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Konradsen Mccormick posted an update 6 months ago
Reversal learning, a component of executive functioning, is commonly impaired among schizophrenia patients and is lacking effective treatment. N-methyl-ᴅ-aspartate (NMDA) receptor antagonists, such as phencyclidine (PCP), impair reversal learning of rodents. Touchscreen-based pairwise visual discrimination and reversal test is a translational tool to assess reversal learning in rodents. However, to fully exploit this task in testing of novel compounds, it is necessary to perform several reversal learning experiments with trained animals. Firstly, we assessed whether PCP-induced deficits in visual reversal learning in rats would be detectable with a short (5 sessions) reversal learning phase, and whether the short reversal phases could be repeated with novel stimulus pairs. Secondly, we assessed whether the PCP-induced deficits in reversal learning could be seen upon repeated PCP challenges with the same animals. Finally, we tested the effect of a novel compound, a selective α2C adrenoceptor antagonist, ORM-13070, to reverse PCP-induced cognitive deficits in this model. A 4-day PCP treatment at a dose of 1.5 mg/kg/day impaired early reversal learning in male Lister Hooded rats without inducing non-specific behavioral effects. We repeated the reversal learning experiment four times using different stimulus pairs with the same animals, and the PCP-induced impairment was evident in every single experiment. The α2C adrenoceptor antagonist ameliorated the PCP-induced cognitive deficits. Our results suggest that repeated PCP challenges in the touchscreen set-up induce schizophrenia-like cognitive deficits in visual reversal learning, improve throughput of the test and provide a protocol for testing novel drugs.Exercise therapy represents an important tool for the treatment of many neurological diseases, including cerebellar degenerations. In mouse models, exercise may decelerate the progression of gradual cerebellar degeneration via potent activation of neuroprotective pathways. However, whether exercise could also improve the condition in mice with already heavily damaged cerebella remains an open question. Here we aimed to explore this possibility, employing a mouse model with dramatic early-onset cerebellar degeneration, the Lurcher mice. The potential of forced physical activity and environmental enrichment (with the possibility of voluntary running) for improvement of behaviour and neuroplasticity was evaluated by a series of behavioural tests, measuring BDNF levels and using stereological histology techniques. Using advanced statistical analysis, we showed that while forced physical activity improved motor learning by ∼26 % in Lurcher mice and boosted BDNF levels in the diseased cerebellum by 57 %, an enriched environment partially alleviated some behavioural deficits related to behavioural disinhibition. Specifically, Lurcher mice exposed to the enriched environment evinced reduced open arm exploration in elevated plus maze test by 18 % and increased immobility almost 9-fold in the forced swim test. However, we must conclude that the overall beneficial effects were very mild and much less clear, compared to previously demonstrated effects in slowly-progressing cerebellar degenerations.
Independent component analysis (ICA) has been widely used for blind source separation in the field of medical imaging. However, despite of previous substantial efforts, the stability of ICA components remains a critical issue which has not been adequately addressed, despite numerous previous efforts. Most critical is the inconsistency of some of the extracted components when ICA is run with different model orders (MOs).
In this study, a novel method of determining the consistency of component analysis (CoCA) is proposed to evaluate the consistency of extracted components with different model orders. In the method, “consistent components” (CCs) are defined as those which can be extracted repeatably over a range of model orders.
The efficacy of the method was evaluated with simulation data and fMRI datasets. Roblitinib cell line With our method, the simulation result showed a clear difference of consistency between ground truths and noise.
The information criteria were implemented to provide suggestions for the optimal model order, where some of the ICs were revealed inconsistent in our proposed method.
This method provided an objective protocol for choosing CCs of an ICA decomposition of a data matrix, independent of model order. This is especially useful with high model orders, where noise or other disturbances could possibly lead to an instability of the components.
This method provided an objective protocol for choosing CCs of an ICA decomposition of a data matrix, independent of model order. This is especially useful with high model orders, where noise or other disturbances could possibly lead to an instability of the components.
Brain herniation is one of the fatal outcomes of increased intracranial pressure (ICP). It is caused due to the presence of hematoma or tumor mass in the brain. Ideal midline (iML) divides the healthy brain into two (right and left) nearly equal hemispheres. In the presence of hematoma, the midline tends to shift from its original position to the contralateral side of the mass and thus develops a deformed midline (dML).
In this study, a convolutional neural network (CNN) was used to predict the deformed left and right hemispheres. The proposed algorithm was validated with non-contrast computed tomography (NCCT) of (n = 45) subjects with two types of brain hemorrhages – epidural hemorrhage (EDH) (n = 5) and intra-parenchymal hemorrhage (IPH) (n = 40)).
The method demonstrated excellent potential in automatically predicting MLS with the average errors of 1.29 mm by location, 66.4 mm
by 2D area, and 253.73 mm
by 3D volume. Estimated MLS could be well correlated with other clinical markers including hematoma volume – R
= 0.86 (EDH); 0.48 (IPH) and a Radiologist-defined severity score (RSS) – R
= 0.62 (EDH); 0.57 (IPH). RSS was found to be even better correlated (R
= 0.98 (EDH); 0.70 (IPH)), hence better predictable by a joint correlation between hematoma volume, midline pixel- or voxel-shift, and minimum distance of (ideal or deformed) midline from the hematoma (boundary or centroid).
All these predictors were computed automatically, which highlighted the excellent clinical potential of the proposed automated method in midline shift (MLS) estimation and severity prediction in hematoma decision support systems.
All these predictors were computed automatically, which highlighted the excellent clinical potential of the proposed automated method in midline shift (MLS) estimation and severity prediction in hematoma decision support systems.
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