Structural Magnetic Resonance Imaging Scans Research Articles

Associations between cortical thickness and anxious/depressive symptoms differ by the quality of early care.

A variety of childhood experiences can lead to anxious/depressed (A/D) symptoms. The aim of the present study was to explore the brain morphological (cortical thickness and surface area) correlates of A/D symptoms and the extent to which these phenotypes vary depending on the quality of the parenting context in which children develop. Structural magnetic resonance imaging (MRI) scans were acquired on 45 children with Child Protective Services (CPS) involvement due to risk of not receiving adequate care (high-risk group) and 25 children without CPS involvement (low-risk group) (rangeage = 8.08-12.14; Mage = 10.05) to assess cortical thickness (CT) and cortical surface area (SA). A/D symptoms were measured using the Child Behavioral Checklist. The association between A/D symptoms and CT, but not SA, differed by risk status such that high-risk children showed decreasing CT as A/D scores increased, whereas low-risk children showed increasing CT as A/D scores increased. This interaction was specific to CT in prefrontal, frontal, temporal, and parietal cortical regions. The groups had marginally different A/D scores, in the direction of higher risk being associated with lower A/D scores. Results suggest that CT correlates of A/D symptoms are differentially shaped by the quality of early caregiving experiences and should be distinguished between high- and low-risk children.

Gray matter structural plasticity in patients with basal ganglia germ cell tumors: A voxel-based morphometry study

BackgroundBasal ganglia germ cell tumors (BGGCTs) are rare intracranial germ cell tumors (iGCTs) that often presents with cognitive impairment. ObjectiveTo assess structural brain plasticity in the presence of unilateral basal ganglia germ cell tumors (BGGCTs), and the correlation between gray matter volume (GMV) changes and cognitive tests. Materials and methodsWe applied voxel-based morphometry (VBM) to structural magnetic resonance imaging (MRI) scans to compare a sample of 41 patients with BGGCTs in the left (n = 22) or right (n = 19) and a sample of 16 patients as control group using a two-sample t-test, correcting for family-wise-errors. A battery of cognitive tests was administered to all BGGCTs patients prior to MRI. We used Pearson correlation analysis to assess the correlation between cognitive test scores and GMV changes. ResultsIn patients with left BGGCTs, whole-brain VBM analysis revealed a large cluster of voxels reflecting an increase in GMV in the left parahippocampal region (k = 529 voxels, T = 4.18, p < 0.01), right middle cingulate cortex (k = 172 voxels, T = 3.96, p < 0.01), and a decrease in volume in the left thalamus (k = 527 voxels, T = −4.88, p < 0.01), right inferior frontal gyrus (k = 495 voxels, T = −4.29, p < 0.01). Pearson correlation analysis showed that the GMV were significantly correlated with the Integrated Visual and Auditory continuous performance test (IVA-CPT) scale (r = 0.637, P = 0.002), abstract reasoning (r = 0.597, P = 0.011), Self-rating Depression Scale (SAS) scale (r = −0.623, P = 0.004) and memory recall (r = 0.648, P = 0.003). ConclusionThese results demonstrate that slow growing but destructive BGGCTs markedly and asymmetrically effect the GMV in left parahippocampal, left thalamus, right middle cingulate cortex, right inferior frontal gyrus and GMV changes were significantly associated with cognitive test.

The Effect of the APOE-ε4 Allele on the Cholinergic Circuitry for Subjects With Different Levels of Cognitive Impairment.

