Neuroscience and biobehavioral reviews
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Neurosci Biobehav Rev · Apr 2018
Serotonergic psychedelics and personality: A systematic review of contemporary research.
Serotonergic psychedelics act as agonists at cortical 5-HT2A receptors and seem to induce personality changes. We conducted a systematic review of studies assessing the effects of these drugs on personality. Papers published from 1985-2016 were included from PubMed, LILACS, and SciELO databases. ⋯ Increases in global brain entropy induced by acute psychedelic administration predicted changes in Openness, and Self-Transcendence was negatively correlated with cortical thinning of the posterior cingulate cortex in long-term religious ayahuasca users. Acute and long-term use of psychedelics is associated with personality changes that appear to be modulated by 5-HT2A receptors. These changes seem to induce therapeutic effects that should be further explored in randomized controlled studies.
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Neurosci Biobehav Rev · Mar 2018
Meta AnalysisMultisensory temporal binding window in autism spectrum disorders and schizophrenia spectrum disorders: A systematic review and meta-analysis.
Multisensory temporal integration could be compromised in both autism spectrum disorders (ASD) and schizophrenia spectrum disorders (SSD) and may play an important role in perceptual and cognitive impairment in these two disorders. This review aimed to quantitatively compare the sensory temporal acuity between healthy controls and the two clinical groups (ASD and SSD). ⋯ Such multisensory dysfunction is associated with symptoms like hallucinations and impaired social communications. Future studies focusing on improving multisensory temporal functions may have important implications for the amelioration of schizophrenia and autistic symptoms.
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Neurosci Biobehav Rev · Jan 2018
ReviewGlymphatic system disruption as a mediator of brain trauma and chronic traumatic encephalopathy.
Traumatic brain injury (TBI) is an increasingly important issue among veterans, athletes and the general public. Difficulties with sleep onset and maintenance are among the most commonly reported symptoms following injury, and sleep debt is associated with increased accumulation of beta amyloid (Aβ) and phosphorylated tau (p-tau) in the interstitial space. Recent research into the glymphatic system, a lymphatic-like metabolic clearance mechanism in the central nervous system (CNS) which relies on cerebrospinal fluid (CSF), interstitial fluid (ISF), and astrocytic processes, shows that clearance is potentiated during sleep. ⋯ Long-term consequences of chronic dysfunction within this system in the context of repetitive brain trauma and insomnia have not been established, but potentially provide one link in the explanatory chain connecting repetitive TBI with later neurodegeneration. Current research has shown p-tau deposition in perivascular spaces and along interstitial pathways in chronic traumatic encephalopathy (CTE), pathways related to glymphatic flow; these are the main channels by which metabolic waste is cleared. This review addresses possible links between mTBI-related damage to glymphatic functioning and physiological changes found in CTE, and proposes a model for the mediating role of sleep disruption in increasing the risk for developing CTE-related pathology and subsequent clinical symptoms following repetitive brain trauma.
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Neurosci Biobehav Rev · Sep 2017
Review Meta AnalysisThe impact of machine learning techniques in the study of bipolar disorder: A systematic review.
Machine learning techniques provide new methods to predict diagnosis and clinical outcomes at an individual level. We aim to review the existing literature on the use of machine learning techniques in the assessment of subjects with bipolar disorder. We systematically searched PubMed, Embase and Web of Science for articles published in any language up to January 2017. ⋯ Most of the included studies used multiple levels of biological data to distinguish the diagnosis of bipolar disorder from other psychiatric disorders or healthy controls. We also found studies that assessed the prediction of clinical outcomes and studies using unsupervised machine learning to build more consistent clinical phenotypes of bipolar disorder. We concluded that given the clinical heterogeneity of samples of patients with BD, machine learning techniques may provide clinicians and researchers with important insights in fields such as diagnosis, personalized treatment and prognosis orientation.
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Neurosci Biobehav Rev · Sep 2017
Review Meta AnalysisThe impact of machine learning techniques in the study of bipolar disorder: A systematic review.
Machine learning techniques provide new methods to predict diagnosis and clinical outcomes at an individual level. We aim to review the existing literature on the use of machine learning techniques in the assessment of subjects with bipolar disorder. We systematically searched PubMed, Embase and Web of Science for articles published in any language up to January 2017. ⋯ Most of the included studies used multiple levels of biological data to distinguish the diagnosis of bipolar disorder from other psychiatric disorders or healthy controls. We also found studies that assessed the prediction of clinical outcomes and studies using unsupervised machine learning to build more consistent clinical phenotypes of bipolar disorder. We concluded that given the clinical heterogeneity of samples of patients with BD, machine learning techniques may provide clinicians and researchers with important insights in fields such as diagnosis, personalized treatment and prognosis orientation.