227 research outputs found
A CAD system for early diagnosis of autism using different imaging modalities.
The term “autism spectrum disorder” (ASD) refers to a collection of neuro-developmental disorders that affect linguistic, behavioral, and social skills. Autism has many symptoms, most prominently, social impairment and repetitive behaviors. It is crucial to diagnose autism at an early stage for better assessment and investigation of this complex syndrome. There have been a lot of efforts to diagnose ASD using different techniques, such as imaging modalities, genetic techniques, and behavior reports. Imaging modalities have been extensively exploited for ASD diagnosis, and one of the most successful ones is Magnetic resonance imaging(MRI),where it has shown promise for the early diagnosis of the ASD related abnormalities in particular. Magnetic resonance imaging (MRI) modalities have emerged as powerful means that facilitate non-invasive clinical diagnostics of various diseases and abnormalities since their inception in the 1980s. After the advent in the nineteen eighties, MRI soon became one of the most promising non- invasive modalities for visualization and diagnostics of ASD-related abnormalities. Along with its main advantage of no exposure to radiation, high contrast, and spatial resolution, the recent advances to MRI modalities have notably increased diagnostic certainty. Multiple MRI modalities, such as different types of structural MRI (sMRI) that examines anatomical changes, and functional MRI (fMRI) that examines brain activity by monitoring blood flow changes,have been employed to investigate facets of ASD in order to better understand this complex syndrome. This work aims at developing a new computer-aided diagnostic (CAD) system for autism diagnosis using different imaging modalities. It mainly relies on making use of structural magnetic resonance images for extracting notable shape features from parts of the brainthat proved to correlate with ASD from previous neuropathological studies. Shape features from both the cerebral cortex (Cx) and cerebral white matter(CWM)are extracted. Fusion of features from these two structures is conducted based on the recent findings suggesting that Cx changes in autism are related to CWM abnormalities. Also, when fusing features from more than one structure, this would increase the robustness of the CAD system. Moreover, fMRI experiments are done and analyzed to find areas of activation in the brains of autistic and typically developing individuals that are related to a specific task. All sMRI findings are fused with those of fMRI to better understand ASD in terms of both anatomy and functionality,and thus better classify the two groups. This is one aspect of the novelty of this CAD system, where sMRI and fMRI studies are both applied on subjects from different ages to diagnose ASD. In order to build such a CAD system, three main blocks are required. First, 3D brain segmentation is applied using a novel hybrid model that combines shape, intensity, and spatial information. Second, shape features from both Cx and CWM are extracted and anf MRI reward experiment is conducted from which areas of activation that are related to the task of this experiment are identified. Those features were extracted from local areas of the brain to provide an accurate analysis of ASD and correlate it with certain anatomical areas. Third and last, fusion of all the extracted features is done using a deep-fusion classification network to perform classification and obtain the diagnosis report. Fusing features from all modalities achieved a classification accuracy of 94.7%, which emphasizes the significance of combining structures/modalities for ASD diagnosis. To conclude, this work could pave the pathway for better understanding of the autism spectrum by finding local areas that correlate to the disease. The idea of personalized medicine is emphasized in this work, where the proposed CAD system holds the promise to resolve autism endophenotypes and help clinicians deliver personalized treatment to individuals affected with this complex syndrome
A Learnable Counter-condition Analysis Framework for Functional Connectivity-based Neurological Disorder Diagnosis
To understand the biological characteristics of neurological disorders with
functional connectivity (FC), recent studies have widely utilized deep
learning-based models to identify the disease and conducted post-hoc analyses
via explainable models to discover disease-related biomarkers. Most existing
frameworks consist of three stages, namely, feature selection, feature
extraction for classification, and analysis, where each stage is implemented
separately. However, if the results at each stage lack reliability, it can
cause misdiagnosis and incorrect analysis in afterward stages. In this study,
we propose a novel unified framework that systemically integrates diagnoses
(i.e., feature selection and feature extraction) and explanations. Notably, we
devised an adaptive attention network as a feature selection approach to
identify individual-specific disease-related connections. We also propose a
functional network relational encoder that summarizes the global topological
properties of FC by learning the inter-network relations without pre-defined
edges between functional networks. Last but not least, our framework provides a
novel explanatory power for neuroscientific interpretation, also termed
counter-condition analysis. We simulated the FC that reverses the diagnostic
information (i.e., counter-condition FC): converting a normal brain to be
abnormal and vice versa. We validated the effectiveness of our framework by
using two large resting-state functional magnetic resonance imaging (fMRI)
datasets, Autism Brain Imaging Data Exchange (ABIDE) and REST-meta-MDD, and
demonstrated that our framework outperforms other competing methods for disease
identification. Furthermore, we analyzed the disease-related neurological
patterns based on counter-condition analysis
Relationship between large-scale structural and functional brain connectivity in the human lifespan
The relationship between the anatomical structure of the brain and its functional organization
is not straightforward and has not been elucidated yet, despite the growing interest this topic
has received in the last decade. In particular, a new area of research has been defined in these
years, called \u2019connectomics\u2019: this is the study of the different kinds of \u2019connections\u2019 existing
among micro- and macro-areas of the brain, from structural connectivity \u2014 described by
white matter fibre tracts physically linking cortical areas \u2014 to functional connectivity \u2014
defined as temporal correlation between electrical activity of different brain regions \u2014 to
effective connectivity\u2014defining causal relationships between functional activity of different
brain areas. Cortical areas of the brain physically linked by tracts of white matter fibres
are known to exhibit a more coherent functional synchronization than areas which are not
anatomically linked, but the absence of physical links between two areas does not imply a
similar absence of functional correspondence. Development and ageing, but also structural
modifications brought on by malformations or pathology, can modify the relation between
structure and function.
The aim of my PhD work has been to further investigate the existing relationship between
structural and functional connectivity in the human brain at different ages of the human
lifespan, in particular in healthy adults and both healthy and pathological neonates and
children. These two \u2019categories\u2019 of subjects are very different in terms of the analysis
techniques which can be applied for their study, due to the different characteristics of the data
obtainable from them: in particular, while healthy adult data can be studied with the most
advanced state-of-the-art methods, paediatric and neonatal subjects pose hard constraints to
the acquisition methods applicable, and thus to the quality of the data which can be analysed.
During this PhD I have studied this relation in healthy adult subjects by comparing structural
connectivity from DWI data with functional connectivity from stereo-EEG recordings
of epileptic patients implanted with intra-cerebral electrodes. I have then focused on the
paediatric age, and in particular on the challenges posed by the paediatric clinical environment
to the analysis of structural connectivity. In collaboration with the Neuroradiology
Unit of the Giannina Gaslini Hospital in Genova, I have adapted and tested advanced DWI analysis methods for neonatal and paediatric data, which is commonly studied with less
effective methods. We applied the same methods to the study of the effects of a specific brain
malformation on the structural connectivity in 5 paediatric patients.
While diffusion weighted imaging (DWI) is recognised as the best method to compute
structural connectivity in the human brain, the most common methods for estimating functional
connectivity data \u2014 functional MRI (fMRI) and electroencephalography (EEG) \u2014
suffer from different limitations: fMRI has good spatial resolution but low temporal resolution,
while EEG has a better temporal resolution but the localisation of each signal\u2019s
originating area is difficult and not always precise. Stereo-EEG (SEEG) combines strong
spatial and temporal resolution with a high signal-to-noise ratio and allows to identify the
source of each signal with precision, but is not used for studies on healthy subjects because
of its invasiveness.
Functional connectivity in children can be computed with either fMRI, EEG or SEEG,
as in adult subjects. On the other hand, the study of structural connectivity in the paediatric
age is met with obstacles posed by the specificity of this data, especially for the application
of the advanced DWI analysis techniques commonly used in the adult age. Moreover, the
clinical environment introduces even more constraints on the quality of the available data,
both in children and adults, further limiting the possibility of applying advanced analysis
methods for the investigation of connectivity in the paediatric age.
