text:
Frontiers in Neuroscience
Brain Imaging Methods
Amir Shmuel
McGill University, Canada
Kevin C. Chan
Langone Medical Center, New York University, United States
Li-Wei Kuo
National Health Research Institutes (Taiwan), Taiwan
Discussion and Conclusion
Front. Neurosci., 23 October 2015 | https://doi.org/10.3389/fnins.2015.00396
Assessing intracortical myelin in the living human brain using myelinated cortical thickness
Christopher D. Rowley1,2, Pierre-Louis Bazin3, Christine L. Tardif3, Manpreet Sehmbi2,4, Eyesha Hashim1, Nadejda Zaharieva1,2, Luciano Minuzzi2,4, Benicio N. Frey2,4 and Nicholas A. Bock1,2*
1Department of Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, ON, Canada
2MiNDS Neuroscience Graduate Program, McMaster University, Hamilton, ON, Canada
3Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
4Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada
Alterations in the myelination of the cerebral cortex may underlie abnormal cortical function in a variety of brain diseases. Here, we describe a technique for investigating changes in intracortical myelin in clinical populations on the basis of cortical thickness measurements with magnetic resonance imaging (MRI) at 3 Tesla. For this, we separately compute the thickness of the shallower, lightly myelinated portion of the cortex and its deeper, heavily myelinated portion (referred to herein as unmyelinated and myelinated cortex, respectively). Our expectation is that the thickness of the myelinated cortex will be a specific biomarker for disruptions in myeloarchitecture. We show representative atlases of total cortical thickness, T, unmyelinated cortical thickness, G, and myelinated cortical thickness, M, for a healthy group of 20 female subjects. We further demonstrate myelinated cortical thickness measurements in a preliminary clinical study of 10 bipolar disorder type-I subjects and 10 healthy controls, and report significant decreases in the middle frontal gyrus in T, G, and M in the disorder, with the largest percentage change occurring in M. This study highlights the potential of myelinated cortical thickness measurements for investigating intracortical myelin involvement in brain disease at clinically relevant field strengths and resolutions.
The cerebral cortex contains a substantial number of myelinated axons whose pattern of distribution describes its myeloarchitecture. Intracortical myelin is found predominantly in the deeper layers of the cortex, and likely serves to speed the propagation of neural signals, as myelin does in the major white matter tracts. It also fine-tunes the timing and synchrony of neural networks, thereby continuously optimizing cortical function (Haroutunian et al., 2014). The density of these myelinated axons, their orientation, and how they are distributed over the cortical layers are regionally dependent in the cortex (Nieuwenhuys, 2013), suggesting that specific features of myeloarchitecture may be related to specific cortical functions. Thus, abnormalities in intracortical myelin may correlate with disrupted function in brain disease.
Magnetic resonance imaging (MRI) is now used in vivo to visualize intracortical myelin (Glasser and van Essen, 2011), since myelin alters MR parameters within the gray matter similarly to how it alters MR parameters in white matter. There have been several MRI studies linking changes in intracortical myelin to changes in function. Imaging studies in large numbers of subjects have confirmed cerebral myelin maturation and aging-related degradation (Grydeland et al., 2013; Shafee et al., 2015) and have indicated that a higher degree of intracortical myelin is associated with greater performance stability on a cognitive task (Grydeland et al., 2013). It has also been shown that the degree of myelination in the left posterior cortex plays a role in error processing and cognitive control (Grydeland et al., 2015), while a study in blind individuals suggests that increased intracortical myelin may represent a mechanism for compensatory functional reorganization in the visual cortex (Voss et al., 2014). Conversely, MRI has identified deficits in cortical myelin in mental disease, with losses of intracortical myelin being observed in patients with schizophrenia (Bartzokis et al., 2009, 2012). Combined, these studies predict a future role for MRI in studies of how intracortical myelin is related to function in health and disease.
While MRI lacks the resolution to visualize the fine details of the myeloarchitecture in living humans, it can map intracortical myelin content grossly across the entire cortex. For intracortical myelin mapping studies, the cortex is imaged with either a quantitative MRI sequence that measures a parameter that is sensitive to myelin [T1 (or R1), T2*, magnetization transfer ratio (MTR); (Cohen-Adad et al., 2012; Marques and Gruetter, 2013; Sereno et al., 2013; Mangeat et al., 2015)], or a qualitative sequence with the contrast optimized such that the image intensity strongly correlates with the presence of myelin (T1-weighted; Bock et al., 2013). We used T1-weighted contrast for this study, because we have previously shown with in vivo MRI and matched histology in non-human primates that T1-weighted signal increases are spatially correlated with myelin density in the cortex (Bock et al., 2009). An image consisting of a combination of weightings (T1 weighted/T2 weighted) may also be used (Glasser and van Essen, 2011). Since the highest density of myelinated axons occurs in the deeper layers of the cortex (Layers IV–VI), the parameter or signal intensity in the resulting image is interpolated and displayed on a surface computed at a specific depth in the cortex to represent best the intracortical myelin content over all regions. Changes in the parameter value or signal intensity are then interpreted as reflecting changes in myelin amounts.
In cortical mapping studies, data sampled at the middle depth of the cortex are often used to summarize cortical myelin; high resolution imaging studies, however, have shown that the appearance of a map of myelin content over the cortex is highly dependent on the cortical depth at which it is made (De Martino et al., 2014; Lutti et al., 2014; Tardif et al., 2015). This means that comparisons of myelin content using MR signal between healthy and disease populations should incorporate cortical depth in their analyses, which could be hampered by the need to find a common cortical depth in diseases where overall cortical thickness is altered.
In this paper, we propose to investigate changes in cortical myelination based on morphology, rather than on MR signal changes. We do this by means of a myelinated cortical thickness technique, where we measure the thickness of lightly and heavily myelinated portions of the cortical depth separately. Separating cortical tissue into lightly and heavily myelinated portions based on MR contrast was previously suggested in 2D for studies of intracortical myelin in schizophrenia (Bartzokis et al., 2009), and here we extend it to map the entire cortex in 3D. Figure 1 shows representative 40 μm thick histological sections of human cortex stained for myelin (adapted from Braitenberg, 1962). In general, the deeper layers of the cortex (IV–VI) are typically the most heavily myelinated and will have an MR signal that is distinct from the signal in superficial gray matter, which has few myelinated fibers. There may be variations in these layers in some regions (where the Bands of Baillarger and the Stria of Gennari are not continuous with deeper heavily myelinated layers), but at the 1 mm isotropic resolution of clinical MRI, the cortical depth divides overall into a lightly myelinated and heavily myelinated portion. Thus, we propose to separate the traditional measure of total cortical thickness from neuroanatomical MRI into measures of the thickness of the lightly myelinated cortex (which we will refer to as "unmyelinated" cortex here for simplicity) and heavily myelinated cortex (referred to as "myelinated" cortex).
Figure 1. Myelinated cortical thickness measurements. The depth of the human cerebral cortex can be roughly subdivided into lightly myelinated and heavily myelinated portions, as seen in representative 40 μm thick histological sections stained for myelin (adapted from Braitenberg, 1962). This leads to intracortical contrast on heavily T1-weighted MRI, which can be used to segment the cerebrum into lightly myelinated gray matter (GM), heavily myelinated gray matter (mGM), and white matter (WM) tissue classes. Measuring the thickness of the GM and mGM tissue classes yields the thickness of the lightly myelinated (G) and heavily myelinated cortical layers (M). Overall cortical thickness (T) is also measured. The mGM class is thicker in regions known to be highly myelinated, such as the primary motor cortex in the precentral gyrus (PG) and the primary auditory cortex in Heschl's gyri (HG), MFG, middle frontal gyrus.
The myelinated cortical thickness measurement proceeds in three general steps:
1. Generate a weighted intensity image or a parametric map of the brain using MRI with strong intracortical contrast.
2. Identify the cerebrum (consisting of cortical gray matter and underlying major white matter tracts) in the brain and use a clustering algorithm to segment this anatomy into three tissue classes based on signal or parameter intensities: unmyelinated gray matter (GM), myelinated gray matter (mGM), and white matter (WM).
3. Use cortical thickness measurement techniques to find the distance between the cerebral spinal fluid (CSF)/GM boundary and the mGM/WM boundary (T), the distance between the CSF/GM and GM/mGM boundaries (G), and the distance between the GM/mGM boundary and the mGM/WM boundary (M).
These measurements yield three thickness parameters as shown in Figure 1: the total cortical thickness, T, the thickness of the unmyelinated layers, G, and the thickness of the myelinated layers, M. One may also calculate the proportional myelinated thickness of the cortex as:
