Functional connectivity-hemodynamic (un)coupling changes in chronic mild brain injury are associated with mental health and neurocognitive indices: a resting state fMRI study.

peer reviewedPURPOSE: To examine hemodynamic and functional connectivity alterations and their association with neurocognitive and mental health indices in patients with chronic mild traumatic brain injury (mTBI). METHODS: Resting-state functional MRI (rs-fMRI) and neuropsychological assessment of 37 patients with chronic mTBI were performed. Intrinsic connectivity contrast (ICC) and time-shift analysis (TSA) of the rs-fMRI data allowed the assessment of regional hemodynamic and functional connectivity disturbances and their coupling (or uncoupling). Thirty-nine healthy age- and gender-matched participants were also examined. RESULTS: Patients with chronic mTBI displayed hypoconnectivity in bilateral hippocampi and parahippocampal gyri and increased connectivity in parietal areas (right angular gyrus and left superior parietal lobule (SPL)). Slower perfusion (hemodynamic lag) in the left anterior hippocampus was associated with higher self-reported symptoms of depression (r =  - 0.53, p = .0006) and anxiety (r =  - 0.484, p = .002), while faster perfusion (hemodynamic lead) in the left SPL was associated with lower semantic fluency (r =  - 0.474, p = .002). Finally, functional coupling (high connectivity and hemodynamic lead) in the right anterior cingulate cortex (ACC)) was associated with lower performance on attention and visuomotor coordination (r =  - 0.50, p = .001), while dysfunctional coupling (low connectivity and hemodynamic lag) in the left ventral posterior cingulate cortex (PCC) and right SPL was associated with lower scores on immediate passage memory (r =  - 0.52, p = .001; r =  - 0.53, p = .0006, respectively). Uncoupling in the right extrastriate visual cortex and posterior middle temporal gyrus was negatively associated with cognitive flexibility (r =  - 0.50, p = .001). CONCLUSION: Hemodynamic and functional connectivity differences, indicating neurovascular (un)coupling, may be linked to mental health and neurocognitive indices in patients with chronic mTBI

Personalized screening and risk profiles for Mild Cognitive Impairment via a Machine Learning Framework: Implications for general practice.

peer reviewedOBJECTIVES: Diagnosis of Mild Cognitive Impairment (MCI) requires lengthy diagnostic procedures, typically available at tertiary Health Care Centers (HCC). This prospective study evaluated a flexible Machine Learning (ML) framework toward identifying persons with MCI or dementia based on information that can be readily available in a primary HC setting. METHODS: Demographic and clinical data, informant ratings of recent behavioral changes, self-reported anxiety and depression symptoms, subjective cognitive complaints, and Mini Mental State Examination (MMSE) scores were pooled from two aging cohorts from the island of Crete, Greece (N = 763 aged 60-93 years) comprising persons diagnosed with MCI (n = 277) or dementia (n = 153), and cognitively non-impaired persons (CNI, n = 333). A Balanced Random Forest Classifier was used for classification and variable importance-based feature selection in nested cross-validation schemes (CNI vs MCI, CNI vs Dementia, MCI vs Dementia). Global-level model-agnostic analyses identified predictors displaying nonlinear behavior. Local level agnostic analyses pinpointed key predictor variables for a given classification result after statistically controlling for all other predictors in the model. RESULTS: Classification of MCI vs CNI was achieved with improved sensitivity (74 %) and comparable specificity (73 %) compared to MMSE alone (37.2 % and 94.3 %, respectively). Additional high-ranking features included age, education, behavioral changes, multicomorbidity and polypharmacy. Higher classification accuracy was achieved for MCI vs Dementia (sensitivity/specificity = 87 %) and CNI vs Dementia (sensitivity/specificity = 94 %) using the same set of variables. Model agnostic analyses revealed notable individual variability in the contribution of specific variables toward a given classification result. CONCLUSIONS: Improved capacity to identify elderly with MCI can be achieved by combining demographic and medical information readily available at the PHC setting with MMSE scores, and informant ratings of behavioral changes. Explainability at the patient level may help clinicians identify specific predictor variables and patient scores to a given prediction outcome toward personalized risk assessment

