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1.
PLoS One ; 15(10): e0237204, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33075046

RESUMO

The amygdala, a subcortical structure known for social and emotional processing, consists of multiple subnuclei with unique functions and connectivity patterns. Tracer studies in adult macaques have shown that the basolateral subnuclei differentially connect to parts of visual cortex, with stronger connections to anterior regions and weaker connections to posterior regions; infant macaques show robust connectivity even with posterior visual regions. Do these developmental differences also exist in the human amygdala, and are there specific functional regions that undergo the most pronounced developmental changes in their connections with the amygdala? To address these questions, we explored the functional connectivity (from resting-state fMRI data) of the basolateral amygdala to occipitotemporal cortex in human neonates scanned within one week of life and compared the connectivity patterns to those observed in young adults. Specifically, we calculated amygdala connectivity to anterior-posterior gradients of the anatomically-defined occipitotemporal cortex, and also to putative occipitotemporal functional parcels, including primary and high-level visual and auditory cortices (V1, A1, face, scene, object, body, high-level auditory regions). Results showed a decreasing gradient of functional connectivity to the occipitotemporal cortex in adults-similar to the gradient seen in macaque tracer studies-but no such gradient was observed in neonates. Further, adults had stronger connections to high-level functional regions associated with face, body, and object processing, and weaker connections to primary sensory regions (i.e., A1, V1), whereas neonates showed the same amount of connectivity to primary and high-level sensory regions. Overall, these results show that functional connectivity between the amygdala and occipitotemporal cortex is not yet differentiated in neonates, suggesting a role of maturation and experience in shaping these connections later in life.


Assuntos
Tonsila do Cerebelo/fisiologia , Complexo Nuclear Basolateral da Amígdala/fisiologia , Mapeamento Encefálico/métodos , Córtex Cerebral/fisiologia , Conectoma , Vias Neurais/fisiologia , Lobo Occipital/fisiologia , Adulto , Feminino , Humanos , Processamento de Imagem Assistida por Computador , Recém-Nascido , Imagem por Ressonância Magnética , Masculino , Adulto Jovem
2.
Nat Commun ; 11(1): 5094, 2020 10 09.
Artigo em Inglês | MEDLINE | ID: mdl-33037225

RESUMO

Brain lesions do not just disable but also disconnect brain areas, which once deprived of their input or output, can no longer subserve behaviour and cognition. The role of white matter connections has remained an open question for the past 250 years. Based on 1333 stroke lesions, here we reveal the human Disconnectome and demonstrate its relationship to the functional segregation of the human brain. Results indicate that functional territories are not only defined by white matter connections, but also by the highly stereotyped spatial distribution of brain disconnections. While the former has granted us the possibility to map 590 functions on the white matter of the whole brain, the latter compels a revision of the taxonomy of brain functions. Overall, our freely available Atlas of White Matter Function will enable improved clinical-neuroanatomical predictions for brain lesion studies and provide a platform for explorations in the domain of cognition.


Assuntos
Encéfalo/patologia , Encéfalo/fisiologia , Conectoma , Acidente Vascular Cerebral/patologia , Comportamento , Humanos , Neuroimagem , Acidente Vascular Cerebral/fisiopatologia
3.
Elife ; 92020 10 08.
Artigo em Inglês | MEDLINE | ID: mdl-33030427

RESUMO

Scientists have created the most detailed map of the fruit fly brain to date, identifying over 25,000 neurons and 20 million synapses.


