Phenomenological model of diffuse global and regional atrophy using finite-element methods

The main goal of this work is the generation of ground-truth data for the validation of atrophy measurement techniques, commonly used in the study of neurodegenerative diseases such as dementia. Several techniques have been used to measure atrophy in cross-sectional and longitudinal studies, but it is extremely difficult to compare their performance since they have been applied to different patient populations. Furthermore, assessment of performance based on phantom measurements or simple scaled images overestimates these techniques' ability to capture the complexity of neurodegeneration of the human brain. We propose a method for atrophy simulation in structural magnetic resonance (MR) images based on finite-element methods. The method produces cohorts of brain images with known change that is physically and clinically plausible, providing data for objective evaluation of atrophy measurement techniques. Atrophy is simulated in different tissue compartments or in different neuroanatomical structures with a phenomenological model. This model of diffuse global and regional atrophy is based on volumetric measurements such as the brain or the hippocampus, from patients with known disease and guided by clinical knowledge of the relative pathological involvement of regions and tissues. The consequent biomechanical readjustment of structures is modelled using conventional physics-based techniques based on biomechanical tissue properties and simulating plausible tissue deformations with finite-element methods. A thermoelastic model of tissue deformation is employed, controlling the rate of progression of atrophy by means of a set of thermal coefficients, each one corresponding to a different type of tissue. Tissue characterization is performed by means of the meshing of a labelled brain atlas, creating a reference volumetric mesh that will be introduced to a finite-element solver to create the simulated deformations. Preliminary work on the simulation of acquisition artefa- - cts is also presented. Cross-sectional and

Multi-Phase Feature Representation Learning for Neurodegenerative Disease Diagnosis

Feature learning with high dimensional neuroimaging features has been explored for the applications on neurodegenerative diseases. Low-dimensional biomarkers, such as mental status test scores and cerebrospinal fluid level, are essential in clinical diagnosis of neurological disorders, because they could be simple and effective for the clinicians to assess the disorder’s progression and severity. Rather than only using the low-dimensional biomarkers as inputs for decision making systems, we believe that such low-dimensional biomarkers can be used for enhancing the feature learning pipeline. In this study, we proposed a novel feature representation learning framework, Multi-Phase Feature Representation (MPFR), with low-dimensional biomarkers embedded. MPFR learns high-level neuroimaging features by extracting the associations between the low-dimensional biomarkers and the high-dimensional neuroimaging features with a deep neural network. We validated the proposed framework using the Mini-Mental-State-Examination (MMSE) scores as a low-dimensional biomarker and multi-modal neuroimaging data as the high-dimensional neuroimaging features from the ADNI baseline cohort. The proposed approach outperformed the original neural network in both binary and ternary Alzheimer’s disease classification tasks

Ventricular volume expansion in presymptomatic genetic frontotemporal dementia

Objective: To characterize the time course of ventricular volume expansion in genetic frontotemporal dementia (FTD) and identify the onset time and rates of ventricular expansion in presymptomatic FTD mutation carriers. Methods: Participants included patients with a mutation in MAPT, PGRN, or C9orf72, or first-degree relatives of mutation carriers from the GENFI study with MRI scans at study baseline and at 1 year follow-up. Ventricular volumes were obtained from MRI scans using FreeSurfer, with manual editing of segmentation and comparison to fully automated segmentation to establish reliability. Linear mixed models were used to identify differences in ventricular volume and in expansion rates as a function of time to expected disease onset between presymptomatic carriers and noncarriers. Results: A total of 123 participants met the inclusion criteria and were included in the analysis (18 symptomatic carriers, 46 presymptomatic mutation carriers, and 56 noncarriers). Ventricular volume differences were observed 4 years prior to symptom disease onset for presymptomatic carriers compared to noncarriers. Annualized rates of ventricular volume expansion were greater in presymptomatic carriers relative to noncarriers. Importantly, time-intensive manually edited and fully automated ventricular volume resulted in similar findings. Conclusions: Ventricular volume differences are detectable in presymptomatic genetic FTD. Concordance of results from time-intensive manual editing and fully automatic segmentation approaches support its value as a measure of disease onset and progression in future studies in both presymptomatic and symptomatic genetic FTD

Probabilistic non-linear registration with spatially adaptive regularisation

This paper introduces a novel method for inferring spatially varying regularisation in non-linear registration. This is achieved through full Bayesian inference on a probabilistic registration model, where the prior on the transformation parameters is parameterised as a weighted mixture of spatially localised components. Such an approach has the advantage of allowing the registration to be more flexibly driven by the data than a traditional globally defined regularisation penalty, such as bending energy. The proposed method adaptively determines the influence of the prior in a local region. The strength of the prior may be reduced in areas where the data better support deformations, or can enforce a stronger constraint in less informative areas. Consequently, the use of such a spatially adaptive prior may reduce unwanted impacts of regularisation on the inferred transformation. This is especially important for applications where the deformation field itself is of interest, such as tensor based morphometry. The proposed approach is demonstrated using synthetic images, and with application to tensor based morphometry analysis of subjects with Alzheimer’s disease and healthy controls. The results indicate that using the proposed spatially adaptive prior leads to sparser deformations, which provide better localisation of regional volume change. Additionally, the proposed regularisation model leads to more data driven and localised maps of registration uncertainty. This paper also demonstrates for the first time the use of Bayesian model comparison for selecting different types of regularisation

Neuronal pentraxin 2 : a synapse-derived CSF biomarker in genetic frontotemporal dementia

Introduction: Synapse dysfunction is emerging as an early pathological event in frontotemporal dementia (FTD), however biomarkers are lacking. We aimed to investigate the value of cerebrospinal fluid (CSF) neuronal pentraxins (NPTXs), a family of proteins involved in homeostatic synapse plasticity, as novel biomarkers in genetic FTD. Methods: We included 106 presymptomatic and 54 symptomatic carriers of a pathogenic mutation in GRN, C9orf72 or MAPT, and 70 healthy non-carriers participating in the Genetic Frontotemporal dementia Initiative (GENFI), all of whom had at least one CSF sample. We measured CSF concentrations of NPTX2 using an in-house ELISA, and NPTX1 and NPTX receptor (NPTXR) by Western blot. We correlated NPTX2 with corresponding clinical and neuroimaging datasets as well as with CSF neurofilament light chain (NfL) using linear regression analyses. Results: Symptomatic mutation carriers had lower NPTX2 concentrations (median 643 pg/mL, IQR (301-872)) than presymptomatic carriers (1003 pg/mL (624-1358), p<0.001) and non-carriers (990 pg/mL (597-1373), p<0.001) (corrected for age). Similar results were found for NPTX1 and NPTXR. Among mutation carriers, NPTX2 concentration correlated with several clinical disease severity measures, NfL and grey matter volume of the frontal, temporal and parietal lobes, insula and whole brain. NPTX2 predicted subsequent decline in phonemic verbal fluency and Clinical Dementia Rating scale plus FTD modules. In longitudinal CSF samples, available in 13 subjects, NPTX2 decreased around symptom onset and in the symptomatic stage. Discussion: We conclude that NPTX2 is a promising synapse-derived disease progression biomarker in genetic FTD

Distinct neuroanatomical correlates of neuropsychiatric symptoms in the three main forms of genetic frontotemporal dementia in the GENFI Cohort

Background: The overlap between frontotemporal dementia (FTD) and primary psychiatric disorders has been brought to light by reports of prominent neuropsychiatric symptoms (NPS) in FTD-related genetic mutations, particularly among C9orf72 and GRN carriers. It has been recently demonstrated that early neuroanatomical changes in genetic FTD may be different across the major disease-causing mutations. Objective: We aimed to identify whether NPS could be driven by distinct structural correlates. Methods: One hundred and sixty-seven mutation carriers (75 GRN, 60 C9orf72, and 32 MAPT) were included from the Genetic FTD Initiative (GENFI) study, a large international cohort of genetic FTD. Neuropsychiatric symptoms including delusions, hallucinations (visual, auditory, and tactile), depression, and anxiety were investigated using a structured interview. Voxel-based morphometry was performed to identify neuroanatomical correlates of NPS. Results: Psychotic symptoms correlated mainly with grey matter (GM) atrophy in the anterior insula, left thalamus, cerebellum, and cortical regions including frontal, parietal, and occipital lobes in GRN mutations carriers. GM atrophy in posterior structures of the default-mode network was associated with anxiety in the GRN group. Delusions in C9orf72 expansion carriers were mainly associated with left frontal cortical atrophy. Cerebellar atrophy was found to be correlated only with anxiety in C9orf72 carriers. NPS in the MAPT group were mainly associated with volume loss in the temporal lobe. Conclusion: Neuroanatomical correlates of NPS appear to be distinct across the main forms of genetic FTD. Overall, our findings support overlapping brain structural changes between FTD and primary psychiatric disorders

Comparison of arterial spin labeling registration strategies in the multi-center GENetic frontotemporal dementia initiative (GENFI)

