Boundary Shift Integral Research Articles

A composite measure of atrophy using volumetric T1‐weighted and T2‐FLAIR imaging using the Boundary Shift Integral

AbstractBackgroundWhole‐brain volume loss using T1‐weighted imaging is an established measurement of neurodegeneration. The Boundary Shift Integral (BSI) produces reliable metrics of longitudinal volume loss, used in research studies and clinical trials. With the increased acquisition of volumetric T2‐FLAIR imaging in studies, there is opportunity to produce atrophy metrics using a composite atrophy measure (T1‐weighted and T2‐FLAIR) that is more precise than either modality alone.MethodData from Alzheimer’s Disease Neuroimaging Initiative (ADNI3) were used. Volumetric T1 and T2‐FLAIR weighted imaging were obtained (n = 269), for baseline and follow‐up; Table 1. T1‐weighted images were bias corrected and underwent Geodesic Information Flow (GIF), producing probabilistic brain masks. Masks were resampled to T2‐FLAIR space. The BSI was performed on serial T1‐weighted (gBSI), and T2‐FLAIR (fBSI) separately. We tested differences by diagnosis (control (CN), mild cognitive impairment (MCI) and Alzheimer’s disease (AD)) between indirect volume change, gBSI, fBSI, and composite BSI, using linear regression and effect size. We further compared metric differences within diagnosis, using pairwise correlation, paired samples t‐test, and Pitman’s test of variance.ResultDemographic information is reported in Table 1. fBSI showed good Pearson’s correlation (r) with both gBSI (CN 0.7, p < 0.001; MCI 0.8, p < 0.001; AD 0.9, p < 0.001), and indirect change (CN 0.6, p < 0.001; MCI 0.6, < 0.001; AD 0.7, < 0.001). The composite BSI further showed good correlation with indirect change (CN 0.8, p < 0.001; MCI 0.8, p < 0.001; AD 0.8, 0.004). Mean values by diagnosis are shown in figure 1, along with paired samples t and variance tests. Pairwise comparisons reported in table 2, highlight good diagnostic group separation in all BSI metrics, with overall strong effect sizes for fBSI, composite BSI, and gBSI respectively.ConclusionCalculating atrophy using T2‐weighted imaging is possible using fBSI. This can be used in a composite atrophy measure using fBSI and gBSI. Both fBSI and composite BSI perform well with group separation, and with good correlations with classical atrophy metrics. Future work will explore the influence of White Matter Hyperintensities on BSI.

A composite measure of atrophy using volumetric T1‐weighted and T2‐FLAIR imaging using the Boundary Shift Integral

AbstractBackgroundWhole‐brain volume loss using T1‐weighted imaging is an established measurement of neurodegeneration. The Boundary Shift Integral (BSI) produces reliable metrics of longitudinal volume loss, used in research studies and clinical trials. With the increased acquisition of volumetric T2‐FLAIR imaging in studies, there is opportunity to produce atrophy metrics using a composite atrophy measure (T1‐weighted and T2‐FLAIR) that is more precise than either modality alone.MethodData from Alzheimer’s Disease Neuroimaging Initiative (ADNI3) were used. Volumetric T1 and T2‐FLAIR weighted imaging were obtained (n = 269), for baseline and follow‐up; Table 1. T1‐weighted images were bias corrected and underwent Geodesic Information Flow (GIF), producing probabilistic brain masks. Masks were resampled to T2‐FLAIR space. The BSI was performed on serial T1‐weighted (gBSI), and T2‐FLAIR (fBSI) separately. We tested differences by diagnosis (control (CN), mild cognitive impairment (MCI) and Alzheimer’s disease (AD)) between indirect volume change, gBSI, fBSI, and composite BSI, using linear regression and effect size. We further compared metric differences within diagnosis, using pairwise correlation, paired samples t‐test, and Pitman’s test of variance.ResultDemographic information is reported in Table 1. fBSI showed good Pearson’s correlation (r) with both gBSI (CN 0.7, p < 0.001; MCI 0.8, p < 0.001; AD 0.9, p < 0.001), and indirect change (CN 0.6, p < 0.001; MCI 0.6, < 0.001; AD 0.7, < 0.001). The composite BSI further showed good correlation with indirect change (CN 0.8, p < 0.001; MCI 0.8, p < 0.001; AD 0.8, 0.004). Mean values by diagnosis are shown in figure 1, along with paired samples t and variance tests. Pairwise comparisons reported in table 2, highlight good diagnostic group separation in all BSI metrics, with overall strong effect sizes for fBSI, composite BSI, and gBSI respectively.ConclusionCalculating atrophy using T2‐weighted imaging is possible using fBSI. This can be used in a composite atrophy measure using fBSI and gBSI. Both fBSI and composite BSI perform well with group separation, and with good correlations with classical atrophy metrics. Future work will explore the influence of White Matter Hyperintensities on BSI.

