Structural Brain Connectivity Research Articles

Subtle microstructural alterations in white matter tracts involved in socio-emotional processing after very preterm birth

Children born very preterm (VPT, < 32 weeks of gestation) have an increased risk of developing socio-emotional difficulties. Possible neural substrates for these socio-emotional difficulties are alterations in the structural connectivity of the social brain due to premature birth. The objective of the current study was to study microstructural white matter integrity in VPT versus full-term (FT) born school-aged children along twelve white matter tracts involved in socio-emotional processing. Diffusion MRI scans were obtained from a sample of 35 VPT and 38 FT 8-to-12-year-old children. Tractography was performed using TractSeg, a state-of-the-art neural network-based approach, which offers investigation of detailed tract profiles of fractional anisotropy (FA). Group differences in FA along the tracts were investigated using both a traditional and complementary functional data analysis approach. Exploratory correlations were performed between the Social Responsiveness Scale (SRS-2), a parent-report questionnaire assessing difficulties in social functioning, and FA along the tract. Both analyses showed significant reductions in FA for the VPT group along the middle portion of the right SLF I and an anterior portion of the left SLF II. These group differences possibly indicate altered white matter maturation due to premature birth and may contribute to altered functional connectivity in the Theory of Mind network which has been documented in earlier work with VPT samples. Apart from reduced social motivation in the VPT group, there were no significant group differences in reported social functioning, as assessed by SRS-2. We found that in the VPT group higher FA values in segments of the left SLF I and right SLF II were associated with better social functioning. Surprisingly, the opposite was found for segments in the right IFO, where higher FA values were associated with worse reported social functioning. Since no significant correlations were found for the FT group, this relationship may be specific for VPT children. The current study overcomes methodological limitations of previous studies by more accurately segmenting white matter tracts using constrained spherical deconvolution based tractography, by applying complementary tractometry analysis approaches to estimate changes in FA more accurately, and by investigating the FA profile along the three components of the SLF.

Identifying potential scalp acupuncture targets for chronic pain and comorbid disorders using functional and anatomical connectivity of critical deep brain structures

Scalp acupuncture is an innovative approach that integrates acupuncture needling stimulation with the modern understanding of brain function. In recent decades, scalp acupuncture has been applied to treat chronic pain and has achieved promising results. This study aimed to identify potential brain surface targets for scalp acupuncture based on the functional and anatomical connectivity of the hippocampus, amygdala, and nucleus accumbens, three deep brain structures that are believed to play an important role in the pathophysiology of chronic pain, as well as multiple comorbid psychiatric and neurological disorders. Resting-state functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) datasets from 119 healthy participants were included in our analysis. We found that the medial prefrontal cortex (mPFC), corresponding to the acupoints EX-HN3 (Yintang) / BL2 (Cuanzhu), is functionally and anatomically connected to all three subcortical regions, while the precuneus, corresponding to the acupoints GV19 (Houding) / GV20 (Baihui) and the MS12 scalp acupuncture line, is connected to the hippocampus and amygdala. Our results suggest that the mPFC and precuneus, two key hubs of the default mode network (DMN), and other cortical areas distributed at the prefrontal, parietal, and temporal cortices may hold potential as novel targets for scalp acupuncture in the treatment of chronic pain and its comorbidities. These identified locations may also be used for the treatment of psychiatric and neurological disorders, such as anxiety, depression, insomnia, and cognitive decline, in which the three corresponding deep brain structures play a crucial role.

The influence of early-life adversity on the coupling of structural and functional brain connectivity across childhood

Early-life adversity (ELA) exposure is suggested to accelerate development. However, the influence of ELA on neurodevelopmental trajectories has not been assessed directly but largely inferred from retrospective reporting in adult cohorts. Using multimodal neuroimaging data from a pediatric cohort study (N = 549), we modeled neurodevelopmental trajectories over childhood with structure–function coupling (SC–FC), the correlation between structural and functional connectivity. A linear decrease in SC–FC was observed from age 4.5 to 7.5 years. When stratified by ELA, only the high-adversity group showed a curvilinear trajectory, with a steep decrease between age 4.5 and 6 years, suggestive of accelerated neurodevelopment. This finding was confirmed by increased DNA-derived epigenetic age acceleration at age 6 years in the high- relative to low-adversity groups. SC–FC at age 4.5 years also positively moderated the associations between ELA and behavioral outcomes assessed in mid-childhood. These results demonstrate the association between ELA and neurodevelopment, and how they interact to influence behavior.

