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Anhedonia is an enduring symptom of subthreshold depression (StD) and predict later onset of major depressive disorder (MDD). Brain structural covariance describes the inter-regional distribution of morphological changes compared to healthy controls (HC) and reflects brain maturation and disease progression. We investigated neural correlates of anhedonia from the structural covariance. T1-weighted brain magnetic resonance images were acquired from 79 young adults (26 StD, 30 MDD, and 23 HC). Intra-individual structural covariance networks of 68 cortical surface area (CSAs), 68 cortical thicknesses (CTs), and 14 subcortical volumes were constructed. Group-level hubs and principal edges were defined using the global and regional graph metrics, compared between groups, and examined for the association with anhedonia severity. Global network metrics were comparable among the StD, MDD, and HC. StD exhibited lower centralities of left pallidal volume than HC. StD showed higher centralities than HC in the CSAs of right rostral anterior cingulate cortex (ACC) and pars triangularis, and in the CT of left pars orbitalis. Less anhedonia was associated with higher centralities of left pallidum and right amygdala, higher edge betweenness centralities in the structural covariance (EBSC) of left postcentral gyrus-parahippocampal gyrus and LIPL-right amygdala. More anhedonia was associated with higher centralities of left inferior parietal lobule (LIPL), left postcentral gyrus, left caudal ACC, and higher EBSC of LIPL-left postcentral gyrus, LIPL-right lateral occipital gyrus, and left caudal ACC-parahippocampal gyrus. This study has a cross-sectional design. Structural covariance of brain morphologies within the salience and limbic networks, and among the salience-limbic-default mode-somatomotor-visual networks, are possible neural correlates of anhedonia in depression. [Display omitted] • The first brain structural covariance(SC) study of anhedonia in subthreshold depression(StD) • Lowered global segregation and integration of SC in StD than healthy control (HC) • Less anhedonia associated with higher centrality of amygdala and pallidum in the SC • More anhedonia with lower centrality of inferior parietal lobule, anterior cingulate, postcentral gyrus • Inter-network SC among the default mode-limbic-somatomotor-visual networks underlie anhedonia [ABSTRACT FROM AUTHOR]

Background In contemporary neuroimaging studies, it has been observed that patients with major depressive disorder (MDD) exhibit aberrant spontaneous neural activity, commonly quantified through the amplitude of low-frequency fluctuations (ALFF). However, the substantial individual heterogeneity among patients poses a challenge to reaching a unified conclusion. Methods To address this variability, our study adopts a novel framework to parse individualized ALFF abnormalities. We hypothesize that individualized ALFF abnormalities can be portrayed as a unique linear combination of shared differential factors. Our study involved two large multi-center datasets, comprising 2424 patients with MDD and 2183 healthy controls. In patients, individualized ALFF abnormalities were derived through normative modeling and further deconstructed into differential factors using non-negative matrix factorization. Results Two positive and two negative factors were identified. These factors were closely linked to clinical characteristics and explained group-level ALFF abnormalities in the two datasets. Moreover, these factors exhibited distinct associations with the distribution of neurotransmitter receptors/transporters, transcriptional profiles of inflammation-related genes, and connectome-informed epicenters, underscoring their neurobiological relevance. Additionally, factor compositions facilitated the identification of four distinct depressive subtypes, each characterized by unique abnormal ALFF patterns and clinical features. Importantly, these findings were successfully replicated in another dataset with different acquisition equipment, protocols, preprocessing strategies, and medication statuses, validating their robustness and generalizability. Conclusions This research identifies shared differential factors underlying individual spontaneous neural activity abnormalities in MDD and contributes novel insights into the heterogeneity of spontaneous neural activity abnormalities in MDD. [ABSTRACT FROM AUTHOR]

Preterm birth is associated with increased risk for a spectrum of neurodevelopmental disabilities. The cerebellum is implicated in a wide range of cognitive functions extending beyond sensorimotor control and plays an increasingly recognized role in brain development. Morphometric studies based on volume analyses have revealed impaired cerebellar development in preterm infants. However, the structural covariance between the cerebellum and cerebral cortex has not been studied during the neonatal period, and the extent to which structural covariance is affected by preterm birth remains unknown. In this study, using the structural MR images of 52 preterm infants scanned at term-equivalent age and 312 full-term controls from the Developing Human Connectome Project, we compared volumetric growth, local cerebellum shape development and cerebello-cerebral structural covariance between the two groups. We found that although there was no significant difference in the overall volume measurements between preterm and full-term infants, the shape measurements were different. Compared with the control infants, preterm infants had significantly larger thickness in the vermis and lower thickness in the lateral portions of the bilateral cerebral hemispheres. The structural covariance between the cerebellum and frontal and parietal lobes was significantly greater in preterm infants than in full-term controls. The findings in this study suggested that cerebellar development and cerebello-cerebral structural covariance may be affected by premature birth. [ABSTRACT FROM AUTHOR]

Structural covariance network disruption has been considered an important pathophysiological indicator for schizophrenia. Here, we introduced a novel individualized structural covariance network measure, referred to as a texture similarity network (TSN), and hypothesized that the TSN could reliably reveal unique intersubject heterogeneity and complex dysconnectivity patterns in schizophrenia. The TSN was constructed by measuring the covariance of 180 three-dimensional voxelwise gray-level co-occurrence matrix feature maps between brain areas in each participant. We first tested the validity and reproducibility of the TSN in characterizing the intersubject variability in 2 longitudinal test-retest healthy cohorts. The TSN was further applied to elucidate intersubject variability and dysconnectivity patterns in 10 schizophrenia case-control datasets (609 schizophrenia cases vs. 579 controls) as well as in a first-episode depression dataset (69 patients with depression vs. 69 control participants). The test-retest analysis demonstrated higher TSN intersubject than intrasubject variability. Moreover, the TSN reliably revealed higher intersubject variability in both chronic and first-episode schizophrenia, but not in depression. The TSN also reproducibly detected coexistent increased and decreased TSN strength in widespread brain areas, increased global small-worldness, and the coexistence of both structural hyposynchronization in the central networks and hypersynchronization in peripheral networks in patients with schizophrenia but not in patients with depression. Finally, aberrant intersubject variability and covariance strength patterns revealed by the TSN showed a missing or weak correlation with other individualized structural covariance network measures, functional connectivity, and regional volume changes. These findings support the reliability of a TSN in revealing unique structural heterogeneity and complex dysconnectivity in patients with schizophrenia. [ABSTRACT FROM AUTHOR]

The morphologic properties of brain regions co-vary or correlate with each other. Here we investigated the structural covariances of cortical thickness and subcortical volumes in the ageing brain, along with their associations with age and cognition, using cross-sectional data from the UK Biobank (N = 42,075, aged 45–83 years, 53% female). As the structural covariance should be estimated in a group of participants, all participants were divided into 84 non-overlapping, equal-sized age groups ranging from the youngest to the oldest. We examined 84 cortical thickness covariances and subcortical covariances. Our findings include: (1) there were significant differences in the variability of structural covariance in the ageing process, including an increased variance, and a decreased entropy. (2) significant enrichment in pairwise correlations between brain regions within the occipital lobe was observed in all age groups; (3) structural covariance in older age, especially after the age of around 64, was significantly different from that in the youngest group (median age 48 years); (4) sixty-two of the total 528 pairs of cortical thickness correlations and 10 of the total 21 pairs of subcortical volume correlations showed significant associations with age. These trends varied, with some correlations strengthening, some weakening, and some reversing in direction with advancing age. Additionally, as ageing was associated with cognitive decline, most of the correlations with cognition displayed an opposite trend compared to age associated patterns of correlations. [ABSTRACT FROM AUTHOR]

