Combination training in aging individuals modifies functional connectivity and cognition, and is potentially affected by dopamine-related genes

Valentina Pieramico, Roberto Esposito, Francesca Sensi, Franco Cilli, Dante Mantini, Peter A Mattei, Valerio Frazzini, Domenico Ciavardelli, Valentina Gatta, Antonio Ferretti, Gian Luca Romani, Stefano L Sensi, Valentina Pieramico, Roberto Esposito, Francesca Sensi, Franco Cilli, Dante Mantini, Peter A Mattei, Valerio Frazzini, Domenico Ciavardelli, Valentina Gatta, Antonio Ferretti, Gian Luca Romani, Stefano L Sensi

Abstract

Background: Aging is a major co-risk factor in many neurodegenerative diseases. Cognitive enrichment positively affects the structural plasticity of the aging brain. In this study, we evaluated effects of a set of structured multimodal activities (Combination Training; CT) on cognitive performances, functional connectivity, and cortical thickness of a group of healthy elderly individuals. CT lasted six months.

Methodology: Neuropsychological and occupational performances were evaluated before and at the end of the training period. fMRI was used to assess effects of training on resting state network (RSN) functional connectivity using Independent Component Analysis (ICA). Effects on cortical thickness were also studied. Finally, we evaluated whether specific dopamine-related genes can affect the response to training.

Principal findings: Results of the study indicate that CT improves cognitive/occupational performances and reorganizes functional connectivity. Intriguingly, individuals responding to CT showed specific dopamine-related genotypes. Indeed, analysis of dopamine-related genes revealed that carriers of DRD3 ser9gly and COMT Val158Met polymorphisms had the greatest benefits from exposure to CT.

Conclusions and significance: Overall, our findings support the idea that exposure to a set of structured multimodal activities can be an effective strategy to counteract aging-related cognitive decline and also indicate that significant capability of functional and structural changes are maintained in the elderly.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1. CT positively affects neuropsychological and…
Figure 1. CT positively affects neuropsychological and occupational performances.
Bar graphs depict results of neuropsychological evaluation in control (white bars) and trained (grey bars) groups at the beginning of the study (T0) and after six months (T6). Graphs show results, expressed as means ± SEM, of: (A) global prose memory test and (B) delayed recall subtest. The trained group shows, at T6, a statistically significant improvement in long-term memory compared to control individuals. In the bottom panel, graphs depict results, always expressed as mean ± SEM, of: (C) OT-E Process Skills and (D) OT-E Time. The training group shows, at T6, a statistically significant improvement in process skills compared to control group for the OT-E.
Figure 2. CT improves neuronal connectivity: effects…
Figure 2. CT improves neuronal connectivity: effects on the Default Mode Network (DMN).
Panel (A) depicts the DMN obtained from functional magnetic resonance (fMRI) data pooled from both groups [map threshold is t>4.08 and processed with independent component analysis (ICA)]. Panel (B) shows t-maps obtained by extrapolating the T6–T0 difference for the trained group. The procedure shows significant changes in the Precuneus (PrC, cluster size: 575 mm3), the Right Angular Gyrus (rAg, cluster size: 201 mm3), and the Posterior Cingulate Cortex (PCC, cluster size: 3081 mm3). Note that the trained group shows significantly increased activation in the PCC, an area that plays a crucial role in memory functioning. Functional maps are overlaid on a conventional inflated cortex with threshold of t>2.1. Graphs (C) show means ± SEM of z-scores of specific DMN areas (PrC, rAg and PCC).
Figure 3. CT improves neuronal connectivity: effects…
Figure 3. CT improves neuronal connectivity: effects on the Dorsal Attention Network (DAN).
Panel (A) depicts the DAN obtained from functional magnetic resonance (fMRI) data pooled from both groups [map threshold is t>4.08 and processed with independent component analysis (ICA)]. Panel (B) shows t-maps obtained by extrapolating the T6-T0 difference for the trained group. The procedure shows significant changes in the Left Frontal Eye Field (lFEF, cluster size: 1381 mm3) an area that plays a critical role in the control of attention. Functional maps are overlaid on a conventional inflated cortex with threshold of t>2.1. Graph (C) shows means ± SEM of z-scores of lFEF.
Figure 4. CT positively affects cortical thickness.
Figure 4. CT positively affects cortical thickness.
Statistical p-maps, employing a threshold ranging from ±0.05 to ±0.005 (uncorrected), depict brain areas that showed a significant change in cortical thickness. In the right hemisphere, these areas are located in the middle temporal, rostral anterior cingulated, pars orbitalis, superior frontal, supramarginal, lateral occipital, isthmus cingulate, superior temporal, and lateral orbitofrontal. In the left hemisphere, these areas are located in the inferior parietal, precuneus, inferior temporal, superior frontal, caudal middle frontal, middle temporal, supramarginal, insula, lateral occipital, pars orbitalis, and inferior parietal. The plot is the graphical distribution of the average thickness for all subjects at the point indicated with a star in the lower left panel. Each red square represents a subject in the control group and the blue square a subject in the training group. The heavy dashed lines are the group means and the lighter dashed lines standard deviations. The pseudocolor bar graphically shows the extension of differences in cortical thickness scores.
Figure 5. Influence of dopaminergic gene functional…
Figure 5. Influence of dopaminergic gene functional polymorphisms on OT-E process skills performance.
Bar graphs show the results of the OT-E Process Skills performance in study participants expressing different dopamine-related gene polymorphisms. No differences were found among carriers of different DRD1 (A), DRD2 (B), DRD4 (D),DRD5 (E), and DAT1 (G) polymorphisms in the trained group. Compared to individuals carrying the DRD3 A1/A1(Ser/Ser) genotype, DRD3 A1/A2(Ser/Gly) carriers showed an increased response to CT (C). COMT L/L-H/L (Val/Val-Val/Met) carriers also benefitted the most from CT (F). Table (H) shows sample size.

