scholarly journals A neuromechanistic model for rhythmic beat generation

2018 ◽  
Author(s):  
Amitabha Bose ◽  
Áine Byrne ◽  
John Rinzel
Keyword(s):  
Experimental Studies ◽  
Brain Regions ◽  
Beat Generation ◽  
Systems Model ◽  
Hard Time ◽  
Wide Range ◽  
Neuronal Network Model ◽  
Musical Traditions ◽  
Algorithmic Approaches ◽  
The Brain

AbstractWhen listening to music, humans can easily identify and move to the beat. Numerous experimental studies have identified brain regions that may be involved with beat perception and representation. Several theoretical and algorithmic approaches have been proposed to account for this ability. Related to, but different from the issue of how we perceive a beat, is the question of how we learn to generate and hold a beat. In this paper, we introduce a neuronal framework for a beat generator that is capable of learning isochronous rhythms over a range of frequencies that are relevant to music and speech. Our approach combines ideas from error-correction and entrainment models to investigate the dynamics of how a biophysically-based neuronal network model synchronizes its period and phase to match that of an external stimulus. The model makes novel use of on-going faster gamma rhythms to form a set of discrete clocks that provide estimates, but not exact information, of how well the beat generator spike times match those of a stimulus sequence. The beat generator is endowed with plasticity allowing it to quickly learn and thereby adjust its spike times to achieve synchronization. Our model makes generalizable predictions about the existence of asymmetries in the synchronization process, as well as specific predictions about resynchronization times after changes in stimulus tempo or phase. Analysis of the model demonstrates that accurate rhythmic time keeping can be achieved over a range of frequencies relevant to music, in a manner that is robust to changes in parameters and to the presence of noise.Author summaryMusic is integral to human experience and is appreciated across a wide range of cultures. Although many features distinguish different musical traditions, rhythm is central to nearly all. Most humans can detect and move along to the beat through finger or foot tapping, hand clapping or other bodily movements. But many people have a hard time “keeping a beat”, or say they have “no sense of rhythm”. There appears to be a disconnect between our ability to perceive a beat versus our ability to produce a beat, as a drummer would do as part of a musical group. Producing a beat requires beat generation, the process by which we learn how to keep track of the specific time intervals between beats, as well as executing the motor movement needed to produce the sound associated with a beat. In this paper, we begin to explore neural mechanisms that may be responsible for our ability to generate and keep a beat. We develop a computational model that includes different neurons and shows how they cooperate to learn a beat and keep it, even after the stimulus is removed, across a range of frequencies relevant to music. Our dynamical systems model leads to predictions for how the brain may react when learning a beat. Our findings and techniques should be widely applicable to those interested in understanding how the brain processes time, particularly in the context of music.

scholarly journals Transmission time delays organize the brain network synchronization

2019 ◽  
Vol 377 (2153) ◽  
pp. 20180132 ◽  
Author(s):  
Spase Petkoski ◽  
Viktor K. Jirsa
Keyword(s):  
Time Delays ◽  
Phase Synchronization ◽  
Brain Regions ◽  
Brain Network ◽  
Temporal Organization ◽  
Delay Systems ◽  
Oscillatory Dynamics ◽  
Wide Range ◽  
Kuramoto Oscillators ◽  
The Brain

The timing of activity across brain regions can be described by its phases for oscillatory processes, and is of crucial importance for brain functioning. The structure of the brain constrains its dynamics through the delays due to propagation and the strengths of the white matter tracts. We use self-sustained delay-coupled, non-isochronous, nonlinearly damped and chaotic oscillators to study how spatio-temporal organization of the brain governs phase lags between the coherent activity of its regions. In silico results for the brain network model demonstrate a robust switching from in- to anti-phase synchronization by increasing the frequency, with a consistent lagging of the stronger connected regions. Relative phases are well predicted by an earlier analysis of Kuramoto oscillators, confirming the spatial heterogeneity of time delays as a crucial mechanism in shaping the functional brain architecture. Increased frequency and coupling are also shown to distort the oscillators by decreasing their amplitude, and stronger regions have lower, but more synchronized activity. These results indicate specific features in the phase relationships within the brain that need to hold for a wide range of local oscillatory dynamics, given that the time delays of the connectome are proportional to the lengths of the structural pathways. This article is part of the theme issue ‘Nonlinear dynamics of delay systems’.

