Your browser doesn't support javascript.
Mostrar: 20 | 50 | 100
Resultados 1 - 20 de 325
Filtrar
Mais filtros










Base de dados
Intervalo de ano de publicação
1.
J Affect Disord ; 260: 281-286, 2020 Jan 01.
Artigo em Inglês | MEDLINE | ID: mdl-31521864

RESUMO

BACKGROUND: White matter abnormalities have been implicated in mental disorders including major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SZ); however, the shared and distinct white matter integrity across mental disorders is still unclear. METHODS: A total of 290 participants (MDD = 85, BD = 42, SZ = 68, and healthy controls = 95) were included in the present study. Tract-based spatial statistics were performed to measure fractional anisotropy (FA) and characterize shared and distinguishing white matter changes across mental disorders. RESULTS: We found that decreased FA converged across MDD, BD and SZ in the body and genu of the corpus callosum, bilateral anterior and posterior corona radiata, and right superior corona radiata. By contrast, diagnosis-specific effect was only found in MDD in the anterior portion of anterior corona radiata. LIMITATIONS: The small and imbalanced sample size, and possible confounding effects of medication. CONCLUSIONS: Our findings suggest that abnormally reduced white matter integrity in the interhemispheric and thalamocortical circuit could be consistently involved in the pathogenesis of MDD, BD and SZ.

2.
Neuroimage ; 205: 116278, 2019 Oct 12.
Artigo em Inglês | MEDLINE | ID: mdl-31614221

RESUMO

Preclinical applications of resting-state functional magnetic resonance imaging (rsfMRI) offer the possibility to non-invasively probe whole-brain network dynamics and to investigate the determinants of altered network signatures observed in human studies. Mouse rsfMRI has been increasingly adopted by numerous laboratories worldwide. Here we describe a multi-centre comparison of 17 mouse rsfMRI datasets via a common image processing and analysis pipeline. Despite prominent cross-laboratory differences in equipment and imaging procedures, we report the reproducible identification of several large-scale resting-state networks (RSN), including a mouse default-mode network, in the majority of datasets. A combination of factors was associated with enhanced reproducibility in functional connectivity parameter estimation, including animal handling procedures and equipment performance. RSN spatial specificity was enhanced in datasets acquired at higher field strength, with cryoprobes, in ventilated animals, and under medetomidine-isoflurane combination sedation. Our work describes a set of representative RSNs in the mouse brain and highlights key experimental parameters that can critically guide the design and analysis of future rodent rsfMRI investigations.

3.
Artigo em Inglês | MEDLINE | ID: mdl-31587084

RESUMO

A common variant (rs53576, G/A) in the oxytocin receptor (OXTR) gene is associated with individual differences in social behavior and may increase the risk for neuropsychiatric disorders characterized by social impairment, especially autism. Although recent functional magnetic resonance imaging (fMRI) studies have identified functional connectivity alteration in some brain regions in risk A allele carriers, it is currently unknown whether this dysfunctional connectivity causes disruption of the topological properties of brain functional networks. We applied a graph-theoretical analysis to investigate the topological properties of brain networks derived from resting-state fMRI in relation to AA homozygotes versus G allele carriers in 290 cognitive normal young adults. We found both AA homozygotes and G allele carriers demonstrated small-world properties; however, male AA homozygotes showed lower normalized clustering coefficient, small-worldness, and local efficiency compared with male G allele carriers, no differences survived after Bonferroni correction; and the inter-group differences of all three metrics exhibited an allele-load-dependent trend (AA < AG < GG), indicating a randomization shift of their brain functional networks. No significant results were observed in any global measures in female AA homozygotes as compared to female G allele carriers. Our results suggested that the topological patterns of brain functional networks were altered in OXTR rs53576 male homozygotes for the risk A allele compared with male G allele carriers, providing evidence for the disruption of integrity in large-scale intrinsic brain networks in a sex-dimorphic manner.

