Brain correlates of depression, post-traumatic distress, and inflammatory biomarkers in COVID-19 survivors: A multimodal magnetic resonance imaging study.
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ABSTRACT:
SUBMITTER: Benedetti F
PROVIDER: S-EPMC8562046 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Cognitive impairment represents a leading residual symptom of COVID-19 infection, which lasts for months after the virus clearance. Up-to-date scientific reports documented a wide spectrum of brain changes in COVID-19 survivors following the illness's resolution, mainly related to neurological and neuropsychiatric consequences. Preliminary insights suggest abnormal brain metabolism, microstructure, and functionality as neural under-layer of post-acute cognitive dysfunction. While previous works focused on brain correlates of impaired cognition as objectively assessed, herein we investigated long-term neural correlates of subjective cognitive decline in a sample of 58 COVID-19 survivors with a multimodal imaging approach. Diffusion Tensor Imaging (DTI) analyses revealed widespread white matter disruption in the sub-group of cognitive complainers compared to the non-complainer one, as indexed by increased axial, radial, and mean diffusivity in several commissural, projection and associative fibres. Likewise, the Multivoxel Pattern Connectivity analysis (MVPA) revealed highly discriminant patterns of functional connectivity in resting-state among the two groups in the right frontal pole and in the middle temporal gyrus, suggestive of inefficient dynamic modulation of frontal brain activity and possible metacognitive dysfunction at rest. Beyond COVID-19 actual pathophysiological brain processes, our findings point toward brain connectome disruption conceivably translating into clinical post-COVID cognitive symptomatology. Our results could pave the way for a potential brain signature of cognitive complaints experienced by COVID-19 survivors, possibly leading to identify early therapeutic targets and thus mitigating its detrimental long-term impact on quality of life in the post-COVID-19 stages.
| S-EPMC9742225 | biostudies-literature
Project description:BackgroundPost-traumatic stress disorder (PTSD) can occur after life-threatening events, including illness, but correlates of PTSD after stroke or transient ischemic attack (TIA) have not been well described.MethodsWe measured the prevalence of stroke-induced PTSD with the PTSD Checklist Specific for stroke (PCL-S) in adults who had a stroke or TIA within 5 years. A PCL-S score of 50 or more indicated likely PTSD. We tested for potential predictors of stroke-associated PTSD, including demographics, stroke history, disability, medical comorbidities, depression, and emotional support and then examined the association between poststroke PTSD and measures of physical and mental health.ResultsOf 535 participants, 95 (18%) had a PCL-S score of 50 or more; the mean score was 35.4 ± 13.7 (range 17-80 of 85). In logistic regression analysis, low income (odds ratio [OR] 1.98, 95% confidence interval [CI] 1.01-3.61), recurrent stroke or TIA (OR 1.86, 1.10-3.16), more disability (OR 1.79, 1.43-2.23), and increased comorbidities (OR 1.90, 1.05-3.45) were independently associated with PTSD. Older age (OR .93, .90-.95), marriage or partnership (OR .52, .28-.98), and having emotional support (OR .25, .11-.54) were protective against developing PTSD. Participants with likely PTSD had worse physical and mental health.ConclusionsIn this racially and ethnically diverse cohort of stroke and TIA survivors, stroke-induced PTSD was associated with younger age, recurrent strokes, greater disability, and comorbidities. PTSD was associated with a substantially increased physical, mental, and quality of life burden in this already vulnerable population. Having social support was protective, suggesting a potential target for intervention.
| S-EPMC3992186 | biostudies-literature
Project description:BackgroundTraumatic brain injury (TBI) can alter brain structure and lead to onset of persistent neuropsychological symptoms. This study investigates the relationship between brain injury and psychological distress after mild TBI using multimodal magnetic resonance imaging.MethodsA total of 89 patients with mild TBI from the TRACK-TBI (Transforming Research and Clinical Knowledge in Traumatic Brain Injury) pilot study were included. Subscales of the Brief Symptoms Inventory 18 for depression, anxiety, and somatization were used as outcome measures of psychological distress approximately 6 months after the traumatic event. Glasgow Coma Scale scores were used to evaluate recovery. Magnetic resonance imaging data were acquired within 2 weeks after injury. Perivascular spaces (PVSs) were segmented using an enhanced PVS segmentation method, and the volume fraction was calculated for the whole brain and white matter regions. Cortical thickness and gray matter structures volumes were calculated in FreeSurfer; diffusion imaging indices and multifiber tracts were extracted using the Quantitative Imaging Toolkit. The analysis was performed considering age, sex, intracranial volume, educational attainment, and improvement level upon discharge as covariates.ResultsPVS fractions in the posterior cingulate, fusiform, and postcentral areas were found to be associated with somatization symptoms. Depression, anxiety, and somatization symptoms were associated with the cortical thickness of the frontal-opercularis and occipital pole, putamen and amygdala volumes, and corticospinal tract and superior thalamic radiation. Analyses were also performed on the two hemispheres separately to explore lateralization.ConclusionsThis study shows how PVS, cortical, and microstructural changes can predict the onset of depression, anxiety, and somatization symptoms in patients with mild TBI.
