Age‐related changes in metabolic profiles of rat hippocampus and cortices

International audienceAbstract The time course of metabolic changes was investigated in the hippocampus and the parietal, rhinal and frontal cortices of rats from 4 to 30 months old. Samples were analysed by the solid‐state high‐resolution magic angle spinning nuclear magnetic resonance method. Quantification was performed with the quest procedure of j mrui software. Eighteen metabolites were identified and separated in the spectrum. Six of them were not age sensitive, in particular alanine, glutamine and lactate. In contrast, choline, glycerophosphocholine, myo ‐inositol, N ‐acetylaspartate, scyllo ‐inositol (s‐Ins) and taurine (Tau) were notably altered over aging. Interestingly, each age group showed a specific metabolic profile. The concentration of metabolites such as Tau was altered in middle‐aged rats only, whereas the s‐Ins level decreased in old rats only. Most metabolites showed progressive alteration during the process of aging, which was initiated during the middle‐aged period (18 months). Taken together, these results suggest that cell membrane integrity is perturbed with age. Each brain region investigated had distinctive qualitative and/or quantitative metabolic age‐related features. These age‐related changes would affect network connectivities and then cognitive functions

A multidimensional model of memory complaints in older individuals and the associated hub regions

International audienceMemory complaints are highly prevalent among middle-aged and older adults, and they are frequently reported in individuals experiencing subjective cognitive decline (SCD). SCD has received increasing attention due to its implications for the early detection of dementia. This study aims to advance our comprehension of individuals with SCD by elucidating potential cognitive/psychologiccontributing factors and characterizing cerebral hubs within the brain network. To identify these potential contributing factors, a structural equation modeling approach was employed to investigate the relationships between various factors, such as metacognitive beliefs, personality, anxiety, depression, self-esteem, and resilience, and memory complaints. Our findings revealed that self-esteem and conscientiousness significantly influenced memory complaints. At the cerebral level, analysis of delta and theta electroencephalographic frequency bands recorded during rest was conducted to identify hub regions using a local centrality metric known as betweenness centrality. Notably, our study demonstrated that certain brain regions undergo changes in their hub roles in response to the pathology of SCD. Specifically, the inferior temporal gyrus and the left orbitofrontal area transition into hubs, while the dorsolateral prefrontal cortex and the middle temporal gyrus lose their hub function in the presence of SCD. This rewiring of the neural network may be interpreted as a compensatory response employed by the brain in response to SCD, wherein functional connectivity is maintained or restored by reallocating resources to other regions

Gene regulation in the rat prefrontal cortex after learning with or without cholinergic insult

International audienceThe prefrontal cortex is essential for a wide variety of higher functions, including attention and memory. Cholinergic neurons are thought to be of prime importance in the modulation of these processes. Degeneration of forebrain cholinergic neurons has been linked to several neurological disorders. The present study was designed to identify genes and networks in rat prefrontal cortex that are associated with learning and cholinergic-loss-memory deficit. Affymetrix microarray technology was used to screen gene expression changes in rats submitted or not to 192 IgG-saporin immunolesion of cholinergic basal forebrain and trained in spatial/object novelty tasks. Results showed learning processes were associated with significant expression of genes, which were organized in several clusters of highly correlated genes and would be involved in biological processes such as intracellular signaling process, transcription regulation, and filament organization and axon guidance. Memory loss following cortical cholinergic deafferentation was associated with significant expression of genes belonging to only one clearly delineated cluster and would be involved in biological processes related to cytoskeleton organization and proliferation, and glial and vascular remodeling, i.e., in processes associated with brain repair after injury. (C) 2011 Elsevier Inc. All rights reserved

Gene expression profile in rat hippocampus with and without memory deficit

International audienceThe cholinergic neuronal system, through its projections to the hippocampus, plays an important role in learning and memory. The aim of the study was to identify genes and networks in rat hippocampus with and without memory deficit. Genome-scale screening was used to analyze gene expression changes in rats submitted or not to intraparenchymal injection of 192 IgG-saporin and trained in spatial/object novelty tasks. Results showed learning processes were associated with significant expression of genes that could be grouped into several clusters of similar expression profiles and that are involved in biological functions, namely lipid metabolism, signal transduction, protein metabolism and modification, and transcription regulation. Memory loss following hippocampal cholinergic deafferentation was associated with significant expression of genes that did not show similar cluster organization. Only one cluster of genes could be identified; it included genes that would be involved in tissue remodeling. More important, most of the genes significantly altered in lesioned rats were down-regulated. (c) 2010 Elsevier Inc. All rights reserved

Benefits of computer-based memory and attention training in healthy older adults

International audienceMultifactorial cognitive training programs have a positive effect on cognition in healthy older adults. Among the age-sensitive cognitive domains, episodic memory is the most affected. In the present study, we evaluated the benefits on episodic memory of a computer-based memory and attention training. We targeted consciously controlled processes at encoding and minimizing processing at retrieval, by using more familiarity than recollection during recognition. Such an approach emphasizes processing at encoding and prevents subjects from reinforcing their own errors. Results showed that the training improved recognition performances and induced near transfer to recall. The largest benefits, however, were for tasks with high mental load. Improvement in free recall depended on the modality to recall; semantic recall was improved but not spatial recall. In addition, a far transfer was also observed with better memory self-perception and self-esteem of the participants. Finally, at 6-month follow up, maintenance of benefits was observed only for semantic free recall. The challenge now is to corroborate far transfer by objective measures of everyday life executive functioning

Predicting creative behavior using resting-state electroencephalography

International audienceNeuroscience research has shown that specific brain patterns can relate to creativity during multiple tasks but also at rest. Nevertheless, the electrophysiological correlates of a highly creative brain remain largely unexplored. This study aims to uncover resting-state networks related to creative behavior using high-density electroencephalography (HD-EEG) and to test whether the strength of functional connectivity within these networks could predict individual creativity in novel subjects. We acquired resting state HD-EEG data from 90 healthy participants who completed a creative behavior inventory. We then employed connectome-based predictive modeling; a machine-learning technique that predicts behavioral measures from brain connectivity features. Using a support vector regression, our results reveal functional connectivity patterns related to high and low creativity, in the gamma frequency band (30-45 Hz). In leave-one-out cross-validation, the combined model of high and low networks predicts individual creativity with very good accuracy (r = 0.36, p = 0.00045). Furthermore, the model’s predictive power is established through external validation on an independent dataset (N = 41), showing a statistically significant correlation between observed and predicted creativity scores (r = 0.35, p = 0.02). These findings reveal large-scale networks that could predict creative behavior at rest, providing a crucial foundation for developing HD-EEG-network-based markers of creativity