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What electrophysiology tells us about Alzheimer’s disease: a window into the synchronization and connectivity of brain neurons
آنچه الکتروفیزیولوژی در مورد بیماری آلزایمر به ما می گوید: پنجره ای برای هماهنگ سازی و اتصال نورون های مغز-2020 Electrophysiology provides a real-time readout of neural functions and network capability in different
brain states, on temporal (fractions of milliseconds) and spatial (micro, meso, and macro) scales unmet
by other methodologies. However, current international guidelines do not endorse the use of electroencephalographic
(EEG)/magnetoencephalographic (MEG) biomarkers in clinical trials performed in
patients with Alzheimer’s disease (AD), despite a surge in recent validated evidence. This position paper
of the ISTAART Electrophysiology Professional Interest Area endorses consolidated and translational
electrophysiological techniques applied to both experimental animal models of AD and patients, to probe
the effects of AD neuropathology (i.e., brain amyloidosis, tauopathy, and neurodegeneration) on
neurophysiological mechanisms underpinning neural excitation/inhibition and neurotransmission as
well as brain network dynamics, synchronization, and functional connectivity, reflecting thalamocortical
and corticocortical residual capacity. Converging evidence shows relationships between abnormalities in
EEG/MEG markers and cognitive deficits in groups of AD patients at different disease stages. The supporting
evidence for the application of electrophysiology in AD clinical research as well as drug discovery
pathways warrants an international initiative to include the use of EEG/MEG biomarkers in the main
multicentric projects planned in AD patients, to produce conclusive findings challenging the present
regulatory requirements and guidelines for AD studies. Keywords: The Alzheimer’s Association International | Society to Advance Alzheimer’s Research | and Treatment (ISTAART) | Alzheimer’s disease (AD) | Electroencephalography and | magnetoencephalography (EEG and MEG) | Resting-state condition | Event-related potentials and magnetic fields | Preclinical and clinical research |
مقاله انگلیسی |
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The release of exosomes in the medial prefrontal cortex and nucleus accumbens brain regions of chronic constriction injury (CCI) model mice could elevate the pain sensation
انتشار اگزوزومها در قشر جلوی مغز داخلی و هسته مجذور مناطق مغزی از آسیب مدل انقباضی مزمن (CCI) موش مدل می تواند احساس درد را بالا ببرد-2020 Background: Brain function relies on the capacity of neurons to locally modulate each other at the level of synapses. Therefore, the exosomal pathway may constitute a well-designed mechanism for local and systemic interneuronal transfer of information within functional brain networks. Exosomes bind to and are endocytosed by neurons of different brain regions to play a definite role. The medial prefrontal cortex (mPFC) and nucleus accumbens (NAc) brain regions are known to involve in pain modulation. Our study observes the roles of exosomal activity in these two dominant regions of the pain-related pathway, and there influence on the analgesic effects in CCI mice.
Methods: We induced pain exosomes in the mPFC and NAc in the mice of chronic constriction injury of the sciatic nerve model to produce neuropathic pain, and
assessed changes that might affect analgesic behaviors. These changes were measured through a combination of behavioral, surgical, and other cellular testings. Results: Our study found that pain expression was elevated in mice given exogenous exosomes isolated from CCI mice, especially at the 2 h and 4 h time interval, in mice given exosomes at the mPFC and NAc, respectively. We also found that inhibiting formation of pain exosomes through GW4869 within the mPFC and NAc can elevate the pain threshold.
Conclusion: Results from our study supported the idea that the release of mPFC and NAc exosomes of CCI model has elevated the pain sensations in the subjected mice. This study will further help in designing new clinical trials, and will revolutionize the drug-induced anesthetic responses. |
مقاله انگلیسی |
3 |
Network properties of healthy and Alzheimer brains
خواص شبکه مغز سالم و آلزایمر-2020 The application of graph theory in diffusion weighted resonance magnetic images have
allowed the description of the brain as a complex network, often called structural network.
