An enduring and richly elaborated dichotomy in cognitive neuroscience is that of reflective versus reflexive decision making and choice. Other literatures refer to the two ends of what is likely to be a spectrum with terms such as goal-directed versus habitual, model-based versus model-free or prospective versus retrospective. One of the most rigorous traditions of experimental work in the field started with studies in rodents and graduated via human versions and enrichments of those experiments to a current state in which new paradigms are probing and challenging the very heart of the distinction. We review four generations of work in this tradition and provide pointers to the forefront of the field’s fifth generation.
Computational models are powerful tools for exploring the properties of complex biological systems. In neuroscience, data-driven models of neural circuits that span multiple scales are increasingly being used to understand brain function in health and disease. But their adoption and reuse has been limited by the specialist knowledge required to evaluate and use them. To address this, we have developed Open Source Brain, a platform for sharing, viewing, analyzing, and simulating standardized models from different brain regions and species. Model structure and parameters can be automatically visualized and their dynamical properties explored through browser-based simulations. Infrastructure and tools for collaborative interaction, development, and testing are also provided. We demonstrate how existing components can be reused by constructing new models of inhibition-stabilized cortical networks that match recent experimental results. These features of Open Source Brain improve the accessibility, transparency, and reproducibility of models and facilitate their reuse by the wider community.
What makes one person more intellectually able than another? Can the entire distribution of human intelligence be accounted for by just one general factor? Is intelligence supported by a single neural system? Here, we provide a perspective on human intelligence that takes into account how general abilities or “factors” reflect the functional organization of the brain. By comparing factor models of individual differences in performance with factor models of brain functional organization, we demonstrate that different components of intelligence have their analogs in distinct brain networks. Using simulations based on neuroimaging data, we show that the higher-order factor “g” is accounted for by cognitive tasks corecruiting multiple networks. Finally, we confirm the independence of these components of intelligence by dissociating them using questionnaire variables. We propose that intelligence is an emergent property of anatomically distinct cognitive systems, each of which has its own capacity.
Multiple sclerosis (MS) is an inflammatory disease characterized by myelin loss and neuronal dysfunction. Despite the aggregation observed in some families, pathogenic mutations have remained elusive. In this study, we describe the identification of NR1H3 p.Arg415Gln in seven MS patients from two multi-incident families presenting severe and progressive disease, with an average age at onset of 34 years. Additionally, association analysis of common variants in NR1H3 identified rs2279238 conferring a 1.35-fold increased risk of developing progressive MS. The p.Arg415Gln position is highly conserved in orthologs and paralogs, and disrupts NR1H3 heterodimerization and transcriptional activation of target genes. Protein expression analysis revealed that mutant NR1H3 (LXRA) alters gene expression profiles, suggesting a disruption in transcriptional regulation as one of the mechanisms underlying MS pathogenesis. Our study indicates that pharmacological activation of LXRA or its targets may lead to effective treatments for the highly debilitating and currently untreatable progressive phase of MS.
The predominant focus in the neurobiological study of memory has been on remembering (persistence). However, recent studies have considered the neurobiology of forgetting (transience). Here we draw parallels between neurobiological and computational mechanisms underlying transience. We propose that it is the interaction between persistence and transience that allows for intelligent decision-making in dynamic, noisy environments. Specifically, we argue that transience (1) enhances flexibility, by reducing the influence of outdated information on memory-guided decision-making, and (2) prevents overfitting to specific past events, thereby promoting generalization. According to this view, the goal of memory is not the transmission of information through time, per se. Rather, the goal of memory is to optimize decision-making. As such, transience is as important as persistence in mnemonic systems.
Sharp wave-ripple (SWR) oscillations play a key role in memory consolidation during non-rapid eye movement sleep, immobility, and consummatory behavior. However, whether temporally modulated synaptic excitation or inhibition underlies the ripples is controversial. To address this question, we performed simultaneous recordings of excitatory and inhibitory postsynaptic currents (EPSCs and IPSCs) and local field potentials (LFPs) in the CA1 region of awake mice in vivo. During SWRs, inhibition dominated over excitation, with a peak conductance ratio of 4.1 ± 0.5. Furthermore, the amplitude of SWR-associated IPSCs was positively correlated with SWR magnitude, whereas that of EPSCs was not. Finally, phase analysis indicated that IPSCs were phase-locked to individual ripple cycles, whereas EPSCs were uniformly distributed in phase space. Optogenetic inhibition indicated that PV(+) interneurons provided a major contribution to SWR-associated IPSCs. Thus, phasic inhibition, but not excitation, shapes SWR oscillations in the hippocampal CA1 region in vivo.
