Subanesthetic ketamine evokes rapid and long-lasting antidepressant effects in human patients. The mechanism for ketamine’s effects remains elusive, but ketamine may broadly modulate brain plasticity ...processes. We show that single-dose ketamine reactivates adult mouse visual cortical plasticity and promotes functional recovery of visual acuity defects from amblyopia. Ketamine specifically induces downregulation of neuregulin-1 (NRG1) expression in parvalbumin-expressing (PV) inhibitory neurons in mouse visual cortex. NRG1 downregulation in PV neurons co-tracks both the fast onset and sustained decreases in synaptic inhibition to excitatory neurons, along with reduced synaptic excitation to PV neurons in vitro and in vivo following a single ketamine treatment. These effects are blocked by exogenous NRG1 as well as PV targeted receptor knockout. Thus, ketamine reactivation of adult visual cortical plasticity is mediated through rapid and sustained cortical disinhibition via downregulation of PV-specific NRG1 signaling. Our findings reveal the neural plasticity-based mechanism for ketamine-mediated functional recovery from adult amblyopia.
•Disinhibition of excitatory cells by ketamine occurs in a fast and sustained manner•Ketamine evokes NRG1 downregulation and excitatory input loss in PV cells•Ketamine induced plasticity is blocked by exogenous NRG1 or its receptor knockout•PV inhibitory cells are the initial functional locus underlying ketamine’s effects
Grieco et al. find that subanesthetic ketamine downregulates NRG1 expression in PV inhibitory cells, resulting in sustained cortical disinhibition to enhance cortical plasticity in adult visual cortex. Their work establishes the neural plasticity-based mechanism for ketamine-mediated functional recovery from amblyopia.
The present study analysed the major features of two important acts and an ordinance in Bangladesh that govern coastal and marine fishery exploitation and conservation. The problems with the ...implementation of these regulations were identified, and the level of compliance among fishers and reasons for their noncompliance were assessed. Based on two case studies on coastal and marine ecosystems, the findings revealed that the level of noncompliance is highly prevalent, particularly in hilsa sanctuaries in the Meghna River estuary. The study identified coastal poverty, the inadequate and improper distribution of incentives, insufficient logistic support, limited alternative occupations, political interference and a lack of awareness regarding fishery regulations as the major limitations in the implementation. The drawbacks of proper implementation and the noncompliance of fishery regulations lead to fishery degradation, directly affect the sustainability of the coastal and marine ecosystem of Bangladesh and may be barriers to achieving Goal 14 of the Sustainable Development Goals (SDGs). Establishing a co-management mechanism for sanctuary management, creating economic opportunities outside of fishery sectors, declaring more protected areas in the coastal and marine ecosystem, enhancing logistic support to the enforcing agencies and building awareness are critical to improving the compliance level among fishers. Finally, the study submits that understanding the fishers’ reasons for compliance and noncompliance of the regulations is important for devising fishery policies through the consultation and engagement of stakeholders at all levels.
Experience‐dependent critical period (CP) plasticity has been extensively studied in the visual cortex. Monocular deprivation during the CP affects ocular dominance, limits visual performance, and ...contributes to the pathological etiology of amblyopia. Neuregulin‐1 (NRG1) signaling through its tyrosine kinase receptor ErbB4 is essential for the normal development of the nervous system and has been linked to neuropsychiatric disorders such as schizophrenia. We discovered recently that NRG1/ErbB4 signaling in PV neurons is critical for the initiation of CP visual cortical plasticity by controlling excitatory synaptic inputs onto PV neurons and thus PV‐cell mediated cortical inhibition that occurs following visual deprivation. Building on this discovery, we review the existing literature of neuregulin signaling in developing and adult cortex and address the implication of NRG/ErbB4 signaling in visual cortical plasticity at the cellular and circuit levels. NRG‐directed research may lead to therapeutic approaches to reactivate plasticity in the adult cortex.
This image is associated with Figure 2 in Grieco et al. in this issue. It is an artistic rendition of their image data illustrating enhanced local excitatory inputs to monocularly deprived PV neurons (white circles) with bath applied NRG1. The eye‐like overlay in black conveys that therapeutic intervention of NRG1/ErbB4 signaling can be developed to help treat critical period‐relevant disorders such as amblyopia. The review article of Grieco et al. addresses a novel and critical role of NRG1/ErbB4 in regulation of visual cortical critical period plasticity.
Discriminative GoDec+ for Classification Guo, Kailing; Xu, Xiangmin; Tao, Dacheng
IEEE transactions on signal processing,
07/2017, Letnik:
65, Številka:
13
Journal Article
Recenzirano
GoDec+ is a robust low-rank representation model, which adopts correntropy to model noise and corruptions. To extend GoDec+ for classification, this paper proposes discriminative GoDec+ (D-GoDec+). ...In the model, each class is represented by a shared underlying subspace and a specific transformation matrix. Structural label information and the Fisher discrimination criterion are incorporated to model the reconstruction errors and coefficients. An efficient solution to D-GoDec+ is proposed based on half-quadratic optimization, and convergence of the solution is rigorously analyzed. Based on D-GoDec+, a simple but effective classification method is presented by combining the discriminability of reconstruction errors and coefficients. Through the use of transformation matrices, the classification method avoids complex encoding computation in the dictionary represented methods and thus is very efficient. Experimental results on face recognition, object classification, scene classification, and action recognition demonstrate the advantages of the proposed model.
We developed and applied a Cre-dependent, genetically modified rabies-based tracing system to map direct synaptic connections to specific CA1 neuron types in the mouse hippocampus. We found common ...inputs to excitatory and inhibitory CA1 neurons from CA3, CA2, the entorhinal cortex (EC), the medial septum (MS), and, unexpectedly, the subiculum. Excitatory CA1 neurons receive inputs from both cholinergic and GABAergic MS neurons, whereas inhibitory neurons receive a great majority of inputs from GABAergic MS neurons. Both cell types also receive weaker input from glutamatergic MS neurons. Comparisons of inputs to CA1 PV+ interneurons versus SOM+ interneurons showed similar strengths of input from the subiculum, but PV+ interneurons received much stronger input than SOM+ neurons from CA3, the EC, and the MS. Thus, rabies tracing identifies hippocampal circuit connections and maps how the different input sources to CA1 are distributed with different strengths on each of its constituent cell types.
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•Connections to specific CA1 neuron types were identified using monosynaptic rabies•Excitatory and inhibitory neurons receive similar inputs that differ in strength•Both neuron types have noncanonical, direct subicular inputs•Septohippocampal connections to CA1 include glutamatergic components
New advances in virology and genetic technology offer powerful tools for mapping cell-type-specific circuit connectivity and function. Sun et al. have developed and applied a new Cre-dependent, genetically modified, rabies-based tracing system to map monosynaptic circuit connections to specific neuron types in hippocampal CA1. This study reveals hippocampal circuit connections and addresses how the different sources of input to CA1 are distributed with different strengths onto each of its constituent cell types.
Electrodermal activity (EDA) sensor is emerging non-invasive equipment in affect detection research, which is used to measure electrical activities of the skin. Knowledge graphs are an effective way ...to learn representation from data. However, few studies analyzed the effect of knowledge-related graph features with physiological signals when subjects are in non-similar mental states. In this paper, we propose a model using deep learning techniques to classify the emotional responses of individuals acquired from physiological datasets. We aim to improve the execution of emotion recognition based on EDA signals. The proposed framework is based on observed gender and age information as embedding feature vectors. We also extract time and frequency EDA features in line with cognitive studies. We then introduce a sophisticated weighted feature fusion method that combines knowledge embedding feature vectors and statistical feature (SF) vectors for emotional state classification. We finally utilize deep neural networks to optimize our approach. Results obtained indicated that the correct combination of Gender-Age Relation Graph (GARG) and SF vectors improve the performance of the valence-arousal emotion recognition system by 4 and 5% on PAFEW and 3 and 2% on DEAP datasets.
