Associations of metabolic syndrome with cognitive function and dementia risk: Evidence from the UK Biobank cohort.

AIM: To investigate the associations of metabolic syndrome (MetS) with cognitive function, dementia and its subtypes. METHODS: Based on the participants recruited by UK Biobank, this study aims to investigate the associations of MetS with cognitive function, dementia and its subtypes. Generalized estimating equations, Cox proportional risk models, and multiple linear regression models were respectively used to assess associations between MetS and dementia-related outcomes. RESULTS: Among the 363,231 participants, 95,713 had MetS at baseline. The results showed that MetS was significantly associated with cognitive function related to fluid intelligence and prospective memory at follow-up. Among participants aged ≥60 years, MetS was correlated with elevated risk of all-cause dementia, particularly vascular dementia (VaD) [hazard ratio 1.115 (95% confidence interval: 1.047, 1.187), hazard ratio 1.393 (95% confidence interval: 1.233, 1.575), respectively]. With increasing MetS components, the risk of all-cause dementia and VaD tended to be elevated. MetS has also been associated with dementia-related structural changes in the brain, including alterations in overall brain volume, white matter volume, grey matter volume and white matter integrity. CONCLUSION: MetS was associated with poorer cognitive performance and might increase the risk of all-cause dementia as well as VaD, but the effect on Alzheimer's disease was not significant. Holistic control of the MetS may benefit the prevention and control of cognitive impairment and dementia.

Does sedentary time and physical activity predict chronic back pain and morphological brain changes? A UK biobank cohort study in 33,402 participants.

BACKGROUND: The relationship between sedentary time, physical activity, and chronic back pain remains unclear. The study aims to investigate whether sedentary time and physical activity predict chronic back pain and morphological brain changes. METHODS: This cohort study recruited adults aged 37-73 years enrolled between 2006 and 2010, with follow-up until 2014. The total cohort comprised 33,402 participants (mean age: 54.53). Data were collected on daily sedentary time, physical activity, lifestyle factors, and health outcomes. RESULTS: After nearly 8-year follow-up, 3,006 individuals (9.00%) reported chronic back pain in total. Individuals with daily sedentary time exceeding 6 h had a 33% higher risk of chronic back pain compared to those with sedentary time of 2 h or less (RR, 1.33, 95%CI, 1.17-1.52). Sedentary time was also associated with decreased grey matter volume in several brain regions, including bilateral primary somatosensory cortex (S1), secondary somatosensory cortex, putamen, primary motor cortex (M1), insula, hippocampus, amygdala, as well as right supplementary motor area, left medial frontal cortex, and right anterior cingulate cortex (FDR-corrected p-value < 0.05). Compared to individuals who sat for more than 6 h with light physical activity, those engaging in moderate physical activity with sedentary time of 2 h or less (RR, 0.71, 95%CI, 0.52-0.99) exhibited a significant decrease in chronic back pain risk. In addition, replacing sedentary time with equivalent amount of physical activity also demonstrated a reduction in the risk of chronic back pain (RR, 0.87, 95%CI, 0.77-0.99) and increased the reginal grey matter volumes including the amygdala, insula, M1, putamen and S1. CONCLUSIONS: Prolonged sedentary time is associated with heightened risks of chronic back pain and deterioration in brain health.

Interconnections of screen time with neuroinflammation.

The increasing prevalence of screen time among modern citizens has raised concerns regarding its potential impact on neuroinflammation and overall brain health. This review examines the complex interconnections between screen time and neuroinflammatory processes, particularly in children and adolescents. We analyze existing literature that explores how excessive digital media use can lead to alterations in neurobiological pathways, potentially exacerbating inflammatory responses in the brain. Key findings suggest that prolonged exposure to screens may contribute to neuroinflammation through mechanisms such as disrupted sleep patterns, diminished cognitive engagement, and increased stress levels. Similarly, we discuss the implications of these findings for mental health and cognitive development, emphasizing the need for a balanced approach to screen time. This review highlights the necessity for further research to elucidate the causal relationships and underlying mechanisms linking screen time and neuroinflammation, thereby informing guidelines for healthy media consumption.

Quantitative assessment of brain structural abnormalities in children with autism spectrum disorder based on artificial intelligence automatic brain segmentation technology and machine learning methods.