White matter hyperintensity volume changes are associated with progressive hippocampal atrophy in Insight 46: the neuroscience sub‐study of the MRC National Survey of Health and Development, the British 1946 birth cohort

AbstractBackgroundWhite matter hyperintensity volume (WMHV) is a presumed marker of cerebrovascular disease associated with ageing, Alzheimer’s disease (AD) and vascular dementia. Higher baseline WMHV is associated with increased brain atrophy rate, with disproportionate effects in the hippocampus – a region affected early in AD. Understanding the relationships between longitudinal change in WMHV and hippocampal atrophy, while accounting for AD and cardiovascular risk factors, will increase our understanding of the multifactorial disease processes occurring during the early stages of AD.MethodRate of change in WMHV was calculated using Bayesian Model Selection (BaMoS) and total hippocampal atrophy rate was calculated using the Boundary Shift Integral (BSI), measured concurrently between two time‐points. Participants with major neurological disorders were excluded. Linear regression was used to test whether rates of WMHV change predicted rates of hippocampal atrophy while adjusting for total intracranial volume (TIV), sex and age. Further models adjusted for baseline PET amyloid‐β (Aβ) standard uptake value ratio (SUVR), ApoE‐e4 carrier status, and Framingham heart study cardiovascular risk (FHS‐CVS) aged 69, and tested for interactions with rate of WMHV change.Result308 individuals with high‐resolution isotropic MRI data at two time‐points were analysed in this study (Table 1). For every 1ml/year increase in WMHV there was an associated 0.013ml/year decrease in total hippocampal volume (Table 2, Model 1). This association remained when adjusting for Aβ SUVR (Table 2, Model 2), ApoE‐e4 status (Table 2, Model 3), and FHS‐CVS (Table 2, Model 4). A separate semi‐partial correlation analysis showed 4.6% of variance in rate of hippocampal atrophy was uniquely explained by rate of WMHV change when accounting for TIV, sex and age. There were no significant interactions between rate of WMHV change and Aβ, ApoE‐e4 or FHS‐CVS in relation to hippocampal atrophy rate.ConclusionIncreases in WMHV were significantly associated with progressive hippocampal atrophy accounting for head size, age and sex. These associations were not materially changed when adjusting for amyloid deposition, ApoE‐e4 or cardiovascular risk factors. These results suggest systemic processes that contribute to WMH development also contribute to hippocampal atrophy. Further work is required to determine the processes that drive this relationship.

Subcortical atrophy imaging biomarker performance across the sporadic and genetic frontotemporal dementias

AbstractBackgroundFrontotemporal dementia (FTD) is a common cause of young‐onset dementia, with promising disease‐modifying candidates. Cortical signatures are well established; however, comprehensive longitudinal investigations of subcortical atrophy are limited and sample size estimates to inform imaging biomarker choice are lacking. Therefore, the current study investigated the relative utility of automated subcortical atrophy measures to detect putative disease‐modifying treatment effects in clinical, genetic and pathologically‐confirmed FTD.MethodLongitudinal registration and segmentation algorithms were applied to a cohort of 262 people, 184 of whom were diagnosed with FTD, to measure atrophy in the amygdala, hippocampus, caudate and thalamus. Clinical diagnoses were behavioural variant FTD (n = 66) and primary progressive aphasia (PPA, n = 118), comprising semantic variant PPA (n = 45), non‐fluent variant PPA (n = 45), logopenic variant PPA (n = 21), and PPA‐not otherwise specified (n = 7). Fifty‐three patients had either a known pathogenic mutation (in the MAPT, C9orf72 or GRN genes) and/or underlying tau or TDP‐43 pathology confirmed at post‐mortem. Seventy‐eight cognitively normal controls were included for comparison. Sample size estimates were computed to explore the relative feasibility of subcortical atrophy measures to track disease progression and detect treatment‐related effects across the different subgroups.ResultRelatively distinct patterns of significantly increased atrophy across the amygdala, hippocampus, caudate and/or thalamus were detected (p < 0.001) for all sporadic, genetic, and pathology‐confirmed FTD subgroups, with a strong left‐dominant pattern evident for PPA‐NOS and GRN patients. The best performing marker (as determined by lowest sample size required to detect a treatment effect) varied by subgroup supporting the importance of informed regional biomarker choice based on the population enrolled. Measures of the caudate and thalamus performed particularly well across all the subgroups, producing estimates well below 100 patients per treatment arm. The left hippocampal BSI (boundary shift integral) measure produced the lowest estimate overall in PPA‐NOS, with 12 individuals required to detect a 30% reduction in atrophy.ConclusionThis work confirms the relative utility of subcortical atrophy measures across the FTD spectrum and provides valuable sample size information to inform non‐invasive biomarker choice for sporadic and genetic FTD trials.

