The use of dynamic susceptibility contrast (DSC) MRI to automatically classify patients with first episode psychosis

Effects of diazepam on hippocampal blood flow in people at clinical high risk for psychosis

Elevated hippocampal perfusion has been observed in people at clinical high risk for psychosis (CHR-P). Preclinical evidence suggests that hippocampal hyperactivity is central to the pathophysiology of psychosis, and that peripubertal treatment with diazepam can prevent the development of psychosis-relevant phenotypes. The present experimental medicine study examined whether diazepam can normalize hippocampal perfusion in CHR-P individuals. Using a randomized, double-blind, placebo-controlled, crossover design, 24 CHR-P individuals were assessed with magnetic resonance imaging (MRI) on two occasions, once following a single oral dose of diazepam (5 mg) and once following placebo. Regional cerebral blood flow (rCBF) was measured using 3D pseudo-continuous arterial spin labeling and sampled in native space using participant-specific hippocampus and subfield masks (CA1, subiculum, CA4/dentate gyrus). Twenty-two healthy controls (HC) were scanned using the same MRI acquisition sequence, but without administration of diazepam or placebo. Mixed-design ANCOVAs and linear mixed-effects models were used to examine the effects of group (CHR-P placebo/diazepam vs. HC) and condition (CHR-P diazepam vs. placebo) on rCBF in the hippocampus as a whole and by subfield. Under the placebo condition, CHR-P individuals (mean [±SD] age: 24.1 [±4.8] years, 15 F) showed significantly elevated rCBF compared to HC (mean [±SD] age: 26.5 [±5.1] years, 11 F) in the hippocampus (F(1,41) = 24.7, pFDR < 0.001) and across its subfields (all pFDR < 0.001). Following diazepam, rCBF in the hippocampus (and subfields, all pFDR < 0.001) was significantly reduced (t(69) = -5.1, pFDR < 0.001) and normalized to HC levels (F(1,41) = 0.4, pFDR = 0.204). In conclusion, diazepam normalized hippocampal hyperperfusion in CHR-P individuals, consistent with evidence implicating medial temporal GABAergic dysfunction in increased vulnerability for psychosis.

Microstructural and Microvascular Alterations in Psychotic Spectrum Disorders: A Three-Compartment Intravoxel Incoherent Imaging and Free Water Model

BACKGROUND AND HYPOTHESIS

Microvascular and inflammatory mechanisms have been hypothesized to be involved in the pathophysiology of psychotic spectrum disorders (PSDs). However, data evaluating these hypotheses remain limited.

STUDY DESIGN

We applied a three-compartment intravoxel incoherent motion free water imaging (IVIM-FWI) technique that estimates the perfusion fraction (PF), free water fraction (FW), and anisotropic diffusion of tissue (FAt) to examine microvascular and microstructural changes in gray and white matter in 55 young adults with a PSD compared to 37 healthy controls (HCs).

STUDY RESULTS

We found significantly increased PF, FW, and FAt in gray matter regions, and significantly increased PF, FW, and decreased FAt in white matter regions in the PSD group versus HC. Furthermore, in patients, but not in the HC group, increased PF, FW, and FAt in gray matter and increased PF in white matter were significantly associated with poor performance on several cognitive tests assessing memory and processing speed. We additionally report significant associations between IVIM-FWI metrics and myo-inositol, choline, and N-acetylaspartic acid magnetic resonance spectroscopy imaging metabolites in the posterior cingulate cortex, which further supports the validity of PF, FW, and FAt as microvascular and microstructural biomarkers of PSD. Finally, we found significant relationships between IVIM-FWI metrics and the duration of psychosis in gray and white matter regions.

CONCLUSIONS

The three-compartment IVIM-FWI model provides metrics that are associated with cognitive deficits and may reflect disease progression.

