{"title":"Pramipexole for the Treatment of Depression: Efficacy and Mechanisms.","authors":"Don Chamith Halahakoon, Michael Browning","doi":"10.1007/7854_2023_458","DOIUrl":"10.1007/7854_2023_458","url":null,"abstract":"<p><p>Dopaminergic mechanisms are a plausible treatment target for patients with clinical depression but are relatively underexplored in conventional antidepressant medications. There is continuing interest in the potential antidepressant effects of the dopamine receptor agonist, pramipexole, with data from both case series and controlled trials indicating that this agent may produce benefit for patients with difficult-to-treat depression. Pramipexole's therapeutic utility in depression is likely to be expressed through alterations in reward mechanisms which are strongly influenced by dopamine pathways and are known to function abnormally in depressed patients. Our work in healthy participants using brain imaging in conjunction with computational modelling suggests that repeated pramipexole facilitates reward learning by inhibiting value decay. This mechanism needs to be confirmed in larger clinical trials in depressed patients. Such studies will also allow assessment of whether baseline performance in reward learning in depression predicts therapeutic response to pramipexole treatment.</p>","PeriodicalId":11257,"journal":{"name":"Current topics in behavioral neurosciences","volume":" ","pages":"49-65"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138046592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jonathan Downar, Shan H Siddiqi, Anish Mitra, Nolan Williams, Conor Liston
{"title":"Mechanisms of Action of TMS in the Treatment of Depression.","authors":"Jonathan Downar, Shan H Siddiqi, Anish Mitra, Nolan Williams, Conor Liston","doi":"10.1007/7854_2024_483","DOIUrl":"10.1007/7854_2024_483","url":null,"abstract":"<p><p>Transcranial magnetic stimulation (TMS) is entering increasingly widespread use in treating depression. The most common stimulation target, in the dorsolateral prefrontal cortex (DLPFC), emerged from early neuroimaging studies in depression. Recently, more rigorous casual methods have revealed whole-brain target networks and anti-networks based on the effects of focal brain lesions and focal brain stimulation on depression symptoms. Symptom improvement during therapeutic DLPFC-TMS appears to involve directional changes in signaling between the DLPFC, subgenual and dorsal anterior cingulate cortex, and salience-network regions. However, different networks may be involved in the therapeutic mechanisms for other TMS targets in depression, such as dorsomedial prefrontal cortex or orbitofrontal cortex. The durability of therapeutic effects for TMS involves synaptic neuroplasticity, and specifically may depend upon dopamine acting at the D1 receptor family, as well as NMDA-receptor-dependent synaptic plasticity mechanisms. Although TMS protocols are classically considered 'excitatory' or 'inhibitory', the actual effects in individuals appear quite variable, and might be better understood at the level of populations of synapses rather than individual synapses. Synaptic meta-plasticity may provide a built-in protective mechanism to avoid runaway facilitation or inhibition during treatment, and may account for the relatively small number of patients who worsen rather than improve with TMS. From an ethological perspective, the antidepressant effects of TMS may involve promoting a whole-brain attractor state associated with foraging/hunting behaviors, centered on the rostrolateral periaqueductal gray and salience network, and suppressing an attractor state associated with passive threat defense, centered on the ventrolateral periaqueductal gray and default-mode network.</p>","PeriodicalId":11257,"journal":{"name":"Current topics in behavioral neurosciences","volume":" ","pages":"233-277"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141283252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Christopher R K Ching, Melody J Y Kang, Paul M Thompson
{"title":"Large-Scale Neuroimaging of Mental Illness.","authors":"Christopher R K Ching, Melody J Y Kang, Paul M Thompson","doi":"10.1007/7854_2024_462","DOIUrl":"10.1007/7854_2024_462","url":null,"abstract":"<p><p>Neuroimaging has provided important insights into the brain variations related to mental illness. Inconsistencies in prior studies, however, call for methods that lead to more replicable and generalizable brain markers that can reliably predict illness severity, treatment course, and prognosis. A paradigm shift is underway with large-scale international research teams actively pooling data and resources to drive consensus findings and test emerging methods aimed at achieving the goals of precision psychiatry. In parallel with large-scale psychiatric genomics studies, international consortia combining neuroimaging data are mapping the transdiagnostic brain signatures of mental illness on an unprecedented scale. This chapter discusses the major challenges, recent findings, and a roadmap for developing better neuroimaging-based tools and markers for mental illness.