scholarly journals Application of Artificial Intelligence in the MRI Classification Task of Human Brain Neurological and Psychiatric Diseases: A Scoping Review

Diagnostics ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1402
Author(s):  
Zhao Zhang ◽  
Guangfei Li ◽  
Yong Xu ◽  
Xiaoying Tang
Keyword(s):  
Artificial Intelligence ◽  
Magnetic Resonance ◽  
Human Brain ◽  
Autism Spectrum ◽  
Magnetic Resonance Images ◽  
Future Research ◽  
Psychiatric Diseases ◽  
Hyperactivity Disorder ◽  
Future Research Directions ◽  
Magnetic Resonance Imaging Mri

Artificial intelligence (AI) for medical imaging is a technology with great potential. An in-depth understanding of the principles and applications of magnetic resonance imaging (MRI), machine learning (ML), and deep learning (DL) is fundamental for developing AI-based algorithms that can meet the requirements of clinical diagnosis and have excellent quality and efficiency. Moreover, a more comprehensive understanding of applications and opportunities would help to implement AI-based methods in an ethical and sustainable manner. This review first summarizes recent research advances in ML and DL techniques for classifying human brain magnetic resonance images. Then, the application of ML and DL methods to six typical neurological and psychiatric diseases is summarized, including Alzheimer’s disease (AD), Parkinson’s disease (PD), major depressive disorder (MDD), schizophrenia (SCZ), attention-deficit/hyperactivity disorder (ADHD), and autism spectrum disorder (ASD). Finally, the limitations of the existing research are discussed, and possible future research directions are proposed.

Self-directed speech and self-regulation in childhood neurodevelopmental disorders: Current findings and future directions

2019 ◽  
Vol 32 (1) ◽  
pp. 205-217 ◽  
Author(s):  
Aisling Mulvihill ◽  
Annemaree Carroll ◽  
Paul E. Dux ◽  
Natasha Matthews
Keyword(s):  
Systematic Review ◽  
Neurodevelopmental Disorders ◽  
Language Disorder ◽  
Self Regulation ◽  
Autism Spectrum ◽  
The Self ◽  
Future Research ◽  
Hyperactivity Disorder ◽  
Future Research Directions ◽  
And Behavior

AbstractSelf-directed speech is considered an important developmental achievement as a self-regulatory mediator of thinking and behavior. Atypical self-directed speech is often implicated in the self-regulatory challenges characteristic of children with neurodevelopmental disorders. A growing body of evidence provides snapshots across age-levels and diagnoses, often presenting conflicting results. This systematic review is undertaken to impose clarity on the nature, extent, and self-regulatory implications of self-directed speech interruption in children with developmental language disorder (DLD), autism spectrum disorder (ASD), and attention deficit hyperactivity disorder (ADHD).A rigorous search process of relevant databases (i.e., PsychInfo, PubMed, CINAHL, ERIC) uncovered 19 relevant peer-reviewed articles that investigate self-directed speech in children with neurodevelopmental disorders. Consistent across the research, children with DLD, ASD, and ADHD present with differential development and use of self-directed speech.In its synthesis of findings, this systematic review clearly explicates the differential ontogenesis of self-directed speech in neurodevelopmental disorders and interprets the self-regulatory implications for children with DLD, ASD, and ADHD. Furthermore, the review spotlights important future research directions to better understand the mechanistic relationship between self-directed speech and self-regulation.

A Review of Supportive Computational Approaches for Neurological Disorder Identification

2020 ◽  
pp. 271-302
Author(s):  
Dulani Meedeniya ◽  
Iresha Rubasinghe
Keyword(s):  
Physical Symptoms ◽  
Autism Spectrum ◽  
Future Research ◽  
Optimal Care ◽  
Medical Specialties ◽  
Hyperactivity Disorder ◽  
Medical Disorders ◽  
Medical Issues ◽  
Future Research Directions ◽  
Disorder Identification

At present there is a growth of physical symptoms with psychological overlays, resulting in neurodevelopment disorders, where both psychiatrist and medical specialties work in collaboration to provide optimal care for the patients. Disorders such as autism spectrum disorder, attention-deficit hyperactivity disorder, Down syndrome, cerebral palsy, sickle cell disease, and Alzheimer disease are more prevalent. These may have a genetic influence and give certain behavioural disturbances due to associated medical issues. The symptoms are observable in early childhood, and they may consist of comorbid medical disorders. This chapter addresses recent studies together with the applied techniques in this context. Further, this chapter shows the limitations, challenges in current practices, and possible future research directions.

