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- Andersson, Gerhard, 1966-, et al.
(författare)
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Internet-Delivered Psychological Treatments
- 2016
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Ingår i: Annual review of clinical psychology. - : Annual Reviews. - 1548-5951 .- 1548-5943. ; 12, s. 157-179
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Tidskriftsartikel (refereegranskat)abstract
- During the past 15 years, much progress has been made in developing and testing Internet-delivered psychological treatments. In particular, therapist-guided Internet treatments have been found to be effective for a wide range of psychiatric and somatic conditions in well over 100 controlled trials. These treatments require (a) a secure web platform, (b) robust assessment procedures, (c) treatment contents that can be text based or offered in other formats, and (d) a therapist role that differs from that in face-to-face therapy. Studies suggest that guided Internet treatments can be as effective as face-to-face treatments, lead to sustained improvements, work in clinically representative conditions, and probably are cost-effective. Despite these research findings, Internet treatment is not yet disseminated in most places, and clinical psychologists should consider using modern information technology and evidence-based treatment programs as a complement to their other services, even though there will always be clients for whom face-to-face treatment is the best option.
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- D'Onofrio, BM, et al.
(författare)
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Accounting for Confounding in Observational Studies
- 2020
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Ingår i: Annual review of clinical psychology. - : Annual Reviews. - 1548-5951 .- 1548-5943. ; 16, s. 25-48
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Tidskriftsartikel (refereegranskat)abstract
- The goal of this review is to enable clinical psychology researchers to more rigorously test competing hypotheses when studying risk factors in observational studies. We argue that there is a critical need for researchers to leverage recent advances in epidemiology/biostatistics related to causal inference and to use innovative approaches to address a key limitation of observational research: the need to account for confounding. We first review theoretical issues related to the study of causation, how causal diagrams can facilitate the identification and testing of competing hypotheses, and the current limitations of observational research in the field. We then describe two broad approaches that help account for confounding: analytic approaches that account for measured traits and designs that account for unmeasured factors. We provide descriptions of several such approaches and highlight their strengths and limitations, particularly as they relate to the etiology and treatment of behavioral health problems.
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- Ge, R, et al.
(författare)
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Normative Modeling of Brain Morphometry Across the Lifespan Using CentileBrain: Algorithm Benchmarking and Model Optimization
- 2023
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Ingår i: bioRxiv : the preprint server for biology. - : Cold Spring Harbor Laboratory.
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Background: Normative modeling is a statistical approach to quantify the degree to which a particular individual-level measure deviates from the pattern observed in a normative reference population. When applied to human brain morphometric measures it has the potential to inform about the significance of normative deviations for health and disease. Normative models can be implemented using a variety of algorithms that have not been systematically appraised. Methods: To address this gap, eight algorithms were compared in terms of performance and computational efficiency using brain regional morphometric data from 37,407 healthy individuals (53% female; aged 3-90 years) collated from 87 international MRI datasets. Performance was assessed with the mean absolute error (MAE) and computational efficiency was inferred from central processing unit (CPU) time. The algorithms evaluated were Ordinary Least Squares Regression (OLSR), Bayesian Linear Regression (BLR), Generalized Additive Models for Location, Scale, and Shape (GAMLSS), Parametric Lambda, Mu, Sigma (LMS), Gaussian Process Regression (GPR), Warped Bayesian Linear Regression (WBLG), Hierarchical Bayesian Regression (HBR), and Multivariable Fractional Polynomial Regression (MFPR). Model optimization involved testing nine covariate combinations pertaining to acquisition features, parcellation software versions, and global neuroimaging measures (i.e., total intracranial volume, mean cortical thickness, and mean cortical surface area). Findings: Statistical comparisons across models at PFDR<0.05 indicated that the MFPR-derived sex- and region-specific models with nonlinear polynomials for age and linear effects of global measures had superior predictive accuracy; the range of the MAE of the models of regional subcortical volumes was 70-520 mm3 and the corresponding ranges for regional cortical thickness and regional cortical surface area were 0.09-0.26 mm and 24-560 mm2, respectively. The MFPR-derived models were also computationally more efficient with a CPU time below one second compared to a range of 2 seconds to 60 minutes for the other algorithms. The performance of all sex- and region-specific MFPR models plateaued at sample sizes exceeding 3,000 and showed comparable MAEs across distinct 10-year age-bins covering the human lifespan. Interpretation: These results provide an empirically benchmarked framework for normative modeling of brain morphometry that is useful for interpreting prior literature and supporting future study designs. The model and tools described here are freely available through CentileBrain (https://centilebrain.org/), a user-friendly web platform.
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- Holmes, Emily A., et al.
