Forty years of structural imaging in psychosis: promises and truth

Glycocalyx shedding patterns identifies antipsychotic-naïve patients with first-episode psychosis

Psychotic disorders have been linked to immune-system abnormalities, increased inflammatory markers, and subtle neuroinflammation. Studies further suggest a dysfunctional blood brain barrier (BBB). The endothelial Glycocalyx (GLX) functions as a protective layer in the BBB, and GLX shedding leads to BBB dysfunction. This study aimed to investigate whether a panel of 11 GLX molecules derived from peripheral blood could differentiate antipsychotic-naïve first-episode psychosis patients (n47) from healthy controls (HC, n49) and whether GLX shedding correlated with symptom severity. Blood samples were collected at baseline and serum was isolated for GLX marker detection. Machine learning models were applied to test whether patterns in GLX markers could classify patient groups. Associations between GLX markers and symptom severity were explored. Patients showed significantly increased levels of three GLX markers compared to HC. Based on the panel of 11 GLX markers, machine learning models achieved a significant mean classification accuracy of 81%. Post hoc analysis revealed associations between increased GLX markers and symptom severity. This study demonstrates the potential of GLX molecules as immuno-neuropsychiatric biomarkers for early diagnosis of psychosis, as well as indicate a compromised BBB. Further research is warranted to explore the role of GLX in the early detection of psychotic disorders.

Brain Abnormalities in Schizophrenia: A Comparative Imagistic Study

Background and Objectives: Neuroimaging reveals a link between psychiatric conditions and brain structural-functional changes, prompting a paradigm shift in viewing schizophrenia as a neurodevelopmental disorder. This study aims to identify and compare structural brain changes found during the first schizophrenia episode with those found after more than 5 years of illness. Materials and Methods: This prospective study involved 149 participants enrolled between 1 January 2019 and 31 December 2021. The participants were categorized into three groups: the first comprises 51 individuals with an initial psychotic episode, the second consists of 49 patients diagnosed with schizophrenia for over 5 years, and a control group comprising 50 individuals without a diagnosis of schizophrenia or any other psychotic disorder. All participants underwent brain CT examinations. Results: The study examined all three groups: first-episode schizophrenia (FES), schizophrenia (SCZ), and the control group. The FES group had a mean age of 26.35 years and a mean duration of illness of 1.2 years. The SCZ group, with a mean age of 40.08 years, had been diagnosed with schizophrenia for an average of 15.12 years. The control group, with a mean age of 34.60 years, had no schizophrenia diagnosis. Structural measurements revealed widening of frontal horns and lateral ventricles in the SCZ group compared to FES and the FES group compared to the control group. Differences in the dimensions of the third ventricle were noted between SCZ and FES, while no distinction was observed between FES and the control group. The fourth ventricle had similar measurements in FES and SCZ groups, both exceeding those of the control group. Our results showed higher densities in the frontal lobe in schizophrenia patients compared to FES and the control group, with the control group consistently displaying the lowest densities. Conclusions: In summary, our comparative imaging analysis of schizophrenia patients, first-episode schizophrenia, and control patients revealed distinct ventricular patterns, with SCZ showing greater widening than FES and FES wider than the control group. Frontal lobe density, assessed via cerebral CT scans, indicated a higher density in the SCZ group in both anterior and posterior cortex portions compared to FES and the control group, while the left posterior cortex in FES had the highest density. These findings highlight unique neuroanatomical features across groups, shedding light on structural differences associated with different stages of schizophrenia.

Fast and functionally specific cortical thickness changes induced by visual stimulation

Structural characteristics of the human brain serve as important markers of brain development, aging, disease progression, and neural plasticity. They are considered stable properties, changing slowly over time. Multiple recent studies reported that structural brain changes measured with magnetic resonance imaging (MRI) may occur much faster than previously thought, within hours or even minutes. The mechanisms behind such fast changes remain unclear, with hemodynamics as one possible explanation. Here we investigated the functional specificity of cortical thickness changes induced by a flickering checkerboard and compared them to blood oxygenation level-dependent (BOLD) functional MRI activity. We found that checkerboard stimulation led to a significant thickness increase, which was driven by an expansion at the gray-white matter boundary, functionally specific to V1, confined to the retinotopic representation of the checkerboard stimulus, and amounted to 1.3% or 0.022 mm. Although functional specificity and the effect size of these changes were comparable to those of the BOLD signal in V1, thickness effects were substantially weaker in V3. Furthermore, a comparison of predicted and measured thickness changes for different stimulus timings suggested a slow increase of thickness over time, speaking against a hemodynamic explanation. Altogether, our findings suggest that visual stimulation can induce structural gray matter enlargement measurable with MRI.

Longitudinal Changes in Cortical Surface Area Associated With Transition to Psychosis in Adolescents at Clinical High Risk for the Disease

OBJECTIVE

Identifying biomarkers of transition to psychosis in individuals at clinical high risk for psychosis (CHR-P) is essential to understanding the mechanisms underlying the disease. Although cross-sectional abnormalities in cortical surface area (CSA) have been demonstrated in individuals at CHR-P who transition to psychosis (CHR-P-T) compared with those who do not (CHR-P-NT), how CSA longitudinally develops remains unclear, especially in younger individuals. We set out to compare CSA in adolescents at CHR-P and healthy controls (HC) over 2 points in time.

