Richard E. Daws

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 included N= 654, N= 535, N= 379, N= 315, and N= 212 respectively. Psychological well-being was increased two weeks after a psychedelic experience and remained at this level after four weeks. This increase was larger for individuals who scored higher for a ‘mystical-type experience’, and smaller for those who scored higher for ‘challenging 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 trait ‘absorption’ and higher drug doses promoted both mystical-type and challenging experiences. When comparing different types of variables, traits variables seemed to explain most variance in 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 reduce risks while maximising potential benefits in an evidence-based manner.

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.

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.

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.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.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.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.

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.

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 N = 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.

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.

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.

Neuroimaging studies suggest greater involvement of the left parietal lobe in sign language compared to speech production. This stronger activation might be linked to the specific demands of sign encoding and proprioceptive monitoring. In Experiment 1 we investigate hemispheric lateralization during sign and speech generation in hearing native users of English and British Sign Language (BSL). Participants exhibited stronger lateralization during BSL than English production. In Experiment 2 we investigated whether this increased lateralization index could be due exclusively to the higher motoric demands of sign production. Sign naïve participants performed a phonological fluency task in English and a non-sign repetition task. Participants were left lateralized in the phonological fluency task but there was no consistent pattern of lateralization for the non-sign repetition in these hearing non-signers. The current data demonstrate stronger left hemisphere lateralization for producing signs than speech, which was not primarily driven by motoric articulatory demands.

Background Gambling disorder is a relatively common psychiatric disorder recently re-classified within the DSM-5 under the category of ‘substance-related and addictive disorders'. Aims To compare white matter integrity in patients with gambling disorder with healthy controls; to explore relationships between white matter integrity and disease severity in gambling disorder. Method In total, 16 participants with treatment-resistant gambling disorder and 15 healthy controls underwent magnetic resonance imaging (MRI). White matter integrity was analysed using tract-based spatial statistics. Results Gambling disorder was associated with reduced fractional anisotropy in the corpus callosum and superior longitudinal fasciculus. Fractional anisotropy in distributed white matter tracts elsewhere correlated positively with disease severity. Conclusions Reduced corpus callosum fractional anisotropy is suggestive of disorganised/damaged tracts in patients with gambling disorder, and this may represent a trait/vulnerability marker for the disorder. Future research should explore these measures in a larger sample, ideally incorporating a range of imaging markers (for example functional MRI) and enrolling unaffected first-degree relatives of patients.

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.

The multicontrast EPImix sequence generates six contrasts, including a T1-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 T1-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 T1-weighted scans. Estimates of both global and voxel-wise tissue volume showed significantly similar results between standard and EPImix-derived T1-weighted scans. Brain-age estimates from both sequences were significantly correlated, although EPImix T1-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 T1-weighted scans. However, this systematic error can be corrected using additional regression of T1-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.

Trichotillomania is a relatively common psychiatric condition, although its neurobiological basis is unknown. Abnormalities of flexible responding have been implicated in the pathophysiology of obsessive-compulsive disorder and thus may be relevant in trichotillomania. The purpose of this study was to probe reversal learning and attentional set-shifting in trichotillomania. Twelve adults with trichotillomania and 13 matched healthy control subjects undertook a functional MRI task of cognitive flexibility. Group-level activation maps for extradimensional and reversal switches were independently parcellated into discrete regions of interest using a custom watershed algorithm. Activation magnitudes were extracted from each region of interest and study subject and compared at the group level. Reversal events evoked the expected patterns of insula and parietal regions and activity in the frontal dorsal cortex extending anterior to the frontal poles, whereas extradimensional shifts evoked the expected frontal dorsolateral and parietal pattern of activity. Trichotillomania was associated with significantly increased right middle frontal and reduced right occipital cortex activation during reversal and set-shifting. Elevated frontal activation coupled with reduced activation in more posterior brain regions was identified. These pilot data suggest potentially important neural dysfunction associated with trichotillomania.

