EEG-EOG based Virtual Keyboard: Toward Hybrid Brain Computer Interface
AbstractThe past twenty years have ignited a new spark in the research of Electroencephalogram (EEG), which was pursued to develop innovative Brain Computer Interfaces (BCIs) in order to help severely disabled people live a better life with a high degree of independence. Current BCIs are more theoretical than practical and are suffering from numerous challenges. New trends of research propose combining EEG to other simple and efficient bioelectric inputs such as Electro-oculography (EOG) resulting from eye movements, to produce more practical and robust Hybrid Brain Computer Interface systems (hBCI) or Brain/Neuronal Compu...
Source: Neuroinformatics - October 27, 2018 Category: Neuroscience Source Type: research
Cocaine-Induced Preference Conditioning: a Machine Vision Perspective
AbstractExisting work on drug-induced synaptic changes has shown that the expression of perineuronal nets (PNNs) at the cerebellar cortex can be regulated by cocaine-related memory. However, these studies on animals have mostly relied on limited manually-driven procedures, and lack some more rigorous statistical approaches and more automated techniques. In this work, established methods from computer vision and machine learning are considered to build stronger evidence of those previous findings. To that end, an image descriptor is designed to characterize PNNs images; unsupervised learning (clustering) is used to automati...
Source: Neuroinformatics - October 24, 2018 Category: Neuroscience Source Type: research
The Residual Center of Mass: An Image Descriptor for the Diagnosis of Alzheimer Disease
AbstractA crucial quest in neuroimaging is the discovery of image features (biomarkers) associated with neurodegenerative disorders. Recent works show that such biomarkers can be obtained by image analysis techniques. However, these techniques cannot be directly compared since they use different databases and validation protocols. In this paper, we present an extensive study of image descriptors for the diagnosis of Alzheimer Disease (AD) and introduce a new one, namedResidual Center of Mass (RCM). The RCM descriptor explores image moments and other techniques to enhance brain regions and select discriminative features for...
Source: Neuroinformatics - October 17, 2018 Category: Neuroscience Source Type: research
Rhesus Macaque Brain Atlas Regions Aligned to an MRI Template
In conclusion, an anatomically defined set of rhesus macaque brain regions has been aligned to an MRI template and has been validated for analysis of PET imaging in a subset of striatal and cortical areas. The entire set of over 200 regions is publicly available athttps://www.nitrc.org/.Graphical Abstractᅟ (Source: Neuroinformatics)
Source: Neuroinformatics - October 5, 2018 Category: Neuroscience Source Type: research
Fused Group Lasso Regularized Multi-Task Feature Learning and Its Application to the Cognitive Performance Prediction of Alzheimer ’s Disease
AbstractAlzheimer ’s disease (AD) is characterized by gradual neurodegeneration and loss of brain function, especially for memory during early stages. Regression analysis has been widely applied to AD research to relate clinical and biomarker data such as predicting cognitive outcomes from MRI measures. Recently, m ulti-task based feature learning (MTFL) methods with sparsity-inducing\( \ell _{2,1} \)-norm have been widely studied to select a discriminative feature subset from MRI features by incorporating inherent correlations among multiple clinical cognitive measures. However, existing MTFL assumes the correlation amo...
Source: Neuroinformatics - October 4, 2018 Category: Neuroscience Source Type: research
Decoding Auditory Saliency from Brain Activity Patterns during Free Listening to Naturalistic Audio Excerpts
AbstractIn recent years, natural stimuli such as audio excerpts or video streams have received increasing attention in neuroimaging studies. Compared with conventional simple, idealized and repeated artificial stimuli, natural stimuli contain more unrepeated, dynamic and complex information that are more close to real-life. However, there is no direct correspondence between the stimuli and any sensory or cognitive functions of the brain, which makes it difficult to apply traditional hypothesis-driven analysis methods (e.g., the general linear model (GLM)). Moreover, traditional data-driven methods (e.g., independent compon...
Source: Neuroinformatics - October 1, 2018 Category: Neuroscience Source Type: research
Cognitive Assessment Prediction in Alzheimer ’s Disease by Multi-Layer Multi-Target Regression
AbstractAccurate and automatic prediction of cognitive assessment from multiple neuroimaging biomarkers is crucial for early detection of Alzheimer ’s disease. The major challenges arise from the nonlinear relationship between biomarkers and assessment scores and the inter-correlation among them, which have not yet been well addressed. In this paper, we propose multi-layer multi-target regression (MMR) which enables simultaneously modeling in trinsic inter-target correlations and nonlinear input-output relationships in a general compositional framework. Specifically, by kernelized dictionary learning, the MMR can effecti...
