China 
Africa 
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Medical device implants for neuromodulation
Published in Ze Zhang, Mahmoud Rouabhia, Simon E. Moulton, Conductive Polymers, 2018
Electrical stimulation is the most common form of device-driven neuromodulation. In neurostimulation, the fundamental interaction is between the electric field generated by the stimulating electrode and the brain tissue surrounding it. For the successful delivery of electrical stimulation, three main issues arise. First, stimulation must be delivered to the targeted anatomical location. The neuronal population closest to the stimulating electrode will receive the greatest effect of electrical excitation; thus, a lower amount of current is needed to stimulate neurons close to the electrode. As the distance between the electrode and the targeted population of neurons increases, however, a larger amount of current is required. In such cases, neurons between the electrode and the targeted population are also activated, potentially yielding side effects or altering global brain function in unintended ways.
Intranets of People, Things, and Services
Published in Claire A. Simmers, Murugan Anandarajan, The Internet of People, Things and Services, 2018
In concluding this chapter, it is clear that new and intriguing technologies are on the horizon. Fifth-generation, or 5G, connectivity will provide much greater planetary coverage of information and communication technology, which is already currently being tested for the potential to revolutionize download speed as well as advanced services, such as robotic surgical operations (Cheng, 2015). Internet linkages will become faster and reach a wider area of the world, bringing more users and devices online. One might argue that the sketch of a global brain might be in place in fifteen years, which mirrors human activity. Cyberspace “may be representative of the way humans think in the way that quantum physics is representative of the way bodies interact” (Glassman & Kang, 2010, p. 1413). IoPTS will continue growing and evolving, as will VHRD.
Mapping the Injured Brain
Published in Yu Chen, Babak Kateb, Neurophotonics and Brain Mapping, 2017
Chandler Sours, Jiachen Zhuo, Rao P. Gullapalli
Using a resting-state paradigm, it is possible to examine functional brain networks to measure the interaction between global brain regions in disparate locations. In this method, participants are instructed to rest in the MRI scanner and are not required to participate in a task. Referred to as resting state functional connectivity (rs-FC), this method measures the strength of functional interactions between brain regions based on temporal correlations between small fluctuations in the BOLD signal (Biswal et al., 1995; Sporns 2011; van den Heuvel and Hulshoff, Pol 2010) (Figure 14.3c). While historically these small fluctuations in BOLD signal were thought to be signal noise, it was noted that regions recruited to perform specific tasks displayed similar temporal patterns of fluctuations during resting conditions (Biswal et al., 1995). Analysis of functional connectivity can be performed using a hypothesis seed-based method or a data-driven independent component analysis method (Calhoun et al., 2009). Regardless of which method is selected, resting-state networks are consistently replicated across studies and often include networks that are associated with sensory systems (auditory, visual, somatosensory, and motor) as well as networks associated with higher-order cognitive processes (Raichle, 2010). Understanding the differences in neural network communications related to postconcussive symptoms among TBI patients will provide valuable information on the precise mechanisms of these deficits.
A Novel Machine Learning Based Framework for Detection of Autism Spectrum Disorder (ASD)
Published in Applied Artificial Intelligence, 2022
Hamza Sharif, Rizwan Ahmed Khan
Until now, biomarkers of ASD are unknown (Del Valle Rubido et al. 2018; Jaliaawala and Khan 2019). Physicians and clinicians are practicing standardized/conventional methods for ASD analysis and diagnosis. Intellectual properties and behavioral characteristics are accessed for the diagnosis of ASD; however, synaptic affiliations of ASD are still unknown and presents a challenging task for cognitive neuroscience and psychological researchers (Kushki et al. 2013). A recent hypothesis in neurology demonstrates that an abnormal trend is associated with different neural regions of the brain among individuals facing ASD (Bourgeron 2009). This variational trend is due to irregularities in neural pattern, disassociation and anti-correlation of cognitive function between different regions that affect the global brain network (Schipul, Keller, and Just 2011).
Radical systems thinking and the future role of computational modelling in ergonomics: an exploration of agent-based modelling
Published in Ergonomics, 2020
Matt Holman, Guy Walker, Terry Lansdown, Adam Hulme
Whilst the theory of SAI provides some assurance against the likelihood of a technological singularity in the near future, it is difficult to ignore the resemblance of the distributed network of IoT capable devices to a ‘global SAI’ system of sorts. A global SAI, enabled by the breadth and depth of the pervasion of IoT devices which gather and transmit data, represents a networked system of decomposed intelligent agents whose collective activity creates a form of emergent intelligence. This idea (which predates 4IR technology) has been referred to as the Global Brain (Bernstein, Klein, and Malone 2012; Mayer‐Kress and Barczys 1995). In this concept, the requisite ‘body’ already exists as the totality of human and technological ‘sensors’ working in tandem to continuously gather and transmit information about the real world, whilst the internet is the functional architecture which links these ‘sensors’ into an interconnected network. The Global Brain hypothesis neatly circumvents the premise that true intelligence requires equally powerful artificial sensorimotor systems; they are already here.
Brain activation associated with eccentric movement: A narrative review of the literature
Published in European Journal of Sport Science, 2018
More recently, four fMRI studies from three independent research labs investigated differences in recruited cortical regions during eccentric and concentric muscle actions with real or imagined movements of the upper limbs (see Table I). The advantage of the fMRI method is its higher spatial resolution compared to EEG. The first BOLD (blood oxygen level dependent) data from fMRI in Yue et al. (2000) have confirmed with a similar experimental design the EEG findings for thumb movements. Global brain activation volume required during eccentric movement was twice higher as compared to concentric movement at submaximal intensity levels. The larger activation volume was observed in the contralateral primary sensorimotor (S1) and M1 cortices and bilateral SMA during thumb eccentric movement. Performing gradual eccentric muscle actions of the wrist extensors resulted in greater fMRI BOLD signal magnitudes as compared to concentric muscle actions for several cortical areas (parietal lobe, pre-SMA, prefrontal cortex -PFC- and cerebellum) (Kwon & Park, 2011). By applying functional brain connectivity method to several BOLD fMRI signals, Yao et al. (2016) were the first to reveal that eccentric movement (right-hand first dorsal interosseous – FDI – muscle actions) presents a weaker cross-correlation of activation between contralateral M1 and other cortical motor regions. Finally, a higher bilateral PFC activation was observed during eccentric movements in all neuroimaging studies that have monitored PFC activity (Fang et al., 2004; Kwon & Park, 2011; Olson, Hedlund, Sojka, Lundström, & Lindström, 2012).