̽��ֱ�� of Cambridge - magnetic resonance imaging (MRI)

Ultra-powered MRI scans show damage to brain’s ‘control centre’ is behind long-lasting Covid-19 symptoms

sc604 — Tue, 08 Oct 2024 01:28:45 +0000

Using ultra-high-resolution scanners that can see the living brain in fine detail, researchers from the Universities of Cambridge and Oxford were able to observe the damaging effects Covid-19 can have on the brain.

̽��ֱ��study team scanned the brains of 30 people who had been admitted to hospital with severe Covid-19 early in the pandemic, before vaccines were available. ̽��ֱ��researchers found that Covid-19 infection damages the region of the brainstem associated with breathlessness, fatigue and anxiety.

̽��ֱ��powerful MRI scanners used for the study, known as 7-Tesla or 7T scanners, can measure inflammation in the brain. Their results, published in the journal Brain, will help scientists and clinicians understand the long-term effects of Covid-19 on the brain and the rest of the body. Although the study was started before the long-term effects of Covid were recognised, it will help to better understand this condition.

̽��ֱ��brainstem, which connects the brain to the spinal cord, is the control centre for many basic life functions and reflexes. Clusters of nerve cells in the brainstem, known as nuclei, regulate and process essential bodily functions such as breathing, heart rate, pain and blood pressure.

“Things happening in and around the brainstem are vital for quality of life, but it had been impossible to scan the inflammation of the brainstem nuclei in living people, because of their tiny size and difficult position.” said first author Dr Catarina Rua, from the Department of Clinical Neurosciences. “Usually, scientists only get a good look at the brainstem during post-mortem examinations.”

“ ̽��ֱ��brainstem is the critical junction box between our conscious selves and what is happening in our bodies,” said Professor James Rowe, also from the Department of Clinical Neurosciences, who co-led the research. “ ̽��ֱ��ability to see and understand how the brainstem changes in response to Covid-19 will help explain and treat the long-term effects more effectively.”

In the early days of the Covid-19 pandemic, before effective vaccines were available, post-mortem studies of patients who had died from severe Covid-19 infections showed changes in their brainstems, including inflammation. Many of these changes were thought to result from a post-infection immune response, rather than direct virus invasion of the brain.

“People who were very sick early in the pandemic showed long-lasting brain changes, likely caused by an immune response to the virus. But measuring that immune response is difficult in living people,” said Rowe. “Normal hospital-type MRI scanners can’t see inside the brain with the kind of chemical and physical detail we need.”

“But with 7T scanners, we can now measure these details. ̽��ֱ��active immune cells interfere with the ultra-high magnetic field, so that we’re able to detect how they are behaving,” said Rua. “Cambridge was special because we were able to scan even the sickest and infectious patients, early in the pandemic.”

Many of the patients admitted to hospital early in the pandemic reported fatigue, breathlessness and chest pain as troubling long-lasting symptoms. ̽��ֱ��researchers hypothesised these symptoms were in part the result of damage to key brainstem nuclei, damage which persists long after Covid-19 infection has passed.

̽��ֱ��researchers saw that multiple regions of the brainstem, in particular the medulla oblongata, pons and midbrain, showed abnormalities consistent with a neuroinflammatory response. ̽��ֱ��abnormalities appeared several weeks after hospital admission, and in regions of the brain responsible for controlling breathing.

“ ̽��ֱ��fact that we see abnormalities in the parts of the brain associated with breathing strongly suggests that long-lasting symptoms are an effect of inflammation in the brainstem following Covid-19 infection,” said Rua. “These effects are over and above the effects of age and gender, and are more pronounced in those who had had severe Covid-19.”

In addition to the physical effects of Covid-19, the 7T scanners provided evidence of some of the psychiatric effects of the disease. ̽��ֱ��brainstem monitors breathlessness, as well as fatigue and anxiety. “Mental health is intimately connected to brain health, and patients with the most marked immune response also showed higher levels of depression and anxiety,” said Rowe. “Changes in the brainstem caused by Covid-19 infection could also lead to poor mental health outcomes, because of the tight connection between physical and mental health.”

̽��ֱ��researchers say the results could aid in the understanding of other conditions associated with inflammation of the brainstem, like MS and dementia. ̽��ֱ��7T scanners could also be used to monitor the effectiveness of different treatments for brain diseases.

“This was an incredible collaboration, right at the peak of the pandemic, when testing was very difficult, and I was amazed how well the 7T scanners worked,” said Rua. “I was really impressed with how, in the heat of the moment, the collaboration between lots of different researchers came together so effectively.”

̽��ֱ��research was supported in part by the NIHR Cambridge Biomedical Research Centre, the NIHR Oxford Biomedical Research Centre, and the ̽��ֱ�� of Oxford COVID Medical Sciences Division Rapid Response Fund.

