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Patients with posttraumatic stress disorder (PTSD) exhibit smaller regional brain volumes in commonly reported regions including the amygdala and hippocampus, regions associated with fear and memory processing. In the current study, we have conducted a voxel-based morphometry (VBM) meta-analysis using whole-brain statistical maps with neuroimaging data from the ENIGMA-PGC PTSD working group.
Methods
T1-weighted structural neuroimaging scans from 36 cohorts (PTSD n = 1309; controls n = 2198) were processed using a standardized VBM pipeline (ENIGMA-VBM tool). We meta-analyzed the resulting statistical maps for voxel-wise differences in gray matter (GM) and white matter (WM) volumes between PTSD patients and controls, performed subgroup analyses considering the trauma exposure of the controls, and examined associations between regional brain volumes and clinical variables including PTSD (CAPS-4/5, PCL-5) and depression severity (BDI-II, PHQ-9).
Results
PTSD patients exhibited smaller GM volumes across the frontal and temporal lobes, and cerebellum, with the most significant effect in the left cerebellum (Hedges’ g = 0.22, pcorrected = .001), and smaller cerebellar WM volume (peak Hedges’ g = 0.14, pcorrected = .008). We observed similar regional differences when comparing patients to trauma-exposed controls, suggesting these structural abnormalities may be specific to PTSD. Regression analyses revealed PTSD severity was negatively associated with GM volumes within the cerebellum (pcorrected = .003), while depression severity was negatively associated with GM volumes within the cerebellum and superior frontal gyrus in patients (pcorrected = .001).
Conclusions
PTSD patients exhibited widespread, regional differences in brain volumes where greater regional deficits appeared to reflect more severe symptoms. Our findings add to the growing literature implicating the cerebellum in PTSD psychopathology.
Recent changes to US research funding are having far-reaching consequences that imperil the integrity of science and the provision of care to vulnerable populations. Resisting these changes, the BJPsych Portfolio reaffirms its commitment to publishing mental science and advancing psychiatric knowledge that improves the mental health of one and all.
To better understand clinicians’ rationale for ordering testing for C. difficile infection (CDI) for patients receiving laxatives and the impact of the implementation of a clinical decision support (CDS) intervention.
Design:
A mixed-methods, case series was performed from March 2, 2017 to December 31, 2018.
Setting:
Yale New Haven Hospital, a 1,541 bed tertiary academic medical center.
Participants:
Hospitalized patients ≥ 18 years old, and clinicians who were alerted by the CDS.
Intervention:
CDS was triggered in real-time when a clinician sought to order testing for CDI for a patient who received one or more doses of laxatives within the preceding 24 hours.
Results:
A total of 3,376 CDS alerts were triggered during the 21-month study period from 2,567 unique clinician interactions. Clinicians bypassed the CDS alert 74.5% of the time, more frequent among residents (48.3% bypass vs. 39.9% accept) and advanced practice providers (APPs) (34.9% bypass vs. 30.6% accept) than attendings (11.3% bypass vs. 22.5% accept). Ordering clinicians noted increased stool frequency/output (48%), current antibiotic exposure (34%), and instructions by an attending physician to test (28%) were among the most common reasons for overriding the alert and proceeding with testing for CDI.
Conclusions:
Testing for CDI despite patient laxative use was associated with an increased clinician concern for CDI, patient risk for CDI, and attending physician instruction for testing. Attendings frequently accepted CDS guidance while residents and APPs often reinstated CDI test orders, suggesting a need for greater empowerment and discretion when ordering tests.
Most people with mental illness in low and middle-income countries (LMICs) do not receive biomedical treatment, though many seek care from traditional healers and faith healers. We conducted a qualitative study in Buyende District, Uganda, using framework analysis. Data collection included interviews with 24 traditional healers, 20 faith healers, and 23 biomedical providers, plus 4 focus group discussions. Interviews explored treatment approaches, provider relationships, and collaboration potential until theoretical saturation was reached. Three main themes emerged: (1) Biomedical providers’ perspectives on traditional and faith healers; (2) Traditional and faith healers’ views on biomedical providers; and (3) Collaboration opportunities and barriers. Biomedical providers viewed faith healers positively but traditional healers as potentially harmful. Traditional and faith healers valued biomedical approaches while feeling variably accepted. Interest in collaboration existed across groups but was complicated by power dynamics, economic concerns, and differing mental illness conceptualizations. Traditional healers and faith healers routinely referred patients to biomedical providers, though reciprocal referrals were rare. The study reveals distinct dynamics among providers in rural Uganda, with historical colonial influences continuing to shape relationships and highlighting the need for integrated, contextually appropriate mental healthcare systems.
Symptoms of complex post-traumatic stress disorder (cPTSD) may play a role in the maintenance of psychotic symptoms. Network analyses have shown interrelationships between post-traumatic sequelae and psychosis, but the temporal dynamics of these relationships in people with psychosis and a history of trauma remain unclear. We aimed to explore, using network analysis, the temporal order of relationships between symptoms of cPTSD (i.e. core PTSD and disturbances of self-organization [DSOs]) and psychosis in the flow of daily life.
Methods
Participants with psychosis and comorbid PTSD (N = 153) completed an experience-sampling study involving multiple daily assessments of psychosis (paranoia, voices, and visions), core PTSD (trauma-related intrusions, avoidance, hyperarousal), and DSOs (emotional dysregulation, interpersonal difficulties, negative self-concept) over six consecutive days. Multilevel vector autoregressive modeling was used to estimate three complementary networks representing different timescales.
