A review of structural neuroimaging in schizophrenia: from connectivity to connectomics

Heterochronicity of white matter development and aging explains regional patient control differences in schizophrenia

BACKGROUND

Altered brain connectivity is implicated in the development and clinical burden of schizophrenia. Relative to matched controls, schizophrenia patients show (1) a global and regional reduction in the integrity of the brain's white matter (WM), assessed using diffusion tensor imaging (DTI) fractional anisotropy (FA), and (2) accelerated age-related decline in FA values. In the largest mega-analysis to date, we tested if differences in the trajectories of WM tract development influenced patient-control differences in FA. We also assessed if specific tracts showed exacerbated decline with aging.

METHODS

Three cohorts of schizophrenia patients (total n = 177) and controls (total n = 249; age = 18-61 years) were ascertained with three 3T Siemens MRI scanners. Whole-brain and regional FA values were extracted using ENIGMA-DTI protocols. Statistics were evaluated using mega- and meta-analyses to detect effects of diagnosis and age-by-diagnosis interactions.

RESULTS

In mega-analysis of whole-brain averaged FA, schizophrenia patients had lower FA (P = 10^-11 ) and faster age-related decline in FA (P = 0.02) compared with controls. Tract-specific heterochronicity measures, that is, abnormal rates of adolescent maturation and aging explained approximately 50% of the regional variance effects of diagnosis and age-by-diagnosis interaction in patients. Interactive, three-dimensional visualization of the results is available at www.enigma-viewer.org.

CONCLUSION

WM tracts that mature later in life appeared more sensitive to the pathophysiology of schizophrenia and were more susceptible to faster age-related decline in FA values. Hum Brain Mapp 37:4673-4688, 2016. © 2016 Wiley Periodicals, Inc.

Limited Evidence for Association of Genome-Wide Schizophrenia Risk Variants on Cortical Neuroimaging Phenotypes

BACKGROUND

There are now over 100 established genetic risk variants for schizophrenia; however, their influence on brain structure and circuitry across the human lifespan are not known.

METHODS

We examined healthy individuals 8-86 years of age, from the Centre for Addiction and Mental Health, the Zucker Hillside Hospital, and the Philadelphia Neurodevelopmental Cohort. Following thorough quality control procedures, we investigated associations of established genetic risk variants with heritable neuroimaging phenotypes relevant to schizophrenia, namely thickness of frontal and temporal cortical regions (n = 565) and frontotemporal and interhemispheric white matter tract fractional anisotropy (FA) (n = 530).

RESULTS

There was little evidence for association of risk variants with imaging phenotypes. No association with cortical thickness of any region was present. Only rs12148337, near a long noncoding RNA region, was associated with white matter FA (splenium of corpus callosum) following multiple comparison correction (corrected p = .012); this single nucleotide polymorphism was also associated with genu FA and superior longitudinal fasciculus FA at p <.005 (uncorrected). There was no association of polygenic risk score with white matter FA or cortical thickness.

CONCLUSIONS

In sum, our findings provide limited evidence for association of schizophrenia risk variants with cortical thickness or diffusion imaging white matter phenotypes. When taken with recent lack of association of these variants with subcortical brain volumes, our results either suggest that structural neuroimaging approaches at current resolution are not sufficiently sensitive to detect effects of these risk variants or that multiple comparison correction in correlated phenotypes is too stringent, potentially "eliminating" biologically important signals.

