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Objective: To investigate the local cortical morphology and individual-based morphological brain networks (MBNs) changes in children with Rolandic epilepsy (RE)., Methods: Based on the structural MRI data of 56 children with RE and 56 healthy controls (HC), we constructed four types of individual-based MBNs using morphological indices (cortical thickness [CT], fractal dimension [FD], gyrification index [GI], and sulcal depth [SD]). The global and nodal properties of the brain networks were analyzed using graph theory. The between-group difference in local morphology and network topology was estimated, and partial correlation analysis was further analyzed., Results: Compared with the HC, children with RE showed regional GI increases in the right posterior cingulate gyrus and SD increases in the right anterior cingulate gyrus and medial prefrontal cortex. Regarding the network level, RE exhibited increased characteristic path length in CT-based and FD-based networks, while decreased FD-based network node efficiency in the right inferior frontal gyrus. No significant correlation between altered morphological features and clinical variables was found in RE., Conclusions: These findings indicated that children with RE have disrupted morphological brain network organization beyond local morphology changes., Significance: The present study could provide more theoretical basis for exploring the neuropathological mechanisms in RE., (Copyright © 2024 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)

Although white matter (WM) accounts for nearly half of adult brain, its wiring diagram is largely unknown. Here, an approach is developed to construct WM networks by estimating interregional morphological similarity based on structural magnetic resonance imaging. It is found that morphological WM networks showed nontrivial topology, presented good-to-excellent test-retest reliability, accounted for phenotypic interindividual differences in cognition, and are under genetic control. Through integration with multimodal and multiscale data, it is further showed that morphological WM networks are able to predict the patterns of hamodynamic coherence, metabolic synchronization, gene co-expression, and chemoarchitectonic covariance, and associated with structural connectivity. Moreover, the prediction followed WM functional connectomic hierarchy for the hamodynamic coherence, is related to genes enriched in the forebrain neuron development and differentiation for the gene co-expression, and is associated with serotonergic system-related receptors and transporters for the chemoarchitectonic covariance. Finally, applying this approach to multiple sclerosis and neuromyelitis optica spectrum disorders, it is found that both diseases exhibited morphological dysconnectivity, which are correlated with clinical variables of patients and are able to diagnose and differentiate the diseases. Altogether, these findings indicate that morphological WM networks provide a reliable and biologically meaningful means to explore WM architecture in health and disease., (© 2024 The Author(s). Advanced Science published by Wiley‐VCH GmbH.)

