Dissociable neuronal substrates of visual feature attention and working memory.

Attention and working memory (WM) are distinct cognitive functions, yet given their close interactions, it is often assumed that they share the same neuronal mechanisms. We show that in macaques performing a WM-guided feature attention task, the activity of most neurons in areas middle temporal (MT), medial superior temporal (MST), lateral intraparietal (LIP), and posterior lateral prefrontal cortex (LPFC-p) displays attentional modulation or WM coding and not both. One area thought to play a role in both functions is LPFC-p. To test this, we optogenetically inactivated LPFC-p bilaterally during different task periods. Attention period inactivation reduced attentional modulation in LPFC-p, MST, and LIP neurons and impaired task performance. In contrast, WM period inactivation did not affect attentional modulation or performance and minimally affected WM coding. Our results suggest that feature attention and WM have dissociable neuronal substrates and that LPFC-p plays a critical role in feature attention, but not in WM.

Dissociable neuronal substrates of visual feature attention and working memory.

Attention and working memory (WM) are distinct cognitive functions, yet given their close interactions, it has been proposed that they share the same neuronal mechanisms. Here we show that in macaques performing a WM-guided feature attention task, the activity of most neurons in areas middle temporal (MT), medial superior temporal (MST), lateral intraparietal (LIP), and posterior lateral prefrontal cortex (LPFC-p) displays either WM coding or attentional modulation, but not both. One area thought to play a role in both functions is LPFC-p. To test this, we optogenetically inactivated LPFC-p bilaterally during the attention or WM periods of the task. Attention period inactivation reduced attentional modulation in LPFC-p, MST, and LIP neurons, and impaired task performance. WM period inactivation did not affect attentional modulation nor performance, and minimally reduced WM coding. Our results suggest that feature attention and WM have dissociable neuronal substrates, and that LPFC-p plays a critical role in attention but not WM.

Widespread and Opponent fMRI Signals Represent Sound Location in Macaque Auditory Cortex.

In primates, posterior auditory cortical areas are thought to be part of a dorsal auditory pathway that processes spatial information. But how posterior (and other) auditory areas represent acoustic space remains a matter of debate. Here we provide new evidence based on functional magnetic resonance imaging (fMRI) of the macaque indicating that space is predominantly represented by a distributed hemifield code rather than by a local spatial topography. Hemifield tuning in cortical and subcortical regions emerges from an opponent hemispheric pattern of activation and deactivation that depends on the availability of interaural delay cues. Importantly, these opponent signals allow responses in posterior regions to segregate space similarly to a hemifield code representation. Taken together, our results reconcile seemingly contradictory views by showing that the representation of space follows closely a hemifield code and suggest that enhanced posterior-dorsal spatial specificity in primates might emerge from this form of coding.

A Potential Role of Auditory Induced Modulations in Primary Visual Cortex.

A biologically relevant event is normally the source of multiple, typically correlated, sensory inputs. To optimize perception of the outer world, our brain combines the independent sensory measurements into a coherent estimate. However, if sensory information is not readily available for every pertinent sense, the brain tries to acquire additional information via covert/overt orienting behaviors or uses internal knowledge to modulate sensory sensitivity based on prior expectations. Cross-modal functional modulation of low-level auditory areas due to visual input has been often described; however, less is known about auditory modulations of primary visual cortex. Here, based on some recent evidence, we propose that an unexpected auditory signal could trigger a reflexive overt orienting response towards its source and concomitantly increase the primary visual cortex sensitivity at the locations where the object is expected to enter the visual field. To this end, we propose that three major functionally specific pathways are employed in parallel. A stream orchestrated by the superior colliculus is responsible for the overt orienting behavior, while direct and indirect (via higher-level areas) projections from A1 to V1 respectively enhance spatiotemporal sensitivity and facilitate object detectability.

Functional MRI of the vocalization-processing network in the macaque brain.

