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Neural Integrators

Valentina Borghesani, Jared Narvid, Giovanni Battistella, Wendy Shwe, Christa Watson, Richard J Binney, Virginia Sturm, Zachary Miller, Maria Luisa Mandelli, Bruce Miller, Maria Luisa Gorno-Tempini
Processing a famous face involves a cascade of steps including detecting the presence of a face, recognizing it as familiar, accessing semantic/biographical information about the person, and finally, if required, production of the proper name. Decades of neuropsychological and neuroimaging studies have identified a network of occipital and temporal brain regions ostensibly comprising the 'core' system for face processing. Recent research has also begun to elucidate upon an 'extended' network, including anterior temporal and frontal regions...
January 23, 2019: Cortex; a Journal Devoted to the Study of the Nervous System and Behavior
Jocelynn R Pearl, Carlo Colantuoni, Dani E Bergey, Cory C Funk, Paul Shannon, Bijoya Basu, Alex M Casella, Rediet T Oshone, Leroy Hood, Nathan D Price, Seth A Ament
Transcriptional regulatory changes in the developing and adult brain are prominent features of brain diseases, but the involvement of specific transcription factors (TFs) remains poorly understood. We integrated brain-specific DNase footprinting and TF-gene co-expression to reconstruct a transcriptional regulatory network (TRN) model for the human brain. We identified key regulator TFs whose predicted target genes were enriched for differentially expressed genes in the prefrontal cortex of individuals with psychiatric and neurodegenerative diseases...
January 31, 2019: Cell Systems
Laura J Batterink, Ken A Paller
Statistical learning, the process of extracting regularities from the environment, plays an essential role in many aspects of cognition, including speech segmentation and language acquisition. A key component of statistical learning in a linguistic context is the perceptual binding of adjacent individual units (e.g., syllables) into integrated composites (e.g., multisyllabic words). A second, conceptually dissociable component of statistical learning is the memory storage of these integrated representations...
January 28, 2019: Cortex; a Journal Devoted to the Study of the Nervous System and Behavior
Maxim Signaevsky, Marcel Prastawa, Kurt Farrell, Nabil Tabish, Elena Baldwin, Natalia Han, Megan A Iida, John Koll, Clare Bryce, Dushyant Purohit, Vahram Haroutunian, Ann C McKee, Thor D Stein, Charles L White, Jamie Walker, Timothy E Richardson, Russell Hanson, Michael J Donovan, Carlos Cordon-Cardo, Jack Zeineh, Gerardo Fernandez, John F Crary
Accumulation of abnormal tau in neurofibrillary tangles (NFT) occurs in Alzheimer disease (AD) and a spectrum of tauopathies. These tauopathies have diverse and overlapping morphological phenotypes that obscure classification and quantitative assessments. Recently, powerful machine learning-based approaches have emerged, allowing the recognition and quantification of pathological changes from digital images. Here, we applied deep learning to the neuropathological assessment of NFT in postmortem human brain tissue to develop a classifier capable of recognizing and quantifying tau burden...
February 15, 2019: Laboratory Investigation; a Journal of Technical Methods and Pathology
Giulio Casali, Daniel Bush, Kate Jeffery
Entorhinal grid cells integrate sensory and self-motion inputs to provide a spatial metric of a characteristic scale. One function of this metric may be to help localize the firing fields of hippocampal place cells during formation and use of the hippocampal spatial representation ("cognitive map"). Of theoretical importance is the question of how this metric, and the resulting map, is configured in 3D space. We find here that when the body plane is vertical as rats climb a wall, grid cells produce stable, almost-circular grid-cell firing fields...
February 15, 2019: Proceedings of the National Academy of Sciences of the United States of America
Gergely David, Maryam Seif, Eveline Huber, Markus Hupp, Jan Rosner, Volker Dietz, Nikolaus Weiskopf, Siawoosh Mohammadi, Patrick Freund
OBJECTIVE: To characterize remote secondary neurodegeneration of spinal tracts and neurons below a cervical spinal cord injury (SCI) and its relation to the severity of injury, the integrity of efferent and afferent pathways, and clinical impairment. METHODS: A comprehensive high-resolution MRI protocol was acquired in 17 traumatic cervical SCI patients and 14 controls at 3T. At the cervical lesion, a sagittal T2-weighted scan provided information on the width of preserved midsagittal tissue bridges...
