Searching for the neurite density with diffusion MRI: Challenges for biophysical modeling

Björn Lampinen; Filip Szczepankiewicz; Mikael Novén; Danielle Westen; Oskar Hansson; Elisabet Englund; Johan Mårtensson; Carl‐Fredrik Westin; Markus Nilsson

doi:10.1002/hbm.24542

Disruption of neurite morphology parallels MS progression

Neurology Neuroimmunology & Neuroinflammation ◽

10.1212/nxi.0000000000000502 ◽

2018 ◽

Vol 5 (6) ◽

pp. e502 ◽

Cited By ~ 14

Author(s):

Barbara Spanò ◽

Giovanni Giulietti ◽

Valerio Pisani ◽

Manuela Morreale ◽

Elisa Tuzzi ◽

...

Keyword(s):

Brain Tissue ◽

Disease Severity ◽

Diffusion Mri ◽

Diffusion Tensor ◽

Compartment Model ◽

Dispersion Index ◽

Brain Areas ◽

Neurite Density ◽

Relapsing Remitting ◽

Diffusion Tensor Imaging Dti

ObjectivesTo apply advanced diffusion MRI methods to the study of normal-appearing brain tissue in MS and examine their correlation with measures of clinical disability.MethodsA multi-compartment model of diffusion MRI called neurite orientation dispersion and density imaging (NODDI) was used to study 20 patients with relapsing-remitting MS (RRMS), 15 with secondary progressive MS (SPMS), and 20 healthy controls. Maps of NODDI were analyzed voxel-wise to assess the presence of abnormalities within the normal-appearing brain tissue and the association with disease severity. Standard diffusion tensor imaging (DTI) parameters were also computed for comparing the 2 techniques.ResultsPatients with MS showed reduced neurite density index (NDI) and increased orientation dispersion index (ODI) compared with controls in several brain areas (p < 0.05), with patients with SPMS having more widespread abnormalities. DTI indices were also sensitive to some changes. In addition, patients with SPMS showed reduced ODI in the thalamus and caudate nucleus. These abnormalities were associated with scores of disease severity (p < 0.05). The association with the MS functional composite score was higher in patients with SPMS compared with patients with RRMS.ConclusionsNODDI and DTI findings are largely overlapping. Nevertheless, NODDI helps interpret previous findings of increased anisotropy in the thalamus of patients with MS and are consistent with the degeneration of selective axon populations.

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Applying biophysical models to understand the role of white matter in cognitive development

10.1101/347872 ◽

2018 ◽

Cited By ~ 1

Author(s):

Elizabeth Huber ◽

Rafael Neto Henriques ◽

Julia P. Owen ◽

Ariel Rokem ◽

Jason D. Yeatman

Keyword(s):

White Matter ◽

Cognitive Development ◽

Diffusion Mri ◽

Reading Skill ◽

Great Promise ◽

Model Parameters ◽

Biophysical Modeling ◽

Tissue Properties ◽

Biophysical Models ◽

Specific Tissue

AbstractDiffusion MRI (dMRI) holds great promise for illuminating the biological changes that underpin cognitive development. The diffusion of water molecules probes the cellular structure of brain tissue, and biophysical modeling of the diffusion signal can be used to make inferences about specific tissue properties that vary over development or predict cognitive performance. However, applying these models to study development requires that the parameters can be reliably estimated given the constraints of data collection with children. Here we collect repeated scans using a multi-shell diffusion MRI protocol in a group of children (ages 7-12) and use two popular biophysical models to characterize axonal properties. We first assess the scan-rescan reliability of model parameters and show that axon water faction can be reliably estimated from a relatively fast acquisition, without applying spatial smoothing or de-noising. We then investigate developmental changes in the white matter, and individual differences in white matter that correlate with reading skill. Specifically, we test the hypothesis that previously reported correlations between reading skill and diffusion anisotropy in the corpus callosum reflect increased axon density in poor readers. Both models support this interpretation, highlighting the utility of biophysical models for testing specific hypotheses about cognitive development.

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An open resource for transdiagnostic research in pediatric mental health and learning disorders

10.1101/149369 ◽

2017 ◽

Cited By ~ 5

Author(s):

Lindsay M. Alexander ◽

Jasmine Escalera ◽

Lei Ai ◽

Charissa Andreotti ◽

Karina Febre ◽

...

Keyword(s):

Mental Health ◽

New York ◽

Diffusion Mri ◽

Large Scale ◽

Brain Network ◽

Learning Disorders ◽

Biophysical Modeling ◽

Video Recordings ◽

Pathologic Processes ◽

Pediatric Mental Health

ABSTRACTTechnological and methodological innovations are equipping researchers with unprecedented capabilities for detecting and characterizing pathologic processes in the developing human brain. As a result, ambitions to achieve clinically useful tools to assist in the diagnosis and management of mental health and learning disorders are gaining momentum. To this end, it is critical to accrue large-scale multimodal datasets that capture a broad range of commonly encountered clinical psychopathology. The Child Mind Institute has launched the Healthy Brain Network (HBN), an ongoing initiative focused on creating and sharing a biobank of data from 10,000 New York area participants (ages 5-21). The HBN Biobank houses data about psychiatric, behavioral, cognitive, and lifestyle phenotypes, as well as multimodal brain imaging (resting and naturalistic viewing fMRI, diffusion MRI, morphometric MRI), electroencephalography, eye-tracking, voice and video recordings, genetics, and actigraphy. Here, we present the rationale, design and implementation of HBN protocols. We describe the first data release (n = 664) and the potential of the biobank to advance related areas (e.g., biophysical modeling, voice analysis).

