Dataset Information

Glutamatergic Dysfunction and Glutamatergic Compounds for Major Psychiatric Disorders: Evidence From Clinical Neuroimaging Studies.

ABSTRACT: Excessive glutamate release has been linked to stress and many neurodegenerative diseases. Evidence indicates abnormalities of glutamatergic neurotransmission or glutamatergic dysfunction as playing an important role in the development of many major psychiatric disorders (e.g., schizophrenia, bipolar disorder, and major depressive disorder). Recently, ketamine, an N-methyl-d-aspartate antagonist, has been demonstrated to have promisingly rapid antidepressant efficacy for treatment-resistant depression. Many compounds that target the glutamate system have also become available that possess potential in the treatment of major psychiatric disorders. In this review, we update evidence from recent human studies that directly or indirectly measured glutamatergic neurotransmission and function in major psychiatric disorders using modalities such as magnetic resonance spectroscopy, positron emission tomography/single-photon emission computed tomography, and paired-pulse transcranial magnetic stimulation. The newer generation of antidepressants that target the glutamatergic system developed in human clinical studies is also reviewed.

SUBMITTER: Li CT

PROVIDER: S-EPMC6353824 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Publications

Glutamatergic Dysfunction and Glutamatergic Compounds for Major Psychiatric Disorders: Evidence From Clinical Neuroimaging Studies.

Li Cheng-Ta CT Yang Kai-Chun KC Lin Wei-Chen WC

Frontiers in psychiatry 20190124

Excessive glutamate release has been linked to stress and many neurodegenerative diseases. Evidence indicates abnormalities of glutamatergic neurotransmission or glutamatergic dysfunction as playing an important role in the development of many major psychiatric disorders (e.g., schizophrenia, bipolar disorder, and major depressive disorder). Recently, ketamine, an <i>N</i>-methyl-d-aspartate antagonist, has been demonstrated to have promisingly rapid antidepressant efficacy for treatment-resista ...[more]

PMID: 30733690

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Project description:In a recent review, it was suggested that much larger cohorts are needed to prove the diagnostic value of neuroimaging biomarkers in psychiatry. While within a sample, an increase of diagnostic accuracy of schizophrenia (SZ) with number of subjects (N) has been shown, the relationship between N and accuracy is completely different between studies. Using data from a recent meta-analysis of machine learning (ML) in imaging SZ, we found that while low-N studies can reach 90% and higher accuracy, above N/2?=?50 the maximum accuracy achieved steadily drops to below 70% for N/2?>?150. We investigate the role N plays in the wide variability in accuracy results in SZ studies (63-97%). We hypothesize that the underlying cause of the decrease in accuracy with increasing N is sample heterogeneity. While smaller studies more easily include a homogeneous group of subjects (strict inclusion criteria are easily met; subjects live close to study site), larger studies inevitably need to relax the criteria/recruit from large geographic areas. A SZ prediction model based on a heterogeneous group of patients with presumably a heterogeneous pattern of structural or functional brain changes will not be able to capture the whole variety of changes, thus being limited to patterns shared by most patients. In addition to heterogeneity (sample size), we investigate other factors influencing accuracy and introduce a ML effect size. We derive a simple model of how the different factors, such as sample heterogeneity and study setup determine this ML effect size, and explain the variation in prediction accuracies found from the literature, both in cross-validation and independent sample testing. From this, we argue that smaller-N studies may reach high prediction accuracy at the cost of lower generalizability to other samples. Higher-N studies, on the other hand, will have more generalization power, but at the cost of lower accuracy. In conclusion, when comparing results from different ML studies, the sample sizes should be taken into account. To assess the generalizability of the models, validation (by direct application) of the prediction models should be tested in independent samples. The prediction of more complex measures such as outcome, which are expected to have an underlying pattern of more subtle brain abnormalities (lower effect size), will require large samples.

Dataset Information

Glutamatergic Dysfunction and Glutamatergic Compounds for Major Psychiatric Disorders: Evidence From Clinical Neuroimaging Studies.

Publications

Glutamatergic Dysfunction and Glutamatergic Compounds for Major Psychiatric Disorders: Evidence From Clinical Neuroimaging Studies.

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