Abstract Importance Psilocybin therapy shows antidepressant potential; our data link its antidepressant effects to decreased brain network modularity post-treatment. Objective To assess the sub-acute impact of psilocybin on brain activity in patients with depression. Design Pre vs post-treatment resting-state functional MRI (fMRI) was recorded in two trials: 1) Open-label treatment-resistant depression (TRD) trial with baseline vs 1 day post-treatment fMRI (April-2015 to April-2016); 2) Two-arm double-blind RCT in major depressive disorder (MDD), fMRI baseline vs 3 week after psilocybin-therapy or 6 weeks of daily escitalopram (January-2019 to March-2020). Setting Study visits occurred at the NIHR Imperial Clinical Research Facility. Participants Adult male and female patients with TRD or MDD. Intervention(s) (for clinical trials) or Exposure(s) (for observational studies) Study 1: Two oral doses of psilocybin (10mg and 25mg, fixed order, 7 days apart). fMRI was recorded at baseline and one day after the 25mg dose. Study 2: either: 2 x 25mg oral psilocybin, 3 weeks apart, plus 6 weeks of daily placebo (‘psilocybin-arm’), or 2 x 1mg oral psilocybin, 3 weeks apart, plus 6 weeks of daily escitalopram [10-20mg] (‘escitalopram-arm’). fMRI was recorded at baseline and 3 weeks after the 2nd psilocybin dose, which was the final day of the 6-week daily capsule ingestion. Main Outcome(s) and Measure(s) Beck Depression Inventory and fMRI network modularity. Results Study 1: In 16 adults (mean age [SD], 42.8 [10.1] years, 4 [25%] female), psilocybin therapy was associated with markedly decreased BDI scores at 1 week (mean difference, -21; 95% CI=[-27.3, -14.7], P <.001) and 6 months (mean difference, -14.19; 95% CI=[-21.3, -7.1], P <.001). Decreased network modularity at one day post-treatment correlated with treatment response at 6 months (Pearson, 0.64; P =.01). Study 2: In 43 adults (42.7 [10.5] years, 14 [33%] female), antidepressant effects favoured the psilocybin-arm at 2 (mean difference, -8.76; 95% CI=[-13.6, -3.9], P =.002) and 6 weeks (mean difference, -8.78; 95% CI=[-15.6, -2.0], P =.01). Specific to the psilocybin-arm, improvements at the 6-week primary endpoint correlated with decreased network modularity (Pearson, -0.42, P =.025). Conclusions and Relevance Consistent efficacy-related functional brain changes correlating with robust and reliable antidepressant effects across two studies suggest a candidate antidepressant mechanism for psilocybin therapy: decreased brain network modularity. Trial registration ClinicalTrials.gov identifier: NCT03429075

Abstract The default-mode network (DMN) has been primarily associated with internally-directed and self-relevant cognition. This perspective is expanding to recognise its importance in executive behaviours like switching. We investigated the effect different task-switching manipulations have on DMN activation in two studies with novel fMRI paradigms. In the first study, the paradigm manipulated visual discriminability, visuo-perceptual distance and sequential predictability during switching. Increased posterior cingulate/precuneus (PCC/PrCC) activity was evident during switching; critically, this was strongest when the occurrence of the switch was predictable. In the second study, we sought to replicate and further investigate this switch-related effect with a fully factorial design manipulating sequential, spatial and visual-feature predictability. Whole-brain analysis again identified a PCC/PrCC-centred cluster that was more active for sequentially predictable versus unpredictable switches, but not for the other predictability dimensions. We propose PCC/PrCC DMN subregions may play a prominent executive role in mapping the sequential structure of complex tasks.

We developed two novel self-ordered switching (SOS) fMRI paradigms to investigate how human behaviour and underlying network resources are optimised when learning to perform complex tasks with multiple goals. SOS was performed with detailed feedback and minimal pretraining (study 1) or with minimal feedback and substantial pretraining (study 2). In study 1, multiple-demand (MD) system activation became less responsive to routine trial demands but more responsive to the executive switching events with practice. Default Mode Network (DMN) activation showed the opposite relationship. Concomitantly, reaction time learning curves correlated with increased connectivity between functional brain networks and subcortical regions. This 'fine-tuning' of network resources correlated with progressively more routine and lower complexity behavioural structure. Furthermore, overall task performance was superior for people who applied structured behavioural routines with low algorithmic complexity. These behavioural and network signatures of learning were less evident in study 2, where task structure was established prior to entering the scanner. Together, these studies demonstrate how detailed feedback monitoring enables network resources to be progressively redeployed in order to efficiently manage concurrent demands.

Abstract Diffusion magnetic resonance imaging (MRI) provides useful information about neuroanatomy and improves detection of neuropathology. As yet, a comprehensive evaluation of the diffusivity parameters within the feline brain has not been documented. In this study, we anesthetized and performed in vivo MRI on the brain of eight neurologically normal felines. A T1‐weighted structural sequence with a resolution of 0.5 mm 3 and a parallel diffusion weighted sequence with 61 directions and a resolution of 1.5 mm 3 was obtained. After correction and processing the diffusion brain data were parcellated into 151 regions of interest using previously published priors. These regions were grouped according to their lobar location within the brain (frontal, occipital, temporal, parietal, thalamus, midbrain, cerebellum, and white matter). The mean and standard deviation of fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) for these 151 individual regions and lobar groups were calculated and averaged across participants, creating a comprehensive distribution range of diffusion tensor values. When regions were statistically evaluated, white matter had significantly higher FA and RD and lower AD and MD diffusivity parameters when compared to other regions. Additionally, thalamic regions had significantly higher FA values than parietal and occipital regions. This information will not only help inform feline neuroanatomy but also will serve as a reference standard for future feline neuroimaging studies.

