Serotonergic influence on depressive symptoms and trait anxiety is mediated by negative life events and frontal activation in children and adolescents.

Depression and anxiety are common in childhood and adolescence. Even though cardinal symptoms differ, there is a considerable overlap regarding the pathogenic influence of serotonergic innervation, negative life experience, disturbed emotion perception/affect regulation, and impaired neural functioning in the fronto-limbic circuit. In this study, we examined the effect of the 5-HTTLPR/rs25531 genotype on depressive symptoms and trait anxiety under the consideration of the amount of negative life events in healthy children and adolescents (N = 389). In a subsample of 49 subjects, we performed fMRI to add fronto-limbic brain activation as a second interacting factor. Across all subjects, negative life events moderated the influence of the 5-HTTLPR/rs25531 genotype on both depressive symptoms and trait anxiety. In the fMRI subsample, 5-HTTLPR/rs25531 S + S/LG + S/LA + LGLA + LGLG genotype-associated left middle frontal gyrus (MFG) activation mediated the influence of 5-HTTLPR/rs25531 genotype on depressive symptoms, however, only in combination with negative life events. Genetic influence on trait anxiety was predominantly mediated by negative life events; only LALA genotype-specific activation in the right MFG worked as a mediator in combination with negative life events. The present findings hint towards distinct mechanisms mediating the influence of 5-HTTLPR/rs25531 genotype on depressive symptoms and anxiety, with negative life events playing a crucial role in both phenotypes. With regard to depressive symptoms, however, this influence was only visible in combination with MFG activation, whereas, in anxiety, it was independent of brain activation.

Multisite Machine Learning Analysis Provides a Robust Structural Imaging Signature of Schizophrenia Detectable Across Diverse Patient Populations and Within Individuals.

Past work on relatively small, single-site studies using regional volumetry, and more recently machine learning methods, has shown that widespread structural brain abnormalities are prominent in schizophrenia. However, to be clinically useful, structural imaging biomarkers must integrate high-dimensional data and provide reproducible results across clinical populations and on an individual person basis. Using advanced multi-variate analysis tools and pooled data from case-control imaging studies conducted at 5 sites (941 adult participants, including 440 patients with schizophrenia), a neuroanatomical signature of patients with schizophrenia was found, and its robustness and reproducibility across sites, populations, and scanners, was established for single-patient classification. Analyses were conducted at multiple scales, including regional volumes, voxelwise measures, and complex distributed patterns. Single-subject classification was tested for single-site, pooled-site, and leave-site-out generalizability. Regional and voxelwise analyses revealed a pattern of widespread reduced regional gray matter volume, particularly in the medial prefrontal, temporolimbic and peri-Sylvian cortex, along with ventricular and pallidum enlargement. Multivariate classification using pooled data achieved a cross-validated prediction accuracy of 76% (AUC = 0.84). Critically, the leave-site-out validation of the detected schizophrenia signature showed accuracy/AUC range of 72-77%/0.73-0.91, suggesting a robust generalizability across sites and patient cohorts. Finally, individualized patient classifications displayed significant correlations with clinical measures of negative, but not positive, symptoms. Taken together, these results emphasize the potential for structural neuroimaging data to provide a robust and reproducible imaging signature of schizophrenia. A web-accessible portal is offered to allow the community to obtain individualized classifications of magnetic resonance imaging scans using the methods described herein.

Prediction of outcome in the psychosis prodrome using neuroanatomical pattern classification.

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 4years 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.

Individualized differential diagnosis of schizophrenia and mood disorders using neuroanatomical biomarkers.

