Clinical utility of magnetic resonance imaging in first-episode psychosis.

BackgroundThere is no consensus as to whether magnetic resonance imaging (MRI) should be used as part of the initial clinical evaluation of patients with first-episode psychosis (FEP).Aims(a) To assess the logistical feasibility of routine MRI; (b) to define the clinical significance of radiological abnormalities in patients with FEP.MethodRadiological reports from MRI scans of two FEP samples were reviewed; one comprised 108 patients and 98 healthy controls recruited to a research study and the other comprised 241 patients scanned at initial clinical presentation plus 66 healthy controls.ResultsIn the great majority of patients, MRI was logistically feasible. Radiological abnormalities were reported in 6% of the research sample and in 15% of the clinical sample (odds ratio (OR)=3.1, 95% CI 1.26-7.57, χ2(1) = 6.63, P = 0.01). None of the findings necessitated a change in clinical management.ConclusionsRates of neuroradiological abnormalities in FEP are likely to be underestimated in research samples that often exclude patients with organic abnormalities. However, the majority of findings do not require intervention.

Does neuroanatomy account for superior temporal dysfunction in early psychosis? A multimodal MRI investigation.

BACKGROUND: Neuroimaging studies of ultra-high risk (UHR) and first-episode psychosis (FEP) have revealed widespread alterations in brain structure and function. Recent evidence suggests there is an intrinsic relationship between these 2 types of alterations; however, there is very little research linking these 2 modalities in the early stages of psychosis. METHODS: To test the hypothesis that functional alteration in UHR and FEP articipants would be associated with corresponding structural alteration, we examined brain function and structure in these participants as well as in a group of healthy controls using multimodal MRI. The data were analyzed using statistical parametric mapping. RESULTS: We included 24 participants in the FEP group, 18 in the UHR group and 21 in the control group. Patients in the FEP group showed a reduction in functional activation in the left superior temporal gyrus relative to controls, and the UHR group showed intermediate values. The same region showed a corresponding reduction in grey matter volume in the FEP group relative to controls. However, while the difference in grey matter volume remained significant after including functional activation as a covariate of no interest, the reduction in functional activation was no longer evident after including grey matter volume as a covariate of no interest. LIMITATIONS: Our sample size was relatively small. All participants in the FEP group and 2 in the UHR group had received antipsychotic medication, which may have impacted neurofunction and/or neuroanatomy. CONCLUSION: Our results suggest that superior temporal dysfunction in early psychosis is accounted for by a corresponding alteration in grey matter volume. This finding has important implications for the interpretation of functional alteration in early psychosis.

Multivariate pattern classification reveals differential brain activation during emotional processing in individuals with psychosis proneness.

Among the general population, individuals with subthreshold psychotic-like experiences, or psychosis proneness (PP), can be psychometrically identified and are thought to have a 10-fold increased risk of psychosis. They also show impairments in measures of emotional functioning parallel to schizophrenia. Whilst previous studies have revealed altered brain activation in patients with schizophrenia during emotional processing, it is unclear whether these alterations are also expressed in individuals with high PP. Here we used Support Vector Machine (SVM) to perform multivariate pattern classification based on brain activation during emotional processing in 20 individuals with high PP and 20 comparison subjects (low PP). In addition, we performed a standard univariate analysis based on the General Linear Model (GLM) on the same data for comparison. The experimental task involved passively viewing negative and neutral pictures from the International Affective Picture System (IAPS). SVM allowed classification of the two groups with statistically significant accuracy (p=0.017) and identified group differences within an emotional circuitry including the amygdala, insula, anterior cingulate and medial prefrontal cortex. In contrast, the standard univariate analysis did not detect any significant between-group differences. Our results reveal a distributed and subtle set of alterations in brain function within the emotional circuitry of individuals with high PP, providing neurobiological support for the notion of dysfunctional emotional circuitry in this group. In addition, these alterations are best detected using a multivariate approach rather than standard univariate methods. Further application of this approach may aid in characterising people at clinical and genetic risk of developing psychosis.

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.

Network-Level Dysconnectivity in Drug-Naïve First-Episode Psychosis: Dissociating Transdiagnostic and Diagnosis-Specific Alterations.