Chronic insomnia (CI) is a complex disease involving multiple factors including genetics, gut microbiota, and brain structure and function. However, there lacks a unified framework to elucidate how these factors interact in CI. By combining data of clinical assessment, sleep behavior recording, cognitive test, multimodal MRI (structural, functional, and perfusion), gene, and gut microbiota, this study demonstrated that enhanced cerebral blood flow (CBF) similarities of the somatomotor network (SMN) acted as a key mediator to link multiple factors in CI. Specifically, we first demonstrated that only CBF but not morphological or functional networks exhibited alterations in patients with CI, characterized by increases within the SMN and between the SMN and higher-order associative networks. Moreover, these findings were highly reproducible and the CBF similarity method was test-retest reliable. Further, we showed that transcriptional profiles explained 60.4 % variance of the pattern of the increased CBF similarities with the most correlated genes enriched in regulation of cellular and protein localization and material transport, and gut microbiota explained 69.7 % inter-individual variance in the increased CBF similarities with the most contributions from Negativicutes and Lactobacillales. Finally, we found that the increased CBF similarities were correlated with clinical variables, accounted for sleep behaviors and cognitive deficits, and contributed the most to the patient-control classification (accuracy = 84.4 %). Altogether, our findings have important implications for understanding the neuropathology of CI and may inform ways of developing new therapeutic strategies for the disease., Competing Interests: Declaration of competing interest The authors declare no competing financial interests., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Morphological features sourced from structural magnetic resonance imaging can be used to infer human brain connectivity. Although integrating different morphological features may theoretically be beneficial for obtaining more precise morphological connectivity networks (MCNs), the empirical evidence to support this supposition is scarce. Moreover, the incorporation of different morphological features remains an open question. In this study, we proposed a method to construct cortical MCNs based on multiple morphological features. Specifically, we adopted a multi-dimensional kernel density estimation algorithm to fit regional joint probability distributions (PDs) from different combinations of four morphological features, and estimated inter-regional similarity in the joint PDs via Jensen-Shannon divergence. We evaluated the method by comparing the resultant MCNs with those built based on different single morphological features in terms of topological organization, test-retest reliability, biological plausibility, and behavioral and cognitive relevance. We found that, compared to MCNs built based on different single morphological features, MCNs derived from multiple morphological features displayed less segregated, but more integrated network architecture and different hubs, had higher test-retest reliability, encompassed larger proportions of inter-hemispheric edges and edges between brain regions within the same cytoarchitectonic class, and explained more inter-individual variance in behavior and cognition. These findings were largely reproducible when different brain atlases were used for cortical parcellation. Further analysis of macaque MCNs revealed weak, but significant correlations with axonal connectivity from tract-tracing, independent of the number of morphological features. Altogether, this paper proposes a new method for integrating different morphological features, which will be beneficial for constructing MCNs., Competing Interests: Declaration of competing interest The authors declare no competing interests., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Hearing impairment is a common condition in the elderly. However, a comprehensive understanding of its neural correlates is still lacking., Methods: We recruited 284 elderly adults who underwent structural MRI, magnetic resonance spectroscopy, audiometry, and cognitive assessments. Individual hearing abilities indexed by pure tone average (PTA) were correlated with multiple structural MRI-derived cortical morphological indices. For regions showing significant correlations, mediation analyses were performed to examine their role in the relationship between hearing ability and cognitive function. Finally, the correlation maps between hearing ability and cortical morphology were linked with publicly available connectomic gradient, transcriptomic, and neurotransmitter maps., Findings: Poorer hearing was related to cortical thickness (CT) reductions in widespread regions and gyrification index (GI) reductions in the right Area 52 and Insular Granular Complex. The GI in the right Area 52 mediated the relationship between hearing ability and executive function. This mediating effect was further modulated by glutamate and N-acetylaspartate levels in the right auditory region. The PTA-CT correlation map followed microstructural connectomic hierarchy, were related to genes involved in certain biological processes (e.g., glutamate metabolic process), cell types (e.g., excitatory neurons and astrocytes), and developmental stages (i.e., childhood to young adulthood), and covaried with dopamine receptor 1, dopamine transporter, and fluorodopa. The PTA-GI correlation map was related to 5-hydroxytryptamine receptor 2a., Interpretation: Poorer hearing is associated with cortical thinning and folding reductions, which may be engaged in the relationship between hearing impairment and cognitive decline in the elderly and have different neurobiological substrates., Funding: See the Acknowledgements section., Competing Interests: Declaration of interests The authors declare no conflict of interest related to this work., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Human costly punishment is rooted in multiple regions across large-scale functional systems, a collection of which constitutes the costly punishment network (CPN). Our previous study found that the CPN is intrinsically organized in an optimized and reliable manner to support individual costly punishment propensity. However, it remains unknown how the CPN is reconfigured in response to external cognitive demands in punishment decision-making. Here, we combined resting-state and task-functional magnetic resonance imaging to examine the task-related reconfigurations of intrinsic organizations of the CPN when participants made decisions of costly punishment in the Ultimatum Game. Although a strong consistency was observed in the overall pattern and each nodal profile between the intrinsic (task-free) and extrinsic (task-evoked) functional connectivity of the CPN, condition-general and condition-specific reconfigurations were also evident. Specifically, both unfair and fair conditions induced increases in functional connectivity between a few specific pairs of regions, and the unfair condition additionally induced increases in network efficiency of the CPN. Intriguingly, the specific changes in global efficiency of the CPN in the unfair condition were associated with individual differences in costly punishment after adjusting for the corresponding results in the fair condition, which were further identified for females but not for males. These findings were largely reproducible on independent samples. Collectively, our findings provide novel insights into how the CPN adaptively reconfigures its network architecture to support costly punishment., (© 2023 Society for Psychophysiological Research.)

