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 is part of the rapidly growing field of computational psychiatry, where mental health data 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-assisted therapy (PATH) 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 investigating the therapeutic potential of psilocybin-assisted psychotherapy (PAP) to treat trauma-related mental health disorders. This work is funded by the newly created PATH Fund through the UMN Foundation, which was created thanks to a generous donation from the community. These, and other data, are part of a larger network of data supporting the potential for psychedelic therapies for the treatment of numerous mental health conditions (see network below).
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 computational psychiatry research projects include combining dimension reduction, causal 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 > 250,000).
- Standardized data can be queried across multiple domains for clinical/phenotype, neurophysiology, imaging, genomics, disorder-specific assessments and completed treatment trials.
2) 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.
3) 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 traumatic brain injury (TBI) patients through the TRACK-TBI study.
- 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.
4) 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
5) 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.
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 therapy is undergoing a renaissance within the scientific arena, where an astounding volume of literature and data are emerging to support this new frontier in 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. While this added attention has helped in the re-birth of the scientific inquiry of psychedelics, it has also given rise to increased "underground" facilities that lead to exploitation of resources and harm to those seeking them out for psychological and spiritual support.
Psychedelic therapy 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. Current projects include the development of a pilot study of psilocybin-assisted psychotherapy for treating trauma-related mental health disorders, and analysis of data from an anonymous online survey of ayahuasca users in naturalistic settings.
The network below is a model of overlapping symptoms of trauma, and the respective psychedelic therapies that have published research (red numbers and reference list) suggesting a therapeutic benefit.
References for psychedelic therapy network
1. LSD and psilocybin for end of life anxiety
2. LSD and psilocybin for 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:
The safety and efficacy of ±3,4-methylenedioxymethamphetamine-assisted psychotherapy in subjects with chronic, treatment-resistant posttraumatic stress disorder: the first randomized controlled pilot study
Durability of improvement in post-traumatic stress disorder symptoms and absence of harmful effects or drug dependency after 3,4-methylenedioxymethamphetamine-assisted psychotherapy: a prospective long-term follow-up study
11. MDMA for social anxiety in autistic adults:
12. Cannabidiol for Dravet's syndrome:
13. Marijuana for PTSD: