Hierarchical Dynamical Model for Multiple Cortical Neural Decoding

2021 ◽  
Vol 33 (5) ◽  
pp. 1372-1401
Author(s):  
Xi Liu ◽  
Xiang Shen ◽  
Shuhang Chen ◽  
Xiang Zhang ◽  
Yifan Huang ◽  
...  
Keyword(s):  
Hierarchical Structure ◽  
Primary Motor Cortex ◽  
Neural Pathway ◽  
Neural Recordings ◽  
Cortical Areas ◽  
The Hierarchical Structure ◽  
Brain States ◽  
Heavy Tailed ◽  
The Brain

Abstract Motor brain machine interfaces (BMIs) interpret neural activities from motor-related cortical areas in the brain into movement commands to control a prosthesis. As the subject adapts to control the neural prosthesis, the medial prefrontal cortex (mPFC), upstream of the primary motor cortex (M1), is heavily involved in reward-guided motor learning. Thus, considering mPFC and M1 functionality within a hierarchical structure could potentially improve the effectiveness of BMI decoding while subjects are learning. The commonly used Kalman decoding method with only one simple state model may not be able to represent the multiple brain states that evolve over time as well as along the neural pathway. In addition, the performance of Kalman decoders degenerates in heavy-tailed nongaussian noise, which is usually generated due to the nonlinear neural system or influences of movement-related noise in online neural recording. In this letter, we propose a hierarchical model to represent the brain states from multiple cortical areas that evolve along the neural pathway. We then introduce correntropy theory into the hierarchical structure to address the heavy-tailed noise existing in neural recordings. We test the proposed algorithm on in vivo recordings collected from the mPFC and M1 of two rats when the subjects were learning to perform a lever-pressing task. Compared with the classic Kalman filter, our results demonstrate better movement decoding performance due to the hierarchical structure that integrates the past failed trial information over multisite recording and the combination with correntropy criterion to deal with noisy heavy-tailed neural recordings.

scholarly journals Nanowire-Based Electrode for Acute In Vivo Neural Recordings in the Brain

PLoS ONE ◽  
2013 ◽  
Vol 8 (2) ◽  
pp. e56673 ◽  
Author(s):  
Dmitry B. Suyatin ◽  
Lars Wallman ◽  
Jonas Thelin ◽  
Christelle N. Prinz ◽  
Henrik Jörntell ◽  
...  
Keyword(s):  
Neural Recordings ◽  
The Brain

scholarly journals The consequences of very late exposure to BSL as an L1

2018 ◽  
Vol 21 (5) ◽  
pp. 936-937 ◽  
Author(s):  
BENCIE WOLL
Keyword(s):  
Early Childhood ◽  
Language Acquisition ◽  
Hierarchical Structure ◽  
Language Processing ◽  
Processing System ◽  
Brain Activation ◽  
Brain Maturation ◽  
The Hierarchical Structure ◽  
Late Exposure ◽  
The Brain

Mayberry and Kluender (2017) make an important contribution to our understanding of the CPL, reporting the striking differences in regions of brain activation in Martin, a deaf man with very late exposure to an L1, compared to other deaf individuals, when processing single signs of ASL. They conclude: “The unique effects of AoA . . . suggest that the hierarchical structure of language and the architecture of the brain language processing system arise from their interaction over the course of early childhood when brain maturation and language acquisition are temporally synchronized.”

scholarly journals Glutamate and GABA concentrations following mild traumatic brain injury: a pilot study

2018 ◽  
Vol 120 (3) ◽  
pp. 1318-1322 ◽  
Author(s):  
Alia L. Yasen ◽  
Jolinda Smith ◽  
Anita D. Christie
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Mild Traumatic Brain Injury ◽  
Primary Motor Cortex ◽  
Brain Regions ◽  
Resonance Spectroscopy ◽  
Multiple Time ◽  
Time Points ◽  
The Brain