Assuntos
Conectoma , Animais , Encéfalo , Drosophila , Proteínas de Drosophila , Neurônios , Sinapses , Ubiquitina-Proteína Ligases
4.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 5653-5656, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33019259

RESUMO

Brain connectivity analysis is a new multidisciplinary approach in neuroscience for determining neurological disorders from brain imaging data. But, there is no end-to-end toolchain that processes raw MRI data and extracts brain connectivity network metrics. Again, the existing method of cortical parcellation from MRI data is mainly based on fixed Brodmann atlas; which does not support neonate's brain or adult's brain with neuroplasticity anomalies. In this work, we design an end-to-end toolchain that processes raw MRI data and generates network metrics for brain connectivity analysis using non-anatomical equal-area parcellation. We process the structural and diffusion MRI data to generate the parcellated and segmented image, extract white matter tracks and build structural connectome and then interface it with Brain Connectivity Toolbox to extract graph theory measures.Clinical relevance An automated tool for end-to-end processing of MRI data to brain connectivity pattern extraction and its quantitative characterisation for diagnosing brain disorder.


Assuntos
Conectoma , Substância Branca , Adulto , Encéfalo/diagnóstico por imagem , Imagem de Difusão por Ressonância Magnética , Humanos , Recém-Nascido , Imagem por Ressonância Magnética
5.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1568-1571, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018292

RESUMO

There is growing evidence that the use of stringent and dichotomic diagnostic categories in many medical disciplines (particularly 'brain sciences' as neurology and psychiatry) is an oversimplification. Although clear diagnostic boundaries remain useful for patients, families, and their access to dedicated NHS and health care services, the traditional dichotomic categories are not helpful to describe the complexity and large heterogeneity of symptoms across many and overlapping clinical phenotypes. With the advent of 'big' multimodal neuroimaging databases, data-driven stratification of the wide spectrum of healthy human physiology or disease based on neuroimages is theoretically become possible. However, this conceptual framework is hampered by severe computational constraints. In this paper we present a novel, deep learning based encode-decode architecture which leverages several parameter efficiency techniques generate latent deep embedding which compress the information contained in a full 3D neuroimaging volume by a factor 1000 while still retaining anatomical detail and hence rendering the subsequent stratification problem tractable. We train our architecture on 1003 brain scan derived from the human connectome project and demonstrate the faithfulness of the obtained reconstructions. Further, we employ a data driven clustering technique driven by a grid search in hyperparameter space to identify six different strata within the 1003 healthy community dwelling individuals which turn out to correspond to highly significant group differences in both physiological and cognitive data. Indicating that the well-known relationships between such variables and brain structure can be probed in an unsupervised manner through our novel architecture and pipeline. This opens the door to a variety of previously inaccessible applications in the realm of data driven stratification of large cohorts based on neuroimaging data.Clinical Relevance -With our approach, each person can be described and classified within a multi-dimensional space of data, where they are uniquely classified according to their individual anatomy, physiology and disease-related anatomical and physiological alterations.


Assuntos
Conectoma , Aprendizado Profundo , Neuroimagem , Encéfalo , Análise por Conglomerados , Bases de Dados Factuais , Humanos
6.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1071-1074, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018171

RESUMO

While Deep Learning methods have been successfully applied to tackle a wide variety of prediction problems, their application has been mostly limited to data structured in a grid-like fashion. However, the study of the human brain "connectome" involves the representation of the brain as a graph with interacting nodes. In this paper, we extend the Graph Attention Network (GAT), a novel neural network (NN) architecture acting on the features of the nodes of a binary graph, to handle a set of graphs provided with node features and non-binary edge weights. We demonstrate the effectiveness of our architecture by training it multimodal data collected from a large homogeneous fMRI dataset (n=1003 individuals with multiple fMRI sessions per subject) made publicly available by the Human Connectome Project (HCP), demonstrating good performance and seamless integration of multimodal neuroimaging data. Our adaptation provides a powerful and flexible deep learning tool to integrate multimodal neuroimaging connectomics data in a predictive context.