Magnetic resonance imaging-based markers of schizophrenia have been repeatedly shown to separate patients from healthy controls at the single-subject level, but it remains unclear whether these markers reliably distinguish schizophrenia from mood disorders across the life span and generalize to new patients as well as to early stages of these illnesses. The current study used structural MRI-based multivariate pattern classification to (i) identify and cross-validate a differential diagnostic signature separating patients with first-episode and recurrent stages of schizophrenia (n = 158) from patients with major depression (n = 104); and (ii) quantify the impact of major clinical variables, including disease stage, age of disease onset and accelerated brain ageing on the signature's classification performance. This diagnostic magnetic resonance imaging signature was then evaluated in an independent patient cohort from two different centres to test its generalizability to individuals with bipolar disorder (n = 35), first-episode psychosis (n = 23) and clinically defined at-risk mental states for psychosis (n = 89). Neuroanatomical diagnosis was correct in 80% and 72% of patients with major depression and schizophrenia, respectively, and involved a pattern of prefronto-temporo-limbic volume reductions and premotor, somatosensory and subcortical increments in schizophrenia versus major depression. Diagnostic performance was not influenced by the presence of depressive symptoms in schizophrenia or psychotic symptoms in major depression, but earlier disease onset and accelerated brain ageing promoted misclassification in major depression due to an increased neuroanatomical schizophrenia likeness of these patients. Furthermore, disease stage significantly moderated neuroanatomical diagnosis as recurrently-ill patients had higher misclassification rates (major depression: 23%; schizophrenia: 29%) than first-episode patients (major depression: 15%; schizophrenia: 12%). Finally, the trained biomarker assigned 74% of the bipolar patients to the major depression group, while 83% of the first-episode psychosis patients and 77% and 61% of the individuals with an ultra-high risk and low-risk state, respectively, were labelled with schizophrenia. Our findings suggest that neuroanatomical information may provide generalizable diagnostic tools distinguishing schizophrenia from mood disorders early in the course of psychosis. Disease course-related variables such as age of disease onset and disease stage as well alterations of structural brain maturation may strongly impact on the neuroanatomical separability of major depression and schizophrenia.

Detecting the psychosis prodrome across high-risk populations using neuroanatomical biomarkers.

To date, the MRI-based individualized prediction of psychosis has only been demonstrated in single-site studies. It remains unclear if MRI biomarkers generalize across different centers and MR scanners and represent accurate surrogates of the risk for developing this devastating illness. Therefore, we assessed whether a MRI-based prediction system identified patients with a later disease transition among 73 clinically defined high-risk persons recruited at two different early recognition centers. Prognostic performance was measured using cross-validation, independent test validation, and Kaplan-Meier survival analysis. Transition outcomes were correctly predicted in 80% of test cases (sensitivity: 76%, specificity: 85%, positive likelihood ratio: 5.1). Thus, given a 54-month transition risk of 45% across both centers, MRI-based predictors provided a 36%-increase of prognostic certainty. After stratifying individuals into low-, intermediate-, and high-risk groups using the predictor's decision score, the high- vs low-risk groups had median psychosis-free survival times of 5 vs 51 months and transition rates of 88% vs 8%. The predictor's decision function involved gray matter volume alterations in prefrontal, perisylvian, and subcortical structures. Our results support the existence of a cross-center neuroanatomical signature of emerging psychosis enabling individualized risk staging across different high-risk populations. Supplementary results revealed that (1) potentially confounding between-site differences were effectively mitigated using statistical correction methods, and (2) the detection of the prodromal signature considerably depended on the available sample sizes. These observations pave the way for future multicenter studies, which may ultimately facilitate the neurobiological refinement of risk criteria and personalized preventive therapies based on individualized risk profiling tools.

Brain size and white matter content of cerebrospinal tracts determine the upper cervical cord area: evidence from structural brain MRI.

INTRODUCTION: Measurement of the upper cervical cord area (UCCA) from brain MRI may be an effective way to quantify spinal cord involvement in neurological disorders such as multiple sclerosis. However, knowledge on the determinants of UCCA in healthy controls (HCs) is limited. METHODS: In two cohorts of 133 and 285 HCs, we studied the influence of different demographic, body-related, and brain-related parameters on UCCA by simple and partial correlation analyses as well as by voxel-based morphometry (VBM) across both cerebral gray matter (GM) and white matter (WM). RESULTS: First, we confirmed the known but moderate effect of age on UCCA in the older cohort. Second, we studied the correlation of UCCA with sex, body height, and total intracranial volume (TIV). TIV was the only variable that correlated significantly with UCCA after correction for the other variables. Third, we studied the correlation of UCCA with brain-related parameters. Brain volume correlated stronger with UCCA than TIV. Both volumes of the brain tissue compartments GM and WM correlated with UCCA significantly. WM volume explained variance of UCCA after correction for GM volume, whilst the opposite was not observed. Correspondingly, VBM did not yield any brain region, whose GM content correlated significantly with UCCA, whilst cerebral WM content of cerebrospinal tracts strongly correlated with UCCA. This latter effect increased along a craniocaudal gradient. CONCLUSION: UCCA is mainly determined by brain volume as well as by WM content of cerebrospinal tracts.

