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  1. Article ; Online: Dissociable neural routes to successful prospective memory.

    McDaniel, Mark A / Lamontagne, Pamela / Beck, Stefanie M / Scullin, Michael K / Braver, Todd S

    Psychological science

    2013  Volume 24, Issue 9, Page(s) 1791–1800

    Abstract: Identifying the processes by which people remember to execute an intention at an appropriate moment (prospective memory) remains a fundamental theoretical challenge. According to one account, top-down attentional control is required to maintain ... ...

    Abstract Identifying the processes by which people remember to execute an intention at an appropriate moment (prospective memory) remains a fundamental theoretical challenge. According to one account, top-down attentional control is required to maintain activation of the intention, initiate intention retrieval, or support monitoring. A diverging account suggests that bottom-up, spontaneous retrieval can be triggered by cues that have been associated with the intention and that sustained attentional processes are not required. We used a specialized experimental design and functional MRI 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 the 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.
    MeSH term(s) Adolescent ; Adult ; Attention/physiology ; Brain/physiology ; Brain Mapping/methods ; Cues ; Humans ; Intention ; Magnetic Resonance Imaging/methods ; Memory/physiology ; Neural Pathways/physiology ; Psychomotor Performance/physiology ; Young Adult
    Language English
    Publishing date 2013-08-01
    Publishing country United States
    Document type Journal Article ; Randomized Controlled Trial ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 2022256-7
    ISSN 1467-9280 ; 0956-7976
    ISSN (online) 1467-9280
    ISSN 0956-7976
    DOI 10.1177/0956797613481233
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Integration of resting state functional MRI into clinical practice - A large single institution experience.

    Leuthardt, Eric C / Guzman, Gloria / Bandt, S Kathleen / Hacker, Carl / Vellimana, Ananth K / Limbrick, David / Milchenko, Mikhail / Lamontagne, Pamela / Speidel, Benjamin / Roland, Jarod / Miller-Thomas, Michelle / Snyder, Abraham Z / Marcus, Daniel / Shimony, Joshua / Benzinger, Tammie L S

    PloS one

    2018  Volume 13, Issue 6, Page(s) e0198349

    Abstract: 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 ... ...

    Abstract 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.
    MeSH term(s) Adolescent ; Adult ; Aged ; Aged, 80 and over ; Algorithms ; Brain Neoplasms/diagnostic imaging ; Cerebral Cortex/diagnostic imaging ; Child ; Child, Preschool ; Drug Resistant Epilepsy/diagnostic imaging ; Female ; Humans ; Image Interpretation, Computer-Assisted ; Magnetic Resonance Imaging/methods ; Male ; Middle Aged ; Nervous System Diseases/diagnostic imaging ; Preoperative Period ; Rest ; Vascular Malformations/diagnostic imaging ; Young Adult
    Language English
    Publishing date 2018-06-22
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ISSN 1932-6203
    ISSN (online) 1932-6203
    DOI 10.1371/journal.pone.0198349
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: The Brain Tumor Segmentation (BraTS) Challenge 2023:

    Li, Hongwei Bran / Conte, Gian Marco / Anwar, Syed Muhammad / Kofler, Florian / Ezhov, Ivan / van Leemput, Koen / Piraud, Marie / Diaz, Maria / Cole, Byrone / Calabrese, Evan / Rudie, Jeff / Meissen, Felix / Adewole, Maruf / Janas, Anastasia / Kazerooni, Anahita Fathi / LaBella, Dominic / Moawad, Ahmed W / Farahani, Keyvan / Eddy, James /
    Bergquist, Timothy / Chung, Verena / Shinohara, Russell Takeshi / Dako, Farouk / Wiggins, Walter / Reitman, Zachary / Wang, Chunhao / Liu, Xinyang / Jiang, Zhifan / Familiar, Ariana / Johanson, Elaine / Meier, Zeke / Davatzikos, Christos / Freymann, John / Kirby, Justin / Bilello, Michel / Fathallah-Shaykh, Hassan M / Wiest, Roland / Kirschke, Jan / Colen, Rivka R / Kotrotsou, Aikaterini / Lamontagne, Pamela / Marcus, Daniel / Milchenko, Mikhail / Nazeri, Arash / Weber, Marc-André / Mahajan, Abhishek / Mohan, Suyash / Mongan, John / Hess, Christopher / Cha, Soonmee / Villanueva-Meyer, Javier / Colak, Errol / Crivellaro, Priscila / Jakab, Andras / Albrecht, Jake / Anazodo, Udunna / Aboian, Mariam / Yu, Thomas / Baid, Ujjwal / Bakas, Spyridon / Linguraru, Marius George / Menze, Bjoern / Iglesias, Juan Eugenio / Wiestler, Benedikt

