Variation In Brain Structure Research Articles

Do total hippocampus and hippocampal subfield volumes relate to navigation ability? A call towards methodological consistency

Despite the need for successful navigation, humans vary greatly in their ability to navigate, and these individual differences may relate to variation in brain structure. While prior research provides support for a correlation between hippocampal volume and navigation ability in both navigation experts and in older individuals, this relationship is under scrutiny for healthy, young adults. We assessed 99 healthy young adults’ ability to navigate in a virtual, desktop maze and correlated their performance with total hippocampal gray matter volume. For a subset of these individuals, we further segmented the medial temporal lobe – including regions of the hippocampus – into anatomically-distinct subregions to uniquely examine the association between volumes of hippocampal subfields and navigation. Given the need to distinguish between similar-looking maze hallways and partially overlapping routes, young adults with stronger pattern separation ability may perform better in this task. Thus, we theorized that successful navigation would positively correlate with hippocampal CA3 and dentate gyrus (DG) subfield volumes due to these regions’ role in pattern separation. CA1 and entorhinal cortex (ERC) are both associated with rodent spatial memory, too, suggesting a possible relationship between their volumes and navigation performance. Consistent with our hypotheses, we observed a positive relationship between volumes of hippocampal subfields and wayfinding accuracy, while ERC and parahippocampal cortex volumes correlated with navigation efficiency. However, when analyzing total hippocampal volume, a nuanced interpretation is warranted. We found evidence of Simpson’s Paradox, where total hippocampal volume and navigation accuracy displayed no correlation in males, a negative correlation in females, yet a positive correlation when considering the full sample of males and females combined. Furthermore, no significant relationship was observed between total hippocampal volume and path efficiency. Given these findings, we urge caution in interpreting the results because these associations differ by analysis techniques (including voxel-based morphometry), after sex stratification, and with anterior and posterior hippocampal subdivisions. Overall, this study enhances our understanding of the relationship between brain volume and navigation ability for young adults but also emphasizes the need for methodological consistency across studies with respect to boundary definitions, neuroimaging techniques, statistical methods, and factors that give rise to individual differences.

A review of artificial intelligence-based brain age estimation and its applications for related diseases.

The study of brain age has emerged over the past decade, aiming to estimate a person's age based on brain imaging scans. Ideally, predicted brain age should match chronological age in healthy individuals. However, brain structure and function change in the presence of brain-related diseases. Consequently, brain age also changes in affected individuals, making the brain age gap (BAG)-the difference between brain age and chronological age-a potential biomarker for brain health, early screening, and identifying age-related cognitive decline and disorders. With the recent successes of artificial intelligence in healthcare, it is essential to track the latest advancements and highlight promising directions. This review paper presents recent machine learning techniques used in brain age estimation (BAE) studies. Typically, BAE models involve developing a machine learning regression model to capture age-related variations in brain structure from imaging scans of healthy individuals and automatically predict brain age for new subjects. The process also involves estimating BAG as a measure of brain health. While we discuss recent clinical applications of BAE methods, we also review studies of biological age that can be integrated into BAE research. Finally, we point out the current limitations of BAE's studies.

Sex differences in prelimbic cortex calcium dynamics during stress and fear learning

In recent years, research has progressively increased the importance of considering sex differences in stress and fear memory studies. Many studies have traditionally focused on male subjects, potentially overlooking critical differences with females. Emerging evidence suggests that males and females can exhibit distinct behavioral and neurophysiological responses to stress and fear conditioning. These differences may be attributable to variations in hormone levels, brain structure, and neural circuitry, particularly in regions such as the prefrontal cortex (PFC). In the present study, we explored sex differences in prelimbic cortex (PL) calcium activity in animals submitted to immobilization stress (IMO), fear conditioning (FC), and fear extinction (FE). While no significant sex differences were found in behavioral responses, we did observe differences in several PL calcium activity parameters. To determine whether these results were related to behaviors beyond stress and fear memory, we conducted correlation studies between the movement of the animals and PL activity during IMO and freezing behavior during FC and FE. Our findings revealed a clear correlation between PL calcium activity with movement during stress exposure and freezing behavior, with no sex differences observed in these correlations. These results suggest a significant role for the PL in movement and locomotion, in addition to its involvement in fear-related processes. The inclusion of both female and male subjects is crucial for studies like this to fully understand the role of the PFC and other brain areas in stress and fear responses. Recognizing sex differences enhances our comprehension of brain function and can lead to more personalized and effective approaches in the study and treatment of stress and fear-related conditions.