Assuntos
Encéfalo , Conectoma , Atenção , Encéfalo/diagnóstico por imagem , Humanos , Imagem por Ressonância Magnética , Neuroimagem
7.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1088-1091, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018175

RESUMO

A unified framework for the analysis of fluorescence data taken by a two-photon imaging system is presented. As in the processing of blood-oxygen-level-dependent signals of functional magnetic resonance imaging, the acquired functional images have to be co-registered with a structural brain atlas before delineating the regions activated by a given stimulus. The voxels whose calcium traces are highly correlated with the predicted responses are demarcated without the need for subjective reasoning. Experimental data acquired while presenting olfactory stimuli are used to demonstrate the efficacy of the proposed schemes. The results indicate that the functional images of a Drosophila individual can be normalized into a standard stereotactic space, and the expected brain regions can be delineated adequately. This framework provides an opportunity to enable the development of a Drosophila functional connectome database.


Assuntos
Conectoma , Drosophila , Animais , Encéfalo/diagnóstico por imagem , Imageamento Tridimensional , Imagem por Ressonância Magnética
8.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1120-1123, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018183

RESUMO

In recent years, the conceptualisation of the brain as a "connectome" as summary measures derived from graph theory analyses, has become increasingly popular. Still, such approaches are inherently limited by the need to condense and simplify temporal fMRI dynamics and architecture into a purely spatial representation. We formulate a novel architecture based on Geometric Deep Learning which is specifically tailored to the one-step integration of spatial relationship between nodes and single-node temporal dynamics. We compare different spatiotemporal modelling mechanisms and demonstrate the effectiveness of our architecture in a binary prediction task based on a large homogeneous fMRI dataset made publicly available by the Human Connectome Project (HCP). As the idea of e.g. a dynamical network connectivity is beginning to make its way into the more mainstream toolset which neuroscientists commonly employ with neuroimaging data, our model can contribute to laying the groundwork for explicitly incorporating spatiotemporal information into every association and prediction problem in neuroscience.


Assuntos
Conectoma , Neurociências , Encéfalo/diagnóstico por imagem , Humanos , Imagem por Ressonância Magnética
9.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1692-1695, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018322

RESUMO

With several initiatives well underway towards amassing large and high-quality population-based neuroimaging datasets, deep learning is set to push the boundaries of what is possible in classification and prediction in neuroimaging studies. This includes those that derive increasingly popular structural connectomes, which map out the connections (and their relative strengths) between brain regions. Here, we test different Convolutional Neural Network (CNN) models in a benchmark sex prediction task in a large sample of N=3,152 structural connectomes acquired from the UK Biobank, and compare results across different connectome processing choices. The best results (76.5% test accuracy) were achieved using Fractional Anisotropy (FA) weighted connectomes, without sparsification, and with a simple weight normalisation through division by the maximum FA value. We also confirm that for structural connectomes, a Graph CNN approach, the recently proposed BrainNetCNN, outperforms an image-based CNN.


Assuntos
Conectoma , Anisotropia , Encéfalo/diagnóstico por imagem , Humanos , Redes Neurais de Computação
10.
Phys Rev Lett ; 125(12): 128102, 2020 Sep 18.
Artigo em Inglês | MEDLINE | ID: mdl-33016724

RESUMO

Neurodegenerative diseases, such as Alzheimer's or Parkinson's disease, show characteristic degradation of structural brain networks. This degradation eventually leads to changes in the network dynamics and degradation of cognitive functions. Here, we model the progression in terms of coupled physical processes: The accumulation of toxic proteins, given by a nonlinear reaction-diffusion transport process, yields an evolving brain connectome characterized by weighted edges on which a neuronal-mass model evolves. The progression of the brain functions can be tested by simulating the resting-state activity on the evolving brain network. We show that while the evolution of edge weights plays a minor role in the overall progression of the disease, dynamic biomarkers predict a transition over a period of 10 years associated with strong cognitive decline.


