scholarly journals The functional organization of human epileptic hippocampus

2016 ◽  
Vol 115 (6) ◽  
pp. 3140-3145 ◽  
Author(s):  
Petr Klimes ◽  
Juliano J. Duque ◽  
Ben Brinkmann ◽  
Jamie Van Gompel ◽  
Matt Stead ◽  
...  
Keyword(s):  
Spatial Scale ◽  
Local Field ◽  
Medial Temporal Lobe ◽  
Slow Wave Sleep ◽  
Functional Organization ◽  
Spectral Power ◽  
Spatial Scales ◽  
Field Potential ◽  
Brain Regions ◽  
Behavioral State

The function and connectivity of human brain is disrupted in epilepsy. We previously reported that the region of epileptic brain generating focal seizures, i.e., the seizure onset zone (SOZ), is functionally isolated from surrounding brain regions in focal neocortical epilepsy. The modulatory effect of behavioral state on the spatial and spectral scales over which the reduced functional connectivity occurs, however, is unclear. Here we use simultaneous sleep staging from scalp EEG with intracranial EEG recordings from medial temporal lobe to investigate how behavioral state modulates the spatial and spectral scales of local field potential synchrony in focal epileptic hippocampus. The local field spectral power and linear correlation between adjacent electrodes provide measures of neuronal population synchrony at different spatial scales, ∼1 and 10 mm, respectively. Our results show increased connectivity inside the SOZ and low connectivity between electrodes in SOZ and outside the SOZ. During slow-wave sleep, we observed decreased connectivity for ripple and fast ripple frequency bands within the SOZ at the 10 mm spatial scale, while the local synchrony remained high at the 1 mm spatial scale. Further study of these phenomena may prove useful for SOZ localization and help understand seizure generation, and the functional deficits seen in epileptic eloquent cortex.

scholarly journals Sensory feedback-dependent coding of arm position in local field potentials of the posterior parietal cortex

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Paul VanGilder ◽  
Ying Shi ◽  
Gregory Apker ◽  
Christopher A. Buneo
Keyword(s):  
Local Field ◽  
Sensory Feedback ◽  
Posterior Parietal Cortex ◽  
Spectral Power ◽  
Field Potential ◽  
Beta Activity ◽  
Beta Band ◽  
Arm Position ◽  
Multisensory Interactions ◽  
Spiking Activity

AbstractAlthough multisensory integration is crucial for sensorimotor function, it is unclear how visual and proprioceptive sensory cues are combined in the brain during motor behaviors. Here we characterized the effects of multisensory interactions on local field potential (LFP) activity obtained from the superior parietal lobule (SPL) as non-human primates performed a reaching task with either unimodal (proprioceptive) or bimodal (visual-proprioceptive) sensory feedback. Based on previous analyses of spiking activity, we hypothesized that evoked LFP responses would be tuned to arm location but would be suppressed on bimodal trials, relative to unimodal trials. We also expected to see a substantial number of recording sites with enhanced beta band spectral power for only one set of feedback conditions (e.g. unimodal or bimodal), as was previously observed for spiking activity. We found that evoked activity and beta band power were tuned to arm location at many individual sites, though this tuning often differed between unimodal and bimodal trials. Across the population, both evoked and beta activity were consistent with feedback-dependent tuning to arm location, while beta band activity also showed evidence of response suppression on bimodal trials. The results suggest that multisensory interactions can alter the tuning and gain of arm position-related LFP activity in the SPL.

scholarly journals Increased neural correlations in primate auditory cortex during slow-wave sleep

2013 ◽  
Vol 109 (11) ◽  
pp. 2732-2738 ◽  
Author(s):  
Elias B. Issa ◽  
Xiaoqin Wang
Keyword(s):  
Auditory Cortex ◽  
Spontaneous Activity ◽  
Slow Wave ◽  
Local Field ◽  
Local Field Potential ◽  
Slow Wave Sleep ◽  
Field Potential ◽  
Sensory Stimulation ◽  
Acoustic Stimulation ◽  
Functional Connections

