��Ī - Duncan Astle

AI system self-organises to develop features of brains of complex organisms

cjb250 — Mon, 20 Nov 2023 16:00:47 +0000

As neural systems such as the brain organise themselves and make connections, they have to balance competing demands. For example, energy and resources are needed to grow and sustain the network in physical space, while at the same time optimising the network for information processing. This trade-off shapes all brains within and across species, which may help explain why many brains converge on similar organisational solutions.

Jascha Achterberg, a Gates Scholar from the Medical Research Council Cognition and Brain Sciences Unit (MRC CBU) at the ��Ī said: “Not only is the brain great at solving complex problems, it does so while using very little energy. In our new work we show that considering the brain’s problem solving abilities alongside its goal of spending as few resources as possible can help us understand why brains look like they do.”

Co-lead author Dr Danyal Akarca, also from the MRC CBU, added: “This stems from a broad principle, which is that biological systems commonly evolve to make the most of what energetic resources they have available to them. The solutions they come to are often very elegant and reflect the trade-offs between various forces imposed on them.”

In a study published today in Nature Machine Intelligence, Achterberg, Akarca and colleagues created an artificial system intended to model a very simplified version of the brain and applied physical constraints. They found that their system went on to develop certain key characteristics and tactics similar to those found in human brains.

Instead of real neurons, the system used computational nodes. Neurons and nodes are similar in function, in that each takes an input, transforms it, and produces an output, and a single node or neuron might connect to multiple others, all inputting information to be computed.

In their system, however, the researchers applied a ‘physical’ constraint on the system. Each node was given a specific location in a virtual space, and the further away two nodes were, the more difficult it was for them to communicate. This is similar to how neurons in the human brain are organised.

The researchers gave the system a simple task to complete – in this case a simplified version of a maze navigation task typically given to animals such as rats and macaques when studying the brain, where it has to combine multiple pieces of information to decide on the shortest route to get to the end point.

One of the reasons the team chose this particular task is because to complete it, the system needs to maintain a number of elements – start location, end location and intermediate steps – and once it has learned to do the task reliably, it is possible to observe, at different moments in a trial, which nodes are important. For example, one particular cluster of nodes may encode the finish locations, while others encode the available routes, and it is possible to track which nodes are active at different stages of the task.

Initially, the system does not know how to complete the task and makes mistakes. But when it is given feedback it gradually learns to get better at the task. It learns by changing the strength of the connections between its nodes, similar to how the strength of connections between brain cells changes as we learn. The system then repeats the task over and over again, until eventually it learns to perform it correctly.

With their system, however, the physical constraint meant that the further away two nodes were, the more difficult it was to build a connection between the two nodes in response to the feedback. In the human brain, connections that span a large physical distance are expensive to form and maintain.

When the system was asked to perform the task under these constraints, it used some of the same tricks used by real human brains to solve the task. For example, to get around the constraints, the artificial systems started to develop hubs – highly connected nodes that act as conduits for passing information across the network.

More surprising, however, was that the response profiles of individual nodes themselves began to change: in other words, rather than having a system where each node codes for one particular property of the maze task, like the goal location or the next choice, nodes developed a flexible coding scheme. This means that at different moments in time nodes might be firing for a mix of the properties of the maze. For instance, the same node might be able to encode multiple locations of a maze, rather than needing specialised nodes for encoding specific locations. This is another feature seen in the brains of complex organisms.

Co-author Professor Duncan Astle, from ��Ī’s Department of Psychiatry, said: “This simple constraint – it’s harder to wire nodes that are far apart – forces artificial systems to produce some quite complicated characteristics. Interestingly, they are characteristics shared by biological systems like the human brain. I think that tells us something fundamental about why our brains are organised the way they are.”

Understanding the human brain

The team are hopeful that their AI system could begin to shed light on how these constraints, shape differences between people’s brains, and contribute to differences seen in those that experience cognitive or mental health difficulties.

Co-author Professor John Duncan from the MRC CBU said: “These artificial brains give us a way to understand the rich and bewildering data we see when the activity of real neurons is recorded in real brains.”

Achterberg added: “Artificial ‘brains’ allow us to ask questions that it would be impossible to look at in an actual biological system. We can train the system to perform tasks and then play around experimentally with the constraints we impose, to see if it begins to look more like the brains of particular individuals.”

Implications for designing future AI systems

The findings are likely to be of interest to the AI community, too, where they could allow for the development of more efficient systems, particularly in situations where there are likely to be physical constraints.