Aging, brain plasticity, and motor learning.

Motor skill learning, the process of acquiring new motor skills, is critically important across the lifespan, from early development through adulthood and into older age, as well as in pathological conditions (i.e., rehabilitation). Extensive research has demonstrated that motor skill acquisition in young adults is accompanied by significant neuroplastic changes, including alterations in brain structure (gray and white matter), function (i.e., activity and connectivity), and neurochemistry (i.e., levels of neurotransmitters). In the aging population, motor performance typically declines, characterized by slower and less accurate movements. However, despite these age-related changes, older adults maintain the capacity for skill improvement through training. In this review, we explore the extent to which the aging brain retains the ability to adapt in response to motor learning, specifically whether skill acquisition is accompanied by neural changes. Furthermore, we discuss the associations between inter-individual variability in brain structure and function and the potential for future learning in older adults. Finally, we consider the use of non-invasive brain stimulation techniques aimed at optimizing motor learning in this population. Our review provides insights into the neurobiological underpinnings of motor learning in older adults and emphasizes strategies to enhance their motor skill acquisition.

Structural and transcriptional signatures of arithmetic abilities in children

Arithmetic ability is critical for daily life, academic achievement, career development, and future economic success. Individual differences in arithmetic skills among children and adolescents are related to variations in brain structures. Most existing studies have used hypothesis-driven region of interest analysis. To identify distributed brain regions related to arithmetic ability, we used data-driven cross-validated predictive models to analyze cross-sectional behavioral and structural MRI data in children and adolescents. The gray matter volume (GMV) of widespread brain regions reliably predicted arithmetic abilities measured by the Comprehensive Mathematical Abilities Test. Furthermore, we applied neuroimaging-transcriptome association analysis to explore transcriptional signatures associated with structural patterns of arithmetic ability. Structural patterns of arithmetic ability primarily correlated with transcriptional profiles enriched for genes involved in transmembrane transport and synaptic signaling. Our findings enhance our understanding of the neural and genetic mechanisms underlying children’s arithmetic ability and offer a practical predictor for arithmetic skills during development.

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

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. 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. 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. 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.

Exploring the genetic architecture of brain structure and ADHD using polygenic neuroimaging-derived scores.

Genome-wide association studies (GWAS) have provided valuable insights into the genetic basis of neuropsychiatric disorders and highlighted their complexity. Careful consideration of the polygenicity and complex genetic architecture could aid in the understanding of the underlying brain mechanisms. We introduce an innovative approach to polygenic scoring, utilizing imaging-derived phenotypes (IDPs) to predict a clinical phenotype. We leveraged IDP GWAS data from the UK Biobank, to create polygenic imaging-derived scores (PIDSs). As a proof-of-concept, we assessed genetic variations in brain structure between individuals with ADHD and unaffected controls across three NeuroIMAGE waves (n = 954). Out of the 94 PIDS, 72 exhibited significant associations with their corresponding IDPs in an independent sample. Notably, several global measures, including cerebellum white matter, cerebellum cortex, and cerebral white matter, displayed substantial variance explained for their respective IDPs, ranging from 3% to 5.7%. Conversely, the associations between each IDP and the clinical ADHD phenotype were relatively weak. These findings highlight the growing power of GWAS in structural neuroimaging traits, enabling the construction of polygenic scores that accurately reflect the underlying polygenic architecture. However, to establish robust connections between PIDS and behavioral or clinical traits such as ADHD, larger samples are needed. Our novel approach to polygenic risk scoring offers a valuable tool for researchers in the field of psychiatric genetics.