Reference:
Catarina Rua et al. ‘7-Tesla quantitative susceptibility mapping in COVID-19: brainstem effects and outcome associations.’ Brain (2024). DOI: 10.1093/brain/awae215

Damage to the brainstem – the brain’s ‘control centre’ – is behind long-lasting physical and psychiatric effects of severe Covid-19 infection, a study suggests.

̽��ֱ�� of Cambridge

3D projections of QSM maps on the rendered brainstem

̽��ֱ��text in this work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Images, including our videos, are Copyright © ̽��ֱ�� of Cambridge and licensors/contributors as identified. All rights reserved. We make our image and video content available in a number of ways – on our main website under its Terms and conditions, and on a range of channels including social media that permit your use and sharing of our content under their respective Terms.

Yes

Ultra-powerful brain scanners offer hope for Parkinson’s disease patients

cjb250 — Tue, 17 May 2022 08:00:09 +0000

7T MRI scanners could be used to help identify those patients with Parkinson’s disease and similar conditions most likely to benefit from new treatments for previously-untreatable symptoms, say scientists.

Imaging technique could replace tissue biopsies in assessing drug resistance in breast cancer patients

cjb250 — Tue, 06 Oct 2020 13:18:49 +0000

In a study published in the journal Cancer Cell, researchers at the Cancer Research UK (CRUK) Cambridge Institute have shown how a new technique known as hyperpolarisation – which involves effectively magnetising molecules in a strong magnetic field – can be used to monitor how effective cancer drugs are at slowing a tumour’s growth.

In healthy tissue, cell proliferation is a tightly controlled process. When this process goes wrong, cell proliferation can run away with itself, leading to unchecked growth and the development of tumours.

All tissue needs to be ‘fed’. As part of this process – known as metabolism – our cells break down glucose and other sugars to produce pyruvate, which is in turn converted into lactate. This is important for producing energy and the building blocks for making new cells.

Tumours have a different metabolism to healthy cells, and often produce more lactate. This metabolic pathway is affected by the presence of a protein known as FOXM1, which controls the production of a metabolic enzyme that converts pyruvate into lactate. FOXM1 also controls the production of many other proteins involved in cell growth and proliferation.

Around 70% of all cases of breast cancer are of a type known as estrogen-receptor (ER) positive. In many ER-positive breast cancer cases, an enzyme known as PI3Ka is activated. This leads to an abundance of FOXM1, enabling the cancer cells to grow uncontrollably – the characteristic sign of a tumour cell.

Drugs that inhibit PI3Ka are currently being tested in breast cancer patients. Such drugs should be able to decrease the amount of FOXM1 and check the tumour’s growth. However, a patient’s tumour may have an innate resistance to PI3Ka inhibitors, or can acquire resistance over time, making the drugs increasingly less effective.

Dr Susana Ros, first author from the CRUK Cambridge Institute, said: “Thanks to advances in cancer treatments, our medicines are becoming more and more targeted, but not all drugs will work in every case – some tumours are resistant to particular drugs. What we need are biomarkers – biological signatures – that tell us whether a drug is working or not.”

̽��ֱ��researchers took breast cancer cells from patients and grew them in mouse ‘avatars’ to allow them to study the tumours in detail. They found that in tumours resistant to PI3Ka inhibitors, cancer cells continue to produce FOXM1 – meaning that this molecule could be used as a biomarker for drug resistance in patients with ER-positive breast cancer.

Checking whether a tumour is continuing to produce FOXM1 – and hence whether the PI3Ka inhibitor is still working – would usually involve an invasive tissue biopsy. However, researchers have used a new imaging technique to monitor this in real time and non-invasively.

̽��ֱ��technique developed and used by the team is known as hyperpolarisation. First, the team produces a form of pyruvate whose carbon atoms are slightly heavier than normal carbon atoms (they carry an additional neutron and are hence known as carbon-13 molecules). ̽��ֱ��researchers then ‘hyperpolarise’ – or magnetise – the carbon-13 pyruvate by cooling it to around one degree above absolute zero (-272°C) and exposing it to extremely strong magnetic fields and microwave radiation. ̽��ֱ��frozen material is then thawed and dissolved into an injectable solution.

Patients are injected with the solution and then receive a regular MRI scan. ̽��ֱ��signal strength from the hyperpolarised carbon-13 pyruvate molecules is 10,000 times stronger than that from normal pyruvate, making the molecules visible on the scan. ̽��ֱ��researchers can use the scan to see how fast pyruvate is being converted into lactate – only the continued presence of FOXM1 would allow this to happen, and this would be a sign that the drugs are not working properly.

Image: False colour image of a breast tumour (outlined) pre- and post-treatment with a PI3Ka inhibitor. Weaker colours post-treatment indicate that the drug is working. (Credit: Brindle Lab)

Dr Ros added: “We’ve been able to detect the presence of FOXM1, our biomarker, by using this new imaging technique in breast cancer models to look for a proxy – that is, how quickly pyruvate is converted to lactate.”