METHOD

A longitudinal multicenter study was performed in adolescents at CHR-P in comparison to HC and according to transition to psychosis. Magnetic resonance imaging scans were acquired at baseline, at 18-month follow-up, or at the time of transition. Images were pre-processed and hemisphere and regional CSA were computed using FreeSurfer. Between-group analyses were performed with linear mixed-effects models.

RESULTS

A total of 313 scans (107 CHR-P and 102 HC) were included in the analysis. At 18 months, the rate of transition to psychosis in CHR-P was 23.4%. Adolescents at CHR-P-T presented greater age-related decrease in CSA in the left parietal and occipital lobes compared with HC, and in the bilateral parietal lobe and right frontal lobe relative to CHR-P-NT. These results were not influenced by antipsychotic treatment, cannabis use, or intelligence quotient (IQ).

CONCLUSION

Adolescents at CHR-P that developed a psychotic disorder presented different developmental trajectories of CSA relative to those who did not. A relatively greater decrease in CSA in the parietal and frontal lobes may index clinical transition to psychosis in adolescents at CHR-P.

Using Crisis Theory in Dealing With Severe Mental Illness-A Step Toward Normalization?

The perception of mental distress varies with time and culture, e.g., concerning its origin as either social or medical. This may be one reason for the moderate reliability of descriptive psychiatric diagnoses. Additionally, the mechanisms of action of most psychiatric treatments and psychotherapeutic interventions are generally unknown. Thus, these treatments have to be labeled as mostly unspecific even if they help in coping with mental distress. The psychiatric concept of mental disorders therefore has inherent limitations of precision and comprises rather fuzzy boundaries. Against this background, many people question the current process of diagnosing and categorizing mental illnesses. However, many scholars reject new approaches discussed in this context. They rather hold on to traditional diagnostic categories which therefore still play a central role in mental health practice and research and. In order to better understand the adherence to traditional psychiatric concepts, we take a closer look at one of the most widely adopted traditional concepts - the Stress-Vulnerability Model. This model has originally been introduced to tackle some problems of biological psychiatry. However, it has been misapplied with the result of drawing attention preferentially to biological vulnerability instead of a wider array of vulnerability factors including social adversity. Thus, in its current use, the Stress-Vulnerability Model provides only a vague theory for understanding mental phenomena. Therefore, we discuss the advantages and allegedly limited applicability of Crisis Theory as an alternative heuristic model for understanding the nature and development of mental distress. We outline the problems of this theory especially in applying it to severe mental disorders. We finally argue that an understanding of Crisis Theory supported by a systemic approach can be applied to most types of severe psychological disturbances implying that such an understanding may prevent or manage some negative aspects of the psychiatrization of psychosocial problems.

Structural brain imaging abnormalities correlate with positive symptom in schizophrenia

Accumulating evidence indicates neuroanatomical mechanisms underlying positive symptoms in schizophrenia; however, the exact structural determinants of positive symptoms remain unclear. This study aimed to investigate associations between positive symptoms and structural brain changes, including alterations in grey matter (GM) volume and cortical thickness, in patients with first-episode schizophrenia (FES). This study included 44 patients with FES and 48 healthy controls (HCs). Clinical symptoms of patients were evaluated and individual-level GM volume and cortical thickness were assessed. Patients with FES showed reduced GM volume in the right superior temporal gyrus (STG) and increased cortical thickness in the left inferior segment of the circular sulcus of the insula (S_circular_insula_inf) compared with HCs. Increased thickness of the left S_circular_insula_inf correlated positively with positive symptoms in patients with FES. Exploratory correlation analysis found that increased thickness of the left S_circular_insula_inf correlated positively with conceptual disorganization and excitement symptoms, and the right STG GM volume correlated negatively with hallucinations. This study suggests that GM abnormalities in the STG and altered cortical thickness of the S_circular_insula_inf, which were detected at the early stage of schizophrenia, may underlie positive symptoms in patients with FES.

Abnormal corneal nerve morphology and brain volume in patients with schizophrenia

Neurodevelopmental and neurodegenerative pathology occur in Schizophrenia. This study compared the utility of corneal confocal microscopy (CCM), an ophthalmic imaging technique with MRI brain volumetry in quantifying neuronal pathology and its relationship to cognitive dysfunction and symptom severity in schizophrenia. Thirty-six subjects with schizophrenia and 26 controls underwent assessment of cognitive function, symptom severity, CCM and MRI brain volumetry. Subjects with schizophrenia had lower cognitive function (P ≤ 0.01), corneal nerve fiber density (CNFD), length (CNFL), branch density (CNBD), CNBD:CNFD ratio (P < 0.0001) and cingulate gyrus volume (P < 0.05) but comparable volume of whole brain (P = 0.61), cortical gray matter (P = 0.99), ventricle (P = 0.47), hippocampus (P = 0.10) and amygdala (P = 0.68). Corneal nerve measures and cingulate gyrus volume showed no association with symptom severity (P = 0.35–0.86 and P = 0.50) or cognitive function (P = 0.35–0.86 and P = 0.49). Corneal nerve measures were not associated with metabolic syndrome (P = 0.61–0.64) or diabetes (P = 0.057–0.54). The area under the ROC curve distinguishing subjects with schizophrenia from controls was 88% for CNFL, 84% for CNBD and CNBD:CNFD ratio, 79% for CNFD and 73% for the cingulate gyrus volume. This study has identified a reduction in corneal nerve fibers and cingulate gyrus volume in schizophrenia, but no association with symptom severity or cognitive dysfunction. Corneal nerve loss identified using CCM may act as a rapid non-invasive surrogate marker of neurodegeneration in patients with schizophrenia.

