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ROIs for measurement in the anatomical areas reported in Table 2, overlaid on the FA-map of one participant, a 29 years old healthy woman. a In the centrum semiovale (1, 2), cingulate (3, 4), and the parietal subcortical white matter (5, 6), in the body of the CC (7, 8), corona radiate (9, 10), posterior SLF (11, 12), splenium of the CC (13) and the posterior IFL (14, 15), b In the genu of the corpus callosum (16) and the frontal subcortical white matter (17, 18), ALIC (19, 20), genu of internal capsule (21, 22), PLIC (23 ,24), external capsule (25, 26) and the temporal subcortical white matter (27, 28), in the caudate head (29, 30), thalamus (31, 32), pallidum (33, 34), and the putamen (35, 36), CST (37, 38) and the IFO (39, 40)
Source publication
Objective:
To provide estimates of the diffusional kurtosis in different anatomical regions of a healthy brain and to assess age dependency of diffusion kurtosis imaging (DKI) and diffusion tensor imaging (DTI) derived parametric values in these regions.
Materials and methods:
Eighty healthy volunteers underwent DKI of the brain with 3.0 T magne...
Citations
... For nearly two decades, diffusion kurtosis imaging (DKI) [55,56], a higher-order extension of DTI, has gained increasing attention as a method for providing sensitive biomarkers of development [57][58][59][60], ageing [61][62][63][64][65][66], and various brain pathologies, such as stroke [67][68][69][70][71][72], neurodegenerative diseases [73][74][75], childhood epilepsy [76][77][78], and attention-deficit/hyperactivity [59,79]. In relation to developmental processes, strong increases in both FA and mean kurtosis (MK) have been reported for multiple WM regions during the first two years of life, with a subsequent slowdown toward a plateau-like behaviour [58]. ...
... Interestingly, in contrast with FA, MK continued to rise beyond the 2year mark and converged to a plateau at a later age, thus reflecting ongoing remodelling of the tissue microstructural environment. A further increase in MK beyond childhood/adolescence, as opposed to an ageing-related decrease [61,64] after the second decade of life, has only been reported in a relatively small number of works [57,59,62,66], emphasising the need for further studies. In particular, Falangola et al. [62] showed that MK in the prefrontal brain region was higher in young adults (26-47 years old) than in adolescents (12-17 years old). ...
... An increase in MK between the ages of 12 to 18 years, which was also limited to the prefrontal cortex, was reported by Helpern et al. [59] for a small group of subjects (n = 13). Age trajectories of DT and kurtosis tensor (KT) metrics in 21 WM and GM anatomic regions were investigated by Das et al. [66] in a work that had ageing processes as the primary focus. ...
Diffusion tensor imaging (DTI) enables the assessment of changes in brain tissue microstructure during maturation and ageing. In general, patterns of cerebral maturation and decline render non-monotonic lifespan trajectories of DTI metrics with age, and, importantly, the rate of microstructural changes is heterochronous for various white matter fibres. Recent studies have demonstrated that diffusion kurtosis imaging (DKI) metrics are more sensitive to microstructural changes during ageing compared to those of DTI. In a previous work, we demonstrated that the Cohen’s d of mean diffusional kurtosis (dMK) represents a useful biomarker for quantifying maturation heterochronicity. However, some inferences on the maturation grades of different fibre types, such as association, projection, and commissural, were of a preliminary nature due to the insufficient number of fibres considered. Hence, the purpose of this follow-up work was to further explore the heterochronicity of microstructural maturation between pre-adolescence and middle adulthood based on DTI and DKI metrics. Using the effect size of the between-group parametric changes and Cohen’s d, we observed that all commissural fibres achieved the highest level of maturity, followed by the majority of projection fibres, while the majority of association fibres were the least matured. We also demonstrated that dMK strongly correlates with the maxima or minima of the lifespan curves of DTI metrics. Furthermore, our results provide substantial evidence for the existence of spatial gradients in the timing of white matter maturation. In conclusion, our data suggest that DKI provides useful biomarkers for the investigation of maturation spatial heterogeneity and heterochronicity.
