Monitoring brain development with quantitative diffusion

Transcription

1 Developmental Science 5:3 (2002), pp Monitoring brain development with quantitative diffusion Blackwell Publishers Ltd tensor imaging Aziz M. Ulu2 Weill Medical College of Cornell University, New York, USA Abstract Quantifying changes that occur during brain maturation may help in diagnosing diseases that affect pediatric patients. By obtaining normative curves that define brain maturation, pathological changes may be easier to recognize. We assessed diffusion changes which are inherently related to the brain structure during maturation and obtained normative curves. MR scans were obtained for 26 pediatric subjects (ages 0 to 11 years) and four adults. The MR images were all normal. Maps of the average diffusion constant (D av ) were calculated for each subject. Changes in D av were determined with distribution analysis for the entire brain and compared with regions of interest measurements from the periventricular white matter and thalamus. The mean diffusion constant of the whole brain changes quite rapidly as the brain matures. The data suggest that at least two distinct processes are responsible for the change. Quantitative diffusion tensor imaging may provide means of quantifying overall human brain maturation that may be useful in diagnosing pathology. Introduction The human brain is incompletely developed at birth. Changes in brain morphology are a normal aspect of brain maturation (Rivkin, 2000 and references within). Since MR has a very good tissue contrast and does not involve ionizing radiation, it has been used extensively to monitor changes in the developing brain (Lee, Lipper, Nass, Ehrlich, de Ciccio-Bloom, Emmanuel & Auld, 1985; Holland, Haas, Norman, Brant-Zawadzki & Newton, 1986; McArdle, 1987; Barkovich, 1988; Martin, 1988; van der Knaap & Valk, 1990; Pfefferbaum, Mathalon, Sullivan, Rawles, Zipursky & Lim, 1994; Ferrie, Barantin, Saliba, Akoka, Tranquart, Sirinelli & Pourcelot, 1999; Thornton, Amess, Penrice, Chong, Wyatt & Ordidge, 1999). During human brain maturation various anatomical changes occur. The water content decreases, the number of cells and number of neuronal connections change and the myelination continues (Dobbing & Sands, 1973). It would be important to quantify these changes in order to understand the natural course of brain development. More importantly, quantifying normal brain development may help early diagnosis of the diseases that involve developmental delay of the central nervous system (Dietrich, Bradley, Zaragoza, Otto, Taira, Wilson & Kangarloo, 1988; Martin, Boesch, Zuerrer, Kikinis, Molinari, Kaelin, Boltshauser & Duc, 1990). MR diffusion tensor imaging is an emerging imaging modality that is sensitive to small-scale incoherent motion of the tissue water in the human brain. The human brain is about 90% water at birth (Dobbing & Sands, 1973). The self-diffusion of water molecules in the human brain is affected by the underlying tissue structure. Water molecules diffusing inside a white matter fiber tract, for example, are more likely to diffuse in the direction of the fiber than diffuse across the fiber where they need to go through the fiber boundaries. A myelinated white matter tract may pose more restriction on the water molecules than an unmyelinated tract. So, by monitoring the diffusion of tissue water with MR diffusion tensor imaging, information on tissue microstructure can be obtained (Le Bihan, 1995; Pierpaoli, Jezzard, Basser, Barnett & Di Chiro, 1996; Ulug & van Zijl, 1999). Part of the brain tissue with a higher diffusion value compared to some other part, may suggest an increased freedom of water molecules due to, for example, increased water content in the tissue concerned. For example, in the case of brain tissue with vasogenic edema, the diffusion constant is known to increase. The sensitivity of MR diffusion tensor imaging, which is a macroscopic method, to the underlying tissue microstructure is very interesting. Up to to now, microscopic information about the tissue could only be obtained using histo-pathological means. There was no non-invasive method that could supply microscopic Address for correspondence: Weill Medical College of Cornell University, Department of Radiology, Box 141, 1300 York Avenue, New York, NY 10021, USA; amu@watts.med.cornell.edu, 108 Cowley Road, Oxford OX4 1JF, UK and 350 Main Street, Malden, MA 02148, USA.

