Characterizing common and dissociable involvement of medial temporal lobe regions during episodic source memory retrieval and analogical reasoning

Westphal, A.J., Reggente, N., Ito, K., Fortuna, W.H., Nawabi, Y., Milstein, M., & Rissman, J.

SfN_2013_Poster

 SfN 2013 Abstract

Episodic memory and analogical reasoning tasks tend to engage many common frontoparietal structures, perhaps owing to their common demands for declarative memory retrieval and relational integration. Regions of the medial temporal lobe (MTL), well known to play a critical role in the encoding and retrieval of episodic memories, have also been shown to contribute to relational reasoning. We aimed to expand upon these findings by performing a direct comparison of memory- and reasoning-related MTL activity profiles and assessing how these regions communicate with distinct cortical networks to support different task demands. We examined fMRI activity and functional connectivity (FC) of the hippocampus (HIP), parahippocampal cortex (PHC), and perirhinal cortex (PRC) in a novel experimental paradigm featuring closely matched memory and reasoning tasks, both requiring judgments on 4-word stimulus arrays. One day prior to fMRI scanning, subjects (N = 20) encoded 80 words under two different mental imagery conditions. During the scanned memory task, subjects were to identify the word they previously studied and specify the encoding context, if possible. During the analogical reasoning task, subjects were to assess if the top and bottom word pairs shared the same semantic relationship or else indicate the number of non-analogous semantic relationships. Univariate parameter estimates extracted from HIP, PHC, and PRC all showed greater activity for source retrieval versus item familiarity. Activity in the PRC was significantly greater for correct versus incorrect source judgments; this effect also trended in HIP and PHC. During the reasoning task, HIP and PHC showed significantly greater activation on trials with valid analogies than on trials with no semantic relationships, whereas PRC activated strongly during all reasoning task conditions where semantic relationships were present. Task-dependent FC contrasting reasoning and memory was analyzed using psychophysiological interactions analysis. Left HIP demonstrated preferential coupling with both default mode and cognitive control network (CCN) structures for memory and bilateral MTL and lateral temporal regions for reasoning. Left PHC showed preferential coupling with CCN structures for memory and the supramarginal gyrus for reasoning. Left PRC demonstrated stronger coupling with precuneus for memory and occipital structures for reasoning. Taken together, these results confirm prior findings of MTL involvement in episodic source retrieval, while also documenting putative MTL contributions to analogical reasoning and distinct profiles of cortical network coupling across task sets.

Decoding cognitive task-sets from rostral prefrontal cortex functional connectivity patterns

Westphal, A.J., Reggente, N., Ito, K., Fortuna, W., & Rissman, J.

HBM_2013_Poster

 HBM 2013 Abstract

Resting state fMRI connectivity analyses have identified a number of distinct functional brain networks, including the fronto-parietal task control network (FPTCN), the dorsal attention network (DAN), and the default mode network (DMN) (e.g., Vincent et al., 2008, Power et al., 2011). While these networks are typically defined based on intrinsically correlated BOLD fluctuations during periods of undirected thought, engagement of these networks is also observed during goal-oriented cognition. For instance, the FPTCN has been shown to co-activate with the DMN to facilitate internally-focused mentation and with the DAN to promote externally-focused attention (Spreng et al., 2010). In the present investigation, we sought to evaluate the degree to which task-set representations, particularly those requiring relational integration such as analogical reasoning and episodic memory retrieval, could be decoded from functional connectivity patterns within and between these networks. We were most interested in examining the representational content of connections originating in the rostral prefrontal cortex (RPFC), since RPFC may play a key role in relational integration, in addition to supporting the maintenance of superordinate goal-states (e.g., Badre & D’Esposito, 2009).

20 subjects healthy adult subjects underwent fMRI scanning (3T Siemens Trim Trio scanner, TR = 2 s, voxel size = 3 x 3 x 3.7 mm), performing alternating blocks of analogical reasoning, episodic source memory retrieval, and visuospatial attention tasks. These tasks were closely matched for reaction times, response demands, and bottom-up visual stimulus processing (all trials involved 4-word arrays, with the tasks only differing in what subjects had to decide about these words). Our data analysis procedure involved calculating the pairwise correlations between the concatenated BOLD time-courses for each task for each of 264 functional areas (10 mm spheres, identified by Power et al., 2011). We then supplied a regularized logistic regression classification algorithm with the full connectivity matrix from a given network (within-network connectivity) or from the set of connections that linked a pair of networks (between-network connectivity). All classification analyses used a leave-one-subject-out procedure, such that the classifier was trained on the connectivity data from 19 of 20 subjects and then applied to predict the task-sets associated with the remaining connectivity matrices from the held-out subject.

