Intermittent theta burst stimulation for poststroke non-spatial attention deficit: a protocol of prospective, double-blinded, single-centre, randomised controlled trial in China

INTRODUCTION

Attention deficit is the most common cognitive impairment after stroke, which can significantly hinder the recovery of both other cognitive domains and motor functions. Increasing evidence suggests that the left dorsolateral prefrontal cortex (DLPFC) is related to non-spatial attention functions, which indicates that it may be a promising target of repetitive transcranial magnetic stimulation (rTMS) for treating poststroke non-spatial attention deficit. Theta burst stimulation (TBS) is a modified pattern of rTMS that delivers shorter stimulation times and exhibits superior therapeutic efficacy. This study aims to provide evidence regarding the efficacy of intermittent TBS (iTBS) over the left DLPFC to improve poststroke non-spatial attention deficits and elucidate the potential neurophysiological mechanisms.

METHODS AND ANALYSIS

In this single-centre, prospective, randomised, sham-controlled clinical trial, patients with non-spatial attention deficits (n=38) received 10 sessions of real iTBS (n=19) or sham iTBS (n=19) over the left DLPFC and a 30-min conventional attention training. Neuropsychological evaluations, electrophysiological examination and neuroimaging scan will be conducted at baseline, postintervention (second week) and 2-week follow-up (fourth week). The primary outcomes are the change in the Montreal Cognitive Assessment scores and the Digital Span Test scores from baseline to the end of the intervention (second week). The secondary outcomes comprise changes in magnetic resonance spectroscopy neuroimaging from baseline to the end of the intervention (second week) as well as attention test batteries (including tests of selective attention, sustained attention, divided attention and shifting attention) and ERP P300 from baseline to endpoint (fourth week).

ETHICS AND DISSEMINATION

This study has been approved by the Institutional Ethical Committee of Tongji Hospital (ID: TJ-IRB20230879). All participants will sign the informed consent. Findings will be published in peer-reviewed journals and conference presentations.

TRIAL REGISTRATION NUMBER

ChiCTR2300068669.

Predicting Attentional Vulnerability to Sleep Deprivation: A Multivariate Pattern Analysis of DTI Data

BACKGROUND

Large individual differences exist in sleep deprivation (SD) induced sustained attention deterioration. Several brain imaging studies have suggested that the activities within frontal-parietal network, cortico-thalamic connections, and inter-hemispheric connectivity might underlie the neural correlates of vulnerability/resistance to SD. However, those traditional approaches are based on average estimates of differences at the group level. Currently, a neuroimaging marker that can reliably predict this vulnerability at the individual level is lacking.

METHODS

Efficient transfer of information relies on the integrity of white matter (WM) tracts in the human brain, we therefore applied machine learning approach to investigate whether the WM diffusion metrics can predict vulnerability to SD. Forty-nine participants completed the psychomotor vigilance task (PVT) both after resting wakefulness (RW) and after 24 h of sleep deprivation (SD). The number of PVT lapse (reaction time > 500 ms) was calculated for both RW condition and SD condition and participants were categorized as vulnerable (24 participants) or resistant (25 participants) to SD according to the change in the number of PVT lapses between the two conditions. Diffusion tensor imaging were acquired to extract four multitype WM features at a regional level: fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity. A linear support vector machine (LSVM) learning approach using leave-one-out cross-validation (LOOCV) was performed to assess the discriminative power of WM features in SD-vulnerable and SD-resistant participants.

RESULTS

LSVM analysis achieved a correct classification rate of 83.67% (sensitivity: 87.50%; specificity: 80.00%; and area under the receiver operating characteristic curve: 0.85) for differentiating SD-vulnerable from SD-resistant participants. WM fiber tracts that contributed most to the classification model were primarily commissural pathways (superior longitudinal fasciculus), projection pathways (posterior corona radiata, anterior limb of internal capsule) and association pathways (body and genu of corpus callosum). Furthermore, we found a significantly negative correlation between changes in PVT lapses and the LSVM decision value.

CONCLUSION

These findings suggest that WM fibers connecting (1) regions within frontal-parietal attention network, (2) the thalamus to the prefrontal cortex, and (3) the left and right hemispheres contributed the most to classification accuracy.

Regional neurodegeneration correlates with sleep-wake dysfunction after stroke

Sleep-wake disruption is a key modifiable risk factor and sequela of stroke. The pathogenesis of poststroke sleep dysfunction is unclear. It is not known whether poststroke sleep pathology is due to focal infarction to sleep-wake hubs or to accelerated poststroke neurodegeneration in subcortical structures after stroke. We characterize the first prospective poststroke regional brain volumetric and whole-brain, fiber-specific, white matter markers of objectively measured sleep-wake dysfunction. We hypothesized that excessively long sleep (>8 h) duration and poor sleep efficiency (<80%) measured using the SenseWear Armband 3-months poststroke (n = 112) would be associated with reduced regional brain volumes of a priori-selected sleep-wake regions of interest when compared to healthy controls with optimal sleep characteristics (n = 35). We utilized a novel technique known as a whole-brain fixel-based analysis to investigate the fiber-specific white matter differences in participants with long sleep duration. Stroke participants with long sleep (n = 24) duration exhibited reduced regional volumes of the ipsilesional thalamus and contralesional amygdala when compared with controls. Poor sleep efficiency after stroke (n = 29) was associated with reduced ipsilesional thalamus, contralesional hippocampus, and contralesional amygdala volumes. Whole-brain fixel-based analyses revealed widespread macrostructural degeneration to the corticopontocerebellar tract in stroke participants with long sleep duration, with fiber reductions of up to 40%. Neurodegeneration to subcortical structures, which appear to be vulnerable to accelerated brain volume loss after stroke, may drive sleep-wake deficiencies poststroke, independent of lesion characteristics and confounding comorbidities. We discuss these findings in the context of the clinicopathological implications of sleep-related neurodegeneration and attempt to corroborate previous mechanistic-neuroanatomical findings.

Predictors of Arm Nonuse in Chronic Stroke: A Preliminary Investigation

Background. Nonuse (NU) after stroke is characterized by failure to use the contralesional arm despite adequate capacity. It has been suggested that NU is a consequence of the greater effort and/or attention required to use the affected limb, but such accounts have not been directly tested, and we have poor understanding of the predictors of NU. Objective. We aimed to provide preliminary evidence regarding demographic, neuropsychological (ie, apraxia, attention/arousal, neglect), and psychological (ie, self-efficacy) factors that may influence NU in chronic stroke. Methods. Twenty chronic stroke survivors with mild to moderate sensory-motor impairment characterized by the Upper-Extremity Fugl-Meyer (UEFM) were assessed for NU with a modified version of the Actual Amount of Use Test (AAUT), which measures the disparity between amount of use in spontaneous versus forced conditions. Participants were also assessed with measures of limb apraxia, spatial neglect, attention/arousal, and self-efficacy. Using stepwise multiple regression, we determined which variables predicted AAUT NU scores. Results. Scores on the UEFM as well as attention/arousal predicted the degree of NU (P < .05). Attention/arousal predicted NU above and beyond UEFM (P < .05). Conclusions. The results are consistent with the importance of attention and engagement necessary to fully incorporate the paretic limb into daily activities. Larger-scale studies that include additional behavioral (eg, sensation, proprioception, spasticity, pain, mental health, motivation) and neuroanatomical measures (eg, lesion volume and white matter connectivity) will be important for future investigations.