Subcortical atrophy imaging biomarker performance across the sporadic and genetic frontotemporal dementias

AbstractBackgroundFrontotemporal dementia (FTD) is a common cause of young‐onset dementia, with promising disease‐modifying candidates. Cortical signatures are well established; however, comprehensive longitudinal investigations of subcortical atrophy are limited and sample size estimates to inform imaging biomarker choice are lacking. Therefore, the current study investigated the relative utility of automated subcortical atrophy measures to detect putative disease‐modifying treatment effects in clinical, genetic and pathologically‐confirmed FTD.MethodLongitudinal registration and segmentation algorithms were applied to a cohort of 262 people, 184 of whom were diagnosed with FTD, to measure atrophy in the amygdala, hippocampus, caudate and thalamus. Clinical diagnoses were behavioural variant FTD (n = 66) and primary progressive aphasia (PPA, n = 118), comprising semantic variant PPA (n = 45), non‐fluent variant PPA (n = 45), logopenic variant PPA (n = 21), and PPA‐not otherwise specified (n = 7). Fifty‐three patients had either a known pathogenic mutation (in the MAPT, C9orf72 or GRN genes) and/or underlying tau or TDP‐43 pathology confirmed at post‐mortem. Seventy‐eight cognitively normal controls were included for comparison. Sample size estimates were computed to explore the relative feasibility of subcortical atrophy measures to track disease progression and detect treatment‐related effects across the different subgroups.ResultRelatively distinct patterns of significantly increased atrophy across the amygdala, hippocampus, caudate and/or thalamus were detected (p < 0.001) for all sporadic, genetic, and pathology‐confirmed FTD subgroups, with a strong left‐dominant pattern evident for PPA‐NOS and GRN patients. The best performing marker (as determined by lowest sample size required to detect a treatment effect) varied by subgroup supporting the importance of informed regional biomarker choice based on the population enrolled. Measures of the caudate and thalamus performed particularly well across all the subgroups, producing estimates well below 100 patients per treatment arm. The left hippocampal BSI (boundary shift integral) measure produced the lowest estimate overall in PPA‐NOS, with 12 individuals required to detect a 30% reduction in atrophy.ConclusionThis work confirms the relative utility of subcortical atrophy measures across the FTD spectrum and provides valuable sample size information to inform non‐invasive biomarker choice for sporadic and genetic FTD trials.

Herpes simplex virus and rates of cognitive decline or whole brain atrophy in the Dominantly Inherited Alzheimer Network.

To investigate whether herpes simplex virus type 1 (HSV-1) infection was associated with rates of cognitive decline or whole brain atrophy among individuals from the Dominantly Inherited Alzheimer Network (DIAN). Among two subsets of the DIAN cohort (age range 19.6-66.6 years; median follow-up 3.0 years) we examined (i) rate of cognitive decline (N = 164) using change in mini-mental state examination (MMSE) score, (ii) rate of whole brain atrophy (N = 149), derived from serial MR imaging, calculated using the boundary shift integral (BSI) method. HSV-1 antibodies were assayed in baseline sera collected from 2009-2015. Linear mixed-effects models were used to compare outcomes by HSV-1 seropositivity and high HSV-1 IgG titres/IgM status. There was no association between baseline HSV-1 seropositivity and rates of cognitive decline or whole brain atrophy. Having high HSV-1 IgG titres/IgM was associated with a slightly greater decline in MMSE points per year (difference in slope - 0.365, 95% CI: -0.958 to -0.072), but not with rate of whole brain atrophy. Symptomatic mutation carriers declined fastest on both MMSE and BSI measures, however, this was not influenced by HSV-1. Among asymptomatic mutation carriers, rates of decline on MMSE and BSI were slightly greater among those who were HSV-1 seronegative. Among mutation-negative individuals, no differences were seen by HSV-1. Stratifying by APOE4 status yielded inconsistent results. We found no evidence for a major role of HSV-1, measured by serum antibodies, in cognitive decline or whole brain atrophy among individuals at high risk of early-onset AD.

Associations of β-Amyloid and Vascular Burden With Rates of Neurodegeneration in Cognitively Normal Members of the 1946 British Birth Cohort.

