Explore the vast range of titles available.

The microbiota-gut-brain axis (MGBA) plays a significant role in the maintenance of brain structure and function. The MGBA serves as a conduit between the CNS and the ENS, facilitating communication between the emotional and cognitive centers of the brain via diverse pathways. In the initial stages of this review, we will examine the way how MGBA affects neurogenesis, neuronal dendritic morphology, axonal myelination, microglia structure, brain blood barrier (BBB) structure and permeability, and synaptic structure. Furthermore, we will review the potential mechanistic pathways of neuroplasticity through MGBA influence. The short-chain fatty acids (SCFAs) play a pivotal role in the MGBA, where they can modify the BBB. We will therefore discuss how SCFAs can influence microglia, neuronal, and astrocyte function, as well as their role in brain disorders such as Alzheimer’s disease (AD), and Parkinson’s disease (PD). Subsequently, we will examine the technical strategies employed to study MGBA interactions, including using germ-free (GF) animals, probiotics, fecal microbiota transplantation (FMT), and antibiotics-induced dysbiosis. Finally, we will examine how particular bacterial strains can affect brain structure and function. By gaining a deeper understanding of the MGBA, it may be possible to facilitate research into microbial-based pharmacological interventions and therapeutic strategies for neurological diseases.

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.

Recent molecular genetic studies have shown that the majority of genes associated with obesity are expressed in the central nervous system. Obesity has also been associated with neurobehavioral factors such as brain morphology, cognitive performance, and personality. Here, we tested whether these neurobehavioral factors were associated with the heritable variance in obesity measured by body mass index (BMI) in the Human Connectome Project (n = 895 siblings). Phenotypically, cortical thickness findings supported the “right brain hypothesis” for obesity. Namely, increased BMI is associated with decreased cortical thickness in right frontal lobe and increased thickness in the left frontal lobe, notably in lateral prefrontal cortex. In addition, lower thickness and volume in entorhinal-parahippocampal structures and increased thickness in parietal-occipital structures in participants with higher BMI supported the role of visuospatial function in obesity. Brain morphometry results were supported by cognitive tests, which outlined a negative association between BMI and visuospatial function, verbal episodic memory, impulsivity, and cognitive flexibility. Personality–BMI correlations were inconsistent. We then aggregated the effects for each neurobehavioral factor for a behavioral genetics analysis and estimated each factor’s genetic overlap with BMI. Cognitive test scores and brain morphometry had 0.25–0.45 genetic correlations with BMI, and the phenotypic correlations with BMI were 77–89% explained by genetic factors. Neurobehavioral factors also had some genetic overlap with each other. In summary, obesity as measured by BMI has considerable genetic overlap with brain and cognitive measures. This supports the theory that obesity is inherited via brain function and may inform intervention strategies.

INTRODUCTION Amyloid precursor protein (APP) undergoes striking changes following traumatic brain injury (TBI). Considering its role in the control of gene expression, we investigated whether APP regulates transcription and translation following TBI. METHODS We assessed brain morphology (n = 4–9 mice/group), transcriptome (n = 3 mice/group), proteome (n = 3 mice/group), and behavior (n = 17–27 mice/group) of wild‐type (WT) and APP knock‐out (KO) mice either untreated or 10‐weeks following TBI. RESULTS After TBI, WT mice displayed transcriptional programs consistent with late stages of brain repair, hub genes were predicted to impact translation and brain proteome showed subtle changes. APP KO mice largely replicated this transcriptional repertoire, but showed no transcriptional nor translational response to TBI. DISCUSSION The similarities between WT mice following TBI and APP KO mice suggest that developmental APP deficiency induces a condition reminiscent of late stages of brain repair, hampering the control of gene expression in response to injury. Highlights 10‐weeks after TBI, brains exhibit transcriptional profiles consistent with late stage of brain repair. Developmental APP deficiency maintains brains perpetually in an immature state akin to late stages of brain repair. APP responds to TBI by changes in gene expression at a transcriptional and translational level. APP deficiency precludes molecular brain changes in response to TBI.

