Biology subjects

Puonti, O.

Publications and source records attributed to Puonti, O..

15 recordsLinked to original sources

bioRxiv2026

Cortical E-fields of deep brain stimulation in Parkinson's disease patients exceed typical E-field magnitudes of transcranial electrical stimulation

BackgroundDeep brain stimulation (DBS) is an established and effective intervention for Parkinsons disease. Although the exact mechanisms of action are still unclear, both therapeutic benefits and side effects are solely attributed to neuromodulation via strong electric fields (E-fields) in the surgical target. Nevertheless, DBS generates E-fields that extend beyond the stimulation site and can be detected throughout the brain. Recent evidence from transcranial electrical stimulation studies shows that weak cortical E-fields even below 1 V/m can have a neuromodulatory effect, raising the question of a physiological relevance of weak cortical fields of DBS. However, the strength of cortical E-fields of DBS is currently unknown. ObjectiveUsing a novel framework, we aimed to quantify the whole-brain E-field distribution in patients with Parkinsons disease receiving therapeutic DBS of the subthalamic nucleus (STN-DBS). MethodsIn this work, we developed a pipeline to simulate DBS E-field distributions throughout the brain, based on the existing open-source toolboxes Lead-DBS and SimNIBS. We constructed patient-specific whole-head models including electrode leads for 25 patients with Parkinsons disease receiving subthalamic DBS, and simulated E-fields using the patients clinical stimulation settings for 49 hemispheres. ResultsWe found that median peak E-field magnitudes exceeded 0.3 V/m in all cortical regions and were greater than 1 V/m in the orbital gyrus, superior temporal gyrus, fusiform gyrus, parahippocampal gyrus, insular gyrus, cingulate gyrus. Most prominently, the orbital and insular gyri showed peak magnitudes ranging from 0.81 to 8.61 V/m and 0.90 to 8.01 V/m. ConclusionsOur results indicate that the E-fields of STN-DBS reach cortical peak magnitudes that exceed typically reported values of transcranial electrical stimulation. This opens the possibility that weak E-fields of DBS could have a direct neuromodulatory effect in wider regions of the brain, for example in cortical regions.

neuroscience↗

bioRxiv2026

Harmonizing the stimulation dose of focal tDCS across target sites

Non-invasive brain stimulation is an established tool for modulating neural activity that holds promise for advancing both cognitive neuroscience and clinical interventions. Focal transcranial direct current stimulation (tDCS), using center-surround electrode montages, enables more region-specific targeting. Although computational models can simulate individual electric fields, no existing approach enables the prospective individualization of electrode placement while standardizing the dose across targeted brain regions. In the current preparatory methodological study, we present a modeling-based framework that harmonizes the electric field strength between different target regions at the group level, but preserves inter-individual variability. This enables systematic examination of dose-response relationships and their regional differences. Positioning of the center-surround electrode montages is individualized to ensure focusing of the electric field on the target regions. We started by defining brain targets for eight cognitive and motor functions using MRI data from 43 participants. Using field simulations, we then estimated a group-average field strength in the target regions that had led to behavioral and physiological effects in prior tDCS studies (resulting in 0.2 V/m). The radii of the center-surround montages were optimized for each target region to achieve the intended field strength while maximizing focality. Validation in an independent sample (n=53) confirmed that the intended target field strength is achieved on average for new participants. The described computational tools are made available as open-source software, allowing other researchers to apply our individualization framework with parameters (target regions and target field strengths) tailored to their specific research questions; and are currently being implemented in a multi-center study involving approximately 1,000 datasets.