Anxiety and depression severity in neuropsychiatric SLE are associated with perfusion and functional connectivity changes of the frontolimbic neural circuit: a resting-state f(unctional) MRI study.

peer reviewed[en] OBJECTIVE: To examine the hypothesis that perfusion and functional connectivity disturbances in brain areas implicated in emotional processing are linked to emotion-related symptoms in neuropsychiatric SLE (NPSLE). METHODS: Resting-state fMRI (rs-fMRI) was performed and anxiety and/or depression symptoms were assessed in 32 patients with NPSLE and 18 healthy controls (HC). Whole-brain time-shift analysis (TSA) maps, voxel-wise global connectivity (assessed through intrinsic connectivity contrast (ICC)) and within-network connectivity were estimated and submitted to one-sample t-tests. Subgroup differences (high vs low anxiety and high vs low depression symptoms) were assessed using independent-samples t-tests. In the total group, associations between anxiety (controlling for depression) or depression symptoms (controlling for anxiety) and regional TSA or ICC metrics were also assessed. RESULTS: Elevated anxiety symptoms in patients with NPSLE were distinctly associated with relatively faster haemodynamic response (haemodynamic lead) in the right amygdala, relatively lower intrinsic connectivity of orbital dlPFC, and relatively lower bidirectional connectivity between dlPFC and vmPFC combined with relatively higher bidirectional connectivity between ACC and amygdala. Elevated depression symptoms in patients with NPSLE were distinctly associated with haemodynamic lead in vmPFC regions in both hemispheres (lateral and medial orbitofrontal cortex) combined with relatively lower intrinsic connectivity in the right medial orbitofrontal cortex. These measures failed to account for self-rated, milder depression symptoms in the HC group. CONCLUSION: By using rs-fMRI, altered perfusion dynamics and functional connectivity was found in limbic and prefrontal brain regions in patients with NPSLE with severe anxiety and depression symptoms. Although these changes could not be directly attributed to NPSLE pathology, results offer new insights on the pathophysiological substrate of psychoemotional symptomatology in patients with lupus, which may assist its clinical diagnosis and treatment

Converging evidence of impaired brain function in systemic lupus erythematosus: changes in perfusion dynamics and intrinsic functional connectivity.

peer reviewed[en] PURPOSE: Τhe study examined changes in hemodynamics and functional connectivity in patients with systemic lupus erythematosus (SLE) with or without neuropsychiatric manifestations. METHODS: Participants were 44 patients with neuropsychiatric SLE (NPSLE), 20 SLE patients without such manifestations (non-NPSLE), and 35 healthy controls. Resting-state functional MRI (rs-fMRI) was used to obtain whole-brain maps of (a) perfusion dynamics derived through time shift analysis (TSA), (b) regional functional connectivity (intrinsic connectivity contrast (ICC) coefficients), and (c) hemodynamic-connectivity coupling. Group differences were assessed through independent samples t-tests, and correlations of rs-fMRI indices with clinical variables and neuropsychological test scores were, also, computed. RESULTS: Compared to HC, NPSLE patients demonstrated intrinsic hypoconnectivity of anterior Default Mode Network (DMN) and hyperconnectivity of posterior DMN components. These changes were paralleled by elevated hemodynamic lag. In NPSLE, cognitive performance was positively related to higher intrinsic connectivity in these regions, and to higher connectivity-hemodynamic coupling in posterior DMN components. Uncoupling between hemodynamics and connectivity in the posterior DMN was associated with worse task performance. Non-NPSLE patients displayed hyperconnectivity in posterior DMN and sensorimotor regions paralleled by relatively increased hemodynamic lag. CONCLUSION: Adaptation of regional brain function to hemodynamic changes in NPSLE may involve locally decreased or locally increased intrinsic connectivity (which can be beneficial for cognitive function). This process may also involve elevated coupling of hemodynamics with functional connectivity (beneficial for cognitive performance) or uncoupling, which may be detrimental for the cognitive skills of NPSLE patients

Chronic Mild Traumatic Brain Injury: Aberrant Static and Dynamic Connectomic Features Identified Through Machine Learning Model Fusion.

peer reviewedTraumatic 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

Perceived Effects of Orthognathic Surgery versus Orthodontic Camouflage Treatment of Convex Facial Profile Patients.

