Addressing Inaccurate Nosology in Mental Health: A Multilabel Data Cleansing Approach for Detecting Label Noise From Structural Magnetic Resonance Imaging Data in Mood and Psychosis Disorders

Rokham, Hooman; Pearlson, Godfrey D.; Abrol, Anees; Falakshahi, Haleh; Plis, Sergey M.; Calhoun, Vince D.

doi:10.1016/j.bpsc.2020.05.008

Cited by 11 publications

(8 citation statements)

References 28 publications

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“…4, http://links.lww.com/CONR/A58). In addition, recent approaches have started to address sub-groups within neuropsychiatric syndromes [62,63], which can further improve prediction [64–66], improve validity and potentially help to redefine both diagnostic criteria [67,68 ▪ ,69 ▪ ] and to improve individual-level diagnosis [70 ▪ ,71]. Although brain-based classification has proven challenging, there has been considerable progress made in recent years and we look forward to seeing the full potential of the brain-based predictome realized [1].…”

Section: Discussionmentioning

confidence: 99%

Data-driven approaches to neuroimaging biomarkers for neurological and psychiatric disorders: emerging approaches and examples

Calhoun

Pearlson

Sui

2021

Current Opinion in Neurology

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Purpose of review The ‘holy grail’ of clinical applications of neuroimaging to neurological and psychiatric disorders via personalized biomarkers has remained mostly elusive, despite considerable effort. However, there are many reasons to continue to be hopeful, as the field has made remarkable advances over the past few years, fueled by a variety of converging technical and data developments. Recent findings We discuss a number of advances that are accelerating the push for neuroimaging biomarkers including the advent of the ‘neuroscience big data’ era, biomarker data competitions, the development of more sophisticated algorithms including ‘guided’ data-driven approaches that facilitate automation of network-based analyses, dynamic connectivity, and deep learning. Another key advance includes multimodal data fusion approaches which can provide convergent and complementary evidence pointing to possible mechanisms as well as increase predictive accuracy. Summary The search for clinically relevant neuroimaging biomarkers for neurological and psychiatric disorders is rapidly accelerating. Here, we highlight some of these aspects, provide recent examples from studies in our group, and link to other ongoing work in the field. It is critical that access and use of these advanced approaches becomes mainstream, this will help propel the community forward and facilitate the production of robust and replicable neuroimaging biomarkers.

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Section: Discussionmentioning

confidence: 99%

Data-driven approaches to neuroimaging biomarkers for neurological and psychiatric disorders: emerging approaches and examples

Calhoun

Pearlson

Sui

2021

Current Opinion in Neurology

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“…There has been a novel attempt using structural magnetic resonance imaging (MRI) measures to mitigate the negative impact caused by the inconsistency rather than discarding all labels indiscriminately. 29 Subjects were used to build multiple support vector machine (SVM) classifiers to relabel subjects that were unanimously mislabeled by all classifiers. The process was repeated using the subjects with refreshed labels until the number of mislabeled subjects fell below a given threshold.…”

Section: Introductionmentioning

confidence: 99%

More reliable biomarkers and more accurate prediction for mental disorders using a label-noise filtering-based dimensional prediction method

Xing,

van Erp,

Pearlson

et al. 2024

iScience

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“…Analyzing and evaluating DSM‐IV and Biotype categories using state‐of‐the‐art artificial intelligence (AI) methods can provide a better understanding of different categorizations and delineate the improvement and shortcomings of new findings. The current nosology has been studied using unsupervised and semi‐supervised approaches applied to structural magnitude resonance imaging (structural MRI) data and showed overlap among different groups (Rokham, Falakshahi, & Calhoun, 2020 ; Rokham, Pearlson, et al, 2020 ). In the current study, we compared the B‐SNIP Biotype categories in order to assess brain‐based differences compared to the subjects' DSM‐IV categories.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of boundaries between mood and psychosis disorder using dynamic functional network connectivity (dFNC) via deep learning classification

Rokham

Falakshahi

et al. 2023

Human Brain Mapping

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The validity and reliability of diagnoses in psychiatry is a challenging topic in mental health. The current mental health categorization is based primarily on symptoms and clinical course and is not biologically validated. Among multiple ongoing efforts, neurological observations alongside clinical evaluations are considered to be potential solutions to address diagnostic problems. The Bipolar‐Schizophrenia Network on Intermediate Phenotypes (B‐SNIP) has published multiple papers attempting to reclassify psychotic illnesses based on biological rather than symptomatic measures. However, the effort to investigate the relationship between this new categorization approach and other neuroimaging techniques, including resting‐state fMRI data, is still limited. This study focused on investigating the relationship between different psychotic disorders categorization methods and resting‐state fMRI‐based measures called dynamic functional network connectivity (dFNC) using state‐of‐the‐art artificial intelligence (AI) approaches. We applied our method to 613 subjects, including individuals with psychosis and healthy controls, which were classified using both the Diagnostic and Statistical Manual of Mental Disorders (DSM‐IV) and the B‐SNIP biomarker‐based (Biotype) approach. Statistical group differences and cross‐validated classifiers were performed within each framework to assess how different categories. Results highlight interesting differences in occupancy in both DSM‐IV and Biotype categorizations compared to healthy individuals, which are distributed across specific transient connectivity states. Biotypes tended to show less distinctiveness in occupancy level and included fewer cellwise differences. Classification accuracy obtained by DSM‐IV and Biotype categories were both well above chance. Results provided new insights and highlighted the benefits of both DSM‐IV and biology‐based categories while also emphasizing the importance of future work in this direction, including employing further data types.

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Addressing Inaccurate Nosology in Mental Health: A Multilabel Data Cleansing Approach for Detecting Label Noise From Structural Magnetic Resonance Imaging Data in Mood and Psychosis Disorders

Cited by 11 publications

References 28 publications

Data-driven approaches to neuroimaging biomarkers for neurological and psychiatric disorders: emerging approaches and examples

Data-driven approaches to neuroimaging biomarkers for neurological and psychiatric disorders: emerging approaches and examples

More reliable biomarkers and more accurate prediction for mental disorders using a label-noise filtering-based dimensional prediction method

Evaluation of boundaries between mood and psychosis disorder using dynamic functional network connectivity (dFNC) via deep learning classification

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