Discriminative analysis of schizophrenia using support vector machine and recursive feature elimination on structural MRI images

Structural abnormalities in schizophrenia (SZ) patients have been well documented with structural magnetic resonance imaging (MRI) data using voxel-based morphometry (VBM) and region of interest (ROI) analyses. However, these analyses can only detect group-wise differences and thus, have a poor predictive value for individuals. In the present study, we applied a machine learning method that combined support vector machine (SVM) with recursive feature elimination (RFE) to discriminate SZ patients from normal controls (NCs) using their structural MRI data. We first employed both VBM and ROI analyses to compare gray matter volume (GMV) and white matter volume (WMV) between 41 SZ patients and 42 age- and sex-matched NCs. The method of SVM combined with RFE was used to discriminate SZ patients from NCs using significant between-group differences in both GMV and WMV as input features. We found that SZ patients showed GM and WM abnormalities in several brain structures primarily involved in the emotion, memory, and visual systems. An SVM with a RFE classifier using the significant structural abnormalities identified by the VBM analysis as input features achieved the best performance (an accuracy of 88.4%, a sensitivity of 91.9%, and a specificity of 84.4%) in the discriminative analyses of SZ patients. These results suggested that distinct neuroanatomical profiles associated with SZ patients might provide a potential biomarker for disease diagnosis, and machine-learning methods can reveal neurobiological mechanisms in psychiatric diseases.

Implicit Timing as the Missing Link between Neurobiological and Self Disorders in Schizophrenia?

Disorders of consciousness and the self are at the forefront of schizophrenia symptomatology. Patients are impaired in feeling themselves as the authors of their thoughts and actions. In addition, their flow of consciousness is disrupted, and thought fragmentation has been suggested to be involved in the patients' difficulties in feeling as being one unique, unchanging self across time. Both impairments are related to self disorders, and both have been investigated at the experimental level. Here we review evidence that both mechanisms of motor control and the temporal structure of signal processing are impaired in schizophrenia patients. Based on this review, we propose that the sequencing of action and perception plays a key role in the patients' impairments. Furthermore, the millisecond time scale of the disorders, as well as the impaired sequencing, highlights the cooperation between brain networks including the cerebellum, as proposed by Andreasen (1999). We examine this possibility in the light of recent knowledge on the anatomical and physiological properties of the cerebellum, its role in timing, and its involvement in known physiological impairments in patients with schizophrenia, e.g., resting states and brain dynamics. A disruption in communication between networks involving the cerebellum, related to known impairments in dopamine, glutamate and GABA transmission, may help to better explain why patients experience reduced attunement with the external world and possibly with themselves.

Bridging the schism of schizophrenia through yoga-Review of putative mechanisms

Schizophrenia patients experience a 'disconnect' at multiple levels-neuronal networks, mental processes, and interpersonal relationships. The resultant poor quality-of-life and functional disability are related to the persistent cognitive deficits and negative symptoms, which are rather resistant to conventional antipsychotic medications. Yoga has emerged as an important therapeutic intervention to improve quality-of-life in schizophrenia. Recent preliminary evidence suggests that effects of yoga on cognitive and negative symptoms may drive this benefit. This study attempts to integrate evidence from neuroscience-based research, which focuses on the neuroplasticity-harnessing effects of yoga to bridge the schizophrenia connectopathy. In an overarching model to study putative neurobiological mechanisms that drive therapeutic effects of yoga, it is proposed that (a) various styles of meditation may help in strengthening the lateral and medial prefrontal brain networks, thus improving neurocognition and mentalizing abilities, and (b) learning and performing co-ordinated physical postures with a teacher facilitates imitation and the process of being imitated, which can improve social cognition and empathy through reinforcement of the premotor and parietal mirror neuron system. Oxytocin may play a role in mediating these processes, leading to better social connectedness and social outcomes. Clinical and heuristic implications of this model are further discussed.

