Reliable Measurement of Cortico-subcortical Functional Connectivity and Task Activations with 7T MRI
Poster No:
1890
Submission Type:
Abstract Submission
Authors:
Nathan Cross1, Jinhan Chen2, He Wang2, Sharon Naismith3, Fernando Calamante4, Zhensen Chen2, Jinglei Lv5
Institutions:
1University of Sydney, Camperdown, New South Wales, 2Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China, 3The University of Sydney, Camperdown, New South Wales, 4University of Sydney, Sydney, New South Wales, 5The University of Sydney, Sydney, New South Wales
First Author:
Co-Author(s):
Jinhan Chen
Institute of Science and Technology for Brain-inspired Intelligence, Fudan University
Shanghai, China
Institute of Science and Technology for Brain-inspired Intelligence, Fudan University
Shanghai, China
He Wang
Institute of Science and Technology for Brain-inspired Intelligence, Fudan University
Shanghai, China
Institute of Science and Technology for Brain-inspired Intelligence, Fudan University
Shanghai, China
Zhensen Chen
Institute of Science and Technology for Brain-inspired Intelligence, Fudan University
Shanghai, China
Institute of Science and Technology for Brain-inspired Intelligence, Fudan University
Shanghai, China
Introduction:
The widely adopted imaging sequences for BOLD fMRI designed on 3T MRI scanners are traditionally biased to capture signal in the neocortex. For instance, SNR tends to be worse in limbic and subcortical brain regions. This is especially important for small subcortical structures, such as the locus coeruleus, affected in various neurodegenerative diseases. As a consequence, the reliability of the calculated cortico-subcortical functional connectivity is concerning. This project assessed the benefits of multi-echo fMRI on intra-subject reliability of cortico-subcortical functional connectivity (FC).
Methods:
Participants (n=6 (3 F), age=24.6±2.4 years) were measured across 4 sessions using functional MRI (2 scans on the same day, 2 scans one the same day after 1 week), with the Siemens Tarra 7T Scanner at Zhangjiang International Brain Imaging Centre. In each session participants underwent resting-state fMRI and the Multi-Source Interference Task[1]. For both the task and rs-fMRI, two separate sequences were employed: 1) single-echo fMRI (TR=1s, TE=22.2ms) and 2) multi-echo fMRI (TR=1s, TE=11, 29.4, 47.8ms). All data were pre-processed (realignment, co-registration, unwarping) using fMRIprep[2] (v.23.0.0). Multi-echo data were further pre-processed in two ways, 1) using an optimal combination of 3 multiple echoes [3] and 2), via a multi-echo ICA approach (meICA; tedana v.23.0.2 [4]). This resulted in 3 sets of data for comparison 1) single-echo (SE), 2) multi-echo (ME) and 3) meICA. Resting State: All sequences were denoised (confound regression, filtering) using XCP_D (0.5.0 [5]). The temporal signal-to-noise ratio (tSNR) was calculated for the timeseries across the whole brain, as well as averaged in subcortical regions including the thalamus, caudate, putamen, hippocampus, nucleus accumbens and locus coeruleus. The reliability of subcortico-cortical FC across sessions and subjects was calculated using the intra-class correlation (ICC3 [6]). Task: First level GLMs were applied to each session using niLearn [7], and second level t-contrasts were conducted to compare task trials to rest-periods.
Results:
There was a significant improvement in overall tSNR in the ME and meICA methods (Figure 1), which was strongest in the orbitofrontal cortex (F(2) = 212.9, p<0.001) and the temporal poles (F(2) = 129.6, p<0.001). An increase in tSNR was also observed across all subcortical regions (F(2) = 16.5-71.4, all p<0.001). The mean ICC across all subcortical-cortical edges did not significantly change (F(2) = 1.9, p = 0.139, Figure 2A), however there were significantly fewer anti-correlations in the multi-echo sequences (χ2 = 299, p<0.001, Figure 2B). There was a significant increase in ICC in the hippocampus (F(2) = 4.7, p = 0.009), amygdala (F(2) = 4.1, p = 0.017), accumbens (F(2) = 16.1, p<0.001) and putamen (F(2) = 4.6, p = 0.01), as shown in Figure 2C. Finally, task-related activations in the visual and motor cortex were were more widespread in the multi-echo sequence (2218 voxels, t = 3.97 ± 0.51) compared to single-echo sequence (553 voxels, t = 3.7 ± 0.36).