Using correlations between all 264 nodes, our classifier was 100% accurate at differentiating between the three cognitive task-sets. When trained solely on the correlations between the 16 RPFC nodes, the classifier was unable to differentiate between the reasoning and memory task-sets, indicating that within-RPFC connectivity patterns are not necessarily diagnostic of task-set. However, when trained on the correlations between RPFC nodes and nodes outside of RPFC, classification accuracy was quite robust (Fig. 1), reaching accuracy levels of up to 85% depending on which network was paired with RPFC. This result provides novel evidence that RPFC flexibly adjusts its interactivity with all three of the core networks to facilitate both internally and externally-oriented cognition.

By measuring the pattern of correlations between distinct nodes in a subject’s brain, one can reliably decode information about that subject’s cognitive task-set, even when a classifier has not been trained on data from that subject. The connection strengths between RPFC nodes and nodes in other core brain networks can be used to predict whether a subject is engaged in analogical reasoning or episodic source memory retrieval, despite the common demands of these tasks for relational integration. Given its position at the apex of a rostral-caudal hierarchy (Badre & D’Esposito, 2009), these data suggest that RPFC may differentially collaborate with posterior networks depending on task goals.

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References:

Vincent, J. L., Kahn, I., Snyder, A. Z., Raichle, M. E., & Buckner, R. L. (2008). Evidence for a frontoparietal control system revealed by intrinsic functional connectivity. Journal of neurophysiology100(6), 3328-3342.

Spreng, R. N., Stevens, W. D., Chamberlain, J. P., Gilmore, A. W., & Schacter, D. L. (2010). Default network activity, coupled with the frontoparietal control network, supports goal-directed cognition. Neuroimage53(1), 303-317.

Badre, D., & D’Esposito, M. (2009). Is the rostro-caudal axis of the frontal lobe hierarchical?. Nature Reviews Neuroscience10(9), 659-669.

Power, J. D., Cohen, A. L., Nelson, S. M., Wig, G. S., Barnes, K. A., Church, J. A., … & Petersen, S. E. (2011). Functional network organization of the human brain. Neuron72(4), 665-678.

Cho, S., Moody, T.D., Fernandino, L., Mumford, J.A., Poldrack, R.A., Cannon, T.D., Knowlton, B.J., & Holyoak, K.J. (2010). Common and Dissociable Prefrontal Loci Associated with Component Mechanisms of Analogical Reasoning. Cerebral Cortex, 20(3),524-533.

Shared and distinct contributions of rostral prefrontal cortex to analogical reasoning and episodic memory retrieval: Insights from fMRI functional connectivity and multivariate pattern analyses

Westphal, A.J., Reggente, N., Nawabi, Y., & Rissman, J.

CNS_2013_Poster

CNS 2013 Abstract

 

The rostral prefrontal cortex (RPFC), positioned at the apex of the prefrontal processing hierarchy, has been implicated in a diverse array of high-level cognitive processes including analogical reasoning and episodic memory retrieval—tasks that may share demands for relational integration. However, because reasoning and memory tasks have not been compared in the same studies, the degree of neuroanatomical overlap is unclear. To address this gap, we developed an fMRI paradigm that required subjects to periodically shift between Reasoning, Memory, and Perception tasks, closely matched for response demands, reaction times, and bottom-up stimulus processing. On all trials, participants were presented with an array of four words, with the cognitive operations to be performed on this array specified by a task set cue provided at the beginning of each block. Although RPFC regions showed highly overlapping recruitment during successfully solved analogy and source memory retrieval trials, without significant univariate differences, multi-voxel pattern analysis identified areas of RPFC wherein local activity patterns could facilitate robust decoding of these trial types. One such prominent cluster in left lateral RPFC was then seeded in a psychophysiological interaction analysis. Strikingly, this region showed divergent profiles of functional connectivity across task blocks, coupling more strongly with frontoparietal control network structures during Reasoning and with default mode network structures during Memory. These findings suggest that common areas of RPFC may differentially contribute to analogical reasoning and episodic retrieval via their coordinated interactions with distinct brain networks that respectively facilitate the integration of complex semantic or episodic relationships.