References

    1. Erickson KI, Voss MW, Prakash RS, Basak C, Szabo A, et al. (2011) Exercise training increases size of hippocampus and improves memory. Proc Natl Acad Sci U S A 108(7): 3017–22.
    1. Pascual-Leone A, Amedi A, Fregni F, Merabet LB (2005) The plastic human brain cortex. Annu Rev Neurosci 28: 377–401.
    1. Smith SM, Fox PT, Miller KL, Glahn DC, Fox PM, et al.. (2009) Correspondence of the brain’s functional architecture during activation and rest. Proc Natl Acad Sci U S A 106, 13040–13045.
    1. Kelly AM, Garavan H (2005) Human functional neuroimaging of brain changes associated with practice. Cereb Cortex 15(8): 1089–102.
    1. Colcombe SJ, Erickson KI, Scalf PE, Kim JS, Prakash R, et al. (2006) Aerobic exercise training increases brain volume in aging humans. J Gerontol A Biol Sci Med Sci. 61(11): 1166–70.
    1. Boyke J, Driemeyer J, Gaser C, Buchel C, May A (2008) Training-induced brain structure changes in the elderly. J Neurosci 28: 7031–7035.
    1. Scarmeas N, Levy G, Tang MX, Manly J, Stern Y (2001) Influence of leisure activity on the incidence of Alzheimer’s disease. Neurology 57: 2236–2242.
    1. Buckner RL, Andrews-Hanna JR, Schacter DL (2008) The brain’s default network: anatomy, function, and relevance to disease. Ann N Y Acad Sci 1124: 1–38.
    1. Lewis CM, Baldassarre A, Committeri G, Romani GL, Corbetta M (2009) Learning sculpts the spontaneous activity of the resting human brain. Proc Natl Acad Sci U S A. 106(41): 17558–63.
    1. Fleisher AS, Sherzai A, Taylor C, Langbaum JB, Chen K, et al. (2009) Resting-state BOLD networks versus task-associated functional MRI for distinguishing Alzheimer’s disease risk groups. Neuroimage 47: 1678–1690.
    1. Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, et al. (2001) A default mode of brain function. Proc Natl Acad Sci U S A 98: 676–682.
    1. Damoiseaux JS, Rombouts SA, Barkhof F, Scheltens P, Stam CJ, et al. (2006) Consistent resting- state networks across healthy subjects. Proc Natl Acad Sci USA 103: 13848–13853.
    1. Mantini D, Perrucci MG, Del Gratta C, Romani GL, Corbetta M (2007) Electrophysiological signatures of resting state networks in the human brain. Proc Natl Acad Sci U S A 104: 13170–13175.
    1. Deco G, Jirka VK, McIntosh AR (2011) Emerging concepts for the dynamical organization of resting state activity in the brain. Nat Rev Neurosci 12(1): 43–56.
    1. Rocca MA, Valsasina P, Absinta M, Riccitelli G, Rodegher ME, et al. (2010) Default-mode network dysfunction and cognitive impairment in progressive MS. Neurology 74: 1252–1259.
    1. Grady CL, Protzner AB, Kovacevic N, Strother SC, Afshin-Pour B, et al. (2009) A multivariate analysis of age-related differences in default mode and task-positive networks across multiple cognitive domains. Cereb Cortex 20: 1432–1447.
    1. Koch W, Teipel S, Mueller S, Buerger K, Bokde ALW, et al. (2010) Effects of aging on default mode network activity in resting state fMRI: does the method of analysis matter? Neuroimage 51: 280–287.
    1. Qi Z, Wu X, Wang Z, Zhang N, Dong H, et al. (2009) Impairment and compensation coexist in amnestic MCI default mode network. Neuroimage 50: 48–55.
