Age-related changes of the functional architecture of the cortico-basal ganglia circuitry during motor task execution

NeuroImage 55 (2011) 194–203

Contents lists available at ScienceDirect

NeuroImage j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / y n i m g

Age-related changes of the functional architecture of the cortico-basal ganglia circuitry during motor task execution William R. Marchand a,b,c,⁎, James N. Lee a,b, Yana Suchy b,c,d, Cheryl Garn a, Susanna Johnson a, Nicole Wood b, Gordon Chelune b,c,d a

George E. Wahlen Veterans Affairs Medical Center, 500 Foothill, Salt Lake City, UT 84148, USA University of Utah, 201 Presidents Circle, Salt Lake City, UT 84112, USA The Brain Institute at the University of Utah, 383 Colorow Drive, Salt Lake City, UT 84108, USA d University of Utah Center on Aging, 10 South 2000 East, Salt Lake City, UT 84112, USA b c

a r t i c l e

i n f o

Article history: Received 30 September 2010 Revised 8 December 2010 Accepted 9 December 2010 Available online 16 December 2010 Keywords: Aging Basal ganglia Motor pathways MRI, functional Striatum

a b s t r a c t Normal human aging is associated with declining motor control and function. It is thought that dysfunction of the cortico-basal ganglia circuitry may contribute to age-related sensorimotor impairment, however the underlying mechanisms are poorly characterized. The aim of this study was to enhance our understanding of age-related changes in the functional architecture of these circuits. Fifty-nine subjects, consisting of a young, middle and old group, were studied using functional MRI and a motor activation paradigm. Functional connectivity analyses and examination of correlations of connectivity strength with performance on the activation task as well as neurocognitive tasks completed outside of magnet were conducted. Results indicated that increasing age is associated with changes in the functional architecture of the cortico-basal ganglia circuitry. Connectivity strength increased between subcortical nuclei and cortical motor and sensory regions but no changes were found between subcortical components of the circuitry. Further, increased connectivity was correlated with poorer performance on a neurocognitive task independently of age. This result suggests that increased connectivity reflects a decline in brain function rather than a compensatory process. These findings advance our understanding of the normal aging process. Further, the methods employed will likely be useful for future studies aimed at disambiguating age-related versus illness progression changes associated with neuropsychiatric disorders that involve the cortico-basal ganglia circuitry. Published by Elsevier Inc.

Introduction Advanced age is associated with declining sensorimotor control and functioning. Fine motor control deficits as well as gait and balance impairments limit the ability of older adults to perform activities of daily living and function independently. Motor performance impairment associated with advancing age is thought to arise as a result of central nervous system, peripheral nervous system and neuromuscular system dysfunction (Seidler et al., 2010). In the human central nervous system, many changes occur related to age that could contribute to motor performance deficits. These include loss of cortical gray matter (Good et al., 2001) particularly in prefrontal regions (Jernigan et al., 2001; Resnick et al., 2003), reductions of white matter (Ge et al., 2002; Ota et al., 2006) and volume reductions in the basal ganglia (Langenecker et al., 2007). Neurochemical changes occur as well; for example altered serotonin transmission has been associated ⁎ Corresponding author. VHASLCHCS 151, 500 Foothill, Salt Lake City, Utah 84148, USA. Fax: + 1 801 998 3818. E-mail address: [email protected] (W.R. Marchand). 1053-8119/$ – see front matter. Published by Elsevier Inc. doi:10.1016/j.neuroimage.2010.12.030

with motor dysfunction in mice (Sibille et al., 2007). Further, decreased striatal dopamine (DA) signaling is associated with age (Haycock et al., 2003) as well as alterations of DA receptors (Suhara et al., 1991) and the dopamine transporter (DAT) (Troiano et al., 2010). Age-related altered basal ganglia response to dopamine has been demonstrated in nonhuman primates (Zhang et al., 2001) and there is evidence that changes in striatal DA transmission is associated with motor deficits (Cham et al., 2007, 2008). While the cortico-basal ganglia circuits (Fig. 1) have been implicated in age-associated motor deficits, the exact underlying mechanisms remain to be elucidated. Functional MRI (fMRI) studies have demonstrated agerelated changes in basal ganglia activation (Langenecker et al., 2007) and functional connectivity (Taniwaki et al., 2007). Other functional connectivity analyses have been successfully used to enhance our understanding of the cortico-basal ganglia circuitry, for example Barnes et al. (2010), Doron and Goelman (2010), Postuma and Dagher (2006), Taniwaki et al. (2006), Williams et al. (2002), Ystad et al. (2010), and Zhang et al. (2008). In the present study, we further extend prior functional connectivity work by also examining correlation of connectivity strength with performance on (a) the activation paradigm comprising a simple motor output and

W.R. Marchand et al. / NeuroImage 55 (2011) 194–203

195

aging. A motor activation paradigm involving hand function was utilized because normal aging is associated with loss of hand motor function (Ranganathan et al., 2001) and because there is a close relationship between hand motor function and the ability to function independently among the elderly (Scherder et al., 2008). A secondary goal of this study was to better characterize a functional MRI motor activation task we have previously found to have robust activation (Marchand et al., 2008) and excellent group reliability (Lee et al., 2010). We have reported that motor paradigms are effective probes of cortico-basal ganglia function in some mood and anxiety disorders (Marchand et al., 2009, 2007a, 2007b) and therefore anticipate using this task in future studies of neuropsychiatric conditions. To that end, we wanted to define how age might impact activation and functional connectivity associated with this paradigm. This information is critical for planned studies of illness progression as well as investigations of geriatric mood and anxiety disorders. Material and methods Subjects

Fig. 1. Schematic of cortico-basal ganglia connectivity evaluated in this study (Albin et al., 1989; Alexander and Crutcher, 1990, 1986; Brendel et al., 2010; Chen et al., 1991; Sakai et al., 2002; Wiesendanger and Wiesendanger, 1985). SMA = supplementary motor area; GPe = globus pallidus external segment; GPi = globus pallidus internal segment; STN = subthalamic nucleus and VA = ventral anterior nucleus of the thalamus. Solid lines = glutamate fibers and dotted lines = GABA fibers.

(b) motor and executive function tasks conducted outside of magnet. A similar approach has been used to correlate connectivity in the basal ganglia with performance on the California Verbal Learning Test (Ystad et al., 2010) and correlate corticostriatal connectivity with symptom severity scores for Obsessive Compulsive Disorder (Harrison et al., 2009). Functional imaging studies have also demonstrated age-related increased cortical recruitment during motor behavior (Calautti et al., 2001; Heuninckx et al., 2005, 2008) and have found relationships between activation and motor performance (Harada et al., 2009; Heuninckx et al., 2008; Mattay et al., 2002; Ward and Frackowiak, 2003). Therefore, we reasoned that fMRI using a motor activation paradigm would be likely to reveal aberrant connectivity involving both cortical–subcortical and subcortical–subcortical regions. The primary aim of this work was to expand our understanding of age-related changes in the functional architecture of the cortico-basal ganglia circuitry as well determine whether such changes might play a direct role in the decline of motor function associated with normal

Participants were 59 females recruited from three age groups. The age ranges of the cohorts were 18–22, 25–35 and 65–75 for “young,” “middle” and “old” groups respectively. The age ranges for the three groups were selected specifically to allow us to examine and compare activation and connectivity patterns of groups representing: (a) a healthy adult brain (the middle group); (b) the period prior to the completion of brain maturation (the young group) and (c) the period after brain functions begin to decline (the old group). It is well documented that the brain, in particular cortico-basal ganglia circuitry, usually does not fully mature until early in the 3rd decade of life, with most maturation typically completed by the age of 25 years and followed by a period of relative stability (Bava et al., 2010; Lebel et al., 2008). Thus, the age range for the middle group was 25 to 35 years. The age range for the young group was 18 to 22 years, a period marked by active maturation process (Bava et al., 2010; Lebel et al., 2008). The gap of three years between the young and the middle groups was designed to allow for individual differences in maturation rates. Lastly, the age range for the old group was selected so as to examine activation at an age that is well known to be associated with measurable declines in motor and executive processes (Gunstad et al., 2006). All subjects were strongly right-handed as evidenced by a score of ≥80 on the Edinburgh Handedness Inventory (Oldfield, 1971) and all female to avoid any possible confound secondary to gender-specific activation patterns (Bell et al., 2006). See Table 1 for participant characteristics. As can be seen from the table, the old group was slightly less educated than the middle group (p = 0.001), although this difference is likely of little practical significance. Additionally, the old group reported somewhat stronger right-handedness than the other two groups (both p values b 0.01), but again, this is likely of minimal practical significance, as all groups were well within the right-handed range. Groups were comparable on IQ (p = 0.705). Potential subjects Table 1 Means, standard deviations (in parentheses) and range of values for subject characteristics. Young (n = 21) Age (years)