Association of serum VEGF levels with prefrontal cortex volume in schizophrenia

A large body of evidence indicates alterations in brain regional cellular energy metabolism and blood flow in schizophrenia. Among the different molecules regulating blood flow, vascular endothelial growth factor (VEGF) is generally accepted as the major factor involved in the process of angiogenesis. In the present study, we examined whether peripheral VEGF levels correlate with changes in the prefrontal cortex (PFC) volume in patients with schizophrenia and in healthy controls. Whole-blood samples were obtained from 96 people with schizophrenia or schizoaffective disorder and 83 healthy controls. Serum VEGF protein levels were analyzed by enzyme-linked immunosorbent assay, whereas quantitative PCR was performed to measure interleukin-6 (IL-6, a pro-inflammatory marker implicated in schizophrenia) mRNA levels in the blood samples. Structural magnetic resonance imaging scans were obtained using a 3T Achieva scanner on a subset of 59 people with schizophrenia or schizoaffective disorder and 65 healthy controls, and prefrontal volumes were obtained using FreeSurfer software. As compared with healthy controls, individuals with schizophrenia had a significant increase in log-transformed mean serum VEGF levels (t(177)=2.9, P=0.005). A significant inverse correlation (r=-0.40, P=0.002) between serum VEGF and total frontal pole volume was found in patients with schizophrenia/schizoaffective disorder. Moreover, we observed a significant positive association (r=0.24, P=0.03) between serum VEGF and IL-6 mRNA levels in patients with schizophrenia. These findings suggest an association between serum VEGF and inflammation, and that serum VEGF levels are related to structural abnormalities in the PFC of people with schizophrenia.

Structural Brain Abnormalities in Youth With Psychosis Spectrum Symptoms

IMPORTANCE

Structural brain abnormalities are prominent in psychotic disorders, including schizophrenia. However, it is unclear when aberrations emerge in the disease process and if such deficits are present in association with less severe psychosis spectrum (PS) symptoms in youth.

OBJECTIVE

To investigate the presence of structural brain abnormalities in youth with PS symptoms.

DESIGN, SETTING, AND PARTICIPANTS

The Philadelphia Neurodevelopmental Cohort is a prospectively accrued, community-based sample of 9498 youth who received a structured psychiatric evaluation. A subsample of 1601 individuals underwent neuroimaging, including structural magnetic resonance imaging, at an academic and children's hospital health care network between November 1, 2009, and November 30, 2011.

MAIN OUTCOMES AND MEASURES

Measures of brain volume derived from T1-weighted structural neuroimaging at 3 T. Analyses were conducted at global, regional, and voxelwise levels. Regional volumes were estimated with an advanced multiatlas regional segmentation procedure, and voxelwise volumetric analyses were conducted as well. Nonlinear developmental patterns were examined using penalized splines within a general additive model. Psychosis spectrum (PS) symptom severity was summarized using factor analysis and evaluated dimensionally.

RESULTS

Following exclusions due to comorbidity and image quality assurance, the final sample included 791 participants aged youth 8 to 22 years. Fifty percent (n = 393) were female. After structured interviews, 391 participants were identified as having PS features (PS group) and 400 participants were identified as typically developing comparison individuals without significant psychopathology (TD group). Compared with the TD group, the PS group had diminished whole-brain gray matter volume (P = 1.8 × 10-10) and expanded white matter volume (P = 2.8 × 10-11). Voxelwise analyses revealed significantly lower gray matter volume in the medial temporal lobe (maximum z score = 5.2 and cluster size of 1225 for the right and maximum z score = 4.5 and cluster size of 310 for the left) as well as in frontal, temporal, and parietal cortex. Volumetric reduction in the medial temporal lobe was correlated with PS symptom severity.

CONCLUSIONS AND RELEVANCE

Structural brain abnormalities that have been commonly reported in adults with psychosis are present early in life in youth with PS symptoms and are not due to medication effects. Future longitudinal studies could use the presence of such abnormalities in conjunction with clinical presentation, cognitive profile, and genomics to predict risk and aid in stratification to guide early interventions.

Diffusion Imaging of White Matter In Schizophrenia: Progress and Future Directions

Diffusion tensor imaging (DTI) is a powerful tool for the in-vivo assessment of white matter microstructure. The application of DTI methodologies to the study of schizophrenia has supported and advanced the hypothesis of schizophrenia as a disorder of disrupted connectivity. In the context of impaired structural connectivity, the extended time frame of white matter development may offer unique opportunities for treatment that can capitalize on the neural flexibility that is still present in the period leading up to and after disease onset. Therefore, it is important to gain a clear understanding of white matter deficits and how they may emerge and change across the illness. However, while there is broad consistency in the findings of white matter deficits in patients with schizophrenia, there is also a great deal of variability in specific findings across studies. In this review, the aim is to move beyond summarizing case-control analyses, to consider the many factors that may impact DTI measures, to explain variability of findings, and to explore future directions for the field. The topics explored include ways to parse DTI patterns associated with different disease subtypes, ways in which novel and established treatments might interact with or enhance white matter, ways of dissociating developmental change from the disease process itself, and understanding the role of emerging analytic methodologies.

