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Brain Sci. 2017, 7(6), 64;

Evaluation of Visual-Evoked Cerebral Metabolic Rate of Oxygen as a Diagnostic Marker in Multiple Sclerosis
Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Children’s Hospital of Philadelphia, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
University of Texas at Dallas, Dallas, TX 75080, USA
University of Texas Southwestern Medical Center, Advanced Imaging Research Center, Dallas, TX 75235, USA
Department of Neurology and Neurothreapeutics, University of Texas Southwestern Medical Center, Dallas, TX 75235, USA
Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX 75235, USA
Author to whom correspondence should be addressed.
Academic Editor: Evanthia Bernitsas
Received: 31 March 2017 / Accepted: 5 June 2017 / Published: 11 June 2017


A multiple sclerosis (MS) diagnosis often relies upon clinical presentation and qualitative analysis of standard, magnetic resonance brain images. However, the accuracy of MS diagnoses can be improved by utilizing advanced brain imaging methods. We assessed the accuracy of a new neuroimaging marker, visual-evoked cerebral metabolic rate of oxygen (veCMRO2), in classifying MS patients and closely age- and sex-matched healthy control (HC) participants. MS patients and HCs underwent calibrated functional magnetic resonance imaging (cfMRI) during a visual stimulation task, diffusion tensor imaging, T1- and T2-weighted imaging, neuropsychological testing, and completed self-report questionnaires. Using resampling techniques to avoid bias and increase the generalizability of the results, we assessed the accuracy of veCMRO2 in classifying MS patients and HCs. veCMRO2 classification accuracy was also examined in the context of other evoked visuofunctional measures, white matter microstructural integrity, lesion-based measures from T2-weighted imaging, atrophy measures from T1-weighted imaging, neuropsychological tests, and self-report assays of clinical symptomology. veCMRO2 was significant and within the top 16% of measures (43 total) in classifying MS status using both within-sample (82% accuracy) and out-of-sample (77% accuracy) observations. High accuracy of veCMRO2 in classifying MS demonstrated an encouraging first step toward establishing veCMRO2 as a neurodiagnostic marker of MS.
calibrated functional magnetic resonance imaging; multiple sclerosis; diagnosis; visual system; metabolism

1. Introduction

Current procedures for diagnosing multiple sclerosis (MS) rely primarily upon clinical presentation and qualitative analysis of standard, medical-grade (e.g., lower resolution) magnetic resonance structural, brain images, e.g., [1]. It has been demonstrated that the diagnostic accuracy of MS can be improved when providers implement advanced neuroimaging techniques and analyses that are not presently common in clinical practice, e.g., [2], see also [3]. Further, research using advanced neuroimaging techniques has demonstrated that these techniques can be more sensitive than their traditional counterparts in detecting subtle changes associated with very early manifestations of MS, e.g., [4,5]. Here, we investigated the accuracy of an advanced neuroimaging technique never before used in MS, calibrated functional magnetic resonance imaging (cfMRI), to classify MS patients and closely age- and sex-matched healthy controls (HCs). Specifically, we focused our analyses upon the ability of a new neuroimaging marker, visual-evoked cerebral metabolic rate of oxygen (veCMRO2), to accurately discriminate between MS patients and HCs.
cfMRI is a relatively new neuroimaging technique that capitalizes upon established relationships between blood-oxygen-level dependent (BOLD) signal and cerebral blood flow (CBF) in order to estimate steady-state, oxygen metabolism [6,7] see [8]. The technique gets its name from the use of a BOLD-calibration parameter, often acquired during a gas-inhalation challenge. The CMRO2 metric permitted by cfMRI offers several advantages over the more commonly used BOLD signal. First, CMRO2 offers physiological specificity. CMRO2 represents a true physiological process, oxygen metabolism, whereas BOLD reflects a confluence of processes and as such, is physiologically non-specific. Second, calibration-derived CMRO2 is strongly tied to electrical and chemical neural activity, e.g., [9,10,11,12,13,14,15], whereas an appreciable component of BOLD signal is unexplained by neural activity, e.g., [16,17,18,19,20], see [21], but see [9]. Finally, CMRO2 measures are not dependent upon the hemodynamic assumptions of BOLD, making them optimal measures of brain function in populations with atypical hemodynamics, like MS, e.g., [22,23], see [24].
Evaluating CMRO2 as a diagnostic marker of MS is particularly relevant for these patients because MS is associated with changes to neurometabolism. Neuroimaging research has produced considerable evidence of altered neurometabolism in MS, e.g., [25,26,27,28,29]. In one study, Ge and colleagues [30] demonstrated decreases in brain-wide resting CMRO2 for MS patients relative to HCs. Some neuroimaging studies have shown that neurometabolic alterations were related to white matter macrostructural (i.e., lesions, e.g., [30]) or microstructural damage in MS, e.g., [27,28]. For example, magnetic resonance spectroscopy in centrum semiovale white matter has shown that N-acetylaspertate (NAA) and NAA: creatine ratios were strongly related to diffusion-weighted indices of white matter structural integrity in MS patients [27].
It is intuitive that MS patients would show differences in in vivo neurometabolism when considering that postmortem analyses have revealed extensive alterations to the mitochondria in lesioned and non-lesioned MS neural tissue [31,32,33], see [34,35,36]. For instance, Singhal and colleagues [33] found decreases in postmortem NAA, a partial marker of neuronal respiratory capacity, and decreases in electron transport subunit proteins across lesioned and non-lesioned MS grey matter, relative to matched control participants’ grey matter. Taken together, the results of postmortem and in vivo neuroimaging studies demonstrate that neurometabolic alterations are generally featured in MS.
Evaluating veCMRO2 should also be particularly relevant as a diagnostic marker of MS because MS is marked by alterations to the neural substrate of the visual system, see [37,38,39,40] see also [5]. The use of advanced imaging techniques such as high-resolution structural brain imaging, optical coherence tomography (OCT), functional magnetic resonance imaging (fMRI), and diffusion tensor imaging (DTI) has revealed that visual system alterations exist even in MS patients without visual disturbances or a history of optic neuritis (a clinical syndrome closely linked to MS and marked by visual impairment and visual pathway insult). Indeed, there are MS-related structural alterations to both early (e.g., retinae) and later (e.g., optic radiations) portions of the afferent visual pathway, and alterations to visuocortical activity in patients without a history of optic neuritis see [39]. For instance, Alshowaier and colleagues [41] used electroencephalogram recordings to show that MS patients without a history of optic neuritis demonstrated delayed inion channel, multifocal visual-evoked electrical potentials relative to age- and sex-matched HCs. Previous work in our laboratory has also revealed alterations to visual cortex BOLD signal during visual stimulation in MS patients with normal or corrected-to-normal vision compared to matched HCs [42], see also [43]. Together, structural and functional imaging results suggest that changes to the visual system are a robust marker of MS pathology.
MS is associated with changes to neurometabolism and alterations to the neural substrate of the visual system. Thus, visual-evoked oxygen metabolism signals in visual cortex (i.e., veCMRO2) should be a diagnostically relevant marker of MS. We assessed the extent to which veCMRO2 signals could be used to discriminate between MS patients and HCs. The classification accuracy of veCMRO2 was examined in the context of other variables commonly assayed in MS, including measures of neurological insult (e.g., gross lesion volume, parenchymal atrophy), neuropsychological change (e.g., Brief Repeatable Battery of Neuropsychological Tests [44]), and self-report symptom measures (e.g., subjective fatigue). We tested the extent to which veCMRO2, and these other measures, could classify MS status using both within-sample and out-of-sample observations.

2. Materials and Methods

2.1. Participants

Participants between the ages of 18 and 65 were recruited for this study. Participants were required to be free of MR-contraindicators, concurrent substance abuse, have normal or corrected-to-normal vision, and speak fluent English. Because study procedures included a gas-inhalation challenge (see Section 2.4), participant selection was limited to non-smokers. Participants did not have histories of respiratory or pulmonary problems, cerebral vascular issues, or cardiac problems. Participants were required to have a score greater than 21 on the telephone interview for cognitive status [45]. Thirty-one participants in total met the inclusion criteria.
Twelve MS patients meeting the above criteria were recruited from the Clinical Center for Multiple Sclerosis at the University of Texas Southwestern Medical Center. Eleven patients had a diagnosis of relapsing-remitting MS and one patient had a diagnosis of secondary-progressive MS. Patients were required to be at least 1 month past their most recent exacerbation and their last corticosteroid treatment. Patients were recruited who did not report a history of optic neuritis. Patients without a history of optic neuritis were specifically selected so as to limit additional variability from attributed to severe, anterior visual pathway damage/dysfunction (e.g., such as that resulting from conduction block) and potential visual impairment. All MS patients’ vision was normal or corrected-to-normal. Two patients withdrew or declined to undergo the gas challenge (total n = 10).
Nineteen HC participants were recruited from the Dallas-Fort Worth Metroplex via email, posted flyers, and word-of-mouth. These participants were evaluated for the general inclusion/exclusion criteria described above. Three HCs did not undergo the scanning protocol because of exclusions discovered after study enrollment (e.g., concussion history revealed after pre-screening, incidental MR finding). Two HCs withdrew or declined to undergo the gas challenge. During imaging processing (see Section 2.5), one HC’s functional images failed to appropriately register to their anatomical image after multiple attempts, so this person was excluded. Thirteen HCs (n = 13) remained for subsequent analyses. These participants were closely age- and sex-matched to the MS patients (see Table 1).

2.2. Study Procedures

Study procedures were approved by the University of Texas Southwestern Medical Center Institutional Review Board. Recruitment numbers were approximated based upon previous research showing sufficient power to demonstrate group changes in calibrated fMRI (cfMRI) contrasts with similar sample sizes [22,23]. Participants meeting inclusion criteria were asked to refrain from caffeine use at least two hours before their scheduled appointment time, e.g., [47]. They were also asked not to consume alcohol on the same calendar day before their scheduled appointment. Participants gave written informed consent before undergoing procedures and were compensated for their time. Participants underwent functional and structural neuroimaging on a Philips 3-Tesla magnet (Philips Medical Systems, Best, The Netherlands) with an 8-channel SENSE radiofrequency head coil. Foam padding was placed around the head to minimize motion during MRI scan acquisition. Participants completed standard neuropsychological tests (e.g., Brief Repeatable Battery of Neuropsychological tests [44]) and self-report measures regarding their general health and symptomology (i.e., SF-36 [48], Modified Fatigue Impact Scale (MFIS, [49]); see Table 2 for a complete list of model variables).

