Biology subjects

Lorenz, R.

Publications and source records attributed to Lorenz, R..

At least 19 recordsLinked to original sources

bioRxiv2026

Coordinate-Based fMRI Meta-Analyses of Episodic Memory Encoding and Retrieval in Depression

BackgroundDepression is a mood disorder frequently associated with episodic memory impairment. However, it remains unclear whether functional brain activity differs between depressed and non-depressed individuals during encoding or retrieval of autobiographical or non-autobiographical memories. Clarifying these differences is important for refining theoretical models of memory impairment in depression and, potentially, for developing targeted interventions. MethodsWe conducted three coordinate-based meta-analyses examining encoding and retrieval of autobiographical and non-autobiographical memory in control participants and individuals with current, remitted, or subthreshold depression, or those at risk for depression. Studies were identified via database searches and analysed using Seed-based d Mapping. ResultsWe included coordinates from 22 fMRI studies. During encoding, depression was associated with reduced activity in the caudate, the salience network, the frontoparietal executive control network, and motor-related areas (11 studies, N = 542). During non-autobiographical retrieval, depression was associated with higher activity in the right inferior frontal gyrus (7 studies, N = 345). During autobiographical retrieval (10 studies, N = 423), depression was associated with reduced activity in the right insula and fusiform gyrus, alongside increased activity in the left anterior cingulate cortex and the left middle frontal gyrus. Between-study heterogeneity was low and no evidence for publication bias was found. DiscussionOur results indicate that depression may be associated with impaired salience integration during encoding and autobiographical retrieval. In contrast, during non-autobiographical retrieval, increased frontal activity suggests a more vigilant or self-monitoring retrieval mode. Functional brain activity changes in depression therefore appear stage- and content-specific.

neuroscience↗

bioRxiv2025

Vascular draining confounds laminar decoding in fMRI

Laminar fMRI using GE-BOLD is vulnerable to spatial blurring from intracortical veins, while multivariate pattern analysis (MVPA) is often assumed to mitigate these biases. Yet, this assumption has not been systematically investigated. We thus developed a mechanistic laminar response model that simulates voxel-wise patterns across cortical depths, incorporating a vascular draining model. We conducted simulations in which the ground-truth signal originated in a single, several, or across all layers, and applied standard MVPA decoding before and after deconvolution of the draining effect. Decoding accuracies were consistently influenced by draining veins: deep-origin signals yielded above-chance decoding in superficial layers, and null scenarios produced false positives in middle or deep layers. Vascular deconvolution enhanced specificity in single-layer cases but did not resolve ambiguities in null decoding profiles. Simulating six thinner layers improved decoding accuracies, especially in the deconvolved signal scenarios. These findings demonstrate that multivariate techniques are not inherently immune to vascular biases, but also demonstrate that careful modeling can help correct draining effects.

neuroscience↗

bioRxiv2025

Challenges in replicating layer-specificity of working memory processes in human dlPFC

Although working memory reliably activates the dorsolateral prefrontal cortex (dlPFC), the functional contributions of its cortical layers in humans remain unclear. A seminal study reported a laminar dissociation in dlPFC--superficial layers engaged during working-memory manipulation and deeper layers during motor response execution. Like many current layer-resolved studies, that work relied on several manual and semi-manual processing steps, including the selection of regions of interest. We conducted a preregistered replication in 21 human participants using an automated and fully reproducible analysis pipeline. While we replicated the superficial-layer effect during manipulation, we found no evidence for preferential deep-layer activation during response execution. This biologically plausible result refines current models of laminar organization in the human prefrontal cortex and aligns human evidence with the more heterogeneous animal literature. By demonstrating reproducibility through preregistration and automation, this work establishes a benchmark for laminar analyses in cognitive neuroscience.

neuroscience↗

bioRxiv2024

Spatial and feature-selective attention interact to drive selective coding in frontoparietal cortex

