Metastable States of Multiscale Brain Networks Are Keys to Crack the Timing Problem

The dynamics of the environment where we live in and the interaction with it, predicting events, provided strong evolutionary pressures for the brain functioning to process temporal information and generate timed responses. As a result, the human brain is able to process temporal information and generate temporal patterns. Despite the clear importance of temporal processing to cognition, learning, communication and sensory, motor and emotional processing, the basal mechanisms of how animals differentiate simple intervals or provide timed responses are still under debate. The lesson we learned from the last decade of research in neuroscience is that functional and structural brain connectivity matter. Specifically, it has been accepted that the organization of the brain in interacting segregated networks enables its function. In this paper we delineate the route to a promising approach for investigating timing mechanisms. We illustrate how novel insight into timing mechanisms can come by investigating brain functioning as a multi-layer dynamical network whose clustered dynamics is bound to report the presence of metastable states. We anticipate that metastable dynamics underlie the real-time coordination necessary for the brain's dynamic functioning associated to time perception. This new point of view will help further clarifying mechanisms of neuropsychiatric disorders

Disruption in structural–functional network repertoire and time-resolved subcortical fronto-temporoparietal connectivity in disorders of consciousness

Understanding recovery of consciousness and elucidating its underlying mechanism is believed to be crucial in the field of basic neuroscience and medicine. Ideas such as the global neuronal workspace (GNW) and the mesocircuit theory hypothesize that failure of recovery in conscious states coincide with loss of connectivity between subcortical and frontoparietal areas, a loss of the repertoire of functional networks states and metastable brain activation. We adopted a time-resolved functional connectivity framework to explore these ideas and assessed the repertoire of functional network states as a potential marker of consciousness and its potential ability to tell apart patients in the unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS). In addition, the prediction of these functional network states by underlying hidden spatial patterns in the anatomical network, that is so-called eigenmodes, was supplemented as potential markers. By analysing time-resolved functional connectivity from functional MRI data, we demonstrated a reduction of metastability and functional network repertoire in UWS compared to MCS patients. This was expressed in terms of diminished dwell times and loss of nonstationarity in the default mode network and subcortical fronto-temporoparietal network in UWS compared to MCS patients. We further demonstrated that these findings co-occurred with a loss of dynamic interplay between structural eigenmodes and emerging time-resolved functional connectivity in UWS. These results are, amongst others, in support of the GNW theory and the mesocircuit hypothesis, underpinning the role of time-resolved thalamo-cortical connections and metastability in the recovery of consciousness