Background: Cholinergic deficiency has been suggested to associate with the abnormal accumulation of Aβ and tau for patients with Alzheimer's disease (AD). However, no studies have investigated the effect of APOE-ε4 and group differences in modulating the cholinergic basal forebrain–amygdala network for subjects with different levels of cognitive impairment. We evaluated the effect of APOE-ε4 on the cholinergic structural association and the neurocognitive performance for subjects with different levels of cognitive impairment.Methods: We used the structural brain magnetic resonance imaging scans from the Alzheimer's Disease Neuroimaging Initiative dataset. The study included cognitively normal (CN, n = 167) subjects and subjects with significant memory concern (SMC, n = 96), early mild cognitive impairment (EMCI, n = 146), late cognitive impairment (LMCI, n = 138), and AD (n = 121). Subjects were further categorized according to the APOE-ε4 allele carrier status. The main effects of APOE-ε4 and group difference on the brain volumetric measurements were assessed. Regression analyses were conducted to evaluate the associations among cholinergic structural changes, APOE-ε4 status, and cognitive performance.Results: We found that APOE-ε4 carriers in the disease group showed higher brain atrophy than non-carriers in the cholinergic pathway, while there is no difference between carriers and non-carriers in the CN group. APOE-ε4 allele carriers in the disease groups also exhibited a stronger cholinergic structural correlation than non-carriers did, while there is no difference between the carriers and non-carriers in the CN subjects. Disease subjects exhibited a stronger structural correlation in the cholinergic pathway than CN subjects did. Moreover, APOE-ε4 allele carriers in the disease group exhibited a stronger correlation between the volumetric changes and cognitive performance than non-carriers did, while there is no difference between carriers and non-carriers in CN subjects. Disease subjects exhibited a stronger correlation between the volumetric changes and cognitive performance than CN subjects did.Conclusion: Our results confirmed the effect of APOE-ε4 on and group differences in the associations with the cholinergic structural changes that may reflect impaired brain function underlying neurocognitive degeneration in AD.

Prenatal Maternal Stress From a Natural Disaster and Hippocampal Volumes: Gene-by-Environment Interactions in Young Adolescents From Project Ice Storm.

Gene-by-environment interactions influence brain development from conception to adulthood. In particular, the prenatal period is a window of vulnerability for the interplay between environmental and genetic factors to influence brain development. Rodent and human research demonstrates that prenatal maternal stress (PNMS) alters hippocampal volumes. Although PNMS affects hippocampal size on average, similar degrees of PNMS lead to different effects in different individuals. This differential susceptibility to the effects of PNMS may be due to genetic variants. Hence, we investigated the role of genetic variants of two SNPs that are candidates to moderate the effects of PNMS on hippocampal volume: COMT (rs4680) and BDNF (rs6265). To investigate this, we assessed 53 children who were in utero during the January 1998 Quebec ice storm. In June 1998 their mothers responded to questionnaires about their objective, cognitive, and subjective levels of stress from the ice storm. When children were 11 1/2 years old, T1-weighted structural magnetic resonance imaging (MRI) scans were obtained using a 3T scanner and analyzed to determine hippocampal volumes. We collected and genotyped the children’s saliva DNA. Moderation analyses were conducted to determine whether either or both of the SNPs moderate the effect of PNMS on hippocampal volumes. We found that objective hardship was associated with right hippocampal volume in girls, and that the BDNF and COMT genotypes were associated with left hippocampal volume in boys and girls. In addition, SNPs located on COMT moderated the effect of maternal objective distress in boys, and subjective distress in girls, on both right hippocampal volume. Thus, we conclude that an individual’s genotype alters their susceptibility to the effects of PNMS.

Multimodal Prediction of Alzheimer's Disease Severity Level Based on Resting-State EEG and Structural MRI

While several biomarkers have been developed for the detection of Alzheimer's disease (AD), not many are available for the prediction of disease severity, particularly for patients in the mild stages of AD. In this paper, we explore the multimodal prediction of Mini-Mental State Examination (MMSE) scores using resting-state electroencephalography (EEG) and structural magnetic resonance imaging (MRI) scans. Analyses were carried out on a dataset comprised of EEG and MRI data collected from 89 patients diagnosed with minimal-mild AD. Three feature selection algorithms were assessed alongside four machine learning algorithms. Results showed that while MRI features alone outperformed EEG features, when both modalities were combined, improved results were achieved. The top-selected EEG features conveyed information about amplitude modulation rate-of-change, whereas top-MRI features comprised information about cortical area and white matter volume. Overall, a root mean square error between predicted MMSE values and true MMSE scores of 1.682 was achieved with a multimodal system and a random forest regression model.

The latent factor structure underlying regional brain volume change and its relation to cognitive change in older adults.

