Publikasjoner
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Karadag, Naz; Fuhrer, Julian; Heuser, Kjell; Andreassen, Ole & Smeland, Olav B.
(2026).
The genetics of common epilepsies.
Genomic Psychiatry.
s. 1–8.
doi:
10.61373/gp026y.0027.
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Common epilepsies are heterogeneous neurological disorders with significant heritability. Recent large-scale molecular genetic studies have identified both common and rare genetic variants affecting the risk of common epilepsies, suggesting the involvement of thousands of genetic variants. Most of these are common and individually have minuscule effects on disease risk. To date, more than 20 genomic loci with genome-wide significant common variant associations have been identified for genetic generalized epilepsies, implicating genes related to excitatory and GABAergic neurons, intracellular signal transduction, and synaptic excitability. While only a few common variant associations have reached genome-wide significance for focal epilepsies, exome-wide sequencing studies have robustly implicated protein-truncating ultrarare variants in genes encoding parts of the GATOR1 complex. Emerging data suggest a partly convergent genetic signal across subtypes of epilepsy and across common and rare variants. Furthermore, the genetic associations for common epilepsies overlap with a wide range of clinically related phenotypes, including cognitive ability and comorbid psychiatric and neurological disorders. Presently, genetic risk prediction tools that summarize an individual's common variant risk burden have limited clinical utility, but may eventually improve as statistical power increases. This review describes recent advances in the genetics of common epilepsies and outlines clinical implications and remaining challenges.
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Akdeniz, Bayram Cevdet; Bahrami, Shahram; Hagen, Espen; Fuhrer, Julian; Fominykh, Vera & Shadrin, Alexey
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(2026).
Polygenic Hazard Score for Predicting Age-associated Risk of Alzheimer’s Disease in European Populations: Development and Validation.
medRxiv.
doi:
10.1101/2025.07.28.25332293.
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Abstract Objectives Polygenic hazard score (PHS) models can be used to predict the age-associated risk for complex diseases, including Alzheimer’s disease (AD). In this study, we present an improved PHS model for AD that incorporates a large number of genetic variants and demonstrates enhanced predictive accuracy for age of onset in European populations compared to alternative models. Methods We used the genotyped European Alzheimer & Dementia Biobank (EADB) sample (n=42,120) to develop and evaluate the performance of the PHS model. We developed a PHS model building on 720 genetic variants, including Apolipoprotein E ( APOE ) ε2 and ε4 alleles. We used Elastic Net-regularized Cox regression approach to develop the PHS model. Results The new PHS model (EADB720) improved prediction accuracy compared to alternative models in European populations, with the Odds Ratio OR80/20 from the highest quintile of risk (80th risk percentile and above) to the lowest quintile of risk (20th risk percentile and below) varying between 5.10 and 13.15 within the range of age of onset from 65 – 85 years. Our model also improved risk stratification across ε3/3 individuals of European ancestry (OR80/20 ranges from 1.95 to 3.52). It was also successfully validated in independent datasets (HUSK, DemGene and ADNI) by achieving OR80/20 up to 10.00 in each independent dataset. Conclusion Our EADB720 model significantly improves the accuracy of age-associated risk of AD across European populations (pval<0.03). Accurately predicting the age of onset of AD is of large clinical importance to implementing new AD medication and early intervention in clinical settings.
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Wiström, Erik David; Fuhrer, Julian; Shadrin, Alexey; Fominykh, Vera; O'Connell, Kevin Sean & Jaholkowski, Piotr Pawel
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(2026).
Dissecting the genetic relationship between severe mental disorders and autoimmune diseases.
Brain, Behavior, and Immunity.
ISSN 0889-1591.
133.
doi:
10.1016/j.bbi.2025.106249.
