Brain Structural Features Research Articles

Predicting individual personality styles using macrostructural information: A multivariate pattern study

Advances in neuroimaging techniques have uncovered the relationships between neuroanatomy and personality traits like neuroticism and extraversion. Nevertheless, few studies have examined the reliable biological markers that identify individuals with different personality styles. Therefore, we evaluated the diagnostic capability of structural magnetic resonance imaging with a sample of 192 healthy adults by using a machine learning algorithm to divide them into four groups according to brain structural characteristics. Our results showed that personality styles, but not personality traits, could be discriminated with the extraversion candidate brain system (e. g., the frontal cortex, hippocampus, amygdala) and the neuroticism candidate brain system (e. g., the insula, the right anterior cingulate cortex). These findings indicate that personality styles that consider person-centered traits may more effectively predict individuals' mental health, providing a potential brain biomarker for early individual psychological risk assessments.

Distinct Clinical Implications of Patient- Versus Clinician-Rated Motor Symptoms in Parkinson's Disease.

Patient-rated motor symptoms (PRMS) and clinician-rated motor symptoms (CRMS) often differ in Parkinson's disease (PD). Our goal was to investigate the determinants and clinical implications of PRMS compared with CRMS in PD. This retrospective, observational cohort study analyzed the cross-sectional associations and longitudinal impacts of PRMS as assessed by the Movement Disorders Society-sponsored Unified PD Rating Scale (MDS-UPDRS) part 2, while controlling for CRMS measured by MDS-UPDRS part 3. Longitudinal analyses used Cox proportional hazards models and multiple linear mixed-effects random intercepts/slope models, adjusting for many clinical predictors. We conducted propensity score matching (PSM) to reinforce our analyses' robustness and surface-based morphometry to investigate neural correlates. We enrolled 442 patients with early-stage PD. At baseline, regardless of CRMS, PRMS were associated with the severity of postural instability and gait disturbance (PIGD). Notably, PRMS independently and more accurately predicted faster long-term deterioration in motor function than CRMS (Hoehn and Yahr 4, adjusted hazard ratio per +1 point = 1.19 [95% confidence intervals, 1.08-1.32]), particularly in PIGD (PIGD subscore, β-interaction = 0.052 [95% confidence intervals, 0.018-0.086]). PSM confirmed these findings' robustness. Surface-based morphometry suggested that enhanced sensory processing was distinctively associated with PRMS. In early-stage PD, PRMS weighed different aspects of symptoms and more effectively predicted motor deterioration compared to CRMS, with distinctive brain structural characteristics. The superior sensitivity of PRMS to subtle declines in drug-refractory symptoms like PIGD likely underlie our results, highlighting the importance of understanding the differential clinical implications of PRMS to prevent long-term motor deterioration. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

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.

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.

Comparative analysis of cortical anatomy inmale participants with internet gaming disorder or tobacco use disorder: Insights fromnormative modeling.

Research on individual differences in brain structural features of internet gaming disorder (IGD) and established addictions such as tobacco use disorder (TUD) is currently limited. This study utilized normative modeling to analyze the cortical thickness (CT) development patterns of male patients with IGD and TUD, aiming to provide further insights into whether IGD qualifies as an addiction. Surface-based brain morphometry (SBM) was used to calculate CT from T1-weighted magnetic resonance imaging data of 804 male participants (665 healthy individuals, 68 IGD and 71 TUD). Gaussian process regression was employed to generate normative models of CT development. Deviation maps were produced to depict deviations of IGD and TUD participants from the typical developmental patterns. Both addiction groups exhibited widespread cortical thinning, particularly in regions such as the bilateral temporal pole and medial orbitofrontal cortex. The TUD group demonstrated a higher degree of individualization and limited spatial overlap compared to the IGD group. Opposite trends in CT changes were observed between the two groups in the bilateral pericalcarine cortex and pars triangularis. These findings regarding the similarities and differences between IGD and TUD provide support for the idea that IGD shares common features with substance-related addictions and contribute to a deeper understanding of the neural mechanisms underlyingIGD.

Brain functional gradient and structure features in adolescent and adult autism spectrum disorders.

