Biology subjects

Fink, G. R.

Publications and source records attributed to Fink, G. R..

At least 19 recordsLinked to original sources

bioRxiv2026

Structure-function coupling reveals hemisphere-specific network reorganization after stroke

MRI is an excellent tool for monitoring functional and structural network reorganization after brain injury and may provide biomarkers for treatment stratification and novel neuromodulatory interventions. In ischemic stroke, functional network changes, particularly within the contralesional hemisphere, and structural integrity, most prominently of the corticospinal tract, have been successfully associated with behavioral deficit and outcome. However, these components are commonly studied separately and at single time points, which does not reflect the highly dynamic nature of brain network plasticity. Here, we characterized structural and functional connectivity (SC-FC) coupling in the healthy mouse brain and longitudinally assessed intra- and inter-hemispheric connections up to 4 weeks after cortical and cortico-striatal stroke. In the healthy brain, intra-hemispheric SC-FC coupling was lateralized in the sensorimotor network (SMN), whereas the default mode network (DMN) showed no significant hemispheric difference. Intra-hemispheric coupling exceeded inter-hemispheric coupling in the SMN. After cortical stroke, regions with high baseline intra-hemispheric SC-FC coupling remained largely stable, whereas initially weakly coupled sensorimotor regions showed delayed increases within the ischemic hemisphere. Contralesional intra-hemispheric SC-FC coupling changed little, but directional inter-hemispheric analyses revealed selective remodeling, including increased contralesional-to-ischemic coupling of primary motor cortex and reduced bidirectional coupling of medial sensorimotor cortex. Cortico-striatal stroke produced fewer longitudinal changes, dominated by reduced inter-hemispheric motor-cortex coupling. These alterations did not simply correspond to regional lesion involvement. Thus, hemisphere- and direction-resolved SC-FC coupling uncovers network- and stroke-model-specific reorganization not detectable when structural and functional connectivity are analyzed independently.

neuroscience↗

bioRxiv2025

Characterizing the frequency-specific and spatiotemporal dynamics of β-γ Phase-Amplitude Coupling in Parkinson's disease

Cross-frequency coupling (CFC) has been proposed to facilitate neural information transfer across spatial and temporal scales. Phase-amplitude coupling (PAC), a type of CFC in which the amplitude of a faster brain oscillation is coupled to the phase of a slower brain oscillation, is implicated in various higher-order cognitive functions and was shown to be pathologically altered in neurological and psychiatric disease. In Parkinsons disease (PD), the coupling between gamma amplitude (50-150 Hz) to beta phase (13-35 Hz) is exaggerated. Enhanced {beta}-{gamma} PAC was found in the subthalamic nucleus and various cortical sources and shown to be responsive to dopaminergic therapy and deep brain stimulation (DBS). Therefore, exaggerated {beta}-{gamma} PAC has been proposed to be a disease marker and a potential target for brain circuit interventions. Despite these promising findings, a significant knowledge gap remains, as the spatial and frequency-specific dynamics of {beta}-{gamma} PAC and its association with motor symptoms and therapy remain elusive. To address this knowledge gap, we employed high-density electroencephalography (EEG) with advanced source localisation techniques for PD patients at rest. We highlight three key findings: (1) a frequency-specific increase in high {beta} (23-35 Hz)-{gamma} PAC within and between sources of the cortical motor network, (2) a link between elevated high {beta}-{gamma} PAC and bradykinesia and rigidity when OFF medication, but not tremor, and (3) a medication-induced reduction in high {beta}-{gamma} PAC in the supplementary motor area correlating with clinical improvement. Altogether, this study provides novel insights into the pathophysiology of PD as an oscillopathy and identifies high {beta}-{gamma} PAC as a potential marker of Parkinsonian symptoms and treatment effects. This has important implications for invasive as well as non-invasive therapeutic strategies as high {beta}-{gamma} PAC targeting might hold greater promise than targeting {beta}-{gamma} PAC per se.

neuroscience↗

bioRxiv2025

Dynamic structural connectivity changes in cortical and cortico-striatal strokes in mice