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Severe mental disorders have been linked to immune system dysfunction. While a genetic association between mental disorders and autoimmune diseases has been suggested, their genetic relationship remains incompletely understood. Utilizing a complementary set of statistical analyses, we conducted a comprehensive investigation of the genetic architecture between severe mental disorders (major depression (MD), bipolar disorder (BD), and schizophrenia (SCZ)) and seven autoimmune diseases (autoimmune thyroiditis, celiac disease, inflammatory bowel disease (IBD), multiple sclerosis, psoriasis, rheumatoid arthritis, and type 1 diabetes), involving a total of 667,518 cases from 10 genome-wide association studies. While MD was positively genetically correlated with five autoimmune diseases, BD and SCZ were only positively correlated with IBD, suggesting differences in the genetic signal shared with autoimmunity across these mental disorders. A considerable fraction of genetic variants influencing autoimmune diseases (range 17.1–88.4 %) was estimated to overlap with mental disorders; however, this constitutes only a minor part of genetic variants influencing the more polygenic mental disorders. Finally, we identified 172 genetic loci jointly affecting mental disorders and autoimmune diseases, implicating both lipid metabolism and TNF signaling. In conclusion, MD, BD, and SCZ have a small but distinct genetic overlap with autoimmune diseases, which may inform new possible immune targets for treatment in mental illness.
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Spiech, Connor; Câmara, Guilherme Schmidt; Fuhrer, Julian & Virginia, Penhune,
(2025).
4/4 and more, rhythmic complexity more strongly predicts groove in common meters.
Communications Psychology.
3.
doi:
10.1038/s44271-025-00360-0.
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The pleasurable urge to move to music, termed “groove,” is thought to arise from the tension between top-down metric expectations or predictions and rhythmic complexity. Specifically, groove ratings are highest for moderately complex rhythms, balancing expectation and surprise. To test this, meter and rhythmic complexity need to be manipulated independently to assess their impact on groove. Thus, we compared Western listeners’ ratings for musical clips of varying rhythmic complexity composed in either the most common Western meter (4/4) or less common meters (e.g., 7/8). In several behavioral studies (Experiment 1, N = 143; Experiment 2, N = 120; Experiment 3, N = 120), we used Bayesian regression to show that groove is greatest for moderately complex rhythms, but only in 4/4. In non-4/4 meters, simpler rhythms elicited the greatest groove. This provides support for the theory that bottom-up rhythmic features interact with meter in a way that shapes the pleasurable urge to move to music.
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Smeland, Olav B.; Kutrolli, Gleda; Bahrami, Shahram; Fominykh, Vera; Parker, Nadine & Fuhrer, Julian
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(2025).
A genome-wide analysis of the shared genetic risk architecture of complex neurological and psychiatric disorders.
Nature Neuroscience.
ISSN 1097-6256.
28(12),
s. 2439–2450.
doi:
10.1038/s41593-025-02090-2.
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Although neurological and psychiatric disorders have historically been
considered to reflect distinct pathogenic entities, recent findings suggest
shared pathophysiological mechanisms. However, the extent to which
these heritable disorders share genetic influences remains unclear. Here
we performed a comprehensive analysis of genome-wide association study
data, involving nearly 1 million cases across ten neurological diseases
and ten psychiatric disorders, to compare their common genetic signal
and biological associations. Using complementary statistical tools, we
demonstrate that a large set of common genetic variants impacts the risk
of multiple neurological and psychiatric disorders, even in the absence of
genetic correlations. Furthermore, genome-wide association studies on
psychiatric disorders consistently implicate neuronal biology, whereas
neurological diseases are associated with diverse neurobiological processes.
Together, this study elucidates the genetic relationship between complex
neurological and psychiatric disorders, indicating a larger degree of genetic
pleiotropy than previously recognized. The findings have implications for
disease classification, precision medicine and clinical practice.
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Fominykh, Vera; Shadrin, Alexey; Jaholkowski, Piotr Pawel; Fuhrer, Julian; Parker, Nadine & Wiström, Erik David
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(2025).