Age-related changes in focal cortical morphology have been well documented in previous literature; however, how interregional coordination patterns of the focal cortical morphology reorganize with advancing age is not well established. In this study, we performed a comprehensive analysis of the topological changes in single-subject morphological brain networks across the adult lifespan. Specifically, we constructed four types of single-subject morphological brain networks for 650 participants (aged from 18 to 88 years old), and characterized their topological organization using graph-based network measures. Age-related changes in the network measures were examined via linear, quadratic, and cubic models. We found profound age-related changes in global small-world attributes and efficiency, local nodal centralities, and interregional similarities of the single-subject morphological brain networks. The age-related changes were mainly embodied in cortical thickness networks, involved in frontal regions and highly connected hubs, concentrated on short-range connections, characterized by linear changes, and susceptible to connections between limbic, frontoparietal, and ventral attention networks. Intriguingly, nonlinear (i.e., quadratic or cubic) age-related changes were frequently found in the insula and limbic regions, and age-related cubic changes preferred long-range morphological connections. Finally, we demonstrated that the morphological similarity in cortical thickness between two frontal regions mediated the relationship between age and cognition measured by Cattell scores. Taken together, these findings deepen our understanding of adaptive changes of the human brain with advancing age, which may account for interindividual variations in behaviors and cognition., (© 2023 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Despite the prevalence of research on single-subject cerebral morphological networks in recent years, whether they can offer a reliable way for multicentric studies remains largely unknown. Using two multicentric datasets of traveling subjects, this work systematically examined the inter-site test-retest (TRT) reliabilities of single-subject cerebral morphological networks, and further evaluated the effects of several key factors. We found that most graph-based network measures exhibited fair to excellent reliabilities regardless of different analytical pipelines. Nevertheless, the reliabilities were affected by choices of morphological index (fractal dimension > sulcal depth > gyrification index > cortical thickness), brain parcellation (high-resolution > low-resolution), thresholding method (proportional > absolute), and network type (binarized > weighted). For the factor of similarity measure, its effects depended on the thresholding method used (absolute: Kullback-Leibler divergence > Jensen-Shannon divergence; proportional: Jensen-Shannon divergence > Kullback-Leibler divergence). Furthermore, longer data acquisition intervals and different scanner software versions significantly reduced the reliabilities. Finally, we showed that inter-site reliabilities were significantly lower than intra-site reliabilities for single-subject cerebral morphological networks. Altogether, our findings propose single-subject cerebral morphological networks as a promising approach for multicentric human connectome studies, and offer recommendations on how to determine analytical pipelines and scanning protocols for obtaining reliable results., (© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Sleep deprivation strongly deteriorates the stability of vigilant maintenance. In previous neuroimaging studies of large-scale networks, neural variations in the resting state after sleep deprivation have been well documented, highlighting that large-scale networks implement efficient cognitive functions and attention regulation in a spatially hierarchical organization. However, alterations of neural networks during cognitive tasks have rarely been investigated., Methods and Purposes: The present study used a within-participant design of 35 healthy right-handed adults and used task-based functional magnetic resonance imaging to examine the neural mechanism of attentional decline after sleep deprivation from the perspective of rich-club architecture during a psychomotor vigilance task., Results: We found that a significant decline in the hub disruption index was related to impaired vigilance due to sleep loss. The hierarchical rich-club architectures were reconstructed after sleep deprivation, especially in the default mode network and sensorimotor network. Notably, the relatively fast alert response compensation was correlated with the feeder organizational hierarchy that connects core (rich-club) and peripheral nodes., Significances: Our findings provide novel insights into understanding the relationship of alterations in vigilance and the hierarchical architectures of the human brain after sleep deprivation, emphasizing the significance of optimal collaboration between different functional hierarchies for regular attention maintenance., (© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permission@oup.com.)

Brain signal variability has been consistently linked to functional integration; however, whether this coupling is associated with cognitive functions and/or psychiatric diseases has not been clarified. Using multiple multimodality datasets, including resting-state functional magnetic resonance imaging (rsfMRI) data from the Human Connectome Project (HCP: N = 927) and a Beijing sample (N = 416) and cerebral blood flow (CBF) and rsfMRI data from a Hangzhou sample (N = 29), we found that, compared with the existing variability measure (i.e., SD BOLD ), the mean-scaled (standardized) fractional standard deviation of the BOLD signal (mfSD BOLD ) maintained very high test-retest reliability, showed greater cross-site reliability and was less affected by head motion. We also found strong reproducible couplings between the mfSD BOLD and functional integration measured by the degree centrality (DC), both cross-voxel and cross-subject, which were robust to scanning and preprocessing parameters. Moreover, both mfSD BOLD and DC were correlated with CBF, suggesting a common physiological basis for both measures. Critically, the degree of coupling between mfSD BOLD and long-range DC was positively correlated with individuals' cognitive total composite scores. Brain regions with greater mismatches between mfSD BOLD and long-range DC were more vulnerable to brain diseases. Our results suggest that BOLD signal variability could serve as a meaningful index of local function that underlies functional integration in the human brain and that a strong coupling between BOLD signal variability and functional integration may serve as a hallmark of balanced brain networks that are associated with optimal brain functions., Competing Interests: Declaration of Competing Interest The authors declare no conflict of interest., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

Although substantial progress has been made in the identification of genetic substrates underlying physiology, neuropsychology, and brain organization, the genotype-phenotype associations remain largely unknown in the context of high-altitude (HA) adaptation. Here, we related HA adaptive genetic variants in three gene loci (EGLN1, EPAS1, and PPARA) to interindividual variance in a set of physiological characteristics, neuropsychological tests, and topological attributes of large-scale structural and functional brain networks in 135 indigenous Tibetan highlanders. Analyses of individual HA adaptive single-nucleotide polymorphisms (SNPs) revealed that specific SNPs selectively modulated physiological characteristics (erythrocyte level, ratio between forced expiratory volume in the first second to forced vital capacity, arterial oxygen saturation, and heart rate) and structural network centrality (the left anterior orbital gyrus) with no effects on neuropsychology or functional brain networks. Further analyses of genetic adaptive scores, which summarized the overall degree of genetic adaptation to HA, revealed significant correlations only with structural brain networks with respect to local interconnectivity of the whole networks, intermodule communication between the right frontal and parietal module and the left occipital module, nodal centrality in several frontal regions, and connectivity strength of a subnetwork predominantly involving in intramodule edges in the right temporal and occipital module. Moreover, the associations were dependent on gene loci, weight types, or topological scales. Together, these findings shed new light on genotype-phenotype interactions under HA hypoxia and have important implications for developing new strategies to optimize organism and tissue responses to chronic hypoxia induced by extreme environments or diseases., (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals, Inc.)

