Psychiatry Research: Neuroimaging最新文献

Impulsivity is a trait associated with several psychiatric conditions, not least addictive disorders. While the neural mechanisms behind certain aspects of impulsivity have been studied extensively, there are few imaging studies examining this neurocircuitry in populations with substance use disorders. Therefore, we aimed to examine the functional connectivity of relevant neural networks, and their possible association with trait impulsivity, in a sample with severe amphetamine use disorder and a control group of healthy subjects. We used data collected in a randomized clinical trial studying the acute effects of oral naltrexone in amphetamine use disorder. Our final sample included 32 amphetamine users and 27 healthy controls. Trait impulsivity was rated with the Barratt Impulsiveness Scale-11, and functional connectivity was measured during resting-state fMRI, looking specifically at networks involving prefrontal regions previously implicated in studies of impulsivity. Amphetamine users had higher subjective ratings of impulsivity as compared to healthy controls, and these scores correlated positively with a wide-spread prefrontal hyperconnectivity that was found among the amphetamine users. These findings highlight the importance of aberrant prefrontal function in severe addiction.

Electroconvulsive therapy (ECT) demonstrates favorable outcomes in the management of severe depressive disorders. ECT has been consistently associated with volumetric increases in the amygdala and hippocampus. However, the underlying mechanisms of these structural changes and their association to clinical improvement remains unclear. In this cross-sectional structural MRI study, we assessed the difference in amygdala subnuclei and hippocampus subfields in n = 37 patients with either unipolar or bipolar disorder immediately after eighth ECT sessions compared to (n = 40) demographically matched patients in partial remission who did not receive ECT (NoECT group). Relative to NoECT, the ECT group showed significantly larger bilateral amygdala volumes post-treatment, with the effect originating from the lateral, basal, and paralaminar nuclei and the left corticoamydaloid transition area. No significant group differences were observed for the hippocampal or cortical volumes. ECT was associated with a significant decrease in depressive symptoms. However, there were no significant correlations between amygdala subnuclei volumes and symptom improvement. Our study corroborates previous reports on increased amygdalae volumes following ECT and further identifies the subnuclei driving this effect. However, the therapeutic effect of ECT does not seem to be directly related to structural changes in the amygdala.

Autism is a neurodevelopmental disorder that manifests in individuals during childhood and has enduring consequences for their social interactions and communication. The prediction of Autism Spectrum Disorder (ASD) in individuals based on the differences in brain networks and activities have been studied extensively in the recent past, however, with lower accuracies. Therefore in this research, identification at the early stage through computer-aided algorithms to differentiate between ASD and TD patients is proposed. In order to identify features, a Multi-Layer Perceptron (MLP) model is developed which utilizes logistic regression on characteristics extracted from connectivity matrices of subjects derived from fMRI images. The features that significantly contribute to the classification of individuals as having Autism Spectrum Disorder (ASD) or typically developing (TD) are identified by the logistic regression model. To enhance emphasis on essential attributes, an AND operation is integrated. This involves selecting features demonstrating statistical significance across diverse logistic regression analyses conducted on various random distributions. The iterative approach contributes to a comprehensive understanding of relevant features for accurate classification. By implementing this methodology, the estimation of feature importance became more dependable, and the potential for overfitting is moderated through the evaluation of model performance on various subsets of data. It is observed from the experimentation that the highly correlated Left Lateral Occipital Cortex and Right Lateral Occipital Cortex ROIs are only found in ASD. Also, it is noticed that the highly correlated Left Cerebellum Tonsil and Right Cerebellum Tonsil are only found in TD participants. Among the MLP classifier, a recall of 82.61 % is achieved followed by Logistic Regression with an accuracy of 72.46 %. MLP also stands out with a commendable accuracy of 83.57 % and AUC of 0.978.

Background

While prior studies have explored the efficacy of Morinda officinalis oligosaccharides (MOs) as a treatment for patients with major depressive disorder (MDD), the mechanistic basis for the effects of MOs on brain function or the default-mode network (DMN) has yet to be characterized. The objective of this was to examine the effects of MOs treatment on functional connectivity in different regions of the DMN.

