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Background: Neuroplasticity refers to the inherently dynamic biological capacity of the central nervous system (CNS) to undergo maturation, change structurally and functionally in response to experience and to adapt following injury. This malleability is achieved by modulating subsets of genetic, molecular and cellular mechanisms that influence the dynamics of synaptic connections and neural circuitry formation culminating in gain or loss of behavior or function. Neuroplasticity in the healthy developing brain exhibits a heterochronus cortex-specific developmental profile and is heightened during "critical and sensitive periods" of pre and postnatal brain development that enable the construction and consolidation of experience-dependent structural and functional brain connections.

Purpose: In this review, our primary goal is to highlight the essential role of neuroplasticity in brain development, and to draw attention to the complex relationship between different levels of the developing nervous system that are subjected to plasticity in health and disease. Another goal of this review is to explore the relationship between plasticity responses of the developing brain and how they are influenced by critical and sensitive periods of brain development. Finally, we aim to motivate researchers in the pediatric neuromodulation field to build on the current knowledge of normal and abnormal neuroplasticity, especially synaptic plasticity, and their dependence on "critical or sensitive periods" of neural development to inform the design, timing and sequencing of neuromodulatory interventions in order to enhance and optimize their translational applications in childhood disorders of the brain.

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Results: We discuss in details five patterns of neuroplasticity expressed by the developing brain: 1) developmental plasticity which is further classified into normal and impaired developmental plasticity as seen in syndromic autism spectrum disorders, 2) adaptive (experience-dependent) plasticity following intense motor skill training, 3) reactive plasticity to pre and post natal CNS injury or sensory deprivation, 4) excessive plasticity (loss of homeostatic regulation) as seen in dystonia and refractory epilepsy, 6) and finally, plasticity as the brain's "Achilles tendon" which induces brain vulnerability under certain conditions such as hypoxic ischemic encephalopathy and epileptic encephalopathy syndromes. We then explore the unique feature of "time-sensitive heightened plasticity responses" in the developing brain in the in the context of neuromodulation.

Conclusion: The different patterns of neuroplasticity and the unique feature of heightened plasticity during critical and sensitive periods are important concepts for researchers and clinicians in the field of pediatric neurology and neurodevelopmental disabilities. These concepts need to be examined systematically in the context of pediatric neuromodulation. We propose that critical and sensitive periods of brain development in health and disease can create "windows of opportunity" for neuromodulatory interventions that are not commonly seen in adult brain and probably augment plasticity responses and improve clinical outcomes.

Assume that you have a multi-site failover cluster that has a file share witness that's located on one of the sites. If connectivity is lost between the sites, the cluster may go into a "split brain" situation in which both sites think that the other side is down.

You can use this topic to learn how to configure DNS policy in Windows Server 2016 for split-brain DNS deployments, where there are two versions of a single zone - one for the internal users on your organization intranet, and one for the external users, who are typically users on the Internet.

Previously, this scenario required that DNS administrators maintain two different DNS servers, each providing services to each set of users, internal and external. If only a few records inside the zone were split-brained or both instances of the zone (internal and external) were delegated to the same parent domain, this became a management conundrum.

Another configuration scenario for split-brain deployment is Selective Recursion Control for DNS name resolution. In some circumstances, the Enterprise DNS servers are expected to perform recursive resolution over the Internet for the internal users, while they also must act as authoritative name servers for external users, and block recursion for them.

A CT scan provides a 3-dimensional map based on variations of density of anatomical structures. This CT data can be presented in different ways to view structures within specific ranges of density. 'Brain windows' are used to view a range of densities close to the average density of the soft tissues of the brain. 'Bone windows' are used to emphasise a narrow range of densities close to the density of bone.

In the past, some organizations would deploy separate DNS servers hosting different copies of the same zone to achieve a split-brain configuration. A DNS server on an internal network would host a version of the zone that had all hostname mappings with the IP addresses that should be returned to internal clients. A DNS server on the perimeter network, or even hosted at the ISP, would host the version of the zone that returned hostnames with public IP addresses.