2.3. cfMRI Parameters and Theory

Dual-echo pseudocontinuous arterial spin labeling (pCASL) and BOLD images (together referred to as dual-echo images) were acquired using an interleaved echo scanning protocol see [7,52]. Together, the perfusion (Echo 1) and BOLD-weighted (Echo 2) images along with biophysical modeling procedures allowed for estimation of CMRO2 and a neural-vascular coupling coefficient (n, see [8]) associated with steady-state, neural stimulation [5,7]. One task run of dual-echo imaging data and one gas-challenge run of dual-echo imaging data were collected using the following parameters: Echo 1: labeling duration 1650 ms, labeling flip angle 18°, labeling gap = 63.5 mm, 3.44 × 3.44 × 5 mm voxel, repetition time (TR) = 4000 ms, echo time (TE) = 14 ms, 1525 ms post-label delay, 0 mm slice gap. Echo 2: 90° flip angle, 3.44 × 3.44 × 5 mm voxel, TR = 4000 ms, TE = 40 ms, 0 mm slice gap. Total scan time for the visual stimulation task = 600 s (72 dual-echo dynamics). Total scan time for the gas challenge = 624 s (75 dual-echo dynamics).
Estimations of CMRO2 and n were based upon the Davis model of BOLD signal change [6,7]:
Δ S S 0 = M ( ( 1 Δ CBF CBF 0 ) β ( Δ CMRO 2 CMRO 2 | 0 ) β )
where ∆x/x0 denotes a change from baseline, α is an empirically derived constant linking cerebral blood flow and cerebral blood volume, and β is an empirically derived constant related to vascular exchange and susceptibility of deoxyhemoglobin at specific field strengths (e.g., [53,54,55]). We assumed α = 0.38 [56] and β = 1.3 [52]; these values were chosen because they have been shown to be sensitive to group differences in neurophysiology [22,23]. Also, these values have previously demonstrated group-equivalence in the estimation of M, e.g., [22,23]. M is a subject-specific scaling factor dependent upon the washout resting deoxyhemoglobin see [8]. M was estimated in each participant, using the gas challenge detailed below.
The measurement of BOLD, CBF, and M allows for the estimation of CMRO2. Here, ∆CMRO2 reflects the visual task-related change in neurometabolism of oxygen from resting baseline:
Δ CMRO 2   CMRO 2 | 0 =   ( 1 Δ BOLD BOLD 0 M ) 1 / β ( Δ CBF CBF 0 ) 1   α / β
where ∆x/x0 reflects percent change of signal during task compared to resting baseline. With the estimation of ∆CMRO2, n, may also be estimated:
n =   Δ CBF CBF 0 Δ CMRO 2 CMRO 2 | 0
thus, n reflects per unit output of ∆CBF per unit input of ∆CMRO2 see [8].

2.4. cfMRI Task and Gas Challenge

Participants completed a visual stimulation task during dual-echo task imaging. This task was chosen for two reasons. First, differences in the functional response to visual stimulation have been observed in MS visual cortex see [42,57]. Second, because this task required minimal effort, group differences in performance were not expected to be a factor.
Participants were trained on the task before entering the MR environment. During the task, participants focused on a fixation cross at the center of their visual field. Participants were required to respond via bilateral, thumb-button press when a change in the luminance of the fixation cross occurred. This task was used in order to control the center of the participants’ visual field [22,23,58]. Change in luminance was jittered and occurred every 2, 3, 4, or 6 s. Visual stimulation occurred in a block format. There were 6 visual stimulation task blocks consisting of 60 s of continual annulus flickering in the participants’ near-foveal visual field. Annuli alternated at orthogonal orientations (0 to 90°) to avoid neural adaptation [58]. Alterations occurred at a constant frequency of 8 Hz because both electrochemical neural activity and BOLD signal have been shown to peak at this frequency, potentially yielding the greatest signal-to-noise estimates, e.g., [59,60]. Rest blocks were jittered at 32, 34, 36, 38, and 40 s intervals (see Figure 1).
Participants also completed a gas-challenge in order to estimate M. Participants breathed 4 min of room air (~0.03% CO2: 21% O2: 78% N2) and 6 min of an iso-oxic, CO2 solution (5% CO2: 21% O2: 74% N2) during dual-echo imaging. Each participant was fitted with a two-way, non-rebreathing valve/mouthpiece and a nose clip. Baseline end-tidal CO2 (EtCO2), O2 saturation, breath rate, and heart rate measures were collected. After the 4 min of room air breathing, a valve was opened to release the CO2 solution from a Douglas airbag which then flowed into the participants’ breathing apparatus [22,23]. The CO2 inhalation lasted 6 min.
Hypercapnic challenge, via the inhaled 5% CO2 solution, increases global CBF, but probably has no or a minimal depressant effect on oxygen metabolism, e.g., [61,62,63]. Hypercapnia acts to wash out local baseline concentrations of deoxyhemoglobin, yielding a local maximum estimate of resting BOLD signal. Potential changes to oxygen metabolism due hypercapnic challenge have not been shown to appreciably alter the estimation of M as relationships between hypercapnia-derived M and M derived from non-hypercapnic techniques show high correspondence [64].

2.5. cfMRI Processing

Task and gas-challenge Echo 1 and Echo 2 data were processed in analysis of functional neuroimages (AFNI [65]) and the Functional MRI of the Brain Software Library (FSL [66]). Data were transformed into cardinal planes. Anomalous data points in each voxel time series were then attenuated using an interpolation method based upon the average signal. Data were volume registered to correct for motion to the fourth functional volume of each dataset’s (task or gas challenge) Echo 2 sequence using a heptic polynomial interpolation method. CBF was estimated from Echo 1 images using the surround subtraction method [67]. Dual-echo BOLD data were also interpolated by pairwise averaging of temporally adjacent images.
For the visual stimulation task, Echo 2 data were linearly registered (12 degrees-of-freedom) to each participant’s anatomical data using AFNI’s program. The transformation matrix from this registration was then applied to Echo 1 data, placing these two datasets in the same space. For gas-challenge data, a binary mask was created for functional voxels in Echo 2 to aid in co-registration. This mask was then registered to the respective participant’s anatomical space using the program. Gas-challenge Echo 2 and Echo 1 data were also aligned to the mask which was registered in native anatomical space. After alignment, Echoes 1 and 2 data from both the visual task and gas challenge were visually inspected for registration errors. One HC participant failed to register correctly after multiple attempts and was discarded from further analyses. Echoes 1 and 2 data from the visual task and gas challenge were then spatially smoothed using a Gaussian kernel (FWHM = 8 mm) and high-pass filtered (0.0039 Hz).
Preprocessed data from Echoes 1 and 2 in the visual stimulation task were analyzed via generalized linear modeling of task versus rest periods using a boxcar reference function. This modeling quantified task-related CBF and BOLD changes from baseline. BOLD and CBF beta-values were scaled to each voxel’s resting baseline signal and were multiplied by 100, yielding percent signal change estimates from baseline (∆BOLD and ∆CBF). Data were averaged from a visual (functional) region of interest (ROI) comprised of overlapping ∆BOLD and ∆CBF suprathreshold signals within occipital lobe (see Structural and Functional ROI; [22,23]). ∆BOLD, ∆CBF, ∆CMRO2, and n results extracted from the functional region of interest were taken as the visual-evoked signals (i.e., veBOLD, veCBF, veCMRO2, and ven).
For the gas challenge, resting baseline BOLD and CBF signals during room air breathing were averaged for each voxel time-series (BOLD0 and CBF0). The first two minutes of hypercapnia BOLD and CBF time-series were discarded to allow participants’ blood flow to stabilize on the CO2 solution, e.g., [22,23]. The last four minutes of hypercapnia BOLD and CBF time-series were averaged to yield BOLDhc and CBFhc respectively. Average values were extracted from a functional region of interest (see Structural and Functional ROI) using overlapping BOLDhc and CBFhc suprathreshold signals within occipital lobe, and were used to calculate M, using the following equation:
M = BOLD hc   BOLD 0 BOLD 0 ( 1 ( 1 + CBF hc   CBF 0 CBF 0 ) α β )
where (xhc−x0)/x0 reflects percent change in signal from normocapnic to hypercapnic states, normalized by the signals during normocapnia and multiplied by 100. Once M was estimated, ∆CMRO2 and n were also estimated (see Equations (2) and (3); see Figure 2) within a functional region of interest (see Structural and Functional ROI).

2.6. Structural and Functional ROIs

First, the magnetization-prepared rapid acquisition gradient-echo (MPRAGE) data were processed to create a native-space, occipital ROI. The skull was removed using an automated command, separating parenchyma and cerebral spinal fluid from the skull. An intensity based automated segmentation algorithm was used to delineate primarily white matter, grey matter, and cerebral spinal fluid voxels yielding a partial volume estimate of each tissue type, for each voxel. A grey matter mask was then created, retaining voxels with only a greater than or equal to grey matter partial volume estimate of 80%. A structural ROI of occipital lobe was manually delineated on each participant’s MPRAGE image. These were drawn in native space because native space analyses tend to allow for more sensitive patient-control contrasts [68]. The structural ROI was drawn using gyral and sulcal landmarks and encompassed most of occipital cortex including calcarine sulcus, cuneus, and occipital portions of lingual gyrus. Several anatomical landmarks were used in the demarcation of this ROI (parieto-occipital sulcus, occipital pole, pre-occipital notch). Within the anatomically defined occipital lobe, only voxels with partial volume estimates of grey matter (≥80%) were retained. These final masks were down-sampled to the functional voxel size.
A visual task functional ROI was created within the structural ROI described above to estimate veBOLD, veCBF, veCMRO2, and ven (see Figure 3). This procedure eschewed noise from inactive voxels, e.g., [68]. Voxels comprising each participant’s functional ROI were the overlapping top 5% of BOLD and top 5% of CBF t-values obtained from the generalized model, within the structural ROI. This ensured that average veBOLD and veCBF estimates were being derived from the same, task-responsive voxels and that veCMRO2 and ven were derived in voxels with both CBF and BOLD task-related increases (see Figure 3).
veCMRO2 was calculated voxel-wise within the functional ROI using ∆BOLD, ∆CBF, M (which was extracted from functional ROI described below). ven was then calculated similarly. The final product of these analyses was average positive veBOLD, veCBF, and veCMRO2, and ven extracted from the functional ROI (see Figure 3).
Because the gas challenge data differed in occipital coverage compared to the visual task data, M was estimated ex situ. To create a functional ROI for the gas challenge, ∆BOLDhc/BOLD0 and ∆CBFhc/CBF0 maps were thresholded and extracted from the structural ROI detailed above. The criteria for retention of a voxel within these maps required that the voxel was within the top 15% (top 20% for one participant) of ∆BOLDhc/BOLD0 and ∆CBFhc/CBF0 voxels in the structural ROI, and that these ∆BOLDhc/BOLD0 and ∆CBFhc/CBF0 voxels overlapped. This procedure ensured complementary maximum ∆BOLDhc/BOLD0 and ∆CBFhc/CBF0 signals in the retained voxels. Average ∆BOLDhc/BOLD0 and ∆CBFhc/CBF0 signals were extracted from this ROI and M was calculated (see Equation (4)).

