beta burst detection algorithm  (MathWorks Inc)

Journal: bioRxiv

Article Title: Reuniens transiently synchronizes memory networks at beta frequencies

doi: 10.1101/2022.06.21.497087

Figure Lengend Snippet: A) A linear track was used with odor ports located at opposite ends where two separate four-odor sequences (A, B, C, D or W, X, Y, Z) were presented. Rats had to correctly identify the odor as either InSeq (hold ≥ 1 s) or OutSeq (hold < 1 s). B) SMI was not significantly different between Seq 1 and Seq 2. Individual rat performance is indicated by circles. C) Average running speeds less than 10 cm/s represent 80% of the data. D) Representative spectrogram with corresponding filtered LFP from theta and beta bands in prefrontal cortex (mPFC, blue) and hippocampus (HC, red) during both sequences interleaved with a short running bout. High power beta is aligned to the odor sampling period. E) Sample raw LFP (two samples from each rat within the experiment) in prefrontal cortex (blue) and hippocampus (red). Each rat is indicated with a different shade of color. Beta bursts are highlighted in gray and can be seen only during the trial (after poke in). F i ) Prefrontal-hippocampal coherence was significantly different between running periods (non-memory) and memory (odor-trials). Inset shows a difference between memory and running. Beta synchrony increases during memory while theta was lower (close to zero). F ii ) AUC shows that theta coherence was not significantly different between running and memory, while beta coherence was significantly different between running and memory. G i ) Sample bandpass beta filtered from prefrontal and hippocampal sites with a sample of 10 trials shows closely matched high amplitude beta ∼100ms after the poke in. G ii ) A zoomed in trial shows the bursty properties of beta. H) The probability of prefrontal and hippocampal beta burst occurrence are not significantly different. I) Average duration of a beta burst was not significantly different between prefrontal and hippocampal sites. J) The latency to the first beta burst was not significantly different between prefrontal and hippocampal sites. K) Prefrontal-hippocampal coherence separated based on sequential context (InSeq vs OutSeq). L) Coherence between InSeq and OutSeq trials separated based on accuracy. M) Beta AUC was significantly different across the four trail types. InSeq correct trials was significantly higher compared to InSeq incorrect , OutSeq correct , and OutSeq incorrect . Theta AUC was not significantly different across the four trial types. Abbreviations: mPFC, medial prefrontal cortex; HC, hippocampus, InSeq, in-sequence; OutSeq, out-of-sequence; SMI, sequence memory index; Seq, sequence; LFP, local field potential; AUC, area under the curve; InSeq correct , in-sequence correct, InSeq incorrect , in-sequence incorrect, OutSeq correct , out-of-sequence correct, OutSeq incorrect , out-of-sequence incorrect; θ, theta; β, beta; ns, not significant.

Article Snippet: These beta cycles were bursty in nature rather than continuous, thus we developed a beta burst detection algorithm (custom-written scripts in MATLAB, see methods).

Techniques: Sampling, Sequencing

Journal: bioRxiv

Article Title: Reuniens transiently synchronizes memory networks at beta frequencies

doi: 10.1101/2022.06.21.497087

Figure Lengend Snippet: A) SMI was not significantly different between Seq 1 and Seq 2. Individual rat performance is indicated by circles. B) Representative spectrogram with corresponding filtered LFP from theta and beta bands in reuniens (RE; purple) during both sequences with a sample running bout in between. High power beta is aligned to the odor sampling period. C i ) Reuniens-hippocampal coherence was significantly different between running periods (non-memory) and memory (odor-trials). Inset shows a difference (memory – running) and show that beta synchrony increases during memory while theta was lower (close to zero). C ii ) AUC shows that theta coherence was not significantly different between running and memory, while beta coherence was significantly different between running and memory. D) Sample raw LFP (two samples from each rat within the experiment) in reuniens (purple) and hippocampus (red). Each rat is indicated with a different shade of color. Beta bursts are highlighted in gray and can be seen only during the trial (after poke in). E i ) Sample bandpass beta filtered from reuniens and hippocampal sites with a sample of 10 trials shows reuniens beta occurs earlier than hippocampus beta. E ii ) A zoomed in trial shows the bursty properties of beta with reuniens beta occurring earlier in the trial. F) The probability of reuniens and hippocampal beta burst occurrence are significantly different with reuniens occurring earlier indicated with purple arrow. G) Average duration of a beta burst was significantly different longer in reuniens than hippocampal sites. H) The latency to the first beta burst was significantly earlier in reuniens than hippocampal sites. I) Reuniens-hippocampal coherence separated based on sequential context (InSeq vs OutSeq). J) Coherence between InSeq and OutSeq trials separated based on accuracy. K) Beta AUC was significantly different across the four trail types. InSeq correct trials were significantly higher compared to OutSeq correct and OutSeq correct trials were significantly lower than InSeq incorrect . Theta AUC was not significantly different across the four trial types. Abbreviations: RE, nucleus reuniens; HC, hippocampus, SMI, sequence memory index; Seq, sequence; LFP, local field potential; AUC, area under the curve; InSeq, in-sequence; OutSeq, out-of-sequence; InSeq correct , in-sequence correct, InSeq incorrect , in-sequence incorrect, OutSeq correct , out-of-sequence correct, OutSeq incorrect , out-of-sequence incorrect; θ, theta; β, beta; ns, not significant.

Article Snippet: These beta cycles were bursty in nature rather than continuous, thus we developed a beta burst detection algorithm (custom-written scripts in MATLAB, see methods).

Techniques: Sampling, Sequencing