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Prefrontal cortex stimulation normalizes deficient adaptive learning from outcome contingencies in low mood.
Depression and anxiety are associated with deficits in adjusting learning behaviour to changing outcome contingencies. This is likely to drive and maintain symptoms, for instance, by perpetuating negative biases or a sense of uncontrollability. Normalising such deficits in adaptive learning might therefore be a novel treatment target for affective disorders. The aim of this experimental medicine study was to test whether prefrontal cortex transcranial direct current stimulation (tDCS) could normalise these aberrant learning processes in depressed mood. To test proof-of-concept, we combined tDCS with a decision-making paradigm that manipulates the volatility of reward and punishment associations. 85 participants with low mood received tDCS during (or before) the task. In two sessions participants received real or sham tDCS in counter-balanced order. Compared to healthy controls (n = 40), individuals with low mood showed significantly impaired adjustment of learning rates to the volatility of loss outcomes. Prefrontal tDCS applied during task performance normalised this deficit, by increasing the adjustment of loss learning rates. As predicted, prefrontal tDCS before task performance (control) had no effect. Thus, the effect was cognitive-state dependent. Our study shows, for the first time, that a candidate depression treatment, prefrontal tDCS, when paired with a task, can reverse deficits in adaptive learning from outcome contingencies in low mood. Thus, combining neurostimulation with a concurrent cognitive manipulation is a potential novel strategy to enhance the effect of tDCS in depression treatment.
Sensorimnemonic decisions: choosing memories versus sensory information.
We highlight a fundamental psychological function that is central to many of our interactions in the environment - when to rely on memories versus sampling sensory information anew to guide behavior. By operationalizing sensorimnemonic decisions we aim to encourage and advance research into this pivotal process for understanding how memories serve adaptive cognition.
Oxford brain health clinic: protocol and research database.
INTRODUCTION: Despite major advances in the field of neuroscience over the last three decades, the quality of assessments available to patients with memory problems in later life has barely changed. At the same time, a large proportion of dementia biomarker research is conducted in selected research samples that often poorly reflect the demographics of the population of patients who present to memory clinics. The Oxford Brain Health Clinic (BHC) is a newly developed clinical assessment service with embedded research in which all patients are offered high-quality clinical and research assessments, including MRI, as standard. METHODS AND ANALYSIS: Here we describe the BHC protocol, including aligning our MRI scans with those collected in the UK Biobank. We evaluate rates of research consent for the first 108 patients (data collection ongoing) and the ability of typical psychiatry-led NHS memory-clinic patients to tolerate both clinical and research assessments. ETHICS AND DISSEMINATION: Our ethics and consenting process enables patients to choose the level of research participation that suits them. This generates high rates of consent, enabling us to populate a research database with high-quality data that will be disseminated through a national platform (the Dementias Platform UK data portal).
Neural signatures of risk-taking adaptions across health, bipolar disorder, and lithium treatment.
Cognitive and neural mechanisms underlying bipolar disorder (BD) and its treatment are still poorly understood. Here we examined the role of adaptations in risk-taking using a reward-guided decision-making task. We recruited volunteers with high (n = 40) scores on the Mood Disorder Questionnaire, MDQ, suspected of high risk for bipolar disorder and those with low-risk scores (n = 37). We also recruited patients diagnosed with BD who were assigned (randomized, double-blind) to six weeks of lithium (n = 19) or placebo (n = 16) after a two-week baseline period (n = 22 for FMRI). Participants completed mood ratings daily over 50 (MDQ study) or 42 (BD study) days, as well as a risky decision-making task and functional magnetic resonance imaging. The task measured adaptation of risk taking to past outcomes (increased risk aversion after a previous win vs. loss, 'outcome history'). While the low MDQ group was risk averse after a win, this was less evident in the high MDQ group and least so in the patients with BD. During fMRI, 'outcome history' was linked to medial frontal pole activation at the time of the decision and this activation was reduced in the high risk MDQ vs. the low risk MDQ group. While lithium did not reverse the pattern of BD in the task, nor changed clinical symptoms of mania or depression, it changed reward processing in the dorsolateral prefrontal cortex. Participants' modulation of risk-taking in response to reward outcomes was reduced as a function of risk for BD and diagnosed BD. These results provide a model for how reward may prime escalation of risk-related behaviours in bipolar disorder and how mood stabilising treatments may work.
