Posts Tagged Neuropsychology

[Abstract] Employment stability in the first 5 years after moderate to severe traumatic brain injury

Abstract

Objective

To characterize employment stability and identify predictive factors of employment stability in working-age individuals after moderate to severe traumatic brain injury (TBI) that may be clinically addressed.

Design

Longitudinal observational study of an inception cohort from the Traumatic Brain Injury Model Systems National Database (TBIMS-NDB) using data at years 1, 2, and 5 post-TBI.

Setting

Inpatient rehabilitation centers with telephone follow-up.

Participants

Individuals enrolled in the TBIMS-NDB since 2001, aged 18 to 59, with employment data at two or more follow-up interviews at years 1, 2, and 5 (N=5,683).

Interventions

Not applicable.

Main Outcome Measure

Employment stability, categorized using post-TBI employment data as no paid employment (53.25%), stably (27.20%), delayed (10.24%), or unstably (9.31%) employed.

Results

Multinomial regression analyses identified predictive factors of employment stability, including younger age, white race, less severe injuries, pre-injury employment, higher annual earnings, male sex, higher education, transportation independence post-injury, and no anxiety or depression at 1-year post-TBI.

Conclusions

Employment stability serves as an important measure of productivity post-TBI. Psychosocial, clinical, environmental, and demographic factors predict employment stability post-TBI. Notable predictors include transportation independence as well as presence of anxiety and depression at year 1 post-TBI as potentially modifiable intervention targets.

via Employment stability in the first 5 years after moderate to severe traumatic brain injury – Archives of Physical Medicine and Rehabilitation

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[Abstract] Improving Cognitive Function in Patients with Stroke: Can Computerized Training Be the Future?

Background

Cognitive impairment after stroke is common and can cause disability with a high impact on quality of life and independence. Cognitive rehabilitation is a therapeutic approach designed to improve cognitive functioning after central nervous system’s injuries. Computerized cognitive rehabilitation (CCR) uses multimedia and informatics resources to optimize cognitive compromised performances. The aim of this study is to evaluate the effects of pc cognitive training with Erica software in patients with stroke.

Methods

We studied 35 subjects (randomly divided into 2 groups), affected by either ischemic or hemorrhagic stroke, having attended from January 2013 to May 2015 the Laboratory of Robotic and Cognitive Rehabilitation of Istituto di Ricerca e Cura a Carattere Scientifico Neurolesi in Messina. Cognitive dysfunctions were investigated through a complete neuropsychological battery, administered before (T0) and after (T1) each different training.

Results

At T0, all the patients showed language and cognitive deficits, especially in attention process and memory abilities, with mood alterations. After the rehabilitation program (T1), we noted a global cognitive improvement in both groups, but a more significant increase in the scores of the different clinical scales we administered was found after CCR.

Conclusions

Our data suggest that cognitive pc training by using the Erica software may be a useful methodology to increase the post-stroke cognitive recovery.

 

via Improving Cognitive Function in Patients with Stroke: Can Computerized Training Be the Future?

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[Abstract] Improving Cognitive Function in Patients with Stroke: Can Computerized Training Be the Future?

Background

Cognitive impairment after stroke is common and can cause disability with a high impact on quality of life and independence. Cognitive rehabilitation is a therapeutic approach designed to improve cognitive functioning after central nervous system’s injuries. Computerized cognitive rehabilitation (CCR) uses multimedia and informatics resources to optimize cognitive compromised performances. The aim of this study is to evaluate the effects of pc cognitive training with Erica software in patients with stroke.

Methods

We studied 35 subjects (randomly divided into 2 groups), affected by either ischemic or hemorrhagic stroke, having attended from January 2013 to May 2015 the Laboratory of Robotic and Cognitive Rehabilitation of Istituto di Ricerca e Cura a Carattere Scientifico Neurolesi in Messina. Cognitive dysfunctions were investigated through a complete neuropsychological battery, administered before (T0) and after (T1) each different training.

Results

At T0, all the patients showed language and cognitive deficits, especially in attention process and memory abilities, with mood alterations. After the rehabilitation program (T1), we noted a global cognitive improvement in both groups, but a more significant increase in the scores of the different clinical scales we administered was found after CCR.

