Contents lists available at ScienceDirect
NeuroImage
journal homepage: www.elsevier.com/locate/neuroimage
Resolution considerations in imaging of the cortical layers
Shlomi Lifshitsa
, Omri Tomerb
, Ittai Shamirc
, Daniel Barazanyd
, Galia Tsarfatye
,
Saharon Rosseta
, Yaniv Assafb,f,⁎
a
Department of Statistics and Operations Research, Faculty of Exact Sciences, Tel-Aviv University, Tel-Aviv, Israel
b
Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
c
Department of mechanical engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel
d
The Strauss Center for Computational Neuroimaging, Tel Aviv University, Tel Aviv, Israel
e
Department of Diagnostic Imaging, Sheba Medical Center, Tel Hashomer, Ramat Gan, Israel
f
Department of Neurobiology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
A R T I C L E I N F O
Keywords:
Cortical layers
T1 relaxation
Partial volume effect
MRI resolution
Brain parcellation
A B S T R A C T
The cortical layers are a finger print of brain development, function, connectivity and pathology. Obviously, the
formation of the layers and their composition is essential to cognition and behavior. The layers were
traditionally measured by histological means but recent studies utilizing MRI suggested that T1 relaxation
imaging consist of enough contrast to separate the layers. Indeed extreme resolution, post mortem, studies
demonstrated this phenomenon. Yet, one of the limiting factors of using T1 MRI to visualize the layers in
neuroimaging research is partial volume effect. This happen when the image resolution is not high enough and
two or more layers resides within the same voxel. In this paper we demonstrate that due to the physical small
thickness of the layers it is highly unlikely that high resolution imaging could resolve the layers. By contrast, we
suggest that low resolution multi T1 mapping conjugate with composition analysis could provide practical
means for measuring the T1 layers. We suggest an acquisition platform that is clinically feasible and could
quantify measures of the layers. The key feature of the suggested platform is that separation of the layers is
better achieved in the T1 relaxation domain rather than in the spatial image domain.
Introduction
The cortical layers are assumed to play an important role in brain
function, development and pathology. The layout of the layers not only
defines the anatomical location of different brain regions but also
affects their functions (Kandel et al., 2000). The layers are formed
through brain development while neurons migrate to form the cortex
(Kandel et al., 2000). Through that migration process, cortical connections
are formed, hence the formation of the layers also affects brain
connectivity. The layers are defined by the arrangement and density of
neurons (cyto-architecture) and myelin (myelo-architecture) in the
cortex (Brodmann and Garey, 1999). With that definition, the common
quantitative measure of the layers is their thickness and its variation
across the cortex (Brodmann and Garey, 1999; Vogt, 1911; von
Economo and Koskinas, 1925). Yet the cyto- and myelo-architectonic
layer thickness can be measured only with histological measures, which
were used to demonstrate that the layout of the layers (both composition
and thickness) overlaps with the functional representation of
cortical regions. It was also demonstrated that abnormal layer composition
leads to functional deficits and cognitive impairments (Hof et al.,
1996; Kalus et al., 1997; Kordower et al., 2001; Masliah et al., 1991).
Despite the assumed great importance of the layers, as no state-ofthe-art
in-vivo imaging bio-markers of the layers exist, large population
studies on the layers role in cognition, behavior, brain physiology and
neurodegeneration are limited. While fMRI and Voxel-based-morphometry
(VBM) provide information on cortical function and thickness
across groups of subjects (typically where the cortex width is represented
by 2–4 voxels), none of these methods are measured in a
resolution that allows visualization of the layers. Recently, meso-scale
resolution MRI of brain function or anatomy, typically using high field
MRI, allowed rough localization (of various parameters from BOLD
response to susceptibility and diffusion properties) within the cortical
stripe from inner (close to the WM) to outer (close to the CSF) parts of
the cortex (Barazany and Assaf, 2011; Barbier et al., 2002; Duyn et al.,
2007; Koopmans et al., 2010; Leuze et al., 2012; Olman et al., 2012).
Of specific interest was the use of T1 contrast to estimate myelin
content along the cortical surface as a neuro-anatomical and functional
indicator for differences between brain regions and cortical sub-
http://dx.doi.org/10.1016/j.neuroimage.2017.02.086
Accepted 27 February 2017
⁎
Corresponding author at: Department of Neurobiology, Faculty of Life Science, Tel Aviv University, Tel Aviv, Israel.
E-mail address: assafyan@gmail.com (Y. Assaf).
NeuroImage 164 (2018) 112–120
Available online 06 March 2017
1053-8119/ © 2017 Elsevier Inc. All rights reserved.
MARK
structures (Dinse et al., 2015; Fracasso et al., 2016; Goncalves et al.,
2015; Lutti et al., 2014; Sereno et al., 2013). The understanding that
investigation of the layers might reveal more detailed information on
brain function triggered, from the early days of MRI, research aimed to
find which MRI contrast demonstrates best the layers (Barbier et al.,
2002; Clark et al., 1992; Eickhoff et al., 2005; Walters et al., 2003).
