Stefania Tognin

Neuroanatomical Abnormalities That Predate the Onset of Psychosis: A Multicenter Study

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

Genetic Vulnerability to Affective Psychopathology in Childhood: A Combined Voxel-Based Morphometry and Functional Magnetic Resonance Imaging Study

BACKGROUND The majority of affective psychopathology is rooted early in life and first emerges during childhood and adolescence. However, little is known about how genetic vulnerability affects brain structure and function in childhood since the vast majority of studies published so far have been conducted on adult participants. The present investigation examined for the first time the effects of catechol-O-methyltransferase (COMT) valine (val) 158 methionine (met) (val158met) polymorphism, which has been shown to moderate predisposition to negative mood and affective disorders, on brain structure and function in children. METHODS Voxel-based morphometry and functional magnetic resonance imaging were used to measure gray matter volume and emotional reactivity in 50 children aged between 10 and 12 years. We tested the hypothesis that met158 allele affects structural brain development and confers heightened reactivity within the affective frontolimbic circuit in children. RESULTS The met158 allele was positively associated with gray matter volume in the left hippocampal head where genotype accounted for 59% of interindividual variance. In addition, the met158 allele was positively associated with neuronal responses to fearful relative to neutral facial expressions in the right parahippocampal gyrus where genotype accounted for 14% of the interindividual variance. CONCLUSIONS These results indicate that the met158 allele is associated with increased gray matter volume and heightened reactivity during emotional processing within the limbic system in children as young as 10 to 12 years of age. These findings are consistent with the notion that genetic factors affect brain function to moderate vulnerability to affective psychopathology from childhood.

Using Structural Neuroimaging to Make Quantitative Predictions of Symptom Progression in Individuals at Ultra-High Risk for Psychosis

Neuroimaging holds the promise that it may one day aid the clinical assessment of individual psychiatric patients. However, the vast majority of studies published so far have been based on average differences between groups, which do not permit accurate inferences at the level of the individual. We examined the potential of structural Magnetic Resonance Imaging (MRI) data for making accurate quantitative predictions about symptom progression in individuals at ultra-high risk for developing psychosis. Forty people at ultra-high risk for psychosis were scanned using structural MRI at first clinical presentation and assessed over a period of 2 years using the Positive and Negative Syndrome Scale. Using a multivariate machine learning method known as relevance vector regression (RVR), we examined the relationship between brain structure at first clinical presentation, characterized in terms of gray matter (GM) volume and cortical thickness (CT), and symptom progression at 2-year follow-up. The application of RVR to whole-brain CT MRI data allowed quantitative prediction of clinical scores with statistically significant accuracy (correlation = 0.34, p = 0.026; Mean Squared-Error = 249.63, p = 0.024). This prediction was informed by regions traditionally associated with schizophrenia, namely the right lateral and medial temporal cortex and the left insular cortex. In contrast, the application of RVR to GM volume did not allow prediction of symptom progression with statistically significant accuracy. These results provide proof-of-concept that it could be possible to use structural MRI to inform quantitative prediction of symptom progression in individuals at ultra-high risk of developing psychosis. This would enable clinicians to target those individuals at greatest need of preventative interventions thereby resulting in a more efficient use of health care resources.

Diagnostic and Prognostic Significance of Brief Limited Intermittent Psychotic Symptoms (BLIPS) in Individuals at Ultra High Risk.

Background: Brief Limited Intermittent Psychotic Symptoms (BLIPS) are key inclusion criteria to define individuals at ultra high risk for psychosis (UHR). Their diagnostic and prognostic significance is unclear. Objectives: To address the baseline diagnostic relationship between BLIPS and the ICD-10 categories and examine the longitudinal prognostic impact of clinical and sociodemographic factors. Methods: Prospective long-term study in UHR individuals meeting BLIPS criteria. Sociodemographic and clinical data, including ICD-10 diagnoses, were automatically drawn from electronic health records and analyzed using Kaplan–Meier failure function (1-survival), Cox regression models, bootstrapping methods, and Receiver Operating Characteristics (ROC) curve. Results: Eighty BLIPS were included. At baseline, two-thirds (68%) of BLIPS met the diagnostic criteria for ICD-10 Acute and Transient Psychotic Disorder (ATPD), most featuring schizophrenic symptoms. The remaining individuals met ICD-10 diagnostic criteria for unspecified nonorganic psychosis (15%), mental and behavioral disorders due to use of cannabinoids (11%), and mania with psychotic symptoms (6%). The overall 5-year risk of psychosis was 0.54. Recurrent episodes of BLIPS were relatively rare (11%) but associated with a higher risk of psychosis (hazard ratio [HR] 3.98) than mono-episodic BLIPS at the univariate analysis. Multivariate analysis revealed that seriously disorganizing or dangerous features increased greatly (HR = 4.39) the risk of psychosis (0.89 at 5-year). Bootstrapping confirmed the robustness of this predictor (area under the ROC = 0.74). Conclusions: BLIPS are most likely to fulfill the ATPD criteria, mainly acute schizophrenic subtypes. About half of BLIPS cases develops a psychotic disorder during follow-up. Recurrent BLIPS are relatively rare but tend to develop into psychosis. BLIPS with seriously disorganizing or dangerous features have an extreme high risk of psychosis.

