Clinical Applications of Neuroimaging in Psychiatric Disorders.
Ontology highlight
ABSTRACT:
SUBMITTER: First MB
PROVIDER: S-EPMC6583905 | biostudies-literature | 2018 Sep
REPOSITORIES: biostudies-literature
Publications
Clinical Applications of Neuroimaging in Psychiatric Disorders.
First Michael B MB Drevets Wayne C WC Carter Cameron C Dickstein Daniel P DP Kasoff Lauren L Kim Kerri L KL McConathy Jonathan J Rauch Scott S Saad Ziad S ZS Savitz Jonathan J Seymour Karen E KE Sheline Yvette I YI Zubieta Jon-Kar JK
The American journal of psychiatry 20180901 9
Similar Datasets
Project description:In psychiatry, neuroimaging facilitates the diagnosis of psychiatric disorders and the development of new medications. It is used to detect structural lesions causing psychosis and to differentiate depression from neurodegenerative disorders or brain tumors. Functional neuroimaging, mostly in the form of molecular neuroimaging with positron emission tomography or single photon emission tomography, facilitates the identification of therapeutic targets, the determination of the dose of a new drug needed to occupy its target in the brain, and the selection of patients for clinical trials.
| S-EPMC3052989 | biostudies-literature
Project description:Deep learning (DL) methods have been increasingly applied to neuroimaging data to identify patients with psychiatric and neurological disorders. This review provides an overview of the different DL applications within psychiatry and compares DL model accuracy to standard machine learning (SML). Fifty-three articles were included for qualitative analysis, primarily investigating autism spectrum disorder (ASD; n = 22), schizophrenia (SZ; n = 22) and attention-deficit/hyperactivity disorder (ADHD; n = 9). Thirty-two of the thirty-five studies that directly compared DL to SML reported a higher accuracy for DL. Only sixteen studies could be included in a meta-regression to quantitatively compare DL and SML performance. This showed a higher odds ratio for DL models, though the comparison attained significance only for ASD. Our results suggest that deep learning of neuroimaging data is a promising tool for the classification of individual psychiatric patients. However, it is not yet used to its full potential: most studies use pre-engineered features, whereas one of the main advantages of DL is its ability to learn representations of minimally processed data. Our current evaluation is limited by minimal reporting of performance measures to enable quantitative comparisons, and the restriction to ADHD, SZ and ASD as current research focusses on large publicly available datasets. To truly uncover the added value of DL, we need carefully designed comparisons of SML and DL models which are yet rarely performed.
| S-EPMC8209481 | biostudies-literature
Project description: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.
| S-EPMC6353824 | biostudies-literature
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.
| S-EPMC4814515 | biostudies-literature
Project description:Traumatic brain injury (TBI) has traditionally been associated with cognitive and behavioral changes during both the acute and chronic phases of injury. Because of its noninvasive nature, neuroimaging has the potential to provide unique information on underlying macroscopic and microscopic biological mechanisms that may serve as causative agents for these neuropsychiatric sequelae. This broad scoping review identifies at least 4 common macroscopic pathways that exist between TBI and new-onset psychiatric disorders, as well as several examples of how neuroimaging is currently being utilized in clinical research. The review then critically examines the strengths and limitations of neuroimaging for elucidating TBI-related microscopic pathology, such as microstructural changes, neuroinflammation, proteinopathies, blood-brain barrier damage, and disruptions in cellular signaling. A summary is then provided for how neuroimaging is currently being used to investigate TBI-related pathology in new-onset neurocognitive disorders, depression, and posttraumatic stress disorder. Identified gaps in the literature include a lack of prospective studies to definitively associate imaging findings with the development of new-onset psychiatric disorders, as well as antemortem imaging studies subsequently confirmed with postmortem correlates in the same study cohort. Although the spatial resolution and specificity of imaging biomarkers has greatly improved over the last 2 decades, we conclude that neuroimaging biomarkers do not yet exist for the definitive in vivo diagnosis of cellular pathology. This represents a necessary next step for further elucidating causal relationships between TBI and new-onset psychiatric disorders.