20.19 (1.17) 18–22 Education (years) 14.21 (1.10) 12–16 IQ estimate 110.71 (11.73) 88–127 Edinburg Handedness Inventory 90.50 (7.93) 80–100

Middle (n = 20)

Old (n = 18)

27.70 (2.41) 25–33 14.90 (1.33) 12–16 108.05 (11.55) 89–125 90.25 (8.19) 80–100

67.72 (3.08) 65–74 13.42 (1.31) 12–16 110.78 (11.78) 81–123 96.94 (5.46) 80–100

196

W.R. Marchand et al. / NeuroImage 55 (2011) 194–203

were excluded if they were either not native English speakers or string or keyboard musicians because of the possible impact on communicating with study staff and motor task execution, respectively. Additional exclusionary criteria were any history of psychiatric illness, head injury, neurological disorder, dementia or medical disorder, including hypertension. A radiologist reviewed all structural scans. None of the subjects exhibited white matter hyperintensities or changes other than those related to normal aging. Those with any current use of medications that could impact the central nervous system or contraindications to fMRI as well as those with any history of psychiatric disorders, substance abuse, treatment with psychiatric medications or any first-degree relative with any psychiatric disorder were also excluded. All subjects received a study evaluation that included administration of several neuropsychological tasks (described below) as well as the administration of the Structured Clinical Interview for DSM-IV-TR Axis I Disorders-Research Version to rule out psychiatric illness. After a complete description of the study was given to the subjects, written informed consent was obtained, as approved by both the Institutional Review Board at the University of Utah and the Research Review Committee of the George E. Whalen Veterans Administration Medical Center. Neurocognitive testing instruments and procedures All neurocognitive measures were administered on a separate day from and prior to scanning. The tasks used in the present study were administered as part of a longer battery (used for another study). The entire battery took approximately 45 min and was conducted in a quiet testing room by a trained technician. Intellectual functioning was estimated using the Wechsler Test of Adult Reading (WTAR), a 3–5 minute measure of oral reading consisting of 50 words that have irregular grapheme-to-phoneme translation but do not require text comprehension or knowledge of word meaning (Corporation, 2001). The WTAR was specifically developed as a tool for estimating premorbid intellectual functioning in adults aged 16–89, and was co-normed with the Wechsler Adult Intelligence Scale-III (Wechsler, 1997) to provide direct comparisons between predicted and observed intelligence scores. Motor and executive functioning was assessed using the Alphanumeric Sequencing and the Push–Turn–Taptap subtests from the Behavioral Dyscontrol Scale-electronic version (BDS-EV) battery (Suchy et al., 2005). These are described in turn below. The Alphanumeric Sequencing (AS) task is an electronic counterpart to the Trail Making Test-Part B (TMT-B) (Reitan, 1958), which is a well known and extensively validated measure of executive functioning thought to specifically assess cognitive flexibility (Arbuthnott and Frank, 2000; Moll et al., 2002; Stuss et al., 2001; Zakzanis et al., 2005). On the AS task, participants are required to push buttons on a specialized response console marked with letters of the alphabet (A through H) and numerals one through nine. The buttons are to be pressed in alphanumeric sequence (i.e., 1A, 2B, 3C, etc.). Speed of performance is measured electronically in ms. The AS task has been found to correlate highly with the original TMT-B (Suchy et al., 2005), and its reliability has been found to be superior to the original trail making task (Eastvold et al., 2004). The Push–Turn–Taptap (PTT) task is an electronic parallel to the well-known “Fist–Edge–Palm” task developed by Alexander Luria (Luria and Majovski, 1979). Participants are required to learn four different sequences of increasing length that consist of different permutations of three different hand movements executed on a specialized response console. The three hand movements are “Push” — pushing the joystick on the response console forward; “Turn” — turning the joystick clockwise; and “Taptap” — double-tapping on a large dome button on the response console. The task begins with Block 1, in which a twomovement sequence is presented on the computer screen. The participant performs the indicated task until three correct trials are

achieved. Following these three learning trials, participants continue to perform the sequence from memory, until accomplishing five additional correct trials. This completes the Block 1 of the task. After completing Block 1, participants move on to the next block in which they follow the same learning procedures. There is a total of four Blocks, each characterized by different and progressively longer sequences. Mistakes are followed by an audible tone, along with the presentation of the correct sequence on the computer screen and the highlighting of the next movement to be performed. This task was selected for the study because it allows assessment of various discrete components of motor output. First, the PTT task allows assessment of the “motor planning” component of motor output, which has been described as an internal strategy that precedes an intended movement (Banich, 2004). Specifically, prior to initiating a sequence of coordinated movements, an abstract plan is generated that contains both general information about the intended goal and specific information about the neuromuscular control that will be required (Keele, 1968). As such, motor planning may represent an example of motor–executive integration. Motor planning was assessed by measuring the mean latency before initiation of a correctly executed sequence. Second, the PTT task allows assessment of performance speed, reflected in the time required to complete a given sequence from start to finish. This “motor speed” variable was measured by computing the mean speed of completing correct sequences across the four blocks. Because motor speed contributes to performance of many tasks purported to measure executive functioning, this variable served not only the purpose of assessing the efficacy of the motor system, but also of controlling for motor speed on the AS task. Lastly, the PTT task allows assessment of the “motor learning” aspect of motor output, by computing the total number of errors committed across the four blocks, as participants learn new and progressively more complex sequences. The overall performance on this task has been shown to correlate with measures of executive functioning above and beyond participants' demographic characteristics and simple motor speed, with the motor planning latencies showing the strongest association with executive functions (Kraybill and Suchy, 2008; Suchy et al., 2005, 2010; Suchy and Kraybill, 2007). Functional MRI tasks and experimental procedure A block-design motor activation paradigm was used to probe cortico-basal ganglia function. The task, which we have used previously (Lee et al., 2010; Marchand et al., 2008), was a self-paced paradigm performed with the non-dominant hand. In this task, subjects alternated pressing buttons with the middle finger alone and then the index and ring finger simultaneously during a four minute run with six blocks of rest and six blocks of activity presented in pseudorandom order. The task was self-paced and subjects were instructed to complete repetitions at a consistent but comfortable pace during each run. Visual stimuli for the task were presented on a translucent slide screen at the back of the magnet, which was viewed through a mirror mounted on top of the head coil. Stimulus presentation and response recordings were controlled by E-prime software (Psychology Software Tools, Inc., Pittsburgh, USA; www.pstnet.com/eprime). Subjects were trained on the task immediately prior to scanning utilizing a computer to display the visual stimuli while instructions were given. Subjects practiced the task using the actual button boxes used during the scan. Training and orientation to the scan required approximately 10 min per subject. Task compliance was confirmed during scanning by way of a remote button control box that indicated subject button presses by illuminating a light color coded for each button. Functional imaging Subjects were scanned on a Siemens 3 T Trio MR scanner with a 12-channel head coil. Functional MRI data were acquired with a susceptibility weighted gradient echo EPI sequence (field-of-view

W.R. Marchand et al. / NeuroImage 55 (2011) 194–203

22 cm, matrix 64 × 64, repetition time TR = 2.08 s, echo time TE = 30 ms, slice thickness 3 mm with 10% gap, flip angle 75º). Thirty-five slices were acquired during each repetition time. The first five image volumes of each task were discarded to ensure signal equilibrium. Distortions caused by variations in magnetic susceptibility were removed during post-processing using fieldmap data acquired with a separate sequence. Anatomic T1-weighted images were acquired using an MPRAGE sequence (field-of-view 22 cm, matrix 192 × 192, repetition time TR = 1.5 s, inversion time TI = 1.1 s, slice thickness 2 mm, flip angle 8º, signal averages = 2). Data processing Preprocessing Preprocessing and statistical analyses were carried out with SPM5 (http://www.fil.ion.ucl.ac.uk/spm). Images were realigned to correct for head motion, unwarped to remove susceptibility distortion, and slicetime corrected. The maximum linear motion for any subject was 1.88 mm, and the maximum angular motion was 1.84º. The meanrealigned EPI image was co-registered with the anatomic image. All images were spatially normalized to the Montreal Neurological Institute (MNI) template, and voxel sizes resampled to 2 × 2 × 2. EPI images were smoothed using isotropic 6 mm Gaussian kernels and statistically analyzed using an epoch design convolved with the hemodynamic response function. Low-frequency noise was removed with a high-pass filter with a cutoff period of 128 s and an autoregressive AR (1) model was fitted to the residuals to account for temporal autocorrelation. Regions of interest Whole brain maps of between-group differences in activation were thresholded at a voxel-wise 0.05 level of significance corrected for multiple comparisons using family wise error correction (FEW). Regions of interest (ROIs) were selected based upon known components of the cortico-basal ganglia circuitry involved with motor task execution. All ROIs were in the right, or controlling hemisphere, as strongly righthanded subjects completed the task with the non-dominant (left) hand. In regard to cortical structures, primary motor cortex (M1) and primary somatosensory cortex (S1) were selected because of their well-known role in motor behavior. The supplementary motor area (SMA) was chosen because of evidence that while this structure is involved with initiating motor responses, there is evidence that contributions continue during motor execution (Brendel et al., 2010) in parallel with M1 (Chen et al., 1991). The ventral anterior (VA) nucleus of the thalamus was included because of evidence of circuit outflow anatomical connectivity from the globus pallidus internal segment (GPi) to SMA through the VA (Sakai et al., 2002; Wiesendanger and Wiesendanger, 1985). The other regions, subthalamic nucleus (STN), caudate, putamen globus pallidus external segment (GPe) and GPi, are well-known subcortical components of the circuitry (Albin et al., 1989; Alexander et al., 1986). See Fig. 1 for a schematic depiction of the circuitry. Volumes and coordinates of all ROIs are provided in Table 2. Fig. 2 shows an activation map with an example ROI (right putamen) overlaid in black. ROIs for connectivity analysis were generated by forming the intersection of activation maps from the three groups of subjects with anatomic ROIs in the “VOI Tool Utility” available at www.ihb.spb.ru/ ~pet_lab. The activation maps used to generate subcortical ROIs were thresholded at a voxel-wise threshold of 0.001, uncorrected for multiple comparisons, and restricted to cluster sizes that achieved p b 0.05 corrected for multiple comparisons with family wise error correction according to SPM random field theory (Friston et al., 1993). The activation maps used to generate cortical ROIs were voxel-wise thresholded at a higher p b 0.05 threshold, corrected for multiple comparisons, because of their stronger activation, and a few protruding spikes were eliminated to produce uniform models. In addition, regions of interest for regressors of no interest were obtained by placing 3 mmradius seeds in both white matter (MNI coordinates 33, −62, 24) and

197

Table 2 Volumes and coordinates of regions of interest. ROI

Caudate Putamen GPe GPi STN VA SMA S1 M1

Coordinates Volume (mm3)

X max

min

max

min

max

min

424 4168 752 88 168 160 904 5520 6136

6 14 14 16 8 10 2 30 26

16 32 28 20 12 14 0 58 52

− 14 − 22 − 20 − 10 − 16 −8 −6 − 46 − 32

22 18 6 −2 − 10 −4 4 − 12 −8

−6 −6 −4 −2 −8 6 50 30 46

22 18 10 2 −2 14 66 64 70

Y

Z

SMA = supplementary motor area; STN = subthalamic nucleus; GPe = globus pallidus, external segment; GPi = globus pallidus, internal segment and VA = ventral anterior nucleus of the thalamus.

CSF (MNI coordinates 6, −2, 19), and using the six motion regressors that result from spatial realignment in SPM5. All image were normalized to a standard MNI template, but we did not attempt to quantify possible volume changes due to age. Some studies focusing on one specific anatomic region attempt to normalize all subject brains to maximize the overlap of that specific region. However, this approach generally produces worse normalization in other areas of the brain. In this study, since we have multiple regions of interest, which are both cortical and subcortical, any attempt to focus on the normalization of one region of interest would likely produce worse results in other regions. Also, our chosen method of combining anatomic ROIs with activation maps should limit any confound associated with age-related changes in the size of the basal ganglia. Since we limit attention to those portions of the ROI that are active for the group as a whole, any portions of the ROI that exhibit large variability across the group are less likely to produce group-level activation, and are unlikely to survive an imposed activation threshold. Functional connectivity Functional connectivity analyses were conducted using partial correlations of multiple ROIs, a method previously used by other groups

Fig. 2. Activation map with overlaid right putamen region of interest. Region of interest overlay shown in black.

198

W.R. Marchand et al. / NeuroImage 55 (2011) 194–203

(Mostofsky et al., 2009; Park et al., 2010). Because we wanted to compare connectivity between groups, we chose this method rather than structural equation modeling (SEM) or dynamic causal modeling (DCM), which are more often used to evaluate the accuracy of competing models (Penny et al., 2004). Waveforms for all ROIs were extracted with Marsbar software (http://marsbar.sourceforge.net), mean corrected, and bandpassfiltered from 0.008 to 0.1 Hz with a second order butterworth filter. Partial correlations between pairs of ROIs were carried out using the Matlab Statistics Toolbox (Mathworks, Natick, Massachusetts), partialing out the influence of white matter, CSF and motion. The resulting partial correlations were normalized with Fisher's z transform to produce functional connectivity values. These values representing the strength of connectivity were generated for all possible combinations of cortical and subcortical structures for which direct anatomical connections are known to exist. Connectivity strengths (ANOVAs) We conducted a separate series of analyses for relative connectivity strengths among (1) cortical–subcortical pairs, and (2) subcortical– subcortical pairs. Each set of analyses began with an omnibus Analysis of Variance (ANOVA), followed by supplementary analyses to allow interpretation of interactions. These are explained in more detail below. First, in order to compare groups on the relative strength of functional connectivity between the cortical and subcortical regions, a repeated measures analysis of variance (ANOVA) was conducted, using the three cortical regions (SMA vs. S1 vs. M1) and the four subcortical regions (STN vs. caudate vs. VA) as two within-subjects factors, and Group as a between-subjects factor. This analysis allowed determination whether (a) specific regions exhibited stronger overall connectivity values than other regions (reflected in statistically significant main effects), (b) specific pairs exhibited stronger connectivity values than other pairs (reflected in a statistically significant interaction between the two within-subjects factors), (c) the groups differed from each other with respect to the connectivity patterns (reflected in a statistically significant interaction between Group and one or both within-subjects factors), and (d) the groups differed with respect to the overall strength of connectivity across the connectivity pairs (reflected in a main effect of Group). Second, functional connectivity of subcortical pairs was examined in a separate set of analyses. For these analyses, connections could not all be examined in a single repeated measures model because the pairs were not all parallel (i.e., each region of interest did not necessarily connect to an equal number of other regions). For that reason, we first conducted a Multivariate Analysis of Variance (MANOVA), comparing the three Groups on the overall profile of connectivity across all six connectivity pairs, followed by a series of univariate ANOVAs to compare the groups on the strength of the individual connectivity pairs. Next, we examined the group differences on the relative strengths of the connectivity of the external and internal segments of the pallidum with the caudate nucleus and the subthalamic nucleus, using a repeated measures ANOVA. In this analysis, we used the connectivity with the external vs. internal pallidum as the first within-subjects factor, the connectivity with the subthalamic vs. caudate nucleus as a second within-subjects factor, and Group as the between-subjects factor. Again, the significant main effect of a withinsubjects factor would show that a particular region had greater overall connectivity values than other regions, whereas a significant interactions would show that specific pairs had stronger connectivity values than other pairs. Main effect of Group would show that the strength of overall connectivity differed among the groups, and an interaction of Group with one or both of the other factors would reflect different patterns of connectivity among the three groups. Behavioral data analyses Groups were compared on behavioral variables using a one-way ANOVA followed by Bonferroni post-hocs. Additionally, analyses were

Table 3 Means, standard deviations (in parentheses) for behavioral assessment variables.

Complex left task (completed repetitions) PTT speed (seconds per sequence) PTT errors PTT motor planning latencies (ms) AS task (seconds)

Young (n = 21)

Middle (n = 20)

Old (n = 18)

17.65 (5.06) 2.20 (.40) 3.05 (2.97) 747.30 (156.76) 27.52 (5.96)

14.06 (4.54) 2.34 (.47) 7.20 (9.31) 825.31 (106.26) 28.40 (8.23)

13.99 (2.84) 1.81 (1.23) 9.33 (11.00) 923.51 (233.05) 55.87 (38.21)

F (df = 2,56)

p

4.80

0.012

2.43

0.097

19.63

b 0.001

5.12

0.009

10.15

b 0.001

completed to determine if correlations existed between connectivity and either performance on neuropsychological testing or activation task performance during the scan. In order to limit the number of correlations, we only examined associations of behavioral data with connectivity coefficients on which group differences emerged. Behavioral results Group performances on behavioral variables are presented in Table 3. Group differences were evident on most variables. Bonferroni post hoc analyses showed that the young group performed better than the middle and old groups on the activation task (Bonferroni p values = 0.030 and .031, respectively; Cohen's d = 0.80 and 0.79); the old group performed more poorly than the young and the middle groups on PTT accuracy and the AS task (all Bonferroni p values b 0.002, Cohen's d ranging from 1.21 to 1.53), and the young group had shorter PTT motor planning latencies than the old group (Bonferroni p = 0.007, Cohen's d = 0.97), with the middle group falling in between. The correlation matrix among the behavioral variables (zero order correlations as well as partial correlations after controlling for age) is presented in Table 4. As can be seen from the table, motor planning latency correlated with performance on the AS task, consistent with prior research (Kraybill and Suchy, 2008; Suchy et al., 2005, 2010; Suchy and Kraybill, 2007). Functional imaging results Between-group comparison of whole brain activation maps There were no significant differences between the middle and young groups at the threshold utilized (voxel-wise 0.05 corrected for multiple comparisons with FWE). Further, comparisons of the young to middle, young to old and middle to old groups revealed no greater activation of a

Table 4 Zero order correlations and partial correlations (after controlling for age, in parentheses) among behavioral variables. Complex left PTT motor PTT task speed accuracy −.161 (−.250) PTT accuracy −.237⁎ (−.091) PTT motor planning −.508⁎⁎ (−.460⁎⁎) AS task −.239⁎

PTT motor AS task planning

PTT motor speed

IQ estimate

(−.130) .079 (.095)

−.454⁎⁎ (−.378⁎) .523⁎⁎ .147 (.697⁎⁎) (−.136) .216 .449⁎⁎ (.426⁎⁎) (.179) −.182 (−.177)

.064 (.045)

.493⁎⁎ (.383⁎) −.214 (−.249)

N = 59; PTT = Push–Turn–Taptap task; AS = Alphanumeric sequencing. ⁎ = p b .05, one tailed. ⁎⁎ = p b .001.

−.106 (−.150)

W.R. Marchand et al. / NeuroImage 55 (2011) 194–203 Table 5 Mean functional connectivity coefficients of right hemisphere cortical with right hemisphere subcortical structures of the cortico-basal ganglia circuitry across the three age groups.

SMA M1 S1

Caudate

STN

VA

Putamen

0.6173 0.5151 0.5845

0.5520 0.4579 0.5250

0.6282 0.4945 0.5624

0.7824 0.6401 0.6962

SMA = supplementary motor area; STN = subthalamic nucleus; GPe = globus pallidus, external segment; GPi = globus pallidus, internal segment and VA = ventral anterior nucleus of the thalamus.

younger group relative to an older group. In contrast, the same comparisons demonstrated greater activation of the old group relative to both the middle and young group. Areas of greater activation for the old group in comparison to both the middle and young groups included the right caudate, bilateral thalamus, bilateral S1, right cingulate and bilateral SMA. Additionally, the old group demonstrated greater activation relative to the middle group in the right cerebellum, bilateral paracentral lobule and left M1, cingulate and temporal/occipital regions. Finally, greater activation in right temporal, anterior cingulate, superior frontal, left middle occipital and precuneus as well as bilateral putamen was found for the old relative to the young group. Functional connectivity of pairs of cortical–subcortical structures Mean connectivity coefficients (across the three groups) of the three cortical regions (M1, S1 and SMA) with subcortical regions (caudate, putamen and STN) for which direct anatomical connectivity is known to exist are presented in Table 5. Unless otherwise stated, conventional cut-offs for statistical significance (i.e., alpha = 0.05) were used. The omnibus analysis of variance (ANOVA) revealed a significant interaction between one of the two within-subjects factors (S1 vs. M1 vs. SMA) and group (young vs. middle vs. old) [F(4,112) = 6.66, p = 0.001], suggesting that the three groups differed from each other with respect to the relative strengths of connectivity with the S1, M1, and SMA regions (see Fig. 3). To tease apart the specific nature of group differences in this overall pattern of connectivity strengths, we conducted follow-up analyses. In these follow-up analyses, we first (1) examined within-subjects differences for each group separately. In other words, for each group separately, we compared the relative strengths of co-activation among regions implicated by the omnibus ANOVA (i.e., S1 vs. M1 vs. SMA). Because these follow-up analyses required the examination of 12 comparisons, we adjusted the critical alpha level to 0.004 (i.e., .05 × 12).

Fig. 3. Strength of functional connectivity of cortical regions with subcortical structures (caudate, STN and VA nucleus of the thalamus) across three age groups. SMA = supplementary motor area.

199

Second (2), we compared the groups (i.e., young vs. middle vs. old) on individual connectivity strengths in regions implicated by the omnibus ANOVA (i.e., S1, M1, and SMA). Again, twelve comparisons were needed, and thus the alpha level was adjusted to 0.004. The results of these follow-up analyses were as follows: First, follow-up within-subjects comparisons demonstrated that both the young and the middle groups' subcortical structures co-activated relatively more strongly with SMA than with M1 [F(1,20) = 33.50, p b 0.001 and F(1,19)= 20.97, p b 0.001, respectively]. Additionally, the young group's subcortical structures co-activated relatively more strongly with S1 than with M1 [F(1,20) = 19.02, p b 0.001]. For the middle group, the same comparison reached a conventional level of significance, but not the level of significance that was adjusted for multiple comparisons [F(1,19)= 7.60, p = 0.013]. In contrast, no differences between SMA and either M1 or S1 were found for the old group (both p values N0.16). Lastly, all three groups' (young, middle, and old) subcortical structures co-activated relatively more strongly with S1 as compared to M1 [F(1,20) = 22.50, pb 0.001; F(1,19) = 10.4, p = 0.004; F(1,17) = 35.91, p b 0.001; respectively] (see Fig. 3). Second, follow-up group comparisons revealed that whereas the groups did not differ on the strength of co-activation of subcortical structures with SMA (all p values N0.40), the groups did differ for M1–VA and S1–VA pairs [F(2,56) = 7.42, p = 0.001; F(2,56) = 8.57, p = 0.001; respectively]. A traditional (but not adjusted) level of significance was also reached for M1–caudate, S1–putamen, and S1–caudate, [F(2,56) = 4.34, p = 0.018; F(2,56) = 4.29, p = 0.019; F(2,56) = 4.29, p = 0.019; respectively]. Bonferroni post-hocs (using Bonferroni p values b0.05) showed that these differences were driven by the old group showing stronger co-activations than the young group (across all co-activation pairs, Cohen's d ranging from 0.31 to 0.41) and than the middle group (across all co-activation pairs except S1–putamen and S1–caudate, Cohen's d ranging from 0.32 to 0.43). This latter set of results suggests that the old group's stronger S1 and M1 co-activations (see Fig. 3) were primarily driven by co-activations with the VA and, to a lesser extent, the putamen and the caudate, but not STN (see Figs. 4 and 5).

Functional connectivity of pairs of subcortical structures Mean functional connectivity among subcortical structures (caudate, putamen, GPe, GPi and VA) across all three groups is presented in Table 6. No between-group differences emerged.

Fig. 4. Functional connectivity between primary motor cortex and input (caudate and STN) and output (VA nucleus of the thalamus) structures of the cortico-basal ganglia circuitry. STN = subthalamic nucleus and VA= ventral anterior nucleus of the thalamus.

200

W.R. Marchand et al. / NeuroImage 55 (2011) 194–203 Table 7 Correlations of functional connectivity with behavioral data.

Fig. 5. Functional connectivity between primary motor cortex and input (caudate and STN) and output (VA nucleus of the thalamus) structures of the cortico-basal ganglia circuitry. STN = subthalamic nucleus and VA= ventral anterior nucleus of the thalamus.

Correlation of behavioral and imaging data To determine whether age-related changes in connectivity could explain age-related decrements in performance, we selected those connectivity pairs on which age-related changes were evident (i.e., M1–caudate, M1–VA, S1–caudate, and S1–VA, S1–putamen) and correlated them with the behavioral variables on which age-related changes were apparent (i.e., the AS task, PTT errors, and PTT motor planning). The results are presented in Table 7. As can be seen from the table, greater connectivity values on all four pairs are associated with a greater number of PTT errors. Because PTT errors were strongly related to age (Table 3) and performance speed (Table 4), we wanted to determine whether PTT errors were related to connectivity even after age and performance speed were accounted for. To that end, we conducted a hierarchical regression, using the PTT accuracy as the criterion variable, age as a predictor on Step 1, performance speed as a predictor on Step 2, and the four connectivity pairs as predictors on Step 3. The results are summarized in Table 8. As can be seen, age accounted for nearly 44% of variance in PTT accuracy, speed accounted for an additional 8% and connectivity within the four cortical– subcortical pairs for an additional 8.5% above that. Discussion The primary aim of this study was to enhance our understanding of both the nature of age-related functional changes in cortico-basal ganglia circuitry and the impact of these alterations on motor task performance. Advancing age is known to be associated with changes in the basal ganglia nuclei. These changes include volume reduction, iron accumulation in the striatum and changes in DA signaling (Haycock et al., 2003; Langenecker et al., 2007; Suhara et al., 1991; Troiano et al., 2010). There is some evidence suggesting that each of these could contribute to motor deficits associated with normal aging (Cass et al., 2007; Cham et al., 2007, 2008; Seidler et al., 2010). However, there is limited information about whether age-related alterations occur in the cortico-basal ganglia circuit functional architecture underlying motor Table 6 Mean functional connectivity coefficients of pairs of subcortical structures of the cortico-basal ganglia circuitry of the right hemisphere across the three age groups.

Putamen Caudate STN GPe GPi

GPe

GPi

VA

1.1350 0.8650 0.7458 1.0069 –

0.4836 0.4796 0.4725 0.7733 –

0.8513 1.1917 – – .4914

STN = subthalamic nucleus; GPe = globus pallidus, external segment; GPi = globus pallidus, internal segment and VA = ventral anterior nucleus of the thalamus.

Functional connectivity pairs

Performance on Performance on neuropsychological activation paradigm testing Complex left task

PTT accuracy PTT motor planning AS task

Right M1– caudate Right M1–VA Right S1– caudate Right S1–VA Right S1– putamen

.109

.408⁎⁎

−.153

.052

.105 .111

.512⁎⁎ .414⁎⁎

−.050 −.172

.203 .043

.125 .066

.497⁎⁎ .315⁎

−.078 −.258⁎

.178 −.058

N = 59. AS = Alphanumeric Sequencing task; PTT = Push–Turn–Taptap task; SMA = supplementary motor area; STN = subthalamic nucleus; GPe = globus pallidus, external segment; GPi = globus pallidus, internal segment and VA = ventral anterior nucleus of the thalamus. Higher values on the PTT and AS task reflect poorer (i.e., slower, less accurate) performance. Higher values on the Complex Left reflect better performance as demonstrated by a higher mean number of completed task cycles in a 20-second block. ⁎⁎ p b 0.01, one tailed.

task execution. Further to our knowledge, no studies have attempted to determine if changes in functional connectivity accompanying advancing age might directly contribute to motor deficits. In regard to the question of whether cortico-basal ganglia motor circuit functional connectivity changes with age, one previous study reported that such changes exist (Taniwaki et al., 2007). Our results, with a larger sample, confirm that increasing age is associated with changes in functional architecture. However for the sample studied (adults ≥18 years of age) and the motor paradigm utilized, these changes occur mainly after 30 years of age, as we found minimal differences in functional connectivity between the young and middle groups. Functional connectivity changes could potentially occur between pairs of subcortical structures, between cortical and subcortical structures or both. We found alterations of motor circuit connectivity only between cortical and subcortical structures. The lack of evidence for changes in connectivity between subcortical components is interesting in light of studies that suggest age-related changes of these nuclei (Haycock et al., 2003; Langenecker et al., 2007; Moeller et al., 1996; Petit-Taboue et al., 1998; Suhara et al., 1991; Troiano et al., 2010), which may contribute to motor deficits (Cass et al., 2007; Cham et al., 2007, 2008; Seidler et al., 2010). In particular, decreased striatal dopamine signaling (Haycock et al., 2003) as well as alterations of DA receptors (Suhara et al., 1991) and DAT (Troiano et al., 2010) have been reported. Altered basal ganglia response to dopamine has been demonstrated in non-human primates (Zhang et al., 2001) and there is evidence that changed striatal DA transmission is specifically associated with agerelated motor deficits (Cham et al., 2007, 2008). However, some discrepant results have been reported. For example, PET studies have reported increased (Moeller et al., 1996), decreased (Moeller et al.,

Table 8 Hierarchical regression showing functional connectivity and behavioral predictors of performance accuracy on a motor learning task. Step

Predictors

R square

R square change

F change

df

p

1 2 3

Age PTT speed M1–caudate M1–VA S1–caudate S1–VA S1–putamen

.436 .516 .609

.436 .080 .093

44.06 9.32 2.42

1,57 1,56 5,51

b.001 .003 .048

PTT = Push–Turn–Taptap task and VA = ventral anterior nucleus of the thalamus. Accuracy on the PTT task is the criterion variable.

W.R. Marchand et al. / NeuroImage 55 (2011) 194–203

1996) and no change (Loessner et al., 1995) in basal ganglia metabolism associated with age. One possible explanation for our results is that compensatory mechanisms are able to preserve functional connectivity between subcortical circuit structures. A recent study (Troiano et al., 2010) suggests one such mechanism. Troiano et al. found that age-related reductions in striatal DAT are associated with preservation of motor performance. The authors conclude that their findings imply that biochemical processes associated with healthy aging may offset the natural decline in motor function observed in the elderly. Perhaps decreased DAT could functionally compensate for decreased striatal DA release and contribute to preservation of both subcortical functional architecture and motor function. Future studies will be needed to directly evaluate whether decreased DAT could play a role in the preservation of functional connectivity between subcortical components of the circuitry. Finally, an association between connectivity of subcortical structures and motor performance would support the hypothesis of a compensatory mechanism. We did not find such an association. If future investigations report similar results then this theory is unlikely to be valid. In contrast to the connectivity between subcortical pairs, we found significant age-related changes between some cortical–subcortical pairs (Fig. 3). Further, our novel results demonstrate how specific cortical–subcortical pathways differ between age groups in regard to connectivity strength. We found that SMA–subcortical connectivity is stronger than that of either M1or S1 for the two younger groups (Fig. 3). However, with age, M1 and S1 connectivity becomes much stronger and in the case of S1 becomes relatively stronger than SMA. Further, the stronger S1 and M1 connectivity of the older group was primarily driven by cortical connectivity with the VA and caudate but not STN (Figs. 4 and 5). In summary, these findings suggest that advancing age is associated with a shift in the relative strength of engagement of subpathways of the cortico-basal ganglia motor network, such that the subcortical input (caudate) and output (VA nucleus of the thalamus) nuclei become more strongly connected to both M1 and S1 during motor task execution. In contrast, the SMA– basal ganglia loop connectivity changes relatively little as is the case for the other subcortical input nucleus (STN). This finding is in line with previous research demonstrating that enhanced M1 recruitment bilaterally is required to produce the same motor performance among older subjects (Naccarato et al., 2006). Age-associated changes in functional connectivity could reflect either a decline in brain function or a compensatory process. We found that greater connectivity values for all cortical–subcortical pairs were associated with greater numbers of PTT errors (Table 7). Further, PTT errors were related to connectivity even after age and performance speed were accounted for. Thus, these findings indicate that increased cortical–subcortical connectivity is not a compensatory process and in fact is related to age-associated decline and contributes directly to the loss of motor function. This finding is consistent with evidence that in Parkinson's disease abnormally synchronized oscillatory activity occurs at multiple levels of the basal ganglia–cortical loop and this excessive synchronization correlates with motor deficits (Hammond et al., 2007). Further in normal subjects, decreased phase synchronization has been shown to be associated with movement planning and execution (Gentili et al., 2009). Thus, excessive cortical–subcortical co-activation and synchronization of firing may be a common mechanism underlying motor impairment associated with both normal aging and Parkinson's disease. Increased M1 recruitment among older subjects has been thought to represent a compensatory process (Naccarato et al., 2006). Our results suggest that increased subcortical connectivity with M1 is not compensatory. Age-associated changes in interhemispheric connectivity have also been reported (Langan et al., 2010; Taniwaki et al., 2007) and have found to be related to motor performance (Langan et al., 2010). Additional studies will need to further parse the role of changes in both activation and connectivity in processes that contribute to loss as well as preservation of motor function.

201

In contrast to our results, another group (Taniwaki et al., 2007) found decreased connectivity of the cortico-basal ganglia circuitry in response to a self-paced motor task completed with the nondominant hand. There are a number of differences between their work and ours, including the number of subjects, activation paradigm, subject gender and analyses methods. Thus, a comparison of results must be undertaken with caution and one possibility is that one or more of the differences in methods resulted in discrepant results. An important next step may be to compare various functional and effective connectivity analyses methods in the same subject group. Such a study may not only provide replication but also clarify limitations of the various methods in studies of the impact of age on the cortico-basal ganglia circuitry. Despite differences in results, taken together the two studies do provide relatively compelling evidence that aberrations of the functional connectivity within the corticobasal ganglia circuitry are associated with normal aging. We did not find any associations between functional connectivity and performance of the activation paradigm. This lack of association must be interpreted with caution due to the nature of the paradigm and our behavioral measure of performance. The paradigm was designed to generate robust activation of the basal ganglia. However, the fact that it was self-paced limited our ability to precisely measure performance, for example using reaction times. The measure we used, number of repetitions, has very limited sensitivity. Thus even though significant between-group differences in performance were found (Table 3), the measure may not have been accurate enough to detect more subtle differences that might be associated with strength of connectivity. This must be considered a limitation of this study. Future studies may benefit from the inclusion of a motor paradigm that is more amenable to sensitive measurement of performance. If a lack of an association between connectivity and activation task performance is demonstrated in future studies, it will be important to explore the discrepancy between tasks completed in and out of the magnet. Such a finding would also indicate the need to explore why an association exists between right-hand out of magnet task performance and connectivity but not in magnet left-hand task performance and connectivity. In this study, functional connectivity analyses were utilized. Functional connectivity analyses provide information about statistical dependences among spatially remote regions of brain activation, while effective connectivity analyses provide information in regard to the influence of one brain region on another. We determined functional connectivity using partial correlations of multiple ROIs. Since our goal was to compare connectivity between groups, we chose this method rather than effective connectivity approaches, such as SEM or DCM, both of which are more often used to evaluate the accuracy of competing models (Penny et al., 2004). Also, we did not believe adequate information was available in the literature to build an a-priori model. In fact, we anticipated that this study might generate data that would inform the development of such a model. Our results do provide information that can guide model building. Future studies should incorporate effective connectivity methods to build upon our work by directly exploring influences of one brain region on another for the ROI pairs for which we have demonstrated abnormalities of functional connectivity. The results reported herein have important clinical implications. If replicated, our findings could lead to the development of methods to assess strategies aimed at preservation of motor function in the elderly. For example, there is evidence that exercise contributes to the improved cognitive and motor function in older adults (Dustman et al., 1984; Hatta et al., 2005; Hillman et al., 2002). Our results suggest that the PTT is a neurocognitive task sensitive to age-related changes in the cortico-basal ganglia circuitry. Further, the fMRI motor activation paradigm and functional connectivity analyses we describe are sensitive to ageinduced changes in the connectivity strength. Combining these methods in studies of exercise or other strategies may help elucidate underlying mechanisms of motor function preservation and might even be

202

W.R. Marchand et al. / NeuroImage 55 (2011) 194–203

developed to the point of clinical utility. These methods may also be useful for monitoring illness progression in neuropsychiatric disorders that involve basal ganglia pathology. A secondary aim of this study was to further characterize the complex motor activation paradigm. We have previously found motor paradigms to serve as effective probes of cortico-basal ganglia circuitry in bipolar (Marchand et al., 2007a, 2007b) and panic (Marchand et al., 2009) disorder. We have also reported that the complex motor task used in this study has robust activation (Marchand et al., 2008) and excellent group reliability (Lee et al., 2010). It is anticipated that this task will be useful for additional studies of circuit function in neuropsychiatric disorders. The results of the present study suggest that this task can be used across a relatively wide range of subject ages (our young and middle groups with a combined age range of 18–33) without concern about age-related variations in either activation or functional connectivity. Further, we now have data indicating the activation and connectivity changes associated with aging for older cohorts. This information will allow the use of the task with older groups as we now have a means of disentangling the effects of normal aging versus illness progression. Finally, the results reported herein have implications for functional connectivity studies of the cortico-basal ganglia circuitry in neuropsychiatric disorders. Our finding that increased connectivity is associated with decreased performance suggests that future studies should evaluate whether such changes might be a useful marker for a variety of disease processes. Conclusions It has been thought that age-related changes in cortico-basal ganglia circuitry contribute to motor deficits among the elderly. However, changes in functional architecture accompanying advancing age have been incompletely characterized. The findings reported herein expand our understanding of these changes. Our novel finding that increased cortical–subcortical connectivity contributes to the decline of motor function provides the first evidence of how circuit functional architecture changes associated with age directly impact motor performance. Further, the methods reported herein may have utility in studies of strategies aimed at the preservation of motor function as well as investigations of illness progression in basal ganglia disorders. Acknowledgments This work was supported by a University of Utah Faculty Incentive Seed grant and a Department of Veterans Affairs Career Development Award (Marchand). Additional support was provided by the resources and the use of facilities at the VA Salt Lake City Health Care System. References Albin, R.L., Young, A.B., Penney, J.B., 1989. The functional anatomy of basal ganglia disorders. Trends Neurosci. 12, 366–375. Alexander, G.E., Crutcher, M.D., 1990. Functional architecture of basal ganglia circuits: neural substrates of parallel processing. Trends Neurosci. 13, 266–271. Alexander, G.E., DeLong, M.R., Strick, P.L., 1986. Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Annu. Rev. Neurosci. 9, 357–381. Arbuthnott, K., Frank, J., 2000. Trail making test, part B as a measure of executive control: validation using a set-switching paradigm. J. Clin. Exp. Neuropsychol. 22, 518–528. Banich, M., 2004. Cognitive Neuroscience and Neuropsychology, 2nd ed. Houghton Mifflin Co., Boston, MA. Barnes, K.A., Cohen, A.L., Power, J.D., Nelson, S.M., Dosenbach, Y.B., Miezin, F.M., Petersen, S.E., Schlaggar, B.L., 2010. Identifying basal ganglia divisions in individuals using resting-state functional connectivity MRI. Front. Syst. Neurosci. 4, 18. Bava, S., Thayer, R., Jacobus, J., Ward, M., Jernigan, T.L., Tapert, S.F., 2010. Longitudinal characterization of white matter maturation during adolescence. Brain Res. 1327, 38–46. Bell, E.C., Willson, M.C., Wilman, A.H., Dave, S., Silverstone, P.H., 2006. Males and females differ in brain activation during cognitive tasks. Neuroimage 30, 529–538. Brendel, B., Hertrich, I., Erb, M., Lindner, A., Riecker, A., Grodd, W., Ackermann, H., 2010. The contribution of mesiofrontal cortex to the preparation and execution of repetitive syllable productions: an fMRI study. Neuroimage 50, 1219–1230.

Calautti, C., Serrati, C., Baron, J.C., 2001. Effects of age on brain activation during auditory-cued thumb-to-index opposition: a positron emission tomography study. Stroke 32, 139–146. Cass, W.A., Grondin, R., Andersen, A.H., Zhang, Z., Hardy, P.A., Hussey-Andersen, L.K., Rayens, W.S., Gerhardt, G.A., Gash, D.M., 2007. Iron accumulation in the striatum predicts aging-related decline in motor function in rhesus monkeys. Neurobiol. Aging 28, 258–271. Cham, R., Perera, S., Studenski, S.A., Bohnen, N.I., 2007. Striatal dopamine denervation and sensory integration for balance in middle-aged and older adults. Gait Posture 26, 516–525. Cham, R., Studenski, S.A., Perera, S., Bohnen, N.I., 2008. Striatal dopaminergic denervation and gait in healthy adults. Exp. Brain Res. 185, 391–398. Chen, D.F., Hyland, B., Maier, V., Palmeri, A., Wiesendanger, M., 1991. Comparison of neural activity in the supplementary motor area and in the primary motor cortex in monkeys. Somatosens. Mot. Res. 8, 27–44. Corporation, T.P., 2001. Wechsler Test of Adult Reading: Manual. The Psychological Corporation, San Antonio, TX. Doron, O., Goelman, G., 2010. Evidence for asymmetric intra substantia nigra functional connectivity-application to basal ganglia processing. Neuroimage 49, 2940–2946. Dustman, R.E., Ruhling, R.O., Russell, E.M., Shearer, D.E., Bonekat, H.W., Shigeoka, J.W., Wood, J.S., Bradford, D.C., 1984. Aerobic exercise training and improved neuropsychological function of older individuals. Neurobiol. Aging 5, 35–42. Eastvold, A., Derbidge, C., Cope, C., Suchy, Y., 2004. Test–retest reliability of an electronic alternative to the Trail Making Test. Arch. Clin. Neuropsychol. 19, 901. Friston, K.J., Worsley, K.J., Frackowiak, R., Mazziotta, J.C., Evans, A.C., 1993. Assessing the significance of focal activation using their spatial extent. Hum. Brain Mapp. 1, 210–220. Ge, Y., Grossman, R.I., Babb, J.S., Rabin, M.L., Mannon, L.J., Kolson, D.L., 2002. Age-related total gray matter and white matter changes in normal adult brain. Part II: quantitative magnetization transfer ratio histogram analysis. AJNR Am. J. Neuroradiol. 23, 1334–1341. Gentili, R.J., Bradberry, T.J., Hatfield, B.D., Contreras-Vidal, J.L., 2009. Brain biomarkers of motor adaptation using phase synchronization. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2009, 5930–5933. Good, C.D., Johnsrude, I.S., Ashburner, J., Henson, R.N., Friston, K.J., Frackowiak, R.S., 2001. A voxel-based morphometric study of ageing in 465 normal adult human brains. Neuroimage 14, 21–36. Gunstad, J., Paul, R.H., Brickman, A.M., Cohen, R.A., Arns, M., Roe, D., Lawrence, J.J., Gordon, E., 2006. Patterns of cognitive performance in middle-aged and older adults: a cluster analytic examination. J. Geriatr. Psychiatry Neurol. 19, 59–64. Hammond, C., Bergman, H., Brown, P., 2007. Pathological synchronization in Parkinson's disease: networks, models and treatments. Trends Neurosci. 30, 357–364. Harada, T., Miyai, I., Suzuki, M., Kubota, K., 2009. Gait capacity affects cortical activation patterns related to speed control in the elderly. Exp. Brain Res. 193, 445–454. Harrison, B.J., Soriano-Mas, C., Pujol, J., Ortiz, H., Lopez-Sola, M., Hernandez-Ribas, R., Deus, J., Alonso, P., Yucel, M., Pantelis, C., Menchon, J.M., Cardoner, N., 2009. Altered corticostriatal functional connectivity in obsessive–compulsive disorder. Arch. Gen. Psychiatry 66, 1189–1200. Hatta, A., Nishihira, Y., Kim, S.R., Kaneda, T., Kida, T., Kamijo, K., Sasahara, M., Haga, S., 2005. Effects of habitual moderate exercise on response processing and cognitive processing in older adults. Jpn J. Physiol. 55, 29–36. Haycock, J.W., Becker, L., Ang, L., Furukawa, Y., Hornykiewicz, O., Kish, S.J., 2003. Marked disparity between age-related changes in dopamine and other presynaptic dopaminergic markers in human striatum. J. Neurochem. 87, 574–585. Heuninckx, S., Wenderoth, N., Debaere, F., Peeters, R., Swinnen, S.P., 2005. Neural basis of aging: the penetration of cognition into action control. J. Neurosci. 25, 6787–6796. Heuninckx, S., Wenderoth, N., Swinnen, S.P., 2008. Systems neuroplasticity in the aging brain: recruiting additional neural resources for successful motor performance in elderly persons. J. Neurosci. 28, 91–99. Hillman, C.H., Weiss, E.P., Hagberg, J.M., Hatfield, B.D., 2002. The relationship of age and cardiovascular fitness to cognitive and motor processes. Psychophysiology 39, 303–312. Jernigan, T.L., Archibald, S.L., Fennema-Notestine, C., Gamst, A.C., Stout, J.C., Bonner, J., Hesselink, J.R., 2001. Effects of age on tissues and regions of the cerebrum and cerebellum. Neurobiol. Aging 22, 581–594. Keele, S., 1968. Movement control in skilled motor performance. Psychol. Bull. 387–403. Kraybill, M.L., Suchy, Y., 2008. Evaluating the role of motor regulation in figural fluency: partialing variance in the Ruff Figural Fluency Test. J. Clin. Exp. Neuropsychol. 1–10. Langan, J., Peltier, S.J., Bo, J., Fling, B.W., Welsh, R.C., Seidler, R.D., 2010. Functional implications of age differences in motor system connectivity. Front. Syst. Neurosci. 4, 17. Langenecker, S.A., Briceno, E.M., Hamid, N.M., Nielson, K.A., 2007. An evaluation of distinct volumetric and functional MRI contributions toward understanding age and task performance: a study in the basal ganglia. Brain Res. 1135, 58–68. Lebel, C., Rasmussen, C., Wyper, K., Walker, L., Andrew, G., Yager, J., Beaulieu, C., 2008. Brain diffusion abnormalities in children with fetal alcohol spectrum disorder. Alcohol. Clin. Exp. Res. 32, 1732–1740. Lee, J.N., Hsu, E.W., Rashkin, E., Thatcher, J.W., Kreitschitz, S., Gale, P., Healy, L., Marchand, W.R., 2010. Reliability of fMRI motor tasks in structures of the corticostriatal circuitry: implications for future studies and circuit function. Neuroimage 49, 1282–1288. Loessner, A., Alavi, A., Lewandrowski, K.U., Mozley, D., Souder, E., Gur, R.E., 1995. Regional cerebral function determined by FDG-PET in healthy volunteers: normal patterns and changes with age. J. Nucl. Med. 36, 1141–1149. Luria, A.R., Majovski, L.V., 1979. A frontal lobe syndrome due to a cerebrovascular accident affecting the corpus callosum. Clin. Neuropsychol. 1, 17–19.