Professor Kevin Brindle, senior author of the study, commented: “In the future, this could provide us with a rapid assessment of how a breast cancer patient is responding to treatment without the need for invasive biopsies. This information could help put an end to giving treatments that are not working and the side effects that accompany them. Currently, patients can wait a long time to find out if a treatment is working. This technique could shorten this time, and help to tailor treatment for individual patients.”

Reference
Ros, S et al. Metabolic Imaging Detects Resistance to PI3Kα Inhibition Mediated by Persistent FOXM1 Expression in ER+ Breast Cancer. Cancer Cell; 24 Sept 2020; DOI: 10.1016/j.ccell.2020.08.016

Imaging techniques could replace the need for invasive tissue biopsies in helping rapidly determine whether cancer treatments are working effectively, according to researchers at the ̽��ֱ�� of Cambridge.

Currently, patients can wait a long time to find out if a treatment is working. This technique could shorten this time, and help to tailor treatment for individual patients

Kevin Brindle

Raw Pixels

Husband supporting a sick wife

̽��ֱ��text in this work is licensed under a Creative Commons Attribution 4.0 International License. Images, including our videos, are Copyright © ̽��ֱ�� of Cambridge and licensors/contributors as identified. All rights reserved. We make our image and video content available in a number of ways – as here, on our main website under its Terms and conditions, and on a range of channels including social media that permit your use and sharing of our content under their respective Terms.

Yes

Improved MRI scans could aid in development of arthritis treatments

sc604 — Tue, 09 Jun 2020 19:00:00 +0000

A team of engineers, radiologists and physicians, led by the ̽��ֱ�� of Cambridge, developed the algorithm, which builds a three-dimensional model of an individual’s knee joint in order to map where arthritis is affecting the knee. It then automatically creates ‘change maps’ which not only tell researchers whether there have been significant changes during the study but allow them to locate exactly where these are.

There are few effective treatments for arthritis, and the technique could be a considerable boost to efforts to develop and monitor new therapies for the condition. ̽��ֱ��results are reported in the Journal of Magnetic Resonance Imaging.

Osteoarthritis is the most common form of arthritis in the UK. It develops when the articular cartilage that coats the ends of bones and allows them to glide smoothly over each other at joints, is worn down, resulting in painful, immobile joints. Currently, there is no recognised cure and the only definitive treatment is surgery for artificial joint replacement.

Osteoarthritis is normally identified on an X-ray by a narrowing of the space between the bones of the joint due to a loss of cartilage. However, X-rays do not have enough sensitivity to detect subtle changes in the joint over time.

“We don’t have a good way of detecting these tiny changes in the joint over time in order to see if treatments are having any effect,” said Dr James MacKay from Cambridge’s Department of Radiology, and the study’s lead author. “In addition, if we’re able to detect the early signs of cartilage breakdown in joints, it will help us understand the disease better, which could lead to new treatments for this painful condition.”

̽��ֱ��current study builds on earlier work from the same team, who developed an algorithm to monitor subtle changes in arthritic joints in CT scans. Now, they are using similar techniques for MRI, which provides more complete information about the composition of tissue – not just information about the thickness of cartilage or bone.

MRI is already widely used to diagnose joint problems, including arthritis, but manually labelling each image is time-consuming, and may be less accurate than automated or semi-automated techniques when detecting small changes over a period of months or years.

“Thanks to the engineering expertise of our team, we now have a better way of looking at the joint,” said MacKay.

̽��ֱ��technique MacKay and his colleagues from Cambridge’s Department of Engineering developed, called 3D cartilage surface mapping (3D-CaSM), was able to pick up changes over a period of six months that weren’t detected using standard X-ray or MRI techniques.

̽��ֱ��researchers tested their algorithm on knee joints from bodies that had been donated for medical research, and a further study with human participants between 40 and 60 years old. All of the participants suffered from knee pain, but were considered too young for a knee replacement. Their joints were then compared with people of a similar age with no joint pain.

“There’s a certain degree of deterioration of the joint that happens as a normal part of aging, but we wanted to make sure that the changes we were detecting were caused by arthritis,” said MacKay. “ ̽��ֱ��increased sensitivity that 3D-CaSM provides allows us to make this distinction, which we hope will make it a valuable tool for testing the effectiveness of new therapies.”

̽��ֱ��software is freely available to download and can be added to existing systems. MacKay says that the algorithm can easily be added to existing workflows and that the training process for radiologists is short and straightforward.

As part of a separate study funded by the European Union, the researchers will also be using the algorithm to test whether it can predict which patients will need a knee replacement, by detecting early signs of arthritis.