노화에서 뇌기능신경망으로부터 추출한 코어복셀 위계구조의 역동적 변화

학위논문(박사) -- 서울대학교대학원 : 융합과학기술대학원 분자의학 및 바이오제약학과, 2023. 2. 이동수.Many methods were developed to assess the functional intervoxel connectivity to discover and better understand the brain function in individuals. In this study, k-core percolation method was assessed to reveal the dynamic hierarchical structure on resting-state fMRI (rsfMRI) on voxel-level and the feasibility of the method was evaluated by applying it in the aging process. Total 70 individuals were included in this study, 32 individuals from Alzheimers Disease Neuroimaging Initiative (ADNI) and 38 individuals from Seoul National University (SNU). K-core percolation method, a voxel-based approach was applied to reveal the hierarchical structure of voxels in the brain. kmax-core and coreness k values derived from k-core percolation characterizing the time-varying core voxels were visualized on various plots to visualize the dynamic hierarchical structure more intuitively. Independent component analysis (ICA) was carried out to label the associated functional independent components (IC) of the identified voxel. Analysis was done in both static and dynamic studies, and in positive and negative correlations. Coreness k value map overlaid on brain T1 MRI was generated for further evaluation of the distribution of coreness k values. Dynamic hierarchical structure of voxels visualized on various plots revealed time-varying change of kmax-core voxels and coreness k values, reflecting the dynamic change of brain function in an individual, which was not fully reflected on static functional connectivity. Dynamic flow pattern was different in positive and negative correlations, portraying the dynamic brain function in different neuronal networks. Coreness k value map revealed altered distribution of coreness k values in the brain. Asymmetric, unsynchronized distribution was deteriorated in the aging process. This asymmetry detected on dynamic coreness k map was assessed quantitatively by measuring asymmetry index, which revealed distinctive difference between young and aged healthy control group. The difference was more evident on dynamic study than static study. Also, as the age increased, coreness k values from static and dynamic studies decreased in all IC regions, which represents decreased connectivity in aging. Investigation of dynamic functional connectivity with k-core percolation on voxel-level revealed dynamic hierarchical structure of voxels, reflecting the time-varying brain function in individuals. Dynamic functional connectivity is more appropriate to investigate ones brain function, since it contains the time-varying information which is not well reflected on static functional connectivity. With this method, characteristics of dynamic hierarchical structure of an individual can be discovered and have shown possibility of further clinical application.