Purpose To investigate the topological organization of functional brain networks in clinically isolated syndrome (CIS) and multiple sclerosis (MS) and examine the clinical relevance. Materials and Methods The institutional review board of Xuanwu Hospital, Capital Medical University, Beijing, People's Republic of China, approved the study, and written informed consent was obtained from each participant. Functional brain networks were constructed for 34 patients with MS, 34 patients with CIS, and 36 matched healthy control subjects by using resting-state functional magnetic resonance (MR) imaging data. Graph-based network measures were then calculated, followed by performance of between-group comparison and brain-behavior correlation analysis. Results Decreased whole-brain network efficiency was observed for patients with MS when compared with healthy control subjects, with intermediate values for the patients with CIS (P < .05, corrected). Regionally, both patient groups showed decreased nodal efficiency in the left rolandic operculum and insula and the superior temporal gyrus of the bilateral temporal pole (P < .05, corrected). Moreover, impaired functional connectivity involving the occipital, temporal, and frontal cortices and the insula was identified in MS (P = .007), and a similar but smaller component was observed in CIS (P = .032). The disrupted functional connectivity correlated with disease duration of the patients (r = 0.312, P = .011) and served to distinguish the patients from healthy control subjects with high performance (area under the curve for MS, 0.825 [P < .001]; area under the curve for CIS, 0.789 [P < .001]). These findings were reproducible across several different analytical strategies and were largely independent of white matter lesions and gray matter atrophy. Conclusion The results of this study demonstrate that disrupted network organization already emerges in CIS, with a lesser degree relative to MS. © RSNA, 2016 Online supplemental material is available for this article.

Introduction: Structural MRI has long been used to characterize local morphological features of the human brain. Coordination patterns of the local morphological features among regions, however, are not well understood. Here, we constructed individual-level morphological brain networks and systematically examined their topological organization and long-term test-retest reliability under different analytical schemes of spatial smoothing, brain parcellation, and network type., Methods: This study included 57 healthy participants and all participants completed two MRI scan sessions. Individual morphological brain networks were constructed by estimating interregional similarity in the distribution of regional gray matter volume in terms of the Kullback-Leibler divergence measure. Graph-based global and nodal network measures were then calculated, followed by the statistical comparison and intra-class correlation analysis., Results: The morphological brain networks were highly reproducible between sessions with significantly larger similarities for interhemispheric connections linking bilaterally homotopic regions. Further graph-based analyses revealed that the morphological brain networks exhibited nonrandom topological organization of small-worldness, high parallel efficiency and modular architecture regardless of the analytical choices of spatial smoothing, brain parcellation and network type. Moreover, several paralimbic and association regions were consistently revealed to be potential hubs. Nonetheless, the three studied factors particularly spatial smoothing significantly affected quantitative characterization of morphological brain networks. Further examination of long-term reliability revealed that all the examined network topological properties showed fair to excellent reliability irrespective of the analytical strategies, but performing spatial smoothing significantly improved reliability. Interestingly, nodal centralities were positively correlated with their reliabilities, and nodal degree and efficiency outperformed nodal betweenness with respect to reliability., Conclusions: Our findings support single-subject morphological network analysis as a meaningful and reliable method to characterize structural organization of the human brain; this method thus opens a new avenue toward understanding the substrate of intersubject variability in behavior and function and establishing morphological network biomarkers in brain disorders.

Purpose: To systematically investigate structural and functional alterations of the thalamus and its subregions through a multimodal magnetic resonance (MR) imaging technique and examine its clinical relevance in multiple sclerosis (MS) and neuromyelitis optica (NMO)., Materials and Methods: The institutional review board approved this study, and written informed consent was obtained from each participant. Thirty-seven patients with MS, 39 patients with NMO, and 40 healthy control subjects were recruited. Six MR imaging measurements were obtained for each participant and compared between groups in the thalamus and its seven subregions, including gray matter (GM) volume, fractional anisotropy, mean diffusivity, amplitude of low-frequency fluctuation, cross-correlation coefficient of spontaneous low frequency, and weighted functional connectivity strength. Partial correlation was used to estimate the MR imaging-clinical relationships., Results: Both MS and NMO exhibited widespread GM atrophy (GM volume in MS, 0.244; NMO, 0.297; and control subjects, 0.329; P < .001) and diffusion abnormalities (fractional anisotropy in MS, 0.293; NMO, 0.323; and control subjects, 0.355; P < .001) in the whole thalamus and several subregions, while MS showed more severe changes than NMO. Decreased cross-correlation coefficient of spontaneous low-frequency and weighted functional connectivity strength was observed in several thalamus subregions in MS (P < .05), but no significant functional abnormalities were identified in NMO. GM volume, fractional anisotropy, and mean diffusivity, not functional changes of the thalamus and thalamic subregions, correlated with the patients' clinical variables and exhibited high discriminative power in distinguishing the three groups., Conclusion: Similar patterns of thalamic structural alteration were identified in MS and NMO, but MS showed more severe pathologic changes. The thalamus is a key node for functional disconnection in MS but not in NMO.

