RESUMO
Evolutionarily relevant networks have been previously described in several mammalian species using time-averaged analyses of fMRI time-series. However, fMRI network activity is highly dynamic and continually evolves over timescales of seconds. Whether the dynamic organization of resting-state fMRI network activity is conserved across mammalian species remains unclear. Using frame-wise clustering of fMRI time-series, we find that intrinsic fMRI network dynamics in awake male macaques and humans is characterized by recurrent transitions between a set of 4 dominant, neuroanatomically homologous fMRI coactivation modes (C-modes), three of which are also plausibly represented in the male rodent brain. Importantly, in all species C-modes exhibit species-invariant dynamic features, including preferred occurrence at specific phases of fMRI global signal fluctuations, and a state transition structure compatible with infraslow coupled oscillator dynamics. Moreover, dominant C-mode occurrence reconstitutes the static organization of the fMRI connectome in all species, and is predictive of ranking of corresponding fMRI connectivity gradients. These results reveal a set of species-invariant principles underlying the dynamic organization of fMRI networks in mammalian species, and offer novel opportunities to relate fMRI network findings across the phylogenetic tree.
Assuntos
Encéfalo , Conectoma , Imageamento por Ressonância Magnética , Rede Nervosa , Animais , Imageamento por Ressonância Magnética/métodos , Masculino , Humanos , Encéfalo/fisiologia , Encéfalo/diagnóstico por imagem , Camundongos , Conectoma/métodos , Rede Nervosa/fisiologia , Rede Nervosa/diagnóstico por imagem , Evolução Biológica , Adulto , Macaca , Especificidade da Espécie , Macaca mulatta , Mapeamento Encefálico/métodos , FilogeniaRESUMO
This study investigated how resting-state functional connectivity (rsFC) of the subgenual anterior cingulate cortex (sgACC) predicts antidepressant response in patients with major depressive disorder (MDD). Eighty-seven medication-free MDD patients underwent baseline resting-state functional MRI scans. After 12 weeks of escitalopram treatment, patients were classified into remission depression (RD, n = 42) and nonremission depression (NRD, n = 45) groups. We conducted two analyses: a voxel-wise rsFC analysis using sgACC as a seed to identify group differences, and a prediction model based on the sgACC rsFC map to predict treatment efficacy. Haufe transformation was used to interpret the predictive rsFC features. The RD group showed significantly higher rsFC between the sgACC and regions in the fronto-parietal network (FPN), including the bilateral dorsolateral prefrontal cortex (DLPFC) and bilateral inferior parietal lobule (IPL), compared to the NRD group. These sgACC rsFC measures correlated positively with symptom improvement. Baseline sgACC rsFC also significantly predicted treatment response after 12 weeks, with a mean accuracy of 72.64% (p < 0.001), mean area under the curve of 0.74 (p < 0.001), mean specificity of 0.82, and mean sensitivity of 0.70 in 10-fold cross-validation. The predictive voxels were mainly within the FPN. The rsFC between the sgACC and FPN is a valuable predictor of antidepressant response in MDD patients. These findings enhance our understanding of the neurobiological mechanisms underlying treatment response and could help inform personalized treatment strategies for MDD.
Assuntos
Transtorno Depressivo Maior , Giro do Cíngulo , Imageamento por Ressonância Magnética , Humanos , Transtorno Depressivo Maior/tratamento farmacológico , Transtorno Depressivo Maior/diagnóstico por imagem , Transtorno Depressivo Maior/fisiopatologia , Giro do Cíngulo/diagnóstico por imagem , Giro do Cíngulo/fisiopatologia , Masculino , Feminino , Adulto , Pessoa de Meia-Idade , Antidepressivos/uso terapêutico , Escitalopram/uso terapêutico , Escitalopram/farmacologia , Resultado do Tratamento , Conectoma , Rede Nervosa/diagnóstico por imagem , Rede Nervosa/fisiopatologia , Rede Nervosa/efeitos dos fármacosRESUMO
Many animals use visual information to navigate1-4, but how such information is encoded and integrated by the navigation system remains incompletely understood. In Drosophila melanogaster, EPG neurons in the central complex compute the heading direction5 by integrating visual input from ER neurons6-12, which are part of the anterior visual pathway (AVP)10,13-16. Here we densely reconstruct all neurons in the AVP using electron-microscopy data17. The AVP comprises four neuropils, sequentially linked by three major classes of neurons: MeTu neurons10,14,15, which connect the medulla in the optic lobe to the small unit of the anterior optic tubercle (AOTUsu) in the central brain; TuBu neurons9,16, which connect the AOTUsu to the bulb neuropil; and ER neurons6-12, which connect the bulb to the EPG neurons. On the basis of morphologies, connectivity between neural classes and the locations of synapses, we identify distinct information channels that originate from four types of MeTu neurons, and we further divide these into ten subtypes according to the presynaptic connections in the medulla and the postsynaptic connections in the AOTUsu. Using the connectivity of the entire AVP and the dendritic fields of the MeTu neurons in the optic lobes, we infer potential visual features and the visual area from which any ER neuron receives input. We confirm some of these predictions physiologically. These results provide a strong foundation for understanding how distinct sensory features can be extracted and transformed across multiple processing stages to construct higher-order cognitive representations.