2 Monitoring brain development 287 information with the resolution that MR diffusion tensor imaging can offer. Studies regarding human brain maturation, diseases of white matter and dementia with known underlying structural causes such as normal pressure hydrocephalus may benefit from the development of MR diffusion tensor imaging methods. Figure 1 illustrates the utility of MR diffusion imaging in visualizing the tissue microstructure that is not otherwise visible in clinical MR images. The diffusion of water in the human brain is restricted by the tissue microstructure and, in general, is an orientation dependent phenomenon. Mathematically, it can be described as a second rank tensor with six independent components. By mathematical manipulation of tensor components structurally relevant parameters can be derived (Basser & Pierpaoli, 1996; Ulug & van Zijl, 1999). The most commonly used diffusion tensor parameter is D av (van Gelderen, de Vleeschouwer, DesPres, Pekar, van Zijl & Moonen, 1994; Ulug, Beauchamp, Bryan & van Zijl, 1997) which is defined as one-third of the trace of the diffusion tensor. Due to this inherent sensitivity to tissue structure, diffusion tensor imaging is being used to image pediatric subjects (Sakuma, Nomura, Takeda, Tagami, Nakagawa, Tamagawa, Ishii & Tsukamoto, 1991; Nomura, Sakuma, Takeda, Tagami, Okuda & Nakagawa, 1994; Huppi, Maier, Peled, Zientara, Barnes, Jolesz & Volpe, 1998; Neill, Shiran, McKinstry, Schefft, Snyder, Almli, Akbudak, Aronovitz, Miller, Lee & Conturo, 1998; Robertson, Maier, Robson, Mulkern, Karas & Barnes, 1999; Morriss, Zimmerman, Bilaniuk, Hunter & Haselgrove, 1999; Ulug, Filippi, Heier & Zimmerman, 2000). Most of these studies used region of interest (ROI) measurements in assessing brain maturation. Recently, we have shown that it is possible to reduce the diffusion information from the entire brain to a few structurally important parameters (Chun, Filippi, Zimmerman & Ulug, 2000; Ulug et al., 2000). Determining the agedependency of these diffusion-derived structural parameters and obtaining normative curves are expected to have utility in the field of brain maturation and may serve as a baseline for future research. Methods Twenty-six subjects younger than 11 years of age (13 male, 13 female) and four adult subjects (2 male, 2 female) are included in the study. Subjects are either normal volunteers or patients who are scanned for clinical reasons. All MR images of the subjects are normal except one pediatric subject who has a small frontal horn cyst. There was excellent agreement between the results of the four adult subjects and results from our previous study on adults. We also studied three subjects with known pathology. MR scans were acquired for all subjects on a 1.5 T scanner. Axial diffusion images (FOV 22 cm and up to 30 slices of thickness 5 mm, interleaved, image matrix ) were obtained with an echo-planar multislice sequence with TE of 100 ms and TR of 10s. Diffusion was measured in three orthogonal directions (Right- Left, Anterior-Posterior, Superior-Inferior), with a b- value of 100,000 s/cm 2 for each direction. In addition, an image without diffusion gradients was acquired. In about one-third of the subjects we obtained images from four more non-collinear directions for further analysis. Using the diffusion weighted images in these three orthogonal directions, an orientation independent diffusion-weighted image (DWI trace ), which is related to the trace of the diffusion tensor was obtained as follows (Chun et al., 2000): DWI = 3 DWI DWI DWI trace RL AP SI where RL is right to left, AP is anterior to posterior, and SI is the superior to inferior direction. This trace-weighted image and the image without diffusion weighting (S o ) was used to calculate the average diffusion constant by employing the following equation pixel-by-pixel basis: DWI trace = S o exp( bd av ) The orientation independent average diffusion