Developmental Changes in Organization of Structural Brain Networks

Khundrakpam, Budhachandra; Reid, Andrew; Bräuer, Jens; Carbonell, Félix; Lewis, John D.; Ameis, Stephanie H.; Karama, Sherif; Lee, Junki; Chen, Zhang; Das, Samir; Evans, Alan C.; Ball, William S.; Byars, Anna W.; Schapiro, Mark B.; Bommer, Wendy A.; Carr, April; German, April; Dunn, Scott; Rivkin, Michael J.; Waber, Deborah P.; Mulkern, Robert V.; Vajapeyam, Sridhar; Chiverton, Abigail; Davis, Peter E.; Koo, Julie; Marmor, Jacki; Mrakotsky, Christine; Robertson, Richard L.; McAnulty, Gloria; Brandt, Michael E.; Fletcher, Jack Μ.; Kramer, Larry A.; Yang, Grace C. H.; McCormack, Cara; Hebert, Kathleen M.; Volero, Hilda; McKinstry, Robert C.; Todd, Richard D.; Constantino, John N.; Alger, J. R.; O'Neil, Joseph; Toga, Arthur W.; Asarnow, Robert F.; Fadale, David; Heinichen, Laura; Ireland, Cedric; Wang, Dah-Jyuu; Moss, E. Daniel; Zimmerman, Robert A.; Bintliff, Brooke; Bradford, Ruth; Newman, Janice; Arnaoutelis, Rozalia; Pike, G. Bruce; Collins, D. Louis; Leonard, Gabriel; Paus, Tomáš; Zijdenbos, Alex P.; Fonov, Vladimir; Fu, Luke; Harlap, Jonathan; Leppert, Ilana R.; Milovan, D.; Vins, Dario; Zeffiro, Thomas; Meter, J. Van; Lange, Nicholas; Froimowitz, Michael P.; Botteron, Kelly N.; Almli, C. Robert; Rainey, Cheryl A.; Henderson, Stan; Nishino, Tomoyuki; Warren, William M.; Edwards, Jennifer; Dubois, D.; Smith, Karla; Singer, Tori; Wilber, Aaron A.; Pierpaoli, Carlo; Basser, Peter J.; Chang, Lin-Ching; Koay, Chen Guan; Walker, Lindsay; Freund, Lisa S.; Rumsey, Judith M.; Baskir, Lauren; Stanford, Laurence; Sirocco, Karen; Gwinn-Hardy, Katrina; Spinella, Giovanna; McCracken, James T.; Alger, Jeffry R.; Levitt, Jennifer G.; O’Neill, Joseph

doi:10.1093/cercor/bhs187

Cited by 205 publications

(213 citation statements)

References 88 publications

(141 reference statements)

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“…One might expect, for example, that over the course of human development, periods during which cognitive capability is enhanced and Hebbian processes are particularly active may also correspond to increased salience of rich-club structures. This idea is supported by preliminary evidence from developmental neuroimaging studies which have revealed an increase in the number and importance of connector hubs in normally growing children and adolescents [45].…”

Section: Discussionmentioning

confidence: 74%

Generative models of rich clubs in Hebbian neuronal networks and large-scale human brain networks

Vértes

Alexander-Bloch

Bullmore

2014

Phil. Trans. R. Soc. B

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Rich clubs arise when nodes that are 'rich' in connections also form an elite, densely connected 'club'. In brain networks, rich clubs incur high physical connection costs but also appear to be especially valuable to brain function. However, little is known about the selection pressures that drive their formation. Here, we take two complementary approaches to this question: firstly we show, using generative modelling, that the emergence of rich clubs in large-scale human brain networks can be driven by an economic trade-off between connection costs and a second, competing topological term. Secondly we show, using simulated neural networks, that Hebbian learning rules also drive the emergence of rich clubs at the microscopic level, and that the prominence of these features increases with learning time. These results suggest that Hebbian learning may provide a neuronal mechanism for the selection of complex features such as rich clubs. The neural networks that we investigate are explicitly Hebbian, and we argue that the topological term in our model of large-scale brain connectivity may represent an analogous connection rule. This putative link between learning and rich clubs is also consistent with predictions that integrative aspects of brain network organization are especially important for adaptive behaviour.

