The processing of numerals as visual objects is supported by an "Inferior Temporal Numeral Area" (ITNA) in the bilateral inferior temporal gyri (ITG). Extant findings suggest some degree of hemispheric asymmetry in how the bilateral ITNAs process numerals. Pollack and Price (2019) reported such a hemispheric asymmetry by which a region in the left ITG was sensitive to digits during a visual search for a digit among letters, and a homologous region in the right ITG that showed greater digit sensitivity in individuals with higher calculation skills. However, the ITG regions were localized with separate analyses without directly contrasting their digit sensitivities and relation to calculation skills. So, the extent of and reasons for these functional asymmetries remain unclear. Here we probe whether the functional and representational properties of the ITNAs are asymmetric by applying both univariate and multivariate region-of-interest analyses to Pollack and Price's (2019) data. Contrary to the implications of the original findings, digit sensitivity did not differ between ITNAs, and digit sensitivity in both left and right ITNAs was associated with calculation skills. Representational similarity analyses revealed that the overall representational geometries of digits in the ITNAs were also correlated, albeit weakly, but the representational contents of the ITNAs were largely inconclusive. Nonetheless, we found a right lateralization in engagement in alphanumeric categorization, and that the right ITNA showed greater discriminability between digits and letters. Greater right lateralization of digit sensitivity and digit discriminability in the left ITNA were also related to higher calculation skills. Our findings thus suggest that the ITNAs may not be functionally identical and should be directly contrasted in future work. Our study also highlights the importance of within-individual comparisons for understanding hemispheric asymmetries, and analyses of individual differences and multivariate features to uncover effects that would otherwise be obscured by averages.
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http://dx.doi.org/10.1016/j.cortex.2023.08.018 | DOI Listing |
Alzheimers Dement
December 2024
Knight Alzheimer Disease Research Center, St. Louis, MO, USA.
Background: The ability to detect cognitive impairment from Alzheimer Disease (AD) in its earliest possible symptomatic stage is a highly desirable characteristic for neuropsychological measures. Because early cognitive changes are often subtle, measures with high sensitivity are of great importance. Variability in attention, often assessed using reaction time (RT) tasks, have been shown to discriminate between cognitively normal older individuals with and without positive AD biomarkers and is correlated with biological markers of neurodegeneration.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
University of North Texas Health Science Center, Fort Worth, TX, USA.
Background: Mild cognitive function (MCI) is associated with a declined short-term memory (STM). This study compared STM between adults with MCI and normal cognition assessed by verbal memory vs visuospatial memory.
Methods: Sixteen subjects with MCI and 11 subjects with normal cognition gave their written consent to participate in the study which was approved by the North Texas Regional IRB.
Alzheimers Dement
December 2024
Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom.
Background: Impaired auditory verbal working memory is a diagnostic hallmark and integral driver of the clinical phenotype in logopenic variant primary progressive aphasia (lvPPA). However, the physiology of the working memory buffer in this syndrome is poorly characterised. Here we addressed the temporal dynamics of auditory verbal working memory in patients with lvPPA and typical Alzheimer's disease (tAD).
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Penn Alzheimer's Disease Research Center, University of Pennsylvania, Philadelphia, PA, USA.
Background: This study assesses the sensitivity of the mobile cognitive app performance platform (mCAPP), a mobile and engaging cognitive assessment tool, to participant sleep duration.
Method: The mCAPP includes three gamified tasks: a memory task ("Concentration"), a stroop-like task ("Brick Drop"), and a digit-symbol coding-like task ("Space Imposters"). For all games, shorter reaction times and fewer guesses indicates better performance.
Mayo Clin Proc Digit Health
December 2024
School of Computed and Augmented Intelligence, Arizona State University, Tempe, AZ.
Objective: To report the development and performance of 2 distinct deep learning models trained exclusively on retinal color fundus photographs to classify Alzheimer disease (AD).
Patients And Methods: Two independent datasets (UK Biobank and our tertiary academic institution) of good-quality retinal photographs derived from patients with AD and controls were used to build 2 deep learning models, between April 1, 2021, and January 30, 2024. ADVAS is a U-Net-based architecture that uses retinal vessel segmentation.
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