Can statistics help historians to identify the events that are most distinctive of a particular era of time? This essay explores the use of a distinctiveness algorithm from library science for measuring the distinctiveness of manuscripts, tf-idf, recast as "tf-ipf" for the study of the terms most distinctive of historical periods. In a case study, tf-ipf is applied to the text of Hansard's Parliamentary Debates, varying the "period" from a 20-year horizon to a 6-month or one-day horizon. It is shown that the algorithm's assessments of what is most distinctive of 20-year and 10-year periods largely matches the consensus of British historians, while debates that held parliament's attention for six months or fewer have largely fallen beneath the threshold of scholarly attention. Attending to concerns that took up parliamentary debate for six months or fewer, the essay argues that tf-ipf thus presents a metric of parliamentary attention that mirrors the exclusions of class, allowing scholars to retrieve a timeline of when the concerns of the distillers, bleachers, dyers, Chartists, crofters, and miners reached a national debate. More generally, tf-ipf represents an important new tool for discovering the distinctive aspects of historical periods based on past experience, not historical bias.

Stressful life events are significant risk factors for depression, and increases in depressive symptoms have been observed during the COVID-19 pandemic. The aim of this study is to explore the neural makers for individuals’ depression during COVID-19, using connectome-based predictive modeling (CPM). Then we tested whether these neural markers could be used to identify groups at high/low risk for depression with a longitudinal dataset. The results suggested that the high-risk group demonstrated a higher level and increment of depression during the pandemic, as compared to the low-risk group. Furthermore, a support vector machine (SVM) algorithm was used to discriminate major depression disorder patients and healthy controls, using neural features defined by CPM. The results confirmed the CPM’s ability for capturing the depression-related patterns with individuals’ resting-state functional connectivity signature. The exploration for the anatomy of these functional connectivity features emphasized the role of an emotion-regulation circuit and an interoception circuit in the neuropathology of depression. In summary, the present study augments current understanding of potential pathological mechanisms underlying depression during an acute and unpredictable life-threatening event and suggests that resting-state functional connectivity may provide potential effective neural markers for identifying susceptible populations.