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\chapter{Discussion}\label{DISCUSSION}

\centering{\textit{“Left open for further thought and research.” \\- William Worrall Mayo, M.D.}}

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\section{Summary}
We sought to better understand the neural circuitry contributing to attentional selection of useful stimuli and how that ultimately leads to the generation of an associated EEG signal. In this series of works, we primarily concerned ourselves with the role of sensory cortex, and sensory processing, in selective attentional processes. We employed a unique combination of neurophysiological recordings across the layers of visual cortical area V4 simultaneous with EEG. Macaque monkeys were trained to perform an attention-demanding task during recordings known to elicit the attention-associated component known as the N2pc. We found that the synaptic activity across the layers of V4 was sufficient to predict the N2pc. Moreover, biophysically plausible forward modeling of the empirical data demonstrated that the same synaptic currents were sufficient to recapitulate the canonical distribution of the N2pc. Further modeling demonstrated that other candidate brain areas for the generation of the N2pc were insufficient. Yet, cortical columns do not just serve to generate EEG, rather the EEG is a consequence of columnar activity. Columns also represent a canonical feature of neurobiology and seemingly play an important role as a fundamental unit of neural computation. In visual processing, the organization of columns across the cortical surface encodes information regarding the location and identity of objects. Accordingly, in our experiments these cortical structures showed robust selection signals for specific stimuli in time and space, and their functional architecture predicted relative contributions of individual cortical columns to the overlying N2pc-EEG signal. This suggests that nuances of cortical organization influence the relative contributions of individual cortical columns to the global EEG signal. While these findings represent substantive progress in our understanding of the contribution of cortical microcircuits to attentional selection, much remains to be considered.

\section{Attention and Sensory Columnar Processing}
From the outset, we sought to investigate whether attention altered sensory processing across cortical columns. Recent work has shown attention manifesting different magnitudes of effects across cortical layers in V4 (e.g., \citep{nandy_laminar_2017}), but whether this occured in visual search tasks had not been investigated. Chapter two represents the bulk of findings in this pursuit and while we ultimately found several layer-specific attentional modulations in V4, one of the most surprising discoveries made here was in how attention seemingly modulated sensory processing across cortical columns in a rhythmic fashion. We know from decades of prior work that sensory cortical columns are topographically organized to represent visual space and features (e.g., \citep{hubel_receptive_1962, hubel_receptive_1968, mountcastle_columnar_1997, kaas_evolution_2012}). Each cortical column shows selectivity for a specific aspect of a (or several) feature dimensions (e.g., the color [feature dimension] red [specific aspect]). And while other work has found that those selectivities can be tuned within the feature space with deployment of attention \citep{treue_feature-based_1999, martinez-trujillo_feature-based_2004, mcadams_effects_1999}, and have been theorized to be critical for execution of visual search \citep{navalpakkam_search_2007}, we discovered a rather interesting, unforeseen dynamic. Specifically, we found that in selecting a pop-out feature, sensory cortical columns selective for that feature vacillated in their preference through time. A red-preferring color column in V4, when presented with a red pop-out item, became rhythmically more and less selective for red while that item was in the column's response field. However, while this is a noteworthy observation, in many ways this findings asks more questions than it answers. For one, is this a response characteristic generated by the columnar structure and its organization of connections? That is, does the sequence of processing happening along the layers of cortical columns result in this rhythmic selectivity? More likely it is driven through horizontal connections between columns of differing preferences given the topographic organization of feature space. If this is the case, it begs the question why do we observe the strongest rhythmic selectivity outside of the superficial cortical layers, where horizontal connections are perhaps most robust \citep{gilbert_morphology_1979, gilbert_clustered_1983, rockland_intrinsic_1983, martin_relationship_1984, mcguire_targets_1991, anderson_form_1993, callaway_local_1998}? Also, if this rhythmic selectivity generated by the topographic organization for features across columns, is the pop-out being of one feature and flanked by differing features in different parts of retinotopic space necessary for this rhythm to occur? If this is the case, this could be thought of more as a unique characteristic of sensory processing under pop-out conditions rather than it being predicated on attentional selection. On the other hand, this sort of peculiarity to sensory processing could be a factor resulting in the phenomenal observation giving 'pop-out' selection its name. Regardless, it remains to be investigated whether this rhythmic selectivity for feature exists when the pop-out feature is not to-be selected or present under differing attention demands. At the very least, these results demonstrate changes in columnar processing of sensory information as a function of attention during visual search tasks. Given the quality of cortical columns and their capacity to biophysically generate electric fields \citep{nunez_electric_2006}, these findings establish that neural activity in sensory cortical columns show the dynamicity necessary to potentially be implicated in a variety of attention signals across measurement scales.

