Neuroimaging in psychiatric disorders.
Ontology highlight
ABSTRACT:
SUBMITTER: Masdeu JC
PROVIDER: S-EPMC3052989 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description: Not available
| S-EPMC6583905 | 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: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: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: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:Functional magnetic resonance imaging (fMRI) studies in psychiatry use various tasks to identify case-control differences in the patterns of task-related brain activation. Differently activated regions are often ascribed disorder-specific functions in an attempt to link disease expression and brain function. We undertook a systematic meta-analysis of data from task-fMRI studies to examine the effect of diagnosis and study design on the spatial distribution and direction of case-control differences on brain activation. We mapped to atlas regions coordinates of case-control differences derived from 537 task-fMRI studies in schizophrenia, bipolar disorder, major depressive disorder, anxiety disorders, and obsessive compulsive disorder comprising observations derived from 21,427 participants. The fMRI tasks were classified according to the Research Domain Criteria (RDoC). We investigated whether diagnosis, RDoC domain or construct and use of regions-of-interest or whole-brain analyses influenced the neuroanatomical pattern of results. When considering all primary studies, we found an effect of diagnosis for the amygdala and caudate nucleus and an effect of RDoC domains and constructs for the amygdala, hippocampus, putamen and nucleus accumbens. In contrast, whole-brain studies did not identify any significant effect of diagnosis or RDoC domain or construct. These results resonate with prior reports of common brain structural and genetic underpinnings across these disorders and caution against attributing undue specificity to brain functional changes when forming explanatory models of psychiatric disorders. Hum Brain Mapp 38:1846-1864, 2017. © 2017 Wiley Periodicals, Inc.
| S-EPMC5347927 | biostudies-literature
Project description:139 samples from cortex and hipocampus of the mice modeling human CNVs associatted with schizophrenia: Df(h15q13)/+, Df(h15q13)/-, Df(h22q11)/+, and Df(h1q21)/+
2019-05-01 | GSE129891 | GEO
Project description:Mutations in either nuclear DNA or mitochondrial DNA can result in disruption of oxidative phosphorylation and lead to mitochondrial dysfunction. Mitochondrial disease manifestations occur predominantly in the central nervous system, peripheral nervous system, and/or involve several organ systems. The consequences range from manifestations of a single organ or tissues, such as muscle fatigue, if confined only to muscle, seizures, intellectual disabilities, dementia, and stroke (if to the central nervous system), leading to disability or even early death. The definitive diagnosis of a mitochondrial disorder can be difficult to establish. Criteria and checklists have been established and are more reflective of adult disease. However, in children, when symptoms suggest a mitochondrial disease, neuroimaging features may have more diagnostic impact and additionally these can be used to follow the course, evolution, and recovery of the disease. This review will demonstrate the common neuroimaging patterns in patients with mitochondrial disorders and point out how various newer neuroimaging modalities may be exploited to glean information as to the different aspects of mitochondrial dysfunction or resulting neurological and cognitive disruption, although reports in the literature using these methods remain sparse.
| S-EPMC3625392 | biostudies-literature
Project description:We conducted proteomic profiling of plasma, identifying both overlapping and unique differential proteins in adolescent patients diagnosed with major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SZ). Our results showed that the protein signatures of adolescent psychiatric disorders differ significantly from those of adults. We propose potential targets for drug development aimed at the prevention or precision therapy of these psychiatric disorders. These findings enhance our understanding of the molecular etiology of adolescent psychiatric disorders. The specific biomolecular signatures of MDD, BD, and SZ could potentially serve as targets for the development of novel interventions aimed at prevention, early diagnosis, and treatment of these mental health conditions.
2023-09-18 | PXD045434 |