Abnormal white matter microstructure in drug-naive first episode schizophrenia patients before and after eight weeks of antipsychotic treatment

BACKGROUND

Abnormal white matter integrity has been reported among first episode schizophrenia patients. However, findings on whether it can be reversed by short-term antipsychotic medications are inconsistent.

METHOD

Diffusion tensor imaging (DTI) was obtained from 55 drug-naive first episode schizophrenia patients and 61 healthy controls, and was repeated among 25 patients and 31 controls after 8 weeks during which patients were medicated with antipsychotics. White matter integrity is measured using fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD). These measures showing a group difference by Tract-based spatial statistics (TBSS) at baseline were extracted for longitudinal comparisons.

RESULTS

At baseline, patients exhibited lower FA, higher MD and higher RD versus controls in forceps, left superior longitudinal fasciculus, inferior fronto-occipital fasciculus, left corticospinal tract, left uncinate fasciculus, left anterior thalamic radiation, and bilateral inferior longitudinal fasciculi. FA values of schizophrenia patients correlated with their negative symptoms (r=-0.412, P=0.002), working memory (r=0.377, P=0.005) and visual learning (r=0.281, P=0.038). The longitudinal changes in DTI indices in these tracts did not differ between patients and controls. However, among the patients the longitudinal changes in FA values in left superior longitudinal fasciculus correlated with the change of positive symptoms (r=-0.560, p=0.004), and the change of processing speed (r=0.469, p=0.018).

CONCLUSIONS

White matter deficits were validated in the present study by a relatively large sample of medication naïve and first episode schizophrenia patients. They could be associated with negative symptoms and cognitive impairment, whereas improvement in white matter integrity of left superior longitudinal fasciculus correlated with improvement in psychosis and processing speed. Further examination of treatment-related changes in white matter integrity may provide clues to the mechanism of antipsychotic response and provide a biomarker for clinical studies.

Connectomics in Schizophrenia: From Early Pioneers to Recent Brain Network Findings

Schizophrenia has been conceptualized as a brain network disorder. The historical roots of connectomics in schizophrenia go back to the late 19th century, when influential scholars such as Theodor Meynert, Carl Wernicke, Emil Kraepelin, and Eugen Bleuler worked on a theoretical understanding of the multifaceted syndrome that is currently referred to as schizophrenia. Their work contributed to the understanding that symptoms such as psychosis and cognitive disorganization might stem from abnormal integration or dissociation due to disruptions in the brain's association fibers. As methods to test this hypothesis were long lacking, the claims of these early pioneers remained unsupported by empirical evidence for almost a century. In this review, we revisit and pay tribute to the old masters and, discussing recent findings from the developing field of disease connectomics, we examine how their pioneering hypotheses hold up in light of current evidence.

The impact of quality assurance assessment on diffusion tensor imaging outcomes in a large-scale population-based cohort

BACKGROUND

Diffusion tensor imaging (DTI) is applied in investigation of brain biomarkers for neurodevelopmental and neurodegenerative disorders. However, the quality of DTI measurements, like other neuroimaging techniques, is susceptible to several confounding factors (e.g., motion, eddy currents), which have only recently come under scrutiny. These confounds are especially relevant in adolescent samples where data quality may be compromised in ways that confound interpretation of maturation parameters. The current study aims to leverage DTI data from the Philadelphia Neurodevelopmental Cohort (PNC), a sample of 1601 youths with ages of 8-21 who underwent neuroimaging, to: 1) establish quality assurance (QA) metrics for the automatic identification of poor DTI image quality; 2) examine the performance of these QA measures in an external validation sample; 3) document the influence of data quality on developmental patterns of typical DTI metrics.