Clinical implications of brain asymmetries.

No two human brains are alike, and with the rise of precision medicine in neurology, we are seeing an increased emphasis on understanding the individual variability in brain structure and function that renders every brain unique. Functional and structural brain asymmetries are a fundamental principle of brain organization, and recent research suggests substantial individual variability in these asymmetries that needs to be considered in clinical practice. In this Review, we provide an overview of brain asymmetries, variations in such asymmetries and their relevance in the clinical context. We review recent findings on brain asymmetries in neuropsychiatric and neurodevelopmental disorders, as well as in specific learning disabilities, with an emphasis on large-scale database studies and meta-analyses. We also highlight the relevance of asymmetries for disease symptom onset in neurodegenerative diseases and their implications for lateralized treatments, including brain stimulation. We conclude that alterations in brain asymmetry are not sufficiently specific to act as diagnostic biomarkers but can serve as meaningful symptom or treatment response biomarkers in certain contexts. On the basis of these insights, we provide several recommendations for neurological clinical practice.

Gray matter volume of functionally relevant primary motor cortex is causally related to learning a hand motor task.

Variability in brain structure is associated with the capacity for behavioral change. However, a causal link between specific brain areas and behavioral change (such as motor learning) has not been demonstrated. We hypothesized that greater gray matter volume of a primary motor cortex (M1) area active during a hand motor learning task is positively correlated with subsequent learning of the task, and that the disruption of this area blocks learning of the task. Healthy participants underwent structural MRI before learning a skilled hand motor task. Next, participants performed this learning task during fMRI to determine M1 areas functionally active during this task. This functional ROI was anatomically constrained with M1 boundaries to create a group-level "Active-M1" ROI used to measure gray matter volume in each participant. Greater gray matter volume in the left hemisphere Active-M1 ROI was related to greater motor learning in the corresponding right hand. When M1 hand area was disrupted with repetitive transcranial stimulation (rTMS), learning of the motor task was blocked, confirming its causal link to motor learning. Our combined imaging and rTMS approach revealed greater cortical volume in a task-relevant M1 area is causally related to learning of a hand motor task in healthy humans.

A Multi-Target mechanism of Withania somnifera bioactive compounds in autism spectrum disorder (ASD) Treatment: Network pharmacology, molecular docking, and molecular dynamics simulations studies

Autism spectrum disorder (ASD) is a developmental disorder resulting from variations in brain structure and function. Individuals with ASD often experience challenges in communication and social interaction, along with engaging in repetitive or restricted patterns of behavior and interests. The lack of documented data and the wide range of pathophysiological processes associated with ASDmake it challenging to work which causes a major financial burden on health care management. Withania somnifera, often referred to as ashwagandha is the tropical winter cherry in the Solanaceae family that can be used to treat several ailments such as asthma, stress, hypertension,diabetes, cancer, arthritic, and neural disorders including ASD. In this investigation, we examined the active compound-target-pathway network and discovered that Withanolide J, Withanone, and Withaferin A have a great role in the onset of ASD by influencing the IL6 gene. Later, the molecular docking method was applied for confirmation of the active compound's effective action against the prospective target. The molecular dynamics simulation exhibited that the complexes of Withanolide J and Withanone had stable intermolecular binding conformation and unveiled very stable dynamics during the simulation time. A combined network pharmacology and molecular docking approach demonstrated that W. somnifera exhibits a promising preventive impact on ASD by targeting relevant signaling pathways associated with the disorder. This establishes a foundation for comprehending the underlying mechanism of the anti-ASD activity of W. somnifera.

Dose-dependent response of prefrontal transcranial direct current stimulation on the heart rate variability: An electric field modeling study.