Stroke is a primary global health concern, leading to significant mortality and long-term disability. Beyond immediate neuronal damage, functional and structural connectivity is altered brain-wide with implications for functional deficits and recovery. It remains unclear, however, if the level of degeneration, i.e. reduced myelination and axonal damage, as well as compensatory plasticity, i.e., axonal sprouting and remyelination, depend on the lesion size and topology. This study compares for the first time two different stroke models in adult male mice, with the aim of uncovering the dynamics in white matter changes. Repetitive diffusion magnetic resonance imaging (dMRI) over four weeks post photothrombotic cortical (1.41{+/-}0.92% of brain volume), and middle cerebral artery occlusion (MCAO) cortico-striatal (11.53{+/-}2.8% of brain volume), strokes were used to map structural connectivity changes at the whole-brain level. We quantified inter- and intra-hemispheric seed strength changes over time, with seed strength reflecting how strongly each region is connected to the rest of the brain. Differences between groups and time points were assessed using a mixed model corrected for multiple comparisons. In conclusion, large cortico-striatal lesions led to increased structural connectivity in sensorimotor regions, whereas small cortical lesions induced asymmetric connectivity changes: an increase extending globally from the ischemic hemisphere and a decrease expanding globally from the healthy hemisphere. These findings highlight that stroke severity and lesion size significantly affect the temporal dynamics and spatial distribution of connectivity disruptions, emphasizing the need for targeted monitoring of neural changes post-stroke. Significance statementPathophysiological changes close to an ischemic lesion are well understood. But structurally connected regions also undergo stroke-related changes potentially contributing to functional deficits. Animal studies using in vivo diffusion MRI to quantify white matter changes have been limited to single connections, time points after stroke, and basic diffusion measures. We provide the first in-depth characterization of whole-brain fiber tracking to compare small cortical and large cortico-striatal strokes. We found a complex dynamic of decreasing and increasing structural connectivity in single sensorimotor-related brain regions within the brain network - even remote areas from the stroke lesion. This study provides a better understanding of the interactions of regions during stroke recovery, which may help in developing therapies targeting localized white matter repair.

neuroscience↗

bioRxiv2024

Computational modelling reveals neurobiological contributions to static and dynamic functional connectivity patterns

Functional connectivity (FC) is a widely used indicator of brain function in health and disease, yet its neurobiological underpinnings still need to be firmly established. Recent advances in computational modelling allow us to investigate the relationship between FC and neurobiology non-invasively. These techniques allow for targeted manipulations to study the effect of network disturbances on FC. Most modelling research has concentrated on replicating empirical static FC (sFC). However, FC changes over time, and its dynamic properties are closely linked to behaviour and symptomatology. In this study, we adapted computational models to reflect both sFC and dynamic FC (dFC) of individuals, allowing for a more comprehensive characterisation of the neurobiological origins of FC. We modelled the brain activity of 200 healthy individuals based on empirical resting-state functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) data. Simulations were conducted using a group-averaged structural connectome and four parameters guiding regional brain activity: i) G, a global coupling scaling parameter; ii) J_i, the local inhibitory current; iii) J_NMDA, the excitatory NMDA synaptic coupling parameter; and iv) w_p, the excitatory population recurrence weight. We evaluated the models based on four metrics: a) the sFC, b) the FC variance, c) the temporal correlation (TC), and d) the node cohesion (NC). The optimal model for each subject was identified by the fit to both sFC and TC. We analysed associations between brain-wide sFC and TC features with optimal model parameters and fits with a univariate correlation approach and multivariate prediction models. In addition, we used a group-average perturbation approach to investigate the effect of coupling in each region on overall network connectivity. Our models could replicate empirical sFC and TC but not the FC variance and NC. Both fits and parameters exhibited strong associations with brain connectivity. G correlated positively and J_NMDA negatively with a range of static and dynamic FC features (|r| > 0.2, p(FDR) < 0.05). TC fit correlated negatively, and sFC fit positively with static and dynamic FC features. TC features were predictive of TC fit, sFC features of sFC fit (R2 > 0.5). Perturbation analysis revealed that the sFC fit was most impacted by coupling changes in the left paracentral gyrus ({Delta}r = 0.07). In contrast, the left pars triangularis impacted the TC fit most strongly ({Delta}r = 0.24). Our findings indicate that neurobiological characteristics are associated with individual variability in sFC and dFC, and that sFC and dFC are shaped by small sets of distinct regions. In addition, we show that brain network modelling can replicate some, but not all, properties of dFC, and model fits are strongly influenced by specific FC patterns. By modelling both sFC and dFC, we could produce new insights into neurobiological mechanisms of brain network configurations.

