**Feedback Regulation**: We evaluated the contributions of reflex-mediated responses to wrist stabilization by analyzing the cross-correlation between wrist angular velocity and measured EMG responses in the *RT* condition. For wrist flexor FCR, we consistently observed increased EMG activity lagging wrist extension by 59.0 ± 44.2 ms (Fig. 5B-d). The timing of this loaddependent activity was within the range of delays expected for transcortical reflex compen‐ sation for muscle stretch (Evarts and Vaughn, 1978; Strick, 1978; Matthews, 1981). For wrist extensor ECR, we observed decreased EMG activity lagging wrist extension by 40.3 ± 23.7 ms (Fig. 5B-c). The sign and latency of these EMG changes are consistent with an unloading response mediated by spinal circuits (Sinkjaer et al., 2000). We also observed an increase in ECR activity with lag of 484.8± 118.3 ms (Fig. 5B-e) in 5 out of 10 subjects. This later response

is consistent with a strategy of voluntary co-activation about the wrist since no contempora‐ neous decrease in flexor activity was observed.

**Figure 5.** (A) Population average of antagonist muscle coactivity*.* Horizontal bars indicate significant differences be‐ tween stabilization conditions (p < 0.05). Hatched bars: co-activity during phases 2, 4, and 5. White and gray bars: coactivity during periods of active stabilization against *CT* and *RT* respectively. Error bars indicate ± 2 SEM. (B) Crosscorrelation of wrist angular velocity (dθ/dt) and EMG from flexor carpi radialis (*FCR;* black) and extensor carpi radiallis (*ECR*; gray) for a representative subject. Significant correlations were observed with EMG both lagging/leading changes in dθ/dt. Horizontal dashed lines: 95% confidence interval about zero correlation. Figure adapted from Su‐ minski et al., 2007b.

were less able to maintain steady hand posture while perturbed by band-limited pseudorandom torques than by constant torques. Paired t-tests found greater errors in *RT* vs. *CT* trials (p <= 0.0005, both sessions). We also observed positional drift over time (a significant slope in the joint angle time series over the final 20 seconds a trial) in 91% of *RT* trials (88 of 96) and 75% of *CT* trials (72 of 95). The absolute magnitude of this drift was 0.155 ± 0.104 °/s and 0.017

**Figure 4.** (A) Single stabilization trials were conducted in 5 phases. See text for details. (B) During each imaging run, subjects stabilized their wrist against constant (gray line) or pseudo-random (black line) extensor torque perturba‐

**Elevated Coactivity**: We quantified coordination between muscles in the arm by estimating the amount of antagonist muscle co-activity at the wrist, elbow and shoulder joints using a coactivity measure also known as 'wasted contraction' (Thoroughman and Shadmehr, 1999) [*C(t)*]. We considered the anterior and postierior deltoid as shoulder antagonists, the biceps and triceps as elbow antagonists, and flexor carpi radialis and extensor carpi radialis as wrist antagonists. ANOVA and post-hoc Dunnet's t-tests found significant elevations in muscle coactivity values (Fig. 5A) during phase 3 *RT* stabilization at each joint (p < 0.0005) and during phase 3 *CT* stabilization at the wrist (p = 0.03). No significant EMG activity was observed at

**Feedback Regulation**: We evaluated the contributions of reflex-mediated responses to wrist stabilization by analyzing the cross-correlation between wrist angular velocity and measured EMG responses in the *RT* condition. For wrist flexor FCR, we consistently observed increased EMG activity lagging wrist extension by 59.0 ± 44.2 ms (Fig. 5B-d). The timing of this loaddependent activity was within the range of delays expected for transcortical reflex compen‐ sation for muscle stretch (Evarts and Vaughn, 1978; Strick, 1978; Matthews, 1981). For wrist extensor ECR, we observed decreased EMG activity lagging wrist extension by 40.3 ± 23.7 ms (Fig. 5B-c). The sign and latency of these EMG changes are consistent with an unloading response mediated by spinal circuits (Sinkjaer et al., 2000). We also observed an increase in ECR activity with lag of 484.8± 118.3 ms (Fig. 5B-e) in 5 out of 10 subjects. This later response

any joint during passive movement (phases 2 and 4) when compared to rest.

± 0.028 °/s in the *RT* and *CT* cases respectively.

tions. Figure adapted from Suminski et al., 2007b.

454 Advanced Brain Neuroimaging Topics in Health and Disease - Methods and Applications

**Discrete Corrections**: Consistent with expectation, we observed many rapid movements that did not appear to be a direct mechanical consequence of moment-by-moment fluctuations in the robot's commanded wrist torque. To identify the onset of discrete corrective movements, we first computed the average wrist angle trajectory for each subject in each condition. We then removed the trial-averaged trajectory from every individual trial to obtain a 'corrected' wrist angle time series. We considered as discrete corrections only those motions wherein the angular velocity of corrected wrist angle trajectories exceeded 5°/s if they occurred >1 second after the start of stabilization. Discrete corrective movements occurred in 95% of *RT* trials (91 of 96) and 19% of *CT* trials (18 of 95). The majority of corrective movements were directed appropriately to reduce positioning errors (56.1% and 80% of movements in the *RT* and *CT* cases, respectively).

In summary, the behavioral and electromyographic data revealed that subjects compensated for environmental perturbations using a combination of three readily identifiable strategies. Subjects modulated limb impedance via co-activation of agonist/antagonist muscle pairs spanning the wrist, elbow and shoulder. Correlation analysis found that subjects also invoke both spinal and supraspinal reflexes to compensate for the perturbations. Finally, subjects generated discrete corrective movements to reduce performance errors that likely accumulated due to the lack of visual feedback during stabilization (cf. Wann and Ibrahim, 1992). We next sought to characterize the neural mechanisms contributing to each of these strategies during experiments conducted within the MR scanner.

resetting of behavioral goals when ongoing performance is deficient (Jahanshahi et al., 1995; Winstein et al., 1997; Jueptner and Weiller, 1998; Sakai et al., 1999). As shown for an ROI in the cerebellum (Fig 6B, CBLM), some regions exhibited an intermediate pattern (i.e. some sensitivity to stabilization type but little sensitivity to passive movement). To quantify these observations, we defined a 2-element feature vector *Ψ* = {*S*¯, *ΔM*} summarizing the average increase in PSC induced by passive movement in phase 2 (*S*¯) and the difference in PSC between *RT* and *CT* trials during stabilization (*ΔM*). The *S*¯ feature was the average of 4 consecutive TR samples from each of the *RT* and *CT* trials, beginning 5 seconds (i.e. 2 TR) prior to the start of the stabilization period. The *ΔM* feature was computed as the difference between the average

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Using these features, we performed a k-means cluster analysis to identify ROIs demonstrating common patterns of sensitivity to trial type and/or passive movement. Two distinct groups were identified: ROIs that were sensitive to both trial type and passive movement (Cluster I; red regions in Fig. 6A), and those demonstrating increased BOLD activation over the duration of the trial with little sensitivity to passive movement (Cluster II; blue regions in Fig. 6A). Group separation and membership was visualized by plotting the coordinates of each ROI on the axes defined by the features of *Ψ* (Fig. 6C; Cluster I = red circles; Cluster II = blue squares). Regions showing significant sensitivity to passive movement (*S*¯≠ 0; p < 0.05) are indicated by filled symbols. These include left M1/S1, SPL/IPL (BA 5 and 7), and SMA/CCZ. The left M1/S1, SPL/IPL (BA 5 and 7), and right STG (BA 39) were also significantly modulated by task type (*ΔM* > 0; p < 0.05). ROIs with membership in Cluster I were characterized by both a high sensitivity to trial type (being more active during RT trials) and a robust response to passive movement of the wrist. Such sensitivity to positioning errors is necessary for regions directly involved in the feedback control of movement kinematics (i.e. limb position, velocity and acceleration). Indeed, responsivity to passive movement has been considered a hallmark of brain regions participating in the optimal feedback control of movement (Scott, 2004). In contrast, Cluster II ROIs exhibited generally elevated BOLD activation during stabilization with little sensitivity to trial type or passive movement. This pattern of activity is expected from regions involved in supervisory aspects of control rather than in the moment-by-moment

We performed a second BOLD regression analysis to identify brain regions involved in generating the discrete corrective movements observed behaviorally. We limited our investi‐ gation to the *CT* stabilization blocks because corrections were unambiguous and most often directed to decrease kinematic error. In particular, we wished to know whether the regions generating corrective movements might be a subset of the networks previously described or different networks. For this event-related analysis, we created input reference functions for each subject indicating the onset times of discrete corrective movements during *CT* stabiliza‐ tion. These time series had a value of 1 during each sampling instant (TR) wherein a discrete corrective movement occurred and 0 otherwise. Additional reference time series were included to mitigate effects of head motion and to model the average activity used to maintain limb position during CT stabilization. Regressors were convolved with a γ-variate function and the resulting functional images were pre-processed as described above. Voxel-wise, one-sided t-

*RT* and *CT* PSC time series during the middle 20 seconds (8 TR) of stabilization.

cancellation of position errors.

#### **3.3. Neural correlates of stabilization**

Functional images were generated and analyzed within the Analysis of Functional NeuroI‐ mages (AFNI) software package (Cox, 1996). The first three images in each run were discarded to allow for equilibration of the magnetic field. Individual run time series were then concaten‐ ated and aligned in three-dimensional space using an interactive, linear, least squares method. Voxel-wise multiple linear regression was used to determine the amount of FMRI signal contrast between the two task conditions (*CT* and *RT* stabilization) and the resting baseline. The resulting functional images for *RT* and *CT* were interpolated to obtain a volumetric grid having 1mm3 voxel volumes, coregistered, and then converted into the Talairach stereotaxic coordinate space (Talairach and Tournoux, 1988). To facilitate group analysis, the Talairached functional images were spatially blurred using a 4-mm Gaussian full-width half-maximum filter to compensate for inter-subject anatomical variability. In all across-subject analyses, a activation volume and thresholding technique was used to correct for multiple comparisons in the group analysis. Appropriate activation volume and individual voxel p-value thresholds (p=0.005) were estimated by performing 5000 iterations in a Monte-Carlo simulation using the AlphaSim tool within AFNI (Cox, 1996). The location of activated regions in group statistical parametric maps was obtained using the Talairach atlas (Talairach and Tournoux, 1988) for cerebral activations and the Schmahmann atlas (Schmahmann, 2000) for activations in the cerebellum and its nuclei. Cortical activations were visualized using CARET (Van Essen et al., 2001); http://brainmap.wustl.edu/caret).