These kinds of studies focused on excised tissue where extreme image
resolution can be obtained at hours of scanning and on striate cortex
where the heavily myelinated layer 4 (stripe of Gennari) can be easily
detected (Barazany and Assaf, 2011; Barbier et al., 2002; Clare and
Bridge, 2005; Turner et al., 2008). It was found that T1 and T2
weighted contrasts enable demonstration of the layers probably due to
differences in the myelin content across the layers. Over the years T1
contrast was preferred over T2 as the differences in T1 spread over a
much large scale and the contrast in T1 (especially with inversion
recovery (IR) sequence) is much higher. Despite this promising
observation, one of the challenges in cortical layer imaging is to go
beyond the level of the striate cortex and provide a robust framework
that will enable characterization of all the layers for the entire brain
and in-vivo (Barazany and Assaf, 2011). Additional studies assumed
that T1 values within the cortex are correlated with the layers myelin
content (Dinse et al., 2015; Fracasso et al., 2016; Lutti et al., 2014;
Sereno et al., 2013). These studies showed that high resolution
quantitative T1 MRI can provide additional insight into cortical
anatomy and provide new means to investigate the cortex. Yet, it
should be noted that even the highest resolution MRI can achieve (submillimeter)
does not allow visualization of the layers but rather
estimation of various gross factors related to the layers (e.g. myelination
content).
In recent years, with the advances in high magnetic field scanners,
it was speculated that in-vivo sub-millimeter imaging of the human
brain will enable cortical layer characterization. Several studies used T1
contrast to explore the cortical layers providing additional proof that
T1 contrast is highly sensitive to the layer composition, implying that
even higher resolution will enable better characterization of the layers
(Barazany and Assaf, 2011; Geyer et al., 2011; Turner et al., 2008). In
this paper we argue that extreme resolution MRI will not allow
adequate and reasonable characterization of the layers. We note that
as the cortex is only 2–4 mm thick (and in many regions some layers’
width is even less than 200 μm), the minimum MRI resolution to
adequately resolve the layers should be in the order of tens of microns
for the entire brain. Such acquisition resolution cannot be currently
achieved and therefore partial volume effects (PVE) is the main
obstacle for comprehensive and robust cortical layer imaging. In this
paper we demonstrate an alternative approach to resolve PVE in
cortical layer imaging using composition analysis of multi T1 components
within a voxel. We show, across species, that in vivo imaging of
the layers can be achieved with good separation between the layers, for
the whole brain and with reasonable scanning time. We suggest that
separation between the layers cannot be achieved by means of
increased resolution, but rather by composition analysis in the T1 time
domain.
Methods
In this work we have compared 4 experiments: high vs. low
resolution on human and rat samples. A comparison between all
experimental conditions is given in details in Table 1 and in the text
below.
Human experiments
Subjects
33 healthy subjects (age range 25–35) recruited for this study, 15 (8
males, 7 females) of them were scanned in the high-resolution
experiment (see details below) and 18 (10 males, 8 females) for the
low-resolution experiment (see details below). Subjects were neurologically
and radiologically healthy with no history of neurological
diseases. The imaging protocol was approved by the institutional
review boards of Tel Aviv Sourasky Medical Center, Sheba Medical
Center and Tel Aviv University. All subject signed informed consent
before enrollment in the study.
Acquisition
We have used 2 different protocols to demonstrate the ability of T1
weighted MRI to characterize the layers:
1. A high resolution protocol that included a series of inversion
recovery prepared fast spin echo images acquired at resolution of
0.43×0.43×1.5 mm3
(matrix size of 512×384 reconstructed to
512×512) covering the left hemisphere in the sagittal plane (data
taken from:(Barazany and Assaf, 2011)). These experiments were
scanned on a 3T Signa general electric scanner (GE Healthcare,
Milwaukee, WI) with an 8-channel RF coil. TR/TE were 11,000/
14 ms and 7 inversion times (TIs) (230, 432, 575, 760, 920, 1080
and 1380 ms). The acquisition time for the inversion recovery
protocol lasted 45 min. In addition we have scanned a spoiled
gradient recalled echo (SPGR) (TR/TE/TI/Flip angle=9.3 ms/
3.8 ms /450 ms/13°) at resolution of 1x1×1 mm3
as an anatomical
reference with high gray/white matter contrast.
2. A low resolution protocol that included a series of inversion recovery
prepared echo planar images acquired at resolution of 3x3×3 mm3
in the axial plane covering the entire brain. These experiments were
scanned on a 3T Magnetom Siemens Prisma (Siemens Medical
Solutions, Erlangen, Germany) scanner with a 64-channel RF coil
and the following parameters: TR/TE=10000/30 ms and 107 inversion
times spread between 50 ms up to 3000 ms), GRAPPA factor of
2 with matrix size of 68×68. The acquisition time for the inversion
recovery data set was approximately 20 min. In addition we have
scanned MPRAGE sequence (TR/TE=1750/2.6 ms, TI=900 ms) at
resolution of 1x1×1 mm3
as an anatomical reference with high gray/
white matter contrast.
Rat experiments
An excised male rat brain aged 20 weeks (Wistar, Harlan labs),
formalin fixated was scanned on a 7T/30 Bruker Biospec scanner
(Bruker, Karlsruhe, Germany) with a transmit volume coil and quadrature
surface coil as receiver. As in the human experiment we used two
protocols:
1. A high resolution a 3D inversion recovery prepared fast spin echo
with the following parameters: TR=4000 ms, TE=5.92 ms (with 16
echoes train length resulting in effective TE of 29 ms), TIs varied
from 100 ms to 3000 ms in 18 steps, 2 averages with cubic image
resolution of 110 μm3
. The acquisition time for each TI was 42 min
and the total scanning time was about 16 h.