Late-life changes in cognition and brain integrity are both highly multivariate, time-dependent processes that are essential for understanding cognitive aging and neurodegenerative disease outcomes. The present study seeks to identify a latent variable model capable of efficiently reducing a multitude of structural brain change magnetic resonance imaging (MRI) measurements into a smaller number of dimensions. We further seek to demonstrate the validity of this model by evaluating its ability to reproduce patterns of coordinated brain volume change and to explain the rate of cognitive decline over time. We used longitudinal cognitive data and structural MRI scans, obtained from a diverse sample of 358 participants (Mage = 74.81, SD = 7.17), to implement latent variable models for measuring brain change and to estimate the effects of these brain change factors on cognitive decline. Results supported a bifactor model for brain change with four group factors (prefrontal, temporolimbic, medial temporal, and posterior association) and one general change factor (global atrophy). Atrophy in the global (β = 0.434, SE = 0.070), temporolimbic (β = 0.275, SE = 0.085), and medial temporal (β = 0.240, SE = 0.085) factors were the strongest predictors of global cognitive decline. Overall, the brain change model explained 59% of the variance in global cognitive slope. The current results suggest that brain change across 27 bilateral regions of interest can be grouped into five change factors, three of which (global gray matter, temporolimbic, and medial temporal lobe atrophy) are strongly associated with cognitive decline. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Structural Brain Changes and Associated Symptoms of ADHD Subtypes in Children.

Attention-deficit/hyperactivity disorder (ADHD) is presumed to be heterogeneous, but the best way to characterize this heterogeneity remains unclear. Although considerable evidence suggests that the 2 different types of ADHD, inattention and combined, have different cognitive and behavioral profiles, and underlying neurobiologies, we currently lack information on whether these subtypes reflect separated brain structure changes. Structural magnetic resonance imaging scans (N = 234), diagnostic, and demographic information were obtained from the ADHD-200 database. Of this sample, 138 were Typically Developing people, 37 were ADHD-Combined, and 59 were ADHD-Inattentive patients. Freesurfer segmentation methods were used to measure cortical thickness, area, and volume, subcortical volume and hipposubfield volume. ADHD-Inattentive patients showed milder clinical symptoms but more serious cognitive injury than ADHD-Combined patients. In addition, dissociable structural brain changes were found in different subtypes of ADHD, particularly in terms of decreased subcortical volume in ADHD-Combined patients compared with Typically Developing people. Clinical symptoms were predominantly related to smaller rh_caudalanteriorcingulate thickness and left-Pallidum volume, whereas verbal IQ injury was correlated strongly with smaller rh_insula area. These findings indicate that there are significant differences in clinical symptoms and gray matter damage between ADHD-Combined and -Inattentive patients. This supports the growing evidence of heterogeneity in the ADHD-Inattentive subtype and the evidence of brain structure differences.

Identifying resting state differences salient for resilience to chronic pain based on machine learning multivariate pattern analysis.

Studies have documented behavior differences between more versus less resilient adults with chronic pain (CP), but the presence and nature of underlying neurophysiological differences have received scant attention. In this study, we attempted to identify regions of interest (ROIs) in which resting state (Rs) brain activity discriminated more from less resilient CP subgroups based on multiple kernel learning (MKL). More and less resilient community-dwellers with chronic musculoskeletal pain (70 women, 39 men) engaged in structural and functional magnetic resonance imaging (MRI) scans, wherein MKL assessed Rs activity based on amplitude of low frequency fluctuations (ALFF), fractional amplitudes of low frequency fluctuations (fALFF), and regional homogeneity (ReHo) modalities to identify ROIs most salient for discriminating more versus less resilient subgroups. Compared to classification based on single modalities, multi-modal classification based on combined fALFF and ReHo features achieved a substantially higher classification accuracy rate (79%). Brain regions with the best discriminative power included those implicated in pain processing, reward, executive function, goal-directed action, emotion regulation and resilience to mood disorders though variation trends were not consistent between more and less resilient subgroups. Results revealed patterns of Rs activity that serve as possible biomarkers for resilience to chronic musculoskeletal pain.

