Diffusion MRI Metrics Characterize Postoperative Clinical Outcomes After Surgery for Cervical Spondylotic Myelopathy.

BACKGROUND AND OBJECTIVES: Advanced diffusion-weighted MRI (DWI) modeling, such as diffusion tensor imaging (DTI) and diffusion basis spectrum imaging (DBSI), may help guide rehabilitation strategies after surgical decompression for cervical spondylotic myelopathy (CSM). Currently, however, postoperative DWI is difficult to interpret, owing to signal distortions from spinal instrumentation. Therefore, we examined the relationship between postoperative DTI/DBSI-extracted from the rostral C3 spinal level-and clinical outcome measures at 2-year follow-up after decompressive surgery for CSM. METHODS: Fifty patients with CSM underwent complete clinical and DWI evaluation-followed by DTI/DBSI analysis-at baseline and 2-year follow-up. Clinical outcomes included the modified Japanese Orthopedic Association score and comprehensive patient-reported outcomes. DTI metrics included apparent diffusion coefficient, fractional anisotropy, axial diffusivity, and radial diffusivity. DBSI metrics evaluated white matter tracts through fractional anisotropy, fiber fraction, axial diffusivity, and radial diffusivity as well as extra-axonal pathology through restricted and nonrestricted fraction. Cross-sectional Spearman's correlations were used to compare postoperative DTI/DBSI metrics with clinical outcomes. RESULTS: Twenty-seven patients with CSM, including 15, 7, and 5 with mild, moderate, and severe disease, respectively, possessed complete baseline and postoperative DWI scans. At 2-year follow-up, there were 10 significant correlations among postoperative DBSI metrics and postoperative clinical outcomes compared with 3 among postoperative DTI metrics. Of the 13 significant correlations, 7 involved the neck disability index (NDI). The strongest relationships were between DBSI axial diffusivity and NDI (r = 0.60, P < .001), DBSI fiber fraction and NDI (rs = -0.58, P < .001), and DBSI restricted fraction and NDI (rs = 0.56, P < .001). The weakest correlation was between DTI apparent diffusion coefficient and NDI (r = 0.35, P = .02). CONCLUSION: Quantitative measures of spinal cord microstructure after surgery correlate with postoperative neurofunctional status, quality of life, and pain/disability at 2 years after decompressive surgery for CSM. In particular, DBSI metrics may serve as meaningful biomarkers for postoperative disease severity for patients with CSM.

Diffusion basis spectrum imaging and diffusion tensor imaging predict persistent black hole formation in multiple sclerosis.

BACKGROUND AND OBJECTIVES: Diffusion basis spectrum imaging (DBSI) extracts multiple anisotropic and isotropic diffusion tensors, providing greater histopathologic specificity than diffusion tensor imaging (DTI). Persistent black holes (PBH) represent areas of severe tissue damage in multiple sclerosis (MS), and a high PBH burden is associated with worse MS disability. This study evaluated the ability of DBSI and DTI to predict which acute contrast-enhancing lesions (CELs) would persist as T1 hypointensities (i.e. PBHs) 12 months later. We expected that a higher radial diffusivity (RD), representing demyelination, and higher DBSI-derived isotropic non-restricted fraction, representing edema and increased extracellular space, of the acute CEL would increase the likelihood of future PBH development. METHODS: In this prospective cohort study, relapsing MS patients with ≥1 CEL(s) underwent monthly MRI scans for 4 to 6 months until gadolinium resolution. DBSI and DTI metrics were quantified when the CEL was most conspicuous during the monthly scans. To determine whether the CEL became a PBH, a follow-up MRI was performed at least 12 months after the final monthly scan. RESULTS: The cohort included 20 MS participants (median age 33 years; 13 women) with 164 CELs. Of these, 59 (36 %) CELs evolved into PBHs. At Gd-max, DTI RD and AD of all CELs increased, and both metrics were significantly elevated for CELs which became PBHs, as compared to non-black holes (NBHs). DTI RD above 0.74 conferred an odds ratio (OR) of 7.76 (CI 3.77-15.98) for a CEL becoming a PBH (AUC 0.80, CI 0.73-0.87); DTI axial diffusivity (AD) above 1.22 conferred an OR of 7.32 (CI 3.38-15.86) for becoming a PBH (AUC 0.75, CI 0.66-0.83). DBSI RD and AD did not predict PBH development in a multivariable model. At Gd-max, DBSI restricted fraction decreased and DBSI non-restricted fraction increased in all CELs, and both metrics were significantly different for CELs which became PBHs, as compared to NBHs. A CEL with a DBSI non-restricted fraction above 0.45 had an OR of 4.77 (CI 2.35-9.66) for becoming a PBH (AUC 0.74, CI 0.66-0.81); a CEL with a DBSI restricted fraction below 0.07 had an OR of 9.58 (CI 4.59-20.02) for becoming a PBH (AUC 0.80, 0.72-0.87). CONCLUSION: Our findings suggest that greater degree of edema/extracellular space in a CEL is a predictor of tissue destruction, as evidenced by PBH evolution.

The Hao-Fountain syndrome protein USP7 regulates neuronal connectivity in the brain via a novel p53-independent ubiquitin signaling pathway.

