Variability In Brain Activity Research Articles

One size does not fit all: notable individual variation in brain activity correlates of antidepressant treatment response.

To date, no robust electroencephalography (EEG) markers of antidepressant treatment response have been identified. Variable findings may arise from the use of group analyses, which neglect individual variation. Using a combination of group and single-participant analyses, we explored individual variability in EEG characteristics of treatment response. Resting-state EEG data and Montgomery-Åsberg Depression Rating Scale (MADRS) symptom scores were collected from 43 patients with depression before, at 1 and 12 weeks of pharmacotherapy. Partial least squares (PLS) was used to: 1) identify group differences in EEG connectivity (weighted phase lag index) and complexity (multiscale entropy) between eventual medication responders and non-responders, and 2) determine whether group patterns could be identified in individual patients. Responders showed decreased alpha and increased beta connectivity, and early, widespread decreases in complexity over treatment. Non-responders showed an opposite connectivity pattern, and later, spatially confined decreases in complexity. Thus, as in previous studies, our group analyses identified significant differences between groups of patients with different treatment outcomes. These group-level EEG characteristics were only identified in ~40-60% of individual patients, as assessed quantitatively by correlating the spatiotemporal brain patterns between groups and individual results, and by independent raters through visualization. Our single-participant analyses suggest that substantial individual variation exists, and needs to be considered when investigating characteristics of antidepressant treatment response for potential clinical applicability. https://clinicaltrials.gov, identifier NCT00519428.

Re-emergent Tremor in Parkinson's Disease: Evidence of Pathologic β and Prokinetic γ Activity.

Re-emergent tremor is characterized as a continuation of resting tremor and is often highly therapy refractory. This study examines variations in brain activity and oscillatory responses between resting and re-emergent tremors in Parkinson's disease. Forty patients with Parkinson's disease (25 males, mean age, 66.78 ± 5.03 years) and 40 age- and sex-matched healthy controls were included in the study. Electroencephalogram and electromyography signals were simultaneously recorded during resting and re-emergent tremors in levodopa on and off states for patients and mimicked by healthy controls. Brain activity was localized using the beamforming technique, and information flow between sources was estimated using effective connectivity. Cross-frequency coupling was used to assess neuronal oscillations between tremor frequency and canonical frequency oscillations. During levodopa on, differences in brain activity were observed in the premotor cortex and cerebellum in both the patient and control groups. However, Parkinson's disease patients also exhibited additional activity in the primary sensorimotor cortex. On withdrawal of levodopa, different source patterns were observed in the supplementary motor area and basal ganglia area. Additionally, levodopa was found to suppress the strength of connectivity (P < 0.001) between the identified sources and influence the tremor frequency-related coupling, leading to a decrease in β (P < 0.001) and an increase in γ frequency coupling (P < 0.001). Distinct variations in cortical-subcortical brain activity are evident in tremor phenotypes. The primary sensorimotor cortex plays a crucial role in the generation of re-emergent tremor. Moreover, oscillatory neuronal responses in pathological β and prokinetic γ activity are specific to tremor phenotypes. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

EEG resting state alpha dynamics predict an individual’s vulnerability to auditory hallucinations

AbstractTask-free brain activity exhibits spontaneous fluctuations between functional states, characterized by synchronized activation patterns in distributed resting-state (RS) brain networks. The temporal dynamics of the networks’ electrophysiological signatures reflect individual variations in brain activity and connectivity linked to mental states and cognitive functions and can predict or monitor vulnerability to develop psychiatric or neurological disorders. In particular, RS alpha fluctuations modulate perceptual sensitivity, attentional shifts, and cognitive control, and could therefore reflect a neural correlate of increased vulnerability to sensory distortions, including the proneness to hallucinatory experiences. We recorded 5 min of RS EEG from 33 non-clinical individuals varying in hallucination proneness (HP) to investigate links between task-free alpha dynamics and vulnerability to hallucinations. To this end, we used a dynamic brain state allocation method to identify five recurrent alpha states together with their spatiotemporal dynamics and most active brain areas through source reconstruction. The dynamical features of a state marked by activation in somatosensory, auditory, and posterior default-mode network areas predicted auditory and auditory-verbal HP, but not general HP, such that individuals with higher vulnerability to auditory hallucinations spent more time in this state. The temporal dynamics of spontaneous alpha activity might reflect individual differences in attention to internally generated sensory events and altered auditory perceptual sensitivity. Altered RS alpha dynamics could therefore instantiate a neural marker of increased vulnerability to auditory hallucinations.