... Also, the extracellular matrix produced by glioma may be another factor that reduces the density of white matter fibers and axons (Zamecnik, 2005). In DKI model, MK value reflects the complexity and structural integrity of brain tissue (Das et al., 2017). Previous studies on the application of DKI to low-grade gliomas (Goryawala et al., 2018) and inflammation have demonstrated lower radial kurtosis (RK) values in lesions in comparison to healthy controls or contralateral normal-appearing white matter, which related to the destructive impact exerted by tumor cells or inflammation on brain tissue. ...
Background and purpose
The differential diagnosis between solid glioma and brain inflammation is necessary but sometimes difficult. We assessed the effectiveness of multiple diffusion metrics of diffusion-weighted imaging (DWI) in differentiating solid glioma from brain inflammation and compared the diagnostic performance of different DWI models.
Materials and methods
Participants diagnosed with either glioma or brain inflammation with a solid lesion on MRI were enrolled in this prospective study from May 2016 to April 2023. Diffusion-weighted imaging was performed using a spin-echo echo-planar imaging sequence with five b values (500, 1,000, 1,500, 2000, and 2,500 s/mm²) in 30 directions for each b value, and one b value of 0 was included. The mean values of multiple diffusion metrics based on diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), mean apparent propagator (MAP), and neurite orientation dispersion and density imaging (NODDI) in the abnormal signal area were calculated. Comparisons between glioma and inflammation were performed. The area under the curve (AUC) of the receiver operating characteristic curve (ROC) of diffusion metrics were calculated.
Results
57 patients (39 patients with glioma and 18 patients with inflammation) were finally included. MAP model, with its metric non-Gaussianity (NG), shows the greatest diagnostic performance (AUC = 0.879) for differentiation of inflammation and glioma with atypical MRI manifestation. The AUC of DKI model, with its metric mean kurtosis (MK) are comparable to NG (AUC = 0.855), followed by NODDI model with intracellular volume fraction (ICVF) (AUC = 0.825). The lowest value was obtained in DTI with mean diffusivity (MD) (AUC = 0.758).
Conclusion
Multiple diffusion metrics can be used in differentiation of inflammation and solid glioma. Non-Gaussianity (NG) from mean apparent propagator (MAP) model shows the greatest diagnostic performance for differentiation of inflammation and glioma.
... These regions were selected to compare with each parameter value in the previous literature. 23 In addition, the connections between the DTI and DKI nerve fibre pathways were compared using TBSS. Figure 2 shows original, the rigid, and nonrigid aligned images to T 1 w and each parameter image (FA, MK, AK, and RK) using FSL software. ...
... Thus, in a WM tract, it can be assumed that the relationship RK > AK. In our study, 4 common regions as well as the previous study by Das et al 23 were measured (eg, GCC, ALIC, PLIC, and EC). In all regions determined in this study, the diffusivities (MD, AK, and RD) were lower than those published in the previous literature, and the RK value for the DKI parameters was higher (Table 1). ...
Objectives
In a clinical study, diffusion kurtosis imaging (DKI) has been used to visualize and distinguish white matter (WM) structures’ details. The purpose of our study is to evaluate and compare the diffusion tensor imaging (DTI) and DKI parameter values to obtain WM structure differences of healthy subjects.
Methods
Thirteen healthy volunteers (mean age, 25.2 years) were examined in this study. On a 3-T MRI system, diffusion dataset for DKI was acquired using an echo-planner imaging sequence, and T1-weghted (T1w) images were acquired. Imaging analysis was performed using Functional MRI of the brain Software Library (FSL). First, registration analysis was performed using the T1w of each subject to MNI152. Second, DTI (eg, fractional anisotropy [FA] and each diffusivity) and DKI (eg, mean kurtosis [MK], radial kurtosis [RK], and axial kurtosis [AK]) datasets were applied to above computed spline coefficients and affine matrices. Each DTI and DKI parameter value for WM areas was compared. Finally, tract-based spatial statistics (TBSS) analysis was performed using each parameter.
Results
The relationship between FA and kurtosis parameters (MK, RK, and AK) for WM areas had a strong positive correlation (FA-MK, R2 = 0.93; FA-RK, R2 = 0.89) and a strong negative correlation (FA-AK, R2 = 0.92). When comparing a TBSS connection, we found that this could be observed more clearly in MK than in RK and FA.
Conclusions
WM analysis with DKI enable us to obtain more detailed information for connectivity between nerve structures.
