Voxelwise evaluation of white matter volumes in first-episode psychosis

Role of glia in prefrontal white matter abnormalities in first episode psychosis or mania detected by diffusion tensor spectroscopy

BACKGROUND

White matter (WM) abnormalities are amongst the most commonly described neuroimaging findings in patients with psychotic disorders including schizophrenia (SZ) and bipolar disorder (BD), and may be central to pathophysiology. Few studies have directly compared WM abnormalities in patients with SZ and BD in the first episode of illness, and no studies to date have attempted to separate abnormalities of axon and myelin using complementary MRI techniques.

METHODS

We examined WM abnormalities in young adults with SZ (n = 19) or BD (n = 16) within the first year of illness onset, and healthy controls (n = 22) using a combination of diffusion tensor spectroscopy to measure NAA, creatine (Cr), and choline (Cho), and magnetization transfer ratio (MTR). MTR reflects myelin content, NAA diffusion is neuron specific, and Cr and Cho diffusion reflect both neuron and glial signal.

RESULTS

We found no differences in MTR or NAA ADC in either patient group compared to controls, but significant elevations of both Cr and Cho diffusion in patients with SZ, and elevations of Cho diffusion in patients with BD. Elevations in Cr and Cho diffusion in the absence of NAA diffusion abnormalities indicate that the aberrant signal arises in glia.

CONCLUSIONS

Glial abnormalities were present and detectable by the first episode of psychosis, whereas major abnormalities in axon and myelin were not. Examination of these neurobiological markers early in the course of illness may clarify the neuroprogressive nature of these distinct aspects of WM, and their associations with early clinical phenotypes.

Cortico-limbic connectivity as a possible biomarker for bipolar disorder: where are we now?

INTRODUCTION

The fronto-limbic network has been suggested as a key circuitry in the pathophysiology and maintenance of bipolar disorder. In the past decade, a disrupted connectivity within prefrontal-limbic structures was identified as a promising candidate biomarker for the disorder. Areas Covered: In this review, the authors examine current literature in terms of the structural, functional and effective connectivity in bipolar disorder, integrating recent findings of imaging genetics and machine learning. This paper profiles the current knowledge and identifies future perspectives to provide reliable and usable neuroimaging biomarkers for bipolar psychopathology in clinical practice. Expert Opinion: The replication and the translation of acquired knowledge into useful and usable tools represents one of the current greatest challenges in biomarker research applied to psychiatry.

Corpus callosum volumes in the 5 years following the first-episode of schizophrenia: Effects of antipsychotics, chronicity and maturation

Background

White matter (WM) structural changes, particularly affecting the corpus callosum (CC), seem to be critically implicated in psychosis. Whether such abnormalities are progressive or static is still a matter of debate in schizophrenia research. Aberrant maturation processes might also influence the longitudinal trajectory of age-related CC changes in schizophrenia patients. We investigated whether patients with first-episode schizophrenia-related psychoses (FESZ) would present longitudinal CC and whole WM volume changes over the 5 years after disease onset.

Method

Thirty-two FESZ patients and 34 controls recruited using a population-based design completed a 5-year assessment protocol, including structural MRI scanning at baseline and follow-up. The linear effects of disease duration, clinical outcome and antipsychotic (AP) use over time on WM and CC volumes were studied using both voxelwise and volume-based morphometry analyses. We also examined maturation/aging abnormalities through cross-sectional analyses of age-related trajectories of total WM and CC volume changes.

Results

No interaction between diagnosis and time was observed, and clinical outcome did not influence CC volumes in patients. On the other hand, FESZ patients continuously exposed to AP medication showed volume increase over time in posterior CC. Curve-estimation analyses revealed a different aging pattern in FESZ patients versus controls: while patients displayed a linear decline of total WM and anterior CC volumes with age, a non-linear trajectory of total WM and relative preservation of CC volumes were observed in controls.

Conclusions

Continuous AP exposure can influence CC morphology during the first years after schizophrenia onset. Schizophrenia is associated with an abnormal pattern of total WM and anterior CC aging during non-elderly adulthood, and this adds complexity to the discussion on the static or progressive nature of structural abnormalities in psychosis.