Mapping the genetic landscape of immune-mediated disorders: potential implications for classification and therapeutic strategies.
Frontiers in Immunology.
16.
doi:
10.3389/fimmu.2025.1543781.
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Objectives: Based on clinical, biomarker, and genetic data, McGonagle and McDermott suggested that autoimmune and autoinflammatory disorders can be classified as a disease continuum from purely autoimmune to autoinflammatory with mixed diseases in between. However, the genetic architecture of this spectrum has not been systematically described. Here, we investigate the continuum of polygenic immune-mediated disorders using genome-wide association studies (GWAS) and statistical genetics methods.
Methods: We mapped the genetic landscape of 15 immune-mediated disorders using GWAS summary statistics and methods including genomic structural equation modeling (genomic SEM), linkage disequilibrium score regression, Local Analysis of [co]Variant Association, and Gaussian causal mixture modeling (MiXeR). We performed enrichment analyses of tissues and biological gene sets using MAGMA.
Results: Genomic SEM suggested a continuum structure with four underlying latent factors from autoimmune diseases at one end to autoinflammatory on the opposite end. Across disorders, we observed a balanced mixture of negative and positive local genetic correlations within the major histocompatibility complex, while outside this region, local genetic correlations were predominantly positive. MiXeR analysis showed large genetic overlap in accordance with the continuum landscape. MAGMA analysis implicated genes associated with known monogenic immune diseases for prominent autoimmune and autoinflammatory component.
Conclusions: Our findings support a polygenic continuum across immune-mediated disorders, with four genetic clusters. The “polygenic autoimmune” and “polygenic autoinflammatory” clusters reside on margins of this continuum. These findings provide insights and lead us to hypothesize that the identified clusters could inform future therapeutical strategies, with patients in the same clusters potentially responding similarly to specific therapies.
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Fuhrer, Julian; Glette, Kyrre; Ivanovic, Jugoslav; Larsson, Pål Gunnar; Bekinschtein, Tristan & Kochen, Silvia
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(2025).
Direct brain recordings reveal implicit encoding of structure in random auditory streams.
Scientific Reports.
15.
doi:
10.1038/s41598-025-98865-5.
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The brain excels at processing sensory input, even in rich or chaotic environments. Mounting evidence attributes this to sophisticated internal models of the environment that draw on statistical structures in the unfolding sensory input. Understanding how and where such modeling proceeds is a core question in statistical learning and predictive processing. In this context, we address the role of transitional probabilities as an implicit structure supporting the encoding of the temporal structure of a random auditory stream. Leveraging information-theoretical principles and the high spatiotemporal resolution of intracranial electroencephalography, we analyzed the trial-by-trial high-frequency activity representation of transitional probabilities. This unique approach enabled us to demonstrate how the brain automatically and continuously encodes structure in random stimuli and revealed the involvement of a network outside of the auditory system, including hippocampal, frontal, and temporal regions. Our work provides a comprehensive picture of the neural correlates of automatic encoding of implicit structure that can be the crucial substrate for the swift detection of patterns and unexpected events in the environment.
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Fuhrer, Julian; Glette, Kyrre; Anais, Llorens; Endestad, Tor; Solbakk, Anne-Kristin & Blenkmann, Alejandro Omar
(2023).
Quantifying evoked responses through information-theoretical measures.
Frontiers in Neuroinformatics.
17.
doi:
10.3389/fninf.2023.1128866.