Surface area in the insula was associated with 28-month functional outcome in first-episode psychosis

Many studies have tested the relationship between demographic, clinical, and psychobiological measurements and clinical outcomes in ultra-high risk for psychosis (UHR) and first-episode psychosis (FEP). However, no study has investigated the relationship between multi-modal measurements and long-term outcomes for >2 years. Thirty-eight individuals with UHR and 29 patients with FEP were measured using one or more modalities (cognitive battery, electrophysiological response, structural magnetic resonance imaging, and functional near-infrared spectroscopy). We explored the characteristics associated with 13- and 28-month clinical outcomes. In UHR, the cortical surface area in the left orbital part of the inferior frontal gyrus was negatively associated with 13-month disorganized symptoms. In FEP, the cortical surface area in the left insula was positively associated with 28-month global social function. The left inferior frontal gyrus and insula are well-known structural brain characteristics in schizophrenia, and future studies on the pathological mechanism of structural alteration would provide a clearer understanding of the disease.

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.

Structural brain volumes of individuals at clinical high risk for psychosis: a meta-analysis

Background

Structural magnetic resonance imaging studies in individuals at clinical high risk (CHR) for psychosis have yielded conflicting results.

Methods

The aims of this study were to compare intracranial and structural brain volumes and variability of CHR individuals with those of healthy control (HC) subjects and to investigate brain volume differences and variability in CHR subjects with and without transition to psychosis. The PubMed and Embase databases were searched for relevant studies published before June 1, 2020.

Results

A total of 34 studies were deemed eligible, which included baseline data of 2111 CHR and 1472 HC participants. In addition, data were included for 401 CHR subjects who subsequently transitioned to psychosis and 1023 nontransitioned CHR participants. Whole-brain and left, right, and bilateral hippocampal volume were significantly smaller in CHR subjects than in HC subjects. Cerebrospinal fluid and lateral ventricle volumes were significantly larger in CHR subjects than in HC subjects. Variability was not significantly different in CHR subjects compared with HC subjects. CHR individuals with and without subsequent transition to psychosis did not show significant differences in any of the volumetric assessments or in variability.

Conclusions

This meta-analysis demonstrates reduced whole-brain and hippocampal volumes and increased cerebrospinal fluid and lateral ventricle volumes in CHR individuals. However, no significant differences were observed in any of the volumetric assessments between CHR individuals with and without subsequent transition to psychosis. These findings suggest that although structural brain alterations are present before the onset of the disorder, they may not significantly contribute to the identification of CHR individuals at the highest risk for the development of psychosis.

First-episode psychosis: How long does it last? A review of evolution and trajectory

Study of first-episode psychosis (FEP), an episode of psychotic nature which manifests for the first time in an individual in the longitudinal continuum of his/her illness, has been study matter of research interest in recent years. A comprehensive review of the literature will help us understand the evolution and trajectory of this concept better. A literature review of available articles addressing the concept, phenomenology, evolution, identification, course, and outcome of FEP was done; the same was subsequently divided into broad topics for better clarity and analyzed. FEP constituted a clinical psychotic phenomenon with underlying significant heterogeneity in diagnosis, stability, course, and outcome. The study has attempted to view FEP both as horizontal spectrum across various diagnoses and longitudinally ranging from asymptomatic individual with unknown risk status to attenuated psychosis to multiple relapses/unremitting illness. Many risk and protective factors have been brought out with varying certainty ranging bio-psycho-social spectrum. Efforts have been made to calculate polygenic risk score based on genes involvement/sharing between various psychotic spectrum disorders; as well as biomarker panels to identify people at risk. FEP may prove to be an important concept to understand psychosis in general; without putting things into the diagnostic rubric. It may help understand multiple risk and protective factors for the course and outcome of psychotic illness and may clear the cloud to sharpen the evidence toward commonality and distinctiveness between various psychotic diagnoses in vogue for more comprehensive concept.

Schizophrenia: A Survey of Artificial Intelligence Techniques Applied to Detection and Classification

Artificial Intelligence in healthcare employs machine learning algorithms to emulate human cognition in the analysis of complicated or large sets of data. Specifically, artificial intelligence taps on the ability of computer algorithms and software with allowable thresholds to make deterministic approximate conclusions. In comparison to traditional technologies in healthcare, artificial intelligence enhances the process of data analysis without the need for human input, producing nearly equally reliable, well defined output. Schizophrenia is a chronic mental health condition that affects millions worldwide, with impairment in thinking and behaviour that may be significantly disabling to daily living. Multiple artificial intelligence and machine learning algorithms have been utilized to analyze the different components of schizophrenia, such as in prediction of disease, and assessment of current prevention methods. These are carried out in hope of assisting with diagnosis and provision of viable options for individuals affected. In this paper, we review the progress of the use of artificial intelligence in schizophrenia.