Automated detection of cerebral microbleeds on MR images using knowledge distillation framework.
INTRODUCTION: Cerebral microbleeds (CMBs) are associated with white matter damage, and various neurodegenerative and cerebrovascular diseases. CMBs occur as small, circular hypointense lesions on T2*-weighted gradient recalled echo (GRE) and susceptibility-weighted imaging (SWI) images, and hyperintense on quantitative susceptibility mapping (QSM) images due to their paramagnetic nature. Accurate automated detection of CMBs would help to determine quantitative imaging biomarkers (e.g., CMB count) on large datasets. In this work, we propose a fully automated, deep learning-based, 3-step algorithm, using structural and anatomical properties of CMBs from any single input image modality (e.g., GRE/SWI/QSM) for their accurate detections. METHODS: In our method, the first step consists of an initial candidate detection step that detects CMBs with high sensitivity. In the second step, candidate discrimination step is performed using a knowledge distillation framework, with a multi-tasking teacher network that guides the student network to classify CMB and non-CMB instances in an offline manner. Finally, a morphological clean-up step further reduces false positives using anatomical constraints. We used four datasets consisting of different modalities specified above, acquired using various protocols and with a variety of pathological and demographic characteristics. RESULTS: On cross-validation within datasets, our method achieved a cluster-wise true positive rate (TPR) of over 90% with an average of <2 false positives per subject. The knowledge distillation framework improves the cluster-wise TPR of the student model by 15%. Our method is flexible in terms of the input modality and provides comparable cluster-wise TPR and better cluster-wise precision compared to existing state-of-the-art methods. When evaluating across different datasets, our method showed good generalizability with a cluster-wise TPR >80 % with different modalities. The python implementation of the proposed method is openly available.
Acute angiotensin receptor blockade and mnemonic discrimination in healthy participants.
BACKGROUND: The renin angiotensin system (RAS) is implicated in various cognitive processes relevant to anxiety. However, the role of the RAS in pattern separation, a hippocampal memory mechanism that enables discrete encoding of similar stimuli, is unclear. Given the proposed role of this mechanism in overgeneralization and the maintenance of anxiety, we explored the influence of the RAS on mnemonic discrimination i.e., the behavioral ability arising from pattern separation. DESIGN: In a double-blind experimental medicine trial, we examined the effect of losartan, an angiotensin receptor blocker, on mnemonic discrimination in N = 60 healthy volunteers aged 18-50. Participants were randomly allocated to a 50 mg losartan or placebo condition, and then completed the Mnemonic Similarity Task (MST), an established measure of pattern separation. Main outcome measures were the lure discrimination index (LDI), calculated as the rate of 'similar' responses to lures minus 'similar' responses to foils, and recognition (REC) memory, calculated as the difference between the rate of 'old' responses to targets minus 'old' responses to foils. RESULTS: Data were available for N = 56 participants (N = 40 females, N = 16 males). Participants in the losartan group (N = 29) achieved significantly higher LDI scores (t(54) = 2.30, p = 0.025) compared to the placebo group (N = 27), indicating better mnemonic discrimination. No significant group differences were found in REC scores (U = 324, z = -1.10, d = 0.08; p = 0.271). CONCLUSIONS: We demonstrate for the first time that losartan improves mnemonic discrimination in healthy individuals, suggesting that the RAS may influence pattern separation and anxiety.
The architecture of the human default mode network explored through cytoarchitecture, wiring and signal flow.
The default mode network (DMN) is implicated in many aspects of complex thought and behavior. Here, we leverage postmortem histology and in vivo neuroimaging to characterize the anatomy of the DMN to better understand its role in information processing and cortical communication. Our results show that the DMN is cytoarchitecturally heterogenous, containing cytoarchitectural types that are variably specialized for unimodal, heteromodal and memory-related processing. Studying diffusion-based structural connectivity in combination with cytoarchitecture, we found the DMN contains regions receptive to input from sensory cortex and a core that is relatively insulated from environmental input. Finally, analysis of signal flow with effective connectivity models showed that the DMN is unique amongst cortical networks in balancing its output across the levels of sensory hierarchies. Together, our study establishes an anatomical foundation from which accounts of the broad role the DMN plays in human brain function and cognition can be developed.
Self-reports map the landscape of task states derived from brain imaging.
