Impact of Amplitude and Phase of fMRI time series for Functional Connectivity Analysis

Priyanka Mittal, Anil K. Sao, Bharat Biswal

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Purpose: Several studies in the field of fMRI have reported the synchrony between the brain regions using instantaneous phase (IP) representation (derived from analytic representation of BOLD time series). We hypothesized that instantaneous amplitude (IA) representation from different brain regions might give additional information to the functional brain networks. To validate this, we explored this representation of resting state BOLD fMRI signal for deriving resting state networks (RSNs) and compared it with the IP representation based RSNs. Method: Resting state fMRI data of 100 healthy adults (age=20–35 years, 54 females) from the population of 500 Subjects of HCP dataset were studied. Data was acquired using a 3T scanner in four runs (15-min each) with the phase encoding directions: Left to Right (LR), Right to Left (RL). These four runs were acquired in two sessions, and subjects were asked to keep their eyes open with a fixation on a white cross. The IA and IP representations were derived from a narrow-band filtered BOLD time series using Hilbert transforms and a seed-based approach is used to compute the RSNs in the brain. Results: The experimental results demonstrate that within the frequency range 0.01–0.1 Hz, IA representation based RSNs have the highest similarity score between the two sessions for the motor network. Whereas for fronto-parietal network, IP based activation maps have the highest similarity score for all the frequency bands. For higher frequency band (0.198–0.25 Hz) consistency of the obtained RSNs across two sessions reduced for both IA and IP representations. Fusion of IA and IP representations based RSNs in comparison to those of IP based representation, leads to 3–10% improvement in the similarity scores between the default mode network obtained for the two sessions. In addition, the same comparison demonstrates 15–20% improvement for the motor network in the frequency bands: 0.01–0.04 Hz, 0.04–0.07 Hz, slow5 (0.01–0.027 Hz) and slow-4 (0.027–0.073 Hz). It is also observed that the similarity score between two sessions using instantaneous frequency (IF) (derivative of unwrapped IP) representation in exploring functional connectivity (FC) networks is comparable with those obtained using IP representation. Conclusion: Our findings suggest that IA-representation based measures can estimate RSNs with the reproducibility between the sessions comparable to that of the IP representation-based methods. This study demonstrates that IA and IP representations contain the complementary information of BOLD signal, and their fusion improves the results of FC.

Original languageEnglish (US)
Pages (from-to)26-37
Number of pages12
JournalMagnetic Resonance Imaging
StatePublished - Oct 2023

All Science Journal Classification (ASJC) codes

  • Biophysics
  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging


  • Functional connectivity
  • Instantaneous amplitude
  • Instantaneous phase
  • Synchronization


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