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Individual differences in perception are widespread. Considering inter-individual variability, synesthetes experience stable additional sensations; schizophrenia patients suffer perceptual deficits in, eg, perceptual organization (alongside hallucinations and delusions). Is there a unifying principle explaining inter-individual variability in perception? There is good reason to believe perceptual experience results from inferential processes whereby sensory evidence is weighted by prior knowledge about the world. Perceptual variability may result from different precision weighting of sensory evidence and prior knowledge. We tested this hypothesis by comparing visibility thresholds in a perceptual hysteresis task across medicated schizophrenia patients (N = 20), synesthetes (N = 20), and controls (N = 26). Participants rated the subjective visibility of stimuli embedded in noise while we parametrically manipulated the availability of sensory evidence. Additionally, precise long-term priors in synesthetes were leveraged by presenting either synesthesia-inducing or neutral stimuli. Schizophrenia patients showed increased visibility thresholds, consistent with overreliance on sensory evidence. In contrast, synesthetes exhibited lowered thresholds exclusively for synesthesia-inducing stimuli suggesting high-precision long-term priors. Additionally, in both synesthetes and schizophrenia patients explicit, short-term priors—introduced during the hysteresis experiment—lowered thresholds but did not normalize perception. Our results imply that perceptual variability might result from differences in the precision afforded to prior beliefs and sensory evidence, respectively.
Sowohl die Lebenserwartung als auch die Prävalenz HIV-infizierter Patient*innen ist stetig ansteigend,aufgrund der HAART und durch verbesserte diagnostische Methoden. Nicht-AIDS-definierenden Erkrankungen sind heutzutage die führenden Todesursachen. Durch verzögerte Diagnosestellung und zurückhaltenden Therapien gynäkologischer Malignome ist die Prognose im Vergleich zur Normalbevölkerung schlechter.
In dieser retrospektiven Fall-Kontroll-Studie des Universitätsklinikums Frankfurt am Mains wurden die Therapie und das Outcome gynäkologischer Malignome von 23 HIV-infizierten Patientinnen aus den Jahren 2009-2019 mit einer Kontrollgruppe aus dem gynäkologischen Krebszentrum der Klinik Essen Mitte verglichen, um herauszufinden, inwiefern eine HIV-Infektion das Outcome der Patientinnen beeinflusst.
Das gynäkologische Malignom, dominierend das Zervixkarzinom, trat durchschnittlich ein Jahrzehnt nach der HIV-Diagnose auf. Im Unterschied zu anderen Studien, ist unser Kollektiv überwiegend hellhäutig. Ein bekannter Drogenabusus ist häufig und zusammenhängend mit weiteren Koinfektionen.
Die HIV-Erkrankung ist bei mehr als der Hälfte der Patientinnen bereits fortgeschritten, jedoch ließ sich kein Zusammenhang zwischen dem Auftreten gynäkologischer Malignome und einer CD4-Zellzahl <500 CD4-Zellen/µl nachweisen. Die antiretrovirale Therapie entsprach größtenteils nicht den aktuellen Leitlinien.
Bis auf fünf Frauen wurden alle Frauen leitliniengerecht therapiert. Eine Korrelation zwischen der Therapie und der Tumorentität, der CD4-Zellzahl, dem Alter oder dem Stadium des Malignoms konnte nicht gezeigt werden.
Insgesamt liegt die 5-Jahresüberlebensrate der Kohorte bei 74.8%. Eine nicht leitliniengerechte Therapie ist nicht direkt mit einem schlechteren Outcome verbunden, jedoch mit einem weitaus kürzeren Follow-Up-Zeitraum von durchschnittlich 0.22 Jahren im Vergleich zu 4.85 Jahren bei leitlinienkonform therapierten Patientinnen. Es liegt ein statistisch signifikanter Unterschied zwischen der Kontrollgruppe und unserer Kohorte vor, sodass angenommen werden kann, dass bei Vorliegen einer HIV-Infektion die Therapie des gynäkologischen Malignoms häufiger nicht leitliniengerecht ist.
Bislang existieren nur wenige Studien, die die Therapie und das Outcome gynäkologischer Malignome bei HIV-infizierten Patientinnen untersuchen. Die Interaktion einer ART mit antineoplastischen Medikamenten und die Anwendung von Checkpointinhibitoren und einer „targeted therapy“ sollten Gegenstand weiterer Untersuchungen sein. Dafür sollten HIV-Patientinnen in Therapiestudien inkludiert werden, sodass geeignete Leitlinien erarbeitet werden können.
