Deans' stroke musings

Changing stroke rehab and research worldwide now.Time is Brain!Just think of all the trillions and trillions of neurons that DIE each day because there are NO effective hyperacute therapies besides tPA(only 12% effective). I have 493 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:

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's quite disgusting that this information is not available from every stroke association and doctors group.
My back ground story is here:http://oc1dean.blogspot.com/2010/11/my-background-story_8.html

Thursday, January 26, 2017

Brain Networks Under Attack: Robustness Properties and the Impact of Lesions

Based on this you will need to ask your doctor how badly damaged your brain network was as a result of your stroke and EXACTLY what stroke protocols will bring that network back to full function.  I don't care how fucking hard this is for your doctor to do, trying living with a stroke. No response means you need to find another doctor, you likely will need to go through thousands before you find one that at least is conversant in the topic of stroke.  A great stroke association would know about and train doctors in these topics but we have fucking failures of stroke associations instead.
http://www.medscape.com/viewarticle/874308?src=wnl_edit_tpal

Abstract

A growing number of studies approach the brain as a complex network, the so-called 'connectome'. Adopting this framework, we examine what types or extent of damage the brain can withstand—referred to as network 'robustness'—and conversely, which kind of distortions can be expected after brain lesions. To this end, we review computational lesion studies and empirical studies investigating network alterations in brain tumour, stroke and traumatic brain injury patients. Common to these three types of focal injury is that there is no unequivocal relationship between the anatomical lesion site and its topological characteristics within the brain network. Furthermore, large-scale network effects of these focal lesions are compared to those of a widely studied multifocal neurodegenerative disorder, Alzheimer's disease, in which central parts of the connectome are preferentially affected. Results indicate that human brain networks are remarkably resilient to different types of lesions, compared to other types of complex networks such as random or scale-free networks. However, lesion effects have been found to depend critically on the topological position of the lesion. In particular, damage to network hub regions—and especially those connecting different subnetworks—was found to cause the largest disturbances in network organization. Regardless of lesion location, evidence from empirical and computational lesion studies shows that lesions cause significant alterations in global network topology. The direction of these changes though remains to be elucidated. Encouragingly, both empirical and modelling studies have indicated that after focal damage, the connectome carries the potential to recover at least to some extent, with normalization of graph metrics being related to improved behavioural and cognitive functioning. To conclude, we highlight possible clinical implications of these findings, point out several methodological limitations that pertain to the study of brain diseases adopting a network approach, and provide suggestions for future research.

Introduction

Throughout the history of cognitive neuroscience, there has been an ongoing debate as to whether cognitive functions are localized within specific regions of the brain or emerge from dynamical interactions between various brain areas (Catani et al., 2012). Recent advances in non-invasive in vivo neuroimaging technology now allow the construction of comprehensive whole-brain maps of the structural and functional connections of the human cerebrum at the individual level. The ensemble of macroscopic brain connections can then be described as a complex network—the 'connectome' (Hagmann, 2005; Sporns et al., 2005). Using graph theory, a powerful framework to characterize diverse properties of complex networks, it has been consistently demonstrated that the human connectome reflects an optimal balance between segregation and integration (Sporns, 2013). Thereby, both perspectives on the origin of cognitive functions have been unified.
Providing a novel perspective to study the brain's organization and functioning in health and disease, connectome analysis has found rapid applications in clinical neuroscience. Disturbed interactions among brain regions have been found in nearly all neurological, developmental and psychiatric disorders (Griffa et al., 2013; van Straaten and Stam, 2013; Cao et al., 2015; Fornito and Bullmore, 2015). In addition, relationships between network topology and cognitive functioning have been revealed. For example, strong positive associations have been found between global efficiency of structural and functional networks and intellectual performance (Li et al., 2009; van den Heuvel et al., 2009). Hence, network analysis could be used to identify biomarkers of specific brain functions and symptoms, thereby carrying the potential to allow more objective diagnosis, to monitor recovery or progression processes over time, and to predict effective treatment options.
In addition, the availability of structural and functional connectomes has enabled the construction and validation of computational models of large-scale neuronal activity (Ghosh et al., 2008; Deco and Kringelbach, 2014). In particular, dynamical models can be implemented on the structural connectome to simulate brain activity, after which predicted and empirical functional connectivity can be compared to evaluate model performance. Overall, it has been demonstrated that brain activity strongly depends on the underlying structural connectivity (Deco and Corbetta, 2011). By virtually lesioning structural connectomes, computational models thus can be used as unique predictive tools to investigate the impact of diverse structural connectivity alterations on brain dynamics. That is, computational modelling enables us to investigate what types or extent of damage the brain can withstand—referred to as network 'robustness'—and conversely, which kind of distortions can be expected after brain lesions, including those purposively induced by surgery. Furthermore, biologically inspired dynamical models can provide insights into the local dynamics underlying large-scale network topology in health and disease. Hence, they may provide an entry point for understanding brain disorders at a causal mechanistic level. This might lead to novel, more effective therapeutic interventions, for example through drug discovery, optimized presurgical planning, and new targets for deep brain stimulation (Deco and Kringelbach, 2014).
In this review, we briefly discuss how the brain can be studied from a complex networks perspective. Adopting this perspective, we focus on the properties of brain networks underlying network robustness. In turn, we review computational lesion studies and empirical studies investigating network alterations in brain tumour, stroke and traumatic brain injury (TBI) patients. Common to these three types of focal injury is that there is no clear mapping between the anatomical lesion site and its topological characteristics within the brain network. Furthermore, large-scale network effects of these focal lesions are compared to those of a widely studied multifocal neurodegenerative disorder, Alzheimer's disease, in which central parts of the connectome are preferentially affected. To conclude, we highlight potential clinical implications of these findings, point out several methodological limitations that pertain to the study of brain diseases adopting a network approach and provide suggestions for future research.

8 more pages to go.

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