Background and ObjectivesThe goals of this work were to quantify the independent and interactive associations of β-amyloid (Aβ) and white matter hyperintensity volume (WMHV), a marker of presumed cerebrovascular disease (CVD), with rates of neurodegeneration and to examine the contributions of APOE ε4 and vascular risk measured at different stages of adulthood in cognitively normal members of the 1946 British Birth Cohort.MethodsParticipants underwent brain MRI and florbetapir-Aβ PET as part of Insight 46, an observational population-based study. Changes in whole-brain, ventricular, and hippocampal volume were directly measured from baseline and repeat volumetric T1 MRI with the boundary shift integral. Linear regression was used to test associations with baseline Aβ deposition, baseline WMHV, APOE ε4, and office-based Framingham Heart Study Cardiovascular Risk Score (FHS-CVS) and systolic blood pressure (BP) at ages 36, 53, and 69 years.ResultsThree hundred forty-six cognitively normal participants (mean [SD] age at baseline scan 70.5 [0.6] years; 48% female) had high-quality T1 MRI data from both time points (mean [SD] scan interval 2.4 [0.2] years). Being Aβ positive at baseline was associated with 0.87–mL/y faster whole-brain atrophy (95% CI 0.03, 1.72), 0.39–mL/y greater ventricular expansion (95% CI 0.16, 0.64), and 0.016–mL/y faster hippocampal atrophy (95% CI 0.004, 0.027), while each 10-mL additional WMHV at baseline was associated with 1.07–mL/y faster whole-brain atrophy (95% CI 0.47, 1.67), 0.31–mL/y greater ventricular expansion (95% CI 0.13, 0.60), and 0.014–mL/y faster hippocampal atrophy (95% CI 0.006, 0.022). These contributions were independent, and there was no evidence that Aβ and WMHV interacted in their effects. There were no independent associations of APOE ε4 with rates of neurodegeneration after adjustment for Aβ status and WMHV, no clear relationships between FHS-CVS or systolic BP and rates of neurodegeneration when assessed across the whole sample, and no evidence that FHS-CVS or systolic BP acted synergistically with Aβ.DiscussionAβ and presumed CVD have distinct and additive effects on rates of neurodegeneration in cognitively normal elderly. These findings have implications for the use of MRI measures as biomarkers of neurodegeneration and emphasize the importance of risk management and early intervention targeting both pathways.

Magnetic resonance imaging measures of brain volumes across the EXPEDITION trials in mild and moderate Alzheimer's disease dementia.

IntroductionSolanezumab is a monoclonal antibody that preferentially binds soluble amyloid beta and promotes its clearance from the brain. The aim of this post hoc analysis was to assess the effect of low‐dose solanezumab (400 mg) on global brain volume measures in patients with mild or moderate Alzheimer's disease (AD) dementia quantified using volumetric magnetic resonance imaging (vMRI) data from the EXPEDITION clinical trial program.MethodsPatients with mild or moderate AD (EXPEDITION and EXPEDITION2) and mild AD (EXPEDITION3), were treated with either placebo or solanezumab (400 mg) every 4 weeks (Q4W) for 76 weeks. vMRI scans were acquired at baseline and at 80 weeks from 427 MRI facilities using a standardized imaging protocol. Whole brain volume (WBV) and ventricle volume (VV) changes were estimated at 80 weeks using either boundary shift integral (EXPEDITION and EXPEDITION2) or tensor‐based morphometry (EXPEDITION3).ResultsThe pooled cohort used for this study consisted of participants with vMRI at baseline and week 80 across the three trials. Analyzed patient subgroups comprised full patient cohort (N = 2933), apolipoprotein E (APOE) ε4+ carriers (N = 1835), and patients with mild (N = 2497) or moderate AD dementia (N = 428). No significant effect (all P‐values ≥.05) of treatment was observed in the pooled sample, individual trials, or subgroups of patients with mild or moderate AD or APOE ε4 carriers, in either WBV or VV change.DiscussionAnalysis of patients with mild or moderate AD dementia from baseline to 80 weeks using vMRI measures of WBV and VV changes suggested that low‐dose solanezumab was not linked to changes in volumes at 80 weeks. Analysis of the pooled cohort did not demonstrate an effect on brain volumes with treatment. Evaluation of a higher dose of solanezumab in the preclinical stage of AD is currently being undertaken.

Towards a fully automatic AI framework to calculate voxel‐wise atrophy in the human brain

AbstractBackgroundLongitudinal changes in volume using co‐registration of two serial T1W MRI scans are used as biomarkers to track the development of Alzheimer’s Disease and monitor potential treatment effects. Current methods (e.g., the boundary shift integral, BSI) may be restricted to certain regions or are computationally intensive and slow (e.g., the Advanced Normalization Tools, ANTs).MethodWe present an AI‐based framework to calculate voxel‐wise atrophy in the human brain. Our method features convolutional neural networks for cross‐sectional semantic segmentation and longitudinal pairwise non‐linear registration which does not require subject‐specific optimization. It warps one image to another by minimizing their difference, while preserving topology. Voxel‐wise volume change is estimated using the Jacobian determinant of the warp field, summed within a specified and automatically segmented region of interest in the baseline image. We evaluate our framework in ADNI (40 HC and 40 MCI) and OASIS3 (100 HC and 100 MCI) participants and assess the method’s measures of annualized atrophy in whole brain, ventricles, and hippocampus using common language effect sizes (CLES) and a Mann‐Whitney U‐test. Retest variability is measured in 30 OASIS3 participants with two serial MR scans not more than 13 days apart. We compare results to BSI as well as the same framework but substituting the non‐linear registration part with ANTs.ResultOur algorithm outperforms ANTs in non‐linear registration accuracy with mean (SD) normalized mutual information of 0.746 (0.055) compared to 0.651 (0.052) for ANTs and 0.646 (0.049) with affine registration only. It also has lower retest variability than ANTs (mean variability range is 0.08%‐0.12% and 0.15%‐0.34% for our algorithm and ANTs respectively). Finally, both BSI and our method show significant group difference for whole brain, ventricles, and hippocampus (all p < 0.002, CLES > 0.69) in both datasets, whereas in ANTs there is no significant group effect for hippocampus (p = 0.12) (see Table 1 for all results).ConclusionOur framework produces fast (seconds) and accurate non‐linear registration and voxel‐wise atrophy calculation for two serial MR images. Our calculated atrophy values show sensitivity to clinical diagnosis in cognitively impaired patients, comparable to current state‐of‐the‐art methods.

