Prospective associations, longitudinal patterns of childhood socioeconomic status, and white matter organization in adulthood.

The association between childhood socioeconomic status (SES) and brain development is an emerging area of research. The primary focus to date has been on SES and variations in gray matter structure with much less known about the relation between childhood SES and white matter structure. Using a longitudinal study of SES, with measures of income-to-needs ratio (INR) at age 9, 13, 17, and 24, we examined the prospective relationship between childhood SES (age 9 INR) and white matter organization in adulthood using diffusion tensor imaging. We also examined how changes in INR from childhood through young adulthood are associated with white matter organization in adult using a latent growth mixture model. Using tract-based spatial statistics (TBSS) we found that there is a significant prospective positive association between childhood INR and white matter organization in the bilateral uncinate fasciculus, bilateral cingulum bundle, bilateral superior longitudinal fasciculus, and corpus callosum (p < .05, FWE corrected). The probability that an individual was in the high-increasing INR profile across development compared with the low-increasing INR profile was positively associated with white matter organization in the bilateral uncinate fasciculus, left cingulum, and bilateral superior longitudinal fasciculus. The results of the current study have potential implications for interventions given that early childhood poverty may have long-lasting associations with white matter structure. Furthermore, trajectories of socioeconomic status during childhood are important-with individuals that belong to the latent profile that had high increases in INR having greater regional white matter organization in adulthood.

Relating anthropometric indicators to brain structure in 2-month-old Bangladeshi infants growing up in poverty: A pilot study.

Anthropometric indicators, including stunting, underweight, and wasting, have previously been associated with poor neurocognitive outcomes. This link may exist because malnutrition and infection, which are known to affect height and weight, also impact brain structure according to animal models. However, a relationship between anthropometric indicators and brain structural measures has not been tested yet, perhaps because stunting, underweight, and wasting are uncommon in higher-resource settings. Further, with diminished anthropometric growth prevalent in low-resource settings, where biological and psychosocial hazards are most severe, one might expect additional links between measures of poverty, anthropometry, and brain structure. To begin to examine these relationships, we conducted an MRI study in 2-3-month-old infants growing up in the extremely impoverished urban setting of Dhaka, Bangladesh. The sample size was relatively small because the challenges of investigating infant brain structure in a low-resource setting needed to be realized and resolved before introducing a larger cohort. Initially, fifty-four infants underwent T1 sequences using 3T MRI, and resulting structural images were segmented into gray and white matter maps, which were carefully evaluated for accurate tissue labeling by a pediatric neuroradiologist. Gray and white matter volumes from 29 infants (79 â€‹± â€‹10 days-of-age; F/M â€‹= â€‹12/17), whose segmentations were of relatively high quality, were submitted to semi-partial correlation analyses with stunting, underweight, and wasting, which were measured using height-for-age (HAZ), weight-for-age (WAZ), and weight-for-height (WHZ) scores. Positive semi-partial correlations (after adjusting for chronological age and sex and correcting for multiple comparisons) were observed between white matter volume and HAZ and WAZ; however, WHZ was not correlated with any measure of brain volume. No associations were observed between income-to-needs or maternal education and brain volumetric measures, suggesting that measures of poverty were not associated with total brain tissue volume in this sample. Overall, these results provide the first link between diminished anthropometric growth and white matter volume in infancy. Challenges of conducting a developmental neuroimaging study in a low-resource country are also described.

The interplay of socio-economic status represented by paternal educational level, white matter structure and reading.

