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Erschienen in: Journal of Neurology 9/2023

Open Access 13.05.2023 | Original Communication

Presymptomatic grey matter alterations in ALS kindreds: a computational neuroimaging study of asymptomatic C9orf72 and SOD1 mutation carriers

verfasst von: Peter Bede, Dorothée Lulé, Hans-Peter Müller, Ee Ling Tan, Johannes Dorst, Albert C. Ludolph, Jan Kassubek

Erschienen in: Journal of Neurology | Ausgabe 9/2023

Abstract

Background

The characterisation of presymptomatic disease-burden patterns in asymptomatic mutation carriers has a dual academic and clinical relevance. The understanding of disease propagation mechanisms is of considerable conceptual interests, and defining the optimal time of pharmacological intervention is essential for improved clinical trial outcomes.

Methods

In a prospective, multimodal neuroimaging study, 22 asymptomatic C9orf72 GGGGCC hexanucleotide repeat carriers, 13 asymptomatic subjects with SOD1, and 54 “gene-negative” ALS kindreds were enrolled. Cortical and subcortical grey matter alterations were systematically appraised using volumetric, morphometric, vertex, and cortical thickness analyses. Using a Bayesian approach, the thalamus and amygdala were further parcellated into specific nuclei and the hippocampus was segmented into anatomically defined subfields.

Results

Asymptomatic GGGGCC hexanucleotide repeat carriers in C9orf72 exhibited early subcortical changes with the preferential involvement of the pulvinar and mediodorsal regions of the thalamus, as well as the lateral aspect of the hippocampus. Volumetric approaches, morphometric methods, and vertex analyses were anatomically consistent in capturing focal subcortical changes in asymptomatic C9orf72 hexanucleotide repeat expansion carriers. SOD1 mutation carriers did not exhibit significant subcortical grey matter alterations. In our study, none of the two asymptomatic cohorts exhibited cortical grey matter alterations on either cortical thickness or morphometric analyses.

Discussion

The presymptomatic radiological signature of C9orf72 is associated with selective thalamic and focal hippocampal degeneration which may be readily detectable before cortical grey matter changes ensue. Our findings confirm selective subcortical grey matter involvement early in the course of C9orf72-associated neurodegeneration.
Hinweise

Supplementary Information

The online version contains supplementary material available at https://​doi.​org/​10.​1007/​s00415-023-11764-5.
Peter Bede and Dorothée Lulé have contributed equally as joint first authors.
Abkürzungen
AAA
Anterior amygdaloid area
ABN
Accessory basal nucleus
ALS
Amyotrophic lateral sclerosis
AN
Attention network
ANCOVA
Analysis of covariance
AV
Anteroventral nuclei
BG
Basal ganglia
BN
Basal nucleus
C9orf72
Chromosome 9 open-reading frame 72
CA
Cornu Ammonis
CAT
Cortico-amygdaloid transition
CeM
Central medial nucleus
CL
Central lateral nucleus
CM
Centromedian nucleus
CN
Cortical nucleus
CST
Corticospinal tract
Cr
Creatine‐phosphocreatine
CT
Cortical thickness
DTI
Diffusion tensor imaging
ECAS
Edinburgh Cognitive ALS Screen
EMM
Estimated marginal mean
EPI
Echo-planar imaging
eTIV
Total intracranial volume estimates
FTD
Frontotemporal dementia
FLAIR
Fluid-attenuated inversion recovery
FOV
Field-of-view
FSL
FMRIB Software Library
FWE
Familywise error
GC-DG
Granule cell layer of dentate gyrus
GM
Grey matter
HARDI
High angular resolution diffusion imaging
HATA
Hippocampus-amygdala transition area
HC
Healthy control
HRE
Hexanucleotide repeat expansions
IR-SPGR
Inversion Recovery prepared Spoiled Gradient Recalled echo
LD
Laterodorsal nuclei
LN
Lateral nucleus
LGN
Lateral geniculate
LMN
Lower motor neuron
LP
Lateral posterior nuclei
L-SG
Limitans/suprageniculate nuclei
Lt
Left
M
Mean
MANCOVA
Multivariate analysis of covariance
MDI
Mediodorsal lateral parvocellular nuclei
MDm
Mediodorsal medial magnocellular nuclei
MGN
Medial geniculate nuclei
ML
Machine learning
MN
Medial nucleus
MND
Motor neuron disease
MNI152
Montreal Neurological Institute 152 standard space
MR
Magnetic resonance
MRS
Magnetic resonance spectroscopy
MT
Magnetisation transfer
MV-re
Reuniens/medial ventral nuclei
Myo
Myoinositol
NAA
N-Acetylaspartate
NODDI
Neurite orientation dispersion and density imaging
PBA
Pseudobulbar affect
Pc
Paracentral nuclei
PCL
Pathological crying and laughing
Pf
Parafascicular nuclei
PLS
Primary lateral sclerosis
PMA
Progressive muscular atrophy
PMC
Primary motor cortex
PN
Paralaminar nucleus
PPZ
Perforant pathway zone
Pt
Paratenial nuclei
pTDP-43
Phosphorylated 43 kDa TAR DNA-binding protein
PuA
Pulvinar anterior nuclei
PuI
Pulvinar inferior nucleus
PuL
Pulvinar lateral nucleus
pTDP-43
Phosphorylated 43 kDa TAR DNA- binding protein
PuM
Pulvinar medial nucleus
QSM
Quantitative susceptibility mapping
RE
Repeat expansion
ROI
Region-of-interest
Rt
Right
SBMA
Spinal and bulbar muscular atrophy / Kennedy's disease
SD
Standard deviation
SE
Standard error
SOD1
Superoxide dismutase 1
SN
Salience network
SPSS
Statistical product and service solutions
T1W
T1-weighted imaging
TE
Echo time
TFCE
Threshold-free cluster enhancement
TI
Inversion time
TIV
Total intracranial volume
TR
Repetition time
UMN
Upper motor neuron
VA
Ventral anterior nuclei
VAmc
Ventral anterior magnocellular nuclei
VBM
Voxel-based morphometry
VLa
Ventral lateral anterior nuclei
VLp
Ventral lateral posterior nuclei
VM
Ventromedial nuclei
VPL
Ventral posterolateral nuclei
WM
White matter

