Genetic architecture of brain morphology and overlap with neuropsychiatric traits.

Uncovering the genetic architectures of brain morphology offers valuable insights into brain development and disease. Genetic association studies of brain morphological phenotypes have discovered thousands of loci. However, interpretation of these loci presents a significant challenge. One potential solution is exploring the genetic overlap between brain morphology and disorders, which can improve our understanding of their complex relationships, ultimately aiding in clinical applications. In this review, we examine current evidence on the genetic associations between brain morphology and neuropsychiatric traits. We discuss the impact of these associations on the diagnosis, prediction, and treatment of neuropsychiatric diseases, along with suggestions for future research directions.

Uncovering neural substrates across Alzheimer's disease stages using contrastive variational autoencoder.

Alzheimer's disease is the most common major neurocognitive disorder. Although currently, no cure exists, understanding the neurobiological substrate underlying Alzheimer's disease progression will facilitate early diagnosis and treatment, slow disease progression, and improve prognosis. In this study, we aimed to understand the morphological changes underlying Alzheimer's disease progression using structural magnetic resonance imaging data from cognitively normal individuals, individuals with mild cognitive impairment, and Alzheimer's disease via a contrastive variational autoencoder model. We used contrastive variational autoencoder to generate synthetic data to boost the downstream classification performance. Due to the ability to parse out the nonclinical factors such as age and gender, contrastive variational autoencoder facilitated a purer comparison between different Alzheimer's disease stages to identify the pathological changes specific to Alzheimer's disease progression. We showed that brain morphological changes across Alzheimer's disease stages were significantly associated with individuals' neurofilament light chain concentration, a potential biomarker for Alzheimer's disease, highlighting the biological plausibility of our results.

Sex-related cortical asymmetry in antipsychotic-naïve first-episode schizophrenia.

Schizophrenia has been considered to exhibit sex-related clinical differences that might be associated with distinctly abnormal brain asymmetries between sexes. One hundred and thirty-two antipsychotic-naïve first-episode patients with schizophrenia and 150 healthy participants were recruited in this study to investigate whether cortical asymmetry would exhibit sex-related abnormalities in schizophrenia. After a 1-yr follow-up, patients were rescanned to obtain the effect of antipsychotic treatment on cortical asymmetry. Male patients were found to show increased lateralization index while female patients were found to exhibit decreased lateralization index in widespread regions when compared with healthy participants of the corresponding sex. Specifically, the cortical asymmetry of male and female patients showed contrary trends in the cingulate, orbitofrontal, parietal, temporal, occipital, and insular cortices. This result suggested male patients showed a leftward shift of asymmetry while female patients showed a rightward shift of asymmetry in these above regions that related to language, vision, emotion, and cognition. Notably, abnormal lateralization indices remained stable after antipsychotic treatment. The contrary trends in asymmetry between female and male patients with schizophrenia together with the persistent abnormalities after antipsychotic treatment suggested the altered brain asymmetries in schizophrenia might be sex-related disturbances, intrinsic, and resistant to the effect of antipsychotic therapy.

Impact of ectopic fat on brain structure and cognitive function: A systematic review and meta-analysis from observational studies.

Ectopic fat, defined as a specific organ or compartment with the accumulation of fat tissue surrounding organs, is highly associated with obesity which has been identified as a risk factor for cognitive impairment and dementia. However, the relationship between ectopic fat and changes in brain structure or cognition is yet to be elucidated. Here, we investigated the effects of ectopic fat on brain structure and cognitive function via systemic review and meta-analysis. A total of 21 studies were included from electronic databases up to July 9, 2022. We found ectopic fat was associated with decreased total brain volumeand increased lateral ventricle volume. In addition, ectopic was associated with decreased cognitive scores and negatively correlated with cognitive function. More specifically, dementia development were correlated with increased levels of visceral fat. Overall, our data suggested that increased ectopic fat was associated with prominent structural changes in the brain and cognitive decline, an effect driven mainly by increases in visceral fat, while subcutaneous fat may be protective. Our results suggest that patients with increased visceral fat are at risk of developing cognitive impairment and, therefore, represent a subset of population in whom appropriate and timely preventive measures could be implemented.