Attention enables the selective processing of relevant information. Two types of selective attention, spatial and feature attention, have separable neural effects but in real life are often used together. Here, we asked how these types of attention interact to affect information coding in a frontoparietal multiple-demand (MD) network, essential for attentional control. Using functional magnetic resonance imaging (fMRI) with multivariate pattern analysis, we examined how covert attention to object features (colour or shape) and spatial locations (left or right) influences coding of task-related stimulus information. We found that spatial and feature attention interacted multiplicatively on information coding in MD and visual regions, such that there was above-chance decoding of the attended feature of the attended object and no detectable coding of visually equivalent but behaviourally irrelevant aspects of the visual display. The attended information had a multidimensional neural representation, with stimulus information (e.g., colour) and discrimination difficulty (distance from the categorical decision boundary) reflected in separate dimensions. Rather than boosting processing of whole objects or relevant features across space, our results suggest neural activity reflects precise tuning to relevant information, indicating a highly selective control process that codes behaviourally relevant information across multiple dimensions.

neuroscience↗

bioRxiv2024

Neural variability compresses with increasing belief precision during Bayesian inference

To make optimal decisions, intelligent agents must learn latent environmental states from discrete observations. Bayesian frameworks argue that integration of evidence over time allows us to refine our state belief by reducing uncertainty about alternate possibilities. How is this increasing belief precision during learning reflected in the brain? We propose that moment-to-moment neural variability should scale with the degree of reduction of uncertainty during learning. In a sample of 47 healthy adults, we found that BOLD signal variability (SDBOLD, as measured with functional MRI) indeed compressed with successive exposure to decision-related evidence. Crucially, more accurate participants expressed greater SDBOLD compression primarily in Default Mode Network regions, possibly reflecting the increasing precision of their latent state belief during more efficient learning. Further, computational modeling of behavior suggested that more accurate subjects held a more unbiased (flatter) prior belief over possible states that allowed for larger uncertainty reduction during learning, which was directly reflected in SDBOLD changes. Our results provide first evidence that moment-to-moment neural variability compresses with increasing belief precision during effective learning, proposing a flexible mechanism for how we come to learn the probabilistic nature of the world around us.

neuroscience↗

bioRxiv2023

Dynamic layer-specific processing in the prefrontal cortex during working memory

The dorsolateral prefrontal cortex (dlPFC) is reliably engaged in working memory (WM). Evidence from non-human primates indicates that the dlPFC comprises different cytoarchitectonic layers that play distinct roles in WM subprocesses; yet the functional role of the dlPFCs laminar circuitry in human WM is not well understood. In this study, participants completed a delayed-match-to-sample WM task while undergoing functional magnetic resonance imaging (fMRI) at ultra-high resolution, which allowed us to examine layer-specific responses of the dlPFC to manipulations in WM load and motor response. We conducted univariate and multivariate analyses across all periods of the WM task: encoding, delay and retrieval. First, we observed that superficial layers activate stronger than deep layers to higher WM load during the delay period. This aligns with earlier work showing preferential superficial layer activation to WM manipulation and as such may indicate lamina-specific activation of the frontoparietal network to heightened task demands more generally. Second, we found that superficial layers show higher decoding of WM load differences than deep layers during the retrieval period. In this context, we could show that decoding of WM load in the superficial layer exhibited dynamic changes across the encoding, delay and retrieval period of the task, indicative of separate WM control processes that occur on the WM content. Last, we found that superficial and deep layers are both non-differentially involved in the motor response, contradicting earlier findings of a preferential deep layer activation in humans. Taken together, our results provide new insights into the functional laminar circuitry of the dlPFC during WM and provide further support for a dynamic account of dlPFC coding.

neuroscience↗

bioRxiv2023

A guide to computational cotranscriptional folding featuring the SRP RNA

Although RNA molecules are synthesized via transcription, little is known about the general impact of cotranscriptional folding in vivo. We present different computational approaches for the simulation of changing structure ensembles during transcription, including interpretations with respect to experimental data from literature. Specifically, we analyze different mutations of the E.coli SRP RNA, which has been studied comparatively well in previous literature, yet the details of which specific metastable structures form, as well as when they form are still under debate. Here, we combine thermodynamic and kinetic, deterministic and stochastic models with automated and visual inspection of those systems to derive the most likely scenario of which substructures form at which point during transcription. The simulations do not only provide explanations for present experimental observations, but also suggest previously unnoticed conformations that may be verified through future experimental studies.