 

 

Predicting individual differences in cognitive gains from videogame training using machine learning analyses of fMRI functional connectivity patterns

CNS 2014 Abstract: Nikolaidis, A., Reggente, N. et al

Summary:

Using the the task-dependent functional connectivity across nodes within established cortical network from both before and after videogame training, we were able to asses the “plasticity” of these networks as a function of training. The plasticity of these networks, combined with graph theoretical metrics were used as features in a leave-one-out Ridge Regression that was able to account for upwards of 80% of the variance in individual difference scores in learning. Each network contributed varying levels of accuracy to classification depending on its involvement in the subject’s instructed priorities within the task. For example, plasticity in the Cingulo-Opercular network preferentially predicted(upwards of 55% of variance accounted for) learning in a training strategy that relied more heavily on executive control of attention and goal directed behavior.

Predicting individual differences in cognitive gains from videogame training using machine learning analyses of fMRI functional connectivity patterns

Aki Nikolaidis1, Nicco Reggente2, Drew Goatz3, Kathryn Hurley3, Andrew Westphal2, Arthur F. Kramer1; 1University of Illinois, Urbana Champaign, Beckman Institute, 2University of California, Los Angeles, Psychology Department, 3University of Illinois, Bioengineering Department

One of the important questions in cognitive training, and learning and memory more broadly, is how pre-existing individual differences in brain connectivity influence the effect of training. In this study, we use the fMRI functional connectivity of multiple networks, including the frontal-parietal and motor networks, to predict individual differences in learning over the course of 30 hours of cognitive training with the Space Fortress videogame. We used various metrics of functional connectivity and graph theory-derived parameters from 45 young adult participants as features to train adaptive multivariate regression models. Using a leave-one-participant-out cross-validation procedure, we find that we can predict a significant percentage of the variance in learning performance (defined as pre-post differences in Space Fortress score). By analyzing the performance of different regression models, we find that distinct brain networks contain different types of information regarding individual differences in learning rate. Furthermore, using both support vector regression and ridge regression we demonstrate how different feature and model parameters have important effects on model performance, and we consider how these parameters may have limited previous research using such techniques. We discuss implications of our results for cognitive training, as well as the continued use of machine learning and graph theoretical analyses in cognitive neuroscience.

Disentangling Disorders of Consciousnes: Insights from DTI and MVPA

CNS 2014 Poster: Zheng, Z., Reggente, N. et al. CNS 2014

Summary:

For this poster we used Diffusion Tensor Imaging (DTI) techniques to compute thalamo-cortical probabilistic tractography maps. That is, for every voxel in the Thalamus, we determined every other voxel in the brain’s probabilist structural connectivity with that voxel. We did this for a total of 23 patients diagnosed with disorders of consciousness that fell into one of three possible clinical stratification (Vegetative State(VS), Minimally Conscious State+(MCS+), Minimally Conscious State-(MCS-)). We utilized a support vector machine to observe the patterns local anatomical connectivity of regions of cortex with the thalamus unique to subjects within each of the three groupings. We then used this classifier to “diagnose” unseen subjects in an extensive cross-validation. At times, particularly when using the patterns of activity local to the left prefrontal cortex, we were able to achieve 100% accuracy when distinguishing between VS and MCS+. We used a searchlight-mapping technique to determine which regions in the brain were most informative to classification. We are hoping that this technology could be found useful as a diagnostic aid that relies on the underlying pathology of patients with disorders of consciousness as opposed to the purely behavioral diagnoses currently utilized.

CNS 2014 Abstract:

Disentangling Disorders of Consciousness: Insights from DTI and MVPA

Zhong A. Zheng1, Nicco Reggente1, Evan S. Lutkenhoff1, Adrian Owen2, Martin M. Monti1; 1University of California, Los Angeles, 2University of Western Ontario

The stratification of individuals surviving severe brain injury in Minimally Conscious State (MCS) and Vegetative State (VS) patients is, currently, entirely based on behavioral criteria. This approach is problematic for at least two reasons: (i) behavioral assessments are known to be susceptible to sizeable misdiagnosis (~40%); (ii) this stratification of patients is entirely blind to the underlying pathology. To address both issues, we employed diffusion probabilistic tractography to assess projections from thalamic nuclei in 8 MCS plus (+) patients, who exhibit high-level behavioral responses, 8 MCS minus (-) patients, who only show low-level responses, and 8 VS patients. Evaluation of thalamo-cortical connectivity revealed more connections from the lateral-group nuclei to prefrontal, motor, and sensory regions in MCS+, as compared to VS. Additionally, tractography maps from thalamic nuclei were used as patterns in a logistic regression classification scheme. Using the ventral lateral nucleus’ whole-brain tractography maps as patterns, a leave-two-patients-out cross-validation correctly classified 6/8 VS patients and 7/8 MCS+ patients. This classification relied mostly on increased thalamo-frontal connections in MCS+ patients, as compared to VS. These results suggest that DTI combined with machine learning classification may facilitate the diagnostic distinction between VS and subcategories of MCS by uncovering the neural markers and pathological changes underlying disorders of consciousness.

