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The central question of the relationship between structure and function in the human brain is still not well understood. In order to investigate this fundamental relationship we create functional probabilistic maps from a large set of mapping experiments and compare the location of functionally localised regions across subjects using different whole-brain alignment schemes. To avoid the major problems associated with meta-analysis approaches, all subjects are scanned using the same paradigms, the same scanner and the same analysis pipeline. We show that an advanced, curvature driven cortex based alignment (CBA) scheme largely removes macro-anatomical variability across subjects. Remaining variability in the observed spatial location of functional regions, thus, reflects the “true” functional variability, i.e. the quantified variability is a good estimator of the underlying structural–functional correspondence. After localising 13 widely studied functional areas, we found a large variability in the degree to which functional areas respect macro-anatomical boundaries across the cortex. Some areas, such as the frontal eye fields (FEF) are strongly bound to a macro-anatomical location. Fusiform face area (FFA) on the other hand, varies in its location along the length of the fusiform gyrus even though the gyri themselves are well aligned across subjects. Language areas were found to vary greatly across subjects whilst a high degree of overlap was observed in sensory and motor areas. The observed differences in functional variability for different specialised areas suggest that a more complete estimation of the structure–function relationship across the whole cortex requires further empirical studies with an expanded test battery. ► Investigation of the structural–functional relationship in the human brain. ► Curvature driven cortex based alignment accurately aligns cortical macro-anatomy. ► Variability across subjects is calculated by functional probabilistic maps. ► Large variability in structural-functional relationship across functional areas.

Registration of diffusion-weighted images is an important step in comparing white matter fibre bundles across subjects, or in the same subject at different time points. Using diffusion-weighted imaging, Spherical Deconvolution enables multiple fibre populations within a voxel to be resolved by computing the fibre orientation distribution (FOD). In this paper, we present a novel method that employs FODs for the registration of diffusion-weighted images. Registration was performed by optimising a symmetric diffeomorphic non-linear transformation model, using image metrics based on the mean squared difference, and cross-correlation of the FOD spherical harmonic coefficients. The proposed method was validated by recovering known displacement fields using FODs represented with maximum harmonic degrees (lmax) of 2, 4 and 6. Results demonstrate a benefit in using FODs at lmax=4 compared to lmax=2. However, a decrease in registration accuracy was observed when lmax=6 was used; this was likely caused by noise in higher harmonic degrees. We compared our proposed method to fractional anisotropy driven registration using an identical code base and parameters. FOD registration was observed to perform significantly better than FA in all experiments. The cross-correlation metric performed significantly better than the mean squared difference. Finally, we demonstrated the utility of this method by computing an unbiased group average FOD template that was used for probabilistic fibre tractography. This work suggests that using crossing fibre information aids in the alignment of white matter and could therefore benefit several methods for investigating population differences in white matter, including voxel based analysis, tensor based morphometry, atlas based segmentation and labelling, and group average fibre tractography. ► A new method for registration of fibre orientation distributions (FOD) is presented. ► Quantitative validation is performed using two FOD image metrics on human brain data. ► The cross-correlation metric outperforms the mean squared difference metric. ► Higher-order information in FODs improves white matter alignment. ► The method is applied to a potential application: group average FOD tractography.

Doppler LiDARs have become flexible and versatile remote sensing devices for wind energy applications. The possibility to measure radial wind speed components contemporaneously at multiple distances is an advantage with respect to meteorological masts. However, these measurements must be filtered due to the measurement geometry, hard targets and atmospheric conditions. To ensure a maximum data availability while producing low measurement errors, we introduce a dynamic data filter approach that conditionally decouples the dependency of data availability with increasing range. The new filter approach is based on the assumption of self-similarity, that has not been used so far for LiDAR data filtering. We tested the accuracy of the dynamic data filter approach together with other commonly used filter approaches, from research and industry applications. This has been done with data from a long-range pulsed LiDAR installed at the offshore wind farm ‘alpha ventus’. There, an ultrasonic anemometer located approximately 2.8 km from the LiDAR was used as reference. The analysis of around 1.5 weeks of data shows, that the error of mean radial velocity can be minimised for wake and free stream conditions.

The core concept within the field of brain mapping is the use of a standardized, or “stereotaxic”, 3D coordinate frame for data analysis and reporting of findings from neuroimaging experiments. This simple construct allows brain researchers to combine data from many subjects such that group-averaged signals, be they structural or functional, can be detected above the background noise that would swamp subtle signals from any single subject. Where the signal is robust enough to be detected in individuals, it allows for the exploration of inter-individual variance in the location of that signal. From a larger perspective, it provides a powerful medium for comparison and/or combination of brain mapping findings from different imaging modalities and laboratories around the world. Finally, it provides a framework for the creation of large-scale neuroimaging databases or “atlases” that capture the population mean and variance in anatomical or physiological metrics as a function of age or disease. However, while the above benefits are not in question at first order, there are a number of conceptual and practical challenges that introduce second-order incompatibilities among experimental data. Stereotaxic mapping requires two basic components: (i) the specification of the 3D stereotaxic coordinate space, and (ii) a mapping function that transforms a 3D brain image from “native” space, i.e. the coordinate frame of the scanner at data acquisition, to that stereotaxic space. The first component is usually expressed by the choice of a representative 3D MR image that serves as target “template” or atlas. The native image is re-sampled from native to stereotaxic space under the mapping function that may have few or many degrees of freedom, depending upon the experimental design. The optimal choice of atlas template and mapping function depend upon considerations of age, gender, hemispheric asymmetry, anatomical correspondence, spatial normalization methodology and disease-specificity. Accounting, or not, for these various factors in defining stereotaxic space has created the specter of an ever-expanding set of atlases, customized for a particular experiment, that are mutually incompatible. These difficulties continue to plague the brain mapping field. This review article summarizes the evolution of stereotaxic space in term of the basic principles and associated conceptual challenges, the creation of population atlases and the future trends that can be expected in atlas evolution.

