Whole-brain connectivity dynamics reflect both task-specific and individual-specific modulation: A multitask study

Xie, Hua; Calhoun, Vince D.; Gonzalez‐Castillo, Javier; Damaraju, Eswar; Miller, Robyn L.; Bandettini, Peter A.; Mitra, Sunanda

doi:10.1016/j.neuroimage.2017.05.050

Cited by 62 publications

(71 citation statements)

References 45 publications

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“…As in previous work, functional brain networks were considered to be dominated by stable group and individual factors (Gratton et al, 2018;Xie et al, 2018). In this section, we utilize a data-driven SDL model to estimate group factors from functional connectomes.…”

Section: Methodsmentioning

confidence: 99%

Refined measure of functional connectomes for improved identifiability and prediction

Cai

Zhang

et al. 2019

Human Brain Mapping

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Brain functional connectome analysis is commonly based on population‐wise inference. However, in this way precious information provided at the individual subject level may be overlooked. Recently, several studies have shown that individual differences contribute strongly to the functional connectivity patterns. In particular, functional connectomes have been proven to offer a fingerprint measure, which can reliably identify a given individual from a pool of participants. In this work, we propose to refine the standard measure of individual functional connectomes using dictionary learning. More specifically, we rely on the assumption that each functional connectivity is dominated by stable group and individual factors. By subtracting population‐wise contributions from connectivity patterns facilitated by dictionary representation, intersubject variability should be increased within the group. We validate our approach using several types of analyses. For example, we observe that refined connectivity profiles significantly increase subject‐specific identifiability across functional magnetic resonance imaging (fMRI) session combinations. Besides, refined connectomes can also improve the prediction power for cognitive behaviors. In accordance with results from the literature, we find that individual distinctiveness is closely linked with differences in neurocognitive activity within the brain. In summary, our results indicate that individual connectivity analysis benefits from the group‐wise inferences and refined connectomes are indeed desirable for brain mapping.

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Section: Methodsmentioning

confidence: 99%

Refined measure of functional connectomes for improved identifiability and prediction

Cai

Zhang

et al. 2019

Human Brain Mapping

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“…This is critical, since a high accuracy of the classifier on the training set is necessary, but not sufficient for high accuracy on the novel testing set. For example, in the study by Xie et al (2017) the performance of the trained classifier was not validated with a novel dataset. Such validation would have been informative of whether the learned parameters can distinguish the brain states due to true differences that hold at a population level or due to noise .…”

Section: Discussionmentioning

confidence: 99%

“…For example dFC can be calculated with the sliding-window approach , where Pearson correlation or covariance is computed between the signals of every pair of region with a small temporal window moving along the time series. A studies using the sliding window concept of dFC could successfully distinguish between the brain states during five different cognitive tasks (Gonzalez-Castillo et al, 2015;Xie et al, 2017). At the opposite end of the spectrum of timescales, FC can be obtained instantaneously with phase coherence (Cabral, Vidaurre, et al, 2017;Senden et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Comparing Task-Relevant Information Across Different Methods of Extracting Functional Connectivity

Insabato

Deco

et al. 2018

Preprint

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186 words Main text: 4176 wordsReferences: 36 AbstractThe concept of brain states, functionally relevant large-scale patterns, has become popular in neuroimaging. Not all components of such patterns are equally characteristic for each brain state, but machine learning provides a possibility of extracting the structure of brain states from functional data. However, the characterization in terms of functional connectivity measures varies widely, from cross-correlation to phase coherence, and the idea that different measures will provide the similar information is a common assumption made in neuroimaging. Here, we compare the performance of phase coherence, pairwise covariance, correlation, model-based covariance and model-based precision for a dataset of subjects performing five different cognitive tasks. We employ multinomial logistic regression for classification and consider two types of cross-validation schemes, between-and within-subjects. Furthermore, we investigate whether classification is robust for different temporal window lengths. We find that informative TASK INFORMATION ACROSS FUNCTIONAL CONNECTIVITY METHODS 1 links for the classification, meaning changes between tasks that are consistent across subjects, are entirely uncorrelated between correlation and covariance. These results indicate that the corresponding FC signature can strongly differ across FC methods used and that interpretation is subject to caution in terms of subnetworks related to a task.

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“…This is called BOLD signal which is also studied in this paper. The BOLD signal is generally modeled as the convolution of the stimulus function with Hemodynamic Response Function (HRF) [33][34][35][36]. The energy due to an influx of oxygenated blood to a local area of neuronal activity produces the BOLD signal.…”

Section: Data Acquisitionmentioning

confidence: 99%

Optimized Naive‐Bayes and Decision Tree Approaches for fMRI Smoking Cessation Classification

Tahmassebi¹,

Gandomi

Schulte

et al. 2018

Complexity

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This paper aims at developing new theory-driven biomarkers by implementing and evaluating novel techniques from restingstate scans that can be used in relapse prediction for nicotine-dependent patients and future treatment efficacy. Two classes of patients were studied. One class took the drug N-acetylcysteine and the other class took a placebo. Then, the patients underwent a double-blind smoking cessation treatment and the resting-state fMRI scans of their brains before and after treatment were recorded. The scientific research goal of this study was to interpret the fMRI connectivity maps based on machine learning algorithms to predict the patient who will relapse and the one who will not. In this regard, the feature matrix was extracted from the image slices of brain employing voxel selection schemes and data reduction algorithms. Then, the feature matrix was fed into the machine learning classifiers including optimized CART decision tree and Naive-Bayes classifier with standard and optimized implementation employing 10-fold cross-validation. Out of all the data reduction techniques and the machine learning algorithms employed, the best accuracy was obtained using the singular value decomposition along with the optimized Naive-Bayes classifier. This gave an accuracy of 93% with sensitivity-specificity of 99% which suggests that the relapse in nicotine-dependent patients can be predicted based on the resting-state fMRI images. The use of these approaches may result in clinical applications in the future.

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Whole-brain connectivity dynamics reflect both task-specific and individual-specific modulation: A multitask study

Cited by 62 publications

References 45 publications

Refined measure of functional connectomes for improved identifiability and prediction

Refined measure of functional connectomes for improved identifiability and prediction

Comparing Task-Relevant Information Across Different Methods of Extracting Functional Connectivity

Optimized Naive‐Bayes and Decision Tree Approaches for fMRI Smoking Cessation Classification

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