Results
Our between-subjects network suggested that, on average over the testing period, most cPTSD symptoms related to at least one positive psychotic symptom. Many average relationships persist in the contemporaneous network, indicating symptoms of cPTSD and psychosis co-occur, especially paranoia with hyperarousal and negative self-concept. The temporal network suggested that paranoia reciprocally predicted, and was predicted by, hyperarousal, negative self-concept, and emotional dysregulation from moment to moment. cPTSD did not directly relate to voices in the temporal network.
Conclusions
cPTSD and positive psychosis symptoms mutually maintain each other in trauma-exposed people with psychosis via the maintenance of current threat, consistent with cognitive models of PTSD. Current threat, therefore, represents a valuable treatment target in phased-based trauma-focused psychosis interventions.
These are all very practical decisions, and the methods of analyzing them make use of Principle 1:A dollar today is not worth the same as a dollar tomorrow. Economists have considered the management of personal financial resources over a lifetime to be a central issue worthy of serious study, and several Nobel Prizes in economics have been awarded for contributions in this area. And, as Box 3.1 shows, financial literacy for a nation’s people is a goal being pursued by countries all over the world.
Most financial decisions boil down to figuring out how much an asset is worth. For example, in deciding whether to invest in a security such as a stock or a bond or in a business opportunity, you have to determine whether the price being asked is high or low relative to other investment opportunities available to you. In addition to investment decisions, there are many other situations in which one needs to determine the value of an asset. For example, suppose that the tax assessor in your town has assessed your house at $500,000 for property tax purposes. Is this value too high or too low? Or suppose you and your siblings inherit some property, and you decide to sell it and share the proceeds equally among yourselves. How do you decide how much it is worth?
In the previous chapters we introduced the concept of valuation, which involved converting cash flows that are expected to happen in the future into today’s terms, and we learned about the returns on various assets and how to analyze the past performance of financial instruments to inform investment decisions. However, the future is not known for sure. The cash flows that occur may be different from what we initially expect, and the value (and rates of return) of financial instruments change over time. In this chapter, we introduce a fundamental concept in finance: Uncertainty about the future can affect valuation and decision making.
We begin by defining what risk is in finance, and how it affects financial decisions. We then dive into how risk can be managed, which includes identifying relevant risks, assessing how they can affect one’s financial situation, and then determining appropriate techniques that can be used to reduce these risks.
Before proceeding with our first steps in valuation, we need to introduce some tools and define some notation that will be used here and throughout the book when valuing assets.
At a fundamental level, the value of an asset comes from the cash flows that are associated with it—that is, from the amounts of money that the owner either receives or pays at various points in time. An essential tool in analyzing cash flows from any financial decision is a diagram known as a timeline, a linear representation of cash outflows and inflows over a period of time. A negative sign in front of a cash flow means that you are paying that amount of money (it’s a cash outflow from you). No sign means that you are receiving an amount of money (it’s a cash inflow to you).
In the last few chapters we have considered financial and strategic decisions made within companies, and whether they improve the value of the company. In this chapter we continue examining company decisions, and focus on a particular financial decision—the payout decision, which considers whether a company keeps the cash it holds or gives it back to investors. As we will show, this decision is important because firms can potentially increase their value—and benefit their investors—through their choice of whether to pay out cash to investors. Furthermore, as we discussed in the previous chapter, agency problems may arise when companies hold onto large amounts of cash due to managerial conflicts of interest. Thus, payout can serve an important role in corporate governance.
In previous chapters we explored how to calculate the value of a company, given decisions that it had already made. In subsequent chapters we then focused on decisions that a manager within a company could make, and how they affect company value, such as project investment decisions. In this chapter we continue to examine decisions made by companies, and focus on a particularly important decision—the financing decision. Capital structure is the mix of financing sources that a firm uses to fund its operations, growth, and investment projects. A firm may choose to use internal funding from operations, or use external funding from issuing debt (bonds) or equity (stock), or other financing instruments.
In the previous chapter we discussed what risk is and how managing risk is an essential element of every financial decision. Risk stems from uncertainty about the future. In this chapter, we introduce and explain financial contracts—options—that help resolve uncertainty by allowing an asset to be traded at a fixed price in the future after observing outcomes. More specifically, put options allow the choice to sell or not sell an underlying asset in the future, while call options allow the choice to buy or not buy an underlying asset in the future. The owner does not have to sell (in the case of a put) or buy (in the case of a call) in the future if it is not beneficial to them. Thus, the value of option contracts is that they embed flexibility—the owner makes the decision after the market price of the underlying asset is observed.
In the previous chapter we went over the process by which investors form portfolios, how to measure the risk and return of a given portfolio, and how an optimal portfolio can be chosen from a riskless asset and a set of risky assets. We saw that the optimal portfolio consists of holding some portion of one’s money in the riskless asset and some portion in the tangency portfolio consisting of the optimal combination of risky assets (OCRA). In this chapter we introduce the capital asset pricing model (CAPM), which specifies exactly what the OCRA should be. The CAPM predicts, under a set of assumptions, that the OCRA consists of holding all assets in the market in proportion to their value. Thus, all investors should hold some combination of the market portfolio and the riskless asset because it is most efficient.