Changes in BOLD signal intensity (relative to rest) correlated with periods of *CT* or *RT* stabilization in many brain regions that contribute to control of the upper extremity (Table 1; Fig. 6A). We visualized the time series of BOLD activations for each ROI during stabilization against each load type for each subject. Two patterns of activation became evident. The first, an example of which is shown for an ROI spanning left primary motor and somatosensory cortices (Fig. 6B; M1/S1), was characterized by increases in percentage BOLD signal intensity change (PSC) throughout the *RT* stabilization periods and during the passive hand movements preceding and following *CT* stabilization. Motion sensitivity during active stabilization and passive movement might be expected from brain regions concerned with the estimation of and on-line feedback correction for - performance errors (Marsden et al., 1972; Lee and Tatton, 1975; Evarts and Vaughn, 1978; Marsden et al., 1978; Strick, 1978; Horne and Butler, 1995; Scott, 2004). This idea is based on the assumption that passive movement of the wrist induces a discrepancy (error) between actual limb position and that expected given the recent history of motor output. The second pattern, an example is shown for the right middle frontal gyrus (Fig. 6B; BA 46), was characterized by a parallel increase in PSC during both stabilization conditions with no sensitivity to passive movement. This pattern might be expected from regions (i.e. prefrontal cortex, anterior cingulate cortex, rostral aspects of dorsal premotor and SMA cortices, and inferior aspects of posterior parietal cortex) involved in supervisory aspects of control such as evaluating the success of an ongoing task (Carter et al., 1998) as well as the resetting of behavioral goals when ongoing performance is deficient (Jahanshahi et al., 1995; Winstein et al., 1997; Jueptner and Weiller, 1998; Sakai et al., 1999). As shown for an ROI in the cerebellum (Fig 6B, CBLM), some regions exhibited an intermediate pattern (i.e. some sensitivity to stabilization type but little sensitivity to passive movement). To quantify these observations, we defined a 2-element feature vector *Ψ* = {*S*¯, *ΔM*} summarizing the average increase in PSC induced by passive movement in phase 2 (*S*¯) and the difference in PSC between *RT* and *CT* trials during stabilization (*ΔM*). The *S*¯ feature was the average of 4 consecutive TR samples from each of the *RT* and *CT* trials, beginning 5 seconds (i.e. 2 TR) prior to the start of the stabilization period. The *ΔM* feature was computed as the difference between the average *RT* and *CT* PSC time series during the middle 20 seconds (8 TR) of stabilization.

sought to characterize the neural mechanisms contributing to each of these strategies during

Functional images were generated and analyzed within the Analysis of Functional NeuroI‐ mages (AFNI) software package (Cox, 1996). The first three images in each run were discarded to allow for equilibration of the magnetic field. Individual run time series were then concaten‐ ated and aligned in three-dimensional space using an interactive, linear, least squares method. Voxel-wise multiple linear regression was used to determine the amount of FMRI signal contrast between the two task conditions (*CT* and *RT* stabilization) and the resting baseline. The resulting functional images for *RT* and *CT* were interpolated to obtain a volumetric grid

coordinate space (Talairach and Tournoux, 1988). To facilitate group analysis, the Talairached functional images were spatially blurred using a 4-mm Gaussian full-width half-maximum filter to compensate for inter-subject anatomical variability. In all across-subject analyses, a activation volume and thresholding technique was used to correct for multiple comparisons in the group analysis. Appropriate activation volume and individual voxel p-value thresholds (p=0.005) were estimated by performing 5000 iterations in a Monte-Carlo simulation using the AlphaSim tool within AFNI (Cox, 1996). The location of activated regions in group statistical parametric maps was obtained using the Talairach atlas (Talairach and Tournoux, 1988) for cerebral activations and the Schmahmann atlas (Schmahmann, 2000) for activations in the cerebellum and its nuclei. Cortical activations were visualized using CARET (Van Essen et al.,

Changes in BOLD signal intensity (relative to rest) correlated with periods of *CT* or *RT* stabilization in many brain regions that contribute to control of the upper extremity (Table 1; Fig. 6A). We visualized the time series of BOLD activations for each ROI during stabilization against each load type for each subject. Two patterns of activation became evident. The first, an example of which is shown for an ROI spanning left primary motor and somatosensory cortices (Fig. 6B; M1/S1), was characterized by increases in percentage BOLD signal intensity change (PSC) throughout the *RT* stabilization periods and during the passive hand movements preceding and following *CT* stabilization. Motion sensitivity during active stabilization and passive movement might be expected from brain regions concerned with the estimation of and on-line feedback correction for - performance errors (Marsden et al., 1972; Lee and Tatton, 1975; Evarts and Vaughn, 1978; Marsden et al., 1978; Strick, 1978; Horne and Butler, 1995; Scott, 2004). This idea is based on the assumption that passive movement of the wrist induces a discrepancy (error) between actual limb position and that expected given the recent history of motor output. The second pattern, an example is shown for the right middle frontal gyrus (Fig. 6B; BA 46), was characterized by a parallel increase in PSC during both stabilization conditions with no sensitivity to passive movement. This pattern might be expected from regions (i.e. prefrontal cortex, anterior cingulate cortex, rostral aspects of dorsal premotor and SMA cortices, and inferior aspects of posterior parietal cortex) involved in supervisory aspects of control such as evaluating the success of an ongoing task (Carter et al., 1998) as well as the

voxel volumes, coregistered, and then converted into the Talairach stereotaxic

experiments conducted within the MR scanner.

456 Advanced Brain Neuroimaging Topics in Health and Disease - Methods and Applications

**3.3. Neural correlates of stabilization**

2001); http://brainmap.wustl.edu/caret).

having 1mm3

Using these features, we performed a k-means cluster analysis to identify ROIs demonstrating common patterns of sensitivity to trial type and/or passive movement. Two distinct groups were identified: ROIs that were sensitive to both trial type and passive movement (Cluster I; red regions in Fig. 6A), and those demonstrating increased BOLD activation over the duration of the trial with little sensitivity to passive movement (Cluster II; blue regions in Fig. 6A). Group separation and membership was visualized by plotting the coordinates of each ROI on the axes defined by the features of *Ψ* (Fig. 6C; Cluster I = red circles; Cluster II = blue squares). Regions showing significant sensitivity to passive movement (*S*¯≠ 0; p < 0.05) are indicated by filled symbols. These include left M1/S1, SPL/IPL (BA 5 and 7), and SMA/CCZ. The left M1/S1, SPL/IPL (BA 5 and 7), and right STG (BA 39) were also significantly modulated by task type (*ΔM* > 0; p < 0.05). ROIs with membership in Cluster I were characterized by both a high sensitivity to trial type (being more active during RT trials) and a robust response to passive movement of the wrist. Such sensitivity to positioning errors is necessary for regions directly involved in the feedback control of movement kinematics (i.e. limb position, velocity and acceleration). Indeed, responsivity to passive movement has been considered a hallmark of brain regions participating in the optimal feedback control of movement (Scott, 2004). In contrast, Cluster II ROIs exhibited generally elevated BOLD activation during stabilization with little sensitivity to trial type or passive movement. This pattern of activity is expected from regions involved in supervisory aspects of control rather than in the moment-by-moment cancellation of position errors.

We performed a second BOLD regression analysis to identify brain regions involved in generating the discrete corrective movements observed behaviorally. We limited our investi‐ gation to the *CT* stabilization blocks because corrections were unambiguous and most often directed to decrease kinematic error. In particular, we wished to know whether the regions generating corrective movements might be a subset of the networks previously described or different networks. For this event-related analysis, we created input reference functions for each subject indicating the onset times of discrete corrective movements during *CT* stabiliza‐ tion. These time series had a value of 1 during each sampling instant (TR) wherein a discrete corrective movement occurred and 0 otherwise. Additional reference time series were included to mitigate effects of head motion and to model the average activity used to maintain limb position during CT stabilization. Regressors were convolved with a γ-variate function and the resulting functional images were pre-processed as described above.  Brain regions exhibiting significant task-related activation during *RT* or *CT* trials.

*Cluster I*

*Cluster II* **Cortical**

Cingulate Gyrus (BA 24,31)

Superior Temporal Gyrus (BA 22,41)

Superior Temporal Gyrus (BA 22)

Supramarginal Gyrus

Angular Gyrus

Cingulate Gyrus (BA 32) Superior Frontal Gyrus (BA 8)

**Subcortical and Cerebellum**

Dentate R

L = Left; R = Right; B = Bilateral; BA = Broadman's Area

**Figure 6.** (A) Population maps showing significantly activated ROIs during periods of random *RT* or constant *CT* stabi‐ lization (p < 0.05; corrected for multiple comparisons). Cluster I ROIs are shown in red while Cluster II ROIs are shown in blue. Upper panels: activations mapped onto inflated representations of the cerebral hemispheres. Lower panels: activations in the basal ganglia, thalamus, cerebellar cortex (lobule IV-VI), and right dentate nucleus. (B) Percent signal change (PSC) of BOLD time series from *RT* (black) and *CT* (gray) stabilization periods. Shaded regions: ±2 SEM. Scale bars above the PSC plots indicate the time intervals used to calculate the components of Ψ = {*S* ¯ , Δ*M*}. (C) Group mem‐ bership for 18 ROIs was visualized by plotting the coordinates of each ROI within the plane defined by the features of Ψ (red: Cluster I; blue: Cluster II). Filled symbols: region where PSC differs significantly from resting baseline. Figure reproduced with permission from Suminski et al., 2007b.

tests found specific regions in Cluster I and Cluster II to be modulated discrete movement corrections. Interestingly, BOLD signal intensity *increased* with respect to baseline in a subset of regions in Cluster II, while a subset of regions in Cluster I exhibited BOLD signal *decreases* (Fig. 7; red and blue areas, respectively). These results highlight the manner in which Cluster I and II regions work together to maintain the position of the limb during stabilization against a deterministic perturbation: During CT trials, discrete corrective movements induce a persistent decrease in the magnitude of kinematic errors, thus reducing the need for feedback regulation of position and likely contributing to the inhibition of Cluster I ROIs. Conversely, we found the BOLD activity increased in a subset of Cluster II ROIs at the specific times when discrete movements were performed. Such behavior would be expected from brain regions involved in the resetting of behavioral goals (reference wrist angle) when performance is deficient.

**Table 1.** Brain regions exhibiting significant task-related activation during *RT* or *CT* trials.

tests found specific regions in Cluster I and Cluster II to be modulated discrete movement corrections. Interestingly, BOLD signal intensity *increased* with respect to baseline in a subset of regions in Cluster II, while a subset of regions in Cluster I exhibited BOLD signal *decreases* (Fig. 7; red and blue areas, respectively). These results highlight the manner in which Cluster I and II regions work together to maintain the position of the limb during stabilization against a deterministic perturbation: During CT trials, discrete corrective movements induce a persistent decrease in the magnitude of kinematic errors, thus reducing the need for feedback regulation of position and likely contributing to the inhibition of Cluster I ROIs. Conversely, we found the BOLD activity increased in a subset of Cluster II ROIs at the specific times when discrete movements were performed. Such behavior would be expected from brain regions involved in the resetting of behavioral goals (reference wrist angle) when performance is

bership for 18 ROIs was visualized by plotting the coordinates of each ROI within the plane defined by the features of Ψ (red: Cluster I; blue: Cluster II). Filled symbols: region where PSC differs significantly from resting baseline. Figure

**Figure 6.** (A) Population maps showing significantly activated ROIs during periods of random *RT* or constant *CT* stabi‐ lization (p < 0.05; corrected for multiple comparisons). Cluster I ROIs are shown in red while Cluster II ROIs are shown in blue. Upper panels: activations mapped onto inflated representations of the cerebral hemispheres. Lower panels: activations in the basal ganglia, thalamus, cerebellar cortex (lobule IV-VI), and right dentate nucleus. (B) Percent signal change (PSC) of BOLD time series from *RT* (black) and *CT* (gray) stabilization periods. Shaded regions: ±2 SEM. Scale

¯ , Δ*M*}. (C) Group mem‐

bars above the PSC plots indicate the time intervals used to calculate the components of Ψ = {*S*

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reproduced with permission from Suminski et al., 2007b.

deficient.

**Figure 7.** Activation maps showing regions where BOLD signal correlated with the generation of discrete corrective movements (p < 0.05, corrected for multiple comparisons). Increased BOLD activiy was observed in a subset of Cluster II regions [red: right pre-PMd, left medial frontal gyrus (BA 8) and rostral cingulate]. BOLD activaions decreased rela‐ tive to resting state activity in Cluster I regions (blue: bilateral SMA and right PMd).

We performed a final set of multiple linear regression analyses to identify brain regions explicitly involved in the moment-by-moment and long-term evaluation and correction for kinematic and/or kinetic performance errors. We wished to know whether the activations identified in the preceding analyses were related to compensation for kinematic errors, generation of wrist torques, or both. Here, we performed four separate regressions using input reference functions corresponding to the magnitude of RMS wrist angle errors and RMS torque both on a trial-by-trial and TR-by-TR basis (*ErrorTrial*, *ErrorTR*, *TorqueTrial*, and *TorqueTR*). Each function was created from error or pressure data measured during the corresponding stabili‐ zation run within the MR-scanner. The value at each (TR) sampling instant for *ErrorTrial* and *TorqueTrial* was set equal to the RMS error (or torque) value computed throughout the corre‐ sponding stabilization period (30 s integration window). The value at each (TR) sampling instant for *ErrorTR* and *TorqueTR* (reference functions that varied on a moment-by-moment basis) was set equal to the RMS error (torque) value computed during that TR sampling period (2.5 s integration window). In all cases, the reference time series were convolved with a γ-variate function to model the temporal filtering properties of the hemodynamic response. Additional reference time series included head motion parameters and functions representing periods of *RT* and *CT* stabilization. The resulting functional images were processed as described above. Voxel-wise, one-sided t-tests were used to identify regions where BOLD signal covaried with *ErrorTrial*, *ErrorTR*, *TorqueTrial*, or *TorqueTR*.

