1,721,179 research outputs found
Time After Time: Electroconvulsive Therapy Modulates the Brain's Functional Network Connectivity Dynamics
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Cerebellar dysconnectivity in schizophrenia and bipolar disorder is associated with cognitive and clinical variables
Full text linkBackground: Abnormal cerebellar functional connectivity (FC) has been implicated in the pathophysiology of schizophrenia (SCZ) and bipolar disorder (BD). However, the patterns of cerebellar dysconnectivity in these two disorders and their association with cognitive functioning and clinical symptoms have not been fully clarified. In this study, we examined cerebellar FC alterations in SCZ and BD-I and their association with cognition and psychotic symptoms. Methods: Resting-state functional magnetic resonance imaging (rs-fMRI) data of 39 SCZ, 43 BD-I, and 61 healthy controls from the Consortium for Neuropsychiatric Phenomics dataset were examined. The cerebellum was parcellated into ten functional networks, and seed-based FC was calculated for each cerebellar system. Principal component analyses were used to reduce the dimensionality of the diagnosis-related FC and cognitive variables. Multiple regression analyses were used to assess the relationship between FC and cognitive and clinical data. Results: We observed decreased cerebellar FC with the frontal, temporal, occipital, and thalamic areas in individuals with SCZ, and a more widespread decrease in cerebellar FC in individuals with BD-I, involving the frontal, cingulate, parietal, temporal, occipital, and thalamic regions. SCZ had increased within-cerebellum and cerebellar frontal FC compared to BD-I. In BD-I, memory and verbal learning performances, which were higher compared to SCZ, showed a greater interaction with cerebellar FC patterns. Additionally, patterns of increased cortico-cerebellar FC were marginally associated with positive symptoms in patients. Conclusions: Our findings suggest that shared and distinct patterns of cortico-cerebellar dysconnectivity in SCZ and BD-I could underlie cognitive impairments and psychotic symptoms in these disorders
Structurally constrained effective brain connectivity
Full text linkThe relationship between structure and function is of interest in many research fields involving the study of complex biological processes. In neuroscience in particular, the fusion of structural and functional data can help to understand the underlying principles of the operational networks in the brain. To address this issue, this paper proposes a constrained autoregressive model leading to a representation of effective connectivity that can be used to better understand how the structure modulates the function. Or simply, it can be used to find novel biomarkers characterizing groups of subjects. In practice, an initial structural connectivity representation is re-weighted to explain the functional co-activations. This is obtained by minimizing the reconstruction error of an autoregressive model constrained by the structural connectivity prior. The model has been designed to also include indirect connections, allowing to split direct and indirect components in the functional connectivity, and it can be used with raw and deconvoluted BOLD signal. The derived representation of dependencies was compared to the well known dynamic causal model, giving results closer to known ground-truth. Further evaluation of the proposed effective network was performed on two typical tasks. In a first experiment the direct functional dependencies were tested on a community detection problem, where the brain was partitioned using the effective networks across multiple subjects. In a second experiment the model was validated in a case-control task, which aimed at differentiating healthy subjects from individuals with autism spectrum disorder. Results showed that using effective connectivity leads to clusters better describing the functional interactions in the community detection task, while maintaining the original structural organization, and obtaining a better discrimination in the case-control classification task
Functional imaging as a tool to investigate the relationship between genetic variation and response to treatment with antipsychotics
No full textRecent evidence suggests that genetic variation is associated with individual variability in response to treatment with antipsychotics. Although numerous studies have been performed for identification of potential genetic variants affecting response to treatment, initial enthusiasm has been tempered by inconsistent results. Along with some specific methodological issues, another plausible explanation for such inconsistencies is lack of sensitivity of the phenotype ( clinical measures) used to define response. In this paper, we review use of Imaging Genetics, a relatively new approach that combines genetic assessment with functional neuroimaging, to explore in vivo neurobiological effects of genetic variation. Moreover, we propose to use Imaging Genetics as a tool to evaluate and predict response to treatment with antipsychotics based on the individual genetic makeu
Late-life suicide: machine learning predictors from a large European longitudinal cohort
Full text linkBackground: People in late adulthood die by suicide at the highest rate worldwide. However, there are still no tools to help predict the risk of death from suicide in old age. Here, we leveraged the Survey of Health, Ageing, and Retirement in Europe (SHARE) prospective dataset to train and test a machine learning model to identify predictors for suicide in late life. Methods: Of more than 16,000 deaths recorded, 74 were suicides. We matched 73 individuals who died by suicide with people who died by accident, according to sex (28.8% female in the total sample), age at death (67 ± 16.4 years), suicidal ideation (measured with the EURO-D scale), and the number of chronic illnesses. A random forest algorithm was trained on demographic data, physical health, depression, and cognitive functioning to extract essential variables for predicting death from suicide and then tested on the test set. Results: The random forest algorithm had an accuracy of 79% (95% CI 0.60-0.92, p = 0.002), a sensitivity of.80, and a specificity of.78. Among the variables contributing to the model performance, the three most important factors were how long the participant was ill before death, the frequency of contact with the next of kin and the number of offspring still alive. Conclusions: Prospective clinical and social information can predict death from suicide with good accuracy in late adulthood. Most of the variables that surfaced as risk factors can be attributed to the construct of social connectedness, which has been shown to play a decisive role in suicide in late life
