The freedom to choose between options is strongly linked to notions of free will. Accordingly, several studies have shown that individuals demonstrate a preference for choice, or the availability of multiple options, over and above utilitarian value. Yet we lack a decision-making framework that integrates preference for choice with traditional utility maximisation in free choice behaviour. Here we test the predictions of an inference-based model of decision-making in which an agent actively seeks states yielding entropy (availability of options) in addition to utility (economic reward). We designed a study in which participants freely navigated a virtual environment consisting of two consecutive choices leading to reward locations in separate rooms. Critically, the choice of one room always led to two final doors while, in the second room, only one door was permissible to choose. This design allowed us to separately determine the influence of utility and entropy on participants' choice behaviour and their self-evaluation of free will. We found that choice behaviour was better predicted by an inference-based model than by expected utility alone, and that both the availability of options and the value of the context positively influenced participants' perceived freedom of choice. Moreover, this consideration of options was apparent in the ongoing motion dynamics as individuals navigated the environment. In a second study, in which participants selected between rooms that gave access to three or four doors, we observed a similar pattern of results, with participants preferring the room that gave access to more options and feeling freer in it. These results suggest that free choice behaviour is well explained by an inference-based framework in which both utility and entropy are optimised and supports the idea that the feeling of having free will is tightly related to options availability.
Humans can implicitly learn complex perceptuo-motor skills over the course of large numbers of trials. This likely depends on our becoming better able to take advantage of ever richer and temporally deeper predictive relationships in the environment. Here, we offer a novel characterization of this process, fitting a non-parametric, hierarchical Bayesian sequence model to the reaction times of human participants’ responses over ten sessions, each comprising thousands of trials, in a serial reaction time task involving higher-order dependencies. The model, adapted from the domain of language, forgetfully updates trial-by-trial, and seamlessly combines predictive information from shorter and longer windows onto past events, weighing the windows proportionally to their predictive power. As the model implies a posterior over window depths, we were able to determine how, and how many, previous sequence elements influenced individual participants’ internal predictions, and how this changed with practice. Already in the first session, the model showed that participants had begun to rely on two previous elements (i.e., trigrams), thereby successfully adapting to the most prominent higher-order structure in the task. The extent to which local statistical fluctuations influenced participants’ responses waned over subsequent sessions, as subjects forgot the trigrams less and evidenced skilled performance. By the eighth session, a subset of participants shifted their prior further to consider a context deeper than two previous elements. Finally, participants showed resistance to interference and slow forgetting of the old sequence when it was changed in the final sessions. Model parameters for individual subjects covaried appropriately with independent measures of working memory. In sum, the model offers the first principled account of the adaptive complexity and nuanced dynamics of humans’ internal sequence representations during long-term implicit skill learning.
Despite the development of large-scale data-acquisition techniques, experimental observations of complex systems are often limited to a tiny fraction of the system under study. This spatial subsampling is particularly severe in neuroscience, in which only a tiny fraction of millions or even billions of neurons can be individually recorded. Spatial subsampling may lead to substantial systematic biases when inferring the collective properties of the entire system naively from a subsampled part. To overcome such biases, powerful mathematical tools have been developed. In this Perspective, we give an overview of some issues arising from subsampling and review approaches developed in recent years to tackle the subsampling problem. These approaches enable one to correctly assess phenomena such as graph structures, collective dynamics of animals, neural network activity or the spread of disease from observing only a tiny fraction of the system. However, existing approaches are still far from having solved the subsampling problem in general, and we also outline what we believe are the main open challenges. Solving these challenges alongside the development of large-scale recording techniques will enable further fundamental insights into the workings of complex and living systems. For many complex or living systems, it is impossible to individually sample all their units, but subsampling can heavily bias the inference about their collective properties. This Perspective presents the subsampling problem and reviews recent developments to overcome this fundamental limitation.