constant maps (D av ) are then generated (Ulug & van Zijl, 1999): D ( ) 1 3 Trace D 1 3 D D D = { } = + + av RL AP SI where D ij is the diffusion constant measured in three orthogonal directions. A computer program was then used to calculate the diffusion distribution maps from the D av maps for the entire brain (see Figure 2). This program distributed the D av pixels according to their diffusion constant between cm 2 /s into 250 bins. The width of the bins was cm 2 /s. This distribution map (D av histogram) was fitted to a triple Gaussian curve. This curve with nine parameters Ce 1 + C2e + Ce 3 2 Dav BDav Dav D Dav D3 σ σ σ3 represents a brain model with two distinct compartments with a third mixing compartment:

3 288 Aziz M. Ulug Figure 1 Axial MR images through the basal ganglia of 29- year-old female volunteer are shown. (a) A T2-weighted image (standard clinical image) is shown with a diffusion-weighted image DWI trace (b). An average diffusion constant (D av ) image (c) and a diffusion anisotropy image (d) are also shown. In the average diffusion constant image, the effect of diffusion anisotropy is removed. Brain tissue has low diffusion constant and cerebrospinal fluid (CSF) has high diffusion constant. In the diffusion anisotropy image, the orientation dependence of diffusion is enhanced. The white matter tracts are white, gray matter and CSF are dark. Both anterior and posterior limbs of internal capsule and genu and splenium of corpus callosum are clearly visible. i. low diffusion compartment: brain tissue; ii. medium diffusion compartment: brain tissue mixed with cerebrospinal fluid (CSF), i.e. partial-volume pixels; iii. high diffusion compartment: CSF and non-brain tissue. The peak location of the tissue compartment as determined by the fit was defined to be BD av. The width of this compartment was defined to be σ. Because the diffusion constant of CSF ( cm 2 /s) is much higher than that of the brain tissue, the small contamination of tissue pixels with CSF would increase the measured diffusion constant of these pixels. These pixels would then Figure 2 Whole brain diffusion distribution map (D av histogram) from a 6-month-old subject. The data (circles) are fitted with a triple Gaussian function to accommodate the twocompartment nature of the data and the mixing between two compartments (solid curve). The brain tissue compartment has a narrow distribution of values around its mean. The second and third compartments have broader distributions and merge into a broad tail. The mean of the brain tissue compartment is recognized as a mean diffusion constant for the entire brain (BD av ). BD av and the distribution width (σ) of the brain tissue compartment is shown. The individual compartments decomposed from the fit are shown. The brain tissue compartment has the lowest mean and the narrowest width. be distributed in the medium diffusion compartment. Hence, the measured BD av and σ characterize the brain tissue pixels that are not contaminated with CSF. Figure 2 shows an average diffusion constant distribution map from a 6-month-old subject and threecompartmental fit to the data using a nine parameter, triple Gaussian curve. The three-compartmental decomposition of the fitted curve is also shown. We also measured the D av values of the periventricular white matter (PVWM) and thalamus using regions of interest. We placed ROIs on average diffusion maps and calculated the mean value for each ROI. PVWM measurements included approximately 30 ROIs placed over 2 3 slices. Thalamic measurements included 2 4 ROIs placed on a single slice. All diffusion data were plotted against age and were then fitted with a five parameter double exponential curve of m 1 exp( m 2 age) + m 3 exp ( m 4 age) + m 5, where m i are the fitted parameters. The data from the adult subjects were included in the fitting to reduce the variability of m 5, which basically determines the adult normal values.

