Factors Affecting Performance on a Screening Tool in Persons with Amyotrophic Lateral Sclerosis.

Persons with amyotrophic lateral sclerosis (PALS) are at risk of developing cognitive impairments and frontotemporal dementia (FTD). This study examined the relationship between performance of the ALS-Cognitive Behavioral Screen (ALS-CBS) and the demographic parameters of sex, education, time post-ALS diagnosis, and severity of symptoms. Data were collected retrospectively from 69 participants seen at the Mayo Clinic. Correlations were conducted on the ALS-CBS total scores and subsection scores and the above listed parameters; t-tests were conducted between participant subgroups. No statistically significant relationships or differences occurred between the ALS-CBS or its subsections and the variables measured with exception of age and the attention subsection. Older participants had lower ALS-CBS attention subsection scores. Based on the ALS-CBS scores, most participants had some degree of cognitive impairments: 43 had suspected cognitive impairment, 8 had suspected FTD; 18 fell within the normal range of cognitive function. Overall, the variables of sex, education, time post-diagnosis, and severity of symptoms do not appear to influence ALS-CBS scores. It is recommended cognitive screenings be completed for all PALS due to the high risk for developing cognitive impairments and FTD. Such knowledge can help clinicians develop assessment and treatment plans.

Derivation of high-resolution MRI atlases of the human cerebellum at 3T and segmentation using multiple automatically generated templates.

The cerebellum has classically been linked to motor learning and coordination. However, there is renewed interest in the role of the cerebellum in non-motor functions such as cognition and in the context of different neuropsychiatric disorders. The contribution of neuroimaging studies to advancing understanding of cerebellar structure and function has been limited, partly due to the cerebellum being understudied as a result of contrast and resolution limitations of standard structural magnetic resonance images (MRI). These limitations inhibit proper visualization of the highly compact and detailed cerebellar foliations. In addition, there is a lack of robust algorithms that automatically and reliably identify the cerebellum and its subregions, further complicating the design of large-scale studies of the cerebellum. As such, automated segmentation of the cerebellar lobules would allow detailed population studies of the cerebellum and its subregions. In this manuscript, we describe a novel set of high-resolution in vivo atlases of the cerebellum developed by pairing MR imaging with a carefully validated manual segmentation protocol. Using these cerebellar atlases as inputs, we validate a novel automated segmentation algorithm that takes advantage of the neuroanatomical variability that exists in a given population under study in order to automatically identify the cerebellum, and its lobules. Our automatic segmentation results demonstrate good accuracy in the identification of all lobules (mean Kappa [κ]=0.731; range 0.40-0.89), and the entire cerebellum (mean κ=0.925; range 0.90-0.94) when compared to "gold-standard" manual segmentations. These results compare favorably in comparison to other publically available methods for automatic segmentation of the cerebellum. The completed cerebellar atlases are available freely online (http://imaging-genetics.camh.ca/cerebellum) and can be customized to the unique neuroanatomy of different subjects using the proposed segmentation pipeline (https://github.com/pipitone/MAGeTbrain).