Summary Objective Hippocampal dentation (HD) is a “toothlike” morphological feature observed on the inferior aspect of the human hippocampus. It has been found that HD varies dramatically in healthy adults and is positively associated with verbal and visual memory. In this work, we evaluate the loss of HD and its association with memory dysfunction in patients with temporal lobe epilepsy (TLE) who have hippocampal sclerosis (HS). Methods Fifty‐eight unilateral HS patients with neuropsychological data were identified from a retrospective database. T1‐weighted magnetization‐prepared rapid acquisition gradient echo images (~1 mm resolution) were upsampled to .25 mm and were processed using ASHS software to obtain ultra‐high‐resolution segmentations and three‐dimensional renderings. Dentes were counted on the epileptic and contralateral sides, and associations were tested between dentation on the epileptic versus contralateral sides and measures of verbal and visuospatial memory with respect to the dominant versus nondominant hemisphere. Results The median number of dentes in epileptic hippocampi was significantly lower than in contralateral hippocampi (p < .0001). Among cases with HS in the dominant hemisphere, verbal memory was significantly correlated with contralateral nondominant hemisphere dentation (r = .43, p = .04). Similarly, among cases of HS in the nondominant hemisphere, visual memory was significantly correlated with contralateral dominant hemisphere dentation (r = .48, p = .04). All other analyses were not significant. Significance This is the first study characterizing dentation in TLE patients with HS and its memory correlates. There is marked loss of dentation in sclerotic hippocampi compared to the unaffected contralateral hippocampi. Material‐specific measures of memory performance are paradoxically correlated with dentation contralateral to the side with HS, suggesting that contralateral functional capacity explains some of the variation in memory across TLE patients. HD is an important variable to consider in understanding memory loss in TLE.

Magnetic resonance imaging of hippocampal internal architecture (HIA) at 3T is challenging. HIA is defined by layers of gray and white matter that are less than 1 mm thick in the coronal plane. To visualize HIA, conventional MRI approaches have relied on sequences with high in-plane resolution (≤0.5 mm) but comparatively thick slices (2–5 mm). However, thicker slices are prone to volume averaging effects that result in loss of HIA clarity and blurring of the borders of the hippocampal subfields in up to 61% of slices as has been reported. In this work we describe an approach to hippocampal imaging that provides consistently high HIA clarity using a commonly available sequence and post-processing techniques that is flexible and may be applicable to any MRI platform. We refer to this approach as High Resolution Multiple Image Co-registration and Averaging (HR-MICRA). This approach uses a variable flip angle turbo spin echo sequence to repeatedly acquire a whole brain T2w image volume with high resolution in three dimensions in a relatively short amount of time, and then co-register the volumes to correct for movement and average the repeated scans to improve SNR. We compared the averages of 4, 9, and 16 individual scans in 20 healthy controls using a published HIA clarity rating scale. In the body of the hippocampus, the proportion of slices with good or excellent HIA clarity was 90%, 83%, and 67% for the 16x, 9x, and 4x HR-MICRA images, respectively. Using the 4x HR-MICRA images as a baseline, the 9x HR-MICRA images were 2.6 times and 16x HR-MICRA images were 3.2 times more likely to have high HIA ratings (p &lt; 0.001) across all hippocampal segments (head, body, and tail). The thin slices of the HR-MICRA images allow reformatting in any plane with clear visualization of hippocampal dentation in the sagittal plane. Clear and consistent visualization of HIA will allow application of this technique to future hippocampal structure research, as well as more precise manual or automated segmentation.