Visualization of iron-rich subcortical structures in non-human primates in vivo by quantitative susceptibility mapping at 3T MRI

Magnetic resonance imaging (MRI) is now an essential tool in the field of neuroscience involving non-human primates (NHP). Structural MRI scanning using T1-weighted (T1w) or T2-weighted (T2w) images provides anatomical information, particularly for experiments involving deep structures such as the basal ganglia and cerebellum. However, for certain subcortical structures, T1w and T2w image contrasts are insufficient for their detection of important anatomical details. To better visualize such structures in the macaque brain, we applied a relatively new method called quantitative susceptibility mapping (QSM), which enhances tissue contrast based on the local tissue magnetic susceptibility. The QSM significantly improved the visualization of important structures, including the ventral pallidum (VP), globus pallidus external and internal segments (GPe and GPi), substantia nigra (SN), subthalamic nucleus (STN) in the basal ganglia and the dentate nucleus (DN) in the cerebellum. We quantified this the contrast enhancement by systematically comparing of contrast-to-noise ratios (CNRs) of QSM images relative to the corresponding T1w and T2w images. In addition, QSM values of some structures were correlated to the age of the macaque subjects. These results identify the QSM method as a straightforward and useful tool for clearly visualizing details of subcortical structures that are invisible with more traditional scanning sequences.

Cortical thickness distinguishes between major depression and schizophrenia in adolescents

BackgroundEarly diagnosis of adolescent psychiatric disorder is crucial for early intervention. However, there is extensive comorbidity between affective and psychotic disorders, which increases the difficulty of precise diagnoses among adolescents.MethodsWe obtained structural magnetic resonance imaging scans from 150 adolescents, including 67 and 47 patients with major depressive disorder (MDD) and schizophrenia (SCZ), as well as 34 healthy controls (HC) to explore whether psychiatric disorders could be identified using a machine learning technique. Specifically, we used the support vector machine and the leave-one-out cross-validation method to distinguish among adolescents with MDD and SCZ and healthy controls.ResultsWe found that cortical thickness was a classification feature of a) MDD and HC with 79.21% accuracy where the temporal pole had the highest weight; b) SCZ and HC with 69.88% accuracy where the left superior temporal sulcus had the highest weight. Notably, adolescents with MDD and SCZ could be classified with 62.93% accuracy where the right pars triangularis had the highest weight.ConclusionsOur findings suggest that cortical thickness may be a critical biological feature in the diagnosis of adolescent psychiatric disorders. These findings might be helpful to establish an early prediction model for adolescents to better diagnose psychiatric disorders.

Age Influences Loss Aversion Through Effects on Posterior Cingulate Cortical Thickness.

Decision-making strategies shift during normal aging and can profoundly affect wellbeing. Although overweighing losses compared to gains, termed “loss aversion,” plays an important role in choice selection, the age trajectory of this effect and how it may be influenced by associated changes in brain structure remain unclear. We therefore investigated the relationship between age and loss aversion, and tested for its mediation by cortical thinning in brain regions that are susceptible to age-related declines and are implicated in loss aversion — the insular, orbitofrontal, and anterior and posterior cingulate cortices. Healthy participants (n = 106, 17–54 years) performed the Loss Aversion Task. A subgroup (n = 78) provided structural magnetic resonance imaging scans. Loss aversion followed a curvilinear trajectory, declining in young adulthood and increasing in middle-age, and thinning of the posterior cingulate cortex mediated this trajectory. The findings suggest that beyond a threshold in middle adulthood, atrophy of the posterior cingulate cortex influences loss aversion.

Three-Dimensional Convolutional Autoencoder Extracts Features of Structural Brain Images With a "Diagnostic Label-Free" Approach: Application to Schizophrenia Datasets.

There has been increasing interest in performing psychiatric brain imaging studies using deep learning. However, most studies in this field disregard three-dimensional (3D) spatial information and targeted disease discrimination, without considering the genetic and clinical heterogeneity of psychiatric disorders. The purpose of this study was to investigate the efficacy of a 3D convolutional autoencoder (3D-CAE) for extracting features related to psychiatric disorders without diagnostic labels. The network was trained using a Kyoto University dataset including 82 patients with schizophrenia (SZ) and 90 healthy subjects (HS) and was evaluated using Center for Biomedical Research Excellence (COBRE) datasets, including 71 SZ patients and 71 HS. We created 16 3D-CAE models with different channels and convolutions to explore the effective range of hyperparameters for psychiatric brain imaging. The number of blocks containing two convolutional layers and one pooling layer was set, ranging from 1 block to 4 blocks. The number of channels in the extraction layer varied from 1, 4, 16, and 32 channels. The proposed 3D-CAEs were successfully reproduced into 3D structural magnetic resonance imaging (MRI) scans with sufficiently low errors. In addition, the features extracted using 3D-CAE retained the relation to clinical information. We explored the appropriate hyperparameter range of 3D-CAE, and it was suggested that a model with 3 blocks may be related to extracting features for predicting the dose of medication and symptom severity in schizophrenia.

