Brain morphometry

This article uses direct referencing.

As a subfield of both morphometry and the brain sciences, brain morphometry is concerned with the quantification of anatomical features in the brain, and changes thereof, particularly from ontogenetic and phylogenetic perspectives. These features include whole-brain properties like shape, mass, volume, encephalization quotient, the distribution of grey matter and white matter as well as cerebrospinal fluid but also derived parameters like gyrification and cortical thickness or quantitative aspects of substructures of the brain, e.g. the volume of the hippocampus, or the amount of neurons in the optic tectum.

There are two major prerequisites for brain morphometry: First, the brain features of interest must be measurable, and second, statistical methods have to be in place to compare the measurements quantitatively. Shape feature comparisons form the basis of Linnaean taxonomy, and even in cases of convergent evolution or brain disorders, they still provide a wealth of information about the nature of the processes involved. Shape comparisons have long been constrained to simple and mainly volume- or slice-based measures but profited enormously from the digital revolution, as now all sorts of shapes in any number of dimensions can be handled numerically.

Besides, though the extraction of morphometric parameters like brain mass or liquor volume may be relatively straightforward in post mortem samples, most studies in living subjects will by necessity have to use an indirect approach: A spatial representation of the brain or its components is obtained by some appropriate neuroimaging technique, and the parameters of interest can then be analysed in such sets of data.

Biological background

The morphology and function of a complex organ like the brain are the result of numerous biochemical and biophysical processes interacting in a highly complex manner across multiple scales in space and time (Vallender et al., 2008). Most of the genes known to control these processes during brain development, maturation and aging are highly conserved (Holland, 2003), whereas pronounced differences at the cognitive level abound even amongst closely related species, or between individuals within a species (Roth and Dicke, 2005).

In contrast, variations in macroscopic brain anatomy (i.e. at a level of detail still discernable by the naked human eye) are sufficiently conserved to allow for comparative analyses, yet diverse enough to reflect variations within and between individuals and species: As morphological analyses that compare brains at different ontogenetic or pathogenetic stages can reveal important information about the progression of normal or abnormal development within a given species, cross-species comparative studies have a similar potential to reveal evolutionary trends and phylogenetic relationships, though the concept of progression has to be used with caution here, especially when considering contemporary species.

Methodologies

With the exception of the usually slice-based histological sections, neuroimaging data are generally stored as matrices of voxels. The most popular morphometric method, thus, is known as Voxel-based morphometry (VBM; cf. Ashburner and Friston, 2000). Yet as an imaging voxel is not a biologically meaningful unit, other approaches have been developed that bear a closer correspondence to biological structures (e.g. Brechbühler et al., 1995; Dale et al., 1999; Fischl et al., 1999): Deformation-based morphometry (DBM), surface-based morphometry (SBM) and tensor-based morphometry (TBM). All four are usually performed based on Magnetic Resonance (MR) imaging data, with the former three using T1-weighted pulse sequences, and TBM diffusion-weighted ones.

T1-weighted MR-based brain morphometry

Voxel-based morphometry

Voxel-based methods have long been used in a variety of studies involving healthy controls (Lüders et al., 2004) and neuropsychiatric patients (Daniels et al., 2006; Etgen et al., 2006; Jatzko et al., 2006; Lasek et al., 2007, 2006; May and Gaser, 2006; Mühlau et al., 2006, 2007; Soriano-Mas et al., 2007).

Deformation-based morphometry

Gaser et al. (1999) developed the first voxel-based DBM approach and applied it to a large sample of schizophrenic patients and healthy controls. It was later extended and validated with conventional volumetric methods (Gaser et al., 2001).

Surface-based morphometry

Surface-based techniques have been developed that allow, e.g., a three-dimensional analysis of local gyrification (Lüders et al., 2006b) and has been used successfully to document correlations between the gyrification pattern on the one hand and intelligence measures or gender on the other (Lüders et al., 2006b,a). Furthermore, the method was used to quantify regional differences in the gyrification patterns of patients with Williams syndrome, an inherited disorder (Gaser et al., 2006).

Diffusion-weighted MR-based brain morphometry

Tensor-based morphometry

Tensor-based techniques are the latest offspring of this suite of MR-based morphological approaches (e.g. Leow et al., 2006). They determine the tract of nerve fibers within the brain by means of diffusion-tensor imaging or diffusion-spectrum imaging (e.g. Douaud et al., 2007 and O'Donnell et al., 2009).

Applications

Currently, most applications of brain morphometry have a clinical focus, i.e. they serve to diagnose and monitor neuropsychiatric disorders, in particular neurodevelopmental disorders (like schizophrenia) or neurodegenerative diseases (like Alzheimer), but brain development and aging as well as brain evolution can also be studied this way.