Temporal Synchronization Analysis: A Model-Free Method for Detecting Robust and Nonlinear Brain Activation in fMRI Data
The sluggishness of the fMRI blood oxygenation level dependent (BOLD) signal has motivated the use of block or trial-based experimental designs that rely on the assumption of linearity, typically modeled using the General Linear Model (GLM). But many non-sensory brain regions and subcortical areas d...
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| Abstract | The sluggishness of the fMRI blood oxygenation level dependent (BOLD) signal has motivated the use of block or trial-based experimental designs that rely on the assumption of linearity, typically modeled using the General Linear Model (GLM). But many non-sensory brain regions and subcortical areas do not correspond to such linearities. We introduce a model-free estimation method called Temporal Synchronization Analysis (TSA) which detects significant brain activations across trials and subjects at an individual time point. We validate it across multiple cognitive tasks (combined n=1600). In constrained task stimuli like visual checkerboard paradigms, we discovered novel nonlinearities not reported previously. In model-free task paradigms like listening to naturalistic auditory stimuli, TSA can detect unique stimuli linked quasi-temporal activations across default mode and language networks. Our user-friendly Python toolkit enables cognitive neuroscience researchers to identify stable and robust brain activation across various cognitive paradigms that are challenging to model with current methods. |
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| AbstractList | The sluggishness of the fMRI blood oxygenation level dependent (BOLD) signal has motivated the use of block or trial-based experimental designs that rely on the assumption of linearity, typically modeled using the General Linear Model (GLM). But many non-sensory brain regions and subcortical areas do not correspond to such linearities. We introduce a model-free estimation method called Temporal Synchronization Analysis (TSA) which detects significant brain activations across trials and subjects at an individual time point. We validate it across multiple cognitive tasks (combined n=1600). In constrained task stimuli like visual checkerboard paradigms, we discovered novel nonlinearities not reported previously. In model-free task paradigms like listening to naturalistic auditory stimuli, TSA can detect unique stimuli linked quasi-temporal activations across default mode and language networks. Our user-friendly Python toolkit enables cognitive neuroscience researchers to identify stable and robust brain activation across various cognitive paradigms that are challenging to model with current methods. |
| Author | Deb, Aniruddha Fialoke, Suruchi Rode, Kushagra Tripathi, Vaibhav Garg, Rahul |
| Author_xml | – sequence: 1 givenname: Suruchi surname: Fialoke fullname: Fialoke, Suruchi organization: National Resource Center for Value Education in Engineering, Indian Institute of Technology – sequence: 2 givenname: Aniruddha surname: Deb fullname: Deb, Aniruddha organization: Department of Computer Science and Engineering, Indian Institute of Technology – sequence: 3 givenname: Kushagra surname: Rode fullname: Rode, Kushagra organization: Department of Computer Science and Engineering, Indian Institute of Technology – sequence: 4 givenname: Vaibhav orcidid: 0000-0001-7520-4188 surname: Tripathi fullname: Tripathi, Vaibhav email: vaibhav.tripathi@iitgn.ac.in organization: Center for Brain Science & Department of Psychology, Harvard University – sequence: 5 givenname: Rahul surname: Garg fullname: Garg, Rahul email: vaibhav.tripathi@iitgn.ac.in organization: School of Information Technology, Indian Institute of Technology |
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| Notes | Competing Interest Statement: The authors have declared no competing interest. |
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