scholarly journals Dynamic connectivity analyses of intracranial EEG during recognition memory reveal various large-scale functional networks

2020 ◽  
Author(s):  
Jakub Kopal ◽  
Jaroslav Hlinka ◽  
Elodie Despouy ◽  
Luc Valton ◽  
Marie Denuelle ◽  
...  
Keyword(s):  
Recognition Memory ◽  
Large Scale ◽  
Visual Recognition ◽  
Effective Connectivity ◽  
Memory Task ◽  
Brain Regions ◽  
Neural Basis ◽  
Intracranial Eeg ◽  
Large Scale Networks ◽  
The Right

Recognition memory is the ability to recognize previously encountered events, objects, or people. It is characterized by its robustness and rapidness. Even this relatively simple ability requires the coordinated activity of a surprisingly large number of brain regions. These spatially distributed, but functionally linked regions are interconnected into large-scale networks. Understanding memory requires an examination of the involvement of these networks and the interactions between different regions while memory processes unfold. However, little is known about the dynamical organization of large-scale networks during the early phases of recognition memory. We recorded intracranial EEG, which affords high temporal and spatial resolution, while epileptic subjects performed a visual recognition memory task. We analyzed dynamic functional and effective connectivity as well as network properties. Various networks were identified, each with its specific characteristics regarding information flow (feedforward or feedback), dynamics, topology, and stability. The first network mainly involved the right visual ventral stream and bilateral frontal regions. It was characterized by early predominant feedforward activity, modular topology, and high stability. It was followed by the involvement of a second network, mainly in the left hemisphere, but notably also involving the right hippocampus, characterized by later feedback activity, integrated topology, and lower stability. The transition between networks was associated with a change in network topology. Overall, these results confirm that several large-scale brain networks, each with specific properties and temporal manifestation, are involved during recognition memory. Ultimately, understanding how the brain dynamically faces rapid changes in cognitive demand is vital to our comprehension of the neural basis of cognition.

scholarly journals Mapping the Visual Recognition Memory Network with PET in the Behaving Baboon

2000 ◽  
Vol 20 (2) ◽  
pp. 213-219 ◽  
Author(s):  
Xavier Blaizot ◽  
Brigitte Landeau ◽  
Jean-Claude Baron ◽  
Chantal Chavoix
Keyword(s):  
Recognition Memory ◽  
Large Scale ◽  
Visual Recognition ◽  
Memory Task ◽  
Magnetic Resonance Images ◽  
Nucleus Basalis ◽  
Healthy Humans ◽  
Visual Recognition Memory ◽  
Memory Network ◽  
First Time

By means of a novel 18F-fluoro-deoxyglucose PET method designed for cognitive activation imaging in the baboon, the large-scale neural network involved in visual recognition memory in the nonhuman primate was mapped for the first time. In this method, the tracer is injected in the awake, unanesthetized, and unrestrained baboon performing the memory task, and brain imaging is performed later under light anesthesia. Brain maps obtained during a computerized trial-unique delayed matching-to-sample task (lists of meaningless geometrical patterns and delay > 9 seconds) were statistically compared pixel-by-pixel to maps obtained during a specially designed visuomotor control task. When displayed onto the baboon's own anatomic magnetic resonance images, foci of significant activation were distributed along the ventral occipitotemporal pathway, the inferomedial temporal lobe (especially the perirhinal cortex and posterior hippocampal region), and the orbitofrontal cortex, consistent with lesion, single-unit, and autoradiographic studies in monkeys, as well as with activation studies in healthy humans. Additional activated regions included the nucleus basalis of Meynert, the globus pallidus and the putamen. The results also document an unexpected left-sided advantage, suggesting hemispheric functional specialization for recognition of figural material in nonhuman primates.

Memory Networks Supporting Retrieval Effort and Retrieval Success Under Conditions of Full and Divided Attention

2009 ◽  
Vol 56 (6) ◽  
pp. 386-396 ◽  
Author(s):  
Erin I. Skinner ◽  
Myra A. Fernandes ◽  
Cheryl L. Grady
Keyword(s):  
Multivariate Analysis ◽  
Recognition Memory ◽  
Divided Attention ◽  
Brain Activity ◽  
Memory Performance ◽  
Memory Task ◽  
Brain Regions ◽  
Analysis Technique ◽  
The Right ◽  
Performance Results