그동안 뇌 기능을 보다 더 잘 이해하고 분석하기 위해 다양한 분석 방법들이 개발되었다. 이 연구에서는 K코어추출법이라는 방식을 적용하여 휴지기 뇌기능자기공명영상에서 역동적 위계 구조를 복셀 단위에서 밝혀내고자 하였다. 그리고 이 분석방법이 임상 분야에서도 적용 가능한지 알아보고자 노화 과정에 적용하여 분석해 보았다. 총 70명을 대상으로 연구가 진행되었는데, 인지 기능이 정상인 대상들을 ADNI에서 32명 그리고 서울대학교에서 38명의 데이터를 받아 분석을 진행하였다. K코어추출법이라는 방법은 복셀 단위로 분석하는 방법으로, 뇌에서 복셀의 위계 구조를 밝혀내기 위해 사용하였다. K코어추출법으로 구한 K최상위코어값, K코어값의 시간에 따른 변화를 히스토그램, 깃발 플롯, 뇌 그림 등의 다양한 방법으로 시각화하였다. 추출된 복셀은 독립 요소 분석법을 적용하여 기능적으로 뇌의 어느 요소에 해당하는지 확인하였다. 이러한 분석은 정적, 동적 연구에 사용되었고 또한 양성, 음성 상관 연구에도 사용되었다. K코어값은 뇌 T1 자기공명영상에 얹어서 뇌 내의 분포에 관해서도 분석을 진행하였다. 복셀의 역동적 위계 구조는 시간에 따라 변화하는 K최상위코어값과 K코어값을 동영상으로 만든 히스토그램, 깃발 플롯, 뇌 그림 등으로 시각화하였는데 각 개인의 동적인 뇌 기능 변화가 그림으로 잘 표현되었다. 이러한 동적인 기능적 연결성은 정적인 기능적 연결성에는 담기지 못한 시간에 따른 뇌 기능의 변화에 대한 정보가 담겨 있었다. 또한, 시간에 따른 역동적 변화는 양성, 음성 상관에 따라서 다르게 나타나 신경계 네트워크에 따라 그 변화도 다르게 나타남을 보였다. K코어값 분포도에서는 뇌에서 K코어값의 분포가 나이가 든 군에서 비대칭적으로 나타나는 것을 발견할 수 있었는데, 이는 노화가 진행됨에 따라 악화되는 경향이 있었다. K코어값 분포도에서 발견한 비대칭적 분포는 비대칭 척도를 이용하여 정량화하였다. 그 결과, 젊은 정상군과 나이가 든 정상군에서 차이가 있다는 것을 확인할 수 있었고 그 차이는 정적인 기능적 연결성보다는 동적인 기능적 연결성에서 더 뚜렷하게 나타나는 것을 볼 수 있었다. 또한, 나이가 들어감에 따라 모든 영역에서 정적, 동적인 K코어값 모두 감소하였는데 이는 노화가 진행되면서 뇌의 기능적 연결성이 떨어지는 것을 나타낸다고 볼 수 있다. K코어추출법으로 복셀 단위로 동적인 기능적 연결성을 분석하였을 때 뇌의 역동적인 위계 구조를 밝혀냈고 이는 시간에 따라 변화하는 각 개인의 뇌 기능을 잘 반영하였다. 정적 기능적 연결성에는 담지 못하는 시간에 따른 정보를 담는다는 점에서 개인의 뇌 기능을 연구할 때 역동적 기능적 연결성을 연구하는 것이 더 적합하다고 볼 수 있다. 이러한 K코어추출법으로 뇌 기능의 역동적인 위계 구조를 밝혀내고, 노화에 적용해서 분석한 것처럼 앞으로 임상에서 다른 분야에도 적용하여 뇌 기능을 연구하는 데에 활용할 수 있을 것으로 기대된다.I. Introduction 1 1-1. Dynamic functional brain connectivity in rsfMRI 1 1-2. k-core percolation 3 1-3. Functional connectivity in aging process 5 II. Purpose 7 III. Materials and methods 8 3-1. Data preprocessing 8 3-2. Sliding-window analysis 10 3-3. Application of k-core percolation 11 3-4. Quantitative analysis of asymmetry index 13 3-5. Voxel-based analysis of coreness k values 14 IV. Results 15 4-1. Generation of dynamic hierarchical structure 15 4-2. Visual assessment of hierarchical structure 17 4-3. Visual assessment of coreness k value map 23 4-4. Quantitative assessment with asymmetry index 31 4-5. Correlation of age and coreness k values 37 4-6. Coreness k values in gender and aging 37 4-7. Validation of coreness k values in young group 40 V. Discussion 42 VI. Conclusion 51 VII. References 52 VIII. Supplementary figures and movies 61 IX. 국문초록 76박