    ArXiv

    2023  

    Abstract: Automated brain tumor segmentation methods have become well-established and reached performance levels offering clear clinical utility. These methods typically rely on four input magnetic resonance imaging (MRI) modalities: T1-weighted images with and ... ...

    Abstract Automated brain tumor segmentation methods have become well-established and reached performance levels offering clear clinical utility. These methods typically rely on four input magnetic resonance imaging (MRI) modalities: T1-weighted images with and without contrast enhancement, T2-weighted images, and FLAIR images. However, some sequences are often missing in clinical practice due to time constraints or image artifacts, such as patient motion. Consequently, the ability to substitute missing modalities and gain segmentation performance is highly desirable and necessary for the broader adoption of these algorithms in the clinical routine. In this work, we present the establishment of the Brain MR Image Synthesis Benchmark (BraSyn) in conjunction with the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2023. The primary objective of this challenge is to evaluate image synthesis methods that can realistically generate missing MRI modalities when multiple available images are provided. The ultimate aim is to facilitate automated brain tumor segmentation pipelines. The image dataset used in the benchmark is diverse and multi-modal, created through collaboration with various hospitals and research institutions.
    Language English
    Publishing date 2023-06-28
    Publishing country United States
    Document type Preprint
    ISSN 2331-8422
    ISSN (online) 2331-8422
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Book ; Online: The Brain Tumor Segmentation (BraTS) Challenge 2023

    Kofler, Florian / Meissen, Felix / Steinbauer, Felix / Graf, Robert / Oswald, Eva / de da Rosa, Ezequiel / Li, Hongwei Bran / Baid, Ujjwal / Hoelzl, Florian / Turgut, Oezguen / Horvath, Izabela / Waldmannstetter, Diana / Bukas, Christina / Adewole, Maruf / Anwar, Syed Muhammad / Janas, Anastasia / Kazerooni, Anahita Fathi / LaBella, Dominic / Moawad, Ahmed W /
    Farahani, Keyvan / Eddy, James / Bergquist, Timothy / Chung, Verena / Shinohara, Russell Takeshi / Dako, Farouk / Wiggins, Walter / Reitman, Zachary / Wang, Chunhao / Liu, Xinyang / Jiang, Zhifan / Familiar, Ariana / Conte, Gian-Marco / Johanson, Elaine / Meier, Zeke / Davatzikos, Christos / Freymann, John / Kirby, Justin / Bilello, Michel / Fathallah-Shaykh, Hassan M / Wiest, Roland / Kirschke, Jan / Colen, Rivka R / Kotrotsou, Aikaterini / Lamontagne, Pamela / Marcus, Daniel / Milchenko, Mikhail / Nazeri, Arash / Weber, Marc-André / Mahajan, Abhishek / Mohan, Suyash / Mongan, John / Hess, Christopher / Cha, Soonmee / Villanueva-Meyer, Javier / Colak, Errol / Crivellaro, Priscila / Jakab, Andras / Albrecht, Jake / Anazodo, Udunna / Aboian, Mariam / Iglesias, Juan Eugenio / Van Leemput, Koen / Bakas, Spyridon / Rueckert, Daniel / Wiestler, Benedikt / Ezhov, Ivan / Piraud, Marie / Menze, Bjoern

    Local Synthesis of Healthy Brain Tissue via Inpainting

    2023  

    Abstract: A myriad of algorithms for the automatic analysis of brain MR images is available to support clinicians in their decision-making. For brain tumor patients, the image acquisition time series typically starts with a scan that is already pathological. This ... ...