Cannabidiolɗs Role as a Potential Target in the Treatment for Schizophrenia

2017 ◽  
Vol 41 (S1) ◽  
pp. S631-S631
Author(s):  
A. Carvalho ◽  
J. Felgueiras ◽  
T. Abreu ◽  
C. Freitas ◽  
J. Silva
Keyword(s):  
Endocannabinoid System ◽  
Brain Regions ◽  
Great Promise ◽  
Novel Therapy ◽  
Considerable Uncertainty ◽  
Wide Range ◽  
Competing Interest ◽  
The Individual ◽  
The Brain

ObjectivesSchizophrenia is a debilitating psychiatric disorder which places a significant emotional and economic strain on the individual and society-at-large. Unfortunately, currently available therapeutic strategies do not provide adequate relief and some patients are treatment-resistant. Therefore there is urgent need for the development of mechanistically different and less side effect prone antipsychotic compounds. Recently, the endocannabinoid system has emerged as a potential therapeutic target for pharmacotherapy that is involved in a wide range of disorders, including schizophrenia. Modulation of this system by the main psychoactive component in cannabis, Δ9tetrahydrocannabinol (THC), induces acute psychotic effects and cognitive impairment. However, the non-psychotropic, plant-derived cannabinoid agent cannabidiol shows great promise for the treatment of psychosis, and is associated with fewer extrapyramidal side effects than conventional antipsychotic drugs.MethodsThe aim of this review is to analyse the involvement of the endocannabinoid system in schizophrenia and the potential role of cannabidiol in its treatment.Results and conclusionsThere is still considerable uncertainty about the mechanism of action of cannabidiol as well as the brain regions which are thought to mediate its putative antipsychotic effect. Further data is warrant before this novel therapy can be introduced into clinical practice.Disclosure of interestThe authors have not supplied their declaration of competing interest

scholarly journals Extrinsic factors underlying food valuation in the human brain

2020 ◽  
Author(s):  
Kosuke Motoki ◽  
Shinsuke Suzuki
Keyword(s):  
Human Brain ◽  
Brain Regions ◽  
Reward Processing ◽  
Extrinsic Factors ◽  
Decision Neuroscience ◽  
Subjective Value ◽  
Wide Range ◽  
Range Of Functions ◽  
Types Of Information ◽  
The Brain

Subjective value for food rewards guide our dietary choices. There is growing evidence that value signals are constructed in the brain by integrating multiple types of information about flavour, taste, and nutritional attributes of the foods. However, much less is known about the influence of food-extrinsic factors such as labels, brands, prices, and packaging designs. In this mini review, we outline recent findings in decision neuroscience, consumer psychology, and food science with regard to the effect of extrinsic factors on food value computations in the human brain. To date, studies have demonstrated that, while the integrated value signal is encoded in the ventromedial prefrontal cortex, information on the extrinsic factors of the food is encoded in diverse brain regions previously implicated in a wide range of functions: cognitive control, memory, emotion and reward processing. We suggest that a comprehensive understanding of food valuation requires elucidation of the mechanisms behind integrating extrinsic factors in the brain to compute an overall subjective value signal.

scholarly journals The Hidden Brain: Uncovering Previously Overlooked Brain Regions by Employing Novel Preclinical Unbiased Network Approaches

