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ABSTRACT Lesion studies (both patients and nonhuman animals) and functional neuroimaging studies of normal, healthy subjects provide complementary, but different types of information. This article suggests that both study types are necessary for deriving inferentially sound conclusions regarding the neural basis of cognition. [ABSTRACT FROM AUTHOR]

Abstract: Episodic memory provides information about the “when” of events as well as “what” and “where” they happened. Using functional imaging, we investigated the domain specificity of retrieval-related processes following encoding of complex, naturalistic events. Subjects watched a 42-min TV episode, and 24h later, made discriminative choices of scenes from the clip during fMRI. Subjects were presented with two scenes and required to either choose the scene that happened earlier in the film (Temporal), or the scene with a correct spatial arrangement (Spatial), or the scene that had been shown (Object). We identified a retrieval network comprising the precuneus, lateral and dorsal parietal cortex, middle frontal and medial temporal areas. The precuneus and angular gyrus are associated with temporal retrieval, with precuneal activity correlating negatively with temporal distance between two happenings at encoding. A dorsal fronto-parietal network engages during spatial retrieval, while antero-medial temporal regions activate during object-related retrieval. We propose that access to episodic memory traces involves different processes depending on task requirements. These include memory-searching within an organised knowledge structure in the precuneus (Temporal task), online maintenance of spatial information in dorsal fronto-parietal cortices (Spatial task) and combining scene-related spatial and non-spatial information in the hippocampus (Object task). Our findings support the proposal of process-specific dissociations of retrieval. [Copyright &y& Elsevier]

The emergence of machine learning (ML) techniques has opened up new avenues for identifying biomarkers associated with schizophrenia (SCZ) using task‐related fMRI (t‐fMRI) designs. To evaluate the effectiveness of this approach, we conducted a comprehensive meta‐analysis of 31 t‐fMRI studies using a bivariate model. Our findings revealed a high overall sensitivity of 0.83 and specificity of 0.82 for t‐fMRI studies. Notably, neuropsychological domains modulated the classification performance, with selective attention demonstrating a significantly higher specificity than working memory (β = 0.98, z = 2.11, P = 0.04). Studies involving older, chronic patients with SCZ reported higher sensitivity (P <0.015) and specificity (P <0.001) than those involving younger, first‐episode patients or high‐risk individuals for psychosis. Additionally, we found that the severity of negative symptoms was positively associated with the specificity of the classification model (β = 7.19, z = 2.20, P = 0.03). Taken together, these results support the potential of using task‐based fMRI data in combination with machine learning techniques to identify biomarkers related to symptom outcomes in SCZ, providing a promising avenue for improving diagnostic accuracy and treatment efficacy. Future attempts to deploy ML classification should consider the factors of algorithm choice, data quality and quantity, as well as issues related to generalization. [ABSTRACT FROM AUTHOR]

• Data sharing of primate neuroimaging offers new opportunities. • The potential of metadata to enrich primate neuroimaging is described. • Illustration of how meta-data can be shared in the BIDS format is provided. Sharing and pooling large amounts of non-human primate neuroimaging data offer new exciting opportunities to understand the primate brain. The potential of big data in non-human primate neuroimaging could however be tremendously enhanced by combining such neuroimaging data with other types of information. Here we describe metadata that have been identified as particularly valuable by the non-human primate neuroimaging community, including behavioural, genetic, physiological and phylogenetic data. [ABSTRACT FROM AUTHOR]

Animal neuroimaging studies can provide unique insights into brain structure and function, and can be leveraged to bridge the gap between animal and human neuroscience. In part, this power comes from the ability to combine mechanistic interventions with brain-wide neuroimaging. Due to their phylogenetic proximity to humans, nonhuman primate neuroimaging holds particular promise. Because nonhuman primate neuroimaging studies are often underpowered, there is a great need to share data amongst translational researchers. Data sharing efforts have been limited, however, by the lack of standardized tools and repositories through which nonhuman neuroimaging data can easily be archived and accessed. Here, we provide an extension of the Neurovault framework to enable sharing of statistical maps and related voxelwise neuroimaging data from other species and template-spaces. Neurovault, which was previously limited to human neuroimaging data, now allows researchers to easily upload and share nonhuman primate neuroimaging results. This promises to facilitate open, integrative, cross-species science while affording researchers the increased statistical power provided by data aggregation. In addition, the Neurovault code-base now enables the addition of other species and template-spaces. Together, these advances promise to bring neuroimaging data sharing to research in other species, for supplemental data, location-based atlases, and data that would otherwise be relegated to a "file-drawer". As increasing numbers of researchers share their nonhuman neuroimaging data on Neurovault, this resource will enable novel, large-scale, cross-species comparisons that were previously impossible. [ABSTRACT FROM AUTHOR]