Cognitive control & the anterior cingulate cortex: necessity & coherence Influential theories of complex behaviour invoke the notion of cognitive control modulated by conflict between counterfactual actions. Medial frontal cortex, notably the anterior cingulate cortex, has been variously posited as critical to such conflict detection, resolution, or monitoring, largely based on correlative data from functional imaging. Examining performance on the most widely used “conflict” task—Stroop—in a large cohort of patients with focal brain injury (N = 176), we compare anatomical patterns of lesion-inferred neural substrate dependence to those derived from functional imaging, meta-analytically summarised. Our results show that whereas performance is sensitive to the integrity of left lateral frontal regions implicated by functional imaging, it does not depend on medial frontal cortex, despite sampling adequate to reveal robust medial effects in the context of phonemic fluency. We suggest that medial frontal cortex is not critically invoked by Stroop and proceed to review the conceptual grounds for rejecting the core notion of conflict-driven cognitive control.
A “Conscious” Loss of Balance: Directing Attention to Movement Can Impair the Cortical Response to Postural Perturbations “Trying too hard” can interfere with skilled movement, such as sports and music playing. Postural control can similarly suffer when conscious attention is directed toward it (“conscious movement processing”; CMP). However, the neural mechanisms through which CMP influences balance remain poorly understood. We explored the effects of CMP on electroencephalographic (EEG) perturbation-evoked cortical responses and subsequent balance performance. Twenty healthy young adults (age = 25.1 ± 5 years; 10 males and 10 females) stood on a force plate-embedded moveable platform while mobile EEG was recorded. Participants completed two blocks of 50 discrete perturbations, containing an even mix of slower (186 mm/s peak velocity) and faster (225 mm/s peak velocity) perturbations. One block was performed under conditions of CMP (i.e., instructions to consciously control balance), while the other was performed under “Control” conditions with no additional instructions. For both slow and fast perturbations, CMP resulted in significantly smaller cortical N1 signals (a perturbation-evoked potential localized to the supplementary motor area) and lower sensorimotor beta EEG activity 200–400 ms postperturbation. Significantly greater peak velocities of the center of pressure (i.e., greater postural instability) were also observed during the CMP condition. Our findings provide the first evidence that disruptions to postural control during CMP may be a consequence of insufficient cortical activation relevant for balance (i.e., insufficient cortical N1 responses followed by enhanced beta suppression). We propose that conscious attempts to minimize postural instability through CMP acts as a cognitive dual-task that dampens the sensitivity of the sensorimotor system for future losses of balance.
Dopamine alters motor learning performance in the presence and absence of feedback Skilled motor performance is essential for survival. Indeed, we often not only choose to learn motor skills because of some external reward, but also because skilled movement, in and of itself, is satisfying. While dopamine is known to drive reward-based motor learning, it remains unclear whether dopamine is implicated in motor learning under conditions ostensibly driven by intrinsic rewards/motivation (i.e., in the absence of extrinsic feedback or reward). Here, we investigated the role of dopamine in motor skill learning guided by internally determined signals of performance success, using a task where learning occurred either in the absence or presence of feedback. We found that dopamine altered performance both in the presence and in the absence of information on task success. This provides direct causal evidence for a role of dopamine in motor learning driven by internal task goals.
Applied motor noise affects specific learning mechanisms during short-term adaptation to novel movement dynamics Short-term motor adaptation to novel movement dynamics has been shown to involve at least two concurrent learning processes: a slow process that responds weakly to error but retains information well, and a fast process that responds strongly to error but has poor retention. This modeling framework can explain several properties of motion-dependent motor adaptation (e.g., 24-hour retention). An important assumption of this computational framework is that learning is only based on the experienced movement error, and the effect of noise (either internally generated or externally applied) is not considered. We examined the respective error sensitivity by quantifying adaptation in three subject groups distinguished by the noise added to the motion-dependent perturbation (magnitudes of 0, 3 or 7N, at a frequency of 10 Hz, 20 subjects/group). We assessed the feedforward adaptive changes in motor output and examined the adaptation rate, retention and decay of learning. Applying a two-state modeling framework showed that the applied noise during training mainly affected the fast learning process, with the slow process largely unaffected; participants in the higher noise groups demonstrated a reduced force profile following training, but the decay rate across groups was similar, suggesting that the slow process was unimpaired across conditions. Collectively, our results provide evidence that noise significantly decreases motor adaptation, but this reduction may be due to its influence over specific learning mechanisms. Importantly, this may have implications for how the motor system compensates for random fluctuations, especially when affected by brain disorders that result in movement tremor (e.g., Essential Tremor).
