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72
Brain Structures Differ Between Musicians and Non-Musicians
- J. Neurosci
, 2003
"... From an early age, musicians learn complex motor and auditory skills (e.g., the translation of visually perceived musical symbols into motor commands with simultaneous auditory monitoring of output), which they practice extensively from childhood throughout their entire careers. Using a voxel-by-vox ..."
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Cited by 145 (5 self)
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From an early age, musicians learn complex motor and auditory skills (e.g., the translation of visually perceived musical symbols into motor commands with simultaneous auditory monitoring of output), which they practice extensively from childhood throughout their entire careers. Using a voxel-by-voxel morphometric technique (VBM), we found gray matter volume differences in motor, auditory and visual-spatial brain regions when comparing professional musicians (keyboard players) with a matched group of amateur musicians and non-musicians. Although some of these multi-regional differences could be attributable to innate predisposition, we believe they may represent structural adaptations in response to long-term skill acquisition and the repetitive rehearsal of those skills. This hypothesis is supported by the strong association we found between structural differences, musician status, and practice intensity, as well as the wealth of supporting animal data showing structural changes in response to long-term motor training. However, only future experiments can determine the relative contribution of predisposition and practice.
Motor learning through the combination of primitives,”
- Philosophical Transactions of the Royal Society B,
, 2000
"... In this paper we discuss a new perspective on how the central nervous system (CNS) represents and solves some of the most fundamental computational problems of motor control. In particular, we consider the task of transforming a planned limb movement into an adequate set of motor commands. To carry ..."
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Cited by 77 (3 self)
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In this paper we discuss a new perspective on how the central nervous system (CNS) represents and solves some of the most fundamental computational problems of motor control. In particular, we consider the task of transforming a planned limb movement into an adequate set of motor commands. To carry out this task the CNS must solve a complex inverse dynamic problem. This problem involves the transformation from a desired motion to the forces that are needed to drive the limb. The inverse dynamic problem is a hard computational challenge because of the need to coordinate multiple limb segments and because of the continuous changes in the mechanical properties of the limbs and of the environment with which they come in contact. A number of studies of motor learning have provided support for the idea that the CNS creates, updates and exploits internal representations of limb dynamics in order to deal with the complexity of inverse dynamics. Here we discuss how such internal representations are likely to be built by combining the modular primitives in the spinal cord as well as other building blocks found in higher brain structures. Experimental studies on spinalized frogs and rats have led to the conclusion that the premotor circuits within the spinal cord are organized into a set of discrete modules. Each module, when activated, induces a speci¢c force ¢eld and the simultaneous activation of multiple modules leads to the vectorial combination of the corresponding ¢elds. We regard these force ¢elds as computational primitives that are used by the CNS for generating a rich grammar of motor behaviours.
Human functional neuroimaging of brain changes associated with practice
- Cereb. Cortex
, 2005
"... The discovery that experience-driven changes in the human brain can occur from a neural to a cortical level throughout the lifespan has stimulated a proliferation of research into how neural function changes in response to experience, enabled by neuroimaging methods such as positron emission tomogra ..."
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Cited by 75 (2 self)
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The discovery that experience-driven changes in the human brain can occur from a neural to a cortical level throughout the lifespan has stimulated a proliferation of research into how neural function changes in response to experience, enabled by neuroimaging methods such as positron emission tomography and functional magnetic resonance imaging. Studies attempt to characterize these changes by examining how practice on a task affects the functional anatomy underlying performance. Results are incongruous, includ-ing patterns of increases, decreases and functional reorganization of regional activations. Following an extensive review of the practice-effects literature, we distinguish a number of factors affecting the pattern of practice effects observed, including the effects of task domain, changes at the level of behavioural and cognitive processes, the time-window of imaging and practice, and of a number of other influences and miscellaneous confounding factors. We make a novel distinction between patterns of re-organization and redistribution as effects of task practice on brain activation, and emphasize the need for careful attention to practice-related changes occurring on the behavioural, cognitive and neural levels of analysis. Finally, we suggest that functional and effective connectivity analyses may make important contributions to our understanding of changes in functional anatomy occurring as a result of practice on tasks.
A functional MRI study of automatic movements in patients with Parkinson’s disease
"... Patients with Parkinson’s disease have great difficulty performing learned movements automatically. The neural contribution to the problem has not been identified. In the current study, we used functional magnetic resonance imaging (fMRI) to investigate the underlying neural mechanisms of movement a ..."