Dr Akarca said: “AI researchers are constantly trying to work out how to make complex, neural systems that can encode and perform in a flexible way that is efficient. To achieve this, we think that neurobiology will give us a lot of inspiration. For example, the overall wiring cost of the system we've created is much lower than you would find in a typical AI system.”

Many modern AI solutions involve using architectures that only superficially resemble a brain. The researchers say their works shows that the type of problem the AI is solving will influence which architecture is the most powerful to use.

Achterberg said: “If you want to build an artificially-intelligent system that solves similar problems to humans, then ultimately the system will end up looking much closer to an actual brain than systems running on large compute cluster that specialise in very different tasks to those carried out by humans. The architecture and structure we see in our artificial ‘brain’ is there because it is beneficial for handling the specific brain-like challenges it faces.”

This means that robots that have to process a large amount of constantly changing information with finite energetic resources could benefit from having brain structures not dissimilar to ours.

Achterberg added: “Brains of robots that are deployed in the real physical world are probably going to look more like our brains because they might face the same challenges as us. They need to constantly process new information coming in through their sensors while controlling their bodies to move through space towards a goal. Many systems will need to run all their computations with a limited supply of electric energy and so, to balance these energetic constraints with the amount of information it needs to process, it will probably need a brain structure similar to ours.”

The research was funded by the Medical Research Council, Gates ��Ī, the James S McDonnell Foundation, Templeton World Charity Foundation and Google DeepMind.

Reference
Achterberg, J & Akarca, D et al. Spatially embedded recurrent neural networks reveal widespread links between structural and functional neuroscience findings. Nature Machine Intelligence; 20 Nov 2023; DOI: 10.1038/s42256-023-00748-9

��Ī scientists have shown that placing physical constraints on an artificially-intelligent system – in much the same way that the human brain has to develop and operate within physical and biological constraints – allows it to develop features of the brains of complex organisms in order to solve tasks.

Not only is the brain great at solving complex problems, it does so while using very little energy

Jascha Achterberg

DeltaWorks

Graphic representing an artificially intelligent brain

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Teenagers at greatest risk of self-harming could be identified almost a decade earlier

cjb250 — Tue, 15 Jun 2021 06:00:46 +0000

The team, based at the MRC Cognition and Brain Sciences Unit, ��Ī, found that while sleep problems and low self-esteem were common risk factors, there were two distinct profiles of young people who self-harm – one with emotional and behavioural difficulties, and a second group without those difficulties but with different risk factors.

Between one in five and one in seven adolescents in England self-harms, for example by deliberately cutting themselves. While self-harm is a significant risk factor for subsequent suicide attempts, many do not intend suicide but face other harmful outcomes, including repeatedly self-harming, poor mental health, and risky behaviours like substance abuse. Despite its prevalence and lifelong consequences, there has been little progress in the accurate prediction of self-harm.

The ��Ī team identified adolescents who reported self-harm at age 14, from a nationally representative UK birth cohort of approximately 11,000 individuals. They then used a machine learning analysis to identify whether there were distinct profiles of young people who self-harm, with different emotional and behavioural characteristics. They used this information to identify risk factors from early and middle childhood. The results are published in the Journal of the American Academy of Child and Adolescent Psychiatry.

Because the data tracked the participants over time, the researchers were able to distinguish factors that appear alongside reported self-harm behaviour, such as low self-esteem, from those that precede it, such as bullying.

The team identified two distinct subgroups among young people who self-harm, with significant risk factors present as early as age five, nearly a decade before they reported self-harming. While both groups were likely to experience sleep difficulties and low self-esteem reported at age 14, other risk factors differed between the two groups.

The first group showed a long history of poor mental health, as well as bullying before they self-harmed. Their caregivers were more likely to have mental health issues of their own.

For the second group, however, their self-harming behaviour was harder to predict early in childhood. One of the key signs was a greater willingness to take part in risk-taking behaviour, which is linked to impulsivity. Other research suggests these tendencies may predispose the individual towards spending less time to consider alternate coping methods and the consequences of self-harm. Factors related to their relationships with their peers were also important for this subgroup, including feeling less secure with friends and family at age 14 and a greater concern about the feelings of others as a risk factor at age 11.

Stepheni Uh, a Gates ��Ī Scholar and first author of the study, said: “Self-harm is a significant problem among adolescents, so it’s vital that we understand the nuanced nature of self-harm, especially in terms of the different profiles of young people who self-harm and their potentially different risk factors.

