Role of Artificial Intelligence for Autism Diagnosis Using DTI and fMRI: A Survey

Autism spectrum disorder (ASD) is a wide range of diseases characterized by difficulties with social skills, repetitive activities, speech, and nonverbal communication. The Centers for Disease Control (CDC) estimates that 1 in 44 American children currently suffer from ASD. The current gold standard for ASD diagnosis is based on behavior observational tests by clinicians, which suffer from being subjective and time-consuming and afford only late detection (a child must have a mental age of at least two to apply for an observation report). Alternatively, brain imaging—more specifically, magnetic resonance imaging (MRI)—has proven its ability to assist in fast, objective, and early ASD diagnosis and detection. With the recent advances in artificial intelligence (AI) and machine learning (ML) techniques, sufficient tools have been developed for both automated ASD diagnosis and early detection. More recently, the development of deep learning (DL), a young subfield of AI based on artificial neural networks (ANNs), has successfully enabled the processing of brain MRI data with improved ASD diagnostic abilities. This survey focuses on the role of AI in autism diagnostics and detection based on two basic MRI modalities: diffusion tensor imaging (DTI) and functional MRI (fMRI). In addition, the survey outlines the basic findings of DTI and fMRI in autism. Furthermore, recent techniques for ASD detection using DTI and fMRI are summarized and discussed. Finally, emerging tendencies are described. The results of this study show how useful AI is for early, subjective ASD detection and diagnosis. More AI solutions that have the potential to be used in healthcare settings will be introduced in the future.


Introduction
Autism spectrum disorder (ASD) is a long-term neurodevelopmental disorder characterized by impaired social communication and interaction, restricted and repetitive stereotypical behavior patterns, and diminished cognitive skills. The World Health Organization (WHO) estimates that ASD affects about 67 million individuals around the world. Males are four times more affected than females [1][2][3]. The exact etiology of ASD is still unclear. Heterogeneous and multi-factorial causes are suggested, including genetic background [4].
Autistic symptoms usually begin to develop within the first two years of life. Early ASD manifestations can be found in 12-month-old infants. However, the average age for diagnosis is around five years [1,5]. The current gold standard in ASD diagnosis is based on behavior observational tests by clinicians such as the Autism Diagnostic Observation Schedule (ADOS) or Autism Diagnostic Interview-Revised (ADI-R) report, but these approaches are subjective and time-consuming. Early and appropriate diagnosis is crucial to help limit the deterioration of the condition and to improve prognostic outcomes [2,6,7].
Magnetic resonance imaging (MRI) is an essential non-invasive method in the detection of brain structure, white-matter (WM) integrity, and functional activity [8]. Structural MRI (sMRI) has been used to describe the morphological brain changes in ASD regarding the shape and volume of different brain regions. Diffusion tensor imaging (DTI) provides an assessment of anatomical connections and has shown disorganized micro-structural WM integrity in the autistic population. Functional MRI (fMRI) relies on the detection of dynamic physiological information from active brain regions. Measuring the change in blood-oxygenation-level-dependent (BOLD) signals in various brain states (resting state or task-evoked) can reveal functional architecture abnormalities in the ASD population [9,10]. Although different MRI modalities have shown promise in distinguishing ASD individuals from healthy controls (HCs), MRI results remain inconsistent and nonreplicable [8]. Therefore, the need for neuroimaging biomarkers remains an ongoing clinical challenge. Several computer-aided design systems (CADS) have been widely applied to integrate multimodal MRI with artificial intelligence (AI). Machine learning (ML) is a subfield within (AI). In neuroimaging, ML is widely used in medical image analysis through extracting informative features and constructing the best-fitting algorithm to provide the desired output [5,11]. The most frequently selected features for ASD include color, shape, texture, and spatial relationship features. These features are computed to study developmental brain abnormalities and can be applied to improve the diagnosis and to classify subtypes and the degree of severity of ASD [12,13].
The availability of large datasets, including those from the Autism Brain Imaging Data Exchange (ABIDE), has led to an increase in publications combining ML with different neuroimaging biomarkers. These studies aim to reduce subjectivity and to establish a more objective, data-driven method for identification, classification, and prognosis of ASD children [5].
In this survey, we aim to review the publications predicting or identifying ASD based on different MRI modalities (i.e., DTI and fMRI) using ML methods. DTI studies since 2011 are presented. There has been a great increase in the number of publications based on functional imaging (fMRI). Here, we focus on recent fMRI studies in the last five years. A manual search is done using electronic databases in PubMed and Google Scholar for articles and papers published in English until July 2022 using search terms as follows: (autism, autism spectrum disorders, or ASD) and (diffusion tensor imaging or DTI) AND (functional magnetic resonance imaging, fMRI, task-based fMRI, T-fMRI, resting state fMRI, rs-fMR, or BOLD) and (artificial intelligence, AI, machine learning, ML, deep learning, or DL) and (detection, diagnosis, or findings). The eligibility criteria included original research articles published, accepted for publication, or available online in English. Age-or sex-based studies were included. Case reports and review articles, including narrative, systematic reviews, and meta-analyses, were excluded from data extraction but were used as reference searches. Studies comparing a group of ASD individuals with a group of typically developed controls were included. However, studies conducted on the comparison of ASD with other neurodevelopmental, cognitive, or psychiatric disorders such as attention-deficit hyperactive disorder (ADHD) were excluded. Given that this review is designed to look at DTI and fMRI studies based on ML findings, ML algorithms with neuroimaging data were used as a biomarker in differentiating ASD individuals from typically developed controls. In addition, other imaging modalities such as structural MRI, MR spectroscopy, or positron emission tomography were excluded as well.