For many years, the small-world properties of brain networks have been studied
and reported. However, few studies have gone beyond of clustering and characteristic
path length. In this work, we compare the structural connection network of a healthy
brain and a brain affected by Alzheimer’s disease with artificial small-world networks.
Based on statistical analysis, we demonstrate how artificial networks can be constructed
using Newman–Watts procedure. The network quantifiers of both structural matrices
are identified inside a probabilistic valley. Despite of similarities between structural
connection matrices and artificial small-world networks, increased assortativity can be
found in the Alzheimer brain. Due to limited experimental data, we cannot define
a direct link between Alzheimer’s disease and assortativity. Nevertheless, we intend
to call attention for an important network quantifier that has been neglected. Our
results indicate that network quantifiers can be helpful to identify abnormalities in real
structural connections, for instance Alzheimer’s disease that disrupts the communication
among neurons. One of our main results is to show that the network indicators of
the Alzheimer brain are almost identical with the small-world network, except the
assortativity. Keywords: Network | Human brain | Alzheimer’s disease | Small-world |
مقاله انگلیسی |
4 |
Dynamic changes of brain networks during feedback-related processing of reinforcement learning in schizophrenia
تغییرات پویا شبکه های مغزی در طی پردازش مربوط به بازخورد یادگیری تقویت در اسکیزوفرنی-2020 Previous studies have reported that schizophrenia (SZ) patients showed selective reinforcement learning deficits
and abnormal feedback-related event-related potential (ERP) components. However, how the brain networks
and their topological properties evolve over time during transient feedback-related cognition processing in SZ
patients has not been investigated so far. In this paper, using publicly available feedback-related ERP data which
were recorded from SZ patients and healthy controls (HC) when they performed a reinforcement learning task,
we carried out an event-related network analysis where topology of brain functional networks was characterized
with some graph measures including clustering coefficient (C), global efficiency (Eglobal) and local efficiency
(Elocal) on a millisecond timescale. Our results showed that the brain functional networks displayed rapid rearrangements
of topological properties during transient feedback-related cognition process for both two groups.
More importantly, we found that SZ patients exhibited significantly reduced theta-band (time window of
170–350 ms after stimuli onset) brain functional connectivity strength, Eglobal, Elocal and C in response to negative
feedback stimuli compared to HC group. The network based statistic (NBS) analysis detected one significantly
decreased theta-band subnetwork in SZ patients mainly involving in frontal-occipital and temporal-occipital
connections compared to HC group. In addition, clozapine treatment seemed to greatly reduce theta-band power
and topological measures of brain networks in SZ patients. Finally, the theta-band power, graph measures and
functional connectivity were extracted to train a support vector machine classifier for classification of HC from
SZ, or Cloz + SZ or Cloz- SZ, and a relatively good classification accuracy of 84.48%, 89.47% and 78.26% was
obtained, respectively. The above results suggested a less optimal organization of theta-band brain network in SZ
patients, and studying the topological parameters of brain networks evolve over time during transient feedbackrelated
processing could be useful for understanding the pathophysiologic mechanisms underlying reinforcement
learning deficits in SZ patients. Keywords: Event-related network analysis | Support vector machine | Graph measures | Reinforcement learning | Schizophrenia |
مقاله انگلیسی |
5 |
Interaction of neuronal and network mechanisms on firing propagation in a feedforward network
تعامل مکانیسم های عصبی و شبکه بر انتشار سریع در یک شبکه feedforward-2020 The mammalian brain has enormously complex neuronal diversity and a highly modular structure.
The propagation of information in the modular brain network can be modeled by a feedforward network
(FFN). Although studies in this area have yielded many important results, neuronal diversity
has rarely been considered. In the current work, we investigate the complex interactions between the
intrinsic properties of neurons and the FFN structure in the propagation of spiking activity. Here,
four typical types of cortical neurons reproduced by the Izhikevich neuron model are introduced. A
homogeneous FFN composed of a single type of excitatory neuron (regular spiking, mixed model,
or tonic bursting) can propagate spiking activity. However, an FFN with fast spiking neurons does
not propagate spiking activity. By modifying the network structure and synaptic weights, the spiking
propagation of the homogeneous FFNs can vary from synchronous transmission (with a high firing
rate) to asynchronous transmission (with a low firing rate). Among the homogeneous FFNs, both the
firing rate and the synchrony of the FFN with tonic bursting neurons are the highest, but those of
the FFN with regular spiking neurons is lowest, even when implementing the same FFN structure.
