confidence that detected effects were not just due to noise and to better characterize the heterogeneity of psychiatric disorders. During this era, we learned with greater confidence which brain regions were activated by tasks, as well as the degree to which regional responses differed in those with mental illness (e.g., auditory oddball or working memory task deficits in schizophrenia). Likewise, we learned more about how brain structure was impacted by disease (e.g., individuals with schizophrenia consistently show reduced temporal lobe and medial frontal gray matter). Approaches based on meta-analysis offered tools to pool results from many small studies, in order to provide more reliable statistical summaries. During this era, the focus was still very much on isolating specific brain regions, rather than considering the brain as a highly interconnected system. The “Network” Era A major shift occurred when our attention turned to brain networks, both at rest and during task performance (referred to as the “network” era). The mathematical tools used to study networks can also be applied to study networks between brain regions. The same approaches Google and Facebook use to leverage the concept of networks to improve search engines and social interactions began to be applied to the brain, which can be thought of as a network of networks. The idea was to identify specific networks of brain regions that are linked to, or correlated with, one another. Among other advantages, this approach enabled us to study the brain even when individuals were resting, rather than performing specified tasks. By mitigating the formidable challenge of ensuring that everyone was performing the same task the same way, the approach allowed researchers to scale up to much larger numbers, combining data across many imaging centers. In Using neuroimaging to identify disrupted brain regions could provide information useful to develop and evaluate new treatments, enable prediction of individual response to such treatments, and help subtype individuals (e.g., schizophrenia and schizoaffective disorder).
The “Small N” Era In the early days of neuroimaging studies, we used a small number of subjects (called “small Ns”). In this era, even though structural and functional MRI studies were initially quite small (e.g., using a "small N" of only 5 to 20 subjects), each promised a potentially revolutionary finding. Initial functional studies had individuals perform a number of carefully designed tasks and tracked how the brain responded to these tasks. But while many studies highlighted specific deficits associated with various mental disorders, differences tended to be relatively small and most studies lacked sufficient controls for the many possible confounds, such as medication or patient movement within the scanner. Studies that followed often yielded promising findings that pointed toward various brain regions that appeared to be important contributors to brain disorders or symptoms. Many of these findings turned out to be hard to replicate. In some cases, the tasks were difficult for those impacted by mental illness to perform, which made it hard to know if the observed changes were due to the disorder itself or were different simply because the tasks were not being performed. So, the small N problem became a large problem, compounded by the heterogeneity of mental disorders. To better understand what researchers faced, consider the development of a vaccine for Covid-19, where 30,000 individuals are required for a study. Testing the efficacy and safety of a vaccine is a much simpler problem than treating depression, for example, since the outcome measures are clear (someone gets ill or not). With psychiatric disorders, by contrast, we are simultaneously trying to clarify the diagnosis and understand how the brain is impacted—which regions and what mechanisms are involved. Despite the challenges, we learned much about how to model neuroimaging data during the small N era, including how to efficiently administer tasks, control for statistical complexities, and compare data. The “dead salmon” paper , which was presented at the Human Brain Mapping conference in 2009, light-heartedly highlighted the already well-known statistical corrections that are needed when studying brain images. The presenters were making an important scientific point regarding the “ multiple comparisons problem .” If one does a lot of different statistical tests, some of them will, just by chance, give interesting results. With this, we gained important information about how mental illness impacts the brain. But this was not enough. The “Large Group” Era In the next phase of neuroimaging (referred to as the “large group” era), researchers focused on increasing group sizes (typically to hundreds of individuals or more) to increase
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