Age-related differences in structural and resting-state functional brain network organization across the adult lifespan: A cross-sectional study

We investigated age-related trends in the topology and hierarchical organization of brain structural and functional networks using diffusion-weighted imaging and resting-state fMRI data from a large cohort of healthy aging adults. At the cross-modal level, we explored age-related patterns in the RC involvement of different functional subsystems using a high-resolution functional parcellation. We further assessed age-related differences in the structure–function coupling as well as the network vulnerability to damage to rich club connectivity.Regardless of age, the structural and functional brain networks exhibited a rich club organization and small-world topology. In older individuals, we observed reduced integration and segregation within the frontal-occipital regions and the cerebellum along the brain's medial axis. Additionally, functional brain networks displayed decreased integration and increased segregation in the prefrontal, centrotemporal, and occipital regions, and the cerebellum. In older subjects, structural networks also exhibited decreased within-network and increased between-network RC connectivity. Furthermore, both within-network and between-network RC connectivity decreased in functional networks with age. An age-related decline in structure–function coupling was observed within sensory-motor, cognitive, and subcortical networks. The structural network exhibited greater vulnerability to damage to RC connectivity within the language-auditory, visual, and subcortical networks. Similarly, for functional networks, increased vulnerability was observed with damage to RC connectivity in the cerebellum, language-auditory, and sensory-motor networks. Overall, the network vulnerability decreased significantly in subjects older than 70 in both networks. Our findings underscore significant age-related differences in both brain functional and structural RC connectivity, with distinct patterns observed across the adult lifespan.

Association between cannabis use and brain structure and function: an observational and Mendelian randomisation study.

Cannabis use during adolescence and young adulthood has been associated with brain harm, yet despite a rapid increase in cannabis use among older adults in the past decade, the impact on brain health in this population remains understudied. To explore observational and genetic associations between cannabis use and brain structure and function. We examined 3641 lifetime cannabis users (mean (SD) age 61.0 (7.1) years) and 12 255 controls (mean (SD) age 64.5 (7.5) years) from UK Biobank. Brain structure and functional connectivity were measured using multiple imaging-derived phenotypes. Associations with cannabis use were assessed using multiple linear regression controlling for potential confounds. Bidirectional two-sample Mendelian randomisation analyses were used to investigate potential causal relationships. Cannabis use was associated with multiple measures of brain structure and function. Participants with a history of cannabis use had poorer white matter integrity, as assessed by lower fractional anisotropy and higher mean diffusivity in the genu of the corpus callosum, as well as weaker resting-state functional connectivity in brain regions underlying the default mode and central executive networks. Mendelian randomisation analyses found no support for causal relationships underlying associations between cannabis use and brain structure or function. Associations between lifetime cannabis use and brain structure and function in later life are probably not causal in nature and might represent residual confounding. Cannabis use is associated with differences in brain structure and function. Further research is needed to understand the mechanisms underlying these associations, which do not appear to be causal.

Brain structural network modular and connectivity alterations in subtypes of patients with migraine and medication overuse headache.

Migraine, one of the most prevalent and debilitating neurological disorders, can be classified based on attack frequency into episodic migraine (EM) and chronic migraine (CM). Medication overuse headache (MOH), a type of chronic headache, arises when painkillers are overused by individuals with untreated or inadequately treated headaches. This study compares regional cortical morphological alterations and brain structural network changes among these headache subgroups. Sixty participants, including 20 in each of the following patient groups (EM, CM, MOH), and healthy controls (HC) completed the study. Our results show that the EM group exhibited cortical thickness (CTs) thinning predominantly in the left limbic, whereas CM patients exhibited CTs thinning across both left and right hemispheres. The MOH group demonstrated the most widespread CTs thinning. Both CM and MOH exhibited comparable patterns of CTs thinning within lobes, leading to reduced intra-lobe connectivity. While there were no significant differences in total inter-lobe connectivity between migraine groups and HC, both CM and MOH groups exhibited significantly decreased inter-limbic connectivity compared to HC and EM groups. In addition, they showed increased inter-frontal and inter-parietal connectivity, suggesting possible compensatory mechanisms to offset the loss of inter-lobe connectivity between the limbic and other lobes. Both CM and MOH groups exhibited a significant loss of global efficiency and a decrease in betweenness centrality in their brain networks, with MOH showing the most pronounced decrease and CM showing the second largest decrease. Our results suggest that aberrant structural brain networks in CM and MOH are less efficient, less centralization, and abnormally segregated.

