The emergence of complex behavior

Cambrian Explosion

Sparked by behavioral imperatives? (Fox, 2016)

  • Behavior requires energy
  • Behavior requires perception at a distance
  • Behavior requires action
  • Actions require
    • Problem solving, (sequence) planning
    • Current + stored information (memory)

Behaviors realized through…

  • Perception at a distance of what/where
  • Locomotion
    • Approach/avoid/explore
  • Object manipulation/consumption
  • Signaling/communication
  • Physiological regulation

Cognition

Combines…

Cognition and the cerebral cortex

Macrostructure

  • Areas
    • Unimodal sensory
    • Polymodal association
    • Motor
  • Connections
    • Association
    • Commissural

Microstructure

  • Columnar structure
  • Cytoarchitectonic differerences (e.g. Brodmann)
Wikipedia

Wikipedia

Layer Connection type Comments
I Few cell bodies
II Efferent Ipsilateral association via large pyramidal cells
III Efferent Contralateral commissural
IV Afferent from thalamus; small stellate & granual cells; V1 has sublayers
V Efferent Superficial -> Basal ganglia; Deep -> brainstem, spinal cord; pyramidal cells
VI Efferent Thalamus

Processing networks

Although it has long been assumed that cognitive functions are attributable to the isolated operations of single brain areas, we demonstrate that the weight of evidence has now shifted in support of the view that cognition results from the dynamic interactions of distributed brain areas operating in large-scale networks….

(Bressler & Menon, 2010)

Data-driven dynamics

  • Cortical states have high dimensionality
  • Is there a lower-dimensional space that maps onto behavior?

(Shine et al., 2019)

  • Data from \(n=200\) adult participants in Human Connectome Project (HCP)
  • 7 cognitive tasks
  • Dimension reduction via principal components analysis (PCA)

Fig. 1: Spatiotemporal PCA across multiple cognitive tasks. a, Spatial maps for the first five principal components (colored according to spatial weight; thresholded for visualization). b, Line plot representing the percentage of variance explained by first ten principal components; bar plot depicting the percentage (single value per component) of false nearest neighbors for first ten principal components. FNN, false nearest neighbors. c, Correspondence between convolved, concatenated task block regressor (gray) and the time course of the first five tPCs (black); color intensities of the blocks reflect the Pearson’s correlation between tPC1−5 and each of the unique task blocks (n = 100 subjects). d, Mean spatial loading of first five PCs, organized according to a set of predefined networks. DAN, dorsal attention; Vis, visual; FPN, frontoparietal; SN, salience; CO, cingulo-opercular; VAN, ventral attention; SM, somatomotor; RSp, retrosplenial; FTP, frontotemporal; DN, default mode; Aud, auditory.

(Shine et al., 2019)

  • Map PCAs to time series…

Fig. 2: The low-dimensional signature across cognitive tasks. a, The procedure used to partition tPC1 into unique phases: low (blue), rise (red), high (orange), and fall (light blue). b, Scatter plot comparing the loading of tPC1 (colored according to the partition defined in a) with a temporal stability measure (defined by the similarity of the BOLD response at adjacent time points); we observed a significant positive Pearson’s correlation (r = 0.58) between |tPC1| and temporal stability (n=1,939 time points), providing heuristic evidence for attractor basins at the extremes of tPC1 engagement. c, A three-dimensional scatter plot comparing the first three tPCs; each node represents one time point (colored according to the phase of tPC1), with time implicitly unfolding across the embedding space (contiguous points connected by black line). d, The low-dimensional manifold traversed by the global brain state across the first three dimensions, with arrows depicting the direction of flow along the manifold.

(Shine et al., 2019)

  • How do these brain states map to cognition?
    • Explore overlap with NeuroSynth ‘topic families’

Fig. 3: The cognitive relevance of the low-dimensional embedding space. a, Four NeuroSynth ‘topic families’: motor (red), cognition (yellow), language (green), and memory (blue). b, Bar plot demonstrating loading (single-value) of topic families onto top five principal components. c, Scatter plot of time points of the first two tPCs, colored according to their loading onto each of the four NeuroSynth topic families. d, Mean value (resampled 100 times) of tPC1−2 for each topic family compared with a block resampled null distribution (5,000 iterations). e, Temporal conjunction between the topic families and the four phases of the tPC1 manifold; bar plots designate a single value (%) and asterisks denote P < 0.01 (block resampled null model; n=5,000 iterations).

(Shine et al., 2019)

The results of our multimodal analysis revealed that the neural activity required for the execution of cognitive tasks corresponds to flow within a low-dimensional state space43. Across multiple, diverse cognitive tasks, the dynamics of large-scale brain activity engage an integrative core of brain regions that maximizes information-processing complexity and facilitates cognitive performance; only to then dissipate as the tasks conclude, flowing towards a more segregated architecture…Across multiple cognitive tasks with markedly different behavioral requirements, the dynamics of human brain activity were found to occupy a low-dimensional state space embedding that may form the functional backbone of cognition in the human brain.

(Shine et al., 2019)

Summing up

[@Powers1973-zn]

(Powers, 1973)

[@Powers1973-zn]

(Powers, 1973)

Fig. 1. Illustration showing the durations of the four stages associated with problem solving. In the four example problems, the arrows denote new mathematical operators that participants had learned. In each stage, the axial slice (x = 0 mm, y = 0 mm, z = 28 mm in Talairach space) highlights brain regions in which activation in that stage was significantly greater than the average activation during problem solving. Brain images are displayed with the left hemisphere on the right-hand side.

(Anderson, Pyke, & Fincham, 2016)

Fig. 4. The four brain signatures placed in a 3-D space where the activity of a stage is a sum of the activity of the signature in the solving stage plus a sum of the three vectors weighted by their coordinates in the space. The heat maps illustrate the proportion of change in activation relative to baseline. The coordinates of the stages are as follows (in Talairach space)—encoding: x = 1.61, y = 0.37, z = 0.58; planning: x = 0.58, y = 0.28, z = 1.38; solving: x = 0, y = 0, z = 0; and responding: x = 0.37, y = 1.78, z = 0.28. Brain images are displayed with the left hemisphere on the right-hand side.

(Anderson, Pyke, & Fincham, 2016)

Language and the brain

Language behavior

Hierarchical structure of language information

Wernicke-Geschwind (WG) model

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Wikipedia

Wernicke’s area (Brodmann Area or BA 42)

  • Adjacent to primary auditory cortex (A1; Heschl’s gyrus; BA 41)
  • Perception
  • Receptive or ‘fluent’ aphasia
Wikipedia

Wikipedia

Wikipedia

Wikipedia

Broca’s area

  • Inferior frontal gyrus, pars opercularis (BA 44) & pars angularis (BA 45)
  • Production
  • Expressive aphasia
Wikipedia

Wikipedia

Dual streams (Hickok & Poeppel, 2007)

Metaanalytic evidence (Hagoort & Indefrey, 2014)

Figure 1. Schematic representation of the brain showing regions with reliably reported activations for sentences compared with nonsentential stimuli (a) and sentences with high syntactic or semantic processing demands compared with simpler sentences (b,c). The left posterior inferior frontal gyrus is further subdivided into Brodmann areas (BA) 44 (above black line), BA 45 (below black line, above AC–PC line) and BA 47 (below AC–PC line). Green regions indicate a reliable number of reports. Pink regions indicate no reports in 53 studies. For details, see Supplemental Tables 2, 3, and 4. Abbreviations: AC, anterior commissure; PC, posterior commissure).

(Hagoort & Indefrey, 2014)


A meta-analysis of numerous neuroimaging studies reveals a clear dorsal/ventral gradient in both left inferior frontal cortex and left posterior temporal cortex, with dorsal foci for syntactic processing and ventral foci for semantic processing. In addition…further networks need to be recruited to realize language-driven communication to its full extent.

(Hagoort & Indefrey, 2014)

Summing up

Figure 2. (a) Summary of activation patterns for sentences with high syntactic or semantic processing demands compared with simpler sentences. (b) Syntactic/semantic gradients in left inferior frontal and posterior temporal cortex based on 28 studies reporting posterior temporal cortex activation for syntactically demanding or semantically demanding sentences compared with less demanding sentences (see Supplemental Figure 13 for details). The centers represent the mean coordinates of the local maxima, and the radii represent the standard deviations of the distance between the local maxima and their means. Abbreviations: GFm, GFi, middle and inferior frontal gyri; BA, Brodmann area; GTs, GTm, GTi, superior, middle, and inferior temporal gyri; STS, ITS, superior and inferior temporal sulci; Gsm, supramarginal gyrus.

(Hagoort & Indefrey, 2014)

References

Anderson, J. R., Pyke, A. A., & Fincham, J. M. (2016). Hidden stages of cognition revealed in patterns of brain activation. Psychological Science, 27(9), 1215–1226. https://doi.org/10.1177/0956797616654912
Bressler, S. L., & Menon, V. (2010). Large-scale brain networks in cognition: Emerging methods and principles. Trends in Cognitive Sciences, 14(6), 277–290. https://doi.org/10.1016/j.tics.2010.04.004
Coltheart, M. (2013). How can functional neuroimaging inform cognitive theories? Perspectives on Psychological Science: A Journal of the Association for Psychological Science, 8(1), 98–103. https://doi.org/10.1177/1745691612469208
Fox, D. (2016). What sparked the Cambrian explosion? Nature, 530(7590), 268–270. https://doi.org/10.1038/530268a
Hagoort, P., & Indefrey, P. (2014). The neurobiology of language beyond single words. Annu. Rev. Neurosci., 37, 347–362. https://doi.org/10.1146/annurev-neuro-071013-013847
Hickok, G., & Poeppel, D. (2007). The cortical organization of speech processing. Nat. Rev. Neurosci., 8(5), 393–402. https://doi.org/10.1038/nrn2113
Powers, W. T. (1973). Behavior: The control of perception. Aldine Chicago. Retrieved from http://www.pctresources.com/Other/Reviews/BCP_book.pdf
Shine, J. M., Breakspear, M., Bell, P. T., Ehgoetz Martens, K. A., Shine, R., Koyejo, O., … Poldrack, R. A. (2019). Human cognition involves the dynamic integration of neural activity and neuromodulatory systems. Nature Neuroscience, 22(2), 289–296. https://doi.org/10.1038/s41593-018-0312-0
Swanson, L. W. (2005). Anatomy of the soul as reflected in the cerebral hemispheres: Neural circuits underlying voluntary control of basic motivated behaviors. Journal of Comparative Neurology, 493(1), 122–131. https://doi.org/10.1002/cne.20733
Swanson, L. W. (2012). Brain architecture: Understanding the basic plan. Oxford University Press.
Tressoldi, P. E., Sella, F., Coltheart, M., & Umiltà, C. (2012). Using functional neuroimaging to test theories of cognition: A selective survey of studies from 2007 to 2011 as a contribution to the decade of the mind initiative. Cortex; a Journal Devoted to the Study of the Nervous System and Behavior, 48(9), 1247–1250. https://doi.org/10.1016/j.cortex.2012.05.024
White, C. N., & Poldrack, R. A. (2013). Using fMRI to constrain theories of cognition. Perspectives on Psychological Science: A Journal of the Association for Psychological Science, 8(1), 79–83. https://doi.org/10.1177/1745691612469029