Transcranial direct current stimulation (tDCS) of the prefrontal cortex (PFC) modulates the autonomic nervous system by activating deeper brain areas via top-down pathway. However, effects on the nervous system are heterogeneous and may depend on the amount of current that penetrates. Therefore, we aimed to investigate the variable effects of tDCS on heart rate variability (HRV), a measure of the functional state of the autonomic nervous system. Using three prefrontal tDCS protocols (1.5, 3 mA and sham), we associated the simulated individual electric field (E-field) magnitude in brain regions of interest with the HRV effects. This was a randomized, double-blinded, sham-controlled and within-subject trial, in which healthy young-adult participants received tDCS sessions separated by 2 weeks. The brain regions of interest were the dorsolateral PFC (DLPFC), anterior cingulate cortex, insula and amygdala. Overall, 37 participants were investigated, corresponding to a total of 111 tDCS sessions. The findings suggested that HRV, measured by root mean squared of successive differences (RMSSD) and high-frequency HRV (HF-HRV), were significantly increased by the 3.0 mA tDCS when compared to sham and 1.5 mA. No difference was found between sham and 1.5 mA. E-field analysis showed that all brain regions of interest were associated with the HRV outcomes. However, this significance was associated with the protocol intensity, rather than inter-individual brain structural variability. To conclude, our results suggest a dose-dependent effect of tDCS for HRV. Therefore, further research is warranted to investigate the optimal current dose to modulate HRV.

Gray and White Matter Metrics Demonstrate Distinct and Complementary Prediction of Differences in Cognitive Performance in Children: Findings from ABCD (N = 11,876).

Individual differences in cognitive performance in childhood are a key predictor of significant life outcomes such as educational attainment and mental health. Differences in cognitive ability are governed in part by variations in brain structure. However, studies commonly focus on either gray or white matter metrics in humans, leaving open the key question as to whether gray or white matter microstructure plays distinct or complementary roles supporting cognitive performance. To compare the role of gray and white matter in supporting cognitive performance, we used regularized structural equation models to predict cognitive performance with gray and white matter measures. Specifically, we compared how gray matter (volume, cortical thickness, and surface area) and white matter measures (volume, fractional anisotropy, and mean diffusivity) predicted individual differences in cognitive performance. The models were tested in 11,876 children (ABCD Study; 5,680 female, 6,196 male) at 10 years old. We found that gray and white matter metrics bring partly nonoverlapping information to predict cognitive performance. The models with only gray or white matter explained respectively 15.4 and 12.4% of the variance in cognitive performance, while the combined model explained 19.0%. Zooming in, we additionally found that different metrics within gray and white matter had different predictive power and that the tracts/regions that were most predictive of cognitive performance differed across metrics. These results show that studies focusing on a single metric in either gray or white matter to study the link between brain structure and cognitive performance are missing a key part of the equation.

A structural heart-brain axis mediates the association between cardiovascular risk and cognitive function

Abstract Elevated vascular disease risk associates with poorer cognitive function, but the mechanism for this link is poorly understood. A leading theory, the structural-functional model argues that vascular risk may drive adverse cardiac remodelling, which, in turn, leads to chronic cerebral hypoperfusion and subsequent brain structural damage. This model predicts that variation in heart and brain structure should associate with both greater vascular risk and lower cognitive function. This study tests that prediction in a large sample of the UK Biobank (N = 11,962). We assemble and summarise vascular risk factors, cardiac magnetic resonance radiomics, brain structural and diffusion MRI indices, and cognitive assessment. We also extract “heart-brain axes” capturing the covariation in heart and brain structure. Many heart and brain measures partially explain the vascular risk—cognitive function association, like left ventricular end-diastolic volume and grey matter volume. Notably, a heart-brain axis, capturing correlation between lower myocardial intensity, lower grey matter volume, and poorer thalamic white matter integrity, completely mediates the association, supporting the structural-functional model. Our findings also complicate this theory by finding that brain structural variation cannot completely explain the heart structure—cognitive function association. Our results broadly offer evidence for the structural functional hypothesis, identify imaging biomarkers for this association by considering covariation in heart and brain structure, and generate novel hypotheses about how cardiovascular risk may link to cognitive function.

