Publications by authors named "H Gurvit"

Clinical cognitive decline, leading to Alzheimer's Disease Dementia (ADD), has long been interpreted as a disconnection syndrome, hindering the information flow capacity of the brain, hence leading to the well-known symptoms of ADD. The structural and functional brain connectome analyses play a central role in studies of brain from this perspective. However, most current research implicitly assumes that the changes accompanying the progression of cognitive decline are monotonous in time, whether measured across the entire brain or in fixed cortical regions.

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Objective: Memory processes known to be impaired in Alzheimer's disease (AD) are maintained by a large-scale neurocognitive network with subcortical components, including the thalamus. Therefore, we aimed to examine the volumetric and functional changes of the thalamic nuclei at different scales across AD stages.

Methods: MRI data of patients diagnosed with 20 AD dementia (ADD), 30 amnestic mild cognitive impairment (MCI), and 30 subjective cognitive impairment (SCI) were used.

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Article Synopsis
  • A multicenter study involving 18 centers in 12 countries examined 360 patients with frontotemporal dementia (FTD) characterized by right anterior temporal lobe (RATL) atrophy to create a unified clinical description of the syndrome.
  • Key symptoms identified in patients included mental rigidity (78%), disinhibition (74%), and naming difficulties (70%), with cognitive tests revealing specific deficits in social interactions and emotional recognition, though lacking in measuring mental rigidity.
  • The findings represent the largest cohort of its kind, highlighting under-acknowledged symptoms of RATL and providing valuable insights for clinicians to improve early diagnosis and management of FTD patients.
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Background And Objective: Alzheimer's disease dementia (ADD) is well known to induce alterations in both structural and functional brain connectivity. However, reported changes in connectivity are mostly limited to global/local network features, which have poor specificity for diagnostic purposes. Following recent advances in machine learning, deep neural networks, particularly Graph Neural Network (GNN) based approaches, have found applications in brain research as well.

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Background And Purpose: Studies have found that up to 73% of COVID-19 patients experience hyposmia. It is unclear if the loss of smell in COVID-19 is due to damage to the peripheral or central mechanisms. This study aimed to explore the impacts of COVID-19-induced hyposmia on brain structure and cognitive functions.

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