cholesterol and lipid biosynthesis were suppressed. Activation of the IL1β cytokine network was prominent in DoxR cells, and CRISPR-Cas9 screens data identified dependencies on genes involved in rRNA biogenesis and metabolism. A 27-gene signature associated with doxorubicin resistance was linked to worse clinical outcomes in a large breast cancer cohort (HR = 1.76, FDR p < 2.0 × 10-13). This study uncovers potential therapeutic strategies for overcoming TNBC resistance, including dormancy reversal and targeting onco-ribosomal pathways and cytokine signaling networks, to improve the efficacy of doxorubicin-based treatments.

Predicting upper limb motor recovery in subacute stroke patients via fNIRS-measured cerebral functional responses induced by robotic training

BackgroundNeural activation induced by upper extremity robot-assisted training (UE-RAT) helps characterize adaptive changes in the brains of poststroke patients, revealing differences in recovery potential among patients. However, it remains unclear whether these task-related neural activities can effectively predict rehabilitation outcomes. In this study, we utilized functional near-infrared spectroscopy (fNIRS) to measure participants’ neural activity profiles during resting and UE-RAT tasks and developed models via machine learning to verify whether task-related functional brain responses can predict the recovery of upper limb motor function.MethodsCortical activation and brain network functional connectivity (FC) in brain regions such as the superior frontal cortex, premotor cortex, and primary motor cortex were measured using fNIRS in 82 subacute stroke patients in the resting state and during UE-RAT. The Fugl-Meyer Upper Extremity Assessment Scale (FMA-UE) was chosen as the index for assessing upper extremity motor function, and clinical information such as demographic and neurophysiological data was also collected. Robust features were screened in 100 randomly divided training sets using the least absolute shrinkage and selection operator (LASSO) method. Based on the selected robust features, machine learning algorithms were used to develop clinical models, fNIRS models, and combined models that integrated both clinical and fNIRS features. Finally, Shapley Additive Explanations (SHAP) was applied to interpret the prediction process and analyze key predictive factors.ResultsCompared to the resting state, task-related FC is a more robust feature for modeling, with screening frequencies above 90%. The combined models built using artificial neural networks (ANNs) and support vector machines (SVMs) significantly outperformed the other algorithms, with an average AUC of 0.861 (± 0.087) for the ANN and an average correlation coefficient (r) of 0.860 (± 0.069) for the SVM. Furthermore, predictive factor analysis of the models revealed that FC measured during tasks is the most important factor for predicting upper limb motor function.ConclusionThis study confirmed that UE-RAT-induced FC can serve as an important predictor of rehabilitation, especially when combined with clinical information, further enhancing the accuracy of model predictions. These findings provide new insights for the early prediction of patients’ recovery potential, which may contribute to personalized rehabilitation decisions.

Reactive oxygen species-mediated signal transduction and utilization strategies in microalgae.

Reactive oxygen species (ROS) are crucial in stress perception, the integration of environmental signals, and the activation of downstream response networks. This review emphasizes ROS-mediated signaling pathways in microalgae and presents an overview of strategies for leveraging ROS. Eight distinct signaling pathways mediated by ROS in microalgae have been summarized, including the calcium signaling pathway, the target of rapamycin signaling pathway, the mitogen-activated protein kinase signaling pathway, the cyclic adenosine monophosphate/protein kinase A signaling pathway, the ubiquitin/protease pathway, the ROS-regulated transcription factors and enzymes, the endoplasmic reticulum stress, and the retrograde ROS signaling. Moreover, this review outlines three strategies for utilizing ROS: two-stage cultivation, combined stress with phytohormones, and strain engineering. The physicochemical properties of various ROS, together with their redox reactions with downstream targets, have been elucidated to reveal the role of ROS in signal transduction processes while delineating the ROS-mediated signal transduction network within microalgae.

