White Matter Structural Connectivity Research Articles

Brain structure and connectivity mediate the association of lifestyle and cognition: the Maastricht study

Abstract Life-course exposure to risk and protective factors impacts brain macro- and micro-structure, which in turn affects cognition. The concept of Brain Age Gap (BAG) assesses brain health by comparing an individual’s neuroimaging-based predicted age with their calendar age. Higher BAG implies accelerated brain aging and is expected to be associated with worse cognition. In this study, we comprehensively modelled mutual associations between brain-health and lifestyle factors, brain age, and cognition in a large, middle-aged population. For this study, cognitive test scores, lifestyle, and 3T MRI data for n=4881 participants (mean age (±SD) = 59.2 (±8.6), 50.1% male) were available from The Maastricht Study, a population-based cohort study with extensive phenotyping. Whole brain volumes (grey matter (GM), cerebrospinal fluid (CSF), white matter hyperintensity (WMH)), cerebral microbleeds (CMB), and structural white matter connectivity were calculated. Lifestyle factors were combined into an adapted LIfestyle for BRAin health (LIBRA) weighted sum score, with higher score indicating greater dementia risk. Cognition was calculated by averaging z-scores across three cognitive domains (memory, information processing speed, executive function and attention). BAG was calculated by comparing calendar age to predictions from a neuroimaging-based multivariable regression model. Paths between LIBRA tertiles, BAG and cognitive function were tested using linear regression and structural equation modelling, adjusting for sociodemographic and clinical confounders. Results show that CSF, GM, WMH and CMB best predicted BAG (R2 = 0.455, RMSE = 6.44). In regression analysis, higher LIBRA scores (greater dementia risk) were associated with higher BAG (standardized regression coefficient ß = 0.126, p < 0.001) and worse cognition (ß = -0.046, p = 0.013), while higher BAG was associated with worse cognition (ß=-0.163, p<0.001). In mediation analysis, 24.7% of the total difference in cognition between the highest and lowest LIBRA tertile was mediated by BAG (ßindirect = -0.049, p < 0.001; ßtotal = -0.198, p < 0.001), and an additional 3.8% was mediated via connectivity (ßindirect = -0.006, p < 0.001; ßtotal = -0.150, p < 0.001). Findings suggest that associations between health- and lifestyle-based risk/protective factors (LIBRA) and cognition can be partially explained by structural brain health markers (BAG) and white matter connectivity markers. Lifestyle interventions targeted at high-risk individuals in mid to late life may be effective in promoting and preserving cognitive function in the general public.

Limbic-predominant age-related TDP-43 encephalopathy neuropathological change (LATE-NC) is associated with abnormalities in white matter structural integrity and connectivity: An ex-vivo diffusion MRI and pathology investigation

Limbic predominant age-related TDP-43 encephalopathy neuropathological change (LATE-NC) is common in older adults and is associated with neurodegeneration, cognitive decline and dementia. In this MRI and pathology investigation we tested the hypothesis that LATE-NC is associated with abnormalities in white matter structural integrity and connectivity of a network of brain regions typically harboring TDP-43 inclusions in LATE, referred to here as the “LATE-NC network”. Ex-vivo diffusion MRI and detailed neuropathological data were collected on 184 community-based older adults. Linear regression revealed an independent association of higher LATE-NC stage with lower diffusion anisotropy in a set of white matter connections forming a pattern of connectivity that is consistent with the stereotypical spread of this pathology in the brain. Graph theory analysis revealed an association of higher LATE-NC stage with weaker integration and segregation in the LATE-NC network. Abnormalities were significant in stage 3, suggesting that they are detectable in later stages of the disease. Finally, LATE-NC network abnormalities were associated with faster cognitive decline, specifically in episodic and semantic memory.

Enhanced amygdala-anterior cingulate white matter structural connectivity in Sahaja Yoga Meditators.