neuroscience↗

bioRxiv2024

AutoGaitA - Automated Gait Analysis in Python

Individual behaviours require the nervous system to execute specialised motor programs, each characterised by unique patterns of coordinated movements across body parts. Deep learning approaches for body-posture tracking have facilitated the analysis of such motor programs. However, translating the resulting time-stamped coordinate datasets into meaningful kinematic representations of motor programs remains a long-standing challenge. We developed the versatile quantitative framework AutoGaitA (Automated Gait Analysis), a Python toolbox that enables comparisons of motor programs at multiple levels of granularity and across tracking methods, species and behaviours. AutoGaitA allowed us to demonstrate that flies, mice, and humans, despite divergent biomechanics, converge on the age-dependent loss of propulsive strength, and that, in mice, locomotor programs adapt as an integrated function of both age and task difficulty. AutoGaitA represents a truly universal framework for robust analyses of motor programs and changes thereof in health and disease, and across species and behaviours.

animal behavior and cognition↗

bioRxiv2024

When visual attention is divided in the flash-lag effect

The flash-lag effect (FLE) occurs when a flashs position appears delayed relative to a continuously moving object, even though both are physically aligned. While several studies have demonstrated that reduced attention increases FLE magnitude, the precise mechanism underlying these attention-dependent effects remains elusive. In this study, we investigated the influence of visual attention on the FLE by manipulating the level of attention allocated to multiple stimuli moving simultaneously in different locations. Participants were cued to either focus on one moving stimulus or split their attention among two, three, or four moving stimuli presented in different quadrants. We measured trial-wise FLE to explore potential changes in the magnitude of perceived displacement and its trial-to-trial variability under different attention conditions. Our results reveal that FLE magnitudes were significantly larger when attention was divided among multiple stimuli compared to when attention was focused on a single stimulus, suggesting that divided attention considerably augments the perceptual illusion. However, FLE variability, measured as the coefficient of variation, did not differ between conditions, indicating that the consistency of the illusion is unaffected by divided attention. We discuss the interpretations and implications of our findings in the context of widely accepted explanations of the FLE within a dynamic environment.

neuroscience↗

bioRxiv2024

Earlier finish of motor planning in the premotor cortex predicts faster motor command in the primary motor cortex: human intracranial EEG evidence

The human motor system has a hierarchical control during finger movements. The non-primary motor cortex (premotor cortex, PM, and supplementary motor area, SMA) organizes motor planning, while the primary motor cortex (M1) is responsible for motor execution. We utilized the human intracranial EEGs high temporal and spatial resolution to investigate how the temporal dynamics of the high-gamma neural oscillations in the hierarchically organized motor sub-regions, during both pre-movement planning and motor execution, correlated with reaction times (RTs) in a cued finger movement task. Our results showed that the high-gamma power of PM, SMA, and M1 activated sequentially. More importantly, the sustained high-gamma power activation in the non-primary motor cortex and the peak latency of high-gamma power in M1 had a significant predictive relationship with the RTs. In particular, the faster the activation of the non-primary motor cortex returned to baseline, the faster the motor command in M1, and accordingly the shorter the RTs. Further, pairwise phase coherence between the motor areas showed that the more sustained the connection between the motor areas, the longer the RTs would be. The current findings illustrate the relationship between the temporal profiles of high-gamma power in human motor areas and response performance.

neuroscience↗

bioRxiv2024

Dynamic properties in functional connectivity changes and striatal dopamine deficiency in Parkinson's disease