Psychological states influence our happiness and productivity; however, estimates of their impact have historically been assumed to be limited by the accuracy with which introspection can quantify them. Over the last two decades, studies have shown that introspective descriptions of psychological states correlate with objective indicators of cognition, including task performance and metrics of brain function, using techniques like functional magnetic resonance imaging (fMRI). Such evidence suggests it may be possible to quantify the mapping between self-reports of experience and objective representations of those states (e.g., those inferred from measures of brain activity). Here, we used machine learning to show that self-reported descriptions of experiences across tasks can reliably map the objective landscape of task states derived from brain activity. In our study, 194 participants provided descriptions of their psychological states while performing tasks for which the contribution of different brain systems was available from prior fMRI studies. We used machine learning to combine these reports with descriptions of brain function to form a 'state-space' that reliably predicted patterns of brain activity based solely on unseen descriptions of experience (N = 101). Our study demonstrates that introspective reports can share information with the objective task landscape inferred from brain activity.
Human cortical high-gamma power scales with movement rate in healthy participants and stroke survivors.
Motor cortical high-gamma oscillations (60-90 Hz) occur at movement onset and are spatially focused over the contralateral primary motor cortex. Although high-gamma oscillations are widely recognized for their significance in human motor control, their precise function on a cortical level remains elusive. Importantly, their relevance in human stroke pathophysiology is unknown. Because motor deficits are fundamental determinants of symptom burden after stroke, understanding the neurophysiological processes of motor coding could be an important step in improving stroke rehabilitation. We recorded magnetoencephalography data during a thumb movement rate task in 14 chronic stroke survivors, 15 age-matched control participants and 29 healthy young participants. Motor cortical high-gamma oscillations showed a strong relation with movement rate as trials with higher movement rate were associated with greater high-gamma power. Although stroke survivors showed reduced cortical high-gamma power, this reduction primarily reflected the scaling of high-gamma power with movement rate, yet after matching movement rate in stroke survivors and age-matched controls, the reduction of high-gamma power exceeded the effect of their decreased movement rate alone. Even though motor skill acquisition was evident in all three groups, it was not linked to high-gamma power. Our study quantifies high-gamma oscillations after stroke, revealing a reduction in movement-related high-gamma power. Moreover, we provide strong evidence for a pivotal role of motor cortical high-gamma oscillations in encoding movement rate. KEY POINTS: Neural oscillations in the high-gamma frequency range (60-90 Hz) emerge in the human motor cortex during movement. The precise function of these oscillations in motor control remains unclear, and they have never been characterized in stroke survivors. In a magnetoencephalography study, we demonstrate that high-gamma oscillations in motor cortical areas scale with movement rate, and we further explore their temporal and spatial characteristics. Stroke survivors exhibit lower high-gamma power during movement than age-matched control participants, even after matching for movement rate. The results contribute to the understanding of the role of high-gamma oscillations in motor control and have important implications for neuromodulation in stroke rehabilitation.
Clean Self-Supervised MRI Reconstruction from Noisy, Sub-Sampled Training Data with Robust SSDU.
Most existing methods for magnetic resonance imaging (MRI) reconstruction with deep learning use fully supervised training, which assumes that a fully sampled dataset with a high signal-to-noise ratio (SNR) is available for training. In many circumstances, however, such a dataset is highly impractical or even technically infeasible to acquire. Recently, a number of self-supervised methods for MRI reconstruction have been proposed, which use sub-sampled data only. However, the majority of such methods, such as Self-Supervised Learning via Data Undersampling (SSDU), are susceptible to reconstruction errors arising from noise in the measured data. In response, we propose Robust SSDU, which provably recovers clean images from noisy, sub-sampled training data by simultaneously estimating missing k-space samples and denoising the available samples. Robust SSDU trains the reconstruction network to map from a further noisy and sub-sampled version of the data to the original, singly noisy, and sub-sampled data and applies an additive Noisier2Noise correction term upon inference. We also present a related method, Noiser2Full, that recovers clean images when noisy, fully sampled data are available for training. Both proposed methods are applicable to any network architecture, are straightforward to implement, and have a similar computational cost to standard training. We evaluate our methods on the multi-coil fastMRI brain dataset with novel denoising-specific architecture and find that it performs competitively with a benchmark trained on clean, fully sampled data.
