Widespread extrahippocampal NAA/(Cr+Cho) abnormalities in TLE with and without mesial temporal sclerosis

MR spectroscopy has demonstrated extrahippocampal NAA/(Cr+Cho) reductions in medial temporal lobe epilepsy with (TLE-MTS) and without (TLE-no) mesial temporal sclerosis. Because of the limited brain coverage of those previous studies, it was, however, not possible to assess differences in the distribution and extent of these abnormalities between TLE-MTS and TLE-no. This study used a 3D whole brain echoplanar spectroscopic imaging (EPSI) sequence to address the following questions: (1) Do TLE-MTS and TLE-no differ regarding severity and distribution of extrahippocampal NAA/(Cr+Cho) reductions? (2) Do extrahippocampal NAA/(Cr+Cho) reductions provide additional information for focus lateralization? Forty-three subjects (12 TLE-MTS, 13 TLE-no, 18 controls) were studied with 3D EPSI. Statistical parametric mapping (SPM2) was used to identify regions of significantly decreased NAA/(Cr+Cho) in TLE groups and in individual patients. TLE-MTS and TLE-no had widespread extrahippocampal NAA/(Cr+Cho) reductions. NAA/(Cr+Cho) reductions had a bilateral fronto-temporal distribution in TLE-MTS and a more diffuse, less well defined distribution in TLE-no. Extrahippocampal NAA/(Cr+Cho) decreases in the single subject analysis showed a large inter-individual variability and did not provide additional focus lateralizing information. Extrahippocampal NAA/(Cr+Cho) reductions in TLE-MTS and TLE-no are neither focal nor homogeneous. This reduces their value for focus lateralization and suggests a heterogeneous etiology of extrahippocampal spectroscopic metabolic abnormalities in TLE

Learning detectors quickly with stationary statistics

Computer vision is increasingly becoming interested in the rapid estimation of object detectors. The canonical strategy of using Hard Negative Mining to train a Support Vector Machine is slow, since the large negative set must be traversed at least once per detector. Recent work has demonstrated that, with an assumption of signal stationarity, Linear Discriminant Analysis is able to learn comparable detectors without ever revisiting the negative set. Even with this insight, the time to learn a detector can still be on the order of minutes. Correlation filters, on the other hand, can produce a detector in under a second. However, this involves the unnatural assumption that the statistics are periodic, and requires the negative set to be re-sampled per detector size. These two methods differ chie y in the structure which they impose on the co- variance matrix of all examples. This paper is a comparative study which develops techniques (i) to assume periodic statistics without needing to revisit the negative set and (ii) to accelerate the estimation of detectors with aperiodic statistics. It is experimentally verified that periodicity is detrimental