Regional variation in the shear modulus of in vivo brain.

<p>All differences between the regions are statistically significant (p<0.001). The boxplot depicts the lower and upper quartiles as well as the median. Full data range (without outliers) is presented by whiskers. Crosses depict outliers.</p

Four image slices from T1-weighted volume MRI data compliant with MRE slice positions (upper row).

<p>Color-coded MRE wave data of 50 Hz vibrations. Blue colors scale vibrations towards the reader, while red to yellow colors scale motion beneath the image plane. The maximum tissue deflection is approximately 80<i> µ</i>m (mid row). Real-part modulus images corresponding to 50 Hz vibration frequency with specific regions of interest (ROIs) investigated in this study. Green lines: <i>ROI</i>_full, blue lines: <i>ROI</i>_inner, red lines: <i>ROI</i>_frontal, magenta lines: <i>ROI</i>_posterior, outer green lines excluding ventricles: <i>ROI</i>_full (bottom line).</p

Brain shear elasticity modulus averaged over the entire parenchyma visible in four image slices of all volunteers.

<p>Linear and quadratic regression is shown to indicate the order of softening of brain tissue with years of age.</p

Decrease in total brain volume and WM volume with age represented by linear regression of MRI volume data.

<p>Decrease in total brain volume and WM volume with age represented by linear regression of MRI volume data.</p

Regional variation in the parameter <i>α</i> representing the slope of the modulus dispersion and according to the springpot model.

<p>As <i>α</i> is sensitive to the microstructure geometry of biological tissue it is named ‘geometry’ parameter. Similar to <i>µ</i> (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0023451#pone-0023451-g003" target="_blank">Figure 3</a>), all regional differences are statistically significant (p<0.001).</p

Description of volume data and viscoelasticity parameters.

<p>The standard deviations (SD) are given in brackets.</p>a<p>dm³,</p>b<p>kPa.</p

Enhanced Adult Neurogenesis Increases Brain Stiffness: <i>In Vivo</i> Magnetic Resonance Elastography in a Mouse Model of Dopamine Depletion

<div><p>The mechanical network of the brain is a major contributor to neural health and has been recognized by in vivo magnetic resonance elastography (MRE) to be highly responsive to diseases. However, until now only brain softening was observed and no mechanism was known that reverses the common decrement of neural elasticity during aging or disease. We used MRE in the 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine hydrochloride (MPTP) mouse model for dopaminergic neurodegeneration as observed in Parkinson’s disease (PD) to study the mechanical response of the brain on adult hippocampal neurogenesis as a robust correlate of neuronal plasticity in healthy and injured brain. We observed a steep transient rise in elasticity within the hippocampal region of up to over 50% six days after MPTP treatment correlating with increased neuronal density in the dentate gyrus, which could not be detected in healthy controls. Our results provide the first indication that new neurons reactively generated following neurodegeneration substantially contribute to the mechanical scaffold of the brain. Diagnostic neuroimaging may thus target on regions of the brain displaying symptomatically elevated elasticity values for the detection of neuronal plasticity following neurodegeneration.</p></div

The influence of noise on MDEV inversion given by eq.(8).

<p>The right-hand side and the left-hand side of eq.(7) are calculated from experimental data at individual drive frequencies from 30 Hz to 60 Hz (note, for demonstration purposes, experiments were performed in a volunteer with 7 excitation frequencies ranging from 30 to 60 Hz in increments of 5 Hz). The triangles correspond to unfiltered curl components, and the circles are obtained by applying the filter described in the Methods section (see also fig. 4c). Data from different frequencies are color-coded with different filling patterns (open symbols: 45–60 Hz; solid gray: 40 Hz; horizontal line pattern: 35 Hz; vertical line pattern: 30 Hz). The slopes of the fit lines correspond to modulus . According to eq.(8), the fit lines are forced to run through the origin, resulting in severe underestimation of . A better implementation of least-squares inversion would account for an offset as done by the fit yielding (here only the consistent high-frequency data [blue symbols] are considered). The noise-related offset is suppressed by an appropriate noise filter as used in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0071807#pone-0071807-g004" target="_blank">figure 4c</a> yielding .</p

Sequence timing diagram for acquisition of wave fields (three Cartesian motion components) in 30 slices at eight time steps () during a vibration period 1/<i>f</i>.

<p>Sequence timing diagram for acquisition of wave fields (three Cartesian motion components) in 30 slices at eight time steps () during a vibration period 1/<i>f</i>.</p

Changes in MDEV maps resulting from increasing noise suppression in a single transverse slice.

<p> and <i>φ</i> are reconstructed from: (a) unsmoothed and unfiltered wave data; (b) smoothed wave data prior to curl calculations; (c) smoothed wave data prior to curl calculations and subsequent noise filter (wave number limit of 100 m⁻¹. The processing steps used for (c) were applied to the rest of the paper.</p