Our results on adult subjects showed a positive correlation between structural and functional
connectivity at different granularity levels, from global networks to community structures
to single nodes, suggesting a correspondence between structural and functional organization
which is maintained at different aggregation levels of brain units. In neonatal and
paediatric subjects, we successfully adapted and applied the same advanced DWI analysis
method used for the investigation in adults, obtaining white matter reconstructions more
precise and anatomically plausible than with methods commonly used in paediatric clinical
environments, and we were able to study the effects of a specific type of brain malformation
on structural connectivity, explaining the different physical and functional manifestation
of this malformation with respect to similar pathologies. This work further elucidates the
relationship between structural and functional connectivity in the adult subject, and poses
the basis for a corresponding work in the neonatal and paediatric subject in the clinical
environment, allowing to study structural connectivity in the healthy and pathological child
with clinical data
Investigating microstructural variation in the human hippocampus using non-negative matrix factorization
In this work we use non-negative matrix factorization to identify patterns of microstructural variance in the human hippocampus. We utilize high-resolution structural and diffusion magnetic resonance imaging data from the Human Connectome Project to query hippocampus microstructure on a multivariate, voxelwise basis. Application of non-negative matrix factorization identifies spatial components (clusters of voxels sharing similar covariance patterns), as well as subject weightings (individual variance across hippocampus microstructure). By assessing the stability of spatial components as well as the accuracy of factorization, we identified 4 distinct microstructural components. Furthermore, we quantified the benefit of using multiple microstructural metrics by demonstrating that using three microstructural metrics (T1-weighted/T2-weighted signal, mean diffusivity and fractional anisotropy) produced more stable spatial components than when assessing metrics individually. Finally, we related individual subject weightings to demographic and behavioural measures using a partial least squares analysis. Through this approach we identified interpretable relationships between hippocampus microstructure and demographic and behavioural measures. Taken together, our work suggests non-negative matrix factorization as a spatially specific analytical approach for neuroimaging studies and advocates for the use of multiple metrics for data-driven component analyses
Neonatal brain resting-state functional connectivity imaging modalities
Infancy is the most critical period in human brain development. Studies demonstrate that subtle brain abnormalities during this state of life may greatly affect the developmental processes of the newborn infants. One of the rapidly developing methods for early characterization of abnormal brain development is functional connectivity of the brain at rest. While the majority of resting-state studies have been conducted using magnetic resonance imaging (MRI), there is clear evidence that resting-state functional connectivity (rs-FC) can also be evaluated using other imaging modalities. The aim of this review is to compare the advantages and limitations of different modalities used for the mapping of infants’ brain functional connectivity at rest. In addition, we introduce photoacoustic tomography, a novel functional neuroimaging modality, as a complementary modality for functional mapping of infants’ brain
Characterising population variability in brain structure through models of whole-brain structural connectivity
Models of whole-brain connectivity are valuable for understanding neurological function. This thesis
seeks to develop an optimal framework for extracting models of whole-brain connectivity from clinically
acquired diffusion data. We propose new approaches for studying these models. The aim is to
develop techniques which can take models of brain connectivity and use them to identify biomarkers
or phenotypes of disease.
The models of connectivity are extracted using a standard probabilistic tractography algorithm, modified
to assess the structural integrity of tracts, through estimates of white matter anisotropy. Connections
are traced between 77 regions of interest, automatically extracted by label propagation from
multiple brain atlases followed by classifier fusion. The estimates of tissue integrity for each tract
are input as indices in 77x77 ”connectivity” matrices, extracted for large populations of clinical data.
These are compared in subsequent studies.
To date, most whole-brain connectivity studies have characterised population differences using graph
theory techniques. However these can be limited in their ability to pinpoint the locations of differences
in the underlying neural anatomy. Therefore, this thesis proposes new techniques. These include
a spectral clustering approach for comparing population differences in the clustering properties of
weighted brain networks. In addition, machine learning approaches are suggested for the first time.
These are particularly advantageous as they allow classification of subjects and extraction of features
which best represent the differences between groups.