2. Low-resolution 3D inversion recovery prepared fast spin echo with
60 inversion times spread between 100 and 3000 ms with high
sampling rate around the TIs that nulls the GM layers. The
resolution was 510 μm cubic with TR of 4000 ms and TE of 4.9 ms
(effective TE of 41.4 ms). The acquisition time for each TI was 80 s
and the total acquisition time was 80 min.
Image analysis
The IR datasets (either human or rats) were fitted to the conventional
inversion recovery decay function (Barral et al., 2010) (using an
in-house code written in Matlab, (Mathworks, Natick, MA)) with
possible multiple T1 components, on a voxel-by-voxel basis:
S. Lifshits et al. NeuroImage 164 (2018) 112–120
113
∑TI M eM( ) = 0 ∙(1 − 2 )i
j
j
TI T− /i j1
(1)
where TIM( )i is the magnetization at the i-th inversion recovery image,
M0j is the predicted magnetization at TI=0 ms for each T1 component
(j) in the voxel, and T j1 is the longitudinal relaxation time for each T1
component. j was set to 1 for the high resolution experiments and to 7
for the low resolution experiments. In the low-resolution experiment, j
was set to 7 following the assumptions that there are 7 T1 components
in the tissue – 1 for CSF, 1 for WM and heavily myelinated layer of the
cortex and additional 5 cortical layers.
Calculation of the T1component probability maps
A histogram of the whole brain T1 values was used to compute T1component
probability maps. A Gaussian mixture model was used to fit
the whole brain T1 values (here we used the gmdistribution fit function
in Matlab). Using a Bayesian index criterion (BIC) we found that the
best fit the T1-histogram provides 7 or 8 distinct T1 components
(presumably 1 for CSF, 1 for WM and 5–6 for GM). Fig. 1 shows such
analysis for the entire dataset: high resolution (A and C) vs lowresolution
(B and D) and human (A and B) compared with rat (C and
D). In general we found 7 Gaussian classes: 1 for white matter and
layer 6 (with T1 values lower than ~600 ms), 5 for additional gray
matter layers (noted L 1–5) and one for CSF (not shown on graph).
Based on the Gaussian mixture analysis we could compute the
probability of each voxel to be assigned as each of the classes.
Eventually it was possible to create class specific probability maps.
Therefore, each image voxel, regardless of the acquisition resolution, is
represented by a vector of the abovementioned T1 class's probabilities.
To visualize this information we projected the T1 class probabilities
onto a surface representation of the brain. Another way we used to
visualize the data is by clustering. K-means clusters employed to
segment the cortex to the different layers based on the class probability
and location along the cortex (k was set to 7 or 8 based on the number
Table 1
Experiment Low-res rat High-res rat Low-res human High-res human
Magnet 7T Biospec Bruker 3T Siemens Prisma
TR 4,000 ms 4,000 ms 11,000 ms 10,000 ms
TE (eff. TE) 4.9 (41.4) ms 5.92 (29) ms 30 ms 14ms
TI rangea
50–3000 ms 100–3000 ms 50–3000 ms 230-1380 ms
No. of TI measurements 60 18 107 7
Resolution 0.51×0.51×0.51 mm3
0.11×0.11×0.11 mm3
3x3×3 mm3
0.43×0.43×1.5 mm3
a
The full list of TI's is given in the Supplementary information.
Fig. 1. T1 histograms of the different experiments performed in this study. (A) Histogram of T1 values for the entire brain of the high resolution human experiments. The histogram
demonstrate a mixture of Gaussians with the gray lines indicates the histograms for each subject and the black line – the average of all subjects. The arrows are used for visual guidance
on the centers of the different Gaussian classes (1–6 for gray matter and their layer classification). It is interesting to note that there is increased variability across subject especially at the
low-T1 range (lower layers of the cortex and white matter). Similar analysis on the T1 data of the low resolution experiments (B) shows that his variability reduces (probably since we
model the partial volume effect). (C) and (D) shows the same but for the rat brain high resolution (C) and low-resolution (D). Note that the centers of the Gaussians remained the same
both in the high resolution (A and C) compared with the low-resolution (B and D) indicating that the low-resolution T1 mapping is adequate for separation between the layers.
S. Lifshits et al. NeuroImage 164 (2018) 112–120
114
of Gaussians in the T1 histogram).
Results
In this paper we wish to explore PVE effects in cortical layers
imaging through T1 relaxation mapping. At first step we acquired a
high resolution (110 μm3
) on excised rat brain to investigate the T1
layers with minimal PVE. Then we demonstrate composition based
analysis on two in-vivo human data sets (one with meso-scale resolution
and one with low resolution).
High resolution rat data
High cubic resolution MRI (110 μm3
) of a fixated rat brain was
acquired to demonstrate the utility of inversion recovery and T1 MRI to
visualize and quantify the layers. The purpose of this experiment was to
demonstrate that: a) Different layers have different and distinct T1
values; b) The layer distribution has neuro-anatomical meaning. Fig. 2
shows analysis of the high resolution IR data set. Fig. 2A depicts the T1
map of one representative slice with an enlargement of the S1BF cortex
demonstrating the variation in T1 perpendicular to the cortex (numerically
demonstrated in Fig. 2B). It is obvious that T1 changes across
the cortex are not just a ‘gradual’ change from CSF's high T1 to white
matter's low T1. The T1 perpendicular to the cortical stripe changes in
a stepwise manner with a trend of reduction in T1 towards the white
matter probably due to increased myelination in deeper layers
(Fig. 2B).