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Information theory is a viable candidate to advance our understanding of how the brain processes information generated in the internal or external environment. With its universal applicability, information theory enables the analysis of complex data sets, is free of requirements about the data structure, and can help infer the underlying brain mechanisms. Information-theoretical metrics such as Entropy or Mutual Information have been highly beneficial for analyzing neurophysiological recordings. However, a direct comparison of the performance of these methods with well-established metrics, such as the t- test, is rare. Here, such a comparison is carried out by evaluating the novel method of Encoded Information with Mutual Information, Gaussian Copula Mutual Information, Neural Frequency Tagging, and t -test. We do so by applying each method to event-related potentials and event-related activity in different frequency bands originating from intracranial electroencephalography recordings of humans and marmoset monkeys. Encoded Information is a novel procedure that assesses the similarity of brain responses across experimental conditions by compressing the respective signals. Such an information-based encoding is attractive whenever one is interested in detecting where in the brain condition effects are present.
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Fuhrer, Julian; Blenkmann, Alejandro Omar; Endestad, Tor; Solbakk, Anne-Kristin & Glette, Kyrre
(2022).
Complexity-based Encoded Information Quantification in Neurophysiological Recordings.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
s. 2319–2323.
doi:
10.1109/EMBC48229.2022.9871501.
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Brain activity differs vastly between sleep, cognitive tasks, and action. Information theory is an appropriate concept to analytically quantify these brain states. Based on neurophysiological recordings, this concept can handle complex data sets, is free of any requirements about the data structure, and can infer the present underlying brain mechanisms. Specifically, by utilizing algorithmic information theory, it is possible to estimate the absolute information contained in brain responses. While current approaches that apply this theory to neurophysiological recordings can discriminate between different brain states, they are limited in directly quantifying the degree of similarity or encoded information between brain responses. Here, we propose a method grounded in algorithmic information theory that affords direct statements about responses' similarity by estimating the encoded information through a compression-based scheme. We validated this method by applying it to both synthetic and real neurophysiological data and compared its efficiency to the mutual information measure. This proposed procedure is especially suited for task paradigms contrasting different event types because it can precisely quantify the similarity of neuronal responses.
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Fuhrer, Julian; Blenkmann, Alejandro Omar; Endestad, Tor; Solbakk, Anne-Kristin & Glette, Kyrre
(2022).
Complexity-Based Encoded Information Quantification in Neurophysiological Recordings.
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Fuhrer, Julian; Blenkmann, Alejandro Omar; Tørresen, Jim; Endestad, Tor & Glette, Kyrre
(2019).
Making Sense of Randomness?
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Jensenius, Alexander Refsum; Erdem, Cagri; Zelechowska, Agata; Lan, Qichao; Fuhrer, Julian Peter & Sanchez, Victor Evaristo Gonzalez
(2019).
Entraining Guitars.
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Fuhrer, Julian Peter; Glette, Kyrre & Jensenius, Alexander Refsum
(2018).
Interactive Animation of the RITMO Logo.
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In this project the logo of RITMO is installed in an interactive animation. It is able to move in accordance with the frequency band of an audio input stream. That is to say, the RITMO logo interacts with the rhythmical streams of music.
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Fuhrer, Julian
(2023).
Implicit Encoding of Seemingly Unstructured Auditory Stimuli.
Universitetet i Oslo.
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We are biased to perceive patterns in random conditions: We see figures in clouds, recognize words within noise, or tend to set everything on a single color given the past sequence when gambling. While there are numerous examples like this falling within the phenomenon termed apophenia, the exact underlying neurophysiological mechanisms are unknown. Presumably, at the core of this phenomenon are internal representations in the brain that map the immediate environment, made possible by automatically drawing on temporal relationships in the unfolding sensory input. Yet, whether and how individual brain regions exploit these statistical structures remains elusive. In this thesis, Julian Fuhrer combines neuroscience and information theory to address the current lack of neurophysiological evidence on this phenomenon. Utilizing the high temporal and spatial precision of intracranial electroencephalography, he found that the brain automatically encodes temporal relationships between events when exposed to random acoustic stimuli. The experimental framework is further developed and tested on different types of signals, demonstrating the potential for future studies on comprehending the unknowns of the brain.
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Publisert
25. jan. 2024 11:41
- Sist endret
3. mai 2024 10:30