Cortex, countercurrent context, and dimensional integration of lifetime memory

The correlation between relative neocortex size and longevity in mammals encourages a search for a cortical function specifically related to the life-span. A candidate in the domain of permanent and cumulative memory storage is proposed and explored in relation to basic aspects of cortical organization. The pattern of cortico-cortical connectivity between functionally specialized areas and the laminar organization of that connectivity converges on a globally coherent representational space in which contextual embedding of information emerges as an obligatory feature of cortical function. This brings a powerful mode of inductive knowledge within reach of mammalian adaptations, a mode which combines item specificity with classificatory generality. Its neural implementation is proposed to depend on an obligatory interaction between the oppositely directed feedforward and feedback currents of cortical activity, in countercurrent fashion. Direct interaction of the two streams along their cortex-wide local interface supports a scheme of "contextual capture" for information storage responsible for the lifelong cumulative growth of a uniquely cortical form of memory termed "personal history." This approach to cortical function helps elucidate key features of cortical organization as well as cognitive aspects of mammalian life history strategies

Movie-driven fMRI Reveals Network Asynchrony and Connectivity Alterations in Temporal Lobe Epilepsy

Mesial temporal lobe epilepsy (TLE) is the most common form of focal epilepsy and is often resistant to medication. Recent studies have noted brain-wide disruptions to several neural networks in so-called “focal” epilepsy, notably TLE, leading to it being recognized as a network disease. We aimed to assess the integrity of functional networks while they were simultaneously activated in an ecologically valid manner, using an actively engaging, richly stimulating audio-visual film clip. This stimulus elicits widespread, dynamic patterns of time-locked brain activity, measurable using functional magnetic resonance imaging. Thirteen persons with drug-resistant TLE (persons with epilepsy; PWE) and 10 demographically matched controls were scanned while at rest and while watching a suspenseful movie clip in a 3T MRI system. We observed idiosyncratic activation in several functional networks among PWE during movie-viewing. Activation time courses among PWE synchronized poorly with the highly stereotyped movie-driven BOLD fluctuations exhibited by controls [i.e., high inter-subject correlation (ISC)]. We also examined coupling (functional connectivity) among 10 canonical functional networks during resting-state and movie-viewing conditions. Whereas functional networks in healthy viewers segregate to support movie processing, the auditory and dorsal attention networks among PWE do not segregate as efficiently. Furthermore, we observed a robust pattern of connectivity alterations in temporal and extratemporal regions during movie viewing in PWE compared to controls. Our findings supplement evidence derived from resting-state fMRI and provide novel insight into how the cognitively engaged brain is altered in TLE

The Aha! Experience of Spatial Reorientation

The experience of spatial re-orientation is investigated as an instance of the wellknown phenomenon of the Aha! moment. The research question is: What are the visuospatial conditions that are most likely to trigger the spatial Aha! experience? The literature suggests that spatial re-orientation relies mainly on the geometry of the environment and a visibility graph analysis is used to quantify the visuospatial information. Theories from environmental psychology point towards two hypotheses. The Aha! experience may be triggered by a change in the amount of visual information, described by the isovist properties of area and revelation, or by a change in the complexity of the visual information associated with the isovist properties of clustering coefficient and visual control. Data from participants’ exploratory behaviour and EEG recordings are collected during wayfinding in virtual reality urban environments. Two types of events are of interest here: (a) sudden changes of the visuospatial information preceding subjects' response to investigate changes in EEG power; and (b) participants brain dynamics (Aha! effect) just before the response to examine differences in isovist values at this location. Research on insights, time-frequency analysis of the P3 component and findings from navigation and orientation studies suggest that the spatial Aha! experience may be reflected by: a parietal alpha power decrease associated with the switch of the representation and a frontocentral theta increase indexing spatial processing during decision-making. Single-trial time-frequency analysis is used to classify trials into two conditions based on the alpha/theta power differences between a 3s time-period before participants’ response and a time-period of equal duration before that. Behavioural results show that participants are more likely to respond at locations with low values of clustering coefficient and high values of visual control. The EEG analysis suggests that the alpha decrease/theta increase condition occurs at locations with significantly lower values of clustering coefficient and higher values of visual control. Small and large decreases in clustering coefficient, just before the response, are associated with significant differences in delta/theta power. The values of area and revelation do not show significant differences. Both behavioural and EEG results suggest that the Aha! experience of re-orientation is more likely to be triggered by a change in the complexity of the visual-spatial environment rather than a change in the amount, as measured by the relevant isovist properties