**Figure 8.** Activation maps showing regions wherein the BOLD response correlated with kinematic performance errors (i.e. state estimation errors) on both a moment-by-moment (*ErrorTR*, red regions) and a trial-by-trial basis (*ErrorTrial*, blue regions) (p < 0.05, corrected for multiple comparisons). Figure reproduced with permission from Suminski et al.,

Inferior Parietal Lobule (BA 40) L 51.3 29.9 28.4 3599 4.7941

Medial Frontal Gyrus (BA 6) B 1.5 4.9 50 3056 4.7047

**Tailarach Coordinates Hem X Y Z Vol Mean T**

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2007b.

*ErrorTR* **Cortical**

Supramarginal Gyrus Postcentral Gyrus (BA 2)

Superior Frontal Gyurs (BA 6) L Cingulate Gyrus (BA 24) R

Insula (BA 13)

Regions demonstrating increased sensitivity to errors that change on a moment-by-moment timescale (*ErrorTR*; Fig. 8 red) were predominantly members of Cluster I, while regions sensitive to kinematic errors on a much longer timescale (*ErrorTrial*; Fig. 8 blue) were more likely to be members of Cluster II (see also Table 2). Activation of the left IPL in the *ErrorTrial* contrast was located more posterior to those activated regions of IPL in the *ErrorTR* contrast. There were no overlapping activations in the *ErrorTrial* and *ErrorTR* contrasts. Importantly, no regions were found to be active in contrasts examining differences between either *TorqueTrial* or *TorqueTR* and resting baseline.

We performed a final set of multiple linear regression analyses to identify brain regions explicitly involved in the moment-by-moment and long-term evaluation and correction for kinematic and/or kinetic performance errors. We wished to know whether the activations identified in the preceding analyses were related to compensation for kinematic errors, generation of wrist torques, or both. Here, we performed four separate regressions using input reference functions corresponding to the magnitude of RMS wrist angle errors and RMS torque both on a trial-by-trial and TR-by-TR basis (*ErrorTrial*, *ErrorTR*, *TorqueTrial*, and *TorqueTR*). Each function was created from error or pressure data measured during the corresponding stabili‐ zation run within the MR-scanner. The value at each (TR) sampling instant for *ErrorTrial* and *TorqueTrial* was set equal to the RMS error (or torque) value computed throughout the corre‐ sponding stabilization period (30 s integration window). The value at each (TR) sampling instant for *ErrorTR* and *TorqueTR* (reference functions that varied on a moment-by-moment basis) was set equal to the RMS error (torque) value computed during that TR sampling period (2.5 s integration window). In all cases, the reference time series were convolved with a γ-variate function to model the temporal filtering properties of the hemodynamic response. Additional reference time series included head motion parameters and functions representing periods of *RT* and *CT* stabilization. The resulting functional images were processed as described above. Voxel-wise, one-sided t-tests were used to identify regions where BOLD signal covaried with

tive to resting state activity in Cluster I regions (blue: bilateral SMA and right PMd).

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**Figure 7.** Activation maps showing regions where BOLD signal correlated with the generation of discrete corrective movements (p < 0.05, corrected for multiple comparisons). Increased BOLD activiy was observed in a subset of Cluster II regions [red: right pre-PMd, left medial frontal gyrus (BA 8) and rostral cingulate]. BOLD activaions decreased rela‐

Regions demonstrating increased sensitivity to errors that change on a moment-by-moment timescale (*ErrorTR*; Fig. 8 red) were predominantly members of Cluster I, while regions sensitive to kinematic errors on a much longer timescale (*ErrorTrial*; Fig. 8 blue) were more likely to be members of Cluster II (see also Table 2). Activation of the left IPL in the *ErrorTrial* contrast was located more posterior to those activated regions of IPL in the *ErrorTR* contrast. There were no overlapping activations in the *ErrorTrial* and *ErrorTR* contrasts. Importantly, no regions were found to be active in contrasts examining differences between either *TorqueTrial* or *TorqueTR* and

*ErrorTrial*, *ErrorTR*, *TorqueTrial*, or *TorqueTR*.

resting baseline.

**Figure 8.** Activation maps showing regions wherein the BOLD response correlated with kinematic performance errors (i.e. state estimation errors) on both a moment-by-moment (*ErrorTR*, red regions) and a trial-by-trial basis (*ErrorTrial*, blue regions) (p < 0.05, corrected for multiple comparisons). Figure reproduced with permission from Suminski et al., 2007b.

computer controlled perturbations of movement and 2) compatible with the MR scanner such that performance of neither the device nor image quality was affected by robot operation. We then demonstrated device utility in a study of wrist posture stabilization against environmen‐ tal perturbation. We provided behavioral evidence that subjects invoke three complementary compensatory responses when stabilizing the wrist in the absence of ongoing visual feedback of task performance. These compensatory responses include feedback regulation via spinal and long-loop reflexes, impedance modulation via antagonist muscle co-activation and feedforward, discrete corrective movements. Analysis of functional neuroimages obtained from the same subjects performing the same tasks revealed two distinct networks that were differentially excited by the task. The first (Cluster I) included a cerebello-thalamo-cortical network previously implicated in the online computation and feedback correction of errors (Marsden et al., 1972; Lee and Tatton, 1975; Evarts and Vaughn, 1978; Marsden et al., 1978; Strick, 1978; Horne and Butler, 1995). BOLD signal changes within these regions were corre‐ lated with moment-by-moment fluctuations in state estimation errors (Fig. 7, red regions; *ErrorTR*, 2.5 s integration period). That is, BOLD activity in these regions was elevated mainly during random torque perturbations and during passive movement of the hand, despite the absence of significant muscle activation during passive manipulation in the behavioral experiments and the production of the same average wrist torques in both tasks. A second network (Cluster II) exhibited similarly elevated BOLD activity during performance of both stabilization tasks. Brain regions demonstrating this pattern include the prefrontal cortex, rostral aspects of dorsal premotor and SMA cortices, and inferior aspects of posterior parietal cortex. These brain regions have previously been reported to contribute to the planning and execution of internally-generated, discrete motor actions (Jahanshahi et al., 1995; Winstein et al., 1997; Jueptner and Weiller, 1998; Sakai et al., 1999). Consistent with those reports, we found that BOLD activity increased within rostral PMd, SMA and cingulate cortex during time periods wherein subjects generated discrete corrective movements. Although discrete corrections were transient events, they gave rise to long-lasting performance improvements in most cases and so, they likely represent a resetting of the reference wrist angle when online (moment-by-moment) feedback control failed to satisfy subjective performance constraints. To do so, however, subjects first needed to determine when error had grown sufficiently large to warrant correction and then plan the direction and magnitude of corrective action. This type of supervisory role likely was supported by Cluster II ROIs demonstrating high sensitivity to

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state estimation errors over a longer, 30 s timeframe (Fig. 7, blue regions).

These results highlight the importance of both postural and movement trajectory control mechanisms in peripheral limb stabilization and suggest a possible neural basis for the distinct postural and trajectory (movement) control mechanisms recently isolated during point-topoint arm movements and movement sequences (Ghez et al., 2007; Scheidt and Ghez, 2007). Additional studies are needed to better understand how the brain combines the different control processes to minimize performance errors and how the brain uses information from multiple sensory feedback modalities to optimize limb stabilization and movement control. Such work will be greatly facilitated by the use of mechanically-active tools that apply physical

perturbations to the limb while subjects undergo concurrent functional MR imaging.

**Table 2.** Brain regions activated in the ErrorTR and ErrorTrial contrasts.

## **4. Summary and future directions**

This chapter has described the development and validation of a novel, 1 DOF pneumatically actuated manipulandum. We demonstrated that the device was: 1) capable of generating computer controlled perturbations of movement and 2) compatible with the MR scanner such that performance of neither the device nor image quality was affected by robot operation. We then demonstrated device utility in a study of wrist posture stabilization against environmen‐ tal perturbation. We provided behavioral evidence that subjects invoke three complementary compensatory responses when stabilizing the wrist in the absence of ongoing visual feedback of task performance. These compensatory responses include feedback regulation via spinal and long-loop reflexes, impedance modulation via antagonist muscle co-activation and feedforward, discrete corrective movements. Analysis of functional neuroimages obtained from the same subjects performing the same tasks revealed two distinct networks that were differentially excited by the task. The first (Cluster I) included a cerebello-thalamo-cortical network previously implicated in the online computation and feedback correction of errors (Marsden et al., 1972; Lee and Tatton, 1975; Evarts and Vaughn, 1978; Marsden et al., 1978; Strick, 1978; Horne and Butler, 1995). BOLD signal changes within these regions were corre‐ lated with moment-by-moment fluctuations in state estimation errors (Fig. 7, red regions; *ErrorTR*, 2.5 s integration period). That is, BOLD activity in these regions was elevated mainly during random torque perturbations and during passive movement of the hand, despite the absence of significant muscle activation during passive manipulation in the behavioral experiments and the production of the same average wrist torques in both tasks. A second network (Cluster II) exhibited similarly elevated BOLD activity during performance of both stabilization tasks. Brain regions demonstrating this pattern include the prefrontal cortex, rostral aspects of dorsal premotor and SMA cortices, and inferior aspects of posterior parietal cortex. These brain regions have previously been reported to contribute to the planning and execution of internally-generated, discrete motor actions (Jahanshahi et al., 1995; Winstein et al., 1997; Jueptner and Weiller, 1998; Sakai et al., 1999). Consistent with those reports, we found that BOLD activity increased within rostral PMd, SMA and cingulate cortex during time periods wherein subjects generated discrete corrective movements. Although discrete corrections were transient events, they gave rise to long-lasting performance improvements in most cases and so, they likely represent a resetting of the reference wrist angle when online (moment-by-moment) feedback control failed to satisfy subjective performance constraints. To do so, however, subjects first needed to determine when error had grown sufficiently large to warrant correction and then plan the direction and magnitude of corrective action. This type of supervisory role likely was supported by Cluster II ROIs demonstrating high sensitivity to state estimation errors over a longer, 30 s timeframe (Fig. 7, blue regions).

 We demonstrated that the device was: 1) capable of generating

Superior Temporal Gyrus (BA 22)

Superior Temporal Gyrus (BA 22)

Supramarginal Gyrus

Middle Frontal Gyurs (BA 10)

**Subcortical and Cerebellum**

Globus Pallidus (Medial/Lateral)

Ventral Posterior Lateral

Supramarginal Gyrus

L = Left; R = Right; B = Bilateral; BA = Broadman's Area

**4. Summary and future directions**

**Table 2.** Brain regions activated in the ErrorTR and ErrorTrial contrasts.

Ventral Lateral

Dentate *ErrorTrial*

Putamen

These results highlight the importance of both postural and movement trajectory control mechanisms in peripheral limb stabilization and suggest a possible neural basis for the distinct postural and trajectory (movement) control mechanisms recently isolated during point-topoint arm movements and movement sequences (Ghez et al., 2007; Scheidt and Ghez, 2007). Additional studies are needed to better understand how the brain combines the different control processes to minimize performance errors and how the brain uses information from multiple sensory feedback modalities to optimize limb stabilization and movement control. Such work will be greatly facilitated by the use of mechanically-active tools that apply physical perturbations to the limb while subjects undergo concurrent functional MR imaging.

## **Acknowledgements**

This work was supported by NSF BES0238442, NIH NCRR M01-RR00058, 2 P01 MH51358, R01 HD053727 and by the Alvin W. and Marion Birnschein Foundation. We thank Kristine Mosier and Steve Rao for constructive feedback on an earlier version of this manuscript, Jeff Goldstein for crafting the manipulandum, Dr. N. Bansal for helpful suggestions regarding the statistical handling of the data, as well as Vanai Roopchansingh, Sally Durgerian, Matthew Verber and the MCW MRI technicians for assistance during FMRI data collection and analysis.

