Effects and mechanisms of computerized cognitive training in Huntington's disease: protocol for a pilot study

Abstract

Huntington's disease (HD) causes progressive cognitive decline, with no available treatments. Computerized cognitive training (CCT) has shown efficacy in other populations, but its effects in HD are largely unknown. This pilot study will explore the effects and neural mechanisms of CCT in HD. The intervention group participants will complete 12 weeks of multidomain CCT. Control group participants will receive lifestyle education and access to CCT after the study. The primary outcome is change in processing speed. Secondary outcomes include – change in other cognitive domains, functional brain network connectivity (derived from MRI) and psychosocial function. Feasibility outcomes include rates of recruitment, adherence and retention. This study may provide insights into the effects of CCT in HD and guide future trials.

Clinical Trial Registration: ACTRN12622000908730 (ClinicalTrials.gov)

Executive summary

• As Huntington's disease (HD) causes progressive cognitive decline, with no currently available treatments, investigation of potential treatment options is crucial.

• Computerized cognitive training (CCT) shows efficacy in improving cognition in other clinical populations.

• However, the effects and mechanisms of CCT in HD require further exploration.

• This pilot study will explore the effects and mechanisms of a multi-domain CCT intervention, compared with lifestyle education, in individuals with pre-manifest or early-stage HD.

• Outcomes include changes in cognitive function, functional brain network connectivity and psychosocial function.

• Feasibility will be assessed according to rates of recruitment, retention and adherence.

• This study may provide valuable information regarding the potential effects of CCT in HD and guide further research in this area.

Keywords: :

• cognition
• cognitive training
• functional connectivity
• Huntington's disease
• randomized controlled trial

Background & rationale

Huntington's disease (HD) is a neurodegenerative disease caused by a CAG repeat expansion on the HTT gene [1]. Individuals with HD experience progressive deterioration of cognitive, motor and psychiatric function due to structural and functional changes in the brain [^2-5].

Cognitive impairment can occur many years prior to diagnosis in the premanifest stage, particularly in processing speed [6]. Attention, working memory, and executive functions are also affected [^7-9]. Cognitive symptoms are reported to be the most debilitating by HD individuals [10]. However, there are no established treatments to delay or manage cognitive decline [11].

Computerized cognitive training (CCT) is a potential intervention for remediating or delaying cognitive decline, involving repeated practice of tasks that engage specific cognitive processes. CCT has been found to improve cognitive function in older adults [12], as well as neurodegenerative conditions such as Parkinson's disease [13] and multiple sclerosis [14]. CCT is hypothesized to lead to increased capacity or efficiency of cognitive processes. Increased capacity refers to increased cognitive resources, while increased efficiency refers to using existing cognitive resources more effectively (e.g., increased strategy use or automatization of processes) [15]. Such changes in cognition may be driven by structural and functional changes in the brain [^16-18]. In particular, studies have reported changes in gray matter volumes, functional activity, and functional connectivity in task-activated [19,20] and resting-state networks, particularly the default mode network and frontoparietal network [16,18].

However, the efficacy and mechanisms of cognitive training in HD are unclear as only small single-arm or feasibility studies have been conducted. For example, Metzler-Baddeley et al. [21] conducted a single-arm study of a rhythm intervention with ten participants and found significant improvements in executive function and increased structural integrity of associated white matter tracts. Sadeghi et al. [22] and Yhnell et al. [23] conducted single-arm and randomized controlled trials of CCT with 9 and 26 participants, respectively, but did not report statistical analyses of outcomes due to the small samples.

Encouragingly, our systematic review of cognitive and physical exercise interventions in HD [24] suggested that studies of cognitive training alone, or combined with exercise, appeared to have small positive effects on cognition. In addition, cognitive training effect sizes on cognition were larger than in studies of exercise alone or when exercise was combined with other cognition-oriented treatments. Therefore, there is promising but limited evidence to support the effects of cognitive training on cognitive function in HD, and its neural mechanisms remain largely unclear.