Introduction

One of the important paradigm shifts in amyotrophic lateral sclerosis (ALS) research is the departure from the concept of “one-drug for all” to the pursuit of precision, genotype-specific pharmacological interventions [1, 2]. The recognition of the fundamental heterogeneity of ALS led to the nuanced characterisation of various ALS genotypes and phenotypes [36]. It is increasingly recognised that symptom manifestation in ALS is preceded by a long presymptomatic phase [7] and degenerative changes may be detected decades before symptom manifestation [8, 9]. The ideal timing of therapeutic intervention should therefore be reconsidered, especially in genetically susceptible cohorts. Antisense oligonucleotide (ASO)-therapies have been approved for the treatment of spinal muscular atrophy and Duchenne muscular dystrophy [1012], and also trialled in ALS [1]. Accordingly, the assessment of disease burden prior to symptom manifestation is of pressing practical relevance. Two most commonly studied genotypes in ALS are the GGGGCC hexanucleotide repeat expansions (HRE) in C9orf72 and SOD1. C9orf72 HRE may lead to a spectrum of clinical manifestations spanning from ALS to frontotemporal dementia (FTD) and clinical manifestations are thought to be closely associated with patterns of phosphorylated 43 kDa TAR DNA-binding protein (pTDP-43) burden. Striatal [13], temporal [14], frontal [15], cerebellar [16], and thalamic [13, 16] grey matter alterations have previously been described in asymptomatic C9orf72 cohorts. Presymptomatic orbitofrontal [17], corpus callosum, cingulate, uncinate [8, 13, 18], and corticospinal tract [9, 13] white matter changes have also been consistently detected and PET studies captured frontotemporal, thalamic, and basal ganglia hypometabolism [19]. Three mechanisms have been proposed for C9orf72 HRE-associated pathophysiology; loss of C9orf72 function through haploinsufficiency, toxic gain-of-function due to the generation of aberrant HRE-containing RNA, and toxic gain-of-function through the accumulation of dipeptide repeat proteins translated from hexanucleotide repeat RNA [20]. These mechanisms are thought to trigger a multitude of cellular responses, of which pTDP-43 accumulation may only be one amongst several processes. Accordingly, cerebral involvement outside the neocortex may represent a distinguishing feature of C9orf72 distinct from other genetic variants, such as SOD1 mutations [21]. The presymptomatic imaging signature of SOD1 is thought to be relatively unique; white matter changes in the posterior limb of the internal capsule [22], reduced superior spinal cord NAA/Cr and NAA/Myo ratios [23], and reduced left frontotemporal junction flumazenil binding [24], and have been reported. While structural, metabolic, and function thalamic [8, 13, 16, 18, 2530] changes have been previously described in presymptomatic C9orf72 hexanucleotide carriers and longitudinal thalamic changes have also been explored [25, 31, 32], the predilection for specific thalamic nuclei remains poorly characterised despite the unique role of thalamic nuclei in relaying specific sensory, cognitive, and behavioural functions [3336]. Presymptomatic amygdalar and hippocampal alterations are also under evaluated despite the selective involvement of these structures in symptomatic mutation carriers [3741]. Accordingly, the principal objective of this study is the nuanced characterisation of cortical and subcortical grey matter changes in two cohorts of presymptomatic mutation carriers using a panel of supplementary imaging techniques. Our hypothesis is that presymptomatic hexanucleotide expansion carriers exhibit focal subcortical degeneration with concomitant alterations in their cortical projection areas.

