scholarly journals Evidence for time division multiplexing: Single neurons may encode simultaneous stimuli by switching between activity patterns

2017 ◽  
Author(s):  
Valeria C. Caruso ◽  
Jeff T. Mohl ◽  
Christopher Glynn ◽  
Jungah Lee ◽  
Shawn M. Willett ◽  
...  
Keyword(s):  
Activity Patterns ◽  
General Strategy ◽  
Field Potentials ◽  
Single Neurons ◽  
Neural Firing ◽  
Data Set ◽  
Brain Processing ◽  
Simultaneous Sounds ◽  
Time Division ◽  
The Brain

ABSTRACTHow the brain preserves information about multiple simultaneous items is poorly understood. We report that single neurons can represent multiple different stimuli by interleaving different signals across time. We record single units in an auditory region, the inferior colliculus, while monkeys localize 1 or 2 simultaneous sounds. During dual-sound trials, we find that some neurons fluctuate between firing rates observed for each single sound, either on a whole-trial or on a sub-trial timescale. These fluctuations are correlated in pairs of neurons, can be predicted by the state of local field potentials prior to sound onset, and, in one monkey, can predict which sound will be reported first. We find corroborating evidence of fluctuating activity patterns in a separate data set involving responses of inferotemporal cortex neurons to multiple visual stimuli. Alternation between activity patterns corresponding to each of multiple items may therefore be a general strategy to enhance the brain processing capacity, potentially linking such disparate phenomena as variable neural firing, neural oscillations, and limits in attentional/memory capacity.

Neuroimaging-based brain-age prediction of first-episode schizophrenia and the alteration of brain age after early medication

2021 ◽  
pp. 1-8
Author(s):  
Yi-Bin Xi ◽  
Xu-Sha Wu ◽  
Long-Biao Cui ◽  
Li-Jun Bai ◽  
Shuo-Qiu Gan ◽  
...  
Keyword(s):  
Diffusion Tensor ◽  
First Episode ◽  
Age Difference ◽  
Healthy Controls ◽  
Data Set ◽  
Brain Ageing ◽  
First Episode Schizophrenia ◽  
Brain Age ◽  
The Brain ◽  
Age Prediction

Background Neuroimaging- and machine-learning-based brain-age prediction of schizophrenia is well established. However, the diagnostic significance and the effect of early medication on first-episode schizophrenia remains unclear. Aims To explore whether predicted brain age can be used as a biomarker for schizophrenia diagnosis, and the relationship between clinical characteristics and brain-predicted age difference (PAD), and the effects of early medication on predicted brain age. Method The predicted model was built on 523 diffusion tensor imaging magnetic resonance imaging scans from healthy controls. First, the brain-PAD of 60 patients with first-episode schizophrenia, 60 healthy controls and 21 follow-up patients from the principal data-set and 40 pairs of individuals in the replication data-set were calculated. Next, the brain-PAD between groups were compared and the correlations between brain-PAD and clinical measurements were analysed. Results The patients showed a significant increase in brain-PAD compared with healthy controls. After early medication, the brain-PAD of patients decreased significantly compared with baseline (P < 0.001). The fractional anisotropy value of 31/33 white matter tract features, which related to the brain-PAD scores, had significantly statistical differences before and after measurements (P < 0.05, false discovery rate corrected). Correlation analysis showed that the age gap was negatively associated with the positive score on the Positive and Negative Syndrome Scale in the principal data-set (r = −0.326, P = 0.014). Conclusions The brain age of patients with first-episode schizophrenia may be older than their chronological age. Early medication holds promise for improving the patient's brain ageing. Neuroimaging-based brain-age prediction can provide novel insights into the understanding of schizophrenia.

scholarly journals Harmonic Brain Modes: A Unifying Framework for Linking Space and Time in Brain Dynamics

2017 ◽  
Vol 24 (3) ◽  
pp. 277-293 ◽  
Author(s):  
Selen Atasoy ◽  
Gustavo Deco ◽  
Morten L. Kringelbach ◽  
Joel Pearson
Keyword(s):  
Neural Activity ◽  
Brain Activity ◽  
Activity Patterns ◽  
Structural Connectivity ◽  
Building Blocks ◽  
Mental States ◽  
Brain Dynamics ◽  
Space And Time ◽  
Wide Range ◽  
The Brain

A fundamental characteristic of spontaneous brain activity is coherent oscillations covering a wide range of frequencies. Interestingly, these temporal oscillations are highly correlated among spatially distributed cortical areas forming structured correlation patterns known as the resting state networks, although the brain is never truly at “rest.” Here, we introduce the concept of harmonic brain modes—fundamental building blocks of complex spatiotemporal patterns of neural activity. We define these elementary harmonic brain modes as harmonic modes of structural connectivity; that is, connectome harmonics, yielding fully synchronous neural activity patterns with different frequency oscillations emerging on and constrained by the particular structure of the brain. Hence, this particular definition implicitly links the hitherto poorly understood dimensions of space and time in brain dynamics and its underlying anatomy. Further we show how harmonic brain modes can explain the relationship between neurophysiological, temporal, and network-level changes in the brain across different mental states ( wakefulness, sleep, anesthesia, psychedelic). Notably, when decoded as activation of connectome harmonics, spatial and temporal characteristics of neural activity naturally emerge from the interplay between excitation and inhibition and this critical relation fits the spatial, temporal, and neurophysiological changes associated with different mental states. Thus, the introduced framework of harmonic brain modes not only establishes a relation between the spatial structure of correlation patterns and temporal oscillations (linking space and time in brain dynamics), but also enables a new dimension of tools for understanding fundamental principles underlying brain dynamics in different states of consciousness.