The Influence of Background Auditory Noise on P50 and N100 Suppression Elicited by the Paired-Click Paradigm

2020 ◽  
Vol 34 (3) ◽  
pp. 171-178
Author(s):  
Samantha Major ◽  
Kimberly Carpenter ◽  
Logan Beyer ◽  
Hannah Kwak ◽  
Geraldine Dawson ◽  
...  
Keyword(s):  
Sensory Gating ◽  
Autism Spectrum ◽  
Inhibitory Response ◽  
Future Research ◽  
Typically Developing ◽  
Optimal Outcomes ◽  
Hyperactivity Disorder ◽  
Auditory Sensory Gating ◽  
Auditory Noise ◽  
P50 Suppression

Abstract. Auditory sensory gating is commonly assessed using the Paired-Click Paradigm (PCP), an electroencephalography (EEG) task in which two identical sounds are presented sequentially and the brain’s inhibitory response to the second sound is measured. Many clinical populations demonstrate reduced P50 and/or N100 suppression. Testing sensory gating in children may help to identify individuals at risk for neurodevelopmental disorders earlier, including autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD), which could lead to more optimal outcomes. Minimal research has been done with children because of the difficulty of performing lengthy EEG experiments with young children, requiring them to sit still for long periods of time. We designed a modified, potentially child-friendly version of the PCP and evaluated it in typically developing adults. The PCP was administered twice, once in a traditional silent room (silent movie condition) and once with an audible movie playing (audible movie condition) to minimize boredom and enhance behavioral compliance. We tested whether P50 and N100 suppression were influenced by the presence of the auditory background noise from the movie. N100 suppression was observed in both hemispheres in the silent movie condition and in the left hemisphere only during the audible movie condition, though suppression was attenuated in the audible movie condition. P50 suppression was not observed in either condition. N100 sensory gating was successfully elicited with an audible movie playing during the PCP, supporting the use of the modified task for future research in both children and adults.

Synthesis

2020 ◽  
pp. 421-428
Author(s):  
Lauren Brookman-Frazee ◽  
Amy Drahota ◽  
Colby Chlebowski ◽  
Yael Koenig ◽  
Katherine Nguyen Williams ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Autism Spectrum ◽  
Future Research ◽  
Research Directions ◽  
Course Assessment ◽  
Academic Partnerships ◽  
Assessment And Treatment ◽  
Important Field ◽  
Future Research Directions ◽  
Psychiatric Conditions

Recent research and clinical attention devoted to co-occurring psychiatric conditions within autism spectrum disorder (ASD) has led to significant advances in the understanding of and ability to assess and treat co-occurring problems effectively. This chapter summarizes those advances while also highlighting the substantial gaps that remain in the understanding of co-occurring problems in people with ASD. The chapter provides recommendations for future research directions in the areas of etiology, developmental course, assessment, and treatment. It also offers suggestions for improving the representativeness of research participants and strengthening community–academic partnerships in this important field of study.

scholarly journals Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Manan Binth Taj Noor ◽  
Nusrat Zerin Zenia ◽  
M Shamim Kaiser ◽  
Shamim Al Mamun ◽  
Mufti Mahmud
Keyword(s):  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Neurological Disorders ◽  
Magnetic Resonance Images ◽  
Future Research ◽  
Comparative Performance

Abstract Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have opened up possibilities to unprecedentedly identify neurological disorders. However, similarities in disease phenotypes make it very difficult to detect such disorders accurately from the acquired neuroimaging data. This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders—focusing on Alzheimer’s disease, Parkinson’s disease and schizophrenia—from MRI data acquired using different modalities including functional and structural MRI. The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting neurological disorders. Towards the end, a number of current research challenges are indicated and some possible future research directions are provided.

