scholarly journals Finding informative neurons in the brain using Multi-Scale Relevance

2018 ◽  
Author(s):  
Ryan John Cubero ◽  
Matteo Marsili ◽  
Yasser Roudi
Keyword(s):  
Mutual Information ◽  
Tuning Curve ◽  
A Priori ◽  
Electrode Array ◽  
Firing Activity ◽  
Significant Information ◽  
Multi Scale ◽  
Time Stamps ◽  
Wide Variability ◽  
The Brain

AbstractWe propose a metric – called Multi-Scale Relevance (MSR) – to score neurons for their prominence in encoding for the animal’s behaviour that is being observed in a multi-electrode array recording experiment. The MSR assumes that relevant neurons exhibit a wide variability in their dynamical state, in response to the external stimulus, across different time scales. It is a non-parametric, fully featureless indicator, in that it uses only the time stamps of the firing activity, without resorting to any a priori covariate or invoking any specific tuning curve for neural activity. We test the method on data from freely moving rodents, where we found that neurons having low MSR tend to have low mutual information and low firing sparsity across the correlates that are believed to be encoded by the region of the brain where the recordings were made. In addition, neurons with high MSR contain significant information on spatial navigation and allow to decode spatial position or head direction as efficiently as those neurons whose firing activity has high mutual information with the covariate to be decoded.

scholarly journals Aggregation-and-Attention Network for brain tumor segmentation

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Chih-Wei Lin ◽  
Yu Hong ◽  
Jinfu Liu
Keyword(s):  
Brain Tumor ◽  
Semantic Information ◽  
Spatial Relationship ◽  
Tumor Segmentation ◽  
Computer Assisted ◽  
Brain Tumor Segmentation ◽  
Attention Network ◽  
Multi Scale ◽  
Assisted Diagnosis ◽  
The Brain

Abstract Background Glioma is a malignant brain tumor; its location is complex and is difficult to remove surgically. To diagnosis the brain tumor, doctors can precisely diagnose and localize the disease using medical images. However, the computer-assisted diagnosis for the brain tumor diagnosis is still the problem because the rough segmentation of the brain tumor makes the internal grade of the tumor incorrect. Methods In this paper, we proposed an Aggregation-and-Attention Network for brain tumor segmentation. The proposed network takes the U-Net as the backbone, aggregates multi-scale semantic information, and focuses on crucial information to perform brain tumor segmentation. To this end, we proposed an enhanced down-sampling module and Up-Sampling Layer to compensate for the information loss. The multi-scale connection module is to construct the multi-receptive semantic fusion between encoder and decoder. Furthermore, we designed a dual-attention fusion module that can extract and enhance the spatial relationship of magnetic resonance imaging and applied the strategy of deep supervision in different parts of the proposed network. Results Experimental results show that the performance of the proposed framework is the best on the BraTS2020 dataset, compared with the-state-of-art networks. The performance of the proposed framework surpasses all the comparison networks, and its average accuracies of the four indexes are 0.860, 0.885, 0.932, and 1.2325, respectively. Conclusions The framework and modules of the proposed framework are scientific and practical, which can extract and aggregate useful semantic information and enhance the ability of glioma segmentation.

scholarly journals Sex-Change Chemicals and their Influence on the Brain

2001 ◽  
Vol 1 ◽  
pp. 681-683 ◽  
Author(s):  
Catherine A. Harris
Keyword(s):  
Endocrine Disrupting Chemicals ◽  
Reproductive Disorders ◽  
Developmental Abnormalities ◽  
Scientific World ◽  
Endocrine Disrupting ◽  
Causative Agents ◽  
Wide Variability ◽  
Wildlife Populations ◽  
Natural Hormones ◽  
The Brain

The potential for man-made chemicals to mimic or antagonise natural hormones is a controversial issue, but one for which increasing amounts of evidence are being gathered worldwide. The controversy surrounds not so much the matter of whether these chemicals can mimic hormones invitro— this phenomenon has been widely accepted in the scientific world — but more whether, as a result, they can disrupt reproduction in a wildlife situation. It has, nevertheless, been acknowledged that many wildlife populations are exhibiting reproductive and/or developmental abnormalities such as intersex gonads in wild roach populations in the U.K.[1] and various reproductive disorders in alligators in Lake Apopka, Florida[2]. However, the causative agents for many of these effects are difficult to specify, due to the extensive mixtures of chemicals — each of which may act via different pathways — to which wild populations are exposed, together with the wide variability observed even in natural (uncontaminated) habitats. As a result, any information detailing fundamental mechanism of action of the so-called endocrine disrupting chemicals (EDCs) is of use in determining whether or not these chemicals, as they are present in the environment, may in fact be capable of causing some of the effects observed in wildlife over recent years.

