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Neurite orientation and dispersion density imaging: clinical utility, efficacy, and role in therapy

Authors Sone D 

Received 28 May 2019

Accepted for publication 20 July 2019

Published 5 August 2019 Volume 2019:12 Pages 17—29

DOI https://doi.org/10.2147/RMI.S194083

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Professor Tarik Massoud



Daichi Sone

Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo, Japan

Abstract: In the field of diffusion magnetic resonance imaging (MRI) for neuroimaging, white matter tracts have traditionally been analyzed using diffusion tensor imaging (DTI) measures, such as fractional anisotropy. However, recent advances in diffusion MRI have provided further information on brain microstructures using multi-shell protocols of diffusion MRI. Neurite orientation dispersion and density imaging (NODDI) is one such emerging advanced diffusion MRI method that enables investigation of the neurite density and neurite orientation dispersion of brain microstructures. NODDI was developed as a practical and clinically feasible diffusion MRI technique to evaluate the microstructural complexity of dendrites and axons. This review shed light on recent studies on the use of NODDI in human brain. Indeed, a growing number of studies are using NODDI to examine neurological and psychiatric disorders, with most reporting its clinical utility. The time has thus come, for us to seriously consider the clinical use of NODDI.

Keywords: neurite orientation dispersion and density imaging, diffusion magnetic resonance imaging, brain image, neurological diseases, psychiatric disorders

Introduction

In the field of diffusion magnetic resonance imaging (MRI) for neuroimaging, white matter tracts have traditionally been analyzed using diffusion tensor imaging (DTI) measures, such as fractional anisotropy.1 However, recent advances in diffusion MRI have provided further information on brain microstructures using multi-shell protocols of diffusion MRI, including diffusion kurtosis imaging (DKI), q-space imaging (QSI), and restriction spectrum imaging (RSI).24 Neurite orientation dispersion and density imaging (NODDI) is one such emerging advanced diffusion MRI method that enables investigation of the neurite density (ND) and neurite orientation dispersion (OD) of brain microstructures.5 Conventional DTI is based on the assumption of Gaussian distribution of diffusion processes, which may be inappropriate for non-Gaussian diffusion in biological tissues.6 Therefore, DKI, which is a common non-Gaussian diffusion model, has been developed as the extension of DTI.3 However, parameters derived from DKI lacked structural specificity, and for this reason, NODDI was proposed to provide more specific indices of tissue microstructures.7 On the other hand, QSI can provide molecular displacement probability maps by using Fourier transformation,2 but the prolonged acquisition time is problematic in clinical use.8 RSI is a newer method than QSI or DKI, and also yields parameters of ND but not of OD.4 Thus, NODDI is a clinically feasible technique to provide tissue-specific information based on non-Gaussian diffusion model. Indeed, a growing number of studies are using NODDI to examine neurological and psychiatric disorders such as Alzheimer’s disease and schizophrenia.9,10 In this article, I review the literature on recent applications of NODDI and discuss its potential clinical utility.

Principles and validations

NODDI was developed as a practical and clinically feasible diffusion MRI technique to evaluate the microstructural complexity of dendrites and axons. While OD, which represents the angular variation of neurites, can be estimated with a single high angular resolution diffusion-weighted imaging (HARDI) shell, ND requires at least two shells (ie b-values).5 NODDI has an advantage over conventional DTI in regions with a complex microstructure and is feasible with 1.5-T MRI scanners.11 Histopathologically, Sepehrband et al12 reported good agreement between the fiber density estimated by NODDI and the values measured by microscopy. According to a more recent histopathological validation study,13 NODDI can adequately describe the overall angular structures of fiber OD but fails to consistently extract discrete measures of the numbers and orientations of fiber OD peaks. Thus, although there is still room for improvement, NODDI may become a useful and reliable method for further investigating the microstructural complexity of the brain.

Alzheimer’s disease

Alzheimer’s disease is the most common cause of dementia. Early detection of Alzheimer’s disease is clinically important in terms of early cognitive intervention14,15,16 as well as future development of disease-modifying therapy,17 and neuroimaging plays key roles in ensuring accurate and early diagnosis, in revealing the underlying pathophysiology, and in monitoring the disease. Thus far, the use of NODDI in Alzheimer’s disease has focused on young-onset Alzheimer’s disease (Table 1).10,18 Using NODDI, Slattery et al10 reported an association between white matter changes and apolipoprotein E (APOE) ε4 status, which is the main inherited risk factor for sporadic Alzheimer’s disease. They also revealed correlations between regional white matter ND and neuropsychological batteries. Another study of young-onset Alzheimer’s disease and NODDI reported a widespread reduction in cortical ND and OD, including mesial and lateral temporal lobe and precuneus.18 Moreover, Fu et al19 reported that ND has detected clearer differences between Alzheimer’s disease and mild cognitive impairment than FA. Thus, NODDI may have better sensitivity to diagnose Alzheimer’s disease than conventional DTI.