RATIONALE AND OBJECTIVES: To explore the characteristics of brain structure in Chinese children with autism spectrum disorder (ASD) using artificial intelligence automatic brain segmentation technique, and to diagnose children with ASD using machine learning (ML) methods in combination with structural magnetic resonance imaging (sMRI) features. METHODS: A total of 60 ASD children and 48 age- and sex-matched typically developing (TD) children were prospectively enrolled from January 2023 to April 2024. All subjects were scanned using 3D-T1 sequences. Automated brain segmentation techniques were utilized to obtain the standardized volume of each brain structure (the ratio of the absolute volume of brain structure to the whole brain volume). The standardized volumes of each brain structure in the two groups were statistically compared, and the volume data of brain areas with significant differences were combined with ML methods to diagnose and predict ASD patients. RESULTS: Compared with the TD group, the volumes of the right lateral orbitofrontal cortex, right medial orbitofrontal cortex, right pars opercularis, right pars triangularis, left hippocampus, bilateral parahippocampal gyrus, left fusiform gyrus, right superior temporal gyrus, bilateral insula, bilateral inferior parietal cortex, right precuneus cortex, bilateral putamen, left pallidum, and right thalamus were significantly increased in the ASD group (P< 0.05). Among six ML algorithms, support vector machine (SVM) and adaboost (AB) had better performance in differentiating subjects with ASD from those TD children, with their average area under curve (AUC) reaching 0.91 and 0.92, respectively. CONCLUSION: Automatic brain segmentation technology based on artificial intelligence can rapidly and directly measure and display the volume of brain structures in children with autism spectrum disorder and typically developing children. Children with ASD show abnormalities in multiple brain structures, and when paired with sMRI features, ML algorithms perform well in the diagnosis of ASD.

Depression, brain structure and socioeconomic status: A UK Biobank study.

BACKGROUND: Depression results from interactions between biological, social, and psychological factors. Literature shows that depression is associated with abnormal brain structure, and that socioeconomic status (SES) is associated with depression and brain structure. However, limited research considers the interaction between each of these factors. METHODS: Multivariate regression analysis was conducted using UK Biobank data on 39,995 participants to examine the relationship between depression and brain volume in 23 cortical regions for the whole sample and then separated by sex. It then examined whether SES affected this relationship. RESULTS: Eight out of 23 brain areas had significant negative associations with depression in the whole population. However, these relationships were abolished in seven areas when SES was included in the analysis. For females, three regions had significant negative associations with depression when SES was not included, but only one when it was. For males, lower volume in six regions was significantly associated with higher depression without SES, but this relationship was abolished in four regions when SES was included. The precentral gyrus was robustly associated with depression across all analyses. LIMITATIONS: Participants with conditions that could affect the brain were not excluded. UK Biobank is not representative of the general population which may limit generalisability. SES was made up of education and income which were not considered separately. CONCLUSIONS: SES affects the relationship between depression and cortical brain volume. Health practitioners and researchers should consider this when working with imaging data in these populations.

Dataset factors associated with age-related changes in brain structure and function in neurodevelopmental conditions.

With brain structure and function undergoing complex changes throughout childhood and adolescence, age is a critical consideration in neuroimaging studies, particularly for those of individuals with neurodevelopmental conditions. However, despite the increasing use of large, consortium-based datasets to examine brain structure and function in neurotypical and neurodivergent populations, it is unclear whether age-related changes are consistent between datasets and whether inconsistencies related to differences in sample characteristics, such as demographics and phenotypic features, exist. To address this, we built models of age-related changes of brain structure (regional cortical thickness and regional surface area; N = 1218) and function (resting-state functional connectivity strength; N = 1254) in two neurodiverse datasets: the Province of Ontario Neurodevelopmental Network and the Healthy Brain Network. We examined whether deviations from these models differed between the datasets, and explored whether these deviations were associated with demographic and clinical variables. We found significant differences between the two datasets for measures of cortical surface area and functional connectivity strength throughout the brain. For regional measures of cortical surface area, the patterns of differences were associated with race/ethnicity, while for functional connectivity strength, positive associations were observed with head motion. Our findings highlight that patterns of age-related changes in the brain may be influenced by demographic and phenotypic characteristics, and thus future studies should consider these when examining or controlling for age effects in analyses.

Alcohol and brain structure across the lifespan: A systematic review of large-scale neuroimaging studies.

