Depression; Functional Connectivity; Traumatic Brain Injury; Verbal Fluency; fMRI; Humans; Brain/diagnostic imaging; Magnetic Resonance Imaging/methods; Brain Concussion; Connectome/methods; Brain Injuries, Traumatic/diagnostic imaging; Brain; Brain Injuries, Traumatic; Connectome; Magnetic Resonance Imaging; Software; Neuroscience (all); Information Systems; General Neuroscience
Abstract :
[en] Traumatic Brain Injury (TBI) is a frequently occurring condition and approximately 90% of TBI cases are classified as mild (mTBI). However, conventional MRI has limited diagnostic and prognostic value, thus warranting the utilization of additional imaging modalities and analysis procedures. The functional connectomic approach using resting-state functional MRI (rs-fMRI) has shown great potential and promising diagnostic capabilities across multiple clinical scenarios, including mTBI. Additionally, there is increasing recognition of a fundamental role of brain dynamics in healthy and pathological cognition. Here, we undertake an in-depth investigation of mTBI-related connectomic disturbances and their emotional and cognitive correlates. We leveraged machine learning and graph theory to combine static and dynamic functional connectivity (FC) with regional entropy values, achieving classification accuracy up to 75% (77, 74 and 76% precision, sensitivity and specificity, respectively). As compared to healthy controls, the mTBI group displayed hypoconnectivity in the temporal poles, which correlated positively with semantic (r = 0.43, p < 0.008) and phonemic verbal fluency (r = 0.46, p < 0.004), while hypoconnectivity in the right dorsal posterior cingulate correlated positively with depression symptom severity (r = 0.54, p < 0.0006). These results highlight the importance of residual FC in these regions for preserved cognitive and emotional function in mTBI. Conversely, hyperconnectivity was observed in the right precentral and supramarginal gyri, which correlated negatively with semantic verbal fluency (r=-0.47, p < 0.003), indicating a potential ineffective compensatory mechanism. These novel results are promising toward understanding the pathophysiology of mTBI and explaining some of its most lingering emotional and cognitive symptoms.
Disciplines :
Neurosciences & behavior
Author, co-author :
Simos, Nikolaos-Ioannis ✱; Université de Liège - ULiège > GIGA > GIGA CRC In vivo Imaging - Sleep and chronobiology
Manolitsi, Katina ✱; Department of Neurosurgery, School of Medicine & University Hospital of Heraklion, University of Crete, Crete, Greece ; Department of Psychiatry, School of Medicine & University Hospital of Heraklion, University of Crete, Crete, Greece
Luppi, Andrea I; Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, UK ; Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, UK
Kagialis, Antonios; Department of Psychiatry, School of Medicine & University Hospital of Heraklion, University of Crete, Crete, Greece
Antonakakis, Marios; Digital Image and Signal Processing Laboratory, School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece
Zervakis, Michalis; Digital Image and Signal Processing Laboratory, School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece
Antypa, Despina; Department of Psychiatry, School of Medicine & University Hospital of Heraklion, University of Crete, Crete, Greece
Kavroulakis, Eleftherios; Department of Radiology, School of Medicine & University Hospital of Heraklion, University of Crete, Crete, Greece
Maris, Thomas G; Computational Bio-Medicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas, 70013, Heraklion, Greece ; Department of Radiology, School of Medicine & University Hospital of Heraklion, University of Crete, Crete, Greece
Vakis, Antonios; Department of Neurosurgery, School of Medicine & University Hospital of Heraklion, University of Crete, Crete, Greece
Stamatakis, Emmanuel A; Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, UK. eas46@cam.ac.uk ; Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, UK. eas46@cam.ac.uk
Papadaki, Efrosini; Computational Bio-Medicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas, 70013, Heraklion, Greece ; Department of Radiology, School of Medicine & University Hospital of Heraklion, University of Crete, Crete, Greece
✱ These authors have contributed equally to this work.
Language :
English
Title :
Chronic Mild Traumatic Brain Injury: Aberrant Static and Dynamic Connectomic Features Identified Through Machine Learning Model Fusion.
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