Structural brain network organization in children with prenatal alcohol exposure

IntroductionThere is growing evidence suggesting that children with prenatal alcohol exposure (PAE) struggle with cognitively demanding tasks, such as learning, attention, and language. Complex structural network analyses can provide insight into the neurobiological underpinnings of these functions, as they may be sensitive for characterizing the effects of PAE on the brain. However, investigations on how PAE affects brain networks are limited. We aim to compare diffusion magnetic resonance imaging (MRI) tractography-based structural networks between children with low-to-moderate PAE in trimester 1 only (T1) or throughout all trimesters (T1-T3) with those without alcohol exposure prenatally. MethodsOur cohort included three groups of children aged 6 to 8 years: 1) no PAE (n = 24), 2) low-to-moderate PAE during T1 only (n = 30), 3) low-to-moderate PAE throughout T1-T3 (n = 36). Structural networks were constructed using the multi-shell multi-tissue constrained spherical deconvolution tractography technique. Quantitative group-wise analyses were conducted at three levels: (a) at the whole-brain network level, using both network-based statistical analyses and network centrality; and then using network centrality at (b) the modular level, and (c) per-region level, including the regions identified as brain hubs. ResultsCompared with the no PAE group, widespread brain network alterations were observed in the PAE T1-T3 group using network-based statistics, but no alterations were observed for the PAE T1 group. Network alterations were also detected at the module level in the PAE T1-T3 compared with the no PAE group, with lower eigenvector centrality in the module that closely represented the right cortico-basal ganglia-thalamo-cortical network. No significant group differences were found in network centrality at the per-region level, including the hub regions. ConclusionsThis study demonstrated that low-to-moderate PAE throughout pregnancy may alter brain structural connectivity, which may explain the neurodevelopmental deficits associated with PAE. It is possible that timing and duration of alcohol exposure are crucial, as PAE in T1 only did not appear to alter brain structural connectivity.

DTI fiber tractography of human brain

DTI fiber tractography is a powerful tool for investigating the human brain's structural connectivity. It enables us to explore the complex network of fiber pathways that connect different regions of the brain and play a crucial role in its function. In this work, I used DWI(Diffusion-Weighted Imaging) data processing software (such as DiffusionToolkit, and Trackvis ) to construct fiber tracks of the human brain based on MRI(Magnetic Resonance Imaging) data and investigated the brain anatomical structure of a human subject using DTI(Diffusion tensor imaging) fiber tractography. The two software I used was Diffusion Toolkit and Trackvis. Diffusion Toolkit did the preparation work for Trackvis, including data reconstruction and fiber tracking on diffusion-weighted MR(Magnetic Resonance) images. Trackvis was utilized for the tractogram visualization and further analyses of the white matter tracts generated by using DTI fiber tractography. In this work, I successfully used Diffusion Toolkit and Trackvis to construct fiber tracks of the human brain, and the results were correct when compared to the standard brain. Besides, I summarized the principle of DTI and the advantages and disadvantages of the technology.

From the reward network to whole-brain metrics: structural connectivity in adolescents and young adults according to body mass index and genetic risk of obesity.

Obesity is a multifactorial condition. Genetic variants, such as the fat mass and obesity related gene (FTO) polymorphism, may increase the vulnerability of developing obesity by disrupting dopamine signaling within the reward network. Yet, the association of obesity, genetic risk of obesity, and structural connectivity of the reward network in adolescents and young adults remains unexplored. We investigate, in adolescents and young adults, the structural connectivity differences in the reward network and at the whole-brain level according to body mass index (BMI) and the FTO rs9939609 polymorphism. One hundred thirty-two adolescents and young adults (age range: [10, 21] years, BMI z-score range: [-1.76, 2.69]) were included. Genetic risk of obesity was determined by the presence of the FTO A allele. Whole-brain and reward network structural connectivity were analyzed using graph metrics. Hierarchical linear regression was applied to test the association between BMI-z, genetic risk of obesity, and structural connectivity. Higher BMI-z was associated with higher (B = 0.76, 95% CI = [0.30, 1.21], P = 0.0015) and lower (B = -0.003, 95% CI = [-0.006, -0.00005], P = 0.048) connectivity strength for fractional anisotropy at the whole-brain level and of the reward network, respectively. The FTO polymorphism was not associated with structural connectivity nor with BMI-z. We provide evidence that, in healthy adolescents and young adults, higher BMI-z is associated with higher connectivity at the whole-brain level and lower connectivity of the reward network. We did not find the FTO polymorphism to correlate with structural connectivity. Future longitudinal studies with larger sample sizes are needed to assess how genetic determinants of obesity change brain structural connectivity and behavior.

