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1.
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
2.
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
3.
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
4.
Proc Natl Acad Sci U S A ; 117(31): 18780-18787, 2020 08 04.
Artigo em Inglês | MEDLINE | ID: mdl-32699144

RESUMO

Macular telangiectasia type 2 (MacTel), a late-onset macular degeneration, has been linked to a loss in the retina of Müller glial cells and the amino acid serine, synthesized by the Müller cells. The disease is confined mainly to a central retinal region called the MacTel zone. We have used electron microscopic connectomics techniques, optimized for disease analysis, to study the retina from a 48-y-old woman suffering from MacTel. The major observations made were specific changes in mitochondrial structure within and outside the MacTel zone that were present in all retinal cell types. We also identified an abrupt boundary of the MacTel zone that coincides with the loss of Müller cells and macular pigment. Since Müller cells synthesize retinal serine, we propose that a deficiency of serine, required for mitochondrial maintenance, causes mitochondrial changes that underlie MacTel development.


Assuntos
Conectoma/métodos , Retina , Doenças Retinianas , Feminino , Humanos , Degeneração Macular/diagnóstico por imagem , Degeneração Macular/patologia , Microscopia Eletrônica , Pessoa de Meia-Idade , Retina/citologia , Retina/diagnóstico por imagem , Retina/patologia , Doenças Retinianas/diagnóstico por imagem , Doenças Retinianas/patologia
5.
PLoS One ; 15(7): e0235039, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32707574

RESUMO

Functional brain network (FBN), estimated with functional magnetic resonance imaging (fMRI), has become a potentially useful way of diagnosing neurological disorders in their early stages by comparing the connectivity patterns between different brain regions across subjects. However, this depends, to a great extent, on the quality of the estimated FBNs, indicating that FBN estimation is a key step for the subsequent task of disorder identification. In the past decades, researchers have developed many methods to estimate FBNs, including Pearson's correlation and (regularized) partial correlation, etc. Despite their widespread applications in current studies, most of the existing methods estimate FBNs only based on the dependency between the measured blood oxygen level dependent (BOLD) signals, which ignores spatial relationship of signals associated with different brain regions. Due to the space and material parsimony principle of our brain, we believe that the spatial distance between brain regions has an important influence on FBN topology. Therefore, in this paper, we assume that spatially neighboring brain regions tend to have stronger connections and/or share similar connections with others; based on this assumption, we propose two novel methods to estimate FBNs by incorporating the information of brain region distance into the estimation model. To validate the effectiveness of the proposed methods, we use the estimated FBNs to identify subjects with mild cognitive impairment (MCI) from normal controls (NCs). Experimental results show that the proposed methods are better than the baseline methods in the sense of MCI identification accuracy.


Assuntos
Disfunção Cognitiva/diagnóstico , Conectoma/métodos , Imagem por Ressonância Magnética/métodos , Rede Nervosa , Idoso , Idoso de 80 Anos ou mais , Algoritmos , Mapeamento Encefálico/métodos , Disfunção Cognitiva/diagnóstico por imagem , Feminino , Humanos , Masculino , Modelos Teóricos , Rede Nervosa/diagnóstico por imagem , Análise Espacial
6.
PLoS One ; 15(7): e0235860, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32645058

RESUMO

The Human Connectome Project (HCP) is a large structural and functional MRI dataset with a rich array of behavioral and genotypic measures, as well as a biologically verified family structure. This makes it a valuable resource for investigating questions about individual differences, including questions about heritability. While its MRI data have been analyzed extensively in this regard, to our knowledge a comprehensive estimation of the heritability of the behavioral dataset has never been conducted. Using a set of behavioral measures of personality, emotion and cognition, we show that it is possible to re-identify the same individual across two testing times (fingerprinting), and to identify identical twins significantly above chance. Standard heritability estimates of 37 behavioral measures were derived from twin correlations, and machine-learning models (univariate linear model, Ridge classifier and Random Forest model) were trained to classify monozygotic twins and dizygotic twins. Correlations between the standard heritability metric and each set of model weights ranged from 0.36 to 0.7, and questionnaire-based and task-based measures did not differ significantly in their heritability. We further explored the heritability of a smaller number of latent factors extracted from the 37 measures and repeated the heritability estimation; in this case, the correlations between the standard heritability and each set of model weights were lower, ranging from 0.05 to 0.43. One specific discrepancy arose for the general intelligence factor, which all models assigned high importance, but the standard heritability calculation did not. We present a thorough investigation of the heritabilities of the behavioral measures in the HCP as a resource for other investigators, and illustrate the utility of machine-learning methods for qualitative characterization of the differential heritability across diverse measures.


