Vision transformer and complex network analysis for autism spectrum disorder classification in T1 structural MRI.

Autism spectrum disorder (ASD) is a multifaceted neurodevelopmental condition that affects millions of individuals globally. This study leverages the publicly available UCI Machine Learning ASD Screening Dataset, which contains behavioral questionnaire responses from children aged 4-11 years, to develop and compare multiple machine learning (ML) models for early ASD diagnosis. This study applies SHAP (SHapley Additive exPlanations) to interpret and analyze the feature importance of various attributes in the dataset. Advanced ML models like Extreme Gradient Boosting (XGBoost), Random Forest (RF), Extra Trees Classifier (ETC), Support Vector Machines (SVM), and Multilayer Perceptrons (MLPs) were implemented to evaluate and compare their performance in predicting early ASD. The developed ASD model underwent a rigorous evaluation through comprehensive performance metrics, including accuracy, precision, recall, F1-score, specificity, and Matthews correlation coefficient. Ranking-based metrics, such as area under the receiver operating characteristic curve (AUROC), provided additional insights into classification robustness, while 10-fold cross-validation ensured reliability and generalizability. Results indicate that XGBoost achieved the highest diagnostic performance (accuracy = 97.8%, AUROC = 0.99), followed closely by MLP, with RF and ETC offering balanced performance and interpretability. SHAP analysis identified key behavioral features influencing model predictions, enabling explainable AI-driven insights. These findings suggest that ML-based tools can support clinicians in identifying ASD earlier, allowing for timelier interventions in disability services, particularly in underserved populations. While results are promising, the use of secondary data and the lack of clinical validation remain limitations, highlighting the need for real-world pilot testing. These findings underscore the transformative potential of ML techniques in enhancing ASD diagnostics. Despite their promise, the results are constrained by reliance on secondary data and the absence of clinical validation, underscoring the need for real-world pilot testing. Nonetheless, incorporating these models into clinical practice could accelerate early detection, facilitate personalized interventions, and optimize resource allocation for improved ASD management outcomes.

This study proposes a Computer-Aided Diagnostic (CAD) system to diagnose subjects with autism spectrum disorder (ASD). The CAD system identifies morphological anomalies within the brain regions of ASD subjects. Cortical features are scored according to their contribution in diagnosing a subject to be ASD or typically developed (TD) based on a trained machine-learning (ML) model. This approach opens the hope for developing a new CAD system for early personalized diagnosis of ASD. We propose a framework to extract the cerebral cortex from structural MRI as well as identifying the altered areas in the cerebral cortex. This framework consists of the following five main steps: (i) extraction of cerebral cortex from structural MRI; (ii) cortical parcellation to a standard atlas; (iii) identifying ASD associated cortical markers; (iv) adjusting feature values according to sex and age; (v) building tailored neuro-atlases to identify ASD; and (vi) artificial neural networks (NN) are trained to classify ASD. The system is tested on the Autism Brain Imaging Data Exchange (ABIDE I) sites achieving an average balanced accuracy score of . This paper demonstrates the ability to develop an objective CAD system using structure MRI and tailored neuro-atlases describing specific developmental patterns of the brain in autism.

Patients with non-small cell lung cancer (NSCLC) and patients with NSCLC combined with chronic obstructive pulmonary disease (COPD) have similar physiological conditions in early stages, and the latter have shorter survival times and higher mortality rates. The purpose of this study was to develop and compare machine learning models to identify future diagnoses of COPD combined with NSCLC patients based on the patient's disease and routine clinical data. Data were obtained from 237 patients with COPD combined with NSCLC as well as NSCLC admitted to Ningxia Hui Autonomous Region People's Hospital from October 2013 to July 2022. Six machine learning algorithms (K-nearest neighbor, logistic regression, eXtreme gradient boosting, support vector machine, naïve Bayes, and artificial neural network) were used to develop prediction models for NSCLC combined with COPD. Sensitivity, specificity, positive predictive value, negative predictive value, accuracy, F1 score, Mathews correlation coefficient (MCC), Kappa, area under the receiver operating characteristic curve (AUROC)and area under the precision-recall curve (AUPRC) were used as performance indicators to evaluate the performance of the models. 135 patients with NSCLC combined with COPD, 102 patients with NSCLC were included in the study. The results showed that pulmonary function and emphysema were important risk factors and that the support vector machine-based identification model showed optimal performance with accuracy:0.946, recall:0.940, specificity:0.955, precision:0.972, npv:0.920, F1 score:0.954, MCC:0.893, Kappa:0.888, AUROC:0.975, AUPRC:0.987. The use of machine learning tools combining clinical symptoms and routine examination data features is suitable for identifying the risk of concurrent NSCLC in COPD patients.