To examine the brain morphological characteristics of 2-to-4-year-olds with ASD, including cortical and subcortical regions, and to investigate their associations with clinical behaviors. A total of 43 toddlers with ASD aged 2-4 years, 44 age- and gender- matched toddlers with developmental delay (DD) and 18 typically developing (TD) children were recruited. All participants underwent whole-brain high-resolution MRI and clinical assessments. Brain structural data were analyzed using Surface-Based Morphometry with FreeSurfer. Linear mixed-effects models were employed to identify differences in brain regions between groups and to explore their links with clinical behaviors. Compared to TD group, ASD group demonstrated increased cortical thickness and volume, particularly within the frontal and parietal brain regions (FDR, p < 0.05). When compared to DD, ASD group exhibited a distinction in the increased cortical volume within the posterior frontal lobe and middle-post cingulate (FDR, p < 0.05). DD children had increased cortical thickness and cortical volume in the superior parietal gyrus compared to TD (FDR, p < 0.05). In the ASD + DD group, increased cortical thickness of the superior frontal sulcus was correlated with lower adaptive behavior, while reduced nucleus accumbens volume and increased cortical thickness of the superior frontal sulcus and precentral sulcus were associated with decreased language development (FDR, p < 0.05). Our study found widespread brain abnormalities in young children with ASD, notably altered cortical volume and thickness in frontal and parietal lobes. These findings may serve as important indicators of altered brain development trajectories in this population.

Abstract Background and Hypothesis Given the heterogeneity and possible disease progression in schizophrenia, identifying the neurobiological subtypes and progression patterns in each patient may lead to novel biomarkers. Here, we adopted data-driven machine-learning techniques to identify the progression patterns of brain morphological changes in schizophrenia and investigate the association with treatment resistance. Study Design In this cross-sectional multicenter study, we included 177 patients with schizophrenia, characterized by treatment response or resistance, with 3D T1-weighted magnetic resonance imaging. Cortical thickness and subcortical volumes calculated by FreeSurfer were converted into z scores using 73 healthy controls data. The Subtype and Stage Inference (SuStaIn) algorithm was used for unsupervised machine-learning analysis. Study Results SuStaIn identified 3 different subtypes: (1) subcortical volume reduction (SC) type (73 patients), in which volume reduction of subcortical structures occurs first and moderate cortical thinning follows, (2) globus pallidus hypertrophy and cortical thinning (GP-CX) type (42 patients), in which globus pallidus hypertrophy initially occurs followed by progressive cortical thinning, and (3) cortical thinning (pure CX) type (39 patients), in which thinning of the insular and lateral temporal lobe cortices primarily happens. The remaining 23 patients were assigned to baseline stage of progression (no change). SuStaIn also found 84 stages of progression, and treatment-resistant schizophrenia showed significantly more progressed stages than treatment-responsive cases (P = .001). The GP-CX type presented earlier stages than the pure CX type (P = .009). Conclusions The brain morphological progressions in schizophrenia can be classified into 3 subtypes, and treatment resistance was associated with more progressed stages, which may suggest a novel biomarker.

Abstract Background Early psychosis in first-episode psychosis (FEP) and clinical high-risk (CHR) individuals has been associated with alterations in mean regional measures of brain morphology. Examination of variability in brain morphology could assist in quantifying the degree of brain structural heterogeneity in clinical relative to healthy control (HC) samples. Methods Structural magnetic resonance imaging data were obtained from CHR (n = 71), FEP (n = 72), and HC individuals (n = 55). Regional brain variability in cortical thickness (CT), surface area (SA), and subcortical volume (SV) was assessed with the coefficient of variation (CV). Furthermore, the person-based similarity index (PBSI) was employed to quantify the similarity of CT, SA, and SV profile of each individual to others within the same diagnostic group. Normative modeling of the PBSI-CT, PBSI-SA, and PBSI-SV was used to identify CHR and FEP individuals whose scores deviated markedly from those of the healthy individuals. Results There was no effect of diagnosis on the CV for any regional measure (P > .38). CHR and FEP individuals differed significantly from the HC group in terms of PBSI-CT (P < .0001), PBSI-SA (P < .0001), and PBSI-SV (P = .01). In the clinical groups, normative modeling identified 32 (22%) individuals with deviant PBSI-CT, 12 (8.4%) with deviant PBSI-SA, and 21 (15%) with deviant PBSI-SV; differences of small effect size indicated that individuals with deviant PBSI scores had lower IQ and higher psychopathology. Conclusions Examination of brain structural variability in early psychosis indicated heterogeneity at the level of individual profiles and encourages further large-scale examination to identify individuals that deviate markedly from normative reference data.

