scholarly journals Manifold Learning of Dynamic Functional Connectivity Reliably Identifies Functionally Consistent Coupling Patterns in Human Brains

2019 ◽  
Vol 9 (11) ◽  
pp. 309
Author(s):  
Yuyuan Yang ◽  
Lubin Wang ◽  
Yu Lei ◽  
Yuyang Zhu ◽  
Hui Shen
Keyword(s):  
Functional Connectivity ◽  
Sleep Deprivation ◽  
Manifold Learning ◽  
Temporal Dynamics ◽  
Functional Coupling ◽  
Dimensional Manifold ◽  
Dynamic Functional Connectivity ◽  
Local Linear Embedding ◽  
Human Connectome Project ◽  
Low Dimensional

Most previous work on dynamic functional connectivity (dFC) has focused on analyzing temporal traits of functional connectivity (similar coupling patterns at different timepoints), dividing them into functional connectivity states and detecting their between-group differences. However, the coherent functional connectivity of brain activity among the temporal dynamics of functional connectivity remains unknown. In the study, we applied manifold learning of local linear embedding to explore the consistent coupling patterns (CCPs) that reflect functionally homogeneous regions underlying dFC throughout the entire scanning period. By embedding the whole-brain functional connectivity in a low-dimensional manifold space based on the Human Connectome Project (HCP) resting-state data, we identified ten stable patterns of functional coupling across regions that underpin the temporal evolution of dFC. Moreover, some of these CCPs exhibited significant neurophysiological meaning. Furthermore, we apply this method to HCP rsfMR and tfMRI data as well as sleep-deprivation data and found that the topological organization of these low-dimensional structures has high potential for predicting sleep-deprivation states (classification accuracy of 92.3%) and task types (100% identification for all seven tasks).In summary, this work provides a methodology for distilling coherent low-dimensional functional connectivity structures in complex brain dynamics that play an important role in performing tasks or characterizing specific states of the brain.

scholarly journals Dynamic Alterations in Neural Networks Supporting Aversive Learning in Children Exposed to Trauma: Neural Mechanisms Underlying Psychopathology

2020 ◽  
Author(s):  
Stephanie N. DeCross ◽  
Kelly Sambrook ◽  
Margaret Sheridan ◽  
Nim Tottenham ◽  
Katie A McLaughlin
Keyword(s):  
Functional Connectivity ◽  
Temporal Dynamics ◽  
Longitudinal Design ◽  
Region Of Interest ◽  
Posterior Cingulate Cortex ◽  
Parahippocampal Gyrus ◽  
Functional Coupling ◽  
Neural Activation ◽  
Aversive Learning ◽  
Frontal Pole

Altered aversive learning represents a potential mechanism through which childhood trauma (CT) might influence risk for psychopathology. This study examines the temporal dynamics of neural activation and patterns of functional connectivity during aversive learning in children with and without CT, and evaluates whether these neural patterns mediate the association of CT with psychopathology in a longitudinal design. 147 children (aged 8-16 years, 77 with CT) completed a fear conditioning procedure during an fMRI scan. Dynamic patterns of neural activation were examined in whole-brain and region-of-interest analyses; functional connectivity was assessed with generalized psychophysiological interaction analyses. We evaluated whether the associations between CT and psychopathology symptoms at baseline and two-year follow-up were mediated by neural activation and connectivity during aversive learning. Children exposed to trauma displayed blunted patterns of neural activation over time during CS+>CS- in right amygdala and during CS->CS+ in right hippocampus and frontal pole. Additionally, during CS+>CS-, CT was associated with elevated functional connectivity of right amygdala with fronto-parietal regions and reduced connectivity with hippocampus, posterior parahippocampal gyrus, and posterior cingulate cortex. The longitudinal association between CT and later externalizing symptoms was mediated by blunted activation in right amygdala and insula. Reduced amygdala-hippocampal connectivity mediated the association of CT with transdiagnostic anxiety symptoms. CT is associated with poor threat-safety discrimination and altered functional coupling between salience and default mode network regions during aversive learning. These altered neural dynamics during learning may be key mechanisms linking CT with internalizing and externalizing psychopathology.

