Sexually dimorphic subcortical brain volumes in emerging psychosis

Based on neural network cascade abnormal texture information dissemination of classification of patients with schizophrenia and depression

This study used MRI brain image segmentation to identify novel magnetic resonance imaging (MRI) biomarkers to distinguish patients with schizophrenia (SCZ), major depressive disorder (MD), and healthy control (HC). Brain texture measurements, including entropy and contrast, were calculated to capture variability in adjacent MRI voxel intensity. These measures are then applied to group classification in deep learning techniques and combined with hierarchical correlations to locate results. Texture feature maps were extracted from segmented brain MRI scans of 141 patients with schizophrenia (SCZ), 103 patients with major depressive disorder (MD) and 238 healthy controls (HC). Gray scale coassociation matrix (GLCM) is a monomer matrix calculated in a voxel cube. Deep learning methods were evaluated to determine the application capability of texture feature mapping in binary classification (SCZ vs. HC, MD vs. HC, SCZ vs. MD). The method is implemented by repeated nesting and cross-validation for feature selection. Regions that show the highest correlation (positive or negative). In this study, the authors successfully classified SCZ, MD and HC. This suggests that texture analysis can be used as an effective feature extraction method to distinguish different disease states. Compared with other methods, texture analysis can capture richer image information and improve classification accuracy in some cases. The classification accuracy of SCZ and HC, MD and HC, SCZ and MD reached 84.6%, 86.4% and 76.21%, respectively. Among them, SCZ and HC are the most significant features with high sensitivity and specificity.

A Causal Analysis of the Effect of Age and Sex Differences on Brain Atrophy in the Elderly Brain

We studied how brain volume loss in old age is affected by age, the APOE gene, sex, and the level of education completed. The quantitative characterization of brain volume loss at an old age relative to a young age requires-at least in principle-two MRI scans, one performed at a young age and one at an old age. There is, however, a way to address this problem when having only one MRI scan obtained at an old age. We computed the total brain losses of elderly subjects as a ratio between the estimated brain volume and the estimated total intracranial volume. Magnetic resonance imaging (MRI) scans of 890 healthy subjects aged 70 to 85 years were assessed. A causal analysis of factors affecting brain atrophy was performed using probabilistic Bayesian modelling and the mathematics of causal inference. We found that both age and sex were causally related to brain atrophy, with women reaching an elderly age with a 1% larger brain volume relative to their intracranial volume than men. How the brain ages and the rationale for sex differences in brain volume losses during the adult lifespan are questions that need to be addressed with causal inference and empirical data. The graphical causal modelling presented here can be instrumental in understanding a puzzling scientific area of study-the biological aging of the brain.

Psychosis in Women: Time for Personalized Treatment

Early detection and prompt treatment of psychosis is of the utmost importance. The great variability in clinical onset, illness course, and response to pharmacological and psychosocial treatment is in great part gender-related. Our aim has been to review narratively the literature focusing on gender related differences in the psychoses, i.e., schizophrenia spectrum disorders. We searched the PubMed/Medline, Scopus, Embase, and ScienceDirect databases on 31 July 2021, focusing on recent research regarding sex differences in early psychosis. Although women, compared to men, tend to have better overall functioning at psychotic symptom onset, they often present with more mood symptoms, may undergo misdiagnosis and delay in treatment and are at a higher risk for antipsychotic drug-induced metabolic and endocrine-induced side effects. Furthermore, women with schizophrenia spectrum disorders have more than double the odds of having physical comorbidities than men. Tailored treatment plans delivered by healthcare services should consider gender differences in patients with a diagnosis of psychosis, with a particular attention to early phases of disease in the context of the staging model of psychosis onset.

Identification of voxel-based texture abnormalities as new biomarkers for schizophrenia and major depressive patients using layer-wise relevance propagation on deep learning decisions

Non-segmented MRI brain images are used for the identification of new Magnetic Resonance Imaging (MRI) biomarkers able to differentiate between schizophrenic patients (SCZ), major depressive patients (MD) and healthy controls (HC). Brain texture measures such as entropy and contrast, capturing the neighboring variation of MRI voxel intensities, were computed and fed into deep learning technique for group classification. Layer-wise relevance was applied for the localization of the classification results. Texture feature map of non-segmented brain MRI scans were extracted from 141 SCZ, 103 MD and 238 HC. The gray level co-occurrence matrix (GLCM) was calculated on a voxel-by-voxel basis in a cube of voxels. Deep learning tested if texture feature map could predict diagnostic group membership of three classes under a binary classification (SCZ vs. HC, MD vs. HC, SCZ vs. MD). The method was applied in a repeated nested cross-validation scheme and cross-validated feature selection. The regions with the highest relevance (positive/negative) are presented. The method was applied on non-segmented images reducing the computation complexity and the error associated with segmentation process.