Table 1 NODDI findings in Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, and epilepsy

Parkinson’s disease

Parkinson’s disease is a common neurodegenerative movement disorder characterized by the degeneration of dopaminergic neurons in the substantia nigra, and the overall prevalence is approximately 300–500 per 100,000 people.20 Through the use of NODDI, Kamagata and colleagues21 first unveiled reduced ND in the substantia nigra and putamen in patients with Parkinson’s disease, which correlated with disease severity. Subsequently, the same group also reported detection of gray matter abnormalities by NODDI and its diagnostic accuracy, as well as retrograde degeneration of the nigrostriatal pathway in patients with Parkinson’s disease.22,23 Thus, NODDI may prove to be useful in the diagnosis and monitoring of Parkinson’s disease. Especially, the potential of early detection has been suggested.7

Multiple sclerosis

Multiple sclerosis (MS) is an autoimmune disease involving damage to the myelin sheaths of the brain and spinal cord, and the global median prevalence is 33 per 100,000 people.24 In NODDI, the demyelinating lesions in white matter are detected as decreased ND (Figure 1). Schneider and colleagues25 reported more sensitive detection of MS lesions with NODDI parameters than with conventional DTI. Similar findings were also reported for spinal lesions.26 In contrast, the cortical lesions of MS are more subtle and difficult to detect on conventional MRI. Accordingly, the finding of reduced ND even in cortical lesions27 may have further clinical implications for MS. Additionally, the further usefulness of NODDI was indicated for detecting the disease progression of MS, including the change from normal-appearing white matter to lesions.28,29 The viability of a 7-T MRI scanner was also confirmed in MS.30

Figure 1 Sample images (A, T1-weighted; B, FLAIR; C, neurite density) of a 48-year-old patient with relapsing-remitting multiple sclerosis. Reduced neurite density can be seen in the demyelinating white matter lesions (arrows).

Epilepsy

Epilepsy is a common neurological disease marked by recurrent seizures associated with abnormal electrical activity in the brain. Patients with drug-resistant focal seizures can benefit from neurosurgical resection, and successful localization of the focus is thus a key clinical role of neuroimaging in epilepsy. For focal epilepsy, Winston et al31 reported the clinical usefulness of reduced ND in focal cortical dysplasia, which was also visible on conventional MRI. Subsequently, a significant reduction in ND was revealed in temporal lobe epilepsy without visible lesions on conventional MRI.32 Additionally, hippocampal sclerosis, which is the most common cause of temporal lobe epilepsy, shows reduced ND and OD.32 Representative images of these findings are shown in Figure 2. Cortical abnormalities on OD images in focal epilepsy have also been reported,33 but a limitation was raised afterward.34 At any rate, these findings would suggest a potential use for NODDI as a novel clinical biomarker for focal epilepsy treatment. Apart from focus detection, NODDI has revealed a brain connectivity dysfunction in temporal lobe epilepsy35 and a frontal lobe abnormality in idiopathic generalized epilepsy.36

Figure 2 (A) In a 46-year-old patient with focal epilepsy, reduced neurite density is evident in the focal cortical dysplasia (circles). (B) A 17-year-old patient with tuberous sclerosis. Reduced neurite density can be seen in the multiple cortical tubers (arrows). (C) A 37-year-old patient with left temporal lobe epilepsy and hippocampal sclerosis. The abnormal hippocampus shows reduced neurite density and orientation dispersion (arrowheads). (D) A 47-year-old patient with right temporal lobe without any visible lesions on conventional MRI. The right temporal lobe shows reduced neurite density in accordance with the hypometabolic areas of 18F-FDG-PET (broken circles).

