Depressed brains may hate differently

Depressed people are often withdrawn and antisocial. This doesn’t necessarily mean that they don’t like other people, but it could mean that their brains don’t process feelings of hate in a normal way, a new study suggests.

Scientists in China and the UK scanned the brains of people with and without depression, and they found a surprising pattern in nearly all of the depressed people: Their brain activity was out of sync in three regions collectively known as the “hate circuit” — so called because in previous experiments they have been shown to light up when people look at photographs of someone they can’t stand.

“We were a bit shocked when we first saw these results,” says lead researcher Jianfeng Feng, a professor of computer science at the University of Warwick who specializes in biology. Feelings of self-hatred are a common feature of depression, he explains, so one would expect those feelings to also be more intense when directed toward other people.

Instead, it’s as if the brains of depressed people hate incorrectly. The brain disruptions the researchers observed could be a sign that people with depression have an impaired ability to cope with — and learn from — social situations in which they feel hate, Feng says. This may explain why they often turn emotions such as hatred and anger inward, instead of handling them in more constructive ways, he adds.

The study, which was published Tuesday in the journal Molecular Psychiatry, is the first to connect disruptions in the hate circuit to depression, and the findings may help doctors understand why depressed people react the way they do to certain circumstances, says Madhukar Trivedi M.D., director of the mood disorders program and clinic at the University of Texas Southwestern Medical Center at Dallas.

“These patients start doubting themselves and they withdraw from social situations,” says Trivedi, who was not involved in the study. “The hate circuit might have something to do with that.”

Discoveries like this warrant a good deal of interest, Trivedi says, though he cautions that the results are preliminary. “It is exciting when something is this novel and this promising, but… I would of course like to see some results replicated by other researchers,” he says.

Feng and his colleagues used functional magnetic resonance imaging (fMRI) to scan the brains of 37 healthy people and 39 people who had received a depression diagnosis but had not sought treatment or responded to antidepressant medication. The makeup of each group was similar in terms of age, sex, and education levels.

In a healthy brain, the waves recorded by fMRI move up and down together in a continuous pattern. But when brain function is disturbed, the waves move out sync with each other — a phenomenon known as “uncoupling.”

The brain waves in the hate circuit were uncoupled in 92% of the depressed patients, the researchers found. Depression was also associated with disruptions in parts of the brain involved in action and risk-taking, emotion and reward-seeking, and attention and memory processing.

Most MRI research in depressed people has treated the brain as a group of discrete regions, by targeting very specific areas or by looking at how regions behave independently. This study, by contrast, observed the entire brain system at once — an approach that helped the researchers spot connections and patterns across multiple regions.

Feng and his colleagues performed the scans while the subjects were resting. This minimized interference from any outside stimuli, but it also means the researchers can’t be sure if the brain disruptions they observed are likely to carry over into active settings, such as social situations.

Further research will be needed to confirm and extend his team’s findings, Feng says. In the future, he says, a focus on the hate circuit may open new avenues for treatment — including new drugs and psychotherapies — that target this and other specific circuits in the brain.

“We might have to think about depression from more wide angles,” he says.


Breastfeeding trust hormone clue

Scientists have for the first time shown how a “trust” hormone is released in the brains of breastfeeding mothers.

It is further proof that breastfeeding promotes the maternal bond through a biochemical process.

The team at Warwick University said the hormone oxytocin was known to be released during breastfeeding but the mechanism in the brain was unclear.

Oxytocin also produces contractions during labour and causes milk to be “let down” from the mammary glands.

The hormone is produced in the hypothalamus – the part of the brain that controls body temperature, thirst, hunger, anger and tiredness.

It has been shown to promote feelings of trust and confidence and to reduce fear.

Co-ordination

The study, published in the journal PLoS Computational Biology, found that in response to a baby suckling, specialised neurons in the mothers’ brain start to release the hormone from the nerve endings.

But surprisingly oxytocin is also released from the part of the cell called the dendrite which is usually the part of a neurone which receives, rather than transmits information.

Using a mathematical model, the researchers worked out that this release from the dendrites allows a massive increase in communication between the neurons, co-ordinating a “swarm” of oxytocin factories producing intense bursts of the hormone.

They is an example of an “emergent process”, the scientists said – a closely co-ordinated action developing without a single leader, in the same as a flock of birds or insects swarms.

