Evaluation of multi-shell diffusion MRI acquisition strategy on quantitative analysis using multi-compartment models

A framework for optimizing the acquisition protocol multishell diffusion-weighted imaging for multimodel assessment

Complementary aspects of tissue microstructure can be studied with diffusion-weighted imaging (DWI). However, there is no consensus on how to design a diffusion acquisition protocol for multiple models within a clinically feasible time. The purpose of this study is to provide a flexible framework that is able to optimize the shell acquisition protocol given a set of DWI models. Eleven healthy subjects underwent an extensive DWI acquisition protocol, including 15 candidate shells, ranging from 10 to 3500 s/mm2. The proposed framework aims to determine the optimized acquisition scheme (OAS) with a data-driven procedure minimizing the squared error of model-estimated parameters. We tested the proposed method over five heterogeneous DWI models exploiting both low and high b-values (i.e., diffusion tensor imaging [DTI], free water, intra-voxel incoherent motion [IVIM], diffusion kurtosis imaging [DKI], and neurite orientation dispersion and density imaging [NODDI]). A voxel-level and region of interest (ROI)-level analysis was conducted over the white matter and in 48 fiber bundles, respectively. Results showed that acquiring data for the five abovementioned models via OAS requires 14 min, compared with 35 min for the joint recommended acquisition protocol. The parameters derived from the reference acquisition scheme and the OAS are comparable in terms of estimated values, noise, and tissue contrast. Furthermore, the power analysis showed that the OAS retains the potential sensitivity to group-level differences in the parameters of interest, with the exception of the free water model. Overall, there is a linear correspondence (R2 = 0.91) between OAS and reference-derived parameters. In conclusion, the proposed framework optimizes the shell acquisition scheme for a given set of DWI models (i.e., DTI, free water, IVIM, DKI, and NODDI), combining low and high b-values while saving acquisition time.

Prefrontal-Limbic Circuitry Is Associated With Reward Sensitivity in Nonhuman Primates

BACKGROUND

Abnormal reward sensitivity is a risk factor for psychiatric disorders, including eating disorders such as overeating and binge-eating disorder, but the brain structural mechanisms that underlie it are not completely understood. Here, we sought to investigate the relationship between multimodal whole-brain structural features and reward sensitivity in nonhuman primates.

METHODS

Reward sensitivity was evaluated through behavioral economic analysis in which monkeys (adult rhesus macaques; 7 female, 5 male) responded for sweetened condensed milk (10%, 30%, 56%), Gatorade, or water using an operant procedure in which the response requirement increased incrementally across sessions (i.e., fixed ratio 1, 3, 10). Animals were divided into high (n = 6) or low (n = 6) reward sensitivity groups based on essential value for 30% milk. Multimodal magnetic resonance imaging was used to measure gray matter volume and white matter microstructure. Brain structural features were compared between groups, and their correlations with reward sensitivity for various stimuli was investigated.

RESULTS

Animals in the high sensitivity group had greater dorsolateral prefrontal cortex, centromedial amygdaloid complex, and middle cingulate cortex volumes than animals in the low sensitivity group. Furthermore, compared with monkeys in the low sensitivity group, high sensitivity monkeys had lower fractional anisotropy in the left dorsal cingulate bundle connecting the centromedial amygdaloid complex and middle cingulate cortex to the dorsolateral prefrontal cortex, and in the left superior longitudinal fasciculus 1 connecting the middle cingulate cortex to the dorsolateral prefrontal cortex.

CONCLUSIONS

These results suggest that neuroanatomical variation in prefrontal-limbic circuitry is associated with reward sensitivity. These brain structural features may serve as predictive biomarkers for vulnerability to food-based and other reward-related disorders.

Emotional Regulation and Adolescent Concussion: Overview and Role of Neuroimaging

Emotional dysregulation symptoms following a concussion are associated with an increased risk for emotional dysregulation disorders (e.g., depression and anxiety), especially in adolescents. However, predicting the emergence or worsening of emotional dysregulation symptoms after concussion and the extent to which this predates the onset of subsequent psychiatric morbidity after injury remains challenging. Although advanced neuroimaging techniques, such as functional magnetic resonance imaging and diffusion magnetic resonance imaging, have been used to detect and monitor concussion-related brain abnormalities in research settings, their clinical utility remains limited. In this narrative review, we have performed a comprehensive search of the available literature regarding emotional regulation, adolescent concussion, and advanced neuroimaging techniques in electronic databases (PubMed, Scopus, and Google Scholar). We highlight clinical evidence showing the heightened susceptibility of adolescents to experiencing emotional dysregulation symptoms following a concussion. Furthermore, we describe and provide empirical support for widely used magnetic resonance imaging modalities (i.e., functional and diffusion imaging), which are utilized to detect abnormalities in circuits responsible for emotional regulation. Additionally, we assess how these abnormalities relate to the emotional dysregulation symptoms often reported by adolescents post-injury. Yet, it remains to be determined if a progression of concussion-related abnormalities exists, especially in brain regions that undergo significant developmental changes during adolescence. We conclude that neuroimaging techniques hold potential as clinically useful tools for predicting and, ultimately, monitoring the treatment response to emotional dysregulation in adolescents following a concussion.