P=MT (1)
which summarizes the proportion of the cortical depth that is myelinated. The approach is similar to that used to measure total cortical thickness in standard software packages such as Freesurfer (http://surfer.nmr.mgh.harvard.edu/), except that it is considering three tissue classes in the cerebrum (GM, mGM, and WM), rather than two (GM and WM). This is only appropriate in the case that the input MRI image has enough intracortical contrast to allow segmentation into three classes.
In typical cortical thickness studies of brain disease, changes in total cortical thickness, T, are interpreted as broadly reflecting changes in cortical substructure caused by neurogenesis, axonal sprouting, dendritic branching, angiogenesis, and myelination (Zatorre et al., 2012). We expect that changes in the thickness of the myelinated portion, M, will be more specific to changes in intracortical myelin in brain disease than T, since that portion contains most of the myelin in the cortex.
Below, we demonstrate the feasibility of myelinated cortical thickness measurements made on T1-weighted images with strong intracortical contrast collected at a clinically-relevant field strength of 3 T. We show patterns of T, G, M, and P in representative atlases made from 20 healthy female subjects. We also demonstrate the utility of myelinated cortical thickness measurements in a preliminary study of 10 subjects with bipolar disorder and 10 healthy controls. The consensus in the literature is that bipolar disorder is associated with decreases in cortical thickness (Lyoo et al., 2006; Elvsashagen et al., 2013; Lan et al., 2014) and we question whether losses in intracortical myelin may underlie that thinning. That is in light of imaging (Marlinge et al., 2014; Lewandowski et al., 2015) and histology (Regenold et al., 2007) studies that indicate losses of myelin in major white matter tracts in bipolar disorder, and neuropathology studies indicating losses of the oligodendrocytes in cortical tissue that maintain myelin (Uranova et al., 2004; Savitz et al., 2014).
We performed two imaging studies: one in healthy controls to generate atlases showing normal myelinated cortical thickness patterns, and one in subjects with bipolar disorder type-I and healthy controls to see if we could detect differences in myelinated thickness in this mental disorder. Both studies used the same imaging procedure, image processing, image registration, and atlas generation techniques. We also performed histology in the cortex of a representative individual to examine myeloarchitecture in frontal regions relevant to bipolar disorder. For the bipolar study, we performed a region-of-interest analysis of T, G, and M in the middle frontal gyrus, a highly myelinated area that has been implicated in bipolar disorder in humans, and also in the precentral gyrus, an area containing motor regions thought to have little involvement in the disorder.
Imaging Subjects
Studies were approved by the Hamilton Integrated Research Ethics Board and informed consent was obtained from each volunteer before enrollment. Images for the first study were collected in 20 healthy females, aged 30 ± 8 years (μ ± σ). Images for the second study were obtained in 10 healthy females, aged 30 ± 9 years, and 10 females with bipolar disorder type-I, aged 33 ± 8 years. Nine bipolar subjects had been clinically stable (euthymic) for at least 2 months and one was in a current depressive episode. All study participants were interviewed with the Structured Clinical Interview for DSM-IV (First et al., 2002) to confirm group status. In terms of treatment, three bipolar subjects were on lithium at the time of the study, four were on anticonvulsants, six were on atypical antipsychotics, two were on antidepressants and four were on anxiolytics. The average medication count across all 10 bipolar subjects was 2.50 (SD: 1.72), which is well-consistent with studies in bipolar disorder. The demographic details are presented in Table 1.
Table 1. Subject demographics for the bipolar study presented as mean ± standard deviation.
Images were acquired on a 3 T General Electric scanner (Software Version 22.0) using a 32-channel receive-only coil for the head (MR Instruments) and a transmit body coil (GE).
Anatomical Reference Image
A 3D T1-weighted whole-head image with 1 mm isotropic resolution was made using a 3D inversion-recovery gradient echo sequence (GE 3D BRAVO) [Inversion time = 450 ms, TE = 3.2 ms, TR in acquisition block = 8.4 ms, flip angle in acquisition block = 12°, field of view (FOV) = 25.6 × 25.6 × 25.6 cm, matrix = 256 × 256 × 256, linear phase encoding, Autocalibrating Reconstruction for Cartesian imaging (ARC) parallel imaging factor of 2 in the second phase encode direction, Number of averages = 1, time = 5 min 32 s]. This image served as an anatomical reference for image registration.
High Intracortical Contrast T1-weighted Image
Another 3D T1-weighted whole-head image with 1 mm isotropic resolution and increased intracortical contrast was made from four separate images collected with an inversion-recovery gradient echo sequence (GE 3D BRAVO) [Inversion time = 1100 ms, Time between end of acquisition block and next 180° pulse (TD) = 1000 ms, TE = 3.2 ms, TR in acquisition block = 8.4 ms, flip angle in acquisition block = 12°, FOV = 24.0 × 10.0 (selective slab in left/right direction) × 24.0 cm, matrix = 240 × 100 × 240, centric phase encoding, ARC factor 2 in second phase-encoding direction, Number of averages = 1, time = 5 min 53 s]. To increase intracortical contrast, each hemisphere was imaged separately. That reduced the matrix dimension in the first phase-encoding direction in the sequence, which in turn shortened the acquisition block following the inversion pulse. This is akin to using multiple segments in a magnetization-prepared rapidly-acquired gradient echo sequence (MP-RAGE; Mugler and Brookeman, 1990), where the shorter acquisition block improves contrast. A true MP-RAGE sequence was not available on our scanner, so we took this reduced-FOV approach instead, employing a long TD to increase intracortical contrast (Bock et al., 2013). Finally, to increase the contrast-to-noise ratio further, each hemisphere was imaged twice. Overall, this image took 24 min to acquire.
Each of the four separate images was registered to the anatomical reference image via a 6-parameter rigid transformation with sinc resampling using the FLIRT tool in FSL (Jenkinson et al., 2012) version 5.0 (fsl.fmrib.ox.ac.uk/fsl/). The magnitude images were then summed to create the final image of the whole head. This image for each individual was visually inspected for artifacts arising from improper registration, and none were found.
Ratio Image
A final 3D highly proton density-weighted whole-head image at 1 mm isotropic resolution was collected to correct B1 inhomogeneity artifacts in the T1-weighted image. The image was made with a 3D gradient-echo sequence (GE 3D SPGR) [parameters: TE = 3.1 ms, TR = 7.9 ms, flip angle = 4°, FOV = 24.0 × 17.4 × 24.0 cm, matrix = 240 × 174 × 240, Number of averages = 1, time = 5 min 29 s].
The proton-density weighted image was registered to the T1-weighted image using a 6-parameter rigid transform (FSL) and filtered with a 3D median filter with a 5 × 5 × 5 mm kernel size. The T1-weighted image was then divided by the filtered proton-density weighted image to create the ratio image, which is a highly T1-weighted image with B1– and some B1+ inhomogeneities removed (Wang et al., 2005; van de Moortele et al., 2009; Marques et al., 2010).
Image processing was performed predominantly in MIPAV v7.0.1 software (mipav.cit.nih.gov) using the JIST v3.0 (www.nitrc.org/projects/jist/), TOADS-CRUISE vR3c (www.nitrc.org/projects/toads-cruise/), and CBS High-Res Brain Processing Tools Version v3.0 (www.nitrc.org/projects/cbs-tools/) plug-ins, and Amira v5.2 software (Visage Imaging).
The first step in processing was to generate labels identifying the cerebrum in an individual's brain. The T1-weighted image was used as input to the SPECTER 2010 algorithm (Carass et al., 2011) in MIPAV, which created a mask identifying the brain and dura matter based on a combination of atlas-based segmentation, tissue classification and region growing. The T1-weighted image was used for this skull stripping rather than the Ratio image because the background in the Ratio image had a high mean value that made it difficult to identify air-tissue boundaries. The mask was then used to skull strip the Ratio image.
The skull-stripped Ratio image was used as input to the Multiple Object Geometric Deformable Model (MGDM) Multi-contrast Brain Segmentation algorithm (Bogovic et al., 2013; Bazin et al., 2014) in MIPAV to generate initial probabilistic labels for tissue classes in the brain. This algorithm evolves multiple boundary surfaces simultaneously according to competing forces derived from a prior intensity, shape and topology model to label the entire brain. The resulting probability labels for cerebral gray matter and white matter were then used as input to the CRUISE algorithm (Han et al., 2004) in MIPAV, which generated smoothed, topologically correct labels for the cerebrum using nested geometric deformable models and an anatomically consistent enhancement of sulcal CSF. This was performed in each hemisphere separately.
Subcortical structures and ventricles (as identified by the MGDM algorithm) were then removed from the labels for the left and right cerebrums and their labels were combined. This label for the entire cerebrum without the subcortical structures was then used to mask the Ratio image such that it contained only the cerebral cortex and underlying white matter tracts.
