Structural Brain Connectivity Research Articles

A cross-sectional case–control study on the structural connectome in recovered hospitalized COVID-19 patients

COVID-19 can induce neurological sequelae, negatively affecting the quality of life. Unravelling this illness's impact on structural brain connectivity, white-matter microstructure (WMM), and cognitive performance may help elucidate its implications. This cross-sectional study aimed to investigate differences in these factors between former hospitalised COVID-19 patients (COV) and healthy controls. Group differences in structural brain connectivity were explored using Welch-two sample t-tests and two-sample Mann–Whitney U tests. Multivariate linear models were constructed (one per region) to examine fixel-based group differences. Differences in cognitive performance between groups were investigated using Wilcoxon Rank Sum tests. Possible effects of bundle-specific FD measures on cognitive performance were explored using a two-group path model. No differences in whole-brain structural organisation were found. Bundle-specific metrics showed reduced fiber density (p = 0.012, Hedges’ g = 0.884) and fiber density cross-section (p = 0.007, Hedges’ g = 0.945) in the motor segment of the corpus callosum in COV compared to healthy controls. Cognitive performance on the motor praxis and digit symbol substitution tests was worse in COV than healthy controls (p < 0.001, r = 0.688; p = 0.013, r = 422, respectively). Associations between the cognitive performance and bundle-specific FD measures differed significantly between groups. WMM and cognitive performance differences were observed between COV and healthy controls.

Atypical brain structure and function in young adults exposed to disaster-related prenatal maternal stress: Project Ice Storm.

Studies have shown that prenatal maternal stress (PNMS) affects brain structure and function in childhood. However, less research has examined whether PNMS effects on brain structure and function extend to young adulthood. We recruited women who were pregnant during or within 3 months following the 1998 Quebec ice storm, assessed their PNMS, and prospectively followed-up their children. T1-weighted magnetic resonance imaging (MRI) and resting-state functional MRI were obtained from 19-year-old young adults with (n = 39) and without (n = 65) prenatal exposure to the ice storm. We examined between-group differences in gray matter volume (GMV), surface area (SA), and cortical thickness (CT). We used the brain regions showing between-group GMV differences as seeds to compare between-group functional connectivity. Within the Ice Storm group, we examined (1) associations between PNMS and the atypical GMV, SA, CT, and functional connectivity, and (2) moderation by timing of exposure. Primarily, we found that, compared to Controls, the Ice Storm youth had larger GMV and higher functional connectivity of the anterior cingulate cortex, the precuneus, the left occipital pole, and the right hippocampus; they also had larger CT, but not SA, of the left occipital pole. Within the Ice Storm group, maternal subjective distress during preconception and mid-to-late pregnancy was associated with atypical left occipital pole CT. These results suggest the long-lasting impact of disaster-related PNMS on child brain structure and functional connectivity. Our study also indicates timing-specific effects of the subjective aspect of PNMS on occipital thickness.

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.

Shared and distinct structural brain networks related to childhood maltreatment and social support: connectome-based predictive modeling

Childhood maltreatment (CM) has been associated with changes in structural brain connectivity even in the absence of mental illness. Social support, an important protective factor in the presence of childhood maltreatment, has been positively linked to white matter integrity. However, the shared effects of current social support and CM and their association with structural connectivity remain to be investigated. They might shed new light on the neurobiological basis of the protective mechanism of social support. Using connectome-based predictive modeling (CPM), we analyzed structural connectomes of N = 904 healthy adults derived from diffusion-weighted imaging. CPM predicts phenotypes from structural connectivity through a cross-validation scheme. Distinct and shared networks of white matter tracts predicting childhood trauma questionnaire scores and the social support questionnaire were identified. Additional analyses were applied to assess the stability of the results. CM and social support were predicted significantly from structural connectome data (all rs ≥ 0.119, all ps ≤ 0.016). Edges predicting CM and social support were inversely correlated, i.e., positively correlated with CM and negatively with social support, and vice versa, with a focus on frontal and temporal regions including the insula and superior temporal lobe. CPM reveals the predictive value of the structural connectome for CM and current social support. Both constructs are inversely associated with connectivity strength in several brain tracts. While this underlines the interconnectedness of these experiences, it suggests social support acts as a protective factor following adverse childhood experiences, compensating for brain network alterations. Future longitudinal studies should focus on putative moderating mechanisms buffering these adverse experiences.

Advancements in Diffusion MRI Tractography for Neurosurgery.