Assuntos
Demência/patologia , Modelos Neurológicos , Doenças Neurodegenerativas/patologia , Animais , Relógios Biológicos , Encéfalo/patologia , Encéfalo/fisiopatologia , Morte Celular/fisiologia , Disfunção Cognitiva/patologia , Disfunção Cognitiva/fisiopatologia , Conectoma/métodos , Demência/fisiopatologia , Humanos , Camundongos , Doenças Neurodegenerativas/fisiopatologia , Neurônios/patologia
11.
J Vis Exp ; (162)2020 08 18.
Artigo em Inglês | MEDLINE | ID: mdl-32894269

RESUMO

In vitro slice electrophysiology techniques measure single-cell activity with precise electrical and temporal resolution. Brain slices must be relatively thin to properly visualize and access neurons for patch-clamping or imaging, and in vitro examination of brain circuitry is limited to only what is physically present in the acute slice. To maintain the benefits of in vitro slice experimentation while preserving a larger portion of presynaptic nuclei, we developed a novel slice preparation. This "wedge slice" was designed for patch-clamp electrophysiology recordings to characterize the diverse monaural, sound-driven inputs to medial olivocochlear (MOC) neurons in the brainstem. These neurons receive their primary afferent excitatory and inhibitory inputs from neurons activated by stimuli in the contralateral ear and corresponding cochlear nucleus (CN). An asymmetrical brain slice was designed which is thickest in the rostro-caudal domain at the lateral edge of one hemisphere and then thins towards the lateral edge of the opposite hemisphere. This slice contains, on the thick side, the auditory nerve root conveying information about auditory stimuli to the brain, the intrinsic CN circuitry, and both the disynaptic excitatory and trisynaptic inhibitory afferent pathways that converge on contralateral MOC neurons. Recording is performed from MOC neurons on the thin side of the slice, where they are visualized using DIC optics for typical patch-clamp experiments. Direct stimulation of the auditory nerve is performed as it enters the auditory brainstem, allowing for intrinsic CN circuit activity and synaptic plasticity to occur at synapses upstream of MOC neurons. With this technique, one can mimic in vivo circuit activation as closely as possible within the slice. This wedge slice preparation is applicable to other brain circuits where circuit analyses would benefit from preservation of upstream connectivity and long-range inputs, in combination with the technical advantages of in vitro slice physiology.


Assuntos
Tronco Encefálico/citologia , Tronco Encefálico/fisiologia , Conectoma/métodos , Neurônios/fisiologia , Animais , Vias Auditivas/fisiologia , Nervo Coclear/fisiologia , Núcleo Coclear/citologia , Núcleo Coclear/fisiologia , Núcleo Olivar/citologia , Núcleo Olivar/fisiologia , Técnicas de Patch-Clamp , Sinapses/fisiologia
12.
PLoS One ; 15(9): e0239475, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32976545

RESUMO

Diffusion-weighted MRI makes it possible to quantify subvoxel brain microstructure and to reconstruct white matter fiber trajectories with which structural connectomes can be created. However, at the border between cerebrospinal fluid and white matter, or in the presence of edema, the obtained MRI signal originates from both the cerebrospinal fluid as well as from the white matter partial volume. Diffusion tractography can be strongly influenced by these free water partial volume effects. Thus, including a free water model can improve diffusion tractography in glioma patients. Here, we analyze how including a free water model influences structural connectivity estimates in healthy subjects as well as in brain tumor patients. During a clinical study, we acquired diffusion MRI data of 35 glioma patients and 28 age- and sex-matched controls, on which we applied an open-source deep learning based free water model. We performed deterministic as well as probabilistic tractography before and after free water modeling, and utilized the tractograms to create structural connectomes. Finally, we performed a quantitative analysis of the connectivity matrices. In our experiments, the number of tracked diffusion streamlines increased by 13% for high grade glioma patients, 9.25% for low grade glioma, and 7.65% for healthy controls. Intra-subject similarity of hemispheres increased significantly for the patient as well as for the control group, with larger effects observed in the patient group. Furthermore, inter-subject differences in connectivity between brain tumor patients and healthy subjects were reduced when including free water modeling. Our results indicate that free water modeling increases the similarity of connectivity matrices in brain tumor patients, while the observed effects are less pronounced in healthy subjects. As the similarity between brain tumor patients and healthy controls also increased, connectivity changes in brain tumor patients may have been overestimated in studies that did not perform free water modeling.


Assuntos
Neoplasias Encefálicas/patologia , Imagem de Difusão por Ressonância Magnética , Glioma/patologia , Água/química , Adolescente , Adulto , Idoso , Idoso de 80 Anos ou mais , Conectoma/métodos , Aprendizado Profundo , Feminino , Humanos , Processamento de Imagem Assistida por Computador/métodos , Masculino , Pessoa de Meia-Idade , Estudos Prospectivos , Substância Branca/patologia , Adulto Jovem
13.
PLoS Comput Biol ; 16(9): e1008144, 2020 09.
Artigo em Inglês | MEDLINE | ID: mdl-32886673

RESUMO

At the macroscale, the brain operates as a network of interconnected neuronal populations, which display coordinated rhythmic dynamics that support interareal communication. Understanding how stimulation of different brain areas impacts such activity is important for gaining basic insights into brain function and for further developing therapeutic neurmodulation. However, the complexity of brain structure and dynamics hinders predictions regarding the downstream effects of focal stimulation. More specifically, little is known about how the collective oscillatory regime of brain network activity-in concert with network structure-affects the outcomes of perturbations. Here, we combine human connectome data and biophysical modeling to begin filling these gaps. By tuning parameters that control collective system dynamics, we identify distinct states of simulated brain activity and investigate how the distributed effects of stimulation manifest at different dynamical working points. When baseline oscillations are weak, the stimulated area exhibits enhanced power and frequency, and due to network interactions, activity in this excited frequency band propagates to nearby regions. Notably, beyond these linear effects, we further find that focal stimulation causes more distributed modifications to interareal coherence in a band containing regions' baseline oscillation frequencies. Importantly, depending on the dynamical state of the system, these broadband effects can be better predicted by functional rather than structural connectivity, emphasizing a complex interplay between anatomical organization, dynamics, and response to perturbation. In contrast, when the network operates in a regime of strong regional oscillations, stimulation causes only slight shifts in power and frequency, and structural connectivity becomes most predictive of stimulation-induced changes in network activity patterns. In sum, this work builds upon and extends previous computational studies investigating the impacts of stimulation, and underscores the fact that both the stimulation site, and, crucially, the regime of brain network dynamics, can influence the network-wide responses to local perturbations.


Assuntos
Encéfalo/fisiologia , Conectoma , Modelos Neurológicos , Humanos , Neurônios/fisiologia
14.
PLoS Comput Biol ; 16(9): e1008186, 2020 09.
Artigo em Inglês | MEDLINE | ID: mdl-32941425

RESUMO

Identifying heterogeneous cognitive impairment markers at an early stage is vital for Alzheimer's disease diagnosis. However, due to complex and uncertain brain connectivity features in the cognitive domains, it remains challenging to quantify functional brain connectomic changes during non-pharmacological interventions for amnestic mild cognitive impairment (aMCI) patients. We present a quantitative method for functional brain network analysis of fMRI data based on the multi-graph unsupervised Gaussian embedding method (MG2G). This neural network-based model can effectively learn low-dimensional Gaussian distributions from the original high-dimensional sparse functional brain networks, quantify uncertainties in link prediction, and discover the intrinsic dimensionality of brain networks. Using the Wasserstein distance to measure probabilistic changes, we discovered that brain regions in the default mode network and somatosensory/somatomotor hand, fronto-parietal task control, memory retrieval, and visual and dorsal attention systems had relatively large variations during non-pharmacological training, which might provide distinct biomarkers for fine-grained monitoring of aMCI cognitive alteration. An important finding of our study is the ability of the new method to capture subtle changes for individual patients before and after short-term intervention. More broadly, the MG2G method can be used in studying multiple brain disorders and injuries, e.g., in Parkinson's disease or traumatic brain injury (TBI), and hence it will be useful to the wider neuroscience community.


