Faculty Bibliography

The paradigm task A-->task B-->task A, which varies the time interval between task A and task B, has been used extensively to investigate the consolidation of motor memory. Consolidation is defined as resistance to retrograde interference (interference by task B on initial learning of task A). Consolidation has been demonstrated for simple skills, motor sequencing, and learning of force fields. In contrast, evidence to date suggests that visuomotor learning does not consolidate. We have shown previously that adaptation to a 30 degrees screen-cursor rotation is faster and more complete on relearning 24 hr later. This improvement is prevented if a 30 degrees counter-rotation is learned 5 min after the original rotation. Here, we sought to identify conditions under which rotation learning becomes resistant to interference by a counter-rotation. In experiment 1, we found that interference persists even when the counter-rotation is learned 24 hr after the initial rotation. In experiment 2, we removed potential anterograde interference (interference by task B on relearning of task A) by introducing washout blocks before all of the learning blocks. In contrast to experiment 1, we found resistance to interference (i.e., consolidation) when the counter-rotation was learned after 24 hr but not after 5 min. In experiment 3, we doubled the amount of initial rotation learning and found resistance to interference even after 5 min. Our results suggest that persistent interference is attributable to anterograde effects on memory retrieval. When anterograde effects are removed, rotation learning consolidates both over time and with increased initial training

In this study, we tested the hypotheses that (1) the acquisition of sequential information is related to the integrity of dopaminergic input to the caudate nucleus; and (2) the integrity of dopaminergic input to the caudate nucleus correlates significantly with brain activation during sequence acquisition. Twelve early stage Parkinson's disease (PD) patients and six age-matched healthy volunteers were scanned using a dual tracer PET imaging design. All subjects were scanned with [(18)F]fluoropropyl-betaCIT (FPCIT) to measure striatal dopamine transporter (DAT) binding and with [(15)O]water to assess activation during a sequence learning task where movements were made to a repeating sequence of eight targets. Caudate and putamen DAT binding in the PD cohort was reduced by 15% and 43%, respectively. In PD, caudate DAT binding correlated with target acquisition (R = 0.57, P < 0.05), while putamen DAT binding did not correlate with performance. In volunteers, caudate DAT binding correlated with learning-related activation (P < 0.05, corrected for multiple comparisons) in the left dorsolateral and ventral prefrontal cortices, the anterior cingulate and premotor regions, and the right cerebellum. A significant correlation with caudate DAT binding was additionally detected in the right anteromedial thalamus, extending into the rostral midbrain. By contrast, in the PD cohort, most of these regional relationships were lost: Only ventral and dorsolateral prefrontal cortex activation correlated with caudate dopaminergic tone. Our findings suggest that sequence learning is normally associated with tight coupling between dopaminergic input to the caudate and thalamo-cortical functional activity. Despite minimal reductions in nigro-caudate input, PD patients demonstrate a loss of this coupling early in the disease

Human sleep is a global state whose functions remain unclear. During much of sleep, cortical neurons undergo slow oscillations in membrane potential, which appear in electroencephalograms as slow wave activity (SWA) of <4 Hz. The amount of SWA is homeostatically regulated, increasing after wakefulness and returning to baseline during sleep. It has been suggested that SWA homeostasis may reflect synaptic changes underlying a cellular need for sleep. If this were so, inducing local synaptic changes should induce local SWA changes, and these should benefit neural function. Here we show that sleep homeostasis indeed has a local component, which can be triggered by a learning task involving specific brain regions. Furthermore, we show that the local increase in SWA after learning correlates with improved performance of the task after sleep. Thus, sleep homeostasis can be induced on a local level and can benefit performance

Previous studies suggest that horizontal reaching movements are planned vectorially with independent specification of direction and extent. The transformation from visual to hand-centered coordinates requires the learning of a task-specific reference frame and scaling factor. We studied learning of a novel reference frame by imposing a screen-cursor rotation and learning of a scaling factor by imposing a novel gain. Previous work demonstrates that rotation and gain learning have different time courses and patterns of generalization. Here we used PET to identify and compare brain areas activated during rotation and gain learning, with a baseline motor-execution task as the subtracted control. Previous work has shown that the time courses of rotation and gain adaptation have a short rapid phase followed by a longer slow phase. We therefore also sought to compare activations associated with the rapid and slower phases of adaptation. We isolated the rapid phase by alternating opposite values of the rotation or gain every 16 movements. The rapid phase of rotation adaptation activated the preSMA. More complete adaptation to the rotation activated right ventral premotor cortex, right posterior parietal cortex, and the left lateral cerebellum. The rapid phase of gain learning only activated subcortical structures: bilateral putamen and left cerebellum. More complete gain learning failed to show any significant activation. We conclude that the time course of rotation adaptation is paralleled by a frontoparietal shift in activated cortical regions. In contrast, early gain adaptation involves only subcortical structures, which we suggest reflects a more automatic process of contextual recalibration of a scaling factor

BACKGROUND: Dopaminergic therapy with levodopa improves motor function in PD patients, but the effects of levodopa on cognition in PD remain uncertain. OBJECTIVE: To use H(2)(15)O and PET to assess the effect of levodopa infusion on motor sequence learning in PD. METHODS: Seven right-handed PD patients were scanned 'on' and 'off' levodopa while performing a sequence learning task. The changes in learning performance and regional brain activation that occurred during this intervention were assessed. RESULTS: During PET imaging, levodopa infusion reduced learning performance as measured by subject report (p < 0.05). This behavioral change was accompanied by enhanced activation during treatment in the right premotor cortex and a decline in the ipsilateral occipital association area (p < 0.01). Levodopa-induced changes in learning-related activation responses in the occipital association cortex correlated with changes in learning indexes (p < 0.01). CONCLUSIONS: Levodopa treatment appears to have subtle detrimental effects on cognitive function in nondemented PD patients. These effects may be mediated through an impairment in brain activation in occipital association cortex

BACKGROUND: Although the pathophysiology remains unknown, most nondemented patients with PD have difficulty with frontal tasks, including trial-and-error sequence learning. If given time, they can perform cognitive tasks of moderate difficulty as well as controls. However, it is not known how brain function is altered during this time period to preserve higher cortical function in the face of PD pathology. METHOD: To evaluate this phenomenon, the authors matched sequence learning between PD and control subjects for the last 30 seconds of a PET scan. Learning during the initial 50 seconds of PET was unconstrained. RESULTS: Learning indices were equivalent between groups during the last 30 seconds of the scan, whereas rates of acquisition, correct movements, and forgetting differed in the first 30 seconds. In normal controls sequence learning was associated with activations in the right prefrontal, premotor, parietal, rostral supplementary motor area, and precuneus regions. To achieve equal performance, the PD group activated greater volume within these same regions, and also their left sided cortical homologs and the lateral cerebellum bilaterally. CONCLUSIONS: Mildly affected patients with PD demonstrated only modest impairment of learning during the first 30 seconds of the task and performed equivalently with controls thereafter. However, the mechanism by which they achieved equiperformance involved considerable changes in brain function. The PD group had to activate four times as much neural tissue as the controls, including recruiting brain from homologous cortical regions and bilateral lateral cerebellum

In a previous H(2) (15)O/PET study of motor sequence learning, we used principal components analysis (PCA) of region of interest (ROI) data to identify performance-related activation patterns in normal subjects and patients with Parkinson's disease (PD). In the present study, we determined whether these patterns predicted learning performance in subsequent normal and untreated PD cohorts. Using a voxel-based PCA approach, we correlated the changes in network activity that occurred during antiparkinsonian treatment and their relationship to learning performance. We found that the previously identified ROI-based patterns correlated with learning performance in the prospective normal (P < 0.01) and untreated PD (P < 0.05) cohorts. Voxel analysis revealed that target retrieval was related to a network characterized by bilateral activation of the dorsolateral prefrontal, premotor and anterior cingulate cortex, the precuneus, and the occipital association areas as well as the right ventral prefrontal and inferior parietal regions. Target acquisition was associated with a different network involving activation of the caudate, putamen, and right dentate nucleus, as well as the left ventral prefrontal and inferior parietal areas. Antiparkinsonian therapy gave rise to changes in retrieval performance that correlated with network modulation (P < 0.01). Increases in network activation and learning performance occurred with internal pallidal deep brain stimulation (GPi DBS); decrements in these measures were present with levodopa. Our findings suggest that network analysis of activation data can provide stable descriptors of learning performance. Network quantification can provide an objective means of assessing the effects of therapy on cognitive functioning in neurodegenerative disorders

Previous positron emission tomography (PET) studies have shown that nonmanifesting carriers of the DYT1 dystonia mutation express an abnormal pattern of resting glucose metabolism. To determine whether motor behavior is impaired in these subjects, we compared movement and sequence learning in 12 clinically unaffected DYT1 carriers with 12 age-matched controls. Regional differences in brain function during task performance were assessed with simultaneous H(2) (15)O/PET. We found that motor performance was similar in the DYT1 and control groups, with no significant differences in movement time and spatial accuracy measured during each of the tasks. In contrast, sequence learning was reduced in gene carriers relative to controls (p < 0.01). PET imaging during motor execution showed increased activation in gene carriers (p < 0.001, uncorrected) in the left premotor cortex and right supplementary motor area, with concomitant reduction in the posterior medial cerebellum. During sequence learning, activation responses in DYT1 carriers were increased in the left ventral prefrontal cortex, and lateral cerebellum. These findings suggest that abnormalities in motor behavior and brain function exist in clinically nonmanifesting DYT1 carriers. Although localized increases in neural activity may enable normal movement execution in these subjects, this mechanism may not compensate for their defect in sequence learning

BACKGROUND: Motor sequence learning is abnormal in PD. However, it is not known whether this defect is present during the earliest stages of the illness or whether it reflects specific limitations in dividing attention between cognitive and motor requirements. METHODS: Fifteen patients with early stage PD and 10 age-matched and 9 younger normal controls moved the right dominant hand on a digitizing tablet to eight targets presented on a screen in synchrony with a tone at 1-second intervals. The tasks were as follows: 1) CCW--a timed-response task where targets appeared in a predictable counterclockwise order; 2) RAN--a reaction time task where targets were random and unpredictable; 3) SEQ--a task with multiple demands emphasizing explicit learning and target anticipation in which subjects learned a sequence while reaching for targets; and 4) VSEQ--subjects learned a visual sequence without moving. RESULTS: CCW and RAN yielded similar results in all groups. In patients with PD, sequence learning was the same in SEQ and VSEQ and was slower compared to both control groups. In older controls, learning was faster in VSEQ than in SEQ, whereas younger controls learned equally fast in both tasks. CONCLUSIONS: Despite normal motor execution, the initial phases of sequence learning are impaired in early PD independent of task requirements, possibly reflecting reduced working memory. Learning was slower in older than younger controls only in tasks with multiple demands, presumably due to reduced attentional resources