Neurobiological support to the diagnosis of <scp>ADHD</scp> in stimulant‐naïve adults: pattern recognition analyses of <scp>MRI</scp> data

Chaim‐Avancini, Tiffany M.; Doshi, Jimit; Zanetti, Marcus V.; Erus, Güray; Silva, M A; Duran, Fábio L.S.; Cavallet, Mikael; Caetano, Sheila C.; Louzã, Mário Rodrigues; Davatzikos, Christos; Busatto, Geraldo F.

doi:10.1111/acps.12824

Cited by 21 publications

(12 citation statements)

References 78 publications

(110 reference statements)

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“…Ten other studies focused only on children and/or adolescents (under age 18). Only three studies examined classification models for older adults (Chaim-Avancini et al, 2017; Wang et al, 2013; Yao et al, 2018). Overall, the difference in accuracy across the three types of age compositions was not significant (F (2, 47) = 2.64, p= 0.08).…”

Section: Resultsmentioning

confidence: 99%

Machine learning and MRI-based diagnostic models for ADHD: are we there yet?

Zhang-James

Hoogman

Franke

et al. 2020

Preprint

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Machine learning (ML) has been applied to develop magnetic resonance imaging (MRI)-based diagnostic classifiers for attention-deficit/hyperactivity disorder (ADHD). This systematic review examines this literature to clarify its clinical significance and to assess the implications of the various analytic methods applied. We found that, although most of studies reported the classification accuracies, they varied in choice of MRI modalities, ML models, cross-validation and testing methods, and sample sizes. We found that the accuracies of cross-validation methods inflated the performance estimation compared with those of a held-out test, compromising the model generalizability. Test accuracies have increased with publication year but were not associated with training sample sizes. Improved test accuracy over time was likely due to the use of better ML methods along with strategies to deal with data imbalances. Ultimately, large multi-modal imaging datasets, and potentially the combination with other types of data, like cognitive data and/or genetics, will be essential to achieve the goal of developing clinically useful imaging classification tools for ADHD in the future.

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Section: Resultsmentioning

confidence: 99%

Machine learning and MRI-based diagnostic models for ADHD: are we there yet?

Zhang-James

Hoogman

Franke

et al. 2020

Preprint

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“…Although several existing multivariate machine learning and ELTs-based studies have commonly reported that the anatomical features in frontal and parietal areas are associated with the classification performance between adults with ADHD and group-matched normal controls (Chaim-Avancini et al, 2017; Zhang-James et al, 2019), no machine learning study has been conducted to identify the classification pattern for discrimination between ADHD persisters and remitters. We further observed that the features of nodal efficiency in right IFG, functional connectivity between right MFG and right IPL, and right amygdala volume were associated with inattentive symptom severity T-score, while the nodal efficiencies of right IFG and MFG and functional connectivity between MFG and IPL in right hemisphere were associated with hyperactive/impulsive symptom severity T-score.…”

Section: Discussionmentioning

confidence: 99%

“…(Brown et al, 2012; Colby et al, 2012; Iannaccone et al, 2015). SVM has also been applied to structural MRI and DTI data collected from adults with ADHD and controls, which reported between-group differences in widespread GM and WM regions in cortices, thalamus, and cerebellum (Chaim-Avancini et al, 2017). Meanwhile, neural network-based techniques, including deep belief network, fully connected cascade artificial neural network, convolutional neural network, extreme learning machine, and hierarchical extreme learning machine, have also been utilized to structural MRI and resting-state functional MRI (fMRI) data in children with ADHD and controls (Deshpande et al, 2015; Kuang and He, 2014; Peng et al, 2013; Qureshi et al, 2016; Qureshi et al, 2017; Zou et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Multimodal Neuroimaging-based Prediction of Adult Outcomes in Childhood-onset ADHD using Ensemble Learning Techniques

