Using brain structural neuroimaging measures to predict psychosis onset for individuals at clinical high-risk.

Machine learning approaches using structural magnetic resonance imaging (sMRI) can be informative for disease classification, although their ability to predict psychosis is largely unknown. We created a model with individuals at CHR who developed psychosis later (CHR-PS+) from healthy controls (HCs) that can differentiate each other. We also evaluated whether we could distinguish CHR-PS+ individuals from those who did not develop psychosis later (CHR-PS-) and those with uncertain follow-up status (CHR-UNK). T1-weighted structural brain MRI scans from 1165 individuals at CHR (CHR-PS+, n = 144; CHR-PS-, n = 793; and CHR-UNK, n = 228), and 1029 HCs, were obtained from 21 sites. We used ComBat to harmonize measures of subcortical volume, cortical thickness and surface area data and corrected for non-linear effects of age and sex using a general additive model. CHR-PS+ (n = 120) and HC (n = 799) data from 20 sites served as a training dataset, which we used to build a classifier. The remaining samples were used external validation datasets to evaluate classifier performance (test, independent confirmatory, and independent group [CHR-PS- and CHR-UNK] datasets). The accuracy of the classifier on the training and independent confirmatory datasets was 85% and 73% respectively. Regional cortical surface area measures-including those from the right superior frontal, right superior temporal, and bilateral insular cortices strongly contributed to classifying CHR-PS+ from HC. CHR-PS- and CHR-UNK individuals were more likely to be classified as HC compared to CHR-PS+ (classification rate to HC: CHR-PS+, 30%; CHR-PS-, 73%; CHR-UNK, 80%). We used multisite sMRI to train a classifier to predict psychosis onset in CHR individuals, and it showed promise predicting CHR-PS+ in an independent sample. The results suggest that when considering adolescent brain development, baseline MRI scans for CHR individuals may be helpful to identify their prognosis. Future prospective studies are required about whether the classifier could be actually helpful in the clinical settings.

Fibre density and fibre-bundle cross-section of the corticospinal tract are distinctly linked to psychosis-specific symptoms in antipsychotic-naïve patients with first-episode schizophrenia.

Multiple lines of research support the dysconnectivity hypothesis of schizophrenia. However, findings on white matter (WM) alterations in patients with schizophrenia are widespread and non-specific. Confounding factors from magnetic resonance image (MRI) processing, clinical diversity, antipsychotic exposure, and substance use may underlie some of the variability. By application of refined methodology and careful sampling, we rectified common confounders investigating WM and symptom correlates in a sample of strictly antipsychotic-naïve first-episode patients with schizophrenia. Eighty-six patients and 112 matched controls underwent diffusion MRI. Using fixel-based analysis (FBA), we extracted fibre-specific measures such as fibre density and fibre-bundle cross-section. Group differences on fixel-wise measures were examined with multivariate general linear modelling. Psychopathology was assessed with the Positive and Negative Syndrome Scale. We separately tested multivariate correlations between fixel-wise measures and predefined psychosis-specific versus anxio-depressive symptoms. Results were corrected for multiple comparisons. Patients displayed reduced fibre density in the body of corpus callosum and in the middle cerebellar peduncle. Fibre density and fibre-bundle cross-section of the corticospinal tract were positively correlated with suspiciousness/persecution, and negatively correlated with delusions. Fibre-bundle cross-section of isthmus of corpus callosum and hallucinatory behaviour were negatively correlated. Fibre density and fibre-bundle cross-section of genu and splenium of corpus callosum were negative correlated with anxio-depressive symptoms. FBA revealed fibre-specific properties of WM abnormalities in patients and differentiated associations between WM and psychosis-specific versus anxio-depressive symptoms. Our findings encourage an itemised approach to investigate the relationship between WM microstructure and clinical symptoms in patients with schizophrenia.

Normative modeling of brain morphometry in Clinical High-Risk for Psychosis.

