Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268

Positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) have been extensively used to explore the functional neuroanatomy of cognitive functions. Here we review 275 PET and fMRI studies of attention (sustained, selective, Stroop, orientation, divided), perception (object, face, space/motion, smell), imagery (object, space/ motion), language (written/spoken word recognition, spoken/ no spoken response), working memory (verbal/numeric, object, spatial, problem solving), semantic memory retrieval (categorization, generation), episodic memory encoding (verbal, object, spatial), episodic memory retrieval (verbal, nonverbal, success, effort, mode, context), priming (perceptual, conceptual), and procedural memory (conditioning, motor, and nonmotor skill learning). To identify consistent activation patterns associated with these cognitive operations, data from 412 contrasts were summarized at the level of cortical Brodmann's areas, insula, thalamus, medial-temporal lobe (including hippocampus), basal ganglia, and cerebellum. For perception and imagery, activation patterns included primary and secondary regions in the dorsal and ventral pathways. For attention and working memory, activations were usually found in prefrontal and parietal regions. For language and semantic memory retrieval, typical regions included left prefrontal and temporal regions. For episodic memory encoding, consistently activated regions included left prefrontal and medial-temporal regions. For episodic memory retrieval, activation patterns included prefrontal, medial-temporal, and posterior midline regions. For priming, deactivations in prefrontal (conceptual) or extrastriate (perceptual) regions were consistently seen. For procedural memory, activations were found in motor as well as in non-motor brain areas. Analysis of regional activations across cognitive domains suggested that several brain regions, including the cerebellum, are engaged by a variety of cognitive challenges. These observations are discussed in relation to functional specialization as well as functional integration.

Imaging Cognition II: An Empirical Review of 275 PET and fMRI Studies

In addition to their well-known role in skeletal movements, the basal ganglia control saccadic eye movements (saccades) by means of their connection to the superior colliculus (SC). The SC receives convergent inputs from cerebral cortical areas and the basal ganglia. To make a saccade to an object purposefully, appropriate signals must be selected out of the cortical inputs, in which the basal ganglia play a crucial role. This is done by the sustained inhibitory input from the substantia nigra pars reticulata (SNr) to the SC. This inhibition can be removed by another inhibition from the caudate nucleus (CD) to the SNr, which results in a disinhibition of the SC. The basal ganglia have another mechanism, involving the external segment of the globus pallidus and the subthalamic nucleus, with which the SNr-SC inhibition can further be enhanced. The sensorimotor signals carried by the basal ganglia neurons are strongly modulated depending on the behavioral context, which reflects working memory, expectation, and attention. Expectation of reward is a critical determinant in that the saccade that has been rewarded is facilitated subsequently. The interaction between cortical and dopaminergic inputs to CD neurons may underlie the behavioral adaptation toward purposeful saccades.

https://www.physiology.org/doi/pdf/10.1152/physrev.2000.80.3.953

Role of the Basal Ganglia in the Control of Purposive Saccadic Eye Movements

One fundamental function of primary motor cortex (MI) is to control voluntary movements. Recent evidence suggests that this role emerges from distributed networks rather than discrete representations and that in adult mammals these networks are capable of modification. Neuronal recordings and activation patterns revealed with neuroimaging methods have shown considerable plasticity of MI representations and cell properties following pathological or traumatic changes and in relation to everyday experience, including motor-skill learning and cognitive motor actions. The intrinsic horizontal neuronal connections in MI are a strong candidate substrate for map reorganization: They interconnect large regions of MI, they show activity-dependent plasticity, and they modify in association with skill learning. These findings suggest that MI cortex is not simply a static motor control structure. It also contains a dynamic substrate that participates in motor learning and possibly in cognitive events as well.

https://www.uta.edu/rfmems/Telemetry/Implant%20Sensor/47185_Plasticity%20and%20primary%20motor%20cortex.pdf

Plasticity and primary motor cortex.

Neuroplasticity subserving motor skill learning.

We examined the dynamic involvement of different brain regions in implicit and explicit motor sequence learning using PET. In a serial reaction time task, subjects pressed each of four buttons with a different finger of the right hand in response to a visually presented number. Test sessions consisted of 10 cycles of the same 10-item sequence. The effects of explicit and implicit learning were assessed separately using a different behavioural parameter for each type of learning: correct recall of the test sequence for explicit learning and improvement of reaction time before the successful recall of any component of the test sequence for implicit learning. Regional cerebral blood flow was measured repeatedly during the task, and a parametric analysis was performed to identify brain regions in which activity was significantly correlated with subjects' performances: i.e. with correct recall of the test sequence or with reaction time. Explicit learning, shown as a positive correlation with the correct recall of the sequence, was associated with increased activity in the posterior parietal cortex, precuneus and premotor cortex bilaterally, also in the supplementary motor area (SMA) predominantly in the left anterior part, left thalamus, and right dorsolateral prefrontal cortex. In contrast, the reaction time showed a different pattern of correlation during different learning phases. During the implicit learning phase, when the subjects were not aware of the sequence, improvement of the reaction time was associated with increased activity in the contralateral primary sensorimotor cortex (SM1). During the explicit learning phase, the reaction time was significantly correlated with activity in a part of the frontoparietal network. During the post-learning phase, when the subjects achieved all components of the sequence explicitly, the reaction time was correlated with the activity in the ipsilateral SM1 and posterior part of the SMA. These results show that different sets of cortical regions are dynamically involved in implicit and explicit motor sequence learning.

/pdf/dynamic-cortical-involvement-in-implicit-and-explicit-motor-pxupdnsjx8.pdf

Ping Zhuang

Papers

Dynamic cortical involvement in implicit and explicit motor sequence learning : A PET study