IntroductionRecent studies in Parkinsons disease (PD) patients reported disruptions in dynamic functional connectivity (dFC, i.e., a characterization of spontaneous fluctuations in functional connectivity over time). Here, we assessed whether the integrity of striatal dopamine terminals directly modulates dFC metrics in separate PD cohorts, indexing dopamine-dependent changes in large-scale brain network dynamics and its implications in clinical features. MethodsWe pooled data from two cohorts reflecting early PD. From the Parkinsons Progression Marker Initiative (PPMI) cohort, resting-state functional magnetic resonance imaging (rsfMRI) and dopamine transporter (DaT) SPECT were available for 63 PD patients and 16 age- and sex-matched healthy controls. From the clinical research group 219 (KFO) cohort, rsfMRI imaging was available for 52 PD patients and 17 age- and sex-matched healthy controls. A subset of 41 PD patients and 13 healthy control subjects additionally underwent 18F-DOPA-PET imaging. The striatal synthesis capacity of 18F-DOPA PET and dopamine terminal quantity of DaT SPECT images were extracted for the putamen and the caudate. After rsfMRI pre-processing, an independent component analysis was performed on both cohorts simultaneously. Based on the derived components, an individual sliding window approach (44s window) and a subsequent k-means clustering were conducted separately for each cohort to derive dFC states (reemerging intra- and interindividual connectivity patterns). From these states we derived temporal metrics, such as average dwell time per state, state attendance, and number of transitions and compared them between groups and cohorts. Further, we correlated these with the respective measures for local dopaminergic impairment and clinical severity. ResultsIn both cohorts, dFC analysis resulted in three distinct states, varying in connectivity patterns and strength. In the PPMI cohort, PD patients showed a lower state attendance for the globally integrated (GI) state (X2(1, N=79) = 5.82, p= 0.016) and a lower number of transitions (U(N=79) = 337.5, z = -2.06 p= .039) than controls. Significantly, worse motor scores (UPDRS-III) and dopaminergic impairment in the putamen and the caudate were associated with low average dwell time in the GI state (UPDRS-III: {tau}b(N=63) = -.281; p =.003, DaT putamen: {tau}b(N=63)=.213, p= .023, DaT caudate: {tau}b(N=63)=.209, p= .025) and a low total number of transitions (UPDRS-III: {tau}b(N=63)= -.308; p = .001, DaT putamen: {tau}b(N=63)=.350, p<.001, DaT caudate: {tau}b(N=63)=.251, p=.007). Additionally, worse motor performance was associated with a low number of bi-directional transitions between the GI and the lesser connected (LC) state ({tau}b(N=63)= -.237; p =.019). These results could not be reproduced in the KFO cohort: No group differences in dFC measures or associations between dFC variables and dopamine synthesis capacity or clinical measure were observed. ConclusionIn early PD, relative preservation of motor performance may be linked to a more dynamic engagement of an interconnected brain state. Specifically, those large-scale network dynamics seem to depend on striatal dopamine availability. Notably, we obtained these results in only one cohort, but not in a replication sample. Key pointsO_LIExploring dopamines role in brain network dynamics in two Parkinsons disease (PD) cohorts, we unraveled PD-specific changes in dynamic functional connectivity (dFC). C_LIO_LIIn the discovery cohort, results suggest striatal dopamine availability influences large-scale network dynamics that are relevant in motor control. C_LIO_LIIn the confirmation cohort, these findings were not replicated, indicating PD-specific dFC changes are dependent on unrecognized cohort features. C_LI

neuroscience↗

bioRxiv2023

Previous motor task performance impacts phase-based EEG resting-state connectivity states

The resting human brain cycles through distinct states that can be analyzed using microstate analysis and electroencephalography (EEG) data. This approach classifies, multichannel EEG data into spontaneously interchanging microstates based on topographic features. These microstates may be valuable biomarkers in neurodegenerative diseases since they reflect the resting brains state. However, microstates do not provide information about the active neural networks during the resting-state. This article presents an alternative and complementary method for analyzing resting-state EEG data and demonstrates its reproducibility and reliability. This method considers cerebral connectivity states defined by phase synchronization and measured using the corrected imaginary phase-locking value (ciPLV) based on source-reconstructed EEG recordings. We analyzed resting-state EEG data from young, healthy participants acquired on five consecutive days before and after a motor task. We show that our data reproduce microstates previously reported. Further, we reveal four stable topographic patterns over the multiple recording sessions in the source connectivity space. While the classical microstates were unaffected by a preceding motor task, the connectivity states were altered, reflecting the suppression of frontal activity in the post-movement resting-state.

