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Jonathan M. Schott

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12 papers

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YNICL Journal 2023 Journal Article

Spatial distributions of white matter hyperintensities on brain MRI: A pooled analysis of individual participant data from 11 memory clinic cohorts

Mirthe Coenen
Geert Jan Biessels
Charles DeCarli
Evan F. Fletcher
Pauline M. Maillard
Frederik Barkhof
Josephine Barnes
Thomas Benke

INTRODUCTION: The spatial distribution of white matter hyperintensities (WMH) on MRI is often considered in the diagnostic evaluation of patients with cognitive problems. In some patients, clinicians may classify WMH patterns as "unusual", but this is largely based on expert opinion, because detailed quantitative information about WMH distribution frequencies in a memory clinic setting is lacking. Here we report voxel wise 3D WMH distribution frequencies in a large multicenter dataset and also aimed to identify individuals with unusual WMH patterns. METHODS: Individual participant data (N = 3525, including 777 participants with subjective cognitive decline, 1389 participants with mild cognitive impairment and 1359 patients with dementia) from eleven memory clinic cohorts, recruited through the Meta VCI Map Consortium, were used. WMH segmentations were provided by participating centers or performed in Utrecht and registered to the Montreal Neurological Institute (MNI)-152 brain template for spatial normalization. To determine WMH distribution frequencies, we calculated WMH probability maps at voxel level. To identify individuals with unusual WMH patterns, region-of-interest (ROI) based WMH probability maps, rule-based scores, and a machine learning method (Local Outlier Factor (LOF)), were implemented. RESULTS: WMH occurred in 82% of voxels from the white matter template with large variation between subjects. Only a small proportion of the white matter (1.7%), mainly in the periventricular areas, was affected by WMH in at least 20% of participants. A large portion of the total white matter was affected infrequently. Nevertheless, 93.8% of individual participants had lesions in voxels that were affected in less than 2% of the population, mainly located in subcortical areas. Only the machine learning method effectively identified individuals with unusual patterns, in particular subjects with asymmetric WMH distribution or with WMH at relatively rarely affected locations despite common locations not being affected. DISCUSSION: Aggregating data from several memory clinic cohorts, we provide a detailed 3D map of WMH lesion distribution frequencies, that informs on common as well as rare localizations. The use of data-driven analysis with LOF can be used to identify unusual patterns, which might serve as an alert that rare causes of WMH should be considered.

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YNIMG Journal 2022 Journal Article

Association between carotid atherosclerosis and brain activation patterns during the Stroop task in older adults: An fNIRS investigation

Sarah A. Mason
Lamia Al Saikhan
Siana Jones
Sarah-Naomi James
Heidi Murray-Smith
Alicja Rapala
Suzanne Williams
Carole Sudre

There is an increasing body of evidence suggesting that vascular disease could contribute to cognitive decline and overt dementia. Of particular interest is atherosclerosis, as it is not only associated with dementia, but could be a potential mechanism through which cardiovascular disease directly impacts brain health. In this work, we evaluated the differences in functional near infrared spectroscopy (fNIRS)-based measures of brain activation, task performance, and the change in central hemodynamics (mean arterial pressure (MAP) and heart rate (HR)) during a Stroop color-word task in individuals with atherosclerosis, defined as bilateral carotid plaques (n = 33) and healthy age-matched controls (n = 33). In the healthy control group, the left prefrontal cortex (LPFC) was the only region showing evidence of activation when comparing the incongruous with the nominal Stroop test. A smaller extent of brain activation was observed in the Plaque group compared with the healthy controls (1) globally, as measured by oxygenated hemoglobin (p = 0.036) and (2) in the LPFC (p = 0.02) and left sensorimotor cortices (LMC)(p = 0.008) as measured by deoxygenated hemoglobin. There were no significant differences in HR, MAP, or task performance (both in terms of the time required to complete the task and number of errors made) between Plaque and control groups. These results suggest that carotid atherosclerosis is associated with altered functional brain activation patterns despite no evidence of impaired performance of the Stroop task or central hemodynamic changes.

