In:
PLOS ONE, Public Library of Science (PLoS), Vol. 17, No. 7 ( 2022-7-27), p. e0268752-
Abstract:
Resting-state blood-oxygen-level-dependent (BOLD) signal acquired through functional magnetic resonance imaging is a proxy of neural activity and a key mechanism for assessing neurological conditions. Therefore, practical tools to filter out artefacts that can compromise the assessment are required. On the one hand, a variety of tailored methods to preprocess the data to deal with identified sources of noise (e.g., head motion, heart beating, and breathing, just to mention a few) are in place. But, on the other hand, there might be unknown sources of unstructured noise present in the data. Therefore, to mitigate the effects of such unstructured noises, we propose a model-based filter that explores the statistical properties of the underlying signal (i.e., long-term memory). Specifically, we consider autoregressive fractional integrative process filters. Remarkably, we provide evidence that such processes can model the signals at different regions of interest to attain stationarity. Furthermore, we use a principled analysis where a ground-truth signal with statistical properties similar to the BOLD signal under the injection of noise is retrieved using the proposed filters. Next, we considered preprocessed (i.e., the identified sources of noise removed) resting-state BOLD data of 98 subjects from the Human Connectome Project. Our results demonstrate that the proposed filters decrease the power in the higher frequencies. However, unlike the low-pass filters, the proposed filters do not remove all high-frequency information, instead they preserve process-related higher frequency information. Additionally, we considered four different metrics (power spectrum, functional connectivity using the Pearson’s correlation, coherence, and eigenbrains) to infer the impact of such filter. We provided evidence that whereas the first three keep most of the features of interest from a neuroscience perspective unchanged, the latter exhibits some variations that could be due to the sporadic activity filtered out.
Type of Medium:
Online Resource
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0268752
DOI:
10.1371/journal.pone.0268752.g001
DOI:
10.1371/journal.pone.0268752.g002
DOI:
10.1371/journal.pone.0268752.g003
DOI:
10.1371/journal.pone.0268752.g004
DOI:
10.1371/journal.pone.0268752.g005
DOI:
10.1371/journal.pone.0268752.g006
DOI:
10.1371/journal.pone.0268752.g007
DOI:
10.1371/journal.pone.0268752.g008
DOI:
10.1371/journal.pone.0268752.g009
DOI:
10.1371/journal.pone.0268752.g010
DOI:
10.1371/journal.pone.0268752.g011
DOI:
10.1371/journal.pone.0268752.g012
DOI:
10.1371/journal.pone.0268752.t001
DOI:
10.1371/journal.pone.0268752.t002
DOI:
10.1371/journal.pone.0268752.s001
DOI:
10.1371/journal.pone.0268752.s002
DOI:
10.1371/journal.pone.0268752.s003
DOI:
10.1371/journal.pone.0268752.s004
DOI:
10.1371/journal.pone.0268752.r001
DOI:
10.1371/journal.pone.0268752.r002
DOI:
10.1371/journal.pone.0268752.r003
DOI:
10.1371/journal.pone.0268752.r004
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2022
detail.hit.zdb_id:
2267670-3
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