Diffusion magnetic resonance imaging tractography is a noninvasive technique that enables the visualization and quantification of white matter tracts within the brain. It is extensively used in preoperative planning for brain tumors, epilepsy, and functional neurosurgical procedures such as deep brain stimulation. Over the past 25 years, significant advancements have been made in imaging acquisition, fiber direction estimation, and tracking methods, resulting in considerable improvements in tractography accuracy. The technique enables the mapping of functionally critical pathways around surgical sites to avoid permanent functional disability. When the limitations are adequately acknowledged and considered, tractography can serve as a valuable tool to safeguard critical white matter tracts and provides insight regarding changes in normal white matter and structural connectivity of the whole brain beyond local lesions. In functional neurosurgical procedures such as deep brain stimulation, it plays a significant role in optimizing stimulation sites and parameters to maximize therapeutic efficacy and can be used as a direct target for therapy. These insights can aid in patient risk stratification and prognosis. This article aims to discuss state-of-the-art tractography methodologies and their applications in preoperative planning and highlight the challenges and new prospects for the use of tractography in daily clinical practice.

Anxiety in Parkinson's Disease Is Associated with Changes in Brain Structural Connectivity.

Anxiety in Parkinson's disease (PD) has been associated with grey matter changes and functional changes in anxiety-related neuronal circuits. So far, no study has analyzed white matter (WM) changes in patients with PD and anxiety. The aim of this study was to identify WM changes by comparing PD patients with and without anxiety, using diffusion-tensor imaging (DTI). 108 non-demented PD patients with (n = 31) and without (n = 77) anxiety as defined by their score on the Parkinson Anxiety Scale participated. DTI was used to determine the fractional anisotropy (FA) and mean diffusivity (MD) in specific tracts within anxiety-related neuronal circuits. Mean FA and MD were compared between groups and correlated with the severity of anxiety adjusted by sex, center, Hoehn & Yahr stage, levodopa equivalent daily dosage, and Hamilton depression rating scale. Compared to patients without anxiety, PD patients with anxiety showed lower FA within the striato-orbitofrontal, striato-cingulate, cingulate-limbic, and caudate-thalamic tracts; higher FA within the striato-limbic and accumbens-thalamic tracts; higher MD within the striato-thalamic tract and lower MD within the striato-limbic tract. Anxiety in PD is associated with microstructural alterations in anxiety-related neuronal circuits within the WM. This result reinforces the view that PD-related anxiety is linked to structural alteration within the anxiety-related brain circuits.

Disrupted resting-state functional connectivity and network topology in mild traumatic brain injury: an arterial spin labelling study.

Mild traumatic brain injury can cause different degrees of cognitive impairment and abnormal brain structure and functional connectivity, but there is still a lack of research on the functional connectivity and topological organization of cerebral blood flow fluctuations. This study explored the cerebral blood flow, functional connectivity and topological organization of the cerebral blood flow network in acute mild traumatic brain injury patients. In total, 48 mild traumatic brain injury patients and 46 well-matched healthy controls underwent resting-state arterial spin labelling perfusion MRI and neuropsychological assessments. The functional connectivity and topological organization of the cerebral blood flow network were analysed. Then, the correlation between the changes in cerebral blood flow network characteristics and cognitive function was explored. Acute mild traumatic brain injury patients showed decreased cerebral blood flow in the right insula and increased cerebral blood flow in the right inferior temporal gyrus and left superior temporal gyrus. Abnormal cerebral blood flow network connection patterns mainly occur in sensorimotor network, default mode network, cingulo-opercular network and occipital network-related regions. Furthermore, mild traumatic brain injury disrupted the topological organization of the whole brain, which manifested as (i) reduced global efficiency; (ii) abnormal degree centrality, betweenness centrality, nodal clustering coefficient and nodal efficiency; and (iii) decreased intermodular connectivity between the occipital network and sensorimotor network. Finally, the change in network topology was correlated with the cognitive score of the mild traumatic brain injury. This study provided evidence of abnormal functional connectivity and network topology based on cerebral blood flow in acute mild traumatic brain injury patients, revealing their potential use as early markers for mild traumatic brain injury, which may contribute to both disease diagnosis and assessment.

