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David J. Hellerstein

7 papers in the library · 388 citations · publishing 2013-2026

Papers

Antidepressants Normalize the Default Mode Network in Patients With Dysthymia

JAMA Psychiatry February 6, 2013 Jonathan Posner, David J. Hellerstein, Inbal Gat et al. 275 citations

People with dysthymic disorder (DD) show greater coherence of neural activity within the brain's default mode network (DMN) compared with healthy controls, similar to abnormalities seen in major depressive disorder. In a 10-week double-blind, placebo-controlled trial of duloxetine, treatment with duloxetine normalized DMN connectivity, while placebo did not. The findings suggest increased DMN connectivity may be important in the pathophysiology of depressive illness, and its normalization may be a causal pathway through which antidepressants reduce depression.

Assessing the risk–benefit profile of classical psychedelics: a clinical review of second-wave psychedelic research

Psychopharmacology January 13, 2022 David Bender, David J. Hellerstein 82 citations

A review of studies since 1991 identified 14 clinical trials of classical psychedelics—11 of psilocybin (257 participants), 1 of LSD (12 participants), and 2 of ayahuasca (46 participants). Published studies largely support the hypothesis that small numbers of treatments with psychedelic-assisted psychotherapy can offer significant and sustained alleviation of symptoms for multiple psychiatric conditions. No serious adverse events attributed to psychedelic therapy have been reported. Existing studies have limitations including small sample sizes, difficulty in blinding, limited follow-up, and highly screened treatment populations. The ideal means of employing these substances to minimize adverse events and maximize therapeutic effects remains controversial.

Psilocybin therapy for treatment resistant depression: prediction of clinical outcome by natural language processing

Psychopharmacology August 22, 2023 Robert F. Dougherty, Patrick Clarke, Merve Atli et al. 21 citations

A machine learning model that analyzes language from therapy sessions can predict which patients with treatment-resistant depression will respond to psilocybin therapy. Researchers used a zero-shot classifier based on the BART large language model to measure sentiment (valence and arousal) in transcripts of therapist-patient conversations one day after COMP360 psilocybin administration. These sentiment scores, combined with the Emotional Breakthrough Index and treatment arm, were fed into multinomial logistic regression models. The models predicted responder status at week 3 and through week 12 with 85% and 88% accuracy, respectively, and AUC values of 88% and 85%. This approach could enable early identification of patients needing alternative treatments.

Single-dose psilocybin alters resting state functional networks in patients with body dysmorphic disorder

Psychedelics. September 24, 2024 Xi Zhu, Chen Zhang, David J. Hellerstein et al. 6 citations

A single 25 mg dose of psilocybin, given with psychological support, led to significant reductions in body dysmorphic disorder symptoms at one week and twelve weeks after dosing in eight adults with moderate-to-severe nondelusional BDD. Resting state functional connectivity measured one day after dosing showed increased connectivity within the Executive Control Network and between the Executive Control Network, Default Mode Network, and Salience Network. These connectivity increases predicted symptom improvement at one week. The authors note the small sample size and uncontrolled design require larger controlled studies to validate the findings.

Psilocybin Therapy for Treatment Resistant Depression: Prediction of Clinical Outcome by Natural Language Processing

September 30, 2022 Robert F. Dougherty, Patrick Clarke, Merve Alti et al. 3 citations preprint

A machine learning model that analyzes language from therapy sessions accurately predicted which patients with treatment-resistant depression would respond to psilocybin treatment. Transcripts of psychological support sessions held one day after COMP360 (a synthetic psilocybin formulation) administration were analyzed using a zero-shot classifier based on the BART large language model to measure sentiment (valence and arousal) for both participant and therapist. These scores, combined with the Emotional Breakthrough Index and treatment arm, were used to predict treatment outcome measured by MADRS scores. Two multinomial logistic regression models predicted responder status at week 3 and through week 12 with 85% and 88% accuracy, and AUC values of 88% and 85%, respectively. The approach enables rapid prognostication of personalized response to psilocybin treatment and insights into therapeutic model optimization.

Trends in the psychedelic renaissance: applying artificial intelligence to measure media portrayal of psychedelic drugs in the 21st century

BJPsych Open February 12, 2026 David A. Bender, Holly Dunn, Amanda Pekau et al. 1 citation

From 2000 to 2025, media coverage of psychedelic drugs increasingly focused on their therapeutic potential, rising from 13.3% of articles in 2000–2009 to 85.3% in 2020–2025. Overall sentiment was positive, with an average score of 78.5 out of 100. However, negative and neutral coverage has grown since 2020: the proportion of articles with sentiment scores of 65 or below rose from 3.6% in 2020 to 20.9% in 2024, and average sentiment dropped significantly in 2024 compared to 2020–2023. Artificial intelligence sentiment ratings closely matched human ratings.