BSI whole‐brain volume change estimation initialised with fully automatic LEAP segmentation

AbstractBackgroundWhole brain atrophy as measured from T1‐wieghted MR images is a key biomarker for progression in Alzheimer's disease (AD) and employed as marker for treatment efficacy in clinical trials. The Boundary Shift Integral (BSI) methodology (Freborough;IEEE;1997) is the widely established standard and well validated method for volume change. BSI requires initialisation with a whole‐brain segmentation at both timepoints, manual segmentations remain the gold standard, however the BSI approach is robust to minor segmentation, potentially facilitating the use of automatic segmentation approaches. Furthermore, to improve sensitivity to volume change and exploit the probabilistic nature of many automatic segmentations a generalised form of the BSI (gBSI) pipeline has been proposed (Prados;NeurobiolAging;2015). The use of a fully automatic pipeline to estimate volume change provides a scalable solution for clinical trial deployment without the burden of manual segmentation or edits and associated variability introduced. Here we assess the use of LEAP (Ledig;ProcISBI;2012), a fully automatic whole brain segmentation tool providing both binary and probabilistic parcellations, to initialise the BSI, as implemented in MIDAS, and gBSI pipelines.MethodWhole brain volume change over 1 year was estimated for 48 participants, acquired as part of a clinical trial, with the BSI and gBSI methods initialised with 1) manually generated whole brain segmentations and 2) automatically estimated whole brain segmentations generate with LEAP. No manual corrections were performed; all LEAP segmentations passed a visual quality control inspection.ResultFigure 1 shows the relationship between the manually initialised BSI and the fully‐automated LEAP‐BSI approach. Table 1 shows average measures of volume change across all tested methods. All measures were significantly correlated with non‐significant differences in reported mean volume change.ConclusionLEAP offers segmentations suitable for initialisation of the BSI method providing a pipeline for fully automatic volume estimation consistent with the semi‐automated BSI approach, providing a scalable solution for clinical trial deployment without the associated variability and resource burden required for fully manual segmentations. Furthermore the probabilistic output of LEAP allows for the use of the gBSI methodology with the associated potential of increased sensitivity to volume change.

White matter hyperintensity increases are a feature of familial AD and are associated with increased brain atrophy

AbstractBackgroundRelatively little is known about how white matter hyperintensity (WMH) changes relate to well‐characterised markers of disease progression such as brain atrophy rates in autosomal dominant familial AD (ADAD). Such analyses may improve models of disease progression and indicate whether WMH accrual and brain atrophy are associated features over the disease course.MethodsWe investigated WMH accrual in the Dominantly Inherited Alzheimer Network (DIAN) cohort. Participants included 72 controls, 95 PSEN1, 7 PSEN2 and 27 APP mutation carriers, with longitudinal multi‐modal imaging. WMH were segmented using a semi‐automated protocol. Brain atrophy rate was calculated using the boundary shift integral (BSI). Three statistical models were used: Model 1 used mixed‐effect linear regression to assess WMH accrual by mutation presence or type, adjusting for evidence of cognitive impairment (symptomatic status as defined using CDR). Model 2 assessed the effect of estimated years to onset (EYO) on WMH accrual. Model 3 estimated correlations between residual rates of change after allowing mean rates of change in WMH and brain volume to depend on proximity to expected onset. All models (1‐3) were allowed to depend upon total intracranial volume.ResultsThe table shows summary statistics and rates of change including annualised rates of WMH accrual and brain atrophy. Results from Model 1 showed differences in WMH accrual between mutation groups after adjustment for symptom status (p<0.001), with APP having higher rates of accrual than all other groups. Model 2 demonstrated WMH increases were associated with EYO in PSEN1 and APP groups (p≤0.001, both tests), with an accelerating rate of accrual observed approaching and passing estimated onset in the PSEN1 and PSEN2 groups (p≤0.005, both tests). Model 3 demonstrated associations between residual rates of change in WMH and brain volume in the PSEN1 (r 0.76, p<0.001) and APP (r 0.55, p=0.03) groups (the figure shows crude rates).ConclusionMutation type has an impact on WMH accrual, with APP mutation carriers in this cohort gaining more WMH over time. WMH changes are an important biomarker in ADAD with burden increasing with EYO. WMH and brain atrophy changes track together across the AD disease course.

