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Martha E. Shenton

Researcher at Brigham and Women's Hospital

Publications -  626
Citations -  48184

Martha E. Shenton is an academic researcher from Brigham and Women's Hospital. The author has contributed to research in topics: Schizophrenia & Fractional anisotropy. The author has an hindex of 106, co-authored 586 publications receiving 44244 citations. Previous affiliations of Martha E. Shenton include Cambridge Health Alliance & Beth Israel Deaconess Medical Center.

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Clinical appraisal of chronic traumatic encephalopathy: current perspectives and future directions.

TL;DR: Several in-vivo procedures each have the potential to contribute unique information about the manifestations of CTE, including clinical and preclinical stages, and more research is needed to develop a set of consensus diagnostic criteria for chronic traumatic encephalopathy.
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An MRI study of temporal lobe abnormalities and negative symptoms in chronic schizophrenia

TL;DR: The reduction in gray matter of the superior temporal gyrus in patients with schizophrenia is consistent with previous findings, and is noteworthy in that it was found in this group of patients with predominantly negative symptoms.
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Button-pressing affects P300 amplitude and scalp topography.

TL;DR: Button-pressing generates smaller P300 than silent-counting and P300 topography in button-pressed tasks is confounded by motor potentials, which can be corrected with a motor potential estimate.
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The Application of DTI to Investigate White Matter Abnormalities in Schizophrenia

TL;DR: Improvements in MR acquisition and image processing, including the introduction of positron emission tomography (PET), followed by functional MR (fMRI), and diffusion tensor imaging (DTI), have led to an appreciation of the critical role that brain abnormalities play in schizophrenia.
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A Hierarchical Algorithm for MR Brain Image Parcellation

TL;DR: An algorithm for segmenting brain magnetic resonance (MR) images into anatomical compartments such as the major tissue classes and neuro-anatomical structures of the gray matter, guided by prior information represented within a tree structure is introduced.