Dynamics of the human brain network revealed by time-frequency effective connectivity in fNIRS

Vergotte, Grégoire; Torre, Kjerstin; Chirumamilla, Venkata Chaitanya; Anwar, Abdul Rauf; Groppa, Sergiu; Perrey, Stéphane; Muthuraman, Muthuraman

doi:10.1364/boe.8.005326

Cited by 26 publications

(18 citation statements)

References 81 publications

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“…These adaptive models are particularly useful for modelling non-linear processes, where autoregressive coefficients and the variance of the process are allowed to change over the course of time 55 . Despite these advantages of using TPDC over other (more conventional) causality methods like CBN, one drawback is that TPDC is time-consuming, owing to the estimation of MVAR coefficients at each time point, which limits its usage depending on the available computational resources 21 . However, despite this, our findings lend support to the increased application of this analysis where possible, especially over longer time periods.…”

Section: Discussionmentioning

confidence: 99%

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Continuous reorganization of cortical information flow in multiple sclerosis: A longitudinal fMRI effective connectivity study

Fleischer

Anwar

González‐Escamilla

et al. 2020

Sci Rep

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are highly relevant for biological network behaviour and can be traced longitudinally to depict brain reorganization processes in brain diseases 7,8. Early EC studies in MS patients demonstrated higher EC levels during Paced Auditory Serial Addition Test (PASAT) performance in the working memory network 9 , during a sensorimotor and information processing speed task within frontal networks 10,11 as well as during Stroop task performance in the sensorimotor cortex 10each in a cross-sectional approach. These differences in EC measures between MS patients and healthy controls (HC) were primarily interpreted as an adaptive response to maintain cognitive and/or sensorimotor function for review see 12. Recently, altered connectivity patterns were found during high cognitive control demands in the executive control network among different MS phenotypes 13 , in particular a loss of top-down connections was seen in patients with progressive MS 14. These task-related EC changes point towards dynamic connectivity patterns that change during the course of the disease. But in contrast to task-dependent approaches, the dynamics of EC in the absence of behavioural measures are largely unknown and existing research still lacks the understanding of how directed connectivity changes over time in MS patients. Hence, longitudinal investigations of brain reorganization in MS patients are necessary to provide maps of brain plasticity processes over time, very likely relevant for disability progression. In our study, we performed two parallel EC analyses based on recently established methods, neither of which require any a priori information on the existence of a connection between two regions as is necessary for dynamic causal modelling 10,15 or structural equation modelling (SEM) 9,16. Specifically, we analysed EC based on Causal Bayesian Networks (CBN) proposed by Smith et al. 17 and, since EC can also be simultaneously estimated in two domains (namely frequency and time), we additionally assessed EC based on the so-called Time-resolved Partial Directed Coherence (TPDC) method 18-23 developed in the context of electrophysiological measurements of frequency-filtered blood oxygen-level dependent (BOLD) fMRI time courses. Ultimately, we aimed to provide a longitudinal outline of EC changes derived from resting-state fMRI with these two methodological approaches. Therefore, relapsing-remitting (RR)MS patients were followed-up over five successive time points at twelve-week intervals. In addition, we validated these findings by comparing our results with longitudinal EC data from HC over the same period of time. Finally, we investigated the link between the strength of EC and clinical disability, patient fatigue and disease duration.

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Section: Discussionmentioning

confidence: 99%

“…The shuffling process is done 1000 times and the 95 th percentile TPDC value is taken as the significance threshold for all of our connections. This process is performed separately for each subject 21 .…”

Section: Effective Connectivity Analysis Causal Bayesian Network (Cbmentioning

confidence: 99%

Continuous reorganization of cortical information flow in multiple sclerosis: A longitudinal fMRI effective connectivity study

Fleischer

Anwar

González‐Escamilla

et al. 2020

Sci Rep

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“…Another potential limitation of the current study is that although we measured the cortical activity of M1, ECC or CON movements can act on more distant sites intra- and interhemispherically. Further studies could utilize more subjects to examine the reproducibility of these first findings and examine the interactions with other areas of the motor network through cortico-cortical connections [ 37 ] both intrahemispherically and across the corpus callosum.…”

