3D Face Recognition with Sparse Spherical Representations

This paper addresses the problem of 3D face recognition using simultaneous sparse approximations on the sphere. The 3D face point clouds are first aligned with a novel and fully automated registration process. They are then represented as signals on the 2D sphere in order to preserve depth and geometry information. Next, we implement a dimensionality reduction process with simultaneous sparse approximations and subspace projection. It permits to represent each 3D face by only a few spherical functions that are able to capture the salient facial characteristics, and hence to preserve the discriminant facial information. We eventually perform recognition by effective matching in the reduced space, where Linear Discriminant Analysis can be further activated for improved recognition performance. The 3D face recognition algorithm is evaluated on the FRGC v.1.0 data set, where it is shown to outperform classical state-of-the-art solutions that work with depth images

Cortical thickness modeling and variability in Alzheimer's disease and frontotemporal dementia

Alzheimer's disease (AD) and frontotemporal dementia (FTD) show different patterns of cortical thickness (CTh) loss compared with healthy controls (HC), even though there is relevant heterogeneity between individuals suffering from each of these diseases. Thus, we developed CTh models to study individual variability in AD, FTD, and HC.We used the baseline CTh measures of 379 participants obtained from the structural MRI processed with FreeSurfer. A total of 169 AD patients (63 ± 9 years, 65 men), 88 FTD patients (64 ± 9 years, 43 men), and 122 HC (62 ± 10 years, 47 men) were studied. We fitted region-wise temporal models of CTh using Support Vector Regression. Then, we studied associations of individual deviations from the model with cerebrospinal fluid levels of neurofilament light chain (NfL) and 14-3-3 protein and Mini-Mental State Examination (MMSE). Furthermore, we used real longitudinal data from 144 participants to test model predictivity.We defined CTh spatiotemporal models for each group with a reliable fit. Individual deviation correlated with MMSE for AD and with NfL for FTD. AD patients with higher deviations from the trend presented higher MMSE values. In FTD, lower NfL levels were associated with higher deviations from the CTh prediction. For AD and HC, we could predict longitudinal visits with the presented model trained with baseline data. For FTD, the longitudinal visits had more variability.We highlight the value of CTh models for studying AD and FTD longitudinal changes and variability and their relationships with cognitive features and biomarkers.© 2023. The Author(s)

Classifying Alzheimer's disease and frontotemporal dementia using machine learning with cross-sectional and longitudinal magnetic resonance imaging data

Alzheimer's disease (AD) and frontotemporal dementia (FTD) are common causes of dementia with partly overlapping, symptoms and brain signatures. There is a need to establish an accurate diagnosis and to obtain markers for disease tracking. We combined unsupervised and supervised machine learning to discriminate between AD and FTD using brain magnetic resonance imaging (MRI). We included baseline 3T-T1 MRI data from 339 subjects: 99 healthy controls (CTR), 153 AD and 87 FTD patients; and 2-year follow-up data from 114 subjects. We obtained subcortical gray matter volumes and cortical thickness measures using FreeSurfer. We used dimensionality reduction to obtain a single feature that was later used in a support vector machine for classification. Discrimination patterns were obtained with the contribution of each region to the single feature. Our algorithm differentiated CTR versus AD and CTR versus FTD at the cross-sectional level with 83.3% and 82.1% of accuracy. These increased up to 90.0% and 88.0% with longitudinal data. When we studied the classification between AD versus FTD we obtained an accuracy of 63.3% at the cross-sectional level and 75.0% for longitudinal data. The AD versus FTD versus CTR classification has reached an accuracy of 60.7%, and 71.3% for cross-sectional and longitudinal data respectively. Disease discrimination brain maps are in concordance with previous results obtained with classical approaches. By using a single feature, we were capable to classify CTR, AD, and FTD with good accuracy, considering the inherent overlap between diseases. Importantly, the algorithm can be used with cross-sectional and longitudinal data.© 2023 The Authors. Human Brain Mapping published by Wiley Periodicals LLC

Inflammation, Amyloid, and Atrophy in The Aging Brain: Relationships with Longitudinal Changes in Cognition

