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Human intelligence has been theorized since the ancient Greeks. Plato and Aristotle incorporated theories of human intelligence into their metaphysical and cosmological theories which informed the social and medical sciences for centuries. With the advent of the 20th century, human intelligence became increasingly standardized based on Intelligence Quotients (IQ). Moreover, multiple theories of human intelligence were posited on morphological features of the human brain, focusing on cranial volume and size of the pre-frontal cortex which was suggestive of superior human cognitive abilities. This article argues that fixation with anatomical features of the brain was tended to ignore the importance of neuro-hormonal regulation which is a more appropriate indicator of human cognitive abilities. The article challenges the correlation between brain size and human cognitive abilities while offering an alternate theory of human cognitive abilities which emphasizes the roles of neurotransmitters, neurotrophins, and enteric gut microbiome (EGM) regulation.

The inherent complexity of brain tissue, with brain cells intertwining locally and projecting to distant regions, has made three-dimensional visualization of intact brains a highly desirable but challenging task in neuroscience. The natural opaqueness of tissue has traditionally limited researchers to techniques short of single cell resolution such as computer tomography or magnetic resonance imaging. By contrast, techniques with single-cell resolution required mechanical slicing into thin sections, which entails tissue distortions that severely hinder accurate reconstruction of large volumes. Recent developments in tissue clearing and light sheet microscopy have made it possible to investigate large volumes at micrometer resolution. The value of tissue clearing has been shown in a variety of tissue types and animal models. However, its potential for examining the songbird brain remains unexplored. Songbirds are an established model system for the study of vocal learning and sensorimotor control. They share with humans the capacity to adapt vocalizations based on auditory input. Song learning and production are controlled in songbirds by the song system, which forms a network of interconnected discrete brain nuclei. Here, we use the CUBIC and iDISCO+ protocols for clearing adult songbird brain tissue. Combined with light sheet imaging, we show the potential of tissue clearing for the investigation of connectivity between song nuclei, as well as for neuroanatomy and brain vasculature studies., Frontiers in Neuroanatomy, 13, ISSN:1662-5129

Expansion microscopy and light sheet imaging (ExLSM) provide a viable alternative to existing tissue clearing and large volume imaging approaches. The analysis of intact volumes of brain tissue presents a distinct challenge in neuroscience. Recent advances in tissue clearing and light sheet microscopy have re-addressed this challenge and blossomed into a plethora of protocols with diverse advantages and disadvantages. While refractive index matching achieves near perfect transparency and allows for imaging at large depths, the resolution of cleared brains is usually limited to the micrometer range. Moreover, the often long and harsh tissue clearing protocols hinder preservation of native fluorescence and antigenicity. Here we image large expanded brain volumes of zebra finch brain tissue in commercially available light sheet microscopes. Our expansion light sheet microscopy (ExLSM) approach presents a viable alternative to many clearing and imaging methods because it improves on tissue processing times, fluorophore compatibility, and image resolution. ISSN:1662-5129

Borderline personality disorder (BPD) is associated with disturbed emotion regulation. Psychotherapeutic interventions using mindfulness elements have shown effectiveness in reducing clinical symptoms, yet little is known about their underlying neurobiology. In this functional magnetic resonance imaging (fMRI) study, 19 female BPD patients and 19 healthy controls were compared during mindful introspection, cognitive self-reflection and a neutral condition. The activation pattern in the right dorsomedial prefrontal cortex (DMPFC) in BPD patients was different from that in healthy subject when directing attention onto their emotions and bodily feelings in contrast to cognitively thinking about themselves. Mindful introspection compared with the neutral condition was associated with higher activations in bilateral motor/pre-motor regions, left inferior frontal gyrus (IFG), and left posterior cingulate cortex (PCC), while cognitive self-reflection activated the right motor and somatosensory cortex, extending into the right supramarginal gyrus (SMG) and superior temporal gyrus (STG) in BPD patients compared with the controls. Results indicate that self-referential cognitive and emotional processes are not clearly differentiated in BPD patients at the neurobiological level. In particular, altered neural mechanism underlying self-referential thinking may be related to some aspects of the typical emotion dysregulation in BPD. Current data support the finding that mindful self-focused attention is effective in regulating amygdala activity in BPD as well as in healthy subjects.

