9 research outputs found
Interpreting Age Effects of Human Fetal Brain from Spontaneous fMRI using Deep 3D Convolutional Neural Networks
Understanding human fetal neurodevelopment is of great clinical importance as
abnormal development is linked to adverse neuropsychiatric outcomes after
birth. Recent advances in functional Magnetic Resonance Imaging (fMRI) have
provided new insight into development of the human brain before birth, but
these studies have predominately focused on brain functional connectivity (i.e.
Fisher z-score), which requires manual processing steps for feature extraction
from fMRI images. Deep learning approaches (i.e., Convolutional Neural
Networks) have achieved remarkable success on learning directly from image
data, yet have not been applied on fetal fMRI for understanding fetal
neurodevelopment. Here, we bridge this gap by applying a novel application of
deep 3D CNN to fetal blood oxygen-level dependence (BOLD) resting-state fMRI
data. Specifically, we test a supervised CNN framework as a data-driven
approach to isolate variation in fMRI signals that relate to younger v.s. older
fetal age groups. Based on the learned CNN, we further perform sensitivity
analysis to identify brain regions in which changes in BOLD signal are strongly
associated with fetal brain age. The findings demonstrate that deep CNNs are a
promising approach for identifying spontaneous functional patterns in fetal
brain activity that discriminate age groups. Further, we discovered that
regions that most strongly differentiate groups are largely bilateral, share
similar distribution in older and younger age groups, and are areas of
heightened metabolic activity in early human development.Comment: 9 page
FMRI correlates in autism spectrum disorder populations: evidence for intolerance of uncertainty
Recent estimates of prevalence of Autism Spectrum Disorders (ASD) in the United States exceeds 1.4%. Identifying neural correlates can provide important insight to help refine diagnosis, treatment, and understanding of ASD. A review of fMRI studies revealed activity and connectivity differences among brains of individuals with ASD compared to those without. Certain regions appear to activate differently based on task. In facial processing, hyperactivity of the prefrontal cortex, anterior cingulate cortex, and insula is seen compared to controls, however the prefrontal cortex of individuals with ASD demonstrates hypoactivity in language processing and inhibition tasks. Studies on functional connectivity revealed both hypoconnectivity and hyperconnectivity of several brain regions.
Intolerance of uncertainty (IU) describes a disposition toward incapacity for enduring that which is unknown or unpredictable. IU has been tied to restricted and repetitive behaviors seen in ASD. A review of fMRI studies on neural correlates of IU revealed hyperactivity of the insula with hypoactivity of the anterior cingulate cortex and prefrontal cortex.
Through independently reviewing fMRI correlates of ASD and IU, it is revealed that the two share some patterns of altered activity and connectivity. It is thus proposed that IU can be an important conceptual framework for understanding ASD
Automatic Autism Spectrum Disorder Detection Using Artificial Intelligence Methods with MRI Neuroimaging: A Review
Autism spectrum disorder (ASD) is a brain condition characterized by diverse
signs and symptoms that appear in early childhood. ASD is also associated with
communication deficits and repetitive behavior in affected individuals. Various
ASD detection methods have been developed, including neuroimaging modalities
and psychological tests. Among these methods, magnetic resonance imaging (MRI)
imaging modalities are of paramount importance to physicians. Clinicians rely
on MRI modalities to diagnose ASD accurately. The MRI modalities are
non-invasive methods that include functional (fMRI) and structural (sMRI)
neuroimaging methods. However, the process of diagnosing ASD with fMRI and sMRI
for specialists is often laborious and time-consuming; therefore, several
computer-aided design systems (CADS) based on artificial intelligence (AI) have
been developed to assist the specialist physicians. Conventional machine
learning (ML) and deep learning (DL) are the most popular schemes of AI used
for diagnosing ASD. This study aims to review the automated detection of ASD
using AI. We review several CADS that have been developed using ML techniques
for the automated diagnosis of ASD using MRI modalities. There has been very
limited work on the use of DL techniques to develop automated diagnostic models
for ASD. A summary of the studies developed using DL is provided in the
appendix. Then, the challenges encountered during the automated diagnosis of
ASD using MRI and AI techniques are described in detail. Additionally, a
graphical comparison of studies using ML and DL to diagnose ASD automatically
is discussed. We conclude by suggesting future approaches to detecting ASDs
using AI techniques and MRI neuroimaging
Going Deep in Medical Image Analysis: Concepts, Methods, Challenges and Future Directions
Medical Image Analysis is currently experiencing a paradigm shift due to Deep
Learning. This technology has recently attracted so much interest of the
Medical Imaging community that it led to a specialized conference in `Medical
Imaging with Deep Learning' in the year 2018. This article surveys the recent
developments in this direction, and provides a critical review of the related
major aspects. We organize the reviewed literature according to the underlying
Pattern Recognition tasks, and further sub-categorize it following a taxonomy
based on human anatomy. This article does not assume prior knowledge of Deep
Learning and makes a significant contribution in explaining the core Deep
Learning concepts to the non-experts in the Medical community. Unique to this
study is the Computer Vision/Machine Learning perspective taken on the advances
of Deep Learning in Medical Imaging. This enables us to single out `lack of
appropriately annotated large-scale datasets' as the core challenge (among
other challenges) in this research direction. We draw on the insights from the
sister research fields of Computer Vision, Pattern Recognition and Machine
Learning etc.; where the techniques of dealing with such challenges have
already matured, to provide promising directions for the Medical Imaging
community to fully harness Deep Learning in the future