The cerebrum-masked Ratio image was then segmented into three tissue classes using the FANTASM algorithm (Pham, 2001) in MIPAV, which is based on a fuzzy c-means clustering algorithm with additional spatial regularization. This produced membership functions for each tissue class, whose voxel values described the probability of that voxel belonging to a particular class. For instance, a value of 0.5 or greater for a membership function would mean there was a greater than 50% probability of that voxel belonging to the given class. In order to define nested boundaries for the WM, mGM and GM, the membership functions were binarized with a series of thresholds: the first label, for the innermost mGM/WM boundary, was initially created to include all voxels with a given WM fuzzy membership threshold above 0.5. This was later refined to include all voxels with a given WM fuzzy membership threshold above 0.1 to reduce partial volume effects at the boundary with the WM. This refinement was performed to match more closely the WM segmentation that would result from a two-class tissue segmentation (see Section Results). Note that the fuzzy classification is independent of this adjustment, which only affects the location of the WM boundary. The mGM/GM boundary and unmyelinated thickness both remain unchanged. The second label, for the GM/mGM boundary, included the remaining voxels with an mGM fuzzy membership above 0.5, and finally the last label for the CSF/GM boundary included the remaining voxels again with a GM fuzzy membership above 0.5. All labels were split back into left and right hemispheres for subsequent processing. The mGM/WM-boundary labels were morphologically processed to remove all tissue not connected to the largest mass. The GM/mGM-boundary labels were slightly smoothed in Amira using 3D morphological smoothing with a kernel size of 2 × 2 × 2 mm. Voxels identified at the outermost surface of the cortex in these labels were removed using a mask made from a 1-voxel morphologically eroded version of the CSF/GM-boundary labels.
For distance measurements, signed distance levelset functions were calculated for the CSF/GM, GM/mGM and mGM/WM tissue boundaries using the Distance Field algorithm from the JIST plugins in MIPAV. Distances between boundaries were calculated as follows:
G=φCSF∕GM-φGM∕mGM (2)
T=φCSF∕GM-φmGM∕WM (3)
where φ is the levelset (the signed distance function perpendicular from a given surface) for a given boundary, T is the total cortical thickness, and G is the unmyelinated cortical thickness. These distances were measured at the CSF/GM (pial) surface and embedded in a 3D image of the same dimensions as the input images. This allowed sampling of the values at a surface generated at the pial surface.
The myelinated cortical thickness was calculated from these distance images as:
M=T-G (4)
such that M was also defined at the pial surface and propagated similarly.
Finally, the proportional myelinated thickness was calculated from the distance images according to Equation (1).
The distance images for individual subjects were visualized on the pial surface in Amira. This surface mesh was created from the CSF/GM boundary labels and the distance images were projected on it using nearest-neighbor interpolation.
Average Atlas Generation
To create average atlases of T, G, M, and P, the distance images for each individual were registered to the ICBM 152 atlas space (Fonov et al., 2011) with FSL Version 5.0. The necessary warp was determined by non-linearly registering the T1-Weighted image to the ICBM 152 atlas using the parameters provided in the T1_2_MNI152_2 mm.cnf file, which is packaged with FSL. This warp was then applied to each distance file with sinc resampling to transform it to the atlas. The individual registered images were combined in Matlab (Mathworks) into a single average atlas image for each metric.
The registered atlas distance images were visualized in Amira using a surface mesh created from the pial surface of the MNI 152 atlas. This surface with the distance maps displayed on it was also inflated in Caret (van Essen et al., 2001; www.nitrc.org/projects/caret/) to show the pattern of myelinated thickness within the cortical folds. The cortical surface was inflated using 500 iterations with smoothing every 10 iterations and an inflation factor of 1.02.
Postmortem brain tissue from a 43 year old male with a past history of depression and substance abuse was obtained from the Stanley Medical Research Institute's Brain Collection (Chevy Chase, MD). The tissue consisted of 10 μm thick paraffin embedded coronal sections of the dorsal lateral prefrontal cortex. For staining, sections were deparafinized and hydrated in 95% ethanol. They were then soaked overnight in Luxol® fast blue solution and differentiated until there was good gray/white matter contrast with light staining remaining in the gray matter. It was important not to differentiate overly the sections and remove all stain in the gray matter, since then the myelinated intracortical axons would not be visible. Whole stained sections were digitized using a Pentax K5 DSLR (Ricoh) with an SMC-A 50 mm f 2.8 macro lens. Captured color images were converted into grayscale in Adobe Photoshop CS4 using only the red channel, to enhance contrast. Magnified views of sections were captured on an EVOS® FL Cell Imaging System (Life Technologies) in gray scale using the Texas Red filter, again to enhance contrast.
Region of Interest Analysis
For the bipolar study, average atlases of T, G, and M were created for control and bipolar groups. T, G, and M were then measured bilaterally in each individual's registered images in a region-of-interest (ROI) corresponding to the cortical ribbon in the middle frontal gyrus (MFG) and precentral gyrus (PCG). A statistical analysis was performed with the software Minitab 17 (Minitab, Inc.) separately on T, G, and M between control and bipolar groups in the MFG and PCG. A two-sided ANOVA tested for significant differences between groups at the p < 0.05 level, following confirmation that the data were normal (using an Anderson–Darling test) and that groups had equal variances. This was followed by a Tukey pairwise comparison test at a significance level of p < 0.05. A power analysis was also performed using the ROI data from the control subjects in Minitab to estimate effect size.
The myelinated cortical thickness technique relies on data-driven clustering of MRI intensity in the voxels of the cerebrum to obtain the three tissue classes: GM, mGM, and WM. The FANTASM technique in our study is based on fuzzy c-means clustering, which means that each voxel can be assigned to multiple tissue classes. In our initial data analysis approach, we chose a fuzzy c-means membership threshold of greater than 0.5 for each tissue class, meaning that each contained voxels predominantly assigned to that class (Figure 2). We found, however, that this threshold underestimated the volume of the WM tissue class in the cerebrum because the additional mGM tissue class resulted in a displaced WM/mGM boundary compared to a two-tissue classification. This underestimation was especially prevalent in the thin gyral blades of white matter where partial voluming effects are strongest (see white arrow in Figure 2). Thus, we used a fuzzy c-means membership of greater than 0.1 for the WM class only, which we found to preserve better the geometry of the original WM boundary (see histograms in Figure 2). This had no effect on the GM/mGM boundary, as can be seen in Figure 2.
Figure 2. Tissue segmentation results shown for a representative individual for WM fuzzy c-means threshold values greater than 0.5 or 0.1. Each histogram plots the number of voxels assigned to each class in the cerebrum against normalized MRI signal intensity and each image shows the resulting segmentation superimposed on a 2D slice from the 3D Ratio MRI. Note the improved definition of the gGM/WM boundary in the thin gyral blade of white matter located at the white arrow.
Figure 3 shows dorsal views of representative atlases of myelinated cortical thickness measurements in 20 healthy female subjects. It illustrates that the pattern of G over the cortex largely follows that of T, suggesting that cortical regions with the greatest total thickness also have the greatest thickness in their unmyelinated layers. The pattern of M does not follow G as closely and some regions that have the smallest total thickness, such as the postcentral gyrus and the calcarine fissure, in fact have the thickest myelinated layers. The figure also shows the atlas for P, which is a summary metric describing the proportion of the cortical thickness that is myelinated. It is interesting to note that regions showing the largest values for M and P are the same regions that have been identified as having high myelin contents from previous MRI studies of intracortical myelin using the MR signal for mapping (Glasser and van Essen, 2011; Cohen-Adad et al., 2012; Lutti et al., 2014). This is shown comprehensively in Figure 4 in views of the average atlas for P over the entire cortex, where the proportional depth of the cortex that is highly myelinated is greatest in the precentral gyrus (the location of M1 and associated motor areas), the postcentral gyrus (S1), Heschel's gyri (A1), around the calcarine fissure in the occipital cortex (the primary visual cortex, V1, and associated visual areas) and in the posterior cingulate gyrus. These regions are also described in the literature as having the highest myelin content as measured from histology (Hopf, 1956; Nieuwenhuys, 2013).
Figure 3. Dorsal views of four thickness measures in an atlas made from 20 female subjects. T, total cortical thickness; G, unmyelinated cortical thickness; M, myelinated cortical thickness; P, proportional myelinated thickness; PreCG, precentral gyrus; PostCG, postcentral gyrus.
Figure 4. Views of proportional myelinated thickness, P, in an atlas made from 20 female subjects.