    Abstract A myriad of algorithms for the automatic analysis of brain MR images is available to support clinicians in their decision-making. For brain tumor patients, the image acquisition time series typically starts with a scan that is already pathological. This poses problems, as many algorithms are designed to analyze healthy brains and provide no guarantees for images featuring lesions. Examples include but are not limited to algorithms for brain anatomy parcellation, tissue segmentation, and brain extraction. To solve this dilemma, we introduce the BraTS 2023 inpainting challenge. Here, the participants' task is to explore inpainting techniques to synthesize healthy brain scans from lesioned ones. The following manuscript contains the task formulation, dataset, and submission procedure. Later it will be updated to summarize the findings of the challenge. The challenge is organized as part of the BraTS 2023 challenge hosted at the MICCAI 2023 conference in Vancouver, Canada.

    Comment: 5 pages, 1 figure
    Keywords Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning
    Subject code 004 ; 006
    Publishing date 2023-05-15
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Book ; Online: The Brain Tumor Segmentation (BraTS) Challenge 2023

    Li, Hongwei Bran / Conte, Gian Marco / Anwar, Syed Muhammad / Kofler, Florian / Ezhov, Ivan / van Leemput, Koen / Piraud, Marie / Diaz, Maria / Cole, Byrone / Calabrese, Evan / Rudie, Jeff / Meissen, Felix / Adewole, Maruf / Janas, Anastasia / Kazerooni, Anahita Fathi / LaBella, Dominic / Moawad, Ahmed W. / Farahani, Keyvan / Eddy, James /
    Bergquist, Timothy / Chung, Verena / Shinohara, Russell Takeshi / Dako, Farouk / Wiggins, Walter / Reitman, Zachary / Wang, Chunhao / Liu, Xinyang / Jiang, Zhifan / Familiar, Ariana / Johanson, Elaine / Meier, Zeke / Davatzikos, Christos / Freymann, John / Kirby, Justin / Bilello, Michel / Fathallah-Shaykh, Hassan M. / Wiest, Roland / Kirschke, Jan / Colen, Rivka R. / Kotrotsou, Aikaterini / Lamontagne, Pamela / Marcus, Daniel / Milchenko, Mikhail / Nazeri, Arash / Weber, Marc André / Mahajan, Abhishek / Mohan, Suyash / Mongan, John / Hess, Christopher / Cha, Soonmee / Villanueva, Javier / Colak, Meyer Errol / Crivellaro, Priscila / Jakab, Andras / Albrecht, Jake / Anazodo, Udunna / Aboian, Mariam / Yu, Thomas / Baid, Ujjwal / Bakas, Spyridon / Linguraru, Marius George / Menze, Bjoern / Iglesias, Juan Eugenio / Wiestler, Benedikt

    Brain MR Image Synthesis for Tumor Segmentation (BraSyn)

    2023  

    Abstract: Automated brain tumor segmentation methods have become well-established and reached performance levels offering clear clinical utility. These methods typically rely on four input magnetic resonance imaging (MRI) modalities: T1-weighted images with and ... ...

    Abstract Automated brain tumor segmentation methods have become well-established and reached performance levels offering clear clinical utility. These methods typically rely on four input magnetic resonance imaging (MRI) modalities: T1-weighted images with and without contrast enhancement, T2-weighted images, and FLAIR images. However, some sequences are often missing in clinical practice due to time constraints or image artifacts, such as patient motion. Consequently, the ability to substitute missing modalities and gain segmentation performance is highly desirable and necessary for the broader adoption of these algorithms in the clinical routine. In this work, we present the establishment of the Brain MR Image Synthesis Benchmark (BraSyn) in conjunction with the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2023. The primary objective of this challenge is to evaluate image synthesis methods that can realistically generate missing MRI modalities when multiple available images are provided. The ultimate aim is to facilitate automated brain tumor segmentation pipelines. The image dataset used in the benchmark is diverse and multi-modal, created through collaboration with various hospitals and research institutions.

    Comment: Technical report of BraSyn
    Keywords Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition
    Subject code 004
    Publishing date 2023-05-15
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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