Youth with Down syndrome display widespread increased functional connectivity during rest

Kelsey D Csumitta, Stephen J Gotts, Liv S Clasen, Alex Martin, Nancy Raitano Lee, Kelsey D Csumitta, Stephen J Gotts, Liv S Clasen, Alex Martin, Nancy Raitano Lee

Abstract

Studies of resting-state functional connectivity in young people with Down syndrome (DS) have yielded conflicting results. Some studies have found increased connectivity while others have found a mix of increased and decreased connectivity. No studies have examined whole-brain connectivity at the voxel level in youth with DS during an eyes-open resting-state design. Additionally, no studies have examined the relationship between connectivity and network selectivity in youth with DS. Thus, the current study sought to fill this gap in the literature. Nineteen youth with DS (Mage = 16.5; range 7-23; 13 F) and 33 typically developing (TD) youth (Mage = 17.5; range 6-24; 18 F), matched on age and sex, completed a 5.25-min eyes-open resting-state fMRI scan. Whole-brain functional connectivity (average Pearson correlation of each voxel with every other voxel) was calculated for each individual and compared between groups. Network selectivity was then calculated and correlated with functional connectivity for the DS group. Results revealed that whole-brain functional connectivity was significantly higher in youth with DS compared to TD controls in widespread regions throughout the brain. Additionally, participants with DS had significantly reduced network selectivity compared to TD peers, and selectivity was significantly related to connectivity in all participants. Exploratory behavioral analyses revealed that regions showing increased connectivity in DS predicted Verbal IQ, suggesting differences in connectivity may be related to verbal abilities. These results indicate that network organization is disrupted in youth with DS such that disparate networks are overly connected and less selective, suggesting a potential target for clinical interventions.

Trial registration: ClinicalTrials.gov NCT00001246.

Conflict of interest statement

The authors declare no competing interests.

© 2022. The Author(s).

Figures

Figure 1
Figure 1
18 ROIs where functional connectivity is greater in participants with DS than TD controls, colored by the Yeo networks. Red = Default Mode; Green = Dorsal Attention; Yellow = Frontoparietal; Orange Limbic; Pink = Ventral Attention; Purple = Visual; Black = Cerebellar.
Figure 2
Figure 2
Connectivity matrices for the 18 ROI-ROI connections, each organized by the Yeo network structure. (A) Connectivity matrix (r-values) for participants with DS. (B) Connectivity matrix (r-values) for TD controls. (C) Connectivity differences for DS vs. TD participants t-test results (t-values) that replicated in a motion-matched subsample. (D) Significant (red) and non-significant (blue) connectivity differences from (C) after FDR correction.
Figure 3
Figure 3
Network selectivity differences for participants with DS vs. TD controls. (A) Participants with DS display greater within- and between-network connectivity than TD controls. (B) Network selectivity (the difference between within-network connectivity and between-network connectivity) is reduced in participants with DS compared to TD controls. (C) Reduced selectivity in DS is not due to group differences in participant motion, indicated by a non-significant interaction between motion and group, p > 0.97.
Figure 4
Figure 4
Increased ROI-ROI functional connectivity is significantly (p < 0.015) related to reduced network selectivity after partialling motion in the complete sample of participants.
Figure 5
Figure 5
ROI-ROI correlation measures from participants with DS significantly predicted their VIQ scores by permutation testing using a leave-one-subject-out cross-validation approach (p < 0.05).