Objectives

This pilot study aims to explore the effects of CCT in individuals with HD using a randomized, controlled study design. The primary aim is to assess the change in processing speed in individuals after CCT, compared with a control intervention. The secondary aims are to assess: changes in other cognitive domains, including attention, working memory, and executive functions, changes in functional connectivity of networks activated during cognitive task performance via functional magnetic resonance imaging (fMRI), as well as changes in patient-reported psychosocial outcomes, including subjective cognition, anxiety and depression symptoms, and health-related quality of life. Exploratory analyses will assess changes in functional connectivity of resting-state networks via fMRI, and changes in gray matter volumes using structural magnetic resonance imaging (MRI). Furthermore, feasibility outcomes such as recruitment rates, adherence and retention will also be examined.

We hypothesize that compared with controls, participants who undertake CCT will demonstrate improved processing speed. We also hypothesize that participants who complete CCT will have improved attention, working memory, and executive functions, increased functional connectivity within networks activated during cognitive task performance, and improvements in subjective cognition, anxiety and depression symptoms, and health-related quality of life. In exploratory analyses, we hypothesize increased functional connectivity within resting-state networks and increased regional gray matter volumes.

Methods

This trial is registered on the Australian New Zealand Clinical Trials Registry (ACTRN12622000908730). The study is reported in alignment with the statements of the Consolidated Standards of Reporting Trials [25] and the Standard Protocol Items: Recommendations for Interventional Trials [26].

Trial design

This study has a parallel-group, two-arm, randomized controlled design with a 1:1 allocation ratio to either the intervention group (CCT) or active control group (lifestyle education).

Study setting

Assessment sessions will be completed either at participant homes or at Monash Biomedical Imaging (MBI) in Clayton (Victoria, Australia). Intervention and control conditions will be delivered within participant homes. If participants are in the intervention group, the first in-person supervised training session may occur at MBI or in participant homes.

Eligibility criteria

Inclusion criteria:

▪	Genetically positive for Huntington's disease (HD) (CAG repeat length >35)
▪	Premanifest or early-stage manifest disease according to neurologist assessment
▪	Unified Huntington's Disease Rating Scale (UHDRS) total functional capacity (TFC) score = 7–13 [27]
▪	Access to a computer with internet connection

Exclusion criteria:

▪	Under 18 years of age
▪	Diagnosis of any major neurological or psychiatric condition other than HD (besides mild to moderate anxiety or depression), history of substance abuse or head injury
▪	Severe anxiety or depression symptoms (Hospital Anxiety and Depression Scale [HADS] score >14 on either subscale)
▪	Unstable dose of medication for depression or anxiety in the last 6 months

Participants who meet any of the following criteria will be eligible for the trial but will not undergo MRI:

▪	Left-handed
▪	Currently pregnant
▪	Other MRI contraindications

Participants in premanifest or early disease stage are targeted as they are expected to possess adequate cognitive and neural resources required for neuroplasticity [28]. Furthermore, they often do not have significant cognitive impairment or motor symptoms which may reduce adherence [21,22], and therefore might be more responsive to CCT as a secondary prevention strategy.

Individuals with severe symptoms of depression or anxiety, or with recent changes in dose of depression or anxiety medication, will be excluded as significant mood symptoms can affect cognition [29], and changing doses of medication can significantly influence mood [30].

Interventions

Computerized cognitive training

Participants in the intervention group will complete 60-min CCT sessions, twice a week, for 12 weeks on their own devices within their own homes. Furthermore, one session per week will be supervised remotely, where the student researcher will monitor the session via video teleconferencing, provide feedback on progress and troubleshoot any barriers to adherence. The first session will be supervised in person to ensure participants understand the training program.

