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Mapping the Injured Brain
Published in Yu Chen, Babak Kateb, Neurophotonics and Brain Mapping, 2017
Chandler Sours, Jiachen Zhuo, Rao P. Gullapalli
Using a resting-state paradigm, it is possible to examine functional brain networks to measure the interaction between global brain regions in disparate locations. In this method, participants are instructed to rest in the MRI scanner and are not required to participate in a task. Referred to as resting state functional connectivity (rs-FC), this method measures the strength of functional interactions between brain regions based on temporal correlations between small fluctuations in the BOLD signal (Biswal et al., 1995; Sporns 2011; van den Heuvel and Hulshoff, Pol 2010) (Figure 14.3c). While historically these small fluctuations in BOLD signal were thought to be signal noise, it was noted that regions recruited to perform specific tasks displayed similar temporal patterns of fluctuations during resting conditions (Biswal et al., 1995). Analysis of functional connectivity can be performed using a hypothesis seed-based method or a data-driven independent component analysis method (Calhoun et al., 2009). Regardless of which method is selected, resting-state networks are consistently replicated across studies and often include networks that are associated with sensory systems (auditory, visual, somatosensory, and motor) as well as networks associated with higher-order cognitive processes (Raichle, 2010). Understanding the differences in neural network communications related to postconcussive symptoms among TBI patients will provide valuable information on the precise mechanisms of these deficits.
Dementia Prevention Research Clinic: a longitudinal study investigating factors influencing the development of Alzheimer’s disease in Aotearoa, New Zealand
Published in Journal of the Royal Society of New Zealand, 2023
Lynette J. Tippett, Erin E. Cawston, Catherine A. Morgan, Tracy R. Melzer, Kiri L. Brickell, Christina Ilse, Gary Cheung, Ian J. Kirk, Reece P. Roberts, Jane Govender, Leon Griner, Campbell Le Heron, Sarah Buchanan, Waiora Port, Makarena Dudley, Tim J. Anderson, Joanna M. Williams, Nicholas J. Cutfield, John C. Dalrymple-Alford, Phil Wood
MRI scanning is conducted on a 3T Siemens Skyra in all three DPRC clinics (Centre for Advanced MRI at the University of Auckland; Pacific Radiology Group in Christchurch and Dunedin). Biennial MRI scans are 45 min in length, and incorporate four clinical sequences: T1-weighted, T2-weighted, FLAIR, and susceptibility-weighted imaging. These are viewed and interpreted by a neuroradiologist blind to group status. Additional ‘research’ sequences measuring structural and functional connectivity (diffusion MRI and resting state functional MRI; consistent with the UK Biobanking Imaging study [Thompson et al. 2015]) and perfusion (arterial spin labelling, ASL) are collected. Figure 1 shows examples of a number of the sequences.
Classification based levodopamine response prediction in parkinson’s disorder
Published in Applied Artificial Intelligence, 2021
Aman Jatain, Shalini Bhaskar Bajaj, Ritika Agarwal, Haziq Rahat Bullah
The existing studies elucidating the structural and functional imaging of PD subjects have insinuated the association of palladium and thalamus regions along with caudate and putamen regions with the correlation in PD and L-Dopa response as neuroimaging shreds of evidence. Although the putamen and caudate regions are less prominent contributors when compared to the palladium and thalamus region, this PD-related covariance pattern evaluation itself has unearthed the relationship between regions of basal ganglia and L-Dopa response. The reduced functional connectivity magnetic resonance imaging (fcMRI) between striatum, palladium, and thalamus corresponds to a degraded L-Dopa response. There has been an identified association of the nonconformity in the BG connectivity patterns mapped using resting state functional magnetic resonance imaging (fMRI) with the disparate degrees of response to L-Dopa therapy. This is in line with the rationale that the remapping of functional connectivity in the brain translates into the clinical effects of dopamine. Relatively higher connectivity is exhibited by the networks linked to cognitive motor inhibition as opposed to lower connectivity of networks linked to reactive motor inhibition with an improved dopamine response. Moreover, there is relatively stronger connectivity in-between BG structures having improved dopaminergic response (Harith et al. 2017). The above discussed factors evidence their large medical significance in the robust prediction of L-Dopa treatment response in PD. Machine learning is a scientific endeavor in the ambit of artificial intelligence pertaining to the study and development of systems that can train on large scores of data (Graziella et al. 2012). Recent shreds of evidence are suggestive of the fact that response predictive analysis in psychiatry can benefit from the pervasive use of machine learning at an individual level (McGuire et al. 2012). It is maintained that these methods can be very helpful in informing and assisting medical experts to make more efficient objective choices before the treatment, thereby leading to increased trial success rates and higher response rates. More effective predictive analytics could be beneficial for PD, in particular, because of the clinical heterogeneity, high prevalence, and societal costs associated with the disease (Lang Anthony and Andres 1998). Machine learning techniques are being extensively used in the field of medicine to identify drug responsiveness toward a particular disease. These measures are rising to prevalence due to their noninvasive approach, decreased costs, and reduced risk of inflicting side effects. However, trivial work has been done in mapping the L-Dopa responsiveness in PD patients also, due to the lack of any evident biomarkers for a robust prediction of the drug response. Further, no study has shown up which resorts to finding the response of PD patients to L-Dopa drug using demographic, clinical, and sensor data.