# Anisha Keshavan

## Rationale

When an MS patient is first diagnosed, the physician has no way of knowing if fine motor function will be impaired. As the disease progresses, the lesions seen in an MRI scan are no more informative of this prognosis. Advanced structural imaging methods, such as diffusion imaging, better tracks disease progression, but there is more that needs to be explained. Functional metrics may bridge the gap between structural damage, clinical disability, and disease progression. Identifying patients that are better able or unable to adapt to structural damage may lead to more personalized treatments. Identifying functional network metrics that are biomarkers with prognostic value could lead the way towards more effective treatments of MS.

## Background

Functional MRI tasks have shown regions of abnormal activity from MS patients compared to controls (Cader 2006), suggestive of adaptive or maladaptive mechanisms to damage. In a study of lower motor disease severity and its relationship to task activity, researchers found that increased ipsilateral activation on a hand motor task (Reddy 2002) was related to increased overall lower motor disease severity. This could signify that neurons are recruited from the part of the corticospinal tract that does not cross over. However, task-fMRI results may be influenced by performance in the scanner (Filippi 2013), and therefore, resting state fMRI-derived metrics are preferred due to better standardization, especially for patients with higher disability status.

The BOLD fluctuations of the brain at rest are organized in networks, which are synchronous, spatially distinct regions. In healthy controls, Yeo and colleagues found 7 stable networks, with each network responsible for different tasks (Yeo 2011). These networks are the visual, sensorimotor, dorsal attention, ventral attention, limbic, frontoparietal and the default mode. Of these networks, the default mode network (DMN) has been the most studied (for a review, see (Raichle 2007)). Alterations in the default mode network have been detected in many neurological and psychiatric diseases, such as Alzheimers (Greicius 2004), depression (Sheline 2009), schizophrenia(Garrity 2007), Parkinson’s disease (van Eimeren 2009), and multiple sclerosis (Bonavita 2011).

My preliminary research has shown that functional connectivity, combined with T2 lesion load, explains 40% of the variance in the nine hole peg test (a measure of hand dexterity) score in our cohort of MS patients. This metric was based on increased functional connectivity between nodes in the sensorimotor strip and the precuneus and posterior cingulate, which are important hubs of the default mode network. It is possible that the posterior medial cortex (precuneus and posterior cingulate gyrus), plays an important role in functional adaption for upper extremity motor disability. However, differential connectivity strengths were also detected between the sensorimotor cortex and the frontoparietal, visual, and dorsal attention networks. The frontoparietal and dorsal attention networks are involved in association, executive control, and visuospatial integration. Therefore, poor performance on the nine hole peg test may not be fully attributable to motor pathway damage alone, but rather could be a result of damage to the visual integration pathways.

In order to understand the relationship between functional MRI networks and upper-extremity motor disability, metrics that are more specific to motor pathway damage need to be studied. I propose to collect two metrics that are less dependent on visuospatial ability. These include the finger tapping test and central motor conduction time. The finger tapping test is a simple and reliable measure of upper-extremity motor speed. In MS patients, it was shown to be below average in 55% of patients on the dominant and 65% below average for the non-dominant hand (Zakzanis 2000). Electrophysiological measures, such as the motor evoked potential (MEP) are used for the diagnosis of MS and as a measure of dysfunction of the corticospinal tract (Kallmann 2006). Increased central motor conduction times (CMCT) are seen in MS patients, which is a result of demyelination from the disease, conduction block, or axonal destruction (Fuhr 2001). The motor evoked potential (MEP) and visual evoked potential (VEP) have been shown to be strongly predictive of changes in MS lower motor disability a full 14 years after the initial measurements (Schlaeger 2012), with a spearman’s rank correlation of $$\rho=0.69$$.

In this proposal, I aim to 1) show that the M1-DMN connectivity increases with increasing central motor conduction time and finger tapping speed and 2) to improve on the effect size of functional connectivity measures in the prediction of upper-extremity motor disability by studying the connectivity between the PMC and the motor network using a dynamic functional connectivity analysis.

## Hypothesis

I hypothesize that the prediction of upper extremity motor disability, will be improved with the inclusion of fMRI network features.