The neuroimaging literature provides compelling evidence for functional dysconnectivity in people with psychosis. However, it is likely that at least some of the observed alterations represent secondary effects of illness chronicity and/or antipsychotic medication. In addition, the extent to which these alterations are specific to psychosis or represent a transdiagnostic feature of psychiatric illness remains unclear. The aim of this study was therefore to examine the diagnostic specificity of functional dysconnectivity in drug-naïve first-episode psychosis (FEP). We used resting-state functional magnetic resonance imaging and functional connectivity analysis to estimate network-level connectivity in 50 patients with FEP, 50 patients with major depressive disorder (MDD), 50 patients with post-traumatic stress disorder (PTSD), and 122 healthy controls (HCs). The FEP, MDD, and PTSD groups showed reductions in intranetwork connectivity of the default mode network relative to the HC group (p<0.05 corrected); therefore, intranetwork alterations were expressed across the three diagnostic groups. In addition, the FEP group showed heightened internetwork connectivity between the default mode network, particularly the anterior cingulate cortex, and the central executive network relative to the MDD, PTSD, and HC groups (p<0.05 corrected); therefore, internetwork alterations were specific to the FEP. These findings suggest that network-level alterations are present in individuals with a first episode of psychosis who have not been exposed to antipsychotic medication. In addition, they suggest a dissociation between aberrant internetwork connectivity as a distinctive feature of psychosis and aberrant intranetwork connectivity as a transdiagnostic feature of psychiatric illness.

Auditory verbal hallucinations and brain dysconnectivity in the perisylvian language network: a multimodal investigation.

Neuroimaging studies of schizophrenia have indicated that the development of auditory verbal hallucinations (AVHs) is associated with altered structural and functional connectivity within the perisylvian language network. However, these studies focussed mainly on either structural or functional alterations in patients with chronic schizophrenia. Therefore, they were unable to examine the relationship between the 2 types of measures and could not establish whether the observed alterations would be expressed in the early stage of the illness. We used diffusion tensor imaging and functional magnetic resonance imaging to examine white matter integrity and functional connectivity within the left perisylvian language network of 46 individuals with an at risk mental state for psychosis or a first episode of the illness, including 28 who had developed AVH group and 18 who had not (nonauditory verbal hallucination [nAVH] group), and 22 healthy controls. Inferences were made at P < .05 (corrected). The nAVH group relative to healthy controls showed a reduction of both white matter integrity and functional connectivity as well as a disruption of the normal structure-function relationship along the fronto-temporal pathway. For all measures, the AVH group showed intermediate values between healthy controls and the nAVH group. These findings seem to suggest that, in the early stage of the disorder, a significant impairment of fronto-temporal connectivity is evident in patients who do not experience AVHs. This is consistent with the hypothesis that, whilst mild disruption of connectivity might still enable the emergence of AVHs, more severe alterations may prevent the occurrence of the hallucinatory experience.

The effects of psychotherapy on brain function: a systematic and critical review.

Over the past two decades, the development of neuroimaging techniques has allowed the non-invasive investigation of neuroplastic changes associated with psychotherapeutic treatment. The aim of the present article is to present a systematic and critical review of longitudinal studies addressing the impact of psychotherapy on the brain published to date. After summarizing the results reported in the literature for each psychiatric disorder separately (i.e. obsessive-compulsive disorder, panic disorder, unipolar major depressive disorder, posttraumatic stress disorder, specific phobia, schizophrenia), we discuss the results focusing on three questions of interest: (i) whether neurobiological changes which follow psychotherapy occur in regions that showed significant neurofunctional alteration pre-treatment; (ii) whether these neurobiological changes are similar, or different, to those observed following pharmacological treatment; and (iii) whether neurobiological changes could be used as an objective means of monitoring the progress and outcome of psychotherapy. The evidence reviewed indicates that (i) depending on the disorder under investigation, psychotherapy results in either a normalisation of abnormal patterns of activity, the recruitment of additional areas which did not show altered activation prior to treatment, or a combination of the two; (ii) the effects of psychotherapy on brain function are comparable to those of medication for some but not all disorders; and (iii) there is preliminary evidence that neurobiological changes are associated with the progress and outcome of psychotherapy. It is hoped that a better understanding of the impact of psychotherapy on brain function will eventually inform the development of new biologically informed treatments and allow clinicians to make more effective treatment decisions.

Quantitative prediction of individual psychopathology in trauma survivors using resting-state FMRI.

