Anatomical MRI staging of frontotemporal dementia variants

INTRODUCTION

The three clinical variants of frontotemporal dementia (behavioral variant [bvFTD], semantic dementia, and progressive non-fluent aphasia [PNFA]) are likely to develop over decades, from the preclinical stage to death.

METHODS

To describe the long-term chronological anatomical progression of FTD variants, we built lifespan brain charts of normal aging and FTD variants by combining 8022 quality-controlled MRIs from multiple large-scale data-bases, including 107 bvFTD, 44 semantic dementia, and 38 PNFA.

RESULTS

We report in this manuscript the anatomical MRI staging schemes of the three FTD variants by describing the sequential divergence of volumetric trajectories between normal aging and FTD variants. Subcortical atrophy precedes focal cortical atrophy in specific behavioral and/or language networks, with a "radiological" prodromal phase lasting 8-10 years (time elapsed between the first structural alteration and canonical cortical atrophy).

DISCUSSION

Amygdalar and striatal atrophy can be candidate biomarkers for future preclinical/prodromal FTD variants definitions.

HIGHLIGHTS

We describe the chronological MRI staging of the most affected structures in the three frontotemporal dementia (FTD) syndromic variants. In behavioral variant of FTD (bvFTD): bilateral amygdalar, striatal, and insular atrophy precedes fronto-temporal atrophy. In semantic dementia: bilateral amygdalar atrophy precedes left temporal and hippocampal atrophy. In progressive non-fluent aphasia (PNFA): left striatal, insular, and thalamic atrophy precedes opercular atrophy.

Lifespan Neurodegeneration Of The Human Brain In Multiple Sclerosis

Background

Atrophy related to Multiple Sclerosis (MS) has been found at the early stages of the disease. However, the archetype dynamic trajectories of the neurodegenerative process, even prior to clinical diagnosis, remain unknown.

Methods

We modeled the volumetric trajectories of brain structures across the entire lifespan using 40944 subjects (38295 healthy controls and 2649 MS patients). Then, we estimated the chronological progression of MS by assessing the divergence of lifespan trajectories between normal brain charts and MS brain charts.

Results

Chronologically, the first affected structure was the thalamus, then the putamen and the pallidum (3 years later), followed by the ventral diencephalon (7 years after thalamus) and finally the brainstem (9 years after thalamus). To a lesser extent, the anterior cingulate gyrus, insular cortex, occipital pole, caudate and hippocampus were impacted. Finally, the precuneus and accumbens nuclei exhibited a limited atrophy pattern.

Conclusion

Subcortical atrophy was more pronounced than cortical atrophy. The thalamus was the most impacted structure with a very early divergence in life. It paves the way toward utilization of these lifespan models for future preclinical/prodromal prognosis and monitoring of MS.

Longitudinal detection of new MS lesions using deep learning

The detection of new multiple sclerosis (MS) lesions is an important marker of the evolution of the disease. The applicability of learning-based methods could automate this task efficiently. However, the lack of annotated longitudinal data with new-appearing lesions is a limiting factor for the training of robust and generalizing models. In this study, we describe a deep-learning-based pipeline addressing the challenging task of detecting and segmenting new MS lesions. First, we propose to use transfer-learning from a model trained on a segmentation task using single time-points. Therefore, we exploit knowledge from an easier task and for which more annotated datasets are available. Second, we propose a data synthesis strategy to generate realistic longitudinal time-points with new lesions using single time-point scans. In this way, we pretrain our detection model on large synthetic annotated datasets. Finally, we use a data-augmentation technique designed to simulate data diversity in MRI. By doing that, we increase the size of the available small annotated longitudinal datasets. Our ablation study showed that each contribution lead to an enhancement of the segmentation accuracy. Using the proposed pipeline, we obtained the best score for the segmentation and the detection of new MS lesions in the MSSEG2 MICCAI challenge.

