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<title>Brain morphometry</title>

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<span id="openzim-page-title" class="mw-page-title-main"><span class="mw-page-title-main">Brain morphometry</span></span>

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<p><b>Brain morphometry</b> is a subfield of both <a href="Morphometry" class="mw-redirect" title="Morphometry">morphometry</a> and the <a href="Brain_science" class="mw-redirect" title="Brain science">brain sciences</a>, concerned with the measurement of <a href="Brain" title="Brain">brain</a> structures and changes thereof during <a href="Brain_development" class="mw-redirect" title="Brain development">development</a>, aging, learning, disease and <a href="Brain_evolution" class="mw-redirect" title="Brain evolution">evolution</a>. Since <a href="Autopsy" title="Autopsy">autopsy</a>-like dissection is generally impossible on living <a href="Brain" title="Brain">brains</a>, brain morphometry starts with noninvasive <a href="Neuroimaging" title="Neuroimaging">neuroimaging</a> data, typically obtained from <a href="Magnetic_resonance_imaging" title="Magnetic resonance imaging">magnetic resonance imaging</a> (MRI). These data are <a href="Born_digital" class="mw-redirect" title="Born digital">born digital</a>, which allows researchers to analyze the brain images further by using advanced mathematical and statistical methods such as shape quantification or <a href="Multivariate_analysis" class="mw-redirect" title="Multivariate analysis">multivariate analysis</a>. This allows researchers to quantify anatomical features of the brain in terms of shape, mass, volume (e.g. of the <a href="Hippocampus" title="Hippocampus">hippocampus</a>, or of the primary versus secondary <a href="Visual_cortex" title="Visual cortex">visual cortex</a>), and to derive more specific information, such as the <a href="Encephalization_quotient" title="Encephalization quotient">encephalization quotient</a>, grey matter density and white matter connectivity, <a href="Gyrification" title="Gyrification">gyrification</a>, cortical thickness, or the amount of <a href="Cerebrospinal_fluid" title="Cerebrospinal fluid">cerebrospinal fluid</a>. These variables can then be <a href="Brain_mapping" title="Brain mapping">mapped</a> within the brain volume or on the brain surface, providing a convenient way to assess their pattern and extent over time, across individuals or even between different <a href="Biological_species" class="mw-redirect" title="Biological species">biological species</a>. The field is rapidly evolving along with neuroimaging techniques—which deliver the underlying data—but also develops in part independently from them, as part of the emerging field of <a href="Neuroinformatics" title="Neuroinformatics">neuroinformatics</a>, which is concerned with developing and adapting <a href="Algorithm" title="Algorithm">algorithms</a> to analyze those data.

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<div class="mw-heading mw-heading2"><h2 id="Background">Background</h2></div>

<div class="mw-heading mw-heading3"><h3 id="Terminology">Terminology</h3></div>

<p>The term <a href="Brain_mapping" title="Brain mapping">brain mapping</a> is often used interchangeably with brain morphometry, although <i>mapping</i> in the narrower sense of <a href="Map_projection" title="Map projection">projecting</a> properties of the brain onto a template brain is, strictly speaking, only a subfield of brain morphometry. On the other hand, though much more rarely, neuromorphometry is also sometimes used as a synonym for brain morphometry (particularly in the earlier literature, e.g. <a href="https://en.citizendium.org/wiki/CZ:Ref:Haug_1986_History_of_neuromorphometry" class="extiw external" title="citizendium:CZ:Ref:Haug 1986 History of neuromorphometry">Haug 1986</a>), though technically is only one of its subfields.

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<div class="mw-heading mw-heading3"><h3 id="Biology">Biology</h3></div>

<p>The morphology and function of a complex <a href="Organ_(anatomy)" class="mw-redirect" title="Organ (anatomy)">organ</a> like the brain are the result of numerous <a href="Biochemical" class="mw-redirect" title="Biochemical">biochemical</a> and <a href="Biophysical" class="mw-redirect" title="Biophysical">biophysical</a> processes interacting in a highly complex manner across multiple scales in space and time (<a href="https://en.citizendium.org/wiki/CZ:Ref:Vallender_2008_Genetic_basis_of_human_brain_evolution" class="extiw external" title="citizendium:CZ:Ref:Vallender 2008 Genetic basis of human brain evolution">Vallender et al., 2008</a>). Most of the genes known to control these processes during <a href="Brain_development" class="mw-redirect" title="Brain development">brain development</a>, <a href="Erikson's_stages_of_psychosocial_development" title="Erikson's stages of psychosocial development">maturation</a> and <a href="Aging" class="mw-redirect" title="Aging">aging</a> are highly <a href="Conservation_(biology)" class="mw-redirect" title="Conservation (biology)">conserved</a> (<a href="https://en.citizendium.org/wiki/CZ:Ref:Holland_2003_Early_central_nervous_system_evolution:_an_era_of_skin_brains%3F" class="extiw external" title="citizendium:CZ:Ref:Holland 2003 Early central nervous system evolution: an era of skin brains?">Holland, 2003</a>), though some show <a href="Polymorphism_(biology)" title="Polymorphism (biology)">polymorphisms</a> (cf. <a href="https://en.citizendium.org/wiki/CZ:Ref:Meda_2008_Polymorphism_of_DCDC2_Reveals_Differences_in_Cortical_Morphology_of_Healthy_Individuals%E2%80%94A_Preliminary_Voxel_Based_Morphometry_Study" class="extiw external" title="citizendium:CZ:Ref:Meda 2008 Polymorphism of DCDC2 Reveals Differences in Cortical Morphology of Healthy Individuals—A Preliminary Voxel Based Morphometry Study">Meda et al., 2008</a>), and pronounced differences at the cognitive level abound even amongst closely related <a href="Species" title="Species">species</a>, or between individuals within a species (<a href="https://en.citizendium.org/wiki/CZ:Ref:Roth_2005_Evolution_of_the_brain_and_intelligence" class="extiw external" title="citizendium:CZ:Ref:Roth 2005 Evolution of the brain and intelligence">Roth and Dicke, 2005</a>).

</p><p>In contrast, variations in <a href="Macroscopic" class="mw-redirect" title="Macroscopic">macroscopic</a> brain anatomy (i.e., at a level of detail still discernible by the naked <a href="Human_eye" title="Human eye">human eye</a>) are sufficiently conserved to allow for <a href="Comparative_bullet-lead_analysis" title="Comparative bullet-lead analysis">comparative analyses</a>, yet diverse enough to reflect variations within and between individuals and species: As morphological analyses that compare brains at different onto-genetic or pathogenic 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.

</p><p>Given that the imaging modalities commonly employed for brain morphometric investigations are essentially of a molecular or even sub-atomic nature, a number of factors may interfere with

derived quantification of brain structures. These include all of the parameters mentioned in "Applications" but also the state of hydration, hormonal status, medication and substance abuse.

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<div class="mw-heading mw-heading3"><h3 id="Technical_requirements">Technical requirements</h3></div>

<p>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 <a href="Carl_Linnaeus" title="Carl Linnaeus">Linnaean</a> taxonomy, and even in cases of <a href="Convergent_evolution" title="Convergent evolution">convergent evolution</a> or <a href="Brain_disorders" class="mw-redirect" title="Brain disorders">brain disorders</a>, 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.

</p><p>In addition, though the extraction of morphometric parameters like brain mass or <a href="Liquor" title="Liquor">liquor</a> volume may be relatively straightforward in <a href="Post_mortem" class="mw-redirect" title="Post mortem">post mortem</a> 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 <a href="Neuroimaging" title="Neuroimaging">neuroimaging</a> technique, and the parameters of interest can then be analyzed on that basis. Such a structural representation of the brain is also a prerequisite for the interpretation of <a href="FMRI" class="mw-redirect" title="FMRI">functional</a> <a href="Neuroimaging" title="Neuroimaging">neuroimaging</a>.

