Our Mission

Our lab focuses on identifying the neuroanatomical substrate for symptoms common in autism and other neurodevelopmental conditions with a goal of direct translation into possible targets for noninvasive treatment modalities, e.g., TMS and/or fMRI-based neurofeedback.

This entails three interactive research arms:

  1. Generation of circuit-based hypotheses for specific symptoms from cohorts with lesions, tubers, tumor resections, etc.

  2. Validation of these localizations through prospective neuroimaging study of patients with neurodevelopmental disorders with similar symptoms, and

  3. Testing whether this circuit can be modulated through non-invasive therapy, e.g., behavioral, fMRI-neurofeedback, or potentially TMS-based interventions.

Alexander Cohen, MD, PhD

Alexander Cohen, MD, PhD

Principle Investigator

Boston Children's Hospital

Principal Investigator

Alexander Cohen is an Instructor of Neurology at Boston Children’s Hospital who specializes in translational neuroimaging approaches to understand and develop treatments for neurodevelopmental disorders. He earned his BA, MD, and PhD from Washington University in St. Louis, and did his Child Neurology training at the Mayo Clinic in Minnesota. He joined BCH in 2016 as a Pediatric Behavioral Neurology fellow and did his post-doctoral training in the Computational Radiology Laboratory at BCH and the Laboratory for Brain Network Imaging and Modulation at Beth Israel Deaconess Medical Center.

Now, he sees patients in the Autism Spectrum Center and Behavioral Neurology Clinic at BCH and has created a translational neuroimaging laboratory focusing on identifying the causal neuroanatomy of symptoms in autism that can serve as treatment targets for non-invasive stimulation.

Interests

  • The Brain
  • Playing Sitar

Education

  • Translational Post-doctoral Training in Neurodevelopment (T32) Fellowship, 2017-2019

    Boston Children's Hospital

  • Pediatric Behavioral Neurology Clinical Fellowship, 2016-2018

    Boston Children's Hospital

  • Child and Adolescent Neurology Residency, 2011-2016

    Mayo Clinic, Rochester

  • MD/PhD in Biology and Biomedical Sciences (Neurosciences), 2003-2011

    Washington University in St. Louis School of Medicine

  • BA in Biology and Biomedical Physics, 1999-2003

    Washington University in St. Louis

Projects

  • This project seeks to understand whether there are particular networks of regions impacted by lesions that are associated with particular symptoms that are also seen in Autism Spectrum Disorders. We have begun several projects to help answer this question, starting with the symptom of face processing difficulties:

    1. Studying Face Processing in children with Tuberous Sclerosis Complex:
      • This study investigates whether the pattern of cortical tubers in children with Tuberous Sclerosis Complex predicts their face processing ability. If this is true, it would indicate which networks are causally involved in face processing difficulties.

    2. Studying Face Processing in adolescents with Autism Spectrum Disorder:
      • This study investigates whether face processing difficulties in adolescents with ASD is correlated with specific brain network differences, a hypothesis generated from patients with acquired prosopagnosia, i.e., face blindness after brain injury. If this is true, it would mean that the same brain regions are causally involved in face processing difficulties in patients without brain injury.

    3. Studying the correlation between Face Processing and Social Affect:
      • This study leverages large-scale existing datasets to quantify the relationship between face recognition ability and social affect.

Moving forward, we have already started to investigate additional symptoms and are happy to collaborate with researchers interested in applying lesion network mapping to their data.

Generating Developmental Atlases of Brain Connectivity (2018 - present)

  • This project utilizes publicly available brain connectivity data, currently from more than 20,000 children and adolescents, into single consistently processed and quality-controlled datasets that can be used by medical researchers as a ‘gold-standard’ reference of typical development.

    1. The GSP1000 Processed Connectome is derived from data acquired by the Brain Genomics Superstruct Project (GSP), which contained 1570 subjects in total (ages 18-36). From this dataset, 1000 subjects (1:1 M/F) were chosen and processed using publicly available tools to generate a normative functional connectivity dataset. Click this link to download:‘https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/ILXIKS'

Meet the Team

Faculty

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Alexander Cohen, MD, PhD

Principle Investigator

The Brain, Playing Sitar

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Jurriaan M. Peters, MD, PhD

Investigator

Epilepsy, Triathlon

Researchers

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Ayesha Imran

Undergraduate Research Assistant

Neurology, Yoga, Meditation

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Mallory Kroeck, MA, BCN

Research Specialist III

Neuromodulation, Backpacking

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Peter McManus

Clinical Research Assistant

Neurodegenerative Disorders, Golf

Alumni

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Louis Soussand, BA, MS

Data Scientist

Biostatistics, Big Data

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Brechtje Mulder, BS

Medical Student

Medical Education,

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Ivry Zagurly-Orly, MMedEd

Medical Student

Medical Education,

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Maya Fray-Witzer

Intern

Sleep Disorders, Ski Instructing

Recent Publications

(Draft List, refer to Google Scholar)

Quickly discover relevant content by filtering publications.
(2020). Cortical lesions causing loss of consciousness are anticorrelated with the dorsal brainstem. Human Brain Mapping.

(2020). Mapping migraine to a common brain network. Brain.

(2019). Looking beyond the face area: lesion network mapping of prosopagnosia. Brain.

(2019). Pediatric postoperative cerebellar cognitive affective syndrome follows outflow pathway lesions. Neurology.

(2019). Tubers Associated with Infantile Spasms Impact a Common Brain Network in Tuberous Sclerosis Complex. ANNALS OF NEUROLOGY.

Educational Resources

Quick tutorials for scientific computing

Learn to use Markdown to make ‘modern’ text files (10 minutes):

The ‘command line’ and writing shell scripts (~1 hour each):

An introduction to pandas (an alternative to excel):

An introduction to R (an alternative to SPSS/STATA):

Jupyter Notebooks, a great way to organize your python, matlab, and R code:

Git, a way to keep track of versions of your code and share files:

Good articles to read:

Introduction to MRI-fMRI

Useful Courses (free):

Contact