Biomagnetic Imaging Laboratory

The Biomagnetic Imaging Laboratory (BIL) is focused on improving non-invasive functional brain imaging methods so that we can develop a better understanding of the dynamics of brain networks involved in processing and learning of complex human behaviors, such as speech and language. We use multiple modalities of brain imaging in conjuction with other tools, such as:

Magnetoencephalographic Imaging (MEGI)

Magnetoencephalographic Imaging (MEGI)

Functional Connectivity (fMRI, MEGI)

Functional Connectivity (fMRI, MEGI)

Machine learning algorithm development

Machine learning algorithm development

Electrocorticography (ECoG)

Electrocorticography (ECoG)

Transcranial Magnetic Stimulation (TMS)

Transcranial Magnetic Stimulation (TMS)

BCI, Computer games and training

BCI, Computer games and training

Speech Neuroscience research

Speech Neuroscience Research

The Biomagnetic Imaging Laboratory (BIL) is a shared clinical and research facility established for over 20 years in the Department of Radiology and Biomedical Imaging at UCSF.  The laboratory primarily conducts basic and clinical research on brain function with a focus on Magnetoencephagraphic (MEG) Imaging technology, a non-invasive measurement of magnetic fields.  The laboratory also provides clinical services such as mapping of brain regions involved in eloquent function, in patients with brain tumors and other brain lesions (which is critical in providing neurosurgeons pre-op brain mapping information to minimize any harm to brain functions), and epileptic zone localization in patients with epilepsy.  UCSF Department of Radiology and Biomedical Imaging is amongst of only 25 clinical MEG sites in the US, and the older of only two sites in California. 

Research Areas in the Biomagnetic Imaging Lab

  • Developing machine learning algorithms and tools for functional brain imaging and imaging brain connectivity
  • Developing and evaluating algorithms for brain computer interfaces
  • Developing novel clinical applications for electromagnetic source imaging
  • Imaging speech production and auditory feedback
  • Imaging cortical plasticity associated with learning
  • Integrating multi-modal and multi-scale functional

Development and Validation of Algorithms and Tools for Electromagnetic Source Imaging

MEGIMagnetoencephalographic Imaging (MEGI)

The principal goal here is to improve the use of electromagnetic source imaging (ESI is the combined use of MEG, EEG and MRI) in clinical and research practice through the development of better algorithms for image reconstruction and analysis. Another goal is the development of tools for integration of ESI with other imaging modalities such as DTI and fMRI. Ongoing projects include the development of various algorithms for selective signal cancellation, separation and localization of brain sources from EEG and MEG data. We draw upon advances in statistical signal processing, machine learning, and probabilistic Bayesian inference and apply such techniques to analyze MEG and EEG data. Another ongoing effort is to develop algorithms for automated detection and localization of epileptogenic zones. All Algorithms are validated by comparing ESI reconstructions with electrocorticography (ECoG) recordings in patients with brain tumors and in patients with epilepsy. Algorithm validation is also performed in animal models by comparing imaging data such as ESI reconstructions and DTI connectivity with electrophysiological and neuroanatomical measurements. Clinical applications of this work will be tested in patients with brain tumors and in patients with epilepsy.

Determining the Biophysical Basis for Novel Anatomical and Functional Brain Imaging Methods

ECoGElectrocorticography (ECoG)

The goal here is to determine the biophysical basis of auditory and somatosensory cortical activation and connectivity as assayed by modern functional brain imaging methods such as ESI and DTI. In ongoing experiments in patients, with brain tumors or intractable epilepsy, and in animals, we record magnetoencephalography (MEG), electroencephalography (EEG), electrocorticography (ECoG), functional magnetic resonance imaging (fMRI), diffusion-tensor imaging (DTI) data. In animals, we can also record local field potentials and action-potentials from microelectrode arrays and measure neuroanatomical tracer uptakes. Linking such data obtained in the same subjects in response to the same stimuli across these recording methods enables comparisons of imaging across multiple scales and modalities. Current projects include studies of representations of somatosensory and auditory stimuli (in monkeys and cats) and of speech, language and memory responses in humans.