Cerebral blood flow in schizophrenia: A systematic review and meta-analysis of MRI-based studies

INTRODUCTION

Schizophrenia-spectrum disorders (SSD) represent one of the leading causes of disability worldwide and are usually underpinned by neurodevelopmental brain abnormalities observed on a structural and functional level. Nuclear medicine imaging studies of cerebral blood flow (CBF) have already provided insights into the pathophysiology of these disorders. Recent developments in non-invasive MRI techniques such as arterial spin labeling (ASL) have allowed broader examination of CBF across SSD prompting us to conduct an updated literature review of MRI-based perfusion studies. In addition, we conducted a focused meta-analysis of whole brain studies to provide a complete picture of the literature on the topic.

METHODS

A systematic OVID search was performed in Embase, MEDLINEOvid, and PsycINFO. Studies eligible for inclusion in the review involved: 1) individuals with SSD, first-episode psychosis or clinical-high risk for psychosis, or; 2) had healthy controls for comparison; 3) involved MRI-based perfusion imaging methods; and 4) reported CBF findings. No time span was specified for the database queries (last search: 08/2022). Information related to participants, MRI techniques, CBF analyses, and results were systematically extracted. Whole-brain studies were then selected for the meta-analysis procedure. The methodological quality of each included studies was assessed.

RESULTS

For the systematic review, the initial Ovid search yielded 648 publications of which 42 articles were included, representing 3480 SSD patients and controls. The most consistent finding was that negative symptoms were linked to cortical fronto-limbic hypoperfusion while positive symptoms seemed to be associated with hyperperfusion, notably in subcortical structures. The meta-analysis integrated results from 13 whole-brain studies, across 426 patients and 401 controls, and confirmed the robustness of the hypoperfusion in the left superior and middle frontal gyri and right middle occipital gyrus while hyperperfusion was found in the left putamen.

CONCLUSION

This updated review of the literature supports the implication of hemodynamic correlates in the pathophysiology of psychosis symptoms and disorders. A more systematic exploration of brain perfusion could complete the search of a multimodal biomarker of SSD.

Neurovascular alterations in bipolar disorder: A review of perfusion weighted magnetic resonance imaging studies

BACKGROUND

Bipolar Disorder (BD) is a severe chronic psychiatric disorder whose aetiology is still largely unknown. However, increasing literature reported the involvement of neurovascular factors in the pathophysiology of BD, suggesting that a measure of Cerebral Blood Flow (CBF) could be an important biomarker of the illness. Therefore, since, to date, Magnetic Resonance Perfusion Weighted Imaging (PWI) techniques, such as Dynamic Susceptibility Contrast (DSC) and Arterial Spin Labelling (ASL), are the most common approaches that allow non-invasive in-vivo perfusion measurements,this review aims to summarize the results from all PWI studies that evaluated the CBF in BD.

METHODS

A bibliographic search in PubMed up until May 2021 was performed. 16 PWI studies that used DSC or ASL sequences met our inclusion criteria.

RESULTS

Overall, the results supported the presence of hyper-perfusion in the cingulate cortex and fronto-temporal regions, as well as the presence of hypo-perfusion in the cerebellum in BD, compared with both healthy controls and patients with unipolar depression. CBF changes after cognitive and aerobic training, as well as in relation with other physiological, clinical, and neurocognitive variables were also reported.

LIMITATIONS

The heterogeneity across the studies, in terms of experimental designs, sample selection, and methodological approach employed, limited the studies' comparison.

CONCLUSIONS

These findings showed CBF alterations in the cingulate cortex, fronto-temporal regions, and cerebellum in BD, suggesting that CBF may be an important pathophysiological marker of BD that merits further investigation to clarify the extent of neurovascular alterations.

Insula volumes in first-episode and chronic psychosis: A longitudinal MRI study

BACKGROUND

Alterations in insular grey matter (GM) volume has been consistently reported for affective and non-affective psychoses both in chronic and first-episode patients, ultimately suggesting that the insula might represent a good region to study in order to assess the longitudinal course of psychotic disorders. Therefore, in this longitudinal Magnetic Resonance Imaging (MRI) study, we aimed at further investigating the key role of insular volumes in psychosis.

MATERIAL AND METHODS

68 First-Episode Psychosis (FEP) patients, 68 patients with Schizophrenia (SCZ), 47 Bipolar Disorder (BD) patients, and 94 Healthy Controls (HC) were enrolled and underwent a 1.5 T MRI evaluation. A subsample of 99 subjects (10 HC, 23 BD, 29 SCZ, 37 FEP) was rescanned after 2,53 ± 1,68 years. The insular cortex was manually traced and then divided into an anterior and posterior portion. Group and correlation analyses were then performed both at baseline and at follow-up.

RESULTS

At baseline, greater anterior and lower posterior insular GM volumes were observed in chronic patients. At follow-up, we found that FEP patients had a significant GM volume increase from baseline to follow-up, especially in the posterior insula whereas chronic patients showed a relative stability. Finally, significant negative correlations between illness severity and pharmacological treatment and insular GM volumes were observed in the whole group of psychotic patients.

CONCLUSIONS

The longitudinal assessment of both chronic and first-episode patients allowed us to detect a complex pattern of GM abnormalities in selective sub-portions of insular volumes, ultimately suggesting that this structure could represent a key biological marker of psychotic disorders.

Distinguish bipolar and major depressive disorder in adolescents based on multimodal neuroimaging: Results from the Adolescent Brain Cognitive Development study®

Background

Major depressive disorder and bipolar disorder in adolescents are prevalent and are associated with cognitive impairment, executive dysfunction, and increased mortality. Early intervention in the initial stages of major depressive disorder and bipolar disorder can significantly improve personal health.

Methods

We collected 309 samples from the Adolescent Brain Cognitive Development study, including 116 adolescents with bipolar disorder, 64 adolescents with major depressive disorder, and 129 healthy adolescents, and employed a support vector machine to develop classification models for identification. We developed a multimodal model, which combined functional connectivity of resting-state functional magnetic resonance imaging and four anatomical measures of structural magnetic resonance imaging (cortical thickness, area, volume, and sulcal depth). We measured the performances of both multimodal and single modality classifiers.

Results

The multimodal classifiers showed outstanding performance compared with all five single modalities, and they are 100% for major depressive disorder versus healthy controls, 100% for bipolar disorder versus healthy control, 98.5% (95% CI: 95.4–100%) for major depressive disorder versus bipolar disorder, 100% for major depressive disorder versus depressed bipolar disorder and the leave-one-site-out analysis results are 77.4%, 63.3%, 79.4%, and 81.7%, separately.

Conclusions

The study shows that multimodal classifiers show high classification performances. Moreover, cuneus may be a potential biomarker to differentiate major depressive disorder, bipolar disorder, and healthy adolescents. Overall, this study can form multimodal diagnostic prediction workflows for clinically feasible to make more precise diagnose at the early stage and potentially reduce loss of personal pain and public society.

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.

Classification of first-episode psychosis using cortical thickness: A large multicenter MRI study

Machine learning classifications of first-episode psychosis (FEP) using neuroimaging have predominantly analyzed brain volumes. Some studies examined cortical thickness, but most of them have used parcellation approaches with data from single sites, which limits claims of generalizability. To address these limitations, we conducted a large-scale, multi-site analysis of cortical thickness comparing parcellations and vertex-wise approaches. By leveraging the multi-site nature of the study, we further investigated how different demographical and site-dependent variables affected predictions. Finally, we assessed relationships between predictions and clinical variables. 428 subjects (147 females, mean age 27.14) with FEP and 448 (230 females, mean age 27.06) healthy controls were enrolled in 8 centers by the ClassiFEP group. All subjects underwent a structural MRI and were clinically assessed. Cortical thickness parcellation (68 areas) and full cortical maps (20,484 vertices) were extracted. Linear Support Vector Machine was used for classification within a repeated nested cross-validation framework. Vertex-wise thickness maps outperformed parcellation-based methods with a balanced accuracy of 66.2% and an Area Under the Curve of 72%. By stratifying our sample for MRI scanner, we increased generalizability across sites. Temporal brain areas resulted as the most influential in the classification. The predictive decision scores significantly correlated with age at onset, duration of treatment, and positive symptoms. In conclusion, although far from the threshold of clinical relevance, temporal cortical thickness proved to classify between FEP subjects and healthy individuals. The assessment of site-dependent variables permitted an increase in the across-site generalizability, thus attempting to address an important machine learning limitation.

