M ETHODS

4.2.1. Participants

A total of 67 patients with ALS and 39 healthy controls, partly overlapping with participants from Study 1, were included in this cross-sectional neuroimaging study. Patients were recruited from the Hannover Medical School and the Otto-von-Guericke University Magdeburg, Germany, over a period of three years. Patients were diagnosed according to the revised El Escorial Criteria (Brooks et al., 2000), and physical disability was rated using the revised ALS functional rating scale (ALSFRS-R) (Cedarbaum et al., 1999). Seven ALS patients fulfilled the diagnostic criteria for behavioral variant frontotemporal dementia (bvFTD) (Rascovsky et al., 2011). The remainder of patients was further categorized into patients with and without neuropsychological impairment using the Strong criteria (Strong et al., 2009)(see chapter 1.4.6), and allocated to the groups of “ALS-Plus” and “ALS-Nci”, respectively. A detailed description of classification criteria is given in the next section 4.2.2.

Basic clinical and demographic data are summarized in Table 4. In order to maximize clinical homogeneity, patients with progressive muscular atrophy, primary lateral sclerosis, or flail limb phenotypes were excluded. All patients were screened for the presence of the GGGGCC hexanucleotide repeat expansion in C9orf72 using repeat-primed polymerase chain reaction.

A repeat length greater than 30 was considered positive for the expansion which was further verified by southern blotting. Given the strong imaging signature of the C9orf72 repeat expansions (Bede et al., 2013b; Walhout et al., 2015), mutation carriers were not included in the analyses. Additional exclusion criteria for all participants included cerebrovascular disease, traumatic brain injury, and other neurological or psychiatric conditions. Healthy controls had to score within the normal range of the Montreal Cognitive Assessment (MoCA;

(Nasreddine et al., 2005) to be included in the study.

Study 3

4.2.2. Neuropsychological assessment

All participants underwent a detailed neuropsychological assessment within two days of their brain scan, testing the domains of executive function, verbal memory, language, behavior, and visuo-spatial skills. For details on the neuropsychological test battery see 2.2.2. Performance on the following tests was utilized for neuropsychological classification: letter fluency and flexibility (Aschenbrenner et al., 2000), semantic fluency and flexibility (Aschenbrenner et al., 2000), trail making test (ratio between part B and A, Reitan, 1992), Stroop test (ratio between naming and reading; naming errors, Stroop, 1935), backward digit span (Härting et al., 2000), and the Frontal Systems Behaviour Scale (FrSBe). Patients were classified as ALS with cognitive impairment (“ALSci”) if their performance was two standard deviations below the mean of healthy controls in at least two independent executive tests (Strong et al., 2009).

The diagnosis of ALS with behavioral impairment (ALSbi) was established based on impairment in one of the subscales of the FrSBe, indicating apathy, disinhibition, or executive dysfunction. Questionnaires were filled in by the caregiver. Only those items that indicated a change in behavior after disease onset were considered for classification. Ten patients with

“ALSci” and eight patients with “ALSbi” were merged to form the group “ALS-Plus”. To account for motor impairment, fluency and flexibility indices were computed as suggested by Abrahams and colleagues, where fluency index (fi) = (60 seconds – time needed for reproducing words verbally or in writing) / total number of items generated (Abrahams et al., 2000). Cut-off values for each test and study group specific performance data are reported in Table 5.

Tab. 4: Demographic profile of participants N Age (years) Education

Sex (male – female)

Handedness

(right – left) ALSFRS-R

Disease duration (months)

HC 39 59.6±10.1 13.9±1.8 27-12 38-1 na na

ALS-Nci 42 58.4±9.5 13.7±2.9 25-17 42-0 35.2±8.3 26.6±15.8

ALS-Plus 18 63.6±12.1 12.3±2.2 12-6 16-2 35.2±6.3 24.7±19.3

ALS-FTD 7 63.0±11.1 13.6±1.9 5-2 6-1 39.4±3.0 32.9±42.0

p Value 0.284 0.105 0.799 0.086 0.370 0.669

Study 3

. 5: Group-specific neuropsychological performance. Cut-off

Nr. of impaired patients

ALS-Nci (Mean±SD)NALS-Plus (Mean±SD)NALS-FTD (Mean±SD)NF value p value ALS-Nci vs. ALS-Plus p value ALS-Nci vs. ALS- FTD

p value ALS-Plus vs. ALS-FTD tter fluency6.784.1±1.9366.8±2.5147.5±1.8412.1<.0010.007ns ter flexibility7.3104.6±1.5348.9±6.9148.8±2.047.20.002nsns antic fluency3.531.9±0.5342.8±1.1143.8±1.1417.10.001<.0010.055 antic flexibility5.153.2±0.8344.6±1.2146.3±2.247.1<.001<.0010.024 oop ratio1.521.1±0.1351.5±0.6121.3±0.35ns--- oop errors2.4111.6±3.9352.3±2.0126.0±10.35ns--- itive flexibility3.982.3±1.0413.0±1.7183.6±0.964.2ns0.052ns t span backwards3.166.0±1.7404.9±1.3164.3±0.545.00.039nsns Be Apathy36.2925.8±7.62539.2±7.11042.7±11.9314.4<.0010.003ns Be Disinhibiton35.1223.2±5.82530.0±4.41030.9±10.436.10.012nsns Be Executive Function55.7031.4±8.72539.4±8.31050.4±5.438.70.0470.013ns MT Learningnana51.1±8.73440.5±11.81133.3±14.538.20.0100.002ns MT Immediate recallnana10.9±2.4347.2±4.0112.7±3.839.20.002<.001ns MT Delayed recallnana10.7±2.4347.2±4.2116.0±5.63ns--- MT Recognitionnana11.8±3.0347.6±5.8113.3±12.93ns---

Study 3

4.2.3. MRI data acquisition

T1-weighted structural MRI scans were acquired on a 3T Siemens Magnetom VERIO scanner with a 32-channel head coil using a 3D-MPRAGE sequence (echo time (TE) = 4.82ms, repetition time (TR) = 2500ms, inversion time (TI) = 1100ms, flip angle = 7°, isotropic voxel size = 1mm³). To rule out confounding pathological findings, T2-weighted (gradient echo sequence: TE = 19.9ms, TR = 620ms, flip angle = 20°, voxel size = 1.1x0.9x5.0 mm³) and FLAIR (turbo spin-echo sequence: TE = 94.0ms, TR = 9000ms, TI = 2500ms, flip angle = 150°, voxel size = 1.0x0.9x5.0 mm³) sequences were also acquired and individually inspected.

4.2.4. Volumetric analyses

The volumes of seven subcortical structures were estimated separately for each hemisphere using the segmentation and registration tool FIRST, which is part of the FMRIB´s Software Library (FSL). These structures included the caudate nucleus, thalamus, nucleus accumbens, hippocampus, amygdala, putamen, and pallidum. Brain-extracted T1-weighted images were used for registration and subcortical segmentation (Figure 8) that was carried out as previously described in section 3.2.5. The resulting subcortical volumes were adjusted for total intracranial volume (TIV) using the covariance method (Jack et al., 1989). Image segmentation for TIV calculation was carried out based on a hidden Markov random field model using an expectation-maximization algorithm as implemented in the FSL tool FAST.

TIV was calculated as the sum of partial volume estimates of the three main tissue components. To check for the asymmetry of the structural changes, an asymmetry index (AI) was calculated for each subcortical structure that did not differ between the study groups (Table 7). Accordingly, the pairwise sum of the left and right nuclei was used for each structure in subsequent analyses. A one-way analysis of covariance (ANCOVA) was conducted to explore intergroup differences in subcortical volumes using patient categorization as independent variable and age as a covariate of no interest. Post-hoc comparisons between groups were adjusted for multiple comparisons using Bonferroni correction. The significance threshold was set to 0.05. Additionally, a linear discriminant analysis was performed to evaluate the predictive value of subcortical volumes for phenotypic classification. The volumes of all subcortical structures were included in a stepwise analysis where at each step the predictor with the largest F value that exceeds the entry criteria

Study 3 (F = 3.84) is added to the model (removal criteria: F = 2.71). The resulting model was cross-validated using leave-one-out classification. The overall a priori group prediction is 47.6%.

Fig. 8: Subcortical gray matter segmentation and registration example using FSL FIRST.

In order to rule out a confounding relationship between subcortical volumes and disease duration or patients’ physical disability (ALSFRS-R), Pearson correlations were calculated within each patient group. In addition, the relationship between subcortical volumes and anatomically linked neuropsychological performance was explored, i.e. memory performance with hippocampal volumes, and apathy scores with nucleus accumbens volumes.

4.2.5. Shape analyses

Vertex-wise statistics for each structure were performed using the subcortical segmentation outputs generated by FIRST, as previously described in section 3.2.4. The vertex locations from each subject were projected onto the surface of an average template shape as scalar values, where a positive value is outside the surface and a negative is inside. TIV and age were included as covariates of no interest. Intergroup differences were assessed using permutation-based non-parametric testing as implemented in FSL "Randomise" (Winkler et al., 2014). Vertex-wise threshold-free cluster enhanced (TFCE) parameters (Smith and Nichols, 2009) were estimated and permuted to account for age and TIV. Results were corrected for multiple comparisons across space (FWE < 0.05).

4.2.6. Density analyses

In order to comprehensively characterize the extent and nature of subcortical involvement in ALS, we performed additional density analyses using FSL-VBM. Brain-extracted T1-weighted images of all participants were tissue-type segmented and a study-specific template

Study 3 was created, consisting of seven brain scans of each group that did not differ in age, gender, education, and handedness. Gray matter partial volume estimates of all participants were non-linearly registered to the study-specific template, modulated by a Jacobian field warp to compensate for registration bias, and smoothed with an isotropic Gaussian kernel with a sigma of 3 mm. A supplementary region-of-interest (ROI) gray matter density analysis was also performed within a combined basal ganglia mask, which included the bilateral caudate nuclei, thalami, accumbens nuclei, hippocampi, amygdalae, putamina, and pallida (Figure 9).

To explore focal gray matter differences between groups (using age as a covariate), permutation-based non-parametric testing was applied using “Randomise”, as implemented in FSL. Briefly, TFCE parameters were estimated for each voxel and permuted using the Freedman-Lane procedure to account for age (Freedman and Lane, 1983). This approach was used for both the whole-brain and ROI analyses. Results were corrected for multiple comparisons across space (FWE < 0.05).

Fig. 9: Basal ganglia region-of-interest (ROI) mask. The mask includes the bilateral caudate nuclei, thalami, accumbens nuclei, hippocampi, amygdalae, putamina, and pallida and was created based on the Harvard-Oxford subcortical atlas.