Psilocybin therapy shows antidepressant potential, but its therapeutic actions are not well understood. We assessed the subacute impact of psilocybin on brain function in two clinical trials of ...depression. The first was an open-label trial of orally administered psilocybin (10 mg and 25 mg, 7 d apart) in patients with treatment-resistant depression. Functional magnetic resonance imaging (fMRI) was recorded at baseline and 1 d after the 25-mg dose. Beck's depression inventory was the primary outcome measure ( MR/J00460X/1 ). The second trial was a double-blind phase II randomized controlled trial comparing psilocybin therapy with escitalopram. Patients with major depressive disorder received either 2 × 25 mg oral psilocybin, 3 weeks apart, plus 6 weeks of daily placebo ('psilocybin arm') or 2 × 1 mg oral psilocybin, 3 weeks apart, plus 6 weeks of daily escitalopram (10-20 mg) ('escitalopram arm'). fMRI was recorded at baseline and 3 weeks after the second psilocybin dose ( NCT03429075 ). In both trials, the antidepressant response to psilocybin was rapid, sustained and correlated with decreases in fMRI brain network modularity, implying that psilocybin's antidepressant action may depend on a global increase in brain network integration. Network cartography analyses indicated that 5-HT2A receptor-rich higher-order functional networks became more functionally interconnected and flexible after psilocybin treatment. The antidepressant response to escitalopram was milder and no changes in brain network organization were observed. Consistent efficacy-related brain changes, correlating with robust antidepressant effects across two studies, suggest an antidepressant mechanism for psilocybin therapy: global increases in brain network integration.
Responses to psychedelics are notoriously difficult to predict, yet significant work is currently underway to assess their therapeutic potential and the level of interest in psychedelics among the ...general public appears to be increasing. We aimed to collect prospective data in order to improve our ability to predict acute- and longer-term responses to psychedelics. Individuals who planned to take a psychedelic through their own initiative participated in an online survey (www.psychedelicsurvey.com). Traits and variables relating to set, setting and the acute psychedelic experience were measured at five different time points before and after the experience. Principle component and regression methods were used to analyse the data. Sample sizes for the five time points were
= 654,
= 535,
= 379,
= 315, and
= 212 respectively. Psychological well-being was increased 2 weeks after a psychedelic experience and remained at this level after 4 weeks. Higher ratings of a "mystical-type experience" had a positive effect on the change in well-being after a psychedelic experience, whereas the other acute psychedelic experience measures, i.e., "challenging experience" and "visual effects", did not influence the change in well-being after the psychedelic experience. Having "clear intentions" for the experience was conducive to mystical-type experiences. Having a positive "set" as well as having the experience with intentions related to "recreation" were both found to decrease the likelihood of having a challenging experience. The baseline trait "absorption" and higher drug doses promoted all aspects of the acute experience, i.e., mystical-type and challenging experiences, as well as visual effects. When comparing the relative contribution of different types of variables in explaining the variance in the change in well-being, it seemed that baseline trait variables had the strongest effect on the change in well-being after a psychedelic experience. These results confirm the importance of extra-pharmacological factors in determining responses to a psychedelic. We view this study as an early step towards the development of empirical guidelines that can evolve and improve iteratively with the ultimate purpose of guiding crucial clinical decisions about whether, when, where and how to dose with a psychedelic, thus helping to mitigate risks while maximizing potential benefits in an evidence-based manner.
Despite a century of research, it remains unclear whether human intelligence should be studied as one dominant, several major, or many distinct abilities, and how such abilities relate to the ...functional organisation of the brain. Here, we combine psychometric and machine learning methods to examine in a data-driven manner how factor structure and individual variability in cognitive-task performance relate to dynamic-network connectomics. We report that 12 sub-tasks from an established intelligence test can be accurately multi-way classified (74%, chance 8.3%) based on the network states that they evoke. The proximities of the tasks in behavioural-psychometric space correlate with the similarities of their network states. Furthermore, the network states were more accurately classified for higher relative to lower performing individuals. These results suggest that the human brain uses a high-dimensional network-sampling mechanism to flexibly code for diverse cognitive tasks. Population variability in intelligence test performance relates to the fidelity of expression of these task-optimised network states.
It is well established that religiosity correlates inversely with intelligence. A prominent hypothesis states that this correlation reflects behavioral biases toward intuitive problem solving, which ...causes errors when intuition conflicts with reasoning. We tested predictions of this hypothesis by analyzing data from two large-scale Internet-cohort studies (combined
= 63,235). We report that atheists surpass religious individuals in terms of reasoning but not working-memory performance. The religiosity effect is robust across sociodemographic factors including age, education and country of origin. It varies significantly across religions and this co-occurs with substantial cross-group differences in religious dogmatism. Critically, the religiosity effect is strongest for tasks that explicitly manipulate conflict; more specifically, atheists outperform the most dogmatic religious group by a substantial margin (0.6 standard deviations) during a color-word conflict task but not during a challenging matrix-reasoning task. These results support the hypothesis that behavioral biases rather than impaired general intelligence underlie the religiosity effect.