Source: Neuroinformatics - October 1, 2018 Category: Neuroscience Source Type: research
Learning Efficient Spatial-Temporal Gait Features with Deep Learning for Human Identification
AbstractThe integration of the latest breakthroughs in bioinformatics technology from one side and artificial intelligence from another side, enables remarkable advances in the fields of intelligent security guard computational biology, healthcare, and so on. Among them, biometrics based automatic human identification is one of the most fundamental and significant research topic. Human gait, which is a biometric features with the unique capability, has gained significant attentions as the remarkable characteristics of remote accessed, robust and security in the biometrics based human identification. However, the existed me...
Source: Neuroinformatics - October 1, 2018 Category: Neuroscience Source Type: research
GPU Accelerated Browser for Neuroimaging Genomics
AbstractNeuroimaging genomics is an emerging field that provides exciting opportunities to understand the genetic basis of brain structure and function. The unprecedented scale and complexity of the imaging and genomics data, however, have presented critical computational bottlenecks. In this work we present our initial efforts towards building an interactive visual exploratory system for mining big data in neuroimaging genomics. A GPU accelerated browsing tool for neuroimaging genomics is created that implements the ANOVA algorithm for single nucleotide polymorphism (SNP) based analysis and the VEGAS algorithm for gene-ba...
Source: Neuroinformatics - October 1, 2018 Category: Neuroscience Source Type: research
Patch-Based Label Fusion with Structured Discriminant Embedding for Hippocampus Segmentation
AbstractAutomatic and accurate segmentation of hippocampal structures in medical images is of great importance in neuroscience studies. In multi-atlas based segmentation methods, to alleviate the misalignment when registering atlases to the target image, patch-based methods have been widely studied to improve the performance of label fusion. However, weights assigned to the fused labels are usually computed based on predefined features (e.g. image intensities), thus being not necessarily optimal. Due to the lack of discriminating features, the original feature space defined by image intensities may limit the description ac...
Source: Neuroinformatics - October 1, 2018 Category: Neuroscience Source Type: research
Neuronal Activities in the Mouse Visual Cortex Predict Patterns of Sensory Stimuli
AbstractVisual cortex forms the basis of visual processing and plays important roles in visual encoding. By using the recently published Allen Brain Observatory dataset consisting of large-scale calcium imaging of mouse V1 activities under visual stimuli, we were able to obtain high-quality data capturing simultaneous neuronal activities at multiple sub-areas and cortical depths of V1. Using prediction models, we analyzed the activity profiles related to static and drifting grating stimuli. We conducted a comprehensive survey of the coding ability of multiple cortical locations toward different stimulus attributes. Specifi...
Source: Neuroinformatics - October 1, 2018 Category: Neuroscience Source Type: research
Predict MiRNA-Disease Association with Collaborative Filtering
AbstractThe era of human brain science research is dawning. Researchers utilize the various multi-disciplinary knowledge to explore the human brain,such as physiology and bioinformatics. The emerging disease association prediction technology can speed up the study of diseases, so as to better understanding the structure and function of human body. There are increasing evidences that miRNA plays a significant role in nervous system development, adult function, plasticity, and vulnerability to neurological disease states. In this paper ,we proposed the novel improved collaborative filtering-based miRNA-disease association pr...
Source: Neuroinformatics - October 1, 2018 Category: Neuroscience Source Type: research
Large-scale Exploration of Neuronal Morphologies Using Deep Learning and Augmented Reality
AbstractRecently released large-scale neuron morphological data has greatly facilitated the research in neuroinformatics. However, the sheer volume and complexity of these data pose significant challenges for efficient and accurate neuron exploration. In this paper, we propose an effective retrieval framework to address these problems, based on frontier techniques of deep learning and binary coding. For the first time, we develop a deep learning based feature representation method for the neuron morphological data, where the 3D neurons are first projected into binary images and then learned features using an unsupervised d...
Source: Neuroinformatics - October 1, 2018 Category: Neuroscience Source Type: research
Field of View Normalization in Multi-Site Brain MRI
AbstractMulti-site brain MRI analysis is needed in big data neuroimaging studies, but challenging. The challenges lie in almost every analysis step including skull stripping. The diversities in multi-site brain MR images make it difficult to tune parameters specific to subjects or imaging protocols. Alternatively, using constant parameter settings often leads to inaccurate, inconsistent and even failed skull stripping results. One reason is that images scanned at different sites, under different scanners or protocols, and/or by different technicians often have very different fields of view (FOVs). Normalizing FOV is curren...
Source: Neuroinformatics - October 1, 2018 Category: Neuroscience Source Type: research
