es/iode’s Post

📃Scientific paper: Brain State-Dependent Modulation of Thalamic Visual Processing by Cortico-Thalamic Feedback Abstract: The behavioral state of a mammal impacts how the brain responds to visual stimuli as early as in the dorsolateral geniculate nucleus of the thalamus (dLGN), the primary relay of visual information to the cortex. A clear example of this is the markedly stronger response of dLGN neurons to higher temporal frequencies of the visual stimulus in alert as compared with quiescent animals. The dLGN receives strong feedback from the visual cortex, yet whether this feedback contributes to these state-dependent responses to visual stimuli is poorly understood. Here, we show that in male and female mice, silencing cortico-thalamic feedback profoundly reduces state-dependent differences in the response of dLGN neurons to visual stimuli. This holds true for dLGN responses to both temporal and spatial features of the visual stimulus. These results reveal that the state-dependent shift of the response to visual stimuli in an early stage of visual processing depends on cortico-thalamic feedback. SIGNIFICANCE STATEMENT Brain state affects even the earliest stages of sensory processing. A clear example of this phenomenon is the change in thalamic responses to visual stimuli depending on whether the animal’s brain is in an alert or quiescent state. Despite the radical impact that brain state has on sensory processing, the underlying circuits are still poorly understood. Here, we show that both the temporal and spatial response properties of thalamic neurons to visual stimuli depend on... Continued on ES/IODE ➡️ https://etcse.fr/9p ------- If you find this interesting, feel free to follow, comment and share. We need your help to enhance our visibility, so that our platform continues to serve you.

📃Scientific paper: Brain State-Dependent Modulation of Thalamic Visual Processing by Cortico-Thalamic Feedback Abstract: The behavioral state of a mammal impacts how the brain responds to visual stimuli as early as in the dorsolateral geniculate nucleus of the thalamus (dLGN), the primary relay of visual information to the cortex. A clear example of this is the markedly stronger response of dLGN neurons to higher temporal frequencies of the visual stimulus in alert as compared with quiescent animals. The dLGN receives strong feedback from the visual cortex, yet whether this feedback contributes to these state-dependent responses to visual stimuli is poorly understood. Here, we show that in male and female mice, silencing cortico-thalamic feedback profoundly reduces state-dependent differences in the response of dLGN neurons to visual stimuli. This holds true for dLGN responses to both temporal and spatial features of the visual stimulus. These results reveal that the state-dependent shift of the response to visual stimuli in an early stage of visual processing depends on cortico-thalamic feedback. SIGNIFICANCE STATEMENT Brain state affects even the earliest stages of sensory processing. A clear example of this phenomenon is the change in thalamic responses to visual stimuli depending on whether the animal’s brain is in an alert or quiescent state. Despite the radical impact that brain state has on sensory processing, the underlying circuits are still poorly understood. Here, we show that both the temporal and spatial response properties of thalamic neurons to visual stimuli depend on... Continued on ES/IODE ➡️ https://etcse.fr/9p ------- If you find this interesting, feel free to follow, comment and share. We need your help to enhance our visibility, so that our platform continues to serve you.

📃Scientific paper: Brain State-Dependent Modulation of Thalamic Visual Processing by Cortico-Thalamic Feedback Abstract: The behavioral state of a mammal impacts how the brain responds to visual stimuli as early as in the dorsolateral geniculate nucleus of the thalamus (dLGN), the primary relay of visual information to the cortex. A clear example of this is the markedly stronger response of dLGN neurons to higher temporal frequencies of the visual stimulus in alert as compared with quiescent animals. The dLGN receives strong feedback from the visual cortex, yet whether this feedback contributes to these state-dependent responses to visual stimuli is poorly understood. Here, we show that in male and female mice, silencing cortico-thalamic feedback profoundly reduces state-dependent differences in the response of dLGN neurons to visual stimuli. This holds true for dLGN responses to both temporal and spatial features of the visual stimulus. These results reveal that the state-dependent shift of the response to visual stimuli in an early stage of visual processing depends on cortico-thalamic feedback. SIGNIFICANCE STATEMENT Brain state affects even the earliest stages of sensory processing. A clear example of this phenomenon is the change in thalamic responses to visual stimuli depending on whether the animal’s brain is in an alert or quiescent state. Despite the radical impact that brain state has on sensory processing, the underlying circuits are still poorly understood. Here, we show that both the temporal and spatial response properties of thalamic neurons to visual stimuli depend on... Continued on ES/IODE ➡️ https://etcse.fr/9p ------- If you find this interesting, feel free to follow, comment and share. We need your help to enhance our visibility, so that our platform continues to serve you.