Methods

The study was approved by the Ethics Committee of the University of Ulm (reference 68/19), in accordance with the ethical standards of the current version of the revised Helsinki declaration. All participants gave informed consent prior to enrolment. Recruitment strategy and genetic testing have been previously described [17].

Neuroimaging

T1-weighted data were acquired on a 1.5 Tesla Magnetom Symphony (Siemens Medical) with a 12-channel head coil. Acquisition parameters have been described previously [42]. T2-weighted and fluid-attenuated inversion recovery (FLAIR) images were systematically reviewed for confounding vascular or neuroinflammatory pathologies. Raw T1-weighted MR data were screened for artifacts, developmental malformations, arachnoid or porencephalic cysts, hydrocephalus, or other pathologies that could impact on quantitative morphometric analyses prior to pre-processing.

Volumetric analyses

The standard pre-processing steps of the FreeSurfer image analysis suite [43] were first implemented, including removal of non-brain tissue, segmentation of the subcortical white matter and deep grey matter structures, intensity normalization, tessellation of the grey matter–white matter boundary, and automated topology correction. Following quality control steps for segmentation accuracy, overall volumes of subcortical structures and total intracranial volume estimates (eTIV) were retrieved from each subject. Total volume estimates of the following structures were generated in the left and right hemispheres separately: thalamus, caudate, putamen, pallidum, hippocampus, amygdala, and nucleus accumbens.

Nuclear segmentation of the thalamus and amygdala

The thalamus was segmented into 25 subregions using Bayesian inference based on a probabilistic atlas developed upon histological data [44]. The thalamus was first parcellated into the following nuclei in each hemisphere: anteroventral (AV), laterodorsal (LD), lateral posterior (LP), ventral anterior (VA), ventral anterior magnocellular (VA mc), ventral lateral anterior (VLa), ventral lateral posterior (VLp), ventral posterolateral (VPL), ventromedial (VM), central medial (CeM), central lateral (CL), paracentral (Pc), centromedian (CM), parafascicular (Pf), paratenial (Pt), reuniens/medial ventral (MV-re), mediodorsal medial magnocellular (MDm), mediodorsal lateral parvocellular (MDl), lateral geniculate (LGN), medial geniculate (MGN), limitans/suprageniculate (L-SG), pulvinar anterior (PuA), pulvinar medial (PuM), pulvinar lateral (PuL), and pulvinar inferior (PuI). The raw volume estimates of the above nuclei were averaged between left and right and merged into the following 10 core group of nuclei defined based on their distinctive physiological function: “anteroventral”, “lateral geniculate”, “medial geniculate”, “pulvinar-limitans” (PuA, PuM, PuL, PuI, L-SG), “laterodorsal”, “lateroposterior”, “mediodorsal-paratenial-reuniens” (MDm, MDl, MV-re, Pt), “motor nuclei” (VA, VAmc, VLa, VLp), “sensory nuclei” (VPL, VM), and “intralaminar” (CeM, CL, Pc, CM, Pf). “Total thalamic volume” was defined as the mean of the left and right thalamus volume estimates and used as a covariate in the relevant statistical models. Post hoc statistics were corrected for demographic variables, total thalamic volume, and multiple testing.
A Bayesian inference was also used to parcellate the amygdala into nine subregions using a probabilistic atlas developed based on histological data [44, 45]. The amygdala was segmented into the following nuclei in each hemisphere: lateral nucleus (LN), basal nucleus (BN), accessory basal nucleus (ABN), anterior amygdaloid area (AAA), central nucleus (CN), medial nucleus (MN), cortical nucleus (CN), cortico-amygdaloid transition (CAT), and paralaminar nucleus (PN). The raw volume estimates of the above nuclei were averaged between left and right. “Total amygdala volume” was defined as the mean of the left and right total amygdala volume estimates. Post hoc statistics were corrected for demographic variables, total amygdala volume, and multiple testing.