4.
Cereb Cortex ; 2019 Jul 29.
Artigo em Inglês | MEDLINE | ID: mdl-31364696

RESUMO

Scores on intelligence tests are strongly predictive of various important life outcomes. However, the gender discrepancy on intelligence quotient (IQ) prediction using brain imaging variables has not been studied. To this aim, we predicted individual IQ scores for males and females separately using whole-brain functional connectivity (FC). Robust predictions of intellectual capabilities were achieved across three independent data sets (680 subjects) and two intelligence measurements (IQ and fluid intelligence) using the same model within each gender. Interestingly, we found that intelligence of males and females were underpinned by different neurobiological correlates, which are consistent with their respective superiority in cognitive domains (visuospatial vs verbal ability). In addition, the identified FC patterns are uniquely predictive on IQ and its sub-domain scores only within the same gender but neither for the opposite gender nor on the IQ-irrelevant measures such as temperament traits. Moreover, females exhibit significantly higher IQ predictability than males in the discovery cohort. This findings facilitate our understanding of the biological basis of intelligence by demonstrating that intelligence is underpinned by a variety of complex neural mechanisms that engage an interacting network of regions-particularly prefrontal-parietal and basal ganglia-whereas the network pattern differs between genders.

5.
EBioMedicine ; 47: 543-552, 2019 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-31420302

RESUMO

BACKGROUND: Current fMRI-based classification approaches mostly use functional connectivity or spatial maps as input, instead of exploring the dynamic time courses directly, which does not leverage the full temporal information. METHODS: Motivated by the ability of recurrent neural networks (RNN) in capturing dynamic information of time sequences, we propose a multi-scale RNN model, which enables classification between 558 schizophrenia and 542 healthy controls by using time courses of fMRI independent components (ICs) directly. To increase interpretability, we also propose a leave-one-IC-out looping strategy for estimating the top contributing ICs. FINDINGS: Accuracies of 83·2% and 80·2% were obtained respectively for the multi-site pooling and leave-one-site-out transfer classification. Subsequently, dorsal striatum and cerebellum components contribute the top two group-discriminative time courses, which is true even when adopting different brain atlases to extract time series. INTERPRETATION: This is the first attempt to apply a multi-scale RNN model directly on fMRI time courses for classification of mental disorders, and shows the potential for multi-scale RNN-based neuroimaging classifications. FUND: Natural Science Foundation of China, the Strategic Priority Research Program of the Chinese Academy of Sciences, National Institutes of Health Grants, National Science Foundation.

6.
Neuroimage ; 200: 562-574, 2019 10 15.
Artigo em Inglês | MEDLINE | ID: mdl-31276799

RESUMO

The precuneus (PCun) is one of the most expanded areas of the association cortex and plays an important role in integrating information from different modalities. However, whether the functional architecture of PCun is shared by humans and macaques is an open question. We used both anatomical connectivity and task-dependent coactivation patterns to parcellate the human PCun and consistently identified three subregions in the human PCun using two independent datasets. Two subregions were located in the dorsal PCun and one subregion was located in the ventral PCun. This parcellation scheme for the PCun was supported by identifying the subregion-specific networks and by functional characterization. Then, the absolute and relative gray matter volume of precuneus in human and macaque was calculated and significantly smaller absolute and relative gray matter volume in macaque was identified. Next, three macaque PCun subregions were defined based on our tractographic atlas. Finally, the whole brain anatomical connectivity patterns and connectivity fingerprints with 17 predefined homologous target brain areas were mapped for each PCun subregion and revealed that the PCun shares similar anatomical connectivity patterns in humans and macaques. The similar anatomical connectivity patterns of PCun were validated by an independent in-house dataset. Our findings demonstrated that anatomical connectivity patterns can reflect the functional architecture of the PCun in humans and that the functional architecture of the PCun is similar in humans and macaques.

7.
Brain Struct Funct ; 224(8): 2619-2630, 2019 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-31332515

RESUMO

Catechol-O-methyltransferase (COMT) affects brain connectivity via modulating the dopamine system, with an expected greater effect of haplotypes than single-nucleotide polymorphism (SNP). The action pathway from COMT to dopamine to connectivity is theoretically dependent on the gene expression of dopamine receptors. Here, we aimed to investigate the impact of COMT haplotypes on brain functional connectivity density (FCD) in hundreds of healthy young subjects, and to disclose the association between the COMT-FCD statistical map and the spatial expression of the dopamine receptor genes. We found an inverted U-shaped modulation of COMT haplotypes on FCD in the left inferior parietal lobule that is mainly connected to the frontal and parietal cortices, with APS homozygotes exhibiting greater FCD than the other five groups. However, we failed to identify any significant effect of any SNP on FCD. Utilizing gene expression data collected from Allen human brain atlas, we found the COMT-FCD statistical map was significantly associated with the expression patterns of the dopamine receptor genes. Our results suggest that COMT haplotypes have greater impact on functional connectivity than a single genetic variation and that the association between COMT and functional connectivity may be dependent on the gene expression of dopamine receptors.