This work was a collaboration with Zhong (Amy) Sheng Zheng.

 

Memory recall for high value items correlates with individual differences in white matter pathways associated with reward processing and fronto-temporal communication

Publication: Coming Soon

SfN 2013 Poster: Reggente_SfN_2013

Summary:

We used DTI to explore anatomical accounts for individual differences in the behavioral effects of value-directed remembering. We examined connectivity within the mesolimbic system by using probabilistic tractography algorithms to compute anatomical connectivity between each subject’s nucleus accumbens(NAcc) and ventral tegmental area(VTA). We also extracted mean fractional anisotropy (FA) values from each subject’s uncinate fasciculus(UF). Subject’s engaged in a value-directed remembering task where each word was paired with either high or low values. Subject’s mean FA along their UF was strongly correlated with the mean number of high value words they reported during recall (r=.746, p=.0001), but not with number of low value words recalled(r=.219, p=.81). The difference between these two correlations was statistically significant (Z=2.46, p=.006). The number of streamlines (i.e, the Anatomical Connectivity Index) from left NAcc to left VTA correlated with Selectivity Index(i.e how preferentially selective they were in selectively recalling high value words more so than low value words) (r=.482, p=.018).

SfN 2013 Abstract:

Memory recall for high value items correlates with individual differences in white matter pathways associated with reward processing and fronto-temporal communication

Reggente, N., Cohen, M.S., Zheng, Z., De Shetler N.G., Castel, A.D., Knowlton, B.J., Rissman, J.

When given a long list of items to remember, people will often prioritize the memorization of the most important items. In an experimental setting, importance can be operationalized by reward values assigned to each item. Prior neuroimaging studies (e.g., Adcock et al., 2006) have found that high value cues engage the mesolimbic dopamingeric reward circuitry of the brain, including the nucleus accumbens (NAcc) and the ventral tegmental area (VTA), which it turn leads to an up-regulation of medial temporal lobe encoding processes and better memory for the high value items. Value cues may also trigger the use of elaborative semantic encoding strategies, which depend on interactions between frontal and temporal lobe structures. In the present study, we used diffusion tensor imaging (DTI) to examine whether individual differences in anatomical connectivity within these circuits are predictive of value-induced modulation of memory. DTI data were collected from 19 healthy adult subjects, who also underwent fMRI scanning as they performed a value-directed memory task. In this task, subjects encoded lists of words with arbitrarily assigned point values and then completed a free recall test after each list. Our fMRI results revealed that subjects whose recall performance exhibited the greatest sensitivity to item value preferentially recruited left ventrolateral prefrontal cortex (VLPFC) duringthe encoding of high value items relative to low value items. While this effect may partially be driven by individual differences in the cognitive strategies that foster deep semantic encoding, we predicted that the robustness of the white matter pathways connecting the VLPFC with the temporal lobe might also be a determinant of recall performance for high value items. To explore this possibility, we measured the mean fractional anisotropy (FA) of each subject’s left uncinate fasciculus, a pathway thought to play a critical role in semantic processing (Harvey et al., 2013). This measure showed a significant positive correlation with the mean number of high value items that a subject recalled, but did not correlate with the mean number of low value items recalled. Given prior findings on reward-induced modulation of memory, we also examined the white matter connections between reward-related regions such as the NAcc and VTA using probabilistic tractography. As predicted, the number of fibers projecting from left NAcc to VTA correlated with individual differences in high value but not low value memory. Together, these findings provide novel insights into the neuroanatomical pathways that support verbal memory encoding and its value-incentivized modulation.

MVPA (Multi Variate/Voxel Pattern Analysis): The Basics

Often times I feel restrained by the unfortunate, inherent shortcomings of medical and functional imaging data. The entire process is subject to a vast array of variability, assumptions, and “warpings” in many senses of the word. These, however minute the calculations and interpolations may be, carry an overarching sentiment of being haphazard. However, differences between sets of such arrays can account and point to “effects”. Enter functional imaging.