Hippocampal subfield parcellation

The hippocampus was segmented into cytologically-defined subfields (Fig. 1A) using the FreeSurfer image analysis suite [43]. The pre-processing pipeline included the removal of non-brain tissue, segmentation of the subcortical white matter and deep grey matter structures, intensity normalization, tessellation of the grey matter–white matter boundary, and automated topology correction. The hippocampal stream of the FreeSurfer package was used for the delineation of the following hippocampal subfields: CA1, CA2/3, CA4, fimbria, hippocampal fissure, presubiculum, subiculum, hippocampal tail, parasubiculum, molecular layer; granule cell layer of the dentate gyrus (GC-DG), and hippocampal–amygdala transition area (HATA) [46].

Vertex analyses

Surface projected patterns of atrophy were evaluated using vertex analyses. As described previously [47], FMRIB’s subcortical segmentation and registration tool FIRST [48] was utilised to characterise focal thalamic shape deformations. Vertex locations of each participant were projected on the surface of an average thalamic shape template as scalar values, positive value being outside the surface, and negative values inside. Using study-specific design matrices specifying group membership and covariates, permutation-based non-parametric inference was implemented for group comparisons using FMRIB’s ‘RANDOMISE’ module [49]. The design matrices included demeaned age, sex, education, and total intracranial volumes as covariates [49].

Subcortical morphometry

FMRIB’s software library was used for brain extraction and tissue-type segmentation. Resulting grey-matter partial volume images were then aligned to MNI152 standard space using affine registration. A study-specific template was subsequently created, to which the grey matter images from each subject were non-linearly coregistered. Group membership and covariates were specified in study-specific design matrices and demeaned covariates included age, sex, education, and TIV. A voxelwise generalized linear model and permutation-based non-parametric testing was used to highlight density alterations in a merged subcortical grey matter mask accounting for multiple testing, age, sex, and education. [49, 50] Labels of the Harvard–Oxford subcortical probabilistic structural atlas was used to generate a merged subcortical grey mask incorporating the left and right caudate, thalamus, accumbens, hippocampus, amygdala, putamen, and pallidum. [51, 52]

Cortical grey matter analyses

A dual pipeline was implemented exploring (1) cortical thickness alterations and (2) morphometric changes using voxel-based morphometry (VBM). Following pre-processing and cortical segmentation in FreeSurfer, average cortical thickness values have been retrieved from 34 cortical regions in each hemisphere separately as per the Desikan–Killiany atlas. Cortical thickness values in the following lobes and corresponding subregions regions were appraised, Frontal lobe (13 ROIs): Superior Frontal, Rostral and Caudal Middle Frontal, Pars Opercularis, Pars Triangularis, and Pars Orbitalis, Lateral and Medial Orbitofrontal, Precentral, Paracentral, Frontal Pole, Rostral Anterior cingulate, Caudal Anterior cingulate, Parietal lobe (7 ROIs): Superior Parietal, Inferior Parietal, Supramarginal, Postcentral, Precuneus, Posterior cingulate, cingulate isthmus, Temporal lobe (9 ROIs): Superior, Middle, and Inferior Temporal, Banks of the Superior Temporal Sulcus, Fusiform, Transverse Temporal, Entorhinal, Temporal Pole, Parahippocampal, Occipital lobe (4 ROIs): lateral Occipital, Lingual, Cuneus, Pericalcarine, and the Insula (1 ROI). In addition to cortical thickness analyses, voxel-based morphometry was also performed to evaluate anatomical patterns of signal intensity reductions in mutation carriers. Cortical grey matter morphometry analyses were conducted using FSL-VBM [53, 54]. Following brain extraction, motion-correction, and tissue-type segmentation, the resulting grey-matter partial volume images were aligned to MNI152 standard space using affine registration. A study-specific template was generated to which the grey matter images from each subject were non-linearly coregistered. Permutation-based non-parametric inference and the threshold-free cluster enhancement (TFCE) approach were utilised to test for differences between study groups controlling for age, sex, TIV, and education.