Objective: To investigate spinal cord and brain atrophy in neuromyelitis optica (NMO), and its relationship with other MRI measurements and clinical disability, compared with patients with multiple sclerosis (MS) and healthy controls (HC)., Methods: We recruited 35 patients with NMO, 35 patients with MS, and 35 HC, who underwent both spinal cord and brain MRI. Mean upper cervical cord area (MUCCA), brain parenchymal fraction (BPF), gray matter fraction (GMF), white matter fraction (WMF), and spinal cord and brain lesion loads were measured and compared among groups. Multivariate associations between MUCCA and brain volume measurement and clinical variables were assessed by partial correlations and multiple linear regression., Results: Patients with NMO showed smaller MUCCA than HC (p = 0.004), and patients with MS had a trend of smaller MUCCA compared to HC (p = 0.07), with no significant difference between the patient groups. Patients with NMO showed lower BPF than HC, and patients with MS had lower BPF and GMF than patients with NMO. In NMO, MUCCA was correlated with Expanded Disability Status Scale score (EDSS), number of relapses, and total spinal cord lesion length, while in MS, MUCCA was correlated with WMF and EDSS. MUCCA was the only independent variable for predicting clinical disability measured by EDSS in NMO (R(2) = 0.55, p < 0.001) and MS (R(2) = 0.17, p = 0.013)., Conclusion: NMO showed predominately spinal cord atrophy with mild brain atrophy, while MS demonstrated more brain atrophy, especially in the gray matter. MUCCA is the main MRI-derived parameter for explaining clinical disability in NMO and MS, and may serve as a potential biomarker for further clinical trials, especially in NMO., (© 2015 American Academy of Neurology.)

Background: Few studies have used neuroimaging to characterize treatment-refractory obsessive-compulsive disorder (OCD). This study sought to explore gray matter structure in patients with treatment-refractory OCD and compare it with that of healthy controls., Methods: A total of 18 subjects with treatment-refractory OCD and 26 healthy volunteers were analyzed by MRI using a 3.0-T scanner and voxel-based morphometry (VBM). Diffeomorphic anatomical registration using exponentiated Lie algebra (DARTEL) was used to identify structural changes in gray matter associated with treatment-refractory OCD. A partial correlation model was used to analyze whether morphometric changes were associated with Yale-Brown Obsessive-Compulsive Scale scores and illness duration., Results: Gray matter volume did not differ significantly between the two groups. Treatment-refractory OCD patients showed significantly lower gray matter density than healthy subjects in the left posterior cingulate cortex (PCC) and mediodorsal thalamus (MD) and significantly higher gray matter density in the left dorsal striatum (putamen). These changes did not correlate with symptom severity or illness duration., Conclusions: Our findings provide new evidence of deficits in gray matter density in treatment-refractory OCD patients. These patients may show characteristic density abnormalities in the left PCC, MD and dorsal striatum (putamen), which should be verified in longitudinal studies., (© 2013. Published by Elsevier Inc. All rights reserved.)

The present study aimed to investigate changes in structural gray matter (GM) volume and functional amplitude of spontaneous low-frequency oscillations (LFO) and functional connectivity density in patients with subcortical vascular mild cognitive impairment (svMCI). Structural MRI and resting-sate functional MRI data were collected from 26 svMCI patients and 28 age- and gender-matched healthy controls. Structurally, widespread GM atrophy was found in the svMCI patients that resided primarily in frontal (e.g., the superior and middle frontal gyri and medial prefrontal cortex) and temporal (the superior and inferior temporal gyri) brain regions as well as several subcortical brain sites (e.g., the thalamus and the caudate). Functionally, svMCI-related changes were predominantly found in the default mode network (DMN). Compared with the healthy controls, the svMCI patients exhibited decreased LFO amplitudes in the anterior part of the DMN (e.g., the medial prefrontal cortex), whereas increased LFO amplitudes in the posterior part of the DMN (e.g., the posterior cingulate/precuneus). As for functional connectivity density, the DMN regions (e.g., the posterior cingulate/precuneus, the medial prefrontal cortex and the middle temporal gyrus) consistently exhibited decreased functional connectivity. Finally, the overall patterns of functional alterations in LFO amplitudes and functional connectivity density remained little changed after controlling for structural GM volume losses, which suggests that functional abnormalities can be only partly explained by morphological GM volume changes. Together, our results indicate that svMCI patients exhibit widespread abnormalities in both structural GM volume and functional intrinsic brain activity, which have important implications in understanding the pathophysiological mechanism of svMCI.