8.
Sci China Life Sci ; 2019 Jul 08.
Artigo em Inglês | MEDLINE | ID: mdl-31290094

RESUMO

Different patterns of brain activity are observed in various subjects across a wide functional domain. However, these individual differences, which are often neglected through the group average, are not yet completely understood. Based on the fundamental assumption that human behavior is rooted in the underlying brain function, we speculated that the individual differences in brain activity are reflected in the individual differences in behavior. Adopting 98 behavioral measures and assessing the brain activity induced at seven task functional magnetic resonance imaging states, we demonstrated that the individual differences in brain activity can be used to predict behavioral measures of individual subjects with high accuracy using the partial least square regression model. In addition, we revealed that behavior-relevant individual differences in brain activity transferred between different task states and can be used to reconstruct individual brain activity. Reconstructed individual brain activity retained certain individual differences which were lost in the group average and could serve as an individual functional localizer. Therefore, our results suggest that the individual differences in brain activity contain behavior-relevant information and should be included in group averaging. Moreover, reconstructed individual brain activity shows a potential use in precise and personalized medicine.

9.
Brain Imaging Behav ; 2019 Jul 05.
Artigo em Inglês | MEDLINE | ID: mdl-31278651

RESUMO

Intelligence is a socially and scientifically interesting topic because of its prominence in human behavior, yet there is little clarity on how the neuroimaging and neurobiological correlates of intelligence differ between males and females, with most investigations limited to using either mass-univariate techniques or a single neuroimaging modality. Here we employed connectome-based predictive modeling (CPM) to predict the intelligence quotient (IQ) scores for 166 males and 160 females separately, using resting-state functional connectivity, grey matter cortical thickness or both. The identified multimodal, IQ-predictive imaging features were then compared between genders. CPM showed high out-of-sample prediction accuracy (r > 0.34), and integrating both functional and structural features further improved prediction accuracy by capturing complementary information (r = 0.45). Male IQ demonstrated higher correlations with cortical thickness in the left inferior parietal lobule, and with functional connectivity in left parahippocampus and default mode network, regions previously implicated in spatial cognition and logical thinking. In contrast, female IQ was more correlated with cortical thickness in the right inferior parietal lobule, and with functional connectivity in putamen and cerebellar networks, regions previously implicated in verbal learning and item memory. Results suggest that the intelligence generation of males and females may rely on opposite cerebral lateralized key brain regions and distinct functional networks consistent with their respective superiority in cognitive domains. Promisingly, understanding the neural basis of gender differences underlying intelligence may potentially lead to optimized personal cognitive developmental programs and facilitate advancements in unbiased educational test design.

10.
Psychol Med ; : 1-9, 2019 Jun 26.
Artigo em Inglês | MEDLINE | ID: mdl-31239006

RESUMO

BACKGROUND: Genome-wide association studies (GWAS) have consistently revealed that a variant of microRNA 137 (MIR137) shows a quite significant association with schizophrenia. Identifying the network of genes regulated by MIR137 could provide insights into the biological processes underlying schizophrenia. In addition, DLPFC functional connectivity, a robust correlate of MIR137, may provide plausible endophenotypes. However, the regulatory role of the MIR137 gene network in the disrupted functional connectivity remains unclear. Here, we tested the effects of the MIR137 regulated genes on the risk for schizophrenia and DLPFC functional connectivity. METHODS: To evaluate the additive effects of the MIR137 regulated genes (N = 1274), we calculated a MIR137 polygenic risk score (PRS) for schizophrenia and tested its association with the risk for schizophrenia in the genomic data of a Han Chinese population that included schizophrenia patients (N = 589) and normal controls (N = 575). We then investigated the association between MIR137 PRS and DLPFC functional connectivity in two independent young healthy cohorts (N = 356 and N = 314). RESULTS: We found that the MIR137 PRS successfully captured the differences in genetic structure between the patients and controls, but the single gene MIR137 did not. We then consistently found that a higher MIR137 PRS was correlated with lower functional connectivities between the DLPFC and both the superior parietal cortex and the inferior temporal cortex in two independent cohorts. CONCLUSION: The findings suggested that these two functional connectivities of the DLPFC could be important endophenotypes linking the MIR137-regulated genetic structure to schizophrenia.