Cognitive Function Is Associated with the Genetically Determined Efficiency of DNA Repair Mechanisms.

Several modifiable risk factors for neurodegeneration and dementia have been identified, although individuals vary in their vulnerability despite a similar risk of exposure. This difference in vulnerability could be explained at least in part by the variability in DNA repair mechanisms' efficiency between individuals. Therefore, the aim of this study was to test associations between documented, prevalent genetic variation (single nucleotide polymorphism, SNP) in DNA repair genes, cognitive function, and brain structure. Community-living participants (n = 488,159; 56.54 years (8.09); 54.2% female) taking part in the UK Biobank study and for whom cognitive and genetic measures were available were included. SNPs in base excision repair (BER) genes of the bifunctional DNA glycosylases OGG1 (rs1052133, rs104893751), NEIL1 (rs7402844, rs5745906), NEIL2 (rs6601606), NEIL3 (rs10013040, rs13112390, rs13112358, rs1395479), MUTYH (rs34612342, rs200165598), NTHL1 (rs150766139, rs2516739) were considered. Cognitive measures included fluid intelligence, the symbol-digit matching task, visual matching, and trail-making. Hierarchical regression and latent class analyses were used to test the associations between SNPs and cognitive measures. Associations between SNPs and brain measures were also tested in a subset of 39,060 participants. Statistically significant associations with cognition were detected for 12 out of the 13 SNPs analyzed. The strongest effects amounted to a 1-6% difference in cognitive function detected for NEIL1 (rs7402844), NEIL2 (rs6601606), and NTHL1 (rs2516739). Associations varied by age and sex, with stronger effects detected in middle-aged women. Weaker associations with brain measures were also detected. Variability in some BER genes is associated with cognitive function and brain structure and may explain variability in the risk for neurodegeneration and dementia.

Brain structure variation and individual differences in theory of mind among older adults

The theory of mind (ToM) is not substantially influenced by aging, suggesting the emergence of various compensatory mechanisms. To identify brain regions subserving ToM in older adults, we investigated the associations of individual differences in brain structure with performance on the Reading the Mind in the Eyes Test (RMET), a widely used measure of ToM, using voxel-based morphometry (VBM) and tract-based spatial statistics (TBSS). In contrast to findings obtained from young adults, where multiple cortical regions are implicated in ToM, VBM analysis revealed a significant positive correlation between RMET score and gray matter (GM) volume only in the right middle temporal gyrus, a region implicated in social cognition. Alternatively, TBSS revealed significant positive correlations between RMET score and the fractional anisotropy (FA) values in widespread white matter (WM) tracts, including the bilateral uncinate fasciculus, a region previously linked to RMET performance in young adults. We speculate that individual differences in WM integrity are strong influences on ToM among older adults, whereas the impact of individual differences in GM volumes is relatively limited.

Enhanced Brain Tumor Segmentation and Size Estimation in MRI Samples using Hybrid Optimization

The area of medical imaging specialization, specifically in the context of brain tumor segmentation, has long been challenged by the inherent complexity and variability of brain structures. Traditional segmentation methods often struggle to accurately differentiate between the diverse types of tissues within the brain, such as white matter, grey matter, and cerebrospinal fluid, leading to suboptimal results in tumor identification and delineation. These limitations necessitate the development of more advanced and precise segmentation techniques to enhance diagnostic accuracy and treatment planning. In response to these challenges, the proposed study introduces a novel segmentation approach that combines the Grey Wolf Optimization approach and the Cuckoo Search approach within a Fuzzy C-Means (FCM) framework. The integration of GWO and CS is designed to leverage their respective strengths in optimizing the segmentation of brain tissues. This hybrid approach was rigorously tested across multiple Magnetic Resonance Imaging (MRI) datasets, demonstrating significant enhancements over existing segmentation methods. The study observed a 4,9 % improvement in accuracy, 3,5 % increase in precision, 4,5 % higher recall, 3,2 % less delay, and 2,5 % better specificity in tumor segmentation. The implications of these advancements are profound. By achieving higher precision and accuracy in brain tumor segmentation, the proposed method can substantially aid in early diagnosis and accurate staging of brain tumors, eventually leading to more effective treatment planning and improved patient outcomes. Furthermore, the integration of GWO and CS within the FCM process sets a new benchmark in medical imaging, paving the way for future investigation in the field of study