A child's school achievement is influenced by environmental factors. The environmental factors, when represented by socio-economic status (SES) of the family, have been demonstrated to be related to the reading skills of a child. The neural correlates of the relation between SES and reading have been less thoroughly investigated. The present study expands current research by exploring the relation between SES, quantified by paternal educational level, reading of the offspring and the structure of white matter pathways in the left hemisphere as derived from DTI-based tractography analyses. Therefore, three dorsal white matter pathways, i.e. the long, anterior and posterior segments of the arcuate fasciculus (AF), and three ventral white matter pathways, i.e. the inferior fronto-occipital fasciculus (IFOF), the inferior longitudinal fasciculus (ILF) and the uncinate fasciculus (UF), were manually dissected in the left hemisphere of 34 adolescents with a wide range of reading skills. The results demonstrated a relation between word reading, SES quantified by paternal educational level, and fractional anisotropy (FA) within the left dorsal AF segment and the left ventral UF. Thus, the present study proposes a relationship between paternal educational level and a specific white matter pathway that is important for reading, aiming to guide future research that can determine processes underlying this relationship.

Towards microstructure fingerprinting: Estimation of tissue properties from a dictionary of Monte Carlo diffusion MRI simulations.

Many closed-form analytical models have been proposed to relate the diffusion-weighted magnetic resonance imaging (DW-MRI) signal to microstructural features of white matter tissues. These models generally make assumptions about the tissue and the diffusion processes which often depart from the biophysical reality, limiting their reliability and interpretability in practice. Monte Carlo simulations of the random walk of water molecules are widely recognized to provide near groundtruth for DW-MRI signals. However, they have mostly been limited to the validation of simpler models rather than used for the estimation of microstructural properties. This work proposes a general framework which leverages Monte Carlo simulations for the estimation of physically interpretable microstructural parameters, both in single and in crossing fascicles of axons. Monte Carlo simulations of DW-MRI signals, or fingerprints, are pre-computed for a large collection of microstructural configurations. At every voxel, the microstructural parameters are estimated by optimizing a sparse combination of these fingerprints. Extensive synthetic experiments showed that our approach achieves accurate and robust estimates in the presence of noise and uncertainties over fixed or input parameters. In an in vivo rat model of spinal cord injury, our approach provided microstructural parameters that showed better correspondence with histology than five closed-form models of the diffusion signal: MMWMD, NODDI, DIAMOND, WMTI and MAPL. On whole-brain in vivo data from the human connectome project (HCP), our method exhibited spatial distributions of apparent axonal radius and axonal density indices in keeping with ex vivo studies. This work paves the way for microstructure fingerprinting with Monte Carlo simulations used directly at the modeling stage and not only as a validation tool.

White-Matter Pathways for Statistical Learning of Temporal Structures.

Extracting the statistics of event streams in natural environments is critical for interpreting current events and predicting future ones. The brain is known to rapidly find structure and meaning in unfamiliar streams of sensory experience, often by mere exposure to the environment (i.e., without explicit feedback). Yet, we know little about the brain pathways that support this type of statistical learning. Here, we test whether changes in white-matter (WM) connectivity due to training relate to our ability to extract temporal regularities. By combining behavioral training and diffusion tensor imaging (DTI), we demonstrate that humans adapt to the environment's statistics as they change over time from simple repetition to probabilistic combinations. In particular, we show that learning relates to the decision strategy that individuals adopt when extracting temporal statistics. We next test for learning-dependent changes in WM connectivity and ask whether they relate to individual variability in decision strategy. Our DTI results provide evidence for dissociable WM pathways that relate to individual strategy: extracting the exact sequence statistics (i.e., matching) relates to connectivity changes between caudate and hippocampus, while selecting the most probable outcomes in a given context (i.e., maximizing) relates to connectivity changes between prefrontal, cingulate and basal ganglia (caudate, putamen) regions. Thus, our findings provide evidence for distinct cortico-striatal circuits that show learning-dependent changes of WM connectivity and support individual ability to learn behaviorally-relevant statistics.

SIENA-XL for improving the assessment of gray and white matter volume changes on brain MRI.

