Does Second Language Learning Promote Neuroplasticity in Aging? A Systematic Review of Cognitive and Neuroimaging Studies.
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ABSTRACT:
SUBMITTER: Ware C
PROVIDER: S-EPMC8633567 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The number of the elderly across the globe will approximate 2.1 billion by 2050. Juxtaposed against this burgeoning segment of the population is evidence that nonpathological aging is associated with an increased risk for cognitive decline in a variety of domains, changes that can cause mild disability even before the onset of dementia. Given that pharmacological treatments that mitigate dementia are still outstanding, alternative therapeutic options are being investigated increasingly. The results from translational studies have shown that modifiable lifestyle factors-including physical activity, cognitive engagement, and diet-are a key strategy for maintaining brain health during aging. Indeed, a multiplicity of studies has demonstrated relationships between lifestyle factors, brain structure and function, and cognitive function in aging adults. For example, physical activity and diet modulate common neuroplasticity substrates (neurotrophic signaling, neurogenesis, inflammation, stress response, and antioxidant defense) in the brain whereas cognitive engagement enhances brain and cognitive reserve. The aims of this review are to evaluate the relationship between modifiable lifestyle factors, neuroplasticity, and optimal brain health during aging; to identify putative mechanisms that contribute positive brain aging; and to highlight future directions for scientists and clinicians. Undoubtedly, the translation of cutting-edge knowledge derived from the field of cognitive neuroscience will advance our understanding and enhance clinical treatment interventions as we endeavor to promote brain health during aging.
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Project description:Mild cognitive impairment (MCI) is a multifactorial and complex central neurodegenerative disease. Acupuncture appears to be an effective method for cognitive function improvement in MCI patients. Neural plasticity remaining in the MCI brain implies that acupuncture-associated benefits may not be limited to the cognitive function. Instead, neurological alternations in the brain play a vital role in corresponding to the cognitive improvement. However, previous studies have mainly focused on the effects of cognitive function, leaving neurological findings relatively unclear. This systematic review summarized existing studies that used various brain imaging techniques to explore the neurological effect regarding acupuncture use for MCI treatment. Potential neuroimaging trials were searched, collected, and identified independently by two researchers. Four Chinese databases, four English databases, and additional sources were searched to identify studies reporting the use of acupuncture for MCI from the inception of databases until 1 June 2022. Methodological quality was appraised using the Cochrane risk-of-bias tool. In addition, general, methodological, and brain neuroimaging information was extracted and summarized to investigate the potential neural mechanisms by which acupuncture affects patients with MCI. In total, 22 studies involving 647 participants were included. The methodological quality of the included studies was moderate to high. The methods used included functional magnetic resonance imaging, diffusion tensor imaging, functional near-infrared spectroscopy, and magnetic resonance spectroscopy. Acupuncture-induced brain alterations observed in those patients with MCI tended to be observable in the cingulate cortex, prefrontal cortex, and hippocampus. The effect of acupuncture on MCI may play a role in regulating the default mode network, central executive network, and salience network. Based on these studies, researchers could extend the recent research focus from the cognitive domain to the neurological level. Future researches should develop additional relevant, well-designed, high-quality, and multimodal neuroimaging researches to detect the effects of acupuncture on the brains of MCI patients.
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Project description:Research on proficient bilinguals has demonstrated that both languages are always active, even when only one is required. The coactivation of the two languages creates both competition and convergence, facilitating the processing of cognate words, but slowing lexical access when there is a requirement to engage control mechanisms to select the target language. Critically, these consequences are evident in the native language (L1) as well as in the second language (L2). The present study questioned whether L1 changes can be detected at early stages of L2 learning and how they are modulated by L2 proficiency. Native English speakers learning Spanish performed an English (L1) lexical decision task that included cognates while event-related potentials were recorded. They also performed verbal fluency, working memory, and inhibitory control tasks. A group of matched monolinguals performed the same tasks in English only. The results revealed that intermediate learners demonstrate a reduced N400 for cognates compared with noncognates in English (L1), and an emerging effect is visually present in beginning learners as well; however, no behavioral cognate effect was present for either group. In addition, slower reaction times in English (L1) are related to a larger cognate N400 magnitude in English (L1) and Spanish (L2), and to better inhibitory control for learners but not for monolinguals. The results suggest that contrary to the claim that L2 affects L1 only when L2 speakers are highly proficient, L2 learning begins to impact L1 early in the development of the L2 skill.