Background Reward processing has been proposed to underpin atypical social behavior, a core feature of autism spectrum disorder (ASD). However, previous neuroimaging studies have yielded inconsistent results regarding the specificity of atypicalities for social rewards in ASD. Utilizing a large sample, we aimed to assess altered reward processing in response to reward type (social, monetary) and reward phase (anticipation, delivery) in ASD.
Methods Functional magnetic resonance imaging during social and monetary reward anticipation and delivery was performed in 212 individuals with ASD (7.6-30.5 years) and 181 typically developing (TD) participants (7.6-30.8 years).
Results Across social and monetary reward anticipation, whole-brain analyses (p<0.05, family-wise error-corrected) showed hypoactivation of the right ventral striatum (VS) in ASD. Further, region of interest (ROI) analysis across both reward types yielded hypoactivation in ASD in both the left and right VS. Across delivery of social and monetary reward, hyperactivation of the VS in individuals with ASD did not survive correction for multiple comparisons. Reward type by diagnostic group interactions, and a dimensional analysis of autism trait scores were not significant during anticipation or delivery. Levels of attention-deficit/hyperactivity disorder (ADHD) symptoms did not affect reward processing in ASD.
Conclusions Our results do not support current theories linking atypical social interaction in ASD to specific alterations in processing of social rewards. Instead, they point towards a generalized hypoactivity of VS in ASD during anticipation of both social and monetary rewards. We suggest that this indicates attenuated subjective reward value in ASD independent of social content and ADHD symptoms.
Background: Autism Spectrum Disorder (henceforth ‘autism’) is a highly heterogeneous neurodevelopmental condition with few effective treatments for core and associated features. To make progress we need to both identify and validate neural markers that help to parse heterogeneity to tailor therapies to specific neurobiological profiles. Atypical hemispheric lateralization is a stable feature across studies in autism, however its potential of lateralization as a neural stratification marker has not been widely examined.
Methods: In order to dissect heterogeneity in lateralization in autism, we used the large EU-AIMS Longitudinal European Autism Project dataset comprising 352 individuals with autism and 233 neurotypical (NT) controls as well as a replication dataset from ABIDE (513 autism, 691 NT) using a promising approach that moves beyond mean-group comparisons. We derived grey matter voxelwise laterality values for each subject and modelled individual deviations from the normative pattern of brain laterality across age using normative modeling.
Results: Results showed that individuals with autism had highly individualized patterns of both extreme right- and leftward deviations, particularly in language-, motor- and visuospatial regions, associated with symptom severity. Language delay (LD) explained most variance in extreme rightward patterns, whereas core autism symptom severity explained most variance in extreme leftward patterns. Follow-up analyses showed that a stepwise pattern emerged with individuals with autism with LD showing more pronounced rightward deviations than autism individuals without LD.
Conclusion: Our analyses corroborate the need for novel (dimensional) approaches to delineate the heterogeneous neuroanatomy in autism, and indicate atypical lateralization may constitute a neurophenotype for clinically meaningful stratification in autism.
Background: Marked sex differences in autism prevalence accentuate the need to understand the role of biological sex-related factors in autism. Efforts to unravel sex differences in the brain organization of autism have, however, been challenged by the limited availability of female data.
Methods: We addressed this gap by using a large sample of males and females with autism and neurotypical (NT) control individuals (ABIDE; Autism: 362 males, 82 females; NT: 409 males, 166 females; 7-18 years). Discovery analyses examined main effects of diagnosis, sex and their interaction across five resting-state fMRI (R-fMRI) metrics (voxel-level Z > 3.1, cluster-level P < 0.01, gaussian random field corrected). Secondary analyses assessed the robustness of the results to different pre-processing approaches and their replicability in two independent samples: the EU-AIMS Longitudinal European Autism Project (LEAP) and the Gender Explorations of Neurogenetics and Development to Advance Autism Research (GENDAAR).
Results: Discovery analyses in ABIDE revealed significant main effects across the intrinsic functional connectivity (iFC) of the posterior cingulate cortex, regional homogeneity and voxel-mirrored homotopic connectivity (VMHC) in several cortical regions, largely converging in the default network midline. Sex-by-diagnosis interactions were confined to the dorsolateral occipital cortex, with reduced VMHC in females with autism. All findings were robust to different pre-processing steps. Replicability in independent samples varied by R-fMRI measures and effects with the targeted sex-by-diagnosis interaction being replicated in the larger of the two replication samples – EU-AIMS LEAP.
Limitations: Given the lack of a priori harmonization among the discovery and replication datasets available to date, sample-related variation remained and may have affected replicability.
Conclusions: Atypical cross-hemispheric interactions are neurobiologically relevant to autism. They likely result from the combination of sex-dependent and sex-independent factors with a differential effect across functional cortical networks. Systematic assessments of the factors contributing to replicability are needed and necessitate coordinated large-scale data collection across studies.
Competing Interest Statement: ADM receives royalties from the publication of the Italian version of the Social Responsiveness Scale Child Version by Organization Speciali, Italy. JKB has been a consultant to, advisory board member of, and a speaker for Takeda/Shire, Medice, Roche, and Servier. He is not an employee of any of these companies and not a stock shareholder of any of these companies. He has no other financial or material support, including expert testimony, patents, or royalties. CFB is director and shareholder in SBGneuro Ltd. TC has received consultancy from Roche and Servier and received book royalties from Guildford Press and Sage. DM has been a consultant to, and advisory board member, for Roche and Servier. He is not an employee of any of these companies, and not a stock shareholder of any of these companies. TB served in an advisory or consultancy role for Lundbeck, Medice, Neurim Pharmaceuticals, Oberberg GmbH, Shire, and Infectopharm. He received conference support or speakers fee by Lilly, Medice, and Shire. He received royalties from Hogrefe, Kohlhammer, CIP Medien, Oxford University Press; the present work is unrelated to these relationships. JT is a consultant to Roche. The remaining authors declare no competing interests.
Individual differences in perception are widespread. Considering inter-individual variability, synesthetes experience stable additional sensations; schizophrenia patients suffer perceptual deficits in e.g. perceptual organization (alongside hallucinations and delusions). Is there a unifying principle explaining inter-individual variability in perception? There is good reason to believe perceptual experience results from inferential processes whereby sensory evidence is weighted by prior knowledge about the world. Different perceptual phenotypes may result from different precision weighting of sensory evidence and prior knowledge. We tested this hypothesis by comparing visibility thresholds in a perceptual hysteresis task across medicated schizophrenia patients, synesthetes, and controls. Participants rated the subjective visibility of stimuli embedded in noise while we parametrically manipulated the availability of sensory evidence. Additionally, precise long-term priors in synesthetes were leveraged by presenting either synesthesia-inducing or neutral stimuli. Schizophrenia patients showed increased visibility thresholds, consistent with overreliance on sensory evidence. In contrast, synesthetes exhibited lowered thresholds exclusively for synesthesia-inducing stimuli suggesting high-precision long-term priors. Additionally, in both synesthetes and schizophrenia patients explicit, short-term priors – introduced during the hysteresis experiment – lowered thresholds but did not normalize perception. Our results imply that distinct perceptual phenotypes might result from differences in the precision afforded to prior beliefs and sensory evidence, respectively.
Electrocardiograms (ECG) record the heart activity and are the most common and reliable method to detect cardiac arrhythmias, such as atrial fibrillation (AFib). Lately, many commercially available devices such as smartwatches are offering ECG monitoring. Therefore, there is increasing demand for designing deep learning models with the perspective to be physically implemented on these small portable devices with limited energy supply. In this paper, a workflow for the design of small, energy-efficient recurrent convolutional neural network (RCNN) architecture for AFib detection is proposed. However, the approach can be well generalized to every type of long time series. In contrast to previous studies, that demand thousands of additional network neurons and millions of extra model parameters, the logical steps for the generation of a CNN with only 114 trainable parameters are described. The model consists of a small segmented CNN in combination with an optimal energy classifier. The architectural decisions are made by using the energy consumption as a metric in an equally important way as the accuracy. The optimization steps are focused on the software which can be embedded afterwards on a physical chip. Finally, a comparison with some previous relevant studies suggests that the widely used huge CNNs for similar tasks are mostly redundant and unessentially computationally expensive.