Increased facial profile convexity has a common occurrence in the population and is a primary reason for seeking orthodontic treatment. The present study aimed to compare the perceived changes in facial profile appearance between patients treated with combined orthognathic/orthodontic treatment versus only orthodontic camouflage treatment. For this reason, 18 pairs of before- and after-treatment facial profile photos per treatment group (n = 36 patients) were presented to four types of assessors (surgeons, orthodontists, patients, laypeople). Ratings were recorded on 100 mm visual analogue scales depicted in previously validated questionnaires. All rater groups identified minor positive changes in the facial profile appearance after exclusively orthodontic treatment, in contrast to substantial positive changes (14% to 18%) following combined orthodontic and orthognathic surgery. The differences between the two treatment approaches were slightly larger in the lower face and the chin than in the lips. The combined orthodontic and orthognathic surgery interventions were efficient in improving the facial appearance of patients with convex profile, whereas orthodontic treatment alone was not. Given the significant influence of facial aesthetics on various life aspects and its pivotal role in treatment demand and patient satisfaction, healthcare providers should take these findings into account when consulting adult patients with a convex facial profile

Evidence of Age-Related Hemodynamic and Functional Connectivity Impairment: A Resting State fMRI Study.

peer reviewedPurpose: To assess age-related changes in intrinsic functional brain connectivity and hemodynamics during adulthood in the context of the retrogenesis hypothesis, which states that the rate of age-related changes is higher in late-myelinating (prefrontal, lateral-posterior temporal) cerebrocortical areas as compared to early myelinating (parietal, occipital) regions. In addition, to examine the dependence of age-related changes upon concurrent subclinical depression symptoms which are common even in healthy aging. Methods: Sixty-four healthy adults (28 men) aged 23-79 years (mean 45.0, SD = 18.8 years) were examined. Resting-state functional MRI (rs-fMRI) time series were used to compute voxel-wise intrinsic connectivity contrast (ICC) maps reflecting the strength of functional connectivity between each voxel and the rest of the brain. We further used Time Shift Analysis (TSA) to estimate voxel-wise hemodynamic lead or lag for each of 22 ROIs from the automated anatomical atlas (AAL). Results: Adjusted for depression symptoms, gender and education level, reduced ICC with age was found primarily in frontal, temporal regions, and putamen, whereas the opposite trend was noted in inferior occipital cortices (p < 0.002). With the same covariates, increased hemodynamic lead with advancing age was found in superior frontal cortex and thalamus, with the opposite trend in inferior occipital cortex (p < 0.002). There was also evidence of reduced coupling between voxel-wise intrinsic connectivity and hemodynamics in the inferior parietal cortex. Conclusion: Age-related intrinsic connectivity reductions and hemodynamic changes were demonstrated in several regions-most of them part of DMN and salience networks-while impaired neurovascular coupling was, also, found in parietal regions. Age-related reductions in intrinsic connectivity were greater in anterior as compared to posterior cortices, in line with implications derived from the retrogenesis hypothesis. These effects were affected by self-reported depression symptoms, which also increased with age

Changes in resting-state functional connectivity in neuropsychiatric lupus: A dynamic approach based on recurrence quantification analysis

peer reviewedThere is growing interest in dynamic approaches to functional brain connectivity (FC), and their potential applications in understanding atypical brain function. In this study, we assess the relative sensitivity of cross recurrence quantification analysis CRQA) to identify aberrant FC in patients with neuropsychiatric systemic lupus erythematosus (NPSLE) in comparison with conventional static and dynamic bivariate FC measures, as well as univariate (nodal) RQA. This technique was applied to resting-state fMRI data obtained from 45 NPSLE patients and 35 healthy volunteers (HC). Cross recurrence plots were computed for all pairs of 16 frontoparietal brain regions known to be critically involved in visuomotor control and suspected to show hemodynamic disturbance in NPSLE. Multivariate group comparisons revealed that the combination of six CRQA measures differentiated the two groups with large effect sizes (.214>η2>.061) in 40 out of the 120 region pairs. The majority of brain regions forming these pairs also showed group differences on nodal RQA indices (.146>η2>.09) Overall, larger values were found in NPSLE patients vs. HC with the exception of FC formed by the paracentral lobule. Determinism within five pairs of right-hemisphere sensorimotor regions (paracentral lobule, primary somatosensory, primary motor, and supplementary motor areas), correlated positively with visuomotor performance among NPSLE patients (pη2>.061), none of which correlated significantly with visuomotor performance. Indices derived from dynamic, temporal-based FC analyses displayed large effect sizes in 11/120 region pairs (.11>η2>.063). These findings further support the importance of feature-based dFC in advancing current knowledge on correlates of cognitive dysfunction in a clinically challenging disorder, such as NPSLE

