Publikasjoner
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Corfield, Elizabeth Claire; Shadrin, Alexey; Frei, Oleksandr; Rahman, Mohammad Zillur; Akdeniz, Bayram Cevdet & Filiz, Tahir Tekin
[Vis alle 29 forfattere av denne artikkelen]
(2026).
Family genetic designs in MoBa provide insights into health and functioning.
Nature.
ISSN 0028-0836.
doi:
10.1038/s41586-026-10926-5.
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Genome-wide association studies using large, population-based samples of unrelated individuals have discovered thousands of genetic associations with health and disease1. These studies can help explain genetic and environmental risks. However, increasing evidence suggests that population-based estimates, while precise, can also reflect confounding that affects their use and interpretation. This confounding can be overcome using data from genotyped family members, such as nuclear mother–father–child trios2,3. However, samples of genotyped families are rare4,5,6,7,8,9,10,11. Here we illustrate some of the advantages of familial data using the Norwegian Mother, Father and Child Cohort Study (MoBa), a population-based cohort of parents and offspring with extensive genotype data (n ≈ 230,000) (ref. 3), along with broad and longitudinal phenotyping of health and functioning. We provide an overview of MoBa and describe the quality control of genotype data tailored to this extensively related sample. We then use trio data to illustrate how family-based genomic designs can identify distinct direct and indirect sources of genetic influence and structural confounding. As examples, we analyse children’s height, educational achievement, depressive symptoms and sleep duration. These demonstrations highlight MoBa as a broadly valuable resource for advancing understanding of health and functioning across the lifecourse and generations.
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Frei, Evgeniia; Frei, Oleksandr; Hagen, Espen; Shadrin, Alexey; Bakken, Nora Refsum & Birkenæs, Viktoria
[Vis alle 9 forfattere av denne artikkelen]
(2026).
Prediction of adolescent internalizing disorder risk: Evidence from the Norwegian mother, father, and child cohort study.
Journal of Affective Disorders.
ISSN 0165-0327.
404,
s. 1–9.
doi:
10.1016/j.jad.2026.121481.
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Background
Internalizing disorders are among the most common psychiatric conditions in adolescence, often associated with long-term adverse outcomes. Early identification of at-risk youth is important for effective intervention, though it remains challenging due to the multifactorial nature of risk. Machine learning (ML) offers opportunities to integrate multiple data sources and improve risk prediction for internalizing disorders.
Methods
We used data from 13,743 adolescents (mean age 14.45 years; 52.7% female) participating in the Norwegian Mother, Father and Child Cohort Study (MoBa), linked to national health registries. Logistic regression with elastic net regularization was applied to predict the risk of an internalizing disorder (mood, anxiety or stress-related) occurring within one to five years after assessment. Nested models of increasing complexity incorporated sociodemographic, clinical, lifestyle, mental health, psychosocial, and genetic predictors. Model performance was evaluated in a hold-out test set. Simplified models combining three questionnaire scales were also evaluated.
Results
Test-set performance increased with model complexity, reaching area under the receiver operating characteristic curve (AUC) of 0.731 for the full model. Mental health self-reported symptoms and psychosocial predictors contributed most to the discrimination. Simplified models using three questionnaire scales, alongside age and sex, achieved AUCs up to 0.718 and effectively stratified adolescents into high- and low-risk groups (OR80/20 ranged 6.11–9.35).
Conclusion
Multimodal ML models integrating registry information, mental health symptoms, psychosocial factors, and genetic data demonstrated moderate predictive performance. Simplified models with three questionnaire items reached comparable performance, highlighting their potential utility in the early identification of adolescents at elevated internalizing disorder risk.
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Shadrin, Alexey; Hindley, Guy; Hagen, Espen; Parker, Nadine; Tesfaye, Markos & Jaholkowski, Piotr Pawel
[Vis alle 16 forfattere av denne artikkelen]
(2025).
Distinct patterns of genetic overlap among multimorbidities revealed with trivariate MiXeR.
Genome Medicine.
17.
doi:
10.1186/s13073-025-01528-3.
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Background: Multimorbidities are a global health challenge. Accumulating evidence indicates that overlapping genetic architectures underlie comorbid complex human traits and disorders. This can be quantified for a pair of phenotypes using various techniques. Still, the pattern of genetic overlap between three distinct complex phenotypes, which is important for understanding multimorbidities, has not been possible to quantify.
Methods: Here, we present and validate the novel trivariate MiXeR tool, which disentangles the pattern of genetic overlap between three complex phenotypes using summary statistics from genome-wide association studies. Our simulations show that trivariate MiXeR can reliably reconstruct different patterns of genetic overlap and estimate the proportions of genetic overlap between three phenotypes.
Results: We found substantial genetic overlap between gastro-intestinal and brain diseases supporting a genetic basis of the gut-brain axis—the pattern consistent with pairwise analysis. However, the pattern of genetic overlap between three diverse cardiometabolic and renal health indicators and three immune-linked disorders revealed a much larger genomic component shared between all phenotypes than expected from separate pairwise analyses. This suggests the existence of core pathways underlying distinct but related chronic conditions.
Conclusions: Overall, trivariate MiXeR offers a novel and efficient tool for investigating patterns of genetic overlap among three complex phenotypes. This contributes to a better understanding of genetic relationships between complex traits and disorders, potentially providing new insights into the mechanisms underlying common multimorbidities. Trivariate MiXeR is freely available at https://github.com/precimed/mix3r .
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Destexhe, Alain; Goldman, Jennifer S; Tort-Colet, Nuria; Roques, Axel; Fousek, Jan & Petkoski, Spase
[Vis alle 35 forfattere av denne artikkelen]
(2025).
State-dependent brain responsiveness, from local circuits to the whole brain.
eLIFE.
14,
s. 1–36.
doi:
10.7554/elife.108768.1.
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The objective of this paper is to review physiological and computational aspects of the responsiveness of the cerebral cortex to stimulation, and how responsiveness depends on the state of the system. This correspondence between brain state and brain responsiveness (statedependent responses) is outlined at different scales from the cellular and circuit level, to the mesoscale and macroscale level. At each scale, we review how quantitative methods can be used to characterize network states based on brain responses, such as the Perturbational Complexity Index (PCI). This description will compare data and models, systematically and at multiple scales, with a focus on the mechanisms that explain how brain responses depend on brain states.
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Shorter, John R.; Pasman, Joëlle A.; Kurvits, Siim; Jangmo, Andreas; Naamanka, Joonas & Harder, Arvid
[Vis alle 31 forfattere av denne artikkelen]
(2025).
Genome-wide association analyses identify distinct genetic architectures for early-onset and late-onset depression.
Nature Genetics.
ISSN 1061-4036.
57,
s. 2972–2979.
doi:
10.1038/s41588-025-02396-8.
