Poster No:
268
Submission Type:
Abstract Submission
Authors:
Costanza Iester1, Monica Biggio1, Laura Bonzano1, Sabrina Brigadoi2, Ludovico Pedullà3, Simone Cutini2, Giampaolo Brichetto3, Marco Bove4
Institutions:
1Department of Neuroscience, DINOGMI, University of Genoa, Genoa, Italy, Genova, Italy, 2Department of Developmental Psychology, University of Padova, Padova, Italy, Padova, Italy, 3Italian Multiple Sclerosis Foundation, Genoa, Italy, 4DIMES, University of Genoa, Genova, Italy
First Author:
Costanza Iester
Department of Neuroscience, DINOGMI, University of Genoa, Genoa, Italy
Genova, Italy
Co-Author(s):
Monica Biggio
Department of Neuroscience, DINOGMI, University of Genoa, Genoa, Italy
Genova, Italy
Laura Bonzano
Department of Neuroscience, DINOGMI, University of Genoa, Genoa, Italy
Genova, Italy
Sabrina Brigadoi
Department of Developmental Psychology, University of Padova, Padova, Italy
Padova, Italy
Simone Cutini
Department of Developmental Psychology, University of Padova, Padova, Italy
Padova, Italy
Marco Bove
DIMES, University of Genoa
Genova, Italy
Introduction:
Resting-state functional connectivity (RSFC) has predominantly been explored using functional Magnetic Resonance Imaging (fMRI)1. Existing literature highlights alterations in RSFC among specific neurodegenerative conditions, including multiple sclerosis (MS)2,3. Functional near-infrared spectroscopy (fNIRS) emerges as a potential alternative for RSFC analysis4. In contrast to fMRI, fNIRS boasts several advantages, such as portability, noiselessness, and resistance to motion artifacts. These advantages ensure unrestricted participation of all subjects, eliminating constraints associated with factors like magnetic fields and enhancing overall comfort during data acquisition. This study aims to investigate RSFC patterns using fNIRS in both healthy controls and people with multiple sclerosis (PwMS).
Methods:
We enrolled 18 control participants (mean age = 55.0 ± 3.1 years) and 18 PwMS (mean age = 59.4 ± 1.7 years) for this study. The experimental protocol included a 15-minute resting-state session while recording fNIRS data. Changes in oxy-hemoglobin concentration were measured across 44 standard channel (3cm) and 8 short-separation channels (8mm). The fNIRS array covered premotor, sensorimotor, associative, parietal, and frontal areas. After signal acquisition, noisy channels were removed, and the remaining channels were converted into changes in optical density. Motion artifacts were identified, and motion-free segments were segregated5. Subsequently, motion-free segments exceeding a duration of 20 seconds were individually analysed. They were band-passed (0.009–0.08Hz)6, and the optical density data were converted into concentration changes. At the end, the short-separation channels were regressed out, and the free segments were combined. Channel signals within the same Brodmann area (BA) were averaged for each subject, resulting in 18 regions of interest (nine for each hemisphere). Subsequently, the Pearson correlation was employed to calculate the correlation matrix for each subject. Group correlation matrices were then computed by averaging individual correlation matrices within each group. Finally, to assess the statistical difference between groups, individual correlation matrices were Z-transformed and then each box of the matrix was compared between the two groups through a non-parametric test (Wilcoxon rank sum test, p <0.05)7.
Results:
Results revealed a robust inter-hemispheric correlation specific to homologous areas in the control group, and clusters in prefrontal, sensorimotor, and associative intra-hemispheric regions. Conversely, PwMS generally exhibited a loss or reduction in correlations compared to the control group. Specifically, PwMS demonstrated diminished connections between homologous areas (BA40, p=.001; BA7, p=.017; BA3, p= 0.0016) and, more broadly, among inter-hemispheric connections. Additionally, reductions were observed in intra-hemispheric connections related to sensorimotor and parietal areas (e.g., Left BA3-BA40, p=.014; Left BA4-BA40, p=.005; Left BA3-BA4, p=.005).
Conclusions:
The decrease in functional inter-hemispheric connections could be attributed to the loss of integrity of the corpus callosum, which is typical in PwMS8. Impaired RSFC can lead to inadequate performance of daily life tasks. Therefore, exploiting the advantages of fNIRS, such as portability, quietness, and non-invasiveness, to acquire data immediately before the execution of a specific motor or cognitive task, it may be possible to investigate potential associations between the state of the brain (resting-state) and following behavioral outcomes (task).
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
Task-Independent and Resting-State Analysis
Novel Imaging Acquisition Methods:
NIRS 2
Keywords:
Acquisition
ADULTS
Cortex
Degenerative Disease
Near Infra-Red Spectroscopy (NIRS)
1|2Indicates the priority used for review
Provide references using author date format
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