Distinguishing prodromal from first-episode psychosis using neuroanatomical single-subject pattern recognition.

BACKGROUND: The at-risk mental state for psychosis (ARMS) and the first episode of psychosis have been associated with structural brain abnormalities that could aid in the individualized early recognition of psychosis. However, it is unknown whether the development of these brain alterations predates the clinical deterioration of at-risk individuals, or alternatively, whether it parallels the transition to psychosis at the single-subject level. METHODS: We evaluated the performance of an magnetic resonance imaging (MRI)-based classification system in classifying disease stages from at-risk individuals with subsequent transition to psychosis (ARMS-T) and patients with first-episode psychosis (FE). Pairwise and multigroup biomarkers were constructed using the structural MRI data of 22 healthy controls (HC), 16 ARMS-T and 23 FE subjects. The performance of these biomarkers was measured in unseen test cases using repeated nested cross-validation. RESULTS: The classification accuracies in the HC vs FE, HC vs ARMS-T, and ARMS-T vs FE analyses were 86.7%, 80.7%, and 80.0%, respectively. The neuroanatomical decision functions underlying these discriminative results particularly involved the frontotemporal, cingulate, cerebellar, and subcortical brain structures. CONCLUSIONS: Our findings suggest that structural brain alterations accumulate at the onset of psychosis and occur even before transition to psychosis allowing for the single-subject differentiation of the prodromal and first-episode stages of the disease. Pattern regression techniques facilitate an accurate prediction of these structural brain dynamics at the early stage of psychosis, potentially allowing for the early recognition of individuals at risk of developing psychosis.

Multivariate patterns of brain-cognition associations relating to vulnerability and clinical outcome in the at-risk mental states for psychosis.

BACKGROUND: Neuropsychological deficits are a core feature of established psychosis and have been previously linked to fronto-temporo-limbic brain alterations. Both neurocognitive and neuroanatomical abnormalities characterize clinical at-risk mental states (ARMS) for psychosis. However, structure-cognition relationships in the ARMS have not been directly explored using multivariate neuroimaging techniques. METHODS: Voxel-based morphometry and partial least squares were employed to study system-level covariance patterns between whole-brain morphological data and processing speed, working memory, verbal learning/IQ, and executive functions in 40 ARMS subjects and 30 healthy controls (HC). The detected structure-cognition covariance patterns were tested for significance and reliability using non-parametric permutation and bootstrap resampling. RESULTS: We identified ARMS-specific covariance patterns that described a generalized association of neurocognitive measures with predominantly prefronto-temporo-limbic and subcortical structures as well as the interconnecting white matter. In the conversion group, this generalized profile particularly involved working memory and verbal IQ and was positively correlated with limbic, insular and subcortical volumes as well as negatively related to prefrontal, temporal, parietal, and occipital cortices. Conversely, the neurocognitive profiles in the HC group were confined to working memory, learning and IQ, which were diffusely associated with cortical and subcortical brain regions. CONCLUSIONS: These findings suggest that the ARMS and prodromal phase of psychosis are characterized by a convergent mapping from multi-domain neurocognitive measures to a set of prefronto-temporo-limbic and subcortical structures. Furthermore, a neuroanatomical separation between positive and negative brain-cognition correlations may not only point to a biological process determining the clinical risk for disease transition, but also to possible compensatory or dysmaturational neural processes.

Variation within the Huntington's disease gene influences normal brain structure.

Genetics of the variability of normal and diseased brain structure largely remains to be elucidated. Expansions of certain trinucleotide repeats cause neurodegenerative disorders of which Huntington's disease constitutes the most common example. Here, we test the hypothesis that variation within the IT15 gene on chromosome 4, whose expansion causes Huntington's disease, influences normal human brain structure. In 278 normal subjects, we determined CAG repeat length within the IT15 gene on chromosome 4 and analyzed high-resolution T1-weighted magnetic resonance images by the use of voxel-based morphometry. We found an increase of GM with increasing long CAG repeat and its interaction with age within the pallidum, which is involved in Huntington's disease. Our study demonstrates that a certain trinucleotide repeat influences normal brain structure in humans. This result may have important implications for the understanding of both the healthy and diseased brain.

Early recognition and disease prediction in the at-risk mental states for psychosis using neurocognitive pattern classification.

BACKGROUND: Neuropsychological deficits predate overt psychosis and overlap with the impairments in the established disease. However, to date, no single neurocognitive measure has shown sufficient power for a prognostic test. Thus, it remains to be determined whether multivariate neurocognitive pattern classification could facilitate the diagnostic identification of different at-risk mental states (ARMS) for psychosis and the individualized prediction of illness transition. METHODS: First, classification of 30 healthy controls (HC) vs 48 ARMS individuals subgrouped into 20 "early," 28 "late" ARMS subjects was performed based on a comprehensive neuropsychological test battery. Second, disease prediction was evaluated by categorizing the neurocognitive baseline data of those ARMS individuals with transition (n = 15) vs non transition (n = 20) vs HC after 4 years of follow-up. Generalizability of classification was estimated by repeated double cross-validation. RESULTS: The 3-group cross-validated classification accuracies in the first analysis were 94.2% (HC vs rest), 85.0% (early at-risk subjects vs rest), and, 91.4% (late at-risk subjects vs rest) and 90.8% (HC vs rest), 90.8% (converters vs rest), and 89.0% (nonconverters vs rest) in the second analysis. Patterns distinguishing the early or late ARMS from HC primarily involved the verbal learning/memory domains, while executive functioning and verbal IQ deficits were particularly characteristic of the late ARMS. Disease transition was mainly predicted by executive and verbal learning impairments. CONCLUSIONS: Different ARMS and their clinical outcomes may be reliably identified on an individual basis by evaluating neurocognitive test batteries using multivariate pattern recognition. These patterns may have the potential to substantially improve the early recognition of psychosis.

Disease prediction in the at-risk mental state for psychosis using neuroanatomical biomarkers: results from the FePsy study.

BACKGROUND: Reliable prognostic biomarkers are needed for the early recognition of psychosis. Recently, multivariate machine learning methods have demonstrated the feasibility to predict illness onset in clinically defined at-risk individuals using structural magnetic resonance imaging (MRI) data. However, it remains unclear whether these findings could be replicated in independent populations. METHODS: We evaluated the performance of an MRI-based classification system in predicting disease conversion in at-risk individuals recruited within the prospective FePsy (Früherkennung von Psychosen) study at the University of Basel, Switzerland. Pairwise and multigroup biomarkers were constructed using the MRI data of 22 healthy volunteers, 16/21 at-risk subjects with/without a subsequent disease conversion. Diagnostic performance was measured in unseen test cases using repeated nested cross-validation. RESULTS: The classification accuracies in the "healthy controls (HCs) vs converters," "HCs vs nonconverters," and "converters vs nonconverters" analyses were 92.3%, 66.9%, and 84.2%, respectively. A positive likelihood ratio of 6.5 in the converters vs nonconverters analysis indicated a 40% increase in diagnostic certainty by applying the biomarker to an at-risk population with a transition rate of 43%. The neuroanatomical decision functions underlying these results particularly involved the prefrontal perisylvian and subcortical brain structures. CONCLUSIONS: Our findings suggest that the early prediction of psychosis may be reliably enhanced using neuroanatomical pattern recognition operating at the single-subject level. These MRI-based biomarkers may have the potential to identify individuals at the highest risk of developing psychosis, and thus may promote informed clinical strategies aiming at preventing the full manifestation of the disease.

Association between brain structure and psychometric schizotypy in healthy individuals.

