Abstract
The ability to form decisions is a foundational cognitive function which is impaired across many psychiatric and neurological conditions. Understanding the neural processes underpinning clinical deficits may provide insights into the fundamental mechanisms of decision-making. The N2c has been identified as an EEG signal indexing the efficiency of early target selection, which subsequently influences the timing of perceptual reports through modulating neural evidence accumulation rates. Evidence for the contribution of the N2c to human decision-making however has thus far come from correlational research in neurologically healthy individuals. Here, we capitalized on the superior temporal resolution of EEG to show that unilateral brain lesions in male and female humans were associated with specific deficits in both the timing and strength of the N2c in the damaged hemisphere, with corresponding deficits in the timing of perceptual reports contralaterally. The extent to which the N2c influenced clinical deficits in perceptual reporting speed depended on neural rates of evidence accumulation. This work provides causal evidence that the N2c indexes an early, hemisphere-specific process supporting human decision-making. This noninvasive EEG marker could be used to monitor novel approaches for remediating clinical deficits in perceptual decision-making across a range of brain disorders.
Significance Statement
Understanding how particular brain processes contribute to decision-making is crucial for our treatment of psychiatric and neurological disorders. This study provides causal evidence linking deficits in speed of visual processing to specific well-delineated EEG signals representing early target selection and evidence accumulation, in individuals with brain lesions. By showing how these lesions disrupt perceptual decisions, this work identifies a potential biomarker for decision-making deficits. This EEG measure offers a promising, noninvasive tool to track and refine treatments aimed at restoring decision-making abilities in affected patients.
Introduction
The speed at which individuals can make simple perceptual decisions is a foundational aspect of cognition which is negatively impacted by a wide range of neurological and psychiatric disorders (Huang et al., 2015; Fosco et al., 2017; Fish et al., 2018). Understanding the neural processes underpinning these clinical deficits can provide insights into the fundamental mechanisms of perceptual decision-making, aided by advances in human electroencephalography (EEG). A substantial body of research has demonstrated that a slow-building centroparietal positivity EEG signal (the “CPP”) tracks the accumulation of sensory evidence and the formation of a decision across a wide range of tasks with varying sensory, perceptual, and higher cognitive requirements (Donner et al., 2009; De Lange et al., 2010; Wyart et al., 2012; Philiastides et al., 2014; Murphy et al., 2015; Loughnane et al., 2016; Spitzer et al., 2017; Afacan-Seref et al., 2018; Rungratsameetaweemana et al., 2018; Steinemann et al., 2018; Herding et al., 2019; Tagliabue et al., 2019; van Vugt et al., 2019; von Lautz et al., 2019). This signal bears remarkable similarities to evidence accumulation signals observed invasively in nonhuman primates (Kim and Shadlen, 1999; Shadlen and Newsome, 2001; Ding and Gold, 2012; Heitz and Schall, 2012; Mante et al., 2013).
Temporally preceding the evidence accumulation process is an earlier, hemisphere-specific posterior negativity, labeled the N2c, which indexes the selection of contralateral sensory targets from the environment (Loughnane et al., 2016; Newman et al., 2017; Brosnan et al., 2020). The N2c bears some similarity to the the family of EEG event-related potentials (ERPs) referred to as the N2/N2pc (Luck and Hillyard, 1994; Eimer, 1996, 2014; Woodman and Luck, 1999; Kappenman and Luck, 2011) which are typically elicited using a target detection task and calculated as a subtraction waveform between trial conditions (Kappenman and Luck, 2011). The N2pc signal has been shown to relate to both distractor suppression (Luck and Hillyard, 1994; Luck, 2012) and target enhancement (Kiss et al., 2008; Hickey et al., 2009; Mazza et al., 2009). Using a task design that eliminated the typical visual evoked potentials which appear in response to sudden-onset stimuli, Loughnane et al. (2016) isolated the specific aspect of the N2 related to target enhancement, contralateral to the target. The contralateral N2c predicted the timing of perceptual reports via both the onset and buildup rates of neural evidence accumulation (i.e., dynamics of the CPP; see also Nunez et al., 2019; Ghaderi-Kangavari et al., 2023).
A crucial aspect in identifying the relative importance of these EEG components along the sensorimotor transformation is to show whether they contribute to behavior in a causal manner (O’Connell and Kelly, 2021). In nonhuman primates, several studies have used invasive methods such as inactivation and microstimulation to show the causal importance of certain neural populations to perceptual decision-making (Hanks et al., 2006; Fleming et al., 2015; Katz et al., 2016; Yartsev et al., 2018; Derosiere et al., 2019; Zhou and Freedman, 2019). To date, however, no such causal contribution has been investigated in humans with respect to the N2 component on behavior. Yet this is important if we are to establish bona fide neural signals that might help to understand decision-making deficits in clinical disorders.
Here, we used a stroke patient lesion model to investigate the potential causal link between the N2 component and the timing of perceptual reports. A reliable phenomenon following unilateral stroke damage is slowed responses to sensory stimuli presented to the contralesional space, independently of response modality (Ballard et al., 2003; Johansson and Rönnbäck, 2012; Hurford et al., 2013). Using a bilateral variant of the random dot motion task (Kelly and O’Connell, 2013; Loughnane et al., 2016; Newman et al., 2017; Brosnan et al., 2020), we looked at whether contralesional slowing of response time (RT) in stroke patients would be related to a disruption in the dynamics of early target selection signals.
Materials and Methods
Participants
This research was approved by the Oxford Central University (MSD IDREC reference R61968/RE001), Monash Health (MHREC reference #14408A), and Monash University Human Research Ethics Committees (MUHREC reference #2492). All participants volunteered without knowledge of the hypotheses and provided written informed consent.