Figure 5A shows a representative distribution of myelinated fibers from histology in an individual in the dorsal frontal cortex. On the middle frontal gyrus (MFG), a dense distribution of myelinated fibers extends substantially through the cortical depth, implying that M would be thick in this region. That is confirmed in the M average atlas, which shows the MFG as having the greatest thickness of myelinated cortex in the dorsal frontal cortex (Figure 5A bottom). Figure 5B shows the myeloarchitecture in the MFG at a 10X magnification, revealing that most myelinated fibers exist in small bundles through the cortex. On the nearby anterior cingulate gyrus, the dense distribution of myelinated fibers does not extend as far over the cortical depth, implying that M is thinner in this region, which we also note in the average atlas. Finally, the dense distribution of myelinated fibers in the inferior frontal sulcus is closely localized at the WM boundary, suggesting that M is very thin in this region. Such is generally true of the depths of sulci in the cortex, which have been shown to contain far fewer myelinated fibers than the gyral crowns (Annese et al., 2004) and is again confirmed by low values for M in our average atlas.
Figure 5. (A) Representative coronal section of the dorsal frontal cortex stained for myelin showing the distribution of bundles of myelinated fibers on the middle frontal gryus (MFG) [Brodman's area 46 (BA 46)], on the anterior cingulate gyrus (aCG), and in the inferior frontal sulcus (IFS). The white dotted line shows the rough extent of bundles over the three regions. The asterisk denotes the same blood vessel at each magnification. The bottom panel shows the location of regions on an inflated atlas of M in the right hemisphere in healthy controls on medial and dorsal views, respectively, pCG, posterior cingulate gyrus. (B) 10X magnification in the MFG illustrating bundles of myelinated fibers and distinctly darker blood vessels.
Figure 6 shows average atlases for T, G, and M in 10 control and 10 bipolar subjects from a preliminary study of myelinated cortical thickness in bipolar disorder. We did not include P in the analysis, because it includes both M and T, and thus is not an independent metric. The small sample size in this study does not produce adequate power for whole-brain statistical parametric mapping, but the trend in the maps of T suggests that total thickness is generally thinner in the bipolar subjects—more so in the association regions of the cortex than in the primary motor and sensory regions. The maps for G show largely the same trend, with visible decreases again generally in the association regions. The maps for M also show a trend toward thinner myelinated cortex in the bipolar subjects, although the decreases appear more regionally confined than for T and G.
Figure 6. Thickness measurements in controls and bipolar disorder type-I subjects (n = 10 in each group).
Since the MFG in the dorsal lateral prefrontal cortex was shown to be highly myelinated in our normal average atlas of M and our histology study, we investigated changes there in a region of interest (ROI) analysis study of T, G, and M (Figure 7, Table 1, and Supplemental Figure 1). We also investigated changes in the precentral gyrus (PCG), a region that contains the primary motor cortex and is not thought to be involved in bipolar disorder. We found statistically significant decreases in all three metrics in bipolar subjects in both the left and right MFG (p < 0.05), but not in the PCG. Our observations of a reduced total cortical thickness in the MFG are consistent with other reports of cortical thinning in the DLPFC in bipolar disorder (Lyoo et al., 2006; Hartberg et al., 2011; Elvsashagen et al., 2013). Interestingly, the percentage difference change was largest for M of all the metrics, which could suggest that disruptions in intracortical myelin are implicated in the overall cortical thinning observed in other studies of the MFG in bipolar disorder.
Figure 7. Region-of-interest thickness measurements (μ ± σ, n = 10 in each group) in the middle frontal gyrus (MFG) and precentral gyrus (PCG). An asterisk denotes statistical significance between control and bipolar groups at p < 0.05.
Finally, we used the data from the ROI analysis for the 10 control subjects to estimate effect sizes for the myelinated cortical thickness technique. In the MFG, with a two-way t-test at a significance level of 0.05 and a power of 0.8, one could detect significant differences in mean T of 6%, G of 7%, and M of 11%. In the PCG, one could detect significant differences in the mean of T of 3%, of G of 6%, and of M of 5%. This suggests that the technique produces useful effect sizes for clinical studies, even in relatively small groups of individuals, provided the variance in thickness metrics in the clinical subjects was the same as the controls. We were actually able to detect smaller significant effect sizes in this study because the variance of the thickness values in the bipolar subjects was actually lower than in controls.
Here we demonstrate that the cerebrum in an MRI image with high intracortical contrast can be segmented into three tissue classes using a clustering algorithm, which, based on brain histology, we hypothesize to represent lightly myelinated gray matter (GM), heavily myelinated gray matter (mGM), and white matter (WM). The mGM tissue class appears continuous across the deeper portion of the cortex at our imaging resolution of 1 mm isotropic resolution. Thus, with our technique we could define two unique thickness measures for shallower, unmyelinated gray matter and deeper, myelinated gray matter. This myelinated cortical thickness measurement provides a new approach to complement traditional cortical thickness studies where it is now possible to ascribe changes in cortical thickness in disease studies to one or both of the two tissue classes in the cerebral cortex.
Although 1 mm isotropic resolution may seem low for measuring meaningful changes in cortical thickness (since the cortex itself is only a few millimeters thick), it is still assumed to be possible to measure sub-millimeter changes in cortical thickness on 3D MRIs (Fischl and Dale, 2000). This is because the true underlying pial and white matter surfaces in the brain are smooth. Thus, if the radius of curvature of these surfaces and the thickness of the tissue classes is greater than the size of the imaging resolution, then the reconstructed surfaces can be interpolated to achieve sub-voxel accuracy in thickness measurements. The same argument holds true for myelinated thickness measurements, even though the unmyelinated and myelinated portions of the cortex are thinner than the overall cortex. This was validated by the power analysis in our study which showed similar effect sizes for measurements of T, G, and M.
The thickness of the myelinated layers, M, may be specific to changes in cortical myelination, and changes in this metric may indicate intracortical myelin involvement in brain diseases. For instance, M could change if either the number of myelinated fibers in the cortex, or the amount of myelin ensheathing their axons changed. Recent studies in mice have demonstrated the later, with socially isolated animals having thinner myelin sheaths around axons in the prefrontal cortex than control animals (Liu et al., 2012; Makinodan et al., 2012). Interestingly, the thinning of the myelin sheaths preceded changes in behavior, suggesting that changes in intracortical myelin represent plasticity in the cortex of isolated mice that leads to abnormal behavior. We confirmed reports of overall cortical thinning in the MFG in bipolar disorder, and further showed that the largest percentage change in thickness occurs in the myelinated depths of that region. The MFG area is located in the dorsal lateral prefrontal cortex, and is known to be associated with executive function. Since a number of meta-analyses have shown that individuals with bipolar disorder display poor performance in executive function even during periods of clinical remission of symptoms (Robinson et al., 2006; Bora et al., 2009; Bourne et al., 2013), these findings raise the possibility that disruptions in intracortical myelin may underlie in part the neurocognitive deficits seen in bipolar patients. With future studies in larger numbers of subjects, it will be possible to perform whole-brain analyses to see if other brain regions show similar thinning of M in the bipolar population. It will also be interesting to extend myelinated cortical thickness measurements to other brain diseases with noted changes in overall cortical thickness. Our preliminary findings of decreased intracortical myelin in bipolar disorder are in line with several studies using a variety of brain imaging techniques such as diffusion-tension imaging (Nortje et al., 2013), quantitative T1ρ mapping (Johnson et al., 2015), and magnetization transfer ratio (Lewandowski et al., 2015) showing abnormalities in the structure and myelin content of subcortical white matter in bipolar disorder. Because previous studies have found an association between white matter tracts and cognitive performance in bipolar disorder (Linke et al., 2013; Poletti et al., 2015), we speculate that changes in intracortical myelin in bipolar disorder may also reflect cortical plasticity that underlies some of the behavioral deficits and disorganized thought seen in bipolar disorder. A prospective study with repeated measures of intracortical myelin and clinical assessments over time would be necessary to test this hypothesis. While the underlying mechanisms of decreased intracortical myelin are yet to be determined, proposed mechanisms include increased oxidative stress, inflammation, loss of oligodendrocytes and blood-brain barrier dysfunction (Konradi et al., 2012; Andreazza et al., 2013; Patel and Frey, 2015).
