Age-related neural dedifferentiation in the sensorimotor system and its behavioral consequences
Kaitlin Cassady, University of Michigan, Department of Psychology
Aging is typically associated with declines in sensorimotor performance. Previous studies have linked some age-related behavioral declines to reductions in network segregation. For example, compared to young adults, older adults typically exhibit weaker functional connectivity within the same functional network but stronger functional connectivity between different networks. Based on previous animal studies, we hypothesized that such reductions of network segregation are linked to age-related reductions in the brain’s major inhibitory transmitter, gamma aminobutyric acid (GABA). To investigate this hypothesis, we conducted graph theoretical analyses of resting state functional MRI data to measure sensorimotor network segregation in both young and old adults. We also used magnetic resonance spectroscopy to measure GABA levels in the sensorimotor cortex and collected a battery of sensorimotor behavioral measures. We report four findings. First, relative to young adults, old adults exhibit both less segregated sensorimotor brain networks and reduced sensorimotor GABA levels. Second, less segregated networks are associated with lower GABA levels. Third, less segregated networks and lower GABA levels predict worse sensorimotor performance. Fourth, network segregation mediates the relationship between GABA and performance. These findings link age-related differences in network segregation to age-related differences in GABA levels and sensorimotor performance.
Advanced age is typically associated with declines in sensorimotor functioning. Such declines affect the ability of older adults to perform activities of daily living and maintain their functional independence1. Evidence suggests that some of these age-related declines in behavior are related to changes in the brain, including alterations in neurochemistry, gray matter atrophy, and changes in the functional organization of large-scale brain networks1–6. Understanding these brain-behavior relationships is important for our efforts to prolong the functional independence of older adults as our society continues to age.
Among such age-related brain changes, one consistent finding is that older adults exhibit weaker functional connectivity between brain regions within the same functional network but stronger functional connectivity between regions belonging to different networks. In other words, their functional networks are less segregated (i.e., dedifferentiated)7–9. Many of these studies have also found that less segregated brain networks are associated with worse cognitive performance, independent of age7,8,10,11. Fewer studies have investigated the relationship between network segregation and sensorimotor behavior. One such study found that reduced segregation of several large-scale resting state brain networks was associated with poorer bimanual motor performance12. In sum, there are now a large number of findings suggesting that age-related changes in functional connectivity may contribute to age-related declines in cognitive and sensorimotor performance.
Previous animal studies have linked neural dedifferentiation of activation patterns to changes at the neurotransmitter level. In particular, studies by Leventhal and colleagues suggested that age differences in the brain’s major inhibitory neurotransmitter, gammaaminobutyric acid (GABA), may play an important and potentially causal role in age-related dedifferentiation (i.e., reductions in the specificity of neural activation patterns). More specifically, they demonstrated that manipulations of GABA levels led to changes in the orientation-selectivity of neurons in the visual cortex13. The application of GABA or a GABA agonist made visual cortex neurons in old monkeys more orientation-selective, thereby making them similar to neurons in young monkeys. In contrast, the application of a GABA antagonist reduced the orientation-selectivity of visual cortex neurons in young monkeys, thereby making them similar to neurons in old monkeys. These results demonstrate that manipulations of GABA cause changes in neural selectivity in animals, raising the possibility that age declines in GABA might contribute to age-related neural dedifferentiation and associated behavioral declines in humans.
In the present study, we performed graph theoretical analysis of resting state functional MRI data to measure sensorimotor network segregation and used magnetic resonance spectroscopy (MRS) to measure GABA levels in the sensorimotor cortex. We also collected a battery of sensorimotor behavioral measures to determine whether network segregation and/or GABA levels predict individual differences in performance. We tested three hypotheses: 1) The sensorimotor resting state brain network would be less segregated and sensorimotor cortex GABA levels would be reduced in older compared to young adults; 2) lower levels of GABA would be associated with less segregated networks, independent of age; and 3) lower levels of GABA and less segregated networks would be associated with worse sensorimotor performance, independent of age.
