Biology subjects

Kuceyeski, A.

Publications and source records attributed to Kuceyeski, A..

39 records · Page 3Linked to original sources

bioRxiv2018

Identifiability in connectome based neural mass models

The spatial-temporal patterns of neuronal dynamics emerge from the network of coordinated brain regions, this structure-function relationship of the brain can be described mathematically by biophysical models of coupled brain regions connected by white matter tractography. Implementations of such models have focused on reproducing functional connectivity extracted from functional magnetic resonance imaging (fMRI), but these efforts are limited by the temporal resolution of fMRI data and the reduction of time course recordings into phenomenological functional connectivity maps. Here, we optimize parameters of a neural mass model (NMM) to best fit region-wise power spectra across the whole brain estimated from source localized electroencephalography (EEG). NMM models with global parameters were not able to fully reproduce region-wise power spectra, with or without the inclusion of structural connectivity information. In contrast, without the inclusion of structural connectivity information, independent oscillators at each brain region are able to reproduce region-wise power spectra. But the addition of structural connectivity and transmission delays to the NMM does not improve overall power spectra fit. Connectome-based NMM implementations with regional parameters lead to high dimensional network models that produce non-unique results. Inherent parameter identifiability problem in network models poses challenges for using such models as diagnostic tools for neurological diseases.

neuroscience↗

bioRxiv2018

Predicting response to motor therapy in chronic stroke patients using Machine Learning

Accurate predictions of motor improvement resulting from intensive therapy in chronic stroke patients is a difficult task for clinicians, but is key in prescribing appropriate therapeutic strategies. Statistical methods, including machine learning, are a highly promising avenue with which to improve prediction accuracy in clinical practice. The first main objective of this study was to use machine learning methods to predict a chronic stroke individuals motor function improvement after 6 weeks of intervention using pre-intervention demographic, clinical, neurophysiological and imaging data. The second main objective was to identify which data elements were most important in predicting chronic stroke patients impairment after 6 weeks of intervention. Data from one hundred and two patients (Female: 31%, age 61{+/-}11 years) who suffered first ischemic stroke 3-12 months prior were included in this study. After enrollment, patients underwent 6 weeks of intensive motor and transcranial magnetic stimulation therapy. Age, gender, handedness, time since stroke, pre-intervention Fugl-Meyer Assessment, stroke lateralization, the difference in motor threshold between the unaffected and affected hemispheres, absence or presence of motor evoked potential in the affected hemisphere and various imaging metrics were used as predictors of post-intervention Fugl-Meyer Assessment. Five machine learning methods, including Elastic-Net, Support Vector Machines, Artificial Neural Networks, Classification and Regression Trees, and Random Forest, were used to predict post-intervention Fugl-Meyer Assessment based on either demographic, clinical and neurophysiological data alone or in combination with the imaging metrics. Cross-validated R-squared and root of mean squared error were used to assess the prediction accuracy and compare the performance of methods. Elastic-Net performed significantly better than the other methods for the model containing pre-intervention Fugl-Meyer Assessment, demographic, clinical and neurophysiological data as predictors of post-intervention Fugl-Meyer Assessment ([Formula]). Pre-intervention Fugl-Meyer Assessment and difference in motor threshold between affected and unaffected hemispheres were commonly found as the strongest two predictors in the clinical model. The difference in motor threshold had greater importance than the absence or presence of motor evoked potential in the affected hemisphere. The various imaging metrics, including lesion overlap with the spinal cord, largely did not improve the model performance. The approach implemented here may enable clinicians to more accurately predict a chronic stroke patients individual response to intervention. The predictive models used in this study could assist clinicians in making treatment decisions and improve the accuracy of prognosis in chronic stroke patients.

neuroscience↗

bioRxiv2018

Functional rerouting via the structural connectome is associated with better recovery after mild TBI

Traumatic brain injury damages white matter pathways that connect brain regions, disrupting transmission of electrochemical signals and causing cognitive and emotional dysfunction. Connectome-level mechanisms for how the brain compensates for injury have not been fully characterized. Here, we collected serial MRI-based structural and functional connectome metrics and neuropsychological scores in 26 mild traumatic brain injury subjects (29.4{+/-}8.0 years, 20 male) at 1 and 6 months post-injury. We quantified the relationship between functional and structural connectomes using network diffusion model propagation time, a measure that can be interpreted as how much of the structural connectome is being utilized for the spread of functional activation, as captured via the functional connectome. Overall cognition showed significant improvement from 1 to 6 months (t25=-2.15, p=0.04). None of the structural or functional global connectome metrics were significantly different between 1 and 6 months, or when compared to 34 age- and gender-matched controls (28.6{+/-}8.8 years, 25 male). We predicted longitudinal changes in overall cognition from changes in global connectome measures using a partial least squares regression model (cross-validated R2 = 0.27). We observe that increased network diffusion model propagation time, increased structural connectome segregation and increased functional connectome integration were related to better cognitive recovery. We interpret these findings as suggesting two connectome-based post-injury recovery mechanisms: one of neuroplasticity that increases functional connectome integration and one of remote white matter degeneration that increases structural connectome segregation. We hypothesize that our inherently multi-modal measure of network diffusion model propagation time captures the interplay between these two mechanisms.\n\nAbbreviationsmild traumatic brain injury (mTBI), structural connectome (SC), functional connectome (FC), network diffusion (ND), functional MRI (fMRI), diffusion MRI (dMRI), principal component analysis (PCA), partial least squares regression (PLSR), confidence interval (CI), Attention Network Test (ANT), California Verbal Learning Test II (CVLT-II), Coma Recovery Scale - Revised (CRS-R)

neuroscience↗