Changing stroke rehab and research worldwide now.Time is Brain! trillions and trillions of neurons that DIE each day because there are NO effective hyperacute therapies besides tPA(only 12% effective). I have 523 posts on hyperacute therapy, enough for researchers to spend decades proving them out. These are my personal ideas and blog on stroke rehabilitation and stroke research. Do not attempt any of these without checking with your medical provider. Unless you join me in agitating, when you need these therapies they won't be there.

What this blog is for:

My blog is not to help survivors recover, it is to have the 10 million yearly stroke survivors light fires underneath their doctors, stroke hospitals and stroke researchers to get stroke solved. 100% recovery. The stroke medical world is completely failing at that goal, they don't even have it as a goal. Shortly after getting out of the hospital and getting NO information on the process or protocols of stroke rehabilitation and recovery I started searching on the internet and found that no other survivor received useful information. This is an attempt to cover all stroke rehabilitation information that should be readily available to survivors so they can talk with informed knowledge to their medical staff. It lays out what needs to be done to get stroke survivors closer to 100% recovery. It's quite disgusting that this information is not available from every stroke association and doctors group.

Saturday, October 26, 2019

Assessment of longitudinal hippocampal atrophy in the first year after ischemic stroke using automatic segmentation techniques

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


Highlights

First-year hippocampal atrophy in stroke is more accelerated ipsi-lesionally
Volume estimation is not impacted by hemisphere side, study group, or scan timepoint
Segmentation method-hippocampal size interaction determines volume estimation
FreeSurfer/Subfields and fsl/FIRST segmentations agreed best with manual tracing

Abstract

We assessed first-year hippocampal atrophy in stroke patients and healthy controls using manual and automated segmentations: AdaBoost, FIRST (fsl/v5.0.8), FreeSurfer/v5.3 and v6.0, and Subfields (in FreeSurfer/v6.0). We estimated hippocampal volumes in 39 healthy controls and 124 stroke participants at three months, and 38 controls and 113 stroke participants at one year. We used intra-class correlation, concordance, and reduced major axis regression to assess agreement between automated and ‘Manual’ estimations. A linear mixed-effect model was used to characterize hippocampal atrophy.
Overall, hippocampal volumes were reduced by 3.9% in first-ever stroke and 9.2% in recurrent stroke at three months post-stroke, with comparable ipsi-and contra-lesional reductions in first-ever stroke. Mean atrophy rates between time points were 0.5% for controls and 1.0% for stroke patients (0.6% contra-lesionally, 1.4% ipsi-lesionally). Atrophy rates in left and right-hemisphere strokes were comparable. All methods revealed significant volume change in first-ever and ipsi-lesional stroke (p < 0.001).
Hippocampal volume estimation was not impacted by hemisphere, study group, or scan time point, but rather, by the interaction between the automated segmentation method and hippocampal size. Compared to Manual, Subfields and FIRST recorded the lowest bias. FreeSurfer/v5.3 overestimated volumes the most for large hippocampi, while FIRST was the most accurate in estimating small volumes. AdaBoost performance was average.
Our findings suggest that first-year ipsi-lesional hippocampal atrophy rate especially in first-ever stroke, is greater than atrophy rates in healthy controls and contra-lesional stroke. Subfields and FIRST can complementarily be effective in characterizing the hippocampal atrophy in healthy and stroke cohorts.

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.
More at link.

 

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