In this article, SIENA-XL, a new segmentation-based longitudinal pipeline is introduced, for: (i) increasing the precision of longitudinal volume change estimation for white (WM) and gray (GM) matter separately, compared with cross-sectional segmentation methods such as SIENAX; and (ii) avoiding potential biases in registration-based methods when Jacobians are used, with a smoothing extent larger than spatial scale between tissue-interfaces, which is where atrophy usually occurs. SIENA-XL implements a new brain extraction procedure and a multi-time-point intensity equalization step before performing the final segmentation that also includes separate segmentation of deep GM structures by using FMRIB's Integrated Registration and Segmentation Tool. The detection of GM and WM volume changes with SIENA-XL was evaluated using different healthy control (HC) and multiple sclerosis (MS) MRI datasets and compared with the traditional SIENAX and two Jacobian-based approaches, SPM12 and SIENAX-JI (a version of SIENAX including Jacobian integration - JI). In scan-rescan data from HCs, SIENA-XL showed: (i) a significant decrease in error, of 50-70% when compared with SIENAX; (ii) no significant differences in error when compared with SIENAX-JI and SPM12 in a scan-rescan HC dataset that included repositioning. When tested in a HC dataset with scan-rescan both at baseline and after 1 year of follow-up, SIENA-XL showed: (i) significantly higher precision (P < 0.01) than SIENAX; (ii) no significant differences to SIENAX-JI and SPM12. Finally, in a dataset of 79 MS patients with a 2 years follow-up, SIENA-XL showed a substantial reduction of sample size, by comparison with SIENAX, SIENAX-JI, and SPM12, for detecting treatment effects of 25, 30, and 50%. Hum Brain Mapp 39:1063-1077, 2018. © 2017 Wiley Periodicals, Inc.

Partial volume model for brain MRI scan using MP2RAGE.

MP2RAGE is a T1 weighted MRI sequence that estimates a composite image providing much reduction of the receiver bias, has a high intensity dynamic range, and provides an estimate of T1 mapping. It is, therefore, an appealing option for brain morphometry studies. However, previous studies have reported a difference in cortical thickness computed from MP2RAGE compared with widely used Multi-Echo MPRAGE. In this article, we demonstrated that using standard segmentation and partial volume estimation techniques on MP2RAGE introduces systematic errors, and we proposed a new model to estimate partial volume of the cortical gray matter. We also included in their model a local estimate of tissue intensity to take into account the natural variation of tissue intensity across the brain. A theoretical framework is provided and validated using synthetic and physical phantoms. A repeatability experiment comparing MPRAGE and MP2RAGE confirmed that MP2RAGE using our model could be considered for structural imaging in brain morphology study, with similar cortical thickness estimate than that computed with MPRAGE. Hum Brain Mapp 38:5115-5127, 2017. © 2017 Wiley Periodicals, Inc.

Diffusion tensor imaging tensor shape analysis for assessment of regional white matter differences.

Purpose The purpose of this study was to investigate a novel tensor shape plot analysis technique of diffusion tensor imaging data as a means to assess microstructural differences in brain tissue. We hypothesized that this technique could distinguish white matter regions with different microstructural compositions. Methods Three normal canines were euthanized at seven weeks old. Their brains were imaged using identical diffusion tensor imaging protocols on a 7T small-animal magnetic resonance imaging system. We examined two white matter regions, the internal capsule and the centrum semiovale, each subdivided into an anterior and posterior region. We placed 100 regions of interest in each of the four brain regions. Eigenvalues for each region of interest triangulated onto tensor shape plots as the weighted average of three shape metrics at the plot's vertices: CS, CL, and CP. Results The distribution of data on the plots for the internal capsule differed markedly from the centrum semiovale data, thus confirming our hypothesis. Furthermore, data for the internal capsule were distributed in a relatively tight cluster, possibly reflecting the compact and parallel nature of its fibers, while data for the centrum semiovale were more widely distributed, consistent with the less compact and often crossing pattern of its fibers. This indicates that the tensor shape plot technique can depict data in similar regions as being alike. Conclusion Tensor shape plots successfully depicted differences in tissue microstructure and reflected the microstructure of individual brain regions. This proof of principle study suggests that if our findings are reproduced in larger samples, including abnormal white matter states, the technique may be useful in assessment of white matter diseases.