neuroscience↗

bioRxiv2022

Crossmodal reorganization in deaf auditory cortices compensates for the impaired body-centered visuomotor transformation after early deafness

Early deafness leads to the reorganization of large-scale brain networks, involving and extending beyond the auditory system. Body-centered visuomotor transformation is impaired after early auditory deprivation, associated with a hyper-crosstalk between the task-critical frontoparietal network (FPN) and the default-mode network (DMN). It remains to be elucidated, how the reorganized functional connectivity between the auditory system, the FPN, and the DMN contributes to the impaired visuomotor transformation after early deafness. In this fMRI study, we asked early deaf participants and hearing controls to judge the spatial location of a visual target, either about the middle-sagittal line of their own body (the egocentric task) or another background object (the allocentric task). The bilateral superior temporal gyrus (STG) in the deaf group exhibited cross-modal reorganization, with generally enhanced neural activity during the visual tasks, compared to hearing controls. Moreover, the STG showed significantly increased functional connectivity with both the FPN and the DMN in the deaf group compared to hearing controls, specifically during the egocentric task. The increased STG-FPN and STG-DMN coupling, however, showed antagonistic effects on the egocentric performance of the deaf participants. The increased STG-FPN connectivity was associated with improved (i.e., a beneficial role) while the increased STG-DMN with deteriorated (i.e., a detrimental role) egocentric performance in the deaf participants. No such effect was observed in hearing controls. Therefore, the auditory cortex is reorganized to functionally resemble the FPN in the deaf brain, representing compensatory neuroplasticity to mitigate the impaired visuomotor transformation after early deafness. Significance StatementOur brain constantly plans vision-guided actions, transforming visuospatial representations of external visual targets into visuomotor representations. The frontoparietal network (FPN) critically supports this visuomotor transformation process, which is impaired after early deafness. To mitigate the impaired visuomotor transformation, the deaf auditory cortex in the bilateral superior temporal gyrus (STG) shows compensatory cross-modal reorganization that functionally resembles the FPN regions. Specifically, the deaf auditory cortex becomes functionally coupled with the dorsal FPN regions. The stronger the STG-FPN coupling, the more improved the deaf adults visuomotor transformation performance, indicating the reorganized STG as a critical node of the task-critical network. Correspondingly, increased coupling between the task-critical deaf STG and the default-mode network impairs the visuomotor transformation.

neuroscience↗

bioRxiv2022

The relationship between response dynamics and the formation of confidence varies across the lifespan

Accurate metacognitive judgements, such as forming a confidence judgement, are crucial for goaldirected behaviour but decline with older age. Besides changes in the sensory processing of stimulus features, there might also be changes in the motoric aspects of giving responses that account for age-related changes in confidence. In order to assess the association between confidence and response parameters across the adult lifespan, we measured response times and peak forces in a four-choice flanker task with subsequent confidence judgements. In 65 healthy adults from 20 to 76 years of age, we showed divergent associations of each measure with confidence, depending on decision accuracy. Participants indicated higher confidence after faster responses in correct but not incorrect trials. They also indicated higher confidence after less forceful responses in errors but not in correct trials. Notably, these associations were age-dependent as the relationship between confidence and response time was more pronounced in older participants, while the relationship between confidence and response force decayed with age. Our results add to the notion that confidence is related to response parameters and demonstrate noteworthy changes in the observed associations across the adult lifespan. These changes potentially constitute an expression of general age-related deficits in performance monitoring or, alternatively, index a failing mechanism in the computation of confidence in older adults.

neuroscience↗

bioRxiv2022

Selectively expressed RNA molecules: a new dimension infunctionalized cell targeting