During sleep, changes in brain rhythms and neuromodulator levels in cortex modify the properties of individual neurons and the network as a whole. In principle, network-level interactions during sleep can be studied by observing covariation in spontaneous activity between neurons. Spontaneous activity, however, reflects only a portion of the effective functional connectivity that is activated by external and internal inputs (e.g., sensory stimulation, motor behavior, and mental activity), and it has been shown that neural responses are less correlated during external sensory stimulation than during spontaneous activity. Here, we took advantage of the unique property that the auditory cortex continues to respond to sounds during sleep and used external acoustic stimuli to activate cortical networks for studying neural interactions during sleep. We found that during slow-wave sleep (SWS), local (neuron-neuron) correlations are not reduced by acoustic stimulation remaining higher than in wakefulness and rapid eye movement sleep and remaining similar to spontaneous activity correlations. This high level of correlations during SWS complements previous work finding elevated global (local field potential-local field potential) correlations during sleep. Contrary to the prediction that slow oscillations in SWS would increase neural correlations during spontaneous activity, we found little change in neural correlations outside of periods of acoustic stimulation. Rather, these findings suggest that functional connections recruited in sound processing are modified during SWS and that slow rhythms, which in general are suppressed by sensory stimulation, are not the sole mechanism leading to elevated network correlations during sleep.

scholarly journals A comprehensive neural simulation of slow-wave sleep and highly responsive wakefulness dynamics

2021 ◽  
Author(s):  
Jennifer S Goldman ◽  
Lionel Kusch ◽  
Bahar Hazal Yalcinkaya ◽  
Damien Depannemaecker ◽  
Trang-Anh Estelle Nghiem ◽  
...  
Keyword(s):  
Open Access ◽  
Human Brain ◽  
Slow Wave Sleep ◽  
Spatial Scales ◽  
Mean Field ◽  
Brain Regions ◽  
Inhibitory Neurons ◽  
Neural Dynamics ◽  
Healthy Human ◽  
Brain States

Hallmarks of neural dynamics during healthy human brain states span spatial scales from neuromodulators acting on microscopic ion channels to macroscopic changes in communication between brain regions. Developing a scale-integrated understanding of neural dynamics has therefore remained challenging. Here, we perform the integration across scales using mean-field modeling of Adaptive Exponential (AdEx) neurons, explicitly incorporating intrinsic properties of excitatory and inhibitory neurons. We report that when AdEx mean-field neural populations are connected via structural tracts defined by the human connectome, macroscopic dynamics resembling human brain activity emerge. Importantly, the model can qualitatively and quantitatively account for properties of empirical spontaneous and stimulus-evoked dynamics in the space, time, phase, and frequency domains. Remarkably, the model also reproduces brain-wide enhanced responsiveness and capacity to encode information particularly during wake-like states, as quantified using the perturbational complexity index. The model was run using The Virtual Brain (TVB) simulator, and is open-access in EBRAINS. This approach not only provides a scale-integrated understanding of brain states and their underlying mechanisms, but also open access tools to investigate brain responsiveness, toward producing a more unified, formal understanding of experimental data from conscious and unconscious states, as well as their associated pathologies.

scholarly journals Hippocampal theta bursting and waveform shape reflect CA1 spiking patterns

2018 ◽  
Author(s):  
Scott Cole ◽  
Bradley Voytek
Keyword(s):  
Local Field ◽  
Pyramidal Neurons ◽  
Field Potential ◽  
Brain Regions ◽  
Neuronal Firing ◽  
Detectable Effect ◽  
Field Recordings ◽  
Cognitive States ◽  
Open Questions ◽  
Hippocampal Theta

AbstractBrain rhythms are nearly always analyzed in the spectral domain in terms of their power, phase, and frequency. While this conventional approach has uncovered spike-field coupling, as well as correlations to normal behaviors and pathological states, emerging work has highlighted the physiological and behavioral importance of multiple novel oscillation features. Oscillatory bursts, for example, uniquely index a variety of cognitive states, and the nonsinusoidal shape of oscillations relate to physiological changes, including Parkinson’s disease. Open questions remain regarding how bursts and nonsinusoidal features relate to circuit-level processes, and how they interrelate. By analyzing unit and local field recordings in the rodent hippocampus, we uncover a number of significant relationships between oscillatory bursts, nonsinusoidal waveforms, and local inhibitory and excitatory spiking patterns. Bursts of theta oscillations are surprisingly related to a decrease in pyramidal neuron synchrony, and have no detectable effect on firing sequences, despite significant increases in neuronal firing rates during periods of theta bursting. Theta burst duration is predicted by the asymmetries of its first cycle, and cycle asymmetries relate to firing rate, synchrony, and sequences of pyramidal neurons and interneurons. These results provide compelling physiological evidence that time-domain features, of both nonsinusoidal hippocampal theta waveform and the theta bursting state, reflects local circuit properties. These results point to the possibility of inferring circuit states from local field potential features in the hippocampus and perhaps other brain regions with other rhythms.

scholarly journals Chronic Stability of Single-Channel Neurophysiological Correlates of Gross and Fine Reaching Movements in the Rat