For the FFN with mixed neuronal types, interestingly, the spiking propagation is very sensitive to
the composition of the four types of neurons. By introducing fast spiking neurons into the homogeneous
FFN composed of excitatory neurons, spiking propagation can be modified from synchronous
to asynchronous. Similarly, changing the proportion of any of the types of neuron affects the spiking
propagation, even for very small changes. The underlying mechanism of these observed results has
also been discussed. Keywords: Feedforward network | Neuronal diversity | Spiking propagation | Izhikevich neuron |
مقاله انگلیسی |
6 |
EEG intentions recognition in dynamic complex object control task byfunctional brain networks and regularized discriminant analysis
تشخیص اهداف EEG در شبکه کنترل عملکردی پیچیده پویا شبکه های مغزی و عملکرد تجزیه و تحلیل تبعیض آمیز منظم-2020 Most of the tasks involved in neurological rehabilitation are generally constrained. However the complex-ity of experimental paradigms with constraints for acquiring electroencephalography (EEG) are relativelylow. In order to improve the level of arousal in the brain, we propose an energy-constrained dynamicand complex experimental paradigm. In this novel experimental paradigm, EEG signals collected underthe novel paradigm have different motor intentions. In this study, we combine the phase synchroniza-tion method and common spatial pattern (CSP) to build functional brain networks of subjects. Based onthis method, we investigate the correlation of the global and local features of the functional brain net-works and use regularized discriminant analysis (RDA) to recognize features. The accuracy obtained bythe proposed method for EEG intention recognition can reach 93.47% (p < 0.01). The results show that theproposed method can decode EEG intention with high recognition performance during the manipulationof complex objects, which lays a foundation for the study of neurological rehabilitation. Keywords:Dynamic complex model | EEG | Regularized discriminant analysis | Functional brain network |
مقاله انگلیسی |
7 |
Macaques Exhibit Implicit Gaze Bias Anticipating Others’ False-Belief-Driven Actions via Medial Prefrontal Cortex
Macaques Exhibit Implicit Gaze Bias Anticipating Others’ False-Belief-Driven Actions via Medial Prefrontal Cortex-2020 The ability to infer others’ mental states is essential to
social interactions. This ability, critically evaluated by
testing whether one attributes false beliefs (FBs) to
others, has been considered to be uniquely hominid
and to accompany the activation of a distributed
brain network. We challenge the taxon specificity of
this ability and identify the causal brain locus by
introducing an anticipatory-looking FB paradigm
combined with chemogenetic neuronal manipulation
in macaque monkeys. We find spontaneous gaze
bias of macaques implicitly anticipating others’ FBdriven
actions. Silencing of the medial prefrontal
neuronal activity with inhibitory designer receptor
exclusively activated by designer drugs (DREADDs)
specifically eliminates the implicit gaze bias while
leaving the animals’ visually guided and memoryguided
tracking abilities intact. Thus, neuronal activity
in the medial prefrontal cortex could have a causal
role in FB-attribution-like behaviors in the primate
lineage, emphasizing the importance of probing the
neuronal mechanisms underlying theory of mind
with relevant macaque animal models. |
مقاله انگلیسی |
8 |
Sacral Neuromodulation: Mechanism of Action
Sacral عصب زدایی : مکانیسم عمل-2020 Although the mechanism of action of sacral neuromodulation (SNM) is still not fully
elucidated, it seems to involve modulation of spinal cord reflexes and brain networks by
peripheral afferents according to findings from neurophysiology, electroencephalogra-
phy, positron emission tomography, and magnetic resonance imaging studies. Moreover,
motor effects mediated via efferents on direct stimulation cannot be fully excluded. In
this mini-review, we summarize current knowledge on the mechanism of action of SNM.