Hyposmia and apathy in early, de novo Parkinson's disease: Lessons from structural brain connectivity

IntroductionThe neuroanatomical structures implicated in olfactory and emotional processing overlap significantly. Our understanding of the relationship between hyposmia and apathy, common manifestations of early Parkinson's disease (PD), is inadequate. Materials and methodsWe analyzed data on 40 patients with early de-novo idiopathic PD enrolled within 2 years of motor symptom onset in the Parkinson's Progression Markers Initiative (PPMI) study. To be included in the analysis, patients must have smell dysfunction but no apathy at the baseline visit and had completed a diffusion MRI (dMRI) at the baseline visit and at the 48-month follow-up visit. We used the FMRIB Software Library's diffusion tool kit to measure fractional anisotropy (FA) in six regions of interest on dMRI: bilateral anterior corona radiata, left cingulum, left superior corona radiata, genu and body of the corpus callosum. We compared the FA in each region from the dMRI done at the beginning of the study with the follow up studies at 4 years. ResultsWe found a significant decrease of FA at the bilateral anterior corona radiata, and the genu and body of the corpus callosum comparing baseline scans with follow up images at 4-years after starting the study. ConclusionStructural connectivity changes associated with apathy can be seen early in PD patients with smell dysfunction.

Diffusion tensor imaging tractography in the one-humped camel (Camelus dromedarius) brain.

Tractography is a technique used to trace the pathways of the brain using noninvasive diffusion tensor imaging (DTI) data. It is becoming increasingly popular for investigating the brains of domestic mammals and other animals with myelinated fibers but the principle of DTI can also apply for those with unmyelinated fibers. In the case of camels, DTI tractography is a promising method for enhancing current knowledge of the brain's structural connectivity and identifying white-matter tract changes potentially linked to neurodegenerative pathologies. The present study was therefore designed to describe representative white-matter tracts in the one-humped camel DTI tractography. Post mortem DTI was used to obtain images of two one-humped camel brains using a 3 Tesla system. T2-weighted images were also acquired to identify regions of interest for each fiber tract and a fiber dissection technique was used to complement the DT images. The main association, commissural, and projection fibers were reconstructed and superimposed on T2-weighted images or fractional anisotropy maps. The results of the present study show the reconstruction of the most representative tracts, ie the cingulum, the corpus callosum and the internal capsule, in the one-humped camel brain using DTI data acquired post mortem. These DTI results were compared to those from fiber dissection. Anatomy of the cingulum, corpus callosum and internal capsule correlates well with the description in anatomical textbooks and appears to be similar to fibers describe in large animals. Further research will be required to improve and validate these findings and to generate a tractography atlas based on MRI and histological data, as such an atlas would be a valuable resource for future neuroimaging research.

Effects of family income on brain functional connectivity in US children: associations with cognition.

Higher family income (FI) is associated with larger cortical gray matter volume and improved cognitive performance in children. However, little is known about the effects of FI on brain functional and structural connectivity. This cross-sectional study investigates the effects of FI on brain connectivity and cognitive performance in 9- to 11-years old children (n = 8739) from the Adolescent Brain Cognitive Development (ABCD) study. Lower FI was associated with decreased global functional connectivity density (gFCD) in the default-mode network (DMN), inferior and superior parietal cortices and in posterior cerebellum, and increased gFCD in motor, auditory, and extrastriate visual areas, and in subcortical regions both for girls and boys. Findings demonstrated high reproducibility in Discovery and Reproducibility samples. Cognitive performance partially mediated the association between FI and DMN connectivity, whereas DMN connectivity did not mediate the association between FI and cognitive performance. In contrast, there was no significant association between FI and structural connectivity. Findings suggest that poor cognitive performance, which likely reflects multiplefactors (genetic, nutritional, the level and quality of parental interactions, and educational exposure [1]), contributes to reduced DMN functional connectivity in children from low-income families. Follow-up studies are needed to help clarify if this leads to reductions in structural connectivity as these children age.

The neurobiology of openness as a personality trait.

Openness is a multifaceted behavioral disposition that encompasses personal, interpersonal, and cultural dimensions. It has been suggested that the interindividual variability in openness as a personality trait is influenced by various environmental and genetic factors, as well as differences in brain functional and structural connectivity patterns along with their various associated cognitive processes. Alterations in degree of openness have been linked to several aspects of health and disease, being impacted by both physical and mental health, substance use, and neurologic conditions. This review aims to explore the current state of knowledge describing the neurobiological basis of openness and how individual differences in openness can manifest in brain health and disease.