Assuntos
Conectoma , Imagem por Ressonância Magnética , Cognição , Conectoma/métodos , Emoções , Genótipo , Humanos , Aprendizado de Máquina , Imagem por Ressonância Magnética/métodos , Personalidade , Fenótipo , Gêmeos Monozigóticos
7.
Nat Commun ; 11(1): 2650, 2020 05 27.
Artigo em Inglês | MEDLINE | ID: mdl-32461583

RESUMO

Although the feeling of stress is ubiquitous, the neural mechanisms underlying this affective experience remain unclear. Here, we investigate functional hippocampal connectivity throughout the brain during an acute stressor and use machine learning to demonstrate that these networks can specifically predict the subjective feeling of stress. During a stressor, hippocampal connectivity with a network including the hypothalamus (known to regulate physiological stress) predicts feeling more stressed, whereas connectivity with regions such as dorsolateral prefrontal cortex (associated with emotion regulation) predicts less stress. These networks do not predict a subjective state unrelated to stress, and a nonhippocampal network does not predict subjective stress. Hippocampal networks are consistent, specific to the construct of subjective stress, and broadly informative across measures of subjective stress. This approach provides opportunities for relating hypothesis-driven functional connectivity networks to clinically meaningful subjective states. Together, these results identify hippocampal networks that modulate the feeling of stress.


Assuntos
Conectoma , Emoções/fisiologia , Hipocampo/fisiologia , Rede Nervosa/fisiologia , Adulto , Conectoma/métodos , Conectoma/psicologia , Feminino , Hipocampo/diagnóstico por imagem , Humanos , Hipotálamo/diagnóstico por imagem , Imagem por Ressonância Magnética/métodos , Masculino , Modelos Teóricos , Neurociências/métodos , Córtex Pré-Frontal/diagnóstico por imagem , Estresse Fisiológico/fisiologia , Adulto Jovem
8.
Brain ; 143(4): 1249-1260, 2020 04 01.
Artigo em Inglês | MEDLINE | ID: mdl-32176777

RESUMO

There is both clinical and neuroanatomical variability at the single-subject level in Alzheimer's disease, complicating our understanding of brain-behaviour relationships and making it challenging to develop neuroimaging biomarkers to track disease severity, progression, and response to treatment. Prior work has shown that both group-level atrophy in clinical dementia syndromes and complex neurological symptoms in patients with focal brain lesions localize to brain networks. Here, we use a new technique termed 'atrophy network mapping' to test the hypothesis that single-subject atrophy maps in patients with a clinical diagnosis of Alzheimer's disease will also localize to syndrome-specific and symptom-specific brain networks. First, we defined single-subject atrophy maps by comparing cortical thickness in each Alzheimer's disease patient versus a group of age-matched, cognitively normal subjects across two independent datasets (total Alzheimer's disease patients = 330). No more than 42% of Alzheimer's disease patients had atrophy at any given location across these datasets. Next, we determined the network of brain regions functionally connected to each Alzheimer's disease patient's location of atrophy using seed-based functional connectivity in a large (n = 1000) normative connectome. Despite the heterogeneity of atrophied regions at the single-subject level, we found that 100% of patients with a clinical diagnosis of Alzheimer's disease had atrophy functionally connected to the same brain regions in the mesial temporal lobe, precuneus cortex, and angular gyrus. Results were specific versus control subjects and replicated across two independent datasets. Finally, we used atrophy network mapping to define symptom-specific networks for impaired memory and delusions, finding that our results matched symptom networks derived from patients with focal brain lesions. Our study supports atrophy network mapping as a method to localize clinical, cognitive, and neuropsychiatric symptoms to brain networks, providing insight into brain-behaviour relationships in patients with dementia.