Machine learning classification of neuropsychiatric systemic lupus erythematosus patients using resting-state fmri functional connectivity

peer reviewedIn this study we explored the robustness of machine learning algorithms for the classification of Neuropsychiatric systemic lupus erythematosus (NPSLE) patients and healthy controls using resting-state fMRI functional connectivity matrices. NPSLE, which is driven by systemic autoimmune inflammation in the context of lupus, involves a wide range of focal and diffuse central and peripheral nervous system symptoms and poses significant diagnostic challenges. Machine learning applications on clinical data may enhance the existing workflow for NPSLE classification as there is no established method of applying neuroimaging data to the diagnosis of NPSLE. Feature selection methods were applied prior to the classification process in order to perform the classification process on a lower dimension feature space. The Connectivity Matrix used consisted of pairwise regional functional associations of the fMRI signals (ROI to ROI correlations) within each of three predetermined brain networks in 41 NPSLE patients and 31 healthy control subjects. Support Vector Machines (SVM) was utilized in the final model. Results were evaluated using a nested cross validation methodology to prevent overfitting, and enhance generalization. Regions of Interest (ROI's) that contributed most in the final model were: Right Inferior Temporal, Thalamus, Left Angular Gyrus, Right Precuneus, Left Primary Motor Cortex, SMA, Left and Right Primary Motor Cortex. With a final F1 score of up to 77%, the results demonstrate the potential for the future implementation of similar methods in the diagnosis of NPSLE

Μη-γραμμική λειτουργική συνδεσιμότητα σε δεδομένα λειτουργικής μαγνητικής τομογραφίας κατάστασης ηρεμίας (re-fMRI) ασθενών με προσβολές του νευρικού συστήματος: Μια δυναμική προσέγγιση

