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Multidimensional progressive declines in the absence of standard biomarkers for neurodegeneration are observed commonly in the development of schizophrenia, and are accepted as consistent with neurodevelopmental etiological hypotheses to explain the origins of the disorder. Far less accepted is the possibility that neurodegenerative processes are involved as well, or even that key dimensions of function, such as cognition and aspects of biological integrity, such as white matter function, decline in chronic schizophrenia beyond levels associated with normal aging. We propose that recent research germane to these issues warrants a current look at the question of neurodegeneration. We propose the view that a neurodegenerative hypothesis provides a better explanation of some features of chronic schizophrenia, including accelerated aging, than is provided by neurodevelopmental hypotheses. Moreover, we suggest that neurodevelopmental influences in early life, including those that may extend to later life, do not preclude the development of neurodegenerative processes in later life, including some declines in cognitive and biological integrity. We evaluate these views by integrating recent findings in representative domains such as cognition and white and gray matter integrity with results from studies on accelerated aging, together with functional implications of neurodegeneration for our understanding of chronic schizophrenia.

Risk calculators for prediction of conversion of Clinical High-Risk (CHR) individuals to syndromal psychosis have recently been developed and have generated considerable clinical use and research interest. Predictor variables in these calculators have been clinical rather than biological, and our goal was to incorporate a neurochemical imaging measure into this framework and assess its impact on prediction. We combined striatal glutamate 1H MRS data with the SIPS symptoms identified by the Columbia Risk Calculator as having the greatest predictive value in order to develop an imaging-based risk calculator for conversion to psychosis. We evaluated the calculator in 19 CHR individuals, 7 (36.84%) of whom converted to syndromal psychosis during the 2-year follow up. The receiver operating characteristic (ROC) curve for the logistic model including only striatal glutamate and visual perceptual abnormalities showed an AUC = 0.869 (95% CI = [0.667, 1.000]) and AUCoa = 0.823, with sensitivity of 0.714, specificity of 0.917, positive predictive value of 0.833, and negative predictive value of 0.846. These results represent modest improvements over each of the individual ROC curves based on either striatal glutamate or visual perceptual abnormalities alone. The preliminary model building and evaluation presented here in a small CHR sample suggests that the approach of incorporating predictive imaging measures into risk classification is not only feasible but offers the potential of enhancing risk assessment.

Risk calculators for prediction of conversion of Clinical High-Risk (CHR) individuals to syndromal psychosis have recently been developed and have generated considerable clinical use and research interest. Predictor variables in these calculators have been clinical rather than biological, and our goal was to incorporate a neurochemical imaging measure into this framework and assess its impact on prediction. We combined striatal glutamate (1)H MRS data with the SIPS symptoms identified by the Columbia Risk Calculator as having the greatest predictive value in order to develop an imaging-based risk calculator for conversion to psychosis. We evaluated the calculator in 19 CHR individuals, 7 (36.84%) of whom converted to syndromal psychosis during the 2-year follow up. The receiver operating characteristic (ROC) curve for the logistic model including only striatal glutamate and visual perceptual abnormalities showed an AUC=0.869 (95% CI = [0.667, 1.000]) and AUC(oa)=0.823, with sensitivity of 0.714, specificity of 0.917, positive predictive value of 0.833, and negative predictive value of 0.846. These results represent modest improvements over each of the individual ROC curves based on either striatal glutamate or visual perceptual abnormalities alone. The preliminary model building and evaluation presented here in a small CHR sample suggests that the approach of incorporating predictive imaging measures into risk classification is not only feasible but offers the potential of enhancing risk assessment.

The last fifteen years have seen a great increase in our understanding of the role of glutamate in schizophrenia (SCZ). The glutamate hypothesis focuses on disturbances in brain glutamatergic pathways and impairment in signaling at glutamate receptors. Proton Magnetic Resonance Spectroscopy ((1)H-MRS) is an MR-based technique that affords investigators the ability to study glutamate function by measuring in vivo glutamatergic indices in the brains of individuals with SCZ. (1)H-MRS studies have been performed comparing glutamatergic levels of individuals with SCZ and healthy control subjects or studying the effect of antipsychotic medications on glutamatergic levels. In this article we summarize the results of these studies by brain region. We will review the contribution of (1)H-MRS studies to our knowledge about glutamatergic abnormalities in the brains of individuals with SCZ and discuss the implications for future research and clinical care.