The human circadian system responds to light as low as 30 photopic lux. Furthermore, recent evidence shows that there are huge individual differences in light sensitivity, which may help to explain why some people are more susceptible to sleep and circadian disruption than others. The biological mechanisms underlying the differences in light sensitivity remain largely unknown. A key variable of interest in understanding these individual differences in light sensitivity is biological sex. It is possible that in humans, males and females differ in their sensitivity to light, but the evidence is inconclusive. This is in part due to the historic exclusion of women in biomedical research. Hormonal fluctuations across the menstrual cycle in women has often been cited as a confound by researchers. Attitudes, however, are changing with funding and publication agencies advocating for more inclusive research frameworks and mandating that women and minorities participate in scientific research studies. In this article, we distill the existing knowledge regarding the relationship between light and the menstrual cycle. There is some evidence of a relationship between light and the menstrual cycle, but the nature of this relationship seems dependent on the timing of the light source (sunlight, moonlight, and electric light at night). Light sensitivity may be influenced by biological sex and menstrual phase but there might not be any effect at all. To better understand the relationship between light, the circadian system, and the menstrual cycle, future research needs to be designed thoughtfully, conducted rigorously, and reported transparently.
Almost all functional processing in the cortex strongly depends on thalamic interactions. However, in terms of functional interactions with the cerebral cortex, the human thalamus nuclei still partly constitute a terra incognita. Hence, for a deeper understanding of thalamic-cortical cooperation, it is essential to know how the different thalamic nuclei are associated with cortical networks. The present work examines network-specific connectivity and task-related topical mapping of cortical areas with the thalamus. The study finds that the relay and higher-order thalamic nuclei show an intertwined functional association with different cortical networks. In addition, the study indicates that relay-specific thalamic nuclei are not only involved with relay-specific behavior but also in higher-order functions. The study enriches our understanding of interactions between large-scale cortical networks and the thalamus, which may interest a broader audience in neuroscience and clinical research.
Ethical frameworks are the foundation for any research with humans or nonhuman animals. Human research is guided by overarching international ethical principles, such as those defined in the Helsinki Declaration by the World Medical Association. However, for nonhuman animal research, because there are several sets of ethical principles and national frameworks, it is commonly thought that there is substantial variability in animal research approaches internationally and a lack of an animal research ‘Helsinki Declaration’, or the basis for one. We first overview several prominent sets of ethical principles, including the 3Rs, 3Ss, 3Vs, 4Fs and 6Ps. Then using the 3Rs principles, originally proposed by Russell & Burch, we critically assess them, asking if they can be Replaced, Reduced or Refined. We find that the 3Rs principles have survived several replacement challenges, and the different sets of principles (3Ss, 3Vs, 4Fs and 6Ps) are complementary, a natural refinement of the 3Rs and are ripe for integration into a unified set of principles, as proposed here. We also overview international frameworks and documents, many of which incorporate the 3Rs, including the Basel Declaration on animal research. Finally, we propose that the available animal research guidance documents across countries can be consolidated, to provide a similar structure as seen in the Helsinki Declaration, potentially as part of an amended Basel Declaration on animal research. In summary, we observe substantially greater agreement on and the possibility for unification of the sets of ethical principles and documents that can guide animal research internationally.
Purpose: In this work, we investigated the ability of neural networks to rapidly and robustly predict Lorentzian parameters of multi-pool CEST MRI spectra at 7 T with corresponding uncertainty maps to make them quickly and easily available for routine clinical use. Methods: We developed a deepCEST 7 T approach that generates CEST contrasts from just 1 scan with robustness against B1 inhomogeneities. The input data for a neural feed-forward network consisted of 7 T in vivo uncorrected Z-spectra of a single B1 level, and a B1 map. The 7 T raw data were acquired using a 3D snapshot gradient echo multiple interleaved mode saturation CEST sequence. These inputs were mapped voxel-wise to target data consisting of Lorentzian amplitudes generated conventionally by 5-pool Lorentzian fitting of normalized, denoised, B0 - and B1 -corrected Z-spectra. The deepCEST network was trained with Gaussian negative log-likelihood loss, providing an uncertainty quantification in addition to the Lorentzian amplitudes. Results: The deepCEST 7 T network provides fast and accurate prediction of all Lorentzian parameters also when only a single B1 level is used. The prediction was highly accurate with respect to the Lorentzian fit amplitudes, and both healthy tissues and hyperintensities in tumor areas are predicted with a low uncertainty. In corrupted cases, high uncertainty indicated wrong predictions reliably. Conclusion: The proposed deepCEST 7 T approach reduces scan time by 50% to now 6:42 min, but still delivers both B0 - and B1 -corrected homogeneous CEST contrasts along with an uncertainty map, which can increase diagnostic confidence. Multiple accurate 7 T CEST contrasts are delivered within seconds.