neuroscience↗

bioRxiv2025

Fast segmentation with the NextBrain histological atlas

Structural brain analysis at the subregion level offers critical insights into healthy aging and neurodegenerative diseases. The NextBrain histological atlas was recently introduced to support such fine-grained investigations, but its existing Bayesian segmentation framework remains computationally prohibitive, particularly for large-scale studies. We present a new, open-source tool that dramatically accelerates segmentation using a hybrid approach combining: machine learning, contrast-adaptive segmentation; target-specific image synthesis; and fast diffeomorphic registration (all three with GPU support). Our method enables highly granular segmentation of brain MRI scans of any resolution and contrast (in vivo or ex vivo) at a fraction of the computational cost of the original method (<5 minutes on a GPU). We validate our tool on four different modalities (in vivo MRI, ex vivo MRI, HiP-CT, and photography) across a total of approximately 4,000 brain scans. Our results demonstrate that the accelerated approach achieves comparable accuracy to the original method in terms of Dice scores, while reducing runtime by over an order of magnitude. This work enables high-resolution anatomical analysis at unprecedented scale and flexibility, providing a practical solution for large neuroimaging studies. Our tool is publicly available in FreeSurfer (https://surfer.nmr.mgh.harvard.edu/fswiki/HistoAtlasSegmentation).

neuroscience↗

bioRxiv2025

Variable E-field properties of dual-site tACS

BackgroundDual-site transcranial alternating current stimulation (ds-tACS) enables the modulation of interregional functional connectivity by introducing a phase lag between the stimulating currents. However, overlapping electric fields (E-fields), particularly in closely spaced cortical targets like the primary motor cortices (M1s), may unintentionally alter E-field characteristics and confound the interpretation of functional connectivity modulation. ObjectiveWe aimed to systematically evaluate how different phase lags affect key E-field characteristics when using high-definition ds-tACS, particularly when targeting the M1s. We sought to determine which montage configuration best preserved stable E-field characteristics and investigated whether indi-vidualised montage selection could enhance control over E-field consistency. MethodsWe used individualised finite-element method simulations based on MRI-derived head models to quantify the effects of different phase lags on E-field characteristics. E-field magnitude, normal component, spatial distribution, directionality, and effective stimulation area were assessed for nine montages and eight phase lags. ResultsAll E-field properties, including E-field peak magnitude, peak magnitude of the normal component, redistribution, difference to optimal directionality, and effective area of stimulation, were modulated significantly across differ-ent phase lags for all tested montages. Furthermore, we found substantial inter-individual variability in all E-field properties. Individual selection of montages improved critical properties, particularly the E-field directionality. ConclusionsIn contrast to common assumptions, variations in the phase lag can significantly affect key E-field properties of high-definition ds-tACS. Therefore, we recommend considering modulations of the E-field characteristics when comparing physiological or behavioural effects of ds-tACS at different phase lags. Moreover, given the high inter-individual variability, we suggest the individualisation of montages to the most relevant E-field property. HighlightsO_LIDual-site tACS is often used to modulate functional connectivity. C_LIO_LIChanges in E-field characteristics with varying phase lags are undesirable. C_LIO_LIWe used FEM models to quantify these changes across arbitrary phase lags. C_LIO_LIE-field characteristics of all montages varied significantly with the phase lag. C_LIO_LIIndividualised montage selection was able to improve critical characteristics. C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/668900v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@1c666baorg.highwire.dtl.DTLVardef@8257e4org.highwire.dtl.DTLVardef@1584d7corg.highwire.dtl.DTLVardef@472447_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2024

