Trajectories of subcortical volume change in schizophrenia: A 5-year follow-up

Borderline shades: Morphometric features predict borderline personality traits but not histrionic traits

Borderline personality disorder (BPD) is one of the most diagnosed disorders in clinical settings. Besides the fully diagnosed disorder, borderline personality traits (BPT) are quite common in the general population. Prior studies have investigated the neural correlates of BPD but not of BPT. This paper investigates the neural correlates of BPT in a subclinical population using a supervised machine learning method known as Kernel Ridge Regression (KRR) to build predictive models. Additionally, we want to determine whether the same brain areas involved in BPD are also involved in subclinical BPT. Recent attempts to characterize the specific role of resting state-derived macro networks in BPD have highlighted the role of the default mode network. However, it is not known if this extends to the subclinical population. Finally, we wanted to test the hypothesis that the same circuitry that predicts BPT can also predict histrionic personality traits. Histrionic personality is sometimes considered a milder form of BPD, and making a differential diagnosis between the two may be difficult. For the first time KRR was applied to structural images of 135 individuals to predict BPT, based on the whole brain, on a circuit previously found to correctly classify BPD, and on the five macro-networks. At a whole brain level, results show that frontal and parietal regions, as well as the Heschl's area, the thalamus, the cingulum, and the insula, are able to predict borderline traits. BPT predictions increase when considering only the regions limited to the brain circuit derived from a study on BPD, confirming a certain overlap in brain structure between subclinical and clinical samples. Of all the five macro networks, only the DMN successfully predicts BPD, confirming previous observations on its role in the BPD. Histrionic traits could not be predicted by the BPT circuit. The results have implications for the diagnosis of BPD and a dimensional model of personality.

The contribution of the cingulate cortex: treating depressive symptoms in first-episode drug naïve schizophrenia

Background

Our previous study has shown the cingulate cortex abnormalities in first-episode drug naïve (FEDN) schizophrenia patients with comorbid depressive symptoms. However, it remains largely unknown whether antipsychotics may induce morphometric change in cingulate cortex and its relationship with depressive symptoms. The purpose of this study was to further clarify the important role of cingulate cortex in the treatment on depressive symptoms in FEDN schizophrenia patients.

Method

In this study, 42 FEDN schizophrenia patients were assigned into depressed patients group (DP, n = 24) and non-depressed patients group (NDP, n = 18) measured by the 24-item Hamilton Depression Rating Scale (HAMD). Clinical assessments and anatomical images were obtained from all patients before and after 12-week treatment with risperidone.

Results

Although risperidone alleviated psychotic symptoms in all patients, depressive symptoms were decreased only in DP. Significant group by time interaction effects were found in the right rostral anterior cingulate cortex (rACC) and other subcortical regions in the left hemisphere. After risperidone treatment, the right rACC were increased in DP. Further, the increasing volume of right rACC was negatively associated with improvement in depressive symptoms.

Conclusion

These findings suggested that the abnormality of the rACC is the typical characteristics in schizophrenia with depressive symptoms. It's likely key region contributing to the neural mechanisms underlying the effects of risperidone treatment on depressive symptoms in schizophrenia.

A multicentre study on grey matter morphometric biomarkers for classifying early schizophrenia and parkinson's disease psychosis