One limitation of the proposed approach is that errors propagate from segmentation and registration
steps prior to tractography. This can cumulate in the assignment of false positive connections, where
the contribution of these factors may vary across populations, causing the appearance of population
differences where there are none. The final contribution of this thesis is therefore to develop a common
co-ordinate space approach. This combines probabilistic models of voxel-wise diffusion for each subject
into a single probabilistic model of diffusion for the population. This allows tractography to be
performed only once, ensuring that there is one model of connectivity. Cross-subject differences can
then be identified by mapping individual subjects’ anisotropy data to this model. The approach is
used to compare populations separated by age and gender
Fast and robust hybrid framework for infant brain classification from structural MRI : a case study for early diagnosis of autism.
The ultimate goal of this work is to develop a computer-aided diagnosis (CAD) system for early autism diagnosis from infant structural magnetic resonance imaging (MRI). The vital step to achieve this goal is to get accurate segmentation of the different brain structures: whitematter, graymatter, and cerebrospinal fluid, which will be the main focus of this thesis. The proposed brain classification approach consists of two major steps. First, the brain is extracted based on the integration of a stochastic model that serves to learn the visual appearance of the brain texture, and a geometric model that preserves the brain geometry during the extraction process. Secondly, the brain tissues are segmented based on shape priors, built using a subset of co-aligned training images, that is adapted during the segmentation process using first- and second-order visual appearance features of infant MRIs. The accuracy of the presented segmentation approach has been tested on 300 infant subjects and evaluated blindly on 15 adult subjects. The experimental results have been evaluated by the MICCAI MR Brain Image Segmentation (MRBrainS13) challenge organizers using three metrics: Dice coefficient, 95-percentile Hausdorff distance, and absolute volume difference. The proposed method has been ranked the first in terms of performance and speed
SEARCHING NEUROIMAGING BIOMARKERS IN MENTAL DISORDERS WITH GRAPH AND MULTIMODAL FUSION ANALYSIS OF FUNCTIONAL CONNECTIVITY
Mental disorders such as schizophrenia (SZ), bipolar (BD), and major depression disorders (MDD) can cause severe symptoms and life disruption. They share some symptoms, which can pose a major clinical challenge to their differentiation. Objective biomarkers based on neuroimaging may help to improve diagnostic accuracy and facilitate optimal treatment for patients. Over the last decades, non-invasive in-vivo neuroimaging techniques such as magnetic resonance imaging (MRI) have been increasingly applied to measure structure and function in human brains. With functional MRI (fMRI) or structural MRI (sMRI), studies have identified neurophysiological deficits in patients’ brain from different perspective. Functional connectivity (FC) analysis is an approach that measures functional integration in brains. By assessing the temporal coherence of the hemodynamic activity among brain regions, FC is considered capable of characterizing the large-scale integrity of neural activity.
In this work, we present two data analysis frameworks for biomarker detection on brain imaging with FC, 1) graph analysis of FC and 2) multimodal fusion analysis, to better understand the human brain. Graph analysis reveals the interaction among brain regions based on graph theory, while the multimodal fusion framework enables us to utilize the strength of different imaging modalities through joint analysis. Four applications related to FC using these frameworks were developed. First, FC was estimated using a model-based approach, and revealed altered the small-world network structure in SZ. Secondly, we applied graph analysis on functional network connectivity (FNC) to differentiate BD and MDD during resting-state. Thirdly, two functional measures, FNC and fractional amplitude of low frequency fluctuations (fALFF), were spatially overlaid to compare the FC and spatial alterations in SZ. And finally, we utilized a multimodal fusion analysis framework, multi-set canonical correlation analysis + joint independent component analysis (mCCA+jICA) to link functional and structural abnormalities in BD and MDD. We also evaluated the accuracy of predictive diagnosis through classifiers generated on the selected features. In summary, via the two frameworks, our work has made several contributions to advance FC analysis, which improves our understanding of underlying brain function and structure, and our findings may be ultimately useful for the development of biomarkers of mental disease
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