Objective: Although extensive structural and functional abnormalities have been reported in schizophrenia, the gray matter volume (GMV) covariance of the amygdala remain unknown. The amygdala contains several subregions with different connection patterns and functions, but it is unclear whether the GMV covariance of these subregions are selectively affected in schizophrenia. Methods: To address this issue, we compared the GMV covariance of each amygdala subregion between 807 schizophrenia patients and 845 healthy controls from 11 centers. The amygdala was segmented into nine subregions using FreeSurfer (v7.1.1), including the lateral (La), basal (Ba), accessory-basal (AB), anterior-amygdaloid-area (AAA), central (Ce), medial (Me), cortical (Co), corticoamygdaloid-transition (CAT), and paralaminar (PL) nucleus. We developed an operational combat harmonization model for 11 centers, subsequently employing a voxel-wise general linear model to investigate the differences in GMV covariance between schizophrenia patients and healthy controls across these subregions and the entire brain, while adjusting for age, sex and TIV. Results: Our findings revealed that five amygdala subregions of schizophrenia patients, including bilateral AAA, CAT, and right Ba, demonstrated significantly increased GMV covariance with the hippocampus, striatum, orbitofrontal cortex, and so on (permutation test, P< 0.05, corrected). These findings could be replicated in most centers. Rigorous correlation analysis failed to identify relationships between the altered GMV covariance with positive and negative symptom scale, duration of illness, and antipsychotic medication measure. Conclusion: Our research is the first to discover selectively impaired GMV covariance patterns of amygdala subregion in a large multicenter sample size of patients with schizophrenia. [ABSTRACT FROM AUTHOR]

INTRODUCTION: The neuroanatomical changes driving both cognitive and mobility impairments, an emerging preclinical dementia syndrome, are not fully understood. We examined gray‐matter volumes (GMVs) and structural covariance networks (SCNs) abnormalities in community‐based older people preceding the conversion to physio‐cognitive decline syndrome (PCDS). METHODS: Voxel‐wise brain GMV and established SCNs were compared between PCDS and non‐PCDS converters. RESULTS: The study included 343 individuals (60.2 ± 6.9 years, 49.6% men) with intact cognitive and mobility functions. Over an average 5.6‐year follow‐up, 116 transitioned to PCDS. Identified regions with abnormal GMVs in PCDS converters were over cerebellum and caudate, which served as seeds for SCNs establishment. Significant differences in cerebellum‐based (to right frontal pole and left middle frontal gyrus) and caudate‐based SCNs (to right caudate putamen, right planum temporale, left precentral gyrus, right postcentral gyrus, and left parietal operculum) between converters and nonconverters were observed. DISCUSSION: This study reveals early neuroanatomic changes, emphasizing the cerebellum's role, in dual cognitive and mobility impairments. Highlights: Neuroanatomic precursors of dual cognitive and mobility impairments are identified.Cerebellar GMV reductions and increased right caudate GMV precede the onset of PCDS.Altered cerebellum‐ and caudate‐based SCNs drive PCDS transformation.This research establishes a foundation for understanding PCDS as a specific dementia syndrome. [ABSTRACT FROM AUTHOR]

Previous neuroimaging studies on arithmetic development have mainly focused on functional activation or functional connectivity between brain regions. It remains largely unknown how brain structures support arithmetic development. The present study investigated whether early gray matter structural covariance contributes to later gain in arithmetic ability in children. We used a public longitudinal sample comprising 63 typically developing children. The participants received structural magnetic resonance imaging scanning when they were 11 years old and were tested with a multiplication task at 11 years old (time 1) and 13 years old (time 2), respectively. Mean gray matter volumes were extracted from eight brain regions of interest to anchor salience network (SN), frontal-parietal network (FPN), motor network (MN), and default mode network (DMN) at time 1. We found that longitudinal gain in arithmetic ability was associated with stronger structural covariance of the SN seed with frontal and parietal regions and stronger structural covariance of the FPN seed with insula, but weaker structural covariance of the FPN seed with motor and temporal regions, weaker structural covariance of the MN seed with frontal and motor regions, and weaker structural covariance of the DMN seed with temporal region. However, we did not detect correlation between longitudinal gain in arithmetic ability and behavioral measure or regional gray matter volume at time 1. Our study provides novel evidence for a specific contribution of gray matter structural covariance to longitudinal gain in arithmetic ability in childhood. [ABSTRACT FROM AUTHOR]

Background and Hypothesis Schizophrenia is associated with widespread cortical thinning and abnormality in the structural covariance network, which may reflect connectome alterations due to treatment effect or disease progression. Notably, patients with treatment-resistant schizophrenia (TRS) have stronger and more widespread cortical thinning, but it remains unclear whether structural covariance is associated with treatment response in schizophrenia. Study Design We organized a multicenter magnetic resonance imaging study to assess structural covariance in a large population of TRS and non-TRS, who had been resistant and responsive to non-clozapine antipsychotics, respectively. Whole-brain structural covariance for cortical thickness was assessed in 102 patients with TRS, 77 patients with non-TRS, and 79 healthy controls (HC). Network-based statistics were used to examine the difference in structural covariance networks among the 3 groups. Moreover, the relationship between altered individual differentiated structural covariance and clinico-demographics was also explored. Study Results Patients with non-TRS exhibited greater structural covariance compared with HC, mainly in the fronto-temporal and fronto-occipital regions, while there were no significant differences in structural covariance between TRS and non-TRS or HC. Higher individual differentiated structural covariance was associated with lower general scores of the Positive and Negative Syndrome Scale in the non-TRS group, but not in the TRS group. Conclusions These findings suggest that reconfiguration of brain networks via coordinated cortical thinning is related to treatment response in schizophrenia. Further longitudinal studies are warranted to confirm if greater structural covariance could serve as a marker for treatment response in this disease. [ABSTRACT FROM AUTHOR]

The identification of prognostic markers in early multiple sclerosis (MS) is challenging and requires reliable measures that robustly predict future disease trajectories. Ideally, such measures should make inferences at the individual level to inform clinical decisions. This study investigated the prognostic value of longitudinal structural networks to predict 5-year Expanded Disability Status Scale (EDSS) progression in patients with relapsing-remitting MS (RRMS). We hypothesized that network measures, derived from MRI, outperform conventional MRI measurements at identifying patients at risk of developing disability progression. This longitudinal, multicentre study within the Magnetic Resonance Imaging in MS (MAGNIMS) network included 406 patients with RRMS (mean age = 35.7 ± 9.1 years) followed up for 5 years (mean follow-up = 5.0 ± 0.6 years). EDSS was determined to track disability accumulation. A group of 153 healthy subjects (mean age = 35.0 ± 10.1 years) with longitudinal MRI served as controls. All subjects underwent MRI at baseline and again 1 year after baseline. Grey matter atrophy over 1 year and white matter lesion load were determined. A single-subject brain network was reconstructed from T1-weighted scans based on grey matter atrophy measures derived from a statistical parameter mapping-based segmentation pipeline. Key topological measures, including network degree, global efficiency and transitivity, were calculated at single-subject level to quantify network properties related to EDSS progression. Areas under receiver operator characteristic (ROC) curves were constructed for grey matter atrophy and white matter lesion load, and the network measures and comparisons between ROC curves were conducted. The applied network analyses differentiated patients with RRMS who experience EDSS progression over 5 years through lower values for network degree [H(2) = 30.0, P < 0.001] and global efficiency [H(2) = 31.3, P < 0.001] from healthy controls but also from patients without progression. For transitivity, the comparisons showed no difference between the groups [H(2) = 1.5, P = 0.474]. Most notably, changes in network degree and global efficiency were detected independent of disease activity in the first year. The described network reorganization in patients experiencing EDSS progression was evident in the absence of grey matter atrophy. Network degree and global efficiency measurements demonstrated superiority of network measures in the ROC analyses over grey matter atrophy and white matter lesion load in predicting EDSS worsening (all P -values < 0.05). Our findings provide evidence that grey matter network reorganization over 1 year discloses relevant information about subsequent clinical worsening in RRMS. Early grey matter restructuring towards lower network efficiency predicts disability accumulation and outperforms conventional MRI predictors. [ABSTRACT FROM AUTHOR]

Normal and pathologic neurobiological processes influence brain morphology in coordinated ways that give rise to patterns of structural covariance (PSC) across brain regions and individuals during brain aging and diseases. The genetic underpinnings of these patterns remain largely unknown. We apply a stochastic multivariate factorization method to a diverse population of 50,699 individuals (12 studies and 130 sites) and derive data-driven, multi-scale PSCs of regional brain size. PSCs were significantly correlated with 915 genomic loci in the discovery set, 617 of which are newly identified, and 72% were independently replicated. Key pathways influencing PSCs involve reelin signaling, apoptosis, neurogenesis, and appendage development, while pathways of breast cancer indicate potential interplays between brain metastasis and PSCs associated with neurodegeneration and dementia. Using support vector machines, multi-scale PSCs effectively derive imaging signatures of several brain diseases. Our results elucidate genetic and biological underpinnings that influence structural covariance patterns in the human brain. [ABSTRACT FROM AUTHOR]