Intra- and interhemispheric symmetry of subcortical brain structures: a volumetric analysis in the aging human brain

Here, we address the hemispheric interdependency of subcortical structures in the aging human brain. In particular, we investigated whether subcortical volume variations can be explained by the adjacency of structures in the same hemisphere or are due to the interhemispheric development of mirror subcortical structures in the brain. Seven subcortical structures in each hemisphere were automatically segmented in a large sample of 3312 magnetic resonance imaging (MRI) studies of elderly individuals in their 70s and 80s. We performed Eigenvalue analysis, and found that anatomic volumes in the limbic system and basal ganglia show similar statistical dependency whether considered in the same hemisphere (intrahemispherically) or different hemispheres (interhemispherically). Our results indicate that anatomic bilaterality of subcortical volumes is preserved in the aging human brain, supporting the hypothesis that coupling between non-adjacent subcortical structures might act as a mechanism to compensate for the deleterious effects of aging.

Lower Hippocampal Volume in Patients with Schizophrenia and Bipolar Disorder: A Quantitative MRI Study

Since patients with schizophrenia (SZ) and bipolar disorder (BD) share many biological features, detecting biomarkers that differentiate SZ and BD patients is crucial for optimized treatments. High-resolution magnetic resonance imaging (MRI) is suitable for detecting subtle brain structural differences in patients with psychiatric disorders. In the present study, we adopted a neuroanatomically defined and manually delineated region of interest (ROI) method to evaluate the amygdalae, hippocampi, Heschl’s gyrus (HG), and planum temporale (PT), because these regions are crucial in the development of SZ and BD. ROI volumes were measured using high resolution MRI in 31 healthy subjects (HS), 23 SZ patients, and 21 BD patients. Right hippocampal volumes differed significantly among groups (HS > BD > SZ), whereas left hippocampal volumes were lower in SZ patients than in HS and BD patients (HS = BD > SZ). Volumes of the amygdalae, HG, and PT did not differ among the three groups. For clinical correlations, there were no significant associations between ROI volumes and demographics/clinical symptoms. Our study revealed significant lower hippocampal volume in patients with SZ and BD, and we suggest that the right hippocampal volume is a potential biomarker for differentiation between SZ and BD.

Beta-band modulation in the human hippocampus during a conflict response task

Objective Identify the role of beta-band (13-30 Hz) power modulation in the human hippocampus during conflict processing. Approach We investigated changes in the spectral power of the beta band (13-30 Hz) as measured by depth electrode leads in the hippocampus during a modified Stroop task in six patients with medically-refractory epilepsy. Previous work done with direct electrophysiological recordings in humans has shown hippocampal theta-band (3-8 Hz) modulation during conflict processing. Local field potentials (LFP) sampled at 2k Hz were used for analysis and a non-parametric cluster-permutation t-test was used to identify the time period and frequency ranges of significant power change during cue processing (i.e. post-stimulus, pre-response). Main Results In five of the six patients, we observe a statistically significant increase in hippocampal beta-band power during successful conflict processing in the incongruent trial condition (cluster-based correction for multiple comparisons, p < 0.05). There was no significant beta-band power change observed during the cue processing period of the congruent condition in the hippocampus of these patients. Significance The beta-power changes during conflict processing represented here are consistent with previous studies suggesting that the hippocampus plays a role in conflict processing, but it is the first time that the beta band has been shown to be involved in humans with direct electrophysiological evidence. We propose that beta-band modulation plays a role in successful conflict detection and automatic response inhibition in the human hippocampus as studied during a conflict response task.

Influence of BDNF and MTHFR polymorphisms on hippocampal volume in first-episode psychosis

BACKGROUND

The BDNF and MTHFR genes are independently linked to the pathogenesis of schizophrenia and its neuroimaging correlates. The aim of this study was to explore, for the first time, the individual and interactional effects of the Val66Met and C677T polymorphisms on hippocampal atrophy in first-episode psychosis (FEP).

