To cut or not to cut? Assessing the modular structure of brain networks

Chang, Yu-Teng; Pantazis, Dimitrios; Leahy, Richard M.

doi:10.1016/j.neuroimage.2014.01.010

“…To reach some standard of replicability, researchers have instead come to rely on permuting the algorithm for the maximum

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, or modularity quotient. We have therefore sought to employ a more robust modularity algorithm [Chang et al, ] that relies on a transformed Tracy‐Widom distribution to more adequately model the null distribution in a modularity computation. Furthermore, while it is possible to estimate modularity within individuals [e.g., Simpson et al, ], modularity based on an average are naturally consistent across subjects, and widely used [Hagmann et al, ; Lim et al, ].…”

Section: Data Analysesmentioning

confidence: 99%

Frequency‐specific neuromodulation of local and distant connectivity in aging and episodic memory function

Davis

¹

,

Luber

²

,

Murphy

³

et al. 2017

Human Brain Mapping

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A growing literature has focused on the brain's ability to augment processing in local regions by recruiting distant communities of neurons in response to neural decline or insult. In particular, both younger and older adult populations recruit bilateral prefrontal cortex (PFC) as a means of compensating for increasing neural effort to maintain successful cognitive function. However, it remains unclear how local changes in neural activity affect the recruitment of this adaptive mechanism. To address this problem, we combined graph theoretical measures from functional MRI with diffusion weighted imaging and repetitive transcranial magnetic stimulation (rTMS) to resolve a central hypothesis: how do aged brains flexibly adapt to local changes in cortical activity? Specifically, we applied neuromodulation to increase or decrease local activity in a cortical region supporting successful memory encoding (left dorsolateral PFC or DLPFC) using 5 or 1 Hz rTMS, respectively. We then assessed a region's local within-module degree, or the distributed between-module degree (BMD) between distant cortical communities. We predicted that (1) local stimulation-related deficits may be counteracted by boosting BMD between bilateral PFC, and that this effect should be (2) positively correlated with structural connectivity. Both predictions were confirmed; 5 Hz rTMS increased local success-related activity and local increases in PFC connectivity, while 1 Hz rTMS decreases local activity and triggered a more distributed pattern of bilateral PFC connectivity to compensate for this local inhibitory effect. These results provide an integrated, causal explanation for the network interactions associated with successful memory encoding in older adults. Hum Brain Mapp 38:5987-6004, 2017. © 2017 Wiley Periodicals, Inc.

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“…To reach some standard of replicability, researchers have instead come to rely on permuting the algorithm for the maximum , or modularity quotient. We have therefore sought to employ a more robust modularity algorithm (Chang et al, 2014) that relies on a transformed Tracy-Widom distribution in order to more adequately model the null distribution in a modularity computation. Furthermore, while it is possible to estimate modularity within individuals (e.g., Simpson et al, 2012), modularity based on an average are naturally consistent across subjects, and widely used (Hagmann et al, 2008;Lim et al, 2015).…”

Section: Modularitymentioning

confidence: 99%

Frequency-specific neuromodulation of local and distant connectivity in aging & episodic memory function