Regional Brain Structure Mediates Age‐Related Decline in Eye Movement Control

AbstractBackgroundEye movements (EM) are considered potential biomarkers of neurodegeneration because they are altered in patients with neurodegenerative disease, including Alzheimer’s disease (AD). Alterations in EM control have also been observed in old age, with particularly strong age effects on saccadic latency, inhibitory errors in the antisaccade task, and smooth pursuit accuracy. The goal of our study was to characterize the neural pathways of age‐related changes in these EM outcomes.MethodThe analysis was based on 8,318 participants (aged 30 to 95 years) from the population‐based Rhineland Study. EMs were recorded using video‐based infrared oculography at 1,000 Hz. Volumes and thickness measures of selected brain structures were obtained using FreeSurfer from 3 Tesla T1‐weighted MR images. Relations of brain structure with EM outcomes were quantified using multivariable linear regression models. Brain structure variables were also analyzed as potential mediators in the relation between age and EM outcomes using causal mediation analysis under a counterfactual framework.ResultIn women, higher volume of the right thalamus and right globus pallidus was associated with lower antisaccade latency, whereas higher cortical thickness in the anterior cingulate cortex was associated with higher antisaccade latency. Higher cortical thickness in right supramarginal gyrus and right paracentral lobule and sulcus was associated with fewer antisaccade errors in women. There were no significant associations between regional brain structure and antisaccade performance in men. Higher cortical thickness in the right calcarine sulcus was associated with higher smooth pursuit velocity gain in both men and women. Additionally, individual brain structures mediated up to 11% of the age‐related decline in EM performance.ConclusionOur results support that EMs indeed reflect neurodegenerative processes as changes in brain structure partly account for age‐related decline in oculomotor performance. Relevant brain regions include both brain regions vulnerable to AD pathology and brain regions that are typically spared, suggesting that both “normal” age‐related and pathological mechanisms may be causative for decline in EM control. Our results highlight the importance of taking into account normal age‐related changes when considering EMs as early biomarkers for AD. Further work is needed to better distinguish normal age‐related changes from pathological change.

Genetic predisposition to Alzheimer’s disease is differentially associated with joint volumetric variations in the disease continuum

AbstractBackgroundImaging genetics studies commonly assess the individual association between genetic information and brain features without considering joint brain modulations. The aim of this study was to explore the structural variation of the brain at different stages of Alzheimer’s disease (AD), depending on the genetic predisposition to AD.MethodA total of 351 cognitively unimpaired (CU) middle‐age participants from the ALFA (Alzheimer’s and FAmilies) study and 330 cognitively impaired participants from ADNI (Alzheimer’s Disease Neuroimaging Initiative) with magnetic resonance imaging scans, genetic information and CSF‐amyloid status were included. They were classified as CU Aβ‐, CU Aβ+, (mild cognitive impairment) MCI Aβ+ and AD Aβ+[Table 1]. Freesurfer was used to obtain cortical and subcortical parcellations using the Desikan‐Killiany atlas. A polygenic risk score estimating each participant’s genetic predisposition to AD (PRS‐AD) was calculated including genetic variants at the genome‐wide suggestive level (5×10‐6). Individuals were classified into high/low genetic predisposition groups. Statistical analysis was based on compositional data analysis. First, we computed the optimal multivariate brain structural variation signature (elastic net selection). Then, a logistic regression was performed to determine the association between the optimal signature and the PRS‐AD. Models were stratified by disease stage and adjusted for age and sex.ResultIndividuals at different stages on the AD continuum displayed different brain volumetric modulations associated with higher genetic predisposition to AD [Figure 1]. For instance, the optimal signature associated with an increased risk of AD was mainly characterized by increased hippocampal volumes in Aβ‐ individuals, but decreased volumes in MCI individuals along with the modulation of other temporal regions. Moreover, in AD patients at higher genetic predisposition of AD, the optimal signature was characterized by increased and decreased volumes in frontal regions (e.g. pars triangularis, lateral orbitofrontal vs. medial orbitofrontal, respectively) [Figure 2].ConclusionResults showed AD stage‐specific multivariate volumetric variation associated with an increased genetic risk of AD. Main results suggested that regions that modulated together, although differing according to AD stage, were implicated in the structural brain topology of specific memory networks. The analysis of the joint volumetric variation of brain subregions brings an innovative modeling perspective for neurogenetic studies of AD.

