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Tiemann, L.

Publications and source records attributed to Tiemann, L..

15 recordsLinked to original sources

EEG Reveals Robust Within-Person but Unstable Between-Person Neural Encoding of Pain

The perception of pain varies both within and between individuals, even when sensory input remains constant. Understanding how the brain encodes these intra- and interindividual variations is essential for elucidating the neural mechanisms of pain in health and disease. Yet, previous findings have been inconsistent, and their robustness, in terms of both repeatability and replicability, has remained unclear. Here, we used electroencephalography (EEG) in 161 healthy participants to re-investigate the neural correlates of intra- and inter-individual variations in the perception of brief painful stimuli, independent of stimulus intensity. Using Bayesian multivariate multi-model regression, we related pain ratings to evoked (N1, N2, P2) and induced oscillatory responses (alpha, beta, gamma). To assess the robustness of our findings, the experiment was repeated after four weeks in the same participants and replicated in an independent cohort (n = 111). This design allowed us to examine within-person variability at both short (moment-to-moment) and long (day-to-day) timescales. Inter-individual differences in pain were primarily associated with the P2 response, an effect that was repeatable in the same but not replicable in the independent cohort. In contrast, intra-individual variations were explained by a multicomponent EEG pattern that was both repeatable across time and replicable across cohorts. These findings demonstrate that intra- and inter-individual variability in pain is differentially encoded in the human brain and reveal greater robustness of within-person brain-behavior associations. EEG markers may therefore be more suitable for tracking longitudinal changes in pain within individuals than for comparing pain sensitivity across individuals.

neuroscience↗

EEG-based analysis of intrinsic brain network function in chronic pain: Insights from a comprehensive multi-data set study

Chronic pain is associated with alterations in brain function. A better understanding of these alterations might help to develop new approaches for the diagnosis, prediction, and treatment of chronic pain. Here, we analyzed associations between chronic pain and alterations of intrinsic brain network function using resting-state electroencephalography. We included data from 537 people with chronic pain obtained from various research groups worldwide. We found strong evidence for associations between pain intensity and intrinsic brain network connectivity, but the replicability of these associations in independent data was mostly low. However, a mega-analysis revealed associations of chronic pain with salience-somatomotor network connectivity at theta frequencies and with more complex patterns of intrinsic brain network connectivity. These findings provide novel insights into brain network function in chronic pain. Moreover, they highlight the need for collaborative multi-center studies, which can be guided by the present approach to promote replicability and consistency of findings.

neuroscience↗

Assessing the balance between excitation and inhibition in chronic pain through the aperiodic component of EEG

Chronic pain is a prevalent and debilitating condition whose neural mechanisms are incompletely understood. An imbalance of cerebral excitation and inhibition (E/I), particularly in the medial prefrontal cortex (mPFC), is believed to represent a crucial mechanism in the development and maintenance of chronic pain. Thus, identifying a non-invasive, scalable marker of E/I could provide valuable insights into the neural mechanisms of chronic pain and aid in developing clinically useful biomarkers. Recently, the aperiodic component of the electroencephalography (EEG) power spectrum has been proposed to represent a non-invasive proxy for E/I. We, therefore, assessed the aperiodic component in the mPFC of resting-state EEG recordings in 149 people with chronic pain and 115 healthy participants. We found robust evidence against differences in the aperiodic component in the mPFC between people with chronic pain and healthy participants, and no correlation between the aperiodic component and pain intensity. These findings were consistent across different subtypes of chronic pain and were similarly found in a whole-brain analysis. Their robustness was supported by preregistration and multiverse analyses across many different methodological choices. Together, our results suggest that the EEG aperiodic component does not differentiate between people with chronic pain and healthy individuals. These findings and the rigorous methodological approach can guide future studies investigating non-invasive, scalable markers of cerebral dysfunction in people with chronic pain and beyond.

neuroscience↗

Assessing the predictive value of peak alpha frequency for the sensitivity to pain

