A Computational Model for the Automatic Diagnosis of Attention Deficit Hyperactivity Disorder Based on Functional Brain Volume

Multiclass classification of Autism Spectrum Disorder, attention deficit hyperactivity disorder, and typically developed individuals using fMRI functional connectivity analysis

Neurodevelopmental conditions, such as Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD), present unique challenges due to overlapping symptoms, making an accurate diagnosis and targeted intervention difficult. Our study employs advanced machine learning techniques to analyze functional magnetic resonance imaging (fMRI) data from individuals with ASD, ADHD, and typically developed (TD) controls, totaling 120 subjects in the study. Leveraging multiclass classification (ML) algorithms, we achieve superior accuracy in distinguishing between ASD, ADHD, and TD groups, surpassing existing benchmarks with an area under the ROC curve near 98%. Our analysis reveals distinct neural signatures associated with ASD and ADHD: individuals with ADHD exhibit altered connectivity patterns of regions involved in attention and impulse control, whereas those with ASD show disruptions in brain regions critical for social and cognitive functions. The observed connectivity patterns, on which the ML classification rests, agree with established diagnostic approaches based on clinical symptoms. Furthermore, complex network analyses highlight differences in brain network integration and segregation among the three groups. Our findings pave the way for refined, ML-enhanced diagnostics in accordance with established practices, offering a promising avenue for developing trustworthy clinical decision-support systems.

Digital assessments for children and adolescents with ADHD: a scoping review

Introduction

In spite of rapid advances in evidence-based treatments for attention deficit hyperactivity disorder (ADHD), community access to rigorous gold-standard diagnostic assessments has lagged far behind due to barriers such as the costs and limited availability of comprehensive diagnostic evaluations. Digital assessment of attention and behavior has the potential to lead to scalable approaches that could be used to screen large numbers of children and/or increase access to high-quality, scalable diagnostic evaluations, especially if designed using user-centered participatory and ability-based frameworks. Current research on assessment has begun to take a user-centered approach by actively involving participants to ensure the development of assessments that meet the needs of users (e.g., clinicians, teachers, patients).

Methods

The objective of this mapping review was to identify and categorize digital mental health assessments designed to aid in the initial diagnosis of ADHD as well as ongoing monitoring of symptoms following diagnosis.

Results

Results suggested that the assessment tools currently described in the literature target both cognition and motor behaviors. These assessments were conducted using a variety of technological platforms, including telemedicine, wearables/sensors, the web, virtual reality, serious games, robots, and computer applications/software.

Discussion

Although it is evident that there is growing interest in the design of digital assessment tools, research involving tools with the potential for widespread deployment is still in the early stages of development. As these and other tools are developed and evaluated, it is critical that researchers engage patients and key stakeholders early in the design process.

Decreased gray matter volume in the anterior cerebellar of attention deficit/hyperactivity disorder comorbid oppositional defiant disorder children with associated cerebellar-cerebral hyperconnectivity: insights from a combined structural MRI and resting-state fMRI study

Attention deficit/hyperactivity disorder (ADHD) and oppositional defiant disorder (ODD) are highly comorbid. Many prior investigations have found that ADHD relates to anatomical abnormalities in gray matter. The abnormal gray matter of ADHD comorbid ODD is still poorly understood. This study aimed to explore the effect of comorbid ODD on gray matter volume (GMV) and functional alterations in ADHD. All data were provided by the ADHD-200 Preprocessed Repository, including 27 ADHD-only children, 27 ADHD + ODD children, and 27 healthy controls aged 9-14 years. Voxel-based morphometry (VBM) and functional connectivity (FC) of resting-state functional magnetic resonance imaging (fMRI) were used to compare the difference in GMV and FC between ADHD + ODD, ADHD-only, and healthy children. The results showed that ADHD children with comorbid ODD had a more significant reduction in cerebellar volume, mainly in the anterior regions of the cerebellum (Cerebellum_4_5). The Cerebellum_4_5 showed increased functional connectivity with multiple cortical regions. These brain regions include numerous executive functioning (EF) and brain default mode network (DMN) nodes. The GMV abnormalities and excessive connectivity between brain regions may further exacerbate the emotional and cognitive deficits associated with ADHD.

Structural or/and functional MRI-based machine learning techniques for attention-deficit/hyperactivity disorder diagnosis: A systematic review and meta-analysis

BACKGROUND

The aim of this study was to investigate the diagnostic value of ML techniques based on sMRI or/and fMRI for ADHD.