References

    1. Parker SE, Mai CT, Canfield MA, Rickard R, Wang Y, Meyer RE, Anderson P, Mason CA, Collins JS, Kirby RS, Correa A. Updated national birth prevalence estimates for selected birth defects in the United States, 2004–2006. Birth Defects Res. A. 2010;88:1008–1016. doi: 10.1002/bdra.20735.
    1. Martin GE, Klusek J, Estigarribia B, Roberts JE. Language characteristics of individuals with Down syndrome. Top. Lang. Disord. 2009;29:112–132. doi: 10.1097/TLD.0b013e3181a71fe1.
    1. Daunhauer LA, Gerlach-Mcdonald B, Will E, Fidler DJ. Performance and ratings based measures of executive function in school-aged children with Down syndrome. Dev. Neuropsychol. 2017;42:351–368. doi: 10.1080/87565641.2017.1360303.
    1. Lee NR, Fidler DJ, Blakeley-Smith A, Daunhauer L, Robinson C, Hepburn SL. Caregiver report of executive functioning in a population-based sample of young children with Down syndrome. Am. J. Intellect. Dev. Disabil. 2011;116:290–304. doi: 10.1352/1944-7558-116.4.290.
    1. Loveall SJ, Conners FA, Tungate AS, Hahn LJ, Osso TD. A cross-sectional analysis of executive function in Down syndrome from 2 to 35 years. J. Intellect. Disabil. Res. 2017;61:877–887. doi: 10.1111/jir.12396.
    1. Malamud N. Neuropathology of organic brain syndromes associated with aging. In: Gaitz CM, editor. Book Neuropathology of Organic Brain Syndromes Associated with Aging. Springer; 1972.
    1. Carter JC, Capone GT, Kaufmann WE. Neuroanatomic correlates of autism and stereotypy in children with Down syndrome. NeuroReport. 2008;16:653–656. doi: 10.1097/WNR.0b013e3282faa8d8.
    1. Kates WR, et al. Cerebral growth in Fragile X syndrome: Review and comparison with Down syndrome. Microsc. Res. Tech. 2002;57:159–167. doi: 10.1002/jemt.10068.
    1. Lee NR, et al. Dissociations in cortical morphometry in youth with down syndrome: Evidence for reduced surface area but increased thickness. Cereb. Cortex. 2016;26:2982–2990. doi: 10.1093/cercor/bhv107.
    1. Pinter JD, et al. Neuroanatomy of Down's syndrome: A high-resolution MRI study. Am. J. Psychiatry. 2001;158:1659–1665. doi: 10.1176/appi.ajp.158.10.1659.
    1. Smigielska-Kuzia J, et al. A volumetric magnetic resonance imaging study of brain structures in children with Down syndrome. Neurol. Neurochir. Pol. 2011;45:363–369. doi: 10.1016/S0028-3843(14)60107-9.
    1. Lee NR, et al. A preliminary examination of brain morphometry in youth with Down syndrome with and without parent-reported sleep difficulties. Res. Dev. Disabil. 2020;99:103575. doi: 10.1016/j.ridd.2020.103575.
    1. Aylward EH, et al. MRI volumes of the hippocampus and amygdala in adults with Down's syndrome with and without dementia. Am. J. Psychiatry. 1999;156:564–568. doi: 10.1176/ajp.156.4.564.
    1. Pinter JD, et al. Amygdala and hippocampal volumes in children with Down syndrome: A high-resolution MRI study. Neurology. 2001;56:972–974. doi: 10.1212/WNL.56.7.972.
    1. Miyake AFN, Emerson MJ, Witzki AH, Howerter A, Wager TD. The unity and diversity of executive functions and their contributions to complex “frontal lobe” tasks: A latent variable analysis. Cogn. Psychol. 2000;41:49–100. doi: 10.1006/cogp.1999.0734.
    1. Bogen JE. Wernicke’s region: Where is it? Ann. N. Y. Acad. Sci. 1976;290:834–843. doi: 10.1111/j.1749-6632.1976.tb25546.x.
    1. Koenig KA, et al. High resolution structural and functional MRI of the hippocampus in young adults with Down syndrome. Brain Commun. 2021;3:088. doi: 10.1093/braincomms/fcab088.