Molecular architecture of synaptic vesicles Synaptic vesicles (SVs) store and transport neurotransmitters to the presynaptic active zone for release by exocytosis. After release, SV proteins and excess membrane are recycled via endocytosis, and new SVs can be formed in a clathrin-dependent manner. This process maintains complex molecular composition of SVs through multiple recycling rounds. Previous studies explored the molecular composition of SVs through proteomic analysis and fluorescent microscopy, proposing a model for an average SV (1). However, the structural heterogeneity and molecular architecture of individual SVs are not well described. Here, we used cryoelectron tomography to visualize molecular details of SVs isolated from mouse brains and inside cultured neurons. We describe several classes of small proteins on the SV surface and long proteinaceous densities inside SVs. We identified V-ATPases, determined a structure using subtomogram averaging, and showed them forming a complex with the membrane-embedded protein synaptophysin (Syp). Our bioluminescence assay revealed pairwise interactions between vesicle-associated membrane protein 2 and Syp and V-ATPase Voe1 domains. Interestingly, V-ATPases were randomly distributed on the surface of SVs irrespective of vesicle size. A subpopulation of isolated vesicles and vesicles inside neurons contained a partially assembled clathrin coat with an icosahedral symmetry. We observed V-ATPases under clathrin cages in several isolated clathrin-coated vesicles (CCVs). Additionally, from isolated SV preparations and within hippocampal neurons we identified clathrin baskets without vesicles. We determined their and CCVs preferential location in proximity to the cell membrane. Our analysis advances the understanding of individual SVs’ diversity and their molecular architecture.
Projections from subfornical organ to bed nucleus of the stria terminalis modulate inflammation-induced anxiety-like behaviors in mice Peripheral inflammation is closely related to the pathogenesis of sickness behaviors and psychiatric disorders such as anxiety and depression. The circumventricular organs (CVOs) are important brain sites to perceive peripheral inflammatory signals, but few studies have reported their role in inflammation-induced anxiety or depression. Using a mouse model of lipopolysaccharide (LPS)–induced inflammation, we identified a previously unreported role of the subfornical organ (SFO), one of the CVOs, in combating inflammation-induced anxiety. LPS treatment induced anxiety-like and sickness behaviors in mice. Although both the SFO and the organum vasculosum of the lamina terminalis (a CVO) neurons were activated after LPS treatment, only manipulating SFO neurons modulated LPS-induced anxiety-like behaviors. Activating or inhibiting SFO neurons alleviated or aggravated LPS-induced anxiety-like behaviors. In addition, SFO exerted this effect through glutamatergic projections to the bed nucleus of the stria terminalis. Manipulating SFO neurons did not affect LPS-induced sickness behaviors. Thus, we uncovered an active role of SFO neurons in counteracting peripheral inflammation-induced anxiety.
Brain Activity: Unifying networks of a rhythm Our brains constantly receive and respond to information from the world around us. This ability largely relies on communication between different areas of the brain via networks of neurons, as well as the release of molecules known as neurotransmitters. The interplay between neural networks and neurotransmitters manifests in waxing and waning patterns of electrical activity, known as brain rhythms, which oscillate at different frequencies.
However, understanding how areas of the brain communicate with one another has remained a major challenge in neuroscience, partly because directly measuring the activity of neurons in the cortex requires invasive procedures. This means that such measurements are typically only possible in animals or in patients undergoing surgery, and are usually limited to a few localized sites in the cortex.
Brain health is essential for smooth economic transitions: towards socio-economic sustainability, productivity and well-being Optimal brain health is essential to smoothing major global skill-intensive economic transitions, such as the bioeconomy, green, care economy and digital transitions. Good brain health is vital to socio-economic sustainability, productivity and well-being. The care transition focuses on recognizing and investing in care services and care work as essential for economic growth and social well-being. The green transition involves shifting towards environmentally sustainable and fairer societies to combat climate change and environmental degradation. The digital transition aims to unlock digital growth potential and deploy innovative solutions for businesses and citizens, and to improve the accessibility and efficiency of services. The bioeconomy transition refers to the shift towards an economy based on products, services and processes derived from biological resources, such as plants and microorganisms. Brain capital, which encompasses brain health and brain skills, is a critical economic asset for the success of economies of the future. The brain economy transition from a brain-negative (brain-unhealthy) economy, which depletes brain capital, to a brain-positive (brain-healthy) economy, which arrests and reverses the loss of brain capital, will be foundational to these major transitions. Increased brain capital is vital to educational attainment, upskilling and reskilling. In this paper, we provide a detailed roadmap for the brain economy transition.