Patient summary: We reviewed the literature on the mechanism of action of sacral
neuromodulation, in which electrical stimulation is applied to the nerves that regulate
bladder activity. The mechanism seems to involve modulation of spinal cord reflexes and
brain networks by peripheral sensory and possibly motor neurons. Keywords: neuromodulation | sacral |
مقاله انگلیسی |
9 |
A network view on brain regions involved in experts’ object and pattern recognition: Implications for the neural mechanisms of skilled visual perception
نمای شبکه در مورد مناطق مغز درگیر در تشخیص موضوع و الگوی متخصصان: پیامدهای مکانیسم های عصبی درک بصری ماهر-2019 Skilled visual object and pattern recognition form the basis of many everyday behaviours. The game of chess has
often been used as a model case for studying how long-term experience aides in perceiving objects and their
spatio-functional interrelations. Earlier research revealed two brain regions, posterior middle temporal gyrus
(pMTG) and collateral sulcus (CoS), to be linked to chess experts’ superior object and pattern recognition, respectively.
Here we elucidated the brain networks these two expertise-related regions are embedded in, employing
resting-state functional connectivity analysis and meta-analytic connectivity modelling with the
BrainMap database. pMTG was preferentially connected with dorsal visual stream areas and a parieto-prefrontal
network for action planning, while CoS was preferentially connected with posterior medial cortex and hippocampus,
linked to scene perception, perspective-taking and navigation. Functional profiling using BrainMap
meta-data revealed that pMTG was linked to semantic processing as well as inhibition and attention, while CoS
was linked to face and shape perception as well as passive viewing. Our findings suggest that pMTG subserves
skilled object recognition by mediating the link between object identity and object affordances, while CoS
subserves skilled pattern recognition by linking the position of individual objects with typical spatio-functional
layouts of their environment stored in memory. Keywords: Skilled perception | Chess expertise | Functional connectivity | Resting-state fMRI | MACM | Functional decoding |
مقاله انگلیسی |
10 |
Exploring the fatigue affecting electroencephalography based functional brain networks during real driving in young males
بررسی خستگی مؤثر بر شبکه های عملکردی مغزی مبتنی بر الکترونسفالوگرافی در هنگام رانندگی واقعی در مردان جوان-2019 In recent years, a large proportion of traffic accidents are caused by driver fatigue. The brain has been conceived
as a complex network, whose function can be assessed with EEG. Hence, in this research, fourteen subjects
participated in the real driving experiments, and a comprehensive EEG-based expert system was designed for
detecting driver fatigue. Collected EEG signals were first decomposed into delta-range, theta-range, alpha-range
and beta-range by wavelet packet transform (WPT). Unlike other approaches, a multi-channel network construction
method based on Phase Lag Index (PLI) was then proposed in this paper. Finally, the functional
connectivity between alert state (at the beginning of the drive) and fatigue state (at the end of the drive) in
multiple frequency bands were analyzed. The results indicate that functional connectivity of the brain area was
significantly different between alert and fatigue states, especially in alpha-range and beta-range. Particularly, the
frontal-to-parietal functional connectivity was weakened. Meanwhile, lower clustering coefficient (C) values and
higher characteristic path length (L) values were observed in fatigue state in comparison with alert state. Based
on this, two new EEG feature selection approaches, C and L in the corresponding sub-frequency range were
applied to feature recognition and classification system. Using a support vector machine (SVM) machine learning
algorithm, these features were combined to distinguish between alert and fatigue states, achieving an accuracy
of 94.4%, precision of 94.3%, sensitivity of 94.6% and false alarm rate of 5.7%. The results suggest that brain
network analysis approaches combined with SVM are helpful to alert drivers while being sleepy or even fatigue. Keywords: Electroencephalography (EEG) | Driver fatigue | Phase lag index | Graph theory | Functional connectivity | Brain network |
مقاله انگلیسی |