Imaging Cortical Spatiotemporal Plasticity Associated with Learning

ischemic strokeResting-state network connectivity predicts recovery in ischemic Stroke.

The goal here is to examine cortical plasticity due to learning and experience in normal adult humans using ESI. We are specifically interested in cortical plasticity in response to dynamic stimuli in the time-scale of tens to hundreds of milliseconds. We have been examining plasticity, associated with perceptual learning, in representations of simple and complex acoustic stimuli as well as speech and language stimuli. We are also examining plasticity of somatosensory and motor representation changes due to training and perceptual learning.

Neuroimaging of Speech, Language and Memory

schizophreniaResting MEGI Functional Connectivity in Schizophrenia predicts symptoms.

The goal here is to examine the spatiotemporal dynamics of brain networks involved in speech, language and memory processes. We specifically focus on overt speech production, its interaction with auditory feedback processing, and with language and memory processes. Several recent studies have shown that speaking causes "speaking-induced suppression" or SIS - a suppressed response to self-produced speech when compared to identical speech from an external source - in auditory cortex and associative regions. In our own recent work, we have shown that SIS is present in auditory cortex, and does not result from overall inhibition of auditory cortex during speaking. Rather, SIS results from a comparison between actual auditory input and an internal "speaking-induced prediction" (SIP) of that auditory input. What is the functional significance of SIS and SIP? Based on several lines of evidence, we have developed a conceptual working model for SIS and SIP. The principal goal of this research is to test predictions from this model. Our overall approach capitalizes on unique real-time speech feedback alteration methods developed by our research team, the excellent spatial resolution of functional magnetic resonance imaging (fMRI), the excellent temporal resolution of electromagnetic source imaging (ESI) and advanced analyses methods that we have developed for reconstructing spatiotemporal dynamics and connectivity of distributed cortical networks.

Developing Novel Clinical Applications for ESI

How MSI worksIn addition to existing clinical research projects in brain tumor and epilepsy patients, we are also in the process of developing several protocols and procedures for examining novel clinical populations. Ongoing projects including conducting ESI studies on patients with Schizophrenia, Mild Cognitive Impairments, Parkinson's disease, Autism, Traumatic Brain Injury, Stroke, Focal-Hand Dystonia and Agenesis of the Corpus-Collosum.

Center versus peripheral vowel productions in a sample subject.
A, Acoustic variation
across repeated productions, shown in 2D formant frequency space. Green represents center productions; red represents peripheral productions; black represents remaining productions.
B, A source localization algorithm (Owen et al., 2012) determined the coordinates and field strength of the M100 peak (MNI56,24, 8 in this subject).
C, MEG traces aligned to
vowel onset, separated into center and peripheral trials as determined by A. Shaded regions represent SE (n100); vertical bars on the y-axis represent SIS magnitude.


A complete list of BIL publications can be found at the bottom of Srikantan Nagarajan's bio page, or Srikantan Nagarajan's Google Scholar's page.

The Biomagnetic Imaging Laboratory (BIL) provides the following:

Measure magnetic and electric brain activity

Measure magnetic and electric brain activity• Clinical patients for presurgical work up (brain tumor, epilepsy)
• Research patients with various neurologic conditions (traumatic brain injury, dementia, autism, schizophrenia, stroke, tinnitus, spasmodic dysphonia, focal hand dystonia, primary progressive aphasia, agenesis of the corpus callosum, dyslexia, etc.)


MEG/MSI Support and Service for Clinical & Research Purposes

MEG/MSI• MEG - Magnetoencephalography
• MSI – Magnetic Source Imaging  (MEG + MRI = MSI)

MEG Referrals Instructions




Perform Source Analysis

source analysis• Standard analysis software/protocols
• Specialized source analysis algorithms developed in house



Administrative Support

BIL groupCoordination of patient schedules, scheduling, screening, consenting, recharging, CHR management, reimbursements, etc.





Internships can provide opportunities for people at all levels of training (i.e. undergraduate, graduate, and resident).


