Diffusion tensor imaging and resting state functional connectivity as advanced imaging biomarkers of outcome in infants with hypoxic-ischaemic encephalopathy treated with hypothermia

Therapeutic hypothermia confers significant benefit in term neonates with hypoxic-ischaemic encephalopathy (HIE). However, despite the treatment nearly half of the infants develop an unfavourable outcome. Intensive bench-based and early phase clinical research is focused on identifying treatments that augment hypothermic neuroprotection. Qualified biomarkers are required to test these promising therapies efficiently. This thesis aims to assess advanced magnetic resonance imaging (MRI) techniques, including diffusion tensor imaging (DTI) and resting state functional MRI (fMRI) as imaging biomarkers of outcome in infants with HIE who underwent hypothermic neuroprotection. FA values in the white matter (WM), obtained in the neonatal period and assessed by tract-based spatial statistics (TBSS), correlated with subsequent developmental quotient (DQ). However, TBSS is not suitable to study grey matter (GM), which is the primary site of injury following an acute hypoxic-ischaemic event. Therefore, a neonatal atlas-based automated tissue labelling approach was applied to segment central and cortical grey and whole brain WM. Mean diffusivity (MD) in GM structures, obtained in the neonatal period correlated with subsequent DQ. Although the central GM is the primary site of injury on conventional MRI following HIE; FA within WM tissue labels also correlated to neurodevelopmental performance scores. As DTI does not provide information on functional consequences of brain injury functional sequel of HIE was studied with resting state fMRI. Diminished functional connectivity was demonstrated in infants who suffered HIE, which associated with an unfavourable outcome. The results of this thesis suggest that MD in GM tissue labels and FA either determined within WM tissue labels or analysed with TBSS correlate to subsequent neurodevelopmental performance scores in infants who suffered HIE treated with hypothermia and may be applied as imaging biomarkers of outcome in this population. Although functional connectivity was diminished in infants with HIE, resting state fMRI needs further study to assess its utility as an imaging biomarker following a hypoxic-ischaemic brain injury.Open Acces

Interacting Turing-Hopf Instabilities Drive Symmetry-Breaking Transitions in a Mean-Field Model of the Cortex: A Mechanism for the Slow Oscillation

Electrical recordings of brain activity during the transition from wake to anesthetic coma show temporal and spectral alterations that are correlated with gross changes in the underlying brain state. Entry into anesthetic unconsciousness is signposted by the emergence of large, slow oscillations of electrical activity (≲1  Hz) similar to the slow waves observed in natural sleep. Here we present a two-dimensional mean-field model of the cortex in which slow spatiotemporal oscillations arise spontaneously through a Turing (spatial) symmetry-breaking bifurcation that is modulated by a Hopf (temporal) instability. In our model, populations of neurons are densely interlinked by chemical synapses, and by interneuronal gap junctions represented as an inhibitory diffusive coupling. To demonstrate cortical behavior over a wide range of distinct brain states, we explore model dynamics in the vicinity of a general-anesthetic-induced transition from “wake” to “coma.” In this region, the system is poised at a codimension-2 point where competing Turing and Hopf instabilities coexist. We model anesthesia as a moderate reduction in inhibitory diffusion, paired with an increase in inhibitory postsynaptic response, producing a coma state that is characterized by emergent low-frequency oscillations whose dynamics is chaotic in time and space. The effect of long-range axonal white-matter connectivity is probed with the inclusion of a single idealized point-to-point connection. We find that the additional excitation from the long-range connection can provoke seizurelike bursts of cortical activity when inhibitory diffusion is weak, but has little impact on an active cortex. Our proposed dynamic mechanism for the origin of anesthetic slow waves complements—and contrasts with—conventional explanations that require cyclic modulation of ion-channel conductances. We postulate that a similar bifurcation mechanism might underpin the slow waves of natural sleep and comment on the possible consequences of chaotic dynamics for memory processing and learning

Fractals in the Nervous System: conceptual Implications for Theoretical Neuroscience

This essay is presented with two principal objectives in mind: first, to document the prevalence of fractals at all levels of the nervous system, giving credence to the notion of their functional relevance; and second, to draw attention to the as yet still unresolved issues of the detailed relationships among power law scaling, self-similarity, and self-organized criticality. As regards criticality, I will document that it has become a pivotal reference point in Neurodynamics. Furthermore, I will emphasize the not yet fully appreciated significance of allometric control processes. For dynamic fractals, I will assemble reasons for attributing to them the capacity to adapt task execution to contextual changes across a range of scales. The final Section consists of general reflections on the implications of the reviewed data, and identifies what appear to be issues of fundamental importance for future research in the rapidly evolving topic of this review

Multimodal functional and structural brain connectivity analysis in autism: A preliminary integrated approach with EEG, fMRI and DTI

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record.This paper proposes a novel approach of integrating different neuroimaging techniques to characterize an autistic brain. Different techniques like EEG, fMRI and DTI have traditionally been used to find biomarkers for autism, but there have been very few attempts for a combined or multimodal approach of EEG, fMRI and DTI to understand the neurobiological basis of autism spectrum disorder (ASD). Here, we explore how the structural brain network correlate with the functional brain network, such that the information encompassed by these two could be uncovered only by using the latter. In this paper, source localization from EEG using independent component analysis (ICA) and dipole fitting has been applied first, followed by selecting those dipoles that are closest to the active regions identified with fMRI. This allows translating the high temporal resolution of EEG to estimate time varying connectivity at the spatial source level. Our analysis shows that the estimated functional connectivity between two active regions can be correlated with the physical properties of the structure obtained from DTI analysis. This constitutes a first step towards opening the possibility of using pervasive EEG to monitor the long-term impact of ASD treatment without the need for frequent expensive fMRI or DTI investigations

Changes in Electroencephalogram Coherence in Adolescents with Autism Spectrum Disorder after a Social Skills Intervention

Autism spectrum disorder (ASD) is a developmental disorder that affects social communication and behavior. There is a consensus that neurological differences present in individuals with ASD. Further, theories emphasize the mixture of hypo- and hyper-connectivity as a neuropathology in ASD (O’Reilly, Lewis, & Elsabbagh, 2017), however, there is a paucity of studies specifically testing neurological underpinnings as predictors of success on social skills interventions. This study examined functional neural connectivity (electroencephalogram, EEG, coherence) of adolescents with ASD before and after the Program for the Education and Enrichment of Relational Skills (PEERS®) intervention. Two groups were utilized in this randomized controlled trial (RCT): an Experimental ASD Group (EXP ASD; n = 74) and a Waitlist Control ASD Group (WL ASD; n = 74). The study had 2 purposes. Aim 1 was to determine whether changes in EEG coherence differed in adolescents with ASD receiving PEERS® compared to a waitlist control group of ASD adolescents that did not receive the intervention. Results revealed a statistically significant difference between groups in EEG coherence in the occipital left to temporal left pair; indicating an increase of connectivity between the occipital left and temporal left regions after intervention. Aim 2 was to determine if changes in EEG coherence related to changes in behavior, friendships, and social skills via the Social Skills Improvement System (SSIS: Gresham, 2009), Social Responsiveness Scale (SRS: Constantino, 2005), Quality of Socialization Questionnaire-Adolescent (QSQ-A: Laugeson, 2010), and Test of Adolescent Social Skills (TASSK: Laugeson, 2010). Results indicated a positive change in frontal right to parietal right coherence was linked to an increase in SSIS Social Skills scores at post-test. Positive changes in occipital right to temporal right coherence and occipital left to parietal left coherence were linked to an increase in the total number of get-togethers via the QSQ-A. Results of this study have implications for the importance of assessing response to treatment in ASD using neurobehavioral domains

Studying brain connectivity: a new multimodal approach for structure and function integration \u200b

Il cervello \ue8 un sistema che integra organizzazioni anatomiche e funzionali. Negli ultimi dieci anni, la comunit\ue0 neuroscientifica si \ue8 posta la domanda sulla relazione struttura-funzione. Essa pu\uf2 essere esplorata attraverso lo studio della connettivit\ue0. Nello specifico, la connettivit\ue0 strutturale pu\uf2 essere definita dal segnale di risonanza magnetica pesato in diffusione seguito dalla computazione della trattografia; mentre la correlazione funzionale del cervello pu\uf2 essere calcolata a partire da diversi segnali, come la risonanza magnetica funzionale o l\u2019elettro-/magneto-encefalografia, che consente la cattura del segnale di attivazione cerebrale a una risoluzione temporale pi\uf9 elevata. Recentemente, la relazione struttura-funzione \ue8 stata esplorata utilizzando strumenti di elaborazione del segnale sui grafi, che estendono e generalizzano le operazioni di elaborazione del segnale ai grafi. In specifico, alcuni studi utilizzano la trasformata di Fourier applicata alla connettivit\ue0 strutturale per misurare la decomposizione del segnale funzionale in porzioni che si allineano (\u201caligned\u201d) e non si allineano (\u201cliberal\u201d) con la sottostante rete di materia bianca. Il relativo allineamento funzionale con l\u2019anatomia \ue8 stato associato alla flessibilit\ue0 cognitiva, sottolineando forti allineamenti di attivit\ue0 corticali, e suggerendo che i sistemi sottocorticali contengono pi\uf9 segnali liberi rispetto alla corteccia. Queste relazioni multimodali non sono, per\uf2, ancora chiare per segnali con elevata risoluzione temporale, oltre ad essere ristretti a specifiche zone cerebrali. Oltretutto, al giorno d'oggi la ricostruzione della trattografia \ue8 ancora un argomento impegnativo, soprattutto se utilizzata per l'estrazione della connettivit\ue0 strutturale. Nel corso dell'ultimo decennio si \ue8 vista una proliferazione di nuovi modelli per ricostruire la trattografia, ma il loro conseguente effetto sullo strumento di connettivit\ue0 non \ue8 ancora chiaro. In questa tesi, ho districato i dubbi sulla variabilit\ue0 dei trattogrammi derivati da diversi metodi di trattografia, confrontandoli con un paradigma di test-retest, che consente di definire la specificit\ue0 e la sensibilit\ue0 di ciascun modello. Ho cercato di trovare un compromesso tra queste, per definire un miglior metodo trattografico. Inoltre, ho affrontato il problema dei grafi pesati confrontando alcune possibili stime, evidenziando la sufficienza della connettivit\ue0 binaria e la potenza delle propriet\ue0 microstrutturali di nuova generazione nelle applicazioni cliniche. Qui, ho sviluppato un modello di proiezione che consente l'uso dei filtri aligned e liberal per i segnali di encefalografia. Il modello estende i vincoli strutturali per considerare le connessioni indirette, che recentemente si sono dimostrate utili nella relazione struttura-funzione. I risultati preliminari del nuovo modello indicano un\u2019implicazione dinamica di momenti pi\uf9 aligned e momenti pi\uf9 liberal, evidenziando le fluttuazioni presenti nello stato di riposo. Inoltre, viene presentata una relazione specifica di periodi pi\uf9 allineati e liberali per il paradigma motorio. Questo modello apre la prospettiva alla definizione di nuovi biomarcatori. Considerando che l\u2019encefalografia \ue8 spesso usata nelle applicazioni cliniche, questa integrazione multimodale applicata su dati di Parkinson o di ictus potrebbe combinare le informazioni dei cambiamenti strutturali e funzionali nelle connessioni cerebrali, che al momento sono state dimostrate individualmente.The brain is a complex system of which anatomical and functional organization is both segregated and integrated. A longstanding question for the neuroscience community has been to elucidate the mutual influences between structure and function. To that aim, first, structural and functional connectivity need to be explored individually. Structural connectivity can be measured by the Diffusion Magnetic Resonance signal followed by successive computational steps up to virtual tractography. Functional connectivity can be established by correlation between the brain activity time courses measured by different modalities, such as functional Magnetic Resonance Imaging or Electro/Magneto Encephalography. Recently, the Graph Signal Processing (GSP) framework has provided a new way to jointly analyse structure and function. In particular, this framework extends and generalizes many classical signal-processing operations to graphs (e.g., spectral analysis, filtering, and so on). The graph here is built by the structural connectome; i.e., the anatomical backbone of the brain where nodes represent brain regions and edge weights strength of structural connectivity. The functional signals are considered as time-dependent graph signals; i.e., measures associated to the nodes of the graph. The concept of the Graph Fourier Transform then allows decomposing regional functional signals into, on one side, a portion that strongly aligned with the underlying structural network (\u201caligned"), and, on the other side, a portion that is not well aligned with structure (\u201cliberal"). The proportion of aligned-vs-liberal energy in functional signals has been associated with cognitive flexibility. However, the interpretation of these multimodal relationships is still limited and unexplored for higher temporal resolution functional signals such as M/EEG. Moreover, the construction of the structural connectome itself using tractography is still a challenging topic, for which, in the last decade, many new advanced models were proposed, but their impact on the connectome remains unclear. In the first part of this thesis, I disentangled the variability of tractograms derived from different tractography methods, comparing them with a test-retest paradigm, which allows to define specificity and sensitivity of each model. I want to find the best trade-off between specificity and sensitivity to define the best model that can be deployed for analysis of functional signals. Moreover, I addressed the issue of weighing the graph comparing few estimates, highlighting the sufficiency of binary connectivity, and the power of the latest-generation microstructural properties in clinical applications. In the second part, I developed a GSP method that allows applying the aligned and liberal filters to M/EEG signals. The model extends the structural constraints to consider indirect connections, which recently demonstrated to be powerful in the structure/function link. I then show that it is possible to identify dynamic changes in aligned-vs-liberal energy, highlighting fluctuations present motor task and resting state. This model opens the perspective of novel biomarkers. Indeed, M/EEG are often used in clinical applications; e.g., multimodal integration in data from Parkinson\u2019s disease or stroke could combine changes of both structural and functional connectivity