2019 ◽  
Author(s):  
David T. Bundy ◽  
David J Guggenmos ◽  
Maxwell D Murphy ◽  
Randolph J. Nudo
Keyword(s):  
Motor Cortex ◽  
Local Field ◽  
Neural Activity ◽  
Local Field Potential ◽  
Primary Motor Cortex ◽  
Spectral Power ◽  
Premotor Cortex ◽  
Field Potential ◽  
Motor Recovery ◽  
Reaching Movements

AbstractFollowing injury to motor cortex, reorganization occurs throughout spared brain regions and is thought to underlie motor recovery. Unfortunately, the standard neurophysiological and neuroanatomical measures of post-lesion plasticity are only indirectly related to observed changes in motor execution. While substantial task-related neural activity has been observed during motor tasks in rodent primary motor cortex and premotor cortex, the long-term stability of these responses in healthy rats is uncertain, limiting the interpretability of longitudinal changes in the specific patterns of neural activity during motor recovery following injury. This study examined the stability of task-related neural activity associated with execution of reaching movements in healthy rodents. Rats were trained to perform a novel reaching task combining a ‘gross’ lever press and a ‘fine’ pellet retrieval. In each animal, two chronic microelectrode arrays were implanted in motor cortex spanning the caudal forelimb area (rodent primary motor cortex) and the rostral forelimb area (rodent premotor cortex). We recorded multiunit spiking and local field potential activity from 10 days to 7-10 weeks post-implantation to characterize the patterns of neural activity observed during each task component and analyzed the consistency of channel-specific task-related neural activity. Task-related changes in neural activity were observed on the majority of channels. While the task-related changes in multi-unit spiking and local field potential spectral power were consistent over several weeks, spectral power changes were more stable, despite the trade-off of decreased spatial and temporal resolution. These results show that rodent primary and premotor cortex are both involved in reaching movements with stable patterns of task-related activity across time, establishing the relevance of the rodent for future studies designed to examine changes in task-related neural activity during recovery from focal cortical lesions.

scholarly journals Large- and multi-scale networks in the rodent brain during novelty exploration

2020 ◽  
Author(s):  
Michael X Cohen ◽  
Bernhard Englitz ◽  
Arthur S C França
Keyword(s):  
Prefrontal Cortex ◽  
Parietal Cortex ◽  
Local Field ◽  
Neural Activity ◽  
Large Scale ◽  
Field Potential ◽  
Brain Regions ◽  
Data Availability ◽  
Data Matrix ◽  
Temporal Scales

AbstractNeural activity is coordinated across multiple spatial and temporal scales, and these patterns of coordination are implicated in both healthy and impaired cognitive operations. However, empirical cross-scale investigations are relatively infrequent, due to limited data availability and to the difficulty of analyzing rich multivariate datasets. Here we applied frequency-resolved multivariate source-separation analyses to characterize a large-scale dataset comprising spiking and local field potential activity recorded simultaneously in three brain regions (prefrontal cortex, parietal cortex, hippocampus) in freely-moving mice. We identified a constellation of multidimensional, inter-regional networks across a range of frequencies (2-200 Hz). These networks were reproducible within animals across different recording sessions, but varied across different animals, suggesting individual variability in network architecture. The theta band (~4-10 Hz) networks had several prominent features, including roughly equal contribution from all regions and strong inter-network synchronization. Overall, these findings demonstrate a multidimensional landscape of large-scale functional activations of cortical networks operating across multiple spatial, spectral, and temporal scales during open-field exploration.Significance statementNeural activity is synchronized over space, time, and frequency. To characterize the dynamics of large-scale networks spanning multiple brain regions, we recorded data from the prefrontal cortex, parietal cortex, and hippocampus in awake behaving mice, and pooled data from spiking activity and local field potentials into one data matrix. Frequency-specific multivariate decomposition methods revealed a cornucopia of neural networks defined by coherent spatiotemporal patterns over time. These findings reveal a rich, dynamic, and multivariate landscape of large-scale neural activity patterns during foraging behavior.

scholarly journals Multi-scale mapping along the auditory hierarchy using high-resolution functional UltraSound in the awake ferret

2018 ◽  
Author(s):  
Célian Bimbard ◽  
Charlie Demené ◽  
Constantin Girard ◽  
Susanne Radtke-Schuller ◽  
Shihab Shamma ◽  
...  
Keyword(s):  
Cerebral Blood Volume ◽  
Multiple Scales ◽  
Functional Organization ◽  
Brain Activity ◽  
Spatial Scales ◽  
Auditory Pathway ◽  
Brain Regions ◽  
Sensory Response ◽  
Response Modulation ◽  
Sensory Responses