[7] Evarts EV, Vaughn WJ (1978) Intended Arm Movements in Response to Externally Produced Arm Displacements in Man. In: Cerebral Motor Control in Man: Long

Feedback Regulation of Limb Position Characterized Using FMRI

http://dx.doi.org/10.5772/30740

465

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## **Author details**

Aaron J. Suminski1,2 and Robert A. Scheidt1,3,4

1 Biomedical Engineering, Marquette University, Milwaukee, WI, USA

4 Physical Medicine and Rehabilitation, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA

#### **References**

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**Acknowledgements**

**Author details**

University, Chicago, IL, USA

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Aaron J. Suminski1,2 and Robert A. Scheidt1,3,4

This work was supported by NSF BES0238442, NIH NCRR M01-RR00058, 2 P01 MH51358, R01 HD053727 and by the Alvin W. and Marion Birnschein Foundation. We thank Kristine Mosier and Steve Rao for constructive feedback on an earlier version of this manuscript, Jeff Goldstein for crafting the manipulandum, Dr. N. Bansal for helpful suggestions regarding the statistical handling of the data, as well as Vanai Roopchansingh, Sally Durgerian, Matthew Verber and

the MCW MRI technicians for assistance during FMRI data collection and analysis.

1 Biomedical Engineering, Marquette University, Milwaukee, WI, USA

3 Neurology, Medical College of Wisconsin, Milwaukee, WI, USA

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4 Physical Medicine and Rehabilitation, Feinberg School of Medicine, Northwestern

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**Chapter 18**

**Active and Passive fMRI for Presurgical Mapping of**

Functional magnetic resonance imaging (fMRI) using the blood-oxygenation level dependent (BOLD) contrast measures local changes in the concentration of paramagnetic deoxyhemo‐ globin associated with an increase in blood flow to active regions of the brain (Bandettini, Wong, Hinks, Tikofsky, & Hyde, 1992; Kwong, et al., 1992; Ogawa, Lee, Kay, & Tank, 1990). Together with structural MRI, BOLD fMRI is increasingly being used for presurgical functional mapping of epilepsy and brain tumor patients (Binder, et al., 1996; Haberg, Kvistad, Unsgard, & Haraldseth, 2004; Hall & Truwit, 2008; Krings, et al., 2001; Lee, et al., 1999; Van Westen, Skagerberg, Olsrud, Fransson, & Larsson, 2005; Liebenthal, 2011). Presurgical fMRI provides patient-specific functional information that may facilitate maximal tumor or epileptic tissue surgical resection, with minimal damage to surrounding gray and white matter structures that support vital sensory and cognitive functions. Presurgical mapping of brain regions and their spatial relationship to the lesion can in some cases help predict possible deficits in sensory, motor or cognitive functions due to surgery or due to continued lesion growth (Haberg, Kvistad, Unsgard, & Haraldseth, 2004). In this way, presurgical mapping can guide the decision for course of intervention (extent of resection, alternative treatment, etc.) and the decision to conduct additional intra-operative functional mapping (e.g., intra-cortical stimu‐ lation-ICS). Finally, presurgical mapping is also valuable for planning the pathway of surgical

The main advantages of fMRI in particular for clinical applications are its high spatial resolu‐ tion, non-invasive nature, relative safety, and the good access to MRI scanners in most medical

> © 2014 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**Motor and Language Cortex**

Victoria A.L. Mosher, Einat Liebenthal and

Additional information is available at the end of the chapter

**1.1. Overview of fMRI and its use in presurgical mapping**

Bradley G. Goodyear

**1. Introduction**

approach.

http://dx.doi.org/10.5772/30844

## **Active and Passive fMRI for Presurgical Mapping of Motor and Language Cortex**

Victoria A.L. Mosher, Einat Liebenthal and Bradley G. Goodyear

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/30844

## **1. Introduction**

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468 Advanced Brain Neuroimaging Topics in Health and Disease - Methods and Applications

form Assoc 8:443-459.

Neurophysiol 77:1581-1594.

Trans 64:759-768.

91:162-166.

#### **1.1. Overview of fMRI and its use in presurgical mapping**

Functional magnetic resonance imaging (fMRI) using the blood-oxygenation level dependent (BOLD) contrast measures local changes in the concentration of paramagnetic deoxyhemo‐ globin associated with an increase in blood flow to active regions of the brain (Bandettini, Wong, Hinks, Tikofsky, & Hyde, 1992; Kwong, et al., 1992; Ogawa, Lee, Kay, & Tank, 1990). Together with structural MRI, BOLD fMRI is increasingly being used for presurgical functional mapping of epilepsy and brain tumor patients (Binder, et al., 1996; Haberg, Kvistad, Unsgard, & Haraldseth, 2004; Hall & Truwit, 2008; Krings, et al., 2001; Lee, et al., 1999; Van Westen, Skagerberg, Olsrud, Fransson, & Larsson, 2005; Liebenthal, 2011). Presurgical fMRI provides patient-specific functional information that may facilitate maximal tumor or epileptic tissue surgical resection, with minimal damage to surrounding gray and white matter structures that support vital sensory and cognitive functions. Presurgical mapping of brain regions and their spatial relationship to the lesion can in some cases help predict possible deficits in sensory, motor or cognitive functions due to surgery or due to continued lesion growth (Haberg, Kvistad, Unsgard, & Haraldseth, 2004). In this way, presurgical mapping can guide the decision for course of intervention (extent of resection, alternative treatment, etc.) and the decision to conduct additional intra-operative functional mapping (e.g., intra-cortical stimu‐ lation-ICS). Finally, presurgical mapping is also valuable for planning the pathway of surgical approach.

The main advantages of fMRI in particular for clinical applications are its high spatial resolu‐ tion, non-invasive nature, relative safety, and the good access to MRI scanners in most medical

© 2014 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

centers. Compared to functional mapping using ICS, or the intracarotid sodium amobarbital procedure (Wada test) to assess hemispheric language and memory dominance, fMRI is far less unpleasant to the patient and it is also less expensive (Medina et al., 2004). Unlike ICS, fMRI is performed prior to the surgery, without the time constraints and limitations on patient cognitive ability imposed by the surgery. The Wada test, named after Canadian neurologist and epileptologist Juhn Atsushi Wada, was developed 50 years ago to assess the hemispheric dominance of language and thereby the risk of language decline in patients undergoing brain surgery (Wada & Rasmussen, 1960). The procedure involves some risk to the patient, however, and it is unpleasant, consisting of intracarotid injection of a barbiturate to one brain hemi‐ sphere, during which time the other hemisphere is examined for language and memory functions.

of several fMRI language tasks, but specificity remains low (Pouratian, Bookheimer, Rex,

Active and Passive fMRI for Presurgical Mapping of Motor and Language Cortex

http://dx.doi.org/10.5772/30844

471

Perhaps the most serious concern with regard to fMRI presurgical mapping (and functional mapping in general) is that the predictive value of this technique for patient outcome has not yet systematically been established in different patient types and using specific activation paradigms. Several studies in small cohorts of brain tumor patients suggest that fMRI mapping can be predictive of surgical outcome. A distance of 10 mm or more between tumor and functional cortex has been suggested to carry reduced risk for postsurgical neurological deficits (Haberg, et al., 2004; Krishnan, et al., 2004). In epilepsy, fMRI of language has been demon‐ strated to be strongly correlated with Wada language asymmetry (Binder, et al., 1996; Lehericy, Cohen, et al., 2000; Liegeois, et al., 2002) and to predict language (Sabsevitz, et al., 2003) and verbal memory (Binder et al., 2008) outcome after left anterior temporal lobe resection. Despite these recent advances, more work is required to establish guidelines for the use of fMRI in

Factors related to the choice of fMRI activation paradigm (active or passive experimental task, control task, level of task performance, and the number of trials per task condition) have a critical impact on the resulting functional maps and their interpretation, particularly for mapping cognitive functions such as language and memory (Binder, Swanson, Hammeke, & Sabsevitz, 2008; Bookheimer, 2007). In terms of motor paradigms, simple hand/finger move‐ ments are usually sufficient to get a reliable map of motor cortex activity. These types of paradigms will be presented later in the chapter when we compare task-based fMRI with

In terms of language tasks, active tasks requiring an overt response are generally considered more reliable because they allow monitoring of the patient's performance, and are thought to engage targeted brain regions more strongly (Lee, Jack, & Riederer, 1998). An active control task rather than passive rest (i.e., no task) is recommended as a baseline for contrasting with an active language task, because activation in the control condition can account for general executive, attentional and motor processes that are nonspecific to the tested cognitive function (Binder, Swanson, et al., 2008; Demonet, et al., 1992). In addition, activation during passive rest can be dominated by continuous processing of linguistically-mediated thoughts (whether day dreaming or intense mental processing), thereby effectively reducing the differential activation of language networks between the active and control tasks (McKiernan, D'Angelo, Kaufman,

Finally, the task demands should be adjusted according to the patient's level of performance. For example, shorter and/or easier versions of the tasks may be needed for young children who tend to have a shorter attention span and a different knowledge base. As will be elaborated in the second part of this chapter, an interesting alternative approach to active task paradigms is passive resting-state fMRI. The resting-state paradigm is independent of patient perform‐

Martin, & Toga, 2002; Rutten, Ramsey, van Rijen, Noordmans, & van Veelen, 2002).

presurgical mapping.

**1.2. Selection of fMRI paradigms**

passive resting-state paradigms.

& Binder, 2006; Stark & Squire, 2001).

One concern sometimes raised with respect to fMRI presurgical mapping is that it identifies neuronal networks involved in a specific function, including regions that may actually be expendable (Desmond & Annabel Chen, 2002). The problem is that, surgical resection of expendable areas of a functional network identified with fMRI should not cause a post-surgical functional decline, and it would therefore be safe and desirable for achieving the surgery goals to resect them. In contrast, ICS is thought to indicate strictly regions that are essential for a function, and should therefore be preserved during surgery in order to prevent functional damage. However it is important to understand that, as with fMRI, a similar concern actually applies to ICS in that regions that are remote from the stimulated region but connected to it may also be affected by the stimulation and 'erroneously' considered essential.

Another difficulty relates to the high inter-and intra-subject variability in the extent of activation foci observed with fMRI even with constant scanning parameters and consistent task performance (Liu, Zhang, Brown, Yue, 2004). This problem usually arises because the extent of active areas is determined by a statistical significance threshold that depends on the quality and quantity of the acquired signal, irrespective of the physiological boundaries of functional fields. In an effort to address this issue, recent work examined the spatial and temporal reproducibility of the relative amplitude of the fMRI signal and found it to be more stable than the absolute amplitude (Voyvodic, 2006). It was further determined empirically that thresholding individual activation maps at 40% of the most active voxels resulted in highly reproducible activity in the sensorimotor hand region during a finger tapping task, both within and across subjects (Voyvodic, Petrella, Friedman, 2009). Thus, relative amplitude maps can be used to improve the reliability and anatomical specificity of functional MR mapping.

Despite the differences between the techniques, there is generally very good concordance between fMRI and ICS mapping of somatosensory and motor functions (Bartos, et al., 2009; Hirsch, et al., 2000; Holodny, et al., 2000; Jack, et al., 1994; Krishnan, et al., 2004; Lehericy, Duffau, et al., 2000; Pujol, et al., 1996; Yetkin, et al., 1997). Nevertheless, in language mapping, reported levels of task sensitivity and specificity are variable and strongly dependent on the particular task that is used (Bookheimer, 2007). High sensitivity (i.e., fMRI activates all or most sites identified by ICS) but low specificity (i.e., fMRI activates additional sites not identified by ICS) has been reported for auditory tasks and in particular word generation to auditory cues (FitzGerald, et al., 1997). Higher sensitivity can generally be achieved with the conjunction of several fMRI language tasks, but specificity remains low (Pouratian, Bookheimer, Rex, Martin, & Toga, 2002; Rutten, Ramsey, van Rijen, Noordmans, & van Veelen, 2002).

Perhaps the most serious concern with regard to fMRI presurgical mapping (and functional mapping in general) is that the predictive value of this technique for patient outcome has not yet systematically been established in different patient types and using specific activation paradigms. Several studies in small cohorts of brain tumor patients suggest that fMRI mapping can be predictive of surgical outcome. A distance of 10 mm or more between tumor and functional cortex has been suggested to carry reduced risk for postsurgical neurological deficits (Haberg, et al., 2004; Krishnan, et al., 2004). In epilepsy, fMRI of language has been demon‐ strated to be strongly correlated with Wada language asymmetry (Binder, et al., 1996; Lehericy, Cohen, et al., 2000; Liegeois, et al., 2002) and to predict language (Sabsevitz, et al., 2003) and verbal memory (Binder et al., 2008) outcome after left anterior temporal lobe resection. Despite these recent advances, more work is required to establish guidelines for the use of fMRI in presurgical mapping.