The validity of the myelinated cortical thickness measurement technique depends on two main premises: (1) whether discrete boundaries between the proposed tissue classifications GM, mGM, and WM actually exist in the cortex and (2) whether one can image those boundaries accurately. Currently, there is no dataset in the literature quantifying the myelin content over the layers for the entire cortex to confirm the first point. A few studies exist, however, that measured the optical density of a myelin stain over the layers of the cortex in selected regions of the brain to quantify myelin content (Braitenberg, 1962; Hopf, 1968, 1969, 1970). These suggest that, for the examined regions of the brain, there is a division between unmyelinated and myelinated layers of the cortex that would approximately segregate into two discrete voxel intensities when imaged with MRI at least at a coarse resolution of 1 mm isotropic. We observed a similar division in our histology in the frontal cortex, where myelinated fibers were only seen to extend at a high density to a specific depth in each region. Whether this holds over the entire cortex is not clear, and there may be regions where the boundaries between unmyelinated and myelinated cortical layers are diffuse, making the proposed description of two cortical tissue classes too simplistic. The approximation of two classes of cortical tissue is worth exploring here, however, as a first step toward characterizing cortical myelination at clinically relevant resolutions in MRI. More elaborate models to describe cortical myelination are possible (Dinse et al., 2015), but currently these require sub-millimeter resolutions achievable only with ultra-high field MRI systems.
Even if discrete boundaries between tissue classes are present, the low image resolution in MRI leads to a mixing of tissue classifications, causing a partial voluming artifact in studies of cortical thickness. While partial voluming can cause any tissue boundary in thickness measurements to be incorrectly identified, the error is most pronounced at the mGM/WM boundary because of its high curvature and the thin geometric features of the WM volume. These effects can be lessened in sub-1 mm isotropic resolution images acquired with high field MRI (Lusebrink et al., 2013; Tardif et al., 2015). It would also be useful in future implementations of myelinated cortical thickness mapping to correct for partial volume effects using more sophisticated strategies than simply changing the WM fuzzy c-means membership function, as we did in this study (Pham and Bazin, 2004; Duche et al., 2014; Shafee et al., 2015).
There is also potential to refine further the myelinated cortical thickness technique and improve the quality of the thickness maps. For instance, although in this study we used an MRI protocol that was optimized to produce strong intracortical contrast, it was not specifically optimized for myelinated cortical thickness measures. Any segmentation routine based on clustering will perform more robustly the better the contrast-to-noise ratio (CNR) is between the three tissue classes; thus, different MR contrasts that are sensitive to myelin should be investigated to find the one that produces the best intracortical CNR.
Overall, measurements of myelinated cortical thickness provide a new description of intracortical myelination which can be measured at 3 Tesla in clinically achievable times. With the outlined methods, we were able to separate the cortex into lightly and heavily myelinated portions and this has the potential to disentangle changes in cortical myelination and overall cortical thickness in studies of aging, disease, and plasticity.
This project was supported by a 2014 NARSAD Independent Investigator Grant from the Brain & Behavior Research Foundation (Dr. B. Frey), and an AFP Innovations Award, Department of Psychiatry and Behavioural Neurosciences, McMaster University (Dr. L. Minuzzi). This work was partially supported by the Marie Curie IRG grant 276684 (HIRESBRAIN7T).
The authors are grateful to the Stanley Foundation for postmortem tissue donations and to Dr. David Chettle for editing the manuscript.
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/article/10.3389/fnins.2015.00396
Supplemental Figure 1. Individual region-of-interest thickness measures in the middle frontal gyrus (MFG) and precentral gyrus (PCG). The blue colored dot indicates the mean for each group.
Andreazza, A. C., Wang, J. F., Salmasi, F., Shao, L., and Young, L. T. (2013). Specific subcellular changes in oxidative stress in prefrontal cortex from patients with bipolar disorder. J. Neurochem. 127, 552–561. doi: 10.1111/jnc.12316
Annese, J., Pitiot, A., Dinov, I. D., and Toga, A. W. (2004). A myelo-architectonic method for the structural classification of cortical areas. Neuroimage 21, 15–26. doi: 10.1016/j.neuroimage.2003.08.024
Bartzokis, G., Lu, P. H., Raven, E. P., Amar, C. P., Detore, N. R., Couvrette, A. J., et al. (2012). Impact on intracortical myelination trajectory of long acting injection versus oral risperidone in first-episode schizophrenia. Schizophr. Res. 140, 122–128. doi: 10.1016/j.schres.2012.06.036
Bartzokis, G., Lu, P. H., Stewart, S. B., Oluwadara, B., Lucas, A. J., Pantages, J., et al. (2009). In vivo evidence of differential impact of typical and atypical antipsychotics on intracortical myelin in adults with schizophrenia. Schizophr. Res. 113, 322–331. doi: 10.1016/j.schres.2009.06.014
Bazin, P. L., Weiss, M., Dinse, J., Schafer, A., Trampel, R., and Turner, R. (2014). A computational framework for ultra-high resolution cortical segmentation at 7 Tesla. Neuroimage 93(Pt 2), 201–209. doi: 10.1016/j.neuroimage.2013.03.077
Bock, N. A., Hashim, E., Janik, R., Konyer, N. B., Weiss, M., Stanisz, G. J., et al. (2013). Optimizing T1-weighted imaging of cortical myelin content at 3.0 T. Neuroimage 65, 1–12. doi: 10.1016/j.neuroimage.2012.09.051
Bock, N. A., Kocharyan, A., Liu, J. V., and Silva, A. C. (2009). Visualizing the entire cortical myelination pattern in marmosets with magnetic resonance imaging. J. Neurosci. Methods 185, 15–22. doi: 10.1016/j.jneumeth.2009.08.022
Bogovic, J. A., Prince, J. L., and Bazin, P. L. (2013). A multiple object geometric deformable model for image segmentation. Comput. Vis. Image Underst. 117, 145–157. doi: 10.1016/j.cviu.2012.10.006
Bora, E., Yucel, M., and Pantelis, C. (2009). Cognitive endophenotypes of bipolar disorder: a meta-analysis of neuropsychological deficits in euthymic patients and their first-degree relatives. J. Affect. Disord. 113, 1–20. doi: 10.1016/j.jad.2008.06.009
Bourne, C., Aydemir, Ö., Balanzá-Martínez, V., Bora, E., Brissos, S., Cavanagh, J. T., et al. (2013). Neuropsychological testing of cognitive impairment in euthymic bipolar disorder: an individual patient data meta-analysis. Acta Psychiatr. Scand. 128, 149–162. doi: 10.1111/acps.12133
Braitenberg, V. (1962). A note on myeloarchitectonics. J. Comp. Neurol. 118, 114–156. doi: 10.1002/cne.901180202
Carass, A., Cuzzocreo, J., Wheeler, M. B., Bazin, P. L., Resnick, S. M., and Prince, J. L. (2011). Simple paradigm for extra-cerebral tissue removal: algorithm and analysis. Neuroimage 56, 1982–1992. doi: 10.1016/j.neuroimage.2011.03.045
Cohen-Adad, J., Polimeni, J. R., Helmer, K. G., Benner, T., McNab, J. A., Wald, L. L., et al. (2012). T(2)* mapping and B(0) orientation-dependence at 7 T reveal cyto- and myeloarchitecture organization of the human cortex. Neuroimage 60, 1006–1014. doi: 10.1016/j.neuroimage.2012.01.053
De Martino, F., Moerel, M., Xu, J., van de Moortele, P. F., Ugurbil, K., Goebel, R., et al. (2014). High-resolution mapping of myeloarchitecture in vivo: localization of auditory areas in the human brain. Cereb. Cortex 10, 3394–3405. doi: 10.1093/cercor/bhu150
Dinse, J., Hartwich, N., Waehnert, M. D., Tardif, C. L., Schäfer, A., Geyer, S., et al. (2015). A cytoarchitecture-driven myelin model reveals area-specific signatures in human primary and secondary areas using ultra-high resolution in-vivo brain MRI. Neuroimage 114, 71–87. doi: 10.1016/j.neuroimage.2015.04.023
Duche, Q. R. P., Egan, G., Acosta, O., Gambarota, G., Salvado, O., and Saint-Jalmes, H. (2014). New partial volume estimation methods for MRI MP2RAGE. Med. Image Comput. Comput. Assist. Interv. 17, 129–136. doi: 10.1007/978-3-319-10443-0_17
Elvsåshagen, T., Westlye, L. T., Bøen, E., Hol, P. K., Andreassen, O. A., Boye, B., et al. (2013). Bipolar II disorder is associated with thinning of prefrontal and temporal cortices involved in affect regulation. Bipolar Disord. 15, 855–864. doi: 10.1111/bdi.12117
First, M. B. Spitzer. R. L Gibbon, M., and Williams, J. B. W. (2002). SCID-I/P (for DSM-IV-TR) Patient Edition. New York, NY: Biometrics Research, New York State Psychiatric Institute.
Fischl, B., and Dale, A. M. (2000). Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc. Natl. Acad. Sci. U.S.A. 97, 11050–11055. doi: 10.1073/pnas.200033797
Fonov, V., Evans, A. C., Botteron, K., Almli, C. R., McKinstry, R. C., Collins, D. L., et al. (2011). Unbiased average age-appropriate atlases for pediatric studies. Neuroimage 54, 313–327. doi: 10.1016/j.neuroimage.2010.07.033
Glasser, M. F., and van Essen, D. C. (2011). Mapping human cortical areas in vivo based on myelin content as revealed by T1- and T2-weighted MRI. J. Neurosci. 31, 11597–11616. doi: 10.1523/JNEUROSCI.2180-11.2011
Grydeland, H., Walhovd, K. B., Tamnes, C. K., Westlye, L. T., and Fjell, A. M. (2013). Intracortical myelin links with performance variability across the human lifespan: results from T1- and T2-weighted MRI myelin mapping and diffusion tensor imaging. J. Neurosci. 33, 18618–18630. doi: 10.1523/JNEUROSCI.2811-13.2013
Grydeland, H., Westlye, L. T., Walhovd, K. B., and Fjell, A. M. (2015). Intracortical posterior cingulate myelin content relates to error processing: results from T1- and T2-weighted MRI myelin mapping and electrophysiology in healthy adults. Cereb. Cortex. doi: 10.1093/cercor/bhv065. [Epub ahead of print].
