[HTML][HTML] Potential pitfalls of using fractional anisotropy, axial diffusivity, and radial diffusivity as biomarkers of cerebral white matter microstructure
Fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD) are commonly
used as MRI biomarkers of white matter microstructure in diffusion MRI studies of …
used as MRI biomarkers of white matter microstructure in diffusion MRI studies of …
Safe surgery for glioblastoma: Recent advances and modern challenges
JKW Gerritsen, MLD Broekman… - Neuro-oncology …, 2022 - academic.oup.com
One of the major challenges during glioblastoma surgery is balancing between maximizing
extent of resection and preventing neurological deficits. Several surgical techniques and …
extent of resection and preventing neurological deficits. Several surgical techniques and …
[HTML][HTML] White matter plasticity in healthy older adults: the effects of aerobic exercise
White matter deterioration is associated with cognitive impairment in healthy aging and
Alzheimer's disease. It is critical to identify interventions that can slow down white matter …
Alzheimer's disease. It is critical to identify interventions that can slow down white matter …
[HTML][HTML] Diffusional kurtosis imaging in the diffusion imaging in python project
Diffusion-weighted magnetic resonance imaging (dMRI) measurements and models provide
information about brain connectivity and are sensitive to the physical properties of tissue …
information about brain connectivity and are sensitive to the physical properties of tissue …
Neuroplasticity and aphasia treatments: new approaches for an old problem
Given the profound impact of language impairment after stroke (aphasia), neuroplasticity
research is garnering considerable attention as means for eventually improving aphasia …
research is garnering considerable attention as means for eventually improving aphasia …
[HTML][HTML] Diffusion weighted/tensor imaging, functional MRi and Perfusion weighted imaging in glioblastoma—foundations and future
GR Salama, LA Heier, P Patel, R Ramakrishna… - Frontiers in …, 2018 - frontiersin.org
In this article, we review the basics of diffusion tensor imaging and functional MRI, their
current utility in preoperative neurosurgical mapping, and their limitations. We also discuss …
current utility in preoperative neurosurgical mapping, and their limitations. We also discuss …
[HTML][HTML] Cross-scanner reproducibility and harmonization of a diffusion MRI structural brain network: A traveling subject study of multi-b acquisition
Abstract Characterization of brain networks by diffusion MRI (dMRI) has rapidly evolved, and
there are ongoing movements toward data sharing and multi-center studies. To extract …
there are ongoing movements toward data sharing and multi-center studies. To extract …
Repetitive model of mild traumatic brain injury produces cortical abnormalities detectable by magnetic resonance diffusion imaging, histopathology, and behavior
F Yu, DK Shukla, RC Armstrong, CM Marion… - Journal of …, 2017 - liebertpub.com
Noninvasive detection of mild traumatic brain injury (mTBI) is important for evaluating acute
through chronic effects of head injuries, particularly after repetitive impacts. To better detect …
through chronic effects of head injuries, particularly after repetitive impacts. To better detect …
[HTML][HTML] Types of naming errors in chronic post-stroke aphasia are dissociated by dual stream axonal loss
The types of errors during speech production can vary across individuals with chronic post-
stroke aphasia, possibly due to the location and extent of brain damage. In this study, we …
stroke aphasia, possibly due to the location and extent of brain damage. In this study, we …
Classification of cognitively normal controls, mild cognitive impairment and Alzheimer's disease using transfer learning approach
VPS Rallabandi, K Seetharaman… - … Signal Processing and …, 2023 - Elsevier
Automated classification of dementia stage using imaging will be useful for clinical diagnosis
and the classification accuracy will be biased for highly imbalanced samples in each class …
and the classification accuracy will be biased for highly imbalanced samples in each class …