Fast FEM-based Electric Field Calculations for Transcranial Magnetic Stimulation

ObjectiveTo provide a Finite-Element Method (FEM) for rapid, repeated evaluations of the electric field induced by transcranial magnetic stimulation (TMS) in the brain for changing coil positions. ApproachPreviously, we introduced a first-order tetrahedral FEM enhanced by super- convergent patch recovery (SPR), striking a good balance between computational efficiency and accuracy (Saturnino et al 2019 J. Neural Eng. 16 066032). In this study, we refined the method to accommodate repeated simulations with varying TMS coil position. Starting from a fast direct solver, streamlining the pre- and SPR-based post-calculation steps using weight matrices computed during initialization strongly improved the computational efficiency. Additional speedups were achieved through efficient multi-core and GPU acceleration, alongside the optimization of the volume conductor model of the head for TMS. Main ResultsFor an anatomically detailed head model with [~]4.4 million tetrahedra, the optimized implementation achieves update rates above 1 Hz for electric field calculations in bilateral gray matter, resulting in a 60-fold speedup over the previous method with identical accuracy. An optimized model without neck and with adaptive spatial resolution scaled in dependence to the distance to brain grey matter, resulting in [~]1.9 million tetrahedra, increased update rates up to 10 Hz, with [~]3% numerical error and [~]4% deviation from the standard model. Region-of-interest (ROI) optimized models focused on the left motor, premotor and dorsolateral prefrontal cortices reached update rates over 20 Hz, maintaining a difference of <4% from standard results. Our approach allows efficient switching between coil types and ROI during runtime which greatly enhances the flexibility. SignificanceThe optimized FEM enhances speed, accuracy and flexibility and benefits various applications. This includes the planning and optimization of coil positions, pre-calculation and training procedures for real-time electric field simulations based on surrogate models as well as targeting and dose control during neuronavigated TMS.

neuroscience↗

bioRxiv2024

Head And Shoulders - The Impact Of An Extended Head Model On The Simulation And Optimization Of Transcranial Electric Stimulation

Electric field calculations are increasingly used for dose characterization of transcranial electrical stimulation (tES), but existing open-source head models are inaccurate for extracephalic montages that include electrodes placed on the neck or shoulder. We introduce the "Ernie Extended" model, an MRI- and CT-derived open-source head model extending to the upper shoulder region. Simulations of extracephalic tES targeting the cerebellum and supplementary motor area show significant differences in electric fields when using Ernie Extended compared to the non-extended Ernie model. Additionally, we propose an electrode layout that complements the electroencephalography 10- 20 system with extracephalic electrode positions. We demonstrate the use of this layout for optimizing multi-electrode tES montages for cerebellar stimulation, enhancing focality and reducing off-target stimulation, particularly of the spinal cord. Our results highlight the practical value of the Ernie Extended model for accurately characterizing doses produced by extracephalic tES montages and when targeting more caudal brain regions.

neuroscience↗

bioRxiv2024

A next-generation, histological atlas of the human brain and its application to automated brain MRI segmentation

Magnetic resonance imaging (MRI) is the standard tool to image the human brain in vivo. In this domain, digital brain atlases are essential for subject-specific segmentation of anatomical regions of interest (ROIs) and spatial comparison of neuroanatomy from different subjects in a common coordinate frame. High-resolution, digital atlases derived from histology (e.g., Allen atlas [7], BigBrain [13], Julich [15]), are currently the state of the art and provide exquisite 3D cytoarchitectural maps, but lack probabilistic labels throughout the whole brain. Here we present NextBrain, a next - generation probabilistic atlas of human brain anatomy built from serial 3D histology and corresponding highly granular delineations of five whole brain hemispheres. We developed AI techniques to align and reconstruct [~]10,000 histological sections into coherent 3D volumes with joint geometric constraints (no overlap or gaps between sections), as well as to semi-automatically trace the boundaries of 333 distinct anatomical ROIs on all these sections. Comprehensive delineation on multiple cases enabled us to build the first probabilistic histological atlas of the whole human brain. Further, we created a companion Bayesian tool for automated segmentation of the 333 ROIs in any in vivo or ex vivo brain MRI scan using the NextBrain atlas. We showcase two applications of the atlas: automated segmentation of ultra-high-resolution ex vivo MRI and volumetric analysis of Alzheimers disease and healthy brain ageing based on [~]4,000 publicly available in vivo MRI scans. We publicly release: the raw and aligned data (including an online visualisation tool); the probabilistic atlas; the segmentation tool; and ground truth delineations for a 100 m isotropic ex vivo hemisphere (that we use for quantitative evaluation of our segmentation method in this paper). By enabling researchers worldwide to analyse brain MRI scans at a superior level of granularity without manual effort or highly specific neuroanatomical knowledge, NextBrain holds promise to increase the specificity of MRI findings and ultimately accelerate our quest to understand the human brain in health and disease.

