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Negative symptoms are a key feature of several psychiatric disorders. Difficulty identifying common neurobiological mechanisms that cut across diagnostic boundaries might result from equifinality (i.e., multiple mechanistic pathways to the same clinical profile), both within and across disorders. This study used a data-driven approach to identify unique subgroups of participants with distinct reward processing profiles to determine which profiles predicted negative symptoms.
Methods
Participants were a transdiagnostic sample of youth from a multisite study of psychosis risk, including 110 individuals at clinical high-risk for psychosis (CHR; meeting psychosis-risk syndrome criteria), 88 help-seeking participants who failed to meet CHR criteria and/or who presented with other psychiatric diagnoses, and a reference group of 66 healthy controls. Participants completed clinical interviews and behavioral tasks assessing four reward processing constructs indexed by the RDoC Positive Valence Systems: hedonic reactivity, reinforcement learning, value representation, and effort–cost computation.
Results
k-means cluster analysis of clinical participants identified three subgroups with distinct reward processing profiles, primarily characterized by: a value representation deficit (54%), a generalized reward processing deficit (17%), and a hedonic reactivity deficit (29%). Clusters did not differ in rates of clinical group membership or psychiatric diagnoses. Elevated negative symptoms were only present in the generalized deficit cluster, which also displayed greater functional impairment and higher psychosis conversion probability scores.
Conclusions
Contrary to the equifinality hypothesis, results suggested one global reward processing deficit pathway to negative symptoms independent of diagnostic classification. Assessment of reward processing profiles may have utility for individualized clinical prediction and treatment.
Accurate diagnosis of bipolar disorder (BPD) is difficult in clinical practice, with an average delay between symptom onset and diagnosis of about 7 years. A depressive episode often precedes the first manic episode, making it difficult to distinguish BPD from unipolar major depressive disorder (MDD).
Aims
We use genome-wide association analyses (GWAS) to identify differential genetic factors and to develop predictors based on polygenic risk scores (PRS) that may aid early differential diagnosis.
Method
Based on individual genotypes from case–control cohorts of BPD and MDD shared through the Psychiatric Genomics Consortium, we compile case–case–control cohorts, applying a careful quality control procedure. In a resulting cohort of 51 149 individuals (15 532 BPD patients, 12 920 MDD patients and 22 697 controls), we perform a variety of GWAS and PRS analyses.
Results
Although our GWAS is not well powered to identify genome-wide significant loci, we find significant chip heritability and demonstrate the ability of the resulting PRS to distinguish BPD from MDD, including BPD cases with depressive onset (BPD-D). We replicate our PRS findings in an independent Danish cohort (iPSYCH 2015, N = 25 966). We observe strong genetic correlation between our case–case GWAS and that of case–control BPD.
Conclusions
We find that MDD and BPD, including BPD-D are genetically distinct. Our findings support that controls, MDD and BPD patients primarily lie on a continuum of genetic risk. Future studies with larger and richer samples will likely yield a better understanding of these findings and enable the development of better genetic predictors distinguishing BPD and, importantly, BPD-D from MDD.
The malicious use of artificial intelligence is growing rapidly, creating major security threats for individuals and the healthcare sector. Individuals with mental illness may be especially vulnerable. Healthcare provider data are a prime target for cybercriminals. There is a need to improve cybersecurity to detect and prevent cyberattacks against individuals and the healthcare sector, including the use of artificial intelligence predictive tools.
Anxiety is a common comorbid feature of late-life depression (LLD) and is associated with poorer global cognitive functioning independent of depression severity. However, little is known about whether comorbid anxiety is associated with a domain-specific pattern of cognitive dysfunction. We therefore examined group differences (LLD with and without comorbid anxiety) in cognitive functioning performance across multiple domains.
Method:
Older adults with major depressive disorder (N = 228, ages 65–91) were evaluated for anxiety and depression severity, and cognitive functioning (learning, memory, language, processing speed, executive functioning, working memory, and visuospatial functioning). Ordinary least squares regression adjusting for age, sex, education, and concurrent depression severity examined anxiety group differences in performance on tests of cognitive functioning.