Neurologically healthy controls
Thirty-one (31) neurologically healthy adults (57–90 years, hereafter “controls”) were recruited from the community in Melbourne, Australia, of which 27 (13 males, 14 females) were included in the study (exclusions of 4 participants due to one each of the following: left handedness, an extensive medical history, a score lower than 23/30 on the Montreal Cognitive Assessment, and EEG data quality issues). Participants reported normal or corrected-to-normal visual acuity, self-reported being right-handed, were not taking arousal-modulating medications (such as tranquilizers), and reported no prior history of neurological or psychiatric conditions. All controls scored higher than 23/30 on the Montreal Cognitive Assessment (MoCA; Nasreddine et al., 2005) after adjusting for education.
Poststroke adults
Seventy-one (71) individuals who had experienced a focal, first-ever clinically significant unilateral cerebral stroke (hereafter “patients”) were recruited from Melbourne, Australia, and Oxford, UK. Of these, N = 41 patients were excluded (n = 16 did not successfully complete the task, n = 21 had past history of neurological disorder on medical record review, and n = 4 were removed due to data collection issues), leaving 30 patients (16 males, 14 females) for analysis. Patients were right-handed prior to their stroke, had a medically confirmed diagnosis of stroke, had no prior neurological or psychiatric history, reported normal or corrected-to-normal visual acuity, and had no reported use of arousal-altering medications. Participants did not have inferior visual field defects (i.e., where the visual stimuli were presented), as measured by a simple assessment [within the Oxford cognitive screen (OCS); Demeyere et al., 2015] and did not have a primary extinction syndrome on a computerized extinction task (Bender, 2011). Of these, n = 2 participants used a left-hand response due to right-sided hemiplegia, and n = 3 had upper quadrant visual field defects. See Tables 1 and 2 for participant demographics and stroke characteristics, respectively.
Demographic information across groups
Stroke characteristics
Procedure and materials
All participants completed the bilateral random dot motion (BRDM) task (Loughnane et al., 2016; Brosnan et al., 2023; Fig. 1) while EEG was recorded. Eye movements were monitored with an EyeLink 1000 remote infrared eye-tracker (SR Research). To ensure that all patients and controls were fixating centrally (and not moving their eyes toward stimuli in either hemifield), pupils were monitored in real time, and if fixation was broken, the trial ended and restarted once fixation was reestablished. The task was controlled via a Windows PC using MATLAB (MathWorks) and the Psychophysics Toolbox extensions (Brainard, 1997; Pelli, 1997; Cornelissen et al., 2002), and stimuli were displayed on either a 21-inch CRT monitor (Melbourne), or on a 23- or 27-inch LED monitor (Oxford). All monitors were set to a refresh rate of 85 Hz and a screen resolution of 1,024 × 768. Participants viewed the task from 57 cm in a darkened, sound-attenuated room, with their head supported by a chin rest. Prior to completion of the BRDM EEG task, stroke patients completed a larger battery of cognitive tasks (Table 3).
Cognitive test data by group
Assessment of perceptual decision-making
The BRDM task (Newsome et al., 1989; Loughnane et al., 2016) is a computerized task that allows examination of how quickly and accurately responses are made separately for targets in each visual hemifield. Participants maintained central fixation while monitoring two peripheral circular patches that were positioned 10° to either side and 4° below the fixation square, covering an 8° visual angle (Fig. 1). These patches consisted of 150 9 × 9 pixel randomly moving dots which were displaced by 5° per second. At intervals of 1.8, 2.8, or 3.8 s, 90% of the dots in either patch transitioned to moving uniformly either upward or downward. These target events occurred with equal probability in the left and right patches. Participants responded via a speeded single mouse click with their index finger upon detection of coherent motion targets. Trials restarted if a blink or gaze deviation of >4° left or right of center was detected. Coherent motion pulses persisted for 3 s or until a response was made.
Practice trials were administered until the participant understood the task, irrespective of their accuracy. Participants then completed 7–15 blocks of 24 trials. There were 12 possible trial types which varied according to the interstimulus interval (1.8, 2.8, or 3.8 s), hemifield of target (left or right), and motion direction (up or down), with each presented twice per block in pseudorandom order. Rest breaks up to 180 s followed each block.
Electroencephalography (EEG)
Continuous EEG data were acquired from either 64 scalp electrodes using a BrainAmp DC system (Brain Products), digitized at 500 Hz (Melbourne), or 64 electrodes using a Neuroscan SynAmps RT system, digitized at 1,000 Hz (Oxford), while participants completed the BRDM. We used a combination of custom scripts (available at https://osf.io/mvehu/?view_only=12aef65f46e246fcb94b1e8bd6dba040) and EEGLAB routines (Delorme and Makeig, 2004) implemented in MATLAB (MathWorks) to process the data.
Preprocessing
First, we manually inspected channel variances for artifacts. We then accounted for cross-site differences in data collection by realigning the channels according to a common subset of 58 electrodes and resampling Oxford data from 1,000 to 500 Hz. Thereafter, all data were treated consistently. Data were detrended, and we eliminated line noise by applying notch filters at 50, 100, and 150 Hz. We then applied a 0.1 Hz Hamming windowed sinc FIR high-pass filter. Channels previously identified as demonstrating zero or extreme variance were interpolated via spherical spline (mean channels interpolated = 1.95, median = 0). The interpolated data were then low-pass filtered at 8 Hz (120 Hz for spectral analyses) and rereferenced to the average.
A trial was defined as the period between central fixation and either a response, a fixation break, or the onset of a new random motion stimulus, as identified using EEG-recorded triggers. We extracted epochs from −200 ms pretarget onset to 3,000 ms post-target onset (i.e., target offset), while response-locked epochs were extracted from −500 ms preresponse to 100 ms postresponse. We then removed trials meeting any of the following conditions: (1) one or more EEG channels exceeded 100 µV between baseline (200 ms pretarget onset) and 100 ms postresponse or between baseline and 1,000 ms post-target onset or (2) the participant blinked or deviated their gaze by >3° left or right of center during the same time window. Finally, we applied a current source density transformation with a spline flexibility of 4 to disentangle overlapping ERP components (Kayser and Tenke, 2006) and baselined the stimulus- and response-locked ERP waveforms relative to −200 to 0 ms prestimulus onset.
Event-related potential (ERP) extraction
CPP and N2 waveforms were derived by aggregating the baseline-corrected epochs at relevant electrodes separately for left and right hemifield targets. Consistent with our previous publications (Loughnane et al., 2016; Newman et al., 2017; Brosnan et al., 2020, 2023), we extracted the N2c contralateral to the target location at peak electrodes P8 and P7 for left and right hemifield targets, respectively. The ipsilateral N2i component was extracted at peak electrodes P8/TP8 and P7/TP7. The CPP was measured centrally at peak electrode Pz irrespective of target hemifield (O’Connell et al., 2012; Kelly and O’Connell, 2013; Twomey et al., 2015; Loughnane et al., 2016; Newman et al., 2017; Brosnan et al., 2023).
We subsequently derived several metrics for each of the stimulus-locked ERP waveforms on a single-trial basis for each participant. The peak latency of the N2c was derived according to the mean stimulus-locked waveform and defined as the time point that reflected the most negative amplitude value between 150 and 400 ms poststimulus onset (Loughnane et al., 2016; Brosnan et al., 2023). We calculated N2c amplitude as the mean amplitude within a 10 ms window centered on the N2c's grand average peak (Loughnane et al., 2016; Brosnan et al., 2023). The same was done to calculate peak amplitude and latency of the N2i, but from ipsilateral electrodes. To increase the accuracy of N2c/N2i amplitude and decrease any influence from slow drifts, we subtracted a baseline amplitude up to 180 ms post-target, given the lack of ERP response in that period (Loughnane et al., 2016). To derive measures of CPP onset and peak latency, we first applied a 2 Hz low-pass filter to the stimulus-locked CPP waveform to smooth out noise that might interfere with these calculations. Peak latency was measured as the time point at which the CPP waveform from 400 to 1,000 ms poststimulus onset (the end of the EEG artifact rejection window) reached its peak amplitude. CPP onset was defined as the timepoint at which the CPP waveform reached half of its peak amplitude.
Additional metrics of buildup rate and peak amplitude were calculated using the response-locked CPP waveform. We first fitted a straight line to each participant's waveform (O’Connell et al., 2012; Loughnane et al., 2016; Brosnan et al., 2020, 2023) and defined the CPP buildup rate as the slope within a −150 to −50 ms window around the response (Brosnan et al., 2020, 2023; Stefanac et al., 2021). Finally, CPP amplitude was defined as the mean amplitude of the response-locked waveform from −25 to 25 ms postresponse (Kelly and O’Connell, 2013; Van Kempen et al., 2019; Brosnan et al., 2023).
Imaging data
We acquired clinical brain imaging data from hospital records for n = 21 patients (n = 9 CT, n = 12 MRI). For those with MRI data, we manually selected the image that best demonstrated the lesion (n = 1 DWI, n = 7 FLAIR, n = 2 T2, n = 2 T1). Recent research has verified that similar routine clinical imaging data are of sufficient quality to accurately localize established neural correlates in statistical lesion mapping analyses (Moore and Demeyere, 2022; Moore et al., 2023). Lesion masks were derived and processed in line with a standard analysis pipeline (Moore, n.d.). Specifically, lesions were manually delineated on the axial plane of native-space scans by a trained expert (MJM) using MRIcron (Rorden et al., 2007). These native-space masks were smoothed at 5 mm full width at half maximum in the z-direction, reoriented to the anterior commissure, and normalized to 1 × 1 × 1 MNI space. This spatial normalization was completed using statistical parametric mapping (Penny et al., 2007) and standard, age-matched templates (Clinical Toolbox; Rorden et al., 2012). All normalized scans and lesions were visually inspected to ensure accuracy.
Visual inspection of lesion distribution at the group level suggested insufficient overlap to support statistical lesion mapping analyses (Moore et al., 2023). For this reason, this study does not aim to draw definitive conclusions about the anatomical correlates of human perceptual decision-making after stroke, but instead broadly compares lesion characteristics between groups.
To this end, lesion maps are presented for each patient group in Figure 1. Individual lesion masks, network disconnection summaries, and cortical integrity statistics can be found in Figures S1 and S2 and Table S3, respectively.
Patient competency rating scale
Self-reported psychosocial functioning was examined using the 30-item patient competency rating scale (PCRS; Prigatano, 1986). Participants rated the ease with which they could perform a range of common activities (e.g., dressing themselves or remembering their daily schedule) on a five-point Likert scale from 1 (“can't do”) to 5 (“can do with ease”). Scores were aggregated such that higher scores suggest higher functioning. The PCRS has been validated for characterizing psychosocial functioning following stroke (Barskova and Wilz, 2006).
Experimental design and statistical analyses
Many of the conducted analyses utilized the same structure, investigating the existence of a Group (Control, Left Stroke, Right Stroke) × Target Hemifield (Left, Right) interaction. For RT, N2c amplitude, N2i amplitude, and CPP peak amplitude, we used single-trial analysis using linear mixed-effects (LME) modeling, predicting the given outcome measure with fixed effects of Group and Target Hemifield and random slopes and intercepts for Target Hemifield × Participant. Thus, the equation for those models was the following: [RT/N2c/N2i/CPP] ∼ Target Hemifield × Group + (Target Hemifield|Participant). This was the maximal model that could be fitted and permitted individual participant effects to vary, while still revealing group-level effects. Other analyses were conducted on a participant-level to increase signal-to-noise ratio before measurement: N2c peak latency, N2i peak latency, CPP onset latency, CPP peak latency, and CPP buildup rate. These analyses were performed using a mixed-effects ANOVA, with factors of Target Hemifield and Group. When using LME, we performed Bonferroni–Holm-adjusted post hoc analyses comparing Target Hemifield within each Group using further LMEs, whereas when using ANOVA, we performed post hoc analyses using within-group t tests and calculated Bonferroni–Holm-adjusted p-values to account for multiple comparisons.
Next, for Left and Right Stroke Groups, we calculated Left Hemifield minus Right Hemifield asymmetry indices for both RT and our ERP metrics of interest via the following formula: (Left Target − Right Target) / ((Left Target + Right Target)/2). Thus, for RT and ERP latency variables, lower indices denoted earlier timing to left hemifield targets, whereas higher indices reflected earlier timing for right hemifield targets. There was one exception to this strategy for N2c/N2i amplitude asymmetry indices, which were calculated using the following formula: (Left Target − Right Target)((abs(Left Target) + (abs(Right Target)) / 2). Here, the absolute value of the signals was used in the denominator for these indices due to the fact that the N2 signals could be negative. As such, for these amplitude metrics, lower indices were indicative of greater (i.e., more negative) N2c/N2i amplitude in response to left hemifield targets, whereas higher indices denoted greater amplitude in response to right hemifield targets. To ascertain the relationship between these indices, we performed a linear regression, covarying for Stroke Group to ensure that effects were not simply driven by differences between groups.
We followed up these analyses with two serial mediation analyses to investigate whether the effect of Stroke Group on RT asymmetry was mediated via N2c amplitude asymmetry and then CPP onset (and peak latency in a secondary mediation) asymmetry. We performed two, two-stage serial mediations, with Stroke Group as a predictor, N2c amplitude asymmetry and CPP onset/CPP peak latency asymmetry as serial mediators, and RT asymmetry as the dependent variable.
Finally, we tested the relationship between RT asymmetry and quality of life indices, measured by the PCRS. Given that PCRS is an absolute measure, we calculated a slightly different RT asymmetry index which denoted worse contralesional RTs: (Contra Target − Ipsi Target)((Contra Target + Ipsi Target) / 2). A lower index here denoted faster RT to targets contralateral to the lesioned hemisphere, whereas a higher index denoted slower RT to contralesional targets, i.e., the effect one might expect poststroke. We then performed a linear regression of RT asymmetry index on PCRS, again covarying for Stroke Group.
Results
Unilateral stroke compromises the speed of contralesional responding
Hit rate on the BRDM task was at ceiling across most participants, suggesting that, in general, participants did not have any difficulty with the task requirements. Four right hemisphere patients had a hit rate lower than 90% for left hemifield targets. To examine group differences in the timing of perceptual reports across hemifields, we first utilized a linear mixed-effects model (LMM) to examine group and target hemifield differences in RT. RT was measured as the time (in milliseconds) from the onset of target motion to a response. The model included Group (control, left hemisphere patients, and right hemisphere patients), Target Hemifield (left and right), and a Group × Target Hemifield interaction as fixed effects, with a random effect to account for within-subject effects. All post hoc comparisons were Bonferroni–Holm adjusted.
The LMM revealed a significant main effect of Group (F(2,11,275) = 3.87, p = 0.02; see Table S1 for all models), but no significant main effect of Target Hemifield (F(1,11,275) = 2.27, p = 0.13). Critically, there was a significant interaction between Group and Target Hemifield (F(2,11,275) = 7.21, p < 0.001). The interaction effect was driven by the two patient groups who showed significantly slowed responding to contralesional, relative to ipsilesional, stimuli. On average, the right hemisphere patients responded 137 ms more slowly to left compared with right hemifield targets (left, M = 794 ms, SD = 327; right, M = 657 ms, SD = 120; t(3,769) = −2.38, pcorr = 0.03, model estimate difference = −136, 95% CI [−248.41, −24.05]), whereas the left hemisphere patients responded 44 ms more slowly to right compared with left hemifield stimuli (left, M = 538 ms, SD = 129; right, M = 582 ms, SD = 147; t(2,514) = 3.43, pcorr = 0.01, model estimate difference = 44.76, 95% CI [16.36, 73.16]). There was no significant asymmetry in response times for the controls (left, M = 632 ms, SD = 165; right, M = 632 ms, SD = 130; t(4,992) = 0.07, pcorr = 0.94, model estimate difference = 0.73, 95% CI [−19.57, 21.04]). Overall, these results indicate that lesions to either hemisphere compromise the speed of responses to contralesional stimuli. These results are shown in Figure 1.
Perceptual decision-making task reveals slowed contralesional responding poststroke. A, Schematic of the BRDM task. Beginning with central fixation, each dot moved randomly at a rate of 5° per second. Following a period of random motion lasting either 1.8, 2.8, or 3.8 s, 90% of the dots in one patch began moving uniformly, either up or down. Participants were 1 to respond to these coherent motion targets with a single mouse click with their right index finger (left index finger for n = 2 patients with right-sided hemiplegia). B, Performance on the BRDM task, with trials in which target motion was presented in the left or right hemifield plotted separately. Histograms depict the response time for each valid trial (i.e., where central fixation was maintained, there were no artifacts in the EEG/eye-tracking data, and a response was made between 150 ms poststimulus onset and stimulus offset) across all participants within a given group. Note that responses up to 3,000 ms were permitted, but for clarity, only responses up to 2,000 ms are visualized here. The mean hit rate for each participant is depicted via inset scatterplots. C, Mean reaction time for left and right hemifield target trials for each group, with individual participant means plotted as single points. D, Lesions overlaid for n = 21 patients with available clinical neuroimaging (n = 11 left hemisphere, n = 10 right hemisphere). Voxel color indicates the number of overlapping lesions present at each region (range = 1–4). Lesions are colored according to the number of overlapping voxels between participants. Individual lesion masks, network disconnection summaries, and cortical integrity statistics can be found in Figures S1 and S2 and Table S3, respectively. Significant (p < 0.05) within-group differences are denoted by asterisks. Within-subject error bars are depicted.
Next, we sought to understand the sequence of neural events that might give rise to the contralesional slowing observed in both stroke patient groups, in order to demonstrate the related signals’ causal importance. We hypothesized that contralesional slowing of response times could be influenced by variation in the dynamics of the N2 and/or the CPP.
Impaired contralateral target selection in the damaged hemisphere
N2c amplitude
We computed an LMM based on single-trial estimates of peak amplitude to examine group and target hemifield differences in N2c magnitude. As with RT, the LMM showed a significant Group × Target Hemifield interaction (F(2,8,599) = 6.25, p < 0.01; Fig. 2A; Table S1), whereby damage to either hemisphere was associated with reduced amplitudes for contralesional, compared with ipsilesional, stimuli (i.e., attenuated signal in the lesioned hemisphere; Table S1). The right hemisphere patients had 9.96 µV/m2 greater N2c amplitude for right, compared with left, hemifield targets (left, M = −8.39, SD = 8.99; right, M = −18.34, SD = 13.75; t(3,131) = 2.73, pcorr = 0.027, model estimate difference = −10.02, 95% CI [−17.21, −2.83]), whereas the left hemisphere patients showed an 7.61 µV/m2 greater (i.e., more negative) N2c amplitude for left relative to right hemifield targets (left, M = −19.17, SD = 8.28; right, M = −11.56, SD = 9.76; t(1,800) = −3.48, pcorr = 0.01, model estimate difference = 8.31, 95% CI [3.08, 13.54]). In contrast, we did not find evidence for a difference in N2c amplitude between left and right hemifield targets for the controls (left, M = −19.21, SD = 13.20; right, M = −17.28, SD = 15.44; t(3,668) = −0.65, pcorr = 0.52, 95% CI [−4.41, 8.48]). Finally, there was no significant main effect of Group (F(2,8,599) = 1.35, p = 0.26) or Target Hemifield (F(1,8,599) = 0.005, p = 0.99). These results show that unilateral brain damage acquired through stroke negatively impacts the strength of early target selection processes in the damaged hemisphere.
Reduced N2c, but not N2i, amplitude in the stroke-affected hemisphere indexes slowed responding to contralesional space. A, N2c waveforms extracted from temporal electrodes contralateral to target hemifield (T7 and T8 for right and left hemifield targets, respectively). Inset topoplots show average scalp electrical activity during 250–280 ms windows poststimulus onset for each group separately. Note that “I” and “C” denote ipsilateral and contralateral signals (such that ipsilateral shows the grand average of left hemisphere activity for left hemifield targets, and right hemisphere activity for right hemifield targets, and vice versa). B, N2i waveforms extracted from temporal electrodes ipsilateral to target hemifield (T8 and T7 for right and left hemifield targets, respectively). C, D, The relationship between RT asymmetry index and N2c amplitude asymmetry index for patients. A least squares regression line is fitted for reference. Significant results are represented by *.
Relationship with the timing of perceptual reports
Given evidence that the N2c is predictive of RT in younger (Loughnane et al., 2016) and older adults (Brosnan et al., 2023), we explored whether stroke-related changes in this metric were consequential for behavior. Specifically, to examine whether this difference indexes task-relevant behavior in the two stroke groups, we first calculated asymmetry indices (see Materials and Methods for details). Next, we conducted a multiple regression to examine the relationship between the asymmetry of RT and N2c amplitude in patients, with group included as a covariate. There was a significant positive relationship, whereby higher N2c asymmetries (i.e., lower amplitude for left hemifield targets) were associated with more positive RT asymmetries (i.e., slower RT for left hemifield targets; F(1,27) = 11.84, p = 0.002; Fig. 2C; Table S2). These data demonstrate that stroke disrupts the strength of early target selection processes in the lesioned hemisphere and that this effect is relevant to downstream behavioral responses.
N2c peak latency
Next, we used a two-way mixed-effects ANOVA on participant-level means to examine the effects of Group and Target Hemifield on the peak latency of the N2c (i.e., the time from stimulus onset until the peak amplitude of the average waveform). There were no significant main effects of Group (F(2,54) = 0.50, p = 0.61) or Target Hemifield (F(1,54) = 0.08, p = 0.78) and no significant interaction effect between Group and Target Hemifield (F(2,54) = −0.09, p = 0.91). As such, and in contrast to the differences in strength of the N2c component poststroke described above, we did not find evidence for altered timing of early target selection processes following unilateral damage to the brain.
Impaired ipsilesional target selection specifically following right hemisphere damage
N2i amplitude
To examine group and target hemifield differences in the magnitude of the ipsilateral target selection signal, N2i (Loughnane et al., 2016), we computed an LMM based on single-trial estimates of peak amplitude. There was a significant Group × Target Hemifield interaction (F(2,8,599) = 4.43, p = 0.01; Fig. 2B; Table S1). Controls (left, M = −6.22 µV/m2, SD = 4.82; right, M = −11.43 µV/m2, SD = 4.82) showed a significantly stronger right hemisphere N2i compared with the left hemisphere (t(3,668) = 3.23, pcorr = 0.002, model estimate difference = 2.38, 95% CI [0.88, 3.88]), consistent with previous work showing that the N2i is right lateralized in healthy individuals (Loughnane et al., 2016). Similar to the controls, the left hemisphere patients had a stronger N2i in the right hemisphere (left, M = −5.43 µV/m2, SD = 3.42; right, M = −12.47 µV/m2, SD = 3.42; t(1,800) = 4.16, pcorr = 0.001, model estimate difference = 3.52, 95% CI [1.53, 5.51]). In contrast to the other two groups, we did not find evidence for a difference across hemispheres for the right hemisphere patients (left, M = −6.76 µV/m2, SD = 6.67; right, M = −6.49 µV/m2, SD = 6.99; t(3,131) = −0.07, p = 0.94, model estimate difference = −0.078 95% CI [−2.05, 1.90]). These results thus reflect a strong right hemisphere dominance of the N2i in neurologically healthy individuals and patients with left hemisphere damage, which is absent in patients with right hemisphere lesions.
Relationship with the timing of perceptual reports
Consistent with our approach for the N2c, we next conducted a multiple regression to examine the relationship between the asymmetry of RT and N2i amplitude in our patients, with Group included as a covariate. We did not find evidence for a relationship between the two (F(1,27) = 0.04, p = 0.85; Fig. 2C; Table S2). Taken together, these results demonstrate that the typical right lateralization of the N2i is reduced in right hemisphere stroke, but we did not find evidence to suggest that this impacts behavior.
N2i peak latency
We subsequently employed a two-way mixed-effects ANOVA on participant-level means to examine the effects of Group and Target Hemifield on the peak latency of the N2i. As observed with the N2c, there were no significant main effects of Group (F(2,54) = 0.36, p = 0.70) or Target Hemifield (F(1,54) = 1.95, p = 0.17) and no significant interaction effect between Group and Target Hemifield (F(2,54) = 0.14, p = 0.87). Together, these data do not provide evidence that the timing of ipsilateral target selection ERPs is affected following unilateral stroke.
Delayed CPP for stimuli in the contralesional hemifield
Stimulus-locked signal
Two-way mixed ANOVAs were used to investigate differences in onset and peak latency of evidence accumulation rates between groups and target hemifields.
CPP onset
There was a significant Group × Target Hemifield interaction for CPP onset, F(2,53) = 11.30, p < 0.001. Bonferroni–Holm-adjusted post hoc analyses revealed that the right hemisphere patients (t(16) = 3.22, pcorr < 0.01) had a later onset of evidence accumulation for left, compared with right hemifield targets. We did not find evidence for a difference in CPP onset for left hemisphere patients (t(11) = −2.45, pcorr = 0.06) or controls (t(26) = −1.31, pcorr = 0.20). There were no significant main effects of Group (F(2,53) = 0.11, p = 0.90) or Target Hemifield (F(1,53) = 0.60, p = 0.44).
CPP peak latency
Similar results were found for the peak latency of the CPP. There were no significant main effects of Group (F(2,54) = 0.03, p = 0.97) or Target Hemifield (F(1,54) = 1.26, p = 0.27), but there was a significant interaction (F(2,54) = 6.34, p < 0.01), again driven by differential target hemifield effects as a function of stroke group. For right hemisphere patients, left hemifield targets elicited a later CPP peak compared with right hemifield targets (t(16) = 2.69, pcorr < 0.05). We did not find evidence for differences in CPP peak latency as a function of target hemifield in the left hemisphere patients (t(11) = −2.32, pcorr = 0.08) or controls (t(26) = 0.66, pcorr = 0.52).
We further examined the relevance of these signals to task-related RT asymmetry exclusively in the stroke groups using two multiple linear regressions (one for each of CPP onset and CPP peak latency asymmetries), with Group included as a covariate. RT asymmetry was related to both CPP onset asymmetry and CPP peak latency asymmetry, such that later onsets (F(1,27) = 20.76, p < 0.001) and peaks (F(1,27) = 53.54, p < 0.001) predicted slower behavioral responses. Overall, evidence accumulation in the right hemisphere stroke group was slower to begin and end for contralesional, compared with ipsilesional, stimuli. As per findings in healthy adults (O’Connell et al., 2012; Brosnan et al., 2023), markers of delayed evidence accumulation were found to predict slowed responding in both stroke groups (Fig. 3).
Delayed CPP onset and later peak latency index slowed contralesional responding. A, Stimulus- and response-locked CPP waveforms extracted from electrode Pz, presented separately for each group and for left and right hemifield targets. Inset topoplots reflect average activity between 600 and 700 ms poststimulus onset for contra- (C) and ipsilateral (I) targets. Dashed lines signify calculated CPP onset, and solid vertical lines indicate CPP peak latency. B, Relationship between the CPP onset asymmetry index (where more positive values suggest delayed onset for left hemifield targets) and RT asymmetry index (where more positive values suggest slower responding to left hemifield targets). A least squares regression line is fitted for reference. C, Scatter plot of participant means depicting the relationship between CPP peak latency index (where more positive values suggest delayed peaks for left hemifield targets) and RT asymmetry index (where more positive values suggest slower responding to left hemifield stimuli). Significant results are indicated by *.
Response-locked signal
We examined group and target hemifield differences in CPP slope using a two-way mixed ANOVA, and peak amplitude using an LMM with fixed effects of Group, Target Hemifield, and a Group × Target Hemifield interaction. There was no significant main effect of Group (F(2,55) = 0.17, p = 0.85) or Target Hemifield (F(1,55) = 1.29, p = 0.26), nor a significant Group × Target Hemifield interaction for the CPP slope (F(2,54) = 0.73, p = 0.48). Similarly, the interaction effect of Group × Target Hemifield was not significant for CPP peak amplitude (F(2,11,091) = 2.45, p = 0.09), nor were the main effects of Group (F(2,11,091) = 2.42, p = 0.09) or Hemifield (F(1,11,091) = 2.85, p = 0.09). Overall, stroke did not appear to alter the dynamics of the response-locked CPP, suggesting that evidence is accumulated at similar rates in contralesional and ipsilesional hemispheres.
Neurophysiological asymmetries mediate the relationship between stroke hemisphere and RT asymmetry
The findings above suggest that slowed contralesional responding is indexed by diminished markers of contralateral target selection (N2c) and delayed evidence accumulation (CPP). Given previous work demonstrating that these ERPs represent an interrelated temporal sequence of events (Loughnane et al., 2016), we sought to determine whether they could serially mediate the relationship between stroke hemisphere and RT asymmetry. To test this possibility, we performed a mediation analysis, whereby the degree to which RT asymmetry is predicted by stroke hemisphere is mediated serially by the N2c, followed by the CPP onset. Mediation analyses suggested a full mediation of stroke hemisphere on RT asymmetry via both N2c amplitude and CPP onset asymmetries (indirect effect = 0.08, p = 0.04, 95% CI [0.003, 0.15]). A similar mediation was found when the model was repeated with CPP peak latency asymmetry instead of CPP onset (indirect effect = 0.14, p < 0.01, 95% CI [0.04, 0.25]). Full mediation results are presented diagrammatically in Figure 4.
Relationship between stroke side and contralesional slowing is mediated by N2c and CPP asymmetries. Serial mediation models of lesion side → RT asymmetry including CPP onset latency (A) and CPP peak latency (B) asymmetries. *p < 0.05, **p < 0.01, ***p < 0.001. Also depicted is a scatterplot demonstrating the relationship between the self-reported everyday functioning measure (PCRS) and RT asymmetry (C).