Animal models of mild traumatic brain injury (mTBI) suggest that metabolic changes in the brain occur immediately after a mechanical injury to the head. Proton magnetic resonance spectroscopy (1H-MRS) can be used to determine relative concentrations of metabolites in vivo in the human brain. The purpose of this study was to determine concentrations of glutamate and GABA in the brain acutely after mTBI and throughout 2 mo of recovery. Concentrations of glutamate and GABA were obtained using 1H-MRS in nine individuals who had suffered an mTBI and nine control individuals in two brain regions of interest: the primary motor cortex (M1), and the dorsolateral prefrontal cortex (DLPFC), and at three different time points postinjury: 72 h, 2 wk, and 2 mo postinjury. There were no differences between groups in concentrations of glutamate or GABA, or the ratio of glutamate to GABA, in M1. In the DLPFC, glutamate concentration was lower in the mTBI group compared with controls at 72 h postinjury (d = 1.02), and GABA concentration was lower in the mTBI group at 72 h and 2 wk postinjury (d = 0.81 and d = 1.21, respectively). The ratio of glutamate to GABA in the DLPFC was higher in the mTBI group at 2 wk postinjury (d = 1.63). These results suggest that changes in glutamate and GABA concentrations in the brain may be region-specific and may depend on the amount of time that has elapsed postinjury. NEW & NOTEWORTHY To our knowledge, this is the first study to examine neurotransmitter concentrations in vivo at multiple time points throughout recovery from mild traumatic brain injury in humans.

scholarly journals Gut-to-brain signals in feeding control

2020 ◽  
Author(s):  
Alexandre Moura-Assis ◽  
Jeffrey M. Friedman ◽  
Licio A. Velloso
Keyword(s):  
Neural Control ◽  
Neural Circuits ◽  
Neural Recordings ◽  
Technical Developments ◽  
Brain Signals ◽  
Feedback Correction ◽  
Motivated Behaviors ◽  
Feeding Control ◽  
The Brain

Interoceptive signals from gut and adipose tissue and sensory cues from the environment are integrated by hubs in the brain to regulate feeding behavior and maintain homeostatic control of body weight. In vivo neural recordings have revealed that these signals control the activity of multiple layers of hunger neurons and that eating is not only the result of feedback correction to a set point, but can also be under the influence of anticipatory regulations. A series of recent technical developments have revealed how peripheral and sensory signals in particular from the gut are conveyed to the brain to integrate neural circuits. Here, we describe the mechanisms involved in gastrointestinal stimulation by nutrients and how these signals act on the hindbrain to generate motivated behaviors. We also consider the organization of multidirectional intra- and extra-hypothalamic circuits and how this has created a framework for understanding neural control of feeding.

The Brain Circuits and Dynamics of Curiosity-Driven Behavior in Naturally Curious Marmosets

2021 ◽  
Author(s):  
Xiaoguang Tian ◽  
Afonso C Silva ◽  
Cirong Liu
Keyword(s):  
Behavioral Responses ◽  
Brain Regions ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Cortical Areas ◽  
Brain Circuits ◽  
New Information ◽  
Brain States ◽  
Main Barrier ◽  
The Brain

Abstract Curiosity is a fundamental nature of animals for adapting to changing environments, but its underlying brain circuits and mechanisms remain poorly understood. One main barrier is that existing studies use rewards to train animals and motivate their engagement in behavioral tasks. As such, the rewards become significant confounders in interpreting curiosity. Here, we overcame this problem by studying research-naïve and naturally curious marmosets that can proactively and persistently participate in a visual choice task without external rewards. When performing the task, the marmosets manifested a strong innate preference towards acquiring new information, associated with faster behavioral responses. Longitudinally functional magnetic resonance imaging revealed behavior-relevant brain states that reflected choice preferences and engaged several brain regions, including the cerebellum, the hippocampus, and cortical areas 19DI, 25, and 46D, with the cerebellum being the most prominent. These results unveil the essential brain circuits and dynamics underlying curiosity-driven activity.

