A year in research · 30 selected publications · five research threads
In 2020, we turned cognitive concepts into measurable neural representations and used stimulation, pharmacology and machine learning to test how those representations are controlled.
The work demonstrated that event files, proactive states and network communication are not abstract metaphors. They can be decoded from EEG, altered by experience and stimulation, and used to explain why alcohol, ADHD, autism and Tourette syndrome affect some forms of control but spare others.
01 · EVENT CODING
Temporal EEG decomposition isolated distractor-response bindings and linked them to temporoparietal updating and frontal conflict resolution.[1] The same approach connected the P3 and inferior-parietal activity directly to event-file binding during response selection.[2] Multivariate decoding showed why this decomposition matters: mixed EEG signals can conceal partly opposing representational dynamics, whereas separated components revealed bindings that activate and decay gradually.[3]
Somatosensory binding depended primarily on response selection in superior parietal cortex rather than on early stimulus processing.[4] In Tourette syndrome, perception-action binding was increased, and behavioural therapy reduced this excessive binding while improving inhibition.[5,6]
In plain terms: Event files became empirically traceable objects whose formation, stability and clinical alteration could be measured rather than inferred only from behaviour.
02 · PREPARATORY DYNAMICS
Pre-trial theta in ventromedial prefrontal cortex covaried with inhibition-related theta in right inferior frontal cortex, and their relationship strengthened with inhibitory demand.[7] Resting theta was linked specifically to stimulus-related coding within later conflict processing.[8] Perceptual components in middle frontal regions contributed to trial-to-trial conflict adaptation.[9]
Causal manipulation supported a role for superior-frontal theta in combining subliminal and conscious conflict.[10] During sequential task switching, theta and pupil measures pointed to norepinephrine-related modulation of backward inhibition.[11] Lower-level perceptual properties also changed norepinephrine-related effects on stopping, and passive perceptual learning altered later inhibition in superior frontal regions.[12,13]
In plain terms: Baseline and pre-trial activity are part of the control process: they configure how incoming information will be interpreted and acted upon.
03 · PERTURBATION AND COMPUTATION
Deep learning identified single-trial EEG dynamics that connected complementary theories of action control.[14] Anodal stimulation increased theta-network efficiency during auditory attention even when theta power itself did not change, separating communication efficiency from local amplitude.[15] Frequency-specific stimulation of superior parietal cortex altered automatic tactile response selection in different ways.[16]
Learning experience reversed the behavioural effect of catecholaminergic enhancement and could even eliminate drug effects after the task became familiar.[17,18] In ADHD, one feedback protocol affected opposing execution and inhibition problems across presentations, suggesting that it acted on a superordinate control system rather than a single symptom.[19]
In plain terms: Causal and computational tools reveal which level changes: local activity, network efficiency, representation or behaviour.
04 · CLINICAL PROFILES
A comprehensive account reframed Tourette syndrome as altered action-perception integration, and stimulation-triggered tic exacerbation suggested an increased tendency to form automatic perception-action links.[20,21] In ADHD, both contextual modulation and basic stimulus-response conflict monitoring could remain intact, challenging a global conflict-control deficit.[22,23]
Adolescents with autism showed a different difficulty: after cognitive-emotional conflict, they relied more strongly on subjective expectations about what would happen next and adjusted less flexibly to objective transition probabilities.[24] These findings separated preserved basic operations from altered deployment under uncertainty or changing context.
In plain terms: Diagnostic groups are not defined by a uniform loss of control; they show selective changes in how intact operations are configured and combined.
05 · ALCOHOL, BODILY STATE AND ADDICTION
Alcohol impaired stopping mainly when the competing response had become automatic, without a clear dependence on baseline GABA or glutamate concentrations.[25] Hangover left model-based and model-free learning largely unchanged, while acute intoxication reduced resting 1/f neural noise.[26,27] A broader synthesis showed that automatic processing was relatively preserved compared with controlled processing under high-dose intoxication.[28]
The cardiac cycle also modulated response-related theta and behaviour, demonstrating that internal bodily timing can influence cognitive-emotional action selection.[29] The ReCoDe consortium translated this state-sensitive perspective into longitudinal monitoring and mechanism-based interventions for losing and regaining control over drug use.[30]
In plain terms: Control is shaped by learned automaticity and by the current state of the body and brain - precisely the factors that real-world addiction research must track.
The 2020 work made theoretical constructs operational. Event files could be decoded, preparatory states linked to later control, and targeted perturbations used to distinguish power from connectivity and learning from pharmacology. These tools supported more specific clinical accounts and a translational approach grounded in real-life changes of state.
Curated from PubMed records returned for Beste C[au] in 2020. Citation numbers in the story link to entries below. DOI links open the publisher landing page; PubMed links open the indexed record.
Opitz A, Beste C, Stock AK. NeuroImage. 2020;209:116524.
DOI: 10.1016/j.neuroimage.2020.116524 · PubMed
Takacs A, Zink N, Wolff N, Münchau A, Mückschel M, Beste C. Human brain mapping. 2020;41(10):2862-2877.
DOI: 10.1002/hbm.24983 · PubMed
Takacs A, Mückschel M, Roessner V, Beste C. Cerebral cortex communications. 2020;1(1):tgaa016.