Conclusions

Our data suggest that cognitive pc training by using the Erica software may be a useful methodology to increase the post-stroke cognitive recovery.

 

via Improving Cognitive Function in Patients with Stroke: Can Computerized Training Be the Future?

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[Systematic review] The potential of real-time fMRI neurofeedback for stroke rehabilitation – Full Text

Abstract

Real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback aids the modulation of neural functions by training self-regulation of brain activity through operant conditioning. This technique has been applied to treat several neurodevelopmental and neuropsychiatric disorders, but its effectiveness for stroke rehabilitation has not been examined yet. Here, we systematically review the effectiveness of rt-fMRI neurofeedback training in modulating motor and cognitive processes that are often impaired after stroke. Based on predefined search criteria, we selected and examined 33 rt-fMRI neurofeedback studies, including 651 healthy individuals and 15 stroke patients in total. The results of our systematic review suggest that rt-fMRI neurofeedback training can lead to a learned modulation of brain signals, with associated changes at both the neural and the behavioural level. However, more research is needed to establish how its use can be optimized in the context of stroke rehabilitation.

1. Introduction

The number of stroke survivors is continuously increasing with the ageing of the population: about 15 million people worldwide suffer from stroke every year, of whom 5 million die, whereas another 5 million become chronically disabled (WHO, 2012). Behavioural deficits in cognitive and motor domains are highly prevalent and persistent in stroke survivors (Bickerton et al., 2014; Demeyere, Riddoch, Slavkova, Bickerton, & Humphreys, 2015; Demeyere et al., 2016; Jaillard, Naegele, Trabucco-Miguel, LeBas, & Hommel, 2009; Planton et al., 2012; Verstraeten, Mark, & Sitskoorn, 2016). Neurophysiological and neuroimaging studies suggested that stroke causes network-wide changes across structurally intact regions, directly or indirectly connected to the site of infarction (Carrera & Tononi, 2014; Carter et al., 2010; Gillebert & Mantini, 2013; Grefkes et al., 2008; Ward & Cohen, 2004). Disruptions in even one of the many networks or brain regions implicated in the different aspects of motor function and cognition can have a major impact on quality of life (Achten, Visser-Meily, Post, & Schepers, 2012; Hochstenbach, Mulder, Limbeek, Donders, & Schoonderwaldt, 1998). Accordingly, both local tissue damage and secondary changes in brain function should be considered when developing rehabilitation strategies to improve the recovery rate and generally increase the quality of life in stroke survivors (Chechlacz, Mantini, Gillebert, & Humphreys, 2015; Chechlacz et al., 2013; Corbetta et al., 2015; Gillebert & Mantini, 2013). In this regard, the use of neurofeedback may be a promising approach.

1.1. Neurofeedback

Neurofeedback works as a closed loop system that provides real-time information regarding the participant’s own brain activity and/or connectivity, which can be used to develop self-learning strategies to modulate these brain signals (Weiskopf, Mathiak, et al., 2004). It follows the principle of operant conditioning, a method of learning that occurs through reinforcing specific behaviour with rewards and punishments (Skinner, 1938). If the participant learns to control activity of the brain areas targeted through neurofeedback, this may ultimately lead to a measurable behavioural change that is related to the function of those areas (DeCharms et al., 2005; Haller, Birbaumer, & Veit, 2010; Hartwell et al., 2016).

The origins of neurofeedback are rooted in electroencephalography (EEG), which measures dynamic changes of electrical potentials over the participant’s scalp (Nowlis & Kamiya, 1970). This technique is portable and inexpensive, and provides estimates of brain activity at high temporal resolution. EEG neurofeedback has been widely used over the years to induce long-lasting behavioural changes, both in healthy volunteers and in patients (Gruzelier, 2014; Nelson, 2007). However, because of the low spatial resolution associated with this technique, it is very challenging to selectively target brain areas of interest. As such, the effects of EEG neurofeedback are often not specific (Rogala et al., 2016; Scharnowski & Weiskopf, 2015). Other neuroimaging techniques used for neurofeedback include magnetoencephalography (MEG) (Buch et al., 2012; Okazaki et al., 2015) and functional near-infrared spectroscopy (fNIRS) (Kober et al., 2014; Mihara et al., 2013). However, as also for EEG, their spatial resolution is relatively limited and they do not permit to target precise brain regions.