Precise control of protein ubiquitination is essential for brain development, and hence, disruption of ubiquitin signaling networks can lead to neurological disorders. Mutations of the deubiquitinase USP7 cause the Hao-Fountain syndrome (HAFOUS), characterized by developmental delay, intellectual disability, autism, and aggressive behavior. Here, we report that conditional deletion of USP7 in excitatory neurons in the mouse forebrain triggers diverse phenotypes including sensorimotor deficits, learning and memory impairment, and aggressive behavior, resembling clinical features of HAFOUS. USP7 deletion induces neuronal apoptosis in a manner dependent of the tumor suppressor p53. However, most behavioral abnormalities in USP7 conditional mice persist despite p53 loss. Strikingly, USP7 deletion in the brain perturbs the synaptic proteome and dendritic spine morphogenesis independently of p53. Integrated proteomics analysis reveals that the neuronal USP7 interactome is enriched for proteins implicated in neurodevelopmental disorders and specifically identifies the RNA splicing factor Ppil4 as a novel neuronal substrate of USP7. Knockdown of Ppil4 in cortical neurons impairs dendritic spine morphogenesis, phenocopying the effect of USP7 loss on dendritic spines. These findings reveal a novel USP7-Ppil4 ubiquitin signaling link that regulates neuronal connectivity in the developing brain, with implications for our understanding of the pathogenesis of HAFOUS and other neurodevelopmental disorders.

Diffusion basis spectrum imaging detects subclinical traumatic optic neuropathy in a closed-head impact mouse model of traumatic brain injury.

Introduction: Traumatic optic neuropathy (TON) is the optic nerve injury secondary to brain trauma leading to visual impairment and vision loss. Current clinical visual function assessments often fail to detect TON due to slow disease progression and clinically silent lesions resulting in potentially delayed or missed treatment in patients with traumatic brain injury (TBI). Methods: Diffusion basis spectrum imaging (DBSI) is a novel imaging modality that can potentially fill this diagnostic gap. Twenty-two, 16-week-old, male mice were equally divided into a sham or TBI (induced by moderate Closed-Head Impact Model of Engineered Rotational Acceleration device) group. Briefly, mice were anesthetized with isoflurane (5% for 2.5 min followed by 2.5% maintenance during injury induction), had a helmet placed over the head, and were placed in a holder prior to a 2.1-joule impact. Serial visual acuity (VA) assessments, using the Virtual Optometry System, and DBSI scans were performed in both groups of mice. Immunohistochemistry (IHC) and histological analysis of optic nerves was also performed after in vivo MRI. Results: VA of the TBI mice showed unilateral or bilateral impairment. DBSI of the optic nerves exhibited bilateral involvement. IHC results of the optic nerves revealed axonal loss, myelin injury, axonal injury, and increased cellularity in the optic nerves of the TBI mice. Increased DBSI axon volume, decreased DBSI λ||, and elevated DBSI restricted fraction correlated with decreased SMI-312, decreased SMI-31, and increased DAPI density, respectively, suggesting that DBSI can detect coexisting pathologies in the optic nerves of TBI mice. Conclusion: DBSI provides an imaging modality capable of detecting subclinical changes of indirect TON in TBI mice.

Feasibility of postoperative diffusion-weighted imaging to assess representations of spinal cord microstructure in cervical spondylotic myelopathy.

OBJECTIVE: Diffusion basis spectrum imaging (DBSI) has shown promise in evaluating cervical spinal cord structural changes in patients with cervical spondylotic myelopathy (CSM). DBSI may also be valuable in the postoperative setting by serially tracking spinal cord microstructural changes following decompressive cervical spine surgery. Currently, there is a paucity of studies investigating this topic, likely because of challenges in resolving signal distortions from spinal instrumentation. Therefore, the objective of this study was to assess the feasibility of DBSI metrics extracted from the C3 spinal level to evaluate CSM patients postoperatively. METHODS: Fifty CSM patients and 20 healthy controls were enrolled in a single-center prospective study between 2018 and 2020. All patients and healthy controls underwent preoperative and postoperative diffusion-weighted MRI (dMRI) at a 2-year follow-up. All CSM patients underwent decompressive cervical surgery. The modified Japanese Orthopaedic Association (mJOA) score was used to categorize CSM patients as having mild, moderate, or severe myelopathy. DBSI metrics were extracted from the C3 spinal cord level to minimize image artifact and reduce partial volume effects. DBSI anisotropic tensors evaluated white matter tracts through fractional anisotropy, axial diffusivity, radial diffusivity, and fiber fraction. DBSI isotropic tensors assessed extra-axonal pathology through restricted and nonrestricted fractions. RESULTS: Of the 50 CSM patients, both baseline and postoperative dMR images with sufficient quality for analysis were obtained in 27 patients. These included 15 patients with mild CSM (mJOA scores 15-17), 7 with moderate CSM (scores 12-14), and 5 with severe CSM (scores 0-11), who were followed up for a mean of 23.5 (SD 4.1, range 11-31) months. All preoperative C3-level DBSI measures were significantly different between CSM patients and healthy controls (p < 0.05), except DBSI fractional anisotropy (p = 0.31). At the 2-year follow-up, the same significance pattern was found between CSM patients and healthy controls, except DBSI radial diffusivity was no longer statistically significant (p = 0.75). When assessing change (i.e., postoperative - preoperative values) in C3-level DBSI measures, CSM patients exhibited significant decreases in DBSI radial diffusivity (p = 0.02), suggesting improvement in myelin integrity (i.e., remyelination) at the 2-year follow-up. Among healthy controls, there was no significant difference in DBSI metrics over time. CONCLUSIONS: DBSI metrics derived from dMRI at the C3 spinal level can be used to provide meaningful insights into representations of the spinal cord microstructure of CSM patients at baseline and 2-year follow-up. DBSI may have the potential to characterize white matter tract recovery and inform outcomes following decompressive cervical surgery for CSM.

Diffusion basis spectrum imaging detects pathological alterations in substantia nigra and white matter tracts with early-stage Parkinson's disease.

OBJECTIVES: Using diffusion basis spectrum imaging (DBSI) to examine the microstructural changes in the substantia nigra (SN) and global white matter (WM) tracts of patients with early-stage PD. METHODS: Thirty-seven age- and sex-matched patients with early-stage PD and 22 healthy controls (HCs) were enrolled in this study. All participants underwent clinical assessments and diffusion-weighted MRI scans, analyzed by diffusion tensor imaging (DTI) and DBSI to assess the pathologies of PD in SN and global WM tracts. RESULTS: The lower DTI fraction anisotropy (FA) was seen in SN of PD patients (PD: 0.316 ± 0.034 vs HCs: 0.331 ± 0.019, p = 0.015). The putative cells marker-DBSI-restricted fraction (PD: 0.132 ± 0.051 vs HCs: 0.105 ± 0.039, p = 0.031) and the edema/extracellular space marker-DBSI non-restricted-fraction (PD: 0.150 ± 0.052 vs HCs: 0.122 ± 0.052, p = 0.020) were both significantly higher and the density of axons/dendrites marker-DBSI fiber-fraction (PD: 0.718 ± 0.073 vs HCs: 0.773 ± 0.071, p = 0.003) was significantly lower in SN of PD patients. DBSI-restricted fraction in SN was negatively correlated with HAMA scores (r = - 0.501, p = 0.005), whereas DTI-FA was not correlated with any clinical scales. In WM tracts, only higher DTI axial diffusivity (AD) among DTI metrics was found in multiple WM regions in PD, while lower DBSI fiber-fraction and higher DBSI non-restricted-fraction were detected in multiple WM regions. DBSI non-restricted-fraction in both left fornix (cres)/stria terminalis (r = -0.472, p = 0.004) and right posterior thalamic radiation (r = - 0.467, p = 0.005) was negatively correlated with MMSE scores. CONCLUSION: DBSI could potentially detect and quantify the extent of inflammatory cell infiltration, fiber/dendrite loss, and edema in both SN and WM tracts in patients with early-stage PD, a finding remains to be further investigated through more extensive longitudinal DBSI analysis. CLINICAL RELEVANCE STATEMENT: Our study shows that DBSI indexes can potentially detect early-stage PD's pathological changes, with a notable ability to distinguish between inflammation and edema. This implies that DBSI has the potential to be an imaging biomarker for early PD diagnosis. KEY POINTS: • Diffusion basis spectrum imaging detected higher restricted-fraction in Parkinson's disease, potentially reflecting inflammatory cell infiltration. • Diffusion basis spectrum imaging detected higher non-restricted-fraction and lower fiber-fraction in Parkinson's disease, indicating the presence of edema and/or dopaminergic neuronal/dendritic loss. • Diffusion basis spectrum imaging metrics correlated with non-motor symptoms, suggesting its potential diagnostic role to detect early-stage PD dysfunctions.

Childhood obesity is linked to putative neuroinflammation in brain white matter, hypothalamus, and striatum.

Neuroinflammation is both a consequence and driver of overfeeding and weight gain in rodent obesity models. Advances in magnetic resonance imaging (MRI) enable investigations of brain microstructure that suggests neuroinflammation in human obesity. To assess the convergent validity across MRI techniques and extend previous findings, we used diffusion basis spectrum imaging (DBSI) to characterize obesity-associated alterations in brain microstructure in 601 children (age 9-11 years) from the Adolescent Brain Cognitive DevelopmentSM Study. Compared with children with normal-weight, greater DBSI restricted fraction (RF), reflecting neuroinflammation-related cellularity, was seen in widespread white matter in children with overweight and obesity. Greater DBSI-RF in hypothalamus, caudate nucleus, putamen, and, in particular, nucleus accumbens, correlated with higher baseline body mass index and related anthropometrics. Comparable findings were seen in the striatum with a previously reported restriction spectrum imaging (RSI) model. Gain in waist circumference over 1 and 2 years related, at nominal significance, to greater baseline RSI-assessed restricted diffusion in nucleus accumbens and caudate nucleus, and DBSI-RF in hypothalamus, respectively. Here we demonstrate that childhood obesity is associated with microstructural alterations in white matter, hypothalamus, and striatum. Our results also support the reproducibility, across MRI methods, of findings of obesity-related putative neuroinflammation in children.

Diffusion basis spectrum imaging as an adjunct to conventional MRI leads to earlier diagnosis of high-grade glioma tumor progression versus treatment effect.