Importance: The lack of robust neuroanatomical markers of psychosis risk has been traditionally attributed to heterogeneity. A complementary hypothesis is that variation in neuroanatomical measures in the majority of individuals at psychosis risk may be nested within the range observed in healthy individuals. Objective: To quantify deviations from the normative range of neuroanatomical variation in individuals at clinical high-risk for psychosis (CHR-P) and evaluate their overlap with healthy variation and their association with positive symptoms, cognition, and conversion to a psychotic disorder. Design Setting and Participants: Clinical, IQ and FreeSurfer-derived regional measures of cortical thickness (CT), cortical surface area (SA), and subcortical volume (SV) from 1,340 CHR-P individuals [47.09% female; mean age: 20.75 (4.74) years] and 1,237 healthy individuals [44.70% female; mean age: 22.32 (4.95) years] from 29 international sites participating in the ENIGMA Clinical High Risk for Psychosis Working Group. Main Outcomes and Measures: For each regional morphometric measure, z-scores were computed that index the degree of deviation from the normative means of that measure in a healthy reference population (N=37,407). Average deviation scores (ADS) for CT, SA, SV, and globally across all measures (G) were generated by averaging the respective regional z-scores. Regression analyses were used to quantify the association of deviation scores with clinical severity and cognition and two-proportion z-tests to identify case-control differences in the proportion of individuals with infranormal (z<-1.96) or supranormal (z>1.96) scores. Results: CHR-P and healthy individuals overlapped in the distributions of the observed values, regional z-scores, and all ADS vales. The proportion of CHR-P individuals with infranormal or supranormal values in any metric was low (<12%) and similar to that of healthy individuals. CHR-P individuals who converted to psychosis compared to those who did not convert had a higher percentage of infranormal values in temporal regions (5-7% vs 0.9-1.4%). In the CHR-P group, only the ADSSA showed significant but weak associations (|ß|<0.09; PFDR<0.05) with positive symptoms and IQ. Conclusions and Relevance: The study findings challenge the usefulness of macroscale neuromorphometric measures as diagnostic biomarkers of psychosis risk and suggest that such measures do not provide an adequate explanation for psychosis risk.

Cortico-cognition coupling in treatment resistant schizophrenia.

BACKGROUND: Brain structural alterations and cognitive dysfunction are independent predictors for poor clinical outcome in schizophrenia, and the associations between these domains remains unclear. We employed a novel, multiblock partial least squares correlation (MB-PLS-C) technique and investigated multivariate cortico-cognitive patterns in patients with treatment-resistant schizophrenia (TRS) and matched healthy controls (HC). METHOD: Forty-one TRS patients (age 38.5 ± 9.1, 30 males (M)), and 45 HC (age 40.2 ± 10.6, 29 M) underwent 3T structural MRI. Volumes of 68 brain regions and seven variables from CANTAB covering memory and executive domains were included. Univariate group differences were assessed, followed by the MB-PLS-C analyses to identify group-specific multivariate patterns of cortico-cognitive coupling. Supplementary three-group analyses, which included 23 non-affected first-degree relatives (NAR), were also conducted. RESULTS: Univariate tests demonstrated that TRS patients showed impairments in all seven cognitive tasks and volume reductions in 12 cortical regions following Bonferroni correction. The MB-PLS-C analyses revealed two significant latent variables (LVs) explaining > 90% of the sum-of-squares variance. LV1 explained 78.86% of the sum-of-squares variance, describing a shared, widespread structure-cognitive pattern relevant to both TRS patients and HCs. In contrast, LV2 (13.47% of sum-of-squares variance explained) appeared specific to TRS and comprised a differential cortico-cognitive pattern including frontal and temporal lobes as well as paired associates learning (PAL) and intra-extra dimensional set shifting (IED). Three-group analyses also identified two significant LVs, with NARs more closely resembling healthy controls than TRS patients. CONCLUSIONS: MB-PLS-C analyses identified multivariate brain structural-cognitive patterns in the latent space that may provide a TRS signature.

Changes in negative symptoms are linked to white matter changes in superior longitudinal fasciculus in individuals at ultra-high risk for psychosis.

AIM: Growing evidence suggests that subtle white matter (WM) alterations are associated with psychopathology in individuals at ultra-high risk for psychosis (UHR). However, the longitudinal relationship between symptom progression and WM changes over time remains under-explored. Here, we examine associations between changes in clinical symptoms and changes in WM over six months in a large UHR-cohort. METHODS: 110 UHR-individuals and 59 healthy controls underwent diffusion weighted imaging at baseline and after six months. Group × time effects on fractional anisotropy (FA) were tested globally and in four predefined regions of interest (ROIs) bilaterally using linear modelling with repeated measures. Correlations between the changes in clinical symptoms and FA changes in the ROIs were examined with Pearson's correlation. A partial least squares correlation-technique (PLS-C) explored multivariate associations between patterns of changes in psychopathology, regional FA and additional WM indices. RESULTS: At baseline, UHR-individuals displayed significantly lower FA globally (p = 0.018; F = 12.274), in right superior longitudinal fasciculus (p = 0.02; Adj R2 = 0.07) and in left uncinate fasciculus (p = 0.048; Adj R2 = 0.058) compared to controls (corrected). We identified a group × time interaction in global FA and right superior longitudinal fasciculus, but the finding did not survive multiple comparisons. However, an increase of negative symptoms in UHR-individuals correlated with FA increase in right superior longitudinal fasciculus (p = 0.048, corrected, r = 0.357), and this finding was supported by the multivariate PLS-C. CONCLUSION: We found a positive correlation with a moderate effect between change in negative symptoms and FA change over 6 months in right superior longitudinal fasciculus. This link appeared mainly to reflect a subgroup of UHR-individuals, which already at baseline presented as vulnerable.

Investigation of cortical thickness and volume during spontaneous attacks of migraine without aura: a 3-Tesla MRI study.

BACKGROUND: Structural imaging has revealed changes in cortical thickness in migraine patients compared to healthy controls is reported, but presence of dynamic cortical and subcortical changes during migraine attack versus inter-ictal phase is unknown. The aim of the present study was to investigate possible changes in cortical thickness during spontaneous migraine attacks. We hypothesized that pain-related cortical area would be affected during the attack compared to an inter-ictal phase. METHODS: Twenty-five patients with migraine without aura underwent three-dimensional T1-weighted imaging on a 3-Tesla MRI scanner during spontaneous and untreated migraine attacks. Subsequently, 20 patients were scanned in the inter-ictal phase, while 5 patients did not show up for the inter-ictal scan. Four patients were excluded from the analysis because of bilateral migraine pain and another one patient was excluded due to technical error in the imaging. Longitudinal image processing was done using FreeSurfer. Repeated measures ANOVA was used for statistical analysis and to control for multiple comparison the level of significance was set at p = 0.025. RESULTS: In a total of 15 patients, we found reduced cortical thickness of the precentral (p = 0.023), pericalcarine (p = 0.024), and temporal pole (p = 0.017) cortices during the attack compared to the inter-ictal phase. Cortical volume was reduced in prefrontal (p = 0.018) and pericalcarine (p = 0.017) cortices. Hippocampus volume was increased during attack (p = 0.007). We found no correlations between the pain side or any other clinical parameters and the reduced cortical size. CONCLUSION: Spontaneous migraine attacks are accompanied by transient reduced cortical thickness and volume in pain-related areas. The findings constitute a fingerprint of acute pain in migraine patients, which can be used as a possible biomarker to predict antimigraine treatment effect in future studies. TRIAL REGISTRATION: The study was registered at ClinicalTrials.gov ( NCT02202486 ).

Global fractional anisotropy predicts transition to psychosis after 12 months in individuals at ultra-high risk for psychosis.

OBJECTIVE: Psychosis spectrum disorders are associated with cerebral changes, but the prognostic value and clinical utility of these findings are unclear. Here, we applied a multivariate statistical model to examine the predictive accuracy of global white matter fractional anisotropy (FA) for transition to psychosis in individuals at ultra-high risk for psychosis (UHR). METHODS: 110 UHR individuals underwent 3 Tesla diffusion-weighted imaging and clinical assessments at baseline, and after 6 and 12 months. Using logistic regression, we examined the reliability of global FA at baseline as a predictor for psychosis transition after 12 months. We tested the predictive accuracy, sensitivity and specificity of global FA in a multivariate prediction model accounting for potential confounders to FA (head motion in scanner, age, gender, antipsychotic medication, parental socioeconomic status and activity level). In secondary analyses, we tested FA as a predictor of clinical symptoms and functional level using multivariate linear regression. RESULTS: Ten UHR individuals had transitioned to psychosis after 12 months (9%). The model reliably predicted transition at 12 months (χ2  = 17.595, p = 0.040), accounted for 15-33% of the variance in transition outcome with a sensitivity of 0.70, a specificity of 0.88 and AUC of 0.87. Global FA predicted level of UHR symptoms (R2  = 0.055, F = 6.084, p = 0.016) and functional level (R2  = 0.040, F = 4.57, p = 0.036) at 6 months, but not at 12 months. CONCLUSION: Global FA provided prognostic information on clinical outcome and symptom course of UHR individuals. Our findings suggest that the application of prediction models including neuroimaging data can inform clinical management on risk for psychosis transition.