Collectively, these findings characterize the nature and temporal sequence of discrete neural events that give rise to contralesional slowing after stroke. Specifically, we provide evidence for a mechanism whereby weakened target selection signals (N2c asymmetry) due to stroke delay time to threshold of evidence accumulation (CPP onset and peak latency asymmetries) and thence slow contralesional RT.
Asymmetries in response time predict poorer everyday functioning
Finally, to determine whether the behavioral asymmetry described above has relevance to daily function following stroke, we used a multiple linear regression with an asymmetry measure of task RT (where greater RT asymmetries indicate slower responses to contralesional stimuli, regardless of lesion side; see Materials and Methods) as a predictor of PCRS scores, with stroke side as a covariate. There was no significant relationship between lesion volume and RT (r = 0.17, p = 0.49) or PCRS score (r = −0.31, p = 0.21), and thus lesion size was not included as a covariate in these analyses.
There was a significant, negative relationship between RT asymmetry on the BRDM task and everyday functioning, such that slower responses to contralesional targets predicted poorer everyday functioning (F(1,24) = 5.67, p = 0.03). Given this result, we conducted additional, exploratory analyses to examine whether asymmetries in our neurophysiological metrics were related to everyday function. There was marginal evidence for the relevance of CPP parameters to poststroke daily function; however, this was not robust to correction for multiple comparisons (Text S1, Fig. S3).
Taken together, these results suggest that the BRDM task is indexing a facet of cognition that has real-life implications for daily functioning poststroke.
Discussion
We leveraged an integrated EEG/perceptual decision-making paradigm in stroke patients to identify the causal contribution of the target-selective N2 signal to behavior. Results revealed that the relationship between lesion hemisphere and spatial RT bias was mediated by attenuation of the N2c in response to contralesional stimuli. Furthermore, we found that this relationship was mediated by delayed evidence accumulation as indicated by a slower onset and thus time to threshold of the CPP. To our knowledge, this is the first time that a temporal sequence of discrete and well-described neural events, i.e., the target-selective N2c and decision-related CPP, has been causally linked to behavior in humans.
Unilateral lesions selectively weakened the contralateral N2 in the affected hemisphere and subsequently slowed responses. The N2c traces the early selection of goal-relevant targets in uncertain environments and increases proportionally to the quality and availability of sensory evidence (Loughnane et al., 2016). Our results show that the N2c amplitude was weaker in the absence of any difference in N2c latency. This may suggest that while a timely onset of early target selection was preserved, this was accompanied by a distinct deficit in the attentional selection of the relevant stimulus. This accords with previous correlational work relating the N2c to behavior (Loughnane et al., 2016; Newman et al., 2017), where a weaker N2c predicted slower responses (Van Kempen et al., 2019). Here we build on this work to show that hemisphere-specific damage to the brain is causally associated with weaker N2c signals with subsequent impacts on behavior (slower responses).
In addition, our observations highlight the consequences of weaker target selection signals for downstream processes (perceptual decision formation), thereby establishing a causal chain of events underpinning variability in behavior. Specifically, we highlight a potential pathway through which the target-selective N2c can impact response times. Stroke-related contralesional slowing was additionally correlated with the time of onset and peak of the CPP, an established marker of evidence accumulation (O’Connell et al., 2012; Philiastides et al., 2014; Murphy et al., 2015; Twomey et al., 2016; Spitzer et al., 2017; McGovern et al., 2018; Steinemann et al., 2018; Herding et al., 2019; van Vugt et al., 2019; von Lautz et al., 2019; Brosnan et al., 2020, 2023; Ruesseler et al., 2023). Previous studies on poststroke contralesional slowing have observed a delayed P300 (Lhermitte et al., 1985; Priftis et al., 2008; Lasaponara et al., 2018)—an ERP that shares many features with the CPP (O’Connell et al., 2012; Twomey et al., 2015). Our findings extend this in three critical ways. First, our models suggested that the timing of the CPP mediated the relationship between N2c amplitude and RT and thus demonstrate that delayed evidence accumulation is driven by earlier disruption of target selection. This establishes a causal role for early target selection signals such as the N2c in facilitating and modulating downstream evidence accumulation processes (Loughnane et al., 2016). Second, we show that asymmetry in the timing of evidence accumulation onset directly related to variance in performance stems from a weaker asymmetry of the N2c, thereby increasing the certainty with which these can be attributed as causal. Finally, we demonstrate a dissociation between the time taken to begin evidence accumulation and the rate at which evidence is sampled: whereas the CPP begins later for contralesional stimuli following stroke, we did not find evidence to suggest that stroke compromises the rate at which the brain accumulates evidence. Thus, following stroke, decision formation is equally efficient for stimuli presented to ipsilesional and contralesional hemifields, but these processes occur later for contralesional stimuli. Collectively, these data suggest a plausible causal temporal pathway by which unilateral stroke dampens the early selection of sensory stimuli in the contralesional hemifield and subsequently delays the onset (and thus peak) of evidence accumulation, thereby slowing responses.
Another benefit of our paradigm was the ability to parse out contralateral and ipsilateral target selection, via the elimination of VEPs in response to sudden-onset stimuli. As noted above, our results suggest that the core deficit following stroke stems from reduced stimulus salience in the contralesional hemifield. Notably, right hemisphere patients demonstrated additional attenuation of the ipsilateral N2 in the affected hemisphere. One possibility is that this effect is specific to lesions in the right hemisphere due to interhemispheric differences in the distribution of attention networks (Corbetta and Shulman, 2002). There was, however, no evidence that disruptions to ipsilateral target selection meaningfully impacted behavior, reaffirming the relative importance of contralateral, as opposed to ipsilateral, visual processing (Loughnane et al., 2016; Newman et al., 2017).