Exact targeting of specific mammalian cell types or diseased cells is one of the most urgently needed prerequisites for a new generation of potent pharmaceuticals. Different approaches have been pursued, failing mainly due to a lack of specific surface markers in most cases. Developing a completely novel RNA-based methodology, we can now ensure exact cell targeting and simultaneously combine this with selective expression of effector proteins, thereby functionalization of the target cell for therapy, diagnostics or cell steering. The specific combination of the molecular properties of antisense technology and mRNA therapy with functional RNA secondary structures allowed us to develop selectively expressed RNA molecules for medical applications. These so-called seRNAs remain inactive in non-target cells and are only activated by partial degradation to induce translation in preselected cell types of interest. Cell type specificity and type of functionalization are easily adaptable based on a simple modular system. In proof of concept in vitro and in vivo studies we used seRNAs as a highly selective platform technology for powerful glioblastoma cancer cell targeting and significantly reduce brain tumors of mice without detectable side effects with just a single treatment within days. Our data open up new potential avenues for the efficient treatment of various cancers and other human diseases.

cell biology↗

bioRxiv2022

Speeded Inference Game: Opening a new chapter in the assessment of error awareness

Influential theories on error processing assume that when we conduct errors adaptive processes are triggered to improve our behaviour and prevent errors in the future. These processes appear to be more effective after participants have detected an error. Therefore, the assessment of error awareness allowing a differential analysis of detected and undetected errors in the context of cognitive control and behavioural adjustments has gained more and more attention in the past decades. A common methodological challenge posed on all studies investigating error detection is that the number of undetected errors is usually relatively low. Here, we introduce a gamified experimental task that uses an adaptive algorithm to generate a robust and stable amount of errors with a high rate of undetected errors. Further, we were able to identify error types, which interestingly differed in terms of their detection rate. Moreover, the game-like appearance of the novel experimental task led to highly motivated participants. The results of the first study were replicated and extended by a second behavioural study. Notably, in study 2, a change in task design specifically modulated error detection, while these changes did not affect the total error rate. Potential applications of the open-source code will be discussed. With this newly developed paradigm, we wish to lay the ground for future research to understand better (neural) processes associated with error awareness.

neuroscience↗

bioRxiv2021

An individualized fMRI-based protocol to assess semantic memory in a heterogeneous multilingual population

The cognitive stimulation induced by multilingualism may slow down age-related memory impairment. However, a suitable neuroscientific framework to assess the influence of multilingualism on age-related memory processes is missing. We propose an experimental paradigm that assesses the effects of semantic congruency on episodic memory using functional magnetic resonance imaging (fMRI). To this end, we modified the picture-word interference (PWI) task to be suitable for the assessment of older multilingual subjects undergoing functional magnetic resonance imaging (fMRI). In particular, stimulus materials were prepared in multiple languages (French, German, Luxembourgish, English) and closely matched in semantic properties, thus enabling participants to perform the experiment in a language of their choice. This paradigm was validated in a group (n = 62) of healthy, older participants (over 64 years) who were multilingual, all practicing three or more languages. Consistent with the engagement of semantic congruency processes, we found that the encoding and recognition of semantically related vs. unrelated picture-word pairs evoked robust differences in behavior and the neural activity of parietal-temporal networks. These effects were negligibly modulated by the language used to perform the task. Based on this validation in a multilingual population, we conclude that the proposed paradigm will allow future studies to evaluate whether multilingualism aptitude engages neural systems in a manner that protects long-term memory from aging-related decline.

neuroscience↗

bioRxiv2021

Divergent effects of absolute evidence magnitude on decision accuracy and confidence in perceptual judgements

Whether people change their mind after making a perceptual judgement may depend on how confident they are in their decision. Recently, it was shown that, when making perceptual judgements about stimuli containing high levels of absolute evidence (i.e., the overall magnitude of sensory evidence across choice options), people make less accurate decisions and are also slower to change their mind and correct their mistakes. Here we report two studies that investigated whether high levels of absolute evidence also lead to increased decision confidence. We used a luminance judgment task in which participants decided which of two dynamic, flickering stimuli was brighter. After making a decision, participants rated their confidence. We manipulated relative evidence (i.e., the mean luminance difference between the two stimuli) and absolute evidence (i.e., the summed luminance of the two stimuli). In the first experiment, we found that higher absolute evidence was associated with decreased decision accuracy but increased decision confidence. In the second experiment, we additionally manipulated the degree of luminance variability to assess whether the observed effects were due to differences in perceived evidence variability. We replicated the results of the first experiment but did not find substantial effects of luminance variability on confidence ratings. Our findings support the view that decisions and confidence judgments are based on partly dissociable sources of information, and suggest that decisions initially made with higher confidence may be more resistant to subsequent changes of mind.

