Back

Temporal fingerprints of TMS-evoked potentials across thalamocortical circuits

Hassan, G.; Gaglioti, G.; Furregoni, G.; Focacci, E.; Porro, M.; Bernardelli, L.; Calcagno, A.; Massimini, M.; Sarasso, S.; Rosanova, M.; Casarotto, S.

2026-07-02 neuroscience
10.64898/2026.06.29.734769 bioRxiv
Show abstract

Background: Electroencephalographic (EEG) potentials evoked by transcranial magnetic stimulation (TMS) offer a direct window into cortical dynamics. Yet, a systematic exploration of their morphological features, analogous to sensory-evoked potentials, is lacking, especially for stimulation outside the motor cortex. Aim: To obtain region-specific properties of frontal, parietal and occipital networks from the time course of TMS-evoked potentials (TEPs). Materials and Methods: We implemented and applied an automatic procedure to compute peak-to-peak amplitude, peak latency, and inter-peak interval of TEPs recorded from 40 neurotypical subjects stimulated over left occipital (n=25), parietal (n=25), and frontal (n=25) cortices. Results: Occipital TEPs showed the largest peak-to-peak amplitude and longest latency of the first waveform component, independently of stimulation intensity and consistent with the recruitment of a large patch of densely interconnected neurons. Concerning later components, both latency and inter-peak interval systematically decreased along the posterior-to-anterior axis, reflecting progressively faster recurrent dynamics from the alpha-dominated occipital circuitry to the tightly coupled loops between frontal cortex and subcortical structures. Parietal TEPs showed intermediate amplitude and latency measures, consistent with the heterogeneous cytoarchitectonic and connectional organization of the superior parietal cortex. Conclusions: Our findings suggest that TEP morphology is shaped by the distinct properties of the stimulated networks, with early amplitude reflecting the extent of local recruitment and later temporal features tracking the rhythm of recurrent activity. This work offers a mechanistically grounded and practically accessible approach, also released as a Python-based tool, that allows to characterize cortical reactivity across different brain-states and populations.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

1
NeuroImage
903 papers in training set
Top 0.3%
26.4%
2
Imaging Neuroscience
282 papers in training set
Top 0.2%
15.0%
3
Brain Topography
29 papers in training set
Top 0.1%
9.7%
50% of probability mass above
4
Brain Stimulation
125 papers in training set
Top 0.4%
5.1%
5
eneuro
439 papers in training set
Top 2%
3.5%
6
Scientific Reports
3612 papers in training set
Top 33%
3.2%
7
Clinical Neurophysiology
56 papers in training set
Top 0.3%
3.2%
8
Human Brain Mapping
329 papers in training set
Top 2%
2.6%
9
Cerebral Cortex
396 papers in training set
Top 2%
2.4%
10
Journal of Neuroscience Methods
122 papers in training set
Top 0.8%
2.4%
11
PLOS Computational Biology
1863 papers in training set
Top 13%
2.1%
12
Cortex
119 papers in training set
Top 1%
1.7%
13
Journal of Neural Engineering
221 papers in training set
Top 2%
1.5%
14
Frontiers in Human Neuroscience
77 papers in training set
Top 1%
1.4%
15
PLOS ONE
5266 papers in training set
Top 53%
1.3%
16
European Journal of Neuroscience
189 papers in training set
Top 2%
1.3%
17
Psychophysiology
77 papers in training set
Top 0.9%
1.1%
18
Network Neuroscience
126 papers in training set
Top 1%
1.0%
19
Neuroinformatics
46 papers in training set
Top 0.7%
1.0%
20
Scientific Data
209 papers in training set
Top 3%
0.8%
21
Frontiers in Systems Neuroscience
22 papers in training set
Top 0.4%
0.6%
22
Communications Biology
993 papers in training set
Top 35%
0.6%
23
eLife
5828 papers in training set
Top 69%
0.6%
24
Peer Community Journal
281 papers in training set
Top 6%
0.6%
25
Journal of Neurophysiology
302 papers in training set
Top 4%
0.6%