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Major depressive disorder (MDD) is a common and heterogeneous disorder of complex etiology. Studying more homogeneous groups stratified according to clinical characteristics, such as age of onset, can improve the identification of the underlying genetic causes and lead to more targeted treatment strategies. We leveraged Nordic biobanks with longitudinal health registries to investigate differences in the genetic architectures of early-onset (eoMDD; n = 46,708 cases) and late-onset (loMDD; n = 37,168 cases) MDD. We identified 12 genomic loci for eoMDD and two for loMDD. Overall, the two MDD subtypes correlated moderately (genetic correlation, r g = 0.58) and differed in their genetic correlations with related traits. These findings suggest that eoMDD and loMDD have partially distinct genetic signatures, with a specific developmental brain signature for eoMDD. Importantly, we demonstrate that polygenic risk scores (PRS) for eoMDD predict suicide attempts within the first 10 years after the initial diagnosis: the absolute risk for suicide attempt was 26% in the top PRS decile, compared to 12% and 20% in the bottom decile and the intermediate group, respectively. Taken together, our findings can inform precision psychiatry approaches for MDD.
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Jaholkowski, Piotr Pawel; Bahrami, Shahram; Fominykh, Vera; Hindley, Guy; Tesfaye, Markos & Parekh, Pravesh
[Vis alle 23 forfattere av denne artikkelen]
(2024).
Charting the shared genetic architecture of Alzheimer's disease, cognition, and educational attainment, and associations with brain development.
Neurobiology of Disease.
ISSN 0969-9961.
203.
doi:
10.1016/j.nbd.2024.106750.
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The observation that the risk of developing Alzheimer's disease is reduced in individuals with high premorbid cognitive functioning, higher educational attainment, and occupational status has led to the ‘cognitive reserve’ hypothesis. This hypothesis suggests that individuals with greater cognitive reserve can tolerate a more significant burden of neuropathological changes before the onset of cognitive decline. The underpinnings of cognitive reserve remain poorly understood, although a shared genetic basis between measures of cognitive reserve and Alzheimer's disease has been suggested. Using the largest samples to date and novel statistical tools, we aimed to investigate shared genetic variants between Alzheimer's disease, and measures of cognitive reserve; cognition and educational attainment to identify molecular and neurobiological foundations. We applied the causal mixture model (MiXeR) to estimate the number of trait-influencing variants shared between Alzheimer's disease, cognition, and educational attainment, and condFDR/conjFDR to identify shared loci. To provide biological insights loci were functionally characterized. Subsequently, we constructed a Structural Equation Model (SEM) to determine if the polygenic foundation of cognition has a direct impact on Alzheimer's disease risk, or if its effect is mediated through established risk factors for the disease, using a case-control sample from the UK Biobank. Univariate MiXeR analysis (after excluding chromosome 19) revealed that Alzheimer's disease was substantially less polygenic (450 trait-influencing variants) compared to cognition (11,100 trait-influencing variants), and educational attainment (12,700 trait-influencing variants). Bivariate MiXeR analysis estimated that Alzheimer's disease shared approximately 70 % of trait-influencing variants with cognition, and approximately 40 % with educational attainment, with mixed effect directions. Using condFDR analysis, we identified 18 loci jointly associated with Alzheimer's disease and cognition and 6 loci jointly associated with Alzheimer's disease and educational attainment. Genes mapped to shared loci were associated with neurodevelopment, expressed in early life, and implicated the dendritic tree and phosphatidylinositol phosphate binding mechanisms. Spatiotemporal gene expression analysis of the identified genes showed that mapped genes were highly expressed during the mid-fetal period, further suggesting early neurodevelopmental stages as critical periods for establishing cognitive reserve which affect the risk of Alzheimer's disease in old age. Furthermore, our SEM analysis showed that genetic variants influencing cognition had a direct effect on the risk of developing Alzheimer's disease, providing evidence in support of the neurodevelopmental hypothesis of the disease.
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Karadag, Naz; Hagen, Espen; Shadrin, Alexey; Meer, Dennis van der; O'Connell, Kevin Sean & Rahman, Mohammad Zillur
[Vis alle 18 forfattere av denne artikkelen]
(2024).
Dissecting the Shared Genetic Architecture of Common Epilepsies With Cortical Brain Morphology.
Neurology: Genetics.
10(3).
doi:
10.1212/NXG.0000000000200143.
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Background and Objectives: Epilepsies are associated with differences in cortical thickness (TH) and surface area (SA). However, the mechanisms underlying these relationships remain elusive. We investigated the extent to which these phenotypes share genetic influences. Methods: We analyzed genome-wide association study data on common epilepsies (n = 69,995) and TH and SA (n = 32,877) using Gaussian mixture modeling MiXeR and conjunctional false discovery rate (conjFDR) analysis to quantify their shared genetic architecture and identify overlapping loci. We biologically interrogated the loci using a variety of resources and validated in independent samples. Results: The epilepsies (2.4 k–2.9 k variants) were more polygenic than both SA (1.8 k variants) and TH (1.3 k variants). Despite absent genome-wide genetic correlations, there was a substantial genetic overlap between SA and genetic generalized epilepsy (GGE) (1.1 k), all epilepsies (1.1 k), and juvenile myoclonic epilepsy (JME) (0.7 k), as well as between TH and GGE (0.8 k), all epilepsies (0.7 k), and JME (0.8 k), estimated with MiXeR. Furthermore, conjFDR analysis identified 15 GGE loci jointly associated with SA and 15 with TH, 3 loci shared between SA and childhood absence epilepsy, and 6 loci overlapping between SA and JME. 23 loci were novel for epilepsies and 11 for cortical morphology. We observed a high degree of sign concordance in the independent samples. Discussion: Our findings show extensive genetic overlap between generalized epilepsies and cortical morphology, indicating a complex genetic relationship with mixed-effect directions. The results suggest that shared genetic influences may contribute to cortical abnormalities in epilepsies.
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Karadag, Naz; Hagen, Espen; Shadrin, Alexey; Meer, Dennis van der; O'Connell, Kevin Sean & Rahman, Mohammad Zillur
[Vis alle 19 forfattere av denne artikkelen]
(2024).
Unraveling the shared genetics of common epilepsies and general cognitive ability.
Seizure.
ISSN 1059-1311.
122,
s. 105–112.
doi:
10.1016/j.seizure.2024.09.016.
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Purpose Cognitive impairment is prevalent among individuals with epilepsy, and increasing evidence indicates that genetic factors can underlie this relationship. However, the extent to which epilepsy subtypes differ in their genetic relationship with cognitive function, and information about the specific genetic variants involved remain largely unknown. Methods We investigated the genetic relationship between epilepsies and general cognitive ability (COG) using complementary statistical tools, including linkage disequilibrium score (LDSC) regression, MiXeR and conjunctional false discovery rate (conjFDR). We analyzed genome-wide association study data on COG (n = 269,867) and common epilepsies (n = 27,559 cases, 42,436 controls), including the broad phenotypes ‘all epilepsy’, focal epilepsies and genetic generalized epilepsies (GGE), as well as specific subtypes. We functionally annotated the identified loci using several biological resources and validated the results in independent samples. Results Using MiXeR, COG (11.2k variants) was estimated to be almost four times more polygenic than ‘all epilepsy’, GGE, juvenile myoclonic epilepsy (JME), and childhood absence epilepsy (CAE) (2.5k – 2.9k variants). The other epilepsy phenotypes were insufficiently powered for MiXeR analysis. We quantified extensive genetic overlap between COG and epilepsy types, but with varying negative genetic correlations (-0.23 to -0.04). COG was estimated to share 2.9k variants with both GGE and ‘all epilepsy’, and 2.3k variants with both JME and CAE. Using conjFDR, we identified 66 distinct loci shared between COG and epilepsies, including novel associations for GGE (27), ‘all epilepsy’ (5), JME (5) and CAE (5). The implicated genes were significantly expressed in multiple brain regions. The results were validated in independent samples (COG: p = 3.62 × 10–7; ‘all epilepsy’: p = 2.58 × 10–3). Conclusion Our study further dissects the substantial genetic basis shared between epilepsies and COG and identifies novel shared loci. An improved understanding of the genetic relationship between epilepsies and COG may lead to the development of novel comorbidity-targeted epilepsy treatments.