Temporal changes in brain morphology related to inflammation and schizophrenia: an omnigenic Mendelian randomization study.

BACKGROUND: Over the past several decades, more research focuses have been made on the inflammation/immune hypothesis of schizophrenia. Building upon synaptic plasticity hypothesis, inflammation may contribute the underlying pathophysiology of schizophrenia. Yet, pinpointing the specific inflammatory agents responsible for schizophrenia remains a complex challenge, mainly due to medication and metabolic status. Multiple lines of evidence point to a wide-spread genetic association across genome underlying the phenotypic variations of schizophrenia. METHOD: We collected the latest genome-wide association analysis (GWAS) summary data of schizophrenia, cytokines, and longitudinal change of brain. We utilized the omnigenic model which takes into account all genomic SNPs included in the GWAS of trait, instead of traditional Mendelian randomization (MR) methods. We conducted two round MR to investigate the inflammatory triggers of schizophrenia and the resulting longitudinal changes in the brain. RESULTS: We identified seven inflammation markers linked to schizophrenia onset, which all passed the Bonferroni correction for multiple comparisons (bNGF, GROA(CXCL1), IL-8, M-CSF, MCP-3 (CCL7), TNF-ß, CRP). Moreover, CRP were found to significantly influence the linear rate of brain morphology changes, predominantly in the white matter of the cerebrum and cerebellum. CONCLUSION: With an omnigenic approach, our study sheds light on the immune pathology of schizophrenia. Although these findings need confirmation from future studies employing different methodologies, our work provides substantial evidence that pervasive, low-level neuroinflammation may play a pivotal role in schizophrenia, potentially leading to notable longitudinal changes in brain morphology.

Comparative Brain Morphology of Cleaning and Sponge-Dwelling Elacatinus Gobies.

INTRODUCTION: Comparative studies of brain anatomy between closely related species have been very useful in demonstrating selective changes in brain structure. Within-species comparisons can be particularly useful for identifying changes in brain structure caused by contrasting environmental selection pressures. Here, we aimed to understand whether differences within and between species in habitat use and foraging behaviour influence brain morphology, on both ecological and evolutionary time scales. METHODS: We used as a study model three species of the Elacatinus genus that differ in their habitat-foraging mode. The obligatory cleaning goby Elacatinus evelynae inhabits mainly corals and feeds mostly on ectoparasites removed from larger fish during cleaning interactions. In contrast, the obligatory sponge-dwelling goby Elacatinus chancei inhabits tubular sponges and feeds on microinvertebrates buried in the sponges' tissues. Finally, in the facultatively cleaning goby Elacatinus prochilos, individuals can adopt either phenotype, the cleaning or the sponge-dwelling habitat-foraging mode. By comparing the brains of the facultative goby phenotypes to the brains of the obligatory species we can test whether brain morphology is better predicted by phylogenetic relatedness or the habitat-foraging modes (cleaning × sponge dwelling). RESULTS: We found that E. prochilos brains from both types (cleaning and sponge dwelling) were highly similar to each other. Their brains were in general more similar to the brains of the most closely related species, E. evelynae (obligatory cleaning species), than to the brains of E. chancei (sponge-dwelling species). In contrast, we found significant brain structure differences between the cleaning species (E. evelynae and E. prochilos) and the sponge-dwelling species (E. chancei). These differences revealed independent changes in functionally correlated brain areas that might be ecologically adaptive. E. evelynae and E. prochilos had a relatively larger visual input processing brain axis and a relatively smaller lateral line input processing brain axis than E. chancei. CONCLUSION: The similar brain morphology of the two types of E. prochilos corroborates other studies showing that individuals of both types can be highly plastic in their social and foraging behaviours. Our results in the Elacatinus species suggest that morphological adaptations of the brain are likely to be found in specialists whereas species that are more flexible in their habitat may only show behavioural plasticity without showing anatomical differences.

Adherence to unhealthy diets is associated with altered frontal gamma-aminobutyric acid and glutamate concentrations and grey matter volume: preliminary findings.

