{'fraction_non_alphanumeric': 0.030145768144699686, 'fraction_numerical': 0.008395024040296115, 'mean_word_length': 5.175306314797361, 'pattern_counts': {'":': 0, '<': 2, '<?xml version=': 0, '>': 1, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Task Performance Changes the Amplitude and Timing of the BOLD Signal

Abstract Translational studies comparing imaging data of animals and humans have gained increasing scientific interests. With this upcoming translational approach, however, identifying harmonized statistical analysis as well as shared data acquisition protocols and/or combined statistical approaches is necessary. Following this idea, we applied Bayesian Adaptive Regression Splines (BARS), which have until now mainly been used to model neural responses of electrophysiological recordings from rodent data, on human hemodynamic responses as measured via fMRI. Forty-seven healthy subjects were investigated while performing the Attention Network Task in the MRI scanner. Fluctuations in the amplitude and timing of the BOLD response were determined and validated externally with brain activation using GLM and also ecologically with the influence of task performance (i.e. good vs. bad performers). In terms of brain activation, bad performers presented reduced activation bilaterally in the parietal lobules, right prefrontal cortex (PFC) and striatum. This was accompanied by an enhanced left PFC recruitment. With regard to the amplitude of the BOLD-signal, bad performers showed enhanced values in the left PFC. In addition, in the regions of reduced activation such as the parietal and striatal regions, the temporal dynamics were higher in bad performers. Based on the relation between BOLD response and neural firing with the amplitude of the BOLD signal reflecting gamma power and timing dynamics beta power, we argue that in bad performers, an enhanced left PFC recruitment hints towards an enhanced functioning of gamma-band activity in a compensatory manner. This was accompanied by reduced parieto-striatal activity, associated with increased and potentially conflicting beta-band activity.


Introduction
In recent years, translational studies comparing imaging data of animals and humans have gained increasing scientific interests with crucial findings stemming from both human and animal works. In order to harmonize statistical analysis of data from different species and to optimize the transfer of knowledge between them, shared data acquisition protocols and/ or combined statistical approaches have to be identified. Following this idea, we applied a statistical approach, which has until now mainly been used to model neural responses of electrophysiological recordings from rodent data, on human hemodynamic responses (i.e. Blood-Oxygen-Level-Dependent BOLD signal) as measured via fMRI. The statistical approach is Bayesian Adaptive Regression Splines (BARS).
BARS is a smoothing algorithm/curve fitting technique. In neuroscience it is used to smooth neural time courses, spike trains and tuning curves from neurophysiological recordings [1][2][3], as well as regional fMRI time courses [4]. The graphical data representation of peristimulus-time histograms (PSTH) of a data set, for example spike trains of a single neuron is accumulated for all trials under a particular set of experimental conditions to show the firing rate varies over time. One reason the PSTH works well is that our eye is able to smooth the PSTH so that we see the temporal evolution of the firing rate. However, once we have articulated the goal of estimating the firing rate, it is possible to improve the PSTH by smoothing [5]. Estimating the firing rate in this context means producing an estimate of the instantaneous firing rate, which we write as λ(t), at each time t, where t varies across a whole range of experimental values of interest.
In other words, we are interested in estimating the curve described by λ(t) [5]. BARS uses cubic splines (piecewise cubic polynomials) which are joined at selected points called 'knots' [4,6], with the number of knots and their locations being based on a posterior probability distribution. The expectation of the unknown function of time is then taken to be the fitted curve [2,5,[7][8][9].
In our study, BARS describes the fluctuations of the amplitude of the BOLD signal over the course of the entire measurement. The resulting curve is described by estimating the expectation value of a certain event (e.g. neural firing, increase of the amplitude of the BOLD signal), which is written as λ at each time point t (λ(t)) and its fluctuation over the time course in terms of peaks and valleys.
The relation between electrophysiological recordings and BOLD response has been empirically proven by Logothetis and coworkers in numerous studies. They have found that local field potentials (LFPs) reflect the best hemodynamic responses and it is mainly the component of the gamma band (60-120 Hz) which correlated positively with fMRI data [10]. Subsequent studies were able to differentiate between amplitude and timing characteristics the way the amplitude

TASK PERFORMANCE CHANGES THE AMPLITUDE AND TIMING OF THE BOLD SIGNAL
of the BOLD signal reliably reflected both the increases and decreases in gamma power.
In the cognitive domain, neural oscillations in the gamma frequency band play a crucial role in the synchronization of neural firing as well as in conscious cognitive information processing and focused attention [12]. On the other hand, beta band oscillations have been associated with selective attention [e.g. 13], where a decrease of activity reflects a state of increased processing capabilities [14]. In order to prove that BARS when applied on fMRI time series corresponds to neural processing like it does on neurophysiological recordings, we have additionally performed: i) conventional analyses of brain activation patterns using GLM in terms of an external validation and, ii) task performance of an attention task which the volunteers had to peform in the MRI scanner for behavioral correlate/ecological validation.
The present sample of healthy volunteers underwent a task-fMRI measurement using the Attentional Network Task (ANT) by Fan et al. (2005) [15]. The ANT has been used in numerous fMRI as well as EEG-studies to show most robust responses in the fronto-parietal regions as well as the striatum in terms of increased brain activation [16,17], as well as reflected by the event-related potential P300 in parietally-located electrodes [e.g. 18]. In addition, behavioral attention network scores significant correlation of the beta and gamma powers in the fronto-central regions [19]. Within a fronto-parieto-striatal attention network, the fronto-striatal loop has been associated with inhibiting response of the prefrontal cortex (PFC) representing a top-down control, which means the ability to focus on the current task and not being distracted by further stimulations [20], and the striatum being responsible for motor and impulse control. The parietal lobe (PL), in turn, is understood as a bottom-up structure, perceiving task-relevant and irrelevant stimuli and then reporting these perceptions to higherorder cortical structures such as the PFC.
Within the fronto-parieto-striatal attention network, brain activation patterns and λ(t) were determined. Task performance was operationalized by splitting the subjects into groups of good and bad performers according to their overall accuracy. Group differences were addressed in the behavioral data, brain activation maps and expectation values λ(t). We expected to differentiate good performers from bad performers:

