Abstract:Brain structure varies between people in a markedly organized fashion. Communities of brain regions co-vary in their morphological properties. For example, cortical thickness in one region influences the thickness of structurally and functionally connected regions. Such networks of structural co-variance partially recapitulate the functional networks of healthy individuals and the foci of grey matter loss in neurodegenerative disease. This architecture is genetically heritable, is associated with behavioural a… Show more
“…Unlike most previous studies of gray matter structural covariance networks, the similarity‐based extraction method we applied resulted in individual level networks that allowed us to examine correlations with functional connectomes. Gray matter structural covariance networks are believed to reflect underlying axonal connections as well as common genetic, neurotrophic, and neuroplastic processes (Alexander‐Bloch, Giedd, & Bullmore, 2013; Mechelli, Friston, Frackowiak, & Price, 2005). Our group and others have previously demonstrated, in healthy adults, that structural covariance networks are consistent with intrinsic functional networks with respect to connectivity pattern, although not in all brain regions (Damoiseaux & Greicius, 2009; Hosseini & Kesler, 2013).…”
IntroductionSeveral previous studies have demonstrated that cancer chemotherapy is associated with brain injury and cognitive dysfunction. However, evidence suggests that cancer pathogenesis alone may play a role, even in non‐CNS cancers.MethodsUsing a multimodal neuroimaging approach, we measured structural and functional connectome topology as well as functional network dynamics in newly diagnosed patients with breast cancer. Our study involved a novel, pretreatment assessment that occurred prior to the initiation of any cancer therapies, including surgery with anesthesia. We enrolled 74 patients with breast cancer age 29–65 and 50 frequency‐matched healthy female controls who underwent anatomic and resting‐state functional MRI as well as cognitive testing.ResultsCompared to controls, patients with breast cancer demonstrated significantly lower functional network dynamics (p = .046) and cognitive functioning (p < .02, corrected). The breast cancer group also showed subtle alterations in structural local clustering and functional local clustering (p < .05, uncorrected) as well as significantly increased correlation between structural global clustering and functional global clustering compared to controls (p = .03). This hyper‐correlation between structural and functional topologies was significantly associated with cognitive dysfunction (p = .005).ConclusionsOur findings could not be accounted for by psychological distress and suggest that non‐CNS cancer may directly and/or indirectly affect the brain via mechanisms such as tumor‐induced neurogenesis, inflammation, and/or vascular changes, for example. Our results also have broader implications concerning the importance of the balance between structural and functional connectome properties as a potential biomarker of general neurologic deficit.
“…Unlike most previous studies of gray matter structural covariance networks, the similarity‐based extraction method we applied resulted in individual level networks that allowed us to examine correlations with functional connectomes. Gray matter structural covariance networks are believed to reflect underlying axonal connections as well as common genetic, neurotrophic, and neuroplastic processes (Alexander‐Bloch, Giedd, & Bullmore, 2013; Mechelli, Friston, Frackowiak, & Price, 2005). Our group and others have previously demonstrated, in healthy adults, that structural covariance networks are consistent with intrinsic functional networks with respect to connectivity pattern, although not in all brain regions (Damoiseaux & Greicius, 2009; Hosseini & Kesler, 2013).…”
IntroductionSeveral previous studies have demonstrated that cancer chemotherapy is associated with brain injury and cognitive dysfunction. However, evidence suggests that cancer pathogenesis alone may play a role, even in non‐CNS cancers.MethodsUsing a multimodal neuroimaging approach, we measured structural and functional connectome topology as well as functional network dynamics in newly diagnosed patients with breast cancer. Our study involved a novel, pretreatment assessment that occurred prior to the initiation of any cancer therapies, including surgery with anesthesia. We enrolled 74 patients with breast cancer age 29–65 and 50 frequency‐matched healthy female controls who underwent anatomic and resting‐state functional MRI as well as cognitive testing.ResultsCompared to controls, patients with breast cancer demonstrated significantly lower functional network dynamics (p = .046) and cognitive functioning (p < .02, corrected). The breast cancer group also showed subtle alterations in structural local clustering and functional local clustering (p < .05, uncorrected) as well as significantly increased correlation between structural global clustering and functional global clustering compared to controls (p = .03). This hyper‐correlation between structural and functional topologies was significantly associated with cognitive dysfunction (p = .005).ConclusionsOur findings could not be accounted for by psychological distress and suggest that non‐CNS cancer may directly and/or indirectly affect the brain via mechanisms such as tumor‐induced neurogenesis, inflammation, and/or vascular changes, for example. Our results also have broader implications concerning the importance of the balance between structural and functional connectome properties as a potential biomarker of general neurologic deficit.