Structural properties of the human corpus callosum: Multimodal assessment and sex differences.

A number of structural properties of white matter can be assessed in vivo using multimodal magnetic resonance imaging (MRI). We measured profiles of R1 and R2 relaxation rates, myelin water fraction (MWF) and diffusion tensor measures (fractional anisotropy [FA], mean diffusivity [MD]) across the mid-sagittal section of the corpus callosum in two samples of young individuals. In Part 1, we compared histology-derived axon diameter (Aboitiz et al., 1992) to MRI measures obtained in 402 young men (19.55 ± 0.84 years) recruited from the Avon Longitudinal Study on Parents and Children. In Part 2, we examined sex differences in FA, MD and magnetization transfer ratio (MTR) across the corpus callosum in 433 young (26.50 ± 0.51 years) men and women recruited from the Northern Finland Birth Cohort 1986. We found that R1, R2, and MWF follow the anterior-to-posterior profile of small-axon density. Sex differences in mean MTR were similar across the corpus callosum (males > females) while these in FA differed by the callosal segment (Body: M>F; Splenium: F>M). We suggest that the values of R1, R2 and MWF are driven by high surface area of myelin in regions with high density of "small axons".

Prospective assessment of white matter integrity in adult stem cell transplant recipients.

Hematopoietic stem cell transplantation (HSCT) is often used in the treatment of hematologic disorders. Although it can be curative, the pre-transplant conditioning regimen can be associated with neurotoxicity. In this prospective study, we examined white matter (WM) integrity with diffusion tensor imaging (DTI) and neuropsychological functioning before and one year after HSCT in twenty-two patients with hematologic disorders and ten healthy controls evaluated at similar intervals. Eighteen patients received conditioning treatment with high-dose (HD) chemotherapy, and four had full dose total body irradiation (fTBI) and HD chemotherapy prior to undergoing an allogeneic or autologous HSCT. The results showed a significant decrease in mean diffusivity (MD) and axial diffusivity (AD) in diffuse WM regions one year after HSCT (p-corrected <0.05) in the patient group compared to healthy controls. At baseline, patients treated with allogeneic HSCT had higher MD and AD in the left hemisphere WM than autologous HSCT patients (p-corrected <0.05). One year post-transplant, patients treated with allogeneic HSCT had lower fractional anisotropy (FA) and higher radial diffusivity (RD) in the right hemisphere and left frontal WM compared to patients treated with autologous HSCT (p-corrected <0.05).There were modest but significant correlations between MD values and cognitive test scores, and these were greatest for timed tests and in projection tracts. Patients showed a trend toward a decline in working memory, and had lower cognitive test scores than healthy controls at the one-year assessment. The findings suggest a relatively diffuse pattern of alterations in WM integrity in adult survivors of HSCT.

Neuromarkers of the common angiotensinogen polymorphism in healthy older adults: A comprehensive assessment of white matter integrity and cognition.

The common angiotensinogen (AGT) M268T polymorphism (rs699; historically referred to as M235T) has been identified as a significant risk factor for cerebrovascular pathologies, yet it is unclear if healthy older adults carrying the threonine amino acid variant have a greater risk for white matter damage in specific fiber tracts. Further, the impact of the threonine variant on cognitive function remains unknown. The present study utilized multiple indices of diffusion tensor imaging (DTI) and neuropsychological assessment to examine the integrity of specific white matter tracts and cognition between individuals with homozygous genotypes of M268T (MetMet n=27, ThrThr n=27). Differences in subcortical hyperintensity (SH) volume were also examined between groups. Results indicated that the threonine variant was associated with significantly reduced integrity in the superior longitudinal fasciculus (SLF) and the cingulate gyrus segment of the cingulum bundle (cingulum CG) compared to those with the methionine variant, and poorer cognitive performance on tests of attention/processing speed and language. Despite these associations, integrity of these tracts did not significantly mediate relationships between cognition and genetic status, and SH did not differ significantly between groups. Collectively our results suggest that the threonine variant of M268T is a significant risk factor for abnormalities in specific white matter tracts and cognitive domains in healthy older adults, independent of SH burden.