Results

The main study groups were matched for age and education and differed in sex ratios (Table 1). We note that all statistical models were corrected for age, sex, and education.
Table 1
The demographic profile of study groups
 
Gene negative
(n = 54)
C9orf72
(n = 22)
SOD1
(n = 13)
Statistics
(ANOVA, χ2; p value)
Age (yrs)
41.61 ± 11.75
45.00 ± 11.62
48.31 ± 14.45
F (2,86) = 1.842; p = 0.165
Gender (M / F)
24/30
5/17
9/4
χ2 (2) = 7.394; p = 0.025
Education (yrs)
14.89 ± 3.37
14.59 ± 3.02
13.23 ± 2.68
F (2,86) = 1.407; p = 0.251
While differences in overall subcortical volume differences did not reach significance (Table 2), mediodorsal-paratenial-reuniens in the left thalamus and pulvinar-limitans atrophy in the right thalamus was detected in asymptomatic C9orf72 hexanucleotide repeat expansion carriers compared to gene-negative family members (Table 3.). Furthermore, higher sensory nuclei volumes were identified in C9orf72 hexanucleotide repeat expansion carriers compared to both gene-negative controls and SOD1 mutation carriers in both thalami. Effect sizes are illustrated in Fig. 1. Differences in amygdalar nuclei and hippocampal subfield volumes did not reach significance. Relevant output statistics, univariate p value, and effect sizes are summarised in supplementary tables 1–2.
Table 2
The volumetric profile (mm3) of evaluated structures [estimated marginal means ± standard error] (covariates: age, gender, education, and TIV)
 
“Gene Negative”
C9orf72
SOD1
Univariate p value and effect size
Left
 Thalamus
8028.79 ± 76.15
7592.38 ± 121.20
8188.57 ± 160.65
p = 0.004; η2p = 0.125
 Caudate
3438.21 ± 39.25
3365.10 ± 62.48
3312.67 ± 82.82
p = 0.325; η2p = 0.027
 Putamen
4520.49 ± 60.39
4573.78 ± 96.12
4610.60 ± 127.40
p = 0.779; η2p = 0.006
 Pallidum
1880.10 ± 24.82
1944.05 ± 39.50
1957.33 ± 52.36
p = 0.244; η2p = 0.034
 Hippocampus
3985.09 ± 57.33
3833.27 ± 91.26
4073.76 ± 120.96
p = 0.236; η2p = 0.035
 Amygdala
1489.68 ± 25.98
1462.62 ± 41.36
1592.96 ± 54.82
p = 0.160; η2p = 0.044
 Accumbens
457.18 ± 9.66
433.20 ± 15.38
472.04 ± 20.38
p = 0.271; η2p = 0.031
Right
 Thalamus
7864.32 ± 72.02
7512.04 ± 114.63
8105.19 ± 151.93
p = 0.006; η2p = 0.117
 Caudate
3476.46 ± 39.28
3460.89 ± 62.52
3388.91 ± 82.86
p = 0.644; η2p = 0.011
 Putamen
4579.26 ± 57.41
4555.90 ± 91.38
4727.42 ± 121.12
p = 0.495; η2p = 0.017
 Pallidum
1865.01 ± 24.24
1922.27 ± 38.58
1924.18 ± 51.14
p = 0.354; η2p = 0.025
 Hippocampus
4090.66 ± 56.34
3986.73 ± 89.68
4250.73 ± 118.87
p = 0.220; η2p = 0.036
 Amygdala
1664.20 ± 23.58
1645.97 ± 37.53
1726.34 ± 49.75
p = 0.430; η2p = 0.020
 Accumbens
484.14 ± 10.67
470.90 ± 16.98
480.98 ± 22.51
p = 0.810; η2p = 0.005
Age = 43.43; Sex = 1.57; Education = 14.57; Total Intracranial Volume = 1585cm3. Post hoc comparisons were not performed, because the multivariate omnibus test was not significant: Wilks’ Lambda = 0.634; F (28, 138) = 1.260; p = 0.192. Partial η2 effect size is interpreted as small (η2p = 0.01), medium (η2p = 0.06), and large (η2p = 0.14)
Table 3
Thalamic nuclei volumes mm3 [estimated marginal means ± standard error] (covariates: age, sex, education, and thalamic volume)
 
