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The wiring of visual circuits requires that retinal neurons functionally connect to specific brain targets, a process that involves activity-dependent signaling between retinal axons and their postsynaptic targets. Vision loss in various ophthalmological and neurological diseases is caused by damage to the connections from the eye to the brain. How postsynaptic brain targets influence retinal ganglion cell (RGC) axon regeneration and functional reconnection with the brain targets remains poorly understood. Here, we established a paradigm in which the enhancement of neural activity in the distal optic pathway, where the postsynaptic visual target neurons reside, promotes RGC axon regeneration and target reinnervation and leads to the rescue of optomotor function. Furthermore, selective activation of retinorecipient neuron subsets is sufficient to promote RGC axon regeneration. Our findings reveal a key role for postsynaptic neuronal activity in the repair of neural circuits and highlight the potential to restore damaged sensory inputs via proper brain stimulation.

BACKGROUND: Sensory over-responsivity (SOR) is an impairing sensory processing challenge in autism spectrum disorder (ASD) which shows heterogenous developmental trajectories and appears to improve into adulthood in some but not all autistic individuals. However, the neural mechanisms underlying interindividual differences in these trajectories are currently unknown. METHODS: Here, we used functional magnetic resonance imaging (fMRI) to investigate the association between age and neural activity linearly and nonlinearly in response to mildly aversive sensory stimulation as well as how SOR severity moderates this association. Participants included 52 ASD (14F) and 41 (13F) typically developing (TD) youth, aged 8.6-18.0 years. RESULTS: We found that in pre-teens, ASD children showed widespread activation differences in sensorimotor, frontal and cerebellar regions compared to TD children, while there were fewer differences between ASD and TD teens. In TD youth, older age was associated with less activation in the prefrontal cortex. In contrast, in ASD youth, older age was associated with more engagement of sensory integration and emotion regulation regions. In particular, orbitofrontal and medial prefrontal cortices showed a nonlinear relationship with age in ASD, with an especially steep increase in sensory-evoked neural activity during the mid-to-late teen years. There was also an interaction between age and SOR severity in ASD youth such that these age-related trends were more apparent in youth with higher SOR. LIMITATIONS: The cross-sectional design limits causal interpretations of the data. Future longitudinal studies will be instrumental in determining how prefrontal engagement and SOR co-develop across adolescence. CONCLUSIONS: Our results suggest that enhanced recruitment of prefrontal regions may underlie age-related decreases in SOR for a subgroup of ASD youth.

While listening to meaningful speech, auditory input is processed more rapidly near the end (vs. beginning) of sentences. Although several studies have shown such word-to-word changes in auditory input processing, it is still unclear from which processing level these word-to-word dynamics originate. We investigated whether predictions derived from sentential context can result in auditory word-processing dynamics during sentence tracking. We presented healthy human participants with auditory stimuli consisting of word sequences, arranged into either predictable (coherent sentences) or less predictable (unstructured, random word sequences) 42-Hz amplitude-modulated speech, and a continuous 25-Hz amplitude-modulated distractor tone. We recorded RTs and frequency-tagged neuroelectric responses (auditory steady-state responses) to individual words at multiple temporal positions within the sentences, and quantified sentential context effects at each position while controlling for individual word characteristics (i.e., phonetics, frequency, and familiarity). We found that sentential context increasingly facilitates auditory word processing as evidenced by accelerated RTs and increased auditory steady-state responses to later-occurring words within sentences. These purely top-down contextually driven auditory word-processing dynamics occurred only when listeners focused their attention on the speech and did not transfer to the auditory processing of the concurrent distractor tone. These findings indicate that auditory word-processing dynamics during sentence tracking can originate from sentential predictions. The predictions depend on the listeners' attention to the speech, and affect only the processing of the parsed speech, not that of concurrently presented auditory streams.

This study investigates the relationship between deep learning models and the human brain, specifically focusing on the prediction of brain activity in response to static visual stimuli using functional magnetic resonance imaging (fMRI). By leveraging intermediate outputs of pre-trained convolutional neural networks (CNNs) with feature-weighted receptive fields, it becomes possible to estimate brain activity in the visual cortex. The primary objective of this research is to analyze how different CNN architectures affect the accuracy of predicting brain activity. To accomplish this, we utilize the novel fMRI Natural Scenes Dataset, which provides a large-scale data set for comprehensive analysis. Through this investigation, we aim to gain insights into the impact of CNN architectures on the prediction accuracy of brain activity in the context of visual stimuli., CSE3000 Research Project, Computer Science and Engineering

In humans and animals, spatial and temporal information from the nervous system are translated into muscle force enabling movements of body segments. To gain deeper understanding of this translation of information into movements, we investigated the motor control dynamics of isometric contractions in children, adolescents, young adults and older adults. Twelve children, thirteen adolescents, fourteen young adults, and fifteen older adults completed two minutes of submaximal isometric plantar- and dorsiflexion. Simultaneously, sensorimotor cortex EEG, tibialis anterior and soleus EMG and plantar- and dorsiflexion force was recorded. Surrogate analysis suggested that all signals were from a deterministic origin. Multiscale entropy analysis revealed an inverted U-shape relationship between age and complexity for the force but not for the EEG and EMG signals. This suggests that temporal information in from the nervous system is modulated by the musculoskeletal system during the transmission into force. The entropic half-life analyses indicated that this modulation increases the time scale of the temporal dependency in the force signal compared to the neural signals. Together this indicates that the information embedded in produced force does not exclusively reflect the information embedded in the underlying neural signal.

Parkinson’s disease (PD), like other neurodegenerative diseases, lacks a disease-modifying therapy. While we have solutions to counteract the dopaminergic depletion that ensues at the level of the striatum following the progressive loss of substantia nigra neurons in the midbrain, and therefore, are capable of reversing the motor symptoms experienced by patients, our understanding of the molecular mechanisms that trigger degeneration is minimal. Even following the discovery of single mutations in several genes that can cause familial forms of PD, like PINK1 and PRKN, we are still deprived of a deep appreciation of the basic biological mechanisms that drive disease initiation and progression. In Chapter 1 of this thesis, I describe the remarkable research progress that was made in the last two centuries following the original description of the disease. I detail how the mitochondrial hypothesis of PD emerged from brilliant discoveries, the advent of new biochemical and genomic tools, and even a few accidents, and how it may underlie the selective vulnerability of midbrain dopamine neurons that die in PD. Finally, I introduce some key unanswered questions in the field of PD research that I begin to address in this thesis. In Chapter 2, I describe the novel approach we developed to study a pathway of mitochondrial turnover that is critical in PD, which allowed us to track individual mitochondria at every step of the degradation process in neurons. We gained unprecedented insight into the fate of these mitochondria once they reach the lysosomal compartment which led to us to identify new steps of PINK1/PRKN-mediated mitochondrial degradation. These findings may have important implications for both basic biology and disease pathophysiology. In Chapter 3, I describe a mouse model we developed to assess the impact that chronic changes in neural activity have on the function and survival of midbrain dopamine neurons. We find that the substantia nigra dopamine neurons are pr

Parkinson’s disease (PD), like other neurodegenerative diseases, lacks a disease-modifying therapy. While we have solutions to counteract the dopaminergic depletion that ensues at the level of the striatum following the progressive loss of substantia nigra neurons in the midbrain, and therefore, are capable of reversing the motor symptoms experienced by patients, our understanding of the molecular mechanisms that trigger degeneration is minimal. Even following the discovery of single mutations in several genes that can cause familial forms of PD, like PINK1 and PRKN, we are still deprived of a deep appreciation of the basic biological mechanisms that drive disease initiation and progression. In Chapter 1 of this thesis, I describe the remarkable research progress that was made in the last two centuries following the original description of the disease. I detail how the mitochondrial hypothesis of PD emerged from brilliant discoveries, the advent of new biochemical and genomic tools, and even a few accidents, and how it may underlie the selective vulnerability of midbrain dopamine neurons that die in PD. Finally, I introduce some key unanswered questions in the field of PD research that I begin to address in this thesis. In Chapter 2, I describe the novel approach we developed to study a pathway of mitochondrial turnover that is critical in PD, which allowed us to track individual mitochondria at every step of the degradation process in neurons. We gained unprecedented insight into the fate of these mitochondria once they reach the lysosomal compartment which led to us to identify new steps of PINK1/PRKN-mediated mitochondrial degradation. These findings may have important implications for both basic biology and disease pathophysiology. In Chapter 3, I describe a mouse model we developed to assess the impact that chronic changes in neural activity have on the function and survival of midbrain dopamine neurons. We find that the substantia nigra dopamine neurons are pr