Understanding how function and structure are organized and their coupling with clinical traits in individuals with autism spectrum disorder (ASD) is a primary goal in network neuroscience research for ASD. Atypical brain functional networks and structures in individuals with ASD have been reported, but whether these associations show heterogeneous hierarchy modeling in adolescents and adults with ASD remains to be clarified. In this study, 176 adolescent and 74 adult participants with ASD without medication or comorbidities and sex, age matched healthy controls (HCs) from 19 research groups from the openly shared Autism Brain Imaging Data Exchange II database were included. To investigate the relationship between the functional gradient, structural changes, and clinical symptoms of brain networks in adolescents and adults with ASD, functional gradient and voxel-based morphometry (VBM) analyses based on 1000 parcels defined by Schaefer mapped to Yeo's seven-network atlas were performed. Pearson's correlation was calculated between the gradient scores, gray volume and density, and clinical traits. The subsystem-level analysis showed that the second gradient scores of the default mode networks and frontoparietal network in patients with ASD were relatively compressed compared to adolescent HCs. Adult patients with ASD showed an overall compression gradient of 1 in the ventral attention networks. In addition, the gray density and volumes of the subnetworks showed no significant differences between the ASD and HC groups at the adolescent stage. However, adults with ASD showed decreased gray density in the limbic network. Moreover, numerous functional gradient parameters, but not VBM parameters, in adolescents with ASD were considerably correlated with clinical traits in contrast to those in adults with ASD. Our findings proved that the atypical changes in adolescent ASD mainly involve the brain functional network, while in adult ASD, the changes are more related to brain structure, including gray density and volume. These changes in functional gradients or structures are markedly correlated with clinical traits in patients with ASD. Our study provides a novel understanding of the pathophysiology of the structure-function hierarchy in ASD.

Brain structure, amyloid, and behavioral features for predicting clinical progression in subjective cognitive decline.

As a potential preclinical stage of Alzheimer's dementia, subjective cognitive decline (SCD) reveals a higher risk of future cognitive decline and conversion to dementia. However, it has not been clear whether SCD status increases the clinical progression of older adults in the context of amyloid deposition, cerebrovascular disease (CeVD), and psychiatric symptoms. We identified 99 normal controls (NC), 15 SCD individuals who developed mild cognitive impairment in the next 2 years (P-SCD), and 54 SCD individuals who did not (S-SCD) from ADNI database with both baseline and 2-year follow-up data. Total white matter hyperintensity (WMH), WMH in deep (DWMH) and periventricular (PWMH) regions, and voxel-wise grey matter volumes were compared among groups. Furthermore, using structural equation modelling method, we constructed path models to explore SCD-related brain changes longitudinally and to determine whether baseline SCD status, age, and depressive symptoms affect participants' clinical outcomes. Both SCD groups showed higher baseline amyloid PET SUVR, baseline PWMH volumes, and larger increase of PWMH volumes over time than NC. In contrast, only P-SCD had higher baseline DWMH volumes and larger increase of DWMH volumes over time than NC. No longitudinal differences in grey matter volume and amyloid was observed among NC, S-SCD, and P-SCD. Our path models demonstrated that SCD status contributed to future WMH progression. Further, baseline SCD status increases the risk of future cognitive decline, mediated by PWMH; baseline depressive symptoms directly contribute to clinical outcomes. In conclusion, both S-SCD and P-SCD exhibited more severe CeVD than NC. The CeVD burden increase was more pronounced in P-SCD. In contrast with the direct association of depressive symptoms with dementia severity progression, the effects of SCD status on future cognitive decline may manifest via CeVD pathologies. Our work highlights the importance of multi-modal longitudinal designs in understanding the SCD trajectory heterogeneity, paving the way for stratification and early intervention in the preclinical stage. PRACTITIONER POINTS: Both S-SCD and P-SCD exhibited more severe CeVD at baseline and a larger increase of CeVD burden compared to NC, while the burden was more pronounced in P-SCD. Baseline SCD status increases the risk of future PWMH and DWMH volume accumulation, mediated by baseline PWMH and DWMH volumes, respectively. Baseline SCD status increases the risk of future cognitive decline, mediated by baseline PWMH, while baseline depression status directly contributes to clinical outcome.

