Higher-order interactions in macroscopic functional networks of the brain and its relation to BOLD global signal

Background: Postmortem studies of brains with Alzheimer’s disease (AD) not only find amyloid-beta (Aβ) and neurofibrillary tangles (NFT) in the visual cortex, but also reveal temporally sequential changes in AD pathology from higher-order association areas to lower-order areas and then primary visual area (V1) with disease progression. Objective: This study investigated the effect of AD severity on visual functional network. Methods: Eight severe AD (SAD) patients, 11 mild/moderate AD (MAD), and 26 healthy senior (HS) controls undertook a resting-state fMRI (rs-fMRI) and a task fMRI of viewing face photos. A resting-state visual functional connectivity (FC) network and a face-evoked visual-processing network were identified for each group. Results: For the HS, the identified group-mean face-evoked visual-processing network in the ventral pathway started from V1 and ended within the fusiform gyrus. In contrast, the resting-state visual FC network was mainly confined within the visual cortex. AD disrupted these two functional networks in a similar severity dependent manner: the more severe the cognitive impairment, the greater reduction in network connectivity. For the face-evoked visual-processing network, MAD disrupted and reduced activation mainly in the higher-order visual association areas, with SAD further disrupting and reducing activation in the lower-order areas. Conclusion: These findings provide a functional corollary to the canonical view of the temporally sequential advancement of AD pathology through visual cortical areas. The association of the disruption of functional networks, especially the face-evoked visual-processing network, with AD severity suggests a potential predictor or biomarker of AD progression.

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The novel guanylyl cyclase MsGC-I is strongly expressed in higher-order neuropils in the brain of Manduca sexta

Journal of Experimental Biology ◽

10.1242/jeb.204.2.305 ◽

2001 ◽

Vol 204 (2) ◽

pp. 305-314 ◽

Cited By ~ 1

Author(s):

A. Nighorn ◽

P.J. Simpson ◽

D.B. Morton

Keyword(s):

Nervous System ◽

Manduca Sexta ◽

Guanylyl Cyclase ◽

Higher Order ◽

Adult Brain ◽

Central Complex ◽

Amino Acid Residues ◽

Order Processing ◽

Guanylyl Cyclases ◽

The Brain

Guanylyl cyclases are usually characterized as being either soluble (sGCs) or receptor (rGCs). We have recently cloned a novel guanylyl cyclase, MsGC-I, from the developing nervous system of the hawkmoth Manduca sexta that cannot be classified as either an sGC or an rGC. MsGC-I shows highest sequence identity with receptor guanylyl cyclases throughout its catalytic and dimerization domains, but does not contain the ligand-binding, transmembrane or kinase-like domains characteristic of receptor guanylyl cyclases. In addition, MsGC-I contains a C-terminal extension of 149 amino acid residues. In this paper, we report the expression of MsGC-I in the adult. Northern blots show that it is expressed preferentially in the nervous system, with high levels in the pharate adult brain and antennae. In the antennae, immunohistochemical analyses show that it is expressed in the cell bodies and dendrites, but not axons, of olfactory receptor neurons. In the brain, it is expressed in a variety of sensory neuropils including the antennal and optic lobes. It is also expressed in structures involved in higher-order processing including the mushroom bodies and central complex. This complicated expression pattern suggests that this novel guanylyl cyclase plays an important role in mediating cyclic GMP levels in the nervous system of Manduca sexta.

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Small world in a seismic network: the California case

Nonlinear Processes in Geophysics ◽

10.5194/npg-15-389-2008 ◽

2008 ◽

Vol 15 (3) ◽

pp. 389-395 ◽

Cited By ~ 21

Author(s):

A. Jiménez ◽

K. F. Tiampo ◽

A. M. Posadas

Keyword(s):

Complex Networks ◽

Recent Work ◽

Brain Activity ◽

Social Systems ◽

Small World ◽

Functional Networks ◽

Topological Properties ◽

Southern California Region ◽

Seismic Area ◽

The Brain

Abstract. Recent work has shown that disparate systems can be described as complex networks i.e. assemblies of nodes and links with nontrivial topological properties. Examples include technological, biological and social systems. Among them, earthquakes have been studied from this perspective. In the present work, we divide the Southern California region into cells of 0.1°, and calculate the correlation of activity between them to create functional networks for that seismic area, in the same way that the brain activity is studied from the complex network perspective. We found that the network shows small world features.