Childhood adversity is one of the strongest predictors of adolescent mental illness. Therefore, it is critical that the mechanisms that aid resilient functioning in individuals exposed to childhood adversity are better understood. Here, we examined whether resilient functioning was related to structural brain network topology. We quantified resilient functioning at the individual level as psychosocial functioning adjusted for the severity of childhood adversity in a large sample of adolescents (N = 2406, aged 14–24). Next, we examined nodal degree (the number of connections that brain regions have in a network) using brain-wide cortical thickness measures in a representative subset (N = 275) using a sliding window approach. We found that higher resilient functioning was associated with lower nodal degree of multiple regions including the dorsolateral prefrontal cortex, the medial prefrontal cortex, and the posterior superior temporal sulcus (z > 1.645). During adolescence, decreases in nodal degree are thought to reflect a normative developmental process that is part of the extensive remodeling of structural brain network topology. Prior findings in this sample showed that decreased nodal degree was associated with age, as such our findings of negative associations between nodal degree and resilient functioning may therefore potentially resemble a more mature structural network configuration in individuals with higher resilient functioning. [ABSTRACT FROM AUTHOR]

For information from sensory organs to be processed by the brain, it is usually passed to appropriate areas of the cerebral cortex. Almost all of this information passes through the thalamus, a relay structure that reciprocally connects to the vast majority of the cortex. The thalamus facilitates this information transfer through a set of thalamocortical connections that vary in cellular structure, molecular profiles, innervation patterns, and firing rates. Additionally, corticothalamic connections allow for intracortical information transfer through the thalamus. These efferent and afferent connections between the thalamus and cortex have been the focus of many studies, and the importance of cortical connectivity in defining thalamus anatomy is demonstrated by multiple studies that parcellate the thalamus based on cortical connectivity profiles.Here, we examine correlated morphological variation between the thalamus and cortex, or thalamocortical structural covariance. For each voxel in the thalamus as a seed, we construct a cortical structural covariance map that represents correlated cortical volume variation, and examine whether high structural covariance is observed in cortical areas that are functionally relevant to the seed. Then, using these cortical structural covariance maps as features, we subdivide the thalamus into six non-overlapping regions (clusters of voxels), and assess whether cortical structural covariance is associated with cortical connectivity that specifically originates from these regions.We show that cortical structural covariance is high in areas of the cortex that are functionally related to the seed voxel, cortical structural covariance varies along cortical depth, and sharp transitions in cortical structural covariance profiles are observed when varying seed locations in the thalamus. Subdividing the thalamus based on structural covariance, we additionally demonstrate that the six thalamic clusters of voxels stratify cortical structural covariance along the dorsal–ventral, medial–lateral, and anterior–posterior axes. These cluster-associated structural covariance patterns are prominently detected in cortical regions innervated by fibers projecting out of their related thalamic subdivisions.Together, these results advance our understanding of how the thalamus and the cortex couple in their volumes. Our results indicate that these volume correlations reflect functional organization and structural connectivity, and further provides a novel segmentation of the mouse thalamus that can be used to examine thalamic structural variation and thalamocortical structural covariation in disease models.

The amygdala is divided into functional subnuclei which have been challenging to investigate due to functional magnetic resonance imaging (MRI) limitations in mapping small neural structures. Hence their role in the neurobiology of posttraumatic stress disorder (PTSD) remains poorly understood. Examination of covariance of structural MRI measures could be an alternate approach to circumvent this issue. T1-weighted anatomical scans from a 3 T scanner from non-trauma-exposed controls (NEC; n = 71, 75 % female) and PTSD participants (n = 67, 69 % female) were parcellated into 105 brain regions. Pearson’s r partial correlations were computed for three and nine bilateral amygdala subnuclei and every other brain region, corrected for age, sex, and total brain volume. Pairwise correlation comparisons were performed to examine subnuclei covariance profiles between-groups. Graph theory was employed to investigate subnuclei network topology. Volumetric measures were compared to investigate structural changes.We found differences between amygdala subnuclei in covariance with the hippocampus for both groups, and additionally with temporal brain regions for the PTSD group. Network topology demonstrated the importance of the right basal nucleus in facilitating network communication only in PTSD. There were no between-group differences for any of the three structural metrics. These findings are in line with previous work that has failed to find structural differences for amygdala subnuclei between PTSD and controls. However, differences between amygdala subnuclei covariance profiles observed in our study highlight the need to investigate amygdala subnuclei functional connectivity in PTSD using higher field strength fMRI for better spatial resolution.

Introduction: In this study, we applied multivariate methods to identify brain regions that have a critical role in shaping the connectivity patterns of networks associated with major psychiatric diagnoses, including schizophrenia (SCH), major depressive disorder (MDD) and bipolar disorder (BD) and healthy controls (HC). We used T1w images from 164 subjects: Schizophrenia (n  =  17), bipolar disorder (n  =  25), major depressive disorder (n  =  68) and a healthy control group (n  =  54). Methods: We extracted regions of interest (ROIs) using a method based on the SHOOT algorithm of the SPM12 toolbox. We then performed multivariate structural covariance between the groups. For the regions identified as significant in t term of their covariance value, we calculated their eigencentrality as a measure of the influence of brain regions within the network. We applied a significance threshold of p = 0.001. Finally, we performed a cluster analysis to determine groups of regions that had similar eigencentrality profiles in different pairwise comparison networks in the observed groups. Results: As a result, we obtained 4 clusters with different brain regions that were diagnosis-specific. Cluster 1 showed the strongest discriminative values between SCH and HC and SCH and BD. Cluster 2 had the strongest discriminative value for the MDD patients, cluster 3 – for the BD patients. Cluster 4 seemed to contribute almost equally to the discrimination between the four groups. Discussion: Our results suggest that we can use the multivariate structural covariance method to identify specific regions that have higher predictive value for specific psychiatric diagnoses. In our research, we have identified brain signatures that suggest that degeneracy shapes brain networks in different ways both within and across major psychiatric disorders. [ABSTRACT FROM AUTHOR]

Introduction: Abnormal interactions among distributed brain systems are implicated in the mechanisms of nicotine addiction. However, the relationship between the structural covariance network, a measure of brain connectivity, and smoking severity remains unclear. To fill this gap, this study aimed to investigate the relationship between structural covariance network and smoking severity in smokers. Methods: A total of 101 male smokers and 51 male non-smokers were recruited, and they underwent a T1-weighted anatomical image scan. First, an individualized structural covariance network was derived via a jackknife-bias estimation procedure for each participant. Then, a data-driven machine learning method called connectome-based predictive modeling (CPM) was conducted to infer smoking severity measured with Fagerström Test for Nicotine Dependence (FTND) scores using an individualized structural covariance network. The performance of CPM was evaluated using the leave-one-out cross-validation and a permutation testing. Results: As a result, CPM identified the smoking severity-related structural covariance network, as indicated by a significant correlation between predicted and actual FTND scores (r = 0.23, permutation p = 0.020). Identified networks comprised of edgesmainly located between the subcortical-cerebellumnetwork and networks including the frontoparietal default model and motor and visual networks. Discussion: These results identified smoking severity-related structural covariance networks and provided a new insight into the neural underpinnings of smoking severity. [ABSTRACT FROM AUTHOR]

IntroductionIn this study, we applied multivariate methods to identify brain regions that have a critical role in shaping the connectivity patterns of networks associated with major psychiatric diagnoses, including schizophrenia (SCH), major depressive disorder (MDD) and bipolar disorder (BD) and healthy controls (HC). We used T1w images from 164 subjects: Schizophrenia (n = 17), bipolar disorder (n = 25), major depressive disorder (n = 68) and a healthy control group (n = 54).MethodsWe extracted regions of interest (ROIs) using a method based on the SHOOT algorithm of the SPM12 toolbox. We then performed multivariate structural covariance between the groups. For the regions identified as significant in t term of their covariance value, we calculated their eigencentrality as a measure of the influence of brain regions within the network. We applied a significance threshold of p = 0.001. Finally, we performed a cluster analysis to determine groups of regions that had similar eigencentrality profiles in different pairwise comparison networks in the observed groups.ResultsAs a result, we obtained 4 clusters with different brain regions that were diagnosis-specific. Cluster 1 showed the strongest discriminative values between SCH and HC and SCH and BD. Cluster 2 had the strongest discriminative value for the MDD patients, cluster 3 – for the BD patients. Cluster 4 seemed to contribute almost equally to the discrimination between the four groups.DiscussionOur results suggest that we can use the multivariate structural covariance method to identify specific regions that have higher predictive value for specific psychiatric diagnoses. In our research, we have identified brain signatures that suggest that degeneracy shapes brain networks in different ways both within and across major psychiatric disorders.