EEG brain network variability is correlated with other pathophysiological indicators of critical patients in neurology intensive care unit

Continuous electroencephalogram (cEEG) plays a crucial role in monitoring and postoperative evaluation of critical patients with extensive EEG abnormalities. Recently, the temporal variability of dynamic resting-state functional connectivity has emerged as a novel approach to understanding the pathophysiological mechanisms underlying diseases. However, little is known about the underlying temporal variability of functional connections in critical patients admitted to neurology intensive care unit (NICU). Furthermore, considering the emerging field of network physiology that emphasizes the integrated nature of human organisms, we hypothesize that this temporal variability in brain activity may be potentially linked to other physiological functions. Therefore, this study aimed to investigate network variability using fuzzy entropy in 24-hour dynamic resting-state networks of critical patients in NICU, with an emphasis on exploring spatial topology changes over time. Our findings revealed both atypical flexible and robust architectures in critical patients. Specifically, the former exhibited denser functional connectivity across the left frontal and left parietal lobes, while the latter showed predominantly short-range connections within anterior regions. These patterns of network variability deviating from normality may underlie the altered network integrity leading to loss of consciousness and cognitive impairment observed in these patients. Additionally, we explored changes in 24-hour network properties and found simultaneous decreases in brain efficiency, heart rate, and blood pressure between approximately 1 pm and 5 pm. Moreover, we observed a close relationship between temporal variability of resting-state network properties and other physiological indicators including heart rate as well as liver and kidney function. These findings suggest that the application of a temporal variability-based cEEG analysis method offers valuable insights into underlying pathophysiological mechanisms of critical patients in NICU, and may present novel avenues for their condition monitoring, intervention, and treatment.

Personality Moderates Intra-Individual Variability in EEG Microstates and Spontaneous Thoughts.

Variability in brain activity that persists after accounting for overt behavioral and physiological states is often considered noise and controlled as a covariate in research. However, studying intra-individual variability in brain function can provide valuable insights into the dynamic nature of the brain. To explore this, we conducted a study on 43 participants analyzing the EEG microstate dynamics and self-reported spontaneous mental activity during five-minute resting-state recordings on two separate days with a twenty days average delay between recordings. Our results showed that the associations between EEG microstates and spontaneous cognition significantly changed from one day to another. Moreover, microstate changes were associated with changes in spontaneous cognition. Specifically, inter-day changes in Verbal thoughts about Others and future Planning were positively related to bottom-up sensory network-related microstate changes and negatively associated with top-down, attention, and salience network-related microstates. In addition, we find that personality traits are related to inter-day changes in microstates and spontaneous thoughts. Specifically, extraversion, neuroticism, agreeableness, and openness to experience moderated the relationship between inter-day changes in EEG microstates and spontaneous thoughts. Our study provides valuable information on the dynamic changes in the EEG microstate-spontaneous cognition organization, which could be essential for developing interventions and treatments for neuropsychiatric disorders.

Modulation of smoker brain activity and functional connectivity by tDCS: A go/no-go task-state fMRI study

BackgroundTranscranial direct current stimulation (tDCS) applied to particular brain areas may reduce a smoker's smoking cravings. Most studies on tDCS mechanisms are performed on brains in the resting state. Therefore, brain activity changes induced by tDCS during tasks need to be further studied. MethodsForty-six male smokers were randomised to receive anodal tDCS of the left/right dorsolateral prefrontal cortex (DLPFC) or sham tDCS. A go/no-go task was performed before and after stimulation, respectively. Brain activity and functional connectivity (FC) changes during the task state before and after tDCS were used for comparison. ResultsThis study revealed that the anodal stimulation over one DLPFC area caused decreased activity in the ipsilateral precuneus during the go task state. Right DLPFC stimulation increased the FC between the bilateral DLPFCs and the right anterior cingulate cortex (ACC), which is closely associated with cognition and inhibition of executive functions. Additionally, the study showed variations in brain activity depending on whether the anode was positioned over the right or left DLPFC (R-DLPFC or L-DLPFC). ConclusionDuring the go task, tDCS might exert a suppressive effect on some brain areas, such as the precuneus. Stimulation on the R-DLPFC might strengthen the FC between the right ACC and the bilateral DLPFCs, which could enhance the ability of behavioural decision-making and inhibition to solve conflicts effectively. Stimulating the L-DLPFC alone could increase the FC of bilateral DLPFCs with some brain regions associated with response inhibition.

Using Convolutional Neural Network to Recognize Emotions from Scalp Electroencephalography Plot Images

Introduction: Real-time variations in brain activity are determined by electroencephalogram (EEG) data. EEG signals are commonly used in studies to analyze human emotional states. Emotions EEG signals vary from person to person because they each have different emotional responses to the same stimuli. The objective of this study was using EEG signals in emotion recognition.Material and Methods: We specifically focused on employing convolutional neural network (CNN) for detecting image-based emotions in long-term EEG data. After filtering, the EEG data is divided into short sections based on a certain time window and they are converted into EEG plot images. Each of these is classified by convolutional neural networks.Results: In comparison with the existing methods, the error rate has been reduced and the accuracy rate is better than the existing methods. The mean accuracy of the compared articles is 62.87, 70.50, 74.88, 82.88 and 68.11, but the average accuracy of the proposed method is 85.13.Conclusion: This research demonstrates the potential and accuracy of CNN in recognizing emotions from scalp EEG plot images. The study contributes to the growing field of emotion recognition and paves the way for future advancements in utilizing CNN for analyzing EEG signals, ultimately aiming to use as an effective method for computer-aided recognition.

Culture shapes spontaneous brain dynamics – Shared versus idiosyncratic neural features among Chinese versus Canadian subjects

ABSTRACTEnvironmental factors, such as culture, are known to shape individual variation in brain activity including spontaneous activity, but less is known about their population-level effects. Eastern and Western cultures differ strongly in their cultural norms about relationships between individuals. For example, the collectivism, interdependence and tightness of Eastern cultures relative to the individualism, independence and looseness of Western cultures, promote interpersonal connectedness and coordination. Do such cultural contexts therefore influence the group-level variability of their cultural members’ spontaneous brain activity? Using novel methods adapted from studies of inter-subject neural synchrony, we compare the group-level variability of resting state EEG dynamics in Chinese and Canadian samples. We observe that Chinese subjects show significantly higher inter-subject correlation and lower inter-subject distance in their EEG power spectra than Canadian subjects, as well as lower variability in theta power and alpha peak frequency. We demonstrate, for the first time, different relationships among subjects’ resting state brain dynamics in Chinese and Canadian samples. These results point to more idiosyncratic neural dynamics among Canadian participants, compared with more shared neural features in Chinese participants.

Brain signatures indexing variation in internal processing during perceptual decision-making

Brain activity is highly variable during a task. Discovering, characterizing, and linking variability in brain activity to internal processes has primarily relied on experimental manipulations. However, changes in internal processing could arise from many factors independent of experimental conditions. Here we utilize a data-driven clustering method based on modularity-maximation to identify consistent spatial-temporal EEG activity patterns across individual trials. Subjects (N= 25) performed a motion discrimination task with six interleaved levels of coherence. Clustering identified two discrete subtypes of trials with different patterns of activity. Surprisingly, Subtype 1 occurred more frequently in trials with lower motion coherence but was associated with faster response times. Computational modeling suggests that Subtype 1 was characterized by a lower threshold for reaching a decision. These results highlight across-trial variability in decision processes traditionally hidden to experimenters and provide a method for identifying endogenous brain state variability relevant to cognition and behavior.

Kullback-Leibler Divergence of Sleep-Wake Patterns Related with Depressive Severity in Patients with Epilepsy.

(1) Objective: Whether abnormal sleep-wake rhythms were associated with depressive symptoms in patents with epilepsy had remained unclear. Our study aimed to establish relative entropy for the assessment of sleep-wake patterns and to explore the relationship between this index and the severity of depressive symptoms in patients with epilepsy. (2) Methods: We recorded long-term scalp electroencephalograms (EEGs) and Hamilton Depression Rating Scale-17 (HAMD-17) questionnaire scores from 64 patients with epilepsy. Patients with HAMD-17 scores of 0-7 points were defined as the non-depressive group, while patients with scores of 8 or higher were defined as the depression group. Sleep stages were firstly classified based on EEG data. We then quantified sleep-wake rhythm variations in brain activity using the Kullback-Leibler divergence (KLD) of daytime wakefulness and nighttime sleep. The KLD at different frequency bands in each brain region was analyzed between the depression and non-depression groups. (3) Results: Of the 64 patients with epilepsy included in our study, 32 had depressive symptoms. It was found that patients with depression had significantly decreased KLD for high-frequency oscillations in most brain areas, especially the frontal lobe. A detailed analysis was conducted in the right frontal region (F4) because of the significant difference in the high-frequency band. We found that the KLDs at the gamma bands were significantly decreased in the depression groups compared to the non-depression group (KLDD = 0.35 ± 0.05, KLDND = 0.57 ± 0.05, p = 0.009). A negative correlation was displayed between the KLD of gamma band oscillation and HAMD-17 score (r = -0.29, p = 0.02). (4) Conclusions: Sleep-wake rhythms can be assessed using the KLD index calculated from long-term scalp EEGs. Moreover, the KLD of high-frequency bands had a negative correlation with HAMD-17 scores in patients with epilepsy, which indicates a close relationship between abnormal sleep-wake patterns and depressive symptoms in patients with epilepsy.

Changes in local and network brain activity after stereotactic thermocoagulation in patients with drug-resistant epilepsy.

Stereoelectroencephalography-guided radiofrequency thermocoagulation (SEEG-guided RF-TC) aims to reduce seizure frequency by modifying epileptogenic networks through local thermocoagulative lesions. Although RF-TC is hypothesized to functionally modify brain networks, reports of changes in functional connectivity (FC) following the procedure are missing. We evaluated, by means of SEEG recordings, whether variation in brain activity after RF-TC is related to clinical outcome. Interictal SEEG recordings from 33 patients with drug-resistant epilepsy (DRE) were analyzed. Therapeutic response was defined as a >50% reduction in seizure frequency for at least 1 month following RF-TC. Local (power spectral density [PSD]) and FC changes were evaluated in 3-min segments recorded shortly before (baseline), shortly after, and 15 min after RF-TC. The PSD and FC strength values after thermocoagulation were compared with baseline as well as between the responder and nonresponder groups. In responders, we found a significant reduction in PSD after RF-TC in channels that were thermocoagulated for all frequency bands (p = .007 for broad, delta and theta, p <.001 for alpha and beta bands). However, we did not observe such PSD decrease in nonresponders. At the network level, nonresponders displayed a significant FC increase in all frequency bands except theta (broad, delta, beta band: p <.001; alpha band: p <.01), although responders showed a significant FC decrease in delta (p <.001) and alpha bands (p <.05). Nonresponders showed stronger FC changes with respect to responders exclusively in TC channels (broad, alpha, theta, beta: p >.05; delta: p = .001). Thermocoagulation induces both local and network-related (FC) changes in electrical brain activity of patients with DRE lasting for at least 15 min. This study demonstrates that the observed short-term modifications in brain network and local activity significantly differ between responders and nonresponders and opens new perspectives for studying the longer-lasting FC changes after RF-TC.

Intraindividual variability in brain activation—A novel correlate of obesity risk among female college students

There are published data describing impairments in the brain function of adolescents or young adults who have a genetic or familial predisposition for obesity. From these descriptions, it is often assumed that the impairments are appropriately captured by a central tendency estimate and therefore consistently detectable. The present study questions this assumption and shows that the variability in brain function over the time course of a cognitive task is a better predictor of familial risk than its central tendency. Sixty-nine female young adults lacking an obese parent and 24 female young adults with an obese parent were compared on the average amplitude and inter-trial variability (ITV) in amplitude of their P300 electroencephalographic responses to rarely-occurring stimuli during a selective attention task. Simple group comparisons revealed statistically significant findings with effect sizes that were markedly greater for analyses of P300 ITV versus P300 average amplitude. It is suggested that the elevation in P300 ITV among young adults with familial risk indicates temporal instability in systems responsible for the maintenance of attention. These fluctuations may episodically disrupt their attention to satiety cues as well as other cues that influence behavior regulation.

Cortex-wide spontaneous activity non-linearly steers propagating sensory-evoked activity in awake mice.

