The study of neuropsychological disorders has been greatly facilitated by the localization of brain lesions on MRI scans. Current popular approaches for the assessment of MRI brain scans mostly depend on the successful segmentation of the brain into grey and white matter. These methods cannot be used effectively with large lesions because lesions usually impair segmentation. We propose a novel, fully automated approach for the delineation of brain lesions on MR scans. This method involves comparing a skull stripped, smoothed, unsegmented T1 images to a control group using the general linear model. We tested this method by using images with simulated lesions of different sizes and images containing real lesions from patients with language deficits. We also tested how varying the size of the Gaussian smoothing kernel affects detection. The simulation was informed by findings of a lesion morphological study also presented here. The proposed method detected simulated lesions effectively in the range of 30--90%<normal signal. Smoothing kernels in the range of 8--12 mm resulted in the most accurate lesion detection. Both artificial and real lesions were optimally detected when the results were uncorrected for multiple comparisons at p<.001. This proposed method produced highly satisfactory results and can be used to generate reproducible detection of lesions.