Ivan I. Maximov

“Early to bed, early to rise”: Diffusion tensor imaging identifies chronotype-specificity

Sleep and wakefulness are crucial prerequisites for cognitive efficiency, the disturbances of which severely impact performance and mood as present e.g. after time zone traveling, in shift workers or patients with sleep or affective disorders. Based on their individual disposition to sleep and wakefulness, humans can be categorized as early (EC), late (LC) or intermediate (IC) chronotypes. While ECs tend to wake up early in the morning and find it difficult to remain awake beyond their usual bedtime, LCs go to bed late and have difficulties getting up. Beyond sleep/wake timings, chronotypes show distinct patterns of cognitive performance, gene expression, endocrinology and lifestyle. However, little is known about brain structural characteristics potentially underlying differences. Specifically, white matter (WM) integrity is crucial for intact brain function and has been related to various lifestyle habits, suggesting differences between chronotypes. Hence, the present study draws on Diffusion Tensor Imaging as a powerful tool to non-invasively probe WM architecture in 16 ECs, 23 LCs and 20 ICs. Track-based spatial statistics highlight that LCs were characterized by WM differences in the frontal and temporal lobes, cingulate gyrus and corpus callosum. Results are discussed in terms of findings reporting late chronotypes to exhibit a chronic form of jet lag accompanied with sleep disturbances, vulnerability to depression and higher consumption of nicotine and alcohol. This study has far-reaching implications for health and the economy. Ideally, work schedules should fit in with chronotype-specificity whenever possible.

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.

Robust tensor estimation in diffusion tensor imaging

The signal response measured in diffusion tensor imaging is subject to detrimental influences caused by noise. Noise fields arise due to various contributions such as thermal and physiological noise and sources related to the hardware imperfection. As a result, diffusion tensors estimated by different linear and non-linear least squares methods in absence of a proper noise correction tend to be substantially corrupted. In this work, we propose an advanced tensor estimation approach based on the least median squares method of the robust statistics. Both constrained and non-constrained versions of the method are considered. The performance of the developed algorithm is compared to that of the conventional least squares method and of the alternative robust methods proposed in the literature. Two examples of simulated diffusion attenuations and experimental in vivo diffusion data sets were used as a basis for comparison. The robust algorithms were shown to be advantageous compared to the least squares method in the cases where elimination of the outliers is desirable. Additionally, the constraints were applied in order to prevent generation of the non-positive definite tensors and reduce related artefacts in the maps of fractional anisotropy. The developed method can potentially be exploited also by other MR techniques where a robust regression or outlier localisation is required.

Dynamic nuclear polarization and optimal control spatial-selective 13C MRI and MRS

Aimed at (13)C metabolic magnetic resonance imaging (MRI) and spectroscopy (MRS) applications, we demonstrate that dynamic nuclear polarization (DNP) may be combined with optimal control 2D spatial selection to simultaneously obtain high sensitivity and well-defined spatial restriction. This is achieved through the development of spatial-selective single-shot spiral-readout MRI and MRS experiments combined with dynamic nuclear polarization hyperpolarized [1-(13)C]pyruvate on a 4.7 T pre-clinical MR scanner. The method stands out from related techniques by facilitating anatomic shaped region-of-interest (ROI) single metabolite signals available for higher image resolution or single-peak spectra. The 2D spatial-selective rf pulses were designed using a novel Krotov-based optimal control approach capable of iteratively fast providing successful pulse sequences in the absence of qualified initial guesses. The technique may be important for early detection of abnormal metabolism, monitoring disease progression, and drug research.

Diffusion kurtosis metrics as biomarkers of microstructural development: A comparative study of a group of children and a group of adults

ABSTRACT The most common modality of diffusion MRI used in the ageing and development studies is diffusion tensor imaging (DTI) providing two key measures, fractional anisotropy and mean diffusivity. Here, we investigated diffusional changes occurring between childhood (average age 10.3 years) and mitddle adult age (average age 54.3 years) with the help of diffusion kurtosis imaging (DKI), a recent novel extension of DTI that provides additional metrics quantifying non‐Gaussianity of water diffusion in brain tissue. We performed voxelwise statistical between‐group comparison of diffusion tensor and kurtosis tensor metrics using two methods, namely, the tract‐based spatial statistics (TBSS) and the atlas‐based regional data analysis. For the latter, fractional anisotropy, mean diffusivity, mean diffusion kurtosis, and other scalar diffusion tensor and kurtosis tensor parameters were evaluated for white matter fibres provided by the Johns‐Hopkins‐University Atlas in the FSL toolkit (http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Atlases). Within the same age group, all evaluated parameters varied depending on the anatomical region. TBSS analysis showed that changes in kurtosis tensor parameters beyond adolescence are more widespread along the skeleton in comparison to the changes of the diffusion tensor metrics. The regional data analysis demonstrated considerably larger between‐group changes of the diffusion kurtosis metrics than of diffusion tensor metrics in all investigated regions. The effect size of the parametric changes between childhood and middle adulthood was quantified using Cohens d. We used Cohens d related to mean diffusion kurtosis to examine heterogeneous maturation of various fibres. The largest changes of this parameter (interpreted as reflecting the lowest level of maturation by the age of children group) were observed in the association fibres, cingulum (gyrus) and cingulum (hippocampus) followed by superior longitudinal fasciculus and inferior longitudinal fasciculus. The smallest changes were observed in the commissural fibres, forceps major and forceps minor. In conclusion, our data suggest that DKI is sensitive to developmental changes in local microstructure and environment, and is particularly powerful to unravel developmental differences in major association fibres, such as the cingulum and superior longitudinal fasciculus.