Glucose is the main energy source in the brain and its regulated uptake and utilization are important biomarkers of pathological brain function. Glucose Chemical Exchange Saturation Transfer (GlucoCEST) and its time-resolved version Dynamic Glucose-Enhanced MRI (DGE) are promising approaches to monitor glucose and detect tumors, since it is radioactivity-free, does not require ¹³C labelling and it is easily translatable to the clinics. The main principle of DGE is clear. However, what remains to be established is to which extent the signal reflects vascular, extracellular or intracellular glucose. To elucidate the compartmental contributions to the DGE signal, we coupled it with FRET-based fiber photometry of genetically encoded sensors, a technique that combines quantitative glucose readout with cellular specificity. The glucose sensor FLIIP was used with fiber photometry to measure astrocytic and neuronal glucose changes upon injection of D-glucose, 3OMG and L-glucose, in the anaesthetized murine brain. By correlating the kinetic profiles of the techniques, we demonstrate the presence of a vascular contribution to the signal, especially at early time points after injection. Furthermore, we show that, in the case of the commonly used contrast agent 3OMG, the DGE signal actually anticorrelates with the glucose concentration in neurons and astrocytes.
Neuronal cultures are a prominent experimental tool to understand complex functional organization in neuronal assemblies. However, neurons grown on flat surfaces exhibit a strongly coherent bursting behavior with limited functionality. To approach the functional richness of naturally formed neuronal circuits, here we studied neuronal networks grown on polydimethylsiloxane (PDMS) topographical patterns shaped as either parallel tracks or square valleys. We followed the evolution of spontaneous activity in these cultures along 20 days in vitro using fluorescence calcium imaging. The networks were characterized by rich spatiotemporal activity patterns that comprised from small regions of the culture to its whole extent. Effective connectivity analysis revealed the emergence of spatially compact functional modules that were associated to both the underpinned topographical features and predominant spatiotemporal activity fronts. Our results show the capacity of spatial constraints to mold activity and functional organization, bringing new opportunities to comprehend the structure-function relationship in living neuronal circuits.
Paying attention to particular aspects of the world or being more vigilant in general can be interpreted as forms of 'internal' action. Such arousal-related choices come with the benefit of increasing the quality and situational appropriateness of information acquisition and processing, but incur potentially expensive energetic and opportunity costs. One implementational route for these choices is widespread ascending neuromodulation, including by acetylcholine (ACh). The key computational question that elective attention poses for sensory processing is when it is worthwhile paying these costs, and this includes consideration of whether sufficient information has yet been collected to justify the higher signal-to-noise ratio afforded by greater attention and, particularly if a change in attentional state is more expensive than its maintenance, when states of heightened attention ought to persist. We offer a partially observable Markov decision-process treatment of optional attention in a detection task, and use it to provide a qualitative model of the results of studies using modern techniques to measure and manipulate ACh in rodents performing a similar task.
Current powertrains in vehicles offer a broad spectrum of driving modes that shape the driving experience. One crucial factor for determining the human factors aspects of this driving experience is the perception and evaluation of acceleration-a complex, multisensory process that integrates auditory, vestibular, and visual input. Two important questions that need to be answered in order to better characterize this percept include: How do individual sensory inputs influence acceleration perception? and How does acceleration perception change at different acceleration levels? To address these questions, here we used a unique setup based on a cable-robot simulator that allowed us to manipulate the different modalities at different levels of acceleration with real-world, in-car data for maximum realism. Specifically, we measured the just noticeable differences (JNDs) of acceleration perception in five different modality combinations with the same set of participants. Our results showed that auditory acceleration perception was less sensitive compared to other modality combinations. In addition, we found evidence for the validity of Weber’s law with JNDs increasing linearly with increasing acceleration level. Interestingly, the multisensory data showed little evidence for effective cue integration of auditory information in this setup. These findings lay the groundwork for a better understanding of how different modalities work together in acceleration perception.