Psychotic symptoms occur in a majority of schizophrenia patients and in ~50% of all Parkinson's disease (PD) patients. Altered grey matter (GM) structure within several brain areas and networks may contribute to their pathogenesis. Little is known, however, about transdiagnostic similarities when psychotic symptoms occur in different disorders, such as in schizophrenia and PD. The present study investigated a large, multicenter sample containing 722 participants: 146 patients with first episode psychosis, FEP; 106 individuals in at-risk mental state for developing psychosis, ARMS; 145 healthy controls matching FEP and ARMS, Con-Psy; 92 PD patients with psychotic symptoms, PDP; 145 PD patients without psychotic symptoms, PDN; 88 healthy controls matching PDN and PDP, Con-PD. We applied source-based morphometry in association with receiver operating curves (ROC) analyses to identify common GM structural covariance networks (SCN) and investigated their accuracy in identifying the different patient groups. We assessed group-specific homogeneity and variability across the different networks and potential associations with clinical symptoms. SCN-extracted GM values differed significantly between FEP and Con-Psy, PDP and Con-PD, PDN and Con-PD, as well as PDN and PDP, indicating significant overall grey matter reductions in PD and early schizophrenia. ROC analyses showed that SCN-based classification algorithms allow good classification (AUC ~0.80) of FEP and Con-Psy, and fair performance (AUC ~0.72) when differentiating PDP from Con-PD. Importantly, the best performance was found in partly the same networks, including the thalamus. Alterations within selected SCNs may be related to the presence of psychotic symptoms in both early schizophrenia and PD psychosis, indicating some commonality of underlying mechanisms. Furthermore, results provide evidence that GM volume within specific SCNs may serve as a biomarker for identifying FEP and PDP.

Is change over time in psychotic symptoms related to social functioning?

OBJECTIVE

In psychosis, treatment often focuses on symptom reduction whereas social functioning is also essential. In this study, we investigate positive psychotic symptoms and medication use in relation to social functioning over a 3-year time-period in 531 patients diagnosed with psychosis. Furthermore, relations of positive symptoms with needs for care and quality of life were also investigated.

METHOD

Using repeated measures analysis, changes were measured over time. Hereafter, mixed model analyses were performed to determine the associations of social functioning, needs for care, and quality of life with psychotic symptoms and patient characteristics. Finally, we assessed differences in symptoms and medication dose between those with an increase and those with a decrease in social functioning.

RESULTS

Patients significantly improved in social functioning, while psychotic symptoms increased. Improvement in social functioning was associated with younger age, higher IQ, and lower social functioning at T1, but not with positive symptoms. Also, improvement in social functioning was found to be related to a decrease in the dose of clozapine. Improvement in social functioning occurs despite worsening of positive symptoms.

CONCLUSIONS

The findings suggest the need to further explore the relation between symptomatology, social functioning, and medication use. In the treatment of psychotic disorders, one should reconsider the strong focus on reducing psychotic symptoms. The current focus needs to shift much more toward improving functional outcome, especially when the patient expresses a desire for change in this respect.

Cortical morphometric correlational networks associated with cognitive deficits in first episode schizophrenia

Schizophrenia (SCZ) is a chronic cognitive and behavioral disorder associated with abnormal cortical activity during information processing. Several brain structures associated with the seven performance domains evaluated using the MATRICS (Measurement and Treatment Research to Improve Cognition in Schizophrenia) Consensus Cognitive Battery (MCCB) have shown cortical volume loss in first episode schizophrenia (FES) patients. However, the relationship between morphological organization and MCCB performance remains unclear. Therefore, in the current observational study, high-resolution structural MRI scans were collected from 50 FES patients, and the morphometric correlation network (MCN) using cortical volume was established to characterize the cortical pattern associated with poorer MCCB performance. We also investigated topological properties, such as the modularity, the degree and the betweenness centrality. Our findings show structural volume was directly and strongly associated with the cognitive deficits of FES patients in the precuneus, anterior cingulate, and fusiform gyrus, as well as the prefrontal, parietal, and sensorimotor cortices. The medial orbitofrontal, fusiform, and superior frontal gyri were not only identified as the predominant nodes with high degree and betweenness centrality in the MCN, but they were also found to be critical in performance in several of the MCCB domains. Together, these results suggest a widespread cortical network is altered in FES patients and that performance on the MCCB domains is associated with the core pathophysiology of SCZ.

Brain age prediction in schizophrenia: Does the choice of machine learning algorithm matter?

