Multivariate pattern recognition approaches have recently facilitated the search for reliable neuroimaging-based biomarkers in psychiatric disorders such as schizophrenia. By taking into account the ...multivariate nature of brain functional and structural changes as well as their distributed localization across the whole brain, they overcome drawbacks of traditional univariate approaches. To evaluate the overall reliability of neuroimaging-based biomarkers, we conducted a comprehensive literature search to identify all studies that used multivariate pattern recognition to identify patterns of brain alterations that differentiate patients with schizophrenia from healthy controls. A bivariate random-effects meta-analytic model was implemented to investigate the sensitivity and specificity across studies as well as to assess the robustness to potentially confounding variables. In the total sample of n=38 studies (1602 patients and 1637 healthy controls), patients were differentiated from controls with a sensitivity of 80.3% (95% CI: 76.7-83.5%) and a specificity of 80.3% (95% CI: 76.9-83.3%). Analysis of neuroimaging modality indicated higher sensitivity (84.46%, 95% CI: 79.9-88.2%) and similar specificity (76.9%, 95% CI: 71.3-81.6%) of rsfMRI studies as compared with structural MRI studies (sensitivity: 76.4%, 95% CI: 71.9-80.4%, specificity of 79.0%, 95% CI: 74.6-82.8%). Moderator analysis identified significant effects of age (p=0.029), imaging modality (p=0.019), and disease stage (p=0.025) on sensitivity as well as of positive-to-negative symptom ratio (p=0.022) and antipsychotic medication (p=0.016) on specificity. Our results underline the utility of multivariate pattern recognition approaches for the identification of reliable neuroimaging-based biomarkers. Despite the clinical heterogeneity of the schizophrenia phenotype, brain functional and structural alterations differentiate schizophrenic patients from healthy controls with 80% sensitivity and specificity.
Abstract Background Altered serotonin transporter levels have been reported in blood and brain of patients with major depressive disorders. However, the strength and consistency of the evidence for ...altered serotonin transporter availability in major depressive disorder is not clear. Methods To address this, a comprehensive meta-analysis was conducted of all available in vivo neuroimaging and post mortem studies reporting serotonin transporter availability in patients with depression compared with healthy controls. Results The final sample consisted of fifty ( n =27 in vivo and n =25 post mortem) studies including 877 patients with depression (mean age: 42.9 years) and 968 healthy controls (mean age: 42.7 years). In vivo neuroimaging studies indicated reduced serotonin transporter binding in the striatum ( g =−0.39, p =0.01), the amygdala ( g =−0.37, p =0.01) and the brainstem ( g =−0.31, p =0.01), including the midbrain ( g =−0.27, p =0.02), but no significant alteration in the thalamus or the hippocampus. The post mortem findings indicated no significant change in serotonin transporter binding in depression in the brainstem ( p =0.64), the frontal cortex ( p =0.75) and the hippocampus ( p =0.32, corrected for publication bias). Although there were too few studies for a meta-analysis, the post mortem studies in the amygdala and striatum showed reduced SERT binding in MDD in absolute terms, consistent with the imaging findings. Limitations A number of potential factors might have biased the results of the present meta-analysis such as the imaging modality (post mortem or in vivo neuroimaging), partial volume effects, susceptibility of some radiotracers to synaptic serotonin levels or binding to other monoamine transporters. Conclusions The results indicate that serotonin transporter availability in depressed patients is reduced in key regions of the limbic system. This provides direct support for the serotonin hypothesis of depression, and underlines the importance of the serotonin transporter as a target of pharmacological treatments.
Brain derived neurotrophic factor (BDNF) is a critical component of the molecular mechanism of memory formation. Variation in the BDNF gene, particularly the rs6265 (val(66)met) single nucleotide ...polymorphism (SNP), has been linked to variability in human memory performance and to both the structure and physiological response of the hippocampus, which plays a central role in memory processing. However, these effects have not been consistently reported, which may reflect the modest size of the samples studied to date. Employing a meta-analytic approach, we examined the effect of the BDNF val(66)met polymorphism on human memory (5922 subjects) and hippocampal structure (2985 subjects) and physiology (362 subjects). Our results suggest that variations in the rs6265 SNP of the BDNF gene have a significant effect on memory performance, and on both the structure and physiology of the hippocampus, with carriers of the met allele being adversely affected. These results underscore the role of BDNF in moderating variability between individuals in human memory performance and in mediating some of the neurocognitive impairments underlying neuropsychiatric disorders.
