Deep learning for neuroimaging: a validation study
Authors: Sergey M. Plis, Devon R. Hjelm, Ruslan Salakhutdinov, Vince D. Calhoun
ICLR 2014

Abstract: Deep learning methods have recently enjoyed a number of successes in the tasks of classification and representation learning. These tasks are very important for brain imaging and neuroscience discovery, making the methods attractive candidates for porting to a neuroimager's toolbox. Successes are, in part, explained by a great flexibility of deep learning models. This flexibility makes the process of porting to new areas a difficult parameter optimization problem. In this work we demonstrate our results (and feasible parameter ranges) in application of deep learning methods to structural and functional brain imaging data. We also describe a novel constraint-based approach to visualizing high dimensional data. We use it to analyze the effect of parameter choices on data transformations. Our results show that deep learning methods are able to learn physiologically important representations and detect latent relations in neuroimaging data.


Peer review:
[Review]:
The English in the paper is poor making it hard to read. 

There is a lot of material and the changing architectures, datasets and tasks make the whole thing hard to follow. 

You introduce sMRI without explaining it. 

You don't describe the temporal nature of your data. (I presume it's one volume every T ms, but you have to say that and tell us what T is. 

The datasets are specialist and results aren't compared to other authors, so it's hard to really understand performance. 

The task in 2.2 is not explained. What are the classes in the GT. is this really an unsupervised task? The results (what is being measured) in Fig 1c are not clear. Please elucidate. 

Conventionally I believe a DBN is a stacked RBM - when you add a classification layer, please call it a DNN.
For the RBM / DBN please specify the connectivity. Are inputs / hidden connected all:all?


By adding a second layer you don't explicitly show that depth helps the improvement could equally be because adding more parameters helps. You need to show the correct control experiment. Adding a 3rd layer may similarly be causing you to overfit because you added too many parameters. 

You don't explain the reasoning behind applying ICA spatially and the DBN temporally. 

Is the ICA implementation on a GPU too? 

You say 'The training accuracy at the fine tuning stage for the 335 subjects was: 82%, 87%, and 86.5% respectively.'
What are these 3 numbers? You can't say respectively in this sentence.