Four-way multimodal fusion of 7 T imaging data using an mCCA+jICA model in first-episode schizophrenia

Acquisition of multimodal brain imaging data for the same subject has become more common leading to a growing interest in determining the intermodal relationships between imaging modalities to further elucidate the pathophysiology of schizophrenia. Multimodal data have previously been individually analyzed and subsequently integrated; however, these analysis techniques lack the ability to examine true modality inter-relationships. The utilization of a multiset canonical correlation and joint independent component analysis (mCCA + jICA) model for data fusion allows shared or distinct abnormalities between modalities to be examined. In this study, first-episode schizophrenia patients (n_SZ =19) and matched controls (n_HC =21) completed a resting-state functional magnetic resonance imaging (fMRI) scan at 7 T. Grey matter (GM), white matter (WM), cerebrospinal fluid (CSF), and amplitude of low frequency fluctuation (ALFF) maps were used as features in a mCCA + jICA model. Results of the mCCA + jICA model indicated three joint group-discriminating components (GM-CSF, WM-ALFF, GM-ALFF) and two modality-unique group-discriminating components (GM, WM). The joint component findings are highlighted by GM basal ganglia, somatosensory, parietal lobe, and thalamus abnormalities associated with ventricular CSF volume; WM occipital and frontal lobe abnormalities associated with temporal lobe function; and GM frontal, temporal, parietal, and occipital lobe abnormalities associated with caudate function. These results support and extend major findings throughout the literature using independent single modality analyses. The multimodal fusion of 7 T data in this study provides a more comprehensive illustration of the relationships between underlying neuronal abnormalities associated with schizophrenia than examination of imaging data independently.

Meta-analysis of regional white matter volume in bipolar disorder with replication in an independent sample using coordinates, T-maps, and individual MRI data

Converging evidence suggests that bipolar disorder (BD) is associated with white matter (WM) abnormalities. Meta-analyses of voxel based morphometry (VBM) data is commonly performed using published coordinates, however this method is limited since it ignores non-significant data. Obtaining statistical maps from studies (T-maps) as well as raw MRI datasets increases accuracy and allows for a comprehensive analysis of clinical variables. We obtained coordinate data (7-studies), T-Maps (12-studies, including unpublished data) and raw MRI datasets (5-studies) and analysed the 24 studies using Seed-based d Mapping (SDM). A VBM analysis was conducted to verify the results in an independent sample. The meta-analysis revealed decreased WM volume in the posterior corpus callosum extending to WM in the posterior cingulate cortex. This region was significantly reduced in volume in BD patients in the independent dataset (p=0.003) but there was no association with clinical variables. We identified a robust WM volume abnormality in BD patients that may represent a trait marker of the disease and used a novel methodology to validate the findings.

Brain structure, function, and neurochemistry in schizophrenia and bipolar disorder-a systematic review of the magnetic resonance neuroimaging literature

Since Emil Kraepelin's conceptualization of endogenous psychoses as dementia praecox and manic depression, the separation between primary psychotic disorders and primary affective disorders has been much debated. We conducted a systematic review of case-control studies contrasting magnetic resonance imaging studies in schizophrenia and bipolar disorder. A literature search in PubMed of studies published between January 2005 and December 2016 was conducted, and 50 structural, 29 functional, 7 magnetic resonance spectroscopy, and 8 combined imaging and genetic studies were deemed eligible for systematic review. Structural neuroimaging studies suggest white matter integrity deficits that are consistent across the illnesses, while gray matter reductions appear more widespread in schizophrenia compared to bipolar disorder. Spectroscopy studies in cortical gray matter report evidence of decreased neuronal integrity in both disorders. Functional neuroimaging studies typically report similar functional architecture of brain networks in healthy controls and patients across the psychosis spectrum, but find differential extent of alterations in task related activation and resting state connectivity between illnesses. The very limited imaging-genetic literature suggests a relationship between psychosis risk genes and brain structure, and possible gene by diagnosis interaction effects on functional imaging markers. While the existing literature suggests some shared and some distinct neural markers in schizophrenia and bipolar disorder, it will be imperative to conduct large, well designed, multi-modal neuroimaging studies in medication-naïve first episode patients that will be followed longitudinally over the course of their illness in an effort to advance our understanding of disease mechanisms.

A Correlative Classification Study of Schizophrenic Patients with Results of Clinical Evaluation and Structural Magnetic Resonance Images

Patients with schizophrenia suffer from symptoms such as hallucination and delusion. There are currently a number of publications that discuss the treatment, diagnosis, prognosis, and damage in schizophrenia. This study utilized joint independent component analysis to process the images of GMV and WMV and incorporated the Wisconsin card sorting test (WCST) and the positive and negative syndrome scale (PANSS) to examine the correlation of obtained brain characteristics. We also used PANSS score to classify schizophrenic patients into acute and subacute cases, to analyze the brain structure differences. Finally, we used brain structure images and the error rate of the WCST as eigenvalues in support vector machine learning and classification. The results of this study showed that the frontal and temporal lobes of a normal brain are more apparent than those of a schizophrenia brain. The highest level of classification recognition reached 91.575%, indicating that the WCST error rate and characteristic changes in brain structure volume can be used to effectively distinguish schizophrenia and normal brains. Similarly, this result confirmed that the WCST and brain structure volume are correlated with the differences between schizophrenia and normal participants.

