inTUS - Transcranial Ultrasound Resource - streamlined version

Citation information

Please cite the original paper: Caffaratti et al., eLife 2024

https://elifesciences.org/reviewed-preprints/100827

Instructions on use

1. Please find a copy of this file that can be modified in RStudio and run section by section on the Laboratory of Comparative Neuropsychology data share on Open Science Framework under ‘Cafferatti_et_al_inTUS_Resource’:

   https://osf.io/arqp8/

2. Please use the Qualtrics form to submit TUS parameters from a paper not on the datasets. We’ll aim to update them semi-annually:

   https://uiowa.qualtrics.com/jfe/form/SV_4VOvb0fdwvACDkO

3. We hope you find the inTUS resource useful. We will be working on evaluating it with multivariate pattern analyses once the sample sizes grow, and welcome your input.

4. Apologies we are not able to provide more support than making the resource documents available for R and RStudio users to use. Please contacts R team for support in using R.

5. Email the corresponding authors or contact us via the Qualtrics form with other queries: Hugo Caffaratti, Ben Slater & Chris Petkov.

Boxplots

Plots to show the distribution of the data for each of the continuous variables.

I_sppa (brain - W/cm²)

Duty Cycle (%)

Pulse Repetition Frequency (Hz)

Sonication Duration (SD)

Summary statistics

Overview of each key variable split by whether the resulting effect was ‘disruptive’ or ‘enhanced’.

Effect	variable	n	mean	sd
Enhancement	Isppa_brain	28	5.119	4.682
Enhancement	DC	28	25.714	21.909
Enhancement	PRF	28	576.786	732.707
Enhancement	SD	28	26.357	35.721
Disruption	Isppa_brain	25	9.231	10.710
Disruption	DC	25	17.630	16.052
Disruption	PRF	25	464.821	485.038
Disruption	SD	25	30.692	40.648

Correlation Matrix

A simplified correlation matrix, this time the coloured scale refers to strength of correlation:

• Blue refers to a negative correlation

• Red refers to a positive correlation

Logistic Regression

A binomial logistic regression to understand the relationship between the key variables and the main categorical variables:

• Effect: Disruption vs. Enhancement

• Mode: Online vs. Offline stimulation

I_sppa (brain - W/cm²)

## 
## Call:
## glm(formula = Effect ~ 1 + Mode + Isppa_brain, family = "binomial", 
##     data = df_values)
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)  
## (Intercept) -0.31268    0.47933  -0.652   0.5142  
## ModeOffline -0.62749    0.57684  -1.088   0.2767  
## Isppa_brain  0.07515    0.04338   1.732   0.0832 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 73.304  on 52  degrees of freedom
## Residual deviance: 68.470  on 50  degrees of freedom
## AIC: 74.47
## 
## Number of Fisher Scoring iterations: 4

Sonication Duration (sec)

## 
## Call:
## glm(formula = Effect ~ 1 + Mode + SD, family = "binomial", data = df_values)
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)
## (Intercept)  0.078018   0.399674   0.195    0.845
## ModeOffline -0.981828   0.697176  -1.408    0.159
## SD           0.010517   0.009227   1.140    0.254
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 73.304  on 52  degrees of freedom
## Residual deviance: 71.045  on 50  degrees of freedom
## AIC: 77.045
## 
## Number of Fisher Scoring iterations: 4

Duty Cycle (%)

## 
## Call:
## glm(formula = Effect ~ 1 + Mode + DC, family = "binomial", data = df_values)
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)  
## (Intercept)  1.15534    0.68475   1.687   0.0916 .
## ModeOffline -1.05139    0.66289  -1.586   0.1127  
## DC          -0.03451    0.01843  -1.873   0.0611 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 73.304  on 52  degrees of freedom
## Residual deviance: 68.228  on 50  degrees of freedom
## AIC: 74.228
## 
## Number of Fisher Scoring iterations: 3

Pulse Repetition Frequency (Hz)

## 
## Call:
## glm(formula = Effect ~ 1 + Mode + PRF, family = "binomial", data = df_values)
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)
## (Intercept)  0.6088535  0.5686211   1.071    0.284
## ModeOffline -0.8276136  0.6251253  -1.324    0.186
## PRF         -0.0005864  0.0005273  -1.112    0.266
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 73.304  on 52  degrees of freedom
## Residual deviance: 71.053  on 50  degrees of freedom
## AIC: 77.053
## 
## Number of Fisher Scoring iterations: 4