# 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.

## Aim 1: Develop an fMRI metric that relates to CMCT and FT using a seed-based analysis

### Experimental Procedure

#### Patient Selection

50 patients and 25 controls will be selected for this part of the study. To improve on power, we are interested in studying patients at the extremes of the distribution of nine hole peg test scores (i.e. High and Low nine hole peg groups), and matching for age, gender, and lesion load. These patients are part of the UCSF EPIC study and have been scanned previously, so we know ahead of time the lesion load of these patients. We will also collect data from 25 age and gender matched healthy controls.

#### Motor Evoked Potential with TMS

Motor evoked potentials (MEP) will be recorded from MS subjects and healthy controls with a single coil MagStim transcranial magnetic stimulation (TMS) device at the maximal strength. First, we will perform a nerve conduction study of the right and left ulnar nerve to make sure the nerves are functioning properly. Next, the MagStim device will be placed over the right and left motor cortices, and the time between the stimulus and beginning of the evoked response in the pre-innervated ulnar muscles will be recorded. We will stimulate 8 times on each side of the motor cortex, where four stimuli are administered with one side of the coil, and then the coil is flipped (i.e. the direction of the magnetic field is switched). Next, the cervical cord (C7) will be stimulated, at 60% of the maximum strength, four times in total with flipping of the coil. The central motor conduction time is defined as the difference between the latency from the motor cortex and the latency from the cervical spine. We will calculate the test-retest reliability on the patients and controls.

#### Acquisition of MS patients at UCSF

Resting state fMRI(3mm isotropic voxel size, TR = 2.1s, TE = 27ms, 27 slices interleaved, 240 volumes) and MPRAGE(Sagittal, 1mm isotropic voxel size, TR=2.3s, TE=2.98ms, TI=900ms) scans were acquired from 250 patients (aged 26-74, mean age = 50) diagnosed with MS from the UCSF EPIC study. The high and low disability groups will be selected from this set of patients. The upper motor disability of MS patients was evaluated by the nine-hole-peg-test (NHPT) (Fischer 1999). Briefly, the patients were asked to place nine pegs in a grid of 9 holes, one at a time using 1 hand, and then remove the pegs one at a time, all while being timed. Both hands were evaluated twice and the times were averaged and z-scored against healthy controls.

#### Data Preprocessing

FreeSurfer version 5.3(Fischl 2012) will be run on MPRAGE structural scans of the healthy controls and MS patients. Careful editing of the the pial and white matter surfaces will ensure proper affine registration of the resting state scan to the structural surface. Resting state preprocessing steps include the following: simultaneous slice timing and motion correction(Roche 2011), artifact detection, nuisance filtering of motion parameters, artifacts, anatomical and temporal CompCor(Behzadi 2007) from the fMRI timeseries, bandpass filtering(0.01-0.1 Hz), isotropic volume smoothing (6mm), cortical surface smoothing(10mm), mapping the timeseries to the fsaverage5 surface via FreeSurfer, and finally normalization to MNI space via ANTS(Avants 2010).

### Central Motor Conduction Time and Resting State fMRI

A seed-voxel analysis will be run with connectivity maps based on the left and right precentral gyrus seeds in the hand area. Connectivity maps will be created by calculating the correlation coefficient between the seed region and each voxel, and then fisher-z-transformed. A voxel-wise OLS regression will be run on the connectivity maps to predict CMCT, EDSS, and NHPT. Next cross-validation and permutation testing will be implemented to avoid overfitting the data.

## Expected Results and Significance

I expect these voxel-wise analysis to yield regions of the brain that are differentially connected to the motor cortex based on neurophysiology and clinical evaluations. I predict that the patients with less motor deficits have adapted to damage caused by MS, and that this is reflected in the resting state connectivity map of the motor cortex. The regions of the brain highlighted by the connectivity maps could be regions that contribute to adaptive or maladaptive mechanisms to damage, and are regions that could be monitored during a clinical trial. If the relationship between the functional connectivity between M1 and the posteriomedial cortex (PMC) is still present with simpler motor tasks, it is possible that this region plays a role in motor adaptation from MS-related damage.

## Aim 2

### Experimental Procedure

#### Dynamic Functional Connectivity

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 (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 (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 (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 (Chang 2010), where a graph is estimated from a section of the timeseries, and that section slides across the whole timeseries. In (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.

## Expected Results and Significance

It has been shown that a dynamic functional connectivity analysis could be more sensitive to pathology, as shown in (Wee 2015). In that study, researchers reported a nearly 80% classification accuracy in detecting mild cognitive impairment versus normal controls, which was a larger effect compared to when they ran a stationary functional connectivity analysis. I expect the results of this study to show a similar improvement in classification accuracy of mild disability from high disability subjects. The results would signify that the posteriomedial cortex (PMC) may play a role in the functional adaptation for upper-extremity motor disability in MS patients, and its role in functional adaptation should be further studied.

