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Machine learning (ML) and deep learning (DL) systems, currently employed in medical image analysis, are data-driven models often considered as black boxes . However, improved transparency is needed to translate automated decision-making to clinical practice. To this aim, we propose a strategy to open the black box by presenting to the radiologist the annotated cases (ACs) proximal to the current case (CC), making decision rationale and uncertainty more explicit. The ACs, used for training, validation, and testing in supervised methods and for validation and testing in the unsupervised ones, could be provided as support of the ML/DL tool. If the CC is localised in a classification space and proximal ACs are selected by proper metrics, the latter ones could be shown in their original form of images, enriched with annotation to radiologists, thus allowing immediate interpretation of the CC classification. Moreover, the density of ACs in the CC neighbourhood, their image saliency maps, classification confidence, demographics, and clinical information would be available to radiologists. Thus, encrypted information could be transmitted to radiologists, who will know model output (what) and salient image regions (where) enriched by ACs, providing classification rationale (why). Summarising, if a classifier is data-driven, let us make its interpretation data-driven too.

•QSM is characterised by a good level of repeatability across repeated MRI scans.•Using 8 or 7 echoes to derive the final QSM maps provides more consistent results than using fewer echoes.•Small regions show the highest amount of variability. Quantitative Susceptibility Mapping (QSM) is a recent MRI-technique able to quantify the bulk magnetic susceptibility of myelin, iron, and calcium in the brain. Its variability across different acquisition parameters has prompted the need for standardisation across multiple centres and MRI vendors. However, a high level of agreement between repeated imaging acquisitions is equally important. With this study we aimed to assess the inter-scan repeatability of an optimised multi-echo GRE sequence in 28 healthy volunteers. We extracted and compared the susceptibility measures from the scan and rescan acquisitions across 7 bilateral brain regions (i.e., 14 regions of interest (ROIs)) relevant for neurodegeneration. Repeatability was first assessed while reconstructing QSM with a fixed number of echo times (i.e., 8). Excellent inter-scan repeatability was found for putamen, globus pallidus and caudate nucleus, while good performance characterised the remaining structures. An increased variability was instead noted for small ROIs like red nucleus and substantia nigra. Secondly, we assessed the impact exerted on repeatability by the number of echoes used to derive QSM maps. Results were impacted by this parameter, especially in smaller regions. Larger brain structures, on the other hand, showed more consistent performance. Nevertheless, with either 8 or 7 echoes we managed to obtain good inter-scan repeatability on almost all ROIs. These findings indicate that the designed acquisition/reconstruction protocol has wide applicability, particularly in clinical or research settings involving longitudinal acquisitions (e.g. rehabilitation studies).

•Voxel-wise GLM analysis failed to capture fMRI signal variability.•ROI-based ICC analysis differentiated fMRI signal reliability between stimuli.•Stimuli with negative emotional valence are more reliable than positive valence ones.•Emotional valence more than stimulus habituation impacts fMRI signal reliability. The emotional domain is often impaired across many neurological diseases, for this reason it represents a relevant target of rehabilitation interventions. Functional changes in neural activity related to treatment can be assessed with functional MRI (fMRI) using emotion-generation tasks in longitudinal settings. Previous studies demonstrated that within-subject fMRI signal reliability can be affected by several factors such as repetition suppression, type of task and brain anatomy. However, the differential role of repetition suppression and emotional valence of the stimuli on the fMRI signal reliability and reproducibility during an emotion-generation task involving the vision of emotional pictures is yet to be determined. Sixty-two healthy subjects were enrolled and split into two groups: group A (21 subjects, test-retest reliability on same-day and with same-task-form), group B (30 subjects, test-retest reproducibility with 4-month-interval using two equivalent-parallel forms of the task). Test-retest reliability and reproducibility of fMRI responses and patterns were evaluated separately for positive and negative emotional valence conditions in both groups. The analyses were performed voxel-wise, using the general linear model (GLM), and via a region-of-interest (ROI)-based approach, by computing the intra-class correlation coefficient (ICC) on the obtained contrasts. The voxel-wise GLM test yielded no significant differences for both conditions in reliability and reproducibility analyses. As to the ROI-based approach, across all areas with significant main effects of the stimuli, the reliability, as measured with ICC, was poor (<0.4) for the positive condition and ranged from poor to excellent (0.4–0.75) for the negative condition. The ICC-based reproducibility analysis, related to the comparison of two different parallel forms, yielded similar results. The voxel-wise GLM analysis failed to capture the poor reliability of fMRI signal which was instead highlighted using the ROI-based ICC analysis. The latter showed higher signal reliability for negative valence stimuli with respect to positive ones. The implementation of two parallel forms allowed to exclude neural suppression as the predominant effect causing low signal reliability, which could be instead ascribed to the employment of different neural strategies to cope with emotional stimuli over time. This is an invaluable information for a better assessment of treatment and rehabilitation effects in longitudinal studies of emotional neural processing. [Display omitted]