Han, X., Pham, D. L., Tosun, D., Rettmann, M. E., Xu, C., and Prince, J. L. (2004). CRUISE: cortical reconstruction using implicit surface evolution. Neuroimage 23, 997–1012. doi: 10.1016/j.neuroimage.2004.06.043
Haroutunian, V., Katsel, P., Roussos, P., Davis, K. L., Altshuler, L. L., and Bartzokis, G. (2014). Myelination, oligodendrocytes, and serious mental illness. Glia 62, 1856–1877. doi: 10.1002/glia.22716
Hartberg, C. B., Sundet, K., Rimol, L. M., Haukvik, U. K., Lange, E. H., Nesvåg, R., et al. (2011). Brain cortical thickness and surface area correlates of neurocognitive performance in patients with schizophrenia, bipolar disorder, and healthy adults. J. Int. Neuropsychol. Soc. 17, 1080–1093. doi: 10.1017/S1355617711001081
Hopf, A. (1956). [Distribution of myeloarchitectonic marks in the frontal cerebral cortex in man]. J. Hirnforsch. 2, 311–333.
Hopf, A. (1968). Photometric studies on the myeloarchitecture of the human temporal lobe. J. Hirnforsch. 10, 285–297.
Hopf, A. (1969). Photometric studies on the myeloarchitecture of the human parietal lobe. I. Parietal region. J. Hirnforsch. 11, 253–265.
Hopf, A. (1970). Photometric studies on the myeloarchitecture of the human parietal lobe. II. Postcentral region. J. Hirnforsch. 12, 135–141.
Jenkinson, M., Beckmann, C. F., Behrens, T. E., Woolrich, M. W., and Smith, S. M. (2012). Fsl. Neuroimage 62, 782–790. doi: 10.1016/j.neuroimage.2011.09.015
Johnson, C. P., Follmer, R. L., Oguz, I., Warren, L. A., Christensen, G. E., Fiedorowicz, J. G., et al. (2015). Brain abnormalities in bipolar disorder detected by quantitative T1rho mapping. Mol. Psychiatry 20, 201–206. doi: 10.1038/mp.2014.157
Konradi, C., Sillivan, S. E., and Clay, H. B. (2012). Mitochondria, oligodendrocytes and inflammation in bipolar disorder: evidence from transcriptome studies points to intriguing parallels with multiple sclerosis. Neurobiol. Dis. 45, 37–47. doi: 10.1016/j.nbd.2011.01.025
Lan, M. J., Chhetry, B. T., Oquendo, M. A., Sublette, M. E., Sullivan, G., Mann, J. J., et al. (2014). Cortical thickness differences between bipolar depression and major depressive disorder. Bipolar Disord. 16, 378–388. doi: 10.1111/bdi.12175
Lewandowski, K. E., Ongur, D., Sperry, S. H., Cohen, B. M., Sehovic, S., Goldbach, J. R., et al. (2015). Myelin vs axon abnormalities in white matter in bipolar disorder. Neuropsychopharmacology 40, 1243–1249. doi: 10.1038/npp.2014.310
Linke, J., King, A. V., Poupon, C., Hennerici, M. G., Gass, A., and Wessa, M. (2013). Impaired anatomical connectivity and related executive functions: differentiating vulnerability and disease marker in bipolar disorder. Biol. Psychiatry 74, 908–916. doi: 10.1016/j.biopsych.2013.04.010
Liu, J., Dietz, K., DeLoyht, J. M., Pedre, X., Kelkar, D., Kaur, J., et al. (2012). Impaired adult myelination in the prefrontal cortex of socially isolated mice. Nat. Neurosci. 15, 1621–1623. doi: 10.1038/nn.3263
Lüsebrink, F., Wollrab, A., and Speck, O. (2013). Cortical thickness determination of the human brain using high resolution 3T and 7T MRI data. Neuroimage 70, 122–131. doi: 10.1016/j.neuroimage.2012.12.016
Lutti, A., Dick, F., Sereno, M. I., and Weiskopf, N. (2014). Using high-resolution quantitative mapping of R1 as an index of cortical myelination. Neuroimage 93(Pt 2), 176–188. doi: 10.1016/j.neuroimage.2013.06.005
Lyoo, I. K., Sung, Y. H., Dager, S. R., Friedman, S. D., Lee, J. Y., Kim, S. J., et al. (2006). Regional cerebral cortical thinning in bipolar disorder. Bipolar Disord. 8, 65–74. doi: 10.1111/j.1399-5618.2006.00284.x
Makinodan, M., Rosen, K. M., Ito, S., and Corfas, G. (2012). A critical period for social experience-dependent oligodendrocyte maturation and myelination. Science 337, 1357–1360. doi: 10.1126/science.1220845
Mangeat, G., Govindarajan, S. T., Mainero, C., and Cohen-Adad, J. (2015). Multivariate combination of magnetization transfer, T* and B0 orientation to study the myelo-architecture of the in vivo human cortex. Neuroimage 119, 89–102. doi: 10.1016/j.neuroimage.2015.06.033
Marlinge, E., Bellivier, F., and Houenou, J. (2014). White matter alterations in bipolar disorder: potential for drug discovery and development. Bipolar Disord. 16, 97–112. doi: 10.1111/bdi.12135
Marques, J. P., and Gruetter, R. (2013). New developments and applications of the MP2RAGE sequence–focusing the contrast and high spatial resolution R1 mapping. PLoS ONE 8:e69294. doi: 10.1371/journal.pone.0069294
Marques, J. P., Kober, T., Krueger, G., van der Zwaag, W., van de Moortele, P. F., and Gruetter, R. (2010). MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field. Neuroimage 49, 1271–1281. doi: 10.1016/j.neuroimage.2009.10.002
Mugler, J. P. III, and Brookeman, J. R. (1990). Three-dimensional magnetization-prepared rapid gradient-echo imaging (3D MP RAGE). Magn. Reson. Med. 15, 152–157. doi: 10.1002/mrm.1910150117
Nieuwenhuys, R. (2013). The myeloarchitectonic studies on the human cerebral cortex of the Vogt-Vogt school, and their significance for the interpretation of functional neuroimaging data. Brain Struct. Funct. 218, 303–352. doi: 10.1007/s00429-012-0460-z
Nortje, G., Stein, D. J., Radua, J., Mataix-Cols, D., and Horn, N. (2013). Systematic review and voxel-based meta-analysis of diffusion tensor imaging studies in bipolar disorder. J. Affect. Disord. 150, 192–200. doi: 10.1016/j.jad.2013.05.034
Patel, J. P., and Frey, B. N. (2015). Disruption in the blood-brain barrier: the missing link between brain and body inflammation in bipolar disorder? Neural Plast. 2015:708306. doi: 10.1155/2015/708306
Pham, D. L. (2001). "Robust fuzzy segmentation of magnetic resonance images," in Proceedings of the 14th IEEE Symposium on Computer-based Medical Systems (Bethesda, MD), 127–131.
Pham, D. L., and Bazin, P.-L. (2004). "Simultaneous boundary and partial volume estimation in medical images," in Proceedings of the 7th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'04) (Rennes, FR), 119–126.
Poletti, S., Bollettini, I., Mazza, E., Locatelli, C., Radaelli, D., Vai, B., et al. (2015). Cognitive performances associate with measures of white matter integrity in bipolar disorder. J. Affect. Disord. 174, 342–352. doi: 10.1016/j.jad.2014.12.030
Regenold, W. T., Phatak, P., Marano, C. M., Gearhart, L., Viens, C. H., and Hisley, K. C. (2007). Myelin staining of deep white matter in the dorsolateral prefrontal cortex in schizophrenia, bipolar disorder, and unipolar major depression. Psychiatry Res. 151, 179–188. doi: 10.1016/j.psychres.2006.12.019
Robinson, L. J., Thompson, J. M., Gallagher, P., Goswami, U., Young, A. H., Ferrier, I. N., et al. (2006). A meta-analysis of cognitive deficits in euthymic patients with bipolar disorder. J. Affect. Disord. 93, 105–115. doi: 10.1016/j.jad.2006.02.016
Savitz, J. B., Price, J. L., and Drevets, W. C. (2014). Neuropathological and neuromorphometric abnormalities in bipolar disorder: view from the medial prefrontal cortical network. Neurosci. Biobehav. Rev. 42, 132–147. doi: 10.1016/j.neubiorev.2014.02.008
Sereno, M. I., Lutti, A., Weiskopf, N., and Dick, F. (2013). Mapping the human cortical surface by combining quantitative T(1) with retinotopy. Cereb. Cortex 23, 2261–2268. doi: 10.1093/cercor/bhs213
Shafee, R., Buckner, R. L., and Fischl, B. (2015), Gray matter myelination of 1555 human brains using partial volume corrected MRI images. Neuroimage 105, 473–485. doi: 10.1016/j.neuroimage.2014.10.054
Tardif, C. L., Schäfer, A., Waehnert, M., Dinse, J., Turner, R., and Bazin, P. L. (2015). Multi-contrast multi-scale surface registration for improved alignment of cortical areas. Neuroimage 111, 107–122. doi: 10.1016/j.neuroimage.2015.02.005
Uranova, N. A., Vostrikov, V. M., Orlovskaya, D. D., and Rachmanova, V. I. (2004). Oligodendroglial density in the prefrontal cortex in schizophrenia and mood disorders: a study from the Stanley Neuropathology Consortium. Schizophr. Res. 67, 269–275. doi: 10.1016/S0920-9964(03)00181-6
van de Moortele, P. F., Auerbach, E. J., Olman, C., Yacoub, E., Ugurbil, K., and Moeller, S. (2009). T1 weighted brain images at 7 Tesla unbiased for Proton Density, T2* contrast and RF coil receive B1 sensitivity with simultaneous vessel visualization. Neuroimage 46, 432–446. doi: 10.1016/j.neuroimage.2009.02.009
van Essen, D. C., Drury, H. A., Dickson, J., Harwell, J., Hanlon, D., and Anderson, C. H. (2001). An integrated software suite for surface-based analyses of cerebral cortex. J. Am. Med. Inform. Assoc. 8, 443–459. doi: 10.1136/jamia.2001.0080443
Voss, P., Pike, B. G., and Zatorre, R. J. (2014). Evidence for both compensatory plastic and disuse atrophy-related neuroanatomical changes in the blind. Brain 137(Pt 4), 1224–1240. doi: 10.1093/brain/awu030
Wang, J., Qiu, M., and Constable, R. T. (2005). In vivo method for correcting transmit/receive nonuniformities with phased array coils. Magn. Reson. Med. 53, 666–674. doi: 10.1002/mrm.20377
Zatorre, R. J., Fields, R. D., and Johansen-Berg, H. (2012). Plasticity in gray and white: neuroimaging changes in brain structure during learning. Nat. Neurosci. 15, 528–536. doi: 10.1038/nn.3045
Keywords: cerebral cortex, myelin, magnetic resonance imaging, bipolar disorder, cortical thickness
Citation: Rowley CD, Bazin P-L, Tardif CL, Sehmbi M, Hashim E, Zaharieva N, Minuzzi L, Frey BN and Bock NA (2015) Assessing intracortical myelin in the living human brain using myelinated cortical thickness. Front. Neurosci. 9:396. doi: 10.3389/fnins.2015.00396
Received: 12 August 2015; Accepted: 08 October 2015;
Published: 23 October 2015.
Amir Shmuel, McGill University, Canada
Kevin C. Chan, University of Pittsburgh, USA
Li-Wei Kuo, National Health Research Institutes, Taiwan
Copyright © 2015 Rowley, Bazin, Tardif, Sehmbi, Hashim, Zaharieva, Minuzzi, Frey and Bock. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Nicholas A. Bock, bockn@mcmaster.ca