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Cited by 53 (0 self)
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Patients with Parkinson’s disease have great difficulty performing learned movements automatically. The neural contribution to the problem has not been identified. In the current study, we used functional magnetic resonance imaging (fMRI) to investigate the underlying neural mechanisms of movement automaticity in Parkinson’s disease patients. Fifteen patients with Parkinson’s disease were recruited. Three patients were finally excluded because they could not achieve automaticity. The remaining 12 patients were aged from 52 to 67 years, with a mean age of 61.2 years. Controls included 14 age-matched normal subjects. The subjects were asked to practise four tasks, including two self-initiated, self-paced sequences of finger movements with different complexity until they could perform the tasks automatically. Two dual tasks were used to evaluate automaticity. For dual tasks, subjects performed a visual letter-counting task simultaneously with the sequential movements. Twelve normal subjects performed all sequences automatically. All patients performed sequences correctly; 12 patients could perform the simpler sequence automatically; and only 3 patients could perform the more complex sequence automatically. fMRI results showed that for both groups, sequential movements activated similar brain regions before and after automaticity was achieved. No additional activity was observed in the automatic condition. In normal subjects, many areas had reduced activity at the automatic stage, whereas in patients, only the bilateral superior parietal lobes and left insular cortex were less activated.
Differential involvement of parietal and precentral regions in movement preparation and motor intention
- J. Neurosci
, 2002
"... Flexible goal-oriented behavior relies on spatial coordinate transformations and motor control mechanisms, but also on the capability to take advantage of contextual information for steer-ing the sensorimotor machinery. Although accurate perfor-mance of a sensorimotor task requires parietal and fron ..."
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Cited by 39 (7 self)
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Flexible goal-oriented behavior relies on spatial coordinate transformations and motor control mechanisms, but also on the capability to take advantage of contextual information for steer-ing the sensorimotor machinery. Although accurate perfor-mance of a sensorimotor task requires parietal and frontal regions, their differential contribution and functional relationship with other associative regions remains obscure. We have used event-related functional magnetic resonance imaging to measure human cerebral activity associated with motor cognitive processes in the context of delayed perfor-mance of an associative visuomotor task. Movement instruc-tion (specified by visual cues) and motor performance (speci-fied by an auditory cue) were separated by a variable delay period. By manipulating the predictive value of the instruction cue, we distinguished delay-related activity influenced by re-
Neural correlates of motor recovery after stroke: a longitudinal fMRI study
- Brain
, 2003
"... Recovery of motor function after stroke may occur over weeks or months and is often attributed to cerebral reorganization. We have investigated the longitudinal relationship between recovery after stroke and task-related brain activation during a motor task as meas-ured using functional MRI (fMRI). ..."
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Cited by 39 (0 self)
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Recovery of motor function after stroke may occur over weeks or months and is often attributed to cerebral reorganization. We have investigated the longitudinal relationship between recovery after stroke and task-related brain activation during a motor task as meas-ured using functional MRI (fMRI). Eight ®rst-ever stroke patients presenting with hemiparesis resulting from cerebral infarction sparing the primary motor cortex, and four control subjects were recruited. Subjects were scanned on a number of occasions whilst performing an isometric dynamic visually paced hand grip task. Recovery in the patient group was assessed using a battery of outcome measures at each time point. Task-related brain activations decreased over sessions as a function of recovery in a number of primary and
Direct comparison of neural systems mediating conscious and unconscious skill learning
- Journal of Neurophysiology
, 2002
"... Direct comparison of neural systems mediating conscious and unconscious skill learning. J Neurophysiol 88: 1451–1460, 2002; 10.1152/jn.00461.2001. Procedural learning, such as perceptual-mo-tor sequence learning, has been suggested to be an obligatory conse-quence of practiced performance and to ref ..."