Twenty-two young adults (age range 19 to 29 years; 13 females) and 23 older adults (age range 65 to 81; 12 females) participated. All participants were right-handed, native English speakers.
Participants completed three separate testing sessions: one resting state fMRI session, one MRS session, and one behavioral session. The order of the fMRI and behavioral sessions was counterbalanced across participants.
We used a sensorimotor test battery that included both motor and somatosensory components. The motor measures included a 9-hole pegboard dexterity test, grip strength, and a 2-minute walk endurance test. The somatosensory (tactile) measures included vibrotactile simple and choice reaction time (RT), static and dynamic vibrotactile detection thresholds, and a functional tactile object recognition test (fTORT).
Anatomical and functional brain images were acquired using a GE Discovery MR750 3- Tesla MRI scanner. Imaging sessions included the acquisition of T1-weighted anatomical images, high-resolution anatomical images using spoiled 3D gradient-echo acquisition (SPGR), and T2*–weighted functional images.
MRS data were acquired on the same scanner on a different day. Data were collected from 30 mm3 voxels placed in the left and right sensorimotor cortex (See Figure 6A). The placement of voxels in each participant corresponded to the region of maximal sensorimotor activity in that same individual from their previous task-based fMRI session. Briefly, participants performed motor (finger tapping on right vs. left hands) and somatosensory (vibrotactile stimulation to right vs. left hands) tasks in the MRI scanner. The MRS voxel was subsequently placed in each participant to maximize overlap with fMRI activation from both of these tasks.
Resting state data preprocessing included slice-time correction, realignment, segmentation of structural images, normalization into standard Montreal Neurological Institute (MNI) space and spatial smoothing using a Gaussian kernel of 8mm full width at half-maximum (FWHM). Because functional connectivity measurements are influenced by head motion in the scanner49, we detected and rejected motion artifacts using the artifact detection toolbox (ART; http://www.nitrc.org/projects/artifact_detect).
First-level ROI-to-ROI functional connectivity MRI (fcMRI) analysis was performed with the CONN toolbox50. For this analysis, we created ROIs (using 5mm diameter spheres) using coordinates published in Power et al. (2011)52. The cross-correlation of each ROI’s time course with every other ROI’s time course was computed, creating a 214 x 214 ROI-to-ROI correlation matrix. Correlation coefficients (i.e., graph edges) were converted into z-values using Fisher’s r-to-z transformation53. A measure of system segregation was calculated to examine within-network correlations in relation to between-network correlations. Network segregation was defined as the difference in mean within-network connectivity and mean between-network connectivity as a proportion of mean within-network connectivity, as depicted in the following formula:
where 𝑍̅w is the mean Fisher z-transformed correlation between ROIs within the same network and 𝑍̅b is the mean Fisher z-transformed correlation between ROIs of one network to all ROIs in other networks7. GABA levels were measured using the GABA analysis toolbox, Gannet55. The 3-ppm GABA peak in the difference spectrum was fit using a Gaussian model and quantified relative to water in institutional units (See Figure 6B).
An exploratory factor analysis of the sensorimotor behavioral measures identified two factors, one corresponding to grip strength and one corresponding to all of the other sensorimotor measures. These two sensorimotor factors were used in all further statistical analyses. Significant age differences were observed in the general sensorimotor factor (t(39)=7.24, p<.001; Figure 1A), whereas there was no significant effect of age on the grip strength factor (t(39)=1.24, p=.22; Figure 1B).
Sensorimotor network segregation (t(41)=2.23, p=.031, see Figure 1C) and GABA+ levels in sensorimotor cortex (t(40)=4.97, p<.001 (See Figure 1D) were also significantly reduced in older compared to younger adults.
Controlling for age and gray matter (GM) volume differences, we observed a positive relationship between sensorimotor network segregation and GABA+ levels across all subjects, r(38)=.41, p=.008 (See Figure 2A). This finding was also observed when examining the older adult group alone (r(21)=.45, p=.042; Figure 2B), but did not reach significance in the younger adult group (r(21)=.37, p=.10).