“We found two distinct subgroups of young people who self-harm. The first was much as expected – young people who experience symptoms of depression and low self-esteem, face problems with their families and friends, and are bullied. The second, much larger group was much more surprising as they don’t show the usual traits that are associated with those who self-harm.”

The researchers say that their findings suggest that it may be possible to predict which individuals are most at risk of self-harm up to a decade ahead of time, providing a window to intervene.

Dr Duncan Astle said: “The current approach to supporting mental health in young people is to wait until problems escalate. Instead, we need a much better evidence base so we can identify who is at most risk of mental health difficulties in the future, and why. This offers us the opportunity to be proactive, and minimise difficulties before they start.

“Our results suggest that boosting younger children’s self-esteem, making sure that schools implement anti-bullying measures, and providing advice on sleep training, could all help reduce self-harm levels years later.

“Our research gives us potential ways of helping this newly-identified second subgroup. Given that they experience difficulties with their peers and are more willing to engage in risky behaviours, then providing access to self-help and problem-solving or conflict regulation programmes may be effective.”

Professor Tamsin Ford from the Department of Psychiatry added: “We might also help at-risk adolescents by targeting interventions at mental health leaders and school-based mental health teams. Teachers are often the first people to hear about self-harm but some lack confidence in how to respond. Providing them with training could make a big difference.”

The research was supported by the Gates ��Ī Trust, Templeton World Charity Foundation, and the UK Medical Research Council.

Reference
Uh, S et al. Two pathways to self-harm in adolescence. Journal of the American Academy of Child and Adolescent Psychiatry; 14 June 2021; DOI: 10.1016/j.jaac.2021.03.010

Researchers have identified two subgroups of adolescents who self-harm and have shown that it is possible to predict those individuals at greatest risk almost a decade before they begin self-harming.

The current approach to supporting mental health in young people is to wait until problems escalate. Instead, we need a much better evidence base so we can identify who is at most risk of mental health difficulties in the future, and why

Duncan Astle

Adrien Olichon

A man sitting in front of a screen

The text in this work is licensed under a Creative Commons Attribution 4.0 International License. Images, including our videos, are Copyright ©��Ī 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.

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The neuroscientist who champions LGBT+ equal rights

cg605 — Fri, 26 Feb 2021 10:05:03 +0000

Duncan Astle is a neuroscientist on a mission to understand why the brains of some children develop differently to others – and how it affects not just their education but their lives. He also chairs the University’s LGBT+ Staff Network. He talks about the Network’s recent decision to sign a declaration that trans rights are human rights.

Significant increase in depression seen among children during first lockdown

cjb250 — Tue, 08 Dec 2020 23:30:57 +0000

In response to the COVID-19 pandemic, the UK Government implemented a national 'lockdown' involving school closures and social distancing. There has been widespread concern that these measures would negatively impact child and adolescent mental health. To date, however, there is relatively little direct evidence of this.

The most direct way of measuring the association between the onset of lockdown and children’s mental health is to follow the same individuals over a length of time and look for changes – so-called ‘longitudinal’ changes.

To test whether changes in emotional wellbeing, anxiety and depression symptoms occurred during lockdown since their initial assessment, a team at the Medical Research Council (MRC) Cognition and Brain Sciences Unit, ��Ī, examined data from mental health assessments on 168 children (aged 8-12 years) before and during the UK lockdown. These assessments included self-reports, caregiver-reports, and teacher-reports.

The results of their study are published today in Archives of Disease in Childhood.

Relative to their own pre-pandemic scores, children tended to show more symptoms of depression during lockdown. Even though these symptoms are variable across children, the impact of lockdown can still be seen because the effect size is large. The researchers used the variability in scores to estimate how big an increase this is.

“To give an indication of how large this effect is, imagine ranking the children into 100 ‘centiles’ depending on their scores,” explained Dr Duncan Astle from the MRC Cognition and Brain Sciences Unit and senior author of the study. “A child in the 50th centile would be exactly at the middle of the distribution. But a child at this position before the pandemic, could expect to be at the equivalent of the 77th centile during the lockdown.

“Put differently, if you randomly selected a child from the sample there is a 70% chance that their depression symptoms were worse during lockdown than before the pandemic.”

“National lockdowns with mass school closures are unprecedented, so going into this crisis, no one could say definitively what impact it would have on children,” said Giacomo Bignardi, a PhD student at the MRC Cognition and Brain Sciences Unit.