The dose and supervision methods are chosen based on previously published protocols of CCT [31], and evidence of efficacy and feasibility. A previous meta-analysis showed that studies with CCT sessions longer than 30 min and up to three times per week report larger effect sizes compared with shorter or more frequent sessions [32]. A prior feasibility study also showed that premanifest and early-stage HD individuals can tolerate 40–50 min sessions of CCT, five times a week [22]. Supervised sessions are also implemented as it is associated with greater efficacy and adherence in other populations [32,33], and higher motivation and adherence in HD [22].

CCT will be conducted on the BrainHQ online platform [34], which has a relatively large body of supportive evidence for efficacy and feasibility across various neurodegenerative disorders [13,14]. The training program is adaptive, such that the difficulty level adjusts to user performance. As it is commercially available, participants have the option to continue their training even after the study period, if they wish.

As the effects of CCT are most pronounced in the trained domains [^35-37], the training schedule involves visual and auditory tasks that target cognitive domains most affected in HD, including processing speed, attention, working memory and executive functions (inhibition, cognitive flexibility) [38,39]. Furthermore, by training multiple cognitive domains, there is greater variety in the training schedule and less risk of cognitive fatigue. The selection of cognitive exercises and the cognitive processes targeted is described in Supplementary Material 1 (Section 1). Participants will be asked to complete 24 levels per session, with each level taking approximately 2.5 min. Data on adherence and performance of the exercises will be extracted and reported.

Control

The control group will receive three monthly newsletters over 12 weeks that provide information on lifestyle factors associated with cognitive health based on public health recommendations, namely physical activity, cognitive and social engagement and diet [40]. Lifestyle education is a common control condition in studies of CCT in other populations [35]. They will also receive monthly check-in calls where they can report whether any lifestyle changes have been made following the newsletters (although they are not requested to make lifestyle modifications). Control group participants will receive access to the CCT program for 12 weeks after follow-up assessments.

Outcomes

Outcomes focus on the difference between intervention and control groups in mean change from baseline (immediately before commencement) to follow-up (approximately 12 weeks after commencement).

1.	Primary outcome: Change in processing speed, measured by score on Symbol Digits Modalities Test (SDMT) [41]
2.	Secondary outcomes: (a) Change in other cognitive domains, measured by: a. Time to complete Trail Making Test A and B [42] b. Score on Digit Span forwards and backwards [43] c. Score on Spatial Span forwards and backwards [44] d. Score on Stroop word, color and interference conditions, interference score [45] e. Reaction time and accuracy on switch and repeat trials, and switch costs (in reaction time and accuracy) on letter-number task switching paradigm [46] f. Reaction time and accuracy on modified SDMT [47] (b) Change in subjective cognitive function, measured by total score on Cognitive Difficulties Scale [48] (c) Change in neuropsychiatric function, measured by total score on Hospital Anxiety and Depression Scale (HADS) [49] (d) Change in health-related quality of life, measured by total score on HD-PRO-TRIAD [50] (e) Change in functional connectivity within a frontoparietal network (dorsolateral prefrontal cortex, inferior parietal cortex and anterior cingulate cortex) activated during letter-number task-switching paradigm [51] (f) Change in functional connectivity within a frontoparietal, occipital and cerebellar network (lingual gyrus, cuneus, declive, superior parietal lobule, middle frontal gyrus and inferior frontal gyrus) activated during modified SDMT [47,52]
3.	Exploratory outcomes (a) Change in functional connectivity within default mode and frontoparietal resting state networks (b) Change in regional gray matter volumes across the whole brain
4.	Feasibility outcomes: Rates of recruitment, retention and adherence

Outcome measures

Cognitive assessments

The cognitive battery includes the SDMT, Trail Making Test A and B, Digit Span, Spatial Span and Stroop Test. These measures show moderate-large effect size differences between premanifest and early manifest HD patients, compared with controls [6]. In particular, the SDMT is chosen as the primary outcome measure as it is able to detect subtle cognitive deficits in the premanifest stages of the disease and shows the large effect sizes in the early stages of manifest disease when compared with controls [6,53]. It is also predictive of disease progression, including time to diagnosis (during premanifest stage) and decline in functional capacity after diagnosis, even after controlling for age and CAG length [54,55].

