Evidence against altered excitatory/inhibitory balance in the posteromedial cortex of young adult APOE E4 carriers: a resting state 1H-MRS study

A strategy to gain insight into early changes that may predispose people to Alzheimer's disease (AD) is to study the brains of younger cognitively healthy people that are at increased genetic risk of AD. The Apolipoprotein (APOE) E4 allele is the strongest genetic risk factor for AD, and several neuroimaging studies comparing APOE E4 carriers with non-carriers at age ∼20–30 years have detected hyperactivity (or reduced deactivation) in posteromedial cortex (PMC), a key hub of the default network (DN), which has a high susceptibility to early amyloid deposition in AD. Transgenic mouse models suggest such early network activity alterations may result from altered excitatory/inhibitory (E/I) balance, but this is yet to be examined in humans. Here we test the hypothesis that PMC fMRI hyperactivity could be underpinned by altered levels of excitatory (glutamate) and/or inhibitory (GABA) neurotransmitters in this brain region. Forty-seven participants (20 APOE E4 carriers and 27 non-carriers) aged 18–25 years underwent resting-state proton magnetic resonance spectroscopy (1H-MRS), a non-invasive neuroimaging technique to measure glutamate and GABA in vivo. Metabolites were measured in a PMC voxel of interest and in a comparison voxel in the occipital cortex (OCC). There was no difference in either glutamate or GABA between the E4 carriers and non-carriers in either MRS voxel, or in the ratio of glutamate to GABA, a measure of E/I balance. Default Bayesian t-tests revealed evidence in support of this null finding. Our findings suggest that PMC hyperactivity in APOE E4 carriers is unlikely to be associated with, or possibly may precede, alterations in local resting-state PMC neurotransmitters, thus informing our understanding of the spatio-temporal sequence of early network alterations underlying APOE E4 related AD risk

Extrinsic and default mode networks in psychiatric conditions: Relationship to excitatory-inhibitory transmitter balance and early trauma.

Over the last three decades there has been an accumulation of Magnetic Resonance Imaging (MRI) studies reporting that aberrant functional networks may underlie cognitive deficits and other symptoms across a range of psychiatric diagnoses. The use of pharmacological MRI and H-1-Magnetic Resonance Spectroscopy (H-1-MRS) has allowed researchers to investigate how changes in network dynamics are related to perturbed excitatory and inhibitory neurotransmission in individuals with psychiatric conditions. More recently, changes in functional network dynamics and excitatory/inhibitory (E/I) neurotransmission have been linked to early childhood trauma, a major antecedents for psychiatric illness in adulthood. Here we review studies investigating whether perturbed network dynamics seen across psychiatric conditions are related to changes in E/I neurotransmission, and whether such changes could be linked to childhood trauma. Whilst there is currently a paucity of studies relating early traumatic experiences to altered E/I balance and network function, the research discussed here lead towards a plausible mechanistic hypothesis, linking early traumatic experiences to cognitive dysfunction and symptoms mediated by E/I neurotransmitter imbalances

Default mode network segregation and social deficits in autism spectrum disorder: Evidence from non-medicated children DMN in children with ASD

AbstractFunctional pathology of the default mode network is posited to be central to social-cognitive impairment in autism spectrum disorders (ASD). Altered functional connectivity of the default mode network's midline core may be a potential endophenotype for social deficits in ASD. Generalizability from prior studies is limited by inclusion of medicated participants and by methods favoring restricted examination of network function. This study measured resting-state functional connectivity in 22 8–13 year-old non-medicated children with ASD and 22 typically developing controls using seed-based and network segregation functional connectivity methods. Relative to controls the ASD group showed both under- and over-functional connectivity within default mode and non-default mode regions, respectively. ASD symptoms correlated negatively with the connection strength of the default mode midline core—medial prefrontal cortex–posterior cingulate cortex. Network segregation analysis with the participation coefficient showed a higher area under the curve for the ASD group. Our findings demonstrate that the default mode network in ASD shows a pattern of poor segregation with both functional connectivity metrics. This study confirms the potential for the functional connection of the midline core as an endophenotype for social deficits. Poor segregation of the default mode network is consistent with an excitation/inhibition imbalance model of ASD