11.
Br J Psychiatry ; : 1-8, 2019 Jun 06.
Artigo em Inglês | MEDLINE | ID: mdl-31169117

RESUMO

BACKGROUND: Schizophrenia is a complex mental disorder with high heritability and polygenic inheritance. Multimodal neuroimaging studies have also indicated that abnormalities of brain structure and function are a plausible neurobiological characterisation of schizophrenia. However, the polygenic effects of schizophrenia on these imaging endophenotypes have not yet been fully elucidated.AimsTo investigate the effects of polygenic risk for schizophrenia on the brain grey matter volume and functional connectivity, which are disrupted in schizophrenia. METHOD: Genomic and neuroimaging data from a large sample of Han Chinese patients with schizophrenia (N = 509) and healthy controls (N = 502) were included in this study. We examined grey matter volume and functional connectivity via structural and functional magnetic resonance imaging, respectively. Using the data from a recent meta-analysis of a genome-wide association study that comprised a large number of Chinese people, we calculated a polygenic risk score (PGRS) for each participant. RESULTS: The imaging genetic analysis revealed that the individual PGRS showed a significantly negative correlation with the hippocampal grey matter volume and hippocampus-medial prefrontal cortex functional connectivity, both of which were lower in the people with schizophrenia than in the controls. We also found that the observed neuroimaging measures showed weak but similar changes in unaffected first-degree relatives of patients with schizophrenia. CONCLUSIONS: These findings suggested that genetically influenced brain grey matter volume and functional connectivity may provide important clues for understanding the pathological mechanisms of schizophrenia and for the early diagnosis of schizophrenia.Declaration of interestNone.

12.
Neural Netw ; 118: 140-147, 2019 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-31254768

RESUMO

The brain is highly plastic, with synaptic weights changing across a wide range of time scales, from hundreds of milliseconds to days. Changes occurring at different temporal scales are believed to serve different purposes, with long-term changes for learning and memory and short-term changes for adaptation and synaptic computation. By studying the performance of reservoir computing (RC) models in a memory task, we revealed that short-term synaptic plasticity is fundamentally important for long-term synaptic changes in neural networks. Specifically, short-term depression (STD) greatly expands the operational range of a neural network in which it can accommodate long-term synaptic changes while maintaining system performance. This is achieved by dynamically adjusting neural networks close to a critical state. The effects of STD can be further strengthened by synaptic weight heterogeneity, resulting in networks that can tolerate very large, long-term changes in synaptic weights. Our results highlight a potential mechanism used by the brain to organize plasticity at different time scales, thereby maintaining optimal information processing while allowing internal structural changes necessary for learning and memory.

13.
Neuroscience ; 412: 190-206, 2019 08 01.
Artigo em Inglês | MEDLINE | ID: mdl-31181368

RESUMO

Spatially separated brain areas interact with each other to form networks with coordinated activities, supporting various brain functions. Interaction structures among brain areas have been widely investigated through pairwise measures. However, interactions among multiple (e.g., triple and quadruple) areas cannot be reduced to pairwise interactions. Such higher order interactions (HOIs), e.g., exclusive-or (XOR) operation, are widely implemented in computation systems and are crucial for effective information processing. However, it is currently unclear whether any HOIs are present in large-scale brain functional networks when subjects are executing specific tasks. Here we analyzed functional magnetic resonance imaging (fMRI) data collected from human subjects executing various perceptual, motor, and cognitive tasks. We found that HOI strength in the macroscopic functional networks was very weak for all tasks, suggesting that major brain activities do not rely on HOIs on the macroscopic level at the timescale of hundreds of milliseconds. These weak HOIs during tasks were further investigated with a neural network model activated by external inputs, which suggested that weak pairwise interactions among brain areas organized the system without involving HOIs. Taken together, these results demonstrated the dominance of pairwise interactions in organizing coordinated activities among different brain areas to support various brain functions.

