Biology subjects

Ilmoniemi, R.

Publications and source records attributed to Ilmoniemi, R..

8 recordsLinked to original sources

bioRxiv2026

Disentangling evoked and induced activity from single-trial neurophysiological time series

Evoked and induced responses are fundamental neurophysiological readouts in the development and assessment of treatments for disorders with impaired neural function. Distinguishing these responses is essential for interpreting single-trial variability, identifying the determinants of these responses, and enabling real-time probing of cortical responsiveness. However, because of dynamic overlap, separating evoked and induced activity remains unsolved at the single-trial level when instantaneous fluctuations of cortical responsiveness are of interest. We developed the Evoked-Ongoing/Induced Separator (EOIS), a framework for separating event-evoked, ongoing, and event-modulated induced activity from single-trial data. EOIS recovers evoked and ongoing/induced components by leveraging their distinct phase-consistency features in MEG and real-time EEG, enabling subject-agnostic single-trial time-series analysis and attribution of cortical response variability to specific components. By delivering component-resolved time series, EOIS enables biomarker refinement, pharmacodynamic readouts, and closed-loop neuromodulation, facilitating translational deployment across individuals and care settings.

neuroscience↗

bioRxiv2026

The effect of EEG lead configuration on early TMS-EEG artifacts

Transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG) has strong potential for recording cortical reactivity and connectivity. However, this promise is hampered by TMS-induced EEG artifacts. Here, we examine the origins of these artifacts with phantom TMS-EEG recordings and simulations. We focus on two major types of artifacts: (1) the TMS pulse artifact during each [~]0.2 ms TMS pulse and (2) the decay artifact that may last tens of milliseconds. We examine how these artifacts change as a function of the relative position between TMS coil windings and EEG electrode leads. We also examine the hypothesis that certain EEG lead configurations may reduce or even cancel out these artifacts. In experimental results across 23 different TMS coil / EEG lead configurations, the amplitudes between the TMS pulse artifact and the decay artifact were highly correlated (Spearman {rho} = 0.86, p < 0.001), suggesting that the decay artifact is caused by the TMS pulse artifact. As predicted, in certain EEG lead configurations, both the TMS pulse and decay artifacts were minimized. The simulations confirmed that the TMS pulse artifacts depended on the electromagnetic induction from the TMS coil windings to the EEG leads. These results illuminate the generator mechanisms of--and possible means to reduce--both artifacts.

neuroscience↗

bioRxiv2025

Low-dimensional brain-symptom associations delineate depression phenotypes with distinct connectivity biomarkers and symptom profiles

Depression is neurobiologically and clinically heterogeneous. New approaches using resting-state functional MRI (rs-fMRI) functional connectivity (FC) data have modeled the neural basis of depression heterogeneity and revealed unique neural phenotypes. Yet, no studies have identified depression phenotypes from electrophysiological magnetoencephalography (MEG) data although MEG measures human brain dynamics at millisecond precision. We demonstrate here unique depression phenotypes based on MEG-oscillation FC. We collected resting-state MEG, MRI, and clinical symptom data from 263 patients with unipolar depression and 75 healthy controls. We assessed MEG-FC with two oscillatory coupling-mode measures that are fundamental for information processing. To define normative phenotypes, we computed their latent-space low-dimensional brain-symptom associations, and used these components to identify phenotypes using unsupervised machine learning. We identified five stable depression phenotypes that were characterized by unique symptom profiles and distinct spectral patterns. Our results demonstrate new neural underpinnings of depression heterogeneity and reveal unique neural phenotypes with potential personalized diagnostic value.

neuroscience↗

bioRxiv2025

Predictive modeling of TMS-evoked responses: Unraveling instantaneous excitability states

Transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG) and electromyography (EMG) provides a unique window into instantaneous cortical and corticospinal excitability states. We investigated 50 healthy participants to determine how fluctuations in pre-stimulus brain activity influence single-trial TMS-evoked potentials (TEPs) and motor-evoked potentials (MEPs). We developed a novel automated source-level TEP extraction method using individualized spatiotemporal priors that is robust against poor single-trial signal-to-noise ratios (SNRs) and ongoing oscillations. TEP and MEP amplitudes were predicted with linear mixed-effects models based on pre-stimulation EEG band-powers (theta to gamma), while accounting for temporal drifts (within-session trends), coil control, and inter-subject differences. We found that higher pre-stimulus sensorimotor alpha, beta, and gamma power were each associated with larger TEPs, indicating a more excitable cortical state. Increases in alpha and gamma power immediately before stimulation specifically predicted larger MEPs, reflecting increased corticospinal excitability. These results reveal relationships between ongoing oscillatory brain states and TMS response amplitudes, identifying EEG biomarkers of high- and low-excitability states. In conclusion, our study demonstrates the feasibility of single-trial source-level TMS-EEG analysis and shows that spontaneous alpha-, beta-, and gamma-band oscillations modulate motor cortical and corticospinal responsiveness. These findings pave the way for EEG-informed, brain-state-dependent TMS protocols to optimize neuromodulatory interventions in clinical and research applications.