반응 억제의 개인차와 관련한 대규모 휴지기 뇌네트워크의 특성

학위논문 (박사) -- 서울대학교 대학원 : 인문대학 협동과정 인지과학전공, 2020. 8. 이동수.Response inhibition is one of the essential cognitive functions and suppresses inappropriate responses for goal-directed behavior. When a brain is cognitively engaged, it enters a cognitive state that task-positive regions are activated, and the default mode network is deactivated (DMN). In contrast, DMN is activated, and task-positive regions are deactivated at rest. The transition between the states is important for the cognitive function, and recent studies have found that the salience network (SN) plays a crucial role in detecting and processing a salient signal and suppressing DMN at rest. It can be assumed that there exists optimized connectivity to perform response inhibition successfully and that it will also appear in resting-state requiring no cognitive effort. It was hypothesized that lower functional connectivity within SN and higher functional connectivity within DMN and greater anti-correlation between then is related to better response inhibition. The response inhibition of individuals was measured by the stop-signal task and the Stroop task. The correlation between intra-/inter-component functional connectivity derived from independent component analysis with dual regression and task performances were examined to test the hypothesis. The intra-/inter-component structural connectivity analysis using diffusion tensor imaging was conducted to provide a deeper understanding of functional connectivity. Topological characteristics of inter-component functional connectivity were also examined using the minimum spanning tree (MST) of each individual to provide a heuristic insight from the topological view. The results indicate that the functional connectivity within SN, but not DMN components, and the functional and structural connectivity between SN and DMN components are critical to elucidate individual differences in response inhibition. Higher structural connectivity but low functional connectivity of SN at rest was an important feature for superior response inhibition. The stronger structural connectivity and stronger anti-correlation between SN and DMN components were also indicative of better response inhibition. MST of a subject with the best performance showed direct connections between SN and anterior DMN/pDMN, whereas the MST of the one with the worst performance does not. These intra-/inter components connectivities reflect the organization of the brain that enables competent response inhibition and account for individual differences. This study might suggest that the individuals characteristics of large-scale network components at rest provide evidence to illustrate response inhibition of an individual without any experimental scan.반응 억제는 가장 주요한 인지 기능 중 하나이며 이상행동을 동반하는 다양한 정신 질환과도 깊은 관련이 있다. 따라서 이와 관련된 신경적 특성을 탐구하는 것은 매우 중요하다. 우리의 뇌는 어떠한 인지 기능을 수행할 때, 작업 관련 영역들을 활성화하고 자기 참조적 처리를 하는 디폴트 모드 네트워크 영역들은 비활성화한다. 휴지기에는 반대로 작업 관련 영역들은 비활성화하고 디폴트 모드 네트워크 영역은 활성화한다. 이처럼 인지 기능을 수행하기 위해서는 상태를 효율적으로 전환하는 것이 중요하다. 현출성 네트워크 (salience network)는 어떠한 과제를 할 때 중요한 자극을 탐지하여 처리하며 또한 디폴트 모드 네트워크의 활성을 억제하기 때문에 상태 간 전환에 핵심적인 역할을 하는 대규모 뇌네트워크이다. 