ASD Findings Using Diffusion Tensor Imaging (DTI)
DTI is a non-invasive, in vivo tool that measures water diffusion within WM tracts, thus providing a macroscopic picture of WM ultrastructure within the imaged voxel. The most important DTI metrics are mean diffusivity (MD) and fractional anisotropy (FA). MD measures the overall amount of diffusion and is related to cellular density. FA captures the directional changes of diffusion and represents the degree of alignment of WM tracts and cellular structure, ranging from 0 (random or isotropic) to 1 (unidirectional or anisotropic). Other parameters include axial diffusivity (AD) and radial diffusivity (RD). AD measures the diffusion in a direction parallel to WM tracts and represents axon integrity, whereas RD is the perpendicular diffusion and is related to myelin integrity [14][15][16].
White-matter tracts are composed of bundles of axons that carry the communication signals between brain regions. Alteration in synaptogenesis caused by dysmaturation of myelination has been reported in the ASD population. Myelin alteration leads to changes in axonal fiber density, caliber, and homogeneity with subsequent impairment of WM microstructural organization and integrity [10,17,18].
Many studies have shown reduced FA and increased MD in widespread WM tracts and brain regions of the ASD population when compared to typically developed controls (TD), denoting reduced WM integrity. The most-commonly involved tracts are long-range association fibers that directly and indirectly connect the brain regions responsible for social cognition and verbal communication. Those tracts include the superior longitudinal fasciculus, occipitofrontal fasciculus, arcuate fasciculus, uncinate fasciculus, inferior longitudinal fasciculus, and cingulum [1,[19][20][21]. A study by Jung et al. [19] revealed the correlation between impaired connectivity at the occipital cortex in ASD boys with the core symptoms and clinical outcomes of ASD. A significant negative correlation was found between tract length (left cingulum cingulate gyrus and right uncinate fasciculus) and the total score of the Social Communication Questionnaire (SCQ).
Valenti [22] presented a comprehensive review of several published articles that used DTI in the evaluation of corpus callosum (CC) integrity in ASD. They found a significant difference in DTI and tractography findings between the ASD group and healthy controls, indicating both micro-and macro-structure alterations in CC. In addition, those structural alterations are correlated with socio-communicative deficits. Shukla et al. [23] found increased RD and reduced FA in CC and the internal capsule (IC). Others have shown altered IC connectivity with the correlation of DTI changes with the core ASD symptoms [24][25][26]. A study focused on language-related tracts (arcuate fasciculus) to differentiate ASD patients from non-ASD individuals with a developmental language disorder found a significant reduction in FA of the arcuate fasciculus in ASD individuals [27].
Moreover, other studies reported age-related differences in the widespread WM microstructure of ASD population when compared to healthy controls. For example, in [28,29], the authors found increased FA in autistic infants and toddlers, while indices decreased in elder autistic children. This was attributed to better tract coherence and alignment in infancy. Other studies have shown the opposite, as there was a significant positive correlation of FA with increasing age of autistic children while MD and RD measures showed a significant negative correlation with age [30,31]. These studies suggest that neurodevelopmental maturation of WM trajectories with increased age is associated with changes in diffusivity parameters.
Apart from using DTI biomarkers alone, several imaging-based ML studies have been applied to overcome the limitations found in DTI studies alone. DTI lacks a full description of crossing WM trajectories. Despite the sensitivity of DTI metrics to capture microstructural changes, DTI is less specific for other WM disorders affecting myelination and axonal density [32]. In addition, there is still limited integration between clinical and imaging biomarkers. The need for informative data relevant to diagnosis and treatment decisions is challenging. Therefore, ML has been developed to aid the identification and classification of ASD children using clinical, behavioral, and imaging biomarkers [10].

ASD Findings Using fMRI
Functional neuroimaging is used to investigate the functional connectivity and activity of brain regions. Electroencephalography (EEG) has been used as a basic method to record electrical activities of the brain from the scalp with high temporal resolution (in milliseconds) [33]. Further, fMRI detects brain activity by measuring the associated variations in blood-oxygenation-level-dependent spontaneous signals (BOLD) in response to various stimuli [34]. It is a four-dimensional technique (4D) that captures the three-dimensional brain volume (3D) repeatedly over a period of time. This technique has high spatial resolution (in millimeters) but low temporal resolution [35]. This can be explained by the time limitations of fMRI, which cannot record the fast dynamics of brain activity and the slow response of the brain hemodynamic system, thus requiring multiple scans over time. Moreover, fMRI is sensitive to motion artifacts [36]. Techniques based on fMRI include two broad categories: event-related or task-based (T-fMRI) and resting-state (rs-fMRI). Taskbased (T-fMRI) measures brain function after performing specific tasks, while (rs-fMRI) measures brain function in the absence of task demands.