2.7. Structural Images

One T1-weighted MPRAGE image was acquired for each participant: 160 slices, TE = 3.7 ms, repetition time TR = 8.1 ms, sagittal slice orientation, 1 × 1 × 1 mm3 voxel, 12° flip angle. SIENAX [15,69] was used to obtain measures of grey matter, white matter, and total brain volume normalized by participant’s head size. This technique uses partial volume estimation to calculate volume of differing tissue types (see Figure 4B,C). Further, this technique takes into account lesioned tissue, as demarcated by lesion masks (see below), in order to avoid misclassification of this tissue. The final products of these analyses were scaled estimates of each participant’s grey matter, white matter, and total brain volume (mm3).
A T2 fluid attenuated inversion recovery (FLAIR) scan was also acquired for each participant: 33 slices, TE = 125 ms, TR = 11,000 ms, no slice gap, transverse slice orientation, 0.45 × 0.45 × 5.00 mm3 voxel, 120° refocusing angle. FLAIR images were used to estimate the extent of gross lesion burden for each participant. Hyperintense voxels were demarcated using in-house MATLAB code based upon slice-wise, signal intensity (i.e., voxels that were ≥1.25 SD over the slice mean intensity). Next, lesions were manually delineated from the hyperintense tissue by two trained researchers (L.H., S.F.). Manual delineation ruled out false positives in lesion classification due to fat signals, motion, ventricular edge effects, skull, or signal inhomogeneites [70]. Lesion burden was estimated by extracting the number of voxels that were demarcated by the automated and manual procedures. Inter-rater agreement of lesion burden was calculated using a Dice ratio (κ) of the lesion burden estimates made by the two researchers on a sample of several subjects [71]. After the researchers were trained on lesion classification, inter-rater agreement was found to be high, κ = 0.89; where κ > 0.70 is generally thought to reflect excellent inter-rater agreement [72]. Lesion burden was quantified using absolute (total mm3 of lesioned tissue; see Figure 4E) and relative scales (percent of total mm3 of lesioned tissue scaled by uncorrected white matter volume in mm3). Spatially distinct lesion count was also obtained by counting the number of non-touching lesions for each subject (see Figure 4F), e.g., [73]. A lesion was required to have at least 3 mm3 volume in order to be added to the total lesion count. Thus, the final products of these analyses were absolute lesion volume, relative lesion volume, and spatially distinct lesion count.

2.8. Diffusion Images

DTI images were acquired using a single-shot, echo-planar imaging sequence with a Sensitivity Encoding parallel imaging scheme (reduction factor = 2.3), 112 × 112 matrix, field of view = 224 × 224 mm2 (nominal resolution of 2 mm), 65 slices (0 mm gap), slice thickness = 2 mm, TR = 7.78 s, TE = 97 ms. The diffusion weighting was encoded along 30 independent orientations [74] and the b value was 1000 s/mm2. Imaging time was 5 min and 15 s. Two HCs did not undergo DTI (nHC = 11).
Automatic Image Registration [75] was performed on raw diffusion-weighted images to correct distortion caused by eddy currents. Six elements of the 3 × 3 diffusion tensor were determined by multivariate least-squares fitting. The tensor was diagonalized to obtain three eigenvalues (λ1–3) and eigenvectors (v1–3). Standard tensor fitting was conducted with DTIStudio [76] to generate the most common DTI-derived diffusion characteristics, fractional anisotropy (FA), axial diffusivity (AD), mean diffusivity (MD), and radial diffusivity (RD).
DTI measurements were obtained at the skeletons of the white matter using FSL [77] to alleviate partial volume effects with tract-based spatial statistics (see Figure 4F–H) [77]. Participant FA maps were registered nonlinearly to the EVE single-subject FA template [78,79,80] for better alignment with a digital white matter atlas (JHU ICBM-DTI-81) [81]. Registered FA maps of all subjects were averaged to generate a mean FA map, from which an FA skeleton mask was created. Skeletonized FA images of all subjects were obtained by projecting the registered FA images onto the mean FA skeleton mask. Skeletonized AD, MD, and RD metrics were obtained by applying the same registration, projection, and skeletonization procedures. We extracted skeleton-wide averages of each DTI metric (i.e., AD, FA, MD, RD), wherein an average of each metric is calculated across all voxels within the white matter skeleton (see Figure 4A).

2.9. Statistical Analyses

All analyses were performed on distributions free of outliers (≥±2 SD from group mean for simple group comparisons, ≥±3 MAD from group median for classification modeling see [82]). Binary logistic regression was used for classifying MS status. A description of model variables can be found in Table 2. The accuracies of these models were computed as the proportion of correct classification outcomes over all outcomes. Accuracy was chosen as the metric of interest because it combines sensitivity and specificity in binary classification analysis by taking into account both true positives and true negatives relative to all outcomes. We used resampling-based hypothesis testing to examine both within-sample and out-of-sample classification of patient status see [83]. Because we used relatively conservative analytic techniques, inherently reducing the likelihood of Type I error and increasing the generalizability of our results, the criterion for a rejection of the null hypothesis was not corrected for multiple comparisons and all models were evaluated at the field-standard α = 0.05. We also denote which hypothesis tests survived Benjamini-Hotchberg correction (Table 4; Figure 7).
Within-sample classification analyses obtained bias-corrected and accelerated (BCa) bootstrapped-resampled (B = 10,000) 95% confidence intervals of the accuracy of binary logistic regression models. The BCa procedure was used because it is robust to both skewness and sampling bias in the bootstrap distribution [84]. To avoid unstable classification, we stratified all resamples to match the original sample’s constitution of patients and controls, 56.5% and 43.5%, respectively. If the BCa-derived 95% confidence interval did not contain a value at or below 0.50 (binary chance), this would demonstrate the measure’s accuracy was significantly greater than chance to classify MS patients and HCs.
Out-of-sample classification analyses used a leave-one-out cross-validation approach [85]. This technique used training and sample iterations to test the ability of the model derived from the training set to predict an observation in the test (out-of-sample) set, thus, circumventing problems of sample bias, model over fitting, and lending a true predictive element to these analyses. Briefly, the leave-one-out cross validation (LOOCV) approach fitted N models, where N was proportional to our sample size. Each model was trained on N-1 samples and then the accuracy of the training model was assessed on the left-out sample. The N accuracies were then averaged to attain a representative and generalizable measure of the average out-of-sample classification accuracy. Permutation based p-values (5000 permutations) were computed to assess the significance of the LOOCV-derived accuracy statistics. The test permuted patient status labels and recomputed the accuracy of the model at each iteration, thus building the null distribution. The p-values were calculated from the percentage of the accuracy estimates of the permuted samples that were better than actual LOOCV-derived accuracy statistic of each model. This procedure was slightly modified according to Ojala and Garriga [86].

3. Results

3.1. Visual Task Performance

MS patients (92.75 ± 1.11%) did not significantly differ from HCs (94.86 ± 0.44%) on accuracy on the visual stimulation secondary task, t(10.54) = −1.76, p = 0.108. Patients (492.06 ms ± 31.15) also did not significantly differ from HCs (487.19 ms ± 24.10) on their average correct response time to press the button on the secondary task, t(16.22) = 0.12, p = 0.903.

3.2. Group Physiology, Cerebrovascular Response to Gas Challenge, and M

MS and HCs did not significantly differ in breath rate, end-tidal CO2, heart rate, or O2 saturation at baseline or during CO2 solution breathing (all ps > 0.05; see Table 3). We tested whether MS patients differed in their CBF response to the CO2 solution ((CBFhc−CBF0)/CBF0) and M in their respective gas challenge ROIs within occipital lobe see [87]. MS patients did not significantly differ in CBF response to the CO2 solution (167.48 ± 19.8%) compared to HCs (146.90 ± 14.64%), t(15.70) = 0.83, p = 0.417. MS patients (3.88 ± 0.48%) did not significantly differ in M compared to HCs (5.11 ± 0.39%), t(18.90) = −1.98, p = 0.062.

3.3. Group Comparisons on Visual Task cfMRI Measures

MS patients (1.12 ± 0.77%) did not significantly differ from HCs (1.18 ± 0.66%) on veBOLD response to visual stimulation, t(19.18) = −0.60, p = 0.555. MS patients (4.08 ± 0.35) did not show significant changes in ven compared to HCs (4.23 ± 0.23), t(16.16) = −0.35, p = 0.731. MS patients (48.06 ± 12.58%) had significant decreases in veCBF compared to HCs (92.68 ± 17.29%), t(19.76) = −2.09, p = 0.050. MS patients (9.59 ± 0.90%) also showed significant decreases in veCMRO2 compared to HCs (17.85 ± 1.97%), t(16.45) = −3.81, p = 0.002 (see Figure 5).

3.4. Within-Sample Classification Analyses

Measures are ranked on original accuracy and presented in Table 4. Accuracy and smoothed density distributions for the significant and bottom 5 measures can be found in Figure 6.

3.5. Out-of-Sample Classification Analyses

Predictors presented in Figure 7 are ranked on LOOCV-derived accuracy.