2021 ◽  
Vol 15 ◽  
Author(s):  
Sierra Simpson ◽  
Yueyi Chen ◽  
Emma Wellmeyer ◽  
Lauren C. Smith ◽  
Brianna Aragon Montes ◽  
...  
Keyword(s):  
Neurodegenerative Diseases ◽  
Computational Neuroscience ◽  
Brain Function ◽  
Neuropsychiatric Disorders ◽  
Brain Regions ◽  
Whole Brain ◽  
Model Studies ◽  
Wide Range ◽  
Network Approaches ◽  
The Brain

A large focus of modern neuroscience has revolved around preselected brain regions of interest based on prior studies. While there are reasons to focus on brain regions implicated in prior work, the result has been a biased assessment of brain function. Thus, many brain regions that may prove crucial in a wide range of neurobiological problems, including neurodegenerative diseases and neuropsychiatric disorders, have been neglected. Advances in neuroimaging and computational neuroscience have made it possible to make unbiased assessments of whole-brain function and identify previously overlooked regions of the brain. This review will discuss the tools that have been developed to advance neuroscience and network-based computational approaches used to further analyze the interconnectivity of the brain. Furthermore, it will survey examples of neural network approaches that assess connectivity in clinical (i.e., human) and preclinical (i.e., animal model) studies and discuss how preclinical studies of neurodegenerative diseases and neuropsychiatric disorders can greatly benefit from the unbiased nature of whole-brain imaging and network neuroscience.

scholarly journals Neuroprotective Effects of Pomegranate Juice against Parkinson’s Disease and Presence of Ellagitannins-Derived Metabolite—Urolithin A—In the Brain

2019 ◽  
Vol 21 (1) ◽  
pp. 202 ◽  
Author(s):  
Małgorzata Kujawska ◽  
Michael Jourdes ◽  
Monika Kurpik ◽  
Michał Szulc ◽  
Hanna Szaefer ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Oxidative Damage ◽  
Control Level ◽  
Experimental Studies ◽  
Pomegranate Juice ◽  
Neuroprotective Effects ◽  
Urolithin A ◽  
Wide Range ◽  
The Brain

Pomegranate juice is a rich source of ellagitannins (ETs) believed to contribute to a wide range of pomegranate’s health benefits. While a lot of experimental studies have been devoted to Alzheimer disease and hypoxic-ischemic brain injury, our knowledge of pomegranate’s effects against Parkinson’s disease (PD) is very limited. It is suggested that its neuroprotective effects are mediated by ETs-derived metabolites—urolithins. In this study, we examined the capability of pomegranate juice for protection against PD in a rat model of parkinsonism induced by rotenone. To evaluate its efficiency, assessment of postural instability, visualization of neurodegeneration, determination of oxidative damage to lipids and α-synuclein level, as well as markers of antioxidant defense status, inflammation, and apoptosis, were performed in the midbrain. We also check the presence of plausible active pomegranate ETs-derived metabolite, urolithin A, in the plasma and brain. Our results indicated that pomegranate juice treatment provided neuroprotection as evidenced by the postural stability improvement, enhancement of neuronal survival, its protection against oxidative damage and α-synuclein aggregation, the increase in mitochondrial aldehyde dehydrogenase activity, and maintenance of antiapoptotic Bcl-xL protein at the control level. In addition, we have provided evidence for the distribution of urolithin A to the brain.

scholarly journals Limb Preference in Animals: New Insights into the Evolution of Manual Laterality in Hominids

Symmetry ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 96
Author(s):  
Grégoire Boulinguez-Ambroise ◽  
Juliette Aychet ◽  
Emmanuelle Pouydebat
Keyword(s):  
Functional Neuroimaging ◽  
Hemispheric Specialization ◽  
Brain Regions ◽  
Brain Lateralization ◽  
Intentional Communication ◽  
Wide Range ◽  
Specific Trait ◽  
Communicative Gestures ◽  
The Brain ◽  
Human Specific