The Influence of Maternal and Paternal Parenting on Adolescent Brain Structure

BackgroundAdolescents raised in families with different maternal and paternal parenting combinations exhibit variations in neurocognition and psychopathology; however, whether neural differences exist remains unexplored. This study used a longitudinal twin sample to delineate how different parenting combinations influence adolescent brain structure and to elucidate the genetic contribution. MethodsA cohort of 216 twins participated in parenting assessments during early adolescence and underwent magnetic resonance imaging scanning during middle adolescence. We utilized latent profile analysis to distinguish between various maternal and paternal parenting profiles and subsequently investigated their influences on brain anatomy. Biometric analysis was applied to assess genetic influences on brain structure, and associations with internalizing symptoms were explored. ResultsIn early adolescence, 4 parenting profiles emerged, which were characterized by levels of harshness and hostility in one or both parents. Compared with adolescents in “catparent” families (low harshness/hostility in both parents), those raised in “tigermom” families (harsh/hostile mother only) exhibited a smaller nucleus accumbens volume and larger temporal cortex surface area; those in “tigerdad” families demonstrated larger thalamus volumes; and those in “tigerparent” families displayed smaller volumes in the midanterior corpus callosum. Genetic risk factors contributed significantly to the observed brain structural heterogeneity and internalizing symptoms. However, the influences of parenting profiles and brain structure on internalizing symptoms were not significant. ConclusionsThe findings underscore distinct brain structural features linked to maternal and paternal parenting combinations, particularly in terms of subcortical volume and cortical surface area. This study suggests an interdependent role of maternal and paternal parenting in shaping adolescent neurodevelopment.

Prediction of Adolescents' Fluid Intelligence Scores based on Deep Learning with Reconstruction Regularization.

The aim of this study was to develop a predictive model for uncorrected/actual fluid intelligence scores in 9-10 year old children using magnetic resonance T1-weighted imaging. Explore the predictive performance of an autoencoder model based on reconstruction regularization for fluid intelligence in adolescents. We collected actual fluid intelligence scores and T1-weighted MRIs of 11,534 adolescents who completed baseline tasks from ABCD Data Release 3.0. A total of 148 ROIs were selected and 604 features were proposed by FreeSurfer segmentation. The training and testing sets were divided in a ratio of 7:3. To predict fluid intelligence scores, we used AE, MLP and classic machine learning models, and compared their performance on the test set. In addition, we explored their performance across gender subpopulations. Moreover, we evaluated the importance of features using the SHapley Additive Explain method. Results: The proposed model achieves optimal performance on the test set for predicting actual fluid intelligence scores (PCC = 0.209 ± 0.02, MSE = 105.212 ± 2.53). Results show that autoencoders with refactoring regularization are significantly more effective than MLPs and classical machine learning models. In addition, all models performed better on female adolescents than on male adolescents. Further analysis of relevant characteristics in different populations revealed that this may be related to gender differences in underlying fluid intelligence mechanisms. We construct a weak but stable correlation between brain structural features and raw fluid intelligence using autoencoders. Future research may need to explore ensemble regression strategies utilizing multiple machine learning algorithms on multimodal data in order to improve the predictive performance of fluid intelligence based on neuroimaging features.

Early identification of Parkinson's disease with anxiety based on combined clinical and MRI features.

To identify cortical and subcortical volume, thickness and cortical area features and the networks they constituted related to anxiety in Parkinson's disease (PD) using structural magnetic resonance imaging (sMRI), and to integrate multimodal features based on machine learning to identify PD-related anxiety. A total of 219 patients with PD were retrospectively enrolled in the study. 291 sMRI features including cortical volume, subcortical volume, cortical thickness, and cortical area, as well as 17 clinical features, were extracted. Graph theory analysis was used to explore structural networks. A support vector machine (SVM) combination model, which used both sMRI and clinical features to identify participants with PD-related anxiety, was developed and evaluated. The performance of SVM models were evaluated. The mean impact value (MIV) of the feature importance evaluation algorithm was used to rank the relative importance of sMRI features and clinical features within the model. 17 significant sMRI variables associated with PD-related anxiety was used to build a brain structural network. And seven sMRI and 5 clinical features with statistically significant differences were incorporated into the SVM model. The comprehensive model achieved higher performance than clinical features or sMRI features did alone, with an accuracy of 0.88, a precision of 0.86, a sensitivity of 0.81, an F1-Score of 0.83, a macro-average of 0.85, a weighted-average of 0.92, an AUC of 0.88, and a result of 10-fold cross-validation of 0.91 in test set. The sMRI feature right medialorbitofrontal thickness had the highest impact on the prediction model. We identified the brain structural features and networks related to anxiety in PD, and developed and internally validated a comprehensive model with multimodal features in identifying.

A brain subcortical segmentation tool based on anatomy attentional fusion network for developing macaques

Magnetic Resonance Imaging (MRI) plays a pivotal role in the accurate measurement of brain subcortical structures in macaques, which is crucial for unraveling the complexities of brain structure and function, thereby enhancing our understanding of neurodegenerative diseases and brain development. However, due to significant differences in brain size, structure, and imaging characteristics between humans and macaques, computational tools developed for human neuroimaging studies often encounter obstacles when applied to macaques. In this context, we propose an Anatomy Attentional Fusion Network (AAF-Net), which integrates multimodal MRI data with anatomical constraints in a multi-scale framework to address the challenges posed by the dynamic development, regional heterogeneity, and age-related size variations of the juvenile macaque brain, thus achieving precise subcortical segmentation. Specifically, we generate a Signed Distance Map (SDM) based on the initial rough segmentation of the subcortical region by a network as an anatomical constraint, providing comprehensive information on positions, structures, and morphology. Then we construct AAF-Net to fully fuse the SDM anatomical constraints and multimodal images for refined segmentation. To thoroughly evaluate the performance of our proposed tool, over 700 macaque MRIs from 19 datasets were used in this study. Specifically, we employed two manually labeled longitudinal macaque datasets to develop the tool and complete four-fold cross-validations. Furthermore, we incorporated various external datasets to demonstrate the proposed tool’s generalization capabilities and promise in brain development research. We have made this tool available as an open-source resource at https://github.com/TaoZhong11/Macaque_subcortical_segmentation for direct application.

Neurophysiological and brain structural insights into cyclin-dependent kinase-like 5 deficiency disorder: Visual and auditory evoked potentials and MRI analysis

ObjectiveCDKL5 deficiency disorder (CDD), an epileptic encephalopathy for which novel therapeutics are under development, lacks valid and reliable measures of therapeutic efficacy. We aimed to elucidate the neurophysiological and brain structural features of CDD patients and identify objective indicators reflecting the clinical severity. MethodsTwelve CDD patients and 12 healthy controls (HCs) participated. The clinical severity of CDD was scored using the CDD severity assessment (CDD-SA). The participants underwent visual evoked potential (VEP), auditory brainstem response (ABR), structural MRI, and diffusion tensor imaging (DTI) analyses. Measurements from each modality were compared with normal values of age-matched cohorts (VEP and ABR) or statistically compared between CDD patients and HCs (MRI). ResultsVEP showed a significant correlation between P100 latency and CDD-SA in CDD patients. ABR showed abnormalities in six patients (50%), including prolonged V-wave latency (n = 2), prolonged inter-peak latency between waves I and V (n = 3), and mild hearing loss (n = 4). Structural MRI showed a significant reduction in cortical volume in the left pars triangularis and right cerebellum compared with HCs. DTI showed a widespread decrease in fractional anisotropy and an increase in mean and radial diffusivity compared with HCs. ConclusionCDD patients had reduced cortical volume in the left pars triangularis, a brain region crucial for speech, and one-third of patients had mild hearing loss. These changes may be involved in language impairments in CDD patients. Additionally, P100 latency significantly correlated with the clinical severity. These features can be used to assess the clinical severity of CDD.

STF-Net: sparsification transformer coding guided network for subcortical brain structure segmentation.

Subcortical brain structure segmentation plays an important role in the diagnosis of neuroimaging and has become the basis of computer-aided diagnosis. Due to the blurred boundaries and complex shapes of subcortical brain structures, labeling these structures by hand becomes a time-consuming and subjective task, greatly limiting their potential for clinical applications. Thus, this paper proposes the sparsification transformer (STF) module for accurate brain structure segmentation. The self-attention mechanism is used to establish global dependencies to efficiently extract the global information of the feature map with low computational complexity. Also, the shallow network is used to compensate for low-level detail information through the localization of convolutional operations to promote the representation capability of the network. In addition, a hybrid residual dilated convolution (HRDC) module is introduced at the bottom layer of the network to extend the receptive field and extract multi-scale contextual information. Meanwhile, the octave convolution edge feature extraction (OCT) module is applied at the skip connections of the network to pay more attention to the edge features of brain structures. The proposed network is trained with ahybrid loss function. The experimental evaluation on two public datasets: IBSR and MALC, shows outstanding performance in terms of objective and subjective quality.

Structural Brain MR Imaging Alterations in Patients with Systemic Lupus Erythematosus with and without Neuropsychiatric Events.

Systemic lupus erythematosus is a complex autoimmune disease known for its diverse clinical manifestations, including neuropsychiatric systemic lupus erythematosus, which impacts a patient's quality of life. Our aim was to explore the relationships among brain MR imaging morphometric findings, neuropsychiatric events, and laboratory values in patients with systemic lupus erythematosus, shedding light on potential volumetric biomarkers and diagnostic indicators for neuropsychiatric systemic lupus erythematosus. Twenty-seven patients with systemic lupus erythematosus (14 with neuropsychiatric systemic lupus erythematosus, 13 with systemic lupus erythematosus), 24 women and 3 men (average age, 43 years, ranging from 21 to 62 years) were included in this cross-sectional study, along with 10 neuropsychiatric patients as controls. An MR imaging morphometric analysis, with the VolBrain online platform, to quantitatively assess brain structural features and their differences between patients with neuropsychiatric systemic lupus erythematosus and systemic lupus erythematosus, was performed. Correlations and differences between MR imaging morphometric findings and laboratory values, including disease activity scores, such as the Systemic Lupus Erythematosus Disease Activity Index and the Systemic Lupus International Collaborating Clinics Damage Index, were explored. An ordinary least squares regression analysis further explored the Systemic Lupus Erythematosus Disease Activity Index and Systemic Lupus International Collaborating Clinics Damage Index relationship with MR imaging features. For neuropsychiatric systemic lupus erythematosus and non-neuropsychiatric systemic lupus erythematosus, the brain regions with the largest difference in volumetric measurements were the insular central operculum volume (P value = .003) and the occipital cortex thickness (P = .003), which were lower in neuropsychiatric systemic lupus erythematosus. The partial correlation analysis showed that the most correlated morphometric features with neuropsychiatric systemic lupus erythematosus were subcallosal area thickness asymmetry (P < .001) and temporal pole thickness asymmetry (P = .011). The ordinary least squares regression analysis yielded an R 2 of 0.725 for the Systemic Lupus Erythematosus Disease Activity Index score, with calcarine cortex volume as a significant predictor, and an R 2 of 0.715 for the Systemic Lupus International Collaborating Clinics Damage Index score, with medial postcentral gyrus volume as a significant predictor. The MR imaging volumetric analysis, along with the correlation study and the ordinary least squares regression analysis, revealed significant differences in brain regions and their characteristics between patients with neuropsychiatric systemic lupus erythematosus and those with systemic lupus erythematosus, as well as between patients with different Systemic Lupus Erythematosus Disease Activity Index and Systemic Lupus International Collaborating Clinics Damage Index scores.

