Establishing spatial correspondence between subject and template images is necessary in neuroimaging research and clinical applications such as brain mapping and stereotactic neurosurgery. Our anatomical fiducial (AFID) framework has recently been validated to serve as a quantitative measure of image registration based on salient anatomical features. In this study, we sought to apply the AFIDs protocol to the clinic, focusing on structural magnetic resonance images obtained from patients with Parkinson’s disease (PD). We confirmed AFIDs could be placed to millimetric accuracy in the PD dataset with results comparable to those in normal control subjects. We evaluated subject-to-template registration using this framework by aligning the clinical scans to standard template space using a robust open preprocessing workflow. We found that registration errors measured using AFIDs were higher than previously reported, suggesting the need for optimization of image processing pipelines for clinical grade datasets. Finally, we examined the utility of using point-to-point distances between AFIDs as a morphometric biomarker of PD, finding evidence of reduced distances between AFIDs that circumscribe regions known to be affected in PD including the substantia nigra. Overall, we provide evidence that AFIDs can be successfully applied in a clinical setting and utilized to provide localized and quantitative measures of registration error. AFIDs provide clinicians and researchers with a common, open framework for quality control and validation of spatial correspondence and the location of anatomical structures, facilitating aggregation of imaging datasets and comparisons between various neurological conditions.

Cells selectively activated by a particular view of an environment have been found in the primate hippocampus (HPC). Whether view cells are present in other brain areas, and how view selectivity interacts with other variables such as object features and place remain unclear. Here, we explore these issues by recording the responses of neurons in the HPC and the lateral prefrontal cortex (LPFC) of rhesus macaques performing a task in which they learn new context‐object associations while navigating a virtual environment using a joystick. We measured neuronal responses at different locations in a virtual maze where animals freely directed gaze to different regions of the visual scenes. We show that specific views containing task relevant objects selectively activated a proportion of HPC units, and an even higher proportion of LPFC units. Place selectivity was scarce and generally dependent on view. Many view cells were not affected by changing the object color or the context cue, two task relevant features. However, a small proportion of view cells showed selectivity for these two features. Our results show that during navigation in a virtual environment with complex and dynamic visual stimuli, view cells are found in both the HPC and the LPFC. View cells may have developed as a multiarea specialization in diurnal primates to encode the complexities and layouts of the environment through gaze exploration which ultimately enables building cognitive maps of space that guide navigation.

Establishing spatial correspondence between subject and template images is necessary in neuroimaging research and clinical applications such as brain mapping and stereotactic neurosurgery. In the absence of other quantitative approaches, a point-based set of anatomical fiducials (AFIDs) was recently developed and validated to serve as a quantitative measure of image registration based on salient anatomical features. In this study, we sought to apply the AFIDs protocol to the clinic, specifically focussing on structural magnetic resonance images (MRI) obtained from patients with Parkinson’s Disease (PD). We first confirmed that AFIDs could be placed to millimetric accuracy in the PD dataset with results comparable to those in normal control subjects. With localization error established, we evaluated subject-to-template registration using this framework by aligning the clinical scans to standard template space using a robust open preprocessing workflow for MRI scans. We found that registration errors from this workflow as measured using AFIDs were higher than previously reported suggesting the need for optimization of image processing pipelines for clinical grade datasets. Finally, we examined the utility of using point-to-point distances between AFID points as a morphometric biomarker of PD, finding evidence of reduced distances between AFIDs around the left temporal horn, brainstem and pineal gland in the clinical group, structures that circumscribe regions known to be affected in PD including the substantia nigra. Overall, we provide evidence that AFIDs can be successfully applied in a clinical setting and utilized to provide localized and quantitative measures of registration error. AFIDs provide clinicians and researchers with a common, open framework for quality control and validation of spatial correspondence and the location of anatomical structures, facilitating accurate aggregation of imaging datasets and comparisons between various neurological conditions.