To study the white matter connections between anterior cingulate cortex, anterior insula and amygdala as key regions of the frontal-limbic network that have been related to meditation. Twenty experienced practitioners of Sahaja Yoga Meditation and twenty nonmeditators matched on age, gender and education level, were scanned using Diffusion Weighted Imaging, using a 3T scanner, and their white matter connectivity was compared using diffusion tensor imaging analyses. There were five white matter fiber paths in which meditators showed a larger number of tracts, two of them connecting the same area in both hemispheres: the left and right amygdalae and the left and right anterior insula; and the other three connecting left anterior cingulate with the right anterior insula, the right amygdala and the left amygdala. On the other hand, non-meditators showed larger number of tracts in two paths connecting the left anterior insula with the left amygdala, and the left anterior insula with the left anterior cingulate. The study shows that long-term practice of Sahaja Yoga Meditation is associated with larger white matter tracts strengthening interhemispheric connections between limbic regions and connections between cingulo-amygdalar and cingulo-insular brain regions related to top-down attentional and emotional processes as well as between top-down control functions that could potentially be related to the witness state perceived through the state of mental silence promoted with this meditation. On the other hand, reduced connectivity strength in left anterior insula in the meditation group could be associated to reduced emotional processing affecting top-down processes.

Genetic architecture of the structural connectome

Myelinated axons form long-range connections that enable rapid communication between distant brain regions, but how genetics governs the strength and organization of these connections remains unclear. We perform genome-wide association studies of 206 structural connectivity measures derived from diffusion magnetic resonance imaging tractography of 26,333 UK Biobank participants, each representing the density of myelinated connections within or between a pair of cortical networks, subcortical structures or cortical hemispheres. We identify 30 independent genome-wide significant variants after Bonferroni correction for the number of measures studied (126 variants at nominal genome-wide significance) implicating genes involved in myelination (SEMA3A), neurite elongation and guidance (NUAK1, STRN, DPYSL2, EPHA3, SEMA3A, HGF, SHTN1), neural cell proliferation and differentiation (GMNC, CELF4, HGF), neuronal migration (CCDC88C), cytoskeletal organization (CTTNBP2, MAPT, DAAM1, MYO16, PLEC), and brain metal transport (SLC39A8). These variants have four broad patterns of spatial association with structural connectivity: some have disproportionately strong associations with corticothalamic connectivity, interhemispheric connectivity, or both, while others are more spatially diffuse. Structural connectivity measures are highly polygenic, with a median of 9.1 percent of common variants estimated to have non-zero effects on each measure, and exhibited signatures of negative selection. Structural connectivity measures have significant genetic correlations with a variety of neuropsychiatric and cognitive traits, indicating that connectivity-altering variants tend to influence brain health and cognitive function. Heritability is enriched in regions with increased chromatin accessibility in adult oligodendrocytes (as well as microglia, inhibitory neurons and astrocytes) and multiple fetal cell types, suggesting that genetic control of structural connectivity is partially mediated by effects on myelination and early brain development. Our results indicate pervasive, pleiotropic, and spatially structured genetic control of white-matter structural connectivity via diverse neurodevelopmental pathways, and support the relevance of this genetic control to healthy brain function.

White Matter Structural Connectivity Associated With Visual Field Recovery After Stroke.

White Matter Structural Connectivity Associated With Visual Field Recovery After Stroke.