February 15, 2019: Neurology
A Del Vecchio, F Negro, A Holobar, A Casolo, J P Folland, F Felici, D Farina
KEY POINTS: We propose and validate a method to accurately identify the activity of populations of motor neurons during contractions at maximal rate of force development in humans. The behaviour of the motor neuron pool during rapid voluntary contractions in humans is presented. We show with this approach that the motor neuron recruitment speed and maximal motor unit discharge rate largely explains the individual ability in generating rapid force contractions. The results also indicate that the synaptic inputs received by the motor neurons before force is generated dictate human potential to generate force rapidly...
February 15, 2019: Journal of Physiology
Ardi Tampuu, Tambet Matiisen, H Freyja Ólafsdóttir, Caswell Barry, Raul Vicente
Place cells in the mammalian hippocampus signal self-location with sparse spatially stable firing fields. Based on observation of place cell activity it is possible to accurately decode an animal's location. The precision of this decoding sets a lower bound for the amount of information that the hippocampal population conveys about the location of the animal. In this work we use a novel recurrent neural network (RNN) decoder to infer the location of freely moving rats from single unit hippocampal recordings...
February 15, 2019: PLoS Computational Biology
Mostafa Karimi, Di Wu, Zhangyang Wang, Yang Shen
MOTIVATION: Drug discovery demands rapid quantification of compound-protein interaction (CPI). However, there is a lack of methods that can predict compound-protein affinity from sequences alone with high applicability, accuracy, and interpretability. RESULTS: We present a seamless integration of domain knowledges and learning-based approaches. Under novel representations of structurally-annotated protein sequences, a semi-supervised deep learning model that unifies recurrent and convolutional neural networks has been proposed to exploit both unlabeled and labeled data, for jointly encoding molecular representations and predicting affinities...
February 2, 2019: Bioinformatics
Yilong Yang, Zhuyifan Ye, Yan Su, Qianqian Zhao, Xiaoshan Li, Defang Ouyang
Current pharmaceutical formulation development still strongly relies on the traditional trial-and-error methods of pharmaceutical scientists. This approach is laborious, time-consuming and costly. Recently, deep learning has been widely applied in many challenging domains because of its important capability of automatic feature extraction. The aim of the present research is to apply deep learning methods to predict pharmaceutical formulations. In this paper, two types of dosage forms were chosen as model systems...
January 2019: Acta Pharmaceutica Sinica. B
Matteo di Volo, Alberto Romagnoni, Cristiano Capone, Alain Destexhe
Accurate population models are needed to build very large-scale neural models, but their derivation is difficult for realistic networks of neurons, in particular when nonlinear properties are involved, such as conductance-based interactions and spike-frequency adaptation. Here, we consider such models based on networks of adaptive exponential integrate-and-fire excitatory and inhibitory neurons. Using a master equation formalism, we derive a mean-field model of such networks and compare it to the full network dynamics...
February 14, 2019: Neural Computation
Naoki Masuyama, Chu Kiong Loo, Stefan Wermter
This paper attempts to solve the typical problems of self-organizing growing network models, i.e. (a) an influence of the order of input data on the self-organizing ability, (b) an instability to high-dimensional data and an excessive sensitivity to noise, and (c) an expensive computational cost by integrating Kernel Bayes Rule (KBR) and Correntropy-Induced Metric (CIM) into Adaptive Resonance Theory (ART) framework. KBR performs a covariance-free Bayesian computation which is able to maintain a fast and stable computation...
October 30, 2018: International Journal of Neural Systems
Stuart B Mazzone, Michael J Farrell
Cough is an important protective mechanism for clearing the airways but becomes a troublesome, and often difficult to treat, symptom in respiratory disease. Although cough can be produced as a reflex in response to the presence of irritants within the airways, emerging research demonstrates an unappreciated complexity in the peripheral and central neural systems that regulate cough. This complexity includes multiple primary sensory neurons that can induce or facilitate reflex coughing, different ascending central circuits in the brain that contribute to cough sensory discrimination and the perception of the urge-to-cough, and several descending brain systems for inducing, facilitating and inhibiting cough responses...
February 11, 2019: Pulmonary Pharmacology & Therapeutics
Casey L Roark, Lori L Holt
Human category learning appears to be supported by dual learning systems. Previous research indicates the engagement of distinct neural systems in learning categories that require selective attention to dimensions versus those that require integration across dimensions. This evidence has largely come from studies of learning across perceptually separable visual dimensions, but recent research has applied dual system models to understanding auditory and speech categorization. Since differential engagement of the dual learning systems is closely related to selective attention to input dimensions, it may be important that acoustic dimensions are quite often perceptually integral and difficult to attend to selectively...