Study leader, Professor Jianfeng Feng said: “We knew that these pulses arise because, during suckling, oxytocin neurons fire together in dramatic synchronised bursts.

“But exactly how these bursts arise has been a major problem that has until now eluded explanation.

“The model gives us a possible explanation of an important event in the brain that could be used to study and explain many other similar brain activities.”

A spokesperson for the National Childbirth Trust (NCT) said breastfeeding for up to two years can have “significant health benefits” for mother and baby.


Brain, 'Heal Thyself': New Insight Into Schizophrenia

A new study using specialized MRI scans led by Professor Jianfeng Feng and his colleagues provides evidence that patients with schizophrenia actually possess the ability to reorganize and battle the mental illness. This is the first time that imaging scans have been employed to demonstrate the ability of the brain to actually reverse the devastating effects of schizophrenia.

Although schizophrenia is typically associated with a global reduction in the volume of brain tissue, recent evidence indicates that there is actually a small increase in tissue and volume that may occur in specific areas of the brain.

The study, “Dynamic cerebral reorganization in the pathophysiology of schizophrenia: a MRI-derived cortical thickness study,” was published online in Psychology Medicine.

The researchers studied 98 patients with schizophrenia and compared them to 83 patients without schizophrenia. Using Magnetic Resonance Imaging (MRI) and a specialized approach known as covariance analysis, researchers noted an increase in the gray matter tissue in the brains of those patients with schizophrenia. This was difficult to demonstrate in the past, researchers say, due to a wide distribution of perceived increases in brain volume in such patients.


The Research Team Developed a New Methodology:Brain-Wide Association Analysis(BWAS)

The functional differences between autistic and non-autistic brains have been isolated for the first time, following the development of a new methodology for analysing MRI scans.

Developed by researchers at the University of Warwick, the methodology, called Brain-Wide Association Analysis (BWAS), is the first capable of creating panoramic views of the whole brain and provides scientists with an accurate 3D model to study.

The researchers used BWAS to identify regions of the brain that may make a major contribution to the symptoms of autism.

BWAS does so by analysing 1,134,570,430 individual pieces of data; covering the 47,636 different areas of the brain, called voxels, which comprise a functional MRI (fMRI) scan and the connections between them.

Previous methodologies were process this level of data and were restricted to modelling only limited areas.

The ability to analyse the entire data set from an fMRI scan provided the Warwick researchers the opportunity to compile, compare and contrast accurate computer models for both autistic and non-autistic brains.

Led by BWAS developer Professor Jianfeng Feng, from the University of Warwick’s Department of Computer Science, the researchers collected the data from hundreds of fMRI scans of autistic and non-autistic brains.

By comparing the two subsequent models the researchers isolated twenty examples of difference, where the connections between voxels of the autistic brain were stronger or weaker than the non-autistic[1].

The identified differences include key systems involved with brain functions relating to autism. Professor Feng explained the findings:

“We identified in the autistic model a key system in the temporal lobe visual cortex with reduced cortical functional connectivity. This region is involved with the face expression processing involved in social behaviour. This key system has reduced functional connectivity with the ventromedial prefrontal cortex, which is implicated in emotion and social communication”.

The researchers also identified in autism a second key system relating to reduced cortical functional connectivity, a part of the parietal lobe implicated in spatial functions.

They propose that these two types of functionality, face expression-related, and of one’s self and the environment, are important components of the computations involved in theory of mind, whether of oneself or of others, and that reduced connectivity within and between these regions may make a major contribution to the symptoms of autism.

The researchers argue that the methodology can potentially isolate the areas of the brain involved with other cognitive problems, including Obsessive Compulsive Disorder, ADHD and schizophrenia.

By using meta-analysis and a rigorous statistics approach the Warwick researchers were able to collect and use a big data set to obtain significant results, the likes of which have not been seen in autistic literature before. Professor Feng explains:

“We used BWAS to analyse resting state fMRI data collected from 523 autistic people and 452 controls. The amount of data analysed helped to achieve the sufficient statistical power necessary for this first voxel-based, comparison of whole autistic and non-autistic brains. Until the development of BWAS this had not been possible.

“BWAS tests for differences between patients and controls in the connectivity of every pair of voxels at a whole brain level. Unlike previous seed-based or independent components-based approaches, this method has the great advantage of being fully unbiased in that the connectivity of all brain voxels can be compared, not just selected brain regions.”