Reduced brain connectivity along the autism spectrum controlled for familial confounding by co-twin design

Previous studies on brain connectivity correlates of autism have often focused on selective connections and yielded inconsistent results. By applying global fiber tracking and utilizing a within-twin pair design, we aimed to contribute to a more unbiased picture of white matter connectivity in association with clinical autism and autistic traits. Eighty-seven twin pairs (n = 174; 55% monozygotic; 24 with clinical autism) underwent diffusion tensor imaging. Linear regressions assessed within-twin pair associations between structural brain connectivity of anatomically defined brain regions and both clinical autism and autistic traits. These were explicitly adjusted for IQ, other neurodevelopmental/psychiatric conditions and multiple testing, and implicitly for biological sex, age, and all genetic and environmental factors shared by twins. Both clinical autism and autistic traits were associated with reductions in structural connectivity. Twins fulfilling diagnostic criteria for clinical autism had decreased brainstem-cuneus connectivity compared to their co-twins without clinical autism. Further, twins with higher autistic traits had decreased connectivity of the left hippocampus with the left fusiform and parahippocampal areas. These associations were also significant in dizygotic twins alone. Reduced brainstem-cuneus connectivity might point towards alterations in low-level visual processing in clinical autism while higher autistic traits seemed to be more associated with reduced connectivity in networks involving the hippocampus and the fusiform gyrus, crucial especially for processing of faces and other (higher order) visual processing. The observed associations were likely influenced by both genes and environment.

Different degrees of nodes behind obsessive-compulsive symptoms of schizophrenia.

Obsessive-compulsive symptoms are frequently observed in various psychiatric disorders, including obsessive-compulsive disorder, schizophrenia, depression, and anxiety. However, the underlying anatomical basis of these symptoms remains unclear. In this study, we aimed to investigate the mechanism of schizophrenia with obsessive-compulsive symptoms by using diffusion tensor imaging (DTI)-based structural brain connectivity analysis to assess the network differences between patients with obsessive-compulsive disorder (OCD), patients with schizophrenia showing obsessive-compulsive symptoms (SCH), schizophrenia patients with obsessive-compulsive symptoms due to clozapine (LDP), and healthy controls (CN). We included 21 patients with OCD, 20 patients with SCH, 12 patients with LDP, and 25 CN. All subjects underwent MRI scanning, and structural brain connections were estimated using diffusion tensor imaging for further analysis of brain connectivity. The topology and efficiency of the network and the characteristics of various brain regions were investigated. We assessed baseline YALE-BROWN OBSESSIVE COMPULSIVE SCALE (Y-BOCS), Positive and Negative Syndrome Scale (PANSS), and 24-item Hamilton Depression Scale (HAMD-24) scores. Our results showed significant differences among the SCH, OCD, and CN groups (p < 0.05) in the MRI-measured degree of the following nodes: the superior orbitofrontal gyrus (25Frontal_Med_Orb_L), lingual gyrus (47Lingual_L), postcentral gyrus (58Postcentral_R), and inferior temporal gyrus (90Temporal_Inf_R). Additionally, we found significant differences in the degree of the brain regions 02Precentral_R, 47Lingual_L, 58Postcentral_R, and 90Temporal_Inf_R between the CN, OCD, SCH, and LDP groups (p < 0.05). These findings suggest that alterations in the degree of nodes might be the mechanism behind obsessive-compulsive symptoms in schizophrenia.

Bayesian inference of a spectral graph model for brain oscillations

The relationship between brain functional connectivity and structural connectivity has caught extensive attention of the neuroscience community, commonly inferred using mathematical modeling. Among many modeling approaches, spectral graph model (SGM) is distinctive as it has a closed-form solution of the wide-band frequency spectra of brain oscillations, requiring only global biophysically interpretable parameters. While SGM is parsimonious in parameters, the determination of SGM parameters is non-trivial. Prior works on SGM determine the parameters through a computational intensive annealing algorithm, which only provides a point estimate with no confidence intervals for parameter estimates. To fill this gap, we incorporate the simulation-based inference (SBI) algorithm and develop a Bayesian procedure for inferring the posterior distribution of the SGM parameters. Furthermore, using SBI dramatically reduces the computational burden for inferring the SGM parameters. We evaluate the proposed SBI-SGM framework on the resting-state magnetoencephalography recordings from healthy subjects and show that the proposed procedure has similar performance to the annealing algorithm in recovering power spectra and the spatial distribution of the alpha frequency band. In addition, we also analyze the correlations among the parameters and their uncertainty with the posterior distribution which cannot be done with annealing inference. These analyses provide a richer understanding of the interactions among biophysical parameters of the SGM. In general, the use of simulation-based Bayesian inference enables robust and efficient computations of generative model parameter uncertainties and may pave the way for the use of generative models in clinical translation applications.

