You have described a problem but offered NO SOLUTION. What the fuck was the research for? What exactly are you going to do when YOU are the 1 in 4 that the WHO predicts will have a stroke?
Assessment of longitudinal hippocampal atrophy in the first year after ischemic stroke using automatic segmentation techniques
Under a Creative Commons license
open access
Keywords
Hippocampal atrophy
FreeSurfer
Linear mixed-effect model
Magnetic resonance imaging
Stroke
1. Introduction
Stroke is a major global cause of disability and death (Strong, Mathers et al. 2007, Maier, Schroder et al. 2015, Aerts, Fias et al. 2016). People with stroke have a higher incidence of dementia (Makin, Turpin et al. 2013),
and up to one-third of stroke survivors develop post-stroke dementia
(PSD) in the years following the initial stroke incident (Mok, Lam et al. 2017).
Understanding the trajectory of cognitive decline and associated brain
changes over the first year after stroke is crucial for developing
treatments to delay the onset of PSD and modify its course.
Magnetic resonance imaging (MRI) has been successfully used for the detection of structural changes in PSD (Mijajlović, Pavlović et al. 2017). Combined with neuropsychological testing, quantitative MRI methods show promise as important diagnostic tools (Ystad, Lundervold et al. 2009). In many quantitative MRI studies of diseased populations (Frodl, Schaub et al. 2006, M Meisenzahl, Seifert et al. 2009, Woon, Sood et al. 2010, Apfel, Ross et al. 2011), including subcortical ischemic vascular dementia (Fein, Di Sclafani et al. 2000),
smaller hippocampal volumes have been reported. In major stroke
survivors, hippocampal atrophy has been associated with cognitive
decline (Gemmell, Bosomworth et al. 2012, Kliper, Bashat et al. 2013).
The
segmentation of structures characterized by morphological complexity –
like the hippocampi – is challenging. Despite serious efforts to
automate hippocampal segmentation, this is still commonly performed by
human experts (Maglietta, Amoroso et al. 2016).
However, manual segmentation is restricted by time and is costly for
large datasets, has lower reproducibility unless protocols are strictly
adhered to, and its results may be influenced by rater bias (Colon-Perez, Triplett et al. 2016).
The high reproducibility of automated methods reduces bias and
facilitates the replication of findings between studies, in addition to
allowing for faster processing. However, most longitudinal validation
studies of automated segmentation have focused on healthy (Morey, Petty et al. 2009, Perlaki, Horvath et al. 2017) and Alzheimer's disease (AD) cohorts (Morra, Tu et al. 2010, Mulder, de Jong et al. 2014, Cover, van Schijndel et al. 2016, Maglietta, Amoroso et al. 2016, Cover, van Schijndel et al. 2018). Automated segmentation has not been validated for the characterization of hippocampal atrophy in stroke.
Assessing
longitudinal brain atrophy in conditions with destructive brain
lesions, such as stroke, can be particularly challenging given the
dynamic structural alterations before and after the stroke. The timing
and sample size of MRI datasets pose challenges to conclusions about
structural atrophy. There is also the challenge posed by the inclusion
of patients with recurrent (i.e., prior) stroke who may already have
suffered significant regional brain atrophy including in the
hippocampus. While we have looked at brain atrophy in the first three
months post-onset (Brodtmann, Pardoe et al. 2012, Brodtmann, Pardoe et al. 2013, Li, Pardoe et al. 2015),
relatively longer term trajectories of hippocampal volume changes have
not been examined. Given that most functional recovery occurs in the
first three months after stroke (Lee, Lim et al. 2015), the three-month time point may serve as a better baseline for assessing longitudinal brain atrophy.
Researchers have examined whole-brain (Seghier, Ramsden et al. 2014) and hippocampal (Schaapsmeerders, Tuladhar et al. 2015)
atrophy in young and old patients many years after stroke. They
reported lower volumes ipsi-lesionally in the whole-brain and in the
hippocampus. However, the profile and rates of hippocampal atrophy over
the first year remain largely unknown.
Previously (Khlif, Egorova et al. 2018),
we assessed the agreement between a number of automated segmentation
methods and manual tracing in estimating hippocampal volumes in healthy
participants and stroke patients at three months post-stroke. The top
performers (AdaBoost, FIRST, and Subfields) in that study, in addition
to FreeSurfer/v5.3 and v6.0, were used in the current study. The latter
two methods were included in order to validate the improvement in
hippocampal volume estimation sought with the evolution of FreeSurfer
algorithms. We expected the algorithms’ performance at 12 months to be
consistent with their previous performance at three months. Moreover, we
needed to evaluate their performance in assessing hippocampal atrophy
between three and 12 months to better inform future studies.
Accordingly, our aims were to:
- 1
- Quantify hippocampal volume change in healthy and ischemic stroke participants in the period between three and 12 months after stroke (based on volumes estimated using manual and automated segmentation methods).
- 2
- Quantify hippocampal volume change in left-hemisphere stroke compared to right-hemisphere stroke, in first-ever stroke compared to recurrent stroke, and in ipsi-lesional hippocampus compared to contra-lesional hippocampus.
- 3
- Evaluate the agreement between manual tracing and automated segmentation in estimating hippocampal volumes at 12 months post-stroke.
- 4
- Assess the sensitivity of automated algorithms in detecting longitudinal hippocampal volume change between three and 12 months post-stroke.
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