Background: Following chemoradiotherapy for high-grade glioma (HGG), it is often challenging to distinguish treatment changes from true tumor progression using conventional MRI. The diffusion basis spectrum imaging (DBSI) hindered fraction is associated with tissue edema or necrosis, which are common treatment-related changes. We hypothesized that DBSI hindered fraction may augment conventional imaging for earlier diagnosis of progression versus treatment effect. Methods: Adult patients were prospectively recruited if they had a known histologic diagnosis of HGG and completed standard-of-care chemoradiotherapy. DBSI and conventional MRI data were acquired longitudinally beginning 4 weeks post-radiation. Conventional MRI and DBSI metrics were compared with respect to their ability to diagnose progression versus treatment effect. Results: Twelve HGG patients were enrolled between August 2019 and February 2020, and 9 were ultimately analyzed (5 progression, 4 treatment effect). Within new or enlarging contrast-enhancing regions, DBSI hindered fraction was significantly higher in the treatment effect group compared to progression group (P = .0004). Compared to serial conventional MRI alone, inclusion of DBSI would have led to earlier diagnosis of either progression or treatment effect in 6 (66.7%) patients by a median of 7.7 (interquartile range = 0-20.1) weeks. Conclusions: In the first longitudinal prospective study of DBSI in adult HGG patients, we found that in new or enlarging contrast-enhancing regions following therapy, DBSI hindered fraction is elevated in cases of treatment effect compared to those with progression. Hindered fraction map may be a valuable adjunct to conventional MRI to distinguish tumor progression from treatment effect.

Twelve Weeks of Intermittent Caloric Restriction Diet Mitigates Neuroinflammation in Midlife Individuals with Multiple Sclerosis: A Pilot Study with Implications for Prevention of Alzheimer's Disease.

BACKGROUND: Multiple sclerosis (MS) is a prototype neuroinflammatory disorder with increasingly recognized role for neurodegeneration. Most first-line treatments cannot prevent the progression of neurodegeneration and the resultant disability. Interventions can improve symptoms of MS and might provide insights into the underlying pathology. OBJECTIVE: To investigate the effect of intermittent caloric restriction on neuroimaging markers of MS. METHODS: We randomized ten participants with relapsing remitting MS to either a 12-week intermittent calorie restriction (iCR) diet (n = 5) or control (n = 5). Cortical thickness and volumes were measured through FreeSurfer, cortical perfusion was measured by arterial spin labeling and neuroinflammation through diffusion basis spectrum imaging. RESULTS: After 12 weeks of iCR, brain volume increased in the left superior and inferior parietal gyri (p: 0.050 and 0.049, respectively) and the banks of the superior temporal sulcus (p: 0.01). Similarly in the iCR group, cortical thickness improved in the bilateral medial orbitofrontal gyri (p: 0.04 and 0.05 in right and left, respectively), the left superior temporal gyrus (p: 0.03), and the frontal pole (p: 0.008) among others. Cerebral perfusion decreased in the bilateral fusiform gyri (p: 0.047 and 0.02 in right and left, respectively) and increased in the bilateral deep anterior white matter (p: 0.03 and 0.013 in right and left, respectively). Neuroinflammation, demonstrated through hindered and restricted water fractions (HF and RF), decreased in the left optic tract (HF p: 0.02), and the right extreme capsule (RF p: 0.007 and HF p: 0.003). CONCLUSION: These pilot data suggest therapeutic effects of iCR in improving cortical volume and thickness and mitigating neuroinflammation in midlife adults with MS.

Diffusion Basis Spectrum Imaging Identifies Clinically Relevant Disease Phenotypes of Cervical Spondylotic Myelopathy.

STUDY DESIGN: Prospective cohort study. OBJECTIVE: Apply a machine learning clustering algorithm to baseline imaging data to identify clinically relevant cervical spondylotic myelopathy (CSM) patient phenotypes. SUMMARY OF BACKGROUND DATA: A major shortcoming in improving care for CSM patients is the lack of robust quantitative imaging tools to guide surgical decision-making. Advanced diffusion-weighted magnetic resonance imaging (MRI) techniques, such as diffusion basis spectrum imaging (DBSI), may help address this limitation by providing detailed evaluations of white matter injury in CSM. METHODS: Fifty CSM patients underwent comprehensive clinical assessments and diffusion-weighted MRI, followed by DBSI modeling. DBSI metrics included fractional anisotropy, axial and radial diffusivity, fiber fraction, extra-axonal fraction, restricted fraction, and nonrestricted fraction. Neurofunctional status was assessed by the modified Japanese Orthopedic Association, myelopathic disability index, and disabilities of the arm, shoulder, and hand. Quality-of-life was measured by the 36-Item Short Form Survey physical component summary and mental component summary. The neck disability index was used to measure self-reported neck pain. K-means clustering was applied to baseline DBSI measures to identify 3 clinically relevant CSM disease phenotypes. Baseline demographic, clinical, radiographic, and patient-reported outcome measures were compared among clusters using one-way analysis of variance (ANOVA). RESULTS: Twenty-three (55%) mild, 9 (21%) moderate, and 10 (24%) severe myelopathy patients were enrolled. Eight patients were excluded due to MRI data of insufficient quality. Of the remaining 42 patients, 3 groups were generated by k-means clustering. When compared with clusters 1 and 2, cluster 3 performed significantly worse on the modified Japanese Orthopedic Association and all patient-reported outcome measures (P<0.001), except the 36-Item Short Form Survey mental component summary (P>0.05). Cluster 3 also possessed the highest proportion of non-Caucasian patients (43%, P=0.04), the worst hand dynamometer measurements (P<0.05), and significantly higher intra-axonal axial diffusivity and extra-axonal fraction values (P<0.001). CONCLUSIONS: Using baseline imaging data, we delineated a clinically meaningful CSM disease phenotype, characterized by worse neurofunctional status, quality-of-life, and pain, and more severe imaging markers of vasogenic edema. LEVEL OF EVIDENCE: II.