Undersampled single-shell to MSMT fODF reconstruction using CNN-based ODE solver

BACKGROUND AND OBJECTIVE

Diffusion MRI (dMRI) has been considered one of the most popular non-invasive techniques for studying the human brain's white matter (WM). dMRI is used to delineate the brain's microstructure by approximating the WM region's fiber tracts. The achieved fiber tracts can be utilized to assess mental diseases like Multiple sclerosis, ADHD, Seizures, Intellectual disability, and others. New techniques such as high angular resolution diffusion-weighted imaging (HARDI) have been developed, providing precise fiber directions, and overcoming the limitation of traditional DTI. Unlike Single-shell, Multi-shell HARDI provides tissue fractions for white matter, gray matter, and cerebrospinal fluid, resulting in a Multi-shell Multi-tissue fiber orientation distribution function (MSMT fODF). This MSMT fODF comes up with more precise fiber directions than a Single-shell, which helps to get correct fiber tracts. In addition, various multi-compartment diffusion models, including as CHARMED and NODDI, have been developed to describe the brain tissue microstructural information. This type of model requires multi-shell data to obtain more specific tissue microstructural information. However, a major concern with multi-shell is that it takes a longer scanning time restricting its use in clinical applications. In addition, most of the existing dMRI scanners with low gradient strengths commonly acquire a single b-value (shell) upto b=1000s/mm² due to SNR (Signal-to-noise ratio) reasons and severe imaging artifacts.

METHODS

To address this issue, we propose a CNN-based ordinary differential equations solver for the reconstruction of MSMT fODF from under-sampled and fully sampled Single-shell (b=1000s/mm²) dMRI. The proposed architecture consists of CNN-based Adams-Bash-forth and Runge-Kutta modules along with two loss functions, including L₁ and total variation.

RESULTS

We have shown quantitative results and visualization of fODF, fiber tracts, and structural connectivity for several brain regions on the publicly available HCP dataset. In addition, the obtained angular correlation coefficients for white matter and full brain are high, showing the proposed network's utility.Finally, we have also demonstrated the effect of noise by adjusting SNR from 5 to 50 and observed the network robustness.

CONCLUSION

We can conclude that our model can accurately predict MSMT fODF from under-sampled or fully sampled Single-shell dMRI volumes.

Diffusion radiomics for subtyping and clustering in autism spectrum disorder: A preclinical study

Autism spectrum disorder (ASD) is a highly prevalent, heterogenous neurodevelopmental disorder. Neuroimaging methods such as functional, structural, and diffusion MRI have been used to identify candidate imaging biomarkers for ASD, but current findings remain non-specific and likely arise from the heterogeneity present in ASD. To account for this, efforts to subtype ASD have emerged as a potential strategy for both the study of ASD and advancement of tailored behavioral therapies and therapeutics. Towards these ends, to improve upon current neuroimaging methods, we propose combining biologically sensitive neurite orientation dispersion and density index (NODDI) diffusion MR imaging with radiomics image processing to create a new methodological approach that, we hypothesize, can sensitively and specifically capture neurobiology. We demonstrate this method can sensitively distinguish differences between four genetically distinct rat models of ASD (Fmr1, Pten, Nrxn1, Disc1). Further, we demonstrate diffusion radiomic analyses hold promise for subtyping in ASD as we show unsupervised clustering of NODDI radiomic data generates clusters specific to the underlying genetic differences between the animal models. Taken together, our findings suggest the unique application of radiomic analysis on NODDI diffusion MRI may have the capacity to sensitively and specifically disambiguate the neurobiological heterogeneity present in the ASD population.