(författare)
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Mental Imagery in Depression : Phenomenology, Potential Mechanisms, and Treatment Implications
- 2016
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Ingår i: ANNUAL REVIEW OF CLINICAL PSYCHOLOGY, VOL 12. - : ANNUAL REVIEWS. - 1548-5951 .- 1548-5943. - 9780824339128 ; , s. 249-280
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Bokkapitel (refereegranskat)abstract
- Mental imagery is an experience like perception in the absence of a percept. It is a ubiquitous feature of human cognition, yet it has been relatively neglected in the etiology, maintenance, and treatment of depression. Imagery abnormalities in depression include an excess of intrusive negative mental imagery; impoverished positive imagery; bias for observer perspective imagery; and overgeneral memory, in which specific imagery is lacking. We consider the contribution of imagery dysfunctions to depressive psychopathology and implications for cognitive behavioral interventions. Treatment advances capitalizing on the representational format of imagery (as opposed to its content) are reviewed, including imagery rescripting, positive imagery generation, and memory specificity training. Consideration of mental imagery can contribute to clinical assessment and imagery-focused psychological therapeutic techniques and promote investigation of underlying mechanisms for treatment innovation. Research into mental imagery in depression is at an early stage. Work that bridges clinical psychology and neuroscience in the investigation of imagery-related mechanisms is recommended.
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- Nunes, A, et al.
(författare)
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Using structural MRI to identify bipolar disorders - 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group
- 2020
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Ingår i: Molecular psychiatry. - : Springer Science and Business Media LLC. - 1476-5578 .- 1359-4184. ; 25:9, s. 2130-2143
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Tidskriftsartikel (refereegranskat)abstract
- Bipolar disorders (BDs) are among the leading causes of morbidity and disability. Objective biological markers, such as those based on brain imaging, could aid in clinical management of BD. Machine learning (ML) brings neuroimaging analyses to individual subject level and may potentially allow for their diagnostic use. However, fair and optimal application of ML requires large, multi-site datasets. We applied ML (support vector machines) to MRI data (regional cortical thickness, surface area, subcortical volumes) from 853 BD and 2167 control participants from 13 cohorts in the ENIGMA consortium. We attempted to differentiate BD from control participants, investigated different data handling strategies and studied the neuroimaging/clinical features most important for classification. Individual site accuracies ranged from 45.23% to 81.07%. Aggregate subject-level analyses yielded the highest accuracy (65.23%, 95% CI = 63.47–67.00, ROC-AUC = 71.49%, 95% CI = 69.39–73.59), followed by leave-one-site-out cross-validation (accuracy = 58.67%, 95% CI = 56.70–60.63). Meta-analysis of individual site accuracies did not provide above chance results. There was substantial agreement between the regions that contributed to identification of BD participants in the best performing site and in the aggregate dataset (Cohen’s Kappa = 0.83, 95% CI = 0.829–0.831). Treatment with anticonvulsants and age were associated with greater odds of correct classification. Although short of the 80% clinically relevant accuracy threshold, the results are promising and provide a fair and realistic estimate of classification performance, which can be achieved in a large, ecologically valid, multi-site sample of BD participants based on regional neurostructural measures. Furthermore, the significant classification in different samples was based on plausible and similar neuroanatomical features. Future multi-site studies should move towards sharing of raw/voxelwise neuroimaging data.
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- Tiihonen, J, et al.
(författare)
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Molecular pathways underlying schizophrenia
- 2020
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Ingår i: EUROPEAN NEUROPSYCHOPHARMACOLOGY. - : Elsevier BV. - 0924-977X. ; 45:SUPPL 1, s. 245-246
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)
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| 27. |
- Tiihonen, J, et al.
(författare)
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Sex-specific transcriptional and proteomic signatures in schizophrenia
- 2019
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Ingår i: Nature communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 10:1, s. 3933-
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Tidskriftsartikel (refereegranskat)abstract
- It has remained unclear why schizophrenia typically manifests after adolescence and which neurobiological mechanisms are underlying the cascade leading to the actual onset of the illness. Here we show that the use of induced pluripotent stem cell-derived neurons of monozygotic twins from pairs discordant for schizophrenia enhances disease-specific signal by minimizing genetic heterogeneity. In proteomic and pathway analyses, clinical illness is associated especially with altered glycosaminoglycan, GABAergic synapse, sialylation, and purine metabolism pathways. Although only 12% of all 19,462 genes are expressed differentially between healthy males and females, up to 61% of the illness-related genes are sex specific. These results on sex-specific genes are replicated in another dataset. This implies that the pathophysiology differs between males and females, and may explain why symptoms appear after adolescence when the expression of many sex-specific genes change, and suggests the need for sex-specific treatments.
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