Graph theoretical analysis of brain networks based on resting-state functional MRI (R-fMRI) has attracted a great deal of attention in recent years. These analyses often involve the selection of correlation metrics and specific preprocessing steps. However, the influence of these factors on the topological properties of functional brain networks has not been systematically examined. Here, we investigated the influences of correlation metric choice (Pearson's correlation versus partial correlation), global signal presence (regressed or not) and frequency band selection [slow-5 (0.01-0.027 Hz) versus slow-4 (0.027-0.073 Hz)] on the topological properties of both binary and weighted brain networks derived from them, and we employed test-retest (TRT) analyses for further guidance on how to choose the "best" network modeling strategy from the reliability perspective. Our results show significant differences in global network metrics associated with both correlation metrics and global signals. Analysis of nodal degree revealed differing hub distributions for brain networks derived from Pearson's correlation versus partial correlation. TRT analysis revealed that the reliability of both global and local topological properties are modulated by correlation metrics and the global signal, with the highest reliability observed for Pearson's-correlation-based brain networks without global signal removal (WOGR-PEAR). The nodal reliability exhibited a spatially heterogeneous distribution wherein regions in association and limbic/paralimbic cortices showed moderate TRT reliability in Pearson's-correlation-based brain networks. Moreover, we found that there were significant frequency-related differences in topological properties of WOGR-PEAR networks, and brain networks derived in the 0.027-0.073 Hz band exhibited greater reliability than those in the 0.01-0.027 Hz band. Taken together, our results provide direct evidence regarding the influences of correlation metrics and specific preprocessing choices on both the global and nodal topological properties of functional brain networks. This study also has important implications for how to choose reliable analytical schemes in brain network studies.

Here we utilized resting-state functional magnetic resonance imaging (R-fMRI) to measure the amplitude of low-frequency fluctuations (ALFF) and fractional ALFF (fALFF) in 24 patients with amnestic mild cognitive impairment (aMCI) and 24 age- and sex-matched healthy controls. Two different frequency bands (slow-5: 0.01-0.027 Hz; slow-4: 0.027-0.073 Hz) were analyzed. We showed that there were widespread differences in ALFF/fALFF between the two bands in many brain regions, predominantly including the medial prefrontal cortex (MPFC), posterior cingulate cortex/precuneus (PCC/PCu), basal ganglia, and hippocampus/parahippocampal gyrus (PHG). Compared to controls, the aMCI patients had decreased ALFF/fALFF values in the PCC/PCu, MPFC, hippocampus/PHG, basal ganglia, and prefrontal regions, and increased ALFF/fALFF values mainly in several occipital and temporal regions. Specifically, we observed that the ALFF/fALFF abnormalities in the PCC/PCu, PHG, and several occipital regions were greater in the slow-5 band than in the slow-4 band. Finally, our results of functional analysis were not significantly influenced by the gray matter loss in the MCI patients, suggesting that the results reflect functional differences between groups. Together, our data suggest that aMCI patients have widespread abnormalities in intrinsic brain activity, and the abnormalities depend on the studied frequency bands of R-fMRI data., (Copyright © 2010 Elsevier Inc. All rights reserved.)

We employed resting-state functional MRI (R-fMRI) to investigate hemisphere- and gender-related differences in the topological organization of human brain functional networks. Brain networks were first constructed by measuring inter-regional temporal correlations of R-fMRI data within each hemisphere in 86 young, healthy, right-handed adults (38 males and 48 females) followed by a graph-theory analysis. The hemispheric networks exhibit small-world attributes (high clustering and short paths) that are compatible with previous results in the whole-brain functional networks. Furthermore, we found that compared with females, males have a higher normalized clustering coefficient in the right hemispheric network but a lower clustering coefficient in the left hemispheric network, suggesting a gender-hemisphere interaction. Moreover, we observed significant hemisphere-related differences in the regional nodal characteristics in various brain regions, such as the frontal and occipital regions (leftward asymmetry) and the temporal regions (rightward asymmetry), findings that are consistent with previous studies of brain structural and functional asymmetries. Together, our results suggest that the topological organization of human brain functional networks is associated with gender and hemispheres, and they provide insights into the understanding of functional substrates underlying individual differences in behaviors and cognition., (Copyright © 2010 Elsevier Inc. All rights reserved.)

Aims: To explore large‐scale brain network alterations and examine their clinical and neuropsychological relevance in patients with anti‐N‐methyl‐D‐aspartate receptor (NMDAR) encephalitis. Methods: Twenty‐four patients with anti‐NMDAR encephalitis and 26 matched healthy controls (HCs) were enrolled in our study. Based on the multimodal MRI dataset, individual morphological, structural, and functional brain networks were constructed and compared between the two groups at multiple levels. The associations with clinical/neuropsychological variables and the discriminant ability of significant alterations were further studied. Results: Multimodal network analysis revealed that anti‐NMDAR encephalitis mainly affected morphological and structural networks, but subtle alterations were observed in functional networks. Intriguingly, decreased network local efficiency was observed for both morphological and structural networks and increased nodal centrality in the lateral orbital gyrus was convergently observed among the three types of networks in the patients. Moreover, the alterations, particularly those from structural networks, accounted largely for cognitive deficits of the patients and could distinguish the diseased individuals from the HCs with excellent performance (area under the curve =0.933). Conclusions: The current study provides a comprehensive view of characteristic multimodal network dysfunction in anti‐NMDAR encephalitis, which is crucial to establish new diagnostic biomarkers and promising therapeutic targets for the disease. [ABSTRACT FROM AUTHOR]