Generalised boundary shift integral for longitudinal assessment of spinal cord atrophy

Spinal cord atrophy measurements obtained from structural magnetic resonance imaging (MRI) are associated with disability in many neurological diseases and serve as in vivo biomarkers of neurodegeneration. Longitudinal spinal cord atrophy rate is commonly determined from the numerical difference between two volumes (based on 3D surface fitting) or two cross-sectional areas (CSA, based on 2D edge detection) obtained at different time-points. Being an indirect measure, atrophy rates are susceptible to variable segmentation errors at the edge of the spinal cord. To overcome those limitations, we developed a new registration-based pipeline that measures atrophy rates directly. We based our approach on the generalised boundary shift integral (GBSI) method, which registers 2 scans and uses a probabilistic XOR mask over the edge of the spinal cord, thereby measuring atrophy more accurately than segmentation-based techniques. Using a large cohort of longitudinal spinal cord images (610 subjects with multiple sclerosis from a multi-centre trial and 52 healthy controls), we demonstrated that GBSI is a sensitive, quantitative and objective measure of longitudinal spinal cord volume change. The GBSI pipeline is repeatable, reproducible, and provides more precise measurements of longitudinal spinal cord atrophy than segmentation-based methods in longitudinal spinal cord atrophy studies.

Longitudinal spinal cord atrophy in multiple sclerosis using the generalized boundary shift integral.

Spinal cord atrophy is a clinically relevant feature of multiple sclerosis (MS), but longitudinal assessments on magnetic resonance imaging using segmentation-based methods suffer from measurement variability, especially in multicenter studies. We compared the generalized boundary shift integral (GBSI), a registration-based method, with a standard segmentation-based method. Baseline and 1-year spinal cord 3-dimensional T1-weighted images (1mm isotropic) were obtained from 282 patients (52 clinically isolated syndrome [CIS], 196 relapsing-remitting MS [RRMS], 34 progressive MS [PMS]), and 82 controls from 8 MAGNIMS (Magnetic Resonance Imaging in Multiple Sclerosis) sites on multimanufacturer and multi-field-strength scans. Spinal Cord Toolbox was used for C2-5 segmentation and cross-sectional area (CSA) calculation. After cord straightening and registration, GBSI measured atrophy based on the probabilistic boundary-shift region of interest. CSA and GBSI percentage annual volume change was calculated. GBSI provided similar rates of atrophy, but reduced measurement variability compared to CSA in all MS subtypes (CIS: -0.95 ± 2.11% vs -1.19 ± 3.67%; RRMS: -1.74 ± 2.57% vs -1.74 ± 4.02%; PMS: -2.29 ± 2.40% vs -1.29 ± 3.20%) and healthy controls (0.02 ± 2.39% vs -0.56 ± 3.77%). GBSI performed better than CSA in differentiating healthy controls from CIS (area under the curve [AUC] = 0.66 vs 0.53; p = 0.03), RRMS (AUC = 0.73 vs 0.59; p < 0.001), PMS (AUC = 0.77 vs 0.53; p < 0.001), and patients with disability progression from patients without progression (AUC = 0.59 vs 0.50; p = 0.04). Sample size to detect 60% treatment effect on spinal cord atrophy over 1 year was lower for GBSI than CSA (CIS: 106 vs 830; RRMS: 95 vs 335; PMS: 44 vs 215; power = 80%; alpha = 5%). The registration-based method (GBSI) allowed better separation between MS patients and healthy controls and improved statistical power, when compared with a conventional segmentation-based method (CSA), although it is still far from perfect. ANN NEUROL 2019 ANN NEUROL 2019;86:704-713.

Registration-based methods applied to serial high-resolution T1-weighted magnetic resonance imaging for the assessment of brain volume changes in anorexia nervosa of the restricting type

We aimed to determine whether variation in the body mass index (BMI)—a marker of anorexia nervosa (AN) severity—is associated with brain volume changes longitudinally estimated using registration-based methods on serial high-resolution T1-weighted magnetic resonance images (MRI). Fifteen female patients (mean age = 21 years; standard deviation [SD] = 5.7; range: 15–33 years) with the diagnosis of AN of the restricting type (AN-r)—according to the Diagnostic and Statistic Manual of Mental Disorders, 5th edition criteria—underwent T1-weighted MRI at baseline and after a mean follow-up period of 11 months (SD = 6.4). We used the brain boundary shift integral (BSI) and the ventricular BSI (VBSI) to estimate volume changes after registering voxels of follow-up onto baseline MRI. Very significant and strong correlations were found between BMI variation and the brain BSI, as well as between BMI variation and the VBSI. After adjustment for age at onset, duration of illness, and the BMI rate of change before baseline MRI, the statistical significance of both associations persisted. Registration-based methods on serial MRI represent an additional tool to estimate AN severity, because they provide measures of brain volume change strongly associated with BMI variation.