Experimental tasks

Complete details of each task and practice sessions outside of the scanner are provided in Supplementary Material 1 (Section 2). Experimental tasks include a letter-number task-switching paradigm and modified SDMT on which HD individuals show deficits [6,8,56].

The letter-number task switching paradigm involves participants switching randomly between completing either a letter task or a number task (Figure 1). In the letter task, participants are required to classify the letter as a vowel (A, E, I, U) or consonant (G, K, M, R). For the number task, participants are required to classify the number as odd (3, 5, 7, 9) or even (2, 4, 6, 8). Participants will be asked to respond as quickly and as accurately as possible using their left and right index fingers. Average accuracy and reaction times on switch and repeat trials and switch costs (difference in accuracy and reaction time between switch and repeat trials) will be calculated.

Figure 1. Design of the letter-number task-switching paradigm.

ITI: Intertrial interval.

The modified SDMT requires indicating whether a digit-symbol probe matches a digit-symbol pair in a coding table (Figure 2). A symbol-digit probe is presented below the coding table and participants have to indicate whether the probe matches or does not match a symbol-digit pair in the table. Participants will be asked to respond as quickly as possible using their left or right index fingers. Average accuracy and reaction time will be calculated across the trials.

Figure 2. Design of the modified Symbol Digits Modalities Test.

ITI: Intertrial interval.

Patient-reported psychosocial outcomes

Subjective cognition will be measured using a revised version of the Cognitive Difficulties Scale, which measures perceived everyday difficulties in memory and other cognitive functions [48]. Neuropsychiatric function will be measured using the HADS [49]. The HADS is a widely used measure of anxiety and depression symptoms in HD as it is not confounded by physical symptoms [57]. Health-related quality of life will be measured using HD-PRO-TRIAD, a disease-specific measure with high validity and reliability [50].

MRI scanning

For a subset of participants who consent to MRI scanning, imaging data will be collected on a 3T Siemens (Erlangen, Germany) Skyra MRI scanner. Acquisition parameters are specified in Supplementary Material 1 (Section 3). If participants have not consented to MRI scanning, they will complete the experimental tasks on a computer.

Assessments will be administered at three time points (pre-baseline, baseline and follow-up). The pre-baseline assessment will include cognitive tests and practice on experimental tasks on a computer to reduce practice effects [6]. Baseline and follow-up assessments will include cognitive tests, questionnaires and a 1-h MRI scan (for a subset of participants). The assessment schedule is shown in Table 1.

Table 1. Participant assessment schedule.

	Timepoint
	Pre-baseline	Baseline	Follow-up
Outcome measure	-7 to -1 day from baseline	0	1 to 7 days from the end of intervention
Demographics and medical history form	x
National Adult Reading Test	x
Cognitive battery
Symbol Digits Modalities Test	x	x	x
Digit Span	x	x	x
Stroop Test	x	x	x
Trails A and B	x	x	x
Spatial Span	x	x	x
Experimental tasks
Letter-number task switching		x	x
Modified Symbol Digits Modalities Test		x	x
Questionnaires
Cognitive Difficulties Scale		x	x
Hospital Anxiety and Depression Scale		x	x
HD-PRO-TRIAD		x	x
MRI scan (for a subset of participants)
Structural T1 scan		x	x
Resting-state fMRI		x	x
Task fMRI scans^†		x	x

† Note: For the subset of participants completing MRI scanning, functional scans are acquired during the performance of the letter-number task switching and modified Symbol Digit Modalities Test in the scanner.

fMRI: Functional magnetic resonance imaging; MRI: Magnetic resonance imaging.