bioinformatics↗

bioRxiv2023

Salt corrections for RNA secondary structures in the ViennaRNA package

BackgroundRNA features a highly negatively charged phosphate backbone that attracts a of cloud counter-ions that reduce the electrostatic repulsion in a concentration dependent manner. Ion concentrations thus have a large influence on folding and stability of RNA structures. Despite their well-documented effects, salt effects are not handled by currently available secondary stucture prediction algorithms. Combining Debye-Huckel potentials for line charges and Mannings counter-ion condensation theory, Einert et al. [Biophys. J. 100: 2745-2753 (2011)] modeled the energetic effects contributions monovalent cations on loops and helices. ResultsThe model of Einert et al. is adapted to match the structure of the dynamic programming recursion of RNA secondary structure prediction algorithms. An empirical term describing the dependence salt dependence of the duplex initiation energy is added to improve co-folding predictions for two or more RNA strands. The slightly modified model is implemented in the ViennaRNA package in such way that only the energy parameters but not the algorithmic structure is affected. A comparison with data from the literature show that predicted free energies and melting temperatures are in reasonable agreement with experiments. ConclusionThe new feature in the ViennaRNA package makes it possible to study effects of salt concentrations on RNA folding in a systematic manner. Strictly speaking, the model pertains only to mono-valent cations, and thus covers the most important parameter, i.e., the NaCl concentration. It remains a question for future research to what extent unspecific effects of bi- and tri-valent cations can be approximated in a similar manner. AvailabilityCorrections for the concentration of monovalent cations are available in the ViennaRNA package starting from version 2.6.0.

bioinformatics↗

bioRxiv2022

DrTransformer: Heuristic cotranscriptional RNA folding using the nearest neighbor energy model

BackgroundFolding during transcription can have an important influence on the structure and function of [R]NA molecules, as regions closer to the 5 end can fold into metastable structures before potentially stronger interactions with the 3 end become available. Thermodynamic [R]NA folding models are not suitable to analyze this problem, as they can only calculate properties of the equilibrium distribution. Other software packages that simulate the kinetic process of [R]NA folding during transcription exist, but they are mostly applicable for short sequences. ResultsWe present a new algorithm that tracks changes to the [R]NA secondary structure ensemble during transcription. At every transcription step, new representative local minima are identified, a neighborhood relation is defined and transition rates are estimated for kinetic simulations. After every simulation, a part of the ensemble is removed and the remainder is used to search for new potentially relevant structures. The presented algorithm is deterministic (up to numeric instabilities of simulations), fast (in comparison with existing methods), and it is capable of folding [R]NAs much longer than 200 nucleotides. AvailabilityThis software is open-source and available at https://github.com/ViennaRNA/drtransformer.

bioinformatics↗

bioRxiv2022

The AI Neuropsychologist: Automatic scoring of memory deficits with deep learning

BackgroundMemory deficits are a hallmark of many different neurological and psychiatric conditions. The Rey-Osterrieth complex figure (ROCF) is the state-of-the-art assessment tool for neuropsychologists across the globe to assess the degree of non-verbal visual memory deterioration. To obtain a score, a trained clinician inspects a patients ROCF drawing and quantifies deviations from the original figure. This manual procedure is time-consuming, slow and scores vary depending on the clinicians experience, motivation and tiredness. MethodsHere, we leverage novel deep learning architectures to automatize the rating of memory deficits. For this, we collected more than 20k hand-drawn ROCF drawings from patients with various neurological and psychiatric disorders as well as healthy participants. Unbiased ground truth ROCF scores were obtained from crowdsourced human intelligence. This dataset was used to train and evaluate a multi-head convolutional neural network. ResultsThe model performs highly unbiased as it yielded predictions very close to the ground truth and the error was similarly distributed around zero. The neural network outperforms both online raters and clinicians. The scoring system can reliably identify and accurately score individual figure elements in previously unseen ROCF drawings, which facilitates explainability of the AI-scoring system. To ensure generalizability and clinical utility, the model performance was successfully replicated in a large independent prospective validation study that was pre-registered prior to data collection. ConclusionsOur AI-powered scoring system provides healthcare institutions worldwide with a digital tool to assess objectively, reliably and time-efficiently the performance in the ROCF test from hand-drawn images.

