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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.

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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.

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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.

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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.

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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.

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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.

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OBJECTIVE: To investigate whether parenting or neonatal brain volumes mediate associations between prenatal social disadvantage (SD) and cognitive/language abilities and whether these mechanisms vary by level of disadvantage. STUDY DESIGN: Pregnant women were prospectively recruited from obstetric clinics in St Louis, Missouri. Prenatal SD 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-III). Generalized additive and mediation models tested hypotheses. RESULTS: Greater prenatal SD associated nonlinearly with poorer cognitive/language scores. Associations between parenting and cognition/language were moderated by disadvantage, such that supportive and non-supportive parenting behaviors related only to cognition/language in children with lesser prenatal SD. Parenting mediation effects differed by level of disadvantage: both supportive and non-supportive parenting mediated prenatal SD-cognition/language associations in children with lesser disadvantage, but not in children with greater disadvantage. Prenatal SD-associated reductions in neonatal subcortical grey matter (ß=.19, q=.03), white matter (ß=.23, q=.02), and total brain volume (ß=.18, q=.03) were associated with lower cognition, but did not mediate associations between prenatal SD and cognition. CONCLUSIONS: Parenting moderates and mediates associations between prenatal SD 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.

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Optimal glucose control is crucial for maintaining brain health and preventing metabolic and cognitive disorders in the general population. Glycosylated hemoglobin (HbA1c) serves as a key marker for assessing glucose intolerance and its impact on brain structure and function in healthy individuals. However, existing literature presents conflicting findings, necessitating a systematic review to consolidate current knowledge in this domain. This systematic review examines 26 English-language studies involving participants aged 15 years and above, investigating the relationship between HbA1c levels and brain health. Studies focusing on normal/general populations and utilizing magnetic resonance imaging (MRI) as the imaging modality were included. Exclusion criteria encompassed review articles, abstracts, letters, animal studies, and research involving neuropsychiatric or metabolic diseases. Data were gathered from PubMed, Scopus, and Web of Science databases up to November 2023. Analysis reveals significant associations between HbA1c levels and various brain metrics, including volume, cortical thickness, fractional anisotropy, mean diffusivity, activity, and connectivity. However, findings exhibit inconsistency, likely attributed to disparities in sample characteristics and study sizes. Notably, hippocampal volume, white matter hyperintensity, and ventral attention network connectivity emerge as frequently affected structures and functions, mirroring trends observed in diabetic populations. Despite inconclusive evidence, glucose intolerance appears to exert considerable influence on select brain structures and functions in individuals without diagnosed metabolic disorders. Understanding these associations is critical for mitigating the risk of cognitive decline and dementia in healthy populations. Future investigations should aim to elucidate the intricate relationship between HbA1c concentrations and brain health parameters in normoglycemic individuals.

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In cities, the incidence of mental disorders is higher, while visits to nature have been reported to benefit mental health and brain function. However, there is a lack of knowledge about how exposure to natural and urban environments affects brain structure. To explore the causal relationship between exposure to these environments and the hippocampal formation, 60 participants were sent on a one hour walk in either a natural (forest) or an urban environment (busy street), and high-resolution hippocampal imaging was performed before and after the walks. We found that the participants who walked in the forest had an increase in subiculum volume, a hippocampal subfield involved in stress response inhibition, while no change was observed after the urban walk. However, this result did not withstand Bonferroni correction for multiple comparisons. Furthermore, the increase in subiculum volume after the forest walk was associated with a decrease in self-reported rumination. These results indicate that visits to nature can lead to observable alterations in brain structure, with potential benefits for mental health and implications for public health and urban planning policies.

