Towards prognostic biomarkers from BOLD fluctuations to differentiate a first epileptic seizure from new‐onset epilepsy

Gupta, Lalit; Janssens, Rick; Vlooswijk, Mariëlle C.G.; Rouhl, Rob P.W.; Louw, Anton de; Aldenkamp, Albert P.; Ulman, Shrutin; Besseling, René M.H.; Hofman, Paul; Kranen-Mastenbroek, Vivianne H. van; Hilkman, Danny; Jansen, Jacobus F.A.; Backes, Walter H.

doi:10.1111/epi.13658

Cited by 16 publications

(15 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The gradual accumulation of [K + ] o has been suggested to initiate a positive-feedback loop resulting the heightened neuronal excitability and development of seizure discharges (28-30, 34, 37). Studies in patients with epilepsy revealed abnormal resting-state fluctuations (20, 22, 23). Indeed, it has been demonstrated that the amplitude of resting-state fluctuations in epileptic patients is increased as compared to the healthy individuals (22).…”

Section: Discussionmentioning

confidence: 99%

“…Studies in patients with epilepsy revealed abnormal resting-state fluctuations (20, 22, 23). Indeed, it has been demonstrated that the amplitude of resting-state fluctuations in epileptic patients is increased as compared to the healthy individuals (22). Interestingly, our previous work demonstrated that homeostatic up-regulation of excitatory connections, following trauma, may lead to rewiring long-range cortical connectivity (37).…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Origin of Slow Spontaneous Resting-State Neuronal Fluctuations in Brain Networks

Krishnan¹,

González

Bazhenov

2017

Preprint

View full text Add to dashboard Cite

Resting or baseline state low frequency (0.01-0.2 Hz) brain activity has been observed in fMRI, EEG and LFP recordings. These fluctuations were found to be correlated across brain regions, and are thought to reflect neuronal activity fluctuations between functionally connected areas of the brain. However, the origin of these infra-slow fluctuations remains unknown. Here, using a detailed computational model of the brain network, we show that spontaneous infra-slow (< 0.05 Hz) fluctuations could originate due to the ion concentration dynamics. The computational model implemented dynamics for intra and extracellular K + and Na + and intracellular Cl -ions, Na + /K + exchange pump, and KCC2 co-transporter. In the network model representing resting awake-like brain state, we observed slow fluctuations in the extracellular K + concentration, Na + /K + pump activation, firing rate of neurons and local field potentials. Holding K + concentration constant prevented generation of these fluctuations. The amplitude and peak frequency of this activity were modulated by Na + /K + pump, AMPA/GABA synaptic currents and glial properties. Further, in a large-scale network with long-range connections based on CoCoMac connectivity data, the infra-slow fluctuations became synchronized among remote clusters similar to the resting-state networks observed in vivo. Overall, our study proposes that ion concentration dynamics mediated by neuronal and glial activity may contribute to the generation of very slow spontaneous fluctuations of brain activity that are observed as the resting-state fluctuations in fMRI and EEG recordings.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Origin of Slow Spontaneous Resting-State Neuronal Fluctuations in Brain Networks

Krishnan¹,

González

Bazhenov

2017

Preprint

View full text Add to dashboard Cite

show abstract

“…The resting-state activity across wide brain regions forms functional networks, such as the defaultmode network, that vary with brain state and type of cognitive activity (2,5,15,18). Several neurological and psychiatric disorders, such as epilepsy and schizophrenia, have been shown to correlate with altered resting-state fluctuations and functional connectivity (4,(18)(19)(20)(21)(22). Although there is growing interest in understanding the resting-state fluctuations, the underlying neural mechanisms by which these oscillations arise remain unknown.Previous experimental work showed that infra-slow fluctuations in the local field potential gamma power, neuronal firing rate, and slow cortical potentials exhibit a correlational relationship with the resting-state fMRI blood-oxygen-level-dependent (BOLD) fluctuations (7,8,10,(13)(14)(15).…”

