Chapter 3 Freesurfer

There are three main components to FreeSurfer’s output: quality control measure, cortical parcellation, subcortical segmentation. If available, longitudinal analyses of all the regions can also be provided.

  1. FreeSurfer’s Euler number summarizes the topological complexity of the reconstructed cortical surface. The Euler number can be used to identify images as unusable and is a highly reliable measure of image quality.
  2. From FreeSurfer’s cortical parcellation several measures can be obtained from within each region: surface area, cortical thickness, gray matter volume, white matter volume, and cortical mean curvature.
  3. From FreeSurfer’s subcortical segmentation, volumes can be obtained.
  4. Percentage change and raw values can also be obtained through longitudinal pipelines.

3.1 Euler

After Leonhard Euler (1707-83), the Euler number is a topological invariant of a surface that can be computed from the number of edges, vertices and faces in a polygonal tessellation. For a completely flat and smooth surface with no handles or holes, the Euler value should be 2. For a surface that contains a lot of holes or handles, the Euler number is 2-2n, where n is the number of defects. The higher the Euler number, the higher the data quality for FreeSurfer cortical reconstruction. In other words, the less negative the number the better. Unfortunately, there’s no set number for determining quality as the number varies between populations (i.e., adolescents vs. adults), and across scanners and sequences. The Euler value can only be used within a study to determine quality control issues (Rosen et al., 2018). However, it has been proposed that the Euler value may be used towards optimization and standardization of scans across sites (Chalavi, Simmons, Dijkstra, Barker, & Reinders, 2012).

subjid lhSurfaceEuler rhSurfaceEuler
sub-001 -20 -12
sub-002 -36 -58
sub-003 -40 -32
sub-004 -20 -16
sub-005 -44 -36
sub-006 -96 -72
sub-007 -16 -58
sub-008 -42 -36
sub-009 -22 -28

3.2 Cortical Parcellation

subjid region hemi area curv thick gm.vol
sub-001 bankssts lh 992 0.078 2.972 2835
sub-001 caudalanteriorcingulate lh 396 0.097 2.984 1334
sub-001 caudalmiddlefrontal lh 1948 0.122 3.003 6401
sub-001 cuneus lh 1378 0.144 1.973 2768
sub-001 entorhinal lh 402 0.097 3.641 1654
sub-001 fusiform lh 3128 0.133 3.037 10256
sub-001 inferiorparietal lh 3616 0.133 2.910 11735
sub-001 inferiortemporal lh 3139 0.115 3.086 11201
sub-001 isthmuscingulate lh 928 0.129 2.885 2883
sub-001 lateraloccipital lh 3902 0.135 2.479 10358
sub-001 lateralorbitofrontal lh 2132 0.132 2.945 6530
sub-001 lingual lh 3331 0.141 2.184 7313
sub-001 medialorbitofrontal lh 1481 0.123 2.674 4533
sub-001 middletemporal lh 2508 0.125 3.042 9190
sub-001 parahippocampal lh 618 0.078 2.863 1955
sub-001 paracentral lh 991 0.099 2.475 2723
sub-001 parsopercularis lh 1384 0.105 2.850 4609
sub-001 parsorbitalis lh 561 0.125 3.087 2265
sub-001 parstriangularis lh 1051 0.126 2.645 3059
sub-001 pericalcarine lh 1071 0.165 1.732 1490
sub-001 postcentral lh 3596 0.112 2.387 9384
sub-001 posteriorcingulate lh 874 0.136 2.949 2740
sub-001 precentral lh 4056 0.102 3.003 13427
sub-001 precuneus lh 3574 0.119 2.624 9670
sub-001 rostralanteriorcingulate lh 655 0.126 3.054 2175
sub-001 rostralmiddlefrontal lh 4105 0.128 2.760 13301
sub-001 superiorfrontal lh 6181 0.118 3.081 22231
sub-001 superiorparietal lh 4982 0.130 2.559 14203
sub-001 superiortemporal lh 3040 0.104 3.015 10767
sub-001 supramarginal lh 3631 0.125 2.908 11550
sub-001 frontalpole lh 227 0.157 2.943 974
sub-001 temporalpole lh 436 0.131 3.718 2361
sub-001 transversetemporal lh 355 0.108 2.847 1050
sub-001 insula lh 2360 0.127 3.160 7051
sub-001 BrainSegVolNotVent lh 1127167 1127167.000 1127167.000 1127167
sub-001 eTIV lh 1441608 1441608.074 1441608.074 1441608

Frontal

  • Superior Frontal
  • Rostral and Caudal Middle Frontal
  • Pars Opercularis, Pars Triangularis, and Pars Orbitalis
  • Lateral and Medial Orbitofrontal
  • Precentral
  • Paracentral
  • Frontal Pole