Thalamic Subregions and Obsessive-Compulsive Symptoms in 2,500 Children From the General Population

Pediatric obsessive-compulsive disorder (OCD) and clinically relevant obsessive-compulsive symptoms in the general population are associated with increased thalamic volume. It is unknown whether this enlargement is explained by specific thalamic subregions. The relation between obsessive-compulsive symptoms and volume of thalamic subregions was investigated in a population-based sample of children. Obsessive-compulsive symptoms were measured in children (9-12 years of age) from the Generation R Study using the Short Obsessive-Compulsive Disorder Screener (SOCS). Thalamic nuclei volumes were extracted from structural 3T magnetic resonance imaging scans using the ThalamicNuclei pipeline and regrouped into anterior, ventral, intralaminar/medial, lateral, and pulvinar subregions. Volumes were compared between children with symptoms above clinical cutoff (probable OCD cases, SOCS≥ 6, n= 156) and matched children without symptoms (n= 156). Linear regression models were fitted to investigate the association between continuous SOCS score and subregional volume in the whole sample (N= 2500). Children with probable OCD had larger ventral nuclei compared with children without symptoms (d= 0.25, p= .025, false discovery rateadjusted p= .126). SOCS score showed a negative association with pulvinar volume when accounting for overall thalamic volume (β=-0.057, p= .009, false discovery rate adjusted p= .09). However, these associations did not survive multiple testing correction. The results suggest that individual nuclei groups contribute in varying degrees to overall thalamic volume in children with probable OCD, although this did not survive multiple comparisons correction. Understanding the role of thalamic nuclei and their associated circuits in pediatric OCD could lead toward treatment strategies targeting these circuits.

Subcortical brain segment volumes in Gulf War Illness and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome

AimsThere is controversy about brain volumes in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (CFS) and Gulf War Illness (GWI). Subcortical regions were assessed because of significant differences in blood oxygenation level dependent signals in the midbrain between these diseases. Materials and methodMagnetization-prepared rapid acquisition with gradient echo (MPRAGE) images from 3 Tesla structural magnetic resonance imaging scans from sedentary control (n = 34), CFS (n = 38) and GWI (n = 90) subjects were segmented in FreeSurfer. Segmented subcortical volumes were regressed against intracranial volume and age, then iteratively analyzed by multivariate general linear modeling with disease status, gender and demographics as independent co-variates. Key findingsThe optimal model for all subjects used disease status and gender as fixed factors with independent variables eliminated after iteration. Volumes of anterior and midanterior corpus callosum were significantly larger in GWI than CFS. Gender was a significant variable for many segment volumes, and so female and male subjects were analyzed separately. CFS females had smaller left putamen, right caudate and left cerebellum white matter than control women. CFS males had larger left hippocampus than GWI males. Orthostatic status and posttraumatic distress syndrome were not significant covariates. SignificanceCFS and GWI were appropriate “illness controls” for each other. The different patterns of adjusted segment volumes suggested that sexual dimorphisms contributed to pathological changes. Previous volumetric studies may need to be reevaluated to account for gender differences. The findings are framed by comparison to the spectrum of magnetic resonance imaging outcomes in the literature.

Subcortical brain volume and cortical thickness in adolescent girls and women with binge eating.

Alterations in brain structure have been implicated in the onset and acute phases of several forms of psychopathology. However, there is a dearth of research investigating brain structure in persons with binge eating, contributing to poor understanding of mechanisms associated with binge eating. Adolescent girls and women (aged 14-35 years) with binge eating (n=56) and group age-matched girls and women without binge eating (n=26) completed structural magnetic resonance imaging (MRI) scans and interview-based and self-report assessments of eating disorder and general psychopathology. MRI data were processed using FreeSurfer. Analysis of covariance tested mean differences in subcortical volume and cortical thickness of a priori selected regions of interest between binge-eating and non-binge-eating groups, controlling for age, body mass index, purging frequency, depression, and medication use. Exploratory partial correlations tested associations between brain structure and eating disorder symptoms within participants with binge eating. We did not observe differences in regional subcortical volume and cortical thickness between girls and women with and without binge eating. Within participants with binge eating, severity of attitudinal eating disorder symptoms was inversely associated with caudal middle frontal gyrus, right precentral gyrus, right postcentral gyrus, superior parietal, left inferior parietal thickness, and left accumbens volume; however, these associations would not survive multiple-comparison corrections. Correlations between attitudinal eating disorder symptoms and frontoparietal thinning may represent a state marker of binge eating. Future research could investigate whether frontoparietal thinning worsens with illness duration or persists beyond binge eating cessation.