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Senk, Johanna; Hagen, Espen; Albada, Sacha J. van & Diesmann, Markus
(2024).
Reconciliation of weak pairwise spike–train correlations and highly coherent local field potentials across space.
Cerebral Cortex.
ISSN 1047-3211.
34(10).
doi:
10.1093/cercor/bhae405.
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Multi-electrode arrays covering several square millimeters of neural tissue provide simultaneous access to population signals such as extracellular potentials and spiking activity of one hundred or more individual neurons. The interpretation of the recorded data calls for multiscale computational models with corresponding spatial dimensions and signal predictions. Multi-layer spiking neuron network models of local cortical circuits covering about 1mm2 have been developed, integrating experimentally obtained neuron-type-specific connectivity data and reproducing features of observed in-vivo spiking statistics. Local field potentials can be computed from the simulated spiking activity. We here extend a local network and local field potential model to an area of 4x4mm2, preserving the neuron density and introducing distance-dependent connection probabilities and conduction delays. We find that the upscaling procedure preserves the overall spiking statistics of the original model and reproduces asynchronous irregular spiking across populations and weak pairwise spike–train correlations in agreement with experimental recordings from sensory cortex. Also compatible with experimental observations, the correlation of local field potential signals is strong and decays over a distance of several hundred micrometers. Enhanced spatial coherence in the low-gamma band around 50 Hz may explain the recent report of an apparent band-pass filter effect in the spatial reach of the local field potential.
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Bakken, Nora Refsum; Parker, Nadine; Hannigan, Laurie John; Hagen, Espen; Parekh, Pravesh & Shadrin, Alexey
[Vis alle 15 forfattere av denne artikkelen]
(2024).
Childhood trajectories of emotional and behavioral difficulties are related to polygenic liability for mood and anxiety disorders.
Journal of Child Psychology and Psychiatry.
ISSN 0021-9630.
66(3),
s. 350–365.
doi:
10.1111/jcpp.14063.
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Background Symptoms related to mood and anxiety disorders (emotional disorders) often present in childhood and adolescence. Some of the genetic liability for mental disorders, and emotional and behavioral difficulties seems to be shared. Yet, it is unclear how genetic liability for emotional disorders and related traits influence trajectories of childhood behavioral and emotional difficulties, and if specific developmental patterns are associated with higher genetic liability for these disorders. Methods This study uses data from a genotyped sample of children (n = 54,839) from the Norwegian Mother, Father, and Child Cohort Study (MoBa). We use latent growth models (1.5–5 years) and latent profile analyses (1.5–8 years) to quantify childhood trajectories and profiles of emotional and behavioral difficulties and diagnoses. We examine associations between these trajectories and profiles with polygenic scores for bipolar disorder (PGSBD), anxiety (PGSANX), depression (PGSDEP), and neuroticism (PGSNEUR). Results Associations between PGSDEP, PGSANX, and PGSNEUR, and emotional and behavioral difficulties in childhood were more persistent than age-specific across early childhood (1.5–5 years). Higher PGSANX and PGSDEP were associated with steeper increases in behavioral difficulties across early childhood. Latent profile analyses identified five profiles with different associations with emotional disorder diagnosis. All PGS were associated with the probability of classification into profiles characterized by some form of difficulties (vs. a normative reference profile), but only PGSBD was uniquely associated with a single developmental profile. Conclusions Genetic risk for mood disorders and related traits contribute to both a higher baseline level of, and a more rapid increase in, emotional and behavioral difficulties across early and middle childhood, with some indications for disorder-specific profiles. Our findings may inform research on developmental pathways to emotional disorders and the improvement of initiatives for early identification and targeted intervention.
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Akdeniz, Bayram Cevdet; Frei, Oleksandr; Hagen, Espen; Filiz, Tahir Tekin; Karthikeyan, Sandeep & Pasman, Joëlle
[Vis alle 18 forfattere av denne artikkelen]
(2024).
COSGAP: COntainerized Statistical Genetics Analysis Pipelines.
Bioinformatics Advances.
4(1).
doi:
10.1093/bioadv/vbae067.
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The collection and analysis of sensitive data in large-scale consortia for statistical genetics is hampered by multiple challenges, due to their non-shareable nature. Time-consuming issues in installing software frequently arise due to different operating systems, software dependencies, and limited internet access. For federated analysis across sites, it can be challenging to resolve different problems, including format requirements, data wrangling, setting up analysis on high-performance computing (HPC) facilities, etc. Easier, more standardized, automated protocols and pipelines can be solutions to overcome these issues. We have developed one such solution for statistical genetic data analysis using software container technologies. This solution, named COSGAP: “COntainerized Statistical Genetics Analysis Pipelines,” consists of already established software tools placed into Singularity containers, alongside corresponding code and instructions on how to perform statistical genetic analyses, such as genome-wide association studies, polygenic scoring, LD score regression, Gaussian Mixture Models, and gene-set analysis. Using provided helper scripts written in Python, users can obtain auto-generated scripts to conduct the desired analysis either on HPC facilities or on a personal computer. COSGAP is actively being applied by users from different countries and projects to conduct genetic data analyses without spending much effort on software installation, converting data formats, and other technical requirements.
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Rimehaug, Atle Eskeland; Stasik, Alexander Johannes; Hagen, Espen; Billeh, Yazan N.; Siegle, Josh H. & Dai, Kael
[Vis alle 10 forfattere av denne artikkelen]
(2023).
Uncovering circuit mechanisms of current sinks and sources with biophysical simulations of primary visual cortex.
eLIFE.
12.
doi:
10.7554/eLife.87169.
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Local field potential (LFP) recordings reflect the dynamics of the current source density (CSD) in brain tissue. The synaptic, cellular, and circuit contributions to current sinks and sources are ill-understood. We investigated these in mouse primary visual cortex using public Neuropixels recordings and a detailed circuit model based on simulating the Hodgkin–Huxley dynamics of >50,000 neurons belonging to 17 cell types. The model simultaneously captured spiking and CSD responses and demonstrated a two-way dissociation: firing rates are altered with minor effects on the CSD pattern by adjusting synaptic weights, and CSD is altered with minor effects on firing rates by adjusting synaptic placement on the dendrites. We describe how thalamocortical inputs and recurrent connections sculpt specific sinks and sources early in the visual response, whereas cortical feedback crucially alters them in later stages. These results establish quantitative links between macroscopic brain measurements (LFP/CSD) and microscopic biophysics-based understanding of neuron dynamics and show that CSD analysis provides powerful constraints for modeling beyond those from considering spikes.
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Holen, Børge; Shadrin, Alexey; Icick, Romain; Filiz, Tahir Tekin; Hindley, Guy Frederick Lanyon & Rødevand, Linn
[Vis alle 19 forfattere av denne artikkelen]
(2023).
Genome-wide analyses reveal novel opioid use disorder loci and genetic overlap with schizophrenia, bipolar disorder, and major depression.
Addiction Biology.
ISSN 1355-6215.
28(6).
doi:
10.1111/adb.13282.
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Opioid use disorder (OUD) and mental disorders are often comorbid, with increased morbidity and mortality. The causes underlying this relationship are poorly understood. Although these conditions are highly heritable, their shared genetic vulnerabilities remain unaccounted for. We applied the conditional/conjunctional false discovery rate (cond/conjFDR) approach to analyse summary statistics from independent genome wide association studies of OUD, schizophrenia (SCZ), bipolar disorder (BD) and major depression (MD) of European ancestry. Next, we characterized the identified shared loci using biological annotation resources. OUD data were obtained from the Million Veteran Program, Yale-Penn and Study of Addiction: Genetics and Environment (SAGE) (15 756 cases, 99 039 controls). SCZ (53 386 cases, 77 258 controls), BD (41 917 cases, 371 549 controls) and MD (170 756 cases, 329 443 controls) data were provided by the Psychiatric Genomics Consortium. We discovered genetic enrichment for OUD conditional on associations with SCZ, BD, MD and vice versa, indicating polygenic overlap with identification of 14 novel OUD loci at condFDR < 0.05 and 7 unique loci shared between OUD and SCZ (n = 2), BD (n = 2) and MD (n = 7) at conjFDR < 0.05 with concordant effect directions, in line with estimated positive genetic correlations. Two loci were novel for OUD, one for BD and one for MD. Three OUD risk loci were shared with more than one psychiatric disorder, at DRD2 on chromosome 11 (BD and MD), at FURIN on chromosome 15 (SCZ, BD and MD) and at the major histocompatibility complex region (SCZ and MD). Our findings provide new insights into the shared genetic architecture between OUD and SCZ, BD and MD, indicating a complex genetic relationship, suggesting overlapping neurobiological pathways.
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Næss, Solveig; Halnes, Geir; Hagen, Espen; Hagler, Donald J.; Dale, Anders M. & Einevoll, Gaute
[Vis alle 7 forfattere av denne artikkelen]
(2021).
Biophysically detailed forward modeling of the neural origin of EEG and MEG signals.
NeuroImage.
ISSN 1053-8119.
225.
doi:
10.1016/j.neuroimage.2020.117467.
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Electroencephalography (EEG) and magnetoencephalography (MEG) are among the most important techniques for non-invasively studying cognition and disease in the human brain. These signals are known to originate from cortical neural activity, typically described in terms of current dipoles. While the link between cortical current dipoles and EEG/MEG signals is relatively well understood, surprisingly little is known about the link between different kinds of neural activity and the current dipoles themselves. Detailed biophysical modeling has played an important role in exploring the neural origin of intracranial electric signals, like extracellular spikes and local field potentials. However, this approach has not yet been taken full advantage of in the context of exploring the neural origin of the cortical current dipoles that are causing EEG/MEG signals. Here, we present a method for reducing arbitrary simulated neural activity to single current dipoles. We find that the method is applicable for calculating extracranial signals, but less suited for calculating intracranial electrocorticography (ECoG) signals. We demonstrate that this approach can serve as a powerful tool for investigating the neural origin of EEG/MEG signals. This is done through example studies of the single-neuron EEG contribution, the putative EEG contribution from calcium spikes, and from calculating EEG signals from large-scale neural network simulations. We also demonstrate how the simulated current dipoles can be used directly in combination with detailed head models, allowing for simulated EEG signals with an unprecedented level of biophysical details. In conclusion, this paper presents a framework for biophysically detailed modeling of EEG and MEG signals, which can be used to better our understanding of non-inasively measured neural activity in humans.
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Hagen, Espen; Chambers, Anna; Einevoll, Gaute; Pettersen, Klas Henning; Enger, Rune & Stasik, Alexander Johannes
(2021).
RippleNet: a Recurrent Neural Network for Sharp Wave Ripple (SPW-R) Detection.
Neuroinformatics.
ISSN 1539-2791.
19,
s. 493–514.
doi:
10.1007/s12021-020-09496-2.
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Hippocampal sharp wave ripples (SPW-R) have been identified as key bio-markers of important brain functions such as memory consolidation and decision making. Understanding their underlying mechanisms in healthy and pathological brain function and behaviour rely on accurate SPW-R detection. In this multidisciplinary study, we propose a novel, self-improving artificial intelligence (AI) detection method in the form of deep Recurrent Neural Networks (RNN) with Long Short-Term memory (LSTM) layers that can learn features of SPW-R events from raw, labeled input data. The approach contrasts conventional routines that typically relies on hand-crafted, heuristic feature extraction and often laborious manual curation. The algorithm is trained using supervised learning on hand-curated data sets with SPW-R events obtained under controlled conditions. The input to the algorithm is the local field potential (LFP), the low-frequency part of extracellularly recorded electric potentials from the CA1 region of the hippocampus. Its output predictions can be interpreted as time-varying probabilities of SPW-R events for the duration of the inputs. A simple thresholding applied to the output probabilities is found to identify times of SPW-R events with high precision. The non-causal, or bidirectional variant of the proposed algorithm demonstrates consistently better accuracy compared to the causal, or unidirectional counterpart. Reference implementations of the algorithm, named ‘RippleNet’, are open source, freely available, and implemented using a common open-source framework for neural networks (tensorflow.keras) and can be easily incorporated into existing data analysis workflows for processing experimental data.
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Senk, Johanna; Korvasová, Karolína; Schuecker, Jannis; Hagen, Espen; Tetzlaff, Tom & Diesmann, Markus
[Vis alle 7 forfattere av denne artikkelen]
(2020).
Conditions for wave trains in spiking neural networks.
Physical Review Research (PRResearch).
2(2).
doi:
10.1103/PhysRevResearch.2.023174.
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Spatiotemporal patterns such as traveling waves are frequently observed in recordings of neural activity. The mechanisms underlying the generation of such patterns are largely unknown. Previous studies have investigated the existence and uniqueness of different types of waves or bumps of activity using neural-field models, phenomenological coarse-grained descriptions of neural-network dynamics. But it remains unclear how these insights can be transferred to more biologically realistic networks of spiking neurons, where individual neurons fire irregularly. Here, we employ mean-field theory to reduce a microscopic model of leaky integrate-and-fire (LIF) neurons with distance-dependent connectivity to an effective neural-field model. In contrast to existing phenomenological descriptions, the dynamics in this neural-field model depends on the mean and the variance in the synaptic input, both determining the amplitude and the temporal structure of the resulting effective coupling kernel. For the neural-field model we employ linear stability analysis to derive conditions for the existence of spatial and temporal oscillations and wave trains, that is, temporally and spatially periodic traveling waves. We first prove that wave trains cannot occur in a single homogeneous population of neurons, irrespective of the form of distance dependence of the connection probability. Compatible with the architecture of cortical neural networks, wave trains emerge in two-population networks of excitatory and inhibitory neurons as a combination of delay-induced temporal oscillations and spatial oscillations due to distance-dependent connectivity profiles. Finally, we demonstrate quantitative agreement between predictions of the analytically tractable neural-field model and numerical simulations of both networks of nonlinear rate-based units and networks of LIF neurons.
