Research Area: Cognitive Neuroscience

​perception, memory, language, social cognition, attention, empathy, brain disorders, aging, cognitive flexibility, developing brain

Halassa named Max Planck Fellow

by Sabbi Lall |

Michael Halassa was just appointed as one of the newest Max Planck Fellows. His appointment comes through the Max Planck Florida Institute for Neuroscience (MPFI), which aims to forge collaborations between exceptional neuroscientists from around the world to answer fundamental questions about brain development and function. The Max Planck Society selects cutting edge, active researchers from other institutions to fellow positions for a five-year period to promote interactions and synergies. While the program is a longstanding feature of the Max Planck Society, Halassa, and fellow appointee Yi Guo of the University of California, Santa Cruz, are the first selected fellows that are based at U.S. institutions.

Michael Halassa is an associate investigator at the McGovern Institute and an assistant professor in the Department of Brain and Cognitive Sciences at MIT. Halassa’s research focuses on the neural architectures that underlie complex cognitive processes. He is particularly interested in goal-directed attention, our ability to rapidly switch attentional focus based on high level objectives. For example, when you are in a roomful of colleagues, the mention of your name in a distant conversation can quickly trigger your ‘mind’s ear’ to eavesdrop into that conversation. This contrasts with hearing a name that sounds like yours on television, which does not usually grab your attention in the same way. In certain mental disorders such as schizophrenia, the ability to generate such high-level objectives, while also accounting for context, is perturbed. Recent evidence strongly suggests that impaired function of the prefrontal cortex and its interactions with a region of the brain called the thalamus may be altered in such disorders. It is this thalamocortical network that Halassa has been studying in mice, where his group has uncovered how the thalamus supports the ability of the prefrontal cortex to generate context-appropriate attentional signals.

The fellowship will support extending Halassa’s work into the tree shrew (Tupaia belangeri), which has been shown to have advanced cognitive abilities compared to mice while also offering many of the circuit-interrogation tools that make the mouse an attractive experimental model.

The Max Planck Florida Institute for Neuroscience (MPFI), a not-for-profit research organization, is part of the world-renowned Max Planck Society, Germany’s most successful research organization. The Max Planck Society was founded in 1911, and comprises 84 institutes and research facilities. While primarily located in Germany, there are 4 institutes and one research facility located aboard, including the Florida Institute that Halassa will collaborate with. The fellow positions were created with the goal of increasing interactions between the Max Planck Society and its institutes with faculty engaged in active research at other universities and institutions, which with this appointment now include MIT.

How does the brain focus?

by Sabbi Lall |

This is a very interesting question, and one that researchers at the McGovern Institute for Brain Research are actively pursuing. It’s also important for understanding what happens in conditions such as ADHD. There are constant distractions in the world, a cacophony of noise and visual stimulation. How and where we focus our attention, and what the brain attends to vs. treating as background information, is a big question in neuroscience. Thanks to work from researchers, including Robert Desimone, we understand quite a bit about how this works in the visual system in particular. What his lab has found is that when we pay attention to something specific, neurons in the visual cortex responding to the object we’re focusing upon fire in synchrony, whereas those responding to irrelevant information become suppressed. It’s almost as if this synchrony “increases the volume” so that the responding neurons rise above general noise.

Synchronized activity of neurons occurs as they oscillate together at a particular frequency, but the frequency of oscillation really matters when it comes to attention and focus vs. inattention and distraction. To find out more about this, I asked a postdoc in the Desimone lab, Yasaman Bagherzadeh about the role of different “brainwaves,” or oscillations at different frequencies, in attention.

“Studies in humans have shown that enhanced synchrony between neurons in the alpha range –8–12 Hz— is actually associated with inattention and distracting information,” explains Bagherzadeh, “whereas enhanced gamma synchrony (about 30-150 Hz) is associated with attention and focus on a target. For example, when a stimulus (through the ears or eyes) or its location (left vs. right) is intentionally ignored, this is preceded by a relative increase in alpha power, while a stimulus you’re attending to is linked to an increase in gamma power.”

Attention in the Desimone lab (no pun intended) has also recently been focused on covert attention. This type of spatial attention was traditionally thought to occur through a mental shift without a glance, but the Desimone lab recently found that even during these mental shifts, animal sneakily glance at objects that attention becomes focused on. Think now of something you know is nearby (a cup of coffee for example), but not in the center of your field of vision. Chances are that you just sneakily glanced at that object.

Previously these sneaky glances/small eye movements, called microsaccades (MS for short), were considered to be involuntary movements without any functional role. However, in the recent Desimone lab study, it was found that a MS significantly modulates neural activity during the attention period. This means that when you glance at something, even sneakily, it is intimately linked to attention. In other words, when it comes to spatial attention, eye movements seem to play a significant role.