Towards a Standard Psychometric Diagnostic Interview for Subjects at Ultra High Risk of Psychosis: CAARMS versus SIPS

Background. Several psychometric instruments are available for the diagnostic interview of subjects at ultra high risk (UHR) of psychosis. Their diagnostic comparability is unknown. Methods. All referrals to the OASIS (London) or CAMEO (Cambridgeshire) UHR services from May 13 to Dec 14 were interviewed for a UHR state using both the CAARMS 12/2006 and the SIPS 5.0. Percent overall agreement, kappa, the McNemar-Bowker χ 2 test, equipercentile methods, and residual analyses were used to investigate diagnostic outcomes and symptoms severity or frequency. A conversion algorithm (CONVERT) was validated in an independent UHR sample from the Seoul Youth Clinic (Seoul). Results. There was overall substantial CAARMS-versus-SIPS agreement in the identification of UHR subjects (n = 212, percent overall agreement = 86%; kappa = 0.781, 95% CI from 0.684 to 0.878; McNemar-Bowker test = 0.069), with the exception of the brief limited intermittent psychotic symptoms (BLIPS) subgroup. Equipercentile-linking table linked symptoms severity and frequency across the CAARMS and SIPS. The conversion algorithm was validated in 93 UHR subjects, showing excellent diagnostic accuracy (CAARMS to SIPS: ROC area 0.929; SIPS to CAARMS: ROC area 0.903). Conclusions. This study provides initial comparability data between CAARMS and SIPS and will inform ongoing multicentre studies and clinical guidelines for the UHR psychometric diagnostic interview.

Are we really mapping psychosis risk? Neuroanatomical signature of affective disorders in subjects at ultra high risk.

BACKGROUND The majority of people at ultra high risk (UHR) of psychosis also present with co-morbid affective disorders such as depression or anxiety. The neuroanatomical and clinical impact of UHR co-morbidity is unknown. METHOD We investigated group differences in grey matter volume using baseline magnetic resonance images from 121 participants in four groups: UHR with depressive or anxiety co-morbidity; UHR alone; major depressive disorder; and healthy controls. The impact of grey matter volume on baseline and longitudinal clinical/functional data was assessed with regression analyses. RESULTS The UHR-co-morbidity group had lower grey matter volume in the anterior cingulate cortex than the UHR-alone group, with an intermediate effect between controls and patients with major depressive disorder. In the UHR-co-morbidity group, baseline anterior cingulate volume was negatively correlated with baseline suicidality/self-harm and obsessive-compulsive disorder symptoms. CONCLUSIONS Co-morbid depression and anxiety disorders contributed distinctive grey matter volume reductions of the anterior cingulate cortex in people at UHR of psychosis. These volumetric deficits were correlated with baseline measures of depression and anxiety, suggesting that co-morbid depressive and anxiety diagnoses should be carefully considered in future clinical and imaging studies of the psychosis high-risk state.

False positive rates in Voxel-based Morphometry studies of the human brain: Should we be worried?

Voxel-based Morphometry (VBM) is a widely used automated technique for the analysis of neuroanatomical images. Despite its popularity within the neuroimaging community, there are outstanding concerns about its potential susceptibility to false positive findings. Here we review the main methodological factors that are known to influence the results of VBM studies comparing two groups of subjects. We then use two large, open-access data sets to empirically estimate false positive rates and how these depend on sample size, degree of smoothing and modulation. Our review and investigation provide three main results: (i) when groups of equal size are compared false positive rate is not higher than expected, i.e. about 5%; (ii) the sample size, degree of smoothing and modulation do not appear to influence false positive rate; (iii) when they exist, false positive findings are randomly distributed across the brain. These results provide reassurance that VBM studies comparing groups are not vulnerable to the higher than expected false positive rates that are evident in single case VBM.

