HelfrichLab

𝗪𝗵𝗮𝘁 𝗶𝘀 𝗻𝗼𝗶𝘀𝗲 𝗶𝗻 𝘁𝗵𝗲 𝗯𝗿𝗮𝗶𝗻? Almost always we average responses thus equating response variability with noise. Well, we shouldn't because variability is also signal, not noise to be entirely discarded. doi.org/10.1016/j.neur…

Our new paper is out in @NatureNeuro How do large-scale brain networks route information about where/what to attend at fast timescales? High-frequency bursts facilitate fast communication for human spatial attention nature.com/articles/s4159… Using intracranial EEG recordings and spiking neural network modeling we demonstrate that transient bursts of high-frequency activity (HFAbs) in the human brain facilitate attentional performance and support fast, large-scale information routing. We show: - HFAbs are synchronized brain-wide. - HFAbs are strongly coupled to slower brain rhythms but transiently decouple during active processing, such as cue and target periods. - Network-level synchronization patterns of HFAbs reveal functional subnetworks for cue and target processing. - HFAbs in cue subnetworks predict behavioral accuracy following cue onset, and precede target subnetworks following target onset when cues are informative for target detection. - Using spiking neural network models, we show that HFAbs reflect state transitions in population activity, and a four-network model demonstrates attention-mediated fast HFAb communications enhances probability of target detection. We demonstrate that transient bursts of high-frequency activity (HFAbs) in the human brain facilitate attentional performance and support fast, large-scale information routing. Using intracranial EEG recordings and spiking neural network modeling we show: We demonstrate that transient bursts of high-frequency activity (HFAbs) in the human brain facilitate attentional performance and support fast, large-scale information routing. Using intracranial EEG recordings and spiking neural network modeling we show:

New work from the lab now published in @NeuroCellPress

#eNeuro: Kopf and colleagues directly tested whether MEG background activity reflects hyperexcitability in a patient cohort suffering from generalized epilepsy. Their results demonstrate that background activity does not constitute random noise, but reflects excitability dynamics, which define the current brain state. @HelfrichLab @TueNeuroCampus @HIHTuebingen eneuro.org/lookup/DOI/10.…

Work conducted at @TueNeuroCampus @uktuebingen @HIHTuebingen @uni_tue and funded by @HertieNeuroSci, @JungStiftung, @dfg_public and @MedTuebingen. In collab. with Pål Larsson and @linjj4. Thanks everyone! (7/7)

For more background on timescales, see @roxana_zeraati and @_rdgao excellent recent review on timescales. Also check out the summary in the ‘This Week in the Journal’ for a summary: jneurosci.org/content/44/42/… (6/7)

Lastly, several other groups recently investigated sleep timescales, including @mo_s_ameen and @Tomdonoghue or @AthinaTzovara who preprinted cool work on @biorxiv_neursci that jointly suggests that intrinsic timescales shape the brain state during sleep. (5/7)

We devised an iterative fitting approach to estimate these spectral ‘knees’ in 3 independent iEEG datasets, including publicly shared data by @Kai_J_Miller published in @PLOSCompBiol and @NatureHumBehav, which enabled validating and replicating our approach. Thanks! (4/7)

Are different sleep stages only defined by neural oscillations? Our new work led by @JannaLendner now out in JNeuro @SfNJournals demonstrates that several aperiodic (intrinsic) timescales shape the iEEG power spectrum during sleep: jneurosci.org/content/44/42/… (1/7)

Turns out: It depends… The PSD exhibits multiple intrinsic timescales (‘knees’) that shape the 1/f decay function. Some of them are state-invariant, while others were state-specific. Moreover, 1/f behaviors differ between different brain regions, such as the MTL and PFC. (3/7)

We and others (e.g., @smpzzz) had previously observed that the spectral slope tracks the hypnogram, but results across studies varied with different fitting ranges (1-20 vs 30-50 Hz). We wondered where should we fit the spectral slope to define distinct sleep stages? (2/7)

A tradeoff between efficiency and robustness in the hippocampal-neocortical memory network during human and rodent sleep doi.org/10.1016/j.pneu… #neuroscience

Work funded by @DAAD_Germany, @JungStiftung and @HertieNeuroSci at @uni_tue, @MedTuebingen, @HIHTuebingen, @TueNeuroCampus in collab. with @JannaLendner, @linjj4 and friends (6/6)

Critically, we observed that sleep rebalanced information processing capacities. Population activity was also more efficient during task engagement, while it was drastically reduced during propofol anesthesia (5/6).

Does sleep free cognitive resources for efficient next-day processing? Are processing capacities similar in human and rodent sleep? How does the sleeping brain balance efficiency and robustness? New paper led by @HahnMic to answer these questions. sciencedirect.com/science/articl… (1/6)

Check by a 5yr journey of the lab in mice & humans, just up on bioRxiv! Post-ictal symptoms can be life-threatening, & basic mechanisms are mostly unclear. 1/2 biorxiv.org/content/10.110…

For more details and background information, see some of our recent work in @TrendsCognSci sciencedirect.com/science/articl… and @ScienceAdvances science.org/doi/full/10.11… (4/4)

We provide a perspective on how their findings in rodents might translate to humans and how nonoscillatory (aperiodic) activity complements sleep oscillations. Work at @HIHTuebingen, @uktuebingen, @TueNeuroCampus and funded by @HertieNeuroSci, @JungStiftung @dfg_public (3/4)

What defines the brain state during #sleep? Can slow wave sleep be detected without slow waves? New Spotlight in @TrendsNeuro with @JannaLendner. Just in time for @ESRS_Sleep #SleepEurope2024 cell.com/trends/neurosc… (1/4)