Characterizing Structural Changes With Evolving Remyelination Following Experimental Demyelination Using High Angular Resolution Diffusion MRI and Texture Analysis

Luo, Tim; Oladosu, Olayinka; Rawji, Khalil S.; Zhai, Peng; Pridham, Glen; Hossain, Shahnewaz; Zhang, Yunyan

doi:10.1002/jmri.26328

Cited by 16 publications

(11 citation statements)

References 31 publications

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“…This work focused on a mouse model of toxic demyelination (injection of lysolecithin), characterized mainly by myelin content changes rather than inflammatory activity. 13 Most interesting, even in this model, focal lesions showed higher ODI values compared with healthy WM only at the peak of demyelination. These values, then, decreased with time to values similar to those in healthy WM.…”

Section: Nodi As a Marker Of Acute Inflammationmentioning

confidence: 69%

“…These values, then, decreased with time to values similar to those in healthy WM. 13 This change suggests that the increased nODI within lesions in the acute phase might be connected not only with inflammatory changes but also the active demyelination detectable during gadolinium enhancement. 22 Despite its possible association with inflammation, increased nODI is not unique to acute (gadolinium-enhancing) lesions but can sometimes also be detected in nonenhancing lesions.…”

Section: Nodi As a Marker Of Acute Inflammationmentioning

confidence: 98%

“…For animal studies, only a single work, based on a murine model, longitudinally assessed induced demyelinated lesions, correlating NODDI abnormalities with histopathologic changes. 13 The authors suggested that after a demyelinating event, the combination of decreasing ODI and increasing NDI with time might reflect improvement in fiber coherency due to remyelination.…”

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confidence: 99%

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Neurite Orientation Dispersion and Density Imaging for Assessing Acute Inflammation and Lesion Evolution in MS

Sacco¹,

Caverzasi²,

Papinutto³

et al. 2020

AJNR Am J Neuroradiol

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BACKGROUND AND PURPOSE: MR imaging is essential for MS diagnosis and management, yet it has limitations in assessing axonal damage and remyelination. Gadolinium-based contrast agents add value by pinpointing acute inflammation and blood-brain barrier leakage, but with drawbacks in safety and cost. Neurite orientation dispersion and density imaging (NODDI) assesses microstructural features of neurites contributing to diffusion imaging signals. This approach may resolve the components of MS pathology, overcoming conventional MR imaging limitations. MATERIALS AND METHODS: Twenty-one subjects with MS underwent serial enhanced MRIs (12.6 6 9 months apart) including NODDI, whose key metrics are the neurite density and orientation dispersion index. Twenty-one age-and sex-matched healthy controls underwent unenhanced MR imaging with the same protocol. Fifty-eight gadolinium-enhancing and non-gadolinium-enhancing lesions were semiautomatically segmented at baseline and follow-up. Normal-appearing WM masks were generated by subtracting lesions and dirty-appearing WM from the whole WM. RESULTS: The orientation dispersion index was higher in gadolinium-enhancing compared with non-gadolinium-enhancing lesions; logistic regression indicated discrimination, with an area under the curve of 0.73. At follow-up, in the 58 previously enhancing lesions, we identified 2 subgroups based on the neurite density index change across time: Type 1 lesions showed increased neurite density values, whereas type 2 lesions showed decreased values. Type 1 lesions showed greater reduction in size with time compared with type 2 lesions. CONCLUSIONS: NODDI is a promising tool with the potential to detect acute MS inflammation. The observed heterogeneity among lesions may correspond to gradients in severity and clinical recovery after the acute phase. ABBREVIATIONS: AD ¼ axial diffusivity; DAWM ¼ dirty-appearing white matter; FA ¼ fractional anisotropy; FU ¼ follow-up; GEL ¼ gadolinium-enhancing lesion; HC ¼ healthy control; MD ¼ mean diffusivity; NAWM ¼ normal-appearing white matter; NDI ¼ neurite density index; NGEL ¼ non-gadolinium-enhancing lesion; NODDI ¼ neurite orientation dispersion and density Imaging; nODI ¼ normalized orientation dispersion index; ODI ¼ orientation dispersion index; RD ¼ radial diffusivity; VEC ¼ extra-neurite compartment C onventional MR imaging is essential for MS diagnosis and management, specifically for demonstrating WM lesion dissemination in space (involvement of .1 CNS region) and time (across time accumulation). 1 Conventional MR imaging, however, lacks specificity in characterizing MS WM lesions after the acute phase; all lesions show a similar radiologic appearance on T2WI, irrespective of the degree of inflammation, axonal loss, gliosis,