Results:
Significant group differences emerged for confrontation naming and visuospatial functioning, as well as for verbal fluency, working memory, and inhibition with lower performance for LLD with comorbid anxiety compared to LLD only, controlling for depression severity.
Conclusions:
Performance patterns identified among older adults with LLD and comorbid anxiety resemble neuropsychological profiles typically seen in neurodegenerative diseases of aging. These findings have potential implications for etiological considerations in the interpretation of neuropsychological profiles.
Clinical outcomes of repetitive transcranial magnetic stimulation (rTMS) for treatment of treatment-resistant depression (TRD) vary widely and there is no mood rating scale that is standard for assessing rTMS outcome. It remains unclear whether TMS is as efficacious in older adults with late-life depression (LLD) compared to younger adults with major depressive disorder (MDD). This study examined the effect of age on outcomes of rTMS treatment of adults with TRD. Self-report and observer mood ratings were measured weekly in 687 subjects ages 16–100 years undergoing rTMS treatment using the Inventory of Depressive Symptomatology 30-item Self-Report (IDS-SR), Patient Health Questionnaire 9-item (PHQ), Profile of Mood States 30-item, and Hamilton Depression Rating Scale 17-item (HDRS). All rating scales detected significant improvement with treatment; response and remission rates varied by scale but not by age (response/remission ≥ 60: 38%–57%/25%–33%; <60: 32%–49%/18%–25%). Proportional hazards models showed early improvement predicted later improvement across ages, though early improvements in PHQ and HDRS were more predictive of remission in those < 60 years (relative to those ≥ 60) and greater baseline IDS burden was more predictive of non-remission in those ≥ 60 years (relative to those < 60). These results indicate there is no significant effect of age on treatment outcomes in rTMS for TRD, though rating instruments may differ in assessment of symptom burden between younger and older adults during treatment.
Late-life depression (LLD) is common and frequently co-occurs with neurodegenerative diseases of aging. Little is known about how heterogeneity within LLD relates to factors typically associated with neurodegeneration. Varying levels of anxiety are one source of heterogeneity in LLD. We examined associations between anxiety symptom severity and factors associated with neurodegeneration, including regional brain volumes, amyloid beta (Aβ) deposition, white matter disease, cognitive dysfunction, and functional ability in LLD.
Participants and Measurements:
Older adults with major depression (N = 121, Ages 65–91) were evaluated for anxiety severity and the following: brain volume (orbitofrontal cortex [OFC], insula), cortical Aβ standardized uptake value ratio (SUVR), white matter hyperintensity (WMH) volume, global cognition, and functional ability. Separate linear regression analyses adjusting for age, sex, and concurrent depression severity were conducted to examine associations between anxiety and each of these factors. A global regression analysis was then conducted to examine the relative associations of these variables with anxiety severity.
Results:
Greater anxiety severity was associated with lower OFC volume (β = −68.25, t = −2.18, p = .031) and greater cognitive dysfunction (β = 0.23, t = 2.46, p = .016). Anxiety severity was not associated with insula volume, Aβ SUVR, WMH, or functional ability. When examining the relative associations of cognitive functioning and OFC volume with anxiety in a global model, cognitive dysfunction (β = 0.24, t = 2.62, p = .010), but not OFC volume, remained significantly associated with anxiety.
Conclusions:
Among multiple factors typically associated with neurodegeneration, cognitive dysfunction stands out as a key factor associated with anxiety severity in LLD which has implications for cognitive and psychiatric interventions.
With the recent advances in artificial intelligence (AI), patients are increasingly exposed to misleading medical information. Generative AI models, including large language models such as ChatGPT, create and modify text, images, audio and video information based on training data. Commercial use of generative AI is expanding rapidly and the public will routinely receive messages created by generative AI. However, generative AI models may be unreliable, routinely make errors and widely spread misinformation. Misinformation created by generative AI about mental illness may include factual errors, nonsense, fabricated sources and dangerous advice. Psychiatrists need to recognise that patients may receive misinformation online, including about medicine and psychiatry.
