Neuroscience
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Tastes and odors influence the perception of a meal. Especially food aromas can act as potent signals to modulate our eating behavior with strong dependency on the reward produced by food. In this investigation we aimed to study the electrophysiological response to food- and non-food-related odors in healthy volunteers. Analyses revealed specific scalp potential maps for the two conditions; in particular the source of the map in the "food" condition seemed to be associated with the processing of rewards; the specific map in the "non-food" condition reflects odor characteristics excluding the reward.
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Spinocerebellar ataxia type 1 (SCA1) is an incurable, dominantly inherited neurodegenerative disease of the cerebellum caused by a polyglutamine-repeat expansion in the protein ataxin-1 (ATXN1). While analysis of human autopsy material indicates significant glial pathology in SCA1, previous research has focused on characterizing neuronal dysfunction. ⋯ Glial activation occurred in the absence of neuronal death, suggesting that glial activation results from signals emanating from dysfunctional neurons. Finally, in all different models examined glial activation closely correlated with disease progression, supporting the development of glial-based biomarkers to follow disease progression.
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Cerebral malaria (CM) is a severe complication resulting from Plasmodium falciparum infection that might cause permanent neurological deficits. Cannabidiol (CBD) is a nonpsychotomimetic compound of Cannabis sativa with neuroprotective properties. In the present work, we evaluated the effects of CBD in a murine model of CM. ⋯ On 5dpi, TNF-α and IL-6 increased in the hippocampus, while only IL-6 increased in the prefrontal cortex. CBD treatment resulted in an increase in BDNF expression in the hippocampus and decreased levels of proinflammatory cytokines in the hippocampus (TNF-α) and prefrontal cortex (IL-6). Our results indicate that CBD exhibits neuroprotective effects in CM model and might be useful as an adjunctive therapy to prevent neurological symptoms following this disease.
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Altered expression of neuronal cytoskeletal proteins are known to play an important role in hyper-excitability of neurons in patients and animal models of epilepsy. Our previous work showed that cell division cycle 42 GTP-binding protein (Cdc42), a small GTPase of the Rho-subfamily, is significantly increased in the brain tissue of patients with temporal lobe epilepsy (TLE) and in the brain tissues of the epileptic model of rats. However, whether inhibition of Cdc42 can modify epileptic seizures has not been investigated. ⋯ Whole-cell patch-clamp recording on CA1 pyramidal neurons in hippocampal slices from pilocarpine-induced epileptic model demonstrated that ML141 significantly inhibits the frequency of action potentials (APs), increases the amplitude and frequency of miniature inhibitory postsynaptic currents (mIPSCs), and increases the amplitude of evoked inhibitory postsynaptic currents (eIPSCs). However, ML141 did not have an impact on the miniature excitatory postsynaptic currents (mEPSCs). Our results are the first to indicate that Cdc42 plays an important role in the onset and progression of epileptic-seizures by regulating synaptic inhibition.
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Recent studies of electromagnetic ultra-slow waves (⩽0.1Hz) have suggested that they play a role in the integration of otherwise disassociated brain regions supporting vital functions (Ackermann and Borbely, 1997; Picchioni et al., 2010; Knyazev, 2012; Le Bon et al., 2012). We emphasize this spectral domain in probing sensor coherence issues raised by these studies using Hilbert phase coherences in the human MEG. In addition, we ask: will temporal-spatial phase coherence in regional brain oscillations obtained from the ultraslow spectral bands of multi-channel magnetoencephalograms (MEG) differentiate resting, "task-free" MEG records of normal control and schizophrenic subjects? The goal of the study is a comparison of the relative persistence of intra-regional phase locking values (PLVs), among 10, region-defined, sensors in examined in the resting multichannel, MEG records as a function of spectral frequency bands and diagnostic category. ⋯ Leave one out, bootstrapping of the PLVs via a support vector machine (SVM), classified clinical status with 97.3% accuracy. It was generally the case that spectral bands ⩽1.0Hz generated the highest values of the PLVs and discriminated best between control and patient populations. We conclude that PLV analysis of the oscillatory patterns of MEG recordings in the ultraslow frequency bands suggest their functional significance in intra-regional signal coherence and provide a higher rate of classification of patients and normal subjects than the other spectral domains examined.