#### **1.2. Selection of fMRI paradigms**

centers. Compared to functional mapping using ICS, or the intracarotid sodium amobarbital procedure (Wada test) to assess hemispheric language and memory dominance, fMRI is far less unpleasant to the patient and it is also less expensive (Medina et al., 2004). Unlike ICS, fMRI is performed prior to the surgery, without the time constraints and limitations on patient cognitive ability imposed by the surgery. The Wada test, named after Canadian neurologist and epileptologist Juhn Atsushi Wada, was developed 50 years ago to assess the hemispheric dominance of language and thereby the risk of language decline in patients undergoing brain surgery (Wada & Rasmussen, 1960). The procedure involves some risk to the patient, however, and it is unpleasant, consisting of intracarotid injection of a barbiturate to one brain hemi‐ sphere, during which time the other hemisphere is examined for language and memory

470 Advanced Brain Neuroimaging Topics in Health and Disease - Methods and Applications

One concern sometimes raised with respect to fMRI presurgical mapping is that it identifies neuronal networks involved in a specific function, including regions that may actually be expendable (Desmond & Annabel Chen, 2002). The problem is that, surgical resection of expendable areas of a functional network identified with fMRI should not cause a post-surgical functional decline, and it would therefore be safe and desirable for achieving the surgery goals to resect them. In contrast, ICS is thought to indicate strictly regions that are essential for a function, and should therefore be preserved during surgery in order to prevent functional damage. However it is important to understand that, as with fMRI, a similar concern actually applies to ICS in that regions that are remote from the stimulated region but connected to it

Another difficulty relates to the high inter-and intra-subject variability in the extent of activation foci observed with fMRI even with constant scanning parameters and consistent task performance (Liu, Zhang, Brown, Yue, 2004). This problem usually arises because the extent of active areas is determined by a statistical significance threshold that depends on the quality and quantity of the acquired signal, irrespective of the physiological boundaries of functional fields. In an effort to address this issue, recent work examined the spatial and temporal reproducibility of the relative amplitude of the fMRI signal and found it to be more stable than the absolute amplitude (Voyvodic, 2006). It was further determined empirically that thresholding individual activation maps at 40% of the most active voxels resulted in highly reproducible activity in the sensorimotor hand region during a finger tapping task, both within and across subjects (Voyvodic, Petrella, Friedman, 2009). Thus, relative amplitude maps can be used to improve the reliability and anatomical specificity of functional MR mapping.

Despite the differences between the techniques, there is generally very good concordance between fMRI and ICS mapping of somatosensory and motor functions (Bartos, et al., 2009; Hirsch, et al., 2000; Holodny, et al., 2000; Jack, et al., 1994; Krishnan, et al., 2004; Lehericy, Duffau, et al., 2000; Pujol, et al., 1996; Yetkin, et al., 1997). Nevertheless, in language mapping, reported levels of task sensitivity and specificity are variable and strongly dependent on the particular task that is used (Bookheimer, 2007). High sensitivity (i.e., fMRI activates all or most sites identified by ICS) but low specificity (i.e., fMRI activates additional sites not identified by ICS) has been reported for auditory tasks and in particular word generation to auditory cues (FitzGerald, et al., 1997). Higher sensitivity can generally be achieved with the conjunction

may also be affected by the stimulation and 'erroneously' considered essential.

functions.

Factors related to the choice of fMRI activation paradigm (active or passive experimental task, control task, level of task performance, and the number of trials per task condition) have a critical impact on the resulting functional maps and their interpretation, particularly for mapping cognitive functions such as language and memory (Binder, Swanson, Hammeke, & Sabsevitz, 2008; Bookheimer, 2007). In terms of motor paradigms, simple hand/finger move‐ ments are usually sufficient to get a reliable map of motor cortex activity. These types of paradigms will be presented later in the chapter when we compare task-based fMRI with passive resting-state paradigms.

In terms of language tasks, active tasks requiring an overt response are generally considered more reliable because they allow monitoring of the patient's performance, and are thought to engage targeted brain regions more strongly (Lee, Jack, & Riederer, 1998). An active control task rather than passive rest (i.e., no task) is recommended as a baseline for contrasting with an active language task, because activation in the control condition can account for general executive, attentional and motor processes that are nonspecific to the tested cognitive function (Binder, Swanson, et al., 2008; Demonet, et al., 1992). In addition, activation during passive rest can be dominated by continuous processing of linguistically-mediated thoughts (whether day dreaming or intense mental processing), thereby effectively reducing the differential activation of language networks between the active and control tasks (McKiernan, D'Angelo, Kaufman, & Binder, 2006; Stark & Squire, 2001).

Finally, the task demands should be adjusted according to the patient's level of performance. For example, shorter and/or easier versions of the tasks may be needed for young children who tend to have a shorter attention span and a different knowledge base. As will be elaborated in the second part of this chapter, an interesting alternative approach to active task paradigms is passive resting-state fMRI. The resting-state paradigm is independent of patient perform‐ ance, is time efficient, and can therefore serve as an alternative for patients who cannot comply with active task demands.

gyrus, IFG; middle temporal gyrus, MTG; and angular gyrus, AG) were defined using a digital atlas based on cytoarchitectonic and macro anatomical labels (Eickhoff, et al., 2005). An adaptive thresholding scheme, consisting of mapping the activation in each subject as a percentage of local excitation, was used as a means to reduce the variability in the extent of activation between subjects (Voyvodic, 2006). The amplitude threshold each ROI was set individually at 40% of the peak positive activation within that ROI. This level of thresholding was selected because it was shown to yield highly reproducible and specific individual motor activation maps in a large cohort of subjects (Voyvodic, et al., 2009). Individual maps were then grouped using a random effects model to account for intersubject variability and capture effects in the whole population. Sensitivity measures across the group for each task were computed based on the individual mean value of the t-statistic in all voxels exceeding the threshold in the target ROIs. Specificity was measured across subjects as the proportion of the above-threshold positive activation volume (computed as the mean t-value weighted by the number of voxels exceeding the threshold) in the target ROI relative to the whole brain. Lateralization indices (LI) were computed for each of the ROIs in the language tasks because of the clinical relevance of language dominance as a predictor of post surgical neurological deficit, and because quantitative measures of laterality show better within-subject reproduci‐ bility than quantitative measures of the volume of activation (Harrington, Buonocore, & Farias, 2006). The LI was computed as the ratio of the difference and the sum of the activation in each left and right region of interest (LI=[QLHQRH]/[QLH+QRH]), where the activation in the left and right hemispheres (QLH and QRH, respectively) was represented by the sensitivity or specificity measures described above. In this formulation, LI varies between 1 and-1, with positive values reflecting left hemisphere dominance and negative values reflecting right hemisphere dominance. LI measures incorporating both the magnitude and extent of activa‐ tion were selected here because they have been shown to be more robust and reproducible (Fernandez et al., 2003; Jansen et al., 2006). Note that the size of the contralateral ROIs defined in the atlas were similar though not identical, but this did not bias the LI comparison between

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tasks because any ROI asymmetries were consistent across tasks.

largest for DNT when all three regions were combined.

The group maps of fMRI activation in the language ROIs for the three language tasks are shown in Figure 1, and the corresponding sensitivity and specificity measures in each ROI and task are shown in Figures 2 and 3. Activation was observed in all ROIs for all tasks. The LI based on the sensitivity measure (Figure 2) was similar for all tasks in IFG. However, it was larger for SWD and DNT in AG, with a similar trend in MTG. LI was largest for DNT when all three regions were combined. The LI based on the specificity measure (Figure 3) was larger for SWG in IFG, and larger for SWD and DNT in AG, with no differences observed in MTG. The LI was

Figure 4 shows an example of presurgical fMRI language maps using the SWD and SWG tasks in a patient with right frontotemporal low-grade glioma. Activation for both the SWD and SWG tasks was left lateralized. Only the SWD task activated the angular gyrus. The SWG task produced stronger activation in IFG and MFG. Figure 5 shows an example of fMRI language maps in a neurologically healthy left handed volunteer. In this subject, the three maps

#### **1.3. Comparative evaluation of three fMRI language tasks**

To demonstrate the impact of the choice of paradigm on resulting fMRI maps, we compared three language tasks in terms of sensitivity and specificity in regions of interest for language. These tasks are: an auditory semantic word decision task (Binder, et al., 1997), an auditory definition naming task, and a silent word generation task.

The semantic task consists of a dual-criteria go/no-go semantic word decision (SWD) to auditorily presented animal names (i.e., press the keypad if "this animal is found in North America and used by people"), contrasted with a dual-criteria go/no-go auditory decision task (press the keypad if "this tone sequence contains two high-pitch tones"). Sixteen blocks of 8 trials alternating between the tasks are presented in two separate runs. This task has been shown to reliably activate regions in left inferior and middle frontal gyri, left middle temporal gyrus, left fusiform gyrus, and left angular gyrus, involved in phonological and lexicosemantic functions (Binder, et al., 1997). The degree of lateralization of these activated areas has been shown to correspond with Wada language asymmetry in individual epilepsy patients (Binder, et al., 1996) and predicts language and verbal memory outcome after left temporal lobectomy (Binder, et al., 2008; Sabsevitz, et al., 2003).

The auditory definition (or responsive) naming task (DNT) consists of overtly naming (speaking the name of) an object, animal, famous place, or famous person, in response to an auditory description (for example, "a horse with stripes" or "what the king wears on his head") (Hamberger & Seidel, 2003). This task is contrasted with an auditory control task consisting of speaking the number of siren sounds embedded in a sequence of white noise. This task uses an event-related design with 128 naming trials and 128 control trials, presented intermixed in 4 separate runs. Relative to the SWD, the DNT places additional demands on the language system because it involves word retrieval and overt speech. This task is expected to engage stronger, or more extensive activation within language areas, particularly in the left inferior frontal, middle temporal and parietal cortex (Okada & Hickok, 2006; Tomaszewki Farias, Harrington, Broomand, & Seyal, 2005).

The silent word generation (SWG) task consists of covertly finding words that start with certain letters, each displayed on a screen for 5 seconds. The control task consists of visually fixating on letters from foreign alphabets. We compared the SWG task with the SWD and DNT tasks because it is simple to implement, and therefore often used for fMRI language mapping in clinical settings. Nevertheless, a significant drawback of this task is that there is no monitoring of task performance. In addition, the task engages primarily phonological processing, but imposes fewer demands on the semantic system.