Dynamic network analysis of electrophysiological task data
Abstract An important approach for studying the human brain is to use functional neuroimaging combined with a task. In electrophysiological data, this often involves a time-frequency analysis, in which recorded brain activity is time-frequency transformed and epoched around task events of interest, followed by trial-averaging of the power. While this simple approach can reveal fast oscillatory dynamics, the brain regions are analysed one at a time. This causes difficulties for interpretation and a debilitating number of multiple comparisons. In addition, it is now recognised that the brain responds to tasks through the coordinated activity of networks of brain areas. As such, techniques that take a whole-brain network perspective are needed. Here, we show how the oscillatory task responses from conventional time-frequency approaches can be represented more parsimoniously at the network level using two state-of-the-art methods: the HMM (Hidden Markov Model) and DyNeMo (Dynamic Network Modes). Both methods reveal frequency-resolved networks of oscillatory activity with millisecond resolution. Comparing DyNeMo, HMM, and traditional oscillatory response analysis, we show DyNeMo can identify task activations/deactivations that the other approaches fail to detect. DyNeMo offers a powerful new method for analysing task data from the perspective of dynamic brain networks.
Multi-level encoding of reward, effort, and choice across the frontal cortex and basal ganglia during cost-benefit decision-making.
Adaptive value-guided decision-making requires weighing up the costs and benefits of pursuing an available opportunity. Though neurons across frontal cortical-basal ganglia circuits have been repeatedly shown to represent decision-related parameters, it is unclear whether and how this information is coordinated. To address this question, we performed large-scale single-unit recordings simultaneously across 5 medial/orbital frontal and basal ganglia regions as rats decided whether to pursue varying reward payoffs available at different effort costs. Single neurons encoding combinations of decision variables (reward, effort, and choice) were represented within all recorded regions. Coactive cell assemblies, ensembles of neurons that repeatedly coactivated within short time windows (<25 ms), represented the same decision variables despite the members often having diverse individual coding properties. Together, these findings demonstrate a multi-level encoding structure for cost-benefit computations where individual neurons are coordinated into larger assemblies that can represent task variables independently of their constituent components.
Lipid-mediated resolution of inflammation and survival in amyotrophic lateral sclerosis.
Neuroinflammation impacts on the progression of amyotrophic lateral sclerosis (ALS), a fatal neurodegenerative disorder. Specialized pro-resolving mediators trigger the resolution of inflammation. We investigate the specialized pro-resolving mediator blood profile and their receptors' expression in peripheral blood mononuclear cells in relation to survival in ALS. People living with ALS (pwALS) were stratified based on bulbar versus limb onset and on key progression metrics using a latent class model, to separate faster progressing from slower progressing ALS. Specialized pro-resolving mediator blood concentrations were measured at baseline and in one additional visit in 20 pwALS and 10 non-neurological controls (Cohort 1). Flow cytometry was used to study the GPR32 and GPR18 resolvin receptors' expression in peripheral blood mononuclear cells from 40 pwALS and 20 non-neurological controls (Cohort 2) at baseline and in two additional visits in 17 pwALS. Survival analysis was performed using Cox proportional hazards models, including known clinical predictors and GPR32 and GPR18 mononuclear cell expression. Differential expression and linear discriminant analyses showed that plasma resolvins were able to distinguish phenotypic variants of ALS from non-neurological controls. RvE3 was elevated in blood from pwALS, whilst RvD1, RvE3, RvT4 and RvD1n-3 DPA were upregulated in A-S and RvD2 in A-F. Compared to non-neurological controls, GPR32 was upregulated in monocytes expressing the active inflammation-suppressing CD11b+ integrin from fast-progressing pwALS, including those with bulbar onset disease (P < 0.0024), whilst GPR32 and GPR18 were downregulated in most B and T cell subtypes. Only GPR18 was upregulated in naïve double positive Tregs, memory cytotoxic Tregs, senescent late memory B cells and late senescent CD8+ T cells from pwALS compared to non-neurological controls (P < 0.0431). Higher GPR32 and GPR18 median expression in blood mononuclear cells was associated with longer survival, with GPR32 expression in classical monocytes (hazard ratio: 0.11, P = 0.003) and unswitched memory B cells (hazard ratio: 0.44, P = 0.008) showing the most significant association, along with known clinical predictors. Low levels of resolvins and downregulation of their membrane receptors in blood mononuclear cells are linked to a faster progression of ALS. Higher mononuclear cell expression of resolvin receptors is a predictor of longer survival. These findings suggest a lipid-mediated neuroprotective response that could be harnessed to develop novel therapeutic strategies and biomarkers for ALS.