Parietal

  • Superior Parietal
  • Inferior Parietal
  • Supramarginal
  • Postcentral
  • Precuneus

Temporal

  • Superior, Middle, and Inferior Temporal
  • Banks of the Superior Temporal Sulcus
  • Fusiform
  • Transverse Temporal
  • Entorhinal
  • Temporal Pole
  • Parahippocampal

Occipital

  • Lateral Occipital
  • Lingual
  • Cuneus
  • Pericalcarine

Cingulate (if you want to include in a lobe)

  • Rostral Anterior (Frontal)
  • Caudal Anterior (Frontal)
  • Posterior (Parietal)
  • Isthmus (Parietal)

Other

  • Insula

3.3 Subcortical Segmentation

Besides the default subcortical segmentations, FreeSurfer v6.0 has advanced segmentations for the hippocampal subregions, amygdala and thalamic nuclei, and brainstem segmentation. Volume is the only measure from these segmentations.

  • Cerebral White Matter
  • Lateral Ventricle
  • Inferior Lateral Ventricle
  • Cerebellum White Matter
  • Cerebellum Cortex
  • Caudate
  • Putamen
  • Pallidum
  • Lesions
  • Accumbens area
  • Vessel
  • Third Ventricle
  • Fourth Ventricle
  • Cerebrospinal Fluid
  • Corpus Callosum (Posterior, Mid Posterior, Central, Mid Anterior, Anterior)
  • …and more

Hippocampus

If you have used results from this software for a publication, please use this citation (Iglesias, Augustinack, et al., 2015).

  • Hippocampal Tail
  • Hippocampal Body
  • Hippocampal Head
  • Whole Hippocampus
  • Subiculum
  • Presubiculum
  • Parasubiclum
  • CA1
  • CA3
  • CA4
  • Molecular layer
  • Fimbria
  • Hippocampal fissure
  • HATA

Amygdala

In addition, if you have used the segmentation of the nuclei of the amygdala, please also cite (Saygin et al., 2017).

  • Whole amygdala
  • Lateral nucleus
  • Basal nucleus
  • Accessory basal nucleus
  • Anterior amygdaloid area
  • Central nucleus
  • Medial nucleus
  • Cortical nucleus
  • Corticoamygdaloid transition area
  • Paralaminar nucleus

Brainstem

If you have used results from this software for a publication, please use this citation (Iglesias, Van Leemput, et al., 2015).

  • Whole brainstem
  • Medulla
  • Pons
  • Midbrain
  • Superior cerebellar peduncles

Thalamus

If you have used results from this software for a publication, please use this citation (Iglesias et al., 2018).

3.4 Longitudinal Analyses

As quoted from the FreeSurfer website:

Cortical reconstruction and volumetric segmentation was performed with the Freesurfer image analysis suite, which is documented and freely available for download online (http://surfer.nmr.mgh.harvard.edu/). The technical details of these procedures are described in prior publications (Dale, Fischl, & Sereno, 1999; Fischl & Dale, 2000; Fischl, Kouwe, et al., 2004; Fischl, Liu, & Dale, 2001; Fischl et al., 2002, 1999a, 1999b; Fischl, Salat, et al., 2004; Han et al., 2006; Jovicich et al., 2006; Reuter & Fischl, 2011; Reuter, Schmansky, Rosas, & Fischl, 2012; Segonne et al., 2004). Briefly, this processing includes motion correction and averaging (Reuter, Rosas, & Fischl, 2010) of multiple volumetric T1 weighted images (when more than one is available), removal of non-brain tissue using a hybrid watershed/surface deformation procedure (Segonne et al., 2004), automated Talairach transformation, segmentation of the subcortical white matter and deep gray matter volumetric structures (including hippocampus, amygdala, caudate, putamen, ventricles) (Fischl et al., 2002; Fischl, Salat, et al., 2004) intensity normalization (Sled, Zijdenbos, & Evans, 1998), tessellation of the gray matter white matter boundary, automated topology correction (Fischl et al., 2001; Segonne, Pacheco, & Fischl, 2007), and surface deformation following intensity gradients to optimally place the gray/white and gray/cerebrospinal fluid borders at the location where the greatest shift in intensity defines the transition to the other tissue class (Dale et al., 1999; Fischl & Dale, 2000). Once the cortical models are complete, a number of deformable procedures can be performed for further data processing and analysis including surface inflation (Fischl et al., 1999a), registration to a spherical atlas which is based on individual cortical folding patterns to match cortical geometry across subjects (Fischl et al., 1999b), parcellation of the cerebral cortex into units with respect to gyral and sulcal structure (Desikan et al., 2006; Fischl, Kouwe, et al., 2004), and creation of a variety of surface based data including maps of curvature and sulcal depth. This method uses both intensity and continuity information from the entire three dimensional MR volume in segmentation and deformation procedures to produce representations of cortical thickness, calculated as the closest distance from the gray/white boundary to the gray/CSF boundary at each vertex on the tessellated surface (Fischl & Dale, 2000). The maps are created using spatial intensity gradients across tissue classes and are therefore not simply reliant on absolute signal intensity. The maps produced are not restricted to the voxel resolution of the original data thus are capable of detecting submillimeter differences between groups. Procedures for the measurement of cortical thickness have been validated against histological analysis (Rosas et al., 2002) and manual measurements (Kuperberg et al., 2003; Salat et al., 2004). Freesurfer morphometric procedures have been demonstrated to show good test-retest reliability across scanner manufacturers and across field strengths (Han et al., 2006; Reuter et al., 2012).