ObjectivesTo investigate the added diagnostic value of arterial spin labelling (ASL) and diffusion tensor imaging (DTI) to structural MRI for computer-aided classification of Alzheimer's disease (AD), frontotemporal dementia (FTD), and controls.MethodsThis retrospective study used MRI data from 24 early-onset AD and 33 early-onset FTD patients and 34 controls (CN). Classification was based on voxel-wise feature maps derived from structural MRI, ASL, and DTI. Support vector machines (SVMs) were trained to classify AD versus CN (AD-CN), FTD-CN, AD-FTD, and AD-FTD-CN (multi-class). Classification performance was assessed by the area under the receiver-operating-characteristic curve (AUC) and accuracy. Using SVM significance maps, we analysed contributions of brain regions.ResultsCombining ASL and DTI with structural MRI resulted in higher classification performance for differential diagnosis of AD and FTD (AUC = 84%; p = 0.05) than using structural MRI by itself (AUC = 72%). The performance of ASL and DTI themselves did not improve over structural MRI. The classifications were driven by different brain regions for ASL and DTI than for structural MRI, suggesting complementary information.ConclusionsASL and DTI are promising additions to structural MRI for classification of early-onset AD, early-onset FTD, and controls, and may improve the computer-aided differential diagnosis on a single-subject level.Key points• Multiparametric MRI is promising for computer-aided diagnosis of early-onset AD and FTD.• Diagnosis is driven by different brain regions when using different MRI methods.• Combining structural MRI, ASL, and DTI may improve differential diagnosis of dementia.

Acute kidney injury (AKI) after liver transplantation has been reported to be associated with increased mortality. Recently, machine learning approaches were reported to have better predictive ability than the classic statistical analysis. We compared the performance of machine learning approaches with that of logistic regression analysis to predict AKI after liver transplantation. We reviewed 1211 patients and preoperative and intraoperative anesthesia and surgery-related variables were obtained. The primary outcome was postoperative AKI defined by acute kidney injury network criteria. The following machine learning techniques were used: decision tree, random forest, gradient boosting machine, support vector machine, naïve Bayes, multilayer perceptron, and deep belief networks. These techniques were compared with logistic regression analysis regarding the area under the receiver-operating characteristic curve (AUROC). AKI developed in 365 patients (30.1%). The performance in terms of AUROC was best in gradient boosting machine among all analyses to predict AKI of all stages (0.90, 95% confidence interval [CI] 0.86–0.93) or stage 2 or 3 AKI. The AUROC of logistic regression analysis was 0.61 (95% CI 0.56–0.66). Decision tree and random forest techniques showed moderate performance (AUROC 0.86 and 0.85, respectively). The AUROC of support the vector machine, naïve Bayes, neural network, and deep belief network was smaller than that of the other models. In our comparison of seven machine learning approaches with logistic regression analysis, the gradient boosting machine showed the best performance with the highest AUROC. An internet-based risk estimator was developed based on our model of gradient boosting. However, prospective studies are required to validate our results.

Autism Spectrum Disorder (ASD) is a lifelong neurodevelopmental condition characterized by challenges in social communication and repetitive behaviors, making early diagnosis essential for effective intervention. This preliminary study examines the feasibility of classifying ASD using 2D slices extracted from 3D brain MRI volumes, focusing on the comparative diagnostic value of axial, coronal, and sagittal planes within a machine learning framework. The goal is to identify the most informative anatomical plane to guide future neuroimaging protocols and improve automated ASD detection. We analyzed 20,992 slices derived from 82 MRI scans (41 ASD and 41 non-ASD) from the Autism Brain Imaging Data Exchange (ABIDE), each scan averaging 256 × 256 pixels across 256 slices. Preprocessing included skull stripping and conversion to NIFTI-GZ format. Texture features were extracted using the Gray Level Co-occurrence Matrix (GLCM), and classification was performed using the K-Nearest Neighbors (KNN) algorithm with varying K values (3, 5, and 7). The best performance was achieved with K = 3 and GLCM parameters at distance d = 0 and angle 45°, yielding an accuracy of 83.90%, precision of 79%, recall of 78%, and F1-score of 78%. These findings provide initial evidence that 2D slice-based texture analysis can support early ASD classification and offer practical insights for optimizing neuroimaging-based diagnostic tools. Future work will explore deep learning approaches and multimodal integration to enhance robustness and clinical applicability.

