Parcellating cortical functional networks in individuals.
Ontology highlight
ABSTRACT:
SUBMITTER: Wang D
PROVIDER: S-EPMC4661084 | biostudies-literature | 2015 Dec
REPOSITORIES: biostudies-literature
Publications
Parcellating cortical functional networks in individuals.
Wang Danhong D Buckner Randy L RL Fox Michael D MD Holt Daphne J DJ Holmes Avram J AJ Stoecklein Sophia S Langs Georg G Pan Ruiqi R Qian Tianyi T Li Kuncheng K Baker Justin T JT Stufflebeam Steven M SM Wang Kai K Wang Xiaomin X Hong Bo B Liu Hesheng H
Nature neuroscience 20151109 12
The capacity to identify the unique functional architecture of an individual's brain is a crucial step toward personalized medicine and understanding the neural basis of variation in human cognition and behavior. Here we developed a cortical parcellation approach to accurately map functional organization at the individual level using resting-state functional magnetic resonance imaging (fMRI). A population-based functional atlas and a map of inter-individual variability were employed to guide the ...[more]
Similar Datasets
Project description:Subjective cognitive decline (SCD) is considered the earliest stage of the clinical manifestations of the continuous progression of Alzheimer's Disease (AD). Previous studies have suggested that multimodal brain networks play an important role in the early diagnosis and mechanisms underlying SCD. However, most of the previous studies focused on a single modality, and lacked correlation analysis between different modal biomarkers and brain regions. In order to further explore the specific characteristic of the multimodal brain networks in the stage of SCD, 22 individuals with SCD and 20 matched healthy controls (HCs) were recruited in the present study. We constructed the individual morphological, structural and functional brain networks based on 3D-T1 structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI), respectively. A t-test was used to select the connections with significant difference, and a multi-kernel support vector machine (MK-SVM) was applied to combine the selected multimodal connections to distinguish SCD from HCs. Moreover, we further identified the consensus connections of brain networks as the most discriminative features to explore the pathological mechanisms and potential biomarkers associated with SCD. Our results shown that the combination of three modal connections using MK-SVM achieved the best classification performance, with an accuracy of 92.68%, sensitivity of 95.00%, and specificity of 90.48%. Furthermore, the consensus connections and hub nodes based on the morphological, structural, and functional networks identified in our study exhibited abnormal cortical-subcortical connections in individuals with SCD. In addition, the functional networks presented more discriminative connections and hubs in the cortical-subcortical regions, and were found to perform better in distinguishing SCD from HCs. Therefore, our findings highlight the role of the cortical-subcortical circuit in individuals with SCD from the perspective of a multimodal brain network, providing potential biomarkers for the diagnosis and prediction of the preclinical stage of AD.
| S-EPMC8299728 | biostudies-literature
Project description:During normal childhood development, functional brain networks evolve over time in parallel with changes in neuronal oscillations. Previous studies have demonstrated differences in network topology with age, particularly in neonates and in cohorts spanning from birth to early adulthood. Here, we evaluate the developmental changes in EEG functional connectivity with a specific focus on the first 2 years of life. Functional connectivity networks (FCNs) were calculated from the EEGs of 240 healthy infants aged 0-2 years during wakefulness and sleep using a cross-correlation-based measure and the weighted phase lag index. Topological features were assessed via network strength, global clustering coefficient, characteristic path length, and small world measures. We found that cross-correlation FCNs maintained a consistent small-world structure, and the connection strengths increased after the first 3 months of infancy. The strongest connections in these networks were consistently located in the frontal and occipital regions across age groups. In the delta and theta bands, weighted phase lag index networks decreased in strength after the first 3 months in both wakefulness and sleep, and a similar result was found in the alpha and beta bands during wakefulness. However, in the alpha band during sleep, FCNs exhibited a significant increase in strength with age, particularly in the 21-24 months age group. During this period, a majority of the strongest connections in the networks were located in frontocentral regions, and a qualitatively similar distribution was seen in the beta band during sleep for subjects older than 3 months. Graph theory analysis suggested a small world structure for weighted phase lag index networks, but to a lesser degree than those calculated using cross-correlation. In general, graph theory metrics showed little change over time, with no significant differences between age groups for the clustering coefficient (wakefulness and sleep), characteristics path length (sleep), and small world measure (sleep). These results suggest that infant FCNs evolve during the first 2 years with more significant changes to network strength than features of the network structure. This study quantifies normal brain networks during infant development and can serve as a baseline for future investigations in health and neurological disease.