4. Discussion

In the present study, we used a neuroimaging approach novel to MS research (cfMRI) to assess the accuracy of veCMRO2 in classifying MS patients and closely age- and sex-matched HC participants. MS patients showed similar responses to HCs in veBOLD and ven, however showed decreased veCBF and a pronounced decrease in veCMRO2 relative to HCs. Groups were similar on visual task performance and on physiological measures pertaining to the CO2 challenge, indicating that potential MS-related changes in physiological response to carbon dioxide, e.g., [87] or visual attention were not likely contributors to group CMRO2 differences. Within-sample classification analyses demonstrated that veCMRO2 was significant and one of the top measures to accurately classify MS status, discriminating between MS patients and HCs with exceptional accuracy (82%). Results also showed that within-sample classification accuracy by veCMRO2 was comparable to neuroimaging measures often used to gauge MS pathology, such as T2-FLAIR lesion burden (80% accuracy) and T1 grey matter volume (81% accuracy). veCMRO2 was also significantly accurate in MS classification using out-of-sample observations (77% accuracy). The use of such out-of-sample modeling afforded a predictive element to this study and demonstrated that veCMRO2 can accurately classify new observations of MS and HC participants, offering support for its potential diagnostic utility.
One question that arises from these results is whether veCMRO2 can add predictive value over other advanced imaging techniques not studied here. For instance, measurements of multifocal visual-evoked potentials have been of great interest to the MS research community. This technique, which uses visual stimulation and electroencephalogram signals in occipital channels proximal to the inion has been demonstrated to (1) more sensitively and specifically detect visual abnormalities in MS eyes relative to other visual-system measurements [88], (2) predict conversion to an MS diagnosis in persons with optic neuritis [89], and (3) relate to the extent of MS-related damage to visual white matter tracts [41]. Not surprisingly, this technique can also accurately discriminate between MS patients and HCs, e.g., [90]. For example, one study showed that measurements gathered from multifocal visual-evoked potentials were on average 74.76% accurate (range: 62.7%–96.1%) in classifying within-sample observations of MS patients without optic neuritis and HCs ([90], average calculated from Figure 5 and Figure 6, pp. 910–911). We can compare these figures with the within-sample accuracy of veCMRO2 observed here (82%). This suggests that veCMRO2 accuracy is in about the same range as multifocal evoked potentials. However, it performs appreciably better than the average multifocal evoked potential measure. Future research directly comparing veCMRO2 to electroencephalogram and other measures is necessary to more faithfully adjudicate claims about the relative performance of this technique.
A second avenue for future research could involve examining whether the integration of evoked CMRO2 from other neural systems could maximize MS classification accuracy. Here, we showed significant decreases in MS patients’ veCMRO2 relative to HCs. This variable was also largely accurate in the prediction of MS status. We looked at veCMRO2 specifically because of robust alterations to the visual system in MS see [37,38,39,40]. However, because (1) mitochondrial alterations are found in multiple forms of neural tissue in MS [31,33] and (2) global brain decreases in oxygen metabolism have been found in MS patients relative to HCs [30], it is likely that evoked CMRO2 is affected in other neural systems as well. Our work and others’ have shown altered patterns of brain activity in MS patients in motor, e.g., [42,91,92] and association cortices [43,93,94,95], see [96]. It is possible that the addition of measures of evoked CMRO2 in these areas could lend improvements in the accuracy of MS classification. One advantage of the cfMRI approach over other advanced imaging approaches in MS, like OCT or visual-evoked potentials, is that this technique can specifically and simultaneously assay multiple neural systems. Work underway in our laboratories is examining the extent to which evoked motor and executive system CMRO2 differs between MS patients and age- and sex-matched healthy HCs, and whether these changes, along with veCMRO2, can help build optimal neurodiagnostic models of MS.
The utility of imaging biomarkers in MS is not limited to assisting in diagnosis see [97]. For instance, OCT measures have been shown to be effective in predicting brain atrophy and visual acuity loss in MS see [38]. The retinal nerve fiber thickness and macular volume measures from OCT might also be useful in differentiating different subtypes of MS [98]. Other imaging-based measures, such as T2-lesion burden, have shown prognostic ability by prediction of future MS disability, e.g., [99], see also [100,101,102]. One potential avenue for future research is to evaluate the use of oxygen metabolism signals in MS prognosis. For example, Ge and colleagues’ [27] research showed that lower resting brain-wide levels of oxygen metabolism were associated with both increased neurological disability and increased lesion burden in MS patients. Although these findings were cross-sectional, they suggested that oxygen metabolism could be a marker of the trajectory of disease course. To wit, future longitudinal work should examine whether measures of oxygen metabolism in early MS can predict future disease progression cf. [89]. veCMRO2 or resting oxygen metabolic markers could also be evaluated for their abilities to predict the transition from risk states (such as clinically or radiologically isolated syndrome) to clinically definite MS see [100,102,103].
A recent wave of findings related to metabolic dysfunction in MS has led to metabolic hypotheses to explain the pathophysiology of MS see [34,35,36]. For instance, Paling and colleagues furthered an energy failure hypothesis of the pathophysiology of MS [35,104]. These authors postulated a link between white matter damage and energy demand in MS, wherein this damage causes neuroenergetic demand to exceed the supply of metabolic substrate. This hypothesis is largely consistent with the findings of the present study, wherein the observed relative decrease in veCBF (the supply of oxygen and glucose) in MS might have limited the neurometabolic response (veCMRO2) relative to HCs. Further, issues of oxygen extraction due to mitochondrial damage/dysfunction could have also contributed to the relative decrease in veCMRO2 for MS patients relative to HCs see [34,35,36].
Imaging techniques here and elsewhere have produced convincing biomarkers of MS see [38,97,100]. However, MS is a complex, multifaceted disease. Thus, it is not surprising that our results revealed a diverse array of measures that were accurate in classifying MS patients and HCs. The goal of this work was to examine the ability of a new marker (veCMRO2) to accurately classify MS. However, a truly prodigious advance in MS diagnostics will likely evolve from models that combine many relevant factors. It is possible that a “gold-standard” model of MS diagnostics would contain information about evoked CMRO2, along with other information like lesion count, self-reported symptomology, neuropsychological performance, and potentially other strong associates of MS not examined here (e.g., low-contrast letter acuity performance see [105], oligoclonal band status [106], retinal nerve fiber layer thickness see [38]). For instance, research from the Alzheimer’s Disease Neuroimaging Initiative showed that a complement of multimodal neuroimaging, cerebrospinal fluid proteins, along with standard clinical evaluations allow for optimal prediction of conversion from mild cognitive impairment to Alzheimer’s disease [107]; see also [108] for application in psychiatry.

5. Conclusions

This study was the first to apply cfMRI in an MS sample. Presently, the intricacies of cfMRI acquisition and post-acquisition processing probably hinder it from having an immediate impact upon routine diagnosis or tracking of MS. However, acquisition continues to be optimized and research is showing promise toward eliminating the gas-challenge component of this method, see [8], which should increase the ease of cfMRI administration and the diversity of patients in which it can be applied. With contemporary research highlighting the importance of neurometabolism in the pathophysiology of MS and continued optimization of this technique, cfMRI shows promise as a translational diagnostic/prognostic tool for MS.
Our findings demonstrated that veCMRO2 was accurate in classifying both within- and out-of-sample observations of MS patients and HCs. Out-of-sample analyses suggested that predictive models using veCMRO2 could be useful in MS diagnostics and potentially new cases of MS. Although out-of-sample analyses provide confidence in the generalizability of our findings, larger, independent samples are desirable to confirm the robustness of these effects. However, the present findings represent an encouraging first step in realizing the diagnostic relevance of veCMRO2 in MS.


This work was supported by grants from the National Multiple Sclerosis Society (to DTO and BR; number RG-1507-04951; and to BR RG-1510-06687) and from the National Institutes of Health (to BR and HL; number 5RO1AG047972-02). The authors wish to thank Hannah Grotzinger and Judith Gallagher for their contributions to manuscript preparation.

Author Contributions

N.A.H. contributed to study design, data analysis, and wrote the manuscript. Y.S.A. and C.C. contributed to statistical analysis. M.P.T., L.H. and B.P.T. contributed to study design, data collection and analyses, and manuscript writing. M.O. and H.H., contributed to diffusion tensor imaging analyses. S.F. contributed to data collection and data processing. J.H., Jr., D.T.O. and B.R. contributed to study design, conceptualization, and manuscript writing.

Conflicts of Interest

N.A.H., Y.S.A., C.C., M.O., M.P.T., L.H., S.F., B.P.T., J.H., Jr., H.H. and B.R. declare no perceived conflicts of interests. D.T.O. received lecture fees from Acorda, Genzyme, and TEVA Neuroscience, consulting and advisory board fees from EMD Serono, Genentech, Genzyme, Novartis and TEVA Neuroscience, and research support from Biogen not related to this study.


