Neural oscillations: beta band activity across motor networks

Khanna, Preeya; Carmena, Jose M.

doi:10.1016/j.conb.2014.11.010

Cited by 118 publications

(86 citation statements)

References 61 publications

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“…Different frequency bands are mediated by complex neurochemistry and oscillations of frequencies 12-80 Hz are linked to pyramidal neurons, regulated by GABA A inhibitory interneurons (Khanna & Carmena, 2015). Loss of GABAergic interneurons, together with pyramidal neurons, has been observed in both motor and nonmotor areas in ALS (Nihei, McKee, & Kowall, 1993); consequently, the decrease in the lower frequency spectral power can be attributed to structural degeneration of pyramidal cells and/or loss of interneurons that entrain them.…”

Section: Spectral Power Changes In Als Diseasementioning

confidence: 99%

Patterned functional network disruption in amyotrophic lateral sclerosis

Dukic

McMackin

Buxó

et al. 2019

Human Brain Mapping

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Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease primarily affecting motor function, with additional evidence of extensive nonmotor involvement. Despite increasing recognition of the disease as a multisystem network disorder characterised by impaired connectivity, the precise neuroelectric characteristics of impaired cortical communication remain to be fully elucidated. Here, we characterise changes in functional connectivity using beamformer source analysis on resting‐state electroencephalography recordings from 74 ALS patients and 47 age‐matched healthy controls. Spatiospectral characteristics of network changes in the ALS patient group were quantified by spectral power, amplitude envelope correlation (co‐modulation) and imaginary coherence (synchrony). We show patterns of decreased spectral power in the occipital and temporal (δ‐ to β‐band), lateral/orbitofrontal (δ‐ to θ‐band) and sensorimotor (β‐band) regions of the brain in patients with ALS. Furthermore, we show increased co‐modulation of neural oscillations in the central and posterior (δ‐, θ‐ and γl‐band) and frontal (δ‐ and γl‐band) regions, as well as decreased synchrony in the temporal and frontal (δ‐ to β‐band) and sensorimotor (β‐band) regions. Factorisation of these complex connectivity patterns reveals a distinct disruption of both motor and nonmotor networks. The observed changes in connectivity correlated with structural MRI changes, functional motor scores and cognitive scores. Characteristic patterned changes of cortical function in ALS signify widespread disease‐associated network disruption, pointing to extensive dysfunction of both motor and cognitive networks. These statistically robust findings, that correlate with clinical scores, provide a strong rationale for further development as biomarkers of network disruption for future clinical trials.

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Section: Spectral Power Changes In Als Diseasementioning

confidence: 99%

Patterned functional network disruption in amyotrophic lateral sclerosis

Dukic

McMackin

Buxó

et al. 2019

Human Brain Mapping

View full text Add to dashboard Cite

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“…3b–e). In addition, an increasingly suggested hypothesis is that neural information is coded —and transmitted between different cortical and subcortical areas— using LFP oscillations81 and that neurons may exhibit different preferred oscillatory activities for different behavioral situations82. With regard to these suggestions, we have shown here that there are statistically different oscillations in the five structures included in the study to form a cognitive functional state corresponding to the unpredicted situation.…”

Section: Discussionmentioning

confidence: 56%

Functional states of rat cortical circuits during the unpredictable availability of a reward-related cue

Fernández-Lamo

Sánchez-Campusano

Gruart

et al. 2016

Sci Rep

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Proper performance of acquired abilities can be disturbed by the unexpected occurrence of external changes. Rats trained with an operant conditioning task (to press a lever in order to obtain a food pellet) using a fixed-ratio (1:1) schedule were subsequently placed in a Skinner box in which the lever could be removed randomly. Field postsynaptic potentials (fPSPs) were chronically evoked in perforant pathway-hippocampal CA1 (PP-CA1), CA1-subiculum (CA1-SUB), CA1-medial prefrontal cortex (CA1-mPFC), mPFC-nucleus accumbens (mPFC-NAc), and mPFC-basolateral amygdala (mPFC-BLA) synapses during lever IN and lever OUT situations. While lever presses were accompanied by a significant increase in fPSP slopes at the five synapses, the unpredictable absence of the lever were accompanied by decreased fPSP slopes in all, except PP-CA1 synapses. Spectral analysis of local field potentials (LFPs) recorded when the animal approached the corresponding area in the lever OUT situation presented lower spectral powers than during lever IN occasions for all recording sites, apart from CA1. Thus, the unpredictable availability of a reward-related cue modified the activity of cortical and subcortical areas related with the acquisition of operant learning tasks, suggesting an immediate functional reorganization of these neural circuits to address the changed situation and to modify ongoing behaviors accordingly.

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“…In primate premotor cortex, the local field potential (LFP) shows prominent oscillations in the beta frequency range (13-40 Hz) during movement preparation, which are correlated with spike timing (Murthy and Fetz 1992; Sanes and Donoghue 1993; Reimer and Hatsopoulos 2010). While recent work has elucidated the statistical relationships between LFP and behavior (Khanna and Carmena 2015; Khanna and Carmena 2017; Chandrasekaran et al 2019), the impact of beta oscillations on population-level spiking activity is still poorly understood. Recent work used a complex, black box model of neural dynamics to detect oscillatory structure in high-dimensional spike trains (Pandarinath et al 2018).…”

Section: Resultsmentioning

confidence: 99%

Discovering precise temporal patterns in large-scale neural recordings through robust and interpretable time warping

Williams

Poole

Maheswaranathan

et al. 2019

Preprint

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1Though the temporal precision of neural computation has been studied intensively, a data-driven determination 2 of this precision remains a fundamental challenge. Reproducible spike time patterns may be obscured on single 3 trials by uncontrolled temporal variability in behavior and cognition, or may not even be time locked to measurable 4 signatures in either behavior or local field potentials (LFP). To overcome these challenges, we describe a general-5 purpose time warping framework that reveals precise spike-time patterns in an unsupervised manner, even when 6 spiking is decoupled from behavior or is temporally stretched across single trials. We demonstrate this method 7 across diverse systems: cued reaching in nonhuman primates, motor sequence production in rats, and olfaction in 8 mice. This approach flexibly uncovers diverse dynamical firing patterns, including pulsatile responses to behavioral 9 events, LFP-aligned oscillatory spiking, and even unanticipated patterns, like 7 Hz oscillations in rat motor cortex 10 that are not time-locked to measured behaviors or LFP. 11 14 Amarasingham et al. 2015; Brette 2015; Denève and Machens 2016), engendering intense debates in systems 15neuroscience over the last several decades. Empirically determining the degree of temporal precision from data is 16 challenging because multi-neuronal spike trains may contain highly structured temporal patterns that are completely 17 1 masked by temporal variations in behavioral and cognitive variables not under direct experimental control. For 18 example, precise spike patterns may not be temporally locked to naïvely chosen sensory or behavioral events. 19 Indeed, the fidelity of olfactory coding may be underestimated by factors of two to four when spike times are aligned 20 to stimulus delivery instead of inhalation onset (Shusterman et al. 2011; Cury and Uchida 2010; Shusterman et al. 21 2018). 22 Thus, experimental estimates of spike time precision hinge on the choice of an alignment point, which defines the 23 origin of the time axis on each trial. This choice can often be challenging and subjective. Even in relatively simple 24 behavioral tasks, animals can experience a sequence of stimuli, actions, and rewards, each of which occur with 25 varying latencies on different trials. Such tasks thus provide multiple choices for aligning multineuronal spike trains 26 to measurable events marking an origin of time. Moreover, in addition to choosing an origin of time, we must also 27 choose its units. Should spike times be measured in absolute clock time relative to some measured event, or in 28 units of fractional time between two events? Should the units of time change between successive pairs of events? 29 Could any one of these choices unmask spike-timing precision that is otherwise invisible? 30 Past studies have addressed these challenges in a number of ways: grouping trials together with similar durations 31 before averaging spike counts (Murakami et al. 2014; Starkweather et al. 2017; Wang et al. 2018), manually 32...

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Neural oscillations: beta band activity across motor networks

Cited by 118 publications

References 61 publications

Patterned functional network disruption in amyotrophic lateral sclerosis

Patterned functional network disruption in amyotrophic lateral sclerosis

Functional states of rat cortical circuits during the unpredictable availability of a reward-related cue

Discovering precise temporal patterns in large-scale neural recordings through robust and interpretable time warping

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