Tag Archives: Brain structure

Advances in brain imaging

Klein et al 2017The diagnosis of human brain abnormalities depends on knowing the norm and yet defining the range of normal variation is still far from resolved. Understanding what is within the normal human range has been limited by samples and the constraints of producing accurate brain mapping. Access to large brain imaging databases has been possible for a while but producing reliable atlases of key structures including folding patterns (sulci, gyri and fundii), volumes and major shape changes has not had large enough sample sizes to reliably grasp the range of normal brain variation. Current approaches have relied on highly skilled professionals to assess neuroanatomy. While this approach is adequate, it does introduce an inherent level of subjectivity and potential bias with each neuroanatomist dependent on the individual level of experience. To begin reducing this error while increasing sample sizes, new computational technologies allow more automated imaging processes that combine speed and quality.

Mindboggle is a new software platform recently released after development through a long-term research project addressing a need for integrating morphometry (measurements of morphology) to assess the quantitative differences in brain structure. Mindboggle relies on specially developed algorithms to segment brain tissue in MRI images, produce volumetric and structural parallelization of the brain and asses shape variation. Klein and colleagues highlighted issues with similar algorithm-based software that produced errors in segmenting brain from non-brain tissue. Freesurfer was shown to underestimate grey matter while overestimating white matter, while ANTs included more grey matter yet sometimes excluded white matter that extended deep in gyral folds. To resolve this issue, Mindboggle employed a hybrid algorithm that overlays the Freesurfer and ANTs segmentation imaging then combines these to produce a more faithful imaging set negating any errors in volume estimates, folding patterns or shape differences. Further results indicated the geodesic algorithm produced an exaggerated depth for brain regions like the insula, while the time depth algorithm unique to Mindboggle produced more valid results for shallow brain structures than other comparable algorithms. Finally, Mindboggle was shown to be reliable with minimal error estimate showing a consistently greater shape difference between left and right hemispheres than the difference between repeated scans of the same individuals.

Mindboggle also introduced many new and innovative features for extracting and measuring fundii but these algorithms have not yet been thoroughly evaluated. Additionally, the Mindboggle algorithms are developed for human brain anatomy and expansion into non-human neuroanatomy has not yet been fully developed. The potential of Mindboggle and similar platforms lies in the allowance to expand knowledge of normal human brain variation by using much larger samples to more accurately capture the normal range in human neuroanatomy to better inform diagnoses of brain abnormalities.

Alannah Pearson

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Structural MRI artifacts

Magnetic resonance imaging (MRI) is a valuable and increasingly used method for studying brain anatomy as it allows large-scale, high-quality in vivo analyses. However, some artifacts might influence the digital results, and thus require cautious interpretation. In a recent review, these issues are addressed along with possible solutions. First, we need to keep in mind that the images acquired are not mere photographs of the brain, but reflect some biophysical properties of the tissues, by measuring the radio-frequency signals emitted by hydrogen atoms (present in water and fat) after being excited by magnetic waves. Thus, MRI is an indirect analysis of the brain anatomy and depends greatly on specific tissue properties. Second, researchers can choose from a variety of methods, depending on the aim of the survey. Macrostructure, i.e. the size and shape across voxels, can be studied through manual volumetry or automatic segmentation, voxel- or deformation-based morphometry, surface- based algorithms, or diffusion tractography. Microstructure, i.e. within-voxel contents, is usually analyzed through diffusion MRI, but also magnetization transfer imaging, or quantitative susceptibility mapping.

When making inferences on the biological significance of the outputs, the researcher must account for the possible digital artifacts. These can occur both during image acquisition and processing and can be subject-related and methodological-related. A common problem is subject motion, which might contaminate or influence the results, as the amount of motion varies with other factors influencing brain changes (age, sex, and disease status), or can even correlate with a specific effect being studied. For instance, motion induces gray matter reduction, which might be perceived as brain atrophy. Subject motion is unavoidable, but its influence can be reduced by using a motion detector during acquisition, or by estimating the amount of motion allowing statistical adjustments, also useful to  detect outliers. The difficulty in manipulating the magnetic and radio-frequency fields might also introduce deformation. The main magnetic field should be spatially uniform, but it is dispersed by brain tissue while concentrated by air. This can be partially compensated by applying additional fields. The radiofrequency field is not homogeneous, which affects MRI contrast and intensity. Combining multiple transmit coils might help reduce this caveat, although the contribution and sensitivity of each coil must be taken into account when processing the image.

A particular case that can affect estimates of cortical volume and thickness is the difficulty in discriminating the dura and gray matter due to the similar intensity and anatomical proximity. In this case,  MRI parameters can be manipulated in order to increase the contrast between these tissues, without reducing the contrast between gray and white matter. Individual variability in folding patterns is a further major issue in voxel-based morphometry studies because it complicates the mapping of correspondences between subjects. Registration might be enhanced by analyzing regions with larger variation to find possible anatomical alterations, aligning cortical folding patterns to locate corresponding areas, and mapping sulcal changes to improve sulci identification. Finally, researchers should continuously keep track of the constant advances and innovations in the field. The authors conclude acknowledging the importance of structural MRI when coupled with other biological information, like genetic expression (Allen Brain Atlas), cytoarchitecture (JuBrain), and cognitive associations (Neurosynth).

Sofia Pedro