</p><p>The design of a brain morphometric study depends on multiple factors that can be roughly categorized as follows: First, depending on whether ontogenetic, pathological or phylogenetic issues are targeted, the study can be designed as <a href="Longitudinal_study" title="Longitudinal study">longitudinal</a> (within the same brain, measured at different times), or <a href="Cross-sectional_study" title="Cross-sectional study">cross-sectional</a> (across brains). Second, brain image data can be acquired using different <a href="Neuroimaging" title="Neuroimaging">neuroimaging</a> modalities. Third, brain properties can be analyzed at different scales (e.g. in the whole brain, <a href="Region_of_interest" title="Region of interest">regions of interest</a>, cortical or subcortical structures). Fourth, the data can be subjected to different kinds of processing and analysis steps. Brain morphometry as a discipline is mainly concerned with the development of tools addressing this fourth point and integration with the previous ones.

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<div class="mw-heading mw-heading2"><h2 id="Methodologies">Methodologies</h2></div>

<p>With the exception of the usually slice-based <a href="Histology" title="Histology">histology</a> of the brain, neuroimaging data are generally stored as <a href="Matrix_(mathematics)" title="Matrix (mathematics)">matrices</a> of <a href="Voxel" title="Voxel">voxels</a>. The most popular morphometric method, thus, is known as <a href="Voxel-based_morphometry" title="Voxel-based morphometry">Voxel-based morphometry</a> (VBM; cf. <a href="https://en.citizendium.org/wiki/CZ:Ref:Wright_1995_A_Voxel-Based_Method_for_the_Statistical_Analysis_of_Gray_and_White_Matter_Density_Applied_to_Schizophrenia" class="extiw external" title="citizendium:CZ:Ref:Wright 1995 A Voxel-Based Method for the Statistical Analysis of Gray and White Matter Density Applied to Schizophrenia">Wright et al., 1995</a>; <a href="https://en.citizendium.org/wiki/CZ:Ref:Ashburner_2000_Voxel-Based_Morphometry%E2%80%94The_Methods" class="extiw external" title="citizendium:CZ:Ref:Ashburner 2000 Voxel-Based Morphometry—The Methods">Ashburner and Friston, 2000</a>; <a href="https://en.citizendium.org/wiki/CZ:Ref:Good_2001_A_Voxel-Based_Morphometric_Study_of_Ageing_in_465_Normal_Adult_Human_Brains" class="extiw external" title="citizendium:CZ:Ref:Good 2001 A Voxel-Based Morphometric Study of Ageing in 465 Normal Adult Human Brains">Good et al., 2001</a>). Yet as an imaging voxel is not a biologically meaningful unit, other approaches have been developed that potentially bear a closer correspondence to biological structures: Deformation-based morphometry (DBM), surface-based morphometry (SBM) and fiber tracking based on <a href="Diffusion-weighted_imaging" class="mw-redirect" title="Diffusion-weighted imaging">diffusion-weighted imaging</a> (DTI or DSI). All four are usually performed based on <a href="Magnetic_resonance_imaging" title="Magnetic resonance imaging">Magnetic Resonance (MR) imaging</a> data, with the former three commonly using T1-weighted (e.g. Magnetization Prepared Rapid Gradient Echo, MP-RAGE) and sometimes T2-weighted <a href="Pulse_sequence_(NMR)" class="mw-redirect" title="Pulse sequence (NMR)">pulse sequences</a>, while DTI/DSI use <a href="Diffusion" title="Diffusion">diffusion</a>-weighted ones. However, recent evaluation of morphometry algorithms/software demonstrates inconsistency among several of them.<sup id="cite_ref-Gao_1-0" class="reference"><a href="#cite_note-Gao-1"><span class="cite-bracket">[</span>1<span class="cite-bracket">]</span></a></sup> This renders a need for systematic and quantitative validation and evaluation of the field.

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<div class="mw-heading mw-heading3"><h3 id="T1-weighted_MR-based_brain_morphometry">T1-weighted MR-based brain morphometry</h3></div>

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</style><div role="note" class="hatnote navigation-not-searchable">Main article: <a href="Magnetic_Resonance_Imaging" class="mw-redirect" title="Magnetic Resonance Imaging">Magnetic Resonance Imaging</a></div>

<div class="mw-heading mw-heading4"><h4 id="Preprocessing">Preprocessing</h4></div>

<div role="note" class="hatnote navigation-not-searchable">Main article: <a href="Image_registration" title="Image registration">Image registration</a></div>

<p>MR images are generated by a complex interaction between static and dynamic electromagnetic fields and the tissue of interest, namely the brain that is encapsulated in the head of the subject. Hence, the raw images contain noise from various sources—namely head movements (a scan suitable for morphometry typically takes on the order of 10 min) that can hardly be corrected or modeled, and bias fields (neither of the electromagnetic fields involved is homogeneous across the whole head nor brain) which can be modeled.

</p><p>In the following, the image is segmented into non-brain and brain tissue, with the latter usually being sub-segmented into at least gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF). Since

image voxels near the class boundaries do not generally contain just one kind of tissue, partial volume effects ensue that can be corrected for.

</p><p>For comparisons across different scans (within or across subjects), differences in brain size and shape are eliminated by spatially normalizing (i.e. registering) the individual images to the stereotactic space of a template brain.

Registration can be performed using low-resolution (i.e. rigid-body or <a href="Affine_transformations" class="mw-redirect" title="Affine transformations">affine transformations</a>) or high-resolution (i.e. highly non-linear) methods, and templates can be generated from the study's pool of brains, from a <a href="Brain_atlas" title="Brain atlas">brain atlas</a> or a derived <a href="Template_generator" title="Template generator">template generator</a>.

</p><p>Both the registered images and the deformation fields generated upon registration can be used for morphometric analyses, thereby providing the basis for Voxel-Based Morphometry (VBM) and Deformation-Based Morphometry (DBM). Images segmented into tissue classes can also be used to convert segmentation boundaries into parametric surfaces, the analysis of which is the focus of Surface-Based Morphometry (SBM).

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<div class="mw-heading mw-heading4"><h4 id="Voxel-based_morphometry">Voxel-based morphometry</h4></div>

<div role="note" class="hatnote navigation-not-searchable">Main article: <a href="Voxel-based_morphometry" title="Voxel-based morphometry">Voxel-based morphometry</a></div>

<p>After the individual images are segmented, they are <a href="Image_registration" title="Image registration">registered</a> to the template. Each voxel then contains a measure of the probability, according to which it belongs to a specific segmentation class. For gray matter, this quantity is usually referred to as gray matter density (GMD) or gray matter concentration (GMC), or gray matter probability (GMP).

</p><p>In order to correct for the volume changes due to the registration, the gray matter volume (GMV) in the original brain can be calculated by multiplying the GMD with the Jacobian determinants of the deformations used to register the brain to the template. Class-specific volumes for WM and CSF are defined analogously.

</p><p>The local differences in the density or volume of the different segmentation classes can then be statistically analyzed across scans and interpreted in anatomical terms (e.g. as gray matter atrophy). Since VBM is available for many of the major neuroimaging software packages (e.g. <a href="FMRIB_Software_Library" title="FMRIB Software Library">FSL</a> and <a href="Statistical_parametric_mapping" title="Statistical parametric mapping">SPM</a>), it provides an efficient tool to test or generate specific hypotheses about brain changes over time. It is noteworthy, that unlike DBM, considerable criticism and words of caution regarding the correct interpretation of VBM results has been leveled by the medical image computing community <sup id="cite_ref-2" class="reference"><a href="#cite_note-2"><span class="cite-bracket">[</span>2<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-3" class="reference"><a href="#cite_note-3"><span class="cite-bracket">[</span>3<span class="cite-bracket">]</span></a></sup>

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<div class="mw-heading mw-heading4"><h4 id="Deformation-based_morphometry">Deformation-based morphometry</h4></div>

<p>In DBM, highly non-linear registration algorithms are used, and the statistical analyses are not performed on the registered voxels but on the deformation fields used to register them <sup id="cite_ref-4" class="reference"><a href="#cite_note-4"><span class="cite-bracket">[</span>4<span class="cite-bracket">]</span></a></sup> (which requires multivariate approaches) or derived scalar properties thereof, which allows for univariate approaches.<sup id="cite_ref-5" class="reference"><a href="#cite_note-5"><span class="cite-bracket">[</span>5<span class="cite-bracket">]</span></a></sup> One common variant—sometimes referred to as Tensor-based morphometry (TBM)—is based on the <a href="Jacobian_determinant" class="mw-redirect" title="Jacobian determinant">Jacobian determinant</a> of the deformation matrix.