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Section: Nodi As a Marker Of Acute Inflammationmentioning

confidence: 69%

Section: Nodi As a Marker Of Acute Inflammationmentioning

confidence: 98%

mentioning

confidence: 99%

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Neurite Orientation Dispersion and Density Imaging for Assessing Acute Inflammation and Lesion Evolution in MS

Sacco¹,

Caverzasi²,

Papinutto³

et al. 2020

AJNR Am J Neuroradiol

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“…A higher ODI, for instance, suggests a loss of coherence in the organization of neurite tissue (Zhang et al, 2012) or increases in microglial density (Yi et al, 2019) in football players relative to other athletes and non-athletes. A higher NDI suggests that the neurites might be swollen (Dowell et al, 2017;Wang et al, 2019) or under a process of remyelination (Jespersen et al, 2010;Luo et al, 2018;Sepehrband et al, 2015) .b Finally, a higher ISOVF suggests that neuroinflammatory processes is occurring (Zhang et al, 2012) . All of these processes have been documented previously in investigations of the effects of repetitive head impacts on brain tissue (Giza and Hovda, 2014;Israelsson et al, 2008;Johnson et al, 2013;LaPlaca et al, 2005;Shultz et al, 2012) .…”

Section: Collision Sports Participation and Socioeconomic Background mentioning

confidence: 99%

“…For example, differences in ODI can be attributed to a variety of biophysical or biomechanical factors including formation of new spines, microglial density, and biomechanical pulling of white matter tissue (Alexander et al, 2010;Dyrby et al, 2013;Mayer et al, 2017;Yi et al, 2019;Zhang et al, 2012) . Changes in NDI can be interpreted as changes in the density of myelinated neurites (Zhang et al, 2012) but have also been associated with hyper-phosphorylated tau-deposition in cortex (Colgan et al, 2016) , axonal beading (Skinner et al, 2015) , alterations in ionic balance , and myelin density (Luo et al, 2018;Sepehrband et al, 2015) . Finally, ISOVF relates to changes in intra-voxel fluid content (Zhang et al, 2012) and can be potentially related to changes in microscopic fluid content for example due to inflammatory processes (Fick et al, 2017;Suzuki et al, 2017) such as those observed following head impacts.…”

Section: Introductionmentioning

confidence: 99%

Advanced mapping of the human white matter microstructure better separates elite sports participation

Caron¹,

Bullock²,

Kitchell³

et al. 2020

Preprint

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Collision-sport athletes, especially football players, are exposed to a higher number of repetitive head impacts. Little is known, however, regarding the effects of long-term exposure to repetitive head impacts on brain tissue structure and the locations (i.e. superficial or deep tissue structures) affected. On top of this, little is known about the effects of highly competitive athletics on brain tissue structure. We investigated this relationship, including the baseline effect of collegiate athletic participation, by mapping measures of microstructure to cortical and subcortical white matter parcels and major white matter tracts of varsity IU football players, cross-country runners, and non-athlete students using advanced microstructural mapping techniques and machine-learning. We tested a series of hypotheses on the differences between the subject groups. To do so, we implemented an innovative approach to statistical hypothesis testing combining advanced white matter tissue properties and machine-learning classifiers. We used cross-validation to select the best subject group model and best machine-learning classifier. Wide-spread differences in brain tissue microstructure across cortical, subcortical, and major white matter structures were documented. The tissue properties of the major white matter tracts were found to best predict collision-sport participation and sports participation in general, however, cortical and subcortical white matter parcels were also found to predict collision-sports participation. The biggest differences in brain tissue microstructure is between the athletes (football and cross country combined) and the non-athletes students. The rewards and risks of playing competitive sports at the highest collegiate level may account for the differences in brain microstructure. This was the first investigation into the effects of repetitive head impacts to use an open-source data processing platform brainlife.io. The data and code for these analyses are available via brainlife.io.

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Long-term coordinated microstructural disruptions of the developing neocortex and subcortical white matter after early postnatal systemic inflammation

Prasad

Looij

Gunn

et al. 2021

Brain, Behavior, and Immunity

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Characterizing Structural Changes With Evolving Remyelination Following Experimental Demyelination Using High Angular Resolution Diffusion MRI and Texture Analysis

Abstract: 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018.

Cited by 16 publications

References 31 publications

Neurite Orientation Dispersion and Density Imaging for Assessing Acute Inflammation and Lesion Evolution in MS

Neurite Orientation Dispersion and Density Imaging for Assessing Acute Inflammation and Lesion Evolution in MS

Advanced mapping of the human white matter microstructure better separates elite sports participation

Long-term coordinated microstructural disruptions of the developing neocortex and subcortical white matter after early postnatal systemic inflammation

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