| S-EPMC10382710 | biostudies-literature
Project description:Post-traumatic stress disorder (PTSD) is a debilitating psychiatric disorder. It can be difficult to discern the symptoms of PTSD and obtain an accurate diagnosis. Different magnetic resonance imaging (MRI) modalities focus on different aspects, which may provide complementary information for PTSD discrimination. However, none of the published studies assessed the diagnostic potential of multimodal MRI in identifying individuals with and without PTSD. In the current study, we investigated whether the complementary information conveyed by multimodal MRI scans could be combined to improve PTSD classification performance. Structural and resting-state functional MRI (rs-fMRI) scans were conducted on 17 PTSD patients, 20 trauma-exposed controls without PTSD (TEC) and 20 non-traumatized healthy controls (HC). Gray matter volume (GMV), amplitude of low-frequency fluctuations (ALFF), and regional homogeneity were extracted as classification features, and in order to integrate the information of structural and functional MRI data, the extracted features were combined by a multi-kernel combination strategy. Then a support vector machine (SVM) classifier was trained to distinguish the subjects at individual level. The performance of the classifier was evaluated using the leave-one-out cross-validation (LOOCV) method. In the pairwise comparison of PTSD, TEC, and HC groups, classification accuracies obtained by the proposed approach were 2.70, 2.50, and 2.71% higher than the best single feature way, with the accuracies of 89.19, 90.00, and 67.57% for PTSD vs. HC, TEC vs. HC, and PTSD vs. TEC respectively. The proposed approach could improve PTSD identification at individual level. Additionally, it provides preliminary support to develop the multimodal MRI method as a clinical diagnostic aid.
| S-EPMC4919361 | biostudies-literature
Project description:Background and Objectives: The annual global incidence of traumatic brain injury (TBI) is over 10 million. An estimated 29% of TBI patients with negative computed tomography (CT-) have positive magnetic resonance imaging (MRI+) findings. Judicious use of serum biomarkers with MRI may aid in diagnosis of CT-occult TBI. The current manuscript aimed to evaluate the diagnostic, therapeutic and risk-stratification utility of known biomarkers and intracranial MRI pathology. Materials and Methods: The PubMed database was queried with keywords (plasma OR serum) AND (biomarker OR marker OR protein) AND (brain injury/trauma OR head injury/trauma OR concussion) AND (magnetic resonance imaging/MRI) (title/abstract) in English. Seventeen articles on TBI biomarkers and MRI were included: S100 calcium-binding protein B (S100B; N = 6), glial fibrillary acidic protein (GFAP; N = 3), GFAP/ubiquitin carboxyl-terminal hydrolase-L1 (UCH-L1; N = 2), Tau (N = 2), neurofilament-light (NF-L; N = 2), alpha-synuclein (N = 1), and alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor peptide (AMPAR; N = 1). Results: Acute GFAP distinguished CT-/MRI+ from CT-/MRI- (AUC = 0.777, 0.852 at 9-16 h). GFAP discriminated CT-/diffuse axonal injury (DAI+) from controls (AUC = 0.903). Tau correlated directly with number of head strikes and inversely with white matter fractional anisotropy (FA), and a cutoff > 1.5 pg/mL discriminated between DAI+ and DAI- (sensitivity = 74%/specificity = 69%). NF-L had 100% discrimination of DAI in severe TBI and correlated with FA. Low alpha-synuclein was associated with poorer functional connectivity. AMPAR cutoff > 0.4 ng/mL had a sensitivity of 91% and a specificity of 92% for concussion and was associated with minor MRI findings. Low/undetectable S100B had a high negative predictive value for CT/MRI pathology. UCH-L1 showed no notable correlations with MRI. Conclusions: An acute circulating biomarker capable of discriminating intracranial MRI abnormalities is critical to establishing diagnosis for CT-occult TBI and can triage patients who may benefit from outpatient MRI, surveillance and/or follow up with TBI specialists. GFAP has shown diagnostic potential for MRI findings such as DAI and awaits further validation. Tau shows promise in detecting DAI and disrupted functional connectivity. Candidate biomarkers should be evaluated within the context of analytical performance of the assays used, as well as the post-injury timeframe for blood collection relative to MRI abnormalities.