\section{On the Form of Attentional Selection}
In pursuit of identifying laminar contributions to attentional selection, we chose to use a pop-out visual search task. For one, it allowed for the most parsimonious experimental design to study the contributions of and consequences to sensory cortical feature maps with attention as discussed in the previous section. From a more practical perspective, it allowed for rapid training of and reliable performance in macaque monkeys. And crucially, this paradigm is often used to study attentional selection and has been shown to elicit the N2pc (e.g., \citep{eimer_priming_2010,purcell_origin_2013}), the primary target for our more translational investigations in Chapters four and five. However, this approach is limited with respect to how it may or may not generalize to other, and in particular, less efficient forms of attentional selection. Less efficient search, in the simplest form, refers to search conditions where the attentional target does not pop-out from its surroundings. These searches are therefore more difficult and take longer to resolve \citep{treisman_feature-integration_1980}. Thus, while color-based pop-out search afforded certain opportunities for scientific discovery as discussed in Chapter two, much remains in examining cortical microcircuitry for attentional selection in different visual search tasks.

Most notable of these visual search task distinctions is perhaps the difference between efficient or pop-out (e.g., red among green) and less efficient (e.g., T among L's) or multidimensional feature-based (e.g., red T among green T's and red and green L's) searches. For instance, previous work has repeatedly demonstrated that purely feedforward neural networks can reliably select salient stimuli \citep{itti_saliency-based_2000, itti_computational_2001}. This salience-based mechanism for selection does not function as robustly for less efficient forms of search which do not rely on an obvious difference between the target of attention and distracting stimuli. As such, one could conceive that the selection process might unravel differently as a function of the form of visual search being performed just from the phenomenal description alone. In fact, the early component of attentional selection shown in Chapter three conforms to the purely feedforward, salience-based mechanisms. In relating back to the previous section, this purely feedforward selection could be intertwined with the rhythmic feature selectivity, thereby supporting the notion this might not be observed under different attention conditions.

Even so, while the sensory processing of an isolated feature among a homogeneous set of distractors could feasibly be resolved in sensory processing alone, the simplest multidimensional feature searches complicate this process. That is because the obvious feature gradient that makes up the salience difference in pop-out search is no longer present. As such, search for the target could not be resolved (with 100\%, one-shot accuracy) without prior knowledge of the identity of the target. It remains to be investigated whether processing along visual cortical columns could be modulated in such a to promote feedforward selection of a such a template or if early feedforward attentional selection does not exist and we only observe a later feedback-driven selection signal. In Chapter three, some evidence exists for promotion of certain features with knowledge of the attentional target. Speculatively, this might suggest that cortical columnar activity in sensory cortex might be able to be primed before the presentation of a search array which could in turn yield feedforward selection even without a salient feature gradient. However, that remains to be demonstrated. While not investigating the role of columnar circuits, other work demonstrated that the behavioral selection of the relevant multidimensional stimulus in V4 is secondary to feedback activation \citep{ogawa_target_2004}, seemingly from area FEF \citep{ogawa_neuronal_2006}, and not present in the feedforward selection process. Perhaps the lack of laminar information skews this finding. Nonetheless, this highlights an important direction for attention research in cortical circuits: Can we disentangle feedforward vs. feedback contributions to attentional selection processes across different forms of search tasks.

The putative dissociation between early feedforward selection and that of a feedback modulation seems a next logical investigation for the application of laminar neurophysiology. Given models such as the canonical cortical microcircuit \citep{douglas_canonical_1989, douglas_functional_1991, bastos_canonical_2012}, can we can perhaps infer something about the processing stages for visual search as a factor of the exact form of visual search? One hypothesis would be that pop-out visual search is resolved feedforward as observed through selective signals present in the granular input layers in Chapter three, but less efficient forms of search (e.g., T among L's) require selection outside of sensory cortex which then influence sensory processing through activation of the feedback recipient extragranular layers in sensory cortex. This secondary feedback modulation is likely still present in the pop-out case and is perhaps observed best in Chapter four with the extragranular currents demonstrating attentional selective signaling later in the search. In sum, a finer grain understanding of the laminar microcircuitry would likely shed further light on the attentional selection process. However, as one ultimate aim of this research program is to understand the neuronal circuits giving rise to EEG, these considerations highlight a couple of important questions. Which processing stage is being indexed by the N2pc and what does that mean for our interpretation of that signal if there are multiple stages to between dissociated between? 

\section{Biophysical vs. Computational Generators of EEG Signals}
An important observation from this series of experiments is with respect to the interpretability of neural signals, especially at the level of their extracranial manifestations. With the increasing interest and prevalence in brain-machine interface and EEG for medical diagnosis (as will be discussed below), an understanding of the exact information content of individual EEG components is crucial. We confirmed from these experiments that visual cortical area V4 is sufficient to generate an attention-associated signal measurable extracranially, namely the N2pc (Chapters four and five). However, we know that many other brain areas are involved in the selection of attentional targets. A well-documented example being the frontal eye fields (FEF) \citep{schall_visuomotor_2015}. However, FEF is one such area that was deemed unlikely capable of contributing to the biophysical generation of the N2pc in our simulations (Chapter five). Yet, we know from previous work that the attentional target selection process in FEF precedes and predicts the N2pc \citep{cohen_origin_2009} and FEF has causal power in influencing V4 activity \citep{moore_selective_2003, armstrong_changes_2006, armstrong_rapid_2007}. How then should we approach interpreting the N2pc from a neural circuits perspective? 

The attentional modulation indexed by the N2pc may well be secondary. That is, given the timing of the modulation and known pathways for attention, another area like FEF could be instantiating the selection of a stimulus with modulations across a wider range of areas predicated on that initial stage. This would suggest that the N2pc-generating V4 activations are potentially one or more stages removed from the behavior-dictating selection circuitry. Why do we not see FEF contributing to the N2pc, then? As expanded on in Chapter five, the biophysical geometry of FEF (it being within the arcuate sulcus and parallel, rather than perpendicular, to the brain surface) makes it unlikely that FEF-generated electric fields would be measurable via surface EEG. Perhaps the 'best' extracortical N2pc exists within the intrasulcal space of the arcuate. Nonetheless, this model would indicate a notable distinction between the brain areas instantiating the cognitive or perceptual process indexed by an EEG component and those brain areas with the biophysical geometry conducive for the generation of an extracranially measurable signal. 

\section{A Mosaic for EEG}
While the deeper appreciation we gained of computational vs. biophysical contributors to EEG from these studies, in particular from Chapter five, an equally intriguing aspect was observed in Chapter four. The same EEG event-related potential from one trial to the next can be generated by functionally distinct configurations of mesoscopic neural circuits. In other words, the N2pc on trial 1 can look qualitatively indistinct from the N2pc on trial 2, but depending on what exactly was attended to, the cortical columns primarily generating the N2pc on 1 can be entirely different than on 2. In Chapter four this was observed as differential contributions of red- vs. green-preferring sensory cortical columns to the N2pc when a red vs. green item was being attended to. As such, we come to a hypothesis regarding the macrostructure of EEG production, at least for the N2pc. The N2pc can be generated from functionally different combinations of cortical columns depending on the attention conditions. In this sense, you have a mosaic of cortical columns with varying contribution to the overlying EEG signal. As such, this puts forward considerations for interpreting the EEG. For instance, the variability in the EEG signal need not necessary stem from variability in the process being studied. Such variability could stem from the exact configuration of cortical columns contributing on a trial-by-trial basis. Much is left to explore in how this discovery could, if at all, be practically applied for EEG research. At the very least it demonstrates that cortical functional organization impacts even global signals like the EEG.