Functional near-infrared spectroscopy (fNIRS) is an important non-invasive technique used to monitor cortical activity. However, a varying sensitivity of surface channels vs. cortical structures may suggest integrating the fNIRS with the subject-specific anatomy (SSA) obtained from routine MRI. Actual processing tools permit the computation of the SSA forward problem (i.e., cortex to channel sensitivity) and next, a regularized solution of the inverse problem to map the fNIRS signals onto the cortex. The focus of this study is on the analysis of the forward problem to quantify the effect of inter-subject variability. Thirteen young adults (six males, seven females, age 29.3 ± 4.3) underwent both an MRI scan and a motor grasping task with a continuous wave fNIRS system of 102 measurement channels with optodes placed according to a 10/5 system. The fNIRS sensitivity profile was estimated using Monte Carlo simulations on each SSA and on three major atlases (i.e., Colin27, ICBM152 and FSAverage) for comparison. In each SSA, the average sensitivity curves were obtained by aligning the 102 channels and segmenting them by depth quartiles. The first quartile (depth < 11.8 (0.7) mm, median (IQR)) covered 0.391 (0.087)% of the total sensitivity profile, while the second one (depth < 13.6 (0.7) mm) covered 0.292 (0.009)%, hence indicating that about 70% of the signal was from the gyri. The sensitivity bell-shape was broad in the source–detector direction (20.953 (5.379) mm FWHM, first depth quartile) and steeper in the transversal one (6.082 (2.086) mm). The sensitivity of channels vs. different cortical areas based on SSA were analyzed finding high dispersions among subjects and large differences with atlas-based evaluations. Moreover, the inverse cortical mapping for the grasping task showed differences between SSA and atlas based solutions. In conclusion, integration with MRI SSA can significantly improve fNIRS interpretation.

Background Cephalometric analysis is traditionally performed on skull lateral teleradiographs for orthodontic diagnosis and treatment planning. However, the skull flattened over a 2D film presents projection distortions and superimpositions to various extents depending on landmarks relative position. When a CBCT scan is indicated for mixed reasons, cephalometric assessments can be performed directly on CBCT scans with a distortion free procedure. The aim of the present study is to compare these two methods for orthodontic cephalometry. Methods 114 CBCTs were selected, reconstructed lateral cephalometries were obtained by lateral radiographic projection of the entire volume from the right and left sides. 2D and 3D cephalometric tracings were performed. Since paired t-tests between left and right-side measurements found no statistically significant differences, mean values between sides were considered for both 2D and 3D values. The following measurements were evaluated: PNS-A; S-N; N-Me; N-ANS; ANS-Me; Go-Me; Go-S; Go-Co; SNA, SNB, ANB; BaŜN; S-N^PNS-ANS; PNS-ANS^Go-Me; S-N^Go-Me. Intraclass correlation coefficients, paired t-test, correlation coefficient and Bland–Altman analysis were performed to compare these techniques. Results The values of intra- and inter-rater ICC showed excellent repeatability and reliability: the average (± SD) intraobserver ICCs were 0.98 (± 0.01) and 0.97(± 0.01) for CBCT and RLCs, respectively; Inter-rater reliability resulted in an average ICC (± SD) of 0.98 (± 0.01) for CBCT and 0.94 (± 0.03) for RLC. The paired t-tests between CBCT and reconstructed lateral cephalograms revealed that Go-Me, Go-S, PNS-ANS^Go-Me and S-N^Go-Me measurements were statistically different between the two modalities. All the evaluated sets of measurements showed strong positive correlation; the bias and ranges for the 95% Limits of Agreement showed higher levels of agreement between the two modalities for unpaired measurements with respect to bilateral ones. Conclusion The cephalometric measurements laying on the mid-sagittal plane can be evaluated on CBCT and used for orthodontic diagnosis as they do not show statistically significant differences with those measured on 2D lateral cephalograms. For measurements that are not in the mid-sagittal plane, the future development of specific algorithms for distortion correction could help clinicians deduct all the information needed for orthodontic diagnosis from the CBCT scan.

Artifact removal in resting state fMRI (rfMRI) data remains a serious challenge, with even subtle head motion undermining reliability and reproducibility. Here we compared some of the most popular single-echo de-noising methods-regression of Motion parameters, White matter and Cerebrospinal fluid signals (MWC method), FMRIB's ICA-based X-noiseifier (FIX) and ICA-based Automatic Removal Of Motion Artifacts (ICA-AROMA)-with a multi-echo approach (ME-ICA) that exploits the linear dependency of BOLD on the echo time. Data were acquired using a clinical scanner and included 30 young, healthy participants (minimal head motion) and 30 Attention Deficit Hyperactivity Disorder patients (greater head motion). De-noising effectiveness was assessed in terms of data quality after each cleanup procedure, ability to uncouple BOLD signal and motion and preservation of default mode network (DMN) functional connectivity. Most cleaning methods showed a positive impact on data quality. However, based on the investigated metrics, ME-ICA was the most robust. It minimized the impact of motion on FC even for high motion participants and preserved DMN functional connectivity structure. The high-quality results obtained using ME-ICA suggest that using a multi-echo EPI sequence, reliable rfMRI data can be obtained in a clinical setting.

Functional near-infrared spectroscopy (fNIRS) is increasingly employed as an ecological neuroimaging technique in assessing age-related chronic neurological disorders, such as Parkinson’s disease (PD), mainly providing a cross-sectional characterization of clinical phenotypes in ecological settings. Current fNIRS studies in PD have investigated the effects of motor and non-motor impairment on cortical activity during gait and postural stability tasks, but no study has employed fNIRS as an ecological neuroimaging tool to assess PD at different stages. Therefore, in this work, we sought to investigate the cortical activity of PD patients during a motor grasping task and its relationship with both the staging of the pathology and its clinical variables. This study considered 39 PD patients (age 69.0 ± 7.64, 38 right-handed), subdivided into two groups at different stages by the Hoehn and Yahr (HY) scale: early PD (ePD; N = 13, HY = [1; 1.5]) and moderate PD (mPD; N = 26, HY = [2; 2.5; 3]). We employed a whole-head fNIRS system with 102 measurement channels to monitor brain activity. Group-level activation maps and region of interest (ROI) analysis were computed for ePD, mPD, and ePD vs. mPD contrasts. A ROI-based correlation analysis was also performed with respect to contrasted subject-level fNIRS data, focusing on age, a Cognitive Reserve Index questionnaire (CRIQ), disease duration, the Unified Parkinson’s Disease Rating Scale (UPDRS), and performances in the Stroop Color and Word (SCW) test. We observed group differences in age, disease duration, and the UPDRS, while no significant differences were found for CRIQ or SCW scores. Group-level activation maps revealed that the ePD group presented higher activation in motor and occipital areas than the mPD group, while the inverse trend was found in frontal areas. Significant correlations with CRIQ, disease duration, the UPDRS, and the SCW were mostly found in non-motor areas. The results are in line with current fNIRS and functional and anatomical MRI scientific literature suggesting that non-motor areas—primarily the prefrontal cortex area—provide a compensation mechanism for PD motor impairment. fNIRS may serve as a viable support for the longitudinal assessment of therapeutic and rehabilitation procedures, and define new prodromal, low-cost, and ecological biomarkers of disease progression.

The assessment of signal quality has been a research topic since the late 1970s, as it is mainly related to the problem of false alarms in bedside monitors in the intensive care unit (ICU), the incidence of which can be as high as 90 %, leading to alarm fatigue and a drop in the overall level of nurses and clinicians attention. The development of efficient algorithms for the quality control of long diagnostic electrocardiographic (ECG) recordings, both single- and multi-lead, and of the arterial blood pressure (ABP) signal is therefore essential for the enhancement of care quality. The ECG signal is often corrupted by noise, which can be within the frequency band of interest and can manifest similar morphologies as the ECG itself. Similarly to ECG, also the ABP signal is often corrupted by non-Gaussian, nonlinear and non-stationary noise and artifacts, especially in ICU recordings. Moreover, the reliability of several important parameters derived from ABP such as systolic blood pressure or pulse pressure is strongly affected by the quality of the ABP waveform. In this work, several up-to-date algorithms for the quality scoring of a single- or multi-lead ECG recording, based on time-domain approaches, frequency-domain approaches or a combination of the two will be reviewed, as well as methods for the quality assessment of ABP. Additionally, algorithms exploiting the relationship between ECG and pulsatile signals, such as ABP and photoplethysmographic recordings, for the reduction in the false alarm rate will be presented. Finally, some considerations will be drawn taking into account the large heterogeneity of clinical settings, applications and goals that the reviewed algorithms have to deal with.

Background: Parkinson’s disease (PD) is a progressive neurodegenerative disorder that is mainly characterized by movement dysfunction. Neurovascular unit (NVU) disruption has been proposed to be involved in the disease, but its role in PD neurodegenerative mechanisms is still unclear. The aim of this study was to investigate cerebral blood flow (CBF) and cerebrovascular reactivity (CVR) within the regions belonging to the motor network, in patients with mild to moderate stages of PD. Methods: Twenty-eight PD patients (66.6 ± 8.6 years, 22 males, median [interquartile range, IQR] Hoehn & Yahr = 1.5 [1–1.9]) and 32 age- and sex-matched healthy controls (HCs) were scanned with arterial spin labeling (ASL) magnetic resonance imaging (MRI) for CBF assessment. ASL MRI was also acquired in hypercapnic conditions to induce vasodilation and subsequently allow for CVR measurement in a subgroup of 13 PD patients and 13 HCs. Median CBF and CVR were extracted from cortical and subcortical regions belonging to the motor network and compared between PD patients and HCs. In addition, the correlation between these parameters and the severity of PD motor symptoms [quantified with Unified Parkinson’s Disease Rating Scale part III (UPDRS III)] was assessed. The false discovery rate (FDR) method was used to correct for multiple comparisons. Results: No significant differences in terms of CBF and CVR were found between PD patients and HCs. Positive significant correlations were observed between CBF and UPDRS III within the precentral gyrus, postcentral gyrus, supplementary motor area, striatum, pallidum, thalamus, red nucleus, and substantia nigra (pFDR < 0.05). Conversely, significant negative correlation between CVR and UPDRS III was found in the corpus striatum (pFDR < 0.05). Conclusion: CBF and CVR assessment provides information about NVU integrity in an indirect and noninvasive way. Our findings support the hypothesis of NVU involvement at the mild to moderate stages of PD, suggesting that CBF and CVR within the motor network might be used as either diagnostic or prognostic markers for PD.

Functional Near-Infrared Spectroscopy (fNIRS) captures activations and inhibitions of cortical areas and implements a viable approach to neuromonitoring in clinical research. Compared to more advanced methods, continuous wave fNIRS (CW-fNIRS) is currently used in clinics for its simplicity in mapping the whole sub-cranial cortex. Conversely, it often lacks hardware reduction of confounding factors, stressing the importance of a correct signal processing. The proposed pipeline includes movement artifact reduction (MAR), bandpass filtering (BPF), and principal component analysis (PCA). Eight MAR algorithms were compared among 23 young adult volunteers under motor-grasping task. Single-subject examples are shown followed by the percentage in energy reduction (ERD%) statistics by single steps and cumulative values. The block average of the hemodynamic response function was compared with generalized linear model fitting. Maps of significant activation/inhibition were illustrated. The mean ERD% of pre-processed signals concerning the initial raw signal energy reached 4%. A tested multichannel MAR variant showed overcorrection on 4-fold more expansive windows. All of the MAR algorithms found similar activations in the contralateral motor area. In conclusion, single channel MAR algorithms are suggested followed by BPF and PCA. The importance of whole cortex mapping for fNIRS integration in clinical applications was also confirmed by our results.