Miller KL, Alfaro-Almagro F, Bangerter NK, Thomas DL, Yacoub E, Xu J, Bartsch AJ, Jbabdi S, Sotiropoulos SN, Andersson JL, Griffanti L, Douaud G, Okell TW, Weale P, Dragonu I, Garratt S, Hudson S, Collins R, Jenkinson M, Matthews PM, Smith SM. Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat Neurosci. 2016 Sep 19; PubMed.

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  1. The U.K. Biobank is a fantastic project, and I congratulate the investigators and staff involved with designing, collecting, and analyzing its data. Considering that this is just the first step, the results are impressive and demonstrate the feasibility and statistical power of this hugely ambitious effort.

    There are clearly many differences between ADNI and the U.K. Biobank. ADNI’s goal is to validate biomarkers for Alzheimer’s disease clinical trials, while the U.K. Biobank has much broader goals. ADNI includes amyloid tau PET imaging as well as analysis of cerebrospinal fluid obtained by lumbar punctures. The present paper from the U.K. Biobank project currently does not include amyloid or tau PET imaging on a large scale, but I believe there are plans to include these modalities in the future on a considerable number of subjects.

    From the onset, we in ADNI recognized that the subject population in ADNI is not representative of the “general population”; in other words, ADNI is not an “epidemiologically sampled study.” The Olmsted County Study of Aging is a good example of a study, which uses MRI and PET imaging, that aims to represent the population living near the Mayo Clinic in Rochester, Minnesota. In contrast, ADNI is designed to represent a typical AD clinical trial in the United States, and its subjects are more educated and more Caucasian than the general population. For ADNI we rule out people with cerebrovascular disease or cognitive impairment caused by disorders other than AD, because AD clinical trials typically used these same exclusion criteria. Therefore, investigators must be careful not to overgeneralize ADNI findings when interpreting its results.

    The relationships between age, cognition, brain volumes, brain amyloid, etc. in ADNI may or may not reflect that of the general population. One big advantage of the U.K. Biobank study is that its investigators have access to the large amount of health data that is collected by the U.K. universal health care system. In ADNI we lack access to such data.

    Of course caveats apply to all research studies, especially those with lots of intensive measurements such as MRI. Even the U.K. Biobank can only study those subjects who are willing to enter into the study. People who are homeless, or who have severe diseases, or wish to avoid participating in research will most likely not be in the U.K. Biobank. It certainly will be very interesting to compare the results of the U.K. Biobank with those of ADNI and the Olmsted County Study of Aging.

    Having been involved closely with ADNI for more than 12 years, I can share one other concern about these very large studies. It is a general concern about the results coming out of “big data” studies in general. The most robust scientific findings come when a carefully formulated a priori hypothesis is being tested. In fact, in the U.K. Biobank paper, a number of a priori hypotheses are tested, and that is terrific. Sometimes the most exciting discoveries occur outside of the process of a priori hypothesis testing, but we all must be very careful about false positive results emerging because of insufficient correction for multiple comparisons. At the same time, sometimes real results are obscured because corrections for multiplicity prevent assignment of statistical significance to them.

    This general concern applies to many large studies, including ADNI and the U.K. Biobank. In my view, a key challenge to medical science is to find more robust ways to reduce false-positive and false-negative results from analysis of large data sets.

    Nevertheless, this recent report from the U.K. Biobank is a “tour de force,” and all of us in the field look forward to seeing more exciting discoveries coming from this project.

    View all comments by Michael Weiner

  2. The U.K. Biobank will be a tremendous resource for studies on neurodegeneration, both for data discovery and for replication. In addition, it will be a useful reference set for single-subject imaging markers. We have not used the data set ourselves, but many of our colleagues either have already obtained data or plan to request data. Because data collection is not focused on a single disease, it will be possible to find common mechanisms across different brain disorders.

    A limitation of the current data set is the lack of molecular brain markers, for example in cerebrospinal fluid or from PET imaging, but this type of data will possibly be collected in the future. The inclusion of subjects above 80 would also have been useful. In such a huge data set correlations may not always be meaningful. For example, the analysis in table 9 might provide an incentive to eat more cheese and less yogurt. The huge data set urges for the development of new statistical techniques for data mining. To avoid selective reporting of results, one could ask data requesters to submit a protocol specifying the analysis, which later can be linked to a publication.

    View all comments by Betty Tijms

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