Halassa named Max Planck Fellow

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.

Elephant or chair? How the brain IDs objects

As visual information flows into the brain through the retina, the visual cortex transforms the sensory input into coherent perceptions. Neuroscientists have long hypothesized that a part of the visual cortex called the inferotemporal (IT) cortex is necessary for the key task of recognizing individual objects, but the evidence has been inconclusive.

In a new study, MIT neuroscientists have found clear evidence that the IT cortex is indeed required for object recognition; they also found that subsets of this region are responsible for distinguishing different objects.

In addition, the researchers have developed computational models that describe how these neurons transform visual input into a mental representation of an object. They hope such models will eventually help guide the development of brain-machine interfaces (BMIs) that could be used for applications such as generating images in the mind of a blind person.

“We don’t know if that will be possible yet, but this is a step on the pathway toward those kinds of applications that we’re thinking about,” says James DiCarlo, the head of MIT’s Department of Brain and Cognitive Sciences, a member of the McGovern Institute for Brain Research, and the senior author of the new study.

Rishi Rajalingham, a postdoc at the McGovern Institute, is the lead author of the paper, which appears in the March 13 issue of Neuron.

Distinguishing objects

In addition to its hypothesized role in object recognition, the IT cortex also contains “patches” of neurons that respond preferentially to faces. Beginning in the 1960s, neuroscientists discovered that damage to the IT cortex could produce impairments in recognizing non-face objects, but it has been difficult to determine precisely how important the IT cortex is for this task.

The MIT team set out to find more definitive evidence for the IT cortex’s role in object recognition, by selectively shutting off neural activity in very small areas of the cortex and then measuring how the disruption affected an object discrimination task. In animals that had been trained to distinguish between objects such as elephants, bears, and chairs, they used a drug called muscimol to temporarily turn off subregions about 2 millimeters in diameter. Each of these subregions represents about 5 percent of the entire IT cortex.

These experiments, which represent the first time that researchers have been able to silence such small regions of IT cortex while measuring behavior over many object discriminations, revealed that the IT cortex is not only necessary for distinguishing between objects, but it is also divided into areas that handle different elements of object recognition.

The researchers found that silencing each of these tiny patches produced distinctive impairments in the animals’ ability to distinguish between certain objects. For example, one subregion might be involved in distinguishing chairs from cars, but not chairs from dogs. Each region was involved in 25 to 30 percent of the tasks that the researchers tested, and regions that were closer to each other tended to have more overlap between their functions, while regions far away from each other had little overlap.

“We might have thought of it as a sea of neurons that are completely mixed together, except for these islands of “face patches.” But what we’re finding, which many other studies had pointed to, is that there is large-scale organization over the entire region,” Rajalingham says.

The features that each of these regions are responding to are difficult to classify, the researchers say. The regions are not specific to objects such as dogs, nor easy-to-describe visual features such as curved lines.

“It would be incorrect to say that because we observed a deficit in distinguishing cars when a certain neuron was inhibited, this is a ‘car neuron,’” Rajalingham says. “Instead, the cell is responding to a feature that we can’t explain that is useful for car discriminations. There has been work in this lab and others that suggests that the neurons are responding to complicated nonlinear features of the input image. You can’t say it’s a curve, or a straight line, or a face, but it’s a visual feature that is especially helpful in supporting that particular task.”

Bevil Conway, a principal investigator at the National Eye Institute, says the new study makes significant progress toward answering the critical question of how neural activity in the IT cortex produces behavior.

“The paper makes a major step in advancing our understanding of this connection, by showing that blocking activity in different small local regions of IT has a different selective deficit on visual discrimination. This work advances our knowledge not only of the causal link between neural activity and behavior but also of the functional organization of IT: How this bit of brain is laid out,” says Conway, who was not involved in the research.

Brain-machine interface

The experimental results were consistent with computational models that DiCarlo, Rajalingham, and others in their lab have created to try to explain how IT cortex neuron activity produces specific behaviors.

“That is interesting not only because it says the models are good, but because it implies that we could intervene with these neurons and turn them on and off,” DiCarlo says. “With better tools, we could have very large perceptual effects and do real BMI in this space.”

The researchers plan to continue refining their models, incorporating new experimental data from even smaller populations of neurons, in hopes of developing ways to generate visual perception in a person’s brain by activating a specific sequence of neuronal activity. Technology to deliver this kind of input to a person’s brain could lead to new strategies to help blind people see certain objects.

“This is a step in that direction,” DiCarlo says. “It’s still a dream, but that dream someday will be supported by the models that are built up by this kind of work.”

The research was funded by the National Eye Institute, the Office of Naval Research, and the Simons Foundation.

Ila Fiete

Neural Coding and Dynamics

Ila Fiete builds tools and mathematical models to expand our knowledge of the brain’s computations. Specifically, her lab focuses on how the brain develops and reshapes its neural connections to perform high-level computations, like those involved in memory and learning. The Fiete lab applies cutting-edge theoretical and quantitative methods—wielding the vast capabilities of computational models, informed by mathematics, machine learning, and physics—digging deeper into how the brain represents and manipulates information. Through these strategies, Fiete hopes to shed new light onto the neural ensembles behind learning, integration of new information, inference-making, and spatial navigation.

Her lab’s findings are pushing the frontiers of neuroscience—while advancing the utility of computational tools in this space—and are building a more robust understanding of complex brain processes.

Alan Jasanoff

Next Generation Brain Imaging

One of the greatest challenges of modern neuroscience is to relate high-level operations of the brain and mind to well-defined biological processes that arise from molecules and cells. The Jasanoff lab is creating a suite of experimental approaches designed to achieve this by permitting brain-wide dynamics of neural signaling and plasticity to be imaged for the first time, with molecular specificity. These potentially transformative approaches use novel probes detectable by magnetic resonance imaging (MRI) and other noninvasive readouts. The probes afford qualitatively new ways to study healthy and pathological aspects of integrated brain function in mechanistically-informative detail, in animals and possibly also people.

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.

Michale Fee

Song Circuits

Michale Fee studies how the brain learns and generates complex sequential behaviors, focusing on the songbird as a model system. Birdsong is a complex behavior that young birds learn from their fathers and it provides an ideal system to study the neural basis of learned behavior. Because the parts of the bird’s brain that control song learning are closely related to human circuits that are disrupted in brain disorders such as Parkinson’s and Huntington’s disease, Fee hopes the lessons learned from birdsong will provide new clues to the causes and possible treatment of these conditions.

Guoping Feng

Listening to Synapses

Guoping Feng is interested in how synapses — the connections between neurons — contribute to neurodevelopmental and psychiatric diseases, including autism spectrum disorder (ASD) and schizophrenia. He leads research that uses molecular genetics combined with behavioral and electrophysiological methods to study the components of the synapse.

Feng is perhaps best known for pioneering a gene-based therapy that could reverse a severe form of autism that is caused by a single mutation in the SHANK3 gene. After genetically engineering the SHANK3 mutation in animal models using CRISPR-based technology, Feng’s gene-correction therapy greatly reduced SHANK3 symptoms, restoring the animals’ cognitive, behavioral, and motor functions.

Additionally, the lab has leveraged genetic technologies to help map the cellular diversity in the brain—a valuable tool in neuroscience research. Through understanding the molecular, cellular, and circuit changes underlying brain diseases and disorders, the Feng lab hopes to eventually inform new and more effective treatments for neurodevelopmental and psychiatric disorders.

Ann Graybiel

Probing the Deep Brain

Ann Graybiel studies the basal ganglia, forebrain structures that are profoundly important for normal brain function. Dysfunction in these regions is implicated in neurologic and neuropsychiatric disorders ranging from Parkinson’s disease and Huntington’s disease to obsessive-compulsive disorder, anxiety and depression, and addiction. Graybiel’s laboratory is uncovering circuits underlying both the neural deficits related to these disorders, as well as the role that the basal ganglia play in guiding normal learning, motivation, and behavior.

Mark Harnett

Listening to Neurons

Mark Harnett studies how the biophysical features of individual neurons, including ion channels, receptors, and membrane electrical properties, endow neural circuits with the ability to process information and perform the complex computations that underlie behavior. As part of this work, the Harnett lab was the first to describe the physiological properties of human dendrites, the elaborate tree-like structures through which neurons receive the vast majority of their synaptic inputs. Harnett also examines how computations are instantiated in neural circuits to produce complex behaviors such as spatial navigation.

Virtual Tour of Harnett Lab

Ian Wickersham

Making Connections

Ian Wickersham develops genetic tools that provide more powerful and precise ways to study the organization of the brain. His lab invents techniques for targeting neurons based on their synaptic connectivity and gene expression patterns in order to cause them to express genes that allow the neurons to be studied and controlled by neuroscientists and clinicians. The goal of Wickersham’s work is to provide neuroscience with more effective ways of studying the brain, and potentially to provide clinical neurology with more effective ways of treating disorders of the brain.