Consciousness in a multilevel architecture: Evidence from the right side of the brain

Velichkovsky, Boris M.; Krotkova, O. A.; Kotov, Artemy; Orlov, Vyacheslav A.; Verkhlyutov, Vitaly; Ushakov, Vadim L.; Sharaev, Maxim

doi:10.1016/j.concog.2018.06.004

Cited by 30 publications

(10 citation statements)

References 64 publications

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“…Finally, correlation analysis does not reflect directional interactions between brain regions. Research combining behavioral tasks with tools such as dynamic causal modeling and Granger causality analysis may reveal directional functional connectivities of the amygdala ( Ide and Li, 2011 ; Hu et al, 2015 ; Zhang et al, 2015 ; Sato et al, 2017 ; Zhang Y. et al, 2017 ; Velichkovsky et al, 2018 ; Wang et al, 2018 ). This research may also benefit from studies combining functional and spectroscopy imaging of neurotransmitters ( Delli Pizzi et al, 2017a , b ) and animal work that employs pharmacological manipulations to investigate the neurochemical mechanisms underlying amygdala interaction with other brain regions ( Andolina et al, 2013 ; Chang and Ho, 2017 ; Chang and Grace, 2018 ).…”

Section: Conclusion and Limitationsmentioning

confidence: 99%

Exploring Age-Related Changes in Resting State Functional Connectivity of the Amygdala: From Young to Middle Adulthood

Xiao

Zhang

Lee

et al. 2018

Front. Aging Neurosci.

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Functional connectivities of the amygdala support emotional and cognitive processing. Life-span development of resting-state functional connectivities (rsFC) of the amygdala may underlie age-related differences in emotion regulatory mechanisms. To date, age-related changes in amygdala rsFC have been reported through adolescence but not as thoroughly for adulthood. This study investigated age-related differences in amygdala rsFC in 132 young and middle-aged adults (19–55 years). Data processing followed published routines. Overall, amygdala showed positive rsFC with the temporal, sensorimotor and ventromedial prefrontal cortex (vmPFC), insula and lentiform nucleus, and negative rsFC with visual, frontoparietal, and posterior cingulate cortex and caudate head. Amygdala rsFC with the cerebellum was positively correlated with age, and rsFCs with the dorsal medial prefrontal cortex (dmPFC) and somatomotor cortex were negatively correlated with age, at voxel p < 0.001 in combination with cluster p < 0.05 FWE. These age-dependent changes in connectivity appeared to manifest to a greater extent in men than in women, although the sex difference was only evident for the cerebellum in a slope test of age regressions (p = 0.0053). Previous studies showed amygdala interaction with the anterior cingulate cortex (ACC) and vmPFC during emotion regulation. In region of interest analysis, amygdala rsFC with the ACC and vmPFC did not show age-related changes. These findings suggest that intrinsic connectivity of the amygdala evolved from young to middle adulthood in selective brain regions, and may inform future studies of age-related emotion regulation and maladaptive development of the amygdala circuits as an etiological marker of emotional disorders.

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Section: Conclusion and Limitationsmentioning

confidence: 99%

Exploring Age-Related Changes in Resting State Functional Connectivity of the Amygdala: From Young to Middle Adulthood

Xiao

Zhang

Lee

et al. 2018

Front. Aging Neurosci.

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“…2017 ) was more active in the right hemisphere. Besides, the right dominance of prefrontal activation in poststroke patients with DOC ( Moriya and Sakatani 2018 ) and the rightward lateralization of excitatory interactions during consciousness organization ( Velichkovsky et al. 2018 ) suggested the preferential contribution of the right hemisphere towards consciousness, which was hampered in DOC patients.…”

Section: Discussionmentioning

confidence: 99%

Individualized Thalamic Parcellation Reveals Alterations in Shape and Microstructure of Thalamic Nuclei in Patients with Disorder of Consciousness