neuroscience↗

bioRxiv2021

Caveats to deep learning approaches to RNA secondary structure prediction

Machine learning (ML) and in particular deep learning techniques have gained popularity for predicting structures from biopolymer sequences. An interesting case is the prediction of RNA secondary structures, where well established biophysics based methods exist. The accuracy of these classical methods is limited due to lack of experimental parameters and certain simplifying assumptions and has seen little improvement over the last decade. This makes RNA folding an attractive target for machine learning and consequently several deep learning models have been proposed in recent years. However, for ML approaches to be competitive for de-novo structure prediction, the models must not just demonstrate good phenomenological fits, but be able to learn a (complex) biophysical model. In this contribution we discuss limitations of current approaches, in particular due to biases in the training data. Furthermore, we propose to study capabilities and limitations of ML models by first applying them on synthetic data (obtained from a simplified biophysical model) that can be generated in arbitrary amounts and where all biases can be controlled. We assume that a deep learning model that performs well on these synthetic, would also perform well on real data, and vice versa. We apply this idea by testing several ML models of varying complexity. Finally, we show that the best models are capable of capturing many, but not all, properties of RNA secondary structures. Most severely, the number of predicted base pairs scales quadratically with sequence length, even though a secondary structure can only accommodate a linear number of pairs.

bioinformatics↗

bioRxiv2021

Two helices control the dynamic crosstalk between the catalytic domains of LRRK2

The two major molecular switches in biology, kinases and GTPases, are both contained in the Parkinsons Disease-related Leucine-rich repeat kinase 2 (LRRK2). Using hydrogen-deuterium exchange mass spectrometry (HDX-MS) and Molecular Dynamics (MD) simulations, we generated a comprehensive dynamic allosteric portrait of the C-terminal domains of LRRK2 (LRRK2RCKW). We identified two helices that shield the kinase domain and regulate LRRK2 conformation and function. One docking helix in COR-B (Dk-Helix) tethers the COR-B domain to the C helix of the kinase domain and faces its Activation Loop, while the C-terminal helix (Ct-Helix) extends from the WD40 domain and interacts with both kinase lobes. The Ct-Helix and the N-terminus of the Dk-Helix create a "cap" that regulates the N-Lobe of the kinase domain. Our analyses reveal allosteric sites for pharmacological intervention and confirm the kinase domain as the central hub for conformational control.

biochemistry↗

bioRxiv2021

TemplateFlow: a community archive of imaging templates and atlases for improved consistency in neuroimaging

Reference anatomies of the brain and corresponding atlases play a central role in experimental neuroimaging workflows and are the foundation for reporting standardized results. The choice of such references --i.e., templates-- and atlases is one relevant source of methodological variability across studies, which has recently been brought to attention as an important challenge to reproducibility in neuroscience. TemplateFlow is a publicly available framework for human and nonhuman brain models. The framework combines an open database with software for access, management, and vetting, allowing scientists to distribute their resources under FAIR --findable, accessible, interoperable, reusable-- principles. TemplateFlow supports a multifaceted insight into brains across species, and enables multiverse analyses testing whether results generalize across standard references, scales, and in the long term, species, thereby contributing to increasing the reliability of neuroimaging results.

neuroscience↗

bioRxiv2020

Mapping the common and distinct neural correlates of visual, rule and motor conflict

Flexible behaviour requires cognitive-control mechanisms to efficiently resolve conflict between competing information and alternative actions. Whether a global neural resource mediates all forms of conflict or this is achieved within domainspecific systems remains debated. We use a novel fMRI paradigm to orthogonally manipulate rule, response and stimulus-based conflict within a full-factorial design. Whole-brain voxelwise analyses show that activation patterns associated with these conflict types are distinct but partially overlapping within Multiple Demand Cortex (MDC), the brain regions that are most commonly active during cognitive tasks. Region of interest analysis shows that most MDC sub-regions are activated for all conflict types, but to significantly varying levels. We propose that conflict resolution is an emergent property of distributed brain networks, the functional-anatomical components of which place on a continuous, not categorical, scale from domain-specialised to domain general. MDC brain regions place towards one end of that scale but display considerable functional heterogeneity.

neuroscience↗

bioRxiv2020

Preferential activation of the posterior Default-Mode Network with sequentially predictable task switches.