Understanding Wellbeing Profiles According to White Matter Structural Connectivity Sub-types in Early Adolescents: The First Hundred Brains Cohort from the Longitudinal Adolescent Brain Study

Wellbeing is protective against the emergence of psychopathology. Neurobiological markers associated with mental wellbeing during adolescence are important to understand. Limited research has examined neural networks (white matter tracts) and mental wellbeing in early adolescence specifically. A cross-sectional diffusion tensor imaging analysis approach was conducted, from the Longitudinal Adolescent Brain study, First Hundred Brains cohort (N = 99; 46.5% female; Mage = 13.01, SD = 0.55). Participants completed self-report measures including wellbeing, quality-of-life, and psychological distress. Potential neurobiological profiles using fractional anisotropy, axial, and radial diffusivity were determined via a whole brain voxel-wise approach, and hierarchical cluster analysis of fractional anisotropy values, obtained from 21 major white matter tracts. Three cluster groups with significantly different neurobiological profiles were distinguished. No significant differences were found between the three cluster groups and measures of wellbeing, but two left lateralized significant associations between white matter tracts and wellbeing measures were found. These results provide preliminary evidence for potential neurobiological markers of mental health and wellbeing in early adolescence and should be tracked longitudinally to provide more detailed and robust findings.

Structural connectivity from DTI to predict mild cognitive impairment in de novo Parkinson’s disease

BackgroundEarly detection of Parkinson's disease (PD) patients at high risk for mild cognitive impairment (MCI) can help with timely intervention. White matter structural connectivity is considered an early and sensitive indicator of neurodegenerative disease. ObjectivesTo investigate whether baseline white matter structural connectivity features from diffusion tensor imaging (DTI) of de novo PD patients can help predict PD-MCI conversion at an individual level using machine learning methods. MethodsWe included 90 de novo PD patients who underwent DTI and 3D T1-weighted imaging. Elastic net-based feature consensus ranking (ENFCR) was used with 1000 random training sets to select clinical and structural connectivity features. Linear discrimination analysis (LDA), support vector machine (SVM), K-nearest neighbor (KNN) and naïve Bayes (NB) classifiers were trained based on features selected more than 500 times. The area under the ROC curve (AUC), accuracy (ACC), sensitivity (SEN) and specificity (SPE) were used to evaluate model performance. ResultsA total of 57 PD patients were classified as PD-MCI nonconverters, and 33 PD patients were classified as PD-MCI converters. The models trained with clinical data showed moderate performance (AUC range: 0.62–0.68; ACC range: 0.63–0.77; SEN range: 0.45–0.66; SPE range: 0.64–0.84). Models trained with structural connectivity (AUC range, 0.81–0.84; ACC range, 0.75–0.86; SEN range, 0.77–0.91; SPE range, 0.71–0.88) performed similar to models that were trained with both clinical and structural connectivity data (AUC range, 0.81–0.85; ACC range, 0.74–0.85; SEN range, 0.79–0.91; SPE range, 0.70–0.89). ConclusionsBaseline white matter structural connectivity from DTI is helpful in predicting future MCI conversion in de novo PD patients.

Brain and Neural Reserve Mediate Associations Between Lifestyle and Cognition: The Maastricht Study