jetzt downloaden and Online lesen Bücher Un compagnon inattendu Online ePub/PDF/Audible/Kindle, its easy way to streaming Un compagnon inattendu Bücher for multiple devices.
jetzt downloaden Un compagnon inattendu Bücher PDF Toni Griffin Büchers en ligne PDF Un compagnon inattendu. jetzt downloaden et lire des Büchers en ligne Un compagnon inattendu Online ePub/PDF/Audible/Kindle, son moyen facile de diffuser Un compagnon inattendu Büchers pour plusieurs appareils.
Un compagnon inattendu was written by best authors whom known as an author and have wrote many interesting Bücher with great story telling. Un compagnon inattendu was one of the most wanted Bücher on 2019. It contains 202 pages. This book was very surprised because of its top rating and got about best user reviews. So, after finishing reading this book, I recommend to readers to not underestimate this great book. You should take Un compagnon inattendu as your reading list or you will be regret because you have not reading it yet in your live.


text:
We have transitioned from a literate culture to one that values speed, immediacy and the decoding of small grabs of words in the search for information. But old and new ways of reading can co-exist, argues Judith Seaboyer.
Judith Seaboyer
Public anxiety about the capacity of digital-age children and young adults to read anything longer than a screen grab has come to feel like moral panic. But there is plenty of evidence to suggest we must take such unease seriously.
In 2016, the US National Endowment for the Arts reported the proportion of American adults who read at least one novel in 2015 had dropped to 43.1 per cent from 56.9 per cent in 1982.
In 2018, a US academic reported that in 1980, 60 per cent of 18-year-old school students read a book, newspaper or magazine every day that wasn't assigned for school. By 2016, the number had plummeted to 16 per cent.
Those same 12th graders reported spending "six hours a day texting, on social media and online".
American literacy expert and neuroscientist Maryanne Wolf describes the threat screen reading poses to our capacity for "the slower cognitive processes such as critical thinking, personal reflection, imagination, and empathy that are all part of deep reading".
She asks:
Will the mix of continuously stimulating distractions of children's attention and immediate access to multiple sources of information give young readers less incentive either to build their own storehouses of knowledge or to think critically for themselves?
But rather than taking up defensive positions on either side of the digital-analogue reading divide, Wolf encourages us to embrace both. As parents and teachers we can help our children develop a bi-literate reading brain. There are several ways we can do this.
Reading pathways
Reading is a learned skill that requires the development of particular neural networks. And different reading platforms encourage the development of different aspects of those networks.
Screen-reading children, immersed from toddlerhood in the pleasures and instant gratification of skimming, clicking and linking, develop cognitive skills that make them adept power browsers, good at the useful ability to scan for information and analyse data.
But Wolf suggests this kind of reading "can short-circuit the development of the slower, more cognitively demanding comprehension processes that go into the formation of deep reading and deep thinking".
Unless the cognitive skills required for deep reading are similarly developed and nurtured, new generations of readers – distracted by the ready availability of digital information – may not learn to venture beyond the shallows of the reading experience.
We can help children gain a love of reading on paper from an early age. Photo: shutterstock.com
Along with others concerned with early childhood education Wolf advises encouraging paper literacy from infancy. She doesn't recommend forbidding devices. Instead we should regularly turn them off and make the time and space to read books on paper with children.
We can model our own reading practices by setting aside our own smart phones to lose ourselves in a book.
But how can secondary and tertiary teachers help inexperienced readers? The problem is likely to be aliteracy, meaning students can read but they choose not to because they don't see it to be important for learning. And because they haven't read much, it's hard work. The problem can seem intractable. But it can be done.
Turn off the phone and read
My first venture into helping tertiary students read better was a 2011-2013 cross-university government-funded project that set out to foster what we termed "reading resilience". We found if students were persuaded to prioritise reading as they did a test or an essay, they would invest the time to get into the zone that is the other world of the text.
We complemented complex texts with a guide that encouraged students to think critically as they read and to keep going when the language seemed impenetrable, the narrative incomprehensible (or dull) and the length endless. Or when the siren call of the smart phone became irresistible.
They experimented with switching off their devices for blocks of two hours while they simply read. And they did read.
Students prioritised this difficult work because we rewarded pre-class reading with marks. Some classes uploaded one-page, carefully argued responses; others answered complex feedback-rich quizzes.
I surveyed a large first-year introduction to literary studies at the University of Queensland in 2013 before testing a version of the same "reading resilience" course in 2014. The rise in reading rates was exponential.
The number of students who completed all 10 primary texts (including the poem Beowulf and Toni Morrison's Beloved) more than tripled, and the number who completed the 10 accompanying secondary texts (selected chapters from an introduction to literary theory and criticism) went up by more than six times.
Reported student satisfaction for this course from 2008 to 2012 had ranged between 64 per cent and 75 per cent. Once reading resilience was introduced, many complained about the reading load yet the level of overall satisfaction jumped to 86 per cent.
We can all do it
It's not just readers raised in a digital-age who have difficulty with long-form text. Have you have lost the skill of deep reading? Are you finding it increasingly difficult to stay with, say, a literary novel? You are not alone.
Wolf, who despite having two degrees in literature, confesses to the shocking discovery that recently she found herself struggling to stick with a beloved Herman Hesse novel.
We, too, can switch off our devices and set aside a space and time to revitalise the neural pathways that once made us immersive readers.
As Wolf argues, the skills of "deep reading" that involve "slower, more time-consuming cognitive processes […] are vital for contemplative life". Deep readers are likely to be more thoughtful members of the community at a time when good citizenship may never have been more important.
Senior lecturer Judith Seaboyer has taught literary studies at the University of Queensland since 1998. This article is republished from The Conversation under a Creative Commons licence. Read the original article.
More Books & Poetry stories
Books & Poetry Books & Poetry
Compiled by John Miles Wednesday, January 22
Mix Tape: from Sheffield to Adelaide, with love
Kurtis Eichler Monday, January 13
Arts & Culture Arts & Culture
Queering South Australia's museums