Section: Discussionmentioning

confidence: 99%

Different Hemodynamic Responses of the Primary Motor Cortex Accompanying Eccentric and Concentric Movements: A Functional NIRS Study

Borot¹,

Vergotte²,

Perrey³

2018

Brain Sciences

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The literature contains limited evidence on how our brains control eccentric movement. A higher activation is expected in the contralateral motor cortex (M1) but consensus has not yet been reached. Therefore, the present study aimed to compare patterns of M1 activation between eccentric and concentric movements. Nine healthy participants performed in a randomized order three sets of five repetitions of eccentric or concentric movement with the dominant elbow flexors over a range of motion of 60° at two velocities (30°/s and 60°/s). The tests were carried out using a Biodex isokinetic dynamometer with the forearm supported in the horizontal plane. The peak torque values were not significantly different between concentric and eccentric movements (p = 0.42). Hemodynamic responses of the contralateral and ipsilateral M1 were measured with a near-infrared spectroscopy system (Oxymon MkIII, Artinis). A higher contralateral M1 activity was found during eccentric movements (p = 0.04, η² = 0.47) and at the velocity of 30°/s (p = 0.039, η² = 0.48). These preliminary findings indicate a specific control mechanism in the contralateral M1 to produce eccentric muscle actions at the angular velocities investigated, although the role of other brain areas in the motor control network cannot be excluded.

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“…Participants were sitting comfortably on an adjustable chair, with their dominant side forearm and palm of the hand resting on a customized plinth (570 × 160 × 50 mm) on a table in front of them. Subjects realized a tapping task according to a conventional synchronization-continuation paradigm (Wing and Kristofferson, 1973 ; Vergotte et al, 2017 ): during the initial synchronization phase, the tempo was prescribed by a PC-driven auditory metronome delivering 20 signals at a frequency of 1.5 Hz (0.666 s inter-tap intervals), known as a comfortable tapping frequency (Fraisse, 1966 ; Torre and Delignières, 2008 ). Once the metronome stopped, participants had to continue tapping by maintaining the prescribed tempo as accurately and regularly as possible for the whole trial duration.…”

Section: Methodsmentioning

confidence: 99%

Concurrent Changes of Brain Functional Connectivity and Motor Variability When Adapting to Task Constraints

et al. 2018

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In behavioral neuroscience, the adaptability of humans facing different constraints has been addressed on one side at the brain level, where a variety of functional networks dynamically support the same performance, and on the other side at the behavioral level, where fractal properties in sensorimotor variables have been considered as a hallmark of adaptability. To bridge the gap between the two levels of observation, we have jointly investigated the changes of network connectivity in the sensorimotor cortex assessed by modularity analysis and the properties of motor variability assessed by multifractal analysis during a prolonged tapping task. Four groups of participants had to produce the same tapping performance while being deprived from 0, 1, 2, or 3 sensory feedbacks simultaneously (auditory and/or visual and/or tactile). Whereas tapping performance was not statistically different across groups, the number of brain networks involved and the degree of multifractality of the inter-tap interval series were significantly correlated, increasing as a function of feedback deprivation. Our findings provide first evidence that concomitant changes in brain modularity and multifractal properties characterize adaptations underlying unchanged performance. We discuss implications of our findings with respect to the degeneracy properties of complex systems, and the entanglement of adaptability and effective adaptation.

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Dynamics of the human brain network revealed by time-frequency effective connectivity in fNIRS

Cited by 26 publications

References 81 publications

Continuous reorganization of cortical information flow in multiple sclerosis: A longitudinal fMRI effective connectivity study

Continuous reorganization of cortical information flow in multiple sclerosis: A longitudinal fMRI effective connectivity study

Different Hemodynamic Responses of the Primary Motor Cortex Accompanying Eccentric and Concentric Movements: A Functional NIRS Study

Concurrent Changes of Brain Functional Connectivity and Motor Variability When Adapting to Task Constraints

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