The T1 class probability maps were used to segment the cortex to
the different layers based on the values and location along the cortex
(using k-means clustering). Following clustering, the thickness of each
layer perpendicular to the cortical surface could be calculated. Fig. 2C
shows the projection of each layer thickness onto a surface representation
of cortex. The layer thickness surfaces show different regional
distribution across the cortex (and hence demonstrate the sensitivity of
the T1 layers). By comparing the layer probability maps with the cytoarchitectonic
atlas of brain regions (Fig. 2D), it is possible to observe
that different cyto-architectonic areas also differ in their T1 layer
thickness. Noteworthy are the missing layer 4 from the motor cortex
(arrow 1), the thick layer 4 in visual cortex and sensory cortex (arrows
2 and 3) and thick layer 2 in the cingulate cortex (arrow 4).
Meso-scale human data
Cubic resolution of 110 μm in the human brain is in-achievable, yet
the cortical width in humans is about twice that of the rat. Therefore
comparable resolution to Fig. 2 in the human brain would be in the
order of 200–300 μm – which cannot be achieved in-vivo for this
protocol even with high-field magnets (7T and above). Alternatively, we
have scanned human subjects with the best achievable resolution in an
acceptable scanning time (~1 h) of 430×430 μm in-plane resolution
and slice thickness of 1.5 mm. Analysis of T1-layers in such setup were
reported previously (Barazany and Assaf, 2011). Yet, it is well accepted
that at such resolution significant PVE between the layers and adjacent
tissues is apparent and a more sophisticated model should be used to
infer sub-voxel layers distribution.
In the following analysis, instead of computing single T1 value for
each voxel and then assigning each T1/voxel to a layer, we performed a
multi T1 analysis revealing the sub-voxel T1 composition.
Conventionally such multi-component analysis can be achieved by
acquiring multiple inversion recovery images (with large number of
inversion times) to allow multi T1 analysis (as in Eq. (1) when j is set to
2 or higher). Yet, as only 7 TIs were acquired in the meso-scale
resolution human data, we could not fit more than T1 value per voxel.
To overcome this obstacle we used the T1 classes as shown in Fig. 1.
Here, we used the T1 classes as a-priori knowledge and simulated, per
class, the IR signal dependency (sampled several time over the
Gaussian distribution of each class). That signal dependency was used
as a training set. Once this classifier was trained, we could compute the
Fig. 2. High resolution T1 analysis for the rat brain. (A) T1 map of one representative slice along with enlargement of the S1BF area. (B) A profile of T1 values perpendicular to the s1BF
area showing the borders between the layers. (C) Projection of the layer class probability maps (L1-L6) onto a cortical surface. Arrows indicate areas of interest: 1 – M1 cortex, 2 – V1
cortex, 3 – S1 cortex, 4 – Cingulate cortex. (D) shows the orientation surface projection for a cyto-architectonic atlas of the rat brain indicating the entorhinal and perirhinal cortices
(dark blue), visual cortex (blue), cingulate cortex (cyan), temporal cortex (mainly auditory cortex, green), sensory cortex (all parts, orange), motor cortex (all parts, red), piriform cortex
and adjacent regions (olive green), and insular cortex (yellow).
S. Lifshits et al. NeuroImage 164 (2018) 112–120
115
class's probability values for each voxel. Such analysis is shown in Fig. 3
where 5 T1 classes were identified. In this analysis, the T1 component
in Fig. 1 with the longest T1 values has the highest probability at the
superficial parts of the cortex and the probability of the components
with the shorter T1 values increases at voxels that are closer to the
white matter surface.
The T1 classes' probability maps represent the sub-voxel composition
of each class (i.e., layer). The sub-voxel composition can be used to
enhance the image resolution (similar to super-resolution) by solving a
regularized optimization problem. In this analysis each voxel was
divided into 4-sub voxels and each one of them was assigned to a class
based on the composition analysis of the parent voxel. The class of each
sub-voxel was assigned by keeping the mean value of all sub-voxels
with minimum deviation from the parent voxel. This process provided
new classified layer images at interpolated in-plane resolution of
215 μm. In this analysis we used a total variation regularization
function, which preserves the edges in the image and ensure continuity
of the classes in the spatial space. The optimization problem was solved
by using the FISTA algorithm (Beck and Teboulle, 2009) providing the
enhanced resolution images (Fig. 4).
Fig. 4A shows a segmentation of the cortex into 5 classes based on
majority vote of computed T1 values (i.e. the class that received the
highest probability). The white circles in Fig. 4A represent areas where
the continuity of some layer classes was disturbed as a consequence of
PVE. In these voxels, a class was assigned by majority vote but the
probability for other classes was not insignificant. The enhanced
resolution image (Fig. 4B) shows that even with subtle division into
4 sub-voxel – continuity of the layers can be obtained providing better
Fig. 3. Layer class probability maps. Following training a classifier based on the Gaussian mixture model applied on the data shown in Fig. 1, the probability for belonging to each class
was computed. Class 1 shows high probability and superficial parts of the cortex while the probability of the other classes increases approaching the inner surface of the cortex.
Fig. 4. Enhanced cortical layer resolution. (A) Majority votes of the 5 Gy matter classes in the original resolution. Each color represent a class who had the highest probability in each
voxel. (B) Enhanced resolution majority vote image where each voxel in (A) was sub-divided into 4 voxels and a class was assigned based on the probabilities (composition) of the classes.
Circles represents areas where low probability classes could have been visualized only in the enhanced resolution image (see class 4 in cyan).
S. Lifshits et al. NeuroImage 164 (2018) 112–120
116
mean to visualize the layers. Fig. 5 shows the same analysis along with
FreeSurfer segmentation into neuro-anatomical regions zoomed at the
bottom panel. Here we focused on an area where transition between
two neuro-anatomically distinct regions occurs. The transition, that is
also reflected by a change in the layer thickness distribution is better
observed in the enhanced image (Fig. 5B) compared with the original
resolution image (Fig. 5A).
Low resolution human data
Figs. 1–5 show that T1 can be used to characterize layers in the
cortex yet PVE remains a major obstacle before establishing this
method for whole brain layer visualization. In addition, the issue of
scanning time provides another drawback for this method. While the
high resolution rat experiment nicely demonstrated the ability of IRMRI
and T1 mapping to visualize layer and identify the neuroanatomical
brain regions, similar high resolution data, in the human
brain (ex-vivo or in-vivo) will be extremely difficult to achieve,
scanning time wise. Even the combination of meso-scale resolution
and composition analysis (Figs. 4 and 5) does not provide optimal
framework for layer analysis. Therefore, in the following analysis we
focused on extracting more accurately the T1 layers from composition
analysis. To have enough data points to allow robust and accurate
composition analysis, image resolution had to be reduced to remain
within acceptable scanning time-scale. Here we have sampled 107
inversion times per voxel, while each voxel was scanned at 3 mm3
resolution obviously containing multiple layers (and even all of them in
some cases) within a single voxel.
Multi T1 component function was fitted to each voxel with j=7 (in
Eq. (1)). We choose 7 as in a preliminary test we didn’t find voxels with
more than 7 distinct T1 components. Eventually the T1 maps consisted
of up to 7 T1s per voxels - ranging from one T1 class in homogeneous
voxel such as in the CSF up to 7 classes in voxels that include the entire
cortical stripe. In each voxel, aside from the T1 values, we also
computed the relative fraction of each class (M0j in Eq. (1)). Using
this data we computed a whole brain T1 histogram (similar to the one
shown in Fig. 1) where at least 8 Gaussian classes were revealed − 1 for
white matter, 1 for CSF (very broad distribution) and 6 different
Gaussians in the gray matter T1 regime. As in the previous analyses
given in Figs. 2–5, we used the Gaussian mixture analysis to assign the
T1 values to the different classes. Once assigned, we could compute
class specific maps where the values in each voxel represent the volume
fraction of this class from the multi T1 analysis. These class weighted
maps were then projected onto a surface representation of the cortex
(Fig. 6). The layer surface representation reveals interesting features on
the T1 layers – For example the high intensity of layer 4 around the
occipital cortex (arrow 1) and primary visual areas (Fig. 6A), the
missing layer 4 from the primary motor area (arrow 2), the high
intensity of layer 6 in temporal regions (arrow 3), the small fraction of
layers 1 and 2 compared to the others and the almost even distribution
of layer 3 across the cortex.
Quantification of the observations shown in Fig. 6 was performed
by segmenting the cortex into 6 types: Allocortex, agranular cortex, and
additional 4 types with increasing amount of granularity of the cortex
as suggested previously (Barbas and Rempel-Clower, 1997; Brodmann
and Garey, 1999; Triarhou, 2007; von Economo and Koskinas, 1925).
Fig. 7 depicts the layer composition in each of the 6 cortical types. In
this analysis we extracted, per cortical type (see Figure caption for the
list of regions), the relative volume fraction of each layer out of the total
gray matter volume area in that region (i.e. relative layer width). The
total volume fraction of a layer was computed by summing the
probability of a certain layer for each region (from the layer probability
maps as shown in Fig. 6) and multiplying it by the voxel volume. This
number was then divided by the total volume of gray matter for each
region to produce the relative weights of the 6 layers in each cortical
region. This kind of analysis avoids complicated computation of layer
Fig. 5. Comparison of layer class distribution with neuro-anatomical atlas. (A) Shows the FreeSurfer atlas segmentation into neuro-anatomical regions with a section of the frontal
cortex enlarged below. (B) Majority vote of the different classes in the original and (C) enhanced resolution. The transition between the two areas (blue and red in (A)) is marked in the
white arrows where class 4 almost disappear in the red region and class 1 is dramatically narrowed in the blue region.
S. Lifshits et al. NeuroImage 164 (2018) 112–120
117
variation along the folded cortex and also minimize the bias of
variation in cortical thickness across different regions. This figure
nicely demonstrate the ability to distinguish between the types based of
the differences in layer 2 (decreasing in relative width with increase in
cortical granularity) and layer 4 (increase in relative width with the
increase in granularity).
In order to investigate the information embedded in the layer
composition analysis, we have compared the layer class probabilities in
different Brodmann areas (BAs). This was achieved by registering both
to the same volume space and averaging each layer class probability
over each BA (see bottom panel in Fig. 6). Analysis of variance
(ANOVA) of the 6 layer class probabilities x 37 Brodmann areas (BA)
revealed that most of them are distinguishable from the others (red
square in Fig. 8). More importantly, out of the 76 regions pairs (blue
outlined squares) that are adjacent, we could separate between 42 pairs
(p < 0.05) (red squares with blue outline) with additional 11 showing a
trend of difference (orange square with blue outline). Analysis of the
variance across subjects revealed that the highest layer variability exists
in the following regions: prefrontal cortex, orbitofrontal cortex, and
cingulate cortex. The most homogenous regions (layer probability wise)
were: angular gyrus, infer frontal gyrus, and associative visual cortex.
Discussion
The cortical layers can be resolved by T1 MRI – a phenomenon that
was already demonstrated several times in numerous papers, most of
them on a limited region of the brain (Barazany and Assaf, 2011;
Barbier et al., 2002; Clare and Bridge, 2005; Geyer et al., 2011; Turner
et al., 2008). Yet the spatial resolution is the main limitation for cortical
layer imaging since accurate visualization and quantification of the
layers requires acquisition resolution that is far beyond the limits of
current MR technology. Indeed, high-resolution MRI does not allow
adequate characterization of the layers, yet it does allow the extraction
of laminar features (e.g. myelination content) as demonstrated in
several works (Dinse et al., 2015; Fracasso et al., 2016; Lutti et al.,
2014; Sereno et al., 2013; Waehnert et al., 2016, 2014) underscoring
the utility of cortical sub-component characterization. In this work we
wish to push the limits of MRI even further and provide better and
potentially more accurate quantification of the layers themselves but
with a different approach. Reverting to the conventional definition of
resolution might help to understand the concept behind the approach
used in this work: how close objects can be to one another and still be
resolved or the capability of making parts of an object distinguishable
Fig. 6. Layer specific surface projection maps of the human brain. (A) Occipital view of the layer class specific probability maps showing high probability values in the expected regions
for layer 4 (especially in V1, see Brodmann atlas surface projection at the bottom). (B) Right side view of the layer class specific probability maps. Arrows indicate areas of interest: high
probability for layer 4 in the visual cortex (arrow 1), low probability for layer 4 in the motor cortex (arrow 2), and high probability for layer 6 in the temporal cortex (layer 3).
Fig. 7. Quantification of layer composition at different types of cortices. In this figure we
quantified the normalized layer fraction out of the total gray matter volume (relative layer
width) of the Piriform cortex (as a representative of the allo-cortex, BA27), Pre-motor
cortex (BA6 representing the agranular cortex) and in addition BA20 (Inferior temporal
gyrus, S. Granular 1), BA11 (Orbitofrontal cortex, S. Granular 2), BA19 (Associative
visual corte, S. Granular 3) and BA17 (Primary visual cortex, Granular cortex)
representing increasing degrees of cortical granularity). The graph demonstrate the
ability the proposed analysis and specifically the quantification of layers 2 and 4 to
distinguish between the different cortical types. The brain surface projection on the top
right side represent different brain regions that correspond to each of the cortex types
(see color index on the left). This map is based on similar segmentation shown in Beul
and Hilgetag (2014) based on Von Economo (2009).
S. Lifshits et al. NeuroImage 164 (2018) 112–120
118
(and quantifiable). In this paper we argue that distinguishing and
quantifying the cortical layers (via their T1 differences) is feasible in the
relaxation time domain rather than in the spatial image domain. While
the best demonstration of high spatial resolution in-vivo was resolving
some properties of the layers (e.g. the stripe of Gennari), accurate T1
measurements are able to distinguish between all layers regardless the
spatial resolution, once measured in the time domain and observed in
the spatial domain.
With the technological advanced in MRI (i.e. high magnetic field
scanners, efficient r.f. excitation and receiving schemes, strong gradient
capacity) signal to noise ratio (SNR) increased dramatically. Trivial
consequence of SNR improvement is increasing image resolution
leading to visualization of finer details in the image. Indeed better
resolution is a key feature in imaging as summarized in the well-known
phrase: ‘seeing’ is ‘believing’. Yet, let's consider one of the most popular
MRI methods: diffusion imaging – which measure a spatial parameter
(translational displacement) whose scale is orders of magnitude
smaller than high-resolution imaging (μm vs. mm). This is achieved
by measuring the signal in q-space (reciprocal spatial domain) rather
than in the k-space (the spatial frequency domain). The micron scale
resolution of diffusion imaging is achieved not by making an image in
the micron scale but rather characterizing a phenomenon that happen
on the micron-scale. We suggest the same solution for cortical layer
imaging - measuring the signal in poor spatial resolution but with high
resolution in the T1 relaxation domain and achieving good separation
between the layers there.
Continuing the analogy to diffusion imaging – one of the main
difficulties in diffusion imaging is providing biological interpretation to
the diffusion measures as the relation between brain anatomy/physiology
and biophysical properties of water motion is indirect. The same
happen to some extent with T1 measures of the layers. The cortical
layers are defined, traditionally, as a measure of cyto-architecture and/
or myelo-architecture. The relation between T1 and these histological
properties is, as expected, complicated. Yet, there are many evidences
in the literature that T1 is highly influenced by the degree of
myelination (Barazany and Assaf, 2011; Barbier et al., 2002; Turner
et al., 2008) therefore the observed T1 layers might be highly correlated
with myelo-architecture. Indeed previous studies (Dinse et al., 2015;
Fracasso et al., 2016; Lutti et al., 2014; Sereno et al., 2013), as well as
the comparison with the cyto-architectonic atlases shown in this paper
(Figs. 2, 5–7) indicate that the T1 layers resemble the properties of the
cyto and myelo-architecture. An excellent demonstration of that was
provided very recently by Waehnert et al. (2016) indicating the T1
mapping provide invaluable, subject specific, information of cortical
architecture. However, one cannot claim that T1 is a measure of any of
these histological properties as it is not – it measure the relaxation rate
of excited spins to the surrounding. While the ‘surrounding’ indeed
represents the cyto and myelo architecture, it is also represents many
other factors. Therefore, we suggest to refer to cortical layer imaging by
T1 as T1 layers bearing in mind that they resemble the traditional
cortical layers but not a direct measure of them.