Electrocardiograms (ECG) record the heart activity and are the most common and reliable method to detect cardiac arrhythmias, such as atrial fibrillation (AFib). Lately, many commercially available devices such as smartwatches are offering ECG monitoring. Therefore, there is increasing demand for designing deep learning models with the perspective to be physically implemented on these small portable devices with limited energy supply. In this paper, a workflow for the design of small, energy-efficient recurrent convolutional neural network (RCNN) architecture for AFib detection is proposed. However, the approach can be well generalized to every type of long time series. In contrast to previous studies, that demand thousands of additional network neurons and millions of extra model parameters, the logical steps for the generation of a CNN with only 114 trainable parameters are described. The model consists of a small segmented CNN in combination with an optimal energy classifier. The architectural decisions are made by using the energy consumption as a metric in an equally important way as the accuracy. The optimisation steps are focused on the software which can be embedded afterwards on a physical chip. Finally, a comparison with some previous relevant studies suggests that the widely used huge CNNs for similar tasks are mostly redundant and unessentially computationally expensive.
Das Verständnis von Tumorerkrankungen wurde durch neue technologische und ökonomische Verbesserungen für die Next-Generation-Sequencing Analyse (NGS) gefördert. Die Komplexität der Interpretation genomischer Daten erschwert jedoch die Anwendung von NGS-Analysen im klinischen Kontext. Die Herausforderung besteht darin bei stetig wachsendem medizinischem Wissen dieses im klinischen Kontext zu interpretieren und eine personalisierte Therapieempfehlung abzugeben. Einen ressourcensparenden Ansatz können KI-unterstützende Software-Programme bieten, welche die genomischen Varianten mit der aktuellen Literatur vergleichen, eine Bewertung der Therapieoptionen geben und klinische Studien empfehlen können. In dieser retrospektiven Arbeit wurden Patient:innen mit metastasiertem Brustkrebs (n=77) mittels gezielter NGS-Analyse anhand von sogenannten Genpanels mit 126 bzw. 540 krebsrelevanten Genen im Zeitraum von 01/2019-02/2022 untersucht. Mit Hilfe von bioinformatischen Methoden wurden patientenspezifische genomische Veränderungen mit Behandlungsoptionen abgeglichen. Diese Methoden stützen sich vollständig auf öffentliche Datenbanken über somatische Varianten mit prädiktiver Evidenz für das Ansprechen auf bestimmte Medikamente. Diese Versorgungsforschung einer repräsentativen Kohorte des Universitätsklinikums Frankfurt in Kooperation mit Regionalverbund OncoNet Rhein-Main wurden systematisch ausgewertet inklusive der Bedeutung genomischer Varianten. Das OncoNet Rhein-Main ist eine Kooperation aus führenden onkologischen Zentren und Praxen im Rhein-Main-Gebiet, welche sich als Netzwerk der Aufgabe angenommen haben Patient:innen optimal therapeutisch zu versorgen. Für 51% (39/77) der Patient:innen konnte mindestens eine gezielte Therapieoption mit einem effektivem Biomarker im gleichen Tumortyp gemäß Zulassung der Europäischen Arzneimittelbehörde (EMA) gefunden werden.
Bei 12/77 (16%) wurde mindestens eine Alteration mit einem effektivem Biomarker und einer OFF-Label Therapieoption gefunden. Bei 30% der Patient:innen wurden Veränderungen in optionalen Biomarkern gefunden, welche Resistenzmechanismen erklären. Die umfassende molekulare Analyse von Patient:innen mit fortgeschrittenem Brustkrebs erlaubt die Behandlungsoption zu verbessern und ermöglicht durch die Analyse von bekannten Resistenzmarkern auch den klinischen Verlauf besser zu verstehen. Die interdisziplinäre Besprechung der Befunde im molekularem Tumorboard ist im Hinblick auf kontinuierliches Lernen aller Beteiligten sowie zur Qualitätssicherung eine entscheidende obligate Maßnahme.
We studied oscillatory mechanisms of memory formation in 48 younger and 51 older adults in an intentional associative memory task with cued recall. While older adults showed lower memory performance than young adults, we found subsequent memory effects (SME) in alpha/beta and theta frequency bands in both age groups. Using logistic mixed effects models, we investigated whether interindividual differences in structural integrity of key memory regions could account for interindividual differences in the strength of the SME. Structural integrity of inferior frontal gyrus (IFG) and hippocampus was reduced in older adults. SME in the alpha/beta band were modulated by the cortical thickness of IFG, in line with its hypothesized role for deep semantic elaboration. Importantly, this structure–function relationship did not differ by age group. However, older adults were more frequently represented among the participants with low cortical thickness and consequently weaker SME in the alpha band. Thus, our results suggest that differences in the structural integrity of the IFG contribute not only to interindividual, but also to age differences in memory formation.