Between-hemifield asymmetries in the timing of responses were correlated with daily function, suggesting that the asymmetries displayed here meaningfully influence behavior. Asymmetrical sensory processing has been previously shown to deleteriously affect the performance of everyday activities such as driving (Van Kessel et al., 2013). This may be attributable to the relationship between neural decision-making and higher cognitive functioning (Shadlen and Kiani, 2013) such that hemisphere-specific alterations to the brain's capacity to select, process, and thus respond to relevant stimuli may more broadly impact one's ability to function within one's environment. Exploratory analyses exploring the relationship between the CPP and everyday functioning showed marginal support for this hypothesis, though this effect was not robust to multiple comparisons correction and will require replication. Importantly, many of our participants were well beyond the expected critical time points of functional recovery poststroke (Jørgensen et al., 1995; Xi et al., 2006; Bernhardt et al., 2017; Hankey, 2017), indicating that these impacts are persistent and resistant to current rehabilitation methods.
Notably, asymmetries in the neurophysiological signatures of selection and decision processes appeared to be robust determinants of behavioral asymmetries across participants, independent of stroke hemisphere and time since stroke. Furthermore, the lesions of our sample were heterogeneous, with diverse network disconnection profiles and stroke etiologies, and yet commonalities emerged. Such generalizability speaks to the relevance of neural perceptual decision-making to broader cognition, and the involvement of large, interconnected networks in decision-making (Gherman et al., n.d.; Shadlen and Kiani, 2013; Brosnan et al., 2020). Collectively, our framework captures behavioral and neural metrics that are relevant to functioning and are generalizable across a wide range of lesions. This is notable, as lesion characteristics are often imperfect predictors of outcome, necessitating complex multivariate analyses to achieve suboptimal structure–function alignment (for discussion of these issues, see Price et al., 2017). Given the relevance of these neurophysiological metrics to the behavior and broader functioning, a focus of future work will be to establish whether they have utility in clinical assessment or management over and above that of existing procedures.
In evaluating these results, we acknowledge the inherent simplicity of the BRDM task, as evidenced by the relatively strong performance of our clinical sample. This simplicity raises the question of whether the influence of sensory-driven processes (i.e., the N2c) shown here would extend to more complex tasks that are more dependent on higher-order cognitive processing. However, the BRDM has demonstrated the capacity to elicit meaningful variation in both evidence accumulation signals (CPP) and behavior (RT), even though hit rate (accuracy) is at the ceiling (Newman et al., 2017; Brosnan et al., 2020, 2023). Correspondingly, in this cohort of stroke patients, we capture meaningful variability in both neural and behavioral indices of decision-making, suggesting adequate engagement of higher-order processing. Despite this, our mediation analyses suggest that the N2c is preceding and determining the trajectory of evidence accumulation and subsequent decision speed. This suggests that the role of sensory-driven processes has a cascading influence on more complex cognitive operations to subsequently guide decision-making. Future work could further test this causal trajectory by combining our EEG approach with additional tasks which necessitate greater cognitive demands.
An additional consideration is the risk of bias within our sample conferred by the exclusion of a large proportion of the recruited participants. Most notably, patients unable to complete sufficient BRDM trials and those with bilateral lesions on neuroimaging review were not included. Cognitive, physical, and/or visual disabilities are extremely common in stroke patients (Sun et al., 2014; do Carmo et al., 2015), and the experience of one stroke increases the risk of another in the following 5 years ninefold (Burn et al., 1994). Although such selectivity has enabled insights into the impact of unilateral stroke, as a result, our cohort accounts for only a small subset of stroke patients, limiting the generalisability of our results. Positively, our findings of dysfunctional target selection and evidence accumulation signals align with previous electrophysiological (Lhermitte et al., 1985; Tachibana et al., 1993; Priftis et al., 2008; e.g., Lasaponara et al., 2018) and limited theoretical modeling studies (Ulrichsen et al., 2020), although significant methodological differences and variability in results make direct comparison challenging. Our findings will benefit from replication in larger, more heterogeneous samples of stroke survivors.
In summary, here we apply an integrated EEG paradigm to fractionate the key neural determinants of perceptual decision-making after stroke. We define a temporal sequence of neurophysiological events from weakened attentional selection in the damaged hemisphere through to delayed evidence accumulation, which culminates in slowed perceptual decision-making for contralesional stimuli. These results were robust to heterogeneity in the lesion characteristics of our sample. Taken together, our results point to a causal role for contralateral target selection signals in modulating downstream decision-making and behavioral processes.
Data Availability
Ethical clearance to make data publicly available was not obtained for all participating sites. Custom MATLAB scripts used to process and analyze EEG data are available at https://osf.io/mvehu/?view_only=12aef65f46e246fcb94b1e8bd6dba040.
Footnotes
↵*D.J.P., G.M.L., M.B.B., and M.A.B. contributed equally to this work.
The authors declare no competing financial interests.
This work was supported by a National Health and Medical Research Council (NHMRC) Project Grant to M.A.B., J.B.M., and R.G.O. (#1140707); Australian Research Council Grants DP150100986 and DP180102066 to M.A.B and R.G.O; a European Commission Grant #844246 and a Cullen Junior Research Fellowship at Corpus Christi College, University of Oxford, to M.B.B.; a National Institute for Health Research Oxford Health Biomedical Research Center and Wellcome Centre for Integrative Neuroimaging Wellcome Trust Grant 203139/Z/16/Z; an Australian NHMRC Senior Research Fellowship (Level B; APP1154378) to M.A.B.; a European Research Council Grant #865474 to R.G.O.; an Australian Research Council Grant FTFT220100294 to T.T-J.C.; a National Institute for Health Research Advanced Fellowship NIHR302224 to N.D.; an NHMRC Investigator Grant (Leadership 3; GNT2010141) to J.B.M.; an NHMRC Ideas Grant (APP1186955) to D.R.; and an Australian Government Research Training Program (RTP) Scholarship to D.J.P.
This paper contains supplemental material available at: https://doi.org/10.1523/JNEUROSCI.2048-24.2025
- Correspondence should be addressed to Daniel J. Pearce at daniel.pearce1{at}monash.edu or Mark A. Bellgrove at mark.bellgrove{at}monash.edu.