Assuntos
Conectoma , Drosophila melanogaster , Neurônios , Neurópilo , Navegação Espacial , Sinapses , Vias Visuais , Animais , Drosophila melanogaster/fisiologia , Drosophila melanogaster/citologia , Vias Visuais/fisiologia , Navegação Espacial/fisiologia , Neurônios/fisiologia , Neurópilo/citologia , Masculino , Feminino , Lobo Óptico de Animais não Mamíferos/citologia , Microscopia EletrônicaRESUMO
The recent assembly of the adult Drosophila melanogaster central brain connectome, containing more than 125,000 neurons and 50 million synaptic connections, provides a template for examining sensory processing throughout the brain1,2. Here we create a leaky integrate-and-fire computational model of the entire Drosophila brain, on the basis of neural connectivity and neurotransmitter identity3, to study circuit properties of feeding and grooming behaviours. We show that activation of sugar-sensing or water-sensing gustatory neurons in the computational model accurately predicts neurons that respond to tastes and are required for feeding initiation4. In addition, using the model to activate neurons in the feeding region of the Drosophila brain predicts those that elicit motor neuron firing5-a testable hypothesis that we validate by optogenetic activation and behavioural studies. Activating different classes of gustatory neurons in the model makes accurate predictions of how several taste modalities interact, providing circuit-level insight into aversive and appetitive taste processing. Additionally, we applied this model to mechanosensory circuits and found that computational activation of mechanosensory neurons predicts activation of a small set of neurons comprising the antennal grooming circuit, and accurately describes the circuit response upon activation of different mechanosensory subtypes6-10. Our results demonstrate that modelling brain circuits using only synapse-level connectivity and predicted neurotransmitter identity generates experimentally testable hypotheses and can describe complete sensorimotor transformations.
Assuntos
Encéfalo , Drosophila melanogaster , Modelos Neurológicos , Paladar , Animais , Drosophila melanogaster/fisiologia , Encéfalo/fisiologia , Encéfalo/citologia , Paladar/fisiologia , Comportamento Alimentar/fisiologia , Asseio Animal/fisiologia , Sinapses/fisiologia , Simulação por Computador , Optogenética , Neurônios Motores/fisiologia , Feminino , Masculino , ConectomaRESUMO
Walking is a complex motor programme involving coordinated and distributed activity across the brain and the spinal cord. Halting appropriately at the correct time is a critical component of walking control. Despite progress in identifying neurons driving halting1-6, the underlying neural circuit mechanisms responsible for overruling the competing walking state remain unclear. Here, using connectome-informed models7-9 and functional studies, we explain two fundamental mechanisms by which Drosophila implement context-appropriate halting. The first mechanism ('walk-OFF') relies on GABAergic neurons that inhibit specific descending walking commands in the brain, whereas the second mechanism ('brake') relies on excitatory cholinergic neurons in the nerve cord that lead to an active arrest of stepping movements. We show that two neurons that deploy the walk-OFF mechanism inhibit distinct populations of walking-promotion neurons, leading to differential halting of forward walking or turning. The brake neurons, by constrast, override all walking commands by simultaneously inhibiting descending walking-promotion neurons and increasing the resistance at the leg joints. We characterized two behavioural contexts in which the distinct halting mechanisms were used by the animal in a mutually exclusive manner: the walk-OFF mechanism was engaged for halting during feeding and the brake mechanism was engaged for halting and stability during grooming.