Aperiodic and oscillatory systems underpinning human domain-general cognition

Domain-general cognitive systems are essential for adaptive human behaviour, supporting various cognitive tasks through flexible neural mechanisms. While fMRI studies link frontoparietal network activation to increasing demands across various tasks, the electrophysiological mechanisms underlying this domain-general response to demand remain unclear. Here, we used MEG/EEG, and separated the aperiodic and oscillatory components of the signals to examine their roles in domain-general cognition across three cognitive tasks using multivariate analysis. We found that both aperiodic (broadband power, slope, and intercept) and oscillatory (theta, alpha, and beta power) components coded task demand and content across all subtasks. Aperiodic broadband power in particular strongly coded task demand, in a manner that generalised across all subtasks. Source estimation suggested that increasing cognitive demand decreased aperiodic broadband power across the brain, with the strongest modulations overlapping with the frontoparietal network. In contrast, oscillatory activity showed more localised patterns of modulation, primarily in frontal or occipital regions. These results provide insights into the electrophysiological underpinnings of human domain-general cognition, highlighting the critical role of aperiodic broadband power.

Youth Generalized Anxiety and Brain Activation States During Socioemotional Processing

The brain enters distinct activation states to support differential cognitive and emotional processes, but little is known about how brain activation states differ in youths with clinical anxiety. To characterize brain activation states during socioemotional processing (movie stimuli) and assess associations between state characteristics and movie features and anxiety symptoms. The Healthy Brain Network is an ongoing cross-sectional study of individuals aged 5 to 21 years experiencing difficulties in school, of whom approximately 45% met criteria for a lifetime anxiety disorder diagnosis. Data used in this study are from the first 9 releases (collected in a nonclinical research setting in the New York City metropolitan area from 2015 to 2020) and include 620 youths aged 5 to 15 years (53% of whom met criteria for a lifetime anxiety disorder diagnosis) who watched an emotional video during functional magnetic resonance imaging and completed questionnaires and clinical evaluation. Of those with functional magnetic resonance imaging data, 432 youths aged 7 to 15 years also self-reported on anxiety symptoms. Data were processed and analyzed between February 2020 and August 2024. A hidden Markov model was trained to identify brain activation states across participants during video watching. Time spent in each state and the moment-to-moment probability of being in each state were extracted. Videos were annotated for emotion-specific and nonspecific information using the EmoCodes system. Self-reported anxiety symptoms were assessed using the Screen for Child Anxiety Related Disorders. Time spent in each state across the video and during and outside of peaks in negative content correlated with generalized and social anxiety scores. Among the 620 youths in the overall analysis, 369 were male and the mean (SD) age was 10.4 (2.8) years. In the anxiety symptom analysis, 263 of 432 youths were male and the mean (SD) age was 11.5 (2.2) years. Three brain activation states were identified: a high somatomotor activation state (state 1), a high cingulo-opercular network activation state (state 2), and a high ventral attention and default mode state (state 3). The probability of being in state 3 was correlated with video content that was more negative, quieter, and with less visual motion (ρ < 0.08; P < .001). Increased generalized anxiety was associated with greater time in state 3 (B, 0.10; 95% CI, 0.01 to 0.20; false discovery rate [FDR]-corrected P = .048) and less time in state 2 (B, -0.11; 95% CI, -0.21 to -0.02; FDR-corrected P = .048) when negative social cues were present. Youths entered 3 distinct brain activation states during movie watching, and youths with anxiety spent more time in a state with high ventral attention and default activation during negative socioemotional processing. Youths high in generalized anxiety may be more engaged in deeply processing negative emotional content, which may influence self-regulation. Interventions that focus on changing physiological and psychological state during negative social interactions in youths with anxiety should be considered.

Optimized energy management and small cell activation in ultra-dense networks through a data-driven approach

Optimized energy management and small cell activation in ultra-dense networks through a data-driven approach

Interspecies differences in the transcriptome response of corals to acute heat stress.

Rising sea surface temperatures threaten the survival of corals worldwide, with coral bleaching events becoming more commonplace. However, different coral species are known to exhibit variable levels of susceptibility to thermal stress. To elucidate genetic mechanisms that may underlie these differences, we compared the gene repertoire of four coral species, Favites colemani, Montipora digitata, Acropora digitifera, and Seriatopora caliendrum, that were previously demonstrated to have differing responses to acute thermal stress. We found that more tolerant species, like F.colemani and M. digitata, possess a greater abundance of antioxidant protein families and chaperones. Under acute thermal stress conditions, only S. caliendrum showed a significant bleaching response, which was accompanied by activation of the DNA damage response network and drastic upregulation of stress response genes (SRGs). This suggests that differences in SRG orthologs, as well as the mechanisms that control SRG expression response, contribute to the ability of corals to maintain stability of physiological functions required to survive shifts in seawater temperature.