OBJECTIVES: Common mental disorders (CMD) are associated with impaired frontal excitatory/inhibitory (E/I) balance and reduced grey matter volume (GMV). Larger GMV (in the areas that are implicated in CMD-pathology) and improved CMD-symptomatology have been observed in individuals who adhere to high quality diets. Moreover, preclinical studies have shown altered neurometabolites (primarily gamma-aminobutyric acid: GABA and glutamate: GLU) in relation to diet quality. However, neurochemical correlates of diet quality and how these neurobiological changes are associated with CMD and with its transdiagnostic factor, rumination, is unknown in humans. Therefore, in this study, we examined the associations between diet quality and frontal cortex neuro-chemistry and structure, as well as CMD and rumination in humans. METHODS: Thirty adults were classified into high and low diet quality groups and underwent 1H-MRS to measure medial prefrontal cortex (mPFC) metabolite concentrations and volumetric imaging to measure GMV. RESULTS: Low (vs High) diet quality group had reduced mPFC-GABA and elevated mPFC-GLU concentrations, as well as reduced right precentral gyrus (rPCG) GMV. However, CMD and rumination were not associated with diet quality. Notably, we observed a significant negative correlation between rumination and rPCG-GMV and a marginally significant association between rumination and mPFC-GLU concentrations. There was also a marginally significant association between mPFC-GLU concentrations and rPCG-GMV. DISCUSSION: Adhering to unhealthy dietary patterns may be associated with compromised E/I balance, and this could affect GMV, and subsequently, rumination.

3D Tortuosity computation as a shape descriptor and its application to brain structure analysis.

In this study, we propose a novel method for quantifying tortuosity in 3D voxelized objects. As a shape characteristic, tortuosity has been widely recognized as a valuable feature in image analysis, particularly in the field of medical imaging. Our proposed method extends the two-dimensional approach of the Slope Chain Code (SCC) which creates a one-dimensional representation of curves. The utility of 3D tortuosity ( τ 3 D ) as a shape descriptor was investigated by characterizing brain structures. The results of the τ 3 D computation on the central sulcus and the main lobes revealed significant differences between Alzheimer's disease (AD) patients and control subjects, suggesting its potential as a biomarker for AD. We found a p < 0.05 for the left central sulcus and the four brain lobes.

Risk factor patterns define social anxiety subtypes in adolescents with brain and clinical feature differences.

Social anxiety disorder (SAD) is one of the most common psychiatric disorders in adolescents. The heterogeneity of both symptoms and etiology is an essential source of difficulties in the treatment and prevention of SAD. The study aimed to identify subtypes of adolescent SAD based on etiology-related phenotype dimensions and examine symptom and brain associations of the subtypes. We used a deeply phenotyped sample (47 phenotype subscales from 13 measures) of adolescents with SAD (n = 196) and healthy controls (n = 109) to extract etiology-relevant risk factors, based on which we identified subtypes of SAD. We compared the subtypes on clinical characteristics and brain morphometrics and functional connectivity, and examined subtype-specific links between risk factors, brain aberrance, and clinical characteristics. We identified six etiology-relevant risk factors and two subtypes of adolescent SAD. One subtype showed mainly elevated negative emotionality trait and coping style and diminished positive emotionality trait and coping style, while the other additionally had significantly high environmental risk factors, more severe impairments in social functioning, and significant abnormalities in brain structure and function. There were subtype-specific links between the risk factor profiles, brain aberrance, and clinical characteristics. The finding suggests two etiology-based subtypes of adolescent SAD, providing novel insights to the diversity of pathological pathways and precise intervention strategies.

Deep learning classification of reading disability with regional brain volume features.