W.R. Marchand et al. / NeuroImage 55 (2011) 194–203 Marchand, W.R., Lee, J.N., Thatcher, G.W., Jensen, C., Stewart, D., Dilda, V., Thatcher, J., Creem-Regehr, S.H., 2007a. A functional MRI study of a paced motor activation task to evaluate frontal–subcortical circuit function in bipolar depression. Psychiatry Res. 155, 221–230. Marchand, W.R., Lee, J.N., Thatcher, J., Thatcher, G.W., Jensen, C., Starr, J., 2007b. A preliminary longitudinal fMRI study of frontal–subcortical circuits in bipolar disorder using a paced motor activation paradigm. J. Affect. Disord. 103, 237–241. Marchand, W.R., Lee, J.N., Thatcher, J.W., Hsu, E.W., Rashkin, E., Suchy, Y., Chelune, G., Starr, J., Barbera, S.S., 2008. Putamen coactivation during motor task execution. NeuroReport 19, 957–960. Marchand, W.R., Lee, J.N., Healy, L., Thatcher, J.W., Rashkin, E., Starr, J., Hsu, E., 2009. An fMRI motor activation paradigm demonstrates abnormalities of putamen activation in females with panic disorder. J. Affect. Disord. 116, 121–125. Mattay, V.S., Fera, F., Tessitore, A., Hariri, A.R., Das, S., Callicott, J.H., Weinberger, D.R., 2002. Neurophysiological correlates of age-related changes in human motor function. Neurology 58, 630–635. Moeller, J.R., Ishikawa, T., Dhawan, V., Spetsieris, P., Mandel, F., Alexander, G.E., Grady, C., Pietrini, P., Eidelberg, D., 1996. The metabolic topography of normal aging. J. Cereb. Blood Flow Metab. 16, 385–398. Moll, J., de Oliveira-Souza, R., Moll, F.T., Bramati, I.E., Andreiuolo, P.A., 2002. The cerebral correlates of set-shifting: an fMRI study of the trail making test. Arquivos de NeuroPsiquiatria. Arquivos de Neuro-Psiquiatria, Brazil, Brazil, pp. 900–905. Mostofsky, S.H., Powell, S.K., Simmonds, D.J., Goldberg, M.C., Caffo, B., Pekar, J.J., 2009. Decreased connectivity and cerebellar activity in autism during motor task performance. Brain 132, 2413–2425. Naccarato, M., Calautti, C., Jones, P.S., Day, D.J., Carpenter, T.A., Baron, J.C., 2006. Does healthy aging affect the hemispheric activation balance during paced index-to-thumb opposition task? An fMRI study. Neuroimage 32, 1250–1256. Oldfield, R.C., 1971. The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia 9, 97–113. Ota, M., Obata, T., Akine, Y., Ito, H., Ikehira, H., Asada, T., Suhara, T., 2006. Age-related degeneration of corpus callosum measured with diffusion tensor imaging. Neuroimage 31, 1445–1452. Park, D.C., Polk, T.A., Hebrank, A.C., Jenkins, L.J., 2010. Age differences in default mode activity on easy and difficult spatial judgment tasks. Front. Hum. Neurosci. 3, 75. Penny, W.D., Stephan, K.E., Mechelli, A., Friston, K.J., 2004. Modelling functional integration: a comparison of structural equation and dynamic causal models. Neuroimage 23 (Suppl 1), S264–S274. Petit-Taboue, M.C., Landeau, B., Desson, J.F., Desgranges, B., Baron, J.C., 1998. Effects of healthy aging on the regional cerebral metabolic rate of glucose assessed with statistical parametric mapping. Neuroimage 7, 176–184. Postuma, R.B., Dagher, A., 2006. Basal ganglia functional connectivity based on a metaanalysis of 126 positron emission tomography and functional magnetic resonance imaging publications. Cereb. Cortex 16, 1508–1521. Ranganathan, V.K., Siemionow, V., Sahgal, V., Yue, G.H., 2001. Effects of aging on hand function. J. Am. Geriatr. Soc. 49, 1478–1484. Reitan, R.M., 1958. Trail Making Test: Manual for Administration, Scoring, and Interpretation. Indiana University Medical Center, Indianapolis, IN. Resnick, S.M., Pham, D.L., Kraut, M.A., Zonderman, A.B., Davatzikos, C., 2003. Longitudinal magnetic resonance imaging studies of older adults: a shrinking brain. J. Neurosci. 23, 3295–3301. Sakai, S.T., Inase, M., Tanji, J., 2002. The relationship between MI and SMA afferents and cerebellar and pallidal efferents in the macaque monkey. Somatosens. Mot. Res. 19, 139–148. Scherder, E., Dekker, W., Eggermont, L., 2008. Higher-level hand motor function in aging and (preclinical) dementia: its relationship with (instrumental) activities of daily life—a mini-review. Gerontology 54, 333–341.

203

Seidler, R.D., Bernard, J.A., Burutolu, T.B., Fling, B.W., Gordon, M.T., Gwin, J.T., Kwak, Y., Lipps, D.B., 2010. Motor control and aging: links to age-related brain structural, functional, and biochemical effects. Neurosci. Biobehav. Rev. 34, 721–733. Sibille, E., Su, J., Leman, S., Le Guisquet, A.M., Ibarguen-Vargas, Y., Joeyen-Waldorf, J., Glorioso, C., Tseng, G.C., Pezzone, M., Hen, R., Belzung, C., 2007. Lack of serotonin1B receptor expression leads to age-related motor dysfunction, early onset of brain molecular aging and reduced longevity. Mol. Psychiatry 12 (1042–1056), 1975. Stuss, D.T., Bisschop, S.M., Alexander, M.P., Levine, B., Katz, D., Izukawa, D., 2001. The trail making test: a study in focal lesion patients. Psychol. Assess. 13, 230–239. Suchy, Y., Kraybill, M.L., 2007. The relationship between motor programming and executive abilities: constructs measured by the Push-Turn-Taptap task from the Behavioral Dyscontrol Scale-Electronic Version. J. Clin. Exp. Neuropsychol. 29, 648–659. Suchy, Y., Derbidge, C., Cope, C., 2005. Behavioral Dyscontrol Scale-Electronic Version: first examination of reliability, validity, and incremental utility. Clin. Neuropsychol. 19, 4–26. Suchy, Y., Kraybill, M., Larson, J.G.L., 2010. Understanding design fluency: motor and executive contributions. J. Int. Neuropsychol. Soc. 16, 26–37. Suhara, T., Fukuda, H., Inoue, O., Itoh, T., Suzuki, K., Yamasaki, T., Tateno, Y., 1991. Agerelated changes in human D1 dopamine receptors measured by positron emission tomography. Psychopharmacology (Berl) 103, 41–45. Taniwaki, T., Okayama, A., Yoshiura, T., Togao, O., Nakamura, Y., Yamasaki, T., Ogata, K., Shigeto, H., Ohyagi, Y., Kira, J., Tobimatsu, S., 2006. Functional network of the basal ganglia and cerebellar motor loops in vivo: different activation patterns between self-initiated and externally triggered movements. Neuroimage 31, 745–753. Taniwaki, T., Okayama, A., Yoshiura, T., Togao, O., Nakamura, Y., Yamasaki, T., Ogata, K., Shigeto, H., Ohyagi, Y., Kira, J., Tobimatsu, S., 2007. Age-related alterations of the functional interactions within the basal ganglia and cerebellar motor loops in vivo. Neuroimage 36, 1263–1276. Troiano, A.R., Schulzer, M., de la Fuente-Fernandez, R., Mak, E., McKenzie, J., Sossi, V., McCormick, S., Ruth, T.J., Stoessl, A.J., 2010. Dopamine transporter PET in normal aging: dopamine transporter decline and its possible role in preservation of motor function. Synapse 64, 146–151. Ward, N.S., Frackowiak, R.S., 2003. Age-related changes in the neural correlates of motor performance. Brain 126, 873–888. Wechsler, D., 1997. WAIS-III Administration and Scoring Manual. The Psychological Corporation, San Antonio, TX. Wiesendanger, R., Wiesendanger, M., 1985. The thalamic connections with medial area 6 (supplementary motor cortex) in the monkey (Macaca fascicularis). Exp. Brain Res. 59, 91–104. Williams, D., Tijssen, M., Van Bruggen, G., Bosch, A., Insola, A., Di Lazzaro, V., Mazzone, P., Oliviero, A., Quartarone, A., Speelman, H., Brown, P., 2002. Dopamine-dependent changes in the functional connectivity between basal ganglia and cerebral cortex in humans. Brain 125, 1558–1569. Ystad, M., Eichele, T., Lundervold, A.J., Lundervold, A., 2010. Subcortical functional connectivity and verbal episodic memory in healthy elderly—a resting state fMRI study. Neuroimage 52, 379–388. Zakzanis, K.K., Mraz, R., Graham, S.J., 2005. An fMRI study of the Trail Making Test. Neuropsychologia 43, 1878–1886. Zhang, Z., Andersen, A., Grondin, R., Barber, T., Avison, R., Gerhardt, G., Gash, D., 2001. Pharmacological MRI mapping of age-associated changes in basal ganglia circuitry of awake rhesus monkeys. Neuroimage 14, 1159–1167. Zhang, D., Snyder, A.Z., Fox, M.D., Sansbury, M.W., Shimony, J.S., Raichle, M.E., 2008. Intrinsic functional relations between human cerebral cortex and thalamus. J. Neurophysiol. 100, 1740–1748.