Background Disproportionately enlarged subarachnoid space hydrocephalus (DESH) is one of the neuroradiological characteristics of idiopathic normal pressure hydrocephalus (iNPH), which makes statistical analyses of brain images difficult. This study aimed to develop and validate methods of accurate brain segmentation and spatial normalisation in patients with DESH by using the Computational Anatomy Toolbox (CAT12). Methods Two hundred ninety-eight iNPH patients with DESH and 25 healthy controls (HCs) who underwent cranial MRI were enrolled in this study. We selected the structural images of 169 patients to create customised tissue probability maps and diffeomorphic anatomical registration through exponentiated Lie algebra (DARTEL) templates for patients with DESH (DESH-TPM and DESH-Template). The structural images of 38 other patients were used to evaluate the validity of the DESH-TPM and DESH-Template. DESH-TPM and DESH-Template were created using the 114 well-segmented images after the segmentation processing of CAT12. In the validation study, we compared the accuracy of brain segmentation and spatial normalisation among three conditions: customised condition, applying DESH-TPM and DESH-Template to CAT12 and patient images; standard condition, applying the default setting of CAT12 to patient images; and reference condition, applying the default setting of CAT12 to HC images. Results In the validation study, we identified three error types during segmentation. (1) The proportions of misidentifying the dura and/or extradural structures as brain structures in the customised, standard, and reference conditions were 10.5%, 44.7%, and 13.6%, respectively; (2) the failure rates of white matter hypointensity (WMH) cancellation in the customised, standard, and reference conditions were 18.4%, 44.7%, and 0%, respectively; and (3) the proportions of cerebrospinal fluid (CSF)-image deficits in the customised, standard, and reference conditions were 97.4%, 84.2%, and 28%, respectively. The spatial normalisation accuracy of grey and white matter images in the customised condition was the highest among the three conditions, especially in terms of superior convexity. Conclusions Applying the combination of the DESH-TPM and DESH-Template to CAT12 could improve the accuracy of grey and white matter segmentation and spatial normalisation in patients with DESH. However, this combination could not improve the CSF segmentation accuracy. Another approach is needed to overcome this challenge.

Diffeomorphic mappings are central to image registration due largely to their topological properties and success in providing biologically plausible solutions to deformation and morphological estimation problems. Popular diffeomorphic image registration algorithms include those characterized by time-varying and constant velocity fields, and symmetrical considerations. Prior information in the form of regularization is used to enforce transform plausibility taking the form of physics-based constraints or through some approximation thereof, e.g., Gaussian smoothing of the vector fields [a la Thirion's Demons (Thirion, 1998)]. In the context of the original Demons' framework, the so-called directly manipulated free-form deformation (DMFFD) (Tustison et al., 2009) can be viewed as a smoothing alternative in which explicit regularization is achieved through fast B-spline approximation. This characterization can be used to provide B-spline \"flavored\" diffeomorphic image registration solutions with several advantages. Implementation is open source and available through the Insight Toolkit and our Advanced Normalization Tools (ANTs) repository. A thorough comparative evaluation with the well-known SyN algorithm (Avants et al., 2008), implemented within the same framework, and its B-spline analog is performed using open labeled brain data and open source evaluation tools.

Diffusion MRI (dMRI) offers unique insights into the microstructure of fetal brain tissue in utero. Longitudinal and cross-sectional studies of fetal dMRI have the potential to reveal subtle but crucial changes associated with normal and abnormal neurodevelopment. However, these studies depend on precise spatial alignment of data across scans and subjects, which is particularly challenging in fetal imaging due to the low data quality, rapid brain development, and limited anatomical landmarks for accurate registration. Existing registration methods, primarily developed for superior-quality adult data, are not well-suited for addressing these complexities. To bridge this gap, we introduce FetDTIAlign, a deep learning approach tailored to fetal brain dMRI, enabling accurate affine and deformable registration. FetDTIAlign integrates a novel dual-encoder architecture and iterative feature-based inference, effectively minimizing the impact of noise and low resolution to achieve accurate alignment. Additionally, it strategically employs different network configurations and domain-specific image features at each registration stage, addressing the unique challenges of affine and deformable registration, enhancing both robustness and accuracy. We validated FetDTIAlign on a dataset covering gestational ages centered between 23 and 36 weeks, encompassing 60 white matter tracts. For all age groups, FetDTIAlign consistently showed superior anatomical correspondence and the best visual alignment in both affine and deformable registration, outperforming two classical optimization-based methods and a deep learning-based pipeline. Further validation on external data from the Developing Human Connectome Project demonstrated the generalizability of our method to data collected with different acquisition protocols. Our results show the feasibility of using deep learning for fetal brain dMRI registration, providing a more accurate and reliable alternative to classical techniques. By enabling precise cross-subject and tract-specific analyses, FetDTIAlign paves the way for new discoveries in early brain development. The code is available at https://gitlab.com/blibli/fetdtialign. •The first machine learning method for aligning diffusion MRI of the fetal brain.•Superior accuracy and robustness to standard and state-of-the-art techniques.•Supports cross-sectional and longitudinal studies of fetal brain development.

