Brain ventricular volume changes in schizophrenia. A narrative review

Machine learning in prenatal MRI predicts postnatal ventricular abnormalities in fetuses with isolated ventriculomegaly

OBJECTIVES

To evaluate the intracranial structures and brain parenchyma radiomics surrounding the occipital horn of the lateral ventricle in normal fetuses (NFs) and fetuses with ventriculomegaly (FVs), as well as to predict postnatally enlarged lateral ventricle alterations in FVs.

METHODS

Between January 2014 and August 2023, 141 NFs and 101 FVs underwent 1.5 T balanced steady-state free precession (BSSFP), including 68 FVs with resolved lateral ventricles (FVM-resolved) and 33 FVs with stable lateral ventricles (FVM-stable). Demographic data and intracranial structures were analyzed. To predict the enlarged ventricle alterations of FVs postnatally, logistic regression models with 5-fold cross-validation were developed based on lateral ventricle morphology, blended-cortical or/and subcortical radiomics characteristics. Validation of the models' performance was conducted using the receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA).

RESULTS

Significant alterations in cerebral structures were observed between NFs and FVs (p < 0.05), excluding the maximum frontal horn diameter (FD). However, there was no notable distinction between the FVM-resolved and FVM-stable groups (all p > 0.05). Based on subcortical-radiomics on the aberrant sides of FVs, this approach exhibited high efficacy in distinguishing NFs from FVs in the training/validation set, yielding an impressive AUC of 1/0.992. With an AUC value of 0.822/0.743 in the training/validation set, the Subcortical-radiomics model demonstrated its ability to predict lateral ventricle alterations in FVs, which had the greatest predictive advantages indicated by DCA.

CONCLUSIONS

Microstructural alterations in subcortical parenchyma associated with ventriculomegaly can serve as predictive indicators for postnatal lateral ventricle variations in FVs.

CLINICAL RELEVANCE STATEMENT

It is critical to gain pertinent information from a solitary fetal MRI to anticipate postnatal lateral ventricle alterations in fetuses with ventriculomegaly. This approach holds the potential to diminish the necessity for recurrent prenatal ultrasound or MRI examinations.

KEY POINTS

Fetal ventriculomegaly is a dynamic condition that affects postnatal neurodevelopment. Machine learning and subcortical-radiomics can predict postnatal alterations in the lateral ventricle. Machine learning, applied to single-fetal MRI, might reduce required antenatal testing.

Unveiling Promising Neuroimaging Biomarkers for Schizophrenia Through Clinical and Genetic Perspectives

Schizophrenia is a complex and serious brain disorder. Neuroscientists have become increasingly interested in using magnetic resonance-based brain imaging-derived phenotypes (IDPs) to investigate the etiology of psychiatric disorders. IDPs capture valuable clinical advantages and hold biological significance in identifying brain abnormalities. In this review, we aim to discuss current and prospective approaches to identify potential biomarkers for schizophrenia using clinical multimodal neuroimaging and imaging genetics. We first described IDPs through their phenotypic classification and neuroimaging genomics. Secondly, we discussed the applications of multimodal neuroimaging by clinical evidence in observational studies and randomized controlled trials. Thirdly, considering the genetic evidence of IDPs, we discussed how can utilize neuroimaging data as an intermediate phenotype to make association inferences by polygenic risk scores and Mendelian randomization. Finally, we discussed machine learning as an optimum approach for validating biomarkers. Together, future research efforts focused on neuroimaging biomarkers aim to enhance our understanding of schizophrenia.

Longitudinal assessment of ventricular volume trajectories in early-stage schizophrenia: evidence of both enlargement and shrinkage

BACKGROUND

Lateral ventricular enlargement represents a canonical morphometric finding in chronic patients with schizophrenia; however, longitudinal studies elucidating complex dynamic trajectories of ventricular volume change during critical early disease stages are sparse.

METHODS

We measured lateral ventricular volumes in 113 first-episode schizophrenia patients (FES) at baseline visit (11.7 months after illness onset, SD = 12.3) and 128 age- and sex-matched healthy controls (HC) using 3T MRI. MRI was then repeated in both FES and HC one year later.