A One-Dimensional Depthwise Separable Convolutional Neural Network for Bearing Fault Diagnosis Implemented on FPGA.

This paper presents a hardware implementation of a one-dimensional convolutional neural network using depthwise separable convolution (DSC) on the VC707 FPGA development board. The design processes the one-dimensional rolling bearing current signal dataset provided by Paderborn University (PU), employing minimal preprocessing to maximize the comprehensiveness of feature extraction. To address the high parameter demands commonly associated with convolutional neural networks (CNNs), the model incorporates DSC, significantly reducing computational complexity and parameter load. Additionally, the DoReFa-Net quantization method is applied to compress network parameters and activation function outputs, thereby minimizing memory usage. The quantized DSC model requires approximately 22 KB of storage and performs 1,203,128 floating-point operations in total. The implementation achieves a power consumption of 527 mW at a clock frequency of 50 MHz, while delivering a fault diagnosis accuracy of 96.12%.

A study on Physics-Informed Neural Network’s parameters influences in solid mechanics problems.

In recent years, Physics-Informed Neural Networks (PINNs) have introduced a novel approach to solving partial differential equations (PDEs) using deep learning techniques. Despite the promising results and rapid advancements in the field, there is a lack of more accurate studies concerning properties related to modeling choices within the deep learning framework, especially, but not exclusively, in solid mechanics problems. In this study, we aim to explore whether the neural network architecture, activation function, optimizer, and sampling method can influence the accuracy of results and training speed for solid mechanics problems. We will focus on elasticity and hyperelasticity problems in 1D, 2D, and 3D dimensions to lay the groundwork for more complex investigations.

Lean Change Network Design and Activation

Organizational change initiatives often hinge on the effective design and activation of change networks - the interconnected web of individuals, teams, and units responsible for driving transformative efforts. By adopting a lean approach, leaders can streamline this process and foster greater agility. Key principles include customer-centric network mapping to understand stakeholder needs, a minimalist network structure to eliminate waste, and an agile activation strategy with iterative refinements based on continuous feedback. Practical examples from the retail and software industries demonstrate how lean change network design has enabled organizations to modernize operations, enhance customer experiences, and navigate agile transformations. Embracing a lean mindset empowers change agents, promotes cross-functional collaboration, and cultivates a culture of experimentation - critical capabilities for navigating the complexities of organizational change in today's fast-paced, digitally-driven business environment.

Disrupted working memory event-related network dynamics in multiple sclerosis

In multiple sclerosis (MS), working memory (WM) impairment can occur soon after disease onset and significantly affects the patient’s quality of life. Functional imaging research in MS aims to investigate the neurophysiological underpinnings of WM impairment. In this context, we utilize a data-driven technique, the time delay embedded-hidden Markov model, to extract spectrally defined functional networks in magnetoencephalographic (MEG) data acquired during a WM visual-verbal n-back task. Here, we show that the activation of two networks is altered in relapsing remitting-MS patients. First, the activation of an early theta prefrontal network linked to stimulus encoding and attentional control significantly decreases in MS compared to HC. This diminished activation correlates with reduced accuracy and higher reaction time, suggesting that impaired attention control impacts task performance in MS patients. Secondly, a frontoparietal network characterized by beta coupling is activated between 300 and 600 ms post-stimulus, resembling the event-related P300, a cognitive marker extensively explored in EEG studies. The activation of this network is amplified in patients treated with benzodiazepine, in line with the well-known benzodiazepine-induced beta enhancement. Altogether, the TDE-HMM technique extracts task-relevant functional networks showing disease-specific and treatment-related alterations, revealing potential new markers to assess and track WM impairment in MS.

Integrative bioinformatics analysis of immune activation and gene networks in pediatric septic arthritis