Standardized acquisition and post-processing of dynamic susceptibility contrast perfusion in patients with brain tumors, cerebrovascular disease and dementia: comparability of post-processing software

OBJECTIVE

MR-perfusion post-processing still lacks standardization. This study evaluates the results of perfusion analysis with two established software solutions in a large series of patients with different diseases when a highly standardized processing workflow is ensured.

METHODS

Multicenter data of 260 patients (80 with brain tumors, 124 with cerebrovascular disease and 56 with dementia examined with the same MR protocol) were analyzed. Raw data sets were processed with two software suites: Olea sphere and NordicICE. Group differences were analyzed with paired t-tests and one-way ANOVA.

RESULTS

Perfusion metrics were significantly different for all examined diseases in the unaffected brain for both software suites [ratio cortex/white matter left hemisphere: mean transit time (MTT) 0.991 vs 0.847, p < 0.05; relative cerebral bloodflow (rBF) 3.23 vs 4.418, p < 0.001; relative cerebral bloodvolume (rBVc) 2.813 vs 3.884, p < 0.001; right hemisphere: MTT 1.079 vs 0.854, p < 0.05; rBF 3.262 vs 4.378, p < 0.001; rBVc 2.762 vs 3.935, p < 0.001)]. Perfusion results were also significantly different in patients with stroke (ratio cortex/white matter affected hemisphere: MTT 1.058 vs 0.784; p < 0.001), dementia (ratio cortex/white matter left hemisphere: rBVc 1.152 vs 1.795, p < 0.001; right hemisphere: rBVc 1.396 vs 1.662, p < 0.05) and brain tumors (ratio cortex/whole tumor rBVc: 0.778 vs 0.919, p < 0.001 and ratio cortex/tumor hotspot rBVc: 0.529 vs 0.512, p < 0.05).

CONCLUSION

Despite a highly standardized workflow, parametric perfusion maps are depended on the chosen software. Radiologists should consider software related variances when using dynamic susceptibility contrast perfusion for clinical imaging and research.

ADVANCES IN KNOWLEDGE

This multicenter study compared perfusion parameters calculated by two commercial dynamic susceptibility contrast perfusion post-processing software solutions in different central nervous system disorders with a large sample size and a highly standardized processing workflow. Despite, parametric perfusion maps are depended on the chosen software which impacts clinical imaging and research.

Support vector machine-based classification of first episode drug-naïve schizophrenia patients and healthy controls using structural MRI

Although regional brain deficits have been demonstrated in schizophrenia patients by structural MRI studies, one important question that remains largely unanswered is whether the complex and subtle deficits revealed by MRI could be used as objective biomarkers to discriminate patients from healthy controls individually. To address this question, a total of 326 right-handed participants were recruited, including 163 drug-naïve first-episode schizophrenia (FES) patients and 163 demographically matched healthy controls. High-resolution anatomic data were acquired from all subjects and processed via Freesurfer software to obtain cortical thickness and surface area measurements. Subsequently, the Support Vector Machine (SVM) was used to explore the potential utility for cortical thickness and surface area measurements in the differentiation of individual patients and healthy controls. The accuracy of correct classification of patients and controls was 85.0% (specificity 87.0%, sensitivity 83.0%) for surface area and 81.8% (specificity 85.0%, sensitivity 76.9%) for cortical thickness (p<0.001 after permutation testing). Regions contributing to classification accuracy mainly included the gray matter in default mode, central executive, salience, and visual networks. Current findings, in a sample of never-treated FES patients, suggest that the patterns of illness-related gray matter changes has potential as a biomarker for identifying structural brain alterations in individuals with schizophrenia. Future prospective studies are needed to evaluate the utility of imaging biomarkers for research and potentially for clinical purpose.

Automated cortical thickness and skewness feature selection in bipolar disorder using a semi-supervised learning method

BACKGROUND

Bipolar disorder (BD) broadly affects brain structure, in particular areas involved in emotion processing and cognition. In the last years, the psychiatric field's interest in machine learning approaches has been steadily growing, thanks to the potentiality of automatically discriminating patients from healthy controls.