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JBHI Journal 2020 Journal Article

Augmenting Dementia Cognitive Assessment With Instruction-Less Eye-Tracking Tests

Kyriaki Mengoudi
Daniele Ravi
Keir X. X. Yong
Silvia Primativo
Ivanna M. Pavisic
Emilie Brotherhood
Kirsty Lu
Jonathan M. Schott

Eye-tracking technology is an innovative tool that holds promise for enhancing dementia screening. In this work, we introduce a novel way of extracting salient features directly from the raw eye-tracking data of a mixed sample of dementia patients during a novel instruction-less cognitive test. Our approach is based on self-supervised representation learning where, by training initially a deep neural network to solve a pretext task using well-defined available labels (e. g. recognising distinct cognitive activities in healthy individuals), the network encodes high-level semantic information which is useful for solving other problems of interest (e. g. dementia classification). Inspired by previous work in explainable AI, we use the Layer-wise Relevance Propagation (LRP) technique to describe our network's decisions in differentiating between the distinct cognitive activities. The extent to which eye-tracking features of dementia patients deviate from healthy behaviour is then explored, followed by a comparison between self-supervised and handcrafted representations on discriminating between participants with and without dementia. Our findings not only reveal novel self-supervised learning features that are more sensitive than handcrafted features in detecting performance differences between participants with and without dementia across a variety of tasks, but also validate that instruction-less eye-tracking tests can detect oculomotor biomarkers of dementia-related cognitive dysfunction. This work highlights the contribution of self-supervised representation learning techniques in biomedical applications where the small number of patients, the non-homogenous presentations of the disease and the complexity of the setting can be a challenge using state-of-the-art feature extraction methods.

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YNICL Journal 2019 Journal Article

Differences in hippocampal subfield volume are seen in phenotypic variants of early onset Alzheimer's disease

Thomas D. Parker
Catherine F. Slattery
Keir X.X. Yong
Jennifer M. Nicholas
Ross W. Paterson
Alexander J.M. Foulkes
Ian B. Malone
David L. Thomas

The most common presentation of early onset Alzheimer's disease (EOAD - defined as symptom onset <65 years) is with progressive episodic memory impairment - amnestic or typical Alzheimer's disease (tAD). However, EOAD is notable for its phenotypic heterogeneity, with posterior cortical atrophy (PCA) - characterised by prominent higher-order visual processing deficits and relative sparing of episodic memory - the second most common canonical phenotype. The hippocampus, which comprises a number of interconnected anatomically and functionally distinct subfields, is centrally involved in Alzheimer's disease and is a crucial mediator of episodic memory. The extent to which volumes of individual hippocampal subfields differ between different phenotypes in EOAD is unclear. The aim of this analysis was to investigate the hypothesis that patients with a PCA phenotype will exhibit differences in specific hippocampal subfield volumes compared to tAD. We studied 63 participants with volumetric T1-weighted MRI performed on the same 3T scanner: 39 EOAD patients [27 with tAD and 12 with PCA] and 24 age-matched controls. Volumetric estimates of the following hippocampal subfields for each participant were obtained using Freesurfer version 6.0: CA1, CA2/3, CA4, presubiculum, subiculum, hippocampal tail, parasubiculum, the molecular and granule cell layers of the dentate gryus (GCMLDG), the molecular layer, and the hippocampal amygdala transition area (HATA). Linear regression analyses comparing mean hippocampal subfield volumes between groups, adjusting for age, sex and head size, were performed. Using a Bonferonni-corrected p-value of p < 0.0025, compared to controls, tAD was associated with atrophy in all hippocampal regions, except the parasubiculum. In PCA patients compared to controls, the strongest evidence for volume loss was in the left presubiclum, right subiculum, right GCMLDG, right molecular layer and the right HATA. Compared to PCA, patients with tAD had strong evidence for smaller volumes in left CA1 and left hippocampal tail. In conclusion, these data provide evidence that hippocampal subfield volumes differ in different phenotypes of EOAD.

