The value of diffusion tensor imaging for differentiating autism spectrum disorder with language delay from developmental language disorder among toddlers

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.

Brain MRI in Autism Spectrum Disorder: Narrative Review and Recent Advances

Autism spectrum disorder (ASD) is neuropsychiatric continuum of disorders characterized by persistent deficits in social communication and restricted repetitive patterns of behavior which impede optimal functioning. Early detection and intervention in ASD children can mitigate the deficits in social interaction and result in a better outcome. Various non-invasive imaging methods and molecular techniques have been developed for the early identification of ASD characteristics. There is no general consensus on specific neuroimaging features of autism; however, quantitative magnetic resonance techniques have provided valuable structural and functional information in understanding the neuropathophysiology of ASD and how the autistic brain changes during childhood, adolescence, and adulthood. In this review of decades of ASD neuroimaging research, we identify the structural, functional, and molecular imaging clues that most accurately point to the diagnosis of ASD vs. typically developing children. These studies highlight the 1) exaggerated synaptic pruning, 2) anomalous gyrification, 3) interhemispheric under- and overconnectivity, and 4) excitatory glutamate and inhibitory GABA imbalance theories of ASD. The application of these various theories to the analysis of a patient with ASD is mitigated often by superimposed comorbid neuropsychological disorders, evolving brain maturation processes, and pharmacologic and behavioral interventions that may affect the structure and function of the brain. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 3.

Diffusion Tensor Imaging Studies on Spontaneous Subarachnoid Hemorrhage-Related Brain Injury: A Mini-Review

Accurate diagnosis of the presence and severity of neural injury in patients with subarachnoid hemorrhage (SAH) is important in neurorehabilitation because it is essential for establishing appropriate therapeutic strategies and developing a prognosis. Diffusion tensor imaging has a unique advantage in the identification of microstructural white matter abnormalities which are not usually detectable on conventional brain magnetic resonance imaging. In this mini-review article, 12 diffusion tensor imaging studies on SAH-related brain injury were reviewed. These studies have demonstrated SAH-related brain injuries in various neural tracts or structures including the cingulum, fornix, hippocampus, dorsolateral prefrontal region, corticospinal tract, mamillothalamic tract, corticoreticular pathway, ascending reticular activating system, Papez circuit, optic radiation, and subcortical white matter. We believe that these reviewed studies provide information that would be helpful in science-based neurorehabilitation of patients with SAH. Furthermore, the results of these reviewed studies would also be useful for clarification of the pathophysiological mechanisms associated with SAH-related brain injury. However, considering the large number of neural tracts or neural structures in the brain, more research on SAH-related brain injury in other neural tracts or structures should be encouraged.