February 13, 2019: Attention, Perception & Psychophysics
Ping Luo, Yulian Ding, Xiujuan Lei, Fang-Xiang Wu
With the advances in high-throughput technologies, millions of somatic mutations have been reported in the past decade. Identifying driver genes with oncogenic mutations from these data is a critical and challenging problem. Many computational methods have been proposed to predict driver genes. Among them, machine learning-based methods usually train a classifier with representations that concatenate various types of features extracted from different kinds of data. Although successful, simply concatenating different types of features may not be the best way to fuse these data...
2019: Frontiers in Genetics
Linard Filli, Christian Meyer, Tim Killeen, Lilla Lörincz, Beat Göpfert, Michael Linnebank, Vinzenz von Tscharner, Armin Curt, Marc Bolliger, Björn Zörner
Locomotion relies on the fine-tuned coordination of different muscles which are controlled by particular neural circuits. Depending on the attendant conditions, walking patterns must be modified to optimally meet the demands of the task. Assessing neuromuscular control during dynamic conditions is methodologically highly challenging and prone to artifacts. Here we aim at assessing corticospinal involvement during different locomotor tasks using non-invasive surface electromyography. Activity in tibialis anterior (TA) and gastrocnemius medialis (GM) muscles was monitored by electromyograms (EMGs) in 27 healthy volunteers (11 female) during regular walking, walking while engaged in simultaneous cognitive dual tasks, walking with partial visual restriction, and skilled, targeted locomotion...
2019: Frontiers in Neurology
Garam Lee, Kwangsik Nho, Byungkon Kang, Kyung-Ah Sohn, Dokyoon Kim
Alzheimer's disease (AD) is a progressive neurodegenerative condition marked by a decline in cognitive functions with no validated disease modifying treatment. It is critical for timely treatment to detect AD in its earlier stage before clinical manifestation. Mild cognitive impairment (MCI) is an intermediate stage between cognitively normal older adults and AD. To predict conversion from MCI to probable AD, we applied a deep learning approach, multimodal recurrent neural network. We developed an integrative framework that combines not only cross-sectional neuroimaging biomarkers at baseline but also longitudinal cerebrospinal fluid (CSF) and cognitive performance biomarkers obtained from the Alzheimer's Disease Neuroimaging Initiative cohort (ADNI)...
February 13, 2019: Scientific Reports
Qiuling Luo, Mengxia Yu, You Li, Lei Mo
Facial beauty and moral beauty have been suggested to be two significant forms of social aesthetics. However, it remains unknown the extent to which there are neural underpinnings of the integration of these two forms of beauty. In the present study, participants were asked to make general aesthetic judgments of facial portraits and moral descriptions while collecting fMRI data. The facial portrait and moral description were randomly paired. Neurally, the appreciation of facial beauty and moral beauty recruited a common network involving the middle occipital gyrus (MOG) and medial orbitofrontal cortex (mOFC)...
February 13, 2019: Scientific Reports
Beate E Muehlroth, Myriam C Sander, Yana Fandakova, Thomas H Grandy, Björn Rasch, Yee Lee Shing, Markus Werkle-Bergner
Memory consolidation during sleep relies on the precisely timed interaction of rhythmic neural events. Here, we investigate differences in slow oscillations (SO; 0.5-1 Hz), sleep spindles (SP), and their coupling across the adult human lifespan and ask whether observed alterations relate to the ability to retain associative memories across sleep. We demonstrate that older adults do not show the fine-tuned coupling of fast SPs (12.5-16 Hz) to the SO peak present in younger adults but, instead, are characterized most by a slow SP power increase (9-12...
February 13, 2019: Scientific Reports
Eloise Hudry, Jacob Klickstein, Claudia Cannavo, Rosemary Jackson, Alona Muzikansky, Sheetal Gandhi, David Urick, Taylie Sargent, Lauren Wrobleski, Allyson D Roe, Steven S Hou, Kishore V Kuchibhotla, Rebecca A Betensky, Tara Spires-Jones, Bradley T Hyman
Apolipoprotein E (APOE) effects on brain function remain controversial. Removal of APOE not only impairs cognitive functions but also reduces neuritic amyloid plaques in mouse models of Alzheimer's disease (AD). Can APOE simultaneously protect and impair neural circuits? Here, we dissociated the role of APOE in AD versus aging to determine its effects on neuronal function and synaptic integrity. Using two-photon calcium imaging in awake mice to record visually evoked responses, we found that genetic removal of APOE improved neuronal responses in adult APP/PSEN1 mice (8-10 mo)...
February 2019: Life science alliance
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