Brain-predicted age difference (brainPAD) has been used in schizophrenia to assess individual-level deviation in the biological age of the patients' brain (i.e., brain-age) from normative reference brain structural datasets. There is marked inter-study variation in brainPAD in schizophrenia which is commonly attributed to sample heterogeneity. However, the potential contribution of the different machine learning algorithms used for brain-age estimation has not been systematically evaluated. Here, we aimed to assess variation in brain-age estimated by six commonly used algorithms [ordinary least squares regression, ridge regression, least absolute shrinkage and selection operator regression, elastic-net regression, linear support vector regression, and relevance vector regression] when applied to the same brain structural features from the same sample. To assess reproducibility we used data from two publically available samples of healthy individuals (n = 1092 and n = 492) and two further samples, from the Icahn School of Medicine at Mount Sinai (ISMMS) and the Center of Biomedical Research Excellence (COBRE), comprising both patients with schizophrenia (n = 90 and n = 76) and healthy individuals (n = 200 and n = 87). Performance similarity across algorithms was compared within each sample using correlation analyses and hierarchical clustering. Across all samples ordinary least squares regression, the only algorithm without a penalty term, performed markedly worse. All other algorithms showed comparable performance but they still yielded variable brain-age estimates despite being applied to the same data. Although brainPAD was consistently higher in patients with schizophrenia, it varied by algorithm from 3.8 to 5.2 years in the ISMMS sample and from to 4.5 to 11.7 years in the COBRE sample. Algorithm choice introduces variations in brain-age and may confound inter-study comparisons when assessing brainPAD in schizophrenia.

Thalamic, Amygdalar, and hippocampal nuclei morphology and their trajectories in first episode psychosis: A preliminary longitudinal study✰

The thalamus, amygdala, and hippocampus play important pathophysiologic roles in psychosis. Few studies have prospectively examined subcortical nuclei in relation to predicting clinical outcomes after a first-episode of psychosis (FEP). Here, we examined volumetric differences and trajectories among subcortical nuclei in FEP patients and their associations with illness severity. Clinical and brain volume measures were collected using a 1.5T MRI scanner and processed using FreeSurfer 6.0 from a prospective study of antipsychotic-naïve FEP patients of FEP-schizophrenia (FEP-SZ) (baseline, n = 38; follow-up, n = 17), FEP non-schizophrenia (FEP-NSZ) (baseline, n = 23; follow-up, n = 13), and healthy controls (HCs) (baseline, n = 47; follow-up, n = 29). Compared to FEP-NSZ and HCs, FEP-SZ had significantly smaller thalamic anterior nuclei volume at baseline. Longitudinally, FEP-SZ showed a positive rate of change in the amygdala compared to controls or FEP-NSZ, as well as in the basal, central and accessory basal nuclei compared to FEP-NSZ. Enlargement in the thalamic anterior nuclei predicted a worsening in overall psychosis symptoms. Baseline thalamic anterior nuclei alterations further specify key subcortical regions associated with FEP-SZ pathophysiology. Longitudinally, anterior nuclei volume enlargement may signal symptomatic worsening. The amygdala and thalamus structures may show diagnostic differences between schizophrenia and non-schizophrenia psychoses, while the thalamus changes may reflect disease or treatment related changes in clinical outcome.

Hippocampal volume in early psychosis: a 2-year longitudinal study

Cross-sectional studies suggest that hippocampal volume declines across stages of psychosis. In contrast, longitudinal studies indicate that hippocampal volume is stable in the critical period following illness onset. How can these seemingly disparate sets of findings be resolved? In the present study, we examine two previously unexplored reasons for this discrepancy. First, only specific subregions of the hippocampus may change during the early stage of psychosis. Second, there is diagnostic heterogeneity in the early stage of psychosis and cross-sectional analysis does not permit examination of illness trajectory. Some early stage individuals will have persistent illness leading to a diagnosis of schizophrenia, whereas in others, psychosis will remit. Hippocampal volume may be reduced only in individuals who will ultimately be diagnosed with schizophrenia. We acquired longitudinal structural MRI data from 63 early psychosis and 63 healthy control participants, with up to 4 time points per participant collected over 2 years. Subfield volumes were measured in the anterior and posterior hippocampus using automated segmentation specialized for longitudinal analysis. We observed a volume deficit in early psychosis participants compared to healthy controls that was most pronounced in the anterior hippocampus, but this deficit did not change over 2 years. Importantly, we found that anterior cornu ammonis volume is smaller at baseline in individuals who were diagnosed with schizophrenia at follow-up, but normal in those who maintained a diagnosis of schizophreniform disorder over 2 years. Smaller hippocampal volume is not diagnostic of psychosis, but is instead prognostic of clinical outcome.