Diffusion Basis Spectrum Imaging Provides Insights Into Cervical Spondylotic Myelopathy Pathology.

BACKGROUND: Diffusion basis spectrum imaging (DBSI) is a noninvasive quantitative imaging modality that may improve understanding of cervical spondylotic myelopathy (CSM) pathology through detailed evaluations of spinal cord microstructural compartments. OBJECTIVE: To determine the utility of DBSI as a biomarker of CSM disease severity. METHODS: A single-center prospective cohort study enrolled 50 patients with CSM and 20 controls from 2018 to 2020. All patients underwent clinical evaluation and diffusion-weighted MRI, followed by diffusion tensor imaging and DBSI analyses. Diffusion-weighted MRI metrics assessed white matter integrity by fractional anisotropy, axial diffusivity, radial diffusivity, and fiber fraction. In addition, DBSI further evaluates extra-axonal changes by isotropic restricted and nonrestricted fraction. Including an intra-axonal diffusion compartment, DBSI improves estimations of axonal injury through intra-axonal axial diffusivity. Patients were categorized into mild, moderate, and severe CSM using modified Japanese Orthopedic Association classifications. Imaging parameters were compared among patient groups using independent samples t tests and ANOVA. RESULTS: Twenty controls, 27 mild (modified Japanese Orthopedic Association 15-17), 12 moderate (12-14), and 11 severe (0-11) patients with CSM were enrolled. Diffusion tensor imaging and DBSI fractional anisotropy, axial diffusivity, and radial diffusivity were significantly different between control and patients with CSM ( P < .05). DBSI fiber fraction, restricted fraction, and nonrestricted fraction were significantly different between groups ( P < .01). DBSI intra-axonal axial diffusivity was lower in mild compared with moderate (mean difference [95% CI]: 1.1 [0.3-2.1], P < .01) and severe (1.9 [1.3-2.4], P < .001) CSM. CONCLUSION: DBSI offers granular data on white matter tract integrity in CSM that provide novel insights into disease pathology, supporting its potential utility as a biomarker of CSM disease progression.

Diffusion basis spectrum imaging predicts long-term clinical outcomes following surgery in cervical spondylotic myelopathy.

BACKGROUND CONTEXT: A major shortcoming in improving care for cervical spondylotic myelopathy (CSM) patients is the lack of robust quantitative imaging tools to guide surgical decision-making. Diffusion basis spectrum imaging (DBSI), an advanced diffusion-weighted MRI technique, provides objective assessments of white matter tract integrity that may help prognosticate outcomes in patients undergoing surgery for CSM. PURPOSE: To examine the ability of DBSI to predict clinically important CSM outcome measures at 2-years follow-up. STUDY DESIGN/SETTING: Prospective cohort study. PATIENT SAMPLE: Patients undergoing decompressive cervical surgery for CSM. OUTCOME MEASURES: Neurofunctional status was assessed by the mJOA, MDI, and DASH. Quality-of-life was measured by the SF-36 PCS and SF-36 MCS. The NDI evaluated self-reported neck pain, and patient satisfaction was assessed by the NASS satisfaction index. METHODS: Fifty CSM patients who underwent cervical decompressive surgery were enrolled. Preoperative DBSI metrics assessed white matter tract integrity through fractional anisotropy, fiber fraction, axial diffusivity, and radial diffusivity. To evaluate extra-axonal diffusion, DBSI measures restricted and nonrestricted fractions. Patient-reported outcome measures were evaluated preoperatively and up to 2-years follow-up. Support vector machine classification algorithms were used to predict surgical outcomes at 2-years follow-up. Specifically, three feature sets were built for each of the seven clinical outcome measures (eg, mJOA), including clinical only, DBSI only, and combined feature sets. RESULTS: Twenty-seven mild (mJOA 15-17), 12 moderate (12-14) and 11 severe (0-11) CSM patients were enrolled. Twenty-four (60%) patients underwent anterior decompressive surgery compared with 16 (40%) posterior approaches. The mean (SD) follow-up was 23.2 (5.6, range 6.1-32.8) months. Feature sets built on combined data (ie, clinical+DBSI metrics) performed significantly better for all outcome measures compared with those only including clinical or DBSI data. When predicting improvement in the mJOA, the clinically driven feature set had an accuracy of 61.9 [61.6, 62.5], compared with 78.6 [78.4, 79.2] in the DBSI feature set, and 90.5 [90.2, 90.8] in the combined feature set. CONCLUSIONS: When combined with key clinical covariates, preoperative DBSI metrics predicted improvement after surgical decompression for CSM with high accuracy for multiple outcome measures. These results suggest that DBSI may serve as a noninvasive imaging biomarker for CSM valuable in guiding patient selection and informing preoperative counseling. LEVEL OF EVIDENCE: II.

Utility of Diffusion Basis Spectrum Imaging in Quantifying Baseline Disease Severity and Prognosis of Cervical Spondylotic Myelopathy.