METHOD

Multi-site case-control study based on clinical, genetic (rs 6265, rs 1801133) and structural magnetic resonance imaging data from 98 non-affective FEP patients and 117 matched healthy controls (HC). Hippocampal volume was estimated using FreeSurfer software and this volume was compared between diagnostic (FEP vs HC) and genotype (Val66Met, C677T) groups. The BDNF Val66Met x MTHFR C677T effect on hippocampal volume was further evaluated through stratified analyses.

RESULTS

After applying Bonferroni correction, diagnosis showed a significant effect for adjusted left and right hippocampal volume (FEP < HC). Stratified analyses showed that the interactive effect contributed to adjusted hippocampal size in both the HC (left and right hippocampus) and FEP groups (right hippocampus); among BDNF Met carriers, those with the CT-TT genotype exhibited decreased hippocampal volume compared to individuals with the homozygous normal CC genotype.

CONCLUSIONS

Our results provide preliminary evidence indicating that the Val66Met x C677T interaction may be a potential genetic risk factor for reduced hippocampal size in both healthy controls and in patients with FEP. Further research in independent samples including different ethnic groups is warranted to confirm this new finding.

Deep learning identifies morphological determinants of sex differences in the pre-adolescent brain

The application of data-driven deep learning to identify sex differences in developing brain structures of pre-adolescents has heretofore not been accomplished. Here, the approach identifies sex differences by analyzing the minimally processed MRIs of the first 8144 participants (age 9 and 10 years) recruited by the Adolescent Brain Cognitive Development (ABCD) study. The identified pattern accounted for confounding factors (i.e., head size, age, puberty development, socioeconomic status) and comprised cerebellar (corpus medullare, lobules III, IV/V, and VI) and subcortical (pallidum, amygdala, hippocampus, parahippocampus, insula, putamen) structures. While these have been individually linked to expressing sex differences, a novel discovery was that their grouping accurately predicted the sex in individual pre-adolescents. Another novelty was relating differences specific to the cerebellum to pubertal development. Finally, we found that reducing the pattern to a single score not only accurately predicted sex but also correlated with cognitive behavior linked to working memory. The predictive power of this score and the constellation of identified brain structures provide evidence for sex differences in pre-adolescent neurodevelopment and may augment understanding of sex-specific vulnerability or resilience to psychiatric disorders and presage sex-linked learning disabilities.

Hippocampal subfield volumes and change in body mass over 12 months of treatment in first-episode schizophrenia spectrum disorders

In this study, we explored the relationship between baseline hippocampal subfield volumes and change in body mass over 12 months of treatment in 90 first-episode schizophrenia spectrum disorder patients (66 males, 24 females; mean age= 24.7 ± 6.8 years). Body mass index was assessed in patients at baseline, and at months 3, 6, 9 and 12. Hippocampal subfields of interest were assessed at baseline using a segmentation algorithm included in the FreeSurfer 6.0 software program. Linear regression revealed a significant interactive effect between sex and anterior hippocampus size as predictors of change in body mass over 12 months, adjusting for age, substance use, and treatment duration. In an exploratory post-hoc sub-analysis, partial correlations showed a significant association between weight gain and smaller CA1, CA3 and subiculum volumes in females, but not males, adjusting for age and substance use, with similar trends evident for the CA4 and presubiculum subfields. In conclusion, our findings suggest that smaller anterior hippocampal subfields at baseline are associated with the development of weight gain over the course of treatment in first-episode schizophrenia spectrum disorders in a sex-specific fashion. This may be related to the greater increase in body mass evident for female patients in our study.

Sex and gender differences in schizophrenic psychoses-a critical review

Many sex and gender differences in schizophrenic psychoses have been reported, but few have been soundly replicated. A stable finding is the later age of onset in women compared to men. Gender differences in symptomatology, comorbidity, and neurocognition seem to reflect findings in the general population. There is increasing evidence for estrogens being psychoprotective in women and for hypothalamic-pituitary-gonadal dysfunction in both sexes.More methodologically sound, longitudinal, multi-domain, interdisciplinary research investigating both sex (biological) and gender (psychosocial) factors is required to better understand the different pathogenesis and etiologies of schizophrenic psychoses in women and men, thereby leading to better tailored treatments and improved outcomes.