Support Vector Machine-Based Schizophrenia Classification Using Morphological Information from Amygdaloid and Hippocampal Subregions

Structural changes in the hippocampus and amygdala have been demonstrated in schizophrenia patients. However, whether morphological information from these subcortical regions could be used by machine learning algorithms for schizophrenia classification were unknown. The aim of this study was to use volume of the amygdaloid and hippocampal subregions for schizophrenia classification. The dataset consisted of 57 patients with schizophrenia and 69 healthy controls. The volume of 26 hippocampal and 20 amygdaloid subregions were extracted from T1 structural MRI images. Sequential backward elimination (SBE) algorithm was used for feature selection, and a linear support vector machine (SVM) classifier was configured to explore the feasibility of hippocampal and amygdaloid subregions in the classification of schizophrenia. The proposed SBE-SVM model achieved a classification accuracy of 81.75% on 57 patients and 69 healthy controls, with a sensitivity of 84.21% and a specificity of 81.16%. AUC was 0.8241 (p < 0.001 tested with 1000-times permutation). The results demonstrated evidence of hippocampal and amygdaloid structural changes in schizophrenia patients, and also suggested that morphological features from the amygdaloid and hippocampal subregions could be used by machine learning algorithms for the classification of schizophrenia.

Subcortical Brain Volume Abnormalities in Individuals With an At-risk Mental State

Previous structural magnetic resonance imaging studies of psychotic disorders have demonstrated volumetric alterations in subcortical (ie, the basal ganglia, thalamus) and temporolimbic structures, which are involved in high-order cognition and emotional regulation. However, it remains unclear whether individuals at high risk for psychotic disorders with minimal confounding effects of medication exhibit volumetric changes in these regions. This multicenter magnetic resonance imaging study assessed regional volumes of the thalamus, caudate, putamen, nucleus accumbens, globus pallidus, hippocampus, and amygdala, as well as lateral ventricular volume using FreeSurfer software in 107 individuals with an at-risk mental state (ARMS) (of whom 21 [19.6%] later developed psychosis during clinical follow-up [mean = 4.9 years, SD = 2.6 years]) and 104 age- and gender-matched healthy controls recruited at 4 different sites. ARMS individuals as a whole demonstrated significantly larger volumes for the left caudate and bilateral lateral ventricles as well as a smaller volume for the right accumbens compared with controls. In male subjects only, the left globus pallidus was significantly larger in ARMS individuals. The ARMS group was also characterized by left-greater-than-right asymmetries of the lateral ventricle and caudate nucleus. There was no significant difference in the regional volumes between ARMS groups with and without later psychosis onset. The present study suggested that significant volume expansion of the lateral ventricle, caudate, and globus pallidus, as well as volume reduction of the accumbens, in ARMS subjects, which could not be explained only by medication effects, might be related to general vulnerability to psychopathology.

Functional, Anatomical, and Morphological Networks Highlight the Role of Basal Ganglia-Thalamus-Cortex Circuits in Schizophrenia

Evidence from electrophysiological, functional, and structural research suggests that abnormal brain connectivity plays an important role in the pathophysiology of schizophrenia. However, most previous studies have focused on single modalities only, each of which is associated with its own limitations. Multimodal combinations can more effectively utilize various information, but previous multimodal research mostly focuses on extracting local features, rather than carrying out research based on network perspective. This study included 135 patients with schizophrenia and 148 sex- and age-matched healthy controls. Functional magnetic resonance imaging, diffusion tensor imaging, and structural magnetic resonance imaging data were used to construct the functional, anatomical, and morphological networks of each participant, respectively. These networks were used in combination with machine learning to identify more consistent biomarkers of brain connectivity and explore the relationships between different modalities. We found that although each modality had divergent connectivity biomarkers, the convergent pattern was that all were mostly located within the basal ganglia-thalamus-cortex circuit. Furthermore, using the biomarkers of these 3 modalities as a feature yielded the highest classification accuracy (91.75%, relative to a single modality), suggesting that the combination of multiple modalities could be effectively utilized to obtain complementary information regarding different mode networks; furthermore, this information could help distinguish patients. These findings provide direct evidence for the disconnection hypothesis of schizophrenia, suggesting that abnormalities in the basal ganglia-thalamus-cortex circuit can be used as a biomarker of schizophrenia.