scholarly journals Classification of Infrared Objects in Manifold Space Using Kullback-Leibler Divergence of Gaussian Distributions of Image Points

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 434 ◽  
Author(s):  
Huilin Ge ◽  
Zhiyu Zhu ◽  
Kang Lou ◽  
Wei Wei ◽  
Runbang Liu ◽  
...  
Keyword(s):  
Manifold Learning ◽  
Gaussian Distribution ◽  
Classification Accuracy ◽  
Infrared Image ◽  
Data Sets ◽  
Dimensional Manifold ◽  
Infrared Images ◽  
Leibler Divergence ◽  
Data Points ◽  
Low Dimensional

Infrared image recognition technology can work day and night and has a long detection distance. However, the infrared objects have less prior information and external factors in the real-world environment easily interfere with them. Therefore, infrared object classification is a very challenging research area. Manifold learning can be used to improve the classification accuracy of infrared images in the manifold space. In this article, we propose a novel manifold learning algorithm for infrared object detection and classification. First, a manifold space is constructed with each pixel of the infrared object image as a dimension. Infrared images are represented as data points in this constructed manifold space. Next, we simulate the probability distribution information of infrared data points with the Gaussian distribution in the manifold space. Then, based on the Gaussian distribution information in the manifold space, the distribution characteristics of the data points of the infrared image in the low-dimensional space are derived. The proposed algorithm uses the Kullback-Leibler (KL) divergence to minimize the loss function between two symmetrical distributions, and finally completes the classification in the low-dimensional manifold space. The efficiency of the algorithm is validated on two public infrared image data sets. The experiments show that the proposed method has a 97.46% classification accuracy and competitive speed in regards to the analyzed data sets.

Study of Fault Diagnosis Based on Manifold Learning

2013 ◽  
Vol 312 ◽  
pp. 650-654 ◽  
Author(s):  
Yi Lin He ◽  
Guang Bin Wang ◽  
Fu Ze Xu
Keyword(s):  
Fault Diagnosis ◽  
Manifold Learning ◽  
Linear Manifold ◽  
Rotating Machinery ◽  
Dimensional Manifold ◽  
Advantages And Disadvantages ◽  
Non Linear ◽  
Future Direction ◽  
Low Dimensional ◽  
Machinery Fault Diagnosis

Characteristic signals in rotating machinery fault diagnosis with the issues of complex and difficult to deal with, while the use of non-linear manifold learning method can effectively extract low-dimensional manifold characteristics embedded in the high-dimensional non-linear data. It greatly maintains the overall geometric structure of the signals and improves the efficiency and reliability of the rotating machinery fault diagnosis. According to the development prospects of manifold learning, this paper describes four classical manifold learning methods and each advantages and disadvantages. It reviews the research status and application of fault diagnosis based on manifold learning, as well as future direction of researches in the field of manifold learning fault diagnosis.

scholarly journals Unsupervised Metric Learning Using Low Dimensional Embedding

2018 ◽  
Author(s):  
Parag Jain
Keyword(s):  
Manifold Learning ◽  
Euclidean Distance ◽  
Metric Learning ◽  
Special Property ◽  
Dimensional Manifold ◽  
Laplacian Eigenmaps ◽  
Learning Techniques ◽  
Out Of Sample ◽  
Low Dimensional ◽  
Project Data