Various questions arise about the mechanisms of spatial attention as a result this study, as outlined by Karthik Srinivasan, a postdoctoral associate in the Desimone lab.

“How are eye movement signals and attentional processing coordinated? What’s the role of the different frequencies of oscillation for such coordination? Is there a role for them or are they just the frequency domain representation (i.e., an epiphenomenon) of a temporal/dynamical process? Is attention a sustained process or rhythmic or something more dynamic?” Srinivasan lists some of the questions that come out of his study and goes on to explain the implications of the study further. “It is hard to believe that covert attention is a sustained process (the so-called ‘spotlight theory of attention’), given that neural activity during the attention period can be modulated by covert glances. A few recent studies have supported the idea that attention is a rhythmic process that can be uncoupled from eye movements. While this is an idea made attractive by its simplicity, it’s clear that small glances can affect neural activity related to attention, and MS are not rhythmic. More work is thus needed to get to a more unified theory that accounts for all of the data out there related to eye movements and their close link to attention.”

Answering some of the questions that Bagherzadeh, Srinivasan, and others are pursuing in the Desimone lab, both experimentally and theoretically, will clear up some of the issues above, and improve our understanding of how the brain focuses attention.

Do you have a question for The Brain? Ask it here.

 

How motion conveys emotion in the face

by Sabbi Lall |

While a static emoji can stand in for emotion, in real life we are constantly reading into the feelings of others through subtle facial movements. The lift of an eyebrow, the flicker around the lips as a smile emerges, a subtle change around the eyes (or the sudden rolling of the eyes), are all changes that feed into our ability to understand the emotional state, and the attitude, of others towards us. Ben Deen and Rebecca Saxe have now monitored changes in brain activity as subjects followed face movements in movies of avatars. Their findings argue that we can generalize across individual face part movements in other people, but that a particular cortical region, the face-responsive superior temporal sulcus (fSTS), is also responding to isolated movements of individual face parts. Indeed, the fSTS seems to be tied to kinematics, individual face part movement, more than the implied emotional cause of that movement.

We know that the brain responds to dynamic changes in facial expression, and that these are associated with activity in the fSTS, but how do calculations of these movements play out in the brain?

Do we understand emotional changes by adding up individual features (lifting of eyebrows + rounding of mouth= surprise), or are we assessing the entire face in a more holistic way that results in more generalized representations? McGovern Investigator Rebecca Saxe and her graduate student Ben Deen set out to answer this question using behavioral analysis and brain imaging, specifically fMRI.

“We had a good sense of what stimuli the fSTS responds strongly to,” explains Ben Deen, “but didn’t really have any sense of how those inputs are processed in the region – what sorts of features are represented, whether the representation is more abstract or more tied to visual features, etc. The hope was to use multivoxel pattern analysis, which has proven to be a remarkably useful method for characterizing representational content, to address these questions and get a better sense of what the region is actually doing.”

Facial movements were conveyed to subjects using animated “avatars.” By presenting avatars that made isolated eye and eyebrow movements (brow raise, eye closing, eye roll, scowl) or mouth movements (smile, frown, mouth opening, snarl), as well as composites of these movements, the researchers were able to assess whether our interpretation of the latter is distinct from the sum of its parts. To do this, Deen and Saxe first took a behavioral approach where people reported on what combinations of eye and mouth movements in a whole avatar face, or one where the top and bottom parts of the face were misaligned. What they found was that movement in the mouth region can influence perception of movement in the eye region, arguably due to some level of holistic processing. The authors then asked whether there were cortical differences upon viewing isolated vs. combined face part movements. They found that changes in fSTS, but not other brain regions, had patterns of activity that seemed to discriminate between different facial movements. Indeed, they could decode which part of the avatar’s face is being perceived as moving from fSTS activity. The researchers could even model the fSTS response to combined features linearly based on the response to individual face parts. In short, though the behavorial data indicate that there is holistic processing of complex facial movement, it is also clear that isolated parts-based representations are also present, a sort of intermediate state.

As part of this work, Deen and Saxe took the important step of pre-registering their experimental parameters, before collecting any data, at the Open Science Framework. This step allows others to more easily reproduce the analysis they conducted, since all parameters (the task that subjects are carrying out, the number of subjects needed, the rationale for this number, and the scripts used to analyze data) are openly available.

“Preregistration had a big impact on our workflow for the study,” explained Deen. “More of the work was done up front, in coming up with all of the analysis details and agonizing over whether we were choosing the right strategy, before seeing any of the data. When you tie your hands by making these decisions up front, you start thinking much more carefully about them.”