Task-Based (T-fMRI)
Task-based protocols employ paradigms that map the core behavioral symptoms of ASD patients, namely, facial emotional recognition, response to social stimuli, and reward behavior [1]. Other tasks include motor, visual processing, language, auditory, and executive functions. Social communication skills are supported by a distributed brain network within different brain regions collectively named the "social brain" [37]. Many T-fMRI studies have notably demonstrated atypical activity in social brain regions among ASD individuals during social tasks (primarily hypoactivation).
Social communication deficits in ASD include variable manifestations such as impaired recognition of faces, making inferences about others' intentions, and diminished social responsiveness. In addition, there is reduced attention to social cues, human voices, and biological motions [1,37,38]. A study that examined the activity of social brain regions in response to the visual perception of generic faces showed hypoactivation in the fusiform gyrus and amygdala among ASD children. These regions are responsible for such tasks. However, similar activation was found in ASD patients as well as TD controls when the faces were familiar [39]. Another study employed a task to distinguish between attention to biological motions (eye gaze, walking, hand, or mouth movements) versus mechanical motions (clock or wheel). This study revealed reduced activation of the superior temporal sulcus and ventrolateral prefrontal cortex in ASD children compared to TD controls. The study clarified that ASD children are easily distracted by non-facial stimuli and cannot fixate on faces to the same degree as normal children [40]. Investigating brain activity in response to cognition of facial expressions, such as sad facies (visual tasks), revealed increased activation in the amygdala, ventral prefrontal cortex, and striatum in the adolescent ASD group compared to the control group [41].
Regarding the response to reward or positive feedback behavior, ASD children are less responsive than normal children. Normally, a reward activates the visual striatum region and engages the frontostriatal network [1]. A study employed a social reward task such as a smiling face or a momentary reward task such as gold coins. ASD boys showed a nonactivated visual striatum, while it was activated in TD boys Scott-Van Zeeland AA. Moreover, a study comparing the response of both ASD boys and girls revealed more activation in the lateral frontal cortex and insula of ASD girls, denoting that suppressed reward center activation is a distinctive feature of ASD boys [42].
A meta-analysis of fMRI studies was proposed by Philip et al. [43]. They reviewed T-fMRI studies that investigated the functional brain response to auditory and languagerelated tasks. They revealed reduced activation in clusters of brain regions in ASD children, adolescents, and adults compared to TD controls. Those regions are both superior temporal gyri, the right pyramids of the cerebellar vermis, and the left middle cingulate gyrus. The superior temporal gyrus is activated with receptive language, so reduced activation in response to spoken language denotes the underlying verbal communication difficulties in ASD individuals. On the other hand, relative over-activation in ASD adults compared to controls was found in the posterior cingulate gyrus, the motor cortex, and the cerebellar declive. The exact cause of increased activation in ASD groups was not clear; this may suggest the use of atypical language processing strategies [43].