</p>","PeriodicalId":11257,"journal":{"name":"Current topics in behavioral neurosciences","volume":" ","pages":"371-397"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140329685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Genetic Architecture of Neurological Disorders and Their Endophenotypes: Insights from Genetic Association Studies.","authors":"Muralidharan Sargurupremraj","doi":"10.1007/7854_2024_513","DOIUrl":"10.1007/7854_2024_513","url":null,"abstract":"<p><p>Population-scale genetic association studies of complex neurologic diseases have identified the underlying genetic architecture as multifactorial. Despite the study sample sizes reaching the millions, the identified disease-related genes explain only a small fraction of the phenotypic variance. Notable advancements in statistical methods now enable researchers to gain insights even from genomic regions where genotype-phenotype associations do not reach statistical significance. Such studies confirm a highly interconnected molecular network comprising a core group of genes directly involved in the disease process, alongside an expanded peripheral network, each contributing a small but potentially important (modulatory) effect. Additionally, causal inference methods, utilizing genetic instruments, have shed light on putative causal links between risk factors and clinical endpoints. In light of the pervasive genetic overlap or pleiotropy, however, caution is warranted in interpreting causal relationships inferred from these analyses. In this chapter, I will introduce the genetic association model, provide insights into the current state of genetic association studies, and discuss potential future directions.</p>","PeriodicalId":11257,"journal":{"name":"Current topics in behavioral neurosciences","volume":" ","pages":"109-128"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141975292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J Balsa-Barreiro, M Cebrián, M Menéndez, K Axhausen
{"title":"Leveraging Generative AI Models in Urban Science.","authors":"J Balsa-Barreiro, M Cebrián, M Menéndez, K Axhausen","doi":"10.1007/7854_2024_482","DOIUrl":"10.1007/7854_2024_482","url":null,"abstract":"<p><p>Since the late 2000s, cities have emerged as the primary human habitat across the globe, and this trend is anticipated to continue strengthening in the coming decades. As we increasingly inhabit human-designed urban spaces, it becomes crucial to understanding better how these environments influence human behavior and how individuals perceive the city. In this chapter, we begin by examining the interplay between urban form and social behavior, highlighting key indicators of urban morphology, and presenting state-of-the-art methodologies for data collection. Subsequently, we harness the computational capability of foundation models, the latest Artificial Intelligence (AI) generation, to simulate interactions between individuals and urban built environments in a diverse group of 21 cities across the globe. Through this exploration, we scrutinize the models' capacity to encapsulate the intricate complexities of how individuals behave and perceive cities. These examples demonstrate the potential of advanced AI systems to assist urban scientists in understanding cities, emphasizing the necessity for a meticulous evaluation of their capabilities and limitations for the optimal application of Generative AI in urban research and policymaking.</p>","PeriodicalId":11257,"journal":{"name":"Current topics in behavioral neurosciences","volume":" ","pages":"239-275"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141733754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J Carson Smith, Daniel D Callow, Gabriel S Pena, Yash Kommula, Naomi Arnold-Nedimala, Junyeon Won, Kristy A Nielson
{"title":"Exercise and Protection from Age-Related Cognitive Decline.","authors":"J Carson Smith, Daniel D Callow, Gabriel S Pena, Yash Kommula, Naomi Arnold-Nedimala, Junyeon Won, Kristy A Nielson","doi":"10.1007/7854_2024_501","DOIUrl":"10.1007/7854_2024_501","url":null,"abstract":"<p><p>In this chapter, we review the cross-sectional evidence in healthy human subjects for physical activity and cardiorespiratory fitness to offer neuroprotection and moderate cognitive decline in older age. The role of exercise training on cognition in healthy older adults and those diagnosed with mild cognitive impairment (MCI) is also discussed, including the evidence from neuroimaging studies that document changes to brain structure and function after a period of exercise training and improved fitness. Finally, in reference to animal models, the potential neurophysiological mechanisms for physical activity and exercise to impact human brain health are highlighted.</p>","PeriodicalId":11257,"journal":{"name":"Current topics in behavioral neurosciences","volume":" ","pages":"263-280"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141855095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Influence of Regular Physical Activity on Sleep.","authors":"Melissa J McGranahan, Patrick J O'Connor","doi":"10.1007/7854_2024_503","DOIUrl":"10.1007/7854_2024_503","url":null,"abstract":"<p><p>Good sleep and adequate physical activity are essential to health. Yet, large numbers of people are chronically deficient in sleep and physical activity. About 1 in 3 Americans get less than 7 h of sleep per night and only 1 of 4 adults regularly complete weekly physical activity in amounts recommended for good health. This chapter reviews research that has examined relationships between regular physical activity and sleep. The overall weight of evidence supports that regular physical activity is associated with