Relationship of topology, multiscale phase synchronization, and state transitions in human brain networks

How the brain reconstitutes consciousness and cognition after a major perturbation like general anesthesia is an important question with significant neuroscientific and clinical implications. Recent empirical studies in animals and humans suggest that the recovery of consciousness after anesthesia is not random but ordered. Emergence patterns have been classified as progressive and abrupt transitions from anesthesia to consciousness, with associated differences in duration and electroencephalogram(EEG) properties. We hypothesized that the progressive and abrupt emergence patterns from the unconscious state are associated with, respectively, continuous and discontinuous synchronization transitions in functional brain networks. The discontinuous transition is explainable with the concept of explosive synchronization, which has been studied almost exclusively in network science. We used the Kuramato model, a simple oscillatory network model, to simulate progressive and abrupt transitions in anatomical human brain networks acquired from diffusion tensor imaging (DTI) of 82 brain regions. To facilitate explosive synchronization, distinct frequencies for hub nodes with a large frequency disassortativity (i.e., higher frequency nodes linking with lower frequency nodes, or vice versa) were applied to the brain network. In this simulation study, we demonstrated that both progressive and abrupt transitions follow distinct synchronization processes at the individual node, cluster, and global network levels. The characteristic synchronization patterns of brain regions that are ��progressive and earlier�� or ��abrupt but delayed�� account for previously reported behavioral responses of gradual and abrupt emergence from the unconscious state. The characteristic network synchronization processes observed at different scales provide new insights into how regional brain functions are reconstituted during progressive and abrupt emergence from the unconscious state. This theoretical approach also offers a principled explanation of how the brain reconstitutes consciousness and cognitive functions after physiologic (sleep), pharmacologic (anesthesia), and pathologic (coma) perturbations. ? 2017 Kim, Kim, Mashour and Lee.115sciescopu