METHODS

In this work, we employed cortical thickness of 58 regions of interest obtained from magnetic resonance imaging scans of 41 BD patients and 34 healthy controls, to automatically identify the regions which are mostly involved with the disease. We used a semi-supervised method, addressing the criticisms on supervised methods, related to the fact that the diagnosis is not unaffected by uncertainty.

RESULTS

Our results confirm findings in previous studies, with a classification accuracy of about 75% when mean thickness and skewness of up to five regions are considered. We obtained that the parietal lobe and some areas in the temporal sulcus were the regions which were the most involved with BD.

LIMITATIONS

The major limitation of our work is the limited size or our dataset, but in line with other recent machine learning works in the field. Moreover, we considered chronic patients, whose brain characteristics may thus be affected.

CONCLUSIONS

The automatic selection of the brain regions most involved in BD may be of great importance when dealing with the pathogenesis of the disorder. Our method selected regions which are known to be involved with BD, indicating that damage to the identified areas can be considered as a marker of disease.

Machine learning in mental health: a scoping review of methods and applications

BACKGROUND

This paper aims to synthesise the literature on machine learning (ML) and big data applications for mental health, highlighting current research and applications in practice.

METHODS

We employed a scoping review methodology to rapidly map the field of ML in mental health. Eight health and information technology research databases were searched for papers covering this domain. Articles were assessed by two reviewers, and data were extracted on the article's mental health application, ML technique, data type, and study results. Articles were then synthesised via narrative review.

RESULTS

Three hundred papers focusing on the application of ML to mental health were identified. Four main application domains emerged in the literature, including: (i) detection and diagnosis; (ii) prognosis, treatment and support; (iii) public health, and; (iv) research and clinical administration. The most common mental health conditions addressed included depression, schizophrenia, and Alzheimer's disease. ML techniques used included support vector machines, decision trees, neural networks, latent Dirichlet allocation, and clustering.

CONCLUSIONS

Overall, the application of ML to mental health has demonstrated a range of benefits across the areas of diagnosis, treatment and support, research, and clinical administration. With the majority of studies identified focusing on the detection and diagnosis of mental health conditions, it is evident that there is significant room for the application of ML to other areas of psychology and mental health. The challenges of using ML techniques are discussed, as well as opportunities to improve and advance the field.

Gray matter differences between affective and non-affective first episode psychosis: A review of Magnetic Resonance Imaging studies: Special Section on "Translational and Neuroscience Studies in Affective Disorders" Section Editor, Maria Nobile MD, PhD. This Section of JAD focuses on the relevance of translational and neuroscience studies in providing a better understanding of the neural basis of affective disorders. The main aim is to briefly summaries relevant research findings in clinical neuroscience with particular regards to specific innovative topics in mood and anxiety disorders

BACKGROUND

Non-affective and affective psychoses are very common mental disorders. However, their neurobiological underpinnings are still poorly understood. Therefore, the goal of the present review was to evaluate structural Magnetic Resonance Imaging (MRI) studies exploring brain deficits in both non-affective (NA-FEP) and affective first episode psychosis (A-FEP).

METHODS

A bibliographic search on PUBMED of all MRI studies exploring gray matter (GM) differences between NA-FEP and A-FEP was conducted.

RESULTS

Overall, the results from the available evidence showed that the two diagnostic groups share common GM alterations in fronto-temporal regions and anterior cingulate cortex. In contrast, unique GM deficits have also been observed, with reductions in amygdala for A-FEP and in hippocampus and insula for NA-FEP.

LIMITATIONS

Few small MRI studies with heterogeneous methodology.

CONCLUSIONS

Although the evidences are far to be conclusive, they suggest the presence of common and distinct pattern of GM alterations in NA-FEP and A-FEP. Future larger longitudinal studies are needed to further characterize specific neural biomarkers in homogenous NA-FEP and A-FEP samples.