“Our study is one of the first to follow the same children over time during lockdown and suggests that symptoms of depression among children got much worse during this period. This effect was independent of children’s age, gender and family socio-economic status.”

The team found only very small and not statistically significant changes in children’s scores for emotional problems and anxiety during lockdown.

Dr Astle added: “Childhood is a period where mental health may be particularly vulnerable to reduced peer interaction and loneliness. Now that children have returned to school, their wellbeing, socialisation and enjoyment are crucial. Teachers may need additional resources and training to help them support children with low mood.

“Even before lockdown resources for Child and Adolescent Mental Health Services were stretched thin – and that was against a backdrop of worsening mental health among children. Our findings suggest that lockdown measures will likely exacerbate this. The education sector will bear the initial brunt of this.”

The researchers say that how the lockdown measures impact children’s mental health may depend on a variety of factors. A recent study found that loneliness in children was associated with subsequent mental health problems, particularly depression. Also, during lockdown children had fewer opportunities to engage in play and other fun activities that help improve mood.

The research was supported by the Templeton World Charity Foundation and the Medical Research Council.

Reference
Bignardi, G et al. Longitudinal increases in childhood depression symptoms during the COVID-19 lockdown. Archives of Disease in Childhood; 9 Dec 2020; DOI: 10.1136/archdischild-2020-320372

The first lockdown led to a significant increase in symptoms of depression among children, highlighting the unintended consequences of school closures, according to a new study from the ��Ī.

Our study is one of the first to follow the same children over time during lockdown and suggests that symptoms of depression among children got much worse during this period

Giacomo Bignardi

Rod Long

Child looking out of a window

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Vice-Chancellor’s awards showcase impact and engagement during the pandemic

ta385 — Mon, 05 Oct 2020 12:38:48 +0000

Now in their fifth year, the awards were made in five categories: collaboration, early career, established academic, professional service, online and remote.

The winners of the collaboration category are Dr Michael Weekes from ��Ī Institute for Medical Research, and Dr Steven Baker from ��Ī Institute for Therapeutic Immunology and Infectious Disease. They collaborated to establish a comprehensive rapid turn-around COVID-19 testing platform for ��Ī University Hospitals healthcare workers, University staff and students.

The newly-established ‘online and remote engagement’ award goes to Dr Michael Ramage and team from the Department of Architecture for their HappyShield project. This involved developing, testing, and disseminating a novel open-source medical face shield to help tackle severe PPE shortages caused by the COVID-19 pandemic, focussing in particular on production in Low and Middle Income Countries.

The winner of the early career researcher award is Chioma Achi from the Department of Veterinary Medicine. Achi organised an engagement programme across Nigeria to strengthen the participation of poultry farmers in the fight against antimicrobial resistance.

The winner of the established researcher award is Dr Duncan Astle from the MRC Cognition and Brain Sciences Unit. Working in partnership with children’s charities, local education authorities, academy chains and local schools, Astle led an engagement programme providing teachers with robust evidence to help young people overcome cognitive and behavioural barriers to learning.

The winner of the professional services award is Dr Rosalyn Wade from the Museum of Zoology. Wade reimagined the Museum’s learning and public programme following COVID-19 lockdown and the venue’s temporary closure. She designed and released a new blog and developed an innovative online festival (Zoology Live!).

The awards were announced on 5th October by the University’s Public Engagement team on Twitter.

Professor Stephen Toope, Vice-Chancellor of the ��Ī, says:

“The University’s mission is to contribute to society. One of the ways we do it is by undertaking research with real social, cultural and economic impact.

“These awards celebrate research that best demonstrates social, cultural and economic impact through engagement. From advances in healthcare and industrial processes, to rapid responses to the global pandemic; from cultural activities that recognise diversity in our societies, to new knowledge that improves teaching and increases social mobility. This year’s panel of judges was inspired and uplifted by the quality of applications.”

The Vice-Chancellor’s Research Impact and Engagement Awards were established to recognise and reward outstanding achievement, innovation and creativity in devising and implementing ambitious engagement and impact plans that have the potential to create significant economic, social and cultural impact from and engagement with and for research. Each winner is offered a bursary to support their project.

