NeuroImage
-
Automatic hippocampal segmentation in temporal lobe epilepsy: impact of developmental abnormalities.
In drug-resistant temporal lobe epilepsy (TLE), detecting hippocampal atrophy on MRI is important as it allows defining the surgical target. The performance of automatic segmentation in TLE has so far been considered unsatisfactory. In addition to atrophy, about 40% of patients present with developmental abnormalities (referred to as malrotation) characterized by atypical morphologies of the hippocampus and collateral sulcus. ⋯ In addition, they tended to lateralize the seizure focus less accurately in the presence of malrotation (manual: 64%; ANIMAL-multi: 55%, p = 0.4; SACHA: 50%, p = 0.1; FreeSurfer: 41%, p = 0.05). Hippocampal developmental anomalies and atrophy had a negative impact on the segmentation performance of three state-of-the-art automated methods. These shape variants should be taken into account when designing segmentation algorithms.
-
We argue that the emerging practice of using the author byline to acknowledge shared data is incompatible with current established standards for academic authorship. Non-author contributors, whether groups or individuals, should not be added to the author list of published papers. ⋯ Such dilution of authorship standards is problematic because it can compromise fair evaluations in the scientific community. We briefly discuss viable alternatives for crediting contributors, such as citations of papers describing shared data, reference to dataset publications, inclusion in the Acknowledgments section, or credit of individuals for sharing data in an Appendix, a solution that has been used in academic evaluation.
-
In Multiple Sclerosis (MS), detection of T2-hyperintense white matter (WM) lesions on magnetic resonance imaging (MRI) has become a crucial criterion for diagnosis and predicting prognosis in early disease. Automated lesion detection is not only desirable with regard to time and cost effectiveness but also constitutes a prerequisite to minimize user bias. Here, we developed and evaluated an algorithm for automated lesion detection requiring a three-dimensional (3D) gradient echo (GRE) T1-weighted and a FLAIR image at 3 Tesla (T). ⋯ We found good agreement with lesions determined by manual tracing (R2 values of over 0.93 independent of FLAIR slice thickness up to 6mm). These results require validation with data from other protocols based on a conventional FLAIR sequence and a 3D GRE T1-weighted sequence. Yet, we believe that our tool allows fast and reliable segmentation of FLAIR-hyperintense lesions, which might simplify the quantification of lesions in basic research and even clinical trials.
-
Post-stroke microglial activation (MA) may have both neurotoxic and pro-repair effects, particularly in the salvaged penumbra. Mapping MA in vivo is therefore an important goal. 11C-PK11195, a ligand for the 18 kDa translocator protein, is the reference radioligand for MA imaging, but a correlation between the regional distributions of in vivo tracer binding and post mortem MA after stroke, as assessed with PET and immunohistochemistry, respectively, has not been demonstrated so far. Here we performed 11C-PK11195 microPET in a rat model previously shown to induce extensive cortical MA, and determined the correlation between 11C-PK11195 and immunostaining with the CD11 antibody OX42, so as to verify the presence of activated microglia, in a template of PET-resolution size regions-of-interest (ROIs) spanning the whole affected hemisphere. ⋯ The correlation between Day 14 11C-PK11195 and OX42 across the affected hemisphere from the same brain regions and animals further supports the validity of 11C-PK11195 as an in vivo imaging marker of MA following stroke. The finding of statistically significant increases in 11C-PK11195 as early as 48 h after stroke is novel. These results have implications for mapping MA after stroke, with potential therapeutic applications.
-
MRI estimates of brain iron concentration in normal aging using quantitative susceptibility mapping.
Quantifying tissue iron concentration in vivo is instrumental for understanding the role of iron in physiology and in neurological diseases associated with abnormal iron distribution. Herein, we use recently-developed Quantitative Susceptibility Mapping (QSM) methodology to estimate the tissue magnetic susceptibility based on MRI signal phase. To investigate the effect of different regularization choices, we implement and compare ℓ1 and ℓ2 norm regularized QSM algorithms. ⋯ However, QSM values are influenced by factors such as the myelin content, whereas FDRI is a more specific indicator of iron content. Hence, FDRI demonstrated higher specificity to iron yet yielded noisier data despite longer scan times and lower spatial resolution than QSM. The robustness, practicality, and demonstrated ability of predicting the change in iron deposition in adult aging suggest that regularized QSM algorithms using single-field-strength data are possible alternatives to tissue iron estimation requiring two field strengths.