Artificial intelligence and machine learning in emergency medicine

Interest in artificial intelligence (AI) research has grown rapidly over the past few years, in part thanks to the numerous successes of modern machine learning techniques such as deep learning, the availability of large datasets and improvements in computing power. AI is proving to be increasingly applicable to healthcare and there is a growing list of tasks where algorithms have matched or surpassed physician performance. Despite the successes there remain significant concerns and challenges surrounding algorithm opacity, trust and patient data security. Notwithstanding these challenges, AI technologies will likely become increasingly integrated into emergency medicine in the coming years. This perspective presents an overview of current AI research relevant to emergency medicine.

The impact of psychosis on brain anatomy in bipolar disorder: A structural MRI study

BACKGROUND

Bipolar disorder (BD) is a major psychiatric illness characterized by heterogeneous symptoms including psychotic features. Up until now, neuroimaging studies investigating cerebral morphology in patients with BD have underestimated the potential impact of psychosis on brain anatomy in BD patients. In this regard, psychotic and non-psychotic BD may represent biologically different subtypes of the disorder, being possibly associated with specific cerebral features.

METHODS

In the present study, magnetic resonance imaging (MRI) at 3T was used to identify the neuroanatomical correlates of psychosis in an International sample of BD patients. A large sample of structural MRI data from healthy subjects (HC) and BD patients was collected across two research centers. Voxel based morphometry was used to compare gray matter (GM) volume among psychotic and non-psychotic BD patients and HC.

RESULTS

We found specific structural alterations in the two patient groups, more extended in the psychotic sample. Psychotic patients showed GM volume deficits in left frontal cortex compared to HC, and in right temporo-parietal cortex compared to both HC and non-psychotic patients (p < 0.001, > 100 voxels). Psychotic patients also exhibited enhanced age-related GM volume deficits in a set of subcortical and cortical regions.

LIMITATIONS

The integration of multiple datasets may have affected the results.

CONCLUSIONS

Overall, our results confirm the importance of classifying BD based on psychosis. The knowledge of the neuronal bases of psychotic symptomatology in BD can provide a more comprehensive picture of the determinants of BD, in the light of the continuum characteristic of major psychoses.

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.

Common and distinct structural features of schizophrenia and bipolar disorder: The European Network on Psychosis, Affective disorders and Cognitive Trajectory (ENPACT) study

INTRODUCTION

Although schizophrenia (SCZ) and bipolar disorder (BD) share elements of pathology, their neural underpinnings are still under investigation. Here, structural Magnetic Resonance Imaging (MRI) data collected from a large sample of BD and SCZ patients and healthy controls (HC) were analyzed in terms of gray matter volume (GMV) using both voxel based morphometry (VBM) and a region of interest (ROI) approach.

METHODS

The analysis was conducted on two datasets, Dataset1 (802 subjects: 243 SCZ, 176 BD, 383 HC) and Dataset2, a homogeneous subset of Dataset1 (301 subjects: 107 HC, 85 BD and 109 SCZ). General Linear Model analyses were performed 1) at the voxel-level in the whole brain (VBM study), 2) at the regional level in the anatomical regions emerged from the VBM study (ROI study). The GMV comparison across groups was integrated with the analysis of GMV correlates of different clinical dimensions.

RESULTS

The VBM results of Dataset1 showed 1) in BD compared to HC, GMV deficits in right cingulate, superior temporal and calcarine cortices, 2) in SCZ compared to HC, GMV deficits in widespread cortical and subcortical areas, 3) in SCZ compared to BD, GMV deficits in insula and thalamus (p<0.05, cluster family wise error corrected). The regions showing GMV deficits in the BD group were mostly included in the SCZ ones. The ROI analyses confirmed the VBM results at the regional level in most of the clusters from the SCZ vs. HC comparison (p<0.05, Bonferroni corrected). The VBM and ROI analyses of Dataset2 provided further evidence for the enhanced GMV deficits characterizing SCZ. Based on the clinical-neuroanatomical analyses, we cannot exclude possible confounding effects due to 1) age of onset and medication in BD patients, 2) symptoms severity in SCZ patients.