Temporal lobe epilepsy (TLE) is the most common type of intractable epilepsy in adults. Although brain myelination alterations have been observed in TLE, it remains unclear how the myelination network changes in TLE. This study developed a novel method in characterization of myelination structural covariance network (mSCN) by T1‐weighted and T2‐weighted magnetic resonance imaging (MRI). The mSCNs were estimated in 42 left TLE (LTLE), 42 right TLE (RTLE) patients, and 41 healthy controls (HCs). The topology of mSCN was analyzed by graph theory. Voxel‐wise comparisons of myelination laterality were also examined among the three groups. Compared to HC, both patient groups showed decreased myelination in frontotemporal regions, amygdala, and thalamus; however, the LTLE showed lower myelination in left medial temporal regions than RTLE. Moreover, the LTLE exhibited decreased global efficiency compared with HC and more increased connections than RTLE. The laterality in putamen was differently altered between the two patient groups: higher laterality at posterior putamen in LTLE and higher laterality at anterior putamen in RTLE. The putamen may play a transfer station role in damage spreading induced by epileptic seizures from the hippocampus. This study provided a novel workflow by combination of T1‐weighted and T2‐weighted MRI to investigate in vivo the myelin‐related microstructural feature in epileptic patients first time. Disconnections of mSCN implicate that TLE is a system disorder with widespread disruptions at regional and network levels. [ABSTRACT FROM AUTHOR]

The differential structural covariance of nucleus accumbens (NAcc), playing a vital role in etiology and treatment, remains unclear in depression. We aimed to investigate whether structural covariance of NAcc was altered and how it was modulated by illness duration and severity of symptom measured with Hamilton Depression scale (HAMD). T1-weighted anatomical images of never-treated first-episode patients with depression (n = 195) and matched healthy controls (HCs, n = 78) were acquired. Gray matter volumes were calculated using voxel-based morphometry analysis for each subject. Then, we explored abnormal structural covariance of NAcc and how the abnormality was modulated by illness duration and severity of symptom. Patients with depression exhibited altered structural covariance of NAcc connected to key brain regions in reward system including the medial orbitofrontal cortex, amygdala, insula, parahippocampa gyrus, precuneus, thalamus, hippocampus and cerebellum. In addition, the structural covariance of the NAcc was distinctly modulated by illness duration and the severity of symptom in patients with depression. What is more, the structural covariance of the NAcc connected to hippocampus was modulated by these two factors at the same time. These results elucidate altered structural covariance of the NAcc and its distinct modulation of illness duration and severity of symptom. • The structural covariance of the NAcc was distinctly modulated by illness duration and the severity of symptom in patients with depression. • The structural covariance of the NAcc connected to hippocampus was modulated by these two factors at the same time. [ABSTRACT FROM AUTHOR]

Introduction: Stressful experiences such as violence can affect mental health severely. The effects are associated with changes in structural and functional brain networks. The current study aimed to investigate brain network changes in four large-scale brain networks, the default mode network, the salience network, the fronto-parietal network, and the dorsal attention network in self-identified victims of violence and controls who did not identify themselves as victims. Materials and methods: The control group (n = 32) was matched to the victim group (n = 32) by age, gender, and primary psychiatric disorder. Sparse inverse covariance maps were derived from functional resting-state measurements and from T1 weighted structural data for both groups. Results: Our data underlined that mostly the salience network was affected in the sample of self-identified victims. In self-identified victims with a current psychiatric diagnosis, the dorsal attention network was mostly affected underlining the potential role of psychopathological alterations on attention-related processes. Conclusion: The results showed that individuals who identify themselves as victim demonstrated significant differences in all considered networks, both within- and between-network. [ABSTRACT FROM AUTHOR]

IntroductionStressful experiences such as violence can affect mental health severely. The effects are associated with changes in structural and functional brain networks. The current study aimed to investigate brain network changes in four large-scale brain networks, the default mode network, the salience network, the fronto-parietal network, and the dorsal attention network in self-identified victims of violence and controls who did not identify themselves as victims.Materials and methodsThe control group (n = 32) was matched to the victim group (n = 32) by age, gender, and primary psychiatric disorder. Sparse inverse covariance maps were derived from functional resting-state measurements and from T1 weighted structural data for both groups.ResultsOur data underlined that mostly the salience network was affected in the sample of self-identified victims. In self-identified victims with a current psychiatric diagnosis, the dorsal attention network was mostly affected underlining the potential role of psychopathological alterations on attention-related processes.ConclusionThe results showed that individuals who identify themselves as victim demonstrated significant differences in all considered networks, both within- and between-network.

Background and purpose: Patients with asymptomatic carotid stenosis, even without stroke, are at high risk for cognitive impairment, and the neuroanatomical basis remains unclear. Using a novel edge-centric structural connectivity (eSC) analysis from individualized single-subject cortical thickness networks, we aimed to examine eSC and network measures in severe (> 70%) asymptomatic carotid stenosis (SACS). Methods: Twenty-four SACS patients and 24 demographically- and comorbidities-matched controls were included, and structural MRI and multidomain cognitive data were acquired. Individual eSC was estimated via the Manhattan distances of pairwise cortical thickness histograms. Results: In the eSC analysis, SACS patients showed longer interhemispheric but shorter intrahemispheric Manhattan distances seeding from left lateral temporal regions; in network analysis the SACS patients had a decreased system segregation paralleling with white matter hyperintensity burden and recall memory. Further network-based statistic analysis identified several eSC and subgraph features centred around the Perisylvian regions that predicted silent lesion load and cognitive tests. Conclusion: We conclude that SACS exhibits abnormal eSC and a lessoptimized trade-off between physical cost and network segregation, providing a reference and perspective for identifying high-risk individuals. [ABSTRACT FROM AUTHOR]

Objectives: Chronic occupational manganese (Mn) exposure is characterized by motor and cognitive dysfunction. This study aimed to investigate structural abnormalities in Mn-exposed welders compared to healthy controls (HCs). Methods: Thirty-five HCs and forty Mn-exposed welders underwent magnetic resonance imaging (MRI) scans in this study. Based on T1-weighted MRI, the voxel-based morphometry (VBM), structural covariance, and receiver operating characteristic (ROC) curve were applied to examine whole-brain structural changes in Mn-exposed welders. Results: Compared to HCs, Mn-exposed welders had altered gray matter volume (GMV) mainly in the medial prefrontal cortex, lentiform nucleus, hippocampus, and parahippocampus. ROC analysis indicated the potential highest classification power of the hippocampus/parahippocampus. Moreover, distinct structural covariance patterns in the two groups were associated with regions, mainly including the thalamus, insula, amygdala, sensorimotor area, and middle temporal gyrus. No significant relationships were found between the findings and clinical characteristics. Conclusions: Our findings showed Mn-exposed welders had changed GMV and structural covariance patterns in some regions, which implicated in motivative response, cognitive control, and emotional regulation. These results might provide preliminary evidence for understanding the pathophysiology of Mn overexposure. Key Points: • Chronic Mn exposure might be related to abnormal brain structural neural mechanisms. • Mn-exposed welders had morphological changes in brain regions implicated in emotional modulation, cognitive control, and motor-related response. • Altered gray matter volume in the hippocampus/parahippocampus and putamen might serve as potential biomarkers for Mn overexposure. [ABSTRACT FROM AUTHOR]