Hypoxemia is a common complication associated with anesthesia in painless gastroscopy. With the aging of the social population, the number of cases of hypoxemia among middle-aged and elderly patients is increasing. However, tools for predicting hypoxemia in middle-aged and elderly patients are lacking. In this study, we investigated the risk factors for hypoxemia in middle-aged and elderly outpatients undergoing painless gastroscopy based on machine learning and constructed a risk prediction model. In this retrospective study, we included the data on 1,348 outpatients undergoing painless gastroscopy. In total, 26 characteristic variables, including demographic information, past medical history, and clinical data of the patients were included, and BorutaShap was used for feature selection. Five machine learning algorithm models, including logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGB), and light gradient boosting machine (LightGBM), were selected. The best models were selected based on the area under the receiver operating characteristic curve (AUROC). Model feature importance was explained and analyzed using Shapley Additive Explanations (SHAP). The endpoint event of this study was considered to be hypoxemia during the procedure, defined as at least one occurrence of pulse oxygen saturation below 90% without probe misalignment or interference from the beginning of anesthesia induction to the end of painless gastroscopy. In the final cohort of 984 patients, 11% of patients (108/984) experienced hypoxemia during the painless gastroscopy procedure. The AUROCs of the five models were as follows: Logistic Regression (AUROC = 0.893, 95CI: 0.881–0.899), SVM (AUROC = 0.855, 95CI: 0.812–0.884), Random Forest (AUROC = 0.914, 95CI: 0.889–0.924), XGB (AUROC = 0.902, 95CI: 0.865–0.919), and LightGBM (AUROC = 0.891, 95CI: 0.847–0.917). Regarding the explanation of the importance of SHAP features, preoperative variables (baseline SpO2, body mass index, and micrognathia) and intraoperative variables (operating time of gastroscopy, induction dose of etomidate and propofol mixture, append anesthetic, cough, and repeated pharyngeal irritation) significantly contributed to the model. We identified eight potential risk factors related to the occurrence of hypoxemia in middle-aged and elderly patients undergoing painless gastroscopy, based on machine learning feature engineering. Among the five machine learning algorithms, RF exhibited the best predictive performance in the internal test set and had a certain degree of generalization ability in the external validation set, which indicated that the RF model was more suitable for the data framework of this study. This model was more likely to enhance the accuracy of hypoxemia prediction in middle-aged and elderly patients undergoing painless gastroscopy, and thus, it is suitable for assisting anesthesiologists in clinical decision-making.

Autism spectrum disorder (ASD) is a neurodevelopmental disability with global implication. Altered brain connectivity in the language network has frequently been reported in ASD patients using task-based functional magnetic resonance imaging (fMRI) compared to typically developing (TD) participants. Most of these studies have focused on a specific age group or mixed age groups with ASD. In the current study, we investigated age-related changes in functional connectivity related measure, degree centrality (DC), in the language network across three age groups with ASD (113 children, 113 adolescents and 103 adults) using resting-state fMRI data collected from the autism brain imaging data exchange repository. We identified regions with significant group-wise differences between ASD and TD groups for three age cohorts using DC based on graph theory. We found that both children and adolescents with ASD showed decreased DC in Broca’s area compared to age-matched TD groups. Adults with ASD showed decreased DC in Wernicke’s area compared to TD adults. We also observed increased DC in the left inferior parietal lobule (IPL) and left middle temporal gyrus (MTG) for children with ASD compared to TD children and for adults with ASD compared to TD adults, respectively. Overall, functional differences occurred in key language processing regions such as the left inferior frontal gyrus (IFG) and superior temporal gyrus (STG) related to language production and comprehension across three age cohorts. We explored correlations between DC values of our findings with autism diagnostic observation schedule (ADOS) scores related to severity of ASD symptoms in the ASD group. We found that DC values of the left IFG demonstrated negative correlations with ADOS scores in children and adolescents with ASD. The left STG showed significant negative correlations with ADOS scores in adults with ASD. These results might shed light on the language network regions that should be further explored for prognosis, diagnosis, and monitoring of ASD in three age groups.