Pre-registration does remove post-hoc researcher subjectivity from the analysis. As an example, because Deen and Saxe predicted that the people would be accurately able to discriminate between faces per se, they decided ahead of the experiment to focus on analyzing reaction time, rather than looking at the collected data and deciding to focus on this number after the fact. This adds to the overall objectivity of the experiment and is increasingly seen as a robust way to conduct such experiments.

Josh McDermott

The Science of Hearing

Hearing enables us to make sense of our whereabouts, understand the emotional state of others, and enjoy musical experiences. Acoustic information relays vital cues about the world—yet much of the sophisticated brain system that decodes this information is poorly understood.

Josh McDermott’s research is in search of foundational principles of sound perception. Groundbreaking discoveries from the McDermott lab have clarified how people hear and process sounds. His research informs new treatments for those with hearing loss, and paves the way for machine systems that emulate the human ability to recognize and interpret sound. McDermott’s lab has also pioneered new approaches for understanding music perception. His lab deconstructs the neural ensembles that allow us to appreciate music, while also studying the often striking variation that can occur across cultures.

Virtual Tour of McDermott Lab

Rebecca Saxe

Mind Reading

How do we think about the thoughts of other people? How are some thoughts universal and others specific to a culture or an individual?

Rebecca Saxe is tackling these and other thorny questions surrounding human thought in adults, children, and infants. Leveraging behavioral testing, brain imaging, and computational modeling, her lab is focusing on a diverse set of research questions including what people learn from punishment, the role of generosity in social relationships, and the navigation and language abilities in toddlers. The team is also using computational models to deconstruct complex thought processes, such as how humans predict the emotions of others. This research not only expands the junction of sociology and neuroscience, but also unravels—and gives clarity to—the social threads that form the fabric of society.

Virtual Tour of Saxe Lab

Mehrdad Jazayeri

Neurobiology of Mental Computations

How does the brain give rise to the mind? How do neurons, circuits, and synapses in the brain encode knowledge about objects, events, and other structural and causal relationships in the environment? Research in Mehrdad Jazayeri’s lab brings together ideas from cognitive science, neuroscience, and machine learning with experimental data in humans, animals, and computer models to develop a computational understanding of how the brain create internal representations, or models, of the external world.

Nancy Kanwisher

Architecture of the Mind

What is the nature of the human mind? Philosophers have debated this question for centuries, but Nancy Kanwisher approaches this question empirically, using brain imaging to look for components of the human mind that reside in particular regions of the brain. Her lab has identified cortical regions that are selectively engaged in the perception of faces, places, and bodies, and other regions specialized for uniquely human functions including the music, language, and thinking about other people’s thoughts. More recently, her lab has begun using artificial neural networks to unpack these findings and examine why, from a computational standpoint, the brain exhibits functional specification in the first place.

John Gabrieli

Images of Mind

John Gabrieli’s goal is to understand the organization of memory, thought, and emotion in the human brain. In collaboration with clinical colleagues, Gabrieli uses brain imaging to better understand, diagnose, and select treatments for neurological and psychiatric diseases.

A major focus of the Gabrieli lab is the neural basis of learning in children. His team found structural differences in the brains of young children who are at risk for reading difficulties, even before they start learning to read. By studying these differences in children, Gabrieli hopes to identify ways to improve learning in the classroom and inform effective educational policies and practices.

Gabrieli is also interested in using the tools of neuroscience to personalize medicine. His team showed that brain scans can identify children who are vulnerable to depression before symptoms even appear, opening the possibility of earlier interventions to prevent episodes of depression. Brain scans may also help help predict which individuals with social anxiety disorder are most likely to benefit from a particular therapeutic intervention. Gabrieli’s team continues to explore the role of neuroimaging in other brain disorders, including schizophrenia, addiction, and bipolar disorder.

His team also studies a range of other research topics, including new strategies to cope with emotional stress, the benefits of mindfulness for academic performance and mental health, and the value of embracing neurodiversity to better understand autism.

James DiCarlo

Rapid Recognition

DiCarlo’s research goal is to reverse engineer the brain mechanisms that underlie human visual intelligence. He and his collaborators have revealed how population image transformations carried out by a deep stack of interconnected neocortical brain areas — called the primate ventral visual stream — are effortlessly able to extract object identity from visual images. His team uses a combination of large-scale neurophysiology, brain imaging, direct neural perturbation methods, and machine learning methods to build and test neurally-mechanistic computational models of the ventral visual stream and its support of cognition and behavior. Such an engineering-based understanding is likely to lead to new artificial vision and artificial intelligence approaches, new brain-machine interfaces to restore or augment lost senses, and a new foundation to ameliorate disorders of the mind.

Brain activity pattern may be early sign of schizophrenia

Anne Trafton |

Schizophrenia, a brain disorder that produces hallucinations, delusions, and cognitive impairments, usually strikes during adolescence or young adulthood. While some signs can suggest that a person is at high risk for developing the disorder, there is no way to definitively diagnose it until the first psychotic episode occurs.

MIT neuroscientists working with researchers at Beth Israel Deaconess Medical Center, Brigham and Women’s Hospital, and the Shanghai Mental Health Center have now identified a pattern of brain activity correlated with development of schizophrenia, which they say could be used as a marker to diagnose the disease earlier.

“You can consider this pattern to be a risk factor. If we use these types of brain measurements, then maybe we can predict a little bit better who will end up developing psychosis, and that may also help tailor interventions,” says Guusje Collin, a visiting scientist at MIT’s McGovern Institute for Brain Research and the lead author of the paper.

The study, which appeared in the journal Molecular Psychiatry on Nov. 8, was performed at the Shanghai Mental Health Center. Susan Whitfield-Gabrieli, a visiting scientist at the McGovern Institute and a professor of psychology at Northeastern University, is one of the principal investigators for the study, along with Jijun Wang of the Shanghai Mental Health Center, William Stone of Beth Israel Deaconess Medical Center, the late Larry Seidman of Beth Israel Deaconess Medical Center, and Martha Shenton of Brigham and Women’s Hospital.

Abnormal connections

Before they experience a psychotic episode, characterized by sudden changes in behavior and a loss of touch with reality, patients can experience milder symptoms such as disordered thinking. This kind of thinking can lead to behaviors such as jumping from topic to topic at random, or giving answers unrelated to the original question. Previous studies have shown that about 25 percent of people who experience these early symptoms go on to develop schizophrenia.

The research team performed the study at the Shanghai Mental Health Center because the huge volume of patients who visit the hospital annually gave them a large enough sample of people at high risk of developing schizophrenia.

The researchers followed 158 people between the ages of 13 and 34 who were identified as high-risk because they had experienced early symptoms. The team also included 93 control subjects, who did not have any risk factors. At the beginning of the study, the researchers used functional magnetic resonance imaging (fMRI) to measure a type of brain activity involving “resting state networks.” Resting state networks consist of brain regions that preferentially connect with and communicate with each other when the brain is not performing any particular cognitive task.

“We were interested in looking at the intrinsic functional architecture of the brain to see if we could detect early aberrant brain connectivity or networks in individuals who are in the clinically high-risk phase of the disorder,” Whitfield-Gabrieli says.

One year after the initial scans, 23 of the high-risk patients had experienced a psychotic episode and were diagnosed with schizophrenia. In those patients’ scans, taken before their diagnosis, the researchers found a distinctive pattern of activity that was different from the healthy control subjects and the at-risk subjects who had not developed psychosis.

For example, in most people, a part of the brain known as the superior temporal gyrus, which is involved in auditory processing, is highly connected to brain regions involved in sensory perception and motor control. However, in patients who developed psychosis, the superior temporal gyrus became more connected to limbic regions, which are involved in processing emotions. This could help explain why patients with schizophrenia usually experience auditory hallucinations, the researchers say.

Meanwhile, the high-risk subjects who did not develop psychosis showed network connectivity nearly identical to that of the healthy subjects.

Early intervention

This type of distinctive brain activity could be useful as an early indicator of schizophrenia, especially since it is possible that it could be seen in even younger patients. The researchers are now performing similar studies with younger at-risk populations, including children with a family history of schizophrenia.

“That really gets at the heart of how we can translate this clinically, because we can get in earlier and earlier to identify aberrant networks in the hopes that we can do earlier interventions, and possibly even prevent psychiatric disorders,” Whitfield-Gabrieli says.

She and her colleagues are now testing early interventions that could help to combat the symptoms of schizophrenia, including cognitive behavioral therapy and neural feedback. The neural feedback approach involves training patients to use mindfulness meditation to reduce activity in the superior temporal gyrus, which tends to increase before and during auditory hallucinations.

The researchers also plan to continue following the patients in the current study, and they are now analyzing some additional data on the white matter connections in the brains of these patients, to see if those connections might yield additional differences that could also serve as early indicators of disease.

The research was funded by the National Institutes of Health, the Ministry of Science and Technology of China, and the Poitras Center for Psychiatric Disorders Research at MIT. Collin was supported by a Marie Curie Global Fellowship grant from the European Commission.