Sensitive outcome measures are required to test novel therapies designed to target neurodegeneration in MS. The MS Functional Composite (MSFC)¹, developed to address the shortcomings of the EDSS by assessing ambulation, but also upper extremity function, cognition (information processing speed), and vision, may also be insensitive to neurodegeneration-related changes over short follow-up intervals. An alternative method involves neuroimaging. We have previously shown that functional connectivity using resting-state fMRI (rs-fMRI) and anatomic connectivity using DTI are related in the transcallosal motor pathway and along the memory pathway connecting hippocampus (Hipp) to posterior cingulate (PCC).^2,3 We propose a combined metric incorporating anatomic and functional connectivity along these pathways as a potential biomarker of disease progression in MS.

AIFCI: Our metric, called the anatomic impairment functional connectivity index (AIFCI), is constructed as an analogy to the MSFC. The AIFCI is the sum of z-scored measures of pathway-dependent DTI and RS-fMRI measures. Transverse diffusivity (TD), based on Lowe et al.³, is a good measure of anatomic connectivity in the pathways of interest here. Two systems are assessed: the motor system and the episodic memory system.

Motor AIFCI measure

The motor AIFCI is calculated according to equation 1 in Figure 1. <f_c>, σ_c^pop and <TD>_pop, and σ_RD^pop can be either population or sample means and standard deviations for RS-fMRI and TD in the transcallosal motor pathway. The AIFCI metric is constructed such that the measure will decrease with increased disability.

Memory AIFCI measure

Because the PCC-AMTL pathway is intrahemispheric and bilateral, the memory AIFCI measure is constructed to be sensitive to memory pathway impairment in either hemisphere. It is constructed by first z-scoring each hemisphere measure according to equation 2 in Figure 1.

The AIFCI is then calculated by combining the component Z’s according to equation 3 in Figure 1.

Data Acquisition: Nine MS patients were scanned at 4 time-points over one year in an IRB approved protocol (age: 51.44±6.6, 1 male, EDSS: 4.06±1.6). Data were acquired on a Siemens TIM Trio 3T MRI scanner (Erlangen, Germany) using a 12-channel receive-only head array. HARDI data were acquired⁴ (TE/TR=102/7700msec, 128x128x48 matrix, FOV=256x256x96mm), 71 b=1000 sec/mm2, and 8 b=0. Resting state scan: 132 repetitions of 31-4mm thick axial slices acquired with TE/TR=29ms/2800 ms, 128x128 matrix, 256mm x 256mm FOV, receive bandwidth=1954Hz/pixel.

Data analysis

The analysis proceeded as described in reference 2. We measure anatomic and functional connectivity in the transcallosal motor pathway (SMC), right hemisphere posterior cingulate-hippocampal pathway (RH PCC-AMTL), and left hemisphere posterior cingulate-hippocampal pathway (LH PCC-AMTL) in all 9 subjects.

Seed region definition

Seed regions for the SMC pathway are defined as the 9-voxel in-plane region centered on the maximum activated voxel in a finger tapping fMRI study performed in the same scan session. We define the seed region in each of the right and left hemisphere PCC by selecting a finite region in each hemisphere proximal to retrosplenial cortex and use the InstaCorr⁷ based method (see ref 3).

Fiber tracking

Using the seed and target regions defined above, probabilistic fiber tracking is performed on each study². Pathway dependent diffusion measures are calculated using the track density map, the scalar diffusion values, and a white matter mask. The result is a pathway TD for every subject.

Functional connectivity

The RS-fMRI metric, f_c, calculated as: Right and left hemisphere SMC, hippocampal and PCC ROI’s are defined using method described above. A reference timeseries is calculated from the arithmetic average of the nine pixels centered on the seed voxel. The cross correlation is calculated between r/l hemisphere SMC, RH PCC-AMTL, and LH PCC-AMTL. The result is a correlation for each of the three pathways. The cross correlation is converted to a Student t. This is f_c.

Figure 2 shows the evolution of rs-fMRI and transverse diffusivity across the four time-points measured and the three pathways. Figure 3 shows the combined measures for each of the pathways and the total combined AIFCI. A repeated measures anova indicates a significant change in AIFCI over time with p<0.01, supporting our hypothesis that our combined measure is sensitive to disease progression over time.Based on prior observations of the relationship of anatomic and functional connectivity measures in MS, we propose a combined connectivity metric that assesses connectivity in motor and cognitive pathways. In a study performed over 4 time-points across an interval of one year in 9 MS patients, AIFCI shows a significant and systematic decline over that interval. This suggests that combined imaging-based metrics of anatomic and functional connectivity may be sensitive biomarkers for disease progression in MS.