Pain perception varies considerably between and within individuals. How the brain determines these variations has yet to be fully understood. The peak frequency of alpha oscillations (PAF) has recently been shown to predict an individuals sensitivity to longer-lasting experimental and clinical pain. PAF is, thus, discussed as a potential biomarker and novel target for neuromodulatory treatments of pain. Here, we scrutinized the generalizability of the relation between PAF and pain. We applied brief painful laser stimuli to 159 healthy participants and related inter- and intra-individual variations of pain perception to PAF measured with electroencephalography. Comprehensive multiverse analyses across two sessions did not provide consistent evidence for a predictive role of PAF for brief experimental pain. This indicates that the relationship between PAF and pain does not generalize to all types of pain and calls for a systematic exploration of the relationship between PAF, pain perception, and other neuropsychiatric symptoms.

neuroscience↗

Effects of Centrally Acting Analgesics on Resting-State Electroencephalography Biomarker Candidates of Chronic Pain

Resting-state electroencephalography (rsEEG) holds promise as a biomarker of chronic pain. However, the impact of centrally acting analgesics like opioids, antiepileptics, and antidepressants on rsEEG remains unclear. This confounds and limits the interpretability of previous studies and questions the validity of rsEEG biomarker candidates of chronic pain. We, therefore, aimed to elucidate the effects of opioids, antiepileptics, and antidepressants on common rsEEG biomarker candidates of chronic pain. To this end, we analyzed two large, independent rsEEG datasets, including 220 people with chronic pain. We performed preregistered multivariate Bayesian analyses and controlled for the potential confounds of age, pain intensity, and depression. The results predominantly provided evidence against effects of centrally acting analgesics on peak alpha frequency, oscillatory power in different frequency bands, and connectivity-based network measures. Although these findings do not rule out any effects of analgesics on rsEEG, they argue against medium to large effects of centrally acting analgesics on rsEEG. Thus, the findings strengthen the validity of rsEEG biomarker candidates of chronic pain and might thereby help to develop clinically valuable biomarkers of chronic pain.

neuroscience↗

NFixDB (Nitrogen Fixation DataBase) - A Comprehensive Integrated Database for Robust 'Omics Analysis of Diazotrophs

Biological nitrogen fixation is a fundamental biogeochemical process that transforms that provides fixed biologically available nitrogen by diazotrophic microbes. Diazotrophs anaerobically fix nitrogen using the nitrogenase enzyme which has three different gene clusters: 1) molybdenum nitrogenase (nifDHK) is the most abundant, followed by its alternatives 2) vanadium nitrogenase (vnfDHK), and 3) iron nitrogenase (anfDHK). Multiple databases have been constructed as resources for diazotrophic omics analysis; however, an integrated database based on whole genome references does not exist. Here, we present NFixDB (Nitrogen Fixation DataBase), a comprehensive integrated whole genome based database for diazotrophs, which includes all nitrogenases (nifDHK, vnfDHK, anfDHK) and nitrogenase-like enzymes (e.g., nflDH) linked to ribosomal operons (16S-5.8S-23S). NFixDB was computed using Hidden Markov Models (HMMs) against the entire whole genome based Genome Taxonomy Database (GTDB R214), providing searchable reference HMMs for all nitrogenase and nitrogenase-like genes, complete ribosomal operons, both GTDB and NCBI/RefSeq taxonomy, and an SQL database for querying matches. We compared NFixDB to nifH databases from Buckley, Zehr, Mise, and FunGene finding extensive evidence of nifH, in addition to vnfH and nflH. NFixDB contains more than 4,000 verified nifHDK sequences contained on 50 unique phyla of bacteria and archaea. NFixDB offers the first comprehensive nitrogenase database available to researchers.

bioinformatics↗

DISCOVER-EEG: an open, fully automated EEG pipeline for biomarker discovery in clinical neuroscience