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Skaar, Jan-Eirik Welle; Stasik, Alexander Johannes; Hagen, Espen; Ness, Torbjørn V & Einevoll, Gaute
(2020).
Estimation of neural network model parameters from local field potentials (LFPs).
PLoS Computational Biology.
ISSN 1553-734X.
16(3).
doi:
10.1371/journal.pcbi.1007725.
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Most modeling in systems neuroscience has been descriptive where neural representations such as ‘receptive fields’, have been found by statistically correlating neural activity to sensory input. In the traditional physics approach to modelling, hypotheses are represented by mechanistic models based on the underlying building blocks of the system, and candidate models are validated by comparing with experiments. Until now validation of mechanistic cortical network models has been based on comparison with neuronal spikes, found from the high-frequency part of extracellular electrical potentials. In this computational study we investigated to what extent the low-frequency part of the signal, the local field potential (LFP), can be used to validate and infer properties of mechanistic cortical network models. In particular, we asked the question whether the LFP can be used to accurately estimate synaptic connection weights in the underlying network. We considered the thoroughly analysed Brunel network comprising an excitatory and an inhibitory population of recurrently connected integrate-and-fire (LIF) neurons. This model exhibits a high diversity of spiking network dynamics depending on the values of only three network parameters. The LFP generated by the network was computed using a hybrid scheme where spikes computed from the point-neuron network were replayed on biophysically detailed multicompartmental neurons. We assessed how accurately the three model parameters could be estimated from power spectra of stationary ‘background’ LFP signals by application of convolutional neural nets (CNNs). All network parameters could be very accurately estimated, suggesting that LFPs indeed can be used for network model validation.
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Hagen, Espen; Chambers, Anna; Einevoll, Gaute; Pettersen, Klas Henning; Enger, Rune & Stasik, Alexander Johannes
(2020).
RippleNet: A Recurrent Neural Network for Sharp Wave Ripple (SPW-R) Detection.
BioRxiv.
ISSN 0362-4331.
doi:
10.1101/2020.05.11.087874.
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Hippocampal sharp wave ripples (SPW-R) have been identified as key bio-markers of important brain functions such as memory consolidation and decision making. SPW-R detection typically relies on hand-crafted feature extraction, and laborious manual curation is often required. In this multidisciplinary study, we propose a novel, self-improving artificial intelligence (AI) method in the form of deep Recurrent Neural Networks (RNN) with Long Short-Term memory (LSTM) layers that can learn features of SPW-R events from raw, labeled input data. The algorithm is trained using supervised learning on hand-curated data sets with SPW-R events. The input to the algorithm is the local field potential (LFP), the low-frequency part of extracellularly recorded electric potentials from the CA1 region of the hippocampus. The output prediction can be interpreted as the time-varying probability of SPW-R events for the duration of the input. A simple thresholding applied to the output probabilities is found to identify times of events with high precision. The reference implementation of the algorithm, named ‘RippleNet’, is open source, freely available, and implemented using a common open-source framework for neural networks (tensorflow.keras) and can be easily incorporated into existing data analysis workflows for processing experimental data.
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Mäki-Marttunen, Tuomo; Kaufmann, Tobias; Elvsåshagen, Torbjørn; Devor, Anna; Djurovic, Srdjan & Westlye, Lars Tjelta
[Vis alle 23 forfattere av denne artikkelen]
(2019).
Biophysical Psychiatry—How Computational Neuroscience Can Help to Understand the Complex Mechanisms of Mental Disorders.
Frontiers in Psychiatry.
10.
doi:
10.3389/fpsyt.2019.00534.
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The brain is the most complex of human organs, and the pathophysiology underlying abnormal brain function in psychiatric disorders is largely unknown. Despite the rapid development of diagnostic tools and treatments in most areas of medicine, our understanding of mental disorders and their treatment has made limited progress during the last decades. While recent advances in genetics and neuroscience have a large potential, the complexity and multidimensionality of the brain processes hinder the discovery of disease mechanisms that would link genetic findings to clinical symptoms and behavior. This applies also to schizophrenia, for which genome-wide association studies have identified a large number of genetic risk loci, spanning hundreds of genes with diverse functionalities. Importantly, the multitude of the associated variants and their prevalence in the healthy population limit the potential of a reductionist functional genetics approach as a stand-alone solution to discover the disease pathology. In this review, we outline the key concepts of a “biophysical psychiatry,” an approach that employs large-scale mechanistic, biophysics-founded computational modelling to increase transdisciplinary understanding of the pathophysiology and strive toward robust predictions. We discuss recent scientific advances that allow a synthesis of previously disparate fields of psychiatry, neurophysiology, functional genomics, and computational modelling to tackle open questions regarding the pathophysiology of heritable mental disorders. We argue that the complexity of the increasing amount of genetic data exceeds the capabilities of classical experimental assays and requires computational approaches. Biophysical psychiatry, based on modelling diseased brain networks using existing and future knowledge of basic genetic, biochemical, and functional properties on a single neuron to a microcircuit level, may allow a leap forward in deriving interpretable biomarkers and move the field toward novel treatment options.
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Mäki-Marttunen, Tuomo; Krull, Florian; Bettella, Francesco; Hagen, Espen; Næss, Solveig & Ness, Torbjørn V
[Vis alle 16 forfattere av denne artikkelen]
(2019).
Alterations in schizophrenia-associated genes can lead to increased power in delta oscillations.
Cerebral Cortex.
ISSN 1047-3211.
29(2),
s. 875–891.
doi:
10.1093/cercor/bhy291.
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Genome-wide association studies have implicated many ion channels in schizophrenia pathophysiology. Although the functions of these channels are relatively well characterized by single-cell studies, the contributions of common variation in these channels to neurophysiological biomarkers and symptoms of schizophrenia remain elusive. Here, using computational modeling, we show that a common biomarker of schizophrenia, namely, an increase in delta-oscillation power, may be a direct consequence of altered expression or kinetics of voltage-gated ion channels or calcium transporters. Our model of a circuit of layer V pyramidal cells highlights multiple types of schizophrenia-related variants that contribute to altered dynamics in the delta-frequency band. Moreover, our model predicts that the same membrane mechanisms that increase the layer V pyramidal cell network gain and response to delta-frequency oscillations may also cause a deficit in a single-cell correlate of the prepulse inhibition, which is a behavioral biomarker highly associated with schizophrenia.
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Senk, Johanna; Hagen, Espen; Albada, Sacha van & Diesmann, Markus
(2018).
Reconciliation of weak pairwise spike-train correlations and highly coherent local field potentials across space.
arXiv.org.