Preventive psychiatry: a blueprint for improving the mental health of young people

Preventive approaches have latterly gained traction for improving mental health in young people. In this paper, we first appraise the conceptual foundations of preventive psychiatry, encompassing the public health, Gordon's, US Institute of Medicine, World Health Organization, and good mental health frameworks, and neurodevelopmentally-sensitive clinical staging models. We then review the evidence supporting primary prevention of psychotic, bipolar and common mental disorders and promotion of good mental health as potential transformative strategies to reduce the incidence of these disorders in young people. Within indicated approaches, the clinical high-risk for psychosis paradigm has received the most empirical validation, while clinical high-risk states for bipolar and common mental disorders are increasingly becoming a focus of attention. Selective approaches have mostly targeted familial vulnerability and non-genetic risk exposures. Selective screening and psychological/psychoeducational interventions in vulnerable subgroups may improve anxiety/depressive symptoms, but their efficacy in reducing the incidence of psychotic/bipolar/common mental disorders is unproven. Selective physical exercise may reduce the incidence of anxiety disorders. Universal psychological/psychoeducational interventions may improve anxiety symptoms but not prevent depressive/anxiety disorders, while universal physical exercise may reduce the incidence of anxiety disorders. Universal public health approaches targeting school climate or social determinants (demographic, economic, neighbourhood, environmental, social/cultural) of mental disorders hold the greatest potential for reducing the risk profile of the population as a whole. The approach to promotion of good mental health is currently fragmented. We leverage the knowledge gained from the review to develop a blueprint for future research and practice of preventive psychiatry in young people: integrating universal and targeted frameworks; advancing multivariable, transdiagnostic, multi-endpoint epidemiological knowledge; synergically preventing common and infrequent mental disorders; preventing physical and mental health burden together; implementing stratified/personalized prognosis; establishing evidence-based preventive interventions; developing an ethical framework, improving prevention through education/training; consolidating the cost-effectiveness of preventive psychiatry; and decreasing inequalities. These goals can only be achieved through an urgent individual, societal, and global level response, which promotes a vigorous collaboration across scientific, health care, societal and governmental sectors for implementing preventive psychiatry, as much is at stake for young people with or at risk for emerging mental disorders.

Cortical gray matter reduction precedes transition to psychosis in individuals at clinical high-risk for psychosis: A voxel-based meta-analysis

Gray matter and cortical thickness reductions have been documented in individuals at clinical high-risk for psychosis and may be more pronounced in those who transition to psychosis. However, these findings rely on small samples and are inconsistent across studies. In this review and meta-analysis we aimed to investigate neuroanatomical correlates of clinical high-risk for psychosis and potential predictors of transition, using a novel meta-analytic method (Seed-based d Mapping with Permutation of Subject Images) and cortical mask, combining data from surface-based and voxel-based morphometry studies. Individuals at clinical high-risk for psychosis who later transitioned to psychosis were compared to those who did not and to controls, and included three statistical maps. Overall, individuals at clinical high-risk for psychosis did not differ from controls, however, within the clinical high-risk for psychosis group, transition to psychosis was associated with less cortical gray matter in the right temporal lobe (Hedges' g = -0.377), anterior cingulate and paracingulate (Hedges' g = -0.391). These findings have the potential to help refine prognostic and etiopathological research in early psychosis.

Clinical use of neuro-imaging in psychiatric patients at the Charlotte Maxeke Johannesburg Academic Hospital

Background

Neuro-imaging is relatively new in psychiatry. Although the actual role of neuro-imaging in psychiatry remains unclear, it is used to strengthen clinical evidence in making psychiatric diagnoses.

Aim

To analyse the records of inpatients referred for neuro-imaging (computerised tomography [CT] and/or magnetic resonance imaging [MRI] scans) to determine the proportion of abnormal neuro-imaging results and, if any, factors associated with abnormal neuro-imaging results.

Setting

This study was conducted at the Charlotte Maxeke Johannesburg Academic Hospital (CMJAH) situated in Johannesburg, South Africa.

Methods

This was a quantitative retrospective record review. All adult psychiatric inpatients who had undergone a CT and/or MRI scan during 01 January 2014 to 31 December 2015 were included. Out-patients or patients admitted in the medical wards were excluded from the study. All neuro-imaging referrals were identified from hospital records and their demographics, scan characteristics and diagnoses were subsequently captured.

Results

A total of 1040 patients were admitted to the CMJAH psychiatric unit, of which 213 (20.5%) underwent neuro-imaging tests. Of the 213 scans performed, 74 were abnormal, representing a yield of 34.7%. The most common reported pathology was atrophy (n = 22, 29.7%). There was no statistically significant association between age group (χ² = 3.9, p = 0.8), gender (χ² = 1.3; p = 0.5), psychiatric diagnoses and abnormal scans. However, there were trends towards an association with comorbid HIV infection (χ² = 3.476, p = 0.062) and comorbid substance abuse (χ² = 2.286, p = 0.091).

Conclusion

This study supports the need for clear clinical indications to justify the cost-effective use of neuro-imaging in psychiatry. This study’s high yield of abnormal CT scans, although similar to other studies, advocates that HIV positive testing and the presence of focal neurological signs will improve the yield further.

Predicting onset of early- and late-treatment resistance in first-episode schizophrenia patients using advanced shrinkage statistical methods in a small sample

Evidence suggests there are two treatment-resistant schizophrenia subtypes (i.e. early treatment resistant (E-TR) and late-treatment resistant (L-TR)). We aimed to develop prediction models for estimating individual risk for these outcomes by employing advanced statistical shrinkage methods. 239 first-episode schizophrenia (FES) patients were followed-up for approximately 5 years after first presentation to psychiatric services; of these, n=56 (25.2%) were defined as E-TR and n=24 (12.6%) were defined as L-TR. Using known risk factors for poor schizophrenia outcomes, we developed prediction models for E-TR and L-TR using LASSO and RIDGE logistic regression models. Models' internal validation was performed employing Harrell's optimism-correction with repeated cross-validation; their predictive accuracy was assessed through discrimination and calibration. Both LASSO and RIDGE models had high discrimination, good calibration. While LASSO had moderate sensitivity for estimating an individual risk for E-TR and L-TR, sensitivity estimated for RIDGE model for these outcomes was extremely low, which was due to having a very large estimated optimism. Although it was possible to discriminate with sufficient accuracy who would meet criteria for E-TR and L-TR during the 5-year follow-up after first contact with mental health services for schizophrenia, further work is necessary to improve sensitivity for these models.