Assuntos
Neurônios Colinérgicos , Drosophila melanogaster , Neurônios GABAérgicos , Caminhada , Animais , Caminhada/fisiologia , Drosophila melanogaster/fisiologia , Neurônios GABAérgicos/fisiologia , Neurônios GABAérgicos/metabolismo , Feminino , Neurônios Colinérgicos/fisiologia , Masculino , Conectoma , Encéfalo/fisiologia , Encéfalo/citologia , Vias Neurais/fisiologia , Comportamento Alimentar/fisiologia , Modelos Neurológicos , Medula Espinal/fisiologia , Medula Espinal/citologiaRESUMO
The fruit fly Drosophila melanogaster has emerged as a key model organism in neuroscience, in large part due to the concentration of collaboratively generated molecular, genetic and digital resources available for it. Here we complement the approximately 140,000 neuron FlyWire whole-brain connectome1 with a systematic and hierarchical annotation of neuronal classes, cell types and developmental units (hemilineages). Of 8,453 annotated cell types, 3,643 were previously proposed in the partial hemibrain connectome2, and 4,581 are new types, mostly from brain regions outside the hemibrain subvolume. Although nearly all hemibrain neurons could be matched morphologically in FlyWire, about one-third of cell types proposed for the hemibrain could not be reliably reidentified. We therefore propose a new definition of cell type as groups of cells that are each quantitatively more similar to cells in a different brain than to any other cell in the same brain, and we validate this definition through joint analysis of FlyWire and hemibrain connectomes. Further analysis defined simple heuristics for the reliability of connections between brains, revealed broad stereotypy and occasional variability in neuron count and connectivity, and provided evidence for functional homeostasis in the mushroom body through adjustments of the absolute amount of excitatory input while maintaining the excitation/inhibition ratio. Our work defines a consensus cell type atlas for the fly brain and provides both an intellectual framework and open-source toolchain for brain-scale comparative connectomics.
Assuntos
Encéfalo , Conectoma , Drosophila melanogaster , Neurônios , Animais , Drosophila melanogaster/citologia , Drosophila melanogaster/fisiologia , Neurônios/citologia , Neurônios/fisiologia , Neurônios/classificação , Encéfalo/citologia , Encéfalo/fisiologia , Reprodutibilidade dos Testes , Masculino , Curadoria de Dados , Feminino , Contagem de Células , Corpos Pedunculados/citologia , Corpos Pedunculados/fisiologiaRESUMO
A goal of neuroscience is to obtain a causal model of the nervous system. The recently reported whole-brain fly connectome1-3 specifies the synaptic paths by which neurons can affect each other, but not how strongly they do affect each other in vivo. To overcome this limitation, we introduce a combined experimental and statistical strategy for efficiently learning a causal model of the fly brain, which we refer to as the 'effectome'. Specifically, we propose an estimator for a linear dynamical model of the fly brain that uses stochastic optogenetic perturbation data to estimate causal effects and the connectome as a prior to greatly improve estimation efficiency. We validate our estimator in connectome-based linear simulations and show that it recovers a linear approximation to the nonlinear dynamics of more biophysically realistic simulations. We then analyse the connectome to propose circuits that dominate the dynamics of the fly nervous system. We discover that the dominant circuits involve only relatively small populations of neurons-thus, neuron-level imaging, stimulation and identification are feasible. This approach also re-discovers known circuits and generates testable hypotheses about their dynamics. Overall, we provide evidence that fly whole-brain dynamics are generated by a large collection of small circuits that operate largely independently of each other. This implies that a causal model of a brain can be feasibly obtained in the fly.
Assuntos
Encéfalo , Conectoma , Drosophila melanogaster , Modelos Neurológicos , Neurônios , Animais , Encéfalo/fisiologia , Drosophila melanogaster/fisiologia , Neurônios/fisiologia , Optogenética , Modelos Lineares , Masculino , Feminino , Processos Estocásticos , Reprodutibilidade dos TestesRESUMO
Connections between neurons can be mapped by acquiring and analysing electron microscopic brain images. In recent years, this approach has been applied to chunks of brains to reconstruct local connectivity maps that are highly informative1-6, but nevertheless inadequate for understanding brain function more globally. Here we present a neuronal wiring diagram of a whole brain containing 5 × 107 chemical synapses7 between 139,255 neurons reconstructed from an adult female Drosophila melanogaster8,9. The resource also incorporates annotations of cell classes and types, nerves, hemilineages and predictions of neurotransmitter identities10-12. Data products are available for download, programmatic access and interactive browsing and have been made interoperable with other fly data resources. We derive a projectome-a map of projections between regions-from the connectome and report on tracing of synaptic pathways and the analysis of information flow from inputs (sensory and ascending neurons) to outputs (motor, endocrine and descending neurons) across both hemispheres and between the central brain and the optic lobes. Tracing from a subset of photoreceptors to descending motor pathways illustrates how structure can uncover putative circuit mechanisms underlying sensorimotor behaviours. The technologies and open ecosystem reported here set the stage for future large-scale connectome projects in other species.
Assuntos
Encéfalo , Conectoma , Drosophila melanogaster , Neurônios , Sinapses , Animais , Drosophila melanogaster/fisiologia , Drosophila melanogaster/citologia , Feminino , Encéfalo/citologia , Encéfalo/fisiologia , Neurônios/fisiologia , Neurônios/citologia , Vias Neurais/fisiologia , Vias Neurais/citologia , Neurotransmissores/metabolismo , Lobo Óptico de Animais não Mamíferos/citologia , Lobo Óptico de Animais não Mamíferos/fisiologia , Vias Eferentes/fisiologia , Vias Eferentes/citologia , Células Fotorreceptoras de Invertebrados/fisiologia , Células Fotorreceptoras de Invertebrados/citologiaRESUMO
A catalogue of neuronal cell types has often been called a 'parts list' of the brain1, and regarded as a prerequisite for understanding brain function2,3. In the optic lobe of Drosophila, rules of connectivity between cell types have already proven to be essential for understanding fly vision4,5. Here we analyse the fly connectome to complete the list of cell types intrinsic to the optic lobe, as well as the rules governing their connectivity. Most new cell types contain 10 to 100 cells, and integrate information over medium distances in the visual field. Some existing type families (Tm, Li, and LPi)6-10 at least double in number of types. A new serpentine medulla (Sm) interneuron family contains more types than any other. Three families of cross-neuropil types are revealed. The consistency of types is demonstrated by analysing the distances in high-dimensional feature space, and is further validated by algorithms that select small subsets of discriminative features. We use connectivity to hypothesize about the functional roles of cell types in motion, object and colour vision. Connectivity with 'boundary types' that straddle the optic lobe and central brain is also quantified. We showcase the advantages of connectomic cell typing: complete and unbiased sampling, a rich array of features based on connectivity and reduction of the connectome to a substantially simpler wiring diagram of cell types, with immediate relevance for brain function and development.
Assuntos
Conectoma , Drosophila melanogaster , Neurônios , Lobo Óptico de Animais não Mamíferos , Vias Visuais , Animais , Lobo Óptico de Animais não Mamíferos/citologia , Drosophila melanogaster/citologia , Drosophila melanogaster/fisiologia , Vias Visuais/fisiologia , Neurônios/fisiologia , Neurônios/citologia , Interneurônios/fisiologia , Interneurônios/citologia , Feminino , Visão de Cores/fisiologia , Neurópilo/citologia , Neurópilo/fisiologia , Percepção de Movimento/fisiologia , Masculino , Algoritmos , Modelos Neurológicos , Campos Visuais/fisiologiaRESUMO
Brains comprise complex networks of neurons and connections, similar to the nodes and edges of artificial networks. Network analysis applied to the wiring diagrams of brains can offer insights into how they support computations and regulate the flow of information underlying perception and behaviour. The completion of the first whole-brain connectome of an adult fly, containing over 130,000 neurons and millions of synaptic connections1-3, offers an opportunity to analyse the statistical properties and topological features of a complete brain. Here we computed the prevalence of two- and three-node motifs, examined their strengths, related this information to both neurotransmitter composition and cell type annotations4,5, and compared these metrics with wiring diagrams of other animals. We found that the network of the fly brain displays rich-club organization, with a large population (30% of the connectome) of highly connected neurons. We identified subsets of rich-club neurons that may serve as integrators or broadcasters of signals. Finally, we examined subnetworks based on 78 anatomically defined brain regions or neuropils. These data products are shared within the FlyWire Codex ( https://codex.flywire.ai ) and should serve as a foundation for models and experiments exploring the relationship between neural activity and anatomical structure.
Assuntos
Encéfalo , Conectoma , Drosophila melanogaster , Neurônios , Animais , Encéfalo/fisiologia , Encéfalo/citologia , Encéfalo/anatomia & histologia , Drosophila melanogaster/fisiologia , Drosophila melanogaster/anatomia & histologia , Neurônios/fisiologia , Rede Nervosa/fisiologia , Rede Nervosa/anatomia & histologia , Rede Nervosa/citologia , Feminino , Masculino , Neurópilo/fisiologia , Neurópilo/citologia , Vias Neurais/fisiologia , Modelos NeurológicosRESUMO
OBJECTIVE: Autism spectrum disorder (ASD) is a neurodevelopmental condition that is associated with atypical brain network organization, with prior work suggesting differential connectivity alterations with respect to functional connection length. Here, we tested whether functional connectopathy in ASD specifically relates to disruptions in long- relative to short-range functional connections. Our approach combined functional connectomics with geodesic distance mapping, and we studied associations to macroscale networks, microarchitectural patterns, as well as socio-demographic and clinical phenotypes. METHODS: We studied 211 males from three sites of the ABIDE-I dataset comprising 103 participants with an ASD diagnosis (mean ± SD age = 20.8 ± 8.1 years) and 108 neurotypical controls (NT, 19.2 ± 7.2 years). For each participant, we computed cortex-wide connectivity distance (CD) measures by combining geodesic distance mapping with resting-state functional connectivity profiling. We compared CD between ASD and NT participants using surface-based linear models, and studied associations with age, symptom severity, and intelligence scores. We contextualized CD alterations relative to canonical networks and explored spatial associations with functional and microstructural cortical gradients as well as cytoarchitectonic cortical types. RESULTS: Compared to NT, ASD participants presented with widespread reductions in CD, generally indicating shorter average connection length and thus suggesting reduced long-range connectivity but increased short-range connections. Peak reductions were localized in transmodal systems (i.e., heteromodal and paralimbic regions in the prefrontal, temporal, and parietal and temporo-parieto-occipital cortex), and effect sizes correlated with the sensory-transmodal gradient of brain function. ASD-related CD reductions appeared consistent across inter-individual differences in age and symptom severity, and we observed a positive correlation of CD to IQ scores. LIMITATIONS: Despite rigorous harmonization across the three different acquisition sites, heterogeneity in autism poses a potential limitation to the generalizability of our results. Additionally, we focussed male participants, warranting future studies in more balanced cohorts. CONCLUSIONS: Our study showed reductions in CD as a relatively stable imaging phenotype of ASD that preferentially impacted paralimbic and heteromodal association systems. CD reductions in ASD corroborate previous reports of ASD-related imbalance between short-range overconnectivity and long-range underconnectivity.
Assuntos
Conectoma , Imageamento por Ressonância Magnética , Humanos , Masculino , Adulto Jovem , Adulto , Adolescente , Transtorno do Espectro Autista/fisiopatologia , Transtorno do Espectro Autista/diagnóstico por imagem , Transtorno Autístico/fisiopatologia , Transtorno Autístico/diagnóstico por imagem , Encéfalo/diagnóstico por imagem , Encéfalo/fisiopatologia , Estudos de Casos e Controles , Criança , Rede Nervosa/diagnóstico por imagem , Rede Nervosa/fisiopatologia , Vias Neurais/fisiopatologia , Vias Neurais/diagnóstico por imagemRESUMO
Neural consequences of social disparities are not yet rigorously investigated. How socioeconomic conditions influence children's connectome development remains unknown. This paper endeavors to gauge how precisely the connectome structure of the brain can predict an individual's social environment, thereby inversely assessing how social influences are engraved in the neural development of the Adolescent brain. Utilizing Adolescent Brain and Cognition Development (ABCD) data (9099 children residing in the United States), we found that social conditions both at the household and neighborhood levels are significantly associated with specific neural connections. Solely with brain connectome data, we train a linear support vector machine (SVM) to predict socio-economic conditions of those adolescents. The classification performance generally improves when the thresholds of the advantageous and disadvantageous environments compartmentalize the extreme cases. Among the tested thresholds, the 20th and 80th percentile thresholds using the dual combination of household income and neighborhood education yielded the highest Area Under the Precision-Recall Curve (AUPRC) of 0.8224. We identified 8 significant connections that critically contribute to predicting social environments in the parietal lobe and frontal lobe. Insights into social factors that contribute to early brain connectome development is critical to mitigate the disadvantages of children growing up in unfavorable neighborhoods.
Assuntos
Encéfalo , Conectoma , Humanos , Adolescente , Masculino , Feminino , Encéfalo/diagnóstico por imagem , Encéfalo/crescimento & desenvolvimento , Criança , Fatores Socioeconômicos , Máquina de Vetores de Suporte , Estados Unidos , Cognição/fisiologia , Imageamento por Ressonância Magnética , Meio SocialRESUMO
BACKGROUND: Microglial activation is one hallmark of Alzheimer disease (AD) neuropathology but the impact of the regional interplay of microglia cells in the brain is poorly understood. We hypothesized that microglial activation is regionally synchronized in the healthy brain but experiences regional desynchronization with ongoing neurodegenerative disease. We addressed the existence of a microglia connectome and investigated microglial desynchronization as an AD biomarker. METHODS: To validate the concept, we performed microglia depletion in mice to test whether interregional correlation coefficients (ICCs) of 18 kDa translocator protein (TSPO)-PET change when microglia are cleared. Next, we evaluated the influence of dysfunctional microglia and AD pathophysiology on TSPO-PET ICCs in the mouse brain, followed by translation to a human AD-continuum dataset. We correlated a personalized microglia desynchronization index with cognitive performance. Finally, we performed single-cell radiotracing (scRadiotracing) in mice to ensure the microglial source of the measured desynchronization. RESULTS: Microglia-depleted mice showed a strong ICC reduction in all brain compartments, indicating microglia-specific desynchronization. AD mouse models demonstrated significant reductions of microglial synchronicity, associated with increasing variability of cellular radiotracer uptake in pathologically altered brain regions. Humans within the AD-continuum indicated a stage-depended reduction of microglia synchronicity associated with cognitive decline. scRadiotracing in mice showed that the increased TSPO signal was attributed to microglia. CONCLUSION: Using TSPO-PET imaging of mice with depleted microglia and scRadiotracing in an amyloid model, we provide first evidence that a microglia connectome can be assessed in the mouse brain. Microglia synchronicity is closely associated with cognitive decline in AD and could serve as an independent personalized biomarker for disease progression.
Assuntos
Doença de Alzheimer , Encéfalo , Disfunção Cognitiva , Microglia , Animais , Microglia/metabolismo , Doença de Alzheimer/metabolismo , Doença de Alzheimer/patologia , Camundongos , Disfunção Cognitiva/metabolismo , Humanos , Encéfalo/metabolismo , Encéfalo/patologia , Modelos Animais de Doenças , Tomografia por Emissão de Pósitrons , Receptores de GABA/metabolismo , Masculino , Camundongos Transgênicos , Conectoma/métodos , FemininoRESUMO
The biological mechanisms that contribute to cocaine and other substance use disorders involve an array of cortical and subcortical systems. Prior work on the development and maintenance of substance use has largely focused on cortico-striatal circuits, with relatively less attention on alterations within and across large-scale functional brain networks, and associated aspects of the dopamine system. Here, we characterize patterns of functional connectivity in cocaine use disorder and their spatial association with neurotransmitter receptor densities and transporter bindings assessed through PET. Profiles of functional connectivity in cocaine use disorder reliably linked with spatial densities of dopamine D2/3 receptors across independent datasets. These findings demonstrate that the topography of dopamine receptor densities may underlie patterns of functional connectivity in cocaine use disorder, as assessed through fMRI.
Assuntos
Transtornos Relacionados ao Uso de Cocaína , Conectoma , Imageamento por Ressonância Magnética , Receptores de Dopamina D2 , Receptores de Dopamina D3 , Transtornos Relacionados ao Uso de Cocaína/metabolismo , Transtornos Relacionados ao Uso de Cocaína/fisiopatologia , Humanos , Receptores de Dopamina D2/metabolismo , Receptores de Dopamina D3/metabolismo , Masculino , Adulto , Feminino , Encéfalo/metabolismo , Encéfalo/diagnóstico por imagem , Tomografia por Emissão de Pósitrons , Pessoa de Meia-IdadeRESUMO
Connectomics approaches are fundamentally changing the way scientists investigate the brain. Recently published connectomes have enabled dissection of the intricate motor circuits in the fly's version of the spinal cord on a synaptic level. This has allowed reconstruction of complete sensorimotor pathways in Drosophila.
Assuntos
Conectoma , Animais , Drosophila/fisiologia , Neurociências , Drosophila melanogaster/fisiologia , Medula Espinal/fisiologiaRESUMO
Functional connectivity patterns in the human brain, like the friction ridges of a fingerprint, can uniquely identify individuals. Does this "brain fingerprint" remain distinct even during Alzheimer's disease (AD)? Using fMRI data from healthy and pathologically ageing subjects, we find that individual functional connectivity profiles remain unique and highly heterogeneous during mild cognitive impairment and AD. However, the patterns that make individuals identifiable change with disease progression, revealing a reconfiguration of the brain fingerprint. Notably, connectivity shifts towards functional system connections in AD and lower-order cognitive functions in early disease stages. These findings emphasize the importance of focusing on individual variability rather than group differences in AD studies. Individual functional connectomes could be instrumental in creating personalized models of AD progression, predicting disease course, and optimizing treatments, paving the way for personalized medicine in AD management.
Assuntos
Doença de Alzheimer , Encéfalo , Conectoma , Imageamento por Ressonância Magnética , Doença de Alzheimer/diagnóstico por imagem , Doença de Alzheimer/fisiopatologia , Doença de Alzheimer/patologia , Humanos , Idoso , Encéfalo/diagnóstico por imagem , Encéfalo/fisiopatologia , Encéfalo/patologia , Masculino , Feminino , Disfunção Cognitiva/fisiopatologia , Disfunção Cognitiva/diagnóstico por imagem , Idoso de 80 Anos ou mais , Progressão da Doença , Pessoa de Meia-IdadeRESUMO
With brain structure and function undergoing complex changes throughout childhood and adolescence, age is a critical consideration in neuroimaging studies, particularly for those of individuals with neurodevelopmental conditions. However, despite the increasing use of large, consortium-based datasets to examine brain structure and function in neurotypical and neurodivergent populations, it is unclear whether age-related changes are consistent between datasets and whether inconsistencies related to differences in sample characteristics, such as demographics and phenotypic features, exist. To address this, we built models of age-related changes of brain structure (regional cortical thickness and regional surface area; N = 1218) and function (resting-state functional connectivity strength; N = 1254) in two neurodiverse datasets: the Province of Ontario Neurodevelopmental Network and the Healthy Brain Network. We examined whether deviations from these models differed between the datasets, and explored whether these deviations were associated with demographic and clinical variables. We found significant differences between the two datasets for measures of cortical surface area and functional connectivity strength throughout the brain. For regional measures of cortical surface area, the patterns of differences were associated with race/ethnicity, while for functional connectivity strength, positive associations were observed with head motion. Our findings highlight that patterns of age-related changes in the brain may be influenced by demographic and phenotypic characteristics, and thus future studies should consider these when examining or controlling for age effects in analyses.
Assuntos
Conjuntos de Dados como Assunto , Imageamento por Ressonância Magnética , Humanos , Feminino , Masculino , Criança , Adolescente , Adulto Jovem , Adulto , Transtornos do Neurodesenvolvimento/diagnóstico por imagem , Transtornos do Neurodesenvolvimento/fisiopatologia , Transtornos do Neurodesenvolvimento/patologia , Conectoma , Encéfalo/diagnóstico por imagem , Encéfalo/crescimento & desenvolvimento , Encéfalo/anatomia & histologia , Córtex Cerebral/diagnóstico por imagem , Córtex Cerebral/crescimento & desenvolvimento , Córtex Cerebral/anatomia & histologia , Envelhecimento/fisiologiaRESUMO
Both cortical and cerebellar developmental differences have been implicated in attention-deficit/hyperactivity disorder (ADHD). Recently accumulating neuroimaging studies have highlighted hierarchies as a fundamental principle of brain organization, suggesting the importance of assessing hierarchy abnormalities in ADHD. A novel gradient-based resting-state functional connectivity analysis was applied to investigate the cerebro-cerebellar disturbed hierarchy in children and adolescents with ADHD. We found that the interaction of functional gradient between diagnosis and age was concentrated in default mode network (DMN) and visual network (VN). At the same time, we also found that the opposite gradient changes of DMN and VN caused the compression of the cortical main gradient in ADHD patients, implicating the co-occurrence of both low- (visual processing) and high-order (self-related thought) cognitive dysfunction manifesting in abnormal cerebro-cerebellar organizational hierarchy in ADHD. Our study provides a neurobiological framework to better understand the co-occurrence and interaction of both low-level and high-level functional abnormalities in the cortex and cerebellum in ADHD.