Summarization: Mild traumatic brain injuries (mTBI) are a fairly common type of brain injury in which 90% of brain injury cases are classified as mild. However, existing diagnostic criteria for mTBI often fail to identify positive cases due to the inefficiency of diagnostic methods. For this reason, it has become necessary to develop techniques of greater diagnostic accuracy. In this paper, we thus propose the use of resting-state functional magnetic resonance imaging (rs-fMRI), a promising technique with positive research and diagnostic samples in various clinical groups. In the current utilization of rs-fMRI, an innovative combination being developed with the use of static and dynamic functional connectivity networks (SFC and DFC, respectively). The proposed methodology is applied to a comparable number of subjects/patients to accurately identify and separate chronic mTBI from normal brain activity, which to our knowledge has not been explored previously. SFC and DFC are calculated using bivariate linear and non-linear correlation indices. An approach based on temporal connectivity states was employed to characterize the networks of each time frame as either integrated or segregated. A reduction of the generated brain networks using Orthogonal Minimum Spanning Trees was applied to produce networks that maximize efficient information flow. Graph metrics were used to quantify various functional and topological features in SFC and DFC networks. Different types of features representing regional connectivity estimated by SFC/DFC are combined in the final stage to produce a reliable and efficient model. A novel machine learning model combination technique was compared with existing methods. The proposed diagnostic methodology resulted in a classification accuracy of 80% achieved by XGBoost models combined with logistic regression, with nested cross-validation of consensus type and built-in feature selection technique. Most of the regions derived from the machine learning model are in agreement with previous research in the field with the addition of some new findings with interesting interpretations. This experimental combination of approaches seems to offer promising results towards neurodiagnostic imaging of mTBI with high-precision tools, opening new windows of exploration.Περίληψη: Οι ήπιες κρανιοεγκεφαλικές κακώσεις (mild traumatic brain injuries - mTBI) είναι ένα αρκετά συχνό περιστατικό εγκεφαλικού τραύματος κατά το οποίο ένα 90% των περιπτώσεων κρανιοεγκεφαλικών κακώσεων να χαρακτηρίζονται ως ήπιες. Ωστόσο, τα υπάρχοντα διαγνωστικά κριτήρια για mTBI συχνά αποτυγχάνουν να εντοπίσουν τις θετικές περιπτώσεις λόγω της αναποτελεσματικότητας των διαγνωστικών μεθόδων. Για αυτό το λόγο, κατέστη αναγκαίο η ανάπτυξη τεχνικών μεγαλύτερης διαγνωστικής ακρίβειας. Στην παρούσα εργασία, προτείνεται έτσι η χρήση λειτουργικής μαγνητικής τομογραφίας σε κατάστασης ηρεμίας (rs-fMRI), μία πολλά υποσχόμενη τεχνική με θετικά ερευνητικά και διαγνωστικά δείγματα σε διάφορες κλινικές ομάδες. Πέραν όμως από την τωρινή αξιοποίηση του rs-fMRI, ένας καινοτόμος συνδυασμός που αναπτύσσεται είναι η χρήση στατικών και δυναμικών δικτύων συνδεσιμότητας (static and dynamic functional connectivity networks, SFC and DFC, αντίστοιχα). Η προτεινόμενη μεθοδολογία εφαρμόζεται σε ένα συγκρίσιμό αριθμό ατόμων/ασθενών για τον ακριβή εντοπισμό και διαχωρισμό χρόνιων mTBI από φυσιολογική εγκεφαλική δραστηριότητα, κάτι που από όσο γνωρίζουμε δεν έχει εξερευνηθεί στο παρελθόν. Οι SFC και DFC υπολογίζονται χρησιμοποιώντας διμεταβλητούς γραμμικούς και μη-γραμμικούς δείκτες συσχέτισης. Μια προσέγγιση βασισμένη σε διακριτές χρονικές καταστάσεις συνδεσιμότητας αναπτύχθηκε με σκοπό τον χαρακτηρισμό των δικτύων κάθε χρονικού πλαισίου ως διάχυτα (integrated) ή διαχωρισμένα (segregated) ενεργοποιημένα. Εφαρμόστηκε μείωση των παραγόμενων εγκεφαλικών δικτύων με χρήση Orthogonal Minimum Spanning Trees, με σκοπό την παραγωγή δικτύων που μεγιστοποιούν την αποδοτική μεταφορά πληροφορίας. Μετρικές γράφων χρησιμοποιήθηκαν για την ποσοτικοποίηση διαφόρων λειτουργικών και τοπολογικών χαρακτηριστικών σε δίκτυα SFC και DFC. Διαφορετικοί τύποι χαρακτηριστικών που εκπροσωπούν σε βάθος της συνδέσεις των εκτιμωμένων SFC/DFC συνδυάζονται στο τελικό στάδιο για την παραγωγή ενός αξιόπιστου και αποτελεσματικού μοντέλου. Μια καινοτόμα τεχνική συνδυασμού μοντέλων μηχανικής μάθησης συγκρίθηκε με υπάρχουσες τεχνικές. Η προτεινόμενη διαγνωστική μεθοδολογία κατέληξε ακρίβεια διαχωρισμού ύψους 80% που επιτεύχθηκε από μοντέλα XGBoost σε συνδυασμό με λογιστική παλινδρόμηση, με διασταυρωμένη επικύρωση τύπου consensus και ενσωματωμένη τεχνική επιλογής χαρακτηριστικών. Οι περισσότερες επιλαχούσες περιοχές από το μοντέλο μηχανικής μάθησης είναι σε συμφωνία με προηγούμενες έρευνες του τομέα με την προσθήκη κάποιων νέων ευρημάτων με ενδιαφέρουσες ερμηνείες. Αυτός ο πειραματικός συνδυασμός προσεγγίσεων φαίνεται να προσφέρει πολλά υποσχόμενα και ελπιδοφόρα αποτελέσματα προς την κατεύθυνση της νευροδιαγνωστικής απεικόνισης mTBI με εργαλεία υψηλής ακρίβειας, ανοίγοντας νέα παράθυρα στην εξερεύνηση