This year’s winners and runners up are:

Established Academic Award

Winner: Dr Duncan Astle (MRC Cognition and Brain Sciences Unit, School of Clinical Medicine) – Breaking barriers to learning in the classroom

Runners up: Dr Joseph Webster (Faculty of Divinity, School of Arts and Humanities) – Sectarianism in Scotland and the repeal of the Offensive Behaviour at Football Act

Professor Peter Hutchinson (with Professor David Menon) (Clinical Neurosciences / Medicine, School of Clinical Medicine) – Reshaping the treatment of traumatic brain injury

Early Career Researcher Award

Winner: Chioma Achi (Department of Veterinary Medicine, School of Biological Sciences) – Strengthening participation of poultry farmers in the fight against antimicrobial resistance

Runners up: Emma Soneson (Department of Psychiatry, School of Clinical Medicine) – Public health approaches to identifying and responding to mental health difficulties in children and young people

Dr Naures Atto (Asian and Middle Eastern Studies/Middle Eastern Studies, School of Arts and Humanities) – Endangered Middle Eastern Cultures and their Vulnerability in Migration Contexts

Dr Nicki Kindersley (Faculty of History, School of Humanities and Social Sciences) – Militarised political economies in South Sudan

Professional Services Award

Winner: Dr Rosalyn Wade (Museum of Zoology, School of Biological Sciences) – Learning and Public Programme of the Museum of Zoology: blending contemporary zoological research with active and online learning experiences for public audiences

Collaboration Award

Winner: Dr Michael Weekes and Dr Steven Baker (��Ī Institute for Medical Research / ��Ī Institute for Therapeutic Immunology and Infectious Disease, School of Clinical Medicine) – A comprehensive COVID-19 screening programme for ��Ī University Hospitals healthcare workers, ��Ī University staff and students

Runners up: Dr Victoria Avery, Dr Melissa Calaresu and Dr Miranda Stearn (Fitzwilliam Museum / Faculty of History / Fitzwilliam Museum) – Feast & Fast: The Art of Food in Europe, 1500–1800 Research Project

Online and Remote Engagement Award

Winner: Dr Michael Ramage and team (Department of Architecture, School of Arts and Humanities) – The HappyShield

Runners up: Centre for Geopolitics (Department of Politics and International Studies, School of Humanities and Social Sciences) – Centre for Geopolitics Coronavirus Response

Academics, students and professional members of staff from across the University have been recognised in this year’s Vice-Chancellor’s Research Impact and Engagement Awards for their work in areas including COVID-19 testing, PPE production and online engagement.

These awards celebrate research that best demonstrates social, cultural and economic impact through engagement

Stephen Toope

Happyshield

The Happyshield face shield

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Learning difficulties due to poor connectivity, not specific brain regions, study shows

cjb250 — Thu, 27 Feb 2020 16:00:25 +0000

Between 14-30% of children and adolescents worldwide have learning difficulties severe enough to require additional support. These difficulties are often associated with cognitive and/or behavioural problems. In some cases, children who are struggling at school receive a formal diagnosis of a specific learning difficulty or disability, such as dyslexia, dyscalculia or developmental language disorder, or of a developmental disorder such as attention deficit and hyperactivity disorder (ADHD), dyspraxia, or autism spectrum disorder.

Scientists have struggled to identify specific areas of the brain that might give rise to these difficulties, with studies implicating myriad brain regions. ADHD, for example, has been linked to the anterior cingulate cortex, caudate nucleus, pallidum, striatum, cerebellum, prefrontal cortex, the premotor cortex and most parts of the parietal lobe.

One potential explanation is that each diagnosis differs so much between one individual and the next, that each involves different combinations of brain regions. However, a more provocative explanation has been proposed by a team of scientists at the MRC Cognition and Brain Sciences Unit, ��Ī: there are, in fact, no specific brain areas that cause these difficulties.

To test their hypothesis, the researchers used machine learning to map the brain differences across a group of almost 479 children, 337 of whom had been referred with learning-related cognitive problems and 142 from a comparison sample. The algorithm interpreted data taken from a large battery of cognitive, learning, and behavioural measures, as well as from brain scans taken using magnetic resonance imaging (MRI). The results are published today in Current Biology.

The researchers found that the brain differences did not map onto any labels the children had been given – in other words, there were no brain regions that predicted having ASD or ADHD, for example. More surprisingly, they found that the different brain regions did not even predict specific cognitive difficulties – there was no specific brain deficit for language problems or memory difficulties, for example.

Instead, the team found that the children’s brains were organised around hubs, like an efficient traffic system or social network. Children who had well-connected brain hubs had either very specific cognitive difficulties, such as poor listening skills, or had no cognitive difficulties at all. By contrast, children with poorly connected hubs – like a train station with few or poor connections – had widespread and severe cognitive problems.

