Approaches For Capturing Time-Varying Functional Network Connectivity With Application to Normative Development and Mental Illness

Since the beginning of medical science, the human brain has remained an unsolved puzzle; an illusive organ that controls everything- from breathing to heartbeats, from emotion to anger, and more. With the power of advanced neuroimaging techniques, scientists have now started to solve this nearly impossible puzzle, piece by piece. Over the past decade, various in vivo techniques, including functional magnetic resonance imaging (fMRI), have been increasingly used to understand brain functions. fMRI is extensively being used to facilitate the identification of various neuropsychological disorders such as schizophrenia (SZ), bipolar disorder (BP) and autism spectrum disorder (ASD). These disorders are currently diagnosed based on patients’ self-reported experiences, and observed symptoms and behaviors over the course of the illnesses. Therefore, efficient identification of biological-based markers (biomarkers) can lead to early diagnosis of these mental disorders, and provide a trajectory for disease progression. By applying advanced machine learning techniques on fMRI data, significant differences in brain function among patients with mental disorders and healthy controls can be identified. Moreover, by jointly estimating information from multiple modalities, such as, functional brain data and genetic factors, we can now investigate the relationship between brain function and genes. Functional connectivity (FC) has become a very common measure to characterize brain functions, where FC is defined as the temporal covariance of neural signals between multiple spatially distinct brain regions. Recently, researchers are studying the FC among functionally specialized brain networks which can be defined as a higher level of FC, and is termed as functional network connectivity (FNC, defined as the correlation value that summarizes the overall connection between brain ‘networks’ over time). Most functional connectivity studies have made the limiting assumption that connectivity is stationary over multiple minutes, and ignore to identify the time-varying and reoccurring patterns of FNC among brain regions (known as time-varying FNC). In this dissertation, we demonstrate the use of time-varying FNC features as potential biomarkers to differentiate between patients with mental disorders and healthy subjects. The developmental characteristics of time-varying FNC in children with typically developing brain and ASD have been extensively studies in a cross-sectional framework, and age-, sex- and disease-related FNC profiles have been proposed. Also, time-varying FNC is characterized in healthy adults and patients with severe mental disorders (SZ and BP). Moreover, an efficient classification algorithm is designed to identify patients and controls at individual level. Finally, a new framework is proposed to jointly utilize information from brain’s functional network connectivity and genetic features to find the associations between them. The frameworks that we presented here can help us understand the important role played by time-varying FNC to identify potential biomarkers for the diagnosis of severe mental disorders

Autism as a disorder of neural information processing: directions for research and targets for therapy

The broad variation in phenotypes and severities within autism spectrum disorders suggests the involvement of multiple predisposing factors, interacting in complex ways with normal developmental courses and gradients. Identification of these factors, and the common developmental path into which theyfeed, is hampered bythe large degrees of convergence from causal factors to altered brain development, and divergence from abnormal brain development into altered cognition and behaviour. Genetic, neurochemical, neuroimaging and behavioural findings on autism, as well as studies of normal development and of genetic syndromes that share symptoms with autism, offer hypotheses as to the nature of causal factors and their possible effects on the structure and dynamics of neural systems. Such alterations in neural properties may in turn perturb activity-dependent development, giving rise to a complex behavioural syndrome many steps removed from the root causes. Animal models based on genetic, neurochemical, neurophysiological, and behavioural manipulations offer the possibility of exploring these developmental processes in detail, as do human studies addressing endophenotypes beyond the diagnosis itself

Informatics for EEG biomarker discovery in clinical neuroscience

Neurological and developmental disorders (NDDs) impose an enormous burden of disease on children throughout the world. Two of the most common are autism spectrum disorder (ASD) and epilepsy. ASD has recently been estimated to affect 1 in 68 children, making it the most common neurodevelopmental disorder in children. Epilepsy is also a spectrum disorder that follows a developmental trajectory, with an estimated prevalence of 1%, nearly as common as autism. ASD and epilepsy co-occur in approximately 30% of individuals with a primary diagnosis of either disorder. Although considered to be different disorders, the relatively high comorbidity suggests the possibility of common neuropathological mechanisms. Early interventions for NDDs lead to better long-term outcomes. But early intervention is predicated on early detection. Behavioral measures have thus far proven ineffective in detecting autism before about 18 months of age, in part because the behavioral repertoire of infants is so limited. Similarly, no methods for detecting emerging epilepsy before seizures begin are currently known. Because atypical brain development is likely to precede overt behavioral manifestations by months or even years, a critical developmental window for early intervention may be opened by the discovery of brain based biomarkers. Analysis of brain activity with EEG may be under-utilized for clinical applications, especially for neurodevelopment. The hypothesis investigated in this dissertation is that new methods of nonlinear signal analysis, together with methods from biomedical informatics, can extract information from EEG data that enables detection of atypical neurodevelopment. This is tested using data collected at Boston Children’s Hospital. Several results are presented. First, infants with a family history of ASD were found to have EEG features that may enable autism to be detected as early as 9 months. Second, significant EEG-based differences were found between children with absence epilepsy, ASD and control groups using short 30-second EEG segments. Comparison of control groups using different EEG equipment supported the claim that EEG features could be computed that were independent of equipment and lab conditions. Finally, the potential for this technology to help meet the clinical need for neurodevelopmental screening and monitoring in low-income regions of the world is discussed

The prodrome of autism: early behavioral and biological signs, regression, peri- and post-natal development and genetics

Autism is one of the most heritable neurodevelopmental conditions and has an early onset, with symptoms being required to be present in the first 3 years of life in order to meet criteria for the ‘core’ disorder in the classification systems. As such, the focus on identifying a prodrome over the past 20 years has been on pre-clinical signs or indicators that will be present very early in life, certainly in infancy. A number of novel lines of investigation have been used to this end, including retrospective coding of home videos, prospective population screening and ‘high risk’ sibling studies; as well as the investigation of pre- and peri-natal, brain developmental and other biological factors. Whilst no single prodromal sign is expected to be present in all cases, a picture is emerging of indicative prodromal signs in infancy and initial studies are being undertaken to attempt to ameliorate the early presentation and even ‘prevent’ emergence of the full syndrome

Neurobiological abnormalities in the first few years of life in individuals later diagnosed with Autistic Spectrum Disorder : A review of recent data

Peer reviewedPublisher PD

Autism: a world changing too fast for a mis-wired brain ?

International audienceDisorders in verbal and emotional communication and imitation, social reciprocity and higher order cognition observed in individuals with autism spectrum disorders (ASD) are presented here as phenotypic expressions of temporo-spatial processing disorders (TSPDs). TSPDs include various degrees of disability in (i) processing multi-sensory dynamic stimuli online, (ii) associating them into meaningful and coherent patterns and (iii) producing real-time sensory-motor adjustments and motor outputs. In line with this theory, we found that slowing down the speed opf facial and vocal events enhanced imitative, verbal and cognitive abilities in some ASD children, particularly those with low functioning autism. We then argue that TSPDs may result from Multi-system Brain Disconnectivity-Dissynchrony (MBD), defined as an increase or decrease in functional connectivity and neuronal synchronization within/between multiple neurofunctional territories and pathways. Recent functional magnetic resonance imaging (fMRI) and electrophysiological studies supporting MBD are outlined. Finally, we review the suspected underlying neurobiological mechanisms of MBD as evidenced in neuroimaging, genetic, environmental and epigenetic studies. Overall, our TSPD/MBD approach to ASD may open new promising avenues for a better understanding of neuro-physio-psychopathology of ASD and clinical rehabilitation of people affected by these syndromes

Neural Plasticity in Response to Intervention in Adolescents with Autism Spectrum Disorders

Current theories of Autism Spectrum Disorders (ASD) suggest that they may develop from the transactional interaction between biological risk factors and environmental processes (Dawson et al., 2009). Due to the brain’s experience-expectant nature, one’s degree of social exposure may have a significant impact on their brain development and behavioral presentation. In addition to the primary critical neurodevelopmental period identified in early childhood, recent research has demonstrated a second period of substantial neurodevelopment during the adolescent period (Sisk & Foster, 2004). This study investigated the neural and behavioral impact of participation in an empirically validated behavioral intervention (The Program for the Education and Enrichment of Relational Skills; Laugeson & Frankel, 2010) during the adolescent years among individuals with ASD. Prior to intervention adolescents with ASD (n=21) differed from their neurotypical peers (n=24) with regard to amount of EEG spectral power across brain locations within the theta and beta frequency bands but not the delta, alpha or gamma frequency bands. Participation in the intervention resulted in increased EEG power in both frequency bands to a degree rendering adolescents with ASD statistically indiscernible from their typically developing peers. Waitlist control subjects (n=22) continued to differ statistically from their neurotypical peers at follow-up assessment. Behavioral change also was observed in response to the intervention, namely increased social exposure and social skills knowledge. No direct correlations could be drawn, however, between neural and behavioral outcomes, suggesting the presence of mediating factors not examined here. A secondary aim of the study was to examine new EEG methodology. Standard continuous EEG procedures complete data collection with subjects in a resting state with no stimuli present. A novel condition involving video and audio presentation of a neurotypical peer providing autobiographical information normally shared in social settings was examined here. No differences were noted between subjects with and without ASD during the novel condition that were not observed in the resting state condition. Taken together, results suggest continued use of standard EEG procedures in the assessment of neurodevelopment in ASD. They also point to adolescence as a crucial period of neural and behavioral development sensitive to behavioral intervention