Effective Intracerebral Connectivity in Acute Stroke: A TMS–EEG Study
Abstract
:1. Introduction
Aim
2. Methods
2.1. Subjects
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- Age 18–90;
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- NIH stroke scale (NIHSS) range 6–24;
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- Single ischemic stroke in the middle cerebral artery territory of the left hemisphere within 10 days;
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- Upper arm paresis (upper arm at least NIHSS > 1).
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- Exclusion criteria were:
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- Symptom onset more distant than 10 days;
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- Associated neurological diseases;
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- Multiple ischemic strokes;
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- Previous ischemic or hemorrhagic stroke;
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- TMS contraindication, according to the recommendations of the International Federation of Clinical Neurophysiology (IFCN, [24]);
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- Compromised vigilance or severe hemodynamic, neurological, or respiratory conditions;
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- Poor middle cerebral artery insonation through transcranial Doppler;
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- Hemodynamic carotid stenosis (it could determine a compensatory dilatation in the distal circulation, with a consequent reduction of basal VMR);
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- Refusal to sign the informed consent.
2.2. TMS–EEG Experimental Setup and Protocol
2.3. Data Analysis
2.3.1. MEP and EEG Data
2.3.2. Data-Processing Pipeline for TMS–EEG Data
- A typical EEG recording during TMS is presented in Figure 1A,B. The data are averaged over the repeated TMS stimuli (t = 0 ms). As can be seen, the TMS artifact is several orders of magnitude higher than the brain signal. Via software, we simulate the hardware intervention of the sample-and-hold amplifiers [27] (as our TMS system is not equipped with this) by replacing the 5 ms interval around the TMS stimulus (−3 to 2 ms) with the 5 ms interval of the baseline (−8 to −3 ms) (Figure 1).Figure 1. Example of an EEG recording during TMS. (A). Original data averaged over the TMS stimulus (t = 0). (B). The data after replacing the [−3, 2] ms interval with the 5-ms interval of the baseline [−8, −3] ms.
- A channel that has been minimally corrupted by a TMS stimulus artifact is chosen through visual inspection. The data are re-referenced against this channel. In our case, the Cz channel is the channel that is least corrupted by the TMS artifact.
- Application of the SOurce-Utilized Noise Discarding (SOUND) algorithm for cleaning the channels separately for the baseline and the data after the TMS stimulus [26]. TMS–EEG data can contain various other noise sources that corrupt individual channels or cause strange voltage patterns on the scalp. The SOUND algorithm cross-validates EEG channels via consecutive inverse and forward computations. The cross-validation outputs an estimate for the noise distribution across the EEG channels, which is used to form a spatial Wiener filter that highlights the neuronal EEG signals. SOUND filters out those signal components that are not likely to originate from intracranial post-synaptic currents, e.g., electrode-polarization, line-noise, and electrode-movement artifacts [26,28,29].
- 4.
- Re-referencing the data to the mean reference. At this point, the data are processed by the SSP–SIR (Signal-Space Projection—Source-Informed Reconstruction) algorithm for automatic cleaning of residual artifacts, such as muscle artifacts [28,30]. SSP–SIR substantially improved the signal quality of artifactual TMS–EEG data, causing minimal distortion in the neuronal signal components. In the SSP–SIR approach, the artifact signal subspace containing TMS-evoked muscle artifacts is estimated from the high-pass-filtered (cutoff frequency 100 Hz) data using principal component analysis. The rationale is that EEG signals above 100 Hz mainly consist of non-neuronal signals. The estimated artifact subspace is projected out using SSP [30]. The remaining artifact-suppressed signals are used to estimate an equivalent source distribution model exploiting anatomical brain constraints. When solving the inverse problem, the artifact dimensions must be projected out also from the lead field (or gain) matrix. In the final SIR step, the obtained source estimates are projected back onto the original signal space.
- 5.
- Finally, independent component analysis using the fastICA [31] algorithm is applied to the averaged data to eliminate eye blinking and suppress random noise (first and fourth rows of Figure 3). Using data cleaned by SOUND, which is most efficient in eliminating the TMS and the motor artifacts, we used a deep experience in exploiting statistical features of ocular artifacts, thus well identified by the ICA approach [32,33].
2.3.3. Stroke vs. Healthy Volunteer Comparison
3. Results
3.1. Enrolled Population
3.2. MEP Amplitude
3.3. Quality of Artefact Removal
3.4. Global Intracerebral Effective M1 Connectivity
4. Discussion
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Wave | Topography | Functional Connectivity | Synaptic Substrate |
---|---|---|---|
N7 | F3 | ipsi-lateral motor associative, PM | NMDA |
P13 | Fp2 F4 F8 C4 T4 T6 | contra-lateral homolog M1 | GABAA |
N18 | P3 | ipsi-lateral PPC | |
P30 | ExtendedC | contra-lateral thalamo-cortical nodes | |
N44 | ExtendedC&I | ipsi- and contral-lateral M1 | |
P60 | T5 | GABAB | |
N100 | ExtendedI | ipsi-lateral M1 |
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Tecchio, F.; Giambattistelli, F.; Porcaro, C.; Cottone, C.; Mutanen, T.P.; Pizzella, V.; Marzetti, L.; Ilmoniemi, R.J.; Vernieri, F.; Rossini, P.M. Effective Intracerebral Connectivity in Acute Stroke: A TMS–EEG Study. Brain Sci. 2023, 13, 233. https://doi.org/10.3390/brainsci13020233
Tecchio F, Giambattistelli F, Porcaro C, Cottone C, Mutanen TP, Pizzella V, Marzetti L, Ilmoniemi RJ, Vernieri F, Rossini PM. Effective Intracerebral Connectivity in Acute Stroke: A TMS–EEG Study. Brain Sciences. 2023; 13(2):233. https://doi.org/10.3390/brainsci13020233
Chicago/Turabian StyleTecchio, Franca, Federica Giambattistelli, Camillo Porcaro, Carlo Cottone, Tuomas P. Mutanen, Vittorio Pizzella, Laura Marzetti, Risto J. Ilmoniemi, Fabrizio Vernieri, and Paolo Maria Rossini. 2023. "Effective Intracerebral Connectivity in Acute Stroke: A TMS–EEG Study" Brain Sciences 13, no. 2: 233. https://doi.org/10.3390/brainsci13020233