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Neurite density imaging versus imaging of microscopic anisotropy in diffusion MRI: A model comparison using spherical tensor encoding

NeuroImage ◽

10.1016/j.neuroimage.2016.11.053 ◽

2017 ◽

Vol 147 ◽

pp. 517-531 ◽

Cited By ~ 104

Author(s):

Björn Lampinen ◽

Filip Szczepankiewicz ◽

Johan Mårtensson ◽

Danielle van Westen ◽

Pia C. Sundgren ◽

...

Keyword(s):

Diffusion Mri ◽

Model Comparison ◽

Neurite Density

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Biophysical modeling of high field diffusion MRI demonstrates micro-structural aberration in chronic mild stress rat brain

NeuroImage ◽

10.1016/j.neuroimage.2016.07.001 ◽

2016 ◽

Vol 142 ◽

pp. 421-430 ◽

Cited By ~ 29

Author(s):

Ahmad Raza Khan ◽

Andrey Chuhutin ◽

Ove Wiborg ◽

Christopher D. Kroenke ◽

Jens R. Nyengaard ◽

...

Keyword(s):

Rat Brain ◽

Diffusion Mri ◽

High Field ◽

Chronic Mild Stress ◽

Mild Stress ◽

Biophysical Modeling ◽

Field Diffusion ◽

Structural Aberration

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Loss and Reorganisation of Superficial White Matter in Alzheimer's disease: A Diffusion MRI study

10.1101/2021.05.07.21256728 ◽

2021 ◽

Author(s):

Thomas Veale ◽

Ian B Malone ◽

Teresa Poole ◽

Thomas D Parker ◽

Catherine F Slattery ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

White Matter ◽

Fractional Anisotropy ◽

Diffusion Mri ◽

Grey Matter ◽

Diffusion Tensor ◽

Mean Diffusivity ◽

Young Onset ◽

Neurite Density

Pathological involvement of cerebral white matter in Alzheimer's disease has been shown using diffusion tensor imaging. Superficial white matter (SWM) changes have been relatively understudied despite their importance in cortico-cortical connections. Measuring SWM degeneration using diffusion tensor imaging is challenging due to its complex structure and proximity to the cortex. To overcome this we investigated diffusion MRI changes in young-onset Alzheimer's disease using standard diffusion tensor imaging and Neurite Orientation Dispersion and Density Imaging to distinguish between disease-related changes that are due to degeneration (e.g. loss of myelinated fibres) and those due to reorganisation (e.g. increased fibre dispersion). Twenty-nine young-onset Alzheimer's disease patients and 22 healthy controls had both single-shell and multi-shell diffusion MRI. We calculated fractional anisotropy, mean diffusivity, neurite density index, orientation dispersion index and tissue fraction (1-free water fraction). Diffusion metrics were sampled in 15 a priori regions of interest at four points along the cortical profile: cortical grey matter, the grey/white boundary, SWM (1mm below grey/white boundary) and SWM/deeper white matter (2mm below grey/white boundary). To estimate cross-sectional group differences, we used average marginal effects from linear mixed effect models of participants' diffusion metrics along the cortical profile. The SWM of young-onset Alzheimer's disease individuals had lower neurite density index compared to controls in five regions (superior and inferior parietal, precuneus, entorhinal and parahippocampus) (all P<0.05), and higher orientation dispersion index in three regions (fusiform, entorhinal and parahippocampus) (all P<0.05). Young-onset Alzheimer's disease individuals had lower fractional anisotropy in the SWM of two regions (entorhinal and parahippocampus) (both P<0.05) and higher fractional anisotropy within the postcentral region (P<0.05). Mean diffusivity in SWM was higher in the young-onset Alzheimer's disease group in the parahippocampal region (P<0.05) and lower in three regions (postcentral, precentral and superior temporal) (all P<0.05). In the overlying grey matter, disease-related changes were largely consistent with SWM findings when using neurite density index and fractional anisotropy, but appeared at odds with orientation dispersion and mean diffusivity SWM changes. Tissue fraction was significantly lower across all grey matter regions in young-onset Alzheimer's disease individuals (all P<0.001) but group differences reduced in magnitude and coverage when moving towards the SWM. These results show that microstructural changes occur within SWM and along the cortical profile in individuals with young-onset Alzheimer's disease. Lower neurite density and higher orientation dispersion suggests underlying SWM fibres undergo neurodegeneration and reorganisation, two effects previously indiscernible using standard diffusion tensor metrics in SWM.

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