diff --git a/docs/resting_state/ica_pnm.md b/docs/resting_state/ica_pnm.md
index 5894bed511adfb96564e15f657653cf680e23175..1e37b0707901206723334dd7f0393d43d8004944 100644
--- a/docs/resting_state/ica_pnm.md
+++ b/docs/resting_state/ica_pnm.md
@@ -11,7 +11,7 @@ Before combining your sources of noise you should follow the relevant pipelines
 The final line of your ICA-labels file contains the numbers of all noise components. This file is obtained either via manual classification using FSLeyes, or from automated classification using ICA-FIX or ICA-AROMA.
 
 
-<img src="resting_state/ica_pnm_Figure1.png" alt="Red box highlights noise components" width="500" height=550 />
+<img src="resting_state/ica_pnm_Figure1.png" alt="Red box highlights noise components" width="500" />
 
 These numbers can be used to index the file containing the time-series information for each component – ordinarily named melodic_Tmodes, located within the filtered_func_data.ica folder of your MELODIC directory. To run ICA-PNM clean up, we need to generate a file that contains only the noise component timecourses from melodic_Tmodes. This new noise time series text file (named for example: ICA_noise.txt) should be space separated. In addition to ICA identified noise components, signal associated with white matter, CSF or motion outliers can also be appended to this text file.
 The fsl_ENTS tool can prepare this stage for you with the following usage:
@@ -30,17 +30,17 @@ Where
 
 If you have carried out both PNM and ICA you should now have 2 text files – one for your PNM noise output, which should be input into the Voxel Confound List in FEAT.
 
-<img src="resting_state/ica_pnm_Figure2.png" alt="Red box highlights noise components" width="500" height=550 />
+<img src="resting_state/ica_pnm_Figure2.png" alt="Red box highlights noise components" width="500" />
 
 And one containing the time series information of ICA noise components along with any other sources of noise that you wish to model. This file should be space delimited and should be input into the Add additional confound EVs box. The input within the data tab should be your raw data and FEAT can be run from scratch, including all pre-processing steps.
 
-<img src="resting_state/ica_pnm_Figure3.png" alt="Red box highlights noise components" width="500" height=550 />
+<img src="resting_state/ica_pnm_Figure3.png" alt="Red box highlights noise components" width="500" />
 
 # The model
 
 The PNM noise text file and FIX noise text file have been combined by FEAT and input into your model as one long series of regressors of no interest. Many of these components are likely to not be independent and therefore we do not expect the degrees of freedom to be overly reduced.
 
-<img src="resting_state/ica_pnm_Figure4.png" alt="Red box highlights noise components" width="500" height=550 />
+<img src="resting_state/ica_pnm_Figure4.png" alt="Red box highlights noise components" width="500" />
 
 If resting state data are entered into the ICA-PNM pipeline, there will be no task EVs in the model. In this case, res4d.nii.gz file obtained from running Feat as described above is the ‘cleaned’ data, which can be used for subsequent resting state analysis