Neuro Imaging Researc

Distinct patterns of brain atrophy in Genetic Frontotemporal Dementia Initiative (GENFI) cohort revealed by visual rating scales

Background: In patients with frontotemporal dementia, it has been shown that brain atrophy occurs earliest in the anterior cingulate, insula and frontal lobes. We used visual rating scales to investigate whether identifying atrophy in these areas may be helpful in distinguishing symptomatic patients carrying different causal mutations in the microtubule-associated protein tau (MAPT), progranulin (GRN) and chromosome 9 open reading frame (C9ORF72) genes. We also analysed asymptomatic carriers to see whether it was possible to visually identify brain atrophy before the appearance of symptoms. Methods: Magnetic resonance imaging of 343 subjects (63 symptomatic mutation carriers, 132 presymptomatic mutation carriers and 148 control subjects) from the Genetic Frontotemporal Dementia Initiative study were analysed by two trained raters using a protocol of six visual rating scales that identified atrophy in key regions of the brain (orbitofrontal, anterior cingulate, frontoinsula, anterior and medial temporal lobes and posterior cortical areas). Results: Intra- and interrater agreement were greater than 0.73 for all the scales. Voxel-based morphometric analysis demonstrated a strong correlation between the visual rating scale scores and grey matter atrophy in the same region for each of the scales. Typical patterns of atrophy were identified: symmetric anterior and medial temporal lobe involvement for MAPT, asymmetric frontal and parietal loss for GRN, and a more widespread pattern for C9ORF72. Presymptomatic MAPT carriers showed greater atrophy in the medial temporal region than control subjects, but the visual rating scales could not identify presymptomatic atrophy in GRN or C9ORF72 carriers. Conclusions: These simple-to-use and reproducible scales may be useful tools in the clinical setting for the discrimination of different mutations of frontotemporal dementia, and they may even help to identify atrophy prior to onset in those with MAPT mutations

Presymptomatic cognitive and neuroanatomical changes in genetic frontotemporal dementia in the Genetic Frontotemporal dementia Initiative (GENFI) study: A cross-sectional analysis

Background: Frontotemporal dementia is a highly heritable neurodegenerative disorder. In about a third of patients, the disease is caused by autosomal dominant genetic mutations usually in one of three genes: progranulin (. GRN), microtubule-associated protein tau (. MAPT), or chromosome 9 open reading frame 72 (. C9orf72). Findings from studies of other genetic dementias have shown neuroimaging and cognitive changes before symptoms onset, and we aimed to identify whether such changes could be shown in frontotemporal dementia. Methods: We recruited participants to this multicentre study who either were known carriers of a pathogenic mutation in GRN, MAPT, or C9orf72, or were at risk of carrying a mutation because a first-degree relative was a known symptomatic carrier. We calculated time to expected onset as the difference between age at assessment and mean age at onset within the family. Participants underwent a standardised clinical assessment and neuropsychological battery. We did MRI and generated cortical and subcortical volumes using a parcellation of the volumetric T1-weighted scan. We used linear mixed-effects models to examine whether the association of neuropsychology and imaging measures with time to expected onset of symptoms differed between mutation carriers and non-carriers. Findings: Between Jan 30, 2012, and Sept 15, 2013, we recruited participants from 11 research sites in the UK, Italy, the Netherlands, Sweden, and Canada. We analysed data from 220 participants: 118 mutation carriers (40 symptomatic and 78 asymptomatic) and 102 non-carriers. For neuropsychology measures, we noted the earliest significant differences between mutation carriers and non-carriers 5 years before expected onset, when differences were significant for all measures except for tests of immediate recall and verbal fluency. We noted the largest Z score differences between carriers and non-carriers 5 years before expected onset in tests of naming (Boston Naming Test -0·7; SE 0·3) and executive function (Trail Making Test Part B, Digit Span backwards, and Digit Symbol Task, all -0·5, SE 0·2). For imaging measures, we noted differences earliest for the insula (at 10 years before expected symptom onset, mean volume as a percentage of total intracranial volume was 0·80% in mutation carriers and 0·84% in non-carriers; difference -0·04, SE 0·02) followed by the temporal lobe (at 10 years before expected symptom onset, mean volume as a percentage of total intracranial volume 8·1% in mutation carriers and 8·3% in non-carriers; difference -0·2, SE 0·1). Interpretation: Structural imaging and cognitive changes can be identified 5-10 years before expected onset of symptoms in asymptomatic adults at risk of genetic frontotemporal dementia. These findings could help to define biomarkers that can stage presymptomatic disease and track disease progression, which will be important for future therapeutic trials. Funding: Centres of Excellence in Neurodegenerati