“Gene Negative”
C9orf72
SOD1
Univariate p value and effect size
Significant post hoc contrasts (Bonferroni)
Left thalamusa
 AV
137.06 ± 2.00
130.87 ± 3.25
138.34 ± 4.20
p = 0.240; η2p = 0.034
 
 LGN
166.12 ± 3.36
163.11 ± 5.47
165.70 ± 7.06
p = 0.899; η2p  = 0.003
 
 MGN
119.64 ± 1.89
122.31 ± 3.08
121.84 ± 3.97
p = 0.724; η2p  = 0.008
 
 Pulvinar limitans
1459.01 ± 12.86
1423.92 ± 20.91
1498.39 ± 26.98
p = 0.105; η2p  = 0.054
 
 LD
33.19 ± 0.89
33.24 ± 1.44
36.30 ± 1.86
p = 0.315; η2p  = 0.028
 
 LP
138.34 ± 1.75
141.55 ± 2.85
136.20 ± 3.67
p = 0.494; η2p  = 0.017
 
 Mediodorsal paratenial reuniens
1024.70 ± 8.10
963.41 ± 13.17
1014.00 ± 16.99
p < 0.001; η2p  = 0.156
C9orf72 < GeneNegative (p < 0.001);
C9orf72 < SOD1 (p = 0.072)
 Motor nuclei
1925.53 ± 12.99
1962.99 ± 21.13
1894.25 ± 6.39
p = 0.138; η2p  = 0.047
 
 Sensory nuclei
869.66 ± 6.39
925.78 ± 10.40
868.47 ± 13.41
p < 0.001; η2p  = 0.207
C9orf72 > GeneNegative (p < 0.001);
C9orf72 > SOD1 (p = 0.004)
 Intralaminar
438.53 ± 3.35
444.59 ± 5.44
438.28 ± 7.02
p = 0.634; η2p  = 0.011
 
Right thalamusb
 AV
146.04 ± 2.12
149.92 ± 3.46
140.89 ± 4.48
p = 0.305; η2p  = 0.029
 
 LGN
195.57 ± 3.40
199.43 ± 5.56
189.18 ± 7.19
p = 0.552; η2p  = 0.014
 
 MGN
128.02 ± 1.76
131.18 ± 2.88
123.64 ± 3.72
p = 0.303; η2p  = 0.029
 
 Pulvinar limitans
1672.23 ± 14.36
1601.58 ± 23.48
1654.62 ± 30.35
p = 0.047; η2p  = 0.072
C9orf72 < GeneNegative (p = 0.041)
 LD
30.80 ± 0.91
32.86 ± 1.49
32.28 ± 1.92
p = 0.456; η2p  = 0.019
 
 LP
127.60 ± 1.65
128.67 ± 2.69
133.53 ± 3.48
p = 0.312; η2p  = 0.028
 
 Mediodorsal paratenial reuniens
1041.07 ± 7.94
1018.39 ± 12.98
1069.44 ± 16.78
p = 0.070; η2p  = 0.063
 
 Motor nuclei
1950.55 ± 13.03
1967.71 ± 21.30
1929.43 ± 27.54
p = 0.565; η2p  = 0.014
 
 Sensory nuclei
929.49 ± 6.36
974.89 ± 10.40
936.08 ± 13.44
p = 0.002; η2p  = 0.141
C9orf72 > GeneNegative (p < 0.001);
C9orf72 > SOD1 (p = 0.088);
 Intralaminar
434.56 ± 3.63
451.30 ± 5.94
446.84 ± 7.68
p = 0.042; η2p  = 0.075
C9orf72 > GeneNegative (p = 0.062);
Age = 43.43; Sex = 1.57; Education = 14.57; Left total thalamic volume = 6311.78; Right total thalamic volume = 6655.93. Post hoc univariate comparisons across groups were performed only in case of a significant multivariate omnibus test: aWilks’ Lambda = 0.584; F (18, 148) = 2.535; p = 0.001, bWilks’ Lambda = 0.590; F (18, 148) = 2.481; p = 0.001. Partial η2 effect size is interpreted as small (η2p  = 0.01), medium (η2p  = 0.06) and large (η2p  = 0.14)
Vertex analyses identified shape deformations in C9orf72 hexanucleotide repeat expansion carriers compared to gene-negative controls in the anterior, superior, and posterior surface of both thalami as well as the lateral aspect of the left hippocampus (Fig. 2). Vertex analyses in SOD1 carriers did not identify shape deformation compared to gene-negative controls. ROI morphometric analyses revealed bi-thalamic and left pulvinar signal reductions in C9orf72 hexanucleotide repeat expansion carriers compared to gene-negative controls (Fig. 3). In our voxelwise analyses, the contrasts between SOD1 carriers and gene-negative controls did not reach significance.