FULLY AUTOMATIC WHOLE BRAIN LONGITUDINAL SEGMENTATION, VALIDATION AND OPERATIONALIZATION

Whole-brain atrophy is a well established biomarker for clinical trials in AD and other neurodegenerative diseases. Widely used methods of whole-brain atrophy estimation, such as the boundary shift integral (BSI) (Freeborough; IEEE, 1997), require manual intervention. As such, these methods are susceptible to intra/inter-segmentor variability and require trained resource for quality control (QC). Here we present a fully automated pipeline for estimation of whole-brain atrophy with subsequent automatic QC testing, minimising sources of potential variability and need for visual inspection. We employed a subset (n=372) of the ADNI1 cohort with 1.5T T1 images and KMN-BSI values available (Fox Lab, ADNI) at Baseline, 12 month and a 24 month follow-up as validation data set. Baseline images were brain extracted with PinCram (Heckemann;PLOS,2015) and N4 Bias field corrected (Tustison;IEEE,2010). Longitudinal images were registered to the baseline image, bias corrected and intensity normalised to the baseline intensity profile. Segmentation was performed on baseline images using a whole-brain multi-atlas segmentation approach (WB LEAP; Wolz, NeuroImage, 2010; Ledig, Proc ISBI,2012). From this, we obtain subject probabilistic maps of cerebral tissue types used to initialise a probabilistic temporospatial segmentation using expectation maximisation based optimisation (Ledig, IEEE,2014). To ensure data quality is maintained, a visual inspection of the input baseline tissue segmentations is required only. QC of image registrations and longitudinal segmentations is performed automatically through tolerance testing on the spatial cross-correlation between registered images, annualised atrophy and normalised volumetric change. Resultant atrophy estimations were compared to KMN-BSI values for validation. The proposed automatic QC reported a 90% pass rate for longitudinal segmentations; upon inspection, all automatic QC pass grades were found to be correct. Segmentations failing automatic QC were visually inspected, 8 subjects failed due to poor registrations. Good agreement was observed between atrophy estimated with KNM-BSI and the proposed method; significant correlation was observed in atrophy estimations at month 12 (r2=0.62, p<<0.01) and month 24 (r2=0.73,p<<0.01). Reported effect and sample sizes were not significantly different between methods for any clinical group or follow-up. We have proposed a fully automatic method for longitudinal assessment of whole-brain atrophy with semi-automatic QC, reducing potential segmentor/rater variance and reporting atrophy consistent with the BSI method.

A Comparison of Accelerated and Non-accelerated MRI Scans for Brain Volume and Boundary Shift Integral Measures of Volume Change: Evidence from the ADNI Dataset

The aim of this study was to assess whether the use of accelerated MRI scans in place of non-accelerated scans influenced brain volume and atrophy rate measures in controls and subjects with mild cognitive impairment and Alzheimer’s disease. We used data from 861 subjects at baseline, 573 subjects at 6 months and 384 subjects at 12 months from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). We calculated whole-brain, ventricular and hippocampal atrophy rates using the k-means boundary shift integral (BSI). Scan quality was visually assessed and the proportion of good quality accelerated and non-accelerated scans compared. We also compared MMSE scores, vascular burden and age between subjects with poor quality scans with those with good quality scans. Finally, we estimated sample size requirements for a hypothetical clinical trial when using atrophy rates from accelerated scans and non-accelerated scans. No significant differences in whole-brain, ventricular and hippocampal volumes and atrophy rates were found between accelerated and non-accelerated scans. Twice as many non-accelerated scan pairs suffered from at least some motion artefacts compared with accelerated scan pairs (p ≤ 0.001), which may influence the BSI. Subjects whose accelerated scans had significant motion had a higher mean vascular burden and age (p ≤ 0.05) whilst subjects whose non-accelerated scans had significant motion had poorer MMSE scores (p ≤ 0.05). No difference in estimated sample size requirements was found when using accelerated vs. non-accelerated scans. Accelerated scans reduce scan time and are better tolerated. Therefore it may be advantageous to use accelerated over non-accelerated scans in clinical trials that use ADNI-type protocols, especially in more cognitively impaired subjects.

Brain Atrophy Rates in Normal Aging and Alzheimer Disease

The objectives of this study were to (1) compare atrophy rates associated with normal aging and Alzheimer disease (AD) using the semi-automated Boundary Shift Integral (BSI) method and manual tracing of the entorhinal cortex (ERC) and hippocampus and (2) calculate power of BSI vs. ERC and hippocampal volume changes for clinical trials in AD. We quantified whole brain and ventricular BSI atrophy rates and ERC and hippocampal atrophy rates from longitudinal MRI data in 20 AD patients and 22 age-matched healthy controls.