Effect of pH and inhibitors on beta-amyloid fibril assembly

1996 ◽  
Vol 54 ◽  
pp. 752-753
Author(s):  
Beverly E. Maleeff ◽  
Timothy K. Hart ◽  
Stephen J. Wood ◽  
Ronald Wetzel
Keyword(s):  
Amyloid Fibril ◽  
Peptide Fragment ◽  
Hydrophobic Peptides ◽  
Amyloid Fibril Formation ◽  
Effects Of Ph ◽  
Severity Of The Disease ◽  
Kinetics Of ◽  
The Brain

Alzheimer's disease is characterized post-mortem in part by abnormal extracellular neuritic plaques found in brain tissue. There appears to be a correlation between the severity of Alzheimer's dementia in vivo and the number of plaques found in particular areas of the brain. These plaques are known to be the deposition sites of fibrils of the protein β-amyloid. It is thought that if the assembly of these plaques could be inhibited, the severity of the disease would be decreased. The peptide fragment Aβ, a precursor of the p-amyloid protein, has a 40 amino acid sequence, and has been shown to be toxic to neuronal cells in culture after an aging process of several days. This toxicity corresponds to the kinetics of in vitro amyloid fibril formation. In this study, we report the biochemical and ultrastructural effects of pH and the inhibitory agent hexadecyl-N-methylpiperidinium (HMP) bromide, one of a class of ionic micellar detergents known to be capable of solubilizing hydrophobic peptides, on the in vitro assembly of the peptide fragment Aβ.

Microvascular features that suggest effectors of blood-flow regulation in the brain: Evidence by SEM of corrosion casts of intracerebral vessels

1990 ◽  
Vol 48 (3) ◽  
pp. 274-275
Author(s):  
Enrico D.F. Motti ◽  
Hans-Georg Imhof ◽  
Gazi M. Yasargil
Keyword(s):  
Blood Flow ◽  
Perfusion Pressure ◽  
Corrosion Casts ◽  
Cerebral Microcirculation ◽  
Pial Arteries ◽  
Random Occurrence ◽  
Brain Arteries ◽  
Homogeneous Network ◽  
The Brain

Physiologists have devoted most attention in the cerebrovascular tree to the arterial side of the circulation which has been subdivided in three levels: 1) major brain arteries which keep microcirculation constant despite changes in perfusion pressure; 2) pial arteries supposed to be effectors regulating microcirculation; 3) intracerebral arteries supposed to be deprived of active cerebral blood flow regulating devices.The morphological search for microvascular effectors in the cerebrovascular bed has been elusive. The opaque substance of the brain confines in vivo investigation to the superficial pial arteries. Most morphologists had to limit their observation to the random occurrence of a favorable site in the practically two-dimensional thickness of diaphanized histological sections. It is then not surprising most investigators of the cerebral microcirculation refer to an homogeneous network of microvessels interposed between arterioles and venules.We have taken advantage of the excellent depth of focus afforded by the scanning electron microscope (SEM) to investigate corrosion casts obtained injecting a range of experimental animals with a modified Batson's acrylic mixture.

NON-INVASIVE BCI METHOD: EEG - ELECTROENCEPHALOGRAPHY

2019 ◽  
pp. 139-145
Author(s):  
Selma Büyükgöze
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Electrode Placement ◽  
Eeg Signals ◽  
Non Invasive ◽  
Brain Signals ◽  
Brain States ◽  
The Brain

Brain Computer Interface consists of hardware and software that convert brain signals into action. It changes the nerves, muscles, and movements they produce with electro-physiological signs. The BCI cannot read the brain and decipher the thought in general. The BCI can only identify and classify specific patterns of activity in ongoing brain signals associated with specific tasks or events. EEG is the most commonly used non-invasive BCI method as it can be obtained easily compared to other methods. In this study; It will be given how EEG signals are obtained from the scalp, with which waves these frequencies are named and in which brain states these waves occur. 10-20 electrode placement plan for EEG to be placed on the scalp will be shown.

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