scholarly journals Sensory over-responsivity is related to GABAergic inhibition in thalamocortical circuits

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Emily T. Wood ◽  
Kaitlin K. Cummings ◽  
Jiwon Jung ◽  
Genevieve Patterson ◽  
Nana Okada ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Sensory Information ◽  
Network Connectivity ◽  
Sensory Stimulation ◽  
Autism Spectrum ◽  
Future Research ◽  
Resonance Spectroscopy ◽  
Gamma Aminobutyric Acid ◽  
Sensory Stimuli ◽  
Brain Responses

AbstractSensory over-responsivity (SOR), extreme sensitivity to or avoidance of sensory stimuli (e.g., scratchy fabrics, loud sounds), is a highly prevalent and impairing feature of neurodevelopmental disorders such as autism spectrum disorders (ASD), anxiety, and ADHD. Previous studies have found overactive brain responses and reduced modulation of thalamocortical connectivity in response to mildly aversive sensory stimulation in ASD. These findings suggest altered thalamic sensory gating which could be associated with an excitatory/inhibitory neurochemical imbalance, but such thalamic neurochemistry has never been examined in relation to SOR. Here we utilized magnetic resonance spectroscopy and resting-state functional magnetic resonance imaging to examine the relationship between thalamic and somatosensory cortex inhibitory (gamma-aminobutyric acid, GABA) and excitatory (glutamate) neurochemicals with the intrinsic functional connectivity of those regions in 35 ASD and 35 typically developing pediatric subjects. Although there were no diagnostic group differences in neurochemical concentrations in either region, within the ASD group, SOR severity correlated negatively with thalamic GABA (r = −0.48, p < 0.05) and positively with somatosensory glutamate (r = 0.68, p < 0.01). Further, in the ASD group, thalamic GABA concentration predicted altered connectivity with regions previously implicated in SOR. These variations in GABA and associated network connectivity in the ASD group highlight the potential role of GABA as a mechanism underlying individual differences in SOR, a major source of phenotypic heterogeneity in ASD. In ASD, abnormalities of the thalamic neurochemical balance could interfere with the thalamic role in integrating, relaying, and inhibiting attention to sensory information. These results have implications for future research and GABA-modulating pharmacologic interventions.

scholarly journals Detection and Diagnosis of Breast Cancer Using Artificial Intelligence Based Assessment of Maximum Intensity Projection Dynamic Contrast-Enhanced Magnetic Resonance Images

Diagnostics ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 330
Author(s):  
Mio Adachi ◽  
Tomoyuki Fujioka ◽  
Mio Mori ◽  
Kazunori Kubota ◽  
Yuka Kikuchi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Magnetic Resonance ◽  
Diagnostic Performance ◽  
Maximum Intensity Projection ◽  
Breast Mri ◽  
Magnetic Resonance Images ◽  
Maximum Intensity ◽  
Validation Data ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced

We aimed to evaluate an artificial intelligence (AI) system that can detect and diagnose lesions of maximum intensity projection (MIP) in dynamic contrast-enhanced (DCE) breast magnetic resonance imaging (MRI). We retrospectively gathered MIPs of DCE breast MRI for training and validation data from 30 and 7 normal individuals, 49 and 20 benign cases, and 135 and 45 malignant cases, respectively. Breast lesions were indicated with a bounding box and labeled as benign or malignant by a radiologist, while the AI system was trained to detect and calculate possibilities of malignancy using RetinaNet. The AI system was analyzed using test sets of 13 normal, 20 benign, and 52 malignant cases. Four human readers also scored these test data with and without the assistance of the AI system for the possibility of a malignancy in each breast. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were 0.926, 0.828, and 0.925 for the AI system; 0.847, 0.841, and 0.884 for human readers without AI; and 0.889, 0.823, and 0.899 for human readers with AI using a cutoff value of 2%, respectively. The AI system showed better diagnostic performance compared to the human readers (p = 0.002), and because of the increased performance of human readers with the assistance of the AI system, the AUC of human readers was significantly higher with than without the AI system (p = 0.039). Our AI system showed a high performance ability in detecting and diagnosing lesions in MIPs of DCE breast MRI and increased the diagnostic performance of human readers.

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