IcoConv : Explainable brain cortical surface analysis for ASD classification.

In this study, we introduce a novel approach for the analysis and interpretation of 3D shapes, particularly applied in the context of neuroscientific research. Our method captures 2D perspectives from various vantage points of a 3D object. These perspectives are subsequently analyzed using 2D Convolutional Neural Networks (CNNs), uniquely modified with custom pooling mechanisms. We sought to assess the efficacy of our approach through a binary classification task involving subjects at high risk for Autism Spectrum Disorder (ASD). The task entailed differentiating between high-risk positive and high-risk negative ASD cases. To do this, we employed brain attributes like cortical thickness, surface area, and extra-axial cerebral spinal measurements. We then mapped these measurements onto the surface of a sphere and subsequently analyzed them via our bespoke method. One distinguishing feature of our method is the pooling of data from diverse views using our icosahedron convolution operator. This operator facilitates the efficient sharing of information between neighboring views. A significant contribution of our method is the generation of gradient-based explainability maps, which can be visualized on the brain surface. The insights derived from these explainability images align with prior research findings, particularly those detailing the brain regions typically impacted by ASD. Our innovative approach thereby substantiates the known understanding of this disorder while potentially unveiling novel areas of study.

Regional brain atrophy and cognitive decline depend on definition of subjective cognitive decline.

BACKGROUND: People with subjective cognitive decline (SCD) may be at increased risk for Alzheimer's disease (AD). However, not all studies have observed this increased risk. This project examined whether four common methods of defining SCD yields different patterns of atrophy and future cognitive decline between cognitively normal older adults with (SCD+ ) and without SCD (SCD-). METHODS: Data from 273 Alzheimer's Disease Neuroimaging Initiative cognitively normal older adults were examined. To operationalize SCD we used four common methods: Cognitive Change Index (CCI), Everyday Cognition Scale (ECog), ECog + Worry, and Worry. Voxel-based logistic regressions were applied to deformation-based morphology results to determine if regional atrophy between SCD- and SCD+ differed by SCD definition. Linear mixed-effects models were used to evaluate differences in future cognitive decline. RESULTS: Results varied between the four methods of defining SCD. Left hippocampal grading was more similar to AD in SCD+ than SCD- when using the CCI (p = .041) and Worry (p = .021) definitions. The right (p=.008) and left (p=.003) superior temporal regions had smaller volumes in SCD+ than SCD-, but only with the ECog. SCD+ was associated with greater future cognitive decline measured by Alzheimer's Disease Assessment Scale, but only with the CCI definition. In contrast, only the ECog definition of SCD was associated with future decline on the Montreal Cognitive Assessment. CONCLUSION: These findings suggest that the various methods used to differentiate between SCD- and SCD+ influence whether volume differences and findings of cognitive decline are observed between groups in this retrospective analysis.

Open science datasets from PREVENT-AD, a longitudinal cohort of pre-symptomatic Alzheimer's disease.

To move Alzheimer Disease (AD) research forward it is essential to collect data from large cohorts, but also make such data available to the global research community. We describe the creation of an open science dataset from the PREVENT-AD (PResymptomatic EValuation of Experimental or Novel Treatments for AD) cohort, composed of cognitively unimpaired older individuals with a parental or multiple-sibling history of AD. From 2011 to 2017, 386 participants were enrolled (mean age 63 years old ± 5) for sustained investigation among whom 349 have retrospectively agreed to share their data openly. Repositories are findable through the unified interface of the Canadian Open Neuroscience Platform and contain up to five years of longitudinal imaging data, cerebral fluid biochemistry, neurosensory capacities, cognitive, genetic, and medical information. Imaging data can be accessed openly at https://openpreventad.loris.ca while most of the other information, sensitive by nature, is accessible by qualified researchers at https://registeredpreventad.loris.ca. In addition to being a living resource for continued data acquisition, PREVENT-AD offers opportunities to facilitate understanding of AD pathogenesis.