Methods

In total, 27 MDD patients and 29 healthy control subjects (HCs) underwent resting-state functional magnetic resonance imaging. The patients were then treated with MOs for 8 weeks, and scanning was performed at baseline and the end of the 8-week treatment period. Changes in DMN homogeneity associated with MOs treatment were assessed using network homogeneity (NH) analyses of the imaging data, and pattern classification approaches were employed to determine whether abnormal baseline NH deficits could differentiate between MDD patients and controls. The ability of NH abnormalities to predict patient responses to MOs treatment was also evaluated.

Results

Relative to HCs, patients exhibited a baseline reduction in NH values in the right precuneus (PCu). At the end of the 8-week treatment period, the MDD patients showed reduced and increased NH values in the right PCu and left superior medial frontal gyrus (SMFG), respectively. Compared to these patients at baseline, the 8-week MOs treatment was associated with reduced NH values in the right angular gyrus and increased NH values in the left middle temporal gyrus and the right PCu. Support vector machine (SVM) analyses revealed that NH abnormalities in the right PCu and left SMFG were the most accurate (87.50%) for differentiating between MDD patients and HCs. Conclusion: These results indicated that MOs treatment could alter default-mode NH in patients with MDD. The results provide a foundation for elucidation of the effects of MOs on brain function and suggest that the distinctive NH patterns observed in this study may be useful as imaging biomarkers for distinguishing between patients with MDD and healthy subjects.

Transcranial magnetic stimulation (TMS) is an FDA-approved neuromodulation treatment for major depressive disorder (MDD), thought to work by altering dysfunctional brain connectivity pathways, or by indirectly modulating the activity of subcortical brain regions. Clinical response to TMS remains highly variable, highlighting the need for baseline predictors of response and for understanding brain changes associated with response. This systematic review examined brain connectivity features, and changes in connectivity features, associated with clinical improvement following TMS in MDD. Forty-one studies met inclusion criteria, including 1097 people with MDD. Most studies delivered one of two types of TMS to left dorsolateral prefrontal cortex and measured connectivity using resting-state functional MRI. The subgenual anterior cingulate cortex was the most well-studied brain region, particularly its connectivity with the TMS target or with the “executive control network” of brain regions. There was marked heterogeneity in findings. There is a need for greater understanding of how cortical TMS modulates connectivity with, and the activity of, subcortical regions, and how these effects change within and across treatment sessions.

Background

The incidence rate of Posttraumatic stress disorder (PTSD) is currently increasing due to wars, terrorism, and pandemic disease situations. Therefore, accurate detection of PTSD is crucial for the treatment of the patients, for this purpose, the present study aims to classify individuals with PTSD versus healthy control.

Methods

The resting-state functional MRI (rs-fMRI) scans of 19 PTSD and 24 healthy control male subjects have been used to identify the activation pattern in most affected brain regions using group-level independent component analysis (ICA) and t-test. To classify PTSD-affected subjects from healthy control six machine learning techniques including random forest, Naive Bayes, support vector machine, decision tree, K-nearest neighbor, linear discriminant analysis, and deep learning three-dimensional 3D-CNN have been performed on the data and compared.

Results

The rs-fMRI scans of the most commonly investigated 11 regions of trauma-exposed and healthy brains are analyzed to observe their level of activation. Amygdala and insula regions are determined as the most activated regions from the regions-of-interest in the brain of PTSD subjects. In addition, machine learning techniques have been applied to the components extracted from ICA but the models provided low classification accuracy. The ICA components are also fed into the 3D-CNN model, which is trained with a 5-fold cross-validation method. The 3D-CNN model demonstrated high accuracies, such as 98.12%, 98.25 %, and 98.00 % on average with training, validation, and testing datasets, respectively.

Conclusion

The findings indicate that 3D-CNN is a surpassing method than the other six considered techniques and it helps to recognize PTSD patients accurately.