BackgroundWith the increasing popularity of calcium imaging in neuroscience research, choosing the right methods to analyze calcium imaging data is critical to address various scientific questions. Unlike spike trains measured using electrodes, fluorescence intensity traces provide an indirect and noisy measurement of the underlying neuronal activities. The observed calcium traces are either analyzed directly or deconvolved to spike trains to infer neuronal activities. When both approaches are applicable, it is unclear whether deconvolving calcium traces is a necessary step.MethodsIn this article, we compare the performance of using calcium traces or their deconvolved spike trains for three common analyses: clustering, principal component analysis (PCA), and population decoding.ResultsWe found that (1) the two approaches lead to diverging results; (2) estimated spike trains, when smoothed or binned appropriately, usually lead to satisfactory performances, such as more accurate estimation of cluster membership; (3) although estimate spike train produce results more similar to true spike data than trace data, we found that the PCA results from trace data might better reflect the underlying neuronal ensembles (clusters); and (4) for both approaches, decobability can be improved by using denoising or smoothing methods.Comparison with existing methodsOur simulations and applications to real data suggest that estimated spike data outperform trace data in cluster analysis and give comparable results for population decoding. In addition, the decobability of estimated spike data can be slightly better than that of calcium trace data with appropriate filtering / smoothing methods.ConclusionWe conclude that spike detection might be a useful pre-processing step for certain problems such as clustering; however, the continuous nature of calcium imaging data provides a natural smoothness that might be helpful for problems such as dimensional reduction.

Oligodendrocytes, which form the myelin sheaths that insulate axons, regulate conduction velocity. Myelinated axons make up the brain’s white matter and contribute to the efficiency of information processing by regulating the timing of neural activity. Traditionally, it has been thought that myelin is a static, inactive insulator around the axon. However, recent studies in humans using magnetic resonance imaging have shown that structural changes in the white matter occur during learning and training, suggesting that 1) white matter change depends on neural activity and 2) activity-dependent changes in white matter are essential for learning and behavior. Furthermore, suppression of oligodendrocytes and their progenitor cells leads to deficits in motor learning and remote fear memory consolidation, suggesting a causal relationship between glial function and the learning process. However, for technical reasons, it remains unclear how myelin-generating glia modulate neural circuitry and what underlying mechanisms they employ to affect learning and behavior. Recent advances in optical and genetic techniques have helped elucidate this mechanism. In this review, we highlight evidence that neural activities regulated by myelin plasticity play a pivotal role in learning and behavior and provide further insight into possible therapeutic targets for treating diseases accompanied by myelin impairment.

Developmental neural activity is a common feature of neural circuit assembly. Although glia have established roles in synapse development, the contribution of neuron-glia interactions to developmental activity remains largely unexplored. Here we show that astrocytes are necessary for developmental activity during synaptogenesis in Drosophila. Using wide-field epifluorescence and two-photon imaging, we show that the glia of the central nervous system participate in developmental activity with type-specific patterns of intracellular calcium dynamics. Genetic ablation of astrocytes, but not of cortex or ensheathing glia, leads to severe attenuation of neuronal activity. Similarly, inhibition of neuronal activity results in the loss of astrocyte calcium dynamics. By altering these dynamics, we show that astrocytic calcium cycles can influence neuronal activity but are not necessary per se. Taken together, our results indicate that, in addition to their recognized role in the structural maturation of synapses, astrocytes are also necessary for the function of synapses during development.

Inner Ear biology is broad and multidisciplinary, ranging from studies on cell and tissue organization mechanisms during development, the causes of hearing loss, and novel treatment therapies for repair. These questions are tackled using state-of-the-art methodologies and different animal model systems, some being highlighted in this Research Topic.

Reinforcement-learning (RL)-based brain-machine interfaces (BMIs) interpret dynamic neural activity into movement intention without patients’ real limb movements, which is promising for clinical applications. A movement task generally requires the subjects to reach the target within one step and rewards the subjects instantaneously. However, a real BMI scenario involves tasks that require multiple steps, during which sensory feedback is provided to indicate the status of the prosthesis, and the reward is only given at the end of the trial. Actually, subjects internally evaluate the sensory feedback to adjust motor activity. Existing RL-BMI tasks have not fully utilized the internal evaluation from the brain upon the sensory feedback to guide the decoder training, and there lacks an effective tool to assign credit for the multi-step decoding task. We propose first to extract intermediate guidance from the medial prefrontal cortex (mPFC) to assist the learning of multi-step decoding in an RL framework. To effectively explore the neural-action mapping in a large state-action space, a temporal difference (TD) method is incorporated into quantized attention-gated kernel reinforcement learning (QAGKRL) to assign the credit over the temporal sequence of movement, but also discriminate spatially in the Reproducing Kernel Hilbert Space (RKHS). We test our approach on the data collected from the primary motor cortex (M1) and the mPFC of rats when they brain control the cursor to reach the target within multiple steps. Compared with the models which only utilize the final reward, the intermediate evaluation interpreted from the mPFC can help improve the prediction accuracy by 10.9% on average across subjects, with faster convergence and more stability. Moreover, our proposed algorithm further increases 18.2% decoding accuracy compared with existing TD-RL methods. The results reveal the possibility of achieving better multi-step decoding performance for more complicated

Using still pictures of emotional facial expressions as experimental stimuli, reduced amygdala responses or impaired recognition of basic emotions were repeatedly found in people with psychopathic traits. The amygdala also plays an important role in short-latency facial mimicry responses. Since dynamic emotional facial expressions may have higher ecological validity than still pictures, we compared short-latency facial mimicry responses to dynamic and static emotional expressions between adolescents with psychopathic traits and normal controls. Facial EMG responses to videos or still pictures of emotional expressions (happiness, anger, sadness, fear) were measured. Responses to 500-ms dynamic expressions in videos, as well as the subsequent 1500-ms phase of maximal (i.e., static) expression, were compared between male adolescents with disruptive behavior disorders and high (n = 14) or low (n = 17) callous-unemotional (CU) traits, and normal control subjects (n = 32). Responses to still pictures were also compared between groups. EMG responses to dynamic expressions were generally significantly smaller in the high-CU group than in the other two groups, which generally did not differ. These group differences gradually emerged during the 500-ms stimulus presentation period but in general they were already seen a few hundred milliseconds after stimulus onset. Group differences were absent during the 1500-ms phase of maximal expression and during exposure to still pictures. Subnormal short-latency mimicry responses to dynamic emotional facial expressions in the high-CU group might have negative consequences for understanding emotional facial expressions of others during daily life when human facial interactions are primarily dynamic.

Aesthetic experiences have an influence on many aspects of life. Interest in the neural basis of aesthetic experiences has grown rapidly in the past decade, and fMRI studies have identified several brain systems supporting aesthetic experiences. Work on the rapid neuronal dynamics of aesthetic experience, however, is relatively scarce. This study adds to this field by investigating the experience of being aesthetically moved by means of ERP and time–frequency analysis. Participants' electroencephalography (EEG) was recorded while they viewed a diverse set of artworks and evaluated the extent to which these artworks moved them. Results show that being aesthetically moved is associated with a sustained increase in gamma activity over centroparietal regions. In addition, alpha power over right frontocentral regions was reduced in high- and low-moving images, compared to artworks given intermediate ratings. We interpret the gamma effect as an indication for sustained savoring processes for aesthetically moving artworks compared to aesthetically less-moving artworks. The alpha effect is interpreted as an indication of increased attention for aesthetically salient images. In contrast to previous works, we observed no significant effects in any of the established ERP components, but we did observe effects at latencies longer than 1 sec. We conclude that EEG time–frequency analysis provides useful information on the neuronal dynamics of aesthetic experience.

