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Schuler, A.-L.

Publications and source records attributed to Schuler, A.-L..

3 recordsLinked to original sources

Executive and language systems competitively shape predictive processing during reading

When we read, our brains constantly generate predictions about upcoming words, which is thought to make comprehension faster and more efficient. Yet it remains unclear whether this predictive mechanism relies on the same domain-general executive resources the brain uses for other demanding cognitive tasks, or operates largely independently within the language system. Here we tested this question using transcranial magnetic stimulation (TMS) in twenty-five healthy adults, who performed self-paced naturalistic reading, an n-back task, and a dual task of concurrent reading and n-back, allowing us to place competing demands on cognitive resources. In three sessions, we stimulated the left angular gyrus alone, the angular gyrus together with dorsolateral prefrontal cortex, a hub of executive control, or delivered sham stimulation, and used computational modeling of decision-making to isolate how each brain state altered the underlying cognitive processes. Combined stimulation of the angular gyrus and prefrontal cortex selectively weakened the reading-time costs of unexpected words, but only when reading competed with the working-memory task, revealing that linguistic prediction draws on executive resources specifically under cognitive load. Computational models showed that the relationship was bidirectional: Unpredictable words also disrupted concurrent working-memory decisions, slowing the accumulation of evidence and increasing response caution. Together, these findings show that predictive language processing is not a fixed, encapsulated function but is dynamically coupled to executive control when cognitive demands compete. Our results provide evidence that prediction during language comprehension is embedded within a broader, demand-sensitive cognitive architecture that flexibly coordinates linguistic and executive resources.

neuroscience↗

Assessing whole-cortex excitability from electromagnetic brain signals

Cortical excitability, the propensity of neural circuits to respond to internal or external perturbations, is a fundamental property of brain functioning, shaped by local microcircuitry, large-scale networks, and neurochemical architecture. In humans, excitability is inferred from multiple indirect metrics derived from spontaneous and stimulus-driven electromagnetic activity, yet it remains unclear whether these measures reflect a common underlying construct or distinct physiological processes. Here, we systematically compared ten previously validated excitability metrics using whole-head magnetoencephalography (MEG) recorded at rest and during 40 Hz auditory stimulation in a large sample of healthy adults. The measures spanned stimulus-driven synchronization, spectral power, 1/f activity (hereafter defined as aperiodic), signal complexity, long-range temporal correlations, and phase-gamma synchronization. Hierarchical clustering revealed six separable excitability dimensions with limited redundancy, demonstrating that cortical excitability is inherently multidimensional. Aperiodic and alpha-band measures showed the strongest mutual coupling and the furthest spatial correspondence to stimulus-related responses, whereas temporal, complexity, and synchronization-based metrics were largely independent. The similarity between measures varied across cortical regions and functional networks, with maximal cluster separability in integrative and internally driven regions. These regions included orbitofrontal, temporal, anterior cingulate, and default mode areas, as well as visual and somatomotor networks. Finally, excitability dimensions showed distinct relationships with cortical morphology and neurotransmitter receptor density, implicating heterogeneous neurobiological substrates. Together, these findings provide a unified framework for interpreting excitability metrics and highlight the need for multimodal approaches when probing cortical excitability in health and disease.

neuroscience↗

Assessing cortical excitability with electroencephalography: a pilot study with EEG-iTBS.

Cortical excitability measures neural reactivity to stimuli, usually delivered via Transcranial Magnetic Stimulation (TMS). Excitation/inhibition balance (E/I) is the ongoing equilibrium between excitatory and inhibitory activity of neural circuits. According to some studies, E/I could be estimated in-vivo and non-invasively through the modeling of electroencephalography (EEG) signals. Several measures have been proposed (phase consistency in the gamma band, sample entropy, exponent of the power spectral density 1/f curve, E/I index extracted from detrend fluctuation analysis, and alpha power). It remains to be investigated to what extent they scale with excitability and how they relate to each other. Intermittent theta burst stimulation (iTBS) of the primary motor cortex (M1) is a non-invasive neuromodulation technique allowing controlled and focal enhancement of cortical excitability and E/I of the stimulated hemisphere. M1 excitability and several E/I estimates extracted from resting state EEG recordings were assessed before and after iTBS in a cohort of healthy subjects. Enhancement of M1 excitability, as measured through motor-evoked potentials (MEPs), and phase consistency of the cortex in high gamma band correlated with each other. Other measures of E/I showed some expected results, but no correlation with TMS excitability measures or consistency with each other. EEG E/I estimates offer an intriguing opportunity to map cortical excitability non-invasively, with high spatio-temporal resolution and with a stimulus independent approach. While different EEG E/I estimates may reflect the activity of diverse excitatory-inhibitory circuits, spatial phase synchrony in the gamma band is the measure that best captures excitability changes in the primary motor cortex.

neuroscience↗