The recent achievement of fusion ignition with laser-driven technologies at the National Ignition Facility sets a historic accomplishment in fusion energy research. This accomplishment paves the way for using laser inertial fusion as a viable approach for future energy production. Europe has a unique opportunity to empower research in this field internationally, and the scientific community is eager to engage in this journey. We propose establishing a European programme on inertial-fusion energy with the mission to demonstrate laser-driven ignition in the direct-drive scheme and to develop pathway technologies for the commercial fusion reactor. The proposed roadmap is based on four complementary axes: (i) the physics of laser–plasma interaction and burning plasmas; (ii) high-energy high repetition rate laser technology; (iii) fusion reactor technology and materials; and (iv) reinforcement of the laser fusion community by international education and training programmes. We foresee collaboration with universities, research centres and industry and establishing joint activities with the private sector involved in laser fusion. This project aims to stimulate a broad range of high-profile industrial developments in laser, plasma and radiation technologies along with the expected high-level socio-economic impact.
The U.S. Department of Agriculture–Agricultural Research Service (USDA-ARS) has been a leader in weed science research covering topics ranging from the development and use of integrated weed management (IWM) tactics to basic mechanistic studies, including biotic resistance of desirable plant communities and herbicide resistance. ARS weed scientists have worked in agricultural and natural ecosystems, including agronomic and horticultural crops, pastures, forests, wild lands, aquatic habitats, wetlands, and riparian areas. Through strong partnerships with academia, state agencies, private industry, and numerous federal programs, ARS weed scientists have made contributions to discoveries in the newest fields of robotics and genetics, as well as the traditional and fundamental subjects of weed–crop competition and physiology and integration of weed control tactics and practices. Weed science at ARS is often overshadowed by other research topics; thus, few are aware of the long history of ARS weed science and its important contributions. This review is the result of a symposium held at the Weed Science Society of America’s 62nd Annual Meeting in 2022 that included 10 separate presentations in a virtual Weed Science Webinar Series. The overarching themes of management tactics (IWM, biological control, and automation), basic mechanisms (competition, invasive plant genetics, and herbicide resistance), and ecosystem impacts (invasive plant spread, climate change, conservation, and restoration) represent core ARS weed science research that is dynamic and efficacious and has been a significant component of the agency’s national and international efforts. This review highlights current studies and future directions that exemplify the science and collaborative relationships both within and outside ARS. Given the constraints of weeds and invasive plants on all aspects of food, feed, and fiber systems, there is an acknowledged need to face new challenges, including agriculture and natural resources sustainability, economic resilience and reliability, and societal health and well-being.
The IntCal family of radiocarbon (14C) calibration curves is based on research spanning more than three decades. The IntCal group have collated the 14C and calendar age data (mostly derived from primary publications with other types of data and meta-data) and, since 2010, made them available for other sorts of analysis through an open-access database. This has ensured transparency in terms of the data used in the construction of the ratified calibration curves. As the IntCal database expands, work is underway to facilitate best practice for new data submissions, make more of the associated metadata available in a structured form, and help those wishing to process the data with programming languages such as R, Python, and MATLAB. The data and metadata are complex because of the range of different types of archives. A restructured interface, based on the “IntChron” open-access data model, includes tools which allow the data to be plotted and compared without the need for export. The intention is to include complementary information which can be used alongside the main 14C series to provide new insights into the global carbon cycle, as well as facilitating access to the data for other research applications. Overall, this work aims to streamline the generation of new calibration curves.
Every year, there are over 200 traumatic deaths at work in Australia. A government safety inspector usually investigates each incident. The investigation may lead to prosecution of the employer or another party deemed to have breached relevant legislation. However, little systematic research has examined the needs and interests of grieving families in this process. Drawing on interviews with 48 representatives of institutions that deal with deaths at work (including regulators, unions, employers, police and coronial officers), this article examines how they view the problems and experiences of families. Notwithstanding some recent improvements, findings indicate ongoing shortcomings in meeting the needs of families regarding information provision, involvement and securing justice.
The rift setting of eastern Africa preserves exceptional records of mammalian (including hominin) fossils and archeology. The Afar region is host to multiple deposits with sediments ranging in age from>9 Ma to the present (Chorowicz, 2005; Katoh et al., 2016) and plays a major role in our understanding of human origins. The Gona project area contains fossiliferous deposits that span ca. 6.3 to <0.15 Ma (Quade et al., 2008); the duration of this record means that it can make a distinct contribution to understanding the environmental context for human evolution within the Afar and in eastern Africa (Figures 17.1 and 17.2). The primary units at Gona include the late Miocene Adu-Asa Formation, which contains fossils of Ardipithecus kaddaba; the early Pliocene Sagantole Formation with fossils of Ardipithecus ramidus; the mid- to late-Pliocene Hadar Formation; and the Busidima Formation (ca. 2.7 Ma to <0.15 Ma), which contains a record of the earliest Oldowan stone tools, fossils of Homo erectus, and Acheulean artifacts (Figure 17.2).
Response to lithium in patients with bipolar disorder is associated with clinical and transdiagnostic genetic factors. The predictive combination of these variables might help clinicians better predict which patients will respond to lithium treatment.
Aims
To use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder.
Method
This study utilised genetic and clinical data (n = 1034) collected as part of the International Consortium on Lithium Genetics (ConLi+Gen) project. Polygenic risk scores (PRS) were computed for schizophrenia and major depressive disorder, and then combined with clinical variables using a cross-validated machine-learning regression approach. Unimodal, multimodal and genetically stratified models were trained and validated using ridge, elastic net and random forest regression on 692 patients with bipolar disorder from ten study sites using leave-site-out cross-validation. All models were then tested on an independent test set of 342 patients. The best performing models were then tested in a classification framework.
Results
The best performing linear model explained 5.1% (P = 0.0001) of variance in lithium response and was composed of clinical variables, PRS variables and interaction terms between them. The best performing non-linear model used only clinical variables and explained 8.1% (P = 0.0001) of variance in lithium response. A priori genomic stratification improved non-linear model performance to 13.7% (P = 0.0001) and improved the binary classification of lithium response. This model stratified patients based on their meta-polygenic loadings for major depressive disorder and schizophrenia and was then trained using clinical data.
Conclusions
Using PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction. When used with other PRS and biological markers in the future this approach may help inform which patients are most likely to respond to lithium treatment.
Approximately one-third of individuals in a major depressive episode will not achieve sustained remission despite multiple, well-delivered treatments. These patients experience prolonged suffering and disproportionately utilize mental and general health care resources. The recently proposed clinical heuristic of ‘difficult-to-treat depression’ (DTD) aims to broaden our understanding and focus attention on the identification, clinical management, treatment selection, and outcomes of such individuals. Clinical trial methodologies developed to detect short-term therapeutic effects in treatment-responsive populations may not be appropriate in DTD. This report reviews three essential challenges for clinical intervention research in DTD: (1) how to define and subtype this heterogeneous group of patients; (2) how, when, and by what methods to select, acquire, compile, and interpret clinically meaningful outcome metrics; and (3) how to choose among alternative clinical trial design options to promote causal inference and generalizability. The boundaries of DTD are uncertain, and an evidence-based taxonomy and reliable assessment tools are preconditions for clinical research and subtyping. Traditional outcome metrics in treatment-responsive depression may not apply to DTD, as they largely reflect the only short-term symptomatic change and do not incorporate durability of benefit, side effect burden, or sustained impact on quality of life or daily function. The trial methodology will also require modification as trials will likely be of longer duration to examine the sustained impact, raising complex issues regarding control group selection, blinding and its integrity, and concomitant treatments.
Health disparities between Appalachia and the rest of the country are widening. To address this, the Appalachian Translational Research Network (ATRN) organizes an annual ATRN Health Summit. The most recent Summit was held online September 22–23, 2020, and hosted by Wake Forest Clinical and Translational Science Institute in partnership with the Northwest Area Health Education Center. The Summit, titled “Community-Engaged Research in Translational Science: Innovations to Improve Health in Appalachia,” brought together a diverse group of 141 stakeholders from communities, academic institutions, and the National Center for Advancing Translational Science (NCATS) to highlight current research, identify innovative approaches to translational science and community-engaged research, develop cross-regional research partnerships, and establish and disseminate priorities for future Appalachian-focused research. The Summit included three plenary presentations and 39 presentations within 12 concurrent breakout sessions. Here, we describe the Summit planning process and implementation, highlight some of the research presented, and outline nine emergent themes to guide future Appalachian-focused research.
Studying phenotypic and genetic characteristics of age at onset (AAO) and polarity at onset (PAO) in bipolar disorder can provide new insights into disease pathology and facilitate the development of screening tools.
Aims
To examine the genetic architecture of AAO and PAO and their association with bipolar disorder disease characteristics.
Method
Genome-wide association studies (GWASs) and polygenic score (PGS) analyses of AAO (n = 12 977) and PAO (n = 6773) were conducted in patients with bipolar disorder from 34 cohorts and a replication sample (n = 2237). The association of onset with disease characteristics was investigated in two of these cohorts.
Results
Earlier AAO was associated with a higher probability of psychotic symptoms, suicidality, lower educational attainment, not living together and fewer episodes. Depressive onset correlated with suicidality and manic onset correlated with delusions and manic episodes. Systematic differences in AAO between cohorts and continents of origin were observed. This was also reflected in single-nucleotide variant-based heritability estimates, with higher heritabilities for stricter onset definitions. Increased PGS for autism spectrum disorder (β = −0.34 years, s.e. = 0.08), major depression (β = −0.34 years, s.e. = 0.08), schizophrenia (β = −0.39 years, s.e. = 0.08), and educational attainment (β = −0.31 years, s.e. = 0.08) were associated with an earlier AAO. The AAO GWAS identified one significant locus, but this finding did not replicate. Neither GWAS nor PGS analyses yielded significant associations with PAO.
Conclusions
AAO and PAO are associated with indicators of bipolar disorder severity. Individuals with an earlier onset show an increased polygenic liability for a broad spectrum of psychiatric traits. Systematic differences in AAO across cohorts, continents and phenotype definitions introduce significant heterogeneity, affecting analyses.
In the Cordillera Darwin, southernmost South America, we used 10Be and 14C dating, dendrochronology, and historical observations to reconstruct the glacial history of the Dalla Vedova valley from deglacial time to the present. After deglacial recession into northeastern Darwin and Dalla Vedova, by ~16 ka, evidence indicates a glacial advance at ~13 ka coeval with the Antarctic Cold Reversal. The next robustly dated glacial expansion occurred at 870 ± 60 calendar yr ago (approximately AD 1150), followed by less-extensive dendrochronologically constrained advances from shortly before AD 1836 to the mid-twentieth century. Our record is consistent with most studies within the Cordillera Darwin that show that the Holocene glacial maximum occurred during the last millennium. This pattern contrasts with the extensive early- and mid-Holocene glacier expansions farther north in Patagonia; furthermore, an advance at 870 ± 60 yr ago may suggest out-of-phase glacial advances occurred within the Cordillera Darwin relative to Patagonia. We speculate that a southward shift of westerlies and associated climate regimes toward the southernmost tip of the continent, about 900–800 yr ago, provides a mechanism by which some glaciers advanced in the Cordillera Darwin during what is generally considered a warm and dry period to the north in Patagonia.
Over the years, attempts have been made to classify depressive syndromes based on various criteria. For several decades, the term reactive depression was used to describe cases involving an obvious precipitant, whereas endogenous depression lacked a recent stressor. Alternatively, the term secondary depression has been used in reference to cases related to a defined medical condition, as opposed to examples of primary depression. In the current classification scheme, the Diagnostic and Statistics Manual of Mental Disorders, 5th edition (DSM-V) lists fifteen distinct diagnoses related to disorders of mood, which are shown in Table 4.1. Eight of these disorders are considered depressive disorders, whereas seven categorize patients within the bipolar spectrum of illness.