OBJECTIVES: Schizophrenia is associated with replicable grey matter volume reductions in fronto-temporo-limbic and subcortical regions. Psychometric schizotypy refers to a set of behavioural traits and cognitions thought to represent the subclinical manifestation of schizophrenia in the general population. While there is evidence of a continuum between schizophrenia and schizotypy at phenotypic, genetic and cognitive levels, no previous study has observed grey matter volume reductions associated with increased psychometric schizotypy levels in healthy individuals. Such evidence would provide further support for a relationship between non-clinical schizophrenia-like traits in the general population and the full-blown clinical condition of schizophrenia. METHODS: We used magnetic resonance imaging to investigate the relationship between psychometric schizotypy and brain structure in 55 clinically unaffected and unmedicated volunteers. We performed a voxel-based morphometry analysis of grey matter volume data obtained at 1.5 Tesla. RESULTS: Covarying for age and gender, higher scores of self-report positive schizotypy were significantly associated with reduced grey matter volume in medial prefrontal, orbitofrontal, and temporal cortical regions. CONCLUSIONS: These findings show that psychometric schizotypy in healthy individuals is associated with volume reductions in cortical areas known to be altered in schizophrenia, thereby providing neurobiological evidence of a continuum between schizotypy and schizophrenia.

Neuroanatomical abnormalities that predate the onset of psychosis: a multicenter study.

CONTEXT: People experiencing possible prodromal symptoms of psychosis have a very high risk of developing the disorder, but it is not possible to predict, on the basis of their presenting clinical features, which individuals will subsequently become psychotic. Recent neuroimaging studies suggest that there are volumetric differences between individuals at ultra-high risk (UHR) for psychosis who later develop psychotic disorder and those who do not. However, the samples examined to date have been small, and the findings have been inconsistent. OBJECTIVE: To assess brain structure in individuals at UHR for psychosis in a larger and more representative sample than in previous studies by combining magnetic resonance imaging data from 5 different scanning sites. DESIGN: Case-control study. SETTING: Multisite. PARTICIPANTS: A total of 182 individuals at UHR and 167 healthy controls. Participants were observed clinically for a mean of 2 years. Forty-eight individuals (26.4%) in the UHR group developed psychosis and 134 did not. MAIN OUTCOME MEASURES: Magnetic resonance images were acquired from each participant. Group differences in gray matter volume were examined using optimized voxel-based morphometry. RESULTS: The UHR group as a whole had less gray matter volume than did controls in the frontal regions bilaterally. The UHR subgroup who later developed psychosis had less gray matter volume in the left parahippocampal cortex than did the UHR subgroup who did not. CONCLUSIONS: Individuals at high risk for psychosis show alterations in regional gray matter volume regardless of whether they subsequently develop the disorder. In the UHR population, reduced left parahippocampal volume was specifically associated with the later onset of psychosis. Alterations in this region may, thus, be crucial to the expression of illness. Identifying abnormalities that specifically predate the onset of psychosis informs the development of clinical investigations designed to predict which individuals at high risk will subsequently develop the disorder.

Electroconvulsive therapy and corpus callosum aplasia: a case report.

The use of electroconvulsive therapy (ECT) in mental retardation has been discussed in several case reports and case series. In this case, a 35-year-old patient with corpus callosum aplasia and severe therapy-resistant catatonia was treated with a series of unilateral ECT and improved considerably. Electroencephalographic recordings during ECT showed a complete interhemispheric synchronicity due to regular anterior and posterior commissural fibers. After ECT, the patient received long-term medication with quetiapine and lorazepam. Electroconvulsive therapy turned out to be a powerful tool for treating catatonic syndromes in patients with mental retardation and should be considered as a potent treatment option in otherwise therapy-resistant cases.

Neuroanatomical correlates of executive dysfunction in the at-risk mental state for psychosis.