neuroscience↗

bioRxiv2023

Segmentation of supragranular and infragranular layers in ultra-high resolution 7T ex vivo MRI of the human cerebral cortex

Accurate labeling of specific layers in the human cerebral cortex is crucial for advancing our understanding of neurodevelopmental and neurodegenerative disorders. Lever-aging recent advancements in ultra-high resolution ex vivo MRI, we present a novel semi-supervised segmentation model capable of identifying supragranular and infragranular layers in ex vivo MRI with unprecedented precision. On a dataset consisting of 17 whole-hemisphere ex vivo scans at 120 {micro}m, we propose a multi-resolution U-Nets framework (MUS) that integrates global and local structural information, achieving reliable segmentation maps of the entire hemisphere, with Dice scores over 0.8 for supra- and infragranular layers. This enables surface modeling, atlas construction, anomaly detection in disease states, and cross-modality validation, while also paving the way for finer layer segmentation. Our approach offers a powerful tool for comprehensive neuroanatomical investigations and holds promise for advancing our mechanistic understanding of progression of neurodegenerative diseases.

neuroscience↗

bioRxiv2023

Exploring an EM-algorithm for banded regression in computational neuroscience

Regression is a principal tool for relating brain responses to stimuli or tasks in computational neuroscience. This often involves fitting linear models with predictors that can be divided into groups, such as distinct stimulus feature subsets in encoding models or features of different neural response channels in decoding models. When fitting such models, it can be relevant to allow differential shrinkage of the different groups of regression weights. Here, we explore a framework that allow for straightforward definition and estimation of such models. We present an expectation-maximization algorithm for tuning hyperparameters that control shrinkage of groups of weights. We highlight properties, limitations, and potential use-cases of the model using simulated data. Next, we explore the model in the context of a BOLD fMRI encoding analysis and an EEG decoding analysis. Finally, we discuss cases where the model can be useful and scenarios where regularization procedures complicate model interpretation.

neuroscience↗

bioRxiv2022

Systematic evaluation of anatomical details on transcranial electric stimulation and transcranial magnetic stimulation induced electric fields in a non-human primate model

Non-human primates (NHPs) have become key for translational research in noninvasive brain stimulation (NIBS). However, in order to create comparable stimulation conditions to humans it is vital to evaluate and match electric fields across species. Numerical models to simulate electric fields are an important tool for experimental planning in NHPs and translation to human studies. It is thus essential to evaluate how anatomical details in NHP models affect NIBS electric fields. Here, we create a highly accurate head model of a non-human primate (NHP) from 10.5 T MR data. We systematically evaluate how muscle tissue and head size (due to different MRI fields of view) affect simulation results in transcranial electric and magnetic stimulation (TES and TMS). Our findings indicate that the inclusion of muscle can affect TES electric field strength up to 29.5% while TMS is largely unaffected. Additionally, comparing a full head model to a cropped head model illustrates the impact of head size on electric fields for both TES and TMS. We find opposing effects between TES and TMS with an increase up to 13.5% for TES and a decrease up to 21.5% for TMS for the cropped head model compared to the full head model. Our results provide important insights into the level of anatomical detail needed for NHP head models and can inform future translational efforts for NIBS studies. HighlightsO_LIWe created a high resolution non-human primate FEM head model from 10.5 T MR data. C_LIO_LIWe ran transcranial electric and magnetic stimulation (TES and TMS) simulations to evaluate the effects of muscle and head size on the induced electric field in the brain. C_LIO_LIWe simulated both isotropic and anisotropic muscle conductivities. C_LIO_LIMuscle tissue can greatly affect simulation results for TES (increase electric field strength by up to 29.5%), but not for TMS. C_LIO_LIThere are opposing, but large effects of using a full head model versus a cropped head model for both TES (increase up to 13.5%) and TMS (decrease of up to 21.5%) C_LI

neuroscience↗

bioRxiv2022

Evaluating the Influence of Anatomical Accuracy and Electrode Positions on EEG Forward Solutions

Generating realistic volume conductor models for forward calculations in electroencephalography (EEG) is not trivial and several factors contribute to the accuracy of such models, two of which are its anatomical accuracy and the accuracy with which electrode positions are known. Here, we investigate effects of anatomical accuracy by comparing forward solutions from SimNIBS, a tool which allows state-of-the-art anatomical modeling, with well-established pipelines in MNE-Python and FieldTrip. We also compare different ways of specifying electrode locations when digitized positions are not available such as transformation of measured positions from standard space and transformation of a manufacturer layout. Substantial effects of anatomical accuracy were seen throughout the entire brain both in terms of field topography and magnitude with SimNIBS generally being more accurate than the pipelines in MNE-Python and FieldTrip. Topographic and magnitude effects were particularly pronounced for MNE-Python which uses a three-layer boundary element method (BEM) model. We attribute these mainly to differences in skull and cerebrospinal fluid (CSF) modeling. Effects of electrode specification method were evident in occipital and posterior areas when using a transformed manufacturer layout whereas transforming measured positions from standard space generally resulted in smaller errors. We suggest modeling the anatomy of the volume conductor as accurately possible and we hope to facilitate this by making it easy to export simulations from SimNIBS to MNE-Python and FieldTrip for further analysis. Likewise, if digitized electrode positions are not available, a set of measured positions on a standard head template may be preferable to those specified by the manufacturer.

neuroscience↗

bioRxiv2022

tDCS induced GABA change is associated with the simulated electric field in M1, an effect mediated by grey matter volume in the MRS voxel

Background and ObjectiveTranscranial direct current stimulation (tDCS) has wide ranging applications in neuro-behavioural and physiological research, and in neurological rehabilitation. However, it is currently limited by substantial inter-subject variability in responses, which may be explained, at least in part, by anatomical differences that lead to variability in the electric field (E-field) induced in the cortex. Here, we tested whether the variability in the E-field in the stimulated cortex during tDCS, estimated using computational simulations, explains the variability in tDCS induced changes in GABA, a neurophysiological marker of stimulation effect. MethodsData from five previously conducted MRS studies were combined. The anode was placed over the left primary motor cortex (M1, 3 studies, N = 24) or right temporal cortex (2 studies, N = 32), with the cathode over the contralateral supraorbital ridge. Single voxel spectroscopy was performed in a 2x2x2cm voxel under the anode in all cases. MRS data were acquired before and either during or after 1mA tDCS using either a sLASER sequence (7T) or a MEGA-PRESS sequence (3T). sLASER MRS data were analysed using LCModel, and MEGA-PRESS using FID-A and Gannet. E-fields were simulated in a finite element model of the head, based on individual MPRAGE images, using SimNIBS. Separate linear mixed effects models were run for each E-field variable (mean and 95th percentile; magnitude, and components normal and tangential to grey matter surface, within the MRS voxel). The model included effects of time (pre or post tDCS), E-field, grey matter volume in the MRS voxel, and a 3-way interaction between time, E-field and grey matter volume. Additionally, we ran a permutation analysis using PALM to determine whether E-field anywhere in the brain, not just in the MRS voxel, correlated with GABA change. ResultsIn M1, higher mean E-field magnitude was associated with greater tDCS-induced decreases in GABA (t(24) = 3.24, p = 0.003). Further, the association between mean E-field magnitude and GABA change was moderated by the grey matter volume in the MRS voxel (t(24) = -3.55, p =0.002). These relationships were consistent across all E-field variables except the mean of the normal component. No significant relationship was found between tDCS-induced GABA decrease and E-field in the temporal voxel. No significant clusters were found in the whole brain analysis. ConclusionsOur data suggest that the electric field induced by tDCS within the brain is variable, and is significantly related to tDCS-induced decrease in GABA, a key neurophysiological marker of stimulation. These findings strongly support individualised dosing of tDCS, at least in M1. Further studies examining E-fields in relation to other outcome measures, including behaviour, will help determine the optimal E-fields required for any desired effects. HighlightsO_LIWe study the link between individually simulated electric field dose and tDCS-induced change in GABA in the cortex. C_LIO_LIThe electric field strength in the brain correlates with a decrease in GABA in the motor cortex. C_LIO_LIThe correlation between the electric field and GABA change is modulated by the amount of grey matter in the MRS voxel. C_LIO_LIWe find no association between the electric field and GABA in the temporal cortex. C_LI

neuroscience↗

bioRxiv2021

On the Reconstruction of Magnetic ResonanceCurrent Density Images of the Human Brain:Pitfalls and Perspectives