The reliability of each of the three fMRI language tasks was examined in eleven right handed neurologically-healthy volunteers, using sensitivity and specificity measures described previously (Hirsch, et al., 2000). Target regions of interest (ROIs) for language (inferior frontal gyrus, IFG; middle temporal gyrus, MTG; and angular gyrus, AG) were defined using a digital atlas based on cytoarchitectonic and macro anatomical labels (Eickhoff, et al., 2005). An adaptive thresholding scheme, consisting of mapping the activation in each subject as a percentage of local excitation, was used as a means to reduce the variability in the extent of activation between subjects (Voyvodic, 2006). The amplitude threshold each ROI was set individually at 40% of the peak positive activation within that ROI. This level of thresholding was selected because it was shown to yield highly reproducible and specific individual motor activation maps in a large cohort of subjects (Voyvodic, et al., 2009). Individual maps were then grouped using a random effects model to account for intersubject variability and capture effects in the whole population. Sensitivity measures across the group for each task were computed based on the individual mean value of the t-statistic in all voxels exceeding the threshold in the target ROIs. Specificity was measured across subjects as the proportion of the above-threshold positive activation volume (computed as the mean t-value weighted by the number of voxels exceeding the threshold) in the target ROI relative to the whole brain. Lateralization indices (LI) were computed for each of the ROIs in the language tasks because of the clinical relevance of language dominance as a predictor of post surgical neurological deficit, and because quantitative measures of laterality show better within-subject reproduci‐ bility than quantitative measures of the volume of activation (Harrington, Buonocore, & Farias, 2006). The LI was computed as the ratio of the difference and the sum of the activation in each left and right region of interest (LI=[QLHQRH]/[QLH+QRH]), where the activation in the left and right hemispheres (QLH and QRH, respectively) was represented by the sensitivity or specificity measures described above. In this formulation, LI varies between 1 and-1, with positive values reflecting left hemisphere dominance and negative values reflecting right hemisphere dominance. LI measures incorporating both the magnitude and extent of activa‐ tion were selected here because they have been shown to be more robust and reproducible (Fernandez et al., 2003; Jansen et al., 2006). Note that the size of the contralateral ROIs defined in the atlas were similar though not identical, but this did not bias the LI comparison between tasks because any ROI asymmetries were consistent across tasks.

ance, is time efficient, and can therefore serve as an alternative for patients who cannot comply

To demonstrate the impact of the choice of paradigm on resulting fMRI maps, we compared three language tasks in terms of sensitivity and specificity in regions of interest for language. These tasks are: an auditory semantic word decision task (Binder, et al., 1997), an auditory

The semantic task consists of a dual-criteria go/no-go semantic word decision (SWD) to auditorily presented animal names (i.e., press the keypad if "this animal is found in North America and used by people"), contrasted with a dual-criteria go/no-go auditory decision task (press the keypad if "this tone sequence contains two high-pitch tones"). Sixteen blocks of 8 trials alternating between the tasks are presented in two separate runs. This task has been shown to reliably activate regions in left inferior and middle frontal gyri, left middle temporal gyrus, left fusiform gyrus, and left angular gyrus, involved in phonological and lexicosemantic functions (Binder, et al., 1997). The degree of lateralization of these activated areas has been shown to correspond with Wada language asymmetry in individual epilepsy patients (Binder, et al., 1996) and predicts language and verbal memory outcome after left temporal

The auditory definition (or responsive) naming task (DNT) consists of overtly naming (speaking the name of) an object, animal, famous place, or famous person, in response to an auditory description (for example, "a horse with stripes" or "what the king wears on his head") (Hamberger & Seidel, 2003). This task is contrasted with an auditory control task consisting of speaking the number of siren sounds embedded in a sequence of white noise. This task uses an event-related design with 128 naming trials and 128 control trials, presented intermixed in 4 separate runs. Relative to the SWD, the DNT places additional demands on the language system because it involves word retrieval and overt speech. This task is expected to engage stronger, or more extensive activation within language areas, particularly in the left inferior frontal, middle temporal and parietal cortex (Okada & Hickok, 2006; Tomaszewki Farias,

The silent word generation (SWG) task consists of covertly finding words that start with certain letters, each displayed on a screen for 5 seconds. The control task consists of visually fixating on letters from foreign alphabets. We compared the SWG task with the SWD and DNT tasks because it is simple to implement, and therefore often used for fMRI language mapping in clinical settings. Nevertheless, a significant drawback of this task is that there is no monitoring of task performance. In addition, the task engages primarily phonological processing, but

The reliability of each of the three fMRI language tasks was examined in eleven right handed neurologically-healthy volunteers, using sensitivity and specificity measures described previously (Hirsch, et al., 2000). Target regions of interest (ROIs) for language (inferior frontal

with active task demands.

**1.3. Comparative evaluation of three fMRI language tasks**

472 Advanced Brain Neuroimaging Topics in Health and Disease - Methods and Applications

definition naming task, and a silent word generation task.

lobectomy (Binder, et al., 2008; Sabsevitz, et al., 2003).

Harrington, Broomand, & Seyal, 2005).

imposes fewer demands on the semantic system.

The group maps of fMRI activation in the language ROIs for the three language tasks are shown in Figure 1, and the corresponding sensitivity and specificity measures in each ROI and task are shown in Figures 2 and 3. Activation was observed in all ROIs for all tasks. The LI based on the sensitivity measure (Figure 2) was similar for all tasks in IFG. However, it was larger for SWD and DNT in AG, with a similar trend in MTG. LI was largest for DNT when all three regions were combined. The LI based on the specificity measure (Figure 3) was larger for SWG in IFG, and larger for SWD and DNT in AG, with no differences observed in MTG. The LI was largest for DNT when all three regions were combined.

Figure 4 shows an example of presurgical fMRI language maps using the SWD and SWG tasks in a patient with right frontotemporal low-grade glioma. Activation for both the SWD and SWG tasks was left lateralized. Only the SWD task activated the angular gyrus. The SWG task produced stronger activation in IFG and MFG. Figure 5 shows an example of fMRI language maps in a neurologically healthy left handed volunteer. In this subject, the three maps consistently indicate right hemisphere dominance for language. As in previous examples, only the SWD and DNT tasks activated the angular gyrus. The SWG task activated specifically the frontal cortex.

Taken together, these results suggest that an important advantage of the semantic word decision (SWD) and definition naming (DNT) tasks over the silent word generation (SWG) task, is that the former tasks activate the angular gyrus (AG) with high sensitivity and specificity. The AG activation can be used to compute more robust lateralization indices. In contrast, the SWG task activates the inferior frontal gyrus (IFG) with high specificity, such that lateralization patterns in this area are more robust with the SWG compared to the SWD task. Thus, an optimal set of paradigms for presurgical language mapping would consist of a combination of the SWG task with the SWD or DNT tasks.

**Figure 2.** The sensitivity measure (expressed as mean *t*-value of supra threshold voxels) and corresponding lateraliza‐ tion index (LI) in the regions of interest for language (A. left and right inferior frontal gyri, B. left and right middle temporal gyri, C. left and right angular gyri – LIFG, RIFG, LMTG, RMTG, LAG, RAG, respectively), during semantic word

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**Figure 3.** The specificity measure (expressed as mean *t*-value of supra threshold voxels weighted by voxel count in each ROI, normalized to the whole brain) and corresponding LI in each language task. Abbreviations as in Figure 2.

\*\*p < 0.05.

decision (SWD), silent word generation (SWG), and definition naming (DNT). \*p<0.1; \*\*p < 0.05.

**Figure 1.** FMRI activation within the three language ROIs for a group of right handed, neurologically healthy adults (n=11), during auditory semantic word decision (SWD), auditory definition naming (DNT), and silent word generation (SWG) tasks. The individual activation maps in each task were thresholded at 40% of the maximum activation in each ROI, projected into standard stereotaxic space (Talairach & Tournoux, 1988), and combined across subjects using ran‐ dom effects analysis. The fMRI activity in the three language ROIs is displayed on sagittal slices of a template brain (Colin N27), at 49 mm to the left (49L, left panels) and to the right (49R, right panels) of the midsagittal plane. In this map, t=2.57 corresponds to p<0.01 (orange), and t=3.3 corresponds to p<0.001 (yellow).

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consistently indicate right hemisphere dominance for language. As in previous examples, only the SWD and DNT tasks activated the angular gyrus. The SWG task activated specifically the

Taken together, these results suggest that an important advantage of the semantic word decision (SWD) and definition naming (DNT) tasks over the silent word generation (SWG) task, is that the former tasks activate the angular gyrus (AG) with high sensitivity and specificity. The AG activation can be used to compute more robust lateralization indices. In contrast, the SWG task activates the inferior frontal gyrus (IFG) with high specificity, such that lateralization patterns in this area are more robust with the SWG compared to the SWD task. Thus, an optimal set of paradigms for presurgical language mapping would consist of a

**Figure 1.** FMRI activation within the three language ROIs for a group of right handed, neurologically healthy adults (n=11), during auditory semantic word decision (SWD), auditory definition naming (DNT), and silent word generation (SWG) tasks. The individual activation maps in each task were thresholded at 40% of the maximum activation in each ROI, projected into standard stereotaxic space (Talairach & Tournoux, 1988), and combined across subjects using ran‐ dom effects analysis. The fMRI activity in the three language ROIs is displayed on sagittal slices of a template brain (Colin N27), at 49 mm to the left (49L, left panels) and to the right (49R, right panels) of the midsagittal plane. In this

map, t=2.57 corresponds to p<0.01 (orange), and t=3.3 corresponds to p<0.001 (yellow).

combination of the SWG task with the SWD or DNT tasks.

474 Advanced Brain Neuroimaging Topics in Health and Disease - Methods and Applications

frontal cortex.

**Figure 2.** The sensitivity measure (expressed as mean *t*-value of supra threshold voxels) and corresponding lateraliza‐ tion index (LI) in the regions of interest for language (A. left and right inferior frontal gyri, B. left and right middle temporal gyri, C. left and right angular gyri – LIFG, RIFG, LMTG, RMTG, LAG, RAG, respectively), during semantic word decision (SWD), silent word generation (SWG), and definition naming (DNT). \*p<0.1; \*\*p < 0.05.

**Figure 3.** The specificity measure (expressed as mean *t*-value of supra threshold voxels weighted by voxel count in each ROI, normalized to the whole brain) and corresponding LI in each language task. Abbreviations as in Figure 2. \*\*p < 0.05.

**2. The origins of resting-state fMRI**

As with many major scientific advances, the use of fMRI to investigate the resting state of the human brain was discovered essentially by accident. In 1995, Biswal and colleagues (Biswal, Yetkin, Haughton, & Hyde, 1995) initially meant to isolate physiological noise in fMRI data collected from subjects during rest; however, it was observed that a prominent low-frequency signal (< 0.1 Hz) remained after removing the physiological noise. To investigate this further, high frequencies were filtered from all voxels in the brain, and the signal from the left primary sensorimotor cortex was compared to the signal from all other voxels in the brain. A high level of correlation was observed with the right primary sensorimotor cortex and other motor areas. It was thus hypothesized that spontaneous low-frequency fluctuations in the BOLD signal

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With fMRI itself still in its infancy and no clear explanation of how the resting-state signal was generated, there remained some controversy as to whether the resting-state signal was neurologically significant. One study found that the strength of left/right inter-hemispheric correlations during the resting state decreased when subjects held their breath, and returned to normal levels once the subjects began breathing again (Biswal, Hudetz, Yetkin, Haughton, & Hyde, 1997). This study provided initial evidence that spontaneous low-frequency fluctua‐ tions were indeed blood-oxygenation dependent, like the BOLD signal. It was later observed using voltage sensitive dyes that neurons in the visual cortex exhibit patterns of spontaneous activity similar to the activation patterns elicited in response to stimuli of a specific visual orientation angle (Kenet, Bibitchkov, Tsodyks, Grinvald, & Arieli, 2003). This was one of the first studies to provide evidence that the resting-state signal had a physiological basis. In recent years, a number of studies have demonstrated a link between physiological, electrophysio‐ logical, and BOLD processes and the resting-state signal (Laufs, et al., 2003; Leopold, Mur‐

ayama, & Logothetis, 2003; Mantini, Perrucci, Del Gratta, Romani, & Corbetta, 2007).

individual clinical cases (Freilich & Gaillard, 2010; Sakoglu, et al., 2011).

Through the validation of the resting-state phenomenon, resting-state fMRI has become a valuable tool to investigate how the brain functions at rest and how brain networks at rest are organized; however, the physiological mechanisms underlying the resting-state signal are still under investigation. Higher field scanners (3 Tesla and above) with improved signal-to-noise ratios, improved imaging sequences and head coils, and a better understanding of fMRI in general, have also contributed to the ubiquity of resting-state research. Many resting-state studies have been published since the first study, investigating a multitude of disease and normal brain states (e.g., Lowe, 2010; van den Heuvel & Hulshoff Pol, 2010). Since resting-state fMRI is still a relatively new concept, appropriate methods of collecting and analyzing restingstate data are still under development. With these advances in methodology, resting-state fMRI will continue to be an invaluable research tool for investigations of normal and pathological human brain function. However, as we will discuss in the remainder of this chapter, restingstate fMRI has the added potential of becoming a powerful diagnostic and assessment tool for

were the result of functional connections between these brain regions.

**Figure 4.** Presurgical fMRI language maps for the semantic word decision (SWD) and silent word generation (SWG) tasks, in a patient with right frontotemporal low-grade glioma (37 year old, right handed, female).

**Figure 5.** FMRI language maps in the three language tasks, in a neurologically healthy, left handed volunteer (Edin‐ burgh Handedness Inventory score=-0.8). In these maps, the activation was thresholded at 40% of the maximal activa‐ tion in the entire brain. The three maps are consistent with one another in suggesting right hemisphere dominance for language.