METHODS

All diffusion-weighted images were acquired on the same scanner. Visual QA was performed on all subjects completing DTI; images were manually categorized as Poor, Good, or Excellent. Four image quality metrics were automatically computed and used to predict manual QA status: Mean voxel intensity outlier count (MEANVOX), Maximum voxel intensity outlier count (MAXVOX), mean relative motion (MOTION) and temporal signal-to-noise ratio (TSNR). Classification accuracy for each metric was calculated as the area under the receiver-operating characteristic curve (AUC). A threshold was generated for each measure that best differentiated visual QA status and applied in a validation sample. The effects of data quality on sensitivity to expected age effects in this developmental sample were then investigated using the traditional MRI diffusion metrics: fractional anisotropy (FA) and mean diffusivity (MD). Finally, our method of QA is compared with DTIPrep.

RESULTS

TSNR (AUC=0.94) best differentiated Poor data from Good and Excellent data. MAXVOX (AUC=0.88) best differentiated Good from Excellent DTI data. At the optimal threshold, 88% of Poor data and 91% Good/Excellent data were correctly identified. Use of these thresholds on a validation dataset (n=374) indicated high accuracy. In the validation sample 83% of Poor data and 94% of Excellent data was identified using thresholds derived from the training sample. Both FA and MD were affected by the inclusion of poor data in an analysis of an age, sex and race matched comparison sample. In addition, we show that the inclusion of poor data results in significant attenuation of the correlation between diffusion metrics (FA and MD) and age during a critical neurodevelopmental period. We find higher correspondence between our QA method and DTIPrep for Poor data, but we find our method to be more robust for apparently high-quality images.

CONCLUSION

Automated QA of DTI can facilitate large-scale, high-throughput quality assurance by reliably identifying both scanner and subject induced imaging artifacts. The results present a practical example of the confounding effects of artifacts on DTI analysis in a large population-based sample, and suggest that estimates of data quality should not only be reported but also accounted for in data analysis, especially in studies of development.

White matter structural integrity differs between people with schizophrenia and healthy groups as a function of cognitive control

A behavioral hallmark of schizophrenia is poor cognitive control. Recent evidence suggests that problems with cognitive control in schizophrenia are related to disconnectivity along major white matter fibers. Although deficits of cognitive control are common in schizophrenia, a proportion of otherwise healthy subjects show poor cognitive control performance. The present study sought to address this potential confound by comparing white matter integrity between a group with schizophrenia and otherwise healthy individuals with either high or low levels of cognitive control (based on working memory span performance). Diffusion tensor imaging was used to evaluate white matter integrity in 24 participants with schizophrenia, 24 healthy participants with high cognitive control (HCC), and 25 healthy participants with low cognitive control (LCC). To test for differences in fractional anisotropy (FA) across major white matter fiber tracts, a voxelwise region of interest analysis was conducted in standardized brain space. In a separate analysis, regions of interest were manually drawn in native brain space to isolate superior longitudinal fasciculus (SLF), a tract implicated in cognitive control performance. The voxelwise analysis demonstrated widespread lower FA in the schizophrenia group compared to the HCC group. With a high degree of concordance, the manual ROI analysis revealed lower FA in the schizophrenia group compared to the HCC group. Taken together, these results provide evidence to suggest that structural differences identified between healthy groups and schizophrenia may not be entirely specific to the disease process and can vary as a function of cognitive control capacity in the comparison group.

Neuroimaging predictors of functional outcomes in schizophrenia at baseline and 6-month follow-up

PURPOSE

Studies show that deficit syndrome schizophrenia patients, characterized by primary negative symptoms and poor functional outcome, have impairment in specific neural circuits. We assessed whether these same neural circuits are directly linked to functional outcomes across schizophrenia patients.