Rotations provide an opportunity to explore research opportunities in the lab that can lead to a commitment of several years to develop and complete dissertation research in conjunction with enrollment in a Graduate Program or Professional School, such as the following:



Srikantan Nagarajan, Ph.D.Srikantan Nagarajan, PhD
Director, Professor in Residence
Department of Radiology & Biomedical Imaging


Heidi Kirsch, M.D.Heidi Kirsch, MD, MS
Clinical Director and Associate Research Director
Clinical Neurology, Department of Neurology and Radiology & Biomedical Imaging


Steven W. Cheung, M.DSteven W. Cheung, MD
Department of Otolaryngology


John Houde, Ph.D.John Houde, PhD
Associate Professor
Department of Otolaryngology


Phiroz Tarapore, MD
Assistant Professor
Department of Neurological Surgery


Pratik Mukherjee, MD, PhD
Professor in Residence
Department of Radiology & Biomedical Imaging



  • Zarinah Agnew, PhD, Specialist, Department of Otolaryngology
  • Lucia Bulubas, MD, Visiting Scientist
  • Corby Dale, PhD, MPH, Department of Radiology & Biomedical Imaging
  • Carly Demopoulos, PhD, Postdoctoral Scholar, Department of Radiology & Biomedical Imaging
  • Ana Silva De Souza, PhD, Visiting Scholar, Department of Otolaryngology 
  • Elke De Witte, PhD, Postdoctoral Scholar, Department of Neurological Surgery
  • Roeland Hancock, PhD, Postdoctoral Scholar, Department of Psychiatry
  • Leighton Hinkley, PhD, Specialist, Department of Radiology & Biomedical Imaging
  • Zachary Miller, MD, Assistant Professor, Department of Neurology
  • Nayara Mota, PhD, Visiting Scholar, Department of Radiology & Biomedical Imaging
  • Kamalini Ranasinghe, MD, PhD, Postdoctoral Scholar, Department of Neurology
  • Yingying Shang, MD, Visiting Scholar, Department of Otolaryngology 
  • Karuna Subramaniam, PhD, Assistant Adjunct Professor, Department of Psychiatry



  • Megan Thompson, UCSF/UCB Bioengineering Graduate Student
  • Inez Raharjo, UCSF/UCB Bioengineering Graduate Student
  • Velmurugan Jayabal
  • Chang Cai



  • Joshua Bear, MD, University of Colorado, Denver
  • Ethan Brown, MD, UCSF
  • Edward Chang, MD, UCSF 
  • Elizabeth Disbrow, PhD, Louisiana State University, Shreveport 
  • Dario Englot, MD, PhD, Vanderbilt University
  • Adrian Guggisberg, PhD, University of Geneva
  • Ilana Hairston, PhD, 
  • Kenneth Hild, PhD,
  • Kitti Kaiboriboon, MD, Case Western Reserve University
  • Tal Kenet, PhD, Harvard Medical School – Massachusetts General Hospital
  • Juan Martino, MD, PhD, Hospital Universitario Marques de Valdecilla
  • David McGonigle, Cardiff University
  • Caroline Niziolek, PhD, Boston University
  • Maneesh Sahani, PhD, Gatsby Computational Neuroscience Unit, University College London
  • Carsten Stahlhut, PhD, Technical University of Denmark
  • Noriko Tanigawa, University of Oxford
  • Phiroz Tarapore, MD, UCSF
  • Virginie Van Wassenhove, Neurospin, France
  • Maria Ventura, PhD, UCSF
  • Kelly Westlake, PhD, University of Maryland, Baltimore
  • David Wipf, PhD, Amazon AI Lab in Shanghai
  • Zhao Zhu, PhD, South San Francisco

Past Graduate Students:

  • Sarang Dalal, PhD, Aarhus University
  • Alexander Herman, MD, PhD, UCSF
  • Naomi Kort, PhD, UCSF
  • Julia Owen, PhD, UCSF
  • Johanna Zumer, PhD, University of Birmingham
Director, Neuroimaging RIG
Director, Biomagnetic Imaging Lab