  1. Polman, C.H.; Reingold, S.C.; Banwell, B.; Clanet, M.; Cohen, J.A.; Filippi, M.; Fujihara, K.; Havrdova, E.; Hutchison, M.; Kappos, L.; et al. Diagnostic Criteria for Multiple Sclerosis: 2010 Revisions to the McDonald Criteria. Ann. Neurol. 2011, 69, 292–302. [Google Scholar] [CrossRef] [PubMed]
  2. George, I.C.; Sati, P.; Absinta, M.; Cortese, I.C.M.; Sweeney, E.M.; Shea, C.D.; Reich, D.S. Clinical 3-Tesla FLAIR* MRI Improves Diagnostic Accuracy in Multiple Sclerosis. Mult. Scler. 2016, 22, 1578–1586. [Google Scholar] [CrossRef] [PubMed]
  3. Wattjes, M.P.; Barkhof, F. High Field MRI in the Diagnosis of Multiple Sclerosis: High Field-High Yield? Neuroradiology 2009, 51, 279–292. [Google Scholar] [CrossRef] [PubMed]
  4. Metcalf, M.; Xu, D.; Okuda, D.T.; Carvajal, L.; Srinivasan, R.; Kelley, D.A.C.; Mukherjee, P.; Nelson, S.J.; Vigneron, D.B.; Pelletier, D. High-Resolution Phased-Array MRI of the Human Brain and 7 Tesla: Initial Experience in Multiple Sclerosis Patients. J. Neuroimaging 2010, 20, 141–147. [Google Scholar] [CrossRef] [PubMed]
  5. Oberwahrenbrok, T.; Ringelstein, M.; Jentschke, S.; Deuschle, K.; Klumbies, K.; Bellmann-Strobl, J.; Harmel, J.; Ruprecht, K.; Schippling, S.; Hartung, H.P.; et al. Retinal Ganglion Cell and Inner Plexiform Layer Thinning in Clinically Isolated Syndrome. Mult. Scler. 2013, 19, 1887–1895. [Google Scholar] [CrossRef] [PubMed]
  6. Davis, T.L.; Kwong, K.K.; Weiskoff, R.M.; Rosen, B.R. Calibrated Functional MRI: Mapping the Dynamics of Oxidative Metabolism. Proc. Natl. Acad. Sci. USA 1998, 95, 1834–1839. [Google Scholar] [CrossRef] [PubMed]
  7. Hoge, S.A.; Atkinson, J.; Gill, B.; Crelier, G.R.; Marrett, S.; Pike, G.B. Linear Coupling Between Cerebral Blood Flow and Oxygen Consumption in Activated Human Cortex. Proc. Natl. Acad. Sci. USA 1999, 96, 9403–9408. [Google Scholar] [CrossRef] [PubMed]
  8. Hoge, R.D. Calibrated fMRI. NeuroImage 2012, 62, 930–937. [Google Scholar] [CrossRef] [PubMed]
  9. Herman, P.; Sanganahalli, B.G.; Blumenfeld, H.; Hyder, F. Cerebral Oxygen Demand for Short-Lived and Steady-State Events. J. Neurochem. 2009, 109, 73–79. [Google Scholar] [CrossRef] [PubMed]
  10. Herman, P.; Sanganahalli, B.G.; Blumenfeld, H.; Rothman, D.L.; Hyder, F. Quantitative Basis for Neuroimaging of Cortical Laminae with Calibrated Functional MRI. Proc. Natl. Acad. Sci. USA 2013, 110, 15115–15120. [Google Scholar] [CrossRef] [PubMed]
  11. Hyder, F.; Kida, I.; Behar, K.L.; Kennan, R.P.; Maciejewski, P.K.; Rothman, D.L. Quantitative Functional Imaging of the Brain: Towards Mapping Neuronal Activity by BOLD fMRI. NMR Biomed. 2001, 14, 413–431. [Google Scholar] [CrossRef] [PubMed]
  12. Hyder, F.; Rothman, D.L.; Shulman, R.G. Total Neuroenergetics Support Localized Brain Activity: Implications for the Interpretation of fMRI. Proc. Natl. Acad. Sci. USA 2002, 99, 10771–10776. [Google Scholar] [CrossRef] [PubMed]
  13. Hyder, F. Neuroimaging with Calibrated FMRI. Stroke 2004, 35, 2635–2641. [Google Scholar] [CrossRef] [PubMed]
  14. Lin, A.-L.; Fox, P.T.; Hardies, J.; Duong, T.Q.; Gao, J.H. Nonlinear Coupling Between Cerebral Blood Flow, Oxygen Consumption, and ATP Production in Human Visual Cortex. Proc. Natl. Acad. Sci. USA 2010, 107, 8446–8451. [Google Scholar] [CrossRef] [PubMed]
  15. Smith, A.J.; Blumenfeld, H.; Behar, K.J.; Rothman, D.L.; Shulman, R.G.; Hyder, F. Cerebral Energetics and Spiking Frequency: The Neurophysiological Basis of fMRI. Proc. Natl. Acad. Sci. USA 2002, 99, 10765–10770. [Google Scholar] [CrossRef] [PubMed]
  16. He, B.J.; Snyder, A.Z.; Zempel, J.M.; Smyth, M.D.; Raichle, M.E. Electrophysiological Correlates of the Brain’s Intrinsic Large-Scale Functional Architecture. Proc. Natl. Acad. Sci. USA 2008, 105, 16039–16044. [Google Scholar] [CrossRef] [PubMed]
  17. Leopold, D.A.; Maier, A. Ongoing Physiological Processes in the Cerebral Cortex. NeuroImage 2012, 62, 2190–2200. [Google Scholar] [CrossRef] [PubMed]
  18. Logothetis, N.K.; Pauls, J.; Augath, M.; Trinath, T.; Oeltermann, A. Neurophysiological Investigation of the Basis of the fMRI Signal. Nature 2001, 412, 150–157. [Google Scholar] [CrossRef] [PubMed]
  19. Lu, H.; Zuo, Y.; Gu, H.; Waltz, J.A.; Zhan, W.; Scholl, C.A.; Rea, W.; Yang, W.; Stein, E.A. Synchronized Delta Oscillations Correlate with the Resting-State Functional MRI Signal. Proc. Natl. Acad. Sci. USA 2007, 104, 18265–18269. [Google Scholar] [CrossRef] [PubMed]
  20. Zhu, Z.; Johnson, N.F.; Kim, C.; Gold, B.T. Reduced Frontal Cortex Efficiency is Associated with Lower White Matter Integrity in Aging. Cereb. Cortex 2015, 25, 138–146. [Google Scholar] [CrossRef] [PubMed]
  21. Mark, C.I.; Mazerolle, E.L.; Chen, J.J. Metabolic and Vascular Origins of the BOLD Effect: Implications for Imaging Pathology and Resting-State Brain Function. J. Magn. Reson. Imaging 2015, 42, 231–246. [Google Scholar] [CrossRef] [PubMed]
  22. Hutchison, J.L.; Lu, H.; Rypma, B. Neural Mechanisms of Age-Related Slowing: The ΔCBF/ΔCMRO2 Ratio Mediates Age-Differences in BOLD Signal and Human Performance. Cereb. Cortex 2013, 23, 2337–2346. [Google Scholar] [CrossRef] [PubMed]
  23. Hutchison, J.L.; Shokri-Kojori, E.; Lu, H.; Rypma, B. A BOLD Perspective on Age-Related Neurometabolic-Flow Coupling and Neural Efficiency Changes in Human Visual Cortex. Front. Psychol. 2013, 4, 1–12. [Google Scholar] [CrossRef] [PubMed]
  24. Iannetti, G.D.; Wise, R.G. BOLD Functional MRI in Disease and Pharmacological Studies: Room for Improvement? Magn. Reson. Imaging 2007, 25, 978–988. [Google Scholar] [CrossRef] [PubMed]
  25. Cader, S.; Johansen-Berg, H.; Wylezinska, M.; Palace, J.; Behrens, T.E.; Smith, S.; Matthews, P.M. Discordant White Matter N-acetylasparate and Diffusion MRI Measure Suggest that Chronic Metabolic Dysfunction Contributes to Axonal Pathology in Multiple Sclerosis. NeuroImage 2007, 36, 19–27. [Google Scholar] [CrossRef] [PubMed]
  26. Pfueller, C.F.; Brandt, A.U.; Schubert, F.; Bock, M.; Walaszek, B.; Waiszies, H.; Schwenteck, T.; Dörr, J.; Bellmann-Strobl, J.; Mohr, C.; et al. Metabolic Changes in the Visual Cortex are Linked to Retinal Nerve Fiber Layer Thinning in Multiple Sclerosis. PLoS ONE 2011, 6, e18019. [Google Scholar] [CrossRef] [PubMed]
  27. Hannoun, S.; Bagory, M.; Durand-Dubief, F.; Ibarrola, D.; Comte, J.C.; Confavreux, C.; Cotton, F.; Sappey-Marinier, D. Correlation of diffusion and Metabolic Alterations in Different Clinical Forms of Multiple Sclerosis. PLoS ONE 2012, 7, e32525. [Google Scholar] [CrossRef]
  28. Sijens, P.E.; Irwan, R.; Potze, J.H.; Mostert, J.P.; De Keyser, J.; Ouderk, M. Analysis of the Human Brain in Primary Progressive Multiple Sclerosis with Mapping of the Spatial Distributions Using 1H MR Spectroscopy and Diffusion Tensor Imaging. Eur. Radiol. 2005, 15, 1686–1693. [Google Scholar] [CrossRef] [PubMed]
  29. Sun, X.; Tanaka, M.; Kondo, S. Clinical Significance of Reduced Cerebral Metabolism in Multiple Sclerosis: A Combined PET and MRI Study. Ann. Nucl. Med. 1998, 12, 89–94. [Google Scholar] [CrossRef] [PubMed]
  30. Ge, Y.; Zhang, Z.; Lu, H.; Tang, L.; Jaggi, H.; Herbert, J.; Babb, J.S.; Rusinek, H.; Grossman, R.I. Characterizing Brain Oxygen Metabolism in Patients with Multiple Sclerosis with T2-Relaxation-Under-Spin-Tagging MRI. J. Cereb. Blood Flow Metab. 2012, 32, 403–412. [Google Scholar] [CrossRef] [PubMed]
  31. Dutta, R.; McDonough, J.; Yin, X.; Peterson, J.; Chang, A.; Torres, T.; Gudz, T.; Macklin, W.B.; Lewis, D.A.; Fox, R.J.; et al. Mitochondrial Dysfunction as a Cause of Axonal Degeneration in Multiple Sclerosis Patients. Ann. Neurol. 2006, 59, 478–489. [Google Scholar] [CrossRef] [PubMed]
  32. Mahad, D.J.; Ziabreva, I.; Campbell, G.; Lax, N.; White, K.; Hanson, P.S.; Lassmann, H.; Turnbull, D.M. Mitochondrial Changes Within Axons in Multiple Sclerosis. Brain 2009, 132, 1161–1174. [Google Scholar] [CrossRef] [PubMed]
  33. Singhal, N.K.; Li, S.; Arning, E.; Alkhayer, K.; Clements, R.; Sarcyk, Z.; Dassanayake, R.S.; Brasch, N.E.; Freeman, E.J.; Bottiglieri, T.; et al. Changes in Methionine Metabolism and Histone H3 Trimethylation are Linked to Mitochondrial Defects in Multiple Sclerosis. J. Neurosci. 2015, 35, 15170–15186. [Google Scholar] [CrossRef] [PubMed]
  34. Cambron, M.; D’haeseleer, M.; Laureys, G.; Clinckers, R.; Debruyne, J.; De Keyser, J. White-Matter Astrocytes, Axonal Energy Metabolism, and Axonal Degeneration in Multiple Sclerosis. J. Cereb. Blood Flow Metab. 2012, 32, 413–424. [Google Scholar] [CrossRef] [PubMed]
  35. Paling, D.; Golay, X.; Wheeler-Kingshott, C.; Kapoor, R.; Miller, D. Energy Failure in Multiple Sclerosis and its Investigation Using MR Techniques. J. Neurol. 2011, 258, 2113–2127. [Google Scholar] [CrossRef] [PubMed]
  36. Su, K.; Bourdette, D.; Forte, M. Mitochondrial Dysfunction and Neurodegeneration in Multiple Sclerosis. Front. Physiol. 2013, 4, 1–10. [Google Scholar] [CrossRef] [PubMed]
  37. Frohman, E.M.; Frohman, T.C.; Zee, D.S.; McColl, R.; Galetta, S. The Neuro-Ophthalmology of Multiple Sclerosis. Lancet Neurol. 2005, 4, 111–121. [Google Scholar] [CrossRef]
  38. Frohman, E.M.; Fujimoto, J.G.; Frohman, T.C.; Calabresi, P.A.; Cutter, G.; Balcer, L.J. Optical Coherence Tomography: A Window Into the Mechanisms of Multiple Sclerosis. Nat. Clin. Pract. Neurol. 2008, 4, 664–675. [Google Scholar] [CrossRef] [PubMed]
  39. Graham, S.L.; Klistorner, A. Afferent Visual Pathways in Multiple Sclerosis: A Review. Clin. Exp. Ophthalmol. 2017, 45, 62–72. [Google Scholar] [CrossRef] [PubMed]
  40. Kolappan, M.; Henderson, A.P.D.; Jenkins, T.M.; Wheeler-Kingshott, C.A.; Plant, G.T.; Miller, D.H. Assessing Structure and Function of the Afferent Visual Pathway in Multiple Sclerosis and Associated Optic Neuritis. J. Neurol. 2009, 256, 305–319. [Google Scholar] [CrossRef] [PubMed]