Both normal aging and neurodegenerative disorders such as Alzheimer's disease (AD) cause morphological changes of the brain. It is generally difficult to distinguish these two causes of morphological change by visual inspection of magnetic resonance (MR) images. To facilitate making this distinction and thus aid the diagnosis of neurodegenerative disorders, we propose a method for developing a spatio-temporal model of morphological differences in the brain due to normal aging. The method utilizes groupwise image registration to characterize morphological variation across brain scans of people with different ages. To extract the deformations that are due to normal aging we use partial least squares regression, which yields modes of deformations highly correlated with age, and corresponding scores for each input subject. Subsequently, we determine a distribution of morphologies as a function of age by fitting smooth percentile curves to these scores. This distribution is used as a reference to which a person's morphology score can be compared. We validate our method on two different datasets, using images from both cognitively normal subjects and patients with Alzheimer disease (AD). Results show that the proposed framework extracts the expected atrophy patterns. Moreover, the morphology scores of cognitively normal subjects are on average lower than the scores of AD subjects, indicating that morphology differences between AD subjects and healthy subjects can be partly explained by accelerated aging. With our methods we are able to assess accelerated brain aging on both population and individual level. A spatio-temporal aging brain model derived from 988 T1-weighted MR brain scans from a large population imaging study (age range 45.9–91.7y, mean age 68.3y) is made publicly available at www.agingbrain.nl. •A model to assess morphological differences in the brain due to aging is developed.•The method can assess accelerated brain aging on population and individual level.•The model derived from 988 MR brain is made publicly available at www.agingbrain.nl.

The brain entropy (BEN) reflects the randomness of brain activity and is inversely related to its temporal coherence. In recent years, BEN has been found to be associated with a number of neurocognitive, biological, and sociodemographic variables such as fluid intelligence, age, sex, and education. However, evidence regarding the potential relationship between BEN and brain structure is still lacking. In this study, we use resting-state fMRI (rsfMRI) data to estimate BEN and investigate its associations with three structural brain metrics: gray matter volume (GMV), surface area (SA), and cortical thickness (CT). We performed separate analyses on BEN maps derived from four distinct rsfMRI runs, and used a voxelwise as well as a regions-of-interest (ROIs) approach. Our findings consistently showed that lower BEN was related to increased GMV and SA in the lateral frontal and temporal lobes, inferior parietal lobules, and precuneus. We hypothesize that lower BEN and higher SA might reflect higher brain reserve as well as increased information processing capacity.

In this study we propose a deformation-based framework to jointly model the influence of aging and Alzheimer's disease (AD) on the brain morphological evolution. Our approach combines a spatio-temporal description of both processes into a generative model. A reference morphology is deformed along specific trajectories to match subject specific morphologies. It is used to define two imaging progression markers: 1) a morphological age and 2) a disease score. These markers can be computed regionally in any brain region. The approach is evaluated on brain structural magnetic resonance images (MRI) from the ADNI database. The model is first estimated on a control population using longitudinal data, then, for each testing subject, the markers are computed cross-sectionally for each acquisition. The longitudinal evolution of these markers is then studied in relation with the clinical diagnosis of the subjects and used to generate possible morphological evolutions. In the model, the morphological changes associated with normal aging are mainly found around the ventricles, while the Alzheimer's disease specific changes are located in the temporal lobe and the hippocampal area. The statistical analysis of these markers highlights differences between clinical conditions even though the inter-subject variability is quite high. The model is also generative since it can be used to simulate plausible morphological trajectories associated with the disease. Our method quantifies two interpretable scalar imaging biomarkers assessing respectively the effects of aging and disease on brain morphology, at the individual and population level. These markers confirm the presence of an accelerated apparent aging component in Alzheimer's patients but they also highlight specific morphological changes that can help discriminate clinical conditions even in prodromal stages. More generally, the joint modeling of normal and pathological evolutions shows promising results to describe age-related brain diseases over long time scales.