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Evolutionary modifications in human brain connectivity associated with schizophrenia

Brain ◽

10.1093/brain/awz330 ◽

2019 ◽

Vol 142 (12) ◽

pp. 3991-4002 ◽

Cited By ~ 13

Author(s):

Martijn P van den Heuvel ◽

Lianne H Scholtens ◽

Siemon C de Lange ◽

Rory Pijnenburg ◽

Wiepke Cahn ◽

...

Keyword(s):

Human Brain ◽

Brain Connectivity ◽

Brain Evolution ◽

Higher Order ◽

Brain Functions ◽

Human Brain Evolution ◽

The Brain

See Vértes and Seidlitz (doi:10.1093/brain/awz353) for a scientific commentary on this article. Is schizophrenia a by-product of human brain evolution? By comparing the human and chimpanzee connectomes, van den Heuvel et al. demonstrate that connections unique to the human brain show greater involvement in schizophrenia pathology. Modifications in service of higher-order brain functions may have rendered the brain more vulnerable to dysfunction.

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The role of thickness inhomogeneities in hierarchical cortical folding

10.1101/2020.04.01.020172 ◽

2020 ◽

Cited By ~ 1

Author(s):

Lucas da Costa Campos ◽

Raphael Hornung ◽

Gerhard Gompper ◽

Jens Elgeti ◽

Svenja Caspers

Keyword(s):

Fetal Brain ◽

Epileptic Seizures ◽

Quantitative Model ◽

Higher Order ◽

Mammalian Brain ◽

Cortical Folding ◽

Differential Growth ◽

The Brain ◽

Short Life Expectancy

AbstractThe morphology of the mammalian brain cortex is highly folded. For long it has been known that specific patterns of folding are necessary for an optimally functioning brain. On the extremes, lissencephaly, a lack of folds in humans, and polymicrogyria, an overly folded brain, can lead to severe mental retardation, short life expectancy, epileptic seizures, and tetraplegia. The construction of a quantitative model on how and why these folds appear during the development of the brain is the first step in understanding the cause of these conditions. In recent years, there have been various attempts to understand and model the mechanisms of brain folding. Previous works have shown that mechanical instabilities play a crucial role in the formation of brain folds, and that the geometry of the fetal brain is one of the main factors in dictating the folding characteristics. However, modeling higher-order folding, one of the main characteristics of the highly gyrencephalic brain, has not been fully tackled. The effects of thickness inhomogeneity in the gyrogenesis of the mammalian brain are studied in silico. Finite-element simulations of rectangular slabs are performed. The slabs are divided into two distinct regions, where the outer layer mimics the gray matter, and the inner layer the underlying white matter. Differential growth is introduced by growing the top layer tangentially, while keeping the underlying layer untouched. The brain tissue is modeled as a neo-Hookean hyperelastic material. Simulations are performed with both, homogeneous and inhomogeneous cortical thickness. The homogeneous cortex is shown to fold into a single wavelength, as is common for bilayered materials, while the inhomogeneous cortex folds into more complex conformations. In the early stages of development of the inhomogeneous cortex, structures reminiscent of the deep sulci in the brain are obtained. As the cortex continues to develop, secondary undulations, which are shallower and more variable than the structures obtained in earlier gyrification stage emerge, reproducing well-known characteristics of higher-order folding in the mammalian, and particularly the human, brain.

