P177 Resting-state functional connectivity pattern predicts the outcome in cognitive training combined with transcranial electrical stimulation

Resting-state functional connectivity is used throughout neuroscience to study brain organization and to generate biomarkers of development, disease, and cognition. The processes that give rise to correlated activity are, however, poorly understood. Here we decompose resting-state functional connectivity using a temporal unwrapping procedure to assess the contributions of moment-to-moment activity cofluctuations to the overall connectivity pattern. This approach temporally resolves functional connectivity at a timescale of single frames, which enables us to make direct comparisons of cofluctuations of network organization with fluctuations in the blood oxygen level-dependent (BOLD) time series. We show that surprisingly, only a small fraction of frames exhibiting the strongest cofluctuation amplitude are required to explain a significant fraction of variance in the overall pattern of connection weights as well as the network’s modular structure. These frames coincide with frames of high BOLD activity amplitude, corresponding to activity patterns that are remarkably consistent across individuals and identify fluctuations in default mode and control network activity as the primary driver of resting-state functional connectivity. Finally, we demonstrate that cofluctuation amplitude synchronizes across subjects during movie watching and that high-amplitude frames carry detailed information about individual subjects (whereas low-amplitude frames carry little). Our approach reveals fine-scale temporal structure of resting-state functional connectivity and discloses that frame-wise contributions vary across time. These observations illuminate the relation of brain activity to functional connectivity and open a number of directions for future research.

Download Full-text

Transfer learning improves resting-state functional connectivity pattern analysis using convolutional neural networks

GigaScience ◽

10.1093/gigascience/giy130 ◽

2018 ◽

Vol 7 (12) ◽

Cited By ~ 5

Author(s):

Pál Vakli ◽

Regina J Deák-Meszlényi ◽

Petra Hermann ◽

Zoltán Vidnyánszky

Keyword(s):

Neural Networks ◽

Functional Connectivity ◽

Transfer Learning ◽

Convolutional Neural Networks ◽

Resting State ◽

Pattern Analysis ◽

Connectivity Pattern ◽

Resting State Functional Connectivity

Download Full-text

Effects of Cognitive Training on Resting-State Functional Connectivity of Default Mode, Salience, and Central Executive Networks

Frontiers in Aging Neuroscience ◽

10.3389/fnagi.2016.00070 ◽

2016 ◽

Vol 8 ◽

Cited By ~ 44

Author(s):

Weifang Cao ◽

Xinyi Cao ◽

Changyue Hou ◽

Ting Li ◽

Yan Cheng ◽

...

Keyword(s):

Functional Connectivity ◽

Cognitive Training ◽

Resting State ◽

Central Executive ◽

Resting State Functional Connectivity ◽

Default Mode

Download Full-text

fMRI Informed Montage Selection for Transcranial Electrical Stimulation: Frontoparietal Synchronization for Drug Cue Reactivity

10.1101/2021.04.12.439544 ◽

2021 ◽

Author(s):

Ghazaleh Soleimani ◽

Rayus Kupliki ◽

Jerzy Bodurka ◽

Martin Paulus ◽

Hamed Ekhtiari

Keyword(s):

Electrical Stimulation ◽

Functional Connectivity ◽

Resting State ◽

Cue Reactivity ◽

Transcranial Electrical Stimulation ◽

List Type ◽

Whole Brain ◽

Resting State Connectivity ◽

Dual Site ◽

Drug Cue

AbstractBackgroundFrontoparietal network (FPN) with multiple cortical nodes is involved in executive functions. Transcranial electrical stimulation (tES) can potentially modulate interactions between these nodes using frontoparietal synchronization (FPS). Here we used fMRI and computational head models (CHMs) to inform electrode montage and dosage selection in FPS.MethodsSixty methamphetamine users completed an fMRI drug cue-reactivity task. Two sets of 4×1 HD electrodes with anode over F3 and F4 were simulated and spheres around maximum electric field in each hemisphere were defined as frontal seeds. Using frontal seeds, a task-based functional connectivity analysis was conducted based on a seed-to-whole brain generalized psychophysiological interaction (gPPI). Electrode placement for parietal sites was selected based on gPPI results. Task-based and resting-state connectivity were compared between fMRI-informed and classic F3-P3/F4-P4 montages.ResultsWhole-brain gPPI showed two significant clusters (left: 506 voxels P=0.006, right: 455 voxels P=0.016), located in the inferior parietal lobule under the CP5 and CP6 electrode location. Pair-wise ROI-based gPPI comparing informed (F3-CP5/F4-CP6) and classic (F3-P3/F4-P4) montages showed significant increased PPI and resting-state connectivity only in the informed montage. Cue-induced craving score was also correlated with left (F3-CP5) frontoparietal connectivity in the fMRI-informed montage.ConclusionThis study proposes an analytic pipeline to select electrode montage and dosage in dual site tES using CHMs and task-based connectivity. Stimulating F3-F4 can tap into both FPN and saliency network (SN) based on the montage selection. Using CHM and fMRI will be essential to navigating ample parameter space in the stimulation protocols for future tES studies.HighlightsWe demonstrated a methodology for montage selection in network-based tESTask-based functional connectivity can inform dual-site tES montage selectionHead models can help to induce balance tES dose in targeted brain regionsTargeting DLPFC with tES can tap into both saliency and frontoparietal networksLower resting-state frontoparietal connectivity before cue exposure followed by a greater craving