Luo

Alvarez

Halperin

et al. 2019

Preprint

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Attention deficit/hyperactivity disorder (ADHD) is a highly prevalent and heterogeneous neurodevelopmental disorder, currently relaying on subjective symptom observations for diagnosis.Machine learning classifiers have been utilized to assist the development of neuroimaging-based biomarkers for objective diagnosis of ADHD. However, the existing basic model-based studies in ADHD reported suboptimal classification performances and inconclusive results, mainly due to the limited flexibility for each type of basic classifiers to appropriately handle multi-dimensional source features with various properties. In this study, we proposed to apply ensemble learning techniques (ELTs) in multimodal neuroimaging data collected from 72 young adults, including 36 probands (18 remitters and 18 persisters of childhood ADHD) and 36 group-matched controls. All the currently available optimization strategies for ELTs (i.e., voting, bagging, boosting and stacking techniques) were tested in a pool of semi-final classification results generated by seven basic classifiers. The highdimensional neuroimaging features for classification included regional cortical gray matter (GM) thickness and surface area, GM volume of subcortical structures, volume and fractional anisotropy of major white matter fiber tracts, pair-wise regional connectivity and global/nodal topological properties of the functional brain network for cue-evoked attention process. As a result, the bagging-based ELT with the base model of support vector machine achieved the best results, with the most significant improvement of the area under the receiver of operating characteristic curve (0.89 for ADHD vs. controls, and 0.9 for ADHD persisters vs. remitters). We found that features of nodal efficiency in right inferior frontal gyrus, right middle frontal (MFG)-inferior parietal (IPL) functional connectivity, and right amygdala volume significantly contributed to accurate discrimination between ADHD probands and controls; higher nodal efficiency of right MFG greatly contributed to inattentive and hyperactive/impulsive symptom remission, while higher right MFG-IPL functional connectivity strongly linked to symptom persistence in adults with childhood ADHD. Our study also suggested that considering their solidly improved robustness than the commonly implemented basic classifiers, ELTs may have the potential to identify more reliable neurobiological markers for severe brain disorders.structures estimated from structural MRI data, volume and FA of major WM fiber tracts derived from DTI data, the pair-wise regional connectivity and global/nodal topological properties (i.e., global-, local-, and nodal-efficiency, etc.) of the cue-evoked attention processing network computed from task-based fMRI data. Based on findings from existing studies from our group and other teams, we hypothesized that structural and functional alterations in frontal, parietal, and subcortical areas and their interactions would significantly contribute to accurate discrimination of ADHD probands (adults diag...

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“…The LiNC group at UNIFESP has also pioneered studies using 99m Tc-TRODAT to evaluate the density of striatal dopaminergic terminals with SPECT. 80 Concerning MRI, several new image acquisition methods of interest to psychiatry have been incorporated in studies led by Brazilian research groups in the past few years, including diffusion tensor imaging (DTI) for investigations of the microstructural integrity of white matter fibers and tracts, with acquisition of diffusion-weighted imaging (DWI) data (which measures the motion of water molecules within minute tissue portions), 163 and restingstate fMRI methods for the investigation of intra-and internetwork patterns of functional connectivity in the brain at rest. 164 There is continuous innovation in MRI acquisition methods.…”

Section: New Approaches For the Extraction Of Quantitative Neuroimage Indicesmentioning

confidence: 99%

“…The hyperplane is defined using information from one or multiple imaging modalities to generate a signature that differentiates groups with the greatest possible accuracy. 198,200 Psychiatric neuroimaging groups in Brazil have been involved in ML studies of brain aging and dementia, 151,199,201 mood disorders, 200 ADHD, 163 personality disorders, 202 obsessive-compulsive disorder, 203 autism, 74 and schizophrenia-spectrum disorders. 177,204 In general, the neuroimaging signatures identified in ML studies evaluating psychiatric disorders have not produced clinically meaningful indices of diagnostic accuracy.…”

Section: New Statistical Approachesmentioning

confidence: 99%

Psychiatric neuroimaging research in Brazil: historical overview, current challenges, and future opportunities

Busatto

Rosa

Squarzoni

et al. 2021

Braz. J. Psychiatry

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The last four decades have witnessed tremendous growth in research studies applying neuroimaging methods to evaluate pathophysiological and treatment aspects of psychiatric disorders around the world. This article provides a brief history of psychiatric neuroimaging research in Brazil, including quantitative information about the growth of this field in the country over the past 20 years. Also described are the various methodologies used, the wealth of scientific questions investigated, and the strength of international collaborations established. Finally, examples of the many methodological advances that have emerged in the field of in vivo neuroimaging are provided, with discussion of the challenges faced by psychiatric research groups in Brazil, a country of limited resources, to continue incorporating such innovations to generate novel scientific data of local and global relevance.

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Neurobiological support to the diagnosis of ADHD in stimulant‐naïve adults: pattern recognition analyses of MRI data

Abstract: Introvert personality traits showed independent risk effects on suicidality regardless of diagnosis status. Among high risk individuals with suicidal thoughts, higher neuroticism tendency is further associated with increased risk of suicide attempt.

Cited by 21 publications

References 78 publications

Machine learning and MRI-based diagnostic models for ADHD: are we there yet?

Machine learning and MRI-based diagnostic models for ADHD: are we there yet?

Multimodal Neuroimaging-based Prediction of Adult Outcomes in Childhood-onset ADHD using Ensemble Learning Techniques

Psychiatric neuroimaging research in Brazil: historical overview, current challenges, and future opportunities

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