Stroke

In the field of stroke, NODDI is mainly being investigated for its ability to predict and monitor recovery after stroke (Table 2). The global prevalence of stroke is approximately 500 per 100,000 people and it increases up to 4,835 in the elderly.37 To achieve a better course of treatment, prognosis of post-stroke recovery is desirable but still imprecise.38 Diffusion MRI is an expected biomarker,39 and thus NODDI may expand the predictability by providing additional information on microstructural complexity after stroke. An initial preliminary study showed greater specificity of NODDI for white matter reconstruction after brain infarction.40 Subsequently, Hodgson et al41 showed the usefulness of NODDI for predicting upper extremity motor function recovery after stroke, by investigating NODDI parameters within the corticospinal tracts. Another study revealed an increase of OD in the ipsilateral corticospinal tract in subacute stroke, which persisted in the chronic phase.42 Additionally, NODDI was reported to have improved sensitivity for detecting microstructural changes in the brain during ischemic stroke compared with DTI and DKI.43 Beyond usual stroke, Hara et al44 investigated patients with moyamoya disease, which is a progressive cerebrovascular disease caused by blocked main arteries of the brain, and found that NODDI parameters are significantly correlated with PET and clinical severity.

Table 2 NODDI findings in stroke, tumor, and trauma

Tumors

The clinical application of NODDI is also expected in the field of brain tumors, particularly for differentiating gliomas. After the discovery of unique contrast of NODDI maps within gliomas with a 7-T MRI scanner,45 Maximov et al46 reported the reliable and feasible differentiation of glioma grading by NODDI parameters. Additionally, although there is no significant additional utility of NODDI for detecting isocitrate dehydrogenase-1 (IDH-1) mutation status in gliomas,47,48 quantitative NODDI metrics in tumoral and peritumoral regions are suggested to be useful for glioma grading.48 ND seems to have the best discriminative power for distinguishing normal, tumoral, and peritumoral edematous areas.49 In addition, Kadota and colleagues50 showed a potential differentiation between glioblastoma and solitary metastasis with NODDI.

Trauma

For the use of NODDI in traumatic brain injury, Wu et al51 demonstrated improved sensitivity of the NODDI parameter (axonal density) for white matter changes shortly after mild traumatic brain injury. Additionally, Churchill et al52 reported a reduced ND and increased OD index in athletes with concussion. Afterward, the same group also found NODDI abnormalities at both phases in injury and recovery from concussion using longitudinal data.53 These findings may help us to understand the microstructural complexity of brain injuries over time.

Psychiatric disorders

Psychiatric disorders are also an important clinical target of advanced neuroimaging modalities. For NODDI, Nazeri and colleagues9 found significantly lower ND in gray matter in the temporal pole, anterior parahippocampal gyrus, and hippocampus of patients with schizophrenia. Additionally, significantly reduced fractional anisotropy and ND in several white matter tracts were also reported in patients with first-episode psychosis.54 In schizophrenia, another study showed increased OD in the posterior limb of internal capsule and negative correlation with subsequent drug response.55 Moreover, Spray et al56,57 reported correlations between OD and hallucination proneness in otherwise healthy individuals. For mood disorders, reduced ND and OD were found in major depressive disorder with a correlation between OD and disease severity.58 In bipolar affective disorder, Ota et al59 reported reduced ND in the right posterior cingulate cortex, although a previous study did not find any significant changes in bipolar affective disorder.9 This discrepancy could be related with treatment, since another study reported that patient with bipolar affective disorder without lithium therapy showed lower ND in the left frontal cortex than those with lithium as well as than healthy controls.60 Thus, there are already several studies of NODDI and several common psychiatric disorders, suggesting a future clinical use for NODDI. However, psychiatric disorders and symptoms are highly diverse and remain to be fully elucidated in neuroimaging studies.

Brain development

NODDI is also suggested to be useful for estimating brain development from the neonatal period to adulthood (Table 3). In healthy infants, NODDI in combination with myelin content information may provide a good indicator of brain myelination with age.61 In addition, in preadolescence, ND is strongly correlated with age in many brain regions, showing an even better correlation than fractional anisotropy.62 Age-related increases of ND were also reported during late childhood and adolescence, suggesting axonal packing in this term.63 For adults, Nazeri and colleagues64 revealed age-related OD decreases in frontoparietal regions and increases in the hippocampus, as well as a relationship with executive function. Ota et al65 also reported age-related changes in healthy adults in terms of NODDI and DTI/DKI parameters.