CONCLUSION

Our study reported both shared and specific neuroanatomical characteristics between the two disorders, suggesting more severe and generalized GMV deficits in SCZ, with a specific role for insula and thalamus.

A review of altered biochemistry in the anterior cingulate cortex of first-episode psychosis

Relevant biochemicals of the brain can be quantified in vivo, non-invasively, using proton Magnetic Resonance Spectroscopy (¹H MRS). This includes metabolites associated with neural general functioning, energetics, membrane phospholipid metabolism and neurotransmission. Moreover, there is substantial evidence of implication of the frontal and prefrontal areas in the pathogenesis of psychotic disorders such as schizophrenia. In particular, the anterior cingulate cortex (ACC) plays an important role in cognitive control of emotional and non-emotional processes. Thus the study of its extent of biochemistry dysfunction in the early stages of psychosis is of particular interest in gaining a greater understanding of its aetiology. In this review, we selected ¹H MRS studies focused on the ACC of first-episode psychosis (FEP). Four studies reported increased glutamatergic levels in FEP, while other four showed preserved concentrations. Moreover, findings on FEP do not fully mirror those in chronic patients. Due to conflicting findings, larger longitudinal ¹H MRS studies are expected to further explore glutamatergic neurotransmission in ACC of FEP in order to have a better understanding of the glutamatergic mechanisms underlying psychosis, possibly using ultra high field MR scanners.

Association of serum VEGF levels with prefrontal cortex volume in schizophrenia

A large body of evidence indicates alterations in brain regional cellular energy metabolism and blood flow in schizophrenia. Among the different molecules regulating blood flow, vascular endothelial growth factor (VEGF) is generally accepted as the major factor involved in the process of angiogenesis. In the present study, we examined whether peripheral VEGF levels correlate with changes in the prefrontal cortex (PFC) volume in patients with schizophrenia and in healthy controls. Whole-blood samples were obtained from 96 people with schizophrenia or schizoaffective disorder and 83 healthy controls. Serum VEGF protein levels were analyzed by enzyme-linked immunosorbent assay, whereas quantitative PCR was performed to measure interleukin-6 (IL-6, a pro-inflammatory marker implicated in schizophrenia) mRNA levels in the blood samples. Structural magnetic resonance imaging scans were obtained using a 3T Achieva scanner on a subset of 59 people with schizophrenia or schizoaffective disorder and 65 healthy controls, and prefrontal volumes were obtained using FreeSurfer software. As compared with healthy controls, individuals with schizophrenia had a significant increase in log-transformed mean serum VEGF levels (t(177)=2.9, P=0.005). A significant inverse correlation (r=-0.40, P=0.002) between serum VEGF and total frontal pole volume was found in patients with schizophrenia/schizoaffective disorder. Moreover, we observed a significant positive association (r=0.24, P=0.03) between serum VEGF and IL-6 mRNA levels in patients with schizophrenia. These findings suggest an association between serum VEGF and inflammation, and that serum VEGF levels are related to structural abnormalities in the PFC of people with schizophrenia.

Classification of first-episode psychosis in a large cohort of patients using support vector machine and multiple kernel learning techniques

First episode psychosis (FEP) patients are of particular interest for neuroimaging investigations because of the absence of confounding effects due to medications and chronicity. Nonetheless, imaging data are prone to heterogeneity because for example of age, gender or parameter setting differences. With this work, we wanted to take into account possible nuisance effects of age and gender differences across dataset, not correcting the data as a pre-processing step, but including the effect of nuisance covariates in the classification phase. To this aim, we developed a method which, based on multiple kernel learning (MKL), exploits the effect of these confounding variables with a subject-depending kernel weighting procedure. We applied this method to a dataset of cortical thickness obtained from structural magnetic resonance images (MRI) of 127 FEP patients and 127 healthy controls, who underwent either a 3Tesla (T) or a 1.5T MRI acquisition. We obtained good accuracies, notably better than those obtained with standard SVM or MKL methods, up to more than 80% for frontal and temporal areas. To our best knowledge, this is the largest classification study in FEP population, showing that fronto-temporal cortical thickness can be used as a potential marker to classify patients with psychosis.