How is your doctor going to use this information to prevent your upcoming dementia?
Your chances of getting dementia.
1. A documented 33% dementia chance post-stroke from an Australian study? May 2012.
2. Then this study came out and seems to have a range from 17-66%. December 2013.
3. A 20% chance in this research. July 2013.
4. Dementia Risk Doubled in Patients Following Stroke September 2018
5. Parkinson’s Disease May Have Link to Stroke March 2017
The latest here:
How dementia spreads: new study says it’s through neural networks
Scientists have found out a way to forecast the spread of brain
atrophy, using prior research that shows how brain networks connect with
each other to form a continuously linked pathway. The research
published in the journal Neuron
on October 14, 2019, was carried out in patients with frontotemporal
dementia (FTD) and shores up earlier findings that patients with
dementia lose brain cells to atrophy in a systematic pattern, that
follows the synaptic connections between neurons in already well-defined
networks. This tells us more about the progress of neuronal
degeneration – where the nerve cell or pathway is at greatest risk for
atrophy – as well as helping thereby to evolve better tools, in the
foreseeable future, to help measure how efficiently new treatments can
block this process.
Dementia
Dementia is an umbrella term for any condition that is caused by the
degeneration of brain cells resulting in altered behavior and
difficulties in language use – the most well-known condition in this
category being Alzheimer’s disease. FTD is the most common form of
dementia to affect people below the age of 60 years. FTD affects
different people in different ways, the variation in the effects being
mediated by the route of spread of the atrophy through the brain in each
case. This high degree of unpredictability makes it harder to
specifically point to any one factor as the underlying biological reason
for atrophy. It also makes it harder to design a clinical trial to see
whether a new treatment is actually working in any way, or is superior
to previous treatments.
Mapping functionally connected neural networks
The first sign of change in this research field came from the current
team’s senior researcher William Seeley, who showed how atrophy
actually follows the pathway of an already existing brain network in
use. A brain network is a community of neuronal clusters that are
related to the same function, such as speaking, hearing, language
processing and the like. These networks communicate and work together
because of the connections shared by the neurons within them – the
synapses. These clusters may be nearby or far apart from each other.
Thus the progression of cell death in neurodegenerative conditions is
not random, nor is it like a wave of degeneration spreading outwards in
all directions from a central focus. Instead, it is like a telegraphic
message spreading along a communication pathway, composed of the various
physical devices that make up the circuit.
The study
The current study takes forward the same concept by showing that they
can predict how brain atrophy will progress, in a patient with FTD,
based on the maps of neural networks in the brain, derived from brain
scans in healthy people. The researchers had already created a
standardized map of 175 neural functional pathways in different parts of
the brain, based on functional MRI scanning of 75 healthy people.
Functional MRI uses vascular flow detected by MRI in various parts of
the brain, to assess which part of the brain is functioning during a
given activity.
The research included 42 patients with FTD who had altered behavior –
a type called behavioral variant FTD – in the form of inappropriate
social behavior; and 30 patients with FTD of the semantic variant that
showed up as primary progressive aphasia – where the main symptom is the
marked deterioration in the patient’s ability to use language.
On an initial baseline MRI scan, these patients were assessed to see
how much of the brain had already degenerated. One year later, or so, a
repeat MRI was taken to evaluate the progression of brain atrophy in
this period.
Once the maps were in place, the researchers matched the maps from
the FTD patients one by one to the standardized neural network maps
based on best-fit – looking at which of the standardized networks
matched the atrophy pattern in the affected individual’s baseline MRI.
They then arbitrarily assigned the position of ‘origin of atrophy’ to
the center of the best-matching network. The underlying assumption was
that neuronal atrophy begins at a especially high-risk location in the
network, and then spreads to other neural circuits through the
pre-existing synapses. Thus the ‘hub’ of any anatomically connected
network is the logical place for the initial atrophy to begin.
Using
these standardized maps, they predicted the most likely route of spread
of atrophy over the following year. They then tested their predictions
against the follow-up set of MRI scans done a year later. They also
compared the accuracy of these predictions against those which were done
independently of functional connectivity within the affected network.
The findings
The researchers found that they needed to include two steps, in
particular, to make the most accurate prediction of whether any given
brain region would be involved in the atrophy over the following year
after the baseline MRI scan.
The first is the “shortest path to the epicenter,” and is taken as
the number of synapses crossed by the atrophic process from the supposed
epicenter of the degeneration, up to the region where a prediction is
sought. This corresponds to how many synapses link the two regions via
the neural network. In other words, the more synapses there are, the
slower the speed of progression to this region.
The second step to be factored in is the “nodal hazard”, or the
number of regions within the connectivity range that are already showing
significant degeneration. This is important because the more regions
that are already affected, the greater the chance of the target region
being affected as well.
As researcher Jesse Brown says in his analogy, “It's like with an
infectious disease, where your chances of becoming infected can be
predicted by how many degrees of separation you have from 'Patient Zero'
but also by how many people in your immediate social network are
already sick.”
The scientists saw a lot of hope in their method, but also pointed
out that a lot of work is still awaiting them. They need to make their
predictions much more precise, such as by using individualized maps to
show the neural network connectivity for each patient rather than a
standardized map, or by classifying maps by subtype and developing more
narrow models for each type.
Importance of this study
The present study tells scientists more about how brain atrophy
spreads in FTD via the biological underpinnings of the disease
progression. This will, of course, eventually trigger the development of
drugs to avoid or slow down such spread. In an apt comment, Brown sums
up: “Just like epidemiologists rely on models of how infectious diseases
spread to develop interventions targeted to key hubs or choke points.
Neurologists need to understand the underlying biological mechanisms of
neurodegeneration to develop ways of slowing or halting the spread of
the disease.”
Moreover, it can help to build tools that will tell researchers how
well a particular therapy works – is it actually changing the predicted
route or speed or distance of progression over the network?
As the atrophic process destroys increasing numbers of brain neurons
and areas, the devastating changes in the patient’s personality and
behavior will continue to alter. The heavy load this imposes on the
loved ones and caregivers can be somewhat alleviated by bringing down
the degree of unpredictability, since this model could help them foresee
some of the likely changes ahead with advanced disease. This could also
help them take proper decisions and to prepare for such changes.
William Seeley says, “We are excited about this result because it
represents an important first step towards a more precision medicine
type of approach to predicting progression and measuring treatment
effects in neurodegenerative disease.”
Journal reference:
Patient-Tailored,
Connectivity-Based Forecasts of Spreading Brain Atrophy Brown, Jesse A.
et al., Neuron, DOI:https://doi.org/10.1016/j.neuron.2019.08.037, https://www.cell.com/neuron/fulltext/S0896-6273(19)30743-3
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