Table 3 NODDI findings in psychiatry and brain development

As a comparison with normal development, several developmental abnormalities have been investigated by NODDI. In preterm and very preterm children, NODDI detected several regional microstructural changes, partly associated with language function or intelligence quotient.66,67,68 Additionally, Karmacharya, et al investigated neonates with congenital heart disease and reported bilateral reduction in several tracts.69 In developmental dyslexia, age-related differences in local gyrification were correlated with NODDI parameters.70 Thus, NODDI may enable us to investigate human brain development, aging, and developmental disorders in greater detail.

Other brain disorders

There are many applications of NODDI in other brain disorders (Table 4). Broad et al71 found reduced ND in extensive regions of the corticospinal tract in amyotrophic lateral sclerosis, which is consistent with the core pathology of these areas. The corticospinal tract is also a target of research in idiopathic normal pressure hydrocephalus.72,73 In the corticospinal tract, ND and OD are reduced in idiopathic normal pressure hydrocephalus,72 and axon density was unchanged after surgical treatment.73 In preclinical Huntington’s disease, widespread reductions in axonal density have already been found and correlated with clinical disease progression.74 Song et al75 used NODDI to investigate patients with Wilson disease and found a significant reduction in ND and OD in the basal ganglia and thalamus. In galactosemia, ND was decreased in bilateral anterior areas and OD was increased in the left hemisphere.76 Another study investigating patients with diabetes mellitus and mild cognitive impairment reported reduced ND and its correlations with the hemoglobin A1C level, disease duration, and neuropsychological scores.77 The effect of systemic medical conditions on the brain was also investigated using NODDI for hypertension,78 sickle cell anemia,79 and interferon-alpha–induced fatigue.80 Abnormal NODDI parameters were also reported in patients with myalgic encephalomyelitis/chronic fatigue syndrome.81 Billiet and colleagues82 found reduced ND in the “unidentified bright objects” in neurofibromatosis type 1. Other studies focused on perinatal encephalopathy,83 SYN1Q555X mutation,84 C9orf72 disease,85 and sarcoma survivors.86

Table 4 NODDI findings in other brain diseases and spine

Spine

NODDI is also feasible for evaluating the human spinal cord.87 In addition to its use for the spinal lesions of MS,26,88 some studies have focused on cervical spondylotic myelopathy.89,90,91 In general, ND showed good correlations with disease severity. In particular, preoperative ND may be a strong indicator of neurological dysfunction and postoperative recovery in cervical spondylotic myelopathy.91

Future directions

Because the number of studies of NODDI is increasing, we should seriously consider the use of NODDI in daily clinical practice. However, there are several issues to be addressed. First, the MRI protocols and scanners are still inconsistent, and no standards have been established. To achieve the clinical utility of NODDI across different institutes, standard settings should be needed. Although optimal b-values and more directions may yield better images, a clinical protocol with a shorter acquisition time is preferable. Second, we have to consider which NODDI parameters to use. Beyond ND and OD, we can also obtain more advanced metrics such as axon density. Similarly, regarding the targeted areas of brain (eg gray matter or white matter tract), there’s some variability among studies. We may need to understand the basic and clinical meanings of these parameters to correctly interpret patients’ findings. Third, most of the clinical studies on NODDI are from single centers and have not been reproduced in other studies. Novelty is an important consideration for study and publication, but clinical application also strongly requires further accumulation of evidence from repeated confirmation of results.

In addition, there are some useful tools or improved methodologies. Cacerzasi et al92 reported the use of color maps of NODDI for the visual assessment of several neurological diseases. On the other hand, the criticisms to NODDI are mainly associated with the bias derived from the fitting model and its assumption.93,94,95 To overcome this limitation, there are improved methodologies for diffusion MRI, such as NODDI with diffusivity assessment (NODDIDA),94 spherical mean technique (SMT),95 NODDI-DTI model96 and gamma metrics.97 These methodological advances may also deepen our basic and clinical knowledge and promote the clinical use of NODDI.

Conclusion

This review shed light on recent studies on the use of NODDI in humans. The number of studies using NODDI is growing, with most reporting its clinical utility. The time has thus come for us to seriously consider the clinical use of NODDI. Especially, NODDI may directly affect the treatment and recovery prediction in several diseases including stroke, epilepsy and glioma. Setting standard protocols and parameters will accelerate the clinical applications and highly benefit patients with neurological and psychiatric disorders.

Acknowledgment

I am grateful to my colleagues in the Integrative Brain Imaging Center and Department of Radiology at National Center of Neurology and Psychiatry, Japan.

Disclosure

The author reports no conflicts of interest in this work.

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