Genetic predisposition to Alzheimer’s disease is differentially associated with joint volumetric variations in the disease continuum

AbstractBackgroundImaging genetics studies commonly assess the individual association between genetic information and brain features without considering joint brain modulations. The aim of this study was to explore the structural variation of the brain at different stages of Alzheimer’s disease (AD), depending on the genetic predisposition to AD.MethodA total of 351 cognitively unimpaired (CU) middle‐age participants from the ALFA (Alzheimer’s and FAmilies) study and 330 cognitively impaired participants from ADNI (Alzheimer’s Disease Neuroimaging Initiative) with magnetic resonance imaging scans, genetic information and CSF‐amyloid status were included. They were classified as CU Aβ‐, CU Aβ+, (mild cognitive impairment) MCI Aβ+ and AD Aβ+[Table 1]. Freesurfer was used to obtain cortical and subcortical parcellations using the Desikan‐Killiany atlas. A polygenic risk score estimating each participant’s genetic predisposition to AD (PRS‐AD) was calculated including genetic variants at the genome‐wide suggestive level (5×10‐6). Individuals were classified into high/low genetic predisposition groups. Statistical analysis was based on compositional data analysis. First, we computed the optimal multivariate brain structural variation signature (elastic net selection). Then, a logistic regression was performed to determine the association between the optimal signature and the PRS‐AD. Models were stratified by disease stage and adjusted for age and sex.ResultIndividuals at different stages on the AD continuum displayed different brain volumetric modulations associated with higher genetic predisposition to AD [Figure 1]. For instance, the optimal signature associated with an increased risk of AD was mainly characterized by increased hippocampal volumes in Aβ‐ individuals, but decreased volumes in MCI individuals along with the modulation of other temporal regions. Moreover, in AD patients at higher genetic predisposition of AD, the optimal signature was characterized by increased and decreased volumes in frontal regions (e.g. pars triangularis, lateral orbitofrontal vs. medial orbitofrontal, respectively) [Figure 2].ConclusionResults showed AD stage‐specific multivariate volumetric variation associated with an increased genetic risk of AD. Main results suggested that regions that modulated together, although differing according to AD stage, were implicated in the structural brain topology of specific memory networks. The analysis of the joint volumetric variation of brain subregions brings an innovative modeling perspective for neurogenetic studies of AD.

Difficulty of Energy Expenditure of Brain When Using Written Speech

The structural organization of the brain matures between 16 and 22 years old when spontaneous contradictions in individual behavior appear with the greatest frequency which is caused by the duality of human consciousness based on the early maturing instinctive-hormonal centers of the limbic system and the late differentiating neocortical fields of voluntary thinking. When creating a written speech, limbic and cortical mechanisms of motivational control are used and individualize the resulting text. Personalization of writing meaningful texts is enhanced by the variability of brain structures involved in this process. We examined evolutionary morphofunctional systems to evaluate their biological value. Depending on individual priorities and motivational choices, written composition was carried out in two strategies. The first was the most general principle of saving the energy expenditure of the brain during any voluntary activity. The second was the mobilization of cortical centers with extremely high energy costs. To identify these differences, a pragmalinguistic analysis technique was developed in this study, supporting both strategies.