Biology subjects

Kokkinos, V.

Publications and source records attributed to Kokkinos, V..

6 recordsLinked to original sources

bioRxiv2026

Monosynaptic connections link functionally similar regions in human cortex

The functional organization of human cortex has been mapped in considerable detail1-5, yet the axonal connections linking these areas remain largely unknown. This gap precludes inferring either the computational pathways through cortical networks or the principles governing why each area connects to its particular target regions. Here, in patients undergoing intracranial monitoring for epilepsy, we used concurrent electrical stimulation and functional magnetic resonance imaging (es-fMRI), validated previously against tracer studies in nonhuman primates6, to map anatomical connectivity of cortical sites across the whole brain, and combined these maps with task and resting-state fMRI in the same individuals. Es-fMRI revealed four main findings. First, connectivity followed an asymmetric functional-similarity principle: connected sites tended to share similar functional profiles, but, because connectivity is sparse, most pairs of functionally similar sites were not connected. Second, although connectivity also declines with distance7, the functional profile explained three times as much variance in connectivity as distance did, and long-range connections were the most functionally specific. Third, es-fMRI revealed direct monosynaptic links between established functional regions, including between the fusiform face area (FFA)2 and the temporoparietal junction (TPJ)3 for social cognition. Fourth, resting-state functional connectivity (rsFC) of a stimulation site was only weakly correlated with that sites es-fMRI connectivity. Together, these results provide a first principled link between anatomical connectivity and functional organization in the human cortex, and establish es-fMRI as a scalable approach to building a tracer-grade connectome of the human brain.

neuroscience↗

bioRxiv2025

Targeting intracranial electrical stimulation to network regions defined within individuals causes network-level effects

Intracranial electrical stimulation (ES) is routinely used therapeutically, diagnostically, and to provide causal evidence in neuroscience studies. However, our understanding of the brain network-level effects of ES remains limited. We applied precision functional mapping (PFM), based on functional magnetic resonance imaging (fMRI), to define large-scale networks within individual epilepsy patients. We show that single-pulse electrical stimulation (SPES) and high-frequency electrical stimulation (HFES) are more likely to evoke within-network responses and elicit network-related behavioral effects, respectively, when applied near to a PFM-defined network region. Network-level effects were more likely when stimulating sites in white matter, in close proximity to the targeted network, and within a region predominantly occupied by the targeted network. Further, network-specific modulation may be achievable by applying lower current intensities at these sites. Our findings support that modulation of specific networks is achievable by targeting ES to a functional anatomic "sweet spot" that can be identified using PFM.

neuroscience↗

bioRxiv2023

Broadband aperiodic components of local field potentials reflect inherent differences between cortical and subcortical activity

Information flow in brain networks is reflected in intracerebral local field potential (LFP) measurements that have both periodic and aperiodic components. The 1/f{chi} broadband aperiodic component of the power spectra has been shown to track arousal level and to correlate with other physiological and pathophysiological states, with consistent patterns across cortical regions. Previous studies have focused almost exclusively on cortical neurophysiology. Here we explored the aperiodic activity of subcortical nuclei from the human thalamus and basal ganglia, in relation to simultaneously recorded cortical activity. We elaborated on the FOOOF (fitting of one over f) method by creating a new parameterization of the aperiodic component with independent and more easily interpretable parameters, which allows seamlessly fitting spectra with and without an aperiodic knee, a component of the signal that reflects the dominant timescale of aperiodic fluctuations. First, we found that the aperiodic exponent from sensorimotor cortex in Parkinsons disease (PD) patients correlated with disease severity. Second, although the aperiodic knee frequency changed across cortical regions as previously reported, no aperiodic knee was detected from subcortical regions across movement disorders patients, including the ventral thalamus (VIM), globus pallidus internus (GPi) and subthalamic nucleus (STN). All subcortical region studied exhibited a relatively low aperiodic exponent ({chi}STN=1.3{+/-}0.2, {chi}VIM=1.4{+/-}0.1, {chi}GPi=1.4{+/-}0.1) that differed markedly from cortical values ({chi}Cortex=3.2{+/-}0.4, fkCortex=17{+/-}5 Hz). These differences were replicated in a second dataset from epilepsy patients undergoing intracranial monitoring that included thalamic recordings. The consistently lower aperiodic exponent and lack of an aperiodic knee from all subcortical recordings may reflect cytoarchitectonic and/or functional differences between subcortical nuclei and the cortex. Significance StatementThe broadband aperiodic component of local field potentials is a useful and reproducible index of neural activity. Here we refined a widely used phenomenological model for extracting aperiodic parameters, with which we fit cortical, basal ganglia and thalamic intracranial local field potentials, recorded from unique cohorts of movement disorders and epilepsy patients. We found that the aperiodic exponent in motor cortex is higher in Parkinsons disease patients with more severe motor symptoms, suggesting that aperiodic features may have potential as electrophysiological biomarkers for movement disorders symptoms. Remarkably, we found conspicuous differences in the broadband aperiodic components of basal ganglia and thalamic signals compared to those from neocortex, suggesting that the aperiodic neural timescale of subcortical LFPs is slower than that in cortex.