Developmental reading disability is a prevalent and often enduring problem with varied mechanisms that contribute to its phenotypic heterogeneity. This mechanistic and phenotypic variation, as well as relatively modest sample sizes, may have limited the development of accurate neuroimaging-based classifiers for reading disability, including because of the large feature space of neuroimaging datasets. An unsupervised learning model was used to reduce deformation-based data to a lower-dimensional manifold and then supervised learning models were used to classify these latent representations in a dataset of 96 reading disability cases and 96 controls (mean age: 9.86 ± 1.56 years). A combined unsupervised autoencoder and supervised convolutional neural network approach provided an effective classification of cases and controls (accuracy: 77%; precision: 0.75; recall: 0.78). Brain regions that contributed to this classification accuracy were identified by adding noise to the voxel-level image data, which showed that reading disability classification accuracy was most influenced by the superior temporal sulcus, dorsal cingulate, and lateral occipital cortex. Regions that were most important for the accurate classification of controls included the supramarginal gyrus, orbitofrontal, and medial occipital cortex. The contribution of these regions reflected individual differences in reading-related abilities, such as non-word decoding or verbal comprehension. Together, the results demonstrate an optimal deep learning solution for classification using neuroimaging data. In contrast with standard mass-univariate test results, results from the deep learning model also provided evidence for regions that may be specifically affected in reading disability cases.

The association between genetic variations and morphology-based brain networks changes in Alzheimer's disease.

Alzheimer's disease (AD) is a highly heritable disease. The morphological changes of cortical cortex (such as, cortical thickness and surface area) in AD always accompany by the change of the functional connectivity to other brain regions and influence the short- and long-range brain network connections, causing functional deficits of AD. In this study, the first hypothesis is that genetic variations might affect morphology-based brain networks, leading to functional deficits; the second hypothesis is that protein-protein interaction (PPI) between the candidate proteins and known interacting proteins to AD might exist and influence AD. 600 470 variants and structural magnetic resonance imaging scans from 175 AD patients and 214 healthy controls were obtained from the Alzheimer's Disease Neuroimaging Initiative-1 database. A co-sparse reduced-rank regression model was fit to study the relationship between non-synonymous mutations and morphology-based brain networks. After that, PPIs between selected genes and BACE1, an enzyme that was known to be related to AD, are explored by using molecular dynamics (MD) simulation and co-immunoprecipitation (Co-IP) experiments. Eight genes affecting morphology-based brain networks were identified. The results of MD simulation showed that the PPI between TGM4 and BACE1 was the strongest among them and its interaction was verified by Co-IP. Hence, gene variations influence morphology-based brain networks in AD, leading to functional deficits. This finding, validated by MD simulation and Co-IP, suggests that the effect is robust.

A comprehensive anatomical network analysis of human brain topology.

A network approach to the macroscopic anatomy of the human brain can be used to model physical interactions among regions in order to study their topological properties, as well as the topological properties of the overall system. Here, a comprehensive model of human brain topology is presented, based on traditional macroanatomical divisions of the whole brain, which includes its subcortical regions. The aim was to localise anatomical elements that are essential for the geometric balance of the brain, as to identify underlying phenotypic patterns of spatial arrangement and understand how these patterns may influence brain morphology in ontogeny and phylogeny. The model revealed that the parahippocampal gyrus, the anterior lobe of the cerebellum and the ventral portion of the midbrain are subjected to major topological constraints that are likely to limit or channel their morphological evolution. The present model suggests that the brain can be divided into a superior and an inferior morphological block, linked with extrinsic topological constraints imposed by the surrounding braincase. This information should be considered duly both in ontogenetic and phylogenetic studies of primate neuroanatomy.

The brain of Homo habilis: Three decades of paleoneurology.

In 1987, Phillip Tobias published a comprehensive anatomical analysis of the endocasts attributed to Homo habilis, discussing issues dealing with brain size, sulcal patterns, and vascular traces. He suggested that the neuroanatomy of this species evidenced a clear change toward many cerebral traits associated with our genus, mostly when concerning the morphology of the frontal and parietal cortex. After more than 30 years, the fossil record associated with this taxon has not grown that much, but we have much more information on cranial and brain biology, and we are using a larger array of digital methods to investigate the paleoneurological variation observed in the human genus. Brain volume, the size of the frontal lobe, or the gross hemispheric asymmetries are still relevant issues, but they are considered to be less central than before. More attention is instead being paid to the cortical organization, the relationships with the cranial architecture, and the influence of molecular or ecological factors. Although the field of paleoneurology can currently count on a larger range of tools and principles, there is still a general lack of anatomical information on many endocranial traits. This aspect is probably crucial for the agenda of paleoneurology. More importantly, the whole science is undergoing a delicate change, because of the growing influence of the social environment. In this sense, the disciplines working with fossils (and, in particular, with brain evolution) should take particular care to maintain a healthy professional situation, avoiding an excess of speculation and overstatement.