  41. Alshowaeir, D.; Yiannikas, C.; Garrick, R.; Paratt, J.; Barnett, M.H.; Graham, S.L.; Klistorner, A. Latency of Multifocal Visual Evoked Potentials in Nonoptic Neuritis Eyes of Multiple Sclerosis Patients Associated with Optic Radiation Lesions. Investig. Ophthalmol. Vis. Sci. 2014, 55, 3758–3764. [Google Scholar] [CrossRef] [PubMed]
  42. Hubbard, N.A.; Turner, M.; Hutchison, J.L.; Ouyang, A.; Strain, J.; Oasay, L.; Sundaram, S.; Davis, S.; Remington, G.; Brigante, R.; et al. Multiple Sclerosis-Related White Matter Microstructural Change Alters the BOLD Hemodynamic Response. J. Cereb. Blood Flow Metab. 2016, 36, 1872–1884. [Google Scholar] [CrossRef] [PubMed]
  43. Hubbard, N.A.; Hutchison, J.L.; Turner, M.P.; Sundaram, S.; Oasay, L.; Robinson, D.; Strain, J.; Weaver, T.; Davis, S.L.; Remington, G.M.; et al. Asynchrony in Executive Networks Predicts Cognitive Slowing in Multiple Sclerosis. Neuropsychology 2016, 30, 75. [Google Scholar] [CrossRef] [PubMed]
  44. Rao, S.M. Cognitive Function Study Group of the National Multiple Sclerosis Society. In A Manual for the Brief Repeatable Battery of Neuropsychological Tests in Multiple Sclerosis; Medical College of Wisconsin: Milwaukee, WI, USA, 1990. [Google Scholar]
  45. Brandt, J.; Spencer, M.; Folstein, M. The Telephone Interview for Cognitive Status. Neuropsychiatry Neuropsychol. Behav. Neurol. 1988, 1, 111–117. [Google Scholar]
  46. Verdier-Taillefer, M.H.; Roullet, E.; Cesaro, P.; Alpérovitch, A. Validation of Self-Reported Neurological Disability in Multiple Sclerosis. Int. J. Epidemiol. 1994, 23, 148–154. [Google Scholar] [CrossRef] [PubMed]
  47. Perthen, J.E.; Lansing, A.E.; Liau, J.; Liu, T.T.; Buxton, R.B. Caffeine-Induced Uncoupling of Cerebral Blood Flow and Oxygen Metabolism: A Calibrated BOLD fMRI Study. NeuroImage 2008, 40, 237–247. [Google Scholar] [CrossRef] [PubMed]
  48. Ware, J.E.; Kosinski, M.; Keller, S.D. SF-36 Physical and Mental Health Summary Scales: A Users’ Manual; The Health Institute: Scarborough, ON, Canada; New England Medical Center: Boston, MA, USA, 1994. [Google Scholar]
  49. Fisk, J.D.; Pontefract, A.; Ritvo, P.G.; Archibald, C.J.; Muarray, T.J. The impact of fatigue on patients with multiple sclerosis. Can. J. Neurol. Sci. 1994, 21, 9–14. [Google Scholar] [CrossRef] [PubMed]
  50. Boringa, J.B.; Lazeron, R.H.C.; Reuling, I.E.W.; Adèr, H.J.; Pfennings, L.E.M.A.; Lindeboom, J.; de Sonneville, L.M.J.; Kalkers, N.F.; Polman, C.H. The Brief Repeatable Battery of Neuropsychological Tests: Normative Values Allow Application in Multiple Sclerosis Clinical Practice. Mult. Scler. 2001, 7, 263–267. [Google Scholar] [CrossRef] [PubMed]
  51. Strauss, E.; Sherman, E.M.S.; Spreen, O. A Compendium of Neuropsychological Tests: Administration, Norms, and Commentary; American Chemical Society: Washington, DC, USA, 2006. [Google Scholar]
  52. Lu, H.; van Zijl, P. Experimental Measurement of Extravascular Parenchymal BOLD Effects and Tissue Oxygen Extraction Fractions Using Multi-Echo VASO fMRI at 1.5 and 3.0 T. Magn. Reson. Med. 2005, 53, 808–816. [Google Scholar] [CrossRef] [PubMed]
  53. Ances, B.M.; Liang, C.L.; Leontiev, O.; Perthen, J.E.; Fleisher, A.S.; Lansing, A.E.; Buxton, R.B. Effects of Aging on Cerebral Blood Flow, Oxygen Metabolism, and Blood Oxygen Level Dependent Responses to Visual Stimulation. Hum. Brain Mapp. 2009, 30, 1120–1132. [Google Scholar] [CrossRef] [PubMed]
  54. Buxton, R.B. Interpreting Oxygenation-Based Neuroimaging Signals: The Importance and the Challenge of Understanding Brain Oxygen Metabolism. Front. Neuroenerg. 2010. [Google Scholar] [CrossRef] [PubMed]
  55. Leontiev, O.; Buxton, R.B. Reproducibility of BOLD, Perfusion, and CMRO2 Measurements with Calibrated-BOLD fMRI. NeuroImage 2007, 35, 175–184. [Google Scholar] [CrossRef] [PubMed]
  56. Grubb, R.L.; Raichle, M.E.; Eichling, J.O.; Ter-Pogossian, M.M. The Effects of Changes in PaCO2 Cerebral Blood Volume, Blood Flow, and Vascular Mean Transit Time. Stroke 1974, 5, 630–639. [Google Scholar] [CrossRef] [PubMed]
  57. Hubbard, N.A.; Turner, M.P.; Robinson, D.M.; Sundaram, S.; Oasay, L.; Hutchison, J.L.; Ouyang, A.; Huang, H.; Rypma, B. Attenuated BOLD Hemodynamic Response Predicted by Degree of White Matter Insult, Slows Cognition in Multiple Sclerosis. Mult. Scler. J. 2014, 20, 267. [Google Scholar]
  58. Pasley, B.N.; Inglis, B.A.; Freeman, R.D. Analysis of Oxygen Metabolism Implies a Neural Origin for the Negative BOLD Response in Human Visual Cortex. NeuroImage 2007, 36, 269–276. [Google Scholar] [CrossRef] [PubMed]
  59. Lin, A.; Fox, P.T.; Yang, Y.; Lu, J.; Tan, L.H.; Gao, J.H. Evaluation of MRI Models in the Measurement of CMRO2 and Its Relationship with CBF. Magn. Reson. Med. 2008, 60, 380–389. [Google Scholar] [CrossRef] [PubMed]
  60. Singh, M.; Kim, S.; Kim, T. Correlation Between BOLD-fMRI and EEG Signal Changes in Response to Visual Stimulus Frequency in Humans. Magn. Reson. Med. 2003, 49, 108–114. [Google Scholar] [CrossRef] [PubMed]
  61. Peng, S.L.; Ravi, H.; Sheng, M.; Thomas, B.P.; Lu, H. Searching for a Truly “Iso-Metabolic” Gas Challenge in Physiological MRI. J. Cereb. Blood Flow Metab. 2017, 37, 715–725. [Google Scholar] [CrossRef] [PubMed]
  62. Xu, F.; Uh, J.; Brier, M.R.; Hart, J., Jr.; Yezhuvath, U.S.; Gu, H.; Yang, Y.; Lu, H. The Influence of Carbon Dioxide on Brain Activity and Metabolism in Conscious Humans. J. Cereb. Blood Flow Metab. 2011, 31, 58–67. [Google Scholar] [CrossRef] [PubMed]
  63. Zappe, A.C.; Uludağ, K.; Oeltermann, A.; Uğurbil, K.; Logothetis, N.L. The Influence of Moderate Hypercapnia on Neural Activity in the Anesthetized Nonhuman Primate. Cereb. Cortex 2008, 18, 2666–2673. [Google Scholar] [CrossRef] [PubMed]
  64. Yucel, M.A.; Evans, K.C.; Selb, J.; Huppert, T.J.; Boas, D.A.; Gagnon, L. Validation of the Hypercapnic Calibrated fMRI Method Using DOT-fMRI Fusion Imaging. NeuroImage 2014, 102, 729–735. [Google Scholar] [CrossRef] [PubMed]
  65. Cox, R.W. AFNI: Software for Analysis and Visualization of Functional Magnetic Resonance Neuroimages. Comput. Biomed. Res. 1996, 29, 162–173. [Google Scholar] [CrossRef] [PubMed]
  66. FMRIB Analysis Group. FMRIB Software Library v5.0. Available online: (accessed on 3 June 2017).
  67. Liu, T.T.; Wong, E.C. A Signal Processing Model for Arterial Spin Labeling Functional MRI. NeuroImage 2005, 24, 207–215. [Google Scholar] [CrossRef] [PubMed]
  68. Hutchison, J.L.; Hubbard, N.A.; Brigante, R.M.; Turner, M.; Sandoval, T.I.; Hillis, G.A.; Weaver, T.; Rypma, B. The Efficiency of fMRI Region of Interest Analysis Methods for Detecting Group Differences. J. Neurosci. Methods 2014, 226, 57–65. [Google Scholar] [CrossRef] [PubMed]
  69. Smith, S.M.; De Stefano, N.; Jenkinson, M.; Matthews, P.M. Normalised Accurate Measurement of Longitudinal Brain Change. J. Comput. Assist. Tomogr. 2001, 25, 466–475. [Google Scholar] [CrossRef] [PubMed]
  70. Hart, J., Jr.; Kraut, M.A.; Womack, K.B.; Strain, J.; Didehbani, N.; Bartz, E.; Conover, H.; Mansinghani, S.; Lu, H.; Cullum, C.M. Neuroimaging of Cognitive Dysfunction and Depression in Aging Retired National Football League Players. JAMA Neurol. 2013, 70, 326–335. [Google Scholar] [CrossRef] [PubMed]
  71. Dice, L.R. Measures of the Amount of Ecologic Association between Species. Ecology 1945, 26, 297–302. [Google Scholar] [CrossRef]
  72. Zhang, L.; Dean, D.; Liu, J.Z.; Sahgal, V.; Wang, X.; Yue, G.H. Quantifying Degeneration of White Matter in Normal Aging Using Fractal Dimension. Neurobiol. Aging 2007, 28, 1543–1555. [Google Scholar] [CrossRef] [PubMed]
  73. Ghassemi, R.; Narayana, S.; Banwell, B.; Sled, J.G.; Shroff, M.; Arnold, D.L. Quantitative Determination of Regional Lesion Volume and distribution in Children and Adults with Relapsing-Remitting Multiple Sclerosis. PLoS ONE 2014, 9, e85741. [Google Scholar] [CrossRef] [PubMed]
  74. Jones, D.K.; Simmons, A.; Williams, S.C.; Horsfield, M.A. Non-invasive Assessment of Axonal Fiber Connectivity in the Human Brain via Diffusion Tensor MRI. Magn. Reson. Med. 1999, 42, 37–41. [Google Scholar] [CrossRef]
  75. Woods, R.P.; Grafton, S.T.; Holmes, C.J.; Cherry, S.R.; Mazziotta, J.C. Automated Image Registration: I. General Methods and Intrasubject, Intramodality Validation. J. Comput. Assist. Tomogr. 1998, 22, 139–152. [Google Scholar] [CrossRef] [PubMed]
  76. Jiang, H.; van Zijl, P.C.J.K.; Pearlson, G.D.; Mori, S. DtiStudio: Resource Program for Diffusion Tensor Computation and Fiber Bundle Tracking. Comput. Methods Programs Biomed. 2006, 81, 106–116. [Google Scholar] [CrossRef] [PubMed]
  77. Smith, S.M.; Jenkinson, M.; Johansen-Berg, H.; Rueckert, D.; Nichols, T.E.; Mackay, C.E.; Watkins, K.E.; Ciccarelli, O.; Cader, M.Z.; Matthews, P.M.; et al. Tract-Based Spatial Statistics: Voxelwise Analysis of Multi-Subject Diffusion Data. NeuroImage 2006, 3, 1487–1505. [Google Scholar] [CrossRef] [PubMed]
  78. Huang, H.; Gundapuneedi, T.; Rao, U. White Matter Disruptions in Adolescents Exposed to Childhood Maltreatment and Vulnerability to Psychopathology. Neuropsychopharmacology 2012, 37, 2693–2701. [Google Scholar] [CrossRef] [PubMed]
  79. Huang, H.; Fan, X.; Weiner, M.; Martin-Cook, K.; Xiao, G.; Davis, J.; Devous, M.; Rosenberg, R. Distinctive Disruption Patterns of White Matter Tracts in Alzheimer’s Disease with Full Diffusion Tensor Characterization. Neurobiol. Aging 2012, 33, 2029–2045. [Google Scholar] [CrossRef] [PubMed]
  80. Ouyang, M.; Cheng, H.; Mishra, V.; Gong, G.; Mosconi, M.; Sweeney, J.; Peng, Y.; Huang, H. Atypical age-dependent effects of autism on white matter microstructure in children of 2–7 years. Hum. Brain Mapp. 2016, 37, 819–832. [Google Scholar] [CrossRef] [PubMed]
  81. Mori, S.; Oishi, K.; Jiang, H.; Jiang, L.; Li, X.; Akhter, K.; Hua, K.; Faria, A.V.; Mahmood, A.; Woods, R.; et al. Stereotaxic White Matter Atlas Based on Diffusion Tensor Imaging in an ICBM Template. NeuroImage 2008, 40, 570–582. [Google Scholar] [CrossRef] [PubMed]