    1. Aylward EH, et al. Cerebellar volume in adults with Down syndrome. Arch. Neurol. 1997;54:209–212. doi: 10.1001/archneur.1997.00550140077016.
    1. Lee NR, et al. Hypoplasia of cerebellar afferent networks in Down syndrome revealed by DTI-driven tensor based morphometry. Sci. Rep. 2020;10:5447. doi: 10.1038/s41598-020-61799-1.
    1. Carbo-Carrete M, et al. Using fMRI to assess brain activity in people with down syndrome: A systematic review. Front. Hum. Neurosci. 2020;14:147. doi: 10.3389/fnhum.2020.00147.
    1. Jacola LM, et al. Functional magnetic resonance imaging of cognitive processing in young adults with Down syndrome. Am. J. Intellect. Dev. Disabil. 2011;116:344–359. doi: 10.1352/1944-7558-116.5.344.
    1. Jacola LM, et al. Functional magnetic resonance imaging of story listening in adolescents and young adults with Down syndrome: Evidence for atypical neurodevelopment. J. Intellect. Disabil. Res. 2014;58:892–902. doi: 10.1111/jir.12089.
    1. Losin EA, et al. Abnormal fMRI activation pattern during story listening in individuals with Down syndrome. Am. J. Intellect. Dev. Disabil. 2009;114:369–380. doi: 10.1352/1944-7558-114.5.369.
    1. Seyffert M, Castellanos FX. functional MRI in pediatric neurobehavioral disorders. Int. Rev. Neurobiol. 2005;67:239–284. doi: 10.1016/S0074-7742(05)67008-0.
    1. Fox MD, Greicius M. Clinical applications of resting state functional connectivity. Front. Syst. Neurosci. 2010;4:19.
    1. Power JD, Schlaggar BL, Petersen SE. Studying brain organization via spontaneous fMRI signal. Neuron. 2014;84:681–696. doi: 10.1016/j.neuron.2014.09.007.
    1. Pujol J, et al. Anomalous brain functional connectivity contributing to poor adaptive behavior in Down syndrome. Cortex. 2015;64:148–156. doi: 10.1016/j.cortex.2014.10.012.
    1. Anderson JS, et al. Abnormal brain synchrony in Down syndrome. Neuroimage Clin. 2013;2:703–715. doi: 10.1016/j.nicl.2013.05.006.
    1. Vega JN, et al. Resting-state functional connectivity in individuals with Down syndrome and Williams syndrome compared with typically developing controls. Brain Connect. 2015;5:461–475. doi: 10.1089/brain.2014.0266.
    1. Wilson LR, et al. Differential effects of Down's syndrome and Alzheimer's neuropathology on default mode connectivity. Hum. Brain Mapp. 2019;40:4551–4563. doi: 10.1002/hbm.24720.
    1. Rosas HD, et al. Altered connectivity of the default mode network in cognitively stable adults with Down syndrome: "Accelerated aging" or a prelude to Alzheimer's disease? Alzheimers Dement. (Amst.) 2021;13:e12105.
    1. Wisniewski KE, Wisniewski HM, Wen GY. Occurrence of neuropathological changes and dementia of Alzheimer's disease in Down's syndrome. Ann. Neurol. 1985;17:278–282. doi: 10.1002/ana.410170310.
    1. Yeo BT, et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophysiol. 2011;106:1125–1165. doi: 10.1152/jn.00338.2011.
    1. Figueroa-Jimenez MD, et al. Resting-state default mode network connectivity in young individuals with Down syndrome. Brain Behav. 2021;11:e01905. doi: 10.1002/brb3.1905.
    1. Koenig KA, et al. High-resolution functional connectivity of the default mode network in young adults with Down syndrome. Brain Imaging Behav. 2021;15:2051–2060. doi: 10.1007/s11682-020-00399-z.
    1. Saad ZS, et al. Trouble at rest: How correlation patterns and group differences become distorted after global signal regression. Brain Connect. 2012;2:25–32. doi: 10.1089/brain.2012.0080.
    1. Saad ZS, et al. Correcting brain-wide correlation differences in resting-state FMRI. Brain Connect. 2013;3:339–352. doi: 10.1089/brain.2013.0156.