redpajama_set_name: RedPajamaCommonCrawl


text:
2,566Grants to
1,625(Sub)Species
Sign into My Projects
How Does The Fund Work?
The Donor
Grant Application Form
Case Studies & Projects
Supported Grants
By species type
Guinea's Gentle Giants: African manatee
Dolphin Quest: Guinea
Community Conservation of Sea Turtles
Protecting Thailand's River Monsters
Indonesia's Seagrass and Coral Nursery
Wild Cats at the Edge
Saving the Spotted Greenshank
About Project Sponsor
Fan mussel (Pinna nobilis)
Mohamed bin Zayed Species project number 212527457
Assisted recruitment of the critically endangered fan mussel Pinna nobilis in Cyprus (eastern Mediterranean)
Pinna nobilis is an endemic species and the largest bivalve of the Mediterranean Sea.
Due to an on-going mass mortality event (MME) affecting P. nobilis populations across the Mediterranean Sea caused by a pathogen infection, it has been listed as Critically Endangered on the IUCN's Red List in 2019.
P. nobilis is a well-studied organism, however the majority of the information come from the central and western Mediterranean. Accurate and relevant data concerning the current status, distribution and density of P. nobilis in Cyprus are not available or is in the process to be generated.
Historically, P. nobilis was present in almost all the Levantine Basin and until at least early 2018, Cyprus had large P. nobilis populations [1]. Between 2016 and 2017 only few individuals (<3-5%) of various populations around the island were affected by the mass mortality event [2]. However, in late 2018 and 2019, selected areas that were revisited reported almost 100% mortality at least until 40 m depth at all sites (C. Jiménez and L. Hadjioannou, unpubl. data).
The possible recovery of impacted populations will rely on the existence of unimpacted populations, resistant individuals and recruitment. The overall objectives of the project are:
to collect and gather information on the current status of P. nobilis in Cyprus
to enhance the larval recruitment of P. nobilis, through installation of larval collectors, and support eventual recoveries of the species
to produce solid scientific results with implications towards the conservation of P. nobilis in Cyprus and elsewhere in the Mediterranean Sea
to raise public awareness.
[1] Katsanevakis S et al. 2021. The Fan Mussel Pinna nobilis on the Brink of Extinction in the Mediterranean. Reference Module in Earth Systems and Environmental Sciences, doi.org/10.1016/B978-0-12-821139-7.00070-2
[2] Kersting D et al. 2019. Pinna nobilis. The IUCN Red List of Threatened Species 2019: e.T160075998A160081499.
Project 212527457 location - Cyprus, Europe
Project number: 212527457
Awarded amount: $11,000
Awarded date: 05-11-2021
Country: Cyprus
Valentina Fossati
Enalia Physis Environmental Research Center
Acropoleos 2, Aglantzia
The text and images for this case study are uploaded by the grant recipient to raise awareness of the conservation work being done. Through its website the Fund provides the platform, but is not responsible for text or image content of case studies.
How The Fund works
Resume my application
Sign in to my projects
© Mohamed bin Zayed Species Conservation Fund 2013, all rights reserved.
Website by Intex Digital


text:
#ifndef PROC_COMMON_H
#define PROC_COMMON_H

/******************************************************************************
 * Helper Functions
 */

/* does useless computation */
void compute(int count);

/* Does nothing and never returns. */
void wait_forever(void);

/*
 * Print a nice diagnostic based on {pid, status}
 * as returned by wait() or waitpid().
 */
void explain_wait_status(pid_t pid, int status);

/*
 * This function makes sure a number of children processes
 * have raised SIGSTOP, by using waitpid() with the WUNTRACED flag.
 *
 * This will NOT work if children use pause() to wait for SIGCONT.
 */
void wait_for_ready_children(int cnt);

/* Change the name of the process. */
void change_pname(const char *new_name);

/* Print the process tree rooted at process with PID p. */
void show_pstree(pid_t p);

/*
 * Create a shared memory area, usable by all descendants of the calling process.
 */
void *create_shared_memory_area(unsigned int numbytes);

#endif /* PROC_COMMON_H */

redpajama_set_name: RedPajamaGithub


text:
Q: Use react-i18next Trans component with parameters and typescript I have this component:
<Trans t={t} i18nKey="welcome_user" name={user.name}>
    Welcome {{name}}
</Trans>

and this translation, for example in italian:
{
  "welcome_user": "Benvenuto <1>{{name}}</1>"
}

The page is rendered correctly (translated, with the user name, in strong), however typescript complains:
TS2322: Type '{ name: void; }' is not assignable to type 'ReactI18NextChild | Iterable<ReactI18NextChild>'.
  Object literal may only specify known properties, and 'name' does not exist in type 'ReactElement<any, string | JSXElementConstructor<any>> | ReactFragment | ReactPortal | Iterable<ReactI18NextChild>'.
    53 |                     <Trans t={t} i18nKey="welcome_user" name={user.name}>
  > 54 |                         Welcome {{name}}
       |                                           ^^^^
    55 |
                             </Trans>

What is the correct way to do this with typescript?

redpajama_set_name: RedPajamaStackExchange


text:
In a time when large ensembles find it even harder to occupy and claim their place in the music scene, the impulse was to continue going against the stream. The "Joe Dallas and The Monks" project was born in this unusual context, a collective of musicians who primarily wish to express themselves through multiple melodic nuances, meticulously blending the timbres and sounds of several instruments.
After taking part in an extremely ambitious technical challenge producing a pilot episode for a TV show – recorded and filmed in a town square – Joe Dallas became the resident band for two months in a live streaming radio show. After this incredibly educational experience, they took part as a guest band in a live national broadcast transmitted on New Year's Eve. A series of experiences that proved to be very important in learning how to work effectively while maintaining a collective spirit, becoming familiar with composite and complex technical realities.
The pursuit of musical collaborations is one of the key prerogatives of the project, which aims to develop its own musical curriculum characterized by original compositions and arrangements. Taking inspiration on the personalities, as well as the culture and nationality of each musician involved in the project, Joe Dallas is determined to offer an impactful musical experience, full of unexpected twists and turns and aiming to constantly enrich the collective experience.
Firmly rooted in engaging rhythms, jazz and latin sounds are mixed with Funk, Soul and RnB. Joe Dallas offers a variety of home-made compositions where the vocal lines take their inspiration from the pop world and then develop into melodies of various influences.
Rythm section
Daniel Macullo
Composition / Arrangement / Guitar (Switzerland)
danielmacullo.ch rock.idschool.ch
Marco Cuzzovaglia
Drums (Italy)
rock.idschool.ch
André Sampaio
Bass (Portugal)
Matteo Ballabio
Keyboards (Switzerland)
Silvano Detomaso
Percussions (Switzerland)
Horns section
Juan Munguía
Trumpet and flugelhorn (Spain)
Gendrickson Mena
Trumpet and flugelhorn (Cuba)
Mirko Roccato
Tenor sax and soprano (Italy)
Olmo Antezana
Alto sax and flute (Chile)
Giordana Lang
Vox and BK vocals (Switzerland)
Thaís Gomes
Vox and BK vocals (Brazil)
Davide De Vita
Jonas Macullo
Sound engineer (Switzerland)
Let's connect! Contact us
© 2023 Joe Dallas & The Monks. All rights reserved


avocado pound cake — urban. apron.
Yep. I put avocado in a loaf pan and baked it.
And yep. It was awesome.
I smash the green stuff on toast daily.
Or make an avocado sandwich: toast Ezekiel bread and smother in avocado, whole grain spicy mustard, fresh basil, tomatoes/zucchini/spinach and cracked black pepper…the ultimate veg sandwich.
But, when I find out ways that you can bake with it… I am joining that team too.
This is another Joy the Baker inspired recipe that I cut out of a magazine last year, and I really haven't stopped thinking about it since.
My taste can be crazy at times, but trust me…this is really good!
It is similar to a cornbread, in both taste and texture.
Avocados have a mild flavor, so it is some-what hidden in this bread. But, you can catch flecks of green throughout the loaf, as well as a cool green tint.
Cut slices and serve with a dollop of yogurt and honey.
- Preheat oven to 350 degrees F. Grease and flour two loaf pans and set aside.
- In a medium bowl, sift together flour, cornmeal, salt, baking powder and baking soda. Set aside. In a mixer, beat the butter until creamy and add the sugar at medium speed until light and fluffy, about 4 minutes. Add the yogurt.
- Add the avocado and beat another minute to incorporate. Scrape down the sides of the bowl to make sure it is all mixed.
- Add the eggs one at a time, beating 1 minute after the addition of each egg. Beat in vanilla extract.
- Reduce the mixer speed to low and add half of the flour mixture, all of the milk, and the rest of the flour mixture. Beat just until combined.
- Divide the dough between the two loaf pans and place in the oven. Turn the oven down to 325 degrees F. Bake for 40-45 minutes or until a skewer inserted in the center of the cakes comes out clean. Keep checking them after the 30 minute mark.
- Let cool in the pan for 10 minutes, then invert onto a cooling rack to cool completely.
- Store at room temperature. This also freezes very well.