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OBJECTIVE: Previous studies have reported contradictory findings regarding the relationship between obstructive sleep apnea (OSA) and abnormal brain morphology. Furthermore, the causal relationship between OSA and brain morphology has not been clearly established. The aim of this study was to utilize Mendelian randomization (MR) analysis to investigate the impact of obstructive sleep apnea (OSA) on brain morphology and determine its potential causal relationship. METHODS: Firstly, the inverse-variance weighted (IVW) method was employed to assess the causal effects of OSA on cortical surface area and brain structure volume. Additionally, two additional MR methods, namely weighted median and MR-Egger, were used to supplement the results from IVW. Subsequently, a reverse MR analysis was conducted to determine the direction of causality. Furthermore, sensitivity analyses were performed including Cochrane's Q test, MR-Egger intercept test, MR-PRESSO global test, and leave-one-out analysis. RESULTS: The results of the study showed that OSA patients had a tendency towards decreased cortical surface area and hippocampal volume in the precuneus region compared to individuals without OSA, while the superior temporal cortical surface area showed an increase. The results from the weighted median and MR-Egger analyses were consistent with those from the IVW analysis. Sensitivity tests confirmed the reliability of the causal estimates. CONCLUSIONS: This study provides preliminary evidence of an association between OSA and brain structure using large-scale genome-wide association data. The results demonstrate that OSA is associated with changes in brain structure. Therefore, individuals with OSA should be vigilant about the risks of related diseases due to alterations in brain tissue.

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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.

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BACKGROUND: Medications prescribed for chronic diseases can lead to short-term neuropsychiatric symptoms, but their long-term effects on brain structures and psychiatric conditions remain unclear. METHODS: We comprehensively analyzed the FDA Adverse Event Reporting System database and conducted drug target Mendelian Randomization (MR) studies on six categories of common drugs, 477 brain imaging-derived phenotypes (IDPs) and eight psychiatric disorders. Genetic instruments were extracted from expression quantitative trait loci (eQTLs) in blood, brain, and other target tissues, protein quantitative trait loci (pQTLs) in blood, and genome-wide association studies (GWAS) of hemoglobin and cholesterol. Summary statistics for brain IDPs, psychiatric disorders, and gut microbiome were obtained from the BIG40, Psychiatric Genomics Consortium, and MiBioGen. A two-step MR and mediation analysis were employed to screen possible mediators of drug-IDP effects from 119 gut microbiota genera and identify their mediation proportions. FINDINGS: Among 19 drug classes, six drugs were found to be associated with higher risks of psychiatric adverse events, while 11 drugs were associated with higher risks of gastrointestinal adverse events in the FAERS analysis. We identified ten drug-psychiatric disorder associations, 202 drug-IDP associations, 16 drug-microbiota associations, and four drug-microbiota-IDP causal links. For example, PPARG activation mediated HbA1c reduction caused a higher risk of bipolar disorder (BD) II. Genetically proxied GLP-1R agonists were significantly associated with an increase in the volume of the CA3-head of the right hippocampus and the area of the left precuneus cortex, both of which have been shown to correlate with cognition in previous studies. INTERPRETATION: Common drugs may affect brain structure and risk of psychiatric disorder. Oral medications in particular may exert some of these effects by influencing gut microbiota. This study calls for greater attention to be paid to the neuropsychiatric adverse effects of drugs and encourages drug repurposing. FUNDING: National Natural Science Foundation of China (grant No. 82330035, 82130043, 82172685, and 82001223), National Natural Science Foundation of Hunan Province (grant No. 2021SK1010), and the Science Foundation for Distinguished Young Scholars of Changsha (grant No. kq2209006).