neuroscience↗

bioRxiv2021

Statistical learning of frequent distractor locations in visual search involves regional signal suppression in early visual cortex

Observers can learn the locations where salient distractors appear frequently to reduce potential interference - an effect attributed to better suppression of distractors at frequent locations. But how distractor suppression is implemented in the visual cortex and frontoparietal attention networks remains unclear. We used fMRI and a regional distractor-location learning paradigm (Sauter et al. 2018, 2020) with two types of distractors defined in either the same (orientation) or a different (colour) dimension to the target to investigate this issue. fMRI results showed that BOLD signals in early visual cortex were significantly reduced for distractors (as well as targets) occurring at the frequent versus rare locations, mirroring behavioural patterns. This reduction was more robust with same-dimension distractors. Crucially, behavioural interference was correlated with distractor-evoked visual activity only for same- (but not different-) dimension distractors. Moreover, with different- (but not same-) dimension distractors, a colour-processing area within the fusiform gyrus was activated more when a colour distractor was present versus absent and with a distractor occurring at a rare versus frequent location. These results support statistical learning of frequent distractor locations involving regional suppression in the early visual cortex and point to differential neural mechanisms of distractor handling with different-versus same-dimension distractors.

neuroscience↗

bioRxiv2021

Neural correlates of metacognition across the adult lifespan

Metacognitive accuracy describes the degree of overlap between the subjective perception of ones decision accuracy (i.e., confidence) and objectively observed performance. With older age, the need for accurate metacognitive evaluation increases; however, error detection rates typically decrease. We investigated the effect of ageing on metacognitive accuracy using event-related potentials (ERPs) reflecting error detection and confidence: the error/correct negativity (Ne/c) and the error/correct positivity (Pe/c). Sixty-five healthy adults (20 to 76 years) completed a complex perceptual task and provided confidence ratings. We found that metacognitive accuracy declined with age beyond the expected decline in task performance, while the adaptive adjustment of behaviour was well preserved. Pe/c amplitudes varied by confidence rating, but they did not mirror the reduction in metacognitive accuracy. Ne/c amplitudes decreased with age except for high confidence correct responses. The results suggest that age-related difficulties in metacognitive evaluation could be related to an impaired integration of decision accuracy and confidence information processing. Ultimately, training the metacognitive evaluation of fundamental decisions in older adults might constitute a promising endeavour.

neuroscience↗

bioRxiv2020

Simultaneous Modeling of Reaction Times and Brain Dynamics in a Spatial Cuing Task

Understanding how brain activity translates into behavior is a grand challenge in neuroscientific research. Simultaneous computational modeling of both measures offers to address this question. The extension of the dynamic causal modeling (DCM) framework for BOLD responses to behavior (bDCM) constitutes such a modeling approach. However, only very few studies have employed and evaluated bDCM, and its application has been restricted to binary behavioral responses, limiting more general statements about its validity. This study used bDCM to model reaction times in a spatial attention task, which involved two separate runs with either horizontal or vertical stimulus configurations. We recorded fMRI data and reaction times (n=29) and compared bDCM to classical DCM and a behavioral Rescorla-Wagner model using goodness of fit-statistics and machine learning methods. Data showed that bDCM performed equally well as classical DCM when modeling BOLD responses and better than the Rescorla Wagner model when modeling reaction times. Notably, only using bDCMs parameters enabled classification of the horizontal and vertical runs suggesting that bDCM seems to be more sensitive than the other models. Although our data also revealed practical limitations of the current bDCM approach that warrant further investigation, we conclude that bDCM constitutes a promising method for investigating the link between brain activity and behavior.

neuroscience↗