| S-EPMC10013075 | biostudies-literature
Project description:We describe methods for parcellating an individual subject's cortical and subcortical brain structures using resting-state functional correlations (RSFCs). Inspired by approaches from social network analysis, we first describe the application of snowball sampling on RSFC data (RSFC-Snowballing) to identify the centers of cortical areas, subdivisions of subcortical nuclei, and the cerebellum. RSFC-Snowballing parcellation is then compared with parcellation derived from identifying locations where RSFC maps exhibit abrupt transitions (RSFC-Boundary Mapping). RSFC-Snowballing and RSFC-Boundary Mapping largely complement one another, but also provide unique parcellation information; together, the methods identify independent entities with distinct functional correlations across many cortical and subcortical locations in the brain. RSFC parcellation is relatively reliable within a subject scanned across multiple days, and while the locations of many area centers and boundaries appear to exhibit considerable overlap across subjects, there is also cross-subject variability-reinforcing the motivation to parcellate brains at the level of individuals. Finally, examination of a large meta-analysis of task-evoked functional magnetic resonance imaging data reveals that area centers defined by task-evoked activity exhibit correspondence with area centers defined by RSFC-Snowballing. This observation provides important evidence for the ability of RSFC to parcellate broad expanses of an individual's brain into functionally meaningful units.
| S-EPMC4089380 | biostudies-literature
Project description:A common goal in biological sciences is to model a complex web of connections using a small number of interacting units. We present a general approach for dividing up elements in a spatial map based on their connectivity properties, allowing for the discovery of local regions underlying large-scale connectivity matrices. Our method is specifically designed to respect spatial layout and identify locally-connected clusters, corresponding to plausible coherent units such as strings of adjacent DNA base pairs, subregions of the brain, animal communities, or geographic ecosystems. Instead of using approximate greedy clustering, our nonparametric Bayesian model infers a precise parcellation using collapsed Gibbs sampling. We utilize an infinite clustering prior that intrinsically incorporates spatial constraints, allowing the model to search directly in the space of spatially-coherent parcellations. After showing results on synthetic datasets, we apply our method to both functional and structural connectivity data from the human brain. We find that our parcellation is substantially more effective than previous approaches at summarizing the brain's connectivity structure using a small number of clusters, produces better generalization to individual subject data, and reveals functional parcels related to known retinotopic maps in visual cortex. Additionally, we demonstrate the generality of our method by applying the same model to human migration data within the United States. This analysis reveals that migration behavior is generally influenced by state borders, but also identifies regional communities which cut across state lines. Our parcellation approach has a wide range of potential applications in understanding the spatial structure of complex biological networks.
| S-EPMC4338796 | biostudies-literature
Project description:Even though sleep modification is a hallmark of the aging process, age-related changes in functional connectivity using functional Magnetic Resonance Imaging (fMRI) during sleep, remain unknown. Here, we combined electroencephalography and fMRI to examine functional connectivity differences between wakefulness and light sleep stages (N1 and N2 stages) in 16 young (23.1 ± 3.3y; 7 women), and 14 older individuals (59.6 ± 5.7y; 8 women). Results revealed extended, distributed (inter-between) and local (intra-within) decreases in network connectivity during sleep both in young and older individuals. However, compared to the young participants, older individuals showed lower decreases in connectivity or even increases in connectivity between thalamus/basal ganglia and several cerebral regions as well as between frontal regions of various networks. These findings reflect a reduced ability of the older brain to disconnect during sleep that may impede optimal disengagement for loss of responsiveness, enhanced lighter and fragmented sleep, and contribute to age effects on sleep-dependent brain plasticity.
| S-EPMC7921592 | biostudies-literature
Project description:BackgroundObsessive-compulsive disorder (OCD) is characterized by intrusive thoughts and repetitive, compulsive behaviors. Neuroimaging studies have implicated altered connectivity among the functional networks of the cerebral cortex in the pathophysiology of OCD. However, there has been no comprehensive investigation of the cross-talk between the cerebellum and functional networks in the cerebral cortex.MethodsThis functional neuroimaging study was completed by 44 adult participants with OCD and 43 healthy control participants. We performed large-scale data-driven brain network analysis to identify functional connectivity patterns using resting-state functional magnetic resonance imaging data.ResultsParticipants with OCD showed lower functional connectivity within the somatomotor network and greater functional connectivity among the somatomotor network, cerebellum, and subcortical network (e.g., thalamus and pallidum; all p < .005). Network-based statistics analyses demonstrated one component comprising connectivity within the somatomotor network that showed lower connectivity and a second component comprising connectivity among the somatomotor network, and motor regions in particular, and the cerebellum that showed greater connectivity in participants with OCD relative to healthy control participants. In participants with OCD, abnormal connectivity across both network-based statistics-derived components positively correlated with OCD symptom severity (p = .006).ConclusionsTo our knowledge, this study is the first comprehensive investigation of large-scale network alteration across the cerebral cortex, subcortical regions, and cerebellum in OCD. Our findings highlight a critical role of the cerebellum in the pathophysiology of OCD.
| S-EPMC7150632 | biostudies-literature
Project description:Experimental work across species has demonstrated that spontaneously generated behaviors are robustly coupled to variations in neural activity within the cerebral cortex. Functional magnetic resonance imaging data suggest that temporal correlations in cortical networks vary across distinct behavioral states, providing for the dynamic reorganization of patterned activity. However, these data generally lack the temporal resolution to establish links between cortical signals and the continuously varying fluctuations in spontaneous behavior observed in awake animals. Here, we used wide-field mesoscopic calcium imaging to monitor cortical dynamics in awake mice and developed an approach to quantify rapidly time-varying functional connectivity. We show that spontaneous behaviors are represented by fast changes in both the magnitude and correlational structure of cortical network activity. Combining mesoscopic imaging with simultaneous cellular-resolution two-photon microscopy demonstrated that correlations among neighboring neurons and between local and large-scale networks also encode behavior. Finally, the dynamic functional connectivity of mesoscale signals revealed subnetworks not predicted by traditional anatomical atlas-based parcellation of the cortex. These results provide new insights into how behavioral information is represented across the neocortex and demonstrate an analytical framework for investigating time-varying functional connectivity in neural networks.
