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      Functional connectivity alterations in migraineurs with Alice in Wonderland syndrome

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          Abstract

          <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" class="first" dir="auto" id="d3167193e166">Alice in Wonderland syndrome (AIWS) is a neurological disorder characterized by erroneous perception of the body schema or surrounding space. Migraine is the primary cause of AIWS in adults. The pathophysiology of AIWS is largely unknown, especially regarding functional abnormalities. In this study, we compared resting-state functional connectivity (FC) of migraine patients experiencing AIWS, migraine patients with typical aura (MA) and healthy controls (HCs). </p>

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            The organization of the human cerebral cortex estimated by intrinsic functional connectivity.

            Information processing in the cerebral cortex involves interactions among distributed areas. Anatomical connectivity suggests that certain areas form local hierarchical relations such as within the visual system. Other connectivity patterns, particularly among association areas, suggest the presence of large-scale circuits without clear hierarchical relations. In this study the organization of networks in the human cerebrum was explored using resting-state functional connectivity MRI. Data from 1,000 subjects were registered using surface-based alignment. A clustering approach was employed to identify and replicate networks of functionally coupled regions across the cerebral cortex. The results revealed local networks confined to sensory and motor cortices as well as distributed networks of association regions. Within the sensory and motor cortices, functional connectivity followed topographic representations across adjacent areas. In association cortex, the connectivity patterns often showed abrupt transitions between network boundaries. Focused analyses were performed to better understand properties of network connectivity. A canonical sensory-motor pathway involving primary visual area, putative middle temporal area complex (MT+), lateral intraparietal area, and frontal eye field was analyzed to explore how interactions might arise within and between networks. Results showed that adjacent regions of the MT+ complex demonstrate differential connectivity consistent with a hierarchical pathway that spans networks. The functional connectivity of parietal and prefrontal association cortices was next explored. Distinct connectivity profiles of neighboring regions suggest they participate in distributed networks that, while showing evidence for interactions, are embedded within largely parallel, interdigitated circuits. We conclude by discussing the organization of these large-scale cerebral networks in relation to monkey anatomy and their potential evolutionary expansion in humans to support cognition.
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              Functional connectivity in the motor cortex of resting human brain using echo-planar MRI.

              An MRI time course of 512 echo-planar images (EPI) in resting human brain obtained every 250 ms reveals fluctuations in signal intensity in each pixel that have a physiologic origin. Regions of the sensorimotor cortex that were activated secondary to hand movement were identified using functional MRI methodology (FMRI). Time courses of low frequency (< 0.1 Hz) fluctuations in resting brain were observed to have a high degree of temporal correlation (P < 10(-3)) within these regions and also with time courses in several other regions that can be associated with motor function. It is concluded that correlation of low frequency fluctuations, which may arise from fluctuations in blood oxygenation or flow, is a manifestation of functional connectivity of the brain.
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Neurological Sciences
                Neurol Sci
                Springer Science and Business Media LLC
                1590-1874
                1590-3478
                January 2023
                September 17 2022
                January 2023
                : 44
                : 1
                : 305-317
                Article
                10.1007/s10072-022-06404-1
                36114397
                6b00d2e8-8110-49e7-b610-7a679f5b744f
                © 2023

                https://www.springernature.com/gp/researchers/text-and-data-mining

                https://www.springernature.com/gp/researchers/text-and-data-mining

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