Unsupervised metric learning has been generally studied as a byproduct of dimensionality reduction or manifold learning techniques. Manifold learning techniques like Diusion maps, Laplacian eigenmaps has a special property that embedded space is Euclidean. Although laplacian eigenmaps can provide us with some (dis)similarity information it does not provide with a metric which can further be used on out-of-sample data. On other hand supervised metric learning technique like ITML which can learn a metric needs labeled data for learning. In this work propose methods for incremental unsupervised metric learning. In rst approach Laplacian eigenmaps is used along with Information Theoretic Metric Learning(ITML) to form an unsupervised metric learning method. We rst project data into a low dimensional manifold using Laplacian eigenmaps, in embedded space we use euclidean distance to get an idea of similarity between points. If euclidean distance between points in embedded space is below a threshold t1 value we consider them as similar points and if it is greater than a certain threshold t2 we consider them as dissimilar points. Using this we collect a batch of similar and dissimilar points which are then used as a constraints for ITML algorithm and learn a metric. To prove this concept we have tested our approach on various UCI machine learning datasets. In second approach we propose Incremental Diusion Maps by updating SVD in a batch-wise manner.

scholarly journals Time-varying Dynamic Network Model For Dynamic Resting State Functional Connectivity in fMRI and MEG imaging

2021 ◽  
Author(s):  
Fei Jiang ◽  
Huaqing Jin ◽  
Yijing Bao ◽  
Xihe Xie ◽  
Jennifer Cummings ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Dynamic Network ◽  
Dynamic State ◽  
State Transitions ◽  
Dimensional Manifold ◽  
Time Varying ◽  
Resting State Functional Connectivity ◽  
Brain Signals ◽  
Low Dimensional

Dynamic resting state functional connectivity (RSFC) characterizes fluctuations that occurs over time in functional brain networks. Existing methods to extract dynamic RSFCs, such as sliding-window and clustering methods, have various limitations due to their inherent non-adaptive nature and high-dimensionality including an inability to reconstruct brain signals, insufficiency of data for reliable estimation, insensitivity to rapid changes in dynamics, and a lack of generalizability across multi- modal functional imaging datasets. To overcome these deficiencies, we develop a novel and unifying time-varying dynamic network (TVDN) framework for examining dynamic resting state functional connectivity. TVDN includes a generative model that describes the relation between low-dimensional dynamic RSFC and the brain signals, and an inference algorithm that automatically and adaptively learns to detect dynamic state transitions in data and a low-dimensional manifold of dynamic RSFC. TVDN is generalizable to handle multimodal functional neuroimaging data (fMRI and MEG/EEG). The resulting estimated low-dimensional dynamic RSFCs manifold directly links to the frequency content of brain signals. Hence we can evaluate TVDN performance by examining whether learnt features can reconstruct observed brain signals. We conduct comprehensive simulations to evaluate TVDN under hypothetical settings. We then demonstrate the application of TVDN with real fMRI and MEG data, and compare the results with existing benchmarks. Results demonstrate that TVDN is able to correctly capture the dynamics of brain activity and more robustly detect brain state switching both in resting state fMRI and MEG data.

scholarly journals Imaging the spontaneous flow of thought: Distinct periods of cognition contribute to dynamic functional connectivity during rest

2019 ◽  
Author(s):  
Javier Gonzalez-Castillo ◽  
César Caballero-Gaudes ◽  
Natasha Topolski ◽  
Daniel A. Handwerker ◽  
Francisco Pereira ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Task Engagement ◽  
Head Motion ◽  
Clinical Value ◽  
Dynamic Functional Connectivity ◽  
Brain Maintenance ◽  
And Performance ◽  
Low Dimensional ◽  
Brain Functional Connectivity ◽  
And Task

AbstractBrain functional connectivity (FC) changes have been measured across seconds using fMRI. This is true for both rest and task scenarios. Moreover, it is well accepted that task engagement alters FC, and that dynamic estimates of FC during and before task events can help predict their nature and performance. Yet, when it comes to dynamic FC (dFC) during rest, there is no consensus about its origin or significance. Some argue that rest dFC reflects fluctuations in on-going cognition, or is a manifestation of intrinsic brain maintenance mechanisms, which could have predictive clinical value. Conversely, others have concluded that rest dFC is mostly the result of sampling variability, head motion or fluctuating sleep states. Here, we present novel analyses suggesting that rest dFC is influenced by short periods of distinct mental processing, and that the cognitive nature of such mental processes can be inferred blindly from the data. As such, several different behaviorally relevant whole-brain FC configurations may occur during a single rest scan even when subjects were continuously awake and displayed minimal motion. In addition, using low dimensional embeddings as visualization aids, we show how FC states—commonly used to summarize and interpret resting dFC—can accurately and robustly reveal periods of externally imposed tasks; however, they may be less effective in capturing periods of distinct cognition during rest.

