Biology subjects

Hadjipapas, A.

Publications and source records attributed to Hadjipapas, A..

3 recordsLinked to original sources

bioRxiv2023

Intermuscular coherences of plantarflexors during walking suggest distinct neural origin and function for alpha and beta/low-gamma bands after stroke

Plantarflexors provide propulsion during walking (late stance) and receive input from both corticospinal tract (CST) and corticoreticulospinal tract (CReST). Both descending motor tracts exhibit some frequency-specificity, which allows potential differentiation of neural drive from each tract using intermuscular coherence (IMC). Stroke may differentially affect each tract, thus impair the function of plantarflexors. However, the evidence concerning this frequency-specificity and its relation to plantarflexors neuromechanics post-stroke remains very limited. Here, we investigated the intermuscular coherences of alpha, beta, and low-gamma bands between the Soleus (SOL), Lateral Gastrocnemius (LG), and Medial Gastrocnemius (MG) muscles and their relationships with walking-specific measures (propulsive impulse; speed). Fourteen individuals with chronic stroke walked on a treadmill at self-selected and fast walking speed (SSWS and FWS, respectively). Inter-limb IMC comparisons revealed that beta LG-MG (SSWS) and low-gamma SOL-LG (FWS) IMCs were degraded on the paretic side. At the same time, within each limb, the IMCs, which were significantly different to a surrogate dataset denoting random coherence, were in the alpha band (both speeds). Further, alpha LG-MG IMC was positively correlated with propulsive impulse in the paretic limb (SSWS). Findings suggest differential functional role of alpha and beta/low-gamma, which may be related to the frequency-specificity of the underlying descending drives. The persistence of alpha in plantarflexors and its strong positive relationship with propulsive impulse suggests relative preservation and/or upregulation of CReST. Future research should address whether entraining motor system at alpha frequencies via neuromodulation can improve the neuromechanical function of paretic plantarflexors and subsequently promote post-stroke walking recovery. Key Points SummaryO_LICortical and subcortical motor drives may be frequency-specific, have a role in walking, and be degraded after stroke. C_LIO_LIWhether this frequency-specificity exists and how it is related to neuromechanical function of ankle plantarflexors post-stroke remains to be determined. C_LIO_LIHere, we investigated bilaterally the intermuscular coherences of alpha, beta, and low-gamma bands for the Soleus (SOL), Lateral Gastrocnemius (LG), and Medial Gastrocnemius (MG) muscles and their relationships with walking-specific measures (propulsive impulse; self-selected and fast speed) during treadmill walking in individuals post-stroke. C_LIO_LIThe beta LG-MG (self-selected speed) and low-gamma SOL-LG (fast speed) were degraded on the paretic side. C_LIO_LIAlpha coherence was significantly present across plantarflexors mainly on the non-paretic side (both speeds). C_LIO_LIParetic alpha LG-MG was positively correlated with paretic propulsive impulse (self-selected speed). C_LIO_LIGiven that paretic propulsive impulse is impaired post-stroke, entraining the motor system at alpha frequency via neuromodulation may improve propulsive impulse and subsequently promote post-stroke walking recovery. C_LI

neuroscience↗

bioRxiv2019

Empirically Constrained Network Models for Contrast-dependent Modulation of Gamma Rhythm in V1

Here we present experimentally constrained computational models of gamma rhythm and use these to investigate gamma oscillation instability. To this end, we extracted empirical constraints for PING (Pyramidal Interneuron Network Gamma) models from monkey single-unit and LFP responses recorded during contrast variation. These constraints implied weak rather than strong PING, connectivity between excitatory (E) and inhibitory (I) cells within specific bounds, and input strength variations that modulated E but not I cells. Constrained models showed valid behaviours, including gamma frequency increases with contrast and power saturation or decay at high contrasts. The route to gamma instability involved increased heterogeneity of E cells with increasing input triggering a breakdown of I cell pacemaker function. We illustrate the models capacity to resolve disputes in the literature. Our work is relevant for the range of cognitive operations to which gamma oscillations contribute and could serve as a basis for future, more complex models.

neuroscience↗

bioRxiv2017

Multi-scale periodicities in the functional brain networks of patients with epilepsy and their effect on seizure detection

The task of automated epileptic seizure detection and prediction by using non-invasive measurements such as scalp EEG signals or invasive, intracranial recordings, has been at the heart of epilepsy studies for at least three decades. By far, the most common approach for tackling this problem is to examine short-length recordings around the occurrence of a seizure - normally ranging between several seconds and up to a few minutes before and after the epileptic event - and identify any significant changes that occur before or during the event. An inherent assumption in these studies is the presence of a relatively constant EEG activity in the interictal period, which is presumably interrupted by the occurrence of a seizure. Here, we examine this assumption by using long-duration scalp EEG data (ranging between 21 and 94 hours) in patients with epilepsy, based on which we construct functional brain networks. Our results suggest that not only these networks vary over time, but they do so in a periodic fashion, exhibiting multiple peaks at periods ranging between around one and 24 hours. The effects of seizure onset on the functional brain network properties were found to be considerably smaller in magnitude compared to the changes due to the inherent periodic cycles of these networks. Importantly, the properties of the identified network periodic components (instantaneous phase, particularly that of short-term periodicities around 3 and 5 h) were found to be strongly correlated to seizure onset. These correlations were found to be largely absent between EEG signal periodicities and seizure onset, suggesting that higher specificity may be achieved by using network-based metrics. In turn, this suggests that to achieve more robust seizure detection and/or prediction, the evolution of the underlying longer term functional brain network periodic variations should be taken into account.\n\nHighlights- We have examined the long-term characteristics of EEG functional brain networks and their correlations to seizure onset\n\n- We show periodicities over multiple time scales in network summative properties (degree, efficiency, clustering coefficient)\n\n- We also show that, in addition to average network properties, similar periodicities exist in network topology using a novel measure based on the graph edit distance, suggesting that specific connectivity patterns recur over time\n\n- These periodic patterns were preserved when we corrected for the effects of volume conduction and were found to be of much larger magnitude compared to seizure-induced modulations\n\n- For the first time to our knowledge, we demonstrate that seizure onset occurs preferentially at specific phases of network periodic components that were consistently observed across subjects, particularly for shorter periodicities (around 3 and 5 hours)\n\n- These correlations between the phase of network periodic components and seizure onset were nearly absent when examining univariate properties (EEG signal power), suggesting that network-based measures are more tightly coupled with seizure onset compared to EEG signal-based measures\n\n- Our findings suggest that seizure detection and prediction algorithms may benefit significantly by taking into account longer-term variations in brain network properties\n\n- As we show strong evidence that shorter network-based periodicities (3-5 hours) are tightly coupled with seizure onset, our results pave the way for further investigation into the pathophysiology of seizure generation mechanisms beyond the well-known effects of circadian rhythms

bioengineering↗