## **2. The origins of resting-state fMRI**

**Figure 4.** Presurgical fMRI language maps for the semantic word decision (SWD) and silent word generation (SWG)

**Figure 5.** FMRI language maps in the three language tasks, in a neurologically healthy, left handed volunteer (Edin‐ burgh Handedness Inventory score=-0.8). In these maps, the activation was thresholded at 40% of the maximal activa‐ tion in the entire brain. The three maps are consistent with one another in suggesting right hemisphere dominance

for language.

tasks, in a patient with right frontotemporal low-grade glioma (37 year old, right handed, female).

476 Advanced Brain Neuroimaging Topics in Health and Disease - Methods and Applications

As with many major scientific advances, the use of fMRI to investigate the resting state of the human brain was discovered essentially by accident. In 1995, Biswal and colleagues (Biswal, Yetkin, Haughton, & Hyde, 1995) initially meant to isolate physiological noise in fMRI data collected from subjects during rest; however, it was observed that a prominent low-frequency signal (< 0.1 Hz) remained after removing the physiological noise. To investigate this further, high frequencies were filtered from all voxels in the brain, and the signal from the left primary sensorimotor cortex was compared to the signal from all other voxels in the brain. A high level of correlation was observed with the right primary sensorimotor cortex and other motor areas. It was thus hypothesized that spontaneous low-frequency fluctuations in the BOLD signal were the result of functional connections between these brain regions.

With fMRI itself still in its infancy and no clear explanation of how the resting-state signal was generated, there remained some controversy as to whether the resting-state signal was neurologically significant. One study found that the strength of left/right inter-hemispheric correlations during the resting state decreased when subjects held their breath, and returned to normal levels once the subjects began breathing again (Biswal, Hudetz, Yetkin, Haughton, & Hyde, 1997). This study provided initial evidence that spontaneous low-frequency fluctua‐ tions were indeed blood-oxygenation dependent, like the BOLD signal. It was later observed using voltage sensitive dyes that neurons in the visual cortex exhibit patterns of spontaneous activity similar to the activation patterns elicited in response to stimuli of a specific visual orientation angle (Kenet, Bibitchkov, Tsodyks, Grinvald, & Arieli, 2003). This was one of the first studies to provide evidence that the resting-state signal had a physiological basis. In recent years, a number of studies have demonstrated a link between physiological, electrophysio‐ logical, and BOLD processes and the resting-state signal (Laufs, et al., 2003; Leopold, Mur‐ ayama, & Logothetis, 2003; Mantini, Perrucci, Del Gratta, Romani, & Corbetta, 2007).

Through the validation of the resting-state phenomenon, resting-state fMRI has become a valuable tool to investigate how the brain functions at rest and how brain networks at rest are organized; however, the physiological mechanisms underlying the resting-state signal are still under investigation. Higher field scanners (3 Tesla and above) with improved signal-to-noise ratios, improved imaging sequences and head coils, and a better understanding of fMRI in general, have also contributed to the ubiquity of resting-state research. Many resting-state studies have been published since the first study, investigating a multitude of disease and normal brain states (e.g., Lowe, 2010; van den Heuvel & Hulshoff Pol, 2010). Since resting-state fMRI is still a relatively new concept, appropriate methods of collecting and analyzing restingstate data are still under development. With these advances in methodology, resting-state fMRI will continue to be an invaluable research tool for investigations of normal and pathological human brain function. However, as we will discuss in the remainder of this chapter, restingstate fMRI has the added potential of becoming a powerful diagnostic and assessment tool for individual clinical cases (Freilich & Gaillard, 2010; Sakoglu, et al., 2011).

## **3. Resting-state fMRI: The basics**

#### **3.1. The resting-state signal**

The resting-state fMRI signal resides within a narrow frequency range of 0.01 to 0.1 Hz (De Luca, et al., 2006; Damoiseaux, et al., 2006). The similarity of these low-frequency fluctuations between spatially distinct brain regions implies a functional connection. The degree of similarity, which quantifies the strength of the functional connection, is called *functional connectivity*, and the collection of brain regions identified as connected constitute a *resting-state network*. These resting-state networks are consistent with our knowledge of structural connec‐ tions via the connective white matter tracts. In a study of patients with multiple sclerosis, a significant correlation was found between the integrity of the white matter tracts (i.e., struc‐ tural connectivity) within the motor pathway and functional connectivity of the motor network (Lowe, et al., 2008). This suggests that when white matter cannot functional properly, func‐ tional connectivity is diminished or even lost in the specific resting-state network. Even in healthy control subjects, the strength of functional connections within resting-state networks seems to be correlated with the strength of anatomical connections in the same area, as measured by diffusion tensor imaging (Skudlarski, et al., 2008). These studies have helped to establish a link between our knowledge of the anatomical connections in the brain and the resting-state fMRI signal.

every other voxel in the brain, usually by some variant of the General Linear Model including nuisance signals from cerebral spinal fluid, white matter, the average signal from the whole brain and physiological recordings of cardiac and respiratory signals (Glover, Li, & Ress, 2000; Murphy, Birn, Handwerker, Jones, & Bandettini, 2009). The voxels whose time courses are strongly correlated to the seed region are considered to be functionally connected to the seed region, and constitute a whole-brain map of the resting-state network. In contrast, a model-independent analysis is performed when there is no specific hypothesis about a brain region and its inclusion in a resting-state network. One common example of this approach is independent component analysis (ICA) (Hu, Yang, LaConte, & Weng, 2008). The ICA algo‐ rithm identifies groupings of spatial clusters (i.e., components) that are maximally independ‐ ent and whose BOLD signal fluctuations are similar; however, knowledge of brain networks is still required to select and group the components to form a final map of the desired restingstate network. Whether the approach to resting-state connectivity analysis is ICA or a seed region, there is good agreement as to the number and location of these networks in the healthy human brain across studies (Damoiseaux, et al., 2006; De Luca, Beckmann, De Stefano,

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The ease of data collection from the subject's perspective is just one of the many reasons why resting-state is becoming a popular brain imaging method. As we will discuss in the next few sections, the resting-state method is being applied to a number of neurological and psychiatric disease states, which could eventually advance resting-state fMRI towards actual clinical use.

Because there is no effort required by the subject during imaging, resting-state fMRI is quickly becoming a popular research tool. The majority of resting-state investigations are conducted in tandem with task-related fMRI, to gain complementary information about the brain's functional networks. For some patient populations, however, task-based fMRI may not be possible because patients are non-compliant. Resting-state fMRI may permit the investigation of patients who would otherwise not be studied using fMRI. Thus, resting-state fMRI may be a good candidate for translation into the clinical realm (Freilich & Gaillard, 2010; Sakoglu, et al., 2011). A few of the current uses and techniques of resting-state fMRI will be outlined in

To better understand the nature and mechanisms of functional connectivity, current studies combine resting-state fMRI with diffusion tensor imaging (DTI), an MR technique that provides an anatomical map of white matter tract integrity and quantifies structural connec‐ tivity (Skudlarski, et al., 2008). This combination of techniques permits a direct investigation of the relationship between structural and functional connectivity, since white matter tracts may provide the means for distant brain areas to remain functionally connected through correlated fluctuations in the BOLD signal. Studies have shown a strong correlation between structural and functional connectivity in the brain on a whole-brain scale (Skudlarski, et al., 2008) and for individual functional networks (Greicius, Supekar, Menon, & Dougherty, 2009). Hence, these types of studies provide insight into how structure and function are directly

Matthews, & Smith, 2006; Kiviniemi, et al., 2009).

**3.3. Current uses of resting-state fMRI**

this section.

related in the human brain.

#### **3.2. The procedure**

Acquiring resting-state fMRI data is relatively simple because of its non-demanding nature in terms of effort required from the subject. The subject is asked to remain still inside the MR scanner for a period of five to ten minutes, think of nothing in particular, and stare at a fixation cross or keep their eyes closed.  Conversely, it has been shown that the functional connections are stronger within the "default mode" network (consisting of the posterior cingulate cortex, precuneus, medial prefrontal cortex, ventral anterior cingulate cortex and lateral parietal cortex) when subjects have their eyes open (Yan, et al., 2009). Taken together, these studies suggest that the benefit of having subjects stare at a fixation cross or keep their eyes closed differs between resting-state networks, and must be taken into account when a study is planned.

Once the data has been collected, it is subjected to a pre-processing pipeline the same as that for task fMRI: motion correction, spatial and temporal smoothing, and spatial normalization to a standard template (i.e., the Montreal Neurological Institute brain or the Talairach atlas). In terms of the analysis of resting-state data, there are two broad categories: model-dependent or model-independent (Joel, Caffo, van Zijl, & Pekar, 2011; Margulies, et al., 2010). A modeldependent analysis assumes *a priori* knowledge of the approximate location of a brain region within the resting-state network under investigation. Using this knowledge, a seed region (consisting of a voxel or voxels) is typically defined either anatomically or by an apposite task. The average time course of this seed region is extracted and compared to the time course of every other voxel in the brain, usually by some variant of the General Linear Model including nuisance signals from cerebral spinal fluid, white matter, the average signal from the whole brain and physiological recordings of cardiac and respiratory signals (Glover, Li, & Ress, 2000; Murphy, Birn, Handwerker, Jones, & Bandettini, 2009). The voxels whose time courses are strongly correlated to the seed region are considered to be functionally connected to the seed region, and constitute a whole-brain map of the resting-state network. In contrast, a model-independent analysis is performed when there is no specific hypothesis about a brain region and its inclusion in a resting-state network. One common example of this approach is independent component analysis (ICA) (Hu, Yang, LaConte, & Weng, 2008). The ICA algo‐ rithm identifies groupings of spatial clusters (i.e., components) that are maximally independ‐ ent and whose BOLD signal fluctuations are similar; however, knowledge of brain networks is still required to select and group the components to form a final map of the desired restingstate network. Whether the approach to resting-state connectivity analysis is ICA or a seed region, there is good agreement as to the number and location of these networks in the healthy human brain across studies (Damoiseaux, et al., 2006; De Luca, Beckmann, De Stefano, Matthews, & Smith, 2006; Kiviniemi, et al., 2009).

The ease of data collection from the subject's perspective is just one of the many reasons why resting-state is becoming a popular brain imaging method. As we will discuss in the next few sections, the resting-state method is being applied to a number of neurological and psychiatric disease states, which could eventually advance resting-state fMRI towards actual clinical use.

#### **3.3. Current uses of resting-state fMRI**

**3. Resting-state fMRI: The basics**

478 Advanced Brain Neuroimaging Topics in Health and Disease - Methods and Applications

The resting-state fMRI signal resides within a narrow frequency range of 0.01 to 0.1 Hz (De Luca, et al., 2006; Damoiseaux, et al., 2006). The similarity of these low-frequency fluctuations between spatially distinct brain regions implies a functional connection. The degree of similarity, which quantifies the strength of the functional connection, is called *functional connectivity*, and the collection of brain regions identified as connected constitute a *resting-state network*. These resting-state networks are consistent with our knowledge of structural connec‐ tions via the connective white matter tracts. In a study of patients with multiple sclerosis, a significant correlation was found between the integrity of the white matter tracts (i.e., struc‐ tural connectivity) within the motor pathway and functional connectivity of the motor network (Lowe, et al., 2008). This suggests that when white matter cannot functional properly, func‐ tional connectivity is diminished or even lost in the specific resting-state network. Even in healthy control subjects, the strength of functional connections within resting-state networks seems to be correlated with the strength of anatomical connections in the same area, as measured by diffusion tensor imaging (Skudlarski, et al., 2008). These studies have helped to establish a link between our knowledge of the anatomical connections in the brain and the

Acquiring resting-state fMRI data is relatively simple because of its non-demanding nature in terms of effort required from the subject. The subject is asked to remain still inside the MR scanner for a period of five to ten minutes, think of nothing in particular, and stare at a fixation cross or keep their eyes closed.  Conversely, it has been shown that the functional connections are stronger within the "default mode" network (consisting of the posterior cingulate cortex, precuneus, medial prefrontal cortex, ventral anterior cingulate cortex and lateral parietal cortex) when subjects have their eyes open (Yan, et al., 2009). Taken together, these studies suggest that the benefit of having subjects stare at a fixation cross or keep their eyes closed differs between resting-state networks, and must be taken into account when a

Once the data has been collected, it is subjected to a pre-processing pipeline the same as that for task fMRI: motion correction, spatial and temporal smoothing, and spatial normalization to a standard template (i.e., the Montreal Neurological Institute brain or the Talairach atlas). In terms of the analysis of resting-state data, there are two broad categories: model-dependent or model-independent (Joel, Caffo, van Zijl, & Pekar, 2011; Margulies, et al., 2010). A modeldependent analysis assumes *a priori* knowledge of the approximate location of a brain region within the resting-state network under investigation. Using this knowledge, a seed region (consisting of a voxel or voxels) is typically defined either anatomically or by an apposite task. The average time course of this seed region is extracted and compared to the time course of

**3.1. The resting-state signal**

resting-state fMRI signal.