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Section: Discussionmentioning

confidence: 74%

Generative models of rich clubs in Hebbian neuronal networks and large-scale human brain networks

Vértes

Alexander-Bloch

Bullmore

2014

Phil. Trans. R. Soc. B

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“…Acquisition of 1.5 T MRI data was collected according to a standardized protocol (Jack et al, 2008). The scanning protocol included a high- resolution T1-weighted, rapid gradient echo (MP-RAGE) sequence.…”

Section: Adni Participants and Mri Acquisitionmentioning

confidence: 99%

“…This network is characterized by high intrinsic functional connectivity between posterior cingulate cortex (PCC), medial prefrontal cortex, inferior parietal lobule, and lateral and medial temporal lobes (Buckner et al, 2008). The functional integrity of the default network is reduced in older adults (Hafkemeijer et al, 2012), with further reductions in Alzheimer's disease (AD) (Greicius et al, 2004;Damoiseaux et al, 2012;Hafkemeijer et al, 2012;Sheline and Raichle, 2013). Functional connectivity within the default network differs in APOE 4 allele carriers, a risk factor for late-life pathological changes (Filippini et al, 2009;Sheline et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

“…Functional connectivity within the default network differs in APOE 4 allele carriers, a risk factor for late-life pathological changes (Filippini et al, 2009;Sheline et al, 2010). Age-related functional changes to the default network have been reliably observed; regions of the network are also susceptible to AD neuropathology (Buckner et al, 2005;Hafkemeijer et al, 2012;Sheline and Raichle, 2013). The structural covariance of the default network, however, remains relatively unexplored.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Structural Covariance of the Default Network in Healthy and Pathological Aging

2013

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Significant progress has been made uncovering functional brain networks, yet little is known about the corresponding structural covariance networks. The default network's functional architecture has been shown to change over the course of healthy and pathological aging. We examined cross-sectional and longitudinal datasets to reveal the structural covariance of the human default network across the adult lifespan and through the progression of Alzheimer's disease (AD). We used a novel approach to identify the structural covariance of the default network and derive individual participant scores that reflect the covariance pattern in each brain image. A seed-based multivariate analysis was conducted on structural images in the cross-sectional OASIS (N ϭ 414) and longitudinal Alzheimer's Disease Neuroimaging Initiative (N ϭ 434) datasets. We reproduced the distributed topology of the default network, based on a posterior cingulate cortex seed, consistent with prior reports of this intrinsic connectivity network. Structural covariance of the default network scores declined in healthy and pathological aging. Decline was greatestintheADcohortandinthosewhoprogressedfrommildcognitiveimpairmenttoAD.Structuralcovarianceofthedefaultnetworkscores were positively associated with general cognitive status, reduced in APOE4 carriers versus noncarriers, and associated with CSF biomarkers of AD. These findings identify the structural covariance of the default network and characterize changes to the network's gray matter integrity across the lifespan and through the progression of AD. The findings provide evidence for the large-scale network model of neurodegenerative disease, in which neurodegeneration spreads through intrinsically connected brain networks in a disease specific manner.

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“…In this sense, while global brain volume is largely genetically deter mined, regional brain volumes, and therefore structural co variance measurements, are more flexibly determined by a number of factors. 3,4 Such factors range from genetic 5 and other developmental influences [6][7][8] to aging effects. 9,10 Other aspects related to the basic principles of brain organ ization, such as the existence of functional connectivity, or cor related spontaneous activity across time between distant struc tures, may also influence the patterns of structural covariance.…”

Section: Introductionmentioning

confidence: 99%