Structural progression of Alzheimer’s disease over decades: the MRI staging scheme

The chronological progression of brain atrophy over decades, from pre-symptomatic to dementia stages, has never been formally depicted in Alzheimer’s disease. This is mainly due to the lack of cohorts with long enough MRI follow-ups in cognitively unimpaired young participants at baseline. To describe a spatiotemporal atrophy staging of Alzheimer’s disease at the whole-brain level, we built extrapolated lifetime volumetric models of healthy and Alzheimer’s disease brain structures by combining multiple large-scale databases (n = 3512 quality controlled MRI from 9 cohorts of subjects covering the entire lifespan, including 415 MRI from ADNI1, ADNI2 and AIBL for Alzheimer’s disease patients). Then, we validated dynamic models based on cross-sectional data using external longitudinal data. Finally, we assessed the sequential divergence between normal aging and Alzheimer’s disease volumetric trajectories and described the following staging of brain atrophy progression in Alzheimer’s disease: (i) hippocampus and amygdala; (ii) middle temporal gyrus; (iii) entorhinal cortex, parahippocampal cortex and other temporal areas; (iv) striatum and thalamus and (v) middle frontal, cingular, parietal, insular cortices and pallidum. We concluded that this MRI scheme of atrophy progression in Alzheimer’s disease was close but did not entirely overlap with Braak staging of tauopathy, with a ‘reverse chronology’ between limbic and entorhinal stages. Alzheimer’s disease structural progression may be associated with local tau accumulation but may also be related to axonal degeneration in remote sites and other limbic-predominant associated proteinopathies.

DeepLesionBrain: Towards a broader deep-learning generalization for multiple sclerosis lesion segmentation

Recently, segmentation methods based on Convolutional Neural Networks (CNNs) showed promising performance in automatic Multiple Sclerosis (MS) lesions segmentation. These techniques have even outperformed human experts in controlled evaluation conditions such as Longitudinal MS Lesion Segmentation Challenge (ISBI Challenge). However, state-of-the-art approaches trained to perform well on highly-controlled datasets fail to generalize on clinical data from unseen datasets. Instead of proposing another improvement of the segmentation accuracy, we propose a novel method robust to domain shift and performing well on unseen datasets, called DeepLesionBrain (DLB). This generalization property results from three main contributions. First, DLB is based on a large group of compact 3D CNNs. This spatially distributed strategy aims to produce a robust prediction despite the risk of generalization failure of some individual networks. Second, we propose a hierarchical specialization learning (HSL) by pre-training a generic network over the whole brain, before using its weights as initialization to locally specialized networks. By this end, DLB learns both generic features extracted at global image level and specific features extracted at local image level. Finally, DLB includes a new image quality data augmentation to reduce dependency to training data specificity (e.g., acquisition protocol). DLB generalization was validated in cross-dataset experiments on MSSEG'16, ISBI challenge, and in-house datasets. During experiments, DLB showed higher segmentation accuracy, better segmentation consistency and greater generalization performance compared to state-of-the-art methods. Therefore, DLB offers a robust framework well-suited for clinical practice.

AssemblyNet: A large ensemble of CNNs for 3D whole brain MRI segmentation

Whole brain segmentation of fine-grained structures using deep learning (DL) is a very challenging task since the number of anatomical labels is very high compared to the number of available training images. To address this problem, previous DL methods proposed to use a single convolution neural network (CNN) or few independent CNNs. In this paper, we present a novel ensemble method based on a large number of CNNs processing different overlapping brain areas. Inspired by parliamentary decision-making systems, we propose a framework called AssemblyNet, made of two "assemblies" of U-Nets. Such a parliamentary system is capable of dealing with complex decisions, unseen problem and reaching a relevant consensus. AssemblyNet introduces sharing of knowledge among neighboring U-Nets, an "amendment" procedure made by the second assembly at higher-resolution to refine the decision taken by the first one, and a final decision obtained by majority voting. During our validation, AssemblyNet showed competitive performance compared to state-of-the-art methods such as U-Net, Joint label fusion and SLANT. Moreover, we investigated the scan-rescan consistency and the robustness to disease effects of our method. These experiences demonstrated the reliability of AssemblyNet. Finally, we showed the interest of using semi-supervised learning to improve the performance of our method.

Cerebellar parcellation in schizophrenia and bipolar disorder

OBJECTIVE

The cerebellum is involved in cognitive processing and emotion control. Cerebellar alterations could explain symptoms of schizophrenia spectrum disorder (SZ) and bipolar disorder (BD). In addition, literature suggests that lithium might influence cerebellar anatomy. Our aim was to study cerebellar anatomy in SZ and BD, and investigate the effect of lithium.