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Project description:The effects of acupuncture facilitating neural plasticity for treating diseases have been identified by clinical and experimental studies. In the last two decades, the application of neuroimaging techniques in acupuncture research provided visualized evidence for acupuncture promoting neuroplasticity. Recently, the integration of machine learning (ML) and neuroimaging techniques becomes a focus in neuroscience and brings a new and promising approach to understand the facilitation of acupuncture on neuroplasticity at the individual level. This review is aimed at providing an overview of this rapidly growing field by introducing the commonly used ML algorithms in neuroimaging studies briefly and analyzing the characteristics of the acupuncture studies based on ML and neuroimaging, so as to provide references for future research.
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Project description:We currently observe a disconcerting phenomenon in machine learning studies in psychiatry: While we would expect larger samples to yield better results due to the availability of more data, larger machine learning studies consistently show much weaker performance than the numerous small-scale studies. Here, we systematically investigated this effect focusing on one of the most heavily studied questions in the field, namely the classification of patients suffering from Major Depressive Disorder (MDD) and healthy controls based on neuroimaging data. Drawing upon structural MRI data from a balanced sample of N = 1868 MDD patients and healthy controls from our recent international Predictive Analytics Competition (PAC), we first trained and tested a classification model on the full dataset which yielded an accuracy of 61%. Next, we mimicked the process by which researchers would draw samples of various sizes (N = 4 to N = 150) from the population and showed a strong risk of misestimation. Specifically, for small sample sizes (N = 20), we observe accuracies of up to 95%. For medium sample sizes (N = 100) accuracies up to 75% were found. Importantly, further investigation showed that sufficiently large test sets effectively protect against performance misestimation whereas larger datasets per se do not. While these results question the validity of a substantial part of the current literature, we outline the relatively low-cost remedy of larger test sets, which is readily available in most cases.
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Project description:BackgroundWhile it is well established that second language (L2) learning success changes with age and across individuals, the underlying neural mechanisms responsible for this developmental shift and these individual differences are largely unknown. We will study the behavioral and neural factors that subserve new grammar and word learning in a large cross-sectional developmental sample. This study falls under the NWO (Nederlandse Organisatie voor Wetenschappelijk Onderzoek [Dutch Research Council]) Language in Interaction consortium (website: https://www.languageininteraction.nl/ ).MethodsWe will sample 360 healthy individuals across a broad age range between 8 and 25 years. In this paper, we describe the study design and protocol, which involves multiple study visits covering a comprehensive behavioral battery and extensive magnetic resonance imaging (MRI) protocols. On the basis of these measures, we will create behavioral and neural fingerprints that capture age-based and individual variability in new language learning. The behavioral fingerprint will be based on first and second language proficiency, memory systems, and executive functioning. We will map the neural fingerprint for each participant using the following MRI modalities: T1-weighted, diffusion-weighted, resting-state functional MRI, and multiple functional-MRI paradigms. With respect to the functional MRI measures, half of the sample will learn grammatical features and half will learn words of a new language. Combining all individual fingerprints allows us to explore the neural maturation effects on grammar and word learning.DiscussionThis will be one of the largest neuroimaging studies to date that investigates the developmental shift in L2 learning covering preadolescence to adulthood. Our comprehensive approach of combining behavioral and neuroimaging data will contribute to the understanding of the mechanisms influencing this developmental shift and individual differences in new language learning. We aim to answer: (I) do these fingerprints differ according to age and can these explain the age-related differences observed in new language learning? And (II) which aspects of the behavioral and neural fingerprints explain individual differences (across and within ages) in grammar and word learning? The results of this study provide a unique opportunity to understand how the development of brain structure and function influence new language learning success.
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