</p><p>Of course, multiple solutions exist for such non-linear warping procedures, and to balance appropriately between the potentially opposing requirements for global and local shape fit, ever more sophisticated registration algorithms are being developed. Most of these, however, are computationally expensive if applied with a high-resolution grid. The biggest advantage of DBM with respect to VBM is its ability to detect subtle changes in longitudinal studies. However, due to the vast variety of registration algorithms, no widely accepted standard for DBM exists, which also prevented its incorporation into major neuroimaging software packages.

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<div class="mw-heading mw-heading4"><h4 id="Pattern_based_morphometry">Pattern based morphometry</h4></div>

<p>Pattern based morphometry (PBM) is a method of brain morphometry first put forth in PBM.<sup id="cite_ref-6" class="reference"><a href="#cite_note-6"><span class="cite-bracket">[</span>6<span class="cite-bracket">]</span></a></sup> It builds upon DBM and VBM. PBM is based on the application of <a href="Sparse_dictionary_learning" title="Sparse dictionary learning">sparse dictionary learning</a> to morphometry. As opposed to typical voxel based approaches which depend on univariate statistical tests at specific voxel locations, PBM extracts multivariate patterns directly from the entire image. The advantage of this is that the inferences are not made locally as in VBM or DBM but globally. This allows the method to detect if combinations of voxels are better suited to separate the groups being studied rather than single voxels. Also the method is more robust to variations in the underlying registration algorithms as compared to typical DBM analysis

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<div class="mw-heading mw-heading4"><h4 id="Surface-based_morphometry">Surface-based morphometry</h4></div>

<p>Once the brain is segmented, the boundary between different classes of tissue can be <a href="Surface_reconstruction" title="Surface reconstruction">reconstructed as a surface</a> on which morphometric analysis can proceed (e.g. towards <a href="Gyrification" title="Gyrification">gyrification</a>), or onto which results of such analyses can be <a href="Brain_mapping" title="Brain mapping">projected</a>.

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<div class="mw-heading mw-heading3"><h3 id="Diffusion-weighted_MR-based_brain_morphometry">Diffusion-weighted MR-based brain morphometry</h3></div>

<div class="mw-heading mw-heading4"><h4 id="Fiber-tracking_techniques">Fiber-tracking techniques</h4></div>

<p>Nerve fiber-tracking techniques are the latest offspring of this suite of MR-based morphological approaches. They determine the tract of <a href="Nerve_fiber" class="mw-redirect" title="Nerve fiber">nerve fibers</a> within the brain by means of <a href="Diffusion_tensor_imaging" class="mw-redirect" title="Diffusion tensor imaging">diffusion tensor imaging</a> or diffusion-spectrum imaging (e.g. <a href="https://en.citizendium.org/wiki/CZ:Ref:Douaud_2007_Anatomically_related_grey_and_white_matter_abnormalities_in_adolescent-onset_schizophrenia" class="extiw external" title="citizendium:CZ:Ref:Douaud 2007 Anatomically related grey and white matter abnormalities in adolescent-onset schizophrenia">Douaud et al., 2007</a> and <a href="https://en.citizendium.org/wiki/CZ:Ref:O%27Donnell_2009_Tract-based_morphometry_for_white_matter_group_analysis" class="extiw external" title="citizendium:CZ:Ref:O'Donnell 2009 Tract-based morphometry for white matter group analysis">O'Donnell et al., 2009</a>).

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<div class="mw-heading mw-heading2"><h2 id="Diffeomorphometry">Diffeomorphometry</h2></div>

<p><b><a href="Diffeomorphometry" title="Diffeomorphometry">Diffeomorphometry</a></b><sup id="cite_ref-7" class="reference"><a href="#cite_note-7"><span class="cite-bracket">[</span>7<span class="cite-bracket">]</span></a></sup> is the focus on comparison of shapes and forms with a metric structure based on diffeomorphisms, and is central to the field of <a href="Computational_anatomy" title="Computational anatomy">computational anatomy</a>.<sup id="cite_ref-8" class="reference"><a href="#cite_note-8"><span class="cite-bracket">[</span>8<span class="cite-bracket">]</span></a></sup> Diffeomorphic registration,<sup id="cite_ref-9" class="reference"><a href="#cite_note-9"><span class="cite-bracket">[</span>9<span class="cite-bracket">]</span></a></sup> introduced in the 90's, is now an important player that uses computational procedures for constructing correspondences between coordinate systems based on sparse features and dense images, such as ANTS,<sup id="cite_ref-10" class="reference"><a href="#cite_note-10"><span class="cite-bracket">[</span>10<span class="cite-bracket">]</span></a></sup> DARTEL,<sup id="cite_ref-11" class="reference"><a href="#cite_note-11"><span class="cite-bracket">[</span>11<span class="cite-bracket">]</span></a></sup> DEMONS,<sup id="cite_ref-12" class="reference"><a href="#cite_note-12"><span class="cite-bracket">[</span>12<span class="cite-bracket">]</span></a></sup> <a href="Large_deformation_diffeomorphic_metric_mapping" title="Large deformation diffeomorphic metric mapping">LDDMM</a>,<sup id="cite_ref-13" class="reference"><a href="#cite_note-13"><span class="cite-bracket">[</span>13<span class="cite-bracket">]</span></a></sup> or StationaryLDDMM.<sup id="cite_ref-14" class="reference"><a href="#cite_note-14"><span class="cite-bracket">[</span>14<span class="cite-bracket">]</span></a></sup> <a href="Voxel-based_morphometry" title="Voxel-based morphometry">Voxel-based morphometry</a> (VBM) is an important method built on many of these principles. Methods based on diffeomorphic flows are used in For example, deformations could be diffeomorphisms of the ambient space, resulting in the LDDMM (<a href="Large_deformation_diffeomorphic_metric_mapping" title="Large deformation diffeomorphic metric mapping">Large Deformation Diffeomorphic Metric Mapping</a>) framework for shape comparison.<sup id="cite_ref-LDDMM_15-0" class="reference"><a href="#cite_note-LDDMM-15"><span class="cite-bracket">[</span>15<span class="cite-bracket">]</span></a></sup> One such deformation is the right invariant metric of <a href="Computational_anatomy" title="Computational anatomy">computational anatomy</a> which generalizes the metric of non-compressible Eulerian flows to include the Sobolev norm, ensuring smoothness of the flows.<sup id="cite_ref-16" class="reference"><a href="#cite_note-16"><span class="cite-bracket">[</span>16<span class="cite-bracket">]</span></a></sup> Metrics have also been defined that are associated to Hamiltonian controls of diffeomorphic flows.<sup id="cite_ref-17" class="reference"><a href="#cite_note-17"><span class="cite-bracket">[</span>17<span class="cite-bracket">]</span></a></sup>

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<div class="mw-heading mw-heading2"><h2 id="Applications">Applications</h2></div>

<p>The qualitatively largest changes within an individual generally occur during early development and more subtle ones during aging and learning, while pathological changes can vary highly in their extent and interindividual differences increase both during and across lifetimes. The above-described morphometric methods provide the means to analyze such changes quantitatively, and MR imaging has been applied to ever more brain populations relevant to these time scales, both within humans and across species.

Currently, however, most applications of MR-based brain morphometry have a clinical focus, i.e. they help to diagnose and monitor neuropsychiatric disorders, in particular neurodegenerative diseases (like Alzheimer) or psychotic disorders (like schizophrenia).

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<div class="mw-heading mw-heading3"><h3 id="Brain_development">Brain development</h3></div>

<div role="note" class="hatnote navigation-not-searchable">Main article: <a href="Brain_development" class="mw-redirect" title="Brain development">Brain development</a></div>

<p>MR imaging is rarely performed during pregnancy and the neonatal period, in order to avoid stress for mother and child. In the cases of birth complications and other clinical events, however, such data are being acquired. For instance, <a href="https://en.citizendium.org/wiki/CZ:Ref:Dubois_2008_Primary_cortical_folding_in_the_human_newborn:_an_early_marker_of_later_functional_development" class="extiw external" title="citizendium:CZ:Ref:Dubois 2008 Primary cortical folding in the human newborn: an early marker of later functional development">Dubois et al., 2008</a> analyzed gyrification in premature newborns at birth and found it to be predictive of a functional score at term-equivalent age, and Serag et al.<sup id="cite_ref-18" class="reference"><a href="#cite_note-18"><span class="cite-bracket">[</span>18<span class="cite-bracket">]</span></a></sup> built a 4D atlas of the developing neonatal brain which has led to the construction of brain growth curves from 28–44 weeks’ postmenstrual age. Beyond preterms, there have been a number of large-scale longitudinal MR-morphometric studies (often combined with cross-sectional approaches and other neuroimaging modalities) of normal brain development in humans.<sup id="cite_ref-19" class="reference"><a href="#cite_note-19"><span class="cite-bracket">[</span>19<span class="cite-bracket">]</span></a></sup>

Using voxel-based and a number of complementary approaches, these studies revealed (or non-invasively confirmed, from the perspective of previous histological studies which cannot be longitudinal) that brain maturation involves differential growth of gray and white matter, that the time course of the maturation is not linear and that it differs markedly across brain regions.<sup id="cite_ref-20" class="reference"><a href="#cite_note-20"><span class="cite-bracket">[</span>20<span class="cite-bracket">]</span></a></sup> In order to interpret these findings, cellular processes have to be taken into consideration, especially those governing the pruning of axons, dendrites and synapses until an adult pattern of whole-brain connectivity is achieved (which can best be monitored using diffusion-weighted techniques).

</p>

<div class="mw-heading mw-heading3"><h3 id="Aging">Aging</h3></div>

<div role="note" class="hatnote navigation-not-searchable">Main article: <a href="Aging" class="mw-redirect" title="Aging">Aging</a></div>

<p>While white matter increases throughout early development and adolescence, and gray matter decreases in that period generally do not involve neuronal cell bodies, the situation is different beyond the age of about 50 years when atrophy affects gray and possibly also white matter. The most convincing explanation for this is that individual neurons die, leading to the loss of both their cell bodies (i.e. gray matter) and their myelinated axons (i.e. white matter). The gray matter changes can be observed via both gray matter density and gyrification.

That the white matter loss is not nearly as clear as that for gray matter indicates that changes also occur in non-neural tissue, e.g. the vasculature or microglia.

</p>

<div class="mw-heading mw-heading3"><h3 id="Learning_and_plasticity">Learning and plasticity</h3></div>

<div role="note" class="hatnote navigation-not-searchable">Main article: <a href="Brain_plasticity" class="mw-redirect" title="Brain plasticity">Brain plasticity</a></div>

<p>Perhaps the most profound impact to date of brain morphometry on our understanding of the relationships between brain structure and function has been provided by a series of VBM studies targeted at proficiency in various performances: Licensed <a href="Taxicab" class="mw-redirect" title="Taxicab">taxicab</a> drivers in <a href="London" title="London">London</a> were found to exhibit bilaterally increased gray matter volume in the posterior part of the <a href="Hippocampus" title="Hippocampus">hippocampus</a>, both relative to controls from the general population<sup id="cite_ref-21" class="reference"><a href="#cite_note-21"><span class="cite-bracket">[</span>21<span class="cite-bracket">]</span></a></sup> and to London <a href="Bus" title="Bus">bus</a> drivers matched for driving experience and <a href="Workplace_stress" class="mw-redirect" title="Workplace stress">stress</a> levels. Similarly, gray matter changes were also found to correlate with professional experience in musicians, mathematicians and meditators, and with second language proficiency.

</p><p>What is more, bilateral gray matter changes in the posterior and lateral parietal cortex of medical students memorizing for an intermediate exam could be detected over a period of just three months.

</p><p>These studies of professional training inspired questions about the limits of MR-based morphometry in terms of time periods over which structural brain changes can be detected. Important determinants of these limits are the speed and spatial extent of the changes themselves. Of course, some events like accidents, a stroke, a tumor metastasis or a surgical intervention can profoundly change brain structure during very short periods, and these changes can be visualized with MR and other neuroimaging techniques. Given the time constraints under such conditions, brain morphometry is rarely involved in diagnostics but rather used for progress monitoring over periods of weeks and months and longer.

</p><p>One study found that <a href="Juggling" title="Juggling">juggling</a> novices showed a bilateral gray matter expansion in the medial temporal visual area (also known as V5) over a three-month period during which they had learned to sustain a three-ball cascade for at least a minute. No changes were observed in a control group that did not engage in juggling. The extent of these changes in the jugglers reduced during a subsequent three-month period in which they did not practice juggling. To further resolve the time course of these changes, the experiment was repeated with another young cohort scanned in shorter intervals, and the by then typical changes in V5 could already be found after just seven days of juggling practice. The observed changes were larger in the initial learning phase than during continued training.

</p><p>Whereas the former two studies involved students in their early twenties, the experiments were recently repeated with an elderly cohort, revealing the same kind of structural changes, although attenuated by lower juggling performance of this group.<sup id="cite_ref-22" class="reference"><a href="#cite_note-22"><span class="cite-bracket">[</span>22<span class="cite-bracket">]</span></a></sup>

</p><p>Using a completely different kind of intervention—application of <a href="Transcranial_Magnetic_Stimulation" class="mw-redirect" title="Transcranial Magnetic Stimulation">Transcranial Magnetic Stimulation</a> in daily sessions over five days—changes were observed in and near the TMS target areas as well as in the basal ganglia of volunteers in their mid-twenties, compared to a control group that had received placebo. It is possible, though, that these changes simply reflect vascularization effects.

</p><p>Taken together, these morphometric studies strongly support the notion that brain plasticity—changes of brain structure—remains possible throughout life and may well be an adaptation to changes in brain function which has also been shown to change with experience. The title of this section was meant to emphasize this, namely that plasticity and learning provide two perspectives—functional and structural—at the same phenomenon, a brain that changes over time.

</p>

<div class="mw-heading mw-heading3"><h3 id="Brain_disease">Brain disease</h3></div>

<div role="note" class="hatnote navigation-not-searchable">Main article: <a href="Brain_disease" class="mw-redirect" title="Brain disease">Brain disease</a></div>

<p>Brain diseases are the field to which brain morphometry is most often applied, and the volume of the literature on this is vast.

</p>

<div class="mw-heading mw-heading3"><h3 id="Brain_evolution">Brain evolution</h3></div>

<div role="note" class="hatnote navigation-not-searchable">Main article: <a href="Brain_evolution" class="mw-redirect" title="Brain evolution">Brain evolution</a></div>

<p>Brain changes also accumulate over periods longer than an individual life but even though twin studies have established that human brain structure is highly heritable, brain morphometric studies with such a broadened scope are rare.

However, in the context of disorders with a known or suspected hereditary component, a number of studies have compared the brain morphometry of patients with both that of non-affected controls and that of subjects at high risk for developing the disorder. The latter group usually includes family members.