The purpose of this study was to assess the functional connectivity of the posterior cingulate cortex in autism spectrum disorder (ASD). We used resting-state functional magnetic resonance imaging (rsfMRI) brain scans of adolescents diagnosed with ASD and a neurotypical control group. The Autism Brain Imaging Data Exchange (ABIDE) consortium was utilized to acquire data from the University of Michigan (145 subjects) and data from the New York University (183 subjects). The posterior cingulate cortex showed reduced connectivity with the anterior cingulate cortex for the ASD group compared to the control group. These two brain regions have previously both been linked to ASD symptomology. Specifically, the posterior cingulate cortex has been associated with behavioral control and executive functions, which appear to be responsible for the repetitive and restricted behaviors (RRB) in ASD. Our findings support previous data indicating a neurobiological basis of the disorder, and the specific functional connectivity changes involving the posterior cingulate cortex and anterior cingulate cortex may be a potential neurobiological biomarker for the observed RRBs in ASD.

We aimed to examine the hippocampus and amygdala volumes in patients with schizoaffective disorder with the notion that schizoaffective disorder has strong resemblance of clinical presentation with schizophrenia and bipolar disorder and that there have been studies on regions of interest volumes in patients with schizophrenia and bipolar disorder but not in patients with schizoaffective disorder. Eighteen patients with schizoaffective disorder and nineteen healthy controls were included into the study. Hippocampus and amygdala volumes were examined by using the MRI. Both hippocampus and amygdala volumes were statistically significantly reduced in patients with schizoaffective disorder compared to those of the healthy control comparisons (p<0.001 for the hippocampus and p<0.001 for the amygdala). In summary, our findings of the present study suggest that patients with schizoaffective disorder seem to have smaller volumes of the hippocampus and amygdala regions and that our results were in accordance with those obtained both in patients with schizophrenia and bipolar disorder, considering that schizoaffective disorder might have neuroanatomic similarities with both schizophrenia and bipolar disorder. Beacuse of some limitations aforementioned especially age, it is required to replicate our present results in this patient group.

A substantial portion of schizophrenia spectrum disorder (SSD) patients exhibit resistance to antipsychotic treatments, emphasizing the need for reliable treatment response biomarkers. Previous magnetic resonance imaging (MRI) studies have identified various imaging predictors in SSD. This study focuses on evaluating the effectiveness of diffusion MRI sequences, diffusion tensor imaging (DTI) and diffusion-weighted imaging (DWI), in predicting antipsychotic response in SSD patients. A systematic search for relevant articles was conducted in PubMed, Embase, Scopus, and Web of Science on February 11, 2024. Twelve studies involving a total of 742 patients were systematically reviewed. The baseline DTI/DWI biomarkers revealed significant associations with antipsychotic treatment response. Notably a consistent negative link was found between response and baseline fractional anisotropy (FA) in fronto-temporo-limbic white matter tracts, specifically the superior longitudinal fasciculus, providing moderate-level evidence. In addition, weak-level evidence was found for the negative association between the treatment response and baseline FA in the corpus callosum, internal, and external capsule tracts. Collectively, this review demonstrated that obtaining pre-treatment brain diffusion MRI scans, particularly from white matter tracts of fronto-temporo-limbic network, can assist in delineating the treatment response trajectory in patients with SSD. However, additional larger randomized controlled trials are required to further substantiate these findings.

Schizophrenia is associated with robust white matter (WM) abnormalities but influences of potentially confounding variables and relationships with cognitive performance and symptom severity remain to be fully determined. This study was designed to evaluate WM abnormalities based on diffusion tensor imaging (DTI) in individuals with schizophrenia, and their relationships with cognitive performance and symptom severity. Data from individuals with schizophrenia (SZ; n=138, mean age$\pm$SD=39.02$\pm$11.82; 105 males) and healthy controls (HC; n=143, mean age$\pm$SD=37.07$\pm$10.84; 102 males) were collected as part of the Function Biomedical Informatics Research Network Phase 3 study. Fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD), and mean diffusivity (MD) were compared between individuals with schizophrenia and healthy controls, and their relationships with neurocognitive performance and symptomatology assessed. Individuals with SZ had significantly lower FA in forceps minor and the left inferior fronto-occipital fasciculus compared to HC. FA in several tracts were associated with speed of processing and attention/vigilance and the severity of the negative symptom alogia. This study suggests that regional WM abnormalities are fundamentally involved in the pathophysiology of schizophrenia and may contribute to cognitive performance deficits and symptom expression observed in schizophrenia.