Biomarker discovery in neurological and psychiatric disorders critically depends on reproducible and transparent methods applied to large-scale datasets. Electroencephalography (EEG) is a promising tool for identifying biomarkers. However, recording, preprocessing, and analysis of EEG data is time-consuming and researcher- dependent. Therefore, we developed DISCOVER-EEG, an open and fully automated pipeline that enables easy and fast preprocessing, analysis, and visualization of resting state EEG data. Data in the Brain Imaging Data Structure (BIDS) standard are automatically preprocessed, and physiologically meaningful features of brain function (including oscillatory power, connectivity, and network characteristics) are extracted and visualized using two open-source and widely used Matlab toolboxes (EEGLAB and FieldTrip). We tested the pipeline in two large, openly available datasets, the LEMON dataset, containing 213 EEG recordings of healthy participants, and the TDBRAIN dataset, containing 1274 EEG recordings, mainly from patients with a psychiatric condition. Additionally, we performed an exploratory analysis of the LEMON dataset that could inspire biomarkers of healthy aging. Thus, the DISCOVER-EEG pipeline facilitates the aggregation, reuse, and analysis of large EEG datasets, promoting open and reproducible research on brain function.

neuroscience↗

MerCat2: a versatile k-mer counter and diversity estimator for database-independent property analysis obtained from omics data

SummaryMerCat2 ("Mer - Catenate2") is a versatile, parallel, scalable and modular property software package for robustly analyzing features in omics data. Using massively parallel sequencing raw reads, assembled contigs, and protein sequences from any platform as input, MerCat2 performs k-mer counting of any length k, resulting in feature abundance counts tables, quality control reports, protein feature metrics, ecological diversity metrics, and graphical representation (i.e., PCA). MerCat2 allows for direct analysis of data properties in a database-independent manner that initializes all data, which other profilers and assembly-based methods cannot perform. MerCat2 represents an integrated tool to illuminate omics data within a sample for rapid cross-examination and comparisons. Availability and implementationMerCat2 is written in Python and distributed under a BSD-3 license. The source code of MerCat2 is freely available at https://github.com/raw-lab/mercat2. MerCat2 is compatible with Python 3 on Mac OS X and Linux. MerCat2 can also be easily installed using bioconda: conda install MerCat2. ContactRichard Allen White III, UNC Charlotte, rwhit101@uncc.edu Supplementary informationSupplementary data are available online.

bioinformatics↗

Local brain oscillations and inter-regional connectivity differentially serve sensory and expectation effects on pain

Pain emerges from the integration of sensory information about threats and contextual information such as an individuals expectations. However, how sensory and contextual effects on pain are served by the brain is not fully understood so far. To address this question, we applied brief painful stimuli to 40 healthy human participants and independently varied stimulus intensity and expectations. Concurrently, we recorded electroencephalography. We assessed local oscillatory brain activity and inter-regional functional connectivity in a network of six brain regions playing key roles in the processing of pain. We found that sensory information predominantly influenced local brain oscillations. In contrast, expectations exclusively influenced inter-regional connectivity. Specifically, expectations altered connectivity at alpha (8-12 Hz) frequencies from prefrontal to somatosensory cortex. Moreover, discrepancies between sensory information and expectations, i.e., prediction errors, influenced connectivity at gamma (60-100 Hz) frequencies. These findings reveal how fundamentally different brain mechanisms serve sensory and contextual effects on pain. TeaserSensory and expectation effects on pain are implemented by fundamentally different brain mechanisms.

neuroscience↗

Temporal-spectral signaling of sensory information and expectations in the cerebral processing of pain