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Neuropeptides in modulation of Drosophila behavior: how to get a grip on their pleiotropic actions

10.7287/peerj.preprints.27531 ◽

2019 ◽

Author(s):

Dick R Nässel ◽

Dennis Pauls ◽

Wolf Huetteroth

Keyword(s):

Expression Pattern ◽

Internal State ◽

Neurosecretory Cells ◽

Higher Order ◽

Specific Expression ◽

Specific Expression Pattern ◽

Pleiotropic Functions ◽

Pleiotropic Actions ◽

The Brain ◽

Different Levels

Neuropeptides constitute a large and diverse class of signaling molecules that are produced by many types of neurons, neurosecretory cells, endocrines and other cells. Many neuropeptides display pleiotropic actions either as neuromodulators, co-transmitters or circulating hormones, while some play these roles concurrently. Here, we highlight pleiotropic functions of neuropeptides and different levels of neuropeptide signaling in the brain, from context-dependent orchestrating signaling by higher order neurons, to local executive modulation in specific circuits. Additionally, orchestrating neurons receive peptidergic signals from neurons conveying organismal internal state cues and relay these to executive circuits. We exemplify these levels of signaling with four neuropeptides, SIFamide, short neuropeptide F, allatostatin-A and leucokinin, each with a specific expression pattern and level of complexity in signaling.

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Ask and You Shall Receive: A closer look on unsolved consciousness issue

Basic and Clinical Neuroscience Journal ◽

10.32598/bcn.2021.2308.1 ◽

2021 ◽

Author(s):

Ali Motavalli ◽

◽

Javad Mahmoudi ◽

Alireza Majdi ◽

Saeed Sadigh-Eteghad ◽

...

Keyword(s):

Middle Ages ◽

Brain Function ◽

Neural Correlates ◽

Higher Order ◽

Narrative Review ◽

Historical Aspects ◽

Neural Correlates Of Consciousness ◽

States Of Consciousness ◽

Mind And Body ◽

The Brain

Although there are numerous views about the concept of consciousness, no consensus exists regarding the meaning. However, with the aid of the latest neuroscientific developments, the misleading obstacles related to consciousness have been removed. Over the last few decades, neuroscientific efforts in determining the function of the brain and merging these findings with philosophical theories, have brought a more comprehensive perception of the notion of consciousness. In addition to metaphysical/ontological views of consciousness e.g., higher-order theories, reflexive theories, and representationalist theories, there are some brain directed topics in this matter which include but not are limited to neural correlates of consciousness (NCC), brain loop connectivity, and lateralization. This narrative review sheds light on cultural and historical aspects of consciousness in old and middle ages and introduces some of the prominent philosophical discussions related to mind and body. Also, it illustrates the correlation of brain function with states of consciousness with a focus on the roles of function and connectivity.

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Unsupervised seizure localisation with attention-based graph neural networks

10.1101/2020.12.03.409979 ◽

2020 ◽

Author(s):

Daniele Grattarola ◽

Lorenzo Livi ◽

Cesare Alippi ◽

Richard Wennberg ◽

Taufik Valiante

Keyword(s):

Neural Networks ◽

Brain Activity ◽

Functional Networks ◽

Seizure Onset ◽

The Past ◽

Seizure Onset Zone ◽

Brain States ◽

Graph Neural Networks ◽

Intracranial Electroencephalography ◽

The Brain

Abstract Graph neural networks (GNNs) and the attention mechanism are two of the most significant advances in artificial intelligence methods over the past few years. The former are neural networks able to process graph-structured data, while the latter learns to selectively focus on those parts of the input that are more relevant for the task at hand. In this paper, we propose a methodology for seizure localisation which combines the two approaches. Our method is composed of several blocks. First, we represent brain states in a compact way by computing functional networks from intracranial electroencephalography recordings, using metrics to quantify the coupling between the activity of different brain areas. Then, we train a GNN to correctly distinguish between functional networks associated with interictal and ictal phases. The GNN is equipped with an attention-based layer which automatically learns to identify those regions of the brain (associated with individual electrodes) that are most important for a correct classification. The localisation of these regions is fully unsupervised, meaning that it does not use any prior information regarding the seizure onset zone. We report results both for human patients and for simulators of brain activity. We show that the regions of interest identified by the GNN strongly correlate with the localisation of the seizure onset zone reported by electroencephalographers. We also show that our GNN exhibits uncertainty on those patients for which the clinical localisation was also unsuccessful, highlighting the robustness of the proposed approach.

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