  82. Iglewicz, B.; Hoaglin, D. Volume 16: How to Detect and Handle Outliers. In The ASQC Basic References in Quality Control: Statistical Techniques; Mykytka, E.F., Ed.; American Society for Quality Control, Statistics Division: Milwaukee, WI, USA, 1993. [Google Scholar]
  83. Gabrieli, J.D.E.; Ghosh, S.S.; Whitfield-Gabrieli, S. Prediction as a Humanitarian and Pragmatic Contribution from Human Cognitive Neuroscience. Neuron 2015, 85, 11–26. [Google Scholar] [CrossRef] [PubMed]
  84. Efron, B. Better Bootstrap Confidence Intervals. J. Am. Stat. Assoc. 1987, 82, 171–185. [Google Scholar] [CrossRef]
  85. Kohavi, R. A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. In Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, Montreal, QC, Canada, 20–25 August 1995; Morgan Kaufmann Publishers: San Mateo, CA, USA, 1995; Volume 2, pp. 1137–1143. [Google Scholar]
  86. Ojala, M.; Garriga, G.C. Permutation Tests for Studying Classifier Performance. J. Mach. Learn. Res. 2010, 11, 1833–1863. [Google Scholar]
  87. Marshall, O.; Lu, H.; Brisset, J.C.; Xu, F.; Liu, P.; Herbert, J.; Grossman, R.I.; Ge, Y. Impaired Cerebrovascular Reactivity in Multiple Sclerosis. JAMA Neurol. 2014, 71, 1275–1281. [Google Scholar] [CrossRef] [PubMed]
  88. Laron, M.; Cheng, H.; Zhang, B.; Schiffman, J.S.; Tang, R.A.; Frishman, L.J. Comparison of Multifocal Visual Evoked Potential, Standard Automated Perimetry and Optical Coherence Tomography in Assessing Visual Pathways in Multiple Sclerosis Patients. Mult. Scler. 2010, 16, 412–426. [Google Scholar] [CrossRef] [PubMed]
  89. Fraser, C.; Klistorner, A.; Graham, S.; Garrick, R.; Billson, F.; Grigg, J. Multifocal Visual Evoked Potential Latency Analysis: Predicting Progression to Multiple Sclerosis. Arch. Neurol. 2006, 63, 847–850. [Google Scholar] [CrossRef] [PubMed]
  90. Ruseckaite, R.; Maddess, T.; Danta, G.; Lueck, C.J.; James, A.C. Sparse Multifocal Stimuli for the Detection of Multiple Sclerosis. Ann. Neurol. 2005, 57, 904–913. [Google Scholar] [CrossRef] [PubMed]
  91. Pantano, P.; Mainero, C.; Caramia, F. Functional Brain Reorganization in Multiple Sclerosis: Evidence from fMRI Studies. J. Neuroimaging 2006, 16, 104–114. [Google Scholar] [CrossRef] [PubMed]
  92. White, A.T.; Lee, J.N.; Light, A.R.; Light, K.C. Brain Activation in Multiple Sclerosis: A BOLD fMRI Study of the Effects of Fatiguing Hand Exercise. Mult. Scler. 2009, 15, 580–586. [Google Scholar] [CrossRef] [PubMed]
  93. Chiaravalloti, N.D.; Hillary, F.G.; Ricker, J.H.; Christodoulou, C.; Kalnin, A.J.; Liu, W.C.; Steffener, J.; DeLuca, J. Cerebral Activation Patterns During Working Memory Performance in Multiple Sclerosis using fMRI. J. Clin. Exp. Neuropsychol. 2005, 27, 33–54. [Google Scholar] [CrossRef] [PubMed]
  94. Genova, H.M.; Sumowski, J.F.; Chiaravalloti, N.; Voelbel, G.T.; DeLuca, J. Cognition in Multiple Sclerosis: A Review of Neuropsychological and fMRI Research. Front. Biosci. 2009, 14, 1730–1744. [Google Scholar] [CrossRef]
  95. Sweet, L.H.; Rao, S.M.; Primeau, M.; Durgerian, S.; Cohen, R.A. Functional Magnetic Resonance Imaging Response to Increased Verbal Working Memory Demands Among Patients with Multiple Sclerosis. Hum. Brain. Mapp. 2006, 27, 28–36. [Google Scholar] [CrossRef] [PubMed]
  96. Genova, H.M.; Hillary, F.G.; Wylie, G.; Rypma, B.; DeLuca, J. Examination of Processing Speed Deficits in Multiple Sclerosis Using Functional Magnetic Resonance Imaging. J. Int. Neuropsychol. Soc. 2009, 15, 383–393. [Google Scholar] [CrossRef] [PubMed]
  97. Comabella, M.; Sastre-Garriga, J.; Montalban, X. Precision Medicine in Multiple Sclerosis: Biomarkers for Diagnosis, Prognosis, and Treatment Response. Curr. Opin. Neurol. 2016, 29, 254–262. [Google Scholar] [CrossRef] [PubMed]
  98. Pulicken, M.; Gordon-Lipkin, E.; Balcer, L.J.; Frohman, E.; Cutter, G.; Calabresi, P.A. Optical Coherence Tomography and Disease Subtype in Multiple Sclerosis. Neurology 2007, 69, 2085–2092. [Google Scholar] [CrossRef] [PubMed]
  99. Fisniku, L.K.; Brex, P.A.; Altmann, D.R.; Miszkiel, K.A.; Benton, C.E.; Lanyon, R.; Thompson, A.J.; Miller, D.H. Disability and T2 MRI Lesions: A 20-Year Follow-Up of Patients with Relapse Onset of Multiple Sclerosis. Brain 2008, 131, 808–817. [Google Scholar] [CrossRef] [PubMed]
  100. Lebrun, C.; Bensa, C.; Debouverie, M.; Wiertlevski, S.; Brassat, D.; de Seze, J.; Rumbach, L.; Pelletier, J.; Labauge, P.; Brochet, B.; et al. Association Between Clinical Conversion to Multiple Sclerosis in Radiologically Isolated Syndrome and Magnetic Resonance Imaging, Cerebrospinal Fluid, and Visual Evoked Potential. Arch. Neurol. 2009, 66, 841–846. [Google Scholar] [CrossRef] [PubMed]
  101. Leocanti, L.; Rocca, M.A.; Comi, G. MRI and Neurophysiological Measures to Predict Course, Disability and Treatment Response in Multiple Sclerosis. Curr. Opin. Neurol. 2016, 29, 243–253. [Google Scholar] [CrossRef] [PubMed]
  102. Okuda, D.T.; Mowry, E.M.; Cree, B.A.C.; Crabtree, E.C.; Goodin, D.S.; Waubant, E.; Pelletier, D. Asymptomatic Spinal Cord Lesions Predict Disease Progression in Radiologically Isolated Syndrome. Neurology 2011, 76, 686–692. [Google Scholar] [CrossRef] [PubMed]
  103. Stromillo, M.L.; Giorgio, A.; Rossi, F.; Battaglini, M.; Hakiki, B.; Malentacchi, G.; Santangelo, M.; Gasperini, C.; Bartolozzi, M.L.; Portaccio, E.; et al. Brain metabolic changes suggestive of axonal damage in radiologically isolated syndrome. Neurology 2013, 80, 2090–2094. [Google Scholar] [CrossRef] [PubMed]
  104. Campbell, G.R.; Worrall, J.T.; Mahad, D.J. The Central Role of Mitochondrial in Axonal Degeneration in Multiple Sclerosis. Mult. Scler. 2014, 20, 1806–1813. [Google Scholar] [CrossRef] [PubMed]
  105. Balcer, L.J.; Raynowska, J.; Nolan, R.; Galetta, S.L.; Kapoor, R.; Benedict, R.; Phillips, G.; LaRocca, N.; Hudson, L.; Rudick, R.; et al. Validity of Low-Contrast Letter Acuity as a Visual Performance Outcome Measure for Multiple Sclerosis. Mult. Scler. 2017, 23, 734–747. [Google Scholar] [CrossRef] [PubMed]
  106. Link, H.; Huang, Y.-M. Oligoclonal Bands in Multiple Sclerosis Cerebrospinal Fluid: An Update on Methodology and Clinical Usefulness. J. Immunol. 2006, 180, 17–28. [Google Scholar] [CrossRef] [PubMed]
  107. Shaffer, J.L.; Petrella, J.R.; Sheldon, F.C.; Choudhury, K.R.; Calhoun, V.D.; Coleman, R.E.; Doraiswamy, P.M. Predicting Cognitive Decline in Subjects at Risk for Alzheimer Disease by Using Combined Cerebrospinal Fluid, MR Imaging, and PET Biomarkers. Radiology 2013, 266, 583–591. [Google Scholar] [CrossRef] [PubMed]
  108. Whitfield-Gabrieli, S.; Ghosh, S.S.; Nieto-Castanon, A.; Saygin, Z.; Doehrmann, O.; Chai, X.J.; Reynolds, G.O.; Hofmann, S.G.; Pollack, M.H.; Gabrieli, J.D.E. Brain Connectomics Predict Response to Treatment in Social Anxiety Disorder. Mol. Psychiatry 2015, 21, 680–685. [Google Scholar] [CrossRef] [PubMed]
Figure 1. Example of three-trial visual stimulation task. Participants viewed a fixation cross at the center of the screen. This cross changed color at jittered intervals throughout task. Rest periods were also jittered. Continuous stimulation blocks lasted 60 s with 0° to 90° flickering annuli (at 8 Hz). Note: fixation cross was presented during task and rest periods however it cannot be seen in the task example periods here.
Figure 1. Example of three-trial visual stimulation task. Participants viewed a fixation cross at the center of the screen. This cross changed color at jittered intervals throughout task. Rest periods were also jittered. Continuous stimulation blocks lasted 60 s with 0° to 90° flickering annuli (at 8 Hz). Note: fixation cross was presented during task and rest periods however it cannot be seen in the task example periods here.
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Figure 2. Examples of oxygen metabolism changes (∆CMRO2)in occipital lobe. (A) HC ∆CMRO2; (B) MS patient ∆CMRO2. x = right-left, z = superior-inferior.
Figure 2. Examples of oxygen metabolism changes (∆CMRO2)in occipital lobe. (A) HC ∆CMRO2; (B) MS patient ∆CMRO2. x = right-left, z = superior-inferior.
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Figure 3. Graphical overview of masking procedure. For each participant, their top 5%, overlapping BOLD and CBF t-statistics (middle) within the anatomical ROI (left, yellow) were used to create the functional ROI mask (right, yellow). Functional measures (veBOLD, veCBF, veCMRO2, and ven) were extracted from each participant’s functional ROI mask.
Figure 3. Graphical overview of masking procedure. For each participant, their top 5%, overlapping BOLD and CBF t-statistics (middle) within the anatomical ROI (left, yellow) were used to create the functional ROI mask (right, yellow). Functional measures (veBOLD, veCBF, veCMRO2, and ven) were extracted from each participant’s functional ROI mask.
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Figure 4. Diffusion and Structural Image Processing Examples. (A) Diffusion tensor imaging white matter skeleton. (B) T1 image. (C) T1 image segmented into white matter (yellow), grey matter (orange), and cerebral spinal fluid (red) using SIENAX. (D) T2-FLAIR image. (E) Lesions demarcated (yellow) on T2-FLAIR image used for calculating lesion burden. (F) Spatially distinct lesions demarcated on T2-FLAIR image.
Figure 4. Diffusion and Structural Image Processing Examples. (A) Diffusion tensor imaging white matter skeleton. (B) T1 image. (C) T1 image segmented into white matter (yellow), grey matter (orange), and cerebral spinal fluid (red) using SIENAX. (D) T2-FLAIR image. (E) Lesions demarcated (yellow) on T2-FLAIR image used for calculating lesion burden. (F) Spatially distinct lesions demarcated on T2-FLAIR image.