The field of neurofeedback has rapidly developed and delved into new avenues by the introduction of real-time functional magnetic resonance imaging (rt-fMRI) technology (Cox, Jesmanowicz, & Hyde, 1995). Accordingly, in the past years there has been a steady increase of studies focussing on rt-fMRI neurofeedback applications to induce behavioural changes (Sulzer et al., 2013). Rt-fMRI neurofeedback uses the blood-oxygenation level-dependent (BOLD) signal to present contingent feedback to the participant and to enable modulation of brain activity (Fig. 1). Various acquisition parameters are available, and chosen based on a trade-off between spatial and temporal resolution, and signal-to-noise ratio (Weiskopf, Scharnowski, et al., 2004). The analysis is performed almost immediately or with a delay of a few seconds depending on the available computational resources. With a much higher spatial resolution than EEG, fMRI allows for a refined delineation of both cortical and subcortical target regions. These properties can be valuable for neurofeedback applications (Stoeckel et al., 2014). Recent studies suggest that rt-fMRI is a mature technology to use in the context of neurofeedback training (for a review, see e.g., Ruiz, Buyukturkoglu, Rana, Birbaumer, & Sitaram, 2014; Weiskopf, 2012). As a result, doors are being opened to the application of rt-fMRI neurofeedback in ameliorating disrupted brain functions in stroke survivors.[…]

Fig. 1

Fig. 1. Real-time fMRI neurofeedback is a closed-loop system that can be used to voluntarily modulate brain-activity through the principle of operant conditioning. (A) The participants use self-generated or prior instructed strategies to attempt to change their brain activity. (B) fMRI data are acquired and (C) processed in real-time. Computer programs select the relevant signals and (D) return these to the participants after varied degrees of pre-processing to allow them to adjust their control strategies.

Continue —>  The potential of real-time fMRI neurofeedback for stroke rehabilitation: A systematic review – ScienceDirect

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[BOOK] The Role of Technology in Clinical Neuropsychology – Google Books

Front CoverNeuropsychology as a field has been slow to embrace and exploit the potential offered by technology to either make the assessment process more efficient or to develop new capabilities that augment the assessment of cognition.

The Role of Technology in Clinical Neuropsychology details current efforts to use technology to enhance cognitive assessment with an emphasis on developing expanded capabilities for clinical assessment. The first sections of the book provide an overview of current approaches to computerized assessment along with newer technologies to assess behavior. The next series of chapters explores the use of novel technologies and approaches in cognitive assessment as they relate to developments in telemedicine, mobile health, and remote monitoring including developing smart environments. While still largely office-based, health care is increasingly moving out of the office with an increased emphasis on connecting patients with providers, and providers with other providers, remotely.

Chapters also address the use of technology to enhance cognitive rehabilitation by implementing conceptually-based games to teach cognitive strategies and virtual environments to measure outcomes. Next, the chapters explore the use of virtual reality and scenario-based assessment to capture critical aspects of performance not assessed by traditional means and the implementation of neurobiological metrics to enhance patient assessment. Chapters also address the use of imaging to better define cognitive skills and assessment methods along with the integration of cognitive assessment with imaging to define the functioning of brain networks. The final section of the book discusses the ethical and methodological considerations needed for adopting advanced technologies for neuropsychological assessment.

Authored by numerous leading figures in the field of neuropsychology, this volume emphasizes the critical role that virtual environments, neuroimaging, and data analytics will play as clinical neuropsychology moves forward in the future.

Source: The Role of Technology in Clinical Neuropsychology – Google Books

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[BOOK] Neuropsychology: A Review of Science and Practice – Google Books

 Front Cover
Neuropsychology: A Review of Science and Practice, Volume 2
 edited by Emeritus Associate Professor Sandra Koffler, Sandra Koffler, Adjunct Associate Professor Joel Morgan, Joel Morgan, Bernice Marcopulos, Manfred F. Greiffenstein

Neuropsychology: A Review of Science and Practice – Google Books.

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