## Future Directions

The cohort that will be studied for this proposal returns every year to get an MRI scan and to be evaluated by clinicians. In the future, a longitudinal study that examines the functional metrics extracted from this proposal would help us understand if functional imaging changes across time correlate with disease progression. Compared to a cross-sectional study, longitudinal studies require larger sample sizes because longitudinal effect sizes tend to be smaller. To overcome this, we could sample the patients whose disease progressed the most and compare them to an age and gender matched set of patients whose symptoms did not change. If we observe similar patterns in the resting-state fMRI motor network of MS patients longitudinally and cross-sectionally, then these rsFMRI metrics could be used as biomarkers for clinical trials on MS drugs. In particular, a drug that targets the posteriomedial cortex and its functional connectivity to the motor network could potentially improve the ability of a subject to adapt to damage from multiple sclerosis.

### References

1. Frederik Barkhof. The clinico-radiological paradox in multiple sclerosis revisited. Current opinion in neurology 15, 239–245 LWW, 2002.

2. M Filippi, DW Paty, L Kappos, F Barkhof, DAS Compston, AJ Thompson, GJ Zhao, CM Wiles, WI McDonald, DH Miller. Correlations between changes in disability and T2-weighted brain MRI activity in multiple sclerosis A follow-up study. Neurology 45, 255–260 AAN Enterprises, 1995.

3. J Furby, T Hayton, D Altmann, R Brenner, J Chataway, KJ Smith, DH Miller, R Kapoor. A longitudinal study of MRI-detected atrophy in secondary progressive multiple sclerosis. Journal of neurology 257, 1508–1516 Springer, 2010.

4. Ludwig Kappos, David Moeri, Ernst Wilhelm Radue, Andreas Schoetzau, Kati Schweikert, Frederik Barkhof, David Miller, Charles RG Guttmann, Howard L Weiner, Claudio Gasperini, others. Predictive value of gadolinium-enhanced magnetic resonance imaging for relapse rate and changes in disability or impairment in multiple sclerosis: a meta-analysis. The Lancet 353, 964–969 Elsevier, 1999.

5. Arnaud Charil, Alex P Zijdenbos, Jonathan Taylor, Cyrus Boelman, Keith J Worsley, Alan C Evans, Alain Dagher. Statistical mapping analysis of lesion location and neurological disability in multiple sclerosis: application to 452 patient data sets. NeuroImage 19, 532–544 Elsevier BV, 2003. Link

6. Arnaud Charil, Alain Dagher, Jason P. Lerch, Alex P. Zijdenbos, Keith J. Worsley, Alan C. Evans. Focal cortical atrophy in multiple sclerosis: Relation to lesion load and disability. NeuroImage 34, 509–517 Elsevier BV, 2007. Link

7. Maria A Rocca, Paola Valsasina, Vittorio Martinelli, Paolo Misci, Andrea Falini, Giancarlo Comi, Massimo Filippi. Large-scale neuronal network dysfunction in relapsing-remitting multiple sclerosis. Neurology 79, 1449–1457 AAN Enterprises, 2012.

8. KAVIRAJA Udupa, ROBERT Chen. Central motor conduction time. Handb Clin Neurol 16, 375–86 (2013).

9. K. K. Zakzanis. Distinct Neurocognitive Profiles in Multiple Sclerosis Subtypes. Archives of Clinical Neuropsychology 15, 115–136 Oxford University Press (OUP), 2000. Link

10. Sarah Cader, Alberto Cifelli, Yasir Abu-Omar, Jacqueline Palace, Paul M Matthews. Reduced brain functional reserve and altered functional connectivity in patients with multiple sclerosis. Brain 129, 527–537 Oxford Univ Press, 2006.

11. H. Reddy. Functional brain reorganization for hand movement in patients with multiple sclerosis: defining distinct effects of injury and disability. Brain 125, 2646–2657 Oxford University Press (OUP), 2002. Link

12. Massimo Filippi, Federica Agosta, Edoardo G Spinelli, Maria Assunta Rocca. Imaging resting state brain function in multiple sclerosis. Journal of neurology 260, 1709–1713 Springer, 2013.

13. BT Thomas Yeo, Fenna M Krienen, Jorge Sepulcre, Mert R Sabuncu, Danial Lashkari, Marisa Hollinshead, Joshua L Roffman, Jordan W Smoller, Lilla Zöllei, Jonathan R Polimeni, others. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of neurophysiology 106, 1125–1165 Am Physiological Soc, 2011.

