Anatomical connectivity changes in bipolar disorder and schizophrenia investigated using whole-brain tract-based spatial statistics and machine learning approaches

Bipolar Disorders. 2020;22:334-355. wileyonlinelibrary.com/journal/bdi | INTRODUC TI ONBipolar disorder (BD) is a severe chronic mood disorder affecting more than 1% of the general adult population 1 and is associated with a high socio-economic burden. 2,3 Many challenges persist regarding BD management and growing evidence suggests that BD is under-recognized in clinical practice. Currently, the establishment of the diagnosis is solely based on clinical assessments. Therefore, the Abstract Objectives: The existence of anatomofunctional brain abnormalities in bipolar disorder (BD) is now well established by magnetic resonance imaging (MRI) studies. To create diagnostic and prognostic tools, as well as identifying biologically valid subtypes of BD, research has recently turned towards the use of machine learning (ML) techniques. We assessed both supervised ML and unsupervised ML studies in BD to evaluate their robustness, reproducibility and the potential need for improvement. Method:We systematically searched for studies using ML algorithms based on MRI data of patients with BD until February 2019. Result:We identified 47 studies, 45 using supervised ML techniques and 2 including unsupervised ML analyses. Among supervised studies, 43 focused on diagnostic classification. The reported accuracies for classification of BD ranged between (a) 57% and 100%, for BD vs healthy controls; (b) 49.5% and 93.1% for BD vs patients with major depressive disorder; and (c) 50% and 96.2% for BD vs patients with schizophrenia. Reported accuracies for discriminating subjects genetically at risk for BD (either from control or from patients with BD) ranged between 64.3% and 88.93%. Conclusions:Although there are strong methodological limitations in previous studies and an important need for replication in large multicentric samples, the conclusions of our review bring hope of future computer-aided diagnosis of BD and pave the way for other applications, such as treatment response prediction. To reinforce the reliability of future results we provide methodological suggestions for good practice in conducting and reporting MRI-based ML studies in BD. K E Y W O R D Sbipolar disorders, machine learning, magnetic resonance imaging, precision medicine | 335 CLAUDE Et AL.

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“…Two combined sMRI and fMRI and yield 90% 77 and 82% ACC (Palaniyappan et al 78 ). One study used only DTI input data and yield 58.75% ACC 79 …”

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confidence: 99%

Will machine learning applied to neuroimaging in bipolar disorder help the clinician? A critical review and methodological suggestions

et al. 2020

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“…For instance, in one of our previous studies, we analyzed DTI data of individuals with BD and schizophrenia and found that the two disorders largely shared anatomical connectivity alterations. 34 On the other hand, a machine-learning algorithm differentiated two groups with high accuracy (81%) and the connectivity in the right hemisphere mainly contributed to that accuracy. Therefore, in future studies patients from several different disorder groups should also be included in the comparison.…”

Section: Discussionmentioning

confidence: 99%

The Deep Learning Method Differentiates Patients with Bipolar Disorder from Controls with High Accuracy Using EEG Data

et al. 2022

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Background: Bipolar disorder (BD) is a mental disorder characterized by depressive and manic or hypomanic episodes. The complexity in the diagnosis of Bipolar disorder (BD) due to its overlapping symptoms with other mood disorders prompted researchers and clinicians to seek new and advanced techniques for the precise detection of Bipolar disorder (BD). One of these methods is the use of advanced machine learning algorithms such as deep learning (DL). However, no study of BD has previously adopted DL techniques using EEG signals. Method: EEG signals of 169 BD patients and 45 controls were cleaned from the artifacts and processed using two different DL methods: a one-dimensional convolutional neural network (1D-CNN) combined with the long-short term memory (LSTM) and a two-dimensional convolutional neural network (2D-CNN). Additionally, Class Activation Maps (CAMs) acquired from the bipolar and control groups were used to obtain distinctive regions to specify a particular class in an image. Results: Group identifications were confirmed with 95.91% overall accuracy through the 2D-CNN method, demonstrating very high sensitivity and lower specificity. Also, the overall accuracy obtained from the 1D-CNN + LSTM method was 93%. We also found that F4, C3, F7, and F8 electrode activities produce predominant features to detect the bipolar group. Conclusion: To our knowledge, this study used EEG-based DL analysis for the first time in BD. Our results suggest that the raw EEG-based DL algorithm can successfully differentiate individuals with BD from controls. Class Activation Map (CAM) analysis suggests that prefrontal changes are predominant in EEG data of patients with BD.

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“…Modelos patofisiológicos do TAB, que implicam na alteração de conectividade entre as estruturas CPF-límbico, foram diretamente estimados com a utilização do método de DTI, que permite a avaliação da integridade da microestrutura de SB dos tratos que interconectam tais regiões cerebrais(Bellani et al, 2016). Embora o grande número de publicações de estudos sobre DTI em TAB, até o presente momento, os resultados são heterogêneos, com valores baixo, alto ou mesmo similar de FA em amostra TAB versus controle saudável(Sutcubasi et al, 2019;Bellani et al, 2016; Vederine, Wessa, Leboyer e límbico, como amídala e tálamo, sendo esses tratos o UNC, CCG e ATR(Acuff et al, 2019;Ji et al, 2017;Bellani et al, 2016; Benedetti et al, 2011) (Figura 4). O…”

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Tractografia de substância branca em portadores de transtorno afetivo bipolar e relação com biomarcadores de sangue periférico

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Anatomical connectivity changes in bipolar disorder and schizophrenia investigated using whole-brain tract-based spatial statistics and machine learning approaches

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References 43 publications

Will machine learning applied to neuroimaging in bipolar disorder help the clinician? A critical review and methodological suggestions

Will machine learning applied to neuroimaging in bipolar disorder help the clinician? A critical review and methodological suggestions

The Deep Learning Method Differentiates Patients with Bipolar Disorder from Controls with High Accuracy Using EEG Data

Tractografia de substância branca em portadores de transtorno afetivo bipolar e relação com biomarcadores de sangue periférico

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