Reduced parahippocampal cortical thickness in subjects at ultra-high risk for psychosis

Background Grey matter volume and cortical thickness represent two complementary aspects of brain structure. Several studies have described reductions in grey matter volume in people at ultra-high risk (UHR) of psychosis; however, little is known about cortical thickness in this group. The aim of the present study was to investigate cortical thickness alterations in UHR subjects and compare individuals who subsequently did and did not develop psychosis. Method We examined magnetic resonance imaging data collected at four different scanning sites. The UHR subjects were followed up for at least 2 years. Subsequent to scanning, 50 UHR subjects developed psychosis and 117 did not. Cortical thickness was examined in regions previously identified as sites of neuroanatomical alterations in UHR subjects, using voxel-based cortical thickness. Results At baseline UHR subjects, compared with controls, showed reduced cortical thickness in the right parahippocampal gyrus (p < 0.05, familywise error corrected). There were no significant differences in cortical thickness between the UHR subjects who later developed psychosis and those who did not. Conclusions These data suggest that UHR symptomatology is characterized by alterations in the thickness of the medial temporal cortex. We did not find evidence that the later progression to psychosis was linked to additional alterations in cortical thickness, although we cannot exclude the possibility that the study lacked sufficient power to detect such differences.

Using structural neuroanatomy to identify trauma survivors with and without post-traumatic stress disorder at the individual level

Background At present there are no objective, biological markers that can be used to reliably identify individuals with post-traumatic stress disorder (PTSD). This study assessed the diagnostic potential of structural magnetic resonance imaging (sMRI) for identifying trauma-exposed individuals with and without PTSD. Method sMRI scans were acquired from 50 survivors of the Sichuan earthquake of 2008 who had developed PTSD, 50 survivors who had not developed PTSD and 40 healthy controls who had not been exposed to the earthquake. Support vector machine (SVM), a multivariate pattern recognition technique, was used to develop an algorithm that distinguished between the three groups at an individual level. The accuracy of the algorithm and its statistical significance were estimated using leave-one-out cross-validation and permutation testing. Results When survivors with PTSD were compared against healthy controls, both grey and white matter allowed discrimination with an accuracy of 91% (p < 0.001). When survivors without PTSD were compared against healthy controls, the two groups could be discriminated with accuracies of 76% (p < 0.001) and 85% (p < 0.001) based on grey and white matter, respectively. Finally, when survivors with and without PTSD were compared directly, grey matter allowed discrimination with an accuracy of 67% (p < 0.001); in contrast the two groups could not be distinguished based on white matter. Conclusions These results reveal patterns of neuroanatomical alterations that could be used to inform the identification of trauma survivors with and without PTSD at the individual level, and provide preliminary support to the development of SVM as a clinically useful diagnostic aid.

Genetic Variation in Neuregulin1 is Associated with Differences in Prefrontal Engagement in Children

The majority of psychopathology is rooted early in life and first emerges during childhood and adolescence. However, little is known about how risk genes affect brain function to increase biological vulnerability to psychopathology in childhood, because most imaging genetic studies published so far have been conducted on adult participants. We examined the impact of neuregulin1 (NRG1), a probable susceptibility gene for schizophrenia and bipolar disorder, on brain function in a sample of 102 ten‐ to twelve‐year‐old children. Each participant performed a Go/Nogo task, whereas brain responses were measured using functional magnetic resonance imaging. Statistical parametric mapping was used to estimate the impact of genetic variation in NRG1 on brain activation. Response accuracy and reaction times did not differ as a function of NRG1 genotype. However, individuals with the high‐risk variant expressed greater brain activation for both Go and Nogo stimuli in the right posterior orbital gyrus, where NRG1 genotype accounted for 11% of interindividual variance. There were no regions showing a significant interaction between NRG1 genotype and stimulus type even at trend level, suggesting that the impact of NRG1 on brain activation was not specific to either response inhibition or motor execution. These results suggest that that genetic variation in NRG1 is associated with different levels of prefrontal engagement in children as young as 10–12 years of age. Our investigation provides support to the idea that genetic factors may affect brain function to moderate vulnerability to psychopathology from childhood. Hum Brain Mapp, 2009.