Nervous System Commons^™

Partial Least Squares Multimodal Analysis Of Brain Network Correlates Of Language Deficits In Aphasia, Sigfus Kristinsson, Dirk B. Den Ouden, Chris Rorden, Roger Newman-Norlund, Lisa Johnson, Janina Wilmskoetter, Ezequiel Gleichgerrcht, Argye E. Hillis, Gregory Hickok, Julius Fridriksson, Leonardo Bonilha

Communication Sciences and Disorders Faculty Articles and Research

Lesion-symptom mapping techniques are essential to determine brain regions critical for language functions. However, high collinearity in neuroimaging and behavioural data remains a challenge for distinguishing neural substrates supporting multiple language domains (shared variance) and those subserving specific language functions (unique variance). Here, we employed a novel approach to multimodal lesion-symptom mapping using multivariate partial least squares regression to delineate the latent structure of lesion-behavioural mapping in aphasia and decompose the shared and unique neural determinants of language impairments. A total of 86 participants with chronic (>12-month post-stroke) aphasia after left hemisphere strokes were examined. Language impairment was assessed …

Benchmarking Machine Learning Models In Lesion-Symptom Mapping For Predicting Language Outcomes In Stroke Survivors, Deepa Tilwani, Christian O'Reilly, Nicholas Riccardi, Valerie L. Shalin, Dirk B. Den Ouden, Julius Fridriksson, Svetlana V. Shinkareva, Amit P. Sheth, Rutvik H. Desai

Communication Sciences and Disorders Faculty Articles and Research

Several decades of research have investigated the neural connections between stroke-induced brain damage and language difficulties. Typically, lesion-symptom mapping (LSM) studies that address this connection have relied on mass univariate statistics, which do not account for multidimensional relationships between variables. Machine learning (ML) techniques, which can capture these intricate connections, offer a promising complement to LSM methods. To test this promise, we benchmarked ML models on structural and functional MRI to predict aphasia severity (N = 238) and naming impairment (N = 191) for a cohort of chronic-stage stroke survivors. We used nested cross-validation to examine performance along …

Automatic Hemorrhage Segmentation In Brain Ct Scans Using Curriculum-Based Semi-Supervised Learning, Solayman H. Emon, Tzu-Liang (Bill) Tseng, Michael Pokojovy, Peter Mccaffrey, Scott Moen, Md Fashiar Rahman

Mathematics & Statistics Faculty Publications

One of the major neuropathological consequences of traumatic brain injury (TBI) is intracranial hemorrhage (ICH), which requires swift diagnosis to avert perilous outcomes. We present a new automatic hemorrhage segmentation technique via curriculum-based semi-supervised learning. It employs a pre-trained lightweight encoder-decoder framework (MobileNetV2) on labeled and unlabeled data. The model integrates consistency regularization for improved generalization, offering steady predictions from original and augmented versions of unlabeled data. The training procedure employs curriculum learning to progressively train the model at diverse complexity levels. We utilize the PhysioNet dataset to train and evaluate the proposed approach. The performance results surpass those of …

Identification Of Structural Brain Alterations In Adolescents With Depressive Symptomatology, J. Bashford‑Largo, R. James R. Blair, Karina S. Blair, Matthew Dobbertin, Ahria Dominguez, Melissa Hatch, Sahil Bajaj

Center for Brain, Biology, and Behavior: Faculty Publications

Introduction: Depressive symptoms can emerge as early as childhood and may lead to adverse situations in adulthood. Studies have examined structural brain alternations in individuals with depressive symptoms, but findings remain inconclusive. Furthermore, previous studies have focused on adults or used a categorical approach to assess depression. The current study looks to identify grey matter volumes (GMV) that predict depressive symptomatology across a clinically concerning sample of adolescents.

Methods: Structural MRI data were collected from 338 clinically concerning adolescents (mean age = 15.30 SD=2.07; mean IQ = 101.01 SD=12.43; 132 F). Depression symptoms were indexed via the Mood …

Supraspinal Reorganization After Pediatric-Onset Spinal Cord Injury., Luis Alvarado

Electronic Theses and Dissertations

Pediatric spinal cord injury (SCI) disrupts the efferent and afferent flow of the developing brain, leading to devastating functional impairments below the injury site, yet our understanding of its impact on the brain remains limited. This study examines supraspinal reorganization in children with SCI using electrophysiology and neuroimaging techniques to understand the relationship between residual spinal transmission and supraspinal reorganization. Chapter 2 discusses the development of a child-centric approach using ‘learn, play, and practice’ to foster a trusting relationship with each child and increase compliance with experimental protocols. Chapter 3 evaluates the residual neural transmission of three spinal pathways in …

Brain Age Predicts Long-Term Recovery In Post-Stroke Aphasia, Sigfus Kristinsson, Natalie Busby, Christopher Rorden, Roger Newman-Norlund, Dirk B. Den Ouden, Sigridur Magnusdottir, Haukur Hjaltason, Helga Thors, Argye E. Hillis, Olafur Kjartansson, Leonardo Bonilha, Julius Fridriksson

Communication Sciences and Disorders Faculty Articles and Research

The association between age and language recovery in stroke remains unclear. Here, we used neuroimaging data to estimate brain age, a measure of structural integrity, and examined the extent to which brain age at stroke onset is associated with (i) cross-sectional language performance, and (ii) longitudinal recovery of language function, beyond chronological age alone. A total of 49 participants (age: 65.2 ± 12.2 years, 25 female) underwent routine clinical neuroimaging (T1) and a bedside evaluation of language performance (Bedside Evaluation Screening Test-2) at onset of left hemisphere stroke. Brain age was estimated from enantiomorphically reconstructed brain scans using a machine …

Extended Functional Connectivity Of Convergent Structural Alterations Among Individuals With Ptsd: A Neuroimaging Meta-Analysis, Brianna S. Pankey, Michael C. Riedel, Isis Cowan, Jessica E. Bartley, Rosario Pintos Lobo, Lauren D. Hill-Bowen, Taylor Sato, Erica D. Musser, Matthew T. Sutherland, Angela R. Laird

Psychology Faculty Publications

Background: Post-traumatic stress disorder (PTSD) is a debilitating disorder defined by the onset of intrusive, avoidant, negative cognitive or affective, and/or hyperarousal symptoms after witnessing or experiencing a traumatic event. Previous voxel-based morphometry studies have provided insight into structural brain alterations associated with PTSD with notable heterogeneity across these studies. Furthermore, how structural alterations may be associated with brain function, as measured by task-free and task-based functional connectivity, remains to be elucidated.

Methods: Using emergent meta-analytic techniques, we sought to first identify a consensus of structural alterations in PTSD using the anatomical likelihood estimation (ALE) approach. Next, we generated functional …

Neural Function, Injury, And Stroke Subtype Predict Treatment Gains After Stroke, Erin Burke Quinlan, Lucy Dodakian, Jill See, Alison Mackenzie, Vu Le, Mike Wojnowicz, Babak Shahbaba, Steven C. Cramer

Physical Therapy Faculty Articles and Research

Objective

This study was undertaken to better understand the high variability in response seen when treating human subjects with restorative therapies poststroke. Preclinical studies suggest that neural function, neural injury, and clinical status each influence treatment gains; therefore, the current study hypothesized that a multivariate approach incorporating these 3 measures would have the greatest predictive value.

Methods

Patients 3 to 6 months poststroke underwent a battery of assessments before receiving 3 weeks of standardized upper extremity robotic therapy. Candidate predictors included measures of brain injury (including to gray and white matter), neural function (cortical function and cortical connectivity), and clinical …