Mapping distinct timescales of functional interactions among brain networks
Brain processes occur at various timescales, ranging from milliseconds (neurons) to minutes and hours (behavior). Characterizing functional coupling among brain regions at these diverse timescales is key to understanding how the brain produces behavior. Here, we apply instantaneous and lag-based measures of conditional linear dependence, based on Granger-Geweke causality (GC), to infer network connections at distinct timescales from functional magnetic resonance imaging (fMRI) data. Due to the slow sampling rate of fMRI, it is widely held that GC produces spurious and unreliable estimates of functional connectivity when applied to fMRI data. We challenge this claim with simulations and a novel machine learning approach. First, we show, with simulated fMRI data, that instantaneous and lag-based GC identify distinct timescales and complementary patterns of functional connectivity. Next, we analyze fMRI scans from 500 subjects and show that a linear classifier trained on either instantaneous or lag-based GC connectivity reliably distinguishes task versus rest brain states, with ∼80-85% cross-validation accuracy. Importantly, instantaneous and lag-based GC exploit markedly different spatial and temporal patterns of connectivity to achieve robust classification. Our approach enables identifying functionally connected networks that operate at distinct timescales in the brain.
The authors apply instantaneous and lag-based Granger-Geweke causality (iGC and dGC) to infer brain network connections at distinct timescales from functional MRI data. With simulated fMRI data and through analysis of real fMRI recordings, they presented the following novel insights. First, iGC and dGC provide robust measures of brain functional connectivities, resolving over long-term controversy in the field. Second, iGC and dGC can identify complementary functional connections, in particular, the latter can find excretory-inhibitory connections. Third, downsampling the time series to different extents provides an efficient method for recovering connections at different timescales.
Clarity:
The paper is well organised and clearly written with nice figures. The result of real data analysis may be difficult to understand without background knowledge in neuroscience.
Significance:
I think that the insights written in this paper have significant impact on researchers working on this topic. Recently analyses of resting-state fMRI data have led to many interesting results in neuroscience. However, most publications employed correlation matrices for measuring functional connectivities. So, if iGC and dGC would become alternative tools, we could extract/grasp multi timescale processing in the brain. In order to convince more researchers of practical usefulness of iGC and dGC, it would be necessary to compare with existing techniques further and to work on more real fMRI examples.
On the other hand, the scope of this paper is rather narrow and impacts on other research topics in the NIPS community are rather limited.
Correctness:
Since the authors applied existing connectivity measures, I don't expect any technical errors.
Originality:
I feel that a  certain amount of researchers are sceptical about applications of GC measures to fMRI data. I have not seen the messages of this paper which may resolve over long-term controversy in the field.
To sum up, the novel insights derived from analysis of simulated and real fMRI data with GC measures are interesting and could have potential to advance the research field. Since further real-world examples would be preferable to convince other researchers in the field, I would say that this paper is weak accept.
There are a few minor comments.
- p.3 Eq.(2): I know a MVAR model with variable time lag for each connection which is determined by DTI fibre length. The simulation model in Section 2 assumes variable timescale at each node. I wonder whether we can set timescale of each connections separately at a predetermined value in this formulation or we have to further extend the model in Section 2 to construct more realistic model incorporating knowledge from structural MRI data.
- Section 3: I guess that the language task could contain slow brain processing and might be a good example to show multi timescale network structures by fMRI downsampling. I wonder whether there exist other tasks in HCP which we can see similar results.