The stereotyped synaptic connections that define neural circuit function are established during development. Neuronal activity independent of environmental stimulus contributes to neural circuit assembly in vertebrates, but its precise role remains an open question, particularly at the level of defined cell types. By contrast, invertebrate neurodevelopment was thought to proceed in the absence of such developmental activity. Here I show that developmental activity accompanies synaptogenesis in the Drosophila brain. Using Drosophila genetics, epifluorescence microscopy, two-photon microscopy, immunohistochemistry, and visual behavior, I characterize this developmental activity, determine its role in synapse formation, and identify regulatory mechanisms at the cellular and molecular level. This patterned-stimulus independent neural activity occurs in a brain-wide fashion, and is characterized by stereotyped oscillations and cellular dynamics that reflect synaptic connectivity. Activity patterns instruct synapse development in a cell-type-specific manner. A transient and genetically specified population of neurons propagates this activity throughout the brain. Glia participate with complementary cycles of activity in a cell-type-specific fashion, and astrocytes are necessary for activity to occur. These data indicate that developmental activity is an evolutionarily conserved and fundamental component of neural circuit assembly that is coordinated by multiple molecular and cellular mechanisms.

Numerous brain regions have been identified as contributing to withdrawal behaviors, but it is unclear the way in which these brain regions as a whole lead to withdrawal. The search for a final common brain pathway that is involved in withdrawal remains elusive. To address this question, we implanted osmotic minipumps containing either saline, nicotine (24 mg/kg/d), cocaine (60 mg/kg/d), or methamphetamine (4 mg/kg/d) for one week in male C57BL/6J mice. After one week, the minipumps were removed and brains collected 8 h (saline, nicotine, and cocaine) or 12 h (methamphetamine) after removal. We then performed single-cell whole-brain imaging of neural activity during the withdrawal period when brains were collected. We used hierarchical clustering and graph theory to identify similarities and differences in brain functional architecture. Although methamphetamine and cocaine shared some network similarities, the main common neuroadaptation between these psychostimulant drugs was a dramatic decrease in modularity, with a shift from a cortical-driven to subcortical-driven network, including a decrease in total hub brain regions. These results demonstrate that psychostimulant withdrawal produces the drug-dependent remodeling of functional architecture of the brain and suggest that the decreased modularity of brain functional networks and not a specific set of brain regions may represent the final common pathway associated with withdrawal.

Mean-field models describe bulk neural activity in terms of population-average action potential (spike) rates, but do not attempt to address the variations in the firing activity of individual neurons and the interactions among them. In 2016, Waikato Cortical Modelling group published a purely theoretical model [Steyn-Ross et al, Phys. Rev. E 93.2 (2016): 022402] that provides a more accurate mapping of spiking dynamics, scaling from single neuron to the macroscopic level by regridding the system using a spatial blocking: a bottom-up neural regridding referred to as True-field. A 2D continuum of identical neurons is constructed from a lattice of spiking neurons that are coupled both synaptically (via chemical synapses) and diffusively (via electrical synapses). The spiking behaviour at the single-neuron level is modelled using the Wilson point neuron equations, steered by incoming electrical impulses from adjacent neurons. These equations are then reblocked to form a coarser-spatial resolution by eliminating the high-frequency spatial modes. The existence of diffusive terms in voltage and recovery equations is crucial for this spatial coarse-graining procedure. The purpose of this thesis is to conduct a preliminary analysis of the True-field model, which has never been tested in simulations before. The coarse-graining procedure employed in this framework results in a set of nonlinear corrections in the model equations. My first challenge is to recover those corrections and conduct numerical investigations to identify the most significant ones. My next challenge is to tune parameters of the True-field model via a comprehensive series of simulations, looking for “realistic” cortical behaviours. Two approaches are used: point simulations of the homogeneous two-neuron cortex, and full 2D grid simulations of a sheet of cortical tissue. Point simulations are straightforward and time efficient but do not provide any information about spatial variations in firing activity

Electrical activity is omnipresent throughout the brain, and it varies dependant on the brain region. Areal hierarchy has been suggested to be one of the main principles of the organization of the brain, but there is not a lot of evidence available related to the specialization of the brain’s regions in the temporal domain, that is, how the activity evolves over time. It has been suggested that there is a relationship between spatial location and timescale [1] and that the timescales of neuronal activity in rodents change according to the hierarchical position (derived from anatomical connectivity measurements) of the brain region [2]. Timescale is related to to the capability of a neuron to maintain the same firing rate over a time period. This firing rate can be measured as decay time constant of an auto-correlation matrix of spiking activity, referred to as the timescale of a single neuron [3]. In this thesis, timescales of spontaneous brain activity were measured in eight regions of the mouse prefrontal cortex (PFC) (data obtained in the Carlén Laboratory) and compared to the timescales of eight visual areas (Neuropixels Visual Coding dataset, Allen Institute for Brain Science) [4]. The results showed that cortical regions hold varying timescales, but that there is no clear correspondence of the timescales of spontaneous activity to the anatomical hierarchies. Instead, we show that the PFC regions have a greater variability in their respective timescales compared to visual cortical regions. The analysis was done using two different approaches, where for some regions the measured timescales significantly differs, due to the difference in the use of the magnitudes of the correlation. This work highlights how neuronal timescales measurements can be approached in cortical regions and used for the future work investigating their functional role and the mechanisms of generation of distinct neuronal timescales in the brain., Elektrisk aktivitet är allestädes närvarande i hela hjärnan, och den varierar beroende på hjärnregionen. Arealhierarki har föreslagits vara en av huvudprinciperna för hjärnans organisation, men det finns inte mycket bevis tillgängligt relaterat till specialiseringen av hjärnans regioner i den temporala domänen, det vill säga hur aktiviteten utvecklas över tiden . Det har föreslagits att det finns ett samband mellan rumslig plats och tidsskala [1] och att tidsskalorna för neuronal aktivitet hos gnagare ändras beroende på den hierarkiska positionen (härledd från anatomiska anslutningsmätningar) i hjärnregionen [2]. Tidsskala är relaterat till förmågan hos ett neuron att bibehålla samma fyrningshastighet under en tidsperiod. Denna avfyrningshastighet kan mätas som fallstidskonstant för en autokorrelationsmatris av spikaktivitet, kallad tidsskalan för en enda neuron [3]. I denna avhandling mättes tidsskalor för spontan hjärnaktivitet i åtta regioner i musens prefrontala kortex (PFC) (data erhållen av Carlén Laboratory) och jämfört med tidsskalorna för åtta visuella områden (Neuropixels Visual Coding dataset, Allen Institute for Brain Science) [4]. Resultaten visade att kortikala regioner har olika tidsskalor, men att det inte finns någon tydlig överensstämmelse mellan tidsskalorna för spontan aktivitet med de anatomiska hierarkierna. Istället visar vi att PFC-regionerna har större variation i sina respektive tidsskalor jämfört med visuella kortikala regioner. Analysen gjordes med hjälp av två olika tillvägagångssätt, där de uppmätta tidsskalorna för vissa regioner skiljer sig avsevärt på grund av skillnaden i användning av storleken på korrelationen. Detta arbete belyser hur neuronala tidsskalemätningar kan beaktas i kortikala regioner och användas för det framtida arbetet med att undersöka deras funktionella roll och mekanismerna för generering av distinkta neuronala tidsskalor i hjärnan.

The stereotyped synaptic connections that define neural circuit function are established during development. Neuronal activity independent of environmental stimulus contributes to neural circuit assembly in vertebrates, but its precise role remains an open question, particularly at the level of defined cell types. By contrast, invertebrate neurodevelopment was thought to proceed in the absence of such developmental activity. Here I show that developmental activity accompanies synaptogenesis in the Drosophila brain. Using Drosophila genetics, epifluorescence microscopy, two-photon microscopy, immunohistochemistry, and visual behavior, I characterize this developmental activity, determine its role in synapse formation, and identify regulatory mechanisms at the cellular and molecular level. This patterned-stimulus independent neural activity occurs in a brain-wide fashion, and is characterized by stereotyped oscillations and cellular dynamics that reflect synaptic connectivity. Activity patterns instruct synapse development in a cell-type-specific manner. A transient and genetically specified population of neurons propagates this activity throughout the brain. Glia participate with complementary cycles of activity in a cell-type-specific fashion, and astrocytes are necessary for activity to occur. These data indicate that developmental activity is an evolutionarily conserved and fundamental component of neural circuit assembly that is coordinated by multiple molecular and cellular mechanisms.

Visual working memory (WM) storage is largely independent between the left and right visual hemifields/cerebral hemispheres, yet somehow WM feels seamless. We studied how WM is integrated across hemifields by recording neural activity bilaterally from lateral prefrontal cortex. An instructed saccade during the WM delay shifted the remembered location from one hemifield to the other. Before the shift, spike rates and oscillatory power showed clear signatures of memory laterality. After the shift, the lateralization inverted, consistent with transfer of the memory trace from one hemisphere to the other. Transferred traces initially used different neural ensembles from feedforward-induced ones, but they converged at the end of the delay. Around the time of transfer, synchrony between the two prefrontal hemispheres peaked in theta and beta frequencies, with a directionality consistent with memory trace transfer. This illustrates how dynamics between the two cortical hemispheres can stitch together WM traces across visual hemifields.

Numerous brain regions have been identified as contributing to withdrawal behaviors, but it is unclear the way in which these brain regions as a whole lead to withdrawal. The search for a final common brain pathway that is involved in withdrawal remains elusive. To address this question, we implanted osmotic minipumps containing either saline, nicotine (24 mg/kg/d), cocaine (60 mg/kg/d), or methamphetamine (4 mg/kg/d) for one week in male C57BL/6J mice. After one week, the minipumps were removed and brains collected 8 h (saline, nicotine, and cocaine) or 12 h (methamphetamine) after removal. We then performed single-cell whole-brain imaging of neural activity during the withdrawal period when brains were collected. We used hierarchical clustering and graph theory to identify similarities and differences in brain functional architecture. Although methamphetamine and cocaine shared some network similarities, the main common neuroadaptation between these psychostimulant drugs was a dramatic decrease in modularity, with a shift from a cortical-driven to subcortical-driven network, including a decrease in total hub brain regions. These results demonstrate that psychostimulant withdrawal produces the drug-dependent remodeling of functional architecture of the brain and suggest that the decreased modularity of brain functional networks and not a specific set of brain regions may represent the final common pathway associated with withdrawal.

Mean-field models describe bulk neural activity in terms of population-average action potential (spike) rates, but do not attempt to address the variations in the firing activity of individual neurons and the interactions among them. In 2016, Waikato Cortical Modelling group published a purely theoretical model [Steyn-Ross et al, Phys. Rev. E 93.2 (2016): 022402] that provides a more accurate mapping of spiking dynamics, scaling from single neuron to the macroscopic level by regridding the system using a spatial blocking: a bottom-up neural regridding referred to as True-field. A 2D continuum of identical neurons is constructed from a lattice of spiking neurons that are coupled both synaptically (via chemical synapses) and diffusively (via electrical synapses). The spiking behaviour at the single-neuron level is modelled using the Wilson point neuron equations, steered by incoming electrical impulses from adjacent neurons. These equations are then reblocked to form a coarser-spatial resolution by eliminating the high-frequency spatial modes. The existence of diffusive terms in voltage and recovery equations is crucial for this spatial coarse-graining procedure. The purpose of this thesis is to conduct a preliminary analysis of the True-field model, which has never been tested in simulations before. The coarse-graining procedure employed in this framework results in a set of nonlinear corrections in the model equations. My first challenge is to recover those corrections and conduct numerical investigations to identify the most significant ones. My next challenge is to tune parameters of the True-field model via a comprehensive series of simulations, looking for “realistic” cortical behaviours. Two approaches are used: point simulations of the homogeneous two-neuron cortex, and full 2D grid simulations of a sheet of cortical tissue. Point simulations are straightforward and time efficient but do not provide any information about spatial variations in firing activity

Electrical activity is omnipresent throughout the brain, and it varies dependant on the brain region. Areal hierarchy has been suggested to be one of the main principles of the organization of the brain, but there is not a lot of evidence available related to the specialization of the brain’s regions in the temporal domain, that is, how the activity evolves over time. It has been suggested that there is a relationship between spatial location and timescale [1] and that the timescales of neuronal activity in rodents change according to the hierarchical position (derived from anatomical connectivity measurements) of the brain region [2]. Timescale is related to to the capability of a neuron to maintain the same firing rate over a time period. This firing rate can be measured as decay time constant of an auto-correlation matrix of spiking activity, referred to as the timescale of a single neuron [3]. In this thesis, timescales of spontaneous brain activity were measured in eight regions of the mouse prefrontal cortex (PFC) (data obtained in the Carlén Laboratory) and compared to the timescales of eight visual areas (Neuropixels Visual Coding dataset, Allen Institute for Brain Science) [4]. The results showed that cortical regions hold varying timescales, but that there is no clear correspondence of the timescales of spontaneous activity to the anatomical hierarchies. Instead, we show that the PFC regions have a greater variability in their respective timescales compared to visual cortical regions. The analysis was done using two different approaches, where for some regions the measured timescales significantly differs, due to the difference in the use of the magnitudes of the correlation. This work highlights how neuronal timescales measurements can be approached in cortical regions and used for the future work investigating their functional role and the mechanisms of generation of distinct neuronal timescales in the brain., Elektrisk aktivitet är allestädes närvarande i hela hjärnan, och den varierar beroende på hjärnregionen. Arealhierarki har föreslagits vara en av huvudprinciperna för hjärnans organisation, men det finns inte mycket bevis tillgängligt relaterat till specialiseringen av hjärnans regioner i den temporala domänen, det vill säga hur aktiviteten utvecklas över tiden . Det har föreslagits att det finns ett samband mellan rumslig plats och tidsskala [1] och att tidsskalorna för neuronal aktivitet hos gnagare ändras beroende på den hierarkiska positionen (härledd från anatomiska anslutningsmätningar) i hjärnregionen [2]. Tidsskala är relaterat till förmågan hos ett neuron att bibehålla samma fyrningshastighet under en tidsperiod. Denna avfyrningshastighet kan mätas som fallstidskonstant för en autokorrelationsmatris av spikaktivitet, kallad tidsskalan för en enda neuron [3]. I denna avhandling mättes tidsskalor för spontan hjärnaktivitet i åtta regioner i musens prefrontala kortex (PFC) (data erhållen av Carlén Laboratory) och jämfört med tidsskalorna för åtta visuella områden (Neuropixels Visual Coding dataset, Allen Institute for Brain Science) [4]. Resultaten visade att kortikala regioner har olika tidsskalor, men att det inte finns någon tydlig överensstämmelse mellan tidsskalorna för spontan aktivitet med de anatomiska hierarkierna. Istället visar vi att PFC-regionerna har större variation i sina respektive tidsskalor jämfört med visuella kortikala regioner. Analysen gjordes med hjälp av två olika tillvägagångssätt, där de uppmätta tidsskalorna för vissa regioner skiljer sig avsevärt på grund av skillnaden i användning av storleken på korrelationen. Detta arbete belyser hur neuronala tidsskalemätningar kan beaktas i kortikala regioner och användas för det framtida arbetet med att undersöka deras funktionella roll och mekanismerna för generering av distinkta neuronala tidsskalor i hjärnan.