Evidence-based umbrella review of 162 peripheral biomarkers for major mental disorders

The literature on non-genetic peripheral biomarkers for major mental disorders is broad, with conflicting results. An umbrella review of meta-analyses of non-genetic peripheral biomarkers for Alzheimer's disease, autism spectrum disorder, bipolar disorder (BD), major depressive disorder, and schizophrenia, including first-episode psychosis. We included meta-analyses that compared alterations in peripheral biomarkers between participants with mental disorders to controls (i.e., between-group meta-analyses) and that assessed biomarkers after treatment (i.e., within-group meta-analyses). Evidence for association was hierarchically graded using a priori defined criteria against several biases. The Assessment of Multiple Systematic Reviews (AMSTAR) instrument was used to investigate study quality. 1161 references were screened. 110 met inclusion criteria, relating to 359 meta-analytic estimates and 733,316 measurements, on 162 different biomarkers. Only two estimates met a priori defined criteria for convincing evidence (elevated awakening cortisol levels in euthymic BD participants relative to controls and decreased pyridoxal levels in participants with schizophrenia relative to controls). Of 42 estimates which met criteria for highly suggestive evidence only five biomarker aberrations occurred in more than one disorder. Only 15 meta-analyses had a power >0.8 to detect a small effect size, and most (81.9%) meta-analyses had high heterogeneity. Although some associations met criteria for either convincing or highly suggestive evidence, overall the vast literature of peripheral biomarkers for major mental disorders is affected by bias and is underpowered. No convincing evidence supported the existence of a trans-diagnostic biomarker. Adequately powered and methodologically sound future large collaborative studies are warranted.

Neuroimaging in Schizophrenia

Schizophrenia is a chronic psychotic disorder with a lifetime prevalence of about 1%. Onset is typically in adolescence or early adulthood; characteristic symptoms include positive symptoms, negative symptoms, and impairments in cognition. Neuroimaging studies have shown substantive evidence of brain structural, functional, and neurochemical alterations that are more pronounced in the association cortex and subcortical regions. These abnormalities are not sufficiently specific to be of diagnostic value, but there may be a role for imaging techniques to provide predictions of outcome. Incorporating multimodal imaging datasets using machine learning approaches may offer better diagnostic and predictive value in schizophrenia.

The clinical utility of structural neuroimaging in first-episode psychosis: A systematic review

BACKGROUND

Australian and US guidelines recommend routine brain imaging, either computed tomography or magnetic resonance imaging, to exclude structural lesions in presentations for first-episode psychosis. The aim of this review was to examine the evidence for the appropriateness and clinical utility of this recommendation by assessing the frequency of abnormal radiological findings in computed tomography and magnetic resonance imaging scans among patients with first-episode psychosis.

METHODS

PubMed and Embase database were searched from inception to April 2018 using appropriate MeSH or Emtree terms. Studies were included in the review if they reported data on computed tomography or magnetic resonance imaging scan findings of individuals with first-episode psychosis. No restriction on the geographical location of the study or the age of participants was applied. We calculated the percentage of abnormal radiological findings in each study, separately by the two diagnostic methods.

RESULTS

There were 16 suitable studies published between 1988 and 2017, reporting data on an overall 2312 patients with first-episode psychosis. Most were observational studies with a retrospective design and the majority examined patients with computed tomography. While structural abnormalities were a relatively common finding, these rarely required clinical intervention (range across studies: 0-60.7%; median: 3.5%) and were very rarely the cause of the psychotic symptoms (range: 0-3.3%; median: 0%). Only 2 of the 16 studies concluded that brain imaging should be routinely ordered in first-episode psychosis.

CONCLUSION

There is insufficient evidence to suggest that brain imaging should be routinely ordered for patients presenting with first-episode psychosis without associated neurological or cognitive impairment. The appropriate screening procedure for structural brain lesions is conventional history-taking, mental status and neurological examination. If intracranial pathology is suspected clinically, a magnetic resonance imaging or computed tomography scan should be performed depending on the clinical signs, the acuity and the suspected pathology. National guidelines should reflect evidence-based data.

Clinical-learning versus machine-learning for transdiagnostic prediction of psychosis onset in individuals at-risk

Predicting the onset of psychosis in individuals at-risk is based on robust prognostic model building methods including a priori clinical knowledge (also termed clinical-learning) to preselect predictors or machine-learning methods to select predictors automatically. To date, there is no empirical research comparing the prognostic accuracy of these two methods for the prediction of psychosis onset. In a first experiment, no improved performance was observed when machine-learning methods (LASSO and RIDGE) were applied-using the same predictors-to an individualised, transdiagnostic, clinically based, risk calculator previously developed on the basis of clinical-learning (predictors: age, gender, age by gender, ethnicity, ICD-10 diagnostic spectrum), and externally validated twice. In a second experiment, two refined versions of the published model which expanded the granularity of the ICD-10 diagnosis were introduced: ICD-10 diagnostic categories and ICD-10 diagnostic subdivisions. Although these refined versions showed an increase in apparent performance, their external performance was similar to the original model. In a third experiment, the three refined models were analysed under machine-learning and clinical-learning with a variable event per variable ratio (EPV). The best performing model under low EPVs was obtained through machine-learning approaches. The development of prognostic models on the basis of a priori clinical knowledge, large samples and adequate events per variable is a robust clinical prediction method to forecast psychosis onset in patients at-risk, and is comparable to machine-learning methods, which are more difficult to interpret and implement. Machine-learning methods should be preferred for high dimensional data when no a priori knowledge is available.