The cortex and cerebellum are densely connected through reciprocal input/output projections that form segregated circuits. These circuits are shown to differentially connect anterior lobules of the cerebellum to sensorimotor regions, and lobules Crus I and II to prefrontal regions. This differential connectivity pattern leads to the hypothesis that individual differences in structure should be related, especially for connected regions. To test this hypothesis, we examined covariation between the volumes of anterior sensorimotor and lateral cognitive lobules of the cerebellum and measures of cortical thickness (CT) and surface area (SA) across the whole brain in a sample of 270 young adults drawn from the HCP dataset. We observed that patterns of cerebellar-cortical covariance differed between sensorimotor and cognitive networks. Anterior motor lobules of the cerebellum showed greater covariance with sensorimotor regions of the cortex, while lobules Crus I and Crus II showed greater covariance with frontal and temporal regions. Interestingly, cerebellar volume showed predominantly negative relationships with CT and predominantly positive relationships with SA. Individual differences in SA are thought to be largely under genetic control while CT is thought to be more malleable by experience. This suggests that cerebellar-cortical covariation for SA may be a more stable feature, whereas covariation for CT may be more affected by development. Additionally, similarity metrics revealed that the pattern of covariance showed a gradual transition between sensorimotor and cognitive lobules, consistent with evidence of functional gradients within the cerebellum. Taken together, these findings are consistent with known patterns of structural and functional connectivity between the cerebellum and cortex. They also shed new light on possibly differing relationships between cerebellar volume and cortical thickness and surface area. Finally, our findings are consistent with the interactive specialization framework which proposes that structurally and functionally connected brain regions develop in concert., (© 2025 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC.)

Periventricular leukomalacia is a common neuroimaging finding in patients with spastic cerebral palsy. Myelin damage disrupts neuronal connectivity. However, specific alterations in the grey matter structure and their impact on the whole brain remain unclear, particularly when differentiating between preterm and full-term periventricular leukomalacia. This study investigated the grey matter network alterations following early white matter injury in infants and young children. High-resolution T 1 -weighted 3 T brain magnetic resonance imaging, clinical data and motor function scores were collected from 42 children with periventricular leukomalacia and 38 age- and sex-matched healthy controls. Based on gestational age, the periventricular leukomalacia group was stratified into preterm ( n = 27) and full-term ( n = 15) groups. Voxel-based morphometry was used to analyse whole-brain structural metrics, and motor-related regions were selected as nodes for network construction. Structural covariance analysis was used to quantify the strength of the structural connections between grey matter regions, and graph theory metrics were used to assess network properties. Motor assessments included gross and fine motor skills, and their associations with brain regions were analysed. Both preterm and full-term periventricular leukomalacia groups exhibited abnormal motor networks. Preterm periventricular leukomalacia showed more extensive central grey matter nuclei atrophy, whereas full-term periventricular leukomalacia was predominantly localized to the motor cortex. Children with periventricular leukomalacia displayed decreased connectivity between the central grey matter nuclei and other regions, coupled with increased connectivity between the motor cortex and cerebellar hemispheres. Thalamic volume correlated with gross motor scores in preterm infants. These findings suggest that ischaemic-hypoxic injury disrupts motor grey matter networks, with preterm infants being more severely affected. This study highlights the potential of structural covariance patterns for monitoring brain development and advancing our understanding of aberrant brain development in children with periventricular leukomalacia., Competing Interests: The authors declare no conflicts of interest regarding the publication of this paper., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Guarantors of Brain.)

There is considerable interest in understanding the developmental origins and health implications of individual differences in brain structure and function. In this pre-registered study we demonstrate that a hidden subgroup within the general population-people with synesthesia (e.g. who "hear" colors)-show a distinctive behavioral phenotype and wide-ranging differences in brain structure and function. We assess the performance of 13 different brain-based biomarkers (structural and functional MRI) for classifying synesthetes against general population samples, using machine learning models. The features in these models were derived from subject-specific parcellations of the cortex using the Human Connectome Project approach. All biomarkers performed above chance with intracortical myelin being a particularly strong predictor that has not been implicated in synesthesia before. Resting state data show widespread changes in the functional connectome (including less hub-based connectivity). These brain-based individual differences within the neurotypical population can be as large as those that differentiate neurotypical from clinical brain states., (© The Author(s) 2024. Published by Oxford University Press.)

It is widely assumed that our actions shape our brains and that the resulting connections determine who we are. To test this idea in a reductionist setting, in which genes and environment are controlled, we investigated differences in neuroanatomy and structural covariance by ex vivo structural magnetic resonance imaging in mice whose behavioral activity was continuously tracked for 3 months in a large, enriched environment. We confirmed that environmental enrichment increases mouse hippocampal volumes. Stratifying the enriched group according to individual longitudinal behavioral trajectories, however, revealed striking differences in mouse brain structural covariance in continuously highly active mice compared to those whose trajectories showed signs of habituating activity. Network-based statistics identified distinct subnetworks of murine structural covariance underlying these differences in behavioral activity. Together, these results reveal that differentiated behavioral trajectories of mice in an enriched environment are associated with differences in brain connectivity.

There is an ongoing debate about differential clinical outcome and associated adverse effects of deep brain stimulation (DBS) in Parkinson’s disease (PD) targeting the subthalamic nucleus (STN) or the globus pallidus pars interna (GPi). Given that functional connectivity profiles suggest beneficial DBS effects within a common network, the empirical evidence about the underlying anatomical circuitry is still scarce. Therefore, we investigate the STN and GPi-associated structural covariance brain patterns in PD patients and healthy controls.We estimate GPi’s and STN’s whole-brain structural covariance from magnetic resonance imaging (MRI) in a normative mid- to old-age community-dwelling cohort (n = 1184) across maps of grey matter volume, magnetization transfer (MT) saturation, longitudinal relaxation rate (R1), effective transversal relaxation rate (R2*) and effective proton density (PD*). We compare these with the structural covariance estimates in patients with idiopathic PD (n = 32) followed by validation using a reduced size controls’ cohort (n = 32).In the normative data set, we observed overlapping spatially distributed cortical and subcortical covariance patterns across maps confined to basal ganglia, thalamus, motor, and premotor cortical areas. Only the subcortical and midline motor cortical areas were confirmed in the reduced size cohort. These findings contrasted with the absence of structural covariance with cortical areas in the PD cohort.We interpret with caution the differential covariance maps of overlapping STN and GPi networks in patients with PD and healthy controls as correlates of motor network disruption. Our study provides face validity to the proposed extension of the currently existing structural covariance methods based on morphometry features to multiparameter MRI sensitive to brain tissue microstructure.

Purpose: To investigate cortical differences, age-related cortical differences, and structural covariance differences between children with intermittent exotropia (IXT) and healthy controls (HCs) using high-resolution magnetic resonance imaging (MRI). Methods: Sixteen IXT patients and 16 HCs underwent MRI using a 3-T MR scanner. FreeSurfer software was used to obtain measures of cortical volume, thickness, and surface area. Group differences in cortical thickness, volume and surface area were examined using a general linear model with intracranial volume (ICV), age and sex as covariates. Then, the age-related cortical differences between the two groups and structural covariance in abnormal morphometric changes were examined. Results: Compared to HCs, IXT patients demonstrated significantly decreased surface area in the left primary visual cortex (PVC), and increased surface area in the left inferior temporal cortex (ITC). We also found increased cortical thickness in the left orbitofrontal cortex (OFC), right middle temporal cortex (MT), and right inferior frontal cortex (IFC). No significant differences were found in cortical volume between the two groups. There were several negative correlations between neuroanatomic measurements and age in the HC group that were not observed in the IXT group. In addition, we identified altered patterns of structural correlations across brain regions in patients with IXT. Conclusion: To our knowledge, this study is the first to characterize the cortical morphometry of the children and adolescents with IXT. Based on our results, children and adolescents with IXT exhibited significant alterations in the PVC and association cortices, different cortical morphometric development patterns, and disrupted structural covariance across brain regions. [ABSTRACT FROM AUTHOR]