Impact of 36 h of total sleep deprivation on resting-state dynamic functional connectivity

2018 ◽  
Vol 1688 ◽  
pp. 22-32 ◽  
Author(s):  
Huaze Xu ◽  
Hui Shen ◽  
Lubin Wang ◽  
Qi Zhong ◽  
Yu Lei ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Sleep Deprivation ◽  
Resting State ◽  
Dynamic Functional Connectivity ◽  
Total Sleep Deprivation ◽  
State Dynamic

scholarly journals Age-related abnormalities of thalamic shape and dynamic functional connectivity after three hours of sleep restriction

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10751
Author(s):  
Zhiliang Long ◽  
Jia Zhao ◽  
Danni Chen ◽  
Xu Lei
Keyword(s):  
Young Adults ◽  
Functional Connectivity ◽  
Sleep Deprivation ◽  
Parietal Cortex ◽  
Shape Analysis ◽  
Sleep Restriction ◽  
Dynamic Functional Connectivity ◽  
Age Related ◽  
Superior Parietal Cortex ◽  
Left Thalamus

Background Previous neuroimaging studies have detected abnormal activation and intrinsic functional connectivity of the thalamus after total sleep deprivation. However, very few studies have investigated age-related changes in the dynamic functional connectivity of the thalamus and the abnormalities in the thalamic shape following partial sleep deprivation. Methods Fifty-five participants consisting of 23 old adults (mean age: 68.8 years) and 32 young adults (mean age: 23.5 years) were included in current study. A vertex-based shape analysis and a dynamic functional connectivity analysis were used to evaluate the age-dependent structural and functional abnormalities after three hours of sleep restriction. Results Shape analysis revealed the significant main effect of deprivation with local atrophy in the left thalamus. In addition, we observed a significant age deprivation interaction effect with reduced variability of functional connectivity between the left thalamus and the left superior parietal cortex following sleep restriction. This reduction was found only in young adults. Moreover, a significantly negative linear correlation was observed between the insomnia severity index and the changes of variability (post-deprivation minus pre-deprivation) in the functional connectivity of the left thalamus with the left superior parietal cortex. Conclusions The results indicated that three hours of sleep restriction could affect both the thalamic structure and its functional dynamics. They also highlighted the role of age in studies of sleep deprivation.

scholarly journals Modular slowing of resting-state dynamic functional connectivity as a marker of cognitive dysfunction induced by sleep deprivation

NeuroImage ◽  
2020 ◽  
Vol 222 ◽  
pp. 117155 ◽  
Author(s):  
Diego Lombardo ◽  
Catherine Cassé-Perrot ◽  
Jean-Philippe Ranjeva ◽  
Arnaud Le Troter ◽  
Maxime Guye ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Sleep Deprivation ◽  
Cognitive Dysfunction ◽  
Resting State ◽  
Dynamic Functional Connectivity ◽  
State Dynamic

scholarly journals Impact of acute sleep deprivation on dynamic functional connectivity states

2019 ◽  
Vol 41 (4) ◽  
pp. 994-1005 ◽  
Author(s):  
Changhong Li ◽  
Judith Fronczek‐Poncelet ◽  
Denise Lange ◽  
Eva Hennecke ◽  
Tina Kroll ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Sleep Deprivation ◽  
Dynamic Functional Connectivity ◽  
Acute Sleep Deprivation
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