Assuntos
Doença de Alzheimer/diagnóstico por imagem , Doença de Alzheimer/patologia , Encéfalo/diagnóstico por imagem , Encéfalo/patologia , Conectoma/métodos , Idoso , Idoso de 80 Anos ou mais , Atrofia/diagnóstico por imagem , Atrofia/patologia , Feminino , Humanos , Interpretação de Imagem Assistida por Computador/métodos , Imagem por Ressonância Magnética , Masculino
9.
PLoS One ; 15(2): e0228334, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32074115

RESUMO

The brain works as a large-scale complex network, known as the connectome. The strength of the connections between two brain regions in the connectome is commonly estimated by calculating the correlations between their patterns of activation. This approach relies on the assumption that the activation of connected regions occurs together and at the same time. However, there are delays between the activation of connected regions due to excitatory and inhibitory connections. Here, we propose a method to harvest this additional information and reconstruct the structural brain connectome using delayed correlations. This delayed-correlation method correctly identifies 70% to 80% of connections of simulated brain networks, compared to only 5% to 25% of connections detected by the standard methods; this result is robust against changes in the network parameters (small-worldness, excitatory vs. inhibitory connection ratio, weight distribution) and network activation dynamics. The delayed-correlation method predicts more accurately both the global network properties (characteristic path length, global efficiency, clustering coefficient, transitivity) and the nodal network properties (nodal degree, nodal clustering, nodal global efficiency), particularly at lower network densities. We obtain similar results in networks derived from animal and human data. These results suggest that the use of delayed correlations improves the reconstruction of the structural brain connectome and open new possibilities for the analysis of the brain connectome, as well as for other types of networks.


Assuntos
Encéfalo/fisiologia , Conectoma/métodos , Animais , Gatos , Humanos , Macaca , Camundongos , Modelos Biológicos
10.
PLoS One ; 15(2): e0229083, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32092107

RESUMO

Learning synaptic weights of spiking neural network (SNN) models that can reproduce target spike trains from provided neural firing data is a central problem in computational neuroscience and spike-based computing. The discovery of the optimal weight values can be posed as a supervised learning task wherein the weights of the model network are chosen to maximize the similarity between the target spike trains and the model outputs. It is still largely unknown whether optimizing spike train similarity of highly recurrent SNNs produces weight matrices similar to those of the ground truth model. To this end, we propose flexible heuristic supervised learning rules, termed Pre-Synaptic Pool Modification (PSPM), that rely on stochastic weight updates in order to produce spikes within a short window of the desired times and eliminate spikes outside of this window. PSPM improves spike train similarity for all-to-all SNNs and makes no assumption about the post-synaptic potential of the neurons or the structure of the network since no gradients are required. We test whether optimizing for spike train similarity entails the discovery of accurate weights and explore the relative contributions of local and homeostatic weight updates. Although PSPM improves similarity between spike trains, the learned weights often differ from the weights of the ground truth model, implying that connectome inference from spike data may require additional constraints on connectivity statistics. We also find that spike train similarity is sensitive to local updates, but other measures of network activity such as avalanche distributions, can be learned through synaptic homeostasis.


Assuntos
Conectoma/métodos , Modelos Neurológicos , Rede Nervosa/fisiologia , Aprendizado de Máquina Supervisionado , Potenciais de Ação/fisiologia , Animais , Simulação por Computador , Terminações Pré-Sinápticas/fisiologia
11.
Ann Neurol ; 87(5): 725-738, 2020 05.
Artigo em Inglês | MEDLINE | ID: mdl-32072667

