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

Introduction: Neurodegenerative diseases (NDDs) present substantial challenges due to their impact on movement, emphasizing the critical role of biomedical engineering research in clinical diagnosis. Measuring the biomechanical properties of gait during walking can provide valuable insights into the movement pattern of NDDs and has great promise for developing non-invasive automated NDD classification techniques.  Methods: Based on the GaitNDD database, two experimental trials were conducted on healthy controls (HCs) and three NDDs: Parkinson disease (PD), amyotrophic lateral sclerosis (ALS), and Huntington disease (HD), showcasing a comprehensive analysis of 1-dimensional and 2-dimensional force gait features. In the first trial, two time-frequency feature sequences were extracted from right, left, and combined feet during a walking task, feeding a bidirectional long short-term memory (BiLSTM) network. The second trial involves constructing spectrogram images of the gait signal as input for 3 popular pre-trained convolutional neural networks (CNNs): AlexNet, GoogLeNet, and VGG16.  Results: VGG16 emerges as the standout performer, achieving a remarkable accuracy of 99.91%, sensitivity of 99.93%, and specificity of 99.97% for automatic 4-class NDD detection using high-level features from the right foot gait signal. BiLSTM performance significantly improved when fed with VGG16-extracted high-level features, surpassing hand-crafted features. Conclusion: The study underscores the superiority of CNNs, particularly VGG16, in extracting high-level features from spectrogram-derived vertical ground reaction force (vGRF) signals for robust NDD classification. The hybrid VGG16-BiLSTM approach demonstrates enhanced performance, affirming the synergistic benefits of combining deep learning techniques. Overall, the CNN high-level features derived from vGRF signal spectrograms provide valuable insights into NDD groups, offering a promising avenue for understanding diverse mechanisms underlying gait-related conditions. #NeurodegenerativeDisease #GaitAnalysis, #Spectrogram, #TimeFrequencyFeatures, #ShortTimeFourierTransform

A special developmental program in the human brain drives the disproportionate thickening of cortical layer 2/3. This suggests that the expansion of layer 2/3, along with its numerous neurons and their large dendrites, may contribute to what makes us human. Gidon et al. thus investigated the dendritic physiology of layer 2/3 pyramidal neurons in slices taken from surgically resected brain tissue in epilepsy patients. Dual somatodendritic recordings revealed previously unknown classes of action potentials in the dendrites of these neurons, which make their activity far more complex than has been previously thought. These action potentials allow single neurons to solve two long-standing computational problems in neuroscience that were considered to require multilayer neural networks https://lnkd.in/dd8ehhGJ

📃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.

The next #BMAS paper of the month is "A novel deep learning method for large-scale analysis of bone marrow adiposity using UK Biobank Dixon #MRI data" from William Cawthorn's lab The University of Edinburgh, and published in Computational and Structural Biotech Journal (https://lnkd.in/ebtHH8cv). Using a new high-throughput #DeepLearning method to measure bone marrow fat fraction (#BMFF) in several skeletal sites from a large sample population (n=627). By this approach, they confirmed expected relationships between BMFF, age, sex and #bonedensity. Even more exciting...they identified new site- and sex-specific characteristics. Using this tool on other large cohorts will be very useful to better understand the impact of @BoneMarrowAdipocytes on human health and disease.

A new study has located that, non-neural human cells can store memories - similar to brain cells. Learning and memory in animals exhibit a peculiar feature known as the massed-spaced effect: training distributed across multiple sessions (spaced training) produces stronger memory than the same amount of training applied in a single episode (massed training). The massed-spaced effect, also known as the spacing effect and first documented by Hermann Ebbinghaus, is characterized by the existence of an optimal intertrial interval (ITI) between training sessions. While the spacing effect is typically associated with neural systems, it was hypothesized that it might also be observable in non-neural cells, given that much of the molecular toolkit for memory formation is conserved across cell types. During this study, to monitor the memory and learning process, the non-brain cells were engineered to make a glowing protein, which indicated when the memory gene was on and when it was off. The results showed that these cells could determine when the chemical pulses, which imitated bursts of neurotransmitter in the brain, were repeated rather than simply prolonged—just as neurons in our brain can register when we learn with breaks rather than cramming all the material in one sitting. Overall, these findings show that canonical features of memory do not necessarily depend on neural circuitry, but can be embedded in the dynamics of signaling cascades conserved across different cell types. Learn more: https://lnkd.in/gcWCkqVT One love #memory #brain

📃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.