What Do We Know About Neuropsychological Aspects Of Schizophrenia?

Application of a neuropsychological perspective to the study of schizophrenia has established a number of important facts about this disorder. Some of the key findings from the existing literature are that, while neurocognitive impairment is present in most, if not all, persons with schizophrenia, there is both substantial interpatient heterogeneity and remarkable within-patient stability of cognitive function over the long-term course of the illness. Such findings have contributed to the firm establishment of neurobiologic models of schizophrenia, and thereby help to reduce the social stigma that was sometimes associated with purely psychogenic models popular during parts of the 20th century. Neuropsychological studies in recent decades have established the primacy of cognitive functions over psychopathologic symptoms as determinants of functional capacity and independence in everyday functioning. Although the cognitive benefits of both conventional and even second generation antipsychotic medications appear marginal at best, recognition of the primacy of cognitive deficits as determinants of functional disability in schizophrenia has catalyzed recent efforts to develop targeted treatments for the cognitive deficits of this disorder. Despite these accomplishments, however, some issues remain to be resolved. Efforts to firmly establish the specific neurocognitive/neuropathologic systems responsible for schizophrenia remain elusive, as do efforts to definitively demonstrate the specific cognitive deficits underlying specific forms of functional impairment. Further progress may be fostered by recent initiatives to integrate neuropsychological studies with experimental neuroscience, perhaps leading to measures of deficits in cognitive processes more clearly associated with specific, identifiable brain systems

Use of brain MRI atlases to determine boundaries of age-related pathology: the importance of statistical method

Neurodegenerative disease diagnoses may be supported by the comparison of an individual patient's brain magnetic resonance image (MRI) with a voxel-based atlas of normal brain MRI. Most current brain MRI atlases are of young to middle-aged adults and parametric, e.g., mean ± standard deviation (SD); these atlases require data to be Gaussian. Brain MRI data, e.g., grey matter (GM) proportion images, from normal older subjects are apparently not Gaussian. We created a nonparametric and a parametric atlas of the normal limits of GM proportions in older subjects and compared their classifications of GM proportions in Alzheimer's disease (AD) patients.Using publicly available brain MRI from 138 normal subjects and 138 subjects diagnosed with AD (all 55-90 years), we created: a mean ± SD atlas to estimate parametrically the percentile ranks and limits of normal ageing GM; and, separately, a nonparametric, rank order-based GM atlas from the same normal ageing subjects. GM images from AD patients were then classified with respect to each atlas to determine the effect statistical distributions had on classifications of proportions of GM in AD patients.The parametric atlas often defined the lower normal limit of the proportion of GM to be negative (which does not make sense physiologically as the lowest possible proportion is zero). Because of this, for approximately half of the AD subjects, 25-45% of voxels were classified as normal when compared to the parametric atlas; but were classified as abnormal when compared to the nonparametric atlas. These voxels were mainly concentrated in the frontal and occipital lobes.To our knowledge, we have presented the first nonparametric brain MRI atlas. In conditions where there is increasing variability in brain structure, such as in old age, nonparametric brain MRI atlases may represent the limits of normal brain structure more accurately than parametric approaches. Therefore, we conclude that the statistical method used for construction of brain MRI atlases should be selected taking into account the population and aim under study. Parametric methods are generally robust for defining central tendencies, e.g., means, of brain structure. Nonparametric methods are advisable when studying the limits of brain structure in ageing and neurodegenerative disease

Intersectoral wage linkages: the case of Sweden

Sectoral wage linkages, Wage leadership, Wage adaptability, Scandinavian model of inflation, Exposed and sheltered sectors, Vector error correction (VEC) models, C32, J30, J51, J52,