DOI: 10.1093/texcom/tgaa016 · PubMed
Friedrich J, Verrel J, Kleimaker M, Münchau A, Beste C, Bäumer T. Scientific reports. 2020;10(1):14794.
DOI: 10.1038/s41598-020-71779-0 · PubMed
Kleimaker M, Takacs A, Conte G, Onken R, et al.. Brain : a journal of neurology. 2020;143(6):1934-1945.
DOI: 10.1093/brain/awaa111 · PubMed
Petruo V, Bodmer B, Bluschke A, Münchau A, Roessner V, Beste C. Scientific reports. 2020;10(1):1174.
DOI: 10.1038/s41598-020-58269-z · PubMed
Adelhöfer N, Beste C. NeuroImage. 2020;219:117052.
DOI: 10.1016/j.neuroimage.2020.117052 · PubMed
Pscherer C, Bluschke A, Prochnow A, Eggert E, Mückschel M, Beste C. Human brain mapping. 2020;41(18):5114-5127.
DOI: 10.1002/hbm.25178 · PubMed
Adelhöfer N, Beste C. Journal of cognitive neuroscience. 2020;32(7):1381-1393.
DOI: 10.1162/jocn_a_01558 · PubMed
Giller F, Bensmann W, Mückschel M, Stock AK, Beste C. Cortex; a journal devoted to the study of the nervous system and behavior. 2020;132:15-28.
DOI: 10.1016/j.cortex.2020.08.003 · PubMed
Giller F, Mückschel M, Ziemssen T, Beste C. Cortex; a journal devoted to the study of the nervous system and behavior. 2020;128:22-34.
DOI: 10.1016/j.cortex.2020.03.008 · PubMed
Mückschel M, Ziemssen T, Beste C. Biological psychology. 2020;152:107862.
DOI: 10.1016/j.biopsycho.2020.107862 · PubMed
Friedrich J, Beste C. Human brain mapping. 2020;41(3):726-738.
DOI: 10.1002/hbm.24835 · PubMed
Vahid A, Mückschel M, Stober S, Stock AK, Beste C. Communications biology. 2020;3(1):112.
DOI: 10.1038/s42003-020-0846-z · PubMed
Zink N, Kang K, Li SC, Beste C. Journal of neurophysiology. 2020;124(1):207-217.
DOI: 10.1152/jn.00074.2020 · PubMed
Friedrich J, Beste C. Scientific reports. 2020;10(1):3954.
DOI: 10.1038/s41598-020-61025-y · PubMed
Mückschel M, Eggert E, Prochnow A, Beste C. The international journal of neuropsychopharmacology. 2020;23(1):12-19.
DOI: 10.1093/ijnp/pyz058 · PubMed
Mückschel M, Roessner V, Beste C. European neuropsychopharmacology : the journal of the European College of Neuropsychopharmacology. 2020;35:89-99.
DOI: 10.1016/j.euroneuro.2020.03.013 · PubMed
Bluschke A, Schreiter ML, Friedrich J, Adelhöfer N, Roessner V, Beste C. Developmental science. 2020;23(6):e12956.
DOI: 10.1111/desc.12956 · PubMed
Kleimaker A, Kleimaker M, Bäumer T, Beste C, Münchau A. Frontiers in neurology. 2020;11:597898.
DOI: 10.3389/fneur.2020.597898 · PubMed
Weissbach A, Kleimaker M, Bäumer T, Beste C, Münchau A. Tremor and other hyperkinetic movements (New York, N.Y.). 2020;10:41.
DOI: 10.5334/tohm.547 · PubMed
Bluschke A, Chmielewski WX, Roessner V, Beste C. Journal of attention disorders. 2020;24(11):1503-1510.
DOI: 10.1177/1087054716643388 · PubMed
Bluschke A, Mückschel M, Roessner V, Beste C. Journal of clinical medicine. 2020;9(1):234.
DOI: 10.3390/jcm9010234 · PubMed
Schreiter ML, Beste C. Cortex; a journal devoted to the study of the nervous system and behavior. 2020;130:231-245.
DOI: 10.1016/j.cortex.2020.06.002 · PubMed
Bensmann W, Zink N, Werner A, Beste C, Stock AK. Journal of clinical medicine. 2020;9(2):481.
DOI: 10.3390/jcm9020481 · PubMed
Berghäuser J, Bensmann W, Zink N, Endrass T, Beste C, Stock AK. Journal of clinical medicine. 2020;9(5):1453.
DOI: 10.3390/jcm9051453 · PubMed
Stock AK, Pertermann M, Mückschel M, Beste C. Addiction biology. 2020;25(6):e12818.
DOI: 10.1111/adb.12818 · PubMed
Chmielewski WX, Zink N, Chmielewski KY, Beste C, Stock AK. Addiction biology. 2020;25(1):e12700.
DOI: 10.1111/adb.12700 · PubMed
Adelhöfer N, Schreiter ML, Beste C. NeuroImage. 2020;222:117275.
DOI: 10.1016/j.neuroimage.2020.117275 · PubMed
Heinz A, Kiefer F, Smolka MN, Endrass T, et al.. Addiction biology. 2020;25(2):e12866.
DOI: 10.1111/adb.12866 · PubMed