Enhanced resting-state functional connectivity between core memory-task activation peaks is associated with memory impairment in MCI

Abstract Both functional magnetic resonance imaging (fMRI) and transcranial magnetic stimulation (TMS) have been used to non-invasively localize the human motor functional area. These locations can be clinically used as stimulation target of TMS treatment. However, it has been reported that the finger tapping fMRI activation and TMS hotspot were not well-overlapped. The aim of the current study was to measure the distance between the finger tapping fMRI activation and the TMS hotspot, and more importantly, to compare the network difference by using resting-state fMRI. Thirty healthy participants underwent resting-state fMRI, task fMRI, and then TMS hotspot localization. We found significant difference of locations between finger tapping fMRI activation and TMS hotspot. Specifically, the finger tapping fMRI activation was more lateral than the TMS hotspot in the premotor area. The fMRI activation peak and TMS hotspot were taken as seeds for resting-state functional connectivity analyses. Compared with TMS hotspot, finger tapping fMRI activation peak showed more intensive functional connectivity with, e.g., the bilateral premotor, insula, putamen, and right globus pallidus. The findings more intensive networks of finger tapping activation than TMS hotspot suggest that TMS treatment targeting on the fMRI activation area might result in more remote effects and would be more helpful for TMS treatment on movement disorders.

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IC-P-095: Increased resting state functional connectivity associated with episodic memory impairment in mild cognitive impairment

Alzheimer s & Dementia ◽

10.1016/j.jalz.2015.06.116 ◽

2015 ◽

Vol 11 (7S_Part_2) ◽

pp. P66-P66

Author(s):

Yifei Zhang ◽

Miguel Ángel Araque Caballero ◽

Benno Gesierich ◽

Alexander N.W. Taylor ◽

Lee Simon-Vermot ◽

...

Keyword(s):

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Functional Connectivity ◽

Episodic Memory ◽

Resting State ◽

Memory Impairment ◽

Resting State Functional Connectivity

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Individual-Specific Areal-Level Parcellations Improve Functional Connectivity Prediction of Behavior

10.1101/2021.01.16.426943 ◽

2021 ◽

Author(s):

Ru Kong ◽

Qing Yang ◽

Evan Gordon ◽

Aihuiping Xue ◽

Xiaoxuan Yan ◽

...

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Distributed Networks ◽

Resting State Functional Connectivity ◽

Out Of Sample ◽

Spatially Distributed ◽

Behavioral Prediction ◽

Better Than ◽

And Task ◽

Task Activation

AbstractResting-state functional MRI (rs-fMRI) allows estimation of individual-specific cortical parcellations. We have previously developed a multi-session hierarchical Bayesian model (MS-HBM) for estimating high-quality individual-specific network-level parcellations. Here, we extend the model to estimate individual-specific areal-level parcellations. While network-level parcellations comprise spatially distributed networks spanning the cortex, the consensus is that areal-level parcels should be spatially localized, i.e., should not span multiple lobes. There is disagreement about whether areal-level parcels should be strictly contiguous or comprise multiple non-contiguous components, therefore we considered three areal-level MS-HBM variants spanning these range of possibilities. Individual-specific MS-HBM parcellations estimated using 10min of data generalized better than other approaches using 150min of data to out-of-sample rs-fMRI and task-fMRI from the same individuals. Resting-state functional connectivity (RSFC) derived from MS-HBM parcellations also achieved the best behavioral prediction performance. Among the three MS-HBM variants, the strictly contiguous MS-HBM (cMS-HBM) exhibited the best resting-state homogeneity and most uniform within-parcel task activation. In terms of behavioral prediction, the gradient-infused MS-HBM (gMS-HBM) was numerically the best, but differences among MS-HBM variants were not statistically significant. Overall, these results suggest that areal-level MS-HBMs can capture behaviorally meaningful individual-specific parcellation features beyond group-level parcellations. Multi-resolution trained models and parcellations are publicly available (GITHUB_LINK).

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