4 Monitoring brain development PVWM D av (10 5 cm 2 /s) BD av (10 5 cm 2 /s) Figure 3 The mean brain diffusion constants (BD av ) measured from the entire brain distribution maps are plotted against age of the subjects (circles). A double exponential fit to the data (solid line) is also shown (BD av = exp( 2.48 age) exp( age) , R = 0.991). Results from three patients are also shown (squares): a 10-year-old patient with sickle cell disease has an increased BD av ; a 4-year-old patient with growth delay and a 5-month-old developmentally delayed patient have decreased BD av values compared to the normative curve. Results Figure 3 and Table 1 summarize the diffusion constant measurements for the entire brain. The mean diffusion constant of the brain is high at birth and decreases quite rapidly in the first year of life. The rate of decrease slows down after this age. At the age of 12, the mean diffusion constant is still above (6%) the adult normal value (see Table 2). The double exponential fit to the data gave (Table 1): Figure 4 The PVWM D av is plotted against BD av. A linear fit through the data (solid line) is shown (PVWM D av = BD av 0.407, R = 0.977). There is a strong linear correlation. BD av = exp( 2.48 age) exp( age) where age is in years and BD av in 10 5 cm 2 /s. The ROI measurements of PVWM and thalami also show similar age dependency with an initial sharp decrease and a slow decrease afterwards (see Table 1). Since ROI measurements have less signal noise compared to the distribution analysis, there is more scatter in ROI measurements of PVWM and thalamus results (smaller R values, see Table 1). Age dependency of the diffusion measurements are summarized in Table 1. When we plot PVWM D av against BD av (Figure 4), we observe a strong linear correlation. The linear relationship is also observed when thalamic measurements are plotted against BD av or PVWM D av (see Table 1). There were no statistically significant trends with age for the D av distribution width (σ) in this small subject Table 1 Age dependent diffusion changes for the pediatric subjects. Age range 0 11 years (n = 26, 13 male, 13 female) BD av = exp( 2.48 age) exp( age) , R = PVWM D av = exp( 7.36 age) exp( age) , R = Thalamic D av = exp( 7.20 age) exp( age) , R = σ = ± cm 2 /s PVWM D av = BD av 0.407, R = Thalamic D av = BD av , R = Thalamic D av = PVWM D av , R = Note: Age is in years and D av and BD av in 10 5 cm 2 /s. BD av is the mean average diffusion constant for brain tissue, σ is width of brain tissue D av distribution. R is Pearson coefficient.

5 290 Aziz M. Ulug Table 2 Diffusion measurements for adults. Age range years (n = 4, 2 male, 2 female) BD av ± cm 2 /s PVWM D av ± cm 2 /s Thalamic D av ± cm 2 /s σ ± cm 2 /s Adult values from the reference (Chun et al., 2000). Age range years (n = 38, 20 male, 18 female) BD av 0.74 ± cm 2 /s PVWM D av 0.76 ± cm 2 /s Thalamic D av 0.75 ± cm 2 /s σ 0.18 ± χµ 2 /σ Note: Values reported as mean ± standard deviation. BD av is the mean average diffusion constant for brain tissue, σ is width of brain tissue D av distribution. population. For the entire pediatric population, the D av distribution width σ was measured to be ± cm 2 /s. In Figure 3, we have also included data from three patients with known pathology: a 5-month-old patient with developmental delay and a 4-year-old patient with growth delay had BD av values below the normative curve; a 10-year-old sickle cell disease patient had a BD av value significantly higher than the normative curve. Table 2 summarizes the adult diffusion measurements. There is excellent agreement with the measured adult values and previously reported values (Chun et al., 2000). Discussion Our results showed that the mean diffusion constant in the human brain changes quite rapidly as the brain matures. The data can be fitted by a double exponential model suggesting that at least two distinct processes are at work. The first process, which is responsible in the first year, is much faster than the second process which becomes dominant after age 1 year and continues to the early teens. In contrast, in the adult population the mean diffusion constant is reported to be fairly constant during adulthood (Chun et al., 2000). Our diffusion results are in agreement with the results of Dobbing and Sands (1973) who measured the water content of the human brain during maturation using pathological means. Their results show an initial sharp decline of water content before 1 year of age and then a slower decrease following. At age 8 (the oldest datum shown in the reference) the water content is still higher than the adult normal value. The diffusion constant of the brain tissue water is expected to correlate with the water content of the tissue. Less available tissue water would translate into more restricted diffusion and more tortuosity, hence a decreased measured diffusion constant. The diffusion of water molecules in the human brain is restricted by the tissue microstructure. A decrease in diffusion may be also be explained by increased restriction due to brain development (myelination and perhaps increased synaptic density). Although the time constants of age dependency were different for distribution analysis determined (BD av ) and the ROI measured curves (PVWM and Thalamic D av ), when the results were plotted against each other there was a strong linear correlation suggesting that distribution analysis results can determine the overall brain maturation process. The strong linear relationship (see Figure 4) between the measurements of PVWM which is going through myelination and the whole brain that includes both gray matter and white matter is interesting. One might have expected from the myelination-induced changes in white matter diffusion would dominate white matter results and would be different from the diffusional changes in gray matter. One should notice that the average diffusion constant (D av ) that we used is related to the trace of the diffusion tensor which is insensitive to the directional dependence of the diffusion phenomenon. The D av values measured in the adult population do not differ in gray and white matter (Ulug & van Zijl, 1999), hence the similarity of white matter and whole brain results should not be surprising. The ROI measurements of PVWM and thalamus have more scatter compared with the BD av results. This may come from two distinct sources: 1. One important source of the scatter is the fact that signal-to-noise ratio in a given measurement is proportional to the square root of the number of pixels measured. There is much more scatter in thalamic measurements compared to PVWM measurements since thalamic measurements used small regions of interest due to the anatomy compared to the PVWM regions which were measured from up to 30 different ROIs. In contrast, the distribution analysis uses all the pixels that are in the entire imaging data set. 2. The second important source of the scatter is that in ROI measurements the regions have to be correctly identified by the operator before measurement. If the operator mistakenly included regions other than that intended that would increase the error of the measurement. For example in some thalamic measurements, it is possible that parts of ventricles might be inadvertently included. If so, this would increase the measured diffusion constant and introduce additional scatter in the data. The distribution analysis described here does not suffer from these shortcomings of the ROI measurements.

6 Monitoring brain development 291 An additional benefit of this type of analysis is that distributing pixels according to signal intensity should theoretically remove the contaminating effects of blood vessel and small CSF spaces. On D av maps, these phenomena appear as foci of high diffusion. These regions may be unintentionally included in ROI measurements, causing abnormal elevation of D av values. Distribution maps distribute the pixels of these high diffusion areas to the right and remove their contribution to brain tissue D av measurements (Chun et al., 2000). The three patients with pathology that we included in Figure 3 indicate that in certain diseases the use of diffusion curves might be useful in determining pathology. Developmental delay, for example, is difficult to diagnose at an early age. If one can obtain an accurate diffusional maturation curve for the early age, deviation from the normative curve may help in diagnosing the disease. We are currently studying developmentally delayed infants using quantitative diffusion tensor imaging to determine the utility of this technique (Filippi, Ulug, Lin, Heier & Zimmerman, 1999). In this study we assessed the diffusion characteristics of the entire brain as a function of age using distribution analysis and modeling. The use of distribution analysis of average diffusion constant (D av histograms) provides an effective method for measuring mean diffusion for the entire brain. Conclusions We have quantified the MR diffusion tensor images in order to assess total brain diffusion. We have shown in this study that distribution analysis and modeling can be used to accurately assess age dependency of brain diffusion. We found a rapid decrease of diffusion which was constant during brain maturation, which may be explained by the decrease in water content of the brain tissue. We have provided a database of age-specific diffusion values of the entire brain, periventricular white matter and thalamus that can be used as normative curves. Further development in this methodology will likely increase its use for evaluating a number of brain pathologies affecting pediatric patients. Acknowledgements We would like to thank Dr Kathleen Thomas and her colleagues at Sackler Institute for collecting some of the diffusion data. We would like to thank Dr Peter Barker of Johns Hopkins University for providing the IMAX2 data display and processing program that we used in the region of interest measurements. Part of the preliminary data were presented during the 8th annual ISMRM meeting held in Denver, April 2000 (Ulug et al., 2000). This work is supported in part by a grant from NICHD (1R03-HD ). References Barkovich, A.J., Kjos, B.O., Jackson, D.E., & Norman, D. (1988). Normal maturation of the neonatal and infant brain: MR imaging at 1.5 T. 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