14.
BMC Bioinformatics ; 20(1): 219, 2019 Apr 30.
Artigo em Inglês | MEDLINE | ID: mdl-31039742

RESUMO

BACKGROUND: Data from genome-wide association studies (GWASs) have been used to estimate the heritability of human complex traits in recent years. Existing methods are based on the linear mixed model, with the assumption that the genetic effects are random variables, which is opposite to the fixed effect assumption embedded in the framework of quantitative genetics theory. Moreover, heritability estimators provided by existing methods may have large standard errors, which calls for the development of reliable and accurate methods to estimate heritability. RESULTS: In this paper, we first investigate the influences of the fixed and random effect assumption on heritability estimation, and prove that these two assumptions are equivalent under mild conditions in the theoretical aspect. Second, we propose a two-stage strategy by first performing sparse regularization via cross-validated elastic net, and then applying variance estimation methods to construct reliable heritability estimations. Results on both simulated data and real data show that our strategy achieves a considerable reduction in the standard error while reserving the accuracy. CONCLUSIONS: The proposed strategy allows for a reliable and accurate heritability estimation using GWAS data. It shows the promising future that reliable estimations can still be obtained with even a relatively restricted sample size, and should be especially useful for large-scale heritability analyses in the genomics era.


Assuntos
Estudo de Associação Genômica Ampla/métodos , Genômica/métodos , Modelos Genéticos , Humanos
15.
Brain Imaging Behav ; 2019 Mar 01.
Artigo em Inglês | MEDLINE | ID: mdl-30820860

RESUMO

Previous meta-analyses found abnormal brain activations in schizophrenia patients compared with normal controls when performing working memory tasks. Although most studies focused on dysfunction of the working memory activation network in schizophrenia patients, deactivation abnormalities of the working memory in the default mode network have also been reported in schizophrenia but have received less attention. Our goal was to discover whether deactivation abnormalities can also be consistently found in schizophrenia during working memory tasks and, further, to consider both activation and deactivation abnormalities. Fifty-two English language peer-reviewed studies were included in this meta-analysis. Compared with normal controls, the schizophrenia patients showed activation dysfunction of the bilateral dorsolateral prefrontal cortex and posterior parietal cortex as well as the anterior insula, anterior cingulate cortex, and supplementary motor area, which are core nodes of the central executive and salience network. In addition to dysfunction of the activation networks, the patients showed deactivation abnormalities in the ventral medial prefrontal cortex and posterior cingulate cortex, which are core nodes of the default mode network. These results suggest that both activation and deactivation abnormalities exist in schizophrenia patients and that these abnormalities should both be considered when investigating the pathophysiological mechanism of schizophrenia.

16.
Hum Brain Mapp ; 40(9): 2800-2812, 2019 06 15.
Artigo em Inglês | MEDLINE | ID: mdl-30854745

RESUMO

Working memory (WM) is a complex and pivotal cognitive system underlying the performance of many cognitive behaviors. Although individual differences in WM performance have previously been linked to the blood oxygenation level-dependent (BOLD) response across several large-scale brain networks, the unique and shared contributions of each large-scale brain network to efficient WM processes across different cognitive loads remain elusive. Using a WM paradigm and functional magnetic resonance imaging (fMRI) from the Human Connectome Project, we proposed a framework to assess the association and shared-association strength between imaging biomarkers and behavioral scales. Association strength is the capability of individual brain regions to modulate WM performance and shared-association strength measures how different regions share the capability of modulating performance. Under higher cognitive load (2-back), the frontoparietal executive control network (FPN), dorsal attention network (DAN), and salience network showed significant positive activation and positive associations, whereas the default mode network (DMN) showed the opposite pattern, namely, significant deactivation and negative associations. Comparing the different cognitive loads, the DMN and FPN showed predominant associations and globally shared-associations. When investigating the differences in association from lower to higher cognitive loads, the DAN demonstrated enhanced association strength and globally shared-associations, which were significantly greater than those of the other networks. This study characterized how brain regions individually and collaboratively support different cognitive loads.