**3.2. The procedure**

study is planned.

Because there is no effort required by the subject during imaging, resting-state fMRI is quickly becoming a popular research tool. The majority of resting-state investigations are conducted in tandem with task-related fMRI, to gain complementary information about the brain's functional networks. For some patient populations, however, task-based fMRI may not be possible because patients are non-compliant. Resting-state fMRI may permit the investigation of patients who would otherwise not be studied using fMRI. Thus, resting-state fMRI may be a good candidate for translation into the clinical realm (Freilich & Gaillard, 2010; Sakoglu, et al., 2011). A few of the current uses and techniques of resting-state fMRI will be outlined in this section.

To better understand the nature and mechanisms of functional connectivity, current studies combine resting-state fMRI with diffusion tensor imaging (DTI), an MR technique that provides an anatomical map of white matter tract integrity and quantifies structural connec‐ tivity (Skudlarski, et al., 2008). This combination of techniques permits a direct investigation of the relationship between structural and functional connectivity, since white matter tracts may provide the means for distant brain areas to remain functionally connected through correlated fluctuations in the BOLD signal. Studies have shown a strong correlation between structural and functional connectivity in the brain on a whole-brain scale (Skudlarski, et al., 2008) and for individual functional networks (Greicius, Supekar, Menon, & Dougherty, 2009). Hence, these types of studies provide insight into how structure and function are directly related in the human brain.

Resting-state fMRI is also being used to investigate a number of brain diseases and disorders. Disrupted resting-state networks have been identified in neurological disorders such as Alzheimer's disease (Jiang, et al., 2006) and psychopathologies such as schizophrenia (Liu, et al., 2007). One group has even used resting-state fMRI to distinguish patients with major depressive disorder from healthy controls (Craddock, Holtzheimer, Hu, & Mayberg, 2009), demonstrating that resting-state fMRI has the potential for individual patient diagnosis. Studies have also demonstrated changes in resting-state connectivity over the course of mild cognitive impairment (Wang, et al., 2011) and recovery after stroke (Park, et al., 2011). Hence, there is potential for resting-state fMRI in the clinical assessment of disease progression and recovery, as well as in the assessment of treatment strategies.

transient cognitive processes and inner monologues. These states interfere with the accurate determination of resting-state networks, especially those that involve the frontal cortices (Squire & Stark, 2001; McKiernan, et al., 2006). Indeed, it is impossible to achieve a state of "language rest". However, as we will demonstrate later in this chapter, this disadvantage can actually be exploited for the purposes of presurgical mapping of language processing.

Active and Passive fMRI for Presurgical Mapping of Motor and Language Cortex

http://dx.doi.org/10.5772/30844

481

Unless a seed region for resting-state analysis can be defined easily using an anatomical atlas, like motor cortex (Yousry, et al., 1995), an appropriate task-based experiment designed to activate a particular area of the brain is still required. This becomes more difficult if restingstate maps of higher cognitive functions are desired. A recent study, however, has demon‐ strated that ROIs can be defined using the resting-state data by selecting the voxels that exhibit the greatest inter-voxel cross-correlation (Goodyear & Golestani, 2011), thus removing the

Of course, there are several disadvantages to fMRI in general.  This is because the hemodynamic response takes approximately two seconds to initiate, 6 to 8 seconds to reach its peak, and tens of seconds to return to baseline. In addition, because the BOLD signal is dependent on blood oxygenation and blood flow, there are many factors that can influence the generated signal such as age, brain pathology, and medications (Haller & Bartsch,

The downside to using any functional MRI technique for presurgical mapping, however, is that the brain shifts following tissue resection due to gravity, brain swelling and cerebrospinal fluid drainage (Nimsky, et al., 2000). This complicates the interpretation of functional locali‐ zation post-surgically. Inaccuracy in brain function localization associated with this brain shift can be avoided using intraoperative MRI to assist in anatomical registration of fMRI data (Nimsky, Ganslandt, Hastreiter, & Fahlbusch, 2001). However, brain shift remains a problem

As noted above, resting-state fMRI circumvents the limitations associated with task-based fMRI: no task is necessary to isolate a specific brain region, it can be used on any patient regardless of their level of impairment, and multiple maps can be created from one short scan. This technique may also provide an easy means to track plasticity or recruitment of brain function that may occur following surgery. One advantage specific to epilepsy research is that resting-state fMRI can be used to study epileptogenic networks and foci, potentially assisting

To date, very few studies have investigated resting-state fMRI for the purposes of presurgical mapping. Therefore, its feasibility and effectiveness as a presurgical tool is yet to be deter‐ mined. For resting-state fMRI to become a clinically useful tool, it must provide reliable information on a single subject level. One study demonstrated good concordance between

need for the task-based scan.

**4. Resting-state fMRI for presurgical mapping**

for centers that do not have access to intraoperative MR scanners.

presurgical plannning (Guye, et al., 2010; Moeller, et al., 2011).

2009).

#### **3.4. Advantages of resting-state fMRI**

Resting-state fMRI has a number of advantages over task-based fMRI and other imaging modalities. Unlike PET, fMRI in general is noninvasive and unlike EEG, deep-brain activity can be more easily detected. In comparison to task-based fMRI, there are little or no practice effects associated with multiple resting-state sessions. The main advantage of resting-state fMRI, however, is that it requires no effort from the patient. This is especially helpful when studying patients with physical or psychological deficits who cannot perform or understand the required task. For example, most task-based fMRI studies of stroke involving the upper extremities require patients to perform repetitive finger or hand movements (Buma, Linde‐ man, Ramsey, & Kwakkel, 2010; Calautti & Baron, 2003). For many stroke patients, it is difficult to move a single finger or hand, and often other fingers or body parts move in unison (i.e. mirror movements) (Nelles, et al., 1998). As a result, it is difficult to obtain a reliable anatomical map of brain activity in response to the desired movement. Typically, these patients, who make up a large proportion of stroke patients, are simply not studied, creating a significant patient selection bias in research studies. As a result, this poses severe limits on the translation of taskbased fMRI into clinical use. Since resting-state fMRI does not require any movement or action from the patient, this problem can be avoided. All patients, even the most severely impaired, can undergo a resting-state study, thus increasing the likelihood of gaining significant insight into the disease.

#### **3.5. Disadvantages of resting-state fMRI**

Despite continual advances in resting-state fMRI, there are still some drawbacks to the method. The most significant problem is that the physiological basis of the resting-state signal is still not completely understood. As discussed above, a number of studies support the notion that the resting-state BOLD signal has a neuronal basis (e.g., Lowe, et al., 2008; Skudlarski, et al., 2008). However, because of the inherent difficulty associated with recording neuronal activity from multiple spatially distinct sites, no studies have explicitly established the link between spontaneous fluctuations of the BOLD signal associated with the resting-state signal and neural activity.

Another problem is that although subjects are asked to lie still and not think of anything in particular, it is likely that periods of rest actually possess frequent states of unrest, such as transient cognitive processes and inner monologues. These states interfere with the accurate determination of resting-state networks, especially those that involve the frontal cortices (Squire & Stark, 2001; McKiernan, et al., 2006). Indeed, it is impossible to achieve a state of "language rest". However, as we will demonstrate later in this chapter, this disadvantage can actually be exploited for the purposes of presurgical mapping of language processing.

Unless a seed region for resting-state analysis can be defined easily using an anatomical atlas, like motor cortex (Yousry, et al., 1995), an appropriate task-based experiment designed to activate a particular area of the brain is still required. This becomes more difficult if restingstate maps of higher cognitive functions are desired. A recent study, however, has demon‐ strated that ROIs can be defined using the resting-state data by selecting the voxels that exhibit the greatest inter-voxel cross-correlation (Goodyear & Golestani, 2011), thus removing the need for the task-based scan.

Of course, there are several disadvantages to fMRI in general.  This is because the hemodynamic response takes approximately two seconds to initiate, 6 to 8 seconds to reach its peak, and tens of seconds to return to baseline. In addition, because the BOLD signal is dependent on blood oxygenation and blood flow, there are many factors that can influence the generated signal such as age, brain pathology, and medications (Haller & Bartsch, 2009).

## **4. Resting-state fMRI for presurgical mapping**

Resting-state fMRI is also being used to investigate a number of brain diseases and disorders. Disrupted resting-state networks have been identified in neurological disorders such as Alzheimer's disease (Jiang, et al., 2006) and psychopathologies such as schizophrenia (Liu, et al., 2007). One group has even used resting-state fMRI to distinguish patients with major depressive disorder from healthy controls (Craddock, Holtzheimer, Hu, & Mayberg, 2009), demonstrating that resting-state fMRI has the potential for individual patient diagnosis. Studies have also demonstrated changes in resting-state connectivity over the course of mild cognitive impairment (Wang, et al., 2011) and recovery after stroke (Park, et al., 2011). Hence, there is potential for resting-state fMRI in the clinical assessment of disease progression and

Resting-state fMRI has a number of advantages over task-based fMRI and other imaging modalities. Unlike PET, fMRI in general is noninvasive and unlike EEG, deep-brain activity can be more easily detected. In comparison to task-based fMRI, there are little or no practice effects associated with multiple resting-state sessions. The main advantage of resting-state fMRI, however, is that it requires no effort from the patient. This is especially helpful when studying patients with physical or psychological deficits who cannot perform or understand the required task. For example, most task-based fMRI studies of stroke involving the upper extremities require patients to perform repetitive finger or hand movements (Buma, Linde‐ man, Ramsey, & Kwakkel, 2010; Calautti & Baron, 2003). For many stroke patients, it is difficult to move a single finger or hand, and often other fingers or body parts move in unison (i.e. mirror movements) (Nelles, et al., 1998). As a result, it is difficult to obtain a reliable anatomical map of brain activity in response to the desired movement. Typically, these patients, who make up a large proportion of stroke patients, are simply not studied, creating a significant patient selection bias in research studies. As a result, this poses severe limits on the translation of taskbased fMRI into clinical use. Since resting-state fMRI does not require any movement or action from the patient, this problem can be avoided. All patients, even the most severely impaired, can undergo a resting-state study, thus increasing the likelihood of gaining significant insight

Despite continual advances in resting-state fMRI, there are still some drawbacks to the method. The most significant problem is that the physiological basis of the resting-state signal is still not completely understood. As discussed above, a number of studies support the notion that the resting-state BOLD signal has a neuronal basis (e.g., Lowe, et al., 2008; Skudlarski, et al., 2008). However, because of the inherent difficulty associated with recording neuronal activity from multiple spatially distinct sites, no studies have explicitly established the link between spontaneous fluctuations of the BOLD signal associated with the resting-state signal and

Another problem is that although subjects are asked to lie still and not think of anything in particular, it is likely that periods of rest actually possess frequent states of unrest, such as

recovery, as well as in the assessment of treatment strategies.

480 Advanced Brain Neuroimaging Topics in Health and Disease - Methods and Applications

**3.4. Advantages of resting-state fMRI**

into the disease.

neural activity.

**3.5. Disadvantages of resting-state fMRI**

The downside to using any functional MRI technique for presurgical mapping, however, is that the brain shifts following tissue resection due to gravity, brain swelling and cerebrospinal fluid drainage (Nimsky, et al., 2000). This complicates the interpretation of functional locali‐ zation post-surgically. Inaccuracy in brain function localization associated with this brain shift can be avoided using intraoperative MRI to assist in anatomical registration of fMRI data (Nimsky, Ganslandt, Hastreiter, & Fahlbusch, 2001). However, brain shift remains a problem for centers that do not have access to intraoperative MR scanners.