Deficits in executive functioning have been described as a core feature of schizophrenia and have been linked to patterns of fronto-temporo-limbic brain alterations. To date, such structure-cognition relationships have not been explored in a clinically defined at-risk mental state (ARMS) for psychosis using whole-brain neuroimaging techniques. Therefore, we used voxel-based morphometry in 40 ARMS and 30 matched healthy control (HC) individuals to investigate whether gray and white matter volumes (1) correlated with the performance in the Trail-Making Test B (TMT-B), an established measure of executive functioning, and (2) were volumetrically linked to the ventromedial prefrontal cortex (VMPFC), found to be associated with TMT-B in the ARMS during the first analysis step. We found the ARMS subjects to be specifically impaired in their TMT-B performance versus HC. Brain-cognition associations involving the insular cortices were observed in the HC, but not in the ARMS individuals. Conversely, TMT-B correlations in the VMPFC, the cerebellum, the fronto-callosal white matter were detected in the ARMS, but not the HC group. The VMPFC was linked to the temporo-limbic cortices in HC, whereas the connectivity pattern in the ARMS involved the left temporal and dorsolateral prefrontal cortex, the cerebellum, the right SMA and extended portions of the fronto-callosal white matter. These findings suggest that executive deficits are already present in the ARMS for psychosis and may be subserved by structurally altered networks of interconnected cortical and subcortical brain regions in line with the disconnectivity hypothesis of schizophrenia.

Use of neuroanatomical pattern regression to predict the structural brain dynamics of vulnerability and transition to psychosis.

BACKGROUND: The at-risk mental state for psychosis (ARMS) has been associated with abnormal structural brain dynamics underlying disease transition or non-transition. To date, it is unknown whether these dynamic brain changes can be predicted at the single-subject level prior to disease transition using MRI-based machine-learning techniques. METHODS: First, deformation-based morphometry and partial-least-squares (PLS) was used to investigate patterns of volumetric changes over time in 25 ARMS individuals versus 28 healthy controls (HC) (1) irrespective of the clinical outcome and (2) according to illness transition or non-transition. Then, the baseline MRI data were employed to predict the expression of these volumetric changes at the individual level using support-vector regression (SVR). RESULTS: PLS revealed a pattern of pronounced morphometric changes in ARMS versus HC that affected predominantly the right prefrontal, as well as the perisylvian, parietal and periventricular structures (p<0.011), and that was more pronounced in the converters versus the non-converters (p<0.010). The SVR analysis facilitated a reliable prediction of these longitudinal brain changes in individual out-of training cases (HC vs ARMS: r=0.83, p<0.001; HC vs converters vs non-converters: r=0.83, p<0.001) by relying on baseline patterns that involved ventricular enlargements, as well as prefrontal, perisylvian, limbic, parietal and subcortical volume reductions. CONCLUSIONS: Abnormal brain changes over time may underlie an elevated vulnerability for psychosis and may be most pronounced in subsequent converters to psychosis. Pattern regression techniques may facilitate an accurate prediction of these structural brain dynamics, potentially allowing for an early recognition of individuals at risk of developing psychosis-associated neuroanatomical changes over time.

Anterior cingulum volumetry, auditory P300 in schizophrenia with negative symptoms.

The anterior cingulate cortex (ACC) is located at the rostum of the corpus callosum and involved in both cognitive and emotional brain processes. It has been suggested to be involved in P300 event-related potential generation. A large sample of schizophrenia inpatients and controls was examined in order to assess the potential relationship between ACC volumes and P300 characteristics in patients with more pronounced negative symptoms. In 50 male schizophrenia patients and 50 matched controls, auditory P300 and structural magnetic resonance imaging volume measurements of the ACC were obtained. Patients' negative symptoms were assessed using the PANSS (Positive and Negative Syndrome Scale). Volumetry of ACC subregions revealed a volume reduction in patients with schizophrenia compared with controls in right hemispheric rostral ACC subregions that were most pronounced in more negative schizophrenia patients. There was a positive correlation between PZ P300 amplitude and total ACC volume in the right hemisphere in schizophrenia patients with less negative symptoms. The results support the assumption that structural changes of the ACC are more pronounced in subgroups of schizophrenia patients with more negative psychopathology. In addition, while right hemisphere ACC volumes significantly differ between schizophrenia subgroups, combining measures of event-related potential (ERP) and ACC volumetry does not add additional information.

Orbitofrontal volume reductions during emotion recognition in patients with major depression.

