Pineal gland (PG) is a structure located in the midline of the brain, and is considered as a main part of the epithalamus. There are numerous reports on the facilitatory role of this area for brain function; hormone secretion and its role in sleep cycle are the major reports. However, reports are rarely available on the direct role of this structure in brain cognition and in information processing. A suggestion for the limited number of such studies is the lack of a standard atlas for the PG; none of the available MRI templates and atlases has provided parcellations for this structure. In this study, we used the three-dimensional (3D) T1-weighted MRI data of 152 healthy young volunteers, and provided a probabilistic map of the PG in the standard Montreal Neurologic Institute (MNI) space. The methods included collecting the data using a 64-channel head coil on a 3-Tesla Prisma MRI Scanner, manual delineation of the PG by two experts, and robust template and atlas construction algorithms. This atlas is freely accessible, and we hope importing this atlas in the well-known neuroimaging software packages would help to identify other probable roles of the PG in brain function. It could also be used to study pineal cysts, for volumetric analyses, and to test any associations between the cognitive abilities of the human and the structure of the PG.

Brain maturation studies typically examine relationships linking a single morphometric feature with aspects of cognition, behavior, age, or other demographic characteristics. However, the coordinated spatiotemporal arrangement of morphological features across development and their associations with behavior are unclear. Here, we examine covariation across multiple cortical features (cortical thickness [CT], surface area [SA], local gyrification index [GI], and mean curvature [MC]) using magnetic resonance images from the long-running National Institute of Mental Health developmental cohort (ages 5-25). Neuroanatomical covariance was examined using non-negative matrix factorization (NMF), which decomposes covariance resulting in a parts-based representation. Cross-sectionally, we identified six components of covariation which demonstrate differential contributions of CT, GI, and SA in hetero- vs. unimodal areas. We sought to use this technique longitudinally to examine covariance in rates of change, which highlighted a preserved SA in unimodal areas and changes in CT and GI in heteromodal areas. Using behavioral partial least squares (PLS), we identified a single latent variable (LV; 96 % covariance explained) that recapitulated patterns of reduced CT, GI, and SA that are generally related to older age, with limited contributions of IQ and SES. Longitudinally, PLS revealed three LVs that demonstrated a nuanced developmental pattern that highlighted a higher rate of maturational change in SA and CT in higher IQ and SES females. This novel characterization of brain maturation provides an important understanding of the interdependencies between morphological measures, their coordinated development, and their relationship to biological sex, cognitive ability, and the resources of the local environment.

Background: Single suture craniosynostosis (SSC) is a disorder, affecting brain growth. Reviewing literature reveals controversialists of papers in this field.Methods: This prospective study was conducted from 2014 to 2016. All the individuals, aged 2 to 16 years, whose medical records files were complete, with SSC from 1999 to 2013 were included. All patients had undergone cranial vault remodeling at Mofid Hospital, Tehran, Iran. Wechsler questionnaires, WPPSI-III and WISC-IV, were completed for each child based on his/her age.Results: Seventy children were included, with the mean age of 6.7 (±2.9) years. Forty-six (65.7%) children were boys while 24 (34.3%) were girls. Mean FSIQ for all of children was 95.5 (±13.2). Mean verbal IQ, performance IQ, verbal comprehension, perceptual reasoning, processing speed, and working memory are 93.4 (±14.1), 96.1 (±13.3), 97.5 (±13.9), 102.2 (±12.5), 94.5 (±9.8), and 97.5 (±12.9), respectively. There was statistically significant difference between FSIQ of children with SSC and that of unaffected children (P-value<0.05). There was significant difference between verbal IQ of children with SSC and that of unaffected ones (P-value< 0.007). There was significant difference between in processing speed between affected children and unaffected children (P-value<0.012). Conclusion:Children, aged 2 to 6 years, with SSC had a significantly lower Verbal IQ, and children, aged 6 to 16 years, with SSC had a significantly lower processing speed than their healthy counterparts. Though FSIQ of children with SSC falls within normal range, it is a little lower than healthy peers.