The diagnostic process for Autism Spectrum Disorder (ASD) typically involves time-consuming assessments conducted by specialized physicians. To improve the efficiency of ASD screening, intelligent solutions based on machine learning have been proposed in the literature. However, many existing ML models lack the incorporation of medical tests and demographic features, which could potentially enhance their detection capabilities by considering affected features through traditional feature selection approaches. This study aims to address the aforementioned limitation by utilizing a real dataset containing 45 features and 983 patients. To achieve this goal, a two-phase methodology is employed. The first phase involves data preparation, including handling missing data through model-based imputation, normalizing the dataset using the Min-Max method, and selecting relevant features using traditional feature selection approaches based on affected features. In the second phase, seven ML classification techniques recommended by the literature, including Decision Trees (DT), Random Forest (RF), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), AdaBoost, Gradient Boosting (GB), and Neural Network (NN), are utilized to develop ML models. These models are then trained and tested on the prepared dataset to evaluate their performance in detecting ASD. The performance of the ML models is assessed using various metrics, such as Accuracy, Recall, Precision, F1-score, AUC, Train time, and Test time. These metrics provide insights into the models' overall accuracy, sensitivity, specificity, and the trade-off between true positive and false positive rates. The results of the study highlight the effectiveness of utilizing traditional feature selection approaches based on affected features. Specifically, the GB model outperforms the other models with an accuracy of 87%, Recall of 87%, Precision of 86%, F1-score of 86%, AUC of 95%, Train time of 21.890, and Test time of 0.173. Additionally, a benchmarking analysis against five other studies reveals that the proposed methodology achieves a perfect score across three key areas. By considering affected features through traditional feature selection approaches, the developed ML models demonstrate improved performance and have the potential to enhance ASD screening and diagnosis processes.

Screening for autism spectrum disorder (ASD) is constrained by limited resources, particularly trained professionals to conduct evaluations. Individuals with ASD have structural retinal changes that potentially reflect brain alterations, including visual pathway abnormalities through embryonic and anatomic connections. Whether deep learning algorithms can aid in objective screening for ASD and symptom severity using retinal photographs is unknown. To develop deep ensemble models to differentiate between retinal photographs of individuals with ASD vs typical development (TD) and between individuals with severe ASD vs mild to moderate ASD. This diagnostic study was conducted at a single tertiary-care hospital (Severance Hospital, Yonsei University College of Medicine) in Seoul, Republic of Korea. Retinal photographs of individuals with ASD were prospectively collected between April and October 2022, and those of age- and sex-matched individuals with TD were retrospectively collected between December 2007 and February 2023. Deep ensembles of 5 models were built with 10-fold cross-validation using the pretrained ResNeXt-50 (32×4d) network. Score-weighted visual explanations for convolutional neural networks, with a progressive erasing technique, were used for model visualization and quantitative validation. Data analysis was performed between December 2022 and October 2023. Autism Diagnostic Observation Schedule-Second Edition calibrated severity scores (cutoff of 8) and Social Responsiveness Scale-Second Edition T scores (cutoff of 76) were used to assess symptom severity. The main outcomes were participant-level area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity. The 95% CI was estimated through the bootstrapping method with 1000 resamples. This study included 1890 eyes of 958 participants. The ASD and TD groups each included 479 participants (945 eyes), had a mean (SD) age of 7.8 (3.2) years, and comprised mostly boys (392 [81.8%]). For ASD screening, the models had a mean AUROC, sensitivity, and specificity of 1.00 (95% CI, 1.00-1.00) on the test set. These models retained a mean AUROC of 1.00 using only 10% of the image containing the optic disc. For symptom severity screening, the models had a mean AUROC of 0.74 (95% CI, 0.67-0.80), sensitivity of 0.58 (95% CI, 0.49-0.66), and specificity of 0.74 (95% CI, 0.67-0.82) on the test set. These findings suggest that retinal photographs may be a viable objective screening tool for ASD and possibly for symptom severity. Retinal photograph use may speed the ASD screening process, which may help improve accessibility to specialized child psychiatry assessments currently strained by limited resources.