AbstractBackgroundLife‐course exposure to risk and protective factors impacts brain macro‐ and micro‐structure, which in turn affects cognition. Brain Age Gap (BAG) directly measures brain reserve by comparing an individual’s neuroimaging‐based predicted age with their calendar age. Higher BAG implies accelerated brain aging and lower brain reserve. In this study, we comprehensively modeled mutual associations between brain risk and protective factors, reserve, and cognition in a large, middle‐aged population using structural equation modelling (SEM).MethodsCognitive test scores, lifestyle, and 3T MRI data for n = 4881 participants (mean age(±SD) = 59.2(±8.6), 50.1% male) were available from The Maastricht Study, a population‐based cohort study with extensive phenotyping. Whole brain volumes (gray matter (GM), cerebrospinal fluid (CSF), white matter hyperintensity (WMH)), cerebral microbleeds (CMB), and structural white matter connectivity were calculated. Lifestyle factors were combined into the Lifestyle for BRAin health (LIBRA) weighted sum score, with higher score indicating greater dementia risk (Figure 1). Cognition was calculated by averaging z‐scores across three cognitive domains (executive function and attention, memory, information processing speed). BAG was calculated by comparing calendar age to predictions from a neuroimaging‐based multivariable regression model. Paths between LIBRA, BAG and cognitive function were tested using linear regression and SEM, adjusting for confounders. LIBRA was split into tertiles for SEM analysis.ResultsCSF, GM, WMH and CMB best predicted BAG (R2 = 0.455, RMSE = 6.44). In regression analysis higher LIBRA scores (greater dementia risk) were associated with higher BAG (standardized regression coefficient ß = 0.127, p<0.001), lower connectivity (ß = ‐0.071, p = 0.002) and worse cognition (ßlinear = ‐0.044, p = 0.017, ßquadratic = ‐0.047, p = 0.018). Higher BAG was associated with lower connectivity (ß = ‐0.418, p<0.001) and worse cognition (ß = ‐0.165, p<0.001). Lower connectivity was associated with worse cognition (ß = 0.086, p<0.001). In mediation analysis (Figure 2), 21% of the total differences between the highest and lowest LIBRA tertile on cognition was mediated by BAG (ßindirect = ‐0.019, p<0.001; ßtotal = ‐0.092, p<0.001), and an additional 4.1% was mediated via connectivity (ßindirect = ‐0.003, p<0.001; ßtotal = ‐0.073, p<0.001).ConclusionAssociations between health‐ and lifestyle‐based risk/protective factors (LIBRA) and cognition is partially explained by maintenance of brain reserve (BAG) and neural reserve (connectivity). Lifestyle interventions promoting brain and neural reserve may help maintain cognitive function into older age.

Altered White Matter Structural Network Connectivity Associated with Cognitive Declines in Psychogenic Erectile Dysfunction.

It has been reported that individuals with psychogenic erectile dysfunction (pED) potentially suffer from cognitive declines. Despite that increasing neuroimaging studies have demonstrated abnormalities of cerebral structural changes in pED, the association between altered white matter (WM) structural network and cognitive impairments remains unclear. Hence, this study aimed to explore the relationship between WM structural network connectivity and cognitive performance in patients with pED. Forty pED patients and 33 healthy controls (HCs) were recruited to perform cognitive assessments, and diffusion tensor imaging scans. We firstly constructed the WM structural network and applied the machine learning method to identify the important features. Then, we examined group differences in cognitive assessments, WM structural network connectivity within the identified features, and associations between altered WM structural network connectivity and cognitive scores in pED patients. From 26,896 features of DTI data, 24 important features were identified by K-Nearest Neighbor classification with a satisfactory accuracy (78%). Compared with HCs, we found that pED patients showed higher fractional anisotropy (FA) values between left transverse temporal sulcus and left supramarginal gyrus, and lower FA values between left suborbital sulcus and left para-hippocampal part of the medial occipito-temporal gyrus in pED patients. Furthermore, the increased FA between left transverse temporal sulcus and left supramarginal gyrus was observed to be negatively associated with impaired delayed memory. Overall, our findings provide new insights into WM network alterations associated with impaired cognitive functions in pED, which may unravel the potential neural mechanisms underlying the cognitive impairments of pED patients.

Maternal childhood maltreatment: associations to offspring brain volume and white matter connectivity.

The deleterious effects of adversity are likely intergenerational, such that one generation's adverse experiences can affect the next. Epidemiological studies link maternal adversity to offspring depression and anxiety, possibly via transmission mechanisms that influence offspring fronto-limbic connectivity. However, studies have not thoroughly disassociated postnatal exposure effects nor considered the role of offspring sex. We utilized infant neuroimaging to test the hypothesis that maternal childhood maltreatment (CM) would be associated with increased fronto-limbic connectivity in infancy and tested brain-behavior associations in childhood. Ninety-two dyads participated (32 mothers with CM, 60 without; 52 infant females, 40 infant males). Women reported on their experiences of CM and non-sedated sleeping infants underwent MRIs at 2.44 ± 2.74 weeks. Brain volumes were estimated via structural MRI and white matter structural connectivity (fiber counts) via diffusion MRI with probabilistic tractography. A subset of parents (n = 36) reported on children's behaviors at age 5.17 ± 1.73 years. Males in the maltreatment group demonstrated greater intra-hemispheric fronto-limbic connectivity (b = 0.96, p= 0.008, [95%CI 0.25, 1.66]), no differences emerged for females. Fronto-limbic connectivity was related to somatic complaints in childhood only for males (r = 0.673, p = 0.006). Our findings suggest that CM could have intergenerational associations to offspring brain development, yet mechanistic studies are needed.