Zheng

Tan

Liu

et al. 2021

Cerebral Cortex Communications

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The thalamus plays crucial roles in consciousness generation and information processing. Previous evidence suggests that disorder of consciousness (DOC) caused by severe brain injury, is potentially related to thalamic abnormalities. However, how the morphology and microstructure change in thalamic subfields and thalamocortical fiber pathways in patients with DOC, and the relationships between these changes and the consciousness status remain unclear. Here, we generated the individual-specific thalamic parcellation in 10 DOC patients and 10 healthy controls (HC) via a novel thalamic segmentation framework based on the fiber orientation distribution (FOD) derived from 7-Tesla diffusion MRI, and investigated the shape deformation of thalamic nuclei as well as the microstructural changes associated with thalamic nuclei and thalamocortical pathways in patients with DOC. Enlargement of dorsal posterior nucleus and atrophy of anterior nucleus in the right thalamus were observed in DOC cohort relative to the HCs, and the former was closely linked to the consciousness level of the patients. We also found significant reductions of fiber density, but not fiber bundle cross-section, within several thalamic nuclei and most of the thalamocortical fiber pathways, suggesting that loss of axons might take primary responsibility for the impaired thalamocortical connections in patients with DOC rather than the change in fiber-bundle morphology. Furthermore, the individual-specific thalamic parcellation achieved 80% accuracy in classifying patients at the minimally conscious state from the vegetative state, compared to around 60% accuracy based on group-level parcellations. Our findings provide the first evidence for the shape deformation of thalamic nuclei in DOC patients and the microstructural basis of the disrupted thalamocortical connections.

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“…In this view, and since the proposed algorithm runs independently of the nature of the input data, we tried to apply the same graphlet approach to the negative part of the fMRI matrices. Our guess was that some subset of the so-called “anti-DMN” (see for example Di Perri et al, 2016 ; Velichkovsky et al, 2018 ) would have been selected. Differently, no stable orbits in fact emerged, and consequently no subset of topologically relevant nodes could be clearly determined.…”

Section: Discussionmentioning

confidence: 99%

A Graphlet-Based Topological Characterization of the Resting-State Network in Healthy People

et al. 2021

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In this paper, we propose a graphlet-based topological algorithm for the investigation of the brain network at resting state (RS). To this aim, we model the brain as a graph, where (labeled) nodes correspond to specific cerebral areas and links are weighted connections determined by the intensity of the functional magnetic resonance imaging (fMRI). Then, we select a number of working graphlets, namely, connected and non-isomorphic induced subgraphs. We compute, for each labeled node, its Graphlet Degree Vector (GDV), which allows us to associate a GDV matrix to each one of the 133 subjects of the considered sample, reporting how many times each node of the atlas “touches” the independent orbits defined by the graphlet set. We focus on the 56 independent columns (i.e., non-redundant orbits) of the GDV matrices. By aggregating their count all over the 133 subjects and then by sorting each column independently, we obtain a sorted node table, whose top-level entries highlight the nodes (i.e., brain regions) most frequently touching each of the 56 independent graphlet orbits. Then, by pairwise comparing the columns of the sorted node table in the top-k entries for various values of k, we identify sets of nodes that are consistently involved with high frequency in the 56 independent graphlet orbits all over the 133 subjects. It turns out that these sets consist of labeled nodes directly belonging to the default mode network (DMN) or strongly interacting with it at the RS, indicating that graphlet analysis provides a viable tool for the topological characterization of such brain regions. We finally provide a validation of the graphlet approach by testing its power in catching network differences. To this aim, we encode in a Graphlet Correlation Matrix (GCM) the network information associated with each subject then construct a subject-to-subject Graphlet Correlation Distance (GCD) matrix based on the Euclidean distances between all possible pairs of GCM. The analysis of the clusters induced by the GCD matrix shows a clear separation of the subjects in two groups, whose relationship with the subject characteristics is investigated.

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Consciousness in a multilevel architecture: Evidence from the right side of the brain

Cited by 30 publications

References 64 publications

Exploring Age-Related Changes in Resting State Functional Connectivity of the Amygdala: From Young to Middle Adulthood

Exploring Age-Related Changes in Resting State Functional Connectivity of the Amygdala: From Young to Middle Adulthood

Individualized Thalamic Parcellation Reveals Alterations in Shape and Microstructure of Thalamic Nuclei in Patients with Disorder of Consciousness

A Graphlet-Based Topological Characterization of the Resting-State Network in Healthy People

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