Unimpaired social cognition in adult patients with ADHD: brain volumetric and behavioral results.

The present study aimed to investigate whether adult patients with attention deficit hyperactivity disorder (ADHD) show deficits in social cognition and to identify the structural neural correlates of social cognitive skills in ADHD. Twenty-six adult patients with ADHD and 26 matched healthy control participants performed the Movie for the Assessment of Social Cognition and underwent a structural magnetic resonance imaging scan. We compared theory of mind (ToM) performance between ADHD patients and healthy controls. Using voxel-based morphometry, we further compared gray matter volumes in regions that are critical for social cognition between the two groups and examined whether ToM performance was correlated with brain morphometry measures. We did not observe any between-group differences in ToM abilities or regional gray matter volumes. Across both groups, performance on affective aspects of ToM correlated positively with gray matter volumes in the medial part of the superior frontal gyri, which is typically involved in social cognition. This study is the first to relate brain structure to social cognitive abilities in adult patients with ADHD. Although our sample was small and heterogeneous, with half of the patients showing mild-to-moderate psychiatric comorbidities, our results may encourage longitudinal studies that relate social cognitive development in childhood and adolescence to brain maturation of ADHD patients.

Identifying Subgroups of Major Depressive Disorder Using Brain Structural Covariance Networks and Mapping of Associated Clinical and Cognitive Variables

BackgroundIdentifying data-driven subtypes of major depressive disorder (MDD) holds promise for parsing the heterogeneity of MDD in a neurobiologically informed way. However, limited studies have used brain structural covariance networks (SCNs) for subtyping MDD. MethodsThis study included 145 unmedicated patients with MDD and 206 demographically matched healthy control subjects, who underwent a structural magnetic resonance imaging scan and a comprehensive neurocognitive battery. Patterns of structural covariance were identified using source-based morphometry across both patients with MDD and healthy control subjects. K-means clustering algorithms were applied on dysregulated structural networks in MDD to identify potential MDD subtypes. Finally, clinical and neurocognitive measures were compared between identified subgroups to elucidate the profile of these MDD subtypes. ResultsSource-based morphometry across all individuals identified 28 whole-brain SCNs that encompassed the prefrontal, anterior cingulate, and orbitofrontal cortices; basal ganglia; and cerebellar, visual, and motor regions. Compared with healthy control subjects, individuals with MDD showed lower structural network integrity in three networks including default mode, ventromedial prefrontal cortical, and salience networks. Clustering analysis revealed two MDD subtypes based on the patterns of structural network abnormalities in these three networks. Further profiling revealed that patients in subtype 1 had younger age of onset and more symptom severity as well as greater deficits in cognitive performance than patients in subtype 2. ConclusionsOverall, we identified two MDD subtypes based on SCNs that differed in their clinical and cognitive profile. Our results represent a proof-of-concept framework for leveraging these large-scale SCNs to parse heterogeneity in MDD.

Detecting genetic associations with brain imaging phenotypes in Alzheimer's disease via a novel structured KCCA approach.

Neuroimaging genetics has become an important research topic since it can reveal complex associations between genetic variants (i.e. single nucleotide polymorphisms (SNPs) and the structures or functions of the human brain. However, existing kernel mapping is difficult to directly use the sparse representation method in the kernel feature space, which makes it difficult for most existing sparse canonical correlation analysis (SCCA) methods to be directly promoted in the kernel feature space. To bridge this gap, we adopt a novel alternating projected gradient approach, gradient KCCA (gradKCCA) model to develop a powerful model for exploring the intrinsic associations among genetic markers, imaging quantitative traits (QTs) of interest. Specifically, this model solves kernel canonical correlation (KCCA) with an additional constraint that projection directions have pre-images in the original data space, a sparsity-inducing variant of the model is achieved through controlling the [Formula: see text]-norm of the preimages of the projection directions. We evaluate this model using Alzheimer's disease Neuroimaging Initiative (ADNI) cohort to discover the relationships among SNPs from Alzheimer's disease (AD) risk gene APOE, imaging QTs extracted from structural magnetic resonance imaging (MRI) scans. Our results show that the algorithm not only outperforms the traditional KCCA method in terms of Root Mean Square Error (RMSE) and Correlation Coefficient (CC) but also identify the meaningful and relevant biomarkers of SNPs (e.g. rs157594 and rs405697), which are positively related to right Postcentral and right SupraMarginal brain regions in this study. Empirical results indicate its promising capability in revealing biologically meaningful neuroimaging genetics associations and improving the disease-related mechanistic understanding of AD.

