Knowledge Vault 3/34 - G.TEC BCI & Neurotechnology Spring School 2024 - Day 3
Challenges and opportunities in studying effective
connectivity through TMS-EEG coregistration
Marta Bortoletto, IRCCS Centro San Giovanni di Dio Fatebenefratelli (IT)
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Concept Graph & Resume using Claude 3 Opus | Chat GPT4 | Llama 3:

graph LR classDef blue fill:#d4d4f9, font-weight:bold, font-size:14px; classDef green fill:#d4f9d4, font-weight:bold, font-size:14px; classDef red fill:#f9d4d4, font-weight:bold, font-size:14px; classDef purple fill:#f9d4f9, font-weight:bold, font-size:14px; A[Marta Bortoletto] --> B[ TMS expert
joins panel. 1] B --> C[Marta impressed, questions
for speaker. 2] B --> D[Marta presents: TMS-EEG
connectivity challenges. 4] A --> E[Eliminating sound: source
vs. recording. 3] A --> F[TEPs measure brain
connectivity indexes. 5] F --> G[TEPs: waveforms reflecting
stimulated area. 6] F --> H[TEPs detect remote
activity, connectivity. 7] H --> I[Early TEPs: activity
spreads directly. 8] I --> J[P15 relates to
transcallosal inhibition. 9] F --> K[TEPs affected by
stimulation parameters. 10] K --> L[TMS parameters modulate
network signals. 11] K --> M[Current direction, coil
orientation affect TEPs. 12] M --> N[Monophasic TMS alters
activated tracts. 13] F --> O[TEPs reveal task-related
connectivity changes. 14] O --> P[Early TEPs show
transcallosal task changes. 15] O --> Q[Middle TEPs modulated
by task, movement. 16] O --> R[Late TEPs reflect
general arousal. 17] O --> S[TEPs demonstrate action-based
connectivity differences. 18] A --> T[TEPs: biomarkers for
network degeneration. 19] T --> U[AD alters default
mode network early. 20] T --> V[Study tested AD,
MCI, controls. 21] V --> W[Patients showed atrophy,
network changes. 22] W --> X[N20 frontal component
differed in MCI. 23] X --> Y[N20 distinguished healthy
from patients. 24] T --> Z[Controlling TMS improves
TEP diagnostic utility. 25] A --> AA[Future TMS-EEG: task
connectivity, biomarkers. 26] AA --> AB[TMS-EEG studies most
cortical regions. 27] AB --> AC[Easier TMS-EEG enables
clinical biomarker use. 28] A --> AD[Intracranial EEG-TMS provides
high-resolution data. 29] AD --> AE[Comparing non-invasive, intracranial
TMS-EEG is rare. 30] class B,C,D red; class E,F,G,H,I,J,K,L,M,N,O,P,Q,R,S green; class T,U,V,W,X,Y,Z blue; class AA,AB,AC,AD,AE purple;


1.- Marta Bortoletto, an expert in TMS for short-latency evoked potentials, joins the panel discussion.

2.- Marta saw the previous presentation and has many questions for the speaker, finding the work impressive.

3.- They discuss eliminating sound at the source versus after recording it, and the challenges involved with each approach.

4.- Marta presents on challenges and opportunities in studying effective connectivity through TMS-EEG co-registration.

5.- TMS-evoked potentials (TEPs) can measure connectivity in the brain, revealing what can be measured with these indexes.

6.- TEPs are waveforms time-locked to stimulation, with features specific to the stimulated area reflecting its physiological activity.

7.- TEPs can detect activity from areas remote from the stimulated area, providing information on effective connectivity.

8.- Early TEPs correspond to activity spreading from the stimulated area to directly connected ones, similar to MEPs.

9.- The amplitude of the P15 component correlates with transcallosal inhibition, and its latency relates to corpus callosum integrity.

10.- TEPs provide measures of specific cortical connections but are heavily affected by stimulation parameters like intensity, pulse type, and coil orientation.

11.- Changing TMS parameters can modulate signal within the same network or activate different networks.

12.- TMS current direction and coil orientation affect TEP peaks, reflecting changes in activated pathways.

13.- Topographic analysis suggests monophasic TMS parameter changes can alter the specific tracts activated.

14.- TEPs can reveal unique information about target area connectivity changes during task performance.

15.- Early TEPs show transcallosal connectivity changes during bimanual tasks, with P15 amplitude higher for complex tasks.

16.- Middle latency TEPs (30-60ms), associated with premotor and somatosensory feedback, are modulated by both bimanual task complexity and hand movement.

17.- Late TEPs change with any task and may reflect general arousal rather than specific task representation.

18.- TEPs demonstrate effective connectivity differences based on participant actions, allowing inferences about specific pathway changes.

19.- TEPs are used as biomarkers of network degeneration in conditions like Alzheimer's disease (AD).

20.- AD involves structural and functional alterations in networks like the default mode network, even before clinical symptoms.

21.- The study tested AD, mild cognitive impairment, and healthy control groups using neuropsychological assessment, MRI, and TMS-EEG.

22.- Patients showed cortical atrophy, white matter alterations, and individualized changes in default mode and executive control networks.

23.- Analysis focused on early TEPs (0-50ms) linked to direct connectivity; an N20 frontal component differed in MCI patients.

24.- N20 frontal amplitude correlated with patients' cognitive state and distinguished healthy from patient groups with 80% accuracy.

25.- Network-based TEPs may be useful biomarkers; controlling TMS parameters could reduce variability and improve diagnostic utility.

26.- Future advancements in TMS-EEG include studying task-dependent effective connectivity and developing clinically useful biomarkers.

27.- Most cortical regions within 3cm of the coil can be studied with TMS-EEG.

28.- Making TMS-EEG technically easier to apply would facilitate clinical use of promising biomarker data.

29.- Intracranial EEG with TMS provides high-resolution data on local and remote activations, complementing non-invasive studies.

30.- Comparing non-invasive TMS-EEG with intracranial recordings in the same subjects could yield interesting insights but is rarely done.

Knowledge Vault built byDavid Vivancos 2024