“Scientists have argued for decades that there are specific brain regions that predict having a particular learning disorder or difficulty, but we’ve shown that this isn’t the case,” said Dr Duncan Astle, senior author on the study. “In fact, it’s much more important to consider how these brain areas are connected – specifically, whether they are connected via hubs. The severity of learning difficulties was strongly associated with the connectedness of these hubs, we think because these hubs play a key role in sharing information between brain areas.”

Dr Astle said that one implication of their work is that it suggests that interventions should be less reliant on diagnostic labels.

“Receiving a diagnosis is important for families. It can give professional recognition for a child’s difficulties and open the door to specialist support. But in terms of specific interventions, for example from the child’s teachers, they can be a distraction.

“It’s better to look at their areas of cognitive difficulties and how these can be supported, for example using specific interventions to improve listening skills or language competencies, or at interventions that would be good for the whole class, like how to how to reduce working memory demands during learning.”

The findings may explain why drugs treatments have not proven effective for developmental disorders. Methylphenidate (Ritalin), for example, which is used to treat ADHD, appears to reduce hyperactivity, but does not remediate cognitive difficulties or improve educational progress. Drugs tend to target specific types of nerve cells, but would have little impact on a ‘hub-based’ organisation that has emerged over many years.

While this is the first time that hubs and their connections have been shown to play a key role in learning difficulties and developmental disorders, their importance in brain disorders is becoming increasingly clear in recent years. ��Ī researchers have previously shown that they also play an important role in mental health disorders that begin to emerge during adolescence, such as schizophrenia.

The study was funded by the Medical Research Council.

Reference
Siugzdaite, R et al. Transdiagnostic brain mapping in developmental disorders. Current Biology; 27 Feb 2020; DOI: 10.1016/j.cub.2020.01.078

Different learning difficulties do not correspond to specific regions of the brain, as previously thought, say researchers at the ��Ī. Instead poor connectivity between ‘hubs’ within the brain is much more strongly related to children’s difficulties.

Roma Siugzdaite

Brain map showing examples of networks and hubs

Researcher profile: Dr Roma Siugzdaite

Matematika – tai proto gimnastika

Dr Roma Siugzdaite describes her mother, Marijona Siugzdiene, as the best maths teacher in her school in Kaisiadorys, Lithuania. This phrase was written on the wall in her classroom: it means ‘Mathematics is a gymnastics to your mind’.

“Looking back, it seems like it was my destiny written on that wall,” says Roma. “My background studies in mathematics brought me to study the brains and minds of children and people with certain diseases and disorders.”

Nowadays, Roma is based at the MRC Cognition and Brain Sciences Unit at ��Ī, which means she can impress people at parties by describing herself as a brain scientist. “I am fascinated by the complexity of the brain,” she says.

Her research is aimed at helping children to overcome learning difficulties, but to achieve this she must first understand what happens in the brains of these children.

“Every time I have a hypothesis I need to get some data to test it, whether that’s by old-fashioned, pen and paper tests, using iPads or – as it mostly is – using magnetic resonance imaging (MRI). That’s when the fun part begins – data analysis. I love it: it feels like searching for an order in a chaos.”

Fortunately, ��Ī is the ideal place to be doing research on children with learning difficulties, Roma says, in part because of the huge dataset held by the Centre for Attention, Learning and Memory (CALM) at her Unit, but also because of the Unit’s expertise working with MRI data.

Outside of the Unit, Roma – together with her family – will most likely be seen playing basketball. “I’ve been playing basketball my whole life. My husband is a basketball coach and now my daughter is playing basketball, too. We love the game!”

Yes

Taking pride in our researchers

cjb250 — Fri, 05 Jul 2019 07:04:58 +0000

To mark LGBTSTEM Day, celebrating lesbian, gay, bisexual, transgender and queer scientists, engineers and mathematicians around the world, our researchers tell us why celebrating diversity is important – and why identities really do matter.

Inside the mind of a young person

cjb250 — Thu, 15 Nov 2018 17:18:17 +0000

�������������Ī - Duncan Astle

AI system self-organises to develop features of brains of complex organisms

Understanding the human brain

Implications for designing future AI systems

Teenagers at greatest risk of self-harming could be identified almost a decade earlier

The neuroscientist who champions LGBT+ equal rights

Significant increase in depression seen among children during first lockdown

Vice-Chancellor’s awards showcase impact and engagement during the pandemic

Learning difficulties due to poor connectivity, not specific brain regions, study shows

Taking pride in our researchers

Inside the mind of a young person

��Ī - Duncan Astle