Fudan’s Jianfeng Feng’s Group Maps Out Brain Functional Network

The Institute of Science and Technology for Brain-Inspired Intelligence (ISTBI) at Fudan University released the results of its most recent research project on July 25. Entitled “Neural, electrophysiological and anatomical basis of brain-network variability and its characteristic changes in mental disorders” and published in the field’s leading journal BRAIN online, this project earns Fudan the distinction of becoming the first university to map out the brain functional network.

Jianfeng Feng, president of the institute and also the leader of this project, explained that the research was originally intended to study the dynamic variation of the brain in mental diseases such as dissociation and depression. However, the research unexpectedly made a breakthrough on the analysis of human intelligence.

Via applying MRI technology, this research measured the dynamic interaction patterns between different brain areas and unveiled the mechanisms underneath to generate these interactions. According to the research, the areas of the brain responsible for studying and memory are highly changeable. Meanwhile, the areas weakly correlated with intelligence include the visual, auditory and sensorimotor areas, which all have low changeability and adaptability. The research shows that high changeability leads to high intelligence and creativity.

These findings are poised to have a revolutionary impact on the development of artificial intelligence. Currently, artificial intelligence devices are neither changeable nor adaptable. Now, the institute’s brain functional network graph can be used to build advanced artificial neural networks, which would enable computers to study, grow and adapt. The discovery will also help in the treatment and prevention of mental illnesses. 

Since its essential importance in relevant scientific fields, this work was selected as Brain’s Editorial Choice and the cover story of the jounal’s August issue. This work also grabbed the attention of the public, as it instantly became the focus of coverage of dozens of oversea media including the Daily Mail in UK.

Editor: Hongyu,Bianji, Lin Lu, Xiong Xu, Lei Zhao


Forget the IQ test, MRI scans could reveal how smart you REALLY are - and lead to AI that can learn like a human

Forget the IQ test, MRI scans could reveal how smart you REALLY are – and lead to AI that can learn like a human

The IQ test has long been dismissed as an inaccurate way to discern how intelligent a person really is – but now scientists may have found a better way.

Researchers say MRI scans can measure human intelligence, and define exactly what it is.

This could lead to radical leaps in AI with machines programmed to think in the same way we do.

‘Human intelligence is a widely and hotly debated topic and only recently have advanced brain imaging techniques, such as those used in our current study, given us the opportunity to gain sufficient insights to resolve this and inform developments in artificial intelligence, as well as help establish the basis for understanding and diagnosis of debilitating human mental disorders such as schizophrenia and depression,’ said Professor Jianfeng Feng of the University of Warwick, who led the research.

Together with a team in China he has been working to quantify the brain’s dynamic functions, and identify how different parts of the brain interact with each other at different times – to discover how intellect works.

Professor Jianfeng found the more variable a brain is, and the more its different parts frequently connect with each other, the higher a person’s IQ and creativity are.

The team believe the work could lead to a breakthrough in AI systems.  

Currently, AI systems do not process the variability and adaptability that is vital, as evidenced by Professor Jianfeng’s research, to the human brain for growth and learning. 


The Editors’Choice section of Science in October 2014 introduced the work led by Prof. David Waxman

The Editors’Choice section of Science in October 2014 has introduced a work of Professor David Waxman from ISTBI, Fudan University, in cooperation with one of his PhD students, etc. The work entitled “Exact simulation of conditioned Wright–Fisher models” was published in Journal of Theoretical Biology, and it is commented by the editor of Science as followings: Most complex systems have an element of randomness—Even if we can describe them exactly at one point in time, we don’t know for certain where they will be next. Scientists use stochastic equations to compute the many possible trajectories these systems can take. If we constrain both end points of such trajectories, the usual approach to calculating all trajectories and rejecting those that do not end at the desired final point can be very inefficient. Zhao et al. introduced an approach in which they modified the probabilities for moving from one state to the next, so that the system was guaranteed to reach the set final state. The authors used examples from population genetics to illustrate the power of the method. David Waxman is a Chinese Government thousand talent Professor at Fudan University, and he has contributed a lot to the field of Theoretical Population Genetics. For more details, please see: http://www.sciencemag.org/content/346/6207/311.8.short