An Automated Pipeline for the Analysis of PET Data on the Cortical Surface

Iron Overload in Brain: Transport Mismatches, Microbleeding Events, and How Nanochelating Therapies May Counteract Their Effects

Iron overload in many brain regions is a common feature of aging and most neurodegenerative diseases. In this review, the causes, mechanisms, mathematical models, and possible therapies are summarized. Indeed, physiological and pathological conditions can be investigated using compartmental models mimicking iron trafficking across the blood-brain barrier and the Cerebrospinal Fluid-Brain exchange membranes located in the choroid plexus. In silico models can investigate the alteration of iron homeostasis and simulate iron concentration in the brain environment, as well as the effects of intracerebral iron chelation, determining potential doses and timing to recover the physiological state. Novel formulations of non-toxic nanovectors with chelating capacity are already tested in organotypic brain models and could be available to move from in silico to in vivo experiments.

Cortical surface analysis for focal cortical dysplasia diagnosis by using PET images

Focal cortical dysplasia (FCD) is a neurological disorder distinguished by faulty brain cell structure and development. Repetitive and uncontrollable seizures may be linked to FCD's aberrant cortical thickness, gyrification, and sulcal depth. Quantitative cortical surface analysis is a crucial alternative to ineffective visual inspection. This study recruited 42 subjects including 22 FCD patients who underwent surgery and 20 healthy controls (HC). For the FCD patients, T1-weighted and PET images were obtained by a PET-MRI scanner, and the confirmed epileptogenic zone (EZ) was collected from postsurgical follow-up. For the HCs, CT and PET images were obtained by a PET-CT scanner. Cortical thickness, gyrification index, and sulcal depth were calculated using a computational anatomical toolbox (CAT12). A cluster-based analysis is carried out to determine each FCD patient's aberrant cortical surface. After parcellating the cerebral cortex into 68 regions by the Desikan-Killiany atlas, a region of interest (ROI) analysis was conducted to know whether the feature in the FCD group is significantly different from that in the HC group. Finally, the features of all ROIs were utilised to train a support vector machine classifier (SVM). The classification performance is evaluated by the leave-one-out cross-validation. The cluster-based analysis can localize the EZ cluster with the highest accuracy of 54.5 % (12/22) for cortical thickness, 40.9 % (9/22) and 13.6 % (3/22) for sulcal depth and gyrification, respectively. Moderate concordance (Kappa, 0.6) is observed between the confirmed EZs and identified clusters by using the cortical thickness. Fair concordance (Kappa, 0.3) and no concordance (Kappa, 0.1) is found by using sulcal depth and gyrification. Significant differences are found in 46 of 68 regions (67.7 %) for the three measures. The trained SVM classifier achieved a prediction accuracy of 95.5 % for the cortical thickness, while the sulcal depth and the gyrification obtained 86.0 % and 81.5 %. Cortical thickness, as determined by quantitative cortical surface analysis of PET data, has a greater ability than sulcal depth and gyrification to locate aberrant EZ clusters in FCD. Surface measures might be different in many regions for FCD and HC. By integrating machine learning and cortical morphologies features, individual prediction of FCD seems to be feasible.

Psychiatric Illness or Immune Dysfunction-Brain Perfusion Imaging Providing the Answer in a Case of Anti-NMDAR Encephalitis

BACKGROUND

We investigated the potential use of SPECT quantification in addition to qualitative brain perfusion analysis for the detection of anti-NMDAR encephalitis. The question is how to normalize brain activity to be able to quantitatively detect perfusion patterns. Usually, brain activity is normalized to a structure considered unaffected by the disease.

METHODS

Brain [99mTc]-HMPAO SPECT was performed as a method to detect brain perfusion patterns. The patterns of abnormal brain perfusion cannot always be reliably and qualitatively assessed when dealing with rare diseases. Recent advances in SPECT quantification using commercial software have enabled more objective and detailed analysis of brain perfusion. The cerebellum and whole brain were used as the normalization structures and were compared with visual analysis.