    1. Bäckman L, Lindenberger U, Li SC, Nyberg L (2010) Linking cognitive aging to alterations in dopamine neurotransmitter functioning: recent data and future avenues. Neurosci Biobehav Rev. 34(5): 670–7.
    1. Wong AH, Buckle CE, Van Tol HH (2000) Polymorphisms in dopamine receptors: what do they tell us? Eur J Pharmacol 410: 183–203.
    1. McNab F, Varrone A, Farde L, Jucaite A, Bystritsky P, et al. (2009) Changes in cortical dopamine D1 receptor binding associated with cognitive training. Science 323(5915): 800–2.
    1. Fisher AG, Atler K, Potts A (2007) Effectiveness of occupational therapy with frail community living older adults. Scand J Occup Ther 14: 240–249.
    1. McKeown MJ, Makeig S, Brown GG, Jung TP, Kindermann SS, et al. (1998) Analysis of fMRI data by blind separation into independent spatial components. Hum Brain Mapp. 6(3): 160–88.
    1. Hyvarinen A (1999) Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans Neural Netw. 10: 626–634.
    1. Esposito F, Scarabino T, Hyvarinen A, Himberg J, Formisano E, et al. (2005) Independent component analysis of fMRI group studies by self-organizing clustering. Neuroimage. 25(1): 193–205.
    1. Mantini D, Caulo M, Ferretti A, Romani GL, Tartaro A (2009) Noxious somatosensory stimulation affects the default mode of brain function: evidence from functional MR imaging. Radiology 253: 797–804.
    1. Grandy DK, Zhang Y, Civelli O (1993) PCR detection of the TaqA RFLP at the DRD2 locus. Hum Mol Genet 2: 2197.
    1. Beischlag TV, Marchese A, Meador-Woodruff JH, Damask SP, O’Dowd BF, et al. (1995) The human dopamine D5 receptor gene: cloning and characterization of the 5’-flanking and promoter region. Biochemistry 34: 5960–5970.
    1. Daniels JK, Williams NM, Williams J, Jones LA, Cardno AG, et al. (1996) No evidence for allelic association between schizophrenia and a polymorphism determining high or low catechol-O- methyltransferase activity. Am J Psychiatry 153: 268–270.
    1. Retz W, Rosler M, Supprian T, Retz-Junginger P, Thome J (2003) Dopamine D3 receptor gene polymorphism and violent behavior: relation to impulsiveness and ADHD-related psychopathology. J Neural Transm 110: 561–572.
    1. Ho AM, Tang NL, Cheung BK, Stadlin A (2008) Dopamine receptor D4 gene -521C/T polymorphism is associated with opioid dependence through cold-pain responses. Ann N Y Acad Sci 1139: 20–26.
    1. Mill J, Curran S, Richards S, Taylor E, Asherson P (2004) Polymorphisms in the dopamine D5 receptor (DRD5) gene and ADHD. Am J Med Genet B Neuropsychiatr Genet 125B: 38–42.
    1. Gill M, Daly G, Heron S, Hawi Z, Fitzgerald M (1997) Confirmation of association between attention deficit hyperactivity disorder and a dopamine transporter polymorphism. Mol Psychiatry 2(4): 311–3.
    1. Folstein MF, Folstein SE, McHugh PR (1975) “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 12: 189–198.
    1. Horner MD, Teichner G, Kortte KB, Harvey RT (2002) Construct validity of the Babcock Story Recall Test. Appl Neuropsychol 9: 114–116.
    1. Rossini ED, Karl MA (1994) The Trail Making Test A and B: a technical note on structural nonequivalence. Percept Mot Skills 78: 625–626.