Brain age predicted using graph convolutional neural network explains neurodevelopmental trajectory in preterm neonates.

OBJECTIVES: Dramatic brain morphological changes occur throughout the third trimester of gestation. In this study, we investigated whether the predicted brain age (PBA) derived from graph convolutional network (GCN) that accounts for cortical morphometrics in third trimester is associated with postnatal abnormalities and neurodevelopmental outcome. METHODS: In total, 577 T1 MRI scans of preterm neonates from two different datasets were analyzed; the NEOCIVET pipeline generated cortical surfaces and morphological features, which were then fed to the GCN to predict brain age. The brain age index (BAI; PBA minus chronological age) was used to determine the relationships among preterm birth (i.e., birthweight and birth age), perinatal brain injuries, postnatal events/clinical conditions, BAI at postnatal scan, and neurodevelopmental scores at 30 months. RESULTS: Brain morphology and GCN-based age prediction of preterm neonates without brain lesions (mean absolute error [MAE]: 0.96 weeks) outperformed conventional machine learning methods using no topological information. Structural equation models (SEM) showed that BAI mediated the influence of preterm birth and postnatal clinical factors, but not perinatal brain injuries, on neurodevelopmental outcome at 30 months of age. CONCLUSIONS: Brain morphology may be clinically meaningful in measuring brain age, as it relates to postnatal factors, and predicting neurodevelopmental outcome. CLINICAL RELEVANCE STATEMENT: Understanding the neurodevelopmental trajectory of preterm neonates through the prediction of brain age using a graph convolutional neural network may allow for earlier detection of potential developmental abnormalities and improved interventions, consequently enhancing the prognosis and quality of life in this vulnerable population. KEY POINTS: •Brain age in preterm neonates predicted using a graph convolutional network with brain morphological changes mediates the pre-scan risk factors and post-scan neurodevelopmental outcomes. •Predicted brain age oriented from conventional deep learning approaches, which indicates the neurodevelopmental status in neonates, shows a lack of sensitivity to perinatal risk factors and predicting neurodevelopmental outcomes. •The new brain age index based on brain morphology and graph convolutional network enhances the accuracy and clinical interpretation of predicted brain age for neonates.

Evolutionary divergence of developmental plasticity and learning of mating tactics in Trinidadian guppies.

Behavioural plasticity is a major driver in the early stages of adaptation, but its effects in mediating evolution remain elusive because behavioural plasticity itself can evolve. In this study, we investigated how male Trinidadian guppies (Poecilia reticulata) adapted to different predation regimes diverged in behavioural plasticity of their mating tactic. We reared F2 juveniles of high- or low-predation population origins with different combinations of social and predator cues and assayed their mating behaviour upon sexual maturity. High-predation males learned their mating tactic from conspecific adults as juveniles, while low-predation males did not. High-predation males increased courtship when exposed to chemical predator cues during development; low-predation males decreased courtship in response to immediate chemical predator cues, but only when they were not exposed to such cues during development. Behavioural changes induced by predator cues were associated with developmental plasticity in brain morphology, but changes acquired through social learning were not. We thus show that guppy populations diverged in their response to social and ecological cues during development, and correlational evidence suggests that different cues can shape the same behaviour via different neural mechanisms. Our study demonstrates that behavioural plasticity, both environmentally induced and socially learnt, evolves rapidly and shapes adaptation when organisms colonize ecologically divergent habitats.