Resting-State (rs-fMRI)
The complexity and different varieties of fMRI tasks in addition to the unique social skills and intellectual condition of ASD children can limit some task-based experiments. Another point is the difficulty of tasks with potential language barriers that preclude some children, particularly infants, from participation [1,44].
Resting-state (rs-fMRI) is a promising alternative to T-fMRI. The technique is suitable for infants and toddlers. It helps examine functional brain connectivity in the absence of task performance. The total time of the examination is about 5-6 min. The participants just lie in the MRI scanner with their eyes closed or with their vision fixed on a crosshair [44]. Several rs-fMRI studies aim to explore large-scale resting brain networks (RBNs). These are organized brain regions that show cortical synchronization patterns with coherent spontaneous fluctuations in neural activity during rest [45]. RBNs include the default mode network, dorsal and ventral attention network, salient network, visual network, and sensorimotor networks [45].
It has been found that the most common region with altered brain connectivity is the default mode network (DMN). It becomes activated during the resting state, whereas it becomes less activated with the engagement of cognitively-demanding tasks [46]. DMN is a large-scale network composed mainly of the posterior cingulate gyrus, precuneus, and medial prefrontal cortex. It has shown reduced connectivity in ASD patients compared with TD controls [46][47][48]. The dorsal attention network (DAN) is located in the intraparietal sulcus and frontal eye field. This network is activated to reorient the attention towards relevant stimuli. Sun et al. [45] showed increased functional connectivity in the superficial temporal gyrus and cerebellum, indicating the presence of circuit connections between the DAN and cerebellum. The ventral attention network (VAN) also has been studied by SUN [45]. The authors found increased connectivity in the insula, which is a critical region in VAN responsible for social emotions. The salience network is another RSN that has been examined. It is composed primarily of the dorsal anterior cingulate cortex and the anterior insula. It is linked with the detection and filtering of salient stimuli [1]. Uddin et al. [49] observed a reduction in functional connectivity in this network with 83% accuracy for differentiating autistic children from TD controls. Additionally, a study by Wang et al. [50] investigated the functional connectivity of sensory networks in autistic children, including auditory, visual, and sensorimotor networks. They found increased functional connectivity in all networks in ASD children that was correlated with the severity of social impairment of the children.
Several other networks have been examined by rs-fMRI and have provided support to the theory of "brain hypoconnectivity" in ASD. Long-range reduced brain connectivity was found in the superior temporal region of autistic individuals when compared with TD controls [51]. In addition, other studies have observed underconnectivity in the laterobasal subregion of the amygdala, interhemispheric connectivity in the sensorimotor and occipital cortices, and underconnectivity in connections between the anterior and posterior cingulate gyrus and the precuneus [52]. Overconnectivity has been also observed in some studies; researchers found increased connectivity in some areas, such as the frontal, temporal, and occipital regions [48,53,54]. Kleinhans and his colleagues [55] observed areas of overconnectivity within the amygdala (superficial and centro-medial subregions). However, the latero-basal subregion showed underconnectivity; this region stands for the presentation and severity of ASD symptoms. The mixed pattern or the inconsistent overand underconnectivity can be attributed to the small sample size used in these studies, phenotypic heterogeneity among ASD individuals, or an adjustment mechanism by the brain to bypass the underconnected regions [1,44].
Overall, both DTI and fMRI have revealed important findings that are associated with ASD. These findings are summarized in Figure 1.

The Role of AI in ASD Diagnosis
Artificial intelligence (AI) involves the replication of human thinking and problemsolving using artificially intelligent components. Machine learning (ML) is an integral aspect of AI that involves utilizing image processing tools to extract features from an input database. The data are then categorized through unsupervised learning or classified into grades through supervised learning. Supervised learning uses labeled input-output pairs to classify data, with classifiers such as SVM, random forest, and traditional neural networks being commonly used. Deep learning (DL), a subset of ML, has become increasingly popular in the medical field. The most commonly employed deep learning networks are convolutional neural networks (CNNs). They consist of many convolutional and fully connected layers to perform feature extraction and classification. Conversely, unsupervised learning categorizes data based on input data patterns without the need for labeled inputoutput pairs. In modern times, AI has become a significant factor in numerous applications, including the early identification and diagnosis of ASD (see Figure 2). This paper briefly reviews various AI-based techniques for the early detection and diagnosis of ASD. To evaluate the effectiveness of AI components, diverse metrics are utilized to address medical concerns such as the classification, diagnosis, and early detection of eye diseases. A short summary of these metrics is provided below. True positive (TP), true negative (TN), false negative (FN), and false positive (FP) are represented by the abbreviations. The ensuing performance metrics are defined in the following manner: The AUC is the region beneath the receiver operating characteristics (ROC) curve that shows the relationship between the false positive rate (1-specificity, shown on the x-axis) and the true positive rate (sensitivity, on the y-axis). The AUC value ranges from 0 to 1, with a higher AUC value indicating better performance.

