Defining brain-based OCD patient profiles using task-based fMRI and unsupervised machine learning

Nadai, Alessandro S. De; Fitzgerald, Kate D.; Norman, Luke; Block, Stefanie Russman; Mannella, Kristin A.; Himle, Joseph A.; Taylor, Stephan F.

doi:10.1038/s41386-022-01353-x

Cited by 8 publications

(3 citation statements)

References 58 publications

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“…Following correction for false discovery rates, it was observed that the interference-dominant cluster exhibited significantly longer reaction times compared to the other patient clusters, although no other covariate differences were detected between the clusters. These findings enhance the precision of patient characterization, redefining previous neurobehavioral studies of OCD, and providing a starting point for neuroimaging-guided treatment selection (De Nadai et al, 2023).…”

Section: Functional Magnetic Resonance Imagingmentioning

confidence: 77%

A comprehensive review for machine learning on neuroimaging in obsessive-compulsive disorder

Li,

Kang,

2023

Front. Hum. Neurosci.

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Obsessive-compulsive disorder (OCD) is a common mental disease, which can exist as a separate disease or become one of the symptoms of other mental diseases. With the development of society, statistically, the incidence rate of obsessive-compulsive disorder has been increasing year by year. At present, in the diagnosis and treatment of OCD, The clinical performance of patients measured by scales is no longer the only quantitative indicator. Clinical workers and researchers are committed to using neuroimaging to explore the relationship between changes in patient neurological function and obsessive-compulsive disorder. Through machine learning and artificial learning, medical information in neuroimaging can be better displayed. In this article, we discuss recent advancements in artificial intelligence related to neuroimaging in the context of Obsessive-Compulsive Disorder.

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Section: Functional Magnetic Resonance Imagingmentioning

confidence: 77%

A comprehensive review for machine learning on neuroimaging in obsessive-compulsive disorder

Li,

Kang,

2023

Front. Hum. Neurosci.

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“…Our findings indicate the IVA-S3 offers the potential for improved subgroup identification with fMRI data, as we demonstrated that the IVA-S3 is able to better preserve subject variability. A more detailed investigation of subgroup identification is beyond the scope of this work but is an emerging new area of research [4,[42][43][44][45]. Furthermore, as discussed in the previous section, the IVA might be prone to overfitting when analyzing large numbers of datasets.…”

Section: Discussionmentioning

confidence: 98%

A Scalable Approach to Independent Vector Analysis by Shared Subspace Separation for Multi-Subject fMRI Analysis

Sun

Gabrielson

Akhonda

et al. 2023

Sensors

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Joint blind source separation (JBSS) has wide applications in modeling latent structures across multiple related datasets. However, JBSS is computationally prohibitive with high-dimensional data, limiting the number of datasets that can be included in a tractable analysis. Furthermore, JBSS may not be effective if the data’s true latent dimensionality is not adequately modeled, where severe overparameterization may lead to poor separation and time performance. In this paper, we propose a scalable JBSS method by modeling and separating the “shared” subspace from the data. The shared subspace is defined as the subset of latent sources that exists across all datasets, represented by groups of sources that collectively form a low-rank structure. Our method first provides the efficient initialization of the independent vector analysis (IVA) with a multivariate Gaussian source prior (IVA-G) specifically designed to estimate the shared sources. Estimated sources are then evaluated regarding whether they are shared, upon which further JBSS is applied separately to the shared and non-shared sources. This provides an effective means to reduce the dimensionality of the problem, improving analyses with larger numbers of datasets. We apply our method to resting-state fMRI datasets, demonstrating that our method can achieve an excellent estimation performance with significantly reduced computational costs.

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“…Another limitation of the present study was the relatively small number of participants to obtain reliable brain conclusions (Turner et al, 2018). Fourth, emerging clustering analysis studies reported various profiles that cut across categorical nosologies in ADHD (Bergwerff et al, 2019; De Nadai et al, 2023; Fernández-Martín et al, 2023). We cannot rule out the existence of OCD and ADHD patterns that might explain these results and, perhaps, the heterogeneous decision-making strategies found in the previous literature (Apergis-Schoute et al, 2023; Hauser et al, 2014; Mazurki et al, 2021; Sethi et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

Contingency-based flexibility mechanisms through a reinforcement learning model in adults with Attention-Deficit/Hyperactivity Disorder and Obsessive-Compulsive Disorder

Rodríguez-Herrera,

León,

Fernández-Martína

et al. 2024

Preprint

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AbtractThe probabilistic reversal learning paradigm is one of the most used to assess cognitive flexibility during a contingency-based learning process. Lack of cognitive flexibility is related to the symptomatology that characterizes disorders such as Attention-Deficit/Hyperactivity Disorder (ADHD) and Obsessive-Compulsive Disorder (OCD). Resting-state functional connectivity (rsFC) could be a specific predictor of performance in contingency-based cognitive flexibility. However, the mechanisms underlying learning and flexibility in an environment of uncertainty with OCD or ADHD adults have not been widely explored. Computational modelling may be helpful to separate components of the behavioural processes and identify the source of the disorders. In the present study, we aimed to identify the mechanisms underlying contingency-based cognitive flexibility in a sample of the impulsive-compulsive spectrum and healthy controls, and explore the rsFC between the frontoparietal networks (FPN) regions as a possible neuromarker. 148 Spanish-speaking participants (43 patients with OCD, 53 with ADHD, and 52 healthy controls) completed a probabilistic reversal learning task (PRLT). Previously, we obtained a record of FNP rsFC using functional near-infrared spectroscopy (fNIRS). For this purpose, we applied the reinforcement learning model in combination with Bayesian GLM. We found that groups showed an optimal performance in the acquisition phase of the PRLT and higher performance of HC compared to the diagnostic groups in the reversal block, although performance is still optimal in all groups. Likewise, we found that the parameters studied (reinforcement learning rate, punishment learning rate and inverse temperature) predict task performance differently by phase and group. Regarding FNP rsFC, we found that rsFC between left posterior parietal cortex (lpPC) and right posterior parietal cortex (rpPC) seems to credibly predict performance in the acquisition block in the healthy controls. These findings suggest that reducing the uncertainty between action-outcome may help to improve the adaptation of ADHD and OCD patients to changing environments. Thus, understanding sensitivity to punishment or reinforcement and its influence on the decision-making may be important for designing case-specific interventions. According to our data, rsFC between lpPC and rpPC could be important for optimal learning of our task.

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Defining brain-based OCD patient profiles using task-based fMRI and unsupervised machine learning

Cited by 8 publications

References 58 publications

A comprehensive review for machine learning on neuroimaging in obsessive-compulsive disorder

A comprehensive review for machine learning on neuroimaging in obsessive-compulsive disorder

A Scalable Approach to Independent Vector Analysis by Shared Subspace Separation for Multi-Subject fMRI Analysis

Contingency-based flexibility mechanisms through a reinforcement learning model in adults with Attention-Deficit/Hyperactivity Disorder and Obsessive-Compulsive Disorder

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