Reference:
James W. MacKay et al. ‘Three-dimensional Surface-based Analysis of Cartilage MRI data in Knee Osteoarthritis: Validation and Initial Clinical Application.’ Journal of Magnetic Resonance Imaging (2020). DOI: 10.1002/jmri.27193

An algorithm that analyses MRI images and automatically detects small changes in knee joints over time could be used in the development of new treatments for arthritis.

Thanks to the engineering expertise of our team, we now have a better way of looking at the joint

James MacKay

3D model of a knee with osteoarthritis

Yes

AI techniques in medical imaging may lead to incorrect diagnoses

sc604 — Tue, 12 May 2020 07:13:24 +0000

A team of researchers, led by the ̽��ֱ�� of Cambridge and Simon Fraser ̽��ֱ��, designed a series of tests for medical image reconstruction algorithms based on AI and deep learning, and found that these techniques result in myriad artefacts, or unwanted alterations in the data, among other major errors in the final images. ̽��ֱ��effects were typically not present in non-AI based imaging techniques.

̽��ֱ��phenomenon was widespread across different types of artificial neural networks, suggesting that the problem will not be easily remedied. ̽��ֱ��researchers caution that relying on AI-based image reconstruction techniques to make diagnoses and determine treatment could ultimately do harm to patients. Their results are reported in the Proceedings of the National Academy of Sciences.

"There’s been a lot of enthusiasm about AI in medical imaging, and it may well have the potential to revolutionise modern medicine: however, there are potential pitfalls that must not be ignored," said Dr Anders Hansen from Cambridge’s Department of Applied Mathematics and Theoretical Physics, who led the research with Dr Ben Adcock from Simon Fraser ̽��ֱ��. "We’ve found that AI techniques are highly unstable in medical imaging, so that small changes in the input may result in big changes in the output."

A typical MRI scan can take anywhere between 15 minutes and two hours, depending on the size of the area being scanned and the number of images being taken. ̽��ֱ��longer the patient spends inside the machine, the higher resolution the final image will be. However, limiting the amount of time patients spend inside the machine is desired, both to reduce the risk to individual patients and to increase the overall number of scans that can be performed.

Using AI techniques to improve the quality of images from MRI scans or other types of medical imaging is an attractive possibility for solving the problem of getting the highest quality image in the smallest amount of time: in theory, AI could take a low-resolution image and make it into a high-resolution version. AI algorithms ‘learn’ to reconstruct images based on training from previous data, and through this training procedure aim to optimise the quality of the reconstruction. This represents a radical change compared to classical reconstruction techniques that are solely based on mathematical theory without dependency on previous data. In particular, classical techniques do not learn.

Any AI algorithm needs two things to be reliable: accuracy and stability. An AI will usually classify an image of a cat as a cat, but tiny, almost invisible changes in the image might cause the algorithm to instead classify the cat as a truck or a table, for instance. In this example of image classification, the one thing that can go wrong is that the image is incorrectly classified. However, when it comes to image reconstruction, such as that used in medical imaging, there are several things that can go wrong. For example, details like a tumour may get lost or may falsely be added. Details can be obscured and unwanted artefacts may occur in the image.

"When it comes to critical decisions around human health, we can’t afford to have algorithms making mistakes," said Hansen. "We found that the tiniest corruption, such as may be caused by a patient moving, can give a very different result if you’re using AI and deep learning to reconstruct medical images – meaning that these algorithms lack the stability they need."

Hansen and his colleagues from Norway, Portugal, Canada and the UK designed a series of tests to find the flaws in AI-based medical imaging systems, including MRI, CT and NMR. They considered three crucial issues: instabilities associated with tiny perturbations, or movements; instabilities with respect to small structural changes, such as a brain image with or without a small tumour; and instabilities with respect to changes in the number of samples.

They found that certain tiny movements led to myriad artefacts in the final images, details were blurred or completely removed, and that the quality of image reconstruction would deteriorate with repeated subsampling. These errors were widespread across the different types of neural networks.

According to the researchers, the most worrying errors are the ones that radiologists might interpret as medical issues, as opposed to those that can easily be dismissed due to a technical error.

"We developed the test to verify our thesis that deep learning techniques would be universally unstable in medical imaging," said Hansen. " ̽��ֱ��reasoning for our prediction was that there is a limit to how good a reconstruction can be given restricted scan time. In some sense, modern AI techniques break this barrier, and as a result become unstable. We’ve shown mathematically that there is a price to pay for these instabilities, or to put it simply: there is still no such thing as a free lunch."

̽��ֱ��researchers are now focusing on providing the fundamental limits to what can be done with AI techniques. Only when these limits are known will we be able to understand which problems can be solved. "Trial and error-based research would never discover that the alchemists could not make gold: we are in a similar situation with modern AI," said Hansen. "These techniques will never discover their own limitations. Such limitations can only be shown mathematically."

Reference:
Vegard Antun et al. ‘On instabilities of deep learning in image reconstruction and the potential costs of AI.’ Proceedings of the National Academy of Sciences (2020). DOI: 10.1073/pnas.1907377117

Machine learning and AI are highly unstable in medical image reconstruction, and may lead to false positives and false negatives, a new study suggests.

To put it simply: there is still no such thing as a free lunch

Anders Hansen

Yes

Opinion: Why medical technology often doesn’t make it from drawing board to hospital

ljm67 — Wed, 15 Feb 2017 18:58:40 +0000

If there’s something wrong with your brain, how do you spot that in an MRI? Of course, if it’s something obvious, such as a major aneurysm or a tumour, anyone can see it. But what if it’s something more subtle, such as a neural pathway that is more deteriorated than normal? This might be hard to spot by simply looking at an image. However, there is a range of medical image analysis software that can detect something like this.

You may take a diffusion-weighted MRI – a type of MRI that displays white matter extremely well (think of white matter as the neural roadways that connect areas of grey matter). Then, after processing that MRI, you can use tractography to view the white matter road system as a 3D computer model. You can then measure deterioration across these white matter pathways by looking at a measurement called the fractional anisotropy. After someone uses a software tool to bend the image of your brain to a standard shape, its fractional anisotropy can be compared to a database of hundreds of other diffusion-weighted MRIs to find any abnormalities.

But that probably won’t happen in a hospital. All of the methods described above exists in the research world – but in the clinical world, a radiologist will likely just eyeball your MRI and make a diagnosis based on that.

Why? One major reason is that this software is only really usable by experts in the research world, not clinicians. ̽��ֱ��incentives to create medical imaging software are considerable, but the incentives to improve it to a final product – in the way that, say, Microsoft is organically inclined to improve its products after user feedback – are nonexistent. If your operating system crashes, Microsoft has the resources and infrastructure to debug it and put that into the next release. But science labs are barely inclined, funded, or skilled enough (from a software engineering perspective) to improve their software after an initial release.

Beta or worse

FSL, a software toolbox created by Oxford for analysing MRIs, is one of the best of its kind out there. Virtually every medical imaging researcher uses it – and yet, with a graphical user interface that resembles something from the 1990s (plagued with hard-to-follow acronyms) such an item would be impossible or dangerous for the average clinician to use without six months of training. Most researchers don’t even bother to use its graphical user interface, even when they’re just starting to learn it, instead opting to use it as a command-line tool (that is to say, entirely text-based – virtually all computers were like this before the early 1980s). This is not an anomaly. AFNI, another image analysis package, is worse; upon start up, five windows pop up immediately and start flashing like an old GeoCities site.

FSL’s graphical user interface.

In some cases, problems with research software can go beyond the user’s learning curve – and those problems are less obvious and more dangerous when not caught. Both AFNI and FSL suffered from a bug that risked invalidating 40,000 fMRI studies from the past 15 years. Such bugs, unaccounted for, could further inhibit the potential use of research software in clinics.

No business model

Why do people still use this software despite all this? Well, in the vast majority of cases, they do work. But the incentives to improve them enough to make them easier to use – or simply to make better products – are not there. Apple makes a profit off its computers and so is inclined to constantly improve them and make them as easy to use as possible. If they don’t do this, people will just go to Windows, a product that essentially does the same thing.

But while you or I will pay money for a computer, FSL is open source and scientists pay nothing for it. ̽��ֱ��monetary incentive, rather, comes from the publications resulting from using this software – which can lead to grant funding for further research.

Neurovault, an open library for MRIs used in previous research studies, requests in its FAQs that researchers cite the original paper about Neurovault if they make any new discoveries with it, so that Neurovault can obtain more grant money to continue its work. Databases such as Neurovault are excellent and very necessary initiatives – but do you notice Google asking people to cite its original Pagerank algorithm paper if they make a discovery from Google?

̽��ֱ��five windows that make up AFNI’s graphical user interface.

Even Kitware, a medical imaging software company, is totally open-source and makes its money from grants and donations, rather than by selling its software and actively seeking feedback from users. Kitware has better graphical user interfaces, but it’s still essentially a research company; most of its tools would still not be suitable to use without several months of specialised training.

Medical imaging is a brilliant field filled with brilliant minds, but the incentives to drive its proof-of-concepts into final products, suitable for use by clinicians instead of just researchers, are not in place yet. While this remains the case, the road from the lab to the hospital will continue to be stagnant.

Matthew Leming, PhD candidate in Psychiatry, ̽��ֱ�� of Cambridge

This article was originally published on ̽��ֱ��Conversation. Read the original article.