La plasticidad conductual es un factor importante en las primeras fases de adaptación, pero se conocen poco sus efectos sobre la evolución porque la plasticidad conductual en sí puede evolucionar. En este estudio, investigamos cómo los machos del guppy de Trinidad (Poecilia reticulata) adaptados a regímenes de depredación diferentes, han divergido en la plasticidad de su táctica de apareamiento. Criamos juveniles provenientes de poblaciones de alta y baja depredación hasta segunda generación (F2) bajo diferentes combinaciones de señales sociales y de depredación, y evaluamos su comportamiento de apareamiento al llegar a la madurez sexual. Los machos de alta depredación aprendieron su táctica de apareamiento de sus conespecíficos adultos, mientras que los machos de baja depredación no. Los machos de alta depredación aumentaron su cortejo al ser expuestos a señales de depredadores durante su desarrollo; mientras que los machos de baja depredación redujeron su cortejo en respuesta a señales inmediatas de depredadores, pero tan solo cuando no fueron expuestos a tales señales durante el desarrollo. Los cambios conductuales observados inducidos por las señales de depredación están asociados con una plasticidad en el desarrollo de la morfología cerebral, pero los cambios adquiridos por aprendizaje social no. En conclusión, demostramos que las poblaciones de guppy han divergido en su respuesta a señales sociales y ecológicas durante su desarrollo, y mostramos evidencia correlativa que sugiere que diferentes tipos de señales pueden influenciar el mismo comportamiento via mecanismos neuronales diferentes. Nuestro estudio muestra que la plasticidad conductual, tanto inducida por el medio ambiente combo aprendida socialmente, evoluciona rápidamente e influencia la adaptación durante la colonización de hábitats ecológicamente divergentes.

Associations between neuropsychiatric symptoms of affective and vegetative domains and brain morphology in aging people with mild cognitive impairment and Alzheimer's disease.

OBJECTIVE: Neuropsychiatric symptoms (NPS) are common in mild cognitive impairment (MCI) and even more in Alzheimer's disease (AD). The symptom-based cluster including nighttime disturbances, depression, appetite changes, anxiety, and apathy (affective and vegetative symptoms) was associated with an increased risk of dementia in MCI and has common neuroanatomical associations. Our objective was to investigate the differences in brain morphology associations with affective and vegetative symptoms between three groups: cognitively normal older adults (CN), MCI and AD. MATERIAL AND METHODS: Alzheimer's Disease Neuroimaging Initiative data of 223 CN, 367 MCI and 175 AD, including cortical volumes, surface areas and thicknesses and severity scores of the five NPS were analyzed. A whole-brain vertex-wise general linear model was performed to test for intergroup differences (CN-MCI, CN-AD, AD-MCI) in brain morphology associations with five NPS. Multiple regressions were conducted to investigate cortical change as a function of NPS severity in the AD-MCI contrast. RESULTS: We found (1) signature differences in nighttime disturbances associations with prefrontal regions in AD-MCI, (2) signature differences in NPS associations with temporal regions in AD-MCI for depression and in CN-AD for anxiety, (3) decreased temporal metrics in MCI as nighttime disturbances and depression severity increased, (4) decreased pars triangularis metrics in AD as nighttime disturbances and apathy severity increased. CONCLUSION: Each NPS seems to have a signature on brain morphology. Affective and vegetative NPS were primarily associated with prefrontal and temporal regions. These signatures open the possibility of potential future assessments of links between brain morphology and NPS on an individual level.

Morphological brain alterations in dialysis- and non-dialysis-dependent patients with chronic kidney disease.

To 1) investigate the morphological brain-tissue changes in patients with dialysis- and non-dialysis-dependent chronic kidney disease (CKD); 2) analyze the effects of CKD on whole-brain cortical thickness, cortical volume, surface area, and surface curvature; and 3) analyze the correlation of these changes with clinical and biochemical indices. This study included normal controls (NCs, n = 34) and patients with CKD who were divided into dialysis (dialysis-dependent chronic kidney disease [DD-CKD], n = 26) and non-dialysis (non-dialysis patients who underwent cranial magnetic resonance imaging scans [NDD-CKD], n = 26) groups. Cortical thickness, volume, surface area, and surface curvature in each group were calculated using FreeSurfer software. Brain morphological indicators with statistical differences were correlated with clinical and biochemical indicators. Patients with CKD exhibited a significant and widespread decrease in cortical thickness and volume compared with NCs. Among the brain regions associated with higher neural activity, patients with CKD exhibited more significant morphological changes in the paracentral gyrus, transverse temporal gyrus, and lateral occipital cortex than in other brain regions. Cortical thickness and volume in patients with CKD correlated with blood pressure, lipid, hemoglobin, creatinine, and urea nitrogen levels. The extent of brain atrophy was further increased in the DD-CKD group compared with that in the NDD-CKD group. Patients with CKD potentially exhibit a certain degree of structural brain-tissue imaging changes, with morphological changes more pronounced in patients with DD-CKD, suggesting that blood urea nitrogen and dialysis may be influential factors in brain morphological changes in patients with CKD.