</p><p>Even larger time gaps can be bridged by comparing human populations with a sufficiently long history of genetic separation, such as Central Europeans and Japanese. One surface-based study compared the brain shape between these two groups and found a difference in their gender-dependent brain asymmetries. Neuroimaging studies of this kind, combined with functional ones and behavioural data, provide promising and so far largely unexplored avenues to understand similarities and differences between different groups of people.

</p><p>Like morphological analyses that compare brains at different ontogenetic or pathogenetic stages can reveal important information about normal or abnormal development within a given species, cross-species comparative studies have a similar potential to reveal evolutionary trends and phylogenetic relationships. Indeed, shape comparisons (though historically with an emphasis on qualitative criteria) formed the basis of biological taxonomy before the era of genetics.

Three principal sources exist for comparative evolutionary investigations: Fossils, fresh-preserved post-mortem or <a href="In_vivo" title="In vivo">in vivo</a> studies.

</p><p>The fossil record is dominated by structures that were already biomineralized during the lifetime of the respective organism (in the case of vertebrates, mainly teeth and bones).

Brains, like other soft tissues, rarely fossilize, but occasionally they do. The probably oldest vertebrate brain known today belonged to a ratfish that lived around 300 million years ago.<sup id="cite_ref-23" class="reference"><a href="#cite_note-23"><span class="cite-bracket">[</span>23<span class="cite-bracket">]</span></a></sup> While the technique most widely used to image fossils is <a href="Computed_tomography" class="mw-redirect" title="Computed tomography">computed tomography</a> (CT), this particular specimen was imaged by synchrotron tomography, and recent MR imaging studies with fossils suggest that the method may be used to image at least a subset of fossilized brains.

</p><p>MR images have also been obtained from the brain of a 3200-year-old <a href="Ancient_Egypt" title="Ancient Egypt">Egyptian</a> <a href="Mummy" title="Mummy">mummy</a>. The perspectives are slim, however, that any three-dimensional imaging dataset of a fossil, semi-fossil or mummified brain will ever be of much use to morphometric analyses of the kind described here, since the processes of mummification and fossilization heavily alter the structure of soft tissues in a way specific to the individual specimen and subregions therein.

</p><p>Postmortem samples of living or recently extinct species, on the other hand, generally allow to obtain MR image qualities sufficient for morphometric analyses, though preservation artifacts would have to be taken into account. Previous MR imaging studies include specimens

preserved in formalin,<sup id="cite_ref-24" class="reference"><a href="#cite_note-24"><span class="cite-bracket">[</span>24<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-25" class="reference"><a href="#cite_note-25"><span class="cite-bracket">[</span>25<span class="cite-bracket">]</span></a></sup> by freezing,<sup id="cite_ref-26" class="reference"><a href="#cite_note-26"><span class="cite-bracket">[</span>26<span class="cite-bracket">]</span></a></sup> or in alcohol.<sup id="cite_ref-27" class="reference"><a href="#cite_note-27"><span class="cite-bracket">[</span>27<span class="cite-bracket">]</span></a></sup>

</p><p>The third line of comparative evidence would be cross-species in vivo MR imaging studies like the one by Rilling & Insel (1998), who investigated brains from eleven primate species by VBM in order to shed new light on primate brain evolution.

Other studies have combined morphometric with behavioural measures, and brain evolution does not only concern primates: Gyrification occurs across mammalian brains if they reach a size of several centimeters—with cetaceans dominating the upper end of the spectrum—and generally increases slowly with overall brain size, following a power law.

</p>

<div class="mw-heading mw-heading2"><h2 id="References">References</h2></div>

<ul><li><i>This article incorporates material from the <a href="Citizendium" title="Citizendium">Citizendium</a> article "<a href="https://en.citizendium.org/wiki/Brain_morphometry" class="extiw external" title="citizendium:Brain morphometry">Brain morphometry</a>", which is licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported License but not under the GFDL.</i></li></ul>

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<li id="cite_note-10"><span class="mw-cite-backlink"><b><a href="#cite_ref-10">^</a></b></span> <span class="reference-text"><cite class="citation web cs1"><a rel="nofollow" class="external text" href="https://github.com/stnava/ANTs/blob/master/Scripts/antsIntroduction.sh">"stnava/ANTs"</a>. <i>GitHub</i><span class="reference-accessdate">. Retrieved <span class="nowrap">2015-12-11</span></span>.</cite></span>

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<li id="cite_note-11"><span class="mw-cite-backlink"><b><a href="#cite_ref-11">^</a></b></span> <span class="reference-text"><cite id="CITEREFAshburner2007" class="citation journal cs1">Ashburner, John (2007-10-15). "A fast diffeomorphic image registration algorithm". <i>NeuroImage</i>. <b>38</b> (1): <span class="nowrap">95–</span>113. <a href="Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1016%2Fj.neuroimage.2007.07.007">10.1016/j.neuroimage.2007.07.007</a>. <a href="ISSN_(identifier)" class="mw-redirect" title="ISSN (identifier)">ISSN</a> <a rel="nofollow" class="external text" href="https://search.worldcat.org/issn/1053-8119">1053-8119</a>. <a href="PMID_(identifier)" class="mw-redirect" title="PMID (identifier)">PMID</a> <a rel="nofollow" class="external text" href="https://pubmed.ncbi.nlm.nih.gov/17761438">17761438</a>. <a href="S2CID_(identifier)" class="mw-redirect" title="S2CID (identifier)">S2CID</a> <a rel="nofollow" class="external text" href="https://api.semanticscholar.org/CorpusID:545830">545830</a>.</cite></span>

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<li id="cite_note-12"><span class="mw-cite-backlink"><b><a href="#cite_ref-12">^</a></b></span> <span class="reference-text"><cite class="citation web cs1"><a rel="nofollow" class="external text" href="https://sites.google.com/site/tomvercauteren/software">"Software - Tom Vercauteren"</a>. <i>sites.google.com</i><span class="reference-accessdate">. Retrieved <span class="nowrap">2015-12-11</span></span>.</cite></span>

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<li id="cite_note-13"><span class="mw-cite-backlink"><b><a href="#cite_ref-13">^</a></b></span> <span class="reference-text"><cite class="citation web cs1"><a rel="nofollow" class="external text" href="https://www.nitrc.org/projects/lddmm-volume/">"NITRC: LDDMM: Tool/Resource Info"</a>. <i>www.nitrc.org</i><span class="reference-accessdate">. Retrieved <span class="nowrap">2015-12-11</span></span>.</cite></span>

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<li id="cite_note-14"><span class="mw-cite-backlink"><b><a href="#cite_ref-14">^</a></b></span> <span class="reference-text"><cite class="citation web cs1"><a rel="nofollow" class="external text" href="https://web.archive.org/web/20160216022906/https://www.openaire.eu/search/publication?articleId=dedup_wf_001::ea7b28db1d4570e248acdffb6211d98d">"Publication:Comparing algorithms for diffeomorphic registration: Stationary LDDMM and Diffeomorphic Demons"</a>. <i>www.openaire.eu</i>. Archived from <a rel="nofollow" class="external text" href="https://www.openaire.eu/search/publication?articleId=dedup_wf_001::ea7b28db1d4570e248acdffb6211d98d">the original</a> on 2016-02-16<span class="reference-accessdate">. Retrieved <span class="nowrap">2015-12-11</span></span>.</cite></span>

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<li id="cite_note-LDDMM-15"><span class="mw-cite-backlink"><b><a href="#cite_ref-LDDMM_15-0">^</a></b></span> <span class="reference-text"><cite id="CITEREFF._BegM._MillerA._TrouvéL._Younes2005" class="citation journal cs1">F. Beg; M. Miller; A. Trouvé; L. Younes (February 2005). "Computing Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms". <i>International Journal of Computer Vision</i>. <b>61</b> (2): <span class="nowrap">139–</span>157. <a href="Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1023%2Fb%3Avisi.0000043755.93987.aa">10.1023/b:visi.0000043755.93987.aa</a>. <a href="S2CID_(identifier)" class="mw-redirect" title="S2CID (identifier)">S2CID</a> <a rel="nofollow" class="external text" href="https://api.semanticscholar.org/CorpusID:17772076">17772076</a>.</cite></span>