Medical imaging is a brilliant field filled with brilliant minds, writes Matthew Leming, PhD candidate in Psychiatry for ̽��ֱ��Conversation. So why don’t we see more new technologies making it into hospitals?

digital cat

̽��ֱ��text in this work is licensed under a Creative Commons Attribution 4.0 International License. For image use please see separate credits above.

Yes

Cambridge extends world leading role for medical imaging with powerful new brain and body scanners

cjb250 — Mon, 24 Oct 2016 07:22:25 +0000

̽��ֱ��equipment, funded by the Medical Research Council (MRC), Wellcome Trust and Cancer Research UK, sits within the newly-refurbished Wolfson Brain Imaging Centre (WBIC), which today celebrates two decades at the forefront of medical imaging.

At the heart of the refurbishment are three cutting-edge scanners, of which only a very small handful exist at institutions outside Cambridge – and no institution other than the ̽��ֱ�� of Cambridge has all three. These are:

a Siemens 7T Terra Magnetic Resonance Imaging (MRI) scanner, which will allow researchers to see detail in the brain as tiny as a grain of sand
a GE Healthcare PET/MR scanner that will enable researchers to collect critical data to help understand how cancers grow, spread and respond to treatment, and how dementia progresses
a GE Healthcare hyperpolarizer that enables researchers to study real-time metabolism of cancers and other body tissues, including whether a cancer therapy is effective or not

These scanners, together with refurbished PRISMA and Skyra 3T MRI scanners at the WBIC and at the Medical Research Council Cognition and Brain Sciences Unit, will make the Cambridge Biomedical Campus the best-equipped medical imaging centre in Europe.

Professor Ed Bullmore, Co-Chair of Cambridge Neuroscience and Scientific Director of the WBIC, says: “This is an exciting day for us as these new scanners will hopefully provide answers to questions that we have been asking for some time, as well as opening up new areas for us to explore in neuroscience, mental health research and cancer medicine.

“By bringing together these scanners, the research expertise in Cambridge, and the latest in ‘big data’ informatics, we will be able to do sophisticated analyses that could revolutionise our understanding of the brain – and how mental health disorders and dementias arise – as well of cancers and how we treat them. This will be a powerful research tool and represents a big step in the direction of personalised treatments.”

Dr Rob Buckle, Director of Science Programmes at the MRC, adds: “ ̽��ֱ��MRC is proud to sponsor this exciting suite of new technologies at the ̽��ֱ�� of Cambridge. They will play an important role in advancing our strategy in stratified medicine, ultimately ensuring that the right patient gets the right treatment at the right time.”

Slide show: Click on images to expand

7T Medical Resonance Imaging (MRI) scanner

̽��ֱ��Siemens 7T Terra scanner – which refers to the ultrahigh strength of its magnetic field at 7 Tesla – will allow researchers to study at unprecedented levels of detail the workings of the brain and how it encodes information such as individual memories. Current 3T MRI scanners can image structures 2-3mm in size, whereas the new scanner has a resolution of just 0.5mm, the size of a coarse grain of sand.

“Often, the early stages of diseases of the brain, such as Alzheimer’s and Parkinson’s, occur in very small structures – until now too small for us to see,” explains Professor James Rowe, who will be leading research using the new 7T scanner. “ ̽��ֱ��early seeds of dementia for example, which are often sown in middle age, have until now been hidden to less powerful MRI scanners.”

̽��ֱ��scanner will also be able to pick up unique signatures of neurotransmitters in the brain, the chemicals that allow its cells to communicate with each other. Changes in the amount of these neurotransmitters affect how the brain functions and can underpin mental health disorders such as depression and schizophrenia.

“How a patient responds to a particular drug may depend on how much of a particular neurotransmitter present is currently present,” says Professor Rowe. “We will be looking at whether this new scanner can help provide this information and so help us tailor treatments to individual patients.”

̽��ֱ��scanner will begin operating at the start of December, with research projects lined up to look at dementias caused by changes to the brain almost undetectable by conventional scanners, and to look at how visual and sound information is converted to mental representations in the brain.

PET/MR scanner

̽��ֱ��new GE Healthcare PET/MR scanner brings together two existing technologies: positron emission tomography (PET), which enables researchers to visualise cellular activity and metabolism, and magnetic resonance (MR), which is used to image soft tissue for structural and functional details.

Purchased as part of the Dementias Platform UK, a network of imaging centres across the UK, the scanner will enable researchers to simultaneously collect information on physiological and disease-related processes in the body, reducing the need for patients to return for multiple scans. This will be particularly important for dementia patients.

Professor Fiona Gilbert, who will lead research on the PET/MR scanner, explains: “Dementia patients are often frail, which can present challenges when they need separate PET and MR scanners. So, not only will this new scanner provide us with valuable information to help improve understanding and diagnosis of dementia, it will also be much more patient-friendly.”

