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da Silva Castanheira, J.

Publications and source records attributed to da Silva Castanheira, J..

4 recordsLinked to original sources

Evidence for shared resources in visuospatial attention: Endogenous attention misleads the decoding of exogenous attention in alpha rhythms

Prevailing accounts of visuospatial attention differentiate exogenous (involuntary shifts) from endogenous (voluntary control) orienting of attention. While these two forms of attentional processes are functionally separable, their interactions have been at the center of ongoing debates for more than two decades. One hypothesis is that exogenous and endogenous attention interfere because they share processing resources. Here, we confirm that endogenous attention alters exogenous attention processing, and examine the role of alpha-band neurophysiological activity in such interference events. We contrast the effects of exogenous attention across two experimental conditions: a single-cueing condition where exogenous attention is engaged alone, and a double-cueing condition where exogenous attention is concurrently engaged with endogenous attention. Our results show that the engagement of endogenous attention alters the emergence of exogenous attention across cue-related and target-related brain processes. Importantly, we also report that classifiers trained to decode exogenous attention from the power and phase of alpha-band brain activity in the single-cueing condition fail to do so in the doublecueing condition, where endogenous attention is also engaged. Taken together, our observations challenge the idea that exogenous attention operates independently from top-down processes and demonstrate that both forms of attention orienting engage shared brain processes, which constrain their interactions. Significance StatementVisuospatial attention is often dichotomized into top-down and bottom-up components: Top-down attention reflects slow voluntary shifts of attention orienting, while bottom-up attention is recruited by emerging demands from the environment. A large body of previous findings support the view that these two forms of attention orienting are functionally separable, with some interactions. The current study examines such interactions between top-down and bottom-up attention. Using electroencephalography (EEG) and multivariate pattern classification techniques, the researchers show that top-down attention interferes with the brain activity patterns of bottom-up attention. Moreover, machine learning classifiers trained to detect bottom-up attention based on brain activity in the alpha band (8-12 Hz), a marker of visuospatial attention, fail systematically when top-down attention is also engaged. The authors therefore conclude that both forms of visuospatial orienting are supported by overlapping processes that share brain resources.

neuroscience↗

Time-resolved parameterization of aperiodic and periodic brain activity

Macroscopic neural dynamics comprise both aperiodic and periodic signal components. Recent advances in parameterizing neural power spectra offer practical tools for evaluating these features separately. Although neural signals vary dynamically and express non-stationarity in relation to ongoing behaviour and perception, current methods yield static spectral decompositions. Here, we introduce Spectral Parameterization Resolved in Time (SPRiNT) as a novel method for decomposing complex neural dynamics into periodic and aperiodic spectral elements in a time- resolved manner. First, we demonstrate with naturalistic synthetic data SPRiNTs capacity to reliably recover time-varying spectral features. We emphasize SPRiNTs specific strengths compared to other time-frequency parameterization approaches based on wavelets. Second, we use SPRiNT to illustrate how aperiodic spectral features fluctuate across time in empirical resting-state electroencephalography data (n = 178), and relate the observed changes in aperiodic parameters over time to participants demographics and behaviour. Lastly, we use SPRiNT to demonstrate how aperiodic dynamics relate to movement behaviour in intracranial recordings in rodents. We foresee SPRiNT responding to growing neuroscientific interests in the parameterization of time-varying neural power spectra and advancing the quantitation of complex neural dynamics at the natural time scales of behaviour. Significance StatementThe new method and reported findings address a growing interest in neuroscience for research tools that can reliably decompose brain activity at the mesoscopic scale into interpretable components. We show that the new approach proposed is capable of tracking transient, dynamic spectral (aperiodic and periodic) components across time, both in synthetic data and in in vivo experimental data. We anticipate that this novel technique, SPRiNT, will enable new neuroscience inquiries that reconcile multifaceted neural dynamics with complex behaviour.

neuroscience↗

Stability of spectral estimates in resting-state magnetoencephalography: recommendations for minimal data duration with neuroanatomical specificity

The principle of resting-state paradigms is appealing and practical for collecting data from impaired patients and special populations, especially if data collection times can be minimized. To achieve this goal, researchers need to ensure estimated signal features of interest are robust. In electro- and magnetoencephalography (EEG, MEG) we are not aware of studies of the minimal length of recording required to yield a robust one-session snapshot of the frequency-spectrum derivatives that are typically used to characterize the complex dynamics of the brains resting-state. We aimed to fill this knowledge gap by studying the stability of common spectral measures of resting-state MEG source time series obtained from large samples of single-session recordings from shared data repositories featuring different recording conditions and instrument technologies (OMEGA: N = 107; Cam-CAN: N = 50). We discovered that the rhythmic and arrhythmic spectral properties of intrinsic brain activity can be robustly estimated in most cortical regions when derived from relatively short recordings of 30-s to 120-s of resting-state data, regardless of instrument technology and resting-state paradigm. Using an adapted leave-one-out approach and Bayesian analysis, we also provide evidence that the stability of spectral features over time is unaffected by age, sex, handedness, and general cognitive function. In summary, short MEG sessions are sufficient to yield robust estimates of frequency-defined brain activity during resting-state. This study may help guide future empirical designs in the field, particularly when recording times need to be minimized, such as with patient or special populations.

neuroscience↗

MEG, myself, and I: individual identification from neurophysiological brain activity

Large, openly available datasets and current analytic tools promise the emergence of population neuroscience. The considerable diversity in personality traits and behaviour between individuals is reflected in the statistical variability of neural data collected in such repositories. This amount of variability challenges the sensitivity and specificity of analysis methods to capture the personal characteristics of a putative neural portrait. Recent studies with functional magnetic resonance imaging (fMRI) have concluded that patterns of resting-state functional connectivity can both successfully identify individuals within a cohort and predict some individual traits, yielding the notion of a neural fingerprint. Here, we aimed to clarify the neurophysiological foundations of individual differentiation from features of the rich and complex dynamics of resting-state brain activity using magnetoencephalography (MEG) in 158 participants. Akin to fMRI approaches, neurophysiological functional connectomes enabled the identification of individuals, with identifiability rates similar to fMRIs. We also show that individual identification was equally successful from simpler measures of the spatial distribution of neurophysiological spectral signal power. Our data further indicate that identifiability can be achieved from brain recordings as short as 30 seconds, and that it is robust over time: individuals remain identifiable from recordings performed weeks after their baseline reference data was collected. Based on these results, we can anticipate a vast range of further research and practical applications of individual differentiation from neural electrophysiology in personalized, clinical, and basic neuroscience.

neuroscience↗