Elderly people at risk of developing cognitive decline; e.g., following surgery, may benefit from structured, challenging, and repetitive cognitive video training. This study assessed usability and acute effects of a newly developed bedside console (COPHYCON). Fifteen healthy elderly individuals performed a one-time 80-min intervention, including cognitive video games aimed at improving awareness and selective attention. Perceived usefulness and perceived ease of use (Technology Acceptance Model) were assessed together with measures of the achieved game level, reaction times, (in-) correct responses during ALERT and SELECT game play. Further, prefrontal cortical involvement of the regional cerebral hemoglobin saturation (rS02%) assessed with functional near infrared spectroscopy (fNIRS) (n = 5) and EEG power (n = 10) was analyzed. All participants completed the study without any adverse events. Perceived usefulness and perceived ease of use (TAM scores range 1-7) of the system varied between 3.9 and 6.3. The game levels reached for awareness varied between 9 and 11 (initial score 8-10), for reaction speed between 439 and 469 ms, and for correct responses between 74.1 and 78.8%. The highest level for the selective attention games was 2 (initial score 1), where reaction speed varied between 439 and 469 ms, correct responses between 96.2 and 98.5%, respectively. The decrease of rS02% in the right prefrontal cortex during gameplay was significantly (p < 0.001) lower, compared to the left prefrontal cortex. Four participants yielded significant lower rS02% measures after exergaming with the ALERT games (p < 0.000), but not with the SELECT games. EEG recordings of theta power significantly decreased in the averaged ~0.25-0.75 time interval for the left prefrontal cortex sensor across the cognitive game levels between the ALERT 1 and SELECT 1, as well as between SELECT 1 and 2 games. Participants rated the usability of the COPHYCON training positively. Further results indicate that video gaming may be an effective measure to affect prefrontal cortical functioning in elderly. The results warrant a clinical explorative study investigating the feasibility of the COPHYCON in a clinical setting., Frontiers in Systems Neuroscience, 11, ISSN:1662-5137

Dynamic neural fields (DNFs) are dynamical systems models that approximate the activity of large, homogeneous, and recurrently connected neural networks based on a mean field approach. Within dynamic field theory, the DNFs have been used as building blocks in architectures to model sensorimotor embedding of cognitive processes. Typically, the parameters of a DNF in an architecture are manually tuned in order to achieve a specific dynamic behavior (e.g., decision making, selection, or working memory) for a given input pattern. This manual parameters search requires expert knowledge and time to find and verify a suited set of parameters. The DNF parametrization may be particular challenging if the input distribution is not known in advance, e.g., when processing sensory information. In this paper, we propose the autonomous adaptation of the DNF resting level and gain by a learning mechanism of intrinsic plasticity (IP). To enable this adaptation, an input and output measure for the DNF are introduced, together with a hyper parameter to define the desired output distribution. The online adaptation by IP gives the possibility to pre-define the DNF output statistics without knowledge of the input distribution and thus, also to compensate for changes in it. The capabilities and limitations of this approach are evaluated in a number of experiments., Frontiers in Computational Neuroscience, 11 (AUG), ISSN:1662-5188

Anxiety disorders are characterized by deficient emotion regulation prior to and in anxiety-evoking situations. Patients with social anxiety disorder (SAD) have increased brain activation also during the anticipation and perception of non-specific emotional stimuli pointing to biased general emotion processing. In the current study we addressed the neural correlates of emotion regulation by cognitive control during the anticipation and perception of non-specific emotional stimuli in patients with SAD. Thirty-two patients with SAD underwent functional magnetic resonance imaging during the announced anticipation and perception of emotional stimuli. Half of them were trained and instructed to apply reality-checking as a control strategy, the others anticipated and perceived the stimuli. Reality checking significantly (p0.01) reduced activity in insular, amygdalar and medial thalamic areas during the anticipation and perception of negative emotional stimuli. The medial prefrontal cortex was comparably active in both groups (p0.50). The results suggest that cognitive control in patients with SAD influences emotion processing structures, supporting the usefulness of emotion regulation training in the psychotherapy of SAD. In contrast to studies in healthy subjects, cognitive control was not associated with increased activation of prefrontal regions in SAD. This points to possibly disturbed general emotion regulating circuits in SAD.

fMRI studies in mice typically require the use of anesthetics. Yet, it is known that anesthesia alters responses to stimuli or functional networks at rest. In this work, we have used Dual Regression analysis Network Modeling to investigate the effects of two commonly used anesthetics, isoflurane and medetomidine, on rs-fMRI derived functional networks, and in particular to what extent anesthesia affected the interaction within and between these networks. Experimental data have been used from a previous study (Grandjean et al., 2014). We applied multivariate ICA analysis and Dual Regression to infer the differences in functional connectivity between isoflurane- and medetomidine-anesthetized mice. Further network analysis was performed to investigate within- and between-network connectivity differences between these anesthetic regimens. The results revealed five major networks in the mouse brain: lateral cortical, associative cortical, default mode, subcortical, and thalamic network. The anesthesia regime had a profound effect both on within- and between-network interactions. Under isoflurane anesthesia predominantly intra- and inter-cortical interactions have been observed, with only minor interactions involving subcortical structures and in particular attenuated cortico-thalamic connectivity. In contrast, medetomidine-anesthetized mice displayed subcortical functional connectivity including interactions between cortical and thalamic ICA components. Combining the two anesthetics at low dose resulted in network interaction that constituted the superposition of the interaction observed for each anesthetic alone. The study demonstrated that network modeling is a promising tool for analyzing the brain functional architecture in mice and comparing alterations therein caused by different physiological or pathological states. Understanding the differential effects of anesthetics on brain networks and their interaction is essential when interpreting fMRI data recorded under specific physiological and pathological conditions., Frontiers in Neural Circuits, 11, ISSN:1662-5110