The Role of AI for ASD Diagnosis Using DTI
Currently, ML has been implemented with DTI metrics to evaluate structural connectivity changes in ASD population. Several ML studies based on DTI metrics have been performed by applying different features obtained using different algorithms for image processing. For example, Ingalhalikar et al. [56] extracted DTI-based features such as FA and MD metrics in each ROI to classify ASD patients and controls by learning the pattern of the disease. Further, they correlated the degree of ASD with the clinical score of each subject to aid in the diagnosis. A nonlinear SVM was used for classification. Li et al. [57] used brain connectivity network features obtained from DTI to identify faulty sub-networks to distinguish ASD subjects from the HC group. The detection was done by an SVM-recursive feature elimination (RFE) algorithm. Jin et al. [58] used features extracted from ROI-based WM connectivity and DTI metrics such as as FA, MD, and fiber length. A multi-kernel SVM was used for the discrimination of ASD 6-month-old high-risk infants from low-risk infants. Zhang et al. [59] used whole brain fiber clustering analysis with multi-fiber tractography, FA, and MD features. An SVM classifier was used to distinguish ASD from HCs. Qin et al. [60] used graph theory to analyze the topology of the white-matter network of ASD preschool children. Edges and nodes were defined as FA and 90 brain regions, respectively. They found disturbed topology of the structural networks of ASD subjects as compared to HCs. Payabvash [32] used features extracted from edge density imaging (EDI) as well as from conventional DTI metrics such as FA, MD, and RD. Variable ML classifiers were used to discriminate ASD children from HCs. The best accuracy was achieved with the EDI-based random forest model. Saad et al. [61] used DTI-based connectivity features with graph theory to classify ASD and HCs. The classification was performed by an SVM and a linear discriminant analysis (LDA). The principle component analysis (PCA) approach was used to reduce noisy features. Better accuracy was obtained with SVM and two PCA features. ElNakieb et al. [62] used global and local extraction of FA, MD, AD, RD, and skewness features to examine the performance of CAD systems in ASD diagnosis. The detection was performed using an SVM classifier. In [63], the authors used global and local extraction of FA, MD, AD, RD, and skewness features to identify significantly different paired WM areas in ASD and then to classify ASD individuals and HCs. They used an SVM model for classification. In [64], the authors also used global and local extraction of FA, MD, AD, RD, and skewness features to classify ASD individuals and HCs. They used linear and nonlinear SVM classifiers. The best classification accuracy was achieved with linear SVM (LSVM).
Other hybrid studies have combined DTI features with features extracted either from functional MRI (fMRI) or structural MRI (sMRI). For example, An et al. [65] used region-to-region fiber connectivity DTI features and ROI-based functional connectivity (FC) features from fMRI to validate connectivity patterns and then classify ASD subgroups using a multiview expectation maximization formulation (mv-EM). Deshpande et al. [66] used FA and FC values with a multi-variate autoregressive model (MVAR). They investigated the differences between brain regions that may underpin the theory of mind in young ASD patients and HCs. The classification was done by an SVM classifier. Crimi et al. [67] used structural and functional connectivity features with a constrained multivariate auto-regressive model (CMAR) that allows fusing the structural connectivity with the information from the functional time series to represent effective brain connectivity. The classification between ASD subjects and HCs was performed by an SVM classifier. D'Souza et al. [68] used phenotypic measures of rs-MRI connectivity and DTI tractography features with a multimodal graph convolutional network (M-GCN) to extract predictive biomarkers from both ASD individuals and HCs. Irimia et al. [69] used structural morphometric features such as cortical thickness, volume, area, and mean curvature as well as connectivity features to distinguish ASD individuals from HCs. Moreover, they distinguished ASD males and females. The used classifier was an SVM model. Eill et al. [70] combined the anatomical features (surface area, mean curvature, cortical thickness, volume, and local gyrification index), DTI metrics (FA, MD, RD, and AD), and ROI-based FC matrices. They applied a conditional random forest algorithm (CRF) to assess the role of each modality and to explore the more informative one in diagnostic prediction. The use of combined variables achieved higher accuracy (92.5%), and rs-fMRI connectivity variables provided better performance than other anatomical modalities in the classification of ASD individuals from HCs. Figure 3 highlights the different extracted features and the most-used classifiers in DTI autism diagnostic systems. Table 1 summarizes ASD studies of DTI with ML models and briefly outlines the dataset used and its demographics, the feature selection, the ML classifier used, findings, and the accuracy whenever reported.