Parental status and markers of brain and cellular age: A 3D convolutional network and classification study

Recent research shows prominent effects of pregnancy and the parenthood transition on structural brain characteristics in humans. Here, we present a comprehensive study of how parental status and number of children born/fathered links to markers of brain and cellular ageing in 36,323 UK Biobank participants (age range 44.57–82.06 years; 52% female). To assess global effects of parenting on the brain, we trained a 3D convolutional neural network on T1-weighted magnetic resonance images, and estimated brain age in a held-out test set. To investigate regional specificity, we extracted cortical and subcortical volumes using FreeSurfer, and ran hierarchical clustering to group regional volumes based on covariance. Leukocyte telomere length (LTL) derived from DNA was used as a marker of cellular ageing. We employed linear regression models to assess relationships between number of children, brain age, regional brain volumes, and LTL, and included interaction terms to probe sex differences in associations. Lastly, we used the brain measures and LTL as features in binary classification models, to determine if markers of brain and cellular ageing could predict parental status. The results showed associations between a greater number of children born/fathered and younger brain age in both females and males, with stronger effects observed in females. Volume-based analyses showed maternal effects in striatal and limbic regions, which were not evident in fathers. We found no evidence for associations between number of children and LTL. Classification of parental status showed an Area under the ROC Curve (AUC) of 0.57 for the brain age model, while the models using regional brain volumes and LTL as predictors showed AUCs of 0.52. Our findings align with previous population-based studies of middle- and older-aged parents, revealing subtle but significant associations between parental experience and neuroimaging-based surrogate markers of brain health. The findings further corroborate results from longitudinal cohort studies following parents across pregnancy and postpartum, potentially indicating that the parenthood transition is associated with long-term influences on brain health.

Predictability of intelligence and age from structural connectomes.

In this study, structural images of 1048 healthy subjects from the Human Connectome Project Young Adult study and 94 from ADNI-3 study were processed by an in-house tractography pipeline and analyzed together with pre-processed data of the same subjects from braingraph.org. Whole brain structural connectome features were used to build a simple correlation-based regression machine learning model to predict intelligence and age of healthy subjects. Our results showed that different forms of intelligence as well as age are predictable to a certain degree from diffusion tensor imaging detecting anatomical fiber tracts in the living human brain. Though we did not identify significant differences in the prediction capability for the investigated features depending on the imaging feature extraction method, we did find that crystallized intelligence was consistently better predictable than fluid intelligence from structural connectivity data through all datasets. Our findings suggest a practical and scalable processing and analysis framework to explore broader research topics employing brain MR imaging.

Revealing the heterogeneity of plasma protein and cognitive decline trajectory among Mild Cognitive Impairment patients by clustering of brain atrophy features

Alzheimer's disease (AD) is suggested to be a heterogeneous disorder, but limited studies explore the heterogeneity of the Mild Cognitive Impairment (MCI) stage. This study aimed to tackle such problems using the CIMLR (Cancer Integration via Multikernel Learning) algorithm to cluster brain structural features extracted from T1-weighted Magnetic Resonance Images of MCI patients from Alzheimer's Disease Neuroimaging Initiative. The demographic and cognitive results, characteristics of brain structural features, plasma biomarkers, and longitudinal cognitive trajectory were analyzed for each cluster. The CIMLR clustering analysis revealed four distinct clusters. Cluster 1 is the oldest group but has had mild atrophy and moderate progression with elevated Tumor Necrosis Factor Receptor 2 level; and low Brain-Derived Neurotrophic Factor and CD40 Ligand levels. Cluster 2 showed the highest risk for aggressive MCI progression, with abnormal Leptin, Adiponectin, and Creatine kinase-MB values. Cluster 3 exhibited a low level of Monokine Induced by Gamma Interferon and mild atrophy that shared similar patterns with Cluster 1. Cluster 4 represented the healthiest group during longitudinal tracking, with the mildest Parahippocampal atrophy, which was found to be positively correlated with cognitive impairment and amino acid levels. The longitudinal analyses showed the potential of Hepatocyte Growth Factor as a marker for slow cognitive impairment; Cortisol and Neurofilament Light Polypeptide as prognosis markers for aggressive MCI progression. These findings may lay out new suggestions for further research contributing to the accurate diagnosis and precision medicine for dementia and AD.