In order to understand how the mammalian neocortex is performing computations, two things are necessary; we need to have a good understanding of the available neuronal processing units and mechanisms, and we need to gain a better understanding of how those mechanisms are combined to build functioning systems. Therefore, in recent years there is an increasing interest in how spiking neural networks (SNN) can be used to perform complex computations or solve pattern recognition tasks. However, it remains a challenging task to design SNNs which use biologically plausible mechanisms (especially for learning new patterns), since most such SNN architectures rely on training in a rate-based network and subsequent conversion to a SNN. We present a SNN for digit recognition which is based on mechanisms with increased biological plausibility, i.e., conductance-based instead of current-based synapses, spike-timing-dependent plasticity with time-dependent weight change, lateral inhibition, and an adaptive spiking threshold. Unlike most other systems, we do not use a teaching signal and do not present any class labels to the network. Using this unsupervised learning scheme, our architecture achieves 95% accuracy on the MNIST benchmark, which is better than previous SNN implementations without supervision. The fact that we used no domain-specific knowledge points toward the general applicability of our network design. Also, the performance of our network scales well with the number of neurons used and shows similar performance for four different learning rules, indicating robustness of the full combination of mechanisms, which suggests applicability in heterogeneous biological neural networks., Frontiers in Computational Neuroscience, 9, ISSN:1662-5188

Winner-Take-All (WTA) networks are recurrently connected populations of excitatory and inhibitory neurons that represent promising candidate microcircuits for implementing cortical computation. WTAs can perform powerful computations, ranging from signal-restoration to state-dependent processing. However, such networks require fine-tuned connectivity parameters to keep the network dynamics within stable operating regimes. In this article, we show how such stability can emerge autonomously through an interaction of biologically plausible plasticity mechanisms that operate simultaneously on all excitatory and inhibitory synapses of the network. A weight-dependent plasticity rule is derived from the triplet spike-timing dependent plasticity model, and its stabilization properties in the mean-field case are analyzed using contraction theory. Our main result provides simple constraints on the plasticity rule parameters, rather than on the weights themselves, which guarantee stable WTA behavior. The plastic network we present is able to adapt to changing input conditions, and to dynamically adjust its gain, therefore exhibiting self-stabilization mechanisms that are crucial for maintaining stable operation in large networks of interconnected subunits. We show how distributed neural assemblies can adjust their parameters for stable WTA function autonomously while respecting anatomical constraints on neural wiring., Frontiers in Computational Neuroscience, 8, ISSN:1662-5188

Functional magnetic resonance imaging in a Go/Nogo task was employed to investigate the relationship between trait impulsivity and brain activation during motor response inhibition. We found a positive correlation between motor impulsivity and activation of bilateral ventrolateral prefrontal cortex during successful inhibitions, which suggests stronger recruitment to maintain task performance.

Two-photon calcium imaging enables functional analysis of neuronal circuits by inferring action potential (AP) occurrence (“spike trains”) from cellular fluorescence signals. It remains unclear how experimental parameters such as signal-to-noise ratio (SNR) and acquisition rate affect spike inference and whether additional information about network structure can be extracted. Here we present a simulation framework for quantitatively assessing how well spike dynamics and network topology can be inferred from noisy calcium imaging data. For simulated AP-evoked calcium transients in neocortical pyramidal cells, we analyzed the quality of spike inference as a function of SNR and data acquisition rate using a recently introduced peeling algorithm. Given experimentally attainable values of SNR and acquisition rate, neural spike trains could be reconstructed accurately and with up to millisecond precision. We then applied statistical neuronal network models to explore how remaining uncertainties in spike inference affect estimates of network connectivity and topological features of network organization. We define the experimental conditions suitable for inferring whether the network has a scale-free structure and determine how well hub neurons can be identified. Our findings provide a benchmark for future calcium imaging studies that aim to reliably infer neuronal network properties., Frontiers in Neural Circuits, 7, ISSN:1662-5110

Memory reactivations in hippocampal brain areas are critically involved in memory consolidation processes during sleep. In particular, specific firing patterns of hippocampal place cells observed during learning are replayed during subsequent sleep and rest in rodents. In humans, experimentally inducing hippocampal memory reactivations during slow-wave sleep (but not during wakefulness) benefits consolidation and immediately stabilizes declarative memories against future interference. Importantly, spontaneous hippocampal replay activity can also be observed during rapid-eye movement (REM) sleep and some authors have suggested that replay during REM sleep is related to processes of memory consolidation. However, the functional role of reactivations during REM sleep for memory stability is still unclear. Here, we reactivated memories during REM sleep and examined its consequences for the stability of declarative memories. After three hours of early, slow-wave sleep (SWS) rich sleep, 16 healthy young adults learned a 2-D object location task in the presence of a contextual odor. During subsequent REM sleep, participants were either re-exposed to the odor or to an odorless vehicle, in a counterbalanced within subject design. Reactivation was followed by an interference learning task to probe memory stability after awakening. We show that odor-induced memory reactivation during REM sleep does not stabilize memories against future interference. We propose that the beneficial effect of reactivation during sleep on memory stability might be critically linked to processes characterizing SWS including, e.g., slow oscillatory activity, sleep spindles or low cholinergic tone, which are required for a successful redistribution of memories from medial temporal lobe regions to neocortical long-term stores.