Davis

¹

,

Luber²,

Murphy

³

et al. 2016

Preprint

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A growing literature has focused on the brain's ability to augment processing in local regions by recruiting distant communities of neurons in response to neural decline or insult. In particular, both younger and older adult populations recruit bilateral prefrontal cortex (PFC) as a means of compensating for increasing neural effort to maintain successful cognitive function. However, it remains unclear how local changes in neural activity affect the recruitment of this adaptive mechanism. To address this problem, we combined graph theoretical measures from functional MRI (fMRI) with diffusion weighted imaging (DWI) and repetitive transcranial magnetic stimulation (rTMS) in order to resolve a central hypothesis: how do aged brains flexibly adapt to local changes in cortical activity? Specifically, we applied neuromodulation to increase or decrease local activity in a cortical region supporting successful memory encoding (left dorsolateral prefrontal cortex or DLPFC) using 5Hz or 1Hz rTMS, respectively. We then assessed a region's local within-module degree (WMD), or the distributed between-module degree (BMD) between distant cortical communities. We predicted that (1) local stimulationrelated deficits may be counteracted by boosting BMD between bilateral PFC, and that this effect should be (2) positively correlated with structural connectivity. Both predictions were confirmed; 5Hz rTMS increased local success-related activity and local increases in PFC connectivity, while 1Hz rTMS decreases local activity and triggered a more distributed pattern of bilateral PFC connectivity to compensate for this local inhibitory effect. These results provide an integrated, causal explanation for the network interactions associated with successful memory encoding in older adults. 2012), motor coordination (Heuninckx et al., 2008), and working memory (Reuter-Lorenz et al., 2000). This effect follows the more general observation of age-related reductions in core tasknetwork regions coupled with an increase in secondary task-network areas Nyberg et al., 2010), but may also be a specific example of adaptive efficiency in the aging brain. While the prevalence of this pattern in aging studies is widespread, the evidence that this effect benefits performance is mixed, and contralateral recruitment may only be effective if the additional cortical processors brought to bear on the task can play a complementary role in task performance (Colcombe et al., 2005). Such a model of the aging brain proposes that over the years, cortical messages are increasingly dominated by top-down predictions, and less by sensory inputs (Moran et al., 2014). This is consistent with the agerelated shift to PFC activation, and bilateral PFC activity is a likely consequence of this adaptive shift, given that every PFC region is connected to its contralateral homologue by at least one synapse (Aboitiz et al., 1992;Aboitiz and Montiel, 2003). Nonetheless, while correlational studies have observed that PFC regions over-recruited by OAs often show stronger long-ran...

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“…The proposed Empirical Bayes method brings together both the frequentist and Bayesian approaches, in the sense that we can report and control the estimated local FDR at each test, which is defined as the posterior probability that a test belongs to null group, and controlling it. The locFDR approach is readily applicable to the other available methods for measuring synchrony, e.g., mutual information, generalized synchronization [ 42 ], single-trial phase locking [ 43 ], structural synchrony [ 13 ], empirical mode detection PLV [ 44 ], phase resetting [ 45 , 46 ], a method based on Cohens class of time-frequency distributions [ 47 ], and a recently published graph partitioning method for modeling brain connectivity [ 48 ].…”

Section: Discussionmentioning

confidence: 99%

Statistical Detection of EEG Synchrony Using Empirical Bayesian Inference

Singh

¹

,

Asoh

²

,

Takeda

³

et al. 2015

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There is growing interest in understanding how the brain utilizes synchronized oscillatory activity to integrate information across functionally connected regions. Computing phase-locking values (PLV) between EEG signals is a popular method for quantifying such synchronizations and elucidating their role in cognitive tasks. However, high-dimensionality in PLV data incurs a serious multiple testing problem. Standard multiple testing methods in neuroimaging research (e.g., false discovery rate, FDR) suffer severe loss of power, because they fail to exploit complex dependence structure between hypotheses that vary in spectral, temporal and spatial dimension. Previously, we showed that a hierarchical FDR and optimal discovery procedures could be effectively applied for PLV analysis to provide better power than FDR. In this article, we revisit the multiple comparison problem from a new Empirical Bayes perspective and propose the application of the local FDR method (locFDR; Efron, 2001) for PLV synchrony analysis to compute FDR as a posterior probability that an observed statistic belongs to a null hypothesis. We demonstrate the application of Efron's Empirical Bayes approach for PLV synchrony analysis for the first time. We use simulations to validate the specificity and sensitivity of locFDR and a real EEG dataset from a visual search study for experimental validation. We also compare locFDR with hierarchical FDR and optimal discovery procedures in both simulation and experimental analyses. Our simulation results showed that the locFDR can effectively control false positives without compromising on the power of PLV synchrony inference. Our results from the application locFDR on experiment data detected more significant discoveries than our previously proposed methods whereas the standard FDR method failed to detect any significant discoveries.

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To cut or not to cut? Assessing the modular structure of brain networks

Cited by 10 publications

References 58 publications

Frequency‐specific neuromodulation of local and distant connectivity in aging and episodic memory function

Frequency‐specific neuromodulation of local and distant connectivity in aging and episodic memory function

Frequency-specific neuromodulation of local and distant connectivity in aging & episodic memory function

Statistical Detection of EEG Synchrony Using Empirical Bayesian Inference

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