| S-EPMC7074393 | biostudies-literature
Project description:Depressive symptoms are common in multiple psychiatric disorders and are frequent sequelae of trauma. A dimensional conceptualization of depression suggests that symptoms should be associated with a continuum of deficits in specific neural circuits. However, most prior investigations of abnormalities in functional connectivity have typically focused on a single diagnostic category using hypothesis-driven seed-based analyses. Here, using a sample of 105 adult female participants from three diagnostic groups (healthy controls, n=17; major depression, n=38; and post-traumatic stress disorder, n=50), we examine the dimensional relationship between resting-state functional dysconnectivity and severity of depressive symptoms across diagnostic categories using a data-driven analysis (multivariate distance-based matrix regression). This connectome-wide analysis identified foci of dysconnectivity associated with depression severity in the bilateral amygdala. Follow-up seed analyses using subject-specific amygdala segmentations revealed that depression severity was associated with amygdalo-frontal hypo-connectivity in a network of regions including bilateral dorsolateral prefrontal cortex, anterior cingulate and anterior insula. In contrast, anxiety was associated with elevated connectivity between the amygdala and the ventromedial prefrontal cortex. Taken together, these results emphasize the centrality of the amygdala in the pathophysiology of depressive symptoms, and suggest that dissociable patterns of amygdalo-frontal dysconnectivity are a critical neurobiological feature across clinical diagnostic categories.
| S-EPMC4840084 | biostudies-literature
Project description:Contemporary symptom-based diagnosis of post-traumatic stress disorder (PTSD) largely overlooks related neurobehavioral mechanisms and relies entirely on subjective interpersonal reporting. Previous studies associating biomarkers with PTSD have mostly used symptom-based diagnosis as the main outcome measure, disregarding the wide variability and richness of PTSD phenotypical features. Here, we aimed to computationally derive potential biomarkers that could efficiently differentiate PTSD subtypes among recent trauma survivors. A three-staged semi-unsupervised method ("3C") was used to firstly categorize individuals by current PTSD symptom severity, then derive clusters based on clinical features related to PTSD (e.g. anxiety and depression), and finally to classify participants' cluster membership using objective multi-domain features. A total of 256 features were extracted from psychometrics, cognitive functioning, and both structural and functional MRI data, obtained from 101 adult civilians (age = 34.80 ± 11.95; 51 females) evaluated within 1 month of trauma exposure. The features that best differentiated cluster membership were assessed by importance analysis, classification tree, and ANOVA. Results revealed that entorhinal and rostral anterior cingulate cortices volumes (structural MRI domain), in-task amygdala's functional connectivity with the insula and thalamus (functional MRI domain), executive function and cognitive flexibility (cognitive testing domain) best differentiated between two clusters associated with PTSD severity. Cross-validation established the results' robustness and consistency within this sample. The neural and cognitive potential biomarkers revealed by the 3C analytics offer objective classifiers of post-traumatic morbidity shortly following trauma. They also map onto previously documented neurobehavioral mechanisms associated with PTSD and demonstrate the usefulness of standardized and objective measurements as differentiating clinical sub-classes shortly after trauma.
| S-EPMC7320966 | biostudies-literature
Project description:BACKGROUND:Disorders with psychotic features, including schizophrenia and some bipolar disorders, are associated with impairments in regulation of goal-directed behavior, termed cognitive control. Cognitive control-related neural alterations have been studied in psychosis. However, studies are typically unimodal, and relationships across modalities of brain function and structure remain unclear. Thus, we performed transdiagnostic multimodal analyses to examine cognitive control-related neural variation in psychosis. METHODS:Structural, resting, and working memory task imaging for 31 control participants, 27 participants with bipolar disorder, and 23 participants with schizophrenia were collected and processed identically to the Human Connectome Project, enabling identification of relationships with prior multimodal work. Two cognitive control-related independent components (ICs) derived from the Human Connectome Project using multiset canonical correlation analysis with joint IC analysis were used to predict performance in psychosis. De novo multiset canonical correlation analysis with joint IC analysis was performed, and the results were correlated with cognitive control. RESULTS:A priori working memory and cortical thickness maps significantly predicted cognitive control in psychosis. De novo multiset canonical correlation analysis with joint IC analysis identified an IC correlated with cognitive control that also discriminated groups. Structural contributions included insular and cingulate regions; task contributions included precentral, posterior parietal, cingulate, and visual regions; and resting-state contributions highlighted canonical network organization. Follow-up analyses suggested that correlations with cognitive control were primarily influenced by participants with schizophrenia. CONCLUSIONS:A priori and de novo imaging replicably identified a set of interrelated patterns across modalities and the healthy-to-psychosis spectrum, suggesting robustness of these features. Relationships between imaging and cognitive control performance suggest that shared symptomatology may be key to identifying transdiagnostic relationships in psychosis.