STUDY DESIGN: Prospective cohort study. OBJECTIVE: The aim was to assess the association between diffusion tensor imaging (DTI) and diffusion basis spectrum imaging (DBSI) measures and cervical spondylotic myelopathy (CSM) clinical assessments at baseline and two-year follow-up. SUMMARY OF BACKGROUND DATA: Despite advancements in diffusion-weighted imaging, few studies have examined associations between diffusion magnetic resonance imaging (MRI) markers and CSM-specific clinical domains at baseline and long-term follow-up. MATERIALS AND METHODS: A single-center prospective cohort study enrolled 50 CSM patients who underwent surgical decompression and 20 controls from 2018 to 2020. At initial evaluation, all patients underwent diffusion-weighted MRI acquisition, followed by DTI and DBSI analyses. Diffusion-weighted MRI metrics assessed white matter integrity by fractional anisotropy, axial diffusivity, radial diffusivity, and fiber fraction. To improve estimations of intra-axonal anisotropic diffusion, DBSI measures intra-/extra-axonal fraction and intra-axonal axial diffusivity. DBSI also evaluates extra-axonal isotropic diffusion by restricted and nonrestricted fraction. Clinical assessments were performed at baseline and two-year follow-up and included the modified Japanese Orthopedic Association (mJOA); 36-Item Short Form Survey physical component summary (SF-36 PCS); SF-36 mental component summary; neck disability index; myelopathy disability index; and disability of the arm, shoulder, and hand. Pearson correlation coefficients were computed to compare associations between DTI/DBSI and clinical measures. A False Discovery Rate correction was applied for multiple comparisons testing. RESULTS: At baseline presentation, of 36 correlations analyzed between DTI metrics and CSM clinical measures, only DTI fractional anisotropy showed a positive correlation with SF-36 PCS ( r =0.36, P =0.02). In comparison, there were 30/81 (37%) significant correlations among DBSI and clinical measures. Increased DBSI axial diffusivity, intra-axonal axial diffusivity, intra-axonal fraction, restricted fraction, and extra-axonal anisotropic fraction were associated with worse clinical presentation (decreased mJOA; SF-36 PCS/mental component summary; and increased neck disability index; myelopathy disability index; disability of the arm, shoulder, and hand). At latest follow-up, increased preoperative DBSI intra-axonal axial diffusivity and extra-axonal anisotropic fraction were significantly correlated with improved mJOA. CONCLUSIONS: This findings demonstrate that DBSI measures may reflect baseline disease burden and long-term prognosis of CSM as compared with DTI. With further validation, DBSI may serve as a noninvasive biomarker following decompressive surgery. LEVEL OF EVIDENCE: 3.

Analysis of combined clinical and diffusion basis spectrum imaging metrics to predict the outcome of chronic cervical spondylotic myelopathy following cervical decompression surgery.

OBJECTIVE: Cervical spondylotic myelopathy (CSM) is the most common cause of chronic spinal cord injury, a significant public health problem. Diffusion tensor imaging (DTI) is a neuroimaging technique widely used to assess CNS tissue pathology and is increasingly used in CSM. However, DTI lacks the needed accuracy, precision, and recall to image pathologies of spinal cord injury as the disease progresses. Thus, the authors used diffusion basis spectrum imaging (DBSI) to delineate white matter injury more accurately in the setting of spinal cord compression. It was hypothesized that the profiles of multiple DBSI metrics can serve as imaging outcome predictors to accurately predict a patient's response to therapy and his or her long-term prognosis. This hypothesis was tested by using DBSI metrics as input features in a support vector machine (SVM) algorithm. METHODS: Fifty patients with CSM and 20 healthy controls were recruited to receive diffusion-weighted MRI examinations. All spinal cord white matter was identified as the region of interest (ROI). DBSI and DTI metrics were extracted from all voxels in the ROI and the median value of each patient was used in analyses. An SVM with optimized hyperparameters was trained using clinical and imaging metrics separately and collectively to predict patient outcomes. Patient outcomes were determined by calculating changes between pre- and postoperative modified Japanese Orthopaedic Association (mJOA) scale scores. RESULTS: Accuracy, precision, recall, and F1 score were reported for each SVM iteration. The highest performance was observed when a combination of clinical and DBSI metrics was used to train an SVM. When assessing patient outcomes using mJOA scale scores, the SVM trained with clinical and DBSI metrics achieved accuracy and an area under the curve of 88.1% and 0.95, compared with 66.7% and 0.65, respectively, when clinical and DTI metrics were used together. CONCLUSIONS: The accuracy and efficacy of the SVM incorporating clinical and DBSI metrics show promise for clinical applications in predicting patient outcomes. These results suggest that DBSI metrics, along with the clinical presentation, could serve as a surrogate in prognosticating outcomes of patients with CSM.

Characterization of multiple sclerosis neuroinflammation and neurodegeneration with relaxation and diffusion basis spectrum imaging.