METHODS

We conducted a comprehensive search (from database creation date to March 2024) for relevant English articles on sMRI or/and fMRI-based ML techniques for diagnosing ADHD. The pooled sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-), summary receiver operating characteristic (SROC) curve and area under the curve (AUC) were calculated to assess the diagnostic value of sMRI or/and fMRI-based ML techniques. The I2 test was used to assess heterogeneity and the source of heterogeneity was investigated by performing a meta-regression analysis. Publication bias was assessed using the Deeks funnel plot asymmetry test.

RESULTS

Forty-three studies were included in the systematic review, 27 of which were included in our meta-analysis. The pooled sensitivity and specificity of sMRI or/and fMRI-based ML techniques for the diagnosis of ADHD were 0.74 (95 % CI 0.65-0.81) and 0.75 (95 % CI 0.67-0.81), respectively. SROC curve showed that AUC was 0.81 (95 % CI 0.77-0.84). Based on these findings, the sMRI or/and fMRI-based ML techniques have relatively good diagnostic value for ADHD.

LIMITATIONS

Our meta-analysis specifically focused on ML techniques based on sMRI or/and fMRI studies. Since EEG-based ML techniques are also used for diagnosing ADHD, further systematic analyses are necessary to explore ML methods based on multimodal medical data.

CONCLUSION

sMRI or/and fMRI-based ML technique is a promising objective diagnostic method for ADHD.

Multiple measurement analysis of resting-state fMRI for ADHD classification in adolescent brain from the ABCD study

Attention deficit hyperactivity disorder (ADHD) is one of the most common psychiatric disorders in school-aged children. Its accurate diagnosis looks after patients' interests well with effective treatment, which is important to them and their family. Resting-state functional magnetic resonance imaging (rsfMRI) has been widely used to characterize the abnormal brain function by computing the voxel-wise measures and Pearson's correlation (PC)-based functional connectivity (FC) for ADHD diagnosis. However, exploring the powerful measures of rsfMRI to improve ADHD diagnosis remains a particular challenge. To this end, this paper proposes an automated ADHD classification framework by fusion of multiple measures of rsfMRI in adolescent brain. First, we extract the voxel-wise measures and ROI-wise time series from the brain regions of rsfMRI after preprocessing. Then, to extract the multiple functional connectivities, we compute the PC-derived FCs including the topographical information-based high-order FC (tHOFC) and dynamics-based high-order FC (dHOFC), the sparse representation (SR)-derived FCs including the group SR (GSR), the strength and similarity guided GSR (SSGSR), and sparse low-rank (SLR). Finally, these measures are combined with multiple kernel learning (MKL) model for ADHD classification. The proposed method is applied to the Adolescent Brain and Cognitive Development (ABCD) dataset. The results show that the FCs of dHOFC and SLR perform better than the others. Fusing multiple measures achieves the best classification performance (AUC = 0.740, accuracy = 0.6916), superior to those from the single measure and the previous studies. We have identified the most discriminative FCs and brain regions for ADHD diagnosis, which are consistent with those of published literature.

Machine Learning and MRI-based Diagnostic Models for ADHD: Are We There Yet?

OBJECTIVE

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.

METHODS

A comprehensive literature search on MRI-based diagnostic classifiers for ADHD was performed and data regarding the utilized models and samples were gathered.

RESULTS

We found that, although most 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.

CONCLUSION

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.

A Review of Machine Learning and Deep Learning Approaches on Mental Health Diagnosis

Combating mental illnesses such as depression and anxiety has become a global concern. As a result of the necessity for finding effective ways to battle these problems, machine learning approaches have been included in healthcare systems for the diagnosis and probable prediction of the treatment outcomes of mental health conditions. With the growing interest in machine and deep learning methods, analysis of existing work to guide future research directions is necessary. In this study, 33 articles on the diagnosis of schizophrenia, depression, anxiety, bipolar disorder, post-traumatic stress disorder (PTSD), anorexia nervosa, and attention deficit hyperactivity disorder (ADHD) were retrieved from various search databases using the preferred reporting items for systematic reviews and meta-analysis (PRISMA) review methodology. These publications were chosen based on their use of machine learning and deep learning technologies, individually assessed, and their recommended methodologies were then classified into the various disorders included in this study. In addition, the difficulties encountered by the researchers are discussed, and a list of some public datasets is provided.

Graph-Based Disease Prediction in Neuroimaging: Investigating the Impact of Feature Selection

In biomedical machine learning, data often appear in the form of graphs. Biological systems such as protein interactions and ecological or brain networks are instances of applications that benefit from graph representations. Geometric deep learning is an arising field of techniques that has extended deep neural networks to non-Euclidean domains such as graphs. In particular, graph convolutional neural networks have achieved advanced performance in semi-supervised learning in those domains. Over the last years, these methods have gained traction in neuroscience as they could be the key to a deeper understanding in clinical diagnosis at the systems or network level (for an individual brain but also for across a cohort of subjects). As a proof-of-principle, we study and validate a previous implementation of graph-based semi-supervised classification using a ridge classifier and graph convolutional neural networks. The models are trained on population graphs that integrate imaging and phenotypic information. Our analysis employs neuroimaging data of structural and functional connectivity for prediction of neurodevelopmental and neurodegenerative disorders. Here, we particularly study the effect of different strategies to reduce the dimensionality of the neuroimaging features on the graph nodes on the classification performance.

Structural and diffusion-weighted brain imaging predictors of attention-deficit/hyperactivity disorder and its symptomology in very young (4- to 7-year-old) children

The current study aimed to identify the key neurobiology of attention-deficit/hyperactivity disorder (ADHD), as it relates to ADHD diagnostic category and symptoms of hyperactive/impulsive behaviour and inattention. To do so, we adapted a predictive modelling approach to identify the key structural and diffusion-weighted brain imaging measures and their relative standing with respect to teacher ratings of executive function (EF) (measured by the Metacognition Index of the Behavior Rating Inventory of Executive Function [BRIEF]) and negativity and emotion regulation (ER) (measured by the Emotion Regulation Checklist [ERC]), in a critical young age range (ages 4 to 7, mean age 5.52 years, 82.2% Hispanic/Latino), where initial contact with educators and clinicians typically take place. Teacher ratings of EF and ER were predictive of both ADHD diagnostic category and symptoms of hyperactive/impulsive behaviour and inattention. Among the neural measures evaluated, the current study identified the critical importance of the largely understudied diffusion-weighted imaging measures for the underlying neurobiology of ADHD and its associated symptomology. Specifically, our analyses implicated the inferior frontal gyrus as a critical predictor of ADHD diagnostic category and its associated symptomology, above and beyond teacher ratings of EF and ER. Collectively, the current set of findings have implications for theories of ADHD, the relative utility of neurobiological measures with respect to teacher ratings of EF and ER, and the developmental trajectory of its underlying neurobiology.

Machine Learning Techniques for the Diagnosis of Attention-Deficit/Hyperactivity Disorder from Magnetic Resonance Imaging: A Concise Review

Background

Attention-deficit/hyperactivity disorder (ADHD) is a neuro-developmental disease commonly seen in children and it is diagnosed via extensive interview procedures, behavioral studies, third-party observations, and comprehensive personal history. ADHD causes regional atrophy in brain regions and alters the pattern of functional brain connectivity networks. Automated/computerized methods based on magnetic resonance imaging (MRI) can replace subjective methods for the identification of ADHD.

Objectives

The aim of this study was to analyze various machine-learning algorithms for ADHD by feeding in vital input features extracted from functional brain connectivity and different existing methods and to review factors crucial for the diagnosis of ADHD.

Methods

This paper is a concise review of machine learning methods for the diagnosis of ADHD from MRI. Techniques for feature extraction, dimensionality reduction/feature selection, and classification, employed in the computerized techniques for the diagnosis of ADHD from MRI and the accuracy of classification offered by the individual methods, are focussed on the review.

Conclusions

Machine learning algorithms with features of functional brain connectivity networks as input, with hierarchical sparse feature elimination, exhibits the highest accuracy. Augmentation of the behavioral features does not contribute much to increased accuracy. The level of accuracy offered by the frameworks meant for the computer-aided diagnosis of ADHD, available in the literature, does not justify their feasibility in clinical practice. Computerized methods that exploit highly specific biomarkers of ADHD like brain iron concentration in Globus Pallidus, Putamen, Caudate nucleus, and thalamus as features are not available.