METHODS

Participants from 7 centers worldwide underwent a 3T MRI. We included 182 patients with SZ, 144 patients with BD, and 322 controls. We automatically segmented the cerebellum using the CERES pipeline. All outputs were visually inspected.

RESULTS

Patients with SZ showed a smaller global cerebellar gray matter volume compared to controls, with most of the changes located to the cognitive part of the cerebellum (Crus II and lobule VIIb). This decrease was present in the subgroup of patients with recent-onset SZ. We did not find any alterations in the cerebellum in patients with BD. However, patients medicated with lithium had a larger size of the anterior cerebellum, compared to patients not treated with lithium.

CONCLUSION

Our multicenter study supports a distinct pattern of cerebellar alterations in SZ and BD.

Regional hippocampal vulnerability in early multiple sclerosis: Dynamic pathological spreading from dentate gyrus to CA1

BACKGROUND

Whether hippocampal subfields are differentially vulnerable at the earliest stages of multiple sclerosis (MS) and how this impacts memory performance is a current topic of debate.

METHOD

We prospectively included 56 persons with clinically isolated syndrome (CIS) suggestive of MS in a 1-year longitudinal study, together with 55 matched healthy controls at baseline. Participants were tested for memory performance and scanned with 3 T MRI to assess the volume of 5 distinct hippocampal subfields using automatic segmentation techniques.

RESULTS

At baseline, CA4/dentate gyrus was the only hippocampal subfield with a volume significantly smaller than controls (p < .01). After one year, CA4/dentate gyrus atrophy worsened (-6.4%, p < .0001) and significant CA1 atrophy appeared (both in the stratum-pyramidale and the stratum radiatum-lacunosum-moleculare, -5.6%, p < .001 and -6.2%, p < .01, respectively). CA4/dentate gyrus volume at baseline predicted CA1 volume one year after CIS (R²  = 0.44 to 0.47, p < .001, with age, T2 lesion-load, and global brain atrophy as covariates). The volume of CA4/dentate gyrus at baseline was associated with MS diagnosis during follow-up, independently of T2-lesion load and demographic variables (p < .05). Whereas CA4/dentate gyrus volume was not correlated with memory scores at baseline, CA1 atrophy was an independent correlate of episodic verbal memory performance one year after CIS (ß = 0.87, p < .05).

CONCLUSION

The hippocampal degenerative process spread from dentate gyrus to CA1 at the earliest stage of MS. This dynamic vulnerability is associated with MS diagnosis after CIS and will ultimately impact hippocampal-dependent memory performance.

Hippocampal microstructural damage correlates with memory impairment in clinically isolated syndrome suggestive of multiple sclerosis

OBJECTIVE

We investigated whether diffusion tensor imaging (DTI) could reveal early hippocampal damage and clinically relevant correlates of memory impairment in persons with clinically isolated syndrome (CIS) suggestive of multiple sclerosis (MS).

METHODS

A total of 37 persons with CIS, 32 with MS and 36 controls prospectively included from 2011 to 2014 were tested for cognitive performances and scanned with 3T-magnetic resonance imaging (MRI) to assess volumetric and DTI changes within the hippocampus, whole brain volume and T2-lesion load.

RESULTS

While there was no hippocampal atrophy in the CIS group, hippocampal fractional anisotropy (FA) was significantly decreased compared to controls. Decrease in hippocampal FA together with increased mean diffusivity (MD) was even more prominent in MS patients. In CIS, hippocampal MD was correlated with episodic verbal memory performance ( r = -0.57, p = 0.0002 and odds ratio (OR) = 0.058, 95% confidence interval (CI) = 0.0057-0.59, p = 0.016 adjusted for age, gender, depression and T2-lesion load), but not with cognitive tasks unrelated to hippocampal functions. Hippocampal MD was the only variable discriminating memory-impaired from memory-preserved persons with CIS (area under the curve (AUC) = 0.77, sensitivity = 90.0%, specificity = 70.3%, positive predictive value (PPV) = 52.9%, negative predictive value (NPV) = 95.0%).

CONCLUSION

DTI alterations within the hippocampus might reflect early neurodegenerative processes that are correlated with episodic memory performance, discriminating persons with CIS according to their memory status.