Functional connectivity hemispheric contrast (FC-HC): A new metric for language mapping.
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ABSTRACT:
SUBMITTER: Mbwana JS
PROVIDER: S-EPMC8102641 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:We determined the utility of electrocorticography (ECoG) and stimulation for detecting language-related sites in patients with left-hemispheric language-dominance on Wada test.We studied 13 epileptic patients who underwent language mapping using event-related gamma-oscillations on ECoG and stimulation via subdural electrodes. Sites showing significant gamma-augmentation during an auditory-naming task were defined as language-related ECoG sites. Sites at which stimulation resulted in auditory perceptual changes, failure to verbalize a correct answer, or sensorimotor symptoms involving the mouth were defined as language-related stimulation sites. We determined how frequently these methods revealed language-related sites in the superior-temporal, inferior-frontal, dorsolateral-premotor, and inferior-Rolandic regions.Language-related sites in the superior-temporal and inferior-frontal gyri were detected by ECoG more frequently than stimulation (p < 0.05), while those in the dorsolateral-premotor and inferior-Rolandic regions were detected by both methods equally. Stimulation of language-related ECoG sites, compared to the others, more frequently elicited language symptoms (p < 0.00001). One patient developed dysphasia requiring in-patient speech therapy following resection of the dorsolateral-premotor and inferior-Rolandic regions containing language-related ECoG sites not otherwise detected by stimulation.Language-related gamma-oscillations may serve as an alternative biomarker of underlying language function in patients with left-hemispheric language-dominance.Measurement of language-related gamma-oscillations is warranted in presurgical evaluation of epileptic patients.
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Project description:In temporal lobe epilepsy (TLE), determining the hemispheric specialization for language before surgery is critical to preserving a patient's cognitive abilities post-surgery. To date, the major techniques utilized are limited by the capacity of patients to efficiently realize the task. We determined whether resting-state functional connectivity (rsFC) is a reliable predictor of language hemispheric dominance in right and left TLE patients, relative to controls. We chose three subregions of the inferior frontal cortex (pars orbitalis, pars triangularis, and pars opercularis) as the seed regions. All participants performed both a verb generation task and a resting-state fMRI procedure. Based on the language task, we computed a laterality index (LI) for the resulting network. This revealed that 96% of the participants were left-hemisphere dominant, although there remained a large degree of variability in the strength of left lateralization. We tested whether LI correlated with rsFC values emerging from each seed. We revealed a set of regions that was specific to each group. Unique correlations involving the epileptic mesial temporal lobe were revealed for the right and left TLE patients, but not for the controls. Importantly, for both TLE groups, the rsFC emerging from a contralateral seed was the most predictive of LI. Overall, our data depict the broad patterns of rsFC that support strong versus weak left hemisphere language laterality. This project provides the first evidence that rsFC data may potentially be used on its own to verify the strength of hemispheric dominance for language in impaired or pathologic populations.
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Project description:Brain networks with energy-efficient hubs might support the high cognitive performance of humans and a better understanding of their organization is likely of relevance for studying not only brain development and plasticity but also neuropsychiatric disorders. However, the distribution of hubs in the human brain is largely unknown due to the high computational demands of comprehensive analytical methods. Here we propose a 10(3) times faster method to map the distribution of the local functional connectivity density (lFCD) in the human brain. The robustness of this method was tested in 979 subjects from a large repository of MRI time series collected in resting conditions. Consistently across research sites, a region located in the posterior cingulate/ventral precuneus (BA 23/31) was the area with the highest lFCD, which suggest that this is the most prominent functional hub in the brain. In addition, regions located in the inferior parietal cortex (BA 18) and cuneus (BA 18) had high lFCD. The variability of this pattern across subjects was <36% and within subjects was 12%. The power scaling of the lFCD was consistent across research centers, suggesting that that brain networks have a "scale-free" organization.
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Project description:PurposeCSF plays important roles in clearing brain waste and homeostasis. However, mapping whole-brain CSF flow in the rodents is difficult, primarily due to its assumed very low velocity. Therefore, we aimed to develop a novel phase-contrast MRI method to map whole-brain CSF flow in the mouse brain.MethodsA novel generalized Hadamard encoding-based multi-band scheme (dubbed HEAP-METRIC, Hadamard Encoding APproach of Multi-band Excitation for short TR Imaging aCcelerating) using complex Hadamard matrix was developed and incorporated into conventional phase contrast (PC)-MRI to significantly increase SNR.ResultsSlow flow phantom imaging validated HEAP-METRIC PC-MRI's ability to achieve fast and accurate mapping of slow flow velocities (~102 µm/s). With the SNR gain afforded by HEAP-METRIC scheme, high-resolution (0.08 × 0.08 mm in-plane resolution and 36 0.4 mm slices) PC-MRI was completed in 21 min for whole-brain CSF flow mapping in the mouse. Using this novel method, we provide the first report of whole-brain CSF flow in the awake mouse brain with an average flow velocity of ~200 µm/s. Furthermore, HEAP-METRIC PC-MRI revealed CSF flow was reduced by isoflurane anesthesia, accompanied by reduction of glymphatic function as measured by dynamic contrast-enhanced MRI.ConclusionWe developed and validated a generalized HEAP-METRIC PC-MRI for mapping low velocity flow. With this method, we have achieved the first whole-brain mapping of awake mouse CSF flow and have further revealed that anesthesia reduces CSF flow velocity.
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Project description:In patients with gliomas, changes in hemispheric specialization for language determined by magnetoencephalography (MEG) were analyzed to elucidate the impact of treatment and tumor recurrence on language networks. Demonstration of reorganization of language networks in these patients has significant implications on the prevention of postoperative functional loss and recovery. Whole-brain activity during an auditory verb generation task was estimated from MEG recordings in a group of 73 patients with recurrent gliomas. Hemisphere of language dominance was estimated using the language laterality index (LI), a measure derived from the task. The initial scan was performed prior to resection; patients subsequently underwent surgery and adjuvant treatment. A second scan was performed upon recurrence prior to repeat resection. The relationship between the shift in LI between scans and demographics, anatomic location, pathology, and adjuvant treatment was analyzed. Laterality shifts were observed between scans; the median percent change was 29.1% across all patients. Laterality shift magnitude and relative direction were associated with the initial position of language dominance; patients with increased lateralization experienced greater shifts than those presenting more bilateral representation. A change in LI from left or right to bilateral (or vice versa) occurred in 23.3% of patients; complete switch occurred in 5.5% of patients. Patients with tumors within the language-dominant hemisphere experienced significantly greater shifts than those with contralateral tumors. The majority of patients with glioma experience shifts in language network organization over time which correlate with the relative position of language lateralization and tumor location.
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