Voxel-based morphometry for separation of schizophrenia from other types of psychosis in first episode psychosis

BACKGROUND

Schizophrenia is a psychiatric disorder which involves distortions in thought and perception, blunted affect, and behavioural disturbances. The longer psychosis goes unnoticed and untreated, the more severe the repercussions for relapse and recovery. There is some evidence that early intervention services can help, and diagnostic techniques that could contribute to early intervention may offer clinical utility in these situations. The index test being evaluated in this review is the structural magnetic resonance imaging (MRI) analysis technique known as voxel-based morphometry (VBM) that estimates the distribution of grey matter tissue volume across several brain regions. This review is an exploratory examination of the diagnostic 'potential' of VBM for use as an additional tool in the clinical examination of patients with first episode psychosis to establish whether an individual will progress on to developing schizophrenia as opposed to other types of psychosis.

OBJECTIVES

To determine whether VBM applied to the brain can be used to differentiate schizophrenia from other types of psychosis in participants who have received a clinical diagnosis of first episode psychosis.

SEARCH METHODS

In December 2013, we updated a previous search (May 2012) of MEDLINE, EMBASE, and PsycInfo using OvidSP.

SELECTION CRITERIA

We included retrospective and prospective studies that consecutively or randomly selected adolescent and adult participants (< 45 years) with a first episode of psychosis; and that evaluated the diagnostic accuracy of VBM for differentiating schizophrenia from other psychoses compared with a clinical diagnosis made by a qualified mental health professional, with or without the use of standard operational criteria or symptom checklists. We excluded studies in children, and in adult participants with organic brain disorders or who were at high risk for schizophrenia, such as people with a genetic predisposition.

DATA COLLECTION AND ANALYSIS

Two review authors screened all references for inclusion. We assessed the quality of studies using the QUADAS-2 instrument. Due to a lack of data, we were not able to extract 2 x 2 data tables for each study nor undertake any meta-analysis.

MAIN RESULTS

We included four studies with a total of 275 participants with first episode psychosis. VBM was not used to diagnose schizophrenia in any of the studies, instead VBM was used to quantify the magnitude of differences in grey matter volume. Therefore, none of the included studies reported data that could be used in the analysis, and we summarised the findings narratively for each study.

AUTHORS' CONCLUSIONS

There is no evidence to currently support diagnosing schizophrenia (as opposed to other psychotic disorders) using the pattern of brain changes seen in VBM studies in patients with first episode psychosis. VBM has the potential to discriminate between diagnostic categories but the methods to do this reliably are currently in evolution. In addition, the lack of applicability of the use of VBM to clinical practice in the studies to date limits the usefulness of VBM as a diagnostic aid to differentiate schizophrenia from other types of psychotic presentations in people with first episode of psychosis.

Relationship between white matter microstructure abnormalities and ADHD symptomatology in adolescents

The present study sought to evaluate whether white matter microstructure abnormalities observed in a cohort of adolescents with attention-deficit/hyperactivity disorder (ADHD) have specific relationships with either or both Hyperactivity/Impulsivity and Inattentive ADHD symptom domains that would support a dimensional view of ADHD as adopted in the DSM-V. Diffusion tensor imaging (DTI) data were acquired on 22 adolescents diagnosed with ADHD. Multiple regression analyses were performed to determine whether scalar DTI measures in 13 tracts-of-interest demonstrated meaningful associations with Hyperactivity/Impulsivity or Inattentive symptom severity. Fractional anisotropy and radial diffusivity measures of white matter integrity exhibited significant linear relationships with Hyperactivity/Impulsivity and Inattentive symptom severity. However, only radial diffusivity in the right superior longitudinal fasciculus was specifically linked to Inattentive symptom severity and not Hyperactivity/Impulsivity symptom severity. Our results provide preliminary evidence that symptom domains in ADHD are linked to neuroanatomical substrates and confirm the value in examining ADHD from a dimensional perspective.