We used a multivariate analysis technique, partial least squares (PLS), to identify distributed patterns of brain activity associated with retrieval effort and retrieval success. Participants performed a recognition memory task under full attention (FA) or two different divided attention (DA) conditions during retrieval. Behaviorally, recognition was disrupted when a word, but not digit-based distracting task, was performed concurrently with retrieval. PLS was used to identify patterns of brain activation that together covaried with the three memory conditions and which were functionally connected with activity in the right hippocampus to produce successful memory performance. Results indicate that activity in the right dorsolateral frontal cortex increases during conditions of DA at retrieval, and that successful memory performance in the DA-digit condition is associated with activation of the same network of brain regions functionally connected to the right hippocampus, as under FA, which increases with increasing memory performance. Finally, DA conditions that disrupt successful memory performance (DA-word) interfere with recruitment of both retrieval-effort and retrieval-success networks.

scholarly journals The social cerebellum: A large-scale investigation of functional and structural specificity and connectivity

2021 ◽  
Author(s):  
Athanasia Metoki ◽  
Yin Wang ◽  
Ingrid R. Olson
Keyword(s):  
Functional Role ◽  
Large Scale ◽  
Effective Connectivity ◽  
Mental States ◽  
Brain Regions ◽  
Cerebellar Hemisphere ◽  
Affective Processing ◽  
Domain Specific ◽  
The Social ◽  
The Right

AbstractThe cerebellum has been traditionally disregarded in relation to non-motor functions, but recent findings indicate it may be involved in language, affective processing, and social functions. Mentalizing is the ability to infer mental states of others and this skill relies on a distributed network of brain regions. Here, we leveraged large-scale multimodal neuroimaging data to elucidate the structural and functional role of the cerebellum in mentalizing. We used functional activations to determine whether the cerebellum has a domain-general or domain-specific functional role, and effective connectivity and probabilistic tractography to map the cerebello-cerebral mentalizing network. We found that the cerebellum is organized in a domain-specific way and that there is a left cerebellar effective and structural lateralization, with more and stronger effective connections from the left cerebellar hemisphere to the right cerebral mentalizing areas, and greater cerebello-thalamo-cortical (CTC) and cortico-ponto-cerebellar (CPC) streamline counts from and to the left cerebellum. Our study provides novel insights to the network organization of the cerebellum, an overlooked brain structure, and mentalizing, one of humans’ most essential abilities to navigate the social world.

scholarly journals Effects of amnesia on processing in the hippocampus and default mode network during a naturalistic memory task: a case study

2018 ◽  
Author(s):  
Christiane Oedekoven ◽  
James L. Keidel ◽  
Stuart Anderson ◽  
Angus Nisbet ◽  
Chris Bird
Keyword(s):  
Functional Connectivity ◽  
Episodic Memory ◽  
Memory Performance ◽  
Memory Task ◽  
Structural Mri ◽  
Brain Regions ◽  
Left Hippocampus ◽  
Cortical Regions ◽  
The Right ◽  
Encoding And Retrieval

Despite their severely impaired episodic memory, individuals with amnesia are able to comprehend ongoing events. Online representations of a current event are thought to be supported by a network of regions centred on the posterior midline cortex (PMC). By contrast, episodic memory is widely believed to be supported by interactions between the hippocampus and these cortical regions. In this MRI study, we investigated the encoding and retrieval of lifelike events (video clips) in a patient with severe amnesia likely resulting from a stroke to the right thalamus, and a group of 20 age-matched controls. Structural MRI revealed grey matter reductions in left hippocampus and left thalamus in comparison to controls. We first characterised the regions activated in the controls while they watched and retrieved the videos. There were no differences in activation between the patient and controls in any of the regions. We then identified a widespread network of brain regions, including the hippocampus, that were functionally connected with the PMC in controls. However, in the patient there was a specific reduction in functional connectivity between the PMC and a region of left hippocampus when both watching and attempting to retrieve the videos. A follow up analysis revealed that in controls the functional connectivity between these regions when watching the videos was correlated with memory performance. Taken together, these findings support the view that the interactions between the PMC and the hippocampus enable the encoding and retrieval of multimodal representations of the contents of an event.