The vague biological interpretation is one limitation of using lowresolution
multi T1 analysis to quantify the layers. Yet, the biggest
limitation of this approach is in its concept: the need to ‘see’ the layers.
The use of low resolution which does not allow direct imaging of the
layers may raise doubts about the validity of this observations. This is a
conceptual limitation, yet there are some technical issues: 1) the need
for dozens of inversion time data points to allow robust estimation of
the T1 values. 2) Errors in the estimation might lead to misclassification
of a T1 component and “noise” in the measured layers. Finally, we
argue that the layers can be better separated in the T1 relaxation
domain rather than the spatial domain. This is true, yet even in the
relaxation domain the separation is not optimal and there is some
overlap between the T1 components. Shifting to higher magnetic fields
(7T and higher) might reduce this overlap as higher field spreads the
T1 values.
Fig. 8. ANOVA of layer distribution x Brodmann areas. Analysis of variance between the averaged probability for each layer class x 37 Brodmann areas revealed significant differences
(p < 0.05) in the red squares and trend towards significance in the orange squares (0.1 < p < 0.05). Blue squares represent region that adjacent to one another.
S. Lifshits et al. NeuroImage 164 (2018) 112–120
119
The need to ‘see’ the layers is an obstacle that neuroimagers need to
overcome in order to be able to measure the layers. Even when ultrahigh
magnetic fields will be constructed, imaging the human brain at
50 μm resolution (which might allow direct visualization of the layers)
will lead to additional computational and statistical challenges, as each
brain will be represented by an enormous amount of data.
Alternatively, combined with surface projection (as shown in Figs. 2
and 6), low-resolution T1 bears great potential for large population
studies with moderate computational needs and low statistical risks (in
terms of multiple comparisons). The high sensitivity of the T1 layers to
the known neuroanatomy and even some kind of across species
homology in the layer distribution (compare Figs. 2 and 6) strengthen
the need to implement this methodology to further study its sensitivity
to various conditions (and its relation to cognition and pathological
conditions).
Acknowledgements
The authors wish to thank the NOFAR program of the Israel
innovation authority, the Israeli ministry of Economy and Industry
(50647) who funded part of the project (High resolution composition
analysis).
Appendix A. Supporting information
Supplementary data associated with this article can be found in the
online version at doi:10.1016/j.neuroimage.2017.02.086.
References
Barazany, D., Assaf, Y., 2011. Visualization of cortical lamination patterns with magnetic
resonance imaging. Cereb. Cortex.
Barbas, H., Rempel-Clower, N., 1997. Cortical structure predicts the pattern of
corticocortical connections. Cereb. Cortex 7, 635–646.
Barbier, E.L., Marrett, S., Danek, A., Vortmeyer, A., van Gelderen, P., Duyn, J.,
Bandettini, P., Grafman, J., Koretsky, A.P., 2002. Imaging cortical anatomy by highresolution
MR at 3.0T: detection of the stripe of Gennari in visual area 17. Magn.
Reson. Med. 48, 735–738.
Barral, J.K., Gudmundson, E., Stikov, N., Etezadi-Amoli, M., Stoica, P., Nishimura, D.G.,
2010. A robust methodology for in vivo T1 mapping. Magn. Reson. Med. 64,
1057–1067.
Beck, A., Teboulle, M., 2009. Fast gradient-based algorithms for constrained total
variation image denoising and deblurring problems. IEEE Trans. Image Process. 18,
2419–2434.
Beul, S.F., Hilgetag, C.C., 2014. Towards a "canonical" agranular cortical microcircuit.
Front. Neuroanat. 8, 165.
Brodmann, K., Garey, L., 1999. Brodmann's Localisation in the cerebral cortex. Imperial
College Press, London, River Edge, NJ.
Clare, S., Bridge, H., 2005. Methodological issues relating to in vivo cortical myelography
using MRI. Hum. Brain Mapp. 26, 240–250.
Clark, V.P., Courchesne, E., Grafe, M., 1992. In vivo myeloarchitectonic analysis of
human striate and extrastriate cortex using magnetic resonance imaging. Cereb.
Cortex 2, 417–424.
Dinse, J., Hartwich, N., Waehnert, M.D., Tardif, C.L., Schafer, A., Geyer, S., Preim, B.,
Turner, R., Bazin, P.L., 2015. A cytoarchitecture-driven myelin model reveals areaspecific
signatures in human primary and secondary areas using ultra-high
resolution in-vivo brain MRI. Neuroimage 114, 71–87.
Duyn, J.H., van Gelderen, P., Li, T.Q., de Zwart, J.A., Koretsky, A.P., Fukunaga, M., 2007.
High-field MRI of brain cortical substructure based on signal phase. Proc. Natl. Acad.