Planning Method for Safety Neurosurgical and Computed Tomography Contrast-Data Set Visualization

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Husham Farouk Ismail Saied
Keyword(s):  
Computed Tomography ◽  
Tracking System ◽  
Optical Tracking ◽  
Planning System ◽  
Surgical Instrument ◽  
Optical Tracking System ◽  
Data Set ◽  
Strict Adherence ◽  
Surgical Access ◽  
The Brain

Discussed the issues' associated with the development of a computed neurosurgery planning system. An important part is to determine the value of invasive surgical access. The study purpose is to design a methodology for finding the shortest distance between surgical target and peripheral point of the brain tissue with strict adherence considering the type of the brain anatomical structure existing in the path of surgical track (risk map), these two condition used in companion to determine the risk value of the surgical access. The study method consists of two algorithms for calculating the shortest surgical access to the target and assuring the safety by avoiding high-density tissues identification method “internal map” describing the anatomy of the brain such as bones. An algorithm for automatic identification of brain vascular system also was designed. The structural diagram of the contrast data visualization system, using computed tomography data, was thoroughly discussed. Also, trying to contribute in solving issues facing developers of modern medical imaging visualization systems to select the most appropriate method from the whole arsenal of algorithms and processing models concerning displaying brain surgical zone using image registration and optical tracking system. The visualization of the target zone is carried out according to an internal reference landmark points inside the center of the brain as well as an automatic algorithm for contour recognition was applied. Moreover, the optical tracking system was used to assess the navigation accuracy of determining the position of the surgical instrument outside the patient head. Algorithms necessary for operational planning also was included, and the proposed method was applied in a pilot study with simulation mode to human brain model, in order to target a specific surgical zone, and as a result, the system suggested (24) possible surgical track, among them, were selected the best and safest access. The total error of a surgical instrument targeting was less than 3 mm (in average 2.6 mm).

scholarly journals On the methods for reactivation and replay analysis

2020 ◽  
Vol 375 (1799) ◽  
pp. 20190231 ◽  
Author(s):  
David Tingley ◽  
Adrien Peyrache
Keyword(s):  
Neural Activity ◽  
Temporal Structure ◽  
Activity Patterns ◽  
Large Body ◽  
Neural Data ◽  
History Of ◽  
Methodological Approaches ◽  
Neuronal Patterns ◽  
External Stimuli ◽  
The Brain

A major task in the history of neurophysiology has been to relate patterns of neural activity to ongoing external stimuli. More recently, this approach has branched out to relating current neural activity patterns to external stimuli or experiences that occurred in the past or future. Here, we aim to review the large body of methodological approaches used towards this goal, and to assess the assumptions each makes with reference to the statistics of neural data that are commonly observed. These methods primarily fall into two categories, those that quantify zero-lag relationships without examining temporal evolution, termed reactivation , and those that quantify the temporal structure of changing activity patterns, termed replay . However, no two studies use the exact same approach, which prevents an unbiased comparison between findings. These observations should instead be validated by multiple and, if possible, previously established tests. This will help the community to speak a common language and will eventually provide tools to study, more generally, the organization of neuronal patterns in the brain. This article is part of the Theo Murphy meeting issue ‘Memory reactivation: replaying events past, present and future’.

scholarly journals Stimulus-specific information is represented as local activity patterns across the brain

NeuroImage ◽  
2020 ◽  
Vol 223 ◽  
pp. 117326 ◽  
Author(s):  
Amirouche Sadoun ◽  
Tushar Chauhan ◽  
Samir Mameri ◽  
Yi  Fan Zhang ◽  
Pascal Barone ◽  
...  
Keyword(s):  
Activity Patterns ◽  
Specific Information ◽  
Local Activity ◽  
The Brain

Spinomuscular Coherence in Monkeys Performing a Precision Grip Task

2008 ◽  
Vol 99 (4) ◽  
pp. 2012-2020 ◽  
Author(s):  
Tomohiko Takei ◽  
Kazuhiko Seki
Keyword(s):  
Spinal Cord ◽  
Sensorimotor Integration ◽  
Unique Feature ◽  
Cervical Spinal Cord ◽  
Precision Grip ◽  
Muscle Physiology ◽  
Field Potentials ◽  
Spinal Interneurons ◽  
Arm Muscles ◽  
The Brain

We recorded local field potentials (LFPs) from cervical spinal cord (C5–C8) in monkeys performing a precision grip task and examined their coherence with electromyographic (EMG) activities (spinomuscular coherence) recorded from hand and arm muscles. Among 164 LFP-EMG pairs, significant coherence was found in 34 pairs (21%). We classified the coherence into two groups based on its frequency range, narrowband coherence, and broadband coherence. The narrowband coherence was restricted to discrete frequencies in the range of 14–55 Hz and was widespread throughout the superficial and deep gray matter. In contrast, the broadband coherence distributed between 10 and 95 Hz and was found only in the ventral half of the spinal cord. The narrowband coherence suggests that oscillations, which have been described in many motor control areas of the brain, could also pass though spinal interneurons to affect motor output and sensorimotor integration. On the other hand, the broadband coherence could be a unique feature of spinal motoneuron-muscle physiology.

