Biology subjects

Frohlich, F.

Publications and source records attributed to Frohlich, F..

6 recordsLinked to original sources

bioRxiv2018

Low-frequency direct cortical stimulation of left superior frontal gyrus enhances working memory performance

The neural substrates of working memory are spread across prefrontal, parietal and cingulate cortices and are thought to be coordinated through low frequency cortical oscillations in the theta (3 - 8 Hz) and alpha (8 - 12 Hz) frequency bands. While the functional role of many subregions have been elucidated using neuroimaging studies, the role of superior frontal gyrus (SFG) is not yet clear. Here, we combined electrocorticography and direct cortical stimulation in three patients implanted with subdural electrodes to assess if superior frontal gyrus is indeed involved in working memory. We found left SFG exhibited task-related modulation of oscillations in the theta and alpha frequency bands specifically during the encoding epoch. Stimulation at the frequency matched to the endogenous oscillations resulted in reduced reaction times in all three participants. Our results support the causal role of SFG in working memory and suggest that SFG may coordinate working memory through low-frequency oscillations thus bolstering the feasibility of targeting oscillations for restoring cognitive function.

neuroscience↗

bioRxiv2018

Non-linear transfer characteristics of stimulation and recording hardware account for spurious low-frequency artifacts during amplitude modulated transcranial alternating current stimulation (AM-tACS)

Amplitude modulated transcranial alternating current stimulation (AM-tACS) has been recently proposed as a possible solution to overcome the pronounced stimulation artifact encountered when recording brain activity during tACS. In theory, AM-tACS does not entail power at its modulating frequency, thus avoiding the problem of spectral overlap between brain signal of interest and stimulation artifact. However, the current study demonstrates how weak non-linear transfer characteristics inherent in stimulation and recording hardware can reintroduce spurious artifacts at the modulation frequency. The input-output transfer functions (TFs) of different stimulation setups were measured. The setups included basic recordings of signal-generator and stimulator outputs as well as M/EEG phantom measurements. 6th-degree polynomial regression models were fitted to model the input-output TFs of each setup. The resulting TF models were applied to digitally generated AM-tACS signals to predict the location of spurious artifacts in the spectrum. All four setups measured for the study exhibited low-frequency artifacts at the modulation frequency and its harmonics when recording AM-tACS. Fitted TF models showed non-linear contributions significantly different from zero (all p < .05) and successfully predicted the frequency of artifacts observed in AM-signal recordings. Results suggest that even weak non-linearities of stimulation and recording hardware can lead to spurious artifacts at the modulation frequency and its harmonics. Thus, findings emphasize the need for more linear stimulation devices for AM-tACS and careful analysis procedures, which take into account these low-frequency artifacts to avoid confusion with effects of AM-tACS on the brain.

neuroscience↗

bioRxiv2017

Theta Oscillations Organize Spiking Activity in Higher-Order Visual Thalamus during Sustained Attention

Higher-order visual thalamus plays a fundamental but poorly understood role in attention-demanding tasks. To investigate how neuronal dynamics in higher-order visual thalamus are modulated by sustained attention, we performed multichannel electrophysiological recordings in the lateral posterior-pulvinar complex (LP/pulvinar) in the ferret (Mustela putorius furo). We recorded single unit activity and local field potential during the performance of the 5-choice serial reaction time task (5-CSRTT) that is used in both humans and animals as an assay of sustained attention. We found that half of the units exhibited an increasing firing rate during the delay period before stimulus onset (attention-modulated units). In contrast, the non-attention-modulated units responded to the stimulus, but not during the delay period. Spike-field coherence of only the attention-modulated neurons significantly increased from the start of the delay period until screen touch, predominantly in the theta frequency band. In addition, theta power and theta-gamma phase-amplitude coupling were elevated throughout the delay period. Our findings suggest that the theta oscillation plays a central role in orchestrating thalamic signaling during sustained attention.\n\nSignificanceImpaired sustained attention can be deadly, as illustrated by the number of motor vehicle accidents that are caused by drivers not reacting quickly enough to unexpected events on the road. Understanding how electrical signaling in higher-order visual nuclei, such as the LP/pulvinar, is modulated during tasks that require sustained attention is an important step in achieving a mechanistic understanding of sustained attention, which will eventually lead to new strategies to prevent and treat impairment in sustained attention.