Autism spectrum disorder (ASD) is a collection of a way of continuing neurodevelopment illnesses identified by limited and repetitive behavioral patterns, as well as social and communication impairments. Despite the fact that symptoms are most common in childhood, diagnosis is sometimes delayed. Because the current ASD diagnostic technique is solely uncertain and questionnaire, requiring the physician to review the behavior and developmental history of a child. It's been suggested that behavioral symptoms in ASD are linked to brain findings of increased short-distance and diminished long-distance connections. The suggested approach makes use of brain imaging data from Autism Brain Imaging Data Exchange (ABIDE), a global multi-site database. For ASD identification, the proposed approach uses functional connectivity characteristics extracted from resting state functional MRI data. ASD patients’ brain connections could be disturbed. The presented method extracts time series from 122 regions of interest defined by the Bootstrap Analysis of Stable Clusters (BASC) and also extracts time series from 48 regions of interest defined by the Harvard Oxford (HO) brain atlas to create an efficient functional connectivity matrix for all individuals. Synthetic Minority Over-sampling Technique (SMOTE) an oversampling procedure employed for artificial data generation. The classification challenge is carried out using a Recurrent Convolutional Neural Network (RCNN) and Convolutional Neural Network (CNN) and results were compared. In both models, fivefold Cross-Validation is applied. The model performed better in the modified experiment, with RCNN achieving an accuracy of nearly 85%.

Anxiety disorders are common in autism spectrum disorder (ASD) and associated with social-communication impairment and repetitive behavior symptoms. The neurobiology of anxiety in ASD is unknown, but amygdala dysfunction has been implicated in both ASD and anxiety disorders. Using resting-state functional magnetic resonance imaging, we compared amygdala-prefrontal and amygdala-striatal connections across three demographically matched groups studied in the Autism Brain Imaging Data Exchange (ABIDE): ASD with a comorbid anxiety disorder (N = 25; ASD + Anxiety), ASD without a comorbid disorder (N = 68; ASD-NoAnx), and typically developing controls (N = 139; TD). Relative to ASD-NoAnx and TD controls, ASD + Anxiety individuals had decreased connectivity between the amygdala and dorsal/rostral anterior cingulate cortex (dACC/rACC). The functional connectivity of these connections was not affected in ASD-NoAnx, and amygdala connectivity with ventral ACC/medial prefrontal cortex (mPFC) circuits was not different in ASD + Anxiety or ASD-NoAnx relative to TD. Decreased amygdala-dorsomedial prefrontal cortex (dmPFC)/rACC connectivity was associated with more severe social impairment in ASD + Anxiety; amygdala-striatal connectivity was associated with restricted, repetitive behavior (RRB) symptom severity in ASD-NoAnx individuals. These findings suggest comorbid anxiety in ASD is associated with disrupted emotion-monitoring processes supported by amygdala-dACC/mPFC pathways, whereas emotion regulation systems involving amygdala-ventromedial prefrontal cortex (vmPFC) are relatively spared. Our results highlight the importance of accounting for comorbid anxiety for parsing ASD neurobiological heterogeneity.