To extract reliable volume and thickness estimates, images are automatically processed with the longitudinal stream (Reuter et al., 2012) in FreeSurfer. Specifically an unbiased within-subject template space image is created using robust, inverse consistent registration (Reuter et al., 2010). Several processing steps, such as skull stripping, Talairach transforms, atlas registration as well as spherical surface maps and parcellations are then initialized with common information from the within-subject template, significantly increasing reliability and statistical power (Reuter et al., 2012).

Please cite at least (Reuter et al., 2012) if the longitudinal stream was used!

subjid ses hemi area wm.vol
sub-001 1 lh bankssts 3458.5
sub-001 2 lh bankssts 3502.4
sub-002 1 lh bankssts 3994.3
sub-002 2 lh bankssts 3933.2
sub-003 1 lh bankssts 3414.6
sub-003 2 lh bankssts 3427.2
sub-004 1 lh bankssts 3352.9
sub-004 2 lh bankssts 3403.0
sub-005 1 lh bankssts 3511.6
sub-005 2 lh bankssts 3611.9
sub-001 1 lh caudalanteriorcingulate 2213.9
sub-001 2 lh caudalanteriorcingulate 2205.7
sub-002 1 lh caudalanteriorcingulate 2271.9
sub-002 2 lh caudalanteriorcingulate 2243.4
sub-003 1 lh caudalanteriorcingulate 2513.7
sub-003 2 lh caudalanteriorcingulate 2496.6
sub-004 1 lh caudalanteriorcingulate 2678.1
sub-004 2 lh caudalanteriorcingulate 3006.6
sub-005 1 lh caudalanteriorcingulate 3780.7
sub-005 2 lh caudalanteriorcingulate 3811.6
sub-001 1 lh fusiform 7382.8
sub-001 2 lh fusiform 7457.0
sub-002 1 lh fusiform 7536.4
sub-002 2 lh fusiform 7637.2
sub-003 1 lh fusiform 7438.7
sub-003 2 lh fusiform 7497.3
sub-004 1 lh fusiform 7941.5
sub-004 2 lh fusiform 7918.5
sub-005 1 lh fusiform 8160.0
sub-005 2 lh fusiform 8152.7

Percent change is also calculated.

subjid hemi area percentchange
sub-001 lh bankssts -1.269
sub-002 lh bankssts 1.530
sub-003 lh bankssts -0.369
sub-004 lh bankssts -1.494
sub-005 lh bankssts -2.856
sub-001 lh caudalanteriorcingulate 0.370
sub-002 lh caudalanteriorcingulate 1.254
sub-003 lh caudalanteriorcingulate 0.680
sub-004 lh caudalanteriorcingulate -12.266
sub-005 lh caudalanteriorcingulate -0.817
sub-001 lh fusiform -1.005
sub-002 lh fusiform -1.338
sub-003 lh fusiform -0.788
sub-004 lh fusiform 0.290
sub-005 lh fusiform 0.089

3.5 Files

3.5.1 Freesurfer Subjects Directory

If the dataset does not contain multiple sessions, the freesurfer directory will be organized in the following manner:

./freesurfer_subjects
$ Analysis of sub-001
├── sub-001
│   ├── label
│   ├── mri
│   ├── scripts
│   ├── stats
│   ├── surf
│   ├── tmp
│   ├── touch
│   └── trash
$ Analysis of sub-002
└── sub-002
    ├── label
    ├── mri
    ├── scripts
    ├── stats
    ├── surf
    ├── tmp
    ├── touch
    └── trash

However if you have multiple timepoints and the longitudinal stream was run, then you’ll have multiple directories for each participant. First, each timepoint is processed independently, which outputs cross-sectional results. In the FreeSurfer directory the time points will be named:

./freesurfer_subjects
$ Cross sectional analysis of sub-001 at timepoint 1
├── sub-001_ses-1
│   ├── label
│   ├── mri
│   ├── scripts
│   ├── stats
│   ├── surf
│   ├── tmp
│   ├── touch
│   └── trash
$ Cross sectional analysis of sub-001 at timepoint 2
└── sub-001_ses-2
    ├── label
    ├── mri
    ├── scripts
    ├── stats
    ├── surf
    ├── tmp
    ├── touch
    └── trash

Next, a within subject template is created across all time points. This is called the base directory and will just consist of the subject ID and not contain any session information. There should be only one base directory per subject. The data contained in this directory is not to be used for any analyses!