Participant timeline

Participant timeline is shown in Figure 3. After screening and enrolment, participants complete the pre-baseline visit, which occurs 1–7 days before the baseline assessment. After the baseline assessment, participants are randomly allocated to an intervention arm. For intervention group participants, the first training session is supervised and conducted 2–7 days after their baseline assessment. They will then complete training sessions over 12 weeks and receive weekly calls. Control group participants will receive newsletters at the start of Week 1, Week 5 and Week 9, and calls at the end of Week 4, Week 8 and Week 12. Follow-up assessments for either group will be conducted within 7 days of their final call.

Figure 3. Timeline for participants for enrolment, allocation, intervention and assessments.

Sample size

Using G*Power, we conducted an a priori power analysis for a significant interaction effect in a 2 × 2 ANOVA with 2 time points (baseline and follow-up) and 2 groups (intervention and control), ∝ = 0.05, power = 0.80 and g = 0.40, based on meta-analyses of CCT trials in other populations [12]. This produced a required sample size of 200 participants, which is unfeasible as HD is a rare genetically inherited disease. As such, we calculated post hoc power analyses using n = 50 (n = 25 per arm), ∝ = 0.05, and g = 0.40. This produced an estimate of power = 0.23. A subgroup of participants (n = 30, n = 15 per arm) will undergo MRI scanning to investigate secondary neuroimaging outcomes.

Recruitment

Participants will be primarily recruited by the student researcher by screening an existing research database of HD individuals at Monash University in Clayton (Victoria, Australia), who have previously expressed to be contacted for research. Potentially eligible participants will be contacted by email or phone. Participants will also be introduced to the study by clinicians and staff at hospitals in Melbourne, Victoria, Australia, either during an in-person visit or via phone. Interested participants will be given a study flyer that contains contact details of the student researcher, and if the individual consents, their contact details may also be provided to the student researcher to contact the individual.

The existing research database contains previously provided clinical information such as CAG repeat length, disease stage and UHDRS TFC and TMS scores from participation in ENROLL-HD, an international observational study on HD [58]. If relevant clinical information is not available from the existing research database, it will be requested from the participant's HD clinician or specialist (e.g., neurologist) with the participant's consent. If up-to-date UHDRS TFC and TMS scores are not available from their clinician, they will be assessed by a trained researcher.

Recruitment and data collection will be conducted over approximately 18 months on a rolling basis. To facilitate recruitment, participants will be given the option to opt out of MRI scanning, in which case all assessments can be conducted at their homes to reduce participant burden.

Assignment of interventions

Sequence generation

Each participant will be allocated to a group using minimization with a biased coin method [59]. Minimization will be conducted to balance the following variables between groups: age, sex, years of education, anxiety/depression symptoms, consent for MRI. A high probability weighting for the allocation to minimize differences between groups will be used to incorporate a random element into the allocation procedure. Minimization is superior to traditional randomization procedures for minimizing imbalances between groups in small samples [60]. It is also accepted as a valid randomization procedure by CONSORT randomized trial guidelines [61].

Allocation concealment & implementation

A separate researcher (not involved in enrolling the participant) will be responsible for randomizing the participant to either group. Following baseline assessment, the researcher responsible for baseline testing will contact the off-site researcher to generate the allocation of the participant.

Masking

Masking of participants and the researcher administering the intervention is impractical due to obvious differences between the intervention and control groups [62]. However, bias in follow-up assessments and analyses will be minimized by masking the researcher conducting follow-up assessments and masking the statistician to group allocation.

Data collection, management and analysis

Data collection methods

Cognitive testing data will be acquired by researchers trained in administering the neuropsychological tests and cognitive tasks. Neuroimaging data will be acquired by a clinical radiographer, and incidental neuroanatomical abnormalities will be reviewed by a radiologist.