RESUMO

OBJECTIVE: Clinical trials in amyotrophic lateral sclerosis (ALS) continue to rely on survival or functional scales as endpoints, despite the emergence of quantitative biomarkers. Neuroimaging-based biomarkers in ALS have been shown to detect ALS-associated pathology in vivo, although anatomical patterns of disease spread are poorly characterized. The objective of this study is to simulate disease propagation using network analyses of cerebral magnetic resonance imaging (MRI) data to predict disease progression. METHODS: Using brain networks of ALS patients (n = 208) and matched controls across longitudinal time points, network-based statistics unraveled progressive network degeneration originating from the motor cortex and expanding in a spatiotemporal manner. We applied a computational model to the MRI scan of patients to simulate this progressive network degeneration. Simulated aggregation levels at the group and individual level were validated with empirical impairment observed at later time points of white matter and clinical decline using both internal and external datasets. RESULTS: We observe that computer-simulated aggregation levels mimic true disease patterns in ALS patients. Simulated patterns of involvement across cortical areas show significant overlap with the patterns of empirically impaired brain regions on later scans, at both group and individual levels. These findings are validated using an external longitudinal dataset of 30 patients. INTERPRETATION: Our results are in accordance with established pathological staging systems and may have implications for patient stratification in future clinical trials. Our results demonstrate the utility of computational models in ALS to predict disease progression and underscore their potential as a prognostic biomarker. ANN NEUROL 2020;87:725-738.


Assuntos
Esclerose Amiotrófica Lateral/patologia , Conectoma/métodos , Aprendizado Profundo , Neuroimagem/métodos , Idoso , Esclerose Amiotrófica Lateral/diagnóstico por imagem , Progressão da Doença , Feminino , Humanos , Imagem por Ressonância Magnética/métodos , Masculino , Pessoa de Meia-Idade
12.
Physiol Rev ; 100(3): 1181-1228, 2020 07 01.
Artigo em Inglês | MEDLINE | ID: mdl-32078778

RESUMO

For more than one century, brain processing was mainly thought in a localizationist framework, in which one given function was underpinned by a discrete, isolated cortical area, and with a similar cerebral organization across individuals. However, advances in brain mapping techniques in humans have provided new insights into the organizational principles of anatomo-functional architecture. Here, we review recent findings gained from neuroimaging, electrophysiological, as well as lesion studies. Based on these recent data on brain connectome, we challenge the traditional, outdated localizationist view and propose an alternative meta-networking theory. This model holds that complex cognitions and behaviors arise from the spatiotemporal integration of distributed but relatively specialized networks underlying conation and cognition (e.g., language, spatial cognition). Dynamic interactions between such circuits result in a perpetual succession of new equilibrium states, opening the door to considerable interindividual behavioral variability and to neuroplastic phenomena. Indeed, a meta-networking organization underlies the uniquely human propensity to learn complex abilities, and also explains how postlesional reshaping can lead to some degrees of functional compensation in brain-damaged patients. We discuss the major implications of this approach in fundamental neurosciences as well as for clinical developments, especially in neurology, psychiatry, neurorehabilitation, and restorative neurosurgery.


Assuntos
Córtex Cerebral/anatomia & histologia , Córtex Cerebral/fisiologia , Rede Nervosa , Conectoma/métodos , Humanos , Vias Neurais/fisiologia , Plasticidade Neuronal/fisiologia
14.
Brain Connect ; 10(2): 72-82, 2020 03.
Artigo em Inglês | MEDLINE | ID: mdl-32056450

RESUMO

Hierarchical organization of brain function has been an established concept in the neuroscience field for a long time, however, it has been rarely demonstrated how such hierarchical macroscale functional networks are actually organized in the human brain. In this study, to answer this question, we propose a novel methodology to provide an evidence of hierarchical organization of functional brain networks. This article introduces the hybrid spatiotemporal deep learning (HSDL), by jointly using deep belief networks (DBNs) and deep least absolute shrinkage and selection operator (LASSO) to reveal the temporal hierarchical features and spatial hierarchical maps of brain networks based on the Human Connectome Project 900 functional magnetic resonance imaging (fMRI) data sets. Briefly, the key idea of HSDL is to extract the weights between two adjacent layers of DBNs, which are then treated as the hierarchical dictionaries for deep LASSO to identify the corresponding hierarchical spatial maps. Our results demonstrate that both spatial and temporal aspects of dozens of functional networks exhibit multiscale properties that can be well characterized and interpreted based on existing computational tools and neuroscience knowledge. Our proposed novel hybrid deep model is used to provide the first insightful opportunity to reveal the potential hierarchical organization of time series and functional brain networks, using task-based fMRI signals of human brain.