Associations between cognition and white matter microstructure in first-episode antipsychotic-naïve patients with schizophrenia and healthy controls: A multivariate pattern analysis.

BACKGROUND: Cognitive functions have been associated with white matter (WM) microstructure in schizophrenia, but most studies are limited by examining only select cognitive measures and single WM tracts in chronic, medicated patients. It is unclear if the cognition-WM relationship differs between antipsychotic-naïve patients with schizophrenia and healthy controls, as differential associations have not been directly examined. Here we examine if there are differential patterns of associations between cognition and WM microstructure in first-episode antipsychotic-naïve patients with schizophrenia and healthy controls, and we characterize reliable contributors to the pattern of associations across multiple cognitive domains and WM regions, in order to elucidate white matter contribution to the neural underpinnings of cognitive deficits. METHODS: Thirty-six first-episode antipsychotic-naïve patients with schizophrenia and 52 matched healthy controls underwent cognitive tests and diffusion-weighted imaging on a 3T Magnetic Resonance Imaging scanner. Using a multivariate partial least squares correlation analysis, we included 14 cognitive variables and mean fractional anisotropy values of 48 WM regions. RESULTS: Initial analyses showed significant group differences in both measures of WM and cognition. There was no group interaction effect in the pattern of associations between cognition and WM microstructure. The combined analysis of patients and controls lead to a significant pattern of associations (omnibus test p = .015). Thirty-four regions and seven cognitive functions contributed reliably to the associations. CONCLUSIONS: The lack of an interaction effect suggests similar associations in first-episode antipsychotic-naïve patients with schizophrenia and healthy controls. This, together with the differences in both WM and cognitive measurements, supports the involvement of WM in cognitive deficits in schizophrenia. Our findings add to the field by showing a coherent picture of the overall pattern of association between cognition and WM. These findings increase our understanding of the impact of WM on cognition, contributing to the search for neuromarkers of cognitive deficits in schizophrenia.

Multimodal assessment of white matter microstructure in antipsychotic-naïve schizophrenia patients and confounding effects of recreational drug use.

Cerebral white matter (WM) aberrations in schizophrenia have been linked to multiple neurobiological substrates but the underlying mechanisms remain unknown. Moreover, antipsychotic treatment and substance use constitute potential confounders. Multimodal studies using diffusion tensor imaging (DTI) and magnetization transfer imaging (MTI) may provide deeper insight into the whole brain WM pathophysiology in schizophrenia. We combined DTI and MTI to investigate WM integrity in 51 antipsychotic-naïve, first-episode schizophrenia patients and 55 matched healthy controls, using 3 T magnetic resonance imaging (MRI). Psychopathology was assessed with the positive and negative syndrome scale (PANSS). A whole brain partial least squares correlation (PLSC) method was used to conjointly analyze DTI-derived measures (fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD), mode of anisotropy (MO)) and the magnetization transfer ratio (MTR) to identify group differences, and associations with psychopathology. In secondary analyses, we excluded recreational substance users from both groups resulting in 34 patients and 51 healthy controls. The primary PLSC group difference analysis identified a significant pattern of lower FA, AD, MO and higher RD in patients (p = 0.04). This pattern suggests disorganized WM microstructure in patients. The secondary PLSC group difference analysis without recreational substance users revealed a significant pattern of lower FA and higher AD, RD, MO, MTR in patients (p = 0.04). This pattern in the substance free patients is consistent with higher extracellular free-water concentrations, which may reflect neuroinflammation. No significant associations with psychopathology were observed. Recreational substance use appears to be a confounding issue, which calls for attention in future WM studies.

No Effects of Cognitive Remediation on Cerebral White Matter in Individuals at Ultra-High Risk for Psychosis-A Randomized Clinical Trial.