Objective: To investigate structural and functional alterations of gray matter (GM) and examine their clinical relevance in neuromyelitis optica (NMO) using multimodal magnetic resonance imaging (MRI) techniques. Methods: A total of 35 NMO and 36 healthy controls (HC) were recruited in this study. Cortical lesions were investigated by double inversion recovery technique. Five voxel-wise MRI measurements were obtained for each participant in the GM including gray matter volume (GMV), fractional anisotropy (FA), mean diffusivity (MD), amplitude of low-frequency fluctuation (ALFF), and weighted functional connectivity strength (wFCS). Between-group differences, cross-modality relationships, and MRI-clinical correlations were examined. Results: No cortical lesions were found in NMO. Compared to HC, NMO patients exhibited significantly decreased GMV in deep GM and cortical regions involving visual function and cognition. Diffusion GM abnormalities were widespread in the patients. Decreased ALFF and wFCS were observed in the patients in sensorimotor, visual, cognition, and cerebellar sites. GM structural alterations were correlated with cognitive but not physical disability scores of the patients. Conclusion: Despite the lack of focal cortical lesions, patients with NMO exhibit both structural and functional alterations of GM in cerebrum and cerebellum that predominantly involve deep GM, visual, motor, and cognitive regions. GM alterations are associated with cognitive impairment but not physical disability. [ABSTRACT FROM AUTHOR]

Although previous studies reported altered topology of brain functional networks in patients with Parkinson's disease (PD), the modular organization of brain functional networks in PD patients remains largely unknown. Using the resting-state functional MRI (R-fMRI) and graph theory, we examined the modular organization of brain functional networks in 32 unmedicated patients with early-to-mid motor stage PD and 31 healthy controls. Compared to the controls, the PD patients tended to show decreased integrity and segregation, both within and between modules. This was inferred by significantly increased intra-modular characteristic path length (L p) within four modules: mPFC, SN, SMN, and FPN, decreased inter-modular functional connectivity (FC) between mPFC and SN, SMN, and VN, and decreased intra-modular clustering in the PD patients. Intra-modular characteristic path length within the mPFC showed significantly positive correlation with general cognitive ability in the PD group. Receiver operating characteristic (ROC) analysis revealed that FC between mPFC and SN had the highest significant accuracy in differentiating the patients from the controls. Our findings may provide new insight in understanding the pathological changes that underlie impairment in cognition and movement in Parkinson's disease. [ABSTRACT FROM AUTHOR]

Background: Despite accumulating evidence for the hippocampus as a key dysfunctional node in major depressive disorder (MDD), previous findings are controversial possibly due to heterogeneous and small clinical samples, complicated hippocampal structure, and different imaging modalities and analytical methods.Methods: We collected structural and resting-state functional MRI data from 100 first-episode, drug-naïve MDD patients and 99 healthy controls. A subset of the participants (34 patients and 33 controls) also completed a battery of neuropsychological tests and childhood trauma questionnaires. Seed-based morphological and functional (static and dynamic) connectivity were calculated for ten hippocampal subregions, followed by analyses of dynamic functional connectivity states (k-means clustering), connectivity cross-modality relationships (cosine similarity), and connectivity associations with clinical and neuropsychological variables (Spearman correlation).Results: Between-group comparisons revealed abnormal hippocampal connectivity in the patients that depended on 1) hippocampal subdivisions: the cornu ammonis (CA) was the most seriously affected subregion, in particular the right CA1 for functional connectivity alterations; 2) imaging modality: morphological connectivity revealed seldom and sporadic alterations with different lobes, while functional connectivity identified numerous and convergent alterations with prefrontal regions; and 3) time scale: dynamic functional connectivity was more sensitive than static functional connectivity, in particular in revealing alterations between the right CA1 and contralateral prefrontal cortex. Among the 34 patients, functional connectivity alterations of the CA1 were related to the history of childhood trauma in the patients.Limitations: Only a subset of the patients completed the neuropsychological tests, which may cause underestimation of cognitive relevance of hippocampal connectivity alterations.Conclusions: Disrupted hippocampal CA1 functional connectivity plays key roles in the pathophysiology of MDD and may act as a potential diagnostic biomarker for the disease. [ABSTRACT FROM AUTHOR]