RESULTS

The quantification analysis performed with whole brain normalization confirmed right parietal lobe hypoperfusion while also detecting statistically significant left-to-right perfusion differences between the temporal lobe and thalamus. Whole brain normalization further described bilateral frontal lobe hyperperfusion, predominantly of the left lobe, and was in accordance with visual analysis.

CONCLUSION

SPECT quantitative brain perfusion analysis, using the whole brain as the normalization structure rather than the cerebellum, in this case, improved confidence in the visual detection of anti-NMDAR encephalitis and provided unexpected solutions to atypical psychiatric dilemmas.

Distinct cerebral cortical perfusion patterns in idiopathic normal-pressure hydrocephalus

The aims of the study are to evaluate idiopathic normal-pressure hydrocephalus (INPH)-related cerebral blood flow (CBF) abnormalities and to investigate their relation to cortical thickness in INPH patients. We investigated cortical CBF utilizing surface-based early-phase ¹⁸ F-florbetaben (E-FBB) PET analysis in two groups: INPH patients and healthy controls. All 39 INPH patients and 20 healthy controls were imaged with MRI, including three-dimensional volumetric images, for automated surface-based cortical thickness analysis across the entire brain. A subgroup with 37 participants (22 INPH patients and 15 healthy controls) that also underwent ¹⁸ F-fluorodeoxyglucose (FDG) PET imaging was further analyzed. Compared with age- and gender-matched healthy controls, INPH patients showed statistically significant hyperperfusion in the high convexity of the frontal and parietal cortical regions. Importantly, within the INPH group, increased perfusion correlated with cortical thickening in these regions. Additionally, significant hypoperfusion mainly in the ventrolateral frontal cortex, supramarginal gyrus, and temporal cortical regions was observed in the INPH group relative to the control group. However, this hypoperfusion was not associated with cortical thinning. A subgroup analysis of participants that also underwent FDG PET imaging showed that increased (or decreased) cerebral perfusion was associated with increased (or decreased) glucose metabolism in INPH. A distinctive regional relationship between cerebral cortical perfusion and cortical thickness was shown in INPH patients. Our findings suggest distinct pathophysiologic mechanisms of hyperperfusion and hypoperfusion in INPH patients.

Characterizing the hyper- and hypometabolism in temporal lobe epilepsy using multivariate machine learning

Mesial temporal lobe epilepsy (MTLE) is the most common type of focal epilepsy, presenting both structural and metabolic abnormalities in the ipsilateral mesial temporal lobe. While it has been demonstrated that the metabolic abnormalities in MTLE actually extend beyond the epileptogenic zone, how such multidimensional information is associated with the diagnosis of MTLE remains to be tested. Here, we explore the whole-brain metabolic patterns in 23 patients with MTLE and 24 healthy controls using [¹⁸ F]fluorodeoxyglucose PET imaging. Based on a multivariate machine learning approach, we demonstrate that the brain metabolic patterns can discriminate patients with MTLE from controls with a superior accuracy (>95%). Importantly, voxels showing the most extreme contributing weights to the classification (i.e., the most important regional predictors) distribute across both hemispheres, involving both ipsilateral negative weights over the anterior part of lateral and medial temporal lobe, posterior insula, and lateral orbital frontal gyrus, and contralateral positive weights over the anterior frontal lobe, temporal lobe, and lingual gyrus. Through region-of-interest analyses, we verify that in patients with MTLE, the negatively weighted regions are hypometabolic, and the positively weighted regions are hypermetabolic, compared to controls. Interestingly, despite that both hypo- and hypermetabolism have mutually contributed to our model, they may reflect different pathological and/or compensative responses. For instance, patients with earlier age at epilepsy onset present greater hypometabolism in the ipsilateral inferior temporal gyrus, while we find no evidence of such association with hypermetabolism. In summary, quantitative models utilizing multidimensional brain metabolic information may provide additional assistance to presurgical workups in TLE.