Fluorescence calcium imaging using a range of microscopy approaches, such as two-photon excitation or head-mounted "miniscopes," is one of the preferred methods to record neuronal activity and glial signals in various experimental settings, including acute brain slices, brain organoids, and behaving animals. Because changes in the fluorescence intensity of genetically encoded or chemical calcium indicators correlate with action potential firing in neurons, data analysis is based on inferring such spiking from changes in pixel intensity values across time within different regions of interest. However, the algorithms necessary to extract biologically relevant information from these fluorescent signals are complex and require significant expertise in programming to develop robust analysis pipelines. For decades, the only way to perform these analyses was for individual laboratories to write their custom code. These routines were typically not well annotated and lacked intuitive graphical user interfaces (GUIs), which made it difficult for scientists in other laboratories to adopt them. Although the panorama is changing with recent tools like CaImAn, Suite2P, and others, there is still a barrier for many laboratories to adopt these packages, especially for potential users without sophisticated programming skills. As two-photon microscopes are becoming increasingly affordable, the bottleneck is no longer the hardware, but the software used to analyze the calcium data optimally and consistently across different groups. We addressed this unmet need by incorporating recent software solutions, namely NoRMCorre and CaImAn, for motion correction, segmentation, signal extraction, and deconvolution of calcium imaging data into an open-source, easy to use, GUI-based, intuitive and automated data analysis software package, which we named EZcalcium.

The study of the physiological effects underlying brain response to transcranial magnetic stimulation is important to understand its impact on neurorehabilitation. We aim to analyze the impact of a transcranial magnetic stimulation protocol, the continuous theta burst (cTBS), on human neurophysiology, particularly on contralateral motor rhythms. cTBS was applied in 20 subjects over the primary motor cortex. We recorded brain electrical activity pre- and post-cTBS with electroencephalography both at rest and while performing motor tasks, to evaluate changes in brain oscillatory patterns such as mu and beta rhythms. Moreover, we measured motor-evoked potentials before and after cTBS to assess its impact on brain's excitability. On the hemisphere contralateral to the protocol, we did observe a significant increase in mu (p = 0.027) and beta (p = 0.006) rhythms from pre- to post-cTBS, at the beginning of arm elevation. The topology of action planning and motor execution suggests that cTBS produced an inhibitory effect that propagated to the contralateral hemisphere, thereby precluding the expected/desired excitation for therapy purposes. This novel approach provides support for the notion that this protocol induces inhibitory changes in contralateral motor rhythms, by decreasing desynchronization, contradicting the ipsilateral inhibition vs. contralateral disinhibition hypothesis. Our results have implications for personalized cTBS usage as a rehabilitation intervention, suggesting that an unexpected propagation of inhibition can occur.

Fluorescence calcium imaging using a range of microscopy approaches, such as two-photon excitation or head-mounted "miniscopes," is one of the preferred methods to record neuronal activity and glial signals in various experimental settings, including acute brain slices, brain organoids, and behaving animals. Because changes in the fluorescence intensity of genetically encoded or chemical calcium indicators correlate with action potential firing in neurons, data analysis is based on inferring such spiking from changes in pixel intensity values across time within different regions of interest. However, the algorithms necessary to extract biologically relevant information from these fluorescent signals are complex and require significant expertise in programming to develop robust analysis pipelines. For decades, the only way to perform these analyses was for individual laboratories to write their custom code. These routines were typically not well annotated and lacked intuitive graphical user interfaces (GUIs), which made it difficult for scientists in other laboratories to adopt them. Although the panorama is changing with recent tools like CaImAn, Suite2P, and others, there is still a barrier for many laboratories to adopt these packages, especially for potential users without sophisticated programming skills. As two-photon microscopes are becoming increasingly affordable, the bottleneck is no longer the hardware, but the software used to analyze the calcium data optimally and consistently across different groups. We addressed this unmet need by incorporating recent software solutions, namely NoRMCorre and CaImAn, for motion correction, segmentation, signal extraction, and deconvolution of calcium imaging data into an open-source, easy to use, GUI-based, intuitive and automated data analysis software package, which we named EZcalcium.

The study of the physiological effects underlying brain response to transcranial magnetic stimulation is important to understand its impact on neurorehabilitation. We aim to analyze the impact of a transcranial magnetic stimulation protocol, the continuous theta burst (cTBS), on human neurophysiology, particularly on contralateral motor rhythms. cTBS was applied in 20 subjects over the primary motor cortex. We recorded brain electrical activity pre- and post-cTBS with electroencephalography both at rest and while performing motor tasks, to evaluate changes in brain oscillatory patterns such as mu and beta rhythms. Moreover, we measured motor-evoked potentials before and after cTBS to assess its impact on brain's excitability. On the hemisphere contralateral to the protocol, we did observe a significant increase in mu (p = 0.027) and beta (p = 0.006) rhythms from pre- to post-cTBS, at the beginning of arm elevation. The topology of action planning and motor execution suggests that cTBS produced an inhibitory effect that propagated to the contralateral hemisphere, thereby precluding the expected/desired excitation for therapy purposes. This novel approach provides support for the notion that this protocol induces inhibitory changes in contralateral motor rhythms, by decreasing desynchronization, contradicting the ipsilateral inhibition vs. contralateral disinhibition hypothesis. Our results have implications for personalized cTBS usage as a rehabilitation intervention, suggesting that an unexpected propagation of inhibition can occur.

Fluorescence calcium imaging using a range of microscopy approaches, such as two-photon excitation or head-mounted miniscopes, is one of the preferred methods to record neuronal activity and glial signals in various experimental settings, including acute brain slices, brain organoids, and behaving animals. Because changes in the fluorescence intensity of genetically encoded or chemical calcium indicators correlate with action potential firing in neurons, data analysis is based on inferring such spiking from changes in pixel intensity values across time within different regions of interest. However, the algorithms necessary to extract biologically relevant information from these fluorescent signals are complex and require significant expertise in programming to develop robust analysis pipelines. For decades, the only way to perform these analyses was for individual laboratories to write their custom code. These routines were typically not well annotated and lacked intuitive graphical user interfaces (GUIs), which made it difficult for scientists in other laboratories to adopt them. Although the panorama is changing with recent tools like CaImAn, Suite2P, and others, there is still a barrier for many laboratories to adopt these packages, especially for potential users without sophisticated programming skills. As two-photon microscopes are becoming increasingly affordable, the bottleneck is no longer the hardware, but the software used to analyze the calcium data optimally and consistently across different groups. We addressed this unmet need by incorporating recent software solutions, namely NoRMCorre and CaImAn, for motion correction, segmentation, signal extraction, and deconvolution of calcium imaging data into an open-source, easy to use, GUI-based, intuitive and automated data analysis software package, which we named EZcalcium.

The aim of the present study is to optimize parameters for inhibiting neuronal activity safely and investigating thermal inhibition of rat cortex neural networks in vitro by continuous infrared (IR) laser. Rat cortex neurons were cultured on multi-electrode arrays until neural networks were formed with spontaneous neural activity. Neurons were then irradiated to inhibit the activity of the networks using different powers of 1550 nm IR laser light. A finite element heating model, calibrated by the open glass pipette method, was used to calculate temperature increases at different laser irradiation intensities. A damage signal ratio (DSR) was evaluated to avoid excessive heating that may damage cells. The DSR predicted that cortex neurons should be safe at temperatures up to 49.6 degrees C for 30 seconds, but experiments suggested that cortex neurons should not be exposed to temperatures over 46 degrees C for 30 seconds. Neural response experiments showed that the inhibition of neural activity is temperature dependent. The normal neural activity could be inhibited safely with an inhibition degree up to 80% and induced epileptiform activity could be suppressed. These results show that continuous IR laser radiations provide a possible way to safely inhibit the neural network activity., QC 20191105

The cortex is a massively recurrent network, characterized by feedforward and feedback connections between brain areas as well as lateral connections within an area. Feedforward, horizontal and feedback responses largely activate separate layers of a cortical unit, meaning they can be dissociated by lamina-resolved neurophysiological techniques. Such techniques are invasive and are therefore rarely used in humans. However, recent developments in high spatial resolution fMRI allow for non-invasive, in vivo measurements of brain responses specific to separate cortical layers. This provides an important opportunity to dissociate between feedforward and feedback brain responses, and investigate communication between brain areas at a more fine- grained level than previously possible in the human species. In this review, we highlight recent studies that successfully used laminar fMRI to isolate layer-specific feedback responses in human sensory cortex. In addition, we review several areas of cognitive neuroscience that stand to benefit from this new technological development, highlighting contemporary hypotheses that yield testable predictions for laminar fMRI. We hope to encourage researchers with the opportunity to embrace this development in fMRI research, as we expect that many future advancements in our current understanding of human brain function will be gained from measuring lamina-specific brain responses.