scholarly journals Sea of Electrodes Array (SEA): Extremely Dense and High-Count Silicon-Based Electrode Array Technology for High-Resolution High-Bandwidth Interfacing with 3D Neural Structures

2021 ◽  
Author(s):  
Amin Sandoughsaz Zardini ◽  
Behnoush Rostami ◽  
Khalil Najafi ◽  
Vaughn L. Hetrick ◽  
Omar J. Ahmed
Keyword(s):  
Aspect Ratio ◽  
High Aspect Ratio ◽  
Electrode Array ◽  
High Count ◽  
Array Technology ◽  
High Bandwidth ◽  
Length Width ◽  
Silicon Based ◽  
The Brain

AbstractIn this work, we propose a new silicon-based micro-fabrication technology to fabricate 3D high-density high-electrode-count neural micro-probe arrays scalable to thousands and even millions of individual electrodes with user-defined length, width, shape, and tip profile. This unique technology utilizes DRIE of ultra-high aspect-ratio holes in silicon and refilling them with multiple films to form thousands of individual needles with metal tips making up the “sea-of-electrodes” array (SEA). World-record density of 400 electrodes/mm2 in a 5184-needle array is achieved. The needles are ~0.5-1.2mm long, <20μm wide at the base, and <1μm at the tip. The silicon-based structure of these 3D array probes with sharp tips, makes them stiff enough and easily implantable in the brain to reach a targeted region without failing. Moreover, the high aspect ratio of these extremely fine needles reduces the tissue damage and improves the chronic stability. Functionality of the electrodes is investigated using acute in vivo recording in a rat barrel field cortex under isoflurane anesthesia.

scholarly journals Comparing supervised and unsupervised approaches to emotion categorization in the human brain, body, and subjective experience.

2020 ◽  
Author(s):  
Bahar Azari ◽  
Christiana Westlin ◽  
Ajay Satpute ◽  
J. Benjamin Hutchinson ◽  
Philip A. Kragel ◽  
...  
Keyword(s):  
Human Brain ◽  
Subjective Experience ◽  
A Priori ◽  
Ground Truth ◽  
Test Case ◽  
Physical Measurements ◽  
Supervised Classifiers ◽  
Emotional Episodes ◽  
Emotion Labels ◽  
The Brain

Machine learning methods provide powerful tools to map physical measurements to scientific categories. But are such methods suitable for discovering the ground truth about psychological categories? We use the science of emotion as a test case to explore this question. In studies of emotion, researchers use supervised classifiers, guided by emotion labels, to attempt to discover biomarkers in the brain or body for the corresponding emotion categories. This practice relies on the assumption that the labels refer to objective categories that can be discovered. Here, we critically examine this approach across three distinct datasets collected during emotional episodes- measuring the human brain, body, and subjective experience- and compare supervised classification studies with those from unsupervised clustering in which no a priori labels are assigned to the data. We conclude with a set of recommendations to guide researchers towards meaningful, data-driven discoveries in the science of emotion and beyond.

scholarly journals Integrating Multiple and Fine Scale Predictors of Habitat Selection and Landscape Resistance for Amphibian Distribution Modelling at Large Scale

2021 ◽  
Author(s):  
Florence Matutini ◽  
Jacques Baudry ◽  
Marie-Josée Fortin ◽  
Guillaume Pain ◽  
Joséphine Pithon
Keyword(s):  
Large Scale ◽  
A Priori ◽  
Species Level ◽  
Fine Scale ◽  
Landscape Resistance ◽  
Amphibian Species ◽  
Distribution Modelling ◽  
Multi Scale ◽  
Functional Grain ◽  
Level Analysis