Clinica: An Open-Source Software Platform for Reproducible Clinical Neuroscience Studies

We present Clinica (www.clinica.run), an open-source software platform designed to make clinical neuroscience studies easier and more reproducible. Clinica aims for researchers to (i) spend less time on data management and processing, (ii) perform reproducible evaluations of their methods, and (iii) easily share data and results within their institution and with external collaborators. The core of Clinica is a set of automatic pipelines for processing and analysis of multimodal neuroimaging data (currently, T1-weighted MRI, diffusion MRI, and PET data), as well as tools for statistics, machine learning, and deep learning. It relies on the brain imaging data structure (BIDS) for the organization of raw neuroimaging datasets and on established tools written by the community to build its pipelines. It also provides converters of public neuroimaging datasets to BIDS (currently ADNI, AIBL, OASIS, and NIFD). Processed data include image-valued scalar fields (e.g., tissue probability maps), meshes, surface-based scalar fields (e.g., cortical thickness maps), or scalar outputs (e.g., regional averages). These data follow the ClinicA Processed Structure (CAPS) format which shares the same philosophy as BIDS. Consistent organization of raw and processed neuroimaging files facilitates the execution of single pipelines and of sequences of pipelines, as well as the integration of processed data into statistics or machine learning frameworks. The target audience of Clinica is neuroscientists or clinicians conducting clinical neuroscience studies involving multimodal imaging, and researchers developing advanced machine learning algorithms applied to neuroimaging data.

Evaluation of the early-phase [18F]AV45 PET as an optimal surrogate of [18F]FDG PET in ageing and Alzheimer's clinical syndrome

Dual-phase [18F]AV45 positron emission tomography (PET) is highly promising in the assessment of neurodegenerative diseases, allowing to obtain information on both neurodegeneration (early-phase; eAV45) and amyloid deposition (late-phase; lAV45) which are highly complementary; yet eAV45 needs further evaluation. This study aims at validating eAV45 as an optimal proxy of [18F]FDG PET in a large mixed-population of healthy ageing and Alzheimer's clinical syndrome participants (n = 191) who had [18F]FDG PET, eAV45 and lAV45 scans. We found early time frame 0-4 min to give maximal correlation with [18F]FDG PET and minimal correlation with lAV45. Moreover, maximal overlap of [18F]FDG PET versus eAV45 associations with clinical diagnosis and cognition was obtained with pons scaling. Across reference regions, classification performance between clinical subgroups was similar for both eAV45 and [18F]FDG PET. These findings highlight the optimal use of eAV45 to assess neurodegeneration as a validated proxy of [18F]FDG PET. On top of this purpose, this study showed that combined [18F]AV45 PET dual-biomarker even outperformed [18F]FDG PET or lAV45 alone.

Identifying gaps in using artificial intelligence to support students with intellectual disabilities from education and health perspectives

Purpose

Students with developmental/intellectual disabilities (ID/DD) often have serious health issues that require additional medical care and supervision. Serious health issues also mean increased absence and additional lags in academic achievement and development of adaptive and social skills. The incorporation of artificial intelligence in the education of a child with ID/DD could ameliorate the educational, adaptive and social skill gaps that occur as a direct result of persistent health problems.

Design/methodology/approach

The literature regarding the use of artificial intelligence in education for students with ID/DD was collected systematically from international online databases based on specific inclusion and exclusion criteria. The collected articles were analyzed deductively, looking for the different gaps in the domain. Based on the literature, an artificial intelligence–based architecture is proposed and sketched.

Findings

The findings show that there are many gaps in supporting students with ID/DD through the utilization of artificial intelligence. Given that the majority of students with ID/DD often have serious and chronic and comorbid health conditions, the potential use of health information in artificial intelligence is even more critical. Therefore, there is a clear need to develop a system that facilitates communication and access to health information for students with ID/DD, one that provides information to caregivers and education providers, limits errors, and, therefore, improves these individuals' education and quality of life.