| S-EPMC8665933 | biostudies-literature
Project description:Purpose of reviewThe 'holy grail' of clinical applications of neuroimaging to neurological and psychiatric disorders via personalized biomarkers has remained mostly elusive, despite considerable effort. However, there are many reasons to continue to be hopeful, as the field has made remarkable advances over the past few years, fueled by a variety of converging technical and data developments.Recent findingsWe discuss a number of advances that are accelerating the push for neuroimaging biomarkers including the advent of the 'neuroscience big data' era, biomarker data competitions, the development of more sophisticated algorithms including 'guided' data-driven approaches that facilitate automation of network-based analyses, dynamic connectivity, and deep learning. Another key advance includes multimodal data fusion approaches which can provide convergent and complementary evidence pointing to possible mechanisms as well as increase predictive accuracy.SummaryThe search for clinically relevant neuroimaging biomarkers for neurological and psychiatric disorders is rapidly accelerating. Here, we highlight some of these aspects, provide recent examples from studies in our group, and link to other ongoing work in the field. It is critical that access and use of these advanced approaches becomes mainstream, this will help propel the community forward and facilitate the production of robust and replicable neuroimaging biomarkers.
| S-EPMC8263510 | biostudies-literature
Project description: Not available
| S-EPMC9235346 | biostudies-literature
Project description:The evaluation of neuropathologies using MRI methods that leverage tissue susceptibility have become standard practice, especially to detect blood products or mineralization. Additionally, emerging MRI techniques have the ability to provide new information based on tissue susceptibility properties in a robust and quantitative manner. This paper discusses these advanced susceptibility imaging techniques and their clinical applications.
| S-EPMC5501163 | biostudies-literature
Project description:Myoclonus dystonia syndrome is a childhood onset hyperkinetic movement disorder characterized by predominant alcohol responsive upper body myoclonus and dystonia. A proportion of cases are due to mutations in the maternally imprinted SGCE gene. Previous studies have suggested that patients with SGCE mutations may have an increased rate of psychiatric disorders. We established a cohort of patients with myoclonus dystonia syndrome and SGCE mutations to determine the extent to which psychiatric disorders form part of the disease phenotype. In all, 89 patients with clinically suspected myoclonus dystonia syndrome were recruited from the UK and Ireland. SGCE was analysed using direct sequencing and for copy number variants. In those patients where no mutation was found TOR1A (GAG deletion), GCH1, THAP1 and NKX2-1 were also sequenced. SGCE mutation positive cases were systematically assessed using standardized psychiatric interviews and questionnaires and compared with a disability-matched control group of patients with alcohol responsive tremor. Nineteen (21%) probands had a SGCE mutation, five of which were novel. Recruitment of family members increased the affected SGCE mutation positive group to 27 of whom 21 (77%) had psychiatric symptoms. Obsessive-compulsive disorder was eight times more likely (P < 0.001) in mutation positive cases, compulsivity being the predominant feature (P < 0.001). Generalized anxiety disorder (P = 0.003) and alcohol dependence (P = 0.02) were five times more likely in mutation positive cases than tremor controls. SGCE mutations are associated with a specific psychiatric phenotype consisting of compulsivity, anxiety and alcoholism in addition to the characteristic motor phenotype. SGCE mutations are likely to have a pleiotropic effect in causing both motor and specific psychiatric symptoms.
| S-EPMC4052887 | biostudies-literature
Project description:Corticosteroids are extensively used in medicine for their powerful anti-inflammatory and immunosuppressive effects. However, their psychiatric side effects-such as mood disturbances, anxiety, and psychosis-are significant yet often underappreciated. This review provides a comprehensive exploration of corticosteroid-induced psychiatric disorders, with a focus on their underlying mechanisms and clinical implications. We examine how corticosteroids influence the hypothalamic-pituitary-adrenal (HPA) axis, leading to the dysregulation of stress responses and alterations in neurotransmitter levels, particularly dopamine, serotonin, and glutamate. These changes are linked to structural abnormalities in key brain areas such as the hippocampus and amygdala, which are implicated in mood and anxiety disorders, psychosis, and conditions like post-traumatic stress disorder (PTSD) and eating disorders. This review highlights the need for healthcare providers to be vigilant in recognizing and managing corticosteroid-induced psychiatric symptoms, especially in vulnerable populations with pre-existing mental health conditions. The complex relationship between corticosteroid type, dose, duration, and mental health outcomes is explored, emphasizing the importance of personalized treatment approaches to mitigate psychiatric risks. Given the widespread use of corticosteroids, there is an urgent need for more focused research on their psychiatric side effects. This review underscores the importance of patient education and careful monitoring to ensure optimal therapeutic outcomes while minimizing mental health risks associated with corticosteroid therapy.
| S-EPMC11675195 | biostudies-literature