Learning is shaped by abrupt changes in neural engagement.

Internal states such as arousal, attention and motivation modulate brain-wide neural activity, but how these processes interact with learning is not well understood. During learning, the brain modifies its neural activity to improve behavior. How do internal states affect this process? Using a brain-computer interface learning paradigm in monkeys, we identified large, abrupt fluctuations in neural population activity in motor cortex indicative of arousal-like internal state changes, which we term 'neural engagement.' In a brain-computer interface, the causal relationship between neural activity and behavior is known, allowing us to understand how neural engagement impacted behavioral performance for different task goals. We observed stereotyped changes in neural engagement that occurred regardless of how they impacted performance. This allowed us to predict how quickly different task goals were learned. These results suggest that changes in internal states, even those seemingly unrelated to goal-seeking behavior, can systematically influence how behavior improves with learning.

The Impact of Mild Cognitive Impairment on Gait and Balance: A Systematic Review and Meta-Analysis of Studies Using Instrumented Assessment.

BACKGROUND: In addition to cognitive deficits, people with mild cognitive impairment (MCI) can experience motor dysfunction, including deficits in gait and balance. Objective, instrumented motor performance assessment may allow the detection of subtle MCI-related motor deficits, allowing early diagnosis and intervention. Motor assessment under dual-task conditions may increase diagnostic accuracy; however, the sensitivity of different cognitive tasks is unclear. OBJECTIVE: To systematically review the extant literature focusing on instrumented assessment of gait and balance parameters for discriminating MCI patients from cognitively intact peers. METHODS: Database searches were conducted in PubMed, EMBASE, Cochrane Library, PsycINFO and Web of Science. Inclusion criteria were: (1) clinically confirmed MCI; (2) instrumented measurement of gait and/or balance; (3) English language, and (4) reporting gait or balance parameters which could be included in a meta-analysis for discriminating between MCI patients and cognitively intact individuals based on weighted effect size (d). RESULTS: Fourteen studies met the inclusion criteria and reported quantitative gait (n = 11) or postural balance (n = 4) parameters to be included in the meta-analysis. The meta-analysis revealed that several gait parameters including velocity (d = -0.74, p < 0.01), stride length (d = -0.65, p < 0.01), and stride time (mean: d = 0.56, p = 0.02; coefficient of variation: d = 0.50, p < 0.01) discriminated best between MCI and healthy controls under single-task conditions. Importantly, dual-task assessment increased the discriminative power of gait variables wherein gait variables with counting tasks appeared to be more sensitive (range d = 0.84-1.35) compared to verbal fluency tasks such as animal naming (range d = 0.65-0.94). Balance parameters identified as significant discriminators were anterior-posterior (d = 0.49, p < 0.01) and mediolateral (d = -0.34, p = 0.04) sway position in the eyes-open condition but not eyes-closed condition. CONCLUSION: Existing studies provide evidence that MCI affects specific gait parameters. MCI-related gait changes were most pronounced when subjects are challenged cognitively (i.e., dual task), suggesting that gait assessment with an additional cognitive task is useful for diagnosis and outcome analysis in the target population. Static balance seems to also be affected by MCI, although limited evidence exists. Instrumented motor assessment could provide a critical opportunity for MCI diagnosis and tailored intervention targeting specific deficits and potentially slowing progression to dementia. Further studies are required to confirm our findings.