The perception of pain is shaped by somatosensory information about threat. However, pain is also influenced by an individuals expectations. Such expectations can result in clinically relevant modulations and abnormalities of pain. In the brain, sensory information, expectations (predictions), and discrepancies thereof (prediction errors) are signaled by an extended network of brain areas. These brain areas generate evoked potentials and oscillatory responses at different latencies and frequencies. Recent evidence has provided first insights into how oscillatory responses at different frequencies signal predictions and prediction errors. However, a comprehensive picture of how evoked and oscillatory brain responses signal sensory information, predictions, and prediction errors in the processing of pain is lacking so far. We therefore built upon a neuroimaging study which investigated the spatial signalling of sensory information, predictions and predictions errors in the processing of pain (Geuter et al., 2017). To complement and extend this study, we applied brief painful stimuli to 48 healthy human participants and independently modulated sensory information (stimulus intensity) and expectations of pain intensity while measuring brain activity using electroencephalography (EEG). Pain ratings confirmed that pain intensity was shaped by both sensory information and expectations. In contrast, Bayesian analyses revealed that stimulus-induced EEG responses at different latencies (the N1, N2, and P2 components) and frequencies (alpha, beta, and gamma oscillations) were shaped by sensory information but not by expectations. Expectations, however, shaped alpha and beta oscillations before the painful stimuli. These findings indicate that commonly analyzed EEG responses to painful stimuli are more involved in signaling sensory information than in signaling expectations or mismatches of sensory information and expectations. Moreover, they indicate that the effects of expectations on pain are served by brain mechanisms which differ from those conveying effects of sensory information on pain.

neuroscience↗

Biochar alters soil properties, microbial community diversity, and enzyme activities, while decreasing conifer performance

Biochars are porous charcoal-like materials that can enhance soil health and plant growth, but its use has not been adequately evaluated in woody cropping systems. We set up an experimental Christmas tree plantation on a Marlette series soil amended with two types of biochar and conducted two studies on the impacts of biochar on the agroecosystem over three years following establishment. The first study investigated the effects of biochar on plant performance, soil physicochemical properties and extracellular enzyme activities, while the second investigated the response of the root-associated fungal community. Both biochars stimulated five extracellular enzyme activities, with increases of between 67% and 446%. Structural equation modelling identified increases to dissolved organic carbon and soil moisture as potential mechanisms of biochars effects on enzyme activities. Tree growth and survival were negatively affected by biochar application, depending on the tree species and biochar applied, which may have been due to induced nitrogen limitation. High-throughput sequencing showed that biochar decreased the diversity of root-associated fungal communities, with the ectomycorrhizal species Wilcoxina mikolae reaching levels of hyper-dominance on balsam fir in response to one of the biochars. Further studies should investigate how biochar can be harnessed to remediate specific soil quality issues or restructure soil ecosystems in ways that improve crop performance.

ecology↗

Dynamics of brain function in chronic pain patients assessed by microstate analysis of resting-state electroencephalography

Chronic pain is a highly prevalent and severely disabling disease, which is associated with substantial changes of brain function. Such changes have mostly been observed when analyzing static measures of brain activity during the resting-state. However, brain activity varies over time and it is increasingly recognized that the temporal dynamics of brain activity provide behaviorally relevant information in different neuropsychiatric disorders. Here, we therefore investigated whether the temporal dynamics of brain function are altered in chronic pain. To this end, we applied microstate analysis to eyes-open and eyes-closed resting-state electroencephalography (EEG) data of 101 patients suffering from chronic pain and 88 age- and gender-matched healthy controls. Microstate analysis describes EEG activity as a sequence of a limited number of topographies termed microstates, which remain stable for tens of milliseconds. Our results revealed that sequences of 5 microstates, labelled with the letters A to E, described resting-state brain activity in both groups and conditions. Bayesian analysis of the temporal characteristics of microstates revealed that microstate D has a less predominant role in patients than in healthy participants. This difference was consistently found in eyes-open and eyes-closed EEG recordings. No evidence for differences in other microstates was found. As microstate D has been previously related to attentional networks and functions, abnormalities of microstate D might relate to dysfunctional attentional processes in chronic pain. These findings add to the understanding of the pathophysiology of chronic pain and might eventually contribute to the development of an EEG-based biomarker of chronic pain.