Using functional magnetic resonance imaging in awake behaving monkeys we investigated how species-specific vocalizations are represented in auditory and auditory-related regions of the macaque brain. We found clusters of active voxels along the ascending auditory pathway that responded to various types of complex sounds: inferior colliculus (IC), medial geniculate nucleus (MGN), auditory core, belt, and parabelt cortex, and other parts of the superior temporal gyrus (STG) and sulcus (STS). Regions sensitive to monkey calls were most prevalent in the anterior STG, but some clusters were also found in frontal and parietal cortex on the basis of comparisons between responses to calls and environmental sounds. Surprisingly, we found that spectrotemporal control sounds derived from the monkey calls ("scrambled calls") also activated the parietal and frontal regions. Taken together, our results demonstrate that species-specific vocalizations in rhesus monkeys activate preferentially the auditory ventral stream, and in particular areas of the antero-lateral belt and parabelt.

Automatic isotropic fractionation for large-scale quantitative cell analysis of nervous tissue.

Isotropic fractionation is a quantitative technique that allows reliable estimates of absolute numbers of neuronal and non-neuronal brain cells. However, being fast for single small brains, it requires a long time for processing large brains or many small ones, if done manually. To solve this problem, we developed a machine to automate the method, and tested its efficiency, consistency, and reliability as compared with manual processing. The machine consists of a set of electronically controlled rotation and translation motors coupled to tissue grinders, which automatically transform fixed tissue into homogeneous nuclei suspensions. Speed and torque of the motors can be independently regulated by electronic circuits, according to the volume of tissue being processed and its mechanical resistance to fractionation. To test the machine, twelve paraformaldehyde-fixed rat brains and eight human cerebella were separated into two groups, respectively: one processed automatically and the other, manually. Both pairs of groups (rat and human tissue) followed the same, published protocol of the method. We compared the groups according to nuclei morphology, degree of clustering and number of cells. The machine proved superior for yielding faster results due to simultaneous processing in multiple grinders. Quantitative analysis of machine-processed tissue resulted in similar average numbers of total brain cells, neurons, and non-neuronal cells, statistically similar to the manually processed tissue and equivalent to previously published data. We concluded that the machine is more efficient because it utilizes many homogenizers simultaneously, equally consistent in producing high quality material for counting, and quantitatively reliable as compared to manual processing.

How many neurons do you have? Some dogmas of quantitative neuroscience under revision.

Owing to methodological shortcomings and a certain conservatism that consolidates wrong assumptions in the literature, some dogmas have become established and reproduced in papers and textbooks, derived from quantitative features of the brain. The first dogma states that the cerebral cortex is the pinnacle of brain evolution - based on the observations that its volume is greater in more 'intelligent' species, and that cortical surface area grows more than any other brain region, to reach the largest proportion in higher primates and humans. The second dogma claims that the human brain contains 100 billion neurons, plus 10-fold more glial cells. These round numbers have become widely adopted, although data provided by different authors have led to a broad range of 75-125 billion neurons in the whole brain. The third dogma derives from the second, and states that our brain is structurally special, an outlier as compared with other primates. Being so large and convoluted, it is a special construct of nature, unrelated to evolutionary scaling. Finally, the fourth dogma appeared as a tentative explanation for the considerable growth of the brain throughout development and evolution - being modular in structure, the brain (and particularly the cerebral cortex) grows by tangential addition of modules that are uniform in neuronal composition. In this review, we sought to examine and challenge these four dogmas, and propose other interpretations or simply their replacement with alternative views.

Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain.

The human brain is often considered to be the most cognitively capable among mammalian brains and to be much larger than expected for a mammal of our body size. Although the number of neurons is generally assumed to be a determinant of computational power, and despite the widespread quotes that the human brain contains 100 billion neurons and ten times more glial cells, the absolute number of neurons and glial cells in the human brain remains unknown. Here we determine these numbers by using the isotropic fractionator and compare them with the expected values for a human-sized primate. We find that the adult male human brain contains on average 86.1 +/- 8.1 billion NeuN-positive cells ("neurons") and 84.6 +/- 9.8 billion NeuN-negative ("nonneuronal") cells. With only 19% of all neurons located in the cerebral cortex, greater cortical size (representing 82% of total brain mass) in humans compared with other primates does not reflect an increased relative number of cortical neurons. The ratios between glial cells and neurons in the human brain structures are similar to those found in other primates, and their numbers of cells match those expected for a primate of human proportions. These findings challenge the common view that humans stand out from other primates in their brain composition and indicate that, with regard to numbers of neuronal and nonneuronal cells, the human brain is an isometrically scaled-up primate brain.