better sleep quality among healthy adults, with epidemiological studies showing moderate-sized effects and more well-controlled randomized controlled trial experiments often showing larger effects. Large epidemiology studies suggest that the relationship between regular physical activity and better sleep quality may partially mediate the well-established associations between physical activity and reduced risk of mortality, cardiovascular diseases, and dementia. There is evidence that the completion of regular physical activity also is associated with better sleep quality among those with certain sleep disorders (i.e., insomnia, obstructive sleep apnea, and restless legs syndrome), mental health disorders (i.e., depression and posttraumatic stress disorder), and medical illnesses (i.e., breast cancer survivors). The evidence is inadequate to support that regular physical activity substantially improves sleep quality either (i) in children, adolescents, and older adults, (ii) in those with cancers except for breast cancer, (iii) in those with fibromyalgia, or (iv) among those with chronic kidney disease. Also, there is inadequate evidence to conclude that sleep quality is disrupted during weeks when competitive athletes engage in periods of overtraining.</p>","PeriodicalId":11257,"journal":{"name":"Current topics in behavioral neurosciences","volume":" ","pages":"309-328"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141855125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Aging Brain from a Lifespan Perspective.","authors":"Anders Martin Fjell","doi":"10.1007/7854_2024_476","DOIUrl":"10.1007/7854_2024_476","url":null,"abstract":"<p><p>Research during the last two decades has shown that the brain undergoes continuous changes throughout life, with substantial heterogeneity in age trajectories between regions. Especially, temporal and prefrontal cortices show large changes, and these correlate modestly with changes in the corresponding cognitive abilities such as episodic memory and executive function. Changes seen in normal aging overlap with changes seen in neurodegenerative conditions such as Alzheimer's disease; differences between what reflects normal aging vs. a disease-related change are often blurry. This calls for a dimensional view on cognitive decline in aging, where clear-cut distinctions between normality and pathology cannot be always drawn. Although much progress has been made in describing typical patterns of age-related changes in the brain, identifying risk and protective factors, and mapping cognitive correlates, there are still limits to our knowledge that should be addressed by future research. We need more longitudinal studies following the same participants over longer time intervals with cognitive testing and brain imaging, and an increased focus on the representativeness vs. selection bias in neuroimaging research of aging.</p>","PeriodicalId":11257,"journal":{"name":"Current topics in behavioral neurosciences","volume":" ","pages":"349-370"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141155125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Area-level Measures of the Social Environment: Operationalization, Pitfalls, and Ways Forward.","authors":"Marco Helbich, Yi Zeng, Abeed Sarker","doi":"10.1007/7854_2024_464","DOIUrl":"10.1007/7854_2024_464","url":null,"abstract":"<p><p>People's mental health is intertwined with the social environment in which they reside. This chapter explores approaches for quantifying the area-level social environment, focusing specifically on socioeconomic deprivation and social fragmentation. We discuss census data and administrative units, egocentric and ecometric approaches, neighborhood audits, social media data, and street view-based assessments. We close the chapter by discussing possible paths forward from associations between social environments and health to establishing causality, including longitudinal research designs and time-series social environmental indices.</p>","PeriodicalId":11257,"journal":{"name":"Current topics in behavioral neurosciences","volume":" ","pages":"277-296"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140058916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Foundations of Exercise and Physical Activity Research.","authors":"Angelique G Brellenthin, Zoe Sirotiak","doi":"10.1007/7854_2024_488","DOIUrl":"10.1007/7854_2024_488","url":null,"abstract":"<p><p>While recognition of the link between physical activity and mental health dates back nearly two millennia, the academic study of physical activity is a relatively young discipline emerging over the last 50 years. This chapter provides an overview of key terms and measurement techniques in physical activity, exercise, and mental health research. The most common study designs in physical activity research include cross-sectional, cohort, randomized controlled trials, systematic reviews, and meta-analytic studies. Examples from the literature as well as the advantages and disadvantages of various methodological approaches are discussed throughout the chapter in the context of mental health research.</p>","PeriodicalId":11257,"journal":{"name":"Current topics in behavioral neurosciences","volume":" ","pages":"3-22"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141855096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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