| S-EPMC11316935 | biostudies-literature
Project description:Individuals who have prior history of childhood traumatic experiences are at a high risk for a variety of psychological and behavioral problems throughout their lifetime. This study aimed to investigate whether such individuals exhibit altered cortical functional networks during a behavioral inhibition task. One hundred fifty-three non-clinical individuals were recruited and instructed to perform a Go/NoGo task during an electroencephalograph. Source-level weighted functional networks based on the graph theory were analyzed for NoGo-P3 processing. Based on their total scores on the childhood trauma questionnaire (CTQ) participants were divided into three groups: low CTQ, middle CTQ, and high CTQ. Results at the global level indicated decreased strength, clustering coefficient, and efficiency for the low and gamma bands in the high CTQ group. In addition, the path length of the low beta band was observed to be longer in the high CTQ group than the low CTQ group. At the nodal level, the nodal clustering coefficient of high CTQ group was decreased in left primary somatosensory cortex and middle occipital gyrus for the low beta band, and in left superior temporal gyrus for the gamma band. The nodal clustering coefficient of the left primary somatosensory cortex showed a significant negative correlation with the total CTQ score for the low beta band. In addition, the nodal clustering coefficient of the left middle occipital gyrus for the low beta band and superior temporal gyrus for the gamma band showed significant negative correlations with the emotional neglect score. Our results demonstrate an altered cortical functional network in individuals who experienced childhood trauma. In particular, the left primary somatosensory cortex, middle occipital gyrus, and superior temporal gyrus were found to be vulnerable in individuals who experienced childhood trauma, especially emotional neglect.
| S-EPMC6031680 | biostudies-literature
Project description:BackgroundTranscranial direct current stimulation (tDCS) is known to modulate spontaneous neural network excitability. The cognitive improvement observed in previous trials raises the potential of this technique as a possible therapeutic tool for use in attention-deficit/hyperactivity disorder (ADHD) population. However, to explore the potential of this technique as a treatment approach, the functional parameters of brain connectivity and the extent of its effects need to be more fully investigated.ObjectiveThe aim of this study was to investigate a functional cortical network (FCN) model based on electroencephalographic activity for studying the dynamic patterns of brain connectivity modulated by tDCS and the distribution of its effects in individuals with ADHD.MethodsSixty ADHD patients participated in a parallel, randomized, double-blind, sham-controlled trial. Individuals underwent a single session of sham or anodal tDCS at 1 mA of current intensity over the left dorsolateral prefrontal cortex for 20 min. The acute effects of stimulation on brain connectivity were assessed using the FCN model based on electroencephalography activity.ResultsComparing the weighted node degree within groups prior to and following the intervention, a statistically significant difference was found in the electrodes located on the target and correlated areas in the active group (p < 0.05), while no statistically significant results were found in the sham group (p ≥ 0.05; paired-sample Wilcoxon signed-rank test).ConclusionAnodal tDCS increased functional brain connectivity in individuals with ADHD compared to data recorded in the baseline resting state. In addition, although some studies have suggested that the effects of tDCS are selective, the present findings show that its modulatory activity spreads. Further studies need to be performed to investigate the dynamic patterns and physiological mechanisms underlying the modulatory effects of tDCS.Trial registrationClinicalTrials.gov NCT01968512.
| S-EPMC4524049 | biostudies-literature
Project description:Understanding how the mammalian neocortex creates cognition largely depends on knowledge about large-scale cortical organization. Accumulated evidence has illuminated cortical substrates of cognition across the lifespan, but how topological properties of cortical networks support structure-function relationships in normal aging remains an open question. Here we investigate the role of connections (i.e., short/long and direct/indirect) and node properties (i.e., centrality and modularity) in predicting functional-structural connectivity coupling in healthy elderly subjects. Connectivity networks were derived from correlations of cortical thickness and cortical glucose consumption in resting state. Local-direct connections (i.e., nodes separated by less than 30 mm) and node modularity (i.e., a set of nodes highly interconnected within a topological community and sparsely interconnected with nodes from other modules) in the functional network were identified as the main determinants of coupling between cortical networks, suggesting that the structural network in aging is mainly constrained by functional topological properties involved in the segregation of information, likely due to aging-related deficits in functional integration. This hypothesis is supported by an enhanced connectivity between cortical regions of different resting-state networks involved in sensorimotor and memory functions in detrimental to associations between fronto-parietal regions supporting executive processes. Taken collectively, these findings open new avenues to identify aging-related failures in the anatomo-functional organization of the neocortical mantle, and might contribute to early detection of prevalent neurodegenerative conditions occurring in the late life.
| S-EPMC6869592 | biostudies-literature