The Role of AI for ASD Diagnosis Using fMRI
Recent progress in ML algorithms combined with fMRI techniques has been established for the diagnosis of ASD and has shown promising results. In this review, we include several high-impact publications from the last five years. For example, Abraham et al. [71] used features selected from ROI-based resting-state FC matrices to differentiate ASD from HCs. They used an SVM classifier and found an increase in the predictive power with the increase in participant numbers. Emerson et al. [72] used extracted features from ROI-based resting-state FC matrices for the prediction of ASD in high-risk 6-month-old infants based on correlated brain metrics with 24-month ASD-related behaviors. They achieved 96.6% classification accuracy of ASD individuals at 24 months. The classification was performed by an SVM classifier. Guo et al. [73] used ROI-based resting-state FC matrices with deep neural networks with feature selection (DNN-FS) to select the most relevant features from FCs related to ASD from the default mode to identify ASD individuals from HCs. Jahedi A [74] obtained ROI-based FC features as biomarkers to identify ASD patients from HCs. Combined use of conditional random forest (CRF) and random forest classifiers achieved the best prediction accuracy. Kam et al. [75] used extracted features from seed-based FC matrices with discriminative restricted Boltzmann machines (DRBM) to identify dominant FCs to differentiate ASD from HCs. An ensemble classifier was applied and obtained high accuracy with multiple clusters using hierarchicallevel clustering of networks. Sadeghi et al. [76] used features from ROI-based FC nodal matrices to extract local and global parameters of brain networks to identify ASD from HCs. Multiple classifiers were used, but SVM showed superiority to other classifiers. Subbaraju et al. [77] used extracted features from ROI-based resting-state FC matrices with a spatial-feature-based detection method (SFM) to extract the most-discriminative blood-oxygenation-level-dependent (BOLD) signals. An SVM classifier was used in the classification of ASD individuals versus HCs. Tejwani et al. [78] used extracted features from ROI-based enhanced FC variability across brain regions to distinguish between ASD subjects and HCs. They used SVM, RF, Naïve Bayes, and multi-layer perception algorithms for classification. Heinsfeld et al. [79] used extracted features from ROI-based FC matrices with a DNN algorithm. The proposed method achieved better accuracy than SVM and RF classifiers in classifying ASD and control subjects. Bi et al. [80] used ROI-based FC features to differentiate between autistic individuals and HCs. They applied a random SVM cluster and achieved high classification accuracy.
Furthermore, Fredo et al. [81] used features extracted from ROI-based FC matrices to classify ASD individuals and HCs. The detection was done by CRF. Li et al. [82] conducted a two-stage pipeline method composed of DNN and prediction distribution analysis to investigate the accuracy in classifying two datasets of ASD and HC subjects. They extracted features from ROI-based FC matrices from both resting-state and task-fMRI. Bernas et al. [83] extracted temporal neurodynamic fMRI biomarkers for ASD diagnosis with wavelet coherence maps. The detection was done by an SVM classifier. Bhaumik et al. [84] used features extracted from ROI-based FC matrices for the prediction and diagnosis of ASD subjects versus HCs. The detection was done by an SVM and partial least square regression (PLS) algorithms. Dekhil et al. [85] used power spectral densities as extracted features to classify ASD individuals and HCs. An SVM was used and achieved high diagnostic accuracy and prediction of clinical phenotypes. Xiao et al. [86] used the extracted time courses of each subject with NN algorithms. The extracted features were inputted into a stacked autoencoder (SAE) and then into a subsequent softmax classifier for the identification of school-aged ASD children versus HC school-aged children. Yang et al. [87] used features extracted from ROI-based FC matrices to classify ASD and HC subjects. The detection was performed by SVM, LR, and ridge classifiers. They found that accuracy is improved with combined classifiers. Wang et al. [88] used extracted features from ROI-based FC matrices to classify ASD and HC subjects. In [89], they used an SVM recursive feature model to achieve high classification accuracy of ASD and HC individuals on both global and across-site datasets. They also used extracted features from ROI-based FC matrices to identify ASD subjects and HCs. An SVM-recursive feature model and a stacked sparse auto-encoder (SSAE) were used to eliminate some meaningless features to enable the SSAE to extract insightful features. Aghdam et al. [90] used extracted features from fast Fourier transformation with the CNN method in order to classify ASD subjects and HCs. Huang et al. [91] used extracted features from rs-fMRI multiple-group sparse networks. They used an SVM classifier to distinguish ASD individuals. Jun et al. [92] used extracted features from rs-fMRI local functional characteristics based on the estimated likelihood of ROI by hidden Markov models (HMMs) to identify meaningful information for ASD detection. The detection was performed by an SVM classifier. Eslami et al. [93] used extracted features from ROI-based FC matrices with ASD-DiagNet in order to classify ASD and HC subjects, and the proposed method achieved high classification accuracy. Mostafa et al. [94] extracted features from ROI-based FC matrices and then the eigenvalues of the Laplacian matrix of the brain network with a sequential feature selection algorithm. Linear discriminant analysis (LDA) was used to classify ASD and HC subjects. Song et al. [95] used extracted features from community pattern analysis of FC to classify the ASD population and HCs in addition to the prediction of the clinical classes of ASD individuals. The detection was performed by LDA. Spera et al. [96] used extracted features from ROI-based FC matrices to classify ASD and HC subjects. They selected a homogeneous cohort of young ASD males to lessen the impact of confounding factors. The detection was by linear kernel SVM. Tang et al. [97] used extracted features from ROI-based FC from DMN and the whole brain with joint symmetrical non-negative matrix factorization (JSNMF). An SVM classifier was used to classify ASD and HC subjects. Yamagata et al. [98] used extracted features from ROI-based FC matrices with a multivariate ML approach. A sparse logistic regression (SLR) classifier was used to classify pairs of ASD patients and their unaffected siblings from pairs of HCs and their siblings according to the endophenotype.
Recently, Chaitra et al. [99] used extracted features from ROI-based FC matrices to classify ASD patients and controls. They employed a recursive-cluster-elimination SVM algorithm. Fan et al. [100] used extracted features from maps based on estimated likelihood values of ROI by HMM to identify ASD individuals from HCs. The detection was performed by an SVM classifier. Liu et al. [101] employed the elastic network method to extract features from ROI-based FC matrices to distinguish ASD individuals from HCs. They obtained high classification accuracy using an SVM classifier in the automatic diagnosis of ASD compared to LASSO and RR algorithms. Hu et al. [102] utilized extracted features from ROI-based FC matrices with a fully connected neural network (FCNN) model in the classification of the ASD population versus HCs. Sherkatghanad et al. [103] used extracted features from ROIbased FC matrices to classify ASD individuals and HCs using a CNN model. A CNN model is computationally less intensive as it uses fewer parameters than state-of-the-art methods and can be used in prescreening of ASD patients. Thomas et al. [104] used the temporal statistics of rs-fMRI data with 3D-CNN to classify ASD individuals. The classification was also performed by an SVM model on the same dataset. The best classification accuracy obtained by the SVM algorithm was 66%, while 3D-CNN achieved 64%, denoting that 3D-CNN could not learn additional information in classifying ASD and HCs. Jiao et al. [105] utilized extracted features from ROI-based FC matrices with the CapsNET method to classify ASD individuals and HCs. Moreover, they stratified ASD subjects into groups based on distinct FC measures. Liu et al. [106] used the extracted features from ROI-based FC matrices with an elastic network method to classify ASD individuals versus HCs. Liu et al. [107] utilized the extracted dynamic features from ROI-based FC matrices with a multi-task feature selection method. A multi-kernel SVM classifier was used to classify ASD individuals versus HCs. Zhang et al. [108] utilized the rs-fMRI dataset of ASD subjects and HCs with a fast entropy method that included approximate entropy (ApEn) and sample entropy (SampEn). The SVM classifier was used to diagnose ASD. Ronicko et al. [109] used extracted features from ROI-based FC matrices with partial and full correlation methods. Classification of ASD individuals and HCs was built by different models, namely, SVM, RF, Oblique RF, and CNN. Khan et al. [110] analyzed the extracted features from ROI-based FC matrices with a teacher-student-neural-network-based feature selection method. Different classifiers were used in classifying ASD and HC subjects, such as SVM, RF, LR, decision trees, and linear discriminant classifiers.
More recently, Reiter et al. [111] used extracted features from ROI-based FC matrices of ASD subjects and HCs. An RF classifier was used to investigate the effect of heterogeneity of ASD samples on classification accuracy. They found that the most-homogeneous samples achieved better RF classifier performance. Devika and Oruganti [112] utilized extracted data from FC matrices to distinguish ASD and HC subjects. The classification was performed by an SVM model. Ahammed et al. [113] used the extracted features from ROI-based FC matrices with the bag-of-features extraction (BoF) method. The SVM was used as a classifier for identifying ASD and HC subjects. Ahammed et al. [114] used the extracted features from ROI-based FC matrices. They applied a DarkASDNet method to classify ASD and HC subjects. Graña and Silva [115] utilized the extracted features from ROI-based FC matrices of ASD subjects and HCs using nine different classifiers. They explored the impact of choices during building up the ML pipelines on the predictive performance. They found that the selection of some feature extraction methods can strengthen the classifier performance, such as classical principal component analysis (PCA) and factor analysis (FA). Al-Hiyali et al. [116] used the temporal dynamic features from default mode network regions (DMNs) with several deep learning models for the diagnosis and classification of ASD. SVM and K-nearest neighbors (KNN) were used for ASD classification, and KNN achieved the highest classification accuracy. Pominova et al. [117] utilized extracted features of FC matrices and full-size MRI series with a 3D convolutional autoencoder method. To classify ASD and HC subjects, the SVM classifier was used. Yin et al. [118] used extracted features from ROI-based FC matrices with graph theory and autoencoders to distinguish ASD subjects from HCs. SVM, K-nearest neighbor (KNN), and DNN algorithms were used for classification. Chu et al. [119] used extracted features from ROI-based FC network regions with a multi-scale graph convolutional network (GCN) to classify ASD patients and HCs by learning the distinctive FC features. Yang et al. [120] used extracted features from FC matrices to distinguish ASD individuals from HCs. They used different classifiers such as LR, KSVM, DNN, and supervised learning classifiers; among these, KSVM achieved the best classification accuracy. Figure 4 highlights the different extracted features and the most-used classifiers in fMRI autism diagnostic systems. Table 2 summarizes ASD studies of fMRI with ML models and briefly outlines the dataset used and its demographics, the feature selection, the ML classifier used, findings, and the accuracy whenever reported.