Neural activation can be relayed and inferred due to one image of the brain varying from another image, as due to some cause. That’s the bare bones of what is called “univariate analysis”. Data collected during one time, say when a subject is resting, can be shown to differ, significantly, from when they are engaged in a task. Fitting the brain and all the data into a matrix allows for comparisons and differences to be spatially represented. Very similar to to “Photo Hunt” based games, where players compare two side by side, macroscopically identical pictures and circle the regions which contain some sort of shift (i.e in one picture a person may have a ring on their finger, but in the other they do not[For those deprived of the bar bound touch screens which frequently feature the past-time, here is an online version]), comparing differences in brain data allow for scientists to see which regions in the brain can be related to which causes as determined by the design of their study. Until recently, this type of straightforward comparison has been the go-to method for fMRI imaging. The regions showing the most change between two states are considered to be most correlated, and thus, to please the physicalists, most responsible for such perception, memories, or any other such study defined cognitive functioning.

Thanks to exponential progress in the field of machine learning, we can now see that there is more than initially meets the eye as to which regions of the brain can be deemed “responsible” for correlating brain activity with performance, task, memory, etc. No longer is it just the difference, space by space between images, but it is the difference in the patterns of activation between images. The previous “Photo Hunt” analogy would have to be transposed as such: instead of being able to just detect that one picture has a person with a ring and the other not, but also being able to extract which material that ring is made of to the point where even if both photos had the ring, a pattern assessment of the photo would reveal the differences between the two based on the makeup of the ring. This hypothetical situation is meant solely to be conceptual. With just a photo, it is not feasible to infer the makeup of an object when all other variables are constant. However, the idea of seemingly identical presentations having different content is key in understanding exactly what it is that pattern assessment differentiation between images is doing.

Take a simple matrix below:

It’s easy to see that there is nothing in this matrix. Let’s call this Matrix of Rest.

In this matrix, it’s immediately apparent that there are six blue squares now colored in. Let’s say that these are akin to each being a ring, to stay steady with the Photo Hunt example.

Therefore, it’s easy to say that The first image is different from the second, as per the fact that within this region, there are 6 rings where the first had none. The first is significantly different from the second.

Easy enough. To parlay this to brain images, images each cell of the matriz as a small region of the brain (voxels, which are cubic regions of brain space), and in the first image, these voxels are operating at baseline, but in the second, there are six voxels which operate at a level significantly higher than baseline. Therefore, we could say that this particular 4×6 regions of space is more active in the second image. If the second image is taken at a time when there is a particular, measurable task going on that wasn’t going on during the first image, then you have yourself a univariate, spatial finding of supposed causal relationship between neuronal processes and task related behavior. Granted there is a lot being inferred here, mainly due to the ad-hoc assumptions of BOLD signals, but that’s a debate for another time.

Now, presenting the next image may make it seem like the explanation that follows as somewhat intuitive, but that’s only due to its inherent ingenuity. This is a spectacular and fascinating concept that has only struck the field of neuroscience applicably for the last ten or so years.

This matrix differs from the first one exactly in the same way that the second one does. It has six “rings” in it, where the first one did not. Just as we said with the second image, this one could be correlated with any task related change in brain activity. Essentially, this 4×6 region of space in the brain could be deemed “responsible” for the task at hand.

However, the pattern of the arrangement of which positions in the matrix these six rings differs between the second and third matrices. This is where there is even more information. These arrangement patterns can, essentially, further differentiate between two sets of images. For example, now we may know that there are six rings initially, but upon assessment of the arrangement of the patterns, we can then know whether the rings in the images are made of gold or made of silver. To translate into the actual cog neuro applications, it would be the same as identifying this region of brain space as being responsible for perceiving objects. However, when assessing more than just the summation of the activity in this particular region and differentiating the patterns, one could theoretically differentiate between different types of objects. Fors example, and umbrellas might trigger six voxels of activation in this region and a bookshelf may recruit six voxels in this region as well. Therefore, it would be safe to say that this particular region recruits six voxels when the subject is viewing objects. However, additionally, since the six voxels may arrange themselves differently, it would be possible to identify, through the patterns only, which type of object the subject is viewing, as opposed to just the fact that they are viewing an object instead of nothing.

A favorite scene of mine comes to mind when trying to conceptualize this idea. It’s from I ❤ Huckabees, when the “detective” is trying to explain existentialism. Here’s a clip of it below.

Think of the flat sheet as a brain in resting state as a flat image; a single slice of the brain. Then, each object which is placed under the sheet can be thought of as a task that the subject is doing, which causes a spike in activation in that region, or a raise in the sheet as seen physically by the demonstration.