Until the 1990s, the notion of brain lateralization—the division of labor between the two hemispheres—and its more visible behavioral manifestation, handedness, remained fiercely defined as a human specific trait. Since then, many studies have evidenced lateralized functions in a wide range of species, including both vertebrates and invertebrates. In this review, we highlight the great contribution of comparative research to the understanding of human handedness’ evolutionary and developmental pathways, by distinguishing animal forelimb asymmetries for functionally different actions—i.e., potentially depending on different hemispheric specializations. Firstly, lateralization for the manipulation of inanimate objects has been associated with genetic and ontogenetic factors, with specific brain regions’ activity, and with morphological limb specializations. These could have emerged under selective pressures notably related to the animal locomotion and social styles. Secondly, lateralization for actions directed to living targets (to self or conspecifics) seems to be in relationship with the brain lateralization for emotion processing. Thirdly, findings on primates’ hand preferences for communicative gestures accounts for a link between gestural laterality and a left-hemispheric specialization for intentional communication and language. Throughout this review, we highlight the value of functional neuroimaging and developmental approaches to shed light on the mechanisms underlying human handedness.

scholarly journals Neuronal Sequence Models for Bayesian Online Inference

2021 ◽  
Vol 4 ◽  
Author(s):  
Sascha Frölich ◽  
Dimitrije Marković ◽  
Stefan J. Kiebel
Keyword(s):  
Machine Learning ◽  
Sensory Input ◽  
Brain Activity ◽  
Brain Regions ◽  
Hierarchical Networks ◽  
Bayesian Brain ◽  
Fast Recognition ◽  
Wide Range ◽  
Human Ethology ◽  
The Brain

Various imaging and electrophysiological studies in a number of different species and brain regions have revealed that neuronal dynamics associated with diverse behavioral patterns and cognitive tasks take on a sequence-like structure, even when encoding stationary concepts. These neuronal sequences are characterized by robust and reproducible spatiotemporal activation patterns. This suggests that the role of neuronal sequences may be much more fundamental for brain function than is commonly believed. Furthermore, the idea that the brain is not simply a passive observer but an active predictor of its sensory input, is supported by an enormous amount of evidence in fields as diverse as human ethology and physiology, besides neuroscience. Hence, a central aspect of this review is to illustrate how neuronal sequences can be understood as critical for probabilistic predictive information processing, and what dynamical principles can be used as generators of neuronal sequences. Moreover, since different lines of evidence from neuroscience and computational modeling suggest that the brain is organized in a functional hierarchy of time scales, we will also review how models based on sequence-generating principles can be embedded in such a hierarchy, to form a generative model for recognition and prediction of sensory input. We shortly introduce the Bayesian brain hypothesis as a prominent mathematical description of how online, i.e., fast, recognition, and predictions may be computed by the brain. Finally, we briefly discuss some recent advances in machine learning, where spatiotemporally structured methods (akin to neuronal sequences) and hierarchical networks have independently been developed for a wide range of tasks. We conclude that the investigation of specific dynamical and structural principles of sequential brain activity not only helps us understand how the brain processes information and generates predictions, but also informs us about neuroscientific principles potentially useful for designing more efficient artificial neuronal networks for machine learning tasks.

scholarly journals A brain atlas of synapse protein lifetime across the mouse lifespan.

2021 ◽  
Author(s):  
Edita Bulovaite ◽  
Zhen Qiu ◽  
Maximillian Kratschke ◽  
Adrianna Zgraj ◽  
David Fricker ◽  
...  
Keyword(s):  
Brain Regions ◽  
Brain Atlas ◽  
Excitatory Synapses ◽  
Brain Repair ◽  
Postsynaptic Protein ◽  
Behavior Development ◽  
Wide Range ◽  
Synapse Maintenance ◽  
Innate Behaviors ◽  
The Brain

