Anatomical and electrophysiological studies have provided us with detailed information regarding the extent and topography of the primary (V1) and secondary (V2) visual areas in primates. The consensus about the V1 and V2 maps, however, is in sharp contrast with controversies regarding the organization of the cortical areas lying immediately rostral to V2. In this review, we address the contentious issue of the extent of the third visual area (V3). Specifically, we will argue for the existence of both ventral (V3v) and dorsal (V3d) segments of V3, which are located, respectively, adjacent to the anterior border of ventral and dorsal V2. V3v and V3d would together constitute a single functional area with a complete representation of both upper and lower visual hemifields. Another contentious issue is the organization of the parietal-occipital (PO) area, which also borders the rostral edge of the medial portion of dorsal V2. Different from V1, V2, and V3, which exhibit a topography based on the defined lines of isoeccentricity and isopolar representation, area PO only has a systematic representation of polar angles, with an emphasis on the peripheral visual field (isoeccentricity lines are not well defined). Based on the connectivity patterns of area PO with distinct cytochrome oxidase modules in V2, we propose a subdivision of the dorsal stream of visual information processing into lateral and medial domains. In this model, area PO constitutes the first processing instance of the dorsal-medial stream, coding for the full-field flow of visual cues during navigation. Finally, we compare our findings with those in other species of Old and New World monkeys and argue that larger animals, such as macaque and capuchin monkeys, have similar organizations of the areas rostral to V2, which is different from that in smaller New World monkeys.

In this chapter, we compare the pattern of pulvinar immunohistochemical staining for the calcium-binding proteins calbindin and parvalbumin and for the neurofilament protein SMI-32 in macaque, capuchin, and squirrel monkeys. This group of New and Old World primates shares five similar pulvinar subdivisions: PI_P, PI_M, PI_C, PI_L, and PI_LS. In the Old World macaque monkey, the inferior-lateral pulvinar can be subdivided into the P1 and P2 fields based on its connectivity with visual area V1. On the other hand, only the P1 field and no P2 was found in the New World capuchin monkey. Notably, the similarities in chemoarchitecture contrast with the distinct connectivity patterns and the different visuotopic organizations found across the species.

Stroke is a neurological condition that often results in long-term motor deficits. Given the high prevalence of motor impairments worldwide, there is a critical need to explore innovative neurorehabilitation strategies that aim to enhance the quality of life of patients. One promising approach involves brain–computer interface (BCI) systems controlled by electroencephalographic (EEG) signals elicited when a subject performs motor imagery (MI), which is the mental simulation of movement without actual execution. Such systems have shown potential for facilitating motor recovery by promoting neuroplastic mechanisms. Controlling BCI systems based on MI-EEG signals involves the following sequential stages: recording the raw signal, preprocessing, feature extraction and selection, and classification. Each of these stages can be executed using several techniques and numerous parameter combinations. In this study, we searched for the combination of feature extraction technique, time window, frequency range, and classifier that could provide the best classification accuracy for the BCI Competition 2008 IV 2a benchmark dataset (BCI-C), characterized by EEG-MI data of different limbs (four classes, of which three were used in this work), and the NeuroSCP EEG-MI dataset, a custom experimental protocol developed in our laboratory, consisting of EEG recordings of different movements with the same limb (three classes—right dominant arm). The mean classification accuracy for BCI-C was 76%. When the subjects were evaluated individually, the best-case classification accuracy was 94% and the worst case was 54%. For the NeuroSCP dataset, the average classification result was 53%. The individual subject’s evaluation best-case was 71% and the worst case was 35%, which is close to the chance level (33%). These results indicate that techniques commonly applied to classify different limb MI based on EEG features cannot perform well when classifying different MI tasks with the same limb. Therefore, we propose other techniques, such as EEG functional connectivity, as a feature that could be tested in future works to classify different MI tasks of the same limb.

We studied the multiunit responses to moving and static stimuli from 585 cell clusters in area MT using multi-electrode arrays. Our aim was to explore if MT columns exhibit any larger-scale tangential organization or clustering based on their response properties. Neurons showing both motion and orientation selectivity were classified into four categories: 1- Type I (orientation selectivity orthogonal to the axis of motion); 2- Type II (orientation selectivity coaxial to the axis of motion); 3- Type DS (significant response to moving stimuli, but non-significant response to static stimuli); and 4- Type OS (significant orientation selectivity, but non-significant direction selectivity). Type I (34%), Type II (24%) and Type DS (32%) clusters were the most predominant and may be associated with different stages of motion processing in MT. On the other hand, the rarer Type OS (9%) may be integrating motion and form processing. Type I and unidirectional sites were the only classes to exhibit significant clustering. Type OS sites showed a trend for clustering, which did not reach statistical significance. We also found a trend for unidirectional sites to have bidirectional sites as neighbors. In conclusion, neuronal clustering associated with these four categories may be related to distinct MT functional circuits.