The cortex is a massively recurrent network, characterized by feedforward and feedback connections between brain areas as well as lateral connections within an area. Feedforward, horizontal and feedback responses largely activate separate layers of a cortical unit, meaning they can be dissociated by lamina-resolved neurophysiological techniques. Such techniques are invasive and are therefore rarely used in humans. However, recent developments in high spatial resolution fMRI allow for non-invasive, in vivo measurements of brain responses specific to separate cortical layers. This provides an important opportunity to dissociate between feedforward and feedback brain responses, and investigate communication between brain areas at a more fine- grained level than previously possible in the human species. In this review, we highlight recent studies that successfully used laminar fMRI to isolate layer-specific feedback responses in human sensory cortex. In addition, we review several areas of cognitive neuroscience that stand to benefit from this new technological development, highlighting contemporary hypotheses that yield testable predictions for laminar fMRI. We hope to encourage researchers with the opportunity to embrace this development in fMRI research, as we expect that many future advancements in our current understanding of human brain function will be gained from measuring lamina-specific brain responses.

В статье исследуется двумерная модель Рулькова нейронной активности в зоне замкнутых инвариантных кривых канардного типа. Изучается эффект расщепления под воздействием случайных возмущений. Рассматривается переход от режима тонического спайкинга к режиму стохастического берстинга. Для исследования этих явлений используются метод прямого численного моделирования и метод функции стохастической чувствительности., In this paper, we investigate the two-dimensional Rulkov model of neural activity in the zone of Canard-type closed invariant curves. We are studying the phenomenon of splitting under random disturbances. We are considered the transition from the tonic spiking regime to the stochastic bursting one. The direct numerical simulation and the stochastic sensitivity function technique are used to study these phenomena.

The blood-brain barrier (BBB) is a set of properties belonging to endothelial cells that make up blood vessels in the central nervous system (CNS). The BBB’s function is to reduce the transport of nonspecific ions and molecules between the blood and brain and import specific molecules to maintain brain homeostasis. The cerebral vasculature is also known to be tightly coupled to neural activity and dynamically changes blood flow to satisfy the energy demand of active brain regions. Although the cerebral vascular blood flow is tightly coupled to neural activity, the question remains whether BBB properties can dynamically respond to neural activity, as well. Through manipulating glutamatergic neurons in mice, we have shown that neural activity can dynamically regulate both the expression and function of BBB genes in the cerebral vasculature, namely, ATP- binding cassette (ABC) efflux transporters. Additionally, we found that neural activity dynamically regulates circadian clock-related genes in endothelial cells. This led us to ask the question of what is the functional role of the circadian clock in endothelial cells of the cerebral vasculature? Through knocking out a core clock gene, Bmal1, specifically in endothelial cells, we found that the vascular clock regulates ABC efflux transport and animal behavior. We propose the idea that neural activity entrains the vascular-specific circadian clock, which regulates ABC transporter activity in a circadian manner, and deletion of the clock in endothelial cells causes dysregulation of ABC transport, thus disrupting the chemical microenvironment of the brain, resulting in complex behavioral phenotypes.

OF THE DISSERTATIONQuantification of Changes of Intensity and Attenuation Coefficient During Neural Activity Using Optical Coherence TomographybyMd. Monirul HasanDoctor of Philosophy, Graduate Program in BioengineeringUniversity of California, Riverside, March 2018Dr. B. Hyle Park, ChairpersonThe nervous system, which receives information through the electrical signal to coordinate and disseminate information about the body and its environment, is an assembly of neurons. Current methods of detection of neural activity are based on electrophysiology, which requires direct or near direct contact, or fluorescence-based techniques, which require the introduction of reporter molecules, raising a concern of toxicity. Optical coherence tomography(OCT) detects neural activity by taking advantage of intrinsic structural changes that accompany neural activity without any exogenous agents. The phase-resolved OCT detected 10-30 nm swelling of axon during neural activity from the functionally stimulated compound eye of the horseshoe crab(limulus) by utilizing the phase-resolved measurement. There was a decrease of backscattered intensity during propagation of action potential due to change of scattering as well. Further study on in vitro murine brain slice showed the changes of the phase (or thickness change) as well as a decrease of back-reflected intensity during propagation of action potential. Above studies with pr-OCT were performed without scanning because scanning introduced phase noise, and the desired signal was buried under noise for the signal to noise ratio of 25~30 dB. However, spectral domain OCT was used to capture 3D volumetric intensity images during the neural activity in in vitro seizure mouse model after a period of baseline data. The average intensity of the hippocampal brain slice decreased by 10~18% from baseline during neural activity. Although intensity can separate neural activity from non-neural activity with 97.27% specificity and 97.60% sens

The formation of synapses is a critical step in the development of the brain. During this developmental stage neural activity propagates across the brain from synapse to synapse. This activity is thought to instruct the precise, topological connectivity found in the sensory central nervous system. In the General Introduction some possible hypotheses for establishing topological connectivity in the brain are put forward. In this thesis I demonstrated that during the period when a giant auditory synapse is formed, the calyx of Held synapse, including both the calyx itself and its postsynaptic target, the principal neuron of the medial nucleus of the trapezoid body, displays bursts of neural firing. With our unique in vivo approach I describe in CHAPTER 2 the patterns of neural activity at the calyx of Held in neonatal rats. Despite the young age of the pup and the early stage of calyx development, very brief intervals between action potentials (APs) were observed with only minor changes in the shape of the calyceal AP. One of the processes that keeps the AP shape invariant was related to the absolute membrane potential attained following the AP, the after-potential. I propose that there might be an essential role for the after-potential to stabilize the AP shape in high frequency firing. In CHAPTER 3 I focus on the developmental changes in the calyx of Held synapse and the postsynaptic neuron. In a few days a relay synapse emerges that reliably drives postsynaptic activity. The other synaptic inputs become less relevant for postsynaptic firing, because of changes in the intrinsic properties of the postsynaptic neuron. I demonstrate a clear correlation between the postsynaptic excitability and the emergence of the relay synapse, possibly indicating a homeostatic matching of the size of the inputs and the size of the input resistance. In the days prior to the appearance of the relay synapse, the activity of many converging synapses caused a continuous depolarization. By

Meditation has been practiced around the world for thousands of years and has during the past decade become increasingly popular in the Western world. Meditation can be seen as a form of mental exercise and refers to a family of complex emotional and attentional regulatory practices that involves different attentional, cognitive monitoring and awareness processes. Clinical research on meditation has demonstrated that meditation seem to reduce stress, anxiety, and depression. Recent interest in how meditation affect the human brain and body have lead to an increase in research regarding the neural correlates of meditation, structural changes induced by meditation, and the potential attentional and emotional benefits mediated by meditation. This thesis investigates expert related changes in neural activity, brain structure, and attentional performance induced by focused attention meditation (FAM) and open monitoring meditation (OMM). The research on meditation and the brain is still in its infancy but despite this, there seem to be some converging evidence of meditation’s impact on the human brain and mind. The results from the included studies in this thesis indicates that expert meditators show greater activation in some meditation related brain areas, as well as less activation in other areas when compared to novice meditators. The results also suggest that long-term meditation practice induce some structural changes in the brain and that meditation seem to enhance the practitioners’ attentional control.