Practical implications

This review highlights the gap in the current literature regarding using artificial intelligence in supporting the education of students with ID/DD. There is an urgent need for an intelligent system in collaboration with the updated health information to improve the quality of services submitted for people with intellectual disabilities and as a result improving their quality of life.

Originality/value

This study contributes to the literature by highlighting the gaps in incorporating artificial intelligence and its service to individuals with ID/DD. The research additionally proposes a solution based on the confounding variables of students’ health and individual characteristics. This solution will provide an automated information flow as a functional diagnostic and intervention tool for teachers, caregivers and parents. It could potentially improve the educational and practical outcomes for individuals with ID/DD and, ultimately, their quality of life.

Advances in multimodal data fusion in neuroimaging: Overview, challenges, and novel orientation

Multimodal fusion in neuroimaging combines data from multiple imaging modalities to overcome the fundamental limitations of individual modalities. Neuroimaging fusion can achieve higher temporal and spatial resolution, enhance contrast, correct imaging distortions, and bridge physiological and cognitive information. In this study, we analyzed over 450 references from PubMed, Google Scholar, IEEE, ScienceDirect, Web of Science, and various sources published from 1978 to 2020. We provide a review that encompasses (1) an overview of current challenges in multimodal fusion (2) the current medical applications of fusion for specific neurological diseases, (3) strengths and limitations of available imaging modalities, (4) fundamental fusion rules, (5) fusion quality assessment methods, and (6) the applications of fusion for atlas-based segmentation and quantification. Overall, multimodal fusion shows significant benefits in clinical diagnosis and neuroscience research. Widespread education and further research amongst engineers, researchers and clinicians will benefit the field of multimodal neuroimaging.

Intensity normalization methods in brain FDG-PET quantification

BACKGROUND

The lack of standardization of intensity normalization methods and its unknown effect on the quantification output is recognized as a major drawback for the harmonization of brain FDG-PET quantification protocols. The aim of this work is the ground truth-based evaluation of different intensity normalization methods on brain FDG-PET quantification output.

METHODS

Realistic FDG-PET images were generated using Monte Carlo simulation from activity and attenuation maps directly derived from 25 healthy subjects (adding theoretical relative hypometabolisms on 6 regions of interest and for 5 hypometabolism levels). Single-subject statistical parametric mapping (SPM) was applied to compare each simulated FDG-PET image with a healthy database after intensity normalization based on reference regions methods such as the brain stem (RRBS), cerebellum (RRC) and the temporal lobe contralateral to the lesion (RRTL), and data-driven methods, such as proportional scaling (PS), histogram-based method (HN) and iterative versions of both methods (iPS and iHN). The performance of these methods was evaluated in terms of the recovery of the introduced theoretical hypometabolic pattern and the appearance of unspecific hypometabolic and hypermetabolic findings.

RESULTS

Detected hypometabolic patterns had significantly lower volumes than the introduced hypometabolisms for all intensity normalization methods particularly for slighter reductions in metabolism . Among the intensity normalization methods, RRC and HN provided the largest recovered hypometabolic volumes, while the RRBS showed the smallest recovery. In general, data-driven methods overcame reference regions and among them, the iterative methods overcame the non-iterative ones. Unspecific hypermetabolic volumes were similar for all methods, with the exception of PS, where it became a major limitation (up to 250 cm³) for extended and intense hypometabolism. On the other hand, unspecific hypometabolism was similar far all methods, and usually solved with appropriate clustering.

CONCLUSIONS

Our findings showed that the inappropriate use of intensity normalization methods can provide remarkable bias in the detected hypometabolism and it represents a serious concern in terms of false positives. Based on our findings, we recommend the use of histogram-based intensity normalization methods. Reference region methods performance was equivalent to data-driven methods only when the selected reference region is large and stable.