Global tractography of multi-shell diffusion-weighted imaging data using a multi-tissue model.

Diffusion-weighted imaging and tractography provide a unique, non-invasive technique to study the macroscopic structure and connectivity of brain white matter in vivo. Global tractography methods aim at reconstructing the full-brain fiber configuration that best explains the measured data, based on a generative signal model. In this work, we incorporate a multi-shell multi-tissue model based on spherical convolution, into a global tractography framework, which allows to deal with partial volume effects. The required tissue response functions can be estimated from and hence calibrated to the data. The resulting track reconstruction is quantitatively related to the apparent fiber density in the data. In addition, the fiber orientation distribution for white matter and the volume fractions of gray matter and cerebrospinal fluid are produced as ancillary results. Validation results on simulated data demonstrate that this data-driven approach improves over state-of-the-art streamline and global tracking methods, particularly in the valid connection rate. Results in human brain data correspond to known white matter anatomy and show improved modeling of partial voluming. This work is an important step toward detecting and quantifying white matter changes and connectivity in healthy subjects and patients.

SIFT2: Enabling dense quantitative assessment of brain white matter connectivity using streamlines tractography.

Diffusion MRI streamlines tractography allows for the investigation of the brain white matter pathways non-invasively. However a fundamental limitation of this technology is its non-quantitative nature, i.e. the density of reconstructed connections is not reflective of the density of underlying white matter fibres. As a solution to this problem, we have previously published the "spherical-deconvolution informed filtering of tractograms (SIFT)" method, which determines a subset of the streamlines reconstruction such that the streamlines densities throughout the white matter are as close as possible to fibre densities estimated using the spherical deconvolution diffusion model; this permits the use of streamline count as a valid biological marker of connection density. Particular aspects of its performance may have however limited its uptake in the diffusion MRI research community. Here we present an alternative to this method, entitled SIFT2, which provides a more logically direct and computationally efficient solution to the streamlines connectivity quantification problem: by determining an appropriate cross-sectional area multiplier for each streamline rather than removing streamlines altogether, biologically accurate measures of fibre connectivity are obtained whilst making use of the complete streamlines reconstruction.

Quantitative assessment of diffusional kurtosis anisotropy.

Diffusional kurtosis imaging (DKI) measures the diffusion and kurtosis tensors to quantify restricted, non-Gaussian diffusion that occurs in biological tissue. By estimating the kurtosis tensor, DKI accounts for higher order diffusion dynamics, when compared with diffusion tensor imaging (DTI), and consequently can describe more complex diffusion profiles. Here, we compare several measures of diffusional anisotropy which incorporate information from the kurtosis tensor, including kurtosis fractional anisotropy (KFA) and generalized fractional anisotropy (GFA), with the diffusion tensor-derived fractional anisotropy (FA). KFA and GFA demonstrate a net enhancement relative to FA when multiple white matter fiber bundle orientations are present in both simulated and human data. In addition, KFA shows net enhancement in deep brain structures, such as the thalamus and the lenticular nucleus, where FA indicates low anisotropy. Thus, KFA and GFA provide additional information relative to FA with regard to diffusional anisotropy, and may be particularly advantageous for the assessment of diffusion in complex tissue environments.

Convergence and divergence across construction methods for human brain white matter networks: an assessment based on individual differences.