    1. Dubois B, Slachevsky A, Litvan I, Pillon B (2000) The FAB: a Frontal Assessment Battery at bedside. Neurology 55: 1621–1626.
    1. Tombaugh TN, Kozak J, Rees L (1999) Normative data stratified by age and education for two measures of verbal fluency: FAS and animal naming. Arch Clin Neuropsychol 14: 167–177.
    1. Rosenzweig MR, Bennett EL, Hebert M, Morimoto H (1978) Social grouping cannot account for cerebral effects of enriched environments. Brain Res. 153: 563–576.
    1. Van Praag H, Kempermann G, Gage FH (2000) Neural consequences of environmental enrichment. Nat Rev Neurosci. 1(3): 191–8.
    1. Kempermann G, Fabel K, Ehninger D, Babu H, Leal-Galicia P, et al. (2010) Why and how physical activity promotes experience-induced brain plasticity. Front Neurosci. 8 4: 189.
    1. Voss MW, Nagamatsu LS, Liu-Ambrose T, Kramer AF (2011) Exercise, brain, and cognition across the life span. J Appl Physiol. 111(5): 1505–13.
    1. Fabel K, Wolf SA, Ehninger D, Babu H, Leal-Galicia P, et al. (2009) Additive effects of physical exercise and environmental enrichment on adult hippocampal neurogenesis in mice. Front Neurosci 10 3: 50.
    1. Kempermann G (2008) The neurogenic reserve hypothesis: what is adult hip­pocampal neurogenesis good for? Trends Neurosci. 31: 163–169.
    1. Bennett S, Shand S, Liddle J (2011) Occupational therapy practice in Australia with people with dementia: a profile in need of change. Aust Occup Ther J. 58(3): 155–63.
    1. Belleville S, Clément F, Mellah S, Gilbert B, Fontaine F, et al. (2011) Training-related brain plasticity in subjects at risk of developing Alzheimer’s disease. Brain 134(Pt 6): 1623–34.
    1. Deweerdt S (2011) Prevention: activity is the best medicine. Nature 475(7355): S16–7.
    1. Reuter-Lorenz PA, Mikels JA (2005) A split-brain model of Alzheimer’s disease? Behavioral evidence for comparable intra and interhemispheric decline. Neuropsychologia 43: 1307–1317.
    1. Wang L, Zang Y, He Y, Liang M, Zhang X, Tian L, et al. (2006) Changes in hippocampal connectivity in the early stages of Alzheimer’s disease: evidence from resting state fMRI. Neuroimage. 31(2): 496–504.
    1. Broyd SJ, Demanuele C, Debener S, Helps SK, James CJ, et al. (2009) Default-mode brain dysfunction in mental disorders: a systematic review. Neurosci Biobehav Rev. 33(3): 279–96.
    1. Sambataro F, Murty VP, Callicott JH, Tan H-Y, Das S, et al. (2010) Age-related alterations in default mode network: impact on working memory performance. Neurobiol Aging 31: 839–852.
    1. Greicius MD, Srivastava G, Reiss AL, Menon V (2004) Default-mode network activity distinguishes Alzheimer’s disease from healthy aging: evidence from functional MRI. Proc Natl Acad Sci U S A 101: 4637–4642.
    1. Valenstein E, Bowers D, Verfaellie M, Heilman KM, Day A, et al. (1987) Retrosplenial amnesia. Brain 110 (6): 1631–1646.
    1. Buckner RL, Snyder AZ, Shannon BJ, LaRossa G, Sachs R, et al. (2005) Molecular, structural, and functional characterization of Alzheimer’s disease: evidence for a relationship between default activity, amyloid, and memory. J Neurosci 25: 7709–7717.