Adherence and retention will be promoted through regular contact with the participants throughout the study period. Participants in the training group will be sent text messages or email reminders to complete training. Participants in the control group will be given monthly phone calls. Participants will also be reimbursed with two $AUD25 gift cards, with one gift card provided at each baseline and follow-up assessment session. Participants will also be provided taxi vouchers to cover the cost of their travel to and from the research facility where assessment sessions may take place.

Data management

After collection, data from paper forms will be entered into a secure electronic database (REDCap). Hard copy data will be kept in locked filing cabinets in a lockable room. Non-neuroimaging electronic data will be password-protected and kept on password-protected computers. Neuroimaging data will be hosted on the secure MBI XNAT server. Electronic and hard-copy data will only be accessible to the research team. Data will be stored for a minimum of 5 years from study completion. After this period, data will be disposed of accordingly. Individual data will not be shared because there is a higher risk of participant identification due to the low incidence of HD.

Statistical methods

Baseline demographic and clinical variables will be compared between groups (intervention and control) using independent sample t-tests and Chi-squared tests. Baseline demographic and clinical variables will also be reported separately for subsets of participants with and without MRI data to consider potential recruitment bias in participants undergoing MRI scanning. Rates of recruitment, retention and adherence to intervention will also be reported.

Analyses of the effects of CCT will assess pre- to post-intervention changes in each outcome measure. Changes will be compared between intervention and control groups. All analyses will be carried out using intention-to-treat principles, in which all participants will be analyzed as randomized, regardless of protocol adherence. Due to the limited statistical power, analyses will focus on effect sizes and confidence intervals to explore the effects of CCT and identify the required sample size in larger randomized controlled trials.

Analyses of cognitive & clinical measures

Changes in cognitive and patient-reported outcomes will be analyzed using a mixed model ANOVA to test for Group (intervention, control) x Time (baseline, follow-up) interaction effects. Baseline variables that differ across groups (age, sex, premorbid IQ [NART score], total HADS score, disease severity [TFC score]) will be included as covariates in an ANCOVA. As experimental tasks may be completed either in the MRI or on a computer outside the scanner, the context will be added as a covariate in an ANCOVA when examining changes in performance on these tasks (task switching or modified SDMT). Additionally, correlations of disease burden score (DBS) [63] with changes in cognitive outcome measures in the intervention group will be conducted to explore whether disease severity moderates the efficacy of CCT. If there is significant missing data, sensitivity analyses will be conducted using complete case data and controlled multiple imputations to determine the robustness of estimates.

Analyses of neuroimaging data

Complete details of image quality control and neuroimaging analyses are provided in Supplementary Material 1 (Section 4).

Pre-processing & quality control

Structural MRI and fMRI data will be pre-processed and analyzed in SPM12 software [64] (RRID: SCR_007037; www.fil.ion.ucl.ac.uk/spm). Pre-processing will include slice timing correction, realignment, co-registration, segmentation, normalization and smoothing. fMRI data will undergo additional denoising including band-pass filtering to remove high-frequency signals and nuisance parameters. The quality of T1 structural images will be examined using the CAT12 toolbox [65] (https://neuro-jena.github.io/cat12-help/#qc) implemented in SPM12, which provides an overall image quality rating based on noise and motion. Participants with T1 images that are of inadequate quality will be excluded from neuroimaging analyses. The quality of fMRI images will be assessed according to realignment parameters and ART outlier detection in CONN [^66-68], which identifies outlier scans based on global BOLD signal changes and framewise displacement. Realignment parameters and outlier scans are added as covariates in subsequent functional neuroimaging analyses.

Analyses of gray matter volume

To determine whether there are significant differences in total gray matter volumes between groups at baseline and control for differences in subsequent fMRI analyses, voxel-based morphometry analyses will be conducted. Each participant's T1 structural image at baseline will be segmented and images of gray matter will be modulated, normalized, and smoothed and an independent sample t-test will be run.