This paper examines the concept, historical evolution and the different schools of thought regarding an aspect of one of the most productive and widely disseminated Operations Research disciplines of our time: Multiple Criteria Decision Making (MCDM). It analyses the development of the scientific method and new tendencies that have emerged in the context of the Knowledge Society. The work also considers future challenges in the field of MCDM, particularly in the context of one of the most popular approaches: the Analytic Hierarchy Process (AHP). Three fundamental problems envisioned for this school of thought by its creator need to be addressed as future challenges in the MCDM field. This article is dedicated to the memory of Professor Thomas L. Saaty, the originator of AHP and one of the most brilliant and ingenious mathematicians of the last 60 years; he passed away in August 2017 at the age of 91., Este trabajo recoge brevemente el concepto, la evolución histórica y las diferentes escuelas de una de las partes de la Investigación Operativa más fructífera y con mayor difusión de los últimos 45 años: la Decisión Multicriterio. Así mismo, analiza la evolución que el método científico ha seguido en este periodo de tiempo y cuáles son las nuevas orientaciones que presenta en el contexto de la Sociedad del Conocimiento. Finalmente, se incluyen una serie de ideas sobre cuáles pueden ser algunos de los retos futuros en el campo de la toma de decisiones multicriterio (TDMC), en particular en el contexto de una de las aproximaciones más populares: el Proceso Analítico Jerárquico (AHP). Tres problemas fundamentales, ya vislumbrados para esta escuela de pensamiento por su creador, necesitan ser abordados como futuros retos en el campo multicriterio. Este trabajo se ha dedicado a la memoria del profesor Thomas L. Saaty, el autor de AHP y uno de los matemáticos más brillantes e ingeniosos de los últimos 60 años, quien acaba de fallecer en agosto de 2017 a los 91 años de edad.

OF THE DISSERTATIONQuantification of Changes of Intensity and Attenuation Coefficient During Neural Activity Using Optical Coherence TomographybyMd. Monirul HasanDoctor of Philosophy, Graduate Program in BioengineeringUniversity of California, Riverside, March 2018Dr. B. Hyle Park, ChairpersonThe nervous system, which receives information through the electrical signal to coordinate and disseminate information about the body and its environment, is an assembly of neurons. Current methods of detection of neural activity are based on electrophysiology, which requires direct or near direct contact, or fluorescence-based techniques, which require the introduction of reporter molecules, raising a concern of toxicity. Optical coherence tomography(OCT) detects neural activity by taking advantage of intrinsic structural changes that accompany neural activity without any exogenous agents. The phase-resolved OCT detected 10-30 nm swelling of axon during neural activity from the functionally stimulated compound eye of the horseshoe crab(limulus) by utilizing the phase-resolved measurement. There was a decrease of backscattered intensity during propagation of action potential due to change of scattering as well. Further study on in vitro murine brain slice showed the changes of the phase (or thickness change) as well as a decrease of back-reflected intensity during propagation of action potential. Above studies with pr-OCT were performed without scanning because scanning introduced phase noise, and the desired signal was buried under noise for the signal to noise ratio of 25~30 dB. However, spectral domain OCT was used to capture 3D volumetric intensity images during the neural activity in in vitro seizure mouse model after a period of baseline data. The average intensity of the hippocampal brain slice decreased by 10~18% from baseline during neural activity. Although intensity can separate neural activity from non-neural activity with 97.27% specificity and 97.60% sens

The blood-brain barrier (BBB) is a set of properties belonging to endothelial cells that make up blood vessels in the central nervous system (CNS). The BBB’s function is to reduce the transport of nonspecific ions and molecules between the blood and brain and import specific molecules to maintain brain homeostasis. The cerebral vasculature is also known to be tightly coupled to neural activity and dynamically changes blood flow to satisfy the energy demand of active brain regions. Although the cerebral vascular blood flow is tightly coupled to neural activity, the question remains whether BBB properties can dynamically respond to neural activity, as well. Through manipulating glutamatergic neurons in mice, we have shown that neural activity can dynamically regulate both the expression and function of BBB genes in the cerebral vasculature, namely, ATP- binding cassette (ABC) efflux transporters. Additionally, we found that neural activity dynamically regulates circadian clock-related genes in endothelial cells. This led us to ask the question of what is the functional role of the circadian clock in endothelial cells of the cerebral vasculature? Through knocking out a core clock gene, Bmal1, specifically in endothelial cells, we found that the vascular clock regulates ABC efflux transport and animal behavior. We propose the idea that neural activity entrains the vascular-specific circadian clock, which regulates ABC transporter activity in a circadian manner, and deletion of the clock in endothelial cells causes dysregulation of ABC transport, thus disrupting the chemical microenvironment of the brain, resulting in complex behavioral phenotypes.

This paper examines the concept, historical evolution and the different schools of thought regarding an aspect of one of the most productive and widely disseminated Operations Research disciplines of our time: Multiple Criteria Decision Making (MCDM). It analyses the development of the scientific method and new tendencies that have emerged in the context of the Knowledge Society. The work also considers future challenges in the field of MCDM, particularly in the context of one of the most popular approaches: the Analytic Hierarchy Process (AHP). Three fundamental problems envisioned for this school of thought by its creator need to be addressed as future challenges in the MCDM field. This article is dedicated to the memory of Professor Thomas L. Saaty, the originator of AHP and one of the most brilliant and ingenious mathematicians of the last 60 years; he passed away in August 2017 at the age of 91., Este trabajo recoge brevemente el concepto, la evolución histórica y las diferentes escuelas de una de las partes de la Investigación Operativa más fructífera y con mayor difusión de los últimos 45 años: la Decisión Multicriterio. Así mismo, analiza la evolución que el método científico ha seguido en este periodo de tiempo y cuáles son las nuevas orientaciones que presenta en el contexto de la Sociedad del Conocimiento. Finalmente, se incluyen una serie de ideas sobre cuáles pueden ser algunos de los retos futuros en el campo de la toma de decisiones multicriterio (TDMC), en particular en el contexto de una de las aproximaciones más populares: el Proceso Analítico Jerárquico (AHP). Tres problemas fundamentales, ya vislumbrados para esta escuela de pensamiento por su creador, necesitan ser abordados como futuros retos en el campo multicriterio. Este trabajo se ha dedicado a la memoria del profesor Thomas L. Saaty, el autor de AHP y uno de los matemáticos más brillantes e ingeniosos de los últimos 60 años, quien acaba de fallecer en agosto de 2017 a los 91 años de edad.

Meditation has been practiced around the world for thousands of years and has during the past decade become increasingly popular in the Western world. Meditation can be seen as a form of mental exercise and refers to a family of complex emotional and attentional regulatory practices that involves different attentional, cognitive monitoring and awareness processes. Clinical research on meditation has demonstrated that meditation seem to reduce stress, anxiety, and depression. Recent interest in how meditation affect the human brain and body have lead to an increase in research regarding the neural correlates of meditation, structural changes induced by meditation, and the potential attentional and emotional benefits mediated by meditation. This thesis investigates expert related changes in neural activity, brain structure, and attentional performance induced by focused attention meditation (FAM) and open monitoring meditation (OMM). The research on meditation and the brain is still in its infancy but despite this, there seem to be some converging evidence of meditation’s impact on the human brain and mind. The results from the included studies in this thesis indicates that expert meditators show greater activation in some meditation related brain areas, as well as less activation in other areas when compared to novice meditators. The results also suggest that long-term meditation practice induce some structural changes in the brain and that meditation seem to enhance the practitioners’ attentional control.

