Brain connectivity
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Deep brain stimulation (DBS) targeting the subthalamic nucleus (STN) represents a powerful clinical tool for the alleviation of many motor symptoms that are associated with Parkinson's disease. Despite its extensive use, the underlying therapeutic mechanisms of STN-DBS remain poorly understood. ⋯ These techniques enable the noninvasive detection of brain regions that are modulated by DBS on a whole-brain scale, representing a key experimental strength given the diffuse and far-reaching effects of electrical field stimulation. By examining these data in the context of multiple hypotheses of DBS action, generally developed through clinical and physiological observations, we define a multitude of consistencies and inconsistencies in the developing literature of this rapidly moving field.
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Review Historical Article
Anatomical brain networks on the prediction of abnormal brain states.
Graph-based brain anatomical network analysis models the brain as a graph whose nodes represent structural/functional regions, whereas the links between them represent nervous fiber connections. Initial studies of brain anatomical networks using this approach were devoted to describe the key organizational principles of the normal brain, while current trends seem to be more focused on detecting network alterations associated to specific brain disorders. ⋯ This article offers an overview from early gross connectional anatomy explorations until more recent advances on anatomical brain network reconstruction approaches, with a specific focus on how the latter move toward the prediction of abnormal brain states. While anatomical graph-based predictor approaches are still at an early stage, they bear promising implications for individualized clinical diagnosis of neurological and psychiatric disorders, as well as for neurodevelopmental evaluations and subsequent assisted creation of educational strategies related to specific cognitive disorders.
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General anesthesia consists of amnesia, hypnosis, analgesia, and areflexia. Of these, the mechanism of hypnosis, or loss of consciousness, has been the most elusive, yet a fascinating problem. How anesthetic agents suppress human consciousness has been investigated with neuroimaging for two decades. ⋯ The changes in functional connectivity during anesthesia induction and emergence do not mirror each other; the recovery from anesthesia may involve increases in functional connectivity above the normal wakeful baseline. Anesthetic loss of consciousness is not a block of corticofugal information transfer, but a disruption of higher-order cortical information integration. The prime candidates for functional networks of the forebrain that play a critical role in maintaining the state of consciousness are those based on the posterior parietal-cingulate-precuneus region and the nonspecific thalamus.
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A primary objective in neuroscience is to determine how neuronal populations process information within networks. In humans and animal models, functional magnetic resonance imaging (fMRI) is gaining increasing popularity for network mapping. Although neuroimaging with fMRI-conducted with or without tasks-is actively discovering new brain networks, current fMRI data analysis schemes disregard the importance of the total neuronal activity in a region. ⋯ Thus, another alternative for quantitative neuroimaging of fMRI-based networks is a perspective in which the activity of a neuronal population is accounted for by the demanded oxidative energy (CMR(O2)). In this article, we argue that network mapping can be improved by including neuronal energy/activity of both the information about baseline and small differences/fluctuations of BOLD signal. Thus, total energy/activity information can be obtained through use of calibrated fMRI to quantify differences of ΔCMR(O2) and through resting-state positron emission tomography/magnetic resonance spectroscopy measurements for average CMR(O2).