Forty years of structural brain imaging in mental disorders: is it clinically useful or not?

Structural brain imaging was introduced into routine clinical practice more than 40 years ago with the hope that it would support the diagnosis and treatment of mental disorders. It is now widely used to exclude organic brain disease (eg, brain tumors, cardiovascular, and inflammatory processes) in mental disorders. However, questions have been raised about whether structural brain imaging is still needed today and whether it could also be clinically useful to apply new biostatistical methods, such as machine learning. Therefore, the current paper not only reviews structural findings in Alzheimer disease, depression, bipolar disorder, and schizophrenia but also discusses the role of structural imaging in supporting diagnostic, prognostic, and therapeutic processes in mental disorders. Thus, it attempts to answer the questions whether, after four decades of use, structural brain imaging is clinically useful in mental disorders or whether it will become so in the future.

Structural covariance and cortical reorganisation in schizophrenia: a MRI-based morphometric study

BACKGROUND

In patients with schizophrenia, distributed abnormalities are observed in grey matter volume. A recent hypothesis posits that these distributed changes are indicative of a plastic reorganisation process occurring in response to a functional defect in neuronal information transmission. We investigated the structural covariance across various brain regions in early-stage schizophrenia to determine if indeed the observed patterns of volumetric loss conform to a coordinated pattern of structural reorganisation.

METHODS

Structural magnetic resonance imaging scans were obtained from 40 healthy adults and 41 age, gender and parental socioeconomic status matched patients with schizophrenia. Volumes of grey matter tissue were estimated at the regional level across 90 atlas-based parcellations. Group-level structural covariance was studied using a graph theoretical framework.

RESULTS

Patients had distributed reduction in grey matter volume, with high degree of localised covariance (clustering) compared with controls. Patients with schizophrenia had reduced centrality of anterior cingulate and insula but increased centrality of the fusiform cortex, compared with controls. Simulating targeted removal of highly central nodes resulted in significant loss of the overall covariance patterns in patients compared with controls.

CONCLUSION

Regional volumetric deficits in schizophrenia are not a result of random, mutually independent processes. Our observations support the occurrence of a spatially interconnected reorganisation with the systematic de-escalation of conventional 'hub' regions. This raises the question of whether the morphological architecture in schizophrenia is primed for compensatory functions, albeit with a high risk of inefficiency.

Neural circuitry and precision medicines for mental disorders: are they compatible?

Given the failure of psychiatry to develop clinically useful biomarkers for psychiatric disorders, and the concomitant failure to develop significant advances in diagnosis and treatment, the National Institute of Mental Health (NIMH) in 2010 launched the Research Domain Criteria (RDoC), a framework for research based on the assumption that mental disorders are disorders of identifiable brain neural circuits, with neural circuitry at the center of units of analysis ranging from genes, molecules, and cells to behavior, self-reports, and paradigms. These were to be integrated with five validated dimensional psychological constructs such as negative and positive valence systems. Four years later, the NIMH stated that the ultimate goal of RDoC is precision medicine for psychiatry, with the assumption that precision medications will normalize dysfunctional neural circuits. How this could be accomplished is not obvious, given that neural circuits are widely distributed, have unclear boundaries, and exhibit a significant degree of neuroplasticity, with multiple circuits present in any given disorder. Moreover, the early focus on neural circuitry has been criticized for its reductionism and neglect of the more recent RDoC emphasis on the integration and equivalence of biological and psychological phenomena. Yet this seems inconsistent with the priorities of the NIMH director, an advocate of the central role of neural circuitry and projects such as the Brain Initiative and the Human Connectome Project. Will such projects, at a cost of at least $10 billion, lead to precision medications for mental disorders, or further diminish funding for clinical care and research?

Development of Neuroimaging-Based Biomarkers in Psychiatry

This chapter presents an overview of accumulating neuroimaging data with emphasis on translational potential. The subject will be described in the context of three disease states, i.e., schizophrenia, bipolar disorder, and major depressive disorder, and for three clinical goals, i.e., disease risk assessment, subtyping, and treatment decision.

The effects of synthetic cannabinoids (SCs) on brain structure and function

There is an increasing use of "Novel Psychoactive Substances" containing synthetic cannabinoids worldwide. Synthetic cannabinoids (SC) are highly addictive and cause severe adverse effects. The purpose of our study was to assess whether chronic use of SC alters brain volume and function. Fifteen SC chronic users and 15 healthy control participants undertook an MRI scan to assess brain volume and function while performing a working memory N-back task and a response-inhibition Go-No-Go task. SC users showed impaired performance on the N-back task but not on the Go-No-Go task. They also showed reduced total gray matter volume compared with control participants, as well as reduced gray matter volume in several cortical regions including the middle frontal gyrus, frontal orbital gyrus, inferior frontal gyrus, insula, anterior cingulate cortex and the precuneus. Moreover, SC users showed diminished brain activations in the precuneus, cuneus, lingual gyrus, hippocampus and cerebellum while performing the N-back task. No differences were found in brain activation while performing the response-inhibition task. This is the first study showing overall reduced grey matter volume and specific reduced grey matter volumes in chronic SC users. Furthermore, this study showed for the first time impairment in the neural brain mechanisms responsible for working memory in SC users. Our results of reduced grey matter density and diminished activation during a working memory task in SC users, may suggest vulnerability of the frontal-parietal network in chronic SC users.