Advances in knowledge
Quantitative indices of neurological diseases were determined using segmenting WM regions using voxel-based morphometry processing of DKI images.
... These initial declines were assumed to be associated with degenerative processes such as fibre demyelination and axonal loss. Studies using DKI showed that the degree of non-Gaussian diffusion increases up to the fifth decade of life (Coutu et al., 2014;Das et al., 2017;Falangola et al., 2008;Gong et al., 2014;Lätt et al., 2013). Since increased degree of non-Gaussian diffusion has been associated with WM maturation processes (Helpern et al., 2011a;Jensen and Helpern, 2010;Paydar et al., 2014), these DKI results are difficult to reconcile with the degeneration suggested by DTI anisotropy metrics. ...
... The different (linear) effects of age in each subgroup imply that the age-related patterns observed across metrics and ROIs is highly dependent on the age ranges of the volunteers included in a study, which might explain some of the inconsistencies in previous studies using DKI/NODDI (Billiet et al., 2015;Chang et al., 2015;Cox et al., 2016;Merluzzi et al., 2016). Overall, our results also confirm that going beyond DTI, more advanced dMRI techniques based on signal representation (e.g., DKI) and microstructural models (e.g., NODDI) can provide different information about microstructural age-associated changes ( Fig. 4 and Fig. 5), consistent with prior claims (Billiet et al., 2015;Chang et al., 2015;Coutu et al., 2014;Das et al., 2017;Falangola et al., 2008;Gong et al., 2014;Kodiweera et al., 2016;Lätt et al., 2013). However, our factor analysis shows that variation in all six diffusion metrics used in this study can be captured by just three main dimensions (Fig. 6), which we have linked to effects of 1) non-Gaussian diffusion, 2) tissue configuration complexity and 3) freewater content. ...
... In this study, we did not consider directional metrics of DTI and DKI such as the axial and radial diffusivities, and the axial and radial kurtosis, since their interpretation may be limited to WM regions comprising of single fibre populations (De Santis et al., 2014;Henriques et al., 2015;Jeurissen et al., 2013;Wheeler-Kingshott et al., 2009). Although our study mainly focused on advanced diffusion MRI metrics that can be generally applied to different WM fibre configurations, an intrinsic pitfall of this work is that it does not explore if directional DTI/DKI metrics can provide additional information that could potentially be used, for example, to distinguish mechanism of fibre loss and demyelination as suggested by previous studies (Beck et al., 2021;Coutu et al., 2014;Das et al., 2017;Davis et al., 2009;Fieremans et al., 2013;Gong et al., 2014;Lätt et al., 2013). Therefore, in future studies, it will be of interest to perform similar factor analysis on directional metrics of DTI/DKI or other metrics from models that use information from directional quantities, such as the White Matter Tract Integrity model (a two-compartmental microstructural model that can be reconstructed from the full DKI tensor under the assumption that fibres are well-aligned; Fieremans et al., 2013Fieremans et al., , 2011Henriques et al., 2021a). ...
Diffusion Magnetic Resonance Imaging (dMRI) is sensitive to white matter (WM) changes across the human lifespan. Several models have been proposed to provide more specific metrics than those provided by the conventional Diffusion Tensor Imaging (DTI) analysis. However, previous results using different metrics have led to contradictory conclusions regarding the effect of age on fibre demyelination and axonal loss in adults. Moreover, it remains unclear whether these metrics provide distinct information about the effects of age. To address this, we analysed dMRI data from 651 adults uniformly aged from 18 to 88 years in the Cam-CAN cohort, using six dMRI metrics: Fractional Anisotropy (FA) from DTI; Mean Signal Diffusion (MSD) and Mean Signal Kurtosis (MSK) from Diffusional Kurtosis Imaging (DKI); and Neurite Density Index (NDI), Orientation Dispersion Index (ODI) and isotropic Free water volume fraction (Fiso) estimated from Neurite Orientation Dispersion and Density Imaging (NODDI). Averaging across WM ROIs, 2nd order polynomial fits revealed that MSD, MSK and Fiso showed the strongest effects of age, with significant quadratic components. Analysing the data in different age subgroups revealed that some apparent discrepancies in previous studies may be explained by cohorts with different age ranges. Factor analysis of the six metrics across all ROIs revealed three independent factors that can be associated to 1) tissue non-Gaussian diffusion effects, 2) free-water contamination, and 3) tissue configuration complexity. While FA captures a combination of different factors, other dMRI metrics are strongly aligned to specific factors (NDI/MSK with Factor 1, Fiso with Factor 2, and ODI with Factor 3). In sum, our study explains previous discrepancies in dMRI ageing studies and provides further insights on the interpretation of dMRI metrics in the context of WM microstructural properties.