Regional changes in gray matter volume between groups will be examined as exploratory analyses. Following pre-processing, a 2 × 2 (time × group) mixed ANOVA interaction effect will be calculated. Covariates will include baseline variables that differ across groups (age, sex, premorbid IQ [NART score], total HADS score, disease severity [TFC score], and total gray matter volume). Significance will be FDR-corrected at the cluster level.

Analyses of task-related functional connectivity

To identify task-activated regions, separate general linear models (GLMs) will be defined for each task. Event-related regressors will be convolved with the canonical hemodynamic response function as implemented in SPM12. Activation contrasts will be estimated for each individual, across baseline and follow-up timepoints. For the task-switching paradigm, the activation contrast will be switch trial > repeat trial, and for the modified SDMT, the activation contrast will be trial > baseline. Group-level contrasts will be estimated by running one-sample t-tests with all participants, FDR-corrected for multiple comparisons at the cluster level.

To analyze task-related functional connectivity, a task-related network will be defined for each paradigm using a priori regions of interest (ROIs) and exploratory ROIs. A priori ROIs will consist of regions reported by previous meta-analyses of fMRI of similar tasks. Spherical ROIs will be created by defining a 10 mm sphere around coordinates reported in previous studies [51,52,69,70]. Exploratory ROIs will be identified as regions that are activated above the threshold at baseline and follow-up in the one-sample t-tests above.

Changes in functional connectivity within each ‘task-related network’ will be compared between groups using ROI-to-ROI analyses. Functional connectivity will be estimated at the individual level using generalized psychophysiological interactions (gPPI) analyses in CONN. The gPPI model is computed for each target ROI [71,72], which includes task regressors for each condition, time series regressors (BOLD time course) for each ROI and interaction terms representing the product of each task regressor and time series regressor for each ROI. The connectivity (gPPI) value is the regression coefficient associated with each interaction term and represents the change in connectivity between regions due to task condition. First-level covariates will include nuisance variables such as realignment parameters. Changes in functional connectivity between groups will be analyzed using a 2 × 2 (time × group) mixed ANOVA interaction, FDR-corrected for multiple comparisons at the ROI-level. Second-level covariates will include baseline variables that differ across groups.

Analyses of resting-state functional connectivity

Changes in functional connectivity within the default mode network and frontoparietal network will be compared using ROI-to-ROI analyses. ROIs will be based on prior literature [73]. Functional connectivity values will be estimated at the individual level using a weighted GLM in CONN, which produces Fisher-transformed bivariate correlation coefficients for each session. Changes in within-network connectivity between groups will be investigated using a 2 × 2 (time × group) mixed ANOVA interaction, FDR-corrected for multiple comparisons at the ROI level. Additionally, correlations between each ROI will be averaged to create a network-level measure of connectivity. Changes between groups will be investigated using a 2 × 2 (time × group) mixed ANOVA. First-level covariates will include nuisance variables such as realignment parameters. Second-level covariates will include baseline variables which differ across groups.

Data monitoring

A data monitoring committee is not used as this study is of relatively short duration and there are no known risks from CCT. No interim data analyses are planned.

Harms

There are no expected risks of participation to the participants. Completion of questionnaires may elicit some distress. Adverse events occurring after providing consent and being enrolled into the study will be recorded. Relatedness to study participation will be determined based on the participant's existing medical history. If there are incidental MRI findings that the radiologist deems as requiring clinical follow-up, the radiologist report will be sent to a nominated doctor or GP for consultation.

Ethics & dissemination

This protocol, along with other study materials (e.g., explanatory statements), has been reviewed and approved by the Monash University Human Research Ethics Committee (MUHREC; approval number: 16420). Any subsequent modifications to the protocol or study materials will also be reviewed by MUHREC. Informed consent will be obtained from participants by researchers via a written consent form.

Results will be disseminated via publication in peer-reviewed journals and research conferences. Group-level summary data may be provided to contribute to pooled meta-analyses upon request from review authors. Individual data will not be shared because there is a higher risk of participant identification due to the low incidence of HD.