Discussion

Our computational image analyses capture thalamic and hippocampal alterations in a cohort asymptomatic C9orf72 hexanucleotide repeat expansion carriers without associated neocortical grey matter atrophy. No cortical or subcortical grey matter pathology was observed in our presymptomatic SOD1 group.
Methodologically, our study benefits from a multiparametric approach, where data were interrogated in multiple pipelines run in different image analyses suites resulting in good anatomical concordance. The results of our post-segmentation volumetric analyses and our ROI-based morphometric subcortical analyses are relatively concordant in identifying bilateral mediodorsal thalamic atrophy (Table 3, Fig. 3.). Our results indicate that as opposed to global thalamic degeneration, selective thalamic involvement characterises the asymptomatic phase of C9orf72. The shape deformation identified on vertex analyses confirms focal thalamic involvement, but the nature of these analyses is that surface-projected changes are captured instead of the intra-thalamic changes described by morphometric and post-segmentation pipelines. Resting-state fMRI studies have consistently described widespread connectivity alterations in C9orf72 mutation carriers including networks relayed through the thalamus [13, 55]. Network integrity alterations were also detected using chronnectomic approaches [56] and thalamic hypometabolism has also been consistently identified by PET studies [57, 58]. MR spectroscopy [59] identified reduced putaminal NAA/Cr, Glu/Cr and Glu/NAA ratios, and reduced thalamic Glu/NAA in asymptomatic C9orf72 mutation carriers [19]. Based on structural data, thalamic atrophy [8, 13, 16, 18, 2528] has been previously described in presymptomatic C9orf72 hexanucleotide carriers as well as disease burden in other subcortical grey matter structures, such as the caudate [14, 60], putamen [14], and striatum, but the predilection for specific nuclei or subregions have not been comprehensively analysed. In our study, the two main thalamic regions identified by both our volumetric and morphometric analysis streams are the mediodorsal and pulvinar regions. The physiological role of cortico-basal networks relayed though specific nuclei are fairly well established [33, 35, 61]. Thalamic atrophy has been highlighted in most genetic variants of FTD [35, 62], but medial pulvinar degeneration is thought to be relatively unique to C9orf72 [62, 63]. The involvement of specific groups of nuclei at a presymptomatic phase is relativity novel as previous studies primarily focused on global thalamus pathology [18] or only described focal changes based on voxelwise analyses. In a particularly elegant recent study, spectral clustering, a graph-based partitioning technique was implemented which revealed posteromedial thalamic changes in asymptomatic C9orf72 carriers. [64] The early degeneration of mediodorsal and pulvinar regions in our C9orf72 cohort may herald future dysfunction in associated cognitive domains, such as limbic, executive, and associative processes.
It is noteworthy that the patterns of grey matter changes identified in this asymptomatic cohort, are also relatively consistent with the “post-symptomatic” signature associated of the genotype. Selective thalamic atrophy in symptomatic C9orf72 hexanucleotide expansion carriers is evidenced by a multitude of large, prospective neuroimaging studies including both ALS and FTD phenotypes [35, 65]. Grey matter involvement in C9orf72 carriers outside the neocortex has a predilection for the thalamus, [6670] although hippocampal, putaminal, striatal, caudate, cerebellar, and nucleus accumbens changes are also commonly reported [14, 40, 71, 72]. Even though grey matter disease burden is thought to be more pronounced in C9orf72-associated ALS [71, 73], considerable subcortical grey matter pathology is also readily identified in C9orf72-negative ALS and PLS [40, 47, 7477]. Basal ganglia changes have also been consistently described in sporadic patients and linked to neuropsychological and extra-pyramidal deficits [7880]. Similar to our findings in this cohort of presymptomatic patients, a study of symptomatic C9orf72 patients with ALS fulfilling the EL Escorial criteria [69] also identified the selective degeneration of thalamic nuclei in C9orf72-positive ALS preferentially involving the mediodorsal–parateniual–reuniens group of nuclei, which play a central role in executive processes. Interestingly, we have detected higher sensory nuclei volumes bilaterally in C9orf72 hexanucleotide carriers compared to both “gene-negative” ALS kindreds and asymptomatic SOD1 mutation carriers. While somatosensory impairment is not classically associated