The Structural Language Network

2017 ◽  
Author(s):  
Angela D. Friederici ◽  
Noam Chomsky
Keyword(s):  
White Matter ◽  
Language Processing ◽  
Sentence Processing ◽  
Information Transfer ◽  
Right Hemisphere ◽  
Brain Regions ◽  
Neural Basis ◽  
Fiber Tracts ◽  
White Matter Fiber Tracts ◽  
The Right

An adequate description of the neural basis of language processing must consider the entire network both with respect to its structural white matter connections and the functional connectivities between the different brain regions as the information has to be sent between different language-related regions distributed across the temporal and frontal cortex. This chapter discusses the white matter fiber bundles that connect the language-relevant regions. The chapter is broken into three sections. In the first, we look at the white matter fiber tracts connecting the language-relevant regions in the frontal and temporal cortices; in the second, the ventral and dorsal pathways in the right hemisphere that connect temporal and frontal regions; and finally in the third, the two syntax-relevant and (at least) one semantic-relevant neuroanatomically-defined networks that sentence processing is based on. From this discussion, it becomes clear that online language processing requires information transfer via the long-range white matter fiber pathways that connect the language-relevant brain regions within each hemisphere and between hemispheres.

Mnemonic firing of neurons in the monkey temporal pole during a visual recognition memory task

1995 ◽  
Vol 74 (1) ◽  
pp. 162-178 ◽  
Author(s):  
K. Nakamura ◽  
K. Kubota
Keyword(s):  
Recognition Memory ◽  
Visual Information ◽  
Visual Recognition ◽  
Discharge Rate ◽  
Memory Task ◽  
Delay Period ◽  
Recognition Memory Task ◽  
Visual Response ◽  
Visual Recognition Memory ◽  
Temporal Pole

1. We examined single-neuronal activity in the temporal pole of monkeys, including the anterior ventromedial temporal (VMT) cortex (the temporopolar cortex, area 36, area 35, and the entorhinal cortex) and the anterior inferotemporal (IT) cortex, during a visual recognition memory task. In the task, a trial began when the monkey pressed a lever. After a waiting period, a visual sample stimulus (S) was presented one to four times on a monitor with an interstimulus delay. Thereafter, a new stimulus (R) was presented. The monkeys were trained to remember S during the delay period and to release the lever in response to R. Colored photographs of natural objects were used as visual stimuli. 2. About 70% of the recorded neurons (225 of 311) responded to at least one of the Ss tested. Thirty percent of these neurons (68 of 225) continued to fire during the subsequent delay periods. In 75% of these neurons (51 of 68), the firing during the delay period strongly correlated with the response to S. 3. The discharge rate during the delay period did not correlate with the monkey's eye movements, pressing or releasing of the lever, or the reaction time. 4. If the monkey erroneously released the lever in response to S or during the delay period, the firing disappeared after the erroneous lever release. If the monkey failed to release the lever in response to R, the firing persisted even after R was withdrawn. The discharge rate in incorrect trials was comparable with that in correct trials. The neurons were considered to fire for as long as the memory of S was necessary. 5. Firing persisted even when an achromatic version or half (even a portion) of S was presented, indicating that the color, a particular portion, or the entire shape of S was not always necessary to elicit firing. 6. An S that elicited firing during the delay period invariably elicited a visual response. Neurons that fired during the delay period showed a higher stimulus selectivity than other visually responsive neurons in the anterior VMT cortex. Thus neurons that fire during the delay period represent a subgroup of visually responsive neurons that are selectively tuned to a certain stimulus. 7. More neurons fired during the delay period in the anterior VMT cortex than in the anterior IT cortex. 8. We conclude that firing during the delay period by neurons in the temporal pole reflects the short-term storage of visual information regarding a particular S.

scholarly journals The neurophysiological architecture of semantic dementia: spectral dynamic causal modelling of a neurodegenerative proteinopathy

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Elia Benhamou ◽  
Charles R. Marshall ◽  
Lucy L. Russell ◽  
Chris J. D. Hardy ◽  
Rebecca L. Bond ◽  
...  
Keyword(s):  
Network Architecture ◽  
Large Scale ◽  
Effective Connectivity ◽  
Brain Regions ◽  
Semantic Dementia ◽  
Older Individuals ◽  
World Knowledge ◽  
Causal Modelling ◽  
Dynamic Causal Modelling ◽  
Semantic Impairment

Abstract The selective destruction of large-scale brain networks by pathogenic protein spread is a ubiquitous theme in neurodegenerative disease. Characterising the circuit architecture of these diseases could illuminate both their pathophysiology and the computational architecture of the cognitive processes they target. However, this is challenging using standard neuroimaging techniques. Here we addressed this issue using a novel technique—spectral dynamic causal modelling—that estimates the effective connectivity between brain regions from resting-state fMRI data. We studied patients with semantic dementia—the paradigmatic disorder of the brain system mediating world knowledge—relative to healthy older individuals. We assessed how the effective connectivity of the semantic appraisal network targeted by this disease was modulated by pathogenic protein deposition and by two key phenotypic factors, semantic impairment and behavioural disinhibition. The presence of pathogenic protein in SD weakened the normal inhibitory self-coupling of network hubs in both antero-mesial temporal lobes, with development of an abnormal excitatory fronto-temporal projection in the left cerebral hemisphere. Semantic impairment and social disinhibition were linked to a similar but more extensive profile of abnormally attenuated inhibitory self-coupling within temporal lobe regions and excitatory projections between temporal and inferior frontal regions. Our findings demonstrate that population-level dynamic causal modelling can disclose a core pathophysiological feature of proteinopathic network architecture—attenuation of inhibitory connectivity—and the key elements of distributed neuronal processing that underwrite semantic memory.