Controlling for age and GM volume differences, we also observed a positive relationship between sensorimotor network segregation and sensorimotor performance across all subjects, r(37)=.38, p=.016 (See Figure 3A). These findings were also observed when examining the older adult group alone (r(20)=.50, p=.026; See Figure 3B), but did not reach significance in the younger adult group (r(21)=.18, p=.44).
Controlling for age and GM volume differences, we observed a positive relationship between GABA+ levels and sensorimotor performance across all participants (r(37)=.32, p=.046; Figure 4A). This finding was also observed when examining the older adult group alone (r(20)=.48, p=.031; Figure 4B), but was not significant in the younger adult group (r(21)=-.14, p=.55).
In order to further explore the potential role of network segregation as a mechanistic link between GABA levels and the sensorimotor performance factor, we performed a mediation analysis on the MR and behavioral data. In this analysis, GABA+ levels were the logical independent variable (X), with sensorimotor network segregation as the mediator (M) and sensorimotor performance as the dependent variable (Y). Mediation was performed using regression with bootstrapping to determine whether segregation accounts for the link between GABA+ levels and performance.
Controlling for age and GM volume differences, we found that network segregation mediates the link between GABA+ and sensorimotor performance, with a percentage mediation (PM) of 70%. This resulted from a significant indirect effect (c) of greater magnitude than the direct effect (c’), which itself was not significant (ab=.13, BCa CI [.0037, .41]; See Figure 5).
In the present study, we investigated whether older and younger adults differ with regard to network segregation, GABA+ levels, and sensorimotor performance, as well as the relationships between these variables. There were four main findings. First, relative to young adults, old adults exhibited less segregated sensorimotor brain networks and reduced sensorimotor GABA+ levels. Second, less segregated networks were associated with lower GABA+ levels. Third, less segregated networks and lower GABA levels predicted worse sensorimotor performance. Fourth, network segregation mediated the relationship between GABA+ and performance. These findings suggest that age-related reductions of GABA+ are a neurochemical substrate of age-related dedifferentiation at the level of large-scale brain networks. We now discuss each of these findings in turn.
Our results revealed significant age differences in the organization of large-scale resting state brain networks, such that the networks of older adults were less segregated than those of their younger counterparts. Our findings are novel in that more segregated sensorimotor networks were associated with higher sensorimotor GABA+ levels and better sensorimotor performance, a finding that will be discussed in more detail in subsequent sections.
The study also demonstrates that GABA+ levels in sensorimotor cortex are reduced in older relative to younger adults. The decline in GABA+ levels in older adults may reflect the loss of GABAergic interneurons during normal aging.
Our findings also revealed a positive relationship between GABA+ levels and sensorimotor network segregation, such that participants with lower GABA+ levels showed less segregated networks. Taken together, these results suggest a neurochemical substrate for age-related dedifferentiation at the level of large-scale resting state brain networks.
One of the main findings from this study was that less segregated sensorimotor networks predict worse sensorimotor performance. Specifically, sensorimotor network segregation was predictive of a summary measure of somatosensory and motor abilities in addition to grip strength.
Our data also revealed a positive correlation between sensorimotor GABA+ levels and sensorimotor performance. This finding is consistent with previous studies linking local GABA+ levels in motor cortex to motor performance15, GABA+ levels in visual cortex to orientation discrimination performance14, and GABA+ levels in sensorimotor cortex to tactile discrimination thresholds16.
Our findings also demonstrate for the first time that sensorimotor network segregation mediates the relationship between GABA+ levels and sensorimotor performance. One possible explanation is that age-related reductions in sensorimotor GABA levels lead to less segregated neural networks, which in turn lead to declines in sensorimotor behavior. Such a causal relationship between age declines in GABA and age-related reductions in neural specificity in visual cortex has been reported in previous animal studies13. Although we cannot make causal inferences from the data in the present study, our findings from the mediation analysis do provide preliminary evidence for directionality in the relationship between GABA, network segregation and behavior.