A body of work spanning neuroscience, economics, and psychology indicates that decision-making is context-dependent, which means that the value of an option depends not only on the option in question, but also on the other options in the choice set—or the ‘context’. While context effects have been observed primarily in small-scale laboratory studies with tightly constrained, artificially constructed choice sets, it remains to be determined whether these context effects take hold in real-world choice problems, where choice sets are large and decisions driven by rich histories of direct experience. Here, we investigate whether valuations are context-dependent in real-world choice by analyzing a massive restaurant rating dataset as well as two independent replication datasets which provide complementary operationalizations of restaurant choice. We find that users make fewer ratings-maximizing choices in choice sets with higher-rated options—a hallmark of context-dependent choice—and that post-choice restaurant ratings also varied systematically with the ratings of unchosen restaurants. Furthermore, in a follow-up laboratory experiment using hypothetical choice sets matched to the real-world data, we find further support for the idea that subjective valuations of restaurants are scaled in accordance with the choice context, providing corroborating evidence for a general mechanistic-level account of these effects. Taken together, our results provide a potent demonstration of context-dependent choice in real-world choice settings, manifesting both in decisions and subjective valuation of options.
Forming a complete picture of the relationship between neural activity and skeletal kinematics requires quantification of skeletal joint biomechanics during free behavior; however, without detailed knowledge of the underlying skeletal motion, inferring limb kinematics using surface-tracking approaches is difficult, especially for animals where the relationship between the surface and underlying skeleton changes during motion. Here we developed a videography-based method enabling detailed three-dimensional kinematic quantification of an anatomically defined skeleton in untethered freely behaving rats and mice. This skeleton-based model was constrained using anatomical principles and joint motion limits and provided skeletal pose estimates for a range of body sizes, even when limbs were occluded. Model-inferred limb positions and joint kinematics during gait and gap-crossing behaviors were verified by direct measurement of either limb placement or limb kinematics using inertial measurement units. Together we show that complex decision-making behaviors can be accurately reconstructed at the level of skeletal kinematics using our anatomically constrained model.
The therapeutic use of noradrenergic drugs makes the evaluation of their effects on cognition of high priority. Norepinephrine (NE) is an important neuromodulator for a variety of cognitive processes, including memory. The NE transmission fluctuates with the behavioral state and influences associated neural activity. Here, we addressed the role of NE during a post-learning period in the sleep-mediated mechanisms of memory consolidation. We treated adult male rats with clonidine (0.05 mg/kg, i.p.), propranolol (10 mg/kg, i.p.), or saline after each of seven daily learning sessions on an 8-arm radial maze. We monitored the prefrontal EEG and population activity in the hippocampus for 2h after the drug administration. Both drugs made spatial learning less efficient and dramatically reduced the occurrence of hippocampal ripples at least for 2h post-injection. Clonidine made the sleep onset faster while prolonging quiet wakefulness. Propranolol increased active wakefulness at the expense of NREM sleep. Clonidine reduced the occurrence of slow oscillations (SO) and sleep spindles during NREM sleep and altered the temporal coupling between SO and sleep spindles. Thus, pharmacological alteration of NE transmission produced a suboptimal brain state for memory consolidation. Our results suggest that the post-learning NE contributes to the efficiency of ripple-associated memory trace replay and hippocampal-cortical communication underlying memory consolidation.
Subcortical brain regions are absolutely essential for normal human function. These phylogenetically early brain regions play critical roles in human behaviors such as the orientation of attention, arousal, and the modulation of sensory signals to cerebral cortex. Despite the critical health importance of subcortical brain regions, there has been a dearth of research on their neurovascular responses. Blood oxygen level dependent (BOLD) functional MRI (fMRI) experiments can help fill this gap in our understanding. The BOLD hemodynamic response function (HRF) evoked by brief (<4 s) neural activation is crucial for the interpretation of fMRI results because linear analysis between neural activity and the BOLD response relies on the HRF. Moreover, the HRF is a consequence of underlying local blood flow and oxygen metabolism, so characterization of the HRF enables understanding of neurovascular and neurometabolic coupling. We measured the subcortical HRF at 9.4T and 3T with high spatiotemporal resolution using protocols that enabled reliable delineation of HRFs in individual subjects. These results were compared with the HRF in visual cortex. The HRF was faster in subcortical regions than cortical regions at both field strengths. There was no significant undershoot in subcortical areas while there was a significant post-stimulus undershoot that was tightly coupled with its peak amplitude in cortex. The different BOLD temporal dynamics indicate different vascular dynamics and neurometabolic responses between cortex and subcortical nuclei.