따라서 이와 관련된 연결적 특성이 인지 기능과 밀접한 관련이 있으며, 그러한 특성은 휴지기의 연결성에도 반영되어 있을 것이라 가정하였다. 즉, 본 연구에서는 반응 억제의 개인차를 휴지기 대규모 뇌네트워크들의 특성을 통해 설명할 수 있을 것이며, 특히 현출성 네트워크의 낮은 기능적 연결성, 디폴트 모드 네트워크의 높은 기능적 연결성, 그리고 그 둘 간의 높은 기능적 역 상관 (anti-correlation)이 반응 억제에 우수한 사람들의 특징적인 휴지기 연결성일 것이라 가설을 세웠다. 개인의 반응 억제는 정지 신호 과제와 스트룹 과제를 통해 측정하였으며, 휴지기 대규모 뇌네트워크들의 특성들과 어떠한 상관을 갖는지 알아보았다. 즉, 기능적 뇌네트워크 내의 연결성과 두 뇌네트워크 간 연결성이 과제 수행과 어떠한 상관을 보이는지를 알아보았다. 또한 기능적 연결성에 대한 보다 깊은 이해를 위해 확산 텐서 영상과 트랙토그래피 기법을 사용하여 구조적 연결성과 반응 억제와의 상관을 알아보았다. 반응 억제와 관련된 토폴로지 특성 역시 함께 알아보기 위해 참여자들의 미니멈 스패닝 트리(MST: minimum spanning tree)를 계산하였다. 분석 결과, 현출성 네트워크, 그리고 현출성 네트워크와 디폴트 모드 네트워크 간의 연결성을 통해 반응 억제의 개인차를 설명할 수 있었다. 현출성 네트워크의 성분 내 구조적 연결성은 강하지만 휴지기의 기능적 연결성이 약한 참여자들일수록 반응 억제 수행이 우수했다. 현출성 네트워크와 디폴트 모드 네트워크 간의 구조적 연결성과 기능적 역 상관은 모두 높을수록 우수한 반응 억제를 보였다. 또한 두 네트워크 간 구조적 연결성이 높을수록 기능적 역 상관이 높은 것으로 나타났다. 토폴로지 분석에서는 가장 수행이 좋은 참여자의 MST는 현출성 네트워크와 디폴트 모드 네트워크들 간에 직접적인 연결이 관찰되었으나 수행이 가장 나쁜 참여자에서는 그러한 직접적인 연결이 관찰되지 않았다. 분석 결과, 휴지기의 현출성 네트워크 내 연결성, 그리고 현출성 네트워크와 디폴트 모드 네트워크 간의 기능적 역 상관과 구조적 연결성이 반응 억제의 개인 차이를 설명하였으나, 디폴트 모드 네트워크 내의 연결성은 그렇지 못했다. 이 연구는 과제 수행 중이 아닌 휴지기 동안의 뇌네트워크의 특성들을 통해 반응 억제의 개인차를 설명할 수 있음을 보여준다.1. Introduction 1 1.1. Response inhibition and its neural correlates 1 1.1.1. Cognitive tasks to measure response inhibition 1 1.1.2. The neural correlates of response inhibition 2 1.1.3. Response inhibition and resting-state brain 3 1.2. Investigations on large-scale networks underlying response inhibition 4 1.2.1. Resting-state networks and response inhibition 4 1.2.2. Structural connectivity 6 1.2.3. Topological characteristics 7 1.2.4. The aim of the present study 8 2. Methods 9 2.1. Subjects 9 2.2. Behavioral tasks to assess response inhibition 11 2.3. Brain imaging data acquisition and preprocessing 14 2.3.1. Resting-state fMRI 14 2.3.2. Diffusion tensor imaging 15 2.4. Resting-state networks and functional connectivity analysis 16 2.4.1. Group independent component analysis to identify resting-state networks 16 2.4.2. Dual regression to obtain subject-specific data of components 17 2.4.3. Estimation of subject-specific intra-/inter-component functional connectivity 21 2.5. Structural connectivity analysis 21 2.5.1. Structural connectivity and response inhibition 21 2.5.2. Relationship between functional connectivity and structural connectivity 22 2.6. Topological data analysis 25 2.6.1. Minimum spanning tree 25 3. Results 27 3.1. The performances of behavioral tasks 27 3.2. Intra-component connectivity and response inhibition 30 3.3. Inter-component connectivity and response inhibition 35 3.4. Relationship between functional connectivity and structural connectivity 41 3.5. Minimum spanning tree 43 4. Discussion 46 4.1. Resting-state network and cognition 46 4.2. Salience network and response inhibition 47 4.3. Connectivity and structural connectivity between SN and DMN 50 4.3.1. Functional connectivity between SN and DMN 50 4.3.2. Structural connectivity between SN and DMN 51 4.3.3. Topological characteristics between SN and DMN 53 4.4. Limitations of the study 54 5. Conclusion 56 References 57 국문 초록 70Docto