The default-mode network (DMN) has been primarily associated with internally-directed and self-relevant cognition. This perspective is expanding to recognise its importance in executive behaviours like switching. We investigated the effect different task-switching manipulations have on DMN activation in two studies with novel fMRI paradigms. In the first study, the paradigm manipulated visual discriminability, visuo-perceptual distance and sequential predictability during switching. Increased posterior cingulate/precuneus (PCC/PrCC) activity was evident during switching; critically, this was strongest when the occurrence of the switch was predictable. In the second study, we sought to replicate and further investigate this switch-related effect with a fully factorial design manipulating sequential, spatial and visual-feature predictability. Whole-brain analysis again identified a PCC/PrCC-centred cluster that was more active for sequentially predictable versus unpredictable switches, but not for the other predictability dimensions. We propose PCC/PrCC DMN subregions may play a prominent executive role in mapping the sequential structure of complex tasks.

neuroscience↗

bioRxiv2020

A Bayesian optimisation approach for rapidly mapping residual network function in stroke

Post-stroke cognitive and linguistic impairments are debilitating conditions, with current therapies only showing small improvements. Domain-general brain networks seem to play a critical role in stroke recovery and characterising their residual function with functional neuroimaging (fMRI) has the potential to yield biomarkers capable of guiding patient-specific rehabilitation. However, this is currently challenging in patients as such detailed characterisation requires too many different cognitive tasks. Here, we use neuroadaptive Bayesian optimisation to overcome this problem, an approach combining real-time fMRI with machine-learning. By intelligently searching across many tasks, this approach rapidly maps out patient-specific profiles of residual domain-general network function. Whereas controls have highly similar profiles, patients show idiosyncratic profiles of network abnormalities that are associated with behavioural performance. This approach can be extended to diverse brain networks and combined with brain stimulation or other therapeutics, thereby opening new avenues for precision medicine targeting diverse neurological and psychiatric conditions.

neuroscience↗

bioRxiv2020

RNAxplorer: Harnessing the Power of Guiding Potentials for Sampling of RNA Landscapes

MotivationPredicting the folding dynamics of RNAs is a computationally difficult problem, first and foremost due to the combinatorial explosion of alternative structures in the folding space. Abstractions are therefore needed to simplify downstream analyses, and thus make them computationally tractable. This can be achieved by various structure sampling algorithms. However, current sampling methods are still time consuming and frequently fail to represent key elements of the folding space. MethodWe introduce RNAxplorer, a novel adaptive sampling method to efficiently explore the structure space of RNAs. RNAxplorer uses dynamic programming to perform an efficient Boltzmann sampling in the presence of guiding potentials, which are accumulated into pseudo-energy terms and reflect similarity to already well-sampled structures. This way, we effectively steer sampling towards underrepresented or unexplored regions of the structure space. ResultsWe developed and applied different measures to benchmark our sampling methods against its competitors. Most of the measures show that RNAxplorer produces more diverse structure samples, yields rare conformations that may be inaccessible to other sampling methods and is better at finding the most relevant kinetic traps in the landscape. Thus, it produces a more representative coarse graining of the landscape, which is well suited to subsequently compute better approximations of RNA folding kinetics. Availabilityhttps://github.com/ViennaRNA/RNAxplorer/ Contactandrea.tanzer@meduniwien.ac.at, ronny@tbi.univie.ac.at

bioinformatics↗

bioRxiv2019

Pubertal Testosterone Correlates with Adolescent Impatience and Dorsal Striatal Activity

Recent self-report and behavioral studies have demonstrated that pubertal testosterone is related to an increase in risky and impulsive behavior. Yet, the mechanisms underlying such a relationship are poorly understood. Findings from both human and rodent studies point towards distinct striatal pathways including the ventral and dorsal striatum as key target regions for pubertal hormones. In this study we investigated task-related impatience of boys between 10 and 15 years of age (N = 75), using an intertemporal choice task combined with measures of functional magnetic resonance imaging and hormonal assessment. Increased levels of testosterone were associated with a greater response bias towards choosing the smaller sooner option. Furthermore, our results show that testosterone specifically modulates the dorsal, not ventral, striatal pathway. These results provide novel insights into our understanding of adolescent impulsive and risky behaviors and how pubertal hormones are related to neural processes.

neuroscience↗