Magnetic resonance imaging of the monkey fetal brain in utero

Non-human primates (NHPs) are the closest living relatives of the human and play a critical role in investigating the effects of maternal viral infection and consumption of medicines, drugs, and alcohol on fetal development. With the advance of contemporary fast MRI techniques with parallel imaging, fetal MRI is becoming a robust tool increasingly used in clinical practice and preclinical studies to examine congenital abnormalities including placental dysfunction, congenital heart disease (CHD), and brain abnormalities non-invasively. Because NHPs are usually scanned under anesthesia, the motion artifact is reduced substantially, allowing multi-parameter MRI techniques to be used intensively to examine the fetal development in a single scanning session or longitudinal studies. In this paper, the MRI techniques for scanning monkey fetal brains in utero in biomedical research are summarized. Also, a fast imaging protocol including T2-weighted imaging, diffusion MRI, resting-state functional MRI (rsfMRI) to examine rhesus monkey fetal brains in utero on a clinical 3T scanner is introduced.

Higher-order multi-shell diffusion measures complement tensor metrics and volume in gray matter when predicting age and cognition

Recent advances in diffusion-weighted imaging have enabled us to probe the microstructure of even gray matter non-invasively. However, these advanced multi-shell protocols are often not included in large-scale studies as they significantly increase scan time. In this study, we investigated whether one set of multi-shell diffusion metrics commonly used in gray matter (as derived from Neurite Orientation Dispersion and Density Imaging, NODDI) provide enough additional information over typical tensor and volume metrics to justify the increased acquisition time, using the cognitive aging framework in the human hippocampus as a testbed. We first demonstrated that NODDI metrics are robust and reliable by replicating previous findings from our lab in a larger population of 79 younger (20.41 ± 1.89 years, 46 females) and 75 older (73.56 ± 6.26 years, 45 females) adults, showing that these metrics in the hippocampal subfields are sensitive to age and memory performance. We then asked how these subfield specific hippocampal NODDI metrics compared with standard tensor metrics and volume in predicting age and memory ability. We discovered that both NODDI and tensor measures separately predicted age and cognition in comparable capacities. However, integrating these modalities together considerably increased the predictive power of our logistic models, indicating that NODDI and tensor measures may be capturing independent microstructural information. We use these findings to encourage neuroimaging data collection consortiums to include a multi-shell diffusion sequence in their protocols since existing NODDI measures (and potential future multi-shell measures) may be able to capture microstructural variance that is missed by traditional approaches, even in studies exclusively examining gray matter.

Automated facial-vestibulocochlear nerve complex identification based on data-driven tractography clustering

Small size and intricate anatomical environment are the main difficulties facing tractography of the facial-vestibulocochlear nerve complex (FVN), and lead to challenges in fiber orientation distribution (FOD) modeling, fiber tracking, region-of-interest selection, and fiber filtering. Experts need rich experience in anatomy and tractography, as well as substantial labor costs, to identify the FVN. Thus, we present a pipeline to identify the FVN automatically, in what we believe is the first study of the automated identification of the FVN. First, we created an FVN template. Forty high-resolution multishell data were used to perform data-driven fiber clustering based on the multishell multitissue constraint spherical deconvolution FOD model and deterministic tractography. We selected the brainstem and cerebellum (BS-CB) region as the seed region and removed the fibers that reach other brain regions. We then performed spectral fiber clustering twice. The first clustering was to create a BS-CB atlas and separate the fibers that pass through the cerebellopontine angle, and the other one was to extract the FVN. Second, we registered the subject-specific fibers in the space of the FVN template and assigned each fiber to the closest cluster to identify the FVN automatically by spectral embedding. We applied the proposed method to different acquirement sites, including two different healthy datasets and two tumor patient datasets. Experimental results showed that our automatic identification results have ideal colocalization with expert manual identification in terms of spatial overlap and visualization. Importantly, we successfully applied our method to tumor patient data. The FVNs identified by the proposed method were in agreement with intraoperative findings.

Impact of the acquisition protocol on the sensitivity to demyelination and axonal loss of clinically feasible DWI techniques: a simulation study

OBJECTIVE

To evaluate: (a) the specific effect that the demyelination and axonal loss have on the DW signal, and (b) the impact of the sequence parameters on the sensitivity to damage of two clinically feasible DWI techniques, i.e. DKI and NODDI.

METHODS

We performed a Monte Carlo simulation of water diffusion inside a novel synthetic model of white matter in the presence of axonal loss and demyelination, with three compartments with permeable boundaries between them. We compared DKI and NODDI in their ability to detect and assess the damage, using several acquisition protocols. We used the F test statistic as an index of the sensitivity for each DWI parameter to axonal loss and demyelination, respectively.

RESULTS

DKI parameters significantly changed with increasing axonal loss, but, in most cases, not with demyelination; all the NODDI parameters showed sensitivity to both the damage processes (at p < 0.01). However, the acquisition protocol strongly affected the sensitivity to damage of both the DKI and NODDI parameters and, especially for NODDI, the parameter absolute values also.