White matter hyperintensities and neuropsychiatric symptoms in mild cognitive impairment and Alzheimer's disease.

Neuropsychiatric symptoms (NPS), such as apathy, irritability and depression, are frequently encountered in patients with Alzheimer's disease (AD). Focal grey matter atrophy has been linked to NPS development. Cerebrovascular disease is common among AD patients and can be detected on MRI as white matter hyperintensities (WMH). In this longitudinal study, the relative contribution of WMH burden and GM atrophy to NPS was evaluated in a cohort of mild cognitive impairment (MCI), AD and normal controls. This study included 121 AD, 315 MCI and 225 normal control subjects from the Alzheimer's Disease Neuroimaging Initiative. NPS were assessed using the Neuropsychiatric Inventory and grouped into hyperactivity, psychosis, affective and apathy subsyndromes. WMH were measured using an automatic segmentation technique and mean deformation-based morphometry (DBM) was used to measure atrophy of grey matter regions. Linear mixed-effects models found focal grey matter atrophy and WMH volume both contributed significantly to NPS subsyndromes in MCI and AD subjects, however, WMH burden played a greater role. This study could provide a better understanding of the pathophysiology of NPS in AD and support the monitoring and control of vascular risk factors.

Newborn amygdalar volumes are associated with maternal prenatal psychological distress in a sex-dependent way.

Maternal psychological distress during pregnancy (PPD)1 has been associated with changes in offspring amygdalar and hippocampal volumes. Studies on child amygdalae suggest that sex moderates the vulnerability of fetal brains to prenatal stress. However, this has not yet been observed in these structures in newborns. Newborn studies are crucial, as they minimize the confounding influence of postnatal life. We investigated the effects of maternal prenatal psychological symptoms on newborn amygdalar and hippocampal volumes and their interactions with newborn sex in 123 newborns aged 2-5 weeks (69 males, 54 females). Based on earlier studies, we anticipated small, but statistically significant effects of PPD on the volumes of these structures. Maternal psychological distress was measured at gestational weeks (GW)2 14, 24 and 34 using Symptom Checklist-90 (SCL-90, anxiety scale)3 and Edinburgh Postnatal Depression Scale (EPDS)4 questionnaires. Newborn sex was found to moderate the relationship between maternal distress symptoms at GW 24 and the volumes of left and right amygdala. This relationship was negative and significant only in males. No significant main effect or sex-based moderation was found for hippocampal volumes. This newborn study provides evidence for a sex-dependent influence of maternal psychiatric symptoms on amygdalar structural development. This association may be relevant to later psychopathology.

Age-specific associations between oestradiol, cortico-amygdalar structural covariance, and verbal and spatial skills.

Oestradiol is known to play an important role in the developing human brain, although little is known about the entire network of potential regions that might be affected and how these effects may vary from childhood to early adulthood, which in turn can explain sexually differentiated behaviours. In the present study, we examined the relationships between oestradiol, cortico-amygdalar structural covariance, and cognitive or behavioural measures typically showing sex differences (verbal/spatial skills, anxious-depressed symptomatology) in 152 children and adolescents (aged 6-22 years). Cortico-amygdalar structural covariance shifted from positive to negative across the age range. Oestradiol was found to diminish the impact of age on cortico-amygdalar covariance for the pre-supplementary motor area/frontal eye field and retrosplenial cortex (across the age range), as well as for the posterior cingulate cortex (in older children). Moreover, the influence of oestradiol on age-related cortico-amygdalar networks was associated with higher word identification and spatial working memory (across the age range), as well as higher reading comprehension (in older children), although it did not impact anxious-depressed symptoms. There were no significant sex effects on any of the above relationships. These findings confirm the importance of developmental timing on oestradiol-related effects and hint at the non-sexually dimorphic role of oestradiol-related cortico-amygdalar structural networks in aspects of cognition distinct from emotional processes.

The relationship between brain atrophy and cognitive-behavioural symptoms in retired Canadian football players with multiple concussions.

Multiple concussions, particularly in contact sports, have been associated with cognitive deficits, psychiatric impairment and neurodegenerative diseases like chronic traumatic encephalopathy. We used volumetric and deformation-based morphometric analyses to test the hypothesis that repeated concussions may be associated with smaller regional brain volumes, poorer cognitive performance and behavioural symptoms among former professional football players compared to healthy controls. This study included fifty-three retired Canadian Football League players, 25 age- and education-matched healthy controls, and controls from the Cambridge Centre for Aging and Neuroscience database for validation. Volumetric analyses revealed greater hippocampal atrophy than expected for age in former athletes with multiple concussions than controls and smaller left hippocampal volume was associated with poorer verbal memory performance in the former athletes. Deformation-based morphometry confirmed smaller bilateral hippocampal volume that was associated with poorer verbal memory performance in athletes. Repeated concussions may lead to greater regional atrophy than expected for age.

SCT: Spinal Cord Toolbox, an open-source software for processing spinal cord MRI data.

For the past 25 years, the field of neuroimaging has witnessed the development of several software packages for processing multi-parametric magnetic resonance imaging (mpMRI) to study the brain. These software packages are now routinely used by researchers and clinicians, and have contributed to important breakthroughs for the understanding of brain anatomy and function. However, no software package exists to process mpMRI data of the spinal cord. Despite the numerous clinical needs for such advanced mpMRI protocols (multiple sclerosis, spinal cord injury, cervical spondylotic myelopathy, etc.), researchers have been developing specific tools that, while necessary, do not provide an integrative framework that is compatible with most usages and that is capable of reaching the community at large. This hinders cross-validation and the possibility to perform multi-center studies. In this study we introduce the Spinal Cord Toolbox (SCT), a comprehensive software dedicated to the processing of spinal cord MRI data. SCT builds on previously-validated methods and includes state-of-the-art MRI templates and atlases of the spinal cord, algorithms to segment and register new data to the templates, and motion correction methods for diffusion and functional time series. SCT is tailored towards standardization and automation of the processing pipeline, versatility, modularity, and it follows guidelines of software development and distribution. Preliminary applications of SCT cover a variety of studies, from cross-sectional area measures in large databases of patients, to the precise quantification of mpMRI metrics in specific spinal pathways. We anticipate that SCT will bring together the spinal cord neuroimaging community by establishing standard templates and analysis procedures.

Surface-based analysis reveals regions of reduced cortical magnetization transfer ratio in patients with multiple sclerosis: a proposed method for imaging subpial demyelination.

The in vivo detection of subpial cortical gray matter lesions in multiple sclerosis is challenging. We quantified the spatial extent of subpial decreases in the magnetization transfer ratio (MTR) of cortical gray matter in subjects with multiple sclerosis, as such reductions may indicate regions of cortical demyelination. We exploited the unique geometry of cortical lesions by using two-dimensional parametric surface models of the cortex instead of traditional three-dimensional voxel-wise analyses. MTR images were mapped onto intermediate surfaces between the pial and white matter surfaces and were used to compute differences between secondary-progressive MS (n = 12), relapsing-remitting MS (n = 12), and normal control (n = 12) groups as well as between each individual patient and the normal controls. We identified large regions of significantly reduced cortical MTR in secondary-progressive patients when compared with normal controls. We also identified large regions of reduced cortical MTR in 11 individual patients (8 secondary-progressive, 3 relapsing-remitting). The secondary-progressive patients showed larger areas of abnormally low MTR compared with relapsing-remitting patients both at the group level and on an individual basis. The spatial distributions of abnormal MTR preferentially involved cingulate cortex, insula, and the depths of sulci, in agreement with pathological descriptions of subpial gray matter lesion distribution. These findings suggest that our method is a plausible in vivo imaging technique for quantifying subpial cortical demyelinating lesions in patients with multiple sclerosis and, furthermore, can be applied at the typical clinical field strength of 1.5 T.