neuroscience↗

bioRxiv2021

The sensitivity of ECG contamination to surgical implantation site in adaptive closed-loop neurostimulation systems

BackgroundBrain sensing devices are approved today for Parkinsons, essential tremor, and epilepsy therapies. Clinical decisions for implants are often influenced by the premise that patients will benefit from using sensing technology. However, artifacts, such as ECG contamination, can render such treatments unreliable. Therefore, clinicians need to understand how surgical decisions may affect artifact probability. ObjectivesInvestigate neural signal contamination with ECG activity in sensing enabled neurostimulation systems, and in particular clinical choices such as implant location that impact signal fidelity. MethodsElectric field modelling and empirical signals from 85 patients were used to investigate the relationship between implant location and ECG contamination.a ResultsThe impact on neural recordings depends on the difference between ECG signal and noise floor of the electrophysiological recording. Empirically, we demonstrate that severe ECG contamination was more than 3.2x higher in left-sided subclavicular implants (48.3%), when compared to right-sided implants (15.3%). Cranial implants did not show ECG contamination. ConclusionsGiven the relative frequency of corrupted neural signals, we conclude that implant location will impact the ability of brain sensing devices to be used for "closed-loop" algorithms. Clinical adjustments such as implant location can significantly affect signal integrity and need consideration. HighlightsO_LIChronic embedded brain sensing promises algorithm-based neurostimulation C_LIO_LIAlgorithms for closed-loop stimulation can be impaired by artifacts C_LIO_LIThe relationship of implant location to cardiac dipole has relevant impact on neural signal fidelity; simple models can provide guidance on the sensitivity C_LIO_LIECG artifacts are present in up to 50% of neural signals from left subclavicular DBS systems C_LIO_LIImplanting DBS in a right subclavicular location significantly reduces frequency of ECG artifacts C_LIO_LICranial-mounted implants are relatively immune to artifacts C_LI

neuroscience↗

bioRxiv2020

Quantifying a Frequency Modulation Response Biomarker in Responsive Neurostimulation

ObjectiveResponsive Neurostimulation (RNS) is an effective treatment for controlling seizures in patients with drug-resistant focal epilepsy who are not suitable candidates for resection surgery. A lack of tools for detecting and characterizing potential response biomarkers, however, contributes to a limited understanding of mechanisms by which RNS improves seizure control. We developed a method to quantify ictal frequency modulation, previously identified as a biomarker of clinical responsiveness to RNS. ApproachFrequency modulation is characterized by shifts in power across spectral bands during ictal events, over several months of neurostimulation. This effect was quantified by partitioning each seizure pattern into segments with distinct spectral content and measuring the extent change from the baseline distribution of spectral content using the squared Earthmovers distance. Main resultsWe analyzed intracranial electroencephalography data from 13 patients who received RNS therapy, six of whom exhibited frequency modulation on expert evaluation. Subjects in the frequency modulation group had, on average, significantly larger and more sustained changes in their Earthmovers distances (mean = 13.97x10-3 {+/-} 1.197x10-3). In contrast, those subjects without expert-identified frequency modulation exhibited statistically insignificant or negligible distances (mean = 4.994x10-3 {+/-} 0.732x10-3). SignificanceThis method is the first step towards a quantitative, feedback-driven system for systematically optimizing RNS stimulation parameters, with an ultimate goal of truly personalized closed-loop therapy for epilepsy.

neuroscience↗

bioRxiv2018

Modulation of electrocorticographic seizure features predicts response to closed-loop brain stimulation

Why does closed-loop invasive brain stimulation improve seizure control in some patients with epilepsy, but not others? The RNS System, the only FDA-approved bi-directional brain-computer interface, has been shown to improve seizure control in patients with refractory epilepsy, although the mechanisms behind this success are undefined. We analyzed recordings from the RNS System and discovered two main categories of electrophysiological signatures of stimulation-induced modulation of the seizure network. Direct effects included ictal inhibition and early frequency modulation but did not correlate with improved clinical outcomes. Only indirect effects, those occurring remote from triggered stimulation, predicted improved clinical outcomes. These indirect effects, which included spontaneous ictal inhibition, frequency modulation, fragmentation, and ictal duration modulation, may reflect progressive local epileptogenic network compartmentalization that hinders the spread of pathological synchrony from recruiting neuronal populations. Our findings suggest that responsiveness to RNS may be explained by chronic stimulation-induced modulation of seizure network activity, rather than direct effects on each detected seizure.

neuroscience↗