Alcohol exposure affects brain structure, but the extent to which its effects differ across development remains unclear. Several countries are considering changes to recommended guidelines for alcohol consumption, so high-quality evidence is needed. Many studies have been conducted among small samples, but recent efforts have been made to acquire large samples to characterize alcohol's effects on the brain on a population level. Several large-scale consortia have acquired such samples, but this evidence has not been synthesized across the lifespan. We conducted a systematic review of large-scale neuroimaging studies examining effects of alcohol exposure on brain structure at multiple developmental stages. We included studies with an alcohol-exposed sample of at least N = 100 from the following consortia: ABCD, ENIGMA, NCANDA, IMAGEN, Framingham Offspring Study, HCP and UK BioBank. Twenty-seven studies were included, examining prenatal (N = 1), adolescent (N = 9), low-to-moderate-level adult (N = 11) and heavy adult (N = 7) exposure. Prenatal exposure was associated with greater brain volume at ages 9-10, but contemporaneous alcohol consumption during adolescence and adulthood was associated with smaller volume/thickness. Both low-to-moderate consumption and heavy consumption were characterized by smaller volume and thickness in frontal, temporal and parietal regions, and reductions in insula, cingulate and subcortical structures. Adolescent consumption had similar effects, with less consistent evidence for smaller cingulate, insula and subcortical volume. In sum, prenatal exposure was associated with larger volume, while adolescent and adult alcohol exposure was associated with smaller volume and thickness, suggesting that regional patterns of effects of alcohol are similar in adolescence and adulthood.

The effects of APOEe4 allele on cerebral structure, function, and related interactions with cognition in young adults.

In the last decade, extensive research has emerged into understanding the impact of risk factors for Alzheimer's Disease (AD) on brain in pre-symptomatic stages. We investigated the neuroimaging correlates of the APOEe4 genetic risk factor for AD in young adulthood, its relationship with cognition, and potential effects of other variables on the findings. While conventional volumetric analyses revealed no consistent differences, more sophisticated analyses identified subtle structural differences between APOEe4 carriers and non-carriers. Findings from diffusion studies were limited, but functional studies demonstrated consistent alterations in connectivity and activity. The complex relationship between APOE genotype, neuroimaging variables, and cognition revealed no consensus on the directionality of findings. Methodological choices, including analytical approaches, sample size, and the influence of other genes, gender, and ethnicity, varied across studies, impacting comparability and generalizability. Recommendations for future research include multimodal and longitudinal imaging, standardisation of pipelines, advanced analytical techniques, and collaborative data pooling.

The importance of timing of socioeconomic disadvantage throughout development for depressive symptoms and brain structure.

Prior studies have reported associations between socioeconomic disadvantage, brain structure and mental health outcomes, but the timing of these relations is not well understood. Using prospective longitudinal data from the Avon Longitudinal Study of Parents and Children (ALSPAC), this preregistered study examined whether socioeconomic disadvantage related differentially to depressive symptoms (n=3012-3530) and cortical and subcortical structures (n=460-733) in emerging adults, depending on the timing of exposure to socioeconomic disadvantage. Family income in early childhood and own income measured concurrently were both significantly related to depressive symptoms in emerging adulthood. Similar results were observed for perceived financial strain. In contrast, only family income in early childhood was associated with brain structure in emerging adulthood, with positive associations with intracranial volume and total and regional cortical surface area. The findings suggest that both objective and subjective aspects of one's financial standing throughout development relate to depressive symptoms in adulthood, but that specifically early life family income is related to brain structural features in emerging adulthood. This suggests that associations between socioeconomic disadvantage and brain structure originate early in neurodevelopment, highlighting the role of timing of socioeconomic disadvantage.

Unsupervised machine learning for identifying attention-deficit/hyperactivity disorder subtypes based on cognitive function and their implications for brain structure.