Promising results from clinical trials have ignited a resurgence of scientific interest in serotonergic psychedelic compounds.Functional neuroimaging investigations of acute psychedelic brain action ...have broadly revealed that they reduce integration within, and segregation between, most large-scale networks. However, spatially specific findings have shown poor convergence.Complex systems approaches characterize the brain as a dynamically evolving system of interacting elements. In doing so, they abstract from localized patterns of activity and instead focus on the dynamical properties of the brain as a whole.Drawing from complexity science, we propose a new ‘meta’ perspective in which psychedelics are catalysts of a distinct mode of brain functioning that is best characterized by dynamical whole-brain features rather than by region- or network-specific changes.
Recent findings suggesting the potential transdiagnostic efficacy of psychedelic-assisted therapy have fostered the need to deepen our understanding of psychedelic brain action. Functional neuroimaging investigations have found that psychedelics reduce the functional segregation of large-scale brain networks. However, beyond this general trend, findings have been largely inconsistent. We argue here that a perspective based on complexity science that foregrounds the distributed, interactional, and dynamic nature of brain function may render these inconsistencies intelligible. We propose that psychedelics induce a mode of brain function that is more dynamically flexible, diverse, integrated, and tuned for information sharing, consistent with greater criticality. This ‘meta’ perspective has the potential to unify past findings and guide intuitions toward compelling mechanistic models.
Abstract
The multicontrast EPImix sequence generates six contrasts, including a T
1
-weighted scan, in ~1 min. EPImix shows comparable diagnostic performance to conventional scans under qualitative ...clinical evaluation, and similarities in simple quantitative measures including contrast intensity. However, EPImix scans have not yet been compared to standard MRI scans using established quantitative measures. In this study, we compared conventional and EPImix-derived T
1
-weighted scans of 64 healthy participants using tissue volume estimates and predicted brain-age. All scans were pre-processed using the SPM12
DARTEL
pipeline, generating measures of grey matter, white matter and cerebrospinal fluid volume. Brain-age was predicted using
brainageR
, a Gaussian Processes Regression model previously trained on a large sample of standard T
1
-weighted scans. Estimates of both global and voxel-wise tissue volume showed significantly similar results between standard and EPImix-derived T
1
-weighted scans. Brain-age estimates from both sequences were significantly correlated, although EPImix T
1
-weighted scans showed a systematic offset in predictions of chronological age. Supplementary analyses suggest that this is likely caused by the reduced field of view of EPImix scans, and the use of a brain-age model trained using conventional T
1
-weighted scans. However, this systematic error can be corrected using additional regression of T
1
-predicted brain-age onto EPImix-predicted brain-age. Finally, retest EPImix scans acquired for 10 participants demonstrated high test-retest reliability in all evaluated quantitative measurements. Quantitative analysis of EPImix scans has potential to reduce scanning time, increasing participant comfort and reducing cost, as well as to support automation of scanning, utilising active learning for faster and individually-tailored (neuro)imaging.
Diverse cortical networks and striatal brain regions are implicated in instruction-based learning (IBL); however, their distinct contributions remain unclear. We use a modified fMRI paradigm to test ...two hypotheses regarding the brain mechanisms that underlie IBL. One hypothesis proposes that anterior caudate and frontoparietal regions transiently co-activate when new rules are being bound in working memory. The other proposes that they mediate the application of the rules at different stages of the consolidation process. In accordance with the former hypothesis, we report strong activation peaks within and increased connectivity between anterior caudate and frontoparietal regions when rule-instruction slides are presented. However, similar effects occur throughout a broader set of cortical and sub-cortical regions, indicating a metabolically costly reconfiguration of the global brain state. The distinct functional roles of cingulo-opercular, frontoparietal and default-mode networks are apparent from their activation throughout, early and late in the practice phase respectively. Furthermore, there is tentative evidence of a peak in anterior caudate activity mid-way through the practice stage. These results demonstrate how performance of the same simple task involves a steadily shifting balance of brain systems as learning progresses. They also highlight the importance of distinguishing between regional specialisation and global dynamics when studying the network mechanisms that underlie cognition and learning.
•We examine regional brain activity at different stages of instruction based learning.•Widespread transient activity occurs in the brain when new rules are presented.•This is consistent with a metabolically costly reconfiguration of the global brain state.•Frontoparietal & default-mode networks activate early vs late in the practice phase.•A small peak in anterior-caudate activity occurs midway through the practice phase.