Experiences of childhood trauma (CT) are associated with increased psychological vulnerability. Past research suggests that CT might alter stress processing with a subsequent negative impact on ...mental health. However, it is currently unclear how different domains of CT exert effects on specific subjective experiences of stress during adulthood.
In the present study, we used network analysis to explore the complex interplay between distinct domains of CT and perceived stress in a large, general-population sample of middle-aged adults (N = 1252). We used a data-driven community-detection algorithm to identify strongly connected subgroups of items within the network. To assess the replicability of the findings, we repeated the analyses in a second sample (N = 862). Combining data from both samples, we evaluated network differences between men (n = 955) and women (n = 1159).
Results indicate specific associations between distinct domains of CT and perceived stress. CT domains reflecting a dimension of deprivation, i.e. experiences of neglect, were associated exclusively to a stress network community representing low perceived self-efficacy. By contrast, CT associated with threat, i.e. experiences of abuse, was specifically related to a stress community reflecting perceived helplessness. Our results replicated with high accordance in the second sample. We found no difference in network structure between men and women, but overall a stronger connected network in women.
Our findings emphasize the unique role of distinct domains of CT in psychological stress processes in adulthood, implying opportunities for targeted interventions following distinct domains of CT.
Previous studies have shown that structural brain changes are among the best-studied candidate markers for schizophrenia (SZ) along with functional connectivity (FC) alterations of resting-state (RS) ...patterns. This study aimed to investigate effects of clinical and sociodemographic variables on the classification by applying multivariate pattern analysis (MVPA) to both gray matter (GM) volume and FC measures in patients with SZ and healthy controls (HC). RS and structural magnetic resonance imaging data (sMRI) from 74 HC and 71 SZ patients were obtained from a Mind Research Network COBRE dataset available via COINS (http://coins.mrn.org/dx). We used a MVPA framework using support-vector machines embedded in a repeated, nested cross-validation to generate a multi-modal diagnostic system and evaluate its generalizability. The dependence of neurodiagnostic performance on clinical and sociodemographic variables was evaluated. The RS classifier showed a slightly higher accuracy (70.5%) compared to the structural classifier (69.7%). The combination of sMRI and RS outperformed single MRI modalities classification by reaching 75% accuracy. The RS based moderator analysis revealed that the neurodiagnostic performance was driven by older SZ patients with an earlier illness onset and more pronounced negative symptoms. In contrast, there was no linear relationship between the clinical variables and neuroanatomically derived group membership measures. This study achieved higher accuracy distinguishing HC from SZ patients by fusing 2 imaging modalities. In addition the results of RS based moderator analysis showed that age of patients, as well as their age at the illness onset were the most important clinical features.
Abstract Introduction Multiple studies have examined functional and structural brain alteration in patients diagnosed with from Major Depressive Disorder (MDD). The introduction of multivariate ...statistical methods allows investigators to utilize data concerning these brain alterations to generate diagnostic models that accurately differentiate patients with MDD from healthy controls. However, there is substantial heterogeneity in the reported results, the methodological approaches, and the clinical characteristics of participants in these studies. Method We conducted a meta-analysis of all studies using neuroimaging (volumetric measures derived from T1 weighted images, task-based functional MRI, resting-state MRI, or diffusion-tensor imaging) in combination with multivariate statistical methods to differentiate patients diagnosed with MDD from healthy controls. Results Thirty-three (k=33) samples including n=912 patients with MDD and n=894 healthy control subjects were included in the meta-analysis. Across all studies, patients with MDD were separated from healthy control subjects with 77% sensitivity and 78% specificity. Classification based on resting-state MRI (sensitivity of 85%, specificity of 83%) and on DTI data (sensitivity of 88%, specificity of 92%) outperformed classification based on structural MRI (sensitivity of 70%, specificity of 71%) and task-based functional MRI (sensitivity 74%, specificity 77%). Discussion Our results demonstrate the high representational capacity of multivariate statistical methods to identify neuroimaging-based biomarkers of depression. Future studies are needed to elucidate whether multivariate neuroimaging analysis has the potential to generate clinically useful tools for the differential diagnosis of affective disorders and the prediction of both treatment response and functional outcome.