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BACKGROUND: We aimed to investigate the association between OA and treatment with dementia risk and structural brain abnormalities. METHODS: We recruited a total of 466,460 individuals from the UK Biobank to investigate the impact of OA on the incidence of dementia. Among the total population, there were 63,081 participants diagnosed with OA. We subsequently categorised the OA patients into medication and surgery groups based on treatment routes. Cox regression models explored the associations between OA/OA treatment and dementia risk, with the results represented as hazard ratios (HRs) and 95% confidence intervals (95% CI). Linear regression models assessed the associations of OA/OA therapy with alterations in cortical structure. RESULTS: During an average of 11.90 (± 1.01) years of follow-up, 5,627 individuals were diagnosed with all-cause dementia (ACD), including 2,438 AD (Alzheimer's disease), and 1,312 VaD (vascular dementia) cases. Results revealed that OA was associated with the elevated risk of ACD (HR: 1.116; 95% CI: 1.039-1.199) and AD (HR: 1.127; 95% CI: 1.013-1.254). OA therapy lowered the risk of dementia in both medication group (HR: 0.746; 95% CI: 0.652-0.854) and surgery group (HR: 0.841; 95% CI: 0.736-0.960). OA was negatively associated with cortical area, especially precentral, postcentral and temporal regions. CONCLUSIONS: Osteoarthritis increased the likelihood of developing dementia, and had an association with regional brain atrophy. OA treatment lowered the dementia risk. OA is a promising modifiable risk factor for dementia.

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OBJECTIVE: We aim to investigate the interplay between mentalization, brain microstructure, and psychological resilience as potential protective factors against mental illness. METHOD: Four hundred and twenty-six participants (mean age 40.12±16.95; 202 males, 224 females), without psychiatric or neurological history, completed assessments: Dissociative Process Scale (DPS), Peace of Mind (PoM), Beck Depression Inventory (BDI), Beck Anxiety Inventory (BAI), Resilience Scale for Adults (RSA), and Magnetic Resonance Imaging (MRI) structures with selected regions of interest, and Diffusion Tensor Imaging (DTI) maps from various tracts in the right hemisphere and connection to the frontal areas, including anterior thalamic radiation (ATR), Cingulum (hippocampus) (CH), Corticospinal tract (CST), Superior longitudinal fasciculus (SLF), Inferior fronto-occipital fasciculus (IFOF), and Uncinate fasciculus (UF) were analyzed. RESULTS: Two clusters, representing hypomentalization (HypoM) and hypermentalization (HyperM), were identified based on DPS, CPSS, and RFQ responses. One-way ANOVA showed no significant age or gender differences between clusters. The HypoM group exhibited lower PoM scores, higher BDI and BAI scores, and lower RSA scores (ps< 0.05). Structural brain metric comparison showed significant differences in GMV in the right caudal middle frontal gyrus (rcMFG), right superior frontal gyrus (rsFG), and right frontal pole (rFP) between groups. In addition, the HyperM individuals with a higher risk of depression and a higher ratio of intrapersonal to interpersonal factors of resilience were found with reduced GMV on the rcMFG. Additionally, analyses of DTI metrics revealed significant differences between two groups in rATR and rSLF in terms of fractional anisotropy (FA) values; rATR, rCST, rUF, rSLF, rCH and rIFOF in terms of mean diffusivity (MD) values, and radial diffusivity (RD) (corrected p = 0.05). Moreover, the positive correlation between different domains of resilience and white matter (WM) integrity implied further enhancement of intrapersonal or interpersonal resilience factors that are different for people with different mentalization. CONCLUSIONS: The findings underscore the importance of considering both intrapersonal and interpersonal factors in understanding the interactions between psychological resilience and mental health conditions relevant to brain mechanisms.

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Cognitive impairments affect functional capacity in individuals with schizophrenia (SZH), but their neural basis remains unclear. The Wisconsin Card Sorting Test (WCST), and the Stroop Task (SCWT), are paradigmatic tests which have been used extensively for examining executive function in SZH. However, few studies have explored how deficits on these tasks link to brain volume differences commonly seen in SZH. Here, for the first time, we tested associations between FreeSurfer-derived frontal brain volumes and performance on both WCST and SCWT, in a well-matched sample of 57 SZH and 32 control subjects. We also explored whether these associations were dissociable from links to symptom severity in SZH. Results revealed correlations between volumes and task performance which were unique to SZH. In SZH only, volumes of right middle frontal regions correlated with both WCST and Stroop performance: correlation coefficients were significantly different to those present in the control group, highlighting their specificity to the patient group. In the Stroop task, superior frontal regions also showed associations with Stroop interference scores which were unique to SZH. These findings provide important detail around how deficits on these two paradigmatic executive function tasks link to brain structural differences in SZH. Results align with converging evidence suggesting that neuropathology within right middle frontal regions (BA9 and BA46) might be of particular import in SZH. No volumetric associations with symptom severity were found, supporting the notion that the structural abnormalities underpinning cognitive deficits in SZH differ from those associated with symptomatology.