Download Full-text

Eyes closed or Eyes open? Exploring the alpha desynchronization hypothesis in resting state functional connectivity networks with intracranial EEG

10.1101/118174 ◽

2017 ◽

Cited By ~ 1

Author(s):

Jaime Gómez-Ramírez ◽

Shelagh Freedman ◽

Diego Mateos ◽

José Luis Pérez-Velázquez ◽

Taufik Valiante

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Connectivity Pattern ◽

Cost Calculation ◽

Resting State Functional Connectivity ◽

Eyes Closed ◽

Regional Specificity ◽

Eyes Open ◽

First Time ◽

Intracranial Recordings

AbstractThis paper addresses a fundamental question, are eyes closed and eyes open resting states equivalent baseline conditions, or do they have consistently different electrophysiological signatures? We compare the functional connectivity patterns in an eyes closed resting state with an eyes open resting state, and show that functional connectivity in the alpha band decreases in the eyes open condition compared to eyes closed. This "alpha desynchronization " or reduction in the number of connections from eyes closed to eyes open, is here, for the first time, studied with intracranial recordings. We provide two calculations of the wiring cost, local and mesoscopic, defined in terms of the distance between the electrodes and the likelihood that they are functionally connected. We find that, in agreement with the "alpha desynchronization" hypothesis, the local wiring cost decreases going from eyes closed to eyes open. However, when the wiring cost calculation takes into account the connectivity pattern, the wiring cost variation from eyes closed to eyes open is not as consistent and shows regional specificity. The wiring cost measure defined here, provides a new avenue for understanding the electrophysiology of resting state.

Download Full-text

High-amplitude co-fluctuations in cortical activity drive functional connectivity

10.1101/800045 ◽

2019 ◽

Cited By ~ 6

Author(s):

Farnaz Zamani Esfahlani ◽

Youngheun Jo ◽

Joshua Faskowitz ◽

Lisa Byrge ◽

Daniel P. Kennedy ◽

...

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Temporal Structure ◽

High Amplitude ◽

Network Activity ◽

Connectivity Pattern ◽

Future Research ◽

Resting State Functional Connectivity ◽

Brain Organization ◽

Fluctuation Amplitude

Resting-state functional connectivity is used throughout neuroscience to study brain organization and to generate biomarkers of development, disease, and cognition. The processes that give rise to correlated activity are, however, poorly understood. Here, we decompose resting-state functional connectivity using a “temporal unwrapping” procedure to assess the contributions of moment-to-moment activity co-fluctuations to the overall connectivity pattern. This approach temporally resolves functional connectivity at a timescale of single frames, which enables us to make direct comparisons of co-fluctuations of network organization with fluctuations in the BOLD time series. We show that, surprisingly, only a small fraction of frames exhibiting the strongest co-fluctuation amplitude are required to explain a significant fraction of variance in the overall pattern of connection weights as well as the network’s modular structure. These frames coincide with frames of high BOLD activity amplitude, corresponding to activity patterns that are remarkably consistent across individuals and identify fluctuations in default mode and control network activity as the primary driver of resting-state functional connectivity. Finally, we demonstrate that co-fluctuation amplitude synchronizes across subjects during movie-watching and that high-amplitude frames carry detailed information about individual subjects (whereas low-amplitude frames carry little). Our approach reveals fine-scale temporal structure of resting-state functional connectivity, and discloses that frame-wise contributions vary across time. These observations illuminate the relation of brain activity to functional connectivity and open a number of new directions for future research.

Download Full-text

Distributed Patterns of Functional Connectivity Underlie Individual Differences in Long-Term Memory Forgetting

10.1101/2021.08.04.455133 ◽

2021 ◽

Author(s):

Yinan Xu ◽

Chantel S. Prat ◽

Florian Sense ◽

Hedderik van Rijn ◽

Andrea Stocco

Keyword(s):

Individual Differences ◽

Functional Connectivity ◽

Resting State ◽

Formal Model ◽

Machine Learning Techniques ◽

Connectivity Pattern ◽

Long Term Memory ◽

Resting State Functional Connectivity ◽

Term Memory

Despite the importance of memories in everyday life and the progress made in understanding how they are encoded and retrieved, the neural processes by which declarative memories are maintained or forgotten remain elusive. Part of the problem is that it is empirically difficult to measure the rate at which memories fade and, without such a measure, it is hard to identify the corresponding neural correlates. This study addresses this problem using a combination of individual differences, model-based inferences, and resting-state functional connectivity. The individual-specific values of rate of forgetting in long-term memory (LTM) were estimated for 33 participants using a formal model fit to data from an adaptive fact learning task. Individual rates of forgetting were then used to examine participant-specific patterns of resting-state fMRI connectivity, using machine-learning techniques to identify the most predictive and generalizable features. Consistent with the existing literature, our results identified a sparse, distributed network of cortical and subcortical regions that underlies forgetting in LTM. Cross-validation showed that individual rates of forgetting were predicted with high accuracy (r = .96) from this connectivity pattern alone. These results open up new opportunities for the study of individual differences in LTM function and dysfunction.

Download Full-text