The hypothesis that cortical dopaminergic alterations underlie aspects of schizophrenia has been highly influential.
To bring together and evaluate the imaging evidence for dopaminergic alterations ...in cortical and other extrastriatal regions in schizophrenia.
Electronic databases were searched for in vivo molecular studies of extrastriatal dopaminergic function in schizophrenia. Twenty-three studies (278 patients and 265 controls) were identified. Clinicodemographic and imaging variables were extracted and effect sizes determined for the dopaminergic measures. There were sufficient data to permit meta-analyses for the temporal cortex, thalamus and substantia nigra but not for other regions.
The meta-analysis of dopamine D2/D3 receptor availability found summary effect sizes of d = -0.32 (95% CI -0.68 to 0.03) for the thalamus, d = -0.23 (95% CI -0.54 to 0.07) for the temporal cortex and d = 0.04 (95% CI -0.92 to 0.99) for the substantia nigra. Confidence intervals were wide and all included no difference between groups. Evidence for other measures/regions is limited because of the small number of studies and in some instances inconsistent findings, although significant differences were reported for D2/D3 receptors in the cingulate and uncus, for D1 receptors in the prefrontal cortex and for dopamine transporter availability in the thalamus.
There is a relative paucity of direct evidence for cortical dopaminergic alterations in schizophrenia, and findings are inconclusive. This is surprising given the wide influence of the hypothesis. Large, well-controlled studies in drug-naive patients are warranted to definitively test this hypothesis.
Cannabis use characteristics, such as earlier initiation and frequent use, have been associated with an increased risk for developing psychotic experiences and psychotic disorders. However, little is ...known how these characteristics relate to specific aspects of sub-clinical psychopathology in the general population. Here, we explore the relationships between cannabis use characteristics and psychopathology in a large general population sample (N = 2,544, mean age 29.2 years, 47% women) by employing a network approach. This allows for the identification of unique associations between two cannabis use characteristics (lifetime cumulative frequency of cannabis use, age of cannabis use initiation), and specific psychotic experiences and affective symptoms, while controlling for early risk factors (childhood trauma, urban upbringing). We found particularly pronounced unique positive associations between frequency of cannabis use and specific delusional experiences (persecutory delusions and thought broadcasting). Age of cannabis use initiation was negatively related to visual hallucinatory experiences and irritability, implying that these experiences become more likely the earlier use is initiated. Earlier initiation, but not lifetime frequency of cannabis use, was related to early risk factors. These findings suggest that cannabis use characteristics may contribute differentially to risk for specific psychotic experiences and affective symptoms in the general population.
Abstract To date, research into the biomarker-aided early recognition of psychosis has focused on predicting the transition likelihood of clinically defined individuals with different at-risk mental ...states (ARMS) based on structural (and functional) brain changes. However, it is currently unknown whether neuroimaging patterns could be identified to facilitate the individualized prediction of symptomatic and functional recovery. Therefore, we investigated whether cortical surface alterations analyzed by means of multivariate pattern recognition methods could enable the single-subject identification of functional outcomes in twenty-seven ARMS individuals. Subjects were dichotomized into ‘good’ vs. ‘poor’ outcome groups on average 4 years after the baseline MRI scan using a Global Assessment of Functioning (GAF) threshold of 70. Cortical surface-based pattern classification predicted good (N = 14) vs. poor outcome status (N = 13) at follow-up with an accuracy of 82% as determined by nested leave-one-cross-validation. Neuroanatomical prediction involved cortical area reductions in superior temporal, inferior frontal and inferior parietal areas and was not confounded by functional impairment at baseline, or antipsychotic medication and transition status over the follow-up period. The prediction model's decision scores were correlated with positive and general symptom scores in the ARMS group at follow-up, whereas negative symptoms were not linked to predicted poorer functional outcome. These findings suggest that poorer functional outcomes are associated with non-resolving attenuated psychosis and could be predicted at the single-subject level using multivariate neuroanatomical risk stratification methods. However, the generalizability and specificity of the suggested prediction model should be thoroughly investigated in future large-scale and cross-diagnostic MRI studies.
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