    1. Gotts SJ, Gilmore AW, Martin A. Brain networks, dimensionality, and global signal averaging in resting-state fMRI: Hierarchical network structure results in low-dimensional spatiotemporal dynamics. Neuroimage. 2020;205:116289. doi: 10.1016/j.neuroimage.2019.116289.
    1. Gotts SJ, et al. The perils of global signal regression for group comparisons: A case study of autism spectrum disorders. Front. Hum. Neurosci. 2013;7:356. doi: 10.3389/fnhum.2013.00356.
    1. Murphy K, et al. The impact of global signal regression on resting state correlations: Are anti-correlated networks introduced? Neuroimage. 2009;44:893–905. doi: 10.1016/j.neuroimage.2008.09.036.
    1. Hahamy A, Calhoun V, Pearlson G, Harel M, Stern N, Attar F, Malach R, Salomon R. Save the global: Global signal connectivity as a tool for studying clinical populations with functional magnetic resonance imaging. Brain Connect. 2014;4:395–403. doi: 10.1089/brain.2014.0244.
    1. Yang GJ, et al. Altered global brain signal in schizophrenia. Proc. Natl. Acad. Sci. U.S.A. 2014;111:7438–7443. doi: 10.1073/pnas.1405289111.
    1. Pueschel SM, Louis S, McKnight P. Seizure disorders in Down syndrome. Arch. Neurol. 1991;48:318–320. doi: 10.1001/archneur.1991.00530150088024.
    1. Cox RW. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput. Biomed. Res. 1996;29:162–173. doi: 10.1006/cbmr.1996.0014.
    1. Talairach J, Tournoux P. Co-planar Stereotaxic Atlas of the Human Brain. Thieme Medical Publishers, Inc.; 1988.
    1. Jo HJ, et al. Effective preprocessing procedures virtually eliminate distance-dependent motion artifacts in resting state FMRI. J. Appl. Math. 2013;2013:1–9. doi: 10.1155/2013/935154.
    1. Jo HJ, et al. Mapping sources of correlation in resting state FMRI, with artifact detection and removal. Neuroimage. 2010;52:571–582. doi: 10.1016/j.neuroimage.2010.04.246.
    1. Gotts SJ, Milleville SC, Martin A. Enhanced inter-regional coupling of neural responses and repetition suppression provide separate contributions to long-term behavioral priming. Commun. Biol. 2021;4:487. doi: 10.1038/s42003-021-02002-7.
    1. Fischl B, et al. Whole brain segmentation: Automated labeling of neuroanatomical structures in the human brain. Neuron. 2002;33:341–355. doi: 10.1016/S0896-6273(02)00569-X.
    1. Birn RM, et al. The respiration response function: The temporal dynamics of fMRI signal fluctuations related to changes in respiration. Neuroimage. 2008;40:644–654. doi: 10.1016/j.neuroimage.2007.11.059.
    1. Glover GH, Li TQ, Ress D. Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR. Magn. Reson. Med. 2000;44:162–167. doi: 10.1002/1522-2594(200007)44:1<162::AID-MRM23>;2-E.
    1. Behzadi Y, et al. A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. Neuroimage. 2007;37:90–101. doi: 10.1016/j.neuroimage.2007.04.042.
    1. Stoddard J, Gotts SJ, Brotman MA, Lever S, Hsu D, Zarate C, Ernst M, Pine DS, Leibenluft E. Aberrant intrinsic functional connectivity within and between corticostriatal and temporal–parietal networks in adults and youth with bipolar disorder. Psychol. Med. 2016;47:1509–1522. doi: 10.1017/S0033291716000143.
    1. Cole MW, Pathak S, Schneider W. Identifying the brain’s most globally connected regions. Neuroimage. 2010;49:3132–3148. doi: 10.1016/j.neuroimage.2009.11.001.
    1. Salomon R, et al. Global functional connectivity deficits in schizophrenia depend on behavioral state. J. Neurosci. 2011;31:12972–12981. doi: 10.1523/JNEUROSCI.2987-11.2011.