Paying taxes demonstrates the well-functioning of State and it is necessary to finance public goods. Despite taxes are defined as the fuel of civilization and the governments underline the crucial importance of taxation, the understanding of why people pay them remains limited. The present research aimed at detecting the cognitive responses during tax compliance experiment, also by considering characteristics of personality. We evaluated the decision-making process to pay taxes of thirty healthy participants during two different phases: individual phase, where the participant is the only one taxpayer who decides unconditionally whether paying taxes on his income; social phase, where the participant pays taxes with four other taxpayers in order to ensure public welfare. Each phase consisted of twenty rounds in which participants had to decide whether paying taxes. Brain oscillations (delta, theta, alpha, beta) were monitoring within the prefrontal area when subjects expressed their payment intention. At the end of the experiment the taxpayers completed a questionnaire, the Italian version of the tax compliance inventory TAX-I. TAX-I assesses tax compliance and distinguishes between two forms of compliance and non-compliance: voluntary compliance (spontaneous willingness to cooperate, emanating from taxpayers’ moral obligation to contribute to the public welfare) and enforced compliance (tax payments according to the law arise from taxpayers’ concern of being audited and fined). Especially in social phase, it was observed higher DLPFC delta and theta activity when enforced taxpayers decided to be compliant. DLPFC was more responsive to social phase when enforced taxpayers paid taxes. Probably this result is driven by the enforced taxpayers’ preference to be social norm compliant. In line with electroencephalographic results, behavioral data showed how enforced taxpayers decided to pay taxes especially in social phase and evade in individual phase. In the presence of o

Objective Paying taxes demonstrates the well-functioning of tax policies and it is necessary to finance public goods. Despite taxes are defined as the fuel of civilizations and the governments underline the crucial importance of taxation, the understanding of why people pay them remains limited. The present research aimed at detecting the cognitive responses during tax compliance experiment by considering characteristics of personality. Participants and methods We evaluated the decision-making process to pay taxes of thirty healthy participants during two different phases: individual phase, where the participant is the only one taxpayer who decides unconditionally whether paying taxes on his income; social phase, where the participant pays taxes with four other taxpayers in order to ensure public welfare. Each phases consisted of twenty rounds where participants had to decide whether paying taxes. Brain oscillations (delta, theta, alpha, beta) were monitored within the dorsolateral prefrontal cortex (DLPFC) when subjects expressed their payment intention. At the end of the experiment the taxpayers completed a questionnaire, the Italian version of the tax compliance inventory TAX-I. TAX-I assesses tax compliance and distinguishes between two forms of compliance and non-compliance: voluntary compliance (spontaneous willingness to cooperate) and enforced compliance (tax payments according to the law arise from taxpayers’ concern of being audited and fined). Results Especially in social phase, it was observed higher DLPFC delta and theta activity when enforced taxpayers decided to be compliant. DLPFC was more responsive to social phase when enforced taxpayers paid taxes. In line with electroencephalographic results, behavioural data showed how enforced taxpayers decided to pay taxes especially in social phase and evade in individual phase. Conclusion In the presence of other taxpayers, enforced subjects chose an empathic and pro-social behaviour. Probably this result is

Threat demands fast and adaptive reactions that are manifested at the physiological, behavioral, and phenomenological level and are responsive to the direction of threat and its severity for the individual. Here, we investigated the effects of threat directed toward or away from the observer on motor corticospinal excitability and explicit recognition. Sixteen healthy right-handed volunteers completed a transcranial magnetic stimulation (TMS) task and a separate three-alternative forced-choice emotion recognition task. Single-pulse TMS to the left primary motor cortex was applied to measure motor evoked potentials from the right abductor pollicis brevis in response to dynamic angry, fearful, and neutral bodily expressions with blurred faces directed toward or away from the observer. Results showed that motor corticospinal excitability increased independent of direction of anger compared with fear and neutral. In contrast, anger was better recognized when directed toward the observer compared with when directed away from the observer, while the opposite pattern was found for fear. The present results provide evidence for the differential effects of threat direction on explicit recognition and motor corticospinal excitability. In the face of threat, motor corticospinal excitability increases independently of the direction of anger, indicative of the importance of more automatic reactions to threat.

Music is generated through vibrations of a medium creating what is perceived as tone. Two frequencies played simultaneously create intervals, and those ratios of frequencies determine what is consonant and dissonant. Studies have shown a greater neural response in human participants to consonant intervals (Bidelman and Heinz 2011, Bidelman and Krishnan 2009). In this study, the ventral nerve cord of Lumbricus terrestris was exposed to six dichotic intervals in the form of vibrations through a bass actuator. Nerve activity was recorded for amplitude (mV), duration (ms), and maximum depolarization (mV) of the action potential. There was no significant difference in amplitude, duration, or maximum depolarization across all six interval treatments.

Studies are described which are intended to improve our understanding of the primary measurements made in non-invasive neural imaging. The blood oxygenation level-dependent signal used in functional magnetic resonance imaging (fMRI) reflects changes in deoxygenated haemoglobin. Tissue oxygen concentration, along with blood flow, changes during neural activation. Therefore, measurements of tissue oxygen together with the use of a neural sensor can provide direct estimates of neural-metabolic interactions. We have used this relationship in a series of studies in which a neural microelectrode is combined with an oxygen micro-sensor to make simultaneous co-localized measurements in the central visual pathway. Oxygen responses are typically biphasic with small initial dips followed by large secondary peaks during neural activation. By the use of established visual response characteristics, we have determined that the oxygen initial dip provides a better estimate of local neural function than the positive peak. This contrasts sharply with fMRI for which the initial dip is unreliable. To extend these studies, we have examined the relationship between the primary metabolic agents, glucose and lactate, and associated neural activity. For this work, we also use a Doppler technique to measure cerebral blood flow (CBF) together with neural activity. Results show consistent synchronously timed changes such that increases in neural activity are accompanied by decreases in glucose and simultaneous increases in lactate. Measurements of CBF show clear delays with respect to neural response. This is consistent with a slight delay in blood flow with respect to oxygen delivery during neural activation.This article is part of the themed issue 'Interpreting BOLD: a dialogue between cognitive and cellular neuroscience'.

Threat demands fast and adaptive reactions that are manifested at the physiological, behavioral, and phenomenological level and are responsive to the direction of threat and its severity for the individual. Here, we investigated the effects of threat directed toward or away from the observer on motor corticospinal excitability and explicit recognition. Sixteen healthy right-handed volunteers completed a transcranial magnetic stimulation (TMS) task and a separate three-alternative forced-choice emotion recognition task. Single-pulse TMS to the left primary motor cortex was applied to measure motor evoked potentials from the right abductor pollicis brevis in response to dynamic angry, fearful, and neutral bodily expressions with blurred faces directed toward or away from the observer. Results showed that motor corticospinal excitability increased independent of direction of anger compared with fear and neutral. In contrast, anger was better recognized when directed toward the observer compared with when directed away from the observer, while the opposite pattern was found for fear. The present results provide evidence for the differential effects of threat direction on explicit recognition and motor corticospinal excitability. In the face of threat, motor corticospinal excitability increases independently of the direction of anger, indicative of the importance of more automatic reactions to threat.

Music is generated through vibrations of a medium creating what is perceived as tone. Two frequencies played simultaneously create intervals, and those ratios of frequencies determine what is consonant and dissonant. Studies have shown a greater neural response in human participants to consonant intervals (Bidelman and Heinz 2011, Bidelman and Krishnan 2009). In this study, the ventral nerve cord of Lumbricus terrestris was exposed to six dichotic intervals in the form of vibrations through a bass actuator. Nerve activity was recorded for amplitude (mV), duration (ms), and maximum depolarization (mV) of the action potential. There was no significant difference in amplitude, duration, or maximum depolarization across all six interval treatments.

The mesoscopic activity of the brain is strongly dynamical, while at the same time exhibits remarkable computational capabilities. In order to examine how these two features coexist, here we show that the patterns of synchronized oscillations displayed by networks of neural mass models, representing cortical columns, can be used as substrates for Boolean-like computations. Our results reveal that the same neural mass network may process different combinations of dynamical inputs as different logical operations or combinations of them. This dynamical feature of the network allows it to process complex inputs in a very sophisticated manner. The results are reproduced experimentally with electronic circuits of coupled Chua oscillators, showing the robustness of this kind of computation to the intrinsic noise and parameter mismatch of the coupled oscillators. We also show that the information-processing capabilities of coupled oscillations go beyond the simple juxtaposition of logic gates., Peer Reviewed, Postprint (published version)