ARE THERE DISCRETE GAMMA SUB-BANDS IN HIPPOCAMPAL NETWORKS DURING SPATIAL LEARNING ?

(1)

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Submitted on 21 Nov 2019

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ARE THERE DISCRETE GAMMA SUB-BANDS IN HIPPOCAMPAL NETWORKS DURING SPATIAL

LEARNING ?

Vincent Douchamps, Demian Battaglia, Romain Goutagny

To cite this version:

Vincent Douchamps, Demian Battaglia, Romain Goutagny. ARE THERE DISCRETE GAMMA SUB-BANDS IN HIPPOCAMPAL NETWORKS DURING SPATIAL LEARNING ?. Society for Neuroscience annual meeting, Oct 2019, Chicago, United States. �hal-02373798�

(2)

Vincent Douchamps ¹ , Demian Battaglia ² , Romain Goutagny ¹

1

Laboratoire de Neurosciences Cognitives et Adaptatives, CNRS UMR 7364, Université de Strasbourg;

²

Institut de Neurosciences des Systèmes, INSERM UMR 1106, Aix-Marseille Université

Are there discrete gamma sub-bands

in hippocampus during spatial learning?

Institut de

Neurosciences des Systèmes

Fonds Paul Mandel pour les neurosciences

Fédération Neuropôle Université de Strasbourg

Power across learning Introduction

Theta (θ) and gamma (γ) oscillations are believed to

organise hippocampal activity, via their cross-freq coupling

Classic view: Slow γ (~30-60 Hz), possibly related to memory retrieval; and Medium γ (~60-100 Hz), possibly related to

sensory infomation and memory encoding

However, recent evidence suggests a wider repertoire of coupling patterns when considering individual θ cycles

Schomburg et al., 2014

AIM OF THE STUDY

Explore theta-gamma oscillatory dynamics in CA1 and DG across learning in an ecologic spatial navigation task and without a priori on frequencies and phases that should be observed.

The Arm-to-Arm task

Subjects: water-restricted mice (n=4) Maze: 8-arm radial arm maze; distal visual cues available

Task: finding the rewarded arm starting from another (semi-random) arm.

Fixed reward arm across the 10-day training, 4 daily trials

Reward: 0.05 ml of water at last arm end

Day 1 - Trial 1 Day 4 - Trial 4

0 50 100

Time to reach target (s)

Learning day

1 2 3 4 5 6 7 8 9 10 F(9,63) = 7.956

p < 0.0001

target

-90 opposite

0 0.2 0.4 0.6

Ratio of visits in arm

+90

* *

*

F(7,49) = 12.38 p < 0.0001

Identification of θ cycles: preservation of θ

asymmetry (1-25 Hz filtering); phase estimation with piece-wise linear interpolation

γ spectral content of each θ cycle: coincident seg- ment of the spectrogram of γ-composite signal

(complex Morlet wavelets; 1-200 Hz; 1 Hz steps)

Mean θ-γ motifs from repre- sentative channels and trials do correspond to the “classical

model”

However, the possibility of multi- ple γ bouts per θ cycle is not

“classical”, as well as the pres-

ence of both medium and slow γ episodes in both dendritic

layers

The large variability of individ- ual cycles and the broad count distributions suggest that the landscape of possible θ-γ cou- plings is better described as a structured continuum

270

90 0 180

At all stages of learning and all anatomical layers, cycles arehighly variable, but phase, freq and power are not uniformly distributed

The “swarms” of cycles morph along

learning (e.g. migration of hi power SLM cycles toward higher frequencies)

Yet, despite complexity, non-trivial

coding of navigation speed or location?

We trained classifiers (Random forests, via RUSBoost method) to predict speed and maze location as a function of individual cycle properties

Decoding is possible, revealing that different

inputs convey different information at different times (but not simply “recall” or “encoding”!)

Raw LFP (hippocampal fissure)

Unsupervised EEMD decomposition

Unsupervised empirical ensemble mode decomposition (EEMD for cycle-by-cycle analy- sis [Wu & Huang, 2009] ; CEEMD for power spectrum [Torres et al., 2011] )

Power spectra of EEMD-derived IMFs is stable along training Need to correct for theta harmonics!

SLM spectra

n.s.

θ cycle-by-cycle analyses

Peak-to-peak segmentation in θ cycles (ref. in LFP from hippocampal fissure)

θ-γ coupling: means vs counts

20 40 60 80 100 120 140 160 180 200

0

-π 0 π

Frequency (Hz) LFP power

max

min

SP

20 40 60 80 100 120 140 160 180 200

0

Frequency (Hz)

20 40 60 80 100 120 140 160 180 200

0

Frequency (Hz)

Theta phase (°)

Hippocampal fissure

CSD amplitude

min max

SR

SLM

Arbitrary units

Mean Count

Frequency (Hz)

Mean Count

0 50 100 150

-π 0 π

15 85 160

Frequency (Hz)

-π 0 π

15 85 160

Frequency (Hz)

15 85 160

Frequency (Hz)

INDIVIDUAL CYCLES MEAN θ CYCLES Mean vs Count

Daunting variability and deviations from “prototypical” case (usually, only ~2% of cycles are similar to mean template)

40 80 120

SLM

Frequency (Hz)

SP

Cycle counts (a.u.)