PET/MR will allow researchers to see early molecular changes in the brain, accurately map them onto structural brain images and follow their progression as disease develops or worsens. This could enable researchers to diagnose dementia before any symptoms have arisen and to understand which treatments may best halt or slow the disease.

As well as being used for dementia research, the scanner will also be applied to cancer research, says Professor Gilbert.

“At the moment, we have to make lots of assumptions about what’s going on in tumour cells. We can take biopsies and look at the different cell types, how aggressive they are, their genetic structure and so on, but we can only guess what’s happening to a tumour at a functional level. Functional information is important for helping us determine how best to treat the cancer – and hence how we can personalise treatment for a particular patient. Using PET/MR, we can get real-time information for that patient’s specific tumour and not have to assume it is behaving in the same way as the last hundred tumours we’ve seen.”

̽��ֱ��PET/MR scanner will begin operation at the start of November, when it will initially be used to study oxygen levels and blood flow in the tumours of breast cancer patients and in studies of brain inflammation in patients with Alzheimer’s disease and depression.

Hyperpolarizer

̽��ֱ��third new piece of imaging equipment to be installed is a GE Healthcare hyperpolarizer, which is already up and running at the facility.

MRI relies on the interaction of strong magnetic fields with a property of atomic nuclei known as ‘spin’. By looking at how these spins differ in the presence of magnetic field gradients applied across the body, scientists are able to build up three-dimensional images of tissues. ̽��ֱ��hyperpolarizer boosts the ‘spin’ signal from tracers injected into the tissue, making the MRI measurement much more sensitive and allowing imaging of the biochemistry of the tissue as well as its anatomy.

“Because of underlying genetic changes in a tumour, not all patients respond in the same way to the same treatment,” explains Professor Kevin Brindle, who leads research using the hyperpolarizer. “Using hyperpolarisation and MRI, we can potentially tell whether a drug is working, from changes in the tumour’s biochemistry, within a few hours of starting treatment. If it’s working you continue, if not you change the treatment.”

̽��ֱ��next generation of imaging technology, newly installed at the ̽��ֱ�� of Cambridge, will give researchers an unprecedented view of the human body – in particular of the myriad connections within our brains and of tumours as they grow and respond to treatment – and could pave the way for development of treatments personalised for individual patients.

By bringing together these scanners, the research expertise in Cambridge, and the latest in ‘big data’ informatics, we will be able to do sophisticated analyses that could revolutionise our understanding of the brain – and how mental health disorders and dementias arise – as well of cancers and how we treat them

Ed Bullmore

̽��ֱ��text in this work is licensed under a Creative Commons Attribution 4.0 International License. For image use please see separate credits above.

Yes

Schizophrenia and the teenage brain: how can imaging help?

lw355 — Wed, 17 Feb 2016 11:02:18 +0000

Restless, disordered, uncertain, impulsive, emotional – the teenage brain can be a confused fury of neural firings and misfirings.

For most 14- to 24-year-olds – the “risky age” as Professor Ed Bullmore describes it – the maelstrom eventually subsides. For some, episodes of depression, low self-esteem, self-harm or paranoia may intensify and become more frequent. For around 1 in 100, the change in mental state is so marked that it will become difficult for them to distinguish their delusions and hallucinations from reality – one of the hallmarks of schizophrenia.

“Schizophrenia is a particularly feared diagnosis,” says Bullmore. “People tend to think it means a chronic lifelong dependency on medication and therapy. It can mean this, but it can also last only a few years. ̽��ֱ��main thing that patients and their families want to know is what does the future hold – am I likely to be able to resume my life, get a job, and so on?”

Bullmore is co-chair of Cambridge Neuroscience, an initiative to enhance multidisciplinary research across the ̽��ֱ��, and leads the Department of Psychiatry, where he and colleagues have been developing imaging techniques that are revealing where and over what timescale abnormalities in the brain develop in people with mental health problems.

This is no easy task. Even being able to show a neural abnormality has been a major and relatively recent advance for understanding a condition that, Bullmore says, has in the past been regarded with prejudice and assumptions. “Demonstrating neural change moves us away from what might be regarded as a blaming approach where someone is made to feel personally responsible for the fact these symptoms exist. Imaging shows you that’s not the case – there is a biological basis.”

̽��ֱ��task is made difficult because there is no single event or area of the brain that underlies schizophrenia. It has only been from the collation of results from imaging studies worldwide that it has become apparent that when it comes to mental health disorders the scientists need to look at the big picture – the changes happening in wiring circuits across the whole brain.

Imaging techniques such as magnetic resonance imaging (MRI) are helping to map the brain in unprecedented detail. Structural MRI follows the movement of water as it diffuses along the pathways forged by neurons – showing the network of connections spread across the brain. Functional MRI measures slow rhythmic activity in the brain; if two areas of the brain show activity at the same time the chances are they are functionally connected. Bullmore and colleagues have developed mathematical methods to calculate the probability of there being such a connection.