Conclusions:
These findings demonstrate a significant improvement in signal-to-noise within regions that are known to suffer from poor signal quality due to inhomogeneities in the magnetic field and tissue relaxation (T2*) times. By leveraging an optimal combination of 3 echo times and ICA to extract BOLD components from noise, these results show that signal can be boosted in these areas. This signal recovery has a significant impact on the reliability of FC between subcortical regions and the cortex, providing evidence that noise is reduced in these regions. Future work will investigate and replicate these findings in 3T MRI where signal quality would be even more greatly reduced in the limbic and subcortical regions.
This methodology has considerable implications for functional neuroimaging research focused on subcortical regions, which is gaining considerable interest in fields such as neurodegeneration.
This methodology has considerable implications for functional neuroimaging research focused on subcortical regions, which is gaining considerable interest in fields such as neurodegeneration.
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 2
Novel Imaging Acquisition Methods:
BOLD fMRI 1
Keywords:
FUNCTIONAL MRI
MRI
MRI PHYSICS
NORMAL HUMAN
1|2Indicates the priority used for review
·Figure 1
·Figure 2
Abstract Information
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2. Esteban O, Markiewicz CJ, Blair RW, Moodie CA, Isik AI, Erramuzpe A, Kent JD, Goncalves M, DuPre E, Snyder M, Oya H, Ghosh SS, Wright J, Durnez J, Poldrack RA, Gorgolewski KJ. (2019). fMRIPrep: a robust preprocessing pipeline for functional MRI. Nat Methods. 16(1):111-116. doi: 10.1038/s41592-018-0235-4. Epub 2018 Dec 10.
3. Posse S, Wiese S, Gembris D, Mathiak K, Kessler C, Grosse-Ruyken ML, Elghahwagi B, Richards T, Dager SR, Kiselev VG. (1999). Enhancement of BOLD-contrast sensitivity by single-shot multi-echo functional MR imaging. Magn Reson Med. 42(1):87-97. doi: 10.1002/(sici)1522-2594(199907)42:1<87::aid-mrm13>3.0.co;2-o.
4. DuPre EM, Salo T, Ahmed Z, Bandettini PA, Bottenhorn KL, Caballero-Gaudes C, Dowdle, LT, Gonzalez-Castillo J, Heunis S, Kundu P, Laird AR, Markello R, Markiewicz CJ, Moia S, Staden I, Teves JB, Uruñuela E, Vaziri-Pashkam M, Whitaker K, & Handwerker D A. (2021). TE-dependent analysis of multi-echo fMRI with tedana. Journal of Open Source Software, 6(66), 3669. doi:10.21105/joss.03669.
5. Ciric R, Wolf DH, Power JD, Roalf DR, Baum GL, Ruparel K, Shinohara RT, Elliott MA, Eickhoff SB, Davatzikos C, Gur RC, Gur RE, Bassett DS, Satterthwaite TD. (2017). Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity. Neuroimage. 1;154:174-187. doi: 10.1016/j.neuroimage.2017.03.020. Shrout, P. E., & Fleiss, J. L. (1979). Intraclass correlations: uses in assessing rater reliability. Psychological bulletin, 86(2), 420.
6. Shrout P, Fleiss J. (1979). Intraclass correlations: uses in assessing rater reliability. Psychological bulletin, 86(2), 420.
7. Nilearn contributors, Chamma, A., Frau-Pascual, A., Rothberg, A., Abadie, A., Abraham, A., Gramfort, A., Savio, A., Cionca, A., Sayal, A., Thual, A., Kodibagkar, A., Kanaan, A., Pinho, A. L., Joshi, A., Idrobo, A. H., Kieslinger, A.-S., Kumari, A., Rokem, A., … Nájera, Ó. (2024). nilearn (0.11.0). Zenodo. https://doi.org/10.5281/zenodo.14259676