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Chronic mild Traumatic Brain Injury: aberrant static and dynamic connectomic features identified through machine learning model fusion

Simos Nikolaos-Ioannis, Manolitsi Katina, Luppi Andrea I., Kagialis Antonios, Antonakakis Marios, Zervakis Michail, Antypa Despina, Kavroulakis Eleftherios, Maris Thomas G., Vakis Antonios, Stamatakis Emmanuel A., Papadaki Efrosini

Απλή Εγγραφή


URIhttp://purl.tuc.gr/dl/dias/D83DB06D-D54B-4D39-9158-34E3D83BA364-
Αναγνωριστικόhttps://doi.org/10.1007/s12021-022-09615-1-
Αναγνωριστικόhttps://link.springer.com/article/10.1007/s12021-022-09615-1-
Γλώσσαen-
Μέγεθος16 pagesen
ΤίτλοςChronic mild Traumatic Brain Injury: aberrant static and dynamic connectomic features identified through machine learning model fusionen
ΔημιουργόςSimos Nikolaos-Ioannisen
ΔημιουργόςΣιμος Νικολαος-Ιωαννηςel
ΔημιουργόςManolitsi Katinaen
ΔημιουργόςLuppi Andrea I.en
ΔημιουργόςKagialis Antoniosen
ΔημιουργόςAntonakakis Mariosen
ΔημιουργόςΑντωνακακης Μαριοςel
ΔημιουργόςZervakis Michailen
ΔημιουργόςΖερβακης Μιχαηλel
ΔημιουργόςAntypa Despinaen
ΔημιουργόςKavroulakis Eleftheriosen
ΔημιουργόςMaris Thomas G.en
ΔημιουργόςVakis Antoniosen
ΔημιουργόςStamatakis Emmanuel A.en
ΔημιουργόςPapadaki Efrosinien
ΕκδότηςSpringeren
ΠερίληψηTraumatic 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.en
ΤύποςPeer-Reviewed Journal Publicationen
ΤύποςΔημοσίευση σε Περιοδικό με Κριτέςel
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by/4.0/en
Ημερομηνία2024-03-04-
Ημερομηνία Δημοσίευσης2023-
Θεματική ΚατηγορίαTraumatic Brain Injuryen
Θεματική ΚατηγορίαfMRIen
Θεματική ΚατηγορίαDepressionen
Θεματική ΚατηγορίαVerbal Fluencyen
Θεματική ΚατηγορίαFunctional Connectivityen
Βιβλιογραφική ΑναφοράN. J. Simos, K. Manolitsi, A. I. Luppi, A. Kagialis, M. Antonakakis, M. Zervakis, D. Antypa, E. Kavroulakis, T. G. Maris, A. Vakis, E. A. Stamatakis and E. Papadaki “Chronic mild Traumatic Brain Injury: aberrant static and dynamic connectomic features identified through machine learning model fusion,” Neuroinform., vol. 21, no. 2, pp. 427–442, Apr. 2023, doi: 10.1007/s12021-022-09615-1.en

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