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      Treatment response prediction and individualized identification of first-episode drug-naïve schizophrenia using brain functional connectivity

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          Abstract

          Identifying biomarkers in schizophrenia during the first episode without the confounding effects of treatment has been challenging. Leveraging these biomarkers to establish diagnosis and make individualized predictions of future treatment responses to antipsychotics would be of great value, but there has been limited progress. In this study, by using machine learning algorithms and the functional connections of the superior temporal cortex, we successfully identified the first-episode drug-naive (FEDN) schizophrenia patients (accuracy 78.6%) and predict their responses to antipsychotic treatment (accuracy 82.5%) at an individual level. The functional connections (FC) were derived using the mutual information and the correlations, between the blood-oxygen-level dependent signals of the superior temporal cortex and other cortical regions acquired with the resting-state functional magnetic resonance imaging. We also found that the mutual information and correlation FC was informative in identifying individual FEDN schizophrenia and prediction of treatment response, respectively. The methods and findings in this paper could provide a critical step toward individualized identification and treatment response prediction in first-episode drug-naive schizophrenia, which could complement other biomarkers in the development of precision medicine approaches for this severe mental disorder.

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          Most cited references 40

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          Undirected graphs of frequency-dependent functional connectivity in whole brain networks.

          We explored properties of whole brain networks based on multivariate spectral analysis of human functional magnetic resonance imaging (fMRI) time-series measured in 90 cortical and subcortical subregions in each of five healthy volunteers studied in the (no-task) resting state. We note that undirected graphs representing conditional independence between multivariate time-series can be more readily approached in the frequency domain than the time domain. Estimators of partial coherency and normalized partial mutual information phi, an integrated measure of partial coherence over an arbitrary frequency band, are applied. Using these tools, we replicate the prior observations that bilaterally homologous brain regions tend to be strongly connected and functional connectivity is generally greater at low frequencies [0.0004, 0.1518 Hz]. We also show that long-distance intrahemispheric connections between regions of prefrontal and parietal cortex were more salient at low frequencies than at frequencies greater than 0.3 Hz, whereas many local or short-distance connections, such as those comprising segregated dorsal and ventral paths in posterior cortex, were also represented in the graph of high-frequency connectivity. We conclude that the partial coherency spectrum between a pair of human brain regional fMRI time-series depends on the anatomical distance between regions: long-distance (greater than 7 cm) edges represent conditional dependence between bilaterally symmetric neocortical regions, and between regions of prefrontal and parietal association cortex in the same hemisphere, are predominantly subtended by low-frequency components.
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            The neuroprogressive nature of major depressive disorder: pathways to disease evolution and resistance, and therapeutic implications.

            In some patients with major depressive disorder (MDD), individual illness characteristics appear consistent with those of a neuroprogressive illness. Features of neuroprogression include poorer symptomatic, treatment and functional outcomes in patients with earlier disease onset and increased number and length of depressive episodes. In such patients, longer and more frequent depressive episodes appear to increase vulnerability for further episodes, precipitating an accelerating and progressive illness course leading to functional decline. Evidence from clinical, biochemical and neuroimaging studies appear to support this model and are informing novel therapeutic approaches. This paper reviews current knowledge of the neuroprogressive processes that may occur in MDD, including structural brain consequences and potential molecular mechanisms including the role of neurotransmitter systems, inflammatory, oxidative and nitrosative stress pathways, neurotrophins and regulation of neurogenesis, cortisol and the hypothalamic-pituitary-adrenal axis modulation, mitochondrial dysfunction and epigenetic and dietary influences. Evidence-based novel treatments informed by this knowledge are discussed.
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              Controlling the familywise error rate in functional neuroimaging: a comparative review

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                Author and article information

                Journal
                Molecular Psychiatry
                Mol Psychiatry
                Springer Nature
                1359-4184
                1476-5578
                June 19 2018
                Article
                10.1038/s41380-018-0106-5
                29921920
                © 2018

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