Effect of Modulating fMRI Time-Series on Fluid Ability and Fluid Intelligence for Healthy Humans

This paper investigates the effect of filtering (or modulating) the functional magnetic resonance imaging (fMRI) time-series on intelligence metrics predicted using dynamic functional connectivity (dFC). Thirteen brain regions that have highest correlation with intelligence are selected and their corresponding time-series are filtered. Using filtered time-series, the modified intelligence metrics are predicted. This experiment investigates whether modulating the time-series of one or two regions of the brain will increase or decrease the fluid ability and fluid intelligence among healthy humans. Two sets of experiments are performed. In the first case, each of the thirteen regions is separately filtered using four different digital filters with passbands: i) 0 - 0.25π, ii) 0.25π - 0.5π, iii) 0.5π - 0.75π, and iv) 0.75π – π, respectively. In the second case, two of the thirteen regions are filtered simultaneously using a low-pass filter of passband 0 - 0.25π. In both cases, the predicted intelligence declined for 45-65% of the subjects after filtering in comparison with the ground truths. In the first case, the low-pass filtering process had the highest predicted intelligence among the four filters. In the second case, it was noticed that the filtering of two regions simultaneously resulted in a higher prediction of intelligence for over 80% of the subjects compared to low-pass filtering of a single region.

[1]  P. Stanzione,et al.  Bilateral deep brain stimulation of the pedunculopontine and subthalamic nuclei in severe Parkinson's disease. , 2007, Brain : a journal of neurology.

[2]  R. Gur,et al.  Development of Abbreviated Nine-Item Forms of the Raven’s Standard Progressive Matrices Test , 2012, Assessment.

[3]  Nikos D. Sidiropoulos,et al.  Tensor Decomposition for Signal Processing and Machine Learning , 2016, IEEE Transactions on Signal Processing.

[4]  A. Lozano,et al.  Deep Brain Stimulation for Treatment-Resistant Depression , 2005, Neuron.

[5]  Bhaskar Sen,et al.  Predicting Biological Gender and Intelligence From fMRI via Dynamic Functional Connectivity , 2020, IEEE Transactions on Biomedical Engineering.

[6]  Dimitri Van De Ville,et al.  The dynamic functional connectome: State-of-the-art and perspectives , 2017, NeuroImage.

[7]  Michael C. Park,et al.  Subthalamic nucleus deep brain stimulation with a multiple independent constant current-controlled device in Parkinson's disease (INTREPID): a multicentre, double-blind, randomised, sham-controlled study , 2020, The Lancet Neurology.

[8]  Rasmus Bro,et al.  The N-way Toolbox for MATLAB , 2000 .

[9]  Steen Moeller,et al.  The Human Connectome Project: A data acquisition perspective , 2012, NeuroImage.

[10]  Anders M. Dale,et al.  An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest , 2006, NeuroImage.

[11]  Essa Yacoub,et al.  The WU-Minn Human Connectome Project: An overview , 2013, NeuroImage.