./freesurfer_subjects
$ Base template for sub-001 across all timepoints
├── sub-001
│   ├── label
│   ├── mri
│   ├── scripts
│   ├── stats
│   ├── surf
│   ├── tmp
│   ├── touch
│   └── trash
$ Cross sectional analysis of sub-001 at timepoint 1
├── sub-001_ses-1
│   ├── label
│   ├── mri
│   ├── scripts
│   ├── stats
│   ├── surf
│   ├── tmp
│   ├── touch
│   └── trash
$ Cross sectional analysis of sub-001 at timepoint 2
└── sub-001_ses-2
    ├── label
    ├── mri
    ├── scripts
    ├── stats
    ├── surf
    ├── tmp
    ├── touch
    └── trash

Finally, there will be longitudinal directories for each session.They contain the final, most reliable and accurate processing results that can be used.

./freesurfer_subjects
$ Base template for sub-001 across all timepoints
├── sub-001
│   ├── label
│   ├── mri
│   ├── scripts
│   ├── stats
│   ├── surf
│   ├── tmp
│   ├── touch
│   └── trash
$ Cross sectional analysis of sub-001 at timepoint 1
├── sub-001_ses-1
│   ├── label
│   ├── mri
│   ├── scripts
│   ├── stats
│   ├── surf
│   ├── tmp
│   ├── touch
│   └── trash
$ Cross sectional analysis of sub-001 at timepoint 2
├── sub-001_ses-2
│   ├── label
│   ├── mri
│   ├── scripts
│   ├── stats
│   ├── surf
│   ├── tmp
│   ├── touch
│   └── trash
$ Longitudinal analysis of sub-001 at timepoint 1
├── sub-001_ses-1.long.sub-001
│   ├── label
│   ├── mri
│   ├── scripts
│   ├── stats
│   ├── surf
│   ├── tmp
│   ├── touch
│   └── trash
$ Longitudinal analysis of sub-001 at timepoint 2
└── sub-001_ses-2.long.sub-001
    ├── label
    ├── mri
    ├── scripts
    ├── stats
    ├── surf
    ├── tmp
    ├── touch
    └── trash

3.5.2 Output Tables found on Github

List of files will include euler values:

  • euler-lh.csv
  • euler-rh.csv

Cortical parcellation:

  • aparc-area-lh-stats.csv
  • aparc-area-rh-stats.csv
  • aparc-meancurv-lh-stats.csv
  • aparc-meancurv-rh-stats.csv
  • aparc-thickness-lh-stats.csv
  • aparc-thickness-rh-stats.csv
  • aparc-volume-lh-stats.csv
  • aparc-volume-rh-stats.csv
  • wmparc-volume-stats.csv

Subcortical volumes:

  • aseg-volume-stats.csv
  • amygdalarnuclei-lh-stats.csv
  • amygdalarnuclei-rh-stats.csv
  • hipposubfields-lh-stats.csv
  • hipposubfields-rh-stats.csv
  • thalamicnuclei-lh-stats.csv
  • thalamicnuclei-rh-stats.csv
  • brainstem-stats.csv

Longitudinal:

  • aparc-area-lh-long-stats.csv
  • aparc-area-lh-percentchange-stats.csv
  • aparc-area-rh-long-stats.csv
  • aparc-area-rh-percentchange-stats.csv
  • aparc-meancurv-lh-long-stats.csv
  • aparc-meancurv-lh-percentchange-stats.csv
  • aparc-meancurv-rh-long-stats.csv
  • aparc-meancurv-rh-percentchange-stats.csv
  • aparc-thickness-lh-long-stats.csv
  • aparc-thickness-lh-percentchange-stats.csv
  • aparc-thickness-rh-long-stats.csv
  • aparc-thickness-rh-percentchange-stats.csv
  • aparc-volume-lh-long-stats.csv
  • aparc-volume-lh-percentchange-stats.csv
  • aparc-volume-rh-long-stats.csv
  • aparc-volume-rh-percentchange-stats.csv
  • aseg-volume-long-stats.csv
  • aseg-volume-percentchange-stats.csv
  • amygdalarnuclei-long-stats.csv
  • amygdalarnuclei-percentchange-stats.csv
  • hipposubfields-long-stats.csv
  • hipposubfields-percentchange-stats.csv
  • wmparc-volume-long-stats.csv
  • wmparc-volume-percentchange-stats.csv

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