BACKGROUND: Major depressive disorder is associated with both structural and functional alterations in the emotion regulation network of the central nervous system. The relation between structural and functional changes is largely unknown. Therefore, we sought to determine the relation between structural differences and functional alterations during the recognition of emotional facial expressions. METHODS: We examined 13 medication-free patients with major depression and 15 healthy controls by use of structural T1-weighted high-resolution magnetic resonance imaging (MRI) and functional MRI during 1 session. We set the statistical threshold for the analysis of imaging data to p < 0.001 (uncorrected). RESULTS: As shown by voxel-based morphometry, depressed patients had reductions in orbitofrontal cortex volume and increases in cerebellar volume. Additionally, depressed patients showed increased activity during emotion recognition in the middle frontal cortex, caudate nucleus, precuneus and lingual gyrus. Within this cerebral network, the orbitofrontal volumes were negatively correlated in depressed patients but not in healthy controls with changes in blood oxygen level-dependent signal in the middle frontal gyrus, caudate nucleus, precuneus and supplementary motor area. LIMITATIONS: Our results are limited by the relatively small sample size. CONCLUSIONS: This combined functional and structural MRI study provides evidence that the orbitofrontal cortex is a key area in major depression and that structural changes result in functional alterations within the emotional circuit. Whether these alterations in the orbitofrontal cortex are also related to persistent emotional dysfunction in remitted mental states and, therefore, are related to the risk of depression needs further exploration.

Childhood stress, serotonin transporter gene and brain structures in major depression.

The underlying neurobiology of major depression (MD) is likely to represent an interaction between genetic susceptibility and environmental factors such as stress. We investigated, in a multimodal high-resolution magnetic resonance imaging (MRI) genetic study, whether reduced hippocampal volumes and other brain alterations are associated with the tri-allelic polymorphism of the serotonin transporter and childhood stress in patients with MD and healthy subjects. Patients with MD and healthy participants were investigated using high-resolution MRI and genotyping for serotonin transporter polymorphism in the promoter region of the serotonin transporter gene (SLC6A4, 5-HTTLPR). Region of interest analysis of the hippocampus, whole-brain voxel-based morphometry (VBM), and assessment of childhood stress were carried out. Patients carrying the risk S-allele developed smaller hippocampal volumes when they had a history of emotional neglect compared with patients who only had one risk factor (environmental or genetic). In patients, childhood stress also predicted further hippocampal white matter alterations independently from the genotype. Moreover, the left prefrontal cortex was smaller in patients, whereby childhood stress resulted in larger prefrontal volumes in those subjects carrying the non-risk L-allele, suggesting preventive effects. The findings indicate that subjects with both environmental and genetic risk factors are susceptible to stress-related hippocampal changes. Structural brain changes due to stress represent part of the mechanism by which the illness risk and outcome might be genetically mediated.

Interaction of childhood stress with hippocampus and prefrontal cortex volume reduction in major depression.

Early emotional stress is associated with a life-long burden of risk for later depression and stressful life events contribute to the development of depressive episodes. In this study we investigated whether childhood stress is associated with structural brain alterations in patients with major depression (MD). Forty-three patients with MD and 44 age as well as gender matched healthy control subjects were investigated using high-resolution magnetic resonance imaging (MRI). Region of interest analysis of the hippocampus, whole brain voxel-based morphometry (VBM) and assessment of childhood stress was carried out. Significantly smaller hippocampal white matter and prefrontal gray matter volume was observed in patients with MD compared to healthy controls. In particular left hippocampal white matter was smaller in patients, who had emotional childhood neglect, compared to those without neglect. For male patients this effect was seen in the left and right hippocampus. Moreover, physical neglect during childhood affected prefrontal gray matter volume in healthy subjects. Both emotional neglect and brain structural abnormalities predicted cumulative illness duration and there was a significant interaction between emotional neglect and prefrontal volumes as well as hippocampal white matter on the illness course. Childhood neglect resulted in hippocampal white matter changes in patients with major depression, pronounced at the left side and in males. Most interestingly, childhood stress and brain structure volumes independently predicted cumulative illness course. Subjects with both, structural brain changes and childhood emotional neglect seem to be at a very high risk to develop a more severe illness course.