Directed Functional Networks in Alzheimer's Disease: Disruption of Global and Local Connectivity Measures

Afshari, Saeedeh; Jalili, Mahdi

doi:10.1109/jbhi.2016.2578954

Cited by 65 publications

(55 citation statements)

References 47 publications

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“…However, local topological measures such as clustering coefficient, efficiency, and betweenness centrality were the highest for populations making indirect connections when compared to populations making direct PAC connections, to populations receiving PAC connections, and to populations not involved on the generation of PAC. This is another factor to consider when studying the origin of PAC during neurodegenerative disorders known to affect both local and global brain circuitry [79–81]. …”

Section: Discussionmentioning

confidence: 99%

Topology, Cross-Frequency, and Same-Frequency Band Interactions Shape the Generation of Phase-Amplitude Coupling in a Neural Mass Model of a Cortical Column

Sotero

2016

PLoS Comput Biol

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Phase-amplitude coupling (PAC), a type of cross-frequency coupling (CFC) where the phase of a low-frequency rhythm modulates the amplitude of a higher frequency, is becoming an important indicator of information transmission in the brain. However, the neurobiological mechanisms underlying its generation remain undetermined. A realistic, yet tractable computational model of the phenomenon is thus needed. Here we analyze a neural mass model of a cortical column, comprising fourteen neuronal populations distributed across four layers (L2/3, L4, L5 and L6). A control analysis showed that the conditional transfer entropy (cTE) measure is able to correctly estimate the flow of information between neuronal populations. Then, we computed cTE from the phases to the amplitudes of the oscillations generated in the cortical column. This approach provides information regarding directionality by distinguishing PAC from APC (amplitude-phase coupling), i.e. the information transfer from amplitudes to phases, and can be used to estimate other types of CFC such as amplitude-amplitude coupling (AAC) and phase-phase coupling (PPC). While experiments often only focus on one or two PAC combinations (e.g., theta-gamma or alpha-gamma), we found that a cortical column can simultaneously generate almost all possible PAC combinations, depending on connectivity parameters, time constants, and external inputs. PAC interactions with and without an anatomical equivalent (direct and indirect interactions, respectively) were analyzed. We found that the strength of PAC between two populations was strongly correlated with the strength of the effective connections between the populations and, on average, did not depend on whether the PAC connection was direct or indirect. When considering a cortical column circuit as a complex network, we found that neuronal populations making indirect PAC connections had, on average, higher local clustering coefficient, efficiency, and betweenness centrality than populations making direct connections and populations not involved in PAC connections. This suggests that their interactions were more effective when transmitting information. Since approximately 60% of the obtained interactions represented indirect connections, our results highlight the importance of the topology of cortical circuits for the generation of the PAC phenomenon. Finally, our results demonstrated that indirect PAC interactions can be explained by a cascade of direct CFC and same-frequency band interactions, suggesting that PAC analysis of experimental data should be accompanied by the estimation of other types of frequency interactions for an integrative understanding of the phenomenon.

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Section: Discussionmentioning

confidence: 99%

Topology, Cross-Frequency, and Same-Frequency Band Interactions Shape the Generation of Phase-Amplitude Coupling in a Neural Mass Model of a Cortical Column

Sotero

2016

PLoS Comput Biol

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“…Specifically, topological statistics have proven to be useful biomarkers of schizophrenia [20, 21, 22, 23, 24], epilepsy [25], Alzheimer’s disease [26, 27, 28], and autism [29, 30, 31, 32]. …”

Section: Abnormal Network and Diseasementioning

confidence: 99%

A network neuroscience of neurofeedback for clinical translation

Murphy

Bassett

2017

Current Opinion in Biomedical Engineering

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In the emerging field of network neuroscience, the brain is represented as a network of discrete yet functionally and structurally interconnected areas. Mathematical and computational tools to characterize the organization of this network can provide insights into the principles guiding brain structure and function, and can pinpoint differences between healthy individuals and individuals suffering from psychiatric disease or neurological disorders. The field is now faced with the question of how to devise clinical interventions that target these network alterations. Potential solutions to this question include the combination of emerging theories of network control with cutting-edge interventions such as neurofeedback. Each of these techniques may now be mature enough to combine to obtain a theoretically-motivated framework informing viable neuropsychiatric therapies.

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“…Dois importantes princípios organizacionais do córtex cerebral são a segregação funcional e a integração funcional, que permitem a rápida extração de informação e a geração de estados cerebrais coerentes 16 . Segregação funcional corresponde ao processamento de informações localmente especializado do cérebro, e integração funcional refere-se tanto às associações de informações das regiões cerebrais distribuídas quanto à eficiência global da transferência de informação 19 , que mede a integração da rede e seu desempenho geral para transferência de informações, e é inversamente relacionada ao caminho menino médio, índice do grafo que revela a facilidade no transporte de informação ou outras entidades dentro da rede 20, 21,22 .…”

Section: Introductionunclassified

Dinâmica Temporal De Redes Funcionais Corticais Em Portadores De Alzheimer

Silva

Netto²,

Toutai³

et al. 2019

rsc

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Alzheimer's Disease (AD) is the main form of dementia in the elderly and its pathologically is characterized by neurofibrillary tangles and amyloid deposition, which causes loss of functional connectivity at pre-clinical stages. This study aims to compare spatial and temporal topological patterns of cortical functional connectivity between healthy individuals and patients with AD at different stages. Brain functional networks (BFN) were built by the Motif Synchronization method and characterized by indices of temporal connectivity and stability. Synchronicity and network topology of BFN were evaluated. We evaluated connectivity as the average weighted degree and found that it follows a trend to decrease over disease progression. We evaluated topological stability as the vertices variation coefficient (VVC) and found that it follows a trend to increase with disease progression. The analysis of the most representative hubs revealed a decreased proportion of hubs in brain regions markedly affected by AD, mostly at later disease stages, and increased proportion at earlier stages, in accordance with previous studies. These data indicate altered connectivity pattern and stability in dynamics of the functional synchronization networks. Individual variability could have masked statistical significance in our data, we suggest future longitudinal studies to address the questions presented in this paper.

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Directed Functional Networks in Alzheimer's Disease: Disruption of Global and Local Connectivity Measures

Cited by 65 publications

References 47 publications

Topology, Cross-Frequency, and Same-Frequency Band Interactions Shape the Generation of Phase-Amplitude Coupling in a Neural Mass Model of a Cortical Column

Topology, Cross-Frequency, and Same-Frequency Band Interactions Shape the Generation of Phase-Amplitude Coupling in a Neural Mass Model of a Cortical Column

A network neuroscience of neurofeedback for clinical translation

Dinâmica Temporal De Redes Funcionais Corticais Em Portadores De Alzheimer

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