THE ASSOCIATIONS OF SOCIOECONOMIC POSITION WITH STRUCTURAL BRAIN DAMAGE AND CONNECTIVITY AND COGNITION

Abstract Socioeconomic inequalities in the risk for cognitive impairment have been reported, which might partly act through structural brain damage and connectivity. This study investigated the extent to which the association of early-life socioeconomic position (SEP) with later-life cognitive performance is mediated by later-life SEP, and whether the association of SEP with later-life cognitive performance can be explained by structural brain damage and connectivity. We used cross-sectional data from the population-based Maastricht study (n=4,839; mean age 59.2±8.7 years, 49.8% women). Early-life SEP was assessed retrospectively by self-reported poverty and parental education. Later-life SEP included education, occupation, and household income. Participants underwent cognitive testing and 3 T magnetic resonance imaging to measure volumes of white matter hyperintensities, gray matter, white matter, cerebrospinal fluid, and structural connectivity. Multiple linear regression analyses tested the associations between SEP, MRI brain markers and cognition. Structural equation modeling tested mediation. Results indicated that higher SEP was associated with higher grey matter volume, lower white matter volume, and higher structural connectivity. Structural brain damage and lower structural connectivity were associated with lower cognition. Both higher early-life and later-life SEP were associated with higher cognition. The association of early-life SEP with later-life cognition was partly mediated by later-life SES (73.8%). However, the extent to which the association of SEP with cognition could be explained by structural brain damage or connectivity was marginal (up to 5.9%). More research is needed to investigate alternative pathways that can explain the associations between SEP with later-life cognition.

Making Connections: Neurodevelopmental Changes in Brain Connectivity After Adverse Experiences in Early Adolescence.

Adverse childhood experiences have been linked to detrimental mental health outcomes in adulthood. This study investigates a potential neurodevelopmental pathway between adversity and mental health outcomes: brain connectivity. We used data from the prospective, longitudinal Adolescent Brain Cognitive Development (ABCD) study (N ≍ 12.000, participants aged 9-13 years, male and female) and assessed structural brain connectivity using fractional anisotropy (FA) of white matter tracts. The adverse experiences modeled included family conflict and traumatic experiences. K-means clustering and latent basis growth models were used to determine subgroups based on total levels and trajectories of brain connectivity. Multinomial regression was used to determine associations between cluster membership and adverse experiences. The results showed that higher family conflict was associated with higher FA levels across brain tracts (e.g., t (3) = -3.81, β = -0.09, p bonf = 0.003) and within the corpus callosum (CC), fornix, and anterior thalamic radiations (ATR). A decreasing FA trajectory across two brain imaging timepoints was linked to lower socioeconomic status and neighborhood safety. Socioeconomic status was related to FA across brain tracts (e.g., t (3) = 3.44, β = 0.10, p bonf = 0.01), the CC and the ATR. Neighborhood safety was associated with FA in the Fornix and ATR (e.g., t (1) = 3.48, β = 0.09, p bonf = 0.01). There is a complex and multifaceted relationship between adverse experiences and brain development, where adverse experiences during early adolescence are related to brain connectivity. These findings underscore the importance of studying adverse experiences beyond early childhood to understand lifespan developmental outcomes.