You can implement split brain DNS on Windows Server 2016 and Windows Server 2019 using two new features known as DNS policies and DNS Zone scopes. DNS policies allow you to customize DNS server responses based on the properties of the requestor. DNS Zone scopes allow you to create different subset collections of DNS zone records, with each zone supporting multiple zone scopes and DNS records being able to be members of multiple zone scopes.

When creating a DNS policy to implement split brain DNS, you need to first configure DNS zone scopes with one zone scope containing the host records that should be returned to an external client and another DNS zone scope containing host records that should be returned to internal clients. Once you have these two zone scopes, you then need to configure DNS policies, one to return records from DNS zone scope to be used by external clients, the other to return records from the DNS zone scope to be used by internal clients.

To get more detail on the process of creating split brain or split horizon zones on DNS servers running Windows Server 2016 or Windows Server 2019, consult the following docs.microsoft.com article: -us/windows-server/networking/dns/deploy/split-brain-dns-deployment?WT....

Due to the cortical plasticity of the brain, signals from implanted prostheses can, after adaptation, be handled by the brain like natural sensor or effector channels.[5] Following years of animal experimentation, the first neuroprosthetic devices implanted in humans appeared in the mid-1990s.

Recently, studies in human-computer interaction via the application of machine learning to statistical temporal features extracted from the frontal lobe (EEG brainwave) data has had high levels of success in classifying mental states (relaxed, neutral, concentrating),[6] mental emotional states (negative, neutral, positive),[7] and thalamocortical dysrhythmia.[8]

Although the term had not yet been coined, one of the earliest examples of a working brain-machine interface was the piece Music for Solo Performer (1965) by the American composer Alvin Lucier. The piece makes use of EEG and analog signal processing hardware (filters, amplifiers, and a mixing board) to stimulate acoustic percussion instruments. To perform the piece one must produce alpha waves and thereby "play" the various percussion instruments via loudspeakers which are placed near or directly on the instruments themselves.[9]

In 1990, a report was given on a closed loop, bidirectional adaptive BCI controlling computer buzzer by an anticipatory brain potential, the Contingent Negative Variation (CNV) potential.[19][20] The experiment described how an expectation state of the brain, manifested by CNV, controls in a feedback loop the S2 buzzer in the S1-S2-CNV paradigm. The obtained cognitive wave representing the expectation learning in the brain is named Electroexpectogram (EXG). The CNV brain potential was part of the BCI challenge presented by Vidal in his 1973 paper.

Neuroprosthetics is an area of neuroscience concerned with neural prostheses, that is, using artificial devices to replace the function of impaired nervous systems and brain-related problems, or of sensory organs or organs itself (bladder, diaphragm, etc.). As of December 2010, cochlear implants had been implanted as neuroprosthetic device in approximately 220,000 people worldwide.[27] There are also several neuroprosthetic devices that aim to restore vision, including retinal implants. The first neuroprosthetic device, however, was the pacemaker.

Studies that developed algorithms to reconstruct movements from motor cortex neurons, which control movement, date back to the 1970s. In the 1980s, Apostolos Georgopoulos at Johns Hopkins University found a mathematical relationship between the electrical responses of single motor cortex neurons in rhesus macaque monkeys and the direction in which they moved their arms (based on a cosine function). He also found that dispersed groups of neurons, in different areas of the monkey's brains, collectively controlled motor commands, but was able to record the firings of neurons in only one area at a time, because of the technical limitations imposed by his equipment.[34]

There has been rapid development in BCIs since the mid-1990s.[35] Several groups have been able to capture complex brain motor cortex signals by recording from neural ensembles (groups of neurons) and using these to control external devices.

In 1999, researchers led by Yang Dan at the University of California, Berkeley decoded neuronal firings to reproduce images seen by cats. The team used an array of electrodes embedded in the thalamus (which integrates all of the brain's sensory input) of sharp-eyed cats. Researchers targeted 177 brain cells in the thalamus lateral geniculate nucleus area, which decodes signals from the retina. The cats were shown eight short movies, and their neuron firings were recorded. Using mathematical filters, the researchers decoded the signals to generate movies of what the cats saw and were able to reconstruct recognizable scenes and moving objects.[36] Similar results in humans have since been achieved by researchers in Japan (see below). 2351a5e196