Assuntos
Encéfalo/diagnóstico por imagem , Conectoma/métodos , Aprendizado Profundo , Imagem por Ressonância Magnética/métodos , Encéfalo/fisiologia , Emoções/fisiologia , Humanos , Idioma , Vias Neurais/diagnóstico por imagem , Vias Neurais/fisiologia , Análise Espaço-Temporal
15.
Brain Connect ; 10(2): 95-104, 2020 03.
Artigo em Inglês | MEDLINE | ID: mdl-32079409

RESUMO

Research suggests that disruption of brain networks might explain cognitive deficits in multiple sclerosis (MS). The reliability and effectiveness of graph theoretic network metrics as measures of cognitive performance were tested in 37 people with MS and 23 controls. Specifically, relationships with cognitive performance (linear regression against the paced auditory serial addition test-3 seconds [PASAT-3], symbol digit modalities test [SDMT], and attention network test) and 1-month reliability (using the intraclass correlation coefficient [ICC]) of network metrics were measured using both resting-state functional and diffusion magnetic resonance imaging data. Cognitive impairment was directly related to measures of brain network segregation and inversely related to network integration (prediction of PASAT-3 by small worldness, modularity, characteristic path length, R2 = 0.55; prediction of SDMT by small worldness, global efficiency, and characteristic path length, R2 = 0.60). Reliability of the measures for 1 month in a subset of nine participants was mostly rated as good (ICC >0.6) for both controls and MS patients in both functional and diffusion data, but was highly dependent on the chosen parcellation and graph density, with the 0.2-0.5 density range being the most reliable. This suggests that disrupted network organization predicts cognitive impairment in MS and its measurement is reliable for a 1-month period. These new findings support the hypothesis of network disruption as a major determinant of cognitive deficits in MS and the future possibility of the application of derived metrics as surrogate outcomes in trials of therapies for cognitive impairment.


Assuntos
Encéfalo/diagnóstico por imagem , Conectoma/métodos , Imagem por Ressonância Magnética/métodos , Esclerose Múltipla/diagnóstico por imagem , Adulto , Encéfalo/fisiopatologia , Cognição , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Esclerose Múltipla/tratamento farmacológico , Esclerose Múltipla/fisiopatologia , Vias Neurais/diagnóstico por imagem , Vias Neurais/fisiopatologia , Testes Neuropsicológicos , Reprodutibilidade dos Testes
16.
BMC Med ; 18(1): 23, 2020 02 06.
Artigo em Inglês | MEDLINE | ID: mdl-32024511

RESUMO

BACKGROUND: The human brain is complex and interconnected structurally. Brain connectome change is associated with Alzheimer's disease (AD) and other neurodegenerative diseases. Genetics and genomics studies have identified molecular changes in AD; however, the results are often limited to isolated brain regions and are difficult to interpret its findings in respect to brain connectome. The mechanisms of how one brain region impacts the molecular pathways in other regions have not been systematically studied. And how the brain regions susceptible to AD pathology interact with each other at the transcriptome level and how these interactions relate to brain connectome change are unclear. METHODS: Here, we compared structural brain connectomes defined by probabilistic tracts using diffusion magnetic resonance imaging data in Alzheimer's Disease Neuroimaging Initiative database and a brain transcriptome dataset covering 17 brain regions. RESULTS: We observed that the changes in diffusion measures associated with AD diagnosis status and the associations were replicated in an independent cohort. The result suggests that disease associated white matter changes are focal. Analysis of the brain connectome by genomic data, tissue-tissue transcriptional synchronization between 17 brain regions, indicates that the regions connected by AD-associated tracts were likely connected at the transcriptome level with high number of tissue-to-tissue correlated (TTC) gene pairs (P = 0.03). And genes involved in TTC gene pairs between white matter tract connected brain regions were enriched in signaling pathways (P = 6.08 × 10-9). Further pathway interaction analysis identified ionotropic glutamate receptor pathway and Toll receptor signaling pathways to be important for tissue-tissue synchronization at the transcriptome level. Transcript profile entailing Toll receptor signaling in the blood was significantly associated with diffusion properties of white matter tracts, notable association between fractional anisotropy and bilateral cingulum angular bundles (Ppermutation = 1.0 × 10-2 and 4.9 × 10-4 for left and right respectively). CONCLUSIONS: In summary, our study suggests that brain connectomes defined by MRI and transcriptome data overlap with each other.