Tens of millions of people experience age-related deficits in sensorimotor function, even in the absence of significant disease. This research provides insight into a potential cause, namely agerelated declines in neural segregation that are caused by declines in GABA. The insights provided by this work could lead to the development of interventions that target the inhibitory GABA system in order to prolong functional independence for older adults.
- Seidler, R. D. et al. Motor control and aging: Links to age-related brain structural, functional, and biochemical effects. Neurosci. Biobehav. Rev. 34, 721–733 (2010).
- Seidler, R. et al. Associations between age, motor function, and resting state sensorimotor network connectivity in healthy older adults. NeuroImage 108, 47–59 (2015).
- Langan, J. et al. Functional implications of age differences in motor system connectivity. Front Syst Neurosci 4, 17 (2010).
- Raz, N. & Rodrigue, K. M. Differential aging of the brain: Patterns, cognitive correlates and modifiers. Neurosci. Biobehav. Rev. 30, 730–748 (2006).
- Goh, J. O. S. Functional Dedifferentiation and Altered Connectivity in Older Adults: Neural Accounts of Cognitive Aging. Aging Dis. 2, 30–48 (2011).
- Ferreira, L. K. & Busatto, G. F. Resting-state functional connectivity in normal brain aging. Neurosci. Biobehav. Rev. 37, 384–400 (2013).
- Chan, M. Y., Park, D. C., Savalia, N. K., Petersen, S. E. & Wig, G. S. Decreased segregation of brain systems across the healthy adult lifespan. Proc Natl Acad Sci U A 111, E4997-5006 (2014).
- Geerligs, L., Renken, R. J., Saliasi, E., Maurits, N. M. & Lorist, M. M. A Brain-Wide Study of Age-Related Changes in Functional Connectivity. Cereb Cortex 25, 1987–99 (2015).
- Damoiseaux, J. S. Effects of aging on functional and structural brain connectivity. Neuroimage (2017). doi:10.1016/j.neuroimage.2017.01.077
- Wang, L. et al. Intrinsic connectivity between the hippocampus and posteromedial cortex predicts memory performance in cognitively intact older individuals. Neuroimage 51, 910– 917 (2010).
- Damoiseaux, J. S. et al. Reduced resting-state brain activity in the ‘default network’ in normal aging. Cereb Cortex 18, 1856–64 (2008).
- King, B. R. et al. Age-Related Declines in Motor Performance are Associated With Decreased Segregation of Large-Scale Resting State Brain Networks. Cereb. Cortex 1–13 doi:10.1093/cercor/bhx297
- Leventhal, A. G., Wang, Y. C., Pu, M. L., Zhou, Y. F. & Ma, Y. Y. GABA and its agonists improved visual cortical function in senescent monkeys. Science 300, 812–815 (2003).
- Edden, R. A. E., Muthukumaraswamy, S. D., Freeman, T. C. A. & Singh, K. D. Orientation Discrimination Performance Is Predicted by GABA Concentration and Gamma Oscillation Frequency in Human Primary Visual Cortex. J. Neurosci. 29, 15721–15726 (2009).
- Boy, F. et al. Individual Differences in Subconscious Motor Control Predicted by GABA Concentration in SMA. Curr. Biol. 20, 1779–1785 (2010).
- Puts, N. A. J., Edden, R. A. E., Evans, C. J., McGlone, F. & McGonigle, D. J. Regionally Specific Human GABA Concentration Correlates with Tactile Discrimination Thresholds. J. Neurosci. 31, 16556–16560 (2011).
- Stagg, C. J. et al. Local GABA concentration is related to network-level resting functional connectivity. eLife 3, e01465 (2014).
- Kapogiannis, D., Reiter, D. A., Willette, A. A. & Mattson, M. P. Posteromedial cortex glutamate and GABA predict intrinsic functional connectivity of the default mode network. NeuroImage 64, 112–119 (2013).
- Song, J. et al. Age-Related Reorganizational Changes in Modularity and Functional Connectivity of Human Brain Networks. Brain Connect. 4, 662–676 (2014).
- Cao, M. et al. Topological organization of the human brain functional connectome across the lifespan. Dev. Cogn. Neurosci. 7, 76–93 (2014).
- Betzel, R. F. et al. Changes in structural and functional connectivity among resting-state networks across the human lifespan. NeuroImage 102, 345–357 (2014).
- Iordan, A. D. et al. Aging and Network Properties: Stability Over Time and Links with Learning during Working Memory Training. Front. Aging Neurosci. 9, (2018).
- Meier, T. B. et al. Support vector machine classification and characterization of age-related reorganization of functional brain networks. NeuroImage 60, 601–613 (2012).
- Onoda, K., Ishihara, M. & Yamaguchi, S. Decreased Functional Connectivity by Aging Is Associated with Cognitive Decline. J. Cogn. Neurosci. 24, 2186–2198 (2012).
- Li, S. C. & Lindenberger, U. Cross-level unification: A computational exploration of the link between deterioration of neurotransmitter systems and dedifferentiation of cognitive abilities in old age. Cogn. Neurosci. Mem. 103–146 (1999).
- Park, D. C. et al. Aging reduces neural specialization in ventral visual cortex. Proc. Natl. Acad. Sci. U. S. A. 101, 13091–13095 (2004).
- Park, J., Carp, J., Hebrank, A., Park, D. C. & Polk, T. A. Neural Specificity Predicts Fluid Processing Ability in Older Adults. J. Neurosci. 30, 9253–9259 (2010).
- Carp, J., Park, J., Polk, T. A. & Park, D. C. Age differences in neural distinctiveness revealed by multi-voxel pattern analysis. NeuroImage 56, 736–743 (2011).
- Carp, J., Gmeindl, L. & Reuter-Lorenz, P. A. Age differences in the neural representation of working memory revealed by multi-voxel pattern analysis. Front Hum Neurosci 4, 217 (2010).
- Carp, J., Park, J., Hebrank, A., Park, D. C. & Polk, T. A. Age-Related Neural Dedifferentiation in the Motor System. Plos One 6, (2011).
- Hermans, L. et al. GABA levels and measures of intracortical and interhemispheric excitability in healthy young and older adults: an MRS-TMS study. Neurobiol. Aging 65, 168–177 (2018).
- Maes, C. et al. Age-related differences in GABA levels are driven by bulk tissue changes. Hum. Brain Mapp. 0,
- Gao, F. et al. Edited magnetic resonance spectroscopy detects an age-related decline in brain GABA levels. NeuroImage 78, 75–82 (2013).
- Porges, E. C. et al. Frontal Gamma-Aminobutyric Acid Concentrations Are Associated With Cognitive Performance in Older Adults. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 2, 38–44 (2017).
- Hua, T., Kao, C., Sun, Q., Li, X. & Zhou, Y. Decreased proportion of GABA neurons accompanies age-related degradation of neuronal function in cat striate cortex. Brain Res. Bull. 75, 119–125 (2008).
- Stanley, E. M., Fadel, J. R. & Mott, D. D. Interneuron loss reduces dendritic inhibition and GABA release in hippocampus of aged rats. Neurobiol. Aging 33, 431.e1–13 (2012).
- Marenco, S. et al. Genetic modulation of GABA levels in the anterior cingulate cortex by GAD1 and COMT. Neuropsychopharmacol. Off. Publ. Am. Coll. Neuropsychopharmacol. 35, 1708–1717 (2010).
- Bachtiar, V., Near, J., Johansen-Berg, H. & Stagg, C. J. Modulation of GABA and resting state functional connectivity by transcranial direct current stimulation. eLife 4, e08789 (2015).