ISSN 2331-8422.
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Senk, Johanna; Carde, Corto; Hagen, Espen; Kuhlen, Torsten W.; Diesmann, Markus & Weyers, Benjamin
(2018).
VIOLA - A multi-purpose and web-based visualization tool for neuronal-network simulation output.
Frontiers in Neuroinformatics.
12:75,
s. 1–25.
doi:
10.3389/fninf.2018.00075.
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Neuronal network models and corresponding computer simulations are invaluable tools to aid the interpretation of the relationship between neuron properties, connectivity, and measured activity in cortical tissue. Spatiotemporal patterns of activity propagating across the cortical surface as observed experimentally can for example be described by neuronal network models with layered geometry and distance-dependent connectivity. In order to cover the surface area captured by today's experimental techniques and to achieve sufficient self-consistency, such models contain millions of nerve cells. The interpretation of the resulting stream of multi-modal and multi-dimensional simulation data calls for integrating interactive visualization steps into existing simulation-analysis workflows. Here, we present a set of interactive visualization concepts called views for the visual analysis of activity data in topological network models, and a corresponding reference implementation VIOLA (VIsualization Of Layer Activity). The software is a lightweight, open-source, web-based, and platform-independent application combining and adapting modern interactive visualization paradigms, such as coordinated multiple views, for massively parallel neurophysiological data. For a use-case demonstration we consider spiking activity data of a two-population, layered point-neuron network model incorporating distance-dependent connectivity subject to a spatially confined excitation originating from an external population. With the multiple coordinated views, an explorative and qualitative assessment of the spatiotemporal features of neuronal activity can be performed upfront of a detailed quantitative data analysis of specific aspects of the data. Interactive multi-view analysis therefore assists existing data analysis workflows. Furthermore, ongoing efforts including the European Human Brain Project aim at providing online user portals for integrated model development, simulation, analysis, and provenance tracking, wherein interactive visual analysis tools are one component. Browser-compatible, web-technology based solutions are therefore required. Within this scope, with VIOLA we provide a first prototype.
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Hagen, Espen; Næss, Solveig; Ness, Torbjørn V & Einevoll, Gaute
(2018).
Multimodal Modeling of Neural Network Activity: Computing LFP, ECoG, EEG, and MEG Signals With LFPy 2.0.
Frontiers in Neuroinformatics.
12(92),
s. 1–36.
doi:
10.3389/fninf.2018.00092.
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Recordings of extracellular electrical, and later also magnetic, brain signals have been the dominant technique for measuring brain activity for decades. The interpretation of such signals is however nontrivial, as the measured signals result from both local and distant neuronal activity. In volume-conductor theory the extracellular potentials can be calculated from a distance-weighted sum of contributions from transmembrane currents of neurons. Given the same transmembrane currents, the contributions to the magnetic field recorded both inside and outside the brain can also be computed. This allows for the development of computational tools implementing forward models grounded in the biophysics underlying electrical and magnetic measurement modalities. LFPy (LFPy.readthedocs.io) incorporated a well-established scheme for predicting extracellular potentials of individual neurons with arbitrary levels of biological detail. It relies on NEURON (neuron.yale.edu) to compute transmembrane currents of multicompartment neurons which is then used in combination with an electrostatic forward model. Its functionality is now extended to allow for modeling of networks of multicompartment neurons with concurrent calculations of extracellular potentials and current dipole moments. The current dipole moments are then, in combination with suitable volume-conductor head models, used to compute non-invasive measures of neuronal activity, like scalp potentials (electroencephalographic recordings; EEG) and magnetic fields outside the head (magnetoencephalographic recordings; MEG). One such built-in head model is the four-sphere head model incorporating the different electric conductivities of brain, cerebrospinal fluid, skull and scalp. We demonstrate the new functionality of the software by constructing a network of biophysically detailed multicompartment neuron models from the Neocortical Microcircuit Collaboration (NMC) Portal (bbp.epfl.ch/nmc-portal) with corresponding statistics of connections and synapses, and compute in vivo-like extracellular potentials (local field potentials, LFP; electrocorticographical signals, ECoG) and corresponding current dipole moments. From the current dipole moments we estimate corresponding EEG and MEG signals using the four-sphere head model. We also show strong scaling performance of LFPy with different numbers of message-passing interface (MPI) processes, and for different network sizes with different density of connections. The open-source software LFPy is equally suitable for execution on laptops and in parallel on high-performance computing (HPC) facilities and is publicly available on GitHub.com.
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Lee, Jin Hyung; Carlson, David E.; Razaghi, Hooshmand Shokri; Yao, Weichi; Goetz, Georges A. & Hagen, Espen
[Vis alle 10 forfattere av denne artikkelen]
(2017).
YASS: Yet another spike sorter.
Advances in Neural Information Processing Systems.
ISSN 1049-5258.
30,
s. 4005–4015.
doi:
10.1101/151928.
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Spike sorting is a critical first step in extracting neural signals from large-scale electrophysiological data. This manuscript describes an efficient, reliable pipeline for spike sorting on dense multi-electrode arrays (MEAs), where neural signals appear across many electrodes and spike sorting currently represents a major computational bottleneck. We present several new techniques that make dense MEA spike sorting more robust and scalable. Our pipeline is based on an efficient multi-stage "triage-then-cluster-then-pursuit" approach that initially extracts only clean, high-quality waveforms from the electrophysiological time series by temporarily skipping noisy or "collided" events (representing two neurons firing synchronously). This is accomplished by developing a neural network detection method followed by efficient outlier triaging. The clean waveforms are then used to infer the set of neural spike waveform templates through nonparametric Bayesian clustering. Our clustering approach adapts a "coreset" approach for data reduction and uses efficient inference methods in a Dirichlet process mixture model framework to dramatically improve the scalability and reliability of the entire pipeline. The "triaged" waveforms are then finally recovered with matching-pursuit deconvolution techniques. The proposed methods improve on the state-of-the-art in terms of accuracy and stability on both real and biophysically-realistic simulated MEA data. Furthermore, the proposed pipeline is efficient, learning templates and clustering much faster than real-time for a 500-electrode dataset, using primarily a single CPU core.
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Hagen, Espen; Fossum, Janne- Christine; Pettersen, Klas; Alonso, Jose-Manuel; Swadlow, Harvey A. & Einevoll, Gaute
(2017).
Focal local field potential signature of the single-axon monosynaptic thalamocortical connection.
Journal of Neuroscience.
ISSN 0270-6474.
37(20),
s. 5123–5143.
doi:
10.1523/JNEUROSCI.2715-16.2017.