Assuntos
Encéfalo , Disfunção Cognitiva , Diagnóstico por Computador/métodos , Distribuição Normal , Encéfalo/diagnóstico por imagem , Encéfalo/fisiopatologia , Disfunção Cognitiva/diagnóstico por imagem , Disfunção Cognitiva/fisiopatologia , Disfunção Cognitiva/terapia , Conectoma , Humanos , Imagem por Ressonância Magnética , Memória/fisiologia , Testes de Estado Mental e Demência , Pessoa de Meia-Idade , Aprendizado de Máquina não Supervisionado
15.
Nat Commun ; 11(1): 4632, 2020 09 15.
Artigo em Inglês | MEDLINE | ID: mdl-32934230

RESUMO

Mapping neuroanatomy is a foundational goal towards understanding brain function. Electron microscopy (EM) has been the gold standard for connectivity analysis because nanoscale resolution is necessary to unambiguously resolve synapses. However, molecular information that specifies cell types is often lost in EM reconstructions. To address this, we devise a light microscopy approach for connectivity analysis of defined cell types called spectral connectomics. We combine multicolor labeling (Brainbow) of neurons with multi-round immunostaining Expansion Microscopy (miriEx) to simultaneously interrogate morphology, molecular markers, and connectivity in the same brain section. We apply this strategy to directly link inhibitory neuron cell types with their morphologies. Furthermore, we show that correlative Brainbow and endogenous synaptic machinery immunostaining can define putative synaptic connections between neurons, as well as map putative inhibitory and excitatory inputs. We envision that spectral connectomics can be applied routinely in neurobiology labs to gain insights into normal and pathophysiological neuroanatomy.


Assuntos
Conectoma/métodos , Microscopia/métodos , Neurônios/fisiologia , Animais , Encéfalo/fisiologia , Camundongos , Camundongos Endogâmicos C57BL , Neuroanatomia , Neurônios/química , Sinapses/química , Sinapses/fisiologia
17.
Proc Natl Acad Sci U S A ; 117(35): 21681-21689, 2020 09 01.
Artigo em Inglês | MEDLINE | ID: mdl-32817555

RESUMO

With the medial frontal cortex (MFC) centrally implicated in several major neuropsychiatric disorders, it is critical to understand the extent to which MFC organization is comparable between humans and animals commonly used in preclinical research (namely rodents and nonhuman primates). Although the cytoarchitectonic structure of the rodent MFC has mostly been conserved in humans, it is a long-standing question whether the structural analogies translate to functional analogies. Here, we probed this question using ultra high field fMRI data to compare rat, marmoset, and human MFC functional connectivity. First, we applied hierarchical clustering to intrinsically define the functional boundaries of the MFC in all three species, independent of cytoarchitectonic definitions. Then, we mapped the functional connectivity "fingerprints" of these regions with a number of different brain areas. Because rats do not share cytoarchitectonically defined regions of the lateral frontal cortex (LFC) with primates, the fingerprinting method also afforded the unique ability to compare the rat MFC and marmoset LFC, which have often been suggested to be functional analogs. The results demonstrated remarkably similar intrinsic functional organization of the MFC across the species, but clear differences between rodent and primate MFC whole-brain connectivity. Rat MFC patterns of connectivity showed greatest similarity with premotor regions in the marmoset, rather than dorsolateral prefrontal regions, which are often suggested to be functionally comparable. These results corroborate the viability of the marmoset as a preclinical model of human MFC dysfunction, and suggest divergence of functional connectivity between rats and primates in both the MFC and LFC.


Assuntos
Vias Neurais/fisiologia , Córtex Pré-Frontal/fisiologia , Animais , Evolução Biológica , Encéfalo/fisiologia , Mapeamento Encefálico/métodos , Callithrix/anatomia & histologia , Conectoma/métodos , Feminino , Lobo Frontal/anatomia & histologia , Lobo Frontal/fisiologia , Substância Cinzenta/fisiologia , Humanos , Imagem por Ressonância Magnética/métodos , Masculino , Vias Neurais/anatomia & histologia , Córtex Pré-Frontal/anatomia & histologia , Ratos , Ratos Wistar
18.
Neuron ; 107(6): 1029-1047, 2020 09 23.
Artigo em Inglês | MEDLINE | ID: mdl-32755550

RESUMO

Viral tracers are important tools for neuroanatomical mapping and genetic payload delivery. Genetically modified viruses allow for cell-type-specific targeting and overcome many limitations of non-viral tracers. Here, we summarize the viruses that have been developed for neural circuit mapping, and we provide a primer on currently applied anterograde and retrograde viral tracers with practical guidance on experimental uses. We also discuss and highlight key technical and conceptual considerations for developing new safer and more effective anterograde trans-synaptic viral vectors for neural circuit analysis in multiple species.


Assuntos
Conectoma/métodos , Técnicas de Rastreamento Neuroanatômico/métodos , Sinapses/fisiologia , Vírus/genética , Animais , Vetores Genéticos/genética , Vetores Genéticos/metabolismo , Humanos , Vias Neurais/citologia , Vias Neurais/fisiologia , Sinapses/metabolismo , Vírus/metabolismo
19.
PLoS Comput Biol ; 16(7): e1007686, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-32735580

RESUMO

The capability of cortical regions to flexibly sustain an "ignited" state of activity has been discussed in relation to conscious perception or hierarchical information processing. Here, we investigate how the intrinsic propensity of different regions to get ignited is determined by the specific topological organisation of the structural connectome. More specifically, we simulated the resting-state dynamics of mean-field whole-brain models and assessed how dynamic multistability and ignition differ between a reference model embedding a realistic human connectome, and alternative models based on a variety of randomised connectome ensembles. We found that the strength of global excitation needed to first trigger ignition in a subset of regions is substantially smaller for the model embedding the empirical human connectome. Furthermore, when increasing the strength of excitation, the propagation of ignition outside of this initial core-which is able to self-sustain its high activity-is way more gradual than for any of the randomised connectomes, allowing for graded control of the number of ignited regions. We explain both these assets in terms of the exceptional weighted core-shell organisation of the empirical connectome, speculating that this topology of human structural connectivity may be attuned to support enhanced ignition dynamics.


Assuntos
Córtex Cerebral , Conectoma/métodos , Algoritmos , Córtex Cerebral/anatomia & histologia , Córtex Cerebral/diagnóstico por imagem , Córtex Cerebral/fisiologia , Biologia Computacional , Humanos , Imagem por Ressonância Magnética , Masculino
20.
PLoS Biol ; 18(7): e3000810, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-32735557

RESUMO

The temporal association cortex is considered a primate specialization and is involved in complex behaviors, with some, such as language, particularly characteristic of humans. The emergence of these behaviors has been linked to major differences in temporal lobe white matter in humans compared with monkeys. It is unknown, however, how the organization of the temporal lobe differs across several anthropoid primates. Therefore, we systematically compared the organization of the major temporal lobe white matter tracts in the human, gorilla, and chimpanzee great apes and in the macaque monkey. We show that humans and great apes, in particular the chimpanzee, exhibit an expanded and more complex occipital-temporal white matter system; additionally, in humans, the invasion of dorsal tracts into the temporal lobe provides a further specialization. We demonstrate the reorganization of different tracts along the primate evolutionary tree, including distinctive connectivity of human temporal gray matter.


Assuntos
Conectoma , Hominidae/anatomia & histologia , Macaca/anatomia & histologia , Lobo Temporal/anatomia & histologia , Substância Branca/anatomia & histologia , Animais , Humanos
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