Protein turnover is required for synapse maintenance and remodelling and may impact memory duration. We quantified the lifetime of postsynaptic protein PSD95 in individual excitatory synapses across the mouse brain and lifespan, generating the Protein Lifetime Synaptome Atlas. Excitatory synapses have a wide range of protein lifetimes that may extend from a few hours to several months, with distinct spatial distributions in dendrites, neuron types and brain regions. Short protein lifetime (SPL) synapses are enriched in developing animals and in regions controlling innate behaviors, whereas long protein lifetime (LPL) synapses accumulate during development, are enriched in the cortex and CA1 where memories are stored, and are preferentially preserved in old age. The protein lifetime synaptome architecture is disrupted in an autism model, with synapse protein lifetime increased throughout the brain. These findings add a further layer to synapse diversity in the brain and enrich prevailing concepts in behavior, development, ageing and brain repair.

Localization of Tissue Thromboplastin in the Human Brain

1977 ◽  
Vol 37 (01) ◽  
pp. 091-097 ◽  
Author(s):  
E Bjørklid ◽  
J Storm-Mathisen ◽  
E Storm ◽  
H Prydz
Keyword(s):  
Human Brain ◽  
Distribution Pattern ◽  
Brain Regions ◽  
Pulmonary Tissue ◽  
Wide Range ◽  
Tissue Thromboplastin ◽  
Average Activity ◽  
Monospecific Antisera ◽  
The Brain ◽  
Distinct Distribution

SummaryMonospecific antisera against the purified protein component of tissue thromboplastin (apoprotein-III) from human brain have been raised in goats and rabbits. The antisera neutralized tissue thromboplastin prepared from brain, thyroid gland and pulmonary tissue, indicating that apoproteins in the various preparations cross-reacted immunologically and therefore were similar or identical.Comparison of the activities of tissue thromboplastin preparations from 34 different areas of the brain demonstrated a characteristic distribution pattern and a wide range of activities. White and grey matter from the same areas had similar activities. Bulbus and tractus olfac-torius, medulla oblongata, corpus pineale, hippocampus and hypothalamus contained 160–270 % of the average activity, whereas cerebellum, globus pallidus, nucleus ruber and substantia nigra contained 30–60 %. The distinct distribution pattern was unrelated to tissue vascularization, and may suggest that apoprotein-III could serve other functions, apart from the coagulation of blood. The predominance in phylogenetically older brain regions would suggest that it represents a primitive or fundamental feature.

scholarly journals A new era for executive function research: On the transition from centralized to distributed executive functioning

2020 ◽  
Author(s):  
Nicolas Zink ◽  
Sebastian Markett ◽  
Agatha Lenartowicz
Keyword(s):  
Brain Connectivity ◽  
Brain Networks ◽  
Brain Regions ◽  
Central Executive ◽  
Control Mechanisms ◽  
Control Functions ◽  
Wide Range ◽  
Spatially Distributed ◽  
Higher Cognitive Functions ◽  
The Brain

“Executive functions” (EFs) is an umbrella term for higher cognitive functions such as working memory, inhibition, and cognitive flexibility. These functions refer to dissociable mechanisms that are also intricately related, justifying the view of EF as a unitary mental faculty. One of the most challenging theoretical problems in this field of research has been to explain how the wide range of cognitive processes subsumed as EFs are controlled without an all-powerful but ill-defined central executive in the brain. Efforts to localize control mechanisms in circumscribed brain regions have not led to breakthrough in understanding how the brain controls and regulates itself, and no single brain system underlying a ‘central executive’ has yet been identified. We discuss how a distributed control network view can help to refine our understanding of the neurophysiological mechanisms underlying EFs. In this view, executive control functions are realized by spatially distributed brain networks, thus precluding the need for a modular central executive. We further discuss how graph-theory driven analysis of brain networks offers a unique lens on this problem by providing a reference frame to study brain connectivity in EFs in a holistic way and how neuroscience network research endeavors to investigate clinical neuropathology of disrupted EFs.

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