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convolutional neural networks cnns gradually recognized neuroimaging community powerful tool image analysis despite outstanding performances aspects cnn functioning still fully understood human operators postulated interpretability cnns applied neuroimaging data could improved investigating behavior fed data known characteristics analyzed ability 3d cnns discriminate original altered wholebrain parametric maps derived diffusionweighted magnetic resonance imaging alteration consisted linearly changing voxel intensity either one monoregion two biregion anatomical regions brain volume without mimicking neuropathology performing tenfold crossvalidation using holdout set testing assessed cnns discrimination ability according intensity altered regions comparing latters size relative position monoregion cnns showed larger modified region smaller intensity increase needed achieve good performances biregion cnns systematically outperformed monoregion cnns could detect one two target regions tested corresponding monoregion images exploiting prior information training data allowed better understanding cnn behavior especially altered regions combined inform complexity cnn pattern retrieval elucidate misclassified examples particularly relevant pathological data proposed analytical approach may serve gain insights cnn behavior guide design enhanced detection systems exploiting prior knowledge