Using diffusion MRI, a number of studies have investigated the properties of whole-brain white matter (WM) networks with differing network construction methods (node/edge definition). However, how the construction methods affect individual differences of WM networks and, particularly, if distinct methods can provide convergent or divergent patterns of individual differences remain largely unknown. Here, we applied 10 frequently used methods to construct whole-brain WM networks in a healthy young adult population (57 subjects), which involves two node definitions (low-resolution and high-resolution) and five edge definitions (binary, FA weighted, fiber-density weighted, length-corrected fiber-density weighted, and connectivity-probability weighted). For these WM networks, individual differences were systematically analyzed in three network aspects: (1) a spatial pattern of WM connections, (2) a spatial pattern of nodal efficiency, and (3) network global and local efficiencies. Intriguingly, we found that some of the network construction methods converged in terms of individual difference patterns, but diverged with other methods. Furthermore, the convergence/divergence between methods differed among network properties that were adopted to assess individual differences. Particularly, high-resolution WM networks with differing edge definitions showed convergent individual differences in the spatial pattern of both WM connections and nodal efficiency. For the network global and local efficiencies, low-resolution and high-resolution WM networks for most edge definitions consistently exhibited a highly convergent pattern in individual differences. Finally, the test-retest analysis revealed a decent temporal reproducibility for the patterns of between-method convergence/divergence. Together, the results of the present study demonstrated a measure-dependent effect of network construction methods on the individual difference of WM network properties.

New insight into the contrast in diffusional kurtosis images: does it depend on magnetic susceptibility?

PURPOSE: In this MRI study, diffusional kurtosis imaging (DKI) and T2 * multiecho relaxometry were measured from the white matter (WM) of human brains and correlated with each other, with the aim of investigating the influence of magnetic-susceptibility (Δχ (H2O-TISSUE) ) on the contrast. METHODS: We focused our in vivo analysis on assessing the dependence of mean, axial, and radial kurtosis (MK, K‖ , K⊥ ), as well as DTI indices on Δχ (H2O-TISSUE) (quantified by T2 *) between extracellular water and WM tissue molecules. Moreover, Monte Carlo (MC) simulations were used to elucidate experimental data. RESULTS: A significant positive correlation was observed between K⊥ , MK and R2 * = 1/T2 *, suggesting that Δχ (H2O-TISSUE) could be a source of DKI contrast. In this view, K⊥ and MK-map contrasts in human WM would not just be due to different restricted diffusion processes of compartmentalized water but also to local Δχ (H2O-TISSUE) . However, MC simulations show a strong dependence on microstructure rearrangement and a feeble dependence on Δχ (H2O-TISSUE) of DKI signal. CONCLUSION: Our results suggests a concomitant and complementary existence of multi-compartmentalized diffusion process and Δχ (H2O-TISSUE) in DKI contrast that might explain why kurtosis contrast is more sensitive than DTI in discriminating between different tissues. However, more realistic numerical simulations are needed to confirm this statement.

A model for extra-axonal diffusion spectra with frequency-dependent restriction.

PURPOSE: In the brain, there is growing interest in using the temporal diffusion spectrum to characterize axonal geometry in white matter because of the potential to be more sensitive to small pores compared to conventional time-dependent diffusion. However, analytical expressions for the diffusion spectrum of particles have only been derived for simple, restricting geometries such as cylinders, which are often used as a model for intra-axonal diffusion. The extra-axonal space is more complex, but the diffusion spectrum has largely not been modeled. We propose a model for the extra-axonal space, which can be used for interpretation of experimental data. THEORY AND METHODS: An empirical model describing the extra-axonal space diffusion spectrum was compared with simulated spectra. Spectra were simulated using Monte Carlo methods for idealized, regularly and randomly packed axons over a wide range of packing densities and spatial scales. The model parameters are related to the microstructural properties of tortuosity, axonal radius, and separation for regularly packed axons and pore size for randomly packed axons. RESULTS: Forward model predictions closely matched simulations. The model fitted the simulated spectra well and accurately estimated microstructural properties. CONCLUSIONS: This simple model provides expressions that relate the diffusion spectrum to biologically relevant microstructural properties.

Longitudinal assessment of neuroanatomical and cognitive differences in young children with type 1 diabetes: association with hyperglycemia.