Back to table of contents Previous article Next article Clinical and Research NewsFull AccessJournal DigestVabren WattsVabren WattsSearch for more papers by this authorPublished Online:26 Oct 2015https://doi.org/10.1176/appi.pn.2015.10b8Study Reveals Differences Between Girls, Boys with AutismJezper/ShutterstockThe brains of girls with autism spectrum disorder (ASD) appear to differ from boys with ASD, according to a study published in Molecular Autism. The study also revealed behavioral differences between the two groups. Researchers conducted two separate analyses, with data extrapolated from the National Database for Autism Research and Autism Brain Imaging Data Exchange. In the first analysis, the severity of ASD symptoms was examined in nearly 128 girls with ASD and 614 boys with the disorder. Compared with the boys, girls with ASD showed less severe repetitive/restricted behaviors and comparable deficits in social and communications domains on the Autism Diagnostic Interview, Revised (ADI-R). The researchers then examined symptom severity and structural imaging data obtained from 25 girls with ASD, 25 boys with ASD, and 19 girls and 19 boys without ASD. The researchers found that gray matter (GM) in the cortex, supplementary motor area (SMA), cerebellum, fusiform gyrus, and amygdala accurately discriminated girls and boys with ASD. They also found that the GM morphometry in the motor cortex, SMA, and cerebellum was correlated with scores on the repetitive/restricted behaviors domain of the ADI-R.“Our findings not only provide evidence for distinct behavioral phenotypes in girls with ASD, compared with boys, but also link behavioral differences to brain structure. Importantly, the severity of repetitive/restricted behaviors is lower in girls with ASD and is associated with sex differences in GM morphometry in cortical and cerebellar regions involved in motor control,” the researchers wrote. Supekar K and Menon V. Sex Differences in Structural Organization of Motor Systems and Their Dissociable Links With Repetitive/Restricted Behaviors in Children With Autism. Mol Autism. 2015 Sep 4;6:50.Smoke-Free Laws, Cigarette Taxes Deter Youth From SmokingPi-Lens/ShutterstockSmoke-free zone laws and higher taxes on cigarettes appear to deter young adults from smoking, according to a recent report in JAMA Pediatrics.Researchers from the University of California, San Francisco, tracked the smoking habits of youth aged 12 to 18 who participated in the 1997 Bureau of Labor Statistics National Longitudinal Survey of Youth for 11 years.The researchers found that youth living in places with laws for 100 percent smoke-free workplaces were 34 percent less likely to initiate smoking than youth living in places without such bans. Youth living in areas with 100 percent smoke-free bar laws were 20 percent less likely to be smokers and those who were smokers, spent fewer days smoking.The researchers also found that tax hikes on cigarettes deterred youth from smoking, with each 10-cent tax hike associated with a 3 percent drop in the odds of starting to smoke.The researchers noted that the current findings suggest that not only do smoking laws “protect bystanders from secondhand smoke, but also contribute to less smoking among adolescents and young adults.” Song A, Dutra L, Neilands T, et al. Association of Smoke-Free Laws With Lower Percentages of New and Current Smokers Among Adolescents and Young Adults: An 11-Year Longitudinal Study. JAMA Pediatr. 2015. 169(9):e152285.Multimorbidity May Be Associated With Greater Risk of MCI, DementiaRobert Kneschke/ShutterstockHaving multiple chronic conditions may be linked to an increased risk of mild cognitive impairment (MCI) or dementia, the Journal of the American Geriatrics Society reports. Researchers from the Mayo Clinic followed 2,176 seniors aged 70 to 89 without dementia for about 5 years to determine if an association existed between multiple chronic conditions and risk of mild cognitive impairment (MCI) and dementia.The results showed that participants with more than one chronic condition were 38 percent more likely to develop MCI or dementia than those with one or no chronic conditions. Participants with four or more chronic conditions had a 61 percent increased risk of MCI or dementia. Additionally, men with multiple chronic conditions were significantly more likely to develop MCI or dementia than women with equivalent numbers of chronic conditions.These findings are “consistent with the hypothesis that multiple etiologies may contribute to MCI and late-life dementia,” the researchers noted. Preventing chronic diseases may be beneficial in delaying or preventing MCI and dementia, they added.Vassilaki M, Aakre J, Cha R, et al. Multimorbidity and Risk of Mild Cognitive Impairment. J Am Geriatr Soc. 2015 Sep;63(9):1783-1790.Study Suggests That People With Intellectual Disabilities May Be Overprescribed Psychotropics r.classen/ShutterstockThe proportion of people with intellectual disabilities in the United Kingdom who have been treated with psychotropic drugs far exceeds the proportion with recorded severe mental illness, according to a recent study published in BMJ. To determine the rates of psychotropic prescriptions made to people with intellectual disabilities with or without comorbid psychiatric disorders, researchers from the University College London analyzed the electronic health records of 33,016 adults living in the United Kingdom with a diagnosed intellectual disability. The participants were followed over five years. Over the course of the trial, 9,135 participants were treated with antipsychotic drugs; 6,503 (71 percent of total prescribed antipsychotics) had no record of having been diagnosed with a severe mental illness. New prescriptions for antipsychotics were significantly more common in older people and those with a record of challenging behavior, autism, and dementia. “Psychotropic drugs are an important element in the management of specific psychiatric conditions,” the researchers noted. “However, we have shown that adults with intellectual disability are treated with psychotropic drugs at a rate far exceeding that of recorded mental illness and that certain subgroups (such as those with challenging behaviors) are significantly more likely to receive antipsychotic drugs.” ■Sheehan R, Hassiotis A, Walters K, et al. Mental Illness, Challenging Behaviour, and Psychotropic Drug Prescribing in People with Intellectual Disability: U.K. Population Based Cohort Study. BMJ. 2015 Sep 1;351:h4326. ISSUES NewArchived