Assuntos
Doença de Alzheimer/diagnóstico por imagem , Encéfalo/diagnóstico por imagem , Conectoma/métodos , Genômica/métodos , Imagem por Ressonância Magnética/métodos , Idoso , Doença de Alzheimer/patologia , Encéfalo/patologia , Feminino , Humanos , Masculino
17.
Brain ; 143(2): 541-553, 2020 02 01.
Artigo em Inglês | MEDLINE | ID: mdl-31919494

RESUMO

Inconsistent findings from migraine neuroimaging studies have limited attempts to localize migraine symptomatology. Novel brain network mapping techniques offer a new approach for linking neuroimaging findings to a common neuroanatomical substrate and localizing therapeutic targets. In this study, we attempted to determine whether neuroanatomically heterogeneous neuroimaging findings of migraine localize to a common brain network. We used meta-analytic coordinates of decreased grey matter volume in migraineurs as seed regions to generate resting state functional connectivity network maps from a normative connectome (n = 1000). Network maps were overlapped to identify common regions of connectivity across all coordinates. Specificity of our findings was evaluated using a whole-brain Bayesian spatial generalized linear mixed model and a region of interest analysis with comparison groups of chronic pain and a neurologic control (Alzheimer's disease). We found that all migraine coordinates (11/11, 100%) were negatively connected (t ≥ ±7, P < 10-6 family-wise error corrected for multiple comparisons) to a single location in left extrastriate visual cortex overlying dorsal V3 and V3A subregions. More than 90% of coordinates (10/11) were also positively connected with bilateral insula and negatively connected with the hypothalamus. Bayesian spatial generalized linear mixed model whole-brain analysis identified left V3/V3A as the area with the most specific connectivity to migraine coordinates compared to control coordinates (voxel-wise probability of ≥90%). Post hoc region of interest analyses further supported the specificity of this finding (ANOVA P = 0.02; pairwise t-tests P = 0.03 and P = 0.003, respectively). In conclusion, using coordinate-based network mapping, we show that regions of grey matter volume loss in migraineurs localize to a common brain network defined by connectivity to visual cortex V3/V3A, a region previously implicated in mechanisms of cortical spreading depression in migraine. Our findings help unify migraine neuroimaging literature and offer a migraine-specific target for neuromodulatory treatment.


Assuntos
Mapeamento Encefálico , Encéfalo/fisiopatologia , Transtornos de Enxaqueca/fisiopatologia , Rede Nervosa/fisiopatologia , Mapeamento Encefálico/métodos , Córtex Cerebral/fisiopatologia , Conectoma/métodos , Feminino , Substância Cinzenta/fisiopatologia , Humanos , Imagem por Ressonância Magnética/métodos , Masculino , Neuroimagem/métodos , Córtex Visual/fisiopatologia
18.
Neuron ; 105(3): 435-445.e5, 2020 02 05.
Artigo em Inglês | MEDLINE | ID: mdl-31806491

RESUMO

The connectomes of organisms of the same species show remarkable architectural and often local wiring similarity, raising the question: where and how is neuronal connectivity encoded? Here, we start from the hypothesis that the genetic identity of neurons guides synapse and gap-junction formation and show that such genetically driven wiring predicts the existence of specific biclique motifs in the connectome. We identify a family of large, statistically significant biclique subgraphs in the connectomes of three species and show that within many of the observed bicliques the neurons share statistically significant expression patterns and morphological characteristics, supporting our expectation of common genetic factors that drive the synapse formation within these subgraphs. The proposed connectome model offers a self-consistent framework to link the genetics of an organism to the reproducible architecture of its connectome, offering experimentally falsifiable predictions on the genetic factors that drive the formation of individual neuronal circuits.


