This aim will expand on the result of my preliminary data, which showed that increased connectivity from the precuneus to the sensorimotor strip was related to poor nine hole peg test performance. Studies of the precuneus and posterior cingulate cortex, referred to as the posterior medial cortex, have shown that this region is anatomically and functional subdivided in humans and primates \cite{Margulies_2009}. The posterior cingulate region is more connected to limbic structures, while the anterior, middle, and posterior sections of the precuneus correspond to sensorimotor, cognitive/associative, and visual regions, respectively. While on average it may seem like the entire PMC structure is functionally connected to the medial prefrontal gyrus (of the default mode network), capitalizing on the functional boundaries of the PMC may result in a larger predictive power of functional MRI, and could be indicative of functional adaptation, in MS.

The analysis of Aim 1 assumes that temporal dynamics stay the same, meaning that the nodes of traditionally defined networks such as the default mode network are stationary. Recently, in \cite{Chang_2010}, researchers found that temporal dynamics are not stationary. In particular, they found that the phase and coherence of the posterior cingulate cortex (PCC, a hub of the default mode network) had variable connectivity with other nodes of not only the DMN, but other networks such as the salience and attention networks. Given this finding, it is possible that a disease pathology like MS may change the normal network dynamics in short periods of time, and that dynamic functional connectivity may be more sensitive to these small changes. For Alzheimers disease, researchers used functional dynamics to build a classifier to differentiate between patients with mild cognitive impairment or normal controls \cite{Wee_2015}. This classifier based on dynamic functional connectivity outperformed a classifier built on stationary functional connectivity metrics.

The analysis consists of a sliding window approach outlined in \cite{Chang_2010}, where a graph is estimated from a section of the timeseries, and that section slides across the whole timeseries. In \cite{Yang_2014}, researchers ran hierarchical clustering on the set of connectomes from the sliding window analysis, in order to classify the connectomes into 5 states across all healthy control subjects. For each time-window, the four PMC regions could be classified into 1 of the 5 states. Yang and colleagues found that the anterior, middle, posterior, and ventral (posterior cingulate) regions spent more time in the sensorimotor, associative/cognitive, visual and limbic states, respectfully, which aligned well with previous studies on the functional subdivisions of the PMC. However, they found that these regions did not consistently connect to their respective state throughout the entire timeseries, but would switch states, spending a nearly even amount of time in each of the remaining 4 states. They also found this result to be scan-rescan reproducible.

I hypothesize that the amount of time spent in the sensorimotor state by each of the four PMC regions will be elevated in MS patients in comparison to healthy controls, and that the time spent in the sensorimotor state of the high disability group will exceed that of the low disability group. An ordinary least squares analysis to predict group membership will be run using age, gender, and lesion load as regressors, along with the amount of time that each of the 4 PMC nodes spent the sensorimotor state, for a total of 75 subjects and 7 features. The goal is to show that the functional connectivity state features contribute the most to the prediction of a simple (CMCT, FT) or complex (NHPT) motor task. This dynamic function connectivity analysis needs the following parameters to tune: 1) the window size and 2) the step size. Parameter tuning will be performed in a nested leave-one-out cross-validation, where the inner cross-validation fold is used to select the parameters and the results from outer fold are reported as the effect size.