Multi-site study of surgical practice in neurosurgery based on surgical process models.

Surgical Process Modelling (SPM) was introduced to improve understanding the different parameters that influence the performance of a Surgical Process (SP). Data acquired from SPM methodology is enormous and complex. Several analysis methods based on comparison or classification of Surgical Process Models (SPMs) have previously been proposed. Such methods compare a set of SPMs to highlight specific parameters explaining differences between populations of patients, surgeons or systems. In this study, procedures performed at three different international University hospitals were compared using SPM methodology based on a similarity metric focusing on the sequence of activities occurring during surgery. The proposed approach is based on Dynamic Time Warping (DTW) algorithm combined with a clustering algorithm. SPMs of 41 Anterior Cervical Discectomy (ACD) surgeries were acquired at three Neurosurgical departments; in France, Germany, and Canada. The proposed approach distinguished the different surgical behaviors according to the location where surgery was performed as well as between the categorized surgical experience of individual surgeons. We also propose the use of Multidimensional Scaling to induce a new space of representation of the sequences of activities. The approach was compared to a time-based approach (e.g. duration of surgeries) and has been shown to be more precise. We also discuss the integration of other criteria in order to better understand what influences the way the surgeries are performed. This first multi-site study represents an important step towards the creation of robust analysis tools for processing SPMs. It opens new perspectives for the assessment of surgical approaches, tools or systems as well as objective assessment and comparison of surgeon's expertise.

New methods for MRI denoising based on sparseness and self-similarity.

This paper proposes two new methods for the three-dimensional denoising of magnetic resonance images that exploit the sparseness and self-similarity properties of the images. The proposed methods are based on a three-dimensional moving-window discrete cosine transform hard thresholding and a three-dimensional rotationally invariant version of the well-known nonlocal means filter. The proposed approaches were compared with related state-of-the-art methods and produced very competitive results. Both methods run in less than a minute, making them usable in most clinical and research settings.

Non-local MRI upsampling.

In Magnetic Resonance Imaging, image resolution is limited by several factors such as hardware or time constraints. In many cases, the acquired images have to be upsampled to match a specific resolution. In such cases, image interpolation techniques have been traditionally applied. However, traditional interpolation techniques are not able to recover high frequency information of the underlying high resolution data. In this paper, a new upsampling method is proposed to recover some of this high frequency information by using a data-adaptive patch-based reconstruction in combination with a subsampling coherence constraint. The proposed method has been evaluated on synthetic and real clinical cases and compared with traditional interpolation methods. The proposed method is shown to outperform classical interpolation methods compared in terms of quantitative measures and visual observation.

Hippocampal shape analysis using medial surfaces.

In magnetic resonance imaging (MRI) research, significant attention has been paid to the analysis of the hippocampus (HC) within the medial temporal lobe because of its importance in memory and learning, and its role in neurodegenerative diseases. Manual segmentation protocols have established a volume decline in the HC in conjunction with Alzheimer's disease, epilepsy, post-traumatic stress disorder, and depression. Furthermore, recent studies have investigated age-related changes of HC volume which show an interaction with gender; in early adulthood, volume reduction of the HC is found in men but not in women. In this paper, we investigated gender differences in normal subjects in young adulthood by employing a shape analysis of the HC using medial surfaces. For each subject, the most prominent medial manifold of the HC was extracted and flattened. The flattened sheets were then registered using both a rigid and a non-rigid alignment technique, and the medial surface radius was expressed as a height function over them. This allowed for an investigation of the association between subject variables and the local width of the HC. With regard to the effects of age and gender, it could be shown that the previously observed gender differences were mostly due to volume loss in males in the lateral areas of the HC head and tail. We suggest that the analysis of HC shape using medial surfaces might thus serve as a complimentary technique to investigate group differences to the established segmentation protocols for volume quantification in MRI.