(Micro)Glia as Effectors of Cortical Volume Loss in Schizophrenia

Contrary to the notion that neurology but not psychiatry is the domain of disorders evincing structural brain alterations, it is now clear that there are subtle but consistent neuropathological changes in schizophrenia. These range from increases in ventricular size to dystrophic changes in dendritic spines. A decrease in dendritic spine density in the prefrontal cortex (PFC) is among the most replicated of postmortem structural findings in schizophrenia. Examination of the mechanisms that account for the loss of dendritic spines has in large part focused on genes and molecules that regulate neuronal structure. But the simple question of what is the effector of spine loss, ie, where do the lost spines go, is unanswered. Recent data on glial cells suggest that microglia (MG), and perhaps astrocytes, play an important physiological role in synaptic remodeling of neurons during development. Synapses are added to the dendrites of pyramidal cells during the maturation of these neurons; excess synapses are subsequently phagocytosed by MG. In the PFC, this occurs during adolescence, when certain symptoms of schizophrenia emerge. This brief review discusses recent advances in our understanding of MG function and how these non-neuronal cells lead to structural changes in neurons in schizophrenia.

Cerebellar volume and cerebellocerebral structural covariance in schizophrenia: a multisite mega-analysis of 983 patients and 1349 healthy controls

Although cerebellar involvement across a wide range of cognitive and neuropsychiatric phenotypes is increasingly being recognized, previous large-scale studies in schizophrenia (SZ) have primarily focused on supratentorial structures. Hence, the across-sample reproducibility, regional distribution, associations with cerebrocortical morphology and effect sizes of cerebellar relative to cerebral morphological differences in SZ are unknown. We addressed these questions in 983 patients with SZ spectrum disorders and 1349 healthy controls (HCs) from 14 international samples, using state-of-the-art image analysis pipelines optimized for both the cerebellum and the cerebrum. Results showed that total cerebellar grey matter volume was robustly reduced in SZ relative to HCs (Cohens's d=-0.35), with the strongest effects in cerebellar regions showing functional connectivity with frontoparietal cortices (d=-0.40). Effect sizes for cerebellar volumes were similar to the most consistently reported cerebral structural changes in SZ (e.g., hippocampus volume and frontotemporal cortical thickness), and were highly consistent across samples. Within groups, we further observed positive correlations between cerebellar volume and cerebral cortical thickness in frontotemporal regions (i.e., overlapping with areas that also showed reductions in SZ). This cerebellocerebral structural covariance was strongest in SZ, suggesting common underlying disease processes jointly affecting the cerebellum and the cerebrum. Finally, cerebellar volume reduction in SZ was highly consistent across the included age span (16-66 years) and present already in the youngest patients, a finding that is more consistent with neurodevelopmental than neurodegenerative etiology. Taken together, these novel findings establish the cerebellum as a key node in the distributed brain networks underlying SZ.

Meta-analytical prognostic accuracy of the Comprehensive Assessment of at Risk Mental States (CAARMS): The need for refined prediction

Primary indicated prevention is reliant on accurate tools to predict the onset of psychosis. The gold standard assessment for detecting individuals at clinical high risk (CHR-P) for psychosis in the UK and many other countries is the Comprehensive Assessment for At Risk Mental States (CAARMS). While the prognostic accuracy of CHR-P instruments has been assessed in general, this is the first study to specifically analyse that of the CAARMS. As such, the CAARMS was used as the index test, with the reference index being psychosis onset within 2 years. Six independent studies were analysed using MIDAS (STATA 14), with a total of 1876 help-seeking subjects referred to high risk services (CHR-P+: n=892; CHR-P-: n=984). Area under the curve (AUC), summary receiver operating characteristic curves (SROC), quality assessment, likelihood ratios, and probability modified plots were computed, along with sensitivity analyses and meta-regressions. The current meta-analysis confirmed that the 2-year prognostic accuracy of the CAARMS is only acceptable (AUC=0.79 95% CI: 0.75-0.83) and not outstanding as previously reported. In particular, specificity was poor. Sensitivity of the CAARMS is inferior compared to the SIPS, while specificity is comparably low. However, due to the difficulties in performing these types of studies, power in this meta-analysis was low. These results indicate that refining and improving the prognostic accuracy of the CAARMS should be the mainstream area of research for the next era. Avenues of prediction improvement are critically discussed and presented to better benefit patients and improve outcomes of first episode psychosis.

Is It Possible to Predict the Future in First-Episode Psychosis?