... Next, to detect microstructural changes in the damaged tissue, we analyzed the diffusion parameters, which are sensitive to structural changes and can predict functional outcome in chronic stroke patients (Stinear et al., 2007;Umesh Rudrapatna et al., 2014;Weber et al., 2015;Arab et al., 2018). The MK parameter represents the average value of the diffusion kurtosis in all gradient directions, with a greater MK value representing more significant limitation of water molecule diffusion (Grinberg et al., 2012;Das et al., 2017). Changes in Kr and Ka values reflect axonal degeneration (Wu and Cheung, 2010;Steven et al., 2014;Chen et al., 2018). ...
Inhibition of Notch1 signaling has been shown to promote astrocyte-derived neurogenesis after stroke. To investigate the regulatory role of Notch1 signaling in this process, in this study, we used a rat model of stroke based on middle cerebral artery occlusion and assessed the behavior of reactive astrocytes post-stroke. We used the γ-secretase inhibitor N-[N-(3,5-diuorophenacetyl)-1-alanyl]-S-phenylglycine t-butylester (DAPT) to block Notch1 signaling at 1, 4, and 7 days after injury. Our results showed that only administration of DAPT at 4 days after stroke promoted astrocyte-derived neurogenesis, as manifested by recovery of white matter fiber bundle integrity on magnetic resonance imaging, which is consistent with recovery of neurologic function. These findings suggest that inhibition of Notch1 signaling at the subacute stage post-stroke mediates neural repair by promoting astrocyte-derived neurogenesis.
... Kurtosis values are a series of indices used to describe the complexity of organizational structure. The higher the kurtosis values are, the more restricted and complex the environment is (Das et al., 2017). RK mainly describes the degree to which water molecules are confined in the radial direction and is sensitive to myelin sheath changes (Liang et al., 2021). ...
Objectives
To compare parameters of diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) to evaluate which can better describe the microstructural changes of anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis patients and to characterize the non-Gaussian diffusion patterns of the whole brain and their correlation with neuropsychological impairments in these patients.
Materials and methods
DTI and DKI parameters were measured in 57 patients with anti-NMDAR encephalitis and 42 healthy controls. Voxel-based analysis was used to evaluate group differences between white matter and gray matter separately. The modified Rankin Scale (mRS) was used to evaluate the severity of the neurofunctional recovery of patients, the Montreal Cognitive Assessment (MoCA) was used to assess global cognitive performance, and the Hamilton Depression Scale (HAMD) and fatigue severity scale (FSS) were used to evaluate depressive and fatigue states.
Results
Patients with anti-NMDAR encephalitis showed significantly decreased radial kurtosis (RK) in the right extranucleus in white matter (P < 0.001) and notably decreased kurtosis fractional anisotropy (KFA) in the right precuneus, the right superior parietal gyrus (SPG), the left precuneus, left middle occipital gyrus, and left superior occipital gyrus in gray matter (P < 0.001). Gray matter regions with decreased KFA overlapped with those with decreased RK in the left middle temporal gyrus, superior temporal gyrus (STG), supramarginal gyrus (SMG), postcentral gyrus (POCG), inferior parietal but supramarginal gyrus, angular gyrus (IPL) and angular gyrus (ANG) (P < 0.001). The KFA and RK in the left ANG, IPL and POCG correlated positively with MoCA scores. KFA and RK in the left ANG, IPL, POCG and SMG correlated negatively with mRS scores. KFA in the left precuneus and right SPG as well as RK in the left STG correlated negatively with mRS scores. No significant correlation between KFA and RK in the abnormal brain regions and HAMD and FSS scores was found.
Conclusion
The microstructural changes in gray matter were much more extensive than those in white matter in patients with anti-NMDAR encephalitis. The brain damage reflected by DKI parameters, which have higher sensitivity than parameters of DTI, correlated with cognitive impairment and the severity of the neurofunctional recovery.
... This indicates that the microstructure complexity of bilateral visual radiation in patients with anisometropic amblyopia may decrease, which may be related to the changes in the density, direction, myelin sheath, and other aspects of the visual radiation fiber. In previous studies [16,17], it was confirmed that the cells of the lateral geniculate body of amblyopia are shrunken and their function is impaired and that optic radiation is composed of fibers emitted after the lateral geniculate body. Therefore, the optic radiation of patients with amblyopia may have dysplasia and abnormal fiber projection, which reduces its complexity. ...
Background
Anisometropic amblyopia results from the unequal ability to focus between the right and left eyes. Blood oxygenation level-dependent functional magnetic resonance imaging (BOLD-fMRI) measures the proportion of oxygenated hemoglobin in specific areas. Diffusion kurtosis imaging (DKI) is a method of diffusion tensor imaging that estimates the skewed distribution of water diffusion probability. We aimed to evaluate and compare 11 adult patients with anisometropic amblyopia (AA) with 13 normally sighted healthy controls (HC) using BOLD-fMRI and DKI.
Material/Methods
Eleven adults with AA (age range 20–49; mean age 29.18±8.089) and 13 HC adults (age range 22–50; mean age 28.00±5.79) were recruited. DKI scanning used a single excitation echo-planar imaging sequence and a region of interest to obtain DKI parameters for optic radiation; the corpus callosum was manually placed, including mean kurtosis (MK), fractional anisotropic (FA), and mean diffusivity (MD) values; and BOLD data used a gradient-echo echo-planar imaging sequence.
Results
The AA group had lower MK and FA of bilateral optic radiation than the HC group (P=0.008 and P=0.006, respectively) and higher MD than the HC group (P=0.005). The MK of the corpus callosum in the AA group was lower than that of HC group (P=0.012).Compared with the non-dominant eyes of the HC group, the amblyopic eyes in the AA group had less activation range and intensity in Brodmann areas 17, 18, and 19.
Conclusions
The combined use of DKI and BOLD-fMRI detected microstructural changes associated with local visual pathways and identified damage to the visual cortex in patients with amblyopia.
... While the exact meaning of and pathological basis for kurtosis have yet to be fully elucidated (24,25). To date, the general reason for kurtosis has been considered microstructure complexity (26), where the more complex and intricate a structure is, the higher the degree of non-Gaussian water molecule diffusion, and the greater the MK value (27,28). Some scholars have postulated that increases and decreases in kurtosis indicate potential disease changes or microstructure, and hence that kurtosis can provide rich microstructure information, with extensive analysis of pathological kurtosis potentially providing more information than diffusion coefficient data (29,30). ...
Objective
To detect the microstructural changes in patients with cognitive impairment after acute cerebral infarction using diffusion kurtosis imaging (DKI).
Materials and Methods
A total of 70 patients with acute cerebral infarction were divided into two groups: 35 patients with cognitive impairment (VCI group), and 35 patients without cognitive impairment (N-VCI group), according to mini-mental state examination (MMSE) score. Healthy individuals (n = 36) were selected as the normal control (NORM) group. DKI parameters from 28 different brain regions of interest (ROIs) were selected, measured, and compared.
Results
VCI group patients had significantly higher mean diffusion (MD) and significantly lower mean kurtosis (MK) values in most ROIs than those in the N-VCI and NORM groups. DKI parameters in some ROIs correlated significantly with MMSE score. The splenium of corpus callosum MD was most correlated with MMSE score, the correlation coefficient was −0.652, and this parameter had good ability to distinguish patients with VCI from healthy controls; at the optimal cut-off MD value (0.9915), sensitivity was 91.4%, specificity 100%, and the area under the curve value 0.964.
Conclusions
Pathological changes in some brain regions may underlie cognitive impairment after acute cerebral infarction, especially the splenium of corpus callosum. These preliminary results suggest that, in patients with VCI, DKI may be useful for assessing microstructural tissue damage.
... Physical diffusion metrics derived from DTI and diffusional kurtosis imaging (DKI), an extension of DTI that accounts for diffusion non-Gaussianity (Jensen and Helpern, 2010), permit inferences about axonal geometry and coherence (i.e., fractional anisotropy; FA), tissue integrity (i.e., mean diffusivity; MD), and the complexity of tissue microstructure (i.e., mean kurtosis; MK). Numerous studies have yielded highly consistent evidence of less restricted diffusion with age, reflected by decreased FA and MK and increased MD (Benitez et al., 2018;Billiet et al., 2015;Coutu et al., 2014;Cox et al., 2016;Das et al., 2017;Falangola et al., 2008;Gong et al., 2014;Madden et al., 2012Madden et al., , 2009Sullivan & Pfefferbaum, 2006). These effects are most pronounced in late-myelinating areas and/or follow an anterior-posterior gradient, providing convergent evidence from in vivo data that WM degenerates in a pattern inverse to myelin development. ...
Age-related white matter degeneration is characterized by myelin breakdown and neuronal fiber loss that preferentially occur in regions that myelinate later in development. Conventional diffusion MRI (dMRI) has demonstrated age-related increases in diffusivity but provides limited information regarding the tissue-specific changes driving these effects. A recently developed dMRI biophysical modeling technique, Fiber Ball White Matter (FBWM) modeling, offers enhanced biological interpretability by estimating microstructural properties specific to the intra-axonal and extra-axonal spaces. We used FBWM to illustrate the biological mechanisms underlying changes throughout white matter in healthy aging using data from 63 cognitively unimpaired adults ages 45–85 with no radiological evidence of neurodegeneration or incipient Alzheimer’s disease. Conventional dMRI and FBWM metrics were computed for two late-myelinating (genu of the corpus callosum and association tracts) and two early-myelinating regions (splenium of the corpus callosum and projection tracts). We examined the associations between age and these metrics in each region and tested whether age was differentially associated with these metrics in late- vs. early-myelinating regions. We found that conventional metrics replicated patterns of age-related increases in diffusivity in late-myelinating regions. FBWM additionally revealed specific intra- and extra-axonal changes suggestive of myelin breakdown and preferential loss of smaller-diameter axons, yielding in vivo corroboration of findings from histopathological studies of aged brains. These results demonstrate that advanced biophysical modeling approaches, such as FBWM, offer novel information about the microstructure-specific alterations contributing to white matter changes in healthy aging. These tools hold promise as sensitive indicators of early pathological changes related to neurodegenerative disease.
... MK and RK played an important role in detecting the development of isotropic tissues (such as gray matter) (25). In the gray matter region, the changes of MK and RK parameters may be related to the increased concentration of mature neuronal macromolecules and the decrease of tissue water content (26,27) or to other special structures occurring in the development of gray matter. As an advanced and sensitive imaging technique, MK and RK can be used to detect the subtle structural changes of thalamic neurons in the premature infants. ...
Objective: Preterm infants are at high risk of the adverse neurodevelopmental outcomes. Our aim is to explore the value of diffusion kurtosis imaging (DKI) in diagnosing brain developmental disorders in premature infants.
Materials and Methods: A total of 52 subjects were included in this study, including 26 premature infants as the preterm group, and 26 full-term infants as the control group. Routine MRI and DKI examinations were performed. Mean kurtosis (MK), radial kurtosis (RK), fractional anisotropy (FA), and mean diffusivity (MD) values were measured in the brain regions including posterior limbs of the internal capsule (PLIC), anterior limb of internal capsule (ALIC), parietal white matter (PWM), frontal white matter (FWM), thalamus (TH), caudate nucleus (CN), and genu of the corpus callosum (GCC). The chi-squared test, t-test, Spearman's correlation analysis, and receiver operating characteristic curve were used for data analyses.
Results: In the premature infant group, the MK and RK values of PLIA, ALIC, and PWM were lower than those in the control group (p < 0.05). The FA values of PWM, FWM, and TH were also lower than those of the control group (p < 0.05). The area under curves of MK in PLIC and ALIC, MD in PWM, and FA in FWM were 0.813, 0.802, 0.842, and 0.867 (p < 0.05). In the thalamus and CN, the correlations between MK, RK values, and postmenstrual age (PMA) were higher than those between FA, MD values, and PMA.
Conclusion: Diffusion kurtosis imaging can be used as an effective tool in detecting brain developmental disorders in premature infants.