Progressive brain structural changes after the first year of treatment in first-episode treatment-naive patients with deficit or nondeficit schizophrenia

Progressive brain volume atrophy has been reported in patients with schizophrenia. However, whether this progress differs between patients with primary negative symptoms (deficit schizophrenia; DS) and those without such symptoms (nondeficit schizophrenia; NDS) is unknown. Here, we examined grey matter volume (GMV) and white matter volume (WMV) changes over 12 months in 34 first-episode treatment-naive patients with schizophrenia (14 DS and 20 NDS) and 32 healthy controls (HCs) using structural magnetic resonance imaging and voxel-based morphometry. At baseline, compared to HCs, patients with DS but not NDS had less WMV in bilateral posterior limb of the internal capsule (PLIC) and cerebellar tonsil (P < 0.05, FDR corrected) and smaller GMV in the cerebellar culmen (P < 0.05, FWE corrected). At follow-up, NDS group showed WMV reduction in bilateral PLIC (P < 0.05, FDR corrected), while DS group showed no progressive WMV changes. While both patient groups exhibited GMV reduction in the hippocampus and insular cortex, patients with NDS showed additional GMV loss in the frontal and cingulate cortex and a selective increase in GMV in the left thalamus (P < 0.05 FWE corrected). Our study revealed double dissociations in developmental brain volume changes in the first year after clinical contact for psychosis in DS versus NDS patients.

Treatment-Resistant Schizophrenia: Genetic and Neuroimaging Correlates

Schizophrenia is a severe neuropsychiatric disorder that affects approximately 0.5–1% of the population. Response to antipsychotic therapy is highly variable, and it is not currently possible to predict those patients who will or will not respond to antipsychotic medication. Furthermore, a high percentage of patients, approximately 30%, are classified as treatment-resistant (treatment-resistant schizophrenia; TRS). TRS is defined as a non-response to at least two trials of antipsychotic medication of adequate dose and duration. These patients are usually treated with clozapine, the only evidence-based pharmacotherapy for TRS. However, clozapine is associated with severe adverse events. For these reasons, there is an increasing interest to identify better targets for drug development of new compounds and to establish better biomarkers for existing medications. The ability of antipsychotics to improve psychotic symptoms is dependent on their antagonist and reverse agonist activities at different neuroreceptors, and some genetic association studies of TRS have focused on different pharmacodynamic factors. Some genetic studies have shown an association between antipsychotic response or TRS and neurodevelopment candidate genes, antipsychotic mechanisms of action (such as dopaminergic, serotonergic, GABAergic, and glutamatergic) or pharmacokinetic factors (i.e., differences in the cytochrome families). Moreover, there is a growing body of literature on the structural and functional neuroimaging research into TRS. Neuroimaging studies can help to uncover the underlying neurobiological reasons for such resistance and identify resistant patients earlier. Studies examining the neuropharmacological mechanisms of antipsychotics, including clozapine, can help to improve our knowledge of their action on the central nervous system, with further implications for the discovery of biomarkers and the development of new treatments. The identification of the underlying mechanisms of TRS is a major challenge for developing personalized medicine in the psychiatric field for schizophrenia treatment. The main goal of precision medicine is to use genetic and brain-imaging information to improve the safety, effectiveness, and health outcomes of patients via more efficiently targeted risk stratification, prevention, and tailored medication and treatment management approaches. The aim of this review is to summarize the state of art of pharmacogenetic, pharmacogenomic and neuroimaging studies in TRS.