Elucidating distinct morphological atrophy patterns of Alzheimer’s disease (AD) and its prodromal stage, namely, mild cognitive impairment (MCI) helps to improve early diagnosis and medical intervention of AD. On that account, we aimed to obtain distinct patterns of voxel-wise morphological atrophy and its further perturbation on structural covariance network in AD and MCI compared with healthy controls (HCs). T1-weighted anatomical images of matched AD, MCI, and HCs were included in this study. Gray matter volume was obtained using voxel-based morphometry and compared among three groups. In addition, structural covariance network of identified brain regions exhibiting morphological difference was constructed and compared between pairs of three groups. Thus, patients with AD have a reduced hippocampal volume and an increased rate of atrophy compared with MCI and HCs. MCI exhibited a decreased trend in bilateral hippocampal volume compared with HCs and the accelerated right hippocampal atrophy rate than HCs. In AD, the hippocampus further exhibited increased structural covariance connected to reward related brain regions, including the anterior cingulate cortex, the putamen, the caudate, and the insula compared with HCs. In addition, the patients with AD exhibited increased structural covariance of left hippocampus with the bilateral insula, the inferior frontal gyrus, the superior temporal gyrus, and the cerebellum than MCI. These results reveal distinct patterns of morphological atrophy in AD and MCI, providing new insights into pathology of AD. [ABSTRACT FROM AUTHOR]

Individual differences among human brains exist at many scales, spanning gene expression, white matter tissue properties, and the size and shape of cortical areas. One notable example is an approximately 3-fold range in the size of human primary visual cortex (V1), a much larger range than is found in overall brain size. A previous study (Andrews et al., 1997) reported a correlation between optic tract (OT) cross-section area and V1 size in postmortem human brains, suggesting that there may be a common developmental mechanism for multiple components of the visual pathways. We evaluated the relationship between properties of the OT and V1 in a much larger sample of living human brains by analyzing the Human Connectome Project (HCP) 7 Tesla Retinotopy Dataset (including 107 females and 71 males). This dataset includes retinotopic maps measured with functional MRI (fMRI) and fiber tract data measured with diffusion MRI (dMRI). We found a negative correlation between OT fractional anisotropy (FA) and V1 surface area (r = −0.19). This correlation, although small, was consistent across multiple dMRI datasets differing in acquisition parameters. Further, we found that both V1 surface area and OT properties were correlated among twins, with higher correlations for monozygotic (MZ) than dizygotic (DZ) twins, indicating a high degree of heritability for both properties. Together, these results demonstrate covariation across individuals in properties of the retina (OT) and cortex (V1) and show that each is influenced by genetic factors. [ABSTRACT FROM AUTHOR]

Objective: Psychosocial functioning in bipolar disorder (BD) persists even during euthymia and has repeatedly been associated with illness progression and cognitive function. Its neurobiological correlates remain largely unexplored. Using a structural covariance approach, we explored whole cortex intracortical myelin (ICM) and psychosocial functioning in 39 BD type I and 58 matched controls. Method: T1‐weighted images (3T) optimized for ICM measurement were analyzed using a surface‐based approach. The ICM signal was sampled at cortical mid‐depth using the MarsAtlas parcellation, and psychosocial functioning was measured via the Functioning Assessment Short Test (FAST). Following construction of structural covariance matrices, graph theoretical measures were calculated for each subject. Within BD and HC groups separately, correlations between network measures and FAST were explored. After accounting for multiple comparisons, significant correlations were tested formally using rank‐based regressions accounting for sex differences. Results: In BD only, psychosocial functioning was associated with global efficiency (β = −0.312, pcorr = 0.03), local efficiency in the right rostral dorsolateral prefrontal cortex (β = 0.545, pcorr = 0.001) and clustering coefficient in this region (β = 0.497, pcorr = 0.0002) as well as in the right ventromedial prefrontal cortex (β = 0.428, pcorr = 0.002). All results excepting global efficiency remained significant after accounting for severity of depressive symptoms. In contrast, no significant associations between functioning and network measures were observed in the HC group. Conclusion: These results uncovered a novel brain–behaviour relationship between intracortical myelin signal changes and psychosocial functioning in BD. [ABSTRACT FROM AUTHOR]

• A longitudinal study identified specific changes of ventral striatum-related brain structural covariance and gray matter volume in first-episode patients with major depression disorder. • Patients with MDD exhibit specific cortico-ventral striatum-related reward network changes over time at the onset and one-year effective treatment after. • These findings facilitate further understanding of the pathogenesis and neuroplasticity in the onset and development of the MDD. Empirical findings suggest reduced cortico-striatal structural connectivity in patients with major depressive disorder (MDD). However, the relationship between the abnormal structural covariance and one-year outcome of first-episode drug-naive patients has not been evaluated. This longitudinal study aimed to identify specific changes of ventral striatum-related brain structural covariance and grey matter volume in forty-two first-episode patients with major depression disorder compared with thirty-seven healthy controls at the baseline and the one-year follow-up conditions. At the baseline, patients showed decreased structural covariance between the left ventral striatum and the bilateral superior frontal gyrus (SFG), bilateral middle frontal gyrus (MFG), right supplementary motor area (SMA) and left precentral gyrus and increased grey matter volume at the left fusiform and left parahippocampus. At the one-year follow-up, patients showed decreased structural covariance between the left ventral striatum and the right SFG, right MFG, left precentral gyrus and left postcentral gyrus, and increased structural covariance between the right ventral striatum and the right amygdala, right hippocampus, right parahippocampus, right superior temporal pole, right insula and right olfactory bulb and decreased volume at the left SMA compared with controls. These findings suggest that specific ventral striatum connectivity changes contribute to the early brain development of the MDD. [ABSTRACT FROM AUTHOR]

Back ground: Subjective cognitive decline (SCD) may be the first clinical sign of Alzheimer's disease (AD). The possible neural mechanisms of SCD are not well known. This study aimed to compare percent amplitude of fluctuation (PerAF) and structural covariance patterns in patients with SCD and healthy controls (HCs). Methods: We enrolled 53 patients with SCD and 65 HCs. Resting-state functional magnetic resonance imaging (MRI) data and T1-weighted anatomical brain 3.0-T MRI scans were collected. The PerAF approach was applied to distinguish altered brain functions between the two groups. A whole-brain voxel-based morphometry analysis was performed, and all significant regions were selected as regions of interest (ROIs) for the structural covariance analysis. Statistical analysis was performed using two-sample t-tests, and multiple regressions were applied to examine the relationships between neuroimaging findings and clinical symptoms. Results: Functional MRI results revealed significantly increased PerAF including the right hippocampus (HIPP) and right thalamus (THA) in patients with SCD relative to HCs. Gray matter volume (GMV) results demonstrated decreased GMV in the bilateral ventrolateral prefrontal cortex (vlPFC) and right insula in patients with SCD relative to HCs. Taking these three areas including the bilateral vlPFC and right insula as ROIs, differences were observed in the structural covariance of the ROIs with several regions between the two groups. Additionally, significant correlations were observed between neuroimaging findings and clinical symptoms. Conclusion: Our study investigated the abnormal PerAF and structural covariance patterns in patients with SCD, which might provide new insights into the pathological mechanisms of SCD. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the structural covariance between the striatum and large-scale brain regions in patients with vascular parkinsonism (VP) compared to Parkinson's disease (PD) and control subjects, and then explore the relationship between brain connectivity and the clinical features of our patients. Forty subjects (13 VP, 15 PD, and 12 age-and-sex-matched healthy controls) were enrolled in this study. They each underwent a careful clinical and neuropsychological evaluation, DAT-SPECT scintigraphy and 3T MRI scan. While there were no differences between PD and VP in the disease duration and severity, nor in terms of the DAT-SPECT evaluations, VP patients had a reduction in structural covariance between the bilateral corpus striatum (both putamen and caudate) and several brain regions, including the insula, thalamus, hippocampus, anterior cingulate cortex and orbito-frontal cortex compared to PD and controls. VP patients also showed lower scores on several neuropsychological tests. Interestingly, in the VP group, structural connectivity alterations were significantly related to cognitive evaluations exploring executive functions, memory, anxiety and depression. This compelling evidence suggests that structural disconnection in the basal ganglia circuits spreading in critical cortical regions may be involved in the pathophysiology of cognitive impairment in VP. [ABSTRACT FROM AUTHOR]