Background: Previous neuroimaging studies have showed that imbalanced functional integration of distributed large-scale brain networks is associated with pathophysiological characteristics of major depressive disorder (MDD). However, the association between network integrative disturbances and clinical features and cognitive functions remains largely unclear in adolescent MDD. This study investigated the neural correlates of abnormal functional connectivity networks with clinical and cognitive characteristics in adolescent MDD.Methods: Twenty-eight first-episode, treatment-naive adolescents with MDD and 24 well-matched healthy controls (HCs) underwent functional magnetic resonance imaging (fMRI) and a battery of cognitive tests. A seed-based functional connectivity (FC) approach was used to depict connectivity patterns of the cognitive control network (CCN), affective network (AN) and default mode network (DMN), whose between-group differences were correlated with clinical variables and cognitive functions in the patients.Results: Compared with the HCs, the MDD patients exhibited impaired executive functions. The FC analysis revealed lower CCN FC with the temporal, parietal and frontal regions and the limbic system, higher AN FC with the temporal and occipital regions and the cerebellum, and lower DMN FC with the cerebellum and insula. Interestingly, the decreased CCN FC was related to disease severity (with the inferior frontal gyrus) and executive dysfunctions (with the middle cingulate gyrus and supramarginal gyrus) in the patients.Limitations: The main limitations were the relatively small sample size and suboptimal imaging parameters.Conclusion: Functional alteration of CCN during the developmentally sensitive period may be important in the neurobiology of adolescent MDD. [ABSTRACT FROM AUTHOR]

• Single-subject morphological brain networks differed between males and females. • Single-subject morphological brain networks were predictive of individual performance in cognition and motor domains. • Single-subject morphological brain networks provided fingerprints for individual identification. • Different types of single-subject morphological brain networks were distinctively related to genetic, cytoarchitectonic and chemoarchitectonic factors. Although single-subject morphological brain networks provide an important way for human connectome studies, their roles and origins are poorly understood. Combining cross-sectional and repeated structural magnetic resonance imaging scans from adults, children and twins with behavioral and cognitive measures and brain-wide transcriptomic, cytoarchitectonic and chemoarchitectonic data, this study examined phenotypic associations and neurobiological substrates of single-subject morphological brain networks. We found that single-subject morphological brain networks explained inter-individual variance and predicted individual outcomes in Motor and Cognition domains, and distinguished individuals from each other. The performance can be further improved by integrating different morphological indices for network construction. Low-moderate heritability was observed for single-subject morphological brain networks with the highest heritability for sulcal depth-derived networks and higher heritability for inter-module connections. Furthermore, differential roles of genetic, cytoarchitectonic and chemoarchitectonic factors were observed for single-subject morphological brain networks. Cortical thickness-derived networks were related to the three factors with contributions from genes enriched in membrane and transport related functions, genes preferentially located in supragranular and granular layers, overall thickness in the molecular layer and thickness of wall in the infragranular layers, and metabotropic glutamate receptor 5 and dopamine transporter; fractal dimension-, gyrification index- and sulcal depth-derived networks were only associated with the chemoarchitectonic factor with contributions from different sets of neurotransmitter receptors. Most results were reproducible across different parcellation schemes and datasets. Altogether, this study demonstrates phenotypic associations and neurobiological substrates of single-subject morphological brain networks, which provide intermediate endophenotypes to link molecular and cellular architecture and behavior and cognition. [ABSTRACT FROM AUTHOR]

• Regions atrophied in MDD are more densely interconnected. • Cytoarchitectonic class of association cortex dominates cortical atrophy in MDD. • Results support the transneuronal spread hypothesis of cortical atrophy in MDD. • MRI-based connectivity reflects gene co-expression and chemoarchitectonic covariance. Major depressive disorder (MDD) is associated with widespread, irregular cortical thickness (CT) reductions across the brain. However, little is known regarding mechanisms that govern spatial distribution of the reductions. We combined multimodal MRI and genetic, cytoarchitectonic and chemoarchitectonic data to examine structural covariance, functional synchronization, gene co-expression, cytoarchitectonic similarity and chemoarchitectonic covariance between regions atrophied in MDD. Regions atrophied in MDD were associated with significantly higher structural covariance, functional synchronization, gene co-expression and chemoarchitectonic covariance. These results were robust against methodological variations in brain parcellation and null model, reproducible in patients and controls, and independent of age at onset of MDD. Despite no significant differences in the cytoarchitectonic similarity, MDD-related CT reductions were susceptible to specific cytoarchitectonic class of association cortex. Further, we found that nodal shortest path lengths to disease epicenters derived from structural (right supramarginal gyrus) and chemoarchitectonic covariance (right sulcus intermedius primus) networks of healthy brains were correlated with the extent to which a region was atrophied in MDD, supporting the transneuronal spread hypothesis that regions closer to the epicenters are more susceptible to MDD. Finally, we showed that structural covariance and functional synchronization among regions atrophied in MDD were mainly related to genes enriched in metabolic and membrane-related processes, driven by genes in excitatory neurons, and associated with specific neurotransmitter transporters and receptors. Altogether, our findings provide empirical evidence for and genetic and molecular insights into connectivity-constrained CT thinning in MDD. [ABSTRACT FROM AUTHOR]