Sample Size Estimation for Alzheimer's Disease Trials from Japanese ADNI Serial Magnetic Resonance Imaging.

Background: Little is known about the sample sizes required for clinical trials of Alzheimer’s disease (AD)-modifying treatments using atrophy measures from serial brain magnetic resonance imaging (MRI) in the Japanese population.Objective: The primary objective of the present study was to estimate how large a sample size would be needed for future clinical trials for AD-modifying treatments in Japan using atrophy measures of the brain as a surrogate biomarker.Methods: Sample sizes were estimated from the rates of change of the whole brain and hippocampus by the k-means normalized boundary shift integral (KN-BSI) and cognitive measures using the data of 537 Japanese Alzheimer’s Neuroimaging Initiative (J-ADNI) participants with a linear mixed-effects model. We also examined the potential use of ApoE status as a trial enrichment strategy.Results: The hippocampal atrophy rate required smaller sample sizes than cognitive measures of AD and mild cognitive impairment (MCI). Inclusion of ApoE status reduced sample sizes for AD and MCI patients in the atrophy measures.Conclusion: These results show the potential use of longitudinal hippocampal atrophy measurement using automated image analysis as a progression biomarker and ApoE status as a trial enrichment strategy in a clinical trial of AD-modifying treatment in Japanese people.

White matter hyperintensities are associated with disproportionate progressive hippocampal atrophy.

ABSTRACTThis study investigates relationships between white matter hyperintensity (WMH) volume, cerebrospinal fluid (CSF) Alzheimer's disease (AD) pathology markers, and brain and hippocampal volume loss. Subjects included 198 controls, 345 mild cognitive impairment (MCI), and 154 AD subjects with serial volumetric 1.5‐T MRI. CSF Aβ42 and total tau were measured (n = 353). Brain and hippocampal loss were quantified from serial MRI using the boundary shift integral (BSI). Multiple linear regression models assessed the relationships between WMHs and hippocampal and brain atrophy rates. Models were refitted adjusting for (a) concurrent brain/hippocampal atrophy rates and (b) CSF Aβ42 and tau in subjects with CSF data. WMH burden was positively associated with hippocampal atrophy rate in controls (P = 0.002) and MCI subjects (P = 0.03), and with brain atrophy rate in controls (P = 0.03). The associations with hippocampal atrophy rate remained following adjustment for concurrent brain atrophy rate in controls and MCIs, and for CSF biomarkers in controls (P = 0.007). These novel results suggest that vascular damage alongside AD pathology is associated with disproportionately greater hippocampal atrophy in nondemented older adults. © 2016 The Authors Hippocampus Published by Wiley Periodicals, Inc.

COMPARATIVE LONGITUDINAL CHANGE OF FUNCTIONAL CONNECTIVITY, DIFFUSION AND STRUCTURAL MRI MARKERS IN A PRODROMAL/MILD ALZHEIMER’S DISEASE POPULATION

Volumetric MRI (vMRI) measures are widely used as metrics of Alzheimer’s disease (AD) progression. Measurements of white matter (WM) integrity by diffusion tensor imaging (DTI) and functional connectivity by resting state functional MRI (rsfMRI) represent potential biomarkers of AD progression, but their utility is less well established. The purpose of this multi-site study was to compare the precision of longitudinal changes in vMRI, DTI and rsfMRI parameters in subjects with mild or prodromal AD over a 12 month period. Clinical entry criteria included objective evidence of significantly impaired episodic memory, clinical dementia rating of 0.5 or 1, memory box score > 0.5 and amyloid positivity. 3T MRI scans were obtained across 10 imaging sites in France at baseline (N=56), 6 months (N=45) and 12 months (N=33). Whole brain and ventricular changes were measured by K-means Brain or Ventricular Boundary Shift Integral (BSI). Hippocampal volume (HV) was measured by learning embeddings for atlas propagation (LEAP). Acquisition parameters for DTI and rsfMRI were derived from ADNI2. For each predefined imaging measure, the longitudinal change and statistical signal-to-noise ratio (SNR) were calculated using a mixed effects model. vMRI measures were highly significant at both time points (p<0.001; Figure). Average HV decreased by 1.7% (SNR=0.8) and 3.0% (SNR=1.3) at 6 and 12 months. Weaker changes in some rsfMRI and DTI measures were also observed. The mean correlation within the posterior default mode network exhibited decreased connectivity relative to baseline at both 6 months (-0.061, SNR=0.33, p=0.04) and 12 months (-0.058, SNR=0.61, p=0.004). Reductions in fractional anisotropy were observed in the corpus callosum (genu), internal capsule, temporal WM, and the uncinate fasiculus (SNR ∼ 0.3 to 0.6, p<0.05), and increases in mean diffusivity were observed in the internal capsule, the posterior cingulum bundle, and temporal WM (SNR ∼ 0.2 to 0.5, p<0.05).