BACKGROUND: Individuals at ultra-high risk for psychosis (UHR) present with subtle alterations in cerebral white matter (WM), which appear to be associated with clinical and functional outcome. The effect of cognitive remediation on WM organization in UHR individuals has not been investigated previously. METHODS: In a randomized, clinical trial, UHR individuals aged 18 to 40 years were assigned to treatment as usual (TAU) or TAU plus cognitive remediation for 20 weeks. Cognitive remediation comprised 20 x 2-h sessions of neurocognitive and social-cognitive training. Primary outcome was whole brain fractional anisotropy derived from diffusion weighted imaging, statistically tested as an interaction between timepoint and treatment group. Secondary outcomes were restricted to five predefined region of interest (ROI) analyses on fractional anisotropy, axial diffusivity, radial diffusivity and mean diffusivity. For significant timepoint and treatment group interactions within these five ROIs, we explored associations between longitudinal changes in WM and cognitive functions/clinical symptoms. Finally, we explored dose-response effects of cognitive remediation on WM. RESULTS: A total of 111 UHR individuals were included. Attrition-rate was 26%. The cognitive remediation group completed on average 12 h of neurocognitive training, which was considerably lower than per protocol. We found no effect of cognitive remediation on whole-brain FA when compared to treatment as usual. Secondary ROI analyses revealed a nominal significant interaction between timepoint*treatment of AD in left medial lemniscus (P=0.016) which did not survive control for multiple comparisons. The exploratory test showed that this change in AD correlated to improvements of mental flexibility in the cognitive remediation group (p=0.001). We found no dose-response effect of neurocognitive training on WM. CONCLUSIONS: Cognitive remediation comprising 12 h of neurocognitive training on average did not improve global or regional WM organization in UHR individuals. Further investigations of duration and intensity of cognitive training as necessary prerequisites of neuroplasticity-based changes are warranted. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov, identifier NCT02098408.

A machine-learning framework for robust and reliable prediction of short- and long-term treatment response in initially antipsychotic-naïve schizophrenia patients based on multimodal neuropsychiatric data.

The reproducibility of machine-learning analyses in computational psychiatry is a growing concern. In a multimodal neuropsychiatric dataset of antipsychotic-naïve, first-episode schizophrenia patients, we discuss a workflow aimed at reducing bias and overfitting by invoking simulated data in the design process and analysis in two independent machine-learning approaches, one based on a single algorithm and the other incorporating an ensemble of algorithms. We aimed to (1) classify patients from controls to establish the framework, (2) predict short- and long-term treatment response, and (3) validate the methodological framework. We included 138 antipsychotic-naïve, first-episode schizophrenia patients with data on psychopathology, cognition, electrophysiology, and structural magnetic resonance imaging (MRI). Perinatal data and long-term outcome measures were obtained from Danish registers. Short-term treatment response was defined as change in Positive And Negative Syndrome Score (PANSS) after the initial antipsychotic treatment period. Baseline diagnostic classification algorithms also included data from 151 matched controls. Both approaches significantly classified patients from healthy controls with a balanced accuracy of 63.8% and 64.2%, respectively. Post-hoc analyses showed that the classification primarily was driven by the cognitive data. Neither approach predicted short- nor long-term treatment response. Validation of the framework showed that choice of algorithm and parameter settings in the real data was successfully guided by results from the simulated data. In conclusion, this novel approach holds promise as an important step to minimize bias and obtain reliable results with modest sample sizes when independent replication samples are not available.

Striatal Volume Increase After Six Weeks of Selective Dopamine D2/3 Receptor Blockade in First-Episode, Antipsychotic-Naïve Schizophrenia Patients.

Patients with chronic schizophrenia often display enlarged striatal volumes, and antipsychotic drugs may contribute via the dopamine D2/3 receptor (D2/3R) blockade. Separating the effects of disease from medication is challenging due to the lack of a proper placebo-group. To address this, we conducted a longitudinal study of antipsychotic-naïve, first-episode schizophrenia patients to test the hypothesis that selective blockade of D2/3R would induce a dose-dependent striatal volume increase. Twenty-one patients underwent structural magnetic resonance imaging (sMRI), single-photon emission computed tomography (SPECT), and symptom severity ratings before and after six weeks of amisulpride treatment. Twenty-three matched healthy controls underwent sMRI and baseline SPECT. Data were analyzed using repeated measures and multiple regression analyses. Correlations between symptom severity decrease, volume changes, dose and receptor occupancy were explored. Striatal volumes did not differ between patients and controls at baseline or follow-up, but a significant group-by-time interaction was found (p = 0.01). This interaction was explained by a significant striatal volume increase of 2.1% in patients (Cohens d = 0.45). Striatal increase was predicted by amisulpride dose, but not by either D2/3R occupancy or baseline symptom severity. A significant reduction in symptom severity was observed at a mean dose of 233.3 (SD = 109.9) mg, corresponding to D2/3R occupancy of 44.65%. Reduction in positive symptoms correlated significantly with striatal volume increase, driven by reductions in hallucinations. Our data demonstrate a clear link between antipsychotic treatment and striatal volume increase in antipsychotic-naïve schizophrenia patients. Moreover, the treatment-induced striatal volume increase appears clinically relevant by correlating to reductions in core symptoms of schizophrenia.