The cognitive deficits associated with Parkinson's disease vary across individuals and change across time, with implications for prognosis and treatment. Key outstanding challenges are to define the distinct behavioural characteristics of this disorder and develop diagnostic paradigms that can assess these sensitively in individuals. In a previous study, we measured different aspects of attentional control in Parkinson's disease using an established fMRI switching paradigm. We observed no deficits for the aspects of attention the task was designed to examine; instead those with Parkinson's disease learnt the operational requirements of the task more slowly. We hypothesized that a subset of people with early-to-mid stage Parkinson's might be impaired when encoding rules for performing new tasks. Here, we directly test this hypothesis and investigate whether deficits in instruction-based learning represent a characteristic of Parkinson's Disease. Seventeen participants with Parkinson's disease (8 male; mean age: 61.2 years), 18 older adults (8 male; mean age: 61.3 years) and 20 younger adults (10 males; mean age: 26.7 years) undertook a simple instruction-based learning paradigm in the MRI scanner. They sorted sequences of coloured shapes according to binary discrimination rules that were updated at two-minute intervals. Unlike common reinforcement learning tasks, the rules were unambiguous, being explicitly presented; consequently, there was no requirement to monitor feedback or estimate contingencies. Despite its simplicity, a third of the Parkinson's group, but only one older adult, showed marked increases in errors, 4 SD greater than the worst performing young adult. The pattern of errors was consistent, reflecting a tendency to misbind discrimination rules. The misbinding behaviour was coupled with reduced frontal, parietal and anterior caudate activity when rules were being encoded, but not when attention was initially oriented to the instruction slides or when discrimination trials were performed. Concomitantly, Magnetic Resonance Spectroscopy showed reduced gamma-Aminobutyric acid levels within the mid-dorsolateral prefrontal cortices of individuals who made misbinding errors. These results demonstrate, for the first time, that a subset of early-to-mid stage people with Parkinson's show substantial deficits when binding new task rules in working memory. Given the ubiquity of instruction-based learning, these deficits are likely to impede daily living. They will also confound clinical assessment of other cognitive processes. Future work should determine the value of instruction-based learning as a sensitive early marker of cognitive decline and as a measure of responsiveness to therapy in Parkinson's disease.

Abstract The multicontrast EPImix sequence generates 6 contrasts, including a T 1 -weighted scan, in ∼1 minute. 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 process 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 holds 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.

Abstract There is an unresolved discrepancy between popular hierarchical and multiple-demand perspectives on the functional organisation of the human frontal lobes. Here, we tested alternative predictions of these perspectives with a novel fMRI switching paradigm. Each trial involved switching attention between stimuli, but at different levels of difficulty and abstraction. As expected, increasing response times were evident when comparing low-level perceptual switching to more abstract dimension, rule and task-switching. However, there was no evidence of an abstraction hierarchy within the prefrontal cortex (PFC). Nor was there recruitment of additional anterior PFC regions under increased switching demand. Instead, switching activated a widespread network of frontoparietal and cerebellar regions. Critically, the activity within PFC sub-regions uniformly increased with behavioural switch costs. We propose that both perspectives have some validity, but neither is complete. Too many studies have reported dissociations within MD for this volume to be functionally uniform, and the recruitment of more anterior regions with increased general difficulty cannot explain those results. Conversely, whilst reproducible evidence for a hierarchical functional organisation has been reported, this cannot be explained in terms of abstraction of representation or reconfiguration per se , because those interpretations generalise poorly to other task contexts.

Abstract 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 T 1 -weighted MRI scans. Subsequently, we apply the selected rapid processing pipeline both to rapidly acquired multicontrast EPImix scans of 95 participants (which include T 1 -FLAIR, T 2 , T 2 *, T 2 -FLAIR, DWI & ADC contrasts, acquired in ∼1 minute), as well as to slower, more standard single-contrast T 1 -weighted scans of a subset of 66 participants. We quantify the correspondence between EPImix and single-contrast T 1 -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. Abstract Figure Graphical abstract.

The psychological deficits associated with Parkinson’s disease vary across individuals and change across time, with implications for prognosis and treatment. Key outstanding challenges are to define the distinct behavioural phenotypes of this disorder and develop diagnostic paradigms that can assess these sensitively in individuals. In a previous study we measured different aspects of attentional control in Parkinson’s disease using an established fMRI switching paradigm (Gruszka et al., 2017). We observed no deficits for the aspects of attention the task was designed to examine; instead those with Parkinson’s disease learnt the operational requirements of the task more slowly. We hypothesised that a subset of people with early to mid-stage Parkinson’s might be impaired when encoding rules for performing new tasks. Here, we directly test this hypothesis and investigate whether deficits in instruction based learning represent a potential phenotype. Seventeen participants with Parkinson’s disease (8 male; mean age: 61.2 years), eighteen older adults (8 male; mean age: 61.3 years) and twenty younger adults (10 males; mean age: 26.7 years) undertook a simple instruction based learning paradigm in the MRI scanner. They sorted sequences of coloured shapes according to binary discrimination rules that were updated at two-minute intervals. Unlike common reinforcement learning tasks, the rules were unambiguous, being explicitly presented; consequently, there was no requirement to monitor feedback or estimate contingencies. Despite its simplicity, a third of the Parkinson’s group, but only one older adult, showed marked increases in errors, 4SD greater than the worst-performing young adult. The pattern of errors was consistent, reflecting a tendency to misbind discrimination rules. The misbinding behaviour was coupled with reduced frontal, parietal and anterior caudate activity when rules were being encoded, but not when attention was initially oriented to the instruction slides or when discrimination trials were performed. Concomitantly, Magnetic Resonance Spectroscopy showed reduced gamma-Aminobutyric acid (GABA) levels within the mid-dorsolateral prefrontal cortices of individuals who made misbinding errors. These results demonstrate, for the first time, that a subset of early to mid-stage people with Parkinson’s have substantial deficits when binding new task rules in working memory. Given the ubiquity of instruction based learning, these deficits are likely to impede daily living. They will also confound clinical assessment of other psychological processes. Future work should determine the value of instruction based learning as a sensitive early marker of cognitive decline and as a measure of responsiveness to therapy in Parkinson disease.

Objective: To examine electroencephalography (EEG) –derived changes in event-related synchronization associated with reduced processing speed in traumatic brain injury (TBI). Background: Reduced processing speed is common following TBI and it is associated with executive dysfunction and disability. Task-related alterations in neural synchronization as indexed by EEG during target detection and speeded responding may reflect modulation in the strength of communication within and between cortical areas, which in turn may determine information processing speed. Design/Methods: 30 subjects, including 16 moderate-severe TBI patients and 14 age-matched healthy controls took part in the study. Participants performed a choice reaction time task (CRT) during 32-channel EEG acquisition. Time-frequency analysis of the EEG signal was carried out both time-locked to the stimulus and to the response for correct trials. Event-related potential (ERP) P300 was also computed. Results: Reaction times (RT) were significantly slower in TBI patients than in healthy controls (U=53, P=0.013). Compared to controls, TBI patients showed significantly reduced event-related synchronisation and phase coherence in the theta band in frontal channels from 200 ms to 50 ms before the stimulus onset and 600 ms before the response. In addition, P300 amplitude was significantly reduced in TBI patients (U=160, P=0.047). Higher P300 amplitude was associated with faster RT in both patients and controls, but there was no interaction by group. Conclusions: Reduced frontal theta event-related synchronisation preceding target detection in a speeded choice reaction time task may be a surrogate marker of slowed information processing following TBI. Study Supported by: Academy of Medical Sciences, National Institute of Health Research (NIHR), Agence regionale de Sante Basse-Normandie (bourse Annee-Recherche) Disclosure: Dr. Dautricourt has nothing to disclose. Dr. Violante has nothing to disclose. Dr. Mallas has nothing to disclose. Dr. Daws has nothing to disclose. Dr. Ross has nothing to disclose. Dr. Jolly has nothing to disclose. Dr. Lorenz has nothing to disclose. Dr. Sharp has nothing to disclose. Dr. Gorgoraptis has nothing to disclose.

This document details an authors' response to a critique of their work entitled: Skepticism About Recent Evidence that Psilocybin Opens Depressed Minds.

Abstract We designed two novel fMRI paradigms to investigate how people self-optimise performance when managing competing demands. We hypothesised that the brain adopts distinct functional states to support different tasks, that switching between them involves a costly process of collapse and reconfiguration of the functional connectome, and that this process is optimised with practice. Accordingly, self-ordered switches (SOS) were associated with transient states of low-connectivity and high-activation. Individuals progressively improved their performance with practice. This learning behaviour was reflected by an ongoing redeployment of the neural resources supporting switching and routine behaviour. Furthermore, those who developed more structured behaviours also scored more points, showed a greater deepening of switching-dysconnectivity and a greater tuning of activity within dorsal frontoparietal cortex to switching events with practice. These results demonstrate that a fundamental property of human neurocognitive systems is concurrent self-optimisation to maximise behavioural outcomes and minimise the use of neural resources.

This zip file represents the data to support our paper in Nature communication titled "Neuroimaging evidence for a network sampling theory of human intelligence"