Emotion recognition from affective brain-computer interfaces (aBCI) has garnered a lot of attention in human-computer interactions. Electroencephalographic (EEG) signals collected and stored in one ...database have been mostly used due to their ability to detect brain activities in real time and their reliability. Nevertheless, large EEG individual differences occur amongst subjects making it impossible for models to share information across. New labeled data is collected and trained separately for new subjects which costs a lot of time. Also, during EEG data collection across databases, different stimulation is introduced to subjects. Audio-visual stimulation (AVS) is commonly used in studying the emotional responses of subjects. In this article, we propose a brain region aware domain adaptation (BRADA) algorithm to treat features from auditory and visual brain regions differently, which effectively tackle subject-to-subject variations and mitigate distribution mismatch across databases. BRADA is a new framework that works with the existing transfer learning method. We apply BRADA to both cross-subject and cross-database settings. The experimental results indicate that our proposed transfer learning method can improve valence-arousal emotion recognition tasks.
Brain extraction and image quality assessment are two fundamental steps in fetal brain magnetic resonance imaging (MRI) 3D reconstruction and quantification. However, the randomness of fetal position ...and orientation, the variability of fetal brain morphology, maternal organs around the fetus, and the scarcity of data samples, all add excessive noise and impose a great challenge to automated brain extraction and quality assessment of fetal MRI slices. Conventionally, brain extraction and quality assessment are typically performed independently. However, both of them focus on the brain image representation, so they can be jointly optimized to ensure the network learns more effective features and avoid overfitting. To this end, we propose a novel two-stage dual-task deep learning framework with a brain localization stage and a dual-task stage for joint brain extraction and quality assessment of fetal MRI slices. Specifically, the dual-task module compactly contains a feature extraction module, a quality assessment head and a segmentation head with feature fusion for simultaneous brain extraction and quality assessment. Besides, a transformer architecture is introduced into the feature extraction module and the segmentation head. We utilize a multi-step training strategy to guarantee a stable and successful training of all modules. Finally, we validate our method by a 5-fold cross-validation and ablation study on a dataset with fetal brain MRI slices in different qualities, and perform a cross-dataset validation in addition. Experiments show that the proposed framework achieves very promising performance.
•A novel two-stage dual-task deep learning framework for fetal MRI slices.•A compact dual-task module for joint brain extraction and quality assessment.•The introduction of a novel transformer architecture for fetal brain MRI processing.•Promising performance on 5-fold cross-validation and cross-dataset validation.
Loss-of-function mutations in CNTNAP2 cause a syndromic form of autism spectrum disorder in humans and produce social deficits, repetitive behaviors, and seizures in mice. However, the functional ...effects of these mutations at cellular and circuit levels remain elusive. Using laser-scanning photostimulation, whole-cell recordings, and electron microscopy, we found a dramatic decrease in excitatory and inhibitory synaptic inputs onto L2/3 pyramidal neurons of the medial prefrontal cortex (mPFC) of Cntnap2 knockout (KO) mice, concurrent with reduced spines and synapses, despite normal dendritic complexity and intrinsic excitability. Moreover, recording of mPFC local field potentials (LFPs) and unit spiking in vivo revealed increased activity in inhibitory neurons, reduced phase-locking to delta and theta oscillations, and delayed phase preference during locomotion. Excitatory neurons showed similar phase modulation changes at delta frequencies. Finally, pairwise correlations increased during immobility in KO mice. Thus, reduced synaptic inputs can yield perturbed temporal coordination of neuronal firing in cortical ensembles.
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•Synaptic inputs onto mPFC L2/3 pyramidal neurons are reduced in Cntnap2 KO mice•The frequency and amplitude of mEPSCs are reduced in the mPFC of Cntnap2 KO neurons•Decreased density of dendritic excitatory and inhibitory synapses in Cntnap2 KO mice•Phase-modulated spiking to slow LFP oscillations is altered in Cntnap2 KO units
Lazaro et al. demonstrate a decrease in synaptic inputs onto mPFC L2/3 pyramidal neurons of Cntnap2 KO mice, concurrent with reduced spines and synapses. These lead to perturbed network activity, with mPFC cells exhibiting reduced phase locking and altered preferred phases to slow LFP oscillations, which may underlie autism-related phenotypes.
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