- Antonenko, D. et al. tDCS-Induced Modulation of GABA Levels and Resting-State Functional Connectivity in Older Adults. J. Neurosci. 37, 4065–4073 (2017).
- Kolasinski, J. et al. A Mechanistic Link from GABA to Cortical Architecture and Perception. Curr. Biol. 27, 1685-1691.e3 (2017).
- Bessaih, T., Higley, M. J. & Contreras, D. Millisecond precision temporal encoding of stimulus features during cortically generated gamma oscillations in the rat somatosensory cortex. J. Physiol. 596, 515–534 (2018).
- van Wijk, B. C. M., Beek, P. J. & Daffertshofer, A. Neural synchrony within the motor system: what have we learned so far? Front. Hum. Neurosci. 6, (2012).
- Brookes, M. J. et al. Investigating the electrophysiological basis of resting state networks using magnetoencephalography. Proc. Natl. Acad. Sci. U. S. A. 108, 16783–16788 (2011).
- Hall, S. D. et al. The role of GABAergic modulation in motor function related neuronal network activity. NeuroImage 56, 1506–1510 (2011).
- Mullins, P. G. et al. Current practice in the use of MEGA-PRESS spectroscopy for the detection of GABA. NeuroImage 86, 43–52 (2014).
- Harris, A. D., Puts, N. A. J., Barker, P. B. & Edden, R. A. E. Spectral-editing measurements of GABA in the human brain with and without macromolecule suppression. Magn. Reson. Med. 74, 1523–1529 (2015).
- Mescher, M., Merkle, H., Kirsch, J., Garwood, M. & Gruetter, R. Simultaneous in vivo spectral editing and water suppression. Nmr Biomed. 11, 266–272 (1998).
- Edden, R. A. E. & Barker, P. B. Spatial effects in the detection of gamma-aminobutyric acid: Improved sensitivity at high fields using inner volume saturation. Magn. Reson. Med. 58, 1276–1282 (2007).
- Van Dijk, K. R. A., Sabuncu, M. R. & Buckner, R. L. The influence of head motion on intrinsic functional connectivity MRI. NeuroImage 59, 431–438 (2012).
- Whitfield-Gabrieli, S. & Nieto-Castanon, A. Conn: A Functional Connectivity Toolbox for Correlated and Anticorrelated Brain Networks. Brain Connect. 2, 125–141 (2012).
- Behzadi, Y., Restom, K., Liau, J. & Liu, T. T. A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. NeuroImage 37, 90–101 (2007).
- Power, J. D. et al. Functional network organization of the human brain. Neuron 72, 665–678 (2011).
- Zar, J. H. Biostatistical Analysis. (Prentice Hall, 1996).
- Chan, M. Y., Alhazmi, F. H., Park, D. C., Savalia, N. K. & Wig, G. S. Resting-State Network Topology Differentiates Task Signals across the Adult Life Span. J. Neurosci. 37, 2734–2745 (2017).
- Edden, R. A. E., Puts, N. A. J., Harris, A. D., Barker, P. B. & Evans, C. J. Gannet: A Batch- Processing Tool for the Quantitative Analysis of Gamma-Aminobutyric Acid-Edited MR Spectroscopy Spectra. J. Magn. Reson. Imaging 40, 1445–1452 (2014).
- Near, J. et al. Frequency and Phase Drift Correction of Magnetic Resonance Spectroscopy Data by Spectral Registration in the Time Domain. Magn. Reson. Med. 73, 44–50 (2015).
- Dale, A. M., Fischl, B. & Sereno, M. I. Cortical Surface-Based Analysis: I. Segmentation and Surface Reconstruction. NeuroImage 9, 179–194 (1999).
- Fischl, B. & Dale, A. M. Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc. Natl. Acad. Sci. 97, 11050–11055 (2000).
- Fischl, B. et al. Automatically parcellating the human cerebral cortex. Cereb. Cortex 14, 11– 22 (2004).