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A resurgence has taken place in recent years in the use of the extracellularly recorded local field potential (LFP) to investigate neural network activity. To probe monosynaptic thalamic activation of cortical postsynaptic target cells, so called spike-trigger-averaged LFP (stLFP) signatures have been measured. In these experiments, the cortical LFP is measured by multielectrodes covering several cortical lamina and averaged on spontaneous spikes of thalamocortical (TC) cells. Using a well established forward-modeling scheme, we investigated the biophysical origin of this stLFP signature with simultaneous synaptic activation of cortical layer-4 neurons, mimicking the effect of a single afferent spike from a single TC neuron. Constrained by previously measured intracellular responses of the main postsynaptic target cell types and with biologically plausible assumptions regarding the spatial distribution of thalamic synaptic inputs into layer 4, the model predicted characteristic contributions to monosynaptic stLFP signatures both for the regular-spiking (RS) excitatory neurons and the fast-spiking (FS) inhibitory interneurons. In particular, the FS cells generated stLFP signatures of shorter temporal duration than the RS cells. Added together, a sum of the stLFP signatures of these two principal synaptic targets of TC cells were observed to resemble experimentally measured stLFP signatures. Outside the volume targeted by TC afferents, the resulting postsynaptic LFP signals were found to be sharply attenuated. This implies that such stLFP signatures provide a very local measure of TC synaptic activation, and that newly developed inverse current-source density (CSD)-estimation methods are needed for precise assessment of the underlying spatiotemporal CSD profiles.
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Heiberg, Thomas; Hagen, Espen; Halnes, Geir & Einevoll, Gaute
(2016).
Biophysical Network Modelling of the dLGN Circuit: Different Effects of Triadic and Axonal Inhibition on Visual Responses of Relay Cells.
PLoS Computational Biology.
ISSN 1553-734X.
12(5).
doi:
10.1371/journal.pcbi.1004929.
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Despite its prominent placement between the retina and primary visual cortex in the early visual pathway, the role of the dorsal lateral geniculate nucleus (dLGN) in molding and regulating the visual signals entering the brain is still poorly understood. A striking feature of the dLGN circuit is that relay cells (RCs) and interneurons (INs) form so-called triadic synapses, where an IN dendritic terminal can be simultaneously postsynaptic to a retinal ganglion cell (GC) input and presynaptic to an RC dendrite, allowing for so-called triadic inhibition. Taking advantage of a recently developed biophysically detailed multicompartmental model for an IN, we here investigate putative effects of these different inhibitory actions of INs, i.e., triadic inhibition and standard axonal inhibition, on the response properties of RCs. We compute and investigate so-called area-response curves, that is, trial-averaged visual spike responses vs. spot size, for circular flashing spots in a network of RCs and INs. The model parameters are grossly tuned to give results in qualitative accordance with previous in vivo data of responses to such stimuli for cat GCs and RCs. We particularly investigate how the model ingredients affect salient response properties such as the receptive-field center size of RCs and INs, maximal responses and center-surround antagonisms. For example, while triadic inhibition not involving firing of IN action potentials was found to provide only a non-linear gain control of the conversion of input spikes to output spikes by RCs, axonal inhibition was in contrast found to substantially affect the receptive-field center size: the larger the inhibition, the more the RC center size shrinks compared to the GC providing the feedforward excitation. Thus, a possible role of the different inhibitory actions from INs to RCs in the dLGN circuit is to provide separate mechanisms for overall gain control (direct triadic inhibition) and regulation of spatial resolution (axonal inhibition) of visual signals sent to cortex.
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Hagen, Espen; Dahmen, David; Stavrinou, Maria; Linden, Henrik; Tetzlaff, Tom & Albada, Sacha Van
[Vis alle 9 forfattere av denne artikkelen]
(2016).
Hybrid Scheme for Modeling Local Field Potentials from Point-Neuron Networks.
Cerebral Cortex.
ISSN 1047-3211.
26(12),
s. 4461–4496.
doi:
10.1093/cercor/bhw237.
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With rapidly advancing multi-electrode recording technology, the local field potential (LFP) has again become a popular measure of neuronal activity in both research and clinical applications. Proper understanding of the LFP requires detailed mathematical modeling incorporating the anatomical and electrophysiological features of neurons near the recording electrode, as well as synaptic inputs from the entire network. Here we propose a hybrid modeling scheme combining efficient point-neuron network models with biophysical principles underlying LFP generation by real neurons. The LFP predictions rely on populations of network-equivalent multicompartment neuron models with layer-specific synaptic connectivity, can be used with an arbitrary number of point-neuron network populations, and allows for a full separation of simulated network dynamics and LFPs. We apply the scheme to a full-scale cortical network model for a ∼1 mm2 patch of primary visual cortex, predict laminar LFPs for different network states, assess the relative LFP contribution from different laminar populations, and investigate effects of input correlations and neuron density on the LFP. The generic nature of the hybrid scheme and its public implementation in hybridLFPy form the basis for LFP predictions from other and larger point-neuron network models, as well as extensions of the current application with additional biological detail.
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Linden, Henrik; Hagen, Espen; Leski, Szymon; Norheim, Eivind Skjønsberg; Pettersen, Klas Henning & Einevoll, Gaute Tomas
(2014).
LFPy: A tool for biophysical simulation of extracellular potentials generated by detailed model neurons.
Frontiers in Neuroinformatics.
7.
doi:
10.3389/fninf.2013.00041.
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Electrical extracellular recordings, i.e., recordings of the electrical potentials in the extracellular medium between cells, have been a main work-horse in electrophysiology for almost a century. The high-frequency part of the signal (≳500 Hz), i.e., the multi-unit activity (MUA), contains information about the firing of action potentials in surrounding neurons, while the low-frequency part, the local field potential (LFP), contains information about how these neurons integrate synaptic inputs. As the recorded extracellular signals arise from multiple neural processes, their interpretation is typically ambiguous and difficult. Fortunately, a precise biophysical modeling scheme linking activity at the cellular level and the recorded signal has been established: the extracellular potential can be calculated as a weighted sum of all transmembrane currents in all cells located in the vicinity of the electrode. This computational scheme can considerably aid the modeling and analysis of MUA and LFP signals. Here, we describe LFPy, an open source Python package for numerical simulations of extracellular potentials. LFPy consists of a set of easy-to-use classes for defining cells, synapses and recording electrodes as Python objects, implementing this biophysical modeling scheme. It runs on top of the widely used NEURON simulation environment, which allows for flexible usage of both new and existing cell models. Further, calculation of extracellular potentials using the line-source-method is efficiently implemented. We describe the theoretical framework underlying the extracellular potential calculations and illustrate by examples how LFPy can be used both for simulating LFPs, i.e., synaptic contributions from single cells as well a populations of cells, and MUAs, i.e., extracellular signatures of action potentials.
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Hagen, Espen; Næss, Solveig; Ness, Torbjørn V & Einevoll, Gaute
(2013).
Multimodal Modeling of Extracellular Neuronal Recordings in Python.
I Jaeger, Dieter & Jung, Ranu (Red.),
Encyclopedia of Computational Neuroscience.
Springer Science+Business Media B.V..
ISSN 9781461466741.
doi:
10.1007/978-1-4614-7320-6.
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LFPy is an open-source tool for calculating brain signals such as extracellular potentials and mag- netic signals from simulated activity in multi- compartment neuron models, ranging from single cells to large neuronal networks. The tool is provided as a Python package and relies on the NEURON simulation environment.
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Hagen, Espen; Næss, Solveig; Ness, Torbjørn Vefferstad & Einevoll, Gaute
(2013).
LFPy: Multimodal Modeling of Extracellular Neuronal Recordings in Python.
