DTI investigations of normal older adults demonstrated the great potential of diffusion studies to detect structural changes occurring in the brain that are associated with changes in cognitive ability. In this study we investigated the so-called anomalous diffusion (AD) stretched exponential γ-imaging model^1,2 to overcome the sensitivity limitations of conventional DTI approach based on the assumption of the Gaussian model with regard of displacements of water molecules in tissues. The benefits of this approach are illustrated with an in-vivo diffusion study of the human brain performed on 18 healthy volunteers in the age range (23-70 years). Mean γ (Mγ) and anisotropic γ (Aγ) maps are obtained and compared with DTI maps. The results are discussed in the context of Mγ and Aγ potential applications for brain aging monitoring.The volunteers (11 men and 7 women, with a mean age +/- SD = 37.8 +/- 15.1 years), after providing informed written consent, underwent MRI examination performed on a 3.0 T Siemens Magnetom Allegra (Siemens Medical Solutions, Erlangen, Germany), equipped with a circularly polarized transmit-receive coil, a maximum gradient strength of 400 mT/m and a maximum slew rate of 400 T/m/s. The same MRI protocol was applied to all the subjects, including Diffusion-Weighted Spin Echo-Echo Planar Imaging (DW SE-EPI) with TR/TE = 6400ms/107ms; Δ/δ = 107ms/35ms; bandwidth = 1860 Hz/px; matrix size = 128 x 128, number of axial slices = 32; in-plane resolution 1.8 x 1.8 mm²; slice thickness 3 mm. The diffusion-encoding gradients were applied along 15 non-collinear directions spanning the entire sphere, and 11 different b-values were used (from 200 to 5000 s/mm²) by varying the gradient strength, plus the b0 image, with an anterior-posterior phase encode direction for all the scans. The acquisition time of the DW-experiment was 42 minutes per subject. The images pre-processing was performed with FSL 5.0 (FMRIB Software Library v5.0, FMRIB, Oxford, UK³). Distortions due to eddy currents were corrected by means of EDDY tool. Mean Diffusivity (MD) and Fractional Anisotropy (FA), together with the 3 diffusion tensor eigenvalues (λ₁, λ₂, λ₃) and eigenvectors (V₁, V₂, V₃) maps were obtained by means of FSL DTIFIT routine, considering b-values comprised in between b0 and b = 1500 s/mm2. Mγ, Aγ, $$$\gamma\bot$$$ and $$$\gamma\parallel$$$ were obtained as described in previous work², by using a custom-made MATLAB script (MATLAB R2012b), which employs a non-linear least square estimation procedure using trust-region reflective algorithm for minimization, and exploits the advantage of parallel computing. The data were spatially smoothed by means of a Gaussian filter with full-width-half-maximum of 3.2 mm. The non linear fit estimates the parameters minimizing the difference between the signal intensity and the theoretical signal, modeled as follows: $$S(b) \propto\prod_1^3\exp\left[-A_ib^\gamma(V_{ix}G_x+V_{iy}G_y+V_{iz}G_z)^{\gamma_{i}}\right]$$ where b, Gx, Gy, and Gz are arrays of dimension 165 x 1 (where 165 derives from the product between the b-values and the diffusion-encoding directions), A_i are the generalized diffusion constants, γ_i the three values of the anomalous exponent projected along the 3 main axes of the DTI reference frame². The 3 γ_i values were used to compute $$$M_{\gamma} = \sum_{i=1}^3\gamma_{i}$$$, $$$A_{\gamma} = \sqrt{\frac{3\sum_{i=1}^3(\gamma_{i}-M_{\gamma})^{2}}{2(\gamma_{1}^{2}+\gamma_{2}^{2}+\gamma_{3}^{2})}}$$$, $$$\gamma_{\parallel} =\gamma_{3}$$$, and $$$\gamma_{\bot} =(\gamma_{1}+\gamma_{2})/2$$$. AD and conventional DTI metrics were computed in selected regions of interest (ROIs)^4,5. Specific Atlases as reference spaces were used with TBSS tool^6,7. In a second step we re-projected the Atlases onto each single subject-space, and eroded them in order to avoid voxels at the boundaries. The correlation between AD and DTI metrics and subjects' age was quantified with Pearson’s correlation coefficient in each ROI. P-values < 0.05 were considered statistically significant.Color-maps showing the R-values of Pearson analysis are displayed in Fig.1-2, respectively. Only zones in which statistical correlation was significant are reported. Hot and cold colors represent positive and negative correlation, respectively. A statistically significant decrease of Mγ and increase of Aγ as a function of age has been observed (see Fig. 3), suggesting that water diffusion becomes more anomalous with aging. This behavior may depend on a global increase of barriers and compartments among white matter fibers and bundles, due to degradation of axons and related macromolecular structures with aging. The current study suggests that Mγ and Aγ are more sensitive to micro-structural changes caused by normal aging, compared to DTI metrics.