RESULTS

Compared to controls, ventricular enlargement was identified in 18.6% of patients with FES (14.1% annual ventricular volume (VV) increase; 95%CI: 5.4; 33.1). The ventricular expansion correlated with the severity of PANSS-negative symptoms at one-year follow-up (p = 0.0078). Nevertheless, 16.8% of FES showed an opposite pattern of statistically significant ventricular shrinkage during ≈ one-year follow-up (-9.5% annual VV decrease; 95%CI: -23.7; -2.4). There were no differences in sex, illness duration, age of onset, duration of untreated psychosis, body mass index, the incidence of Schneiderian symptoms, or cumulative antipsychotic dose among the patient groups exhibiting ventricular enlargement, shrinkage, or no change in VV.

CONCLUSION

Both enlargement and ventricular shrinkage are equally present in the early stages of schizophrenia. The newly discovered early reduction of VV in a subgroup of patients emphasizes the need for further research to understand its mechanisms.

Volume alterations of the hippocampus and amygdala in patients with schizophrenia and persistent auditory hallucinations

INTRODUCTION

Auditory hallucinations (AH) are one of the most prevalent symptoms of schizophrenia. They might cause several brain alterations, especially changes in the volumes of hippocampus and amygdala, regions related to the relay and processing of auditory cues and emotional memories.

MATERIAL AND METHODS

We have recruited 41 patients with schizophrenia and persistent AH, 35 patients without AH, and 55 healthy controls. Using their MRIs, we have performed semiautomatic segmentations of the hippocampus and amygdala using Freesurfer. We have also performed bilateral correlations between the total PSYRATS score and the volumes of affected subregions and nuclei.

RESULTS

In the hippocampus, we found bilateral increases in the volume of its hippocampal fissure and decreases in the right fimbria in patients with and without AH. The volume of the right hippocampal tail and left head of the granule cell layer from the dentate gyrus were decreased in patients with AH. In the amygdala, we found its left total volume was shrunk, and there was a decrease of its left accessory basal nucleus in patients with AH.

CONCLUSIONS

We have detected volume alterations of different limbic structures likely due to the presence of AH. The volumes of the right hippocampal tail and left head of the granule cell layer from the dentate gyrus, and total volume of the amygdala and its accessory basal nucleus, were only affected in patients with AH. Bilateral volume alterations in the hippocampal fissure and right fimbria seem inherent of schizophrenia and due to traits not contemplated in our research.

Constructing the Schizophrenia Recognition Method Employing GLCM Features from Multiple Brain Regions and Machine Learning Techniques

Accurately diagnosing schizophrenia, a complex psychiatric disorder, is crucial for effectively managing the treatment process and methods. Various types of magnetic resonance (MR) images have the potential to serve as biomarkers for schizophrenia. The aim of this study is to numerically analyze differences in the textural characteristics that may occur in the bilateral amygdala, caudate, pallidum, putamen, and thalamus regions of the brain between individuals with schizophrenia and healthy controls via structural MR images. Towards this aim, Gray Level Co-occurence Matrix (GLCM) features obtained from five regions of the right, left, and bilateral brain were classified using machine learning methods. In addition, it was analyzed in which hemisphere these features were more distinctive and which method among Adaboost, Gradient Boost, eXtreme Gradient Boosting, Random Forest, k-Nearest Neighbors, Linear Discriminant Analysis (LDA), and Naive Bayes had higher classification success. When the results were examined, it was demonstrated that the GLCM features of these five regions in the left hemisphere could be classified as having higher performance in schizophrenia compared to healthy individuals. Using the LDA algorithm, classification success was achieved with a 100% AUC, 94.4% accuracy, 92.31% sensitivity, 100% specificity, and an F1 score of 91.9% in healthy and schizophrenic individuals. Thus, it has been revealed that the textural characteristics of the five predetermined regions, instead of the whole brain, are an important indicator in identifying schizophrenia.

Brain ventricles as windows into brain development and disease

Dilation of the fluid-filled cerebral ventricles (ventriculomegaly) characterizes hydrocephalus and is frequently seen in autism and schizophrenia. Recent work suggests that the genomic study of congenital hydrocephalus may be unexpectedly fertile ground for revealing insights into neural stem cell regulation, human cerebrocortical development, and pathogenesis of neuropsychiatric disease.