Central serotonin (5-hydroxytryptophan, 5-HT) modulates somatosensory transduction, but how it achieves sensory modality-specific modulation remains unclear. Here we report that enhancing serotonergic tone via administration of 5-HT potentiates itch sensation, whereas mice lacking 5-HT or serotonergic neurons in the brainstem exhibit markedly reduced scratching behavior. Through pharmacological and behavioral screening, we identified 5-HT1A as a key receptor in facilitating gastrin-releasing peptide (GRP)-dependent scratching behavior. Coactivation of 5-HT1A and GRP receptors (GRPR) greatly potentiates subthreshold, GRP-induced Ca(2+) transients, and action potential firing of GRPR(+) neurons. Immunostaining, biochemical, and biophysical studies suggest that 5-HT1A and GRPR may function as receptor heteromeric complexes. Furthermore, 5-HT1A blockade significantly attenuates, whereas its activation contributes to, long-lasting itch transmission. Thus, our studies demonstrate that the descending 5-HT system facilitates GRP-GRPR signaling via 5-HT1A to augment itch-specific outputs, and a disruption of crosstalk between 5-HT1A and GRPR may be a useful antipruritic strategy. VIDEO ABSTRACT:
Developmental alterations of excitatory synapses are implicated in autism spectrum disorders (ASDs). Here, we report increased dendritic spine density with reduced developmental spine pruning in layer V pyramidal neurons in postmortem ASD temporal lobe. These spine deficits correlate with hyperactivated mTOR and impaired autophagy. In Tsc2+/- ASD mice where mTOR is constitutively overactive, we observed postnatal spine pruning defects, blockade of autophagy, and ASD-like social behaviors. The mTOR inhibitor rapamycin corrected ASD-like behaviors and spine pruning defects in Tsc2+/ mice, but not in Atg7(CKO) neuronal autophagy-deficient mice or Tsc2+/-:Atg7(CKO) double mutants. Neuronal autophagy furthermore enabled spine elimination with no effects on spine formation. Our findings suggest that mTOR-regulated autophagy is required for developmental spine pruning, and activation of neuronal autophagy corrects synaptic pathology and social behavior deficits in ASD models with hyperactivated mTOR.
Humans can resist temptations by exerting willpower, the effortful inhibition of impulses. But willpower can be disrupted by emotions and depleted over time. Luckily, humans can deploy alternative self-control strategies like precommitment, the voluntary restriction of access to temptations. Here, we examined the neural mechanisms of willpower and precommitment using fMRI. Behaviorally, precommitment facilitated choices for large delayed rewards, relative to willpower, especially in more impulsive individuals. While willpower was associated with activation in dorsolateral prefrontal cortex (DLPFC), posterior parietal cortex (PPC), and inferior frontal gyrus, precommitment engaged lateral frontopolar cortex (LFPC). During precommitment, LFPC showed increased functional connectivity with DLPFC and PPC, especially in more impulsive individuals, and the relationship between impulsivity and LFPC connectivity was mediated by value-related activation in ventromedial PFC. Our findings support a hierarchical model of self-control in which LFPC orchestrates precommitment by controlling action plans in more caudal prefrontal regions as a function of expected value.
Older adults do not sleep as well as younger adults. Why? What alterations in sleep quantity and quality occur as we age, and are there functional consequences? What are the underlying neural mechanisms that explain age-related sleep disruption? This review tackles these questions. First, we describe canonical changes in human sleep quantity and quality in cognitively normal older adults. Second, we explore the underlying neurobiological mechanisms that may account for these human sleep alterations. Third, we consider the functional consequences of age-related sleep disruption, focusing on memory impairment as an exemplar. We conclude with a discussion of a still-debated question: do older adults simply need less sleep, or rather, are they unable to generate the sleep that they still need?