Conclusion

While CCT shows promise in improving cognitive function in other populations [12], examination of CCT in HD has been limited to small feasibility studies [24]. Additionally, neural mechanisms of cognitive training have only been investigated in one single-arm study, and functional imaging outcomes have not been explored [21].

This pilot study aims to explore the effects and neural mechanisms of CCT in HD using a randomized, controlled study design, including MRI scanning in a subset of participants. Unlike existing studies of cognitive training in HD with either no control or a passive control, we are including an active control group that will receive frequent social contact to control for increased social interaction due to cognitive training. Furthermore, newsletters will be sent via email to partially control for increased computer use. Control group participants will also benefit from access to CCT after the study period.

Although our study examines a range of cognitive, psychosocial and neuroimaging outcomes, we acknowledge some potential limitations. First, we have restricted the examination of neuropsychiatric function to anxiety and depression symptoms to limit participant burden. We prioritized the examination of anxiety and depression symptoms due to their prevalence in HD [74] and evidence of the benefits of CCT to these symptoms in other populations [12]. Nonetheless, other neuropsychiatric symptoms such as apathy are also prevalent in HD and may interact with the effects of CCT or mediate the effects on cognition. As such, the effects on other neuropsychiatric symptoms will be important to investigate in future research.

Additionally, we acknowledge a relatively small sample size. Regardless, we will include a larger sample than prior studies of cognitive training in HD and are exploring a broader range of outcomes. Given the low prevalence of HD in the population, a large and sufficiently powered trial at this stage is not feasible. However, conclusions from smaller, well-designed pilot studies will support the development of CCT interventions for HD and the conduct of larger multisite trials in the future.

Author contributions

K Huynh: conceptualization of project and methodology, writing of original draft, revision of manuscript and funding acquisition. S Jamadar: conceptualization of project and methodology, revision of manuscript and supervision. J Stout: conceptualization of project and methodology, supervision. K Voigt: Design of neuroimaging analyses.

A Lampit: conceptualization of project and methodology, revision of manuscript and supervision. N Georgiou-Karistianis: conceptualization of project and methodology, revision of the manuscript, funding acquisition and supervision.

Financial disclosure

This study is funded by Monash University (PAG19-0576) and Huntington's Victoria (2022 Peter Walsh Scholarship). K Huynh is supported by an Australian Government Research Training Program (RTP) Scholarship. S Jamadar is supported by an Australian National Health and Medical Research Council (NHMRC) Fellowship (APP1174164). A Lampit is supported by the University of Melbourne. The funders have no involvement in the design, conduct, analysis and reporting of the study. All researchers and personnel have no conflicts of interest to declare. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

Writing disclosure

No writing assistance was utilized in the production of this manuscript.

Ethical conduct of research

The authors state that they have obtained appropriate institutional review board approval (approved by the Monash University Human Research Ethics Committee, MUHREC; approval number: 16420) or have followed the principles outlined in the Declaration of Helsinki for all human or animal experimental investigations. In addition, for investigations involving human subjects, informed consent has been obtained from the participants involved.

Supplementary data

To view the supplementary data that accompany this paper please visit the journal website at: www.futuremedicine.com/doi/suppl/10.2217/nmt-2023-0001

Competing interests disclosure

The authors have no competing interests or relevant affiliations with any organization or entity with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Additional information

Funding

This study is funded by Monash University (PAG19-0576) and Huntington's Victoria (2022 Peter Walsh Scholarship). K Huynh is supported by an Australian Government Research Training Program (RTP) Scholarship. S Jamadar is supported by an Australian National Health and Medical Research Council (NHMRC) Fellowship (APP1174164). A Lampit is supported by the University of Melbourne. The funders have no involvement in the design, conduct, analysis and reporting of the study. All researchers and personnel have no conflicts of interest to declare. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.