Neuroimaging techniques hold the promise that they 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. This study employed a multivariate approach to examine the potential of resting-state functional magnetic resonance imaging (MRI) data for making accurate predictions about psychopathology in survivors of the 2008 Sichuan earthquake at an individual level. Resting-state functional MRI data was acquired for 121 survivors of the 2008 Sichuan earthquake each of whom was assessed for symptoms of post-traumatic stress disorder (PTSD) using the 17-item PTSD Checklist (PCL). Using a multivariate analytical method known as relevance vector regression (RVR), we examined the relationship between resting-state functional MRI data and symptom scores. We found that the use of RVR allowed quantitative prediction of clinical scores with statistically significant accuracy (correlation=0.32, P=0.006; mean squared error=176.88, P=0.001). Accurate prediction was based on functional activation in a number of prefrontal, parietal, and occipital regions. This is the first evidence that neuroimaging techniques may inform the clinical assessment of trauma-exposed individuals by providing an accurate and objective quantitative estimation of psychopathology. Furthermore, the significant contribution of parietal and occipital regions to such estimation challenges the traditional view of PTSD as a disorder specific to the fronto-limbic network.

An empirical comparison of different approaches for combining multimodal neuroimaging data with support vector machine.

In the pursuit of clinical utility, neuroimaging researchers of psychiatric and neurological illness are increasingly using analyses, such as support vector machine, that allow inference at the single-subject level. Recent studies employing single-modality data, however, suggest that classification accuracies must be improved for such utility to be realized. One possible solution is to integrate different data types to provide a single combined output classification; either by generating a single decision function based on an integrated kernel matrix, or, by creating an ensemble of multiple single modality classifiers and integrating their predictions. Here, we describe four integrative approaches: (1) an un-weighted sum of kernels, (2) multi-kernel learning, (3) prediction averaging, and (4) majority voting, and compare their ability to enhance classification accuracy relative to the best single-modality classification accuracy. We achieve this by integrating structural, functional, and diffusion tensor magnetic resonance imaging data, in order to compare ultra-high risk (n = 19), first episode psychosis (n = 19) and healthy control subjects (n = 23). Our results show that (i) whilst integration can enhance classification accuracy by up to 13%, the frequency of such instances may be limited, (ii) where classification can be enhanced, simple methods may yield greater increases relative to more computationally complex alternatives, and, (iii) the potential for classification enhancement is highly influenced by the specific diagnostic comparison under consideration. In conclusion, our findings suggest that for moderately sized clinical neuroimaging datasets, combining different imaging modalities in a data-driven manner is no "magic bullet" for increasing classification accuracy. However, it remains possible that this conclusion is dependent on the use of neuroimaging modalities that had little, or no, complementary information to offer one another, and that the integration of more diverse types of data would have produced greater classification enhancement. We suggest that future studies ideally examine a greater variety of data types (e.g., genetic, cognitive, and neuroimaging) in order to identify the data types and combinations optimally suited to the classification of early stage psychosis.

Pattern classification of brain activation during emotional processing in subclinical depression: psychosis proneness as potential confounding factor.

We used Support Vector Machine (SVM) to perform multivariate pattern classification based on brain activation during emotional processing in healthy participants with subclinical depressive symptoms. Six-hundred undergraduate students completed the Beck Depression Inventory II (BDI-II). Two groups were subsequently formed: (i) subclinical (mild) mood disturbance (n = 17) and (ii) no mood disturbance (n = 17). Participants also completed a self-report questionnaire on subclinical psychotic symptoms, the Community Assessment of Psychic Experiences Questionnaire (CAPE) positive subscale. The functional magnetic resonance imaging (fMRI) paradigm entailed passive viewing of negative emotional and neutral scenes. The pattern of brain activity during emotional processing allowed correct group classification with an overall accuracy of 77% (p = 0.002), within a network of regions including the amygdala, insula, anterior cingulate cortex and medial prefrontal cortex. However, further analysis suggested that the classification accuracy could also be explained by subclinical psychotic symptom scores (correlation with SVM weights r = 0.459, p = 0.006). Psychosis proneness may thus be a confounding factor for neuroimaging studies in subclinical depression.

Attentional modulation of source attribution in first-episode psychosis: a functional magnetic resonance imaging study.