In vivo mapping of the deep and superficial white matter connectivity in the chimpanzee brain

Mapping the chimpanzee brain connectome and comparing it to that of humans is key to our understanding of similarities and differences in primate evolution that occurred after the split from their common ancestor around 6 million years ago. In contrast to studies on macaque species’ brains, fewer studies have specifically addressed the structural connectivity of the chimpanzee brain and its comparison with the human brain. Most comparative studies in the literature focus on the anatomy of the cortex and deep nuclei to evaluate how their morphology and asymmetry differ from that of the human brain, and some studies have emerged concerning the study of brain connectivity among humans, monkeys, and apes. In this work, we established a new white matter atlas of the deep and superficial white matter structural connectivity in chimpanzees. In vivo anatomical and diffusion-weighted magnetic resonance imaging (MRI) data were collected on a 3-Tesla MRI system from 39 chimpanzees. These datasets were subsequently processed using a novel fiber clustering pipeline adapted to the chimpanzee brain, enabling us to create two novel deep and superficial white matter connectivity atlases representative of the chimpanzee brain. These atlases provide the scientific community with an important and novel set of reference data for understanding the commonalities and differences in structural connectivity between the human and chimpanzee brains. We believe this study to be innovative both in its novel approach and in mapping the superficial white matter bundles in the chimpanzee brain, which will contribute to a better understanding of hominin brain evolution.

White Matter Structural Connectivity and Its Impact on Psychogenic Non-Epileptic Seizures: An Evidence-Based Review.

Psychiatric non-epileptic seizure (PNES), also known as a form of functional neurological disorders (FND), is a common but still underrecognized disorder presenting seizure-like symptoms and no electrophysiological abnormality. Despite the significant burden of this disorder, the neurobiological mechanisms are not clearly understood, which hinders the development of better diagnosis and treatment. In the recent neuroimaging research on PNES, brain network analysis has become a relevant topic beyond conventional methodologies. The human brain is a highly intricate system of interconnected regions that collaborate to facilitate a wide range of cognitive and behavioral functions. White matter tracts, which are comprised of bundles of axonal fibers, are the primary means by which information is transmitted between different brain regions. As such, comprehending the organization and structure of the brain's white matter network is critical for gaining insight into its functional architecture. This review article aims to provide an overview of the brain mechanisms underlying PNES, with a special focus on analyzing brain networks.

Intracranial electrophysiological and structural basis of BOLD functional connectivity in human brain white matter

While functional MRI (fMRI) studies have mainly focused on gray matter, recent studies have consistently found that blood-oxygenation-level-dependent (BOLD) signals can be reliably detected in white matter, and functional connectivity (FC) has been organized into distributed networks in white matter. Nevertheless, it remains unclear whether this white matter FC reflects underlying electrophysiological synchronization. To address this question, we employ intracranial stereotactic-electroencephalography (SEEG) and resting-state fMRI data from a group of 16 patients with drug-resistant epilepsy. We find that BOLD FC is correlated with SEEG FC in white matter, and this result is consistent across a wide range of frequency bands for each participant. By including diffusion spectrum imaging data, we also find that white matter FC from both SEEG and fMRI are correlated with white matter structural connectivity, suggesting that anatomical fiber tracts underlie the functional synchronization in white matter. These results provide evidence for the electrophysiological and structural basis of white matter BOLD FC, which could be a potential biomarker for psychiatric and neurological disorders.

Contributions of macrostructural and microstructural white matter damage to apathy in people with Alzheimer’s disease

AbstractBackgroundApathy is the most common neuropsychiatric symptom in Alzheimer’s disease (AD) that affects patients’ quality of life and prognosis. Previous functional neuroimaging studies have found multiple cerebral alterations suggesting a conceptualisation of apathy as a symptom resulting from a communication breakdown among functional neural networks involved in motivational‐affective processing. To test this hypothesis, this study investigated the association between white matter (WM) structural connectivity and apathy in AD.MethodSixty‐one patients with (AD‐AP) and 61 without apathy (AD‐NA) were identified from the ADNI database and matched for diagnosis, cognitive status, age and education. A group of 61 matched healthy older adults (HOA) was also included. Data on cognitive performance, cerebrospinal fluid (CSF) biomarkers, brain volumes and mean DTI indices of 50 WM tracts were extracted for all participants. Cognitive profiles and volumetric/DTI data were compared across groups using general linear models including sex as a covariate. Post hoc analyses were performed to investigate pair‐wise differences in all outcome measures.ResultBoth patient groups showed widespread cognitive deficits and neural alterations, lower amyloid beta and higher phosphorylated tau CSF levels compared to the HOA group. The direct AD‐AP vs AD‐NA comparison showed no significant differences in CSF biomarkers and MRI outcomes, but the AD‐AP group had more severe neuropsychiatric symptoms, lower scores on the Clock Test – copy and relied more on cues in the Logical Memory test – delayed recall. When the patient groups were compared to the HOA independently, the AD‐AP group had more extensive WM damage: 1) reduced WM and larger WM hyperintensity volume; 2) increased radial diffusivity and reduced fractional anisotropy primarily in the cingulum, superior longitudinal and inferior fronto‐occipital fasciculi and genu of the corpus callosum.ConclusionAD‐AP patients had WM damage mildly more severe than that observed in AD‐NA, especially in associative tracts in the frontal lobes and the cingulum. Such disruption in structural connectivity might affect crucial functional inter‐network communication resulting in motivational deficits and worse cognitive decline. These findings warrant further investigations to ascertain the relationship between WM damage and disruption of functional brain network dynamic interactions in AD patients with apathy.

The functional role of the precuneus.

Recent advancements in computational approaches and neuroimaging techniques have refined our understanding of the precuneus. While previously believed to be largely a visual processing region, the importance of the precuneus in complex cognitive functions has been previously less familiar due to a lack of focal lesions in this deeply seated region, but also a poor understanding of its true underlying anatomy. Fortunately, recent studies have revealed significant information on the structural and functional connectivity of this region, and this data has provided a more detailed mechanistic understanding of the importance of the precuneus in healthy and pathologic states. Through improved resting-state functional MRI analyses, it has become clear that the function of the precuneus can be better understood based on its functional association with large scale brain networks. Dual default mode network systems have been well explained in recent years in supporting episodic memory and theory of mind; however, a novel 'para-cingulate' network, which is a subnetwork of the larger central executive network, with likely significant roles in self-referential processes and related psychiatric symptoms is introduced here and requires further clarification. Importantly, detailed anatomic studies on the precuneus structural connectivity inside and beyond the cingulate cortex has demonstrated the presence of large structural white matter connections, which provide an additional layer of meaning to the structural-functional significance of this region and its association with large scale brain networks. Together, the structural-functional connectivity of the precuneus has provided central elements which can model various neurodegenerative diseases and psychiatric disorders, such as Alzheimer's disease and depression.

High-resolution multi-shot diffusion imaging of structural networks in healthy neurocognitive aging

Healthy neurocognitive aging has been associated with the microstructural degradation of white matter pathways that connect distributed gray matter regions, assessed by diffusion-weighted imaging (DWI). However, the relatively low spatial resolution of standard DWI has limited the examination of age-related differences in the properties of smaller, tightly curved white matter fibers, as well as the relatively more complex microstructure of gray matter. Here, we capitalize on high-resolution multi-shot DWI, which allows spatial resolutions < 1 mm3 to be achieved on clinical 3T MRI scanners. We assessed whether traditional diffusion tensor-based measures of gray matter microstructure and graph theoretical measures of white matter structural connectivity assessed by standard (1.5 mm3 voxels, 3.375 μl volume) and high-resolution (1 mm3 voxels, 1μl volume) DWI were differentially related to age and cognitive performance in 61 healthy adults 18–78 years of age. Cognitive performance was assessed using an extensive battery comprising 12 separate tests of fluid (speed-dependent) cognition. Results indicated that the high-resolution data had larger correlations between age and gray matter mean diffusivity, but smaller correlations between age and structural connectivity. Moreover, parallel mediation models including both standard and high-resolution measures revealed that only the high-resolution measures mediated age-related differences in fluid cognition. These results lay the groundwork for future studies planning to apply high-resolution DWI methodology to further assess the mechanisms of both healthy aging and cognitive impairment.

Probabilistic Tractography Between Nucleus Accumbens and Other Reward-related Brain Areas in Malay Female Adolescents

Introduction: Studies show that adolescents are more reward sensitive compared to other age groups. The nucleus accumbens (NAcc) has been identified as a key brain area involved in reward through its connectivity to other reward-related brain areas. Our study aimed to characterise the white matter structural connectivity of nucleus accumbens with brain areas that are most often associated with reward in female adolescents. Methods: Fifteen healthy female Malay adolescents were recruited and underwent diffusion-weighted brain scanning. Two behaviour scales were also given to verify typical reward responsiveness. Then, probabilistic tractography and NAcc segmentation were performed on the data using FMRIB Software Library (FSL). Probabilistic tractography was performed to determine the relative connection probability of nucleus accumbens (NAcc) to areas shown to be associated with reward, namely amygdala, anterior cingulate cortex (ACC), medial orbitofrontal cortex (mOFC), hippocampus, ventrolateral prefrontal cortex (vlPFC) and dorsolateral prefrontal cortex (dlPFC). Connectivity-based segmentation of NAcc was performed to determine the spatial distribution of its connectivity with the target brain areas according to the highest connection probability. Results: The highest relative connection probability was found between NAcc to mOFC, while the NAcc parcellation showed the widest distribution of connection to mOFC compared to the other five targets on both sides of the brain. Conclusion: Our findings demonstrated the strongest structural connectivity and widest distribution between NAcc and mOFC compared with other brain areas related to reward. This study’s findings could be used as baseline to compare with people with atypical reward circuit problems.

Characteristics of white matter structural connectivity in healthy adults with childhood maltreatment

ABSTRACT Background: Childhood maltreatment (CM) is a common psychological stressor associated with multiple mental disorders. While CM is associated with vulnerability to depression and anxiety, little is known about the specific mechanism underlying this relationship. Objective: This study aimed to investigate the white matter (WM) of healthy adults with CM and their relationships with depression and anxiety to provide biological evidence for the development of mental disorders in subjects with childhood trauma. Methods: The CM group included 40 healthy adults with CM. The non-CM group included 40 healthy adults without CM. Diffusion tensor imaging (DTI) data were collected, and tract-based spatial statistics (TBSS) were applied to the whole brain to assess WM differences between the two groups; post-hoc fibre tractography was used to characterise the developmental differences; and mediation analysis was used to assess the relationships among the Child Trauma Questionnaire (CTQ) results, DTI indices, and depression and anxiety scores. Results: Relative to the non-CM group, the CM group revealed significantly lower fractional anisotropy (FA) in the right posterior corona radiata (PCR-R), right anterior corona radiata (ACR-R), left super corona radiata (SCR-L), anterior thalamic radiation (ATR), and right posterior limb of the internal capsule (PLIC-R). Additionally, shorter fibre bundles passed through the PCR-R, ACR-R, and ATR in the CM group compared with the non-CM group. Besides, the length of the ACR-R mediated the relationship between CM and trait anxiety. Conclusions: The alteration of white matter microstructure associated with childhood trauma in healthy adults may reflect biomarkers of childhood trauma. Besides, an alteration of WM microstructure in healthy adults with CM mediates the association between CM and trait anxiety, which may represent the vulnerability to developing mental disorders after childhood trauma experiences.

