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Neuroimaging studies of individuals with Down syndrome
Published in Vee P. Prasher, Down Syndrome and Alzheimer’s Disease, 2018
Felix Beacher, Declan G. M. Murphy
Most volumetric MRI studies of adults with DS have not examined age-related differences in regional brain volume. Those volumetric MRI studies of individuals with DS that have investigated this question provided evidence that non-demented individuals with DS show significant age-related differences in brain morphometry.36–38,40, 60,62 For example, volumetric MRI studies of non-demented individuals with DS reported significant age-related decreases in volume of the medial temporal regions, including the hippocampi,36,60 amygdalae and posterior hippocampal gyrus,37 and also a significant age-related increase in cross-sectional area of the lateral ventricles.36 Thus some MRI studies of non-demented adults with DS have reported decreases in medial temporal volume, consistent with the findings of studies of brain ageing in the healthy population.
Exercise-Induced Improvement in Motor Learning
Published in Henning Budde, Mirko Wegner, The Exercise Effect on Mental Health, 2018
Accordingly, the key notion of this section is that endurance exercise and motor skill learning affect the CNS, at least in part, distinctly (Markham & Greenough 2004; Thomas, Dennis, Bandettini, & Johansen-Berg 2012; Voelcker-Rehage & Niemann 2013) and these differences may be exploited to subsequently facilitate motor learning (Adkins, Boychuk, Remple, & Kleim 2006). First, we deal with structural and functional neuroplastic changes on the systems level of brain organization using novel techniques to study brain morphometry and large-scale structural and functional connectivity. Such changes are in part based on adaptations at the cellular level (Nudo 2008; Zatorre, Fields, & Johansen-Berg 2012), which are in turn promoted, for example, by the action of neuromodulatory transmitters and neurotrophic factors on the molecular level. Throughout, we will relate these changes to the proposed mechanisms of motor learning (e.g. LTP). Brain adaptations to endurance exercise are just treated inasmuch as essential for a basic understanding of the assumed mechanisms. For a more comprehensive view, we refer the reader to Chapter 2 (this volume).
Common Biochemical and Physiological Parameters in Rats
Published in Yanlin Wang-Fischer, Manual of Stroke Models in Rats, 2008
Yanlin Wang-Fischer, Lee Koetzner
Table 25.6 shows the brain morphometry (juvenile).7Table 25.7 shows the brain morphometry (adult).7Table 25.8 shows the proportion by volume (%).7
Modifiable Risk Factors for Brain Health and Dementia and Opportunities for Intervention: A Brief Review
Published in Clinical Gerontologist, 2023
David W. Coon, Abigail Gómez-Morales
In the United States, the Alzheimer’s Association is funding the WW-FINGERS affiliated study named the U.S. study to Protect Brain Health Through Lifestyle Intervention to Reduce Risk (U.S. POINTER) which has adapted FINGER intervention components for U.S. sites (Alzheimer’s Association, 2022b). This ongoing two-year clinical trial aims to enroll approximately 2000 participants from diverse cultural backgrounds age 60 to 79 who are at increased risk for cognitive decline in later life. “Risk” is defined as having a sedentary lifestyle, poor diet, suboptimal cardiovascular health status, family history of memory problems and other factors. U.S. POINTER is presently conducted at five sites (Winston-Salem, NC; Houston, TX; Davis, CA; and Chicago, IL., and Rhode Island) with hopes of engaging more sites in the future. U.S. POINTER will test to see if a self-guided versus a structured multidomain lifestyle intervention (healthy nutrition, physical activity, social and intellectual change, and increased medical monitoring of vascular and metabolic conditions) will result in changes in cognition. The study also includes advanced brain imaging – amyloid positron emission tomography (PET) – to help measure the two proteins (amyloid and tau) responsible for the buildup of plaques and neuronal deterioration over time. Magnetic Resonance Imaging (MRI) will also be collected to investigate cerebrovascular pathophysiology in the brain including brain morphometry, white matter, hyperintensities and microstructure, and cerebral blood flow (Lockhart et al., 2020). These dementia brain markers are significant in evaluating the multi-lifestyle component trial response with the potential to inform healthcare delivery. Moreover, the success of the U.S. POINTER study will provide insights into the social determinants of cognitive health and will have substantial implications for public policy (U.S. National Library of Medicine, 2021).
Precision neuroimaging biomarkers for bipolar disorder
Published in International Review of Psychiatry, 2022
Delfina Janiri, Sophia Frangou
To-date most research effort has been directed towards neuroimaging-based and machine learning aided diagnostic classification. Available studies identified patients with BD from healthy individual or patients with other major psychiatric disorders with an accuracy ranging from random to near perfect (Table 1). This variability probably reflects inter-study heterogeneity in sample sizes, algorithm used and MRI modalities. Figure 1 demonstrates the effect of sample sizes between 20 and 3000 for classification accuracy showing that classification performance is generally higher in studies with small samples (n < 200) regardless of diagnostic labels or modality. In multi-site datasets classification accuracy is typically lower than in single-site studies. This is because models developed and tested in the same dataset over-estimate accuracy due to overfitting. The use of a hold-out subset or an independent sample are the most appropriate methods for testing the generalizability of a model and hence its usefulness in robustly separating diagnostic labels. The study by the ENIGMA Bipolar Working Group (Nunes et al., 2020) currently provides the most reliable estimate of the ability of sMRI data to distinguish patients with BD from healthy individuals because of its large sample size and multisite. The reported accuracy of 65.23% is not ideal either for research or clinical practice and may reflect a true limitation of using macroscale brain morphometric features for diagnostic classification. Multimodal neuroimaging datasets may assist in increasing accuracy compared to those based on a single modality although this possibility has not been meaningfully tested. The same applies to the classification algorithms which have yet to undergo comprehensive comparative evaluation. In sum, there is a major methodological knowledge gap concerning the optimization of classification methods in BD and studies to systematically evaluate the effect of sample size, neuroimaging features, and algorithms on classification accuracy are necessary for further progress.