mentioning

confidence: 99%

mentioning

confidence: 99%

Origin of slow spontaneous resting-state neuronal fluctuations in brain networks

Krishnan

González

Bazhenov

2018

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

Resting- or baseline-state low-frequency (0.01-0.2 Hz) brain activity is observed in fMRI, EEG, and local field potential recordings. These fluctuations were found to be correlated across brain regions and are thought to reflect neuronal activity fluctuations between functionally connected areas of the brain. However, the origin of these infra-slow resting-state fluctuations remains unknown. Here, using a detailed computational model of the brain network, we show that spontaneous infra-slow (<0.05 Hz) activity could originate due to the ion concentration dynamics. The computational model implemented dynamics for intra- and extracellular K and Na and intracellular Cl ions, Na/K exchange pump, and KCC2 cotransporter. In the network model simulating resting awake-like brain state, we observed infra-slow fluctuations in the extracellular K concentration, Na/K pump activation, firing rate of neurons, and local field potentials. Holding K concentration constant prevented generation of the infra-slow fluctuations. The amplitude and peak frequency of this activity were modulated by the Na/K pump, AMPA/GABA synaptic currents, and glial properties. Further, in a large-scale network with long-range connections based on CoCoMac connectivity data, the infra-slow fluctuations became synchronized among remote clusters similar to the resting-state activity observed in vivo. Overall, our study proposes that ion concentration dynamics mediated by neuronal and glial activity may contribute to the generation of very slow spontaneous fluctuations of brain activity that are reported as the resting-state fluctuations in fMRI and EEG recordings.

show abstract

“…Resting‐state functional magnetic resonance imaging (fMRI) is another tool to record spontaneous blood oxygen level–dependent fluctuations in the brain. Fractional amplitude of low‐frequency fluctuations (fALFF) is a sensitive and specific parameter and relates to pathological alteration in epilepsy . In this study, we used DTI to track the probabilistic HTP (pHTP) and investigate its integrity in patients with TLE‐HS and healthy controls.…”

Section: Introductionmentioning

confidence: 99%

Alterations in the hippocampal‐thalamic pathway underlying secondarily generalized tonic–clonic seizures in mesial temporal lobe epilepsy: A diffusion tensor imaging study

Chen

Ding

et al. 2018

Epilepsia

View full text Add to dashboard Cite

Objective: The epileptogenic network underlying secondarily generalized tonicclonic seizures (sGTCS) in mesial temporal lobe epilepsy (mTLE) is not well understood. Here, we investigated alterations in the probabilistic hippocampal-thalamic pathway (pHTP) underlying sGTCS using diffusion tensor imaging and resting-state functional magnetic resonance imaging in a cohort of TLE patients with hippocampal sclerosis (HS). Methods: We consecutively recruited 51 unilateral TLE-HS patients (26 with and 25 without sGTCS) and 22 healthy controls. Probabilistic tractography was used to track the pHTP. Raw fractional anisotropy (FA) and mean diffusivity (MD) of the pHTP were corrected by the FA/MD of the hemispheric white matter on the same side. The volume of the thalamic subregion connected to the hippocampus (TSCH) was investigated. Fractional amplitude of low-frequency fluctuations of the hippocampus, the TSCH, and the thalamic subregion unconnected to the hippocampus in resting-state functional magnetic resonance imaging were also calculated. Results: After correction, the sGTCS group showed lower FA than the non-sGTCS group (P = 0.03), and lower FA as well as higher MD than controls in the ipsilateral pHTP. The non-sGTCS group only showed higher corrected MD in the ipsilateral pHTP relative to controls. Corrected FA or MD in the contralateral pHTP did not differ among groups. The TSCH was located in the mesial aspect of the thalamus, and it was atrophied in the sGTCS group compared to the non-sGTCS group and controls. The sGTCS group had lower fractional amplitude of low-frequency fluctuations in the ipsilateral hippocampus and TSCH compared to controls. Significance: In TLE-HS, sGTCS was associated with impaired integrity of the pHTP as well as structural and functional abnormalities in the medial thalamus.The medial thalamus is important in seizure generalization in mTLE. K E Y W O R D Shippocampal sclerosis, MRI, secondarily generalized tonic-clonic seizures, temporal lobe epilepsy, thalamus

show abstract

Towards prognostic biomarkers from BOLD fluctuations to differentiate a first epileptic seizure from new‐onset epilepsy

Cited by 16 publications

References 33 publications

Origin of Slow Spontaneous Resting-State Neuronal Fluctuations in Brain Networks

Origin of Slow Spontaneous Resting-State Neuronal Fluctuations in Brain Networks

Origin of slow spontaneous resting-state neuronal fluctuations in brain networks

Alterations in the hippocampal‐thalamic pathway underlying secondarily generalized tonic–clonic seizures in mesial temporal lobe epilepsy: A diffusion tensor imaging study

Contact Info

Product

Resources

About