Short-range white matter tracts modulate global parameters for neurocompensation in aging
Poster No:
1145
Submission Type:
Abstract Submission
Authors:
Priyanka Chakraborty1, Suman Saha1, Arpan Banerjee2, Dr Dipanjan Roy3
Institutions:
1National Brain Research Center, Gurgaon, Haryana, 2National Brain Research Centre, Gurgaon, Haryana, 3School of AIDE, Center for Brain Research and Applications, IIT Jodhpur,, Jodhpur, Rajasthan
First Author:
Co-Author(s):
Dr Dipanjan Roy
School of AIDE, Center for Brain Research and Applications, IIT Jodhpur,
Jodhpur, Rajasthan
School of AIDE, Center for Brain Research and Applications, IIT Jodhpur,
Jodhpur, Rajasthan
Introduction:
An overarching question in aging neuroscience is how the brain compensates for age-related white matter deterioration and sustains its normative pattern in neuronal dynamics across lifespan. Neuroplasticity, a recognized neurocompensatory mechanism, involves synaptic modifications and modulation of biological parameters (Park & Reuter-Lorenz 2009) in face of age related structural changes. Increased global coupling strength compensates for structural loss during healthy aging, and enhanced inter-areal coupling preserves neural synchrony (Pathak et al. 2022). Nonetheless, short-range (SR) white matter tracts allow fast and efficient local communication across the brain (Mišić & Betzel 2015), whereas, long-range (LR) tracts are crucial for integrating information among distant areas (Sporns & Zwi 2004). However, how the brain's sub-graphs comprises of SR and LR tracts contribute to this neurocompensatory mechanism keeping functional integrity intact in healthy aging process is not completely understood.
We aim to investigate how two sub-communities comprising SR and LR tracts modulate biophysical parameters to compensate for age related decline and specifically explore their roles in calibrating global interaction strength and conduction delay in age-related neurocompensation. We categorize white matter fibers into SR and LR connections based on their physical length distributions and employ metastability and an anatomically constrained whole-brain network model to assess how these sub-graphs influence the model parameters using structural connectivity data from individuals.
We aim to investigate how two sub-communities comprising SR and LR tracts modulate biophysical parameters to compensate for age related decline and specifically explore their roles in calibrating global interaction strength and conduction delay in age-related neurocompensation. We categorize white matter fibers into SR and LR connections based on their physical length distributions and employ metastability and an anatomically constrained whole-brain network model to assess how these sub-graphs influence the model parameters using structural connectivity data from individuals.
Methods:
The study includes 82 healthy subjects (43 F and 39 M) from CamCAN, which are separated into two groups: 41 young (18-33 years, mean = 25±4 years, 22 F), and 41 old subjects (60-86 years, mean = 74±6.8 years, 21 F). We used Desikan-Killany parcellation of 68 cortical regions. We defined SR and LR connections based on the fiber lengths using two thresholds selected from first quartile (70 mm for SR) and third quartile (140 mm for LR) of the average tract length distribution. The maximum fiber length is set to 250mm.
We utilized the Kuramoto model (Kuramoto 1984) to capture metastability in a network, where the input to the model is SC of individual subjects.
For each pair of the two parameters (κ, τ ), we measure the metastability M(κ, τ ) and generated a metastability map on two-parameter plane. Next, we estimate the optimal parameters utilizing the condition, [κ, τ ]opt= argmax[(M(κ, τ )). The optimal parameters are estimated at the level of individuals. Next, we compared the estimated parameters between young and old groups using independent t-test.
We utilized the Kuramoto model (Kuramoto 1984) to capture metastability in a network, where the input to the model is SC of individual subjects.
For each pair of the two parameters (κ, τ ), we measure the metastability M(κ, τ ) and generated a metastability map on two-parameter plane. Next, we estimate the optimal parameters utilizing the condition, [κ, τ ]opt= argmax[(M(κ, τ )). The optimal parameters are estimated at the level of individuals. Next, we compared the estimated parameters between young and old groups using independent t-test.
Results:
The empirical structural analysis shows a significant reduction in fiber lengths and white matter counts or strengths in old subjects compared to younger participants. We also observed an age-related alteration in empirical functional network properties, whereas the empirical metastability remains constant between the two age groups. Overall, the results indicate a re-organisation in functional network and preservation in whole-brain dynamical repertoire.
The simulated results reveal a significant increase in coupling strength in older adults compared to the young. We observed no significant difference between the optimal coupling strength of older adults and the short-range (SR) sub-community. Hence, the SR sub-community enhanced its coupling strength to maintain the desired set point while preserving functional integrity in the face of lost LR connections.
The simulated results reveal a significant increase in coupling strength in older adults compared to the young. We observed no significant difference between the optimal coupling strength of older adults and the short-range (SR) sub-community. Hence, the SR sub-community enhanced its coupling strength to maintain the desired set point while preserving functional integrity in the face of lost LR connections.
Conclusions:
SR compensate for long-range fiber loss by modulating global coupling, effectively rescaling altered local interaction strengths (SC). Our study is useful for understanding the mechanisms underlying the brain's function in neural disorders, including Alzheimer's and Parkinson's.
Lifespan Development:
Aging 1
Modeling and Analysis Methods:
Diffusion MRI Modeling and Analysis 2
Keywords:
Aging
White Matter
Other - short range and long range tracts, metastability, neurocompensation
1|2Indicates the priority used for review
Provide references using author date format
Mišić, Bratislav, & Betzel, Richard F et al. 2015. Cooperative and competitive spreading dynamics on the
human connectome. Neuron, 86(6), 1518–1529.
Park, Denise C, & Reuter-Lorenz, Patricia. 2009. The adaptive brain: aging and neurocognitive scaffolding.
Annual review of psychology, 60, 173–196.
Pathak, Anagh, Sharma, Vivek, Roy, Dipanjan, & Banerjee, Arpan. 2022. Biophysical mechanism underlying
compensatory preservation of neural synchrony over the adult lifespan. Communications Biology, 5(1), 567.
Sporns, Olaf, & Zwi, Jonathan D. 2004. The small world of the cerebral cortex. Neuroinformatics, 2, 145–162.