I Jaeger, Dieter & Jung, Ranu (Red.),
Encyclopedia of Computational Neuroscience.
Springer Science+Business Media B.V..
ISSN 9781461466741.
doi:
10.1007/978-1-4614-7320-6_100681-1.
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LFPy is an open-source tool for calculating brain signals such as extracellular potentials and magnetic signals from simulated activity in multicompartment neuron models, ranging from single cells to large neuronal networks. The tool is provided as a Python package and relies on the NEURON simulation environment.
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Hagen, Espen; Næss, Solveig; Ness, Torbjørn V & Einevoll, Gaute
(2013).
LFPy: Multimodal Modeling of Extracellular Neuronal Recordings in Python.
I Jaeger, Dieter & Jung, Ranu (Red.),
Encyclopedia of Computational Neuroscience.
Springer Science+Business Media B.V..
ISSN 9781461466741.
doi:
10.1007/978-1-4614-7320-6.
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Einevoll, Gaute & Hagen, Espen
(2022).
Modelling local field potentials (theory and practice).
Fulltekst i vitenarkiv
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Hagen, Espen; Ness, Torbjørn Vefferstad; Halnes, Geir & Einevoll, Gaute
(2022).
Brain signal predictions from multi-scale networks using a linearized framework.
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Stasik, Alexander Johannes; Hagen, Espen; Skaar, Jan Eirik; Ness, Torbjørn V & Einevoll, Gaute
(2019).
Estimating parameters of biological neuronal networks using Convolutional Neural Networks.
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Einevoll, Gaute & Hagen, Espen
(2019).
Tutorial: Biophysical modeling of extracellular potentials (using LFPy).
Fulltekst i vitenarkiv
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Hagen, Espen; Næss, Solveig; Ness, Torbjørn V & Einevoll, Gaute
(2019).
LFPy: Multimodal Modeling of Extracellular Neuronal
Recordings in Python.
Fulltekst i vitenarkiv
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Hagen, Espen
(2019).
Forward-model based generation of test data with ground truth for spike-sorting algorithms using ViSAPy and LFPy.
Fulltekst i vitenarkiv
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Stasik, Alexander Johannes; Hagen, Espen; Skaar, Jan Eirik; Ness, Torbjørn V & Einevoll, Gaute
(2019).
Estimating parameters of biological neuronal networks using Convolutional Neural Networks.
Fulltekst i vitenarkiv
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Stasik, Alexander Johannes; Hagen, Espen; Skaar, Jan Eirik; Ness, Torbjørn V & Einevoll, Gaute
(2019).
Estimating parameters of biological neuronal networks using Convolutional Neural Networks.
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Hagen, Espen; Næss, Solveig; Ness, Torbjørn V & Einevoll, Gaute
(2018).
Multimodal modeling of neural network activity: computing LFP, ECoG, EEG and MEG signals with LFPy 2.0.
Fulltekst i vitenarkiv
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Næss, Solveig; Ness, Torbjørn V; Hagen, Espen & Einevoll, Gaute
(2018).
Biophysical modelling of EEG signals from large neural populations.
Fulltekst i vitenarkiv
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Hagen, Espen; Næss, Solveig; Ness, Torbjørn V & Einevoll, Gaute
(2018).
Multimodal modeling of neural network activity: computing LFP, ECoG, EEG and MEG signals with LFPy2.0.
Fulltekst i vitenarkiv
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Skaar, Jan-Eirik Welle; Hagen, Espen; Stasik, Alexander Johannes; Einevoll, Gaute & Ness, Torbjørn V
(2018).
Estimation of model parameters from LFPs of spiking neuron networks using deep learning.
Fulltekst i vitenarkiv
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Berthet, Pierre; Hagen, Espen; Ness, Torbjørn V & Einevoll, Gaute
(2018).
Selectivity and sensitivity of cortical neurons to electric stimulation using ECoG electrode arrays.
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Senk, Johanna; Hagen, Espen; albada, Sacha Van & Diesmann, Markus
(2018).
A mesoscopic layered cortical network model for spiking activity and local field potentials.
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Stasik, Alexander Johannes; Mobarhan, Milad; Hagen, Espen & Einevoll, Gaute
(2018).
Generating Input from LGN for Simulation of Visual Cortex.
Fulltekst i vitenarkiv
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Hagen, Espen; Næss, Solveig; Ness, Torbjørn V & Einevoll, Gaute
(2017).
Computing Brain Signals (CBra): Concurrent simulation of network activity, extracellular electric potentials and magnetic fields.
Fulltekst i vitenarkiv
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Hagen, Espen; Næss, Solveig; Ness, Torbjørn V & Einevoll, Gaute
(2017).
Computing brain signals (CBra): Concurrent simulation of network activity, extracellular electric potentials and magnetic fields.
Fulltekst i vitenarkiv
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Hagen, Espen; Næss, Solveig; Ness, Torbjørn Vefferstad & Einevoll, Gaute
(2017).
Computing Brain Signals: Concurrent simulation of network activity, extracellular electric poten-
tials and magnetic fields.
Fulltekst i vitenarkiv
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Hagen, Espen; Næss, Solveig; Ness, Torbjørn V & Einevoll, Gaute
(2017).
Computing Brain Signals: concurrent simulation of network activity, extracellular electric potentials and magnetic fields.
Fulltekst i vitenarkiv
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Ness, Torbjørn Bækø; Hagen, Espen; Negwer, Moritz; Bakker, Rembrant; Schubert, Dirk & Einevoll, Gaute Tomas
(2013).
Modeling Extracellular Potentials in Microelectrode Array Recordings.
Fulltekst i vitenarkiv
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Ness, Torbjørn Bækø; Negwer, Moritz; Hagen, Espen; Schubert, Dirk; Bakker, Rembrandt & Einevoll, Gaute Tomas
(2013).
Modeling Extracellular Potentials in Microelectrode Array Recordings.
Fulltekst i vitenarkiv
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Ness, Torbjørn Bækø; Hagen, Espen; Negwer, Moritz; Bakker, Rembrant; Schubert, Dirk & Einevoll, Gaute Tomas
(2013).
Modeling Extracellular Potentials in Microelectrode Array Recording.
Fulltekst i vitenarkiv
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Heiberg, Thomas; Hagen, Espen; Halnes, Geir; Plesser, Hans Ekkehard & Einevoll, Gaute Tomas
(2013).
Biophysically detailed network model for area summation in LGN relay cells.
Fulltekst i vitenarkiv
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Hagen, Espen; Khosrowshahi, Amir; Ness, Torbjørn Bækø; Franke, Felix & Einevoll, Gaute Tomas
(2012).
Modeling realistic extracellular recordings of neuronal populations for the purpose of evaluating automatic spike-sorting algorithms.
Fulltekst i vitenarkiv
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Hagen, Espen; Khosrowshahi, Amir; Ness, Torbjørn Bækø; Franke, Felix & Einevoll, Gaute Tomas
(2012).
Modeling realistic extracellular spiking activity in populations of neurons for the purpose of evaluating automatic spike-sorting algorithms.
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Publisert
2. sep. 2022 14:24
- Sist endret
28. sep. 2022 14:13