Due to anatomical variability across subjects many brain mapping experiments have analysis focused on a few particular regions of interest so as to circumvent the problem of sub-optimal statistics resulting from the lack of anatomical correspondence across subjects. Since the topographic distribution of experimental effects across the cortex is also often of interest, two separate analyses are often conducted, one on the regions of interest alone, as well as a separate ‘whole brain’ analysis with sub-optimal spatial correspondence across brains. In this paper we present a new group alignment procedure which incorporates, from each subject, both macro-anatomical (curvature) information and functional information from standard localizer experiments. After specifying appropriate parameters to weight anatomical and functional alignment forces, we were able to create a group cortical reconstruction which was well aligned in terms of both anatomical and functional areas. We observed an increase in the overlap of functional areas as well as an improvement in group statistics following this integrated alignment procedure. We propose that, using this alignment scheme, two separate analyses may not be necessary as both analyses can be integrated into a single procedure. After an integrated structural and functional alignment one is able to carry out a whole brain analysis with improved statistical sensitivity due to the reduction in spatial variation in the location of functional regions of interest which fCBA accomplishes. Furthermore, regions in the vicinity of localised and aligned regions-of-interest will also benefit from the integrated alignment. •Anatomical alignment cannot bring all functional areas into spatial correspondence.•Functional and anatomical information were integrated to drive group alignment.•We observed increased overlap of function areas after this integrated alignment.•Group statistics also improved due to increased spatial correspondence.•Benefits of ROI and whole brain analyses can be combined using this method.

Spatial normalization of neuroimaging data is a standard step when assessing group effects. As a result of divergent analysis procedures due to different normalization algorithms or templates, not all published coordinates refer to the same neuroanatomical region. Specifically, the literature is populated with results in the form of MNI or Talairach coordinates, and their disparity can impede the comparison of results across different studies. This becomes particularly problematic in coordinate-based meta-analyses, wherein coordinate disparity should be corrected to reduce error and facilitate literature reviews. In this study, a quantitative comparison was performed on two corrections, the Brett transform (i.e., “mni2tal”), and the Lancaster transform (i.e., “icbm2tal”). Functional magnetic resonance imaging (fMRI) data acquired during a standard paired associates task indicated that the disparity between MNI and Talairach coordinates was better reduced via the Lancaster transform, as compared to the Brett transform. In addition, an activation likelihood estimation (ALE) meta-analysis of the paired associates literature revealed that a higher degree of concordance was obtained when using the Lancaster transform in the form of fewer, smaller, and more intense clusters. Based on these results, we recommend that the Lancaster transform be adopted as the community standard for reducing disparity between results reported as MNI or Talairach coordinates, and suggest that future spatial normalization strategies be designed to minimize this variability in the literature.

[ 18 F]-fluorodeoxyglucose (FDG) Positron Emission Tomography (PET) is a widely used diagnostic tool that can detect and quantify pathophysiology, as assessed through changes in cerebral glucose metabolism. [ 18 F]-FDG PET scans can be analyzed using voxel-based statistical methods such as Statistical Parametric Mapping (SPM) that provide statistical maps of brain abnormalities in single patients. In order to perform SPM, a “spatial normalization” of an individual’s PET scan is required to match a reference PET template. The PET template currently used for SPM normalization is based on [ 15 O]-H 2 O images and does not resemble either the specific metabolic features of [ 18 F]-FDG brain scans or the specific morphological characteristics of individual brains affected by neurodegeneration. Thus, our aim was to create a new [ 18 F]-FDG PET aging and dementia-specific template for spatial normalization, based on images derived from both age-matched controls and patients. We hypothesized that this template would increase spatial normalization accuracy and thereby preserve crucial information for research and diagnostic purposes. We investigated the statistical sensitivity and registration accuracy of normalization procedures based on the standard and new template—at the single-subject and group level—independently for subjects with Mild Cognitive Impairment (MCI), probable Alzheimer’s Disease (AD), Frontotemporal lobar degeneration (FTLD) and dementia with Lewy bodies (DLB). We found a significant statistical effect of the population-specific FDG template-based normalisation in key anatomical regions for each dementia subtype, suggesting that spatial normalization with the new template provides more accurate estimates of metabolic abnormalities for single-subject and group analysis, and therefore, a more effective diagnostic measure.