BACKGROUND: Structural anomalies in the frontal lobe and basal ganglia have been reported in patients with attention-deficit/hyperactivity disorder (ADHD). However, these findings have been not always consistent because of ADHD diversity. This study aimed to identify ADHD subtypes based on cognitive function and find their distinct brain structural characteristics. METHODS: Using the data of 656 children with ADHD from the Adolescent Brain Cognitive Development (ABCD) Study, we applied unsupervised machine learning to identify ADHD subtypes using the National Institutes of Health Toolbox Tasks. Moreover, we compared the regional brain volumes between each ADHD subtype and 6601 children without ADHD (non-ADHD). RESULTS: Hierarchical cluster analysis automatically classified ADHD into three distinct subtypes: ADHD-A (n = 212, characterized by high-order cognitive ability), ADHD-B (n = 190, characterized by low cognitive control, processing speed, and episodic memory), and ADHD-C (n = 254, characterized by strikingly low cognitive control, working memory, episodic memory, and language ability). Structural analyses revealed that the ADHD-C type had significantly smaller volumes of the left inferior temporal gyrus and right lateral orbitofrontal cortex than the non-ADHD group, and the right lateral orbitofrontal cortex volume was positively correlated with language performance in the ADHD-C type. However, the volumes of the ADHD-A and ADHD-B types were not significantly different from those of the non-ADHD group. CONCLUSIONS: These results indicate the presence of anomalies in the lateral orbitofrontal cortex associated with language deficits in the ADHD-C type. Subtype specificity may explain previous inconsistencies in brain structural anomalies reported in ADHD.

Plasma metabolomic signature of healthy lifestyle, structural brain reserve and risk of dementia.

Although the association between healthy lifestyle and dementia risk has been documented, the relationship between a metabolic signature indicative of healthy lifestyle and dementia risk and the mediating role of structural brain impairment remain unknown. We retrieved 136 628 dementia-free participants from UK Biobank. Elastic net regression was used to obtain a metabolic signature that represented lifestyle behaviours. Cox proportional hazard models were fitted to explore the associations of lifestyle-associated metabolic signature with incident dementia. Causal associations between identified metabolites and dementia were investigated using Mendelian randomization. Mediation analysis was also conducted to uncover the potential mechanisms involving 19 imaging-derived phenotypes (brain volume, grey matter volume, white matter volume and regional grey matter volumes). During a follow-up of 12.55 years, 1783 incident cases of all-cause dementia were identified, including 725 cases of Alzheimer's dementia and 418 cases of vascular dementia. We identified 83 metabolites that could represent healthy lifestyle behaviours using elastic net regression. The metabolic signature was associated with a lower dementia risk, and for each standard deviation increment in metabolic signature, the hazard ratio was 0.89 [95% confidence interval (CI): 0.85, 0.93] for all-cause dementia, 0.95 (95% CI: 0.88, 1.03) for Alzheimer's dementia and 0.84 (95% CI: 0.77, 0.91) for vascular dementia. Mendelian randomization revealed potential causal associations between the identified metabolites and risk of dementia. In addition, the specific structural brain reserve, including the hippocampus, grey matter in the hippocampus, parahippocampal gyrus and middle temporal gyrus, were detected to mediate the effects of metabolic signature on dementia risk (mediated proportion ranging from 6.21% to 11.98%). The metabolic signature associated with a healthy lifestyle is inversely associated with dementia risk, and greater structural brain reserve plays an important role in mediating this relationship. These findings have significant implications for understanding the intricate connections between lifestyle, metabolism and brain health.

Imaging body-mind crosstalk in young adults.

Objective: There is evidence that complex relationships exist between motor functions, brain structure, and cognitive functions, particularly in the aging population. However, whether such relationships observed in older adults could extend to other age groups (e.g., younger adults) remains to be elucidated. Thus, the current study addressed this gap in the literature by investigating potential associations between motor functions, brain structure, and cognitive functions in a large cohort of young adults. Methods: In the current study, data from 910 participants (22-35 yr) were retrieved from the Human Connectome Project. Interactions between motor functions (i.e., cardiorespiratory fitness, gait speed, hand dexterity, and handgrip strength), brain structure (i.e., cortical thickness, surface area, and subcortical volumes), and cognitive functions were examined using linear mixed-effects models and mediation analyses. The performance of different machine-learning classifiers to discriminate young adults at three different levels (related to each motor function) was compared. Results: Cardiorespiratory fitness and hand dexterity were positively associated with fluid and crystallized intelligence in young adults, whereas gait speed and handgrip strength were correlated with specific measures of fluid intelligence (e.g., inhibitory control, flexibility, sustained attention, and spatial orientation; false discovery rate [FDR] corrected, p < 0.05). The relationships between cardiorespiratory fitness and domains of cognitive function were mediated by surface area and cortical volume in regions involved in the default mode, sensorimotor, and limbic networks (FDR corrected, p < 0.05). Associations between handgrip strength and fluid intelligence were mediated by surface area and volume in regions involved in the salience and limbic networks (FDR corrected, p < 0.05). Four machine-learning classifiers with feature importance ranking were built to discriminate young adults with different levels of cardiorespiratory fitness (random forest), gait speed, hand dexterity (support vector machine with the radial kernel), and handgrip strength (artificial neural network). Conclusions: In summary, similar to observations in older adults, the current study provides empirical evidence (i) that motor functions in young adults are positively related to specific measures of cognitive functions, and (ii) that such relationships are at least partially mediated by distinct brain structures. Furthermore, our analyses suggest that machine-learning classifier has a promising potential to be used as a classification tool and decision support for identifying populations with below-average motor and cognitive functions.