14. Marcus E Raichle, Abraham Z Snyder. A default mode of brain function: a brief history of an evolving idea. Neuroimage 37, 1083–1090 Elsevier, 2007.

15. Michael D Greicius, Gaurav Srivastava, Allan L Reiss, Vinod Menon. Default-mode network activity distinguishes Alzheimer’s disease from healthy aging: evidence from functional MRI. Proceedings of the National Academy of Sciences of the United States of America 101, 4637–4642 National Acad Sciences, 2004.

16. Yvette I Sheline, Deanna M Barch, Joseph L Price, Melissa M Rundle, S Neil Vaishnavi, Abraham Z Snyder, Mark A Mintun, Suzhi Wang, Rebecca S Coalson, Marcus E Raichle. The default mode network and self-referential processes in depression. Proceedings of the National Academy of Sciences 106, 1942–1947 National Acad Sciences, 2009.

17. Abigail G Garrity, Godfrey D Pearlson, Kristen McKiernan, Dan Lloyd, Kent A Kiehl, Vince D Calhoun. Aberrant “default mode” functional connectivity in schizophrenia. Am Psychiatric Assoc, 2007.

18. Thilo van Eimeren, Oury Monchi, Benedicte Ballanger, Antonio P Strafella. Dysfunction of the default mode network in Parkinson disease: a functional magnetic resonance imaging study. Archives of Neurology 66, 877–883 American Medical Association, 2009.

19. Simona Bonavita, Antonio Gallo, Rosaria Sacco, Marida Della Corte, Alvino Bisecco, Renato Docimo, Luigi Lavorgna, Daniele Corbo, Alfonso Di Costanzo, Fabio Tortora, others. Distributed changes in default-mode resting-state connectivity in multiple sclerosis. Multiple sclerosis journal 17, 411–422 SAGE Publications, 2011.

20. BA Kallmann, S Fackelmann, KV Toyka, P Rieckmann, K Reiners. Early abnormalities of evoked potentials and future disability in patients with multiple sclerosis. Multiple sclerosis 12, 58–65 Sage Publications, 2006.

21. Peter Fuhr, L Kappos. Evoked potentials for evaluation of multiple sclerosis. Clinical neurophysiology 112, 2185–2189 Elsevier, 2001.

22. R Schlaeger, M D’Souza, C Schindler, L Grize, S Dellas, EW Radue, L Kappos, P Fuhr. Prediction of long-term disability in multiple sclerosis. Multiple Sclerosis Journal 18, 31–38 Sage Publications, 2012.

23. JS Fischer, RA Rudick, GR Cutter, SC Reingold, others. The Multiple Sclerosis Functional Composite measure (MSFC): an integrated approach to MS clinical outcome assessment. Multiple sclerosis 5, 244–250 Sage Publications, 1999.

24. Bruce Fischl. FreeSurfer. NeuroImage 62, 774–781 Elsevier BV, 2012. Link

25. A. Roche. A Four-Dimensional Registration Algorithm With Application to Joint Correction of Motion and Slice Timing in fMRI. IEEE Transactions on Medical Imaging 30, 1546–1554 Institute of Electrical & Electronics Engineers (IEEE), 2011. Link

26. Yashar Behzadi, Khaled Restom, Joy Liau, Thomas T. Liu. A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. NeuroImage 37, 90–101 Elsevier BV, 2007. Link

27. Brian B. Avants, Paul Yushkevich, John Pluta, David Minkoff, Marc Korczykowski, John Detre, James C. Gee. The optimal template effect in hippocampus studies of diseased populations. NeuroImage 49, 2457–2466 Elsevier BV, 2010. Link

28. D. S. Margulies, J. L. Vincent, C. Kelly, G. Lohmann, L. Q. Uddin, B. B. Biswal, A. Villringer, F. X. Castellanos, M. P. Milham, M. Petrides. Precuneus shares intrinsic functional architecture in humans and monkeys. Proceedings of the National Academy of Sciences 106, 20069–20074 Proceedings of the National Academy of Sciences, 2009. Link

29. Catie Chang, Gary H. Glover. Timefrequency dynamics of resting-state brain connectivity measured with fMRI. NeuroImage 50, 81–98 Elsevier BV, 2010. Link

30. Chong-Yaw Wee, Sen Yang, Pew-Thian Yap, Dinggang Shen. Sparse temporally dynamic resting-state functional connectivity networks for early MCI identification. Brain Imaging and Behavior Springer Science $$\mathplus$$ Business Media, 2015. Link

31. Zhen Yang, R. Cameron Craddock, Daniel S. Margulies, Chao-Gan Yan, Michael P. Milham. Common intrinsic connectivity states among posteromedial cortex subdivisions: Insights from analysis of temporal dynamics. NeuroImage 93, 124–137 Elsevier BV, 2014. Link