Neurodevelopmental disorders comprise a group of neuropsychiatric conditions. Presently, behavior-based diagnostic approaches are utilized in clinical settings, but the overlapping features among these disorders obscure their recognition and management. Attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) have common characteristics across various levels, from genes to symptoms. Designing a computational framework based on the neuroimaging findings could provide a discriminative tool for ultimate more efficient treatment. Machine learning approaches, specifically classification methods are among the most applied techniques to reach this goal. We applied a novel two-level multi-class data maximum dispersion classifier (DMDC) algorithm to classify the functional neuroimaging data (utilizing datasets: ADHD-200 and autism brain imaging data exchange (ABIDE)) into two categories: Neurodevelopmental disorders (ASD and ADHD) or healthy participants, based on calculated functional connectivity values (statistical temporal correlation). Our model achieved a total accuracy of 62% for healthy controls. Specifically, it demonstrated an accuracy of 51% for healthy subjects, 61% for autism spectrum disorder, and 84% for ADHD. The support vector machine (SVM) model achieved an accuracy of 46% for both the healthy control and ASD groups, while the ADHD group classification accuracy was estimated to be 84%. These two models showed similar classification indices for the ADHD group. However, the discrimination power was higher in the ASD class. The method employed in this study demonstrated acceptable performance in classifying disorders and healthy conditions compared to the more commonly used SVM method. Notably, functional connections associated with the cerebellum showed discriminative power.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder usually presenting as reduced social interaction, lessened verbal communication, and repetitive behavior. Diagnosing autism spectrum disorder is extremely difficult because of its wide variety of symptoms, so it can only be diagnosed through behavioral tests and analysis of developmental history. Resting-state functional magnetic resonance imaging (rs-fMRI) can help researchers discover a neural substrate for autism spectrum disorder to diagnose it earlier. One prominent fMRI database for autism spectrum disorder research is the Autism Brain Imaging Data Exchange, a large-scale collection of anonymized functional MRI scans subdivided by age, gender, handedness, and scores on behavioral assessments. This analysis focused on two brain networks: the default mode network (DMN), which is active when minds wander, and the executive network, which is active during the performance of tasks. The medial prefrontal cortex (mPFC), posterior cingulate cortex (PCC), and angular gyrus are nodes of the default mode network, and the dorsolateral prefrontal cortex (DLPFC) is the main node in the executive network. Both networks are affected by autism spectrum disorder. This research used preprocessed resting-state fMRI data to establish neurocognitive phenotypes for autism spectrum disorder. Bivariate correlation was used to compare connectivity in the default mode network and dorsolateral prefrontal cortex between autism spectrum disorder fMRI scans and control fMRI scans, and these differences were analyzed for correlations with each patient’s assessment scores. After the Benjamini-Hochberg procedure was applied to reduce the false discovery rate, analysis of these metrics revealed that in autism spectrum disorder patients there was underconnectivity between the right posterior cingulate cortex and the right medial prefrontal cortex, while in control patients there was overconnectivity between the right angular gyrus and left dorsolateral prefrontal cortex. Autism spectrum disorder is extremely heritable, so phenotypic research is absolutely necessary for discovering more about the genetic causes of autism spectrum disorder, which will speed up autism spectrum disorder diagnosis and help researchers develop more targeted treatments for autism spectrum disorder.