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Query: UMLS:C0036341 (schizophrenia)
 60,220 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Recent investigations have documented abnormalities in working memory related processes in schizophrenics on tasks assessing the central executive component of this cognitive model. This preliminary study investigated the function of another component of the working memory system, the visuospatial scratch pad in schizophrenia. The "scratch pad's" passive visual store--responsible for the temporary retention of visual material--was assessed via a computerized spatial delayed response task, whereas its active spatial rehearsal subsystem--specialized for retaining the temporal properties--was explored through visual block span. To assess elemental visual spatial abilities we used the Judgment of Line Orientation test. Thirty-two schizophrenics and 27 controls were tested. Although we discovered the basic perceptual abilities of patients to be intact, we determined that whenever memory was necessitated on spatial tasks, patients demonstrated marked deficits. This pattern of cognitive dysfunction is consistent with impairments in a neural network involving prefrontal and/or posterior brain regions in schizophrenia.
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PMID:Visuospatial working memory in patients with schizophrenia. 898 94

Individuals with schizophrenia experience a range of cognitive deficits and associated dysfunctions in the neural systems that support cognitive processes. This chapter reviews the literature on disturbances in working memory, executive control, and episodic memory in schizophrenia. Advances in basic cognitive neuroscience are described to help explain the cognitive neuroscience of schizophrenia. For working memory in schizophrenia, evidence is reviewed regarding deficits in the verbal (phonological loop) and nonverbal (visual-spatial scratch pad) buffer systems as well as in the central executive function. In the domain of episodic memory, evidence is reviewed for deficits in recollection versus familiarity processes in episodic memory. Also discussed are conceptual issues and potential confounds relevant to understanding the cognitive neuroscience of schizophrenia, including the role that cognitive deficits play in the developmental course of schizophrenia, relationships to specific symptom domains, behavioral performance confounds, and medication influences on behavioral performance and brain function.
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PMID:The cognitive neuroscience of schizophrenia. 1771 91

Previous studies have demonstrated that LASSBio-579 and LASSBio-581, two N-phenylpiperazine derivatives designed for the treatment of schizophrenia, are presynaptic dopamine D(2) receptor agonists that induce a hypothermic effect in mice that is not mediated by dopamine receptor activation. The aim of the present study was to investigate possible serotonergic mechanisms underlying hypothermia induced by LASSBio-579 and LASSBio-581 in CF1 mice. The reduction in core temperature was dose-dependent (15-60 mg/kg, i.p.) and occurred by the oral route (30 mg/kg). Pretreatment with haloperidol (4 mg/kg, i.p.) resulted in a synergistic hypothermic effect. Pretreatment with (+/-)DOI (0.25 mg/kg, i.p.), a serotonin 5-HT(2A/C) receptor agonist, reduced the hypothermic effect induced by LASSBio-579 and LASSBio-581 at 15 and 30 mg/kg, i.p. In contrast, (+/-)DOI enhanced the hypothermia induced by both compounds at 60 mg/kg, i.p. The serotonin 5-HT1A antagonist WAY 100635 (0.05 mg/kg, s.c.) abolished the hypothermia induced by LASSBio-579 and diminished the hypothermia induced by LASSBio-581. Pretreatment with LASSBio579 (30 and 60 mg/kg, i.p.) and LASSBio-581 (60 mg/kg, i.p.) reduced the number of head-twitches induced by (+/-)DOI (2.5 mg/kg, i.p.). The ear-scratch response induced by (+/-)DOI was inhibited by both LASSBio-579 and LASSBio-581 at 60 mg/kg, i.p. These results indicate that LASSBio-579 and LASSBio-581 have mechanisms of action through the serotonergic neurotransmitter system.
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PMID:Serotonergic neurotransmission mediates hypothermia induced by the N-phenylpiperazine antipsychotic prototypes LASSBio-579 and LASSBio-581. 1808 72

A disease like schizophrenia results from the malfunctioning of a complex, multi-faceted biological system. As a consequence, the root causes of such a disease and the trajectories from health toward the disease are very difficult to comprehend with simple cause-and-effect reasoning. Similarly, reductionistic investigations are crucial for the discovery of specific disease mechanisms, but they are not sufficient for comprehensive assessments and explanations. A promising option for advancing the field is the utilization of mathematical models that can quantitatively account for hundreds of components and their interactions and thus have the potential of truly explaining complex diseases. While the potential of mathematical models is quite evident in principle, their practical implementation is a daunting task. On the one hand, many distinctly different approaches are possible. For instance, in the case of schizophrenia, models could focus on neurological aspects, physiological features, or the biochemical malfunctioning within some cell complexes in the brain, and each model would ultimately be very different. On the other hand, it seems that there are no rules or recommendations that guide the development of a new mathematical model from scratch. We discuss here that, even though mathematical models in biology and medicine may ultimately have a very different appearance, their development can be structured as a sequence of generic steps. Major drivers for many of the details of model development are the goals and objectives of the modeling task and the availability and quality of data that can be used for model design and validation.
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PMID:Steps of modeling complex biological systems. 1875 24

How the human brain develops and adapts with its trillions of functionally integrated synapses remains one of the greatest mysteries of life. With tremendous advances in neuroscience, genetics, and molecular biology, we are beginning to appreciate the scope of this complexity and define some of the parameters of the systems that make it possible. These same tools are also leading to advances in our understanding of the pathophysiology of neurocognitive and neuropsychiatric disorders. Like the substrate for these problems, the etiology is usually complex-involving an array of genetic and environmental influences. To resolve these influences and derive better interventions, we need to reveal every aspect of this complexity and model their interactions and define the systems and their regulatory structure. This is particularly important at the tissue-specific molecular interface between the underlying genetic and environmental influence defined by the transcriptome. Recent advances in transcriptome analysis facilitated by RNA sequencing (RNA-Seq) can provide unprecedented insight into the functional genomics of neurological disorders. In this review, we outline the advantages of this approach and highlight some early application of this technology in the investigation of the neuropathology of schizophrenia. Recent progress of RNA-Seq studies in schizophrenia has shown that there is extraordinary transcriptome dynamics with significant levels of alternative splicing. These studies only scratch the surface of this complexity and therefore future studies with greater depth and samples size will be vital to fully explore transcriptional diversity and its underlying influences in schizophrenia and provide the basis for new biomarkers and improved treatments.
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PMID:Understanding complex transcriptome dynamics in schizophrenia and other neurological diseases using RNA sequencing. 2517 74

Convolutional Neural Network (CNN) has been successfully applied on classification of both natural images and medical images but limited studies applied it to differentiate patients with schizophrenia from healthy controls. Given the subtle, mixed, and sparsely distributed brain atrophy patterns of schizophrenia, the capability of automatic feature learning makes CNN a powerful tool for classifying schizophrenia from controls as it removes the subjectivity in selecting relevant spatial features. To examine the feasibility of applying CNN to classification of schizophrenia and controls based on structural Magnetic Resonance Imaging (MRI), we built 3D CNN models with different architectures and compared their performance with a handcrafted feature-based machine learning approach. Support vector machine (SVM) was used as classifier and Voxel-based Morphometry (VBM) was used as feature for handcrafted feature-based machine learning. 3D CNN models with sequential architecture, inception module and residual module were trained from scratch. CNN models achieved higher cross-validation accuracy than handcrafted feature-based machine learning. Moreover, testing on an independent dataset, 3D CNN models greatly outperformed handcrafted feature-based machine learning. This study underscored the potential of CNN for identifying patients with schizophrenia using 3D brain MR images and paved the way for imaging-based individual-level diagnosis and prognosis in psychiatric disorders.
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PMID:Brain MRI-based 3D Convolutional Neural Networks for Classification of Schizophrenia and Controls. 3301 34