The relationship between stress, HPA axis functioning and brain structure in first episode psychosis over the first 12 weeks of treatment

Stress and abnormal hypothalamic-pituitary-adrenal axis functioning have been implicated in the early phase of psychosis and may partly explain reported changes in brain structure. This study used magnetic resonance imaging to investigate whether biological measures of stress were related to brain structure at baseline and to structural changes over the first 12 weeks of treatment in first episode patients (n=22) compared with matched healthy controls (n=22). At baseline, no significant group differences in biological measures of stress, cortical thickness or hippocampal volume were observed, but a significantly stronger relationship between baseline levels of cortisol and smaller white matter volumes of the cuneus and anterior cingulate was found in patients compared with controls. Over the first 12 weeks of treatment, patients showed a significant reduction in thickness of the posterior cingulate compared with controls. Patients also showed a significant positive relationship between baseline cortisol and increases in hippocampal volume over time, suggestive of brain swelling in association with psychotic exacerbation, while no such relationship was observed in controls. The current findings provide some support for the involvement of stress mechanisms in the pathophysiology of early psychosis, but the changes are subtle and warrant further investigation.

Neuroanatomical classification in a population-based sample of psychotic major depression and bipolar I disorder with 1 year of diagnostic stability

The presence of psychotic features in the course of a depressive disorder is known to increase the risk for bipolarity, but the early identification of such cases remains challenging in clinical practice. In the present study, we evaluated the diagnostic performance of a neuroanatomical pattern classification method in the discrimination between psychotic major depressive disorder (MDD), bipolar I disorder (BD-I), and healthy controls (HC) using a homogenous sample of patients at an early course of their illness. Twenty-three cases of first-episode psychotic mania (BD-I) and 19 individuals with a first episode of psychotic MDD whose diagnosis remained stable during 1 year of followup underwent 1.5 T MRI at baseline. A previously validated multivariate classifier based on support vector machine (SVM) was employed and measures of diagnostic performance were obtained for the discrimination between each diagnostic group and subsamples of age- and gender-matched controls recruited in the same neighborhood of the patients. Based on T1-weighted images only, the SVM-classifier afforded poor discrimination in all 3 pairwise comparisons: BD-I versus HC; MDD versus HC; and BD-I versus MDD. Thus, at the population level and using structural MRI only, we failed to achieve good discrimination between BD-I, psychotic MDD, and HC in this proof of concept study.

Brain connectivity networks in schizophrenia underlying resting state functional magnetic resonance imaging

Schizophrenia (SZ) is a severe neuropsychiatric disorder. A leading hypothesis is that SZ is a brain dysconnection syndrome, involving abnormal interactions between widespread brain networks. Resting state functional magnetic resonance imaging (R-fMRI) is a powerful tool to explore the dysconnectivity of brain networks in SZ and other disorders. Seed-based functional connectivity analysis, spatial independent component analysis (ICA), and graph theory-based analysis are popular methods to quantify brain network connectivity in R-fMRI data. Widespread network dysconnectivity in SZ has been observed using both seed-based analysis and ICA, although most seed-based studies report decreased connectivity while ICA studies report both increases and decreases. Importantly, most of the findings from both techniques are also associated with typical symptoms of the illness. Disrupted topological properties and altered modular community structure of brain system in SZ have been shown using graph theory-based analysis. Overall, the resting-state findings regarding brain networks deficits have advanced our understanding of the underlying pathology of SZ. In this article, we review aberrant brain connectivity networks in SZ measured in R-fMRI by the above approaches, and discuss future challenges.

Neuroanatomical pattern classification in a population-based sample of first-episode schizophrenia

Recent neuroanatomical pattern classification studies have attempted to individually classify cases with psychotic disorders using morphometric MRI data in an automated fashion. However, this approach has not been tested in population-based samples, in which variable patterns of comorbidity and disease course are typically found. We aimed to evaluate the diagnostic accuracy (DA) of the above technique to discriminate between incident cases of first-episode schizophrenia identified in a circumscribed geographical region over a limited period of time, in comparison with next-door healthy controls. Sixty-two cases of first-episode schizophrenia or schizophreniform disorder and 62 age, gender and educationally-matched controls underwent 1.5 T MRI scanning at baseline, and were naturalistically followed-up over 1 year. T1-weighted images were used to train a high-dimensional multivariate classifier, and to generate both spatial maps of the discriminative morphological patterns between groups and ROC curves. The spatial map discriminating first-episode schizophrenia patients from healthy controls revealed a complex pattern of regional volumetric abnormalities in the former group, affecting fronto-temporal-occipital gray and white matter regions bilaterally, including the inferior fronto-occipital fasciculus, as well as the third and lateral ventricles. However, an overall modest DA (73.4%) was observed for the individual discrimination between first-episode schizophrenia patients and controls, and the classifier failed to predict 1-year prognosis (remitting versus non-remitting course) of first-episode schizophrenia (DA=58.3%). In conclusion, using a "real world" sample recruited with epidemiological methods, the application of a neuroanatomical pattern classifier afforded only modest DA to classify first-episode schizophrenia subjects and next-door healthy controls, and poor discriminative power to predict the 1-year prognosis of first-episode schizophrenia.