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Figure 5. Effect sizes of group contrasts on calibrated functional magnetic imaging measures. Effect sizes reflect Cohen’s d. ns = non-significant effect, p > 0.05; * p < 0.05; ** p < 0.01.
Figure 5. Effect sizes of group contrasts on calibrated functional magnetic imaging measures. Effect sizes reflect Cohen’s d. ns = non-significant effect, p > 0.05; * p < 0.05; ** p < 0.01.
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Figure 6. Smoothed density estimates of BCa-bootstap distributions. Distributions of significant (solid lines) and bottom 5 (dashed lines) within-sample predictors of MS status are illustrated. Note: because of smoothing, tails of distributions may exceed 1.
Figure 6. Smoothed density estimates of BCa-bootstap distributions. Distributions of significant (solid lines) and bottom 5 (dashed lines) within-sample predictors of MS status are illustrated. Note: because of smoothing, tails of distributions may exceed 1.
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Figure 7. Leave-one-out cross-validation (LOOCV) out-of-sample classification accuracy of each model. * p < 0.05; ** = p < 0.01; *** = p < 0.001. † p-value also significant using Benjamini-Hotchberg correction (p < 0.05). ‡ p-value marginally significant using Benjamini-Hotchberg correction (p < 0.10).
Figure 7. Leave-one-out cross-validation (LOOCV) out-of-sample classification accuracy of each model. * p < 0.05; ** = p < 0.01; *** = p < 0.001. † p-value also significant using Benjamini-Hotchberg correction (p < 0.05). ‡ p-value marginally significant using Benjamini-Hotchberg correction (p < 0.10).
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Table 1. Group Characteristics.
Table 1. Group Characteristics.
Age50.10 (3.35)50.77 (3.35)0.885 a
MFIS39.10 (7.62)20.54 (4.57)0.046 a
Sex (% female)90.00%84.62%0.704 b
TICS Score27.00 (0.82)28.08 (1.43)0.520 a
Age of MS Onset38.67 (2.42)--
Disease Duration118.80 (19.32)--
Last Flare-up28.60 (11.32)--
Neurological Disability Score15.70 (3.71)--
Disease Modifying Therapies
 Dimethyl fumarate10%--
 Glatiramer acetate10%--
Mean (SEM). Age in years. MFIS = modified fatigue impact score total. Sex in percent female. TICS score = telephone interview for cognitive status score. Age of MS onset in years. Disease duration and last flare-up in months. Neurological disability score measured by self-report [46]. Disease modifying therapies represent percent of participants reporting use of therapy. a p-value based upon independent samples t-test. b p-value based upon Pearson χ2.
Table 2. Predictor Variables.
Table 2. Predictor Variables.
Predictor (Units if Available)Predictor CategoryWhat Predictor Measures
Normalized Grey Matter Volume (mm3)MR ImageTotal grey matter volume normalized to skull
Normalized White Matter Volume (mm3)MR ImageTotal white matter volume normalized to skull
Normalized Whole Brain Volume (mm3)MR ImageTotal brain volume normalized to skull
Skeleton AD (mm2/s)MR ImageDiffusion along primary diffusion axis
Skeleton FA (proportion)MR ImageProportion of anisotropic diffusion
Skeleton MD (mm2/s)MR ImageAverage Diffusion in primary diffusion axes
Skeleton RD (mm2/s)MR ImageDiffusion orthogonal to primary diffusion axis
T2-FLAIR Lesion Burden-absolute lesion volume (mm3)MR ImageTotal volume of lesioned brain tissue
T2-FLAIR Lesion Burden-relative lesion volume (%)MR ImageTotal lesioned brain tissue relative to total white matter volume
T2-FLAIR spatially distinct lesion countMR ImageTotal number of spatially distinct lesions
veBOLD (% signal change)MR ImageVisual cortex BOLD response to visual stimulation task
veCBF (% signal change)MR ImageVisual cortex CBF response to visual stimulation task
veCMRO2 (% signal change)MR ImageVisual cortex CMRO2 response to visual stimulation task
ven (proportion)MR ImageVisual cortex neural-vascular coupling
10/36 Delayed Recall (total correct after 15 min)NeuropsychVisuospatial memory/learning and delayed recall
10/36 Immediate Recall (total correct)NeuropsychVisuospatial memory/learning
25 Foot Walk (s)NeuropsychWalking ability and gait speed
9-Hole Peg Test-Dominant Hand (s)NeuropsychFinger and hand dexterity
9-Hole Peg Test-Non-dominant Hand (s)NeuropsychFinger and hand dexterity
Box Completion (items completed)NeuropsychMotor control
Controlled Oral Word Association Test (total correct)NeuropsychVerbal association fluency
Number Comparison (items completed)NeuropsychProcessing speed
Paced Auditory Serial Addition Test 2 (% correct)NeuropsychProcessing speed and selective/sustained attention
Paced Auditory Serial Addition Test 3 (% correct)NeuropsychProcessing speed and selective/sustained attention
Selective Reminding Task Delayed (items recalled)NeuropsychVerbal learning and memory
Selective Reminding Task Long-term Storage (items recalled)NeuropsychVerbal learning and long-term memory
Symbol-digit Modalities Test (items completed)NeuropsychSustained attention and concentration
Trail Making Task Form A (s)NeuropsychVisual search, attention, mental flexibility, and motor function
Trail Making Task Form B (s)NeuropsychVisual search, attention, mental flexibility, and motor function
Trail Making Task Form B-A (s)NeuropsychVisual search, attention, mental flexibility, and motor function
WAIS-III Digit Span Backward (items completed)NeuropsychShort-term, working memory
WAIS-III Digit Span Forward (items completed)NeuropsychShort-term, working memory
WAIS-III Digit Span Total (items completed)NeuropsychShort-term, working memory
WAIS-III Digit symbol coding (items completed)NeuropsychPerformance subtest of WAIS
Modified Fatigue Impact ScoreSymptomsFatigue symptomology
SF-36 Bodily Pain ScaleSymptomsGeneral measure of bodily pain
SF-36 EmotionSymptomsRole limitations due to emotional problems
SF-36 General Health ScaleSymptomsGeneral measure of health wellbeing
SF-36 Mental Health ScaleSymptomsGeneral measure of mental health
SF-36 Physical Functioning ScaleSymptomsGeneral measure of physical functioning
SF-36 Role Physical Function ScaleSymptomsRole limitations due to physical problems
SF-36 Social Functioning ScaleSymptomsGeneral measure of social functioning
SF-36 Vitality ScaleSymptomsGeneral measure of energy/fatigue
FLAIR = Fluid-attenuated inversion recovery. WAIS = Wechsler adult intelligent scale. SF-36 = Short-form health survey. MR Image = magnetic resonance image; Neuropsych = neuropsychological test; Symptoms = self-report general health and symptom measures. Explanations of neuropsychological tests and symptom measures taken from [44,48,50,51].
Table 3. Sample Physiological Data.
Table 3. Sample Physiological Data.
 Breath Rate11.20 (1.00)10.25 (0.79)0.747 a
 EtCO242.70 (1.81)39.23 (0.74)0.101 b
 Heart Rate66.90 (2.38)72.08 (3.18)0.207 b
 SpO298.10% (0.35%)97.85% (0.32%)0.596 b
5% CO2
 Breath Rate13.35 (1.28)15.42 (1.07)0.236 c
 EtCO248.95 (1.45)49.06 (0.64)0.950 c
 Heart Rate69.67 (2.38)75.04 (2.60)0.147 d
 SpO297.58% (0.39%)98.20% (0.20%)0.139 d
Mean (SEM). Breath Rate in breaths per minute. EtCO2 = end-tidal CO2 in mmHg. Heart Rate in beats per minute. SpO2 = peripheral oxygen saturation in percent hemoglobin saturation. p-values were based on independent samples. a 22 degrees-of-freedom; b 21 degrees-of-freedom; c 16 degrees-of-freedom; d 17 degrees-of-freedom.
Table 4. Accuracy and 95% Confidence Limits of Within-Sample Classification Analyses.
Table 4. Accuracy and 95% Confidence Limits of Within-Sample Classification Analyses.
PredictorPredictor Accuracy95% LCL95% UCLSignificant
SF-36 Physical Functioning Scale0.940.651.00Yes †
SF-36 Social Functioning Scale0.890.610.94Yes †
T2-FLAIR spatially distinct lesion count0.860.570.95Yes †
Box Completion0.860.520.95Yes †
SF-36 Role Physical Function Scale0.850.600.95Yes †
veCMRO20.820.550.91Yes ‡
Normalized Grey Matter Volume0.810.430.95No ‡
T2-FLAIR Lesion Burden-absolute lesion volume0.800.500.90No ‡
T2-FLAIR Lesion Burden-relative lesion volume0.800.500.90No ‡
SF-36 Emotion0.780.560.89Yes
9-Hole Peg Test-Non-dominant Hand0.770.550.91Yes ‡
SF-36 General Health Scale0.770.500.86No ‡
veCBF0.750.450.85No ‡
Normalized Whole Brain Volume0.730.450.86No
9-Hole Peg Test-Dominant Hand0.730.500.82No
SF-36 Bodily Pain Scale0.730.450.86No
Skeleton AD0.710.430.81No
Skeleton MD0.710.480.86No
Paced Auditory Serial Addition Test 2 s0.710.480.86No
Modified Fatigue Impact Score Total0.700.430.78No ‡
Normalized White Matter Volume0.680.450.82No
Paced Auditory Serial Addition Test 3 s0.680.450.82No
Skeleton RD0.670.480.76No
Trail Making Task Form A0.650.430.78No
SF-36 Vitality Scale0.650.430.74No
25 Foot Walk0.640.500.77No
WAIS-III Digit Span Backward0.640.410.77No
WAIS-III Digit Span Total0.640.410.82No
10/36 Delayed Recall0.630.420.74No
Trail Making Task Form B0.620.330.76No
SF-36 Mental Health Scale0.620.380.62No
Selective Reminding Task Delayed0.600.350.60No
Symbol-digit Modalities Test0.600.300.70No
Number Comparison0.590.360.68No
WAIS-III Digit symbol coding0.580.370.58No
Skeleton FA0.570.370.67No
Selective Reminding Task Long-term Storage0.570.350.70No
Controlled Oral Word Association Test0.570.350.57No
10/36 Immediate Recall0.520.300.57No
WAIS-III Digit Span Forward0.500.270.55No
Trail Making Task Form B-A0.480.290.52No
LCL = lower confidence limit. UCL = upper confidence limit. Confidence limits based upon 10,000 iteration BCa-corrected bootstrapping procedure.Yes = 95% confidence interval (CI) does not contain 0.50; No = 95% CI contains 0.50. Note: that the original parameter estimates do not necessarily need to lie within the 95% CI of the BCa-corrected, empirically derived distributions. † permutation p-value significant using Benjamini-Hotchberg correction (p < 0.05). ‡ permutation p-value marginally significant using Benjamini-Hotchberg correction (p < 0.10).
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