Significant regional differences in gray and white matter volume and subtle cognitive differences between young diabetic and nondiabetic children have been observed. Here, we assessed whether these differences change over time and the relation with dysglycemia. Children ages 4 to <10 years with (n = 144) and without (n = 72) type 1 diabetes (T1D) had high-resolution structural MRI and comprehensive neurocognitive tests at baseline and 18 months and continuous glucose monitoring and HbA1c performed quarterly for 18 months. There were no differences in cognitive and executive function scores between groups at 18 months. However, children with diabetes had slower total gray and white matter growth than control subjects. Gray matter regions (left precuneus, right temporal, frontal, and parietal lobes and right medial-frontal cortex) showed lesser growth in diabetes, as did white matter areas (splenium of the corpus callosum, bilateral superior-parietal lobe, bilateral anterior forceps, and inferior-frontal fasciculus). These changes were associated with higher cumulative hyperglycemia and glucose variability but not with hypoglycemia. Young children with T1D have significant differences in total and regional gray and white matter growth in brain regions involved in complex sensorimotor processing and cognition compared with age-matched control subjects over 18 months, suggesting that chronic hyperglycemia may be detrimental to the developing brain.

Influence of noise correction on intra- and inter-subject variability of quantitative metrics in diffusion kurtosis imaging.

Diffusion kurtosis imaging (DKI) is a promising extension of diffusion tensor imaging, giving new insights into the white matter microstructure and providing new biomarkers. Given the rapidly increasing number of studies, DKI has a potential to establish itself as a valuable tool in brain diagnostics. However, to become a routine procedure, DKI still needs to be improved in terms of robustness, reliability, and reproducibility. As it requires acquisitions at higher diffusion weightings, results are more affected by noise than in diffusion tensor imaging. The lack of standard procedures for post-processing, especially for noise correction, might become a significant obstacle for the use of DKI in clinical routine limiting its application. We considered two noise correction schemes accounting for the noise properties of multichannel phased-array coils, in order to improve the data quality at signal-to-noise ratio (SNR) typical for DKI. The SNR dependence of estimated DKI metrics such as mean kurtosis (MK), mean diffusivity (MD) and fractional anisotropy (FA) is investigated for these noise correction approaches in Monte Carlo simulations and in in vivo human studies. The intra-subject reproducibility is investigated in a single subject study by varying the SNR level and SNR spatial distribution. Then the impact of the noise correction on inter-subject variability is evaluated in a homogeneous sample of 25 healthy volunteers. Results show a strong impact of noise correction on the MK estimate, while the estimation of FA and MD was affected to a lesser extent. Both intra- and inter-subject SNR-related variability of the MK estimate is considerably reduced after correction for the noise bias, providing more accurate and reproducible measures. In this work, we have proposed a straightforward method that improves accuracy of DKI metrics. This should contribute to standardization of DKI applications in clinical studies making valuable inferences in group analysis and longitudinal studies.

Assessment of electric field distribution in anisotropic cortical and subcortical regions under the influence of tDCS.

The focus of this study is to estimate the contribution of regional anisotropic conductivity on the spatial distribution of an induced electric field across gray matter (GM), white matter (WM), and subcortical regions under transcranial direct current stimulation (tDCS). The assessment was conducted using a passive high-resolution finite element head model with inhomogeneous and variable anisotropic conductivities derived from the diffusion tensor data. Electric field distribution was evaluated across different cortical as well as subcortical regions under four bicephalic electrode configurations. Results indicate that regional tissue heterogeneity and anisotropy cause the pattern of induced fields to vary in orientation and strength when compared to the isotropic scenario. Different electrode montages resulted in distinct distribution patterns with noticeable variations in field strengths. The effect of anisotropy is highly montage dependent and directional conductivity has a more profound effect in defining the strength of the induced field. The inclusion of anisotropy in the GM and subcortical regions has a significant effect on the strength and spatial distribution of the induced electric field. Under the (C3-Fp2) montage, the inclusion of GM and subcortical anisotropy increased the average percentage difference in the electric field strength of brain from 5% to 34% (WM anisotropy only). In terms of patterns distribution, the topographic errors increased from 9.9% to 40% (WM anisotropy only) across the brain.