Brain volumetric and white matter structural connectivity alterations in autistic children: case–control study

BackgroundAutism spectrum disorder (ASD) is a neurodevelopmental disorder that includes a large heterogeneous constellation of disorders with overlapping symptoms and clinical features. The diagnosis is based mainly on clinical symptoms meeting DSM-5 criteria with no radiologic or laboratory diagnostic investigations available yet. The specific neuropathologic aberrations occurring in ASD are still under investigation. This study aimed at providing a preliminary database for better understanding of the neuropathologic aspects of ASD, regarding both macrostructure and microstructure of the brain using magnetic resonance imaging. This case–control study included total of 40 children, 20 cases (diagnosed with ASD) and 20 control (Typically Developing Children, TDC) aged 2–18 years. 3D-T1 and Diffusion Tensor Images (DTI) were acquired. 3D-T1 images were uploaded to Volbrain and brain segmentation was done using Volbrain 2.0 pipeline. DTI data were analyzed using FSL where Tract-Based Spatial Statistics analysis was carried out and mean fractional anisotropy values obtained. Independent samples t test was used to compare means of both groups.ResultsASD group displayed statistically significant larger intracranial cavity, brain, white matter, grey matter and cerebrospinal fluid volumes (p < 0.001 for all except CSF volume p = 0.01) with the white matter occupying higher percentage of intracranial volume in ASD compared to TDC group (p < 0.001). The cortical thickness showed statistically significant larger volume in entorhinal cortex in ASD group compared to TDC group at both sides (p < 0.001 at right side, p = 0.003 at left side). Widespread statistically significant (p < 0.001) higher mean FA value was observed at multiple white matter tracts.ConclusionThese findings suggest that the main pathology of ASD is within the white matter. It also supports the hypothesis that autistic brain undergoes period of precocious growth in early years of life. Further studies with age and clinical severity stratification are needed to investigate temporal changes and severity related macrostructure and microstructure changes in autistic brains.

Multimodal brain connectome-based prediction of suicide risk in people with late-life depression

Suicidal ideation, plans and behavior are particularly serious health issues among the older population, resulting in a higher likelihood of deaths than in any other age group. The increasing prevalence of depression in late life reflects the urgent need for efficient screening of suicide risk in people with late-life depression. Employing a cross-sectional design, we performed connectome-based predictive modelling using whole-brain resting-state functional connectivity and white matter structural connectivity data to predict suicide risk in late-life depression patients (N = 37 non-suicidal patients, N = 24 patients with suicidal ideation/plan, N = 30 patients who attempted suicide). Suicide risk was measured using three standardized questionnaires. Brain connectivity profiles were used to classify three groups in our dataset and two independent datasets using machine learning. We found that brain patterns could predict suicide risk in the late-life depression population, with the explained variance up to 30.34%. The functional and structural connectivity profiles improved the classification-prediction accuracy compared with using questionnaire scores alone and could be applied to identify depressed patients who had higher suicide risk in two independent datasets. Our findings suggest that multimodal brain connectivity could capture individual differences in suicide risk among late-life depression patients. Our predictive models might be further tested to help clinicians identify patients who need detailed assessments and interventions. The trial registration number for this study is ChiCTR2200066356.