Amyloid deposition occurs in aging, even in individuals free from cognitive symptoms, and is often interpreted as preclinical Alzheimer’s disease (AD) pathophysiology. YKL-40 is a marker of neuroinflammation, being increased in AD, and hypothesized to interact with amyloid-β (Aβ) in causing cognitive decline early in the cascade of AD pathophysiology. Whether and how Aβ and YKL-40 affect brain and cognitive changes in cognitively healthy older adults is still unknown. We studied 89 participants (mean age: 73.1 years) with cerebrospinal fluid samples at baseline, and both MRI and cognitive assessments from two time-points separated by two years. We tested how baseline levels of Aβ42 and YKL-40 correlated with changes in cortical thickness and cognition. Thickness change correlated with Aβ42 only in Aβ42+ participants (<600 pg/mL, n = 27) in the left motor and premotor cortices. Aβ42 was unrelated to cognitive change. Increased YKL-40 was associated with less preservation of scores on the animal naming test in the total sample (r = –0.28, p = 0.012) and less preservation of a score reflecting global cognitive function for Aβ42+ participants (r = –0.58, p = 0.004). Our results suggest a role for inflammation in brain atrophy and cognitive changes in cognitively normal older adults, which partly depended on Aβ accumulation

Neuroinflammation and Tau Interact with Amyloid in Predicting Sleep Problems in Aging Independently of Atrophy

Sleep problems relate to brain changes in aging and disease, but the mechanisms are unknown. Studies suggest a relationship between β-amyloid (Aβ) accumulation and sleep, which is likely augmented by interactions with multiple variables. Here, we tested how different cerebrospinal fluid (CSF) biomarkers for brain pathophysiology, brain atrophy, memory function, and depressive symptoms predicted self-reported sleep patterns in 91 cognitively healthy older adults over a 3-year period. The results showed that CSF levels of total- and phosphorylated (P) tau, and YKL-40—a marker of neuroinflammation/astroglial activation—predicted poor sleep in Aβ positive older adults. Interestingly, although brain atrophy was strongly predictive of poor sleep, the relationships between CSF biomarkers and sleep were completely independent of atrophy. A joint analysis showed that unique variance in sleep was explained by P-tau and the P-tau × Aβ interaction, memory function, depressive symptoms, and brain atrophy. The results demonstrate that sleep relates to a range of different pathophysiological processes, underscoring the importance of understanding its impact on neurocognitive changes in aging and people with increased risk of Alzheimer's disease

Contribution of CSF biomarkers to early-onset Alzheimer's disease and frontotemporal dementia neuroimaging signatures

Prior studies have described distinct patterns of brain gray matter and white matter alterations in Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD), as well as differences in their cerebrospinal fluid (CSF) biomarkers profiles. We aim to investigate the relationship between early‐onset AD (EOAD) and FTLD structural alterations and CSF biomarker levels. We included 138 subjects (64 EOAD, 26 FTLD, and 48 controls), all of them with a 3T MRI brain scan and CSF biomarkers available (the 42 amino acid‐long form of the amyloid‐beta protein [Aβ42], total‐tau protein [T‐tau], neurofilament light chain [NfL], neurogranin [Ng], and 14‐3‐3 levels). We used FreeSurfer and FSL to obtain cortical thickness (CTh) and fraction anisotropy (FA) maps. We studied group differences in CTh and FA and described the “AD signature” and “FTLD signature.” We tested multiple regression models to find which CSF‐biomarkers better explained each disease neuroimaging signature. CTh and FA maps corresponding to the AD and FTLD signatures were in accordance with previous literature. Multiple regression analyses showed that the biomarkers that better explained CTh values within the AD signature were Aβ and 14‐3‐3; whereas NfL and 14‐3‐3 levels explained CTh values within the FTLD signature. Similarly, NfL levels explained FA values in the FTLD signature. Ng levels were not predictive in any of the models. Biochemical markers contribute differently to structural (CTh and FA) changes typical of AD and FTLD

The bounds of education in the human brain connectome

Inter-individual heterogeneity is evident in aging; education level is known to contribute for this heterogeneity. Using a cross-sectional study design and network inference applied to resting-state fMRI data, we show that aging was associated with decreased functional connectivity in a large cortical network. On the other hand, education level, as measured by years of formal education, produced an opposite effect on the long-term. These results demonstrate the increased brain efficiency in individuals with higher education level that may mitigate the impact of age on brain functional connectivity.This work was funded by the European Commission (FP7): “SwitchBox” (Contract HEALTH-F2-2010-259772) and co-financed by the Portuguese North Regional Operational Program (ON.2 – O Novo Norte) under the National Strategic Reference Framework (QREN), through the European Regional Development Fund (FEDER). José M. Soares, Paulo Marques, and Nadine C. Santos are supported by fellowships of the project “SwitchBox”; Ricardo Magalhães is supported by a fellowship from the project FCT ANR/NEU-OSD/0258/2012 funded by FCT/MEC (www.fct.pt) and by ON.2 – ONOVONORTE – North Portugal Regional Operational Programme 2007/2013, of the National Strategic Reference Framework (NSRF) 2007/2013, through FEDER