Longitudinal changes in striatum and sub-threshold positive symptoms in individuals with an 'at risk mental state' (ARMS)

Recent studies have revealed that several psychotic symptom changes observed in the 'at risk mental state' (ARMS) are associated with changes in the striatum. We investigated if structural changes in the striatum are associated with recovery of sub-threshold psychotic symptoms in subjects with an ARMS who did not develop psychosis (ARMS-N). Sixteen healthy controls and 42 subjects with an ARMS participated in this study. Striatal volumes (caudate, putamen, and nucleus accumbens) were analyzed using MRI. The sub-threshold psychotic symptoms of the subjects with an ARMS were measured using the SOPS. Imaging and symptoms were reevaluated after 52 weeks. Significant right putamen volume reduction was observed at the follow-up in ARMS-N subjects. Improvement in sub-threshold positive symptoms significantly correlated with an increase in volume in the right accumbens at follow up. No relationship was found for negative symptoms. From these findings, the association between improvement in sub-threshold positive symptoms and an increase in the volume of the right accumbens may suggest that changes in the accumbens, which is a major site for dopamine innervation, are associated with symptom recovery. These findings may point to neurobiological resilience that may be associated with lower transition to psychosis.

Defining the Locus of Dopaminergic Dysfunction in Schizophrenia: A Meta-analysis and Test of the Mesolimbic Hypothesis

BACKGROUND

Studies using positron emission tomography to image striatal dopamine function, have demonstrated that individuals with schizophrenia display increases in presynaptic function. Mesolimbic dysfunction specifically, has previously been suggested to underlie psychotic symptoms. This has not been directly tested in vivo, and the precise anatomical locus of dopamine dysfunction within the striatum remains unclear. The current article investigates the magnitude of dopaminergic abnormalities in individuals with schizophrenia, and determines how the magnitude of abnormality varies across functional subdivisions of the striatum.

METHODS

EMBASE, PsychINFO, and MEDLINE were searched from January 1, 1960, to December 1, 2016. Inclusion criteria were molecular imaging studies that had measured presynaptic striatal dopamine functioning. Effects sizes for whole striatum and functional subdivisions were calculated separately. The magnitude of difference between functional subdivisions in patients and controls was meta-analyzed.

RESULTS

Twenty-one eligible studies were identified, including 269 patients and 313 controls. Individuals with schizophrenia (Hedges' g = 0.68, P < .001) demonstrated elevated presynaptic dopamine functioning compared to controls. Seven studies examined functional subdivisions. These demonstrated significant increases in patients compared to controls in associative (g = 0.73, P = .002) and sensorimotor (g = 0.54, P = .005) regions, but not limbic (g = 0.29, P = .09). The magnitude of the difference between associative and limbic subdivisions was significantly greater in patients compared to controls (g = 0.39, P = .003).

CONCLUSION

In individuals with schizophrenia dopaminergic dysfunction is greater in dorsal compared to limbic subdivisions of the striatum. This is inconsistent with the mesolimbic hypothesis and identifies the dorsal striatum as a target for novel treatment development.

The effect of duration of illness and antipsychotics on subcortical volumes in schizophrenia: Analysis of 778 subjects

Background

The effect of duration of illness and antipsychotic medication on the volumes of subcortical structures in schizophrenia is inconsistent among previous reports. We implemented a large sample analysis utilizing clinical data from 11 institutions in a previous meta-analysis.

Methods

Imaging and clinical data of 778 schizophrenia subjects were taken from a prospective meta-analysis conducted by the COCORO consortium in Japan. The effect of duration of illness and daily dose and type of antipsychotics were assessed using the linear mixed effect model where the volumes of subcortical structures computed by FreeSurfer were used as a dependent variable and age, sex, duration of illness, daily dose of antipsychotics and intracranial volume were used as independent variables, and the type of protocol was incorporated as a random effect for intercept. The statistical significance of fixed-effect of dependent variable was assessed.