DISCUSSION

This work is expected to impact future choices for investigating white matter microstructure in focusing on specific stages of the disease, and for selecting the appropriate experimental framework to obtain optimal data quality given the purpose of the experiment.

Quantitative assessment of cerebral metabolism and hemodynamics in small-for-gestational-age (SGA) newborns

Background

Small-for-gestational-age (SGA) newborns represent approximately 10% of births worldwide and 45% of births in some countries. It has been suggested that SGA might cause learning difficulties and behavioral abnormalities in childhood, yet the neurobiological basis for this is poorly understood. In this study, we employed several advanced imaging techniques-including T2-relaxation-under-spin-tagging (TRUST) magnetic resonance imaging (MRI), and phase-contrast (PC) MRI-to quantify oxygen extraction fraction (OEF), global cerebral blood flow (CBF), and cerebral metabolic rate of oxygen (CMRO₂) to elucidate pathophysiological vulnerabilities of SGA neonates.

Methods

A total of 41 newborns were enrolled in this study, consisting of 29 SGA and 12 appropriate-for-gestational-age (AGA) neonates. The SGA group was further divided into subgroups with and without abnormalities on structural MRI, denoted as SGA-a (N=17) and SGA-n (N=12). TRUST and PC MRI were performed to determine OEF, CBF, and CMRO₂. Linear regression analyses were performed to examine physiological parameters' dependence on scan age, gender, and group. Similar analyses were conducted for birth weight and brain volume. Receiver operating characteristic (ROC) curves were used to test physiological parameters' ability to different diagnostic groups.

Results

Regression analysis revealed that CMRO₂ was significantly lower (P=0.04) in the SGA group relative to the AGA group. When further stratifying the SGA participants into SGA-a and SGA-n subgroups, the SGA-a subgroup was found to have the most pronounced physiological deficits, with a lower CMRO₂ (P=0.004) and lower CBF (P=0.007) than those in the AGA group. Conversely, CMRO₂ (P=0.40) and CBF (P=0.90) in the SGA-n subgroup were not different from those of the AGA group. Accordingly, CBF in the SGA-a group was significantly lower (P=0.01) than that of the SGA-n group and CMRO2 also showed a difference (P=0.09). Additionally, CMRO₂ (P=0.002) and CBF (P=0.04) showed an age-related increase during this early developmental period. In analyzing the SGA-a subgroup relative to the remaining neonates, the area under curve (AUC) values were 0.6, 0.6, 0.7, 0.8, and 0.5 for birth weight, OEF, CMRO₂, CBF, and brain volume, respectively. In analyzing the SGA-a subgroup relative to the SGA-n subgroup, AUC values were 0.5, 0.6, 0.7, 0.8, and 0.5 for birth weight, OEF, CMRO₂, CBF, and brain volume.

Conclusions

Structural damage in SGA-a neonates is associated with cerebral hemodynamic and metabolic deficits. SGA neonates with normal CBF and CMRO₂reveal minimal structural abnormalities. Physiological imaging may help identify SGA patients at high risk of developing irreversible brain damage.

Cortical degeneration detected by neurite orientation dispersion and density imaging in chronic lacunar infarcts

Background

Although lacunar infarcts are focal lesions, they may also have more widespread effects. A reduction in cortical thickness in the remote cortex after lacunar infarcts has been detected by structural imaging; however, its underlying microstructural changes are yet to be elucidated. This study aimed to investigate the effects of lacunar infarcts on the microstructural abnormalities associated with cortical thickness reduction in the remote cortex.

Methods

Thirty-seven patients with chronic lacunar infarcts were included. Brain structural magnetic resonance images (MRIs) and diffusion tensor images were acquired. We constructed the white matter tracts connecting with the lacunar infarcts and identified the connected cortical area based on a standard brain atlas warped into the subject space. Cortical thickness and microstructural neurite orientation dispersion and density imaging (NODDI) metrics of the ipsilesional and contralesional cortices were compared, and correlations between cortical thickness and NODDI metrics were also investigated.

Results

We found decreased cortical thickness and reduced neurite orientation dispersion index (ODI) in the ipsilesional cortex (2.47 vs. 2.50 mm, P=0.008; 0.451 vs. 0.456, P=0.035, respectively). In patients with precentral gyrus involvement (n=23), we found that ODI in the ipsilesional cortex was correlated with cortical thickness (r=0.437, P=0.037), and ODI in the contralesional cortex was also correlated with contralesional cortical thickness (r=0.440, P=0.036).

Conclusions

NODDI metrics could reflect cortical microstructural changes following lacunar infarcts. The correlation between decreased ODI and reduced cortical thickness suggests that dendrites' loss might contribute to lacunar infarct-related cortical atrophy.

