Abstract Thanks to its safety and accessibility, magnetic resonance imaging (MRI) is extensively used in clinical routine and research field, largely contributing to our understanding of the ...pathophysiology of neurodegenerative disorders such as Alzheimer’s disease (AD). This review aims to provide a comprehensive overview of the main findings in AD and normal aging over the past twenty years, focusing on the patterns of gray and white matter changes assessed in vivo using MRI. Major progresses in the field concern the segmentation of the hippocampus with novel manual and automatic segmentation approaches, which might soon enable to assess also hippocampal subfields. Advancements in quantification of hippocampal volumetry might pave the way to its broader use as outcome marker in AD clinical trials. Patterns of cortical atrophy have been shown to accurately track disease progression and seem promising in distinguishing among AD subtypes. Disease progression has also been associated with changes in white matter tracts. Recent studies have investigated two areas often overlooked in AD, such as the striatum and basal forebrain, reporting significant atrophy, although the impact of these changes on cognition is still unclear. Future integration of different MRI modalities may further advance the field by providing more powerful biomarkers of disease onset and progression.
•A publicly available deep learning tool to segment the hypothalamus and its subunits.•Our tool outperforms inter-rater accuracy and approaches intra-rater precision level.•It can robustly generalise ...to unseen heterogeneous datasets.•It yields a rejection rate of less than 1% in a QC analysis performed on 675 scans.•It detects subtle subunit-specific hypothalamic atrophy in Alzheimer’s Disease.
Despite the crucial role of the hypothalamus in the regulation of the human body, neuroimaging studies of this structure and its nuclei are scarce. Such scarcity partially stems from the lack of automated segmentation tools, since manual delineation suffers from scalability and reproducibility issues. Due to the small size of the hypothalamus and the lack of image contrast in its vicinity, automated segmentation is difficult and has been long neglected by widespread neuroimaging packages like FreeSurfer or FSL. Nonetheless, recent advances in deep machine learning are enabling us to tackle difficult segmentation problems with high accuracy. In this paper we present a fully automated tool based on a deep convolutional neural network, for the segmentation of the whole hypothalamus and its subregions from T1-weighted MRI scans. We use aggressive data augmentation in order to make the model robust to T1-weighted MR scans from a wide array of different sources, without any need for preprocessing. We rigorously assess the performance of the presented tool through extensive analyses, including: inter- and intra-rater variability experiments between human observers; comparison of our tool with manual segmentation; comparison with an automated method based on multi-atlas segmentation; assessment of robustness by quality control analysis of a larger, heterogeneous dataset (ADNI); and indirect evaluation with a volumetric study performed on ADNI. The presented model outperforms multi-atlas segmentation scores as well as inter-rater accuracy level, and approaches intra-rater precision. Our method does not require any preprocessing and runs in less than a second on a GPU, and approximately 10 seconds on a CPU. The source code as well as the trained model are publicly available at https://github.com/BBillot/hypothalamus_seg, and will also be distributed with FreeSurfer.
The human thalamus is a brain structure that comprises numerous, highly specific nuclei. Since these nuclei are known to have different functions and to be connected to different areas of the ...cerebral cortex, it is of great interest for the neuroimaging community to study their volume, shape and connectivity in vivo with MRI. In this study, we present a probabilistic atlas of the thalamic nuclei built using ex vivo brain MRI scans and histological data, as well as the application of the atlas to in vivo MRI segmentation. The atlas was built using manual delineation of 26 thalamic nuclei on the serial histology of 12 whole thalami from six autopsy samples, combined with manual segmentations of the whole thalamus and surrounding structures (caudate, putamen, hippocampus, etc.) made on in vivo brain MR data from 39 subjects. The 3D structure of the histological data and corresponding manual segmentations was recovered using the ex vivo MRI as reference frame, and stacks of blockface photographs acquired during the sectioning as intermediate target. The atlas, which was encoded as an adaptive tetrahedral mesh, shows a good agreement with previous histological studies of the thalamus in terms of volumes of representative nuclei. When applied to segmentation of in vivo scans using Bayesian inference, the atlas shows excellent test-retest reliability, robustness to changes in input MRI contrast, and ability to detect differential thalamic effects in subjects with Alzheimer's disease. The probabilistic atlas and companion segmentation tool are publicly available as part of the neuroimaging package FreeSurfer.
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•A probabilistic atlas of the human thalamus (26 nuclei) derived from histology.•3D histology reconstruction assisted by ex vivo MRI and blockface photographs.•Companion Bayesian method segments the nuclei from in vivo scans of any MRI contrast.•Method has excellent test-retest reliability and detects differential effects in AD.•The method is publicly available as part of FreeSurfer.
TDP-43 type C is one of the pathological forms of frontotemporal lobar degeneration (FTLD) and mainly associated clinically with the semantic variant of primary progressive aphasia (svPPA). We aimed ...to define in vivo the sequential pattern of neuroanatomical involvement in a cohort of patients with FTLD-TDP type C pathology.