Identification of a Serotonin 2A Receptor Subtype of Schizophrenia Spectrum Disorders With Pimavanserin: The Sub-Sero Proof-of-Concept Trial Protocol.

BACKGROUND: All current approved antipsychotic drugs against schizophrenia spectrum disorders share affinity for the dopamine receptor (D2R). However, up to one-third of these patients respond insufficiently, and in some cases, side-effects outweigh symptom reduction. Previous data have suggested that a subgroup of antipsychotic-naïve patients will respond to serotonin 2A receptor (2AR) blockade. AIMS: This investigator-initiated, translational, proof-of-concept study has overall two aims; 1) To test the clinical effectiveness of monotherapy with the newly approved drug against Parkinson's disease psychosis, pimavanserin, in antipsychotic-free patients with first-episode schizophrenia spectrum disorders; 2) To characterize the neurobiological profile of responders to pimavaserin. MATERIALS AND EQUIPMENT: Forty patients will be enrolled in this 6-week open label, one-armed trial with the selective serotonin 2AR antagonist (pimavanserin 34 mg/day). At baseline, patients will undergo: positron emission tomography (PET) imaging of the serotonin 2AR using the radioligand [¹¹C]Cimbi-36; structural magnetic resonance imaging (MRI); MR spectroscopy of cerebral glutamate levels and diffusion tensor imaging; cognitive and psychopathological examinations; electrocardiogram, and blood sampling for genetic- and metabolic analyses. OUTCOME MEASURES: The primary clinical endpoint will be reduction in the Positive and Negative Syndrome Scale (PANSS) positive score. Secondary clinical endpoints comprise multiple clinical ratings (positive and negative symptoms, depressive-, obsessive-compulsive symptoms, quality of life, social functioning, sexual functioning, and side-effects). PET, MRI, and cognitive parameters will be used for in-depth neuropsychiatric characterization of pimavanserin response. ANTICIPATED RESULTS: Clinically, we expect pimavanserin to reduce psychotic symptoms with similar effect as observed with conventional antipsychotics, for which we have comparable historical data. We expect pimavanserin to induce minimal side-effects. Neurobiologically, we expect psychotic symptom reduction to be most prominent in patients with low frontal serotonin 2AR binding potential at baseline. Potential pro-cognitive and brain structural effects of pimavanserin will be explored. PERSPECTIVES: Sub-Sero will provide unique information about the role serotonin 2AR in antipsychotic-free, first-episode psychosis. If successful, Sub-Sero will aid identification of a "serotonergic subtype" of schizophrenia spectrum patients, thereby promoting development of precision medicine in clinical psychiatry. CLINICAL TRIAL REGISTRATION: ClinicalTrials, identifier NCT03994965.

Widespread higher fractional anisotropy associates to better cognitive functions in individuals at ultra-high risk for psychosis.

In schizophrenia patients, cognitive functions appear linked to widespread alterations in cerebral white matter microstructure. Here we examine patterns of associations between regional white matter and cognitive functions in individuals at ultra-high risk for psychosis. One hundred and sixteen individuals at ultra-high risk for psychosis and 49 matched healthy controls underwent 3 T magnetic resonance diffusion-weighted imaging and cognitive assessments. Group differences on fractional anisotropy were tested using tract-based spatial statistics. Group differences in cognitive functions, voxel-wise as well as regional fractional anisotropy were tested using univariate general linear modeling. Multivariate partial least squares correlation analyses tested for associations between patterns of regional fractional anisotropy and cognitive functions. Univariate analyses revealed significant impairments on cognitive functions and lower fractional anisotropy in superior longitudinal fasciculus and cingulate gyrus in individuals at ultra-high risk for psychosis. Partial least squares correlation analysis revealed different associations between patterns of regional fractional anisotropy and cognitive functions in individuals at ultra-high risk for psychosis compared to healthy controls. Widespread higher fractional anisotropy was associated with better cognitive functioning for individuals at ultra-high risk for psychosis, but not for the healthy controls. Furthermore, patterns of cognitive functions were associated with an interaction-effect on regional fractional anisotropy in fornix, medial lemniscus, uncinate fasciculus, and superior cerebellar peduncle. Aberrant associations between patterns of cognitive functions to white matter may be explained by dysmyelination.

