Discrete Structure of the Brain Rhythms

Theta oscillons in behaving rats

Recently discovered constituents of the brain waves-the oscillons -provide high-resolution representation of the extracellular field dynamics. Here we study the most robust, highest-amplitude oscillons that manifest in actively behaving rats and generally correspond to the traditional θ -waves. We show that the resemblances between θ -oscillons and the conventional θ -waves apply to the ballpark characteristics-mean frequencies, amplitudes, and bandwidths. In addition, both hippocampal and cortical oscillons exhibit a number of intricate, behavior-attuned, transient properties that suggest a new vantage point for understanding the θ -rhythms' structure, origins and functions. We demonstrate that oscillons are frequency-modulated waves, with speed-controlled parameters, embedded into a noise background. We also use a basic model of neuronal synchronization to contextualize and to interpret the observed phenomena. In particular, we argue that the synchronicity level in physiological networks is fairly weak and modulated by the animal's locomotion.

Theta oscillons in behaving rats

Recently discovered constituents of the brain waves-the oscillons-provide high-resolution representation of the extracellular field dynamics. Here we study the most robust, highest-amplitude oscillons that manifest in actively behaving rats and generally correspond to the traditional θ -waves. We show that the resemblances between θ -oscillons and the conventional θ -waves apply to the ballpark characteristics-mean frequencies, amplitudes, and bandwidths. In addition, both hippocampal and cortical oscillons exhibit a number of intricate, behavior-attuned, transient properties that suggest a new vantage point for understanding the θ -rhythms' structure, origins and functions. We demonstrate that oscillons are frequency-modulated waves, with speed-controlled parameters, embedded into a noise background. We also use a basic model of neuronal synchronization to contextualize and to interpret the observed phenomena. In particular, we argue that the synchronicity level in physiological networks is fairly weak and modulated by the animal's locomotion.

The contribution of coherence field theory to a model of consciousness: electric currents, EM fields, and EM radiation in the brain

A paradigm in neuroscience is developing which views resonance as the phenomenon responsible for consciousness. Much progress is being made in the investigation of how resonance as oscillating flows within the brain's electric field might result in production of mind from matter. But it's mostly unknown how vibrations among features of matter such as nanoscale atomic structures and photonic waves may participate in forming the basic substance of first-person consciousness, meaning percepts such as colors, textures, sounds, thoughts, feelings et cetera. Initial evidence at the leading edge of quantum biology suggests that light and atoms combine to form synchronously resonating structures of contiguous energy which I have termed coherence fields. My hypothesis is that coherence fields as atomic nodes within expanses of integrating photonic waves are the fundamental unit of first-person percepts insofar as they arise from electromagnetic matter. A concept of quantum coherence is formulated based on a new phenomenology of matter's nanoscale properties, and this is shown to tie what we have thus far discovered of neural anatomy into a comprehensive model of how electrical impulses travel through neurons as electron currents driven by coherence at the quantum scale. Transmembrane electric fields generated by ionic currents, synaptic phase regulation, and perhaps further mechanisms have been hypothesized as responsible for local field potentials (LFP) oscillations. Some insights into how emergent, macroscopic waves in the brain's electric field may reciprocally impact LFP propagation to control arousal, attention, and volition are briefly discussed. Activation of neural tissue is closely linked to temperature variation, and it is hypothesized that this is not merely a waste byproduct but constitutes a signature of coherence field modulation, with photonic waves of a primarily infrared spectral range functioning as an interstitial medium of the basic percept field. A variety of possible routes to coherence field modulation are outlined that derive from the mechanisms of electric currents, EM fields, EM radiation, and entanglement. If future experimental designs continue to validate coherence field theory, this could set science on course to resolve the mind/body problem.

Rapid Spectral Dynamics in Hippocampal Oscillons

Neurons in the brain are submerged into oscillating extracellular potential produced by synchronized synaptic currents. The dynamics of these oscillations is one of the principal characteristics of neurophysiological activity, broadly studied in basic neuroscience and used in applications. However, our interpretation of the brain waves' structure and hence our understanding of their functions depend on the mathematical and computational approaches used for data analysis. The oscillatory nature of the wave dynamics favors Fourier methods, which have dominated the field for several decades and currently constitute the only systematic approach to brain rhythms. In the following study, we outline an alternative framework for analyzing waves of local field potentials (LFPs) and discuss a set of new structures that it uncovers: a discrete set of frequency-modulated oscillatory processes—the brain wave oscillons and their transient spectral dynamics.