The associations of socioeconomic status with structural brain damage and connectivity and cognition: The Maastricht Study

AbstractBackgroundSocioeconomic inequalities in the risk for cognitive impairment have been reported, which might partly act through structural brain damage or differential numbers of brain connections (structural connectivity). This study investigated the extent to which the association of early‐life socioeconomic status (SES) with later‐life cognitive performance is mediated by later‐life SES, and whether the association of SES with later‐life cognitive performance can be explained by structural brain damage and connectivity.MethodWe used cross‐sectional data from the population‐based Maastricht study (n = 4,839; mean age 59.2±8.7 years, 49.8% women). Early‐life SES was assessed retrospectively by self‐reported poverty and parental education. Later‐life SES included education, occupation, and household income. Participants underwent cognitive testing and 3 T magnetic resonance imaging to measure volumes of white matter hyperintensities, gray matter, white matter, cerebrospinal fluid, and structural connectivity. Multiple linear regression analyses tested the associations between SES, brain markers and cognition. Analyses were adjusted for age, sex, and type 2 diabetes (because of oversampling). Structural equation modeling tested mediation.ResultHigher SES was associated with higher grey matter volume, lower white matter volume, and higher structural connectivity. Structural brain damage and lower structural connectivity were associated with lower overall cognitive performance. Both higher early‐life and later‐life SES were associated with overall higher cognitive performance (B [95% CI] medium versus low early‐life SES = 0.17[0.12;0.23]; B high versus low early‐life SES = 0.26[0.20;0.32]; B medium versus low later‐life SES = 0.46[0.41;0.52]; B high versus low later‐life SES = 0.72[0.67;0.78]). The association of early‐life SES with later‐life cognitive performance was partly mediated by later‐life SES (73.8%). The extent to which the association of SES with cognitive performance could be explained by structural brain damage or connectivity was marginal (up to 5.9%).ConclusionThough there was an association between early‐life SES and later‐life cognitive performance independent of later‐life SES, a large part of this association could be explained by later‐life SES. Structural brain damage and connectivity marginally explained socioeconomic inequalities in cognitive performance. More research is needed to investigate alternative pathways that explain associations of life‐course SES with later‐life cognition.

Structural connectivity of cytoarchitectonically distinct human left temporal pole subregions: a diffusion MRI tractography study.

The temporal pole (TP) is considered one of the major paralimbic cortical regions, and is involved in a variety of functions such as sensory perception, emotion, semantic processing, and social cognition. Based on differences in cytoarchitecture, the TP can be further subdivided into smaller regions (dorsal, ventrolateral and ventromedial), each forming key nodes of distinct functional networks. However, the brain structural connectivity profile of TP subregions is not fully clarified. Using diffusion MRI data in a set of 31 healthy subjects, we aimed to elucidate the comprehensive structural connectivity of three cytoarchitectonically distinct TP subregions. Diffusion tensor imaging (DTI) analysis suggested that major association fiber pathways such as the inferior longitudinal, middle longitudinal, arcuate, and uncinate fasciculi provide structural connectivity to the TP. Further analysis suggested partially overlapping yet still distinct structural connectivity patterns across the TP subregions. Specifically, the dorsal subregion is strongly connected with wide areas in the parietal lobe, the ventrolateral subregion with areas including constituents of the default-semantic network, and the ventromedial subregion with limbic and paralimbic areas. Our results suggest the involvement of the TP in a set of extensive but distinct networks of cortical regions, consistent with its functional roles.

A proposed structural connectivity matrices approach for the superior fronto-occipital fascicle in the Harvard-Oxford Atlas comparative framework following the Pandya comparative extrapolation principle.

A key set of connections necessary for the most complex brain functions are the long association cortico-cortical fiber tracts. These pathways have been described by the Dejerines and others using post mortem histological or brain dissection techniques. Given methodological limitations, these fiber connections have not been delineated completely in humans. Although the stem portions of fiber tracts have been identified in humans, their precise origins and terminations remain to be determined. By contrast, the origins and terminations as well as the stems of long cortico-cortical association fiber pathways in monkeys have been detailed in the macaque monkey brain using experimental tract tracing methods. Deepak Pandya made major contributions to the delineation of fiber tracts in the monkey brain. In the early 1990s, he compared his observations in monkeys with the original descriptions in humans by the Dejerines. With the advent of diffusion-weighted imaging, Dr. Pandya extended this line of investigation to the human brain with Dr. Nikos Makris. In this translational analysis of long association cortico-cortical fiber tracts, they applied a principle of extrapolation from monkey to human. In the present study, we addressed the reasoning and the complex methodology in translating brain structural connectivity from monkey to human in one cortico-cortical fiber tract, namely the superior fronto-occipital fascicle, which was delineated in both species by Dr. Pandya and colleagues. Furthermore, we represented this information in the form of connectional matrices in the context of the HOA2.0-ComPaRe framework, a homological monkey-to-human translational system used in neuroimaging.