Sci. USA 104, 11796–11801.
Eickhoff, S., Walters, N.B., Schleicher, A., Kril, J., Egan, G.F., Zilles, K., Watson, J.D.,
Amunts, K., 2005. High-resolution MRI reflects myeloarchitecture and
cytoarchitecture of human cerebral cortex. Hum. Brain Mapp. 24, 206–215.
Fracasso, A., van Veluw, S.J., Visser, F., Luijten, P.R., Spliet, W., Zwanenburg, J.J.,
Dumoulin, S.O., Petridou, N., 2016. Lines of Baillarger in vivo and ex vivo: myelin
contrast across lamina at 7T MRI and histology. Neuroimage 133, 163–175.
Geyer, S., Weiss, M., Reimann, K., Lohmann, G., Turner, R., 2011. Microstructural
parcellation of the human cerebral cortex - from Brodmann's post-mortem map to in
vivo mapping with high-field magnetic resonance imaging. Front Hum. Neurosci. 5,
19.
Goncalves, N.R., Ban, H., Sanchez-Panchuelo, R.M., Francis, S.T., Schluppeck, D.,
Welchman, A.E., 2015. 7 T FMRI reveals systematic functional organization for
binocular disparity in dorsal visual cortex. J Neurosci. 35, 3056–3072.
Hof, P.R., Glannakopoulos, P., Bouras, C., 1996. The neuropathological changes
associated with normal brain aging. Histol. Histopathol. 11, 1075–1088.
Kalus, P., Senitz, D., Beckmann, H., 1997. Cortical layer I changes in schizophrenia: a
marker for impaired brain development? J Neural Transm. 104, 549–559.
Kandel, E.R., Schwartz, J.H., Jessell, T.M., 2000. Principles of Neural Science 4th ed..
McGraw-Hill, Health Professions Division, New York.
Koopmans, P.J., Barth, M., Norris, D.G., 2010. Layer-specific BOLD activation in human
V1. Hum. Brain Mapp. 31, 1297–1304.
Kordower, J.H., Chu, Y., Stebbins, G.T., DeKosky, S.T., Cochran, E.J., Bennett, D.,
Mufson, E.J., 2001. Loss and atrophy of layer II entorhinal cortex neurons in elderly
people with mild cognitive impairment. Ann. Neurol. 49, 202–213.
Leuze, C.W., Anwander, A., Bazin, P.L., Dhital, B., Stuber, C., Reimann, K., Geyer, S.,
Turner, R., 2012. Layer-specific intracortical connectivity revealed with diffusion
MRI. Cereb. Cortex..
Lutti, A., Dick, F., Sereno, M.I., Weiskopf, N., 2014. Using high-resolution quantitative
mapping of R1 as an index of cortical myelination. Neuroimage 93 (Pt 2), 176–188.
Masliah, E., Mallory, M., Hansen, L., Alford, M., Albright, T., DeTeresa, R., Terry, R.,
Baudier, J., Saitoh, T., 1991. Patterns of aberrant sprouting in Alzheimer's disease.
Neuron 6, 729–739.
Olman, C.A., Harel, N., Feinberg, D.A., He, S., Zhang, P., Ugurbil, K., Yacoub, E., 2012.
Layer-specific fMRI reflects different neuronal computations at different depths in
human V1. PLoS One 7, e32536.
Sereno, M.I., Lutti, A., Weiskopf, N., Dick, F., 2013. Mapping the human cortical surface
by combining quantitative T(1) with retinotopy. Cereb. Cortex 23, 2261–2268.
Triarhou, L.C., 2007. A proposed number system for the 107 cortical areas of Economo
and Koskinas, and Brodmann area correlations. Stereotact. Funct. Neurosurg. 85,
204–215.
Turner, R., Oros-Peusquens, A.M., Romanzetti, S., Zilles, K., Shah, N.J., 2008. Optimised
in vivo visualisation of cortical structures in the human brain at 3 T using IR-TSE.
Magn. Reson. Imaging 26, 935–942.
Vogt, O., 1911. Die myeloarchitektonik des isocortex parietalis. J. Psychol. Neurol. 18,
379–390.
Von Economo, C., 2009. Cellular Structure of the Human Cerebral Cortex. KARGER,
Berlin.
von Economo, C.B., Koskinas, G.N., 1925. The Cytoarchitectonics Of The Adult Human
Cortex. Julius Springer Verlag, Vienna and Berlin.
Waehnert, M.D., Dinse, J., Schafer, A., Geyer, S., Bazin, P.L., Turner, R., Tardif, C.L.,
2016. A subject-specific framework for in vivo myeloarchitectonic analysis using high
resolution quantitative MRI. Neuroimage 125, 94–107.
Waehnert, M.D., Dinse, J., Weiss, M., Streicher, M.N., Waehnert, P., Geyer, S., Turner,
R., Bazin, P.L., 2014. Anatomically motivated modeling of cortical laminae.
Neuroimage 93 (Pt 2), 210–220.
Walters, N.B., Egan, G.F., Kril, J.J., Kean, M., Waley, P., Jenkinson, M., Watson, J.D.,
2003. In vivo identification of human cortical areas using high-resolution MRI: an
approach to cerebral structure-function correlation. Proc. Natl. Acad. Sci. USA 100,
2981–2986.
S. Lifshits et al. NeuroImage 164 (2018) 112–120
120