Biopsychosocial factors of gaming disorder: a systematic review employing screening tools with well-defined psychometric properties.

Considering the growing number of gamers worldwide and increasing public concerns regarding the negative consequences of problematic gaming, the aim of the present systematic review was to provide a comprehensive overview of gaming disorder (GD) by identifying empirical studies that investigate biological, psychological, and social factors of GD using screening tools with well-defined psychometric properties. A systematic literature search was conducted through PsycINFO, PubMed, RISS, and KISS, and papers published up to January 2022 were included. Studies were screened based on the GD diagnostic tool usage, and only five scales with well-established psychometric properties were included. A total of 93 studies were included in the synthesis, and the results were classified into three groups based on biological, psychological, and social factors. Biological factors (n = 8) included reward, self-concept, brain structure, and functional connectivity. Psychological factors (n = 67) included psychiatric symptoms, psychological health, emotion regulation, personality traits, and other dimensions. Social factors (n = 29) included family, social interaction, culture, school, and social support. When the excess amount of assessment tools with varying psychometric properties were controlled for, mixed results were observed with regards to impulsivity, social relations, and family-related factors, and some domains suffered from a lack of study results to confirm any relevant patterns. More longitudinal and neurobiological studies, consensus on a diagnostic tool with well-defined psychometric properties, and an in-depth understanding of gaming-related factors should be established to settle the debate regarding psychometric weaknesses of the current diagnostic system and for GD to gain greater legitimacy in the field of behavioral addiction.

Dynamic network properties of the superior temporal gyrus mediate the impact of brain age gap on chronic aphasia severity

Brain structure deteriorates with aging and predisposes an individual to more severe language impairments (aphasia) after a stroke. However, the underlying mechanisms of this relation are not well understood. Here we use an approach to model brain network properties outside the stroke lesion, network controllability, to investigate relations among individualized structural brain connections, brain age, and aphasia severity in 93 participants with chronic post-stroke aphasia. Controlling for the stroke lesion size, we observe that lower average controllability of the posterior superior temporal gyrus (STG) mediates the relation between advanced brain aging and aphasia severity. Lower controllability of the left posterior STG signifies that activity in the left posterior STG is less likely to yield a response in other brain regions due to the topological properties of the structural brain networks. These results indicate that advanced brain aging among individuals with post-stroke aphasia is associated with disruption of dynamic properties of a critical language-related area, the STG, which contributes to worse aphasic symptoms. Because brain aging is variable among individuals with aphasia, our results provide further insight into the mechanisms underlying the variance in clinical trajectories in post-stroke aphasia.

Cortical structural and functional coupling during development and implications for attention deficit hyperactivity disorder

Functional connectivity is scaffolded by the structural connections of the brain. Disruptions of either structural or functional connectivity can lead to deficits in cognitive functions and increase the risk for neurodevelopmental disorders such as attention deficit hyperactivity disorder (ADHD). To date, very little research has examined the association between structural and functional connectivity in typical development, while no studies have attempted to understand the development of structure-function coupling in children with ADHD. 175 individuals (84 typically developing children and 91 children with ADHD) participated in a longitudinal neuroimaging study with up to three waves. In total, we collected 278 observations between the ages 9 and 14 (139 each in typically developing controls and ADHD). Regional measures of structure-function coupling were calculated at each timepoint using Spearman’s rank correlation and mixed effect models were used to determine group differences and longitudinal changes in coupling over time. In typically developing children, we observed increases in structure-function coupling strength across multiple higher-order cognitive and sensory regions. Overall, weaker coupling was observed in children with ADHD, mainly in the prefrontal cortex, superior temporal gyrus, and inferior parietal cortex. Further, children with ADHD showed an increased rate of coupling strength predominantly in the inferior frontal gyrus, superior parietal cortex, precuneus, mid-cingulate, and visual cortex, compared to no corresponding change over time in typically developing controls. This study provides evidence of the joint maturation of structural and functional brain connections in typical development across late childhood to mid-adolescence, particularly in regions that support cognitive maturation. Findings also suggest that children with ADHD exhibit different patterns of structure-function coupling, suggesting atypical patterns of coordinated white matter and functional connectivity development predominantly in the regions overlapping with the default mode network, salience network, and dorsal attention network during late childhood to mid-adolescence.

