Time-frequency based Coherence and Phase Locking Value Analysis of Human Locomotion Data using Generalized Morse Wavelets

Suwansawang, Sopapun; Halliday, David M.

doi:10.5220/0006111800340041

Cited by 9 publications

(6 citation statements)

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“…The PLV was then computed from these phases and was used as an indicator of spike-field synchrony. The PLV was computed as the sum of the phases of the θ oscillation at which spikes were discharged [93]. These phases were treated as phasors and mapped onto the unit circle per the definition of the PLV, giving rise to the calculation: where φ n is the θ phase at which the n th spike discharged for a given neuron during recording.…”

Section: Methodsmentioning

confidence: 99%

Spatial encoding in primate hippocampus during free navigation

et al. 2019

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The hippocampus comprises two neural signals—place cells and θ oscillations—that contribute to facets of spatial navigation. Although their complementary relationship has been well established in rodents, their respective contributions in the primate brain during free navigation remains unclear. Here, we recorded neural activity in the hippocampus of freely moving marmosets as they naturally explored a spatial environment to more explicitly investigate this issue. We report place cells in marmoset hippocampus during free navigation that exhibit remarkable parallels to analogous neurons in other mammalian species. Although θ oscillations were prevalent in the marmoset hippocampus, the patterns of activity were notably different than in other taxa. This local field potential oscillation occurred in short bouts (approximately .4 s)—rather than continuously—and was neither significantly modulated by locomotion nor consistently coupled to place-cell activity. These findings suggest that the relationship between place-cell activity and θ oscillations in primate hippocampus during free navigation differs substantially from rodents and paint an intriguing comparative picture regarding the neural basis of spatial navigation across mammals.

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Section: Methodsmentioning

confidence: 99%

Spatial encoding in primate hippocampus during free navigation

et al. 2019

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“…Thus, for example, setting the symmetry parameter γ = 3 defines a family members ('Airy' wavelets) that can successfully replace the popular Morlet wavelet as the default analytic wavelet for general-purpose use [68]. Besides other applications, Morse wavelets have been proposed in a variety of biomedical studies, such as human locomotion [69], electrocardiography and electromyography [70,71], neural oscillations coupling [60,72], electroencephalographic data classification [73,74] and fNIRS artifactual denoising [75].…”

Section: Time-frequency Decompositionmentioning

confidence: 99%

Identifying ADHD boys by very-low frequency prefrontal fNIRS fluctuations during a rhythmic mental arithmetic task

Ortuño-Miró,

Molina-Rodríguez,

Belmonte

et al. 2023

J. Neural Eng.

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Objective. Computer-aided diagnosis of attention-deficit/hyperactivity disorder (ADHD) aims to provide useful adjunctive indicators to support more accurate and cost-effective clinical decisions. Deep- and machine-learning (ML) techniques are increasingly used to identify neuroimaging-based features for objective assessment of ADHD. Despite promising results in diagnostic prediction, substantial barriers still hamper the translation of the research into daily clinic. Few studies have focused on functional near-infrared spectroscopy (fNIRS) data to discriminate ADHD condition at the individual level. This work aims to develop an fNIRS-based methodological approach for effective identification of ADHD boys via technically feasible and explainable methods. Approach. fNIRS signals recorded from superficial and deep tissue layers of the forehead were collected from 15 clinically referred ADHD boys (average age 11.9 years) and 15 non-ADHD controls during the execution of a rhythmic mental arithmetic task. Synchronization measures in the time-frequency plane were computed to find frequency-specific oscillatory patterns maximally representative of the ADHD or control group. Time series distance-based features were fed into four popular ML linear models (support vector machine, logistic regression (LR), discriminant analysis and naïve Bayes) for binary classification. A ‘sequential forward floating selection’ wrapper algorithm was adapted to pick out the most discriminative features. Classifiers performance was evaluated through five-fold and leave-one-out cross-validation (CV) and statistical significance by non-parametric resampling procedures. Main results. LR and linear discriminant analysis achieved accuracy, sensitivity and specificity scores of near 100% (p <.001) for both CV schemes when trained with only three key wrapper-selected features, arising from surface and deep oscillatory components of very low frequency. Significance. We provide preliminary evidence that very-low frequency fNIRS fluctuations induced/modulated by a rhythmic mental task accurately differentiate ADHD boys from non-ADHD controls, outperforming other similar studies. The proposed approach holds promise for finding functional biomarkers reliable and interpretable enough to inform clinical practice.

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“…Theta-gamma PAC was determined using a modulation index from the normalised mean vector length. Estimates were constructed using analytic Morse wavelets, with significance determined using surrogate data [75][76][77] . Amplitude information in each trial was split into five blocks, which were randomly permuted to yield a surrogate dataset for amplitude.…”

Section: Electrophysiological Data Analysismentioning

confidence: 99%

Dopamine D1-like receptors modulate synchronized oscillations in the hippocampal-prefrontal-amygdala circuit in contextual fear

Stubbendorff

Hale

Bast

et al. 2023

Preprint

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Contextual fear conditioning (CFC) is mediated by a neural circuit that includes the hippocampus, prefrontal cortex, and amygdala, but the neurophysiological mechanisms underlying the regulation of CFC by neuromodulators remain unclear. Dopamine D1-like receptors (D1Rs) in this circuit regulate CFC and local synaptic plasticity, which is facilitated by synchronized oscillations between these areas. In rats, we determined the effects of systemic D1R blockade on CFC and oscillatory synchrony between dorsal hippocampus (DH), prelimbic (PL) cortex, basolateral amygdala (BLA), and ventral hippocampus (VH), which sends hippocampal projections to PL and BLA. D1R blockade altered DH-VH and reduced VH-PL and VH-BLA synchrony during CFC, as inferred from theta and gamma coherence and theta-gamma coupling. D1R blockade also impaired CFC, as indicated by decreased freezing at retrieval, which was characterized by altered DH-VH and reduced VH-PL, VH-BLA, and PL-BLA synchrony. This reduction in VH-PL-BLA synchrony was not fully accounted for by non-specific locomotor effects, as revealed by comparing between epochs of movement and freezing in the controls. These results suggest that D1Rs regulate CFC by modulating synchronized oscillations within the hippocampus-prefrontal-amygdala circuit. They also add to growing evidence indicating that this circuit synchrony at retrieval reflects a neural signature of learned fear.

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Time-frequency based Coherence and Phase Locking Value Analysis of Human Locomotion Data using Generalized Morse Wavelets

Cited by 9 publications

References 0 publications

Spatial encoding in primate hippocampus during free navigation

Spatial encoding in primate hippocampus during free navigation

Identifying ADHD boys by very-low frequency prefrontal fNIRS fluctuations during a rhythmic mental arithmetic task

Dopamine D1-like receptors modulate synchronized oscillations in the hippocampal-prefrontal-amygdala circuit in contextual fear

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