Assuntos
Transtorno do Deficit de Atenção com Hiperatividade , Cerebelo , Córtex Cerebral , Conectoma , Imageamento por Ressonância Magnética , Rede Nervosa , Humanos , Transtorno do Deficit de Atenção com Hiperatividade/diagnóstico por imagem , Transtorno do Deficit de Atenção com Hiperatividade/fisiopatologia , Adolescente , Criança , Masculino , Cerebelo/diagnóstico por imagem , Cerebelo/fisiopatologia , Feminino , Córtex Cerebral/diagnóstico por imagem , Córtex Cerebral/fisiopatologia , Rede Nervosa/diagnóstico por imagem , Rede Nervosa/fisiopatologia , Rede de Modo Padrão/diagnóstico por imagem , Rede de Modo Padrão/fisiopatologiaRESUMO
Network neuroscience explores the brain's connectome, demonstrating that dynamic neural networks support cognitive functions. This study investigates how distinct cognitive abilities-working memory and cognitive inhibitory control-are supported by unique brain network configurations constructed by estimating whole-brain networks using mutual information. The study involved 195 participants who completed the Sternberg Item Recognition task and Flanker tasks while undergoing electroencephalography recording. A mixed-effects linear model analyzed the influence of network metrics on cognitive performance, considering individual differences and task-specific dynamics. The findings indicate that working memory and cognitive inhibitory control are associated with different network attributes, with working memory relying on distributed networks and cognitive inhibitory control on more segregated ones. Our analysis suggests that both strong and weak connections contribute to cognitive processes, with weak connections potentially leading to a more stable and support networks of memory and cognitive inhibitory control. The findings indirectly support the network neuroscience theory of intelligence, suggesting different functional topology of networks inherent to various cognitive functions. Nevertheless, we propose that understanding individual variations in cognitive abilities requires recognizing both shared and unique processes within the brain's network dynamics.
Assuntos
Eletroencefalografia , Inibição Psicológica , Memória de Curto Prazo , Rede Nervosa , Humanos , Memória de Curto Prazo/fisiologia , Masculino , Adulto , Rede Nervosa/fisiologia , Rede Nervosa/diagnóstico por imagem , Feminino , Adulto Jovem , Conectoma , Função Executiva/fisiologia , Desempenho Psicomotor/fisiologia , Encéfalo/fisiologia , Encéfalo/diagnóstico por imagem , AdolescenteRESUMO
Task-related studies have consistently reported that listening to speech sounds activate the temporal and prefrontal regions of the brain. However, it is not well understood how functional organization of auditory and language networks differ when processing speech sounds from its resting state form. The knowledge of language network organization in typically developing infants could serve as an important biomarker to understand network-level disruptions expected in infants with hearing impairment. We hypothesized that topological differences of language networks can be characterized using functional connectivity measures in two experimental conditions (1) complete silence (resting) and (2) in response to repetitive continuous speech sounds (steady). Thirty normal-hearing infants (14 males and 16 females, age: 7.8 ± 4.8 months) were recruited in this study. Brain activity was recorded from bilateral temporal and prefrontal regions associated with speech and language processing for two experimental conditions: resting and steady states. Topological differences of functional language networks were characterized using graph theoretical analysis. The normalized global efficiency and clustering coefficient were used as measures of functional integration and segregation, respectively. We found that overall, language networks of infants demonstrate the economic small-world organization in both resting and steady states. Moreover, language networks exhibited significantly higher functional integration and significantly lower functional segregation in resting state compared to steady state. A secondary analysis that investigated developmental effects of infants aged 6-months or below and above 6-months revealed that such topological differences in functional integration and segregation across resting and steady states can be reliably detected after the first 6-months of life. The higher functional integration observed in resting state suggests that language networks of infants facilitate more efficient parallel information processing across distributed language regions in the absence of speech stimuli. Moreover, higher functional segregation in steady state indicates that the speech information processing occurs within densely interconnected specialized regions in the language network.