\section{Towards Practical Applications: Clinical Utility and Brain-machine Interface}
Sophisticated brain-machine interface (BMI) is rapidly becoming a reality with modern advances in neural recording technologies. Major companies like Neuralink aim to implant humans with high-density intracranial recording devices within the next few years. However, to intelligently use BMI, it will be important to know where to place them to record and possibly affect areas in the brain that are of interest. An example of this might be a device to readout attentional state which may be useful in cases of cognitive disruption such as schizophrenia \citep{luck_speed_2006, verleger_neurophysiological_2013, fuggetta_electrophysiological_2015} or attention-deficit/hyperactivity disorder \citep{wang_attentional_2016, wang_attentional_2017, shushakova_electrophysiological_2018, rodriguez-martinez_neurophysiological_2021}. For example, the electroencephalogram can be used to readout and, coupled with transcranial direct current stimulation, affect performance in cognitive tasks \citep{reinhart_synchronizing_2015}. While we know we can measure and manipulate these cognitive abilities, our understanding is in its infancy. Perhaps the reason the EEG has not had the same impact on neurology and psychiatry that the EKG has had on cardiology is due to our relatively poor understanding of the neuronal generators for EEG. Each polarization of the EKG has a well-defined physiological generator, something we are lacking for most EEG polarizations. This is moreover complicated by the relatively distributed processing the brain uses to perform perceptual and cognitive operations. If we consider again the computational vs. biophysical generators of EEG, it may not be most effective to causally affect areas generating the EEG if we know a precursor area is performing the computation of interest (e.g., affecting V4 vs. FEF for an attention-demanding task). These 'confusion' elements should be carefully considered when thinking to use EEG for practical purposes as affecting the generator would not necessarily affect the computational process. Beginning to understand in more detail where sources of these cognitive signals in the brain, not just the biophysical generators, are localized through invasive neurophysiologyical recordings will allow us to more effectively use EEG in the clinic and BMI in practice.

\section{A Digression to Conscious Perception}
And while the practical applications of this research could be quite broad, there are also broader considerations to basic science. For instance, how might we consider approaching consciousness research. In considering feedforward and feedback contributions to attentional selection and the idea of a speculative sequence of selective processes presented in earlier sections, a question springs to mind. One could ask when does the recognition of an object as the 'target' arise? That is, when, where, and why is an object consciously perceived as the target. This is perhaps the putative selection stage that we are most interested in from a cognitive perspective, distinguishing it from purely sensory selective and motor selective processes. While V4 can perhaps identify the behavioral target in the feedforward sensory response as documented in Chapter three, are we already consciously aware at that earliest selection time that the target is present? On the other hand, it seems too late that we are consciously aware only once we are planning an eye movement to the item. It seems a timely question given the resurgence in consciousness research, when in visual search is the target consciously perceived and how does that relate to when certain brain areas are showing selective responses to that stimulus. Some recent work might suggest that conscious perception of a minimally-visible stimulus requires engagement of frontal cortex \citep{van_vugt_threshold_2018} with prior physiological studies suggesting such a role \citep{thompson_detection_1999, thompson_antecedents_2000, libedinsky_role_2011}, but placing that within the framework of a visual search task remains to be performed. Ultimately, conscious perception during visual search is perhaps a difficult question to ask in the macaque monkey where these recordings are available, but one that has the potential to shed light on one of modern neuroscience's greatest questions: what are the neural substrates of conscious perception.

\section{Conclusions}
In sum, this dissertation sought to provide a better understanding of circuits performing attentional selection and their consequent signals through two aims. A combination of experimentation and computational modeling demonstrated that cortical columns in primates can generate EEG signals through an exploration of attentional selection and the N2pc, leading to a more comprehensive understanding of the meaning of attention-associated signals across spatial scales. Also, in evaluating the functional architecture of cortex we reveal new discoveries underpinning the organization of mesoscopic circuitry comprising EEG production and attentional modulation and selection, more generally. From these results, we made new discoveries in sensory coding during attentional selection and gained insight into the origins and interpretative value of EEG signals, shedding new light on findings past, present, and future.