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Thalamic alterations occur in many neurological disorders including Alzheimer's disease, Parkinson's disease and multiple sclerosis. Routine interventions to improve symptom severity in movement disorders, for example, often consist of surgery or deep brain stimulation to diencephalic nuclei. Therefore, accurate delineation of grey matter thalamic subregions is of the upmost clinical importance. MRI is highly appropriate for structural segmentation as it provides different views of the anatomy from a single scanning session. Though with several contrasts potentially available, it is also of increasing importance to develop new image segmentation techniques that can operate multi-spectrally. We hereby propose a new segmentation method for use with multi-modality data, which we evaluated for automated segmentation of major thalamic subnuclear groups using T1 -weighted, T 2 * -weighted and quantitative susceptibility mapping (QSM) information. The proposed method consists of four steps: Highly iterative image co-registration, manual segmentation on the average training-data template, supervised learning for pattern recognition, and a final convex optimisation step imposing further spatial constraints to refine the solution. This led to solutions in greater agreement with manual segmentation than the standard Morel atlas based approach. Furthermore, we show that the multi-contrast approach boosts segmentation performances. We then investigated whether prior knowledge using the training-template contours could further improve convex segmentation accuracy and robustness, which led to highly precise multi-contrast segmentations in single subjects. This approach can be extended to most 3D imaging data types and any region of interest discernible in single scans or multi-subject templates.

The MR signal from gray matter has been long known to present small differences in intensity that have been attributed to variations in cortical myelin content. Previous studies have shown that the T1-, T2-weighted signal and their ratio are sensitive to these variations. Here, we investigated different combinations of signal from MPRAGE and FLAIR images in multimodal segmentation with parametric models of signal intensity to identify a procedure for the identification of contrast in cortical gray matter and the segmentation of different cortical components at 3T. We show that a three-modal combination of these signals delivers a stable segmentation of the cortical mantle in which two distinct components are reliably identified. The resulting intensity maps correspond well to known regional myeloarchitectural differences between cortical regions. These results confirm that widely available MR sequences contain signal that may be used to reliably detect subtle differences in the composition of gray matter with a segmentation approach.

Delayed recall at the primacy position (first few items on a list) has been shown to predict cognitive decline in cognitively intact elderly participants, with poorer delayed primacy performance associated with more pronounced generalized cognitive decline during follow-up. We have previously suggested that this association is due to delayed primacy performance indexing memory consolidation, which in turn is thought to depend upon hippocampal function. Here, we test the hypothesis that hippocampal size is associated with delayed primacy performance in cognitively intact elderly individuals. Data were analyzed from a group (N=81) of cognitively intact participants, aged 60 or above. Serial position performance was measured with the Buschke selective reminding test (BSRT). Hippocampal size was automatically measured via MRI, and unbiased voxel-based analyses were also conducted to explore further regional specificity of memory performance. We conducted regression analyses of hippocampus volumes on serial position performance; other predictors included age, family history of Alzheimer's disease (AD), APOE ε4 status, education, and total intracranial volume. Our results collectively suggest that there is a preferential association between hippocampal volume and delayed primacy performance. These findings are consistent with the hypothesis that delayed primacy consolidation is associated with hippocampal size, and shed light on the relationship between delayed primacy performance and generalized cognitive decline in cognitively intact individuals, suggesting that delayed primacy consolidation may serve as a sensitive marker of hippocampal health in these individuals.