A major challenge in neuroscience is to longitudinally monitor whole brain activity across multiple spatial scales in the same animal. Functional UltraSound (fUS) is an emerging technology that offers images of cerebral blood volume over large brain portions. Here we show for the first time its capability to resolve the functional organization of sensory systems at multiple scales in awake animals, both within structures by precisely mapping sensory responses, and between structures by elucidating the connectivity scheme of top-down projections. We demonstrate that fUS provides stable (over days), yet rapid, highly-resolved 3D tonotopic maps in the auditory pathway of awake ferrets, with unprecedented sharp functional resolution (100μm). This was performed in four different brain regions, including small (1-2mm3 size), subcortical (8mm deep) and previously undescribed structures in the ferret. Furthermore, we used fUS to map longdistance projections from frontal cortex, a key source of sensory response modulation, to auditory cortex.

scholarly journals Weak representation of awake/sleep states by local field potentials in aged mice

2021 ◽  
Author(s):  
Daichi Konno ◽  
Yuji Ikegaya ◽  
Takuya Sasaki
Keyword(s):  
Local Field ◽  
Field Potential ◽  
Brain Regions ◽  
Machine Learning Algorithms ◽  
Network Activity ◽  
Aged Mice ◽  
Neuronal Network Activity ◽  
Induced Changes ◽  
Old Mice ◽  
Young Mice

Senescence affects various aspects of sleep, and it remains unclear how sleep-related neuronal network activity is altered by senescence. Here, we recorded local field potential signals from multiple brain regions covering the forebrain in young (10-week-old) and aged (2-year-old) mice. Interregional LFP correlations across these brain regions showed smaller differences between awake and sleep states in aged mice. Multivariate analyses with machine learning algorithms with uniform manifold approximation and projection (UMAP) and robust continuous clustering (RCC) demonstrated that these LFP correlational patterns in aged mice less represented awake/sleep states than those in young mice. By housing aged mice in an enriched environment, the LFP patterns were restored to those observed in young mice. Our results demonstrate senescence-induced changes in neuronal activity at the network level and provide insight into the prevention of pathological symptoms associated with sleep disturbance in senescence.

scholarly journals LFP clustering in cortex reveals a taxonomy of Up states and near-millisecond, ordered phase-locking in cortical neurons

2019 ◽  
Vol 122 (4) ◽  
pp. 1794-1809
Author(s):  
Catalin C. Mitelut ◽  
Martin A. Spacek ◽  
Allen W. Chan ◽  
Tim H. Murphy ◽  
Nicholas V. Swindale
Keyword(s):  
Local Field ◽  
Local Field Potential ◽  
Cortical Neurons ◽  
State Transition ◽  
Slow Wave Sleep ◽  
Field Potential ◽  
State Transitions ◽  
Wide Field ◽  
Cortical Function ◽  
Up States

During slow-wave sleep and anesthesia, mammalian cortex exhibits a synchronized state during which neurons shift from a largely nonfiring to a firing state, known as an Up-state transition. Up-state transitions may constitute the default activity pattern of the entire cortex (Neske GT. Front Neural Circuits 9: 88, 2016) and could be critical to understanding cortical function, yet the genesis of such transitions and their interaction with single neurons is not well understood. It was recently shown that neurons firing at rates >2 Hz fire spikes in a stereotyped order during Up-state transitions (Luczak A, McNaughton BL, Harris KD. Nat Rev Neurosci 16: 745–755, 2015), yet it is still unknown if Up states are homogeneous and whether spiking order is present in neurons with rates <2 Hz (the majority). Using extracellular recordings from anesthetized cats and mice and from naturally sleeping rats, we show for the first time that Up-state transitions can be classified into several types based on the shape of the local field potential (LFP) during each transition. Individual LFP events could be localized in time to within 1–4 ms, more than an order of magnitude less than in previous studies. The majority of recorded neurons synchronized their firing to within ±5–15 ms relative to each Up-state transition. Simultaneous electrophysiology and wide-field imaging in mouse confirmed that LFP event clusters are cortex-wide phenomena. Our findings show that Up states are of different types and point to the potential importance of temporal order and millisecond-scale signaling by cortical neurons. NEW & NOTEWORTHY During cortical Up-state transitions in sleep and anesthesia, neurons undergo brief periods of increased firing in an order similar to that occurring in awake states. We show that these transitions can be classified into distinct types based on the shape of the local field potential. Transition times can be defined to <5 ms. Most neurons synchronize their firing to within ±5–15 ms of the transitions and fire in a consistent order.

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