Event-related potential in facial affect recognition: Potential clinical utility in patients with traumatic brain injury

VA Palo Alto Healthcare System, PM&R Service, MS-B117, 3801 Miranda Avenue, Palo Alto, CA 94304, USA.
The Journal of Rehabilitation Research and Development (Impact Factor: 1.43). 01/2005; 42(1):29-34. DOI: 10.1682/JRRD.2004.05.0056
Source: PubMed


Traumatic brain injury (TBI) frequently leads to deficits in social behavior. Prior research suggests that such deficits may result from impaired perception of basic social cues. However, these social-emotional deficits have not been studied electrophysiologically. We measured the P300 event-related potential (ERP), which has been shown to be a sensitive index of cognitive efficiency, in 13 patients with a history of moderate to severe TBI and in 13 healthy controls. The P300 response was measured during detection of 30 pictures of angry faces (rare target) randomly distributed among 120 neutral faces (frequent nontarget). Compared to control subjects, the TBI group's P300 responses were significantly delayed in latency (p = 0.002) and lower in amplitude (p = 0.003). TBI patients also showed slower reaction times and reduced accuracy when manually signaling their detection of angry faces. Coefficients of variation (CV) for the facial P300 response compared favorably to those of many standard clinical assays, suggesting potential clinical utility. For this study, we demonstrated the feasibility of studying TBI patients' P300 responses during the recognition of facial affect. Compared to controls, TBI patients showed significantly impaired electrophysiological and behavioral responses while attempting to detect affective facial cues. Additional studies are required for clinicians to determine whether this measure is related to patients' psychosocial function in the community.

20 Reads
  • Source
    • "Under these circumstances, the P300 has been found to be related to the attention resources allocated to the task (Kok, 2001; Polich, 2003). Anomalies in P300 amplitude and latency have been shown to be present in mental disorders and neurological diseases, such as schizophrenia (Bramon et al., 2004; Coburn et al., 1998; Ford et al., 1992), dementia and Alzheimer's disease (Missonnier et al., 1999; Polich et al., 1985; Polikar et al., 2008; Sumi et al., 2000), attention deficit/hyperactivity disorder (Sangal and Sangal, 2006) and traumatic brain injury (Keren et al., 1998; Lew et al., 2005). Moreover, pharmacological interventions that target the attention domain have also been shown to affect the amplitude and latency of the P300 (Coburn et al., 1998; Sangal and Sangal, 2004, 2006). "
    [Show abstract] [Hide abstract]
    ABSTRACT: To determine whether automated classifiers can be used for correctly identifying target categorization responses from averaged event-related potentials (ERPs) along with identifying appropriate features and classification models for computer-assisted investigation of attentional processes. ERPs were recorded during a target categorization task. Automated classification of average target ERPs versus average non-target ERPs was performed by extracting different combinations of features from the P300 and N200 components, which were used to train six classifiers: Euclidean classifier (EC), Mahalanobis discriminant (MD), quadratic classifier (QC), Fisher linear discriminant (FLD), multi-layer perceptron neural network (MLP) and support vector machine (SVM). The best classification performance (accuracy: 91-92%; sensitivity: 85-86%; specificity: 95-99%) was provided by QC, MLP, SVM on feature vectors extracted from P300 recorded at multiple sites. In general, non-linear and non-parametric classifiers (QC, MLP, SVM) performed better than linear classifiers (EC, MD, FLD). The N200 did not explain variance beyond that of P300 recorded at multiple sites. The results suggest that automatic characterization and classification of average target and non-target ERPs is feasible. Features of P300 recorded at multiple sites used to train non-linear classifiers are recommended for optimal classification performance. Automatic characterization of target ERPs can provide an objective approach for detecting and diagnosing abnormalities and evaluating interventions for clinical populations, paving the way for future real-time monitoring of attentional processes.
    Clinical neurophysiology: official journal of the International Federation of Clinical Neurophysiology 02/2009; 120(2):264-74. DOI:10.1016/j.clinph.2008.10.016 · 3.10 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: A frequent consequence of traumatic brain injury (TBI) is cognitive impairment, which results in significant disruption of an individual‟s everyday living. To date, most clinical rehabilitation interventions still rely on behavioral observation, with little or no quantitative information about physiological changes produced at the brain level. Functional brain imaging modalities have been extensively used in the study of cognitive impairments following TBI. However, the applications of these technologies to rehabilitation have been limited. This is due in part to the expensive or invasive nature of these modalities or because they rely on experimental tasks that are not ecologically valid in reference to real-world behaviors. Additionally, studies of cognitive impairments have most commonly depended on a single imaging modality. However, different modalities glean differ aspects of the brain activity and each may offer distinct and often complementary strengths. Therefore, combining multiple technologies could offer improved understanding of brain-behavior relationship under pathological conditions. The objective of this study is to apply, for the first time, functional near infrared spectroscopy (fNIRS), and its integration with electroencephalography (EEG), to the assessment of working memory impairments following TBI. fNIRS provides a localized measure of prefrontal hemodynamic activation, which is susceptible to TBI, and it does so in a noninvasive, affordable and wearable way, thus partially overcoming the limitations of other modalities. EEG offers a cost-effective and simple measure of brain electrical activity and it has been employed in a few studies on traumatic brain injury, showing abnormal patterns of neural activity. The combination of two modalities therefore offers information about the spatial location of the recorded activity and it takes advantage the good temporal resolution of EEG. Participants included six TBI subjects and eleven healthy controls. Standard neuropsychological tests probing attention and working memory were administered. Brain activation measurements were collected during a visual n-back task, designed to incrementally vary the working memory load and often used in neuroimaging studies. Overall, the results from the research presented in this thesis provide first evidence of the ability of fNIRS to reveal differences between TBI and healthy subjects in working memory tasks, suggesting a dysfunction in the matching between cognitive demands and cortical resources in TBI subjects. Moreover, this study has demonstrated that fNIRS measures can distinguish between the two groups and these data have been investigated relative to the results obtained by EEG alone or by the combination of fNIRS and EEG. Each of the two modalities revealed unique strengths that can contribute to the classification between the two groups. Therefore, successful combination of fNIRS and EEG for this application takes advantage of the complementary strengths of the individual modalities in order to improve the classification between normal and TBI cases.
  • American Journal of Physical Medicine & Rehabilitation 07/2005; 84(6):393-8. DOI:10.1097/01.phm.0000163703.91647.a7 · 2.20 Impact Factor
Show more