The effects of neuroplasticity-based auditory information processing remediation in adults with chronic traumatic brain injury

1 Introduction

Approximately 5.3 million people in the United States currently live with a chronic disability as a result of a traumatic brain injury (TBI; CDC, 2015; Faul & Coronado, 2015; Frieden et al., 2015; Ma et al., 2014). Chronic TBI-related deficits often include impairments in cognition, behavior, emotion, and motor functions (CDC, 2015; Dikmen et al., 2017). Cognitive deficits are the hallmark of a brain injury and constrain employment options and the ability to perform everyday functions (Ponsford et al., 2014; Sherer et al., 2002; Toglia, 1991). Impairments in speed of information processing, attention, and working memory are the most commonly reported cognitive deficits experienced after TBI (Finnanger et al., 2013; Kwak et al., 2020; Slavoaca et al., 2020; Stocchetti & Zanier, 2016).

The main principles of cognitive rehabilitation for persons suffering from TBI are to restore lost cognitive functions and develop compensatory skills to overcome cognitive deficits to improve everyday functioning (Anderson et al., 2010; Cicerone et al., 2006). Restorative approaches toward cognitive rehabilitation are designed to restore cognitive deficits by targeting the training toward systematic engagement of a specific function. Plasticity-based training techniques incorporate strategies for restoration of function by targeting neuromodulatory processes that support plasticity, strengthen processes of working memory and attention, and suppress neurological distractors that degrade the quality of incoming sensory information and interfere with learning and memory (Anderson et al., 2014; Dikmen et al., 2017; Merzenich, 2013; Nahum et al., 2013). Such interventions rely on the notion that neuroplastic change may be stimulated through focused training exercises that aid in the neuroplastic repair of associated neurological circuitry that has been damaged (Dams-O’Connor & Gordon, 2010). Effective interventions facilitate alterations in the structural connectivity of the brain via top-down, Hebbian principles of network plasticity (Hebb, 1949), whereby compensation for lesions occurs through the repetitive activation of nearby tissue, which strengthens damaged synapses that are simultaneously activated (Merzenich et al., 2014; Nahum et al., 2013). Through repetitive training, learning is enhanced and enriched to increase the gains of cognitive rehabilitation.

Over the last 30 years, numerous studies have demonstrated, with strong evidence, that holistic and targeted cognitive rehabilitation strategies can be used to improve functional impairments experienced by adults with a TBI (Ben-Yishay & Diller, 1993; Cicerone et al., 2005; Cicerone et al., 2019; Cicerone et al., 2011; Kumar et al., 2017; Wilson, 1997). Specifically, adults with TBI have benefited from cognitive interventions that target restoration of attention (Cantor et al., 2014; Couillet et al., 2010; Pero et al., 2006), working memory (Richter et al., 2015; Vallat-Azouvi et al., 2009), learning and memory (Goverover et al., 2010; Lesniak et al., 2020; O’Brien et al., 2007; Stringer & Small, 2011), and executive functions (Jacoby et al., 2013; Levine et al., 2011; Vas et al., 2011).

Recently, computer-based cognitive interventions have been shown to be particularly useful, as they allow for a more flexible and personalized experience for the user, can be more cost effective than traditional face-to-face training programs, and can be used in both inpatient and outpatient clinics or, oftentimes, at home (Kueider et al., 2012). Lebowitz et al. (2012) demonstrated the feasibility of a computerized cognitive training programs for individuals with TBI who completed the intervention at home and reported improvements in cognitive functioning. Other studies have also provided evidence for the utility of targeted computerized cognitive training in TBI, where improvements have been demonstrated in attention (Zickefoose et al., 2013) and working memory (Akerlund et al., 2013; Bjorkdahl et al., 2013; Fernández López & Antolí, 2020; Fetta et al., 2017), learning and memory (Fernandez et al., 2012; Li et al., 2013; Tam & Man, 2004), and executive functions (Bogdanova et al., 2016; De Luca et al., 2014).

The ability to efficiently take in and process new information from the environment is particularly problematic after TBI, as the speed at which one processes information directly impacts one’s ability to successfully execute higher-order cognitive functions, such as attention, memory, and executive functions (Asikainen et al., 1999; DeLuca et al., 2000; Salthouse, 1996). Computerized cognitive interventions that target processing speed involve tasks that increase in speed and complexity over time, building on components of the task that were previously learned to increase the users capacity for processing more and increasingly complex information over time, ultimately enhancing one’s ability to succeed in complex environments (Schooler & Mulatu, 2001). Studies of the efficacy of speed of processing training have demonstrated that the improvement of these skills may transfer to other cognitive domains and to the abilities required to perform everyday functions (Ball et al., 2007; Edwards et al., 2002; Edwards et al., 2005).

Since speed and efficiency of auditory processing affects the acquisition of verbal information, the present study examines the effects of a neuroplasticity based computerized cognitive training system that targeted auditory processing on verbal attention and working memory as well as measures of executive functioning in adults with a chronic TBI. We hypothesize that adults with TBI who undergo the intervention will demonstrate objective improvements in auditory processing speed and verbal attention and working memory as well as subjective improvements in functional ability when compared adults with TBI who do not undergo the cognitive intervention.

2 Method

3 Results

The intervention and control groups did not differ in demographic or injury variables (Table 1), and no significant differences were found in objective or subjective measures of function between the two groups at baseline (Table 2).