scholarly journals Meditation-induced effects on whole-brain structural and effective connectivity

2021 ◽  
Author(s):  
Eleonora De Filippi ◽  
Anira Escrichs ◽  
Matthieu Gilson ◽  
Marti Sanchez-Fibla ◽  
Estela Camara ◽  
...  
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Effective Connectivity ◽  
Temporal Structure ◽  
Functional Domain ◽  
Anatomical Connectivity ◽  
Whole Brain ◽  
Spatio Temporal ◽  
Large Scale Networks ◽  
And Function

In the past decades, there has been a growing scientific interest in characterizing neural correlates of meditation training. Nonetheless, the mechanisms underlying meditation remain elusive. In the present work, we investigated meditation-related changes in structural and functional connectivities (SC and FC, respectively). For this purpose, we scanned experienced meditators and control (naive) subjects using magnetic resonance imaging (MRI) to acquire structural and functional data during two conditions, resting-state and meditation (focused attention on breathing). In this way, we aimed to characterize and distinguish both short-term and long-term modifications in the brain's structure and function. First, we performed a network-based analysis of anatomical connectivity. Then, to analyze the fMRI data, we calculated whole-brain effective connectivity (EC) estimates, relying on a dynamical network model to replicate BOLD signals' spatio-temporal structure, akin to FC with lagged correlations. We compared the estimated EC, FC, and SC links as features to train classifiers to predict behavioral conditions and group identity. The whole-brain SC analysis revealed strengthened anatomical connectivity across large-scale networks for meditators compared to controls. We found that differences in SC were reflected in the functional domain as well. We demonstrated through a machine-learning approach that EC features were more informative than FC and SC solely. Using EC features we reached high performance for the condition-based classification within each group and moderately high accuracies when comparing the two groups in each condition. Moreover, we showed that the most informative EC links that discriminated between meditators and controls involved the same large-scale networks previously found to have increased anatomical connectivity. Overall, the results of our whole-brain model-based approach revealed a mechanism underlying meditation by providing causal relationships at the structure-function level.

scholarly journals The locus of recognition memory signals in human cortex depends on the complexity of the memory representations

2021 ◽  
Author(s):  
D. Merika W. Sanders ◽  
Rosemary A. Cowell
Keyword(s):  
Recognition Memory ◽  
Medial Temporal Lobe ◽  
Declarative Memory ◽  
Memory Task ◽  
Representational Content ◽  
Brain Regions ◽  
Complex Stimuli ◽  
Memory Representations ◽  
Memory Signals ◽  
Feature Memory

Representational theories predict that brain regions contribute to cognition according to the information they represent (e.g., simple versus complex), contradicting the traditional notion that brain regions are specialized for cognitive functions (e.g., perception versus memory). In support of representational accounts, substantial evidence now attests that the Medial Temporal Lobe (MTL) is not specialized solely for long-term declarative memory, but underpins other functions including perception and future-imagining for complex stimuli and events. However, a complementary prediction has been less well explored, namely that the cortical locus of declarative memory may fall outside the MTL if the to-be-remembered content is sufficiently simple. Specifically, the locus should coincide with the optimal neural code for the representations being retrieved. To test this prediction, we manipulated the complexity of the to-be-remembered representations in a recognition memory task. First, participants in the scanner viewed novel 3D objects and scenes, and we used multivariate analyses to identify regions in the ventral visual-MTL pathway that preferentially coded for either simple features of the stimuli, or complex conjunctions of those features. Next, in a separate scan, we tested recognition memory for these stimuli and performed neuroimaging contrasts that revealed two memory signals ‒ feature memory and conjunction memory. Feature memory signals were found in visual cortex, while conjunction memory signals emerged in MTL. Further, the regions optimally representing features via preferential feature-coding coincided with those exhibiting feature memory signals. These findings suggest that representational content, rather than cognitive function, is the primary organizing principle in the ventral visual-MTL pathway.

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