Excitatory-inhibitory balance within EEG microstates and resting-state fMRI networks: assessed via simultaneous trimodal PET-MR-EEG imaging

The symbiosis of neuronal activities and glucose energy metabolism is reflected in the generation of functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) signals. However, their association with the balance between neuronal excitation and inhibition (E/I-B), which is closely related to the activities of glutamate and γ-aminobutyric acid (GABA) and the receptor availability (RA) of GABAA and mGluR5, remains unexplored. This research investigates these associations during the resting state (RS) condition using simultaneously recorded PET/MR/EEG (trimodal) data. The trimodal data were acquired from three studies using different radio-tracers such as, [11C]ABP688 (ABP) (N = 9), [11C]Flumazenil (FMZ) (N = 10) and 2-[18F]fluoro-2-deoxy-D-glucose (FDG) (N = 10) targeted to study the mGluR5, GABAA receptors and glucose metabolism respectively. Glucose metabolism and neuroreceptor binding availability (non-displaceable binding potential (BPND)) of GABAA and mGluR5 were found to be significantly higher and closely linked within core resting-state networks (RSNs). The neuronal generators of EEG microstates and the fMRI measures were most tightly associated with the BPND of GABAA relative to mGluR5 BPND and the glucose metabolism, emphasising a predominance of inhibitory processes within in the core RSNs at rest. Changes in the neuroreceptors leading to an altered coupling with glucose metabolism may render the RSNs vulnerable to psychiatric conditions. The paradigm employed here will likely help identify the precise neurobiological mechanisms behind these alterations in fMRI functional connectivity and EEG oscillations, potentially benefitting individualised healthcare treatment measures

Functional network antagonism and consciousness

Spontaneous brain activity changes across states of consciousness. A particular consciousness-mediated configuration is the anticorrelations between the default mode network and other brain regions. What this antagonistic organization implies about consciousness to date remains inconclusive. In this Perspective Article, we propose that anticorrelations are the physiological expression of the concept of segregation, namely the brain’s capacity to show selectivity in the way areas will be functionally connected. We postulate that this effect is mediated by the process of neural inhibition, by regulating global and local inhibitory activity. While recognizing that this effect can also result from other mechanisms, neural inhibition helps the understanding of how network metastability is affected after disrupting local and global neural balance. In combination with relevant theories of consciousness, we suggest that anticorrelations are a physiological prior that can work as a marker of preserved consciousness. We predict that if the brain is not in a state to host anticorrelations, then most likely the individual does not entertain subjective experience. We believe that this link between anticorrelations and the underlying physiology will help not only to comprehend how consciousness happens, but also conceptualize effective interventions for treating consciousness disorders in which anticorrelations seem particularly affected

Electrophysiological network alterations in adults with copy number variants associated with high neurodevelopmental risk

Rare copy number variants associated with increased risk for neurodevelopmental and psychiatric disorders (referred to as ND-CNVs) are characterized by heterogeneous phenotypes thought to share a considerable degree of overlap. Altered neural integration has often been linked to psychopathology and is a candidate marker for potential convergent mechanisms through which ND-CNVs modify risk; however, the rarity of ND-CNVs means that few studies have assessed their neural correlates. Here, we used magnetoencephalography (MEG) to investigate resting-state oscillatory connectivity in a cohort of 42 adults with ND-CNVs, including deletions or duplications at 22q11.2, 15q11.2, 15q13.3, 16p11.2, 17q12, 1q21.1, 3q29, and 2p16.3, and 42 controls. We observed decreased connectivity between occipital, temporal and parietal areas in participants with ND-CNVs. This pattern was common across genotypes and not exclusively characteristic of 22q11.2 deletions, which were present in a third of our cohort. Furthermore, a data-driven graph theory framework enabled us to successfully distinguish participants with ND-CNVs from unaffected controls using differences in node centrality and network segregation. Together, our results point to alterations in electrophysiological connectivity as a putative common mechanism through which genetic factors confer increased risk for neurodevelopmental and psychiatric disorders

