Synopsis

Modulations in brain connectivity by task reveal more insights into complex interaction and neuronal communication occurs between various cortexes. However, assessment of these modulation is limited by dynamic hemodynamic (HRF) spread (3 to 6 sec) occurs at every brain regions by various task stimulus. This dynamic HRF limits methods of resting-state studies to be adopted directly in task-fMRI. Thus, in this study, a novel hemodynamic reorganization method is proposed to rearrange the dynamic HRF of every stimulus such that functional connectivity modulation caused by every stimulus and their mutual correlations in visual search based target detection task can be assessed.

Aim

Method

At first, resting-state and task-fMRI (target detection by visual search) of twenty volunteers (13 Males, 7 Females: Mean-age:24 years) were acquired using 3T MRI scanner and pre-processed. Then, proposed hemodynamic reorganization for network analysis was performed on aquired task-fMRI data $$$\small Y(T)$$$ and hemodynamically reorganised task-fMRI dataset R(T) was estimated as follows:

$$$\small R\left(T\right)=\left\{R_{r,c,s}(T)\right\}=\left\{Mean \left\{Y_{r,c,s}(T)\right\}\mid t_{earlier} <t<t_{later}\right\}$$$

where r, c and s are row, column and slice-number of voxel in $$$ \small R(T) $$$. $$$t_{earlier}$$$ and $$$t_{later}$$$ are earlier and later time point around Amplitude(A) of HRF and estimated through,

$$$\small t_{earlier}=\left\{Firstpoint\left\{t\right\}\mid t>TTP_{r,c,s}^\mu\ and\ h_{r,c,s}^\mu<\frac{A_{r,c,s}^\mu}{2}\right\}$$$

$$$\small t_{later}=\left\{Lastpoint\left\{t\right\}\mid t<TTP_{r,c,s}^\mu \ and\ h_{r,c,s}^\mu<\frac{A_{r,c,s}^\mu}{2}\right\}$$$

where $$$\small h_{r,c,s}^\mu(t)$$$ is HRF estimated for voxel (r,c,s) to given specific stimuli $$$\small \mu$$$ .$$$\small TTP_{r,c,s}^\mu$$$ ,$$$\small A_{r,c,s}^\mu$$$ are Time-to-Peak and Amplitude measured from $$$\small h_{r,c,s}^\mu(t)$$$.The estimation of $$$\small TTP_{r,c,s}^\mu$$$ and $$$\small A_{r,c,s}^\mu$$$ was performed as

$$$\small TTP_{r,c,s}^\mu=min\left\{t\mid\frac{dh_{r,c,s}^\mu(t)}{dt}=0,\ and\ \frac{d^{2} h_{r,c,s}^\mu(t)}{dt^{2}}<0\right\}$$$

$$$\small A_{r,c,s}^\mu=sign(\beta_{r,c,s}^\mu(1))\sqrt{(\beta_{r,c,s}^\mu(1))^{2}+\beta_{r,c,s}^\mu(2))^{2}+\beta_{r,c,s}^\mu(3))^{2}}$$$

$$$\small \beta_{r,c,s}^\mu(1))$$$,$$$\small \beta_{r,c,s}^\mu(2))$$$ and $$$\small \beta_{r,c,s}^\mu(3))$$$ are regression coefficients parameters of canonical HRF and its first, second order derivatives. Reorganized task-fMRI data $$$R_{r,c,s}(T)$$$ was subsequently segregated for every single stimulus independently.

These independently grouped hemodynamic reorganized task dataset along with rsfMRI data were further processed through MELODIC probabilistic ICA¹ in FMRIB Software Library to estimate correlation between DMN, Visual, Frontal-Parietal and Hippocampus/Thalamus networks in task as well as in resting-state.

Results

Discussion

The proposed Hemodynamic-reorganization approach performs better than established approaches^2-4 for task-fMRI studies due to following observations.

a. It reveals deactivation of DMN caused by every task stimulus and provides better insight in to network pathway.

b. It reveals engagement of visual ventral pathway during the target and non-target task stimulus whereas visual medial pathway was engaged during the rest. It is an established hypothesis⁵ that ventral stream pathway is specialized for object identification and activated during target detection task through visual search.

c. Further, effect of task stimuli on hemispherical difference on frontal-parietal pathway^6,7 is clearly revealed as two independent frontal-parietal networks where resting-state frontal-parietal observed to be independent of hemispherical difference.

d. In addition, role of hippocampus in visual target detection task⁸ is also revealed in hippocampus/thalamus network of task. In resting-state, only thalamus network was observed without any role of hippocampus.

Conclusion

Acknowledgements

References

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