Structural and functional multilayer network analysis in restless legs syndrome patients.

The combination of brain structural and functional connectivity offers complementary insights into its organisation. Multilayer network analysis explores various relationships across different layers within a single system. We aimed to investigate changes in the structural and functional multilayer network in 69 patients with primary restless legs syndrome (RLS) compared with 50 healthy controls. Participants underwent diffusion tensor imaging (DTI) and resting state-functional magnetic resonance imaging (rs-fMRI) using a three-tesla MRI scanner. We constructed a structural connectivity matrix derived from DTI using a DSI program and made a functional connectivity matrix based on rs-fMRI using an SPM program and CONN toolbox. A multilayer network analysis, using BRAPH program, was then conducted to assess the connectivity patterns in both groups. At the global level, significant differences there were between the patients with RLS and healthy controls. The average multiplex participation was lower in patients with RLS than in healthy controls (0.804 vs. 0.821, p = 0.042). Additionally, several regions showed significant differences in the nodal level in multiplex participation between patients with RLS and healthy controls, particularly the frontal and temporal lobes. The regions affected included the inferior frontal gyrus, medial orbital gyrus, precentral gyrus, rectus gyrus, insula, superior and inferior temporal gyrus, medial and lateral occipitotemporal gyrus, and temporal pole. These results represent evidence of diversity in interactions between structural and functional connectivity in patients with RLS, providing a more comprehensive understanding of the brain network in RLS. This may contribute to a precise diagnosis of RLS, and aid the development of a biomarker to track treatment effectiveness.

Structural and functional connectivity of the whole brain and subnetworks in individuals with mild traumatic brain injury: predictors of patient prognosis.

Abstract JOURNAL/nrgr/04.03/01300535-202407000-00035/figure1/v/2023-11-20T171125Z/r/image-tiff Patients with mild traumatic brain injury have a diverse clinical presentation, and the underlying pathophysiology remains poorly understood. Magnetic resonance imaging is a non-invasive technique that has been widely utilized to investigate neurobiological markers after mild traumatic brain injury. This approach has emerged as a promising tool for investigating the pathogenesis of mild traumatic brain injury. Graph theory is a quantitative method of analyzing complex networks that has been widely used to study changes in brain structure and function. However, most previous mild traumatic brain injury studies using graph theory have focused on specific populations, with limited exploration of simultaneous abnormalities in structural and functional connectivity. Given that mild traumatic brain injury is the most common type of traumatic brain injury encountered in clinical practice, further investigation of the patient characteristics and evolution of structural and functional connectivity is critical. In the present study, we explored whether abnormal structural and functional connectivity in the acute phase could serve as indicators of longitudinal changes in imaging data and cognitive function in patients with mild traumatic brain injury. In this longitudinal study, we enrolled 46 patients with mild traumatic brain injury who were assessed within 2 weeks of injury, as well as 36 healthy controls. Resting-state functional magnetic resonance imaging and diffusion-weighted imaging data were acquired for graph theoretical network analysis. In the acute phase, patients with mild traumatic brain injury demonstrated reduced structural connectivity in the dorsal attention network. More than 3 months of follow-up data revealed signs of recovery in structural and functional connectivity, as well as cognitive function, in 22 out of the 46 patients. Furthermore, better cognitive function was associated with more efficient networks. Finally, our data indicated that small-worldness in the acute stage could serve as a predictor of longitudinal changes in connectivity in patients with mild traumatic brain injury. These findings highlight the importance of integrating structural and functional connectivity in understanding the occurrence and evolution of mild traumatic brain injury. Additionally, exploratory analysis based on subnetworks could serve a predictive function in the prognosis of patients with mild traumatic brain injury.

Effect of Dialysis Treatment on Structural Brain Connectivity in Patients with ESRD

Effect of Dialysis Treatment on Structural Brain Connectivity in Patients with ESRD

Graph Theoretical Analysis of Brain Structural Connectivity in Patients with Alcohol Dependence.

