Brain Health Across the Life Span Proceedings of a Workshop (2020) / Chapter Skim
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5 Measuring Brain Health
5 Measuring Brain Health
Pages 65-80
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From page 65... ...
(Russell Poldrack) • Small sample sizes in neuroimaging studies undercut the ability to believe a significant number of the results coming out of the brain health literature; mandatory preregistration would contribute substantially to moving toward a neurosci ence that is aimed at translating findings into more effective treatments.
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From page 66... ...
He discussed the methodological techniques and standards for reliability that are necessary for measuring brain health and resilience; he also described how quality metrics and criteria can improve measurement of brain health and resilience in future research. GRAND VIEWS AND POTHOLES ON THE ROAD TO PRECISION NEUROSCIENCE Poldrack sketched an optimistic dream for the use of neuroscience around brain health as well as identifying challenges that may hinder achieving that vision.
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From page 67... ...
In recent years, substantial efforts have been devoted to developing this concept, including the 2015 Precision Medicine Initiative launched by the White House.1 This wave is being driven in part by success stories emerging from precision cancer drugs that are markedly improving outcomes based on genetic targeting and treatment. For example, Gleevec is a precision cancer treatment developed for people with a particular genetic mutation that causes chronic myeloid leukemia.
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From page 68... ...
Insufficient Scanning Time Estimating connectivity reliably requires substantial scan time, but the brain imaging studies currently being carried out are collecting far too little information about each individual. Poldrack estimated that achieving reliable measurements of connectivity requires something in the range of 30–100 minutes of resting-state data from a subject, depending on the degree of reliability desired, but most studies collect less than 10 minutes of resting-state data (Laumann et al., 2015)
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From page 69... ...
In general, published data with a sample size of 20 tends to reflect a more flexible sample size determination based on interim data analysis and other types of problematic analyses. A majority of studies in the meta-analysis had grossly insufficient N
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From page 70... ...
. Methodological Pluralism Methodological flexibility is a major challenge in the field of neuroimaging, because researchers have a large degree of flexibility in how they analyze neuroimaging data.
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From page 71... ...
Faulty Predictive Models Poldrack turned to the pothole of faulty predictive models. Predictive models are essential to precision science, but faulty predictive models are another pothole on the roadway to precision neuroscience.
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From page 72... ...
Poor Understanding of Intraindividual Variability Over Time Poldrack turned to his third pothole on the road to precision neuroscience, which is poor understanding of intraindividual variability over time. An increasing body of knowledge is providing insight into how brain function changes over time on both ends of the spectrum from milliseconds to decades (Sowell et al., 2004)
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From page 73... ...
p This highlights the need to study individual variability in more depth, in order to identify factors that may drive variance in resting-state connectivity within an individual. The My Connectome project revealed that intake of caffeine and food affects large-scale network structure, for example (Poldrack et al., 2015)
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From page 74... ...
Discussion Damien Fair, associate professor of behavioral neuroscience, associate professor of psychiatry, and associate scientist at the Advanced Imaging Research Center at the Oregon Health & Science University, asked about
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From page 75... ...
These types of measures, such as the Stroop task, can be useful and have a robust effect, but the effect is not robust at the level of test reliability; therefore, they are not reliable enough to provide any validity as an individual difference measure. Huda Akil, codirector and research professor of the Molecular and Behavioral Neuroscience Institute and Quarton Professor of Neurosciences at the University of Michigan, added that just because a measure is useful as a group measure, it is not necessarily good as an individual measure; this distinction is an important one that should be more prominently taught to researchers.
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From page 76... ...
When building measures that are intended to be sensitive to learning, for example, it is important to ensure that the measures are reliably sensitive to learning -- not necessarily in the test-retest sense, but perhaps in some other sense. PANEL DISCUSSION ON THE WAY FORWARD IN MEASUREMENT AND RESEARCH Akil asked the panelists to reflect on key issues in measuring brain health.
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From page 77... ...
Lis Nielsen, chief of the Individual Behavioral Processes Branch of the Division of Behavioral and Social Research at the National Institute on Aging, said that measures in psychological domains do not necessarily need to be derived from biology. They can be based on functional or behavioral categories; mental health and subjective states, for instance, can be assessed by self-report or performance-based tasks.
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From page 78... ...
having a variety of neurocognitive tools and networks that can be activated in the context of internal and external environmental and psychological challenges, and (2) being able to adaptively activate these tools and networks to optimize function in response to environmental and psychological challenges.
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From page 79... ...
In this context, resilience can be defined as maintaining access to a sufficient range of cognitive tools and an adequate degree of neuroplasticity over time. The ability to "roll with the punches" and rebound from adversity, for example, partly depends on having more than one coping strategy available.
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