The RM-ANOVAs performed on the objective outcome measures of cognitive functioning revealed significant interactions with moderate effect sizes for group over time on the WJ-III UD subtest [F(1, 46) = 4.84, p = 0.033, η_p² = 0.10) and TMT part A [F(1, 46) = 5.02, p = 0.030, η_p² = 0.09). On both measures, the intervention group improved in performance over time in comparison to the control group (Table 3). No other significant group differences were shown on the cognitive measures, although small effect sizes were observed for improved scores by the intervention group compared to controls on the 3-second trial of the PASAT (η_p² = .03), TMT part B (η_p² = 0.02), and on the WCST (η_p² = 0.01).

Similarly, the RM-ANOVAs conducted on self-reported subjective outcome measures revealed a significant interaction with a moderate effect size for group over time on the Cognitive subscale of the CSRQ [F(1, 46) = 5.58, p = 0.022, η_p² = 0.11), with the intervention group reporting increased function at follow-up relative to control group (Table 4). No other significant interactions were observed for change in subjective function over time, although small effect sizes were observed for improved self-reported function in the intervention group compared to control group on the CSRQ Total scale (η_p² = 0.05) and on the Social subscale of the CSRQ (η_p² = 0.01).

This was a quasi-randomized control trial (RCT) investigating the use of a computerized cognitive remediation program, the BFP, in adults who were at least one-year post-TBI. Both the intervention group and control group were comparable in terms of demographics, premorbid intelligence, clinical indicators and their baseline neuropsychological performance. The intervention group improved on objective neuropsychological measures and a self-report measure of subjective functioning compared to the control after completing the BFP. Specifically, when compared to the control group, the intervention group significantly improved on measures of auditory and visual attention with moderate effect sizes, and small effect sizes were found for improved performance on auditory processing speed, visual working memory, and executive function. In addition, the intervention group reported significant improvements over time, with a moderate effect size, in self-reported cognitive function, and with small effect sizes for self-reported improvements in social and overall functioning. The CSRQ Cognitive domain includes questions regarding one’s perception of their attention, executive functioning, and memory abilities. The objective and subjective improvements were noted in the intervention group compared to the control group with an average of eight years post-injury. Suggesting that adults with a chronic brain injury years after the injury may still benefit from this type of cognitive remediation.

Computerized cognitive training has recently gained much attention, but concern has been raised over the true efficacy of such programs (Max Planck Institute for Human Development and Stanford Center on Longevity, 2014; Owen et al., 2010; Simons et al., 2016), as some lack the scientific evidence necessary to support their claims. In response, the Institute of Medicine (2015) published five guidelines that should be met by a training program in order for it to be considered an evidence-based cognitive training program. To date, the computerized processing speed training programs established by Posit Science (San Francisco, CA, USA), such as the BFP, are the only ones to meet all of these requirements (Merzenich, 2020).

The BFP was designed as a neuroplasticity-inducing “bottom-up” approach for auditory processing with six cognitive exercises that have participants attend and process basic sounds, nonsense syllables, and complete story details (Jahshan et al., 2019; Mahncke et al., 2006; Merzenich et al., 2014). To this end, it is not surprising that our intervention group improved on the WJ-III UD subtest, a measure of verbal attention and working memory, since the BFP targets auditory information processing. The improvements on TMT A and small effect sizes for improvements on the 3-second trial of the PASAT suggests the BFP also improved processing speed (Salthouse, 2011). The improvement on these neuropsychological measures demonstrates the near effects of the intervention, as the functional improvements were seen primarily on measures close to the trained tasks, and they support the findings of Wright et al. (2010), where improving auditory information processing was also shown to improve the acquisition of verbal information. The small effect sizes observed for improvements on tasks of higher-order cognitive abilities, such as cognitive flexibility, suggest that the effects of the intervention may transfer to other abilities; however, similar studies are necessary to replicate these findings.

Numerous studies have shown the benefits of computerized cognitive remediation in older adults (Edwards et al., 2005; Smith et al., 2009), as well as clinical populations such as adults with schizophrenia (Cellard et al., 2016; Fisher et al., 2010; Fisher et al., 2015), and cancer-related cognitive deficits (Bail & Meneses, 2016; Bray et al., 2017; Kesler et al., 2013; Mihuta et al., 2018), MCI associated with Parkinson’s Disease (Bernini et al., 2019; Oh et al., 2020), stroke patients (van de Ven et al., 2015) and neurosurgical patients (Liberta et al., 2020). Likewise, the improvement in neuropsychological performance in the present TBI sample supports prior studies, which have demonstrated the systematic delivery of a computerized cognitive remediation program benefits individuals with TBI (Bogdanova et al., 2016; Cicerone et al., 2019; Fernández López & Antolí, 2020; Fetta et al., 2017).

Quality of life (QOL) is negatively impacted by the cognitive deficits experienced by adults with TBI (Johnson & Ditchman, 2020). Although transfer effects are rarely seen for improved everyday function or increased QOL in the majority of the current cognitive training literature, an exception is seen in the results of studies using Posit Science’s plasticity-based interventions that target cognitive speed of processing (Ball et al., 2002; Ball et al., 2007; Belchior et al., 2019; Edwards et al., 2002; Edwards et al., 2005; Gary et al., 2020; Roenker et al., 2003; Wadley et al., 2006). In the present study, the intervention group reported improvements in overall, cognitive, and social functioning. Along with the improvements demonstrated on objective measures of function, subjective improvement may increase self-efficacy, which has been shown to mediate the relationship between TBI symptom severity and QOL (Stalnacke, 2007). The present findings suggest that neuroplasticity-based speed of processing training may help survivors of TBI to improve their cognitive abilities years post-injury, and this may result in further improvements in the ability to perform everyday activities and increased QOL.