The Neural Correlates of Spatial and Object Working Memory in Elderly and Parkinson’s Disease Subjects

Caminiti, Silvia Paola; Siri, Chiara; Antonini, Angelo; Perani, Daniela

doi:10.1155/2015/123636

Cited by 20 publications

(10 citation statements)

References 69 publications

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“…These regions are consistent with previous accounts of semantic cognition ( Binder et al, 1997 , Demb et al, 1995 , Derrfuss et al, 2004 , Kang et al, 1999 , Vigneau et al, 2006 ). Furthermore, this effect was also seen by Caminiti et al (2015) in a similar (abstract shape based) working memory task. These authors also considered a verbalisation strategy as the likely interpretation.…”

Section: Discussionmentioning

confidence: 65%

Measurement of dynamic task related functional networks using MEG

et al. 2017

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The characterisation of dynamic electrophysiological brain networks, which form and dissolve in order to support ongoing cognitive function, is one of the most important goals in neuroscience. Here, we introduce a method for measuring such networks in the human brain using magnetoencephalography (MEG). Previous network analyses look for brain regions that share a common temporal profile of activity. Here distinctly, we exploit the high spatio-temporal resolution of MEG to measure the temporal evolution of connectivity between pairs of parcellated brain regions. We then use an ICA based procedure to identify networks of connections whose temporal dynamics covary. We validate our method using MEG data recorded during a finger movement task, identifying a transient network of connections linking somatosensory and primary motor regions, which modulates during the task. Next, we use our method to image the networks which support cognition during a Sternberg working memory task. We generate a novel neuroscientific picture of cognitive processing, showing the formation and dissolution of multiple networks which relate to semantic processing, pattern recognition and language as well as vision and movement. Our method tracks the dynamics of functional connectivity in the brain on a timescale commensurate to the task they are undertaking.

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Section: Discussionmentioning

confidence: 65%

Measurement of dynamic task related functional networks using MEG

et al. 2017

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“…These regions are critical in memory encoding and retrieval and semantic cognition (Axmacher et al, 2008;Caminiti et al, 2015;Demb et al, 1995;Derrfuss et al, 2004;Deschamps et al, 2014;Vigneau et al, 2006). In a similar (abstract shape based) working memory task, the interpretation of this network was related to a verbalisation naming strategy employed by participants as a way to aid in memory encoding (Caminiti et al, 2015;O'Neill et al, 2017). Therefore, this network's activation may be possible with the task as it modulates strongly with the probe presentation and response time.…”

Section: Discussionmentioning

confidence: 99%

Dynamics of task-related electrophysiological networks: a benchmarking study

Tabbal

Kabbara

Khalil

et al. 2020

Preprint

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Motor, sensory and cognitive functions rely on dynamic reshaping of functional brain networks. Tracking these rapid changes is crucial to understand information processing in the brain, but challenging due to the random selection of methods and the limited evaluation studies. Using Magnetoencephalography (MEG) combined with Source Separation (SS) methods, we present an integrated framework to track fast dynamics of electrophysiological brain networks. We evaluate nine SS methods applied to three independent MEG databases (N=95) during motor and memory tasks. We report differences between these methods at the group and subject level. We show that the independent component analysis (ICA)-based methods and especially those exploring high order statistics are the most efficient, in terms of spatiotemporal accuracy and subject-level analysis. We seek to help researchers in choosing objectively the appropriate methodology when tracking fast reconfiguration of functional brain networks, due to its enormous benefits in cognitive and clinical neuroscience.

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“…The Kullback-Leibler (KL) divergence was then calculated between two PDFs of each pair of ROIs. This measures the difference between two probability distributions and equates to the information lost when a probability distribution is used to approximate another (Burnham and AnderSon 2002). The standard KL divergence from distribution Q to P is calculated using: However, D KL (P‖Q) is not equal to D KL (Q‖P) .…”

Section: Construction Of Gm Brain Networkmentioning

confidence: 99%

Disrupted morphological grey matter networks in early-stage Parkinson’s disease

Suo

Lei

et al. 2021

Brain Struct Funct

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While previous structural-covariance studies have an advanced understanding of brain alterations in Parkinson's disease (PD), brain–behavior relationships have not been examined at the individual level. This study investigated the topological organization of grey matter (GM) networks, their relation to disease severity, and their potential imaging diagnostic value in PD. Fifty-four early-stage PD patients and 54 healthy controls (HC) underwent structural T1-weighted magnetic resonance imaging. GM networks were constructed by estimating interregional similarity in the distributions of regional GM volume using the Kullback–Leibler divergence measure. Results were analyzed using graph theory and network-based statistics (NBS), and the relationship to disease severity was assessed. Exploratory support vector machine analyses were conducted to discriminate PD patients from HC and different motor subtypes. Compared with HC, GM networks in PD showed a higher clustering coefficient (P = 0.014) and local efficiency (P = 0.014). Locally, nodal centralities in PD were lower in postcentral gyrus and temporal-occipital regions, and higher in right superior frontal gyrus and left putamen. NBS analysis revealed decreased morphological connections in the sensorimotor and default mode networks and increased connections in the salience and frontoparietal networks in PD. Connection matrices and graph-based metrics allowed single-subject classification of PD and HC with significant accuracy of 73.1 and 72.7%, respectively, while graph-based metrics allowed single-subject classification of tremor-dominant and akinetic–rigid motor subtypes with significant accuracy of 67.0%. The topological organization of GM networks was disrupted in early-stage PD in a way that suggests greater segregation of information processing. There is potential for application to early imaging diagnosis.

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The Neural Correlates of Spatial and Object Working Memory in Elderly and Parkinson’s Disease Subjects

Cited by 20 publications

References 69 publications

Measurement of dynamic task related functional networks using MEG

Measurement of dynamic task related functional networks using MEG

Dynamics of task-related electrophysiological networks: a benchmarking study

Disrupted morphological grey matter networks in early-stage Parkinson’s disease

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