“Neuroscience is no longer just about neurons,” he explains. “We can also now talk in terms of hubs, networks and connectomes. If the brain is thought of as a computer, with ‘processors’ in the outer grey matter and ‘wires’ that connect them in the inner white matter, some hub regions are more highly connected than others.”

In schizophrenia, connectivity in the wiring diagram goes awry and highly connected hubs are especially affected – “you could call it a hubopathy,” says Bullmore. His team’s research has demonstrated that those who have suffered decades of schizophrenia have large-scale network abnormalities compared with a healthy brain, which goes some way to explaining the diversity and severity of symptoms experienced in schizophrenia. ̽��ֱ��question is: can imaging be used to chart this progression?

Bullmore and his colleagues believe so: “Roughly a third of patients recover, a third have intermittent symptoms and a third will be affected for decades by schizophrenia. At diagnosis we can’t currently tell which of these outcomes lies in store. But we think one day we will be able to correlate the pattern of network activity with future outcome.”

It’s not only what happens to patients post-diagnosis that interests Bullmore, but also what has happened neurologically in the years before diagnosis.

“For me, one of the most exciting aspects of psychiatry is that we can use imaging to study the ‘risky age’ of brain development to understand how the connectome grows or matures in healthy brains. We can then start to pinpoint which genetic and environmental factors might favour healthy adolescent brain network development and which factors might predispose to abnormal network development, leading to chronic disability or distress.”

In 2012, Bullmore and colleagues Professor Ian Goodyer and Professor Peter Jones in Cambridge’s Department of Psychiatry (in collaboration with Professor Ray Dolan and Professor Peter Fonagy from ̽��ֱ�� College London) launched the NeuroScience in Psychiatry Network, funded by the Wellcome Trust. They have been recruiting a panel of 2,000 healthy volunteers aged 14–24 years, 300 of whom have had brain scans to contribute to one of the most comprehensive ‘circuit diagrams’ of the teenage brain ever attempted.

“Remarkably little is known about how brain networks grow during the crucial transition from childhood dependence to life as independent adults,” adds Bullmore. “ ̽��ֱ��adolescent brain is still a bit of a black box. But it is a big step forward that we can now see healthy human brain development much more clearly, especially with the next-generation brain scanners coming to Cambridge soon [see panel]. It’s very exciting to think that we should then be able to understand and predict the pathways of brain network development that lead to schizophrenia.”

Adolescence is a dangerous time for the onset of mental health problems. Advances in brain imaging are helping to picture how neural changes in these crucial years can lead to chronic debilitating mental illness.

Neuroscience is no longer just about neurons. We can also now talk in terms of hubs, networks and connectomes.

Ed Bullmore

̽��ֱ��District

Scientists are looking at the 'bigger picture' of mental health

Opening the black box

̽��ֱ��arrival of two state-of-the-art MRI machines in Cambridge, thanks to funding from the Medical Research Council (MRC), will revolutionise the study of the brain.

“A brain scan is much more than an image,” contends Ed Bullmore. “It’s really a very large collection of numbers. With the best scanners and some high-performance computing, you can start to think not only about disease mechanisms but also about identifying early risk factors and preventative action.”

Two of the newest scanners in the UK will arrive at the Cambridge Biomedical Campus in 2016, and a new high-speed secure link will be created through to the recently opened £20 million West Cambridge Data Centre, which will analyse the data.

One scanner, a new 7-Tesla ‘ultrahigh-field’ MRI machine, will help researchers see how the human brain works as a whole, yet also with the precision of a grain of sand a fraction of a millimetre across. It will further the study of dementia, brain injury, obesity, addiction, mental health disorders, pain and stroke.

‘7T’ is a collaboration between the ̽��ֱ�� and the MRC’s Cognition and Brain Sciences Unit (CBSU). Professor James Rowe, from the Department of Clinical Neurosciences and the CBSU, explains: “ ̽��ֱ��new scanner is a major advance to study the details of the human brain not only in health but also the effects of age and the origins of brain diseases. ̽��ֱ��unprecedented detail and sensitivity at 7T is essential in the national effort towards a cure for dementia and mental illness.”

Joining the 7T scanner will be a positron emission tomography (PET)–MRI machine, which shows changes in the brain down to the level of individual molecules. Until now only two PET–MRI scanners existed in the UK, but MRC Dementias Platform UK has invested in five more nationally, creating what is thought to be the first nationally coordinated MRI–PET network anywhere in the world.

“Beating dementia is a long-term goal,” adds Rowe. “These scanners will make a very significant contribution to this eventual success and to the lives of patients and their families.”

̽��ֱ��text in this work is licensed under a Creative Commons Attribution 4.0 International License. For image use please see separate credits above.

Yes