| S-EPMC6842450 | biostudies-literature
Project description:Sepsis is a life-threatening condition with high hospital mortality. Elevated mortality has also been observed in patients after hospital discharge, associated with post sepsis syndrome (PSS), causing systemic impairments and reduced life quality. The etiology of PSS is still not completely known but certainly involves inflammation. Extracellular vesicles (EV) are recognized as a notable mechanism of intercellular communication in inflammatory processes. It has been reported that EV microRNA (miRNA) production patterns during the acute phase of the disease may persist until after sepsis resolution and are associated with PSS. Methods: We employed mass spectrometry and qPCR to characterize the protein and miRNA composition of plasma-derived EVs of 35 patients during sepsis-related hospitalization and after discharge (post-sepsis) for up to three years. Findings: Fifteen differentially expressed EVs miRNAs (DEMiRs) were identified in septic patients compared to the control group. Predictive analyses revealed that these DEMiRs could influence inflammation by modulating pathways mediated by the activation of NF-κB, STAT3, and TLR4. Thirteen miRNAs (-15b-5p, -16-5p, -20a-5p, -25-3p, -27a-3p, -29a-3p, -30d-5p, -93-5p, -146a-5p, -148a -3p, -191-5p, -195-5p, -223-3p) were downregulated in the death group compared to the survivor group and are candidates for serving as prognostic markers of survival in the Intensive Care Unit. The expression of 11 miRNAs (-15b-5p, -16-5p, -21-5p, -25-3p, -27a-3p, -29a-3p, -30d-5p, -93-5p, -146a-5p -195- 5p and -223-3p) was lower one year after ICU discharge than the control group. Interpretation: The miRNAs identified in the present study represent potential biomarkers for the survival prognosis of post-sepsis patients.
2022-08-03 | GSE210335 | GEO
Project description:ObjectivePost-traumatic stress disorder (PTSD) is a severe psychiatric disorder, which might develop after a traumatic event, like cancer diagnosis, and threatens the patient's psychological and/or physiological integrity. Anxiety, depression, and mental distress are known to be common in cancer patients; however, the frequency of PTSD was not investigated thoroughly in this patient group so far. Here, we aim to screen cancer patients for PTSD symptoms and determine a possible correlation with anxiety, depression, and distress.MethodsThe study was performed at the Divisions of Hematology and Oncology of the Medical University of Vienna from 2010 to 2018. Following written consent, patients were asked to fill out the validated self-assessment questionnaire for PTSS-10 and HADS. The study was approved by the institutional ethics committee of the Medical University of Vienna (EC Nr: 2255/2016).ResultsA total of 1017 adult cancer patients (513 male, 504 female) were included in a cross-sectional single-center study. Mean age was 57.6 years (SD 14.4 years); 31.7%, 14.6%, 13.2%, and 27.4% of patients outscored the predefined thresholds for self-assessed cases of PTSD, anxiety, depression, and distress, respectively. Compared with men, women showed a higher prevalence of symptoms for PTSD (38.9% vs 24.5%; P < .001) and anxiety (20.4% vs 8.6%; P < .001). The scores of HADS-A, HADS-D, and the combined HADS score (distress) were significantly correlated with PTSS-10 scores (P < .01). No differences in age were observed among the different score groups.ConclusionThe study shows a significant prevalence as well as a correlation of PTSD symptoms with anxiety, depression, and distress among cancer patients. Findings underscore the necessity of a serious screening for psychiatric disorders, especially in female patients. In order to enable multidisciplinary care for cancer patients and to reduce the burden for psychiatric disorders, interdisciplinary screening and treatment concepts, which take into account gender aspects, are urged.
| S-EPMC6916606 | biostudies-literature