Posts Tagged 'brain imaging'

Brainstorm …

As properly remarked in the prequel of the Planet of the Apes, we know everything about our brain, except how it does work. We are aware of such lack of knowledge, at least in theory. In practice, papers are replete of firm sentences and conclusive statements. But we use complex programs and devices, and we should not forget that these tools can only generate models of reality. Models based on algorithms that are trying to represent and simulate only some specific physical or spatial properties. Our brain models are but statistical outputs, not real “brains”. We identify brain activity through indirect blood or metabolic functions, assuming there is a strict correspondence between those signals and our concept of “at work”. A correspondence that is reasonable, but not that strict. Even basic anatomical issues can be blurred after a more detailed scrutiny, mostly when previous knowledge is based on information that has been copy-and-pasted through decades. We are more and more finding strange factors influencing our results. Apparently, the brain undergoes daily variations, and the braincase may suffer seasonal changes. Brain structure and function can be even influenced by head position and posture. These unexpected effects recommend further caution when making too general conclusions from specific and punctual results. Let’s take into account that we still miss much information on gross neuroanatomical components. For example, we still ignore the function of the cerebellum, that has four time the number of neurons of the brain, and we still don’t know all the functions of the glial cells, that may be nine times more numerous than neurons. And, we don’t know how much brain anatomy and functions are the result of genetic programs or environmental influences. In only few weeks, training can easily improve or demote brain complexity. Nothing new under the sun: science is about hypotheses, and hypotheses need to be tested and validated. Our models are tentatively designed with this scope in mind. This summer post is a summary of articles concerning some methodological limitations and some curious result dealing with brain structure and function. And an invitation to interpret results for what they are: evidences supporting or rejecting hypotheses. Remember that those are not neurons: just pixels! Take it easy …


Imaging brains

Brain glass coasters 2016In the last decade neuroscience has experienced an explosion of brain imaging studies, programs, and databases. The advance has been outstanding, indeed. Nonetheless, we have also been surprised by a large and unexpected number of strange and discordant results. There are so many examples in which similar studies reach different or even opposite conclusions. Differences in raw values, comparisons (from sexual variation to hemispheric asymmetries), or correlations and associations between variables, can be pretty frequent when dealing with specific aspects of brain anatomy. In some cases, these studies apparently deal with “simple” factors, like volumes or linear metrics. We have discovered new kinds of uncertainties on functions, software, and even on basic anatomy. A recent study has discussed the problem of reliability in functional MRI (here a comprehensive post). Of course this is not something strictly associated with neuroanatomy and brain imaging. Any analysis in molecular biology has many more methodological and technical passages which can hide some kind of processing problem and generate noise or even confounding outputs. In neuroimaging, there are at least four main steps which can be problematic. First, data sampling (machines, parameters, and so on). Second, the formatting procedures (databases, archives, platforms). Third, the processing of the data (programs, algorithms, etc.). Fourth, statistics (sample size, statistical power, adequacy of the statistical tools). Whatever it is, it is calling our attention. And this is the good part: we are more and more forced to be less superficial. All these unexpected uncertainties require a critical view, as discussed in this manuscript on future challenges for neuroimaging research. The most patent problem is confusion, and an epidemic spread of wrong information. But there is also another risk: too many discordant results can lead to a consistent loss of confidence in these methods (here another post – in Spanish). And this can have both scientific and economic consequences.

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