The outcome of first-episode psychosis (FEP) is highly variable, ranging from early sustained recovery to antipsychotic treatment resistance from the onset of illness. For clinicians, a possibility to predict patient outcomes would be highly valuable for the selection of antipsychotic treatment and in tailoring psychosocial treatments and psychoeducation. This selective review summarizes current knowledge of prognostic markers in FEP. We sought potential outcome predictors from clinical and sociodemographic factors, cognition, brain imaging, genetics, and blood-based biomarkers, and we considered different outcomes, like remission, recovery, physical comorbidities, and suicide risk. Based on the review, it is currently possible to predict the future for FEP patients to some extent. Some clinical features-like the longer duration of untreated psychosis (DUP), poor premorbid adjustment, the insidious mode of onset, the greater severity of negative symptoms, comorbid substance use disorders (SUDs), a history of suicide attempts and suicidal ideation and having non-affective psychosis-are associated with a worse outcome. Of the social and demographic factors, male gender, social disadvantage, neighborhood deprivation, dysfunctional family environment, and ethnicity may be relevant. Treatment non-adherence is a substantial risk factor for relapse, but a small minority of patients with acute onset of FEP and early remission may benefit from antipsychotic discontinuation. Cognitive functioning is associated with functional outcomes. Brain imaging currently has limited utility as an outcome predictor, but this may change with methodological advancements. Polygenic risk scores (PRSs) might be useful as one component of a predictive tool, and pharmacogenetic testing is already available and valuable for patients who have problems in treatment response or with side effects. Most blood-based biomarkers need further validation. None of the currently available predictive markers has adequate sensitivity or specificity used alone. However, personalized treatment of FEP will need predictive tools. We discuss some methodologies, such as machine learning (ML), and tools that could lead to the improved prediction and clinical utility of different prognostic markers in FEP. Combination of different markers in ML models with a user friendly interface, or novel findings from e.g., molecular genetics or neuroimaging, may result in computer-assisted clinical applications in the near future.

What can we learn about brain donors? Use of clinical information in human postmortem brain research

Postmortem studies on the human brain reside at the core of investigations on neurologic and psychiatric disorders. Ground-breaking advances continue to be made on the pathologic basis of many of these disorders, at molecular, cellular, and neural connectivity levels. In parallel, there is increasing emphasis on improving methods to extract relevant demographic and clinical information about brain donors and, importantly, translate it into measures that can reliably and effectively be incorporated in the design and data analysis of postmortem human investigations. Here, we review the main source of information typically available to brain banks and provide examples on how this information can be processed. In particular, we discuss approaches to establish primary and secondary diagnoses, estimate exposure to therapeutic treatment and substance abuse, assess agonal status, and use time of death as a proxy in investigations on circadian rhythms. Although far from exhaustive, these considerations are intended as a contribution to ongoing efforts from tissue banks and investigators aimed at establishing robust, well-validated methods for collecting and standardizing information about brain donors, further strengthening the scientific rigor of human postmortem studies.

Translational Genetic Modelling of 3D Craniofacial Dysmorphology: Elaborating the Facial Phenotype of Neurodevelopmental Disorders Through the "Prism" of Schizophrenia

Purpose of Review

In the context of human developmental conditions, we review the conceptualisation of schizophrenia as a neurodevelopmental disorder, the status of craniofacial dysmorphology as a clinically accessible index of brain dysmorphogenesis, the ability of genetically modified mouse models of craniofacial dysmorphology to inform on the underlying dysmorphogenic process and how geometric morphometric techniques in mutant mice can extend quantitative analysis.

Recent Findings

Mutant mice with disruption of neuregulin-1, a gene associated meta-analytically with risk for schizophrenia, constitute proof-of-concept studies of murine facial dysmorphology in a manner analogous to clinical studies in schizophrenia. Geometric morphometric techniques informed on the topography of facial dysmorphology and identified asymmetry therein.

Summary

Targeted disruption in mice of genes involved in individual components of developmental processes and analysis of resultant facial dysmorphology using geometric morphometrics can inform on mechanisms of dysmorphogenesis at levels of incisiveness not possible in human subjects.

Improving outcomes of first-episode psychosis: an overview

Outcomes of psychotic disorders are associated with high personal, familiar, societal and clinical burden. There is thus an urgent clinical and societal need for improving those outcomes. Recent advances in research knowledge have opened new opportunities for ameliorating outcomes of psychosis during its early clinical stages. This paper critically reviews these opportunities, summarizing the state-of-the-art knowledge and focusing on recent discoveries and future avenues for first episode research and clinical interventions. Candidate targets for primary universal prevention of psychosis at the population level are discussed. Potentials offered by primary selective prevention in asymptomatic subgroups (stage 0) are presented. Achievements of primary selected prevention in individuals at clinical high risk for psychosis (stage 1) are summarized, along with challenges and limitations of its implementation in clinical practice. Early intervention and secondary prevention strategies at the time of a first episode of psychosis (stage 2) are critically discussed, with a particular focus on minimizing the duration of untreated psychosis, improving treatment response, increasing patients' satisfaction with treatment, reducing illicit substance abuse and preventing relapses. Early intervention and tertiary prevention strategies at the time of an incomplete recovery (stage 3) are further discussed, in particular with respect to addressing treatment resistance, improving well-being and social skills with reduction of burden on the family, treatment of comorbid substance use, and prevention of multiple relapses and disease progression. In conclusion, to improve outcomes of a complex, heterogeneous syndrome such as psychosis, it is necessary to globally adopt complex models integrating a clinical staging framework and coordinated specialty care programmes that offer pre-emptive interventions to high-risk groups identified across the early stages of the disorder. Only a systematic implementation of these models of care in the national health care systems will render these strategies accessible to the 23 million people worldwide suffering from the most severe psychiatric disorders.