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Cited by 29 (1 self)
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Direct comparison of neural systems mediating conscious and unconscious skill learning. J Neurophysiol 88: 1451–1460, 2002; 10.1152/jn.00461.2001. Procedural learning, such as perceptual-mo-tor sequence learning, has been suggested to be an obligatory conse-quence of practiced performance and to reflect adaptive plasticity in the neural systems mediating performance. Prior neuroimaging stud-ies, however, have found that sequence learning accompanied with awareness (declarative learning) of the sequence activates entirely different brain regions than learning without awareness of the se-quence (procedural learning). Functional neuroimaging was used to assess whether declarative sequence learning prevents procedural learning in the brain. Awareness of the sequence was controlled by changing the color of the stimuli to match or differ from the color used for random sequences. This allowed direct comparison of brain acti-vation associated with procedural and declarative memory for an identical sequence. Activation occurred in a common neural network whether initial learning had occurred with or without awareness of the sequence, and whether subjects were aware or not aware of the sequence during performance. There was widespread additional acti-vation associated with awareness of the sequence. This supports the view that some types of unconscious procedural learning occurs in the brain whether or not it is accompanied by conscious declarative knowledge.
Sleep-related consolidation of a visuomotor skill: brain mechanisms as assessed by functional magnetic resonance imaging
- J. Neurosci
, 2003
"... Subjects were trained on a pursuit task in which the target trajectory was predictable only on the horizontal axis. Half of them were sleep deprived on the first post-training night (n � 13). Three days later, functional magnetic resonance imaging revealed task-related increases in brain responses t ..."
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Cited by 25 (3 self)
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Subjects were trained on a pursuit task in which the target trajectory was predictable only on the horizontal axis. Half of them were sleep deprived on the first post-training night (n � 13). Three days later, functional magnetic resonance imaging revealed task-related increases in brain responses to the learned trajectory, as compared with a new trajectory. In the sleeping group (n � 12) as compared with the sleep-deprived group, subjects ’ performance was improved, and their brain activity was greater in the superior temporal sulcus (STS). Increased functional connectivity was observed between the STS and the cerebellum and between the supplementary eye field and the frontal eye field. These differences indicate sleep-related plastic changes during motor skill learning in areas involved in smooth pursuit eye movements. Key words: functional neuroimaging; functional magnetic resonance imaging; statistical parametric mapping; functional connectivity; procedural memory; memory consolidation; sleep; sleep deprivation; smooth pursuit eye movements
Frontal Cortex and the Discovery of Abstract . . .
, 2010
"... Although we often encounter circumstances with which we have no prior experience, we rapidly learn how to behave in these novel situations. Such adaptive behavior relies on abstract behavioral rules that are generalizable, rather than concrete rules mapping specific cues to specific responses. Altho ..."
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Cited by 24 (4 self)
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Although we often encounter circumstances with which we have no prior experience, we rapidly learn how to behave in these novel situations. Such adaptive behavior relies on abstract behavioral rules that are generalizable, rather than concrete rules mapping specific cues to specific responses. Although the frontal cortex is known to support concrete rule learning, less well understood are the neural mechanisms supporting the acquisition of abstract rules. Here, we use a reinforcement learning paradigm to demonstrate that more anterior regions along the rostro-caudal axis of frontal cortex support rule learning at higher levels of abstraction. Moreover, these results indicate that when humans confront new rule learning problems, this rostro-caudal division of labor supports the search for relationships between context and action at multiple levels of abstraction simultaneously.
Neural evidence of statistical learning: Efficient detection of visual regularities without awareness
- Journal of Cognitive Neuroscience
, 2009
"... & Our environment contains regularities distributed in space and time that can be detected by way of statistical learning. This unsupervised learning occurs without intent or awareness, but little is known about how it relates to other types of learning, how it affects perceptual processing, and ..."
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Cited by 23 (1 self)
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& Our environment contains regularities distributed in space and time that can be detected by way of statistical learning. This unsupervised learning occurs without intent or awareness, but little is known about how it relates to other types of learning, how it affects perceptual processing, and how quickly it can occur. Here we use fMRI during statistical learning to explore these questions. Participants viewed statistically structured versus unstructured sequences of shapes while performing a task unrelated to the structure. Robust neural responses to statistical structure were observed, and these responses were notable in four ways: First, responses to structure were observed in the striatum and medial temporal lobe, suggesting that statistical learning may be related to other forms of associative learning and relational memory. Second, statistical regularities yielded greater activation in category-specific visual regions (object-selective lateral occipital cortex and word-selective ventral occipito-temporal cortex), demonstrating that these regions are sensitive to information distributed in time. Third, evidence of learning emerged early during familiarization, showing that statistical learning can operate very quickly and with little exposure. Finally, neural signatures of learning were dissociable from subsequent explicit familiarity, suggesting that learning can occur in the absence of awareness. Overall, our findings help elucidate the underlying nature of statistical learning. &