The functional connectivity of the right superior temporal gyrus is associated with psychological risk and resilience factors for suicidality
Full text linkIntroduction: Suicide attempters show increased activation in the right superior temporal gyrus (rSTG). Here, we investigated the rSTG functional connectivity (FC) to identify a functional network involved in suicidality and its associations with psychological suicidality risk and resilience factors. Methods: The resting state functional magnetic resonance imaging data of 151 healthy individuals from the Human Connectome Project Young Adult database were used to explore the FC of the rSTG with itself and with the rest of the brain. The correlation between the rSTG FC and loneliness and purpose in life scores was assessed with the NIH Toolbox. The effect of sex was also investigated. Results: The rSTG had a positive FC with bilateral cortical and subcortical regions, including frontal, temporal, parietal, occipital, limbic, and cerebellar regions, and a negative FC with the medulla oblongata. The FC of the rSTG with itself and with the left central operculum were associated with loneliness scores. The within rSTG FC was also negatively correlated with purpose in life scores, although at a trend level. We did not find any effect of sex on FC and its associations with psychological factors. Limitations: The cross-sectional design, the limited age range, and the lack of measures of suicidality limit the generalizability of our findings. Conclusions: The rSTG functional network is associated with loneliness and purpose in life. Together with the existing literature on suicide, this supports the idea that the neural activity of rSTG may contribute to suicidality by modulating risk and resilience factors associated with suicidality
Static and Dynamic Dysconnectivity in Early Psychosis: Relationship With Symptom Dimensions
No full textBackground and Hypothesis Altered functional connectivity (FC) has been frequently reported in psychosis. Studying FC and its time-varying patterns in early-stage psychosis allows the investigation of the neural mechanisms of this disorder without the confounding effects of drug treatment or illness-related factors.Study Design We employed resting-state functional magnetic resonance imaging (rs-fMRI) to explore FC in individuals with early psychosis (EP), who also underwent clinical and neuropsychological assessments. 96 EP and 56 demographically matched healthy controls (HC) from the Human Connectome Project for Early Psychosis database were included. Multivariate analyses using spatial group independent component analysis were used to compute static FC and dynamic functional network connectivity (dFNC). Partial correlations between FC measures and clinical and cognitive variables were performed to test brain-behavior associations.Study Results Compared to HC, EP showed higher static FC in the striatum and temporal, frontal, and parietal cortex, as well as lower FC in the frontal, parietal, and occipital gyrus. We found a negative correlation in EP between cognitive function and FC in the right striatum FC (pFWE = 0.009). All dFNC parameters, including dynamism and fluidity measures, were altered in EP, and positive symptoms were negatively correlated with the meta-state changes and the total distance (pFWE = 0.040 and pFWE = 0.049).Conclusions Our findings support the view that psychosis is characterized from the early stages by complex alterations in intrinsic static and dynamic FC, that may ultimately result in positive symptoms and cognitive deficits
Multiparametric assessment of sensorimotor abnormalities in vulnerable populations: A window of opportunity
No full textThis commentary suggests that neuroscience research on young healthy heavy cannabis users and patients with cannabis-induced psychosis using multimodal assessment of sensorimotor dysfunction (e.g. neuroimaging, clinical rating scales, and instrumental assessments) may help to identify both biological resistance and vulnerability without constraints and confounder factors imposed by antipsychotic treatment or disease chronicity
Diagnostic value of regional homogeneity and fractional amplitude of low-frequency fluctuations in the classification of schizophrenia and bipolar disorders
Full text linkSchizophrenia (SCZ) and bipolar disorders (BD) show significant neurobiological and clinical overlap. In this study, we wanted to identify indexes of intrinsic brain activity that could differentiate these disorders. We compared the diagnostic value of the fractional amplitude of low-frequency fluctuations (fALFF) and regional homogeneity (ReHo) estimated from resting-state functional magnetic resonance imaging in a support vector machine classification of 59 healthy controls (HC), 40 individuals with SCZ, and 43 individuals with BD type I. The best performance, measured by balanced accuracy (BAC) for binary classification relative to HC was achieved by a stacking model (87.4% and 90.6% for SCZ and BD, respectively), with ReHo performing better than fALFF, both in SCZ (86.2% vs. 79.4%) and BD (89.9% vs. 76.9%). BD were better differentiated from HC by fronto-temporal ReHo and striato-temporo-thalamic fALFF. SCZ were better classified from HC using fronto-temporal-cerebellar ReHo and insulo-tempo-parietal-cerebellar fALFF. In conclusion, we provided evidence of widespread aberrancies of spontaneous activity and local connectivity in SCZ and BD, demonstrating that ReHo features exhibited superior discriminatory power compared to fALFF and achieved higher classification accuracies. Our results support the complementarity of these measures in the classification of SCZ and BD and suggest the potential for multivariate integration to improve diagnostic precision
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