Assuntos
Encéfalo/fisiologia , Conectoma/métodos , Modelos Genéticos , Rede Nervosa/fisiologia , Animais , Caenorhabditis elegans , Ciona intestinalis , Drosophila
19.
World Neurosurg ; 133: e197-e204, 2020 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-31491572

RESUMO

OBJECTIVE: To evaluate the functional connectivity (FC) and resting-state networks (RSNs) in patients under anesthesia operated for resection of intracerebral lesions. METHODS: We performed intraoperative resting-state functional magnetic resonance imaging (irs-fMRI) in 24 patients under anesthesia before and after lesion resection. Correlation matrices were established for each session (a total 48 of sessions). We analyzed the changes in overall FC and in FC of the healthy and operated hemispheres between the first and second sessions. We tested the correlation between changes in FC and clinical outcomes and the duration, rate, and total dosage of anesthesia. We also performed a group analysis to detect topographic changes in RSNs in patients under anesthesia. A single-subject analysis was performed to detect clinically relevant RSNs in each patient. RESULTS: FC decreased significantly in the second session, as did interhemispheric connectivity. The decrease in the pathological hemisphere was significant and significantly greater than the decrease in the intrahemispheric connectivity of the healthy hemisphere. The change in FC was not correlated with clinical outcome or with the duration, rate, or dosage of anesthesia. Group analysis showed topographic changes in RSNs, especially in high-level networks such as default mode and salience networks. Identification of clinically relevant networks was also possible. CONCLUSIONS: FC and RSNs could be identified under anesthesia and used for extended brain mapping. Further studies are needed to optimize the depth of hypnosis to stabilize FC between sessions.


Assuntos
Neoplasias Encefálicas/diagnóstico por imagem , Conectoma/métodos , Glioma/diagnóstico por imagem , Hemangioma Cavernoso/diagnóstico por imagem , Malformações Arteriovenosas Intracranianas/diagnóstico por imagem , Imagem por Ressonância Magnética/métodos , Neuronavegação/métodos , Radiografia Intervencionista/métodos , Cirurgia Assistida por Computador , Adolescente , Adulto , Idoso , Neoplasias Encefálicas/cirurgia , Pré-Escolar , Feminino , Glioma/cirurgia , Hemangioma Cavernoso/cirurgia , Humanos , Malformações Arteriovenosas Intracranianas/cirurgia , Masculino , Pessoa de Meia-Idade , Adulto Jovem
20.
Brain Stimul ; 13(1): 10-14, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-31582301

RESUMO

BACKGROUND: Panic attacks affect a sizeable proportion of the population. The neurocircuitry of panic remains incompletely understood. OBJECTIVE: To investigate the neuroanatomical underpinnings of panic attacks induced by deep brain stimulation (DBS) through (1) connectomic analysis of an obsessive-compulsive disorder patient who experienced panic attacks during inferior thalamic peduncle DBS; (2) appraisal of existing clinical reports on DBS-induced panic attacks. METHODS: Panicogenic, ventral contact stimulation was compared with benign stimulation at other contacts using volume of tissue activated (VTA) modelling. Networks associated with the panicogenic zone were investigated using state-of-the-art normative connectivity mapping. In addition, a literature search for prior reports of DBS-induced panic attacks was conducted. RESULTS: Panicogenic VTAs impinged primarily on the tuberal hypothalamus. Compared to non-panicogenic VTAs, panicogenic loci were significantly functionally coupled to limbic and brainstem structures, including periaqueductal grey and amygdala. Previous studies found stimulation of these areas can also provoke panic attacks. CONCLUSIONS: DBS in the region of the tuberal hypothalamus elicited panic attacks in a single obsessive-compulsive disorder patient and recruited a network of structures previously implicated in panic pathophysiology, reinforcing the importance of the hypothalamus as a hub of panicogenic circuitry.


Assuntos
Conectoma/métodos , Estimulação Encefálica Profunda/métodos , Hipotálamo/fisiopatologia , Rede Nervosa/fisiopatologia , Transtorno Obsessivo-Compulsivo/fisiopatologia , Transtorno Obsessivo-Compulsivo/terapia , Adulto , Tonsila do Cerebelo/fisiopatologia , Conectoma/psicologia , Feminino , Humanos , Transtorno Obsessivo-Compulsivo/psicologia , Tálamo/fisiopatologia
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