BACKGROUND: Advanced magnetic resonance imaging (MRI) methods can provide more specific information about various microstructural tissue changes in multiple sclerosis (MS) brain. Quantitative measurement of T1 and T2 relaxation, and diffusion basis spectrum imaging (DBSI) yield metrics related to the pathology of neuroinflammation and neurodegeneration that occurs across the spectrum of MS. OBJECTIVE: To use relaxation and DBSI MRI metrics to describe measures of neuroinflammation, myelin and axons in different MS subtypes. METHODS: 103 participants (20 clinically isolated syndrome (CIS), 33 relapsing-remitting MS (RRMS), 30 secondary progressive MS and 20 primary progressive MS) underwent quantitative T1, T2, DBSI and conventional 3T MRI. Whole brain, normal-appearing white matter, lesion and corpus callosum MRI metrics were compared across MS subtypes. RESULTS: A gradation of MRI metric values was seen from CIS to RRMS to progressive MS. RRMS demonstrated large oedema-related differences, while progressive MS had the most extensive abnormalities in myelin and axonal measures. CONCLUSION: Relaxation and DBSI-derived MRI measures show differences between MS subtypes related to the severity and composition of underlying tissue damage. RRMS showed oedema, demyelination and axonal loss compared with CIS. Progressive MS had even more evidence of increased oedema, demyelination and axonal loss compared with CIS and RRMS.

Microstructural Periventricular White Matter Injury in Post-Hemorrhagic Ventricular Dilatation.

BACKGROUND AND OBJECTIVES: The neurological deficits of neonatal post-hemorrhagic hydrocephalus (PHH) have been linked to periventricular white matter injury. To improve understanding of PHH-related injury, diffusion basis spectrum imaging (DBSI) was applied in neonates, modeling axonal and myelin integrity, fiber density, and extra-fiber pathologies. Objectives included characterizing DBSI measures in periventricular tracts, associating measures with ventricular size, and examining MRI findings in the context of post-mortem white matter histology from similar cases. METHODS: A prospective cohort of infants born very preterm underwent term equivalent MRI, including infants with PHH, high-grade intraventricular hemorrhage without hydrocephalus (IVH), and controls (VPT). DBSI metrics extracted from the corpus callosum, corticospinal tracts, and optic radiations included fiber axial diffusivity, fiber radial diffusivity, fiber fractional anisotropy, fiber fraction (fiber density), restricted fractions (cellular infiltration), and non-restricted fractions (vasogenic edema). Measures were compared across groups and correlated with ventricular size. Corpus callosum postmortem immunohistochemistry in infants with and without PHH assessed intra- and extra-fiber pathologies. RESULTS: Ninety-five infants born very preterm were assessed (68 VPT, 15 IVH, 12 PHH). Infants with PHH had the most severe white matter abnormalities and there were no consistent differences in measures between IVH and VPT groups. Key tract-specific white matter injury patterns in PHH included reduced fiber fraction in the setting of axonal and/or myelin injury, increased cellular infiltration, vasogenic edema, and inflammation. Specifically, measures of axonal injury were highest in the corpus callosum; both axonal and myelin injury were observed in the corticospinal tracts; and axonal and myelin integrity were preserved in the setting of increased extra-fiber cellular infiltration and edema in the optic radiations. Increasing ventricular size correlated with worse DBSI metrics across groups. On histology, infants with PHH had high cellularity, variable cytoplasmic vacuolation, and low synaptophysin marker intensity. DISCUSSION: PHH was associated with diffuse white matter injury, including tract-specific patterns of axonal and myelin injury, fiber loss, cellular infiltration, and inflammation. Larger ventricular size was associated with greater disruption. Postmortem immunohistochemistry confirmed MRI findings. These results demonstrate DBSI provides an innovative approach extending beyond conventional diffusion MRI for investigating neuropathological effects of PHH on neonatal brain development.

Nucleus accumbens microstructure mediates the relationship between obesity and eating behavior in adults.

OBJECTIVE: Basal ganglia regions are part of the brain's reward-processing networks and are implicated in the neurobiology of obesity and eating disorders. This study examines basal ganglia microstructural properties in adults with and without obesity. METHODS: Diffusion basis spectrum imaging (DBSI) images were analyzed to obtain putative imaging markers of neuroinflammation. Relationships between basal ganglia DBSI metrics and reward sensitivity and eating behaviors were also explored. RESULTS: A total of 46 participants (25 people with obesity; aged 20-40 years; 37 women) were included. Relative to the people in the normal-weight group, people with obesity had smaller caudate and larger nucleus accumbens (NAcc) volumes (p < 0.05) and lower DBSI fiber fraction (reflecting apparent axonal/dendrite density) in NAcc and putamen, higher DBSI nonrestricted fraction (reflecting tissue edema) in NAcc and caudate, and higher DBSI restricted fraction (reflecting tissue cellularity) in putamen (p ≤ 0.01, all). Increased emotional and reward eating behaviors were related to lower NAcc axonal/dendrite density and greater tissue edema (p ≤ 0.002). The relationships between emotional eating and adiposity measures were mediated by NAcc microstructure. CONCLUSIONS: These findings provide evidence that microstructural alterations in basal ganglia relate to obesity and insights linking NAcc microstructure and eating behavior in adults.

Diffusion basis spectrum imaging measures anti-inflammatory and neuroprotective effects of fingolimod on murine optic neuritis.