SR

CYCLE COUNTS (all trials and channels)

LO and HI power together

LO power only

HI power only

A structured continuum of cycles...

180 -90

0

90

40 60

80 100

120

40 60

80 100

120

40 60

80 100

40 60

80 100

120

40 0 60

80 100

120

40 60

80 100

120 40

60 80 100

120

40 60

80 100

120

40 60

80 100

40 60

80 100

120

40 60

80 100

120

40 60

80 100

120

SO

Early Late

SP

Early Late

SR

Early Late

SLM

Early Late

DG mol

Early Late

DG gran

Early Late

CENTER:

low freq cycles

PERIPHERY:

hi freq cycles One dot = one θ/γ event

Cycle peak power

5% quantile 95% quantile

Phase

30 Hz 120 Hz

Frequency

e.g., in SLM,

cycles occurring during high-speed epochs

tend to cluster in patches in this

dimensionally-

reduced projection of all SLM cycles

... which conveys decodable information

Trial group

Early Late Early

Trial group

Late SPEED PREDICTION TARGET ARM

PREDICTION

Correlation between predicted and real speed

0.5

0.3

0.62 0.70 0.78

Area under ROC curve (AUC)

SLM SR

SR confusion matrix (% of classifications)

Other

Target arm Reward

Other Target arm Reward

0% 100%

50%

60%

82%

REPRESENTATIONAL SIMILARITY

Trial group

Early

Late

SLM predicts speed

Trial group

Early

Late

SLM predicts target arm

Trial group

Early Late

low high

Cross-classification performance

predicts

ARE THERE DISCRETE GAMMA SUB-BANDS IN HIPPOCAMPAL NETWORKS DURING SPATIAL LEARNING ?

Vincent Douchamps 1 , Demian Battaglia 2 , Romain Goutagny 1

Laboratoire de Neurosciences Cognitives et Adaptatives, CNRS UMR 7364, Université de Strasbourg;

Institut de Neurosciences des Systèmes, INSERM UMR 1106, Aix-Marseille Université

Are there discrete gamma sub-bands

in hippocampus during spatial learning?

Institut de

Neurosciences des Systèmes

Fonds Paul Mandel pour les neurosciences

Fédération Neuropôle Université de Strasbourg

Power across learning Introduction

Theta (θ) and gamma (γ) oscillations are believed to

organise hippocampal activity, via their cross-freq coupling

Classic view: Slow γ (~30-60 Hz), possibly related to memory retrieval; and Medium γ (~60-100 Hz), possibly related to

sensory infomation and memory encoding

However, recent evidence suggests a wider repertoire of coupling patterns when considering individual θ cycles

Schomburg et al., 2014

AIM OF THE STUDY

Explore theta-gamma oscillatory dynamics in CA1 and DG across learning in an ecologic spatial navigation task and without a priori on frequencies and phases that should be observed.

The Arm-to-Arm task

Subjects: water-restricted mice (n=4) Maze: 8-arm radial arm maze; distal visual cues available

Task: finding the rewarded arm starting from another (semi-random) arm.

Fixed reward arm across the 10-day training, 4 daily trials

Reward: 0.05 ml of water at last arm end

Time to reach target (s)

Learning day

Ratio of visits in arm

Identification of θ cycles: preservation of θ

asymmetry (1-25 Hz filtering); phase estimation with piece-wise linear interpolation

γ spectral content of each θ cycle: coincident seg- ment of the spectrogram of γ-composite signal

(complex Morlet wavelets; 1-200 Hz; 1 Hz steps)

Mean θ-γ motifs from repre- sentative channels and trials do correspond to the “classical

model”

However, the possibility of multi- ple γ bouts per θ cycle is not

“classical”, as well as the pres-

ence of both medium and slow γ episodes in both dendritic

layers

The large variability of individ- ual cycles and the broad count distributions suggest that the landscape of possible θ-γ cou- plings is better described as a structured continuum

270

90

0 180

90

0 180

90

0 180

At all stages of learning and all anatomical layers, cycles arehighly variable, but phase, freq and power are not uniformly distributed

The “swarms” of cycles morph along

learning (e.g. migration of hi power SLM cycles toward higher frequencies)

Yet, despite complexity, non-trivial

coding of navigation speed or location?

We trained classifiers (Random forests, via RUSBoost method) to predict speed and maze location as a function of individual cycle properties

Decoding is possible, revealing that different

inputs convey different information at different times (but not simply “recall” or “encoding”!)

Unsupervised EEMD decomposition

Unsupervised empirical ensemble mode decomposition (EEMD for cycle-by-cycle analy- sis [Wu & Huang, 2009] ; CEEMD for power spectrum [Torres et al., 2011] )

Power spectra of EEMD-derived IMFs is stable along training Need to correct for theta harmonics!

θ cycle-by-cycle analyses

θ-γ coupling: means vs counts

SP

SR

SLM

INDIVIDUAL CYCLES MEAN θ CYCLES Mean vs Count

40 80 120

SLM

SP

SR

CYCLE COUNTS (all trials and channels)

A structured continuum of cycles...

180 -90

0

90

SO

Early Late

SP

Early Late

SR

Early Late

SLM

Early Late

DG mol

Vincent Douchamps ¹ , Demian Battaglia ² , Romain Goutagny ¹