We extracted the volumes of a set of cortical and subcortical regions from MRI scans of 19 patients with post mortem confirmed TDP-43 type C pathology (all with left hemisphere-predominant atrophy at baseline). In the initial development phase, we used w-scores computed from 81 cognitively normal controls to define a set of sequential stages of neuroanatomical involvement within the FTLD-TDP type C cohort where a w-score of < - 1.65 was considered abnormal. In a subsequent validation phase, we used 31 follow-up scans from 14 of the 19 patients in the same cohort to confirm the staging model.
Four sequential stages were identified in the initial development phase. Stage 1 was defined by atrophy in the left amygdala, medial temporal cortex, temporal pole, lateral temporal cortex and right medial temporal cortex; Stage 2 by atrophy in the left supratemporal cortex; Stage 3 by atrophy in the right anterior insula; and Stage 4 by atrophy in the right accumbens. In the validation phase, calculation of w-scores in the longitudinal scans confirmed the staging system, with all patients either staying in the same stage or progressing to a later stage at follow-up.
In vivo imaging is able to detect distinct stages of neuroanatomical involvement in FTLD-TDP type C pathology. Using an imaging-derived staging system allows a more refined stratification of patients with svPPA during life.
•We add diffusion MRI to Bayesian thalamic nuclei segmentation with structural MRI.•Adding fiber tracts to probabilistic atlases enables orientation modelling.•Thalamus segmentation from joint ...structural and diffusion MRI improves accuracy.•Atlas and companion segmentation code are freely distributed with FreeSurfer.
The human thalamus is a highly connected brain structure, which is key for the control of numerous functions and is involved in several neurological disorders. Recently, neuroimaging studies have increasingly focused on the volume and connectivity of the specific nuclei comprising this structure, rather than looking at the thalamus as a whole. However, accurate identification of cytoarchitectonically designed histological nuclei on standard in vivo structural MRI is hampered by the lack of image contrast that can be used to distinguish nuclei from each other and from surrounding white matter tracts. While diffusion MRI may offer such contrast, it has lower resolution and lacks some boundaries visible in structural imaging. In this work, we present a Bayesian segmentation algorithm for the thalamus. This algorithm combines prior information from a probabilistic atlas with likelihood models for both structural and diffusion MRI, allowing segmentation of 25 thalamic labels per hemisphere informed by both modalities. We present an improved probabilistic atlas, incorporating thalamic nuclei identified from histology and 45 white matter tracts surrounding the thalamus identified in ultra-high gradient strength diffusion imaging. We present a family of likelihood models for diffusion tensor imaging, ensuring compatibility with the vast majority of neuroimaging datasets that include diffusion MRI data. The use of these diffusion likelihood models greatly improves identification of nuclear groups versus segmentation based solely on structural MRI. Dice comparison of 5 manually identifiable groups of nuclei to ground truth segmentations show improvements of up to 10 percentage points. Additionally, our chosen model shows a high degree of reliability, with median test-retest Dice scores above 0.85 for four out of five nuclei groups, whilst also offering improved detection of differential thalamic involvement in Alzheimer’s disease (AUROC 81.98%). The probabilistic atlas and segmentation tool will be made publicly available as part of the neuroimaging package FreeSurfer (https://freesurfer.net/fswiki/ThalamicNucleiDTI).
Abstract Late-onset and early-onset Alzheimer's disease (LOAD, EOAD) affect different neural systems and may be separate nosographic entities. The most striking differences are in the medial temporal ...lobe, severely affected in LOAD and relatively spared in EOAD. We assessed amygdalar morphology and volume in 18 LOAD and 18 EOAD patients and 36 aged-matched controls and explored their relationship with the hippocampal volume. Three-dimensional amygdalar shape was reconstructed with the radial atrophy mapping technique, hippocampal volume was measured using a manual method. Atrophy was greater in LOAD than EOAD: 25% versus 17% in the amygdala and 20% versus 13% in the hippocampus. In the amygdala, LOAD showed significantly greater tissue loss than EOAD in the right dorsal central, lateral, and basolateral nuclei (20%–30% loss, p < 0.03), all known to be connected to limbic regions. In LOAD but not EOAD, greater hippocampal atrophy was associated with amygdalar atrophy in the left dorsal central and medial nuclei (r = 0.6, p < 0.05) also part of the limbic system. These findings support the notion that limbic involvement is a prominent feature of LOAD but not EOAD.
Frontotemporal dementia (FTD) is a heterogeneous neurodegenerative disorder, with a strong genetic component. Previous research has shown that medial temporal lobe atrophy is a common feature of FTD. ...However, no study has so far investigated the differential vulnerability of the hippocampal subfields in FTD.
We aimed to investigate hippocampal subfield volumes in genetic FTD.
We in6/2/2018vestigated hippocampal subfield volumes in a cohort of 75 patients with genetic FTD (age: mean (standard deviation) 59.3 (7.7) years; disease duration: 5.1 (3.4) years; 29 with MAPT, 28 with C9orf72, and 18 with GRN mutations) compared with 97 age-matched controls (age: 62.1 (11.1) years). We performed a segmentation of their volumetric T1-weighted MRI scans to extract hippocampal subfields volumes. Left and right volumes were summed and corrected for total intracranial volumes.