This study aimed to compare brain structural connectivity using graph theory between patients with alcohol dependence and social drinkers. The participants were divided into two groups; the alcohol group (N=23) consisting of patients who had been hospitalized and had abstained from alcohol for at least three months and the control group (N=22) recruited through advertisements and were social drinkers. All participants were evaluated using 3T magnetic resonance imaging. A total of 1000 repeated whole-brain tractographies with random parameters were performed using DSI Studio. Four hundred functionally defined cortical regions of interest (ROIs) were parcellated using FreeSurfer based on the Schaefer Atlas. The ROIs were overlaid on the tractography results to generate 1000 structural connectivity matrices per person, and 1000 matrices were averaged into a single matrix per subject. Graph analysis was performed through igraph R package. Graph measures were compared between the two groups using analysis of covariance, considering the effects of age and smoking pack years. The alcohol group showed lower local efficiency than the control group in the whole-brain (F=5.824, p=0.020), somato-motor (F=5.963, p=0.019), and default mode networks (F=4.422, p=0.042). The alcohol group showed a lower global efficiency (F=5.736, p=0.021) in the control network. The transitivity of the alcohol group in the dorsal attention network was higher than that of the control (F=4.257, p=0.046). Our results imply that structural stability of the whole-brain network is affected in patients with alcohol dependence, which can lead to ineffective information processing in cases of local node failure.

Outliers in diffusion-weighted MRI: Exploring detection models and mitigation strategies

Diffusion-weighted MRI (dMRI) is a medical imaging method that can be used to investigate the brain microstructure and structural connections between different brain regions. The method, however, requires relatively complex data processing frameworks and analysis pipelines. Many of these approaches are vulnerable to signal dropout artefacts that can originate from subjects moving their head during the scan.To combat these artefacts and eliminate such outliers, researchers have proposed two approaches: to replace outliers or to downweight outliers during modelling and analysis. With the rising interest in dMRI for clinical research, these types of corrections are increasingly important. Therefore, we set out to investigate the differences between outlier replacement and weighting approaches to help the dMRI community to select the best tool for their data processing pipelines. We evaluated dMRI motion correction registration and single tensor model fit pipelines using Gaussian Process and Spherical Harmonic based replacement approaches and outlier downweighting using highly realistic whole-brain simulations. As a proof of concept, we applied these approaches to dMRI infant data sets that contained varying numbers of dropout artefacts.Based on our results, we concluded that the Gaussian Process based outlier replacement provided similar tensor fit results to Gaussian Process based outlier detection and downweighting. Therefore, if only the least-squares estimate of the single tensor model is of interest, our recommendation is to use outlier replacement. However, outlier downweighting can potentially provide a more accurate estimate of the model precision which could be relevant for applications such as probabilistic tractoraphy.

Population Atlas Analysis of Emerging Brain Structural Connections in the Human Fetus.

A lack of in utero imaging data hampers our understanding of the connections in the human fetal brain. Generalizing observations from postmortem subjects and premature newborns is inaccurate due to technical and biological differences. To evaluate changes in fetal brain structural connectivity between 23 and 35 weeks postconceptional age using a spatiotemporal atlas of diffusion tensor imaging (DTI). Retrospective. Publicly available diffusion atlases, based on 60 healthy women (age 18-45 years) with normal prenatal care, from 23 and 35 weeks of gestation. 3.0 Tesla/DTI acquired with diffusion-weighted echo planar imaging (EPI). We performed whole-brain fiber tractography from DTI images. The cortical plate of each diffusion atlas was segmented and parcellated into 78 regions derived from the Edinburgh Neonatal Atlas (ENA33). Connectivity matrices were computed, representing normalized fiber connections between nodes. We examined the relationship between global efficiency (GE), local efficiency (LE), small-worldness (SW), nodal efficiency (NE), and betweenness centrality (BC) with gestational age (GA) and with laterality. Linear regression was used to analyze changes in GE, LE, NE, and BC throughout gestation, and to assess changes in laterality. The t-tests were used to assess SW. P-values were corrected using Holm-Bonferroni method. A corrected P-value <0.05 was considered statistically significant. Network analysis revealed a significant weekly increase in GE (5.83%/week, 95% CI 4.32-7.37), LE (5.43%/week, 95% CI 3.63-7.25), and presence of SW across GA. No significant hemisphere differences were found in GE (P = 0.971) or LE (P = 0.458). Increasing GA was significantly associated with increasing NE in 41 nodes, increasing BC in 3 nodes, and decreasing BC in 2 nodes. Extensive network development and refinement occur in the second and third trimesters, marked by a rapid increase in global integration and local segregation. 3 TECHNICAL EFFICACY: Stage 2.