Sensory History Matters for Visual Representation: Implications for Autism

How does the brain represent the enormous variety of the visual world? An approach to this question recognizes the types of information that visual representations maintain. The work in this thesis begins by investigating the neural correlates of perceptual similarity & distinctiveness, using EEG measurements of the evoked response to faces. In considering our results, we recognized that the effects being measured shared intrinsic relationships, both in measurement and in their theoretic basis. Using carry-over fMRI designs, we explored this relationship, ultimately demonstrating a new perspective on stimulus relationships based around sensory history that best explains the modulation of brain responses being measured. The result of this collection of experiments is a unified model of neural response modulation based around the integration of recent sensory history into a continually-updated reference; a drifting-norm. With this novel framework for understanding neural dynamics, we tested whether cognitive theories of autism spectrum disorder (ASD) might have a foundation in altered neural coding for perceptual information. Our results suggest ASD brain responses depend on a more moment-to-moment understanding of the visual world relative to neurotypical controls. This application both provides an exciting foothold in the brain for future investigations into the etiology of ASD, and validates the importance of sensory history as a dimension of visual representation

Increased Functional Connectivity in the Default Mode Network in Mild Cognitive Impairment: A Maladaptive Compensatory Mechanism Associated with Poor Semantic Memory Performance

Semantic memory decline and changes of default mode network (DMN) connectivity have been reported in mild cognitive impairment (MCI). Only a few studies, however, have investigated the role of changes of activity in the DMN on semantic memory in this clinical condition. The present study aimed to investigate more extensively the relationship between semantic memory impairment and DMN intrinsic connectivity in MCI. Twenty-one MCI patients and 21 healthy elderly controls matched for demographic variables took part in this study. All participants underwent a comprehensive semantic battery including tasks of category fluency, visual naming and naming from definition for objects, actions and famous people, word-association for early and late acquired words and reading. A subgroup of the original sample (16 MCI patients and 20 healthy elderly controls) was also scanned with resting state functional magnetic resonance imaging and DMN connectivity was estimated using a seed-based approach. Compared with healthy elderly, patients showed an extensive semantic memory decline in category fluency, visual naming, naming from definition, words-association, and reading tasks. Patients presented increased DMN connectivity between the medial prefrontal regions and the posterior cingulate and between the posterior cingulate and the parahippocampus and anterior hippocampus. MCI patients also showed a significant negative correlation of medial prefrontal gyrus connectivity with parahippocampus and posterior hippocampus and visual naming performance. Our findings suggest that increasing DMN connectivity may contribute to semantic memory deficits in MCI, specifically in visual naming. Increased DMN connectivity with posterior cingulate and medio-temporal regions seems to represent a maladaptive reorganization of brain functions in MCI, which detrimentally contributes to cognitive impairment in this clinical population

The white matter connectome as an individualized biomarker of language impairment in temporal lobe epilepsy.

ObjectiveThe distributed white matter network underlying language leads to difficulties in extracting clinically meaningful summaries of neural alterations leading to language impairment. Here we determine the predictive ability of the structural connectome (SC), compared with global measures of white matter tract microstructure and clinical data, to discriminate language impaired patients with temporal lobe epilepsy (TLE) from TLE patients without language impairment.MethodsT1- and diffusion-MRI, clinical variables (CVs), and neuropsychological measures of naming and verbal fluency were available for 82 TLE patients. Prediction of language impairment was performed using a robust tree-based classifier (XGBoost) for three models: (1) a CV-model which included demographic and epilepsy-related clinical features, (2) an atlas-based tract-model, including four frontotemporal white matter association tracts implicated in language (i.e., the bilateral arcuate fasciculus, inferior frontal occipital fasciculus, inferior longitudinal fasciculus, and uncinate fasciculus), and (3) a SC-model based on diffusion MRI. For the association tracts, mean fractional anisotropy was calculated as a measure of white matter microstructure for each tract using a diffusion tensor atlas (i.e., AtlasTrack). The SC-model used measurement of cortical-cortical connections arising from a temporal lobe subnetwork derived using probabilistic tractography. Dimensionality reduction of the SC was performed with principal components analysis (PCA). Each model was trained on 49 patients from one epilepsy center and tested on 33 patients from a different center (i.e., an independent dataset). Randomization was performed to test the stability of the results.ResultsThe SC-model yielded a greater area under the curve (AUC; .73) and accuracy (79%) compared to both the tract-model (AUC: .54, p < .001; accuracy: 70%, p < .001) and the CV-model (AUC: .59, p < .001; accuracy: 64%, p < .001). Within the SC-model, lateral temporal connections had the highest importance to model performance, including connections similar to language association tracts such as links between the superior temporal gyrus to pars opercularis. However, in addition to these connections many additional connections that were widely distributed, bilateral and interhemispheric in nature were identified as contributing to SC-model performance.ConclusionThe SC revealed a white matter network contributing to language impairment that was widely distributed, bilateral, and lateral temporal in nature. The distributed network underlying language may be why the SC-model has an advantage in identifying sub-components of the complex fiber networks most relevant for aspects of language performance