with the core clinical features of ALS, in symptomatic cohorts, imaging studies have consistently confirmed the involvement of somatosensory structures [34, 70]. The marked differences between SOD1 and C9orf72 showcase the notable heterogeneity of ALS, and given the relatively young age profile of participants, the higher volumes detected hexanucleotide repeat carriers may support the role of neurodevelopmental factors [17, 81, 82]. Our vertex analyses also highlight hippocampal atrophy in hexanucleotide carriers which is also consistent with observations from symptomatic patient groups [14, 40, 71] and in line with the reports of early memory impairment in clinical subgroups of ALS [83]. Also, these subjects exhibit nucleus reuniens involvement of a key hub of thalamic afferents to the hippocampus [84]. Thus, C9orf72 carriers display concomitant thalamic and hippocampal alterations which are probably functionally interlinked. However, given that our hippocampal findings are unilateral, further validation is needed by larger studies.
The practical relevance of multimodal presymptomatic studies stems from the prospect of developing accurate predictive models to foretell the approximate time of phenoconversion and the likely clinical phenotype. This would enable precision care planning for individual subjects and optimised timing for clinical trial inclusion. The utility of machine learning (ML) has already been demonstrated in a variety of prognostic and diagnostic applications in ALS [8587]. Imaging-based ML models in ALS increasingly include subcortical measures [8891] in addition to cortical grey matter and cerebral white matter metrics [9295]. Feature importance analyses and cluster analyses consistently confirmed the discriminatory potential of subcortical indices and integrity metrics of networks relayed through subcortical nuclei [96, 97]. In asymptomatic C9orf72 HRE carriers, emerging spinal cord imaging techniques may be particularly useful in delineating incipient ALS from FTD [9, 98]. MRI-based predictive models have already been successfully trialled in symptomatic ALS patients [92, 99] and similar strategies could be adopted in asymptomatic cohorts to foretell likely phenoconversion.
Our results highlight the fundamental heterogeneity of genetic ALS from its earliest stages, by demonstrating the strikingly divergent disease-burden patterns between C9orf72 and SOD1 carriers. Despite the insights generated by a cross-sectional study, ultimately, large, multi-timepoint longitudinal studies are required to track mutation carriers from a very young age until fulfilling diagnostic criteria and beyond. The lack of longitudinal analyses is the biggest limitation of this cross-sectional study. While our descriptive statistics demonstrate focal grey matter changes, they only offer a mere snapshot in time. Characterising the evolution of grey matter alterations from birth to phenoconversion through several timepoints would reveal the full biological trajectory of these processes [100]. Importantly, the subjects of this study have not yet met relevant diagnostic criteria, and therefore, we cannot be sure whether individual hexanucleotide expansion carriers will develop a syndrome primarily consistent with ALS or FTD, although the index cases (affected, symptomatic family members) of the current study all had ALS. Finally, we acknowledge the limitations of the raw data and wish that additional spectroscopic or resting-state fMRI data would be at our disposal to comprehensively interrogate metabolic and connectomic alterations.

Conclusions

C9orf72 hexanucleotide repeat expansions are associated with presymptomatic thalamus and hippocampus alterations which may precede detectable neocortical involvement. The identified radiological changes may be mediated by a multitude of C9orf72-associated pathophysiological processes which take place decades before symptom manifestation.

Acknowledgements

We wholeheartedly acknowledge all study participants for contributing to this research study.

Declarations

Competing interests

The authors declare that they have no conflict of interest.
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Metadaten
Titel
Presymptomatic grey matter alterations in ALS kindreds: a computational neuroimaging study of asymptomatic C9orf72 and SOD1 mutation carriers
verfasst von
Peter Bede
Dorothée Lulé
Hans-Peter Müller
Ee Ling Tan
Johannes Dorst
Albert C. Ludolph
Jan Kassubek
Publikationsdatum
13.05.2023
Verlag
Springer Berlin Heidelberg
Erschienen in
Journal of Neurology / Ausgabe 9/2023
Print ISSN: 0340-5354
Elektronische ISSN: 1432-1459
DOI
https://doi.org/10.1007/s00415-023-11764-5

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