A synergistic workspace for human consciousness revealed by Integrated Information Decomposition.

How is the information-processing architecture of the human brain organised, and how does its organisation support consciousness? Here, we combine network science and a rigorous information-theoretic notion of synergy to delineate a synergistic global workspace, comprising gateway regions that gather synergistic information from specialised modules across the human brain. This information is then integrated within the workspace and widely distributed via broadcaster regions. Through functional MRI analysis, we show that gateway regions of the synergistic workspace correspond to the human brains default mode network, whereas broadcasters coincide with the executive control network. We find that loss of consciousness due to general anaesthesia or disorders of consciousness corresponds to diminished ability of the synergistic workspace to integrate information, which is restored upon recovery. Thus, loss of consciousness coincides with a breakdown of information integration within the synergistic workspace of the human brain. This work contributes to conceptual and empirical reconciliation between two prominent scientific theories of consciousness, the Global Neuronal Workspace and Integrated Information Theory, while also advancing our understanding of how the human brain supports consciousness through the synergistic integration of information

Dynamic functional connectivity and brain metastability during altered states of consciousness

The scientific study of human consciousness has greatly benefited from the development of non-invasive brain imaging methods. The quest to identify the neural correlates of consciousness combined psychophysical experimentation with neuroimaging tools such as functional magnetic resonance imaging (fMRI) to map the changes in neural activity associated with conscious vs. unconscious percepts. Different neuroimaging methods have also been applied to characterize spontaneous brain activity fluctuations during altered states of consciousness, and to develop quantitative metrics for the level of consciousness. Most of these studies, however, have not explored the dynamic nature of the whole-brain imaging data provided by fMRI. A series of empirical and computational studies strongly suggests that the temporal fluctuations observed in this data present a non-trivial structure, and that this structure is compatible with the exploration of a discrete repertoire of states. In this review we focus on how dynamic neuroimaging can be used to address theoretical accounts of consciousness based on the hypothesis of a dynamic core, i.e. a constantly evolving and transiently stable set of coordinated neurons that constitute an integrated and differentiated physical substrate for each conscious experience. We review work exploring the possibility that metastability in brain dynamics leads to a repertoire of dynamic core states, and discuss how it might be modified during altered states of consciousness. This discussion prompts us to review neuroimaging studies aimed to map the dynamic exploration of the repertoire of states as a function of consciousness. Complementary studies of the dynamic core hypothesis using perturbative methods are also discussed. Finally, we propose that a link between metastability in brain dynamics and the level of consciousness could pave the way towards a mechanistic understanding of altered states of consciousness using tools from dynamical systems theory and statistical physics.Fil: Cavanna, Federico Amadeo. Universidad Nacional de San Martín. Escuela de Ciencia y Tecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Gonzalez Vilas, Martina. Universidad Nacional de San Martín. Escuela de Ciencia y Tecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Palmucci, Matías Damian. Universidad Nacional de San Martín. Escuela de Ciencia y Tecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Tagliazucchi, Enzo Rodolfo. Centre de Recherche de I'Institut du Cerveau et de la Moelle Epinière; Francia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

On the phase synchronization and metastability of neural networks

Orientador: Prof. Dr. Sergio Roberto LopesDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Física. Defesa : Curitiba, 24/02/2021Inclui referências: p.93-104Resumo: Regioes cerebrais e neuronios precisam se comunicar eficientemente e coordenar as suas respectivas atividades. Para conseguir isso, dois fenomenos importantes sao a sincronizacao de fase, relevante para comunicacao neural, e a metastabilidade, relevante para atividade neural. Nessa dissertacao, estudamos ambas em uma rede de neuronios bursting acoplados quimicamente sob uma topologia aleatoria. A temperatura desses neuronios influencia seu modo de disparo, que pode ser bursting ou caotico ou periodico. O bursting caotico leva a uma transicao naomonotonica comum, enquanto o periodico leva a transicoes nao-monotonicas mais incomuns. Em todos os casos, observamos que as diferencas de fase entre neuronios mudam intermitentemente ao longo do tempo, mesmo em redes fortemente sincronizadas em fase. Chamamos esse fenomeno promiscuidade, e o medimos diretamente calculando como os tempos de burst dos neuronios flutuam entre si ao longo do tempo. Entao, agrupando neuronios de acordo com suas fases, exploramos como a promiscuidade afeta a composicao desses clusters, e obtemos detalhes aprofundados sobre a sincronizacao de fase dessa rede. Tambem calculamos duas variabilidades neurais, medindo como os tempos de disparo se dispersam ao longo do tempo ou da rede, e encontramos que os dois possuem valores similares e estao fortemente correlacionados com o grau de sincronizacao de fase da rede para acoplamento fraco. Em seguida, expandimos nosso foco para metastabilidade como vista em neurociencia, considerando promiscuidade um tipo de comportamento metastavel. Nos fazemos uma mini-revisao das diferentes definicoes do termo, e discutimos elas. Com isso, categorizamos brevemente os mecanismos dinamicos levando a metastabilidade. Finalmente, usando o conhecimento obtido no estudo de promiscuidade, investigamos novamente a rede promiscua para discutir como metastabilidade pode diferir dependendo das multiplas escalas do sistema. Palavras-chave: Metastabilidade. Sincronizacao de Fase. Redes Neurais.Abstract: Brain regions and neurons need to communicate effectively and coordinate their respective activities. To manage this, two important phenomena are phase synchronization, relevant for neural communication, and metastability, relevant for neural activity. In this dissertation, we aim to study both in a network of chemically coupled Hodgkin-Huxley-type bursting neurons under a random topology. The temperature of these neurons influences their firing mode, which can be either chaotic or periodic bursting. The firing mode in turn influences the transitions from desynchronization to phase synchronization when neurons are coupled in networks. Chaotic bursting leads to a common monotonic transition, while periodic bursting leads to rarer nonmonotonic transitions. In all these cases, we observe that phase differences between neurons change intermittently throughout time, even in strongly phase-synchronized networks. We call this promiscuity, and measure it directly by calculating how neuron's burst times drift from each other across time. Then, grouping neurons according to their phases, we explore how promiscuity affects the composition of these clusters, and obtain detailed knowledge of the network's phase synchronization. We also calculate two neuronal variabilities, measuring how the neuronal firing times disperse over time or over the network, and find that the two have very similar values and are strongly correlated to the network's degree of PS for weak coupling. Next, we expand our focus to metastability as viewed in neuroscience, regarding promiscuity as a type of metastable behavior. We provide a mini-review of the different definitions of metastability, and discuss them. With this, we categorize briefly the dynamical mechanisms leading to metastability. Finally, using the insights gained from studying promiscuity, we investigate the promiscuous network again to discuss how metastability can differ depending on the multiple scales of a system. Keywords: Metastability. Phase synchronization. Neural networks