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YNICL Journal 2019 Journal Article

Prion disease diagnosis using subject-specific imaging biomarkers within a multi-kernel Gaussian process

Liane S. Canas
Carole H. Sudre
Enrico De Vita
Akin Nihat
Tze How Mok
Catherine F. Slattery
Ross W. Paterson
Alexander J.M. Foulkes

Prion diseases are a group of rare neurodegenerative conditions characterised by a high rate of progression and highly heterogeneous phenotypes. Whilst the most common form of prion disease occurs sporadically (sporadic Creutzfeldt-Jakob disease, sCJD), other forms are caused by prion protein gene mutations, or exposure to prions in the diet or by medical procedures, such us surgeries. To date, there are no accurate quantitative imaging biomarkers that can be used to predict the future clinical diagnosis of a healthy subject, or to quantify the progression of symptoms over time. Besides, CJD is commonly mistaken for other forms of dementia. Due to the heterogeneity of phenotypes and the lack of a consistent geometrical pattern of disease progression, the approaches used to study other types of neurodegenerative diseases are not satisfactory to capture the progression of human form of prion disease. In this paper, using a tailored framework, we aim to classify and stratify patients with prion disease, according to the severity of their illness. The framework is initialised with the extraction of subject-specific imaging biomarkers. The extracted biomakers are then combined with genetic and demographic information within a Gaussian Process classifier, used to calculate the probability of a subject to be diagnosed with prion disease in the next year. We evaluate the effectiveness of the proposed method in a cohort of patients with inherited and sporadic forms of prion disease. The model has shown to be effective in the prediction of both inherited CJD (92% of accuracy) and sporadic CJD (95% of accuracy). However the model has shown to be less effective when used to stratify the different stages of the disease, in which the average accuracy is 85%, whilst the recall is 59%. Finally, our framework was extended as a differential diagnosis tool to identify both forms of CJD among another neurodegenerative disease. In summary we have developed a novel method for prion disease diagnosis and prediction of clinical onset using multiple sources of features, which may have use in other disorders with heterogeneous imaging features.

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YNIMG Journal 2015 Journal Article

Assessing atrophy measurement techniques in dementia: Results from the MIRIAD atrophy challenge

David M. Cash
Chris Frost
Leonardo O. Iheme
Devrim Ünay
Melek Kandemir
Jurgen Fripp
Olivier Salvado
Pierrick Bourgeat

Structural MRI is widely used for investigating brain atrophy in many neurodegenerative disorders, with several research groups developing and publishing techniques to provide quantitative assessments of this longitudinal change. Often techniques are compared through computation of required sample size estimates for future clinical trials. However interpretation of such comparisons is rendered complex because, despite using the same publicly available cohorts, the various techniques have been assessed with different data exclusions and different statistical analysis models. We created the MIRIAD atrophy challenge in order to test various capabilities of atrophy measurement techniques. The data consisted of 69 subjects (46 Alzheimer's disease, 23 control) who were scanned multiple (up to twelve) times at nine visits over a follow-up period of one to two years, resulting in 708 total image sets. Nine participating groups from 6 countries completed the challenge by providing volumetric measurements of key structures (whole brain, lateral ventricle, left and right hippocampi) for each dataset and atrophy measurements of these structures for each time point pair (both forward and backward) of a given subject. From these results, we formally compared techniques using exactly the same dataset. First, we assessed the repeatability of each technique using rates obtained from short intervals where no measurable atrophy is expected. For those measures that provided direct measures of atrophy between pairs of images, we also assessed symmetry and transitivity. Then, we performed a statistical analysis in a consistent manner using linear mixed effect models. The models, one for repeated measures of volume made at multiple time-points and a second for repeated “direct” measures of change in brain volume, appropriately allowed for the correlation between measures made on the same subject and were shown to fit the data well. From these models, we obtained estimates of the distribution of atrophy rates in the Alzheimer's disease (AD) and control groups and of required sample sizes to detect a 25% treatment effect, in relation to healthy ageing, with 95% significance and 80% power over follow-up periods of 6, 12, and 24months. Uncertainty in these estimates, and head-to-head comparisons between techniques, were carried out using the bootstrap. The lateral ventricles provided the most stable measurements, followed by the brain. The hippocampi had much more variability across participants, likely because of differences in segmentation protocol and less distinct boundaries. Most methods showed no indication of bias based on the short-term interval results, and direct measures provided good consistency in terms of symmetry and transitivity. The resulting annualized rates of change derived from the model ranged from, for whole brain: −1. 4% to −2. 2% (AD) and −0. 35% to −0. 67% (control), for ventricles: 4. 6% to 10. 2% (AD) and 1. 2% to 3. 4% (control), and for hippocampi: −1. 5% to −7. 0% (AD) and −0. 4% to −1. 4% (control). There were large and statistically significant differences in the sample size requirements between many of the techniques. The lowest sample sizes for each of these structures, for a trial with a 12month follow-up period, were 242 (95% CI: 154 to 422) for whole brain, 168 (95% CI: 112 to 282) for ventricles, 190 (95% CI: 146 to 268) for left hippocampi, and 158 (95% CI: 116 to 228) for right hippocampi. This analysis represents one of the most extensive statistical comparisons of a large number of different atrophy measurement techniques from around the globe. The challenge data will remain online and publicly available so that other groups can assess their methods.

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YNICL Journal 2015 Journal Article

Functional neuroanatomy of auditory scene analysis in Alzheimer's disease

Hannah L. Golden
Jennifer L. Agustus
Johanna C. Goll
Laura E. Downey
Catherine J. Mummery
Jonathan M. Schott
Sebastian J. Crutch
Jason D. Warren

Auditory scene analysis is a demanding computational process that is performed automatically and efficiently by the healthy brain but vulnerable to the neurodegenerative pathology of Alzheimer's disease. Here we assessed the functional neuroanatomy of auditory scene analysis in Alzheimer's disease using the well-known 'cocktail party effect' as a model paradigm whereby stored templates for auditory objects (e.g., hearing one's spoken name) are used to segregate auditory 'foreground' and 'background'. Patients with typical amnestic Alzheimer's disease (n = 13) and age-matched healthy individuals (n = 17) underwent functional 3T-MRI using a sparse acquisition protocol with passive listening to auditory stimulus conditions comprising the participant's own name interleaved with or superimposed on multi-talker babble, and spectrally rotated (unrecognisable) analogues of these conditions. Name identification (conditions containing the participant's own name contrasted with spectrally rotated analogues) produced extensive bilateral activation involving superior temporal cortex in both the AD and healthy control groups, with no significant differences between groups. Auditory object segregation (conditions with interleaved name sounds contrasted with superimposed name sounds) produced activation of right posterior superior temporal cortex in both groups, again with no differences between groups. However, the cocktail party effect (interaction of own name identification with auditory object segregation processing) produced activation of right supramarginal gyrus in the AD group that was significantly enhanced compared with the healthy control group. The findings delineate an altered functional neuroanatomical profile of auditory scene analysis in Alzheimer's disease that may constitute a novel computational signature of this neurodegenerative pathology.

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YNICL Journal 2015 Journal Article

White matter tract signatures of impaired social cognition in frontotemporal lobar degeneration

Laura E. Downey
Colin J. Mahoney
Aisling H. Buckley
Hannah L. Golden
Susie M. Henley
Nicole Schmitz
Jonathan M. Schott
Ivor J. Simpson

Impairments of social cognition are often leading features in frontotemporal lobar degeneration (FTLD) and likely to reflect large-scale brain network disintegration. However, the neuroanatomical basis of impaired social cognition in FTLD and the role of white matter connections have not been defined. Here we assessed social cognition in a cohort of patients representing two core syndromes of FTLD, behavioural variant frontotemporal dementia (bvFTD; n = 29) and semantic variant primary progressive aphasia (svPPA; n = 15), relative to healthy older individuals (n = 37) using two components of the Awareness of Social Inference Test, canonical emotion identification and sarcasm identification. Diffusion tensor imaging (DTI) was used to derive white matter tract correlates of social cognition performance and compared with the distribution of grey matter atrophy on voxel-based morphometry. The bvFTD and svPPA groups showed comparably severe deficits for identification of canonical emotions and sarcasm, and these deficits were correlated with distributed and overlapping white matter tract alterations particularly affecting frontotemporal connections in the right cerebral hemisphere. The most robust DTI associations were identified in white matter tracts linking cognitive and evaluative processing with emotional responses: anterior thalamic radiation, fornix (emotion identification) and uncinate fasciculus (sarcasm identification). DTI associations of impaired social cognition were more consistent than corresponding grey matter associations. These findings delineate a brain network substrate for the social impairment that characterises FTLD syndromes. The findings further suggest that DTI can generate sensitive and functionally relevant indexes of white matter damage in FTLD, with potential to transcend conventional syndrome boundaries.

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YNIMG Journal 2013 Journal Article

MIRIAD—Public release of a multiple time point Alzheimer's MR imaging dataset

Ian B. Malone
David Cash
Gerard R. Ridgway
David G. MacManus
Sebastien Ourselin
Nick C. Fox
Jonathan M. Schott

The Minimal Interval Resonance Imaging in Alzheimer's Disease (MIRIAD) dataset is a series of longitudinal volumetric T1 MRI scans of 46 mild–moderate Alzheimer's subjects and 23 controls. It consists of 708 scans conducted by the same radiographer with the same scanner and sequences at intervals of 2, 6, 14, 26, 38 and 52weeks, 18 and 24months from baseline, with accompanying information on gender, age and Mini Mental State Examination (MMSE) scores. Details of the cohort and imaging results have been described in peer-reviewed publications, and the data are here made publicly available as a common resource for researchers to develop, validate and compare techniques, particularly for measurement of longitudinal volume change in serially acquired MR.

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YNIMG Journal 2011 Journal Article

Algorithms, atrophy and Alzheimer's disease: Cautionary tales for clinical trials

Nick C. Fox
Gerard R. Ridgway
Jonathan M. Schott

Thompson and Holland (2010) highlight a biologically implausible deceleration of atrophy in results previously published in this journal (Hua et al. , 2010); the results were derived using tensor based morphometry on images from the Alzheimer's Disease Neuroimaging Initiative. They speculate that bias may have been introduced due to asymmetric interpolation in global image registration, and/or to the use of a statistically defined region of interest. In their reply, Hua et al. (this issue) acknowledge the presence of a bias, but show that it stems largely from an asymmetry in the local image registration algorithm (an asymmetry common to methods in many published studies using nonlinear registration). Hua et al. demonstrate that the bias can largely be removed using a revised symmetric algorithm. This correspondence raises important issues relating to the lack of ground truth against which image analysis methodologies designed to determine atrophy patterns and rates can be assessed; and the increasing importance of striving to avoid potential biases as these techniques become utilised in clinical trials. In the absence of a “gold standard”, we discuss a number of steps against which methodologies designed to quantify atrophy from serial scans can be assessed.

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YNIMG Journal 2007 Journal Article

Longitudinal and cross-sectional analysis of atrophy in Alzheimer's disease: Cross-validation of BSI, SIENA and SIENAX

Stephen M. Smith
Anil Rao
Nicola De Stefano
Mark Jenkinson
Jonathan M. Schott
Paul M. Matthews
Nick C. Fox

Brain volume loss (atrophy) is widely used as a marker of disease progression. Atrophy has been measured with a variety of methods, some estimating atrophy rate from two temporally separated scans, and others estimating atrophy state from a single scan. Three popular tools for measuring brain atrophy are BSI and SIENA (rate) and SIENAX (state). Previous papers have shown BSI and SIENA to have similar accuracy, but no work has carefully compared both methods using the same data set. Here we compare these methods, using data from patients with Alzheimer's disease and age-matched controls. We also compare the SIENA longitudinal measure with atrophy state estimated by SIENAX using just the earliest scan taken from each subject. We show strong correspondence and similar sensitivity to atrophy between all 3 measures.

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YNIMG Journal 2006 Journal Article

Cerebral atrophy measurements using Jacobian integration: Comparison with the boundary shift integral

Richard G. Boyes
Daniel Rueckert
Paul Aljabar
Jennifer Whitwell
Jonathan M. Schott
Derek L.G. Hill
Nicholas C. Fox

We compared two methods of measuring cerebral atrophy in a cohort of 38 clinically probable Alzheimer's disease (AD) subjects and 22 age-matched normal controls, using metrics of zero atrophy, consistency, scaled atrophy and AD/control group separation. The two methods compared were the boundary shift integral (BSI) and a technique based on the integration of Jacobian determinants from non-rigid registration. For each subject, we used two volumetric magnetic resonance (MR) scans at baseline and a third obtained 1 year later. The case of zero atrophy was established by registering the same-day baseline scan pair, which should approximate zero change. Consistency was established by registering the 1-year follow-up scan to each of the baseline scans, giving two measurements of atrophy that should be very similar, while scaled atrophy was established by reducing one of the same-day scans by a fixed amount, and rigidly registering this to the other same-day scan. Group separation was ascertained by calculating atrophy rates over the two 1-year measures for the control and AD subjects. The results showed the Jacobian integration technique was significantly more accurate in calculating scaled atrophy (P < 0. 001) and was able to distinguish between control and AD subjects more clearly (P < 0. 01).

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