    1. Sperling R, LaViolette PS, O’Keefe K, O’Brien J, Rentz DR, et al. (2009) Amyloid deposition is associated with impaired default network function in older persons without dementia. Neuron 63: 178–188.
    1. Mintun MA, LaRossa GN, Sheline YI, Dence CS, Lee SY, et al. (2006) [11C]PIB in a nondemented population: potential antecedent marker of Alzheimer disease. Neurology 67: 446–452.
    1. Mitsuru K, Tetsu H, Masamichi Y, Shunsuke Y, Norio M, et al. (2011) Effects of brain amyloid deposition and reduced glucose metabolism on the default mode of brain function in normal aging. J Neurosci 31(31): 11193–11199.
    1. Scott AS, Scott AS, Richard BB, Menno PW, Carol AB (2011) A pathophysiological framework of hippocampal dysfunction in ageing and disease. Nat Rev Neurosci. 12(10): 585–601.
    1. Corbetta M, Shulman GL (2002) Control of goal-directed and stimulus-driven attention in the brain. Nat Rev Neurosci 3: 201–215.
    1. Voss MW, Prakash RS, Erickson KI, Basak C, Chaddock L, et al. (2010) Plasticity of brain networks in a randomized intervention trial of exercise training in older adults. Front Aging Neurosci 2: 1–17.
    1. Engvig A, Fjell AM, Westlye LT, Moberget T, Sundseth Ø, et al. (2010) Effects of memory training on cortical thickness in the elderly. Neuroimage 52: 1667–1676.
    1. Izaki Y, Takita M, Akema T (2008) Specific role of the posterior dorsal hippocampus-prefrontal cortex in short-term working memory. Eur J Neurosci 27: 3029–3034.
    1. Black KJ, Hershey T, Koller JM, Videen TO, Mintun MA, et al. (2002) A possible substrate for dopamine-related changes in mood and behavior: prefrontal and limbic effects of a D3-preferring dopamine agonist. Proc Natl Acad Sci U S A 99: 17113–17118.
    1. Rybakowski JK, Borkowska A, Czerski PM, Kapelski P, Dmitrzak-Weglarz M, et al. (2005) An association study of dopamine receptors polymorphisms and the Wisconsin Card Sorting Test in schizophrenia. J Neural Transm 112: 1575–1582.
    1. Joyce JN, Millan MJ (2005) Dopamine D3 receptor antagonists as therapeutic agents. Drug Discov Today 10(13): 917–25.
    1. Swart M, Bruggeman R, Larøi F, Alizadeh BZ, Kema I, et al. (2011) COMT Val158Met polymorphism, verbalizing of emotion and activation of affective brain systems. Neuroimage 55(1): 338–44.
    1. Bertolino A, Rubino V, Sambataro F, Blasi G, Latorre V, et al. (2006) Prefrontal–hippocampal coupling during memory processing is modulated by COMT Val158Met genotype. Biol. Psychiatry 60 (11): 1250–1258.
    1. Ursini G, Bollati V, Fazio L, Porcelli A, Iacovelli L, et al. (2011) Stress-related methylation of the catechol-O methyltransferase Val 158 allele predicts human prefrontal cognition and activity. J Neurosci. 31(18): 6692–8.
    1. Stokes PR, Rhodes RA, Grasby PM, Mehta MA (2011) The Effects of The COMT val(108/158)met Polymorphism on BOLD Activation During Working Memory, Planning, and Response Inhibition: A Role for The Posterior Cingulate Cortex? Neuropsychopharmacology 36(4): 763–71.
    1. Lu Y, Ji Y, Ganesan S, Schloesser R, Martinowich K, et al. (2011) TrkB as a potential synaptic and behavioral tag. J Neurosci. 31 (33): 11762–71.

Source: PubMed

Sponsoren und Mitarbeiter

Krankheiten

Drogeninterventionen

3
Abonnieren