Cognitive Enhancement via Network-Targeted Cortico-cortical Associative Brain Stimulation

Abstract

Fluid intelligence (gf) represents a crucial component of human cognition, as it correlates with academic achievement, successful aging, and longevity. However, it has strong resilience against enhancement interventions, making the identification of gf enhancement approaches a key unmet goal of cognitive neuroscience. Here, we applied a spike-timing-dependent plasticity (STDP)-inducing brain stimulation protocol, named cortico-cortical paired associative stimulation (cc-PAS), to modulate gf in 29 healthy young subjects (13 females-mean ± standard deviation, 25.43 years ± 3.69), based on dual-coil transcranial magnetic stimulation (TMS). Pairs of neuronavigated TMS pulses (10-ms interval) were delivered over two frontoparietal nodes of the gf network, based on individual functional magnetic resonance imaging data and in accordance with cognitive models of information processing across the prefrontal and parietal lobe. cc-PAS enhanced accuracy at gf tasks, with parieto-frontal and fronto-parietal stimulation significantly increasing logical and relational reasoning, respectively. Results suggest the possibility of using SPTD-inducing TMS protocols to causally validate cognitive models by selectively engaging relevant networks and manipulating inter-regional temporal dynamics supporting specific cognitive functions.

Keywords: Hebbian plasticity; abstract reasoning; fluid intelligence; logical reasoning; transcranial magnetic stimulation.

Figure 1

Experimental design. (A) Baseline assessment included a set of neuropsychological tests and resting state fMRI acquisition. A high degree of variability in functional connectivity of prefrontal and parietal TMS targets was present. (B) Pre-cc-PAS evaluation consisted of two gf assessments (Baseline gf 1 and Baseline gf 2) interleaved with a LNG task (near transfer) and VS task (far transfer). TMS parameters were based on individual RMT collected at the beginning of each session. (C) Different TMS conditions were tested on different days: (i) left IPL TMS pulse either preceding (P → F, ISI = +10 ms), (ii) following (F → P, ISI = −10 ms), or (iii) delivered simultaneously (Simultaneous-TMS, ISI = 0 ms) to the TMS pulse over the MFG. Additionally, a (iv) “Prefrontal-TMS” condition was performed by delivering real TMS over MFG and Sham TMS over IPL, while (v) spontaneous learning was tested by longitudinally assessing cognition without stimulation (“NoStim” condition). (D) Post-cc-PAS assessment included two gf evaluations (Post TMS gf 1, Post TMS gf 2), interleaved with an LNG (near transfer) and VS task (far transfer) administrated in reverse order with respect to pre-cc-PAS.

Figure 2

TMS targets based on fMRI data. (A) A meta-analytic map of gf fMRI activation patterns (Santarnecchi et al. 2017) used to derive individual FC maps. (B) Overlap between individual TMS targets of the current study and gf network. (C) Overlap between TMS targets and known resting state networks parcellations.

Figure 3

Effects on gf performance. Significant changes in logical reasoning performance (i.e., pre- and post-delta ACC) were observed after P → F cc-PAS (A), whereas a significant enhancement in relational reasoning was found after F → P cc-PAS (B). All ACC values were normalized to baseline. Error bars represent ±1 standard error of the mean (SEM). For cc-PAS effects on RTs, please refer to the Supplementary Materials. Note: ^* = P < 0.05; ^** = P < 0.01; ^*** = P < 0.001.

Figure 4

Near and far transfer. (A) Average patterns of fMRI activation during the transfer tasks (VA, LNG) and gf tasks, as well as their quantitative overlap (B). No significant changes in ACC and RTs were observed for the transfer tasks after any TMS condition. Error bars represent ±1 SEM.