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<li id="cite_note-16"><span class="mw-cite-backlink"><b><a href="#cite_ref-16">^</a></b></span> <span class="reference-text"><cite id="CITEREFMillerYounes2001" class="citation journal cs1">Miller, M. I.; Younes, L. (2001-01-01). "Group Actions, Homeomorphisms, And Matching: A General Framework". <i>International Journal of Computer Vision</i>. <b>41</b>: <span class="nowrap">61–</span>84. <a href="CiteSeerX_(identifier)" class="mw-redirect" title="CiteSeerX (identifier)">CiteSeerX</a> <span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.37.4816">10.1.1.37.4816</a></span>. <a href="Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1023%2FA%3A1011161132514">10.1023/A:1011161132514</a>. <a href="S2CID_(identifier)" class="mw-redirect" title="S2CID (identifier)">S2CID</a> <a rel="nofollow" class="external text" href="https://api.semanticscholar.org/CorpusID:15423783">15423783</a>.</cite></span>

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<li id="cite_note-17"><span class="mw-cite-backlink"><b><a href="#cite_ref-17">^</a></b></span> <span class="reference-text"><cite id="CITEREFMillerTrouvéYounes2015" class="citation journal cs1">Miller, Michael I.; Trouvé, Alain; Younes, Laurent (2015-01-01). "Hamiltonian Systems and Optimal Control in Computational Anatomy: 100 Years Since D'Arcy Thompson". <i>Annual Review of Biomedical Engineering</i>. <b>17</b>: <span class="nowrap">447–</span>509. <a href="Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1146%2Fannurev-bioeng-071114-040601">10.1146/annurev-bioeng-071114-040601</a>. <a href="ISSN_(identifier)" class="mw-redirect" title="ISSN (identifier)">ISSN</a> <a rel="nofollow" class="external text" href="https://search.worldcat.org/issn/1545-4274">1545-4274</a>. <a href="PMID_(identifier)" class="mw-redirect" title="PMID (identifier)">PMID</a> <a rel="nofollow" class="external text" href="https://pubmed.ncbi.nlm.nih.gov/26643025">26643025</a>.</cite></span>

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<li id="cite_note-18"><span class="mw-cite-backlink"><b><a href="#cite_ref-18">^</a></b></span> <span class="reference-text"><cite id="CITEREFSeragAljabarBallCounsell2012" class="citation journal cs1">Serag, A.; Aljabar, P.; Ball, G.; Counsell, S.J.; Boardman, J.P.; Rutherford, M.A.; Edwards, A.D.; Hajnal, J.V.; Rueckert, D. (2012). "Construction of a consistent high-definition spatio-temporal atlas of the developing brain using adaptive kernel regression". <i>NeuroImage</i>. <b>59</b> (3): <span class="nowrap">2255–</span>2265. <a href="Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1016%2Fj.neuroimage.2011.09.062">10.1016/j.neuroimage.2011.09.062</a>. <a href="PMID_(identifier)" class="mw-redirect" title="PMID (identifier)">PMID</a> <a rel="nofollow" class="external text" href="https://pubmed.ncbi.nlm.nih.gov/21985910">21985910</a>. <a href="S2CID_(identifier)" class="mw-redirect" title="S2CID (identifier)">S2CID</a> <a rel="nofollow" class="external text" href="https://api.semanticscholar.org/CorpusID:9747334">9747334</a>.</cite></span>

</li>

<li id="cite_note-19"><span class="mw-cite-backlink"><b><a href="#cite_ref-19">^</a></b></span> <span class="reference-text">most notably by

<cite id="CITEREFGiedd1999" class="citation journal cs1">Giedd, JAY (1999). "Brain Development, IX". <i>American Journal of Psychiatry</i>. <b>156</b>: 4. <a href="Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1176%2Fajp.156.1.4">10.1176/ajp.156.1.4</a>. <a href="PMID_(identifier)" class="mw-redirect" title="PMID (identifier)">PMID</a> <a rel="nofollow" class="external text" href="https://pubmed.ncbi.nlm.nih.gov/9892290">9892290</a>.</cite> and \citet{Thompson:2000p55997} and, more recently, by

<cite id="CITEREFEvans2006" class="citation journal cs1">Evans, Alan C. (2006). "The NIH MRI study of normal brain development". <i>NeuroImage</i>. <b>30</b>: <span class="nowrap">184–</span>202. <a href="Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1016%2Fj.neuroimage.2005.09.068">10.1016/j.neuroimage.2005.09.068</a>. <a href="PMID_(identifier)" class="mw-redirect" title="PMID (identifier)">PMID</a> <a rel="nofollow" class="external text" href="https://pubmed.ncbi.nlm.nih.gov/16376577">16376577</a>.</cite> and <cite id="CITEREFAlmliRivkinMcKinstry2007" class="citation journal cs1">Almli, C.R.; Rivkin, M.J.; McKinstry, R.C. (2007). "The NIH MRI study of normal brain development (Objective-2): Newborns, infants, toddlers, and preschoolers". <i>NeuroImage</i>. <b>35</b>: <span class="nowrap">308–</span>325. <a href="Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1016%2Fj.neuroimage.2006.08.058">10.1016/j.neuroimage.2006.08.058</a>. <a href="PMID_(identifier)" class="mw-redirect" title="PMID (identifier)">PMID</a> <a rel="nofollow" class="external text" href="https://pubmed.ncbi.nlm.nih.gov/17239623">17239623</a>.</cite></span>

</li>

<li id="cite_note-20"><span class="mw-cite-backlink"><b><a href="#cite_ref-20">^</a></b></span> <span class="reference-text">For reviews of MR morphometric studies of brain maturation, see <cite id="CITEREFPaus2005" class="citation journal cs1">Paus, Tomáš (2005). "Mapping brain maturation and cognitive development during adolescence". <i>Trends in Cognitive Sciences</i>. <b>9</b> (2): <span class="nowrap">60–</span>68. <a href="Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1016%2Fj.tics.2004.12.008">10.1016/j.tics.2004.12.008</a>. <a href="PMID_(identifier)" class="mw-redirect" title="PMID (identifier)">PMID</a> <a rel="nofollow" class="external text" href="https://pubmed.ncbi.nlm.nih.gov/15668098">15668098</a>.</cite> and [from early development onto adolescence]<cite id="CITEREFToga2006" class="citation journal cs1">Toga, Arthur W. (2006). <a rel="nofollow" class="external text" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3113697">"Mapping brain maturation"</a>. <i>Trends in Neurosciences</i>. <b>29</b> (3): <span class="nowrap">148–</span>159. <a href="Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1016%2Fj.tins.2006.01.007">10.1016/j.tins.2006.01.007</a>. <a href="PMC_(identifier)" class="mw-redirect" title="PMC (identifier)">PMC</a> <span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3113697">3113697</a></span>. <a href="PMID_(identifier)" class="mw-redirect" title="PMID (identifier)">PMID</a> <a rel="nofollow" class="external text" href="https://pubmed.ncbi.nlm.nih.gov/16472876">16472876</a>.</cite>; <cite id="CITEREFLenrootGiedd2006" class="citation journal cs1">Lenroot, Rhoshel K.; Giedd, Jay N. (2006). "Brain development in children and adolescents: Insights from anatomical magnetic resonance imaging". <i>Neuroscience & Biobehavioral Reviews</i>. <b>30</b> (6): <span class="nowrap">718–</span>729. <a href="Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1016%2Fj.neubiorev.2006.06.001">10.1016/j.neubiorev.2006.06.001</a>. <a href="PMID_(identifier)" class="mw-redirect" title="PMID (identifier)">PMID</a> <a rel="nofollow" class="external text" href="https://pubmed.ncbi.nlm.nih.gov/16887188">16887188</a>.</cite></span>