Current neuroimaging acquisition and processing approaches tend to be optimised for quality rather than speed. However, rapid acquisition and processing of neuroimaging data can lead to novel ...neuroimaging paradigms, such as adaptive acquisition, where rapidly processed data is used to inform subsequent image acquisition steps. Here we first evaluate the impact of several processing steps on the processing time and quality of registration of manually labelled T1‐weighted MRI scans. Subsequently, we apply the selected rapid processing pipeline both to rapidly acquired multicontrast EPImix scans of 95 participants (which include T1‐FLAIR, T2, T2*, T2‐FLAIR, DWI and ADC contrasts, acquired in ~1 min), as well as to slower, more standard single‐contrast T1‐weighted scans of a subset of 66 participants. We quantify the correspondence between EPImix T1‐FLAIR and single‐contrast T1‐weighted scans, using correlations between voxels and regions of interest across participants, measures of within‐ and between‐participant identifiability as well as regional structural covariance networks. Furthermore, we explore the use of EPImix for the rapid construction of morphometric similarity networks. Finally, we quantify the reliability of EPImix‐derived data using test–retest scans of 10 participants. Our results demonstrate that quantitative information can be derived from a neuroimaging scan acquired and processed within minutes, which could further be used to implement adaptive multimodal imaging and tailor neuroimaging examinations to individual patients.
We first evaluate the impact of several processing steps on the processing time and quality of registration of manually labelled T1‐weighted MRI scans, and apply the selected rapid processing pipeline both to standard T1‐weighted scans, as well as rapidly acquired multicontrast EPImix scans (which include T1‐FLAIR, T2, T2*, T2‐FLAIR, DWI and ADC contrasts, acquired in ~1 min). We then quantify the correspondence between rapidly processed EPImix T1‐FLAIR and single‐contrast T1‐weighted scans using several methods, and explore the use of EPImix for the rapid construction of morphometric similarity networks. Our results demonstrate that quantitative information can be derived from a neuroimaging scan within minutes; this could be used to implement adaptive multimodal imaging, where rapidly processed data is used to inform subsequent image acquisition steps and tailor neuroimaging examinations to individual patients.
Objective
Huntington's disease (HD) gene carriers can be identified before clinical diagnosis; however, statistical models for predicting when overt motor symptoms will manifest are too imprecise to ...be useful at the level of the individual. Perfecting this prediction is integral to the search for disease modifying therapies. This study aimed to identify an imaging marker capable of reliably predicting real‐life clinical diagnosis in HD.
Method
A multivariate machine learning approach was applied to resting‐state and structural magnetic resonance imaging scans from 19 premanifest HD gene carriers (preHD, 8 of whom developed clinical disease in the 5 years postscanning) and 21 healthy controls. A classification model was developed using cross‐group comparisons between preHD and controls, and within the preHD group in relation to “estimated” and “actual” proximity to disease onset. Imaging measures were modeled individually, and combined, and permutation modeling robustly tested classification accuracy.
Results
Classification performance for preHDs versus controls was greatest when all measures were combined. The resulting polymarker predicted converters with high accuracy, including those who were not expected to manifest in that time scale based on the currently adopted statistical models.
Interpretation
We propose that a holistic multivariate machine learning treatment of brain abnormalities in the premanifest phase can be used to accurately identify those patients within 5 years of developing motor features of HD, with implications for prognostication and preclinical trials. Ann Neurol 2018;83:532–543
•DAT-specific radioligands and PET is one measure of nigrostriatal dopaminergic integrity.•Basal ganglia functional connectivity can differentiate patients with PD from healthy controls.•Striatal ...functional connectivity is dependent on the integrity of dopaminergic system.•Basal ganglia functional connectivity is compromised by the dopaminergic pathology of PD.
Resting-state functional magnetic resonance imaging (fMRI) studies have demonstrated that basal ganglia functional connectivity is altered in Parkinson’s disease (PD) as compared to healthy controls. However, such functional connectivity alterations have not been related to the dopaminergic deficits that occurs in PD over time.
To examine whether functional connectivity impairments are correlated with dopaminergic deficits across basal ganglia subdivisions in patients with PD both cross-sectionally and longitudinally.
We assessed resting-state functional connectivity of basal ganglia subdivisions and dopamine transporter density using 11C-PE2I PET in thirty-four PD patients at baseline. Of these, twenty PD patients were rescanned after 19.9 ± 3.8 months. A seed-based approach was used to analyze resting-state fMRI data. 11C-PE2I binding potential (BPND) was calculated for each participant. PD patients were assessed for disease severity.
At baseline, PD patients with greater dopaminergic deficits, as measured with 11C-PE2I PET, showed larger decreases in posterior putamen functional connectivity with the midbrain and pallidum. Reduced functional connectivity of the posterior putamen with the thalamus, midbrain, supplementary motor area and sensorimotor cortex over time were significantly associated with changes in DAT density over the same period. Furthermore, increased motor disability was associated with lower intraregional functional connectivity of the posterior putamen.
Our findings suggest that basal ganglia functional connectivity is related to integrity of dopaminergic system in patients with PD. Application of resting-state fMRI in a large cohort and longitudinal scanning may be a powerful tool for assessing underlying PD pathology and its progression.
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