☑️ *READ ASTRACT BELOW:* Keywords: Deep learning; Dynamic contrast-enhanced MRI; Early recurrence; Hepatocellular carcinoma; Prediction model. Part 1: Rationale and objectives: The proliferative nature of hepatocellular carcinoma (HCC) is closely related to early recurrence following radical resection. This study develops and validates a deep learning (DL) prediction model to distinguish between proliferative and non-proliferative HCCs using dynamic contrast-enhanced MRI (DCE-MRI), aiming to refine preoperative assessments and optimize treatment strategies by assessing early recurrence risk. Materials and methods: In this retrospective study, 355 HCC patients from two Chinese medical centers (April 2018-February 2023) who underwent radical resection were included. Patient data were collected from medical records, imaging databases, and pathology reports. The cohort was divided into a training set (n = 251), an internal test set (n = 62), and external test sets (n = 42). A DL model was developed using DCE-MRI images of primary tumors. Clinical and radiological models were generated from their respective features, and fusion strategies were employed for combined model development. The discriminative abilities of the clinical, radiological, DL, and combined models were extensively analyzed. The performances of these models were evaluated against pathological diagnoses, with independent and fusion DL-based models validated for clinical utility in predicting early recurrence.(...) Qu H, Acad Radiol. 2024 Nov;31(11):4445-4455. doi: 10.1016/j.acra.2024.04.028. Epub 2024 May 15. PMID: 38749868. #Gesundheit #Bildung #Fuehrung #Coaching #Mindset #Motivation #Gehirn #Neuroscience #Psychologie #Persoenlichkeitsentwicklung #Kindheit #KeyNoteSpeaker #Humangenetik #Biochemie #Neuroleadership #Ernaehrung #Transformation #Stress #Demografie #Gender #Age #interkulturelleKompetenz #Epigenetik #Veraenderung #EmotionaleIntelligenz #Change #Gesellschaft #Organisationsentwicklung #Philosophie #Beratung # Quantum

🚨 [AI] Avoiding abuse and misuse of T-test and ANOVA: Regression for categorical responses 👉 An approach using Bayesian regression with brmsIn neuroscience and other biomedical sciences, it is common to use behavioral tests to assess responses to experimental conditions or treatments. We can... https://lnkd.in/dPnRKV9t

# Exciting News: Two Research Papers Published! 🎉📊 I'm thrilled to announce the publication of two research papers I've been working on for over a year. As the first author, I'm proud to share these contributions to the field of EEG analysis and its applications in tinnitus therapy and epilepsy detection. ## 1. Graph-Based Electroencephalography Analysis in Tinnitus Therapy Published in Biomedicines (https://lnkd.in/dgTMQvn8) This study introduces a novel approach to analyzing tinnitus therapy data using EEG signals. Here's what we achieved: - Developed an innovative method to represent EEG channels as graph networks - Applied Graph Neural Networks (GNNs) and Long Short-Term Memory (LSTM) networks for comprehensive analysis - Achieved an impressive 99.41% accuracy in our model Our research aims to contribute to more effective treatment strategies for tinnitus sufferers by providing deeper insights into EEG data during therapy. ## 2. Graphical Insight: Revolutionizing Seizure Detection with EEG Representation Published in Biomedicines (https://lnkd.in/dEmJQRDx) This paper focuses on enhancing epileptic seizure detection using graph representations of EEG signals. Key highlights include: - Constructed graph representations of EEG signals using various features - Employed two models: GCN+LSTM and GCN+BRF for improved seizure detection - Achieved significant improvements in accuracy compared to previous methods - Demonstrated consistent performance even with reduced EEG channels Our approach opens new avenues for EEG analysis in neurodegenerative disease detection, emphasizing the potential of graph representations in advancing our understanding of complex neurological conditions. I'm incredibly grateful for the opportunity to contribute to these important areas of research. Working with EEG data has been challenging but immensely rewarding. I look forward to seeing how these findings may impact future studies and, ultimately, patient care. #Research #EEG #TinnitusTherapy #EpilepsyDetection #MachineLearning #GraphNeuralNetworks

Thanks to Magali Haas and Terry Sejnowski for an exciting and stimulating first day of the “Exploring the Bidirectional Relationship Between Artificial Intelligence and Neuroscience: A Workshop” in DC. The objectives of the workshop include exploring the bidirectional relationship between #neuroscience and #AI, discussing the utility and limitations of AI in basic, #translational, and #clinical neuroscience research, and understand the key role of neuroscience in equipping regulators and the public with the necessary knowledge and resources for responsible use of AI in research. At Beacon, we aim to enhance the utility of our novel, regulatory-grade device (#Dreem3S) and AI/ML-based analytical approaches towards maximal translational value in #clinical and #regulatory #drugdevelopment efforts. Identifying and evaluating novel #sleep and #EEG markers of #diseaseprogression and pathology in various #neurology therapeutic indications will be a key focus. Looking forward to an equally exciting and stimulating day 2 of the workshop today! https://lnkd.in/gBfs94jR Beacon Biosignals Jacob Donoghue, MD PhD David Matthews, PhD Matthew Alkaitis, MD, PhD Alexander Chan Jarrett Revels Eva Childers, Ph.D Sheena Posey Norris Bill Martin, PhD Allyson Gage Lars Lau Raket Antoine Leuzy Gregory Klein Hartmuth Kolb

📃Scientific paper: Decoding Trans-Saccadic Prediction Error Abstract: We are constantly sampling our environment by moving our eyes, but our subjective experience of the world is stable and constant. Stimulus displacement during or shortly after a saccade often goes unnoticed, a phenomenon called the saccadic suppression of displacement. Although we fail to notice such displacements, our oculomotor system computes the prediction errors and adequately adjusts the gaze and future saccadic execution, a phenomenon known as saccadic adaptation. In the present study, we aimed to find a brain signature of the trans-saccadic prediction error that informs the motor system but not explicit perception. We asked participants (either sex) to report whether a visual target was displaced during a saccade while recording electroencephalography (EEG). Using multivariate pattern analysis, we were able to differentiate displacements from no displacements, even when participants failed to report the displacement. In other words, we found that trans-saccadic prediction error is represented in the EEG signal 100 ms after the displacement presentation, mainly in occipital and parieto-occipital channels, even in the absence of explicit perception of the displacement. SIGNIFICANCE STATEMENT Stability in vision occurs even while performing saccades. One suggested mechanism for this counterintuitive visual phenomenon is that external displacement is suppressed during the retinal remapping caused by a saccade. Here, we shed light on the mechanisms of trans-sacc... Continued on ES/IODE ➡️ https://etcse.fr/3FI ------- If you find this interesting, feel free to follow, comment and share. We need your help to enhance our visibility, so that our platform continues to serve you.

In a landmark study published in the journal Nature, Dr. Itzhak Fried and colleagues are unraveling how the human brain interprets the flow of time and experiences. The researchers found that, when they directly recorded neuronal activity in the brain, specific types of brain cells fire in a way that mirrors a person's experience. The brain was found to retain these unique firing patterns even after the experience concluded and would utilize the same patterns when preparing for similar stimuli. These findings offer the first evidence of how brain cells integrate "what" and "when" information to extract and retain representations of experiences through time. The study's conclusions have groundbreaking implications for the development of neuro-prosthetic devices to enhance memory and other cognitive functions and can influence AI's understanding of cognition in the human brain. Read the full paper in Nature here: https://lnkd.in/gMpQcAWP Read the UCLA Health newsroom article here: https://lnkd.in/gpDbFgwr

📃Scientific paper: Decoding Trans-Saccadic Prediction Error Abstract: We are constantly sampling our environment by moving our eyes, but our subjective experience of the world is stable and constant. Stimulus displacement during or shortly after a saccade often goes unnoticed, a phenomenon called the saccadic suppression of displacement. Although we fail to notice such displacements, our oculomotor system computes the prediction errors and adequately adjusts the gaze and future saccadic execution, a phenomenon known as saccadic adaptation. In the present study, we aimed to find a brain signature of the trans-saccadic prediction error that informs the motor system but not explicit perception. We asked participants (either sex) to report whether a visual target was displaced during a saccade while recording electroencephalography (EEG). Using multivariate pattern analysis, we were able to differentiate displacements from no displacements, even when participants failed to report the displacement. In other words, we found that trans-saccadic prediction error is represented in the EEG signal 100 ms after the displacement presentation, mainly in occipital and parieto-occipital channels, even in the absence of explicit perception of the displacement. SIGNIFICANCE STATEMENT Stability in vision occurs even while performing saccades. One suggested mechanism for this counterintuitive visual phenomenon is that external displacement is suppressed during the retinal remapping caused by a saccade. Here, we shed light on the mechanisms of trans-sacc... Continued on ES/IODE ➡️ https://etcse.fr/3FI ------- If you find this interesting, feel free to follow, comment and share. We need your help to enhance our visibility, so that our platform continues to serve you.