Putting aside the utter beauty of the realization that “everything is connected”, brain imaging up until this point was like having this sole sheet, which could identify temporal and spatial rises in sections of the sheet and correlate and compare them with different brain functioning. However, by seeing the patterns which are giving rise to seemingly identical spikes, there is such a deeper realization to be had as to the actual causes of each spike, allowing for more distinct classifications of decoded brain activity and their relevant, functional implications. Now, it’s like having a very large number of sheets which can each lie over separate parts of the formerly whole  object and allow for the topography of the object to emerge. Think of one giant sheet over the Statue of Liberty. Covered in such a manner, it would appear similar to just a cylinder rising in the sky, comprable to many other structures of its height. However, with one sheet for each spike of the crown, one sheet for the torch, one for the arm, etc. until each area of the statue is covered to the same extent that the singular sheet satisfied, it would be much easier to distinguish the Statue of Liberty from another structure which was “sheeted” in the same manner.

Therefore, it is the patterns of activation which carry the greatest amount of information under the current limitations of fMRI imaging. We find these patterns through MVPA.

The Details…. (Coming Soon).

The Explanatory Gap. Where All The Subtleties Are Derived.

I was reading an article in Scientific American this past weekend, tucked away in the “nerds only” section of the already erudite publication. It was about something I’ve thought about a million times, and, after years of proper philosophical thinking and testing on the subject, I’m a little tired of thinking about it.

However, that doesn’t mean I can’t still be amazed by the variations of prose in which the question is being asked and assessed.

Basically the question of the explanatory gap, perhaps first posited in this blatant of a manner by Thomas Nagel’s “What Is It Like To Be A Bat?”, asks what is the causal correlation between mind and body? What is the connection between when I pine over the nostalgic effects of a past memory and the patterns of activation that are relaying their way through my neural circuits? How do exchanges of sodium and potassium “give rise” to sensations and let me feel utterly unique when I embrace a breathtaking sight? You get the picture. It’s the question everyone asks. It’s the question that has been asked countless times. However, now all the sophists are scientists and they give names and apply complex terminology.

However, at the end of the day….the “explanatory gap” is an infinitely simple concept, but inherently enigmatic and, truthfully, minus some work in the field of Quantum Mechanics, its answers are still at large.

That’s why, like how I tackle most of the problems I can’t resolve, I have found beauty in not the pursuit of the answers, but the ways in which the questions are asked. Even attempts at answers seem to me to be more like questions.

That brings me back to the quote from the article I linked to above.

Where is Aunt Millie’s mind when her brain dies of Alzheimer’s? I countered to Chopra. Aunt Millie was an impermanent pattern of behavior of the universe and returned to the potential she emerged from, Chopra rejoined.

mmm. Ponder that.

I feel like this came at a perfectly aligned time for my particular pattern of existence.

Just after reading that quote, I headed out to Yoga. The instructor was really driving home the concept of our own individual uniqueness and being like snowflakes, blah blah blah. However, all cliches aside, it really did make me think.

I’m always trying to be the best or the most memorable. If I make love to a woman, I want to make sure she remembers it as different. If I do research, I want my publication to be influential enough to referenced as a framework for future work. However, my existence, my patterns of behavior, as impermanent as they may be, yield the most significance I could ever ask for. I came from a potential, to which I shall return, but for now, I am not that one. I am this. I am the bearer of these words. And with that, I need not chase the answers (for which I have plenty, previous, cathartic attempts), but instead I can revel in the beauty of the questions.

…..

But, time passes. We cannot revel forever, and it is the subtleties to which me must lay blame for harnessing our inspired minds for the labors that pursue the answers.

Therefore, I’ll be spending a good amount of time tackling this problem, but I think it’s important to start with the inherent sublimity of that which I’m diving into.

I hope to expand on my overarching theories that extend back to the explanatory gap, which rest on my initial axioms:

  • Order was first, like a Rubik’s cube in the initial packaging, but chaos arose as time was introduced. The patterns in this chaos that allowed for a greater permanence of existence found ways to persevere.
  • Consciousness arose as a way to be cognizant of these processes and, with this understanding, the conscious beings can arrange future patterns in a more coherent, time-lasting manner.
  • This is all in pursuit of resolving back to the initial order. To complete the cycle of an attempt of the one attempting to understand itself. However, one cannot understand itself, until they split and communicate with their parts.

I hope to expand upon the dense ideas illustrated in those bullet points. They’ve taken much contemplation and it’s amazing to see how minimal the number of words were to convey the theses properly.

We have time.