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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.

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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.

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Long-duration spaceflight poses a variety of health risks to astronauts, largely resulting from extended exposure to microgravity and radiation. Here, we assessed the prevalence and incidence of cerebral microbleeds in sixteen astronauts before and after a typical 6-month mission on board the International Space Station Cerebral microbleeds are microhemorrhages in the brain, which are typically interpreted as early evidence of small vessel disease and have been associated with cognitive impairment. We identified evidence of higher-than-expected microbleed prevalence in astronauts with prior spaceflight experience. However, we did not identify a statistically significant increase in microbleed burden up to 7 months after spaceflight. Altogether, these preliminary findings suggest that spaceflight exposure may increase microbleed burden, but this influence may be indirect or occur over time courses that exceed 1 year. For health monitoring purposes, it may be valuable to acquire neuroimaging data that are able to detect the occurrence of microbleeds in astronauts following their spaceflight missions.

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Background: The prevalence of Alzheimer's disease (AD) is increasing, therefore, identifying biomarkers to predict those vulnerable to AD is imperative. Type 2 diabetes (T2D) serves as an independent risk factor for AD. Early prediction of T2D patients who may be more susceptible to AD, so as to achieve early intervention, is of great significance to reduce the prevalence of AD. Objective: To establish periphery biomarkers that could predict conversion of T2D into pre-AD-like cognitive decline. Methods: A follow-up study was carried out from 159 T2D patients at baseline. The correlations of cognitive states (by MMSE score) with multi-periphery biomarkers, including APOE genotype, plasma amyloid-ß level, platelet GSK-3ß activity, and olfactory score were analyzed by logistic regression. ROC curve was used for establishing the prediction model. Additionally, MRI acquired from 38 T2D patients for analyzing the correlation among cognitive function, biomarkers and brain structure. Results: Compared with the patients who maintained normal cognitive functions during the follow-up period, the patients who developed MCI showed worse olfactory function, higher platelet GSK-3ß activity, and higher plasma Aß42/Aß40 ratio. We conducted a predictive model which T2D patients had more chance of suffering from pre-AD-like cognitive decline. The MRI data revealed MMSE scores were positively correlated with brain structures. However, platelet GSK-3ß activity was negatively correlated with brain structures. Conclusions: Elevated platelet GSK-3ß activity and plasma Aß42/Aß40 ratio with reduced olfactory function are correlated with pre-AD-like cognitive decline in T2D patients, which used for predicting which T2D patients will convert into pre-AD-like cognitive decline in very early stage.

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Large changes to brain structure (e.g., from damage or disease) can explain alterations in behavior. It is therefore plausible that smaller structural differences in healthy samples can be used to better understand and predict individual differences in behavior. Despite the brain's multivariate and distributed structure-to-function mapping, most studies have used univariate analyses of individual structural brain measures. Here we used a multivariate approach in a multimodal data set composed of volumetric, surface-based, diffusion-based, and functional resting-state MRI measures to predict reliable individual differences in risk and intertemporal preferences. We show that combining twelve brain structure measures led to better predictions across tasks than using any individual measure, and by examining model coefficients, we visualize the relative contribution of different brain measures from different brain regions. Using a multivariate approach to brain structure-to-function mapping that combines across many brain structure properties, along with reliably measured behavior phenotypes, may increase out-of-sample prediction accuracies and insight into neural underpinnings. Furthermore, this methodological approach may be useful to improve predictions and neural insight across basic, translational, and clinical research fields.