Statistical Instability of TBSS Analysis Based on DTI Fitting Algorithm

Voxel‐based DTI analysis is an important approach in the comparison of subject groups by detecting and localizing gray and white matter changes in the brain. One of the principal problems for intersubject comparison is the absence of a “gold standard” processing pipeline. As a result, contradictory results may be obtained from identical data using different data processing pipelines, for example, in the data normalization or smoothing procedures. Tract‐based spatial statistics (TBSS) shows potential to overcome this problem by automatic detection of white matter changes and decreasing variation in the performed analysis. However, skeleton projection approaches, such as TBSS, critically depend on the accuracy of the diffusion scalar metric estimations. In this work, we demonstrate that the agreement and reliability of TBSS results depend on the applied DTI data processing algorithm. Statistical tests have been performed using two in vivo measured datasets and compared with different implementations of the least squares algorithm. As a result, we recommend repeating TBSS analysis using different fitting algorithms, in particular, using on iteratively‐assessed robust estimators, as accurate and more reliable approach in voxel‐based analysis, particularly, for TBSS. Repeating TBSS analysis allows one to detect and localize suspicious regions in white matter which were estimated as the regions with significant difference. Finally, we did not find a favorite fitting algorithm (or class of them) which can be marked as more reliable for group comparison.

Real-time 2D spatially selective MRI experiments: Comparative analysis of optimal control design methods.

There is an increasing need for development of advanced radio-frequency (RF) pulse techniques in modern magnetic resonance imaging (MRI) systems driven by recent advancements in ultra-high magnetic field systems, new parallel transmit/receive coil designs, and accessible powerful computational facilities. 2D spatially selective RF pulses are an example of advanced pulses that have many applications of clinical relevance, e.g., reduced field of view imaging, and MR spectroscopy. The 2D spatially selective RF pulses are mostly generated and optimised with numerical methods that can handle vast controls and multiple constraints. With this study we aim at demonstrating that numerical, optimal control (OC) algorithms are efficient for the design of 2D spatially selective MRI experiments, when robustness towards e.g. field inhomogeneity is in focus. We have chosen three popular OC algorithms; two which are gradient-based, concurrent methods using first- and second-order derivatives, respectively; and a third that belongs to the sequential, monotonically convergent family. We used two experimental models: a water phantom, and an in vivo human head. Taking into consideration the challenging experimental setup, our analysis suggests the use of the sequential, monotonic approach and the second-order gradient-based approach as computational speed, experimental robustness, and image quality is key. All algorithms used in this work were implemented in the MATLAB environment and are freely available to the MRI community.

Using Structural and Functional Brain Imaging to Investigate Responses to Acute Thermal Pain

UNLABELLED Despite a fundamental interest in the relationship between structure and function, the relationships between measures of white matter microstructural coherence and functional brain responses to pain are poorly understood. We investigated whether fractional anisotropy (FA) in 2 white matter regions in pathways associated with pain is related to the functional magnetic resonance imaging (fMRI) blood oxygen level-dependent (BOLD) response to thermal stimulation. BOLD fMRI was measured from 16 healthy male subjects during painful thermal stimulation of the right arm. Diffusion-weighted images were acquired for each subject and FA estimates were extracted from the posterior internal capsule and the cingulum (cingulate gyrus). These values were then included as covariates in the fMRI data analysis. We found BOLD response in the midcingulate cortex (MCC) to be positively related to FA in the posterior internal capsule and negatively related to FA in the cingulum. Our results suggest that the MCCs involvement in processing pain can be further delineated by considering how the magnitude of the BOLD response is related to white matter microstructural coherence and to subjective perception of pain. Considering relationships to white matter microstructural coherence in tracts involved in transmitting information to different parts of the pain network can help interpretation of MCC BOLD activation. PERSPECTIVE Relationships between functional brain responses, white matter microstructural coherence, and subjective ratings are crucial for understanding the role of the MCC in pain. These findings provide a basis for investigating the effect of the reduced white matter microstructural coherence observed in some pain disorders on the functional responses to pain.