deletions | additions       diff --git a/sectionAim_1__subsec.tex b/sectionAim_1__subsec.tex  index 3978c4d..a15379d 100644  --- a/sectionAim_1__subsec.tex  +++ b/sectionAim_1__subsec.tex 
...
\subsubsection*{Dynamic Functional Connectivity}  The analysis This aim will expand on the result  of Aim 1 assumes that temporal dynamics stay my preliminary data, which showed increased connectivity from  the same, meaning that precuneus to  the nodes sensorimotor strip was related to poor nine hole peg test performance. Studies  of traditionally defined networks such the precuneus and posterior cinculate cortex, referred to  as the default mode network are stationary. Recently, in \cite{Chang_2010}, researchers found that temporal dynamics are not stationary. In particular, they found posterior medial cortex, have shown  that the phase this region is anatomically  and coherence of the functional subdivided in humans and primates \cite{Margulies_2009}. The  posterior cingulate cortex (PCC, a hub of region is more connected to limbic structures, while  the default mode network) had variable connectivity with other nodes anterior, middle, and posterior sections  ofnot only the DMN, but other networks such as  the salience precuneus correspond to sensorimotor, cognitive/associative,  and attention networks. Given this finding, visual regions, respectively. While on average  itis possible that a disease pathology like MS  may change seem like the entire PMC structure is functionally connected to the medial prefrontal gyrus (of the default mode network), capitalizing on  the normal network dynamics functional boundaries of the PMC may result  in short periods a larger predictive power  oftime, and that dynamic  functional connectivity may MRI, and could  be more sensitive to these small changes. For Alzheimers disease, researchers used indicatative of  functional dynamics to build a classifier to differentiate between patients with mild cognitive impairment or normal controls \cite{Wee_2015}. 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}.  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{Wee_2015}, \cite{Yang_2014}, researchers ran hierarchical clustering on the set of connectomes from  the method was modified sliding window analysis, in order  to enforce temporal smoothness (meaning that network connectivity strengths 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 topology cannot change drastically between adjacent sliding windows), which is accomplished using a fused multiple group LASSO. From colleagues found that  the graph, network measures, such as anterior, middle, posterior, and ventral (posterior cingulate) regions spent more time in  the clustering coefficient sensorimotor, associative/cognitive, visual  and small-worldness properties, are estimated\cite{Wee_2015}. The new resulting timeseries (clustering coefficient over time, or small-worldness over time) will then be used as features for a random forest classifier, limbic states, respectfully,  which will attempt aligned well with previous studies on the functional subdivisions of the PMC. However, they found that these regions did not consistently connect  to distinguish between their respective state throughout  the High and Low NHPT groups, and healthy controls. 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 prefer random forests to SVMs because there are less parameters 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 go into healthy controls, and that the time spend in the sensorimotor state of the high disability group will exceed that of  the model. low disability group. A regularized least squares analysis will be run using age, gender, and lesion load as regressors, along with a 20-feature vector with the amount of time that each of the 4 PMC nodes spend in each of the 5 states (one of which is the sensorimotor state).  The goal is to show that the coefficients on the amount of time spent in the sensorimotor state is the highest, meaning that it contributes most to the prediction of patient group. This  dynamic function connectivity analysisalready  needs the following parameters to tune: 1) the window size, 2) the step size and 3) the regularization parameters parameter  for the LASSO optimization. regularized least squares.  Parameter tuning is done 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.