About

The overarching purpose of the Nielson lab is to understand and treat trauma. Research projects in the Nielson lab utilize a multidisciplinary approach, merging the fields of neurobiology, psychiatry and informatics to identify more precise "bio-types" of trauma psychopathology than traditional diagnostic criteria, and potential novel targets for treatment. We use established and emerging machine learning methods with multi-modal data spanning across a diverse range of diagnostic categories for neuropsychiatric disorders. Our approach can be used to run hypotheses on in silico models to understand the complexity involved in these disorders. An advantage of such approaches is the minimization for the need to test hypotheses in animal models (in vivo). Dr. Nielson received an Early Stage Investigator (ESI) award from NIMH to apply these methods to large datasets from trauma-exposed patients to identify and validate dimensions of post-traumatic stress (PTS), relevant biological predictors, and precision treatment response trajectories.

Other areas of focus in the Nielson lab are dedicated to psychedelic neuroscience research and drug policy reform. Dr. Nielson has been collecting data through an anonymous online survey to assess benefits and risks of ayahuasca use in naturalistic settings to treat symptoms of trauma. Dr. Nielson is also conducting research into the neurological mechanisms of altered states of consciousness and their role in promoting neuroplasticity and wellness in healthy research participants. This work is funded by the newly created Psychedelic-Assisted THerapy (PATH) Fund through the UMN Foundation, which was created thanks to a generous donation from the local community. These, and other data, are part of a larger network of data supporting the potential for psychedelic therapies and experiences to promote wellness across a diverse range of mental health disorders (see network below).

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Mental Health Informatics Research

Recent efforts to optimize diagnosis and treatment planning for psychiatric disorders through the Research Domain Criteria (RDoC) and ongoing clinical trials have generated large datasets housed in the NIMH Data Archive (NDA). The NDA is a valuable resource for data-driven discovery in mental health research. Data across multiple units of measure from a wide array of disorders are housed in the NDA. We will mine data from the NDA to characterize biotypes that span across diagnostic categories, and the complex constellation of symptoms and functional deficits that can be measured in individual patients.

Current research projects include combining dimension reduction, causal inference analyses, machine learning and topological data analysis (TDA) to characterize subtypes and predictors of syndrome trajectories and treatment response.

Current datasets include:

1) De-identified data from the NIMH Data Archive (NDA)

Contains data from clinical research on a wide range of mental health disorders for transdiagnostic and precision treatment research (N > 360,000).
Standardized data can be queried across multiple domains for clinical/phenotype, neurophysiology, imaging, genomics, disorder-specific assessments and completed treatment trials.
UMN IRB approval on May 1, 2018: STUDY00003370

2) De-identified data from the Federal Interagency on Traumatic Brain Injury Research (FITBIR) Informatics System

Contains data from clinical research on traumatic brain injury in children and adults (N>63,000).
Standardized data can be queried across multiple domains for clinical/phenotype, neurophysiology, imaging, domain-specific assessments and completed treatment trials.
UMN IRB approval on May 1, 2018: STUDY00003370

3) De-identified data from the National Institute of Drug Abuse (NIDA) Data Repository

Data from ~ 26,000 patients enrolled in NIDA-funded clinical trials
Outcomes include substance use, health measures and neuropsychiatric outcomes collected over time in a variety of patient populations struggling with substance abuse.
UMN IRB approval on May 1, 2018: STUDY00003370

4) Data from an anonymous online survey to assess the therapeutic uses of the plant medicine ayahuasca.

Contains structured data from questionnaire-based assessments related to symptoms of trauma, including PTSD, substance use, and depression.
Contains unstructured data from open-ended responses describing dangerous, beneficial and general experiences associated with ayahuasca use.
Collected under UCSF IRB #16-19906, approved for reuse/transferred to UMN IRB RNI00004357

5) Neurotrauma datasets from animal models and de-identified observational clinical studies assessing complications associated with trauma exposure.

