Skip to content

Laith Alexander

Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.

5 papers in the library · 75 citations · publishing 2020-2026

Papers

Preliminary evidence that ketamine alters anterior cingulate resting-state functional connectivity in depressed individuals

Translational Psychiatry December 3, 2023 Laith Alexander, Peter C. T. Hawkins, Jennifer W. Evans et al. 26 citations

Ketamine's antidepressant effects involve changes in brain connectivity that depend on which part of the anterior cingulate cortex (ACC) is examined. In a double-blind, placebo-controlled crossover trial, patients with treatment-resistant depression and healthy volunteers received intravenous ketamine or placebo. Two days later, resting-state functional connectivity between ACC subregions and other brain areas differed between groups. Changes in perigenual ACC connectivity to the insula correlated with improved depression scores. Subgenual ACC connectivity was most altered by ketamine compared to placebo, and changes in its connectivity to other ACC subregions and the ventral striatum correlated with reduced anhedonia. Accurate ACC segmentation is needed to understand ketamine's effects.

A transdiagnostic systematic review and meta-analysis of ketamine’s anxiolytic effects

medRxiv December 11, 2022 Hannah Hartland, Kimia Mahdavi, L. Jelen et al. 22 citations

Ketamine reduces anxiety symptoms within 12 hours of administration, and the effect lasts for 1 to 2 weeks. A systematic review and meta-analysis of 14 randomized controlled trials found significant reductions in anxiety scores compared to placebo at acute (less than 12 hours), subacute (24 hours), and sustained (7–14 days) time points. Improvements in anxiety and depression symptoms were correlated at 24 hours and at 7–14 days. The relationship between peak dissociation and anxiety improvement was not significant. Most studies had a high risk of bias.

Psychedelic science in post-COVID-19 psychiatry

Irish Journal of Psychological Medicine August 19, 2020 John R. Kelly, Matthew Crockett, Laith Alexander et al. 16 citations

The medium- to long-term mental health consequences of COVID-19 are predicted to increase, requiring multidisciplinary strategies. Psilocybin therapy shows promise as a transdiagnostic treatment for disorders with maladaptive habitual patterns, such as depression, addiction, and obsessive compulsive disorder. The COMPASS Pathways phase 2b double-blind trial is testing psilocybin therapy in antidepressant-free treatment-resistant depression (TRD) to determine safety, efficacy, and optimal dose. Results from the Imperial College London Psilodep-RCT comparing psilocybin therapy to the SSRI escitalopram will soon be published. The efficacy and safety of psilocybin therapy with SSRIs in TRD is not yet known; a COMPASS study in Dublin will address this. Psilocybin therapy may play an important role in post-COVID-19 psychiatry, though it is at an early clinical stage.

Preclinical models for evaluating psychedelics in the treatment of major depressive disorder.

British journal of pharmacology October 28, 2024 Laith Alexander, Dasha Anderson, Luke Baxter et al. 11 citations

Psychedelic drugs are being investigated as a new class of rapid-acting antidepressants, but their mechanisms remain unclear—specifically whether antidepressant and psychedelic effects arise from related or independent processes. This review examines behavioral methods used in animal studies to measure both the psychedelic and antidepressant effects of these drugs. It highlights conceptual and methodological challenges, stresses the importance of using doses comparable to those in human clinical use, and calls for attention to potential sex differences in preclinical research. Understanding these mechanisms could help identify new drug targets and improve treatments.

Structural imaging predictors of ketamine response in treatment-resistant depression: a machine learning approach.

Translational psychiatry May 12, 2026 Linda Bryant, Laith Alexander, Sergio Mena et al.

A machine-learning model using structural brain scans predicted which adults with treatment-resistant depression would respond to a single ketamine infusion. The model, trained on 99 participants, achieved 72% balanced accuracy in the discovery sample and 60% in two independent groups, with performance dropping to chance in a saline-treated control group. Greater gray matter volume in frontal regions predicted response, while greater cerebellar volume predicted non-response. The findings suggest that pre-treatment brain structure may help guide personalized treatment decisions for ketamine therapy.