In visual cortex, anatomically distinct patches respond to distinct categories, such as faces or text. New research confirms this parcellation using unsupervised analysis of functional magnetic resonance imaging data obtained from humans viewing tens of thousands of images, discovering one more preference: for food.
Inferring causes of the good and bad events that we experience is part of the process of building models of our own capabilities and of the world around us. Making such inferences can be difficult because of complex reciprocal relationships between attributions of the causes of particular events, and beliefs about the capabilities and skills that influence our role in bringing them about. Abnormal causal attributions have long been studied in connection with psychiatric disorders, notably depression and paranoia; however, the mechanisms behind attributional inferences and the way they can go awry are not fully understood. We administered a novel, challenging, game of skill to a substantial population of healthy online participants, and collected trial-by-trial time series of both their beliefs about skill and attributions about the causes of the success and failure of real experienced outcomes. We found reciprocal relationships that provide empirical confirmation of the attribution-self representation cycle theory. This highlights the dynamic nature of the processes involved in attribution, and validates a framework for developing and testing computational accounts of attribution-belief interactions.
Divisive normalization is a canonical computation in the brain, observed across neural systems, that is often considered to be an implementation of the efficient coding principle. We provide a theoretical result that makes the conditions under which divisive normalization is an efficient code analytically precise: We show that, in a low-noise regime, encoding an n -dimensional stimulus via divisive normalization is efficient if and only if its prevalence in the environment is described by a multivariate Pareto distribution. We generalize this multivariate analog of histogram equalization to allow for arbitrary metabolic costs of the representation, and show how different assumptions on costs are associated with different shapes of the distributions that divisive normalization efficiently encodes. Our result suggests that divisive normalization may have evolved to efficiently represent stimuli with Pareto distributions. We demonstrate that this efficiently encoded distribution is consistent with stylized features of naturalistic stimulus distributions such as their characteristic conditional variance dependence, and we provide empirical evidence suggesting that it may capture the statistics of filter responses to naturalistic images. Our theoretical finding also yields empirically testable predictions across sensory domains on how the divisive normalization parameters should be tuned to features of the input distribution.
Purpose: To evaluate the benefits and challenges of dynamic parallel transmit (pTx) pulses for fat saturation (FS) and water-excitation (WE), in the context of CEST MRI. Methods: "Universal" kT -points (for FS) and spiral non-selective (for WE) trajectories were optimized offline for flip angle (FA) homogeneity. Routines to optimize the pulse shape online, based on the subject's fields maps, were implemented (target FA of 110°/0° for FS, 0°/5° for WE at fat/water frequencies). The pulses were inserted in a CEST sequence with a pTx readout. The different fat suppression schemes and their effects on CEST contrasts were compared in 12 volunteers at 7T. Results: With a 25%-shorter pulse duration, pTx FS largely improved the FA homogeneity (root-mean-square-error (RMSE) = 12.3° vs. 53.4° with circularly-polarized mode, at the fat frequency). However, the spectral selectivity was degraded mainly in the cerebellum and close to the sinuses (RMSE = 5.8° vs. 0.2° at the water frequency). Similarly, pTx WE showed a trade-off between FA homogeneity and spectral selectivity compared to pTx non-selective pulses (RMSE = 0.9° and 1.1° at the fat and water frequencies, vs. 4.6° and 0.5°). In the brain, CEST metrics were reduced by up to 31.9% at -3.3 ppm with pTx FS, suggesting a mitigated lipid-induced bias. Conclusion: This clinically compatible implementation of dynamic pTx pulses improved the fat suppression homogeneity at 7T taking into account the subject-specific B0 heterogeneities online. This study highlights the lipid-induced biases on the CEST z-spectrum. The results are promising for body applications where B0 heterogeneities and fat are more substantial.
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