Background: Externalizing and internalizing behaviors contribute to clinical impairment in children with neurodevelopmental disorders (NDDs). Although associations between externalizing or internalizing behaviors and cortico-amygdalar connectivity have been found in clinical and non-clinical pediatric samples, no previous study has examined whether similar shared associations are present across children with different NDDs. Methods: Multi-modal neuroimaging and behavioral data from the Province of Ontario Neurodevelopmental Disorders (POND) Network were used. POND participants aged 6–18 years with a primary diagnosis of autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD) or obsessive–compulsive disorder (OCD), as well as typically developing children (TDC) with T1-weighted, resting-state fMRI or diffusion weighted imaging (DWI) and parent-report Child Behavioral Checklist (CBCL) data available, were analyzed (total n = 346). Associations between externalizing or internalizing behavior and cortico-amygdalar structural and functional connectivity indices were examined using linear regressions, controlling for age, gender, and image-modality specific covariates. Behavior-by-diagnosis interaction effects were also examined. Results: No significant linear associations (or diagnosis-by-behavior interaction effects) were found between CBCL-measured externalizing or internalizing behaviors and any of the connectivity indices examined. Post-hoc bootstrapping analyses indicated stability and reliability of these null results. Conclusions: The current study provides evidence towards an absence of a shared linear relationship between internalizing or externalizing behaviors and cortico-amygdalar connectivity properties across a transdiagnostic sample of children with different primary NDD diagnoses and TDC. Different methodological approaches, including incorporation of multi-dimensional behavioral data (e.g., task-based fMRI) or clustering approaches may be needed to clarify complex brain-behavior relationships relevant to externalizing/internalizing behaviors in heterogeneous clinical NDD populations. [ABSTRACT FROM AUTHOR]

Current neuroimaging acquisition and processing approaches tend to be optimised for quality rather than speed. However, rapid acquisition and processing of neuroimaging data can lead to novel neuroimaging paradigms, such as adaptive acquisition, where rapidly processed data is used to inform subsequent image acquisition steps. Here we first evaluate the impact of several processing steps on the processing time and quality of registration of manually labelled T1‐weighted MRI scans. Subsequently, we apply the selected rapid processing pipeline both to rapidly acquired multicontrast EPImix scans of 95 participants (which include T1‐FLAIR, T2, T2*, T2‐FLAIR, DWI and ADC contrasts, acquired in ~1 min), as well as to slower, more standard single‐contrast T1‐weighted scans of a subset of 66 participants. We quantify the correspondence between EPImix T1‐FLAIR and single‐contrast T1‐weighted scans, using correlations between voxels and regions of interest across participants, measures of within‐ and between‐participant identifiability as well as regional structural covariance networks. Furthermore, we explore the use of EPImix for the rapid construction of morphometric similarity networks. Finally, we quantify the reliability of EPImix‐derived data using test–retest scans of 10 participants. Our results demonstrate that quantitative information can be derived from a neuroimaging scan acquired and processed within minutes, which could further be used to implement adaptive multimodal imaging and tailor neuroimaging examinations to individual patients. [ABSTRACT FROM AUTHOR]

Abstract: Despite diffuse tenderness, patients with fibromyalgia (FM) have reported a wide range of areas with musculoskeletal pain. This study investigated the neural structures and neuroanatomical networks associated with self-reported widespread pain in FM using magnetic resonance imaging. We collected clinical profiles and brain magnetic resonance imaging data of newly diagnosed patients with FM. A total of 138 patients with FM were divided into 3 subgroups based on the number of pain areas, with 3 to 8, 9 to 12, and 13 to 19 areas, respectively. Using voxel-based morphometry analysis, we first identified the neural structure that showed a trend of volumetric change across the 3 subgroups. We then used it as a candidate seed of interest with a seed-to-voxel analytical approach to explore the structural covariance (SC) networks of the whole brain. Finally, we studied the trend of changes in the distribution and strength of SC networks across subgroups of patients. We found a decreasing trend in the volumes of the right anterior insular cortex (rAIC) across the 3 subgroups that had an increased number of pain areas. An increasing trend in the number of neural substrates over the subcortical regions, especially the basal ganglion, showed SC to the rAIC, and a decreasing trend of SC strength was shown between the rAIC and the precuneus, frontal cortex, anterior and posterior cingulate, and lingual gyri, across the patient subgroups with increased pain areas. The rAIC and its altered connection with specific brain regions indicates widespread pain in patients with FM. [ABSTRACT FROM AUTHOR]

Background: The striatum has been reported to be implicated in various neurological diseases, including lifelong premature ejaculation (LPE). Altered striatum‐related functional connectivity was investigated in LPE patients in previous studies; however, structural abnormalities in the striatum have been less studied in LPE. Purpose To identify the gray matter volume (GMV) and structural covariance patterns of the striatum between LPE patients and healthy controls (HCs). Study Type: Prospective. Subjects: Forty‐three LPE patients and 31 male HCs. Field Strength/Sequence: 3.0 T magnetic resonance imaging (MRI) scanner; T1‐weighted imaging using a spoiled gradient recalled echo sequence. Assessment Preprocessing of structural MRI data and the striatum‐seeded GMV computation were conducted using SPM12. Statistical Tests: Two sample t‐test was used to compare differences in GMV of the striatum between patients and HCs. Regions showing altered between‐group GMV were considered as seeds for structural covariance analysis in two groups. Additionally, correlations between GMV findings and clinical features were assessed with age and total intracranial volume (TIV) as covariates and with age, TIV, anxiety, and depression scores as covariates in the patient group, P < 0.05 was considered statistically significant. Results: Compared to HCs, LPE patients had significantly decreased GMV in four regions located in the bilateral caudate and putamen. Distinct striatum‐based structural covariance patterns in the two groups were mainly related to the thalamus, amygdala, insula, anterior cingulate cortex, middle cingulate cortex, medial prefrontal cortex, primary motor cortex, and precuneus/cuneus. LPE patients showed that GMV in the bilateral caudate negatively correlated with the premature ejaculation diagnostic tool (PEDT) scores (r = −0.369, r = −0.377, respectively). Data Conclusion: Our findings indicated that LPE patients had altered GMV and structural covariance patterns in the striatum compared to HCs. The correlations between abnormal GMV and PEDT were also shown in the present findings. These findings may contribute to enhancing the understanding of the pathophysiology of LPE. Level of Evidence: 1 Technical Efficacy: Stage 3 [ABSTRACT FROM AUTHOR]

To investigate the influence of epileptogenic cortex (Rolandic areas) with executive functions in Rolandic epilepsy using structural covariance analysis of structural magnetic resonance imaging (MRI). Structural MRI data of drug-naive patients with Rolandic epilepsy (n = 70) and typically developing children as healthy controls (n = 83) were analyzed using voxel-based morphometry. Gray matter volumes in the patients were compared with those of healthy controls, and were further correlated with epilepsy duration and cognitive score of executive function, respectively. By applying Granger causal analysis to the sequenced morphometric data according to disease progression information, causal network of structural covariance was constructed to assess the causal influence of structural changes from Rolandic cortices to the regions engaging executive function in the patients. Compared with healthy controls, epilepsy patients showed increased gray matter volume in the Rolandic regions, and also the regions engaging in executive function. Covariance network analyses showed that along with disease progression, the Rolandic regions imposed positive causal influence on the regions engaging in executive function. In the patients with Rolandic epilepsy, epileptogenic regions have causal influence on the structural changes in the regions of executive function, implicating damaging effects of Rolandic epilepsy on human brain. [ABSTRACT FROM AUTHOR]

BackgroundBy calculating cortical thickness (CT) and cortical structural covariance (SC), we aimed to investigate cortical morphology and cortical inter-regional correlation alterations in adolescent bipolar disorder type I (BD-I) and type II (BD-II) patients.MethodsT1-weighted images from 36 BD-I and 22 BD-II patients and 19 healthy controls (HCs) were processed to estimate CT. CT values of the whole brain were compared among three groups. Cortical regions showing CT differences in groups were regarded as seeds for analyzing cortical SC differences between groups. The relationship between CT and clinical indices was further assessed.ResultsBoth BD groups showed cortical thinning in several frontal and temporal areas vs. HCs, and CT showed no significant difference between two BD subtypes. Compared to HCs, both BD groups exhibited reduced SC connections between left superior frontal gyrus (SFG) and right postcentral gyrus (PCG), left superior temporal gyrus (STG) and right pars opercularis, and left STG and right PCG. Compared with HCs, decreased SC connections between left STG and right inferior parietal gyrus (IPG) and right pars opercularis and right STG were only observed in the BD-I group, and left PCG and left SFG only in the BD-II group. CT of right middle temporal gyrus was negatively correlated with number of episodes in BD-II patients.ConclusionsAdolescent BD-I and BD-II showed commonly decreased CT while presenting commonly and distinctly declined SC connections. This study provides a better understanding of cortical morphology and cortical inter-regional correlation alterations in BD and crucial insights into neuroanatomical mechanisms and pathophysiology of different BD subtypes.