Multimodal MR Images-Based Diagnosis of Early Adolescent Attention-Deficit/Hyperactivity Disorder Using Multiple Kernel Learning

Attention-deficit/hyperactivity disorder (ADHD) is one of the most common brain diseases among children. The current criteria of ADHD diagnosis mainly depend on behavior analysis, which is subjective and inconsistent, especially for children. The development of neuroimaging technologies, such as magnetic resonance imaging (MRI), drives the discovery of brain abnormalities in structure and function by analyzing multimodal neuroimages for computer-aided diagnosis of brain diseases. This paper proposes a multimodal machine learning framework that combines the Boruta based feature selection and Multiple Kernel Learning (MKL) to integrate the multimodal features of structural and functional MRIs and Diffusion Tensor Images (DTI) for the diagnosis of early adolescent ADHD. The rich and complementary information of the macrostructural features, microstructural properties, and functional connectivities are integrated at the kernel level, followed by a support vector machine classifier for discriminating ADHD from healthy children. Our experiments were conducted on the comorbidity-free ADHD subjects and covariable-matched healthy children aged 9-10 chosen from the Adolescent Brain and Cognitive Development (ABCD) study. This paper is the first work to combine structural and functional MRIs with DTI for early adolescents of the ABCD study. The results indicate that the kernel-level fusion of multimodal features achieves 0.698 of AUC (area under the receiver operating characteristic curves) and 64.3% of classification accuracy for ADHD diagnosis, showing a significant improvement over the early feature fusion and unimodal features. The abnormal functional connectivity predictors, involving default mode network, attention network, auditory network, and sensorimotor mouth network, thalamus, and cerebellum, as well as the anatomical regions in basal ganglia, are found to encode the most discriminative information, which collaborates with macrostructure and diffusion alterations to boost the performances of disorder diagnosis.

Localization and Diagnosis of Attention-Deficit/Hyperactivity Disorder

In this paper, a random-forest-based method was proposed for the classification and localization of Attention-Deficit/Hyperactivity Disorder (ADHD), a common neurodevelopmental disorder among children. Experimental data were magnetic resonance imaging (MRI) from the public case-control dataset of 3D images for ADHD-200. Each MRI image was a 3D-tensor of 121×145×121 size. All 3D matrices (MRI) were segmented into the slices from each of three orthogonal directions. Each slice from the same position of the same direction in the training set was converted into a vector, and all these vectors were composed into a designed matrix to train the random forest classification algorithm; then, the well-trained RF classifier was exploited to give a prediction label in correspondence direction and position. Diagnosis and location results can be obtained upon the intersection of these three prediction matrices. The performance of our proposed method was illustrated on the dataset from New York University (NYU), Kennedy Krieger Institute (KKI) and full datasets; the results show that the proposed methods can archive more accuracy identification in discrimination of ADHD, and can be extended to the other practices of diagnosis. Moreover, another suspected region was found at the first time.

Classification Accuracy of Neuroimaging Biomarkers in Attention-Deficit/Hyperactivity Disorder: Effects of Sample Size and Circular Analysis

BACKGROUND

Motivated by an inconsistency between reports of high diagnosis-classification accuracies and known heterogeneity in attention-deficit/hyperactivity disorder (ADHD), this study assessed classification accuracy in studies of ADHD as a function of methodological factors that can bias results. We hypothesized that high classification results in ADHD diagnosis are inflated by methodological factors.

METHODS

We reviewed 69 studies (of 95 studies identified) that used neuroimaging features to predict ADHD diagnosis. Based on reported methods, we assessed the prevalence of circular analysis, which inflates classification accuracy, and evaluated the relationship between sample size and accuracy to test if small-sample models tend to report higher classification accuracy, also an indicator of bias.

RESULTS

Circular analysis was detected in 15.9% of ADHD classification studies, lack of independent test set was noted in 13%, and insufficient methodological detail to establish its presence was noted in another 11.6%. Accuracy of classification ranged from 60% to 80% in the 59.4% of reviewed studies that met criteria for independence of feature selection, model construction, and test datasets. Moreover, there was a negative relationship between accuracy and sample size, implying additional bias contributing to reported accuracies at lower sample sizes.

CONCLUSIONS

High classification accuracies in neuroimaging studies of ADHD appear to be inflated by circular analysis and small sample size. Accuracies on independent datasets were consistent with known heterogeneity of the disorder. Steps to resolve these issues, and a shift toward accounting for sample heterogeneity and prediction of future outcomes, will be crucial in future classification studies in ADHD.