The psychiatric and neural effects of L-type calcium channel antagonism: pharmacoepidemiology and experimental medicine studies

L-type calcium channel (LTCC) antagonists are used to manage cardiovascular conditions. However, several factors suggest they may also have therapeutic potential in psychiatry. First, there is evidence for calcium signalling abnormalities in bipolar disorder (BD). Second, there is some evidence, albeit very inconclusive, that LTCC antagonists may have beneficial effects in BD. Third, calcium channel genes are involved in a number of psychiatric conditions. In particular, genome-wide association studies (GWAS) have consistently identified CACNA1C as a gene associated with psychiatric disorders including BD. CACNA1C codes for the CaV1.2 alpha subunit, the primary target of LTCC antagonists, and the genomic data have given new impetus to studying whether and how these drugs affect psychiatric or neural phenotypes. This study used two complementary approaches to investigate this issue. Using a federated network of electronic health records (EHRs), the first part of this thesis aimed to explore the association between LTCC antagonism and psychiatric disorder. Analyses compared LTCC antagonists with other antihypertensives in matched cohorts of patients. Findings demonstrated LTCC antagonists were associated with lower incidence of first-onset psychiatric disorder compared to beta blockers and diuretics, but higher incidence compared to angiotensin receptor blockers (ARBs) and angiotensin-converting enzyme inhibitors (ACEIs). Follow-up analyses specifically compared brain-penetrant LTCC antagonists with non-penetrant variants (amlodipine and verapamil/diltiazem) and with ARBs. These findings demonstrated that brain-penetrant LTCC antagonists were associated with overall lower incidence of first-onset neuropsychiatric disorder compared to amlodipine, verapamil/diltiazem, and ARBs. However, benefits varied across individual disorders, and indications of residual confounding between groups undermined the interpretation of some of the findings. The second part of this thesis aimed to examine the broader effects of LTCC antagonism on human brain and behaviour through an exploratory experimental medicine study. The Oxford Study of Calcium Channel Antagonism, Cognition, Mood Instability and Sleep (OxCaMS) compared the effect of 14 days’ nicardipine (a brain-penetrant LTCC antagonist) with placebo across various parameters, including measures of mood, cognition, and neural activity, using a randomised, double-blind design. While there was no evidence of an effect of LTCC antagonism on mood instability, behavioural and neural findings suggested LTCC antagonism may shift emotional processing in line with an antidepressant effect. Cognitive evidence indicated that, compared to placebo, LTCC antagonism reduced negative bias through changes in the perception of sad and angry faces, while neural evidence suggested that LTCC antagonism decreased amygdala activity in response to fear. However, neural findings were based on small voxel clusters, and therefore further research is warranted to assess LTCC antagonist effects in the brain. In summary, these findings offer insights into the possible associations between LTCC antagonists and neuropsychiatric disorder, as well as the effects of these drugs on mood, cognitive function, and neural activity. Several lines of evidence support the potential of brain-selective LTCC antagonists in psychiatry. However further research is required to fully clarify the therapeutic possibilities of LTCC antagonism in the future