Diffusion MRI-based connectomics features improve the noninvasive prediction of H3K27M mutation in brainstem gliomas

PurposeTo establish an individualized predictive model to identify patients with brainstem gliomas (BSGs) at high risk of H3K27M mutation, with the inclusion of brain structural connectivity analysis based on diffusion MRI (dMRI). Materials and MethodsA primary cohort of 133 patients with BSGs (80 H3K27M-mutant) were retrospectively included. All patients underwent preoperative conventional MRI and dMRI. Tumor radiomics features were extracted from conventional MRI, while two kinds of global connectomics features were extracted from dMRI. A machine learning-based individualized H3K27M mutation prediction model combining radiomics and connectomics features was generated with a nested cross validation strategy. Relief algorithm and SVM method were used in each outer LOOCV loop to select the most robust and discriminative features. Additionally, two predictive signatures were established using the LASSO method, and simplified logistic models were built using multivariable logistic regression analysis. An independent cohort of 27 patients was used to validate the best model. Results35 tumor-related radiomics features, 51 topological properties of brain structural connectivity networks, and 11 microstructural measures along white matter tracts were selected to construct a machine learning-based H3K27M mutation prediction model, which achieved an AUC of 0.9136 in the independent validation set. Radiomics- and connectomics-based signatures were generated and simplified combined logistic model was built, upon which derived nomograph achieved an AUC of 0.8827 in the validation cohort. ConclusiondMRI is valuable in predicting H3K27M mutation in BSGs, and connectomics analysis is a promising approach. Combining multiple MRI sequences and clinical features, the established models have good performance.

Anatomical and Functional Connectivity of Critical Deep Brain Structures and Their Potential Clinical Application in Brain Stimulation

Subcortical structures, such as the hippocampus, amygdala, and nucleus accumbens (NAcc), play crucial roles in human cognitive, memory, and emotional processing, chronic pain pathophysiology, and are implicated in various psychiatric and neurological diseases. Interventions modulating the activities of these deep brain structures hold promise for improving clinical outcomes. Recently, non-invasive brain stimulation (NIBS) has been applied to modulate brain activity and has demonstrated its potential for treating psychiatric and neurological disorders. However, modulating the above deep brain structures using NIBS may be challenging due to the nature of these stimulations. This study attempts to identify brain surface regions as source targets for NIBS to reach these deep brain structures by integrating functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI). We used resting-state functional connectivity (rsFC) and probabilistic tractography (PTG) analysis to identify brain surface stimulation targets that are functionally and structurally connected to the hippocampus, amygdala, and NAcc in 119 healthy participants. Our results showed that the medial prefrontal cortex (mPFC) is functionally and anatomically connected to all three subcortical regions, while the precuneus is connected to the hippocampus and amygdala. The mPFC and precuneus, two key hubs of the default mode network (DMN), as well as other cortical areas distributed at the prefrontal cortex and the parietal, temporal, and occipital lobes, were identified as potential locations for NIBS to modulate the function of these deep structures. The findings may provide new insights into the NIBS target selections for treating psychiatric and neurological disorders and chronic pain.

Preterm birth associated alterations in brain structure, cognitive functioning and behavior in children from the ABCD dataset.

Preterm birth is a global health problem and associated with increased risk of long-term developmental impairments, but findings on the adverse outcomes of prematurity have been inconsistent. Data were obtained from the baseline session of the ongoing longitudinal Adolescent Brain and Cognitive Development (ABCD) Study. We identified 1706 preterm children and 1865 matched individuals as Control group and compared brain structure (MRI data), cognitive function and mental health symptoms. Results showed that preterm children had higher psychopathological risk and lower cognitive function scores compared to controls. Structural MRI analysis indicated that preterm children had higher cortical thickness in the medial orbitofrontal cortex, parahippocampal gyrus, temporal and occipital gyrus; smaller volumes in the temporal and parietal gyrus, cerebellum, insula and thalamus; and smaller fiber tract volumes in the fornix and parahippocampal-cingulum bundle. Partial correlation analyses showed that gestational age and birth weight were associated with ADHD symptoms, picvocab, flanker, reading, fluid cognition composite, crystallized cognition composite and total cognition composite scores, and measures of brain structure in regions involved with emotional regulation, attention and cognition. These findings suggest a complex interplay between psychopathological risk and cognitive deficits in preterm children that is associated with changes in regional brain volumes, cortical thickness, and structural connectivity among cortical and limbic brain regions critical for cognition and emotional well-being.