Research Papers

Probing Mechanical Properties of Brain in a Tuberous Sclerosis Model of Autism

[+] Author and Article Information
Bo Qing

Department of Biological Engineering,
Cambridge, MA 02139

Elizabeth P. Canovic, Anna Jagielska, Matthew J. Whitfield

Department of Materials Science
and Engineering,
Cambridge, MA 02139

Aleksandar S. Mijailovic

Department of Mechanical Engineering,
Cambridge, MA 02139

Alexis L. Lowe

Department of Neuroscience,
Wellesley College,
Wellesley, MA 02481

Elyza H. Kelly, Daria Turner, Mustafa Sahin

Department of Neurology,
The F.M. Kirby Neurobiology Center,
Harvard Medical School,
Boston Children's Hospital,
Boston, MA 02115

Krystyn J. Van Vliet

Department of Biological Engineering,
Cambridge, MA 02139;
Department of Materials Science
and Engineering,
Cambridge, MA 02139
e-mail: krystyn@mit.edu

1B. Qing, E. P. Canovic, and A. S. Mijailovic authors contributed equally.

2Corresponding author.

Manuscript received November 28, 2017; final manuscript received July 12, 2018; published online January 18, 2019. Assoc. Editor: Barclay Morrison.

J Biomech Eng 141(3), 031001 (Jan 18, 2019) (10 pages) Paper No: BIO-17-1558; doi: 10.1115/1.4040945 History: Received November 28, 2017; Revised July 12, 2018

Causes of autism spectrum disorders (ASD) are understood poorly, making diagnosis and treatment challenging. While many studies have investigated the biochemical and genetic aspects of ASD, whether and how mechanical characteristics of the autistic brain can modulate neuronal connectivity and cognition in ASD are unknown. Previously, it has been shown that ASD brains are characterized by abnormal white matter and disorganized neuronal connectivity; we hypothesized that these significant cellular-level structural changes may translate to changes in the mechanical properties of the autistic brain or regions therein. Here, we focused on tuberous sclerosis complex (TSC), a genetic disorder with a high penetrance of ASD. We investigated mechanical differences between murine brains obtained from control and TSC cohorts at various deformation length- and time-scales. At the microscale, we conducted creep-compliance and stress relaxation experiments using atomic force microscope(AFM)-enabled indentation. At the mesoscale, we conducted impact indentation using a pendulum-based instrumented indenter to extract mechanical energy dissipation metrics. At the macroscale, we used oscillatory shear rheology to quantify the frequency-dependent shear moduli. Despite significant changes in the cellular organization of TSC brain tissue, we found no corresponding changes in the quantified mechanical properties at every length- and time-scale explored. This investigation of the mechanical characteristics of the brain has broadened our understanding of causes and markers of TSC/ASD, while raising questions about whether any mechanical differences can be detected in other animal models of ASD or other disease models that also feature abnormal brain structure.

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Grahic Jump Location
Fig. 1

Mechanical properties at the micrometer length scale measured using AFM-enabled indentation in the white matter of healthy control and TSC mice: (a) schematic of a coronal section of mouse brain indicating the location of the corpus callosum, (b) the Young's elastic modulus E of TSC brain tissue is not significantly different than that of control tissue, (c) equilibrium modulus E, (d) instantaneous modulus E0, and (e) relaxation time τr obtained from fitting creep (left) and stress relaxation (right) data with a SLS model. These experiments also show no significant differences in any viscoelastic property between the control and TSC brains. Data are represented as mean ± standard deviation (n > 10 measurements per animal; each data point in Fig. 1(b) represents an animal; in Figs. 1(c)–1(e), four control and three TSC animals were characterized for creep and stress relaxation experiments).

Grahic Jump Location
Fig. 2

Impact energy dissipation response metrics of control and TSC mouse brain tissue. (a) maximum penetration depth xmax, (b) energy dissipation capacity K, and (c) dissipation quality factor Q obtained at different impact velocities show no statistical difference between control and TSC brain tissue. Data are represented as mean ± standard deviation (n > 3 measurements per animal; six control and four TSC animals were characterized).

Grahic Jump Location
Fig. 3

Storage G′ moduli (filled symbols) and loss G″ moduli (open symbols) of control and TSC brain tissue at a range of frequencies. Both G′ and G″ show no statistical difference between control and TSC brain tissue for all frequencies measured here. Data are represented as mean ± standard deviation (n = 7 control and 5 TSC mouse brains).

Grahic Jump Location
Fig. 4

Representative images of (a) control and (b) TSC coronal brain slices analyzed for the expression of fibronectin protein (Fn) using fluorescent immunohistochemistry. (c) Mean fluorescence intensity quantified in the original images shows no statistical difference between control and TSC slices. Data are represented as mean ± standard deviation (n = 4 control and five TSC slices).



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