neuroscience↗

bioRxiv2017

Stochastic Resonance Mediates the State-Dependent Effect of Periodic Stimulation on Cortical Alpha Oscillations

Brain stimulation can be used to engage and modulate rhythmic activity in cortical networks. However, the outcomes have been shown to be impacted by behavioral states and endogenous brain fluctuations. To better understand how this intrinsic oscillatory activity controls the brains susceptibility to stimulation, we analyzed a computational model of the thalamocortical system in both the rest and task states, to identify the mechanisms by which endogenous alpha oscillations (8Hz-12Hz) are impacted by periodic stimulation. Our analysis shows that the differences between different brain states can be explained by a passage through a bifurcation combined to stochastic resonance - a mechanism whereby irregular fluctuations amplify the response of a nonlinear system to weak signals. Indeed, our findings suggest that modulating brain oscillations is best achieved in states of low endogenous rhythmic activity, and that irregular state-dependent fluctuations in thalamic inputs shape the susceptibility of cortical population to periodic stimulation.

bioinformatics↗

bioRxiv2017

Arousal Dependent Modulation Of Thalamo-Cortical Functional Interaction

Summary paragraphCognition and behavior emerge from the dynamic interaction of widely distributed, but functionally specialized brain networks. However, it remains unclear how network-level interactions dynamically reorganize to support ever-shifting cognitive and behavioral demands. Here, we investigate how the interaction between posterior parietal cortex (PPC) and lateral posterior (LP) / Pulvinar is shaped by ongoing fluctuations in pupil-linked arousal, which is a non-invasive measure related to neuromodulatory tone in the brain. We found that fluctuations in pupil-linked arousal tracked the dynamic interaction between PPC and LP/Pulvinar characterized by changes in the direction and carrier frequency of oscillatory interaction. Active visual exploration by saccadic eye movements elicited similar transitions in thalamo-cortical interaction. These findings suggest a common network substrate of both spontaneous activity and active vision. Thus, neuromodulators may play a role in dynamically sculpting the patterns of thalamo-cortical functional interaction that underlie visual processing.

neuroscience↗

bioRxiv2016

Scalable parameter estimation for genome-scale biochemical reaction networks

Mechanistic mathematical modeling of biochemical reaction networks using ordinary differential equation (ODE) models has improved our understanding of small-and medium-scale biological processes. While the same should in principle hold for large-and genome-scale processes, the computational methods for the analysis of ODE models which describe hundreds or thousands of biochemical species and reactions are missing so far. While individual simulations are feasible, the inference of the model parameters from experimental data is computationally too intensive. In this manuscript, we evaluate adjoint sensitivity analysis for parameter estimation in large scale biochemical reaction networks. We present the approach for time-discrete measurement and compare it to state-of-the-art methods used in systems and computational biology. Our comparison reveals a significantly improved computational efficiency and a superior scalability of adjoint sensitivity analysis. The computational complexity is effectively independent of the number of parameters, enabling the analysis of large-and genome-scale models. Our study of a comprehensive kinetic model of ErbB signaling shows that parameter estimation using adjoint sensitivity analysis requires a fraction of the computation time of established methods. The proposed method will facilitate mechanistic modeling of genome-scale cellular processes, as required in the age of omics.\n\nAuthor SummaryIn this manuscript, we introduce a scalable method for parameter estimation for genome-scale biochemical reaction networks. Mechanistic models for genome-scale biochemical reaction networks describe the behavior of thousands of chemical species using thousands of parameters. Standard methods for parameter estimation are usually computationally intractable at these scales. Adjoint sensitivity based approaches have been suggested to have superior scalability but any rigorous evaluation is lacking. We implement a toolbox for adjoint sensitivity analysis for biochemical reaction network which also supports the import of SBML models. We show by means of a set of benchmark models that adjoint sensitivity based approaches unequivocally outperform standard approaches for large-scale models and that the achieved speedup increases with respect to both the number of parameters and the number of chemical species in the model. This demonstrates the applicability of adjoint sensitivity based approaches to parameter estimation for genome-scale mechanistic model. The MATLAB toolbox implementing the developed methods is available from http://ICB-DCM.github.io/AMICI/.

systems biology↗