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<li id="cite_note-21"><span class="mw-cite-backlink"><b><a href="#cite_ref-21">^</a></b></span> <span class="reference-text"><cite id="CITEREFMaguire2000" class="citation journal cs1">Maguire, E. A. (2000). <a rel="nofollow" class="external text" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC18253">"Navigation-related structural change in the hippocampi of taxi drivers"</a>. <i>Proceedings of the National Academy of Sciences</i>. <b>97</b> (8): <span class="nowrap">4398–</span>4403. <a href="Bibcode_(identifier)" class="mw-redirect" title="Bibcode (identifier)">Bibcode</a>:<a rel="nofollow" class="external text" href="https://ui.adsabs.harvard.edu/abs/2000PNAS...97.4398M">2000PNAS...97.4398M</a>. <a href="Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://doi.org/10.1073%2Fpnas.070039597">10.1073/pnas.070039597</a></span>. <a href="PMC_(identifier)" class="mw-redirect" title="PMC (identifier)">PMC</a> <span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC18253">18253</a></span>. <a href="PMID_(identifier)" class="mw-redirect" title="PMID (identifier)">PMID</a> <a rel="nofollow" class="external text" href="https://pubmed.ncbi.nlm.nih.gov/10716738">10716738</a>.</cite></span>

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<li id="cite_note-22"><span class="mw-cite-backlink"><b><a href="#cite_ref-22">^</a></b></span> <span class="reference-text"><cite id="CITEREFBoykeDriemeyerGaserBüchel2008" class="citation journal cs1">Boyke, Janina; Driemeyer, Joenna; Gaser, Christian; Büchel, Christian; May, Arne (2008). <a rel="nofollow" class="external text" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6670504">"Training-Induced Brain Structure Changes in the Elderly"</a>. <i>The Journal of Neuroscience</i>. <b>28</b> (28): <span class="nowrap">7031–</span>7035. <a href="Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1523%2Fjneurosci.0742-08.2008">10.1523/jneurosci.0742-08.2008</a>. <a href="PMC_(identifier)" class="mw-redirect" title="PMC (identifier)">PMC</a> <span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6670504">6670504</a></span>. <a href="PMID_(identifier)" class="mw-redirect" title="PMID (identifier)">PMID</a> <a rel="nofollow" class="external text" href="https://pubmed.ncbi.nlm.nih.gov/18614670">18614670</a>.</cite></span>

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<li id="cite_note-23"><span class="mw-cite-backlink"><b><a href="#cite_ref-23">^</a></b></span> <span class="reference-text"><cite id="CITEREFPradelLangerMaiseyGeffard-Kuriyama2009" class="citation journal cs1">Pradel, Alan; Langer, Max; Maisey, John G.; Geffard-Kuriyama, Didier; Cloetens, Peter; Janvier, Philippe; Tafforeau, Paul (2009). <a rel="nofollow" class="external text" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2653559">"Skull and brain of a 300-million-year-old chimaeroid fish revealed by synchrotron holotomography"</a>. <i>Proceedings of the National Academy of Sciences</i>. <b>106</b> (13): <span class="nowrap">5224–</span>5228. <a href="Bibcode_(identifier)" class="mw-redirect" title="Bibcode (identifier)">Bibcode</a>:<a rel="nofollow" class="external text" href="https://ui.adsabs.harvard.edu/abs/2009PNAS..106.5224P">2009PNAS..106.5224P</a>. <a href="Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://doi.org/10.1073%2Fpnas.0807047106">10.1073/pnas.0807047106</a></span>. <a href="PMC_(identifier)" class="mw-redirect" title="PMC (identifier)">PMC</a> <span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2653559">2653559</a></span>. <a href="PMID_(identifier)" class="mw-redirect" title="PMID (identifier)">PMID</a> <a rel="nofollow" class="external text" href="https://pubmed.ncbi.nlm.nih.gov/19273859">19273859</a>.</cite></span>

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<li id="cite_note-24"><span class="mw-cite-backlink"><b><a href="#cite_ref-24">^</a></b></span> <span class="reference-text"><cite id="CITEREFPfefferbaumSullivanAdalsteinssonGarrick2004" class="citation journal cs1">Pfefferbaum, Adolf; Sullivan, Edith V.; Adalsteinsson, Elfar; Garrick, Therese; Harper, Clive (2004). "Postmortem MR imaging of formalin-fixed human brain". <i>NeuroImage</i>. <b>21</b> (4): <span class="nowrap">1585–</span>1595. <a href="Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1016%2Fj.neuroimage.2003.11.024">10.1016/j.neuroimage.2003.11.024</a>. <a href="PMID_(identifier)" class="mw-redirect" title="PMID (identifier)">PMID</a> <a rel="nofollow" class="external text" href="https://pubmed.ncbi.nlm.nih.gov/15050582">15050582</a>.</cite> </span>

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<li id="cite_note-25"><span class="mw-cite-backlink"><b><a href="#cite_ref-25">^</a></b></span> <span class="reference-text"><cite id="CITEREFHakeemHofSherwoodSwitzer2005" class="citation journal cs1">Hakeem, Atiya Y.; Hof, Patrick R.; Sherwood, Chet C.; Switzer, Robert C.; Rasmussen, L.E.L.; Allman, John M. (2005). "Brain of the African elephant (<i>Loxodonta africana</i>): Neuroanatomy from magnetic resonance images". <i>The Anatomical Record Part A: Discoveries in Molecular, Cellular, and Evolutionary Biology</i>. <b>287A</b> (1): <span class="nowrap">1117–</span>1127. <a href="Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1002%2Far.a.20255">10.1002/ar.a.20255</a>. <a href="PMID_(identifier)" class="mw-redirect" title="PMID (identifier)">PMID</a> <a rel="nofollow" class="external text" href="https://pubmed.ncbi.nlm.nih.gov/16216009">16216009</a>.</cite></span>

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<li id="cite_note-26"><span class="mw-cite-backlink"><b><a href="#cite_ref-26">^</a></b></span> <span class="reference-text"><cite id="CITEREFCorfieldWildCowanParsons2008" class="citation journal cs1">Corfield, Jeremy R; Wild, J Martin; Cowan, Brett R; Parsons, Stuart; Kubke, M Fabiana (2008). "MRI of postmortem specimens of endangered species for comparative brain anatomy". <i>Nature Protocols</i>. <b>3</b> (4): <span class="nowrap">597–</span>605. <a href="Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1038%2Fnprot.2008.17">10.1038/nprot.2008.17</a>.</cite></span>

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<li id="cite_note-27"><span class="mw-cite-backlink"><b><a href="#cite_ref-27">^</a></b></span> <span class="reference-text"><cite id="CITEREFChanetFusellierBaudetMadec2009" class="citation journal cs1">Chanet, B; Fusellier, M; Baudet, J; Madec, S; Guintard, C (2009). "No need to open the jar: a comparative study of Magnetic Resonance Imaging results on fresh and alcohol preserved common carps (Cyprinus carpio (L. 1758), Cyprinidae, Teleostei)". <i>C R Biol</i>. <b>332</b> (4): <span class="nowrap">413–</span>9. <a href="Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1016%2Fj.crvi.2008.12.002">10.1016/j.crvi.2008.12.002</a>. <a href="PMID_(identifier)" class="mw-redirect" title="PMID (identifier)">PMID</a> <a rel="nofollow" class="external text" href="https://pubmed.ncbi.nlm.nih.gov/19304272">19304272</a>.</cite></span>

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</ol></div>

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</style><div id="Anatomy_and_morphology430" style="font-size:114%;margin:0 4em"><a href="Anatomy" title="Anatomy">Anatomy</a> and <a href="Morphology_(biology)" title="Morphology (biology)">morphology</a></div></th></tr><tr><th scope="row" class="navbox-group" style="width:1%">Fields</th><td class="navbox-list-with-group navbox-list navbox-odd hlist" style="width:100%;padding:0"><div style="padding:0 0.25em">