Neurotechnological Approaches to the Diagnosis and Treatment of Alzheimer’s Disease

Alzheimer’s disease (AD) is the most common cause of dementia in the elderly, clinically defined by progressive cognitive decline and pathologically, by brain atrophy, neuroinflammation, and accumulation of extracellular amyloid plaques and intracellular neurofibrillary tangles. Neurotechnological approaches, including optogenetics and deep brain stimulation, have exploded as new tools for not only the study of the brain but also for application in the treatment of neurological diseases. Here, we review the current state of AD therapeutics and recent advancements in both invasive and non-invasive neurotechnologies that can be used to ameliorate AD pathology, including neurostimulation via optogenetics, photobiomodulation, electrical stimulation, ultrasound stimulation, and magnetic neurostimulation, as well as nanotechnologies employing nanovectors, magnetic nanoparticles, and quantum dots. We also discuss the current challenges in developing these neurotechnological tools and the prospects for implementing them in the treatment of AD and other neurodegenerative diseases.

Behavior of olfactory-related frontal lobe oscillations in Alzheimer's disease and MCI: A pilot study

Slow-gamma (35-45 Hz) phase synchronization and the coupling between slow-gamma and low-frequency theta oscillations (4-8 Hz) are closely related to memory retrieval and cognitive functions. In this pilot study, we assess the Phase Amplitude Coupling (PAC) between theta and slow-gamma oscillatory bands and the quality of synchronization in slow-gamma oscillations using Phase Locking Value (PLV) on EEG data from healthy individuals and patients diagnosed with amnestic Mild Cognitive Impairment (aMCI) and Alzheimer's Disease (AD) during an oddball olfactory task. Our study indicates noticeable differences between the PLV and PAC values corresponding to olfactory stimulation in the three groups of participants. These differences can help explain the underlying processes involved in these cognitive disorders and the differences between aMCI and AD patients in performing cognitive tasks. Our study also proposes a diagnosis method for aMCI through comparing the brain's response characteristics during olfactory stimulation and rest. Early diagnosis of aMCI can potentially lead to its timely treatment and prevention from progression to AD.

Review of wearable technologies and machine learning methodologies for systematic detection of mild traumatic brain injuries

Mild traumatic brain injuries (mTBIs) are the most common type of brain injury. Timely diagnosis of mTBI is crucial in making 'go/no-go' decision in order to prevent repeated injury, avoid strenuous activities which may prolong recovery, and assure capabilities of high-level performance of the subject. If undiagnosed, mTBI may lead to various short- and long-term abnormalities, which include, but are not limited to impaired cognitive function, fatigue, depression, irritability, and headaches. Existing screening and diagnostic tools to detect acute andearly-stagemTBIs have insufficient sensitivity and specificity. This results in uncertainty in clinical decision-making regarding diagnosis and returning to activity or requiring further medical treatment. Therefore, it is important to identify relevant physiological biomarkers that can be integrated into a mutually complementary set and provide a combination of data modalities for improved on-site diagnostic sensitivity of mTBI. In recent years, the processing power, signal fidelity, and the number of recording channels and modalities of wearable healthcare devices have improved tremendously and generated an enormous amount of data. During the same period, there have been incredible advances in machine learning tools and data processing methodologies. These achievements are enabling clinicians and engineers to develop and implement multiparametric high-precision diagnostic tools for mTBI. In this review, we first assess clinical challenges in the diagnosis of acute mTBI, and then consider recording modalities and hardware implementation of various sensing technologies used to assess physiological biomarkers that may be related to mTBI. Finally, we discuss the state of the art in machine learning-based detection of mTBI and consider how a more diverse list of quantitative physiological biomarker features may improve current data-driven approaches in providing mTBI patients timely diagnosis and treatment.

Bilateral enucleation at birth modifies calcium spike amplitude, but not frequency, in neurons of the somatosensory thalamus and cortex: Implications for developmental cross-modal plasticity

Bilateral eye enucleation at birth (BE) leads to an expansion of the primary somatosensory cortex (S1) in rat pups. Although increased growth of the somatosensory thalamo-cortical afferents (STCAs) in part explains S1 expansion, timing mechanisms governing S1 formation are also involved. In this work, we begin the search of a developmental clock by intending to document the existence of putative clock neurons in the somatosensory thalamus (VPM) and S1 based upon changes of spontaneous spike amplitude; a biophysical property sensitive to circadian regulation; the latter known to be shifted by enucleation. In addition, we also evaluated whether STCAs growth rate and segregation timing were modified, as parameters the clock might time. We found that spontaneous spike amplitude transiently, but significantly, increased or decreased in VPM and S1 neurons of BE rat pups, respectively, as compared to their control counterparts. The growth rate and segregation timing of STCAs was, however, unaffected by BE. These results support the existence of a developmental clock that ticks differently in the VPM and S1 after BE. This observation, together with the fact that STCAs growth rate and segregation timing is unchanged, suggests that S1 expansion in BE rats may in part be controlled at the cortical level.