BackgroundPediatric septic arthritis, driven by Staphylococcus aureus, leads to substantial morbidity due to the host’s complex inflammatory response. This study integrates bioinformatics analyses to map the genomic and immune profiles of pediatric septic arthritis, aiming to identify key biomarkers and therapeutic targets. MethodsAn integrative bioinformatics approach was adopted to analyze gene expression datasets from the GEO database, focusing on pediatric septic arthritis. DEGs were identified using GEO2R, and gene co-expression networks were generated via GeneMANIA. STRING database and Cytoscape software facilitated PPI network construction. DAVID enabled functional enrichment analysis to elucidate biological processes and pathways, while iRegulon predicted transcription factor regulation. CIBERSORT provided a detailed profile of immune cell alterations in the condition. ResultsFrom the datasets analyzed, 576 DEGs were extracted, with 35 shared between the two datasets, revealing an innate immunity signature with notable hub genes such as MPO and ELANE, indicative of a pronounced neutrophilic response. Functional enrichment analysis highlighted pathways pertinent to antimicrobial defense and NET formation. Key transcription factors, including PBX1, POLR2A, and STAT3, were identified as potential modulators of these pathways. Immune profiling demonstrated significant shifts in cell populations, with increased plasma cells and reduced CD4+ naïve T cells. ConclusionsThis study elucidates the complex genomic and immunological milieu of pediatric septic arthritis, uncovering potential biomarkers and signaling pathways for targeted therapeutic intervention. These findings underscore the preeminence of innate immune mechanisms in the disease's pathology and offer a foundation for future research to explore diagnostic and treatment innovations. Translation of these bioinformatics discoveries into clinical applications requires further validation and consideration of the limitations inherent to gene expression data and its interpretation.

Spinal Neuromodulation for Respiratory Rehabilitation in Patients with Post-Acute COVID-19 Syndrome.

(1) Background: Neurological deficits associated with coronavirus disease (COVID-19) exacerbate respiratory dysfunction, necessitating rehabilitation strategies that address both. Previous studies have demonstrated that spinal cord transcutaneous stimulation (scTS) can facilitate the excitation of respiratory spinal neural networks in patients with post-COVID-19 syndrome. This study evaluates the efficacy of combining scTS with respiratory training (RT) to improve respiratory function in individuals with post-COVID-19 pulmonary deficits; (2) Methods: In this before-after, case-controlled clinical trial, five individuals with post-acute COVID-19 respiratory deficits participated in two interventional programs: 10 daily sessions of respiratory training (RT), followed by 10 daily sessions of scTS combined with RT (scTS + RT). Forced vital capacity (FVC), peak inspiratory flow (PIF), peak expiratory flow (PEF), time-to-peak inspiratory flow (tPIF), and time-to-peak expiratory flow (tPEF) were assessed at baseline and after each program; (3) Results: Compared to RT alone, the scTS + RT intervention resulted in an average effect size that was twice as large, with significant increases in FVC and PEF, and a significant decrease in tPEF; (4) Conclusions: The scTS-induced activation of respiratory neuronal networks, when combined with respiratory training, offers a promising therapeutic approach for treating persistent respiratory deficits in patients with post-acute COVID-19 syndrome.

A novel regimen for pancreatic ductal adenocarcinoma targeting MEK, BCL-xL, and EGFR

Oncogenic KRAS signaling plays a critical role in pancreatic ductal adenocarcinoma (PDAC) biology. Recent studies indicate that the combination of MEK and BCL-xL inhibition is synthetically lethal and holds promise for some types of solid cancers, however, patient response was poorly observed in PDAC predominantly due to amplified EGFR signaling. Here, we leverage the advantage of the combinational treatment strategy and designed a triplet regimen targeting the comprehensive RAS activation networks through simultaneously blocking MEK/BCL-xL/EGFR. The cytotoxicity of trametinib (MEK inhibitor), DT2216 (BCL-xL degrader) and afatinib (pan-EGFR inhibitor) and their combination was tested in patient-derived, primary PDAC cells using a live cell imaging system. Patient-derived xenograft (PDX) model was employed for the evaluation of the therapeutic efficacy and safety of the combinational regimen. Targeted pathway cascades activities were analyzed using multiplex phosphor-immune assays. In vitro comparisons showed the addition of afatinib as a third agent was statistically superior compared to a doublet of trametinib+DT2216 in suppressing cell growth and inducing cell death in all cell lines tested. This triplet similarly demonstrated significant superiority over the doublet of MEK/BCL-xL inhibition in the in vivo murine model. The triplet regimen was well tolerated in vivo. Overall tumor growth rates were significantly reduced in doublet treatment compared to controls, and further reduced in the triplet treatment group. Pathway analysis revealed the addition of afatinib in triplet regimen further inhibited PI3K/AKT effectors of p90RSK, p70S6K, and GSK3α/β along with a secondary pathway of P38 MAPK. Our study identifies an important contribution of EGFR inhibition to elevate the response of PDAC, supporting a clinical assessment of this triplet combination in patients.