Deep learning applications for the classification of psychiatric disorders using neuroimaging data: <i>systematic review and meta-analysis</i>

Quaak, Mirjam; Mortel, Laurens van de; Thomas, Rajat Mani; Wingen, Guido van

doi:10.1101/2020.06.12.20129130

Cited by 2 publications

(3 citation statements)

References 84 publications

(134 reference statements)

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“…Moreover, early RNNs had simple recurring layers but they had a vanishing gradient problem and didn't work for long data sequences (Dua et al, 2020). Long-short-term memory (LSTM) is the most prevalent architecture of RNNs used to solve these problems (Eslami et al, 2021;Quaak et al, 2021).…”

Section: Model Testing and Performance Evaluationmentioning

confidence: 99%

“…Several publications (Zhang L. et al, 2020;Zhang et al, 2021;Quaak et al, 2021) have reviewed the classification of ASD using only ML or DL algorithms. Some representative examples of previous reviews are listed in Table 1.…”

Section: Introductionmentioning

confidence: 99%

“…However, these publications often cover many human tissues or diseases (Quaak et al, 2021). Thus, this survey focuses on ASD and brain imaging only.…”

Section: Introductionmentioning

confidence: 99%

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Machine learning for autism spectrum disorder diagnosis using structural magnetic resonance imaging: Promising but challenging

Bahathiq

Banjar

Bamaga

et al. 2022

Front. Neuroinform.

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Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that affects approximately 1% of the population and causes significant burdens. ASD’s pathogenesis remains elusive; hence, diagnosis is based on a constellation of behaviors. Structural magnetic resonance imaging (sMRI) studies have shown several abnormalities in volumetric and geometric features of the autistic brain. However, inconsistent findings prevented most contributions from being translated into clinical practice. Establishing reliable biomarkers for ASD using sMRI is crucial for the correct diagnosis and treatment. In recent years, machine learning (ML) and specifically deep learning (DL) have quickly extended to almost every sector, notably in disease diagnosis. Thus, this has led to a shift and improvement in ASD diagnostic methods, fulfilling most clinical diagnostic requirements. However, ASD discovery remains difficult. This review examines the ML-based ASD diagnosis literature over the past 5 years. A literature-based taxonomy of the research landscape has been mapped, and the major aspects of this topic have been covered. First, we provide an overview of ML’s general classification pipeline and the features of sMRI. Next, representative studies are highlighted and discussed in detail with respect to methods, and biomarkers. Finally, we highlight many common challenges and make recommendations for future directions. In short, the limited sample size was the main obstacle; Thus, comprehensive data sets and rigorous methods are necessary to check the generalizability of the results. ML technologies are expected to advance significantly in the coming years, contributing to the diagnosis of ASD and helping clinicians soon.

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Section: Model Testing and Performance Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Machine learning for autism spectrum disorder diagnosis using structural magnetic resonance imaging: Promising but challenging

Bahathiq

Banjar

Bamaga

et al. 2022

Front. Neuroinform.

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Deep learning for small and big data in psychiatry

Koppe

Meyer‐Lindenberg

Durstewitz

2020

Neuropsychopharmacol.

126

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Psychiatry today must gain a better understanding of the common and distinct pathophysiological mechanisms underlying psychiatric disorders in order to deliver more effective, person-tailored treatments. To this end, it appears that the analysis of ‘small’ experimental samples using conventional statistical approaches has largely failed to capture the heterogeneity underlying psychiatric phenotypes. Modern algorithms and approaches from machine learning, particularly deep learning, provide new hope to address these issues given their outstanding prediction performance in other disciplines. The strength of deep learning algorithms is that they can implement very complicated, and in principle arbitrary predictor-response mappings efficiently. This power comes at a cost, the need for large training (and test) samples to infer the (sometimes over millions of) model parameters. This appears to be at odds with the as yet rather ‘small’ samples available in psychiatric human research to date (n < 10,000), and the ambition of predicting treatment at the single subject level (n = 1). Here, we aim at giving a comprehensive overview on how we can yet use such models for prediction in psychiatry. We review how machine learning approaches compare to more traditional statistical hypothesis-driven approaches, how their complexity relates to the need of large sample sizes, and what we can do to optimally use these powerful techniques in psychiatric neuroscience.

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Deep learning applications for the classification of psychiatric disorders using neuroimaging data: systematic review and meta-analysis

Cited by 2 publications

References 84 publications

Machine learning for autism spectrum disorder diagnosis using structural magnetic resonance imaging: Promising but challenging

Machine learning for autism spectrum disorder diagnosis using structural magnetic resonance imaging: Promising but challenging

Deep learning for small and big data in psychiatry

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