Results

Daily dose of antipsychotics was positively associated with left globus pallidus volume and negatively associated with right hippocampus. It was also positively associated with laterality index of globus pallidus. Duration of illness was positively associated with bilateral globus pallidus volumes. Type of antipsychotics did not have any effect on the subcortical volumes.

Discussion

A large sample size, uniform data collection methodology and robust statistical analysis are strengths of the current study. This result suggests that we need special attention to discuss about relationship between subcortical regional brain volumes and pathophysiology of schizophrenia because regional brain volumes may be affected by antipsychotic medication.

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The imaging data as well as prescription data and demographics from 778 patients with schizophrenia from 11 institutions were included.

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The effect of protocol was cooperated as random-effect in the linear mixed-effect model.

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Significant positive association were found between daily dose of antipsychotics and left globus pallidus volume.

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Significant negative association was found between daily dose of antipsychotics and right hippocampus volume.

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Significant positive associations were found between duration of illness and bilateral volumes of globus pallidus.

Structural brain changes in schizophrenia at different stages of the illness: A selective review of longitudinal magnetic resonance imaging studies

OBJECTIVE

Schizophrenia is a devastating mental disorder accompanied by aberrant structural brain connectivity. The question whether schizophrenia is a progressive brain disorder is yet to be resolved. Thus, it is not clear when these structural alterations occur and how they develop over time.

METHODS

In our selective review, we summarized recent findings from longitudinal magnetic resonance imaging studies investigating structural brain alterations and its impact on clinical outcome at different stages of the illness: (1) subjects at ultra-high risk of developing psychosis, (2) patients with a first episode psychosis, and (3) chronically ill patients. Moreover, we reviewed studies examining the longitudinal effects of medication on brain structure in patients with schizophrenia.

RESULTS

(1) Studies from pre-clinical stages to conversion showed a more pronounced cortical gray matter loss (i.e. superior temporal and inferior frontal regions) in those individuals who later made transition to psychosis. (2) Studies investigating patients with a first episode psychosis revealed a decline in multiple gray matter regions (i.e. frontal regions and thalamus) over time as well as progressive cortical thinning in the superior and inferior frontal cortex. (3) Studies focusing on patients with chronic schizophrenia showed that gray matter decreased to a greater extent (i.e. frontal and temporal areas, thalamus, and cingulate cortices)-especially in poor-outcome patients. Very few studies reported effects on white matter microstructure in the longitudinal course of the illness.

CONCLUSION

There is adequate evidence to suggest that schizophrenia is associated with progressive gray matter abnormalities particularly during the initial stages of illness. However, causal relationships between structural changes and illness course-especially in chronically ill patients-should be interpreted with caution. Findings might be confounded by longer periods of treatment and higher doses of antipsychotics or epiphenomena related to the illness.

Problem solving, impulse control and planning in patients with early- and late-stage Huntington's disease

Sub-domains of executive functions, including problems with planning, accuracy, impulsivity, and inhibition, are core features of Huntington's disease. It is known that the decline of cognitive function in Huntington's disease is related to the anatomical progression of pathology in the basal ganglia. However, it remains to be determined whether the severity of executive dysfunction depends on the stage of the disease. To examine the severity of sub-domains of executive dysfunction in early- and late-stage Huntington's disease, we studied performance in the Tower of London task of two groups of Huntington's disease patients (Group 1: early, n = 23, and Group 2: late stage, n = 29), as well as a third group of age, education, and IQ matched healthy controls (n = 34). During the task, we measured the total number of problems solved, total planning time, and total number of breaks taken. One aspect of executive function indexed by the number of solved problems seems to progress in the course of the disease. Late-stage Huntington's disease patients scored significantly worse than early-stage patients and controls, and early-stage patients scored significantly worse than controls on this measure of accuracy. In contrast, late- and early-stage HD patients did not differ in terms of planning time and number of breaks. Early- and late-stage HD pathology has a different impact on executive sub-domains. While accuracy differs between early- and late-stage HD patients, other domains like planning time and number of breaks do not. Striatal degeneration, which is a characteristic feature of the disease, might not affect all aspects of executive function in HD.