Loss of brainstem white matter predicts onset and motor neuron symptoms in C9orf72 expansion carriers:a GENFI study

Background and objectives: The C9orf72 expansion is the most common genetic cause of frontotemporal dementia (FTD) and/or motor neuron disease (MND). Corticospinal degeneration has been described in post-mortem neuropathological studies in these patients, especially in those with MND. We used MRI to analyze white matter (WM) volumes in presymptomatic and symptomatic C9orf72 expansion carriers and investigated whether its measure may be helpful in predicting the onset of symptoms. Methods: We studied 102 presymptomatic C9orf72 mutation carriers, 52 symptomatic carriers: 42 suffering from FTD and 11 from MND, and 75 non-carriers from the Genetic Frontotemporal dementia Initiative (GENFI). All subjects underwent T1-MRI acquisition. We used FreeSurfer to estimate the volume proportion of WM in the brainstem regions (midbrain, pons, and medulla oblongata). We calculated group differences with ANOVA tests and performed linear and non-linear regressions to assess group-by-age interactions. Results: A reduced WM ratio was found in all brainstem subregions in symptomatic carriers compared to both noncarriers and pre-symptomatic carriers. Within symptomatic carriers, MND patients presented a lower ratio in pons and medulla oblongata compared with FTD patients. No differences were found between presymptomatic carriers and non-carriers. Clinical severity was negatively associated with the WM ratio. C9orf72 carriers presented greater age-related WM loss than non-carriers, with MND patients showing significantly more atrophy in pons and medulla oblongata. Discussion: We find consistent brainstem WM loss in C9orf72 symptomatic carriers with differences related to the clinical phenotype supporting the use of brainstem measures as neuroimaging biomarkers for disease tracking.</p

Disease-related cortical thinning in presymptomatic granulin mutation carriers

Mutations in the granulin gene (GRN) cause familial frontotemporal dementia. Understanding the structural brain changes in presymptomatic GRN carriers would enforce the use of neuroimaging biomarkers for early diagnosis and monitoring. We studied 100 presymptomatic GRN mutation carriers and 94 noncarriers from the Genetic Frontotemporal dementia initiative (GENFI), with MRI structural images. We analyzed 3T MRI structural images using the FreeSurfer pipeline to calculate the whole brain cortical thickness (CTh) for each subject. We also perform a vertex-wise general linear model to assess differences between groups in the relationship between CTh and diverse covariables as gender, age, the estimated years to onset and education. We also explored differences according to TMEM106B genotype, a possible disease modifier. Whole brain CTh did not differ between carriers and noncarriers. Both groups showed age-related cortical thinning. The group-by-age interaction analysis showed that this age-related cortical thinning was significantly greater in GRN carriers in the left superior frontal cortex. TMEM106B did not significantly influence the age-related cortical thinning. Our results validate and expand previous findings suggesting an increased CTh loss associated with age and estimated proximity to symptoms onset in GRN carriers, even before the disease onset

Disease-related cortical thinning in presymptomatic granulin mutation carriers

Mutations in the granulin gene (GRN) cause familial frontotemporal dementia. Understanding the structural brain changes in presymptomatic GRN carriers would enforce the use of neuroimaging biomarkers for early diagnosis and monitoring. We studied 100 presymptomatic GRN mutation carriers and 94 noncarriers from the Genetic Frontotemporal dementia initiative (GENFI), with MRI structural images. We analyzed 3T MRI structural images using the FreeSurfer pipeline to calculate the whole brain cortical thickness (CTh) for each subject. We also perform a vertex-wise general linear model to assess differences between groups in the relationship between CTh and diverse covariables as gender, age, the estimated years to onset and education. We also explored differences according to TMEM106B genotype, a possible disease modifier. Whole brain CTh did not differ between carriers and noncarriers. Both groups showed age-related cortical thinning. The group-by-age interaction analysis showed that this age-related cortical thinning was significantly greater in GRN carriers in the left superior frontal cortex. TMEM106B did not significantly influence the age-related cortical thinning. Our results validate and expand previous findings suggesting a