Preclinical (animal models) spinal cord injury (SCI) data from the Open Data Commons for SCI (ODC-SCI)
De-identified observational data from patients with post-traumatic stress disorder (PTSD), monitoring cardiovascular health during disease progression from the Mind Your Heart (MYH) study.

6) De-identified data from cognitive training interventions for schizophrenia

Assessments before and after training for cognitive and symptom changes
ELISA data from serum samples before and after training for blood-based biomarker discovery

7) De-identified data from the The National Survey on Drug Use and Health (NSDUH).

NSDUH contains data about the use of illegal drugs, alcohol, and tobacco, diagnosed mental disorders and related treatments within the U.S. population, ages 12 or older.
Data tables available from 1979 to 2016.

8) De-identified data from the Hazelden Betty Ford Foundation in Center City, MN

The lab has begun a collaboration with the Center City location to receive data from ~ 20,000 people who have received treatment for substance use disorders over the past 10 years
Data include medical history, treatment options, and long term follow up of SUD-related outcomes.
Our lab will focus on identifying trauma history risk factors for recovery and relapse trajectories.
UMN IRB approval on May 1, 2018: STUDY00003370

9) Visual surround suppression and perceptual expectation under psilocybin - enrollment starting in Spring 2021

FDA approval on January 2, 2020: IND #146989
UMN IRB approval on August 14, 2020: STUDY00009765/SITE00000856/Pro00045074

The figure below illustrates a data-driven hypothesis that was found regarding functional decline in patients with PTSD following traumatic brain injury (TBI). Using a machine learning method known as topological data analysis (TDA), a gene involved in DNA repair was uncovered to have predictive value on functional outcome trajectories in a subgroup of patients with co-morbid PTSD and mild TBI.

Figure Legend. Network topology of patients with traumatic brain injury differentiates severity of brain pathology measured by both CT (A) and MRI (B), where most patients with a PTSD diagnosis 6 months (C) after injury have little to no obvious brain pathology (circled nodes). Highlighted patients show a decline in function between 3 (D) and 6 months (E) after injury, which was significantly predicted by enrichment for the A/T genotype of the PARP1 SNP (F, green nodes), a gene involved in DNA repair. Published in Nielson et al., 2017, PLoS One.

Psychedelic Research

Psychedelic research is undergoing a renaissance within the scientific arena, where an astounding volume of literature and data are emerging to support this new frontier in science and medicine. There is also increasing attention being paid in the popular press towards their therapeutic potential, including notable mediums like The New Yorker, NY Times, and Rolling Stone. Additionally, compounds such as MDMA and psilocybin have recently been granted breakthrough therapy designation by the FDA for PTSD and depression, respectively, making this area of research an exciting and innovate avenue for continued scientific exploration into their mechanisms of action.

Psychedelic research in my lab focuses on collecting data from completed and ongoing psychedelic research studies to develop an evidence base for drug policy reform and treatment planning, as well as to understand the mechanisms for how these compounds inform new models about information processing in the human brain. Current projects include analysis of data from an anonymous online survey of ayahuasca users in naturalistic settings, and investigating the role of the 5HT2A receptor on neural mechanisms related to visual perception and expectations.

The network below is a model of overlapping symptoms of trauma, and various psychedelic therapies that have published research (red numbers and reference list) suggesting a therapeutic benefit.

References for psychedelic therapy network

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Psychedelic Research

References for psychedelic therapy network

1. LSD, psilocybin or MDMA for end of life anxiety

2. LSD and psilocybin for migraine and cluster headaches

3. Psilocybin for depression

4. Ayahuasca for depression

5. Ayahuasca for addiction

6. Ayahuasca for PTSD

7. Ibogaine for addiction

8. Psilocybin for addiction

9. Psilocybin for OCD

10. MDMA for PTSD

11. MDMA for social anxiety in autistic adults

12. Cannabidiol for Dravet's syndrome

13. Marijuana for PTSD

14. Ketamine for depression (in progress, many trials on this)

15. Ketamine for PTSD

16. MDMA for addiction

17. Ketamine for addiction