Benefits and Meaning of Lipids Profile in Relation to Oxidative Balance and Brain Morphology in Schizophrenia.

Schizophrenia is characterized by complex metabolic dysregulations and their consequences. Until now, numerous theories have explained its pathogenesis, using a spectrum of available technologies. We focused our interest on lipid profile-periphery high-density cholesterol level and lipoproteins in the human brain and compared magnetic resonance imaging (MRI) scans of patients with schizophrenia and the healthy group. Detailed analysis of biochemical parameters was performed using magnetic resonance spectroscopy. Our study aimed to reveal correlations between periphery high-density lipoproteins levels and lipoproteins in the brain, depicted in MRI scans, and parameters of peripheral oxidative stress expressed as paraoxonase. Patients with schizophrenia have decreased levels of high-density lipoproteins, low paraoxonase activity, and slightly raised sodium in the blood. Positive significant correlations between serum high-density cholesterol and anterior cingulate cortex, unique brain area for schizophrenia pathophysiology, MR spectroscopy signals, and diffusion have been revealed. To our knowledge, this is the first study to describe the effect of an anterior cingulate disorder on high-density cholesterol levels on the development of schizophrenia.

Brain morphology, harm avoidance, and the severity of excessive internet use.

As the previous studies have mainly focused on the reward system and the corresponding brain regions, the relationship between brain morphology and excessive internet use (EIU) were not clear; the purpose of the study was to investigate if the brain regions other than the reward system were associated with EIU. Data were acquired from 131 excessive internet users. Psychological measures included internet use, life quality, personality, mental illness symptoms, impulsivity, and thought suppression. The brain was scanned with 3T magnetic resonance imaging (MRI) and six types of brain morphological indexes were calculated. Lasso regression methods were used to select the predictors. Stepwise linear regression methods were used to build the models and verify the model. The variables remaining in the model were left precentral (curve), left superior temporal (surface area), right cuneus (folding index), right rostral anterior cingulate (folding index), and harm avoidance. The independent variable was the EIU score of the worst week in the past year. The study found that the brain morphological indexes other than the reward system, including the left precentral (curve), the left superior temporal (surface area), the right cuneus (folding index), and the right rostral anterior cingulate (folding index), can predict the severity of EIU, suggesting an extensive change in the brain. In this study, a whole-brain data analysis was conducted and it was concluded that the changes in certain brain regions were more predictive than the reward system and psychological measures or more important for EIU.

Disorganized attachment behaviors in infancy as predictors of brain morphology and peer rejection in late childhood.

Studies show robust links between disorganized attachment in infancy and socioemotional maladjustment in childhood. Little is known, however, about the links between disorganized attachment and brain development, and whether attachment-related differences in brain morphology translate into meaningful variations in child socioemotional functioning. This study examined the links between infants' disorganized attachment behaviors toward their mothers, whole-brain regional grey matter volume and thickness, and peer rejection in late childhood. Thirty-three children and their mothers took part in this study. The Strange Situation Procedure was used to assess mother-infant attachment when infants were 18 months old. Magnetic resonance imaging was performed when they were 10 years old to assess cortical thickness and grey matter volumes. Children and teachers reported on peer rejection 1 year later, as an indicator of socioemotional maladjustment. Results indicated that disorganized attachment was not associated with grey matter volumes. However, children who exhibited more disorganized attachment behaviors in infancy had significantly thicker cortices in bilateral middle and superior frontal gyri, and extending to the inferior frontal gyrus, as well as the orbitofrontal and insular cortices in the right hemisphere in late childhood. Moreover, children with thicker cortices in these regions experienced greater peer rejection, as rated by themselves and their teachers. Although preliminary, these results are the first to indicate that disorganized attachment may play a role in cortical thickness development and that changes in cortical thickness are associated with differences in child socioemotional functioning.