18.
Schizophr Res ; 210: 262-269, 2019 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-30587426

RESUMO

Auditory verbal hallucinations (AVH) are one of the cardinal symptoms of schizophrenia, and are proposed to be associated with altered integrity of the left perisylvian language pathways. There is considerable heterogeneity in the pattern of white matter abnormalities across previous studies. We investigated the white matter integrity of the perisylvian language pathways in schizophrenia patients with AVH based on a relatively large sample dataset from four different sites. 113 schizophrenia patients with AVH, 96 patients without AVH (nAVH), and 269 healthy controls (HC) underwent diffusion-weighted imaging. Between-group comparisons were performed on the fractional anisotropy (FA) values of the anterior, posterior, and long segment fasciculi within the perisylvian language network. Analysis of covariance among the 3 groups revealed the long segment of the left perisylvian language pathways was significantly different in FA value. Post hoc analysis showed that compared with the HC group, the AVH group had significantly higher FA measurements in the left long segment. The nAVH group showed intermediate FA values for this segment compared to the AVH and HC group but did not differ significantly from either group. Furthermore, the prospective meta-analyses also revealed that FA value of the left long segment was significantly higher in the AVH group compared to the HC group. Our findings suggest the hyperconnectivity pattern of the left perisylvian language pathways in the presence of AVH in schizophrenia and support the self-monitoring of inner speech model.

19.
Conf Proc IEEE Eng Med Biol Soc ; 2018: 558-561, 2018 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-30440458

RESUMO

Major depressive disorder (MDD) is a complex mood disorder characterized by persistent and overwhelming depression. Previous studies have identified abnormalities in large scale functional brain networks in MDD, yet most of them were based on static functional connectivity. By contrast, here we explored disrupted topological organization of dynamic functional network connectivity (dFNC) in MDD based on graph theory. 182 MDD patients and 218 healthy controls were included in this study, all Chinese Han people. By applying group information guided independent component analysis (GIG-ICA) on resting-state fMRI data, the dFNCs of each subject were estimated using a sliding window method and k-means clustering. Five dynamic functional states were identified, three of which demonstrated significant group difference on the percentage of state occurrence. Interestingly, MDD patients spent much more time in a weakly-connected state 2, which is associated with self-focused thinking, a representative feature of depression. In addition, the abnormal FNCs in MDD were observed connecting different networks, especially among prefrontal, sensorimotor and cerebellum networks. As to network properties, MDD patients exhibited increased node efficiency in prefrontal and cerebellum. Moreover, three dFNCs with disrupted node properties were commonly identified in different states, which are also correlated with depressive symptom severity and cognitive performance. This study is the first attempt to investigate the dynamic functional abnormalities in Chinese MDD using a relatively large sample size, which provides new evidence on aberrant time-varying brain activity and its network disruptions in MDD, which might underscore the impaired cognitive functions in this mental disorder.

20.
Conf Proc IEEE Eng Med Biol Soc ; 2018: 1028-1031, 2018 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-30440566

RESUMO

The combination of graph theoretical approaches and neuroimaging data provides a powerful way to explore the characteristics of brain network. Recently, the temporal variability of spontaneous brain activity and functional connectivity has attracted wide attention. Thus, it is essential to evaluate the reliability of functional network connectivity and properties from the dynamic perspective. However, previous test-retest (TRT) studies have explored this reliability with a static point of view. In this study, using a large rs-fMRI dataset from Human Connectome Project (HCP), we investigated TRT reliability of functional connectivity and graph metrics derived from the most commonly used method- sliding window at three time intervals (short: 72 seconds, middle: 15 minutes and long: >24 hours). The results revealed that reliable connectivities and related brain regions are mainly distributed in primary cortex, such as visual area and sensorimotor area and default mode network. Notably, connectivity strength and global efficiency have better reliability than other metrics. Finally, short scan time interval and long scan duration can increase the TRT reliability of metrics. Findings of present study provide important guidance for searching reliable network markers in future research.

SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA