Mental health progress requires causal diagnostic nosology and scalable causal discovery

Saxe, Glenn; Bickman, Leonard; Ma, Sisi; Aliferis, Constantin F.

doi:10.3389/fpsyt.2022.898789

Cited by 5 publications

(9 citation statements)

References 124 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While the term 'causal epidemiology' has long been used to refer to the aetiological susceptibility-stress model, 4 almost only associational analyses have been conducted, which has been criticised. 5 Addressing the causal objective is often evaded in Psychology and other disciplines by invoking the mantra ‘correlation is not causation’, 2 a stance that has been refuted for ‘conflating the means [no experiment is possible] with the ends [causal assessment is not required]’. 1 The prevailing advice to circumvent causality when no experiment is possible has been condemned as detrimental to science.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Avoidance of causality outside experiments: Hypotheses from cognitive dissonance reduction

Höfler,

Giesche

2024

Science Progress

View full text Add to dashboard Cite

The avoidance of causality in the design, analysis and interpretation of non-experimental studies has often been criticised as an untenable scientific stance, because theories are based on causal relations (and not associations) and a rich set of methodological tools for causal analysis has been developed in recent decades. Psychology researchers (n = 106 with complete data) participated in an online study presenting a causal statement about the results of a fictitious paper on the potential effect of drinking clear water for years on the risk of dementia. Two randomised groups of participants were then asked to reflect on the conflict between the goal of approaching a causal answer and the prevailing norm of avoiding doing so. One of the two groups was also instructed to think about possible benefits of addressing causality. Both groups then responded to a list of 19 items about attitudes to causal questions in science. A control group did this without reflecting on conflict or benefits. Free-text assessments were also collected during reflection, giving some indication of how and why causality is avoided. We condense the exploratory findings of this study into five new hypotheses about the how and why, filtered through what can be explained by cognitive dissonance reduction theory. These concern the cost of addressing causality, the variety of ways in which dissonance can be reduced, the need for profound intervention through teaching and social aspects. Predictions are derived from the hypotheses for confirmation trials in future studies and recommendations for teaching causality. Open data are provided for researchers’ own analyses.

show abstract

Section: Introductionmentioning

confidence: 99%

“… 3 , 9 – 11 Despite several calls for these now not entirely new causal methods, 3 , 12 , 13 the field of Psychology in which the authors of this article work has proved largely resistant to this need. 3 , 5 , 12 , 13 …”

Section: Introductionmentioning

confidence: 99%

Avoidance of causality outside experiments: Hypotheses from cognitive dissonance reduction

Höfler,

Giesche

2024

Science Progress

View full text Add to dashboard Cite

show abstract

“…However, despite intensive effort by the research community and considerable funding, a causal understanding of most mental health disorders remains elusive ( 4 ) and the majority of psychiatric diagnosis and treatment still operates at the level of symptomatic experience, rather than addressing root causes. This is analogous, within the domain of physical health, to physicians selecting treatments for conditions such as pneumonia, Covid-19, cancer, heart disease or diabetes based solely on a patient’s symptoms and sensations such as fever, pain, or fatigue, without having the necessary diagnostic or screening tests to know what’s caused them or what’s going on at a biological level.…”

Section: Introductionmentioning

confidence: 99%

“…However, recent advances in large-scale data acquisition, open datasets and analytical/machine learning approaches present a new era of opportunity within mental health research (4,(29)(30)(31)(32) to deal with the multitude of biological, social and environmental factors which can influence the brain and mental health and unpack their complex relationships. This allows a refocus of the mental health research paradigm to deliver a more coherent understanding of both the causal factors and physiological underpinnings of psychiatric conditions to enable better prevention, diagnosis and treatment.…”

Section: Introductionmentioning

confidence: 99%

Leveraging big data for causal understanding in mental health: a research framework

Newson,

Bala,

Giedd

et al. 2024

Front. Psychiatry

View full text Add to dashboard Cite

Over the past 30 years there have been numerous large-scale and longitudinal psychiatric research efforts to improve our understanding and treatment of mental health conditions. However, despite the huge effort by the research community and considerable funding, we still lack a causal understanding of most mental health disorders. Consequently, the majority of psychiatric diagnosis and treatment still operates at the level of symptomatic experience, rather than measuring or addressing root causes. This results in a trial-and-error approach that is a poor fit to underlying causality with poor clinical outcomes. Here we discuss how a research framework that originates from exploration of causal factors, rather than symptom groupings, applied to large scale multi-dimensional data can help address some of the current challenges facing mental health research and, in turn, clinical outcomes. Firstly, we describe some of the challenges and complexities underpinning the search for causal drivers of mental health conditions, focusing on current approaches to the assessment and diagnosis of psychiatric disorders, the many-to-many mappings between symptoms and causes, the search for biomarkers of heterogeneous symptom groups, and the multiple, dynamically interacting variables that influence our psychology. Secondly, we put forward a causal-orientated framework in the context of two large-scale datasets arising from the Adolescent Brain Cognitive Development (ABCD) study, the largest long-term study of brain development and child health in the United States, and the Global Mind Project which is the largest database in the world of mental health profiles along with life context information from 1.4 million people across the globe. Finally, we describe how analytical and machine learning approaches such as clustering and causal inference can be used on datasets such as these to help elucidate a more causal understanding of mental health conditions to enable diagnostic approaches and preventative solutions that tackle mental health challenges at their root cause.

show abstract

“…Liability to these disorders is determined by a complex interaction of distinct and shared genetic, lifestyle, and environmental factors. Disentangling these disorders is further complicated by the fact that risk factors of one disorder can often be the consequence of another or of its therapy (e.g., disturbed sleep) [3,4].…”

Section: Introductionmentioning

confidence: 99%

Cerebrospinal and Brain Proteins Implicated in Neuropsychiatric and Risk Factor Traits: Evidence from Mendelian Randomization

de La Harpe,

Zagkos,

Gill

et al. 2024

Biomedicines

View full text Add to dashboard Cite

Neuropsychiatric disorders present a global health challenge, necessitating an understanding of their molecular mechanisms for therapeutic development. Using Mendelian randomization (MR) analysis, this study explored associations between genetically predicted levels of 173 proteins in cerebrospinal fluid (CSF) and 25 in the brain with 14 neuropsychiatric disorders and risk factors. Follow-up analyses assessed consistency across plasma protein levels and gene expression in various brain regions. Proteins were instrumented using tissue-specific genetic variants, and colocalization analysis confirmed unbiased gene variants. Consistent MR and colocalization evidence revealed that lower cortical expression of low-density lipoprotein receptor-related protein 8, coupled higher abundance in the CSF and plasma, associated with lower fluid intelligence scores and decreased bipolar disorder risk. Additionally, elevated apolipoprotein-E2 and hepatocyte growth factor-like protein in the CSF and brain were related to reduced leisure screen time and lower odds of physical activity, respectively. Furthermore, elevated CSF soluble tyrosine-protein kinase receptor 1 level increased liability to attention deficit hyperactivity disorder and schizophrenia alongside lower fluid intelligence scores. This research provides genetic evidence supporting novel tissue-specific proteomic targets for neuropsychiatric disorders and their risk factors. Further exploration is necessary to understand the underlying biological mechanisms and assess their potential for therapeutic intervention.

show abstract

Mental health progress requires causal diagnostic nosology and scalable causal discovery

Cited by 5 publications

References 124 publications

Avoidance of causality outside experiments: Hypotheses from cognitive dissonance reduction

Avoidance of causality outside experiments: Hypotheses from cognitive dissonance reduction

Leveraging big data for causal understanding in mental health: a research framework

Cerebrospinal and Brain Proteins Implicated in Neuropsychiatric and Risk Factor Traits: Evidence from Mendelian Randomization

Contact Info

Product

Resources

About