The brain responds highly variably to identical sensory inputs, but there is no consensus on the nature of this variability. We explore this question using cortex-wide optical voltage imaging and whisker stimulation in awake mice. Clustering analysis reveals that the sensory-evoked activity propagates over the cortex via distinct pathways associated with distinct behavioral states. The pathway taken by each trial is independent of the level of primary sensory-evoked activation but is partially predictable by the spatiotemporal features of the preceding cortical spontaneous activity patterns. The sensory inputs reduce trial-to-trial variability in brain activity and alter temporal autocorrelation in spatial activity pattern evolutions, suggesting non-linear interactions between evoked activities and spontaneous activities. Further, evoked activities and spontaneous activities occupy different positions in the state space, suggesting that sensory inputs can intricately interact with the internal state to generate large-scale evoked activity patterns not frequented by spontaneous brain states.

Building bridges: simultaneous multimodal neuroimaging approaches for exploring the organization of brain networks.

.Brain organization is evident across spatiotemporal scales as well as from structural and functional data. Yet, translating from micro- to macroscale (vice versa) as well as between different measures is difficult. Reconciling disparate observations from different modes is challenging because each specializes within a restricted spatiotemporal milieu, usually has bounded organ coverage, and has access to different contrasts. True intersubject biological heterogeneity, variation in experiment implementation (e.g., use of anesthesia), and true moment-to-moment variations in brain activity (maybe attributable to different brain states) also contribute to variability between studies. Ultimately, for a deeper and more actionable understanding of brain organization, an ability to translate across scales, measures, and species is needed. Simultaneous multimodal methods can contribute to bettering this understanding. We consider four modes, three optically based: multiphoton imaging, single-photon (wide-field) imaging, and fiber photometry, as well as magnetic resonance imaging. We discuss each mode as well as their pairwise combinations with regard to the definition and study of brain networks.

Signal in the noise:Altered brain activation among adolescent alcohol users detected via the analysis of intra-individual variability1.

RationaleUnlike its average level, the variability in brain activation over time or trials can capture subtle and brief disruptions likely to occur among participants with low-to-moderate levels of substance use or misuse.ObjectiveThe present study used this intra-individual variability measurement approach to detect neural processing differences associated with light-to-moderate use of alcohol among 14–19-year-old adolescents.MethodA total of 128 participants reporting any level of alcohol use during the previous 6 months and 87 participants reporting no use during this period completed intake questionnaires and interviews as well as an assessment of P300 electroencephalographic responses to novel stimuli recorded during two separate tasks.ResultsIn addition to differing in recent alcohol use, the groups differed in nicotine and cannabis use, risk-taking behavior and conduct disorder symptoms, and P300 amplitude inter-trial variability (ITV) across both tasks. Across all participants, P300 ITV was positively correlated with a family history of depression but not with a family history of alcohol dependence. There were no group differences in P300 amplitude averaged across trials.ConclusionsRecent reports attributing brain volume or brain function differences to an effect of light-to-moderate alcohol use should be viewed with great caution. In the present analysis of brain function differences among substance-using adolescents, the group differences were small, complicated by many factors coinciding with or preceding alcohol use, and not reflected in a stable central tendency.

Subjective difficulty in a verbal recognition-based memory task: Exploring brain-behaviour relationships at the individual level in healthy young adults

The vast majority of fMRI studies of task-related brain activity utilize common levels of task demands and analyses that rely on the central tendencies of the data. This approach does not take into account perceived difficulty nor regional variations in brain activity between people. The results are findings of brain-behavior relationships that weaken as sample sizes increase. Participants of the current study included twenty-six healthy young adults evenly split between the sexes. The current work utilizes five parametrically modulated levels of memory load centered around each individual's predetermined working memory cognitive capacity. Principal components analyses (PCA) identified the group-level central tendency of the data. After removing the group effect from the data, PCA identified individual-level patterns of brain activity across the five levels of task demands. Expression of the group effect significantly differed between the sexes across all load levels. Expression of the individual level patterns demonstrated a significant load by sex interaction. Furthermore, expressions of the individual maps make better predictors of response time behavior than group-derived maps. We demonstrated that utilization of an individual's unique pattern of brain activity in response to increasing a task's perceived difficulty is a better predictor of brain-behavior relationships than study designs and analyses focused on identification of group effects. Furthermore, these methods facilitate exploration into how individual differences in patterns of brain activity relate to individual differences in behavior and cognition.

