This report investigates in what way functional connectivity may explain how two memory systems that share almost all their structures, can function as separate systems. The first series of experiments was aimed at demonstrating the reliability of our experimental design by showing that acquisition of the spatial version of a water cross-maze task (stimulus-stimulus associations) was impaired by dorsal hippocampal lesions whereas the cue version (stimulus-reinforcement association) was altered by amygdala lesion. Then, we evaluated how these two tasks induce different patterns of connectivity. The connectivity was evaluated by calculating the correlations between the zif-268 immunoreactivity of 22 structures composing the hippocampus and the amygdala systems. We designed a new statistical procedure to demonstrate double dissociations on the basis of brain regional intercorrelations. Our data show that the correlations between the hippocampus and the other structures of the memory system are higher in the place-learning group compared to the cue-learning group, whereas they are enhanced with the amygdala in the latter group compared to the former. This demonstrates that the activation of a memory system consists in the focusing of functional connectivity toward the central structure of the system. This may explain how several memory systems can share the same structures while remaining independent.
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http://dx.doi.org/10.1016/j.bbr.2009.06.016 | DOI Listing |
Alzheimers Res Ther
January 2025
Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093, USA.
Background: PSEN1, PSEN2, and APP mutations cause Alzheimer's disease (AD) with an early age at onset (AAO) and progressive cognitive decline. PSEN1 mutations are more common and generally have an earlier AAO; however, certain PSEN1 mutations cause a later AAO, similar to those observed in PSEN2 and APP.
Methods: We examined whether common disease endotypes exist across these mutations with a later AAO (~ 55 years) using hiPSC-derived neurons from familial Alzheimer's disease (FAD) patients harboring mutations in PSEN1, PSEN2, and APP and mechanistically characterized by integrating RNA-seq and ATAC-seq.
BMC Med Res Methodol
January 2025
Systems Engineering & Operations Research, George Mason University, Fairfax, VA, 22030, USA.
Background: In this work, we implement a data-driven approach using an aggregation of several analytical methods to study the characteristics of COVID-19 daily infection and death time series and identify correlations and characteristic trends that can be corroborated to the time evolution of this disease. The datasets cover twelve distinct countries across six continents, from January 22, 2020 till March 1, 2022. This time span is partitioned into three windows: (1) pre-vaccine, (2) post-vaccine and pre-omicron (BA.
View Article and Find Full Text PDFIntroduction: Dual-task (DT) exercises combine both physical and cognitive activities and have the potential to efficiently enhance both physical and cognitive function.
Background/objectives: This study aimed to determine if, compared with exercise-only (EO) and control (C) groups, adults in a DT training program improved measures of cognitive and/or physical functioning.
Methods: Thirty-five participants (Mage = 65.
PLoS One
January 2025
Department of Mathematics and General Sciences, Prince Sultan University, Riyadh, Saudi Arabia.
This study proposes and analyses a revised predator-prey model that accounts for a twofold Allee impact on the rate of prey population expansion. Employing the Caputo fractional-order derivative, we account for memory impact on the suggested model. We proceed to examine the significant mathematical aspects of the suggested model, including the uniqueness, non-negativity, boundedness, and existence of solutions to the noninteger order system.
View Article and Find Full Text PDFChaos
January 2025
AIMdyn, Inc., Santa Barbara, California 93101, USA.
Koopman operator theory has found significant success in learning models of complex, real-world dynamical systems, enabling prediction and control. The greater interpretability and lower computational costs of these models, compared to traditional machine learning methodologies, make Koopman learning an especially appealing approach. Despite this, little work has been performed on endowing Koopman learning with the ability to leverage its own failures.
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