<ul><li><a href="Gross_anatomy" title="Gross anatomy">Gross anatomy</a></li>

<li><a href="Superficial_anatomy" class="mw-redirect" title="Superficial anatomy">Superficial anatomy</a></li>

<li><a href="Neuroanatomy" title="Neuroanatomy">Neuroanatomy</a>

<li><a href="Comparative_anatomy" title="Comparative anatomy">Comparative anatomy</a></li>

<li>Microscopic anatomy

<ul><li><a href="Histology" title="Histology">histology</a></li>

<li><a href="Molecular_anatomy" title="Molecular anatomy">molecular</a></li></ul></li>

<li><a href="Morphometrics" title="Morphometrics">Morphometrics</a></li></ul>

</div></td><td class="noviewer navbox-image" rowspan="9" style="width:1px;padding:0 0 0 2px"><div><span typeof="mw:File"></span></div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">Bacteria and fungi</th><td class="navbox-list-with-group navbox-list navbox-even hlist" style="width:100%;padding:0"><div style="padding:0 0.25em">

<ul><li><a href="Bacterial_cell_structure" title="Bacterial cell structure">Bacterial cell structure</a>

<ul><li><a href="Bacterial_cellular_morphologies" title="Bacterial cellular morphologies">cellular morphologies</a></li>

<li><a href="Bacterial_morphological_plasticity" title="Bacterial morphological plasticity">morphological plasticity</a></li></ul></li>

<li><a href="Colonial_morphology" title="Colonial morphology">Colonial morphology</a></li>

<li><a href="Lichen_morphology" title="Lichen morphology">Lichen morphology</a></li></ul>

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<ul><li>Structures</li></ul>

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<ul><li><a href="Plant_anatomy" title="Plant anatomy">Plant anatomy</a>

<ul><li><a href="Fruit_anatomy" class="mw-redirect" title="Fruit anatomy">fruit</a></li></ul></li>

<li><a href="Plant_habit" class="mw-redirect" title="Plant habit">Plant habit</a></li>

<li><a href="Plant_life-form" title="Plant life-form">Plant life-form</a></li>

<li><a href="Plant_morphology" title="Plant morphology">Plant morphology</a>

<ul><li><a href="Plant_reproductive_morphology" title="Plant reproductive morphology">reproductive</a></li></ul></li>

<li><a href="Soil_morphology" title="Soil morphology">Soil morphology</a></li></ul>

</div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">Invertebrates</th><td class="navbox-list-with-group navbox-list navbox-odd hlist" style="width:100%;padding:0"><div style="padding:0 0.25em">

<ul><li><a href="Decapod_anatomy" title="Decapod anatomy">Decapod anatomy</a></li>

<li><a href="Gastropoda#Anatomy" title="Gastropoda">Gastropod anatomy</a></li>

<li><a href="Insect_morphology" title="Insect morphology">Insect morphology</a>

<ul><li><a href="Morphology_of_Diptera" title="Morphology of Diptera">Diptera</a></li>

<li><a href="External_morphology_of_Odonata" title="External morphology of Odonata">Odonata</a></li></ul></li>

<li><a href="Spider_anatomy" title="Spider anatomy">Spider anatomy</a></li>

<li><a href="Arthropod_cuticle" class="mw-redirect" title="Arthropod cuticle">Arthropod cuticle</a></li></ul>

</div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%"><a href="Mammal_anatomy" class="mw-redirect" title="Mammal anatomy">Mammals</a></th><td class="navbox-list-with-group navbox-list navbox-even hlist" style="width:100%;padding:0"><div style="padding:0 0.25em">

<ul><li><a href="Human_anatomy" title="Human anatomy">Human anatomy</a></li>

<li><a href="Neanderthal_anatomy" title="Neanderthal anatomy">Neanderthal anatomy</a></li>

<li><a href="Cat_anatomy" title="Cat anatomy">Cat anatomy</a></li>

<li><a href="Dog_anatomy" title="Dog anatomy">Dog anatomy</a></li>

<li><a href="Equine_anatomy" title="Equine anatomy">Horse anatomy</a></li>

<li><a href="Elephant#Anatomy_and_morphology" title="Elephant">Elephant anatomy</a></li>

<li><a href="Giraffe#Appearance_and_anatomy" title="Giraffe">Giraffe anatomy</a></li></ul>

</div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%"><a href="Vertebrate_anatomy" class="mw-redirect" title="Vertebrate anatomy">Other vertebrates</a></th><td class="navbox-list-with-group navbox-list navbox-odd hlist" style="width:100%;padding:0"><div style="padding:0 0.25em">

<ul><li><a href="Amphibian_anatomy" class="mw-redirect" title="Amphibian anatomy">Amphibian anatomy</a></li>

<li><a href="Bird_anatomy" title="Bird anatomy">Bird anatomy</a></li>

<li><a href="Fish_anatomy" title="Fish anatomy">Fish anatomy</a></li>

<li><a href="Shark_anatomy" title="Shark anatomy">Shark anatomy</a></li></ul>

</div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">Glossaries</th><td class="navbox-list-with-group navbox-list navbox-even hlist" style="width:100%;padding:0"><div style="padding:0 0.25em">

<ul><li><a href="Anatomical_terminology" title="Anatomical terminology">Anatomical terminology</a></li>

<li><a href="Anatomical_terms_of_location" title="Anatomical terms of location">Anatomical terms of location</a></li>

<li><a href="Glossary_of_dinosaur_anatomy" title="Glossary of dinosaur anatomy">Glossary of dinosaur anatomy</a></li>

<li><a href="Glossary_of_plant_morphology" title="Glossary of plant morphology">Glossary of plant morphology</a>

<ul><li><a href="Glossary_of_leaf_morphology" title="Glossary of leaf morphology">leaf morphology</a></li></ul></li></ul>

</div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">Related topics</th><td class="navbox-list-with-group navbox-list navbox-odd hlist" style="width:100%;padding:0"><div style="padding:0 0.25em">

<ul><li><a href="Allometry" title="Allometry">Allometry</a></li>

<li><a href="Anatomical_variation" title="Anatomical variation">Anatomical variation</a></li>

<li><a href="Anatomical_plane" title="Anatomical plane">Anatomical plane</a></li>

<li><a href="Form_classification" title="Form classification">Form classification</a></li>

<li><a href="Gracility" title="Gracility">Gracility</a></li>

<li><a href="Hertwig_rule" title="Hertwig rule">Hertwig rule</a></li>

<li><a href="History_of_anatomy" title="History of anatomy">History of anatomy</a>

<ul><li><a href="History_of_anatomy_in_the_19th_century" title="History of anatomy in the 19th century">19th century</a></li></ul></li>

<li><a href="Physiognomy" title="Physiognomy">Physiognomy</a></li>

<li><a href="Standard_anatomical_position" title="Standard anatomical position">Standard anatomical position</a></li>

<li><a href="Transcendental_anatomy" title="Transcendental anatomy">Transcendental anatomy</a></li></ul>

</div></td></tr><tr><td class="navbox-abovebelow hlist" colspan="3"><div>

<ul><li><span class="noviewer" typeof="mw:File"><span title="Category"></span></span> Category</li>

<li><span class="noviewer" typeof="mw:File"></span> <a href="Portal%3AAnatomy" title="Portal:Anatomy">Portal</a></li>

<li><a href="Index_of_anatomy_articles" title="Index of anatomy articles">Index of anatomy articles</a></li></ul>

</div></td></tr></tbody></table></div></div><div><div class="zim-footer">

This article is issued from <a class="external text" title="Last edited on 2025-02-19" href="https://en.wikipedia.org/wiki/?title=Brain_morphometry&oldid=1276510725">Wikipedia</a>. The text is available under <a class="external text" href="https://creativecommons.org/licenses/by-sa/4.0/deed.en">Creative Commons Attribution-Share Alike 4.0</a> unless otherwise noted. Additional terms may apply for the media files.

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