Estimating the Effects of Nanoparticles on Neuronal Field Potentials Based on Their Effects on Single Neurons In Vitro

2018 ◽  
pp. 149-169
Author(s):  
Michael Busse ◽  
Narsis Salafzoon ◽  
Annette Kraegeloh ◽  
David R. Stevens ◽  
Daniel J. Strauss
Keyword(s):  
Field Potentials ◽  
Single Neurons

Normal Aging and the Brain

Physiology ◽  
1988 ◽  
Vol 3 (5) ◽  
pp. 197-200
Author(s):  
R Katzman
Keyword(s):  
Episodic Memory ◽  
Normal Aging ◽  
Intelligence Tests ◽  
Neural Basis ◽  
Very Old ◽  
Brain Processing ◽  
The Brain

During normal aging, cognition as measured by intelligence tests is remarkably preserved, although most of the very old show significant slowing of brain processing and mild loss of episodic memory. The neural basis for these changes is poorly understood.

scholarly journals Functional Aspects of Hypothalamic Asymmetry

2020 ◽  
Vol 10 (6) ◽  
pp. 389
Author(s):  
David Sandor Kiss ◽  
Istvan Toth ◽  
Gergely Jocsak ◽  
Zoltan Barany ◽  
Tibor Bartha ◽  
...  
Keyword(s):  
Hemispheric Specialization ◽  
Functional Asymmetry ◽  
Vast Number ◽  
Brain Functions ◽  
Functional Aspects ◽  
Functional Lateralization ◽  
High Level ◽  
Brain Processing ◽  
Higher Brain Functions ◽  
The Brain

Anatomically, the brain is a symmetric structure. However, growing evidence suggests that certain higher brain functions are regulated by only one of the otherwise duplicated (and symmetric) brain halves. Hemispheric specialization correlates with phylogeny supporting intellectual evolution by providing an ergonomic way of brain processing. The more complex the task, the higher are the benefits of the functional lateralization (all higher functions show some degree of lateralized task sharing). Functional asymmetry has been broadly studied in several brain areas with mirrored halves, such as the telencephalon, hippocampus, etc. Despite its paired structure, the hypothalamus has been generally considered as a functionally unpaired unit, nonetheless the regulation of a vast number of strongly interrelated homeostatic processes are attributed to this relatively small brain region. In this review, we collected all available knowledge supporting the hypothesis that a functional lateralization of the hypothalamus exists. We collected and discussed findings from previous studies that have demonstrated lateralized hypothalamic control of the reproductive functions and energy expenditure. Also, sporadic data claims the existence of a partial functional asymmetry in the regulation of the circadian rhythm, body temperature and circulatory functions. This hitherto neglected data highlights the likely high-level ergonomics provided by such functional asymmetry.

Electrophysiology in Neuropsychiatric Research: A Network Perspective

CNS Spectrums ◽  
1999 ◽  
Vol 4 (8) ◽  
pp. 17-29 ◽  
Author(s):  
Georg Winterer ◽  
Werner M. Herrmann ◽  
Richard Coppola
Keyword(s):  
Network Model ◽  
Network Models ◽  
Disease Classification ◽  
Pharmacodynamic Modeling ◽  
Neural Network Models ◽  
New Concepts ◽  
Discrete Processing ◽  
Motor Information ◽  
Brain Processing ◽  
The Brain

ABSTRACTA growing number of anatomic and physiologic studies have shown that parallel sensory and motor information processing occurs in multiple cortical areas. These findings challenge the traditional model of brain processing, which states that the brain is a collection of physically discrete processing modules that pass information to each other by neuronal impulses in a stepwise manner. New concepts based on neural network models suggest that the brain is a dynamically shifting collection of interpenetrating, distributed, and transient neural networks. Neither of these models is necessarily mutually exclusive, but each gives different perspectives on the brain that might be complementary. Each model has its own research methodology, with functional magnetic resonance imaging supporting notions of modular processing, and electrophysiology (eg, electroencephalography) emphasizing the network model. These two technologies might be combined fruitfully in the near future to provide us with a better understanding of the brain. However, this common enterprise can succeed only when the inherent limitations and advantages of both models and technologies are known. After a general introduction about electrophysiology as a research tool and its relation to the network model, several practical examples are given on the generation of pathophysiologic models and disease classification, intermediate phenotyping for genetic investigations, and pharmacodynamic modeling. Finally, proposals are made about how to integrate electrophysiology and neuroimaging methods.

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