All three groups had smaller hippocampi than controls. The MAPT group had the most atrophic hippocampi, with the subfields showing the largest difference from controls being CA1-4 (24-27%, p < 0.0005). For C9orf72, the CA4, CA1, and dentate gyrus regions (8-11%, p < 0.0005), and for GRN the presubiculum and subiculum (10-14%, p < 0.0005) showed the largest differences from controls.
The hippocampus was affected in all mutation types but a different pattern of subfield involvement was found in the three genetic groups, consistent with differential cortical-subcortical network vulnerability.
Frontotemporal dementia (FTD) is a heterogeneous neurodegenerative disorder associated with frontal and temporal atrophy. Subcortical involvement has been described as well, with early thalamic ...atrophy most commonly associated with the C9orf72 expansion. However thalamic involvement has not been comprehensively investigated across the FTD spectrum.
We investigated thalamic volumes in a sample of 341 FTD patients (age: mean(standard deviation) 64.2(8.5) years; disease duration: 4.6(2.7) years) compared with 99 age-matched controls (age: 61.9(11.4) years). We performed a parcellation of T1 MRIs using an atlas propagation and label fusion approach to extract left and right thalamus volumes, which were corrected for total intracranial volumes. We assessed subgroups stratified by clinical diagnosis (141 behavioural variant FTD (bvFTD), 76 semantic dementia (SD), 103 progressive nonfluent aphasia (PNFA), 7 with associated motor neurone disease (FTD-MND) and 14 primary progressive aphasia not otherwise specified (PPA-NOS), genetic diagnosis (24 with MAPT, 24 with C9orf72, and 15 with GRN mutations), and pathological diagnosis (40 tauopathy, 61 TDP-43opathy, 3 FUSopathy). We assessed the diagnostic accuracy based on thalamic volume.
Overall, FTD patients had smaller thalami than controls (8% difference in volume, p < 0.0005, ANCOVA). Stratifying by genetics, C9orf72 group had the smallest thalami (14% difference from controls, p < 0.0005). However, the thalami were also smaller than controls in the other genetic groups: GRN and MAPT groups showed a difference of 11% and 9% respectively (p < 0.0005). ROC analysis showed a relatively poor ability to separate C9orf72 from MAPT (AUC = 0.651, p = 0.073) and from GRN cases (AUC = 0.644, p = 0.133) using thalamic volume. All clinical subtypes had significantly smaller thalami than controls (p < 0.0005), with the FTD-MND group having the smallest (15%), followed by bvFTD (9%), PNFA (8%), PPA-NOS (7%), and lastly SD (5%). In the pathological groups, the TDP-43opathies had an 11% difference from controls, and tauopathies 9%, while the FUSopathies showed only 2% of difference from controls (p < 0.0005). GRN, PPA-NOS and SD were the subgroups showing the highest asymmetry in volumes.
The thalamus was most affected in C9orf72 genetically, TDP-43opathies pathologically and FTD-MND clinically. However, thalamic atrophy is a common feature across all FTD groups.
•Thalamic atrophy is a common feature in frontemporal dementia.•The thalamus was most affected in C9orf72, TDP-43opathies and FTD-MND.•Thalamic volumes cannot accurately discriminate different forms of FTD.•GRN, PPA-NOS and SD were the subgroups with the highest asymmetry in volumes.
PET imaging of glucose metabolism has revealed presymptomatic abnormalities in genetic FTD but has not been explored in MAPT P301L mutation carriers. This study aimed to explore the patterns of ...presymptomatic hypometabolism and atrophy in MAPT P301L mutation carriers.
Eighteen asymptomatic members from five families with a P301L MAPT mutation were recruited to the study, six mutation carriers, and twelve mutation-negative controls. All participants underwent standard behavioural and cognitive assessment as well as
FFDG-PET and 3D T1-weighted MRI brain scans. Regional standardised uptake value ratios (SUVR) for the PET scan and volumes calculated from an automated segmentation for the MRI were obtained and compared between the mutation carrier and control groups.
The mean (standard deviation) estimated years from symptom onset was 12.5 (3.6) in the mutation carrier group with a range of 7 to 18 years. No differences in cognition were seen between the groups, and all mutation carriers had a global CDR plus NACC FTLD of 0. Significant reduction in
F FDG uptake in the anterior cingulate was seen in mutation carriers (mean 1.25 standard deviation 0.07) compared to controls (1.36 0.09). A similar significant reduction was also seen in grey matter volume in the anterior cingulate in mutation carriers (0.60% 0.06%) compared to controls (0.68% 0.08%). No other group differences were seen in other regions.
Anterior cingulate hypometabolism and atrophy are both apparent presymptomatically in a cohort of P301L MAPT mutation carriers. Such a specific marker may prove to be helpful in stratification of presymptomatic mutation carriers in future trials.
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