Subjects
Forty-seven participants (f=23, m=24; mean age: 25.43+2.7 years) were examined in the present fMRI study. This sample has previously been investigated for the genetic influence of brain activation patterns of alerting and executive attention using GLM [16,21]

Paradigm
The used paradigm was the ANT as described alerting= rt no cue -rt double cue , orienting= rt doucle cuert spatial cue and executive attention= rt incongruent targets -rt congruent targets . Performance-based differences were revealed using a two sample t-test with the performance group as a group factor and behavioral parameters as dependent variables.
Results were significant when they passed a threshold of p<.05, FDR-corrected for five comparisons. Data was high-pass filtered with a default size of 120s to reduce data from physiological noise such as breathing, heart rate etc.

fMRI-Data Preprocessing
On the group level, we aimed to identify these regions, which were activated by the task, i.e. the Main Effect of the Attention Network Task defined as 'double cue' + 'spatial cue' + 'no cue' + 'congruent target' + 'incongruent target' > implicit baseline (contrast weights: 1 1 1 1 1). We analysed this contrast over all subjects as well as with regard to potential group differences. Therefore, we performed a one-way ANOVA model using the performance group as an independent factor (good vs. bad performers) and activation maps as the dependent variables. Activation was considered significant when p<.05 FDRcorrected for multiple comparisons. MarsBar [24]. Thus, five ROIs (cf.  Fig. 1C).

BARS: Statistical Analysis
In order to statistically approach the spline, we

Behavioral results
We found that good performers committed

GLM results
Across all subjects, we found a significantly   figure 3B). In the right SPL (n good =2, n bad =2), fluctuations were similar between both groups but were more pronounced in good performers.

Discussion
In this study, we have investigated the influence of task performance on multiple levels of attention processing: behavioral performance, brain activation as well as the amplitude and timing of regional time course. We found that bad performers had longer reaction times and higher executive attention network scores on the behavioral level. On the neural level, altered performance was associated with    [17,27,28], thus the relation between impaired performance and reduced activation in these regions seems plausible. The finding that reduced right fronto-parietal activation was related to an increased beta activity fits nicely into this context, with an increased beta activity hinting towards decreased processing abilities [14]. In detail, bad performers showed higher fluctuations mainly in parietal and striatal processing. Within a fronto-parietal attention network, parietal activity has been associated with bottom-up attentional orienting [29], reflecting screening processes for stimuli [30]. Thus, continuous parietal processing in bad performers might reflect a constant parietal bottom-up processing and indicate a hyperaroused basal attentional state.
With regard to beta oscillations in attention processing, firing rates in the pallidum exhibited a linear decrease in sequences of correct responses in a reward learning task in monkeys [31]. In addition, beta oscillations in the human pallidum have been associated with motor control in healthy volunteers [32], as well as with alterations of the same in patients with Parkinson's Disease [33,34].
In sum, a higher beta power in the parietal and striatal regions is associated with altered attentional performance and motor control. In combination with reduced brain activation in  [42,43], auditory conflict processing [44], and selective attention to lexical and speech sound [45,46]. Thus, it is possible that bad performers in our study used a verbal strategy before or during responding with a button press. A verbal indication of the direction would be mirrored by left PFC activation and gamma power and this might also explain the longer reaction time as verbalization might take some milliseconds.

The combination of GLM and BARS and the transfer of knowledge between species
In this study, we aimed to prove that BARS applied on human fMRI time courses reflects neural processing as it is the case in the modeling of neural responses of electrophysiological recordings on rodents.
We argue that the present combined findings are in line with earlier findings from EEG and fMRI studies as well as from animal and human studies.
However, the application of BARS on fMRI data, to date, has mainly been theoretically in terms of BARS acting as "a flexible denoiser for fMRI time courses, where all smooth sources of variation are combined into the function being estimated", and serving as "a front-end to spatial and regional analyses and group comparisons, automatically incorporating variation in response shape and magnitude across the replicated task blocks in the experiment" [4]. Therefore, this is the first fMRI study empirically applying the BARS approach and their reports predominantly consist of the description of differences between groups in curve shapes [3,8]. In our study, we also found that differences were bin-specific, which processing. Based on the relationship between neural signaling and BOLD response, we argue that bad performance is associated with both increased activation and gamma power in the left PFC, along with a reduced brain activation and an increased beta power in the parietal and striatal areas. With regard to the harmonization of translational study protocols, BARS seems to be a promising tool for data analysis.