OASIS-3 is a compilation of MRI and PET imaging and related clinical data for 1098 participants who were collected across several ongoing studies in the Washington University Knight Alzheimer Disease Research Center over the course of 15 years. Participants include 605 cognitively normal adults and 493 individuals at various stages of cognitive decline ranging in age from 42 to 95 years. The OASIS-3 dataset contains over 2000 MR sessions, including multiple structural and functional sequences. PET metabolic and amyloid imaging includes over 1500 raw imaging scans and the accompanying post-processed files from the PET Unified Pipeline (PUP) are also available in OASIS-3. OASIS-3 also contains post-processed imaging data such as volumetric segmentations and PET analyses. Imaging data is accompanied by dementia and APOE status and longitudinal clinical and cognitive outcomes. OASIS-3 is available as an open access data set to the scientific community to answer questions related to healthy aging and dementia.

Identifying the processes by which people remember to execute an intention at an appropriate moment (prospective memory) remains a fundamental theoretical challenge. One account is that top-down attentional control is required to maintain activation of the intention, self-initiate intention retrieval, or support monitoring. A diverging account suggests bottom-up spontaneous retrieval can be triggered by cues that have been associated with the intention; sustained attentional processes are not required. We used a specialized experimental design and fMRI methods to selectively marshal and identify each process. Results revealed a clear dissociation. One prospective memory task recruited sustained activity in attentional control areas, such as anterior prefrontal cortex; the other engaged purely transient activity in parietal and ventral brain regions associated with attentional capture, target detection, and episodic retrieval. These patterns provide critical evidence that there are two neural routes to prospective memory, with each route emerging under different circumstances.

Evidence suggests that initiation of some forms of hormone therapy (HT) early in the perimenopausal or postmenopausal stage might confer benefit to verbal memory and the neural systems underlying memory, whereas late-life initiation confers no benefit or harm. This "critical window hypothesis" remains a topic of debate. Using functional magnetic resonance imaging (fMRI), we examined the long-term impact of perimenopausal HT use on brain function during performance of verbal and figural memory tasks. Participants were 34 postmenopausal women (mean age 60 years) from the Melbourne Women's Midlife Health Project and included 17 early (perimenopausal) and continuous users of HT and 17 never users matched on age, education, and verbal knowledge. Continuous HT use from the perimenopausal stage versus no use was validated with prospective daily diary records and study visit data. The primary outcome was patterns of brain activation in an a priori region of interest in the medial temporal lobe during verbal encoding and recognition of words. Results indicated that perimenopausal HT users performed better than nonusers on the imaging verbal memory task (p < .05). During verbal recognition, perimenopausal HT users showed increased activation in the left hippocampus and decreased activation in the parahippocampal gyrus bilaterally compared with never users. Each of these patterns of activation was associated with better memory performance on the imaging memory Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal Disclaimers that apply to the journal pertain. NIH Public Access

Brain extraction, or skull-stripping, is an essential pre-processing step in neuro-imaging that has a direct impact on the quality of all subsequent processing and analyses steps. It is also a key requirement in multi-institutional collaborations to comply with privacy-preserving regulations. Existing automated methods, including Deep Learning (DL) based methods that have obtained state-of-the-art results in recent years, have primarily targeted brain extraction without considering pathologically-affected brains. Accordingly, they perform sub-optimally when applied on magnetic resonance imaging (MRI) brain scans with apparent pathologies such as brain tumors. Furthermore, existing methods focus on using only T1-weighted MRI scans, even though multi-parametric MRI (mpMRI) scans are routinely acquired for patients with suspected brain tumors. In this study, we present a comprehensive performance evaluation of recent deep learning architectures for brain extraction, training models on mpMRI scans of pathologically-affected brains, with a particular focus on seeking a practically-applicable, low computational footprint approach, generalizable across multiple institutions, further facilitating collaborations. We identified a large retrospective multi-institutional dataset of n = 3340 mpMRI brain tumor scans, with manually-inspected and approved gold-standard segmentations, acquired during standard clinical practice under varying acquisition protocols, both from private institutional data and public (TCIA) collections. To facilitate optimal utilization of rich mpMRI data, we further introduce and evaluate a novel “modality-agnostic training” technique that can be applied using any available modality, without need for model retraining. Our results indicate that the modality-agnostic approach 1 obtains accurate results, providing a generic and practical tool for brain extraction on scans with brain tumors.

Functional magnetic resonance imaging (fMRI) is an important tool for pre-surgical evaluation of eloquent cortex. Classic task-based paradigms require patient participation and individual imaging sequence acquisitions for each functional domain that is being assessed. Resting state fMRI (rs-fMRI), however, enables functional localization without patient participation and can evaluate numerous functional domains with a single imaging session. To date, post-processing of this resting state data has been resource intensive, which limits its widespread application for routine clinical use. Through a novel automated algorithm and advanced imaging IT structure, we report the clinical application and the large-scale integration of rs-fMRI into routine neurosurgical practice. One hundred and ninety one consecutive patients underwent a 3T rs-fMRI, 83 of whom also underwent both motor and language task-based fMRI. Data were processed using a novel, automated, multi-layer perceptron algorithm and integrated into stereotactic navigation using a streamlined IT imaging pipeline. One hundred eighty-five studies were performed for intracranial neoplasm, 14 for refractory epilepsy and 33 for vascular malformations or other neurological disorders. Failure rate of rs-fMRI of 13% was significantly better than that for task-based fMRI (38.5%,) (p <0.001). In conclusion, at Washington University in St. Louis, rs-fMRI has become an integral part of standard imaging for neurosurgical planning. Resting state fMRI can be used in all patients, and due to its lower failure rate than task-based fMRI, it is useful for patients who are unable to cooperate with task-based studies.