Discussions and Future Trends
The use of artificial intelligence (AI) in medical diagnosis is a rapidly growing field, and there has been increasing interest in using AI for the diagnosis of autism spectrum disorder (ASD). This survey article examines the potential role of AI in ASD diagnosis using diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI). DTI is a neuroimaging technique that can be used to assess the microstructure of white matter in the brain, and fMRI is a technique that measures brain activity by detecting changes in blood flow. Both DTI and fMRI have shown promise in the diagnosis of ASD, as they can provide insights into the neural basis of the disorder. This article reviews the literature on the use of DTI and fMRI in ASD diagnosis and the reported findings, highlighting the different machine-learning techniques that have been employed. We also discuss the strengths and limitations of these techniques and suggest areas for future research.

Summary of ASD Findings Using DTI and fMRI
As summarized in Figure 1, both DTI and fMRI have revealed significant differences in ASD brains. In DTI, altered FA and MD have been reported in different brain regions. In addition, alterations in CC integrity have been reported at both micro-and macro-structural levels. Using fMRI, different reports have used T-fMRI to reveal atypical activities during social tasks and different auditory and language tasks. On the other side, rs-fMRI, being more suitable for infants and toddlers as not involving specific tasks, has been used to report altered brain connectivity in DMN, DAN, and salience networks.
To build more meaningful findings using DTI and fMRI, more reports should be carried out to verify how the age of participants affects these findings. In addition, there is a need to carry out studies that involve large numbers of participants to further verify the reported findings.