neuroscience↗

bioRxiv2024

Optimization of TMS target engagement: novel evidence based on combined TMS-EEG and dMRI tractography of brain circuitry

Neuromodulation is based on the principle that brain stimulation produces plastic changes in cerebral circuitry. Given the intersubject structural and functional variability, neuromodulation has a personalized effect in the brain. Moreover, because of cerebral dominance and interhemispheric functional and structural differences in the same individual, the characterization of specific brain circuitries involved is currently not feasible. This notion is extremely important for neuromodulation treatments applied in neuropsychiatry. Specifically, the efficacy of the neuromodulation treatments is critically dependent on the anatomical precision of the brain target and the circuitry which has been affected. However, a complete understanding of how the brain behaves under stimulation needs a combined characterization of its neurophysiological response. This can be achieved by TMS-EEG guided by current multimodal neuroimaging techniques in real time. Herein, we present novel data based on dMRI tractography-guided TMS-EEG on one healthy young adult volunteer.

neuroscience↗

bioRxiv2024

Characterizing an electronic-robotic targeting platform for precise and fast brain stimulation with multi-locus transcranial magnetic stimulation

1.BackgroundMulti-locus TMS (mTMS) enables precise electronic control of brain stimulation targeting, eliminating the need for physical coil movement. However, with a small number of coils, the stimulation area is constrained, and manually handling the coil array is cumbersome. Combining electronic mTMS targeting with robotics will enable automated, user-independent, and precise brain stimulation protocols. ObjectiveCharacterizing an open-source electronic-robotic mTMS platform for rapid and accurate brain stimulation targeting. MethodsWe developed an automated robotic mTMS positioning platform. The accuracy of the system was quantified with a TMS characterizer that measures the TMS-induced electric field on a spherical cortex model. We used a 5-coil mTMS device equipped with a set of five coils coupled to a collaborative robot. The induced electric-field distortion generated by robot coupling was evaluated for each coil. We compared the accuracy of robotic-electronic targeting by repositioning the mTMS coil set with the robotic and the conventional manual positioning. ResultsOur collaborative robot-based system offers submillimeter precision and autonomy in positioning mTMS coil sets. The electronic-robotic mTMS platform was approximately 1.8 mm and 1.0{degrees} more accurate than the conventional manual positioning. Integrating robotics and mTMS automates brain stimulation procedures, resulting in minimal reliance on user expertise and subjective analysis. ConclusionOur open-source platform combining rapid mTMS targeting with robotic precision enhances the safety and reproducibility of brain stimulation techniques, enabling more efficient and reliable outcomes than previous techniques.

neuroscience↗

bioRxiv2024

Forecasting EEG time series with WaveNet

Forecasting electroencephalography (EEG) signals, i.e., estimating future values of the time series based on the past ones, is essential in many real-time EEG-based applications, such as brain-computer interfaces and closed-loop brain stimulation. As these applications are becoming more and more common, the importance of a good prediction model has increased. Previously, the autoregressive model (AR) has been employed for this task -- however, its prediction accuracy tends to fade quickly as multiple steps are predicted. We aim to improve on this by applying probabilistic deep learning to make robust longer-range forecasts. For this, we applied the probabilistic deep neural network model WaveNet to forecast resting-state EEG in theta- (4-7.5 Hz) and alpha-frequency (8-13 Hz) bands and compared it to the AR model. WaveNet reliably predicted EEG signals in both theta and alpha frequencies over 100 ms ahead, with mean errors of 0.8{+/-}0.6 {micro}V (theta) and 0.7{+/-}0.5 {micro}V (alpha), and outperformed the AR model in estimating the signal amplitude and phase. Furthermore, we found that the probabilistic approach offers a way of forecasting even more accurately while effectively discarding uncertain predictions. We demonstrate for the first time that probabilistic deep learning can be utilised to forecast resting-state EEG time series. In the future, the developed model can enhance the real-time estimation of brain states in brain-computer interfaces and brain stimulation protocols. It may also be useful for answering neuroscientific questions and for diagnostic purposes.

neuroscience↗

bioRxiv2017

Locating highly correlated sources from MEG with (recursive) (R)DS-MUSIC

We introduce a source localization method of the MUltiple Signal Classification (MUSIC) family that can locate brain-signal sources robustly and reliably, irrespective of their temporal correlations. The method, double-scanning (DS) MUSIC, is based on projecting out the topographies of source candidates during topographical scanning in a way that breaks the mutual dependence of highly correlated sources, but keeps the uncorrelated sources intact. We also provide a recursive version of DS-MUSIC (RDS-MUSIC), which overcomes the peak detection problem present in the non-recursive methods. We compare DS-MUSIC and RDS-MUSIC with other localization techniques in numerous simulations with varying source configurations, correlations, and signal-to-noise ratios. DS- and RDS-MUSIC were the most robust localization methods; they had a high success rate and localization accuracy for both uncorrelated and highly correlated sources. In addition, we validated RDS-MUSIC by showing that it successfully locates bilateral synchronous activity from measured auditory-evoked MEG.

neuroscience↗