Structural brain characteristics of epilepsy patients with comorbid migraine without aura.

Migraine is a common bi-directional comorbidity of epilepsy, indicating potential complex interactions between the two conditions. However, no previous studies have used brain morphology analysis to assess possible interactions between epilepsy and migraine. Voxel-based morphometry (VBM), surface-based morphometry (SBM), and structural covariance networks (SCNs) can be used to detect morphological changes with high accuracy. We recruited 30 individuals with epilepsy and comorbid migraine without aura (EM), along with 20 healthy controls (HC) and 30 epilepsy controls (EC) without migraine. We used VBM, SBM, and SCN analysis to compare differences in gray matter volume, cortical thickness, and global level and local level graph theory indexes between the EM, EC, and HC groups to investigate structural brain changes in the EM patients. VBM analysis showed that the EM group had gray matter atrophy in the right temporal pole compared with the HC group (p < 0.001, false discovery rate correction [FDR]). Furthermore, the headache duration in the EM group was negatively correlated with the gray matter volume of the right temporal pole (p < 0.05). SBM analysis showed cortical atrophy in the left insula, left posterior cingulate gyrus, left postcentral gyrus, left middle temporal gyrus, and left fusiform gyrus in the EM compared with the HC group (p < 0.001, family wise error correction). We found a positive correlation between headache frequency and the cortical thickness of the left middle temporal gyrus (p < 0.05). SCN analysis revealed no differences in global parameters between the three groups. The area under the curve (AUC) of the nodal betweenness centrality in the right postcentral gyrus was lower in the EM group compared with the HC group (p < 0.001, FDR correction), and the AUC of the nodal degree in the right fusiform gyrus was lower in the EM group compared with the EC group (p < 0.001, FDR correction). We found clear differences in brain structure in the EM patients compared with the HC group. Accordingly, migraine episodes may influence brain structure in epilepsy patients. Conversely, abnormal brain structure may be an important factor in the development of epilepsy with comorbid migraine without aura. Further studies are needed to investigate the role of brain structure in individuals with epilepsy and comorbid migraine without aura.

A brain structure learning-guided multi-view graph representation learning for brain network analysis.

Background: Resting-state brain networks represent the interconnectivity of different brain regions during rest. Utilizing brain network analysis methods to model these networks can enhance our understanding of how different brain regions collaborate and communicate without explicit external stimuli. However, analyzing resting-state brain networks faces challenges due to high heterogeneity and noise correlation between subjects. This study proposes a brain structure learning-guided multi-view graph representation learning method to address the limitations of current brain network analysis and improve the diagnostic accuracy (ACC) of mental disorders. Methods: We first used multiple thresholds to generate different sparse levels of brain networks. Subsequently, we introduced graph pooling to optimize the brain network representation by reducing noise edges and data inconsistency, thereby providing more reliable input for subsequent graph convolutional networks (GCNs). Following this, we designed a multi-view GCN to comprehensively capture the complexity and variability of brain structure. Finally, we employed an attention-based adaptive module to adjust the contributions of different views, facilitating their fusion. Considering that the Smith atlas offers superior characterization of resting-state brain networks, we utilized the Smith atlas to construct the graph network. Results: Experiments on two mental disorder datasets, the Autism Brain Imaging Data Exchange (ABIDE) dataset and the Mexican Cocaine Use Disorders (SUDMEX CONN) dataset, show that our model outperforms the state-of-the-art methods, achieving nearly 75% ACC and 70% area under the receiver operating characteristic curve (AUC) on both datasets. Conclusions: These findings demonstrate that our method of combining multi-view graph learning and brain structure learning can effectively capture crucial structural information in brain networks while facilitating the acquisition of feature information from diverse perspectives, thereby improving the performance of brain network analysis.