Neural Mechanisms and Psychology of Psychedelic Ego Dissolution

Neuroimaging studies of psychedelics have advanced our understanding of hierarchical brain organization and the mechanisms underlying their subjective and therapeutic effects. The primary mechanism of action of classic psychedelics is binding to serotonergic 5-HT2A receptors. Agonist activity at these receptors leads to neuromodulatory changes in synaptic efficacy that can have a profound effect on hierarchical message-passing in the brain. Here, we review the cognitive and neuroimaging evidence for the effects of psychedelics: in particular, their influence on selfhood and subject-object boundaries—known as ego dissolution—surmised to underwrite their subjective and therapeutic effects. Agonism of 5-HT2A recep-tors, located at the apex of the cortical hierarchy, may have a particularly powerful effect on sentience and consciousness. These effects can endure well after the pharmacological half-life, suggesting that psychedelics may have effects on neural plasticity that may play a role in their therapeutic efficacy. Psychologi-cally, this may be accompanied by a disarming of ego resistance that increases the repertoire of perceptual hypotheses and affords alternate pathways for thought and behavior, including those that undergird selfhood. We consider the interaction between serotonergic neuromodulation and sentience through the lens of hierarchical predictive coding, which speaks to the value of psychedelics in understanding how we make sense of the world and specific predictions about effective connectivity in cortical hierarchies that can be tested using functional neuroimaging. Significance Statement——Classic psychedelics bind to serotonergic 5-HT2A receptors. Their agonist activity at these receptors leads to neuromodulatory changes in synaptic efficacy, resulting in a profound effect on information processing in the brain. Here, we synthesize an abundance of brain imaging research with pharmacological and psychological interpretations informed by the framework of predictive coding. Moreover, predictive coding is suggested to offer more sophisticated interpretations of neuroimaging find-ings by bridging the role between the 5-HT2A receptors and large-scale brain networks

Physiologically informed dynamic causal modeling of fMRI data

AbstractThe functional MRI (fMRI) signal is an indirect measure of neuronal activity. In order to deconvolve the neuronal activity from the experimental fMRI data, biophysical generative models have been proposed describing the link between neuronal activity and the cerebral blood flow (the neurovascular coupling), and further the hemodynamic response and the BOLD signal equation. These generative models have been employed both for single brain area deconvolution and to infer effective connectivity in networks of multiple brain areas. In the current paper, we introduce a new fMRI model inspired by experimental observations about the physiological underpinnings of the BOLD signal and compare it with the generative models currently used in dynamic causal modeling (DCM), a widely used framework to study effective connectivity in the brain. We consider three fundamental aspects of such generative models for fMRI: (i) an adaptive two-state neuronal model that accounts for a wide repertoire of neuronal responses during and after stimulation; (ii) feedforward neurovascular coupling that links neuronal activity to blood flow; and (iii) a balloon model that can account for vascular uncoupling between the blood flow and the blood volume. Finally, we adjust the parameterization of the BOLD signal equation for different magnetic field strengths. This paper focuses on the form, motivation and phenomenology of DCMs for fMRI and the characteristics of the various models are demonstrated using simulations. These simulations emphasize a more accurate modeling of the transient BOLD responses — such as adaptive decreases to sustained inputs during stimulation and the post-stimulus undershoot. In addition, we demonstrate using experimental data that it is necessary to take into account both neuronal and vascular transients to accurately model the signal dynamics of fMRI data. By refining the models of the transient responses, we provide a more informed perspective on the underlying neuronal process and offer new ways of inferring changes in local neuronal activity and effective connectivity from fMRI

Functional neurophysiological biomarkers of early-stage Alzheimer's disease:A perspective of network hyperexcitability in disease progression

Network hyperexcitability (NH) has recently been suggested as a potential neurophysiological biomarker of Alzheimer's disease (AD), as new, more accurate biomarkers of AD are sought. NH has generated interest as a potential biomarker of certain stages in the disease trajectory and even as a disease mechanism by which network dysfunction could be modulated. NH has been demonstrated in several animal models of AD pathology and multiple lines of evidence point to the existence of NH in patients with AD, strongly supporting the physiological and clinical relevance of this indication. Several hypotheses have been put forward to explain the prevalence of NH in animal models through neurophysiological, biochemical, and imaging techniques. However, some of these hypotheses have been built on animal models with limitations and caveats that may have derived NH through other mechanisms or mechanisms without translational validity to sporadic AD patients, potentially leading to an erroneous conclusion of the underlying cause of NH occurring in patients with AD. In this review, we discuss the substantiation for NH in animal models of AD pathology and in human patients, as well as some of the hypotheses considering recently developed animal models that challenge existing hypotheses and mechanisms of NH. In addition, we provide a preclinical perspective on how the development of animal models incorporating AD-specific NH could provide physiologically relevant translational experimental data that may potentially aid the discovery and development of novel therapies for AD