OBJECTIVE: To prospectively determine whether diffusion basis spectrum imaging (DBSI) detects, differentiates and quantitates coexisting inflammation, demyelination, axonal injury and axon loss in mice with optic neuritis (ON) due to experimental autoimmune encephalomyelitis (EAE), and to determine if DBSI accurately measures effects of fingolimod on underlying pathology. METHODS: EAE was induced in 7-week-old C57BL/6 female mice. Visual acuity (VA) was assessed daily to detect onset of ON after which daily oral-treatment with either fingolimod (1 mg/kg) or saline was given for ten weeks. In vivo DBSI scans of optic nerves were performed at baseline, 2-, 6- and 10-weeks post treatment. DBSI-derived metrics including restricted isotropic diffusion tensor fraction (putatively reflecting cellularity), non-restricted isotropic diffusion tensor fraction (putatively reflecting vasogenic edema), DBSI-derived axonal volume, axial diffusivity, λ∥ (putatively reflecting axonal integrity), and increased radial diffusivity, λ⊥ (putatively reflecting demyelination). Mice were killed immediately after the last DBSI scan for immunohistochemical assessment. RESULTS: Optic nerves of fingolimod-treated mice exhibited significantly better (p < 0.05) VA than saline-treated group at each time point. During ten-week of treatment, DBSI-derived non-restricted and restricted-isotropic-diffusion-tensor fractions, and axonal volumes were not significantly different (p > 0.05) from the baseline values in fingolimod-treated mice. Transient DBSI-λ∥ decrease and DBSI-λ⊥ increase were detected during Fingolimod treatment. DBSI-derived metrics assessed in vivo significantly correlated (p < 0.05) with the corresponding histological markers. CONCLUSION: DBSI was used to assess changes of the underlying optic nerve pathologies in EAE mice with ON, exhibiting great potential as a noninvasive outcome measure for monitoring disease progression and therapeutic efficacy for MS.

Diffusion histology imaging differentiates distinct pediatric brain tumor histology.

High-grade pediatric brain tumors exhibit the highest cancer mortality rates in children. While conventional MRI has been widely adopted for examining pediatric high-grade brain tumors clinically, accurate neuroimaging detection and differentiation of tumor histopathology for improved diagnosis, surgical planning, and treatment evaluation, remains an unmet need in their clinical management. We employed a novel Diffusion Histology Imaging (DHI) approach employing diffusion basis spectrum imaging (DBSI) derived metrics as the input classifiers for deep neural network analysis. DHI aims to detect, differentiate, and quantify heterogeneous areas in pediatric high-grade brain tumors, which include normal white matter (WM), densely cellular tumor, less densely cellular tumor, infiltrating edge, necrosis, and hemorrhage. Distinct diffusion metric combination would thus indicate the unique distributions of each distinct tumor histology features. DHI, by incorporating DBSI metrics and the deep neural network algorithm, classified pediatric tumor histology with an overall accuracy of 85.8%. Receiver operating analysis (ROC) analysis suggested DHI's great capability in distinguishing individual tumor histology with AUC values (95% CI) of 0.984 (0.982-0.986), 0.960 (0.956-0.963), 0.991 (0.990-0.993), 0.950 (0.944-0.956), 0.977 (0.973-0.981) and 0.976 (0.972-0.979) for normal WM, densely cellular tumor, less densely cellular tumor, infiltrating edge, necrosis and hemorrhage, respectively. Our results suggest that DBSI-DNN, or DHI, accurately characterized and classified multiple tumor histologic features in pediatric high-grade brain tumors. If these results could be further validated in patients, the novel DHI might emerge as a favorable alternative to the current neuroimaging techniques to better guide biopsy and resection as well as monitor therapeutic response in patients with high-grade brain tumors.

Non-invasive quantification of inflammation, axonal and myelin injury in multiple sclerosis.

The aim of this study was to determine the feasibility of diffusion basis spectrum imaging in multiple sclerosis at 7 T and to investigate the pathological substrates of tissue damage in lesions and normal-appearing white matter. To this end, 43 patients with multiple sclerosis (24 relapsing-remitting, 19 progressive), and 21 healthy control subjects were enrolled. White matter lesions were classified in T1-isointense, T1-hypointense and black holes. Mean values of diffusion basis spectrum imaging metrics (fibres, restricted and non-restricted fractions, axial and radial diffusivities and fractional anisotropy) were measured from whole brain white matter lesions and from both lesions and normal appearing white matter of the corpus callosum. Significant differences were found between T1-isointense and black holes (P ranging from 0.005 to <0.001) and between lesions' centre and rim (P < 0.001) for all the metrics. When comparing the three subject groups in terms of metrics derived from corpus callosum normal appearing white matter and T2-hyperintense lesions, a significant difference was found between healthy controls and relapsing-remitting patients for all metrics except restricted fraction and fractional anisotropy; between healthy controls and progressive patients for all metrics except restricted fraction and between relapsing-remitting and progressive multiple sclerosis patients for all metrics except fibres and restricted fractions (P ranging from 0.05 to <0.001 for all). Significant associations were found between corpus callosum normal-appearing white matter fibres fraction/non-restricted fraction and the Symbol Digit Modality Test (respectively, r = 0.35, P = 0.043; r = -0.35, P = 0.046), and between black holes radial diffusivity and Expanded Disability Status Score (r = 0.59, P = 0.002). We showed the feasibility of diffusion basis spectrum imaging metrics at 7 T, confirmed the role of the derived metrics in the characterization of lesions and normal appearing white matter tissue in different stages of the disease and demonstrated their clinical relevance. Thus, suggesting that diffusion basis spectrum imaging is a promising tool to investigate multiple sclerosis pathophysiology, monitor disease progression and treatment response.