Can an online battery match in-person cognitive testing in providing information about age-related cortical morphology?

Clinical identification of early neurodegenerative changes requires an accurate and accessible characterization of brain and cognition in healthy aging. We assessed whether a brief online cognitive assessment can provide insights into brain morphology comparable to a comprehensive neuropsychological battery. In 141 healthy mid-life and older adults, we compared Creyos, a relatively brief online cognitive battery, to a comprehensive in person cognitive assessment. We used a multivariate technique to study the ability of each test to inform brain morphology as indexed by cortical sulcal width extracted from structural magnetic resonance imaging (sMRI).We found that the online test demonstrated comparable strength of association with cortical sulcal width compared to the comprehensive in-person assessment.These findings suggest that in our at-risk sample online assessments are comparable to the in-person assay in their association with brain morphology. With their cost effectiveness, online cognitive testing could lead to more equitable early detection and intervention for neurodegenerative diseases.

Gray Matter Volumes Mediate the Relationship Between Disease Duration and Balance Control Performance in Chronic Ankle Instability.

The relationship between structural changes in the cerebral gray matter and diminished balance control performance in patients with chronic ankle instability (CAI) has remained unclear. This paper aimed to assess the difference in gray matter volume (GMV) between participants with CAI and healthy controls (HC) and to characterize the role of GMV in the relationship between disease duration and balance performance in CAI. 42 participants with CAI and 33 HC completed the structural brain MRI scans, one-legged standing test, and Y-balance test. Regional GMV was measured by applying voxel-based morphometry methods. The result showed that, compared with HC, participants with CAI exhibited lower GMV in multiple brain regions (familywise error [FWE] corrected p < 0.021). Within CAI only, but not in HC, lower GMV in the thalamus (ß = -0.53, p = 0.003) and hippocampus (ß = -0.57, p = 0.001) was associated with faster sway velocity of the center of pressure (CoP) in eyes closed condition (i.e., worse balance control performance). The GMV in the thalamus (percentage mediated [PM] = 32.02%; indirect effect ß = 0.119, 95% CI = 0.003 to 0.282) and hippocampus (PM = 33.71%; indirect effect ß = 0.122, 95% CI = 0.005 to 0.278) significantly mediated the association between the disease duration and balance performance. These findings suggest that the structural characteristics of the supraspinal elements is critical to the maintenance of balance control performance in individuals suffering from CAI, which deserve careful consideration in the management and rehabilitation programs in this population.

Associations between Parenting and Cognitive and Language Abilities at 2 Years of Age Depend on Prenatal Exposure to Disadvantage.

OBJECTIVE: To investigate whether parenting or neonatal brain volumes mediate associations between prenatal social disadvantage (PSD) and cognitive/language abilities and whether these mechanisms vary by level of disadvantage. STUDY DESIGN: Pregnant women were recruited prospectively from obstetric clinics in St Louis, Missouri. PSD encompassed access to social (eg, education) and material (eg, income to needs, health insurance, area deprivation, and nutrition) resources during pregnancy. Neonates underwent brain magnetic resonance imaging. Mother-child dyads (n = 202) returned at age 1 year for parenting observations and at age 2 years for cognition/language assessments (Bayley Scales of Infant and Toddler Development, Third Edition). Generalized additive and mediation models tested hypotheses. RESULTS: Greater PSD associated nonlinearly with poorer cognitive/language scores. Associations between parenting and cognition/language were moderated by disadvantage, such that supportive and nonsupportive parenting behaviors related only to cognition/language in children with lesser PSD. Parenting mediation effects differed by level of disadvantage: both supportive and nonsupportive parenting mediated PSD-cognition/language associations in children with lesser disadvantage, but not in children with greater disadvantage. PSD-associated reductions in neonatal subcortical grey matter (ß = 0.19; q = 0.03), white matter (ß = 0.23; q = 0.02), and total brain volume (ß = 0.18; q = 0.03) were associated with lower cognition, but did not mediate the associations between PSD and cognition. CONCLUSIONS: Parenting moderates and mediates associations between PSD and early cognition and language, but only in families with less social disadvantage. These findings, although correlational, suggest that there may be a critical threshold of disadvantage, below which mediating or moderating factors become less effective, highlighting the importance of reducing disadvantage as primary prevention.