Longer illness duration is associated with greater individual variability in functional brain activity in Schizophrenia, but not bipolar disorder

BackgroundIndividuals with schizophrenia exhibit greater inter-patient variability in functional brain activity during neurocognitive task performance. Some studies have shown associations of age and illness duration with brain function; however, the association of these variables with variability in brain function activity is not known. In order to better understand the progressive effects of age and illness duration across disorders, we examined the relationship with individual variability in brain activity. MethodsNeuroimaging and behavioural data were extracted from harmonized datasets collectively including 212 control participants, 107 individuals with bipolar disorder, and 232 individuals with schizophrenia (total n = 551). Functional activity in response to an N-back working memory task (2-back vs 1-back) was examined. Individual variability was quantified via the correlational distance of fMRI activity between participants; mean correlational distance of one participant in relation to all others was defined as a ‘variability score’. ResultsGreater individual variability was found in the schizophrenia group compared to the bipolar disorder and control groups (p = 1.52e−09). Individual variability was significantly associated with aging (p = 0.027), however, this relationship was not different across diagnostic groups. In contrast, in the schizophrenia sample only, a longer illness duration was associated with increased variability (p = 0.027). ConclusionAn increase in variability was observed in the schizophrenia group related to illness duration, beyond the effects of normal aging, implying illness-related deterioration of cognitive networks. This has clinical implications for considering long-term trajectories in schizophrenia and progressive neural and cognitive decline which may be amiable to novel treatments.

Altered brain network centrality in patients with retinal vein occlusion: a resting-state fMRI study.

To explore the intrinsic brain activity variations in retinal vein occlusion (RVO) subjects by using the voxel-wise degree centrality (DC) technique. Twenty-one subjects with RVO and twenty-one healthy controls (HCs) were enlisted and underwent the resting-state functional magnetic resonance imaging (rs-fMRI) examination. The spontaneous cerebrum activity variations were inspected using the DC technology. The receiver operating characteristic (ROC) curve was implemented to distinguish the DC values of RVOs from HCs. The relationships between DC signal of definite regions of interest and the clinical characteristics in RVO group were evaluated by Pearson's correlation analysis. RVOs showed notably higher DC signals in right superior parietal lobule, middle frontal gyrus and left precuneus, but decreased DC signals in left middle temporal gyrus and bilateral anterior cingulated (BAC) when comparing with HCs. The mean DC value of RVOs in the BAC were negatively correlated with the anxiety and depression scale. RVO is associated aberrant intrinsic brain activity patterns in several brain areas including pain-related as well as visual-related regions, which might assist to reveal the latent neural mechanisms.

Brain Correlates of Verbal Working Memory in Children with Hearing Loss: Auditory Experience Matters

Brain Correlates of Verbal Working Memory in Children with Hearing Loss: Auditory Experience Matters

Temporal instability in brain activation: a novel paradigm for evaluating the maintenance of attention among substance dependent patients.

Prior studies have demonstrated statistically significant but subtle differences in brain function between patients with a history of substance dependence (SD) and control groups. The goal of the present study was to show that variability in brain activation over the trials of a cognitive task is more useful for revealing the putative impact of SD than analyses focusing on the amplitude of activation averaged over trials. The study also tested the additional contribution of antisocial personality disorder (ASPD)-a prevalent comorbidity that promotes both an early onset and more severe course of SD. Two hundred eleven adults performed two selective attention tasks while P300 event-related electroencephalographic potentials were recorded. They were assigned to one of 3 mutually exclusive groups: no lifetime history of SD or ASPD (n = 67), a SD history but no ASPD (n = 68), or both SD and ASPD (n = 76). The major finding was a statistically significant elevation of P300 amplitude inter-trial variability (ITV) in the SD plus ASPD group in comparison to the group with neither attribute. The elevation was detected during both selective attention tasks and most prominent at electrodes sites located over the frontal brain. There were no group differences in P300 amplitude averaged over trials. We conclude from these findings that the ITV of P300 amplitude is an efficient and sensitive biomarker of the maintenance of attention. It is valuable for revealing group differences associated with substance dependence and ASPD. It may ultimately be valuable for detecting improvements resulting from psychostimulant treatment or other interventions, including cognitive remediation.