    1. Gotts SJ, et al. Fractionation of social brain circuits in autism spectrum disorders. Brain. 2012;135:2711–2725. doi: 10.1093/brain/aws160.
    1. Dosenbach NU, et al. Prediction of individual brain maturity using fMRI. Science. 2010;329:1358–1361. doi: 10.1126/science.1194144.
    1. Fair DA, Cohen LA, Power JD, Dosenbach NUF, Church JA, Miezin FM, Schlaggar BL, Petersen SE. Functional brain networks develop from a “local to distributed” organization. PLoS Comput. Biol. 2009;5:e1000381. doi: 10.1371/journal.pcbi.1000381.
    1. Supekar K, Musen M, Menon V. Development of large-scale functional brain networks in children. PLoS Biol. 2009;7:e1000157. doi: 10.1371/journal.pbio.1000157.
    1. Malagurski B, et al. Functional dedifferentiation of associative resting state networks in older adults—A longitudinal study. Neuroimage. 2020;214:116680. doi: 10.1016/j.neuroimage.2020.116680.
    1. Chan MY, et al. Decreased segregation of brain systems across the healthy adult lifespan. Proc. Natl. Acad. Sci. U.S.A. 2014;111:E4997–E5006. doi: 10.1073/pnas.1415122111.
    1. Smith REW, et al. Sex differences in resting-state functional connectivity of the cerebellum in autism spectrum disorder. Front. Hum. Neurosci. 2019;13:104. doi: 10.3389/fnhum.2019.00104.
    1. Jasmin K, et al. Overt social interaction and resting state in young adult males with autism: Core and contextual neural features. Brain. 2019;142:808–822. doi: 10.1093/brain/awz003.
    1. Choi J, et al. Aberrant development of functional connectivity among resting state-related functional networks in medication-naive ADHD children. PLoS ONE. 2013;8:e83516. doi: 10.1371/journal.pone.0083516.
    1. Watsky RE, et al. Attenuated resting-state functional connectivity in patients with childhood- and adult-onset schizophrenia. Schizophr. Res. 2018;197:219–225. doi: 10.1016/j.schres.2018.01.003.
    1. Berman RA, et al. Disrupted sensorimotor and social-cognitive networks underlie symptoms in childhood-onset schizophrenia. Brain. 2016;139:276–291. doi: 10.1093/brain/awv306.
    1. Bluhm RL, et al. Spontaneous low-frequency fluctuations in the BOLD signal in schizophrenic patients: Anomalies in the default network. Schizophr. Bull. 2007;33:1004–1012. doi: 10.1093/schbul/sbm052.
    1. Li SJ, Li Z, Wu G, Zhang MJ, Franczak M, Antuono PG. Alzheimer disease: Evaluation of a functional MR imaging index as a marker. Radiology. 2002;225:253–259. doi: 10.1148/radiol.2251011301.
    1. Supekar K, et al. Network analysis of intrinsic functional brain connectivity in Alzheimer's disease. PLoS Comput. Biol. 2008;4:e1000100. doi: 10.1371/journal.pcbi.1000100.
    1. Greicius MD, et al. Default-mode network activity distinguishes Alzheimer's disease from healthy aging: Evidence from functional MRI. Proc. Natl. Acad. Sci. U.S.A. 2004;101:4637–4642. doi: 10.1073/pnas.0308627101.
    1. Carr J. Stability and change in cognitive ability over the life span: A comparison of populations with and without Down’s syndrome. J. Intellect. Disabil. Res. 2005;49:915–928. doi: 10.1111/j.1365-2788.2005.00735.x.
    1. Couzens D, Haynes M, Cuskelly M. Individual and environmental characteristics associated with cognitive development in Down syndrome: A longitudinal study. J. Appl. Res. Intellect. Disabil. 2012;25:396–413. doi: 10.1111/j.1468-3148.2011.00673.x.
    1. Power JD, et al. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage. 2012;59:2142–2154. doi: 10.1016/j.neuroimage.2011.10.018.

Source: PubMed

3
Předplatit