P150 Network vs ROI Perspectives: Brain Connectivity Analysis using Complex Principal Component Analysis
Puneet Kumar*1†, Alakhsimar Singh2†, Xiaobai Li1,3, Shella Keilholz4, Eric H. Schumacher5
1University of Oulu, Finland
2National Institute of Technology Jalandhar, India
3Zhejiang University, China
4Emory University, USA
5Georgia Institute of Technology, USA
†Equal Contribution
*Email: puneet.kumar@oulu.fi
Introduction: We implement Complex Principal Component Analysis (CPCA) [1] for brain connectivity analysis. It largely reproduces traditional Quasi-Periodic Patterns (QPP)-like activity [2] and handles tasks of various lengths, while QPP struggles with shorter tasks. We present network- and ROI-level observations for the Human Connectome Project (HCP) data having four 15-min rest scans (TR=0.72s) and seven tasks (1 hour total) [3]. Our focus is on Task-Positive Network (TPN) – defined as the Dorsal Attention Network (DAN) plus Fronto-Parietal Network (FPN), and Default Mode Network (DMN). Our contributions are CPCA implementation and dual (network and ROI) level analysis. The implementation code is at github.com/MIntelligence-Group/DBCATS.
Methods: The data was preprocessed using the Configurable Pipeline for the Analysis of Connectomes (C-PAC) [4], including motion and slice-timing correction, normalization to MNI space, and band-pass filtering (0.01–0.1 Hz). We focus on the working memory (0-back/2-back) task with 405 frames/run. Each run has eight 42.5 s task blocks (10 trials of 2.5 s), four 15 s fixation blocks, and 2 s stimuli followed by a 500 ms ITI. The DMN (36 ROIs), DAN (33 ROIs), and FPN (30 ROIs) were defined using the 7-network parcellation [5]. We adapted CPCA for fMRI by applying the Hilbert transform to introduce a 90° phase shift, capturing amplitude and phase. Seven principal components (PCs) were extracted to reconstruct the dominant activity patterns.
Results: Fig. 1(a) and 1(e) display Blood Oxygenation Level Dependent (BOLD) activation at the global network level for rest and task states. Correlation values between DMN and DAN are -0.99, and between DMN and FPN -0.91, as per Fig. 1(i) and 1(j). Fig. 1(b–d) depicts local ROI-level BOLD activation (from both left and right hemispheres of the brain) during rest, and Fig. 1(f–h) during task. In the rest state, FPN shows 440 positive and 618 negative correlations with DMN, and DAN shows 531 and 629. For the task state, FPN has 439 positive and 620 negative correlations with DMN, and DAN has 532 and 629. Comparing Fig. 1(k–n) indicates slightly shifted connectivity patterns from rest to task, reflecting changes in DMN, DAN, and FPN signals.
Discussion: At the network level, DMN shows anticorrelation with both DAN (-0.99) and FPN (-0.91), as depicted in Fig. 1(i,j). At the ROI level, 44% (1972) of DMN-TPN pairs are positively correlated, while 56% (2496) are negative, indicating local differences. Correlations become more negative from rest to task, though changes are modest. Fig. 1(k–n) shows these changes, highlighting how brain connections adapt at the ROI level and exhibit task-dependent shifts. We are the first to implement CPCA as a potential brain connectivity analysis method comparing rest and task. We aim to extend our implementation to other datasets, seeking visibility for our work and findings and feedback to refine our approach and drive further advancements.
Figure 1. Network-level and ROI-level BOLD time series for DMN, DAN, and FPN during rest (a–d) and task (e–h). Network-level correlation connectivity matrices (CCM) (i, j). ROI-level CCMs for DMN–DAN regions (k, l) and DMN-FPN regions (m, n) for rest and task. (a, e) show average PC1 activity at network level, while (b–d, f–h) show PC1 activity at ROI level, with different colors denoting different ROIs.
Acknowledgements
The authors gratefully acknowledge the collaboration with the CoNTRoL Lab and GSU/GT Center for Advanced Brain Imaging at Georgia Institute of Technology, USA, and the Keilholz Mind Lab at Emory University, USA. We thank the CMVS International Research Visit Program 2024 for funding and the University of Oulu, Eudaimonia Institute, and CSC Finland for support and computational resources.
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