neuroscience↗

MetaFunPrimer: primer design for targeting genes observed in metagenomes

High throughput primer design is needed to simultaneously design primers for multiple genes of interest, such as a group of functional genes. We have developed MetaFunPrimer, a bioinformatic pipeline to design primer targets for genes of interests, with a prioritization based on ranking the presence of gene targets in references, such as metagenomes. MetaFunPrimer takes inputs of protein and nucleotide sequences for gene targets of interest accompanied by a set of reference metagenomes or genomes for determining genes of interest. Its output is a set of primers that may be used to amplify genes of interest. To demonstrate the usage and benefits of MetaFunPrimer, a total of 78 HT-qPCR primer pairs were designed to target observed ammonia monooxygenase subunit A (amoA) genes of ammonia-oxidizing bacteria (AOB) in 1,550 soil metagenomes. We demonstrate that these primers can significantly improve targeting of amoA-AOB genes in soil metagenomes compared to previously published primers. IMPORTANCEAmplification-based gene characterization allows for sensitive and specific quantification of functional genes. Often, there is a large diversity of genes represented for a function of interest, and multiple primers may be necessary to target associated genes. Current primer design tools are limited to designing primers for only a few genes of interest. MetaFunPrimer allows for high throughput primer design for functional genes of interest and also allows for ranking gene targets by their presence and abundance in environmental datasets. This tool enables high throughput qPCR approaches for characterizing functional genes.

bioinformatics↗

Modulating Brain Rhythms of Pain using Transcranial Alternating Current Stimulation (tACS)? A Sham-controlled Study in Healthy Human Participants

Pain protects the body. However, pain can also occur for longer periods without serving protective functions. Such chronic pain conditions are difficult to treat. Thus, a better understanding of the underlying neural mechanisms and new approaches for the treatment of pain are urgently needed. Here, we investigated a causal role of oscillatory brain activity for pain and explored the potential of transcranial alternating current stimulation (tACS) as a new treatment approach for pain. To this end, we investigated whether tACS can modulate pain and pain-related autonomic activity in 29 healthy human participants using a tonic heat pain paradigm as an experimental model of chronic pain. In 6 recording sessions, participants received tACS over prefrontal or somatosensory cortices at alpha or gamma frequencies or sham tACS. During tACS, pain ratings and autonomic responses were collected. TACS did not modulate pain intensity, the stability of pain ratings or the translation of the noxious stimulus into pain. Likewise, tACS did not change autonomic responses. Bayesian statistics further indicated a lack of tACS effects in most conditions. The only exception was alpha tACS over somatosensory cortex where evidence for tACS effects was inconclusive. Taken together, the present study did not find significant tACS effects on tonic experimental pain in healthy human participants. However, considering the conceptual plausibility of using tACS to modulate pain and the urgent need for novel pain treatments, further tACS studies are warranted. Based on the present findings, such studies might apply refined stimulation protocols targeting alpha oscillations in somatosensory cortices.

neuroscience↗

Brain dysfunction in chronic pain patients assessed by resting-state electroencephalography

Chronic pain is a common and severely disabling disease whose treatment is often unsatisfactory. Insights into the brain mechanisms of chronic pain promise to advance the understanding of the underlying pathophysiology and might help to develop disease markers and novel treatments. Here, we systematically and comprehensively exploited the potential of electroencephalography (EEG) to determine abnormalities of brain function during the resting state in chronic pain. To this end, we performed state-of-the-art analyses of oscillatory brain activity, brain connectivity and brain networks in 101 patients of either sex suffering from chronic pain. The results show that global and local measures of brain activity did not differ between chronic pain patients and a healthy control group. However, we observed significantly increased connectivity at theta (4 - 8 Hz) and gamma (> 60 Hz) frequencies in frontal brain areas as well as global network reorganization at gamma frequencies in chronic pain patients. Furthermore, a machine learning algorithm could differentiate between patients and healthy controls with an above-chance accuracy of 57%, mostly based on frontal connectivity. These results implicate increased theta and gamma synchrony in frontal brain areas in the pathophysiology of chronic pain. While substantial challenges concerning accuracy, specificity and validity of potential EEG-based disease markers remain to be overcome, our study identifies abnormal frontal synchrony at theta and gamma frequencies as promising targets for non-invasive brain stimulation and/or neurofeedback approaches.

neuroscience↗