Sex Differences in the Association of Cigarette Craving With Insula Structure.

BackgroundCigarette craving, which can negatively impact smoking cessation, is reportedly stronger in women than in men when they initiate abstinence from smoking. Identifying approaches to counteract craving in people of different sexes may facilitate the development of personalized treatments for Tobacco Use Disorder, which disproportionately affects women. Because cigarette craving is associated with nicotine dependence and structure of the insula, this study addressed whether a person’s sex influences these associations.MethodsThe research participants (n = 99, 48 women) reported daily cigarette smoking and provided self-reports of nicotine dependence. After overnight abstinence from smoking, they underwent structural magnetic resonance imaging scanning to determine cortical thickness of the left and right anterior circular insular sulcus, and self-rated their cigarette craving before and after their first cigarette of the day.ResultsWomen reported stronger craving than men irrespective of smoking condition (i.e., pre- and post-smoking) (P = .048), and smoking reduced craving irrespective of sex (P < .001). A 3-way interaction of sex, smoking condition, and right anterior circular insular sulcus thickness on craving (P = .033) reflected a negative association of cortical thickness with pre-smoking craving in women only (P = .012). No effects of cortical thickness in the left anterior circular insular sulcus were detected. Nicotine dependence was positively associated with craving (P < .001) across groups and sessions, with no sex differences in this association.ConclusionsA negative association of right anterior insula thickness with craving in women only suggests that this region may be a relevant therapeutic target for brain-based smoking cessation interventions in women.

Study Protocol: The Heart and Brain Study.

BackgroundIt is well-established that what is good for the heart is good for the brain. Vascular factors such as hypertension, diabetes, and high cholesterol, and genetic factors such as the apolipoprotein E4 allele increase the risk of developing both cardiovascular disease and dementia. However, the mechanisms underlying the heart–brain association remain unclear. Recent evidence suggests that impairments in vascular phenotypes and cerebrovascular reactivity (CVR) may play an important role in cognitive decline. The Heart and Brain Study combines state-of-the-art vascular ultrasound, cerebrovascular magnetic resonance imaging (MRI) and cognitive testing in participants of the long-running Whitehall II Imaging cohort to examine these processes together. This paper describes the study protocol, data pre-processing and overarching objectives.Methods and DesignThe 775 participants of the Whitehall II Imaging cohort, aged 65 years or older in 2019, have received clinical and vascular risk assessments at 5-year-intervals since 1985, as well as a 3T brain MRI scan and neuropsychological tests between 2012 and 2016 (Whitehall II Wave MRI-1). Approximately 25% of this cohort are selected for the Heart and Brain Study, which involves a single testing session at the University of Oxford (Wave MRI-2). Between 2019 and 2023, participants will undergo ultrasound scans of the ascending aorta and common carotid arteries, measures of central and peripheral blood pressure, and 3T MRI scans to measure CVR in response to 5% carbon dioxide in air, vessel-selective cerebral blood flow (CBF), and cerebrovascular lesions. The structural and diffusion MRI scans and neuropsychological battery conducted at Wave MRI-1 will also be repeated. Using this extensive life-course data, the Heart and Brain Study will examine how 30-year trajectories of vascular risk throughout midlife (40–70 years) affect vascular phenotypes, cerebrovascular health, longitudinal brain atrophy and cognitive decline at older ages.DiscussionThe study will generate one of the most comprehensive datasets to examine the longitudinal determinants of the heart–brain association. It will evaluate novel physiological processes in order to describe the optimal window for managing vascular risk in order to delay cognitive decline. Ultimately, the Heart and Brain Study will inform strategies to identify at-risk individuals for targeted interventions to prevent or delay dementia.