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Several diffusion tensor imaging (DTI) studies have reported disturbed white matter integrity in various brain regions in patients with schizophrenia, whereas only a few studied the effect of antipsychotics on DTI measures. The aim of this study was to investigate the effect of 12 weeks of clozapine treatment on DTI findings in patients with schizophrenia, and to compare the findings with those in unaffected controls. The study included 16 patients with schizophrenia who were assessed with the Positive and Negative Syndrome Scale, a neurocognitive test battery, and DTI at baseline and 12 weeks after the initiation of clozapine treatment. Eight unaffected controls were assessed once with the neurocognitive test battery and DTI. Voxel-wise analysis of DTI data was performed via tract-based spatial statistics (TBSS). Compared with the control group, the patient group exhibited lower fractional anisotropy (FA) in 16 brain regions, including the bilateral superior longitudinal fasciculi, inferior fronto-occipital fasciculi, superior and inferior parietal lobules, cingulate bundles, cerebellum, middle cerebellar peduncles, and left inferior longitudinal fasciculus, whereas the patients had higher FA in six regions, including the right parahippocampus, left anterior thalamic radiation, and right posterior limb of the internal capsule before clozapine treatment. After 12 weeks of treatment with clozapine, white matter FA was increased in widespread brain regions. In two of the regions where FA had initially been lower in patients compared with controls (left inferior fronto-occipital fasciculus and superior parietal lobule), clozapine appeared to increase FA. An improvement in semantic fluency was correlated with the increase in FA value in the left inferior fronto-occipital fasciculus. An increase in FA following 12 weeks of treatment with clozapine suggests that this treatment alters white matter microstructural integrity in patients with schizophrenia previously treated with typical and/or atypical antipsychotics and, in some locations, reverses a previous deficit.

Observer-driven pattern recognition is the standard for interpretation of medical images. To achieve global parity in interpretation, semi-quantitative scoring systems have been developed based on observer assessments; these are widely used in scoring coronary artery disease, the arthritides and neurological conditions and for indicating the likelihood of malignancy. However, in an era of machine learning and artificial intelligence, it is increasingly desirable that we extract quantitative biomarkers from medical images that inform on disease detection, characterisation, monitoring and assessment of response to treatment. Quantitation has the potential to provide objective decision-support tools in the management pathway of patients. Despite this, the quantitative potential of imaging remains under-exploited because of variability of the measurement, lack of harmonised systems for data acquisition and analysis, and crucially, a paucity of evidence on how such quantitation potentially affects clinical decision-making and patient outcome. This article reviews the current evidence for the use of semi-quantitative and quantitative biomarkers in clinical settings at various stages of the disease pathway including diagnosis, staging and prognosis, as well as predicting and detecting treatment response. It critically appraises current practice and sets out recommendations for using imaging objectively to drive patient management decisions.

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Hippocampal volumetry can be used in early diagnosis of cognitive impairment, as well as grading cognitive decline.

Objective Prediction of the prognosis of comatose survivors after cardiopulmonary arrest (CPA) The lower limit of confidence interval (CI) of the area under the curve (AUC) of the ROC was higher than 0.5 for visual, motor and total scores of GCS and FOUR score. Presence of a lesion pattern of multilobar, or diffuse, cortical involvement, termed as "extensive cortical lesion pattern" in MR imaging was a very good predictor of poor prognosis with an AUC of ROC of 0,937. Sensitivity of GCS motor part score and MR was 87.5% (95% CI: 61.6%-92.6%). Motor part of the

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