Prefrontal-Limbic Circuitry Is Associated With Reward Sensitivity in Nonhuman Primates

BackgroundAbnormal reward sensitivity is a risk factor for psychiatric disorders, including eating disorders such as overeating and binge-eating disorder, but the brain structural mechanisms that underlie it are not completely understood. Here, we sought to investigate the relationship between multimodal whole-brain structural features and reward sensitivity in nonhuman primates. MethodsReward sensitivity was evaluated through behavioral economic analysis in which monkeys (adult rhesus macaques; 7 female, 5 male) responded for sweetened condensed milk (10%, 30%, 56%), Gatorade, or water using an operant procedure in which the response requirement increased incrementally across sessions (i.e., fixed ratio 1, 3, 10). Animals were divided into high (n = 6) or low (n = 6) reward sensitivity groups based on essential value for 30% milk. Multimodal magnetic resonance imaging was used to measure gray matter volume and white matter microstructure. Brain structural features were compared between groups, and their correlations with reward sensitivity for various stimuli was investigated. ResultsAnimals in the high sensitivity group had greater dorsolateral prefrontal cortex, centromedial amygdaloid complex, and middle cingulate cortex volumes than animals in the low sensitivity group. Furthermore, compared with monkeys in the low sensitivity group, high sensitivity monkeys had lower fractional anisotropy in the left dorsal cingulate bundle connecting the centromedial amygdaloid complex and middle cingulate cortex to the dorsolateral prefrontal cortex, and in the left superior longitudinal fasciculus 1 connecting the middle cingulate cortex to the dorsolateral prefrontal cortex. ConclusionsThese results suggest that neuroanatomical variation in prefrontal-limbic circuitry is associated with reward sensitivity. These brain structural features may serve as predictive biomarkers for vulnerability to food-based and other reward-related disorders.

Exploring the relationship between environment and brain morphology in anurans: a comparative phylogenetic approach

Abstract The correlation of ecological and behavioural characteristics with morphological features of brain structures has been recurrently corroborated for different vertebrate taxa, such as mammals, birds, and fishes. Here, we asked whether such correlations can also be detected in amphibians, an animal group of great interest due to its great behavioural and ecological diversity. We analysed the shape of two brain regions, cerebellum and optic tectum, of 67 species from 32 families of the order Anura (frogs and toads), applying 2D geometric morphometrics in a phylogenetic context. We found species that share similar environments and daily patterns have a similarly shaped cerebellum and optic tectum, with diurnal and arboreal species with more varied brain shapes. We did not find a correlation between activity mode or lifestyle with shape for both brain regions; however, we found that shared ancestry had an influence in their evolution. Future studies including a higher number of species from each ecological group, and from more families, would help to better clarify whether the patterns found here are a general rule for anurans.

Structural MRI of the Brain in Treatment-Resistant Schizophrenia

Background: despite a significant progress of psychopharmacology, treatment-resistant schizophrenia (TRS) remains a challenge for clinicians. The etiology and pathogenesis of TRS probably differ from schizophrenia susceptible to therapy, which underlies the non-respondence to most antipsychotics.Objective: to establish morphometric gray matter brain structural features in TRS as well as to analyze the association of these parameters with the clinical characteristics of patients.Patients and methods: 21 right-handed male patients diagnosed with paranoid schizophrenia and meeting criteria for treatment resistance and 21 matched healthy controls underwent MRI and clinical examination. T1-weighted images were processed via FreeSurfer 7.1.1. For each subject average values for the cortex thickness and area, volumes of subcortical structures, brain stem structures, and separately volumes of the amygdala nuclei and hippocampal subregions were obtained. Intergroup comparisons and correlations with clinical scales (PANSS, CDSS) and antipsychotic dosage in chlorpromazine equivalent were calculated. Results: TRS patients showed decreased gray matter thickness in frontal, temporal, parietal, occipital, cingulate and insular regions, volumes of the amygdala, hippocampus and nucleus accumbens, as well as a number of amygdala nuclei and hippocampal subregions bilaterally. The volume of the right globus pallidus, on the contrary, was increased.Conclusion: the widespread gray matter thinning in TRS confirmed the other researchs, which described resistance as a more severe form of schizophrenia that affects brain structures worse. The increase of globus pallidus volume is a surprising result, which is not yet clearly explained.