Publications by authors named "K Whittingstall"

Objective: Explore how anatomical measurements and field modeling can be leveraged to improve investigations of transcranial magnetic stimulation (TMS) effects on both motor and non-motor TMS targets.

Methods: TMS motor effects (targeting the primary motor cortex [M1]) were evaluated using the resting motor threshold (rMT), while TMS non-motor effects (targeting the superior temporal gyrus [STG]) were assessed using a pain memory task. Anatomical measurements included scalp-cortex distance (SCD) and cortical thickness (CT), whereas field modeling encompassed the magnitude of the electric field (E) induced by TMS.

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Objective Therapeutic relationship is a cornerstone in the treatment of schizophrenia. However, certain personal factors on the part of psychiatrists can hinder it, notably the lack of experience with auditory hallucinations. Such factors can lead to a decrease in empathy and, consequently, a negative alteration in the therapeutic relationship.

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Background: The basal ganglia are strongly connected to the primary motor cortex (M1) and play a crucial role in movement control. Interestingly, several disorders showing abnormal neurotransmitter levels in basal ganglia also present concomitant anomalies in intracortical function within M1.

Objective/hypothesis: The main aim of this study was to clarify the relationship between neurotransmitter content in the basal ganglia and intracortical function at M1 in healthy individuals.

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Background And Objectives: Rates of cerebrovascular disease increase after menopause, which is often attributed to the absence of hormones. It remains unknown whether the cumulative exposure to hormones across a female person's premenopausal life extends the window of cerebrovascular protection to the postmenopausal period. To investigate this, we examined the relationship between lifetime hormone exposure (LHE) and cerebral small vessel disease in more than 9,000 postmenopausal women in the UK-Biobank.

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Background: The accurate segmentation, labeling and quantification of cerebral blood vessels on MR imaging is important for basic and clinical research, yet results are not generalizable, and often require user intervention. New methods are needed to automate this process.

Purpose: To automatically segment, label and quantify Circle of Willis (CW) arteries on Magnetic Resonance Angiography images using deep convolutional neural networks.

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