METHODS

T1- and diffusion-weighted MR images were obtained for schizophrenia (n=30) and matched healthy control participants (n=30). Negative symptoms and functional outcome were assessed at baseline and 6-month follow-up. Cortical thickness and tract-wise fractional anisotropy (FA) were compared between groups. To assess relationships of neuroimaging measures with functional outcome, principal component analysis (PCA) was performed on tract-wise FA values and components were entered into a multiple regression model for schizophrenia participants.

RESULTS

Consistent with the literature, schizophrenia participants showed frontotemporal reductions in cortical thickness and tract-wise FA compared to controls. The top two components from PCA explained 71% of the variance in tract-wise FA values. The second component (associated with inferior longitudinal and arcuate fasciculus FA) was significantly correlated with functional outcome (baseline: β=0.54, p=0.03; follow-up: β=0.74, p=0.047); further analysis revealed this effect was mediated by negative symptoms. Post-hoc network analysis revealed increased cortical coupling between right inferior frontal and supramarginal gyri (connected by the arcuate fasciculus) in schizophrenia participants with poorer functional outcome.

CONCLUSIONS

Our findings indicate that impairment in the same neural circuitry susceptible in deficit syndrome schizophrenia predicts functional outcome in a continuous manner in schizophrenia participants. This relationship was mediated by negative symptom burden. Our findings provide novel evidence for brain-based biomarkers of longitudinal functional outcome in people with schizophrenia.

Functional and clinical insights from neuroimaging studies in childhood-onset schizophrenia

Childhood-onset schizophrenia is a rare pediatric onset psychiatric disorder continuous with and typically more severe than its adult counterpart. Neuroimaging research conducted on this population has revealed similarly severe neural abnormalities. When taken as a whole, neuroimaging research in this population shows generally decreased cortical gray matter coupled with white matter connectivity abnormalities, suggesting an anatomical basis for deficits in executive function. Subcortical abnormalities are pronounced in limbic structures, where volumetric deficits are likely related to social skill deficits, and cerebellar deficits that have been correlated to cognitive abnormalities. Structures relevant to motor processing also show a significant alteration, with volumetric increase in basal ganglia structures likely due to antipsychotic administration. Neuroimaging of this disorder shows an important clinical image of exaggerated cortical loss, altered white matter connectivity, and differences in structural development of subcortical areas during the course of development and provides important background to the disease state.

Left-hemisphere lateralization for language and interhemispheric fiber tracking in patients with schizophrenia

BACKGROUND

It has been suggested that the degree of hemispheric specialization (HS) depends on the structural connectivity between the two hemispheres, that is to say the corpus callosum (CC). Studies, performed only on healthy participants, investigated this anatomo-functional relationship. Nevertheless, it has never been studied in schizophrenia. We therefore propose to study the anatomo-functional relationships between the integrity of interhemispheric connectivity and leftward functional lateralization for language in patients with schizophrenia compared with healthy participants, driven by a multimodal approach combining fMRI and DTI-based fiber tractography. We hypothesized that reduced leftward functional lateralization for language in patients with schizophrenia could be related to a callosal hypoconnectivity.

MATERIALS AND METHODS

Seventeen patients based on the DSM-IV, and 17 controls were included. The functional laterality index and interhemispheric diffusion values between homologue temporal regions, belonging to the language network, were individually extracted in order to study the anatomo-functional relationships.

RESULTS

In the patients, higher mean and radial diffusivity (RD) values (thicker myelin sheaths) were associated with less leftward lateralization. In contrast, the controls presented higher RD values and lower fractional anisotropy values (axonal loss) with more leftward lateralization.

CONCLUSIONS

Our study revealed a relationship between the CC and the HS for language, but did not provide evidence clarifying the direction of the relationship between callosal connectivity and functional lateralization for language. In particular, the present findings showed that the loss of integrity in interhemispheric callosal fibers was associated with reduced leftward cerebral dominance for language in patients with schizophrenia.