Background: Prenatal stress influences brain development and mood disorder vulnerability. Brain structural covariance network (SCN) properties based on inter-regional volumetric correlations may reflect developmentally-mediated shared plasticity among regions. Childhood trauma is associated with amygdala-centric SCN reorganization patterns, however, the impact of prenatal stress on SCN properties remains unknown. Methods: The study included participants from the European Longitudinal Study of Pregnancy and Childhood (ELSPAC) with archival prenatal stress data and structural MRI acquired in young adulthood (age 23–24). SCNs were constructed based on Freesurfer-extracted volumes of 7 subcortical and 34 cortical regions. We compared amygdala degree centrality, a measure of hubness, between those exposed to high vs. low (median split) prenatal stress, defined by maternal reports of stressful life events during the first (n = 93, 57% female) and second (n = 125, 54% female) half of pregnancy. Group differences were tested across network density thresholds (5–40%) using 10,000 permutations, with sex and intracranial volume as covariates, followed by sex-specific analyses. Finally, we sought to replicate our results in an independent all-male sample (n = 450, age 18–20) from the Avon Longitudinal Study of Parents and Children (ALSPAC). Results: The high-stress during the first half of pregnancy ELSPAC group showed lower amygdala degree particularly in men, who demonstrated this difference at 10 consecutive thresholds, with no significant differences in global network properties. At the lowest significant density threshold, amygdala volume was positively correlated with hippocampus, putamen, rostral anterior and posterior cingulate, transverse temporal, and pericalcarine cortex in the low-stress (p(FDR) 0.882) group. Although amygdala degree was nominally lower across thresholds in the high-stress ALSPAC group, these results were not significant. Conclusion: Unlike childhood trauma, prenatal stress may shift SCN towards a less amygdala-centric SCN pattern, particularly in men. These findings did not replicate in an all-male ALSPAC sample, possibly due to the sample’s younger age and lower prenatal stress exposure.

Background: By calculating cortical thickness (CT) and cortical structural covariance (SC), we aimed to investigate cortical morphology and cortical inter-regional correlation alterations in adolescent bipolar disorder type I (BD-I) and type II (BD-II) patients. Methods: T1-weighted images from 36 BD-I and 22 BD-II patients and 19 healthy controls (HCs) were processed to estimate CT. CT values of the whole brain were compared among three groups. Cortical regions showing CT differences in groups were regarded as seeds for analyzing cortical SC differences between groups. The relationship between CT and clinical indices was further assessed. Results: Both BD groups showed cortical thinning in several frontal and temporal areas vs. HCs, and CT showed no significant difference between two BD subtypes. Compared to HCs, both BD groups exhibited reduced SC connections between left superior frontal gyrus (SFG) and right postcentral gyrus (PCG), left superior temporal gyrus (STG) and right pars opercularis, and left STG and right PCG. Compared with HCs, decreased SC connections between left STG and right inferior parietal gyrus (IPG) and right pars opercularis and right STG were only observed in the BD-I group, and left PCG and left SFG only in the BD-II group. CT of right middle temporal gyrus was negatively correlated with number of episodes in BD-II patients. Conclusions: Adolescent BD-I and BD-II showed commonly decreased CT while presenting commonly and distinctly declined SC connections. This study provides a better understanding of cortical morphology and cortical inter-regional correlation alterations in BD and crucial insights into neuroanatomical mechanisms and pathophysiology of different BD subtypes. [ABSTRACT FROM AUTHOR]

Pediatric traumatic brain injury (TBI) is prevalent and can disrupt ongoing brain maturation. However, the long-term consequences of pediatric TBI on the brain's network architecture are poorly understood. Structural covariance networks (SCN), based on anatomical correlations between brain regions, may provide important insights into brain topology following TBI. Changes in global SCN (default-mode network [DMN], central executive network [CEN], and salience network [SN]) were compared sub-acutely (<90 days) and in the long-term (approximately 12–24 months) after pediatric moderate-severe TBI (n = 16), and compared to typically developing children assessed concurrently (n = 15). Gray matter (GM) volumes from selected seeds (DMN: right angular gyrus [rAG], CEN: right dorsolateral prefrontal cortex [rDLPFC], SN: right anterior insula) were extracted from T1-weighted images at both timepoints. No group differences were found sub-acutely; at the second timepoint, the TBI group showed significantly reduced structural covariance within the DMN seeded from the rAG and the (1) right middle frontal gyrus, (2) left superior frontal gyrus, and (3) left fusiform gyrus. Reduced structural covariance was also found within the CEN, that is, between the rDLPFC and the (1) calcarine sulcus, and (2) right occipital gyrus. In addition, injury severity was positively associated with GM volumes in the identified CEN regions. Over time, there were no significant changes in SCN in either group. The findings, albeit preliminary, suggest for the first time a long-term effect of pediatric TBI on SCN. SCN may be a complementary approach to characterize the global effect of TBI on the developing brain. Future work needs to further examine how disruptions of these networks relate to behavioral and cognitive difficulties. [ABSTRACT FROM AUTHOR]

Adolescence is a period of rapid development of the brain's inherent functional and structural networks; however, little is known about the region-to-region organization of adolescent cerebral blood flow (CBF) or its relationship to neuroanatomy. Here, we investigate both the regional covariation of CBF MRI and the covariation of structural MRI, in adolescents with and without bipolar disorder. Bipolar disorder is a disease with increased onset during adolescence, putative vascular underpinnings, and evidence of anomalous CBF and brain structure. In both groups, through hierarchical clustering, we found CBF covariance was principally described by clusters of regions circumscribed to the left hemisphere, right hemisphere, and the inferior brain; these clusters were spatially reminiscent of cerebral vascular territories. CBF covariance was associated with structural covariance in both the healthy group (n = 56; r = 0.20, p < 0.0001) and in the bipolar disorder group (n = 68; r = 0.36, p < 0.0001), and this CBF-structure correspondence was higher in bipolar disorder (p = 0.0028). There was lower CBF covariance in bipolar disorder compared to controls between the left angular gyrus and pre- and post-central gyri. Altogether, CBF covariance revealed distinct brain organization, had modest correspondence to structural covariance, and revealed evidence of differences in bipolar disorder. [ABSTRACT FROM AUTHOR]

Introduction: Although previous studies have revealed that dysfunctional brain organization is associated with internet gamingdisorder (IGD), the neuroanatomical basis that underlies IGD remains elusive. In this work, we aimed to investigate gray matter (GM) volume alterations and structural covariance patterns in relation to IGD severity.Methods: Structural magnetic resonance imaging data were acquired from two hundred and thirty young adults encompassing a wide range of IGD severity. Voxel-based morphometry (VBM) analysis was applied to examine GM volume changes associated with IGD severity. Furthermore, the organization of whole-brain structural covariance network (SCN) was analyzed using the regions identified as seeds from VBM analysis.Results: Individuals with greater IGD severity had increased GM volumes in the midline components of the default mode network (DMN), namely, the right medial prefrontal cortex (mPFC) and precuneus. More importantly, the SCN results revealed impaired patterns of structural covariance between the DMN-related regions and areas associated with visuospatial attention and reward craving processing as the addiction severity of IGD worsened.Limitations: Only young Chinese adults were enrolled in our study andthe extent to which findings generalize to samples in other age groups and diverse cultures is unclear.Conclusions: These results showed volume expansion of the DMN components and its weakened structural association with visuospatial attention and motivational craving regions with increasing IGD severity. This study deepens our understanding of the underlying neuroanatomical correlates of IGD, which may help to explain why some individuals are more vulnerable to compulsive gaming usage than others. [ABSTRACT FROM AUTHOR]