BACKGROUND: In patients with schizophrenia, the misattribution of self-generated events to an external source is associated with the presence of psychotic symptoms. The aim of this study was to investigate how this misattribution is influenced by dysfunction of attentional processing, which is also impaired in schizophrenia. METHODS: Participants underwent functional Magnetic Resonance Imaging (fMRI) while listening to prerecorded speech. Their expectancies were manipulated using visual cues that were either congruent (valid) or incongruent (invalid) with the speech. The source (self/other) and the acoustic quality (undistorted/distorted) of the speech were also manipulated. Twenty patients with first-episode psychosis (FEP) and 20 matched healthy controls (HC) were tested. RESULTS: When listening to self-generated speech preceded by an invalid (other speech) cue, relative to HC, FEP patients showed a trend to misidentify their own speech as that of another person. Analysis of fMRI data showed that FEP patients had reduced activation in the right middle temporal gyrus (MTG) and left precuneus (Pc) relative to HC. Within the FEP group, the level of activation in the right MTG was negatively correlated with the severity of their positive psychotic symptoms. CONCLUSIONS: Impaired attentional modulation in schizophrenia may contribute to the tendency for FEP patients to misattribute the source of self-generated material, and this may be mediated by the right MTG and Pc, regions that are involved in both self-referential processing and the integration of sensory information.

Elucidating neuroanatomical alterations in the at risk mental state and first episode psychosis: a combined voxel-based morphometry and voxel-based cortical thickness study.

Previous studies have reported alterations in grey matter volume and cortical thickness in individuals at high risk of developing psychosis and patients in the early stages of the disorder. Because these studies have typically focused on either grey matter volume or cortical thickness separately, the relationship between these two types of alterations is currently unclear. In the present investigation we used both voxel-based cortical thickness (VBCT) and voxel-based morphometry (VBM) to examine neuroanatomical differences in 21 individuals with an At Risk Mental State (ARMS) for psychosis, 26 patients with a First Episode of Psychosis (FEP) and 24 healthy controls. Statistical inferences were made at P<0.05 after correction for multiple comparisons. Cortical thinning in the right superior temporal gyrus was observed in both individuals at high risk of developing psychosis and patients with a first episode of the disorder, and therefore is likely to represent a marker of vulnerability. In contrast, the right posterior cingulate cortex showed cortical thinning in FEP patients relative to individuals at high risk, and therefore appears to be implicated in the onset of the disease. These neuroanatomical differences were expressed in terms of cortical thickness but not in terms of grey matter volume, and therefore may reflect specific cortical atrophy as opposed to variations in sulcal and gyral morphology.

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.

Using Support Vector Machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review.

Standard univariate analysis of neuroimaging data has revealed a host of neuroanatomical and functional differences between healthy individuals and patients suffering a wide range of neurological and psychiatric disorders. Significant only at group level however these findings have had limited clinical translation, and recent attention has turned toward alternative forms of analysis, including Support-Vector-Machine (SVM). A type of machine learning, SVM allows categorisation of an individual's previously unseen data into a predefined group using a classification algorithm, developed on a training data set. In recent years, SVM has been successfully applied in the context of disease diagnosis, transition prediction and treatment prognosis, using both structural and functional neuroimaging data. Here we provide a brief overview of the method and review those studies that applied it to the investigation of Alzheimer's disease, schizophrenia, major depression, bipolar disorder, presymptomatic Huntington's disease, Parkinson's disease and autistic spectrum disorder. We conclude by discussing the main theoretical and practical challenges associated with the implementation of this method into the clinic and possible future directions.

Dysconnectivity in schizophrenia: where are we now?

The disconnection hypothesis suggests that the core symptoms of schizophrenia (SZ) are related to aberrant, or 'dys-', connectivity between distinct brain regions. A proliferation of functional and structural neuroimaging studies have been conducted to investigate this hypothesis, across the full course of the disorder; from people at Ultra-High-Risk of developing psychosis to patients with chronic SZ. However the results of these studies have not always been consistent, and to date, there have been no attempts to summarise the results of both methodologies in conjunction. In this article, we systematically review both the structural and functional connectivity literature in SZ. The main trends to emerge are that schizophrenia is associated with connectivity reductions, as opposed to increases, relative to healthy controls, and that this is particularly evident in the connections involving the frontal lobe. These two trends appear to apply across all stages of the disorder, and to be independent of the neuroimaging methodology employed. We discuss the potential implications of these trends, and identify possible future investigative directions.