Convergence and divergence in neurostructural signatures of unipolar and bipolar depressions: Insights from surface-based morphometry and prospective follow-up.

BACKGROUND: Bipolar disorder (BD) is often misidentified as unipolar depression (UD) during its early stages, typically until the onset of the first manic episode. This study aimed to explore both shared and unique neurostructural changes in patients who transitioned from UD to BD during follow-up, as compared to those with UD. METHODS: This study utilized high-resolution structural magnetic resonance imaging (MRI) to collect brain data from individuals initially diagnosed with UD. During the average 3-year follow-up, 24 of the UD patients converted to BD (cBD). For comparison, the study included 48 demographically matched UD patients who did not convert and 48 healthy controls. The MRI data underwent preprocessing using FreeSurfer, followed by surface-based morphometry (SBM) analysis to identify cortical thickness (CT), surface area (SA), and cortical volume (CV) among groups. RESULTS: The SBM analysis identified shared neurostructural characteristics between the cBD and UD groups, specifically thinner CT in the right precentral cortex compared to controls. Unique to the cBD group, there was a greater SA in the right inferior parietal cortex compared to the UD group. Furthermore, no significant correlations were observed between cortical morphological measures and cognitive performance and clinical features in the cBD and UD groups. LIMITATIONS: The sample size is relatively small. CONCLUSIONS: Our findings suggest that while cBD and UD exhibit some common alterations in cortical macrostructure, numerous distinct differences are also present. These differences offer valuable insights into the neuropathological underpinnings that distinguish these two conditions.

Magnetic Resonance Imaging to Detect Structural Brain Changes in Huntington's Disease: A Review of Data from Mouse Models.

Structural magnetic resonance imaging (MRI) is a powerful tool to visualize 3D neuroanatomy and assess pathology and disease progression in neurodegenerative disorders such as Huntington's disease (HD). The development of mouse models of HD that reproduce many of the psychiatric, motor and cognitive impairments observed in human HD has improved our understanding of the disease and provided opportunities for testing novel therapies. Similar to the clinical scenario, MRI of mouse models of HD demonstrates onset and progression of brain pathology. Here, we provided an overview of the articles that used structural MRI in mouse models of HD to date, highlighting the differences between studies and models and describing gaps in the current state of knowledge and recommendations for future studies.

Association of chronotype with language and episodic memory processing in children: implications for brain structure.

Introduction: Chronotype refers to individual preference in circadian cycles and is associated with psychiatric problems. It is mainly classified into early (those who prefer to be active in the morning and sleep and wake up early) and late (those who prefer to be active in the evening and sleep and wake up late) chronotypes. Although previous research has demonstrated associations between chronotype and cognitive function and brain structure in adults, little is known regarding these associations in children. Here, we aimed to investigate the relationship between chronotype and cognitive function in children. Moreover, based on the significant association between chronotype and specific cognitive functions, we extracted regions-of-interest (ROI) and examined the association between chronotype and ROI volumes. Methods: Data from 4,493 children (mean age of 143.06 months) from the Adolescent Brain Cognitive Development Study were obtained, wherein chronotype (mid-sleep time on free days corrected for sleep debt on school days) was assessed by the Munich Chronotype Questionnaire. Subsequently, the associations between chronotype, cognitive function, and ROI volumes were evaluated using linear mixed-effects models. Results: Behaviorally, chronotype was negatively associated with vocabulary knowledge, reading skills, and episodic memory performance. Based on these associations, the ROI analysis focused on language-related and episodic memory-related areas revealed a negative association between chronotype and left precentral gyrus and right posterior cingulate cortex volumes. Furthermore, the precentral gyrus volume was positively associated with vocabulary knowledge and reading skills, while the posterior cingulate cortex volume was positively associated with episodic memory performance. Discussion: These results suggest that children with late chronotype have lower language comprehension and episodic memory and smaller brain volumes in the left precentral gyrus and right posterior cingulate cortex associated with these cognitive functions.