Magnetic resonance current density imaging (MRCDI) of the human brain aims to reconstruct the current density distribution caused by transcranial electric stimulation from MR-based measurements of the current-induced magnetic fields. The reconstruction problem is challenging due to a low signal-to-noise ratio and a limited volume coverage of the MR-based measurements, the lack of data from the scalp and skull regions and because MRCDI is only sensitive to the component of the current-induced magnetic field parallel to the scanner field. Most existing reconstruction approaches have been validated using simulation studies and measurements in phantoms with simplified geometries. Only one reconstruction method, the projected current density algorithm, has been applied to human in-vivo data so far, however resulting in blurred current density estimates even when applied to noise-free simulated data. We analyze the underlying causes for the limited performance of the projected current density algorithm when applied to human brain data. In addition, we compare it with an approach that relies on the optimization of the conductivities of a small number of tissue compartments of anatomically detailed head models reconstructed from structural MR data. Both for simulated ground truth data and human in-vivo MRCDI data, our results indicate that the estimation of current densities benefits more from using a personalized volume conductor model than from applying the projected current density algorithm. In particular, we introduce a hierarchical statistical testing approach as a principled way to test and compare the quality of reconstructed current density images that accounts for the limited signal-to-noise ratio of the human in-vivo MRCDI data and the fact that the ground truth of the current density is unknown for measured data. Our results indicate that the statistical testing approach constitutes a valuable framework for the further development of accurate volume conductor models of the head. Our findings also highlight the importance of tailoring the reconstruction approaches to the quality and specific properties of the available data.

neuroscience↗

bioRxiv2019

Comparing and Validating Automated Tools for Individualized Electric Field Simulations in the Human Head

Comparing electric field simulations from individualized head models against in-vivo recordings is important for direct validation of computational field modeling for transcranial brain stimulation and brain mapping techniques such as electro- and magnetoencephalography. This also helps to improve simulation accuracy by pinning down the factors having the largest influence on the simulations. Here we compare field simulations from four different automated pipelines, against intracranial voltage recordings in an existing dataset of 14 epilepsy patients. We show that ignoring uncertainty in the simulations leads to a strong bias in the estimated linear relationship between simulated and measured fields. In addition, even though the simulations between the pipelines differ notably, this is not reflected in the correlation with the measurements. We discuss potential reasons for this apparent mismatch and propose a new Bayesian regression analysis of the data that yields unbiased estimates enabling robust conclusions to be reached.

bioengineering↗

bioRxiv2018

SimNIBS 2.1: A Comprehensive Pipeline for Individualized Electric Field Modelling for Transcranial Brain Stimulation

Numerical simulation of the electric fields induced by Non-Invasive Brain Stimulation (NIBS), using realistic anatomical head models has gained interest in recent years for understanding the NIBS effects in individual subjects. Although automated tools for generating the head models and performing the electric field simulations have become available, individualized modelling is still not standard practice in NIBS studies. This is likely partly explained by the lack of robustness and usability of the previously available software tools, and partly by the still developing understanding of the link between physiological effects and electric field distributions in the brain. To facilitate individualized modelling in NIBS, we have introduced the SimNIBS (Simulation of NIBS) software package, providing easy-to-use automated tools for electric field modelling. In this article, we give an overview of the modelling pipeline in SimNIBS 2.1, with step-by-step examples of how to run a simulation. Furthermore, we demonstrate a set of scripts for extracting average electric fields for a group of subjects, and finally demonstrate the accuracy of automated placement of standard electrode montages on the head model. SimNIBS 2.1 is freely available at www.simnibs.org.

bioengineering↗