![]() ![]() Samuel Friedman, a machine learning researcher at MIT, came to the psychedelic question via an enigmatic region of the central nervous system called the “default mode network.” Formerly known as the “task-negative network,” the DMN is thought to govern some of our most introspective behaviors. That’s hard to replicate under scientific observation. People usually trip in scenic environments with their friends. Last year, however, a group of interdisciplinary researchers announced a simple but powerful work-around: using AI and brain imaging, they found a way to draw directly from the experiences of some of the internet’s chattiest psychonauts-potentially paving the way for a new class of hybrid drugs. Venture capitalists are pouring millions of dollars into new psychedelic-based antidepressants that move beyond the classic SSRI model, but without a precise index of the neural processes at work, pharmaceutical patents are unlikely to be approved. To the researcher, this same variability represents a bottleneck, and a costly one. ![]() Drug nerds-the true bearers of this occult knowledge-love to share their unique experiences with each other. The temperamental nature of psychedelics is essential to their appeal. That’s not to mention the inherent contradiction of conducting clinical trials on any social (or at least highly contextual) drug. Even now, as some psychedelics pass the threshold to decriminalization, large-scale trials remain few and far between-a problem for research into such a variable experience. Until recently, illegality hampered clinical research. People have been doing psychedelics for at least three thousand years, and yet our understanding of how these substances interact with our nervous systems is still in its infancy. ![]() (PsycInfo Database Record (c) 2021 APA, all rights reserved).“If there is one quick truism about psychedelic drugs it is that anyone who tries to write about them without first-hand experience is a fool and a fraud.” - Hunter S. More generally, the integrative cognitive neuroscience approach used here establishes a framework for directly testing theories of cognitive and brain function using the combined power of behavioral and fMRI data. The model-based analysis suggests that key areas in the dorsal attention network such as the intraparietal sulcus play a central role in change detection rather than working memory maintenance, counter to previous interpretations of fMRI studies. Results reveal neural patterns predicted by the model within regions of the dorsal attention network that have been the focus of much debate. Critically, predictions of the model run counter to cognitive theories of the origin of errors in change detection. The model explains performance on both correct trials and incorrect trials where errors in change detection emerge from neural fluctuations amplified by neural interaction. In an exemplary study of visual working memory, we use multilevel Bayesian statistics to demonstrate that a neural dynamic model simultaneously explains behavioral data and predicts localized patterns of brain activity, outperforming standard analytic approaches to fMRI. Here, we show how an integrative cognitive neuroscience approach may bridge this gap. The impact of this consensus is limited, however, by a gap that exists between data-driven correlational analyses that specify where functional brain activity is localized using functional magnetic resonance imaging (fMRI), and neural process accounts that specify how neural activity unfolds through time to give rise to behavior. There is consensus that activation within distributed functional brain networks underlies human thought.
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