Patterns of Cortical Structures and Cognition in Antipsychotic-Naïve Patients With First-Episode Schizophrenia: A Partial Least Squares Correlation Analysis.

BACKGROUND: Schizophrenia is associated with alterations in cortical structures and cognitive impairments, but antipsychotic medication may affect these measures. We investigated patterns of relationships between cortical structures and cognitive domains in antipsychotic-naïve patients with first-episode schizophrenia. METHODS: T1-weighted 3T magnetic resonance imaging was performed in 105 patients and 136 healthy control subjects. Using FreeSurfer, we obtained measurements of cortical thickness, surface area, and mean curvature. Using an extensive neurocognitive battery including the Danish Adult Reading Test and subtests from the Cambridge Neuropsychological Test Automated Battery, we obtained estimates of premorbid intelligence, spatial working memory, spatial planning, intra-extradimensional set shifting, and reaction and movement times. With univariate analyses, we tested group differences between cortical structures and cognition. With partial least squares correlation analyses, we investigated patterns of associations between cortical structures and cognition. RESULTS: Patients had significantly higher mean curvature and were impaired on 7 of 11 cognitive parameters. The between-group partial least squares correlation analysis revealed two cortical thickness/cognition patterns that differentiated patients and healthy control subjects (omnibus test, p = .011). Most cortical regions contributed reliably to these patterns. In patients, spatial working memory, spatial planning, reaction and movement times, and premorbid intelligence contributed reliably to the pattern; in healthy control subjects, spatial planning and intra-extradimensional set shifting contributed reliably. CONCLUSIONS: Antipsychotic-naïve patients with first-episode schizophrenia displayed a higher mean curvature, but no significant difference in other gray matter indices was found. Nevertheless, the pattern of associations between global cortical thickness and cognitive functions was markedly different between groups. These multivariate analyses reveal a novel linkage between regional cortical brain structure and cognitive deficits at the earliest, never-medicated illness stage.

Accuracy of diagnostic classification algorithms using cognitive-, electrophysiological-, and neuroanatomical data in antipsychotic-naïve schizophrenia patients.

BACKGROUND: A wealth of clinical studies have identified objective biomarkers, which separate schizophrenia patients from healthy controls on a group level, but current diagnostic systems solely include clinical symptoms. In this study, we investigate if machine learning algorithms on multimodal data can serve as a framework for clinical translation. METHODS: Forty-six antipsychotic-naïve, first-episode schizophrenia patients and 58 controls underwent neurocognitive tests, electrophysiology, and magnetic resonance imaging (MRI). Patients underwent clinical assessments before and after 6 weeks of antipsychotic monotherapy with amisulpride. Nine configurations of different supervised machine learning algorithms were applied to first estimate the unimodal diagnostic accuracy, and next to estimate the multimodal diagnostic accuracy. Finally, we explored the predictability of symptom remission. RESULTS: Cognitive data significantly classified patients from controls (accuracies = 60-69%; p values = 0.0001-0.009). Accuracies of electrophysiology, structural MRI, and diffusion tensor imaging did not exceed chance level. Multimodal analyses with cognition plus any combination of one or more of the remaining three modalities did not outperform cognition alone. None of the modalities predicted symptom remission. CONCLUSIONS: In this multivariate and multimodal study in antipsychotic-naïve patients, only cognition significantly discriminated patients from controls, and no modality appeared to predict short-term symptom remission. Overall, these findings add to the increasing call for cognition to be included in the definition of schizophrenia. To bring about the full potential of machine learning algorithms in first-episode, antipsychotic-naïve schizophrenia patients, careful a priori variable selection based on independent data as well as inclusion of other modalities may be required.

Extrastriatal dopamine D2/3 receptors and cortical grey matter volumes in antipsychotic-naïve schizophrenia patients before and after initial antipsychotic treatment.

OBJECTIVES: Long-term dopamine D2/3 receptor blockade, common to all antipsychotics, may underlie progressive brain volume changes observed in patients with chronic schizophrenia. In the present study, we examined associations between cortical volume changes and extrastriatal dopamine D2/3 receptor binding potentials (BPND) in first-episode schizophrenia patents at baseline and after antipsychotic treatment. METHODS: Twenty-two initially antipsychotic-naïve patients underwent magnetic resonance imaging (MRI), [123I]epidepride single-photon emission computerised tomography (SPECT), and psychopathology assessments before and after 3 months of treatment with either risperidone (N = 13) or zuclopenthixol (N = 9). Twenty healthy controls matched on age, gender and parental socioeconomic status underwent baseline MRI and SPECT. RESULTS: Neither extrastriatal D2/3 receptor BPND at baseline, nor blockade at follow-up, was related to regional cortical volume changes. In post-hoc analyses excluding three patients with cannabis use we found that higher D2/3 receptor occupancy was significantly associated with an increase in right frontal grey matter volume. CONCLUSIONS: The present data do not support an association between extrastriatal D2/3 receptor blockade and extrastriatal grey matter loss in the early phases of schizophrenia. Although inconclusive, our exclusion of patients tested positive for cannabis use speaks to keeping attention to potential confounding factors in imaging studies.

Frontal fasciculi and psychotic symptoms in antipsychotic-naive patients with schizophrenia before and after 6 weeks of selective dopamine D2/3 receptor blockade.

BACKGROUND: Psychotic symptoms are core clinical features of schizophrenia. We tested recent hypotheses proposing that psychotic, or positive, symptoms stem from irregularities in long-range white matter tracts projecting into the frontal cortex, and we predicted that selective dopamine D2/3 receptor blockade would restore white matter. METHODS: Between December 2008 and July 2011, antipsychotic-naive patients with first-episode schizophrenia and matched healthy controls underwent baseline examination with 3 T MRI diffusion tensor imaging and clinical assessments. We assessed group differences of fractional anisotropy (FA) using voxelwise tract-based spatial statistics (TBSS) and anatomic region of interest (ROI)-based analyses. Subsequently, patients underwent 6 weeks of antipsychotic monotherapy with amisulpride. We repeated the examinations after 6 weeks. RESULTS: We included 38 patients with first-episode schizophrenia and 38 controls in our analysis, and 28 individuals in each group completed the study. At baseline, whole brain TBSS analyses revealed lower FA in patients in the right anterior thalamic radiation (ATR), right cingulum, right inferior longitudinal fasciculus and right corticospinal tract (CT). Fractional anisotropy in the right ATR correlated with positive symptoms (z = 2.64, p= 0.008). The ROI analyses showed significant associations between positive symptoms and FA of the frontal fasciculi, specifically the right arcuate fasciculus (z = 2.83, p= 0.005) and right superior longitudinal fasciculus (z = -3.31, p= 0.001). At re-examination, all correlations between positive symptoms and frontal fasciculi had resolved. Fractional anisotropy in the ATR increased more in patients than in controls (z = -4.92, p< 0.001). The amisulpride dose correlated positively with FA changes in the right CT (t= 2.52, p= 0.019). LIMITATIONS: Smoking and a previous diagnosis of substance abuse were potential confounders. Long-term effects of amisulpride on white matter were not evaluated. CONCLUSION: Antipsychotic-naive patients with schizophrenia displayed subtle deficits in white matter, and psychotic symptoms appeared specifically associated with frontal fasciculi integrity. Six weeks of amisulpride treatment normalized white matter. Potential remyelinating effects of dopamine D2/3 receptor antagonism warrant further clarification.

[White matter alterations in schizophrenia].

Schizophrenia is a brain disorder characterized by fundamental changes in thinking and beliefs. Alterations in white matter integrity may underlie the characteristic psychotic symptoms. This review focuses on diffusion tensor imaging studies in schizophrenia patients. Overall, schizophrenia appears to be associated with white matter deficits particularly in the fronto-temporal connections. To dissect potential medication effects from myelination deficits related to symptoms, longitudinal studies in initially antipsychotic-naive first-episode patients with schizophrenia are needed.