“…Structural covariance is observed as inter-individual differences in regional brain structure covarying with other brain structures across the population [52][53][54]. Across individuals, intrinsically connected functional brain networks, such as the default network, can be topographically represented in the structural patterns of cortical gray matter.…”
Section: Changes In Structural Brain Networkmentioning
In this opening section of the Aging Brain we set the stage for the contributions that follow by providing a broad overview of the latest advances in our understanding of how the brain changes, both structurally and functionally, across the adult lifespan. We leave domain-specific aspects of brain aging to the subsequent chapters, where contributors will provide more targeted accounts of brain change germane to their particular focus on the aging brain. Here we review the extant, and rapidly expanding literature to provide a brief overview and introduction to structural and functional change that occur with typical brain aging. We begin the chapter by looking back, to review some of the early discoveries about how the brain changes across the adult lifespan. We close the chapter by looking forward, towards new discoveries that challenge our core assumptions about the inevitability or irreversibility of age-related brain changes. These sections serve as bookends for the core of the chapter where we review the latest research advances that continue to uncover the mysteries of the aging brain. Spreng, R.N., Turner, G.R. (2019, forthcoming) Structure and function of the aging brain. In G Samanez-Larkin (Ed.) The aging brain. Washington DC: American Psychological Association.
“…This method provides a precise quantitative description of cortical structure by representing brain morphology as a network in which each cortical area represents a node and nodes are connected by edges when they show as statistical covariance in their morphometric features (local thickness and folding structure of the cortex). Patterns of coordinated grey matter morphology have been proposed to reflect functional co-activation (Alexander-Bloch et al, 2013;Andrews et al, 1997;Bailey et al, 2014;Hopkins, 2004;Krongold et al, 2015), axonal connectivity (Budday et al, 2014;Gong et al, 2012) and/or genetic factors (Chen et al, 2013;Schmitt et al, 2009;2008). Analogously, brain areas that are involved in specific cognitive or behavioral functions seem to deteriorate in a coordinated way (Sepulcre et al, 2012;Voss and Zatorre, 2015).…”
We set out to study whether single-subject grey matter (GM) networks show disturbances that are specific for Alzheimer's disease (AD) (n=90) or behavioral variant Frontotemporal dementia (bvFTD) (n=59), and whether such disturbances would be related to cognitive deficits measured with Mini-mental state examination (MMSE) and a neuropsychological battery, using subjective cognitive decline subjects (SCD) as reference. AD and bvFTD patients had a lower degree, connectivity density, clustering, path length, betweenness centrality and small world values compared to SCD. AD patients had a lower connectivity density than bvFTD patients (F = 5.79, p = 0.02; Mean±SD bvFTD 16.10% ± 1.19; Mean±SD AD 15.64% ± 1.02). Lasso logistic regression showed that connectivity differences between bvFTD and AD were specific to 23 anatomical areas, in terms of local GM volume, degree and clustering. Lower clustering values and lower degree values were specifically associated with worse MMSE scores and lower performance on the neuropsychological tests. GM showed disease-specific alterations, when comparing bvFTD with AD patients, and these alterations were associated with cognitive deficits.
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