Motor impairments are one of the most common adverse outcomes after pediatric arterial ischemic stroke (AIS), affecting approximately half of survivors. The development of motor and cognitive skills is closely interrelated, and they share common neural substrates. The objective of this study was to examine whether motor functioning after the acute phase of stroke is associated with school-age intellectual abilities. We also examined associations between concurrent motor functioning and intellectual abilities. Finally, we explored clinical features associated with motor impairments.

Participants were 64 children, 34 childhood AIS (Meanage= 11.90[2.38]); 30 perinatal AIS (Meanage= 8.75[2.22]), from the Children’s Stroke Program at SickKids Hospital. Motor functioning was assessed with the Pediatric Stroke Outcome Measure sensorimotor subscale at two timepoints, Time 1 or early recovery (childhood group between 30 days post-stroke to 1 year; perinatal group between 2-5 years of age) and Time 2, closest to neuropsychological testing. Intellectual abilities were measured using the Wechsler Intelligence Scale for Children 4th or 5th edition. Associations between motor and intellectual functioning were examined separately in childhood and perinatal AIS groups. Clinical features associated with motor impairment were examined across the full sample.

Motor functioning during early recovery was significantly associated with processing speed (r= -.391, p= .036) in the perinatal group and with overall intellectual functioning (r= -.414, p= .018) verbal intellectual abilities (r= -.444, p= .011), working memory (r= .393, p= .026), and processing speed (r= -.351, p= .042) in the childhood group. There were no associations between concurrent motor and intellectual functioning in the perinatal group, and only with processing speed (r= -.525, p= .002) in the childhood group. When motor functioning was dichotomized as no/mild motor deficit and moderate/severe motor deficit at Time 1, children in the perinatal group with moderate/severe motor deficit had significantly lower perceptual reasoning scores (f[28]= 2.15, p= .040) and participants in the childhood group with moderate/severe motor deficit had significantly lower perceptual reasoning (f[32]= 2.35, p= .025) and processing speed (f[32]= 2.14, p= 0.41) scores. There were no differences between no/mild and moderate/severe motor deficit groups for either perinatal or childhood AIS at Time 2. Clinical features associated with moderate/severe motor deficit at Time 1 were cortical+subcortical infarcts, large lesions, presenting with hemiparesis and seizures at time of neuropsychological assessment, and accessing occupational therapy and physical therapy.

Results suggest that motor functioning during early stroke recovery is associated with intellectual outcome, whereas motor functioning at time closest to neuropsychological assessment is not. This may be related neuroplastic changes post-injury, likely in frontal-subcortical connections, that result in observable motor deficits after stroke and affect subsequent hierarchal brain maturational processes thereby impacting later cognitive outcome. Different patterns of associations between motor functioning and specific intellectual abilities in perinatal and childhood groups suggest possible age-mediated effects on this relationship.

There is growing evidence that the side of brain lesions results in distinct upper extremity deficits in motor control, movement behavior, and emotional and cognitive function poststroke. We investigated self-evaluation errors, which are the differences in scores between patient self-evaluation and clinician evaluations, and compared patients with left hemisphere damage (LHD) and right hemisphere damage (RHD) poststroke.

Twenty-eight patients with chronic stroke (LHD = 16) performed the actual amount of the test twice with a one-week interval. We videotaped the participants' movements, and participants with stroke and evaluators graded the quality of movement scores by watching video recordings.

Self-evaluation errors were significantly lower in patients with LHD than in those with RHD (t = 2.350, p = .019). Interestingly, this error did not change after the clinician provided the correct score as feedback. Chi-squared analysis revealed that more patients with LHD underestimated their movements (χ2 = 9.049, p = .002), while more patients with RHD overestimated (χ2 = 7.429, p = .006) in the send evaluation. Furthermore, there were no correlations between self-evaluation error and age, cognitive function, physical impairment, ability to control emotions, or onset months poststroke.

Patients with stroke and therapists evaluated the same movements differently, and this can be dependent on hemispheric damage. Therapists might need to encourage patients with LHD who underestimate their movement to ensure continuous use of their more-affected arm. Patients with RHD who overestimate their movement might need treatment to overcome impaired self-awareness, such as video recordings, to protect from unexpected dangerous situations.

Identify which NIH Toolbox Cognition Battery (NIHTB-CB) subtest(s) best differentiate healthy controls (HC) from those with amnestic mild cognitive impairment (aMCI) and compare the discriminant accuracy between a model using a priori “Norm Adjusted” scores versus “Unadjusted” standard scores with age, sex, race/ethnicity, and education controlled for within the model. Racial differences were also examined.

Participants were Black/African American (B/AA) and White consensus-confirmed (HC = 96; aMCI = 62) adults 60–85 years old that completed the NIHTB-CB for tablet. Discriminant function analysis (DFA) was used in the Total Sample and separately for B/AA (n = 80) and White participants (n = 78).

Picture Sequence Memory (an episodic memory task) was the highest loading coefficient across all DFA models. When stratified by race, differences were noted in the pattern of the highest loading coefficients within the DFAs. However, the overall discriminant accuracy of the DFA models in identifying HCs and those with aMCI did not differ significantly by race (B/AA, White) or model/score type (Norm Adjusted versus Unadjusted).

Racial differences were noted despite the use of normalized scores or demographic covariates—highlighting the importance of including underrepresented groups in research. While the models were fairly accurate at identifying consensus-confirmed HCs, the models proved less accurate at identifying White participants with an aMCI diagnosis. In clinical settings, further work is needed to optimize computerized batteries and the use of NIHTB-CB norm adjusted scores is recommended. In research settings, demographically corrected scores or within model correction is suggested.