Schizophrenia-like topological changes in the structural connectome of individuals with subclinical psychotic experiences

Schizophrenia is often regarded as a “dysconnectivity” disorder and recent work using graph theory has been used to better characterize dysconnectivity of the structural connectome in schizophrenia. However, there are still little data on the topology of connectomes in less severe forms of the condition. Such analysis will identify topological markers of less severe disease states and provide potential predictors of further disease development. Individuals with psychotic experiences (PEs) were identified from a population‐based cohort without relying on participants presenting to clinical services. Such individuals have an increased risk of developing clinically significant psychosis. 123 individuals with PEs and 125 controls were scanned with diffusion‐weighted MRI. Whole‐brain structural connectomes were derived and a range of global and local GT‐metrics were computed. Global efficiency and density were significantly reduced in individuals with PEs. Local efficiency was reduced in a number of regions, including critical network hubs. Further analysis of functional subnetworks showed differential impairment of the default mode network. An additional analysis of pair‐wise connections showed no evidence of differences in individuals with PEs. These results are consistent with previous findings in schizophrenia. Reduced efficiency in critical core hubs suggests the brains of individuals with PEs may be particularly predisposed to dysfunction. The absence of any detectable effects in pair‐wise connections illustrates that, at less severe stages of psychosis, white‐matter alterations are subtle and only manifest when examining network topology. This study indicates that topology could be a sensitive biomarker for early stages of psychotic illness. Hum Brain Mapp 36:2629–2643, 2015.© 2015 TheAuthors Human Brain Mapping Published by Wiley Periodicals, Inc.

White matter microstructure in schizophrenia: associations to neurocognition and clinical symptomatology

BACKGROUND

Diffusion tensor imaging (DTI) studies in schizophrenia report widespread aberrations in brain white matter (WM). These appear related to poorer neurocognitive performance and higher levels of negative and positive symptomatology. However, identification of the most salient WM aberrations to neurocognition and clinical symptoms is limited by relatively small samples with divergent results.

METHODS

We examined 53 well-characterized patients with schizophrenia and 62 healthy controls. All participants were administered a computerized neurocognitive battery, which evaluated performance in several domains. Patients were assessed for negative and positive symptoms. Fractional anisotropy (FA) of WM cortical regions and WM fiber tracts were compared across the groups. FA values were also used to predict neurocognitive performance and symptoms.

RESULTS

We confirm widespread aberrant WM microstructure in a relatively large sample of well-characterized patients with schizophrenia in comparison to healthy participants. Moreover, we illustrate the utility of FA measures in predicting global neurocognitive performance in healthy participants and schizophrenia patients, especially for reaction time. FA was less predictive of clinical symptomatology.

CONCLUSIONS

Using a standardized computerized neurocognitive battery and diffusion tensor imaging we show that behavioral performance is moderated by a particular constellation of WM microstructure in healthy individuals that differs in schizophrenia.

Abnormal asymmetry of brain connectivity in schizophrenia

Recently, a growing body of data has revealed that beyond a dysfunction of connectivity among different brain areas in schizophrenia patients (SCZ), there is also an abnormal asymmetry of functional connectivity compared with healthy subjects. The loss of the cerebral torque and the abnormalities of gyrification, with an increased or more complex cortical folding in the right hemisphere may provide an anatomical basis for such aberrant connectivity in SCZ. Furthermore, diffusion tensor imaging studies have shown a significant reduction of leftward asymmetry in some key white-matter tracts in SCZ. In this paper, we review the studies that investigated both structural brain asymmetry and asymmetry of functional connectivity in healthy subjects and SCZ. From an analysis of the existing literature on this topic, we can hypothesize an overall generally attenuated asymmetry of functional connectivity in SCZ compared to healthy controls. Such attenuated asymmetry increases with the duration of the disease and correlates with psychotic symptoms. Finally, we hypothesize that structural deficits across the corpus callosum may contribute to the abnormal asymmetry of intra-hemispheric connectivity in schizophrenia.