Clinical characteristics and endovascular treatment in patients with intracranial giant serpentine aneurysms

Background

Giant serpentine aneurysms (GSAs) are a rare subtype of intracranial aneurysm. Recently, GSAs have been successfully treated with endovascular parent artery occlusion with or without distal bypass. The present study retrospectively analyzed the clinical outcomes of endovascular parent artery occlusion for intracranial GSAs.

Methods

Medical records and cerebral angiograms from our endovascular center were retrospectively analyzed. Twenty-two patients with serpentine aneurysms were treated with endovascular occlusion of the parent artery at the site of the aneurysm. These patients had selective treatment. Clinical and angiographic outcomes of the patients were assessed between the 3-month to 3-year stage.

Results

The clinical manifestations of GSA included headache, hemiparesis, SAH, epilepsy, memory loss, right oculomotor palsy, and intracerebral hemorrhage. No cerebral infarction occurred. The average Glasgow Outcome Scale (GOS) score was 5. There were no clinical complications and none of the patients had recurrence after selective embolization. All of the patients recovered well, and no recurrence or rebleeding was noted at the follow-up.

Conclusions

Endovascular parent artery occlusion may be a safe and effective way to treat intracranial GSAs.

In-vivo diffusion MRI protocol optimization for the chimpanzee brain and examination of aging effects on the primate optic nerve at 3T

BACKGROUND

Diffusion MRI (dMRI) data acquisition protocols are well-established on modern high-field clinical scanners for human studies. However, these protocols are not suitable for the chimpanzee (or other large-brained mammals) because of its substantial difference in head geometry and brain volume compared with humans. Therefore, an optimal dMRI data acquisition protocol dedicated to chimpanzee neuroimaging is needed.

METHODS

A multi-shot (4 segments) double spin-echo echo-planar imaging (MS-EPI) sequence and a single-shot double spin-echo EPI (SS-EPI) sequence were optimized separately for in vivo dMRI data acquisition of chimpanzees using a clinical 3T scanner. Correction for severe susceptibility-induced image distortion and signal drop-off of the chimpanzee brain was performed and evaluated using FSL software. DTI indices in different brain regions and probabilistic tractography were compared. A separate DTI data set from n=34 chimpanzees (13 to 56 years old) was collected using the optimal protocol. Age-related changes in diffusivity indices of optic nerve fibers were evaluated.

RESULTS

The SS-EPI sequence acquired dMRI data of the chimpanzee brain with approximately doubled the SNR as the MS-EPI sequence given the same scan time. The quality of white matter fiber tracking from the SS-EPI data was much higher than that from MS-EPI data. However, quantitative analysis of DTI indices showed no difference in most ROIs between the SS-EPI and MS-EPI sequences. The progressive evolution of diffusivity indices of optic nerves indicated mild changes in fiber bundles of chimpanzees aged 40 years and above.

CONCLUSION

The single-shot EPI-based acquisition protocol provided better image quality of dMRI for chimpanzee brains and is recommended for in vivo dMRI study or clinical diagnosis of chimpanzees (or other large animals) using a clinical scanner. Also, the tendency of FA decrease or diffusivity increase in the optic nerve of aged chimpanzees was seen but did not show significant age-related changes, suggesting aging may have less impact on optic nerve fiber integrity of chimpanzees, in contrast to previous results for both macaque monkeys and humans.

Advanced non-invasive MRI of neuroplasticity in ischemic stroke: Techniques and applications

Ischemic stroke represents a serious medical condition which could cause survivors suffer from long-term and even lifetime disabilities. After a stroke attack, the brain would undergo varying degrees of recovery, in which the central nervous system could be reorganized spontaneously or with the help of appropriate rehabilitation. Magnetic resonance imaging (MRI) is a non-invasive technique which can provide comprehensive information on structural, functional and metabolic features of brain tissue. In the last decade, there has been an increased technical advancement in MR techniques such as voxel-based morphological analysis (VBM), diffusion magnetic resonance imaging (dMRI), functional magnetic resonance imaging (fMRI), arterial spin-labeled perfusion imaging (ASL), magnetic sensitivity weighted imaging (SWI), quantitative sensitivity magnetization (QSM) and magnetic resonance spectroscopy (MRS) which have been proven to be a valuable tool to study the brain tissue reorganization. Due to MRI indices of neuroplasticity related to neurological outcome could be translated to the clinic. The ultimate goal of this review is to equip readers with a fundamental understanding of advanced MR techniques and their corresponding clinical application for improving the ability to predict neuroplasticity that are most suitable for stroke management.