Abstract Context – Species distribution modelling is a common tool in conservation biology but two main criticisms remain: (1) the use of simplistic variables that do not account for species movements and/or connectivity and (2) poor consideration of multi-scale processes driving species distributions. Objectives – We aimed to determine if including multi-scale and fine-scale movement processes in SDM predictors would improve accuracy of SDM for low-mobility amphibian species over species-level analysis.Methods – We tested and compared different SDMs for nine amphibian species with four different sets of predictors: (1) simple distance-based predictors; (2) single-scale compositional predictors; (3) multi-scale compositional predictors with a priori selection of scale based on knowledge of species mobility and scale-of-effect (4) multi-scale compositional predictors calculated using a friction-based functional grain to account for resource accessibility with landscape resistance to movement.Results - Using friction-based functional grain predictors produced slight to moderate improvements of SDM performance at large scale. The multi-scale approach, with a priori scale selection led to ambiguous results depending on the species studied, in particular for generalist species.Conclusion - We underline the potential of using a friction-based functional grain to improve SDM predictions for species-level analysis.

Early Experience Determines How the Senses Will Interact

2007 ◽  
Vol 97 (1) ◽  
pp. 921-926 ◽  
Author(s):  
Mark T. Wallace ◽  
Barry E. Stein
Keyword(s):  
Superior Colliculus ◽  
Multisensory Integration ◽  
A Priori ◽  
Spatial Relationship ◽  
Early Experience ◽  
Auditory Stimuli ◽  
Anomalous Form ◽  
Sensory Environment ◽  
External Events ◽  
The Brain

Multisensory integration refers to the process by which the brain synthesizes information from different senses to enhance sensitivity to external events. In the present experiments, animals were reared in an altered sensory environment in which visual and auditory stimuli were temporally coupled but originated from different locations. Neurons in the superior colliculus developed a seemingly anomalous form of multisensory integration in which spatially disparate visual-auditory stimuli were integrated in the same way that neurons in normally reared animals integrated visual-auditory stimuli from the same location. The data suggest that the principles governing multisensory integration are highly plastic and that there is no a priori spatial relationship between stimuli from different senses that is required for their integration. Rather, these principles appear to be established early in life based on the specific features of an animal's environment to best adapt it to deal with that environment later in life.

scholarly journals Brain Network Modeling Based on Mutual Information and Graph Theory for Predicting the Connection Mechanism in the Progression of Alzheimer’s Disease

Entropy ◽  
2019 ◽  
Vol 21 (3) ◽  
pp. 300 ◽  
Author(s):  
Shuaizong Si ◽  
Bin Wang ◽  
Xiao Liu ◽  
Chong Yu ◽  
Chao Ding ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Graph Theory ◽  
Mutual Information ◽  
Brain Networks ◽  
Brain Network ◽  
Network Efficiency ◽  
Characteristic Path Length ◽  
Anatomical Space ◽  
The Brain

Alzheimer’s disease (AD) is a progressive disease that causes problems of cognitive and memory functions decline. Patients with AD usually lose their ability to manage their daily life. Exploring the progression of the brain from normal controls (NC) to AD is an essential part of human research. Although connection changes have been found in the progression, the connection mechanism that drives these changes remains incompletely understood. The purpose of this study is to explore the connection changes in brain networks in the process from NC to AD, and uncovers the underlying connection mechanism that shapes the topologies of AD brain networks. In particular, we propose a mutual information brain network model (MINM) from the perspective of graph theory to achieve our aim. MINM concerns the question of estimating the connection probability between two cortical regions with the consideration of both the mutual information of their observed network topologies and their Euclidean distance in anatomical space. In addition, MINM considers establishing and deleting connections, simultaneously, during the networks modeling from the stage of NC to AD. Experiments show that MINM is sufficient to capture an impressive range of topological properties of real brain networks such as characteristic path length, network efficiency, and transitivity, and it also provides an excellent fit to the real brain networks in degree distribution compared to experiential models. Thus, we anticipate that MINM may explain the connection mechanism for the formation of the brain network organization in AD patients.

Bioresorbable Electrode Array for Electrophysiological and Pressure Signal Recording in the Brain

2019 ◽  
Vol 8 (15) ◽  
pp. 1801649 ◽  
Author(s):  
Kedi Xu ◽  
Shijian Li ◽  
Shurong Dong ◽  
Shaomin Zhang ◽  
Gang Pan ◽  
...  
Keyword(s):  
Electrode Array ◽  
Pressure Signal ◽  
The Brain ◽  
Signal Recording
Sign in / Sign up

Export Citation Format

Share Document