Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2015 May 21:7:90.
doi: 10.3389/fnagi.2015.00090. eCollection 2015.

On the central role of brain connectivity in neurodegenerative disease progression

Yasser Iturria-Medina  1 , Alan C Evans  1
Affiliations
Review

On the central role of brain connectivity in neurodegenerative disease progression

Yasser Iturria-Medina et al. Front Aging Neurosci. .

Abstract

Increased brain connectivity, in all its variants, is often considered an evolutionary advantage by mediating complex sensorimotor function and higher cognitive faculties. Interaction among components at all spatial scales, including genes, proteins, neurons, local neuronal circuits and macroscopic brain regions, are indispensable for such vital functions. However, a growing body of evidence suggests that, from the microscopic to the macroscopic levels, such connections might also be a conduit for in intra-brain disease spreading. For instance, cell-to-cell misfolded proteins (MP) transmission and neuronal toxicity are prominent connectivity-mediated factors in aging and neurodegeneration. This article offers an overview of connectivity dysfunctions associated with neurodegeneration, with a specific focus on how these may be central to both normal aging and the neuropathologic degenerative progression.

Keywords: brain connectivity; deregulated gene networks; disease spreading; metabolic dysfunction; misfolded proteins; neurodegeneration; neuronal activity toxicity; vascular deregulation.

PubMed Disclaimer

Figures

Figure 1
Figure 1
Connectivity distances to pathologic epicenters predict diseases effects. (A) TYROBP causal network in late onset Alzheimer’s disease (AD), (B) differential expression levels of deregulated genes associated with TYROBP at various functional distances from it. Note the negative association (R = −0.82; P < 10−3) implying increasing impact with proximity to TYROBP. Figure adapted from Zhang et al. (2013), with permission from Elsevier.
Figure 2
Figure 2
Anatomically dissociable networks targeted by five different neurodegenerative disorders: AD, behavioral variant frontotemporal dementia (bvFTD), semantic dementia (SD), progressive nonfluent aphasia (PNFA), and corticobasal syndrome (CBS). Figure adapted from Seeley et al. (2009), with permission from Elsevier.
Figure 3
Figure 3
In brain and social networks, effective proximity to an epicenter modulates the propagation of aberrant factors. (A) PET-based regional Aβ deposition probabilities for different clinical groups (healthy control (HC), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI) and AD) vs. effective anatomical distances to the identified Aβ outbreak region (anterior and posterior cingulate cortices). (B) Regional Aβ arriving times vs. effective anatomical distances, for different Aβ probability thresholds (i.e., 0.1, 0.5 and 0.9). (C) N1H1 pandemic arrival time vs. effective distance (Deff) to outbreak country (i.e., Mexico). In (C), the effective distance was computed from the projected global mobility network between countries. Panels (A,B) and (C) were adapted with permission from Iturria-Medina et al. (2014), and Brockmann and Helbing (2013) respectively.
Figure 4
Figure 4
Age-dependent blood-brain barrier (BBB) permeability breakdown may be causally associated with neurodegenerative spreading (Panels (A) and (B) were adapted from Montagne et al. (2015), Iadecola (2015), respectively, with permission from Elsevier). (A) Significant increases in BBB permeability in older compared young group of individuals with no cognitive impairment (NCI), and MCI compared to older NCI group in the entire hippocampus. Multiple sclerosis (MS) patients with no cognitive impairment were comparable with the age-matched young NCI group (Montagne et al., 2015). (B) Hypothetical pathologic mechanisms by which Aβ may induce BBB permeability alterations and hippocampal/cognitive dysfunction (Iadecola, 2015). Aβ affects endothelial cells, damaging pericytes, vesicular transport and Ca+ balance. This contributes to BBB disruption, homeostasis alterations, reduction on misfolded proteins (MP) clearance and tentatively to hippocampal dysfunction, cognitive deficits and intra-brain pathology spreading.
Figure 5
Figure 5
Gray matter lesions identified on 26 clinical brain disorders impact mainly on the structural and functional hub regions. (A) Nodes of the normative structural connectome, represented in anatomical space, with nodes size reflecting connectivity degrees. (B) Spiral representation of the region vulnerability vs. hubness relationship. Nodes of similar degree are arranged in the same circle, and the different circumferences arranged so that the tip of the spiral has the highest degree hub nodes, while the base the most peripheral nodes. Nodes sizes are proportional to their connectional degree, with colors reflecting each region’s lesioned percentage. The strongest 0.1% of edges between nodes, which highlight pairs of nodes with consistently high number of streamlines interconnecting them, are shown for illustrative purposes. (C) Plot of the probability of lesion voxels (y-axis) vs. connectivity degree for structural connectome nodes (x-axis). The red line is a fitted logistic regression model. (D) Plot of the probability of lesion voxels (y-axis) vs. the degree of the functional co-activation network nodes (x-axis). Figure adapted from Crossley et al. (2014), with permission.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Albrecht S., Bourdeau M., Bennett D., Mufson E. J., Bhattacharjee M., LeBlanc A. C. (2007). Activation of caspase-6 in aging and mild cognitive impairment. Am. J. Pathol. 170, 1200–1209. 10.2353/ajpath.2007.060974 - DOI - PMC - PubMed
    1. Al-Chalabi A., Hardiman O. (2013). The epidemiology of ALS: a conspiracy of genes, environment and time. Nat. Rev. Neurol. 9, 617–628. 10.1038/nrneurol.2013.203 - DOI - PubMed
    1. Axer M., Amunts K., Grässel D., Palm C., Dammers J., Axer H., et al. . (2011). A novel approach to the human connectome: ultra-high resolution mapping of fiber tracts in the brain. Neuroimage 54, 1091–1101. 10.1016/j.neuroimage.2010.08.075 - DOI - PubMed
    1. Bernot G., Comet J., Richard A., Chaves M., Gouz J. (2013). “Modeling in computational biology and biomedicine,” in A Multidisciplinary Endeavor, eds Cazals F., Kornprobst P. (Berlin, Heidelberg: Springer Berlin Heidelberg; ), 47–80.
    1. Bero A. W., Yan P., Roh J. H., Cirrito J. R., Stewart F. R., Raichle M. E., et al. . (2011). Neuronal activity regulates the regional vulnerability to amyloid- b deposition. Nat. Neurosci. 14, 750–756. 10.1038/nn.2801 - DOI - PMC - PubMed