Summary of ASD Diagnostic Systems Using DTI and fMRI
Figures 3 and 4 summarize the most-utilized features and classifiers in different reported DTI and fMRI autism diagnostic systems. For DTI, different tractography-based or voxel-based features can be extracted. Tractography-based features include regionto-region fiber connectivity, brain connectivity networks, and fiber lengths. Voxel-based features include FA, MD, RD, AD, and skewness. A limited number of classifiers have been reported in the literature, with SVM being the most frequently used. For fMRI, the ROIbased FC matrices were the most frequently used features. In addition, seed-based, wavelet coherent maps, FFT features, and dynamic FC features have also been investigated. A wide variety of classifiers have been reported using fMRI, including regular ML classifiers, neural networks, autoencoder, CNN, and ensembles of different classifiers.
More diagnostic systems should be investigated to further improve diagnostic accuracy, considering more novel features or the fusion between the reported features. In addition, the recent advances in deep learning methods and explainable AI should be investigated.

Strengths of Using DTI and fMRI for ASD Diagnosis
One of the main strengths of using DTI and fMRI for ASD diagnosis is that these techniques provide non-invasive, objective measures of brain structure and function. They can be used to identify subtle brain abnormalities that may not be visible on structural MRI or other imaging modalities and can provide insights into the neural basis of ASD. Another strength of DTI and fMRI is their ability to capture brain activity in real-time, which allows for the study of dynamic brain processes such as language, attention, and social interaction. This can be particularly useful for understanding the specific brain abnormalities that underlie the core deficits of ASD and for developing targeted interventions. Overall, the use of DTI and fMRI in combination with machine learning techniques has the potential to revolutionize the way ASD is diagnosed and treated by providing a more accurate and personalized approach to care.

Limitations of Using DTI and fMRI for ASD Diagnosis
One limitation of using DTI and fMRI for ASD diagnosis is that these techniques are typically only available at specialized centers and may not be readily accessible to all patients. In addition, these techniques can be time-consuming and costly, which may limit their widespread use. Another limitation is that the findings from DTI and fMRI studies in ASD are often inconsistent, and there is a lack of consensus on the specific brain abnormalities that are associated with the disorder. This may be due in part to the heterogeneous nature of ASD as well as the use of different ML techniques and sample sizes in different studies.
To address these limitations and improve the accuracy of ASD diagnosis using DTI and fMRI, it is important to continue to develop and validate ML techniques and to standardize the acquisition and analysis of neuroimaging data. In addition, future research should focus on identifying the specific brain abnormalities that are most strongly associated with ASD and on developing more precise and predictive biomarkers for the disorder.
Overall, the use of AI for ASD diagnosis using DTI and fMRI shows great promise, but there is still much work to be done in order to fully understand the neural basis of ASD and to develop reliable and accurate diagnostic tools. Further research is needed to validate and optimize these techniques and to determine their clinical utility. More specifically, optimized software prototypes need to be developed and tested on large datasets acquired from different laboratories and institutions before commercialization. The availability and cost of these datasets are considered to be one of the largest potential barriers, especially with the lack of infant-and toddler-related MRI data that are required to prove the ability of these diagnostic-imaging-based systems to provide early autism diagnosis.

Conclusions
The literature reported significant differences in ASD brains using DTI and fMRI. DTI reported altered FA, MD, and CC integrity in different brain regions. T-fMRI reported atypical activity during social tasks and different auditory and language tasks. Finally, rs-fMRI revealed altered brain connectivity in DMN, DAN, and salience networks. These findings encourage researcher to utilize artificial intelligence (AI) and machine learning (ML) techniques for ASD diagnosis using DTI and fMRI. The application of AI to DTI and fMRI mainly involves two basic steps: feature extraction and classification. This survey basically highlights the different databases, features, and classifiers that are used in different autism diagnostic systems. These systems have shown great promise, but there is still much work to be done in order to fully understand the neural basis of ASD and to develop reliable and accurate diagnostic tools. The strengths of DTI and fMRI include their ability to provide non-invasive, objective measures of brain structure and function and their ability to capture brain activity in real-time. However, these techniques also have limitations, including their availability, cost, and the inconsistent findings from different studies. To address these limitations and improve the accuracy of ASD diagnosis using DTI and fMRI, it is important to continue to develop and validate machine learning (ML) techniques and to standardize the acquisition and analysis of neuroimaging data. In addition, future research should focus on identifying the specific brain abnormalities that are most strongly associated with ASD and on developing more precise and predictive biomarkers for the disorder. This requires a concerted effort from researchers across different disciplines, including neuroscience, psychology, and computer science. Overall, the use of AI in ASD diagnosis has the potential to revolutionize the way the disorder is diagnosed and treated by providing a more accurate and personalized approach to care. This will ultimately lead to more effective and personalized treatment strategies for individuals with ASD and could have a significant impact on the quality of life for individuals with this disorder and their families.

Conflicts of Interest:
The authors declare no conflict of interest.

Abbreviations
The following abbreviations are used in this manuscript: