
John A. Assad
· Director of the Program in Neuroscience, Professor of NeurobiologyVerifiedHarvard University · Neuroscience
Active 1985–2025
About
John A. Assad is a Professor of Neurobiology at Harvard Medical School. He completed his PhD at Harvard under the mentorship of David Corey, focusing on the biophysical mechanisms of mechano-electrical transduction in hair cells. Following his PhD, he conducted postdoctoral research at Baylor College of Medicine with John Maunsell, studying higher order visual processing in the primate parietal cortex. In his laboratory at Harvard, Assad has conducted extensive research using non-human primates (NHPs) to investigate a variety of questions including neuronal mechanisms of attention, inference, and flexible coding in the parietal cortex; economic decision making in the orbitofrontal cortex; and movement initiation within basal ganglia-thalamic-cortical circuits. More recently, his lab transitioned to using mice to leverage the advanced genetic tools available in this species. The current research focus is on neuronal mechanisms underlying self-initiated or self-timed movements, which provide insights into voluntary movement control and movement disorders such as Parkinson's disease. The lab employs a combination of electrophysiological, behavioral, optical, and computational approaches to address these questions. In addition to his research, John Assad serves as the director of the PhD Program in Neuroscience at Harvard and is the principal investigator of an NIH R25 Research Education Program training grant supporting the PiNBAC post-baccalaureate program at Harvard.
Research topics
- Neuroscience
- Physics
- Computer Science
- Psychology
- Biology
- Materials science
- Nanotechnology
- Computer hardware
- Endocrinology
- Optoelectronics
Selected publications
bioRxiv (Cold Spring Harbor Laboratory) · 2025-02-19 · 1 citations
preprintOpen accessSenior authorCorrespondingAbstract The basal ganglia are involved in the control of movement, but their exact role is unclear. Paradoxically, most of the inhibitory projection neurons in the main output nuclei increase firing around the time of movement; only a small fraction decrease firing. This antagonistic activity pattern could subserve action selection, with the small “decrease” population selectively disinhibiting the desired movement, and the larger “increase” population inhibiting competing movements. The action-selection hypothesis makes an implicit assumption: neurons that decrease firing to disinhibit a specific action should increase firing to inhibit that action when a different action is desired. To test this hypothesis, we recorded projection neurons in the substantia nigra pars reticulata (SNr) of mice trained to alternate between two different types of movements. Many SNr neurons showed a “ramping” pattern of pre-movement firing-rate modulation, with most neurons increasing firing, consistent with previous findings. However, contrary to the action-selection model, the overwhelming majority of SNr neurons exhibited congruent modulation between the competing actions, either increasing or decreasing their firing rates for both actions; only a small fraction of neurons exhibited opposite signs of modulation. We could not ascribe the congruent modulation to potential uninstructed movements. Our results are not easily reconciled with simple antagonistic mechanisms for action selection in the basal ganglia output nuclei. We also found that ramping activity in SNr neurons typically began hundreds of ms before self-timed and spontaneous movements, in contrast to previous findings suggesting that basal ganglia output is modulated too late to be involved in movement initiation. Our findings suggest constraints – and raise new questions – about the role of the basal ganglia in movement initiation and action selection.
Heart Lung and Circulation · 2025-08-01
articleOpen accessExplaining dopamine through prediction errors and beyond
Nature Neuroscience · 2024-07-25 · 76 citations
reviewZenodo (CERN European Organization for Nuclear Research) · 2022-01-01 · 3 citations
articleOpen accessDeciphering the neural patterns underlying brain functions is essential to understanding how neurons are organized into networks. This deciphering has been greatly facilitated by optogenetics and its combination with optoelectronic devices to control neural activity with millisecond temporal resolution and cell type specificity. However, targeting small brain volumes causes photoelectric artefacts, in particular when light emission and recording sites are close to each other. We take advantage of the photonic properties of tapered fibres to develop integrated ‘fibertrodes’ able to optically activate small brain volumes with abated photoelectric noise. Electrodes are positioned very close to light emitting points by non-planar microfabrication, with angled light emission allowing the simultaneous optogenetic manipulation and electrical read-out of one to three neurons, with no photoelectric artefacts, in vivo. The unconventional implementation of two-photon polymerization on the curved taper edge enables the fabrication of recoding sites all around the implant, making fibertrodes a promising complement to planar microimplants.
Zenodo (CERN European Organization for Nuclear Research) · 2022-04-22 · 1 citations
datasetOpen accessThis dataset contains the raw data for the paper titled "Tapered fibertrodes for opto-electrical neural interfacing in small brain volumes with reduced artefacts".
Zenodo (CERN European Organization for Nuclear Research) · 2022-04-22
datasetOpen accessThis dataset contains the raw data for the paper titled "Tapered fibertrodes for opto-electrical neural interfacing in small brain volumes with reduced artefacts".
Zenodo (CERN European Organization for Nuclear Research) · 2022-04-22
datasetOpen accessThis dataset contains the raw data for the paper titled "Tapered fibertrodes for opto-electrical neural interfacing in small brain volumes with reduced artefacts"
Nature Materials · 2022 · 58 citations
- Computer Science
- Materials science
- Optoelectronics
Dopamine mediates the bidirectional update of interval timing.
Behavioral Neuroscience · 2022-10-01 · 14 citations
articleOpen accessThe role of dopamine (DA) as a reward prediction error (RPE) signal in reinforcement learning (RL) tasks has been well-established over the past decades. Recent work has shown that the RPE interpretation can also account for the effects of DA on interval timing by controlling the speed of subjective time. According to this theory, the timing of the dopamine signal relative to reward delivery dictates whether subjective time speeds up or slows down: Early DA signals speed up subjective time and late signals slow it down. To test this bidirectional prediction, we reanalyzed measurements of dopaminergic neurons in the substantia nigra pars compacta of mice performing a self-timed movement task. Using the slope of ramping dopamine activity as a readout of subjective time speed, we found that trial-by-trial changes in the slope could be predicted from the timing of dopamine activity on the previous trial. This result provides a key piece of evidence supporting a unified computational theory of RL and interval timing. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Interfacing with small brain volumes with tapered fiber-based optrode
Biophotonics Congress 2021 · 2021-01-01
articleCorrespondingOptogenetics offers the possibility of understanding behavior-related neuronal circuits, using light to trigger neuronal response. The exploitation of unconventional microfabrication techniques has led to the development of different optoelectronic devices to deliver light while electrically recording neural activity over small volumes even in deep brain areas.
Recent grants
NIH · $2.1M · 2005
Towards a Unified Framework for Dopamine Signaling in the Striatum
NIH · $7.2M · 2019–2024
NIH · $5.1M · 2017
Frequent coauthors
- 52 shared
Massimo De Vittorio
University of Salento
- 43 shared
Bernardo L. Sabatini
Howard Hughes Medical Institute
- 42 shared
Marco Pisanello
- 41 shared
Leonardo Sileo
- 38 shared
Ferruccio Pisanello
Italian Institute of Technology
- 27 shared
David J. Freedman
University of Chicago
- 26 shared
Rui T. Peixoto
University of Pittsburgh
- 20 shared
Marco Bianco
Italian Institute of Technology
Labs
Neuronal mechanisms of self-initiated or self-timed movements
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